From d3ff0990bb26a7e0fbc37cb76ee730784380bfa6 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Sat, 12 Dec 2020 18:20:17 +0800 Subject: [PATCH 01/39] Removed i3s, added AWS Instance Selector, Cloud9 --- .../awscli.md | 28 +++++ .../cleanup_ownaccount.md | 9 +- .../fleet_config_options.md | 2 +- .../right_sizing_executors.md | 2 +- .../selecting_instance_types.md | 105 ++++++++++++++---- .../workspace.md | 42 +++++++ 6 files changed, 158 insertions(+), 30 deletions(-) create mode 100644 content/running_spark_apps_with_emr_on_spot_instances/awscli.md create mode 100644 content/running_spark_apps_with_emr_on_spot_instances/workspace.md diff --git a/content/running_spark_apps_with_emr_on_spot_instances/awscli.md b/content/running_spark_apps_with_emr_on_spot_instances/awscli.md new file mode 100644 index 00000000..95d0eafb --- /dev/null +++ b/content/running_spark_apps_with_emr_on_spot_instances/awscli.md @@ -0,0 +1,28 @@ +--- +title: "Update to the latest AWS CLI" +chapter: false +weight: 20 +comment: default install now includes aws-cli/1.15.83 +--- + +{{% notice tip %}} +For this workshop, please ignore warnings about the version of pip being used. +{{% /notice %}} + +1. Run the following command to view the current version of aws-cli: +``` +aws --version +``` + +1. Update to the latest version: +``` +curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip" +unzip awscliv2.zip +sudo ./aws/install +. ~/.bash_profile +``` + +1. Confirm you have a newer version: +``` +aws --version +``` diff --git a/content/running_spark_apps_with_emr_on_spot_instances/cleanup_ownaccount.md b/content/running_spark_apps_with_emr_on_spot_instances/cleanup_ownaccount.md index fc2e5be5..cd9e56a1 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/cleanup_ownaccount.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/cleanup_ownaccount.md @@ -6,8 +6,7 @@ hidden: true --- 1. In the EMR Management Console, check that the cluster is in the **Terminated** state. If it isn't, then you can terminate it from the console. -2. Delete the VPC you deployed via CloudFormation, by going to the CloudFormation service in the AWS Management Console, selecting the VPC stack (default name is Quick-Start-VPC) and click the Delete option. Make sure that the deletion has completed successfully (this should take around 1 minute), the status of the stack will be DELETE_COMPLETE (the stack will move to the Deleted list of stacks). -3. Delete your S3 bucket from the AWS Management Console - choose the bucket from the list of buckets and hit the Delete button. This approach will also empty the bucket and delete all existing objects in the bucket. -4. Delete the Athena table by going to the Athena service in the AWS Management Console, find the **emrworkshopresults** Athena table, click the three dots icon next to the table and select **Delete table**. - - +2. Go to the [Cloud9 Dashboard](https://console.aws.amazon.com/cloud9/home) and delete your environment. +3. Delete the VPC you deployed via CloudFormation, by going to the CloudFormation service in the AWS Management Console, selecting the VPC stack (default name is Quick-Start-VPC) and click the Delete option. Make sure that the deletion has completed successfully (this should take around 1 minute), the status of the stack will be DELETE_COMPLETE (the stack will move to the Deleted list of stacks). +4. Delete your S3 bucket from the AWS Management Console - choose the bucket from the list of buckets and hit the Delete button. This approach will also empty the bucket and delete all existing objects in the bucket. +5. Delete the Athena table by going to the Athena service in the AWS Management Console, find the **emrworkshopresults** Athena table, click the three dots icon next to the table and select **Delete table**. \ No newline at end of file diff --git a/content/running_spark_apps_with_emr_on_spot_instances/fleet_config_options.md b/content/running_spark_apps_with_emr_on_spot_instances/fleet_config_options.md index c758f7ee..606147e8 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/fleet_config_options.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/fleet_config_options.md @@ -18,4 +18,4 @@ If my Spark application is memory driven, I can set the total units to the total This option will allow you run your EMR Instance Fleet on Spot Blocks, which are uninterrupted Spot Instances, available for 1-6 hours, at a lower discount compared to Spot Instances. #### Provisioning timeout -You can determine that after a set amount of minutes, if EMR is unable to provision your selected Spot Instances due to lack of capacity, it will either start On-Demand instances instead, or terminate the cluster. This can be determined according to the business definition of the cluster or Spark application - if it is SLA bound and should complete even at On-Demand price, then the "Switch to On-Demand" option might be suitable. However, make sure you diversify the instance types in the fleet when looking to use Spot Instances, before you look into failing over to On-Demand. Also, try to select instance types with lower interruption rates according to the [Spot Instance Advisor] (https://aws.amazon.com/ec2/spot/instance-advisor/) \ No newline at end of file +You can determine that after a set amount of minutes, if EMR is unable to provision your selected Spot Instances due to lack of capacity, it will either start On-Demand instances instead, or terminate the cluster. This can be determined according to the business definition of the cluster or Spark application - if it is SLA bound and should complete even at On-Demand price, then the "Switch to On-Demand" option might be suitable. However, make sure you diversify the instance types in the fleet when looking to use Spot Instances, before you look into failing over to On-Demand. \ No newline at end of file diff --git a/content/running_spark_apps_with_emr_on_spot_instances/right_sizing_executors.md b/content/running_spark_apps_with_emr_on_spot_instances/right_sizing_executors.md index 95d6cd38..4eca770a 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/right_sizing_executors.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/right_sizing_executors.md @@ -25,7 +25,7 @@ r5.xlarge: yarn.scheduler.maximum-allocation-mb 24576\ 2. With the Spark on YARN configuration option which was [introduced in EMR version 5.22] (https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-whatsnew-history.html#emr-5220-whatsnew): spark.yarn.executor.memoryOverheadFactor and defaults to 0.1875 (18.75% of the spark.yarn.executor.memoryOverhead setting ) -So we can conclude that if we decrease our executor size to ~18GB, we'll be able to use r4.xlarge and basically any of the R family instance types (or i3 that have the same vCPU:Mem ratio) as vCPU and Memory grows linearly within family sizes. If EMR will select an r4.2xlarge instance type from the list of supported instance types that we'll provide to EMR Instance Fleets, then it will run more than 1 executor on each instance, due to Spark dynamic allocation being enabled by default. +So we can conclude that if we decrease our executor size to ~18GB, we'll be able to use r4.xlarge and basically any of the R family instance types as vCPU and Memory grows linearly within family sizes. If EMR will select an r4.2xlarge instance type from the list of supported instance types that we'll provide to EMR Instance Fleets, then it will run more than 1 executor on each instance, due to Spark dynamic allocation being enabled by default. ![tags](/images/running-emr-spark-apps-on-spot/sparkmemory.png) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md index 3d8497c0..2f8c782e 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md @@ -6,37 +6,96 @@ weight: 50 Let's use our newly acquired knowledge around Spark executor sizing in order to select the EC2 Instance Types that will be used in our EMR cluster.\ EMR clusters run Master, Core and Task node types. [Click here] (https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-master-core-task-nodes.html) to read more about the different node types. -We determined that in order to be flexible and allow running on multiple instance types, we will submit our Spark application with **"–executor-memory=18GB –executor-cores=4"**, +We determined that in order to be flexible and allow running on multiple instance types across R instance family, we will submit our Spark application with **"–executor-memory=18GB –executor-cores=4"**. -We can use the [Spot Instance Advisor] (https://aws.amazon.com/ec2/spot/instance-advisor/) page to find the relevant instance types with sufficient number of vCPUs and RAM, and use this opportunity to also select instance types with low interruption rates. \ -For example: r5.2xlarge has 8 vCPUs and 64 GB of RAM, so EMR will automatically run 2 executors that will consume 36 GB of RAM and still leave free RAM for the operating system and other processes.\ -However, at the time of writing, when looking at the EU (Ireland) region in the Spot Instance advisor, the r5.2xlarge instance type is showing an interruption rate of >20%.\ -Instead, we'll focus on instance types with lower interruption rates and suitable vCPU/Memory ratio. As an example, at the time of writing, in the EU (Ireland) region, these could be: r4.xlarge, r4.2xlarge, i3.xlarge, i3.2xlarge, r5d.xlarge +We will use **[amazon-ec2-instance-selector](https://github.com/aws/amazon-ec2-instance-selector)** to help us select the relevant instance +types and familes with sufficient number of vCPUs and RAM. +For example: We will indentiy R family instances, so EMR can run executors that will consume 4 vCPUs and 18 GB of RAM and still leave free RAM for the operating system and other processes. We can select different-sized instance types such as r5.xlarge and r5.2xlarge. Second, add previous generation r4.xlarge and other R4 instance sizes. After we have added different sizes within the same family, as well as previous generation instance types, we can add extra instance types with similar hardware characteristics and vCPU to memory ratio, such as the r5a instance family with AMD processors, r5d instance family with locally attached NVMe storage, and more. + +There are over 270 different instance types available on EC2 which can make the process of selecting appropriate instance types difficult. **[amazon-ec2-instance-selector](https://github.com/aws/amazon-ec2-instance-selector)** helps you select compatible instance types for your application to run on. The command line interface can be passed resource criteria like vcpus, memory, network performance, and much more and then return the available, matching instance types. + +Let's first install **amazon-ec2-instance-selector** on Cloud9 IDE: + +``` +curl -Lo ec2-instance-selector https://github.com/aws/amazon-ec2-instance-selector/releases/download/v1.3.0/ec2-instance-selector-`uname | tr '[:upper:]' '[:lower:]'`-amd64 && chmod +x ec2-instance-selector +sudo mv ec2-instance-selector /usr/local/bin/ +ec2-instance-selector --version +``` + +Now that you have ec2-instance-selector installed, you can run +`ec2-instance-selector --help` to understand how you could use it for selecting +instances that match your workload requirements. For the purpose of this workshop +we need to first get a group of instances with sizes between 4vCPU to 16vCPUs and belong to current generation R family. +Run the following command to get the list of instances. + +```bash +ec2-instance-selector --vcpus-min 4 --vcpus-max 16 --allow-list '.*r5.*|.*r4.*|.*r5a.*|.*r5d.*' --deny-list '.*n.*|.*ad.*|.*b.*' +``` + +This should display a list like the one that follows (note results might differ depending on the region). We will use this instances as part of our EMR Core and Task Instance Fleets. + +``` +r4.2xlarge +r4.4xlarge +r4.xlarge +r5.2xlarge +r5.4xlarge +r5.xlarge +r5a.2xlarge +r5a.4xlarge +r5a.xlarge +r5d.2xlarge +r5d.4xlarge +r5d.xlarge +``` + +Internally ec2-instance-selector is making calls to the [DescribeInstanceTypes](https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeInstanceTypes.html) for the specific region and filtering +the intstances based on the criteria selected in the command line, in our case +we did filter for instances that meet the following criteria:\ + * Instances that have minimum 4 vCPUs and maximum 16 vCPUs\ + * Instances of R5, R5A, R5D and R4 generation (4th gen onwards)\ + * Instances that don't meet the regular expresion `.*n.*|.*ad.*|.*b.*` so effectively r5n, r5dn, r5ad and r5b. -![Spot Instance Advisor](/images/running-emr-spark-apps-on-spot/spotinstanceadvisor1.png) {{% notice note %}} -Spot Instance interruption rates are dynamic, the above just provides a real world example from a specific time and would probably be different when you are performing this workshop. +You are encouraged to test what are the options that `ec2-instance-selector` provides and run a few commands with it to familiarize yourself with the tool. +For example, try running the same commands as you did before with the extra parameter **`--output table-wide`**. {{% /notice %}} -To keep our flexibility in place and be able to provide multiple instance types for our EMR cluster, we need to make sure that our executor size will be under the EMR YARN limitation that we saw in the previous step, +### Challenge + +Find out another group that adheres to a 1vCPU:8GB ratio but this time across all instance families with same deny list. -**Your first task**: Find and take note of 5 instance types in the region where you have created your VPC to run your EMR cluster, which will allow running executors with at least 4 vCPUs and 30+ GB of RAM, and also have low Spot interruption rates (maximum 10-15%). +{{%expand "Expand this for an example on the list of instances" %}} -{{%expand "Click here to see a hint for the task" %}} -Instance types with sufficient Memory and vCPUs for our executor size, as well as suitable for our desired vCPU:Mem ratio, and are also under the default memory EMR limitations:\ +That should be easy. You can run the command: -**Recommended for the workshop:**\ -- r4.xlarge and larger\ -- r5.xlarge and larger\ -- r5a.xlarge and larger\ -- r5d.xlarge and larger\ -- i3.xlarge and larger\ +```bash +ec2-instance-selector --vcpus-to-memory-ratio 1:8 --vcpus-min 4 --vcpus-max 32 --gpus 0 --current-generation -a x86_64 --deny-list '.*n.*|.*ad.*|.*b.*' +``` -**Previous generation instance types:**\ -- r3.xlarge and larger\ -- i2.xlarge and larger\ -you will notice that these instance types have double the vCores as they do vCPU, as reflected in the EMR instance selection window - this is an EMR optimization method. Feel free to use these as well, but note that the executor calculations that we're referring to in the workshop will differ. Also, these previous generation instance types will perform slower and the application will take longer to complete.\ -Also note that not all instance types exist in all regions. -{{% /expand%}} +which should yield a list as follows +``` +d2.2xlarge +d2.4xlarge +d2.xlarge +i3.2xlarge +i3.4xlarge +i3.8xlarge +i3.xlarge +r4.2xlarge +r4.4xlarge +r4.8xlarge +r4.xlarge +r5.2xlarge +r5.4xlarge +r5.8xlarge +r5.xlarge +r5a.2xlarge +r5a.4xlarge +r5a.8xlarge +r5a.xlarge +r5d.2xlarge +``` +{{% /expand %}} diff --git a/content/running_spark_apps_with_emr_on_spot_instances/workspace.md b/content/running_spark_apps_with_emr_on_spot_instances/workspace.md new file mode 100644 index 00000000..fa16d39f --- /dev/null +++ b/content/running_spark_apps_with_emr_on_spot_instances/workspace.md @@ -0,0 +1,42 @@ +--- +title: "Create a Workspace" +chapter: false +weight: 15 +--- + +{{% notice warning %}} +If you are running the workshop on your own, the Cloud9 workspace should be built by an IAM user with Administrator privileges, not the root account user. Please ensure you are logged in as an IAM user, not the root +account user. +{{% /notice %}} + +{{% notice info %}} +If you are at an AWS hosted event (such as re:Invent, Kubecon, Immersion Day, or any other event hosted by +an AWS employee) follow the instructions on the region that should be used to launch resources +{{% /notice %}} + +{{% notice tip %}} +Ad blockers, javascript disablers, and tracking blockers should be disabled for +the cloud9 domain, or connecting to the workspace might be impacted. +Cloud9 requires third-party-cookies. You can whitelist the [specific domains]( https://docs.aws.amazon.com/cloud9/latest/user-guide/troubleshooting.html#troubleshooting-env-loading). +{{% /notice %}} + +### Launch Cloud9 in your closest region: + +{{< tabs name="Region" >}} + {{< tab name="Oregon" include="us-west-2.md" />}} + {{< tab name="Ireland" include="eu-west-1.md" />}} + {{< tab name="Ohio" include="us-east-2.md" />}} + {{< tab name="Singapore" include="ap-southeast-1.md" />}} +{{< /tabs >}} + +- Select **Create environment** +- Name it **eksworkshop**, and take all other defaults +- When it comes up, customize the environment by closing the **welcome tab** +and **lower work area**, and opening a new **terminal** tab in the main work area: +![c9before](/images/using_ec2_spot_instances_with_eks/prerequisites/c9before.png) + +- Your workspace should now look like this: +![c9after](/images/using_ec2_spot_instances_with_eks/prerequisites/c9after.png) + +- If you like this theme, you can choose it yourself by selecting **View / Themes / Solarized / Solarized Dark** +in the Cloud9 workspace menu. From 8f2879e386b6403978b5088cd6269d1320898725 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Sat, 12 Dec 2020 18:21:46 +0800 Subject: [PATCH 02/39] capacity-optimized and 15 instance types --- .../emr_allocation_strategies.md | 19 ++++++++++++++++++ .../launching_emr_cluster-2.md | 8 ++++---- .../cliexport.png | Bin 78270 -> 333743 bytes .../emrinstancefleets-task2.png | Bin 50232 -> 149881 bytes 4 files changed, 23 insertions(+), 4 deletions(-) create mode 100644 content/running_spark_apps_with_emr_on_spot_instances/emr_allocation_strategies.md diff --git a/content/running_spark_apps_with_emr_on_spot_instances/emr_allocation_strategies.md b/content/running_spark_apps_with_emr_on_spot_instances/emr_allocation_strategies.md new file mode 100644 index 00000000..fec25637 --- /dev/null +++ b/content/running_spark_apps_with_emr_on_spot_instances/emr_allocation_strategies.md @@ -0,0 +1,19 @@ +--- +title: "EMR Allocation Strategies" +weight: 35 +--- + +As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version 5.12.1 and later. It optimizes the allocation of instance fleet capacity and lets you choose a target strategy for each cluster node. + +* On-Demand instances use a lowest-price strategy, which launches the lowest-priced instances first. +* Spot instances use a capacity-optimized strategy, which launches Spot instances from Spot instance pools that have optimal capacity for the number of instances that are launching. + +{{% notice note %}} +The allocation strategy option also lets you specify up to fifteen EC2 instance types per task node when creating your cluster, as opposed to five maximum allowed by the default EMR cluster instance fleet configuration. +{{% /notice %}} + +The capacity-optimized allocation strategy for Spot instances uses real-time capacity data to allocate instances from the Spot instance pools with the optimal capacity for the number of instances that are launching. This allocation strategy is appropriate for workloads that have a higher cost of interruption. Examples include long-running jobs and multi-tenant persistent clusters running Apache Spark, Apache Hive, and Presto. This allocation strategy lets you specify up to 15 EC2 instance types on task instance fleets to diversify your Spot requests and get steep discounts. Previously, instance fleets allowed a maximum of five instance types. You can now diversify your Spot requests across these 15 pools within each Availability Zone and prioritize deploying into a deeper capacity pool to lower the chance of interruptions. With more instance type diversification, Amazon EMR has more capacity pools to allocate capacity from, and chooses the Spot Instances which are least likely to be interrupted. + +{{% notice info %}} +[Click here] (https://aws.amazon.com/blogs/big-data/optimizing-amazon-emr-for-resilience-and-cost-with-capacity-optimized-spot-instances/) For an in-depth blog post about capacity-optimized allocation strategy for Amazon EMR instance fleets. +{{% /notice %}} \ No newline at end of file diff --git a/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md b/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md index 8b50d105..ee539ec3 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md @@ -15,20 +15,20 @@ The workshop focuses on running Spot Instances across all the cluster node types #### **Master node**: Unless your cluster is very short-lived and the runs are cost-driven, avoid running your Master node on a Spot Instance. We suggest this because a Spot interruption on the Master node terminates the entire cluster. \ For the purpose of this workshop, we will run the Master node on a Spot Instance as we simulate a relatively short lived job running on a transient cluster. There will not be business impact if the job fails due to a Spot interruption and later re-started.\ -Click **Add / remove instance types to fleet** and select two relatively small and cheap instance types - i.e c4.large and m4.large and check Spot under target capacity. EMR will only provision one instance, but will select the best instance type for the Master node based on price and available capacity. +Click **Add / remove instance types to fleet** and select two relatively cheaper instance types - i.e c5.xlarge and m5.xlarge and check Spot under target capacity. EMR will only provision one instance, but will select the best instance type for the Master node based on price and available capacity. ![FleetSelection1](/images/running-emr-spark-apps-on-spot/emrinstancefleets-master.png) #### **Core Instance Fleet**: Avoid using Spot Instances for Core nodes if your Spark applications use HDFS. That prevents a situation where Spot interruptions cause data loss for data that was written to the HDFS volumes on the instances. For short-lived applications on transient clusters, as is the case in this workshop, we are going to run our Core nodes on Spot Instances.\ When using EMR Instance Fleets, one Core node is mandatory. Since we want to scale out and run our Spark application on our Task nodes, let's stick to the one mandatory Core node. We will specify **4 Spot units**, and select instance types that count as 4 units and will allow to run one executor.\ -Under the core node type, Click **Add / remove instance types to fleet** and select instance types that have 4 vCPUs and enough memory to run an executor (given the 18G executor size), for example: +Under the core node type, Click **Add / remove instance types to fleet** and select instance types that you noted before as suitable to run an executor (given the 18G executor size), for example: ![FleetSelection2](/images/running-emr-spark-apps-on-spot/emrinstancefleets-core1.png) #### **Task Instance Fleet**: Our task nodes will only run Spark executors and no HDFS DataNodes, so this is a great fit for scaling out and increasing the parallelization of our application's execution, to achieve faster execution times. -Under the task node type, Click **Add / remove instance types to fleet** and select the 5 instance types you noted before as suitable for our executor size and that had suitable interruption rates in the Spot Instance Advisor.\ -Since our executor size is 4 vCPUs, and each instance counts as the number of its vCPUs towards the total units, let's specify **40 Spot units** in order to run 10 executors, and allow EMR to select the best instance type in the Task Instance Fleet to run the executors on. In this example, it will either start 10 * r4.xlarge / r5.xlarge / i3.xlarge **or** 5 * r5.2xlarge / r4.2xlarge in EMR Task Instance Fleet. +Under the task node type, Click **Add / remove instance types to fleet** and select upto 15 instance types you noted before as suitable for our executor size.\ +Since our executor size is 4 vCPUs, and each instance counts as the number of its vCPUs towards the total units, let's specify **40 Spot units** in order to run 10 executors, and allow EMR to select the best instance type in the Task Instance Fleet to run the executors on. {{% notice warning %}} If you are using your own AWS account (not an account that was created for you in an AWS event), Keep reading: if your account is new, or you've never launched Spot Instances in the account, your ability to launch Spot Instances could be limited. To overcome this, please make sure you launch no more than 3 instances in the Task Instance Fleet. 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fa16d39f..f295adad 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/workspace.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/workspace.md @@ -10,8 +10,7 @@ account user. {{% /notice %}} {{% notice info %}} -If you are at an AWS hosted event (such as re:Invent, Kubecon, Immersion Day, or any other event hosted by -an AWS employee) follow the instructions on the region that should be used to launch resources +If you are at an AWS hosted event, follow the instructions on the region that should be used to launch resources {{% /notice %}} {{% notice tip %}} @@ -20,15 +19,9 @@ the cloud9 domain, or connecting to the workspace might be impacted. Cloud9 requires third-party-cookies. You can whitelist the [specific domains]( https://docs.aws.amazon.com/cloud9/latest/user-guide/troubleshooting.html#troubleshooting-env-loading). {{% /notice %}} -### Launch Cloud9 in your closest region: - -{{< tabs name="Region" >}} - {{< tab name="Oregon" include="us-west-2.md" />}} - {{< tab name="Ireland" include="eu-west-1.md" />}} - {{< tab name="Ohio" include="us-east-2.md" />}} - {{< tab name="Singapore" include="ap-southeast-1.md" />}} -{{< /tabs >}} +### Launch Cloud9: +- Go to [Cloud9 Console](https://console.aws.amazon.com/cloud9/home) - Select **Create environment** - Name it **eksworkshop**, and take all other defaults - When it comes up, customize the environment by closing the **welcome tab** From 55eb74ddb69145fad0ff0bd0410f0bf89a7059e6 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Sat, 12 Dec 2020 18:53:50 +0800 Subject: [PATCH 05/39] Cloud9 Images --- .../workspace.md | 4 ++-- .../running-emr-spark-apps-on-spot/c9after.png | Bin 0 -> 170533 bytes .../c9before.png | Bin 0 -> 424780 bytes 3 files changed, 2 insertions(+), 2 deletions(-) create mode 100644 static/images/running-emr-spark-apps-on-spot/c9after.png create mode 100644 static/images/running-emr-spark-apps-on-spot/c9before.png diff --git a/content/running_spark_apps_with_emr_on_spot_instances/workspace.md b/content/running_spark_apps_with_emr_on_spot_instances/workspace.md index f295adad..8ea29a1a 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/workspace.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/workspace.md @@ -26,10 +26,10 @@ Cloud9 requires third-party-cookies. You can whitelist the [specific domains]( h - Name it **eksworkshop**, and take all other defaults - When it comes up, customize the environment by closing the **welcome tab** and **lower work area**, and opening a new **terminal** tab in the main work area: -![c9before](/images/using_ec2_spot_instances_with_eks/prerequisites/c9before.png) +![c9before](/images/running-emr-spark-apps-on-spot/c9before.png) - Your workspace should now look like this: -![c9after](/images/using_ec2_spot_instances_with_eks/prerequisites/c9after.png) +![c9after](/images/running-emr-spark-apps-on-spot/c9after.png) - If you like this theme, you can choose it yourself by selecting **View / Themes / Solarized / Solarized Dark** in the Cloud9 workspace menu. diff --git a/static/images/running-emr-spark-apps-on-spot/c9after.png b/static/images/running-emr-spark-apps-on-spot/c9after.png new file mode 100644 index 0000000000000000000000000000000000000000..d1298ba62d85f98838b6e50bf8f1a4770a48f812 GIT binary patch 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c44daf86ef2e5fe110fbb328e8d2f061affa774e Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Sat, 12 Dec 2020 19:44:31 +0800 Subject: [PATCH 07/39] Cloud9 --- .../{workspace.md => cloud9-workspace.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename content/running_spark_apps_with_emr_on_spot_instances/{workspace.md => cloud9-workspace.md} (100%) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/workspace.md b/content/running_spark_apps_with_emr_on_spot_instances/cloud9-workspace.md similarity index 100% rename from content/running_spark_apps_with_emr_on_spot_instances/workspace.md rename to content/running_spark_apps_with_emr_on_spot_instances/cloud9-workspace.md From ab761cd12aa3b5544a51ac2d054c5ed4145c5094 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Sat, 12 Dec 2020 19:56:19 +0800 Subject: [PATCH 08/39] Cloud9 --- .../awscli.md | 28 ------------------- 1 file changed, 28 deletions(-) delete mode 100644 content/running_spark_apps_with_emr_on_spot_instances/awscli.md diff --git a/content/running_spark_apps_with_emr_on_spot_instances/awscli.md b/content/running_spark_apps_with_emr_on_spot_instances/awscli.md deleted file mode 100644 index 95d0eafb..00000000 --- a/content/running_spark_apps_with_emr_on_spot_instances/awscli.md +++ /dev/null @@ -1,28 +0,0 @@ ---- -title: "Update to the latest AWS CLI" -chapter: false -weight: 20 -comment: default install now includes aws-cli/1.15.83 ---- - -{{% notice tip %}} -For this workshop, please ignore warnings about the version of pip being used. -{{% /notice %}} - -1. Run the following command to view the current version of aws-cli: -``` -aws --version -``` - -1. Update to the latest version: -``` -curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip" -unzip awscliv2.zip -sudo ./aws/install -. ~/.bash_profile -``` - -1. Confirm you have a newer version: -``` -aws --version -``` From 7bb18664a2265d9a732c3b239595047d22731d26 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Sat, 12 Dec 2020 21:07:08 +0800 Subject: [PATCH 09/39] Savings Summary - 32 Task Spot Unit --- .../cloud9-awscli.md | 28 ++++++++++++++++++ .../savingssummary.png | Bin 197871 -> 92930 bytes 2 files changed, 28 insertions(+) create mode 100644 content/running_spark_apps_with_emr_on_spot_instances/cloud9-awscli.md diff --git a/content/running_spark_apps_with_emr_on_spot_instances/cloud9-awscli.md b/content/running_spark_apps_with_emr_on_spot_instances/cloud9-awscli.md new file mode 100644 index 00000000..95d0eafb --- /dev/null +++ b/content/running_spark_apps_with_emr_on_spot_instances/cloud9-awscli.md @@ -0,0 +1,28 @@ +--- +title: "Update to the latest AWS CLI" +chapter: false +weight: 20 +comment: default install now includes aws-cli/1.15.83 +--- + +{{% notice tip %}} +For this workshop, please ignore warnings about the version of pip being used. +{{% /notice %}} + +1. Run the following command to view the current version of aws-cli: +``` +aws --version +``` + +1. Update to the latest version: +``` +curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip" +unzip awscliv2.zip +sudo ./aws/install +. ~/.bash_profile +``` + +1. Confirm you have a newer version: +``` +aws --version +``` diff --git a/static/images/running-emr-spark-apps-on-spot/savingssummary.png b/static/images/running-emr-spark-apps-on-spot/savingssummary.png index ea455230aa461b1ff96b6b7cd0c8b508a837a3b1..d684538289ef21d1c84c08efe42a3fd40b5e3434 100644 GIT binary patch literal 92930 zcmeFZRa9Ne)-FnLCundD8eAs9Ey10HKyaHl6L$$taA)Ff!QEX06DLS;cL?rqCwuR6 z_FC&d_u)R=r+aCm(FQd}A63<>_p18(*NgAUic%P;#HcVZFc>n@A5>vr-~cc%uvW;3 z&=NKlfH4frD^YU^31t}x2})&q5YXJp1O|p7#zr z)M+8^m*#u;PPAUsUeXXX`VoF`Mn)V*Ny<>-2;o(vK201Y*AyzOA}v{bo_hQ@vz-UO zp&ZCXI1Yl8K5{;j>o)Dd9s?l}ArU+Kw{DW0yK@ZN_ne`2l)d}k)~t~nxq2J}GzQTS zgMMX;vhC3H#=qfU4>4h9KU8CrW|j>Q_;yMnXO~ii9Wc_C?(6IH`}sMuhlp^Fhlub= z_xbtx`U?7s6{@24zE0a!5*Q65ed@|Q1{SDd;mtp3IBF=!^BaMzSq+Rqh9<18*0xYx z!@vl-@i_KlJ{kn2nn9U#~b?3Q=n)C{s#+>`f?nSh-o* zsfAG~DJcc*je-2CA0+=H4*g4r+RV|>mYb8`Mw@-I*RBKgu1ekFTz6R472R3XeE$o4oB2%|^2q7ZzGwe^$i z&H0#M91KeIU$1Xgca|jqI?b*z4AS4=|8`N1;(SxYB`|du3;(B7rxWZat2w;Cz5We; 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z|MlQ+hn)~A;KCJPw9NR=r!HWE_JG$;9w(2V>~H`5&kp4!j0xOYx~q=j|M}M93E|cF z?1KoJOh^CcO$~6!lOcenST Date: Mon, 14 Dec 2020 10:55:28 +0800 Subject: [PATCH 10/39] Removed Warning on old Spot limits --- .../launching_emr_cluster-2.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md b/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md index ee539ec3..1328d589 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md @@ -29,9 +29,6 @@ Under the core node type, Click **Add / remove instance types to fleet** and sel Our task nodes will only run Spark executors and no HDFS DataNodes, so this is a great fit for scaling out and increasing the parallelization of our application's execution, to achieve faster execution times. Under the task node type, Click **Add / remove instance types to fleet** and select upto 15 instance types you noted before as suitable for our executor size.\ Since our executor size is 4 vCPUs, and each instance counts as the number of its vCPUs towards the total units, let's specify **40 Spot units** in order to run 10 executors, and allow EMR to select the best instance type in the Task Instance Fleet to run the executors on. -{{% notice warning %}} -If you are using your own AWS account (not an account that was created for you in an AWS event), Keep reading: if your account is new, or you've never launched Spot Instances in the account, your ability to launch Spot Instances could be limited. To overcome this, please make sure you launch no more than 3 instances in the Task Instance Fleet. You can do this, for example, by only providing instances that count as 8 units, and specify 24 for Spot units.\ -{{% /notice %}} ![FleetSelection3](/images/running-emr-spark-apps-on-spot/emrinstancefleets-task2.png) From a936e934b3b061f5ab6a69d5fa1b3f2cec45d4eb Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Mon, 14 Dec 2020 15:08:33 +0800 Subject: [PATCH 11/39] Updated to 32 instances to support 4xl --- .../emr_allocation_strategies.md | 2 +- .../examining_cluster.md | 4 +- .../fleet_config_options.md | 4 +- .../launching_emr_cluster-2.md | 8 +-- .../selecting_instance_types.md | 56 +++--------------- .../cliexport.png | Bin 333743 -> 360219 bytes .../emrinstancefleets-task2.png | Bin 149881 -> 165091 bytes .../savingssummary.png | Bin 92930 -> 92145 bytes 8 files changed, 18 insertions(+), 56 deletions(-) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/emr_allocation_strategies.md b/content/running_spark_apps_with_emr_on_spot_instances/emr_allocation_strategies.md index fec25637..183fd688 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/emr_allocation_strategies.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/emr_allocation_strategies.md @@ -9,7 +9,7 @@ As an enhancement to the default EMR instance fleets cluster configuration, the * Spot instances use a capacity-optimized strategy, which launches Spot instances from Spot instance pools that have optimal capacity for the number of instances that are launching. {{% notice note %}} -The allocation strategy option also lets you specify up to fifteen EC2 instance types per task node when creating your cluster, as opposed to five maximum allowed by the default EMR cluster instance fleet configuration. +The allocation strategy option also lets you specify up to 15 EC2 instance types per task node when creating your cluster, as opposed to 5 maximum allowed by the default EMR cluster instance fleet configuration. {{% /notice %}} The capacity-optimized allocation strategy for Spot instances uses real-time capacity data to allocate instances from the Spot instance pools with the optimal capacity for the number of instances that are launching. This allocation strategy is appropriate for workloads that have a higher cost of interruption. Examples include long-running jobs and multi-tenant persistent clusters running Apache Spark, Apache Hive, and Presto. This allocation strategy lets you specify up to 15 EC2 instance types on task instance fleets to diversify your Spot requests and get steep discounts. Previously, instance fleets allowed a maximum of five instance types. You can now diversify your Spot requests across these 15 pools within each Availability Zone and prioritize deploying into a deeper capacity pool to lower the chance of interruptions. With more instance type diversification, Amazon EMR has more capacity pools to allocate capacity from, and chooses the Spot Instances which are least likely to be interrupted. diff --git a/content/running_spark_apps_with_emr_on_spot_instances/examining_cluster.md b/content/running_spark_apps_with_emr_on_spot_instances/examining_cluster.md index e0b1c541..7a6ea4eb 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/examining_cluster.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/examining_cluster.md @@ -64,8 +64,8 @@ Some notable metrics:\ When you are done examining the cluster and using the different UIs, terminate the EMR cluster from the EMR management console. This is not the end of the workshop though - we still have some interesting steps to go. #### Number of executors in the cluster -With 40 Spot Units in the Task Instance Fleet, EMR launched either 10 * xlarge (running one executor) or 5 * 2xlarge instances (running 2 executors), so the Task Instance Fleet provides 10 executors / containers to the cluster.\ +With 32 Spot Units in the Task Instance Fleet, EMR launched either 8 * xlarge (running one executor) or 4 * 2xlarge instances (running 2 executors) or 2 * 4xlarge instances (running 4 executors), so the Task Instance Fleet provides 8 executors / containers to the cluster.\ The Core Instance Fleet launched one xlarge instance, able to run one executor. -{{%expand "Question: Did you see more than 11 containers in CloudWatch Metrics and in YARN ResourceManager? if so, do you know why? Click to expand the answer" %}} +{{%expand "Question: Did you see more than 9 containers in CloudWatch Metrics and in YARN ResourceManager? if so, do you know why? Click to expand the answer" %}} Your Spark application was configured to run in Cluster mode, meaning that the **Spark driver is running on the Core node**. Since it is counted as a container, this adds a container to our count, but it is not an executor. {{% /expand%}} diff --git a/content/running_spark_apps_with_emr_on_spot_instances/fleet_config_options.md b/content/running_spark_apps_with_emr_on_spot_instances/fleet_config_options.md index 606147e8..d3d50f12 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/fleet_config_options.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/fleet_config_options.md @@ -11,8 +11,8 @@ While our cluster is starting (7-8 minutes) and the step is running (4-10 minute Since Amazon EC2 Spot Instances [changed the pricing model and bidding is no longer required] (https://aws.amazon.com/blogs/compute/new-amazon-ec2-spot-pricing/), we have an optional "Max-price" field for our Spot requests, which would limit how much we're willing to pay for the instance. It is recommended to leave this value at 100% of the On-Demand price, in order to avoid limiting our instance diversification. We are going to pay the Spot market price regardless of the Maximum price that we can specify, and setting a higher max price does not increase the chance of getting Spot capacity nor does it decrease the chance of getting your Spot Instances interrupted when EC2 needs the capacity back. You can see the current Spot price in the AWS Management Console under EC2 -> Spot Requests -> **Pricing History**. #### Each instance counts as X units -This configuration allows us to give each instance type in our diversified fleet a weight that will count towards our Total units. By default, this weight is configured as the number of YARN VCores that the instance type has by default (this would typically equate to the number of EC2 vCPUs) - this way it's easy to set the Total units to the number of vCPUs we want our cluster to run with, and EMR will select the best instances while taking into account the required number of instances to run. For example, if r4.xlarge is the instance type that EMR found to be the least likely to be interrupted and has the lowest price out of our selection, its weight is 4 and our total units (only Spot) is 40, then 10 * r4.xlarge instances will be launched by EMR in the fleet. -If my Spark application is memory driven, I can set the total units to the total amount of memory I want my cluster to run with, and change the "Each instance counts as" field to the total memory of the instance, leaving aside some memory for the operating system and other processes. For example, for the r4.xlarge I can set its weight to 25. If I then set up the Total units to 500 then EMR will bring up 20 * r4.xlrage instances in the fleet. Since our executor size is 18 GB, one executor will run on this instance type. +This configuration allows us to give each instance type in our diversified fleet a weight that will count towards our Total units. By default, this weight is configured as the number of YARN VCores that the instance type has by default (this would typically equate to the number of EC2 vCPUs) - this way it's easy to set the Total units to the number of vCPUs we want our cluster to run with, and EMR will select the best instances while taking into account the required number of instances to run. For example, if r4.xlarge is the instance type that EMR found to be the least likely to be interrupted, its weight is 4 and our total units (only Spot) is 32, then 8 * r4.xlarge instances will be launched by EMR in the fleet. +If my Spark application is memory driven, I can set the total units to the total amount of memory I want my cluster to run with, and change the "Each instance counts as" field to the total memory of the instance, leaving aside some memory for the operating system and other processes. For example, for the r4.xlarge I can set its weight to 25. If I then set up the Total units to 500 then EMR will bring up 20 * r4.xlarge instances in the fleet. Since our executor size is 18 GB, one executor will run on this instance type. #### Defined duration This option will allow you run your EMR Instance Fleet on Spot Blocks, which are uninterrupted Spot Instances, available for 1-6 hours, at a lower discount compared to Spot Instances. diff --git a/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md b/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md index 1328d589..7a42fbdb 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md @@ -15,20 +15,20 @@ The workshop focuses on running Spot Instances across all the cluster node types #### **Master node**: Unless your cluster is very short-lived and the runs are cost-driven, avoid running your Master node on a Spot Instance. We suggest this because a Spot interruption on the Master node terminates the entire cluster. \ For the purpose of this workshop, we will run the Master node on a Spot Instance as we simulate a relatively short lived job running on a transient cluster. There will not be business impact if the job fails due to a Spot interruption and later re-started.\ -Click **Add / remove instance types to fleet** and select two relatively cheaper instance types - i.e c5.xlarge and m5.xlarge and check Spot under target capacity. EMR will only provision one instance, but will select the best instance type for the Master node based on price and available capacity. +Click **Add / remove instance types to fleet** and select two relatively cheaper instance types - i.e c5.xlarge and m5.xlarge and check Spot under target capacity. EMR will only provision one instance, but will select the best instance type for the Master node from the Spot instance pools with the optimal capacity. ![FleetSelection1](/images/running-emr-spark-apps-on-spot/emrinstancefleets-master.png) #### **Core Instance Fleet**: Avoid using Spot Instances for Core nodes if your Spark applications use HDFS. That prevents a situation where Spot interruptions cause data loss for data that was written to the HDFS volumes on the instances. For short-lived applications on transient clusters, as is the case in this workshop, we are going to run our Core nodes on Spot Instances.\ When using EMR Instance Fleets, one Core node is mandatory. Since we want to scale out and run our Spark application on our Task nodes, let's stick to the one mandatory Core node. We will specify **4 Spot units**, and select instance types that count as 4 units and will allow to run one executor.\ -Under the core node type, Click **Add / remove instance types to fleet** and select instance types that you noted before as suitable to run an executor (given the 18G executor size), for example: +Under the core node type, click **Add / remove instance types to fleet** and select instance types that you noted before as suitable to run an executor (given the 18G executor size), for example: ![FleetSelection2](/images/running-emr-spark-apps-on-spot/emrinstancefleets-core1.png) #### **Task Instance Fleet**: Our task nodes will only run Spark executors and no HDFS DataNodes, so this is a great fit for scaling out and increasing the parallelization of our application's execution, to achieve faster execution times. -Under the task node type, Click **Add / remove instance types to fleet** and select upto 15 instance types you noted before as suitable for our executor size.\ -Since our executor size is 4 vCPUs, and each instance counts as the number of its vCPUs towards the total units, let's specify **40 Spot units** in order to run 10 executors, and allow EMR to select the best instance type in the Task Instance Fleet to run the executors on. +Under the task node type, Click **Add / remove instance types to fleet** and select up to 15 instance types you noted before as suitable for our executor size.\ +Since our executor size is 4 vCPUs, and each instance counts as the number of its vCPUs towards the total units, let's specify **32 Spot units** in order to run 8 executors, and allow EMR to select the best instance type in the Task Instance Fleet to run the executors on. ![FleetSelection3](/images/running-emr-spark-apps-on-spot/emrinstancefleets-task2.png) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md index 2f8c782e..9bf03ff8 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md @@ -9,10 +9,10 @@ EMR clusters run Master, Core and Task node types. [Click here] (https://docs.aw We determined that in order to be flexible and allow running on multiple instance types across R instance family, we will submit our Spark application with **"–executor-memory=18GB –executor-cores=4"**. We will use **[amazon-ec2-instance-selector](https://github.com/aws/amazon-ec2-instance-selector)** to help us select the relevant instance -types and familes with sufficient number of vCPUs and RAM. -For example: We will indentiy R family instances, so EMR can run executors that will consume 4 vCPUs and 18 GB of RAM and still leave free RAM for the operating system and other processes. We can select different-sized instance types such as r5.xlarge and r5.2xlarge. Second, add previous generation r4.xlarge and other R4 instance sizes. After we have added different sizes within the same family, as well as previous generation instance types, we can add extra instance types with similar hardware characteristics and vCPU to memory ratio, such as the r5a instance family with AMD processors, r5d instance family with locally attached NVMe storage, and more. +types and families with sufficient number of vCPUs and RAM. +For example: We identified R family instances, so EMR can run executors that will consume 4 vCPUs and 18 GB of RAM and still leave free RAM for the operating system and other processes. First, we can select different-sized instance types from current generation, such as r5.xlarge, r5.2xlarge and r5.4xlarge. Next, we can select different-sized instance types from previous generation, such as r4.xlarge, r4.2xlarge and r4.4xlarge. Last, we can select different-sized instances from R family local storage and processor variants, such as R5d instance types (local NVMe-based SSDs) and R5a instance types (powered by AMD processors). -There are over 270 different instance types available on EC2 which can make the process of selecting appropriate instance types difficult. **[amazon-ec2-instance-selector](https://github.com/aws/amazon-ec2-instance-selector)** helps you select compatible instance types for your application to run on. The command line interface can be passed resource criteria like vcpus, memory, network performance, and much more and then return the available, matching instance types. +There are over 275 different instance types available on EC2 which can make the process of selecting appropriate instance types difficult. **[amazon-ec2-instance-selector](https://github.com/aws/amazon-ec2-instance-selector)** helps you select compatible instance types for your application to run on. The command line interface can be passed resource criteria like vCPUs, memory, network performance, and much more and then return the available, matching instance types. Let's first install **amazon-ec2-instance-selector** on Cloud9 IDE: @@ -25,11 +25,11 @@ ec2-instance-selector --version Now that you have ec2-instance-selector installed, you can run `ec2-instance-selector --help` to understand how you could use it for selecting instances that match your workload requirements. For the purpose of this workshop -we need to first get a group of instances with sizes between 4vCPU to 16vCPUs and belong to current generation R family. +we need to first get a group of instances with sizes between 4vCPU to 16vCPUs and belong to R5, R4, R5D and R5A instance types. Run the following command to get the list of instances. ```bash -ec2-instance-selector --vcpus-min 4 --vcpus-max 16 --allow-list '.*r5.*|.*r4.*|.*r5a.*|.*r5d.*' --deny-list '.*n.*|.*ad.*|.*b.*' +ec2-instance-selector --vcpus-min 4 --vcpus-max 16 --allow-list '.*r5.*|.*r4.*|.*r5d.*|.*r5a.*' --deny-list '.*n.*|.*ad.*|.*b.*' ``` This should display a list like the one that follows (note results might differ depending on the region). We will use this instances as part of our EMR Core and Task Instance Fleets. @@ -50,52 +50,14 @@ r5d.xlarge ``` Internally ec2-instance-selector is making calls to the [DescribeInstanceTypes](https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeInstanceTypes.html) for the specific region and filtering -the intstances based on the criteria selected in the command line, in our case +the instances based on the criteria selected in the command line, in our case we did filter for instances that meet the following criteria:\ * Instances that have minimum 4 vCPUs and maximum 16 vCPUs\ - * Instances of R5, R5A, R5D and R4 generation (4th gen onwards)\ - * Instances that don't meet the regular expresion `.*n.*|.*ad.*|.*b.*` so effectively r5n, r5dn, r5ad and r5b. + * Instances of R5, R4, R5D and R5A generation\ + * Instances that don't meet the regular expression `.*n.*|.*ad.*|.*b.*`, so effectively r5n, r5dn, r5ad and r5b. {{% notice note %}} You are encouraged to test what are the options that `ec2-instance-selector` provides and run a few commands with it to familiarize yourself with the tool. For example, try running the same commands as you did before with the extra parameter **`--output table-wide`**. -{{% /notice %}} - -### Challenge - -Find out another group that adheres to a 1vCPU:8GB ratio but this time across all instance families with same deny list. - -{{%expand "Expand this for an example on the list of instances" %}} - -That should be easy. You can run the command: - -```bash -ec2-instance-selector --vcpus-to-memory-ratio 1:8 --vcpus-min 4 --vcpus-max 32 --gpus 0 --current-generation -a x86_64 --deny-list '.*n.*|.*ad.*|.*b.*' -``` - -which should yield a list as follows - -``` -d2.2xlarge -d2.4xlarge -d2.xlarge -i3.2xlarge -i3.4xlarge -i3.8xlarge -i3.xlarge -r4.2xlarge -r4.4xlarge -r4.8xlarge -r4.xlarge -r5.2xlarge -r5.4xlarge -r5.8xlarge -r5.xlarge -r5a.2xlarge -r5a.4xlarge -r5a.8xlarge -r5a.xlarge -r5d.2xlarge -``` -{{% /expand %}} +{{% /notice %}} \ No newline at end of file diff --git a/static/images/running-emr-spark-apps-on-spot/cliexport.png b/static/images/running-emr-spark-apps-on-spot/cliexport.png index cffe290e999118fd6c034304d0c4bf006d780027..f0570fd50929847e0594253eece59d275fc02df5 100644 GIT binary patch literal 360219 zcmaHT2Uru^x;Bae0vZI7Py^CIq$!<*qVy(66Esv6Y0^PMBGPL>x>7`%3QAEx6bQZd zE+CQK1R=B}gybLgIp?1H-+Rw?o(G0W)~q$FeCvD57;R#teV(3!o{Ea{Lic}=`MAGo+VQ&EW|IojDV>s%G-w70jj>+BO3 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z^ADx9K0yS_?2gP0x60s34L>Cf0Dw`0v1Lfx`}2=fYzma(y<`(chESn@4K1QZ6s+r4 z>jxQhOc*26%e`@O2+^8enR@K_D5MHJRSq-xz`t`@)rJUl0@z|r>(WX zsPth#WGHm<7ncK-daY~ebG2JU<6ZlN{>A5 zCXz{FAuIbMwZ}OIu*+Z?T8lxN%twm#V-~wf4&|mZL%Z(B_pp&Dhu zB2$*a8A`Zu4g`!N2c=v5uPG$b97abI5rUbGM9f1Mhjqd=I9_;)NKjZp=#cF@2)%rn!AHT$FAkmNMeZYmVI+{3TU^ zl&9nU%p`^}5S>fC9b%(BtRKNs(3~GT5e7Xw)X~{J;+jnRy&l&b@vLd;+0ivOKS$!% zw~UhG`Nm<5;Y8-<9I1F&$#^PVv`kq|H_6Y+C5i{TU;r!Ov&Y);N0|$Wk%JEGSvmiGh)_P0j5Z@8GZY;4;roF zJl=OnnUilbxv`cQ?*Q;TP1VKtXp=%mxsm=m^SkHyZsY_vO)X3EFL0Plue6%40ncqe z3i0^tMHCO7y1)ESI<|qL_4C(XBisuj8vVU_XDBO++S%rF+9&{TCiQr(we#0G^-5yBe?X$BEuv$4@s@Dc)A<~6v!>|j!X6>K@=YvahA*Gt0m`@1s ze_iA|SPqzFXJL%~*x`KHTqviCJgB#2h~STW0|%cInGUNT>GAyYerzO|A~65QUquSA z0Q;#~8|$$D^)&|yz&r~t>|^-Dx%=x70bWRCI7n%Ke+PW_BLN6t|J(fkt>B1P+Kw&` zAmXwQzJf#VY?oB$J`!U>a>Pu*EL@N{A`i30LG%`}H00PvVQLNSTn1 G&;JL)ad7Vd From 6a917a2529523157a7725a2bb80127fdf63a2d48 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Mon, 14 Dec 2020 15:19:05 +0800 Subject: [PATCH 12/39] Cloud9 --- .../cloud9-workspace.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/cloud9-workspace.md b/content/running_spark_apps_with_emr_on_spot_instances/cloud9-workspace.md index 8ea29a1a..6150fd29 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/cloud9-workspace.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/cloud9-workspace.md @@ -23,7 +23,7 @@ Cloud9 requires third-party-cookies. You can whitelist the [specific domains]( h - Go to [Cloud9 Console](https://console.aws.amazon.com/cloud9/home) - Select **Create environment** -- Name it **eksworkshop**, and take all other defaults +- Name it **emrworkshop**, and take all other defaults - When it comes up, customize the environment by closing the **welcome tab** and **lower work area**, and opening a new **terminal** tab in the main work area: ![c9before](/images/running-emr-spark-apps-on-spot/c9before.png) From 9a6ab2f4e66b7c4fa381d068f2c01f0fa14b17fc Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Wed, 6 Jan 2021 18:51:54 +0800 Subject: [PATCH 13/39] PR Review --- .../emr_allocation_strategies.md | 19 -------------- .../emr_instance_fleets.md | 16 +++++++++--- .../selecting_instance_types.md | 25 ++++++++++++------- 3 files changed, 29 insertions(+), 31 deletions(-) delete mode 100644 content/running_spark_apps_with_emr_on_spot_instances/emr_allocation_strategies.md diff --git a/content/running_spark_apps_with_emr_on_spot_instances/emr_allocation_strategies.md b/content/running_spark_apps_with_emr_on_spot_instances/emr_allocation_strategies.md deleted file mode 100644 index 183fd688..00000000 --- a/content/running_spark_apps_with_emr_on_spot_instances/emr_allocation_strategies.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -title: "EMR Allocation Strategies" -weight: 35 ---- - -As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version 5.12.1 and later. It optimizes the allocation of instance fleet capacity and lets you choose a target strategy for each cluster node. - -* On-Demand instances use a lowest-price strategy, which launches the lowest-priced instances first. -* Spot instances use a capacity-optimized strategy, which launches Spot instances from Spot instance pools that have optimal capacity for the number of instances that are launching. - -{{% notice note %}} -The allocation strategy option also lets you specify up to 15 EC2 instance types per task node when creating your cluster, as opposed to 5 maximum allowed by the default EMR cluster instance fleet configuration. -{{% /notice %}} - -The capacity-optimized allocation strategy for Spot instances uses real-time capacity data to allocate instances from the Spot instance pools with the optimal capacity for the number of instances that are launching. This allocation strategy is appropriate for workloads that have a higher cost of interruption. Examples include long-running jobs and multi-tenant persistent clusters running Apache Spark, Apache Hive, and Presto. This allocation strategy lets you specify up to 15 EC2 instance types on task instance fleets to diversify your Spot requests and get steep discounts. Previously, instance fleets allowed a maximum of five instance types. You can now diversify your Spot requests across these 15 pools within each Availability Zone and prioritize deploying into a deeper capacity pool to lower the chance of interruptions. With more instance type diversification, Amazon EMR has more capacity pools to allocate capacity from, and chooses the Spot Instances which are least likely to be interrupted. - -{{% notice info %}} -[Click here] (https://aws.amazon.com/blogs/big-data/optimizing-amazon-emr-for-resilience-and-cost-with-capacity-optimized-spot-instances/) For an in-depth blog post about capacity-optimized allocation strategy for Amazon EMR instance fleets. -{{% /notice %}} \ No newline at end of file diff --git a/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md b/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md index 0104f3a9..0f9cec2d 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md @@ -11,8 +11,18 @@ With EMR instance fleets, you specify target capacities for On-Demand Instances [Click here] (https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-instance-fleet.html) to learn more about EMR Instance Fleets in the official documentation. {{% /notice %}} -**When Amazon EMR launches the cluster, it looks across those subnets to find the instances and purchasing options you specify, and will select the Spot Instances with the lowest chance of getting interrupted, for the lowest cost.** - - While a cluster is running, if Amazon EC2 reclaims a Spot Instance or if an instance fails, Amazon EMR tries to replace the instance with any of the instance types that you specify in your fleet. This makes it easier to regain capacity in case some of the instances get interrupted by EC2 when it needs the Spot capacity back.\ These options do not exist within the default EMR configuration option "Uniform Instance Groups", hence we will be using EMR Instance Fleets only. + +As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version **5.12.1** and later. With allocation strategy - +* On-Demand instances use a lowest-price strategy, which launches the lowest-priced instances first. +* Spot instances use a [capacity-optimized] (https://aws.amazon.com/about-aws/whats-new/2020/06/amazon-emr-uses-real-time-capacity-insights-to-provision-spot-instances-to-lower-cost-and-interruption/) allocation strategy, which allocates instances from most-available Spot Instance pools and lowers the chance of interruptions. This allocation strategy is appropriate for workloads that have a higher cost of interruption such as persistent EMR clusters running Apache Spark, Apache Hive, and Presto. + +{{% notice note %}} +This allocation strategy option also lets you specify **up to 15 EC2 instance types on task instance fleet**. By default, Amazon EMR allows a maximum of 5 instance types for each type of instance fleet. By enabling allocation strategy, you can diversify your Spot request for task instance fleet across 15 instance pools. With more instance type diversification, Amazon EMR has more capacity pools to allocate capacity from, this allows you to get more compute capacity. +{{% /notice %}} + + +{{% notice info %}} +[Click here] (https://aws.amazon.com/blogs/big-data/optimizing-amazon-emr-for-resilience-and-cost-with-capacity-optimized-spot-instances/) For an in-depth blog post about capacity-optimized allocation strategy for Amazon EMR instance fleets. +{{% /notice %}} \ No newline at end of file diff --git a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md index 9bf03ff8..ce07e712 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md @@ -8,11 +8,14 @@ EMR clusters run Master, Core and Task node types. [Click here] (https://docs.aw We determined that in order to be flexible and allow running on multiple instance types across R instance family, we will submit our Spark application with **"–executor-memory=18GB –executor-cores=4"**. -We will use **[amazon-ec2-instance-selector](https://github.com/aws/amazon-ec2-instance-selector)** to help us select the relevant instance -types and families with sufficient number of vCPUs and RAM. -For example: We identified R family instances, so EMR can run executors that will consume 4 vCPUs and 18 GB of RAM and still leave free RAM for the operating system and other processes. First, we can select different-sized instance types from current generation, such as r5.xlarge, r5.2xlarge and r5.4xlarge. Next, we can select different-sized instance types from previous generation, such as r4.xlarge, r4.2xlarge and r4.4xlarge. Last, we can select different-sized instances from R family local storage and processor variants, such as R5d instance types (local NVMe-based SSDs) and R5a instance types (powered by AMD processors). +To apply the instance diversification best practices while meeting the application constraints defined in the previous section, we can add different instances sizes from the latest generation, such as r5.xlarge, r5.2xlarge and r5.4xlarge. We can also add instances from previous generations such as r4.xlarge, r4.2xlarge and r4.4xlarge. There are even variants, such as R5d instance types (local NVMe-based SSDs) and R5a instance types (powered by AMD processors) that can be included. +{{% notice info %}} There are over 275 different instance types available on EC2 which can make the process of selecting appropriate instance types difficult. **[amazon-ec2-instance-selector](https://github.com/aws/amazon-ec2-instance-selector)** helps you select compatible instance types for your application to run on. The command line interface can be passed resource criteria like vCPUs, memory, network performance, and much more and then return the available, matching instance types. +{{% /notice %}} + +We will use **amazon-ec2-instance-selector** to help us select the relevant instance +types and families with sufficient number of vCPUs and RAM. Let's first install **amazon-ec2-instance-selector** on Cloud9 IDE: @@ -25,11 +28,12 @@ ec2-instance-selector --version Now that you have ec2-instance-selector installed, you can run `ec2-instance-selector --help` to understand how you could use it for selecting instances that match your workload requirements. For the purpose of this workshop -we need to first get a group of instances with sizes between 4vCPU to 16vCPUs and belong to R5, R4, R5D and R5A instance types. +we need to first get a group of instances with sizes between 4vCPU to 16vCPUs and belong to current generation of R Family (vCPU to Memory ratio of 1:8). + Run the following command to get the list of instances. ```bash -ec2-instance-selector --vcpus-min 4 --vcpus-max 16 --allow-list '.*r5.*|.*r4.*|.*r5d.*|.*r5a.*' --deny-list '.*n.*|.*ad.*|.*b.*' +ec2-instance-selector --vcpus-min 4 --vcpus-max 16 --vcpus-to-memory-ratio 1:8 --cpu-architecture x86_64 --current-generation --gpus 0 --deny-list '.*[n].*|.*[b].*|^[diz].*' ``` This should display a list like the one that follows (note results might differ depending on the region). We will use this instances as part of our EMR Core and Task Instance Fleets. @@ -44,18 +48,21 @@ r5.xlarge r5a.2xlarge r5a.4xlarge r5a.xlarge +r5ad.2xlarge +r5ad.4xlarge +r5ad.xlarge r5d.2xlarge r5d.4xlarge -r5d.xlarge +r5d.xlarge ``` Internally ec2-instance-selector is making calls to the [DescribeInstanceTypes](https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeInstanceTypes.html) for the specific region and filtering the instances based on the criteria selected in the command line, in our case we did filter for instances that meet the following criteria:\ * Instances that have minimum 4 vCPUs and maximum 16 vCPUs\ - * Instances of R5, R4, R5D and R5A generation\ - * Instances that don't meet the regular expression `.*n.*|.*ad.*|.*b.*`, so effectively r5n, r5dn, r5ad and r5b. - + * Instances of R family with vCPU to Memory ratio of 1:8\ + * Filtering out GPU type instances\ + * Instances that don't meet the regular expression `.*[n].*|.*[b].*|^[zid].*`. Effectively lesser popular R5N and R5B variants and higher priced Z, I and D Instance families. {{% notice note %}} You are encouraged to test what are the options that `ec2-instance-selector` provides and run a few commands with it to familiarize yourself with the tool. From c2cff298b2dd70ea39bff1da3a5e9b82fbd7b37f Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Thu, 7 Jan 2021 16:34:43 +0800 Subject: [PATCH 14/39] PR Review --- .../emr_instance_fleets.md | 6 +-- .../launching_emr_cluster-2.md | 2 +- .../right_sizing_executors.md | 2 +- .../selecting_instance_types.md | 45 +++++++++---------- 4 files changed, 26 insertions(+), 29 deletions(-) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md b/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md index 0f9cec2d..ce73f974 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md @@ -14,8 +14,8 @@ With EMR instance fleets, you specify target capacities for On-Demand Instances While a cluster is running, if Amazon EC2 reclaims a Spot Instance or if an instance fails, Amazon EMR tries to replace the instance with any of the instance types that you specify in your fleet. This makes it easier to regain capacity in case some of the instances get interrupted by EC2 when it needs the Spot capacity back.\ These options do not exist within the default EMR configuration option "Uniform Instance Groups", hence we will be using EMR Instance Fleets only. -As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version **5.12.1** and later. With allocation strategy - -* On-Demand instances use a lowest-price strategy, which launches the lowest-priced instances first. +As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version **5.12.1 and later**. With allocation strategy -/ +* On-Demand instances use a lowest-price strategy, which launches the lowest-priced instances first./ * Spot instances use a [capacity-optimized] (https://aws.amazon.com/about-aws/whats-new/2020/06/amazon-emr-uses-real-time-capacity-insights-to-provision-spot-instances-to-lower-cost-and-interruption/) allocation strategy, which allocates instances from most-available Spot Instance pools and lowers the chance of interruptions. This allocation strategy is appropriate for workloads that have a higher cost of interruption such as persistent EMR clusters running Apache Spark, Apache Hive, and Presto. {{% notice note %}} @@ -24,5 +24,5 @@ This allocation strategy option also lets you specify **up to 15 EC2 instance ty {{% notice info %}} -[Click here] (https://aws.amazon.com/blogs/big-data/optimizing-amazon-emr-for-resilience-and-cost-with-capacity-optimized-spot-instances/) For an in-depth blog post about capacity-optimized allocation strategy for Amazon EMR instance fleets. +[Click here] (https://aws.amazon.com/blogs/big-data/optimizing-amazon-emr-for-resilience-and-cost-with-capacity-optimized-spot-instances/) for an in-depth blog post about capacity-optimized allocation strategy for Amazon EMR instance fleets. {{% /notice %}} \ No newline at end of file diff --git a/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md b/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md index 7a42fbdb..b414db1d 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md @@ -27,7 +27,7 @@ Under the core node type, click **Add / remove instance types to fleet** and sel #### **Task Instance Fleet**: Our task nodes will only run Spark executors and no HDFS DataNodes, so this is a great fit for scaling out and increasing the parallelization of our application's execution, to achieve faster execution times. -Under the task node type, Click **Add / remove instance types to fleet** and select up to 15 instance types you noted before as suitable for our executor size.\ +Under the task node type, click **Add / remove instance types to fleet** and select **up to 15 instance types** you noted before as suitable for your executor size.\ Since our executor size is 4 vCPUs, and each instance counts as the number of its vCPUs towards the total units, let's specify **32 Spot units** in order to run 8 executors, and allow EMR to select the best instance type in the Task Instance Fleet to run the executors on. ![FleetSelection3](/images/running-emr-spark-apps-on-spot/emrinstancefleets-task2.png) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/right_sizing_executors.md b/content/running_spark_apps_with_emr_on_spot_instances/right_sizing_executors.md index 4eca770a..b634d257 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/right_sizing_executors.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/right_sizing_executors.md @@ -25,7 +25,7 @@ r5.xlarge: yarn.scheduler.maximum-allocation-mb 24576\ 2. With the Spark on YARN configuration option which was [introduced in EMR version 5.22] (https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-whatsnew-history.html#emr-5220-whatsnew): spark.yarn.executor.memoryOverheadFactor and defaults to 0.1875 (18.75% of the spark.yarn.executor.memoryOverhead setting ) -So we can conclude that if we decrease our executor size to ~18GB, we'll be able to use r4.xlarge and basically any of the R family instance types as vCPU and Memory grows linearly within family sizes. If EMR will select an r4.2xlarge instance type from the list of supported instance types that we'll provide to EMR Instance Fleets, then it will run more than 1 executor on each instance, due to Spark dynamic allocation being enabled by default. +So we can conclude that if we decrease our executor size to ~18GB, we'll be able to use r4.xlarge and basically any of the R family instance types (`1:8 vCPU:Mem ratio`) as vCPU and Memory grows linearly within family sizes. If EMR will select an r4.2xlarge instance type from the list of supported instance types that we'll provide to EMR Instance Fleets, then it will run more than 1 executor on each instance, due to Spark dynamic allocation being enabled by default. ![tags](/images/running-emr-spark-apps-on-spot/sparkmemory.png) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md index ce07e712..6ec58281 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md @@ -3,21 +3,18 @@ title: "Selecting instance types" weight: 50 --- -Let's use our newly acquired knowledge around Spark executor sizing in order to select the EC2 Instance Types that will be used in our EMR cluster.\ -EMR clusters run Master, Core and Task node types. [Click here] (https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-master-core-task-nodes.html) to read more about the different node types. +Let's use our newly acquired knowledge around Spark executor sizing in order to select the EC2 Instance Types that will be used in our EMR cluster. We determined that in order to be flexible and allow running on multiple instance types, we will submit our Spark application with **"–executor-memory=18GB –executor-cores=4"**. -We determined that in order to be flexible and allow running on multiple instance types across R instance family, we will submit our Spark application with **"–executor-memory=18GB –executor-cores=4"**. - -To apply the instance diversification best practices while meeting the application constraints defined in the previous section, we can add different instances sizes from the latest generation, such as r5.xlarge, r5.2xlarge and r5.4xlarge. We can also add instances from previous generations such as r4.xlarge, r4.2xlarge and r4.4xlarge. There are even variants, such as R5d instance types (local NVMe-based SSDs) and R5a instance types (powered by AMD processors) that can be included. +To apply the instance diversification best practices while meeting the application constraints defined in the previous section, we can add different instances sizes from the current generation, such as R5 and R4. We can even include variants, such as R5d instance types (local NVMe-based SSDs) and R5a instance types (powered by AMD processors). {{% notice info %}} There are over 275 different instance types available on EC2 which can make the process of selecting appropriate instance types difficult. **[amazon-ec2-instance-selector](https://github.com/aws/amazon-ec2-instance-selector)** helps you select compatible instance types for your application to run on. The command line interface can be passed resource criteria like vCPUs, memory, network performance, and much more and then return the available, matching instance types. {{% /notice %}} We will use **amazon-ec2-instance-selector** to help us select the relevant instance -types and families with sufficient number of vCPUs and RAM. +types with sufficient number of vCPUs and RAM. -Let's first install **amazon-ec2-instance-selector** on Cloud9 IDE: +Let's first install amazon-ec2-instance-selector on Cloud9 IDE: ``` curl -Lo ec2-instance-selector https://github.com/aws/amazon-ec2-instance-selector/releases/download/v1.3.0/ec2-instance-selector-`uname | tr '[:upper:]' '[:lower:]'`-amd64 && chmod +x ec2-instance-selector @@ -26,17 +23,28 @@ ec2-instance-selector --version ``` Now that you have ec2-instance-selector installed, you can run -`ec2-instance-selector --help` to understand how you could use it for selecting -instances that match your workload requirements. For the purpose of this workshop -we need to first get a group of instances with sizes between 4vCPU to 16vCPUs and belong to current generation of R Family (vCPU to Memory ratio of 1:8). +`ec2-instance-selector --help`, to understand how you could use it for selecting +instances that match your workload requirements. + +For the purpose of this workshop we will select instances based on below criteria -\ + * Instances that have minimum 4 vCPUs and maximum 16 vCPUs\ + * Instances which have vCPU to Memory ratio of 1:8, same as R Instance family\ + * Instances with CPU Architecture x86_64 and no GPU Instances\ + * Instances that belong to current generation\ + * Instances types that are not supported by EMR such as R5N, R5ad and R5b. Enhanced z, I and D Instance families, which are priced higher that R family. So basically, adding a deny list with the regular expression `.*n.*|.*ad.*|.*b.*|^[zid].*`. + +{{% notice info %}} +[Click here] (https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-supported-instance-types.html) to find out the instance types that Amazon EMR supports . +{{% /notice %}} -Run the following command to get the list of instances. +Run the following command with above mentioned criteria, to get the list of instances. ```bash -ec2-instance-selector --vcpus-min 4 --vcpus-max 16 --vcpus-to-memory-ratio 1:8 --cpu-architecture x86_64 --current-generation --gpus 0 --deny-list '.*[n].*|.*[b].*|^[diz].*' +ec2-instance-selector --vcpus-min 4 --vcpus-max 16 --vcpus-to-memory-ratio 1:8 --cpu-architecture x86_64 --current-generation --gpus 0 --deny-list '.*n.*|.*ad.*|.*b.*|^[zid].*' ``` -This should display a list like the one that follows (note results might differ depending on the region). We will use this instances as part of our EMR Core and Task Instance Fleets. +Internally ec2-instance-selector is making calls to the [DescribeInstanceTypes](https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeInstanceTypes.html) for the specific region and filtering +the instances based on the criteria selected in the command line. Above command should display a list like the one that follows (note results might differ depending on the region). We will use this instances as part of our EMR Core and Task Instance Fleets. ``` r4.2xlarge @@ -48,22 +56,11 @@ r5.xlarge r5a.2xlarge r5a.4xlarge r5a.xlarge -r5ad.2xlarge -r5ad.4xlarge -r5ad.xlarge r5d.2xlarge r5d.4xlarge r5d.xlarge ``` -Internally ec2-instance-selector is making calls to the [DescribeInstanceTypes](https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeInstanceTypes.html) for the specific region and filtering -the instances based on the criteria selected in the command line, in our case -we did filter for instances that meet the following criteria:\ - * Instances that have minimum 4 vCPUs and maximum 16 vCPUs\ - * Instances of R family with vCPU to Memory ratio of 1:8\ - * Filtering out GPU type instances\ - * Instances that don't meet the regular expression `.*[n].*|.*[b].*|^[zid].*`. Effectively lesser popular R5N and R5B variants and higher priced Z, I and D Instance families. - {{% notice note %}} You are encouraged to test what are the options that `ec2-instance-selector` provides and run a few commands with it to familiarize yourself with the tool. For example, try running the same commands as you did before with the extra parameter **`--output table-wide`**. From 91206f2079190d1e289ca7bab44c18008b7ae326 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Thu, 7 Jan 2021 16:36:25 +0800 Subject: [PATCH 15/39] Spell Check --- .../selecting_instance_types.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md index 6ec58281..c6e47f1f 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md @@ -31,7 +31,7 @@ For the purpose of this workshop we will select instances based on below criteri * Instances which have vCPU to Memory ratio of 1:8, same as R Instance family\ * Instances with CPU Architecture x86_64 and no GPU Instances\ * Instances that belong to current generation\ - * Instances types that are not supported by EMR such as R5N, R5ad and R5b. Enhanced z, I and D Instance families, which are priced higher that R family. So basically, adding a deny list with the regular expression `.*n.*|.*ad.*|.*b.*|^[zid].*`. + * Instances types that are not supported by EMR such as R5N, R5ad and R5b. Enhanced z, I and D Instance families, which are priced higher than R family. So basically, adding a deny list with the regular expression `.*n.*|.*ad.*|.*b.*|^[zid].*`. {{% notice info %}} [Click here] (https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-supported-instance-types.html) to find out the instance types that Amazon EMR supports . From 33483172b6d867a6d2c5b097511dd1576af7f2f4 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Thu, 7 Jan 2021 18:02:11 +0800 Subject: [PATCH 16/39] Format Fix --- .../emr_instance_fleets.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md b/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md index ce73f974..2c481214 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md @@ -14,8 +14,8 @@ With EMR instance fleets, you specify target capacities for On-Demand Instances While a cluster is running, if Amazon EC2 reclaims a Spot Instance or if an instance fails, Amazon EMR tries to replace the instance with any of the instance types that you specify in your fleet. This makes it easier to regain capacity in case some of the instances get interrupted by EC2 when it needs the Spot capacity back.\ These options do not exist within the default EMR configuration option "Uniform Instance Groups", hence we will be using EMR Instance Fleets only. -As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version **5.12.1 and later**. With allocation strategy -/ -* On-Demand instances use a lowest-price strategy, which launches the lowest-priced instances first./ +As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version **5.12.1 and later**. With allocation strategy -\ +* On-Demand instances use a lowest-price strategy, which launches the lowest-priced instances first.\ * Spot instances use a [capacity-optimized] (https://aws.amazon.com/about-aws/whats-new/2020/06/amazon-emr-uses-real-time-capacity-insights-to-provision-spot-instances-to-lower-cost-and-interruption/) allocation strategy, which allocates instances from most-available Spot Instance pools and lowers the chance of interruptions. This allocation strategy is appropriate for workloads that have a higher cost of interruption such as persistent EMR clusters running Apache Spark, Apache Hive, and Presto. {{% notice note %}} From 96a8e63b9d8f82c6e6e547ec0701f687b3998707 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Thu, 7 Jan 2021 18:09:34 +0800 Subject: [PATCH 17/39] Format Changes --- .../selecting_instance_types.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md index c6e47f1f..b39eea43 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md @@ -30,7 +30,7 @@ For the purpose of this workshop we will select instances based on below criteri * Instances that have minimum 4 vCPUs and maximum 16 vCPUs\ * Instances which have vCPU to Memory ratio of 1:8, same as R Instance family\ * Instances with CPU Architecture x86_64 and no GPU Instances\ - * Instances that belong to current generation\ + * Instances that belong to current generation\ * Instances types that are not supported by EMR such as R5N, R5ad and R5b. Enhanced z, I and D Instance families, which are priced higher than R family. So basically, adding a deny list with the regular expression `.*n.*|.*ad.*|.*b.*|^[zid].*`. {{% notice info %}} From 7d6ffff73ebef8559e47bfba92e5528a2cedcbf2 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Thu, 7 Jan 2021 18:32:18 +0800 Subject: [PATCH 18/39] Format Changes --- .../emr_instance_fleets.md | 9 ++++++--- .../selecting_instance_types.md | 15 ++++++++++----- 2 files changed, 16 insertions(+), 8 deletions(-) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md b/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md index 2c481214..1bad429e 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md @@ -11,11 +11,14 @@ With EMR instance fleets, you specify target capacities for On-Demand Instances [Click here] (https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-instance-fleet.html) to learn more about EMR Instance Fleets in the official documentation. {{% /notice %}} -While a cluster is running, if Amazon EC2 reclaims a Spot Instance or if an instance fails, Amazon EMR tries to replace the instance with any of the instance types that you specify in your fleet. This makes it easier to regain capacity in case some of the instances get interrupted by EC2 when it needs the Spot capacity back.\ +While a cluster is running, if Amazon EC2 reclaims a Spot Instance or if an instance fails, Amazon EMR tries to replace the instance with any of the instance types that you specify in your fleet. This makes it easier to regain capacity in case some of the instances get interrupted by EC2 when it needs the Spot capacity back. + These options do not exist within the default EMR configuration option "Uniform Instance Groups", hence we will be using EMR Instance Fleets only. -As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version **5.12.1 and later**. With allocation strategy -\ -* On-Demand instances use a lowest-price strategy, which launches the lowest-priced instances first.\ +As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version **5.12.1 and later**. With allocation strategy: + +* On-Demand instances use a lowest-price strategy, which launches the lowest-priced instances first. + * Spot instances use a [capacity-optimized] (https://aws.amazon.com/about-aws/whats-new/2020/06/amazon-emr-uses-real-time-capacity-insights-to-provision-spot-instances-to-lower-cost-and-interruption/) allocation strategy, which allocates instances from most-available Spot Instance pools and lowers the chance of interruptions. This allocation strategy is appropriate for workloads that have a higher cost of interruption such as persistent EMR clusters running Apache Spark, Apache Hive, and Presto. {{% notice note %}} diff --git a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md index b39eea43..41aba220 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md @@ -26,11 +26,16 @@ Now that you have ec2-instance-selector installed, you can run `ec2-instance-selector --help`, to understand how you could use it for selecting instances that match your workload requirements. -For the purpose of this workshop we will select instances based on below criteria -\ - * Instances that have minimum 4 vCPUs and maximum 16 vCPUs\ - * Instances which have vCPU to Memory ratio of 1:8, same as R Instance family\ - * Instances with CPU Architecture x86_64 and no GPU Instances\ - * Instances that belong to current generation\ +For the purpose of this workshop we will select instances based on below criteria: + + * Instances that have minimum 4 vCPUs and maximum 16 vCPUs + + * Instances which have vCPU to Memory ratio of 1:8, same as R Instance family + + * Instances with CPU Architecture x86_64 and no GPU Instances + + * Instances that belong to current generation + * Instances types that are not supported by EMR such as R5N, R5ad and R5b. Enhanced z, I and D Instance families, which are priced higher than R family. So basically, adding a deny list with the regular expression `.*n.*|.*ad.*|.*b.*|^[zid].*`. {{% notice info %}} From ef49d05dae5e2f27c8ec0ba6b5027850a66acd71 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Thu, 7 Jan 2021 18:43:22 +0800 Subject: [PATCH 19/39] Format Changes --- .../emr_instance_fleets.md | 6 ++---- .../selecting_instance_types.md | 15 +++++---------- 2 files changed, 7 insertions(+), 14 deletions(-) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md b/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md index 1bad429e..074654cd 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md @@ -15,10 +15,8 @@ While a cluster is running, if Amazon EC2 reclaims a Spot Instance or if an inst These options do not exist within the default EMR configuration option "Uniform Instance Groups", hence we will be using EMR Instance Fleets only. -As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version **5.12.1 and later**. With allocation strategy: - -* On-Demand instances use a lowest-price strategy, which launches the lowest-priced instances first. - +As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version **5.12.1 and later**. With allocation strategy: +* On-Demand instances use a lowest-price strategy, which launches the lowest-priced instances first. * Spot instances use a [capacity-optimized] (https://aws.amazon.com/about-aws/whats-new/2020/06/amazon-emr-uses-real-time-capacity-insights-to-provision-spot-instances-to-lower-cost-and-interruption/) allocation strategy, which allocates instances from most-available Spot Instance pools and lowers the chance of interruptions. This allocation strategy is appropriate for workloads that have a higher cost of interruption such as persistent EMR clusters running Apache Spark, Apache Hive, and Presto. {{% notice note %}} diff --git a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md index 41aba220..f3ab1ad2 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/selecting_instance_types.md @@ -26,16 +26,11 @@ Now that you have ec2-instance-selector installed, you can run `ec2-instance-selector --help`, to understand how you could use it for selecting instances that match your workload requirements. -For the purpose of this workshop we will select instances based on below criteria: - - * Instances that have minimum 4 vCPUs and maximum 16 vCPUs - - * Instances which have vCPU to Memory ratio of 1:8, same as R Instance family - - * Instances with CPU Architecture x86_64 and no GPU Instances - - * Instances that belong to current generation - +For the purpose of this workshop we will select instances based on below criteria: + * Instances that have minimum 4 vCPUs and maximum 16 vCPUs + * Instances which have vCPU to Memory ratio of 1:8, same as R Instance family + * Instances with CPU Architecture x86_64 and no GPU Instances + * Instances that belong to current generation * Instances types that are not supported by EMR such as R5N, R5ad and R5b. Enhanced z, I and D Instance families, which are priced higher than R family. So basically, adding a deny list with the regular expression `.*n.*|.*ad.*|.*b.*|^[zid].*`. {{% notice info %}} From 9ce6ecec605a13b74fc6cb9e89208b54ad3a42ae Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Fri, 8 Jan 2021 16:11:53 +0800 Subject: [PATCH 20/39] Format Changes, replacing "\" with " " --- .../_index.md | 4 +- .../analyzing_costs.md | 18 +++---- .../automations_monitoring.md | 16 +++--- .../conclusions_and_cleanup.md | 6 +-- .../emr_instance_fleets.md | 7 ++- .../examining_cluster.md | 50 +++++++++---------- .../initial_event.md | 2 +- .../initial_ownaccount.md | 18 +++---- .../launching_emr_cluster-1.md | 16 +++--- .../launching_emr_cluster-2.md | 12 ++--- .../launching_emr_cluster-3.md | 6 +-- .../prerequisites_notes.md | 4 +- .../right_sizing_executors.md | 8 +-- .../simulating_recovery.md | 2 +- .../tracking_spot_interruptions.md | 2 +- 15 files changed, 85 insertions(+), 86 deletions(-) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/_index.md b/content/running_spark_apps_with_emr_on_spot_instances/_index.md index 32bd061d..fa910fd4 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/_index.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/_index.md @@ -7,7 +7,7 @@ pre: "" ## Overview -Welcome! In this workshop you will assume the role of a data engineer, tasked with optimizing the organization's costs for running Spark applications, using Amazon EMR and EC2 Spot Instances.\ +Welcome! In this workshop you will assume the role of a data engineer, tasked with optimizing the organization's costs for running Spark applications, using Amazon EMR and EC2 Spot Instances. {{% notice info %}} The **estimated time** for completing the workshop is 60-90 minutes and the **estimated cost** for running the workshop's resources in your AWS account is less than $2.\ The **learning objective** for the workshop is to become familiar with the best practices and tooling that are available to you for cost optimizing your EMR clusters running Spark applications, using Spot Instances. {{% /notice %}} @@ -20,6 +20,6 @@ The **learning objective** for the workshop is to become familiar with the best * [Amazon EC2 Spot Instances] (https://aws.amazon.com/ec2/spot/) offer spare compute capacity available in the AWS Cloud at steep discounts compared to On-Demand prices. EC2 can interrupt Spot Instances with two minutes of notification when EC2 needs the capacity back. You can use Spot Instances for various fault-tolerant and flexible applications. Some examples are analytics, containerized workloads, high-performance computing (HPC), stateless web servers, rendering, CI/CD, and other test and development workloads. ## About Spot Instances in Analytics workloads -The most important best practice when using Spot Instances is to be flexible with the EC2 instance types that our application can run on, in order to be able to access many spare capacity pools (a combination of EC2 instance type and an Availability Zone), as well as achieve our desired capacity from a different instance type in case some of our Spot capacity in the EMR cluster is interrupted, when EC2 needs the spare capacity back.\ +The most important best practice when using Spot Instances is to be flexible with the EC2 instance types that our application can run on, in order to be able to access many spare capacity pools (a combination of EC2 instance type and an Availability Zone), as well as achieve our desired capacity from a different instance type in case some of our Spot capacity in the EMR cluster is interrupted, when EC2 needs the spare capacity back. It's possible to run Spark applications in a single cluster that is running on multiple different instance types, we'll just need to right-size our executors and use the EMR Instance Fleets configuration option in order to meet the Spot diversification best practice. We'll look into that in detail during this workshop. diff --git a/content/running_spark_apps_with_emr_on_spot_instances/analyzing_costs.md b/content/running_spark_apps_with_emr_on_spot_instances/analyzing_costs.md index 06acd21c..456c368f 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/analyzing_costs.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/analyzing_costs.md @@ -22,19 +22,19 @@ If the Name tag Key was not enabled as a Cost Allocation Tag, you will not be ab {{% /notice %}} -Let's use Cost Explorer to analyze the costs of running our EMR application.\ -1. Navigate to Cost Explorer by opening the AWS Management Console -> Click your username in the top right corner -> click **My Billing Dashboard** -> click **Cost Explorer in the left pane**. or [click here] (https://console.aws.amazon.com/billing/home#/costexplorer) for a direct link.\ -2. We know that we gave our EMR cluster a unique Name tag, so let's filter according to it. In the right pane, click Tags -> Name -> enter "**EMRTransientCluster1**"\ -3. Instead of the default 45 days view, let's narrow down the time span to just the day when we ran the cluster. In the data selection dropdown, mark that day as start and end.\ -4. You are now looking at the total cost to run the cluster (**$0.30**), including: EMR, EC2, EBS, and possible AWS Cross-Region data transfer costs, depending on where you ran your cluster relative to where the S3 dataset is located (in N. Virginia).\ +Let's use Cost Explorer to analyze the costs of running our EMR application. +1. Navigate to Cost Explorer by opening the AWS Management Console -> Click your username in the top right corner -> click **My Billing Dashboard** -> click **Cost Explorer in the left pane**. or [click here] (https://console.aws.amazon.com/billing/home#/costexplorer) for a direct link. +2. We know that we gave our EMR cluster a unique Name tag, so let's filter according to it. In the right pane, click Tags -> Name -> enter "**EMRTransientCluster1**" +3. Instead of the default 45 days view, let's narrow down the time span to just the day when we ran the cluster. In the data selection dropdown, mark that day as start and end. +4. You are now looking at the total cost to run the cluster (**$0.30**), including: EMR, EC2, EBS, and possible AWS Cross-Region data transfer costs, depending on where you ran your cluster relative to where the S3 dataset is located (in N. Virginia). 5. Group by **Usage Type** to get a breakdown of the costs ![costexplorer](/images/running-emr-spark-apps-on-spot/costexplorer1.png) -* EU-SpotUsage:r5.xlarge: This was the instance type that ran in the EMR Task Instance fleet and accrued the largest cost, since EMR launched 10 instances ($0.17)\ -* EU-BoxUsage:r5.xlarge: The EMR costs. [Click here] (https://aws.amazon.com/emr/pricing/) to learn more about EMR pricing. ($0.06)\ -* EU-EBS:VolumeUsage.gp2: EBS volumes that were attached to my EC2 Instances in the cluster - these got tagged automatically. ($0.03)\ -* EU-SpotUsage:r5a.xlarge & EU-SpotUsage:m4.xlarge: EC2 Spot price for the other instances in my cluster (Master and Core) ($0.02 combined)\ +* EU-SpotUsage:r5.xlarge: This was the instance type that ran in the EMR Task Instance fleet and accrued the largest cost, since EMR launched 10 instances ($0.17) +* EU-BoxUsage:r5.xlarge: The EMR costs. [Click here] (https://aws.amazon.com/emr/pricing/) to learn more about EMR pricing. ($0.06) +* EU-EBS:VolumeUsage.gp2: EBS volumes that were attached to my EC2 Instances in the cluster - these got tagged automatically. ($0.03) +* EU-SpotUsage:r5a.xlarge & EU-SpotUsage:m4.xlarge: EC2 Spot price for the other instances in my cluster (Master and Core) ($0.02 combined) If you have access to Cost Explorer, have a look around and see what you can find by slicing and dicing with filtering and grouping. For example, what happens if you filter by **Purchase Option = Spot** & **Group by = Instance Type**? diff --git a/content/running_spark_apps_with_emr_on_spot_instances/automations_monitoring.md b/content/running_spark_apps_with_emr_on_spot_instances/automations_monitoring.md index b29875d8..3b7acc78 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/automations_monitoring.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/automations_monitoring.md @@ -3,7 +3,7 @@ title: "Automations and monitoring" weight: 110 --- -When adopting EMR into your analytics flows and data processing pipelines, you will want to launch EMR clusters and run jobs in a programmatic manner. There are many ways to do so with AWS SDKs that can run in different environments like Lambda Functions, invoked by AWS Data Pipeline or AWS Step Functions, with third party tools like Apache Airflow, and more. \ +When adopting EMR into your analytics flows and data processing pipelines, you will want to launch EMR clusters and run jobs in a programmatic manner. There are many ways to do so with AWS SDKs that can run in different environments like Lambda Functions, invoked by AWS Data Pipeline or AWS Step Functions, with third party tools like Apache Airflow, and more. #### (Optional) Examine the JSON configuration for EMR Instance Fleets In this section we will simply look at a CLI command that can be used to start an identical cluster to the one we started from the console. This makes it easy to configure your EMR clusters with the AWS Management Console and get a CLI runnable command with one click. @@ -16,12 +16,12 @@ In this section we will simply look at a CLI command that can be used to start a #### (Optional) Set up CloudWatch Events for Cluster and/or Step failures Much like we set up a CloudWatch Event rule for EC2 Spot Interruptions to be sent to our email via an SNS notification, we can also set up rules to send out notifications or perform automations when an EMR cluster fails to start, or a Task on the cluster fails. This is useful for monitoring purposes. -In this example, let's set up a notification for when our EMR step failed.\ -1. In the AWS Management Console, go to Cloudwatch -> Events -> Rules and click **Create Rule**.\ -2. Under Service Name select EMR, and under Event Type select **State Change**.\ -3. Check **Specific detail type(s)** and from the dropdown menu, select **EMR Step Status Change**\ -4. Check **Specific states(s)** and from the dropdown menu, select **FAILED**.\ +In this example, let's set up a notification for when our EMR step failed. +1. In the AWS Management Console, go to Cloudwatch -> Events -> Rules and click **Create Rule**. +2. Under Service Name select EMR, and under Event Type select **State Change**. +3. Check **Specific detail type(s)** and from the dropdown menu, select **EMR Step Status Change** +4. Check **Specific states(s)** and from the dropdown menu, select **FAILED**. ![cwemrstep](/images/running-emr-spark-apps-on-spot/emrstatechangecwevent.png) -5. In the targets menu, click **Add target**, select **SNS topic** and from the dropdown menu, select the SNS topic you created and click **Configure details**.\ -6. Provide a name for the rule and click **Create rule**.\ +5. In the targets menu, click **Add target**, select **SNS topic** and from the dropdown menu, select the SNS topic you created and click **Configure details**. +6. Provide a name for the rule and click **Create rule**. 7. You can test that the rule works by following the same steps to start a cluster, but providing a bad parameter when submitting the step, for example - a non existing location for the Spark application or results bucket. \ No newline at end of file diff --git a/content/running_spark_apps_with_emr_on_spot_instances/conclusions_and_cleanup.md b/content/running_spark_apps_with_emr_on_spot_instances/conclusions_and_cleanup.md index 5559b946..de83359f 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/conclusions_and_cleanup.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/conclusions_and_cleanup.md @@ -17,10 +17,10 @@ Select the correct tab, depending on where you are running the workshop: #### Thank you -We hope you found this workshop educational, and that it will help you adopt Spot Instances into your Spark applications running on Amazon EMR, in order to optimize your costs.\ +We hope you found this workshop educational, and that it will help you adopt Spot Instances into your Spark applications running on Amazon EMR, in order to optimize your costs. If you have any feedback or questions, click the "**Feedback / Questions?**" link in the left pane to reach out to the authors of the workshop. #### Other Resources: -Visit the [**Amazon EMR on EC2 Spot Instances**] (https://aws.amazon.com/ec2/spot/use-case/emr/) page for more information, customer case studies and videos. \ -Read the blog post: [**Best practices for running Apache Spark applications using Amazon EC2 Spot Instances with Amazon EMR**] (https://aws.amazon.com/blogs/big-data/best-practices-for-running-apache-spark-applications-using-amazon-ec2-spot-instances-with-amazon-emr/) \ +Visit the [**Amazon EMR on EC2 Spot Instances**] (https://aws.amazon.com/ec2/spot/use-case/emr/) page for more information, customer case studies and videos. +Read the blog post: [**Best practices for running Apache Spark applications using Amazon EC2 Spot Instances with Amazon EMR**] (https://aws.amazon.com/blogs/big-data/best-practices-for-running-apache-spark-applications-using-amazon-ec2-spot-instances-with-amazon-emr/) Watch the AWS Online Tech-Talk: [**Best Practices for Running Spark Applications Using Spot Instances on EMR - AWS Online Tech Talks**] (https://www.youtube.com/watch?v=u5dFozl1fW8) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md b/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md index 074654cd..d2484d0e 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md @@ -5,7 +5,7 @@ weight: 30 When adopting Spot Instances into your workload, it is recommended to be flexible around how to launch your workload in terms of Availability Zone and Instance Types. This is in order to be able to achieve the required scale from multiple Spot capacity pools (a combination of EC2 instance type in an availability zone) or one capacity pool which has sufficient capacity, as well as decrease the impact on your workload in case some of the Spot capacity is interrupted with a 2-minute notice when EC2 needs the capacity back, and allow EMR to replenish the capacity with a different instance type. -With EMR instance fleets, you specify target capacities for On-Demand Instances and Spot Instances within each fleet (Master, Core, Task). When the cluster launches, Amazon EMR provisions instances until the targets are fulfilled. You can specify up to five EC2 instance types per fleet for Amazon EMR to use when fulfilling the targets. You can also select multiple subnets for different Availability Zones.\ +With EMR instance fleets, you specify target capacities for On-Demand Instances and Spot Instances within each fleet (Master, Core, Task). When the cluster launches, Amazon EMR provisions instances until the targets are fulfilled. You can specify up to five EC2 instance types per fleet for Amazon EMR to use when fulfilling the targets. You can also select multiple subnets for different Availability Zones. {{% notice info %}} [Click here] (https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-instance-fleet.html) to learn more about EMR Instance Fleets in the official documentation. @@ -15,15 +15,14 @@ While a cluster is running, if Amazon EC2 reclaims a Spot Instance or if an inst These options do not exist within the default EMR configuration option "Uniform Instance Groups", hence we will be using EMR Instance Fleets only. -As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version **5.12.1 and later**. With allocation strategy: +As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version **5.12.1 and later**. With allocation strategy: * On-Demand instances use a lowest-price strategy, which launches the lowest-priced instances first. -* Spot instances use a [capacity-optimized] (https://aws.amazon.com/about-aws/whats-new/2020/06/amazon-emr-uses-real-time-capacity-insights-to-provision-spot-instances-to-lower-cost-and-interruption/) allocation strategy, which allocates instances from most-available Spot Instance pools and lowers the chance of interruptions. This allocation strategy is appropriate for workloads that have a higher cost of interruption such as persistent EMR clusters running Apache Spark, Apache Hive, and Presto. +* Spot instances use a [capacity-optimized] (https://aws.amazon.com/about-aws/whats-new/2020/06/amazon-emr-uses-real-time-capacity-insights-to-provision-spot-instances-to-lower-cost-and-interruption/) allocation strategy, which allocates instances from most-available Spot Instance pools and lowers the chance of interruptions. This allocation strategy is appropriate for workloads that have a higher cost of interruption such as persistent EMR clusters running Apache Spark, Apache Hive, and Presto. {{% notice note %}} This allocation strategy option also lets you specify **up to 15 EC2 instance types on task instance fleet**. By default, Amazon EMR allows a maximum of 5 instance types for each type of instance fleet. By enabling allocation strategy, you can diversify your Spot request for task instance fleet across 15 instance pools. With more instance type diversification, Amazon EMR has more capacity pools to allocate capacity from, this allows you to get more compute capacity. {{% /notice %}} - {{% notice info %}} [Click here] (https://aws.amazon.com/blogs/big-data/optimizing-amazon-emr-for-resilience-and-cost-with-capacity-optimized-spot-instances/) for an in-depth blog post about capacity-optimized allocation strategy for Amazon EMR instance fleets. {{% /notice %}} \ No newline at end of file diff --git a/content/running_spark_apps_with_emr_on_spot_instances/examining_cluster.md b/content/running_spark_apps_with_emr_on_spot_instances/examining_cluster.md index 7a6ea4eb..b4405e36 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/examining_cluster.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/examining_cluster.md @@ -6,8 +6,8 @@ weight: 95 In this section we will look at the utilization of our EC2 Spot Instances while the application is running, and examine how many Spark executors are running. ### EMR Management Console -To get started, let's check that your EMR cluster and Spark application are running.\ -1. In our EMR Cluster page, the status of the cluster will either be Starting (in which case you can see the status of the hardware in the Summary or Hardware tabs) or Running.\ +To get started, let's check that your EMR cluster and Spark application are running. +1. In our EMR Cluster page, the status of the cluster will either be Starting (in which case you can see the status of the hardware in the Summary or Hardware tabs) or Running. 2. Move to the Steps tab, and your Spark application will either be Pending (for the cluster to start) or Running. {{% notice note %}} @@ -15,56 +15,56 @@ In this step, when you look at the utilization of the EMR cluster, do not expect {{% /notice %}} ### Using Ganglia, YARN ResourceManager and Spark History Server -The recommended approach to connect to the web interfaces running on our EMR cluster is to use SSH tunneling. [Click here] (https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-web-interfaces.html) to learn more about connecting to EMR interfaces.\ +The recommended approach to connect to the web interfaces running on our EMR cluster is to use SSH tunneling. [Click here] (https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-web-interfaces.html) to learn more about connecting to EMR interfaces. For the purpose of this workshop, and since we started our EMR cluster in a VPC public subnet, we can allow access in the EC2 Security Group in order to reach the TCP ports on which the web interfaces are listening on. {{% notice warning %}} Normally you would not run EMR in a public subnet and open TCP access to the master instance, this faster approach is just used for the purpose of the workshop. {{% /notice %}} -To allow access to your IP address to reach the EMR web interfaces via EC2 Security Groups:\ -1. In your EMR cluster page, in the AWS Management Console, go to the **Summary** tab\ -2. Click on the ID of the security under **Security groups for Master**\ -3. Check the Security Group with the name **ElasticMapReduce-master**\ -4. In the lower pane, click the **Inbound tab** and click the **Edit**\ -5. Click **Add Rule**. Under Type, select **All Traffic**, under Source, select **My IP**\ +To allow access to your IP address to reach the EMR web interfaces via EC2 Security Groups: +1. In your EMR cluster page, in the AWS Management Console, go to the **Summary** tab +2. Click on the ID of the security under **Security groups for Master** +3. Check the Security Group with the name **ElasticMapReduce-master** +4. In the lower pane, click the **Inbound tab** and click the **Edit** +5. Click **Add Rule**. Under Type, select **All Traffic**, under Source, select **My IP** 6. Click **Save** {{% notice note %}} While the Ganglia web interface uses TCP port 80, the YARN ResourceManager web interface uses TCP port 8088 and the Spark History Server uses TCP port 18080, which might not allowed for outbound traffic on your Internet connection. If you are using a network connection that blocks these ports (or in other words, doesn't allow non-well known ports) then you will not be able to reach the YARN ResourceManager web interface and Spark History Server. You can either skip that part of the workshop, use a different Internet connection (i.e mobile hotspot) or consider using the more complex method of SSH tunneling described [here] (https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-web-interfaces.html) {{% /notice %}} -Go back to the Summary tab in your EMR cluster page, and you will see links to tools in the **Connections** section (you might need to refresh the page).\ -1. Click **Ganglia** to open the web interface.\ -2. Have a look around. Take notice of the heatmap (**Server Load Distribution**). Notable graphs are:\ -* **Cluster CPU last hour** - this will show you the CPU utilization that our Spark application consumed on our EMR cluster. you should see that utilization varied and reached around 70%.\ -* **Cluster Memory last hour** - this will show you how much memory we started the cluster with, and how much Spark actually consumed.\ -3. Go back to the Summary page and click the **Resource Manager** link.\ -4. On the left pane, click **Nodes**, and in the node table, you should see the number of containers that each node ran.\ -5. Go back to the Summary page and click the **Spark History Server** link.\ -6. Click on the App ID in the table (where App Name = Amazon reviews word count) and go to the **Executors** tab\ +Go back to the Summary tab in your EMR cluster page, and you will see links to tools in the **Connections** section (you might need to refresh the page). +1. Click **Ganglia** to open the web interface. +2. Have a look around. Take notice of the heatmap (**Server Load Distribution**). Notable graphs are: +* **Cluster CPU last hour** - this will show you the CPU utilization that our Spark application consumed on our EMR cluster. you should see that utilization varied and reached around 70%. +* **Cluster Memory last hour** - this will show you how much memory we started the cluster with, and how much Spark actually consumed. +3. Go back to the Summary page and click the **Resource Manager** link. +4. On the left pane, click **Nodes**, and in the node table, you should see the number of containers that each node ran. +5. Go back to the Summary page and click the **Spark History Server** link. +6. Click on the App ID in the table (where App Name = Amazon reviews word count) and go to the **Executors** tab 7. You can again see the number of executors that are running in your EMR cluster under the **Executors table** ### Using CloudWatch Metrics -EMR emits several useful metrics to CloudWatch metrics. You can use the AWS Management Console to look at the metrics in two ways:\ -1. In the EMR console, under the Monitoring tab in your Cluster's page\ +EMR emits several useful metrics to CloudWatch metrics. You can use the AWS Management Console to look at the metrics in two ways: +1. In the EMR console, under the Monitoring tab in your Cluster's page 2. By browsing to the CloudWatch service, and under Metrics, searching for the name of your cluster (copy it from the EMR Management Console) and clicking **EMR > Job Flow Metrics** {{% notice note %}} The metrics will take a few minutes to populate. {{% /notice %}} -Some notable metrics:\ -* **AppsRunning** - you should see 1 since we only submitted one step to the cluster.\ -* **ContainerAllocated** - this represents the number of containers that are running on your cluster, on the Core and Task Instance Fleets. These would the be Spark executors and the Spark Driver.\ -* **MemoryAllocatedMB** & **MemoryAvailableMB** - you can graph them both to see how much memory the cluster is actually consuming for the wordcount Spark application out of the memory that the instances have.\ +Some notable metrics: +* **AppsRunning** - you should see 1 since we only submitted one step to the cluster. +* **ContainerAllocated** - this represents the number of containers that are running on your cluster, on the Core and Task Instance Fleets. These would the be Spark executors and the Spark Driver. +* **MemoryAllocatedMB** & **MemoryAvailableMB** - you can graph them both to see how much memory the cluster is actually consuming for the wordcount Spark application out of the memory that the instances have. #### Terminate the cluster When you are done examining the cluster and using the different UIs, terminate the EMR cluster from the EMR management console. This is not the end of the workshop though - we still have some interesting steps to go. #### Number of executors in the cluster -With 32 Spot Units in the Task Instance Fleet, EMR launched either 8 * xlarge (running one executor) or 4 * 2xlarge instances (running 2 executors) or 2 * 4xlarge instances (running 4 executors), so the Task Instance Fleet provides 8 executors / containers to the cluster.\ +With 32 Spot Units in the Task Instance Fleet, EMR launched either 8 * xlarge (running one executor) or 4 * 2xlarge instances (running 2 executors) or 2 * 4xlarge instances (running 4 executors), so the Task Instance Fleet provides 8 executors / containers to the cluster. The Core Instance Fleet launched one xlarge instance, able to run one executor. {{%expand "Question: Did you see more than 9 containers in CloudWatch Metrics and in YARN ResourceManager? if so, do you know why? Click to expand the answer" %}} Your Spark application was configured to run in Cluster mode, meaning that the **Spark driver is running on the Core node**. Since it is counted as a container, this adds a container to our count, but it is not an executor. diff --git a/content/running_spark_apps_with_emr_on_spot_instances/initial_event.md b/content/running_spark_apps_with_emr_on_spot_instances/initial_event.md index d128143b..bccfca7f 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/initial_event.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/initial_event.md @@ -5,7 +5,7 @@ disableToc: true hidden: true --- -Create an S3 bucket - we will use this for our Spark application code (which will be provided later) and the Spark application's results.\ +Create an S3 bucket - we will use this for our Spark application code (which will be provided later) and the Spark application's results. Refer to the **Create a Bucket** page in the [Amazon S3 Getting Started Guide] (https://docs.aws.amazon.com/AmazonS3/latest/gsg/CreatingABucket.html) You don't need to create a VPC, as the workshop account already has a default VPC that we will use in this workshop. \ No newline at end of file diff --git a/content/running_spark_apps_with_emr_on_spot_instances/initial_ownaccount.md b/content/running_spark_apps_with_emr_on_spot_instances/initial_ownaccount.md index dd67c12f..370831b6 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/initial_ownaccount.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/initial_ownaccount.md @@ -5,15 +5,15 @@ disableToc: true hidden: true --- -1. Create an S3 bucket - we will use this for our Spark application code (which will be provided later) and the Spark application's results.\ +1. Create an S3 bucket - we will use this for our Spark application code (which will be provided later) and the Spark application's results. Refer to the **Create a Bucket** page in the [Amazon S3 Getting Started Guide] (https://docs.aws.amazon.com/AmazonS3/latest/gsg/CreatingABucket.html) -2. Deploy a new VPC that will be used to run your EMR cluster in the workshop.\ -a. Open the ["Modular and Scalable VPC Architecture Quick stage page"] (https://aws.amazon.com/quickstart/architecture/vpc/) and go to the "How to deploy" tab, Click the ["Launch the Quick Start"] (https://fwd.aws/mm853) link.\ -b. Select your desired region to run the workshop from the top right corner of the AWS Management Console and click **Next**.\ -c. Provide a name for the stack or leave it as **Quick-Start-VPC**.\ -d. Under **Availability Zones**, select three availability zones from the list, and set the **Number of Availability Zones** to **3**.\ -e. Under **Create private subnets** select **false**.\ -f. click **Next** and again **Next** in the next screen.\ -g. Click **Create stack**.\ +2. Deploy a new VPC that will be used to run your EMR cluster in the workshop. +a. Open the ["Modular and Scalable VPC Architecture Quick stage page"] (https://aws.amazon.com/quickstart/architecture/vpc/) and go to the "How to deploy" tab, Click the ["Launch the Quick Start"] (https://fwd.aws/mm853) link. +b. Select your desired region to run the workshop from the top right corner of the AWS Management Console and click **Next**. +c. Provide a name for the stack or leave it as **Quick-Start-VPC**. +d. Under **Availability Zones**, select three availability zones from the list, and set the **Number of Availability Zones** to **3**. +e. Under **Create private subnets** select **false**. +f. click **Next** and again **Next** in the next screen. +g. Click **Create stack**. The stack creation should take under 2 minutes and the status of the stack will be **CREATE_COMPLETE**. \ No newline at end of file diff --git a/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-1.md b/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-1.md index fe0f4cff..3871810c 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-1.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-1.md @@ -8,15 +8,15 @@ In this step we'll launch our first cluster, which will run solely on Spot Insta Normally our dataset on S3 would be located in the same region where we are going to run our EMR clusters. In this workshop, it is fine if you are running EMR in a different region, and the Spark application will work against the dataset which is located in the N. Virginia region. This will be negligible in terms of price and performance. {{% /notice %}} -To launch the cluster, follow these steps:\ +To launch the cluster, follow these steps: -1. [Open the EMR console] (https://console.aws.amazon.com/elasticmapreduce/home) in the region where you are looking to launch your cluster.\ -1. Click "**Create Cluster**"\ -1. Click "**Go to advanced options**"\ -1. Select the latest EMR release, and in the list of components, only leave **Hadoop** checked and also check **Spark** and **Ganglia** (we will use it later to monitor our cluster)\ -1. Under "**Steps (Optional)**" -> Step type drop down menu, select "**Spark application**" and click **Add step**, then add the following details in the Add step dialog window:\ +1. [Open the EMR console] (https://console.aws.amazon.com/elasticmapreduce/home) in the region where you are looking to launch your cluster. +1. Click "**Create Cluster**" +1. Click "**Go to advanced options**" +1. Select the latest EMR release, and in the list of components, only leave **Hadoop** checked and also check **Spark** and **Ganglia** (we will use it later to monitor our cluster) +1. Under "**Steps (Optional)**" -> Step type drop down menu, select "**Spark application**" and click **Add step**, then add the following details in the Add step dialog window: -* **Spark-submit options**: here we will configure the memory and core count for each executor, as described in the previous section. Use these settings (make sure you have two '-' chars):\ +* **Spark-submit options**: here we will configure the memory and core count for each executor, as described in the previous section. Use these settings (make sure you have two '-' chars): ``` --executor-memory 18G --executor-cores 4 ``` @@ -35,7 +35,7 @@ exit() Then add the location of the file under the **Application location** field, i.e: s3://\/script.py -* **Arguments**: Here we will configure the location of where Spark will write the results of the job. Enter: s3://\/results/\ +* **Arguments**: Here we will configure the location of where Spark will write the results of the job. Enter: s3://\/results/ * **Action on failure**: Leave this on *Continue* and click **Add** to save the step. ![sparksubmit](/images/running-emr-spark-apps-on-spot/sparksubmitstep1.png) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md b/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md index b414db1d..e3f8b807 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-2.md @@ -13,21 +13,21 @@ The workshop focuses on running Spot Instances across all the cluster node types {{% /notice %}} #### **Master node**: -Unless your cluster is very short-lived and the runs are cost-driven, avoid running your Master node on a Spot Instance. We suggest this because a Spot interruption on the Master node terminates the entire cluster. \ -For the purpose of this workshop, we will run the Master node on a Spot Instance as we simulate a relatively short lived job running on a transient cluster. There will not be business impact if the job fails due to a Spot interruption and later re-started.\ +Unless your cluster is very short-lived and the runs are cost-driven, avoid running your Master node on a Spot Instance. We suggest this because a Spot interruption on the Master node terminates the entire cluster. +For the purpose of this workshop, we will run the Master node on a Spot Instance as we simulate a relatively short lived job running on a transient cluster. There will not be business impact if the job fails due to a Spot interruption and later re-started. Click **Add / remove instance types to fleet** and select two relatively cheaper instance types - i.e c5.xlarge and m5.xlarge and check Spot under target capacity. EMR will only provision one instance, but will select the best instance type for the Master node from the Spot instance pools with the optimal capacity. ![FleetSelection1](/images/running-emr-spark-apps-on-spot/emrinstancefleets-master.png) #### **Core Instance Fleet**: -Avoid using Spot Instances for Core nodes if your Spark applications use HDFS. That prevents a situation where Spot interruptions cause data loss for data that was written to the HDFS volumes on the instances. For short-lived applications on transient clusters, as is the case in this workshop, we are going to run our Core nodes on Spot Instances.\ -When using EMR Instance Fleets, one Core node is mandatory. Since we want to scale out and run our Spark application on our Task nodes, let's stick to the one mandatory Core node. We will specify **4 Spot units**, and select instance types that count as 4 units and will allow to run one executor.\ -Under the core node type, click **Add / remove instance types to fleet** and select instance types that you noted before as suitable to run an executor (given the 18G executor size), for example: +Avoid using Spot Instances for Core nodes if your Spark applications use HDFS. That prevents a situation where Spot interruptions cause data loss for data that was written to the HDFS volumes on the instances. For short-lived applications on transient clusters, as is the case in this workshop, we are going to run our Core nodes on Spot Instances. +When using EMR Instance Fleets, one Core node is mandatory. Since we want to scale out and run our Spark application on our Task nodes, let's stick to the one mandatory Core node. We will specify **4 Spot units**, and select instance types that count as 4 units and will allow to run one executor. +Under the core node type, click **Add / remove instance types to fleet** and select instance types that you noted before as suitable to run an executor (given the 18G executor size), for example: ![FleetSelection2](/images/running-emr-spark-apps-on-spot/emrinstancefleets-core1.png) #### **Task Instance Fleet**: Our task nodes will only run Spark executors and no HDFS DataNodes, so this is a great fit for scaling out and increasing the parallelization of our application's execution, to achieve faster execution times. -Under the task node type, click **Add / remove instance types to fleet** and select **up to 15 instance types** you noted before as suitable for your executor size.\ +Under the task node type, click **Add / remove instance types to fleet** and select **up to 15 instance types** you noted before as suitable for your executor size. Since our executor size is 4 vCPUs, and each instance counts as the number of its vCPUs towards the total units, let's specify **32 Spot units** in order to run 8 executors, and allow EMR to select the best instance type in the Task Instance Fleet to run the executors on. ![FleetSelection3](/images/running-emr-spark-apps-on-spot/emrinstancefleets-task2.png) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-3.md b/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-3.md index f5e4d283..32c77804 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-3.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/launching_emr_cluster-3.md @@ -3,9 +3,9 @@ title: "Launch a cluster - Steps 3&4" weight: 80 --- -Under "**Tags**", tag your instance with a recognizable Name tag so that you'll be able to see it later in the cost reports. For example:\ -Key=Name\ -Value (Optional)=EMRTransientCluster1\ +Under "**Tags**", tag your instance with a recognizable Name tag so that you'll be able to see it later in the cost reports. For example: +Key=Name +Value (Optional)=EMRTransientCluster1 ![tags](/images/running-emr-spark-apps-on-spot/emrtags.png) Click **Next** to go to **Step 4: Security** diff --git a/content/running_spark_apps_with_emr_on_spot_instances/prerequisites_notes.md b/content/running_spark_apps_with_emr_on_spot_instances/prerequisites_notes.md index a7fced03..e380937b 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/prerequisites_notes.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/prerequisites_notes.md @@ -2,13 +2,13 @@ title: "Prerequisites and initial steps" weight: 10 --- -#### General requirements and notes:\ +#### General requirements and notes: 1. This workshop is self-paced. The instructions will walk you through achieving the workshop's goal using the AWS Management Console. 2. While the workshop provides step by step instructions, **please do take a moment to look around and understand what is happening at each step** as this will enhance your learning experience. The workshop is meant as a getting started guide, but you will learn the most by digesting each of the steps and thinking about how they would apply in your own environment and in your own organization. You can even consider experimenting with the steps to challenge yourself. -#### Preparation steps:\ +#### Preparation steps: Select the correct tab, depending on where you are running the workshop: {{< tabs name="EventorOwnAccount" >}} {{< tab name="In your own account" include="initial_ownaccount.md" />}} diff --git a/content/running_spark_apps_with_emr_on_spot_instances/right_sizing_executors.md b/content/running_spark_apps_with_emr_on_spot_instances/right_sizing_executors.md index b634d257..46a7beb7 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/right_sizing_executors.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/right_sizing_executors.md @@ -18,10 +18,10 @@ If we keep approximately the same vCPU:Mem ratio (1:4.5) for our job and avoid g EMR by default places limits on executor sizes in two different ways, this is in order to avoid having the executor consume too much memory and interfere with the operating system and other processes running on the instance. 1. [for each instance type differently] (https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-hadoop-task-config.html#emr-hadoop-task-jvm) in the default YARN configuration options. -Let's have a look at a few examples of instances that have our approximate vCPU:Mem ratio:\ -r4.xlarge: yarn.scheduler.maximum-allocation-mb 23424\ -r4.2xlarge: yarn.scheduler.maximum-allocation-mb 54272\ -r5.xlarge: yarn.scheduler.maximum-allocation-mb 24576\ +Let's have a look at a few examples of instances that have our approximate vCPU:Mem ratio: +r4.xlarge: yarn.scheduler.maximum-allocation-mb 23424 +r4.2xlarge: yarn.scheduler.maximum-allocation-mb 54272 +r5.xlarge: yarn.scheduler.maximum-allocation-mb 24576 2. With the Spark on YARN configuration option which was [introduced in EMR version 5.22] (https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-whatsnew-history.html#emr-5220-whatsnew): spark.yarn.executor.memoryOverheadFactor and defaults to 0.1875 (18.75% of the spark.yarn.executor.memoryOverhead setting ) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/simulating_recovery.md b/content/running_spark_apps_with_emr_on_spot_instances/simulating_recovery.md index 70ada879..fa6fc8be 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/simulating_recovery.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/simulating_recovery.md @@ -15,7 +15,7 @@ This is an optional step that will take approximately 20 minutes more than the o [Click here] (https://aws.amazon.com/blogs/big-data/spark-enhancements-for-elasticity-and-resiliency-on-amazon-emr/) For an in-depth blog post about Spark's resiliency in EMR {{% /notice %}} -#### Step objectives:\ +#### Step objectives: 1. Observe that EMR replenishes the target capacity if some EC2 Instances failed, were terminated/stopped, or received an EC2 Spot Interruption. 2. Observe that the Spark application running in your EMR cluster still completes successfully, despite losing executors due to instance terminations. diff --git a/content/running_spark_apps_with_emr_on_spot_instances/tracking_spot_interruptions.md b/content/running_spark_apps_with_emr_on_spot_instances/tracking_spot_interruptions.md index eeb72d06..1c8b5d15 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/tracking_spot_interruptions.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/tracking_spot_interruptions.md @@ -13,7 +13,7 @@ In most cases, when running fault-tolerant workloads, we don't really need to tr Let's set up an email notification for when Spot interruptions occur, so if there are any failures in our EMR applications, we'll be able to check if the failures correlate to a Spot interruption. #### Creating an SNS topic for the notifications -1. Create a new SNS topic and subscribe to the topic with your email address\ +1. Create a new SNS topic and subscribe to the topic with your email address For guidance, you can follow steps #1 & #2 in the [Amazon SNS getting started guide] (https://docs.aws.amazon.com/sns/latest/dg/sns-getting-started.html) 1. You will receive an email with the subject "AWS Notification - Subscription Confirmation". Click the "**Confirm subscription**" link in the email in order to allow SNS to send email to the endpoint (your email). 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diff --git a/content/using_ec2_spot_instances_with_eks/jenkins/autoscaling_nodes.md b/content/using_ec2_spot_instances_with_eks/080_jenkins/autoscaling_nodes.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/jenkins/autoscaling_nodes.md rename to content/using_ec2_spot_instances_with_eks/080_jenkins/autoscaling_nodes.md diff --git a/content/using_ec2_spot_instances_with_eks/jenkins/increasing_resilience.md b/content/using_ec2_spot_instances_with_eks/080_jenkins/increasing_resilience.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/jenkins/increasing_resilience.md rename to content/using_ec2_spot_instances_with_eks/080_jenkins/increasing_resilience.md diff --git a/content/using_ec2_spot_instances_with_eks/jenkins/jenkins_cleanup.md b/content/using_ec2_spot_instances_with_eks/080_jenkins/jenkins_cleanup.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/jenkins/jenkins_cleanup.md rename to content/using_ec2_spot_instances_with_eks/080_jenkins/jenkins_cleanup.md diff --git a/content/using_ec2_spot_instances_with_eks/jenkins/running_jobs.md b/content/using_ec2_spot_instances_with_eks/080_jenkins/running_jobs.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/jenkins/running_jobs.md rename to content/using_ec2_spot_instances_with_eks/080_jenkins/running_jobs.md diff --git a/content/using_ec2_spot_instances_with_eks/jenkins/setup_agents.md b/content/using_ec2_spot_instances_with_eks/080_jenkins/setup_agents.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/jenkins/setup_agents.md rename to content/using_ec2_spot_instances_with_eks/080_jenkins/setup_agents.md diff --git a/content/using_ec2_spot_instances_with_eks/jenkins/setup_jenkins.md b/content/using_ec2_spot_instances_with_eks/080_jenkins/setup_jenkins.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/jenkins/setup_jenkins.md rename to content/using_ec2_spot_instances_with_eks/080_jenkins/setup_jenkins.md diff --git a/content/using_ec2_spot_instances_with_eks/conclusion/_index.md b/content/using_ec2_spot_instances_with_eks/200_conclusion/_index.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/conclusion/_index.md rename to content/using_ec2_spot_instances_with_eks/200_conclusion/_index.md diff --git a/content/using_ec2_spot_instances_with_eks/conclusion/conclusion.md b/content/using_ec2_spot_instances_with_eks/200_conclusion/conclusion.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/conclusion/conclusion.md rename to content/using_ec2_spot_instances_with_eks/200_conclusion/conclusion.md diff --git a/content/using_ec2_spot_instances_with_eks/cleanup.md b/content/using_ec2_spot_instances_with_eks/300_cleanup.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/cleanup.md rename to content/using_ec2_spot_instances_with_eks/300_cleanup.md From 8bc8533e6e953e0a523d03f8af071a4b4ecd20e3 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Tue, 5 Oct 2021 15:18:32 +0800 Subject: [PATCH 22/39] Matching Index to Folder Name --- .../{300_cleanup.md => 200_cleanup.md} | 2 +- .../{200_conclusion => 300_conclusion}/_index.md | 2 +- .../{200_conclusion => 300_conclusion}/conclusion.md | 0 3 files changed, 2 insertions(+), 2 deletions(-) rename content/using_ec2_spot_instances_with_eks/{300_cleanup.md => 200_cleanup.md} (99%) rename content/using_ec2_spot_instances_with_eks/{200_conclusion => 300_conclusion}/_index.md (98%) rename content/using_ec2_spot_instances_with_eks/{200_conclusion => 300_conclusion}/conclusion.md (100%) diff --git a/content/using_ec2_spot_instances_with_eks/300_cleanup.md b/content/using_ec2_spot_instances_with_eks/200_cleanup.md similarity index 99% rename from content/using_ec2_spot_instances_with_eks/300_cleanup.md rename to content/using_ec2_spot_instances_with_eks/200_cleanup.md index 71f18ea5..6be924bb 100644 --- a/content/using_ec2_spot_instances_with_eks/300_cleanup.md +++ b/content/using_ec2_spot_instances_with_eks/200_cleanup.md @@ -1,7 +1,7 @@ --- title: "Cleanup" date: 2018-08-07T08:30:11-07:00 -weight: 100 +weight: 200 --- {{% notice note %}} diff --git a/content/using_ec2_spot_instances_with_eks/200_conclusion/_index.md b/content/using_ec2_spot_instances_with_eks/300_conclusion/_index.md similarity index 98% rename from content/using_ec2_spot_instances_with_eks/200_conclusion/_index.md rename to content/using_ec2_spot_instances_with_eks/300_conclusion/_index.md index 9539b489..27f8a349 100644 --- a/content/using_ec2_spot_instances_with_eks/200_conclusion/_index.md +++ b/content/using_ec2_spot_instances_with_eks/300_conclusion/_index.md @@ -1,7 +1,7 @@ --- title: "Conclusion" chapter: true -weight: 110 +weight: 300 --- # Conclusion diff --git a/content/using_ec2_spot_instances_with_eks/200_conclusion/conclusion.md b/content/using_ec2_spot_instances_with_eks/300_conclusion/conclusion.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/200_conclusion/conclusion.md rename to content/using_ec2_spot_instances_with_eks/300_conclusion/conclusion.md From ec87edde049225fbdc6d40a4e8fc5fe33ea5fc8a Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Tue, 5 Oct 2021 15:29:41 +0800 Subject: [PATCH 23/39] Updaing folder names in all references --- .../005_introduction/_index.md | 2 +- .../005_introduction/basics/what_is_k8s.md | 2 +- .../005_introduction/eks/eks_control_plane.md | 2 +- .../005_introduction/eks/eks_customers.md | 2 +- .../005_introduction/eks/eks_high_architecture.md | 2 +- .../005_introduction/eks/eks_high_level.md | 2 +- .../010_prerequisites/_index.md | 6 +++--- .../010_prerequisites/at_an_aws_updateiam.md | 4 ++-- .../010_prerequisites/aws_event.md | 14 +++++++------- .../010_prerequisites/on_your_own_updateiam.md | 6 +++--- .../010_prerequisites/self_paced.md | 12 ++++++------ .../010_prerequisites/update_workspace_settings.md | 2 +- .../010_prerequisites/update_workspaceiam.md | 4 ++-- .../010_prerequisites/workspace.md | 4 ++-- .../010_prerequisites/workspace_at_launch.md | 4 ++-- .../020_eksctl/launcheks.md | 4 ++-- .../050_spotworkers/selecting_instance_types.md | 2 +- .../060_deploy/_index.md | 2 +- .../060_deploy/deploy.md | 2 +- .../070_scaling/deploy_ca.md | 2 +- .../070_scaling/test_ca.md | 8 ++++---- .../080_jenkins/setup_agents.md | 4 ++-- 22 files changed, 46 insertions(+), 46 deletions(-) diff --git a/content/using_ec2_spot_instances_with_eks/005_introduction/_index.md b/content/using_ec2_spot_instances_with_eks/005_introduction/_index.md index 71a66be8..2ea5dabd 100644 --- a/content/using_ec2_spot_instances_with_eks/005_introduction/_index.md +++ b/content/using_ec2_spot_instances_with_eks/005_introduction/_index.md @@ -9,7 +9,7 @@ draft: false A walkthrough of basic Kubernetes concepts. -![Title Image](/images/using_ec2_spot_instances_with_eks/introduction/eks-product-page.png) +![Title Image](/images/using_ec2_spot_instances_with_eks/005_introduction/eks-product-page.png) Welcome to the Using EC2 Spot Instances with EKS Workshop! The intent of this workshop is to educate users about how to use EC2 Spot instances on Amazon EKS. Background in EKS, Kubernetes, Docker, and container workflows are not required, but they are recommended. diff --git a/content/using_ec2_spot_instances_with_eks/005_introduction/basics/what_is_k8s.md b/content/using_ec2_spot_instances_with_eks/005_introduction/basics/what_is_k8s.md index 2020271b..171659ff 100644 --- a/content/using_ec2_spot_instances_with_eks/005_introduction/basics/what_is_k8s.md +++ b/content/using_ec2_spot_instances_with_eks/005_introduction/basics/what_is_k8s.md @@ -5,7 +5,7 @@ draft: false weight: 30 --- -![K8s summary image](/images/using_ec2_spot_instances_with_eks/introduction/what_is_k8s_new.png) +![K8s summary image](/images/using_ec2_spot_instances_with_eks/005_introduction/what_is_k8s_new.png) * Builds on over a decade of experience and best practices * Utilizes declarative configuration and automation diff --git a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_control_plane.md b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_control_plane.md index 5577170f..b69d2e03 100644 --- a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_control_plane.md +++ b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_control_plane.md @@ -6,4 +6,4 @@ weight: 140 --- -![EKS Control Plane](/images/using_ec2_spot_instances_with_eks/introduction/eks-k8s-control-plane.svg) +![EKS Control Plane](/images/using_ec2_spot_instances_with_eks/005_introduction/eks-k8s-control-plane.svg) diff --git a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_customers.md b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_customers.md index 72f08bd2..98702722 100644 --- a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_customers.md +++ b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_customers.md @@ -6,4 +6,4 @@ weight: 130 --- -![EKS Customers](/images/using_ec2_spot_instances_with_eks/introduction/eks-customers.svg) +![EKS Customers](/images/using_ec2_spot_instances_with_eks/005_introduction/eks-customers.svg) diff --git a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_architecture.md b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_architecture.md index be3cbf3e..6af0d612 100644 --- a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_architecture.md +++ b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_architecture.md @@ -6,4 +6,4 @@ weight: 150 --- -![EKS Architecture](/images/using_ec2_spot_instances_with_eks/introduction/eks-architecture.svg) +![EKS Architecture](/images/using_ec2_spot_instances_with_eks/005_introduction/eks-architecture.svg) diff --git a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_level.md b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_level.md index 1be57d14..9a672d2c 100644 --- a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_level.md +++ b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_level.md @@ -8,4 +8,4 @@ weight: 150 Once your EKS cluster is ready, you get an API endpoint and you'd [use Kubectl, community developed tool to interact with your cluster.](https://kubernetes.io/docs/reference/kubectl/kubectl/) -![EKS High Level](/images/using_ec2_spot_instances_with_eks/introduction/eks-high-level.svg) +![EKS High Level](/images/using_ec2_spot_instances_with_eks/005_introduction/eks-high-level.svg) diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/_index.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/_index.md index c6bae9b7..ed72eaac 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/_index.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/_index.md @@ -7,7 +7,7 @@ weight: 10 # Getting Started To start the workshop, follow one of the following depending on whether you are... -* ...[running the workshop on your own (in your own account)]({{< ref "/using_ec2_spot_instances_with_eks/prerequisites/self_paced.md" >}}), or -* ...[attending an AWS hosted event (using AWS provided hashes)]({{< ref "/using_ec2_spot_instances_with_eks/prerequisites/aws_event.md" >}}) +* ...[running the workshop on your own (in your own account)]({{< ref "/using_ec2_spot_instances_with_eks/010_prerequisites/self_paced.md" >}}), or +* ...[attending an AWS hosted event (using AWS provided hashes)]({{< ref "/using_ec2_spot_instances_with_eks/010_prerequisites/aws_event.md" >}}) -Once you have completed with either setup, continue with **[Create a Workspace]({{< ref "/using_ec2_spot_instances_with_eks/prerequisites/workspace.md" >}})** +Once you have completed with either setup, continue with **[Create a Workspace]({{< ref "/using_ec2_spot_instances_with_eks/010_prerequisites/workspace.md" >}})** diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/at_an_aws_updateiam.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/at_an_aws_updateiam.md index 3c6808cf..f0a8ecb5 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/at_an_aws_updateiam.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/at_an_aws_updateiam.md @@ -9,6 +9,6 @@ hidden: true 1. Follow this [deep link to find your Cloud9 EC2 instance](https://console.aws.amazon.com/ec2/v2/home?#Instances:tag:Name=aws-cloud9-eksworkshop;sort=desc:launchTime). 1. Select the instance, then choose **Actions / Security / Modify IAM role** -![c9instancerole](/images/using_ec2_spot_instances_with_eks/prerequisites/c9instancerole.png) +![c9instancerole](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9instancerole.png) 1. Choose **TeamRoleInstance** from the **IAM role** drop down, and select **Save** -![c9attachrole](/images/using_ec2_spot_instances_with_eks/prerequisites/c9attachroleee.png) +![c9attachrole](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9attachroleee.png) diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/aws_event.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/aws_event.md index 9d592d81..917dc36f 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/aws_event.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/aws_event.md @@ -9,7 +9,7 @@ weight: 20 {{% notice warning %}} Only complete this section if you are at an AWS hosted event (such as re:Invent, Kubecon, Immersion Day, or any other event hosted by an AWS employee). If you -are running the workshop on your own, go to: [Start the workshop on your own]({{< ref "/using_ec2_spot_instances_with_eks/prerequisites/self_paced.md" >}}). +are running the workshop on your own, go to: [Start the workshop on your own]({{< ref "/using_ec2_spot_instances_with_eks/010_prerequisites/self_paced.md" >}}). {{% /notice %}} ### Login to the AWS Workshop Portal @@ -42,7 +42,7 @@ We have deployed the below resources required to get started with the workshop u + An EKS cluster with the name `eksworkshop-eksctl` and a [EKS managed node group](https://docs.aws.amazon.com/eks/latest/userguide/managed-node-groups.html) with 2 on-demand instances. -{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/eksctl/create_eks_cluster_eksctl_command.md" %}} +{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md" %}} #### Use your resources @@ -53,7 +53,7 @@ In this workshop, you'll need to reference the resources created by the CloudFor 1. In the stack details pane, click the **Outputs** tab. -![cnf_output](/images/using_ec2_spot_instances_with_eks/prerequisites/cnf_output.png) +![cnf_output](/images/using_ec2_spot_instances_with_eks/010_prerequisites/cnf_output.png) It is recommended that you keep this window open so you can easily refer to the outputs and resources throughout the workshop. @@ -65,9 +65,9 @@ you will notice additional Cloudformation stacks were also deployed which is the - Click on the url against `Cloud9IDE` from the outputs -{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/prerequisites/workspace_at_launch.md" %}} +{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/010_prerequisites/workspace_at_launch.md" %}} -{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/prerequisites/update_workspace_settings.md" %}} +{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/010_prerequisites/update_workspace_settings.md" %}} ### Validate the IAM role {#validate_iam} @@ -78,10 +78,10 @@ aws sts get-caller-identity ``` -{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/prerequisites/at_an_aws_validaterole.md" %}} +{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/010_prerequisites/at_an_aws_validaterole.md" %}} {{% notice note %}} -Since we have already setup the prerequisites, **you can head straight to [Test the Cluster]({{< relref "/using_ec2_spot_instances_with_eks/eksctl/test.md" >}})** +Since we have already setup the prerequisites, **you can head straight to [Test the Cluster]({{< relref "/using_ec2_spot_instances_with_eks/020_eksctl/test.md" >}})** {{% /notice %}} {{% /expand%}} diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/on_your_own_updateiam.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/on_your_own_updateiam.md index 5afbf2a7..4b991f4f 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/on_your_own_updateiam.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/on_your_own_updateiam.md @@ -12,12 +12,12 @@ hidden: true 1. Confirm that **AdministratorAccess** is checked, then click **Next: Tags** to assign tags. 1. Take the defaults, and click **Next: Review** to review. 1. Enter **eksworkshop-admin** for the Name, and click **Create role**. -![createrole](/images/using_ec2_spot_instances_with_eks/prerequisites/createrole.png) +![createrole](/images/using_ec2_spot_instances_with_eks/010_prerequisites/createrole.png) ## Attach the IAM role to your Workspace 1. Follow this [deep link to find your Cloud9 EC2 instance](https://console.aws.amazon.com/ec2/v2/home?#Instances:tag:Name=aws-cloud9-eksworkshop;sort=desc:launchTime). 1. Select the instance, then choose **Actions / Security / Modify IAM role** -![c9instancerole](/images/using_ec2_spot_instances_with_eks/prerequisites/c9instancerole.png) +![c9instancerole](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9instancerole.png) 1. Choose **eksworkshop-admin** from the **IAM role** drop down, and select **Save** -![c9attachrole](/images/using_ec2_spot_instances_with_eks/prerequisites/c9attachrole.png) \ No newline at end of file +![c9attachrole](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9attachrole.png) \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/self_paced.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/self_paced.md index d1616205..d2ab95e8 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/self_paced.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/self_paced.md @@ -5,7 +5,7 @@ weight: 10 --- {{% notice warning %}} -Only complete this section if you are running the workshop on your own. If you are at an AWS hosted event (such as re:Invent, Kubecon, Immersion Day, etc), go to [Start the workshop at an AWS event]({{< ref "/using_ec2_spot_instances_with_eks/prerequisites/aws_event.md" >}}). +Only complete this section if you are running the workshop on your own. If you are at an AWS hosted event (such as re:Invent, Kubecon, Immersion Day, etc), go to [Start the workshop at an AWS event]({{< ref "/using_ec2_spot_instances_with_eks/010_prerequisites/aws_event.md" >}}). {{% /notice %}} ### Running the workshop on your own @@ -22,16 +22,16 @@ as an IAM user with administrator access to the AWS account: [Create a new IAM user to use for the workshop](https://console.aws.amazon.com/iam/home?#/users$new) 1. Enter the user details: -![Create User](/images/using_ec2_spot_instances_with_eks/prerequisites/iam-1-create-user.png) +![Create User](/images/using_ec2_spot_instances_with_eks/010_prerequisites/iam-1-create-user.png) 1. Attach the AdministratorAccess IAM Policy: -![Attach Policy](/images/using_ec2_spot_instances_with_eks/prerequisites/iam-2-attach-policy.png) +![Attach Policy](/images/using_ec2_spot_instances_with_eks/010_prerequisites/iam-2-attach-policy.png) 1. Click to create the new user: -![Confirm User](/images/using_ec2_spot_instances_with_eks/prerequisites/iam-3-create-user.png) +![Confirm User](/images/using_ec2_spot_instances_with_eks/010_prerequisites/iam-3-create-user.png) 1. Take note of the login URL and save: -![Login URL](/images/using_ec2_spot_instances_with_eks/prerequisites/iam-4-save-url.png) +![Login URL](/images/using_ec2_spot_instances_with_eks/010_prerequisites/iam-4-save-url.png) -Once you have completed the step above, **you can head straight to [Create a Workspace]({{< ref "/using_ec2_spot_instances_with_eks/prerequisites/workspace.md" >}})** \ No newline at end of file +Once you have completed the step above, **you can head straight to [Create a Workspace]({{< ref "/using_ec2_spot_instances_with_eks/010_prerequisites/workspace.md" >}})** \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/update_workspace_settings.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/update_workspace_settings.md index 1c886b7e..76217181 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/update_workspace_settings.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/update_workspace_settings.md @@ -18,7 +18,7 @@ the EKS IAM authentication, so we will disable it and rely on the IAM role inste - Select **AWS SETTINGS** - Turn off **AWS managed temporary credentials** - Close the Preferences tab -![c9disableiam](/images/using_ec2_spot_instances_with_eks/prerequisites/c9disableiam.png) +![c9disableiam](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9disableiam.png) To ensure temporary credentials aren't already in place we will also remove any existing credentials file: diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/update_workspaceiam.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/update_workspaceiam.md index ac0c0834..11fbdceb 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/update_workspaceiam.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/update_workspaceiam.md @@ -4,7 +4,7 @@ chapter: false weight: 60 --- -{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/prerequisites/update_workspace_settings.md" %}} +{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/010_prerequisites/update_workspace_settings.md" %}} We should configure our aws cli with our current region as default: ``` @@ -17,5 +17,5 @@ aws configure set default.region ${AWS_REGION} aws configure get default.region ``` -{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/prerequisites/validate_workspace_role.md" %}} +{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/010_prerequisites/validate_workspace_role.md" %}} diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace.md index 8c64c21a..5ad654aa 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace.md @@ -34,10 +34,10 @@ Cloud9 requires third-party-cookies. You can whitelist the [specific domains]( h - Name it **eksworkshop**, and take all other defaults - When it comes up, customize the environment by closing the **welcome tab** and **lower work area**, and opening a new **terminal** tab in the main work area: -![c9before](/images/using_ec2_spot_instances_with_eks/prerequisites/c9before.png) +![c9before](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9before.png) - Your workspace should now look like this: -![c9after](/images/using_ec2_spot_instances_with_eks/prerequisites/c9after.png) +![c9after](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9after.png) - If you like this theme, you can choose it yourself by selecting **View / Themes / Solarized / Solarized Dark** in the Cloud9 workspace menu. diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace_at_launch.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace_at_launch.md index bc8505d2..f5fb160c 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace_at_launch.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace_at_launch.md @@ -9,10 +9,10 @@ This markdown file is used as part of another file using 'insert-md-from-file' s --> - When it comes up, customize the environment by closing the **welcome tab** and **lower work area**, and opening a new **terminal** tab in the main work area: -![c9before](/images/using_ec2_spot_instances_with_eks/prerequisites/c9before.png) +![c9before](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9before.png) - Your workspace should now look like this: -![c9after](/images/using_ec2_spot_instances_with_eks/prerequisites/c9after.png) +![c9after](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9after.png) - If you like this theme, you can choose it yourself by selecting **View / Themes / Solarized / Solarized Dark** in the Cloud9 workspace menu. diff --git a/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md b/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md index 6abc101f..c6bf6f91 100644 --- a/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md +++ b/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md @@ -14,7 +14,7 @@ weight: 20 {{%expand "Expand here to see the solution" %}} -{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/prerequisites/validate_workspace_role.md" %}} +{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/010_prerequisites/validate_workspace_role.md" %}} If you do not see the correct role, please go back and **[validate the IAM role]({{< relref "../prerequisites/update_workspaceiam.md" >}})** for troubleshooting. @@ -26,7 +26,7 @@ If you do see the correct role, proceed to next step to create an EKS cluster. The following command will create an eks cluster with the name `eksworkshop-eksctl`. It will also create a nodegroup with 2 on-demand instances. -{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/eksctl/create_eks_cluster_eksctl_command.md" %}} +{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md" %}} eksctl allows us to pass parameters to initialize the cluster. While initializing the cluster, eksctl does also allow us to create nodegroups. diff --git a/content/using_ec2_spot_instances_with_eks/050_spotworkers/selecting_instance_types.md b/content/using_ec2_spot_instances_with_eks/050_spotworkers/selecting_instance_types.md index 430c2da5..590efa62 100644 --- a/content/using_ec2_spot_instances_with_eks/050_spotworkers/selecting_instance_types.md +++ b/content/using_ec2_spot_instances_with_eks/050_spotworkers/selecting_instance_types.md @@ -6,6 +6,6 @@ draft: false --- ### Selecting instances for Spot Instance Diversification -[See "Selecting Instance Types" under Spot managed node groups]({{< relref "/using_ec2_spot_instances_with_eks/spotmanagednodegroups/selecting_instance_types.md" >}}) +[See "Selecting Instance Types" under Spot managed node groups]({{< relref "/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/selecting_instance_types.md" >}}) Once you finish selecting instance types, proceed to "Adding Spot Workers with eksctl" section to continue creating Self Managed Spot node groups. diff --git a/content/using_ec2_spot_instances_with_eks/060_deploy/_index.md b/content/using_ec2_spot_instances_with_eks/060_deploy/_index.md index d9069851..fb09b9d9 100644 --- a/content/using_ec2_spot_instances_with_eks/060_deploy/_index.md +++ b/content/using_ec2_spot_instances_with_eks/060_deploy/_index.md @@ -10,4 +10,4 @@ To illustrate application scaling using [Horizontal Pod Autoscaler](https://kube The microservice we will use as an example, is a trivial web service that uses a [Monte Carlo method to approximate pi](https://en.wikipedia.org/wiki/Monte_Carlo_integration) written in go. You can find the application code in in [this github repo](https://github.com/ruecarlo/eks-workshop-sample-api-service-go) -![Monte Carlo Pi Approximation](/images/using_ec2_spot_instances_with_eks/deploy/monte_carlo_pi.png) \ No newline at end of file +![Monte Carlo Pi Approximation](/images/using_ec2_spot_instances_with_eks/060_deploy/monte_carlo_pi.png) \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/060_deploy/deploy.md b/content/using_ec2_spot_instances_with_eks/060_deploy/deploy.md index dc4abe0e..a3a3bdb9 100644 --- a/content/using_ec2_spot_instances_with_eks/060_deploy/deploy.md +++ b/content/using_ec2_spot_instances_with_eks/060_deploy/deploy.md @@ -51,7 +51,7 @@ monte-carlo-pi-service. When running that in your browser you should be able to see the json response provided by the service: -![Monte Carlo Pi Approximation Response](/images/using_ec2_spot_instances_with_eks/deploy/monte_carlo_pi_output_1.png) +![Monte Carlo Pi Approximation Response](/images/using_ec2_spot_instances_with_eks/060_deploy/monte_carlo_pi_output_1.png) {{% notice note %}} The application takes the named argument **iterations** to define the number of samples used during the diff --git a/content/using_ec2_spot_instances_with_eks/070_scaling/deploy_ca.md b/content/using_ec2_spot_instances_with_eks/070_scaling/deploy_ca.md index ea89714e..8bfa0733 100644 --- a/content/using_ec2_spot_instances_with_eks/070_scaling/deploy_ca.md +++ b/content/using_ec2_spot_instances_with_eks/070_scaling/deploy_ca.md @@ -26,7 +26,7 @@ We have provided a manifest file to deploy the CA. Copy the commands below into ``` mkdir -p ~/environment/cluster-autoscaler -curl -o ~/environment/cluster-autoscaler/cluster_autoscaler.yml https://raw.githubusercontent.com/awslabs/ec2-spot-workshops/master/content/using_ec2_spot_instances_with_eks/scaling/deploy_ca.files/cluster_autoscaler.yml +curl -o ~/environment/cluster-autoscaler/cluster_autoscaler.yml https://raw.githubusercontent.com/awslabs/ec2-spot-workshops/master/content/using_ec2_spot_instances_with_eks/070_scaling/deploy_ca.files/cluster_autoscaler.yml sed -i "s/--AWS_REGION--/${AWS_REGION}/g" ~/environment/cluster-autoscaler/cluster_autoscaler.yml ``` diff --git a/content/using_ec2_spot_instances_with_eks/070_scaling/test_ca.md b/content/using_ec2_spot_instances_with_eks/070_scaling/test_ca.md index a1a98dd4..d3a91112 100644 --- a/content/using_ec2_spot_instances_with_eks/070_scaling/test_ca.md +++ b/content/using_ec2_spot_instances_with_eks/070_scaling/test_ca.md @@ -89,7 +89,7 @@ monte-carlo-pi-service-584f6ddff-fs9x6 1/1 Running 0 103s monte-carlo-pi-service-584f6ddff-jst55 1/1 Running 0 103s ``` You should also be able to visualize the scaling action using kube-ops-view. Kube-ops-view provides an option to highlight pods meeting a regular expression. All pods in green are **monte-carlo-pi-service** pods. -![Scaling up to 10 replicas](/images/using_ec2_spot_instances_with_eks/scaling/scaling-kov-10-replicas.png) +![Scaling up to 10 replicas](/images/using_ec2_spot_instances_with_eks/070_scaling/scaling-kov-10-replicas.png) {{% notice info %}} Given we started from 2 node capacity in both Spot node groups, this should trigger a scaling event for Cluster Autoscaler. Can you predict which size (and type!) of node will be provided? @@ -118,14 +118,14 @@ kubectl get pods --watch ``` Kube-ops-view, will show 3 pending yellow pods outside the node. -![Scale Up](/images/using_ec2_spot_instances_with_eks/scaling/scaling-asg-up-kov.png) +![Scale Up](/images/using_ec2_spot_instances_with_eks/070_scaling/scaling-asg-up-kov.png) When inspecting cluster-autoscaler logs with the command line below ``` kubectl logs -f deployment/cluster-autoscaler -n kube-system ``` you will notice Cluster Autoscaler events similar to: -![CA Scale Up events](/images/using_ec2_spot_instances_with_eks/scaling/scaling-asg-up2.png) +![CA Scale Up events](/images/using_ec2_spot_instances_with_eks/070_scaling/scaling-asg-up2.png) To confirm which type of node has been added you can either use kube-ops-view or execute: @@ -135,7 +135,7 @@ kubectl get node --selector=intent=apps --show-labels You can verify in AWS Management Console to confirm that the Auto Scaling groups are scaling up to meet demand. This may take a few minutes. You can also follow along with the pod deployment from the command line. You should see the pods transition from pending to running as nodes are scaled up. -![Scale Up](/images/using_ec2_spot_instances_with_eks/scaling/scaling-asg-up.png) +![Scale Up](/images/using_ec2_spot_instances_with_eks/070_scaling/scaling-asg-up.png) {{% notice info %}} Cluster Autoscaler expands capacity according to the [Expander](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#what-are-expanders) configuration. By default, Cluster Autoscaler uses the **random** expander. This means that there is equal probability of cluster autoscaler selecting the 4vCPUs 16GB RAM group or the 8vCPUs 32GB RAM group. You may consider also using other expanders like **least-waste**, or the **priority** expander. diff --git a/content/using_ec2_spot_instances_with_eks/080_jenkins/setup_agents.md b/content/using_ec2_spot_instances_with_eks/080_jenkins/setup_agents.md index 7326c5d7..4a190901 100644 --- a/content/using_ec2_spot_instances_with_eks/080_jenkins/setup_agents.md +++ b/content/using_ec2_spot_instances_with_eks/080_jenkins/setup_agents.md @@ -58,9 +58,9 @@ The creation of the workers will take about 3 minutes. 1. On the left hand side click on the **Configure Clouds** link. That will take you to the cloud configuration where Kubernetes. 1. Click on the **Pod Templates...** button to expand the default pod template definition and then click again on **Pod Template Details...*** 1. Change the default pod name, attribute **Name** from `defualt` to `jenkins-agent`. We want to be able to identify the pods that are running in our clusters by name. -![Jenkins Pod Setup 1](/images/using_ec2_spot_instances_with_eks/jenkins/jenkinslabels-1.png) +![Jenkins Pod Setup 1](/images/using_ec2_spot_instances_with_eks/080_jenkins/jenkinslabels-1.png) 1. At the bottom of the page, near the end of the Pod template section, for the **Node Selector** parameter , add the following: `intent=jenkins-agents,lifecycle=Ec2Spot` in order to instruct the Jenkins agent pods to run on the dedicated node group. -![Jenkins Pod Setup 2](/images/using_ec2_spot_instances_with_eks/jenkins/jenkinslabels-2.png) +![Jenkins Pod Setup 2](/images/using_ec2_spot_instances_with_eks/080_jenkins/jenkinslabels-2.png) 1. Click **Save** Now, when Jenkins creates new pods (=agents), these will be created with a Node Selector that instructs the kube-scheduler to only deploy the pods on nodes with the above mentioned labels, which only exist in the dedicated Jenkins nodegroup. From c7fd59bb5c3f01f5a78449f9faf577df8e7ca735 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Tue, 5 Oct 2021 15:35:12 +0800 Subject: [PATCH 24/39] Matching folder names to references --- .../using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md b/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md index c6bf6f91..d3446e69 100644 --- a/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md +++ b/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md @@ -6,7 +6,7 @@ weight: 20 {{% notice warning %}} -**DO NOT PROCEED** with this step unless you have [validated the IAM role]({{< relref "../prerequisites/update_workspaceiam.md#validate_iam" >}}) in use by the Cloud9 IDE. You will not be able to run the necessary kubectl commands in the later modules unless the EKS cluster is built using the IAM role. +**DO NOT PROCEED** with this step unless you have [validated the IAM role]({{< relref "../010_prerequisites/update_workspaceiam.md#validate_iam" >}}) in use by the Cloud9 IDE. You will not be able to run the necessary kubectl commands in the later modules unless the EKS cluster is built using the IAM role. {{% /notice %}} #### Challenge: @@ -16,7 +16,7 @@ weight: 20 {{% insert-md-from-file file="using_ec2_spot_instances_with_eks/010_prerequisites/validate_workspace_role.md" %}} -If you do not see the correct role, please go back and **[validate the IAM role]({{< relref "../prerequisites/update_workspaceiam.md" >}})** for troubleshooting. +If you do not see the correct role, please go back and **[validate the IAM role]({{< relref "../010_prerequisites/update_workspaceiam.md" >}})** for troubleshooting. If you do see the correct role, proceed to next step to create an EKS cluster. {{% /expand %}} From 357855f5520abff1de3760dfb3a9551ac8062f42 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Tue, 5 Oct 2021 15:39:13 +0800 Subject: [PATCH 25/39] Updated HELM to K8S Tools --- .../{030_helm_root => 030_k8s_tools}/_index.md | 2 +- .../{030_helm_root => 030_k8s_tools}/deploy_metric_server.md | 0 .../{030_helm_root => 030_k8s_tools}/helm_deploy.md | 0 .../{030_helm_root => 030_k8s_tools}/install_kube_ops_view.md | 0 4 files changed, 1 insertion(+), 1 deletion(-) rename content/using_ec2_spot_instances_with_eks/{030_helm_root => 030_k8s_tools}/_index.md (97%) rename content/using_ec2_spot_instances_with_eks/{030_helm_root => 030_k8s_tools}/deploy_metric_server.md (100%) rename content/using_ec2_spot_instances_with_eks/{030_helm_root => 030_k8s_tools}/helm_deploy.md (100%) rename content/using_ec2_spot_instances_with_eks/{030_helm_root => 030_k8s_tools}/install_kube_ops_view.md (100%) diff --git a/content/using_ec2_spot_instances_with_eks/030_helm_root/_index.md b/content/using_ec2_spot_instances_with_eks/030_k8s_tools/_index.md similarity index 97% rename from content/using_ec2_spot_instances_with_eks/030_helm_root/_index.md rename to content/using_ec2_spot_instances_with_eks/030_k8s_tools/_index.md index 347dd274..a532ddf2 100644 --- a/content/using_ec2_spot_instances_with_eks/030_helm_root/_index.md +++ b/content/using_ec2_spot_instances_with_eks/030_k8s_tools/_index.md @@ -1,5 +1,5 @@ --- -title: "Helm" +title: "Kubernetes Tools" chapter: true weight: 30 --- diff --git a/content/using_ec2_spot_instances_with_eks/030_helm_root/deploy_metric_server.md b/content/using_ec2_spot_instances_with_eks/030_k8s_tools/deploy_metric_server.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/030_helm_root/deploy_metric_server.md rename to content/using_ec2_spot_instances_with_eks/030_k8s_tools/deploy_metric_server.md diff --git a/content/using_ec2_spot_instances_with_eks/030_helm_root/helm_deploy.md b/content/using_ec2_spot_instances_with_eks/030_k8s_tools/helm_deploy.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/030_helm_root/helm_deploy.md rename to content/using_ec2_spot_instances_with_eks/030_k8s_tools/helm_deploy.md diff --git a/content/using_ec2_spot_instances_with_eks/030_helm_root/install_kube_ops_view.md b/content/using_ec2_spot_instances_with_eks/030_k8s_tools/install_kube_ops_view.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/030_helm_root/install_kube_ops_view.md rename to content/using_ec2_spot_instances_with_eks/030_k8s_tools/install_kube_ops_view.md From 3d3ed014c86f2d28133bab34a1c53376c689b7c7 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Tue, 5 Oct 2021 15:52:18 +0800 Subject: [PATCH 26/39] Corrected to EKS managed node groups with Spot capacity --- .../040_spotmanagednodegroups/_index.md | 6 +++--- .../040_spotmanagednodegroups/spotlifecycle.md | 2 +- .../why_select_managed_node_group.md | 6 +++--- .../050_spotworkers/selecting_instance_types.md | 2 +- .../070_scaling/test_ca.md | 2 +- 5 files changed, 9 insertions(+), 9 deletions(-) diff --git a/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/_index.md b/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/_index.md index 2ee5e249..01c48427 100644 --- a/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/_index.md +++ b/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/_index.md @@ -1,12 +1,12 @@ --- -title: "Creating Spot managed node groups on EKS" +title: "Creating EKS managed node groups with Spot capacity" chapter: true weight: 40 draft: false --- -# Using Spot Instances with EKS (via Spot managed node groups) +# Using Spot Instances with EKS -In this module, you will learn how to provision, manage, and maintain your Kubernetes clusters with Amazon EKS Spot managed node groups on Spot Instances to optimize cost and scale. +In this module, you will learn how to provision, manage, and maintain your Kubernetes clusters with Amazon EKS managed node groups with Spot capacity to optimize cost and scale. ![Title Image](/images/using_ec2_spot_instances_with_eks/spotworkers/eks_spot_managed_architecture.png) diff --git a/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/spotlifecycle.md b/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/spotlifecycle.md index d9011d9d..b4a03508 100644 --- a/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/spotlifecycle.md +++ b/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/spotlifecycle.md @@ -18,7 +18,7 @@ Click on the Auto Scaling Group name in the **Details** tab. Scroll to the Purch ![Spot Best Practices](/images/using_ec2_spot_instances_with_eks/spotworkers/asg_spot_best_practices.png) -### Interruption Handling in Spot Managed Node Groups +### Interruption Handling in EKS managed node groups with Spot capacity To handle Spot interruptions, you do not need to install any extra automation tools on the cluster such as the AWS Node Termination Handler. The managed node group handles Spot interruptions for you in the following way: the underlying EC2 Auto Scaling Group is opted-in to Capacity Rebalancing, which means that when one of the Spot Instances in your node group is at elevated risk of interruption and gets an EC2 instance rebalance recommendation, it will attempt to launch a replacement instance. The more instance types you configure in the managed node group, the more chances EC2 Auto Scaling Group has of launching a replacement Spot Instance. sw replacement Spot node and waits until it successfully joins the cluster. diff --git a/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/why_select_managed_node_group.md b/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/why_select_managed_node_group.md index aafa7d7f..a53e39a3 100644 --- a/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/why_select_managed_node_group.md +++ b/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/why_select_managed_node_group.md @@ -17,11 +17,11 @@ Advantages of running Amazon EKS managed node groups: * Node updates and terminations automatically and gracefully drain nodes to ensure that your applications stay available. * No additional costs to use Amazon EKS managed node groups, pay only for the AWS resources provisioned. -### Why EKS Spot Managed Node Groups? +### Why EKS managed node groups with Spot capacity? -**Amazon EKS Spot managed node groups** enhances the managed node group experience in using EKS managed node groups to easily provision and manage EC2 Spot Instances. EKS managed node group will configure and launch an EC2 Autoscaling group of Spot Instances following Spot best practices and draining Spot worker nodes automatically before the instances are interrupted by AWS. This enables you to take advantage of the steep savings that Spot Instances provide for your interruption tolerant containerized applications. +**Amazon EKS managed node groups with Spot capacity** enhances the managed node group experience in using EKS managed node groups to easily provision and manage EC2 Spot Instances. EKS managed node group will configure and launch an EC2 Autoscaling group of Spot Instances following Spot best practices and draining Spot worker nodes automatically before the instances are interrupted by AWS. This enables you to take advantage of the steep savings that Spot Instances provide for your interruption tolerant containerized applications. -In addition to the advantages of managed node groups, Amazon EKS Spot managed node groups have these additional advantages: +In addition to the advantages of managed node groups, EKS managed node groups with Spot capacity have these additional advantages: * Allocation strategy to provision Spot capacity is set to **Capacity Optimized** to ensure that Spot nodes are provisioned in the optimal Spot capacity pools. * Specify **multiple instance types** during EKS Spot managed Node Group creation, to increase the number of Spot capacity pools available for allocating capacity. diff --git a/content/using_ec2_spot_instances_with_eks/050_spotworkers/selecting_instance_types.md b/content/using_ec2_spot_instances_with_eks/050_spotworkers/selecting_instance_types.md index 590efa62..9ea2f00d 100644 --- a/content/using_ec2_spot_instances_with_eks/050_spotworkers/selecting_instance_types.md +++ b/content/using_ec2_spot_instances_with_eks/050_spotworkers/selecting_instance_types.md @@ -6,6 +6,6 @@ draft: false --- ### Selecting instances for Spot Instance Diversification -[See "Selecting Instance Types" under Spot managed node groups]({{< relref "/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/selecting_instance_types.md" >}}) +[See "Selecting Instance Types" under EKS managed node groups with Spot capacity]({{< relref "/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/selecting_instance_types.md" >}}) Once you finish selecting instance types, proceed to "Adding Spot Workers with eksctl" section to continue creating Self Managed Spot node groups. diff --git a/content/using_ec2_spot_instances_with_eks/070_scaling/test_ca.md b/content/using_ec2_spot_instances_with_eks/070_scaling/test_ca.md index d3a91112..ab8dcad4 100644 --- a/content/using_ec2_spot_instances_with_eks/070_scaling/test_ca.md +++ b/content/using_ec2_spot_instances_with_eks/070_scaling/test_ca.md @@ -37,7 +37,7 @@ kubectl scale deployment/monte-carlo-pi-service --replicas=0 **Question:** Can you predict what would be the result of scaling down to 0 replicas? {{%expand "Show me the answer" %}} -The configuration that we applied to procure our nodegroups states that the minimum number of instances in the auto scaling group is 2 for both Spot managed node groups. +The configuration that we applied to procure our nodegroups states that the minimum number of instances in the auto scaling group is 2 for both EKS managed node groups with Spot capacity. (Self managed node group) The configuration that we applied to procure our nodegroups states that the minimum number of instances in the auto scaling group is 0 for both nodegroups. Starting from 1.14 version Cluster Autoscaler does support scaling down to 0. From 43f1b177b9b8d628509a0ab0f68b505a45b35dc8 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Tue, 5 Oct 2021 15:56:29 +0800 Subject: [PATCH 27/39] Corrected EKS Managed Node Groups Folder --- .../_index.md | 0 .../selecting_instance_types.md | 0 .../spotlifecycle.md | 0 .../why_select_managed_node_group.md | 0 .../workers_eksctl.files/spot_nodegroup_4vcpu_16gb.yml | 0 .../workers_eksctl.files/spot_nodegroup_8vcpu_32gb.yml | 0 .../workers_eksctl.md | 0 .../050_spotworkers/selecting_instance_types.md | 2 +- 8 files changed, 1 insertion(+), 1 deletion(-) rename content/using_ec2_spot_instances_with_eks/{040_spotmanagednodegroups => 040_eksmanagednodegroupswithspot}/_index.md (100%) rename content/using_ec2_spot_instances_with_eks/{040_spotmanagednodegroups => 040_eksmanagednodegroupswithspot}/selecting_instance_types.md (100%) rename content/using_ec2_spot_instances_with_eks/{040_spotmanagednodegroups => 040_eksmanagednodegroupswithspot}/spotlifecycle.md (100%) rename content/using_ec2_spot_instances_with_eks/{040_spotmanagednodegroups => 040_eksmanagednodegroupswithspot}/why_select_managed_node_group.md (100%) rename content/using_ec2_spot_instances_with_eks/{040_spotmanagednodegroups => 040_eksmanagednodegroupswithspot}/workers_eksctl.files/spot_nodegroup_4vcpu_16gb.yml (100%) rename content/using_ec2_spot_instances_with_eks/{040_spotmanagednodegroups => 040_eksmanagednodegroupswithspot}/workers_eksctl.files/spot_nodegroup_8vcpu_32gb.yml (100%) rename content/using_ec2_spot_instances_with_eks/{040_spotmanagednodegroups => 040_eksmanagednodegroupswithspot}/workers_eksctl.md (100%) diff --git a/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/_index.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/_index.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/_index.md rename to content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/_index.md diff --git a/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/selecting_instance_types.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/selecting_instance_types.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/selecting_instance_types.md rename to content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/selecting_instance_types.md diff --git a/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/spotlifecycle.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/spotlifecycle.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/spotlifecycle.md rename to content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/spotlifecycle.md diff --git a/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/why_select_managed_node_group.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/why_select_managed_node_group.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/why_select_managed_node_group.md rename to content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/why_select_managed_node_group.md diff --git a/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/workers_eksctl.files/spot_nodegroup_4vcpu_16gb.yml b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.files/spot_nodegroup_4vcpu_16gb.yml similarity index 100% rename from content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/workers_eksctl.files/spot_nodegroup_4vcpu_16gb.yml rename to content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.files/spot_nodegroup_4vcpu_16gb.yml diff --git a/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/workers_eksctl.files/spot_nodegroup_8vcpu_32gb.yml b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.files/spot_nodegroup_8vcpu_32gb.yml similarity index 100% rename from content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/workers_eksctl.files/spot_nodegroup_8vcpu_32gb.yml rename to content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.files/spot_nodegroup_8vcpu_32gb.yml diff --git a/content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/workers_eksctl.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/workers_eksctl.md rename to content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md diff --git a/content/using_ec2_spot_instances_with_eks/050_spotworkers/selecting_instance_types.md b/content/using_ec2_spot_instances_with_eks/050_spotworkers/selecting_instance_types.md index 9ea2f00d..976b0b6c 100644 --- a/content/using_ec2_spot_instances_with_eks/050_spotworkers/selecting_instance_types.md +++ b/content/using_ec2_spot_instances_with_eks/050_spotworkers/selecting_instance_types.md @@ -6,6 +6,6 @@ draft: false --- ### Selecting instances for Spot Instance Diversification -[See "Selecting Instance Types" under EKS managed node groups with Spot capacity]({{< relref "/using_ec2_spot_instances_with_eks/040_spotmanagednodegroups/selecting_instance_types.md" >}}) +[See "Selecting Instance Types" under EKS managed node groups with Spot capacity]({{< relref "/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/selecting_instance_types.md" >}}) Once you finish selecting instance types, proceed to "Adding Spot Workers with eksctl" section to continue creating Self Managed Spot node groups. From fc70609f05045b25bff63cdc91b75c7a88eb43c3 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Tue, 5 Oct 2021 16:49:52 +0800 Subject: [PATCH 28/39] Renamed to EKS MNG with Spot --- .../040_eksmanagednodegroupswithspot/spotlifecycle.md | 4 ++-- .../why_select_managed_node_group.md | 6 +++--- .../040_eksmanagednodegroupswithspot/workers_eksctl.md | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/spotlifecycle.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/spotlifecycle.md index b4a03508..3bde9ce3 100644 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/spotlifecycle.md +++ b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/spotlifecycle.md @@ -5,9 +5,9 @@ weight: 40 draft: false --- -### View the Spot Managed Node Group Configuration +### View the Managed Node Group Configuration -Use the AWS Management Console to inspect the Spot managed node group deployed in your Kubernetes cluster. Select **Elastic Kubernetes Service**, click on **Clusters**, and then on **eksworkshop-eksctl** cluster. Select the **Configuration** tab and **Compute** sub tab. You can see 3 node groups created - one On-Demand node group and two Spot node groups. +Use the AWS Management Console to inspect the Managed node group deployed in your Kubernetes cluster. Select **Elastic Kubernetes Service**, click on **Clusters**, and then on **eksworkshop-eksctl** cluster. Select the **Configuration** tab and **Compute** sub tab. You can see 3 node groups created - one On-Demand node group and two Spot node groups. Click on **dev-4vcpu-16gb-spot** group and you can see the instance types set from the create command. diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/why_select_managed_node_group.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/why_select_managed_node_group.md index a53e39a3..e9cb8675 100644 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/why_select_managed_node_group.md +++ b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/why_select_managed_node_group.md @@ -1,5 +1,5 @@ --- -title: "Advantages of EKS Spot Managed Node Group" +title: "EKS managed node groups with Spot capacity" date: 2018-08-07T11:05:19-07:00 weight: 10 draft: false @@ -24,6 +24,6 @@ Advantages of running Amazon EKS managed node groups: In addition to the advantages of managed node groups, EKS managed node groups with Spot capacity have these additional advantages: * Allocation strategy to provision Spot capacity is set to **Capacity Optimized** to ensure that Spot nodes are provisioned in the optimal Spot capacity pools. -* Specify **multiple instance types** during EKS Spot managed Node Group creation, to increase the number of Spot capacity pools available for allocating capacity. -* Nodes provisioned under Spot managed node group are automatically tagged with capacity type: **eks.amazonaws.com/capacityType: SPOT**. You can use this label to schedule fault tolerant applications on Spot nodes. +* Specify **multiple instance types** during managed node groups creation, to increase the number of Spot capacity pools available for allocating capacity. +* Nodes provisioned under managed node groups with Spot capacity are automatically tagged with capacity type: **eks.amazonaws.com/capacityType: SPOT**. You can use this label to schedule fault tolerant applications on Spot nodes. * Amazon EC2 Spot **Capacity Rebalancing** enabled to ensure Amazon EKS can gracefully drain and rebalance your Spot nodes to minimize application disruption when a Spot node is at elevated risk of interruption. diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md index 00e1a903..8450e92b 100644 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md +++ b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md @@ -73,7 +73,7 @@ There are a few things to note in the configuration that we just used to create * The configuration setup the nodes under the **managedNodeGroups** section. This is to indicate the node group being created is a managed node group. * Notice that the configuration setup a **minSize** to 1, **maxSize** to 5 and **desiredCapacity** to 2. Spot managed nodegroups are created with 2 nodes, but minimum number of instances for managed node group is 1 instance. - * The configuration setup a configuration **spot: true** to indicate that the node group being created is a Spot managed node group, which implies all nodes in the nodegroup would be **Spot Instances**. + * The configuration setup a configuration **spot: true** to indicate that the node group being created is a managed node group with Spot capacity, which implies all nodes in the nodegroup would be **Spot Instances**. * We did also add an extra label **intent: apps**. We will use this label to force a hard partition of the cluster for our applications. During this workshop we will deploy control applications on nodes that have been labeled with **intent: control-apps** while our applications get deployed to nodes labeled with **intent: apps**. * Notice that the configuration setup 2 node label under **labels** - **alpha.eksctl.io/cluster-name: : eksworkshop-eksctl** to indicate the node label belongs to **eksworkshop-eksctl** cluster, and **alpha.eksctl.io/nodegroup-name: dev-4vcpu-16gb-spot** node group. From 04d3c5b2127e6a2dbafa7ecc1d9ffaeb1532a030 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Wed, 6 Oct 2021 15:29:47 +0800 Subject: [PATCH 29/39] Moved Self Managed to later --- .../040_eksmanagednodegroupswithspot/spotlifecycle.md | 9 ++++++--- .../{060_deploy => 050_deploy}/_index.md | 4 ++-- .../{060_deploy => 050_deploy}/deploy.md | 2 +- .../{060_deploy => 050_deploy}/monte_carlo_pi.md | 0 .../monte-carlo-pi-service-final-self-managed.yml | 0 .../monte-carlo-pi-service-final.yml | 0 .../tolerations_and_affinity.md | 0 .../{070_scaling => 060_scaling}/_index.md | 2 +- .../{070_scaling => 060_scaling}/cleanup.md | 0 .../deploy_ca.files/cluster_autoscaler.yml | 0 .../{070_scaling => 060_scaling}/deploy_ca.md | 2 +- .../{070_scaling => 060_scaling}/deploy_hpa.md | 0 .../{070_scaling => 060_scaling}/test_ca.md | 8 ++++---- .../{070_scaling => 060_scaling}/test_hpa.md | 0 .../{050_spotworkers => 070_spotworkers}/_index.md | 2 +- .../deployhandler.md | 0 .../preferspot.files/deployment-solution.yml | 0 .../selecting_instance_types.md | 0 .../spotworkers.files/cloudformation_mixed_workers.yml | 0 .../spotworkers.files/eksctl_mixed_workers_bootstrap.yml | 0 .../workers_eksctl.md | 0 21 files changed, 16 insertions(+), 13 deletions(-) rename content/using_ec2_spot_instances_with_eks/{060_deploy => 050_deploy}/_index.md (94%) rename content/using_ec2_spot_instances_with_eks/{060_deploy => 050_deploy}/deploy.md (97%) rename content/using_ec2_spot_instances_with_eks/{060_deploy => 050_deploy}/monte_carlo_pi.md (100%) rename content/using_ec2_spot_instances_with_eks/{060_deploy => 050_deploy}/tolerations_and_affinity.files/monte-carlo-pi-service-final-self-managed.yml (100%) rename content/using_ec2_spot_instances_with_eks/{060_deploy => 050_deploy}/tolerations_and_affinity.files/monte-carlo-pi-service-final.yml (100%) rename content/using_ec2_spot_instances_with_eks/{060_deploy => 050_deploy}/tolerations_and_affinity.md (100%) rename content/using_ec2_spot_instances_with_eks/{070_scaling => 060_scaling}/_index.md (98%) rename content/using_ec2_spot_instances_with_eks/{070_scaling => 060_scaling}/cleanup.md (100%) rename content/using_ec2_spot_instances_with_eks/{070_scaling => 060_scaling}/deploy_ca.files/cluster_autoscaler.yml (100%) rename content/using_ec2_spot_instances_with_eks/{070_scaling => 060_scaling}/deploy_ca.md (97%) rename content/using_ec2_spot_instances_with_eks/{070_scaling => 060_scaling}/deploy_hpa.md (100%) rename content/using_ec2_spot_instances_with_eks/{070_scaling => 060_scaling}/test_ca.md (98%) rename content/using_ec2_spot_instances_with_eks/{070_scaling => 060_scaling}/test_hpa.md (100%) rename content/using_ec2_spot_instances_with_eks/{050_spotworkers => 070_spotworkers}/_index.md (97%) rename content/using_ec2_spot_instances_with_eks/{050_spotworkers => 070_spotworkers}/deployhandler.md (100%) rename content/using_ec2_spot_instances_with_eks/{050_spotworkers => 070_spotworkers}/preferspot.files/deployment-solution.yml (100%) rename content/using_ec2_spot_instances_with_eks/{050_spotworkers => 070_spotworkers}/selecting_instance_types.md (100%) rename content/using_ec2_spot_instances_with_eks/{050_spotworkers => 070_spotworkers}/spotworkers.files/cloudformation_mixed_workers.yml (100%) rename content/using_ec2_spot_instances_with_eks/{050_spotworkers => 070_spotworkers}/spotworkers.files/eksctl_mixed_workers_bootstrap.yml (100%) rename content/using_ec2_spot_instances_with_eks/{050_spotworkers => 070_spotworkers}/workers_eksctl.md (100%) diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/spotlifecycle.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/spotlifecycle.md index 3bde9ce3..c3f5575f 100644 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/spotlifecycle.md +++ b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/spotlifecycle.md @@ -20,11 +20,14 @@ Click on the Auto Scaling Group name in the **Details** tab. Scroll to the Purch ### Interruption Handling in EKS managed node groups with Spot capacity -To handle Spot interruptions, you do not need to install any extra automation tools on the cluster such as the AWS Node Termination Handler. The managed node group handles Spot interruptions for you in the following way: the underlying EC2 Auto Scaling Group is opted-in to Capacity Rebalancing, which means that when one of the Spot Instances in your node group is at elevated risk of interruption and gets an EC2 instance rebalance recommendation, it will attempt to launch a replacement instance. The more instance types you configure in the managed node group, the more chances EC2 Auto Scaling Group has of launching a replacement Spot Instance. -sw replacement Spot node and waits until it successfully joins the cluster. +To handle Spot interruptions, you do not need to install any extra automation tools on the cluster such as the AWS Node Termination Handler. A managed node group configures an Amazon EC2 Auto Scaling group on your behalf and handles the Spot interruption in following manner: + +* Amazon EC2 Spot Capacity Rebalancing is enabled so that Amazon EKS can gracefully drain and rebalance your Spot nodes to minimize application disruption when a Spot node is at elevated risk of interruption. For more information, see [Amazon EC2 Auto Scaling Capacity Rebalancing](https://docs.aws.amazon.com/autoscaling/ec2/userguide/capacity-rebalance.html) in the Amazon EC2 Auto Scaling User Guide. + * When a replacement Spot node is bootstrapped and in the Ready state on Kubernetes, Amazon EKS cordons and drains the Spot node that received the rebalance recommendation. Cordoning the Spot node ensures that the node is marked as ‘unschedulable’ and kube-scheduler will not schedule any new pods on it. It also removes it from its list of healthy, active Spot nodes. [Draining](https://kubernetes.io/docs/tasks/administer-cluster/safely-drain-node/) the Spot node ensures that running pods are evicted gracefully. + * If a Spot two-minute interruption notice arrives before the replacement Spot node is in a Ready state, Amazon EKS starts draining the Spot node that received the rebalance recommendation. -This process avoids waiting for new capacity to be available when there is a termination notice, and instead procures capacity in advance, limiting the time that pods might be left pending. + This process avoids waiting for replacement Spot node till Spot interruption arrives, instead it procures replacement in advance and helps in minimizing the scheduling time for pending pods. ![Spot Rebalance Recommendation](/images/using_ec2_spot_instances_with_eks/spotworkers/rebalance_recommendation.png) \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/060_deploy/_index.md b/content/using_ec2_spot_instances_with_eks/050_deploy/_index.md similarity index 94% rename from content/using_ec2_spot_instances_with_eks/060_deploy/_index.md rename to content/using_ec2_spot_instances_with_eks/050_deploy/_index.md index fb09b9d9..ca76bed5 100644 --- a/content/using_ec2_spot_instances_with_eks/060_deploy/_index.md +++ b/content/using_ec2_spot_instances_with_eks/050_deploy/_index.md @@ -1,7 +1,7 @@ --- title: "Deploying the example Microservice" chapter: true -weight: 60 +weight: 50 --- # Deploying the example Microservice @@ -10,4 +10,4 @@ To illustrate application scaling using [Horizontal Pod Autoscaler](https://kube The microservice we will use as an example, is a trivial web service that uses a [Monte Carlo method to approximate pi](https://en.wikipedia.org/wiki/Monte_Carlo_integration) written in go. You can find the application code in in [this github repo](https://github.com/ruecarlo/eks-workshop-sample-api-service-go) -![Monte Carlo Pi Approximation](/images/using_ec2_spot_instances_with_eks/060_deploy/monte_carlo_pi.png) \ No newline at end of file +![Monte Carlo Pi Approximation](/images/using_ec2_spot_instances_with_eks/050_deploy/monte_carlo_pi.png) \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/060_deploy/deploy.md b/content/using_ec2_spot_instances_with_eks/050_deploy/deploy.md similarity index 97% rename from content/using_ec2_spot_instances_with_eks/060_deploy/deploy.md rename to content/using_ec2_spot_instances_with_eks/050_deploy/deploy.md index a3a3bdb9..26309916 100644 --- a/content/using_ec2_spot_instances_with_eks/060_deploy/deploy.md +++ b/content/using_ec2_spot_instances_with_eks/050_deploy/deploy.md @@ -51,7 +51,7 @@ monte-carlo-pi-service. When running that in your browser you should be able to see the json response provided by the service: -![Monte Carlo Pi Approximation Response](/images/using_ec2_spot_instances_with_eks/060_deploy/monte_carlo_pi_output_1.png) +![Monte Carlo Pi Approximation Response](/images/using_ec2_spot_instances_with_eks/050_deploy/monte_carlo_pi_output_1.png) {{% notice note %}} The application takes the named argument **iterations** to define the number of samples used during the diff --git a/content/using_ec2_spot_instances_with_eks/060_deploy/monte_carlo_pi.md b/content/using_ec2_spot_instances_with_eks/050_deploy/monte_carlo_pi.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/060_deploy/monte_carlo_pi.md rename to content/using_ec2_spot_instances_with_eks/050_deploy/monte_carlo_pi.md diff --git a/content/using_ec2_spot_instances_with_eks/060_deploy/tolerations_and_affinity.files/monte-carlo-pi-service-final-self-managed.yml b/content/using_ec2_spot_instances_with_eks/050_deploy/tolerations_and_affinity.files/monte-carlo-pi-service-final-self-managed.yml similarity index 100% rename from content/using_ec2_spot_instances_with_eks/060_deploy/tolerations_and_affinity.files/monte-carlo-pi-service-final-self-managed.yml rename to content/using_ec2_spot_instances_with_eks/050_deploy/tolerations_and_affinity.files/monte-carlo-pi-service-final-self-managed.yml diff --git a/content/using_ec2_spot_instances_with_eks/060_deploy/tolerations_and_affinity.files/monte-carlo-pi-service-final.yml b/content/using_ec2_spot_instances_with_eks/050_deploy/tolerations_and_affinity.files/monte-carlo-pi-service-final.yml similarity index 100% rename from content/using_ec2_spot_instances_with_eks/060_deploy/tolerations_and_affinity.files/monte-carlo-pi-service-final.yml rename to content/using_ec2_spot_instances_with_eks/050_deploy/tolerations_and_affinity.files/monte-carlo-pi-service-final.yml diff --git a/content/using_ec2_spot_instances_with_eks/060_deploy/tolerations_and_affinity.md b/content/using_ec2_spot_instances_with_eks/050_deploy/tolerations_and_affinity.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/060_deploy/tolerations_and_affinity.md rename to content/using_ec2_spot_instances_with_eks/050_deploy/tolerations_and_affinity.md diff --git a/content/using_ec2_spot_instances_with_eks/070_scaling/_index.md b/content/using_ec2_spot_instances_with_eks/060_scaling/_index.md similarity index 98% rename from content/using_ec2_spot_instances_with_eks/070_scaling/_index.md rename to content/using_ec2_spot_instances_with_eks/060_scaling/_index.md index d46e37d9..98e8c4a0 100644 --- a/content/using_ec2_spot_instances_with_eks/070_scaling/_index.md +++ b/content/using_ec2_spot_instances_with_eks/060_scaling/_index.md @@ -1,7 +1,7 @@ --- title: "Autoscaling our Applications and Clusters" chapter: true -weight: 70 +weight: 60 --- # Implement AutoScaling with HPA and CA diff --git a/content/using_ec2_spot_instances_with_eks/070_scaling/cleanup.md b/content/using_ec2_spot_instances_with_eks/060_scaling/cleanup.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/070_scaling/cleanup.md rename to content/using_ec2_spot_instances_with_eks/060_scaling/cleanup.md diff --git a/content/using_ec2_spot_instances_with_eks/070_scaling/deploy_ca.files/cluster_autoscaler.yml b/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.files/cluster_autoscaler.yml similarity index 100% rename from content/using_ec2_spot_instances_with_eks/070_scaling/deploy_ca.files/cluster_autoscaler.yml rename to content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.files/cluster_autoscaler.yml diff --git a/content/using_ec2_spot_instances_with_eks/070_scaling/deploy_ca.md b/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.md similarity index 97% rename from content/using_ec2_spot_instances_with_eks/070_scaling/deploy_ca.md rename to content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.md index 8bfa0733..0db056da 100644 --- a/content/using_ec2_spot_instances_with_eks/070_scaling/deploy_ca.md +++ b/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.md @@ -26,7 +26,7 @@ We have provided a manifest file to deploy the CA. Copy the commands below into ``` mkdir -p ~/environment/cluster-autoscaler -curl -o ~/environment/cluster-autoscaler/cluster_autoscaler.yml https://raw.githubusercontent.com/awslabs/ec2-spot-workshops/master/content/using_ec2_spot_instances_with_eks/070_scaling/deploy_ca.files/cluster_autoscaler.yml +curl -o ~/environment/cluster-autoscaler/cluster_autoscaler.yml https://raw.githubusercontent.com/awslabs/ec2-spot-workshops/master/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.files/cluster_autoscaler.yml sed -i "s/--AWS_REGION--/${AWS_REGION}/g" ~/environment/cluster-autoscaler/cluster_autoscaler.yml ``` diff --git a/content/using_ec2_spot_instances_with_eks/070_scaling/deploy_hpa.md b/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_hpa.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/070_scaling/deploy_hpa.md rename to content/using_ec2_spot_instances_with_eks/060_scaling/deploy_hpa.md diff --git a/content/using_ec2_spot_instances_with_eks/070_scaling/test_ca.md b/content/using_ec2_spot_instances_with_eks/060_scaling/test_ca.md similarity index 98% rename from content/using_ec2_spot_instances_with_eks/070_scaling/test_ca.md rename to content/using_ec2_spot_instances_with_eks/060_scaling/test_ca.md index ab8dcad4..dcc0f3a5 100644 --- a/content/using_ec2_spot_instances_with_eks/070_scaling/test_ca.md +++ b/content/using_ec2_spot_instances_with_eks/060_scaling/test_ca.md @@ -89,7 +89,7 @@ monte-carlo-pi-service-584f6ddff-fs9x6 1/1 Running 0 103s monte-carlo-pi-service-584f6ddff-jst55 1/1 Running 0 103s ``` You should also be able to visualize the scaling action using kube-ops-view. Kube-ops-view provides an option to highlight pods meeting a regular expression. All pods in green are **monte-carlo-pi-service** pods. -![Scaling up to 10 replicas](/images/using_ec2_spot_instances_with_eks/070_scaling/scaling-kov-10-replicas.png) +![Scaling up to 10 replicas](/images/using_ec2_spot_instances_with_eks/060_scaling/scaling-kov-10-replicas.png) {{% notice info %}} Given we started from 2 node capacity in both Spot node groups, this should trigger a scaling event for Cluster Autoscaler. Can you predict which size (and type!) of node will be provided? @@ -118,14 +118,14 @@ kubectl get pods --watch ``` Kube-ops-view, will show 3 pending yellow pods outside the node. -![Scale Up](/images/using_ec2_spot_instances_with_eks/070_scaling/scaling-asg-up-kov.png) +![Scale Up](/images/using_ec2_spot_instances_with_eks/060_scaling/scaling-asg-up-kov.png) When inspecting cluster-autoscaler logs with the command line below ``` kubectl logs -f deployment/cluster-autoscaler -n kube-system ``` you will notice Cluster Autoscaler events similar to: -![CA Scale Up events](/images/using_ec2_spot_instances_with_eks/070_scaling/scaling-asg-up2.png) +![CA Scale Up events](/images/using_ec2_spot_instances_with_eks/060_scaling/scaling-asg-up2.png) To confirm which type of node has been added you can either use kube-ops-view or execute: @@ -135,7 +135,7 @@ kubectl get node --selector=intent=apps --show-labels You can verify in AWS Management Console to confirm that the Auto Scaling groups are scaling up to meet demand. This may take a few minutes. You can also follow along with the pod deployment from the command line. You should see the pods transition from pending to running as nodes are scaled up. -![Scale Up](/images/using_ec2_spot_instances_with_eks/070_scaling/scaling-asg-up.png) +![Scale Up](/images/using_ec2_spot_instances_with_eks/060_scaling/scaling-asg-up.png) {{% notice info %}} Cluster Autoscaler expands capacity according to the [Expander](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#what-are-expanders) configuration. By default, Cluster Autoscaler uses the **random** expander. This means that there is equal probability of cluster autoscaler selecting the 4vCPUs 16GB RAM group or the 8vCPUs 32GB RAM group. You may consider also using other expanders like **least-waste**, or the **priority** expander. diff --git a/content/using_ec2_spot_instances_with_eks/070_scaling/test_hpa.md b/content/using_ec2_spot_instances_with_eks/060_scaling/test_hpa.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/070_scaling/test_hpa.md rename to content/using_ec2_spot_instances_with_eks/060_scaling/test_hpa.md diff --git a/content/using_ec2_spot_instances_with_eks/050_spotworkers/_index.md b/content/using_ec2_spot_instances_with_eks/070_spotworkers/_index.md similarity index 97% rename from content/using_ec2_spot_instances_with_eks/050_spotworkers/_index.md rename to content/using_ec2_spot_instances_with_eks/070_spotworkers/_index.md index 2f931519..a3ff0675 100644 --- a/content/using_ec2_spot_instances_with_eks/050_spotworkers/_index.md +++ b/content/using_ec2_spot_instances_with_eks/070_spotworkers/_index.md @@ -1,7 +1,7 @@ --- title: "(Optional) Creating Spot self managed nodegroups on EKS" chapter: true -weight: 50 +weight: 70 draft: false --- diff --git a/content/using_ec2_spot_instances_with_eks/050_spotworkers/deployhandler.md b/content/using_ec2_spot_instances_with_eks/070_spotworkers/deployhandler.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/050_spotworkers/deployhandler.md rename to content/using_ec2_spot_instances_with_eks/070_spotworkers/deployhandler.md diff --git a/content/using_ec2_spot_instances_with_eks/050_spotworkers/preferspot.files/deployment-solution.yml b/content/using_ec2_spot_instances_with_eks/070_spotworkers/preferspot.files/deployment-solution.yml similarity index 100% rename from content/using_ec2_spot_instances_with_eks/050_spotworkers/preferspot.files/deployment-solution.yml rename to content/using_ec2_spot_instances_with_eks/070_spotworkers/preferspot.files/deployment-solution.yml diff --git a/content/using_ec2_spot_instances_with_eks/050_spotworkers/selecting_instance_types.md b/content/using_ec2_spot_instances_with_eks/070_spotworkers/selecting_instance_types.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/050_spotworkers/selecting_instance_types.md rename to content/using_ec2_spot_instances_with_eks/070_spotworkers/selecting_instance_types.md diff --git a/content/using_ec2_spot_instances_with_eks/050_spotworkers/spotworkers.files/cloudformation_mixed_workers.yml b/content/using_ec2_spot_instances_with_eks/070_spotworkers/spotworkers.files/cloudformation_mixed_workers.yml similarity index 100% rename from content/using_ec2_spot_instances_with_eks/050_spotworkers/spotworkers.files/cloudformation_mixed_workers.yml rename to content/using_ec2_spot_instances_with_eks/070_spotworkers/spotworkers.files/cloudformation_mixed_workers.yml diff --git a/content/using_ec2_spot_instances_with_eks/050_spotworkers/spotworkers.files/eksctl_mixed_workers_bootstrap.yml b/content/using_ec2_spot_instances_with_eks/070_spotworkers/spotworkers.files/eksctl_mixed_workers_bootstrap.yml similarity index 100% rename from content/using_ec2_spot_instances_with_eks/050_spotworkers/spotworkers.files/eksctl_mixed_workers_bootstrap.yml rename to content/using_ec2_spot_instances_with_eks/070_spotworkers/spotworkers.files/eksctl_mixed_workers_bootstrap.yml diff --git a/content/using_ec2_spot_instances_with_eks/050_spotworkers/workers_eksctl.md b/content/using_ec2_spot_instances_with_eks/070_spotworkers/workers_eksctl.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/050_spotworkers/workers_eksctl.md rename to content/using_ec2_spot_instances_with_eks/070_spotworkers/workers_eksctl.md From c780ec641ba16c443e501875ad7eff0fa9757c2d Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Wed, 6 Oct 2021 15:36:08 +0800 Subject: [PATCH 30/39] Image URL corrections --- .../010_prerequisites/workspace.md | 4 ++-- .../010_prerequisites/workspace_at_launch.md | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace.md index 5ad654aa..8c64c21a 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace.md @@ -34,10 +34,10 @@ Cloud9 requires third-party-cookies. You can whitelist the [specific domains]( h - Name it **eksworkshop**, and take all other defaults - When it comes up, customize the environment by closing the **welcome tab** and **lower work area**, and opening a new **terminal** tab in the main work area: -![c9before](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9before.png) +![c9before](/images/using_ec2_spot_instances_with_eks/prerequisites/c9before.png) - Your workspace should now look like this: -![c9after](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9after.png) +![c9after](/images/using_ec2_spot_instances_with_eks/prerequisites/c9after.png) - If you like this theme, you can choose it yourself by selecting **View / Themes / Solarized / Solarized Dark** in the Cloud9 workspace menu. diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace_at_launch.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace_at_launch.md index f5fb160c..bc8505d2 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace_at_launch.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/workspace_at_launch.md @@ -9,10 +9,10 @@ This markdown file is used as part of another file using 'insert-md-from-file' s --> - When it comes up, customize the environment by closing the **welcome tab** and **lower work area**, and opening a new **terminal** tab in the main work area: -![c9before](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9before.png) +![c9before](/images/using_ec2_spot_instances_with_eks/prerequisites/c9before.png) - Your workspace should now look like this: -![c9after](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9after.png) +![c9after](/images/using_ec2_spot_instances_with_eks/prerequisites/c9after.png) - If you like this theme, you can choose it yourself by selecting **View / Themes / Solarized / Solarized Dark** in the Cloud9 workspace menu. From 61f826ea414534dc7e10d3952875c97c6496ee99 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Wed, 6 Oct 2021 15:43:12 +0800 Subject: [PATCH 31/39] Corrected Images URL --- .../005_introduction/_index.md | 2 +- .../005_introduction/basics/what_is_k8s.md | 2 +- .../005_introduction/eks/eks_control_plane.md | 2 +- .../005_introduction/eks/eks_customers.md | 2 +- .../005_introduction/eks/eks_high_architecture.md | 2 +- .../005_introduction/eks/eks_high_level.md | 2 +- .../010_prerequisites/at_an_aws_updateiam.md | 4 ++-- .../010_prerequisites/aws_event.md | 2 +- .../010_prerequisites/on_your_own_updateiam.md | 6 +++--- .../010_prerequisites/self_paced.md | 8 ++++---- .../010_prerequisites/update_workspace_settings.md | 2 +- .../050_deploy/_index.md | 2 +- .../050_deploy/deploy.md | 2 +- .../060_scaling/test_ca.md | 8 ++++---- .../080_jenkins/setup_agents.md | 4 ++-- 15 files changed, 25 insertions(+), 25 deletions(-) diff --git a/content/using_ec2_spot_instances_with_eks/005_introduction/_index.md b/content/using_ec2_spot_instances_with_eks/005_introduction/_index.md index 2ea5dabd..71a66be8 100644 --- a/content/using_ec2_spot_instances_with_eks/005_introduction/_index.md +++ b/content/using_ec2_spot_instances_with_eks/005_introduction/_index.md @@ -9,7 +9,7 @@ draft: false A walkthrough of basic Kubernetes concepts. -![Title Image](/images/using_ec2_spot_instances_with_eks/005_introduction/eks-product-page.png) +![Title Image](/images/using_ec2_spot_instances_with_eks/introduction/eks-product-page.png) Welcome to the Using EC2 Spot Instances with EKS Workshop! The intent of this workshop is to educate users about how to use EC2 Spot instances on Amazon EKS. Background in EKS, Kubernetes, Docker, and container workflows are not required, but they are recommended. diff --git a/content/using_ec2_spot_instances_with_eks/005_introduction/basics/what_is_k8s.md b/content/using_ec2_spot_instances_with_eks/005_introduction/basics/what_is_k8s.md index 171659ff..2020271b 100644 --- a/content/using_ec2_spot_instances_with_eks/005_introduction/basics/what_is_k8s.md +++ b/content/using_ec2_spot_instances_with_eks/005_introduction/basics/what_is_k8s.md @@ -5,7 +5,7 @@ draft: false weight: 30 --- -![K8s summary image](/images/using_ec2_spot_instances_with_eks/005_introduction/what_is_k8s_new.png) +![K8s summary image](/images/using_ec2_spot_instances_with_eks/introduction/what_is_k8s_new.png) * Builds on over a decade of experience and best practices * Utilizes declarative configuration and automation diff --git a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_control_plane.md b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_control_plane.md index b69d2e03..5577170f 100644 --- a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_control_plane.md +++ b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_control_plane.md @@ -6,4 +6,4 @@ weight: 140 --- -![EKS Control Plane](/images/using_ec2_spot_instances_with_eks/005_introduction/eks-k8s-control-plane.svg) +![EKS Control Plane](/images/using_ec2_spot_instances_with_eks/introduction/eks-k8s-control-plane.svg) diff --git a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_customers.md b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_customers.md index 98702722..72f08bd2 100644 --- a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_customers.md +++ b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_customers.md @@ -6,4 +6,4 @@ weight: 130 --- -![EKS Customers](/images/using_ec2_spot_instances_with_eks/005_introduction/eks-customers.svg) +![EKS Customers](/images/using_ec2_spot_instances_with_eks/introduction/eks-customers.svg) diff --git a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_architecture.md b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_architecture.md index 6af0d612..be3cbf3e 100644 --- a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_architecture.md +++ b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_architecture.md @@ -6,4 +6,4 @@ weight: 150 --- -![EKS Architecture](/images/using_ec2_spot_instances_with_eks/005_introduction/eks-architecture.svg) +![EKS Architecture](/images/using_ec2_spot_instances_with_eks/introduction/eks-architecture.svg) diff --git a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_level.md b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_level.md index 9a672d2c..1be57d14 100644 --- a/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_level.md +++ b/content/using_ec2_spot_instances_with_eks/005_introduction/eks/eks_high_level.md @@ -8,4 +8,4 @@ weight: 150 Once your EKS cluster is ready, you get an API endpoint and you'd [use Kubectl, community developed tool to interact with your cluster.](https://kubernetes.io/docs/reference/kubectl/kubectl/) -![EKS High Level](/images/using_ec2_spot_instances_with_eks/005_introduction/eks-high-level.svg) +![EKS High Level](/images/using_ec2_spot_instances_with_eks/introduction/eks-high-level.svg) diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/at_an_aws_updateiam.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/at_an_aws_updateiam.md index f0a8ecb5..3c6808cf 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/at_an_aws_updateiam.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/at_an_aws_updateiam.md @@ -9,6 +9,6 @@ hidden: true 1. Follow this [deep link to find your Cloud9 EC2 instance](https://console.aws.amazon.com/ec2/v2/home?#Instances:tag:Name=aws-cloud9-eksworkshop;sort=desc:launchTime). 1. Select the instance, then choose **Actions / Security / Modify IAM role** -![c9instancerole](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9instancerole.png) +![c9instancerole](/images/using_ec2_spot_instances_with_eks/prerequisites/c9instancerole.png) 1. Choose **TeamRoleInstance** from the **IAM role** drop down, and select **Save** -![c9attachrole](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9attachroleee.png) +![c9attachrole](/images/using_ec2_spot_instances_with_eks/prerequisites/c9attachroleee.png) diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/aws_event.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/aws_event.md index 917dc36f..9ab19005 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/aws_event.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/aws_event.md @@ -53,7 +53,7 @@ In this workshop, you'll need to reference the resources created by the CloudFor 1. In the stack details pane, click the **Outputs** tab. -![cnf_output](/images/using_ec2_spot_instances_with_eks/010_prerequisites/cnf_output.png) +![cnf_output](/images/using_ec2_spot_instances_with_eks/prerequisites/cnf_output.png) It is recommended that you keep this window open so you can easily refer to the outputs and resources throughout the workshop. diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/on_your_own_updateiam.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/on_your_own_updateiam.md index 4b991f4f..5afbf2a7 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/on_your_own_updateiam.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/on_your_own_updateiam.md @@ -12,12 +12,12 @@ hidden: true 1. Confirm that **AdministratorAccess** is checked, then click **Next: Tags** to assign tags. 1. Take the defaults, and click **Next: Review** to review. 1. Enter **eksworkshop-admin** for the Name, and click **Create role**. -![createrole](/images/using_ec2_spot_instances_with_eks/010_prerequisites/createrole.png) +![createrole](/images/using_ec2_spot_instances_with_eks/prerequisites/createrole.png) ## Attach the IAM role to your Workspace 1. Follow this [deep link to find your Cloud9 EC2 instance](https://console.aws.amazon.com/ec2/v2/home?#Instances:tag:Name=aws-cloud9-eksworkshop;sort=desc:launchTime). 1. Select the instance, then choose **Actions / Security / Modify IAM role** -![c9instancerole](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9instancerole.png) +![c9instancerole](/images/using_ec2_spot_instances_with_eks/prerequisites/c9instancerole.png) 1. Choose **eksworkshop-admin** from the **IAM role** drop down, and select **Save** -![c9attachrole](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9attachrole.png) \ No newline at end of file +![c9attachrole](/images/using_ec2_spot_instances_with_eks/prerequisites/c9attachrole.png) \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/self_paced.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/self_paced.md index d2ab95e8..1862d908 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/self_paced.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/self_paced.md @@ -22,16 +22,16 @@ as an IAM user with administrator access to the AWS account: [Create a new IAM user to use for the workshop](https://console.aws.amazon.com/iam/home?#/users$new) 1. Enter the user details: -![Create User](/images/using_ec2_spot_instances_with_eks/010_prerequisites/iam-1-create-user.png) +![Create User](/images/using_ec2_spot_instances_with_eks/prerequisites/iam-1-create-user.png) 1. Attach the AdministratorAccess IAM Policy: -![Attach Policy](/images/using_ec2_spot_instances_with_eks/010_prerequisites/iam-2-attach-policy.png) +![Attach Policy](/images/using_ec2_spot_instances_with_eks/prerequisites/iam-2-attach-policy.png) 1. Click to create the new user: -![Confirm User](/images/using_ec2_spot_instances_with_eks/010_prerequisites/iam-3-create-user.png) +![Confirm User](/images/using_ec2_spot_instances_with_eks/prerequisites/iam-3-create-user.png) 1. Take note of the login URL and save: -![Login URL](/images/using_ec2_spot_instances_with_eks/010_prerequisites/iam-4-save-url.png) +![Login URL](/images/using_ec2_spot_instances_with_eks/prerequisites/iam-4-save-url.png) Once you have completed the step above, **you can head straight to [Create a Workspace]({{< ref "/using_ec2_spot_instances_with_eks/010_prerequisites/workspace.md" >}})** \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/update_workspace_settings.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/update_workspace_settings.md index 76217181..1c886b7e 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/update_workspace_settings.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/update_workspace_settings.md @@ -18,7 +18,7 @@ the EKS IAM authentication, so we will disable it and rely on the IAM role inste - Select **AWS SETTINGS** - Turn off **AWS managed temporary credentials** - Close the Preferences tab -![c9disableiam](/images/using_ec2_spot_instances_with_eks/010_prerequisites/c9disableiam.png) +![c9disableiam](/images/using_ec2_spot_instances_with_eks/prerequisites/c9disableiam.png) To ensure temporary credentials aren't already in place we will also remove any existing credentials file: diff --git a/content/using_ec2_spot_instances_with_eks/050_deploy/_index.md b/content/using_ec2_spot_instances_with_eks/050_deploy/_index.md index ca76bed5..79fb1bbe 100644 --- a/content/using_ec2_spot_instances_with_eks/050_deploy/_index.md +++ b/content/using_ec2_spot_instances_with_eks/050_deploy/_index.md @@ -10,4 +10,4 @@ To illustrate application scaling using [Horizontal Pod Autoscaler](https://kube The microservice we will use as an example, is a trivial web service that uses a [Monte Carlo method to approximate pi](https://en.wikipedia.org/wiki/Monte_Carlo_integration) written in go. You can find the application code in in [this github repo](https://github.com/ruecarlo/eks-workshop-sample-api-service-go) -![Monte Carlo Pi Approximation](/images/using_ec2_spot_instances_with_eks/050_deploy/monte_carlo_pi.png) \ No newline at end of file +![Monte Carlo Pi Approximation](/images/using_ec2_spot_instances_with_eks/deploy/monte_carlo_pi.png) \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/050_deploy/deploy.md b/content/using_ec2_spot_instances_with_eks/050_deploy/deploy.md index 26309916..dc4abe0e 100644 --- a/content/using_ec2_spot_instances_with_eks/050_deploy/deploy.md +++ b/content/using_ec2_spot_instances_with_eks/050_deploy/deploy.md @@ -51,7 +51,7 @@ monte-carlo-pi-service. When running that in your browser you should be able to see the json response provided by the service: -![Monte Carlo Pi Approximation Response](/images/using_ec2_spot_instances_with_eks/050_deploy/monte_carlo_pi_output_1.png) +![Monte Carlo Pi Approximation Response](/images/using_ec2_spot_instances_with_eks/deploy/monte_carlo_pi_output_1.png) {{% notice note %}} The application takes the named argument **iterations** to define the number of samples used during the diff --git a/content/using_ec2_spot_instances_with_eks/060_scaling/test_ca.md b/content/using_ec2_spot_instances_with_eks/060_scaling/test_ca.md index dcc0f3a5..da5b9fe8 100644 --- a/content/using_ec2_spot_instances_with_eks/060_scaling/test_ca.md +++ b/content/using_ec2_spot_instances_with_eks/060_scaling/test_ca.md @@ -89,7 +89,7 @@ monte-carlo-pi-service-584f6ddff-fs9x6 1/1 Running 0 103s monte-carlo-pi-service-584f6ddff-jst55 1/1 Running 0 103s ``` You should also be able to visualize the scaling action using kube-ops-view. Kube-ops-view provides an option to highlight pods meeting a regular expression. All pods in green are **monte-carlo-pi-service** pods. -![Scaling up to 10 replicas](/images/using_ec2_spot_instances_with_eks/060_scaling/scaling-kov-10-replicas.png) +![Scaling up to 10 replicas](/images/using_ec2_spot_instances_with_eks/scaling/scaling-kov-10-replicas.png) {{% notice info %}} Given we started from 2 node capacity in both Spot node groups, this should trigger a scaling event for Cluster Autoscaler. Can you predict which size (and type!) of node will be provided? @@ -118,14 +118,14 @@ kubectl get pods --watch ``` Kube-ops-view, will show 3 pending yellow pods outside the node. -![Scale Up](/images/using_ec2_spot_instances_with_eks/060_scaling/scaling-asg-up-kov.png) +![Scale Up](/images/using_ec2_spot_instances_with_eks/scaling/scaling-asg-up-kov.png) When inspecting cluster-autoscaler logs with the command line below ``` kubectl logs -f deployment/cluster-autoscaler -n kube-system ``` you will notice Cluster Autoscaler events similar to: -![CA Scale Up events](/images/using_ec2_spot_instances_with_eks/060_scaling/scaling-asg-up2.png) +![CA Scale Up events](/images/using_ec2_spot_instances_with_eks/scaling/scaling-asg-up2.png) To confirm which type of node has been added you can either use kube-ops-view or execute: @@ -135,7 +135,7 @@ kubectl get node --selector=intent=apps --show-labels You can verify in AWS Management Console to confirm that the Auto Scaling groups are scaling up to meet demand. This may take a few minutes. You can also follow along with the pod deployment from the command line. You should see the pods transition from pending to running as nodes are scaled up. -![Scale Up](/images/using_ec2_spot_instances_with_eks/060_scaling/scaling-asg-up.png) +![Scale Up](/images/using_ec2_spot_instances_with_eks/scaling/scaling-asg-up.png) {{% notice info %}} Cluster Autoscaler expands capacity according to the [Expander](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#what-are-expanders) configuration. By default, Cluster Autoscaler uses the **random** expander. This means that there is equal probability of cluster autoscaler selecting the 4vCPUs 16GB RAM group or the 8vCPUs 32GB RAM group. You may consider also using other expanders like **least-waste**, or the **priority** expander. diff --git a/content/using_ec2_spot_instances_with_eks/080_jenkins/setup_agents.md b/content/using_ec2_spot_instances_with_eks/080_jenkins/setup_agents.md index 4a190901..7326c5d7 100644 --- a/content/using_ec2_spot_instances_with_eks/080_jenkins/setup_agents.md +++ b/content/using_ec2_spot_instances_with_eks/080_jenkins/setup_agents.md @@ -58,9 +58,9 @@ The creation of the workers will take about 3 minutes. 1. On the left hand side click on the **Configure Clouds** link. That will take you to the cloud configuration where Kubernetes. 1. Click on the **Pod Templates...** button to expand the default pod template definition and then click again on **Pod Template Details...*** 1. Change the default pod name, attribute **Name** from `defualt` to `jenkins-agent`. We want to be able to identify the pods that are running in our clusters by name. -![Jenkins Pod Setup 1](/images/using_ec2_spot_instances_with_eks/080_jenkins/jenkinslabels-1.png) +![Jenkins Pod Setup 1](/images/using_ec2_spot_instances_with_eks/jenkins/jenkinslabels-1.png) 1. At the bottom of the page, near the end of the Pod template section, for the **Node Selector** parameter , add the following: `intent=jenkins-agents,lifecycle=Ec2Spot` in order to instruct the Jenkins agent pods to run on the dedicated node group. -![Jenkins Pod Setup 2](/images/using_ec2_spot_instances_with_eks/080_jenkins/jenkinslabels-2.png) +![Jenkins Pod Setup 2](/images/using_ec2_spot_instances_with_eks/jenkins/jenkinslabels-2.png) 1. Click **Save** Now, when Jenkins creates new pods (=agents), these will be created with a Node Selector that instructs the kube-scheduler to only deploy the pods on nodes with the above mentioned labels, which only exist in the dedicated Jenkins nodegroup. From 860a7a5e7b19dbee9361685bd70d0699fb4449ae Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Thu, 7 Oct 2021 13:27:46 +0800 Subject: [PATCH 32/39] Cloud9 Updates --- .../010_prerequisites/at_an_aws_updateiam.md | 3 ++- .../prerequisites/cloud9-role.png | Bin 0 -> 192726 bytes 2 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 static/images/using_ec2_spot_instances_with_eks/prerequisites/cloud9-role.png diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/at_an_aws_updateiam.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/at_an_aws_updateiam.md index 3c6808cf..469debd0 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/at_an_aws_updateiam.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/at_an_aws_updateiam.md @@ -7,7 +7,8 @@ hidden: true ## Attach the IAM role to your Workspace -1. Follow this [deep link to find your Cloud9 EC2 instance](https://console.aws.amazon.com/ec2/v2/home?#Instances:tag:Name=aws-cloud9-eksworkshop;sort=desc:launchTime). +1. Click the grey circle button (in top right corner) and select **Manage EC2 Instance**. +![cloud9Role](/images/using_ec2_spot_instances_with_eks/prerequisites/cloud9-role.png) 1. Select the instance, then choose **Actions / Security / Modify IAM role** ![c9instancerole](/images/using_ec2_spot_instances_with_eks/prerequisites/c9instancerole.png) 1. 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.../selecting_instance_types.md | 17 ++- .../workers_eksctl.md | 110 +++++++++++++----- 8 files changed, 177 insertions(+), 71 deletions(-) diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/k8stools.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/k8stools.md index 741b57eb..3caa708c 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/k8stools.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/k8stools.md @@ -16,7 +16,7 @@ for the download links.](https://docs.aws.amazon.com/eks/latest/userguide/gettin #### Install kubectl ``` -export KUBECTL_VERSION=v1.20.0 +export KUBECTL_VERSION=v1.21.2 sudo curl --silent --location -o /usr/local/bin/kubectl https://storage.googleapis.com/kubernetes-release/release/${KUBECTL_VERSION}/bin/linux/amd64/kubectl sudo chmod +x /usr/local/bin/kubectl ``` diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/on_your_own_updateiam.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/on_your_own_updateiam.md index 5afbf2a7..8617eb9a 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/on_your_own_updateiam.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/on_your_own_updateiam.md @@ -16,7 +16,8 @@ hidden: true ## Attach the IAM role to your Workspace -1. Follow this [deep link to find your Cloud9 EC2 instance](https://console.aws.amazon.com/ec2/v2/home?#Instances:tag:Name=aws-cloud9-eksworkshop;sort=desc:launchTime). +1. Click the grey circle button (in top right corner) and select **Manage EC2 Instance**. +![cloud9Role](/images/using_ec2_spot_instances_with_eks/prerequisites/cloud9-role.png) 1. Select the instance, then choose **Actions / Security / Modify IAM role** ![c9instancerole](/images/using_ec2_spot_instances_with_eks/prerequisites/c9instancerole.png) 1. Choose **eksworkshop-admin** from the **IAM role** drop down, and select **Save** diff --git a/content/using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md b/content/using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md index edf453d9..4f730800 100644 --- a/content/using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md +++ b/content/using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md @@ -8,14 +8,55 @@ hidden: true This markdown file is used as part of another file using 'insert-md-from-file' shortcode --> +Create an eksctl deployment file (eksworkshop.yaml) use in creating your cluster using the following syntax: + ```bash -eksctl create cluster \ - --version=1.20 \ - --name=eksworkshop-eksctl \ - --node-private-networking \ - --managed --nodes=2 \ - --alb-ingress-access \ - --region=${AWS_REGION} \ - --node-labels="intent=control-apps" \ - --asg-access +cat << EOF > eksworkshop.yaml +--- +apiVersion: eksctl.io/v1alpha5 +kind: ClusterConfig + +metadata: + name: eksworkshop-eksctl + region: ${AWS_REGION} + version: "1.21" + +managedNodeGroups: +- amiFamily: AmazonLinux2 + instanceType: m5.large + name: ng-od-m5large + desiredCapacity: 2 + maxSize: 2 + minSize: 0 + labels: + alpha.eksctl.io/cluster-name: eksworkshop-eksctl + alpha.eksctl.io/nodegroup-name: ng-od-m5large + intent: control-apps + tags: + alpha.eksctl.io/nodegroup-name: ng-od-m5large + alpha.eksctl.io/nodegroup-type: managed + k8s.io/cluster-autoscaler/node-template/label/intent: control-apps + ssh: + enableSsm: true + iam: + withAddonPolicies: + autoScaler: true + privateNetworking: true + +# To enable all of the control plane logs, uncomment below: +# cloudWatch: +# clusterLogging: +# enableTypes: ["*"] + +EOF ``` + +Next, use the file you created as the input for the eksctl cluster creation. + +```bash +eksctl create cluster -f eksworkshop.yaml +``` + +{{% notice info %}} +Launching EKS and all the dependencies will take approximately 15 minutes +{{% /notice %}} \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md b/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md index d3446e69..70df0095 100644 --- a/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md +++ b/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md @@ -24,18 +24,10 @@ If you do see the correct role, proceed to next step to create an EKS cluster. ### Create an EKS cluster -The following command will create an eks cluster with the name `eksworkshop-eksctl`. It will also create a nodegroup with 2 on-demand instances. - {{% insert-md-from-file file="using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md" %}} eksctl allows us to pass parameters to initialize the cluster. While initializing the cluster, eksctl does also allow us to create nodegroups. The managed nodegroup will have two m5.large nodes (m5.large is the default instance type used if no instance types are specified) and it will bootstrap with the label **intent=control-apps**. -Amazon EKS adds the following Kubernetes label to all nodes in your managed node group: **eks.amazonaws.com/capacityType: ON_DEMAND**. You can use this label to schedule stateful or fault intolerant applications on On-Demand nodes. - -{{% notice info %}} -Launching EKS and all the dependencies will take approximately **15 minutes** -{{% /notice %}} - -The command above, created a **Managed Node Group**. [Amazon EKS managed node groups](https://docs.aws.amazon.com/eks/latest/userguide/managed-node-groups.html) automate the provisioning and lifecycle management of nodes. Managed Nodegroups use the latest [EKS-optimized AMIs](https://docs.aws.amazon.com/eks/latest/userguide/eks-optimized-ami.html). The node run in your AWS account provisioned as apart of an EC2 Auto Scaling group that is managed for you by Amazon EKS. This means EKS takes care of the lifecycle management and undifferentiated heavy lifting on operations such as node updates, handling of terminations, gracefully drain of nodes to ensure that your applications stay available. \ No newline at end of file +Amazon EKS adds the following Kubernetes label to all nodes in your managed node group: **eks.amazonaws.com/capacityType: ON_DEMAND**. You can use this label to schedule stateful or fault intolerant applications on On-Demand nodes. \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/020_eksctl/prerequisites.md b/content/using_ec2_spot_instances_with_eks/020_eksctl/prerequisites.md index 12d82789..c7f3acb9 100644 --- a/content/using_ec2_spot_instances_with_eks/020_eksctl/prerequisites.md +++ b/content/using_ec2_spot_instances_with_eks/020_eksctl/prerequisites.md @@ -6,7 +6,7 @@ weight: 10 For this module, we need to download the [eksctl](https://eksctl.io/) binary: ``` -export EKSCTL_VERSION=0.51.0 +export EKSCTL_VERSION=v0.68.0 curl --silent --location "https://github.com/weaveworks/eksctl/releases/download/${EKSCTL_VERSION}/eksctl_$(uname -s)_amd64.tar.gz" | tar xz -C /tmp sudo mv -v /tmp/eksctl /usr/local/bin diff --git a/content/using_ec2_spot_instances_with_eks/020_eksctl/test.md b/content/using_ec2_spot_instances_with_eks/020_eksctl/test.md index 9d979992..d8f3daae 100644 --- a/content/using_ec2_spot_instances_with_eks/020_eksctl/test.md +++ b/content/using_ec2_spot_instances_with_eks/020_eksctl/test.md @@ -40,29 +40,46 @@ You might see **Error loading Namespaces** while exploring the cluster on the AW {{%expand "Click to reveal detailed instructions" %}} -### Add your IAM role Arn as cluster-admin on RBAC +### Console Credentials -Get the ARN for your IAM role, it should look something like +The EKS console allows you to see not only the configuration aspects of your cluster, but also to view Kubernetes cluster objects such as Deployments, Pods, and Nodes. For this type of access, the console IAM User or Role needs to be granted permission within the cluster. -``` -arn:aws:iam:::role/ -``` +By default, the credentials used to create the cluster are automatically granted these permissions. Following along in the workshop, you've created a cluster using temporary IAM credentials from within Cloud9. This means that you'll need to add your AWS Console credentials to the cluster. + +#### Import your EKS Console credentials to your new cluster: + +IAM Users and Roles are bound to an EKS Kubernetes cluster via a ConfigMap named `aws-auth`. We can use `eksctl` to do this with one command. + +You'll need to determine the correct credential to add for your AWS Console access. If you know this already, you can skip ahead to the `eksctl create iamidentitymapping` step below. -Edit the ConfigMap **aws-auth** using the below command +If you've built your cluster from Cloud9 as part of this tutorial, invoke the following within your environment to determine your IAM Role or User ARN. +```bash +c9builder=$(aws cloud9 describe-environment-memberships --environment-id=$C9_PID | jq -r '.memberships[].userArn') +if echo ${c9builder} | grep -q user; then + rolearn=${c9builder} + echo Role ARN: ${rolearn} +elif echo ${c9builder} | grep -q assumed-role; then + assumedrolename=$(echo ${c9builder} | awk -F/ '{print $(NF-1)}') + rolearn=$(aws iam get-role --role-name ${assumedrolename} --query Role.Arn --output text) + echo Role ARN: ${rolearn} +fi ``` -kubectl edit configmap -n kube-system aws-auth + +With your ARN in hand, you can issue the command to create the identity mapping within the cluster. + +```bash +eksctl create iamidentitymapping --cluster eksworkshop-eksctl --arn ${rolearn} --group system:masters --username admin ``` -Add the below snippet at the end, that will add the IAM role to the **masters** group on EKS cluster RBAC, thereby assigning a **cluster-admin** role on the cluster. Please refer the documentation [here](https://docs.aws.amazon.com/eks/latest/userguide/add-user-role.html) +Note that permissions can be restricted and granular but as this is a workshop cluster, you're adding your console credentials as administrator. -Please make sure to replace the `` and `` with your AWS Account Number and IAM Role Name respectively +Now you can verify your entry in the AWS auth map within the console. +```bash +kubectl describe configmap -n kube-system aws-auth ``` - - groups: - - system:masters - rolearn: arn:aws:iam:::role/ - username: -``` + +Now you're all set to move on. For more information, check out the [EKS documentation](https://docs.aws.amazon.com/eks/latest/userguide/add-user-role.html) on this topic. {{% /expand%}} diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/selecting_instance_types.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/selecting_instance_types.md index 5b9aaac9..6134b11c 100644 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/selecting_instance_types.md +++ b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/selecting_instance_types.md @@ -35,7 +35,7 @@ There are over 350 different instance types available on EC2 which can make the Let's first install **amazon-ec2-instance-selector** : ``` -curl -Lo ec2-instance-selector https://github.com/aws/amazon-ec2-instance-selector/releases/download/v1.3.0/ec2-instance-selector-`uname | tr '[:upper:]' '[:lower:]'`-amd64 && chmod +x ec2-instance-selector +curl -Lo ec2-instance-selector https://github.com/aws/amazon-ec2-instance-selector/releases/download/v2.0.3/ec2-instance-selector-`uname | tr '[:upper:]' '[:lower:]'`-amd64 && chmod +x ec2-instance-selector sudo mv ec2-instance-selector /usr/local/bin/ ec2-instance-selector --version ``` @@ -47,7 +47,7 @@ we need to first get a group of instances that meet the 4vCPUs and 16GB of RAM. Run the following command to get the list of instances. ```bash -ec2-instance-selector --vcpus 4 --memory 16384 --gpus 0 --current-generation -a x86_64 --deny-list '.*n.*' +ec2-instance-selector --vcpus 4 --memory 16 --gpus 0 --current-generation -a x86_64 --deny-list '.*z.*|.*i.*' ``` This should display a list like the one that follows (note results might differ depending on the region). We will use this instances as part of one of our node groups. @@ -56,18 +56,22 @@ This should display a list like the one that follows (note results might differ m4.xlarge m5.xlarge m5a.xlarge +m5ad.xlarge m5d.xlarge +m5dn.xlarge +m5n.xlarge t2.xlarge t3.xlarge -t3a.xlarge +t3a.xlarge ``` Internally ec2-instance-selector is making calls to the [DescribeInstanceTypes](https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeInstanceTypes.html) for the specific region and filtering the intstances based on the criteria selected in the command line, in our case we did filter for instances that meet the following criteria: + * Instances with no GPUs * of x86_64 Architecture (no ARM instances like A1 or m6g instances for example) * Instances that have 4 vCPUs and 16GB of Ram * Instances of current generation (4th gen onwards) - * Instances that don't meet the regular expresion `.*n.*`, so effectively m5n, m5dn. + * Instances that don't meet the regular expresion `.*z.*|.*i.*`, so effectively `m5zn`, `m6i` instances which are not supported by EKS. {{% notice warning %}} Your workload may have other constraints that you should consider when selecting instance types. For example. **t2** and **t3** instance types are [burstable instances](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/burstable-performance-instances.html) and might not be appropriate for CPU bound workloads that require CPU execution determinism. Instances such as m5**a** are [AMD Instances](https://aws.amazon.com/ec2/amd/), if your workload is sensitive to numerical differences (i.e: financial risk calculations, industrial simulations) mixing these instance types might not be appropriate. @@ -87,7 +91,7 @@ Find out another group that adheres to a 1vCPU:4GB ratio, this time using instan That should be easy. You can run the command: ```bash -ec2-instance-selector --vcpus 8 --memory 32768 --gpus 0 --current-generation -a x86_64 --deny-list '.*n.*|.*h.*' +ec2-instance-selector --vcpus 8 --memory 32 --gpus 0 --current-generation -a x86_64 --deny-list '.*z.*|.*i.*' ``` which should yield a list as follows @@ -96,7 +100,10 @@ which should yield a list as follows m4.2xlarge m5.2xlarge m5a.2xlarge +m5ad.2xlarge m5d.2xlarge +m5dn.2xlarge +m5n.2xlarge t2.2xlarge t3.2xlarge t3a.2xlarge diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md index 8450e92b..23d2cfd4 100644 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md +++ b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md @@ -10,38 +10,86 @@ In this section we will deploy the instance types we selected and request nodegr Let's first create the configuration file: ```bash -eksctl create nodegroup \ - --cluster=eksworkshop-eksctl \ - --region=$AWS_REGION \ - --dry-run \ - --managed \ - --spot \ - --name=dev-4vcpu-16gb-spot \ - --nodes=2 \ - --nodes-min=1 \ - --nodes-max=5 \ - --node-labels="intent=apps" \ - --tags="k8s.io/cluster-autoscaler/node-template/label/intent=apps" \ - --instance-types m4.xlarge,m5.xlarge,m5a.xlarge,m5ad.xlarge,m5d.xlarge,t2.xlarge,t3.xlarge,t3a.xlarge \ - --asg-access \ - > ~/environment/spot_nodegroup_4vcpu_16gb.yml - -eksctl create nodegroup \ - --cluster=eksworkshop-eksctl \ - --region=$AWS_REGION \ - --dry-run \ - --managed \ - --spot \ - --name=dev-8vcpu-32gb-spot \ - --nodes=2 \ - --nodes-min=1 \ - --nodes-max=5 \ - --node-labels="intent=apps" \ - --tags="k8s.io/cluster-autoscaler/node-template/label/intent=apps" \ - --instance-types m4.2xlarge,m5.2xlarge,m5a.2xlarge,m5ad.2xlarge,m5d.2xlarge,t2.2xlarge,t3.2xlarge,t3a.2xlarge \ - --asg-access \ - > ~/environment/spot_nodegroup_8vcpu_32gb.yml +cat << EOF > addnodegroups-spot.yaml +--- +apiVersion: eksctl.io/v1alpha5 +kind: ClusterConfig +managedNodeGroups: +- name: ng-spot-4vcpu-16gb + amiFamily: AmazonLinux2 + desiredCapacity: 2 + minSize: 0 + maxSize: 5 + spot: true + instanceTypes: + - m4.xlarge + - m5.xlarge + - m5a.xlarge + - m5ad.xlarge + - m5d.xlarge + - m5dn.xlarge + - m5n.xlarge + - t2.xlarge + - t3.xlarge + - t3a.xlarge + iam: + withAddonPolicies: + autoScaler: true + privateNetworking: true + labels: + alpha.eksctl.io/cluster-name: eksworkshop-eksctl + alpha.eksctl.io/nodegroup-name: ng-spot-4vcpu-16gb + intent: apps + tags: + alpha.eksctl.io/nodegroup-name: ng-spot-4vcpu-16gb + alpha.eksctl.io/nodegroup-type: managed + +- name: ng-spot-8vcpu-32gb + amiFamily: AmazonLinux2 + desiredCapacity: 2 + minSize: 0 + maxSize: 5 + spot: true + instanceTypes: + - m4.2xlarge + - m5.2xlarge + - m5a.2xlarge + - m5ad.2xlarge + - m5d.2xlarge + - m5dn.2xlarge + - m5n.2xlarge + - t2.2xlarge + - t3.2xlarge + - t3a.2xlarge + iam: + withAddonPolicies: + autoScaler: true + privateNetworking: true + labels: + alpha.eksctl.io/cluster-name: eksworkshop-eksctl + alpha.eksctl.io/nodegroup-name: ng-spot-8vcpu-32gb + intent: apps + tags: + alpha.eksctl.io/nodegroup-name: ng-spot-8vcpu-32gb + alpha.eksctl.io/nodegroup-type: managed + +metadata: + name: eksworkshop-eksctl + region: ap-southeast-1 + tags: + k8s.io/cluster-autoscaler/node-template/label/intent: apps + version: "1.21" + +EOF +``` +Create the new EKS managed nodegroup with Spot Instances. + +```sh +eksctl create nodegroup --config-file=addnodegroups-spot.yaml ``` +{{% notice info %}} +Creation of node group will take 3-4 minutes. +{{% /notice %}} This will create 2 files, `spot_nodegroups_4vcpu_16gb.yml` and `spot_nodegroups_8vcpu_32gb.yml`, that we will use to instruct eksctl to create two nodegroups, both with a diversified configuration. From ab78de70c5daec132f94aadd603c66b4f80fb707 Mon Sep 17 00:00:00 2001 From: Jagdeep <70941399+jagpk@users.noreply.github.com> Date: Tue, 12 Oct 2021 13:09:56 +0800 Subject: [PATCH 34/39] Updated Cluster Autoscaler to 1.21 --- .../060_scaling/deploy_ca.files/cluster_autoscaler.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.files/cluster_autoscaler.yml b/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.files/cluster_autoscaler.yml index 3b9e6fc9..c36b20f0 100644 --- a/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.files/cluster_autoscaler.yml +++ b/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.files/cluster_autoscaler.yml @@ -132,15 +132,15 @@ spec: nodeSelector: intent: control-apps containers: - - image: k8s.gcr.io/autoscaling/cluster-autoscaler:v1.20.0 + - image: k8s.gcr.io/autoscaling/cluster-autoscaler:v1.21.1 name: cluster-autoscaler resources: limits: - cpu: 100m - memory: 300Mi + cpu: 200m + memory: 600Mi requests: - cpu: 100m - memory: 300Mi + cpu: 200m + memory: 600Mi command: - ./cluster-autoscaler - --v=4 From d4c82bbc9968d63c9c852e9b2d424e3a3f6f97fa Mon Sep 17 00:00:00 2001 From: Jagdeep <70941399+jagpk@users.noreply.github.com> Date: Wed, 13 Oct 2021 16:56:03 +0800 Subject: [PATCH 35/39] Self Managed Node Groups --- .../_index.md | 0 .../deployhandler.md | 0 .../preferspot.files/deployment-solution.yml | 0 .../selecting_instance_types.md | 0 .../spotworkers.files/cloudformation_mixed_workers.yml | 0 .../spotworkers.files/eksctl_mixed_workers_bootstrap.yml | 0 .../workers_eksctl.md | 0 7 files changed, 0 insertions(+), 0 deletions(-) rename content/using_ec2_spot_instances_with_eks/{070_spotworkers => 070_selfmanagednodegroupswithspot}/_index.md (100%) rename content/using_ec2_spot_instances_with_eks/{070_spotworkers => 070_selfmanagednodegroupswithspot}/deployhandler.md (100%) rename content/using_ec2_spot_instances_with_eks/{070_spotworkers => 070_selfmanagednodegroupswithspot}/preferspot.files/deployment-solution.yml (100%) rename content/using_ec2_spot_instances_with_eks/{070_spotworkers => 070_selfmanagednodegroupswithspot}/selecting_instance_types.md (100%) rename content/using_ec2_spot_instances_with_eks/{070_spotworkers => 070_selfmanagednodegroupswithspot}/spotworkers.files/cloudformation_mixed_workers.yml (100%) rename content/using_ec2_spot_instances_with_eks/{070_spotworkers => 070_selfmanagednodegroupswithspot}/spotworkers.files/eksctl_mixed_workers_bootstrap.yml (100%) rename content/using_ec2_spot_instances_with_eks/{070_spotworkers => 070_selfmanagednodegroupswithspot}/workers_eksctl.md (100%) diff --git a/content/using_ec2_spot_instances_with_eks/070_spotworkers/_index.md b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/_index.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/070_spotworkers/_index.md rename to content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/_index.md diff --git a/content/using_ec2_spot_instances_with_eks/070_spotworkers/deployhandler.md b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/deployhandler.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/070_spotworkers/deployhandler.md rename to content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/deployhandler.md diff --git a/content/using_ec2_spot_instances_with_eks/070_spotworkers/preferspot.files/deployment-solution.yml b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/preferspot.files/deployment-solution.yml similarity index 100% rename from content/using_ec2_spot_instances_with_eks/070_spotworkers/preferspot.files/deployment-solution.yml rename to content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/preferspot.files/deployment-solution.yml diff --git a/content/using_ec2_spot_instances_with_eks/070_spotworkers/selecting_instance_types.md b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/selecting_instance_types.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/070_spotworkers/selecting_instance_types.md rename to content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/selecting_instance_types.md diff --git a/content/using_ec2_spot_instances_with_eks/070_spotworkers/spotworkers.files/cloudformation_mixed_workers.yml b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/spotworkers.files/cloudformation_mixed_workers.yml similarity index 100% rename from content/using_ec2_spot_instances_with_eks/070_spotworkers/spotworkers.files/cloudformation_mixed_workers.yml rename to content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/spotworkers.files/cloudformation_mixed_workers.yml diff --git a/content/using_ec2_spot_instances_with_eks/070_spotworkers/spotworkers.files/eksctl_mixed_workers_bootstrap.yml b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/spotworkers.files/eksctl_mixed_workers_bootstrap.yml similarity index 100% rename from content/using_ec2_spot_instances_with_eks/070_spotworkers/spotworkers.files/eksctl_mixed_workers_bootstrap.yml rename to content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/spotworkers.files/eksctl_mixed_workers_bootstrap.yml diff --git a/content/using_ec2_spot_instances_with_eks/070_spotworkers/workers_eksctl.md b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/workers_eksctl.md similarity index 100% rename from content/using_ec2_spot_instances_with_eks/070_spotworkers/workers_eksctl.md rename to content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/workers_eksctl.md From 55e35976474c8001ef448b14c69463bab3ae8027 Mon Sep 17 00:00:00 2001 From: Jagdeep <70941399+jagpk@users.noreply.github.com> Date: Wed, 13 Oct 2021 17:30:43 +0800 Subject: [PATCH 36/39] EKS MNGs --- .../create_eks_cluster_eksctl_command.md | 6 +- .../spot_nodegroup_4vcpu_16gb.yml | 58 ------------------- .../spot_nodegroup_8vcpu_32gb.yml | 58 ------------------- .../workers_eksctl.md | 16 ++--- .../workers_eksctl.md | 10 ++-- 5 files changed, 16 insertions(+), 132 deletions(-) delete mode 100644 content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.files/spot_nodegroup_4vcpu_16gb.yml delete mode 100644 content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.files/spot_nodegroup_8vcpu_32gb.yml diff --git a/content/using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md b/content/using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md index 4f730800..12840682 100644 --- a/content/using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md +++ b/content/using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md @@ -24,16 +24,16 @@ metadata: managedNodeGroups: - amiFamily: AmazonLinux2 instanceType: m5.large - name: ng-od-m5large + name: mng-od-m5large desiredCapacity: 2 maxSize: 2 minSize: 0 labels: alpha.eksctl.io/cluster-name: eksworkshop-eksctl - alpha.eksctl.io/nodegroup-name: ng-od-m5large + alpha.eksctl.io/nodegroup-name: mng-od-m5large intent: control-apps tags: - alpha.eksctl.io/nodegroup-name: ng-od-m5large + alpha.eksctl.io/nodegroup-name: mng-od-m5large alpha.eksctl.io/nodegroup-type: managed k8s.io/cluster-autoscaler/node-template/label/intent: control-apps ssh: diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.files/spot_nodegroup_4vcpu_16gb.yml b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.files/spot_nodegroup_4vcpu_16gb.yml deleted file mode 100644 index a3ce9be6..00000000 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.files/spot_nodegroup_4vcpu_16gb.yml +++ /dev/null @@ -1,58 +0,0 @@ -apiVersion: eksctl.io/v1alpha5 -kind: ClusterConfig -managedNodeGroups: -- amiFamily: AmazonLinux2 - desiredCapacity: 2 - disableIMDSv1: false - disablePodIMDS: false - iam: - withAddonPolicies: - albIngress: false - appMesh: false - appMeshPreview: false - autoScaler: false - certManager: false - cloudWatch: false - ebs: false - efs: false - externalDNS: false - fsx: false - imageBuilder: false - xRay: false - instanceTypes: - - m4.xlarge - - m5.xlarge - - m5a.xlarge - - m5ad.xlarge - - m5d.xlarge - - t2.xlarge - - t3.xlarge - - t3a.xlarge - labels: - alpha.eksctl.io/cluster-name: eksworkshop-eksctl - alpha.eksctl.io/nodegroup-name: dev-4vcpu-16gb-spot - intent: apps - maxSize: 5 - minSize: 1 - name: dev-4vcpu-16gb-spot - privateNetworking: false - securityGroups: - withLocal: null - withShared: null - spot: true - ssh: - allow: false - enableSsm: false - publicKeyPath: "" - tags: - alpha.eksctl.io/nodegroup-name: dev-4vcpu-16gb-spot - alpha.eksctl.io/nodegroup-type: managed - k8s.io/cluster-autoscaler/node-template/label/intent: apps - volumeIOPS: 3000 - volumeSize: 80 - volumeThroughput: 125 - volumeType: gp3 -metadata: - name: eksworkshop-eksctl - region: $AWS_REGION - version: auto diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.files/spot_nodegroup_8vcpu_32gb.yml b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.files/spot_nodegroup_8vcpu_32gb.yml deleted file mode 100644 index b5f43a1b..00000000 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.files/spot_nodegroup_8vcpu_32gb.yml +++ /dev/null @@ -1,58 +0,0 @@ -apiVersion: eksctl.io/v1alpha5 -kind: ClusterConfig -managedNodeGroups: -- amiFamily: AmazonLinux2 - desiredCapacity: 2 - disableIMDSv1: false - disablePodIMDS: false - iam: - withAddonPolicies: - albIngress: false - appMesh: false - appMeshPreview: false - autoScaler: false - certManager: false - cloudWatch: false - ebs: false - efs: false - externalDNS: false - fsx: false - imageBuilder: false - xRay: false - instanceTypes: - - m4.2xlarge - - m5.2xlarge - - m5a.2xlarge - - m5ad.2xlarge - - m5d.2xlarge - - t2.2xlarge - - t3.2xlarge - - t3a.2xlarge - labels: - alpha.eksctl.io/cluster-name: eksworkshop-eksctl - alpha.eksctl.io/nodegroup-name: dev-8vcpu-32gb-spot - intent: apps - maxSize: 5 - minSize: 1 - name: dev-8vcpu-32gb-spot - privateNetworking: false - securityGroups: - withLocal: null - withShared: null - spot: true - ssh: - allow: false - enableSsm: false - publicKeyPath: "" - tags: - alpha.eksctl.io/nodegroup-name: dev-8vcpu-32gb-spot - alpha.eksctl.io/nodegroup-type: managed - k8s.io/cluster-autoscaler/node-template/label/intent: apps - volumeIOPS: 3000 - volumeSize: 80 - volumeThroughput: 125 - volumeType: gp3 -metadata: - name: eksworkshop-eksctl - region: $AWS_REGION - version: auto diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md index 23d2cfd4..7e126410 100644 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md +++ b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md @@ -10,12 +10,12 @@ In this section we will deploy the instance types we selected and request nodegr Let's first create the configuration file: ```bash -cat << EOF > addnodegroups-spot.yaml +cat << EOF > add-mngs-spot.yaml --- apiVersion: eksctl.io/v1alpha5 kind: ClusterConfig managedNodeGroups: -- name: ng-spot-4vcpu-16gb +- name: mng-spot-4vcpu-16gb amiFamily: AmazonLinux2 desiredCapacity: 2 minSize: 0 @@ -38,13 +38,13 @@ managedNodeGroups: privateNetworking: true labels: alpha.eksctl.io/cluster-name: eksworkshop-eksctl - alpha.eksctl.io/nodegroup-name: ng-spot-4vcpu-16gb + alpha.eksctl.io/nodegroup-name: mng-spot-4vcpu-16gb intent: apps tags: - alpha.eksctl.io/nodegroup-name: ng-spot-4vcpu-16gb + alpha.eksctl.io/nodegroup-name: mng-spot-4vcpu-16gb alpha.eksctl.io/nodegroup-type: managed -- name: ng-spot-8vcpu-32gb +- name: mng-spot-8vcpu-32gb amiFamily: AmazonLinux2 desiredCapacity: 2 minSize: 0 @@ -67,10 +67,10 @@ managedNodeGroups: privateNetworking: true labels: alpha.eksctl.io/cluster-name: eksworkshop-eksctl - alpha.eksctl.io/nodegroup-name: ng-spot-8vcpu-32gb + alpha.eksctl.io/nodegroup-name: mng-spot-8vcpu-32gb intent: apps tags: - alpha.eksctl.io/nodegroup-name: ng-spot-8vcpu-32gb + alpha.eksctl.io/nodegroup-name: mng-spot-8vcpu-32gb alpha.eksctl.io/nodegroup-type: managed metadata: @@ -85,7 +85,7 @@ EOF Create the new EKS managed nodegroup with Spot Instances. ```sh -eksctl create nodegroup --config-file=addnodegroups-spot.yaml +eksctl create nodegroup --config-file=add-mngs-spot.yaml ``` {{% notice info %}} Creation of node group will take 3-4 minutes. diff --git a/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/workers_eksctl.md b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/workers_eksctl.md index 817947e8..4672d6b9 100644 --- a/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/workers_eksctl.md +++ b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/workers_eksctl.md @@ -9,14 +9,14 @@ In this section we will deploy the instance types we selected and request nodegr Let's first create the configuration file: ``` -cat < ~/environment/spot_nodegroups.yml +cat < ~/environment/add-ngs-spot.yaml apiVersion: eksctl.io/v1alpha5 kind: ClusterConfig metadata: name: eksworkshop-eksctl region: $AWS_REGION nodeGroups: - - name: dev-4vcpu-16gb-spot + - name: ng-spot-4vcpu-16gb minSize: 0 maxSize: 5 desiredCapacity: 1 @@ -41,7 +41,7 @@ nodeGroups: autoScaler: true cloudWatch: true albIngress: true - - name: dev-8vcpu-32gb-spot + - name: ng-spot-8vcpu-32gb minSize: 0 maxSize: 5 desiredCapacity: 1 @@ -69,10 +69,10 @@ nodeGroups: EoF ``` -This will create a `spot_nodegroups.yml` file that we will use to instruct eksctl to create two nodegroups, both with a diversified configuration. +This will create a `add-ngs-spot.yaml` file that we will use to instruct eksctl to create two nodegroups, both with a diversified configuration. ``` -eksctl create nodegroup -f spot_nodegroups.yml +eksctl create nodegroup -f add-ngs-spot.yaml ``` {{% notice note %}} From 5184846ead45a6b5a39d34f8518829891782f166 Mon Sep 17 00:00:00 2001 From: Jagdeep <70941399+jagpk@users.noreply.github.com> Date: Thu, 14 Oct 2021 18:33:45 +0800 Subject: [PATCH 37/39] EKS Cluster Template --- .../010_prerequisites/aws_event.md | 2 -- .../eks-spot-workshop-quickstart-cnf.yml | 11 +++--- .../prerequisites.files/ekscluster.yaml | 35 +++++++++++++++++++ .../workers_eksctl.md | 34 ++++-------------- 4 files changed, 47 insertions(+), 35 deletions(-) create mode 100644 content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/ekscluster.yaml diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/aws_event.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/aws_event.md index 9ab19005..6d96bb50 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/aws_event.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/aws_event.md @@ -42,8 +42,6 @@ We have deployed the below resources required to get started with the workshop u + An EKS cluster with the name `eksworkshop-eksctl` and a [EKS managed node group](https://docs.aws.amazon.com/eks/latest/userguide/managed-node-groups.html) with 2 on-demand instances. -{{% insert-md-from-file file="using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md" %}} - #### Use your resources diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/eks-spot-workshop-quickstart-cnf.yml b/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/eks-spot-workshop-quickstart-cnf.yml index a94368a0..ee40f5bd 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/eks-spot-workshop-quickstart-cnf.yml +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/eks-spot-workshop-quickstart-cnf.yml @@ -28,22 +28,22 @@ Parameters: C9KubectlVersion: Description: Cloud9 instance kubectl version Type: String - Default: v1.18.12 + Default: v1.21.2 ConstraintDescription: Must be a valid kubectl version C9KubectlVersionTEST: Description: Cloud9 instance kubectl version Type: String - Default: v1.18.12 + Default: v1.21.2 ConstraintDescription: Must be a valid kubectl version C9EKSctlVersion: Description: Cloud9 instance eksctl version Type: String - Default: 0.31.0 + Default: v0.68.0 ConstraintDescription: Must be a valid eksctl version EKSClusterVersion: Description: EKS Cluster Vesion Type: String - Default: 1.18 + Default: 1.21 ConstraintDescription: Must be a valid eks version EKSClusterName: Description: EKS Cluster Name @@ -277,7 +277,8 @@ Resources: - sudo mv -v /tmp/eksctl /usr/local/bin - sudo -H -u ec2-user /usr/local/bin/eksctl version - echo '=== Create EKS Cluster ===' - - !Sub 'sudo -H -u ec2-user /usr/local/bin/eksctl create cluster --version=${EKSClusterVersion} --name=${EKSClusterName} --node-private-networking --managed --nodes=2 --alb-ingress-access --region=${AWS::Region} --node-labels="lifecycle=OnDemand,intent=control-apps" --asg-access' + - curl --silent --location -o /home/ec2-user "https://github.com/weaveworks/eksctl/releases/download/${EKSCTL_VERSION}/eksctl_Linux_amd64.tar.gz" | tar xz -C /tmp + - !Sub 'sudo -H -u ec2-user /usr/local/bin/eksctl create cluster -f /home/ec2-user/eksworkshop.yaml' - sudo -H -u ec2-user /usr/local/bin/kubectl get nodes C9BootstrapAssociation: diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/ekscluster.yaml b/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/ekscluster.yaml new file mode 100644 index 00000000..980af29b --- /dev/null +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/ekscluster.yaml @@ -0,0 +1,35 @@ +--- +apiVersion: eksctl.io/v1alpha5 +kind: ClusterConfig + +metadata: + name: eksworkshop-eksctl + region: ap-southeast-1 + version: "1.21" + +managedNodeGroups: +- amiFamily: AmazonLinux2 + instanceType: m5.large + name: mng-od-m5large + desiredCapacity: 2 + maxSize: 2 + minSize: 0 + labels: + alpha.eksctl.io/cluster-name: eksworkshop-eksctl + alpha.eksctl.io/nodegroup-name: mng-od-m5large + intent: control-apps + tags: + alpha.eksctl.io/nodegroup-name: mng-od-m5large + alpha.eksctl.io/nodegroup-type: managed + k8s.io/cluster-autoscaler/node-template/label/intent: control-apps + ssh: + enableSsm: true + iam: + withAddonPolicies: + autoScaler: true + privateNetworking: true + +# To enable all of the control plane logs, uncomment below: +# cloudWatch: +# clusterLogging: +# enableTypes: ["*"] \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md index 7e126410..f49e4df7 100644 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md +++ b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md @@ -91,39 +91,17 @@ eksctl create nodegroup --config-file=add-mngs-spot.yaml Creation of node group will take 3-4 minutes. {{% /notice %}} -This will create 2 files, `spot_nodegroups_4vcpu_16gb.yml` and `spot_nodegroups_8vcpu_32gb.yml`, that we will use to instruct eksctl to create two nodegroups, both with a diversified configuration. - -Let's edit the 2 configuration files before using it to create the node groups: - -1. Remove the section **instanceSelector:**. - -If you are still struggling with the implementation, the solution files are available here - -Note: Remember to change the region *$AWS_REGION* to *your region* before creating the node groups. - -{{%attachments title="Related files" pattern=".yml"/%}} - -Create the two node groups: - -```bash -eksctl create nodegroup -f spot_nodegroup_4vcpu_16gb.yml -``` - -```bash -eksctl create nodegroup -f spot_nodegroup_8vcpu_32gb.yml -``` - -{{% notice note %}} -The creation of each node group will take about 3 minutes. -{{% /notice %}} There are a few things to note in the configuration that we just used to create these nodegroups. * The configuration setup the nodes under the **managedNodeGroups** section. This is to indicate the node group being created is a managed node group. - * Notice that the configuration setup a **minSize** to 1, **maxSize** to 5 and **desiredCapacity** to 2. Spot managed nodegroups are created with 2 nodes, but minimum number of instances for managed node group is 1 instance. + * Notice that the configuration setup a **minSize** to 0, **maxSize** to 5 and **desiredCapacity** to 2. * The configuration setup a configuration **spot: true** to indicate that the node group being created is a managed node group with Spot capacity, which implies all nodes in the nodegroup would be **Spot Instances**. - * We did also add an extra label **intent: apps**. We will use this label to force a hard partition of the cluster for our applications. During this workshop we will deploy control applications on nodes that have been labeled with **intent: control-apps** while our applications get deployed to nodes labeled with **intent: apps**. - * Notice that the configuration setup 2 node label under **labels** - **alpha.eksctl.io/cluster-name: : eksworkshop-eksctl** to indicate the node label belongs to **eksworkshop-eksctl** cluster, and **alpha.eksctl.io/nodegroup-name: dev-4vcpu-16gb-spot** node group. + * Notice that the configuration setup 3 node labels: + + * **alpha.eksctl.io/cluster-name: : eksworkshop-eksctl** to indicate the node label belongs to **eksworkshop-eksctl** cluster. + * **alpha.eksctl.io/nodegroup-name: mng-spot-8vcpu-32gb** node group. + * We add an extra label **intent: apps**, we will deploy control applications on nodes that have been labeled with **intent: control-apps** while our applications get deployed to nodes labeled with **intent: apps**. {{% notice info %}} If you are wondering at this stage: *Where is spot bidding price ?* you are missing some of the changes EC2 Spot Instances had since 2017. Since November 2017 [EC2 Spot price changes infrequently](https://aws.amazon.com/blogs/compute/new-amazon-ec2-spot-pricing/) based on long term supply and demand of spare capacity in each pool independently. You can still set up a **maxPrice** in scenarios where you want to set maximum budget. By default *maxPrice* is set to the On-Demand price; Regardless of what the *maxPrice* value, Spot Instances will still be charged at the current spot market price. From 60d2f7bf29aea16a46b2a09e6e7cf02c794d840c Mon Sep 17 00:00:00 2001 From: Jagdeep <70941399+jagpk@users.noreply.github.com> Date: Thu, 21 Oct 2021 22:07:58 +0800 Subject: [PATCH 38/39] Upgrade to EKS 1.21 --- .vscode/settings.json | 9 ++ .../010_prerequisites/_index.md | 2 +- .../eks-spot-workshop-quickstart-cnf.yml | 6 +- .../{ekscluster.yaml => eksworkshop.yaml} | 6 +- .../010_prerequisites/sshkey.md | 4 +- .../create_eks_cluster_eksctl_command.md | 10 +- .../020_eksctl/launcheks.md | 12 +- .../020_eksctl/test.md | 11 +- .../030_k8s_tools/_index.md | 19 +-- .../030_k8s_tools/helm_deploy.md | 25 +++- .../_index.md | 4 +- .../selecting_instance_types.md | 53 ++++---- .../spotlifecycle.md | 22 ++-- .../why_select_managed_node_group.md | 14 +-- .../workers_eksctl.md | 109 +++++++++++------ .../050_deploy/_index.md | 4 +- .../050_deploy/deploy.md | 14 +-- .../050_deploy/monte_carlo_pi.md | 17 +-- .../050_deploy/tolerations_and_affinity.md | 115 ++++++++++++++---- .../060_scaling/_index.md | 2 +- .../deploy_ca.files/cluster_autoscaler.yml | 10 +- .../060_scaling/deploy_ca.md | 12 +- .../060_scaling/test_ca.md | 66 ++++------ .../060_scaling/test_hpa.md | 12 +- .../_index.md | 6 +- .../deploy_scale.md | 108 ++++++++++++++++ .../deployhandler.md | 36 ++++-- .../selecting_instance_types.md | 11 -- .../self_managed_cleanup.md | 20 +++ .../workers_eksctl.md | 93 ++++++++------ .../200_cleanup.md | 25 +--- .../scaling/scaling-asg-20.png | Bin 0 -> 77052 bytes .../scaling/scaling-asg-up.png | Bin 71725 -> 0 bytes .../scaling/scaling-to-20replicas.png | Bin 0 -> 15452 bytes .../scaling/scaling-to-3-replicas.png | Bin 0 -> 58165 bytes .../Screenshot 2021-10-21 at 11.09.29 AM.png | Bin 0 -> 142502 bytes .../spotworkers/asg_spot_best_practices.png | Bin 139815 -> 142502 bytes .../spotworkers/spot-self-mng-taint.png | Bin 0 -> 114344 bytes 38 files changed, 544 insertions(+), 313 deletions(-) create mode 100644 .vscode/settings.json rename content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/{ekscluster.yaml => eksworkshop.yaml} (90%) create mode 100644 content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/deploy_scale.md delete mode 100644 content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/selecting_instance_types.md create mode 100644 content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/self_managed_cleanup.md create mode 100644 static/images/using_ec2_spot_instances_with_eks/scaling/scaling-asg-20.png delete mode 100644 static/images/using_ec2_spot_instances_with_eks/scaling/scaling-asg-up.png create mode 100644 static/images/using_ec2_spot_instances_with_eks/scaling/scaling-to-20replicas.png create mode 100644 static/images/using_ec2_spot_instances_with_eks/scaling/scaling-to-3-replicas.png create mode 100644 static/images/using_ec2_spot_instances_with_eks/spotworkers/Screenshot 2021-10-21 at 11.09.29 AM.png create mode 100644 static/images/using_ec2_spot_instances_with_eks/spotworkers/spot-self-mng-taint.png diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..1b4f757c --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,9 @@ +{ + "cSpell.words": [ + "Autoscaler", + "Kubernetes", + "Rebalance", + "Rebalancing", + "Tolerations" + ] +} \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/_index.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/_index.md index ed72eaac..b4c21b19 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/_index.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/_index.md @@ -1,5 +1,5 @@ --- -title: "Start the workshop..." +title: "Start the workshop" chapter: true weight: 10 --- diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/eks-spot-workshop-quickstart-cnf.yml b/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/eks-spot-workshop-quickstart-cnf.yml index ee40f5bd..a103d5f1 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/eks-spot-workshop-quickstart-cnf.yml +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/eks-spot-workshop-quickstart-cnf.yml @@ -277,8 +277,10 @@ Resources: - sudo mv -v /tmp/eksctl /usr/local/bin - sudo -H -u ec2-user /usr/local/bin/eksctl version - echo '=== Create EKS Cluster ===' - - curl --silent --location -o /home/ec2-user "https://github.com/weaveworks/eksctl/releases/download/${EKSCTL_VERSION}/eksctl_Linux_amd64.tar.gz" | tar xz -C /tmp - - !Sub 'sudo -H -u ec2-user /usr/local/bin/eksctl create cluster -f /home/ec2-user/eksworkshop.yaml' + - wget -P /home/ec2-user/environment -O eksworkshop.yaml "https://raw.githubusercontent.com/awslabs/ec2-spot-workshops/master/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/eksworkshop.yaml" + - sed 's/{AWS_REGION}/${AWS::Region}/' /home/ec2-user/environment/eksworkshop.yaml + - sed 's/{EKS_VERSION}/${EKSClusterVersion}/' /home/ec2-user/environment/eksworkshop.yaml + - !Sub 'sudo -H -u ec2-user /usr/local/bin/eksctl create cluster -f /home/ec2-user/environment/eksworkshop.yaml' - sudo -H -u ec2-user /usr/local/bin/kubectl get nodes C9BootstrapAssociation: diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/ekscluster.yaml b/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/eksworkshop.yaml similarity index 90% rename from content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/ekscluster.yaml rename to content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/eksworkshop.yaml index 980af29b..e118cb9b 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/ekscluster.yaml +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/prerequisites.files/eksworkshop.yaml @@ -4,15 +4,15 @@ kind: ClusterConfig metadata: name: eksworkshop-eksctl - region: ap-southeast-1 - version: "1.21" + region: ${AWS_REGION} + version: ${EKS_VERSION} # example "1.21" managedNodeGroups: - amiFamily: AmazonLinux2 instanceType: m5.large name: mng-od-m5large desiredCapacity: 2 - maxSize: 2 + maxSize: 3 minSize: 0 labels: alpha.eksctl.io/cluster-name: eksworkshop-eksctl diff --git a/content/using_ec2_spot_instances_with_eks/010_prerequisites/sshkey.md b/content/using_ec2_spot_instances_with_eks/010_prerequisites/sshkey.md index dedeb8c6..2878c344 100644 --- a/content/using_ec2_spot_instances_with_eks/010_prerequisites/sshkey.md +++ b/content/using_ec2_spot_instances_with_eks/010_prerequisites/sshkey.md @@ -10,7 +10,7 @@ Starting from here, when you see command to be entered such as below, you will e Please run this command to generate SSH Key in Cloud9. This key will be used on the worker node instances to allow ssh access if necessary. -```bash +``` ssh-keygen ``` @@ -20,6 +20,6 @@ Press `enter` 3 times to take the default choices Upload the public key to your EC2 region: -```bash +``` aws ec2 import-key-pair --key-name "eksworkshop" --public-key-material fileb://~/.ssh/id_rsa.pub ``` diff --git a/content/using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md b/content/using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md index 12840682..1605c02d 100644 --- a/content/using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md +++ b/content/using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md @@ -8,9 +8,9 @@ hidden: true This markdown file is used as part of another file using 'insert-md-from-file' shortcode --> -Create an eksctl deployment file (eksworkshop.yaml) use in creating your cluster using the following syntax: +Create an eksctl deployment file (eksworkshop.yaml) to create an EKS cluster: -```bash +``` cat << EOF > eksworkshop.yaml --- apiVersion: eksctl.io/v1alpha5 @@ -26,7 +26,7 @@ managedNodeGroups: instanceType: m5.large name: mng-od-m5large desiredCapacity: 2 - maxSize: 2 + maxSize: 3 minSize: 0 labels: alpha.eksctl.io/cluster-name: eksworkshop-eksctl @@ -36,8 +36,6 @@ managedNodeGroups: alpha.eksctl.io/nodegroup-name: mng-od-m5large alpha.eksctl.io/nodegroup-type: managed k8s.io/cluster-autoscaler/node-template/label/intent: control-apps - ssh: - enableSsm: true iam: withAddonPolicies: autoScaler: true @@ -53,7 +51,7 @@ EOF Next, use the file you created as the input for the eksctl cluster creation. -```bash +``` eksctl create cluster -f eksworkshop.yaml ``` diff --git a/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md b/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md index 70df0095..50280711 100644 --- a/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md +++ b/content/using_ec2_spot_instances_with_eks/020_eksctl/launcheks.md @@ -26,8 +26,14 @@ If you do see the correct role, proceed to next step to create an EKS cluster. {{% insert-md-from-file file="using_ec2_spot_instances_with_eks/020_eksctl/create_eks_cluster_eksctl_command.md" %}} -eksctl allows us to pass parameters to initialize the cluster. While initializing the cluster, eksctl does also allow us to create nodegroups. +`eksctl create cluster` command allows you to create the cluster and managed nodegroups in sequence. There are a few things to note in the configuration that we just used to create the cluster and a managed nodegroup. -The managed nodegroup will have two m5.large nodes (m5.large is the default instance type used if no instance types are specified) and it will bootstrap with the label **intent=control-apps**. + * Nodegroup configurations are set under the **managedNodeGroups** section, this indicates that the node group is managed by EKS. + * Nodegroup instance type is **m5.large** with **minSize** to 0, **maxSize** to 5 and **desiredCapacity** to 2. This nodegroup has capacity type set to On-Demand Instances by default. + * Notice that the we add 3 node labels: -Amazon EKS adds the following Kubernetes label to all nodes in your managed node group: **eks.amazonaws.com/capacityType: ON_DEMAND**. You can use this label to schedule stateful or fault intolerant applications on On-Demand nodes. \ No newline at end of file + * **alpha.eksctl.io/cluster-name**, to indicate the nodes belong to **eksworkshop-eksctl** cluster. + * **alpha.eksctl.io/nodegroup-name**, to indicate the nodes belong to **mng-od-m5large** nodegroup. + * **intent**, to allow you to deploy control applications on nodes that have been labeled with value **control-apps** + + * Amazon EKS adds an additional Kubernetes label **eks.amazonaws.com/capacityType: ON_DEMAND**, to all On-Demand Instances in your managed node group. You can use this label to schedule stateful applications on On-Demand nodes. \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/020_eksctl/test.md b/content/using_ec2_spot_instances_with_eks/020_eksctl/test.md index d8f3daae..c3398e39 100644 --- a/content/using_ec2_spot_instances_with_eks/020_eksctl/test.md +++ b/content/using_ec2_spot_instances_with_eks/020_eksctl/test.md @@ -4,7 +4,7 @@ date: 2018-08-07T13:36:57-07:00 weight: 30 --- #### Test the cluster: -Confirm your Nodes, if we see our 2 nodes, we know we have authenticated correctly: +Confirm your Nodes, if we see 2 nodes then we know we have authenticated correctly: ``` kubectl get nodes @@ -23,9 +23,6 @@ ROLE_NAME=$(aws eks describe-nodegroup --cluster-name eksworkshop-eksctl --nodeg echo "export ROLE_NAME=${ROLE_NAME}" >> ~/.bash_profile ``` - - - #### Congratulations! You now have a fully working Amazon EKS Cluster that is ready to use! @@ -54,7 +51,7 @@ You'll need to determine the correct credential to add for your AWS Console acce If you've built your cluster from Cloud9 as part of this tutorial, invoke the following within your environment to determine your IAM Role or User ARN. -```bash +``` c9builder=$(aws cloud9 describe-environment-memberships --environment-id=$C9_PID | jq -r '.memberships[].userArn') if echo ${c9builder} | grep -q user; then rolearn=${c9builder} @@ -68,7 +65,7 @@ fi With your ARN in hand, you can issue the command to create the identity mapping within the cluster. -```bash +``` eksctl create iamidentitymapping --cluster eksworkshop-eksctl --arn ${rolearn} --group system:masters --username admin ``` @@ -76,7 +73,7 @@ Note that permissions can be restricted and granular but as this is a workshop c Now you can verify your entry in the AWS auth map within the console. -```bash +``` kubectl describe configmap -n kube-system aws-auth ``` diff --git a/content/using_ec2_spot_instances_with_eks/030_k8s_tools/_index.md b/content/using_ec2_spot_instances_with_eks/030_k8s_tools/_index.md index a532ddf2..0d816140 100644 --- a/content/using_ec2_spot_instances_with_eks/030_k8s_tools/_index.md +++ b/content/using_ec2_spot_instances_with_eks/030_k8s_tools/_index.md @@ -1,22 +1,9 @@ --- -title: "Kubernetes Tools" +title: "Install Kubernetes Tools" chapter: true weight: 30 --- -# Kubernetes Helm - -[Helm](https://helm.sh/) is a package manager for Kubernetes that packages multiple Kubernetes resources into a single logical deployment unit called **Chart**. - -Helm is a tool that streamlines installing and managing Kubernetes applications. Think of it like apt/yum/homebrew for Kubernetes. We will use Helm during the workshop to install other components out from the list of available charts. - -Helm helps you to: - -- Achieve a simple (one command) and repeatable deployment -- Manage application dependency, using specific versions of other application and services -- Manage multiple deployment configurations: test, staging, production and others -- Execute post/pre deployment jobs during application deployment -- Update/rollback and test application deployments - -![Helm Logo](/images/using_ec2_spot_instances_with_eks/helm/helm-logo.svg) +# Install Kubernetes tools +In this chapter we will install Kubernetes tools; Helm (package manager), Metric Server (resource usage data aggregator) and Kube-Ops-View (cluster visualization tool). diff --git a/content/using_ec2_spot_instances_with_eks/030_k8s_tools/helm_deploy.md b/content/using_ec2_spot_instances_with_eks/030_k8s_tools/helm_deploy.md index 9281d637..132fe9fe 100644 --- a/content/using_ec2_spot_instances_with_eks/030_k8s_tools/helm_deploy.md +++ b/content/using_ec2_spot_instances_with_eks/030_k8s_tools/helm_deploy.md @@ -4,18 +4,33 @@ date: 2018-08-07T08:30:11-07:00 weight: 10 --- +## Kubernetes Helm + +[Helm](https://helm.sh/) is a package manager for Kubernetes that packages multiple Kubernetes resources into a single logical deployment unit called **Chart**. + +Helm is a tool that streamlines installing and managing Kubernetes applications. Think of it like apt/yum/homebrew for Kubernetes. We will use Helm during the workshop to install other components out from the list of available charts. + +Helm helps you to: + +- Achieve a simple (one command) and repeatable deployment +- Manage application dependency, using specific versions of other application and services +- Manage multiple deployment configurations: test, staging, production and others +- Execute post/pre deployment jobs during application deployment +- Update/rollback and test application deployments + + ## Install the Helm CLI Before we can get started configuring Helm, we'll need to first install the command line tools that you will interact with. To do this, run the following: -```sh +``` curl -sSL https://raw.githubusercontent.com/helm/helm/master/scripts/get-helm-3 | bash ``` We can verify the version -```sh +``` helm version --short ``` @@ -25,20 +40,20 @@ Homebrew on macOS. Download the `stable` repository so we have something to start with: -```sh +``` helm repo add stable https://charts.helm.sh/stable/ helm repo update ``` Once this is installed, we will be able to list the charts you can install: -```sh +``` helm search repo stable ``` Finally, let's configure Bash completion for the `helm` command: -```sh +``` helm completion bash >> ~/.bash_completion . /etc/profile.d/bash_completion.sh . ~/.bash_completion diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/_index.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/_index.md index 01c48427..8d3fdbec 100644 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/_index.md +++ b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/_index.md @@ -1,11 +1,11 @@ --- -title: "Creating EKS managed node groups with Spot capacity" +title: "Add EKS managed Spot workers" chapter: true weight: 40 draft: false --- -# Using Spot Instances with EKS +# Add EKS managed Spot workers In this module, you will learn how to provision, manage, and maintain your Kubernetes clusters with Amazon EKS managed node groups with Spot capacity to optimize cost and scale. diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/selecting_instance_types.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/selecting_instance_types.md index 6134b11c..255e1067 100644 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/selecting_instance_types.md +++ b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/selecting_instance_types.md @@ -1,5 +1,5 @@ --- -title: "Selecting Instance Types" +title: "Select Instance Types for Diversification" date: 2018-08-07T11:05:19-07:00 weight: 20 draft: false @@ -7,30 +7,30 @@ draft: false [Amazon EC2 Spot Instances](https://aws.amazon.com/ec2/spot/) offer spare compute capacity available in the AWS Cloud at steep discounts compared to On-Demand prices. EC2 can interrupt Spot Instances with two minutes of notification when EC2 needs the capacity back. You can use Spot Instances for various fault-tolerant and flexible applications. Some examples are analytics, containerized workloads, high-performance computing (HPC), stateless web servers, rendering, CI/CD, and other test and development workloads. -One of the best practices to successfully adopt Spot Instances is to implement **Spot Instance diversification** as part of your configuration. Spot Instance diversification helps to procure capacity from multiple Spot Instance pools, both for scaling up and for replacing Spot Instances that may receive a Spot Instance termination notification. A Spot Instance pool is a set of unused EC2 instances with the same instance type (for example, m5.large), operating system, Availability Zone. +One of the best practices to successfully adopt Spot Instances is to implement **Spot Instance diversification** as part of your configuration. Spot Instance diversification helps to procure capacity from multiple Spot Instance pools, both for scaling up and for replacing Spot Instances that may receive a Spot Instance termination notification. A Spot Instance pool is a set of unused EC2 instances with the same Instance type, operating system and Availability Zone (for example, m5.large on Red Hat Enterprise Linux in us-east-1a). ### Cluster Autoscaler And Spot Instance Diversification Cluster Autoscaler is a tool that automatically adjusts the size of the Kubernetes cluster when there are pods that fail to run in the cluster due to insufficient resources (Scale Out) or there are nodes in the cluster that have been underutilized for a period of time (Scale In). {{% notice info %}} -When using Spot Instances with [Cluster Autoscaler](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler) there are a few things that [should be considered](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/cloudprovider/aws/README.md). For example Cluster Autoscaler makes the assumption that all nodes within a nodegroup will have the same number of vCPUs and Memory. +When using Spot Instances with [Cluster Autoscaler](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler) there are a few things that [should be considered](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/cloudprovider/aws/README.md). One key consideration is, each Auto Scaling group should be composed of instance types that provide approximately equal capacity. Cluster Autoscaler will attempt to determine the CPU, memory, and GPU resources provided by an Auto Scaling Group based on first override provided in an ASG's Mixed Instances Policy. If any such overrides are found, only the first instance type found will be used. See [Using Mixed Instances Policies and Spot Instances](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/cloudprovider/aws/README.md#Using-Mixed-Instances-Policies-and-Spot-Instances) for details. {{% /notice %}} -When applying Spot Diversification best practices to EKS and K8s clusters, using Cluster Autoscaler to dynamically scale capacity, we must implement diversification in a way that adheres to Cluster Autoscaler expected operational mode. In this workshop we will assume that our cluster nodegroups should be provisioned with instance types that adhere to a 1vCPU:4GB RAM ratio. +When applying Spot Diversification best practices to EKS and K8s clusters, using Cluster Autoscaler to dynamically scale capacity, we must implement diversification in a way that adheres to Cluster Autoscaler expected operational mode. In this workshop we will assume that our cluster node groups should be provisioned with instance types that adhere to a 1vCPU:4GB RAM ratio. We can diversify Spot Instance pools using two strategies: - - By creating multiple node groups, each of different sizes. For example a node group of size 4VCPU's and 16GB Ram, and another node group of 8vCPU's and 32GB Ram. + - By creating multiple node groups, each of different sizes. For example, a node group of size 4 vCPUs and 16GB RAM, and another node group of 8 vCPUs and 32GB RAM. - - By Implementing instance diversification within the node groups, by selecting a mix of instance types and families from different Spot Instance pools that meet the same vCPU's and memory criteria. + - By Implementing instance diversification within the node groups, by selecting a mix of instance types and families from different Spot Instance pools that meet the same vCPUs and memory criteria. -Our goal in this workshop, is to create at least 2 diversified groups of instances that adhere the 1vCPU:4GB RAM ratio. +Our goal in this workshop, is to create 2 diversified node groups that adhere the 1 vCPU:4 GB RAM ratio. We will use **[amazon-ec2-instance-selector](https://github.com/aws/amazon-ec2-instance-selector)** to help us select the relevant instance -types and familes with sufficient number of vCPUs and RAM. +types and families with sufficient number of vCPUs and RAM. -There are over 350 different instance types available on EC2 which can make the process of selecting appropriate instance types difficult. **[amazon-ec2-instance-selector](https://github.com/aws/amazon-ec2-instance-selector)** helps you select compatible instance types for your application to run on. The command line interface can be passed resource criteria like vcpus, memory, network performance, and much more and then return the available, matching instance types. +There are over 350 different instance types available on EC2 which can make the process of selecting appropriate instance types difficult. amazon-ec2-instance-selector helps you select compatible instance types for your application to run on. The command line interface can be passed resource criteria like vcpus, memory, network performance, and much more and then return the available, matching instance types. Let's first install **amazon-ec2-instance-selector** : @@ -43,14 +43,20 @@ ec2-instance-selector --version Now that you have ec2-instance-selector installed, you can run `ec2-instance-selector --help` to understand how you could use it for selecting instances that match your workload requirements. For the purpose of this workshop -we need to first get a group of instances that meet the 4vCPUs and 16GB of RAM. +we need to first get a group of instances that meet the 4 vCPUs and 16 GB of RAM. Run the following command to get the list of instances. -```bash -ec2-instance-selector --vcpus 4 --memory 16 --gpus 0 --current-generation -a x86_64 --deny-list '.*z.*|.*i.*' +{{% notice note %}} +The results might differ if you created Cloud9 in any other region than the five regions (N. Virginia, Oregon, Ireland, Ohio and Singapore) suggested in [Create a Workspace]({{< relref "/using_ec2_spot_instances_with_eks/010_prerequisites/workspace" >}}) chapter. We will use **`--deny-list`** for filtering out the instances that are not supported across these five regions. +{{% /notice %}} + +``` +ec2-instance-selector --vcpus 4 --memory 16 --gpus 0 --current-generation -a x86_64 --deny-list '.*d3en.*' ``` -This should display a list like the one that follows (note results might differ depending on the region). We will use this instances as part of one of our node groups. +This should display a list like the one that follows . We will use this instances as part of one of our node groups. + + ``` m4.xlarge @@ -60,38 +66,41 @@ m5ad.xlarge m5d.xlarge m5dn.xlarge m5n.xlarge +m5zn.xlarge +m6i.xlarge t2.xlarge t3.xlarge t3a.xlarge ``` -Internally ec2-instance-selector is making calls to the [DescribeInstanceTypes](https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeInstanceTypes.html) for the specific region and filtering the intstances based on the criteria selected in the command line, in our case we did filter for instances that meet the following criteria: +Internally ec2-instance-selector is making calls to the [DescribeInstanceTypes](https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_DescribeInstanceTypes.html) for the specific region and filtering the instances based on the criteria selected in the command line, in our case we did filter for instances that meet the following criteria: * Instances with no GPUs * of x86_64 Architecture (no ARM instances like A1 or m6g instances for example) - * Instances that have 4 vCPUs and 16GB of Ram + * Instances that have 4 vCPUs and 16 GB of RAM * Instances of current generation (4th gen onwards) - * Instances that don't meet the regular expresion `.*z.*|.*i.*`, so effectively `m5zn`, `m6i` instances which are not supported by EKS. + * Instances that don’t meet the regular expression .*d3en.*, so effectively d3en. {{% notice warning %}} Your workload may have other constraints that you should consider when selecting instance types. For example. **t2** and **t3** instance types are [burstable instances](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/burstable-performance-instances.html) and might not be appropriate for CPU bound workloads that require CPU execution determinism. Instances such as m5**a** are [AMD Instances](https://aws.amazon.com/ec2/amd/), if your workload is sensitive to numerical differences (i.e: financial risk calculations, industrial simulations) mixing these instance types might not be appropriate. {{% /notice %}} {{% notice note %}} -You are encouraged to test what are the options that `ec2-instance-selector` provides and run a few commands with it to familiarize yourself with the tool. -For example, try running the same commands as you did before with the extra parameter **`--output table-wide`**. +You are encouraged to test what are the options that `ec2-instance-selector` provides and run a few commands with it to familiarize yourself with the tool.
      +
      +For example, try running the same commands as you did before with the extra parameters, like **`--output table-wide`** for a tabular view. {{% /notice %}} ### Challenge -Find out another group that adheres to a 1vCPU:4GB ratio, this time using instances with 8vCPU's and 32GB of RAM. +Find out another group that adheres to a 1 vCPU:4 GB ratio, this time using instances with 8 vCPUs and 32 GB of RAM. {{%expand "Expand this for an example on the list of instances" %}} That should be easy. You can run the command: -```bash -ec2-instance-selector --vcpus 8 --memory 32 --gpus 0 --current-generation -a x86_64 --deny-list '.*z.*|.*i.*' +``` +ec2-instance-selector --vcpus 8 --memory 32 --gpus 0 --current-generation -a x86_64 --deny-list '.*d3en.*' ``` which should yield a list as follows @@ -104,6 +113,8 @@ m5ad.2xlarge m5d.2xlarge m5dn.2xlarge m5n.2xlarge +m5zn.2xlarge +m6i.2xlarge t2.2xlarge t3.2xlarge t3a.2xlarge diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/spotlifecycle.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/spotlifecycle.md index c3f5575f..b51e6399 100644 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/spotlifecycle.md +++ b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/spotlifecycle.md @@ -1,20 +1,20 @@ --- -title: "Spot configuration and lifecycle" +title: "Spot Best Practices and Interruption Handling" date: 2021-04-07T12:00:00-00:00 weight: 40 draft: false --- -### View the Managed Node Group Configuration +### View EKS managed node groups Configurations -Use the AWS Management Console to inspect the Managed node group deployed in your Kubernetes cluster. Select **Elastic Kubernetes Service**, click on **Clusters**, and then on **eksworkshop-eksctl** cluster. Select the **Configuration** tab and **Compute** sub tab. You can see 3 node groups created - one On-Demand node group and two Spot node groups. +Use the AWS Management Console to inspect the managed node groups deployed in your Kubernetes cluster. -Click on **dev-4vcpu-16gb-spot** group and you can see the instance types set from the create command. - -Click on the Auto Scaling Group name in the **Details** tab. Scroll to the Purchase options and instance types settings. Note how Spot best practices are applied out of the box: - -* **Capacity Optimized** allocation strategy, which will launch Spot Instances from the most-available spare capacity pools. This results in minimizing the Spot Interruptions. -* **Capacity Rebalance** helps EKS managed node groups manage the lifecycle of the Spot Instance by proactively replacing instances that are at higher risk of being interrupted. Node groups use Auto Scaling Group's Capacity Rebalance feature to launch replacement nodes in response to Rebalance Recommendation notice, thus proactively maintaining desired node capacity. +* Go to **Elastic Kubernetes Service** >> click on **Clusters** >> select **eksworkshop-eksctl** cluster >> select **Configuration** tab >> go to **Compute** tab in the bottom pane. +* You can see 3 node groups created; one On-Demand node group and two Spot node groups. +* Click on **mng-spot-4vcpu-16gb** node group and you can see the instance types we selected in earlier section. +* Click on the Auto Scaling Group name in the **Details** tab. Scroll to the Purchase options and instance types settings. Note how Spot best practices are applied out of the box: + * **Capacity Optimized** allocation strategy, which will launch Spot Instances from the most-available spare capacity pools. This results in minimizing the Spot Interruptions. + * **Capacity Rebalance** helps EKS managed node groups manage the lifecycle of the Spot Instance by proactively replacing instances that are at higher risk of being interrupted. Node groups use Auto Scaling Group's Capacity Rebalance feature to launch replacement nodes in response to Rebalance Recommendation notice, thus proactively maintaining desired node capacity. ![Spot Best Practices](/images/using_ec2_spot_instances_with_eks/spotworkers/asg_spot_best_practices.png) @@ -24,10 +24,10 @@ To handle Spot interruptions, you do not need to install any extra automation to * Amazon EC2 Spot Capacity Rebalancing is enabled so that Amazon EKS can gracefully drain and rebalance your Spot nodes to minimize application disruption when a Spot node is at elevated risk of interruption. For more information, see [Amazon EC2 Auto Scaling Capacity Rebalancing](https://docs.aws.amazon.com/autoscaling/ec2/userguide/capacity-rebalance.html) in the Amazon EC2 Auto Scaling User Guide. -* When a replacement Spot node is bootstrapped and in the Ready state on Kubernetes, Amazon EKS cordons and drains the Spot node that received the rebalance recommendation. Cordoning the Spot node ensures that the node is marked as ‘unschedulable’ and kube-scheduler will not schedule any new pods on it. It also removes it from its list of healthy, active Spot nodes. [Draining](https://kubernetes.io/docs/tasks/administer-cluster/safely-drain-node/) the Spot node ensures that running pods are evicted gracefully. +* When a replacement Spot node is bootstrapped and in the Ready state on Kubernetes, Amazon EKS cordons and drains the Spot node that received the rebalance recommendation. Cordoning the Spot node ensures that the node is marked as unschedulable and kube-scheduler will not schedule any new pods on it. It also removes it from its list of healthy, active Spot nodes. [Draining](https://kubernetes.io/docs/tasks/administer-cluster/safely-drain-node/) the Spot node ensures that running pods are evicted gracefully. * If a Spot two-minute interruption notice arrives before the replacement Spot node is in a Ready state, Amazon EKS starts draining the Spot node that received the rebalance recommendation. - This process avoids waiting for replacement Spot node till Spot interruption arrives, instead it procures replacement in advance and helps in minimizing the scheduling time for pending pods. +This process avoids waiting for replacement Spot node till Spot interruption arrives, instead it procures replacement in advance and helps in minimizing the scheduling time for pending pods. ![Spot Rebalance Recommendation](/images/using_ec2_spot_instances_with_eks/spotworkers/rebalance_recommendation.png) \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/why_select_managed_node_group.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/why_select_managed_node_group.md index e9cb8675..2d114719 100644 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/why_select_managed_node_group.md +++ b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/why_select_managed_node_group.md @@ -1,13 +1,11 @@ --- -title: "EKS managed node groups with Spot capacity" +title: "EKS managed node groups" date: 2018-08-07T11:05:19-07:00 weight: 10 draft: false --- -### Why EKS Managed Node Groups? - -[Amazon EKS managed node groups](https://docs.aws.amazon.com/eks/latest/userguide/managed-node-groups.html) automate the provisioning and lifecycle management of nodes (Amazon EC2 instances) for Amazon EKS Kubernetes clusters. This greatly simplifies operational activities such as rolling updates for new AMIs or Kubernetes version deployments. +[Amazon EKS managed node groups](https://docs.aws.amazon.com/eks/latest/userguide/managed-node-groups.html) automate the provisioning and lifecycle management of nodes (Amazon EC2 instances) for Amazon EKS clusters. This greatly simplifies operational activities such as rolling updates for new AMIs or Kubernetes version deployments. Advantages of running Amazon EKS managed node groups: @@ -19,11 +17,11 @@ Advantages of running Amazon EKS managed node groups: ### Why EKS managed node groups with Spot capacity? -**Amazon EKS managed node groups with Spot capacity** enhances the managed node group experience in using EKS managed node groups to easily provision and manage EC2 Spot Instances. EKS managed node group will configure and launch an EC2 Autoscaling group of Spot Instances following Spot best practices and draining Spot worker nodes automatically before the instances are interrupted by AWS. This enables you to take advantage of the steep savings that Spot Instances provide for your interruption tolerant containerized applications. +Amazon EKS managed node groups with Spot capacity enhances the managed node group experience with ease to provision and manage EC2 Spot Instances. EKS managed node groups launch an EC2 Auto Scaling group with Spot best practices and handle [Spot Instance interruptions](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-interruptions.html) automatically. This enables you to take advantage of the steep savings that Spot Instances provide for your interruption tolerant containerized applications. In addition to the advantages of managed node groups, EKS managed node groups with Spot capacity have these additional advantages: -* Allocation strategy to provision Spot capacity is set to **Capacity Optimized** to ensure that Spot nodes are provisioned in the optimal Spot capacity pools. -* Specify **multiple instance types** during managed node groups creation, to increase the number of Spot capacity pools available for allocating capacity. +* Allocation strategy to provision Spot capacity is set to [Capacity Optimized](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-best-practices.html#use-capacity-optimized-allocation-strategy) to ensure that Spot nodes are provisioned in the optimal Spot capacity pools. +* Specify [multiple instance types](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-best-practices.html#be-instance-type-flexible) during managed node groups creation, to increase the number of Spot capacity pools available for allocating capacity. * Nodes provisioned under managed node groups with Spot capacity are automatically tagged with capacity type: **eks.amazonaws.com/capacityType: SPOT**. You can use this label to schedule fault tolerant applications on Spot nodes. -* Amazon EC2 Spot **Capacity Rebalancing** enabled to ensure Amazon EKS can gracefully drain and rebalance your Spot nodes to minimize application disruption when a Spot node is at elevated risk of interruption. +* Amazon EC2 Spot [Capacity Rebalancing](https://docs.aws.amazon.com/autoscaling/ec2/userguide/ec2-auto-scaling-capacity-rebalancing.html) enabled to ensure Amazon EKS can gracefully drain and rebalance your Spot nodes to minimize application disruption when a Spot node is at elevated risk of interruption. \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md index f49e4df7..424390b9 100644 --- a/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md +++ b/content/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md @@ -1,25 +1,26 @@ --- -title: "Adding Spot Workers with eksctl" +title: "Create EKS managed node groups with Spot capacity" date: 2018-08-07T11:05:19-07:00 weight: 30 draft: false --- -In this section we will deploy the instance types we selected and request nodegroups that adhere to Spot diversification best practices. For that we will use **[eksctl create nodegroup](https://eksctl.io/usage/managing-nodegroups/)** to generate the ClusterConfig file and save the output. The ClusterConfig file will be edited with additional configuration and use it to add the new nodes to the cluster. +In this section we will deploy the instance types we selected in previous chapter and create managed node groups that adhere to Spot diversification best practices. We will use **[`eksctl create nodegroup`](https://eksctl.io/usage/managing-nodegroups/)** to achieve this. Let's first create the configuration file: -```bash +``` cat << EOF > add-mngs-spot.yaml --- apiVersion: eksctl.io/v1alpha5 kind: ClusterConfig + managedNodeGroups: - name: mng-spot-4vcpu-16gb amiFamily: AmazonLinux2 desiredCapacity: 2 minSize: 0 - maxSize: 5 + maxSize: 4 spot: true instanceTypes: - m4.xlarge @@ -29,6 +30,8 @@ managedNodeGroups: - m5d.xlarge - m5dn.xlarge - m5n.xlarge + - m5zn.xlarge + - m6i.xlarge - t2.xlarge - t3.xlarge - t3a.xlarge @@ -36,6 +39,10 @@ managedNodeGroups: withAddonPolicies: autoScaler: true privateNetworking: true + taints: + - key: spotInstance + value: "true" + effect: PreferNoSchedule labels: alpha.eksctl.io/cluster-name: eksworkshop-eksctl alpha.eksctl.io/nodegroup-name: mng-spot-4vcpu-16gb @@ -43,12 +50,14 @@ managedNodeGroups: tags: alpha.eksctl.io/nodegroup-name: mng-spot-4vcpu-16gb alpha.eksctl.io/nodegroup-type: managed + k8s.io/cluster-autoscaler/node-template/label/intent: apps + k8s.io/cluster-autoscaler/node-template/taint/spotInstance: "true:PreferNoSchedule" - name: mng-spot-8vcpu-32gb amiFamily: AmazonLinux2 - desiredCapacity: 2 + desiredCapacity: 1 minSize: 0 - maxSize: 5 + maxSize: 2 spot: true instanceTypes: - m4.2xlarge @@ -58,6 +67,8 @@ managedNodeGroups: - m5d.2xlarge - m5dn.2xlarge - m5n.2xlarge + - m5zn.2xlarge + - m6i.2xlarge - t2.2xlarge - t3.2xlarge - t3a.2xlarge @@ -65,6 +76,10 @@ managedNodeGroups: withAddonPolicies: autoScaler: true privateNetworking: true + taints: + - key: spotInstance + value: "true" + effect: PreferNoSchedule labels: alpha.eksctl.io/cluster-name: eksworkshop-eksctl alpha.eksctl.io/nodegroup-name: mng-spot-8vcpu-32gb @@ -72,39 +87,60 @@ managedNodeGroups: tags: alpha.eksctl.io/nodegroup-name: mng-spot-8vcpu-32gb alpha.eksctl.io/nodegroup-type: managed - + k8s.io/cluster-autoscaler/node-template/label/intent: apps + k8s.io/cluster-autoscaler/node-template/taint/spotInstance: "true:PreferNoSchedule" + metadata: name: eksworkshop-eksctl - region: ap-southeast-1 - tags: - k8s.io/cluster-autoscaler/node-template/label/intent: apps + region: ${AWS_REGION} version: "1.21" EOF ``` -Create the new EKS managed nodegroup with Spot Instances. +Create new EKS managed node groups with Spot Instances. -```sh +``` eksctl create nodegroup --config-file=add-mngs-spot.yaml ``` {{% notice info %}} -Creation of node group will take 3-4 minutes. +Creation of node groups will take 3-4 minutes. {{% /notice %}} -There are a few things to note in the configuration that we just used to create these nodegroups. +There are a few things to note in the configuration that we just used to create these node groups. + + * Node groups configurations are set under the **managedNodeGroups** section, this indicates that the node groups are managed by EKS. + * First node group has **xlarge** (4 vCPU and 16 GB) instance types with **minSize** 0, **maxSize** 4 and **desiredCapacity** 2. + * Second node group has **2xlarge** (8 vCPU and 32 GB) instance types with **minSize** 0, **maxSize** 2 and **desiredCapacity** 1. + * The configuration **spot: true** indicates that the node group being created is a EKS managed node group with Spot capacity. + * We applied a **[Taint](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/)** `spotInstance: "true:PreferNoSchedule"`. **PreferNoSchedule** is used to indicate we prefer pods not be scheduled on Spot Instances. This is a “preference” or “soft” version of **NoSchedule** – the system will try to avoid placing a pod that does not tolerate the taint on the node, but it is not required. + * Notice that the we added 3 node labels per node: + + * **alpha.eksctl.io/cluster-name**, to indicate the nodes belong to **eksworkshop-eksctl** cluster. + * **alpha.eksctl.io/nodegroup-name**, to indicate the nodes belong to **mng-spot-4vcpu-16gb** or **mng-spot-8vcpu-32gb** node groups. + * **intent**, to allow you to deploy stateless applications on nodes that have been labeled with value **apps** - * The configuration setup the nodes under the **managedNodeGroups** section. This is to indicate the node group being created is a managed node group. - * Notice that the configuration setup a **minSize** to 0, **maxSize** to 5 and **desiredCapacity** to 2. - * The configuration setup a configuration **spot: true** to indicate that the node group being created is a managed node group with Spot capacity, which implies all nodes in the nodegroup would be **Spot Instances**. - * Notice that the configuration setup 3 node labels: + * Notice that the we added 2 cluster autoscaler related tags to node groups: + * **k8s.io/cluster-autoscaler/node-template/label/intent** and **k8s.io/cluster-autoscaler/node-template/taint** are used by cluster autoscaler when node groups scale down to 0 (and scale up from 0). Cluster autoscaler acts on Auto Scaling groups belonging to node groups, therefore it requires same tags on ASG as well. Currently managed node groups do not auto propagate tags to ASG, see this [open issue](https://github.com/aws/containers-roadmap/issues/1524). Therefore, we will be adding these tags to ASG manually. + +Let's add these tags to Auto Scaling groups of each node group using AWS cli. + +``` +ASG_4VCPU_16GB=$(eksctl get nodegroup -n mng-spot-4vcpu-16gb --cluster eksworkshop-eksctl -o json | jq -r '.[].AutoScalingGroupName') +ASG_8VCPU_32GB=$(eksctl get nodegroup -n mng-spot-8vcpu-32gb --cluster eksworkshop-eksctl -o json | jq -r '.[].AutoScalingGroupName') - * **alpha.eksctl.io/cluster-name: : eksworkshop-eksctl** to indicate the node label belongs to **eksworkshop-eksctl** cluster. - * **alpha.eksctl.io/nodegroup-name: mng-spot-8vcpu-32gb** node group. - * We add an extra label **intent: apps**, we will deploy control applications on nodes that have been labeled with **intent: control-apps** while our applications get deployed to nodes labeled with **intent: apps**. +aws autoscaling create-or-update-tags --tags \ +ResourceId=$ASG_4VCPU_16GB,ResourceType=auto-scaling-group,Key=k8s.io/cluster-autoscaler/node-template/label/intent,Value=apps,PropagateAtLaunch=true \ +ResourceId=$ASG_4VCPU_16GB,ResourceType=auto-scaling-group,Key=k8s.io/cluster-autoscaler/node-template/taint/spotInstance,Value="true:PreferNoSchedule",PropagateAtLaunch=true + +aws autoscaling create-or-update-tags --tags \ +ResourceId=$ASG_8VCPU_32GB,ResourceType=auto-scaling-group,Key=k8s.io/cluster-autoscaler/node-template/label/intent,Value=apps,PropagateAtLaunch=true \ +ResourceId=$ASG_8VCPU_32GB,ResourceType=auto-scaling-group,Key=k8s.io/cluster-autoscaler/node-template/taint/spotInstance,Value="true:PreferNoSchedule",PropagateAtLaunch=true + +``` {{% notice info %}} -If you are wondering at this stage: *Where is spot bidding price ?* you are missing some of the changes EC2 Spot Instances had since 2017. Since November 2017 [EC2 Spot price changes infrequently](https://aws.amazon.com/blogs/compute/new-amazon-ec2-spot-pricing/) based on long term supply and demand of spare capacity in each pool independently. You can still set up a **maxPrice** in scenarios where you want to set maximum budget. By default *maxPrice* is set to the On-Demand price; Regardless of what the *maxPrice* value, Spot Instances will still be charged at the current spot market price. +If you are wondering at this stage: *Where is spot bidding price ?* you are missing some of the changes EC2 Spot Instances had since 2017. Since November 2017 [EC2 Spot price changes infrequently](https://aws.amazon.com/blogs/compute/new-amazon-ec2-spot-pricing/) based on long term supply and demand of spare capacity in each pool independently. You can still set up a **maxPrice** in scenarios where you want to set maximum budget. By default *maxPrice* is set to the On-Demand price; Regardless of what the *maxPrice* value, Spot Instances will still be charged at the current Spot market price. {{% /notice %}} ### Confirm the Nodes @@ -115,32 +151,35 @@ Aside from familiarizing yourself with the kubectl commands below to obtain the Confirm that the new nodes joined the cluster correctly. You should see the nodes added to the cluster. -```bash +``` kubectl get nodes ``` Managed node groups automatically create a label **eks.amazonaws.com/capacityType** to identify which nodes are Spot Instances and which are On-Demand Instances so that we can schedule the appropriate workloads to run on Spot Instances. You can use this node label to identify the lifecycle of the nodes -```bash +``` kubectl get nodes \ --label-columns=eks.amazonaws.com/capacityType \ --selector=eks.amazonaws.com/capacityType=SPOT ``` +The output of this command should return nodes running on Spot Instances. The output of the command shows the **CAPACITYTYPE** for the current nodes is set to **SPOT**. -The output of this command should return nodes running on Spot Instances. The output of the command shows the CAPACITYTYPE for the current nodes is set to SPOT. - -![Spot Output](/images/using_ec2_spot_instances_with_eks/spotworkers/spot_get_spot.png) +``` +NAME STATUS ROLES AGE VERSION CAPACITYTYPE +ip-192-168-101-235.ap-southeast-1.compute.internal Ready 14m v1.21.4-eks-033ce7e SPOT +ip-192-168-130-210.ap-southeast-1.compute.internal Ready 14m v1.21.4-eks-033ce7e SPOT +ip-192-168-176-250.ap-southeast-1.compute.internal Ready 14m v1.21.4-eks-033ce7e SPOT +``` -Now we will show all nodes running on On Demand Instances. The output of the command shows the CAPACITYTYPE for the current nodes is set to ON_DEMAND. +Now we will show all nodes running on On Demand Instances. The output of the command shows the **CAPACITYTYPE** for the current nodes is set to **ON_DEMAND**. -```bash +``` kubectl get nodes \ --label-columns=eks.amazonaws.com/capacityType \ --selector=eks.amazonaws.com/capacityType=ON_DEMAND ``` -![OnDemand Output](/images/using_ec2_spot_instances_with_eks/spotworkers/spot_get_od.png) - -{{% notice note %}} -Explore your cluster using kube-ops-view and find out the nodes that have just been created. -{{% /notice %}} - +``` +NAME STATUS ROLES AGE VERSION CAPACITYTYPE +ip-192-168-165-163.ap-southeast-1.compute.internal Ready 51m v1.21.4-eks-033ce7e ON_DEMAND +ip-192-168-99-237.ap-southeast-1.compute.internal Ready 51m v1.21.4-eks-033ce7e ON_DEMAND +``` diff --git a/content/using_ec2_spot_instances_with_eks/050_deploy/_index.md b/content/using_ec2_spot_instances_with_eks/050_deploy/_index.md index 79fb1bbe..32969dc5 100644 --- a/content/using_ec2_spot_instances_with_eks/050_deploy/_index.md +++ b/content/using_ec2_spot_instances_with_eks/050_deploy/_index.md @@ -1,10 +1,10 @@ --- -title: "Deploying the example Microservice" +title: "Deploy an example Microservice" chapter: true weight: 50 --- -# Deploying the example Microservice +# Deploy an example Microservice To illustrate application scaling using [Horizontal Pod Autoscaler](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/) (HPA) and cluster scaling using [Cluster Autoscaler](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler) (CA), we will deploy a microservice that generates CPU load. diff --git a/content/using_ec2_spot_instances_with_eks/050_deploy/deploy.md b/content/using_ec2_spot_instances_with_eks/050_deploy/deploy.md index dc4abe0e..870ef3db 100644 --- a/content/using_ec2_spot_instances_with_eks/050_deploy/deploy.md +++ b/content/using_ec2_spot_instances_with_eks/050_deploy/deploy.md @@ -1,11 +1,8 @@ --- -title: "Deploying the Application" +title: "Deploy Application" date: 2018-08-07T08:30:11-07:00 weight: 30 --- -{{% notice warning %}} -Before proceeding, check that your file `~/environment/monte-carlo-pi-service.yml` looks like: **[monte-carlo-pi-service-final.yml](tolerations_and_affinity.files/monte-carlo-pi-service-final.yml)** -{{% /notice %}} To deploy the application we just need to run: ``` @@ -62,11 +59,4 @@ You can also execute a request with the additional parameter from the console: ``` URL=$(kubectl get svc monte-carlo-pi-service | tail -n 1 | awk '{ print $4 }') time curl ${URL}/?iterations=100000000 -``` - - - - - - - +``` \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/050_deploy/monte_carlo_pi.md b/content/using_ec2_spot_instances_with_eks/050_deploy/monte_carlo_pi.md index 8f351bbf..c4db28ea 100644 --- a/content/using_ec2_spot_instances_with_eks/050_deploy/monte_carlo_pi.md +++ b/content/using_ec2_spot_instances_with_eks/050_deploy/monte_carlo_pi.md @@ -4,9 +4,9 @@ date: 2018-08-07T08:30:11-07:00 weight: 10 --- -## Monte Carlo Pi +## Monte Carlo Pi Template -We will use this base configuration to deploy our application: +Let's create a template configuration file for monte carlo pi application: ``` cat < ~/environment/monte-carlo-pi-service.yml @@ -60,7 +60,7 @@ EoF ``` -This should create a `monte-carlo-pi-service.yml` file that defines a **Service** and a **Deployment**. The configuration instructs the cluster to deploy two replicas of a pod with a single container, that sets up [Resource request and limits](https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/#resource-requests-and-limits-of-pod-and-container) to a fixed value 1vCPU and 512Mi of RAM. You can read more about the differences between Resource requests and limits [here](https://docs.aws.amazon.com/eks/latest/userguide/platform-versions.html). +This should create a `monte-carlo-pi-service.yml` template file that defines a **Service** and a **Deployment**. The configuration instructs the cluster to deploy two replicas of a pod with a single container, that sets up [Resource request and limits](https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/#resource-requests-and-limits-of-pod-and-container) to a fixed value 1vCPU and 512Mi of RAM. You can read more about the differences between Resource requests and limits [here](https://docs.aws.amazon.com/eks/latest/userguide/platform-versions.html). The deployment does not include any toleration or affinities. If deployed as is, it will be scheduled to the on-demand nodes that we created during the cluster creation phase! @@ -72,13 +72,8 @@ There are a few best practices for managing multi-tenant dynamic clusters. One o Before we deploy our application and start scaling it, there are two requirements that we should apply and implement in the configuration file: - 1.- The first requirement is for the application to be deployed only on nodes that have been labeled with `intent: apps` - - 2.- The second requirement is for the application to prefer Spot Instances over on-demand instances. - - -In the next section we will explore how to implement this requirements. - - + 1. The first requirement is for the application to be deployed only on nodes that have been labeled with `intent: apps` + 2. The second requirement is for the application to prefer Spot Instances over on-demand instances. +In the next section we will explore how to implement this requirements. \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/050_deploy/tolerations_and_affinity.md b/content/using_ec2_spot_instances_with_eks/050_deploy/tolerations_and_affinity.md index a0d1bcdc..adee45f0 100644 --- a/content/using_ec2_spot_instances_with_eks/050_deploy/tolerations_and_affinity.md +++ b/content/using_ec2_spot_instances_with_eks/050_deploy/tolerations_and_affinity.md @@ -4,14 +4,36 @@ date: 2018-08-07T08:30:11-07:00 weight: 20 --- -## Adding Affinities +## Add Tolerations + +In the previous chapter [Create EKS managed node groups with Spot capacity]({{< ref "/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/workers_eksctl.md" >}}) we added a taint `spotInstance: "true:PreferNoSchedule"` to both node groups. **PreferNoSchedule** is used to indicate we prefer pods not to be scheduled on Spot Instances. **NoSchedule** can also be used to enforce a hard discrimination as a taint. To overcome this taint, we need to add a toleration in the deployment. Read about how [tolerations](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/) are applied and modify the **monte-carlo-pi-service.yml** file accordingly. + +{{%expand "Show me a hint for implementing this." %}} +As per the [tolerations](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/) documentation +the objective is to add the following section to the `monte-carlo-pi-service.yml`. + +The following toleration must be added at the *spec.template.spec* level + +``` + tolerations: + - key: "spotInstance" + operator: "Equal" + value: "true" + effect: "PreferNoSchedule" +``` + +{{% /expand %}} + +## Add Affinities Our next task before deployment is to add [affinities](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#affinity-and-anti-affinity) to the configuration. -In the previous section we created nodegroups with the label `intent` and values **control-apps** and **apps**. Managed node groups automatically create a label **eks.amazonaws.com/capacityType** and sets its value to *SPOT* and *ON_DEMAND* respectively to identify which nodes are Spot Instances and which are On-Demand Instances. For us to adhere to the criteria above we will need to add two affinity properties: +In the previous chapters we labeled managed node groups with On-Demand capacity with **intent: control-apps** and managed node groups with Spot capacity with **intent: apps**. Additionally EKS adds a label **eks.amazonaws.com/capacityType** and sets its value to **ON_DEMAND** for node groups with On-Demand capacity and **SPOT** for node group with Spot capacity. + +To meet the requirements we defined in previous chapter: *application to be deployed only on nodes that have been labeled with `intent: apps`* and *application to prefer Spot Instances over on-demand Instances*, we need to add two affinity properties: - a *requiredDuringSchedulingIgnoredDuringExecution* affinity: also known as "hard" affinity that will limit our deployment to nodes label with **intent: apps** -- a *preferredDuringSchedulingIgnoredDuringExecution* affinity: also known as "soft" affinity that express our preference for nodes of a specific type. In this case Spot instances labeled with **eks.amazonaws.com/capacityType: SPOT**. +- a *preferredDuringSchedulingIgnoredDuringExecution* affinity: also known as "soft" affinity that express our preference for nodes of a specific type. In this case Spot Instances labeled with **eks.amazonaws.com/capacityType: SPOT**. Read about how affinities can be used to [assign pods to nodes](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#affinity-and-anti-affinity) and modify the **monte-carlo-pi-service.yml** file accordingly. @@ -40,32 +62,81 @@ The following affinities must be added at the *spec.template.spec* level - apps ``` -If you are still struggling with the implementation, the solution file is available here : **[monte-carlo-pi-service-final.yml](tolerations_and_affinity.files/monte-carlo-pi-service-final.yml)** - {{% /expand %}} -{{%attachments title="Related files" pattern="monte-carlo-pi-service-final.yml"/%}} - -## Adding Tolerations (Self Managed Spot Node Group only) - -When creating self managed node groups, we created two nodegroups that were tainted with -`spotInstance: "true:PreferNoSchedule"`. **PreferNoSchedule** is used to indicate we prefer pods not to be scheduled on Spot Instances. **NoSchedule** can also be used to enforce a hard discrimination as a taint. To overcome the `spotInstance: "true:PreferNoSchedule"` taint, we need to create a toleration in the deployment. Read about how [tolerations](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/) are applied and modify the **monte-carlo-pi-service.yml** file accordingly. - -{{%expand "Show me a hint for implementing this." %}} -As per the [tolerations](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/) documentation -the objective is to add the following section to the `monte-carlo-pi-service.yml`. -The following toleration must be added at the *spec.template.spec* level +If you are still struggling with the implementation, then run below command to overwrite `monte-carlo-pi-service.yml` template with the final solution. ``` +cat < ~/environment/monte-carlo-pi-service.yml +--- +apiVersion: v1 +kind: Service +metadata: + name: monte-carlo-pi-service +spec: + type: LoadBalancer + ports: + - port: 80 + targetPort: 8080 + selector: + app: monte-carlo-pi-service +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: monte-carlo-pi-service + labels: + app: monte-carlo-pi-service +spec: + replicas: 2 + selector: + matchLabels: + app: monte-carlo-pi-service + template: + metadata: + labels: + app: monte-carlo-pi-service + spec: tolerations: - key: "spotInstance" operator: "Equal" value: "true" effect: "PreferNoSchedule" -``` - -{{% /expand %}} - -{{%attachments title="Related files" pattern="monte-carlo-pi-service-final-self-managed.yml"/%}} - + affinity: + nodeAffinity: + preferredDuringSchedulingIgnoredDuringExecution: + - weight: 1 + preference: + matchExpressions: + - key: eks.amazonaws.com/capacityType + operator: In + values: + - SPOT + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: intent + operator: In + values: + - apps + containers: + - name: monte-carlo-pi-service + image: ruecarlo/monte-carlo-pi-service + resources: + requests: + memory: "512Mi" + cpu: "1024m" + limits: + memory: "512Mi" + cpu: "1024m" + securityContext: + privileged: false + readOnlyRootFilesystem: true + allowPrivilegeEscalation: false + ports: + - containerPort: 8080 + +EoF + +``` \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/060_scaling/_index.md b/content/using_ec2_spot_instances_with_eks/060_scaling/_index.md index 98e8c4a0..ec6d82b6 100644 --- a/content/using_ec2_spot_instances_with_eks/060_scaling/_index.md +++ b/content/using_ec2_spot_instances_with_eks/060_scaling/_index.md @@ -1,5 +1,5 @@ --- -title: "Autoscaling our Applications and Clusters" +title: "Autoscale Cluster and Application" chapter: true weight: 60 --- diff --git a/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.files/cluster_autoscaler.yml b/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.files/cluster_autoscaler.yml index c36b20f0..7dc2a33e 100644 --- a/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.files/cluster_autoscaler.yml +++ b/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.files/cluster_autoscaler.yml @@ -48,7 +48,7 @@ rules: resources: ["storageclasses"] verbs: ["watch","list","get"] - apiGroups: ["storage.k8s.io"] - resources: ["csinodes"] + resources: ["csinodes","csistoragecapacities","csidrivers"] verbs: ["watch","list","get"] - apiGroups: ["batch"] resources: ["jobs"] @@ -136,11 +136,11 @@ spec: name: cluster-autoscaler resources: limits: - cpu: 200m - memory: 600Mi + cpu: 1024m + memory: 1024Mi requests: - cpu: 200m - memory: 600Mi + cpu: 1024m + memory: 1024Mi command: - ./cluster-autoscaler - --v=4 diff --git a/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.md b/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.md index 0db056da..f0a580ff 100644 --- a/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.md +++ b/content/using_ec2_spot_instances_with_eks/060_scaling/deploy_ca.md @@ -6,20 +6,20 @@ weight: 10 We will start by deploying [Cluster Autoscaler](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler). Cluster Autoscaler for AWS provides integration with Auto Scaling groups. It enables users to choose from four different options of deployment: -* One Auto Scaling group +* One Auto Scaling group * Multiple Auto Scaling groups * **Auto-Discovery** - This is what we will use * Master Node setup -In this workshop we will configure Cluster Autoscaler to scale using **[Cluster Autoscaler Auto-Discovery functionality](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md)**. When configured in Auto-Discovery mode on AWS, Cluster Autoscaler will look for Auto Scaling Groups that match a set of pre-set AWS tags. As a convention we use the tags : `k8s.io/cluster-autoscaler/enabled`, and `k8s.io/cluster-autoscaler/eksworkshop-eksctl` . +In this workshop we will configure Cluster Autoscaler to scale using **[Cluster Autoscaler Auto-Discovery functionality](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md)**. When configured in Auto-Discovery mode on AWS, Cluster Autoscaler will look for Auto Scaling groups that match a set of pre-set AWS tags. As a convention we use the tags : `k8s.io/cluster-autoscaler/enabled`, and `k8s.io/cluster-autoscaler/eksworkshop-eksctl` . -This will select the two Auto Scaling groups that have been created for Spot instances. +This will select the two Auto Scaling groups that have been created for Spot Instances. {{% notice note %}} -The **[following link](https://console.aws.amazon.com/ec2/autoscaling/home?#AutoScalingGroups:filter=eksctl-eksworkshop-eksctl-nodegroup-dev;view=details)** Should take you to the -Auto Scaling Group console and select the two spot node-group we have previously created; You should check that +The **[following link](https://console.aws.amazon.com/ec2autoscaling/home?#/details)** should take you to the +Auto Scaling groups console. Type 'eks' in the search bar to see managed node groups we created previously . Verify the tags `k8s.io/cluster-autoscaler/enabled`, and `k8s.io/cluster-autoscaler/eksworkshop-eksctl` are present -in both groups. This has been done automatically by **eksctl** upon creation of the groups. +in all managed node groups. These tags were automatically added by EKS upon the creation of managed node groups. {{% /notice %}} We have provided a manifest file to deploy the CA. Copy the commands below into your Cloud9 Terminal. diff --git a/content/using_ec2_spot_instances_with_eks/060_scaling/test_ca.md b/content/using_ec2_spot_instances_with_eks/060_scaling/test_ca.md index da5b9fe8..e5fb079d 100644 --- a/content/using_ec2_spot_instances_with_eks/060_scaling/test_ca.md +++ b/content/using_ec2_spot_instances_with_eks/060_scaling/test_ca.md @@ -1,5 +1,5 @@ --- -title: "Scale a Cluster with CA" +title: "Scale Cluster with CA" date: 2018-08-07T08:30:11-07:00 weight: 30 --- @@ -37,9 +37,7 @@ kubectl scale deployment/monte-carlo-pi-service --replicas=0 **Question:** Can you predict what would be the result of scaling down to 0 replicas? {{%expand "Show me the answer" %}} -The configuration that we applied to procure our nodegroups states that the minimum number of instances in the auto scaling group is 2 for both EKS managed node groups with Spot capacity. - -(Self managed node group) The configuration that we applied to procure our nodegroups states that the minimum number of instances in the auto scaling group is 0 for both nodegroups. Starting from 1.14 version Cluster Autoscaler does support scaling down to 0. +The configuration that we applied to procure our node groups states that the minimum number of instances in the auto scaling group is 0 for both node groups. Starting from `1.14` version Cluster Autoscaler supports scaling down to 0. By setting the number of replicas to 0, Cluster Autoscaler will detect that the current instances are idle and can be removed to the minSize of the Auto Scaling Group. This may take up to 3 minutes. Cluster autoscaler will log lines such as the one below flagging that the instance is unneeded. @@ -47,25 +45,21 @@ By setting the number of replicas to 0, Cluster Autoscaler will detect that the I1120 00:22:37.204988 1 static_autoscaler.go:382] ip-192-168-54-241.eu-west-1.compute.internal is unneeded since 2021-03-20 00:21:16.651612719 +0000 UTC m=+4789.747568996 duration 1m20.552551794s ``` -After some time, you should be able to confirm that running `kubectl get nodes` return only our 6 initial nodes: +After some time, you should be able to confirm that running `kubectl get nodes` return only our 2 initial On-Demand nodes: ``` -$ kubectl get nodes -NAME STATUS ROLES AGE VERSION -ip-192-168-100-154.us-west-2.compute.internal Ready 87m v1.20.4-eks-6b7464 -ip-192-168-186-233.us-west-2.compute.internal Ready 87m v1.20.4-eks-6b7464 -ip-192-168-33-105.us-west-2.compute.internal Ready 69m v1.20.4-eks-6b7464 -ip-192-168-37-117.us-west-2.compute.internal Ready 17m v1.20.4-eks-6b7464 -ip-192-168-73-255.us-west-2.compute.internal Ready 73m v1.20.4-eks-6b7464 -ip-192-168-85-132.us-west-2.compute.internal Ready 69m v1.20.4-eks-6b7464 +$ kubectl get nodes --label-columns=eks.amazonaws.com/capacityType +``` +``` +NAME STATUS ROLES AGE VERSION CAPACITYTYPE +ip-192-168-165-163.ap-southeast-1.compute.internal Ready 4h1m v1.21.4-eks-033ce7e ON_DEMAND +ip-192-168-99-237.ap-southeast-1.compute.internal Ready 4h1m v1.21.4-eks-033ce7e ON_DEMAND ``` {{% notice tip %}} -Check in the AWS console that both auto-scaling groups have now the Desired capacity set to 0. You can **[follow this link](https://console.aws.amazon.com/ec2/autoscaling/home?#AutoScalingGroups:filter=eksctl-eksworkshop-eksctl-nodegroup-dev;view=details)** to get into the Auto Scaling Group AWS console. +Check in the AWS console that Spot auto-scaling groups have now the Desired capacity set to 0. You can **[follow this link](https://console.aws.amazon.com/ec2/autoscaling/home?#AutoScalingGroups:filter=eksctl-eksworkshop-eksctl-nodegroup-dev;view=details)** to get into the Auto Scaling Group AWS console. {{% /notice %}} - - {{% /expand %}} @@ -84,23 +78,23 @@ kubectl get pods --watch ``` NAME READY STATUS RESTARTS AGE -monte-carlo-pi-service-584f6ddff-fk2nj 1/1 Running 0 20m21s +monte-carlo-pi-service-584f6ddff-fk2nj 1/1 Running 0 103s monte-carlo-pi-service-584f6ddff-fs9x6 1/1 Running 0 103s monte-carlo-pi-service-584f6ddff-jst55 1/1 Running 0 103s ``` You should also be able to visualize the scaling action using kube-ops-view. Kube-ops-view provides an option to highlight pods meeting a regular expression. All pods in green are **monte-carlo-pi-service** pods. -![Scaling up to 10 replicas](/images/using_ec2_spot_instances_with_eks/scaling/scaling-kov-10-replicas.png) +![Scaling up to 3 replicas](/images/using_ec2_spot_instances_with_eks/scaling/scaling-to-3-replicas.png) {{% notice info %}} -Given we started from 2 node capacity in both Spot node groups, this should trigger a scaling event for Cluster Autoscaler. Can you predict which size (and type!) of node will be provided? +Given we started from 0 nodes in both Spot node groups, this should trigger a scaling event for Cluster Autoscaler. Can you predict which size (and type!) of node will be provided? {{% /notice %}} #### Challenge Try to answer the following questions: - - Could you predict what should happen if we increase the number of replicas to 23? - - How would you scale up the replicas to 23? + - Could you predict what should happen if we increase the number of replicas to 20? + - How would you scale up the replicas to 20? - If you are expecting a new node, which size will it be: (a) 4vCPU's 16GB RAM or (b) 8vCPU's 32GB RAM? - Which EC2 Instance type you would expect to be selected? - How would you confirm your predictions? @@ -109,18 +103,18 @@ Try to answer the following questions: {{%expand "Show me the answers" %}} To scale up the number of replicas run: ``` -kubectl scale deployment/monte-carlo-pi-service --replicas=23 +kubectl scale deployment/monte-carlo-pi-service --replicas=20 ``` -When the number of replicas scales up, there will be a few pods pending. You can confirm which pods are pending running the command below. +When the number of replicas scales up, there will be pods pending. You can confirm which pods are pending by running the command below. ``` kubectl get pods --watch ``` -Kube-ops-view, will show 3 pending yellow pods outside the node. -![Scale Up](/images/using_ec2_spot_instances_with_eks/scaling/scaling-asg-up-kov.png) +Kube-ops-view, will show pending yellow pods outside the node. +![Scale Up](/images/using_ec2_spot_instances_with_eks/scaling/scaling-to-20replicas.png) -When inspecting cluster-autoscaler logs with the command line below +When inspecting cluster-autoscaler logs with the command line below. ``` kubectl logs -f deployment/cluster-autoscaler -n kube-system ``` @@ -135,32 +129,28 @@ kubectl get node --selector=intent=apps --show-labels You can verify in AWS Management Console to confirm that the Auto Scaling groups are scaling up to meet demand. This may take a few minutes. You can also follow along with the pod deployment from the command line. You should see the pods transition from pending to running as nodes are scaled up. -![Scale Up](/images/using_ec2_spot_instances_with_eks/scaling/scaling-asg-up.png) +![Scale Up](/images/using_ec2_spot_instances_with_eks/scaling/scaling-asg-20.png) {{% notice info %}} -Cluster Autoscaler expands capacity according to the [Expander](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#what-are-expanders) configuration. By default, Cluster Autoscaler uses the **random** expander. This means that there is equal probability of cluster autoscaler selecting the 4vCPUs 16GB RAM group or the 8vCPUs 32GB RAM group. You may consider also using other expanders like **least-waste**, or the **priority** expander. +Cluster Autoscaler expands capacity according to the [**Expander**](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#what-are-expanders) configuration. By default, Cluster Autoscaler uses the **random** expander. This means that there is equal probability of cluster autoscaler selecting the 4vCPUs 16GB RAM group or the 8vCPUs 32GB RAM group. You may consider also using other expanders like **least-waste**, or the **priority** expander. {{% /notice %}} -As for the node that was selected, by default the Autoscaling Groups that we created with eksctl use [capacity-optimized allocation strategy](https://docs.aws.amazon.com/en_pv/autoscaling/ec2/userguide/asg-purchase-options.html#asg-allocation-strategies), this makes the Auto Scaling Group procure capacity from the pools that has less chances of interruptions. +As for the EC2 Instance type that was selected, by default the Auto Scaling groups that we created with eksctl use [capacity-optimized allocation strategy](https://docs.aws.amazon.com/en_pv/autoscaling/ec2/userguide/asg-purchase-options.html#asg-allocation-strategies), this makes the Auto Scaling group procure capacity from the pools that has less chances of interruptions. {{% /expand %}} -After you've completed the exercise, scale down your replicas back down in preparation for the configuration of Horizontal Pod Autoscheduler. +After you've completed the exercise, scale down your replicas back down in preparation for the configuration of Horizontal Pod Autoscaler. ``` kubectl scale deployment/monte-carlo-pi-service --replicas=3 ``` -{{% notice info %}} -It is a recommended to use **[capacity-optimized](https://aws.amazon.com/blogs/compute/introducing-the-capacity-optimized-allocation-strategy-for-amazon-ec2-spot-instances/)** as an allocation strategy for your mixed instances EC2 Spot nodegroups. Other Strategies like *[Lowest Price](https://docs.aws.amazon.com/autoscaling/ec2/userguide/asg-purchase-options.html)* might be still considered for nodes that just process [Kubernetes retriable Jobs](https://kubernetes.io/docs/concepts/workloads/controllers/jobs-run-to-completion/) -{{% /notice %}} - ### Optional Exercises {{% notice warning %}} Some of this exercises will take time for Cluster Autoscaler to scale up and down. If you are running this workshop at a AWS event or with limited time, we recommend to come back to this section once you have completed the workshop, and before getting into the **cleanup** section. {{% /notice %}} - * What will happen when modifying Cluster Autoscaler **expander** configuration from **random** to **least-waste**. What happens when we increase the replicas back to 23? What happens if we increase the number of replicas to 30? Can you predict which node group will be expanded in each case: (a) 4vCPUs 16GB RAM (b) 8vCPUs 32GB RAM? What's Cluster Autoscaler log looking like in this case? + * What will happen when modifying Cluster Autoscaler **expander** configuration from **random** to **least-waste**. What happens if we increase the number of replicas to 40? Can you predict which node group will be expanded in each case: (a) 4vCPUs 16GB RAM (b) 8vCPUs 32GB RAM? What's Cluster Autoscaler log looking like in this case? * How would you expect Cluster Autoscaler to Scale in the cluster? How about scaling out? How much time you'll expect for it to take? @@ -168,8 +158,4 @@ Some of this exercises will take time for Cluster Autoscaler to scale up and dow * Scheduling in Kubernetes is the process of binding pending pods to nodes, and is performed by a component of Kubernetes called [kube-scheduler](https://kubernetes.io/docs/concepts/scheduling/kube-scheduler/). When running on Spot the cluster is expected to be dynamic; the state is expected to change over time; The original scheduling decision may not be adequate after the state changes. Could you think or research for a project that could help address this? ([hint_1](https://github.com/kubernetes-sigs/descheduler)) [hint_2](https://github.com/pusher/k8s-spot-rescheduler). If so apply the solution and see what is the impact on scale-in operations. - * During the workshop, we did use nodegroups that expand across multiple AZ's; There are scenarios where might create issues. Could you think which scenarios? ([hint](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler/cloudprovider/aws#common-notes-and-gotchas)). Could you think of ways of mitigating the risk in those scenarios? ([hint 1](https://github.com/aws-samples/amazon-k8s-node-drainer), [hint 2](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#im-running-cluster-with-nodes-in-multiple-zones-for-ha-purposes-is-that-supported-by-cluster-autoscaler)) - - * **Managed Spot node groups only:** EC2 Auto Scaling Group automatically sets the node group to use the [capacity-optimized allocation strategy](https://docs.aws.amazon.com/en_pv/autoscaling/ec2/userguide/asg-purchase-options.html#asg-allocation-strategies). - - * **Self managed Spot node groups only:** At the moment EC2 Auto Scaling Group backing up the node group are setup to use the [capacity-optimized allocation strategy](https://docs.aws.amazon.com/en_pv/autoscaling/ec2/userguide/asg-purchase-options.html#asg-allocation-strategies). What do you think is the trade-off when you switch to [lowest price](https://docs.aws.amazon.com/en_pv/autoscaling/ec2/userguide/asg-purchase-options.html#asg-allocation-strategies) allocation strategy? \ No newline at end of file + * During the workshop, we used node groups that expand across multiple AZ's; There are scenarios where might create issues. Could you think which scenarios? ([hint](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler/cloudprovider/aws#common-notes-and-gotchas)). Could you think of ways of mitigating the risk in those scenarios? ([hint 1](https://github.com/aws-samples/amazon-k8s-node-drainer), [hint 2](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#im-running-cluster-with-nodes-in-multiple-zones-for-ha-purposes-is-that-supported-by-cluster-autoscaler)) \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/060_scaling/test_hpa.md b/content/using_ec2_spot_instances_with_eks/060_scaling/test_hpa.md index bc4e7746..2f604c45 100644 --- a/content/using_ec2_spot_instances_with_eks/060_scaling/test_hpa.md +++ b/content/using_ec2_spot_instances_with_eks/060_scaling/test_hpa.md @@ -1,5 +1,5 @@ --- -title: "Scale the Application" +title: "Stress test the Application" date: 2018-08-07T08:30:11-07:00 weight: 50 --- @@ -42,7 +42,7 @@ kubectl get svc kube-ops-view | tail -n 1 | awk '{ print "Kube-ops-view URL = ht Run the stress test ! This time around we will run 2000 requests each expected to take ~1.3sec or so. ``` -time ~/environment/submit_mc_pi_k8s_requests/submit_mc_pi_k8s_requests.py -p 100 -r 30 -i 30000000 -u "http://${URL}" +time ~/environment/submit_mc_pi_k8s_requests/submit_mc_pi_k8s_requests.py -p 100 -r 20 -i 30000000 -u "http://${URL}" ``` ### Challenge @@ -78,10 +78,10 @@ monte-carlo-pi-service Deployment/monte-carlo-pi-service 92%/50% 4 monte-carlo-pi-service Deployment/monte-carlo-pi-service 92%/50% 4 100 19 37m monte-carlo-pi-service Deployment/monte-carlo-pi-service 94%/50% 4 100 19 38m monte-carlo-pi-service Deployment/monte-carlo-pi-service 85%/50% 4 100 19 39m -monte-carlo-pi-service Deployment/monte-carlo-pi-service 85%/50% 4 100 29 39m -monte-carlo-pi-service Deployment/monte-carlo-pi-service 54%/50% 4 100 29 40m -monte-carlo-pi-service Deployment/monte-carlo-pi-service 0%/50% 4 100 29 41m -monte-carlo-pi-service Deployment/monte-carlo-pi-service 0%/50% 4 100 29 45m +monte-carlo-pi-service Deployment/monte-carlo-pi-service 85%/50% 4 100 19 39m +monte-carlo-pi-service Deployment/monte-carlo-pi-service 54%/50% 4 100 19 40m +monte-carlo-pi-service Deployment/monte-carlo-pi-service 0%/50% 4 100 19 41m +monte-carlo-pi-service Deployment/monte-carlo-pi-service 0%/50% 4 100 19 45m monte-carlo-pi-service Deployment/monte-carlo-pi-service 0%/50% 4 100 12 46m monte-carlo-pi-service Deployment/monte-carlo-pi-service 0%/50% 4 100 12 47m monte-carlo-pi-service Deployment/monte-carlo-pi-service 0%/50% 4 100 4 48m diff --git a/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/_index.md b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/_index.md index a3ff0675..059f4e6b 100644 --- a/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/_index.md +++ b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/_index.md @@ -1,12 +1,12 @@ --- -title: "(Optional) Creating Spot self managed nodegroups on EKS" +title: "(Optional) Add self managed Spot workers" chapter: true weight: 70 draft: false --- -# (Optional) Using Spot Instances with EKS self managed node groups +# (Optional) Add self managed Spot workers -In this module, you will learn how to provision, manage, and maintain your Kubernetes clusters with Amazon EKS self managed node groups at any scale on Spot Instances to optimize cost and scale. +In this module, you will learn how to provision self managed node groups with Spot Instances to optimize cost and scale. ![Title Image](/images/using_ec2_spot_instances_with_eks/spotworkers/eks_self_managed_architecture.png) diff --git a/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/deploy_scale.md b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/deploy_scale.md new file mode 100644 index 00000000..1bd37934 --- /dev/null +++ b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/deploy_scale.md @@ -0,0 +1,108 @@ +--- +title: "Test Autoscaling of Cluster and Application" +date: 2018-08-07T08:30:11-07:00 +weight: 50 +--- + +{{% notice info %}} +We added same labels (**intent: apps** and **eks.amazonaws.com/capacityType: SPOT**) and taint (**spotInstance: "true:PreferNoSchedule"**) to EKS managed node groups and Self managed node groups, therefore the same example service can be scaled on both types of node groups. +{{% /notice %}} + +{{% notice note %}} +If you are starting with **self managed Spot workers** chapter directly and planning to run only self managed node groups with Spot Instances, then complete below chapters and proceed to cleanup chapter directly.
      +
      +[Deploy an example Microservice]({{< relref "/using_ec2_spot_instances_with_eks/050_deploy" >}})
      +[Autoscale Cluster and Application]({{< relref "/using_ec2_spot_instances_with_eks/060_scaling" >}}) +{{% /notice %}} + +{{% notice info %}} + +If you are have already completed **EKS managed Spot workers** chapters and still want to explore self managed node groups with Spot Instances, then continue with this chapter. + +{{% /notice %}} + +At this point we have 5 node groups in our cluster: + +* One **EKS managed** node group with **On-Demand** capacity (mng-od-m5large) +* Two **EKS managed** node groups with **Spot** capacity (mng-spot-4vcpu-16gb and mng-spot-8vcpu-32gb) +* Two **self managed** node groups with **Spot** Instances (ng-spot-4vcpu-16gb and ) + + +### Stress test the application + +Let's do a repeat of earlier Stress test with double the number of requests. We will test to see if stressing the same application can trigger autoscaling of both **EKS managed** node groups with Spot capacity and **self managed** node groups with Spot Instances. + +{{% notice note %}} +Before starting the stress test, predict what would be the expected outcome. Use **kube-ops-view** to verify that the changes you were expecting to happen, do in fact happen over time. +{{% /notice %}} +{{%expand "Show me how to get kube-ops-view url" %}} +Execute the following command on Cloud9 terminal +``` +kubectl get svc kube-ops-view | tail -n 1 | awk '{ print "Kube-ops-view URL = http://"$4 }' +``` +{{% /expand %}} + +Run the stress test ! This time around we will run **4000** requests (as we have twice the capacity) each expected to take ~1.3sec or so. +``` +time ~/environment/submit_mc_pi_k8s_requests/submit_mc_pi_k8s_requests.py -p 100 -r 40 -i 30000000 -u "http://${URL}" +``` + +To display the progress of the rule was setup in Horizontal Pod Autoscaler we can run: +``` +kubectl get hpa -w +``` +This should show the current progress and target pods, and refresh a new line every few seconds. +``` +:~/environment $ kubectl get hpa -w +monte-carlo-pi-service Deployment/monte-carlo-pi-service 0%/50% 3 100 0 11s +monte-carlo-pi-service Deployment/monte-carlo-pi-service 50%/50% 3 100 6 15s +monte-carlo-pi-service Deployment/monte-carlo-pi-service 100%/50% 3 100 3 75s +monte-carlo-pi-service Deployment/monte-carlo-pi-service 100%/50% 3 100 6 90s +monte-carlo-pi-service Deployment/monte-carlo-pi-service 60%/50% 3 100 6 2m16s +monte-carlo-pi-service Deployment/monte-carlo-pi-service 100%/50% 3 100 6 3m16s +monte-carlo-pi-service Deployment/monte-carlo-pi-service 100%/50% 3 100 12 3m31s +monte-carlo-pi-service Deployment/monte-carlo-pi-service 74%/50% 3 100 21 7m17s +monte-carlo-pi-service Deployment/monte-carlo-pi-service 80%/50% 3 100 21 8m17s +monte-carlo-pi-service Deployment/monte-carlo-pi-service 14%/50% 3 100 21 9m17s +monte-carlo-pi-service Deployment/monte-carlo-pi-service 14%/50% 3 100 21 10m +monte-carlo-pi-service Deployment/monte-carlo-pi-service 14%/50% 3 100 21 11m +monte-carlo-pi-service Deployment/monte-carlo-pi-service 14%/50% 3 100 21 14m +monte-carlo-pi-service Deployment/monte-carlo-pi-service 50%/50% 3 100 6 14m +monte-carlo-pi-service Deployment/monte-carlo-pi-service 33%/50% 3 100 6 18m +monte-carlo-pi-service Deployment/monte-carlo-pi-service 30%/50% 3 100 6 21m +monte-carlo-pi-service Deployment/monte-carlo-pi-service 0%/50% 3 100 6 22m +monte-carlo-pi-service Deployment/monte-carlo-pi-service 0%/50% 3 100 6 23m +``` + + +To display the node or pod you can use +``` +kubectl top nodes +``` +or +``` +kubectl top pods +``` + +{{% notice info %}} +Cluster Autoscaler will use default **random** expander to scale both types of node groups providing they have matching labels and taints. +{{% /notice %}} + +After some time, you should be able to confirm that running `kubectl get nodes` return both self managed and EKS managed Spot nodes: + +``` +kubectl get nodes --label-columns=alpha.eksctl.io/nodegroup-name,eks.amazonaws.com/capacityType,type +``` + +``` +NAME STATUS ROLES AGE VERSION NODEGROUP-NAME CAPACITYTYPE TYPE +ip-192-168-111-213.ap-southeast-1.compute.internal Ready 22h v1.21.4-eks-033ce7e mng-od-m5large ON_DEMAND +ip-192-168-140-47.ap-southeast-1.compute.internal Ready 22h v1.21.4-eks-033ce7e mng-od-m5large ON_DEMAND +ip-192-168-189-229.ap-southeast-1.compute.internal Ready 3h2m v1.21.4-eks-033ce7e mng-spot-4vcpu-16gb SPOT +ip-192-168-34-125.ap-southeast-1.compute.internal Ready 3m3s v1.21.4-eks-033ce7e ng-spot-4vcpu-16gb SPOT self-managed-spot +ip-192-168-6-44.ap-southeast-1.compute.internal Ready 3m8s v1.21.4-eks-033ce7e ng-spot-4vcpu-16gb SPOT self-managed-spot +ip-192-168-64-221.ap-southeast-1.compute.internal Ready 6m v1.21.4-eks-033ce7e ng-spot-4vcpu-16gb SPOT self-managed-spot +``` + +You should also be able to visualize the scaling action using kube-ops-view. Kube-ops-view provides an option to highlight pods meeting a regular expression. All pods in green are **monte-carlo-pi-service** pods. +![Scaling up to 10 replicas](/images/using_ec2_spot_instances_with_eks/scaling/scaling-kov-10-replicas.png) diff --git a/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/deployhandler.md b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/deployhandler.md index ce1f34a0..ddf6542d 100644 --- a/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/deployhandler.md +++ b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/deployhandler.md @@ -1,19 +1,35 @@ --- -title: "Deploy The Node Termination Handler" +title: "Deploy AWS Node Termination Handler" date: 2018-08-07T12:32:40-07:00 weight: 40 draft: false --- -When users requests On-Demand instances from a pool to the point that the pool is depleted, the system will select a set of Spot Instances from the pool to be terminated. A Spot Instance pool is a set of unused EC2 instances with the same instance type (for example, m5.large), operating system, Availability Zone, and network platform. The Spot Instance is sent an interruption notice two minutes ahead to gracefully wrap up things. +When users requests On-Demand Instances from a pool to the point that the pool is depleted, the system will select a set of Spot Instances from the pool to be terminated. A Spot Instance pool is a set of unused EC2 instances with the same instance type (for example, m5.large), operating system, Availability Zone, and network platform. The Spot Instance is sent an interruption notice two minutes ahead to gracefully wrap up things. We will deploy a pod on each Spot Instance to detect the instance termination notification signal so that we can both terminate gracefully any pod that was running on that node, drain from load balancers and redeploy applications elsewhere in the cluster. -AWS Node Termination Handler does far more than just capture EC2 Spot Instance notification for terminations. There are other events such as [Scheduled Maintenance Events](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/monitoring-instances-status-check_sched.html) that are taken into consideration. AWS Node Termination handler does also offer a Webhook that can be used to integrate with other applications to monitor and instrument this events. You can find more information about **[AWS Node Termination Handler following this link](https://github.com/aws/aws-node-termination-handler)** +**[AWS Node Termination Handler](https://github.com/aws/aws-node-termination-handler)** + ensures that the Kubernetes control plane responds appropriately to events that can cause your EC2 instance to become unavailable, such as [EC2 maintenance events](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/monitoring-instances-status-check_sched.html), [EC2 Spot interruptions](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-interruptions.html), [ASG Scale-In](https://docs.aws.amazon.com/autoscaling/ec2/userguide/AutoScalingGroupLifecycle.html#as-lifecycle-scale-in), [ASG AZ Rebalance](https://docs.aws.amazon.com/autoscaling/ec2/userguide/auto-scaling-benefits.html#AutoScalingBehavior.InstanceUsage), and EC2 Instance Termination via the API or Console. If not handled, your application code may not stop gracefully, take longer to recover full availability, or accidentally schedule work to nodes that are going down. -The Helm chart we will use to deploy AWS Node Termination Handler on each Spot Instance uses a [DaemonSet](https://kubernetes.io/docs/concepts/workloads/controllers/daemonset/). This will monitor the EC2 meta-data service on each of the EC2 Spot Instances to capture EC2 interruption notices. +The aws-node-termination-handler (NTH) can operate in two different modes: **Instance Metadata Service (IMDS)** or the **Queue Processor**. -Within the Node Termination Handler DaemonSet, the workflow can be summarized as: +* The aws-node-termination-handler **Instance Metadata Service Monitor** will run a small pod on each host to perform monitoring of IMDS paths like /spot or /events and react accordingly to drain and/or cordon the corresponding node. +* The aws-node-termination-handler **Queue Processor** will monitor an SQS queue of events from Amazon EventBridge for ASG lifecycle events, EC2 status change events, Spot Interruption Termination Notice events, and Spot Rebalance Recommendation events. When NTH detects an instance is going down, we use the Kubernetes API to cordon the node to ensure no new work is scheduled there, then drain it, removing any existing work. The termination handler Queue Processor requires AWS IAM permissions to monitor and manage the SQS queue and to query the EC2 API. Review below table to decide on which option to use: + +| Syntax | IMDS Processor | Queue Processor | +| ------------ | ----------- | ----------- | +| K8s DaemonSet | ✅ | ❌ | +| K8s Deployment | ❌ | ✅ | +| Spot Instance Interruptions (ITN) | ✅ | ✅ | +| Scheduled Events | ✅ | ✅ | +| EC2 Instance Rebalance Recommendation | ✅ | ✅ | +| ASG Lifecycle Hooks | ❌ | ✅ | +| EC2 Status Changes | ❌ | ✅ | +| Setup Required | ❌ | ✅ | + + +To keep it simple, we will use Helm chart to deploy aws-node-termination-handler in IMDS mode on each Spot Instance as a [DaemonSet](https://kubernetes.io/docs/concepts/workloads/controllers/daemonset/). Within the aws-node-termination-handler in IMDS mode, the workflow can be summarized as: * Identify that a Spot Instance is being reclaimed. * Use the 2-minute notification window to gracefully prepare the node for termination. @@ -21,15 +37,15 @@ Within the Node Termination Handler DaemonSet, the workflow can be summarized as * [**Drain**](https://kubernetes.io/docs/tasks/administer-cluster/safely-drain-node/) connections on the running pods. * Replace the pods on remaining nodes to maintain the desired capacity. -By default, **[aws-node-termination-handler](https://github.com/aws/aws-node-termination-handler)** will run on all of your nodes (on-demand and spot). -This also is our recommendation. Remember the termination handler does also handle maintenance events that can impact OnDemand instances! +By default, **[aws-node-termination-handler](https://github.com/aws/aws-node-termination-handler)** will run on all of your nodes. Let's limit it's scope to only self managed node groups with Spot Instances. ``` helm repo add eks https://aws.github.io/eks-charts helm install aws-node-termination-handler \ --namespace kube-system \ - --version 0.12.0 \ + --version 0.15.4 \ + --set nodeSelector.type=self-managed-spot \ eks/aws-node-termination-handler ``` @@ -38,6 +54,4 @@ Verify that the pods are running on all nodes: kubectl get daemonsets --all-namespaces ``` -Use **kube-ops-view** to confirm *AWS Node Termination Handler* DaemonSet has been deployed to all nodes. - - +Use **kube-ops-view** to confirm *AWS Node Termination Handler* DaemonSet has been deployed to all nodes. \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/selecting_instance_types.md b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/selecting_instance_types.md deleted file mode 100644 index 976b0b6c..00000000 --- a/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/selecting_instance_types.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: "Selecting Instance Types" -date: 2018-08-07T11:05:19-07:00 -weight: 10 -draft: false ---- -### Selecting instances for Spot Instance Diversification - -[See "Selecting Instance Types" under EKS managed node groups with Spot capacity]({{< relref "/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/selecting_instance_types.md" >}}) - -Once you finish selecting instance types, proceed to "Adding Spot Workers with eksctl" section to continue creating Self Managed Spot node groups. diff --git a/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/self_managed_cleanup.md b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/self_managed_cleanup.md new file mode 100644 index 00000000..e369f6bc --- /dev/null +++ b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/self_managed_cleanup.md @@ -0,0 +1,20 @@ +--- +title: "Self managed Spot workers cleanup" +date: 2018-08-07T08:30:11-07:00 +weight: 100 +--- + +{{% notice note %}} +If you're running in an account that was created for you as part of an AWS event, there's no need to go through the cleanup stage - the account will be closed automatically.\ +If you're running in your own account, make sure you run through these steps to make sure you don't encounter unwanted costs. +{{% /notice %}} + +## Cleaning up NTH +``` +helm delete aws-node-termination-handler --namespace kube-system +``` + +### Removing self managed node groups +``` +eksctl delete nodegroup -f add-ngs-spot.yaml --approve +``` diff --git a/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/workers_eksctl.md b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/workers_eksctl.md index 4672d6b9..8097a52e 100644 --- a/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/workers_eksctl.md +++ b/content/using_ec2_spot_instances_with_eks/070_selfmanagednodegroupswithspot/workers_eksctl.md @@ -1,11 +1,29 @@ --- -title: "Adding Spot Workers with eksctl" +title: "Create self managed node groups with Spot Instances" date: 2018-08-07T11:05:19-07:00 weight: 30 draft: false --- +{{% notice warning %}} + +If you are starting with **self managed Spot workers** chapter directly and planning to run only self managed node groups with Spot Instances, then first complete below chapters and then return back here:
      +
      +[Start the workshop]({{< relref "/using_ec2_spot_instances_with_eks/010_prerequisites" >}})
      +[Launch using eksctl]({{< relref "/using_ec2_spot_instances_with_eks/020_eksctl" >}})
      +[Install Kubernetes Tools]({{< relref "/using_ec2_spot_instances_with_eks/030_k8s_tools" >}})
      +[Select Instance Types for Diversification]({{< relref "/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/selecting_instance_types.md" >}}) + +{{% /notice %}} + +{{% notice info %}} + +If you are have already completed **EKS managed Spot workers** chapters and still want to explore self managed node groups with Spot Instances, then continue with this chapter. + +{{% /notice %}} + +In this section we will create self managed node groups with Spot best practices. To adhere to the best practice of instance diversification we will include instance types we identified in [Select Instance Types for Diversification]({{< relref "/using_ec2_spot_instances_with_eks/040_eksmanagednodegroupswithspot/selecting_instance_types.md" >}}) chapter. + -In this section we will deploy the instance types we selected and request nodegroups that adhere to Spot diversification best practices. For that we will use **[eksctl create nodegroup](https://eksctl.io/usage/managing-nodegroups/)** and eksctl configuration files to add the new nodes to the cluster. Let's first create the configuration file: ``` @@ -18,23 +36,23 @@ metadata: nodeGroups: - name: ng-spot-4vcpu-16gb minSize: 0 - maxSize: 5 - desiredCapacity: 1 + maxSize: 4 + desiredCapacity: 2 instancesDistribution: - instanceTypes: ["m5.xlarge", "m5d.xlarge", "m4.xlarge","t3.xlarge","t3a.xlarge","m5a.xlarge","t2.xlarge"] + instanceTypes: ["m4.xlarge", "m5.xlarge", "m5a.xlarge", "m5ad.xlarge", "m5d.xlarge", "m5dn.xlarge", "m5n.xlarge", "m5zn.xlarge", "m6i.xlarge", "t2.xlarge", "t3.xlarge", "t3a.xlarge"] onDemandBaseCapacity: 0 onDemandPercentageAboveBaseCapacity: 0 spotAllocationStrategy: capacity-optimized labels: - lifecycle: Ec2Spot + eks.amazonaws.com/capacityType: SPOT intent: apps - aws.amazon.com/spot: "true" + type: self-managed-spot taints: spotInstance: "true:PreferNoSchedule" tags: - k8s.io/cluster-autoscaler/node-template/label/lifecycle: Ec2Spot + k8s.io/cluster-autoscaler/node-template/label/eks.amazonaws.com/capacityType: SPOT k8s.io/cluster-autoscaler/node-template/label/intent: apps - k8s.io/cluster-autoscaler/node-template/label/aws.amazon.com/spot: "true" + k8s.io/cluster-autoscaler/node-template/label/type: self-managed-spot k8s.io/cluster-autoscaler/node-template/taint/spotInstance: "true:PreferNoSchedule" iam: withAddonPolicies: @@ -43,23 +61,23 @@ nodeGroups: albIngress: true - name: ng-spot-8vcpu-32gb minSize: 0 - maxSize: 5 + maxSize: 2 desiredCapacity: 1 instancesDistribution: - instanceTypes: ["m5.2xlarge", "m5d.2xlarge", "m4.2xlarge","t3.2xlarge","t3a.2xlarge","m5a.2xlarge","t2.2xlarge"] + instanceTypes: ["m4.2xlarge", "m5.2xlarge", "m5a.2xlarge", "m5ad.2xlarge", "m5d.2xlarge", "m5dn.2xlarge", "m5n.2xlarge", "m5zn.2xlarge", "m6i.2xlarge", "t2.2xlarge", "t3.2xlarge", "t3a.2xlarge"] onDemandBaseCapacity: 0 onDemandPercentageAboveBaseCapacity: 0 spotAllocationStrategy: capacity-optimized labels: - lifecycle: Ec2Spot + eks.amazonaws.com/capacityType: SPOT intent: apps - aws.amazon.com/spot: "true" + type: self-managed-spot taints: spotInstance: "true:PreferNoSchedule" tags: - k8s.io/cluster-autoscaler/node-template/label/lifecycle: Ec2Spot + k8s.io/cluster-autoscaler/node-template/label/eks.amazonaws.com/capacityType: SPOT k8s.io/cluster-autoscaler/node-template/label/intent: apps - k8s.io/cluster-autoscaler/node-template/label/aws.amazon.com/spot: "true" + k8s.io/cluster-autoscaler/node-template/label/type: self-managed-spot k8s.io/cluster-autoscaler/node-template/taint/spotInstance: "true:PreferNoSchedule" iam: withAddonPolicies: @@ -69,27 +87,29 @@ nodeGroups: EoF ``` -This will create a `add-ngs-spot.yaml` file that we will use to instruct eksctl to create two nodegroups, both with a diversified configuration. +This will create a `add-ngs-spot.yaml` file that we will use to instruct eksctl to create two node groups. ``` eksctl create nodegroup -f add-ngs-spot.yaml ``` {{% notice note %}} -The creation of the workers will take about 3 minutes. +The creation of the workers will take about 3-4 minutes. {{% /notice %}} There are a few things to note in the configuration that we just used to create these nodegroups. - * We did set up **minSize** to 0, **maxSize** to 5 and **desiredCapacity** to 1. Nodegroups can be scaled down to 0. - * We did set up **onDemandBaseCapacity** and **onDemandPercentageAboveBaseCapacity** both to **0**. which implies all nodes in the nodegroup would be **Spot instances**. - * We did set up a **lifecycle: Ec2Spot** label so we can identify Spot nodes and use [affinities](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/) and [nodeSelectors](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#nodeselector) later on. - * We did specify **spotAllocationStrategy** pointing it to use **[Capacity Optimized](https://aws.amazon.com/about-aws/whats-new/2019/08/new-capacity-optimized-allocation-strategy-for-provisioning-amazon-ec2-spot-instances/)**. This will ensure the capacity we provision in our nodegroups is procured from the pools that will have less chances of being interrupted. - * We did also add an extra label **intent: apps**. We will use this label to force a hard partition - of the cluster for our applications. During this workshop we will deploy control applications on - nodes that have been labeled with **intent: control-apps** while our applications get deployed to nodes labeled with **intent: apps**. - * We are also applying a **[Taint](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/)** using `spotInstance: "true:PreferNoSchedule"`. **PreferNoSchedule** is used to indicate we prefer pods not be scheduled on Spot Instances. This is a “preference” or “soft” version of **NoSchedule** – the system will try to avoid placing a pod that does not tolerate the taint on the node, but it is not required. - * We did apply **k8s.io/cluster-autoscaler/node-template/label** and **k8s.io/cluster-autoscaler/node-template/taint** tags to the nodegroups. [This tags are used by cluster autoscaler](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#how-can-i-scale-a-node-group-to-0) when nodegroups scale down to 0. They ensure cluster autoscaler considers tolerations and placement preferences of pending pods on nodegroups of 0 size. + * First node group has **xlarge** (4 vCPU and 16 GB) instance types with **minSize** 0, **maxSize** 4 and **desiredCapacity** 2. + * Second node group has **2xlarge** (8 vCPU and 32 GB) instance types with **minSize** 0, **maxSize** 2 and **desiredCapacity** 1. + * **onDemandBaseCapacity** and **onDemandPercentageAboveBaseCapacity** both set to **0**. which implies all nodes in the node group would be **Spot instances**. + * **spotAllocationStrategy** is set as **[Capacity Optimized](https://aws.amazon.com/about-aws/whats-new/2019/08/new-capacity-optimized-allocation-strategy-for-provisioning-amazon-ec2-spot-instances/)**. This will ensure the capacity we provision in our node groups is procured from the pools that will have less chances of being interrupted. + * We applied a **[Taint](https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/)** `spotInstance: "true:PreferNoSchedule"`. **PreferNoSchedule** is used to indicate we prefer pods not be scheduled on Spot Instances. This is a “preference” or “soft” version of **NoSchedule** – the system will try to avoid placing a pod that does not tolerate the taint on the node, but it is not required. + * Notice that the we added 3 node labels per node: + * **eks.amazonaws.com/capacityType: SPOT**, to identify Spot nodes and use as [affinities](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/). + * **intent: apps**, to allow you to deploy stateless applications on nodes that have been labeled with value **apps** + * **type: self-managed-spot**, to identify self managed Spot nodes and use as [nodeSelectors](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#nodeselector). + + * Notice that the we added 4 cluster autoscaler related tags to node groups. These tags are used by cluster autoscaler when node groups scale down to 0 (and scale up from 0). Cluster autoscaler acts on Auto Scaling groups belonging to node groups, therefore it requires same tags on ASG as well. EKSCTL adds these tags to ASG automatically while creating self managed node groups. {{% notice info %}} If you are wondering at this stage: *Where is spot bidding price ?* you are missing some of the changes EC2 Spot Instances had since 2017. Since November 2017 [EC2 Spot price changes infrequently](https://aws.amazon.com/blogs/compute/new-amazon-ec2-spot-pricing/) based on long term supply and demand of spare capacity in each pool independently. You can still set up a **maxPrice** in scenarios where you want to set maximum budget. By default *maxPrice* is set to the On-Demand price; Regardless of what the *maxPrice* value, Spot Instances will still be charged at the current spot market price. @@ -110,26 +130,19 @@ kubectl get nodes You can use the node-labels to identify the lifecycle of the nodes ```bash -kubectl get nodes --show-labels --selector=lifecycle=Ec2Spot | grep Ec2Spot +kubectl get nodes --label-columns=eks.amazonaws.com/capacityType --selector=type=self-managed-spot | grep SPOT ``` -The output of this command should return **Ec2Spot** nodes. At the end of the node output, you should see the node label **lifecycle=Ec2Spot** - -![Spot Output](/images/using_ec2_spot_instances_with_eks/spotworkers/spot_get_spot.png) - -Now we will show all nodes with the **lifecycle=OnDemand**. The output of this command should return OnDemand nodes (the ones that we tagged when -creating the cluster). - -```bash -kubectl get nodes --show-labels --selector=lifecycle=OnDemand | grep OnDemand ``` - -![OnDemand Output](/images/using_ec2_spot_instances_with_eks/spotworkers/spot_get_od.png) +ip-192-168-11-135.ap-southeast-1.compute.internal NotReady 13s v1.21.4-eks-033ce7e SPOT +ip-192-168-78-145.ap-southeast-1.compute.internal NotReady 5s v1.21.4-eks-033ce7e SPOT +ip-192-168-78-41.ap-southeast-1.compute.internal NotReady 12s v1.21.4-eks-033ce7e SPOT +``` You can use the `kubectl describe nodes` with one of the spot nodes to see the taints applied to the EC2 Spot Instances. -![Spot Taints](/images/using_ec2_spot_instances_with_eks/spotworkers/instance_taints.png) +![Spot Taints](/images/using_ec2_spot_instances_with_eks/spotworkers/spot-self-mng-taint.png) {{% notice note %}} Explore your cluster using kube-ops-view and find out the nodes that have just been created. -{{% /notice %}} +{{% /notice %}} \ No newline at end of file diff --git a/content/using_ec2_spot_instances_with_eks/200_cleanup.md b/content/using_ec2_spot_instances_with_eks/200_cleanup.md index 6be924bb..c4da1bb3 100644 --- a/content/using_ec2_spot_instances_with_eks/200_cleanup.md +++ b/content/using_ec2_spot_instances_with_eks/200_cleanup.md @@ -21,36 +21,19 @@ kubectl delete -f monte-carlo-pi-service.yml helm delete kube-ops-view metrics-server ``` -## Removing Spot node groups -```bash -eksctl delete nodegroup -f spot_nodegroup_4vcpu_16gb.yml --approve -eksctl delete nodegroup -f spot_nodegroup_8vcpu_32gb.yml --approve +## Removing EKS managed node groups ``` - -This operation may take 3-5 minutes to complete. - -## (Optional) Removing Self managed node groups -If you have followed the optional path to create self managed node groups, run this command to delete the node group. -```bash -eksctl delete nodegroup -f spot_nodegroups.yml --approve +eksctl delete nodegroup -f add-mngs-spot.yaml --approve ``` - This operation may take 3-5 minutes to complete. -## Removing On-Demand node group -```bash -od_nodegroup=$(eksctl get nodegroup --cluster eksworkshop-eksctl | tail -n 1 | awk '{print $2}') -eksctl delete nodegroup --cluster eksworkshop-eksctl --name $od_nodegroup -``` - -This operation may take some time. Once that it completes you can proceed with the deletion of the cluster. ## Removing the cluster ``` -eksctl delete cluster --name eksworkshop-eksctl +eksctl delete cluster -f eksworkshop.yaml ``` -## Clean Cloud 9 +## Delete SSH Key Pair and Cloud 9 ``` aws ec2 delete-key-pair --key-name eksworkshop CLOUD_9_IDS=$(aws cloud9 list-environments | jq -c ".environmentIds | flatten(0)" | sed -E -e 's/\[|\]|\"|//g' | sed 's/,/ /g') diff --git a/static/images/using_ec2_spot_instances_with_eks/scaling/scaling-asg-20.png b/static/images/using_ec2_spot_instances_with_eks/scaling/scaling-asg-20.png new file mode 100644 index 0000000000000000000000000000000000000000..bd1d9f463edc350bf056c5eb14f2a1d3d536e9d8 GIT binary patch literal 77052 zcmeFZbzGb~(=ZGLN-4z)lv3QixI?kx?yifwJEatNcW7~U7Kh?4#bI%Gw?&q3&n?e= z&U3##-~aFLT`qoCHpyg?$xJetnQW+noH)ws_pf1KU{E9_M3rD*5UgQfV7Xttgyz5_ zSolG|2wRAVC`gKk5Gy#^nOax_U|^_YjSUPiCFrRK3=Ito21e;?Q6Z?lBnnnU$iWv-S>s4Nj#CCReeu!Rk!}@qG>B0R5ET1~11-6Vudt_4XEyi#=hP 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