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Keyhole is a MongoDB performance analytic tool and comes with many features. It can be overwhelmed when you first use the tool. This document provides step by step instructions.
Keyhole uses a MongoDB connection string to connect to a cluster. For a standalone server, you typically have a connection string as follows.
mongodb://<usern>:<passwd>@<hostname>:<port>/?authSource=<db>
Once you have a connection string, connect to your cluster with --info flag.
keyhole --info mongodb://user:password@localhost:27017/?authSource=admin
For a sharded cluster, connect to a mongos and the command is similar to connecting to a standalone server. For a replica set, include all replica nodes and replica set name in the connection string. Below is an example of connecting to a replica set.
keyhole --info "mongodb://user:password@host1,host2,host3/?replicaSet=replset&authSource=admin"
If inflight encryption is required by your cluster, include tlsCAFile and tlsCertificateKeyFile in the connection string. For example,
keyhole --info \
"mongodb://user:password@localhost/?authSource=admin&tlsCAFile=/etc/ssl/certs/ca.pem&tlsCertificateKeyFile=/etc/ssl/certs/client.pem"
In the following sections, I will cover use cases of how Keyhole is used for performance analytic.
To determine the amount of physical memory should be installed, MongoDB recommends that for fastest processing, ensure that your indexes fit entirely in RAM so that the system can avoid reading the index from disk. The output of the keyhole command with --info flag has the storage information, and the total index size is the value of StorageSize.totalIndexSize in bytes.
Seeding data is a very useful feature when you need need to populate data for demo or testing your applications. By default, keyhole generates document from a predefined format for demo purpose. But, keyhole can also generate data by mimicking a sample JSON document.
Keyhole can reads in a sample JSON document and populate data using randomized values. This is especially useful for developers or QA to populate data before performing any tests. For example, your have a document with following structure in file template.json.
{
"_id": "a1b2c3d4e5f6f7e8d9c0b6a8",
"email": "[email protected]",
"hostIP": "192.168.1.1",
"shortString": "Atlanta",
"longString": "This is another string value. You can use any field names",
"number": 123,
"hex": "a1023b435c893d123e567f3487",
"objectId": "a1b2c3d4e5f6f7e8d9c0b6a8",
"lastUpdated": "2018-01-01T01:23:45Z",
"array1": [123, 456, 789],
"array2": [ "little", "cute", "girl" ],
"array3": [
{"city1": "New York", "city2": "Atlanta", "city3": "Miami"},
{"city1": "Chicago", "city2": "Dallas", "city3": "Houston"} ],
"subdocs": {
"attribute1": {"email": "[email protected]"}
}
}
To view an example of document, execute the command below.
keyhole --schema --file template.json
A example of result is shown as following.
{
"_id": ObjectId("de763b679dc88d11243b0bc2"),
"array1": [
8511,
8162,
5089
],
"array2": [
"Olivia",
"Michael",
"Olivia"
],
"array3": [
{
"city1": "Ava",
"city2": "Linda",
"city3": "Ava"
},
{
"city1": "Robert",
"city2": "Mary",
"city3": "Jennifer"
}
],
"email": "[email protected]",
"hex": "dcd554c200aa94d6b11729b39b",
"hostIP": "45.156.103.220",
"lastUpdated": ISODate("1987-09-24T12:43:52-04:00"),
"longString": "There's no place like home. All right, Mr. DeMille, I'm",
"number": 563,
"objectId": ObjectId("d739c08d26448db599b89278"),
"shortString": "Richard",
"subdocs": {
"attribute1": {
"email": "[email protected]"
}
}
}
Using a template, keyhole is intelligent enough to detect data types. More importantly, for the string type, keyhole can detect a few special formats, and they are email address, IP, date/time with yyyy-MM-ddT\.* format, and HEX, respectively and then replace their values with randomized values of their formats accordingly. Note that ObjectID() and ISODate() are not valid JSON syntax and you have to change them to a sub document using a 24 bytes HEX string and ISO date string format begins with "yyyy-MM-ddT", for example 2018-06-06T12:34:56Z.
Keyhole by default seeds 1,000 documents at a time to namespace _KEYHOLE_.examples
keyhole --seed --file template.json "mongodb://user:password@localhost:27017/?authSource=admin"
You can specify the destination database by including the target database in the connection string and the total number of documents to be created with the --total flags. For example,
keyhole --seed --file template.json --total 5000 \
"mongodb://user:password@localhost:27017/EXAMPLEDB?authSource=admin"
The above example writes 5,000 documents to EXAMPLEDB.examples. In addition, keyhole inserts into the destination collection without dropping it first. To drop the _KEYHOLE_.examples before any document insertion, include the --drop flag.
keyhole --seed --file template.json --total 5000 --drop \
"mongodb://user:password@localhost:27017/?authSource=admin"
There are two ways to begin monitoring, and they are with --peek and --monitor options, respectively.
Keyhole can collect server status data by issuing db.serverStatus() to a node or a cluster with the --peek flag. if keyhole connects to a replica set, it will connect to the primary node. Below is an example of connecting to a standalone instance.
keyhole --peek "mongodb://user:password@localhost:27017/?authSource=admin"
Note that, the default database keyhole collects database stats from is _KEYHOLE. You can include the targe database in the connection string as described in the Seed Data section. By default, keyhole runs for a period of 5 minutes and intends to take a "snapshot" of the system performance. To prolong the monitoring time, include the --duration flag. For example, to monitor a cluster for 60 minutes, do
keyhole --peek --duration 60 "mongodb://user:password@localhost:27017/?authSource=admin"
For a sharded cluster, keyhole connects to the primary node of all shards. The reason of doing so, instead of remaining at mongos, is to collect more stats, such as wiredTiger stats data, and provide a partial view of the entire cluster.
At the end of a keyhole, including monitoring and load test, it writes data to a file under system's default temporary directory. Save the file and you can view the summaries again using keyhole with --diag flag. For example,
keyhole --diag /tmp/keyhole_stats.2018-06-04T084021-shard01.gz
Keyhole collects server status every 10 seconds, but by default the granularity is 1 minute when using --diag flag. You can adjust the granularity by adding the --span flag. For example, to view stats in a 10 seconds interval, do
keyhole --diag /tmp/keyhole_stats.2018-06-04T084021-shard01.gz --span 10
With --monitor flag, keyhole collects server status using db.serverStats() every 10 minutes for, by default, 24 hours. At the end of each run, same as using --peek, keyhole prints a summary and stores server status data in a file.
keyhole --monitor "mongodb://user:password@localhost:27017/?authSource=admin"
To change the default duration, include the --duration flag (in minutes).
keyhole --monitor --duration 120 "mongodb://user:password@localhost:27017/?authSource=admin"
Keyhole prints stats along the way during the run and summaries are displayed when keyhole exits or interrupted. The discussion of what these metrics represent is outside the scope of this user's guide. But experienced users should be able identify the performance bottlenecks from these summaries.
Analytic summary includes
- Resident memory
- Virtual memory
- Page faults
- Number of ops from Command
- Number of ops from Delete
- Number of ops from Getmore
- Number of ops from Insert
- Number of ops from Query
- Number of ops from Update
- IOPS
Page faults occurs when mongod has to retrieved data from disks because data was not found in the memory. High number of Page faults indicates the amount physical memory installed is not able to hold your working set. Adding additional memory or scaling MongoDB horizontally is recommended.
Global lock includes
- Total time in milliseconds
- Active Client
- Current Queue
Growing number of Current Queue indicates MongoDB is not processing requests fast enough as they arrive. Scaling up your server can help, such as fast CPU and disk, or scaling MongoDB horizontally by adding additional shards.
Latencies summary includes
- reads latency in millisecond
- writes latency in millisecond
- commands latency in millisecond
Metrics summary includes
- Scanned
- ScannedObj
- ScanAndOrder
- WriteConflicts
- Deleted
- Inserted
- Returned
- Updated
Scanned designates the total number of index items scanned during queries and query-plan evaluation. ScannedObj indicates total number of documents scanned during queries and query-plan evaluation. ScanAndOrder means the total number of queries that return sorted numbers that cannot perform the sort operation using an index. Additional indexes can help if you have high numbers of ScannedObj or ScanAndOrder.
WireTiger cache summary includes
- MaxBytes Configured
- Currently InCache
- Tracked DirtyBytes
- Modified PagesEvicted
- Unmodified PagesEvicted
- PagesRead IntoCache
- PagesWritten FromCache
The value of Currently InCache should not be greater than the MaxBytes Configured value. Tracked DirtyBytes indicates data in the wiredTiger cache that has been modified but not yet flushed to disk, and should be less than the value of Currently in cache. Expanding amounts of dirty data represents a bottleneck where data is written to disk not fast enough. Modified PagesEvicted and Unmodified PagesEvicted reveal page eviction statistics. PagesRead IntoCache and PagesWritten FromCache provide an overview of the I/O activity.
WireTiger cache summary includes
- Read ticket
- Write ticket
WiredTiger storage engine uses tickets to control the number of read/write operations simultaneously processed. When the number of available read or write tickets available reaches zero, new read or write requests are queued until a new read or write ticket is freed up.
Keyhole can be used as a load testing tool on top of the collecting stats functions. To load test a standalone server, simply execute it with the MongoDB connection string. Along the way, keyhole also collect server status as described in Monitoring section and prints summaries at the end of a run.
keyhole --file template.json "mongodb://user:password@localhost:27017/?authSource=admin"
The above command by default spawns 10 connections and generates 300 transactions per second (TPS) for 5 minutes. You can change the number of connections by including the --conn flag, the TPS from --tps flag, and duration from --duration flag.
You can load test with pre-defined transactions and indexes with --tx flag.
keyhole --file template.json --tx transactions.json \
"mongodb://user:password@localhost:27017/?authSource=admin"
In the template file, you can define indexes and transactions. Both single field and compound indexes are supported. In addition, supported commands are
- insertOne
- insertMany
- find
- findOne
- update
- updateAll
- remove
- removeAll
- aggregate
Below is as example.
{
"indexes": [{
"email": 1,
"hostIp": 1
}],
"transactions": [{
"c": "insertOne"
}, {
"c": "insertMany"
}, {
"c": "find",
"filter": {
"email": "[email protected]"
}
}, {
"c": "findOne",
"filter": {
"email": "[email protected]"
}
}, {
"c": "update",
"filter": {
"email": "[email protected]"
},
"op": {
"$set": {
"email": "[email protected]"
}
}
}, {
"c": "updateAll",
"filter": {
"email": "[email protected]"
},
"op": {
"$set": {
"email": "[email protected]"
}
}
}, {
"c": "remove",
"filter": "{'email': '[email protected]'}"
}, {
"c": "removeAll",
"filter": "{'email': '[email protected]'}"
}, {
"c": "aggregate",
"pipe": [{
"$match": {
"email": "[email protected]"
}
}, {
"$project": {
"email": 1,
"lastUpdated": 1
}
}]
}]
}
Load test a replica or a sharded cluster, the connecting behavior works the same way as in monitoring mode. Note that under load test mode, keyhole ignores the target database in the connection string. The reason is at the end of run keyhole will clean up data and thus it always run against the _KEYHOLE_88800.examples collection.
There are different stages in a keyhole load test run, and they are as following
In the first minute of a load test run, keyhole insert documents into database. The purposes of doing such are three folds. First, we want to measure the throughputs of a MongoDB cluster in MB per second. Secondly, by having the first minute activities, we can bring the mongod instances from "cold" to "hot". Lastly, we should have enough data to work with at the end of the first minute.
The minute following data population, It executes half of defined TPS. In other words, with 300 TPS and 10 connections, there are a total of 1,500 (10 * 300 / 2) transactions.
The period follows normal ops and before teardown are the thrashing period. The system is in full throttle of 3,000 (10 * 300) transactions. If your server can't achieve the desired TPS, it will adjust and display a message of adjusted TPS.
The last minute is the teardown period and it removes test data from database.
Keyhole can run in a simulation only mode without populating data first. This is especially useful when you already have data ready to test with. If testing the bandwidth of write throughputs is already done, with pre-populated data, you may want to run keyhole in a simulation only mode and lower the TPS. For example,
keyhole --file template.json --simonly --tps 40 \
"mongodb://user:password@localhost:27017/?authSource=admin"
Keyhole can identify slow operations and queries with collections scans by analyzing a log file, either plain or gzip compressed. Run keyhole command with a --loginfo flag and it will display average execution time by query patterns and sorted by execution in descending order.
keyhole --loginfo /var/log/mongodb/mongod.log.2018-06-07T11-08-32.gz
With --collscan flag, keyhole only prints those with COLLSCAN.
keyhole --loginfo /var/log/mongodb/mongod.log.2018-06-07T11-08-32.gz --collscan
Keyhole is able to connection to retrieve the first document of a collection and returns a masked document as its schema view. For example,
keyhole --schema --collection examples "mongodb://user:password@localhost:27017/KEYHOLEDB?authSource=admin"
Keyhole can print all indexes of collections of a database. This is useful to evaluate redundant indexes of a collection. Below is an example.
$ keyhole --index "mongodb://localhost/keyhole"
keyhole.cars:
{ color: -1 }
{ color: 1 }
{ style: 1 }
{ style: 1, color: 1 }
Note that the index { _id: 1 } is ignore. Include -v to print all indexes.
$ keyhole --index -v "mongodb://localhost/keyhole"
keyhole.cars:
{ _id: 1 }
{ color: -1 }
{ color: 1 }
{ style: 1 }
{ style: 1, color: 1 }
Without including database in the connection string, keyhole prints all indexes from all databases except admin, config, and local databases.
$ keyhole --index mongodb://localhost/