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Use JuiceFS on Hadoop Ecosystem	3	/hadoop_java_sdk

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Use JuiceFS on Hadoop Ecosystem

JuiceFS provides Hadoop-compatible FileSystem by Hadoop Java SDK. Various applications in the Hadoop ecosystem can smoothly use JuiceFS to store data without changing the code.

Requirements

1. Hadoop and related components

JuiceFS Hadoop Java SDK is compatible with Hadoop 2.x and Hadoop 3.x. As well as variety of components in Hadoop ecosystem.

2. User permissions

JuiceFS use local mapping of user and UID. So, you should sync all the needed users and their UIDs across the whole Hadoop cluster to avoid permission error. You can also specify a global user list and user group file, please refer to the relevant configurations.

3. File system

You should first create at least one JuiceFS file system to provide storage for components related to the Hadoop ecosystem through the JuiceFS Java SDK. When deploying the Java SDK, specify the metadata engine address of the created file system in the configuration file.

To create a file system, please refer to JuiceFS Quick Start Guide.

:::note If you want to use JuiceFS in a distributed environment, when creating a file system, please plan the object storage and database to be used reasonably to ensure that they can be accessed by each node in the cluster. :::

4. Memory

JuiceFS Hadoop Java SDK need extra 4 * juicefs.memory-size off-heap memory at most. By default, up to 1.2 GB of additional memory is required (depends on write load).

Install and compile the client

Install the pre-compiled client

Please refer to the "Installation & Upgrade" document to learn how to download the precompiled JuiceFS Hadoop Java SDK.

Compile the client manually

:::note No matter which system environment the client is compiled for, the compiled JAR file has the same name and can only be deployed in the matching system environment. For example, when compiled in Linux, it can only be used in the Linux environment. In addition, since the compiled package depends on glibc, it is recommended to compile with a lower version system to ensure better compatibility. :::

Compilation depends on the following tools:

Go 1.17+
JDK 8+
Maven 3.3+
git
make
GCC 5.4+

Linux and macOS

Clone the repository:

$ git clone https://github.com/juicedata/juicefs.git

Enter the directory and compile:

:::note If Ceph RADOS is used to store data, you need to install librados-dev first and build libjfs.so with -tags ceph. :::

$ cd juicefs/sdk/java
$ make

After the compilation, you can find the compiled JAR file in the sdk/java/target directory, including two versions:

Contains third-party dependent packages: juicefs-hadoop-X.Y.Z.jar
Does not include third-party dependent packages: original-juicefs-hadoop-X.Y.Z.jar

It is recommended to use a version that includes third-party dependencies.

Windows

The client used in the Windows environment needs to be obtained through cross-compilation on Linux or macOS. The compilation depends on mingw-w64, which needs to be installed first.

The steps are the same as compiling on Linux or macOS. For example, on the Ubuntu system, install the mingw-w64 package first to solve the dependency problem:

$ sudo apt install mingw-w64

Clone and enter the JuiceFS source code directory, execute the following code to compile:

$ cd juicefs/sdk/java
$ make win

Deploy the client

To enable each component of the Hadoop ecosystem to correctly identify JuiceFS, the following configurations are required:

Place the compiled JAR file and $JAVA_HOME/lib/tools.jar into the classpath of the component. The installation paths of common big data platforms and components are shown in the table below.
Put JuiceFS configurations into the configuration file of each Hadoop ecosystem component (usually core-site.xml), see Client Configurations for details.

It is recommended to place the JAR file in a fixed location, and the other locations are called it through symbolic links.

Big Data Platforms

Name	Installing Paths
CDH	`/opt/cloudera/parcels/CDH/lib/hadoop/lib` `/opt/cloudera/parcels/CDH/spark/jars` `/var/lib/impala`
HDP	`/usr/hdp/current/hadoop-client/lib` `/usr/hdp/current/hive-client/auxlib` `/usr/hdp/current/spark2-client/jars`
Amazon EMR	`/usr/lib/hadoop/lib` `/usr/lib/spark/jars` `/usr/lib/hive/auxlib`
Alibaba Cloud EMR	`/opt/apps/ecm/service/hadoop//package/hadoop/share/hadoop/common/lib` `/opt/apps/ecm/service/spark//package/spark/jars` `/opt/apps/ecm/service/presto//package/presto/plugin/hive-hadoop2` `/opt/apps/ecm/service/hive//package/apache-hive/lib` `/opt/apps/ecm/service/impala//package/impala/lib`
Tencent Cloud EMR	`/usr/local/service/hadoop/share/hadoop/common/lib` `/usr/local/service/presto/plugin/hive-hadoop2` `/usr/local/service/spark/jars` `/usr/local/service/hive/auxlib`
UCloud UHadoop	`/home/hadoop/share/hadoop/common/lib` `/home/hadoop/hive/auxlib` `/home/hadoop/spark/jars` `/home/hadoop/presto/plugin/hive-hadoop2`
Baidu Cloud EMR	`/opt/bmr/hadoop/share/hadoop/common/lib` `/opt/bmr/hive/auxlib` `/opt/bmr/spark2/jars`

Community Components

Name	Installing Paths
Spark	`${SPARK_HOME}/jars`
Presto	`${PRESTO_HOME}/plugin/hive-hadoop2`
Flink	`${FLINK_HOME}/lib`

Client Configurations

Please refer to the following table to set the relevant parameters of the JuiceFS file system and write it into the configuration file, which is generally core-site.xml.

Core Configurations

Configuration	Default Value	Description
`fs.jfs.impl`	`io.juicefs.JuiceFileSystem`	Specify the storage implementation to be used. By default, `jfs://` scheme is used. If you want to use different scheme (e.g. `cfs://`), just modify it to `fs.cfs.impl`. No matter what sheme you use, it is always access the data in JuiceFS.
`fs.AbstractFileSystem.jfs.impl`	`io.juicefs.JuiceFS`	Specify the storage implementation to be used. By default, `jfs://` scheme is used. If you want to use different scheme (e.g. `cfs://`), just modify it to `fs.AbstractFileSystem.cfs.impl`. No matter what sheme you use, it is always access the data in JuiceFS.
`juicefs.meta`		Specify the metadata engine address of the pre-created JuiceFS file system. You can configure multiple file systems for the client at the same time through the format of `juicefs.{vol_name}.meta`. Refer to "Multiple file systems configuration".

Cache Configurations

Configuration	Default Value	Description
`juicefs.cache-dir`		Directory paths of local cache. Use colon to separate multiple paths. Also support wildcard in path. It's recommended create these directories manually and set `0777` permission so that different applications could share the cache data.
`juicefs.cache-size`	0	Maximum size of local cache in MiB. The default value is 0, which means that caching is disabled. It's the total size when set multiple cache directories.
`juicefs.cache-full-block`	`true`	Whether cache every read blocks, `false` means only cache random/small read blocks.
`juicefs.free-space`	0.1	Min free space ratio of cache directory
`juicefs.attr-cache`	0	Expire of attributes cache in seconds
`juicefs.entry-cache`	0	Expire of file entry cache in seconds
`juicefs.dir-entry-cache`	0	Expire of directory entry cache in seconds
`juicefs.discover-nodes-url`		The URL to discover cluster nodes, refresh every 10 minutes. YARN: `yarn` Spark Standalone: `http://spark-master:web-ui-port/json/` Spark ThriftServer: `http://thrift-server:4040/api/v1/applications/` Presto: `http://coordinator:discovery-uri-port/v1/service/presto/`

I/O Configurations

Configuration	Default Value	Description
`juicefs.max-uploads`	20	The max number of connections to upload
`juicefs.max-deletes`	2	The max number of connections to delete
`juicefs.get-timeout`	5	The max number of seconds to download an object
`juicefs.put-timeout`	60	The max number of seconds to upload an object
`juicefs.memory-size`	300	Total read/write buffering in MiB
`juicefs.prefetch`	1	Prefetch N blocks in parallel
`juicefs.upload-limit`	0	Bandwidth limit for upload in Mbps
`juicefs.download-limit`	0	Bandwidth limit for download in Mbps

Other Configurations

Configuration	Default Value	Description
`juicefs.bucket`		Specify a different endpoint for object storage
`juicefs.debug`	`false`	Whether enable debug log
`juicefs.access-log`		Access log path. Ensure Hadoop application has write permission, e.g. `/tmp/juicefs.access.log`. The log file will rotate automatically to keep at most 7 files.
`juicefs.superuser`	`hdfs`	The super user
`juicefs.users`	`null`	The path of username and UID list file, e.g. `jfs://name/etc/users`. The file format is `<username>:<UID>`, one user per line.
`juicefs.groups`	`null`	The path of group name, GID and group members list file, e.g. `jfs://name/etc/groups`. The file format is `<group-name>:<GID>:<username1>,<username2>`, one group per line.
`juicefs.umask`	`null`	The umask used when creating files and directories (e.g. `0022`), default value is `fs.permissions.umask-mode`.
`juicefs.push-gateway`		Prometheus Pushgateway address, format is `<host>:<port>`.
`juicefs.push-auth`		Prometheus basic auth information, format is `<username>:<password>`.
`juicefs.push-graphite`		Graphite address, format is `<host>:<port>`.
`juicefs.push-interval`	10	Metric push interval (in seconds)
`juicefs.fast-resolve`	`true`	Whether enable faster metadata lookup using Redis Lua script
`juicefs.no-usage-report`	`false`	Whether disable usage reporting. JuiceFS only collects anonymous usage data (e.g. version number), no user or any sensitive data will be collected.

Multiple file systems configuration

When multiple JuiceFS file systems need to be used at the same time, all the above configuration items can be specified for a specific file system. You only need to put the file system name in the middle of the configuration item, such as jfs1 and jfs2 in the following example:

<property>
  <name>juicefs.jfs1.meta</name>
  <value>redis://jfs1.host:port/1</value>
</property>
<property>
  <name>juicefs.jfs2.meta</name>
  <value>redis://jfs2.host:port/1</value>
</property>

Configuration Example

The following is a commonly used configuration example. Please replace the {HOST}, {PORT} and {DB} variables in the juicefs.meta configuration with actual values.

<property>
  <name>fs.jfs.impl</name>
  <value>io.juicefs.JuiceFileSystem</value>
</property>
<property>
  <name>fs.AbstractFileSystem.jfs.impl</name>
  <value>io.juicefs.JuiceFS</value>
</property>
<property>
  <name>juicefs.meta</name>
  <value>redis://{HOST}:{PORT}/{DB}</value>
</property>
<property>
  <name>juicefs.cache-dir</name>
  <value>/data*/jfs</value>
</property>
<property>
  <name>juicefs.cache-size</name>
  <value>1024</value>
</property>
<property>
  <name>juicefs.access-log</name>
  <value>/tmp/juicefs.access.log</value>
</property>

Configuration in Hadoop

Please refer to the aforementioned configuration tables and add configuration parameters to the Hadoop configuration file core-site.xml.

CDH6

If you are using CDH 6, in addition to modifying core-site, you also need to modify mapreduce.application.classpath through the YARN service interface, adding:

$HADOOP_COMMON_HOME/lib/juicefs-hadoop.jar

HDP

In addition to modifying core-site, you also need to modify the configuration mapreduce.application.classpath through the MapReduce2 service interface and add it at the end (variables do not need to be replaced):

/usr/hdp/${hdp.version}/hadoop/lib/juicefs-hadoop.jar

Flink

Add configuration parameters to conf/flink-conf.yaml. If you only use JuiceFS in Flink, you don't need to configure JuiceFS in the Hadoop environment, you only need to configure the Flink client.

Hudi

:::note Hudi supports JuiceFS since v0.10.0, please make sure you are using the correct version. :::

Please refer to "Hudi Official Documentation" to learn how to configure JuiceFS.

Kafka Connect

It is possible to use Kafka Connect and HDFS Sink Connector（HDFS 2 and HDFS 3）to store data on JuiceFS.

First you need to add JuiceFS SDK to classpath in Kafka Connect, e.g., /usr/share/java/confluentinc-kafka-connect-hdfs/lib.

While creating a Connect Sink task, configuration needs to be set up as follows:

Specify hadoop.conf.dir as the directory that contains the configuration file core-site.xml. If it is not running in Hadoop environment, you can create a seperate directory such as /usr/local/juicefs/hadoop, and then add the Juicefs-related configurations to core-site.xml.
Specific store.url as the path jfs://

For example,

# Other configuration items are omitted.
hadoop.conf.dir=/path/to/hadoop-conf
store.url=jfs://path/to/store

Restart Services

When the following components need to access JuiceFS, they should be restarted.

:::note Before restart, you need to confirm JuiceFS related configuration has been written to the configuration file of each component, usually you can find them in core-site.xml on the machine where the service of the component was deployed. :::

Components	Services
Hive	HiveServer Metastore
Spark	ThriftServer
Presto	Coordinator Worker
Impala	Catalog Server Daemon
HBase	Master RegionServer

HDFS, Hue, ZooKeeper and other services don't need to be restarted.

When Class io.juicefs.JuiceFileSystem not found or No FilesSystem for scheme: jfs exceptions was occurred after restart, reference FAQ.

Environmental Verification

After the deployment of the JuiceFS Java SDK, the following methods can be used to verify the success of the deployment.

Hadoop CLI

hadoop fs -ls jfs://{JFS_NAME}/

:::info The JFS_NAME is the volume name when you format JuiceFS file system. :::

Hive

CREATE TABLE IF NOT EXISTS person
(
  name STRING,
  age INT
) LOCATION 'jfs://{JFS_NAME}/tmp/person';

Java/Scala project

Add Maven or Gradle dependencies:

<dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-common</artifactId>
    <version>{HADOOP_VERSION}</version>
    <scope>provided</scope>
</dependency>
<dependency>
    <groupId>io.juicefs</groupId>
    <artifactId>juicefs-hadoop</artifactId>
    <version>{JUICEFS_HADOOP_VERSION}</version>
    <scope>provided</scope>
</dependency>

dependencies {
  implementation 'org.apache.hadoop:hadoop-common:${hadoopVersion}'
  implementation 'io.juicefs:juicefs-hadoop:${juicefsHadoopVersion}'
}

Use the following sample code to verify:

package demo;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;

public class JuiceFSDemo {
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        conf.set("fs.jfs.impl", "io.juicefs.JuiceFileSystem");
        conf.set("juicefs.meta", "redis://127.0.0.1:6379/0");  // JuiceFS metadata engine URL
        Path p = new Path("jfs://{JFS_NAME}/");  // Please replace "{JFS_NAME}" with the correct value
        FileSystem jfs = p.getFileSystem(conf);
        FileStatus[] fileStatuses = jfs.listStatus(p);
        // Traverse JuiceFS file system and print file paths
        for (FileStatus status : fileStatuses) {
            System.out.println(status.getPath());
        }
    }
}

Monitoring metrics collection

Please see the "Monitoring" documentation to learn how to collect and display JuiceFS monitoring metrics.

Benchmark

Here are a series of methods to use the built-in stress testing tool of the JuiceFS client to test the performance of the client environment that has been successfully deployed.

1. Local Benchmark

Metadata

create

hadoop jar juicefs-hadoop.jar nnbench create -files 10000 -baseDir jfs://{JFS_NAME}/tmp/benchmarks/NNBench -local

This command will create 10000 empty files

open

hadoop jar juicefs-hadoop.jar nnbench open -files 10000 -baseDir jfs://{JFS_NAME}/tmp/benchmarks/NNBench -local

This command will open 10000 files without reading data

rename

hadoop jar juicefs-hadoop.jar nnbench rename -files 10000 -baseDir jfs://{JFS_NAME}/tmp/benchmarks/NNBench -local

delete

hadoop jar juicefs-hadoop.jar nnbench delete -files 10000 -baseDir jfs://{JFS_NAME}/tmp/benchmarks/NNBench -local

For reference

Operation TPS Latency (ms)

create 644 1.55

open 3467 0.29

rename 483 2.07

delete 506 1.97

I/O Performance

sequential write

hadoop jar juicefs-hadoop.jar dfsio -write -size 20000 -baseDir jfs://{JFS_NAME}/tmp/benchmarks/DFSIO -local

sequential read
```
hadoop jar juicefs-hadoop.jar dfsio -read -size 20000 -baseDir jfs://{JFS_NAME}/tmp/benchmarks/DFSIO -local
```
When run the cmd for the second time, the result may be much better than the first run. It's because the data was cached in memory, just clean the local disk cache.
For reference

Operation Throughput (MB/s)

write 647

read 111

If the network bandwidth of the machine is relatively low, it can generally reach the network bandwidth bottleneck.

2. Distributed Benchmark

The following command will start the MapReduce distributed task to test the metadata and IO performance. During the test, it is necessary to ensure that the cluster has sufficient resources to start the required map tasks.

Computing resources used in this test:

Server: 4 cores and 32 GB memory, burst bandwidth 5Gbit/s x 3
Database: Alibaba Cloud Redis 5.0 Community 4G Master-Slave Edition

Metadata

create

hadoop jar juicefs-hadoop.jar nnbench create -maps 10 -threads 10 -files 1000 -baseDir jfs://{JFS_NAME}/tmp/benchmarks/NNBench

10 map task, each has 10 threads, each thread create 1000 empty file. 100000 files in total

open

hadoop jar juicefs-hadoop.jar nnbench open -maps 10 -threads 10 -files 1000 -baseDir jfs://{JFS_NAME}/tmp/benchmarks/NNBench

10 map task, each has 10 threads, each thread open 1000 file. 100000 files in total

rename

hadoop jar juicefs-hadoop.jar nnbench rename -maps 10 -threads 10 -files 1000 -baseDir jfs://{JFS_NAME}/tmp/benchmarks/NNBench

10 map task, each has 10 threads, each thread rename 1000 file. 100000 files in total

delete

hadoop jar juicefs-hadoop.jar nnbench delete -maps 10 -threads 10 -files 1000 -baseDir jfs://{JFS_NAME}/tmp/benchmarks/NNBench

10 map task, each has 10 threads, each thread delete 1000 file. 100000 files in total

For reference
- 10 threads
  
  Operation IOPS Latency (ms)
  
  create 4178 2.2
  
  open 9407 0.8
  
  rename 3197 2.9
  
  delete 3060 3.0
- 100 threads
  
  Operation IOPS Latency (ms)
  
  create 11773 7.9
  
  open 34083 2.4
  
  rename 8995 10.8
  
  delete 7191 13.6

I/O Performance

sequential write

hadoop jar juicefs-hadoop.jar dfsio -write -maps 10 -size 10000 -baseDir jfs://{JFS_NAME}/tmp/benchmarks/DFSIO

10 map task, each task write 10000MB random data sequentially

sequential read

hadoop jar juicefs-hadoop.jar dfsio -read -maps 10 -size 10000 -baseDir jfs://{JFS_NAME}/tmp/benchmarks/DFSIO

10 map task, each task read 10000MB random data sequentially

For reference

Operation Average throughput (MB/s) Total Throughput (MB/s)

write 198 1835

read 124 1234

3. TPC-DS

The test dataset is 100GB in size, and both Parquet and ORC file formats are tested.

This test only tests the first 10 queries.

Spark Thrift JDBC/ODBC Server is used to start the Spark resident process and then submit the task via Beeline connection.

Test Hardware

Node Category	Instance Type	CPU	Memory	Disk	Number
Master	Alibaba Cloud ecs.r6.xlarge	4	32GiB	System Disk: 100GiB	1
Core	Alibaba Cloud ecs.r6.xlarge	4	32GiB	System Disk: 100GiB Data Disk: 500GiB Ultra Disk x 2	3

Software Configuration

Spark Thrift JDBC/ODBC Server

${SPARK_HOME}/sbin/start-thriftserver.sh \
  --master yarn \
  --driver-memory 8g \
  --executor-memory 10g \
  --executor-cores 3 \
  --num-executors 3 \
  --conf spark.locality.wait=100 \
  --conf spark.sql.crossJoin.enabled=true \
  --hiveconf hive.server2.thrift.port=10001

JuiceFS Cache Configurations

The 2 data disks of Core node are mounted in the /data01 and /data02 directories, and core-site.xml is configured as follows:

<property>
  <name>juicefs.cache-size</name>
  <value>200000</value>
</property>
<property>
  <name>juicefs.cache-dir</name>
  <value>/data*/jfscache</value>
</property>
<property>
  <name>juicefs.cache-full-block</name>
  <value>false</value>
</property>
<property>
  <name>juicefs.discover-nodes-url</name>
  <value>yarn</value>
</property>
<property>
  <name>juicefs.attr-cache</name>
  <value>3</value>
</property>
<property>
  <name>juicefs.entry-cache</name>
  <value>3</value>
</property>
<property>
  <name>juicefs.dir-entry-cache</name>
  <value>3</value>
</property>

Test

The task submission command is as follows:

${SPARK_HOME}/bin/beeline -u jdbc:hive2://localhost:10001/${DATABASE} \
  -n hadoop \
  -f query{i}.sql

Results

JuiceFS can use local disk as a cache to accelerate data access, the following data is the result (in seconds) after 4 runs using Redis and TiKV as the metadata engine of JuiceFS respectively.

ORC

Queries	JuiceFS (Redis)	JuiceFS (TiKV)	HDFS
q1	20	20	20
q2	28	33	26
q3	24	27	28
q4	300	309	290
q5	116	117	91
q6	37	42	41
q7	24	28	23
q8	13	15	16
q9	87	112	89
q10	23	24	22

Parquet

Queries	JuiceFS (Redis)	JuiceFS (TiKV)	HDFS
q1	33	35	39
q2	28	32	31
q3	23	25	24
q4	273	284	266
q5	96	107	94
q6	36	35	42
q7	28	30	24
q8	11	12	14
q9	85	97	77
q10	24	28	38

FAQ

1. `Class io.juicefs.JuiceFileSystem not found` exception

It means JAR file was not loaded, you can verify it by lsof -p {pid} | grep juicefs.

You should check whether the JAR file was located properly, or other users have the read permission.

Some Hadoop distribution also need to modify mapred-site.xml and put the JAR file location path to the end of the parameter mapreduce.application.classpath.

2. `No FilesSystem for scheme: jfs` exception

It means JuiceFS Hadoop Java SDK was not configured properly, you need to check whether there is JuiceFS related configuration in the core-site.xml of the component configuration.

Queries	JuiceFS (Redis)	JuiceFS (TiKV)	HDFS
q1	20	20	20
q2	28	33	26
q3	24	27	28
q4	300	309	290
q5	116	117	91
q6	37	42	41
q7	24	28	23
q8	13	15	16
q9	87	112	89
q10	23	24	22

Queries	JuiceFS (Redis)	JuiceFS (TiKV)	HDFS
q1	33	35	39
q2	28	32	31
q3	23	25	24
q4	273	284	266
q5	96	107	94
q6	36	35	42
q7	28	30	24
q8	11	12	14
q9	85	97	77
q10	24	28	38

Operation	TPS	Latency (ms)
create	644	1.55
open	3467	0.29
rename	483	2.07
delete	506	1.97

Operation	Throughput (MB/s)
write	647
read	111

Operation	IOPS	Latency (ms)
create	4178	2.2
open	9407	0.8
rename	3197	2.9
delete	3060	3.0

Operation	IOPS	Latency (ms)
create	11773	7.9
open	34083	2.4
rename	8995	10.8
delete	7191	13.6

Queries	JuiceFS (Redis)	JuiceFS (TiKV)	HDFS
q1	20	20	20
q2	28	33	26
q3	24	27	28
q4	300	309	290
q5	116	117	91
q6	37	42	41
q7	24	28	23
q8	13	15	16
q9	87	112	89
q10	23	24	22

Queries	JuiceFS (Redis)	JuiceFS (TiKV)	HDFS
q1	33	35	39
q2	28	32	31
q3	23	25	24
q4	273	284	266
q5	96	107	94
q6	36	35	42
q7	28	30	24
q8	11	12	14
q9	85	97	77
q10	24	28	38

Files

hadoop_java_sdk.md

Latest commit

History

hadoop_java_sdk.md

File metadata and controls

Use JuiceFS on Hadoop Ecosystem

Requirements

1. Hadoop and related components

2. User permissions

3. File system

4. Memory

Install and compile the client

Install the pre-compiled client

Compile the client manually

Linux and macOS

Windows

Deploy the client

Big Data Platforms

Community Components

Client Configurations

Core Configurations

Cache Configurations

I/O Configurations

Other Configurations

Multiple file systems configuration

Configuration Example

Configuration in Hadoop

CDH6

HDP

Flink

Hudi

Kafka Connect

Restart Services

Environmental Verification

Hadoop CLI

Hive

Java/Scala project

Monitoring metrics collection

Benchmark

1. Local Benchmark

Metadata

I/O Performance

2. Distributed Benchmark

Metadata

I/O Performance

3. TPC-DS

Test Hardware

Software Configuration

Spark Thrift JDBC/ODBC Server

JuiceFS Cache Configurations

Test

Results

ORC

Parquet

FAQ

1. Class io.juicefs.JuiceFileSystem not found exception

2. No FilesSystem for scheme: jfs exception

1. `Class io.juicefs.JuiceFileSystem not found` exception

2. `No FilesSystem for scheme: jfs` exception

Queries	JuiceFS (Redis)	JuiceFS (TiKV)	HDFS
q1	20	20	20
q2	28	33	26
q3	24	27	28
q4	300	309	290
q5	116	117	91
q6	37	42	41
q7	24	28	23
q8	13	15	16
q9	87	112	89
q10	23	24	22

Queries	JuiceFS (Redis)	JuiceFS (TiKV)	HDFS
q1	33	35	39
q2	28	32	31
q3	23	25	24
q4	273	284	266
q5	96	107	94
q6	36	35	42
q7	28	30	24
q8	11	12	14
q9	85	97	77
q10	24	28	38