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Gluten with Velox Backend |
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Getting-Started |
Currently, the mvn script can automatically fetch and build all dependency libraries incluing Velox and Arrow. Our nightly build still use Velox under oap-project.
Currently Gluten+Velox backend is only tested on Ubuntu20.04/Ubuntu22.04/Centos8. Other kinds of OS support are still in progress . The long term goal is to support several common OS and conda env deployment.
Gluten builds with Spark3.2.x and Spark3.3.3 now but only fully tested in CI with 3.2.2 and 3.3.1. We will add/update supported/tested versions according to the upstream changes.
Velox uses the script setup-ubuntu.sh to install all dependency libraries, but Arrow's dependency libraries are not installed. Velox also requires ninja for compilation.
So we need to install all of them manually. Also, we need to set up the JAVA_HOME env. Currently, java 8 is required and the support for java 11/17 is not ready.
## run as root
## install gcc and libraries to build arrow
apt-get update && apt-get install -y sudo locales wget tar tzdata git ccache cmake ninja-build build-essential llvm-11-dev clang-11 libiberty-dev libdwarf-dev libre2-dev libz-dev libssl-dev libboost-all-dev libcurl4-openssl-dev openjdk-8-jdk mavenFor x86_64
## make sure jdk8 is used
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64
export PATH=$JAVA_HOME/bin:$PATHFor aarch64
## make sure jdk8 is used
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-arm64
export PATH=$JAVA_HOME/bin:$PATHGet gluten
## config maven, like proxy in ~/.m2/settings.xml
## fetch gluten code
git clone https://github.com/oap-project/gluten.gitIt's recommended to use buildbundle-veloxbe.sh and build gluten in one script. Gluten Usage listed the parameters and their default value of build command for your reference.
For x86_64 build:
cd /path_to_gluten
## The script builds two jars for spark 3.2.2 and 3.3.1.
./dev/buildbundle-veloxbe.sh
## When you have successfully compiled once and changed some codes then compile again.
## you may use following command to skip the arrow, velox and protobuf build
# ./dev/buildbundle-veloxbe.sh --build_arrow_from_source=OFF --build_velox_from_source=OFF --build_protobuf=OFFFor aarch64 build, set the CPU_TARGET to "aarch64":
export CPU_TARGET="aarch64"
cd /path_to_gluten
./dev/builddeps-veloxbe.shAlternatively you may build gluten step by step as below.
## fetch arrow and compile
cd /path_to_gluten/ep/build-arrow/src/
./get_arrow.sh
./build_arrow.sh
## fetch velox
cd /path_to_gluten/ep/build-velox/src/
./get_velox.sh
## compile velox
./build_velox.sh
## compile gluten cpp
cd /path_to_gluten/cpp
mkdir build
cd build
cmake -DBUILD_VELOX_BACKEND=ON ..
make -j
# You can also use CMAKE command to compile
# cd /path_to_gluten/cpp
# mkdir build && cd build && cmake .. && make -j
## compile gluten jvm and package jar
cd /path_to_gluten
# For spark3.2.x
mvn clean package -Pbackends-velox -Pspark-3.2 -DskipTests
# For spark3.3.x
mvn clean package -Pbackends-velox -Pspark-3.3 -DskipTestsnotes：The compilation of Velox using the script of build_velox.sh may fail caused by oom, you can prevent this failure by using the user command of export NUM_THREADS=4 before executing the above scripts.
Once building successfully, the Jar file will be generated in the directory: package/target/ for Spark 3.2.2/Spark 3.3.1.
You can also clone the Velox source from OAP/velox to some other folder then specify it as below.
step 1: recompile velox, set velox_home in build_velox.sh
cd /path_to_gluten/ep/build_velox/src
./build_velox.sh --velox_home=/your_specified_velox_path
step 2: recompile gluten cpp folder, set velox_home in build_velox.sh
cd /path_to_gluten/cpp
mkdir build
cd build
cmake -DBUILD_VELOX_BACKEND=ON -DVELOX_HOME=/your_specified_velox_path ..
make -j
step 3: package jar
cd /path_to_gluten
# For spark3.2.x
mvn clean package -Pbackends-velox -Pspark-3.2 -DskipTests
# For spark3.3.x
mvn clean package -Pbackends-velox -Pspark-3.3 -DskipTestsArrow home can be set as the same of Velox. We will soon switch to upstream Arrow. Currently the shuffle still uses Arrow's IPC interface. You can also clone the Arrow source from OAP/Arrow to some other folder then specify it as below.
step 1: set ARROW_SOURCE_DIR in build_arrow.sh and compile
cd /path_to_gluten/ep/build-arrow/src/
./build_arrow.sh
step 2: set ARROW_ROOT
cd /path_to_gluten/cpp
mkdir build
cd build
cmake -DBUILD_VELOX_BACKEND=ON -DARROW_ROOT=/your_arrow_lib ..
make -j
step 3: package jar
cd /path_to_gluten
# For spark3.2.x
mvn clean package -Pbackends-velox -Pspark-3.2 -DskipTests
# For spark3.3.x
mvn clean package -Pbackends-velox -Pspark-3.3 -DskipTestsHadoop hdfs support is ready via the libhdfs3 library. The libhdfs3 provides native API for Hadoop I/O without the drawbacks of JNI. It also provides advanced authentatication like Kerberos based. Please note this library has serveral depedencies which may require extra installations on Driver and Worker node. On Ubuntu 20.04 the required depedencis are libiberty-dev, libxml2-dev, libkrb5-dev, libgsasl7-dev, libuuid1, uuid-dev. The packages can be installed via below command:
sudo apt install -y libiberty-dev libxml2-dev libkrb5-dev libgsasl7-dev libuuid1 uuid-dev
To build Gluten with HDFS support, below command is provided:
cd /path_to_gluten/ep/build-velox/src
./get_velox.sh --enable_hdfs=ON
./build_velox.sh --enable_hdfs=ON
cd /path_to_gluten/cpp
mkdir build
cd build
cmake -DBUILD_VELOX_BACKEND=ON -DENABLE_HDFS=ON ..
make -j
cd /path_to_gluten
mvn clean package -Pbackends-velox -Pspark-3.2 -Pfull-scala-compiler -DskipTests -Dcheckstyle.skip
It is supported to access data on different HDFS endpoints. The endpoint info (hdfs://host:port) contained in a given hdfs file path will be used to initialize an hdfs client.
If your HDFS is in HA mode, you need to set the LIBHDFS3_CONF environment variable to specify hdfs client configuration file. An alternative, you can also make a hdfs-client.xml and upload it to cluster.
// Spark local mode
export LIBHDFS3_CONF="/path/to/hdfs-client.xml"
// Spark Yarn cluster mode
--conf spark.executorEnv.LIBHDFS3_CONF="/path/to/hdfs-client.xml"
// Spark Yarn cluster mode and upload hdfs config file
cp /path/to/hdfs-client.xml hdfs-client.xml
--files hdfs-client.xml
If Gluten is used in a fully-prepared Hadoop cluster, you can directly use the hdfs-site.xml of the cluster.
In the configuration file, you need to set the HDFS HA-related configuration.
<property>
<name>dfs.nameservices</name>
<value>EXAMPLENAMESERVICE</value>
</property>
<property>
<name>dfs.ha.namenodes.EXAMPLENAMESERVICE</name>
<value>ns1,ns2</value>
</property>
<property>
<name>dfs.namenode.rpc-address.EXAMPLENAMESERVICE.ns1</name>
<value>hdfs://ns1:8020</value>
</property>
<property>
<name>dfs.namenode.rpc-address.EXAMPLENAMESERVICE.ns2</name>
<value>hdfs://ns2:8020</value>
</property>
One typical deployment on Spark/HDFS cluster is to enable short-circuit reading. Short-circuit reads provide a substantial performance boost to many applications.
By default libhdfs3 does not set the default hdfs domain socket path to support HDFS short-circuit read. If this feature is required in HDFS setup, users may need to setup the domain socket path correctly by patching the libhdfs3 source code or by setting the correct config environment. In Gluten the short-circuit domain socket path is set to "/var/lib/hadoop-hdfs/dn_socket" in build_velox.sh So we need to make sure the folder existed and user has write access as below script.
sudo mkdir -p /var/lib/hadoop-hdfs/
sudo chown <sparkuser>:<sparkuser> /var/lib/hadoop-hdfs/
You also need to add configuration to the "hdfs-site.xml" as below:
<property>
<name>dfs.client.read.shortcircuit</name>
<value>true</value>
</property>
<property>
<name>dfs.domain.socket.path</name>
<value>/var/lib/hadoop-hdfs/dn_socket</value>
</property>
Here are two steps to enable kerberos.
- Make sure the hdfs config file contains
<property>
<name>hadoop.security.authentication</name>
<value>kerberos</value>
</property>
- Specify the environment variable KRB5CCNAME and upload the kerberos ticket cache file
--conf spark.executorEnv.KRB5CCNAME=krb5cc_0000 --files /tmp/krb5cc_0000
The ticket cache file can be found by klist.
Hadoop Yarn mode is supported. Note libhdfs3 is used to read from HDFS, all its depedencies should be installed on each worker node. Users may requried to setup extra LD_LIBRARY_PATH if the depedencies are not on system's default library path. On Ubuntu 20.04 the dependencies can be installed with below command:
sudo apt install -y libiberty-dev libxml2-dev libkrb5-dev libgsasl7-dev libuuid1 uuid-dev
Velox supports S3 with the open source AWS C++ SDK and Gluten uses Velox S3 connector to connect with S3. A new build option for S3(enable_s3) is added. Below command is used to enable this feature
cd /path_to_gluten/ep/build-velox/src/
./get_velox.sh --enable_s3=ON
./build_velox.sh --enable_s3=ON
cd /path_to_gluten/cpp
mkdir build
cd build
cmake -DBUILD_VELOX_BACKEND=ON -DENABLE_S3=ON ..
make -j
cd /path_to_gluten
mvn clean package -Pbackends-velox -Pspark-3.2 -Pfull-scala-compiler -DskipTests -Dcheckstyle.skip
Currently to use S3 connector below configurations are required in spark-defaults.conf
spark.hadoop.fs.s3a.impl org.apache.hadoop.fs.s3a.S3AFileSystem
spark.hadoop.fs.s3a.aws.credentials.provider org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider
spark.hadoop.fs.s3a.access.key xxxx
spark.hadoop.fs.s3a.secret.key xxxx
spark.hadoop.fs.s3a.endpoint https://s3.us-west-1.amazonaws.com
spark.hadoop.fs.s3a.connection.ssl.enabled true
spark.hadoop.fs.s3a.path.style.access false
You can also use instance credentials by setting the following config
spark.hadoop.fs.s3a.use.instance.credentials true
If you are using instance credentials you do not have to set the access key or secret key.
Note if testing with local S3-like service(Minio/Ceph), users may need to use different values for these configurations. E.g., on Minio setup, the "spark.hadoop.fs.s3a.path.style.access" need to set to "true".
Gluten with velox backend supports Celeborn as remote shuffle service. Below introduction is used to enable this feature
First refer to this URL(https://github.com/apache/incubator-celeborn) to setup a celeborn cluster.
Currently to use Celeborn following configurations are required in spark-defaults.conf
spark.shuffle.manager org.apache.spark.shuffle.gluten.celeborn.CelebornShuffleManager
# celeborn master
spark.celeborn.master.endpoints clb-master:9097
# we recommend set spark.celeborn.push.replicate.enabled to true to enable server-side data replication
# If you have only one worker, this setting must be false
spark.celeborn.push.replicate.enabled true
spark.celeborn.shuffle.writer hash
spark.shuffle.service.enabled false
spark.sql.adaptive.localShuffleReader.enabled false
# If you want to use dynamic resource allocation,
# please refer to this URL (https://github.com/apache/incubator-celeborn/tree/main/assets/spark-patch) to apply the patch into your own Spark.
spark.dynamicAllocation.enabled false
Spark3.3 has 387 functions in total. ~240 are commonly used. Velox's functions have two category, Presto and Spark. Presto has 124 functions implemented. Spark has 62 functions. Spark functions are verified to have the same result as Vanilla Spark. Some Presto functions have the same result as Vanilla Spark but some others have different. Gluten prefer to use Spark functions firstly. If it's not in Spark's list but implemented in Presto, we currently offload to Presto one until we noted some result mismatch, then we need to reimplement the function in Spark category. Gluten currently offloads 94 functions and 14 operators, more details refer to The Operators and Functions Support Progress.
Velox doesn't support ANSI mode), so as Gluten. Once ANSI mode is enabled in Spark config, Gluten will fallback to Vanilla Spark.
Gluten supports allocating memory on HBM. This feature is optional and is disabled by default. It is implemented on top of Memkind library. You can refer to memkind's readme for more details.
Gluten will internally build and link to a specific version of Memkind library and hwloc. Other dependencies should be installed on Driver and Worker node first:
sudo apt install -y autoconf automake g++ libnuma-dev libtool numactl unzip libdaxctl-devAfter the set-up, you can now build Gluten with HBM. Below command is used to enable this feature
cd /path_to_gluten
## The script builds two jars for spark 3.2.2 and 3.3.1.
./dev/buildbundle-veloxbe.sh --enable_hbm=ONAt runtime, MEMKIND_HBW_NODES enviroment variable is detected for configuring HBM NUMA nodes. For the explaination to this variable, please refer to memkind's manual page. This can be set for all executors through spark conf, e.g. --conf spark.executorEnv.MEMKIND_HBW_NODES=8-15. Note that memory allocation fallback is also supported and cannot be turned off. If HBM is unavailable or fills up, the allocator will use default(DDR) memory.
Velox backend supports spilling-to-disk.
Using the following configuration options to customize spilling:
| Name | Default Value | Description |
|---|---|---|
| spark.gluten.sql.columnar.backend.velox.spillEnabled | true | Whether spill is enabled on Velox backend |
| spark.gluten.sql.columnar.backend.velox.memoryCapRatio | 0.75 | The overall ratio of total off-heap memory Velox is able to allocate from. If this value is set lower, spill will be triggered more frequently. |
| spark.gluten.sql.columnar.backend.velox.aggregationSpillEnabled | true | Whether spill is enabled on aggregations |
| spark.gluten.sql.columnar.backend.velox.joinSpillEnabled | true | Whether spill is enabled on joins |
| spark.gluten.sql.columnar.backend.velox.orderBySpillEnabled | true | Whether spill is enabled on sorts |
| spark.gluten.sql.columnar.backend.velox.spillMemoryThresholdRatio | 0.6 | Overall size ratio (in percentage) in task memory for spilling data. This will automatically set values for options SpillMemoryThreshold if they were not set |
| spark.gluten.sql.columnar.backend.velox.aggregationSpillMemoryThreshold | 0 | Memory limit before spilling to disk for aggregations, per Spark task. Unit: byte |
| spark.gluten.sql.columnar.backend.velox.joinSpillMemoryThreshold | 0 | Memory limit before spilling to disk for joins, per Spark task. Unit: byte |
| spark.gluten.sql.columnar.backend.velox.orderBySpillMemoryThreshold | 0 | Memory limit before spilling to disk for sorts, per Spark task. Unit: byte |
| spark.gluten.sql.columnar.backend.velox.maxSpillLevel | 4 | The max allowed spilling level with zero being the initial spilling level |
| spark.gluten.sql.columnar.backend.velox.maxSpillFileSize | 0 | The max allowed spill file size. If it is zero, then there is no limit |
| spark.gluten.sql.columnar.backend.velox.minSpillRunSize | 268435456 | The min spill run size limit used to select partitions for spilling |
| spark.gluten.sql.columnar.backend.velox.spillStartPartitionBit | 29 | The start partition bit which is used with 'spillPartitionBits' together to calculate the spilling partition number |
| spark.gluten.sql.columnar.backend.velox.spillPartitionBits | 2 | The number of bits used to calculate the spilling partition number. The number of spilling partitions will be power of two |
| spark.gluten.sql.columnar.backend.velox.spillableReservationGrowthPct | 25 | The spillable memory reservation growth percentage of the previous memory reservation size |
Gluten enables spilling-to-disk by default. However, manual tweaking may still be required if user wants to get it working at the best situation.
The following tips may help when you are trying to tune against spilling-related configuration options:
-
spark.gluten.sql.columnar.backend.velox.memoryCapRatioThis is to set the memory cap limit (represented as MEMORY_LIMIT) for Velox backend. The formula is: MEMORY_LIMIT = offHeap.size * memoryCapRatio. OOM will be raised if the memory usage exceeds this limit.
-
spark.gluten.sql.columnar.backend.velox.spillMemoryThresholdRatioThis is to set the spill limit (represented as SPILL_LIMIT) for Velox operators during execution. The formula is: SPILL_LIMIT = MEMORY_LIMIT * spillMemoryThresholdRatio. Once the allocated memory by Velox becomes larger than this size, a spill request will be sent to the current active operator to force spilling-to-disk procedure before it continues processing.
Please refer to the figure below:
You can see that the 25% of off-heap memory (controlled by spark.gluten.sql.columnar.backend.velox.memoryCapRatio) is mainly preserved for storing shuffle data which is not controlled by Velox task.
The 30% of off-heap memory is preserved for the operators in Velox that doesn't yet support spilling so far, for example, the window operator.
It's worth noting that the 45% of off-heap memory is not shared across Velox operators in a single task. This could cause OOM if more than one memory-consuming operators are there in the task. If so, please try decreasing spark.gluten.sql.columnar.backend.velox.spillMemoryThresholdRatio to make each operator manage its own share.
Gluten supports using Intel® QuickAssist Technology (QAT) for data compression during Spark Shuffle. It benefits from QAT Hardware-based acceleration on compression/decompression, and uses Gzip as compression format for higher compression ratio to reduce the pressure on disks and network transmission.
This feature is based on QAT driver library and QATzip library. Please manually download QAT driver for your system, and follow its README to build and install on all Driver and Worker node: Intel® QuickAssist Technology Driver for Linux* – HW Version 2.0.
Gluten will internally build and link to a specific version of QATzip library. Please uninstall QATzip library before building Gluten if it's already installed. Additional environment set-up are also required:
- Setup ICP_ROOT environment variable. This environment variable is required during building Gluten and running Spark applicaitons. It's recommended to put it in .bashrc on Driver and Worker node.
export ICP_ROOT=/path_to_QAT_driver- This step is required if your application is running as Non-root user. The users must be added to the 'qat' group after QAT drvier is installed:
sudo usermod -aG qat username # need to reloginChange the amount of max locked memory for the username that is included in the group name. This can be done by specifying the limit in /etc/security/limits.conf. To set 500MB add a line like this in /etc/security/limits.conf:
cat /etc/security/limits.conf |grep qat
@qat - memlock 500000- Enable huge page as root user. Note that this step is required to execute each time after system reboot.
echo 1024 > /sys/kernel/mm/hugepages/hugepages-2048kB/nr_hugepages
rmmod usdm_drv
insmod $ICP_ROOT/build/usdm_drv.ko max_huge_pages=1024 max_huge_pages_per_process=32After the set-up, you can now build Gluten with QAT. Below command is used to enable this feature
cd /path_to_gluten
## The script builds two jars for spark 3.2.2 and 3.3.1.
./dev/buildbundle-veloxbe.sh --enable_qat=ON- To enable QAT at run-time, first make sure you have the right QAT configuration file at /etc/4xxx_devX.conf. We provide a example configuration file. This configuration sets up to 4 processes that can bind to 1 QAT, and each process can use up to 16 QAT DC instances.
## run as root
## Overwrite QAT configuration file.
cd /etc
for i in {0..7}; do echo "4xxx_dev$i.conf"; done | xargs -i cp -f /path_to_gluten/docs/qat/4x16.conf {}
## Restart QAT after updating configuration files.
adf_ctl restart- Check QAT status and make sure the status is up
adf_ctl statusThe output should be like:
Checking status of all devices.
There is 8 QAT acceleration device(s) in the system:
qat_dev0 - type: 4xxx, inst_id: 0, node_id: 0, bsf: 0000:6b:00.0, #accel: 1 #engines: 9 state: up
qat_dev1 - type: 4xxx, inst_id: 1, node_id: 1, bsf: 0000:70:00.0, #accel: 1 #engines: 9 state: up
qat_dev2 - type: 4xxx, inst_id: 2, node_id: 2, bsf: 0000:75:00.0, #accel: 1 #engines: 9 state: up
qat_dev3 - type: 4xxx, inst_id: 3, node_id: 3, bsf: 0000:7a:00.0, #accel: 1 #engines: 9 state: up
qat_dev4 - type: 4xxx, inst_id: 4, node_id: 4, bsf: 0000:e8:00.0, #accel: 1 #engines: 9 state: up
qat_dev5 - type: 4xxx, inst_id: 5, node_id: 5, bsf: 0000:ed:00.0, #accel: 1 #engines: 9 state: up
qat_dev6 - type: 4xxx, inst_id: 6, node_id: 6, bsf: 0000:f2:00.0, #accel: 1 #engines: 9 state: up
qat_dev7 - type: 4xxx, inst_id: 7, node_id: 7, bsf: 0000:f7:00.0, #accel: 1 #engines: 9 state: up
- Extra Gluten configurations are required when starting Spark application
--conf spark.gluten.sql.columnar.shuffle.codec=gzip
--conf spark.gluten.sql.columnar.shuffle.codecBackend=qat
- You can use below command to check whether QAT is working normally at run-time. The value of fw_counters should continue to increase during shuffle.
while :; do cat /sys/kernel/debug/qat_4xxx_0000:6b:00.0/fw_counters; sleep 1; done
Documentation
README Text Files (README_QAT20.L.1.0.0-00021.txt)
Release Notes
Check out the Intel® QuickAssist Technology Software for Linux* - Release Notes for the latest changes in this release.
Getting Started Guide
Check out the Intel® QuickAssist Technology Software for Linux* - Getting Started Guide for detailed installation instructions.
Programmer's Guide
Check out the Intel® QuickAssist Technology Software for Linux* - Programmer's Guide for software usage guidelines.
For more Intel® QuickAssist Technology resources go to Intel® QuickAssist Technology (Intel® QAT)
Similar to Intel® QAT, Gluten supports using Intel® In-memory Analytics Accelerator (IAA, also called IAX) for data compression during Spark Shuffle. It benefits from IAA Hardware-based acceleration on compression/decompression, and uses Gzip as compression format for higher compression ratio to reduce the pressure on disks and network transmission.
This feature is based on Intel® QPL.
Gluten will internally build and link to a specific version of QPL library, but extra environment setup is still required. Please refer to QPL Installation Guide to install dependencies and configure accelerators.
This step is required if your application is running as Non-root user. Create a group for the users who have privilege to use IAA, and grant group iaa read/write access to the IAA Work-Queues.
sudo groupadd iaa
sudo usermod -aG iaa username # need to relogin
sudo chgrp -R iaa /dev/iax
sudo chmod -R g+rw /dev/iaxAfter the set-up, you can now build Gluten with QAT. Below command is used to enable this feature
cd /path_to_gluten
## The script builds two jars for spark 3.2.2 and 3.3.1.
./dev/buildbundle-veloxbe.sh --enable_iaa=ON- To enable QAT at run-time, first make sure you have configured the IAA Work-Queues correctly, and the file permissions of /dev/iax/wqX.0 are correct.
sudo ls -l /dev/iaxThe output should be like:
total 0
crw-rw---- 1 root iaa 509, 0 Apr 5 18:54 wq1.0
crw-rw---- 1 root iaa 509, 5 Apr 5 18:54 wq11.0
crw-rw---- 1 root iaa 509, 6 Apr 5 18:54 wq13.0
crw-rw---- 1 root iaa 509, 7 Apr 5 18:54 wq15.0
crw-rw---- 1 root iaa 509, 1 Apr 5 18:54 wq3.0
crw-rw---- 1 root iaa 509, 2 Apr 5 18:54 wq5.0
crw-rw---- 1 root iaa 509, 3 Apr 5 18:54 wq7.0
crw-rw---- 1 root iaa 509, 4 Apr 5 18:54 wq9.0
- Extra Gluten configurations are required when starting Spark application
--conf spark.gluten.sql.columnar.shuffle.codec=gzip
--conf spark.gluten.sql.columnar.shuffle.codecBackend=iaa
Intel® IAA Enabling Guide
Check out the Intel® In-Memory Analytics Accelerator (Intel® IAA) Enabling Guide
Intel® QPL Documentation
Check out the Intel® Query Processing Library (Intel® QPL) Documentation
All TPC-H and TPC-DS queries are supported in Gluten Velox backend.
The data generation scripts are TPC-H dategen script and TPC-DS dategen script.
The used TPC-H and TPC-DS queries are the original ones, and can be accessed from TPC-DS queries and TPC-H queries.
Some other versions of TPC-DS and TPC-H queries are also provided, but are not recommended for testing, including:
- the modified TPC-H queries with "Date-to-String" conversions: TPC-H non-date queries (outdated).
- the modified TPC-DS queries with "Decimal-to-Double": TPC-DS non-decimal queries (outdated).
- the modified TPC-DS queries with "Decimal-to-Double" and "Date-to-String" conversions: TPC-DS modified queries (outdated).
Submit test script from spark-shell. You can find the scala code to Run TPC-H as an example. Please remember to modify the location of TPC-H files as well as TPC-H queries in backends-velox/workload/tpch/run_tpch/tpch_parquet.scala before you run the testing.
var parquet_file_path = "/PATH/TO/TPCH_PARQUET_PATH"
var gluten_root = "/PATH/TO/GLUTEN"
Below script shows an example about how to run the testing, you should modify the parameters such as executor cores, memory, offHeap size based on your environment.
export GLUTEN_JAR = /PATH/TO/GLUTEN/backends-velox/target/<gluten-jar>
cat tpch_parquet.scala | spark-shell --name tpch_powertest_velox \
--master yarn --deploy-mode client \
--conf spark.plugins=io.glutenproject.GlutenPlugin \
--conf spark.gluten.sql.columnar.backend.lib=velox \
--conf spark.driver.extraClassPath=${GLUTEN_JAR} \
--conf spark.executor.extraClassPath=${GLUTEN_JAR} \
--conf spark.memory.offHeap.enabled=true \
--conf spark.memory.offHeap.size=20g \
--conf spark.gluten.sql.columnar.forceShuffledHashJoin=true \
--conf spark.shuffle.manager=org.apache.spark.shuffle.sort.ColumnarShuffleManager \
--num-executors 6 \
--executor-cores 6 \
--driver-memory 20g \
--executor-memory 25g \
--conf spark.executor.memoryOverhead=5g \
--conf spark.driver.maxResultSize=32gRefer to Gluten parameters for more details of each parameter used by Gluten.
wholestagetransformer indicates that the offload works.
Below table shows the TPC-H Q1 and Q6 Performance in a multiple-thread test (--num-executors 6 --executor-cores 6) for Velox and vanilla Spark. Both Parquet and ORC datasets are sf1024.
| Query Performance (s) | Velox (ORC) | Vanilla Spark (Parquet) | Vanilla Spark (ORC) |
|---|---|---|---|
| TPC-H Q6 | 13.6 | 21.6 | 34.9 |
| TPC-H Q1 | 26.1 | 76.7 | 84.9 |
TO ease your first-hand experience of using Gluten, we have set up an external reference cluster. If you are interested, please contact Weiting.Chen@intel.com.