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Developer-Guide.md

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Developer Guide

This document is a supplement to the whole OAP Developer Guide for SQL Index and Data Source Cache. After following that document, you can continue more details for SQL Index and Data Source Cache.

Building

Building SQL DS Cache

Building with Apache Maven*.

Before building, install PMem-Common locally:

git clone -b <tag-version> https://github.com/oap-project/pmem-common.git
cd pmem-common
mvn clean install -DskipTests

Build the SQL DS Cache package:

git clone -b <tag-version> https://github.com/oap-project/sql-ds-cache.git
cd sql-ds-cache
mvn clean -DskipTests package

Running Tests

Run all the tests:

mvn clean test

Run a specific test suite, for example OapDDLSuite:

mvn -DwildcardSuites=org.apache.spark.sql.execution.datasources.oap.OapDDLSuite test

NOTE: Log level of unit tests currently default to ERROR, please override oap-cache/oap/src/test/resources/log4j.properties if needed.

Building with Intel® Optane™ DC Persistent Memory Module

Prerequisites for building with PMem support

Install the required packages on the build system:

memkind installation

The memkind library depends on libnuma at the runtime, so it must already exist in the worker node system. Build the latest memkind lib from source:

git clone -b v1.10.1 https://github.com/memkind/memkind
cd memkind
./autogen.sh
./configure
make
make install

vmemcache installation

To build vmemcache library from source, you can (for RPM-based linux as example):

git clone https://github.com/pmem/vmemcache
cd vmemcache
mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=/usr -DCPACK_GENERATOR=rpm
make package
sudo rpm -i libvmemcache*.rpm

Plasma installation

To use optimized Plasma cache with OAP, you need following components:

(1) libarrow.so, libplasma.so, libplasma_java.so: dynamic libraries, will be used in Plasma client.
(2) plasma-store-server: executable file, Plasma cache service.
(3) arrow-plasma-0.17.0.jar: will be used when compile oap and spark runtime also need it.

  • .so file and binary file
    Clone code from Intel-arrow repo and run following commands, this will install libplasma.so, libarrow.so, libplasma_java.so and plasma-store-server to your system path(/usr/lib64 by default). And if you are using Spark in a cluster environment, you can copy these files to all nodes in your cluster if the OS or distribution are same, otherwise, you need compile it on each node.
cd /tmp
git clone https://github.com/Intel-bigdata/arrow.git
cd arrow && git checkout branch-0.17.0-oap-1.0
cd cpp
mkdir release
cd release
#build libarrow, libplasma, libplasma_java
cmake -DCMAKE_INSTALL_PREFIX=/usr/ -DCMAKE_BUILD_TYPE=Release -DARROW_BUILD_TESTS=on -DARROW_PLASMA_JAVA_CLIENT=on -DARROW_PLASMA=on -DARROW_DEPENDENCY_SOURCE=BUNDLED  ..
make -j$(nproc)
sudo make install -j$(nproc)

  • arrow-plasma-0.17.0.jar
    arrow-plasma-0.17.0.jar is provided in Maven central repo, you can download it and copy to $SPARK_HOME/jars dir.

    Or you can manually install it, run following command, this will install arrow jars to your local maven repo. Besides, you need copy arrow-plasma-0.17.0.jar to $SPARK_HOME/jars/ dir, cause this jar is needed when using external cache.

cd /tmp/arrow/java
mvn clean -q  plasma -DskipTests install

Building the package

You need to add -Ppersistent-memory to build with PMem support. For noevict cache strategy, you also need to build with -Ppersistent-memory parameter.

cd <path>/pmem-common
mvn clean install -Ppersistent-memory -DskipTests 
cd <path>/sql-ds-cache
mvn clean -DskipTests package

For vmemcache cache strategy, please build with command:

cd <path>/pmem-common
mvn clean install -Pvmemcache -DskipTests
cd <path>/sql-ds-cache
mvn clean -DskipTests package

Build with this command to use all of them:

cd <path>/pmem-common
mvn clean install -Ppersistent-memory -Pvmemcache -DskipTests
cd <path>/sql-ds-cache
mvn clean -DskipTests package

Enabling NUMA binding for PMem in Spark

Rebuilding Spark packages with NUMA binding patch

When using PMem as a cache medium apply the NUMA binding patch numa-binding-spark-3.0.0.patch to Spark source code for best performance.

  1. Download src for Spark-3.0.0 and clone the src from github.

  2. Apply this patch and rebuild the Spark package.

git apply  numa-binding-spark-3.0.0.patch
  1. Add these configuration items to the Spark configuration file $SPARK_HOME/conf/spark-defaults.conf to enable NUMA binding.
spark.yarn.numa.enabled true

NOTE: If you are using a customized Spark, you will need to manually resolve the conflicts.

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