Compilation from Source Code#

Compile and Use in Docker Container#

This section describes the steps to compile and use OpenMLDB inside its official docker image hybridsql, mainly for quick start and development purposes in the docker container. The docker image has packed the required tools and dependencies, so there is no need to set them up separately. To compile without the official docker image, refer to the section Detailed Instructions for Build below.

Keep in mind that you should always use the same version of both compile image and OpenMLDB version. This section demonstrates compiling for OpenMLDB v0.8.5 under hybridsql:0.8.5 ，If you prefer to compile on the latest code in main branch, pull hybridsql:latest image instead.

  1. Pull the docker image

     docker pull 4pdosc/hybridsql:0.8

  2. Create a docker container

    docker run -it 4pdosc/hybridsql:0.8 bash

  3. Download the OpenMLDB source code inside the docker container, and set the branch into v0.8.5

    cd ~
git clone -b v0.8.5 https://github.com/4paradigm/OpenMLDB.git

  4. Compile OpenMLDB

    cd ~/OpenMLDB
make

  5. Install OpenMLDB that will be installed into ${PROJECT_ROOT}/openmldb by default

    make install

Now you’ve finished the compilation job, you may try running OpenMLDB inside the docker container.

Detailed Instructions for Build#

This chapter discusses compiling source code without relying on pre-built container environments.

Hardware Requirements#

  • Memory: 8GB+ recommended.

  • Disk Space: >=25GB of free disk space for full compilation.

  • Operating System: CentOS 7, Ubuntu 20.04 or macOS >= 10.15, other systems are not carefully tested but issue/PR welcome

  • CPU Architecture: Currently, only x86 architecture is supported, and other architectures like ARM are not supported at the moment (please note that running x86 images on heterogeneous systems like M1 Mac is also not supported at this time).

💡 Note: By default, the parallel build is disabled, and it usually takes an hour to finish all the compile jobs. You can enable the parallel build by tweaking the NPROC option if your machine’s resource is enough. This will reduce the compile time but also consume more memory. For example, the following command sets the number of concurrent build jobs to 4:

make NPROC=4

Dependencies#

  • gcc >= 8 or AppleClang >= 12.0.0

  • cmake 3.20 or later ( recommended < cmake 3.24)

  • jdk 8

  • python3, python setuptools, python wheel

  • If you’d like to compile thirdparty from source, checkout the third-party’s requirement for extra dependencies

Build and Install OpenMLDB#

Building OpenMLDB requires certain thirdparty dependencies. Hence a Makefile is provided as a convenience to setup thirdparty dependencies automatically and run CMake project in a single command make. The make command offers three methods to compile, each manages thirdparty differently:

  • Method One: Download Pre-Compiled Thirdparty: Command is make && make install. It downloads necessary prebuild libraries from hybridsql-assert and zetasql. Currently it supports CentOS 7, Ubuntu 20.04 and macOS.

  • Method Two: Compile Thirdparty from Source: This is the suggested way if the host system is not in the supported list for pre-compiled thirdparty (CentOS 7, Ubuntu 20.04 and macOS). Note that when compiling thirdparty for the first time requires extra time to finish, approximately 1 hour on a 2 core & 8 GB machine. To compile thirdparty from source, please pass BUILD_BUNDLED=ON to make:

    make BUILD_BUNDLED=ON
make install

All of the three methods above will install OpenMLDB binaries into ${PROJECT_ROOT}/openmldb by default, you may tweak the installation directory with the option CMAKE_INSTALL_PREFIX (refer to the following section Extra Parameters for make ).

Extra Parameters for make#

You can customize the make behavior by passing the following arguments, e.g., changing the build mode to Debug instead of Release:

make CMAKE_BUILD_TYPE=Debug

  • OPENMLDB_BUILD_DIR: Binary build directory

    Default: ${PROJECT_ROOT}/build

  • CMAKE_BUILD_TYPE

    Default: RelWithDebInfo

  • CMAKE_INSTALL_PREFIX

    Default: ${PROJECT_ROOT}/openmldb

  • SQL_PYSDK_ENABLE: enabling building the Python SDK

    Default: OFF

  • SQL_JAVASDK_ENABLE: enabling building the Java SDK

    Default: OFF

  • TESTING_ENABLE: enabling building the test targets

    Default: OFF

  • NPROC: the number of parallel build jobs

    Default: 1

  • CMAKE_EXTRA_FLAGS: extra flags passed to cmake

    Default: ‘’

  • BUILD_BUNDLED: compile thirdparty from source instead download pre-compiled

    Default: OFF

  • TCMALLOC_ENABLE: expose application memory info by tcmalloc

    Default: ON

  • OPENMLDB_BUILD_TARGET: If you only want to build some targets, not all, e.g. only build a test ddl_parser_test, you can set it to ddl_parser_test. Multiple targets may be given, separated by spaces. It can reduce build time, reduce build output, and save storage space.

    Default: all

  • THIRD_PARTY_CMAKE_FLAGS: You can use this to configure additional parameters when compiling third-party dependencies. For instance, to specify concurrent compilation for each third-party project, you can set THIRD_PARTY_CMAKE_FLAGS to -DMAKEOPTS=-j8. Please note that NPROC does not affect third-party compilation; multiple third-party projects will be executed sequentially.

    Default: ‘’

Build Java SDK with Multi Processes#

make SQL_JAVASDK_ENABLE=ON NPROC=4

The built jar packages are in the target path of each submodule. If you want to use the jar packages built by yourself, please DO NOT add them by systemPath(may get ClassNotFoundException about Protobuf and so on, requires a little work in compile and runtime phase). The better way is, use mvn install -DskipTests=true -Dscalatest.skip=true -Dwagon.skip=true -Dmaven.test.skip=true -Dgpg.skip to install them in local m2 repository, your project will use them.

Optimized Spark Distribution for OpenMLDB#

OpenMLDB Spark Distribution is the fork of Apache Spark. It adopts specific optimization techniques for OpenMLDB. It provides native LastJoin implementation and achieves 10x~100x performance improvement compared with the original Spark distribution. The Java/Scala/Python/SQL APIs of the OpenMLDB Spark distribution are fully compatible with the standard Spark distribution.

  1. Downloading the pre-built OpenMLDB Spark distribution:

wget https://github.com/4paradigm/spark/releases/download/v3.2.1-openmldb0.8.5/spark-3.2.1-bin-openmldbspark.tgz

Alternatively, you can also download the source code and compile from scratch:

git clone https://github.com/4paradigm/spark.git
cd ./spark/
./dev/make-distribution.sh --name openmldbspark --pip --tgz -Phadoop-2.7 -Pyarn -Pallinone -Phive -Phive-thriftserver

  1. Setting up the environment variable SPARK_HOME to make the OpenMLDB Spark distribution for OpenMLDB or other Spark applications

tar xzvf ./spark-3.2.1-bin-openmldbspark.tgz
cd spark-3.2.1-bin-openmldbspark/
export SPARK_HOME=`pwd`

  1. Now you are all set to run OpenMLDB by enjoying the performance speedup from this optimized Spark distribution.

Build for Other OS#

As previously mentioned, if you want to run OpenMLDB or the SDK on a different OS, you will need to compile from the source code. We provide quick compilation solutions for several operating systems. For other OS, you’ll need to perform source code compilation on your own.

Centos 6 or other glibc Linux OS#

Local Compilation#

To compile a version compatible with CentOS 6, you can use Docker and the steps/centos6_build.sh script. As shown below, we use the current directory as the mount directory and place the compilation output locally.

git clone https://github.com/4paradigm/OpenMLDB.git
cd OpenMLDB
docker run -it -v`pwd`:/root/OpenMLDB ghcr.io/4paradigm/centos6_gcc7_hybridsql bash

Execute the compilation script within the container, and the output will be in the “build” directory. If there are failures while downloading bazel or icu4c during compilation, you can use the image sources provided by OpenMLDB by configuring the environment variable OPENMLDB_SOURCE=true. Various environment variables that can be used with “make” will also work, as shown below.

cd OpenMLDB
bash steps/centos6_build.sh
# THIRD_PARTY_CMAKE_FLAGS=-DMAKEOPTS=-j8 bash steps/centos6_build.sh # run fast when build single project
# OPENMLDB_SOURCE=true bash steps/centos6_build.sh
# SQL_JAVASDK_ENABLE=ON SQL_PYSDK_ENABLE=ON NPROC=8 bash steps/centos6_build.sh # NPROC will build openmldb in parallel, thirdparty should use THIRD_PARTY_CMAKE_FLAGS

For a local compilation with a 2.20GHz CPU, SSD hard drive, and 32 threads to build both third-party libraries and the OpenMLDB core, the approximate timeframes are as follows: THIRD_PARTY_CMAKE_FLAGS=-DMAKEOPTS=-j32 SQL_JAVASDK_ENABLE=ON SQL_PYSDK_ENABLE=ON NPROC=32 bash steps/centos6_build.sh

  • third-party (excluding source code download time): Approximately 40 minutes:

    • Zetasql patch: 13 minutes

    • Compilation of all third-party dependencies: 30 minutes

  • OpenMLDB core, including Python and Java native components: Approximately 12 minutes

Please note that these times can vary depending on your specific hardware and system performance. The provided compilation commands and environment variables are optimized for multi-threaded compilation, which can significantly reduce build times.

Cloud Compilation#

After forking the OpenMLDB repository, you can trigger the Other OS Build workflow in Actions, and the output will be available in the Actions Artifacts. Here’s how to configure the workflow:

  • Do not change the Use workflow from setting to a specific tag; it can be another branch.

  • Choose the desired OS name, which in this case is centos6.

  • If you are not compiling the main branch, provide the name of the branch, tag (e.g., v0.8.5), or SHA you want to compile in the The branch, tag, or SHA to checkout, otherwise use the branch field.

  • The compilation output will be accessible in “runs”, as shown in an example here.

    • The workflow will definitely produce the OpenMLDB binary file.

    • If you don’t need the Java or Python SDK, you can configure java sdk enable or python sdk enable to be “OFF” to save compilation time.

Please note that this compilation process involves building third-party dependencies from source code, and it may take a while to complete due to limited resources. The approximate time for this process is around 3 hours and 5 minutes (2 hours for third-party dependencies and 1 hour for OpenMLDB). However, the workflow caches the compilation output for third-party dependencies, so the second compilation will be much faster, taking approximately 1 hour and 15 minutes for OpenMLDB.

Macos 10.15, 11#

MacOS doesn’t require compiling third-party dependencies from source code, so compilation is relatively faster, taking about 1 hour and 15 minutes. Local compilation is similar to the steps outlined in the Detailed Instructions for Build and does not require compiling third-party dependencies (BUILD_BUNDLED=OFF). For cloud compilation on macOS, trigger the Other OS Build workflow in Actions with the specified macOS version (os name as macos10 or macos11). You can also disable Java or Python SDK compilation if they are not needed, by setting java sdk enable or python sdk enable to OFF.