Build#

1. Quick Start#

This section describes the steps to compile and use OpenMLDB inside its official docker image hybridsql. The docker image has packed 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 v0.7.3 under hybridsql:0.7.3 ，If you prefer to compile on the latest code in main branch, pull hybridsql:latest image instead.

Pull the docker image
```
 docker pull 4pdosc/hybridsql:0.7
```
Create a docker container with the hybridsql docker image
```
docker run -it 4pdosc/hybridsql:0.7 bash
```
Download the OpenMLDB source code inside the docker container, and setting the branch into v0.7.3
```
cd ~
git clone -b v0.7.3 https://github.com/4paradigm/OpenMLDB.git
```
Compile OpenMLDB
```
cd ~/OpenMLDB
make
```
Install OpenMLDB that will be installed into ${PROJECT_ROOT}/openmldb by default
```
make install
```

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

2. Detailed Instructions for Build#

2.1. 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

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 set the number of concurrent build jobs to 4:

make NPROC=4

2.2. Prerequisites#

Make sure those tools are installed

gcc >= 8 or AppleClang >= 12.0.0
cmake 3.20 or later ( < cmake 3.24 is better)
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

2.3. 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: Build and Run Inside Docker: Using hybridsql docker image, the thirdparty is already bundled inside the image and no extra steps are required, refer to above section Quick Start
Method Two: 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 Three: 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 & 7 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 the following section Extra options for make).

2.4. Extra Options for `make`#

You can customize the make behavior by passing 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 the build time, reduce the build output, save the storage space.

Default: all

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.

3. 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.

Downloading the pre-built OpenMLDB Spark distribution:

wget https://github.com/4paradigm/spark/releases/download/v3.2.1-openmldb0.7.3/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

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`

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

Build

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