Our strategy for integration testing between Arrow implementations is as follows:

• Test datasets are specified in a custom human-readable, JSON-based format designed for Arrow

• Each implementation provides a testing executable capable of converting between the JSON and the binary Arrow file representation

• The test executable is also capable of validating the contents of a binary file against a corresponding JSON file

The integration test data generator and runner is written in Python and currently requires Python 3.5 or higher. You can create a standalone Python distribution and environment for running the tests by using miniconda. On Linux this is:

MINICONDA_URL=https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
wget -O miniconda.sh $MINICONDA_URL
bash miniconda.sh -b -p miniconda
export PATH=`pwd`/miniconda/bin:$PATH

conda create -n arrow-integration python=3.6 nomkl numpy six
conda activate arrow-integration

If you are on macOS, instead use the URL:

MINICONDA_URL=https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh

After this, you can follow the instructions in the next section.

First, build the Java and C++ projects. For Java, you must run

mvn package

Now, the integration tests rely on two environment variables which point to the Java arrow-tool JAR and the build path for the C++ executables:

JAVA_DIR=$ARROW_HOME/java
CPP_BUILD_DIR=$ARROW_HOME/cpp/build

VERSION=0.11.0-SNAPSHOT
export ARROW_JAVA_INTEGRATION_JAR=$JAVA_DIR/tools/target/arrow-tools-$VERSION-jar-with-dependencies.jar
export ARROW_CPP_EXE_PATH=$CPP_BUILD_DIR/debug

Here $ARROW_HOME is the location of your Arrow git clone. The $CPP_BUILD_DIR may be different depending on how you built with CMake (in-source or out-of-source).

Once this is done, run the integration tests with (optionally adding --debug for additional output)

python integration_test.py

python integration_test.py --debug  # additional output