This directory contains scripts useful to developers when packaging, testing, or committing to Arrow.
Merging a pull request requires being a committer on the project. In addition you need to have linked your GitHub and ASF accounts on https://gitbox.apache.org/setup/ to be able to push to GitHub as the main remote.
NOTE: It may take some time (a few hours) between when you complete the setup at GitBox, and when your GitHub account will be added as a committer.
Please don't merge PRs using the GitHub Web interface. Instead, run the following command:
dev/merge_arrow_pr.shThis creates a new Python virtual environment under dev/.venv[PY_VERSION]
and installs all the necessary dependencies to run the Arrow merge script.
After installed, it runs the merge script.
(We don't provide a wrapper script for Windows yet, so under Windows
you'll have to install Python dependencies yourself and then run
dev/merge_arrow_pr.py directly.)
The merge script requires tokens for access control. There are two options for configuring your tokens: environment variables or a configuration file.
Note: Arrow only requires a GitHub token. Parquet can use GitHub or JIRA tokens.
The merge script uses the GitHub REST API. You must set a
ARROW_GITHUB_API_TOKEN environment variable to use a
Personal Access Token.
You need to add workflow scope to the Personal Access Token.
You can specify the
Personal Access Token
of your JIRA account in the
APACHE_JIRA_TOKEN environment variable.
If the variable is not set, the script will ask you for it.
cp ./merge.conf.sample ~/.config/arrow/merge.conf
Update your new merge.conf file with your Personal Access Tokens.
Example output:
Which pull request would you like to merge? (e.g. 34):
Type the pull request number (from https://github.com/apache/arrow/pulls) and hit enter:
=== Pull Request #X ===
title GH-#Y: [Component] Title
source repo/branch
target master
url https://api.github.com/apache/arrow/pulls/X
=== GITHUB #Y ===
Summary [Component] Title
Assignee Name
Components Python
Status open
URL https://github.com/apache/arrow/issues/Y
Proceed with merging pull request #X? (y/n): y
If this looks good, type y and hit enter:
Author 1: Name
Pull request #X merged!
Merge hash: #hash
Would you like to update the associated issue? (y/n): y
Enter fix version [11.0.0]:
You can just hit enter and the associated GitHub issue will be resolved with the current fix version.
Successfully resolved #Y!
=== GITHUB #Y ===
Summary [Component] Title
Assignee Name
Components Python
Status closed
URL https://github.com/apache/arrow/issues/Y
Build the following base image used by multiple tests:
docker build -t arrow_integration_xenial_base -f docker_common/Dockerfile.xenial.base .docker-compose build conda-cpp
docker-compose build conda-python
docker-compose build conda-python-hdfs
docker-compose run --rm conda-python-hdfsTests can be run to ensure that the current snapshot of Java and Python Arrow works with Spark. This will run a docker image to build Arrow C++ and Python in a Conda environment, build and install Arrow Java to the local Maven repository, build Spark with the new Arrow artifact, and run Arrow related unit tests in Spark for Java and Python. Any errors will exit with a non-zero value. To run, use the following command:
docker-compose build conda-cpp
docker-compose build conda-python
docker-compose build conda-python-spark
docker-compose run --rm conda-python-sparkIf you already are building Spark, these commands will map your local Maven repo to the image and save time by not having to download all dependencies. Be aware, that docker write files as root, which can cause problems for maven on the host.
docker-compose run --rm -v $HOME/.m2:/root/.m2 conda-python-sparkNOTE: If the Java API has breaking changes, a patched version of Spark might need to be used to successfully build.