tree: 19e552ddce1710126c2046737ca021f6bdd46be2 [path history] [tgz]
  1. archery/
  2. benchmarking/
  3. container/
  4. docker_common/
  5. fuzzit/
  6. gen_apidocs/
  7. hiveserver2/
  8. lint/
  9. release/
  10. run_site/
  11. tasks/
  12. docker-compose.yml
  13. Dockerfile.context
  15. merge.conf.sample

Arrow Developer Scripts

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 to be able to push to GitHub as the main remote.

How to merge a Pull request

git remote add apache

run the following command


This script uses requests and jira libraries. Before running this script, run the following command to prepare them:

pip install requests jira

This uses the GitHub REST API; if you encounter rate limit issues, you may set a ARROW_GITHUB_API_TOKEN environment variable to use a Personal Access Token.

You can specify the username and the password of your JIRA account in APACHE_JIRA_USERNAME and APACHE_JIRA_PASSWORD environment variables. If these aren't supplied, the script will ask you the values of them.

Note that the directory name of your Arrow git clone must be called arrow.

example output:

Which pull request would you like to merge? (e.g. 34):

Type the pull request number (from and hit enter.

=== Pull Request #X ===
title	Blah Blah Blah
source	repo/branch
target	master

Proceed with merging pull request #3? (y/n):

If this looks good, type y and hit enter.

 * [new branch]      master     -> PR_TOOL_MERGE_PR_3_MASTER
Switched to branch 'PR_TOOL_MERGE_PR_3_MASTER'

Merge complete (local ref PR_TOOL_MERGE_PR_3_MASTER). Push to apache? (y/n):

A local branch with the merge has been created. type y and hit enter to push it to apache master

Counting objects: 67, done.
Delta compression using up to 4 threads.
Compressing objects: 100% (26/26), done.
Writing objects: 100% (36/36), 5.32 KiB, done.
Total 36 (delta 17), reused 0 (delta 0)
   b767ac4..485658a  PR_TOOL_MERGE_PR_X_MASTER -> master
Restoring head pointer to b767ac4e
Note: checking out 'b767ac4e'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by performing another checkout.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -b with the checkout command again. Example:

  git checkout -b new_branch_name

HEAD is now at b767ac4... Update
Deleting local branch PR_TOOL_MERGE_PR_X
Deleting local branch PR_TOOL_MERGE_PR_X_MASTER
Pull request #X merged!
Merge hash: 485658a5

Would you like to pick 485658a5 into another branch? (y/n):

For now just say n as we have 1 branch

Verifying Release Candidates

We have provided a script to assist with verifying release candidates:

bash dev/release/ 0.7.0 0

Currently this only works on Linux (patches to expand to macOS welcome!). Read the script for information about system dependencies.

On Windows, we have a script that verifies C++ and Python (requires Visual Studio 2015):

dev/release/verify-release-candidate.bat apache-arrow-0.7.0.tar.gz

Verifying the JavaScript release

For JavaScript-specific releases, use a different verification script:

bash dev/release/ 0.7.0 0

Integration testing

Build the following base image used by multiple tests:

docker build -t arrow_integration_xenial_base -f docker_common/Dockerfile.xenial.base .

HDFS C++ / Python support hdfs_integration

Apache Spark Integration Tests

Tests 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 repositiory, 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:

./ spark_integration

Alternatively, you can build and run the Docker images seperately. If 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. These should be run in a directory one level up from your Arrow repository:

docker build -f arrow/dev/spark_integration/Dockerfile -t spark-arrow .
docker run -v $HOME/.m2:/root/.m2 spark-arrow

NOTE: If the Java API has breaking changes, a patched version of Spark might need to be used to successfully build.