Current committers

Name Organization
Sameer Agarwal Deductive AI
Michael Armbrust Databricks
Dilip Biswal Adobe
Ryan Blue Tabular
Joseph Bradley Databricks
Matthew Cheah Palantir
Felix Cheung NVIDIA
Mosharaf Chowdhury University of Michigan, Ann Arbor
Bryan Cutler IBM
Jason Dai Intel
Tathagata Das Databricks
Ankur Dave Databricks
Aaron Davidson Databricks
Thomas Dudziak Meta
Erik Erlandson Red Hat
Robert Evans NVIDIA
Wenchen Fan Databricks
Huaxin Gao Apple
Max Gekk Databricks
Jiaan Geng DataCyber
Joseph Gonzalez UC Berkeley
Thomas Graves NVIDIA
Stephen Haberman LinkedIn
Mark Hamstra ClearStory Data
Seth Hendrickson Stripe
Herman van Hovell Databricks
Liang-Chi Hsieh Apple
Yin Huai Databricks
Shane Huang Intel
Dongjoon Hyun Apple
Kazuaki Ishizaki IBM
Xingbo Jiang Databricks
Yikun Jiang Huawei
Holden Karau Netflix
Shane Knapp UC Berkeley
Cody Koeninger Nexstar Digital
Andy Konwinski Databricks
Hyukjin Kwon Databricks
Ryan LeCompte Quantifind
Haoyuan Li Alluxio
Xiao Li Databricks
Yinan Li Google
Yuanjian Li Databricks
Davies Liu Juicedata
Cheng Lian Databricks
Yanbo Liang Facebook
Jungtaek Lim Databricks
Sean McNamara Oracle
Xiangrui Meng Databricks
Xinrong Meng Databricks
Mridul Muralidharan LinkedIn
Andrew Or Facebook
Kay Ousterhout LightStep
Sean Owen Databricks
Tejas Patil Meta
Nick Pentreath Automattic
Attila Zsolt Piros Cloudera
Anirudh Ramanathan Signadot
Imran Rashid Cloudera
Charles Reiss University of Virginia
Josh Rosen Databricks
Sandy Ryza Dagster
Kousuke Saruta NTT Data
Saisai Shao Datastrato
Prashant Sharma IBM
Gabor Somogyi Apple
Ram Sriharsha Pinecone
Chao Sun OpenAI
Maciej Szymkiewicz  
Jose Torres Databricks
Peter Toth Cloudera
DB Tsai Apple
Takuya Ueshin Databricks
Marcelo Vanzin Cloudera
Shivaram Venkataraman University of Wisconsin, Madison
Gengliang Wang Databricks
Yuming Wang eBay
Zhenhua Wang Huawei
Patrick Wendell Databricks
Yi Wu Databricks
Andrew Xia Alibaba
Reynold Xin Databricks
Weichen Xu Databricks
Takeshi Yamamuro NTT
Jie Yang Baidu
Kent Yao NetEase
Burak Yavuz Databricks
Xiduo You NetEase
Matei Zaharia Databricks, Stanford
Ruifeng Zheng Databricks
Shixiong Zhu Databricks

Becoming a committer

To get started contributing to Spark, learn how to contribute – anyone can submit patches, documentation and examples to the project.

The PMC regularly adds new committers from the active contributors, based on their contributions to Spark. The qualifications for new committers include:

  1. Sustained contributions to Spark: Committers should have a history of major contributions to Spark. An ideal committer will have contributed broadly throughout the project, and have contributed at least one major component where they have taken an “ownership” role. An ownership role means that existing contributors feel that they should run patches for this component by this person.
  2. Quality of contributions: Committers more than any other community member should submit simple, well-tested, and well-designed patches. In addition, they should show sufficient expertise to be able to review patches, including making sure they fit within Spark’s engineering practices (testability, documentation, API stability, code style, etc). The committership is collectively responsible for the software quality and maintainability of Spark. Note that contributions to critical parts of Spark, like its core and SQL modules, will be held to a higher standard when assessing quality. Contributors to these areas will face more review of their changes.
  3. Community involvement: Committers should have a constructive and friendly attitude in all community interactions. They should also be active on the dev and user list and help mentor newer contributors and users. In design discussions, committers should maintain a professional and diplomatic approach, even in the face of disagreement.

The type and level of contributions considered may vary by project area – for example, we greatly encourage contributors who want to work on mainly the documentation, or mainly on platform support for specific OSes, storage systems, etc.

The PMC also adds new PMC members. PMC members are expected to carry out PMC responsibilities as described in Apache Guidance, including helping vote on releases, enforce Apache project trademarks, take responsibility for legal and license issues, and ensure the project follows Apache project mechanics. The PMC periodically adds committers to the PMC who have shown they understand and can help with these activities.

Review process

All contributions should be reviewed before merging as described in Contributing to Spark. In particular, if you are working on an area of the codebase you are unfamiliar with, look at the Git history for that code to see who reviewed patches before. You can do this using git log --format=full <filename>, by examining the “Commit” field to see who committed each patch.

When to commit/merge a pull request

PRs shall not be merged during active, on-topic discussion unless they address issues such as critical security fixes of a public vulnerability. Under extenuating circumstances, PRs may be merged during active, off-topic discussion and the discussion directed to a more appropriate venue. Time should be given prior to merging for those involved with the conversation to explain if they believe they are on-topic.

Lazy consensus requires giving time for discussion to settle while understanding that people may not be working on Spark as their full-time job and may take holidays. It is believed that by doing this, we can limit how often people feel the need to exercise their veto.

All -1s with justification merit discussion. A -1 from a non-committer can be overridden only with input from multiple committers, and suitable time must be offered for any committer to raise concerns. A -1 from a committer who cannot be reached requires a consensus vote of the PMC under ASF voting rules to determine the next steps within the ASF guidelines for code vetoes.

These policies serve to reiterate the core principle that code must not be merged with a pending veto or before a consensus has been reached (lazy or otherwise).

It is the PMC’s hope that vetoes continue to be infrequent, and when they occur, that all parties will take the time to build consensus prior to additional feature work.

Being a committer means exercising your judgement while working in a community of people with diverse views. There is nothing wrong in getting a second (or third or fourth) opinion when you are uncertain. Thank you for your dedication to the Spark project; it is appreciated by the developers and users of Spark.

It is hoped that these guidelines do not slow down development; rather, by removing some of the uncertainty, the goal is to make it easier for us to reach consensus. If you have ideas on how to improve these guidelines or other Spark project operating procedures, you should reach out on the dev@ list to start the discussion.

How to merge a pull request

Changes pushed to the master branch on Apache cannot be removed; that is, we can’t force-push to it. So please don’t add any test commits or anything like that, only real patches.

Setting up remotes

To use the script described below, you will need to add a git remote called apache at, as well as one called apache-github at git://

The apache (the default value of PUSH_REMOTE_NAME environment variable) is the remote used for pushing the squashed commits and apache-github (default value of PR_REMOTE_NAME) is the remote used for pulling the changes. By using two separate remotes for these two actions the result of the can be tested without pushing it into the official Spark repo just by specifying your fork in the PUSH_REMOTE_NAME variable.

After cloning your fork of Spark you already have a remote origin pointing there. So if correct, your git remote -v contains at least these lines:

apache (fetch)
apache (push)
apache-github (fetch)
apache-github (push)
origin[your username]/spark.git (fetch)
origin[your username]/spark.git (push)

For the apache repo, you will need to set up command-line authentication to GitHub. This may include setting up an SSH key and/or personal access token. See:

To check whether the necessary write access are already granted please visit GitBox.

Ask if you have trouble with these steps, or want help doing your first merge.

Merge script

All merges should be done using the dev/, which squashes the pull request’s changes into one commit.

The script is fairly self explanatory and walks you through steps and options interactively.

If you want to amend a commit before merging – which should be used for trivial touch-ups – then simply let the script wait at the point where it asks you if you want to push to Apache. Then, in a separate window, modify the code and push a commit. Run git rebase -i HEAD~2 and “squash” your new commit. Edit the commit message just after to remove your commit message. You can verify the result is one change with git log. Then resume the script in the other window.

Also, please remember to set Assignee on JIRAs where applicable when they are resolved. The script can do this automatically in most cases.

Once a PR is merged please leave a comment on the PR stating which branch(es) it has been merged with.

Policy on backporting bug fixes

From pwendell:

The trade off when backporting is you get to deliver the fix to people running older versions (great!), but you risk introducing new or even worse bugs in maintenance releases (bad!). The decision point is when you have a bug fix and it’s not clear whether it is worth backporting.

I think the following facets are important to consider:

  • Backports are an extremely valuable service to the community and should be considered for any bug fix.
  • Introducing a new bug in a maintenance release must be avoided at all costs. It over time would erode confidence in our release process.
  • Distributions or advanced users can always backport risky patches on their own, if they see fit.

For me, the consequence of these is that we should backport in the following situations:

  • Both the bug and the fix are well understood and isolated. Code being modified is well tested.
  • The bug being addressed is high priority to the community.
  • The backported fix does not vary widely from the master branch fix.

We tend to avoid backports in the converse situations:

  • The bug or fix are not well understood. For instance, it relates to interactions between complex components or third party libraries (e.g. Hadoop libraries). The code is not well tested outside of the immediate bug being fixed.
  • The bug is not clearly a high priority for the community.
  • The backported fix is widely different from the master branch fix.