Joseph K. Bradley created SPARK-18813:
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Summary: MLlib 2.2 Roadmap
Key: SPARK-18813
URL: https://issues.apache.org/jira/browse/SPARK-18813
Project: Spark
Issue Type: Umbrella
Components: ML, MLlib
Reporter: Joseph K. Bradley
Priority: Blocker
*PROPOSAL: This includes a proposal for the 2.2 roadmap process for MLlib.*
The roadmap process described below is significantly updated since the 2.1
roadmap [SPARK-15581]. Please refer to [SPARK-15581] for more discussion on
the basis for this proposal, and comment in this JIRA if you have suggestions
for improvements.
h1. Roadmap process
This roadmap is a master list for MLlib improvements we are working on during
this release. This includes ML-related changes in PySpark and SparkR.
*What is planned for the next release?*
* This roadmap lists issues which at least one Committer has prioritized. See
details below in "Instructions for committers."
* This roadmap only lists larger or more critical issues.
*How can contributors influence this roadmap?*
* If you believe an issue should be in this roadmap, please discuss the issue
on JIRA and/or the dev mailing list. Make sure to ping Committers since at
least one must agree to shepherd the issue.
* For general discussions, use this JIRA or the dev mailing list. For specific
issues, please comment on those issues or the mailing list.
h2. Target Version and Priority
This section describes the meaning of Target Version and Priority. _These
meanings have been updated in this proposal for the 2.2 process._
|| Category | Target Version | Priority | Shepherd | Put on roadmap? | In next
release? ||
| 1 | next release | Blocker | *must* | *must* | *must* |
| 2 | next release | Critical | *must* | yes, unless small | *best effort* |
| 3 | next release | Major | *must* | optional | *best effort* |
| 4 | next release | Minor | optional | no | maybe |
| 5 | next release | Trivial | optional | no | maybe |
| 6 | (empty) | (any) | yes | no | maybe |
| 7 | (empty) | (any) | no | no | maybe |
The *Category* in the table above has the following meaning:
1. A committer has promised to see this issue to completion for the next
release. Contributions *will* receive attention.
2-3. A committer has promised to see this issue to completion for the next
release. Contributions *will* receive attention. The issue may slip to the
next release if development is slower than expected.
4-5. A committer has promised interest in this issue. Contributions *will*
receive attention. The issue may slip to another release.
6. A committer has promised interest in this issue and should respond, but no
promises are made about priorities or releases.
7. This issue is open for discussion, but it needs a committer to promise
interest to proceed.
h1. Instructions
h2. For contributors
Getting started
* Please read
https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark
carefully. Code style, documentation, and unit tests are important.
* If you are a first-time contributor, please always start with a small
[starter task|https://issues.apache.org/jira/issues/?filter=12333209] rather
than a larger feature.
Coordinating on JIRA
* Never work silently. Let everyone know on the corresponding JIRA page when
you start work. This is to avoid duplicate work. For small patches, you do not
need to get the JIRA assigned to you to begin work.
* For medium/large features or features with dependencies, please get assigned
first before coding and keep the ETA updated on the JIRA. If there is no
activity on the JIRA page for a certain amount of time, the JIRA should be
released for other contributors.
* Do not claim multiple (>3) JIRAs at the same time. Try to finish them one
after another.
* Do not set these fields: Target Version, Fix Version, or Shepherd. Only
Committers should set those.
Writing and reviewing PRs
* Remember to add the `@Since("VERSION")` annotation to new public APIs.
* *Please review others' PRs (https://spark-prs.appspot.com/#mllib). Code
review greatly helps to improve others' code as well as yours.*
h2. For Committers
Adding to this roadmap
* You can update the roadmap by (a) adding issues to this list and (b) setting
Target Versions. Only Committers may make these changes.
* *If you add an issue to this roadmap or set a Target Version, you _must_
assign yourself or another Committer as Shepherd.*
* This list should be actively managed during the release.
* If you target a significant item for the next release, please list the item
on this roadmap.
* If you commit to shepherding a new public API, you implicitly commit to
shepherding the follow-up issues as well (Python/R APIs, docs).
Creating JIRA issues
* Try to break down big features into small and specific JIRA tasks and link
them properly.
* Add a "starter" label to starter tasks.
* Put a rough time estimate for medium/big features and track the progress.
* Set Priority carefully. Priority should not be mixed with size of effort for
implementation.
Managing JIRA issues and PRs
* Please add yourself to the Shepherd field on JIRA if you start reviewing a PR.
* If the code looks good to you, please comment "LGTM". For non-trivial PRs,
please ping a Committer experienced with the relevant code to make a final pass.
Follow-up issues: *After merging a PR, create and link the necessary follow-up
JIRAs.*
* For a new Scala/Java API
** Create issues for adding analogous Python and R APIs
** Create issues for adding example code and documentation
* For a new Python/R API
** Create issues for adding example code and documentation
h1. Roadmap for this release
This roadmap only includes larger, more critical tasks targeted at the next
release. To find all issues targeted for the next release, use the links
listed below.
Notes
* We will prioritize API parity, bug fixes, and improvements over new features.
* The RDD-based API (`spark.mllib`) is in maintenance mode now. We will accept
bug fixes for it, but new features, APIs, and improvements will only be added
to the DataFrame-based API (`spark.ml`).
*WIP: This section is still being updated, pending confirmation of the Roadmap
Process described above.*
h2. Critical feature parity in DataFrame-based API
* Umbrella JIRA: [SPARK-4591]
h2. Persistence
* Complete persistence within MLlib
** Python tuning (SPARK-13786)
* MLlib in R format: compatibility with other languages (SPARK-15572)
* Impose backwards compatibility for persistence (SPARK-15573)
h2. SparkR
* Release SparkR on CRAN [SPARK-15799]
h2. Other prioritized issues: links for searching JIRA
* [All MLlib, SparkR, GraphX JIRAs with Target Version 2.2 |
https://issues.apache.org/jira/issues/?jql=project%20%3D%20SPARK%20AND%20component%20in%20(ML%2C%20MLlib%2C%20SparkR%2C%20GraphX)%20AND%20%22Target%20Version%2Fs%22%20%3D%202.2.0%20AND%20(fixVersion%20is%20EMPTY%20OR%20fixVersion%20!%3D%202.2.0)%20ORDER%20BY%20priority]
h1. Long-term roadmap
This section lists long-term or constant efforts. For example, Python/R API
parity with Scala/Java will always be a priority, but we do not promise exact
parity with each release.
h2. Python and R feature parity
Python feature parity: The main goal of the Python API is to have feature
parity with the Scala/Java API. You can find a [complete list of Python MLlib
issues targeted at the next release here|
https://issues.apache.org/jira/issues/?jql=project%20%3D%20SPARK%20AND%20status%20in%20(Open%2C%20"In%20Progress"%2C%20Reopened)%20AND%20component%20in%20(ML%2C%20MLlib)%20AND%20component%20in%20(PySpark)%20AND%20"Target%20Version%2Fs"%20%3D%202.2.0%20ORDER%20BY%20priority%20DESC].
R feature parity: We are building towards feature parity in SparkR as well. You
can find a [complete list of SparkR MLlib issues targeted at the next release
here|
https://issues.apache.org/jira/issues/?jql=project%20%3D%20SPARK%20AND%20status%20in%20(Open%2C%20"In%20Progress"%2C%20Reopened)%20AND%20component%20in%20(ML%2C%20MLlib)%20AND%20component%20in%20(SparkR)%20AND%20"Target%20Version%2Fs"%20%3D%202.2.0%20ORDER%20BY%20priority%20DESC].
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