I think this will be hard to maintain; we already have JIRA as the de
facto central place to store discussions and prioritize work, and the
2.x stuff is already a JIRA. The wiki doesn't really hurt, just
probably will never be looked at again. Let's point people in all
cases to JIRA.

On Tue, Dec 22, 2015 at 11:52 PM, Reynold Xin <r...@databricks.com> wrote:
> I started a wiki page:
> https://cwiki.apache.org/confluence/display/SPARK/Development+Discussions
>
>
> On Tue, Dec 22, 2015 at 6:27 AM, Tom Graves <tgraves...@yahoo.com> wrote:
>>
>> Do we have a summary of all the discussions and what is planned for 2.0
>> then?  Perhaps we should put on the wiki for reference.
>>
>> Tom
>>
>>
>> On Tuesday, December 22, 2015 12:12 AM, Reynold Xin <r...@databricks.com>
>> wrote:
>>
>>
>> FYI I updated the master branch's Spark version to 2.0.0-SNAPSHOT.
>>
>> On Tue, Nov 10, 2015 at 3:10 PM, Reynold Xin <r...@databricks.com> wrote:
>>
>> I’m starting a new thread since the other one got intermixed with feature
>> requests. Please refrain from making feature request in this thread. Not
>> that we shouldn’t be adding features, but we can always add features in 1.7,
>> 2.1, 2.2, ...
>>
>> First - I want to propose a premise for how to think about Spark 2.0 and
>> major releases in Spark, based on discussion with several members of the
>> community: a major release should be low overhead and minimally disruptive
>> to the Spark community. A major release should not be very different from a
>> minor release and should not be gated based on new features. The main
>> purpose of a major release is an opportunity to fix things that are broken
>> in the current API and remove certain deprecated APIs (examples follow).
>>
>> For this reason, I would *not* propose doing major releases to break
>> substantial API's or perform large re-architecting that prevent users from
>> upgrading. Spark has always had a culture of evolving architecture
>> incrementally and making changes - and I don't think we want to change this
>> model. In fact, we’ve released many architectural changes on the 1.X line.
>>
>> If the community likes the above model, then to me it seems reasonable to
>> do Spark 2.0 either after Spark 1.6 (in lieu of Spark 1.7) or immediately
>> after Spark 1.7. It will be 18 or 21 months since Spark 1.0. A cadence of
>> major releases every 2 years seems doable within the above model.
>>
>> Under this model, here is a list of example things I would propose doing
>> in Spark 2.0, separated into APIs and Operation/Deployment:
>>
>>
>> APIs
>>
>> 1. Remove interfaces, configs, and modules (e.g. Bagel) deprecated in
>> Spark 1.x.
>>
>> 2. Remove Akka from Spark’s API dependency (in streaming), so user
>> applications can use Akka (SPARK-5293). We have gotten a lot of complaints
>> about user applications being unable to use Akka due to Spark’s dependency
>> on Akka.
>>
>> 3. Remove Guava from Spark’s public API (JavaRDD Optional).
>>
>> 4. Better class package structure for low level developer API’s. In
>> particular, we have some DeveloperApi (mostly various listener-related
>> classes) added over the years. Some packages include only one or two public
>> classes but a lot of private classes. A better structure is to have public
>> classes isolated to a few public packages, and these public packages should
>> have minimal private classes for low level developer APIs.
>>
>> 5. Consolidate task metric and accumulator API. Although having some
>> subtle differences, these two are very similar but have completely different
>> code path.
>>
>> 6. Possibly making Catalyst, Dataset, and DataFrame more general by moving
>> them to other package(s). They are already used beyond SQL, e.g. in ML
>> pipelines, and will be used by streaming also.
>>
>>
>> Operation/Deployment
>>
>> 1. Scala 2.11 as the default build. We should still support Scala 2.10,
>> but it has been end-of-life.
>>
>> 2. Remove Hadoop 1 support.
>>
>> 3. Assembly-free distribution of Spark: don’t require building an enormous
>> assembly jar in order to run Spark.
>>
>>
>>
>>
>

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