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From Xiao Li <lix...@databricks.com>
Subject Re: Spark 3.0 branch cut and code freeze on Jan 31?
Date Tue, 24 Dec 2019 17:11:07 GMT
Jan 31 is pretty reasonable. Happy Holidays!

Xiao

On Tue, Dec 24, 2019 at 5:52 AM Sean Owen <srowen@gmail.com> wrote:

> Yep, always happens. Is earlier realistic, like Jan 15? it's all arbitrary
> but indeed this has been in progress for a while, and there's a downside to
> not releasing it, to making the gap to 3.0 larger.
> On my end I don't know of anything that's holding up a release; is it
> basically DSv2?
>
> BTW these are the items still targeted to 3.0.0, some of which may not
> have been legitimately tagged. It may be worth reviewing what's still open
> and necessary, and what should be untargeted.
>
> SPARK-29768 nondeterministic expression fails column pruning
> SPARK-29345 Add an API that allows a user to define and observe arbitrary
> metrics on streaming queries
> SPARK-29348 Add observable metrics
> SPARK-29429 Support Prometheus monitoring natively
> SPARK-29577 Implement p-value simulation and unit tests for chi2 test
> SPARK-28900 Test Pyspark, SparkR on JDK 11 with run-tests
> SPARK-28883 Fix a flaky test: ThriftServerQueryTestSuite
> SPARK-28717 Update SQL ALTER TABLE RENAME  to use TableCatalog API
> SPARK-28588 Build a SQL reference doc
> SPARK-28629 Capture the missing rules in HiveSessionStateBuilder
> SPARK-28684 Hive module support JDK 11
> SPARK-28548 explain() shows wrong result for persisted DataFrames after
> some operations
> SPARK-28264 Revisiting Python / pandas UDF
> SPARK-28301 fix the behavior of table name resolution with multi-catalog
> SPARK-28155 do not leak SaveMode to file source v2
> SPARK-28103 Cannot infer filters from union table with empty local
> relation table properly
> SPARK-27986 Support Aggregate Expressions with filter
> SPARK-28024 Incorrect numeric values when out of range
> SPARK-27936 Support local dependency uploading from --py-files
> SPARK-27780 Shuffle server & client should be versioned to enable smoother
> upgrade
> SPARK-27714 Support Join Reorder based on Genetic Algorithm when the # of
> joined tables > 12
> SPARK-27471 Reorganize public v2 catalog API
> SPARK-27520 Introduce a global config system to replace hadoopConfiguration
> SPARK-24625 put all the backward compatible behavior change configs under
> spark.sql.legacy.*
> SPARK-24941 Add RDDBarrier.coalesce() function
> SPARK-25017 Add test suite for ContextBarrierState
> SPARK-25083 remove the type erasure hack in data source scan
> SPARK-25383 Image data source supports sample pushdown
> SPARK-27272 Enable blacklisting of node/executor on fetch failures by
> default
> SPARK-27296 Efficient User Defined Aggregators
> SPARK-25128 multiple simultaneous job submissions against k8s backend
> cause driver pods to hang
> SPARK-26664 Make DecimalType's minimum adjusted scale configurable
> SPARK-21559 Remove Mesos fine-grained mode
> SPARK-24942 Improve cluster resource management with jobs containing
> barrier stage
> SPARK-25914 Separate projection from grouping and aggregate in logical
> Aggregate
> SPARK-20964 Make some keywords reserved along with the ANSI/SQL standard
> SPARK-26221 Improve Spark SQL instrumentation and metrics
> SPARK-26425 Add more constraint checks in file streaming source to avoid
> checkpoint corruption
> SPARK-25843 Redesign rangeBetween API
> SPARK-25841 Redesign window function rangeBetween API
> SPARK-25752 Add trait to easily whitelist logical operators that produce
> named output from CleanupAliases
> SPARK-25640 Clarify/Improve EvalType for grouped aggregate and window
> aggregate
> SPARK-25531 new write APIs for data source v2
> SPARK-25547 Pluggable jdbc connection factory
> SPARK-20845 Support specification of column names in INSERT INTO
> SPARK-24724 Discuss necessary info and access in barrier mode + Kubernetes
> SPARK-24725 Discuss necessary info and access in barrier mode + Mesos
> SPARK-25074 Implement maxNumConcurrentTasks() in
> MesosFineGrainedSchedulerBackend
> SPARK-23710 Upgrade the built-in Hive to 2.3.5 for hadoop-3.2
> SPARK-25186 Stabilize Data Source V2 API
> SPARK-25376 Scenarios we should handle but missed in 2.4 for barrier
> execution mode
> SPARK-7768 Make user-defined type (UDT) API public
> SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
> SPARK-15694 Implement ScriptTransformation in sql/core
> SPARK-18134 SQL: MapType in Group BY and Joins not working
> SPARK-19842 Informational Referential Integrity Constraints Support in
> Spark
> SPARK-22231 Support of map, filter, withColumn, dropColumn in nested list
> of structures
> SPARK-22386 Data Source V2 improvements
> SPARK-24723 Discuss necessary info and access in barrier mode + YARN
>
>
> On Mon, Dec 23, 2019 at 5:48 PM Reynold Xin <rxin@databricks.com> wrote:
>
>> We've pushed out 3.0 multiple times. The latest release window documented
>> on the website <http://spark.apache.org/versioning-policy.html> says
>> we'd code freeze and cut branch-3.0 early Dec. It looks like we are
>> suffering a bit from the tragedy of the commons, that nobody is pushing for
>> getting the release out. I understand the natural tendency for each
>> individual is to finish or extend the feature/bug that the person has been
>> working on. At some point we need to say "this is it" and get the release
>> out. I'm happy to help drive this process.
>>
>> To be realistic, I don't think we should just code freeze *today*.
>> Although we have updated the website, contributors have all been operating
>> under the assumption that all active developments are still going on. I
>> propose we *cut the branch on **Jan 31**, and code freeze and switch
>> over to bug squashing mode, and try to get the 3.0 official release out in
>> Q1*. That is, by default no new features can go into the branch starting Jan
>> 31.
>>
>> What do you think?
>>
>> And happy holidays everybody.
>>
>>
>>
>>

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