spark-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Dongjoon Hyun <dongjoon.h...@gmail.com>
Subject Re: Spark 3.0 branch cut and code freeze on Jan 31?
Date Tue, 24 Dec 2019 18:56:28 GMT
+1 for January 31st.

Bests,
Dongjoon.

On Tue, Dec 24, 2019 at 7:11 AM Xiao Li <lixiao@databricks.com> wrote:

> 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.
>>>
>>>
>>>
>>>
>
> --
> [image: Databricks Summit - Watch the talks]
> <https://databricks.com/sparkaisummit/north-america>
>

Mime
View raw message