Thoughts on Spark 3 release, or a preview release

classic Classic list List threaded Threaded
26 messages Options
12
Reply | Threaded
Open this post in threaded view
|

Thoughts on Spark 3 release, or a preview release

Sean Owen-3
I'm curious what current feelings are about ramping down towards a
Spark 3 release. It feels close to ready. There is no fixed date,
though in the past we had informally tossed around "back end of 2019".
For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
due.

What are the few major items that must get done for Spark 3, in your
opinion? Below are all of the open JIRAs for 3.0 (which everyone
should feel free to update with things that aren't really needed for
Spark 3; I already triaged some).

For me, it's:
- DSv2?
- Finishing touches on the Hive, JDK 11 update

What about considering a preview release earlier, as happened for
Spark 2, to get feedback much earlier than the RC cycle? Could that
even happen ... about now?

I'm also wondering what a realistic estimate of Spark 3 release is. My
guess is quite early 2020, from here.



SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
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-28372 Document Spark WEB UI
SPARK-28476 Support ALTER DATABASE SET LOCATION
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-28024 Incorrect numeric values when out of range
SPARK-27936 Support local dependency uploading from --py-files
SPARK-27884 Deprecate Python 2 support in Spark 3.0
SPARK-27763 Port test cases from PostgreSQL to Spark SQL
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-24640 size(null) returns null
SPARK-24702 Unable to cast to calendar interval in spark sql.
SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
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 User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
SPARK-25128 multiple simultaneous job submissions against k8s backend
cause driver pods to hang
SPARK-26731 remove EOLed spark jobs from jenkins
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-26022 PySpark Comparison with Pandas
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-23210 Introduce the concept of default value to schema
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-24417 Build and Run Spark on JDK11
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-25390 data source V2 API refactoring
SPARK-7768 Make user-defined type (UDT) API public
SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
SPARK-15691 Refactor and improve Hive support
SPARK-15694 Implement ScriptTransformation in sql/core
SPARK-16217 Support SELECT INTO statement
SPARK-16452 basic INFORMATION_SCHEMA support
SPARK-18134 SQL: MapType in Group BY and Joins not working
SPARK-18245 Improving support for bucketed table
SPARK-19842 Informational Referential Integrity Constraints Support in Spark
SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
list of structures
SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
respect session timezone
SPARK-22386 Data Source V2 improvements
SPARK-24723 Discuss necessary info and access in barrier mode + YARN

---------------------------------------------------------------------
To unsubscribe e-mail: [hidden email]

Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Michael Heuer
I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.


Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.

Then out of curiosity, are the new Spark Graph APIs targeting 3.0?


   michael


On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:

I'm curious what current feelings are about ramping down towards a
Spark 3 release. It feels close to ready. There is no fixed date,
though in the past we had informally tossed around "back end of 2019".
For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
due.

What are the few major items that must get done for Spark 3, in your
opinion? Below are all of the open JIRAs for 3.0 (which everyone
should feel free to update with things that aren't really needed for
Spark 3; I already triaged some).

For me, it's:
- DSv2?
- Finishing touches on the Hive, JDK 11 update

What about considering a preview release earlier, as happened for
Spark 2, to get feedback much earlier than the RC cycle? Could that
even happen ... about now?

I'm also wondering what a realistic estimate of Spark 3 release is. My
guess is quite early 2020, from here.



SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
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-28372 Document Spark WEB UI
SPARK-28476 Support ALTER DATABASE SET LOCATION
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-28024 Incorrect numeric values when out of range
SPARK-27936 Support local dependency uploading from --py-files
SPARK-27884 Deprecate Python 2 support in Spark 3.0
SPARK-27763 Port test cases from PostgreSQL to Spark SQL
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-24640 size(null) returns null
SPARK-24702 Unable to cast to calendar interval in spark sql.
SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
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 User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
SPARK-25128 multiple simultaneous job submissions against k8s backend
cause driver pods to hang
SPARK-26731 remove EOLed spark jobs from jenkins
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-26022 PySpark Comparison with Pandas
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-23210 Introduce the concept of default value to schema
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-24417 Build and Run Spark on JDK11
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-25390 data source V2 API refactoring
SPARK-7768 Make user-defined type (UDT) API public
SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
SPARK-15691 Refactor and improve Hive support
SPARK-15694 Implement ScriptTransformation in sql/core
SPARK-16217 Support SELECT INTO statement
SPARK-16452 basic INFORMATION_SCHEMA support
SPARK-18134 SQL: MapType in Group BY and Joins not working
SPARK-18245 Improving support for bucketed table
SPARK-19842 Informational Referential Integrity Constraints Support in Spark
SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
list of structures
SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
respect session timezone
SPARK-22386 Data Source V2 improvements
SPARK-24723 Discuss necessary info and access in barrier mode + YARN

---------------------------------------------------------------------
To unsubscribe [hidden email]


Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Dongjoon Hyun-2
Thank you, Sean.

I'm also +1 for the following three.

1. Start to ramp down (by the official branch-3.0 cut)
2. Apache Spark 3.0.0-preview in 2019
3. Apache Spark 3.0.0 in early 2020

For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.

After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?


Bests,
Dongjoon.


On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.


Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.

Then out of curiosity, are the new Spark Graph APIs targeting 3.0?


   michael


On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:

I'm curious what current feelings are about ramping down towards a
Spark 3 release. It feels close to ready. There is no fixed date,
though in the past we had informally tossed around "back end of 2019".
For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
due.

What are the few major items that must get done for Spark 3, in your
opinion? Below are all of the open JIRAs for 3.0 (which everyone
should feel free to update with things that aren't really needed for
Spark 3; I already triaged some).

For me, it's:
- DSv2?
- Finishing touches on the Hive, JDK 11 update

What about considering a preview release earlier, as happened for
Spark 2, to get feedback much earlier than the RC cycle? Could that
even happen ... about now?

I'm also wondering what a realistic estimate of Spark 3 release is. My
guess is quite early 2020, from here.



SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
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-28372 Document Spark WEB UI
SPARK-28476 Support ALTER DATABASE SET LOCATION
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-28024 Incorrect numeric values when out of range
SPARK-27936 Support local dependency uploading from --py-files
SPARK-27884 Deprecate Python 2 support in Spark 3.0
SPARK-27763 Port test cases from PostgreSQL to Spark SQL
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-24640 size(null) returns null
SPARK-24702 Unable to cast to calendar interval in spark sql.
SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
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 User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
SPARK-25128 multiple simultaneous job submissions against k8s backend
cause driver pods to hang
SPARK-26731 remove EOLed spark jobs from jenkins
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-26022 PySpark Comparison with Pandas
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-23210 Introduce the concept of default value to schema
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-24417 Build and Run Spark on JDK11
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-25390 data source V2 API refactoring
SPARK-7768 Make user-defined type (UDT) API public
SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
SPARK-15691 Refactor and improve Hive support
SPARK-15694 Implement ScriptTransformation in sql/core
SPARK-16217 Support SELECT INTO statement
SPARK-16452 basic INFORMATION_SCHEMA support
SPARK-18134 SQL: MapType in Group BY and Joins not working
SPARK-18245 Improving support for bucketed table
SPARK-19842 Informational Referential Integrity Constraints Support in Spark
SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
list of structures
SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
respect session timezone
SPARK-22386 Data Source V2 improvements
SPARK-24723 Discuss necessary info and access in barrier mode + YARN

---------------------------------------------------------------------
To unsubscribe [hidden email]


Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Hyukjin Kwon
+1 from me too but I would like to know what other people think too.

2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
Thank you, Sean.

I'm also +1 for the following three.

1. Start to ramp down (by the official branch-3.0 cut)
2. Apache Spark 3.0.0-preview in 2019
3. Apache Spark 3.0.0 in early 2020

For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.

After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?


Bests,
Dongjoon.


On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.


Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.

Then out of curiosity, are the new Spark Graph APIs targeting 3.0?


   michael


On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:

I'm curious what current feelings are about ramping down towards a
Spark 3 release. It feels close to ready. There is no fixed date,
though in the past we had informally tossed around "back end of 2019".
For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
due.

What are the few major items that must get done for Spark 3, in your
opinion? Below are all of the open JIRAs for 3.0 (which everyone
should feel free to update with things that aren't really needed for
Spark 3; I already triaged some).

For me, it's:
- DSv2?
- Finishing touches on the Hive, JDK 11 update

What about considering a preview release earlier, as happened for
Spark 2, to get feedback much earlier than the RC cycle? Could that
even happen ... about now?

I'm also wondering what a realistic estimate of Spark 3 release is. My
guess is quite early 2020, from here.



SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
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-28372 Document Spark WEB UI
SPARK-28476 Support ALTER DATABASE SET LOCATION
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-28024 Incorrect numeric values when out of range
SPARK-27936 Support local dependency uploading from --py-files
SPARK-27884 Deprecate Python 2 support in Spark 3.0
SPARK-27763 Port test cases from PostgreSQL to Spark SQL
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-24640 size(null) returns null
SPARK-24702 Unable to cast to calendar interval in spark sql.
SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
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 User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
SPARK-25128 multiple simultaneous job submissions against k8s backend
cause driver pods to hang
SPARK-26731 remove EOLed spark jobs from jenkins
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-26022 PySpark Comparison with Pandas
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-23210 Introduce the concept of default value to schema
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-24417 Build and Run Spark on JDK11
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-25390 data source V2 API refactoring
SPARK-7768 Make user-defined type (UDT) API public
SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
SPARK-15691 Refactor and improve Hive support
SPARK-15694 Implement ScriptTransformation in sql/core
SPARK-16217 Support SELECT INTO statement
SPARK-16452 basic INFORMATION_SCHEMA support
SPARK-18134 SQL: MapType in Group BY and Joins not working
SPARK-18245 Improving support for bucketed table
SPARK-19842 Informational Referential Integrity Constraints Support in Spark
SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
list of structures
SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
respect session timezone
SPARK-22386 Data Source V2 improvements
SPARK-24723 Discuss necessary info and access in barrier mode + YARN

---------------------------------------------------------------------
To unsubscribe [hidden email]


Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Jean Georges Perrin
As a user/non committer, +1

I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...

jg


On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:

+1 from me too but I would like to know what other people think too.

2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
Thank you, Sean.

I'm also +1 for the following three.

1. Start to ramp down (by the official branch-3.0 cut)
2. Apache Spark 3.0.0-preview in 2019
3. Apache Spark 3.0.0 in early 2020

For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.

After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?


Bests,
Dongjoon.


On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.


Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.

Then out of curiosity, are the new Spark Graph APIs targeting 3.0?


   michael


On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:

I'm curious what current feelings are about ramping down towards a
Spark 3 release. It feels close to ready. There is no fixed date,
though in the past we had informally tossed around "back end of 2019".
For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
due.

What are the few major items that must get done for Spark 3, in your
opinion? Below are all of the open JIRAs for 3.0 (which everyone
should feel free to update with things that aren't really needed for
Spark 3; I already triaged some).

For me, it's:
- DSv2?
- Finishing touches on the Hive, JDK 11 update

What about considering a preview release earlier, as happened for
Spark 2, to get feedback much earlier than the RC cycle? Could that
even happen ... about now?

I'm also wondering what a realistic estimate of Spark 3 release is. My
guess is quite early 2020, from here.



SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
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-28372 Document Spark WEB UI
SPARK-28476 Support ALTER DATABASE SET LOCATION
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-28024 Incorrect numeric values when out of range
SPARK-27936 Support local dependency uploading from --py-files
SPARK-27884 Deprecate Python 2 support in Spark 3.0
SPARK-27763 Port test cases from PostgreSQL to Spark SQL
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-24640 size(null) returns null
SPARK-24702 Unable to cast to calendar interval in spark sql.
SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
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 User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
SPARK-25128 multiple simultaneous job submissions against k8s backend
cause driver pods to hang
SPARK-26731 remove EOLed spark jobs from jenkins
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-26022 PySpark Comparison with Pandas
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-23210 Introduce the concept of default value to schema
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-24417 Build and Run Spark on JDK11
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-25390 data source V2 API refactoring
SPARK-7768 Make user-defined type (UDT) API public
SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
SPARK-15691 Refactor and improve Hive support
SPARK-15694 Implement ScriptTransformation in sql/core
SPARK-16217 Support SELECT INTO statement
SPARK-16452 basic INFORMATION_SCHEMA support
SPARK-18134 SQL: MapType in Group BY and Joins not working
SPARK-18245 Improving support for bucketed table
SPARK-19842 Informational Referential Integrity Constraints Support in Spark
SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
list of structures
SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
respect session timezone
SPARK-22386 Data Source V2 improvements
SPARK-24723 Discuss necessary info and access in barrier mode + YARN

---------------------------------------------------------------------
To unsubscribe [hidden email]


Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Jungtaek Lim
+1 (as a contributor) from me to have preview release on Spark 3 as it would help to test the feature. When to cut preview release is questionable, as major works are ideally to be done before that - if we are intended to introduce new features before official release, that should work regardless of this, but if we are intended to have opportunity to test earlier, ideally it should.

As a one of contributors in structured streaming area, I'd like to add some items for Spark 3.0, both "must be done" and "better to have". For "better to have", I pick some items for new features which committers reviewed couple of rounds and dropped off without soft-reject (No valid reason to stop). For Spark 2.4 users, only added feature for structured streaming is Kafka delegation token. (given we assume revising Kafka consumer pool as improvement) I hope we provide some gifts for structured streaming users in Spark 3.0 envelope.

> must be done
* SPARK-26154 Stream-stream joins - left outer join gives inconsistent output
It's a correctness issue with multiple users reported, being reported at Nov. 2018. There's a way to reproduce it consistently, and we have a patch submitted at Jan. 2019 to fix it.

> better to have
* SPARK-23539 Add support for Kafka headers in Structured Streaming
* SPARK-26848 Introduce new option to Kafka source - specify timestamp to start and end offset
* SPARK-20568 Delete files after processing in structured streaming

There're some more new features/improvements items in SS, but given we're talking about ramping-down, above list might be realistic one.



On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <[hidden email]> wrote:
As a user/non committer, +1

I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...

jg


On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:

+1 from me too but I would like to know what other people think too.

2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
Thank you, Sean.

I'm also +1 for the following three.

1. Start to ramp down (by the official branch-3.0 cut)
2. Apache Spark 3.0.0-preview in 2019
3. Apache Spark 3.0.0 in early 2020

For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.

After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?


Bests,
Dongjoon.


On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.


Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.

Then out of curiosity, are the new Spark Graph APIs targeting 3.0?


   michael


On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:

I'm curious what current feelings are about ramping down towards a
Spark 3 release. It feels close to ready. There is no fixed date,
though in the past we had informally tossed around "back end of 2019".
For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
due.

What are the few major items that must get done for Spark 3, in your
opinion? Below are all of the open JIRAs for 3.0 (which everyone
should feel free to update with things that aren't really needed for
Spark 3; I already triaged some).

For me, it's:
- DSv2?
- Finishing touches on the Hive, JDK 11 update

What about considering a preview release earlier, as happened for
Spark 2, to get feedback much earlier than the RC cycle? Could that
even happen ... about now?

I'm also wondering what a realistic estimate of Spark 3 release is. My
guess is quite early 2020, from here.



SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
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-28372 Document Spark WEB UI
SPARK-28476 Support ALTER DATABASE SET LOCATION
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-28024 Incorrect numeric values when out of range
SPARK-27936 Support local dependency uploading from --py-files
SPARK-27884 Deprecate Python 2 support in Spark 3.0
SPARK-27763 Port test cases from PostgreSQL to Spark SQL
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-24640 size(null) returns null
SPARK-24702 Unable to cast to calendar interval in spark sql.
SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
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 User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
SPARK-25128 multiple simultaneous job submissions against k8s backend
cause driver pods to hang
SPARK-26731 remove EOLed spark jobs from jenkins
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-26022 PySpark Comparison with Pandas
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-23210 Introduce the concept of default value to schema
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-24417 Build and Run Spark on JDK11
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-25390 data source V2 API refactoring
SPARK-7768 Make user-defined type (UDT) API public
SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
SPARK-15691 Refactor and improve Hive support
SPARK-15694 Implement ScriptTransformation in sql/core
SPARK-16217 Support SELECT INTO statement
SPARK-16452 basic INFORMATION_SCHEMA support
SPARK-18134 SQL: MapType in Group BY and Joins not working
SPARK-18245 Improving support for bucketed table
SPARK-19842 Informational Referential Integrity Constraints Support in Spark
SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
list of structures
SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
respect session timezone
SPARK-22386 Data Source V2 improvements
SPARK-24723 Discuss necessary info and access in barrier mode + YARN

---------------------------------------------------------------------
To unsubscribe [hidden email]




--
Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

John Zhuge
+1  Like the idea as a user and a DSv2 contributor.

On Thu, Sep 12, 2019 at 4:10 PM Jungtaek Lim <[hidden email]> wrote:
+1 (as a contributor) from me to have preview release on Spark 3 as it would help to test the feature. When to cut preview release is questionable, as major works are ideally to be done before that - if we are intended to introduce new features before official release, that should work regardless of this, but if we are intended to have opportunity to test earlier, ideally it should.

As a one of contributors in structured streaming area, I'd like to add some items for Spark 3.0, both "must be done" and "better to have". For "better to have", I pick some items for new features which committers reviewed couple of rounds and dropped off without soft-reject (No valid reason to stop). For Spark 2.4 users, only added feature for structured streaming is Kafka delegation token. (given we assume revising Kafka consumer pool as improvement) I hope we provide some gifts for structured streaming users in Spark 3.0 envelope.

> must be done
* SPARK-26154 Stream-stream joins - left outer join gives inconsistent output
It's a correctness issue with multiple users reported, being reported at Nov. 2018. There's a way to reproduce it consistently, and we have a patch submitted at Jan. 2019 to fix it.

> better to have
* SPARK-23539 Add support for Kafka headers in Structured Streaming
* SPARK-26848 Introduce new option to Kafka source - specify timestamp to start and end offset
* SPARK-20568 Delete files after processing in structured streaming

There're some more new features/improvements items in SS, but given we're talking about ramping-down, above list might be realistic one.



On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <[hidden email]> wrote:
As a user/non committer, +1

I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...

jg


On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:

+1 from me too but I would like to know what other people think too.

2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
Thank you, Sean.

I'm also +1 for the following three.

1. Start to ramp down (by the official branch-3.0 cut)
2. Apache Spark 3.0.0-preview in 2019
3. Apache Spark 3.0.0 in early 2020

For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.

After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?


Bests,
Dongjoon.


On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.


Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.

Then out of curiosity, are the new Spark Graph APIs targeting 3.0?


   michael


On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:

I'm curious what current feelings are about ramping down towards a
Spark 3 release. It feels close to ready. There is no fixed date,
though in the past we had informally tossed around "back end of 2019".
For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
due.

What are the few major items that must get done for Spark 3, in your
opinion? Below are all of the open JIRAs for 3.0 (which everyone
should feel free to update with things that aren't really needed for
Spark 3; I already triaged some).

For me, it's:
- DSv2?
- Finishing touches on the Hive, JDK 11 update

What about considering a preview release earlier, as happened for
Spark 2, to get feedback much earlier than the RC cycle? Could that
even happen ... about now?

I'm also wondering what a realistic estimate of Spark 3 release is. My
guess is quite early 2020, from here.



SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
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-28372 Document Spark WEB UI
SPARK-28476 Support ALTER DATABASE SET LOCATION
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-28024 Incorrect numeric values when out of range
SPARK-27936 Support local dependency uploading from --py-files
SPARK-27884 Deprecate Python 2 support in Spark 3.0
SPARK-27763 Port test cases from PostgreSQL to Spark SQL
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-24640 size(null) returns null
SPARK-24702 Unable to cast to calendar interval in spark sql.
SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
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 User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
SPARK-25128 multiple simultaneous job submissions against k8s backend
cause driver pods to hang
SPARK-26731 remove EOLed spark jobs from jenkins
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-26022 PySpark Comparison with Pandas
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-23210 Introduce the concept of default value to schema
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-24417 Build and Run Spark on JDK11
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-25390 data source V2 API refactoring
SPARK-7768 Make user-defined type (UDT) API public
SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
SPARK-15691 Refactor and improve Hive support
SPARK-15694 Implement ScriptTransformation in sql/core
SPARK-16217 Support SELECT INTO statement
SPARK-16452 basic INFORMATION_SCHEMA support
SPARK-18134 SQL: MapType in Group BY and Joins not working
SPARK-18245 Improving support for bucketed table
SPARK-19842 Informational Referential Integrity Constraints Support in Spark
SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
list of structures
SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
respect session timezone
SPARK-22386 Data Source V2 improvements
SPARK-24723 Discuss necessary info and access in barrier mode + YARN

---------------------------------------------------------------------
To unsubscribe [hidden email]




--


--
John Zhuge
Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Matt Cheah

+1 as both a contributor and a user.

 

From: John Zhuge <[hidden email]>
Date: Thursday, September 12, 2019 at 4:15 PM
To: Jungtaek Lim <[hidden email]>
Cc: Jean Georges Perrin <[hidden email]>, Hyukjin Kwon <[hidden email]>, Dongjoon Hyun <[hidden email]>, dev <[hidden email]>
Subject: Re: Thoughts on Spark 3 release, or a preview release

 

+1  Like the idea as a user and a DSv2 contributor.

 

On Thu, Sep 12, 2019 at 4:10 PM Jungtaek Lim <[hidden email]> wrote:

+1 (as a contributor) from me to have preview release on Spark 3 as it would help to test the feature. When to cut preview release is questionable, as major works are ideally to be done before that - if we are intended to introduce new features before official release, that should work regardless of this, but if we are intended to have opportunity to test earlier, ideally it should.

 

As a one of contributors in structured streaming area, I'd like to add some items for Spark 3.0, both "must be done" and "better to have". For "better to have", I pick some items for new features which committers reviewed couple of rounds and dropped off without soft-reject (No valid reason to stop). For Spark 2.4 users, only added feature for structured streaming is Kafka delegation token. (given we assume revising Kafka consumer pool as improvement) I hope we provide some gifts for structured streaming users in Spark 3.0 envelope.

 

> must be done

* SPARK-26154 Stream-stream joins - left outer join gives inconsistent output

It's a correctness issue with multiple users reported, being reported at Nov. 2018. There's a way to reproduce it consistently, and we have a patch submitted at Jan. 2019 to fix it.

 

> better to have

* SPARK-23539 Add support for Kafka headers in Structured Streaming

* SPARK-26848 Introduce new option to Kafka source - specify timestamp to start and end offset

* SPARK-20568 Delete files after processing in structured streaming

 

There're some more new features/improvements items in SS, but given we're talking about ramping-down, above list might be realistic one.

 

 

 

On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <[hidden email]> wrote:

As a user/non committer, +1

 

I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...

 

jg

 


On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:

+1 from me too but I would like to know what other people think too.

 

2019 9 12 () 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:

Thank you, Sean.

 

I'm also +1 for the following three.

 

1. Start to ramp down (by the official branch-3.0 cut)

2. Apache Spark 3.0.0-preview in 2019

3. Apache Spark 3.0.0 in early 2020

 

For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.

 

After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?

 

 

Bests,

Dongjoon.

 

 

On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:

I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.

 

 

Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.

 

Then out of curiosity, are the new Spark Graph APIs targeting 3.0?

 

 

   michael

 



On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:

 

I'm curious what current feelings are about ramping down towards a
Spark 3 release. It feels close to ready. There is no fixed date,
though in the past we had informally tossed around "back end of 2019".
For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
due.

What are the few major items that must get done for Spark 3, in your
opinion? Below are all of the open JIRAs for 3.0 (which everyone
should feel free to update with things that aren't really needed for
Spark 3; I already triaged some).

For me, it's:
- DSv2?
- Finishing touches on the Hive, JDK 11 update

What about considering a preview release earlier, as happened for
Spark 2, to get feedback much earlier than the RC cycle? Could that
even happen ... about now?

I'm also wondering what a realistic estimate of Spark 3 release is. My
guess is quite early 2020, from here.



SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
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-28372 Document Spark WEB UI
SPARK-28476 Support ALTER DATABASE SET LOCATION
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-28024 Incorrect numeric values when out of range
SPARK-27936 Support local dependency uploading from --py-files
SPARK-27884 Deprecate Python 2 support in Spark 3.0
SPARK-27763 Port test cases from PostgreSQL to Spark SQL
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-24640 size(null) returns null
SPARK-24702 Unable to cast to calendar interval in spark sql.
SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
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 User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
SPARK-25128 multiple simultaneous job submissions against k8s backend
cause driver pods to hang
SPARK-26731 remove EOLed spark jobs from jenkins
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-26022 PySpark Comparison with Pandas
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-23210 Introduce the concept of default value to schema
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-24417 Build and Run Spark on JDK11
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-25390 data source V2 API refactoring
SPARK-7768 Make user-defined type (UDT) API public
SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
SPARK-15691 Refactor and improve Hive support
SPARK-15694 Implement ScriptTransformation in sql/core
SPARK-16217 Support SELECT INTO statement
SPARK-16452 basic INFORMATION_SCHEMA support
SPARK-18134 SQL: MapType in Group BY and Joins not working
SPARK-18245 Improving support for bucketed table
SPARK-19842 Informational Referential Integrity Constraints Support in Spark
SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
list of structures
SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
respect session timezone
SPARK-22386 Data Source V2 improvements
SPARK-24723 Discuss necessary info and access in barrier mode + YARN

---------------------------------------------------------------------
To unsubscribe [hidden email]

 


 

--


 

--

John Zhuge


smime.p7s (6K) Download Attachment
Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Holden Karau
In reply to this post by John Zhuge
I like the idea from the PoV of giving folks something to start testing against and exploring so they can raise issues with us earlier in the process and we have more time to make calls around this.

On Thu, Sep 12, 2019 at 4:15 PM John Zhuge <[hidden email]> wrote:
+1  Like the idea as a user and a DSv2 contributor.

On Thu, Sep 12, 2019 at 4:10 PM Jungtaek Lim <[hidden email]> wrote:
+1 (as a contributor) from me to have preview release on Spark 3 as it would help to test the feature. When to cut preview release is questionable, as major works are ideally to be done before that - if we are intended to introduce new features before official release, that should work regardless of this, but if we are intended to have opportunity to test earlier, ideally it should.

As a one of contributors in structured streaming area, I'd like to add some items for Spark 3.0, both "must be done" and "better to have". For "better to have", I pick some items for new features which committers reviewed couple of rounds and dropped off without soft-reject (No valid reason to stop). For Spark 2.4 users, only added feature for structured streaming is Kafka delegation token. (given we assume revising Kafka consumer pool as improvement) I hope we provide some gifts for structured streaming users in Spark 3.0 envelope.

> must be done
* SPARK-26154 Stream-stream joins - left outer join gives inconsistent output
It's a correctness issue with multiple users reported, being reported at Nov. 2018. There's a way to reproduce it consistently, and we have a patch submitted at Jan. 2019 to fix it.

> better to have
* SPARK-23539 Add support for Kafka headers in Structured Streaming
* SPARK-26848 Introduce new option to Kafka source - specify timestamp to start and end offset
* SPARK-20568 Delete files after processing in structured streaming

There're some more new features/improvements items in SS, but given we're talking about ramping-down, above list might be realistic one.



On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <[hidden email]> wrote:
As a user/non committer, +1

I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...

jg


On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:

+1 from me too but I would like to know what other people think too.

2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
Thank you, Sean.

I'm also +1 for the following three.

1. Start to ramp down (by the official branch-3.0 cut)
2. Apache Spark 3.0.0-preview in 2019
3. Apache Spark 3.0.0 in early 2020

For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.

After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?


Bests,
Dongjoon.


On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.


Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.

Then out of curiosity, are the new Spark Graph APIs targeting 3.0?


   michael


On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:

I'm curious what current feelings are about ramping down towards a
Spark 3 release. It feels close to ready. There is no fixed date,
though in the past we had informally tossed around "back end of 2019".
For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
due.

What are the few major items that must get done for Spark 3, in your
opinion? Below are all of the open JIRAs for 3.0 (which everyone
should feel free to update with things that aren't really needed for
Spark 3; I already triaged some).

For me, it's:
- DSv2?
- Finishing touches on the Hive, JDK 11 update

What about considering a preview release earlier, as happened for
Spark 2, to get feedback much earlier than the RC cycle? Could that
even happen ... about now?

I'm also wondering what a realistic estimate of Spark 3 release is. My
guess is quite early 2020, from here.



SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
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-28372 Document Spark WEB UI
SPARK-28476 Support ALTER DATABASE SET LOCATION
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-28024 Incorrect numeric values when out of range
SPARK-27936 Support local dependency uploading from --py-files
SPARK-27884 Deprecate Python 2 support in Spark 3.0
SPARK-27763 Port test cases from PostgreSQL to Spark SQL
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-24640 size(null) returns null
SPARK-24702 Unable to cast to calendar interval in spark sql.
SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
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 User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
SPARK-25128 multiple simultaneous job submissions against k8s backend
cause driver pods to hang
SPARK-26731 remove EOLed spark jobs from jenkins
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-26022 PySpark Comparison with Pandas
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-23210 Introduce the concept of default value to schema
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-24417 Build and Run Spark on JDK11
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-25390 data source V2 API refactoring
SPARK-7768 Make user-defined type (UDT) API public
SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
SPARK-15691 Refactor and improve Hive support
SPARK-15694 Implement ScriptTransformation in sql/core
SPARK-16217 Support SELECT INTO statement
SPARK-16452 basic INFORMATION_SCHEMA support
SPARK-18134 SQL: MapType in Group BY and Joins not working
SPARK-18245 Improving support for bucketed table
SPARK-19842 Informational Referential Integrity Constraints Support in Spark
SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
list of structures
SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
respect session timezone
SPARK-22386 Data Source V2 improvements
SPARK-24723 Discuss necessary info and access in barrier mode + YARN

---------------------------------------------------------------------
To unsubscribe [hidden email]




--


--
John Zhuge


--
Books (Learning Spark, High Performance Spark, etc.): https://amzn.to/2MaRAG9 
Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

rxin
+1! Long due for a preview release.


On Thu, Sep 12, 2019 at 5:26 PM, Holden Karau <[hidden email]> wrote:
I like the idea from the PoV of giving folks something to start testing against and exploring so they can raise issues with us earlier in the process and we have more time to make calls around this.

On Thu, Sep 12, 2019 at 4:15 PM John Zhuge <[hidden email]> wrote:
+1  Like the idea as a user and a DSv2 contributor.

On Thu, Sep 12, 2019 at 4:10 PM Jungtaek Lim <[hidden email]> wrote:
+1 (as a contributor) from me to have preview release on Spark 3 as it would help to test the feature. When to cut preview release is questionable, as major works are ideally to be done before that - if we are intended to introduce new features before official release, that should work regardless of this, but if we are intended to have opportunity to test earlier, ideally it should.

As a one of contributors in structured streaming area, I'd like to add some items for Spark 3.0, both "must be done" and "better to have". For "better to have", I pick some items for new features which committers reviewed couple of rounds and dropped off without soft-reject (No valid reason to stop). For Spark 2.4 users, only added feature for structured streaming is Kafka delegation token. (given we assume revising Kafka consumer pool as improvement) I hope we provide some gifts for structured streaming users in Spark 3.0 envelope.

> must be done
* SPARK-26154 Stream-stream joins - left outer join gives inconsistent output
It's a correctness issue with multiple users reported, being reported at Nov. 2018. There's a way to reproduce it consistently, and we have a patch submitted at Jan. 2019 to fix it.

> better to have
* SPARK-23539 Add support for Kafka headers in Structured Streaming
* SPARK-26848 Introduce new option to Kafka source - specify timestamp to start and end offset
* SPARK-20568 Delete files after processing in structured streaming

There're some more new features/improvements items in SS, but given we're talking about ramping-down, above list might be realistic one.



On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <[hidden email]> wrote:
As a user/non committer, +1

I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...

jg


On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:

+1 from me too but I would like to know what other people think too.

2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
Thank you, Sean.

I'm also +1 for the following three.

1. Start to ramp down (by the official branch-3.0 cut)
2. Apache Spark 3.0.0-preview in 2019
3. Apache Spark 3.0.0 in early 2020

For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.

After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?


Bests,
Dongjoon.


On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.


Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.

Then out of curiosity, are the new Spark Graph APIs targeting 3.0?


   michael


On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:

I'm curious what current feelings are about ramping down towards a
Spark 3 release. It feels close to ready. There is no fixed date,
though in the past we had informally tossed around "back end of 2019".
For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
due.

What are the few major items that must get done for Spark 3, in your
opinion? Below are all of the open JIRAs for 3.0 (which everyone
should feel free to update with things that aren't really needed for
Spark 3; I already triaged some).

For me, it's:
- DSv2?
- Finishing touches on the Hive, JDK 11 update

What about considering a preview release earlier, as happened for
Spark 2, to get feedback much earlier than the RC cycle? Could that
even happen ... about now?

I'm also wondering what a realistic estimate of Spark 3 release is. My
guess is quite early 2020, from here.



SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
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-28372 Document Spark WEB UI
SPARK-28476 Support ALTER DATABASE SET LOCATION
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-28024 Incorrect numeric values when out of range
SPARK-27936 Support local dependency uploading from --py-files
SPARK-27884 Deprecate Python 2 support in Spark 3.0
SPARK-27763 Port test cases from PostgreSQL to Spark SQL
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-24640 size(null) returns null
SPARK-24702 Unable to cast to calendar interval in spark sql.
SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
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 User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
SPARK-25128 multiple simultaneous job submissions against k8s backend
cause driver pods to hang
SPARK-26731 remove EOLed spark jobs from jenkins
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-26022 PySpark Comparison with Pandas
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-23210 Introduce the concept of default value to schema
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-24417 Build and Run Spark on JDK11
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-25390 data source V2 API refactoring
SPARK-7768 Make user-defined type (UDT) API public
SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
SPARK-15691 Refactor and improve Hive support
SPARK-15694 Implement ScriptTransformation in sql/core
SPARK-16217 Support SELECT INTO statement
SPARK-16452 basic INFORMATION_SCHEMA support
SPARK-18134 SQL: MapType in Group BY and Joins not working
SPARK-18245 Improving support for bucketed table
SPARK-19842 Informational Referential Integrity Constraints Support in Spark
SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
list of structures
SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
respect session timezone
SPARK-22386 Data Source V2 improvements
SPARK-24723 Discuss necessary info and access in barrier mode + YARN

---------------------------------------------------------------------
To unsubscribe [hidden email]




--


--
John Zhuge


--
Books (Learning Spark, High Performance Spark, etc.): https://amzn.to/2MaRAG9 

Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Driesprong, Fokko
Michael Heuer, that's an interesting issue.

1.8.2 to 1.9.0 is almost binary compatible (94%): http://people.apache.org/~busbey/avro/1.9.0-RC4/1.8.2_to_1.9.0RC4_compat_report.html. Most of the stuff is removing the Jackson and Netty API from Avro's public API and deprecating the Joda library. I would strongly advise moving to 1.9.1 since there are some regression issues, for Java most important: https://jira.apache.org/jira/browse/AVRO-2400

I'd love to dive into the issue that you describe and I'm curious if the issue is still there with Avro 1.9.1. I'm a bit busy at the moment but might have some time this weekend to dive into it.

Cheers, Fokko Driesprong


Op vr 13 sep. 2019 om 02:32 schreef Reynold Xin <[hidden email]>:
+1! Long due for a preview release.


On Thu, Sep 12, 2019 at 5:26 PM, Holden Karau <[hidden email]> wrote:
I like the idea from the PoV of giving folks something to start testing against and exploring so they can raise issues with us earlier in the process and we have more time to make calls around this.

On Thu, Sep 12, 2019 at 4:15 PM John Zhuge <[hidden email]> wrote:
+1  Like the idea as a user and a DSv2 contributor.

On Thu, Sep 12, 2019 at 4:10 PM Jungtaek Lim <[hidden email]> wrote:
+1 (as a contributor) from me to have preview release on Spark 3 as it would help to test the feature. When to cut preview release is questionable, as major works are ideally to be done before that - if we are intended to introduce new features before official release, that should work regardless of this, but if we are intended to have opportunity to test earlier, ideally it should.

As a one of contributors in structured streaming area, I'd like to add some items for Spark 3.0, both "must be done" and "better to have". For "better to have", I pick some items for new features which committers reviewed couple of rounds and dropped off without soft-reject (No valid reason to stop). For Spark 2.4 users, only added feature for structured streaming is Kafka delegation token. (given we assume revising Kafka consumer pool as improvement) I hope we provide some gifts for structured streaming users in Spark 3.0 envelope.

> must be done
* SPARK-26154 Stream-stream joins - left outer join gives inconsistent output
It's a correctness issue with multiple users reported, being reported at Nov. 2018. There's a way to reproduce it consistently, and we have a patch submitted at Jan. 2019 to fix it.

> better to have
* SPARK-23539 Add support for Kafka headers in Structured Streaming
* SPARK-26848 Introduce new option to Kafka source - specify timestamp to start and end offset
* SPARK-20568 Delete files after processing in structured streaming

There're some more new features/improvements items in SS, but given we're talking about ramping-down, above list might be realistic one.



On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <[hidden email]> wrote:
As a user/non committer, +1

I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...

jg


On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:

+1 from me too but I would like to know what other people think too.

2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
Thank you, Sean.

I'm also +1 for the following three.

1. Start to ramp down (by the official branch-3.0 cut)
2. Apache Spark 3.0.0-preview in 2019
3. Apache Spark 3.0.0 in early 2020

For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.

After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?


Bests,
Dongjoon.


On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.


Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.

Then out of curiosity, are the new Spark Graph APIs targeting 3.0?


   michael


On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:

I'm curious what current feelings are about ramping down towards a
Spark 3 release. It feels close to ready. There is no fixed date,
though in the past we had informally tossed around "back end of 2019".
For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
due.

What are the few major items that must get done for Spark 3, in your
opinion? Below are all of the open JIRAs for 3.0 (which everyone
should feel free to update with things that aren't really needed for
Spark 3; I already triaged some).

For me, it's:
- DSv2?
- Finishing touches on the Hive, JDK 11 update

What about considering a preview release earlier, as happened for
Spark 2, to get feedback much earlier than the RC cycle? Could that
even happen ... about now?

I'm also wondering what a realistic estimate of Spark 3 release is. My
guess is quite early 2020, from here.



SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
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-28372 Document Spark WEB UI
SPARK-28476 Support ALTER DATABASE SET LOCATION
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-28024 Incorrect numeric values when out of range
SPARK-27936 Support local dependency uploading from --py-files
SPARK-27884 Deprecate Python 2 support in Spark 3.0
SPARK-27763 Port test cases from PostgreSQL to Spark SQL
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-24640 size(null) returns null
SPARK-24702 Unable to cast to calendar interval in spark sql.
SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
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 User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
SPARK-25128 multiple simultaneous job submissions against k8s backend
cause driver pods to hang
SPARK-26731 remove EOLed spark jobs from jenkins
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-26022 PySpark Comparison with Pandas
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-23210 Introduce the concept of default value to schema
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-24417 Build and Run Spark on JDK11
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-25390 data source V2 API refactoring
SPARK-7768 Make user-defined type (UDT) API public
SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
SPARK-15691 Refactor and improve Hive support
SPARK-15694 Implement ScriptTransformation in sql/core
SPARK-16217 Support SELECT INTO statement
SPARK-16452 basic INFORMATION_SCHEMA support
SPARK-18134 SQL: MapType in Group BY and Joins not working
SPARK-18245 Improving support for bucketed table
SPARK-19842 Informational Referential Integrity Constraints Support in Spark
SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
list of structures
SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
respect session timezone
SPARK-22386 Data Source V2 improvements
SPARK-24723 Discuss necessary info and access in barrier mode + YARN

---------------------------------------------------------------------
To unsubscribe [hidden email]




--


--
John Zhuge


--
Books (Learning Spark, High Performance Spark, etc.): https://amzn.to/2MaRAG9 

Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Stavros Kontopoulos-3
+1 as a contributor and as a user. Given the amount of testing required for all the new cool stuff like java 11 support, major refactorings/deprecations etc, a preview version would help a lot the community making adoption smoother long term. I would also add to the list of issues, Scala 2.13 support (https://issues.apache.org/jira/browse/SPARK-25075) assuming things will move forward faster the next few months.

On Fri, Sep 13, 2019 at 11:08 AM Driesprong, Fokko <[hidden email]> wrote:
Michael Heuer, that's an interesting issue.

1.8.2 to 1.9.0 is almost binary compatible (94%): http://people.apache.org/~busbey/avro/1.9.0-RC4/1.8.2_to_1.9.0RC4_compat_report.html. Most of the stuff is removing the Jackson and Netty API from Avro's public API and deprecating the Joda library. I would strongly advise moving to 1.9.1 since there are some regression issues, for Java most important: https://jira.apache.org/jira/browse/AVRO-2400

I'd love to dive into the issue that you describe and I'm curious if the issue is still there with Avro 1.9.1. I'm a bit busy at the moment but might have some time this weekend to dive into it.

Cheers, Fokko Driesprong


Op vr 13 sep. 2019 om 02:32 schreef Reynold Xin <[hidden email]>:
+1! Long due for a preview release.


On Thu, Sep 12, 2019 at 5:26 PM, Holden Karau <[hidden email]> wrote:
I like the idea from the PoV of giving folks something to start testing against and exploring so they can raise issues with us earlier in the process and we have more time to make calls around this.

On Thu, Sep 12, 2019 at 4:15 PM John Zhuge <[hidden email]> wrote:
+1  Like the idea as a user and a DSv2 contributor.

On Thu, Sep 12, 2019 at 4:10 PM Jungtaek Lim <[hidden email]> wrote:
+1 (as a contributor) from me to have preview release on Spark 3 as it would help to test the feature. When to cut preview release is questionable, as major works are ideally to be done before that - if we are intended to introduce new features before official release, that should work regardless of this, but if we are intended to have opportunity to test earlier, ideally it should.

As a one of contributors in structured streaming area, I'd like to add some items for Spark 3.0, both "must be done" and "better to have". For "better to have", I pick some items for new features which committers reviewed couple of rounds and dropped off without soft-reject (No valid reason to stop). For Spark 2.4 users, only added feature for structured streaming is Kafka delegation token. (given we assume revising Kafka consumer pool as improvement) I hope we provide some gifts for structured streaming users in Spark 3.0 envelope.

> must be done
* SPARK-26154 Stream-stream joins - left outer join gives inconsistent output
It's a correctness issue with multiple users reported, being reported at Nov. 2018. There's a way to reproduce it consistently, and we have a patch submitted at Jan. 2019 to fix it.

> better to have
* SPARK-23539 Add support for Kafka headers in Structured Streaming
* SPARK-26848 Introduce new option to Kafka source - specify timestamp to start and end offset
* SPARK-20568 Delete files after processing in structured streaming

There're some more new features/improvements items in SS, but given we're talking about ramping-down, above list might be realistic one.



On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <[hidden email]> wrote:
As a user/non committer, +1

I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...

jg


On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:

+1 from me too but I would like to know what other people think too.

2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
Thank you, Sean.

I'm also +1 for the following three.

1. Start to ramp down (by the official branch-3.0 cut)
2. Apache Spark 3.0.0-preview in 2019
3. Apache Spark 3.0.0 in early 2020

For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.

After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?


Bests,
Dongjoon.


On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.


Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.

Then out of curiosity, are the new Spark Graph APIs targeting 3.0?


   michael


On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:

I'm curious what current feelings are about ramping down towards a
Spark 3 release. It feels close to ready. There is no fixed date,
though in the past we had informally tossed around "back end of 2019".
For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
due.

What are the few major items that must get done for Spark 3, in your
opinion? Below are all of the open JIRAs for 3.0 (which everyone
should feel free to update with things that aren't really needed for
Spark 3; I already triaged some).

For me, it's:
- DSv2?
- Finishing touches on the Hive, JDK 11 update

What about considering a preview release earlier, as happened for
Spark 2, to get feedback much earlier than the RC cycle? Could that
even happen ... about now?

I'm also wondering what a realistic estimate of Spark 3 release is. My
guess is quite early 2020, from here.



SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
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-28372 Document Spark WEB UI
SPARK-28476 Support ALTER DATABASE SET LOCATION
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-28024 Incorrect numeric values when out of range
SPARK-27936 Support local dependency uploading from --py-files
SPARK-27884 Deprecate Python 2 support in Spark 3.0
SPARK-27763 Port test cases from PostgreSQL to Spark SQL
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-24640 size(null) returns null
SPARK-24702 Unable to cast to calendar interval in spark sql.
SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
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 User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
SPARK-25128 multiple simultaneous job submissions against k8s backend
cause driver pods to hang
SPARK-26731 remove EOLed spark jobs from jenkins
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-26022 PySpark Comparison with Pandas
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-23210 Introduce the concept of default value to schema
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-24417 Build and Run Spark on JDK11
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-25390 data source V2 API refactoring
SPARK-7768 Make user-defined type (UDT) API public
SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
SPARK-15691 Refactor and improve Hive support
SPARK-15694 Implement ScriptTransformation in sql/core
SPARK-16217 Support SELECT INTO statement
SPARK-16452 basic INFORMATION_SCHEMA support
SPARK-18134 SQL: MapType in Group BY and Joins not working
SPARK-18245 Improving support for bucketed table
SPARK-19842 Informational Referential Integrity Constraints Support in Spark
SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
list of structures
SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
respect session timezone
SPARK-22386 Data Source V2 improvements
SPARK-24723 Discuss necessary info and access in barrier mode + YARN

---------------------------------------------------------------------
To unsubscribe [hidden email]




--


--
John Zhuge


--
Books (Learning Spark, High Performance Spark, etc.): https://amzn.to/2MaRAG9 



Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Thomas Graves
+1, I think having preview release would be great.

Tom

On Fri, Sep 13, 2019 at 4:55 AM Stavros Kontopoulos <[hidden email]> wrote:
+1 as a contributor and as a user. Given the amount of testing required for all the new cool stuff like java 11 support, major refactorings/deprecations etc, a preview version would help a lot the community making adoption smoother long term. I would also add to the list of issues, Scala 2.13 support (https://issues.apache.org/jira/browse/SPARK-25075) assuming things will move forward faster the next few months.

On Fri, Sep 13, 2019 at 11:08 AM Driesprong, Fokko <[hidden email]> wrote:
Michael Heuer, that's an interesting issue.

1.8.2 to 1.9.0 is almost binary compatible (94%): http://people.apache.org/~busbey/avro/1.9.0-RC4/1.8.2_to_1.9.0RC4_compat_report.html. Most of the stuff is removing the Jackson and Netty API from Avro's public API and deprecating the Joda library. I would strongly advise moving to 1.9.1 since there are some regression issues, for Java most important: https://jira.apache.org/jira/browse/AVRO-2400

I'd love to dive into the issue that you describe and I'm curious if the issue is still there with Avro 1.9.1. I'm a bit busy at the moment but might have some time this weekend to dive into it.

Cheers, Fokko Driesprong


Op vr 13 sep. 2019 om 02:32 schreef Reynold Xin <[hidden email]>:
+1! Long due for a preview release.


On Thu, Sep 12, 2019 at 5:26 PM, Holden Karau <[hidden email]> wrote:
I like the idea from the PoV of giving folks something to start testing against and exploring so they can raise issues with us earlier in the process and we have more time to make calls around this.

On Thu, Sep 12, 2019 at 4:15 PM John Zhuge <[hidden email]> wrote:
+1  Like the idea as a user and a DSv2 contributor.

On Thu, Sep 12, 2019 at 4:10 PM Jungtaek Lim <[hidden email]> wrote:
+1 (as a contributor) from me to have preview release on Spark 3 as it would help to test the feature. When to cut preview release is questionable, as major works are ideally to be done before that - if we are intended to introduce new features before official release, that should work regardless of this, but if we are intended to have opportunity to test earlier, ideally it should.

As a one of contributors in structured streaming area, I'd like to add some items for Spark 3.0, both "must be done" and "better to have". For "better to have", I pick some items for new features which committers reviewed couple of rounds and dropped off without soft-reject (No valid reason to stop). For Spark 2.4 users, only added feature for structured streaming is Kafka delegation token. (given we assume revising Kafka consumer pool as improvement) I hope we provide some gifts for structured streaming users in Spark 3.0 envelope.

> must be done
* SPARK-26154 Stream-stream joins - left outer join gives inconsistent output
It's a correctness issue with multiple users reported, being reported at Nov. 2018. There's a way to reproduce it consistently, and we have a patch submitted at Jan. 2019 to fix it.

> better to have
* SPARK-23539 Add support for Kafka headers in Structured Streaming
* SPARK-26848 Introduce new option to Kafka source - specify timestamp to start and end offset
* SPARK-20568 Delete files after processing in structured streaming

There're some more new features/improvements items in SS, but given we're talking about ramping-down, above list might be realistic one.



On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <[hidden email]> wrote:
As a user/non committer, +1

I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...

jg


On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:

+1 from me too but I would like to know what other people think too.

2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
Thank you, Sean.

I'm also +1 for the following three.

1. Start to ramp down (by the official branch-3.0 cut)
2. Apache Spark 3.0.0-preview in 2019
3. Apache Spark 3.0.0 in early 2020

For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.

After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?


Bests,
Dongjoon.


On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.


Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.

Then out of curiosity, are the new Spark Graph APIs targeting 3.0?


   michael


On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:

I'm curious what current feelings are about ramping down towards a
Spark 3 release. It feels close to ready. There is no fixed date,
though in the past we had informally tossed around "back end of 2019".
For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
due.

What are the few major items that must get done for Spark 3, in your
opinion? Below are all of the open JIRAs for 3.0 (which everyone
should feel free to update with things that aren't really needed for
Spark 3; I already triaged some).

For me, it's:
- DSv2?
- Finishing touches on the Hive, JDK 11 update

What about considering a preview release earlier, as happened for
Spark 2, to get feedback much earlier than the RC cycle? Could that
even happen ... about now?

I'm also wondering what a realistic estimate of Spark 3 release is. My
guess is quite early 2020, from here.



SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
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-28372 Document Spark WEB UI
SPARK-28476 Support ALTER DATABASE SET LOCATION
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-28024 Incorrect numeric values when out of range
SPARK-27936 Support local dependency uploading from --py-files
SPARK-27884 Deprecate Python 2 support in Spark 3.0
SPARK-27763 Port test cases from PostgreSQL to Spark SQL
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-24640 size(null) returns null
SPARK-24702 Unable to cast to calendar interval in spark sql.
SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
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 User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
SPARK-25128 multiple simultaneous job submissions against k8s backend
cause driver pods to hang
SPARK-26731 remove EOLed spark jobs from jenkins
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-26022 PySpark Comparison with Pandas
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-23210 Introduce the concept of default value to schema
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-24417 Build and Run Spark on JDK11
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-25390 data source V2 API refactoring
SPARK-7768 Make user-defined type (UDT) API public
SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
SPARK-15691 Refactor and improve Hive support
SPARK-15694 Implement ScriptTransformation in sql/core
SPARK-16217 Support SELECT INTO statement
SPARK-16452 basic INFORMATION_SCHEMA support
SPARK-18134 SQL: MapType in Group BY and Joins not working
SPARK-18245 Improving support for bucketed table
SPARK-19842 Informational Referential Integrity Constraints Support in Spark
SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
list of structures
SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
respect session timezone
SPARK-22386 Data Source V2 improvements
SPARK-24723 Discuss necessary info and access in barrier mode + YARN

---------------------------------------------------------------------
To unsubscribe [hidden email]




--


--
John Zhuge


--
Books (Learning Spark, High Performance Spark, etc.): https://amzn.to/2MaRAG9 



Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

ifilonenko
+1 for preview release 

On Fri, Sep 13, 2019 at 9:58 AM Thomas Graves <[hidden email]> wrote:
+1, I think having preview release would be great.

Tom

On Fri, Sep 13, 2019 at 4:55 AM Stavros Kontopoulos <[hidden email]> wrote:
+1 as a contributor and as a user. Given the amount of testing required for all the new cool stuff like java 11 support, major refactorings/deprecations etc, a preview version would help a lot the community making adoption smoother long term. I would also add to the list of issues, Scala 2.13 support (https://issues.apache.org/jira/browse/SPARK-25075) assuming things will move forward faster the next few months.

On Fri, Sep 13, 2019 at 11:08 AM Driesprong, Fokko <[hidden email]> wrote:
Michael Heuer, that's an interesting issue.

1.8.2 to 1.9.0 is almost binary compatible (94%): http://people.apache.org/~busbey/avro/1.9.0-RC4/1.8.2_to_1.9.0RC4_compat_report.html. Most of the stuff is removing the Jackson and Netty API from Avro's public API and deprecating the Joda library. I would strongly advise moving to 1.9.1 since there are some regression issues, for Java most important: https://jira.apache.org/jira/browse/AVRO-2400

I'd love to dive into the issue that you describe and I'm curious if the issue is still there with Avro 1.9.1. I'm a bit busy at the moment but might have some time this weekend to dive into it.

Cheers, Fokko Driesprong


Op vr 13 sep. 2019 om 02:32 schreef Reynold Xin <[hidden email]>:
+1! Long due for a preview release.


On Thu, Sep 12, 2019 at 5:26 PM, Holden Karau <[hidden email]> wrote:
I like the idea from the PoV of giving folks something to start testing against and exploring so they can raise issues with us earlier in the process and we have more time to make calls around this.

On Thu, Sep 12, 2019 at 4:15 PM John Zhuge <[hidden email]> wrote:
+1  Like the idea as a user and a DSv2 contributor.

On Thu, Sep 12, 2019 at 4:10 PM Jungtaek Lim <[hidden email]> wrote:
+1 (as a contributor) from me to have preview release on Spark 3 as it would help to test the feature. When to cut preview release is questionable, as major works are ideally to be done before that - if we are intended to introduce new features before official release, that should work regardless of this, but if we are intended to have opportunity to test earlier, ideally it should.

As a one of contributors in structured streaming area, I'd like to add some items for Spark 3.0, both "must be done" and "better to have". For "better to have", I pick some items for new features which committers reviewed couple of rounds and dropped off without soft-reject (No valid reason to stop). For Spark 2.4 users, only added feature for structured streaming is Kafka delegation token. (given we assume revising Kafka consumer pool as improvement) I hope we provide some gifts for structured streaming users in Spark 3.0 envelope.

> must be done
* SPARK-26154 Stream-stream joins - left outer join gives inconsistent output
It's a correctness issue with multiple users reported, being reported at Nov. 2018. There's a way to reproduce it consistently, and we have a patch submitted at Jan. 2019 to fix it.

> better to have
* SPARK-23539 Add support for Kafka headers in Structured Streaming
* SPARK-26848 Introduce new option to Kafka source - specify timestamp to start and end offset
* SPARK-20568 Delete files after processing in structured streaming

There're some more new features/improvements items in SS, but given we're talking about ramping-down, above list might be realistic one.



On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <[hidden email]> wrote:
As a user/non committer, +1

I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...

jg


On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:

+1 from me too but I would like to know what other people think too.

2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
Thank you, Sean.

I'm also +1 for the following three.

1. Start to ramp down (by the official branch-3.0 cut)
2. Apache Spark 3.0.0-preview in 2019
3. Apache Spark 3.0.0 in early 2020

For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.

After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?


Bests,
Dongjoon.


On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.


Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.

Then out of curiosity, are the new Spark Graph APIs targeting 3.0?


   michael


On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:

I'm curious what current feelings are about ramping down towards a
Spark 3 release. It feels close to ready. There is no fixed date,
though in the past we had informally tossed around "back end of 2019".
For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
due.

What are the few major items that must get done for Spark 3, in your
opinion? Below are all of the open JIRAs for 3.0 (which everyone
should feel free to update with things that aren't really needed for
Spark 3; I already triaged some).

For me, it's:
- DSv2?
- Finishing touches on the Hive, JDK 11 update

What about considering a preview release earlier, as happened for
Spark 2, to get feedback much earlier than the RC cycle? Could that
even happen ... about now?

I'm also wondering what a realistic estimate of Spark 3 release is. My
guess is quite early 2020, from here.



SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
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-28372 Document Spark WEB UI
SPARK-28476 Support ALTER DATABASE SET LOCATION
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-28024 Incorrect numeric values when out of range
SPARK-27936 Support local dependency uploading from --py-files
SPARK-27884 Deprecate Python 2 support in Spark 3.0
SPARK-27763 Port test cases from PostgreSQL to Spark SQL
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-24640 size(null) returns null
SPARK-24702 Unable to cast to calendar interval in spark sql.
SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
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 User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
SPARK-25128 multiple simultaneous job submissions against k8s backend
cause driver pods to hang
SPARK-26731 remove EOLed spark jobs from jenkins
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-26022 PySpark Comparison with Pandas
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-23210 Introduce the concept of default value to schema
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-24417 Build and Run Spark on JDK11
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-25390 data source V2 API refactoring
SPARK-7768 Make user-defined type (UDT) API public
SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
SPARK-15691 Refactor and improve Hive support
SPARK-15694 Implement ScriptTransformation in sql/core
SPARK-16217 Support SELECT INTO statement
SPARK-16452 basic INFORMATION_SCHEMA support
SPARK-18134 SQL: MapType in Group BY and Joins not working
SPARK-18245 Improving support for bucketed table
SPARK-19842 Informational Referential Integrity Constraints Support in Spark
SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
list of structures
SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
respect session timezone
SPARK-22386 Data Source V2 improvements
SPARK-24723 Discuss necessary info and access in barrier mode + YARN

---------------------------------------------------------------------
To unsubscribe [hidden email]




--


--
John Zhuge


--
Books (Learning Spark, High Performance Spark, etc.): https://amzn.to/2MaRAG9 



Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Sean Owen-2
In reply to this post by rxin
Well, great to hear the unanimous support for a Spark 3 preview
release. Now, I don't know how to make releases myself :) I would
first open it up to our revered release managers: would anyone be
interested in trying to make one? sounds like it's not too soon to get
what's in master out for evaluation, as there aren't any major
deficiencies left, although a number of items to consider for the
final release.

I think we just need one release, targeting Hadoop 3.x / Hive 2.x in
order to make it possible to test with JDK 11. (We're only on Scala
2.12 at this point.)

On Thu, Sep 12, 2019 at 7:32 PM Reynold Xin <[hidden email]> wrote:

>
> +1! Long due for a preview release.
>
>
> On Thu, Sep 12, 2019 at 5:26 PM, Holden Karau <[hidden email]> wrote:
>>
>> I like the idea from the PoV of giving folks something to start testing against and exploring so they can raise issues with us earlier in the process and we have more time to make calls around this.
>>
>> On Thu, Sep 12, 2019 at 4:15 PM John Zhuge <[hidden email]> wrote:
>>>
>>> +1  Like the idea as a user and a DSv2 contributor.
>>>
>>> On Thu, Sep 12, 2019 at 4:10 PM Jungtaek Lim <[hidden email]> wrote:
>>>>
>>>> +1 (as a contributor) from me to have preview release on Spark 3 as it would help to test the feature. When to cut preview release is questionable, as major works are ideally to be done before that - if we are intended to introduce new features before official release, that should work regardless of this, but if we are intended to have opportunity to test earlier, ideally it should.
>>>>
>>>> As a one of contributors in structured streaming area, I'd like to add some items for Spark 3.0, both "must be done" and "better to have". For "better to have", I pick some items for new features which committers reviewed couple of rounds and dropped off without soft-reject (No valid reason to stop). For Spark 2.4 users, only added feature for structured streaming is Kafka delegation token. (given we assume revising Kafka consumer pool as improvement) I hope we provide some gifts for structured streaming users in Spark 3.0 envelope.
>>>>
>>>> > must be done
>>>> * SPARK-26154 Stream-stream joins - left outer join gives inconsistent output
>>>> It's a correctness issue with multiple users reported, being reported at Nov. 2018. There's a way to reproduce it consistently, and we have a patch submitted at Jan. 2019 to fix it.
>>>>
>>>> > better to have
>>>> * SPARK-23539 Add support for Kafka headers in Structured Streaming
>>>> * SPARK-26848 Introduce new option to Kafka source - specify timestamp to start and end offset
>>>> * SPARK-20568 Delete files after processing in structured streaming
>>>>
>>>> There're some more new features/improvements items in SS, but given we're talking about ramping-down, above list might be realistic one.
>>>>
>>>>
>>>>
>>>> On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <[hidden email]> wrote:
>>>>>
>>>>> As a user/non committer, +1
>>>>>
>>>>> I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...
>>>>>
>>>>> jg
>>>>>
>>>>>
>>>>> On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:
>>>>>
>>>>> +1 from me too but I would like to know what other people think too.
>>>>>
>>>>> 2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
>>>>>>
>>>>>> Thank you, Sean.
>>>>>>
>>>>>> I'm also +1 for the following three.
>>>>>>
>>>>>> 1. Start to ramp down (by the official branch-3.0 cut)
>>>>>> 2. Apache Spark 3.0.0-preview in 2019
>>>>>> 3. Apache Spark 3.0.0 in early 2020
>>>>>>
>>>>>> For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.
>>>>>>
>>>>>> After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?
>>>>>>
>>>>>> - https://spark.apache.org/versioning-policy.html
>>>>>>
>>>>>> Bests,
>>>>>> Dongjoon.
>>>>>>
>>>>>>
>>>>>> On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
>>>>>>>
>>>>>>> I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.
>>>>>>>
>>>>>>> https://issues.apache.org/jira/browse/SPARK-25588
>>>>>>> https://issues.apache.org/jira/browse/SPARK-27781
>>>>>>>
>>>>>>> Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.
>>>>>>>
>>>>>>> Then out of curiosity, are the new Spark Graph APIs targeting 3.0?
>>>>>>>
>>>>>>> https://github.com/apache/spark/pull/24851
>>>>>>> https://github.com/apache/spark/pull/24297
>>>>>>>
>>>>>>>    michael
>>>>>>>
>>>>>>>
>>>>>>> On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:
>>>>>>>
>>>>>>> I'm curious what current feelings are about ramping down towards a
>>>>>>> Spark 3 release. It feels close to ready. There is no fixed date,
>>>>>>> though in the past we had informally tossed around "back end of 2019".
>>>>>>> For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
>>>>>>> Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
>>>>>>> due.
>>>>>>>
>>>>>>> What are the few major items that must get done for Spark 3, in your
>>>>>>> opinion? Below are all of the open JIRAs for 3.0 (which everyone
>>>>>>> should feel free to update with things that aren't really needed for
>>>>>>> Spark 3; I already triaged some).
>>>>>>>
>>>>>>> For me, it's:
>>>>>>> - DSv2?
>>>>>>> - Finishing touches on the Hive, JDK 11 update
>>>>>>>
>>>>>>> What about considering a preview release earlier, as happened for
>>>>>>> Spark 2, to get feedback much earlier than the RC cycle? Could that
>>>>>>> even happen ... about now?
>>>>>>>
>>>>>>> I'm also wondering what a realistic estimate of Spark 3 release is. My
>>>>>>> guess is quite early 2020, from here.
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
>>>>>>> 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-28372 Document Spark WEB UI
>>>>>>> SPARK-28476 Support ALTER DATABASE SET LOCATION
>>>>>>> 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-28024 Incorrect numeric values when out of range
>>>>>>> SPARK-27936 Support local dependency uploading from --py-files
>>>>>>> SPARK-27884 Deprecate Python 2 support in Spark 3.0
>>>>>>> SPARK-27763 Port test cases from PostgreSQL to Spark SQL
>>>>>>> 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-24640 size(null) returns null
>>>>>>> SPARK-24702 Unable to cast to calendar interval in spark sql.
>>>>>>> SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
>>>>>>> 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 User Defined Aggregating Functions (UDAFs) have a major
>>>>>>> efficiency problem
>>>>>>> SPARK-25128 multiple simultaneous job submissions against k8s backend
>>>>>>> cause driver pods to hang
>>>>>>> SPARK-26731 remove EOLed spark jobs from jenkins
>>>>>>> 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-26022 PySpark Comparison with Pandas
>>>>>>> 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-23210 Introduce the concept of default value to schema
>>>>>>> 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-24417 Build and Run Spark on JDK11
>>>>>>> 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-25390 data source V2 API refactoring
>>>>>>> SPARK-7768 Make user-defined type (UDT) API public
>>>>>>> SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
>>>>>>> SPARK-15691 Refactor and improve Hive support
>>>>>>> SPARK-15694 Implement ScriptTransformation in sql/core
>>>>>>> SPARK-16217 Support SELECT INTO statement
>>>>>>> SPARK-16452 basic INFORMATION_SCHEMA support
>>>>>>> SPARK-18134 SQL: MapType in Group BY and Joins not working
>>>>>>> SPARK-18245 Improving support for bucketed table
>>>>>>> SPARK-19842 Informational Referential Integrity Constraints Support in Spark
>>>>>>> SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
>>>>>>> list of structures
>>>>>>> SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
>>>>>>> respect session timezone
>>>>>>> SPARK-22386 Data Source V2 improvements
>>>>>>> SPARK-24723 Discuss necessary info and access in barrier mode + YARN
>>>>>>>
>>>>>>> ---------------------------------------------------------------------
>>>>>>> To unsubscribe e-mail: [hidden email]
>>>>>>>
>>>>>>>
>>>>
>>>>
>>>> --
>>>> Name : Jungtaek Lim
>>>> Blog : http://medium.com/@heartsavior
>>>> Twitter : http://twitter.com/heartsavior
>>>> LinkedIn : http://www.linkedin.com/in/heartsavior
>>>
>>>
>>>
>>> --
>>> John Zhuge
>>
>>
>>
>> --
>> Twitter: https://twitter.com/holdenkarau
>> Books (Learning Spark, High Performance Spark, etc.): https://amzn.to/2MaRAG9
>> YouTube Live Streams: https://www.youtube.com/user/holdenkarau
>
>

---------------------------------------------------------------------
To unsubscribe e-mail: [hidden email]

Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Jiang Xingbo
Hi all,

I would like to volunteer to be the release manager of Spark 3 preview, thanks!

Sean Owen <[hidden email]> 于2019年9月13日周五 上午11:21写道:
Well, great to hear the unanimous support for a Spark 3 preview
release. Now, I don't know how to make releases myself :) I would
first open it up to our revered release managers: would anyone be
interested in trying to make one? sounds like it's not too soon to get
what's in master out for evaluation, as there aren't any major
deficiencies left, although a number of items to consider for the
final release.

I think we just need one release, targeting Hadoop 3.x / Hive 2.x in
order to make it possible to test with JDK 11. (We're only on Scala
2.12 at this point.)

On Thu, Sep 12, 2019 at 7:32 PM Reynold Xin <[hidden email]> wrote:
>
> +1! Long due for a preview release.
>
>
> On Thu, Sep 12, 2019 at 5:26 PM, Holden Karau <[hidden email]> wrote:
>>
>> I like the idea from the PoV of giving folks something to start testing against and exploring so they can raise issues with us earlier in the process and we have more time to make calls around this.
>>
>> On Thu, Sep 12, 2019 at 4:15 PM John Zhuge <[hidden email]> wrote:
>>>
>>> +1  Like the idea as a user and a DSv2 contributor.
>>>
>>> On Thu, Sep 12, 2019 at 4:10 PM Jungtaek Lim <[hidden email]> wrote:
>>>>
>>>> +1 (as a contributor) from me to have preview release on Spark 3 as it would help to test the feature. When to cut preview release is questionable, as major works are ideally to be done before that - if we are intended to introduce new features before official release, that should work regardless of this, but if we are intended to have opportunity to test earlier, ideally it should.
>>>>
>>>> As a one of contributors in structured streaming area, I'd like to add some items for Spark 3.0, both "must be done" and "better to have". For "better to have", I pick some items for new features which committers reviewed couple of rounds and dropped off without soft-reject (No valid reason to stop). For Spark 2.4 users, only added feature for structured streaming is Kafka delegation token. (given we assume revising Kafka consumer pool as improvement) I hope we provide some gifts for structured streaming users in Spark 3.0 envelope.
>>>>
>>>> > must be done
>>>> * SPARK-26154 Stream-stream joins - left outer join gives inconsistent output
>>>> It's a correctness issue with multiple users reported, being reported at Nov. 2018. There's a way to reproduce it consistently, and we have a patch submitted at Jan. 2019 to fix it.
>>>>
>>>> > better to have
>>>> * SPARK-23539 Add support for Kafka headers in Structured Streaming
>>>> * SPARK-26848 Introduce new option to Kafka source - specify timestamp to start and end offset
>>>> * SPARK-20568 Delete files after processing in structured streaming
>>>>
>>>> There're some more new features/improvements items in SS, but given we're talking about ramping-down, above list might be realistic one.
>>>>
>>>>
>>>>
>>>> On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <[hidden email]> wrote:
>>>>>
>>>>> As a user/non committer, +1
>>>>>
>>>>> I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...
>>>>>
>>>>> jg
>>>>>
>>>>>
>>>>> On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:
>>>>>
>>>>> +1 from me too but I would like to know what other people think too.
>>>>>
>>>>> 2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
>>>>>>
>>>>>> Thank you, Sean.
>>>>>>
>>>>>> I'm also +1 for the following three.
>>>>>>
>>>>>> 1. Start to ramp down (by the official branch-3.0 cut)
>>>>>> 2. Apache Spark 3.0.0-preview in 2019
>>>>>> 3. Apache Spark 3.0.0 in early 2020
>>>>>>
>>>>>> For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.
>>>>>>
>>>>>> After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?
>>>>>>
>>>>>> - https://spark.apache.org/versioning-policy.html
>>>>>>
>>>>>> Bests,
>>>>>> Dongjoon.
>>>>>>
>>>>>>
>>>>>> On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
>>>>>>>
>>>>>>> I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.
>>>>>>>
>>>>>>> https://issues.apache.org/jira/browse/SPARK-25588
>>>>>>> https://issues.apache.org/jira/browse/SPARK-27781
>>>>>>>
>>>>>>> Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.
>>>>>>>
>>>>>>> Then out of curiosity, are the new Spark Graph APIs targeting 3.0?
>>>>>>>
>>>>>>> https://github.com/apache/spark/pull/24851
>>>>>>> https://github.com/apache/spark/pull/24297
>>>>>>>
>>>>>>>    michael
>>>>>>>
>>>>>>>
>>>>>>> On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:
>>>>>>>
>>>>>>> I'm curious what current feelings are about ramping down towards a
>>>>>>> Spark 3 release. It feels close to ready. There is no fixed date,
>>>>>>> though in the past we had informally tossed around "back end of 2019".
>>>>>>> For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
>>>>>>> Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
>>>>>>> due.
>>>>>>>
>>>>>>> What are the few major items that must get done for Spark 3, in your
>>>>>>> opinion? Below are all of the open JIRAs for 3.0 (which everyone
>>>>>>> should feel free to update with things that aren't really needed for
>>>>>>> Spark 3; I already triaged some).
>>>>>>>
>>>>>>> For me, it's:
>>>>>>> - DSv2?
>>>>>>> - Finishing touches on the Hive, JDK 11 update
>>>>>>>
>>>>>>> What about considering a preview release earlier, as happened for
>>>>>>> Spark 2, to get feedback much earlier than the RC cycle? Could that
>>>>>>> even happen ... about now?
>>>>>>>
>>>>>>> I'm also wondering what a realistic estimate of Spark 3 release is. My
>>>>>>> guess is quite early 2020, from here.
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
>>>>>>> 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-28372 Document Spark WEB UI
>>>>>>> SPARK-28476 Support ALTER DATABASE SET LOCATION
>>>>>>> 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-28024 Incorrect numeric values when out of range
>>>>>>> SPARK-27936 Support local dependency uploading from --py-files
>>>>>>> SPARK-27884 Deprecate Python 2 support in Spark 3.0
>>>>>>> SPARK-27763 Port test cases from PostgreSQL to Spark SQL
>>>>>>> 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-24640 size(null) returns null
>>>>>>> SPARK-24702 Unable to cast to calendar interval in spark sql.
>>>>>>> SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
>>>>>>> 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 User Defined Aggregating Functions (UDAFs) have a major
>>>>>>> efficiency problem
>>>>>>> SPARK-25128 multiple simultaneous job submissions against k8s backend
>>>>>>> cause driver pods to hang
>>>>>>> SPARK-26731 remove EOLed spark jobs from jenkins
>>>>>>> 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-26022 PySpark Comparison with Pandas
>>>>>>> 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-23210 Introduce the concept of default value to schema
>>>>>>> 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-24417 Build and Run Spark on JDK11
>>>>>>> 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-25390 data source V2 API refactoring
>>>>>>> SPARK-7768 Make user-defined type (UDT) API public
>>>>>>> SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
>>>>>>> SPARK-15691 Refactor and improve Hive support
>>>>>>> SPARK-15694 Implement ScriptTransformation in sql/core
>>>>>>> SPARK-16217 Support SELECT INTO statement
>>>>>>> SPARK-16452 basic INFORMATION_SCHEMA support
>>>>>>> SPARK-18134 SQL: MapType in Group BY and Joins not working
>>>>>>> SPARK-18245 Improving support for bucketed table
>>>>>>> SPARK-19842 Informational Referential Integrity Constraints Support in Spark
>>>>>>> SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
>>>>>>> list of structures
>>>>>>> SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
>>>>>>> respect session timezone
>>>>>>> SPARK-22386 Data Source V2 improvements
>>>>>>> SPARK-24723 Discuss necessary info and access in barrier mode + YARN
>>>>>>>
>>>>>>> ---------------------------------------------------------------------
>>>>>>> To unsubscribe e-mail: [hidden email]
>>>>>>>
>>>>>>>
>>>>
>>>>
>>>> --
>>>> Name : Jungtaek Lim
>>>> Blog : http://medium.com/@heartsavior
>>>> Twitter : http://twitter.com/heartsavior
>>>> LinkedIn : http://www.linkedin.com/in/heartsavior
>>>
>>>
>>>
>>> --
>>> John Zhuge
>>
>>
>>
>> --
>> Twitter: https://twitter.com/holdenkarau
>> Books (Learning Spark, High Performance Spark, etc.): https://amzn.to/2MaRAG9
>> YouTube Live Streams: https://www.youtube.com/user/holdenkarau
>
>

---------------------------------------------------------------------
To unsubscribe e-mail: [hidden email]

Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Dongjoon Hyun-2
Ur, Sean.

I prefer a full release like 2.0.0-preview.

https://archive.apache.org/dist/spark/spark-2.0.0-preview/

And, thank you, Xingbo!
Could you take a look at website generation? It seems to be broken on `master`.

Bests,
Dongjoon.


On Fri, Sep 13, 2019 at 11:30 AM Xingbo Jiang <[hidden email]> wrote:
Hi all,

I would like to volunteer to be the release manager of Spark 3 preview, thanks!

Sean Owen <[hidden email]> 于2019年9月13日周五 上午11:21写道:
Well, great to hear the unanimous support for a Spark 3 preview
release. Now, I don't know how to make releases myself :) I would
first open it up to our revered release managers: would anyone be
interested in trying to make one? sounds like it's not too soon to get
what's in master out for evaluation, as there aren't any major
deficiencies left, although a number of items to consider for the
final release.

I think we just need one release, targeting Hadoop 3.x / Hive 2.x in
order to make it possible to test with JDK 11. (We're only on Scala
2.12 at this point.)

On Thu, Sep 12, 2019 at 7:32 PM Reynold Xin <[hidden email]> wrote:
>
> +1! Long due for a preview release.
>
>
> On Thu, Sep 12, 2019 at 5:26 PM, Holden Karau <[hidden email]> wrote:
>>
>> I like the idea from the PoV of giving folks something to start testing against and exploring so they can raise issues with us earlier in the process and we have more time to make calls around this.
>>
>> On Thu, Sep 12, 2019 at 4:15 PM John Zhuge <[hidden email]> wrote:
>>>
>>> +1  Like the idea as a user and a DSv2 contributor.
>>>
>>> On Thu, Sep 12, 2019 at 4:10 PM Jungtaek Lim <[hidden email]> wrote:
>>>>
>>>> +1 (as a contributor) from me to have preview release on Spark 3 as it would help to test the feature. When to cut preview release is questionable, as major works are ideally to be done before that - if we are intended to introduce new features before official release, that should work regardless of this, but if we are intended to have opportunity to test earlier, ideally it should.
>>>>
>>>> As a one of contributors in structured streaming area, I'd like to add some items for Spark 3.0, both "must be done" and "better to have". For "better to have", I pick some items for new features which committers reviewed couple of rounds and dropped off without soft-reject (No valid reason to stop). For Spark 2.4 users, only added feature for structured streaming is Kafka delegation token. (given we assume revising Kafka consumer pool as improvement) I hope we provide some gifts for structured streaming users in Spark 3.0 envelope.
>>>>
>>>> > must be done
>>>> * SPARK-26154 Stream-stream joins - left outer join gives inconsistent output
>>>> It's a correctness issue with multiple users reported, being reported at Nov. 2018. There's a way to reproduce it consistently, and we have a patch submitted at Jan. 2019 to fix it.
>>>>
>>>> > better to have
>>>> * SPARK-23539 Add support for Kafka headers in Structured Streaming
>>>> * SPARK-26848 Introduce new option to Kafka source - specify timestamp to start and end offset
>>>> * SPARK-20568 Delete files after processing in structured streaming
>>>>
>>>> There're some more new features/improvements items in SS, but given we're talking about ramping-down, above list might be realistic one.
>>>>
>>>>
>>>>
>>>> On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <[hidden email]> wrote:
>>>>>
>>>>> As a user/non committer, +1
>>>>>
>>>>> I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...
>>>>>
>>>>> jg
>>>>>
>>>>>
>>>>> On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:
>>>>>
>>>>> +1 from me too but I would like to know what other people think too.
>>>>>
>>>>> 2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
>>>>>>
>>>>>> Thank you, Sean.
>>>>>>
>>>>>> I'm also +1 for the following three.
>>>>>>
>>>>>> 1. Start to ramp down (by the official branch-3.0 cut)
>>>>>> 2. Apache Spark 3.0.0-preview in 2019
>>>>>> 3. Apache Spark 3.0.0 in early 2020
>>>>>>
>>>>>> For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.
>>>>>>
>>>>>> After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?
>>>>>>
>>>>>> - https://spark.apache.org/versioning-policy.html
>>>>>>
>>>>>> Bests,
>>>>>> Dongjoon.
>>>>>>
>>>>>>
>>>>>> On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
>>>>>>>
>>>>>>> I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.
>>>>>>>
>>>>>>> https://issues.apache.org/jira/browse/SPARK-25588
>>>>>>> https://issues.apache.org/jira/browse/SPARK-27781
>>>>>>>
>>>>>>> Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.
>>>>>>>
>>>>>>> Then out of curiosity, are the new Spark Graph APIs targeting 3.0?
>>>>>>>
>>>>>>> https://github.com/apache/spark/pull/24851
>>>>>>> https://github.com/apache/spark/pull/24297
>>>>>>>
>>>>>>>    michael
>>>>>>>
>>>>>>>
>>>>>>> On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:
>>>>>>>
>>>>>>> I'm curious what current feelings are about ramping down towards a
>>>>>>> Spark 3 release. It feels close to ready. There is no fixed date,
>>>>>>> though in the past we had informally tossed around "back end of 2019".
>>>>>>> For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
>>>>>>> Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
>>>>>>> due.
>>>>>>>
>>>>>>> What are the few major items that must get done for Spark 3, in your
>>>>>>> opinion? Below are all of the open JIRAs for 3.0 (which everyone
>>>>>>> should feel free to update with things that aren't really needed for
>>>>>>> Spark 3; I already triaged some).
>>>>>>>
>>>>>>> For me, it's:
>>>>>>> - DSv2?
>>>>>>> - Finishing touches on the Hive, JDK 11 update
>>>>>>>
>>>>>>> What about considering a preview release earlier, as happened for
>>>>>>> Spark 2, to get feedback much earlier than the RC cycle? Could that
>>>>>>> even happen ... about now?
>>>>>>>
>>>>>>> I'm also wondering what a realistic estimate of Spark 3 release is. My
>>>>>>> guess is quite early 2020, from here.
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
>>>>>>> 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-28372 Document Spark WEB UI
>>>>>>> SPARK-28476 Support ALTER DATABASE SET LOCATION
>>>>>>> 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-28024 Incorrect numeric values when out of range
>>>>>>> SPARK-27936 Support local dependency uploading from --py-files
>>>>>>> SPARK-27884 Deprecate Python 2 support in Spark 3.0
>>>>>>> SPARK-27763 Port test cases from PostgreSQL to Spark SQL
>>>>>>> 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-24640 size(null) returns null
>>>>>>> SPARK-24702 Unable to cast to calendar interval in spark sql.
>>>>>>> SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
>>>>>>> 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 User Defined Aggregating Functions (UDAFs) have a major
>>>>>>> efficiency problem
>>>>>>> SPARK-25128 multiple simultaneous job submissions against k8s backend
>>>>>>> cause driver pods to hang
>>>>>>> SPARK-26731 remove EOLed spark jobs from jenkins
>>>>>>> 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-26022 PySpark Comparison with Pandas
>>>>>>> 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-23210 Introduce the concept of default value to schema
>>>>>>> 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-24417 Build and Run Spark on JDK11
>>>>>>> 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-25390 data source V2 API refactoring
>>>>>>> SPARK-7768 Make user-defined type (UDT) API public
>>>>>>> SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
>>>>>>> SPARK-15691 Refactor and improve Hive support
>>>>>>> SPARK-15694 Implement ScriptTransformation in sql/core
>>>>>>> SPARK-16217 Support SELECT INTO statement
>>>>>>> SPARK-16452 basic INFORMATION_SCHEMA support
>>>>>>> SPARK-18134 SQL: MapType in Group BY and Joins not working
>>>>>>> SPARK-18245 Improving support for bucketed table
>>>>>>> SPARK-19842 Informational Referential Integrity Constraints Support in Spark
>>>>>>> SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
>>>>>>> list of structures
>>>>>>> SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
>>>>>>> respect session timezone
>>>>>>> SPARK-22386 Data Source V2 improvements
>>>>>>> SPARK-24723 Discuss necessary info and access in barrier mode + YARN
>>>>>>>
>>>>>>> ---------------------------------------------------------------------
>>>>>>> To unsubscribe e-mail: [hidden email]
>>>>>>>
>>>>>>>
>>>>
>>>>
>>>> --
>>>> Name : Jungtaek Lim
>>>> Blog : http://medium.com/@heartsavior
>>>> Twitter : http://twitter.com/heartsavior
>>>> LinkedIn : http://www.linkedin.com/in/heartsavior
>>>
>>>
>>>
>>> --
>>> John Zhuge
>>
>>
>>
>> --
>> Twitter: https://twitter.com/holdenkarau
>> Books (Learning Spark, High Performance Spark, etc.): https://amzn.to/2MaRAG9
>> YouTube Live Streams: https://www.youtube.com/user/holdenkarau
>
>

---------------------------------------------------------------------
To unsubscribe e-mail: [hidden email]

Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Ryan Blue
+1 for a preview release.

DSv2 is quite close to being ready. I can only think of a couple issues that we need to merge, like getting a fix for stats estimation done. I'll have a better idea once I've caught up from being away for ApacheCon and I'll add this to the agenda for our next DSv2 sync on Wednesday.

On Fri, Sep 13, 2019 at 12:26 PM Dongjoon Hyun <[hidden email]> wrote:
Ur, Sean.

I prefer a full release like 2.0.0-preview.

https://archive.apache.org/dist/spark/spark-2.0.0-preview/

And, thank you, Xingbo!
Could you take a look at website generation? It seems to be broken on `master`.

Bests,
Dongjoon.


On Fri, Sep 13, 2019 at 11:30 AM Xingbo Jiang <[hidden email]> wrote:
Hi all,

I would like to volunteer to be the release manager of Spark 3 preview, thanks!

Sean Owen <[hidden email]> 于2019年9月13日周五 上午11:21写道:
Well, great to hear the unanimous support for a Spark 3 preview
release. Now, I don't know how to make releases myself :) I would
first open it up to our revered release managers: would anyone be
interested in trying to make one? sounds like it's not too soon to get
what's in master out for evaluation, as there aren't any major
deficiencies left, although a number of items to consider for the
final release.

I think we just need one release, targeting Hadoop 3.x / Hive 2.x in
order to make it possible to test with JDK 11. (We're only on Scala
2.12 at this point.)

On Thu, Sep 12, 2019 at 7:32 PM Reynold Xin <[hidden email]> wrote:
>
> +1! Long due for a preview release.
>
>
> On Thu, Sep 12, 2019 at 5:26 PM, Holden Karau <[hidden email]> wrote:
>>
>> I like the idea from the PoV of giving folks something to start testing against and exploring so they can raise issues with us earlier in the process and we have more time to make calls around this.
>>
>> On Thu, Sep 12, 2019 at 4:15 PM John Zhuge <[hidden email]> wrote:
>>>
>>> +1  Like the idea as a user and a DSv2 contributor.
>>>
>>> On Thu, Sep 12, 2019 at 4:10 PM Jungtaek Lim <[hidden email]> wrote:
>>>>
>>>> +1 (as a contributor) from me to have preview release on Spark 3 as it would help to test the feature. When to cut preview release is questionable, as major works are ideally to be done before that - if we are intended to introduce new features before official release, that should work regardless of this, but if we are intended to have opportunity to test earlier, ideally it should.
>>>>
>>>> As a one of contributors in structured streaming area, I'd like to add some items for Spark 3.0, both "must be done" and "better to have". For "better to have", I pick some items for new features which committers reviewed couple of rounds and dropped off without soft-reject (No valid reason to stop). For Spark 2.4 users, only added feature for structured streaming is Kafka delegation token. (given we assume revising Kafka consumer pool as improvement) I hope we provide some gifts for structured streaming users in Spark 3.0 envelope.
>>>>
>>>> > must be done
>>>> * SPARK-26154 Stream-stream joins - left outer join gives inconsistent output
>>>> It's a correctness issue with multiple users reported, being reported at Nov. 2018. There's a way to reproduce it consistently, and we have a patch submitted at Jan. 2019 to fix it.
>>>>
>>>> > better to have
>>>> * SPARK-23539 Add support for Kafka headers in Structured Streaming
>>>> * SPARK-26848 Introduce new option to Kafka source - specify timestamp to start and end offset
>>>> * SPARK-20568 Delete files after processing in structured streaming
>>>>
>>>> There're some more new features/improvements items in SS, but given we're talking about ramping-down, above list might be realistic one.
>>>>
>>>>
>>>>
>>>> On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <[hidden email]> wrote:
>>>>>
>>>>> As a user/non committer, +1
>>>>>
>>>>> I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...
>>>>>
>>>>> jg
>>>>>
>>>>>
>>>>> On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:
>>>>>
>>>>> +1 from me too but I would like to know what other people think too.
>>>>>
>>>>> 2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
>>>>>>
>>>>>> Thank you, Sean.
>>>>>>
>>>>>> I'm also +1 for the following three.
>>>>>>
>>>>>> 1. Start to ramp down (by the official branch-3.0 cut)
>>>>>> 2. Apache Spark 3.0.0-preview in 2019
>>>>>> 3. Apache Spark 3.0.0 in early 2020
>>>>>>
>>>>>> For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.
>>>>>>
>>>>>> After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?
>>>>>>
>>>>>> - https://spark.apache.org/versioning-policy.html
>>>>>>
>>>>>> Bests,
>>>>>> Dongjoon.
>>>>>>
>>>>>>
>>>>>> On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
>>>>>>>
>>>>>>> I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.
>>>>>>>
>>>>>>> https://issues.apache.org/jira/browse/SPARK-25588
>>>>>>> https://issues.apache.org/jira/browse/SPARK-27781
>>>>>>>
>>>>>>> Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.
>>>>>>>
>>>>>>> Then out of curiosity, are the new Spark Graph APIs targeting 3.0?
>>>>>>>
>>>>>>> https://github.com/apache/spark/pull/24851
>>>>>>> https://github.com/apache/spark/pull/24297
>>>>>>>
>>>>>>>    michael
>>>>>>>
>>>>>>>
>>>>>>> On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:
>>>>>>>
>>>>>>> I'm curious what current feelings are about ramping down towards a
>>>>>>> Spark 3 release. It feels close to ready. There is no fixed date,
>>>>>>> though in the past we had informally tossed around "back end of 2019".
>>>>>>> For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
>>>>>>> Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
>>>>>>> due.
>>>>>>>
>>>>>>> What are the few major items that must get done for Spark 3, in your
>>>>>>> opinion? Below are all of the open JIRAs for 3.0 (which everyone
>>>>>>> should feel free to update with things that aren't really needed for
>>>>>>> Spark 3; I already triaged some).
>>>>>>>
>>>>>>> For me, it's:
>>>>>>> - DSv2?
>>>>>>> - Finishing touches on the Hive, JDK 11 update
>>>>>>>
>>>>>>> What about considering a preview release earlier, as happened for
>>>>>>> Spark 2, to get feedback much earlier than the RC cycle? Could that
>>>>>>> even happen ... about now?
>>>>>>>
>>>>>>> I'm also wondering what a realistic estimate of Spark 3 release is. My
>>>>>>> guess is quite early 2020, from here.
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
>>>>>>> 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-28372 Document Spark WEB UI
>>>>>>> SPARK-28476 Support ALTER DATABASE SET LOCATION
>>>>>>> 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-28024 Incorrect numeric values when out of range
>>>>>>> SPARK-27936 Support local dependency uploading from --py-files
>>>>>>> SPARK-27884 Deprecate Python 2 support in Spark 3.0
>>>>>>> SPARK-27763 Port test cases from PostgreSQL to Spark SQL
>>>>>>> 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-24640 size(null) returns null
>>>>>>> SPARK-24702 Unable to cast to calendar interval in spark sql.
>>>>>>> SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
>>>>>>> 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 User Defined Aggregating Functions (UDAFs) have a major
>>>>>>> efficiency problem
>>>>>>> SPARK-25128 multiple simultaneous job submissions against k8s backend
>>>>>>> cause driver pods to hang
>>>>>>> SPARK-26731 remove EOLed spark jobs from jenkins
>>>>>>> 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-26022 PySpark Comparison with Pandas
>>>>>>> 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-23210 Introduce the concept of default value to schema
>>>>>>> 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-24417 Build and Run Spark on JDK11
>>>>>>> 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-25390 data source V2 API refactoring
>>>>>>> SPARK-7768 Make user-defined type (UDT) API public
>>>>>>> SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
>>>>>>> SPARK-15691 Refactor and improve Hive support
>>>>>>> SPARK-15694 Implement ScriptTransformation in sql/core
>>>>>>> SPARK-16217 Support SELECT INTO statement
>>>>>>> SPARK-16452 basic INFORMATION_SCHEMA support
>>>>>>> SPARK-18134 SQL: MapType in Group BY and Joins not working
>>>>>>> SPARK-18245 Improving support for bucketed table
>>>>>>> SPARK-19842 Informational Referential Integrity Constraints Support in Spark
>>>>>>> SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
>>>>>>> list of structures
>>>>>>> SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
>>>>>>> respect session timezone
>>>>>>> SPARK-22386 Data Source V2 improvements
>>>>>>> SPARK-24723 Discuss necessary info and access in barrier mode + YARN
>>>>>>>
>>>>>>> ---------------------------------------------------------------------
>>>>>>> To unsubscribe e-mail: [hidden email]
>>>>>>>
>>>>>>>
>>>>
>>>>
>>>> --
>>>> Name : Jungtaek Lim
>>>> Blog : http://medium.com/@heartsavior
>>>> Twitter : http://twitter.com/heartsavior
>>>> LinkedIn : http://www.linkedin.com/in/heartsavior
>>>
>>>
>>>
>>> --
>>> John Zhuge
>>
>>
>>
>> --
>> Twitter: https://twitter.com/holdenkarau
>> Books (Learning Spark, High Performance Spark, etc.): https://amzn.to/2MaRAG9
>> YouTube Live Streams: https://www.youtube.com/user/holdenkarau
>
>

---------------------------------------------------------------------
To unsubscribe e-mail: [hidden email]



--
Ryan Blue
Software Engineer
Netflix
Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Andrew Melo
Hi Spark Aficionados-

On Fri, Sep 13, 2019 at 15:08 Ryan Blue <[hidden email]> wrote:
+1 for a preview release.

DSv2 is quite close to being ready. I can only think of a couple issues that we need to merge, like getting a fix for stats estimation done. I'll have a better idea once I've caught up from being away for ApacheCon and I'll add this to the agenda for our next DSv2 sync on Wednesday.

What does 3.0 mean for the DSv2 API? Does the API freeze at that point, or would it still be allowed to change? I'm writing a DSv2 plug-in (GitHub.com/spark-root/laurelin) and there's a couple little API things I think could be useful, I've just not had time to write here/open a JIRA about.

Thanks
Andrew 


On Fri, Sep 13, 2019 at 12:26 PM Dongjoon Hyun <[hidden email]> wrote:
Ur, Sean.

I prefer a full release like 2.0.0-preview.

https://archive.apache.org/dist/spark/spark-2.0.0-preview/

And, thank you, Xingbo!
Could you take a look at website generation? It seems to be broken on `master`.

Bests,
Dongjoon.


On Fri, Sep 13, 2019 at 11:30 AM Xingbo Jiang <[hidden email]> wrote:
Hi all,

I would like to volunteer to be the release manager of Spark 3 preview, thanks!

Sean Owen <[hidden email]> 于2019年9月13日周五 上午11:21写道:
Well, great to hear the unanimous support for a Spark 3 preview
release. Now, I don't know how to make releases myself :) I would
first open it up to our revered release managers: would anyone be
interested in trying to make one? sounds like it's not too soon to get
what's in master out for evaluation, as there aren't any major
deficiencies left, although a number of items to consider for the
final release.

I think we just need one release, targeting Hadoop 3.x / Hive 2.x in
order to make it possible to test with JDK 11. (We're only on Scala
2.12 at this point.)

On Thu, Sep 12, 2019 at 7:32 PM Reynold Xin <[hidden email]> wrote:
>
> +1! Long due for a preview release.
>
>
> On Thu, Sep 12, 2019 at 5:26 PM, Holden Karau <[hidden email]> wrote:
>>
>> I like the idea from the PoV of giving folks something to start testing against and exploring so they can raise issues with us earlier in the process and we have more time to make calls around this.
>>
>> On Thu, Sep 12, 2019 at 4:15 PM John Zhuge <[hidden email]> wrote:
>>>
>>> +1  Like the idea as a user and a DSv2 contributor.
>>>
>>> On Thu, Sep 12, 2019 at 4:10 PM Jungtaek Lim <[hidden email]> wrote:
>>>>
>>>> +1 (as a contributor) from me to have preview release on Spark 3 as it would help to test the feature. When to cut preview release is questionable, as major works are ideally to be done before that - if we are intended to introduce new features before official release, that should work regardless of this, but if we are intended to have opportunity to test earlier, ideally it should.
>>>>
>>>> As a one of contributors in structured streaming area, I'd like to add some items for Spark 3.0, both "must be done" and "better to have". For "better to have", I pick some items for new features which committers reviewed couple of rounds and dropped off without soft-reject (No valid reason to stop). For Spark 2.4 users, only added feature for structured streaming is Kafka delegation token. (given we assume revising Kafka consumer pool as improvement) I hope we provide some gifts for structured streaming users in Spark 3.0 envelope.
>>>>
>>>> > must be done
>>>> * SPARK-26154 Stream-stream joins - left outer join gives inconsistent output
>>>> It's a correctness issue with multiple users reported, being reported at Nov. 2018. There's a way to reproduce it consistently, and we have a patch submitted at Jan. 2019 to fix it.
>>>>
>>>> > better to have
>>>> * SPARK-23539 Add support for Kafka headers in Structured Streaming
>>>> * SPARK-26848 Introduce new option to Kafka source - specify timestamp to start and end offset
>>>> * SPARK-20568 Delete files after processing in structured streaming
>>>>
>>>> There're some more new features/improvements items in SS, but given we're talking about ramping-down, above list might be realistic one.
>>>>
>>>>
>>>>
>>>> On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <[hidden email]> wrote:
>>>>>
>>>>> As a user/non committer, +1
>>>>>
>>>>> I love the idea of an early 3.0.0 so we can test current dev against it, I know the final 3.x will probably need another round of testing when it gets out, but less for sure... I know I could checkout and compile, but having a “packaged” preversion is great if it does not take too much time to the team...
>>>>>
>>>>> jg
>>>>>
>>>>>
>>>>> On Sep 11, 2019, at 20:40, Hyukjin Kwon <[hidden email]> wrote:
>>>>>
>>>>> +1 from me too but I would like to know what other people think too.
>>>>>
>>>>> 2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <[hidden email]>님이 작성:
>>>>>>
>>>>>> Thank you, Sean.
>>>>>>
>>>>>> I'm also +1 for the following three.
>>>>>>
>>>>>> 1. Start to ramp down (by the official branch-3.0 cut)
>>>>>> 2. Apache Spark 3.0.0-preview in 2019
>>>>>> 3. Apache Spark 3.0.0 in early 2020
>>>>>>
>>>>>> For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` helps it a lot.
>>>>>>
>>>>>> After this discussion, can we have some timeline for `Spark 3.0 Release Window` in our versioning-policy page?
>>>>>>
>>>>>> - https://spark.apache.org/versioning-policy.html
>>>>>>
>>>>>> Bests,
>>>>>> Dongjoon.
>>>>>>
>>>>>>
>>>>>> On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <[hidden email]> wrote:
>>>>>>>
>>>>>>> I would love to see Spark + Hadoop + Parquet + Avro compatibility problems resolved, e.g.
>>>>>>>
>>>>>>> https://issues.apache.org/jira/browse/SPARK-25588
>>>>>>> https://issues.apache.org/jira/browse/SPARK-27781
>>>>>>>
>>>>>>> Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x.  As far as I know, Parquet has not cut a release based on this new version.
>>>>>>>
>>>>>>> Then out of curiosity, are the new Spark Graph APIs targeting 3.0?
>>>>>>>
>>>>>>> https://github.com/apache/spark/pull/24851
>>>>>>> https://github.com/apache/spark/pull/24297
>>>>>>>
>>>>>>>    michael
>>>>>>>
>>>>>>>
>>>>>>> On Sep 11, 2019, at 1:37 PM, Sean Owen <[hidden email]> wrote:
>>>>>>>
>>>>>>> I'm curious what current feelings are about ramping down towards a
>>>>>>> Spark 3 release. It feels close to ready. There is no fixed date,
>>>>>>> though in the past we had informally tossed around "back end of 2019".
>>>>>>> For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd expect
>>>>>>> Spark 2 to last longer, so to speak, but feels like Spark 3 is coming
>>>>>>> due.
>>>>>>>
>>>>>>> What are the few major items that must get done for Spark 3, in your
>>>>>>> opinion? Below are all of the open JIRAs for 3.0 (which everyone
>>>>>>> should feel free to update with things that aren't really needed for
>>>>>>> Spark 3; I already triaged some).
>>>>>>>
>>>>>>> For me, it's:
>>>>>>> - DSv2?
>>>>>>> - Finishing touches on the Hive, JDK 11 update
>>>>>>>
>>>>>>> What about considering a preview release earlier, as happened for
>>>>>>> Spark 2, to get feedback much earlier than the RC cycle? Could that
>>>>>>> even happen ... about now?
>>>>>>>
>>>>>>> I'm also wondering what a realistic estimate of Spark 3 release is. My
>>>>>>> guess is quite early 2020, from here.
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> SPARK-29014 DataSourceV2: Clean up current, default, and session catalog uses
>>>>>>> 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-28372 Document Spark WEB UI
>>>>>>> SPARK-28476 Support ALTER DATABASE SET LOCATION
>>>>>>> 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-28024 Incorrect numeric values when out of range
>>>>>>> SPARK-27936 Support local dependency uploading from --py-files
>>>>>>> SPARK-27884 Deprecate Python 2 support in Spark 3.0
>>>>>>> SPARK-27763 Port test cases from PostgreSQL to Spark SQL
>>>>>>> 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-24640 size(null) returns null
>>>>>>> SPARK-24702 Unable to cast to calendar interval in spark sql.
>>>>>>> SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more operators
>>>>>>> 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 User Defined Aggregating Functions (UDAFs) have a major
>>>>>>> efficiency problem
>>>>>>> SPARK-25128 multiple simultaneous job submissions against k8s backend
>>>>>>> cause driver pods to hang
>>>>>>> SPARK-26731 remove EOLed spark jobs from jenkins
>>>>>>> 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-26022 PySpark Comparison with Pandas
>>>>>>> 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-23210 Introduce the concept of default value to schema
>>>>>>> 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-24417 Build and Run Spark on JDK11
>>>>>>> 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-25390 data source V2 API refactoring
>>>>>>> SPARK-7768 Make user-defined type (UDT) API public
>>>>>>> SPARK-14922 Alter Table Drop Partition Using Predicate-based Partition Spec
>>>>>>> SPARK-15691 Refactor and improve Hive support
>>>>>>> SPARK-15694 Implement ScriptTransformation in sql/core
>>>>>>> SPARK-16217 Support SELECT INTO statement
>>>>>>> SPARK-16452 basic INFORMATION_SCHEMA support
>>>>>>> SPARK-18134 SQL: MapType in Group BY and Joins not working
>>>>>>> SPARK-18245 Improving support for bucketed table
>>>>>>> SPARK-19842 Informational Referential Integrity Constraints Support in Spark
>>>>>>> SPARK-22231 Support of map, filter, withColumn, dropColumn in nested
>>>>>>> list of structures
>>>>>>> SPARK-22632 Fix the behavior of timestamp values for R's DataFrame to
>>>>>>> respect session timezone
>>>>>>> SPARK-22386 Data Source V2 improvements
>>>>>>> SPARK-24723 Discuss necessary info and access in barrier mode + YARN
>>>>>>>
>>>>>>> ---------------------------------------------------------------------
>>>>>>> To unsubscribe e-mail: [hidden email]
>>>>>>>
>>>>>>>
>>>>
>>>>
>>>> --
>>>> Name : Jungtaek Lim
>>>> Blog : http://medium.com/@heartsavior
>>>> Twitter : http://twitter.com/heartsavior
>>>> LinkedIn : http://www.linkedin.com/in/heartsavior
>>>
>>>
>>>
>>> --
>>> John Zhuge
>>
>>
>>
>> --
>> Twitter: https://twitter.com/holdenkarau
>> Books (Learning Spark, High Performance Spark, etc.): https://amzn.to/2MaRAG9
>> YouTube Live Streams: https://www.youtube.com/user/holdenkarau
>
>

---------------------------------------------------------------------
To unsubscribe e-mail: [hidden email]



--
Ryan Blue
Software Engineer
Netflix
--
It's dark in this basement.
Reply | Threaded
Open this post in threaded view
|

Re: Thoughts on Spark 3 release, or a preview release

Sean Owen-3
I don't think this suggests anything is finalized, including APIs. I
would not guess there will be major changes from here though.

On Fri, Sep 13, 2019 at 4:27 PM Andrew Melo <[hidden email]> wrote:

>
> Hi Spark Aficionados-
>
> On Fri, Sep 13, 2019 at 15:08 Ryan Blue <[hidden email]> wrote:
>>
>> +1 for a preview release.
>>
>> DSv2 is quite close to being ready. I can only think of a couple issues that we need to merge, like getting a fix for stats estimation done. I'll have a better idea once I've caught up from being away for ApacheCon and I'll add this to the agenda for our next DSv2 sync on Wednesday.
>
>
> What does 3.0 mean for the DSv2 API? Does the API freeze at that point, or would it still be allowed to change? I'm writing a DSv2 plug-in (GitHub.com/spark-root/laurelin) and there's a couple little API things I think could be useful, I've just not had time to write here/open a JIRA about.
>
> Thanks
> Andrew
>

---------------------------------------------------------------------
To unsubscribe e-mail: [hidden email]

12