## What changes were proposed in this pull request?
Update Jetty to 9.3.24.v20180605 to pick up security fix
## How was this patch tested?
Existing tests.
Closes#22055 from srowen/Jetty9324.
Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
During upgrading Apache ORC to 1.5.2 ([SPARK-24576](https://issues.apache.org/jira/browse/SPARK-24576)), `sql/core` module overrides the exclusion rules of parent pom file and it causes published `spark-sql_2.1X` artifacts have incomplete exclusion rules ([SPARK-25019](https://issues.apache.org/jira/browse/SPARK-25019)). This PR fixes it by moving the newly added exclusion rule to the parent pom. This also fixes the sbt build hack introduced at that time.
## How was this patch tested?
Pass the existing dependency check and the tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#22003 from dongjoon-hyun/SPARK-25019.
## What changes were proposed in this pull request?
Update Hadoop 2.7 to 2.7.7 to pull in bug and security fixes.
## How was this patch tested?
Existing tests.
Author: Sean Owen <srowen@gmail.com>
Closes#21987 from srowen/SPARK-25015.
## What changes were proposed in this pull request?
Upgrade Apache Avro from 1.7.7 to 1.8.2. The major new features:
1. More logical types. From the spec of 1.8.2 https://avro.apache.org/docs/1.8.2/spec.html#Logical+Types we can see comparing to [1.7.7](https://avro.apache.org/docs/1.7.7/spec.html#Logical+Types), the new version support:
- Date
- Time (millisecond precision)
- Time (microsecond precision)
- Timestamp (millisecond precision)
- Timestamp (microsecond precision)
- Duration
2. Single-object encoding: https://avro.apache.org/docs/1.8.2/spec.html#single_object_encoding
This PR aims to update Apache Spark to support these new features.
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21761 from gengliangwang/upgrade_avro_1.8.
## What changes were proposed in this pull request?
This PR updates maven version from 3.3.9 to 3.5.4. The current build process uses mvn 3.3.9 that was release on 2015, which looks pretty old.
We met [an issue](https://issues.apache.org/jira/browse/SPARK-24895) to need the maven 3.5.2 or later.
The release note of the 3.5.4 is [here](https://maven.apache.org/docs/3.5.4/release-notes.html). Note version 3.4 was skipped.
From [the release note of the 3.5.0](https://maven.apache.org/docs/3.5.0/release-notes.html), the followings are new features:
1. ANSI color logging for improved output visibility
1. add support for module name != artifactId in every calculated URLs (project, SCM, site): special project.directory property
1. create a slf4j-simple provider extension that supports level color rendering
1. ModelResolver interface enhancement: addition of resolveModel(Dependency) supporting version ranges
## How was this patch tested?
Existing tests
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21905 from kiszk/SPARK-24956.
## What changes were proposed in this pull request?
Please see [SPARK-24927][1] for more details.
[1]: https://issues.apache.org/jira/browse/SPARK-24927
## How was this patch tested?
Manually tested.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#21879 from liancheng/spark-24927.
(cherry picked from commit d5f340f277)
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Spotbugs maven plugin was a recently added plugin before 2.4.0 snapshot artifacts were broken. To ensure it does not affect the maven deploy plugin, this change removes it.
## How was this patch tested?
Local build was ran, but this patch will be actually tested by monitoring the apache repo artifacts and making sure metadata is correctly uploaded after this job is ran: https://amplab.cs.berkeley.edu/jenkins/view/Spark%20Packaging/job/spark-master-maven-snapshots/
Author: Eric Chang <eric.chang@databricks.com>
Closes#21865 from ericfchang/SPARK-24895.
## What changes were proposed in this pull request?
Make the integration test script build all modules.
In order to not run all the non-Kubernetes integration tests in the build, support specifying tags and tag all integration tests specifically with "k8s". Supply the k8s tag in the dev/dev-run-integration-tests.sh script.
## How was this patch tested?
The build system will test this.
Author: mcheah <mcheah@palantir.com>
Closes#21800 from mccheah/k8s-integration-tests-maven-fix.
## What changes were proposed in this pull request?
This issue aims to upgrade Apache ORC library from 1.4.4 to 1.5.2 in order to bring the following benefits into Apache Spark.
- [ORC-91](https://issues.apache.org/jira/browse/ORC-91) Support for variable length blocks in HDFS (The current space wasted in ORC to padding is known to be 5%.)
- [ORC-344](https://issues.apache.org/jira/browse/ORC-344) Support for using Decimal64ColumnVector
In addition to that, Apache Hive 3.1 and 3.2 will use ORC 1.5.1 ([HIVE-19669](https://issues.apache.org/jira/browse/HIVE-19465)) and 1.5.2 ([HIVE-19792](https://issues.apache.org/jira/browse/HIVE-19792)) respectively. This will improve the compatibility between Apache Spark and Apache Hive by sharing the common library.
## How was this patch tested?
Pass the Jenkins with all existing tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#21582 from dongjoon-hyun/SPARK-24576.
## What changes were proposed in this pull request?
Apache Avro (https://avro.apache.org) is a popular data serialization format. It is widely used in the Spark and Hadoop ecosystem, especially for Kafka-based data pipelines. Using the external package https://github.com/databricks/spark-avro, Spark SQL can read and write the avro data. Making spark-Avro built-in can provide a better experience for first-time users of Spark SQL and structured streaming. We expect the built-in Avro data source can further improve the adoption of structured streaming.
The proposal is to inline code from spark-avro package (https://github.com/databricks/spark-avro). The target release is Spark 2.4.
[Built-in AVRO Data Source In Spark 2.4.pdf](https://github.com/apache/spark/files/2181511/Built-in.AVRO.Data.Source.In.Spark.2.4.pdf)
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21742 from gengliangwang/export_avro.
## What changes were proposed in this pull request?
This PR enables a Java bytecode check tool [spotbugs](https://spotbugs.github.io/) to avoid possible integer overflow at multiplication. When an violation is detected, the build process is stopped.
Due to the tool limitation, some other checks will be enabled. In this PR, [these patterns](http://spotbugs-in-kengo-toda.readthedocs.io/en/lqc-list-detectors/detectors.html#findpuzzlers) in `FindPuzzlers` can be detected.
This check is enabled at `compile` phase. Thus, `mvn compile` or `mvn package` launches this check.
## How was this patch tested?
Existing UTs
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21542 from kiszk/SPARK-24529.
## What changes were proposed in this pull request?
- disables maven surfire plugin to allow tags function properly, doc here: http://www.scalatest.org/user_guide/using_the_scalatest_maven_plugin
## How was this patch tested?
Manually by adding tags.
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#21697 from skonto/fix-tags.
## What changes were proposed in this pull request?
Upgrade ASM to 6.1 to support JDK9+
## How was this patch tested?
Existing tests.
Author: DB Tsai <d_tsai@apple.com>
Closes#21459 from dbtsai/asm.
## What changes were proposed in this pull request?
Scala is upgraded to `2.11.12` and `2.12.6`.
We used `loadFIles()` in `ILoop` as a hook to initialize the Spark before REPL sees any files in Scala `2.11.8`. However, it was a hack, and it was not intended to be a public API, so it was removed in Scala `2.11.12`.
From the discussion in Scala community, https://github.com/scala/bug/issues/10913 , we can use `initializeSynchronous` to initialize Spark instead. This PR implements the Spark initialization there.
However, in Scala `2.11.12`'s `ILoop.scala`, in function `def startup()`, the first thing it calls is `printWelcome()`. As a result, Scala will call `printWelcome()` and `splash` before calling `initializeSynchronous`.
Thus, the Spark shell will allow users to type commends first, and then show the Spark UI URL. It's working, but it will change the Spark Shell interface as the following.
```scala
➜ apache-spark git:(scala-2.11.12) ✗ ./bin/spark-shell
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.4.0-SNAPSHOT
/_/
Using Scala version 2.11.12 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_161)
Type in expressions to have them evaluated.
Type :help for more information.
scala> Spark context Web UI available at http://192.168.1.169:4040
Spark context available as 'sc' (master = local[*], app id = local-1528180279528).
Spark session available as 'spark'.
scala>
```
It seems there is no easy way to inject the Spark initialization code in the proper place as Scala doesn't provide a hook. Maybe som-snytt can comment on this.
The following command is used to update the dep files.
```scala
./dev/test-dependencies.sh --replace-manifest
```
## How was this patch tested?
Existing tests
Author: DB Tsai <d_tsai@apple.com>
Closes#21495 from dbtsai/scala-2.11.12.
## What changes were proposed in this pull request?
Previously, the scheduler backend was maintaining state in many places, not only for reading state but also writing to it. For example, state had to be managed in both the watch and in the executor allocator runnable. Furthermore, one had to keep track of multiple hash tables.
We can do better here by:
1. Consolidating the places where we manage state. Here, we take inspiration from traditional Kubernetes controllers. These controllers tend to follow a level-triggered mechanism. This means that the controller will continuously monitor the API server via watches and polling, and on periodic passes, the controller will reconcile the current state of the cluster with the desired state. We implement this by introducing the concept of a pod snapshot, which is a given state of the executors in the Kubernetes cluster. We operate periodically on snapshots. To prevent overloading the API server with polling requests to get the state of the cluster (particularly for executor allocation where we want to be checking frequently to get executors to launch without unbearably bad latency), we use watches to populate snapshots by applying observed events to a previous snapshot to get a new snapshot. Whenever we do poll the cluster, the polled state replaces any existing snapshot - this ensures eventual consistency and mirroring of the cluster, as is desired in a level triggered architecture.
2. Storing less specialized in-memory state in general. Previously we were creating hash tables to represent the state of executors. Instead, it's easier to represent state solely by the snapshots.
## How was this patch tested?
Integration tests should test there's no regressions end to end. Unit tests to be updated, in particular focusing on different orderings of events, particularly accounting for when events come in unexpected ordering.
Author: mcheah <mcheah@palantir.com>
Closes#21366 from mccheah/event-queue-driven-scheduling.
These tests were developed in the https://github.com/apache-spark-on-k8s/spark-integration repo
by several contributors. This is a copy of the current state into the main apache spark repo.
The only changes from the current spark-integration repo state are:
* Move the files from the repo root into resource-managers/kubernetes/integration-tests
* Add a reference to these tests in the root README.md
* Fix a path reference in dev/dev-run-integration-tests.sh
* Add a TODO in include/util.sh
## What changes were proposed in this pull request?
Incorporation of Kubernetes integration tests.
## How was this patch tested?
This code has its own unit tests, but the main purpose is to provide the integration tests.
I tested this on my laptop by running dev/dev-run-integration-tests.sh --spark-tgz ~/spark-2.4.0-SNAPSHOT-bin--.tgz
The spark-integration tests have already been running for months in AMPLab, here is an example:
https://amplab.cs.berkeley.edu/jenkins/job/testing-k8s-scheduled-spark-integration-master/
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Sean Suchter <sean-github@suchter.com>
Author: Sean Suchter <ssuchter@pepperdata.com>
Closes#20697 from ssuchter/ssuchter-k8s-integration-tests.
## What changes were proposed in this pull request?
ORC 1.4.4 includes [nine fixes](https://issues.apache.org/jira/issues/?filter=12342568&jql=project%20%3D%20ORC%20AND%20resolution%20%3D%20Fixed%20AND%20fixVersion%20%3D%201.4.4). One of the issues is about `Timestamp` bug (ORC-306) which occurs when `native` ORC vectorized reader reads ORC column vector's sub-vector `times` and `nanos`. ORC-306 fixes this according to the [original definition](https://github.com/apache/hive/blob/master/storage-api/src/java/org/apache/hadoop/hive/ql/exec/vector/TimestampColumnVector.java#L45-L46) and this PR includes the updated interpretation on ORC column vectors. Note that `hive` ORC reader and ORC MR reader is not affected.
```scala
scala> spark.version
res0: String = 2.3.0
scala> spark.sql("set spark.sql.orc.impl=native")
scala> Seq(java.sql.Timestamp.valueOf("1900-05-05 12:34:56.000789")).toDF().write.orc("/tmp/orc")
scala> spark.read.orc("/tmp/orc").show(false)
+--------------------------+
|value |
+--------------------------+
|1900-05-05 12:34:55.000789|
+--------------------------+
```
This PR aims to update Apache Spark to use it.
**FULL LIST**
ID | TITLE
-- | --
ORC-281 | Fix compiler warnings from clang 5.0
ORC-301 | `extractFileTail` should open a file in `try` statement
ORC-304 | Fix TestRecordReaderImpl to not fail with new storage-api
ORC-306 | Fix incorrect workaround for bug in java.sql.Timestamp
ORC-324 | Add support for ARM and PPC arch
ORC-330 | Remove unnecessary Hive artifacts from root pom
ORC-332 | Add syntax version to orc_proto.proto
ORC-336 | Remove avro and parquet dependency management entries
ORC-360 | Implement error checking on subtype fields in Java
## How was this patch tested?
Pass the Jenkins.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#21372 from dongjoon-hyun/SPARK_ORC144.
## What changes were proposed in this pull request?
This updates Parquet to 1.10.0 and updates the vectorized path for buffer management changes. Parquet 1.10.0 uses ByteBufferInputStream instead of byte arrays in encoders. This allows Parquet to break allocations into smaller chunks that are better for garbage collection.
## How was this patch tested?
Existing Parquet tests. Running in production at Netflix for about 3 months.
Author: Ryan Blue <blue@apache.org>
Closes#21070 from rdblue/SPARK-23972-update-parquet-to-1.10.0.
## What changes were proposed in this pull request?
1. Adds a `hadoop-3.1` profile build depending on the hadoop-3.1 artifacts.
1. In the hadoop-cloud module, adds an explicit hadoop-3.1 profile which switches from explicitly pulling in cloud connectors (hadoop-openstack, hadoop-aws, hadoop-azure) to depending on the hadoop-cloudstorage POM artifact, which pulls these in, has pre-excluded things like hadoop-common, and stays up to date with new connectors (hadoop-azuredatalake, hadoop-allyun). Goal: it becomes the Hadoop projects homework of keeping this clean, and the spark project doesn't need to handle new hadoop releases adding more dependencies.
1. the hadoop-cloud/hadoop-3.1 profile also declares support for jetty-ajax and jetty-util to ensure that these jars get into the distribution jar directory when needed by unshaded libraries.
1. Increases the curator and zookeeper versions to match those in hadoop-3, fixing spark core to build in sbt with the hadoop-3 dependencies.
## How was this patch tested?
* Everything this has been built and tested against both ASF Hadoop branch-3.1 and hadoop trunk.
* spark-shell was used to create connectors to all the stores and verify that file IO could take place.
The spark hive-1.2.1 JAR has problems here, as it's version check logic fails for Hadoop versions > 2.
This can be avoided with either of
* The hadoop JARs built to declare their version as Hadoop 2.11 `mvn install -DskipTests -DskipShade -Ddeclared.hadoop.version=2.11` . This is safe for local test runs, not for deployment (HDFS is very strict about cross-version deployment).
* A modified version of spark hive whose version check switch statement is happy with hadoop 3.
I've done both, with maven and SBT.
Three issues surfaced
1. A spark-core test failure —fixed in SPARK-23787.
1. SBT only: Zookeeper not being found in spark-core. Somehow curator 2.12.0 triggers some slightly different dependency resolution logic from previous versions, and Ivy was missing zookeeper.jar entirely. This patch adds the explicit declaration for all spark profiles, setting the ZK version = 3.4.9 for hadoop-3.1
1. Marking jetty-utils as provided in spark was stopping hadoop-azure from being able to instantiate the azure wasb:// client; it was using jetty-util-ajax, which could then not find a class in jetty-util.
Author: Steve Loughran <stevel@hortonworks.com>
Closes#20923 from steveloughran/cloud/SPARK-23807-hadoop-31.
## What changes were proposed in this pull request?
This PR avoids version conflicts of `commons-net` by upgrading commons-net from 2.2 to 3.1. We are seeing the following message during the build using sbt.
```
[warn] Found version conflict(s) in library dependencies; some are suspected to be binary incompatible:
...
[warn] * commons-net:commons-net:3.1 is selected over 2.2
[warn] +- org.apache.hadoop:hadoop-common:2.6.5 (depends on 3.1)
[warn] +- org.apache.spark:spark-core_2.11:2.4.0-SNAPSHOT (depends on 2.2)
[warn]
```
[Here](https://commons.apache.org/proper/commons-net/changes-report.html) is a release history.
[Here](https://commons.apache.org/proper/commons-net/migration.html) is a migration guide from 2.x to 3.0.
## How was this patch tested?
Existing tests
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#20672 from kiszk/SPARK-23509.
## What changes were proposed in this pull request?
This PR updates Apache ORC dependencies to 1.4.3 released on February 9th. Apache ORC 1.4.2 release removes unnecessary dependencies and 1.4.3 has 5 more patches (https://s.apache.org/Fll8).
Especially, the following ORC-285 is fixed at 1.4.3.
```scala
scala> val df = Seq(Array.empty[Float]).toDF()
scala> df.write.format("orc").save("/tmp/floatarray")
scala> spark.read.orc("/tmp/floatarray")
res1: org.apache.spark.sql.DataFrame = [value: array<float>]
scala> spark.read.orc("/tmp/floatarray").show()
18/02/12 22:09:10 ERROR Executor: Exception in task 0.0 in stage 1.0 (TID 1)
java.io.IOException: Error reading file: file:/tmp/floatarray/part-00000-9c0b461b-4df1-4c23-aac1-3e4f349ac7d6-c000.snappy.orc
at org.apache.orc.impl.RecordReaderImpl.nextBatch(RecordReaderImpl.java:1191)
at org.apache.orc.mapreduce.OrcMapreduceRecordReader.ensureBatch(OrcMapreduceRecordReader.java:78)
...
Caused by: java.io.EOFException: Read past EOF for compressed stream Stream for column 2 kind DATA position: 0 length: 0 range: 0 offset: 0 limit: 0
```
## How was this patch tested?
Pass the Jenkins test.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20511 from dongjoon-hyun/SPARK-23340.
## What changes were proposed in this pull request?
This PR upgrade snappy-java from 1.1.2.6 to 1.1.7.1.
1.1.7.1 release notes:
- Improved performance for big-endian architecture
- The other performance improvement in [snappy-1.1.5](https://github.com/google/snappy/releases/tag/1.1.5)
1.1.4 release notes:
- Fix a 1% performance regression when snappy is used in PIE executables.
- Improve compression performance by 5%.
- Improve decompression performance by 20%.
More details:
https://github.com/xerial/snappy-java/blob/master/Milestone.md
## How was this patch tested?
manual tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#20510 from wangyum/SPARK-23336.
## What changes were proposed in this pull request?
This PR proposes to explicitly specify Pandas and PyArrow versions in PySpark tests to skip or test.
We declared the extra dependencies:
b8bfce51ab/python/setup.py (L204)
In case of PyArrow:
Currently we only check if pyarrow is installed or not without checking the version. It already fails to run tests. For example, if PyArrow 0.7.0 is installed:
```
======================================================================
ERROR: test_vectorized_udf_wrong_return_type (pyspark.sql.tests.ScalarPandasUDF)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/.../spark/python/pyspark/sql/tests.py", line 4019, in test_vectorized_udf_wrong_return_type
f = pandas_udf(lambda x: x * 1.0, MapType(LongType(), LongType()))
File "/.../spark/python/pyspark/sql/functions.py", line 2309, in pandas_udf
return _create_udf(f=f, returnType=return_type, evalType=eval_type)
File "/.../spark/python/pyspark/sql/udf.py", line 47, in _create_udf
require_minimum_pyarrow_version()
File "/.../spark/python/pyspark/sql/utils.py", line 132, in require_minimum_pyarrow_version
"however, your version was %s." % pyarrow.__version__)
ImportError: pyarrow >= 0.8.0 must be installed on calling Python process; however, your version was 0.7.0.
----------------------------------------------------------------------
Ran 33 tests in 8.098s
FAILED (errors=33)
```
In case of Pandas:
There are few tests for old Pandas which were tested only when Pandas version was lower, and I rewrote them to be tested when both Pandas version is lower and missing.
## How was this patch tested?
Manually tested by modifying the condition:
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.'
```
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
```
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.'
```
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20487 from HyukjinKwon/pyarrow-pandas-skip.
## What changes were proposed in this pull request?
Spark still use a few years old version 3.2.11. This change is to upgrade json4s to 3.5.3.
Note that this change does not include the Jackson update because the Jackson version referenced in json4s 3.5.3 is 2.8.4, which has a security vulnerability ([see](https://issues.apache.org/jira/browse/SPARK-20433)).
## How was this patch tested?
Existing unit tests and build.
Author: shimamoto <chibochibo@gmail.com>
Closes#20233 from shimamoto/upgrade-json4s.
## What changes were proposed in this pull request?
This patch bumps the master branch version to `2.4.0-SNAPSHOT`.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20222 from gatorsmile/bump24.
Hi all,
I would like to bump the PATCH versions of both the Apache httpclient Apache httpcore. I use the SparkTC Stocator library for connecting to an object store, and I would align the versions to reduce java version mismatches. Furthermore it is good to bump these versions since they fix stability and performance issues:
https://archive.apache.org/dist/httpcomponents/httpclient/RELEASE_NOTES-4.5.x.txthttps://www.apache.org/dist/httpcomponents/httpcore/RELEASE_NOTES-4.4.x.txt
Cheers, Fokko
## What changes were proposed in this pull request?
Update the versions of the httpclient and httpcore. Only update the PATCH versions, so no breaking changes.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Fokko Driesprong <fokkodriesprong@godatadriven.com>
Closes#20103 from Fokko/SPARK-22919-bump-httpclient-versions.
## What changes were proposed in this pull request?
Add tarLongFileMode=posix configuration for the assembly plugin
## How was this patch tested?
Reran build successfully
```
./build/mvn package -Pbigtop-dist -DskipTests -rf :spark-assembly_2.11
[INFO] Spark Project Assembly ............................. SUCCESS [ 23.082 s]
```
Author: Gera Shegalov <gshegalov@salesforce.com>
Closes#20055 from gerashegalov/gera/tarLongFileMode.
## What changes were proposed in this pull request?
Upgrade Spark to Arrow 0.8.0 for Java and Python. Also includes an upgrade of Netty to 4.1.17 to resolve dependency requirements.
The highlights that pertain to Spark for the update from Arrow versoin 0.4.1 to 0.8.0 include:
* Java refactoring for more simple API
* Java reduced heap usage and streamlined hot code paths
* Type support for DecimalType, ArrayType
* Improved type casting support in Python
* Simplified type checking in Python
## How was this patch tested?
Existing tests
Author: Bryan Cutler <cutlerb@gmail.com>
Author: Shixiong Zhu <zsxwing@gmail.com>
Closes#19884 from BryanCutler/arrow-upgrade-080-SPARK-22324.
## What changes were proposed in this pull request?
Update Bouncy Castle to 1.58, and jets3t to 0.9.4 to (sort of) match.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#19859 from srowen/SPARK-22634.
… with Janino when compiling generated code.
## What changes were proposed in this pull request?
Bump up Janino dependency version to fix thread safety issue during compiling generated code
## How was this patch tested?
Check https://issues.apache.org/jira/browse/SPARK-22373 for details.
Converted part of the code in CodeGenerator into a standalone application, so the issue can be consistently reproduced locally.
Verified that changing Janino dependency version resolved this issue.
Author: Min Shen <mshen@linkedin.com>
Closes#19839 from Victsm/SPARK-22373.
## What changes were proposed in this pull request?
This is a stripped down version of the `KubernetesClusterSchedulerBackend` for Spark with the following components:
- Static Allocation of Executors
- Executor Pod Factory
- Executor Recovery Semantics
It's step 1 from the step-wise plan documented [here](https://github.com/apache-spark-on-k8s/spark/issues/441#issuecomment-330802935).
This addition is covered by the [SPIP vote](http://apache-spark-developers-list.1001551.n3.nabble.com/SPIP-Spark-on-Kubernetes-td22147.html) which passed on Aug 31 .
## How was this patch tested?
- The patch contains unit tests which are passing.
- Manual testing: `./build/mvn -Pkubernetes clean package` succeeded.
- It is a **subset** of the entire changelist hosted in http://github.com/apache-spark-on-k8s/spark which is in active use in several organizations.
- There is integration testing enabled in the fork currently [hosted by PepperData](spark-k8s-jenkins.pepperdata.org:8080) which is being moved over to RiseLAB CI.
- Detailed documentation on trying out the patch in its entirety is in: https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html
cc rxin felixcheung mateiz (shepherd)
k8s-big-data SIG members & contributors: mccheah ash211 ssuchter varunkatta kimoonkim erikerlandson liyinan926 tnachen ifilonenko
Author: Yinan Li <liyinan926@gmail.com>
Author: foxish <ramanathana@google.com>
Author: mcheah <mcheah@palantir.com>
Closes#19468 from foxish/spark-kubernetes-3.
## What changes were proposed in this pull request?
Set `-ea` and `-Xss4m` consistently for tests, to fix in particular:
```
OrderingSuite:
...
- GenerateOrdering with ShortType
*** RUN ABORTED ***
java.lang.StackOverflowError:
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:370)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
...
```
## How was this patch tested?
Existing tests. Manually verified it resolves the StackOverflowError this intends to resolve.
Author: Sean Owen <sowen@cloudera.com>
Closes#19820 from srowen/SPARK-22607.
## What changes were proposed in this pull request?
Use repo.maven.apache.org repo address; use latest ASF parent POM version 18
## How was this patch tested?
Existing tests; no functional change
Author: Sean Owen <sowen@cloudera.com>
Closes#19742 from srowen/SPARK-22511.
## What changes were proposed in this pull request?
Spark REPL changes for Scala 2.12.4: use command(), not processLine() in ILoop; remove direct dependence on older jline. Not sure whether this became needed in 2.12.4 or just missed this before. This makes spark-shell work in 2.12.
## How was this patch tested?
Existing tests; manual run of spark-shell in 2.11, 2.12 builds
Author: Sean Owen <sowen@cloudera.com>
Closes#19612 from srowen/SPARK-14650.2.
## What changes were proposed in this pull request?
Using zstd compression for Spark jobs spilling 100s of TBs of data, we could reduce the amount of data written to disk by as much as 50%. This translates to significant latency gain because of reduced disk io operations. There is a degradation CPU time by 2 - 5% because of zstd compression overhead, but for jobs which are bottlenecked by disk IO, this hit can be taken.
## Benchmark
Please note that this benchmark is using real world compute heavy production workload spilling TBs of data to disk
| | zstd performance as compred to LZ4 |
| ------------- | -----:|
| spill/shuffle bytes | -48% |
| cpu time | + 3% |
| cpu reservation time | -40%|
| latency | -40% |
## How was this patch tested?
Tested by running few jobs spilling large amount of data on the cluster and amount of intermediate data written to disk reduced by as much as 50%.
Author: Sital Kedia <skedia@fb.com>
Closes#18805 from sitalkedia/skedia/upstream_zstd.
## What changes were proposed in this pull request?
Scala 2.12's `Future` defines two new methods to implement, `transform` and `transformWith`. These can be implemented naturally in Spark's `FutureAction` extension and subclasses, but, only in terms of the new methods that don't exist in Scala 2.11. To support both at the same time, reflection is used to implement these.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#19561 from srowen/SPARK-22322.
## What changes were proposed in this pull request?
Apache ORC 1.4.1 is released yesterday.
- https://orc.apache.org/news/2017/10/16/ORC-1.4.1/
Like ORC-233 (Allow `orc.include.columns` to be empty), there are several important fixes.
This PR updates Apache ORC dependency to use the latest one, 1.4.1.
## How was this patch tested?
Pass the Jenkins.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19521 from dongjoon-hyun/SPARK-22300.
## What changes were proposed in this pull request?
Move flume behind a profile, take 2. See https://github.com/apache/spark/pull/19365 for most of the back-story.
This change should fix the problem by removing the examples module dependency and moving Flume examples to the module itself. It also adds deprecation messages, per a discussion on dev about deprecating for 2.3.0.
## How was this patch tested?
Existing tests, which still enable flume integration.
Author: Sean Owen <sowen@cloudera.com>
Closes#19412 from srowen/SPARK-22142.2.
## What changes were proposed in this pull request?
Add 'flume' profile to enable Flume-related integration modules
## How was this patch tested?
Existing tests; no functional change
Author: Sean Owen <sowen@cloudera.com>
Closes#19365 from srowen/SPARK-22142.
## What changes were proposed in this pull request?
Un-manage jackson-module-paranamer version to let it use the version desired by jackson-module-scala; manage paranamer up from 2.8 for jackson-module-scala 2.7.9, to override avro 1.7.7's desired paranamer 2.3
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#19352 from srowen/SPARK-22128.
## What changes were proposed in this pull request?
Fix finalizer checkstyle violation by just turning it off; re-disable checkstyle as it won't be run by SBT PR builder. See https://github.com/apache/spark/pull/18887#issuecomment-332580700
## How was this patch tested?
`./dev/lint-java` runs successfully
Author: Sean Owen <sowen@cloudera.com>
Closes#19371 from srowen/HotfixFinalizerCheckstlye.
## What changes were proposed in this pull request?
See https://github.com/apache/spark/pull/19282
Revert scala-maven-plugin to 3.2.2 to work with Maven+zinc again
## How was this patch tested?
Reproduced locally with zinc, and confirmed this removes the problem.
Author: Sean Owen <sowen@cloudera.com>
Closes#19292 from srowen/SPARK-22066.2.
## What changes were proposed in this pull request?
Update plugins, including scala-maven-plugin, to latest versions. Update checkstyle to 8.2. Remove bogus checkstyle config and enable it. Fix existing and new Java checkstyle errors.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#19282 from srowen/SPARK-22066.
## What changes were proposed in this pull request?
Upgrade codahale metrics library so that Graphite constructor can re-resolve hosts behind a CNAME with re-tried DNS lookups. When Graphite is deployed behind an ELB, ELB may change IP addresses based on auto-scaling needs. Using current approach yields Graphite usage impossible, fixing for that use case
- Upgrade to codahale 3.1.5
- Use new Graphite(host, port) constructor instead of new Graphite(new InetSocketAddress(host, port)) constructor
## How was this patch tested?
The same logic is used for another project that is using the same configuration and code path, and graphite re-connect's behind ELB's are no longer an issue
This are proposed changes for codahale lib - https://github.com/dropwizard/metrics/compare/v3.1.2...v3.1.5#diff-6916c85d2dd08d89fe771c952e3b8512R120. Specifically, b4d246d34e/metrics-graphite/src/main/java/com/codahale/metrics/graphite/Graphite.java (L120)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: alexmnyc <project@alexandermarkham.com>
Closes#19210 from alexmnyc/patch-1.
## What changes were proposed in this pull request?
Put Kafka 0.8 support behind a kafka-0-8 profile.
## How was this patch tested?
Existing tests, but, until PR builder and Jenkins configs are updated the effect here is to not build or test Kafka 0.8 support at all.
Author: Sean Owen <sowen@cloudera.com>
Closes#19134 from srowen/SPARK-21893.
