## What changes were proposed in this pull request?
Update breeze to 0.13.1 for an emergency bugfix in strong wolfe line search
https://github.com/scalanlp/breeze/pull/651
## How was this patch tested?
N/A
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#18797 from WeichenXu123/update-breeze.
## What changes were proposed in this pull request?
Taking over https://github.com/apache/spark/pull/18789 ; Closes#18789
Update Jackson to 2.6.7 uniformly, and some components to 2.6.7.1, to get some fixes and prep for Scala 2.12
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#18881 from srowen/SPARK-20433.
## What changes were proposed in this pull request?
Integrate Apache Arrow with Spark to increase performance of `DataFrame.toPandas`. This has been done by using Arrow to convert data partitions on the executor JVM to Arrow payload byte arrays where they are then served to the Python process. The Python DataFrame can then collect the Arrow payloads where they are combined and converted to a Pandas DataFrame. Data types except complex, date, timestamp, and decimal are currently supported, otherwise an `UnsupportedOperation` exception is thrown.
Additions to Spark include a Scala package private method `Dataset.toArrowPayload` that will convert data partitions in the executor JVM to `ArrowPayload`s as byte arrays so they can be easily served. A package private class/object `ArrowConverters` that provide data type mappings and conversion routines. In Python, a private method `DataFrame._collectAsArrow` is added to collect Arrow payloads and a SQLConf "spark.sql.execution.arrow.enable" can be used in `toPandas()` to enable using Arrow (uses the old conversion by default).
## How was this patch tested?
Added a new test suite `ArrowConvertersSuite` that will run tests on conversion of Datasets to Arrow payloads for supported types. The suite will generate a Dataset and matching Arrow JSON data, then the dataset is converted to an Arrow payload and finally validated against the JSON data. This will ensure that the schema and data has been converted correctly.
Added PySpark tests to verify the `toPandas` method is producing equal DataFrames with and without pyarrow. A roundtrip test to ensure the pandas DataFrame produced by pyspark is equal to a one made directly with pandas.
Author: Bryan Cutler <cutlerb@gmail.com>
Author: Li Jin <ice.xelloss@gmail.com>
Author: Li Jin <li.jin@twosigma.com>
Author: Wes McKinney <wes.mckinney@twosigma.com>
Closes#18459 from BryanCutler/toPandas_with_arrow-SPARK-13534.
## What changes were proposed in this pull request?
This PR aims to bump Py4J in order to fix the following float/double bug.
Py4J 0.10.5 fixes this (https://github.com/bartdag/py4j/issues/272) and the latest Py4J is 0.10.6.
**BEFORE**
```
>>> df = spark.range(1)
>>> df.select(df['id'] + 17.133574204226083).show()
+--------------------+
|(id + 17.1335742042)|
+--------------------+
| 17.1335742042|
+--------------------+
```
**AFTER**
```
>>> df = spark.range(1)
>>> df.select(df['id'] + 17.133574204226083).show()
+-------------------------+
|(id + 17.133574204226083)|
+-------------------------+
| 17.133574204226083|
+-------------------------+
```
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#18546 from dongjoon-hyun/SPARK-21278.
## What changes were proposed in this pull request?
Integrate Apache Arrow with Spark to increase performance of `DataFrame.toPandas`. This has been done by using Arrow to convert data partitions on the executor JVM to Arrow payload byte arrays where they are then served to the Python process. The Python DataFrame can then collect the Arrow payloads where they are combined and converted to a Pandas DataFrame. All non-complex data types are currently supported, otherwise an `UnsupportedOperation` exception is thrown.
Additions to Spark include a Scala package private method `Dataset.toArrowPayloadBytes` that will convert data partitions in the executor JVM to `ArrowPayload`s as byte arrays so they can be easily served. A package private class/object `ArrowConverters` that provide data type mappings and conversion routines. In Python, a public method `DataFrame.collectAsArrow` is added to collect Arrow payloads and an optional flag in `toPandas(useArrow=False)` to enable using Arrow (uses the old conversion by default).
## How was this patch tested?
Added a new test suite `ArrowConvertersSuite` that will run tests on conversion of Datasets to Arrow payloads for supported types. The suite will generate a Dataset and matching Arrow JSON data, then the dataset is converted to an Arrow payload and finally validated against the JSON data. This will ensure that the schema and data has been converted correctly.
Added PySpark tests to verify the `toPandas` method is producing equal DataFrames with and without pyarrow. A roundtrip test to ensure the pandas DataFrame produced by pyspark is equal to a one made directly with pandas.
Author: Bryan Cutler <cutlerb@gmail.com>
Author: Li Jin <ice.xelloss@gmail.com>
Author: Li Jin <li.jin@twosigma.com>
Author: Wes McKinney <wes.mckinney@twosigma.com>
Closes#15821 from BryanCutler/wip-toPandas_with_arrow-SPARK-13534.
## What changes were proposed in this pull request?
Update hadoop-2.7 profile's curator version to 2.7.1, more see [SPARK-13933](https://issues.apache.org/jira/browse/SPARK-13933).
## How was this patch tested?
manual tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18247 from wangyum/SPARK-13933.
## What changes were proposed in this pull request?
Upgrade breeze version to 0.13.1, which fixed some critical bugs of L-BFGS-B.
## How was this patch tested?
Existing unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17746 from yanboliang/spark-20449.
## What changes were proposed in this pull request?
- Remove support for Hadoop 2.5 and earlier
- Remove reflection and code constructs only needed to support multiple versions at once
- Update docs to reflect newer versions
- Remove older versions' builds and profiles.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#16810 from srowen/SPARK-19464.
## What changes were proposed in this pull request?
According to the discussion on #16281 which tried to upgrade toward Apache Parquet 1.9.0, Apache Spark community prefer to upgrade to 1.8.2 instead of 1.9.0. Now, Apache Parquet 1.8.2 is released officially last week on 26 Jan. We can use 1.8.2 now.
https://lists.apache.org/thread.html/af0c813f1419899289a336d96ec02b3bbeecaea23aa6ef69f435c142%3Cdev.parquet.apache.org%3E
This PR only aims to bump Parquet version to 1.8.2. It didn't touch any other codes.
## How was this patch tested?
Pass the existing tests and also manually by doing `./dev/test-dependencies.sh`.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#16751 from dongjoon-hyun/SPARK-19409.
**What changes were proposed in this pull request?**
Use Hadoop 2.6.5 for the Hadoop 2.6 profile, I see a bunch of fixes including security ones in the release notes that we should pick up
**How was this patch tested?**
Running the unit tests now with IBM's SDK for Java and let's see what happens with OpenJDK in the community builder - expecting no trouble as it is only a minor release.
Author: Adam Roberts <aroberts@uk.ibm.com>
Closes#16616 from a-roberts/Hadoop265Bumper.
## What changes were proposed in this pull request?
Upgrade Netty to `4.0.43.Final` to add the fix for https://github.com/netty/netty/issues/6153
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16568 from zsxwing/SPARK-18971.
## What changes were proposed in this pull request?
Updates to libthrift 0.9.3 to address a CVE.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#16530 from srowen/SPARK-18997.
## What changes were proposed in this pull request?
I recently hit a bug of com.thoughtworks.paranamer/paranamer, which causes jackson fail to handle byte array defined in a case class. Then I find https://github.com/FasterXML/jackson-module-scala/issues/48, which suggests that it is caused by a bug in paranamer. Let's upgrade paranamer. Since we are using jackson 2.6.5 and jackson-module-paranamer 2.6.5 use com.thoughtworks.paranamer/paranamer 2.6, I suggests that we upgrade paranamer to 2.6.
Author: Yin Huai <yhuai@databricks.com>
Closes#16359 from yhuai/SPARK-18951.
## What changes were proposed in this pull request?
Force update to latest Netty 3.9.x, for dependencies like Flume, to resolve two CVEs. 3.9.2 is the first version that resolves both, and, this is the latest in the 3.9.x line.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#16102 from srowen/SPARK-18586.
## What changes were proposed in this pull request?
org.codehaus.janino:janino depends on org.codehaus.janino:commons-compiler and we have been upgraded to org.codehaus.janino:janino 3.0.0.
However, seems we are still pulling in org.codehaus.janino:commons-compiler 2.7.6 because of calcite. It looks like an accident because we exclude janino from calcite (see here https://github.com/apache/spark/blob/branch-2.1/pom.xml#L1759). So, this PR upgrades org.codehaus.janino:commons-compiler to 3.0.0.
## How was this patch tested?
jenkins
Author: Yin Huai <yhuai@databricks.com>
Closes#16025 from yhuai/janino-commons-compile.
## What changes were proposed in this pull request?
One of the important changes for 4.0.42.Final is "Support any FileRegion implementation when using epoll transport netty/netty#5825".
In 4.0.42.Final, `MessageWithHeader` can work properly when `spark.[shuffle|rpc].io.mode` is set to epoll
## How was this patch tested?
Existing tests
Author: Guoqiang Li <witgo@qq.com>
Closes#15830 from witgo/SPARK-18375_netty-4.0.42.
## What changes were proposed in this pull request?
Try excluding org.json:json from hive-exec dep as it's Cat X now. It may be the case that it's not used by the part of Hive Spark uses anyway.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#15798 from srowen/SPARK-18262.
## What changes were proposed in this pull request?
1) Upgrade the Py4J version on the Java side
2) Update the py4j src zip file we bundle with Spark
## How was this patch tested?
Existing doctests & unit tests pass
Author: Jagadeesan <as2@us.ibm.com>
Closes#15514 from jagadeesanas2/SPARK-17960.
## What changes were proposed in this pull request?
`SerializationUtils.clone()` of commons-lang3 (<3.5) has a bug that breaks thread safety, which gets stack sometimes caused by race condition of initializing hash map.
See https://issues.apache.org/jira/browse/LANG-1251.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#15548 from ueshin/issues/SPARK-17985.
This reverts commit bfe7885aee.
The commit caused build failures on Hadoop 2.2 profile:
```
[error] /scratch/rxin/spark/core/src/main/scala/org/apache/spark/util/Utils.scala:1489: value read is not a member of object org.apache.commons.io.IOUtils
[error] var numBytes = IOUtils.read(gzInputStream, buf)
[error] ^
[error] /scratch/rxin/spark/core/src/main/scala/org/apache/spark/util/Utils.scala:1492: value read is not a member of object org.apache.commons.io.IOUtils
[error] numBytes = IOUtils.read(gzInputStream, buf)
[error] ^
```
## What changes were proposed in this pull request?
`SerializationUtils.clone()` of commons-lang3 (<3.5) has a bug that breaks thread safety, which gets stack sometimes caused by race condition of initializing hash map.
See https://issues.apache.org/jira/browse/LANG-1251.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#15525 from ueshin/issues/SPARK-17985.
## What changes were proposed in this pull request?
Upgraded to a newer version of Pyrolite which supports serialization of a BinaryType StructField for PySpark.SQL
## How was this patch tested?
Added a unit test which fails with a raised ValueError when using the previous version of Pyrolite 4.9 and Python3
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#15386 from BryanCutler/pyrolite-upgrade-SPARK-17808.
## What changes were proposed in this pull request?
This PR includes the changes below:
1. Upgrade Univocity library from 2.1.1 to 2.2.1
This includes some performance improvement and also enabling auto-extending buffer in `maxCharsPerColumn` option in CSV. Please refer the [release notes](https://github.com/uniVocity/univocity-parsers/releases).
2. Remove useless `rowSeparator` variable existing in `CSVOptions`
We have this unused variable in [CSVOptions.scala#L127](29952ed096/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala (L127)) but it seems possibly causing confusion that it actually does not care of `\r\n`. For example, we have an issue open about this, [SPARK-17227](https://issues.apache.org/jira/browse/SPARK-17227), describing this variable.
This variable is virtually not being used because we rely on `LineRecordReader` in Hadoop which deals with only both `\n` and `\r\n`.
3. Set the default value of `maxCharsPerColumn` to auto-expending.
We are setting 1000000 for the length of each column. It'd be more sensible we allow auto-expending rather than fixed length by default.
To make sure, using `-1` is being described in the release note, [2.2.0](https://github.com/uniVocity/univocity-parsers/releases/tag/v2.2.0).
## How was this patch tested?
N/A
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15138 from HyukjinKwon/SPARK-17583.
## What changes were proposed in this pull request?
This patch bumps the Hadoop version in hadoop-2.7 profile from 2.7.2 to 2.7.3, which was recently released and contained a number of bug fixes.
## How was this patch tested?
The change should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#15115 from rxin/SPARK-17558.
## What changes were proposed in this pull request?
Upgrade netty-all to latest in the 4.0.x line which is 4.0.41, mentions several bug fixes and performance improvements we may find useful, see netty.io/news/2016/08/29/4-0-41-Final-4-1-5-Final.html. Initially tried to use 4.1.5 but noticed it's not backwards compatible.
## How was this patch tested?
Existing unit tests against branch-1.6 and branch-2.0 using IBM Java 8 on Intel, Power and Z architectures
Author: Adam Roberts <aroberts@uk.ibm.com>
Closes#14961 from a-roberts/netty.
## What changes were proposed in this pull request?
Upgrades the Snappy version to 1.1.2.6 from 1.1.2.4, release notes: https://github.com/xerial/snappy-java/blob/master/Milestone.md mention "Fix a bug in SnappyInputStream when reading compressed data that happened to have the same first byte with the stream magic header (#142)"
## How was this patch tested?
Existing unit tests using the latest IBM Java 8 on Intel, Power and Z architectures (little and big-endian)
Author: Adam Roberts <aroberts@uk.ibm.com>
Closes#14958 from a-roberts/master.
This patch is using Apache Commons Crypto library to enable shuffle encryption support.
Author: Ferdinand Xu <cheng.a.xu@intel.com>
Author: kellyzly <kellyzly@126.com>
Closes#8880 from winningsix/SPARK-10771.
## What changes were proposed in this pull request?
Update to py4j 0.10.3 to enable JAVA_HOME support
## How was this patch tested?
Pyspark tests
Author: Sean Owen <sowen@cloudera.com>
Closes#14748 from srowen/SPARK-16781.
## What changes were proposed in this pull request?
As of Scala 2.11.x there is no longer a org.scala-lang:jline version aligned to the scala version itself. Scala console now uses the plain jline:jline module. Spark's dependency management did not reflect this change properly, causing Maven to pull in Jline via transitive dependency. Unfortunately Jline 2.12 contained a minor but very annoying bug rendering the shell almost useless for developers with german keyboard layout. This request contains the following chages:
- Exclude transitive dependency 'jline:jline' from hive-exec module
- Remove global properties 'jline.version' and 'jline.groupId'
- Add both properties and dependency to 'scala-2.11' profile
- Add explicit dependency on 'jline:jline' to module 'spark-repl'
## How was this patch tested?
- Running mvn dependency:tree and checking for correct Jline version 2.12.1
- Running full builds with assembly and checking for jline-2.12.1.jar in 'lib' folder of generated tarball
Author: Stefan Schulze <stefan.schulze@pentasys.de>
Closes#14429 from stsc-pentasys/SPARK-16770.
## What changes were proposed in this pull request?
New config var: spark.mesos.docker.containerizer={"mesos","docker" (default)}
This adds support for running docker containers via the Mesos unified containerizer: http://mesos.apache.org/documentation/latest/container-image/
The benefit is losing the dependency on `dockerd`, and all the costs which it incurs.
I've also updated the supported Mesos version to 0.28.2 for support of the required protobufs.
This is blocked on: https://github.com/apache/spark/pull/14167
## How was this patch tested?
- manually testing jobs submitted with both "mesos" and "docker" settings for the new config var.
- spark/mesos integration test suite
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#14275 from mgummelt/unified-containerizer.
## What changes were proposed in this pull request?
Version of derby upgraded based on important security info at VersionEye. Test scope added so we don't include it in our final package anyway. NB: I think this should be backported to all previous releases as it is a security problem https://www.versioneye.com/java/org.apache.derby:derby/10.11.1.1
The CVE number is 2015-1832. I also suggest we add a SECURITY tag for JIRAs
## How was this patch tested?
Existing tests with the change making sure that we see no new failures. I checked derby 10.12.x and not derby 10.11.x is downloaded to our ~/.m2 folder.
I then used dev/make-distribution.sh and checked the dist/jars folder for Spark 2.0: no derby jar is present.
I don't know if this would also remove it from the assembly jar in our 1.x branches.
Author: Adam Roberts <aroberts@uk.ibm.com>
Closes#14379 from a-roberts/patch-4.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
## 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)
Mesos agents by default will not pull docker images which are cached
locally already. In order to run Spark executors from mutable tags like
`:latest` this commit introduces a Spark setting
(`spark.mesos.executor.docker.forcePullImage`). Setting this flag to
true will tell the Mesos agent to force pull the docker image (default is `false` which is consistent with the previous
implementation and Mesos' default
behaviour).
Author: Philipp Hoffmann <mail@philipphoffmann.de>
Closes#14348 from philipphoffmann/force-pull-image.
## What changes were proposed in this pull request?
Mesos agents by default will not pull docker images which are cached
locally already. In order to run Spark executors from mutable tags like
`:latest` this commit introduces a Spark setting
`spark.mesos.executor.docker.forcePullImage`. Setting this flag to
true will tell the Mesos agent to force pull the docker image (default is `false` which is consistent with the previous
implementation and Mesos' default
behaviour).
## How was this patch tested?
I ran a sample application including this change on a Mesos cluster and verified the correct behaviour for both, with and without, force pulling the executor image. As expected the image is being force pulled if the flag is set.
Author: Philipp Hoffmann <mail@philipphoffmann.de>
Closes#13051 from philipphoffmann/force-pull-image.
## What changes were proposed in this pull request?
breeze 0.12 has been released for more than half a year, and it brings lots of new features, performance improvement and bug fixes.
One of the biggest features is ```LBFGS-B``` which is an implementation of ```LBFGS``` with box constraints and much faster for some special case.
We would like to implement Huber loss function for ```LinearRegression``` ([SPARK-3181](https://issues.apache.org/jira/browse/SPARK-3181)) and it requires ```LBFGS-B``` as the optimization solver. So we should bump up the dependent breeze version to 0.12.
For more features, improvements and bug fixes of breeze 0.12, you can refer the following link:
https://groups.google.com/forum/#!topic/scala-breeze/nEeRi_DcY5c
## How was this patch tested?
No new tests, should pass the existing ones.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14150 from yanboliang/spark-16494.
## What changes were proposed in this pull request?
Updating the Hadoop version from 2.7.0 to 2.7.2 if we use the Hadoop-2.7 build profile
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Existing tests
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
I'd like us to use Hadoop 2.7.2 owing to the Hadoop release notes stating Hadoop 2.7.0 is not ready for production use
https://hadoop.apache.org/docs/r2.7.0/ states
"Apache Hadoop 2.7.0 is a minor release in the 2.x.y release line, building upon the previous stable release 2.6.0.
This release is not yet ready for production use. Production users should use 2.7.1 release and beyond."
Hadoop 2.7.1 release notes:
"Apache Hadoop 2.7.1 is a minor release in the 2.x.y release line, building upon the previous release 2.7.0. This is the next stable release after Apache Hadoop 2.6.x."
And then Hadoop 2.7.2 release notes:
"Apache Hadoop 2.7.2 is a minor release in the 2.x.y release line, building upon the previous stable release 2.7.1."
I've tested this is OK with Intel hardware and IBM Java 8 so let's test it with OpenJDK, ideally this will be pushed to branch-2.0 and master.
Author: Adam Roberts <aroberts@uk.ibm.com>
Closes#13556 from a-roberts/patch-2.
## What changes were proposed in this pull request?
This reverts commit c24b6b679c. Sent a PR to run Jenkins tests due to the revert conflicts of `dev/deps/spark-deps-hadoop*`.
## How was this patch tested?
Jenkins unit tests, integration tests, manual tests)
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13417 from zsxwing/revert-SPARK-11753.
## What changes were proposed in this pull request?
This includes minimal changes to get Spark using the current release of Parquet, 1.8.1.
## How was this patch tested?
This uses the existing Parquet tests.
Author: Ryan Blue <blue@apache.org>
Closes#13280 from rdblue/SPARK-9876-update-parquet.
## What changes were proposed in this pull request?
See https://issues.apache.org/jira/browse/SPARK-15523
This PR replaces PR #13293. It's isolated to a new branch, and contains some more squashed changes.
## How was this patch tested?
1. Executed `mvn clean package` in `mllib` directory
2. Executed `dev/test-dependencies.sh --replace-manifest` in the root directory.
Author: Villu Ruusmann <villu.ruusmann@gmail.com>
Closes#13297 from vruusmann/update-jpmml.
## What changes were proposed in this pull request?
The ANTLR4 SBT plugin has been moved from its own repo to one on bintray. The version was also changed from `0.7.10` to `0.7.11`. The latter actually broke our build (ihji has fixed this by also adding `0.7.10` and others to the bin-tray repo).
This PR upgrades the SBT-ANTLR4 plugin and ANTLR4 to their most recent versions (`0.7.11`/`4.5.3`). I have also removed a few obsolete build configurations.
## How was this patch tested?
Manually running SBT/Maven builds.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#13299 from hvanhovell/SPARK-15525.
## What changes were proposed in this pull request?
Jackson suppprts `allowNonNumericNumbers` option to parse non-standard non-numeric numbers such as "NaN", "Infinity", "INF". Currently used Jackson version (2.5.3) doesn't support it all. This patch upgrades the library and make the two ignored tests in `JsonParsingOptionsSuite` passed.
## How was this patch tested?
`JsonParsingOptionsSuite`.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9759 from viirya/fix-json-nonnumric.
## What changes were proposed in this pull request?
This is sort of a hot-fix for https://github.com/apache/spark/pull/13117, but, the problem is limited to Hadoop 2.2. The change is to manage `commons-io` to 2.4 for all Hadoop builds, which is only a net change for Hadoop 2.2, which was using 2.1.
## How was this patch tested?
Jenkins tests -- normal PR builder, then the `[test-hadoop2.2] [test-maven]` if successful.
Author: Sean Owen <sowen@cloudera.com>
Closes#13132 from srowen/SPARK-12972.3.
## What changes were proposed in this pull request?
(Retry of https://github.com/apache/spark/pull/13049)
- update to httpclient 4.5 / httpcore 4.4
- remove some defunct exclusions
- manage httpmime version to match
- update selenium / httpunit to support 4.5 (possible now that Jetty 9 is used)
## How was this patch tested?
Jenkins tests. Also, locally running the same test command of one Jenkins profile that failed: `mvn -Phadoop-2.6 -Pyarn -Phive -Phive-thriftserver -Pkinesis-asl ...`
Author: Sean Owen <sowen@cloudera.com>
Closes#13117 from srowen/SPARK-12972.2.
## What changes were proposed in this pull request?
- update httpcore/httpclient to latest
- centralize version management
- remove excludes that are no longer relevant according to SBT/Maven dep graphs
- also manage httpmime to match httpclient
## How was this patch tested?
Jenkins tests, plus review of dependency graphs from SBT/Maven, and review of test-dependencies.sh output
Author: Sean Owen <sowen@cloudera.com>
Closes#13049 from srowen/SPARK-12972.
## What changes were proposed in this pull request?
This upgrades to Py4J 0.10.1 which reduces syscal overhead in Java gateway ( see https://github.com/bartdag/py4j/issues/201 ). Related https://issues.apache.org/jira/browse/SPARK-6728 .
## How was this patch tested?
Existing doctests & unit tests pass
Author: Holden Karau <holden@us.ibm.com>
Closes#13064 from holdenk/SPARK-15061-upgrade-to-py4j-0.10.1.
## What changes were proposed in this pull request?
Since Jetty 8 is EOL (end of life) and has critical security issue [http://www.securityweek.com/critical-vulnerability-found-jetty-web-server], I think upgrading to 9 is necessary. I am using latest 9.2 since 9.3 requires Java 8+.
`javax.servlet` and `derby` were also upgraded since Jetty 9.2 needs corresponding version.
## How was this patch tested?
Manual test and current test cases should cover it.
Author: bomeng <bmeng@us.ibm.com>
Closes#12916 from bomeng/SPARK-14897.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-15148
Mainly it improves the performance roughtly about 30%-40% according to the [release note](https://github.com/uniVocity/univocity-parsers/releases/tag/v2.1.0). For the details of the purpose is described in the JIRA.
This PR upgrades Univocity library from 2.0.2 to 2.1.0.
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12923 from HyukjinKwon/SPARK-15148.
## What changes were proposed in this pull request?
Replace com.sun.jersey with org.glassfish.jersey. Changes to the Spark Web UI code were required to compile. The changes were relatively standard Jersey migration things.
## How was this patch tested?
I did a manual test for the standalone web APIs. Although I didn't test the functionality of the security filter itself, the code that changed non-trivially is how we actually register the filter. I attached a debugger to the Spark master and verified that the SecurityFilter code is indeed invoked upon hitting /api/v1/applications.
Author: mcheah <mcheah@palantir.com>
Closes#12715 from mccheah/feature/upgrade-jersey.
## What changes were proposed in this pull request?
We had the issue when using snowplow in our Spark applications. Snowplow requires json4s version 3.2.11 while Spark still use a few years old version 3.2.10. The change is to upgrade json4s jar to 3.2.11.
## How was this patch tested?
We built Spark jar and successfully ran our applications in local and cluster modes.
Author: Lining Sun <lining@gmail.com>
Closes#12901 from liningalex/master.
## What changes were proposed in this pull request?
This PR copy the thrift-server from hive-service-1.2 (including TCLIService.thrift and generated Java source code) into sql/hive-thriftserver, so we can do further cleanup and improvements.
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#12764 from davies/thrift_server.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-14787
The possible problems are described in the JIRA above. Please refer this if you are wondering the purpose of this PR.
This PR upgrades Joda-Time library from 2.9 to 2.9.3.
## How was this patch tested?
`sbt scalastyle` and Jenkins tests in this PR.
closes#11847
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12552 from HyukjinKwon/SPARK-14787.
This patch upgrades Chill to 0.8.0 and Kryo to 3.0.3. While we'll likely need to bump these dependencies again before Spark 2.0 (due to SPARK-14221 / https://github.com/twitter/chill/issues/252), I wanted to get the bulk of the Kryo 2 -> Kryo 3 migration done now in order to figure out whether there are any unexpected surprises.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#12076 from JoshRosen/kryo3.
## What changes were proposed in this pull request?
This PR resolves the problem during parsing unescaped quotes in input data. For example, currently the data below:
```
"a"b,ccc,ddd
e,f,g
```
produces a data below:
- **Before**
```bash
["a"b,ccc,ddd[\n]e,f,g] <- as a value.
```
- **After**
```bash
["a"b], [ccc], [ddd]
[e], [f], [g]
```
This PR bumps up the Univocity parser's version. This was fixed in `2.0.2`, https://github.com/uniVocity/univocity-parsers/issues/60.
## How was this patch tested?
Unit tests in `CSVSuite` and `sbt/sbt scalastyle`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12226 from HyukjinKwon/SPARK-14103-quote.
This change modifies the "assembly/" module to just copy needed
dependencies to its build directory, and modifies the packaging
script to pick those up (and remove duplicate jars packages in the
examples module).
I also made some minor adjustments to dependencies to remove some
test jars from the final packaging, and remove jars that conflict with each
other when packaged separately (e.g. servlet api).
Also note that this change restores guava in applications' classpaths, even
though it's still shaded inside Spark. This is now needed for the Hadoop
libraries that are packaged with Spark, which now are not processed by
the shade plugin.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#11796 from vanzin/SPARK-13579.
## What changes were proposed in this pull request?
Upgrade to 2.11.8 (from the current 2.11.7)
## How was this patch tested?
A manual build
Author: Jacek Laskowski <jacek@japila.pl>
Closes#11681 from jaceklaskowski/SPARK-13825-scala-2_11_8.
## What changes were proposed in this pull request?
Upgrade snappy to 1.1.2.4 to improve snappy read/write performance.
## How was this patch tested?
Tested by running a job on the cluster and saw 7.5% cpu savings after this change.
Author: Sital Kedia <skedia@fb.com>
Closes#12096 from sitalkedia/snappyRelease.
### What changes were proposed in this pull request?
This PR removes the ANTLR3 based parser, and moves the new ANTLR4 based parser into the `org.apache.spark.sql.catalyst.parser package`.
### How was this patch tested?
Existing unit tests.
cc rxin andrewor14 yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12071 from hvanhovell/SPARK-14211.
### What changes were proposed in this pull request?
The current ANTLR3 parser is quite complex to maintain and suffers from code blow-ups. This PR introduces a new parser that is based on ANTLR4.
This parser is based on the [Presto's SQL parser](https://github.com/facebook/presto/blob/master/presto-parser/src/main/antlr4/com/facebook/presto/sql/parser/SqlBase.g4). The current implementation can parse and create Catalyst and SQL plans. Large parts of the HiveQl DDL and some of the DML functionality is currently missing, the plan is to add this in follow-up PRs.
This PR is a work in progress, and work needs to be done in the following area's:
- [x] Error handling should be improved.
- [x] Documentation should be improved.
- [x] Multi-Insert needs to be tested.
- [ ] Naming and package locations.
### How was this patch tested?
Catalyst and SQL unit tests.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#11557 from hvanhovell/ngParser.
This patch upgrades Py4J from 0.9.1 to 0.9.2 in order to include a patch which modifies Py4J to use the current thread's ContextClassLoader when performing reflection / class loading. This is necessary in order to fix [SPARK-5185](https://issues.apache.org/jira/browse/SPARK-5185), a longstanding issue affecting the use of `--jars` and `--packages` in PySpark.
In order to demonstrate that the fix works, I removed the workarounds which were added as part of [SPARK-6027](https://issues.apache.org/jira/browse/SPARK-6027) / #4779 and other patches.
Py4J diff: https://github.com/bartdag/py4j/compare/0.9.1...0.9.2
/cc zsxwing tdas davies brkyvz
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11687 from JoshRosen/py4j-0.9.2.
## What changes were proposed in this pull request?
Update snappy to 1.1.2.1 to pull in a single fix -- the OOM fix we already worked around.
Supersedes https://github.com/apache/spark/pull/11524
## How was this patch tested?
Jenkins tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#11631 from srowen/SPARK-13663.
## What changes were proposed in this pull request?
Modifies the dependency declarations of the all the hive artifacts, to explicitly exclude the groovy-all JAR.
This stops the groovy classes *and everything else in that uber-JAR* from getting into spark-assembly JAR.
## How was this patch tested?
1. Pre-patch build was made: `mvn clean install -Pyarn,hive,hive-thriftserver`
1. spark-assembly expanded, observed to have the org.codehaus.groovy packages and JARs
1. A maven dependency tree was created `mvn dependency:tree -Pyarn,hive,hive-thriftserver -Dverbose > target/dependencies.txt`
1. This text file examined to confirm that groovy was being imported as a dependency of `org.spark-project.hive`
1. Patch applied
1. Repeated step1: clean build of project with ` -Pyarn,hive,hive-thriftserver` set
1. Examined created spark-assembly, verified no org.codehaus packages
1. Verified that the maven dependency tree no longer references groovy
Note also that the size of the assembly JAR was 181628646 bytes before this patch, 166318515 after —15MB smaller. That's a good metric of things being excluded
Author: Steve Loughran <stevel@hortonworks.com>
Closes#11449 from steveloughran/fixes/SPARK-13599-groovy-dependency.
It registers more Scala classes, including ListBuffer to support Kryo with FPGrowth.
See https://github.com/twitter/chill/releases for Chill's change log.
Author: mark800 <yky800@126.com>
Closes#11041 from mark800/master.
Phase 1: update plugin versions, test dependencies, some example and third-party versions
Author: Sean Owen <sowen@cloudera.com>
Closes#11206 from srowen/SPARK-13324.
This patch changes Spark's build to make Scala 2.11 the default Scala version. To be clear, this does not mean that Spark will stop supporting Scala 2.10: users will still be able to compile Spark for Scala 2.10 by following the instructions on the "Building Spark" page; however, it does mean that Scala 2.11 will be the default Scala version used by our CI builds (including pull request builds).
The Scala 2.11 compiler is faster than 2.10, so I think we'll be able to look forward to a slight speedup in our CI builds (it looks like it's about 2X faster for the Maven compile-only builds, for instance).
After this patch is merged, I'll update Jenkins to add new compile-only jobs to ensure that Scala 2.10 compilation doesn't break.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10608 from JoshRosen/SPARK-6363.
- Remove Akka dependency from core. Note: the streaming-akka project still uses Akka.
- Remove HttpFileServer
- Remove Akka configs from SparkConf and SSLOptions
- Rename `spark.akka.frameSize` to `spark.rpc.message.maxSize`. I think it's still worth to keep this config because using `DirectTaskResult` or `IndirectTaskResult` depends on it.
- Update comments and docs
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10854 from zsxwing/remove-akka.
This patch adds a Hadoop 2.7 build profile in order to let us automate tests against that version.
/cc rxin srowen
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10775 from JoshRosen/add-hadoop-2.7-profile.
This pull request removes the external block store API. This is rarely used, and the file system interface is actually a better, more standard way to interact with external storage systems.
There are some other things to remove also, as pointed out by JoshRosen. We will do those as follow-up pull requests.
Author: Reynold Xin <rxin@databricks.com>
Closes#10752 from rxin/remove-offheap.
CSV is the most common data format in the "small data" world. It is often the first format people want to try when they see Spark on a single node. Having to rely on a 3rd party component for this leads to poor user experience for new users. This PR merges the popular spark-csv data source package (https://github.com/databricks/spark-csv) with SparkSQL.
This is a first PR to bring the functionality to spark 2.0 master. We will complete items outlines in the design document (see JIRA attachment) in follow up pull requests.
Author: Hossein <hossein@databricks.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#10766 from rxin/csv.
- [x] Upgrade Py4J to 0.9.1
- [x] SPARK-12657: Revert SPARK-12617
- [x] SPARK-12658: Revert SPARK-12511
- Still keep the change that only reading checkpoint once. This is a manual change and worth to take a look carefully. bfd4b5c040
- [x] Verify no leak any more after reverting our workarounds
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10692 from zsxwing/py4j-0.9.1.
The current Spark Streaming kinesis connector references a quite old version 1.9.40 of the AWS Java SDK (1.10.40 is current). Numerous AWS features including Kinesis Firehose are unavailable in 1.9. Those two versions of the AWS SDK in turn require conflicting versions of Jackson (2.4.4 and 2.5.3 respectively) such that one cannot include the current AWS SDK in a project that also uses the Spark Streaming Kinesis ASL.
Author: BrianLondon <brian@seatgeek.com>
Closes#10256 from BrianLondon/master.
This is a hotfix for a build bug introduced by the Netty exclusion changes in #10672. We can't exclude `io.netty:netty` because Akka depends on it. There's not a direct conflict between `io.netty:netty` and `io.netty:netty-all`, because the former puts classes in the `org.jboss.netty` namespace while the latter uses the `io.netty` namespace. However, there still is a conflict between `org.jboss.netty:netty` and `io.netty:netty`, so we need to continue to exclude the JBoss version of that artifact.
While the diff here looks somewhat large, note that this is only a revert of a some of the changes from #10672. You can see the net changes in pom.xml at 3119206b71...5211ab8 (diff-600376dffeb79835ede4a0b285078036)
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10693 from JoshRosen/netty-hotfix.
Netty classes are published under multiple artifacts with different names, so our build needs to exclude the `io.netty:netty` and `org.jboss.netty:netty` versions of the Netty artifact. However, our existing exclusions were incomplete, leading to situations where duplicate Netty classes would wind up on the classpath and cause compile errors (or worse).
This patch fixes the exclusion issue by adding more exclusions and uses Maven Enforcer's [banned dependencies](https://maven.apache.org/enforcer/enforcer-rules/bannedDependencies.html) rule to prevent these classes from accidentally being reintroduced. I also updated `dev/test-dependencies.sh` to run `mvn validate` so that the enforcer rules can run as part of pull request builds.
/cc rxin srowen pwendell. I'd like to backport at least the exclusion portion of this fix to `branch-1.5` in order to fix the documentation publishing job, which fails nondeterministically due to incompatible versions of Netty classes taking precedence on the compile-time classpath.
Author: Josh Rosen <rosenville@gmail.com>
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10672 from JoshRosen/enforce-netty-exclusions.
This PR moves a major part of the new SQL parser to Catalyst. This is a prelude to start using this parser for all of our SQL parsing. The following key changes have been made:
The ANTLR Parser & Supporting classes have been moved to the Catalyst project. They are now part of the ```org.apache.spark.sql.catalyst.parser``` package. These classes contained quite a bit of code that was originally from the Hive project, I have added aknowledgements whenever this applied. All Hive dependencies have been factored out. I have also taken this chance to clean-up the ```ASTNode``` class, and to improve the error handling.
The HiveQl object that provides the functionality to convert an AST into a LogicalPlan has been refactored into three different classes, one for every SQL sub-project:
- ```CatalystQl```: This implements Query and Expression parsing functionality.
- ```SparkQl```: This is a subclass of CatalystQL and provides SQL/Core only functionality such as Explain and Describe.
- ```HiveQl```: This is a subclass of ```SparkQl``` and this adds Hive-only functionality to the parser such as Analyze, Drop, Views, CTAS & Transforms. This class still depends on Hive.
cc rxin
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10583 from hvanhovell/SPARK-12575.
There are a couple of places in the `dev/run-tests-*.py` scripts which deal with Hadoop profiles, but the set of profiles that they handle does not include all Hadoop profiles defined in our POM. Similarly, the `hadoop-2.2` and `hadoop-2.6` profiles were missing from `dev/deps`.
This patch updates these scripts to include all four Hadoop profiles defined in our POM.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10565 from JoshRosen/add-missing-hadoop-profiles-in-test-scripts.
This patch adds a new build check which enumerates Spark's resolved runtime classpath and saves it to a file, then diffs against that file to detect whether pull requests have introduced dependency changes. The aim of this check is to make it simpler to reason about whether pull request which modify the build have introduced new dependencies or changed transitive dependencies in a way that affects the final classpath.
This supplants the checks added in SPARK-4123 / #5093, which are currently disabled due to bugs.
This patch is based on pwendell's work in #8531.
Closes#8531.
Author: Josh Rosen <joshrosen@databricks.com>
Author: Patrick Wendell <patrick@databricks.com>
Closes#10461 from JoshRosen/SPARK-10359.
