## What changes were proposed in this pull request?
This PR aims to update `zstd-jni` library to `1.4.0-1` which improves the `level 1 compression speed` performance by 6% in most scenarios. The following is the full release note.
- https://github.com/facebook/zstd/releases/tag/v1.4.0
## How was this patch tested?
Pass the Jenkins.
Closes#24632 from dongjoon-hyun/SPARK-27755.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR upgrades lz4-java from 1.5.1 to 1.6.0. Lz4-java is available at https://github.com/lz4/lz4-java.
Changes from 1.5.1:
- Upgraded LZ4 to 1.9.1. Updated the JNI bindings, except for the one for Linux/i386. Decompression speed is improved on amd64.
- Deprecated use of LZ4FastDecompressor of a native instance because the corresponding C API function is deprecated. See the release note of LZ4 1.9.0 for details. Updated javadoc accordingly.
- Changed the module name from org.lz4.lz4-java to org.lz4.java to avoid using - in the module name. (severn-everett, Oliver Eikemeier, Rei Odaira)
- Enabled build with Java 11. Note that the distribution is still built with Java 7. (Rei Odaira)
## How was this patch tested?
Existing tests.
Closes#24629 from kiszk/SPARK-27752.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
PR #23890 introduced `org.glassfish.jaxb:jaxb-runtime:2.3.2` as a runtime dependency. As an unexpected side effect, `jakarta.activation:jakarta.activation-api:1.2.1` was also pulled in as a transitive dependency. As a result, for the Maven build, both of the following two jars can be found under `assembly/target/scala-2.12/jars/`:
```
activation-1.1.1.jar
jakarta.activation-api-1.2.1.jar
```
This PR exludes the Jakarta one.
Manually built Spark using Maven and checked files under `assembly/target/scala-2.12/jars/`. After this change, only `activation-1.1.1.jar` is there.
Closes#24507 from liancheng/spark-27611.
Authored-by: Cheng Lian <lian@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Update pyrolite to 4.23 to pick up bug and security fixes.
## How was this patch tested?
Existing tests.
Closes#24381 from srowen/SPARK-27470.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Unify commons-beanutils deps to latest 1.9.3. This resolves the version inconsistency in Hadoop 2.7's build and also picks up security and bug fixes.
## How was this patch tested?
Existing tests.
Closes#24378 from srowen/SPARK-27469.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR upgrades `lz4-java` to 1.5.1 in order to get a patch for avoiding racing with GC.
- https://github.com/lz4/lz4-java/blob/master/CHANGES.md#151
## How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#24363 from dongjoon-hyun/SPARK-LZ4.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR mainly contains:
1. Upgrade hadoop-3's built-in Hive maven dependencies to 2.3.4.
2. Resolve compatibility issues between Hive 1.2.1 and Hive 2.3.4 in the `sql/hive` module.
## How was this patch tested?
jenkins test hadoop-2.7
manual test hadoop-3:
```shell
build/sbt clean package -Phadoop-3.2 -Phive
export SPARK_PREPEND_CLASSES=true
# rm -rf metastore_db
cat <<EOF > test_hadoop3.scala
spark.range(10).write.saveAsTable("test_hadoop3")
spark.table("test_hadoop3").show
EOF
bin/spark-shell --conf spark.hadoop.hive.metastore.schema.verification=false --conf spark.hadoop.datanucleus.schema.autoCreateAll=true -i test_hadoop3.scala
```
Closes#23788 from wangyum/SPARK-23710-hadoop3.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
HighlyCompressedMapStatus uses RoaringBitmap to record the empty blocks. But RoaringBitmap couldn't be ser/deser with unsafe KryoSerializer.
It's a bug of RoaringBitmap-0.5.11 and fixed in latest version.
This is an update of #24157
## How was this patch tested?
Add a UT
Closes#24264 from LantaoJin/SPARK-27216.
Lead-authored-by: LantaoJin <jinlantao@gmail.com>
Co-authored-by: Lantao Jin <jinlantao@gmail.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
(See JIRA for problem statement)
Update snappy 1.1.7.1 -> 1.1.7.3 to pick up an empty-stream and Java 9 fix.
There appear to be no other changes of consequence:
https://github.com/xerial/snappy-java/blob/master/Milestone.md
## How was this patch tested?
Existing tests
Closes#24242 from srowen/SPARK-27267.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR upgrade `hadoop-3` to `3.2.0` to workaround [HADOOP-16086](https://issues.apache.org/jira/browse/HADOOP-16086). Otherwise some test case will throw IllegalArgumentException:
```java
02:44:34.707 ERROR org.apache.hadoop.hive.ql.exec.Task: Job Submission failed with exception 'java.io.IOException(Cannot initialize Cluster. Please check your configuration for mapreduce.framework.name and the correspond server addresses.)'
java.io.IOException: Cannot initialize Cluster. Please check your configuration for mapreduce.framework.name and the correspond server addresses.
at org.apache.hadoop.mapreduce.Cluster.initialize(Cluster.java:116)
at org.apache.hadoop.mapreduce.Cluster.<init>(Cluster.java:109)
at org.apache.hadoop.mapreduce.Cluster.<init>(Cluster.java:102)
at org.apache.hadoop.mapred.JobClient.init(JobClient.java:475)
at org.apache.hadoop.mapred.JobClient.<init>(JobClient.java:454)
at org.apache.hadoop.hive.ql.exec.mr.ExecDriver.execute(ExecDriver.java:369)
at org.apache.hadoop.hive.ql.exec.mr.MapRedTask.execute(MapRedTask.java:151)
at org.apache.hadoop.hive.ql.exec.Task.executeTask(Task.java:199)
at org.apache.hadoop.hive.ql.exec.TaskRunner.runSequential(TaskRunner.java:100)
at org.apache.hadoop.hive.ql.Driver.launchTask(Driver.java:2183)
at org.apache.hadoop.hive.ql.Driver.execute(Driver.java:1839)
at org.apache.hadoop.hive.ql.Driver.runInternal(Driver.java:1526)
at org.apache.hadoop.hive.ql.Driver.run(Driver.java:1237)
at org.apache.hadoop.hive.ql.Driver.run(Driver.java:1227)
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$runHive$1(HiveClientImpl.scala:730)
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$withHiveState$1(HiveClientImpl.scala:283)
at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:221)
at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:220)
at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:266)
at org.apache.spark.sql.hive.client.HiveClientImpl.runHive(HiveClientImpl.scala:719)
at org.apache.spark.sql.hive.client.HiveClientImpl.runSqlHive(HiveClientImpl.scala:709)
at org.apache.spark.sql.hive.StatisticsSuite.createNonPartitionedTable(StatisticsSuite.scala:719)
at org.apache.spark.sql.hive.StatisticsSuite.$anonfun$testAlterTableProperties$2(StatisticsSuite.scala:822)
```
## How was this patch tested?
manual tests
Closes#24106 from wangyum/SPARK-27175.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR aims to update Apache ORC dependency to fix [SPARK-27107](https://issues.apache.org/jira/browse/SPARK-27107) .
```
[ORC-452] Support converting MAP column from JSON to ORC Improvement
[ORC-447] Change the docker scripts to keep a persistent m2 cache
[ORC-463] Add `version` command
[ORC-475] ORC reader should lazily get filesystem
[ORC-476] Make SearchAgument kryo buffer size configurable
```
## How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#24096 from dongjoon-hyun/SPARK-27165.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
**ScalaTest 3.0.5 Release Notes**
**Bug Fixes**
- Fixed the implicit view not available problem when used with compile macro.
- Fixed a stack depth problem in RefSpecLike and fixture.SpecLike under Scala 2.13.
- Changed Framework and ScalaTestFramework to set spanScaleFactor for Runner object instances for different Runners using different class loaders. This fixed a problem whereby an incorrect Runner.spanScaleFactor could be used when the tests for multiple sbt project's were run concurrently.
- Fixed a bug in endsWith regex matcher.
**Improvements**
- Removed duplicated parsing code for -C in ArgsParser.
- Improved performance in WebBrowser.
- Documentation typo rectification.
- Improve validity of Junit XML reports.
- Improved performance by replacing all .size == 0 and .length == 0 to .isEmpty.
**Enhancements**
- Added 'C' option to -P, which will tell -P to use cached thread pool.
- External Dependencies Update
- Bumped up scala-js version to 0.6.22.
- Changed to depend on mockito-core, not mockito-all.
- Bumped up jmock version to 2.8.3.
- Bumped up junit version to 4.12.
- Removed dependency to scala-parser-combinators.
More details:
http://www.scalatest.org/release_notes/3.0.5
## How was this patch tested?
manual tests on local machine:
```
nohup build/sbt clean -Djline.terminal=jline.UnsupportedTerminal -Phadoop-2.7 -Pkubernetes -Phive-thriftserver -Pyarn -Pspark-ganglia-lgpl -Phive -Pkinesis-asl -Pmesos test > run.scalatest.log &
```
Closes#24042 from wangyum/SPARK-27120.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Fasterxml Jackson version before 2.9.8 is affected by multiple [CVEs](https://github.com/FasterXML/jackson-databind/issues/2186), we need to fix bump the dependent Jackson to 2.9.8.
## How was this patch tested?
Existing tests and offline benchmark.
I have run ```SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.datasources.json.JSONBenchmark"``` to check there is no performance degradation for this upgrade.
Closes#23965 from yanboliang/SPARK-27051.
Authored-by: Yanbo Liang <ybliang8@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Update Thrift to 0.12.0 to pick up bug and security fixes.
Changes: https://github.com/apache/thrift/blob/master/CHANGES.md
The important one is for https://issues.apache.org/jira/browse/THRIFT-4506
## How was this patch tested?
Existing tests. A quick local test suggests this works.
Closes#23935 from srowen/SPARK-27029.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Remove a few new JAXB dependencies that shouldn't be necessary now.
See https://github.com/apache/spark/pull/23890#issuecomment-468299922
## How was this patch tested?
Existing tests
Closes#23923 from srowen/SPARK-26986.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add reference JAXB impl for Java 9+ from Glassfish. Right now it's only apparently necessary in MLlib but can be expanded later.
## How was this patch tested?
Existing tests particularly PMML-related ones, which use JAXB.
This works on Java 11.
Closes#23890 from srowen/SPARK-26986.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Changed the `kubernetes-client` version to 4.1.2. Latest version fix error with exec credentials (used by aws eks) and this will be used to talk with kubernetes API server. Users can submit spark job to EKS api endpoint now with this patch.
## How was this patch tested?
unit tests and manual tests.
Closes#23814 from Jeffwan/update_k8s_sdk.
Authored-by: Jiaxin Shan <seedjeffwan@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Update to Parquet Java 1.10.1.
## How was this patch tested?
Added a test from HyukjinKwon that validates the notEq case from SPARK-26677.
Closes#23704 from rdblue/SPARK-26677-fix-noteq-parquet-bug.
Lead-authored-by: Ryan Blue <blue@apache.org>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Co-authored-by: Ryan Blue <rdblue@users.noreply.github.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Upgrade Apache Arrow to version 0.12.0. This includes the Java artifacts and fixes to enable usage with pyarrow 0.12.0
Version 0.12.0 includes the following selected fixes/improvements relevant to Spark users:
* Safe cast fails from numpy float64 array with nans to integer, ARROW-4258
* Java, Reduce heap usage for variable width vectors, ARROW-4147
* Binary identity cast not implemented, ARROW-4101
* pyarrow open_stream deprecated, use ipc.open_stream, ARROW-4098
* conversion to date object no longer needed, ARROW-3910
* Error reading IPC file with no record batches, ARROW-3894
* Signed to unsigned integer cast yields incorrect results when type sizes are the same, ARROW-3790
* from_pandas gives incorrect results when converting floating point to bool, ARROW-3428
* Import pyarrow fails if scikit-learn is installed from conda (boost-cpp / libboost issue), ARROW-3048
* Java update to official Flatbuffers version 1.9.0, ARROW-3175
complete list [here](https://issues.apache.org/jira/issues/?jql=project%20%3D%20ARROW%20AND%20status%20in%20(Resolved%2C%20Closed)%20AND%20fixVersion%20%3D%200.12.0)
PySpark requires the following fixes to work with PyArrow 0.12.0
* Encrypted pyspark worker fails due to ChunkedStream missing closed property
* pyarrow now converts dates as objects by default, which causes error because type is assumed datetime64
* ArrowTests fails due to difference in raised error message
* pyarrow.open_stream deprecated
* tests fail because groupby adds index column with duplicate name
## How was this patch tested?
Ran unit tests with pyarrow versions 0.8.0, 0.10.0, 0.11.1, 0.12.0
Closes#23657 from BryanCutler/arrow-upgrade-012.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR aims to update Apache ORC dependency to the latest version 1.5.4 released at Dec. 20. ([Release Notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12318320&version=12344187]))
```
[ORC-237] OrcFile.mergeFiles Specified block size is less than configured minimum value
[ORC-409] Changes for extending MemoryManagerImpl
[ORC-410] Fix a locale-dependent test in TestCsvReader
[ORC-416] Avoid opening data reader when there is no stripe
[ORC-417] Use dynamic Apache Maven mirror link
[ORC-419] Ensure to call `close` at RecordReaderImpl constructor exception
[ORC-432] openjdk 8 has a bug that prevents surefire from working
[ORC-435] Ability to read stripes that are greater than 2GB
[ORC-437] Make acid schema checks case insensitive
[ORC-411] Update build to work with Java 10.
[ORC-418] Fix broken docker build script
```
## How was this patch tested?
Build and pass Jenkins.
Closes#23364 from dongjoon-hyun/SPARK-26427.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Update to Scala 2.12.8
## How was this patch tested?
Existing tests.
Closes#23218 from srowen/SPARK-26266.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR aims to upgrade Janino compiler to the latest version 3.0.11. The followings are the changes from the [release note](http://janino-compiler.github.io/janino/changelog.html).
- Script with many "helper" variables.
- Java 9+ compatibility
- Compilation Error Messages Generated by JDK.
- Added experimental support for the "StackMapFrame" attribute; not active yet.
- Make Unparser more flexible.
- Fixed NPEs in various "toString()" methods.
- Optimize static method invocation with rvalue target expression.
- Added all missing "ClassFile.getConstant*Info()" methods, removing the necessity for many type casts.
## How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#23250 from dongjoon-hyun/SPARK-26298.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
When I ran spark-shell on JDK11+28(2018-09-25), It failed with the error below.
```
Exception in thread "main" java.lang.ExceptionInInitializerError
at org.apache.hadoop.util.StringUtils.<clinit>(StringUtils.java:80)
at org.apache.hadoop.security.SecurityUtil.getAuthenticationMethod(SecurityUtil.java:611)
at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:273)
at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:261)
at org.apache.hadoop.security.UserGroupInformation.loginUserFromSubject(UserGroupInformation.java:791)
at org.apache.hadoop.security.UserGroupInformation.getLoginUser(UserGroupInformation.java:761)
at org.apache.hadoop.security.UserGroupInformation.getCurrentUser(UserGroupInformation.java:634)
at org.apache.spark.util.Utils$.$anonfun$getCurrentUserName$1(Utils.scala:2427)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.util.Utils$.getCurrentUserName(Utils.scala:2427)
at org.apache.spark.SecurityManager.<init>(SecurityManager.scala:79)
at org.apache.spark.deploy.SparkSubmit.secMgr$lzycompute$1(SparkSubmit.scala:359)
at org.apache.spark.deploy.SparkSubmit.secMgr$1(SparkSubmit.scala:359)
at org.apache.spark.deploy.SparkSubmit.$anonfun$prepareSubmitEnvironment$9(SparkSubmit.scala:367)
at scala.Option.map(Option.scala:146)
at org.apache.spark.deploy.SparkSubmit.prepareSubmitEnvironment(SparkSubmit.scala:367)
at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:143)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:86)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:927)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:936)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.StringIndexOutOfBoundsException: begin 0, end 3, length 2
at java.base/java.lang.String.checkBoundsBeginEnd(String.java:3319)
at java.base/java.lang.String.substring(String.java:1874)
at org.apache.hadoop.util.Shell.<clinit>(Shell.java:52)
```
This is a Hadoop issue that fails to parse some java.version. It has been fixed from Hadoop-2.7.4(see [HADOOP-14586](https://issues.apache.org/jira/browse/HADOOP-14586)).
Note, Hadoop-2.7.5 or upper have another problem with Spark ([SPARK-25330](https://issues.apache.org/jira/browse/SPARK-25330)). So upgrading to 2.7.4 would be fine for now.
## How was this patch tested?
Existing tests.
Closes#23101 from tasanuma/SPARK-26134.
Authored-by: Takanobu Asanuma <tasanuma@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This PR makes Spark's default Scala version as 2.12, and Scala 2.11 will be the alternative version. This implies that Scala 2.12 will be used by our CI builds including pull request builds.
We'll update the Jenkins to include a new compile-only jobs for Scala 2.11 to ensure the code can be still compiled with Scala 2.11.
## How was this patch tested?
existing tests
Closes#22967 from dbtsai/scala2.12.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
…. Other related changes to get JDK 11 working, to test
## What changes were proposed in this pull request?
- Access `sun.misc.Cleaner` (Java 8) and `jdk.internal.ref.Cleaner` (JDK 9+) by reflection (note: the latter only works if illegal reflective access is allowed)
- Access `sun.misc.Unsafe.invokeCleaner` in Java 9+ instead of `sun.misc.Cleaner` (Java 8)
In order to test anything on JDK 11, I also fixed a few small things, which I include here:
- Fix minor JDK 11 compile issues
- Update scala plugin, Jetty for JDK 11, to facilitate tests too
This doesn't mean JDK 11 tests all pass now, but lots do. Note also that the JDK 9+ solution for the Cleaner has a big caveat.
## How was this patch tested?
Existing tests. Manually tested JDK 11 build and tests, and tests covering this change appear to pass. All Java 8 tests should still pass, but this change alone does not achieve full JDK 11 compatibility.
Closes#22993 from srowen/SPARK-24421.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Based on the release description of ANTRL 4.7.1., https://github.com/antlr/antlr4/releases, let us upgrade our parser to 4.7.1.
## How was this patch tested?
N/A
Closes#23005 from gatorsmile/upgradeAntlr4.7.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Upgrade ASM to 7.x to support JDK11
## How was this patch tested?
Existing tests.
Closes#22953 from dbtsai/asm7.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Py4J 0.10.8.1 is released on October 21st and is the first release of Py4J to support Python 3.7 officially. We had better have this to get the official support. Also, there are some patches related to garbage collections.
https://www.py4j.org/changelog.html#py4j-0-10-8-and-py4j-0-10-8-1
## How was this patch tested?
Pass the Jenkins.
Closes#22901 from dongjoon-hyun/SPARK-25891.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Changed the `kubernetes-client` version and refactored code that broke as a result
## How was this patch tested?
Unit and Integration tests
Closes#22820 from ifilonenko/SPARK-25828.
Authored-by: Ilan Filonenko <ifilondz@gmail.com>
Signed-off-by: Erik Erlandson <eerlands@redhat.com>
## What changes were proposed in this pull request?
We find below warnings when build spark project:
```
[warn] * com.google.code.findbugs:jsr305:3.0.0 is selected over 1.3.9
[warn] +- org.apache.hadoop:hadoop-common:2.7.3 (depends on 3.0.0)
[warn] +- org.apache.spark:spark-core_2.11:3.0.0-SNAPSHOT (depends on 1.3.9)
[warn] +- org.apache.spark:spark-network-common_2.11:3.0.0-SNAPSHOT (depends on 1.3.9)
[warn] +- org.apache.spark:spark-unsafe_2.11:3.0.0-SNAPSHOT (depends on 1.3.9)
```
So ideally we need to upgrade jsr305 from 1.3.9 to 3.0.0 to fix this warning
Upgrade one of the dependencies jsr305 version from 1.3.9 to 3.0.0
## How was this patch tested?
sbt "core/testOnly"
sbt "sql/testOnly"
Closes#22803 from daviddingly/master.
Authored-by: xiaoding <xiaoding@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Upgrade netty dependency from 4.1.17 to 4.1.30.
Explanation:
Currently when sending a ChunkedByteBuffer with more than 16 chunks over the network will trigger a "merge" of all the blocks into one big transient array that is then sent over the network. This is problematic as the total memory for all chunks can be high (2GB) and this would then trigger an allocation of 2GB to merge everything, which will create OOM errors.
And we can avoid this issue by upgrade the netty. https://github.com/netty/netty/pull/8038
## How was this patch tested?
Manual tests in some spark jobs.
Closes#22765 from lipzhu/SPARK-25757.
Authored-by: Zhu, Lipeng <lipzhu@ebay.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Remove Hadoop 2.6 references and make 2.7 the default.
Obviously, this is for master/3.0.0 only.
After this we can also get rid of the separate test jobs for Hadoop 2.6.
## How was this patch tested?
Existing tests
Closes#22615 from srowen/SPARK-25016.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR upgrade `lz4-java` to 1.5.0 get speed improvement.
**General speed improvements**
LZ4 decompression speed has always been a strong point. In v1.8.2, this gets even better, as it improves decompression speed by about 10%, thanks in a large part to suggestion from svpv .
For example, on a Mac OS-X laptop with an Intel Core i7-5557U CPU 3.10GHz,
running lz4 -bsilesia.tar compiled with default compiler llvm v9.1.0:
Version | v1.8.1 | v1.8.2 | Improvement
-- | -- | -- | --
Decompression speed | 2490 MB/s | 2770 MB/s | +11%
Compression speeds also receive a welcomed boost, though improvement is not evenly distributed, with higher levels benefiting quite a lot more.
Version | v1.8.1 | v1.8.2 | Improvement
-- | -- | -- | --
lz4 -1 | 504 MB/s | 516 MB/s | +2%
lz4 -9 | 23.2 MB/s | 25.6 MB/s | +10%
lz4 -12 | 3.5 Mb/s | 9.5 MB/s | +170%
More details:
https://github.com/lz4/lz4/releases/tag/v1.8.3
**Below is my benchmark result**
set `spark.sql.parquet.compression.codec` to `lz4` and disable orc benchmark, then run `FilterPushdownBenchmark`.
lz4-java 1.5.0:
```
[success] Total time: 5585 s, completed Sep 26, 2018 5:22:16 PM
```
lz4-java 1.4.0:
```
[success] Total time: 5591 s, completed Sep 26, 2018 5:22:24 PM
```
Some benchmark result:
```
lz4-java 1.5.0 Select 1 row with 500 filters: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Parquet Vectorized 1953 / 1980 0.0 1952502908.0 1.0X
Parquet Vectorized (Pushdown) 2541 / 2585 0.0 2541019869.0 0.8X
lz4-java 1.4.0 Select 1 row with 500 filters: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Parquet Vectorized 1979 / 2103 0.0 1979328144.0 1.0X
Parquet Vectorized (Pushdown) 2596 / 2909 0.0 2596222118.0 0.8X
```
Complete benchmark result:
https://issues.apache.org/jira/secure/attachment/12941360/FilterPushdownBenchmark-lz4-java-140-results.txthttps://issues.apache.org/jira/secure/attachment/12941361/FilterPushdownBenchmark-lz4-java-150-results.txt
## How was this patch tested?
manual tests
Closes#22551 from wangyum/SPARK-25539.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Before ORC 1.5.3, `orc.dictionary.key.threshold` and `hive.exec.orc.dictionary.key.size.threshold` are applied for all columns. This has been a big huddle to enable dictionary encoding. From ORC 1.5.3, `orc.column.encoding.direct` is added to enforce direct encoding selectively in a column-wise manner. This PR aims to add that feature by upgrading ORC from 1.5.2 to 1.5.3.
The followings are the patches in ORC 1.5.3 and this feature is the only one related to Spark directly.
```
ORC-406: ORC: Char(n) and Varchar(n) writers truncate to n bytes & corrupts multi-byte data (gopalv)
ORC-403: [C++] Add checks to avoid invalid offsets in InputStream
ORC-405: Remove calcite as a dependency from the benchmarks.
ORC-375: Fix libhdfs on gcc7 by adding #include <functional> two places.
ORC-383: Parallel builds fails with ConcurrentModificationException
ORC-382: Apache rat exclusions + add rat check to travis
ORC-401: Fix incorrect quoting in specification.
ORC-385: Change RecordReader to extend Closeable.
ORC-384: [C++] fix memory leak when loading non-ORC files
ORC-391: [c++] parseType does not accept underscore in the field name
ORC-397: Allow selective disabling of dictionary encoding. Original patch was by Mithun Radhakrishnan.
ORC-389: Add ability to not decode Acid metadata columns
```
## How was this patch tested?
Pass the Jenkins with newly added test cases.
Closes#22622 from dongjoon-hyun/SPARK-25635.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
Hi all,
Jackson is incompatible with upstream versions, therefore bump the Jackson version to a more recent one. I bumped into some issues with Azure CosmosDB that is using a more recent version of Jackson. This can be fixed by adding exclusions and then it works without any issues. So no breaking changes in the API's.
I would also consider bumping the version of Jackson in Spark. I would suggest to keep up to date with the dependencies, since in the future this issue will pop up more frequently.
## What changes were proposed in this pull request?
Bump Jackson to 2.9.6
## How was this patch tested?
Compiled and tested it locally to see if anything broke.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#21596 from Fokko/fd-bump-jackson.
Authored-by: Fokko Driesprong <fokkodriesprong@godatadriven.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR upgrades Spark's use of Janino from 3.0.9 to 3.0.10.
Note that 3.0.10 is a out-of-band release specifically for fixing an integer overflow issue in Janino's `ClassFile` reader. It is otherwise exactly the same as 3.0.9, so it's a low risk and compatible upgrade.
The integer overflow issue affects Spark SQL's codegen stats collection: when a generated Class file is huge, especially when the constant pool size is above `Short.MAX_VALUE`, Janino's `ClassFile reader` will throw an exception when Spark wants to parse the generated Class file to collect stats. So we'll miss the stats of some huge Class files.
The related Janino issue is: https://github.com/janino-compiler/janino/issues/58
## How was this patch tested?
Existing codegen tests.
Closes#22506 from rednaxelafx/upgrade-janino.
Authored-by: Kris Mok <kris.mok@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
How to reproduce permission issue:
```sh
# build spark
./dev/make-distribution.sh --name SPARK-25330 --tgz -Phadoop-2.7 -Phive -Phive-thriftserver -Pyarn
tar -zxf spark-2.4.0-SNAPSHOT-bin-SPARK-25330.tar && cd spark-2.4.0-SNAPSHOT-bin-SPARK-25330
export HADOOP_PROXY_USER=user_a
bin/spark-sql
export HADOOP_PROXY_USER=user_b
bin/spark-sql
```
```java
Exception in thread "main" java.lang.RuntimeException: org.apache.hadoop.security.AccessControlException: Permission denied: user=user_b, access=EXECUTE, inode="/tmp/hive-$%7Buser.name%7D/user_b/668748f2-f6c5-4325-a797-fd0a7ee7f4d4":user_b:hadoop:drwx------
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.check(FSPermissionChecker.java:319)
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkTraverse(FSPermissionChecker.java:259)
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkPermission(FSPermissionChecker.java:205)
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkPermission(FSPermissionChecker.java:190)
```
The issue occurred in this commit: feb886f209. This pr revert Hadoop 2.7 to 2.7.3 to avoid this issue.
## How was this patch tested?
unit tests and manual tests.
Closes#22327 from wangyum/SPARK-25330.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Upgrade chill to 0.9.3, Kryo to 4.0.2, to get bug fixes and improvements.
The resolved tickets includes:
- SPARK-25258 Upgrade kryo package to version 4.0.2
- SPARK-23131 Kryo raises StackOverflow during serializing GLR model
- SPARK-25176 Kryo fails to serialize a parametrised type hierarchy
More details:
https://github.com/twitter/chill/releases/tag/v0.9.3cc3910d501
## How was this patch tested?
Existing tests.
Closes#22179 from wangyum/SPARK-23131.
Lead-authored-by: Yuming Wang <yumwang@ebay.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Update to janino 3.0.9 to address Java 8 + Scala 2.12 incompatibility. The error manifests as test failures like this in `ExpressionEncoderSuite`:
```
- encode/decode for seq of string: List(abc, xyz) *** FAILED ***
java.lang.RuntimeException: Error while encoding: org.codehaus.janino.InternalCompilerException: failed to compile: org.codehaus.janino.InternalCompilerException: Compiling "GeneratedClass": Two non-abstract methods "public int scala.collection.TraversableOnce.size()" have the same parameter types, declaring type and return type
```
It comes up pretty immediately in any generated code that references Scala collections, and virtually always concerning the `size()` method.
## How was this patch tested?
Existing tests
Closes#22203 from srowen/SPARK-25029.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
When starting spark-shell from Mac terminal (MacOS High Sirra Version 10.13.6), Getting exception
[ERROR] Failed to construct terminal; falling back to unsupported
java.lang.NumberFormatException: For input string: "0x100"
at java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
at java.lang.Integer.parseInt(Integer.java:580)
at java.lang.Integer.valueOf(Integer.java:766)
at jline.internal.InfoCmp.parseInfoCmp(InfoCmp.java:59)
at jline.UnixTerminal.parseInfoCmp(UnixTerminal.java:242)
at jline.UnixTerminal.<init>(UnixTerminal.java:65)
at jline.UnixTerminal.<init>(UnixTerminal.java:50)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at java.lang.Class.newInstance(Class.java:442)
at jline.TerminalFactory.getFlavor(TerminalFactory.java:211)
This issue is due a jline defect : https://github.com/jline/jline2/issues/281, which is fixed in Jline 2.14.4, bumping up JLine version in spark to version >= Jline 2.14.4 will fix the issue
## How was this patch tested?
No new UT/automation test added, after upgrade to latest Jline version 2.14.6, manually tested spark shell features
Closes#22130 from vinodkc/br_UpgradeJLineVersion.
Authored-by: Vinod KC <vinod.kc.in@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Remove jets3t dependency, and bouncy castle which it brings in; update licenses and deps
Note this just takes over https://github.com/apache/spark/pull/21146
## How was this patch tested?
Existing tests.
Closes#22081 from srowen/SPARK-23654.
Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Upgrade Apache Arrow to 0.10.0
Version 0.10.0 has a number of bug fixes and improvements with the following pertaining directly to usage in Spark:
* Allow for adding BinaryType support ARROW-2141
* Bug fix related to array serialization ARROW-1973
* Python2 str will be made into an Arrow string instead of bytes ARROW-2101
* Python bytearrays are supported in as input to pyarrow ARROW-2141
* Java has common interface for reset to cleanup complex vectors in Spark ArrowWriter ARROW-1962
* Cleanup pyarrow type equality checks ARROW-2423
* ArrowStreamWriter should not hold references to ArrowBlocks ARROW-2632, ARROW-2645
* Improved low level handling of messages for RecordBatch ARROW-2704
## How was this patch tested?
existing tests
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#21939 from BryanCutler/arrow-upgrade-010.
## 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?
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 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?
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?
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?
Including the `-Pkubernetes` flag in a few places it was missed.
## How was this patch tested?
checkstyle, mima through manual tests.
Author: foxish <ramanathana@google.com>
Closes#20256 from foxish/SPARK-23063.
## 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.
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?
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?
There was a bug in Univocity Parser that causes the issue in SPARK-22516. This was fixed by upgrading from 2.5.4 to 2.5.9 version of the library :
**Executing**
```
spark.read.option("header","true").option("inferSchema", "true").option("multiLine", "true").option("comment", "g").csv("test_file_without_eof_char.csv").show()
```
**Before**
```
ERROR Executor: Exception in task 0.0 in stage 6.0 (TID 6)
com.univocity.parsers.common.TextParsingException: java.lang.IllegalArgumentException - Unable to skip 1 lines from line 2. End of input reached
...
Internal state when error was thrown: line=3, column=0, record=2, charIndex=31
at com.univocity.parsers.common.AbstractParser.handleException(AbstractParser.java:339)
at com.univocity.parsers.common.AbstractParser.parseNext(AbstractParser.java:475)
at org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anon$1.next(UnivocityParser.scala:281)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
```
**After**
```
+-------+-------+
|column1|column2|
+-------+-------+
| abc| def|
+-------+-------+
```
## How was this patch tested?
The already existing `CSVSuite.commented lines in CSV data` test was extended to parse the file also in multiline mode. The test input file was modified to also include a comment in the last line.
Author: smurakozi <smurakozi@gmail.com>
Closes#19906 from smurakozi/SPARK-22516.
## 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?
- Solves the issue described in the ticket by preserving reservation and allocation info in all cases (port handling included).
- upgrades to 1.4
- Adds extra debug level logging to make debugging easier in the future, for example we add reservation info when applicable.
```
17/09/29 14:53:07 DEBUG MesosCoarseGrainedSchedulerBackend: Accepting offer: f20de49b-dee3-45dd-a3c1-73418b7de891-O32 with attributes: Map() allocation info: role: "spark-prive"
reservation info: name: "ports"
type: RANGES
ranges {
range {
begin: 31000
end: 32000
}
}
role: "spark-prive"
reservation {
principal: "test"
}
allocation_info {
role: "spark-prive"
}
```
- Some style cleanup.
## How was this patch tested?
Manually by running the example in the ticket with and without a principal. Specifically I tested it on a dc/os 1.10 cluster with 7 nodes and played with reservations. From the master node in order to reserve resources I executed:
```for i in 0 1 2 3 4 5 6
do
curl -i \
-d slaveId=90ec65ea-1f7b-479f-a824-35d2527d6d26-S$i \
-d resources='[
{
"name": "cpus",
"type": "SCALAR",
"scalar": { "value": 2 },
"role": "spark-role",
"reservation": {
"principal": ""
}
},
{
"name": "mem",
"type": "SCALAR",
"scalar": { "value": 8026 },
"role": "spark-role",
"reservation": {
"principal": ""
}
}
]' \
-X POST http://master.mesos:5050/master/reserve
done
```
Nodes had 4 cpus (m3.xlarge instances) and I reserved either 2 or 4 cpus (all for a role).
I verified it launches tasks on nodes with reserved resources under `spark-role` role only if
a) there are remaining resources for (*) default role and the spark driver has no role assigned to it.
b) the spark driver has a role assigned to it and it is the same role used in reservations.
I also tested this locally on my machine.
Author: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Closes#19390 from skonto/fix_dynamic_reservation.
## 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?
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?
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?
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?
This PR exposes Netty memory usage for Spark's `TransportClientFactory` and `TransportServer`, including the details of each direct arena and heap arena metrics, as well as aggregated metrics. The purpose of adding the Netty metrics is to better know the memory usage of Netty in Spark shuffle, rpc and others network communications, and guide us to better configure the memory size of executors.
This PR doesn't expose these metrics to any sink, to leverage this feature, still requires to connect to either MetricsSystem or collect them back to Driver to display.
## How was this patch tested?
Add Unit test to verify it, also manually verified in real cluster.
Author: jerryshao <sshao@hortonworks.com>
Closes#18935 from jerryshao/SPARK-9104.
## What changes were proposed in this pull request?
There was a bug in Univocity Parser that causes the issue in SPARK-20978. This was fixed as below:
```scala
val df = spark.read.schema("a string, b string, unparsed string").option("columnNameOfCorruptRecord", "unparsed").csv(Seq("a").toDS())
df.show()
```
**Before**
```
java.lang.NullPointerException
at scala.collection.immutable.StringLike$class.stripLineEnd(StringLike.scala:89)
at scala.collection.immutable.StringOps.stripLineEnd(StringOps.scala:29)
at org.apache.spark.sql.execution.datasources.csv.UnivocityParser.org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$getCurrentInput(UnivocityParser.scala:56)
at org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$convert$1.apply(UnivocityParser.scala:207)
at org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$convert$1.apply(UnivocityParser.scala:207)
...
```
**After**
```
+---+----+--------+
| a| b|unparsed|
+---+----+--------+
| a|null| a|
+---+----+--------+
```
It was fixed in 2.5.0 and 2.5.4 was released. I guess it'd be safe to upgrade this.
## How was this patch tested?
Unit test added in `CSVSuite.scala`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19113 from HyukjinKwon/bump-up-univocity.
…build; fix some things that will be warnings or errors in 2.12; restore Scala 2.12 profile infrastructure
## What changes were proposed in this pull request?
This change adds back the infrastructure for a Scala 2.12 build, but does not enable it in the release or Python test scripts.
In order to make that meaningful, it also resolves compile errors that the code hits in 2.12 only, in a way that still works with 2.11.
It also updates dependencies to the earliest minor release of dependencies whose current version does not yet support Scala 2.12. This is in a sense covered by other JIRAs under the main umbrella, but implemented here. The versions below still work with 2.11, and are the _latest_ maintenance release in the _earliest_ viable minor release.
- Scalatest 2.x -> 3.0.3
- Chill 0.8.0 -> 0.8.4
- Clapper 1.0.x -> 1.1.2
- json4s 3.2.x -> 3.4.2
- Jackson 2.6.x -> 2.7.9 (required by json4s)
This change does _not_ fully enable a Scala 2.12 build:
- It will also require dropping support for Kafka before 0.10. Easy enough, just didn't do it yet here
- It will require recreating `SparkILoop` and `Main` for REPL 2.12, which is SPARK-14650. Possible to do here too.
What it does do is make changes that resolve much of the remaining gap without affecting the current 2.11 build.
## How was this patch tested?
Existing tests and build. Manually tested with `./dev/change-scala-version.sh 2.12` to verify it compiles, modulo the exceptions above.
Author: Sean Owen <sowen@cloudera.com>
Closes#18645 from srowen/SPARK-14280.
Mesos has secrets primitives for environment and file-based secrets, this PR adds that functionality to the Spark dispatcher and the appropriate configuration flags.
Unit tested and manually tested against a DC/OS cluster with Mesos 1.4.
Author: ArtRand <arand@soe.ucsc.edu>
Closes#18837 from ArtRand/spark-20812-dispatcher-secrets-and-labels.
## What changes were proposed in this pull request?
This PR bumps the ANTLR version to 4.7, and fixes a number of small parser related issues uncovered by the bump.
The main reason for upgrading is that in some cases the current version of ANTLR (4.5) can exhibit exponential slowdowns if it needs to parse boolean predicates. For example the following query will take forever to parse:
```sql
SELECT *
FROM RANGE(1000)
WHERE
TRUE
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
```
This is caused by a know bug in ANTLR (https://github.com/antlr/antlr4/issues/994), which was fixed in version 4.6.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#19042 from hvanhovell/SPARK-21830.
## What changes were proposed in this pull request?
Like Parquet, this PR aims to depend on the latest Apache ORC 1.4 for Apache Spark 2.3. There are key benefits for Apache ORC 1.4.
- Stability: Apache ORC 1.4.0 has many fixes and we can depend on ORC community more.
- Maintainability: Reduce the Hive dependency and can remove old legacy code later.
Later, we can get the following two key benefits by adding new ORCFileFormat in SPARK-20728 (#17980), too.
- Usability: User can use ORC data sources without hive module, i.e, -Phive.
- Speed: Use both Spark ColumnarBatch and ORC RowBatch together. This will be faster than the current implementation in Spark.
## How was this patch tested?
Pass the jenkins.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#18640 from dongjoon-hyun/SPARK-21422.
## What changes were proposed in this pull request?
This pr updated `lz4-java` to the latest (v1.4.0) and removed custom `LZ4BlockInputStream`. We currently use custom `LZ4BlockInputStream` to read concatenated byte stream in shuffle. But, this functionality has been implemented in the latest lz4-java (https://github.com/lz4/lz4-java/pull/105). So, we might update the latest to remove the custom `LZ4BlockInputStream`.
Major diffs between the latest release and v1.3.0 in the master are as follows (62f7547abb...6d4693f562);
- fixed NPE in XXHashFactory similarly
- Don't place resources in default package to support shading
- Fixes ByteBuffer methods failing to apply arrayOffset() for array-backed
- Try to load lz4-java from java.library.path, then fallback to bundled
- Add ppc64le binary
- Add s390x JNI binding
- Add basic LZ4 Frame v1.5.0 support
- enable aarch64 support for lz4-java
- Allow unsafeInstance() for ppc64le archiecture
- Add unsafeInstance support for AArch64
- Support 64-bit JNI build on Solaris
- Avoid over-allocating a buffer
- Allow EndMark to be incompressible for LZ4FrameInputStream.
- Concat byte stream
## How was this patch tested?
Existing tests.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18883 from maropu/SPARK-21276.
## 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.
