### What changes were proposed in this pull request?
This:
1. switches Spark to use shaded Hadoop clients, namely hadoop-client-api and hadoop-client-runtime, for Hadoop 3.x.
2. upgrade built-in version for Hadoop 3.x to Hadoop 3.2.2
Note that for Hadoop 2.7, we'll still use the same modules such as hadoop-client.
In order to still keep default Hadoop profile to be hadoop-3.2, this defines the following Maven properties:
```
hadoop-client-api.artifact
hadoop-client-runtime.artifact
hadoop-client-minicluster.artifact
```
which default to:
```
hadoop-client-api
hadoop-client-runtime
hadoop-client-minicluster
```
but all switch to `hadoop-client` when the Hadoop profile is hadoop-2.7. A side affect from this is we'll import the same dependency multiple times. For this I have to disable Maven enforcer `banDuplicatePomDependencyVersions`.
Besides above, there are the following changes:
- explicitly add a few dependencies which are imported via transitive dependencies from Hadoop jars, but are removed from the shaded client jars.
- removed the use of `ProxyUriUtils.getPath` from `ApplicationMaster` which is a server-side/private API.
- modified `IsolatedClientLoader` to exclude `hadoop-auth` jars when Hadoop version is 3.x. This change should only matter when we're not sharing Hadoop classes with Spark (which is _mostly_ used in tests).
### Why are the changes needed?
Hadoop 3.2.2 is released with new features and bug fixes, so it's good for the Spark community to adopt it. However, latest Hadoop versions starting from Hadoop 3.2.1 have upgraded to use Guava 27+. In order to resolve Guava conflicts, this takes the approach by switching to shaded client jars provided by Hadoop. This also has the benefits of avoid pulling other 3rd party dependencies from Hadoop side so as to avoid more potential future conflicts.
### Does this PR introduce _any_ user-facing change?
When people use Spark with `hadoop-provided` option, they should make sure class path contains `hadoop-client-api` and `hadoop-client-runtime` jars. In addition, they may need to make sure these jars appear before other Hadoop jars in the order. Otherwise, classes may be loaded from the other non-shaded Hadoop jars and cause potential conflicts.
### How was this patch tested?
Relying on existing tests.
Closes#30701 from sunchao/test-hadoop-3.2.2.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR aims to update `master` branch version to 3.2.0-SNAPSHOT.
### Why are the changes needed?
Start to prepare Apache Spark 3.2.0.
### Does this PR introduce _any_ user-facing change?
N/A.
### How was this patch tested?
Pass the CIs.
Closes#30606 from dongjoon-hyun/SPARK-3.2.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c936) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes#30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This switches Spark to use shaded Hadoop clients, namely hadoop-client-api and hadoop-client-runtime, for Hadoop 3.x. For Hadoop 2.7, we'll still use the same modules such as hadoop-client.
In order to still keep default Hadoop profile to be hadoop-3.2, this defines the following Maven properties:
```
hadoop-client-api.artifact
hadoop-client-runtime.artifact
hadoop-client-minicluster.artifact
```
which default to:
```
hadoop-client-api
hadoop-client-runtime
hadoop-client-minicluster
```
but all switch to `hadoop-client` when the Hadoop profile is hadoop-2.7. A side affect from this is we'll import the same dependency multiple times. For this I have to disable Maven enforcer `banDuplicatePomDependencyVersions`.
Besides above, there are the following changes:
- explicitly add a few dependencies which are imported via transitive dependencies from Hadoop jars, but are removed from the shaded client jars.
- removed the use of `ProxyUriUtils.getPath` from `ApplicationMaster` which is a server-side/private API.
- modified `IsolatedClientLoader` to exclude `hadoop-auth` jars when Hadoop version is 3.x. This change should only matter when we're not sharing Hadoop classes with Spark (which is _mostly_ used in tests).
### Why are the changes needed?
This serves two purposes:
- to unblock Spark from upgrading to Hadoop 3.2.2/3.3.0+. Latest Hadoop versions have upgraded to use Guava 27+ and in order to adopt the latest Hadoop versions in Spark, we'll need to resolve the Guava conflicts. This takes the approach by switching to shaded client jars provided by Hadoop.
- avoid pulling 3rd party dependencies from Hadoop and avoid potential future conflicts.
### Does this PR introduce _any_ user-facing change?
When people use Spark with `hadoop-provided` option, they should make sure class path contains `hadoop-client-api` and `hadoop-client-runtime` jars. In addition, they may need to make sure these jars appear before other Hadoop jars in the order. Otherwise, classes may be loaded from the other non-shaded Hadoop jars and cause potential conflicts.
### How was this patch tested?
Relying on existing tests.
Closes#29843 from sunchao/SPARK-29250.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.1.0-SNAPSHOT.
### Why are the changes needed?
N/A
### Does this PR introduce any user-facing change?
N/A
### How was this patch tested?
N/A
Closes#27698 from gatorsmile/updateVersion.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
1. Revert "Preparing development version 3.0.1-SNAPSHOT": 56dcd79
2. Revert "Preparing Spark release v3.0.0-preview2-rc2": c216ef1
### Why are the changes needed?
Shouldn't change master.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
manual test:
https://github.com/apache/spark/compare/5de5e46..wangyum:revert-masterCloses#26915 from wangyum/revert-master.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
To push the built jars to maven release repository, we need to remove the 'SNAPSHOT' tag from the version name.
Made the following changes in this PR:
* Update all the `3.0.0-SNAPSHOT` version name to `3.0.0-preview`
* Update the sparkR version number check logic to allow jvm version like `3.0.0-preview`
**Please note those changes were generated by the release script in the past, but this time since we manually add tags on master branch, we need to manually apply those changes too.**
We shall revert the changes after 3.0.0-preview release passed.
### Why are the changes needed?
To make the maven release repository to accept the built jars.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
### What changes were proposed in this pull request?
To push the built jars to maven release repository, we need to remove the 'SNAPSHOT' tag from the version name.
Made the following changes in this PR:
* Update all the `3.0.0-SNAPSHOT` version name to `3.0.0-preview`
* Update the PySpark version from `3.0.0.dev0` to `3.0.0`
**Please note those changes were generated by the release script in the past, but this time since we manually add tags on master branch, we need to manually apply those changes too.**
We shall revert the changes after 3.0.0-preview release passed.
### Why are the changes needed?
To make the maven release repository to accept the built jars.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26243 from jiangxb1987/3.0.0-preview-prepare.
Lead-authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
The Pyspark Kinesis tests are failing, at least in master:
```
======================================================================
ERROR: test_kinesis_stream (pyspark.streaming.tests.test_kinesis.KinesisStreamTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jenkins/workspace/SparkPullRequestBuilder2/python/pyspark/streaming/tests/test_kinesis.py", line 44, in test_kinesis_stream
kinesisTestUtils = self.ssc._jvm.org.apache.spark.streaming.kinesis.KinesisTestUtils(2)
File "/home/jenkins/workspace/SparkPullRequestBuilder2/python/lib/py4j-0.10.8.1-src.zip/py4j/java_gateway.py", line 1554, in __call__
answer, self._gateway_client, None, self._fqn)
File "/home/jenkins/workspace/SparkPullRequestBuilder2/python/lib/py4j-0.10.8.1-src.zip/py4j/protocol.py", line 328, in get_return_value
format(target_id, ".", name), value)
Py4JJavaError: An error occurred while calling None.org.apache.spark.streaming.kinesis.KinesisTestUtils.
: java.lang.NoSuchMethodError: com.amazonaws.regions.Region.getAvailableEndpoints()Ljava/util/Collection;
at org.apache.spark.streaming.kinesis.KinesisTestUtils$.$anonfun$getRegionNameByEndpoint$1(KinesisTestUtils.scala:211)
at org.apache.spark.streaming.kinesis.KinesisTestUtils$.$anonfun$getRegionNameByEndpoint$1$adapted(KinesisTestUtils.scala:211)
at scala.collection.Iterator.find(Iterator.scala:993)
at scala.collection.Iterator.find$(Iterator.scala:990)
at scala.collection.AbstractIterator.find(Iterator.scala:1429)
at scala.collection.IterableLike.find(IterableLike.scala:81)
at scala.collection.IterableLike.find$(IterableLike.scala:80)
at scala.collection.AbstractIterable.find(Iterable.scala:56)
at org.apache.spark.streaming.kinesis.KinesisTestUtils$.getRegionNameByEndpoint(KinesisTestUtils.scala:211)
at org.apache.spark.streaming.kinesis.KinesisTestUtils.<init>(KinesisTestUtils.scala:46)
...
```
The non-Python Kinesis tests are fine though. It turns out that this is because Pyspark tests use the output of the Spark assembly, and it pulls in `hadoop-cloud`, which in turn pulls in an old AWS Java SDK.
Per Steve Loughran (below), it seems like we can just resolve this by excluding the aws-java-sdk dependency. See the attached PR for some more detail about the debugging and other options.
See https://github.com/apache/spark/pull/25558#issuecomment-524042709Closes#25559 from srowen/KinesisTest.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
This patch adds the binding classes to enable spark to switch dataframe output to using the S3A zero-rename committers shipping in Hadoop 3.1+. It adds a source tree into the hadoop-cloud-storage module which only compiles with the hadoop-3.2 profile, and contains a binding for normal output and a specific bridge class for Parquet (as the parquet output format requires a subclass of `ParquetOutputCommitter`.
Commit algorithms are a critical topic. There's no formal proof of correctness, but the algorithms are documented an analysed in [A Zero Rename Committer](https://github.com/steveloughran/zero-rename-committer/releases). This also reviews the classic v1 and v2 algorithms, IBM's swift committer and the one from EMRFS which they admit was based on the concepts implemented here.
Test-wise
* There's a public set of scala test suites [on github](https://github.com/hortonworks-spark/cloud-integration)
* We have run integration tests against Spark on Yarn clusters.
* This code has been shipping for ~12 months in HDP-3.x.
Closes#24970 from steveloughran/cloud/SPARK-23977-s3a-committer.
Authored-by: Steve Loughran <stevel@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.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 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>
## 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 patch is to bump the master branch version to 3.0.0-SNAPSHOT.
## How was this patch tested?
N/A
Closes#22606 from gatorsmile/bump3.0.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In the dev list, we can still discuss whether the next version is 2.5.0 or 3.0.0. Let us first bump the master branch version to `2.5.0-SNAPSHOT`.
## How was this patch tested?
N/A
Closes#22426 from gatorsmile/bumpVersionMaster.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## 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 patch bumps the master branch version to `2.4.0-SNAPSHOT`.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20222 from gatorsmile/bump24.
## What changes were proposed in this pull request?
Add a new `spark-hadoop-cloud` module and maven profile to pull in object store support from `hadoop-openstack`, `hadoop-aws` and `hadoop-azure` (Hadoop 2.7+) JARs, along with their dependencies, fixing up the dependencies so that everything works, in particular Jackson.
It restores `s3n://` access to S3, adds its `s3a://` replacement, OpenStack `swift://` and azure `wasb://`.
There's a documentation page, `cloud_integration.md`, which covers the basic details of using Spark with object stores, referring the reader to the supplier's own documentation, with specific warnings on security and the possible mismatch between a store's behavior and that of a filesystem. In particular, users are advised be very cautious when trying to use an object store as the destination of data, and to consult the documentation of the storage supplier and the connector.
(this is the successor to #12004; I can't re-open it)
## How was this patch tested?
Downstream tests exist in [https://github.com/steveloughran/spark-cloud-examples/tree/master/cloud-examples](https://github.com/steveloughran/spark-cloud-examples/tree/master/cloud-examples)
Those verify that the dependencies are sufficient to allow downstream applications to work with s3a, azure wasb and swift storage connectors, and perform basic IO & dataframe operations thereon. All seems well.
Manually clean build & verify that assembly contains the relevant aws-* hadoop-* artifacts on Hadoop 2.6; azure on a hadoop-2.7 profile.
SBT build: `build/sbt -Phadoop-cloud -Phadoop-2.7 package`
maven build `mvn install -Phadoop-cloud -Phadoop-2.7`
This PR *does not* update `dev/deps/spark-deps-hadoop-2.7` or `dev/deps/spark-deps-hadoop-2.6`, because unless the hadoop-cloud profile is enabled, no extra JARs show up in the dependency list. The dependency check in Jenkins isn't setting the property, so the new JARs aren't visible.
Author: Steve Loughran <stevel@apache.org>
Author: Steve Loughran <stevel@hortonworks.com>
Closes#17834 from steveloughran/cloud/SPARK-7481-current.
