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[SPARK-20343][BUILD] Avoid Unidoc build only if Hadoop 2.6 is explicitly set in SBT build

hyukjinkwon authored 7 years ago

## What changes were proposed in this pull request?

This PR proposes two things as below:

- Avoid Unidoc build only if Hadoop 2.6 is explicitly set in SBT build

  Due to a different dependency resolution in SBT & Unidoc by an unknown reason, the documentation build fails on a specific machine & environment in Jenkins but it was unable to reproduce.

  So, this PR just checks an environment variable `AMPLAB_JENKINS_BUILD_PROFILE` that is set in Hadoop 2.6 SBT build against branches on Jenkins, and then disables Unidoc build. **Note that PR builder will still build it with Hadoop 2.6 & SBT.**

  ```
  ========================================================================
  Building Unidoc API Documentation
  ========================================================================
  [info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments:  -Phadoop-2.6 -Pmesos -Pkinesis-asl -Pyarn -Phive-thriftserver -Phive unidoc
  Using /usr/java/jdk1.8.0_60 as default JAVA_HOME.
  ...
  ```

  I checked the environment variables from the logs (first bit) as below:

  - **spark-master-test-sbt-hadoop-2.6** (this one is being failed) - https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-sbt-hadoop-2.6/lastBuild/consoleFull

  ```
  JAVA_HOME=/usr/java/jdk1.8.0_60
  JAVA_7_HOME=/usr/java/jdk1.7.0_79
  SPARK_BRANCH=master
  AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.6   <- I use this variable
  AMPLAB_JENKINS="true"
  ```
  - spark-master-test-sbt-hadoop-2.7 - https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-sbt-hadoop-2.7/lastBuild/consoleFull

  ```
  JAVA_HOME=/usr/java/jdk1.8.0_60
  JAVA_7_HOME=/usr/java/jdk1.7.0_79
  SPARK_BRANCH=master
  AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.7
  AMPLAB_JENKINS="true"
  ```

  - spark-master-test-maven-hadoop-2.6 - https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.6/lastBuild/consoleFull

  ```
  JAVA_HOME=/usr/java/jdk1.8.0_60
  JAVA_7_HOME=/usr/java/jdk1.7.0_79
  HADOOP_PROFILE=hadoop-2.6
  HADOOP_VERSION=
  SPARK_BRANCH=master
  AMPLAB_JENKINS="true"
  ```

  - spark-master-test-maven-hadoop-2.7 - https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.7/lastBuild/consoleFull

  ```
  JAVA_HOME=/usr/java/jdk1.8.0_60
  JAVA_7_HOME=/usr/java/jdk1.7.0_79
  HADOOP_PROFILE=hadoop-2.7
  HADOOP_VERSION=
  SPARK_BRANCH=master
  AMPLAB_JENKINS="true"
  ```

  - PR builder - https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/75843/consoleFull

  ```
  JENKINS_MASTER_HOSTNAME=amp-jenkins-master
  JAVA_HOME=/usr/java/jdk1.8.0_60
  JAVA_7_HOME=/usr/java/jdk1.7.0_79
  ```

  Assuming from other logs in branch-2.1

    - SBT & Hadoop 2.6 against branch-2.1 https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-branch-2.1-test-sbt-hadoop-2.6/lastBuild/consoleFull

      ```
      JAVA_HOME=/usr/java/jdk1.8.0_60
      JAVA_7_HOME=/usr/java/jdk1.7.0_79
      SPARK_BRANCH=branch-2.1
      AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.6
      AMPLAB_JENKINS="true"
      ```

    - Maven & Hadoop 2.6 against branch-2.1 https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-branch-2.1-test-maven-hadoop-2.6/lastBuild/consoleFull

      ```
      JAVA_HOME=/usr/java/jdk1.8.0_60
      JAVA_7_HOME=/usr/java/jdk1.7.0_79
      HADOOP_PROFILE=hadoop-2.6
      HADOOP_VERSION=
      SPARK_BRANCH=branch-2.1
      AMPLAB_JENKINS="true"
      ```

  We have been using the same convention for those variables. These are actually being used in `run-tests.py` script - here https://github.com/apache/spark/blob/master/dev/run-tests.py#L519-L520

- Revert the previous try

  After https://github.com/apache/spark/pull/17651, it seems the build still fails on SBT Hadoop 2.6 master.

  I am unable to reproduce this - https://github.com/apache/spark/pull/17477#issuecomment-294094092 and the reviewer was too. So, this got merged as it looks the only way to verify this is to merge it currently (as no one seems able to reproduce this).

## How was this patch tested?

I only checked `is_hadoop_version_2_6 = os.environ.get("AMPLAB_JENKINS_BUILD_PROFILE") == "hadoop2.6"` is working fine as expected as below:

```python
>>> import collections
>>> os = collections.namedtuple('os', 'environ')(environ={"AMPLAB_JENKINS_BUILD_PROFILE": "hadoop2.6"})
>>> print(not os.environ.get("AMPLAB_JENKINS_BUILD_PROFILE") == "hadoop2.6")
False
>>> os = collections.namedtuple('os', 'environ')(environ={"AMPLAB_JENKINS_BUILD_PROFILE": "hadoop2.7"})
>>> print(not os.environ.get("AMPLAB_JENKINS_BUILD_PROFILE") == "hadoop2.6")
True
>>> os = collections.namedtuple('os', 'environ')(environ={})
>>> print(not os.environ.get("AMPLAB_JENKINS_BUILD_PROFILE") == "hadoop2.6")
True
```

I tried many ways but I was unable to reproduce this in my local. Sean also tried the way I did but he was also unable to reproduce this.

Please refer the comments in https://github.com/apache/spark/pull/17477#issuecomment-294094092

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #17669 from HyukjinKwon/revert-SPARK-20343.

35378766

35378766 7 years ago

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Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.

Building Spark

Spark is built using Apache Maven. To build Spark and its example programs, run:

build/mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.)

You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".

For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".

Interactive Scala Shell

The easiest way to start using Spark is through the Scala shell:

./bin/spark-shell

Try the following command, which should return 1000:

scala> sc.parallelize(1 to 1000).count()

Interactive Python Shell

Alternatively, if you prefer Python, you can use the Python shell:

./bin/pyspark

And run the following command, which should also return 1000:

>>> sc.parallelize(range(1000)).count()

Example Programs

Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:

./bin/run-example SparkPi

will run the Pi example locally.

You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be a mesos:// or spark:// URL, "yarn" to run on YARN, and "local" to run locally with one thread, or "local[N]" to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:

MASTER=spark://host:7077 ./bin/run-example SparkPi

Many of the example programs print usage help if no params are given.

Running Tests

Testing first requires building Spark. Once Spark is built, tests can be run using:

./dev/run-tests

Please see the guidance on how to run tests for a module, or individual tests.

A Note About Hadoop Versions

Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.

Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.

Configuration

Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.

Contributing

Please review the Contribution to Spark guide for information on how to get started contributing to the project.