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Commit 932d8002 authored by Sean Owen's avatar Sean Owen
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[SPARK-15333][DOCS] Reorganize building-spark.md; rationalize vs wiki 

## What changes were proposed in this pull request?

See JIRA for the motivation. The changes are almost entirely movement of text and edits to sections. Minor changes to text include:

- Copying in / merging text from the "Useful Developer Tools" wiki, in areas of
  - Docker
  - R
  - Running one test
- standardizing on ./build/mvn not mvn, and likewise for ./build/sbt
- correcting some typos
- standardizing code block formatting

No text has been removed from this doc; text has been imported from the https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools wiki

## How was this patch tested?

Jekyll doc build and inspection of resulting HTML in browser.

Author: Sean Owen <sowen@cloudera.com>

Closes #13124 from srowen/SPARK-15333.
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...@@ -7,48 +7,18 @@ redirect_from: "building-with-maven.html" ...@@ -7,48 +7,18 @@ redirect_from: "building-with-maven.html"
* This will become a table of contents (this text will be scraped). * This will become a table of contents (this text will be scraped).
{:toc} {:toc}
Building Spark using Maven requires Maven 3.3.9 or newer and Java 7+. # Building Apache Spark
The Spark build can supply a suitable Maven binary; see below.
# Building with `build/mvn`
Spark now comes packaged with a self-contained Maven installation to ease building and deployment of Spark from source located under the `build/` directory. This script will automatically download and setup all necessary build requirements ([Maven](https://maven.apache.org/), [Scala](http://www.scala-lang.org/), and [Zinc](https://github.com/typesafehub/zinc)) locally within the `build/` directory itself. It honors any `mvn` binary if present already, however, will pull down its own copy of Scala and Zinc regardless to ensure proper version requirements are met. `build/mvn` execution acts as a pass through to the `mvn` call allowing easy transition from previous build methods. As an example, one can build a version of Spark as follows:
{% highlight bash %}
build/mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package
{% endhighlight %}
Other build examples can be found below.
**Note:** When building on an encrypted filesystem (if your home directory is encrypted, for example), then the Spark build might fail with a "Filename too long" error. As a workaround, add the following in the configuration args of the `scala-maven-plugin` in the project `pom.xml`:
<arg>-Xmax-classfile-name</arg>
<arg>128</arg>
and in `project/SparkBuild.scala` add:
scalacOptions in Compile ++= Seq("-Xmax-classfile-name", "128"),
to the `sharedSettings` val. See also [this PR](https://github.com/apache/spark/pull/2883/files) if you are unsure of where to add these lines.
# Building a Runnable Distribution
To create a Spark distribution like those distributed by the ## Apache Maven
[Spark Downloads](http://spark.apache.org/downloads.html) page, and that is laid out so as
to be runnable, use `./dev/make-distribution.sh` in the project root directory. It can be configured
with Maven profile settings and so on like the direct Maven build. Example:
./dev/make-distribution.sh --name custom-spark --tgz -Psparkr -Phadoop-2.4 -Phive -Phive-thriftserver -Pyarn The Maven-based build is the build of reference for Apache Spark.
Building Spark using Maven requires Maven 3.3.9 or newer and Java 7+.
For more information on usage, run `./dev/make-distribution.sh --help`
# Setting up Maven's Memory Usage ### Setting up Maven's Memory Usage
You'll need to configure Maven to use more memory than usual by setting `MAVEN_OPTS`. We recommend the following settings: You'll need to configure Maven to use more memory than usual by setting `MAVEN_OPTS`. We recommend the following settings:
{% highlight bash %} export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"
export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"
{% endhighlight %}
If you don't run this, you may see errors like the following: If you don't run this, you may see errors like the following:
...@@ -65,7 +35,26 @@ You can fix this by setting the `MAVEN_OPTS` variable as discussed before. ...@@ -65,7 +35,26 @@ You can fix this by setting the `MAVEN_OPTS` variable as discussed before.
* For Java 8 and above this step is not required. * For Java 8 and above this step is not required.
* If using `build/mvn` with no `MAVEN_OPTS` set, the script will automate this for you. * If using `build/mvn` with no `MAVEN_OPTS` set, the script will automate this for you.
# Specifying the Hadoop Version ### build/mvn
Spark now comes packaged with a self-contained Maven installation to ease building and deployment of Spark from source located under the `build/` directory. This script will automatically download and setup all necessary build requirements ([Maven](https://maven.apache.org/), [Scala](http://www.scala-lang.org/), and [Zinc](https://github.com/typesafehub/zinc)) locally within the `build/` directory itself. It honors any `mvn` binary if present already, however, will pull down its own copy of Scala and Zinc regardless to ensure proper version requirements are met. `build/mvn` execution acts as a pass through to the `mvn` call allowing easy transition from previous build methods. As an example, one can build a version of Spark as follows:
./build/mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package
Other build examples can be found below.
## Building a Runnable Distribution
To create a Spark distribution like those distributed by the
[Spark Downloads](http://spark.apache.org/downloads.html) page, and that is laid out so as
to be runnable, use `./dev/make-distribution.sh` in the project root directory. It can be configured
with Maven profile settings and so on like the direct Maven build. Example:
./dev/make-distribution.sh --name custom-spark --tgz -Psparkr -Phadoop-2.4 -Phive -Phive-thriftserver -Pyarn
For more information on usage, run `./dev/make-distribution.sh --help`
## Specifying the Hadoop Version
Because HDFS is not protocol-compatible across versions, if you want to read from HDFS, you'll need to build Spark against the specific HDFS version in your environment. You can do this through the `hadoop.version` property. If unset, Spark will build against Hadoop 2.2.0 by default. Note that certain build profiles are required for particular Hadoop versions: Because HDFS is not protocol-compatible across versions, if you want to read from HDFS, you'll need to build Spark against the specific HDFS version in your environment. You can do this through the `hadoop.version` property. If unset, Spark will build against Hadoop 2.2.0 by default. Note that certain build profiles are required for particular Hadoop versions:
...@@ -87,87 +76,63 @@ You can enable the `yarn` profile and optionally set the `yarn.version` property ...@@ -87,87 +76,63 @@ You can enable the `yarn` profile and optionally set the `yarn.version` property
Examples: Examples:
{% highlight bash %} # Apache Hadoop 2.2.X
./build/mvn -Pyarn -Phadoop-2.2 -DskipTests clean package
# Apache Hadoop 2.2.X # Apache Hadoop 2.3.X
mvn -Pyarn -Phadoop-2.2 -DskipTests clean package ./build/mvn -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -DskipTests clean package
# Apache Hadoop 2.3.X # Apache Hadoop 2.4.X or 2.5.X
mvn -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -DskipTests clean package ./build/mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=VERSION -DskipTests clean package
# Apache Hadoop 2.4.X or 2.5.X # Apache Hadoop 2.6.X
mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=VERSION -DskipTests clean package ./build/mvn -Pyarn -Phadoop-2.6 -Dhadoop.version=2.6.0 -DskipTests clean package
# Apache Hadoop 2.6.X # Apache Hadoop 2.7.X and later
mvn -Pyarn -Phadoop-2.6 -Dhadoop.version=2.6.0 -DskipTests clean package ./build/mvn -Pyarn -Phadoop-2.7 -Dhadoop.version=VERSION -DskipTests clean package
# Apache Hadoop 2.7.X and later # Different versions of HDFS and YARN.
mvn -Pyarn -Phadoop-2.7 -Dhadoop.version=VERSION -DskipTests clean package ./build/mvn -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -Dyarn.version=2.2.0 -DskipTests clean package
# Different versions of HDFS and YARN. ## Building With Hive and JDBC Support
mvn -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -Dyarn.version=2.2.0 -DskipTests clean package
{% endhighlight %}
# Building With Hive and JDBC Support
To enable Hive integration for Spark SQL along with its JDBC server and CLI, To enable Hive integration for Spark SQL along with its JDBC server and CLI,
add the `-Phive` and `Phive-thriftserver` profiles to your existing build options. add the `-Phive` and `Phive-thriftserver` profiles to your existing build options.
By default Spark will build with Hive 1.2.1 bindings. By default Spark will build with Hive 1.2.1 bindings.
{% highlight bash %}
# Apache Hadoop 2.4.X with Hive 1.2.1 support
mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -Phive -Phive-thriftserver -DskipTests clean package
{% endhighlight %}
# Building for Scala 2.10
To produce a Spark package compiled with Scala 2.10, use the `-Dscala-2.10` property:
./dev/change-scala-version.sh 2.10
mvn -Pyarn -Phadoop-2.4 -Dscala-2.10 -DskipTests clean package
# PySpark Tests with Maven
If you are building PySpark and wish to run the PySpark tests you will need to build Spark with hive support. # Apache Hadoop 2.4.X with Hive 1.2.1 support
./build/mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -Phive -Phive-thriftserver -DskipTests clean package
{% highlight bash %} ## Packaging without Hadoop Dependencies for YARN
build/mvn -DskipTests clean package -Phive
./python/run-tests
{% endhighlight %}
The run-tests script also can be limited to a specific Python version or a specific module The assembly directory produced by `mvn package` will, by default, include all of Spark's
dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this
./python/run-tests --python-executables=python --modules=pyspark-sql causes multiple versions of these to appear on executor classpaths: the version packaged in
the Spark assembly and the version on each node, included with `yarn.application.classpath`.
**Note:** You can also run Python tests with an sbt build, provided you build Spark with hive support. The `hadoop-provided` profile builds the assembly without including Hadoop-ecosystem projects,
like ZooKeeper and Hadoop itself.
# Spark Tests in Maven
Tests are run by default via the [ScalaTest Maven plugin](http://www.scalatest.org/user_guide/using_the_scalatest_maven_plugin).
Some of the tests require Spark to be packaged first, so always run `mvn package` with `-DskipTests` the first time. The following is an example of a correct (build, test) sequence: ## Building for Scala 2.10
To produce a Spark package compiled with Scala 2.10, use the `-Dscala-2.10` property:
mvn -Pyarn -Phadoop-2.3 -DskipTests -Phive -Phive-thriftserver clean package
mvn -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver test
The ScalaTest plugin also supports running only a specific test suite as follows:
mvn -Dhadoop.version=... -DwildcardSuites=org.apache.spark.repl.ReplSuite test ./dev/change-scala-version.sh 2.10
./build/mvn -Pyarn -Phadoop-2.4 -Dscala-2.10 -DskipTests clean package
# Building submodules individually ## Building submodules individually
It's possible to build Spark sub-modules using the `mvn -pl` option. It's possible to build Spark sub-modules using the `mvn -pl` option.
For instance, you can build the Spark Streaming module using: For instance, you can build the Spark Streaming module using:
{% highlight bash %} ./build/mvn -pl :spark-streaming_2.11 clean install
mvn -pl :spark-streaming_2.11 clean install
{% endhighlight %}
where `spark-streaming_2.11` is the `artifactId` as defined in `streaming/pom.xml` file. where `spark-streaming_2.11` is the `artifactId` as defined in `streaming/pom.xml` file.
# Continuous Compilation ## Continuous Compilation
We use the scala-maven-plugin which supports incremental and continuous compilation. E.g. We use the scala-maven-plugin which supports incremental and continuous compilation. E.g.
mvn scala:cc ./build/mvn scala:cc
should run continuous compilation (i.e. wait for changes). However, this has not been tested should run continuous compilation (i.e. wait for changes). However, this has not been tested
extensively. A couple of gotchas to note: extensively. A couple of gotchas to note:
...@@ -182,86 +147,138 @@ the `spark-parent` module). ...@@ -182,86 +147,138 @@ the `spark-parent` module).
Thus, the full flow for running continuous-compilation of the `core` submodule may look more like: Thus, the full flow for running continuous-compilation of the `core` submodule may look more like:
$ mvn install $ ./build/mvn install
$ cd core $ cd core
$ mvn scala:cc $ ../build/mvn scala:cc
# Building Spark with IntelliJ IDEA or Eclipse ## Speeding up Compilation with Zinc
For help in setting up IntelliJ IDEA or Eclipse for Spark development, and troubleshooting, refer to the [Zinc](https://github.com/typesafehub/zinc) is a long-running server version of SBT's incremental
[wiki page for IDE setup](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools#UsefulDeveloperTools-IDESetup). compiler. When run locally as a background process, it speeds up builds of Scala-based projects
like Spark. Developers who regularly recompile Spark with Maven will be the most interested in
Zinc. The project site gives instructions for building and running `zinc`; OS X users can
install it using `brew install zinc`.
# Running Java 8 Test Suites If using the `build/mvn` package `zinc` will automatically be downloaded and leveraged for all
builds. This process will auto-start after the first time `build/mvn` is called and bind to port
3030 unless the `ZINC_PORT` environment variable is set. The `zinc` process can subsequently be
shut down at any time by running `build/zinc-<version>/bin/zinc -shutdown` and will automatically
restart whenever `build/mvn` is called.
Running only Java 8 tests and nothing else. ## Building with SBT
mvn install -DskipTests Maven is the official build tool recommended for packaging Spark, and is the *build of reference*.
mvn -pl :java8-tests_2.11 test But SBT is supported for day-to-day development since it can provide much faster iterative
compilation. More advanced developers may wish to use SBT.
or The SBT build is derived from the Maven POM files, and so the same Maven profiles and variables
can be set to control the SBT build. For example:
sbt java8-tests/test ./build/sbt -Pyarn -Phadoop-2.3 package
Java 8 tests are automatically enabled when a Java 8 JDK is detected. To avoid the overhead of launching sbt each time you need to re-compile, you can launch sbt
If you have JDK 8 installed but it is not the system default, you can set JAVA_HOME to point to JDK 8 before running the tests. in interactive mode by running `build/sbt`, and then run all build commands at the command
prompt. For more recommendations on reducing build time, refer to the
[wiki page](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools#UsefulDeveloperTools-ReducingBuildTimes).
# Running Docker based Integration Test Suites ## Encrypted Filesystems
Running only docker based integration tests and nothing else. When building on an encrypted filesystem (if your home directory is encrypted, for example), then the Spark build might fail with a "Filename too long" error. As a workaround, add the following in the configuration args of the `scala-maven-plugin` in the project `pom.xml`:
mvn install -DskipTests <arg>-Xmax-classfile-name</arg>
mvn -Pdocker-integration-tests -pl :spark-docker-integration-tests_2.11 <arg>128</arg>
or and in `project/SparkBuild.scala` add:
sbt docker-integration-tests/test scalacOptions in Compile ++= Seq("-Xmax-classfile-name", "128"),
to the `sharedSettings` val. See also [this PR](https://github.com/apache/spark/pull/2883/files) if you are unsure of where to add these lines.
# Packaging without Hadoop Dependencies for YARN ## IntelliJ IDEA or Eclipse
The assembly directory produced by `mvn package` will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with `yarn.application.classpath`. The `hadoop-provided` profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself. For help in setting up IntelliJ IDEA or Eclipse for Spark development, and troubleshooting, refer to the
[wiki page for IDE setup](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools#UsefulDeveloperTools-IDESetup).
# Building with SBT
Maven is the official build tool recommended for packaging Spark, and is the *build of reference*. # Running Tests
But SBT is supported for day-to-day development since it can provide much faster iterative
compilation. More advanced developers may wish to use SBT.
The SBT build is derived from the Maven POM files, and so the same Maven profiles and variables Tests are run by default via the [ScalaTest Maven plugin](http://www.scalatest.org/user_guide/using_the_scalatest_maven_plugin).
can be set to control the SBT build. For example:
build/sbt -Pyarn -Phadoop-2.3 package Some of the tests require Spark to be packaged first, so always run `mvn package` with `-DskipTests` the first time. The following is an example of a correct (build, test) sequence:
To avoid the overhead of launching sbt each time you need to re-compile, you can launch sbt ./build/mvn -Pyarn -Phadoop-2.3 -DskipTests -Phive -Phive-thriftserver clean package
in interactive mode by running `build/sbt`, and then run all build commands at the command ./build/mvn -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver test
prompt. For more recommendations on reducing build time, refer to the
[wiki page](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools#UsefulDeveloperTools-ReducingBuildTimes). The ScalaTest plugin also supports running only a specific Scala test suite as follows:
./build/mvn -P... -Dtest=none -DwildcardSuites=org.apache.spark.repl.ReplSuite test
./build/mvn -P... -Dtest=none -DwildcardSuites=org.apache.spark.repl.* test
or a Java test:
# Testing with SBT ./build/mvn test -P... -DwildcardSuites=none -Dtest=org.apache.spark.streaming.JavaAPISuite
## Testing with SBT
Some of the tests require Spark to be packaged first, so always run `build/sbt package` the first time. The following is an example of a correct (build, test) sequence: Some of the tests require Spark to be packaged first, so always run `build/sbt package` the first time. The following is an example of a correct (build, test) sequence:
build/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver package ./build/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver package
build/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver test ./build/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver test
To run only a specific test suite as follows: To run only a specific test suite as follows:
build/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver "test-only org.apache.spark.repl.ReplSuite" ./build/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver "test-only org.apache.spark.repl.ReplSuite"
./build/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver "test-only org.apache.spark.repl.*"
To run test suites of a specific sub project as follows: To run test suites of a specific sub project as follows:
build/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver core/test ./build/sbt -Pyarn -Phadoop-2.3 -Phive -Phive-thriftserver core/test
# Speeding up Compilation with Zinc ## Running Java 8 Test Suites
[Zinc](https://github.com/typesafehub/zinc) is a long-running server version of SBT's incremental Running only Java 8 tests and nothing else.
compiler. When run locally as a background process, it speeds up builds of Scala-based projects
like Spark. Developers who regularly recompile Spark with Maven will be the most interested in
Zinc. The project site gives instructions for building and running `zinc`; OS X users can
install it using `brew install zinc`.
If using the `build/mvn` package `zinc` will automatically be downloaded and leveraged for all ./build/mvn install -DskipTests
builds. This process will auto-start after the first time `build/mvn` is called and bind to port ./build/mvn -pl :java8-tests_2.11 test
3030 unless the `ZINC_PORT` environment variable is set. The `zinc` process can subsequently be
shut down at any time by running `build/zinc-<version>/bin/zinc -shutdown` and will automatically or
restart whenever `build/mvn` is called.
./build/sbt java8-tests/test
Java 8 tests are automatically enabled when a Java 8 JDK is detected.
If you have JDK 8 installed but it is not the system default, you can set JAVA_HOME to point to JDK 8 before running the tests.
## PySpark Tests with Maven
If you are building PySpark and wish to run the PySpark tests you will need to build Spark with Hive support.
./build/mvn -DskipTests clean package -Phive
./python/run-tests
The run-tests script also can be limited to a specific Python version or a specific module
./python/run-tests --python-executables=python --modules=pyspark-sql
**Note:** You can also run Python tests with an sbt build, provided you build Spark with Hive support.
## Running R Tests
To run the SparkR tests you will need to install the R package `testthat`
(run `install.packages(testthat)` from R shell). You can run just the SparkR tests using
the command:
./R/run-tests.sh
## Running Docker-based Integration Test Suites
In order to run Docker integration tests, you have to install the `docker` engine on your box.
The instructions for installation can be found at [the Docker site](https://docs.docker.com/engine/installation/).
Once installed, the `docker` service needs to be started, if not already running.
On Linux, this can be done by `sudo service docker start`.
./build/mvn install -DskipTests
./build/mvn -Pdocker-integration-tests -pl :spark-docker-integration-tests_2.11
or
./build/sbt docker-integration-tests/test
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