[SPARK-1566] consolidate programming guide, and general doc updates This is a fairly large PR to clean up and update the docs for 1.0. The major changes are:
* A unified programming guide for all languages replaces language-specific ones and shows language-specific info in tabs * New programming guide sections on key-value pairs, unit testing, input formats beyond text, migrating from 0.9, and passing functions to Spark * Spark-submit guide moved to a separate page and expanded slightly * Various cleanups of the menu system, security docs, and others * Updated look of title bar to differentiate the docs from previous Spark versions You can find the updated docs at http://people.apache.org/~matei/1.0-docs/_site/ and in particular http://people.apache.org/~matei/1.0-docs/_site/programming-guide.html. Author: Matei Zaharia <ma...@databricks.com> Closes #896 from mateiz/1.0-docs and squashes the following commits: 03e6853 [Matei Zaharia] Some tweaks to configuration and YARN docs 0779508 [Matei Zaharia] tweak ef671d4 [Matei Zaharia] Keep frames in JavaDoc links, and other small tweaks 1bf4112 [Matei Zaharia] Review comments 4414f88 [Matei Zaharia] tweaks d04e979 [Matei Zaharia] Fix some old links to Java guide a34ed33 [Matei Zaharia] tweak 541bb3b [Matei Zaharia] miscellaneous changes fcefdec [Matei Zaharia] Moved submitting apps to separate doc 61d72b4 [Matei Zaharia] stuff 181f217 [Matei Zaharia] migration guide, remove old language guides e11a0da [Matei Zaharia] Add more API functions 6a030a9 [Matei Zaharia] tweaks 8db0ae3 [Matei Zaharia] Added key-value pairs section 318d2c9 [Matei Zaharia] tweaks 1c81477 [Matei Zaharia] New section on basics and function syntax e38f559 [Matei Zaharia] Actually added programming guide to Git a33d6fe [Matei Zaharia] First pass at updating programming guide to support all languages, plus other tweaks throughout 3b6a876 [Matei Zaharia] More CSS tweaks 01ec8bf [Matei Zaharia] More CSS tweaks e6d252e [Matei Zaharia] Change color of doc title bar to differentiate from 0.9.0 Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/c8bf4131 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/c8bf4131 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/c8bf4131 Branch: refs/heads/master Commit: c8bf4131bc2a2e147e977159fc90e94b85738830 Parents: eeee978 Author: Matei Zaharia <ma...@databricks.com> Authored: Fri May 30 00:34:33 2014 -0700 Committer: Patrick Wendell <pwend...@gmail.com> Committed: Fri May 30 00:34:33 2014 -0700 ---------------------------------------------------------------------- docs/_layouts/global.html | 18 +- docs/bagel-programming-guide.md | 2 +- docs/building-with-maven.md | 90 +- docs/cluster-overview.md | 108 +-- docs/configuration.md | 11 +- docs/css/bootstrap.min.css | 2 +- docs/graphx-programming-guide.md | 8 +- docs/hadoop-third-party-distributions.md | 2 +- docs/index.md | 79 +- docs/java-programming-guide.md | 215 +--- docs/js/api-docs.js | 23 +- docs/js/main.js | 21 + docs/mllib-guide.md | 10 +- docs/mllib-optimization.md | 2 +- docs/monitoring.md | 2 +- docs/programming-guide.md | 1294 +++++++++++++++++++++++++ docs/python-programming-guide.md | 168 +--- docs/quick-start.md | 39 +- docs/running-on-mesos.md | 7 +- docs/running-on-yarn.md | 91 +- docs/scala-programming-guide.md | 445 +-------- docs/security.md | 18 +- docs/spark-standalone.md | 4 +- docs/sql-programming-guide.md | 29 +- docs/streaming-programming-guide.md | 42 +- docs/submitting-applications.md | 153 +++ docs/tuning.md | 6 +- 27 files changed, 1767 insertions(+), 1122 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/c8bf4131/docs/_layouts/global.html ---------------------------------------------------------------------- diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html index fb80812..4ba20e5 100755 --- a/docs/_layouts/global.html +++ b/docs/_layouts/global.html @@ -9,6 +9,11 @@ <title>{{ page.title }} - Spark {{site.SPARK_VERSION_SHORT}} Documentation</title> <meta name="description" content=""> + {% if page.redirect %} + <meta http-equiv="refresh" content="0; url={{page.redirect}}"> + <link rel="canonical" href="{{page.redirect}}" /> + {% endif %} + <link rel="stylesheet" href="css/bootstrap.min.css"> <style> body { @@ -61,15 +66,13 @@ <a href="#" class="dropdown-toggle" data-toggle="dropdown">Programming Guides<b class="caret"></b></a> <ul class="dropdown-menu"> <li><a href="quick-start.html">Quick Start</a></li> - <li><a href="scala-programming-guide.html">Spark in Scala</a></li> - <li><a href="java-programming-guide.html">Spark in Java</a></li> - <li><a href="python-programming-guide.html">Spark in Python</a></li> + <li><a href="programming-guide.html">Spark Programming Guide</a></li> <li class="divider"></li> <li><a href="streaming-programming-guide.html">Spark Streaming</a></li> <li><a href="sql-programming-guide.html">Spark SQL</a></li> <li><a href="mllib-guide.html">MLlib (Machine Learning)</a></li> - <li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li> <li><a href="graphx-programming-guide.html">GraphX (Graph Processing)</a></li> + <li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li> </ul> </li> @@ -86,6 +89,8 @@ <a href="#" class="dropdown-toggle" data-toggle="dropdown">Deploying<b class="caret"></b></a> <ul class="dropdown-menu"> <li><a href="cluster-overview.html">Overview</a></li> + <li><a href="submitting-applications.html">Submitting Applications</a></li> + <li class="divider"></li> <li><a href="ec2-scripts.html">Amazon EC2</a></li> <li><a href="spark-standalone.html">Standalone Mode</a></li> <li><a href="running-on-mesos.html">Mesos</a></li> @@ -99,9 +104,10 @@ <li><a href="configuration.html">Configuration</a></li> <li><a href="monitoring.html">Monitoring</a></li> <li><a href="tuning.html">Tuning Guide</a></li> - <li><a href="hadoop-third-party-distributions.html">Running with CDH/HDP</a></li> - <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li> <li><a href="job-scheduling.html">Job Scheduling</a></li> + <li><a href="security.html">Security</a></li> + <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li> + <li><a href="hadoop-third-party-distributions.html">3<sup>rd</sup>-Party Hadoop Distros</a></li> <li class="divider"></li> <li><a href="building-with-maven.html">Building Spark with Maven</a></li> <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark";>Contributing to Spark</a></li> http://git-wip-us.apache.org/repos/asf/spark/blob/c8bf4131/docs/bagel-programming-guide.md ---------------------------------------------------------------------- diff --git a/docs/bagel-programming-guide.md b/docs/bagel-programming-guide.md index 14f43cb..b280df0 100644 --- a/docs/bagel-programming-guide.md +++ b/docs/bagel-programming-guide.md @@ -21,7 +21,7 @@ To use Bagel in your program, add the following SBT or Maven dependency: # Programming Model -Bagel operates on a graph represented as a [distributed dataset](scala-programming-guide.html) of (K, V) pairs, where keys are vertex IDs and values are vertices plus their associated state. In each superstep, Bagel runs a user-specified compute function on each vertex that takes as input the current vertex state and a list of messages sent to that vertex during the previous superstep, and returns the new vertex state and a list of outgoing messages. +Bagel operates on a graph represented as a [distributed dataset](programming-guide.html) of (K, V) pairs, where keys are vertex IDs and values are vertices plus their associated state. In each superstep, Bagel runs a user-specified compute function on each vertex that takes as input the current vertex state and a list of messages sent to that vertex during the previous superstep, and returns the new vertex state and a list of outgoing messages. For example, we can use Bagel to implement PageRank. Here, vertices represent pages, edges represent links between pages, and messages represent shares of PageRank sent to the pages that a particular page links to. http://git-wip-us.apache.org/repos/asf/spark/blob/c8bf4131/docs/building-with-maven.md ---------------------------------------------------------------------- diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md index 8b44535..55a9e37 100644 --- a/docs/building-with-maven.md +++ b/docs/building-with-maven.md @@ -6,14 +6,16 @@ title: Building Spark with Maven * This will become a table of contents (this text will be scraped). {:toc} -Building Spark using Maven requires Maven 3.0.4 or newer and Java 1.6 or newer. +Building Spark using Maven requires Maven 3.0.4 or newer and Java 6+. -## 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: - export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m" +{% highlight bash %} +export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m" +{% endhighlight %} If you don't run this, you may see errors like the following: @@ -25,9 +27,9 @@ If you don't run this, you may see errors like the following: You can fix this by setting the `MAVEN_OPTS` variable as discussed before. -*Note: For Java 1.8 and above this step is not required.* +**Note:** *For Java 8 and above this step is not required.* -## Specifying the Hadoop version ## +# 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 1.0.4 by default. Note that certain build profiles are required for particular Hadoop versions: @@ -46,14 +48,16 @@ Because HDFS is not protocol-compatible across versions, if you want to read fro For Apache Hadoop versions 1.x, Cloudera CDH "mr1" distributions, and other Hadoop versions without YARN, use: - # Apache Hadoop 1.2.1 - $ mvn -Dhadoop.version=1.2.1 -DskipTests clean package +{% highlight bash %} +# Apache Hadoop 1.2.1 +mvn -Dhadoop.version=1.2.1 -DskipTests clean package - # Cloudera CDH 4.2.0 with MapReduce v1 - $ mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -DskipTests clean package +# Cloudera CDH 4.2.0 with MapReduce v1 +mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -DskipTests clean package - # Apache Hadoop 0.23.x - $ mvn -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package +# Apache Hadoop 0.23.x +mvn -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package +{% endhighlight %} For Apache Hadoop 2.x, 0.23.x, Cloudera CDH, and other Hadoop versions with YARN, you can enable the "yarn-alpha" or "yarn" profile and optionally set the "yarn.version" property if it is different from "hadoop.version". The additional build profile required depends on the YARN version: @@ -69,75 +73,77 @@ For Apache Hadoop 2.x, 0.23.x, Cloudera CDH, and other Hadoop versions with YARN Examples: - # Apache Hadoop 2.0.5-alpha - $ mvn -Pyarn-alpha -Dhadoop.version=2.0.5-alpha -DskipTests clean package +{% highlight bash %} +# Apache Hadoop 2.0.5-alpha +mvn -Pyarn-alpha -Dhadoop.version=2.0.5-alpha -DskipTests clean package - # Cloudera CDH 4.2.0 - $ mvn -Pyarn-alpha -Dhadoop.version=2.0.0-cdh4.2.0 -DskipTests clean package +# Cloudera CDH 4.2.0 +mvn -Pyarn-alpha -Dhadoop.version=2.0.0-cdh4.2.0 -DskipTests clean package - # Apache Hadoop 0.23.x - $ mvn -Pyarn-alpha -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package +# Apache Hadoop 0.23.x +mvn -Pyarn-alpha -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package - # Apache Hadoop 2.2.X - $ mvn -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package +# Apache Hadoop 2.2.X +mvn -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package - # Apache Hadoop 2.3.X - $ mvn -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -DskipTests clean package +# Apache Hadoop 2.3.X +mvn -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -DskipTests clean package - # Apache Hadoop 2.4.X - $ mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package +# Apache Hadoop 2.4.X +mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package - # Different versions of HDFS and YARN. - $ mvn -Pyarn-alpha -Phadoop-2.3 -Dhadoop.version=2.3.0 -Dyarn.version=0.23.7 -DskipTests clean package +# Different versions of HDFS and YARN. +mvn -Pyarn-alpha -Phadoop-2.3 -Dhadoop.version=2.3.0 -Dyarn.version=0.23.7 -DskipTests clean package +{% endhighlight %} -## Spark Tests in Maven ## +# 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 require Spark to be packaged first, so always run `mvn package` with `-DskipTests` the first time. You can then run the tests with `mvn -Dhadoop.version=... test`. The ScalaTest plugin also supports running only a specific test suite as follows: - $ mvn -Dhadoop.version=... -DwildcardSuites=org.apache.spark.repl.ReplSuite test + mvn -Dhadoop.version=... -DwildcardSuites=org.apache.spark.repl.ReplSuite test -## Continuous Compilation ## +# Continuous Compilation We use the scala-maven-plugin which supports incremental and continuous compilation. E.g. - $ mvn scala:cc + mvn scala:cc should run continuous compilation (i.e. wait for changes). However, this has not been tested extensively. -## Using With IntelliJ IDEA ## +# Using With IntelliJ IDEA This setup works fine in IntelliJ IDEA 11.1.4. After opening the project via the pom.xml file in the project root folder, you only need to activate either the hadoop1 or hadoop2 profile in the "Maven Properties" popout. We have not tried Eclipse/Scala IDE with this. -## Building Spark Debian Packages ## +# Building Spark Debian Packages -The maven build includes support for building a Debian package containing the assembly 'fat-jar', PySpark, and the necessary scripts and configuration files. This can be created by specifying the following: +The Maven build includes support for building a Debian package containing the assembly 'fat-jar', PySpark, and the necessary scripts and configuration files. This can be created by specifying the following: - $ mvn -Pdeb -DskipTests clean package + mvn -Pdeb -DskipTests clean package The debian package can then be found under assembly/target. We added the short commit hash to the file name so that we can distinguish individual packages built for SNAPSHOT versions. -## Running java 8 test suites. +# Running Java 8 Test Suites -Running only java 8 tests and nothing else. +Running only Java 8 tests and nothing else. - $ mvn install -DskipTests -Pjava8-tests + mvn install -DskipTests -Pjava8-tests -Java 8 tests are run when -Pjava8-tests profile is enabled, they will run in spite of -DskipTests. +Java 8 tests are run when `-Pjava8-tests` profile is enabled, they will run in spite of `-DskipTests`. For these tests to run your system must have a JDK 8 installation. 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. -## Building for PySpark on YARN ## +# Building for PySpark on YARN -PySpark on YARN is only supported if the jar is built with maven. Further, there is a known problem -with building this assembly jar on Red Hat based operating systems (see SPARK-1753). If you wish to +PySpark on YARN is only supported if the jar is built with Maven. Further, there is a known problem +with building this assembly jar on Red Hat based operating systems (see [SPARK-1753](https://issues.apache.org/jira/browse/SPARK-1753)). If you wish to run PySpark on a YARN cluster with Red Hat installed, we recommend that you build the jar elsewhere, then ship it over to the cluster. We are investigating the exact cause for this. -## Packaging without Hadoop dependencies for deployment on YARN ## +# Packaging without Hadoop Dependencies for YARN -The assembly jar 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. +The assembly jar 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. http://git-wip-us.apache.org/repos/asf/spark/blob/c8bf4131/docs/cluster-overview.md ---------------------------------------------------------------------- diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md index f05a755..6a75d5c 100644 --- a/docs/cluster-overview.md +++ b/docs/cluster-overview.md @@ -4,7 +4,8 @@ title: Cluster Mode Overview --- This document gives a short overview of how Spark runs on clusters, to make it easier to understand -the components involved. +the components involved. Read through the [application submission guide](submitting-applications.html) +to submit applications to a cluster. # Components @@ -50,107 +51,10 @@ The system currently supports three cluster managers: In addition, Spark's [EC2 launch scripts](ec2-scripts.html) make it easy to launch a standalone cluster on Amazon EC2. -# Bundling and Launching Applications - -### Bundling Your Application's Dependencies -If your code depends on other projects, you will need to package them alongside -your application in order to distribute the code to a Spark cluster. To do this, -to create an assembly jar (or "uber" jar) containing your code and its dependencies. Both -[sbt](https://github.com/sbt/sbt-assembly) and -[Maven](http://maven.apache.org/plugins/maven-shade-plugin/) -have assembly plugins. When creating assembly jars, list Spark and Hadoop -as `provided` dependencies; these need not be bundled since they are provided by -the cluster manager at runtime. Once you have an assembled jar you can call the `bin/spark-submit` -script as shown here while passing your jar. - -For Python, you can use the `pyFiles` argument of SparkContext -or its `addPyFile` method to add `.py`, `.zip` or `.egg` files to be distributed. - -### Launching Applications with Spark submit - -Once a user application is bundled, it can be launched using the `spark-submit` script located in -the bin directory. This script takes care of setting up the classpath with Spark and its -dependencies, and can support different cluster managers and deploy modes that Spark supports: - - ./bin/spark-submit \ - --class <main-class> - --master <master-url> \ - --deploy-mode <deploy-mode> \ - ... // other options - <application-jar> - [application-arguments] - - main-class: The entry point for your application (e.g. org.apache.spark.examples.SparkPi) - master-url: The URL of the master node (e.g. spark://23.195.26.187:7077) - deploy-mode: Whether to deploy this application within the cluster or from an external client (e.g. client) - application-jar: Path to a bundled jar including your application and all dependencies. The URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes. - application-arguments: Space delimited arguments passed to the main method of <main-class>, if any - -To enumerate all options available to `spark-submit` run it with the `--help` flag. Here are a few -examples of common options: - -{% highlight bash %} -# Run application locally -./bin/spark-submit \ - --class org.apache.spark.examples.SparkPi - --master local[8] \ - /path/to/examples.jar \ - 100 - -# Run on a Spark standalone cluster -./bin/spark-submit \ - --class org.apache.spark.examples.SparkPi - --master spark://207.184.161.138:7077 \ - --executor-memory 20G \ - --total-executor-cores 100 \ - /path/to/examples.jar \ - 1000 - -# Run on a YARN cluster -HADOOP_CONF_DIR=XX ./bin/spark-submit \ - --class org.apache.spark.examples.SparkPi - --master yarn-cluster \ # can also be `yarn-client` for client mode - --executor-memory 20G \ - --num-executors 50 \ - /path/to/examples.jar \ - 1000 -{% endhighlight %} - -### Loading Configurations from a File - -The `spark-submit` script can load default [Spark configuration values](configuration.html) from a -properties file and pass them on to your application. By default it will read configuration options -from `conf/spark-defaults.conf`. For more detail, see the section on -[loading default configurations](configuration.html#loading-default-configurations). - -Loading default Spark configurations this way can obviate the need for certain flags to -`spark-submit`. For instance, if the `spark.master` property is set, you can safely omit the -`--master` flag from `spark-submit`. In general, configuration values explicitly set on a -`SparkConf` take the highest precedence, then flags passed to `spark-submit`, then values in the -defaults file. - -If you are ever unclear where configuration options are coming from, you can print out fine-grained -debugging information by running `spark-submit` with the `--verbose` option. - -### Advanced Dependency Management -When using `spark-submit`, the application jar along with any jars included with the `--jars` option -will be automatically transferred to the cluster. Spark uses the following URL scheme to allow -different strategies for disseminating jars: - -- **file:** - Absolute paths and `file:/` URIs are served by the driver's HTTP file server, and - every executor pulls the file from the driver HTTP server. -- **hdfs:**, **http:**, **https:**, **ftp:** - these pull down files and JARs from the URI as expected -- **local:** - a URI starting with local:/ is expected to exist as a local file on each worker node. This - means that no network IO will be incurred, and works well for large files/JARs that are pushed to each worker, - or shared via NFS, GlusterFS, etc. - -Note that JARs and files are copied to the working directory for each SparkContext on the executor nodes. -This can use up a significant amount of space over time and will need to be cleaned up. With YARN, cleanup -is handled automatically, and with Spark standalone, automatic cleanup can be configured with the -`spark.worker.cleanup.appDataTtl` property. - -For python, the equivalent `--py-files` option can be used to distribute .egg and .zip libraries -to executors. +# Submitting Applications + +Applications can be submitted to a cluster of any type using the `spark-submit` script. +The [application submission guide](submitting-applications.html) describes how to do this. # Monitoring http://git-wip-us.apache.org/repos/asf/spark/blob/c8bf4131/docs/configuration.md ---------------------------------------------------------------------- diff --git a/docs/configuration.md b/docs/configuration.md index b6e7fd3..2fd6918 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -7,8 +7,8 @@ title: Spark Configuration Spark provides three locations to configure the system: -* [Spark properties](#spark-properties) control most application parameters and can be set by passing - a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) object to SparkContext, or through Java +* [Spark properties](#spark-properties) control most application parameters and can be set by using + a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) object, or through Java system properties. * [Environment variables](#environment-variables) can be used to set per-machine settings, such as the IP address, through the `conf/spark-env.sh` script on each node. @@ -18,8 +18,8 @@ Spark provides three locations to configure the system: Spark properties control most application settings and are configured separately for each application. These properties can be set directly on a -[SparkConf](api/scala/index.html#org.apache.spark.SparkConf) and passed as an argument to your -SparkContext. SparkConf allows you to configure some of the common properties +[SparkConf](api/scala/index.html#org.apache.spark.SparkConf) passed to your +`SparkContext`. `SparkConf` allows you to configure some of the common properties (e.g. master URL and application name), as well as arbitrary key-value pairs through the `set()` method. For example, we could initialize an application as follows: @@ -75,6 +75,7 @@ appear. For all other configuration properties, you can assume the default value Most of the properties that control internal settings have reasonable default values. Some of the most common options to set are: +#### Application Properties <table class="table"> <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr> <tr> @@ -777,7 +778,7 @@ Apart from these, the following properties are also available, and may be useful </tr> </table> -#### Cluster Managers (YARN, Mesos, Standalone) +#### Cluster Managers Each cluster manager in Spark has additional configuration options. Configurations can be found on the pages for each mode:
