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authorTathagata Das <tathagata.das1565@gmail.com>2013-06-24 23:57:47 -0700
committerTathagata Das <tathagata.das1565@gmail.com>2013-06-24 23:57:47 -0700
commitc89af0a7f9eebce22dfe2bb4d8b1676ec7f760f6 (patch)
tree01f9f42f30a4aa2f73cb200c89a71254bf74d80e /docs
parent48c7e373c62b2e8cf48157ceb0d92c38c3a40652 (diff)
parent78ffe164b33c6b11a2e511442605acd2f795a1b5 (diff)
downloadspark-c89af0a7f9eebce22dfe2bb4d8b1676ec7f760f6.tar.gz
spark-c89af0a7f9eebce22dfe2bb4d8b1676ec7f760f6.tar.bz2
spark-c89af0a7f9eebce22dfe2bb4d8b1676ec7f760f6.zip
Merge branch 'master' into streaming
Conflicts: .gitignore
Diffstat (limited to 'docs')
-rw-r--r--docs/README.md2
-rw-r--r--docs/_config.yml6
-rwxr-xr-xdocs/_layouts/global.html23
-rw-r--r--docs/_plugins/copy_api_dirs.rb8
-rw-r--r--docs/building-with-maven.md66
-rw-r--r--docs/configuration.md7
-rw-r--r--docs/css/bootstrap.css30
-rw-r--r--docs/css/bootstrap.min.css2
-rw-r--r--docs/ec2-scripts.md17
-rw-r--r--docs/index.md18
-rw-r--r--docs/java-programming-guide.md2
-rw-r--r--docs/python-programming-guide.md4
-rw-r--r--docs/quick-start.md47
-rw-r--r--docs/running-on-yarn.md62
-rw-r--r--docs/scala-programming-guide.md16
-rw-r--r--docs/streaming-programming-guide.md33
-rw-r--r--docs/tuning.md10
17 files changed, 245 insertions, 108 deletions
diff --git a/docs/README.md b/docs/README.md
index 887f407f18..c2b3497bb3 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -33,4 +33,4 @@ Similarly, you can build just the PySpark epydoc by running `epydoc --config epy
When you run `jekyll` in the docs directory, it will also copy over the scaladoc for the various Spark subprojects into the docs directory (and then also into the _site directory). We use a jekyll plugin to run `sbt/sbt doc` before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc. The jekyll plugin also generates the PySpark docs using [epydoc](http://epydoc.sourceforge.net/).
-NOTE: To skip the step of building and copying over the scaladoc when you build the docs, run `SKIP_SCALADOC=1 jekyll`. Similarly, `SKIP_EPYDOC=1 jekyll` will skip PySpark API doc generation.
+NOTE: To skip the step of building and copying over the Scala and Python API docs, run `SKIP_API=1 jekyll`.
diff --git a/docs/_config.yml b/docs/_config.yml
index 09617e4a1e..5c135a0242 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -3,8 +3,8 @@ markdown: kramdown
# These allow the documentation to be updated with nerw releases
# of Spark, Scala, and Mesos.
-SPARK_VERSION: 0.7.0-SNAPSHOT
-SPARK_VERSION_SHORT: 0.7.0
-SCALA_VERSION: 2.9.2
+SPARK_VERSION: 0.8.0-SNAPSHOT
+SPARK_VERSION_SHORT: 0.8.0
+SCALA_VERSION: 2.9.3
MESOS_VERSION: 0.9.0-incubating
SPARK_ISSUE_TRACKER_URL: https://spark-project.atlassian.net
diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html
index 94baa634aa..f06ab2d5b0 100755
--- a/docs/_layouts/global.html
+++ b/docs/_layouts/global.html
@@ -23,6 +23,22 @@
<script src="js/vendor/modernizr-2.6.1-respond-1.1.0.min.js"></script>
<link rel="stylesheet" href="css/pygments-default.css">
+
+ <!-- Google analytics script -->
+ <script type="text/javascript">
+ /*
+ var _gaq = _gaq || [];
+ _gaq.push(['_setAccount', 'UA-32518208-1']);
+ _gaq.push(['_trackPageview']);
+
+ (function() {
+ var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
+ ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
+ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
+ })();
+ */
+ </script>
+
</head>
<body>
<!--[if lt IE 7]>
@@ -53,11 +69,11 @@
</li>
<li class="dropdown">
- <a href="#" class="dropdown-toggle" data-toggle="dropdown">API (Scaladoc)<b class="caret"></b></a>
+ <a href="#" class="dropdown-toggle" data-toggle="dropdown">API Docs<b class="caret"></b></a>
<ul class="dropdown-menu">
- <li><a href="api/core/index.html">Spark Scala/Java (Scaladoc)</a></li>
+ <li><a href="api/core/index.html">Spark Java/Scala (Scaladoc)</a></li>
<li><a href="api/pyspark/index.html">Spark Python (Epydoc)</a></li>
- <li><a href="api/streaming/index.html">Spark Streaming Scala/Java (Scaladoc) </a></li>
+ <li><a href="api/streaming/index.html">Spark Streaming Java/Scala (Scaladoc) </a></li>
</ul>
</li>
@@ -74,6 +90,7 @@
<li class="dropdown">
<a href="api.html" class="dropdown-toggle" data-toggle="dropdown">More<b class="caret"></b></a>
<ul class="dropdown-menu">
+ <li><a href="building-with-maven.html">Building Spark with Maven</a></li>
<li><a href="configuration.html">Configuration</a></li>
<li><a href="tuning.html">Tuning Guide</a></li>
<li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li>
diff --git a/docs/_plugins/copy_api_dirs.rb b/docs/_plugins/copy_api_dirs.rb
index e400dec619..c10ae595de 100644
--- a/docs/_plugins/copy_api_dirs.rb
+++ b/docs/_plugins/copy_api_dirs.rb
@@ -1,7 +1,8 @@
require 'fileutils'
include FileUtils
-if ENV['SKIP_SCALADOC'] != '1'
+if ENV['SKIP_API'] != '1'
+ # Build Scaladoc for Java/Scala
projects = ["core", "examples", "repl", "bagel", "streaming"]
puts "Moving to project root and building scaladoc."
@@ -17,7 +18,7 @@ if ENV['SKIP_SCALADOC'] != '1'
# Copy over the scaladoc from each project into the docs directory.
# This directory will be copied over to _site when `jekyll` command is run.
projects.each do |project_name|
- source = "../" + project_name + "/target/scala-2.9.2/api"
+ source = "../" + project_name + "/target/scala-2.9.3/api"
dest = "api/" + project_name
puts "echo making directory " + dest
@@ -27,9 +28,8 @@ if ENV['SKIP_SCALADOC'] != '1'
puts "cp -r " + source + "/. " + dest
cp_r(source + "/.", dest)
end
-end
-if ENV['SKIP_EPYDOC'] != '1'
+ # Build Epydoc for Python
puts "Moving to python directory and building epydoc."
cd("../python")
puts `epydoc --config epydoc.conf`
diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md
new file mode 100644
index 0000000000..04cd79d039
--- /dev/null
+++ b/docs/building-with-maven.md
@@ -0,0 +1,66 @@
+---
+layout: global
+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 (the build process is tested with Maven 3.0.4) and Java 1.6 or newer.
+
+Building with Maven requires that a Hadoop profile be specified explicitly at the command line, there is no default. There are two profiles to choose from, one for building for Hadoop 1 or Hadoop 2.
+
+for Hadoop 1 (using 0.20.205.0) use:
+
+ $ mvn -Phadoop1 clean install
+
+
+for Hadoop 2 (using 2.0.0-mr1-cdh4.1.1) use:
+
+ $ mvn -Phadoop2 clean install
+
+It uses the scala-maven-plugin which supports incremental and continuous compilation. E.g.
+
+ $ mvn -Phadoop2 scala:cc
+
+…should run continuous compilation (i.e. wait for changes). However, this has not been tested extensively.
+
+## Spark Tests in Maven ##
+
+Tests are run by default via the scalatest-maven-plugin. With this you can do things like:
+
+Skip test execution (but not compilation):
+
+ $ mvn -DskipTests -Phadoop2 clean install
+
+To run a specific test suite:
+
+ $ mvn -Phadoop2 -Dsuites=spark.repl.ReplSuite test
+
+
+## Setting up JVM Memory Usage Via Maven ##
+
+You might run into the following errors if you're using a vanilla installation of Maven:
+
+ [INFO] Compiling 203 Scala sources and 9 Java sources to /Users/me/Development/spark/core/target/scala-{{site.SCALA_VERSION}}/classes...
+ [ERROR] PermGen space -> [Help 1]
+
+ [INFO] Compiling 203 Scala sources and 9 Java sources to /Users/me/Development/spark/core/target/scala-{{site.SCALA_VERSION}}/classes...
+ [ERROR] Java heap space -> [Help 1]
+
+To fix these, you can do the following:
+
+ export MAVEN_OPTS="-Xmx1024m -XX:MaxPermSize=128M"
+
+
+## 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 ##
+
+It includes support for building a Debian package containing a 'fat-jar' which includes the repl, the examples and bagel. This can be created by specifying the deb profile:
+
+ $ mvn -Phadoop2,deb clean install
+
+The debian package can then be found under repl/target. We added the short commit hash to the file name so that we can distinguish individual packages build for SNAPSHOT versions.
diff --git a/docs/configuration.md b/docs/configuration.md
index 55f1962b18..2de512f896 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -134,6 +134,13 @@ Apart from these, the following properties are also available, and may be useful
</td>
</tr>
<tr>
+ <td>spark.ui.port</td>
+ <td>(random)</td>
+ <td>
+ Port for your application's dashboard, which shows memory usage of each RDD.
+ </td>
+</tr>
+<tr>
<td>spark.shuffle.compress</td>
<td>true</td>
<td>
diff --git a/docs/css/bootstrap.css b/docs/css/bootstrap.css
index 0664207aee..b51ef7b42b 100644
--- a/docs/css/bootstrap.css
+++ b/docs/css/bootstrap.css
@@ -2719,16 +2719,16 @@ table .span24 {
.dropdown-menu .active > a:hover {
color: #ffffff;
text-decoration: none;
- background-color: #0088cc;
- background-color: #0081c2;
- background-image: linear-gradient(to bottom, #0088cc, #0077b3);
- background-image: -moz-linear-gradient(top, #0088cc, #0077b3);
- background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3));
- background-image: -webkit-linear-gradient(top, #0088cc, #0077b3);
- background-image: -o-linear-gradient(top, #0088cc, #0077b3);
+ background-color: #0098cc;
+ background-color: #0098cc;
+ background-image: linear-gradient(to bottom, #0098cc, #0087b3);
+ background-image: -moz-linear-gradient(top, #0098cc, #0087b3);
+ background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0098cc), to(#0087b3));
+ background-image: -webkit-linear-gradient(top, #0098cc, #0087b3);
+ background-image: -o-linear-gradient(top, #0098cc, #0087b3);
background-repeat: repeat-x;
outline: 0;
- filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0);
+ filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ff0098cc', endColorstr='#ff0087b3', GradientType=0);
}
.dropdown-menu .disabled > a,
@@ -4025,18 +4025,18 @@ input[type="submit"].btn.btn-mini {
min-height: 40px;
padding-right: 20px;
padding-left: 20px;
- background-color: #fafafa;
- background-image: -moz-linear-gradient(top, #ffffff, #f2f2f2);
- background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#f2f2f2));
- background-image: -webkit-linear-gradient(top, #ffffff, #f2f2f2);
- background-image: -o-linear-gradient(top, #ffffff, #f2f2f2);
- background-image: linear-gradient(to bottom, #ffffff, #f2f2f2);
+ background-color: #fadafa;
+ background-image: -moz-linear-gradient(top, #ffddff, #f2d2f2);
+ background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffddff), to(#f2d2f2));
+ background-image: -webkit-linear-gradient(top, #ffddff, #f2d2f2);
+ background-image: -o-linear-gradient(top, #ffddff, #f2d2f2);
+ background-image: linear-gradient(to bottom, #ffddff, #f2d2f2);
background-repeat: repeat-x;
border: 1px solid #d4d4d4;
-webkit-border-radius: 4px;
-moz-border-radius: 4px;
border-radius: 4px;
- filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff2f2f2', GradientType=0);
+ filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffffddff', endColorstr='#fff2d2f2', GradientType=0);
-webkit-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065);
-moz-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065);
box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065);
diff --git a/docs/css/bootstrap.min.css b/docs/css/bootstrap.min.css
index 3119038a6b..eb48138e08 100644
--- a/docs/css/bootstrap.min.css
+++ b/docs/css/bootstrap.min.css
@@ -6,4 +6,4 @@
* http://www.apache.org/licenses/LICENSE-2.0
*
* Designed and built with all the love in the world @twitter by @mdo and @fat.
- */article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}audio,canvas,video{display:inline-block;*display:inline;*zoom:1}audio:not([controls]){display:none}html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}a:hover,a:active{outline:0}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-0.5em}sub{bottom:-0.25em}img{height:auto;max-width:100%;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic}#map_canvas img{max-width:none}button,input,select,textarea{margin:0;font-size:100%;vertical-align:middle}button,input{*overflow:visible;line-height:normal}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}button,input[type="button"],input[type="reset"],input[type="submit"]{cursor:pointer;-webkit-appearance:button}input[type="search"]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button{-webkit-appearance:none}textarea{overflow:auto;vertical-align:top}.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;line-height:0;content:""}.clearfix:after{clear:both}.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}body{margin:0;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:20px;color:#333;background-color:#fff}a{color:#08c;text-decoration:none}a:hover{color:#005580;text-decoration:underline}.img-rounded{-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.img-polaroid{padding:4px;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.1);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.1);box-shadow:0 1px 3px rgba(0,0,0,0.1)}.img-circle{-webkit-border-radius:500px;-moz-border-radius:500px;border-radius:500px}.row{margin-left:-20px;*zoom:1}.row:before,.row:after{display:table;line-height:0;content:""}.row:after{clear:both}[class*="span"]{float:left;margin-left:20px}.container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom 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diff --git a/docs/ec2-scripts.md b/docs/ec2-scripts.md
index 931b7a66bd..dc57035eba 100644
--- a/docs/ec2-scripts.md
+++ b/docs/ec2-scripts.md
@@ -45,9 +45,9 @@ identify machines belonging to each cluster in the Amazon EC2 Console.
key pair, `<num-slaves>` is the number of slave nodes to launch (try
1 at first), and `<cluster-name>` is the name to give to your
cluster.
-- After everything launches, check that Mesos is up and sees all the
- slaves by going to the Mesos Web UI link printed at the end of the
- script (`http://<master-hostname>:8080`).
+- After everything launches, check that the cluster scheduler is up and sees
+ all the slaves by going to its web UI, which will be printed at the end of
+ the script (typically `http://<master-hostname>:8080`).
You can also run `./spark-ec2 --help` to see more usage options. The
following options are worth pointing out:
@@ -68,6 +68,9 @@ available.
- `--ebs-vol-size=GB` will attach an EBS volume with a given amount
of space to each node so that you can have a persistent HDFS cluster
on your nodes across cluster restarts (see below).
+- `--spot-price=PRICE` will launch the worker nodes as
+ [Spot Instances](http://aws.amazon.com/ec2/spot-instances/),
+ bidding for the given maximum price (in dollars).
- If one of your launches fails due to e.g. not having the right
permissions on your private key file, you can run `launch` with the
`--resume` option to restart the setup process on an existing cluster.
@@ -80,7 +83,7 @@ permissions on your private key file, you can run `launch` with the
above. (This is just for convenience; you could also use
the EC2 console.)
- To deploy code or data within your cluster, you can log in and use the
- provided script `~/mesos-ec2/copy-dir`, which,
+ provided script `~/spark-ec2/copy-dir`, which,
given a directory path, RSYNCs it to the same location on all the slaves.
- If your job needs to access large datasets, the fastest way to do
that is to load them from Amazon S3 or an Amazon EBS device into an
@@ -106,7 +109,7 @@ You can edit `/root/spark/conf/spark-env.sh` on each machine to set Spark config
as JVM options and, most crucially, the amount of memory to use per machine (`SPARK_MEM`).
This file needs to be copied to **every machine** to reflect the change. The easiest way to do this
is to use a script we provide called `copy-dir`. First edit your `spark-env.sh` file on the master,
-then run `~/mesos-ec2/copy-dir /root/spark/conf` to RSYNC it to all the workers.
+then run `~/spark-ec2/copy-dir /root/spark/conf` to RSYNC it to all the workers.
The [configuration guide](configuration.html) describes the available configuration options.
@@ -152,10 +155,10 @@ If you have a patch or suggestion for one of these limitations, feel free to
# Using a Newer Spark Version
-The Spark EC2 machine images may not come with the latest version of Spark. To use a newer version, you can run `git pull` to pull in `/root/spark` to pull in the latest version of Spark from `git`, and build it using `sbt/sbt compile`. You will also need to copy it to all the other nodes in the cluster using `~/mesos-ec2/copy-dir /root/spark`.
+The Spark EC2 machine images may not come with the latest version of Spark. To use a newer version, you can run `git pull` to pull in `/root/spark` to pull in the latest version of Spark from `git`, and build it using `sbt/sbt compile`. You will also need to copy it to all the other nodes in the cluster using `~/spark-ec2/copy-dir /root/spark`.
# Accessing Data in S3
-Spark's file interface allows it to process data in Amazon S3 using the same URI formats that are supported for Hadoop. You can specify a path in S3 as input through a URI of the form `s3n://<id>:<secret>@<bucket>/path`, where `<id>` is your Amazon access key ID and `<secret>` is your Amazon secret access key. Note that you should escape any `/` characters in the secret key as `%2F`. Full instructions can be found on the [Hadoop S3 page](http://wiki.apache.org/hadoop/AmazonS3).
+Spark's file interface allows it to process data in Amazon S3 using the same URI formats that are supported for Hadoop. You can specify a path in S3 as input through a URI of the form `s3n://<bucket>/path`. You will also need to set your Amazon security credentials, either by setting the environment variables `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` before your program or through `SparkContext.hadoopConfiguration`. Full instructions on S3 access using the Hadoop input libraries can be found on the [Hadoop S3 page](http://wiki.apache.org/hadoop/AmazonS3).
In addition to using a single input file, you can also use a directory of files as input by simply giving the path to the directory.
diff --git a/docs/index.md b/docs/index.md
index c6ef507cb0..0c4add45dc 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -3,15 +3,9 @@ layout: global
title: Spark Overview
---
-{% comment %}
-TODO(andyk): Rewrite to make the Java API a first class part of the story.
-{% endcomment %}
-
Spark is a MapReduce-like cluster computing framework designed for low-latency iterative jobs and interactive use from an interpreter.
It provides clean, language-integrated APIs in [Scala](scala-programming-guide.html), [Java](java-programming-guide.html), and [Python](python-programming-guide.html), with a rich array of parallel operators.
-Spark can run on top of the [Apache Mesos](http://incubator.apache.org/mesos/) cluster manager,
-[Hadoop YARN](http://hadoop.apache.org/docs/r2.0.1-alpha/hadoop-yarn/hadoop-yarn-site/YARN.html),
-Amazon EC2, or without an independent resource manager ("standalone mode").
+Spark can run on the Apache Mesos cluster manager, Hadoop YARN, Amazon EC2, or without an independent resource manager ("standalone mode").
# Downloading
@@ -24,10 +18,12 @@ or you will need to set the `SCALA_HOME` environment variable to point
to where you've installed Scala. Scala must also be accessible through one
of these methods on slave nodes on your cluster.
-Spark uses [Simple Build Tool](https://github.com/harrah/xsbt/wiki), which is bundled with it. To compile the code, go into the top-level Spark directory and run
+Spark uses [Simple Build Tool](http://www.scala-sbt.org), which is bundled with it. To compile the code, go into the top-level Spark directory and run
sbt/sbt package
+Spark also supports building using Maven. If you would like to build using Maven, see the [instructions for building Spark with Maven](building-with-maven.html).
+
# Testing the Build
Spark comes with a number of sample programs in the `examples` directory.
@@ -58,9 +54,9 @@ of `project/SparkBuild.scala`, then rebuilding Spark (`sbt/sbt clean compile`).
* [Quick Start](quick-start.html): a quick introduction to the Spark API; start here!
* [Spark Programming Guide](scala-programming-guide.html): an overview of Spark concepts, and details on the Scala API
-* [Streaming Programming Guide](streaming-programming-guide.html): an API preview of Spark Streaming
* [Java Programming Guide](java-programming-guide.html): using Spark from Java
* [Python Programming Guide](python-programming-guide.html): using Spark from Python
+* [Spark Streaming Guide](streaming-programming-guide.html): using the alpha release of Spark Streaming
**API Docs:**
@@ -78,6 +74,7 @@ of `project/SparkBuild.scala`, then rebuilding Spark (`sbt/sbt clean compile`).
**Other documents:**
+* [Building Spark With Maven](building-with-maven.html): Build Spark using the Maven build tool
* [Configuration](configuration.html): customize Spark via its configuration system
* [Tuning Guide](tuning.html): best practices to optimize performance and memory use
* [Bagel](bagel-programming-guide.html): an implementation of Google's Pregel on Spark
@@ -92,7 +89,8 @@ of `project/SparkBuild.scala`, then rebuilding Spark (`sbt/sbt clean compile`).
[slides](http://ampcamp.berkeley.edu/agenda-2012) and [exercises](http://ampcamp.berkeley.edu/exercises-2012) are
available online for free.
* [Code Examples](http://spark-project.org/examples.html): more are also available in the [examples subfolder](https://github.com/mesos/spark/tree/master/examples/src/main/scala/spark/examples) of Spark
-* [Paper Describing the Spark System](http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf)
+* [Paper Describing Spark](http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf)
+* [Paper Describing Spark Streaming](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf)
# Community
diff --git a/docs/java-programming-guide.md b/docs/java-programming-guide.md
index 37a906ea1c..ae8257b539 100644
--- a/docs/java-programming-guide.md
+++ b/docs/java-programming-guide.md
@@ -189,7 +189,7 @@ We hope to generate documentation with Java-style syntax in the future.
# Where to Go from Here
Spark includes several sample programs using the Java API in
-`examples/src/main/java`. You can run them by passing the class name to the
+[`examples/src/main/java`](https://github.com/mesos/spark/tree/master/examples/src/main/java/spark/examples). You can run them by passing the class name to the
`run` script included in Spark -- for example, `./run
spark.examples.JavaWordCount`. Each example program prints usage help when run
without any arguments.
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index 2012241a6a..3a7a8db4a6 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -109,9 +109,9 @@ Code dependencies can be added to an existing SparkContext using its `addPyFile(
# Where to Go from Here
-PySpark includes several sample programs using the Python API in `python/examples`.
+PySpark includes several sample programs in the [`python/examples` folder](https://github.com/mesos/spark/tree/master/python/examples).
You can run them by passing the files to the `pyspark` script -- for example `./pyspark python/examples/wordcount.py`.
-Each example program prints usage help when run without any arguments.
+Each program prints usage help when run without arguments.
We currently provide [API documentation](api/pyspark/index.html) for the Python API as Epydoc.
Many of the RDD method descriptions contain [doctests](http://docs.python.org/2/library/doctest.html) that provide additional usage examples.
diff --git a/docs/quick-start.md b/docs/quick-start.md
index a4c4c9a8fb..335643536a 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -53,8 +53,8 @@ scala> textFile.filter(line => line.contains("Spark")).count() // How many lines
res3: Long = 15
{% endhighlight %}
-## Transformations
-RDD transformations can be used for more complex computations. Let's say we want to find the line with the most words:
+## More On RDD Operations
+RDD actions and transformations can be used for more complex computations. Let's say we want to find the line with the most words:
{% highlight scala %}
scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a > b) a else b)
@@ -111,18 +111,20 @@ We'll create a very simple Spark job in Scala. So simple, in fact, that it's nam
import spark.SparkContext
import SparkContext._
-object SimpleJob extends Application {
- val logFile = "/var/log/syslog" // Should be some file on your system
- val sc = new SparkContext("local", "Simple Job", "$YOUR_SPARK_HOME",
- List("target/scala-{{site.SCALA_VERSION}}/simple-project_{{site.SCALA_VERSION}}-1.0.jar"))
- val logData = sc.textFile(logFile, 2).cache()
- val numAs = logData.filter(line => line.contains("a")).count()
- val numBs = logData.filter(line => line.contains("b")).count()
- println("Lines with a: %s, Lines with b: %s".format(numAs, numBs))
+object SimpleJob {
+ def main(args: Array[String]) {
+ val logFile = "$YOUR_SPARK_HOME/README.md" // Should be some file on your system
+ val sc = new SparkContext("local", "Simple Job", "YOUR_SPARK_HOME",
+ List("target/scala-{{site.SCALA_VERSION}}/simple-project_{{site.SCALA_VERSION}}-1.0.jar"))
+ val logData = sc.textFile(logFile, 2).cache()
+ val numAs = logData.filter(line => line.contains("a")).count()
+ val numBs = logData.filter(line => line.contains("b")).count()
+ println("Lines with a: %s, Lines with b: %s".format(numAs, numBs))
+ }
}
{% endhighlight %}
-This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the job. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the job, the directory where Spark is installed, and a name for the jar file containing the job's sources. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes.
+This job simply counts the number of lines containing 'a' and the number containing 'b' in the Spark README. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the job. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the job, the directory where Spark is installed, and a name for the jar file containing the job's sources. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes.
This file depends on the Spark API, so we'll also include an sbt configuration file, `simple.sbt` which explains that Spark is a dependency. This file also adds two repositories which host Spark dependencies:
@@ -136,7 +138,7 @@ scalaVersion := "{{site.SCALA_VERSION}}"
libraryDependencies += "org.spark-project" %% "spark-core" % "{{site.SPARK_VERSION}}"
resolvers ++= Seq(
- "Typesafe Repository" at "http://repo.typesafe.com/typesafe/releases/",
+ "Akka Repository" at "http://repo.akka.io/releases/",
"Spray Repository" at "http://repo.spray.cc/")
{% endhighlight %}
@@ -154,7 +156,7 @@ $ find .
$ sbt package
$ sbt run
...
-Lines with a: 8422, Lines with b: 1836
+Lines with a: 46, Lines with b: 23
{% endhighlight %}
This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation, and consider using a distributed input source, such as HDFS.
@@ -171,7 +173,7 @@ import spark.api.java.function.Function;
public class SimpleJob {
public static void main(String[] args) {
- String logFile = "/var/log/syslog"; // Should be some file on your system
+ String logFile = "$YOUR_SPARK_HOME/README.md"; // Should be some file on your system
JavaSparkContext sc = new JavaSparkContext("local", "Simple Job",
"$YOUR_SPARK_HOME", new String[]{"target/simple-project-1.0.jar"});
JavaRDD<String> logData = sc.textFile(logFile).cache();
@@ -189,7 +191,7 @@ public class SimpleJob {
}
{% endhighlight %}
-This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Note that like in the Scala example, we initialize a SparkContext, though we use the special `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend `spark.api.java.function.Function`. The [Java programming guide]("java-programming-guide") describes these differences in more detail.
+This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. As with the Scala example, we initialize a SparkContext, though we use the special `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend `spark.api.java.function.Function`. The [Java programming guide](java-programming-guide.html) describes these differences in more detail.
To build the job, we also write a Maven `pom.xml` file that lists Spark as a dependency. Note that Spark artifacts are tagged with a Scala version.
@@ -207,8 +209,8 @@ To build the job, we also write a Maven `pom.xml` file that lists Spark as a dep
<url>http://repo.spray.cc</url>
</repository>
<repository>
- <id>Typesafe repository</id>
- <url>http://repo.typesafe.com/typesafe/releases</url>
+ <id>Akka repository</id>
+ <url>http://repo.akka.io/releases</url>
</repository>
</repositories>
<dependencies>
@@ -237,7 +239,7 @@ Now, we can execute the job using Maven:
$ mvn package
$ mvn exec:java -Dexec.mainClass="SimpleJob"
...
-Lines with a: 8422, Lines with b: 1836
+Lines with a: 46, Lines with b: 23
{% endhighlight %}
This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation, and consider using a distributed input source, such as HDFS.
@@ -251,7 +253,7 @@ As an example, we'll create a simple Spark job, `SimpleJob.py`:
"""SimpleJob.py"""
from pyspark import SparkContext
-logFile = "/var/log/syslog" # Should be some file on your system
+logFile = "$YOUR_SPARK_HOME/README.md" # Should be some file on your system
sc = SparkContext("local", "Simple job")
logData = sc.textFile(logFile).cache()
@@ -263,9 +265,10 @@ print "Lines with a: %i, lines with b: %i" % (numAs, numBs)
This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file.
-Like in the Scala and Java examples, we use a SparkContext to create RDDs.
+Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed.
+As with the Scala and Java examples, we use a SparkContext to create RDDs.
We can pass Python functions to Spark, which are automatically serialized along with any variables that they reference.
-For jobs that use custom classes or third-party libraries, we can add those code dependencies to SparkContext to ensure that they will be available on remote machines; this is described in more detail in the [Python programming guide](python-programming-guide).
+For jobs that use custom classes or third-party libraries, we can add those code dependencies to SparkContext to ensure that they will be available on remote machines; this is described in more detail in the [Python programming guide](python-programming-guide.html).
`SimpleJob` is simple enough that we do not need to specify any code dependencies.
We can run this job using the `pyspark` script:
@@ -274,7 +277,7 @@ We can run this job using the `pyspark` script:
$ cd $SPARK_HOME
$ ./pyspark SimpleJob.py
...
-Lines with a: 8422, Lines with b: 1836
+Lines with a: 46, Lines with b: 23
{% endhighlight python %}
This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation, and consider using a distributed input source, such as HDFS.
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 6fb81b6004..66fb8d73e8 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -4,25 +4,55 @@ title: Launching Spark on YARN
---
Experimental support for running over a [YARN (Hadoop
-NextGen)](http://hadoop.apache.org/docs/r2.0.1-alpha/hadoop-yarn/hadoop-yarn-site/YARN.html)
-cluster was added to Spark in version 0.6.0. Because YARN depends on version
-2.0 of the Hadoop libraries, this currently requires checking out a separate
-branch of Spark, called `yarn`, which you can do as follows:
+NextGen)](http://hadoop.apache.org/docs/r2.0.2-alpha/hadoop-yarn/hadoop-yarn-site/YARN.html)
+cluster was added to Spark in version 0.6.0. This was merged into master as part of 0.7 effort.
+To build spark core with YARN support, please use the hadoop2-yarn profile.
+Ex: mvn -Phadoop2-yarn clean install
- git clone git://github.com/mesos/spark
- cd spark
- git checkout -b yarn --track origin/yarn
+# Building spark core consolidated jar.
+
+We need a consolidated spark core jar (which bundles all the required dependencies) to run Spark jobs on a yarn cluster.
+This can be built either through sbt or via maven.
+
+- Building spark assembled jar via sbt.
+ It is a manual process of enabling it in project/SparkBuild.scala.
+Please comment out the
+ HADOOP_VERSION, HADOOP_MAJOR_VERSION and HADOOP_YARN
+variables before the line 'For Hadoop 2 YARN support'
+Next, uncomment the subsequent 3 variable declaration lines (for these three variables) which enable hadoop yarn support.
+
+Assembly of the jar Ex:
+
+ ./sbt/sbt clean assembly
+
+The assembled jar would typically be something like :
+`./core/target/spark-core-assembly-0.8.0-SNAPSHOT.jar`
+
+
+- Building spark assembled jar via Maven.
+ Use the hadoop2-yarn profile and execute the package target.
+
+Something like this. Ex:
+
+ mvn -Phadoop2-yarn clean package -DskipTests=true
+
+
+This will build the shaded (consolidated) jar. Typically something like :
+`./repl-bin/target/spark-repl-bin-<VERSION>-shaded-hadoop2-yarn.jar`
# Preparations
-- In order to distribute Spark within the cluster, it must be packaged into a single JAR file. This can be done by running `sbt/sbt assembly`
+- Building spark core assembled jar (see above).
- Your application code must be packaged into a separate JAR file.
If you want to test out the YARN deployment mode, you can use the current Spark examples. A `spark-examples_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}` file can be generated by running `sbt/sbt package`. NOTE: since the documentation you're reading is for Spark version {{site.SPARK_VERSION}}, we are assuming here that you have downloaded Spark {{site.SPARK_VERSION}} or checked it out of source control. If you are using a different version of Spark, the version numbers in the jar generated by the sbt package command will obviously be different.
# Launching Spark on YARN
+Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the hadoop cluster.
+This would be used to connect to the cluster, write to the dfs and submit jobs to the resource manager.
+
The command to launch the YARN Client is as follows:
SPARK_JAR=<SPARK_YAR_FILE> ./run spark.deploy.yarn.Client \
@@ -30,22 +60,28 @@ The command to launch the YARN Client is as follows:
--class <APP_MAIN_CLASS> \
--args <APP_MAIN_ARGUMENTS> \
--num-workers <NUMBER_OF_WORKER_MACHINES> \
+ --master-memory <MEMORY_FOR_MASTER> \
--worker-memory <MEMORY_PER_WORKER> \
- --worker-cores <CORES_PER_WORKER>
+ --worker-cores <CORES_PER_WORKER> \
+ --user <hadoop_user> \
+ --queue <queue_name>
For example:
SPARK_JAR=./core/target/spark-core-assembly-{{site.SPARK_VERSION}}.jar ./run spark.deploy.yarn.Client \
--jar examples/target/scala-{{site.SCALA_VERSION}}/spark-examples_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}.jar \
--class spark.examples.SparkPi \
- --args standalone \
+ --args yarn-standalone \
--num-workers 3 \
+ --master-memory 4g \
--worker-memory 2g \
- --worker-cores 2
+ --worker-cores 1
The above starts a YARN Client programs which periodically polls the Application Master for status updates and displays them in the console. The client will exit once your application has finished running.
# Important Notes
-- When your application instantiates a Spark context it must use a special "standalone" master url. This starts the scheduler without forcing it to connect to a cluster. A good way to handle this is to pass "standalone" as an argument to your program, as shown in the example above.
-- YARN does not support requesting container resources based on the number of cores. Thus the numbers of cores given via command line arguments cannot be guaranteed.
+- When your application instantiates a Spark context it must use a special "yarn-standalone" master url. This starts the scheduler without forcing it to connect to a cluster. A good way to handle this is to pass "yarn-standalone" as an argument to your program, as shown in the example above.
+- We do not requesting container resources based on the number of cores. Thus the numbers of cores given via command line arguments cannot be guaranteed.
+- Currently, we have not yet integrated with hadoop security. If --user is present, the hadoop_user specified will be used to run the tasks on the cluster. If unspecified, current user will be used (which should be valid in cluster).
+ Once hadoop security support is added, and if hadoop cluster is enabled with security, additional restrictions would apply via delegation tokens passed.
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index b98718a553..e9cf9ef36f 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -38,17 +38,23 @@ The first thing a Spark program must do is to create a `SparkContext` object, wh
This is done through the following constructor:
{% highlight scala %}
-new SparkContext(master, jobName, [sparkHome], [jars])
+new SparkContext(master, appName, [sparkHome], [jars])
{% endhighlight %}
-The `master` parameter is a string specifying a [Mesos](running-on-mesos.html) cluster to connect to, or a special "local" string to run in local mode, as described below. `jobName` is a name for your job, which will be shown in the Mesos web UI when running on a cluster. Finally, the last two parameters are needed to deploy your code to a cluster if running in distributed mode, as described later.
+The `master` parameter is a string specifying a [Spark or Mesos cluster URL](#master-urls) to connect to, or a special "local" string to run in local mode, as described below. `appName` is a name for your application, which will be shown in the cluster web UI. Finally, the last two parameters are needed to deploy your code to a cluster if running in distributed mode, as described later.
-In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `MASTER` environment variable. For example, to run on four cores, use
+In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `MASTER` environment variable, and you can add JARs to the classpath with the `ADD_JARS` variable. For example, to run `spark-shell` on four cores, use
{% highlight bash %}
$ MASTER=local[4] ./spark-shell
{% endhighlight %}
+Or, to also add `code.jar` to its classpath, use:
+
+{% highlight bash %}
+$ MASTER=local[4] ADD_JARS=code.jar ./spark-shell
+{% endhighlight %}
+
### Master URLs
The master URL passed to Spark can be in one of the following formats:
@@ -67,6 +73,8 @@ The master URL passed to Spark can be in one of the following formats:
</td></tr>
</table>
+If no master URL is specified, the spark shell defaults to "local".
+
For running on YARN, Spark launches an instance of the standalone deploy cluster within YARN; see [running on YARN](running-on-yarn.html) for details.
### Deploying Code on a Cluster
@@ -76,7 +84,7 @@ If you want to run your job on a cluster, you will need to specify the two optio
* `sparkHome`: The path at which Spark is installed on your worker machines (it should be the same on all of them).
* `jars`: A list of JAR files on the local machine containing your job's code and any dependencies, which Spark will deploy to all the worker nodes. You'll need to package your job into a set of JARs using your build system. For example, if you're using SBT, the [sbt-assembly](https://github.com/sbt/sbt-assembly) plugin is a good way to make a single JAR with your code and dependencies.
-If you run `spark-shell` on a cluster, any classes you define in the shell will automatically be distributed.
+If you run `spark-shell` on a cluster, you can add JARs to it by specifying the `ADD_JARS` environment variable before you launch it. This variable should contain a comma-separated list of JARs. For example, `ADD_JARS=a.jar,b.jar ./spark-shell` will launch a shell with `a.jar` and `b.jar` on its classpath. In addition, any new classes you define in the shell will automatically be distributed.
# Resilient Distributed Datasets (RDDs)
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 0e618a06c7..f5788dc467 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -7,9 +7,9 @@ title: Spark Streaming Programming Guide
{:toc}
# Overview
-A Spark Streaming application is very similar to a Spark application; it consists of a *driver program* that runs the user's `main` function and continuous executes various *parallel operations* on input streams of data. The main abstraction Spark Streaming provides is a *discretized stream* (DStream), which is a continuous sequence of RDDs (distributed collection of elements) representing a continuous stream of data. DStreams can created from live incoming data (such as data from a socket, Kafka, etc.) or it can be generated by transformation of existing DStreams using parallel operators like map, reduce, and window. The basic processing model is as follows:
-(i) While a Spark Streaming driver program is running, the system receives data from various sources and and divides the data into batches. Each batch of data is treated as a RDD, that is a immutable and parallel collection of data. These input data RDDs are automatically persisted in memory (serialized by default) and replicated to two nodes for fault-tolerance. This sequence of RDDs is collectively referred to as an InputDStream.
-(ii) Data received by InputDStreams are processed processed using DStream operations. Since all data is represented as RDDs and all DStream operations as RDD operations, data is automatically recovered in the event of node failures.
+A Spark Streaming application is very similar to a Spark application; it consists of a *driver program* that runs the user's `main` function and continuous executes various *parallel operations* on input streams of data. The main abstraction Spark Streaming provides is a *discretized stream* (DStream), which is a continuous sequence of RDDs (distributed collections of elements) representing a continuous stream of data. DStreams can be created from live incoming data (such as data from a socket, Kafka, etc.) or can be generated by transformong existing DStreams using parallel operators like `map`, `reduce`, and `window`. The basic processing model is as follows:
+(i) While a Spark Streaming driver program is running, the system receives data from various sources and and divides it into batches. Each batch of data is treated as an RDD, that is, an immutable parallel collection of data. These input RDDs are saved in memory and replicated to two nodes for fault-tolerance. This sequence of RDDs is collectively called an InputDStream.
+(ii) Data received by InputDStreams are processed using DStream operations. Since all data is represented as RDDs and all DStream operations as RDD operations, data is automatically recovered in the event of node failures.
This guide shows some how to start programming with DStreams.
@@ -17,16 +17,12 @@ This guide shows some how to start programming with DStreams.
The first thing a Spark Streaming program must do is create a `StreamingContext` object, which tells Spark how to access a cluster. A `StreamingContext` can be created by using
{% highlight scala %}
-new StreamingContext(master, jobName, batchDuration)
+new StreamingContext(master, appName, batchDuration, [sparkHome], [jars])
{% endhighlight %}
-The `master` parameter is either the [Mesos master URL](running-on-mesos.html) (for running on a cluster)or the special "local" string (for local mode) that is used to create a Spark Context. For more information about this please refer to the [Spark programming guide](scala-programming-guide.html). The `jobName` is the name of the streaming job, which is the same as the jobName used in SparkContext. It is used to identify this job in the Mesos web UI. The `batchDuration` is the size of the batches (as explained earlier). This must be set carefully such the cluster can keep up with the processing of the data streams. Starting with something conservative like 5 seconds maybe a good start. See [Performance Tuning](#setting-the-right-batch-size) section for a detailed discussion.
-
-This constructor creates a SparkContext object using the given `master` and `jobName` parameters. However, if you already have a SparkContext or you need to create a custom SparkContext by specifying list of JARs, then a StreamingContext can be created from the existing SparkContext, by using
-{% highlight scala %}
-new StreamingContext(sparkContext, batchDuration)
-{% endhighlight %}
+The `master` parameter is a standard [Spark cluster URL](scala-programming-guide.html#master-urls) and can be "local" for local testing. The `appName` is a name of your program, which will be shown on your cluster's web UI. The `batchDuration` is the size of the batches (as explained earlier). This must be set carefully such the cluster can keep up with the processing of the data streams. Start with something conservative like 5 seconds. See the [Performance Tuning](#setting-the-right-batch-size) section for a detailed discussion. Finally, `sparkHome` and `jars` are necessary when running on a cluster to specify the location of your code, as described in the [Spark programming guide](scala-programming-guide.html#deploying-code-on-a-cluster).
+This constructor creates a SparkContext for your job as well, which can be accessed with `streamingContext.sparkContext`.
# Attaching Input Sources - InputDStreams
@@ -87,7 +83,7 @@ DStreams support many of the transformations available on normal Spark RDD's:
<tr>
<td> <b>groupByKey</b>([<i>numTasks</i>]) </td>
<td> When called on a DStream of (K, V) pairs, returns a new DStream of (K, Seq[V]) pairs by grouping together all the values of each key in the RDDs of the source DStream. <br />
- <b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluser) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.
+ <b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluster) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.
</td>
</tr>
<tr>
@@ -136,7 +132,7 @@ Spark Streaming features windowed computations, which allow you to apply transfo
<td> <b>groupByKeyAndWindow</b>(<i>windowDuration</i>, <i>slideDuration</i>, [<i>numTasks</i>])
</td>
<td> When called on a DStream of (K, V) pairs, returns a new DStream of (K, Seq[V]) pairs by grouping together values of each key over batches in a sliding window. <br />
-<b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluser) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.</td>
+<b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluster) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.</td>
</tr>
<tr>
<td> <b>reduceByKeyAndWindow</b>(<i>func</i>, <i>windowDuration</i>, <i>slideDuration</i>, [<i>numTasks</i>]) </td>
@@ -286,7 +282,9 @@ For input streams that receive data from the network (that is, subclasses of Net
Note that, unlike RDDs, the default persistence level of DStreams keeps the data serialized in memory. This is further discussed in the [Performance Tuning](#memory-tuning) section. More information on different persistence levels can be found in [Spark Programming Guide](scala-programming-guide.html#rdd-persistence).
# RDD Checkpointing within DStreams
-DStreams created by stateful operations like `updateStateByKey` require the RDDs in the DStream to be periodically saved to HDFS files for checkpointing. This is because, unless checkpointed, the lineage of operations of the state RDDs can increase indefinitely (since each RDD in the DStream depends on the previous RDD). This leads to two problems - (i) the size of Spark tasks increase proportionally with the RDD lineage leading higher task launch times, (ii) no limit on the amount of recomputation required on failure. Checkpointing RDDs at some interval by writing them to HDFS allows the lineage to be truncated. Note that checkpointing also incurs the cost of saving to HDFS which may cause the corresponding batch to take longer to process. Hence, the interval of checkpointing needs to be set carefully. At small batch sizes (say 1 second), checkpointing every batch may significantly reduce operation throughput. Conversely, checkpointing too slowly causes the lineage and task sizes to grow which may have detrimental effects. Typically, a checkpoint interval of 5 - 10 times of sliding interval of a DStream is good setting to try.
+A _stateful operation_ is one which operates over multiple batches of data. This includes all window-based operations and the `updateStateByKey` operation.
+
+Because stateful operations have a dependency on previous batches of data, they continuously accumulate metadata over time. To clear this metadata, streaming supports periodic _checkpointing_ by saving intermediate data to HDFS. Note that checkpointing also incurs the cost of saving to HDFS which may cause the corresponding batch to take longer to process. Hence, the interval of checkpointing needs to be set carefully. At small batch sizes (say 1 second), checkpointing every batch may significantly reduce operation throughput. Conversely, checkpointing too slowly causes the lineage and task sizes to grow which may have detrimental effects. Typically, a checkpoint interval of 5 - 10 times of sliding interval of a DStream is good setting to try.
To enable checkpointing, the developer has to provide the HDFS path to which RDD will be saved. This is done by using
@@ -380,15 +378,15 @@ val ssc = new StreamingContext(checkpointDirectory)
On calling `ssc.start()` on this new context, the following steps are taken by the system
-1. Schedule the transformations and output operations for all the time steps between the time when the driver failed and when it was restarted. This is also done for those time steps that were scheduled but not processed due to the failure. This will make the system recompute all the intermediate data from the checkpointed RDD files, etc.
+1. Schedule the transformations and output operations for all the time steps between the time when the driver failed and when it last checkpointed. This is also done for those time steps that were previously scheduled but not processed due to the failure. This will make the system recompute all the intermediate data from the checkpointed RDD files, etc.
1. Restart the network receivers, if any, and continue receiving new data.
In the current _alpha_ release, there are two different failure behaviors based on which input sources are used.
1. _Using HDFS files as input source_ - Since the data is reliably stored on HDFS, all data can re-computed and therefore no data will be lost due to any failure.
-1. _Using any input source that receives data through a network_ - As aforesaid, the received input data is replicated in memory to multiple nodes. Since, all the data in the Spark worker's memory is lost when the Spark driver fails, the past input data will not be accessible and driver recovers. Hence, if stateful and window-based operations are used (like `updateStateByKey`, `window`, `countByValueAndWindow`, etc.), then the intermediate state will not be recovered completely.
+1. _Using any input source that receives data through a network_ - The received input data is replicated in memory to multiple nodes. Since, all the data in the Spark worker's memory is lost when the Spark driver fails, the past input data will not be accessible and driver recovers. Hence, if stateful and window-based operations are used (like `updateStateByKey`, `window`, `countByValueAndWindow`, etc.), then the intermediate state will not be recovered completely.
-In future releases, this behaviour will be fixed for all input sources, that is, all data will be recovered irrespective of which input sources are used. Note that for non-stateful transformations like `map`, `count`, and `reduceByKey`, with _all_ input streams, the system, upon restarting, will continue to receive and process new data.
+In future releases, we will support full recoverability for all input sources. Note that for non-stateful transformations like `map`, `count`, and `reduceByKey`, with _all_ input streams, the system, upon restarting, will continue to receive and process new data.
To better understand the behavior of the system under driver failure with a HDFS source, lets consider what will happen with a file input stream Specifically, in the case of the file input stream, it will correctly identify new files that were created while the driver was down and process them in the same way as it would have if the driver had not failed. To explain further in the case of file input stream, we shall use an example. Lets say, files are being generated every second, and a Spark Streaming program reads every new file and output the number of lines in the file. This is what the sequence of outputs would be with and without a driver failure.
@@ -514,5 +512,6 @@ JavaPairDStream<String, Integer> wordCounts = words.map(
# Where to Go from Here
-* Documentation - [Scala](api/streaming/index.html#spark.streaming.package) and [Java](api/streaming/index.html#spark.streaming.api.java.package)
+* API docs - [Scala](api/streaming/index.html#spark.streaming.package) and [Java](api/streaming/index.html#spark.streaming.api.java.package)
* More examples - [Scala](https://github.com/mesos/spark/tree/master/examples/src/main/scala/spark/streaming/examples) and [Java](https://github.com/mesos/spark/tree/master/examples/src/main/java/spark/streaming/examples)
+* [Paper describing Spark Streaming](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf)
diff --git a/docs/tuning.md b/docs/tuning.md
index 738c530458..32c7ab86e9 100644
--- a/docs/tuning.md
+++ b/docs/tuning.md
@@ -49,7 +49,7 @@ Finally, to register your classes with Kryo, create a public class that extends
{% highlight scala %}
import com.esotericsoftware.kryo.Kryo
-class MyRegistrator extends KryoRegistrator {
+class MyRegistrator extends spark.KryoRegistrator {
override def registerClasses(kryo: Kryo) {
kryo.register(classOf[MyClass1])
kryo.register(classOf[MyClass2])
@@ -213,10 +213,10 @@ but at a high level, managing how frequently full GC takes place can help in red
Clusters will not be fully utilized unless you set the level of parallelism for each operation high
enough. Spark automatically sets the number of "map" tasks to run on each file according to its size
-(though you can control it through optional parameters to `SparkContext.textFile`, etc), but for
-distributed "reduce" operations, such as `groupByKey` and `reduceByKey`, it uses a default value of 8.
-You can pass the level of parallelism as a second argument (see the
-[`spark.PairRDDFunctions`](api/core/index.html#spark.PairRDDFunctions) documentation),
+(though you can control it through optional parameters to `SparkContext.textFile`, etc), and for
+distributed "reduce" operations, such as `groupByKey` and `reduceByKey`, it uses the largest
+parent RDD's number of partitions. You can pass the level of parallelism as a second argument
+(see the [`spark.PairRDDFunctions`](api/core/index.html#spark.PairRDDFunctions) documentation),
or set the system property `spark.default.parallelism` to change the default.
In general, we recommend 2-3 tasks per CPU core in your cluster.
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-24 12:24:49 +0000