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-rw-r--r--docs/README.md2
-rw-r--r--docs/_config.yml5
-rwxr-xr-xdocs/_layouts/global.html22
-rw-r--r--docs/_plugins/copy_api_dirs.rb6
-rw-r--r--docs/configuration.md13
-rw-r--r--docs/contributing-to-spark.md2
-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.md13
-rw-r--r--docs/java-programming-guide.md2
-rw-r--r--docs/python-programming-guide.md6
-rw-r--r--docs/quick-start.md10
-rw-r--r--docs/running-on-yarn.md2
-rw-r--r--docs/scala-programming-guide.md4
-rw-r--r--docs/streaming-custom-receivers.md101
-rw-r--r--docs/streaming-programming-guide.md334
-rw-r--r--docs/tuning.md8
18 files changed, 453 insertions, 126 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 2bd2eecc86..f99d5bb376 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -3,7 +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
+SPARK_VERSION: 0.7.1-SNAPSHOT
+SPARK_VERSION_SHORT: 0.7.1
SCALA_VERSION: 2.9.2
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..280ead0323 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>
diff --git a/docs/_plugins/copy_api_dirs.rb b/docs/_plugins/copy_api_dirs.rb
index e400dec619..d77e53963c 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."
@@ -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/configuration.md b/docs/configuration.md
index f1ca77aa78..17fdbf04d1 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>
@@ -183,7 +190,7 @@ Apart from these, the following properties are also available, and may be useful
</tr>
<tr>
<td>spark.broadcast.factory</td>
- <td>spark.broadcast. HttpBroadcastFactory</td>
+ <td>spark.broadcast.HttpBroadcastFactory</td>
<td>
Which broadcast implementation to use.
</td>
@@ -244,10 +251,10 @@ Apart from these, the following properties are also available, and may be useful
</td>
</tr>
<tr>
- <td>spark.cleaner.delay</td>
+ <td>spark.cleaner.ttl</td>
<td>(disable)</td>
<td>
- Duration (minutes) of how long Spark will remember any metadata (stages generated, tasks generated, etc.).
+ Duration (seconds) of how long Spark will remember any metadata (stages generated, tasks generated, etc.).
Periodic cleanups will ensure that metadata older than this duration will be forgetten. This is
useful for running Spark for many hours / days (for example, running 24/7 in case of Spark Streaming
applications). Note that any RDD that persists in memory for more than this duration will be cleared as well.
diff --git a/docs/contributing-to-spark.md b/docs/contributing-to-spark.md
index 14d0dc856b..50feeb2d6c 100644
--- a/docs/contributing-to-spark.md
+++ b/docs/contributing-to-spark.md
@@ -15,7 +15,7 @@ The Spark team welcomes contributions in the form of GitHub pull requests. Here
But first, make sure that you have [configured a spark-env.sh](configuration.html) with at least
`SCALA_HOME`, as some of the tests try to spawn subprocesses using this.
- Add new unit tests for your code. We use [ScalaTest](http://www.scalatest.org/) for testing. Just add a new Suite in `core/src/test`, or methods to an existing Suite.
-- If you'd like to report a bug but don't have time to fix it, you can still post it to our [issue tracker](https://spark-project.atlassian.net), or email the [mailing list](http://www.spark-project.org/mailing-lists.html).
+- If you'd like to report a bug but don't have time to fix it, you can still post it to our [issue tracker]({{site.SPARK_ISSUE_TRACKER_URL}}), or email the [mailing list](http://www.spark-project.org/mailing-lists.html).
# Licensing of Contributions
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..45facd8e63 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
@@ -58,9 +52,9 @@ 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:**
@@ -92,7 +86,8 @@ 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 4e84d23edf..3a7a8db4a6 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -87,7 +87,7 @@ By default, the `pyspark` shell creates SparkContext that runs jobs locally.
To connect to a non-local cluster, set the `MASTER` environment variable.
For example, to use the `pyspark` shell with a [standalone Spark cluster](spark-standalone.html):
-{% highlight shell %}
+{% highlight bash %}
$ MASTER=spark://IP:PORT ./pyspark
{% endhighlight %}
@@ -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..216f7c9cc5 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -136,7 +136,7 @@ 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 %}
@@ -189,7 +189,7 @@ 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 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.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 +207,8 @@ 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>
@@ -265,7 +265,7 @@ 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.
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:
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 6fb81b6004..c2957e6cb4 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -4,7 +4,7 @@ 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)
+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. 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:
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index b98718a553..2315aadbdf 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -38,10 +38,10 @@ 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
diff --git a/docs/streaming-custom-receivers.md b/docs/streaming-custom-receivers.md
new file mode 100644
index 0000000000..5476c00d02
--- /dev/null
+++ b/docs/streaming-custom-receivers.md
@@ -0,0 +1,101 @@
+---
+layout: global
+title: Tutorial - Spark Streaming, Plugging in a custom receiver.
+---
+
+A "Spark Streaming" receiver can be a simple network stream, streams of messages from a message queue, files etc. A receiver can also assume roles more than just receiving data like filtering, preprocessing, to name a few of the possibilities. The api to plug-in any user defined custom receiver is thus provided to encourage development of receivers which may be well suited to ones specific need.
+
+This guide shows the programming model and features by walking through a simple sample receiver and corresponding Spark Streaming application.
+
+
+## A quick and naive walk-through
+
+### Write a simple receiver
+
+This starts with implementing [Actor](#References)
+
+Following is a simple socket text-stream receiver, which is appearently overly simplified using Akka's socket.io api.
+
+{% highlight scala %}
+
+ class SocketTextStreamReceiver (host:String,
+ port:Int,
+ bytesToString: ByteString => String) extends Actor with Receiver {
+
+ override def preStart = IOManager(context.system).connect(host, port)
+
+ def receive = {
+ case IO.Read(socket, bytes) => pushBlock(bytesToString(bytes))
+ }
+
+ }
+
+
+{% endhighlight %}
+
+All we did here is mixed in trait Receiver and called pushBlock api method to push our blocks of data. Please refer to scala-docs of Receiver for more details.
+
+### A sample spark application
+
+* First create a Spark streaming context with master url and batchduration.
+
+{% highlight scala %}
+
+ val ssc = new StreamingContext(master, "WordCountCustomStreamSource",
+ Seconds(batchDuration))
+
+{% endhighlight %}
+
+* Plug-in the actor configuration into the spark streaming context and create a DStream.
+
+{% highlight scala %}
+
+ val lines = ssc.actorStream[String](Props(new SocketTextStreamReceiver(
+ "localhost",8445, z => z.utf8String)),"SocketReceiver")
+
+{% endhighlight %}
+
+* Process it.
+
+{% highlight scala %}
+
+ val words = lines.flatMap(_.split(" "))
+ val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
+
+ wordCounts.print()
+ ssc.start()
+
+
+{% endhighlight %}
+
+* After processing it, stream can be tested using the netcat utility.
+
+ $ nc -l localhost 8445
+ hello world
+ hello hello
+
+
+## Multiple homogeneous/heterogeneous receivers.
+
+A DStream union operation is provided for taking union on multiple input streams.
+
+{% highlight scala %}
+
+ val lines = ssc.actorStream[String](Props(new SocketTextStreamReceiver(
+ "localhost",8445, z => z.utf8String)),"SocketReceiver")
+
+ // Another socket stream receiver
+ val lines2 = ssc.actorStream[String](Props(new SocketTextStreamReceiver(
+ "localhost",8446, z => z.utf8String)),"SocketReceiver")
+
+ val union = lines.union(lines2)
+
+{% endhighlight %}
+
+Above stream can be easily process as described earlier.
+
+_A more comprehensive example is provided in the spark streaming examples_
+
+## References
+
+1.[Akka Actor documentation](http://doc.akka.io/docs/akka/2.0.5/scala/actors.html)
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index b6da7af654..b30699cf3d 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
@@ -34,34 +30,34 @@ The StreamingContext is used to creating InputDStreams from input sources:
{% highlight scala %}
// Assuming ssc is the StreamingContext
-ssc.networkStream(hostname, port) // Creates a stream that uses a TCP socket to read data from hostname:port
-ssc.textFileStream(directory) // Creates a stream by monitoring and processing new files in a HDFS directory
+ssc.textFileStream(directory) // Creates a stream by monitoring and processing new files in a HDFS directory
+ssc.socketStream(hostname, port) // Creates a stream that uses a TCP socket to read data from hostname:port
{% endhighlight %}
-A complete list of input sources is available in the [StreamingContext API documentation](api/streaming/index.html#spark.streaming.StreamingContext). Data received from these sources can be processed using DStream operations, which are explained next.
+We also provide a input streams for Kafka, Flume, Akka actor, etc. For a complete list of input streams, take a look at the [StreamingContext API documentation](api/streaming/index.html#spark.streaming.StreamingContext).
# DStream Operations
-Once an input DStream has been created, you can transform it using _DStream operators_. Most of these operators return new DStreams which you can further transform. Eventually, you'll need to call an _output operator_, which forces evaluation of the DStream by writing data out to an external source.
+Data received from the input streams can be processed using _DStream operations_. There are two kinds of operations - _transformations_ and _output operations_. Similar to RDD transformations, DStream transformations operate on one or more DStreams to create new DStreams with transformed data. After applying a sequence of transformations to the input streams, you'll need to call the output operations, which writies data out to an external source.
## Transformations
DStreams support many of the transformations available on normal Spark RDD's:
<table class="table">
-<tr><th style="width:25%">Transformation</th><th>Meaning</th></tr>
+<tr><th style="width:30%">Transformation</th><th>Meaning</th></tr>
<tr>
<td> <b>map</b>(<i>func</i>) </td>
- <td> Returns a new DStream formed by passing each element of the source through a function <i>func</i>. </td>
+ <td> Returns a new DStream formed by passing each element of the source DStream through a function <i>func</i>. </td>
</tr>
<tr>
<td> <b>filter</b>(<i>func</i>) </td>
- <td> Returns a new stream formed by selecting those elements of the source on which <i>func</i> returns true. </td>
+ <td> Returns a new DStream formed by selecting those elements of the source DStream on which <i>func</i> returns true. </td>
</tr>
<tr>
<td> <b>flatMap</b>(<i>func</i>) </td>
- <td> Similar to map, but each input item can be mapped to 0 or more output items (so <i>func</i> should return a Seq rather than a single item). </td>
+ <td> Similar to map, but each input item can be mapped to 0 or more output items (so <i>func</i> should return a <code>Seq</code> rather than a single item). </td>
</tr>
<tr>
<td> <b>mapPartitions</b>(<i>func</i>) </td>
@@ -70,73 +66,92 @@ DStreams support many of the transformations available on normal Spark RDD's:
</tr>
<tr>
<td> <b>union</b>(<i>otherStream</i>) </td>
- <td> Return a new stream that contains the union of the elements in the source stream and the argument. </td>
+ <td> Return a new DStream that contains the union of the elements in the source DStream and the argument DStream. </td>
+</tr>
+<tr>
+ <td> <b>count</b>() </td>
+ <td> Returns a new DStream of single-element RDDs by counting the number of elements in each RDD of the source DStream. </td>
+</tr>
+<tr>
+ <td> <b>reduce</b>(<i>func</i>) </td>
+ <td> Returns a new DStream of single-element RDDs by aggregating the elements in each RDD of the source DStream using a function <i>func</i> (which takes two arguments and returns one). The function should be associative so that it can be computed in parallel. </td>
+</tr>
+<tr>
+ <td> <b>countByValue</b>() </td>
+ <td> When called on a DStream of elements of type K, returns a new DStream of (K, Long) pairs where the value of each key is its frequency in each RDD of the source DStream. </td>
</tr>
<tr>
<td> <b>groupByKey</b>([<i>numTasks</i>]) </td>
- <td> When called on a stream of (K, V) pairs, returns a stream of (K, Seq[V]) pairs. <br />
-<b>Note:</b> By default, this uses only 8 parallel tasks to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.
+ <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.
</td>
</tr>
<tr>
<td> <b>reduceByKey</b>(<i>func</i>, [<i>numTasks</i>]) </td>
- <td> When called on a stream of (K, V) pairs, returns a stream of (K, V) pairs where the values for each key are aggregated using the given reduce function. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument. </td>
+ <td> When called on a DStream of (K, V) pairs, returns a new DStream of (K, V) pairs where the values for each key are aggregated using the given reduce function. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument. </td>
</tr>
<tr>
<td> <b>join</b>(<i>otherStream</i>, [<i>numTasks</i>]) </td>
- <td> When called on streams of type (K, V) and (K, W), returns a stream of (K, (V, W)) pairs with all pairs of elements for each key. </td>
+ <td> When called on two DStreams of (K, V) and (K, W) pairs, returns a new DStream of (K, (V, W)) pairs with all pairs of elements for each key. </td>
</tr>
<tr>
<td> <b>cogroup</b>(<i>otherStream</i>, [<i>numTasks</i>]) </td>
- <td> When called on DStream of type (K, V) and (K, W), returns a DStream of (K, Seq[V], Seq[W]) tuples.</td>
-</tr>
-<tr>
- <td> <b>reduce</b>(<i>func</i>) </td>
- <td> Returns a new DStream of single-element RDDs by aggregating the elements of the stream using a function func (which takes two arguments and returns one). The function should be associative so that it can be computed correctly in parallel. </td>
+ <td> When called on DStream of (K, V) and (K, W) pairs, returns a new DStream of (K, Seq[V], Seq[W]) tuples.</td>
</tr>
<tr>
<td> <b>transform</b>(<i>func</i>) </td>
<td> Returns a new DStream by applying func (a RDD-to-RDD function) to every RDD of the stream. This can be used to do arbitrary RDD operations on the DStream. </td>
</tr>
+<tr>
+ <td> <b>updateStateByKey</b>(<i>func</i>) </td>
+ <td> Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values of each key. This can be used to track session state by using the session-id as the key and updating the session state as new data is received.</td>
+</tr>
+
</table>
-Spark Streaming features windowed computations, which allow you to report statistics over a sliding window of data. All window functions take a <i>windowDuration</i>, which represents the width of the window and a <i>slideTime</i>, which represents the frequency during which the window is calculated.
+Spark Streaming features windowed computations, which allow you to apply transformations over a sliding window of data. All window functions take a <i>windowDuration</i>, which represents the width of the window and a <i>slideTime</i>, which represents the frequency during which the window is calculated.
<table class="table">
-<tr><th style="width:25%">Transformation</th><th>Meaning</th></tr>
+<tr><th style="width:30%">Transformation</th><th>Meaning</th></tr>
<tr>
- <td> <b>window</b>(<i>windowDuration</i>, </i>slideTime</i>) </td>
- <td> Return a new stream which is computed based on windowed batches of the source stream. <i>windowDuration</i> is the width of the window and <i>slideTime</i> is the frequency during which the window is calculated. Both times must be multiples of the batch interval.
+ <td> <b>window</b>(<i>windowDuration</i>, </i>slideDuration</i>) </td>
+ <td> Return a new DStream which is computed based on windowed batches of the source DStream. <i>windowDuration</i> is the width of the window and <i>slideTime</i> is the frequency during which the window is calculated. Both times must be multiples of the batch interval.
</td>
</tr>
<tr>
- <td> <b>countByWindow</b>(<i>windowDuration</i>, </i>slideTime</i>) </td>
+ <td> <b>countByWindow</b>(<i>windowDuration</i>, </i>slideDuration</i>) </td>
<td> Return a sliding count of elements in the stream. <i>windowDuration</i> and <i>slideDuration</i> are exactly as defined in <code>window()</code>.
</td>
</tr>
<tr>
- <td> <b>reduceByWindow</b>(<i>func</i>, <i>windowDuration</i>, </i>slideDuration</i>) </td>
+ <td> <b>reduceByWindow</b>(<i>func</i>, <i>windowDuration</i>, <i>slideDuration</i>) </td>
<td> Return a new single-element stream, created by aggregating elements in the stream over a sliding interval using <i>func</i>. The function should be associative so that it can be computed correctly in parallel. <i>windowDuration</i> and <i>slideDuration</i> are exactly as defined in <code>window()</code>.
</td>
</tr>
<tr>
- <td> <b>groupByKeyAndWindow</b>(windowDuration, slideDuration, [<i>numTasks</i>])
+ <td> <b>groupByKeyAndWindow</b>(<i>windowDuration</i>, <i>slideDuration</i>, [<i>numTasks</i>])
</td>
- <td> When called on a stream of (K, V) pairs, returns a stream of (K, Seq[V]) pairs over a sliding window. <br />
-<b>Note:</b> By default, this uses only 8 parallel tasks to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks. <i>windowDuration</i> and <i>slideDuration</i> are exactly as defined in <code>window()</code>.
-</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>
</tr>
<tr>
- <td> <b>reduceByKeyAndWindow</b>(<i>func</i>, [<i>numTasks</i>]) </td>
- <td> When called on a stream of (K, V) pairs, returns a stream of (K, V) pairs where the values for each key are aggregated using the given reduce function over batches within a sliding window. Like in <code>groupByKeyAndWindow</code>, the number of reduce tasks is configurable through an optional second argument.
+ <td> <b>reduceByKeyAndWindow</b>(<i>func</i>, <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, V) pairs where the values for each key are aggregated using the given reduce function <i>func</i> over batches in a sliding window. Like in <code>groupByKeyAndWindow</code>, the number of reduce tasks is configurable through an optional second argument.
<i>windowDuration</i> and <i>slideDuration</i> are exactly as defined in <code>window()</code>.
</td>
</tr>
<tr>
- <td> <b>countByKeyAndWindow</b>([<i>numTasks</i>]) </td>
- <td> When called on a stream of (K, V) pairs, returns a stream of (K, Int) pairs where the values for each key are the count within a sliding window. Like in <code>countByKeyAndWindow</code>, the number of reduce tasks is configurable through an optional second argument.
+ <td> <b>reduceByKeyAndWindow</b>(<i>func</i>, <i>invFunc</i>, <i>windowDuration</i>, <i>slideDuration</i>, [<i>numTasks</i>]) </td>
+ <td> A more efficient version of the above <code>reduceByKeyAndWindow()</code> where the reduce value of each window is calculated
+ incrementally using the reduce values of the previous window. This is done by reducing the new data that enter the sliding window, and "inverse reducing" the old data that leave the window. An example would be that of "adding" and "subtracting" counts of keys as the window slides. However, it is applicable to only "invertible reduce functions", that is, those reduce functions which have a corresponding "inverse reduce" function (taken as parameter <i>invFunc</i>. Like in <code>groupByKeyAndWindow</code>, the number of reduce tasks is configurable through an optional second argument.
<i>windowDuration</i> and <i>slideDuration</i> are exactly as defined in <code>window()</code>.
-</td>
+</td>
+</tr>
+<tr>
+ <td> <b>countByValueAndWindow</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, Long) pairs where the value of each key is its frequency within a sliding window. Like in <code>groupByKeyAndWindow</code>, the number of reduce tasks is configurable through an optional second argument.
+ <i>windowDuration</i> and <i>slideDuration</i> are exactly as defined in <code>window()</code>.
+</td>
</tr>
</table>
@@ -147,7 +162,7 @@ A complete list of DStream operations is available in the API documentation of [
When an output operator is called, it triggers the computation of a stream. Currently the following output operators are defined:
<table class="table">
-<tr><th style="width:25%">Operator</th><th>Meaning</th></tr>
+<tr><th style="width:30%">Operator</th><th>Meaning</th></tr>
<tr>
<td> <b>foreach</b>(<i>func</i>) </td>
<td> The fundamental output operator. Applies a function, <i>func</i>, to each RDD generated from the stream. This function should have side effects, such as printing output, saving the RDD to external files, or writing it over the network to an external system. </td>
@@ -176,11 +191,6 @@ When an output operator is called, it triggers the computation of a stream. Curr
</table>
-## DStream Persistence
-Similar to RDDs, DStreams also allow developers to persist the stream's data in memory. That is, using `persist()` method on a DStream would automatically persist every RDD of that DStream in memory. This is useful if the data in the DStream will be computed multiple times (e.g., multiple DStream operations on the same data). For window-based operations like `reduceByWindow` and `reduceByKeyAndWindow` and state-based operations like `updateStateByKey`, this is implicitly true. Hence, DStreams generated by window-based operations are automatically persisted in memory, without the developer calling `persist()`.
-
-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).
-
# Starting the Streaming computation
All the above DStream operations are completely lazy, that is, the operations will start executing only after the context is started by using
{% highlight scala %}
@@ -192,8 +202,8 @@ Conversely, the computation can be stopped by using
ssc.stop()
{% endhighlight %}
-# Example - NetworkWordCount.scala
-A good example to start off is the spark.streaming.examples.NetworkWordCount. This example counts the words received from a network server every second. Given below is the relevant sections of the source code. You can find the full source code in <Spark repo>/streaming/src/main/scala/spark/streaming/examples/WordCountNetwork.scala.
+# Example
+A simple example to start off is the [NetworkWordCount](https://github.com/mesos/spark/tree/master/examples/src/main/scala/spark/streaming/examples/NetworkWordCount.scala). This example counts the words received from a network server every second. Given below is the relevant sections of the source code. You can find the full source code in `<Spark repo>/streaming/src/main/scala/spark/streaming/examples/NetworkWordCount.scala` .
{% highlight scala %}
import spark.streaming.{Seconds, StreamingContext}
@@ -202,7 +212,7 @@ import spark.streaming.StreamingContext._
// Create the context and set up a network input stream to receive from a host:port
val ssc = new StreamingContext(args(0), "NetworkWordCount", Seconds(1))
-val lines = ssc.networkTextStream(args(1), args(2).toInt)
+val lines = ssc.socketTextStream(args(1), args(2).toInt)
// Split the lines into words, count them, and print some of the counts on the master
val words = lines.flatMap(_.split(" "))
@@ -213,6 +223,8 @@ wordCounts.print()
ssc.start()
{% endhighlight %}
+The `socketTextStream` returns a DStream of lines received from a TCP socket-based source. The `lines` DStream is _transformed_ into a DStream using the `flatMap` operation, where each line is split into words. This `words` DStream is then mapped to a DStream of `(word, 1)` pairs, which is finally reduced to get the word counts. `wordCounts.print()` will print 10 of the counts generated every second.
+
To run this example on your local machine, you need to first run a Netcat server by using
{% highlight bash %}
@@ -260,6 +272,33 @@ Time: 1357008430000 ms
</td>
</table>
+You can find more examples in `<Spark repo>/streaming/src/main/scala/spark/streaming/examples/`. They can be run in the similar manner using `./run spark.streaming.examples....` . Executing without any parameter would give the required parameter list. Further explanation to run them can be found in comments in the files.
+
+# DStream Persistence
+Similar to RDDs, DStreams also allow developers to persist the stream's data in memory. That is, using `persist()` method on a DStream would automatically persist every RDD of that DStream in memory. This is useful if the data in the DStream will be computed multiple times (e.g., multiple operations on the same data). For window-based operations like `reduceByWindow` and `reduceByKeyAndWindow` and state-based operations like `updateStateByKey`, this is implicitly true. Hence, DStreams generated by window-based operations are automatically persisted in memory, without the developer calling `persist()`.
+
+For input streams that receive data from the network (that is, subclasses of NetworkInputDStream like FlumeInputDStream and KafkaInputDStream), the default persistence level is set to replicate the data to two nodes for fault-tolerance.
+
+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
+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
+
+{% highlight scala %}
+ssc.checkpoint(hdfsPath) // assuming ssc is the StreamingContext
+{% endhighlight %}
+
+The interval of checkpointing of a DStream can be set by using
+
+{% highlight scala %}
+dstream.checkpoint(checkpointInterval) // checkpointInterval must be a multiple of slide duration of dstream
+{% endhighlight %}
+
+For DStreams that must be checkpointed (that is, DStreams created by `updateStateByKey` and `reduceByKeyAndWindow` with inverse function), the checkpoint interval of the DStream is by default set to a multiple of the DStream's sliding interval such that its at least 10 seconds.
# Performance Tuning
@@ -273,17 +312,21 @@ Getting the best performance of a Spark Streaming application on a cluster requi
There are a number of optimizations that can be done in Spark to minimize the processing time of each batch. These have been discussed in detail in [Tuning Guide](tuning.html). This section highlights some of the most important ones.
### Level of Parallelism
-Cluster resources maybe underutilized if the number of parallel tasks used in any stage of the computation is not high enough. For example, for distributed reduce operations like `reduceByKey` and `reduceByKeyAndWindow`, the default number of parallel tasks is 8. You can pass the level of parallelism as an argument (see the [`spark.PairDStreamFunctions`](api/streaming/index.html#spark.PairDStreamFunctions) documentation), or set the system property `spark.default.parallelism` to change the default.
+Cluster resources maybe under-utilized if the number of parallel tasks used in any stage of the computation is not high enough. For example, for distributed reduce operations like `reduceByKey` and `reduceByKeyAndWindow`, the default number of parallel tasks is 8. You can pass the level of parallelism as an argument (see the [`spark.PairDStreamFunctions`](api/streaming/index.html#spark.PairDStreamFunctions) documentation), or set the system property `spark.default.parallelism` to change the default.
### Data Serialization
The overhead of data serialization can be significant, especially when sub-second batch sizes are to be achieved. There are two aspects to it.
-* Serialization of RDD data in Spark: Please refer to the detailed discussion on data serialization in the [Tuning Guide](tuning.html). However, note that unlike Spark, by default RDDs are persisted as serialized byte arrays to minimize pauses related to GC.
-* Serialization of input data: To ingest external data into Spark, data received as bytes (say, from the network) needs to deserialized from bytes and re-serialized into Spark's serialization format. Hence, the deserialization overhead of input data may be a bottleneck.
+
+* **Serialization of RDD data in Spark**: Please refer to the detailed discussion on data serialization in the [Tuning Guide](tuning.html). However, note that unlike Spark, by default RDDs are persisted as serialized byte arrays to minimize pauses related to GC.
+
+* **Serialization of input data**: To ingest external data into Spark, data received as bytes (say, from the network) needs to deserialized from bytes and re-serialized into Spark's serialization format. Hence, the deserialization overhead of input data may be a bottleneck.
### Task Launching Overheads
If the number of tasks launched per second is high (say, 50 or more per second), then the overhead of sending out tasks to the slaves maybe significant and will make it hard to achieve sub-second latencies. The overhead can be reduced by the following changes:
-* Task Serialization: Using Kryo serialization for serializing tasks can reduced the task sizes, and therefore reduce the time taken to send them to the slaves.
-* Execution mode: Running Spark in Standalone mode or coarse-grained Mesos mode leads to better task launch times than the fine-grained Mesos mode. Please refer to the [Running on Mesos guide](running-on-mesos.html) for more details.
+
+* **Task Serialization**: Using Kryo serialization for serializing tasks can reduced the task sizes, and therefore reduce the time taken to send them to the slaves.
+
+* **Execution mode**: Running Spark in Standalone mode or coarse-grained Mesos mode leads to better task launch times than the fine-grained Mesos mode. Please refer to the [Running on Mesos guide](running-on-mesos.html) for more details.
These changes may reduce batch processing time by 100s of milliseconds, thus allowing sub-second batch size to be viable.
## Setting the Right Batch Size
@@ -292,22 +335,183 @@ For a Spark Streaming application running on a cluster to be stable, the process
A good approach to figure out the right batch size for your application is to test it with a conservative batch size (say, 5-10 seconds) and a low data rate. To verify whether the system is able to keep up with data rate, you can check the value of the end-to-end delay experienced by each processed batch (in the Spark master logs, find the line having the phrase "Total delay"). If the delay is maintained to be less than the batch size, then system is stable. Otherwise, if the delay is continuously increasing, it means that the system is unable to keep up and it therefore unstable. Once you have an idea of a stable configuration, you can try increasing the data rate and/or reducing the batch size. Note that momentary increase in the delay due to temporary data rate increases maybe fine as long as the delay reduces back to a low value (i.e., less than batch size).
## 24/7 Operation
-By default, Spark does not forget any of the metadata (RDDs generated, stages processed, etc.). But for a Spark Streaming application to operate 24/7, it is necessary for Spark to do periodic cleanup of it metadata. This can be enabled by setting the Java system property `spark.cleaner.delay` to the number of minutes you want any metadata to persist. For example, setting `spark.cleaner.delay` to 10 would cause Spark periodically cleanup all metadata and persisted RDDs that are older than 10 minutes. Note, that this property needs to be set before the SparkContext is created.
+By default, Spark does not forget any of the metadata (RDDs generated, stages processed, etc.). But for a Spark Streaming application to operate 24/7, it is necessary for Spark to do periodic cleanup of it metadata. This can be enabled by setting the Java system property `spark.cleaner.ttl` to the number of seconds you want any metadata to persist. For example, setting `spark.cleaner.ttl` to 600 would cause Spark periodically cleanup all metadata and persisted RDDs that are older than 10 minutes. Note, that this property needs to be set before the SparkContext is created.
This value is closely tied with any window operation that is being used. Any window operation would require the input data to be persisted in memory for at least the duration of the window. Hence it is necessary to set the delay to at least the value of the largest window operation used in the Spark Streaming application. If this delay is set too low, the application will throw an exception saying so.
## Memory Tuning
Tuning the memory usage and GC behavior of Spark applications have been discussed in great detail in the [Tuning Guide](tuning.html). It is recommended that you read that. In this section, we highlight a few customizations that are strongly recommended to minimize GC related pauses in Spark Streaming applications and achieving more consistent batch processing times.
-* <b>Default persistence level of DStreams</b>: Unlike RDDs, the default persistence level of DStreams serializes the data in memory (that is, [StorageLevel.MEMORY_ONLY_SER](api/core/index.html#spark.storage.StorageLevel$) for DStream compared to [StorageLevel.MEMORY_ONLY](api/core/index.html#spark.storage.StorageLevel$) for RDDs). Even though keeping the data serialized incurs a higher serialization overheads, it significantly reduces GC pauses.
+* **Default persistence level of DStreams**: Unlike RDDs, the default persistence level of DStreams serializes the data in memory (that is, [StorageLevel.MEMORY_ONLY_SER](api/core/index.html#spark.storage.StorageLevel$) for DStream compared to [StorageLevel.MEMORY_ONLY](api/core/index.html#spark.storage.StorageLevel$) for RDDs). Even though keeping the data serialized incurs a higher serialization overheads, it significantly reduces GC pauses.
+
+* **Concurrent garbage collector**: Using the concurrent mark-and-sweep GC further minimizes the variability of GC pauses. Even though concurrent GC is known to reduce the overall processing throughput of the system, its use is still recommended to achieve more consistent batch processing times.
+
+# Fault-tolerance Properties
+In this section, we are going to discuss the behavior of Spark Streaming application in the event of a node failure. To understand this, let us remember the basic fault-tolerance properties of Spark's RDDs.
+
+ 1. An RDD is an immutable, and deterministically re-computable, distributed dataset. Each RDD remembers the lineage of deterministic operations that were used on a fault-tolerant input dataset to create it.
+ 1. If any partition of an RDD is lost due to a worker node failure, then that partition can be re-computed from the original fault-tolerant dataset using the lineage of operations.
+
+Since all data transformations in Spark Streaming are based on RDD operations, as long as the input dataset is present, all intermediate data can recomputed. Keeping these properties in mind, we are going to discuss the failure semantics in more detail.
+
+## Failure of a Worker Node
+
+There are two 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_ - For network-based data sources like Kafka and Flume, the received input data is replicated in memory between nodes of the cluster (default replication factor is 2). So if a worker node fails, then the system can recompute the lost from the the left over copy of the input data. However, if the worker node where a network receiver was running fails, then a tiny bit of data may be lost, that is, the data received by the system but not yet replicated to other node(s). The receiver will be started on a different node and it will continue to receive data.
+
+Since all data is modeled as RDDs with their lineage of deterministic operations, any recomputation always leads to the same result. As a result, all DStream transformations are guaranteed to have _exactly-once_ semantics. That is, the final transformed result will be same even if there were was a worker node failure. However, output operations (like `foreach`) have _at-least once_ semantics, that is, the transformed data may get written to an external entity more than once in the event of a worker failure. While this is acceptable for saving to HDFS using the `saveAs*Files` operations (as the file will simply get over-written by the same data), additional transactions-like mechanisms may be necessary to achieve exactly-once semantics for output operations.
+
+## Failure of the Driver Node
+A system that is required to operate 24/7 needs to be able tolerate the failure of the driver node as well. Spark Streaming does this by saving the state of the DStream computation periodically to a HDFS file, that can be used to restart the streaming computation in the event of a failure of the driver node. This checkpointing is enabled by setting a HDFS directory for checkpointing using `ssc.checkpoint(<checkpoint directory>)` as described [earlier](#rdd-checkpointing-within-dstreams). To elaborate, the following state is periodically saved to a file.
+
+1. The DStream operator graph (input streams, output streams, etc.)
+1. The configuration of each DStream (checkpoint interval, etc.)
+1. The RDD checkpoint files of each DStream
+
+All this is periodically saved in the file `<checkpoint directory>/graph`. To recover, a new Streaming Context can be created with this directory by using
+
+{% highlight scala %}
+val ssc = new StreamingContext(checkpointDirectory)
+{% endhighlight %}
+
+On calling `ssc.start()` on this new context, the following steps are taken by the system
-* <b>Concurrent garbage collector</b>: Using the concurrent mark-and-sweep GC further minimizes the variability of GC pauses. Even though concurrent GC is known to reduce the overall processing throughput of the system, its use is still recommended to achieve more consistent batch processing times.
+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.
-# Master Fault-tolerance (Alpha)
-TODO
+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_ - 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, 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.
+
+<table class="table">
+ <!-- Results table headers -->
+ <tr>
+ <th> Time </th>
+ <th> Number of lines in input file </th>
+ <th> Output without driver failure </th>
+ <th> Output with driver failure </th>
+ </tr>
+ <tr>
+ <td>1</td>
+ <td>10</td>
+ <td>10</td>
+ <td>10</td>
+ </tr>
+ <tr>
+ <td>2</td>
+ <td>20</td>
+ <td>20</td>
+ <td>20</td>
+ </tr>
+ <tr>
+ <td>3</td>
+ <td>30</td>
+ <td>30</td>
+ <td>30</td>
+ </tr>
+ <tr>
+ <td>4</td>
+ <td>40</td>
+ <td>40</td>
+ <td>[DRIVER FAILS]<br />no output</td>
+ </tr>
+ <tr>
+ <td>5</td>
+ <td>50</td>
+ <td>50</td>
+ <td>no output</td>
+ </tr>
+ <tr>
+ <td>6</td>
+ <td>60</td>
+ <td>60</td>
+ <td>no output</td>
+ </tr>
+ <tr>
+ <td>7</td>
+ <td>70</td>
+ <td>70</td>
+ <td>[DRIVER RECOVERS]<br />40, 50, 60, 70</td>
+ </tr>
+ <tr>
+ <td>8</td>
+ <td>80</td>
+ <td>80</td>
+ <td>80</td>
+ </tr>
+ <tr>
+ <td>9</td>
+ <td>90</td>
+ <td>90</td>
+ <td>90</td>
+ </tr>
+ <tr>
+ <td>10</td>
+ <td>100</td>
+ <td>100</td>
+ <td>100</td>
+ </tr>
+</table>
+
+If the driver had crashed in the middle of the processing of time 3, then it will process time 3 and output 30 after recovery.
+
+# Java API
+
+Similar to [Spark's Java API](java-programming-guide.html), we also provide a Java API for Spark Streaming which allows all its features to be accessible from a Java program. This is defined in [spark.streaming.api.java] (api/streaming/index.html#spark.streaming.api.java.package) package and includes [JavaStreamingContext](api/streaming/index.html#spark.streaming.api.java.JavaStreamingContext) and [JavaDStream](api/streaming/index.html#spark.streaming.api.java.JavaDStream) classes that provide the same methods as their Scala counterparts, but take Java functions (that is, Function, and Function2) and return Java data and collection types. Some of the key points to note are:
+
+1. Functions for transformations must be implemented as subclasses of [Function](api/core/index.html#spark.api.java.function.Function) and [Function2](api/core/index.html#spark.api.java.function.Function2)
+1. Unlike the Scala API, the Java API handles DStreams for key-value pairs using a separate [JavaPairDStream](api/streaming/index.html#spark.streaming.api.java.JavaPairDStream) class(similar to [JavaRDD and JavaPairRDD](java-programming-guide.html#rdd-classes). DStream functions like `map` and `filter` are implemented separately by JavaDStreams and JavaPairDStream to return DStreams of appropriate types.
+
+Spark's [Java Programming Guide](java-programming-guide.html) gives more ideas about using the Java API. To extends the ideas presented for the RDDs to DStreams, we present parts of the Java version of the same NetworkWordCount example presented above. The full source code is given at `<spark repo>/examples/src/main/java/spark/streaming/examples/JavaNetworkWordCount.java`
+
+The streaming context and the socket stream from input source is started by using a `JavaStreamingContext`, that has the same parameters and provides the same input streams as its Scala counterpart.
+
+{% highlight java %}
+JavaStreamingContext ssc = new JavaStreamingContext(mesosUrl, "NetworkWordCount", Seconds(1));
+JavaDStream<String> lines = ssc.socketTextStream(ip, port);
+{% endhighlight %}
+
+
+Then the `lines` are split into words by using the `flatMap` function and [FlatMapFunction](api/core/index.html#spark.api.java.function.FlatMapFunction).
+
+{% highlight java %}
+JavaDStream<String> words = lines.flatMap(
+ new FlatMapFunction<String, String>() {
+ @Override
+ public Iterable<String> call(String x) {
+ return Lists.newArrayList(x.split(" "));
+ }
+ });
+{% endhighlight %}
+
+The `words` is then mapped to a [JavaPairDStream](api/streaming/index.html#spark.streaming.api.java.JavaPairDStream) of `(word, 1)` pairs using `map` and [PairFunction](api/core/index.html#spark.api.java.function.PairFunction). This is reduced by using `reduceByKey` and [Function2](api/core/index.html#spark.api.java.function.Function2).
+
+{% highlight java %}
+JavaPairDStream<String, Integer> wordCounts = words.map(
+ new PairFunction<String, String, Integer>() {
+ @Override
+ public Tuple2<String, Integer> call(String s) throws Exception {
+ return new Tuple2<String, Integer>(s, 1);
+ }
+ }).reduceByKey(
+ new Function2<Integer, Integer, Integer>() {
+ @Override
+ public Integer call(Integer i1, Integer i2) throws Exception {
+ return i1 + i2;
+ }
+ });
+{% endhighlight %}
-* Checkpointing of DStream graph
-* Recovery from master faults
-* Current state and future directions \ No newline at end of file
+# Where to Go from Here
+* 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..843380b9a2 100644
--- a/docs/tuning.md
+++ b/docs/tuning.md
@@ -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 2024-07-06 06:21:57 +0000