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| author | Matei Zaharia <matei.zaharia@gmail.com> | 2013-08-31 17:49:45 -0700 |
|---|---|---|
| committer | Matei Zaharia <matei.zaharia@gmail.com> | 2013-08-31 17:49:45 -0700 |
| commit | 2b29a1d43f5982333d57498427c83155f531fa93 (patch) | |
| tree | 71b11b1b809d70ff236e0f49de652b637b4c2d33 | |
| parent | 6edef9c833b90f9ac3ad2f677f201ab3ca39a016 (diff) | |
| parent | 7862c4a3c8b900db81f5c2af157bd4564d814bd9 (diff) | |
| download | spark-2b29a1d43f5982333d57498427c83155f531fa93.tar.gz spark-2b29a1d43f5982333d57498427c83155f531fa93.tar.bz2 spark-2b29a1d43f5982333d57498427c83155f531fa93.zip | |
Merge pull request #877 from mateiz/docs
Doc improvements for 0.8
| -rwxr-xr-x | conf/spark-env.sh.template | 25 | ||||
| -rwxr-xr-x | docs/_layouts/global.html | 35 | ||||
| -rw-r--r-- | docs/configuration.md | 85 | ||||
| -rw-r--r-- | docs/css/bootstrap.min.css | 2 | ||||
| -rw-r--r-- | docs/hardware-provisioning.md | 69 | ||||
| -rw-r--r-- | docs/img/incubator-logo.png | bin | 0 -> 11651 bytes | |||
| -rw-r--r-- | docs/index.md | 73 | ||||
| -rw-r--r-- | docs/mllib-guide.md | 6 | ||||
| -rw-r--r-- | docs/python-programming-guide.md | 4 | ||||
| -rw-r--r-- | docs/quick-start.md | 30 | ||||
| -rw-r--r-- | docs/running-on-mesos.md | 3 | ||||
| -rw-r--r-- | docs/running-on-yarn.md | 39 | ||||
| -rw-r--r-- | docs/scala-programming-guide.md | 14 | ||||
| -rw-r--r-- | docs/spark-standalone.md | 52 | ||||
| -rwxr-xr-x | pyspark | 2 | ||||
| -rwxr-xr-x | run-example | 2 | ||||
| -rwxr-xr-x | spark-class | 4 |
17 files changed, 263 insertions, 182 deletions
diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template index c978db00d9..0a35ee7c79 100755 --- a/conf/spark-env.sh.template +++ b/conf/spark-env.sh.template @@ -1,24 +1,21 @@ #!/usr/bin/env bash # This file contains environment variables required to run Spark. Copy it as -# spark-env.sh and edit that to configure Spark for your site. At a minimum, -# the following two variables should be set: -# - SCALA_HOME, to point to your Scala installation, or SCALA_LIBRARY_PATH to -# point to the directory for Scala library JARs (if you install Scala as a -# Debian or RPM package, these are in a separate path, often /usr/share/java) +# spark-env.sh and edit that to configure Spark for your site. +# +# The following variables can be set in this file: +# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node # - MESOS_NATIVE_LIBRARY, to point to your libmesos.so if you use Mesos +# - SPARK_JAVA_OPTS, to set node-specific JVM options for Spark. Note that +# we recommend setting app-wide options in the application's driver program. +# Examples of node-specific options : -Dspark.local.dir, GC options +# Examples of app-wide options : -Dspark.serializer # -# If using the standalone deploy mode, you can also set variables for it: -# - SPARK_MASTER_IP, to bind the master to a different IP address +# If using the standalone deploy mode, you can also set variables for it here: +# - SPARK_MASTER_IP, to bind the master to a different IP address or hostname # - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports # - SPARK_WORKER_CORES, to set the number of cores to use on this machine # - SPARK_WORKER_MEMORY, to set how much memory to use (e.g. 1000m, 2g) # - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT -# - SPARK_WORKER_INSTANCES, to set the number of worker instances/processes -# to be spawned on every slave machine -# - SPARK_JAVA_OPTS, to set the jvm options for executor backend. Note: This is -# only for node-specific options, whereas app-specific options should be set -# in the application. -# Examples of node-speicic options : -Dspark.local.dir, GC related options. -# Examples of app-specific options : -Dspark.serializer +# - SPARK_WORKER_INSTANCES, to set the number of worker processes per node diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html index a76346f428..84749fda4e 100755 --- a/docs/_layouts/global.html +++ b/docs/_layouts/global.html @@ -61,20 +61,25 @@ <a href="#" class="dropdown-toggle" data-toggle="dropdown">Programming Guides<b class="caret"></b></a> <ul class="dropdown-menu"> <li><a href="quick-start.html">Quick Start</a></li> - <li><a href="scala-programming-guide.html">Scala</a></li> - <li><a href="java-programming-guide.html">Java</a></li> - <li><a href="python-programming-guide.html">Python</a></li> + <li><a href="scala-programming-guide.html">Spark in Scala</a></li> + <li><a href="java-programming-guide.html">Spark in Java</a></li> + <li><a href="python-programming-guide.html">Spark in Python</a></li> + <li class="divider"></li> <li><a href="streaming-programming-guide.html">Spark Streaming</a></li> + <li><a href="mllib-guide.html">MLlib (Machine Learning)</a></li> + <li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li> </ul> </li> <li class="dropdown"> <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 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 Java/Scala (Scaladoc) </a></li> - <li><a href="api/mllib/index.html">Spark ML Library (Scaladoc) </a></li> + <li><a href="api/core/index.html">Spark Core for Java/Scala</a></li> + <li><a href="api/pyspark/index.html">Spark Core for Python</a></li> + <li class="divider"></li> + <li><a href="api/streaming/index.html">Spark Streaming</a></li> + <li><a href="api/mllib/index.html">MLlib (Machine Learning)</a></li> + <li><a href="api/bagel/index.html">Bagel (Pregel on Spark)</a></li> </ul> </li> @@ -91,10 +96,10 @@ <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> + <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li> + <li><a href="building-with-maven.html">Building Spark with Maven</a></li> <li><a href="contributing-to-spark.html">Contributing to Spark</a></li> </ul> </li> @@ -136,9 +141,15 @@ <hr>--> - <!--<footer> - <p></p> - </footer>--> + <footer> + <hr> + <p style="text-align: center; veritcal-align: middle; color: #999;"> + Apache Spark is an effort undergoing incubation at the Apache Software Foundation. + <a href="http://incubator.apache.org"> + <img style="margin-left: 20px;" src="img/incubator-logo.png" /> + </a> + </p> + </footer> </div> <!-- /container --> diff --git a/docs/configuration.md b/docs/configuration.md index b125eeb03c..1c0492efb3 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -5,50 +5,14 @@ title: Spark Configuration Spark provides three main locations to configure the system: -* [Environment variables](#environment-variables) for launching Spark workers, which can - be set either in your driver program or in the `conf/spark-env.sh` script. -* [Java system properties](#system-properties), which control internal configuration parameters and can be set either - programmatically (by calling `System.setProperty` *before* creating a `SparkContext`) or through the - `SPARK_JAVA_OPTS` environment variable in `spark-env.sh`. +* [Java system properties](#system-properties), which control internal configuration parameters and can be set + either programmatically (by calling `System.setProperty` *before* creating a `SparkContext`) or through + JVM arguments. +* [Environment variables](#environment-variables) for configuring per-machine settings such as the IP address, + which can be set in the `conf/spark-env.sh` script. * [Logging configuration](#configuring-logging), which is done through `log4j.properties`. -# Environment Variables - -Spark determines how to initialize the JVM on worker nodes, or even on the local node when you run `spark-shell`, -by running the `conf/spark-env.sh` script in the directory where it is installed. This script does not exist by default -in the Git repository, but but you can create it by copying `conf/spark-env.sh.template`. Make sure that you make -the copy executable. - -Inside `spark-env.sh`, you *must* set at least the following two variables: - -* `SCALA_HOME`, to point to your Scala installation, or `SCALA_LIBRARY_PATH` to point to the directory for Scala - library JARs (if you install Scala as a Debian or RPM package, there is no `SCALA_HOME`, but these libraries - are in a separate path, typically /usr/share/java; look for `scala-library.jar`). -* `MESOS_NATIVE_LIBRARY`, if you are [running on a Mesos cluster](running-on-mesos.html). - -In addition, there are four other variables that control execution. These should be set *in the environment that -launches the job's driver program* instead of `spark-env.sh`, because they will be automatically propagated to -workers. Setting these per-job instead of in `spark-env.sh` ensures that different jobs can have different settings -for these variables. - -* `SPARK_JAVA_OPTS`, to add JVM options. This includes any system properties that you'd like to pass with `-D`. -* `SPARK_CLASSPATH`, to add elements to Spark's classpath. -* `SPARK_LIBRARY_PATH`, to add search directories for native libraries. -* `SPARK_MEM`, to set the amount of memory used per node. This should be in the same format as the - JVM's -Xmx option, e.g. `300m` or `1g`. Note that this option will soon be deprecated in favor of - the `spark.executor.memory` system property, so we recommend using that in new code. - -Beware that if you do set these variables in `spark-env.sh`, they will override the values set by user programs, -which is undesirable; if you prefer, you can choose to have `spark-env.sh` set them only if the user program -hasn't, as follows: - -{% highlight bash %} -if [ -z "$SPARK_JAVA_OPTS" ] ; then - SPARK_JAVA_OPTS="-verbose:gc" -fi -{% endhighlight %} - # System Properties To set a system property for configuring Spark, you need to either pass it with a -D flag to the JVM (for example `java -Dspark.cores.max=5 MyProgram`) or call `System.setProperty` in your code *before* creating your Spark context, as follows: @@ -67,7 +31,7 @@ there are at least five properties that you will commonly want to control: <td>spark.executor.memory</td> <td>512m</td> <td> - Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. `512m`, `2g`). + Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. <code>512m</code>, <code>2g</code>). </td> </tr> <tr> @@ -78,7 +42,7 @@ there are at least five properties that you will commonly want to control: in serialized form. The default of Java serialization works with any Serializable Java object but is quite slow, so we recommend <a href="tuning.html">using <code>spark.KryoSerializer</code> and configuring Kryo serialization</a> when speed is necessary. Can be any subclass of - <a href="api/core/index.html#spark.Serializer"><code>spark.Serializer</code></a>). + <a href="api/core/index.html#spark.Serializer"><code>spark.Serializer</code></a>. </td> </tr> <tr> @@ -87,7 +51,7 @@ there are at least five properties that you will commonly want to control: <td> If you use Kryo serialization, set this class to register your custom classes with Kryo. You need to set it to a class that extends - <a href="api/core/index.html#spark.KryoRegistrator"><code>spark.KryoRegistrator</code></a>). + <a href="api/core/index.html#spark.KryoRegistrator"><code>spark.KryoRegistrator</code></a>. See the <a href="tuning.html#data-serialization">tuning guide</a> for more details. </td> </tr> @@ -97,7 +61,7 @@ there are at least five properties that you will commonly want to control: <td> Directory to use for "scratch" space in Spark, including map output files and RDDs that get stored on disk. This should be on a fast, local disk in your system. It can also be a comma-separated - list of multiple directories. + list of multiple directories on different disks. </td> </tr> <tr> @@ -106,7 +70,8 @@ there are at least five properties that you will commonly want to control: <td> When running on a <a href="spark-standalone.html">standalone deploy cluster</a> or a <a href="running-on-mesos.html#mesos-run-modes">Mesos cluster in "coarse-grained" - sharing mode</a>, how many CPU cores to request at most. The default will use all available cores. + sharing mode</a>, how many CPU cores to request at most. The default will use all available cores + offered by the cluster manager. </td> </tr> </table> @@ -321,7 +286,7 @@ Apart from these, the following properties are also available, and may be useful </tr> <tr> <td>spark.cleaner.ttl</td> - <td>(disable)</td> + <td>(infinite)</td> <td> 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 @@ -347,6 +312,32 @@ Apart from these, the following properties are also available, and may be useful </table> +# Environment Variables + +Certain Spark settings can also be configured through environment variables, which are read from the `conf/spark-env.sh` +script in the directory where Spark is installed. These variables are meant to be for machine-specific settings, such +as library search paths. While Java system properties can also be set here, for application settings, we recommend setting +these properties within the application instead of in `spark-env.sh` so that different applications can use different +settings. + +Note that `conf/spark-env.sh` does not exist by default when Spark is installed. However, you can copy +`conf/spark-env.sh.template` to create it. Make sure you make the copy executable. + +The following variables can be set in `spark-env.sh`: + +* `SPARK_LOCAL_IP`, to configure which IP address of the machine to bind to. +* `SPARK_LIBRARY_PATH`, to add search directories for native libraries. +* `SPARK_CLASSPATH`, to add elements to Spark's classpath that you want to be present for _all_ applications. + Note that applications can also add dependencies for themselves through `SparkContext.addJar` -- we recommend + doing that when possible. +* `SPARK_JAVA_OPTS`, to add JVM options. This includes Java options like garbage collector settings and any system + properties that you'd like to pass with `-D` (e.g., `-Dspark.local.dir=/disk1,/disk2`). +* Options for the Spark [standalone cluster scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores + to use on each machine and maximum memory. + +Since `spark-env.sh` is a shell script, some of these can be set programmatically -- for example, you might +compute `SPARK_LOCAL_IP` by looking up the IP of a specific network interface. + # Configuring Logging Spark uses [log4j](http://logging.apache.org/log4j/) for logging. You can configure it by adding a `log4j.properties` diff --git a/docs/css/bootstrap.min.css b/docs/css/bootstrap.min.css index eb48138e08..cf4c519cdc 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 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b/docs/hardware-provisioning.md new file mode 100644 index 0000000000..e5f054cb14 --- /dev/null +++ b/docs/hardware-provisioning.md @@ -0,0 +1,69 @@ +--- +layout: global +title: Hardware Provisioning +--- + +A common question received by Spark developers is how to configure hardware for it. While the right +hardware will depend on the situation, we make the following recommendations. + +# Storage Systems + +Because most Spark jobs will likely have to read input data from an external storage system (e.g. +the Hadoop File System, or HBase), it is important to place it **as close to this system as +possible**. We recommend the following: + +* If at all possible, run Spark on the same nodes as HDFS. The simplest way is to set up a Spark +[standalone mode cluster](spark-standalone.html) on the same nodes, and configure Spark and +Hadoop's memory and CPU usage to avoid interference (for Hadoop, the relevant options are +`mapred.child.java.opts` for the per-task memory and `mapred.tasktracker.map.tasks.maximum` +and `mapred.tasktracker.reduce.tasks.maximum` for number of tasks). Alternatively, you can run +Hadoop and Spark on a common cluster manager like [Mesos](running-on-mesos.html) or +[Hadoop YARN](running-on-yarn.html). + +* If this is not possible, run Spark on different nodes in the same local-area network as HDFS. + +* For low-latency data stores like HBase, it may be preferrable to run computing jobs on different +nodes than the storage system to avoid interference. + +# Local Disks + +While Spark can perform a lot of its computation in memory, it still uses local disks to store +data that doesn't fit in RAM, as well as to preserve intermediate output between stages. We +recommend having **4-8 disks** per node, configured _without_ RAID (just as separate mount points). +In Linux, mount the disks with the [`noatime` option](http://www.centos.org/docs/5/html/Global_File_System/s2-manage-mountnoatime.html) +to reduce unnecessary writes. In Spark, [configure](configuration.html) the `spark.local.dir` +variable to be a comma-separated list of the local disks. If you are running HDFS, it's fine to +use the same disks as HDFS. + +# Memory + +In general, Spark can run well with anywhere from **8 GB to hundreds of gigabytes** of memory per +machine. In all cases, we recommend allocating only at most 75% of the memory for Spark; leave the +rest for the operating system and buffer cache. + +How much memory you will need will depend on your application. To determine how much your +application uses for a certain dataset size, load part of your dataset in a Spark RDD and use the +Storage tab of Spark's monitoring UI (`http://<driver-node>:3030`) to see its size in memory. +Note that memory usage is greatly affected by storage level and serialization format -- see +the [tuning guide](tuning.html) for tips on how to reduce it. + +Finally, note that the Java VM does not always behave well with more than 200 GB of RAM. If you +purchase machines with more RAM than this, you can run _multiple worker JVMs per node_. In +Spark's [standalone mode](spark-standalone.html), you can set the number of workers per node +with the `SPARK_WORKER_INSTANCES` variable in `conf/spark-env.sh`, and the number of cores +per worker with `SPARK_WORKER_CORES`. + +# Network + +In our experience, when the data is in memory, a lot of Spark applications are network-bound. +Using a **10 Gigabit** or higher network is the best way to make these applications faster. +This is especially true for "distributed reduce" applications such as group-bys, reduce-bys, and +SQL joins. In any given application, you can see how much data Spark shuffles across the network +from the application's monitoring UI (`http://<driver-node>:3030`). + +# CPU Cores + +Spark scales well to tens of CPU cores per machine because it performes minimal sharing between +threads. You should likely provision at least **8-16 cores** per machine. Depending on the CPU +cost of your workload, you may also need more: once data is in memory, most applications are +either CPU- or network-bound. diff --git a/docs/img/incubator-logo.png b/docs/img/incubator-logo.png Binary files differnew file mode 100644 index 0000000000..33ca7f6227 --- /dev/null +++ b/docs/img/incubator-logo.png diff --git a/docs/index.md b/docs/index.md index ec9c7dd4f3..0ea0e103e4 100644 --- a/docs/index.md +++ b/docs/index.md @@ -3,50 +3,52 @@ layout: global title: Spark Overview --- -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. +Apache Spark is a cluster computing system that aims to make data analytics faster to run and faster to write. +It provides high-level APIs in [Scala](scala-programming-guide.html), [Java](java-programming-guide.html), and [Python](python-programming-guide.html), and a general execution engine that supports rich operator graphs. Spark can run on the Apache Mesos cluster manager, Hadoop YARN, Amazon EC2, or without an independent resource manager ("standalone mode"). # Downloading -Get Spark by visiting the [downloads page](http://spark-project.org/downloads.html) of the Spark website. This documentation is for Spark version {{site.SPARK_VERSION}}. +Get Spark by visiting the [downloads page](http://spark.incubator.apache.org/downloads.html) of the Apache Spark site. This documentation is for Spark version {{site.SPARK_VERSION}}. # Building -Spark requires [Scala {{site.SCALA_VERSION}}](http://www.scala-lang.org/). You will need to have Scala's `bin` directory in your `PATH`, -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](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 assembly -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). +For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_VERSION}}. If you write applications in Scala, you will need to use this same version of Scala in your own program -- newer major versions may not work. You can get the right version of Scala from [scala-lang.org](http://www.scala-lang.org/download/). # Testing the Build -Spark comes with a number of sample programs in the `examples` directory. +Spark comes with several sample programs in the `examples` directory. To run one of the samples, use `./run-example <class> <params>` in the top-level Spark directory -(the `run` script sets up the appropriate paths and launches that program). -For example, `./run-example spark.examples.SparkPi` will run a sample program that estimates Pi. Each of the -examples prints usage help if no params are given. +(the `run-example` script sets up the appropriate paths and launches that program). +For example, `./run-example spark.examples.SparkPi` will run a sample program that estimates Pi. Each +example prints usage help if no params are given. Note that all of the sample programs take a `<master>` parameter specifying the cluster URL to connect to. This can be a [URL for a distributed cluster](scala-programming-guide.html#master-urls), or `local` to run locally with one thread, or `local[N]` to run locally with N threads. You should start by using `local` for testing. -Finally, Spark can be used interactively from a modified version of the Scala interpreter that you can start through -`./spark-shell`. This is a great way to learn Spark. +Finally, Spark can be used interactively through modified versions of the Scala shell (`./spark-shell`) or +Python interpreter (`./pyspark`). These are a great way to learn Spark. # A Note About Hadoop Versions -Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported +Spark uses the Hadoop-client library to talk to HDFS and other Hadoop-supported storage systems. Because the HDFS protocol has changed in different versions of -Hadoop, you must build Spark against the same version that your cluster runs. -You can change the version by setting the `HADOOP_VERSION` variable at the top -of `project/SparkBuild.scala`, then rebuilding Spark (`sbt/sbt clean compile`). +Hadoop, you must build Spark against the same version that your cluster uses. +By default, Spark links to Hadoop 1.0.4. You can change this by setting the +`SPARK_HADOOP_VERSION` variable when compiling: + + SPARK_HADOOP_VERSION=1.2.1 sbt/sbt assembly + +In addition, if you wish to run Spark on [YARN](running-on-yarn.md), set +`SPARK_YARN` to `true`: + + SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly # Where to Go from Here @@ -54,15 +56,20 @@ 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 -* [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 + * [Java Programming Guide](java-programming-guide.html): using Spark from Java + * [Python Programming Guide](python-programming-guide.html): using Spark from Python +* [Spark Streaming](streaming-programming-guide.html): using the alpha release of Spark Streaming +* [MLlib (Machine Learning)](mllib-guide.html): Spark's built-in machine learning library +* [Bagel (Pregel on Spark)](bagel-programming-guide.html): simple graph processing model **API Docs:** -* [Spark Java/Scala (Scaladoc)](api/core/index.html) -* [Spark Python (Epydoc)](api/pyspark/index.html) -* [Spark Streaming Java/Scala (Scaladoc)](api/streaming/index.html) +* [Spark for Java/Scala (Scaladoc)](api/core/index.html) +* [Spark for Python (Epydoc)](api/pyspark/index.html) +* [Spark Streaming for Java/Scala (Scaladoc)](api/streaming/index.html) +* [MLlib (Machine Learning) for Java/Scala (Scaladoc)](api/mllib/index.html) +* [Bagel (Pregel on Spark) for Scala (Scaladoc)](api/bagel/index.html) + **Deployment guides:** @@ -74,27 +81,27 @@ 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 +* [Hardware Provisioning](hardware-provisioning.html): recommendations for cluster hardware +* [Building Spark with Maven](building-with-maven.html): Build Spark using the Maven build tool * [Contributing to Spark](contributing-to-spark.html) **External resources:** -* [Spark Homepage](http://www.spark-project.org) -* [Mailing List](http://groups.google.com/group/spark-users): ask questions about Spark here -* [AMP Camp](http://ampcamp.berkeley.edu/): a two-day training camp at UC Berkeley that featured talks and exercises - about Spark, Shark, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/agenda-2012), +* [Spark Homepage](http://spark.incubator.apache.org) +* [Mailing Lists](http://spark.incubator.apache.org/mailing-lists.html): ask questions about Spark here +* [AMP Camps](http://ampcamp.berkeley.edu/): a series of training camps at UC Berkeley that featured talks and + exercises about Spark, Shark, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/agenda-2012), [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 +* [Code Examples](http://spark.incubator.apache.org/examples.html): more are also available in the [examples subfolder](https://github.com/mesos/spark/tree/master/examples/src/main/scala/) of Spark * [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 -To get help using Spark or keep up with Spark development, sign up for the [spark-users mailing list](http://groups.google.com/group/spark-users). +To get help using Spark or keep up with Spark development, sign up for the [user mailing list](http://spark.incubator.apache.org/mailing-lists.html). If you're in the San Francisco Bay Area, there's a regular [Spark meetup](http://www.meetup.com/spark-users/) every few weeks. Come by to meet the developers and other users. diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md new file mode 100644 index 0000000000..c897f8b36c --- /dev/null +++ b/docs/mllib-guide.md @@ -0,0 +1,6 @@ +--- +layout: global +title: Machine Learning Library (MLlib) +--- + +Coming soon. diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md index 15d3ebfcae..27e0d10080 100644 --- a/docs/python-programming-guide.md +++ b/docs/python-programming-guide.md @@ -17,8 +17,8 @@ There are a few key differences between the Python and Scala APIs: * Python is dynamically typed, so RDDs can hold objects of different types. * PySpark does not currently support the following Spark features: - - Special functions on RDDs of doubles, such as `mean` and `stdev` - - `lookup`, `sample` and `sort` + - `lookup` + - `sort` - `persist` at storage levels other than `MEMORY_ONLY` - Execution on Windows -- this is slated for a future release diff --git a/docs/quick-start.md b/docs/quick-start.md index 4e9deadbaa..11d4370a1d 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -126,7 +126,7 @@ object SimpleJob { 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: +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 a repository that Spark depends on: {% highlight scala %} name := "Simple Project" @@ -137,12 +137,16 @@ scalaVersion := "{{site.SCALA_VERSION}}" libraryDependencies += "org.spark-project" %% "spark-core" % "{{site.SPARK_VERSION}}" -resolvers ++= Seq( - "Akka Repository" at "http://repo.akka.io/releases/", - "Spray Repository" at "http://repo.spray.cc/") +resolvers += "Akka Repository" at "http://repo.akka.io/releases/" {% endhighlight %} -Of course, for sbt to work correctly, we'll need to layout `SimpleJob.scala` and `simple.sbt` according to the typical directory structure. Once that is in place, we can create a JAR package containing the job's code, then use `sbt run` to execute our example job. +If you also wish to read data from Hadoop's HDFS, you will also need to add a dependency on `hadoop-client` for your version of HDFS: + +{% highlight scala %} +libraryDependencies += "org.apache.hadoop" % "hadoop-client" % "<your-hdfs-version>" +{% endhighlight %} + +Finally, for sbt to work correctly, we'll need to layout `SimpleJob.scala` and `simple.sbt` according to the typical directory structure. Once that is in place, we can create a JAR package containing the job's code, then use `sbt run` to execute our example job. {% highlight bash %} $ find . @@ -205,10 +209,6 @@ To build the job, we also write a Maven `pom.xml` file that lists Spark as a dep <version>1.0</version> <repositories> <repository> - <id>Spray.cc repository</id> - <url>http://repo.spray.cc</url> - </repository> - <repository> <id>Akka repository</id> <url>http://repo.akka.io/releases</url> </repository> @@ -223,6 +223,16 @@ To build the job, we also write a Maven `pom.xml` file that lists Spark as a dep </project> {% endhighlight %} +If you also wish to read data from Hadoop's HDFS, you will also need to add a dependency on `hadoop-client` for your version of HDFS: + +{% highlight xml %} + <dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <version>...</version> + </dependency> +{% endhighlight %} + We lay out these files according to the canonical Maven directory structure: {% highlight bash %} $ find . @@ -281,3 +291,5 @@ 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. + +Also, this example links against the default version of HDFS that Spark builds with (1.0.4). You can run it against other HDFS versions by [building Spark with another HDFS version](index.html#a-note-about-hadoop-versions). diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index f4a3eb667c..b31f78e8bf 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -9,9 +9,8 @@ Spark can run on private clusters managed by the [Apache Mesos](http://incubator 2. Download Mesos {{site.MESOS_VERSION}} from a [mirror](http://www.apache.org/dyn/closer.cgi/incubator/mesos/mesos-{{site.MESOS_VERSION}}/). 3. Configure Mesos using the `configure` script, passing the location of your `JAVA_HOME` using `--with-java-home`. Mesos comes with "template" configure scripts for different platforms, such as `configure.macosx`, that you can run. See the README file in Mesos for other options. **Note:** If you want to run Mesos without installing it into the default paths on your system (e.g. if you don't have administrative privileges to install it), you should also pass the `--prefix` option to `configure` to tell it where to install. For example, pass `--prefix=/home/user/mesos`. By default the prefix is `/usr/local`. 4. Build Mesos using `make`, and then install it using `make install`. -5. Create a file called `spark-env.sh` in Spark's `conf` directory, by copying `conf/spark-env.sh.template`, and add the following lines in it: +5. Create a file called `spark-env.sh` in Spark's `conf` directory, by copying `conf/spark-env.sh.template`, and add the following lines it: * `export MESOS_NATIVE_LIBRARY=<path to libmesos.so>`. This path is usually `<prefix>/lib/libmesos.so` (where the prefix is `/usr/local` by default). Also, on Mac OS X, the library is called `libmesos.dylib` instead of `.so`. - * `export SCALA_HOME=<path to Scala directory>`. 6. Copy Spark and Mesos to the _same_ paths on all the nodes in the cluster (or, for Mesos, `make install` on every node). 7. Configure Mesos for deployment: * On your master node, edit `<prefix>/var/mesos/deploy/masters` to list your master and `<prefix>/var/mesos/deploy/slaves` to list the slaves, where `<prefix>` is the prefix where you installed Mesos (`/usr/local` by default). diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index 678cd57aba..fe5334ffdc 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -3,50 +3,33 @@ layout: global title: Launching Spark on YARN --- -Experimental support for running over a [YARN (Hadoop +Support for running on [YARN (Hadoop 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 with YARN support, please use the hadoop2-yarn profile. -Ex: mvn -Phadoop2-yarn clean install +was added to Spark in version 0.6.0, and improved in 0.7.0 and 0.8.0. -# Building spark core consolidated jar. +# Building a YARN-Enabled Assembly 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. +We need a consolidated Spark JAR (which bundles all the required dependencies) to run Spark jobs on a YARN cluster. +This can be built by setting the Hadoop version and `SPARK_YARN` environment variable, as follows: -- Building spark assembled jar via sbt. -Enable YARN support by setting `SPARK_YARN=true` when invoking sbt: + SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true ./sbt/sbt assembly - SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true ./sbt/sbt clean assembly - -The assembled jar would typically be something like : -`./yarn/target/spark-yarn-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 -Dhadoop.version=2.0.5-alpha clean package -DskipTests=true - - -This will build the shaded (consolidated) jar. Typically something like : -`./yarn/target/spark-yarn-bin-<VERSION>-shaded.jar` +The assembled JAR will be something like this: +`./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly_{{site.SPARK_VERSION}}-hadoop2.0.5.jar`. # Preparations -- Building spark-yarn assembly (see above). +- Building a YARN-enabled assembly (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. +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 assembly`. 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. # Configuration Most of the configs are the same for Spark on YARN as other deploys. See the Configuration page for more information on those. These are configs that are specific to SPARK on YARN. -* `SPARK_YARN_USER_ENV`, to add environment variables to the Spark processes launched on YARN. This can be a comma separated list of environment variables. ie SPARK_YARN_USER_ENV="JAVA_HOME=/jdk64,FOO=bar" +* `SPARK_YARN_USER_ENV`, to add environment variables to the Spark processes launched on YARN. This can be a comma separated list of environment variables, e.g. `SPARK_YARN_USER_ENV="JAVA_HOME=/jdk64,FOO=bar"`. # Launching Spark on YARN diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md index db584d2096..e321b8f5b8 100644 --- a/docs/scala-programming-guide.md +++ b/docs/scala-programming-guide.md @@ -17,15 +17,23 @@ This guide shows each of these features and walks through some samples. It assum # Linking with Spark -To write a Spark application, you will need to add both Spark and its dependencies to your CLASSPATH. If you use sbt or Maven, Spark is available through Maven Central at: +Spark {{site.SPARK_VERSION}} uses Scala {{site.SCALA_VERSION}}. If you write applications in Scala, you'll need to use this same version of Scala in your program -- newer major versions may not work. + +To write a Spark application, you need to add a dependency on Spark. If you use SBT or Maven, Spark is available through Maven Central at: groupId = org.spark-project artifactId = spark-core_{{site.SCALA_VERSION}} version = {{site.SPARK_VERSION}} -For other build systems or environments, you can run `sbt/sbt assembly` to build both Spark and its dependencies into one JAR (`core/target/spark-core-assembly-0.6.0.jar`), then add this to your CLASSPATH. +In addition, if you wish to access an HDFS cluster, you need to add a dependency on `hadoop-client` for your version of HDFS: + + groupId = org.apache.hadoop + artifactId = hadoop-client + version = <your-hdfs-version> + +For other build systems, you can run `sbt/sbt assembly` to pack Spark and its dependencies into one JAR (`assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop*.jar`), then add this to your CLASSPATH. Set the HDFS version as described [here](index.html#a-note-about-hadoop-versions). -In addition, you'll need to import some Spark classes and implicit conversions. Add the following lines at the top of your program: +Finally, you need to import some Spark classes and implicit conversions into your program. Add the following lines: {% highlight scala %} import spark.SparkContext diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md index bb8be276c5..9ab6ba0830 100644 --- a/docs/spark-standalone.md +++ b/docs/spark-standalone.md @@ -3,18 +3,7 @@ layout: global title: Spark Standalone Mode --- -{% comment %} -TODO(andyk): - - Add a table of contents - - Move configuration towards the end so that it doesn't come first - - Say the scripts will guess the resource amounts (i.e. # cores) automatically -{% endcomment %} - -In addition to running on top of [Mesos](https://github.com/mesos/mesos), Spark also supports a standalone mode, consisting of one Spark master and several Spark worker processes. You can run the Spark standalone mode either locally (for testing) or on a cluster. If you wish to run on a cluster, we have provided [a set of deploy scripts](#cluster-launch-scripts) to launch a whole cluster. - -# Getting Started - -Compile Spark with `sbt package` as described in the [Getting Started Guide](index.html). You do not need to install Mesos on your machine if you are using the standalone mode. +In addition to running on the Mesos or YARN cluster managers, Spark also provides a simple standalone deploy mode. You can launch a standalone cluster either manually, by starting a master and workers by hand, or use our provided [deploy scripts](#cluster-launch-scripts). It is also possible to run these daemons on a single machine for testing. # Starting a Cluster Manually @@ -22,8 +11,8 @@ You can start a standalone master server by executing: ./spark-class spark.deploy.master.Master -Once started, the master will print out a `spark://IP:PORT` URL for itself, which you can use to connect workers to it, -or pass as the "master" argument to `SparkContext` to connect a job to the cluster. You can also find this URL on +Once started, the master will print out a `spark://HOST:PORT` URL for itself, which you can use to connect workers to it, +or pass as the "master" argument to `SparkContext`. You can also find this URL on the master's web UI, which is [http://localhost:8080](http://localhost:8080) by default. Similarly, you can start one or more workers and connect them to the master via: @@ -68,7 +57,7 @@ Finally, the following configuration options can be passed to the master and wor To launch a Spark standalone cluster with the deploy scripts, you need to create a file called `conf/slaves` in your Spark directory, which should contain the hostnames of all the machines where you would like to start Spark workers, one per line. The master machine must be able to access each of the slave machines via password-less `ssh` (using a private key). For testing, you can just put `localhost` in this file. -Once you've set up this fine, you can launch or stop your cluster with the following shell scripts, based on Hadoop's deploy scripts, and available in `SPARK_HOME/bin`: +Once you've set up this file, you can launch or stop your cluster with the following shell scripts, based on Hadoop's deploy scripts, and available in `SPARK_HOME/bin`: - `bin/start-master.sh` - Starts a master instance on the machine the script is executed on. - `bin/start-slaves.sh` - Starts a slave instance on each machine specified in the `conf/slaves` file. @@ -85,47 +74,56 @@ You can optionally configure the cluster further by setting environment variable <tr><th style="width:21%">Environment Variable</th><th>Meaning</th></tr> <tr> <td><code>SPARK_MASTER_IP</code></td> - <td>Bind the master to a specific IP address, for example a public one</td> + <td>Bind the master to a specific IP address, for example a public one.</td> </tr> <tr> <td><code>SPARK_MASTER_PORT</code></td> - <td>Start the master on a different port (default: 7077)</td> + <td>Start the master on a different port (default: 7077).</td> </tr> <tr> <td><code>SPARK_MASTER_WEBUI_PORT</code></td> - <td>Port for the master web UI (default: 8080)</td> + <td>Port for the master web UI (default: 8080).</td> </tr> <tr> <td><code>SPARK_WORKER_PORT</code></td> - <td>Start the Spark worker on a specific port (default: random)</td> + <td>Start the Spark worker on a specific port (default: random).</td> </tr> <tr> <td><code>SPARK_WORKER_DIR</code></td> - <td>Directory to run jobs in, which will include both logs and scratch space (default: SPARK_HOME/work)</td> + <td>Directory to run jobs in, which will include both logs and scratch space (default: SPARK_HOME/work).</td> </tr> <tr> <td><code>SPARK_WORKER_CORES</code></td> - <td>Total number of cores to allow Spark jobs to use on the machine (default: all available cores)</td> + <td>Total number of cores to allow Spark jobs to use on the machine (default: all available cores).</td> </tr> <tr> <td><code>SPARK_WORKER_MEMORY</code></td> - <td>Total amount of memory to allow Spark jobs to use on the machine, e.g. 1000M, 2G (default: total memory minus 1 GB); note that each job's <i>individual</i> memory is configured using <code>SPARK_MEM</code></td> + <td>Total amount of memory to allow Spark jobs to use on the machine, e.g. <code>1000m</code>, <code>2g</code> (default: total memory minus 1 GB); note that each job's <i>individual</i> memory is configured using its <code>spark.executor.memory</code> property.</td> </tr> <tr> <td><code>SPARK_WORKER_WEBUI_PORT</code></td> - <td>Port for the worker web UI (default: 8081)</td> + <td>Port for the worker web UI (default: 8081).</td> + </tr> + <tr> + <td><code>SPARK_WORKER_INSTANCES</code></td> + <td> + Number of worker instances to run on each machine (default: 1). You can make this more than 1 if + you have have very large machines and would like multiple Spark worker processes. If you do set + this, make sure to also set <code>SPARK_WORKER_CORES</code> explicitly to limit the cores per worker, + or else each worker will try to use all the cores. + </td> </tr> <tr> <td><code>SPARK_DAEMON_MEMORY</code></td> - <td>Memory to allocate to the Spark master and worker daemons themselves (default: 512m)</td> + <td>Memory to allocate to the Spark master and worker daemons themselves (default: 512m).</td> </tr> <tr> <td><code>SPARK_DAEMON_JAVA_OPTS</code></td> - <td>JVM options for the Spark master and worker daemons themselves (default: none)</td> + <td>JVM options for the Spark master and worker daemons themselves (default: none).</td> </tr> </table> - +**Note:** The launch scripts do not currently support Windows. To run a Spark cluster on Windows, start the master and workers by hand. # Connecting a Job to the Cluster @@ -155,5 +153,5 @@ In addition, detailed log output for each job is also written to the work direct # Running Alongside Hadoop -You can run Spark alongside your existing Hadoop cluster by just launching it as a separate service on the machines. To access Hadoop data from Spark, just use a hdfs:// URL (typically `hdfs://<namenode>:9000/path`, but you can find the right URL on your Hadoop Namenode's web UI). Alternatively, you can set up a separate cluster for Spark, and still have it access HDFS over the network; this will be slower than disk-local access, but may not be a concern if you are still running in the same local area network (e.g. you place a few Spark machines on each rack that you have Hadoop on). +You can run Spark alongside your existing Hadoop cluster by just launching it as a separate service on the same machines. To access Hadoop data from Spark, just use a hdfs:// URL (typically `hdfs://<namenode>:9000/path`, but you can find the right URL on your Hadoop Namenode's web UI). Alternatively, you can set up a separate cluster for Spark, and still have it access HDFS over the network; this will be slower than disk-local access, but may not be a concern if you are still running in the same local area network (e.g. you place a few Spark machines on each rack that you have Hadoop on). @@ -31,7 +31,7 @@ if [ ! -f "$FWDIR/RELEASE" ]; then ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/spark-assembly*hadoop*.jar >& /dev/null if [[ $? != 0 ]]; then echo "Failed to find Spark assembly in $FWDIR/assembly/target" >&2 - echo "You need to compile Spark before running this program" >&2 + echo "You need to build Spark with sbt/sbt assembly before running this program" >&2 exit 1 fi fi diff --git a/run-example b/run-example index ccd4356bdf..24d83ba5cf 100755 --- a/run-example +++ b/run-example @@ -50,7 +50,7 @@ if [ -e "$EXAMPLES_DIR"/target/spark-examples*[0-9T].jar ]; then fi if [[ -z $SPARK_EXAMPLES_JAR ]]; then echo "Failed to find Spark examples assembly in $FWDIR/examples/target" >&2 - echo "You need to compile Spark before running this program" >&2 + echo "You need to build Spark with sbt/sbt assembly before running this program" >&2 exit 1 fi diff --git a/spark-class b/spark-class index 5ef3de9773..244b78b4e1 100755 --- a/spark-class +++ b/spark-class @@ -102,10 +102,10 @@ export JAVA_OPTS if [ ! -f "$FWDIR/RELEASE" ]; then # Exit if the user hasn't compiled Spark - ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/spark-assembly*.jar >& /dev/null + ls "$FWDIR"/assembly/target/scala-$SCALA_VERSION/spark-assembly*hadoop*.jar >& /dev/null if [[ $? != 0 ]]; then echo "Failed to find Spark assembly in $FWDIR/assembly/target" >&2 - echo "You need to compile Spark before running this program" >&2 + echo "You need to build Spark with sbt/sbt assembly before running this program" >&2 exit 1 fi fi |
