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| <h1 class="title">Hardware Provisioning</h1> |
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| <p>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.</p> |
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| <h1 id="storage-systems">Storage Systems</h1> |
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| <p>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 <strong>as close to this system as |
| possible</strong>. We recommend the following:</p> |
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| <li> |
| <p>If at all possible, run Spark on the same nodes as HDFS. The simplest way is to set up a Spark |
| <a href="spark-standalone.html">standalone mode cluster</a> on the same nodes, and configure Spark and |
| Hadoop’s memory and CPU usage to avoid interference (for Hadoop, the relevant options are |
| <code>mapred.child.java.opts</code> for the per-task memory and <code>mapred.tasktracker.map.tasks.maximum</code> |
| and <code>mapred.tasktracker.reduce.tasks.maximum</code> for number of tasks). Alternatively, you can run |
| Hadoop and Spark on a common cluster manager like <a href="running-on-mesos.html">Mesos</a> or |
| <a href="running-on-yarn.html">Hadoop YARN</a>.</p> |
| </li> |
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| <p>If this is not possible, run Spark on different nodes in the same local-area network as HDFS.</p> |
| </li> |
| <li> |
| <p>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.</p> |
| </li> |
| </ul> |
| |
| <h1 id="local-disks">Local Disks</h1> |
| |
| <p>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 <strong>4-8 disks</strong> per node, configured <em>without</em> RAID (just as separate mount points). |
| In Linux, mount the disks with the <a href="http://www.centos.org/docs/5/html/Global_File_System/s2-manage-mountnoatime.html"><code>noatime</code> option</a> |
| to reduce unnecessary writes. In Spark, <a href="configuration.html">configure</a> the <code>spark.local.dir</code> |
| 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.</p> |
| |
| <h1 id="memory">Memory</h1> |
| |
| <p>In general, Spark can run well with anywhere from <strong>8 GB to hundreds of gigabytes</strong> 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.</p> |
| |
| <p>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 (<code>http://<driver-node>:4040</code>) to see its size in memory. |
| Note that memory usage is greatly affected by storage level and serialization format – see |
| the <a href="tuning.html">tuning guide</a> for tips on how to reduce it.</p> |
| |
| <p>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 <em>multiple worker JVMs per node</em>. In |
| Spark’s <a href="spark-standalone.html">standalone mode</a>, you can set the number of workers per node |
| with the <code>SPARK_WORKER_INSTANCES</code> variable in <code>conf/spark-env.sh</code>, and the number of cores |
| per worker with <code>SPARK_WORKER_CORES</code>.</p> |
| |
| <h1 id="network">Network</h1> |
| |
| <p>In our experience, when the data is in memory, a lot of Spark applications are network-bound. |
| Using a <strong>10 Gigabit</strong> 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 (<code>http://<driver-node>:4040</code>).</p> |
| |
| <h1 id="cpu-cores">CPU Cores</h1> |
| |
| <p>Spark scales well to tens of CPU cores per machine because it performs minimal sharing between |
| threads. You should likely provision at least <strong>8-16 cores</strong> 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.</p> |
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