path: root/docs/configuration.md

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title: Spark Configuration
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Spark provides three locations to configure the system:

* [Spark properties](#spark-properties) control most application parameters and can be set by using
  a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) object, or through Java
  system properties.
* [Environment variables](#environment-variables) can be used to set per-machine settings, such as
  the IP address, through the `conf/spark-env.sh` script on each node.
* [Logging](#configuring-logging) can be configured through `log4j.properties`.

# Spark Properties

Spark properties control most application settings and are configured separately for each
application. These properties can be set directly on a
[SparkConf](api/scala/index.html#org.apache.spark.SparkConf) passed to your
`SparkContext`. `SparkConf` allows you to configure some of the common properties
(e.g. master URL and application name), as well as arbitrary key-value pairs through the
`set()` method. For example, we could initialize an application with two threads as follows:

Note that we run with local[2], meaning two threads - which represents "minimal" parallelism, 
which can help detect bugs that only exist when we run in a distributed context. 

{% highlight scala %}
val conf = new SparkConf()
             .setMaster("local[2]")
             .setAppName("CountingSheep")
             .set("spark.executor.memory", "1g")
val sc = new SparkContext(conf)
{% endhighlight %}

Note that we can have more than 1 thread in local mode, and in cases like spark streaming, we may actually
require one to prevent any sort of starvation issues.  

## Dynamically Loading Spark Properties
In some cases, you may want to avoid hard-coding certain configurations in a `SparkConf`. For
instance, if you'd like to run the same application with different masters or different
amounts of memory. Spark allows you to simply create an empty conf:

{% highlight scala %}
val sc = new SparkContext(new SparkConf())
{% endhighlight %}

Then, you can supply configuration values at runtime:
{% highlight bash %}
./bin/spark-submit --name "My app" --master local[4] --conf spark.shuffle.spill=false 
  --conf "spark.executor.extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps" myApp.jar 
{% endhighlight %}

The Spark shell and [`spark-submit`](submitting-applications.html)
tool support two ways to load configurations dynamically. The first are command line options,
such as `--master`, as shown above. `spark-submit` can accept any Spark property using the `--conf`
flag, but uses special flags for properties that play a part in launching the Spark application.
Running `./bin/spark-submit --help` will show the entire list of these options.

`bin/spark-submit` will also read configuration options from `conf/spark-defaults.conf`, in which
each line consists of a key and a value separated by whitespace. For example:

    spark.master            spark://5.6.7.8:7077
    spark.executor.memory   512m
    spark.eventLog.enabled  true
    spark.serializer        org.apache.spark.serializer.KryoSerializer

Any values specified as flags or in the properties file will be passed on to the application
and merged with those specified through SparkConf. Properties set directly on the SparkConf
take highest precedence, then flags passed to `spark-submit` or `spark-shell`, then options
in the `spark-defaults.conf` file.

## Viewing Spark Properties

The application web UI at `http://<driver>:4040` lists Spark properties in the "Environment" tab.
This is a useful place to check to make sure that your properties have been set correctly. Note
that only values explicitly specified through either `spark-defaults.conf` or SparkConf will
appear. For all other configuration properties, you can assume the default value is used.

## Available Properties

Most of the properties that control internal settings have reasonable default values. Some
of the most common options to set are:

#### Application Properties
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
  <td><code>spark.app.name</code></td>
  <td>(none)</td>
  <td>
    The name of your application. This will appear in the UI and in log data.
  </td>
</tr>
<tr>
  <td><code>spark.master</code></td>
  <td>(none)</td>
  <td>
    The cluster manager to connect to. See the list of
    <a href="submitting-applications.html#master-urls"> allowed master URL's</a>.
  </td>
</tr>
<tr>
  <td><code>spark.executor.memory</code></td>
  <td>512m</td>
  <td>
    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>
  <td><code>spark.driver.memory</code></td>
  <td>512m</td>
  <td>
    Amount of memory to use for the driver process, i.e. where SparkContext is initialized.
    (e.g. <code>512m</code>, <code>2g</code>).
  </td>
</tr>
<tr>
  <td><code>spark.driver.maxResultSize</code></td>
  <td>1g</td>
  <td>
    Limit of total size of serialized results of all partitions for each Spark action (e.g. collect).
    Should be at least 1M, or 0 for unlimited. Jobs will be aborted if the total size
    is above this limit. 
    Having a high limit may cause out-of-memory errors in driver (depends on spark.driver.memory
    and memory overhead of objects in JVM). Setting a proper limit can protect the driver from
    out-of-memory errors.
  </td>
</tr>
<tr>
  <td><code>spark.serializer</code></td>
  <td>org.apache.spark.serializer.<br />JavaSerializer</td>
  <td>
    Class to use for serializing objects that will be sent over the network or need to be cached
    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>org.apache.spark.serializer.KryoSerializer</code> and configuring Kryo serialization</a>
    when speed is necessary. Can be any subclass of
    <a href="api/scala/index.html#org.apache.spark.serializer.Serializer">
    <code>org.apache.spark.Serializer</code></a>.
  </td>
</tr>
<tr>
  <td><code>spark.kryo.classesToRegister</code></td>
  <td>(none)</td>
  <td>
    If you use Kryo serialization, give a comma-separated list of custom class names to register
    with Kryo.
    See the <a href="tuning.html#data-serialization">tuning guide</a> for more details.
  </td>
</tr>
<tr>
  <td><code>spark.kryo.registrator</code></td>
  <td>(none)</td>
  <td>
    If you use Kryo serialization, set this class to register your custom classes with Kryo. This
    property is useful if you need to register your classes in a custom way, e.g. to specify a custom
    field serializer. Otherwise <code>spark.kryo.classesToRegister</code> is simpler. It should be
    set to a class that extends
    <a href="api/scala/index.html#org.apache.spark.serializer.KryoRegistrator">
    <code>KryoRegistrator</code></a>.
    See the <a href="tuning.html#data-serialization">tuning guide</a> for more details.
  </td>
</tr>
<tr>
  <td><code>spark.local.dir</code></td>
  <td>/tmp</td>
  <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 on different disks.

    NOTE: In Spark 1.0 and later this will be overriden by SPARK_LOCAL_DIRS (Standalone, Mesos) or
    LOCAL_DIRS (YARN) environment variables set by the cluster manager.
  </td>
</tr>
<tr>
  <td><code>spark.logConf</code></td>
  <td>false</td>
  <td>
    Logs the effective SparkConf as INFO when a SparkContext is started.
  </td>
</tr>
</table>

Apart from these, the following properties are also available, and may be useful in some situations:

#### Runtime Environment
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
  <td><code>spark.executor.extraJavaOptions</code></td>
  <td>(none)</td>
  <td>
    A string of extra JVM options to pass to executors. For instance, GC settings or other
    logging. Note that it is illegal to set Spark properties or heap size settings with this
    option. Spark properties should be set using a SparkConf object or the
    spark-defaults.conf file used with the spark-submit script. Heap size settings can be set
    with spark.executor.memory.
  </td>
</tr>
<tr>
  <td><code>spark.executor.extraClassPath</code></td>
  <td>(none)</td>
  <td>
    Extra classpath entries to append to the classpath of executors. This exists primarily
    for backwards-compatibility with older versions of Spark. Users typically should not need
    to set this option.
  </td>
</tr>
<tr>
  <td><code>spark.executor.extraLibraryPath</code></td>
  <td>(none)</td>
  <td>
    Set a special library path to use when launching executor JVM's.
  </td>
</tr>
<tr>
  <td><code>spark.files.userClassPathFirst</code></td>
  <td>false</td>
  <td>
    (Experimental) Whether to give user-added jars precedence over Spark's own jars when
    loading classes in Executors. This feature can be used to mitigate conflicts between
    Spark's dependencies and user dependencies. It is currently an experimental feature.
    (Currently, this setting does not work for YARN, see <a href="https://issues.apache.org/jira/browse/SPARK-2996">SPARK-2996</a> for more details).
  </td>
</tr>
<tr>
  <td><code>spark.python.worker.memory</code></td>
  <td>512m</td>
  <td>
    Amount of memory to use per python worker process during aggregation, in the same
    format as JVM memory strings (e.g. <code>512m</code>, <code>2g</code>). If the memory
    used during aggregation goes above this amount, it will spill the data into disks.
  </td>
</tr>
<tr>
  <td><code>spark.python.profile</code></td>
  <td>false</td>
  <td>
    Enable profiling in Python worker, the profile result will show up by `sc.show_profiles()`,
    or it will be displayed before the driver exiting. It also can be dumped into disk by
    `sc.dump_profiles(path)`. If some of the profile results had been displayed maually,
    they will not be displayed automatically before driver exiting.
  </td>
</tr>
<tr>
  <td><code>spark.python.profile.dump</code></td>
  <td>(none)</td>
  <td>
    The directory which is used to dump the profile result before driver exiting. 
    The results will be dumped as separated file for each RDD. They can be loaded
    by ptats.Stats(). If this is specified, the profile result will not be displayed
    automatically.
</tr>
<tr>
  <td><code>spark.python.worker.reuse</code></td>
  <td>true</td>
  <td>
    Reuse Python worker or not. If yes, it will use a fixed number of Python workers,
    does not need to fork() a Python process for every tasks. It will be very useful
    if there is large broadcast, then the broadcast will not be needed to transfered
    from JVM to Python worker for every task.
  </td>
</tr>
<tr>
  <td><code>spark.executorEnv.[EnvironmentVariableName]</code></td>
  <td>(none)</td>
  <td>
    Add the environment variable specified by <code>EnvironmentVariableName</code> to the Executor 
    process. The user can specify multiple of these and to set multiple environment variables. 
  </td>
</tr>
<tr>
  <td><code>spark.mesos.executor.home</code></td>
  <td>driver side <code>SPARK_HOME</code></td>
  <td>
    Set the directory in which Spark is installed on the executors in Mesos. By default, the
    executors will simply use the driver's Spark home directory, which may not be visible to
    them. Note that this is only relevant if a Spark binary package is not specified through
    <code>spark.executor.uri</code>.
  </td>
</tr>
<tr>
  <td><code>spark.mesos.executor.memoryOverhead</code></td>
  <td>executor memory * 0.07, with minimum of 384</td>
  <td>
    This value is an additive for <code>spark.executor.memory</code>, specified in MiB,
    which is used to calculate the total Mesos task memory. A value of <code>384</code>
    implies a 384MiB overhead. Additionally, there is a hard-coded 7% minimum
    overhead. The final overhead will be the larger of either
    `spark.mesos.executor.memoryOverhead` or 7% of `spark.executor.memory`.
  </td>
</tr>
</table>

#### Shuffle Behavior
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
  <td><code>spark.shuffle.consolidateFiles</code></td>
  <td>false</td>
  <td>
    If set to "true", consolidates intermediate files created during a shuffle. Creating fewer
    files can improve filesystem performance for shuffles with large numbers of reduce tasks. It
    is recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option
    might degrade performance on machines with many (>8) cores due to filesystem limitations.
  </td>
</tr>
<tr>
  <td><code>spark.shuffle.spill</code></td>
  <td>true</td>
  <td>
    If set to "true", limits the amount of memory used during reduces by spilling data out to disk.
    This spilling threshold is specified by <code>spark.shuffle.memoryFraction</code>.
  </td>
</tr>
<tr>
  <td><code>spark.shuffle.spill.compress</code></td>
  <td>true</td>
  <td>
    Whether to compress data spilled during shuffles. Compression will use
    <code>spark.io.compression.codec</code>.
  </td>
</tr>
<tr>
  <td><code>spark.shuffle.memoryFraction</code></td>
  <td>0.2</td>
  <td>
    Fraction of Java heap to use for aggregation and cogroups during shuffles, if
    <code>spark.shuffle.spill</code> is true. At any given time, the collective size of
    all in-memory maps used for shuffles is bounded by this limit, beyond which the contents will
    begin to spill to disk. If spills are often, consider increasing this value at the expense of
    <code>spark.storage.memoryFraction</code>.
  </td>
</tr>
<tr>
  <td><code>spark.shuffle.compress</code></td>
  <td>true</td>
  <td>
    Whether to compress map output files. Generally a good idea. Compression will use
    <code>spark.io.compression.codec</code>.
  </td>
</tr>
<tr>
  <td><code>spark.shuffle.file.buffer.kb</code></td>
  <td>32</td>
  <td>
    Size of the in-memory buffer for each shuffle file output stream, in kilobytes. These buffers
    reduce the number of disk seeks and system calls made in creating intermediate shuffle files.
  </td>
</tr>
<tr>
  <td><code>spark.reducer.maxMbInFlight</code></td>
  <td>48</td>
  <td>
    Maximum size (in megabytes) of map outputs to fetch simultaneously from each reduce task. Since
    each output requires us to create a buffer to receive it, this represents a fixed memory
    overhead per reduce task, so keep it small unless you have a large amount of memory.
  </td>
</tr>
<tr>
  <td><code>spark.shuffle.manager</code></td>
  <td>sort</td>
  <td>
    Implementation to use for shuffling data. There are two implementations available:
    <code>sort</code> and <code>hash</code>. Sort-based shuffle is more memory-efficient and is
    the default option starting in 1.2.
  </td>
</tr>
<tr>
  <td><code>spark.shuffle.sort.bypassMergeThreshold</code></td>
  <td>200</td>
  <td>
    (Advanced) In the sort-based shuffle manager, avoid merge-sorting data if there is no
    map-side aggregation and there are at most this many reduce partitions.
  </td>
</tr>
<tr>
  <td><code>spark.shuffle.blockTransferService</code></td>
  <td>netty</td>
  <td>
    Implementation to use for transferring shuffle and cached blocks between executors. There
    are two implementations available: <code>netty</code> and <code>nio</code>. Netty-based
    block transfer is intended to be simpler but equally efficient and is the default option
    starting in 1.2.
  </td>
</tr>
</table>

#### Spark UI
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
  <td><code>spark.ui.port</code></td>
  <td>4040</td>
  <td>
    Port for your application's dashboard, which shows memory and workload data.
  </td>
</tr>
<tr>
  <td><code>spark.ui.retainedStages</code></td>
  <td>1000</td>
  <td>
    How many stages the Spark UI and status APIs remember before garbage
    collecting.
  </td>
</tr>
<tr>
  <td><code>spark.ui.retainedJobs</code></td>
  <td>1000</td>
  <td>
    How many stages the Spark UI and status APIs remember before garbage
    collecting.
  </td>
</tr>
<tr>
  <td><code>spark.ui.killEnabled</code></td>
  <td>true</td>
  <td>
    Allows stages and corresponding jobs to be killed from the web ui.
  </td>
</tr>
<tr>
  <td><code>spark.eventLog.enabled</code></td>
  <td>false</td>
  <td>
    Whether to log Spark events, useful for reconstructing the Web UI after the application has
    finished.
  </td>
</tr>
<tr>
  <td><code>spark.eventLog.compress</code></td>
  <td>false</td>
  <td>
    Whether to compress logged events, if <code>spark.eventLog.enabled</code> is true.
  </td>
</tr>
<tr>
  <td><code>spark.eventLog.dir</code></td>
  <td>file:///tmp/spark-events</td>
  <td>
    Base directory in which Spark events are logged, if <code>spark.eventLog.enabled</code> is true.
    Within this base directory, Spark creates a sub-directory for each application, and logs the
    events specific to the application in this directory. Users may want to set this to
    a unified location like an HDFS directory so history files can be read by the history server.
  </td>
</tr>
</table>

#### Compression and Serialization
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
  <td><code>spark.broadcast.compress</code></td>
  <td>true</td>
  <td>
    Whether to compress broadcast variables before sending them. Generally a good idea.
  </td>
</tr>
<tr>
  <td><code>spark.rdd.compress</code></td>
  <td>false</td>
  <td>
    Whether to compress serialized RDD partitions (e.g. for
    <code>StorageLevel.MEMORY_ONLY_SER</code>). Can save substantial space at the cost of some
    extra CPU time.
  </td>
</tr>
<tr>
  <td><code>spark.io.compression.codec</code></td>
  <td>snappy</td>
  <td>
    The codec used to compress internal data such as RDD partitions, broadcast variables and
    shuffle outputs. By default, Spark provides three codecs: <code>lz4</code>, <code>lzf</code>,
    and <code>snappy</code>. You can also use fully qualified class names to specify the codec, 
    e.g. 
    <code>org.apache.spark.io.LZ4CompressionCodec</code>,    
    <code>org.apache.spark.io.LZFCompressionCodec</code>,
    and <code>org.apache.spark.io.SnappyCompressionCodec</code>.
  </td>
</tr>
<tr>
  <td><code>spark.io.compression.snappy.block.size</code></td>
  <td>32768</td>
  <td>
    Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec
    is used. Lowering this block size will also lower shuffle memory usage when Snappy is used.
  </td>
</tr>
<tr>
  <td><code>spark.io.compression.lz4.block.size</code></td>
  <td>32768</td>
  <td>
    Block size (in bytes) used in LZ4 compression, in the case when LZ4 compression codec
    is used. Lowering this block size will also lower shuffle memory usage when LZ4 is used.
  </td>
</tr>
<tr>
  <td><code>spark.closure.serializer</code></td>
  <td>org.apache.spark.serializer.<br />JavaSerializer</td>
  <td>
    Serializer class to use for closures. Currently only the Java serializer is supported.
  </td>
</tr>
<tr>
  <td><code>spark.serializer.objectStreamReset</code></td>
  <td>100</td>
  <td>
    When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches
    objects to prevent writing redundant data, however that stops garbage collection of those
    objects. By calling 'reset' you flush that info from the serializer, and allow old
    objects to be collected. To turn off this periodic reset set it to -1.
    By default it will reset the serializer every 100 objects.
  </td>
</tr>
<tr>
  <td><code>spark.kryo.referenceTracking</code></td>
  <td>true</td>
  <td>
    Whether to track references to the same object when serializing data with Kryo, which is
    necessary if your object graphs have loops and useful for efficiency if they contain multiple
    copies of the same object. Can be disabled to improve performance if you know this is not the
    case.
  </td>
</tr>
<tr>
  <td><code>spark.kryo.registrationRequired</code></td>
  <td>false</td>
  <td>
    Whether to require registration with Kryo. If set to 'true', Kryo will throw an exception
    if an unregistered class is serialized. If set to false (the default), Kryo will write
    unregistered class names along with each object. Writing class names can cause
    significant performance overhead, so enabling this option can enforce strictly that a
    user has not omitted classes from registration.
  </td>
</tr>
<tr>
  <td><code>spark.kryoserializer.buffer.mb</code></td>
  <td>0.064</td>
  <td>
    Initial size of Kryo's serialization buffer, in megabytes. Note that there will be one buffer
     <i>per core</i> on each worker. This buffer will grow up to
     <code>spark.kryoserializer.buffer.max.mb</code> if needed.
  </td>
</tr>
<tr>
  <td><code>spark.kryoserializer.buffer.max.mb</code></td>
  <td>64</td>
  <td>
    Maximum allowable size of Kryo serialization buffer, in megabytes. This must be larger than any
    object you attempt to serialize. Increase this if you get a "buffer limit exceeded" exception
    inside Kryo.
  </td>
</tr>
</table>

#### Execution Behavior
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
  <td><code>spark.default.parallelism</code></td>
  <td>
    For distributed shuffle operations like <code>reduceByKey</code> and <code>join</code>, the
    largest number of partitions in a parent RDD.  For operations like <code>parallelize</code>
    with no parent RDDs, it depends on the cluster manager:
    <ul>
      <li>Local mode: number of cores on the local machine</li>
      <li>Mesos fine grained mode: 8</li>
      <li>Others: total number of cores on all executor nodes or 2, whichever is larger</li>
    </ul>
  </td>
  <td>
    Default number of partitions in RDDs returned by transformations like <code>join</code>,
    <code>reduceByKey</code>, and <code>parallelize</code> when not set by user.
  </td>
</tr>
<tr>
  <td><code>spark.broadcast.factory</code></td>
  <td>org.apache.spark.broadcast.<br />TorrentBroadcastFactory</td>
  <td>
    Which broadcast implementation to use.
  </td>
</tr>
<tr>
  <td><code>spark.broadcast.blockSize</code></td>
  <td>4096</td>
  <td>
    Size of each piece of a block in kilobytes for <code>TorrentBroadcastFactory</code>.
    Too large a value decreases parallelism during broadcast (makes it slower); however, if it is
    too small, <code>BlockManager</code> might take a performance hit.
  </td>
</tr>
<tr>
  <td><code>spark.files.overwrite</code></td>
  <td>false</td>
  <td>
    Whether to overwrite files added through SparkContext.addFile() when the target file exists and
    its contents do not match those of the source.
  </td>
</tr>
<tr>
  <td><code>spark.files.fetchTimeout</code></td>
  <td>60</td>
  <td>
    Communication timeout to use when fetching files added through SparkContext.addFile() from
    the driver, in seconds.
  </td>
</tr>
<tr>
  <td><code>spark.storage.memoryFraction</code></td>
  <td>0.6</td>
  <td>
    Fraction of Java heap to use for Spark's memory cache. This should not be larger than the "old"
    generation of objects in the JVM, which by default is given 0.6 of the heap, but you can
    increase it if you configure your own old generation size.
  </td>
</tr>
<tr>
  <td><code>spark.storage.unrollFraction</code></td>
  <td>0.2</td>
  <td>
    Fraction of <code>spark.storage.memoryFraction</code> to use for unrolling blocks in memory.
    This is dynamically allocated by dropping existing blocks when there is not enough free
    storage space to unroll the new block in its entirety.
  </td>
</tr>
<tr>
  <td><code>spark.tachyonStore.baseDir</code></td>
  <td>System.getProperty("java.io.tmpdir")</td>
  <td>
    Directories of the Tachyon File System that store RDDs. The Tachyon file system's URL is set by
    <code>spark.tachyonStore.url</code>. It can also be a comma-separated list of multiple
    directories on Tachyon file system.
  </td>
</tr>
<tr>
  <td><code>spark.storage.memoryMapThreshold</code></td>
  <td>8192</td>
  <td>
    Size of a block, in bytes, above which Spark memory maps when reading a block from disk.
    This prevents Spark from memory mapping very small blocks. In general, memory
    mapping has high overhead for blocks close to or below the page size of the operating system.
  </td>
</tr>
<tr>
  <td><code>spark.tachyonStore.url</code></td>
  <td>tachyon://localhost:19998</td>
  <td>
    The URL of the underlying Tachyon file system in the TachyonStore.
  </td>
</tr>
<tr>
  <td><code>spark.cleaner.ttl</code></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
    forgotten. 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.
  </td>
</tr>
<tr>
    <td><code>spark.hadoop.validateOutputSpecs</code></td>
    <td>true</td>
    <td>If set to true, validates the output specification (e.g. checking if the output directory already exists)
    used in saveAsHadoopFile and other variants. This can be disabled to silence exceptions due to pre-existing
    output directories. We recommend that users do not disable this except if trying to achieve compatibility with
    previous versions of Spark. Simply use Hadoop's FileSystem API to delete output directories by hand.</td>
</tr>
<tr>
    <td><code>spark.hadoop.cloneConf</code></td>
    <td>false</td>
    <td>If set to true, clones a new Hadoop <code>Configuration</code> object for each task.  This
    option should be enabled to work around <code>Configuration</code> thread-safety issues (see
    <a href="https://issues.apache.org/jira/browse/SPARK-2546">SPARK-2546</a> for more details).
    This is disabled by default in order to avoid unexpected performance regressions for jobs that
    are not affected by these issues.</td>
</tr>
<tr>
    <td><code>spark.executor.heartbeatInterval</code></td>
    <td>10000</td>
    <td>Interval (milliseconds) between each executor's heartbeats to the driver.  Heartbeats let
    the driver know that the executor is still alive and update it with metrics for in-progress
    tasks.</td>
</tr>
</table>

#### Networking
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
  <td><code>spark.driver.host</code></td>
  <td>(local hostname)</td>
  <td>
    Hostname or IP address for the driver to listen on.
    This is used for communicating with the executors and the standalone Master.
  </td>
</tr>
<tr>
  <td><code>spark.driver.port</code></td>
  <td>(random)</td>
  <td>
    Port for the driver to listen on.
    This is used for communicating with the executors and the standalone Master.
  </td>
</tr>
<tr>
  <td><code>spark.fileserver.port</code></td>
  <td>(random)</td>
  <td>
    Port for the driver's HTTP file server to listen on.
  </td>
</tr>
<tr>
  <td><code>spark.broadcast.port</code></td>
  <td>(random)</td>
  <td>
    Port for the driver's HTTP broadcast server to listen on.
    This is not relevant for torrent broadcast.
  </td>
</tr>
<tr>
  <td><code>spark.replClassServer.port</code></td>
  <td>(random)</td>
  <td>
    Port for the driver's HTTP class server to listen on.
    This is only relevant for the Spark shell.
  </td>
</tr>
<tr>
  <td><code>spark.blockManager.port</code></td>
  <td>(random)</td>
  <td>
    Port for all block managers to listen on. These exist on both the driver and the executors.
  </td>
</tr>
<tr>
  <td><code>spark.executor.port</code></td>
  <td>(random)</td>
  <td>
    Port for the executor to listen on. This is used for communicating with the driver.
  </td>
</tr>
<tr>
  <td><code>spark.port.maxRetries</code></td>
  <td>16</td>
  <td>
    Default maximum number of retries when binding to a port before giving up.
  </td>
</tr>
<tr>
  <td><code>spark.akka.frameSize</code></td>
  <td>10</td>
  <td>
    Maximum message size to allow in "control plane" communication (for serialized tasks and task
    results), in MB. Increase this if your tasks need to send back large results to the driver
    (e.g. using <code>collect()</code> on a large dataset).
  </td>
</tr>
<tr>
  <td><code>spark.akka.threads</code></td>
  <td>4</td>
  <td>
    Number of actor threads to use for communication. Can be useful to increase on large clusters
    when the driver has a lot of CPU cores.
  </td>
</tr>
<tr>
  <td><code>spark.akka.timeout</code></td>
  <td>100</td>
  <td>
    Communication timeout between Spark nodes, in seconds.
  </td>
</tr>
<tr>
  <td><code>spark.akka.heartbeat.pauses</code></td>
  <td>6000</td>
  <td>
     This is set to a larger value to disable failure detector that comes inbuilt akka. It can be
     enabled again, if you plan to use this feature (Not recommended). Acceptable heart beat pause
     in seconds for akka. This can be used to control sensitivity to gc pauses. Tune this in
     combination of `spark.akka.heartbeat.interval` and `spark.akka.failure-detector.threshold`
     if you need to.
  </td>
</tr>
<tr>
  <td><code>spark.akka.failure-detector.threshold</code></td>
  <td>300.0</td>
  <td>
     This is set to a larger value to disable failure detector that comes inbuilt akka. It can be
     enabled again, if you plan to use this feature (Not recommended). This maps to akka's
     `akka.remote.transport-failure-detector.threshold`. Tune this in combination of
     `spark.akka.heartbeat.pauses` and `spark.akka.heartbeat.interval` if you need to.
  </td>
</tr>
<tr>
  <td><code>spark.akka.heartbeat.interval</code></td>
  <td>1000</td>
  <td>
    This is set to a larger value to disable failure detector that comes inbuilt akka. It can be
    enabled again, if you plan to use this feature (Not recommended). A larger interval value in
    seconds reduces network overhead and a smaller value ( ~ 1 s) might be more informative for
    akka's failure detector. Tune this in combination of `spark.akka.heartbeat.pauses` and
    `spark.akka.failure-detector.threshold` if you need to. Only positive use case for using
    failure detector can be, a sensistive failure detector can help evict rogue executors really
    quick. However this is usually not the case as gc pauses and network lags are expected in a
    real Spark cluster. Apart from that enabling this leads to a lot of exchanges of heart beats
    between nodes leading to flooding the network with those.
  </td>
</tr>
</table>

#### Scheduling
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
  <td><code>spark.task.cpus</code></td>
  <td>1</td>
  <td>
    Number of cores to allocate for each task.
  </td>
</tr>
<tr>
  <td><code>spark.task.maxFailures</code></td>
  <td>4</td>
  <td>
    Number of individual task failures before giving up on the job.
    Should be greater than or equal to 1. Number of allowed retries = this value - 1.
  </td>
</tr>
<tr>
  <td><code>spark.scheduler.mode</code></td>
  <td>FIFO</td>
  <td>
    The <a href="job-scheduling.html#scheduling-within-an-application">scheduling mode</a> between
    jobs submitted to the same SparkContext. Can be set to <code>FAIR</code>
    to use fair sharing instead of queueing jobs one after another. Useful for
    multi-user services.
  </td>
</tr>
<tr>
  <td><code>spark.cores.max</code></td>
  <td>(not set)</td>
  <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>, the maximum amount of CPU cores to request for the application from
    across the cluster (not from each machine). If not set, the default will be
    <code>spark.deploy.defaultCores</code> on Spark's standalone cluster manager, or
    infinite (all available cores) on Mesos.
  </td>
</tr>
<tr>
  <td><code>spark.mesos.coarse</code></td>
  <td>false</td>
  <td>
    If set to "true", runs over Mesos clusters in
    <a href="running-on-mesos.html#mesos-run-modes">"coarse-grained" sharing mode</a>,
    where Spark acquires one long-lived Mesos task on each machine instead of one Mesos task per
    Spark task. This gives lower-latency scheduling for short queries, but leaves resources in use
    for the whole duration of the Spark job.
  </td>
</tr>
<tr>
  <td><code>spark.speculation</code></td>
  <td>false</td>
  <td>
    If set to "true", performs speculative execution of tasks. This means if one or more tasks are
    running slowly in a stage, they will be re-launched.
  </td>
</tr>
<tr>
  <td><code>spark.speculation.interval</code></td>
  <td>100</td>
  <td>
    How often Spark will check for tasks to speculate, in milliseconds.
  </td>
</tr>
<tr>
  <td><code>spark.speculation.quantile</code></td>
  <td>0.75</td>
  <td>
    Percentage of tasks which must be complete before speculation is enabled for a particular stage.
  </td>
</tr>
<tr>
  <td><code>spark.speculation.multiplier</code></td>
  <td>1.5</td>
  <td>
    How many times slower a task is than the median to be considered for speculation.
  </td>
</tr>
<tr>
  <td><code>spark.locality.wait</code></td>
  <td>3000</td>
  <td>
    Number of milliseconds to wait to launch a data-local task before giving up and launching it
    on a less-local node. The same wait will be used to step through multiple locality levels
    (process-local, node-local, rack-local and then any). It is also possible to customize the
    waiting time for each level by setting <code>spark.locality.wait.node</code>, etc.
    You should increase this setting if your tasks are long and see poor locality, but the
    default usually works well.
  </td>
</tr>
<tr>
  <td><code>spark.locality.wait.process</code></td>
  <td>spark.locality.wait</td>
  <td>
    Customize the locality wait for process locality. This affects tasks that attempt to access
    cached data in a particular executor process.
  </td>
</tr>
<tr>
  <td><code>spark.locality.wait.node</code></td>
  <td>spark.locality.wait</td>
  <td>
    Customize the locality wait for node locality. For example, you can set this to 0 to skip
    node locality and search immediately for rack locality (if your cluster has rack information).
  </td>
</tr>
<tr>
  <td><code>spark.locality.wait.rack</code></td>
  <td>spark.locality.wait</td>
  <td>
    Customize the locality wait for rack locality.
  </td>
</tr>
<tr>
  <td><code>spark.scheduler.revive.interval</code></td>
  <td>1000</td>
  <td>
    The interval length for the scheduler to revive the worker resource offers to run tasks
    (in milliseconds).
  </td>
</tr>
</tr>
  <td><code>spark.scheduler.minRegisteredResourcesRatio</code></td>
  <td>0</td>
  <td>
    The minimum ratio of registered resources (registered resources / total expected resources)
    (resources are executors in yarn mode, CPU cores in standalone mode)
    to wait for before scheduling begins. Specified as a double between 0 and 1.
    Regardless of whether the minimum ratio of resources has been reached,
    the maximum amount of time it will wait before scheduling begins is controlled by config 
    <code>spark.scheduler.maxRegisteredResourcesWaitingTime</code>.
  </td>
</tr>
<tr>
  <td><code>spark.scheduler.maxRegisteredResourcesWaitingTime</code></td>
  <td>30000</td>
  <td>
    Maximum amount of time to wait for resources to register before scheduling begins
    (in milliseconds).  
  </td>
</tr>
<tr>
  <td><code>spark.localExecution.enabled</code></td>
  <td>false</td>
  <td>
    Enables Spark to run certain jobs, such as first() or take() on the driver, without sending
    tasks to the cluster. This can make certain jobs execute very quickly, but may require
    shipping a whole partition of data to the driver.
  </td>
</tr>
</table>

#### Security
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
  <td><code>spark.authenticate</code></td>
  <td>false</td>
  <td>
    Whether Spark authenticates its internal connections. See
    <code>spark.authenticate.secret</code> if not running on YARN.
  </td>
</tr>
<tr>
  <td><code>spark.authenticate.secret</code></td>
  <td>None</td>
  <td>
    Set the secret key used for Spark to authenticate between components. This needs to be set if
    not running on YARN and authentication is enabled.
  </td>
</tr>
<tr>
  <td><code>spark.core.connection.auth.wait.timeout</code></td>
  <td>30</td>
  <td>
    Number of seconds for the connection to wait for authentication to occur before timing
    out and giving up.
  </td>
</tr>
<tr>
  <td><code>spark.core.connection.ack.wait.timeout</code></td>
  <td>60</td>
  <td>
    Number of seconds for the connection to wait for ack to occur before timing
    out and giving up. To avoid unwilling timeout caused by long pause like GC,
    you can set larger value.
  </td>
</tr>
<tr>
  <td><code>spark.ui.filters</code></td>
  <td>None</td>
  <td>
    Comma separated list of filter class names to apply to the Spark web UI. The filter should be a
    standard <a href="http://docs.oracle.com/javaee/6/api/javax/servlet/Filter.html">
    javax servlet Filter</a>. Parameters to each filter can also be specified by setting a
    java system property of: <br />
    <code>spark.&lt;class name of filter&gt;.params='param1=value1,param2=value2'</code><br />
    For example: <br />
    <code>-Dspark.ui.filters=com.test.filter1</code> <br />
    <code>-Dspark.com.test.filter1.params='param1=foo,param2=testing'</code>
  </td>
</tr>
<tr>
  <td><code>spark.acls.enable</code></td>
  <td>false</td>
  <td>
    Whether Spark acls should are enabled. If enabled, this checks to see if the user has
    access permissions to view or modify the job.  Note this requires the user to be known, 
    so if the user comes across as null no checks are done. Filters can be used with the UI
    to authenticate and set the user.
  </td>
</tr>
<tr>
  <td><code>spark.ui.view.acls</code></td>
  <td>Empty</td>
  <td>
    Comma separated list of users that have view access to the Spark web ui. By default only the
    user that started the Spark job has view access.
  </td>
</tr>
<tr>
  <td><code>spark.modify.acls</code></td>
  <td>Empty</td>
  <td>
    Comma separated list of users that have modify access to the Spark job. By default only the
    user that started the Spark job has access to modify it (kill it for example).
  </td>
</tr>
<tr>
  <td><code>spark.admin.acls</code></td>
  <td>Empty</td>
  <td>
    Comma separated list of users/administrators that have view and modify access to all Spark jobs.
    This can be used if you run on a shared cluster and have a set of administrators or devs who
    help debug when things work.
  </td>
</tr>
</table>

#### Spark Streaming
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
  <td><code>spark.streaming.blockInterval</code></td>
  <td>200</td>
  <td>
    Interval (milliseconds) at which data received by Spark Streaming receivers is coalesced
    into blocks of data before storing them in Spark.
  </td>
</tr>
<tr>
  <td><code>spark.streaming.receiver.maxRate</code></td>
  <td>infinite</td>
  <td>
    Maximum rate (per second) at which each receiver will push data into blocks. Effectively,
    each stream will consume at most this number of records per second.
    Setting this configuration to 0 or a negative number will put no limit on the rate.
  </td>
</tr>
<tr>
  <td><code>spark.streaming.unpersist</code></td>
  <td>true</td>
  <td>
    Force RDDs generated and persisted by Spark Streaming to be automatically unpersisted from
    Spark's memory. The raw input data received by Spark Streaming is also automatically cleared.
    Setting this to false will allow the raw data and persisted RDDs to be accessible outside the
    streaming application as they will not be cleared automatically. But it comes at the cost of
    higher memory usage in Spark.
  </td>
</tr>
<tr>
  <td><code>spark.executor.logs.rolling.strategy</code></td>
  <td>(none)</td>
  <td>
    Set the strategy of rolling of executor logs. By default it is disabled. It can
    be set to "time" (time-based rolling) or "size" (size-based rolling). For "time",
    use <code>spark.executor.logs.rolling.time.interval</code> to set the rolling interval.
    For "size", use <code>spark.executor.logs.rolling.size.maxBytes</code> to set
    the maximum file size for rolling.
  </td>
</tr>
<tr>
  <td><code>spark.executor.logs.rolling.time.interval</code></td>
  <td>daily</td>
  <td>
    Set the time interval by which the executor logs will be rolled over.
    Rolling is disabled by default. Valid values are `daily`, `hourly`, `minutely` or
    any interval in seconds. See <code>spark.executor.logs.rolling.maxRetainedFiles</code>
    for automatic cleaning of old logs.
  </td>
</tr>
<tr>
  <td><code>spark.executor.logs.rolling.size.maxBytes</code></td>
  <td>(none)</td>
  <td>
    Set the max size of the file by which the executor logs will be rolled over.
    Rolling is disabled by default. Value is set in terms of bytes.
    See <code>spark.executor.logs.rolling.maxRetainedFiles</code>
    for automatic cleaning of old logs.
  </td>
</tr>
<tr>
  <td><code>spark.executor.logs.rolling.maxRetainedFiles</code></td>
  <td>(none)</td>
  <td>
    Sets the number of latest rolling log files that are going to be retained by the system.
    Older log files will be deleted. Disabled by default.
  </td>
</tr>
</table>

#### Cluster Managers
Each cluster manager in Spark has additional configuration options. Configurations
can be found on the pages for each mode:

 * [YARN](running-on-yarn.html#configuration)
 * [Mesos](running-on-mesos.html)
 * [Standalone Mode](spark-standalone.html#cluster-launch-scripts)

# Environment Variables

Certain Spark settings can be configured through environment variables, which are read from the
`conf/spark-env.sh` script in the directory where Spark is installed (or `conf/spark-env.cmd` on
Windows). In Standalone and Mesos modes, this file can give machine specific information such as
hostnames. It is also sourced when running local Spark applications or submission scripts.

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`:


<table class="table">
  <tr><th style="width:21%">Environment Variable</th><th>Meaning</th></tr>
  <tr>
    <td><code>JAVA_HOME</code></td>
    <td>Location where Java is installed (if it's not on your default `PATH`).</td>
  </tr>
  <tr>
    <td><code>PYSPARK_PYTHON</code></td>
    <td>Python binary executable to use for PySpark.</td>
  </tr>
  <tr>
    <td><code>SPARK_LOCAL_IP</code></td>
    <td>IP address of the machine to bind to.</td>
  </tr>
  <tr>
    <td><code>SPARK_PUBLIC_DNS</code></td>
    <td>Hostname your Spark program will advertise to other machines.</td>
  </tr>
</table>

In addition to the above, there are also options for setting up 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` file in the `conf` directory. One way to start is to copy the existing
`log4j.properties.template` located there.

# Overriding configuration directory

To specify a different configuration directory other than the default "SPARK_HOME/conf",
you can set SPARK_CONF_DIR. Spark will use the the configuration files (spark-defaults.conf, spark-env.sh, log4j.properties, etc)
from this directory.