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<h1 class="title">Spark Configuration</h1>
<ul id="markdown-toc">
<li><a href="#spark-properties">Spark Properties</a> <ul>
<li><a href="#dynamically-loading-spark-properties">Dynamically Loading Spark Properties</a></li>
<li><a href="#viewing-spark-properties">Viewing Spark Properties</a></li>
<li><a href="#available-properties">Available Properties</a> <ul>
<li><a href="#application-properties">Application Properties</a></li>
<li><a href="#runtime-environment">Runtime Environment</a></li>
<li><a href="#shuffle-behavior">Shuffle Behavior</a></li>
<li><a href="#spark-ui">Spark UI</a></li>
<li><a href="#compression-and-serialization">Compression and Serialization</a></li>
<li><a href="#memory-management">Memory Management</a></li>
<li><a href="#execution-behavior">Execution Behavior</a></li>
<li><a href="#networking">Networking</a></li>
<li><a href="#scheduling">Scheduling</a></li>
<li><a href="#dynamic-allocation">Dynamic Allocation</a></li>
<li><a href="#security">Security</a></li>
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<p>Spark provides three locations to configure the system:</p>
<ul>
<li><a href="#spark-properties">Spark properties</a> control most application parameters and can be set by using
a <a href="api/scala/index.html#org.apache.spark.SparkConf">SparkConf</a> object, or through Java
system properties.</li>
<li><a href="#environment-variables">Environment variables</a> can be used to set per-machine settings, such as
the IP address, through the <code>conf/spark-env.sh</code> script on each node.</li>
<li><a href="#configuring-logging">Logging</a> can be configured through <code>log4j.properties</code>.</li>
</ul>
<h1 id="spark-properties">Spark Properties</h1>
<p>Spark properties control most application settings and are configured separately for each
application. These properties can be set directly on a
<a href="api/scala/index.html#org.apache.spark.SparkConf">SparkConf</a> passed to your
<code>SparkContext</code>. <code>SparkConf</code> 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
<code>set()</code> method. For example, we could initialize an application with two threads as follows:</p>
<p>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.</p>
<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">conf</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkConf</span><span class="o">()</span>
<span class="o">.</span><span class="n">setMaster</span><span class="o">(</span><span class="s">"local[2]"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setAppName</span><span class="o">(</span><span class="s">"CountingSheep"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">sc</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">)</span></code></pre></div>
<p>Note that we can have more than 1 thread in local mode, and in cases like Spark Streaming, we may
actually require more than 1 thread to prevent any sort of starvation issues.</p>
<p>Properties that specify some time duration should be configured with a unit of time.
The following format is accepted:</p>
<pre><code>25ms (milliseconds)
5s (seconds)
10m or 10min (minutes)
3h (hours)
5d (days)
1y (years)
</code></pre>
<p>Properties that specify a byte size should be configured with a unit of size.
The following format is accepted:</p>
<pre><code>1b (bytes)
1k or 1kb (kibibytes = 1024 bytes)
1m or 1mb (mebibytes = 1024 kibibytes)
1g or 1gb (gibibytes = 1024 mebibytes)
1t or 1tb (tebibytes = 1024 gibibytes)
1p or 1pb (pebibytes = 1024 tebibytes)
</code></pre>
<h2 id="dynamically-loading-spark-properties">Dynamically Loading Spark Properties</h2>
<p>In some cases, you may want to avoid hard-coding certain configurations in a <code>SparkConf</code>. 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:</p>
<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">sc</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkContext</span><span class="o">(</span><span class="k">new</span> <span class="nc">SparkConf</span><span class="o">())</span></code></pre></div>
<p>Then, you can supply configuration values at runtime:</p>
<div class="highlight"><pre><code class="language-bash" data-lang="bash">./bin/spark-submit --name <span class="s2">"My app"</span> --master <span class="nb">local</span><span class="o">[</span>4<span class="o">]</span> --conf spark.eventLog.enabled<span class="o">=</span><span class="nb">false</span>
--conf <span class="s2">"spark.executor.extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps"</span> myApp.jar</code></pre></div>
<p>The Spark shell and <a href="submitting-applications.html"><code>spark-submit</code></a>
tool support two ways to load configurations dynamically. The first are command line options,
such as <code>--master</code>, as shown above. <code>spark-submit</code> can accept any Spark property using the <code>--conf</code>
flag, but uses special flags for properties that play a part in launching the Spark application.
Running <code>./bin/spark-submit --help</code> will show the entire list of these options.</p>
<p><code>bin/spark-submit</code> will also read configuration options from <code>conf/spark-defaults.conf</code>, in which
each line consists of a key and a value separated by whitespace. For example:</p>
<pre><code>spark.master spark://5.6.7.8:7077
spark.executor.memory 4g
spark.eventLog.enabled true
spark.serializer org.apache.spark.serializer.KryoSerializer
</code></pre>
<p>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 <code>spark-submit</code> or <code>spark-shell</code>, then options
in the <code>spark-defaults.conf</code> file. A few configuration keys have been renamed since earlier
versions of Spark; in such cases, the older key names are still accepted, but take lower
precedence than any instance of the newer key.</p>
<h2 id="viewing-spark-properties">Viewing Spark Properties</h2>
<p>The application web UI at <code>http://<driver>:4040</code> 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 <code>spark-defaults.conf</code>, <code>SparkConf</code>, or the command
line will appear. For all other configuration properties, you can assume the default value is used.</p>
<h2 id="available-properties">Available Properties</h2>
<p>Most of the properties that control internal settings have reasonable default values. Some
of the most common options to set are:</p>
<h4 id="application-properties">Application Properties</h4>
<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.driver.cores</code></td>
<td>1</td>
<td>
Number of cores to use for the driver process, only in cluster mode.
</td>
</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>
<td><code>spark.driver.memory</code></td>
<td>1g</td>
<td>
Amount of memory to use for the driver process, i.e. where SparkContext is initialized.
(e.g. <code>1g</code>, <code>2g</code>).
<br /><em>Note:</em> In client mode, this config must not be set through the <code>SparkConf</code>
directly in your application, because the driver JVM has already started at that point.
Instead, please set this through the <code>--driver-memory</code> command line option
or in your default properties file.
</td>
</tr>
<tr>
<td><code>spark.executor.memory</code></td>
<td>1g</td>
<td>
Amount of memory to use per executor process (e.g. <code>2g</code>, <code>8g</code>).
</td>
</tr>
<tr>
<td><code>spark.extraListeners</code></td>
<td>(none)</td>
<td>
A comma-separated list of classes that implement <code>SparkListener</code>; when initializing
SparkContext, instances of these classes will be created and registered with Spark's listener
bus. If a class has a single-argument constructor that accepts a SparkConf, that constructor
will be called; otherwise, a zero-argument constructor will be called. If no valid constructor
can be found, the SparkContext creation will fail with an exception.
</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 overridden 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>
<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.submit.deployMode</code></td>
<td>(none)</td>
<td>
The deploy mode of Spark driver program, either "client" or "cluster",
Which means to launch driver program locally ("client")
or remotely ("cluster") on one of the nodes inside the cluster.
</td>
</tr>
</table>
<p>Apart from these, the following properties are also available, and may be useful in some situations:</p>
<h4 id="runtime-environment">Runtime Environment</h4>
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.driver.extraClassPath</code></td>
<td>(none)</td>
<td>
Extra classpath entries to prepend to the classpath of the driver.
<br /><em>Note:</em> In client mode, this config must not be set through the <code>SparkConf</code>
directly in your application, because the driver JVM has already started at that point.
Instead, please set this through the <code>--driver-class-path</code> command line option or in
your default properties file.</td>
</tr>
<tr>
<td><code>spark.driver.extraJavaOptions</code></td>
<td>(none)</td>
<td>
A string of extra JVM options to pass to the driver. For instance, GC settings or other logging.
<br /><em>Note:</em> In client mode, this config must not be set through the <code>SparkConf</code>
directly in your application, because the driver JVM has already started at that point.
Instead, please set this through the <code>--driver-java-options</code> command line option or in
your default properties file.</td>
</tr>
<tr>
<td><code>spark.driver.extraLibraryPath</code></td>
<td>(none)</td>
<td>
Set a special library path to use when launching the driver JVM.
<br /><em>Note:</em> In client mode, this config must not be set through the <code>SparkConf</code>
directly in your application, because the driver JVM has already started at that point.
Instead, please set this through the <code>--driver-library-path</code> command line option or in
your default properties file.</td>
</tr>
<tr>
<td><code>spark.driver.userClassPathFirst</code></td>
<td>false</td>
<td>
(Experimental) Whether to give user-added jars precedence over Spark's own jars when loading
classes in the driver. This feature can be used to mitigate conflicts between Spark's
dependencies and user dependencies. It is currently an experimental feature.
This is used in cluster mode only.
</td>
</tr>
<tr>
<td><code>spark.executor.extraClassPath</code></td>
<td>(none)</td>
<td>
Extra classpath entries to prepend 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.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.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.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>
<tr>
<td><code>spark.executor.logs.rolling.maxSize</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. See <code>spark.executor.logs.rolling.maxRetainedFiles</code>
for automatic cleaning of old logs.
</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 <code>daily</code>, <code>hourly</code>, <code>minutely</code> 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.userClassPathFirst</code></td>
<td>false</td>
<td>
(Experimental) Same functionality as <code>spark.driver.userClassPathFirst</code>, but
applied to executor instances.
</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 to set multiple environment variables.
</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 <code>sc.show_profiles()</code>,
or it will be displayed before the driver exiting. It also can be dumped into disk by
<code>sc.dump_profiles(path)</code>. If some of the profile results had been displayed manually,
they will not be displayed automatically before driver exiting.
By default the <code>pyspark.profiler.BasicProfiler</code> will be used, but this can be overridden by
passing a profiler class in as a parameter to the <code>SparkContext</code> constructor.
</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.
</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.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 transferred
from JVM to Python worker for every task.
</td>
</tr>
</table>
<h4 id="shuffle-behavior">Shuffle Behavior</h4>
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.reducer.maxSizeInFlight</code></td>
<td>48m</td>
<td>
Maximum size 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.reducer.maxReqsInFlight</code></td>
<td>Int.MaxValue</td>
<td>
This configuration limits the number of remote requests to fetch blocks at any given point.
When the number of hosts in the cluster increase, it might lead to very large number
of in-bound connections to one or more nodes, causing the workers to fail under load.
By allowing it to limit the number of fetch requests, this scenario can be mitigated.
</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</code></td>
<td>32k</td>
<td>
Size of the in-memory buffer for each shuffle file output stream. These buffers
reduce the number of disk seeks and system calls made in creating intermediate shuffle files.
</td>
</tr>
<tr>
<td><code>spark.shuffle.io.maxRetries</code></td>
<td>3</td>
<td>
(Netty only) Fetches that fail due to IO-related exceptions are automatically retried if this is
set to a non-zero value. This retry logic helps stabilize large shuffles in the face of long GC
pauses or transient network connectivity issues.
</td>
</tr>
<tr>
<td><code>spark.shuffle.io.numConnectionsPerPeer</code></td>
<td>1</td>
<td>
(Netty only) Connections between hosts are reused in order to reduce connection buildup for
large clusters. For clusters with many hard disks and few hosts, this may result in insufficient
concurrency to saturate all disks, and so users may consider increasing this value.
</td>
</tr>
<tr>
<td><code>spark.shuffle.io.preferDirectBufs</code></td>
<td>true</td>
<td>
(Netty only) Off-heap buffers are used to reduce garbage collection during shuffle and cache
block transfer. For environments where off-heap memory is tightly limited, users may wish to
turn this off to force all allocations from Netty to be on-heap.
</td>
</tr>
<tr>
<td><code>spark.shuffle.io.retryWait</code></td>
<td>5s</td>
<td>
(Netty only) How long to wait between retries of fetches. The maximum delay caused by retrying
is 15 seconds by default, calculated as <code>maxRetries * retryWait</code>.
</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.service.enabled</code></td>
<td>false</td>
<td>
Enables the external shuffle service. This service preserves the shuffle files written by
executors so the executors can be safely removed. This must be enabled if
<code>spark.dynamicAllocation.enabled</code> is "true". The external shuffle service
must be set up in order to enable it. See
<a href="job-scheduling.html#configuration-and-setup">dynamic allocation
configuration and setup documentation</a> for more information.
</td>
</tr>
<tr>
<td><code>spark.shuffle.service.port</code></td>
<td>7337</td>
<td>
Port on which the external shuffle service will run.
</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.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>
</table>
<h4 id="spark-ui">Spark UI</h4>
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></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>
<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.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.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.retainedJobs</code></td>
<td>1000</td>
<td>
How many jobs the Spark UI and status APIs remember before garbage
collecting.
</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.worker.ui.retainedExecutors</code></td>
<td>1000</td>
<td>
How many finished executors the Spark UI and status APIs remember before garbage collecting.
</td>
</tr>
<tr>
<td><code>spark.worker.ui.retainedDrivers</code></td>
<td>1000</td>
<td>
How many finished drivers the Spark UI and status APIs remember before garbage collecting.
</td>
</tr>
<tr>
<td><code>spark.sql.ui.retainedExecutions</code></td>
<td>1000</td>
<td>
How many finished executions the Spark UI and status APIs remember before garbage collecting.
</td>
</tr>
<tr>
<td><code>spark.streaming.ui.retainedBatches</code></td>
<td>1000</td>
<td>
How many finished batches the Spark UI and status APIs remember before garbage collecting.
</td>
</tr>
<tr>
<td><code>spark.ui.retainedDeadExecutors</code></td>
<td>100</td>
<td>
How many dead executors the Spark UI and status APIs remember before garbage collecting.
</td>
</tr>
</table>
<h4 id="compression-and-serialization">Compression and Serialization</h4>
<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.io.compression.codec</code></td>
<td>lz4</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.lz4.blockSize</code></td>
<td>32k</td>
<td>
Block size 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.io.compression.snappy.blockSize</code></td>
<td>32k</td>
<td>
Block size 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.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.referenceTracking</code></td>
<td>true (false when using Spark SQL Thrift Server)</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.kryo.registrator</code></td>
<td>(none)</td>
<td>
If you use Kryo serialization, give a comma-separated list of classes that 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 classes that extend
<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.kryoserializer.buffer.max</code></td>
<td>64m</td>
<td>
Maximum allowable size of Kryo serialization buffer. 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>
<tr>
<td><code>spark.kryoserializer.buffer</code></td>
<td>64k</td>
<td>
Initial size of Kryo's serialization buffer. 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</code> if needed.
</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> in Java
and Scala or <code>StorageLevel.MEMORY_ONLY</code> in Python).
Can save substantial space at the cost of some extra CPU time.
</td>
</tr>
<tr>
<td><code>spark.serializer</code></td>
<td>
org.apache.spark.serializer.<br />JavaSerializer (org.apache.spark.serializer.<br />
KryoSerializer when using Spark SQL Thrift Server)
</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.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>
</table>
<h4 id="memory-management">Memory Management</h4>
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.memory.fraction</code></td>
<td>0.75</td>
<td>
Fraction of (heap space - 300MB) used for execution and storage. The lower this is, the
more frequently spills and cached data eviction occur. The purpose of this config is to set
aside memory for internal metadata, user data structures, and imprecise size estimation
in the case of sparse, unusually large records. Leaving this at the default value is
recommended. For more detail, see <a href="tuning.html#memory-management-overview">
this description</a>.
</td>
</tr>
<tr>
<td><code>spark.memory.storageFraction</code></td>
<td>0.5</td>
<td>
Amount of storage memory immune to eviction, expressed as a fraction of the size of the
region set aside by <code>spark.memory.fraction</code>. The higher this is, the less
working memory may be available to execution and tasks may spill to disk more often.
Leaving this at the default value is recommended. For more detail, see
<a href="tuning.html#memory-management-overview">this description</a>.
</td>
</tr>
<tr>
<td><code>spark.memory.offHeap.enabled</code></td>
<td>false</td>
<td>
If true, Spark will attempt to use off-heap memory for certain operations. If off-heap memory use is enabled, then <code>spark.memory.offHeap.size</code> must be positive.
</td>
</tr>
<tr>
<td><code>spark.memory.offHeap.size</code></td>
<td>0</td>
<td>
The absolute amount of memory which can be used for off-heap allocation.
This setting has no impact on heap memory usage, so if your executors' total memory consumption must fit within some hard limit then be sure to shrink your JVM heap size accordingly.
This must be set to a positive value when <code>spark.memory.offHeap.enabled=true</code>.
</td>
</tr>
<tr>
<td><code>spark.memory.useLegacyMode</code></td>
<td>false</td>
<td>
Whether to enable the legacy memory management mode used in Spark 1.5 and before.
The legacy mode rigidly partitions the heap space into fixed-size regions,
potentially leading to excessive spilling if the application was not tuned.
The following deprecated memory fraction configurations are not read unless this is enabled:
<code>spark.shuffle.memoryFraction</code><br />
<code>spark.storage.memoryFraction</code><br />
<code>spark.storage.unrollFraction</code>
</td>
</tr>
<tr>
<td><code>spark.shuffle.memoryFraction</code></td>
<td>0.2</td>
<td>
(deprecated) This is read only if <code>spark.memory.useLegacyMode</code> is enabled.
Fraction of Java heap to use for aggregation and cogroups during shuffles.
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.storage.memoryFraction</code></td>
<td>0.6</td>
<td>
(deprecated) This is read only if <code>spark.memory.useLegacyMode</code> is enabled.
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>
(deprecated) This is read only if <code>spark.memory.useLegacyMode</code> is enabled.
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>
</table>
<h4 id="execution-behavior">Execution Behavior</h4>
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.broadcast.blockSize</code></td>
<td>4m</td>
<td>
Size of each piece of a block 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.executor.cores</code></td>
<td>
1 in YARN mode, all the available cores on the worker in
standalone and Mesos coarse-grained modes.
</td>
<td>
The number of cores to use on each executor.
In standalone and Mesos coarse-grained modes, setting this
parameter allows an application to run multiple executors on the
same worker, provided that there are enough cores on that
worker. Otherwise, only one executor per application will run on
each worker.
</td>
</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>