Spark Configuration

Spark Properties
Environment Variables
Configuring Logging

Spark provides three locations to configure the system:

Spark properties control most application parameters and can be set by using a SparkConf object, or through Java system properties.
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 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 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 as follows:

val conf = new SparkConf()
             .setMaster("local")
             .setAppName("CountingSheep")
             .set("spark.executor.memory", "1g")
val sc = new SparkContext(conf)

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:

val sc = new SparkContext(new SparkConf())

Then, you can supply configuration values at runtime:

./bin/spark-submit --name "My app" --master local[4] --conf spark.shuffle.spill=false 
  --conf "spark.executor.extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps" myApp.jar

The Spark shell and spark-submit 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

Property Name	Default	Meaning
`spark.app.name`	(none)	The name of your application. This will appear in the UI and in log data.
`spark.master`	(none)	The cluster manager to connect to. See the list of allowed master URL's.
`spark.executor.memory`	512m	Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. `512m`, `2g`).
`spark.serializer`	org.apache.spark.serializer. JavaSerializer	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 using `org.apache.spark.serializer.KryoSerializer` and configuring Kryo serialization when speed is necessary. Can be any subclass of `org.apache.spark.Serializer`.
`spark.kryo.registrator`	(none)	If you use Kryo serialization, set this class to register your custom classes with Kryo. It should be set to a class that extends `KryoRegistrator`. See the tuning guide for more details.
`spark.local.dir`	/tmp	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.
`spark.logConf`	false	Logs the effective SparkConf as INFO when a SparkContext is started.

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

Runtime Environment

Property Name	Default	Meaning
`spark.executor.memory`	512m	Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. `512m`, `2g`).
`spark.executor.extraJavaOptions`	(none)	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.
`spark.executor.extraClassPath`	(none)	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.
`spark.executor.extraLibraryPath`	(none)	Set a special library path to use when launching executor JVM's.
`spark.files.userClassPathFirst`	false	(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.
`spark.python.worker.memory`	512m	Amount of memory to use per python worker process during aggregation, in the same format as JVM memory strings (e.g. `512m`, `2g`). If the memory used during aggregation goes above this amount, it will spill the data into disks.
`spark.executorEnv.[EnvironmentVariableName]`	(none)	Add the environment variable specified by `EnvironmentVariableName` to the Executor process. The user can specify multiple of these and to set multiple environment variables.
`spark.mesos.executor.home`	driver side `SPARK_HOME`	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 `spark.executor.uri`.

Shuffle Behavior

Property Name	Default	Meaning
`spark.shuffle.consolidateFiles`	false	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.
`spark.shuffle.spill`	true	If set to "true", limits the amount of memory used during reduces by spilling data out to disk. This spilling threshold is specified by `spark.shuffle.memoryFraction`.
`spark.shuffle.spill.compress`	true	Whether to compress data spilled during shuffles. Compression will use `spark.io.compression.codec`.
`spark.shuffle.memoryFraction`	0.2	Fraction of Java heap to use for aggregation and cogroups during shuffles, if `spark.shuffle.spill` 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 `spark.storage.memoryFraction`.
`spark.shuffle.compress`	true	Whether to compress map output files. Generally a good idea. Compression will use `spark.io.compression.codec`.
`spark.shuffle.file.buffer.kb`	32	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.
`spark.reducer.maxMbInFlight`	48	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.
`spark.shuffle.manager`	HASH	Implementation to use for shuffling data. A hash-based shuffle manager is the default, but starting in Spark 1.1 there is an experimental sort-based shuffle manager that is more memory-efficient in environments with small executors, such as YARN. To use that, change this value to `SORT`.
`spark.shuffle.sort.bypassMergeThreshold`	200	(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.

Spark UI

Property Name	Default	Meaning
`spark.ui.port`	4040	Port for your application's dashboard, which shows memory and workload data
`spark.ui.retainedStages`	1000	How many stages the Spark UI remembers before garbage collecting.
`spark.ui.killEnabled`	true	Allows stages and corresponding jobs to be killed from the web ui.
`spark.eventLog.enabled`	false	Whether to log Spark events, useful for reconstructing the Web UI after the application has finished.
`spark.eventLog.compress`	false	Whether to compress logged events, if `spark.eventLog.enabled` is true.
`spark.eventLog.dir`	file:///tmp/spark-events	Base directory in which Spark events are logged, if `spark.eventLog.enabled` 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.

Compression and Serialization

Property Name	Default	Meaning
`spark.broadcast.compress`	true	Whether to compress broadcast variables before sending them. Generally a good idea.
`spark.rdd.compress`	false	Whether to compress serialized RDD partitions (e.g. for `StorageLevel.MEMORY_ONLY_SER`). Can save substantial space at the cost of some extra CPU time.
`spark.io.compression.codec`	snappy	The codec used to compress internal data such as RDD partitions and shuffle outputs. By default, Spark provides three codecs: `lz4`, `lzf`, and `snappy`. You can also use fully qualified class names to specify the codec, e.g. `org.apache.spark.io.LZ4CompressionCodec`, `org.apache.spark.io.LZFCompressionCodec`, and `org.apache.spark.io.SnappyCompressionCodec`.
`spark.io.compression.snappy.block.size`	32768	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.
`spark.io.compression.lz4.block.size`	32768	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.
`spark.closure.serializer`	org.apache.spark.serializer. JavaSerializer	Serializer class to use for closures. Currently only the Java serializer is supported.
`spark.serializer.objectStreamReset`	100	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.
`spark.kryo.referenceTracking`	true	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.
`spark.kryo.registrationRequired`	false	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.
`spark.kryoserializer.buffer.mb`	0.064	Initial size of Kryo's serialization buffer, in megabytes. Note that there will be one buffer per core on each worker. This buffer will grow up to `spark.kryoserializer.buffer.max.mb` if needed.
`spark.kryoserializer.buffer.max.mb`	64	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.

Execution Behavior

Property Name	Default	Meaning
`spark.default.parallelism`	Local mode: number of cores on the local machine Mesos fine grained mode: 8 Others: total number of cores on all executor nodes or 2, whichever is larger	Default number of tasks to use across the cluster for distributed shuffle operations (`groupByKey`, `reduceByKey`, etc) when not set by user.
`spark.broadcast.factory`	org.apache.spark.broadcast. TorrentBroadcastFactory	Which broadcast implementation to use.
`spark.broadcast.blockSize`	4096	Size of each piece of a block in kilobytes for `TorrentBroadcastFactory`. Too large a value decreases parallelism during broadcast (makes it slower); however, if it is too small, `BlockManager` might take a performance hit.
`spark.files.overwrite`	false	Whether to overwrite files added through SparkContext.addFile() when the target file exists and its contents do not match those of the source.
`spark.files.fetchTimeout`	false	Communication timeout to use when fetching files added through SparkContext.addFile() from the driver.
`spark.storage.memoryFraction`	0.6	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.
`spark.storage.unrollFraction`	0.2	Fraction of `spark.storage.memoryFraction` 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.
`spark.tachyonStore.baseDir`	System.getProperty("java.io.tmpdir")	Directories of the Tachyon File System that store RDDs. The Tachyon file system's URL is set by `spark.tachyonStore.url`. It can also be a comma-separated list of multiple directories on Tachyon file system.
`spark.storage.memoryMapThreshold`	8192	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.
`spark.tachyonStore.url`	tachyon://localhost:19998	The URL of the underlying Tachyon file system in the TachyonStore.
`spark.cleaner.ttl`	(infinite)	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.
`spark.hadoop.validateOutputSpecs`	true	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.
`spark.executor.heartbeatInterval`	10000	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.

Networking

Property Name	Default	Meaning
`spark.driver.host`	(local hostname)	Hostname or IP address for the driver to listen on. This is used for communicating with the executors and the standalone Master.
`spark.driver.port`	(random)	Port for the driver to listen on. This is used for communicating with the executors and the standalone Master.
`spark.fileserver.port`	(random)	Port for the driver's HTTP file server to listen on.
`spark.broadcast.port`	(random)	Port for the driver's HTTP broadcast server to listen on. This is not relevant for torrent broadcast.
`spark.replClassServer.port`	(random)	Port for the driver's HTTP class server to listen on. This is only relevant for the Spark shell.
`spark.blockManager.port`	(random)	Port for all block managers to listen on. These exist on both the driver and the executors.
`spark.executor.port`	(random)	Port for the executor to listen on. This is used for communicating with the driver.
`spark.port.maxRetries`	16	Maximum number of retries when binding to a port before giving up.
`spark.akka.frameSize`	10	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 `collect()` on a large dataset).
`spark.akka.threads`	4	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.
`spark.akka.timeout`	100	Communication timeout between Spark nodes, in seconds.
`spark.akka.heartbeat.pauses`	600	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.
`spark.akka.failure-detector.threshold`	300.0	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.
`spark.akka.heartbeat.interval`	1000	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.

Scheduling

Property Name	Default	Meaning
`spark.task.cpus`	1	Number of cores to allocate for each task.
`spark.task.maxFailures`	4	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.
`spark.scheduler.mode`	FIFO	The scheduling mode between jobs submitted to the same SparkContext. Can be set to `FAIR` to use fair sharing instead of queueing jobs one after another. Useful for multi-user services.
`spark.cores.max`	(not set)	When running on a standalone deploy cluster or a Mesos cluster in "coarse-grained" sharing mode, 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 `spark.deploy.defaultCores` on Spark's standalone cluster manager, or infinite (all available cores) on Mesos.
`spark.mesos.coarse`	false	If set to "true", runs over Mesos clusters in "coarse-grained" sharing mode, 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.
`spark.speculation`	false	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.
`spark.speculation.interval`	100	How often Spark will check for tasks to speculate, in milliseconds.
`spark.speculation.quantile`	0.75	Percentage of tasks which must be complete before speculation is enabled for a particular stage.
`spark.speculation.multiplier`	1.5	How many times slower a task is than the median to be considered for speculation.
`spark.locality.wait`	3000	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 `spark.locality.wait.node`, etc. You should increase this setting if your tasks are long and see poor locality, but the default usually works well.
`spark.locality.wait.process`	spark.locality.wait	Customize the locality wait for process locality. This affects tasks that attempt to access cached data in a particular executor process.
`spark.locality.wait.node`	spark.locality.wait	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).
`spark.locality.wait.rack`	spark.locality.wait	Customize the locality wait for rack locality.
`spark.scheduler.revive.interval`	1000	The interval length for the scheduler to revive the worker resource offers to run tasks. (in milliseconds)
`spark.scheduler.minRegisteredResourcesRatio`	0	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 `spark.scheduler.maxRegisteredResourcesWaitingTime`
`spark.scheduler.maxRegisteredResourcesWaitingTime`	30000	Maximum amount of time to wait for resources to register before scheduling begins (in milliseconds).
`spark.localExecution.enabled`	false	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.

Security

Property Name	Default	Meaning
`spark.authenticate`	false	Whether Spark authenticates its internal connections. See `spark.authenticate.secret` if not running on YARN.
`spark.authenticate.secret`	None	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.
`spark.core.connection.auth.wait.timeout`	30	Number of seconds for the connection to wait for authentication to occur before timing out and giving up.
`spark.core.connection.ack.wait.timeout`	60	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.
`spark.ui.filters`	None	Comma separated list of filter class names to apply to the Spark web UI. The filter should be a standard javax servlet Filter. Parameters to each filter can also be specified by setting a java system property of: `spark.<class name of filter>.params='param1=value1,param2=value2'` For example: `-Dspark.ui.filters=com.test.filter1` `-Dspark.com.test.filter1.params='param1=foo,param2=testing'`
`spark.acls.enable`	false	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.
`spark.ui.view.acls`	Empty	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.
`spark.modify.acls`	Empty	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).
`spark.admin.acls`	Empty	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.

Spark Streaming

Property Name	Default	Meaning
`spark.streaming.blockInterval`	200	Interval (milliseconds) at which data received by Spark Streaming receivers is coalesced into blocks of data before storing them in Spark.
`spark.streaming.receiver.maxRate`	infinite	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.
`spark.streaming.unpersist`	true	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.
`spark.executor.logs.rolling.strategy`	(none)	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 `spark.executor.logs.rolling.time.interval` to set the rolling interval. For "size", use `spark.executor.logs.rolling.size.maxBytes` to set the maximum file size for rolling.
`spark.executor.logs.rolling.time.interval`	daily	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 `spark.executor.logs.rolling.maxRetainedFiles` for automatic cleaning of old logs.
`spark.executor.logs.rolling.size.maxBytes`	(none)	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 `spark.executor.logs.rolling.maxRetainedFiles` for automatic cleaning of old logs.
`spark.executor.logs.rolling.maxRetainedFiles`	(none)	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.

Cluster Managers

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

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:

Environment Variable	Meaning
`JAVA_HOME`	Location where Java is installed (if it's not on your default `PATH`).
`PYSPARK_PYTHON`	Python binary executable to use for PySpark.
`SPARK_LOCAL_IP`	IP address of the machine to bind to.
`SPARK_PUBLIC_DNS`	Hostname your Spark program will advertise to other machines.

In addition to the above, there are also options for setting up the Spark standalone cluster 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 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.