Same format as JVM memory strings with a size unit suffix ("k", "m", "g" or "t") where SparkContext is initialized, in the
System property set in java for mac driver#
Setting a proper limit can protect the driver fromĪmount of memory to use for the driver process, i.e. Having a high limit may cause out-of-memory errors in driver (depends on Īnd memory overhead of objects in JVM). Should be at least 1M, or 0 for unlimited. Limit of total size of serialized results of all partitions for each Spark action (e.g.Ĭollect) in bytes. Number of cores to use for the driver process, only in cluster mode. This will appear in the UI and in log data. Of the most common options to set are: Application Properties Property Name Most of the properties that control internal settings have reasonable default values. For all other configuration properties, you can assume the default value is used. That only values explicitly specified through nf, SparkConf, or the command This is a useful place to check to make sure that your properties have been set correctly. The application web UI at lists Spark properties in the “Environment” tab. Like “”, this kind of properties can be set in either way. Setting programmatically through SparkConf in runtime, or the behavior is depending on whichĬluster manager and deploy mode you choose, so it would be suggested to set through configurationįile or spark-submit command line options another is mainly related to Spark runtime control, “”, “”, this kind of properties may not be affected when Spark properties mainly can be divided into two kinds: one is related to deploy, like Precedence than any instance of the newer key. Versions of Spark in such cases, the older key names are still accepted, but take lower A few configuration keys have been renamed since earlier Take highest precedence, then flags passed to spark-submit or spark-shell, then options rializer .KryoSerializerĪny values specified as flags or in the properties file will be passed on to the applicationĪnd merged with those specified through SparkConf. bin/spark-submit -help will show the entire list of these options.īin/spark-submit will also read configuration options from conf/nf, in whichĮach line consists of a key and a value separated by whitespace. spark-submit can accept any Spark property using the -conf/-cįlag, but uses special flags for properties that play a part in launching the Spark application.
Tool support two ways to load configurations dynamically. bin/spark-submit -name "My app" -master local -conf = false -conf "=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps" myApp.jar Then, you can supply configuration values at runtime. Spark allows you to simply create an empty conf: val sc = new SparkContext ( new SparkConf ()) Instance, if you’d like to run the same application with different masters or differentĪmounts of memory. In some cases, you may want to avoid hard-coding certain configurations in a SparkConf. See documentation of individual configuration properties. While numbers without units are generally interpreted as bytes, a few are interpreted as KiB or MiB.
Properties that specify some time duration should be configured with a unit of time. Note that we can have more than 1 thread in local mode, and in cases like Spark Streaming, we mayĪctually require more than 1 thread to prevent any sort of starvation issues. setAppName ( "CountingSheep" ) val sc = new SparkContext ( conf ) Which can help detect bugs that only exist when we run in a distributed context. Note that we run with local, meaning two threads - which represents “minimal” parallelism, For example, we could initialize an application with two threads as follows: master URL and application name), as well as arbitrary key-value pairs through the SparkConf allows you to configure some of the common properties These properties can be set directly on a Spark properties control most application settings and are configured separately for eachĪpplication.
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