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.

Logging can be configured through log4j2.properties.

The IP address, through the conf/spark-env.sh script on each node.

Environment variables can be used to set per-machine settings, such as.

Spark properties control most application parameters and can be set by using.

Spark provides three locations to configure the system:

External Shuffle service(server) side configuration options.

Custom Resource Scheduling and Configuration Overview.

Inheriting Hadoop Cluster Configuration.

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