Package org.apache.spark

Class SparkConf

Object
org.apache.spark.SparkConf
All Implemented Interfaces:
Serializable, Cloneable, org.apache.spark.internal.Logging, ReadOnlySparkConf
public class SparkConf extends Object implements ReadOnlySparkConf, Cloneable, org.apache.spark.internal.Logging, Serializable
Configuration for a Spark application. Used to set various Spark parameters as key-value pairs.

Most of the time, you would create a SparkConf object with new SparkConf(), which will load values from any spark.* Java system properties set in your application as well. In this case, parameters you set directly on the SparkConf object take priority over system properties.

For unit tests, you can also call new SparkConf(false) to skip loading external settings and get the same configuration no matter what the system properties are.

All setter methods in this class support chaining. For example, you can write new SparkConf().setMaster("local").setAppName("My app").

param: loadDefaults whether to also load values from Java system properties

See Also:
Note:
Once a SparkConf object is passed to Spark, it is cloned and can no longer be modified by the user. Spark does not support modifying the configuration at runtime.

  • Constructor Details

    • SparkConf

      public SparkConf(boolean loadDefaults)

    • SparkConf

      public SparkConf()
      Create a SparkConf that loads defaults from system properties and the classpath

  • Method Details

    • isExecutorStartupConf

      public static boolean isExecutorStartupConf(String name)
      Return whether the given config should be passed to an executor on start-up.

      Certain authentication configs are required from the executor when it connects to the scheduler, while the rest of the spark configs can be inherited from the driver later.

      Parameters:
      name - (undocumented)
      Returns:
      (undocumented)

    • isSparkPortConf

      public static boolean isSparkPortConf(String name)
      Return true if the given config matches either spark.*.port or spark.port.*.
      Parameters:
      name - (undocumented)
      Returns:
      (undocumented)

    • getDeprecatedConfig

      public static scala.Option<String> getDeprecatedConfig(String key, Map<String,String> conf)
      Looks for available deprecated keys for the given config option, and return the first value available.
      Parameters:
      key - (undocumented)
      conf - (undocumented)
      Returns:
      (undocumented)

    • logDeprecationWarning

      public static void logDeprecationWarning(String key)
      Logs a warning message if the given config key is deprecated.
      Parameters:
      key - (undocumented)

    • org$apache$spark$internal$Logging$$log_

      public static org.slf4j.Logger org$apache$spark$internal$Logging$$log_()

    • org$apache$spark$internal$Logging$$log__$eq

      public static void org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)

    • LogStringContext

      public static org.apache.spark.internal.Logging.LogStringContext LogStringContext(scala.StringContext sc)

    • set

      public SparkConf set(String key, String value)
      Set a configuration variable.

    • setMaster

      public SparkConf setMaster(String master)
      The master URL to connect to, such as "local" to run locally with one thread, "local[4]" to run locally with 4 cores, or "spark://master:7077" to run on a Spark standalone cluster.
      Parameters:
      master - (undocumented)
      Returns:
      (undocumented)

    • setAppName

      public SparkConf setAppName(String name)
      Set a name for your application. Shown in the Spark web UI.

    • setJars

      public SparkConf setJars(scala.collection.immutable.Seq<String> jars)
      Set JAR files to distribute to the cluster.

    • setJars

      public SparkConf setJars(String[] jars)
      Set JAR files to distribute to the cluster. (Java-friendly version.)

    • setExecutorEnv

      public SparkConf setExecutorEnv(String variable, String value)
      Set an environment variable to be used when launching executors for this application. These variables are stored as properties of the form spark.executorEnv.VAR_NAME (for example spark.executorEnv.PATH) but this method makes them easier to set.
      Parameters:
      variable - (undocumented)
      value - (undocumented)
      Returns:
      (undocumented)

    • setExecutorEnv

      public SparkConf setExecutorEnv(scala.collection.immutable.Seq<scala.Tuple2<String,String>> variables)
      Set multiple environment variables to be used when launching executors. These variables are stored as properties of the form spark.executorEnv.VAR_NAME (for example spark.executorEnv.PATH) but this method makes them easier to set.
      Parameters:
      variables - (undocumented)
      Returns:
      (undocumented)

    • setExecutorEnv

      public SparkConf setExecutorEnv(scala.Tuple2<String,String>[] variables)
      Set multiple environment variables to be used when launching executors. (Java-friendly version.)
      Parameters:
      variables - (undocumented)
      Returns:
      (undocumented)

    • setSparkHome

      public SparkConf setSparkHome(String home)
      Set the ___location where Spark is installed on worker nodes.
      Parameters:
      home - (undocumented)
      Returns:
      (undocumented)

    • setAll

      public SparkConf setAll(scala.collection.Iterable<scala.Tuple2<String,String>> settings)
      Set multiple parameters together

    • setIfMissing

      public SparkConf setIfMissing(String key, String value)
      Set a parameter if it isn't already configured

    • registerKryoClasses

      public SparkConf registerKryoClasses(Class<?>[] classes)
      Use Kryo serialization and register the given set of classes with Kryo. If called multiple times, this will append the classes from all calls together.
      Parameters:
      classes - (undocumented)
      Returns:
      (undocumented)

    • registerAvroSchemas

      public SparkConf registerAvroSchemas(scala.collection.immutable.Seq<org.apache.avro.Schema> schemas)
      Use Kryo serialization and register the given set of Avro schemas so that the generic record serializer can decrease network IO
      Parameters:
      schemas - (undocumented)
      Returns:
      (undocumented)

    • getAvroSchema

      public scala.collection.immutable.Map<Object,String> getAvroSchema()
      Gets all the avro schemas in the configuration used in the generic Avro record serializer

    • remove

      public SparkConf remove(String key)
      Remove a parameter from the configuration

    • getOption

      public scala.Option<String> getOption(String key)
      Get a parameter as an Option
      Specified by:
      getOption in interface ReadOnlySparkConf

    • getAll

      public scala.Tuple2<String,String>[] getAll()
      Get all parameters as a list of pairs
      Specified by:
      getAll in interface ReadOnlySparkConf

    • getAllWithPrefix

      public scala.Tuple2<String,String>[] getAllWithPrefix(String prefix)
      Get all parameters that start with prefix
      Parameters:
      prefix - (undocumented)
      Returns:
      (undocumented)

    • getExecutorEnv

      public scala.collection.immutable.Seq<scala.Tuple2<String,String>> getExecutorEnv()
      Get all executor environment variables set on this SparkConf

    • getAppId

      public String getAppId()
      Returns the Spark application id, valid in the Driver after TaskScheduler registration and from the start in the Executor.
      Returns:
      (undocumented)

    • contains

      public boolean contains(String key)
      Does the configuration contain a given parameter?
      Specified by:
      contains in interface ReadOnlySparkConf

    • clone

      public SparkConf clone()
      Copy this object

    • toDebugString

      public String toDebugString()
      Return a string listing all keys and values, one per line. This is useful to print the configuration out for debugging.
      Returns:
      (undocumented)