pyspark.SparkConf¶

class pyspark.SparkConf(loadDefaults: bool = True, _jvm: Optional[py4j.java_gateway.JVMView] = None, _jconf: Optional[py4j.java_gateway.JavaObject] = None)[source]¶

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 SparkConf(), which will load values from spark.* Java system properties as well. In this case, any parameters you set directly on the SparkConf object take priority over system properties.

For unit tests, you can also call 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 conf.setMaster("local").setAppName("My app").

Parameters

loadDefaultsbool: whether to load values from Java system properties (True by default)
_jvmclass:py4j.java_gateway.JVMView: internal parameter used to pass a handle to the Java VM; does not need to be set by users
_jconfclass:py4j.java_gateway.JavaObject: Optionally pass in an existing SparkConf handle to use its parameters

Notes

Once a SparkConf object is passed to Spark, it is cloned and can no longer be modified by the user.

Examples

>>> from pyspark.conf import SparkConf
>>> from pyspark.context import SparkContext
>>> conf = SparkConf()
>>> conf.setMaster("local").setAppName("My app")
<pyspark.conf.SparkConf object at ...>
>>> conf.get("spark.master")
'local'
>>> conf.get("spark.app.name")
'My app'
>>> sc = SparkContext(conf=conf)
>>> sc.master
'local'
>>> sc.appName
'My app'
>>> sc.sparkHome is None
True

>>> conf = SparkConf(loadDefaults=False)
>>> conf.setSparkHome("/path")
<pyspark.conf.SparkConf object at ...>
>>> conf.get("spark.home")
'/path'
>>> conf.setExecutorEnv("VAR1", "value1")
<pyspark.conf.SparkConf object at ...>
>>> conf.setExecutorEnv(pairs = [("VAR3", "value3"), ("VAR4", "value4")])
<pyspark.conf.SparkConf object at ...>
>>> conf.get("spark.executorEnv.VAR1")
'value1'
>>> print(conf.toDebugString())
spark.executorEnv.VAR1=value1
spark.executorEnv.VAR3=value3
spark.executorEnv.VAR4=value4
spark.home=/path
>>> for p in sorted(conf.getAll(), key=lambda p: p[0]):
...     print(p)
('spark.executorEnv.VAR1', 'value1')
('spark.executorEnv.VAR3', 'value3')
('spark.executorEnv.VAR4', 'value4')
('spark.home', '/path')
>>> conf._jconf.setExecutorEnv("VAR5", "value5")
JavaObject id...
>>> print(conf.toDebugString())
spark.executorEnv.VAR1=value1
spark.executorEnv.VAR3=value3
spark.executorEnv.VAR4=value4
spark.executorEnv.VAR5=value5
spark.home=/path

Methods

`contains`(key)	Does this configuration contain a given key?
`get`(key[, defaultValue])	Get the configured value for some key, or return a default otherwise.
`getAll`()	Get all values as a list of key-value pairs.
`set`(key, value)	Set a configuration property.
`setAll`(pairs)	Set multiple parameters, passed as a list of key-value pairs.
`setAppName`(value)	Set application name.
`setExecutorEnv`([key, value, pairs])	Set an environment variable to be passed to executors.
`setIfMissing`(key, value)	Set a configuration property, if not already set.
`setMaster`(value)	Set master URL to connect to.
`setSparkHome`(value)	Set path where Spark is installed on worker nodes.
`toDebugString`()	Returns a printable version of the configuration, as a list of key=value pairs, one per line.

pyspark.AccumulatorParam

pyspark.SparkFiles