pyspark.TaskContext¶

class pyspark.TaskContext[source]¶

Contextual information about a task which can be read or mutated during execution. To access the TaskContext for a running task, use: TaskContext.get().

New in version 2.2.0.

Examples

>>> from pyspark import TaskContext

Get a task context instance from RDD.

>>> spark.sparkContext.setLocalProperty("key1", "value")
>>> taskcontext = spark.sparkContext.parallelize([1]).map(lambda _: TaskContext.get()).first()
>>> isinstance(taskcontext.attemptNumber(), int)
True
>>> isinstance(taskcontext.partitionId(), int)
True
>>> isinstance(taskcontext.stageId(), int)
True
>>> isinstance(taskcontext.taskAttemptId(), int)
True
>>> taskcontext.getLocalProperty("key1")
'value'
>>> isinstance(taskcontext.cpus(), int)
True

Get a task context instance from a dataframe via Python UDF.

>>> from pyspark.sql import Row
>>> from pyspark.sql.functions import udf
>>> @udf("STRUCT<anum: INT, partid: INT, stageid: INT, taskaid: INT, prop: STRING, cpus: INT>")
... def taskcontext_as_row():
...    taskcontext = TaskContext.get()
...    return Row(
...        anum=taskcontext.attemptNumber(),
...        partid=taskcontext.partitionId(),
...        stageid=taskcontext.stageId(),
...        taskaid=taskcontext.taskAttemptId(),
...        prop=taskcontext.getLocalProperty("key2"),
...        cpus=taskcontext.cpus())
...
>>> spark.sparkContext.setLocalProperty("key2", "value")
>>> [(anum, partid, stageid, taskaid, prop, cpus)] = (
...     spark.range(1).select(taskcontext_as_row()).first()
... )
>>> isinstance(anum, int)
True
>>> isinstance(partid, int)
True
>>> isinstance(stageid, int)
True
>>> isinstance(taskaid, int)
True
>>> prop
'value'
>>> isinstance(cpus, int)
True

Get a task context instance from a dataframe via Pandas UDF.

>>> import pandas as pd  
>>> from pyspark.sql.functions import pandas_udf
>>> @pandas_udf("STRUCT<"
...     "anum: INT, partid: INT, stageid: INT, taskaid: INT, prop: STRING, cpus: INT>")
... def taskcontext_as_row(_):
...    taskcontext = TaskContext.get()
...    return pd.DataFrame({
...        "anum": [taskcontext.attemptNumber()],
...        "partid": [taskcontext.partitionId()],
...        "stageid": [taskcontext.stageId()],
...        "taskaid": [taskcontext.taskAttemptId()],
...        "prop": [taskcontext.getLocalProperty("key3")],
...        "cpus": [taskcontext.cpus()]
...    })  
...
>>> spark.sparkContext.setLocalProperty("key3", "value")  
>>> [(anum, partid, stageid, taskaid, prop, cpus)] = (
...     spark.range(1).select(taskcontext_as_row("id")).first()
... )  
>>> isinstance(anum, int)
True
>>> isinstance(partid, int)
True
>>> isinstance(stageid, int)
True
>>> isinstance(taskaid, int)
True
>>> prop
'value'
>>> isinstance(cpus, int)
True

Methods

`attemptNumber`()	How many times this task has been attempted.
`cpus`()	CPUs allocated to the task.
`get`()	Return the currently active `TaskContext`.
`getLocalProperty`(key)	Get a local property set upstream in the driver, or None if it is missing.
`partitionId`()	The ID of the RDD partition that is computed by this task.
`resources`()	Resources allocated to the task.
`stageId`()	The ID of the stage that this task belong to.
`taskAttemptId`()	An ID that is unique to this task attempt (within the same `SparkContext`, no two task attempts will share the same attempt ID).

pyspark.StorageLevel pyspark.RDDBarrier