Source code for pyspark.taskcontext

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from __future__ import print_function

[docs]class TaskContext(object): """ .. note:: Experimental Contextual information about a task which can be read or mutated during execution. To access the TaskContext for a running task, use: L{TaskContext.get()}. """ _taskContext = None _attemptNumber = None _partitionId = None _stageId = None _taskAttemptId = None def __new__(cls): """Even if users construct TaskContext instead of using get, give them the singleton.""" taskContext = cls._taskContext if taskContext is not None: return taskContext cls._taskContext = taskContext = object.__new__(cls) return taskContext def __init__(self): """Construct a TaskContext, use get instead""" pass @classmethod def _getOrCreate(cls): """Internal function to get or create global TaskContext.""" if cls._taskContext is None: cls._taskContext = TaskContext() return cls._taskContext
[docs] @classmethod def get(cls): """ Return the currently active TaskContext. This can be called inside of user functions to access contextual information about running tasks. .. note:: Must be called on the worker, not the driver. Returns None if not initialized. """ return cls._taskContext
[docs] def stageId(self): """The ID of the stage that this task belong to.""" return self._stageId
[docs] def partitionId(self): """ The ID of the RDD partition that is computed by this task. """ return self._partitionId
[docs] def attemptNumber(self): """" How many times this task has been attempted. The first task attempt will be assigned attemptNumber = 0, and subsequent attempts will have increasing attempt numbers. """ return self._attemptNumber
[docs] def taskAttemptId(self): """ An ID that is unique to this task attempt (within the same SparkContext, no two task attempts will share the same attempt ID). This is roughly equivalent to Hadoop's TaskAttemptID. """ return self._taskAttemptId