Adds a (Java friendly) listener to be executed on task completion. This will be called in all situations - success, failure, or cancellation. Adding a listener to an already completed task will result in that listener being called immediately.

Two listeners registered in the same thread will be invoked in reverse order of registration if the task completes after both are registered. There are no ordering guarantees for listeners registered in different threads, or for listeners registered after the task completes. Listeners are guaranteed to execute sequentially.

An example use is for HadoopRDD to register a callback to close the input stream.

Exceptions thrown by the listener will result in failure of the task.

Adds a listener in the form of a Scala closure to be executed on task completion. This will be called in all situations - success, failure, or cancellation. Adding a listener to an already completed task will result in that listener being called immediately.

An example use is for HadoopRDD to register a callback to close the input stream.

Exceptions thrown by the listener will result in failure of the task.

Adds a listener to be executed on task failure (which includes completion listener failure, if the task body did not already fail). Adding a listener to an already failed task will result in that listener being called immediately.

Note: Prior to Spark 3.4.0, failure listeners were only invoked if the main task body failed.

Adds a listener to be executed on task failure (which includes completion listener failure, if the task body did not already fail). Adding a listener to an already failed task will result in that listener being called immediately.

Note: Prior to Spark 3.4.0, failure listeners were only invoked if the main task body failed.

:: Experimental :: Blocks until all tasks in the same stage have reached this routine. Each task passes in a message and returns with a list of all the messages passed in by each of those tasks.

CAUTION! The allGather method requires the same precautions as the barrier method

The message is type String rather than Array[Byte] because it is more convenient for the user at the cost of worse performance.

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.

:: Experimental :: Sets a global barrier and waits until all tasks in this stage hit this barrier. Similar to MPI_Barrier function in MPI, the barrier() function call blocks until all tasks in the same stage have reached this routine.

CAUTION! In a barrier stage, each task must have the same number of barrier() calls, in all possible code branches. Otherwise, you may get the job hanging or a SparkException after timeout. Some examples of misuses are listed below: 1. Only call barrier() function on a subset of all the tasks in the same barrier stage, it shall lead to timeout of the function call.

rdd.barrier().mapPartitions { iter =>
    val context = BarrierTaskContext.get()
    if (context.partitionId() == 0) {
        // Do nothing.
    } else {
        context.barrier()
    }
    iter
}

2. Include barrier() function in a try-catch code block, this may lead to timeout of the second function call.

rdd.barrier().mapPartitions { iter =>
    val context = BarrierTaskContext.get()
    try {
        // Do something that might throw an Exception.
        doSomething()
        context.barrier()
    } catch {
        case e: Exception => logWarning("...", e)
    }
    context.barrier()
    iter
}

CPUs allocated to the task.

Get a local property set upstream in the driver, or null if it is missing. See also org.apache.spark.SparkContext.setLocalProperty.

::DeveloperApi:: Returns all metrics sources with the given name which are associated with the instance which runs the task. For more information see org.apache.spark.metrics.MetricsSystem.

:: Experimental :: Returns BarrierTaskInfo for all tasks in this barrier stage, ordered by partition ID.

Returns true if the task has completed.

Returns true if the task has failed.

Returns true if the task has been killed.

Total number of partitions in the stage that this task belongs to.

The ID of the RDD partition that is computed by this task.

Resources allocated to the task. The key is the resource name and the value is information about the resource. Please refer to org.apache.spark.resource.ResourceInformation for specifics.

(java-specific) Resources allocated to the task. The key is the resource name and the value is information about the resource. Please refer to org.apache.spark.resource.ResourceInformation for specifics.

How many times the stage that this task belongs to has been attempted. The first stage attempt will be assigned stageAttemptNumber = 0, and subsequent attempts will have increasing attempt numbers.

The ID of the stage that this task belong to.

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.