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15 changes: 15 additions & 0 deletions core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
Original file line number Diff line number Diff line change
Expand Up @@ -168,6 +168,21 @@ private[spark] object PythonRDD extends Logging {
serveIterator(rdd.collect().iterator, s"serve RDD ${rdd.id}")
}

/**
* A helper function to collect an RDD as an iterator, then serve it via socket.
* This method is similar with `PythonRDD.collectAndServe`, but user can specify job group id,
* job description, and interruptOnCancel option.
*/
def collectAndServeWithJobGroup[T](
rdd: RDD[T],
groupId: String,
description: String,
interruptOnCancel: Boolean): Array[Any] = {
val sc = rdd.sparkContext
sc.setJobGroup(groupId, description, interruptOnCancel)
serveIterator(rdd.collect().iterator, s"serve RDD ${rdd.id}")
}

/**
* A helper function to create a local RDD iterator and serve it via socket. Partitions are
* are collected as separate jobs, by order of index. Partition data is first requested by a
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13 changes: 13 additions & 0 deletions python/pyspark/rdd.py
Original file line number Diff line number Diff line change
Expand Up @@ -877,6 +877,19 @@ def collect(self):
sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
return list(_load_from_socket(sock_info, self._jrdd_deserializer))

def collectWithJobGroup(self, groupId, description, interruptOnCancel=False):
"""
.. note:: Experimental

When collect rdd, use this method to specify job group.

.. versionadded:: 3.0.0
"""
with SCCallSiteSync(self.context) as css:
sock_info = self.ctx._jvm.PythonRDD.collectAndServeWithJobGroup(
self._jrdd.rdd(), groupId, description, interruptOnCancel)
return list(_load_from_socket(sock_info, self._jrdd_deserializer))

def reduce(self, f):
"""
Reduces the elements of this RDD using the specified commutative and
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62 changes: 62 additions & 0 deletions python/pyspark/tests/test_rdd.py
Original file line number Diff line number Diff line change
Expand Up @@ -814,6 +814,68 @@ def assert_request_contents(exec_reqs, task_reqs):
rddWithoutRp = self.sc.parallelize(range(10))
self.assertEqual(rddWithoutRp.getResourceProfile(), None)

def test_multiple_group_jobs(self):
import threading
group_a = "job_ids_to_cancel"
group_b = "job_ids_to_run"

threads = []
thread_ids = range(4)
thread_ids_to_cancel = [i for i in thread_ids if i % 2 == 0]
thread_ids_to_run = [i for i in thread_ids if i % 2 != 0]

# A list which records whether job is cancelled.
# The index of the array is the thread index which job run in.
is_job_cancelled = [False for _ in thread_ids]

def run_job(job_group, index):
"""
Executes a job with the group ``job_group``. Each job waits for 3 seconds
and then exits.
"""
try:
self.sc.parallelize([15]).map(lambda x: time.sleep(x)) \
.collectWithJobGroup(job_group, "test rdd collect with setting job group")
is_job_cancelled[index] = False
except Exception:
# Assume that exception means job cancellation.
is_job_cancelled[index] = True

# Test if job succeeded when not cancelled.
run_job(group_a, 0)
self.assertFalse(is_job_cancelled[0])

# Run jobs
for i in thread_ids_to_cancel:
t = threading.Thread(target=run_job, args=(group_a, i))
t.start()
threads.append(t)

for i in thread_ids_to_run:
t = threading.Thread(target=run_job, args=(group_b, i))
t.start()
threads.append(t)

# Wait to make sure all jobs are executed.
time.sleep(3)
# And then, cancel one job group.
self.sc.cancelJobGroup(group_a)

# Wait until all threads launching jobs are finished.
for t in threads:
t.join()

for i in thread_ids_to_cancel:
self.assertTrue(
is_job_cancelled[i],
"Thread {i}: Job in group A was not cancelled.".format(i=i))

for i in thread_ids_to_run:
self.assertFalse(
is_job_cancelled[i],
"Thread {i}: Job in group B did not succeeded.".format(i=i))


if __name__ == "__main__":
import unittest
from pyspark.tests.test_rdd import *
Expand Down