spark-instrumented-optimizer/python/pyspark/worker.py

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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
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"""
Worker that receives input from Piped RDD.
"""
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import os
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import sys
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import time
import socket
import traceback
[SPARK-3478] [PySpark] Profile the Python tasks This patch add profiling support for PySpark, it will show the profiling results before the driver exits, here is one example: ``` ============================================================ Profile of RDD<id=3> ============================================================ 5146507 function calls (5146487 primitive calls) in 71.094 seconds Ordered by: internal time, cumulative time ncalls tottime percall cumtime percall filename:lineno(function) 5144576 68.331 0.000 68.331 0.000 statcounter.py:44(merge) 20 2.735 0.137 71.071 3.554 statcounter.py:33(__init__) 20 0.017 0.001 0.017 0.001 {cPickle.dumps} 1024 0.003 0.000 0.003 0.000 t.py:16(<lambda>) 20 0.001 0.000 0.001 0.000 {reduce} 21 0.001 0.000 0.001 0.000 {cPickle.loads} 20 0.001 0.000 0.001 0.000 copy_reg.py:95(_slotnames) 41 0.001 0.000 0.001 0.000 serializers.py:461(read_int) 40 0.001 0.000 0.002 0.000 serializers.py:179(_batched) 62 0.000 0.000 0.000 0.000 {method 'read' of 'file' objects} 20 0.000 0.000 71.072 3.554 rdd.py:863(<lambda>) 20 0.000 0.000 0.001 0.000 serializers.py:198(load_stream) 40/20 0.000 0.000 71.072 3.554 rdd.py:2093(pipeline_func) 41 0.000 0.000 0.002 0.000 serializers.py:130(load_stream) 40 0.000 0.000 71.072 1.777 rdd.py:304(func) 20 0.000 0.000 71.094 3.555 worker.py:82(process) ``` Also, use can show profile result manually by `sc.show_profiles()` or dump it into disk by `sc.dump_profiles(path)`, such as ```python >>> sc._conf.set("spark.python.profile", "true") >>> rdd = sc.parallelize(range(100)).map(str) >>> rdd.count() 100 >>> sc.show_profiles() ============================================================ Profile of RDD<id=1> ============================================================ 284 function calls (276 primitive calls) in 0.001 seconds Ordered by: internal time, cumulative time ncalls tottime percall cumtime percall filename:lineno(function) 4 0.000 0.000 0.000 0.000 serializers.py:198(load_stream) 4 0.000 0.000 0.000 0.000 {reduce} 12/4 0.000 0.000 0.001 0.000 rdd.py:2092(pipeline_func) 4 0.000 0.000 0.000 0.000 {cPickle.loads} 4 0.000 0.000 0.000 0.000 {cPickle.dumps} 104 0.000 0.000 0.000 0.000 rdd.py:852(<genexpr>) 8 0.000 0.000 0.000 0.000 serializers.py:461(read_int) 12 0.000 0.000 0.000 0.000 rdd.py:303(func) ``` The profiling is disabled by default, can be enabled by "spark.python.profile=true". Also, users can dump the results into disks automatically for future analysis, by "spark.python.profile.dump=path_to_dump" This is bugfix of #2351 cc JoshRosen Author: Davies Liu <davies.liu@gmail.com> Closes #2556 from davies/profiler and squashes the following commits: e68df5a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 858e74c [Davies Liu] compatitable with python 2.6 7ef2aa0 [Davies Liu] bugfix, add tests for show_profiles and dump_profiles() 2b0daf2 [Davies Liu] fix docs 7a56c24 [Davies Liu] bugfix cba9463 [Davies Liu] move show_profiles and dump_profiles to SparkContext fb9565b [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 116d52a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 09d02c3 [Davies Liu] Merge branch 'master' into profiler c23865c [Davies Liu] Merge branch 'master' into profiler 15d6f18 [Davies Liu] add docs for two configs dadee1a [Davies Liu] add docs string and clear profiles after show or dump 4f8309d [Davies Liu] address comment, add tests 0a5b6eb [Davies Liu] fix Python UDF 4b20494 [Davies Liu] add profile for python
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import cProfile
import pstats
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from pyspark.accumulators import _accumulatorRegistry
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from pyspark.broadcast import Broadcast, _broadcastRegistry
from pyspark.files import SparkFiles
from pyspark.serializers import write_with_length, write_int, read_long, \
write_long, read_int, SpecialLengths, UTF8Deserializer, PickleSerializer, \
CompressedSerializer
from pyspark import shuffle
pickleSer = PickleSerializer()
utf8_deserializer = UTF8Deserializer()
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def report_times(outfile, boot, init, finish):
write_int(SpecialLengths.TIMING_DATA, outfile)
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write_long(1000 * boot, outfile)
write_long(1000 * init, outfile)
write_long(1000 * finish, outfile)
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def add_path(path):
# worker can be used, so donot add path multiple times
if path not in sys.path:
# overwrite system packages
sys.path.insert(1, path)
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def main(infile, outfile):
try:
boot_time = time.time()
split_index = read_int(infile)
if split_index == -1: # for unit tests
return
# initialize global state
shuffle.MemoryBytesSpilled = 0
shuffle.DiskBytesSpilled = 0
_accumulatorRegistry.clear()
# fetch name of workdir
spark_files_dir = utf8_deserializer.loads(infile)
SparkFiles._root_directory = spark_files_dir
SparkFiles._is_running_on_worker = True
# fetch names of includes (*.zip and *.egg files) and construct PYTHONPATH
add_path(spark_files_dir) # *.py files that were added will be copied here
[SPARK-2470] PEP8 fixes to PySpark This pull request aims to resolve all outstanding PEP8 violations in PySpark. Author: Nicholas Chammas <nicholas.chammas@gmail.com> Author: nchammas <nicholas.chammas@gmail.com> Closes #1505 from nchammas/master and squashes the following commits: 98171af [Nicholas Chammas] [SPARK-2470] revert PEP 8 fixes to cloudpickle cba7768 [Nicholas Chammas] [SPARK-2470] wrap expression list in parentheses e178dbe [Nicholas Chammas] [SPARK-2470] style - change position of line break 9127d2b [Nicholas Chammas] [SPARK-2470] wrap expression lists in parentheses 22132a4 [Nicholas Chammas] [SPARK-2470] wrap conditionals in parentheses 24639bc [Nicholas Chammas] [SPARK-2470] fix whitespace for doctest 7d557b7 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to tests.py 8f8e4c0 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to storagelevel.py b3b96cf [Nicholas Chammas] [SPARK-2470] PEP8 fixes to statcounter.py d644477 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to worker.py aa3a7b6 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to sql.py 1916859 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to shell.py 95d1d95 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to serializers.py a0fec2e [Nicholas Chammas] [SPARK-2470] PEP8 fixes to mllib c85e1e5 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to join.py d14f2f1 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to __init__.py 81fcb20 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to resultiterable.py 1bde265 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to java_gateway.py 7fc849c [Nicholas Chammas] [SPARK-2470] PEP8 fixes to daemon.py ca2d28b [Nicholas Chammas] [SPARK-2470] PEP8 fixes to context.py f4e0039 [Nicholas Chammas] [SPARK-2470] PEP8 fixes to conf.py a6d5e4b [Nicholas Chammas] [SPARK-2470] PEP8 fixes to cloudpickle.py f0a7ebf [Nicholas Chammas] [SPARK-2470] PEP8 fixes to rddsampler.py 4dd148f [nchammas] Merge pull request #5 from apache/master f7e4581 [Nicholas Chammas] unrelated pep8 fix a36eed0 [Nicholas Chammas] name ec2 instances and security groups consistently de7292a [nchammas] Merge pull request #4 from apache/master 2e4fe00 [nchammas] Merge pull request #3 from apache/master 89fde08 [nchammas] Merge pull request #2 from apache/master 69f6e22 [Nicholas Chammas] PEP8 fixes 2627247 [Nicholas Chammas] broke up lines before they hit 100 chars 6544b7e [Nicholas Chammas] [SPARK-2065] give launched instances names 69da6cf [nchammas] Merge pull request #1 from apache/master
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num_python_includes = read_int(infile)
for _ in range(num_python_includes):
filename = utf8_deserializer.loads(infile)
add_path(os.path.join(spark_files_dir, filename))
# fetch names and values of broadcast variables
num_broadcast_variables = read_int(infile)
ser = CompressedSerializer(pickleSer)
for _ in range(num_broadcast_variables):
bid = read_long(infile)
[SPARK-3030] [PySpark] Reuse Python worker Reuse Python worker to avoid the overhead of fork() Python process for each tasks. It also tracks the broadcasts for each worker, avoid sending repeated broadcasts. This can reduce the time for dummy task from 22ms to 13ms (-40%). It can help to reduce the latency for Spark Streaming. For a job with broadcast (43M after compress): ``` b = sc.broadcast(set(range(30000000))) print sc.parallelize(range(24000), 100).filter(lambda x: x in b.value).count() ``` It will finish in 281s without reused worker, and it will finish in 65s with reused worker(4 CPUs). After reusing the worker, it can save about 9 seconds for transfer and deserialize the broadcast for each tasks. It's enabled by default, could be disabled by `spark.python.worker.reuse = false`. Author: Davies Liu <davies.liu@gmail.com> Closes #2259 from davies/reuse-worker and squashes the following commits: f11f617 [Davies Liu] Merge branch 'master' into reuse-worker 3939f20 [Davies Liu] fix bug in serializer in mllib cf1c55e [Davies Liu] address comments 3133a60 [Davies Liu] fix accumulator with reused worker 760ab1f [Davies Liu] do not reuse worker if there are any exceptions 7abb224 [Davies Liu] refactor: sychronized with itself ac3206e [Davies Liu] renaming 8911f44 [Davies Liu] synchronized getWorkerBroadcasts() 6325fc1 [Davies Liu] bugfix: bid >= 0 e0131a2 [Davies Liu] fix name of config 583716e [Davies Liu] only reuse completed and not interrupted worker ace2917 [Davies Liu] kill python worker after timeout 6123d0f [Davies Liu] track broadcasts for each worker 8d2f08c [Davies Liu] reuse python worker
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if bid >= 0:
value = ser._read_with_length(infile)
_broadcastRegistry[bid] = Broadcast(bid, value)
else:
bid = - bid - 1
_broadcastRegistry.pop(bid)
[SPARK-3030] [PySpark] Reuse Python worker Reuse Python worker to avoid the overhead of fork() Python process for each tasks. It also tracks the broadcasts for each worker, avoid sending repeated broadcasts. This can reduce the time for dummy task from 22ms to 13ms (-40%). It can help to reduce the latency for Spark Streaming. For a job with broadcast (43M after compress): ``` b = sc.broadcast(set(range(30000000))) print sc.parallelize(range(24000), 100).filter(lambda x: x in b.value).count() ``` It will finish in 281s without reused worker, and it will finish in 65s with reused worker(4 CPUs). After reusing the worker, it can save about 9 seconds for transfer and deserialize the broadcast for each tasks. It's enabled by default, could be disabled by `spark.python.worker.reuse = false`. Author: Davies Liu <davies.liu@gmail.com> Closes #2259 from davies/reuse-worker and squashes the following commits: f11f617 [Davies Liu] Merge branch 'master' into reuse-worker 3939f20 [Davies Liu] fix bug in serializer in mllib cf1c55e [Davies Liu] address comments 3133a60 [Davies Liu] fix accumulator with reused worker 760ab1f [Davies Liu] do not reuse worker if there are any exceptions 7abb224 [Davies Liu] refactor: sychronized with itself ac3206e [Davies Liu] renaming 8911f44 [Davies Liu] synchronized getWorkerBroadcasts() 6325fc1 [Davies Liu] bugfix: bid >= 0 e0131a2 [Davies Liu] fix name of config 583716e [Davies Liu] only reuse completed and not interrupted worker ace2917 [Davies Liu] kill python worker after timeout 6123d0f [Davies Liu] track broadcasts for each worker 8d2f08c [Davies Liu] reuse python worker
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_accumulatorRegistry.clear()
command = pickleSer._read_with_length(infile)
if isinstance(command, Broadcast):
command = pickleSer.loads(command.value)
[SPARK-3478] [PySpark] Profile the Python tasks This patch add profiling support for PySpark, it will show the profiling results before the driver exits, here is one example: ``` ============================================================ Profile of RDD<id=3> ============================================================ 5146507 function calls (5146487 primitive calls) in 71.094 seconds Ordered by: internal time, cumulative time ncalls tottime percall cumtime percall filename:lineno(function) 5144576 68.331 0.000 68.331 0.000 statcounter.py:44(merge) 20 2.735 0.137 71.071 3.554 statcounter.py:33(__init__) 20 0.017 0.001 0.017 0.001 {cPickle.dumps} 1024 0.003 0.000 0.003 0.000 t.py:16(<lambda>) 20 0.001 0.000 0.001 0.000 {reduce} 21 0.001 0.000 0.001 0.000 {cPickle.loads} 20 0.001 0.000 0.001 0.000 copy_reg.py:95(_slotnames) 41 0.001 0.000 0.001 0.000 serializers.py:461(read_int) 40 0.001 0.000 0.002 0.000 serializers.py:179(_batched) 62 0.000 0.000 0.000 0.000 {method 'read' of 'file' objects} 20 0.000 0.000 71.072 3.554 rdd.py:863(<lambda>) 20 0.000 0.000 0.001 0.000 serializers.py:198(load_stream) 40/20 0.000 0.000 71.072 3.554 rdd.py:2093(pipeline_func) 41 0.000 0.000 0.002 0.000 serializers.py:130(load_stream) 40 0.000 0.000 71.072 1.777 rdd.py:304(func) 20 0.000 0.000 71.094 3.555 worker.py:82(process) ``` Also, use can show profile result manually by `sc.show_profiles()` or dump it into disk by `sc.dump_profiles(path)`, such as ```python >>> sc._conf.set("spark.python.profile", "true") >>> rdd = sc.parallelize(range(100)).map(str) >>> rdd.count() 100 >>> sc.show_profiles() ============================================================ Profile of RDD<id=1> ============================================================ 284 function calls (276 primitive calls) in 0.001 seconds Ordered by: internal time, cumulative time ncalls tottime percall cumtime percall filename:lineno(function) 4 0.000 0.000 0.000 0.000 serializers.py:198(load_stream) 4 0.000 0.000 0.000 0.000 {reduce} 12/4 0.000 0.000 0.001 0.000 rdd.py:2092(pipeline_func) 4 0.000 0.000 0.000 0.000 {cPickle.loads} 4 0.000 0.000 0.000 0.000 {cPickle.dumps} 104 0.000 0.000 0.000 0.000 rdd.py:852(<genexpr>) 8 0.000 0.000 0.000 0.000 serializers.py:461(read_int) 12 0.000 0.000 0.000 0.000 rdd.py:303(func) ``` The profiling is disabled by default, can be enabled by "spark.python.profile=true". Also, users can dump the results into disks automatically for future analysis, by "spark.python.profile.dump=path_to_dump" This is bugfix of #2351 cc JoshRosen Author: Davies Liu <davies.liu@gmail.com> Closes #2556 from davies/profiler and squashes the following commits: e68df5a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 858e74c [Davies Liu] compatitable with python 2.6 7ef2aa0 [Davies Liu] bugfix, add tests for show_profiles and dump_profiles() 2b0daf2 [Davies Liu] fix docs 7a56c24 [Davies Liu] bugfix cba9463 [Davies Liu] move show_profiles and dump_profiles to SparkContext fb9565b [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 116d52a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 09d02c3 [Davies Liu] Merge branch 'master' into profiler c23865c [Davies Liu] Merge branch 'master' into profiler 15d6f18 [Davies Liu] add docs for two configs dadee1a [Davies Liu] add docs string and clear profiles after show or dump 4f8309d [Davies Liu] address comment, add tests 0a5b6eb [Davies Liu] fix Python UDF 4b20494 [Davies Liu] add profile for python
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(func, stats, deserializer, serializer) = command
init_time = time.time()
[SPARK-3478] [PySpark] Profile the Python tasks This patch add profiling support for PySpark, it will show the profiling results before the driver exits, here is one example: ``` ============================================================ Profile of RDD<id=3> ============================================================ 5146507 function calls (5146487 primitive calls) in 71.094 seconds Ordered by: internal time, cumulative time ncalls tottime percall cumtime percall filename:lineno(function) 5144576 68.331 0.000 68.331 0.000 statcounter.py:44(merge) 20 2.735 0.137 71.071 3.554 statcounter.py:33(__init__) 20 0.017 0.001 0.017 0.001 {cPickle.dumps} 1024 0.003 0.000 0.003 0.000 t.py:16(<lambda>) 20 0.001 0.000 0.001 0.000 {reduce} 21 0.001 0.000 0.001 0.000 {cPickle.loads} 20 0.001 0.000 0.001 0.000 copy_reg.py:95(_slotnames) 41 0.001 0.000 0.001 0.000 serializers.py:461(read_int) 40 0.001 0.000 0.002 0.000 serializers.py:179(_batched) 62 0.000 0.000 0.000 0.000 {method 'read' of 'file' objects} 20 0.000 0.000 71.072 3.554 rdd.py:863(<lambda>) 20 0.000 0.000 0.001 0.000 serializers.py:198(load_stream) 40/20 0.000 0.000 71.072 3.554 rdd.py:2093(pipeline_func) 41 0.000 0.000 0.002 0.000 serializers.py:130(load_stream) 40 0.000 0.000 71.072 1.777 rdd.py:304(func) 20 0.000 0.000 71.094 3.555 worker.py:82(process) ``` Also, use can show profile result manually by `sc.show_profiles()` or dump it into disk by `sc.dump_profiles(path)`, such as ```python >>> sc._conf.set("spark.python.profile", "true") >>> rdd = sc.parallelize(range(100)).map(str) >>> rdd.count() 100 >>> sc.show_profiles() ============================================================ Profile of RDD<id=1> ============================================================ 284 function calls (276 primitive calls) in 0.001 seconds Ordered by: internal time, cumulative time ncalls tottime percall cumtime percall filename:lineno(function) 4 0.000 0.000 0.000 0.000 serializers.py:198(load_stream) 4 0.000 0.000 0.000 0.000 {reduce} 12/4 0.000 0.000 0.001 0.000 rdd.py:2092(pipeline_func) 4 0.000 0.000 0.000 0.000 {cPickle.loads} 4 0.000 0.000 0.000 0.000 {cPickle.dumps} 104 0.000 0.000 0.000 0.000 rdd.py:852(<genexpr>) 8 0.000 0.000 0.000 0.000 serializers.py:461(read_int) 12 0.000 0.000 0.000 0.000 rdd.py:303(func) ``` The profiling is disabled by default, can be enabled by "spark.python.profile=true". Also, users can dump the results into disks automatically for future analysis, by "spark.python.profile.dump=path_to_dump" This is bugfix of #2351 cc JoshRosen Author: Davies Liu <davies.liu@gmail.com> Closes #2556 from davies/profiler and squashes the following commits: e68df5a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 858e74c [Davies Liu] compatitable with python 2.6 7ef2aa0 [Davies Liu] bugfix, add tests for show_profiles and dump_profiles() 2b0daf2 [Davies Liu] fix docs 7a56c24 [Davies Liu] bugfix cba9463 [Davies Liu] move show_profiles and dump_profiles to SparkContext fb9565b [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 116d52a [Davies Liu] Merge branch 'master' of github.com:apache/spark into profiler 09d02c3 [Davies Liu] Merge branch 'master' into profiler c23865c [Davies Liu] Merge branch 'master' into profiler 15d6f18 [Davies Liu] add docs for two configs dadee1a [Davies Liu] add docs string and clear profiles after show or dump 4f8309d [Davies Liu] address comment, add tests 0a5b6eb [Davies Liu] fix Python UDF 4b20494 [Davies Liu] add profile for python
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def process():
iterator = deserializer.load_stream(infile)
serializer.dump_stream(func(split_index, iterator), outfile)
if stats:
p = cProfile.Profile()
p.runcall(process)
st = pstats.Stats(p)
st.stream = None # make it picklable
stats.add(st.strip_dirs())
else:
process()
except Exception:
try:
write_int(SpecialLengths.PYTHON_EXCEPTION_THROWN, outfile)
write_with_length(traceback.format_exc(), outfile)
outfile.flush()
except IOError:
# JVM close the socket
pass
except Exception:
# Write the error to stderr if it happened while serializing
print >> sys.stderr, "PySpark worker failed with exception:"
print >> sys.stderr, traceback.format_exc()
exit(-1)
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finish_time = time.time()
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report_times(outfile, boot_time, init_time, finish_time)
write_long(shuffle.MemoryBytesSpilled, outfile)
write_long(shuffle.DiskBytesSpilled, outfile)
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# Mark the beginning of the accumulators section of the output
write_int(SpecialLengths.END_OF_DATA_SECTION, outfile)
write_int(len(_accumulatorRegistry), outfile)
for (aid, accum) in _accumulatorRegistry.items():
pickleSer._write_with_length((aid, accum._value), outfile)
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if __name__ == '__main__':
# Read a local port to connect to from stdin
java_port = int(sys.stdin.readline())
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.connect(("127.0.0.1", java_port))
sock_file = sock.makefile("a+", 65536)
main(sock_file, sock_file)