[
https://issues.apache.org/jira/browse/BEAM-8944?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yichi Zhang updated BEAM-8944:
------------------------------
Description:
We are seeing a performance degradation for python streaming word count load
tests.
After some investigation, it appears to be caused by swapping the original
ThreadPoolExecutor to UnboundedThreadPoolExecutor in sdk worker. Suspicion is
that python performance is worse with more threads on cpu-bounded tasks.
A simple test for comparing the multiple thread pool executor performance:
def test_performance(self):
def run_perf(executor):
total_number = 1000000
q = queue.Queue()
def task(number):
hash(number)
q.put(number + 200)
return number
t = time.time()
count = 0
for i in range(200):
q.put\(i\)
while count < total_number:
executor.submit(task, q.get(block=True))
count += 1
print('%s uses %s' % (executor, time.time() - t))
with UnboundedThreadPoolExecutor() as executor:
run_perf(executor)
with futures.ThreadPoolExecutor(max_workers=1) as executor:
run_perf(executor)
with futures.ThreadPoolExecutor(max_workers=12) as executor:
run_perf(executor)
Results:
<apache_beam.utils.thread_pool_executor.UnboundedThreadPoolExecutor object at
0x7fab400dbe50> uses 268.160675049
<concurrent.futures.thread.ThreadPoolExecutor object at 0x7fab40096290> uses
79.904583931
<concurrent.futures.thread.ThreadPoolExecutor object at 0x7fab400dbe50> uses
191.179054976
```
Profiling:
!profiling.png!
was:
We are seeing a performance degradation for python streaming word count load
tests.
After some investigation, it appears to be caused by swapping the original
ThreadPoolExecutor to UnboundedThreadPoolExecutor in sdk worker. Suspicion is
that python performance is worse with more threads on cpu-bounded tasks.
A simple test for comparing the multiple thread pool executor performance:
def test_performance(self):
def run_perf(executor):
total_number = 1000000
q = queue.Queue()
def task(number):
hash(number)
q.put(number + 200)
return number
t = time.time()
count = 0
for i in range(200):
q.put\(i\)
while count < total_number:
executor.submit(task, q.get(block=True))
count += 1
print('%s uses %s' % (executor, time.time() - t))
with UnboundedThreadPoolExecutor() as executor:
run_perf(executor)
with futures.ThreadPoolExecutor(max_workers=1) as executor:
run_perf(executor)
with futures.ThreadPoolExecutor(max_workers=12) as executor:
run_perf(executor)
Results:
<apache_beam.utils.thread_pool_executor.UnboundedThreadPoolExecutor object at
0x7fab400dbe50> uses 268.160675049
<concurrent.futures.thread.ThreadPoolExecutor object at 0x7fab40096290> uses
79.904583931
<concurrent.futures.thread.ThreadPoolExecutor object at 0x7fab400dbe50> uses
191.179054976
```
Profiling:
> Python SDK harness performance degradation with UnboundedThreadPoolExecutor
> ---------------------------------------------------------------------------
>
> Key: BEAM-8944
> URL: https://issues.apache.org/jira/browse/BEAM-8944
> Project: Beam
> Issue Type: Bug
> Components: sdk-py-harness
> Affects Versions: 2.18.0
> Reporter: Yichi Zhang
> Priority: Major
> Attachments: profiling.png
>
>
> We are seeing a performance degradation for python streaming word count load
> tests.
>
> After some investigation, it appears to be caused by swapping the original
> ThreadPoolExecutor to UnboundedThreadPoolExecutor in sdk worker. Suspicion is
> that python performance is worse with more threads on cpu-bounded tasks.
>
> A simple test for comparing the multiple thread pool executor performance:
>
> def test_performance(self):
> def run_perf(executor):
> total_number = 1000000
> q = queue.Queue()
> def task(number):
> hash(number)
> q.put(number + 200)
> return number
> t = time.time()
> count = 0
> for i in range(200):
> q.put\(i\)
> while count < total_number:
> executor.submit(task, q.get(block=True))
> count += 1
> print('%s uses %s' % (executor, time.time() - t))
> with UnboundedThreadPoolExecutor() as executor:
> run_perf(executor)
> with futures.ThreadPoolExecutor(max_workers=1) as executor:
> run_perf(executor)
> with futures.ThreadPoolExecutor(max_workers=12) as executor:
> run_perf(executor)
> Results:
> <apache_beam.utils.thread_pool_executor.UnboundedThreadPoolExecutor object at
> 0x7fab400dbe50> uses 268.160675049
> <concurrent.futures.thread.ThreadPoolExecutor object at 0x7fab40096290> uses
> 79.904583931
> <concurrent.futures.thread.ThreadPoolExecutor object at 0x7fab400dbe50> uses
> 191.179054976
> ```
> Profiling:
> !profiling.png!
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