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From Xinyu Liu <xinyuliu...@gmail.com>
Subject Review Request 53282: SAMZA-1043: Samza performance improvements
Date Sat, 29 Oct 2016 00:59:36 GMT

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This is an automatically generated e-mail. To reply, visit:
https://reviews.apache.org/r/53282/
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Review request for samza.


Repository: samza


Description
-------

In the recent experiments of samza batch job (consuming hdfs data on hadoop), the results
are subpar to map/reduce and spark. By looking at the metrics closely, we found two basic
problems:

1) Not enough data to process. This is spotted as the unprocessed message queue length was
zero for quite a lot of times.

2) Not process fast enough. We found samza performed closely in both median size records (100B)
and small record (10B), while spark can scale very well in the small record (over 1M/s).

The first problem is solved by increasing the buffer size. This ticket is to address the second
problem, which contains three major improvements:

- Option to turn off timer metrics calculation: one of the main time spent in samza processing
turns out to be just keeping the timer metrics. While it is useful in debugging, it becomes
a bottleneck when running a stable job with high performance. In my testing job which consumes
8M mock data, it took 30 secs with timer metrics on. After turning it off, it only took 14
secs.

- Java coding improvements: The AsyncRunLoop code can be further optimized for efficiency.
Some of the thread-safe data structure I am using is not for optimal performance (Collections.synchronizedSet).
I switched to use CopyOnWriteArraySet, which has far better performance due to more reads
and small set size.

- Specific handling for in-order processing improvements: AsyncRunLoop handles the callbacks
regardless of whether it's in-order or out-of-order (max concurrency > 1), which incurs
quite some cost. By simplying the logic for in-order handling, the performance gains.

After all three improvements, my test job with mock input (8M messages) can be processed within
8 sec (down from org 30 secs), so it's 1M/s for one cpu core.


Diffs
-----

  samza-core/src/main/java/org/apache/samza/container/RunLoopFactory.java 609a956a1f2fa97419c2f66fe2fb6876aaaeecd0

  samza-core/src/main/java/org/apache/samza/task/AsyncRunLoop.java 8fac8155c7f64e67d4a39ec6943f98da1e1d63d9

  samza-core/src/main/java/org/apache/samza/task/CoordinatorRequests.java 052b3b91ec609ca6288662cfa2d3e71b0273d020

  samza-core/src/main/java/org/apache/samza/task/TaskCallbackImpl.java 9b700998d2af040c6734289f7f28bbd78c36bd2c

  samza-core/src/main/java/org/apache/samza/task/TaskCallbackManager.java 132cf59eb593524a4cac134aeceeeb37a4c74b1f

  samza-core/src/main/java/org/apache/samza/util/TimerClock.java PRE-CREATION 
  samza-core/src/main/java/org/apache/samza/util/Utils.java 472e0a59d5aa992b136292c8a3347c311e2cd606

  samza-core/src/main/scala/org/apache/samza/config/MetricsConfig.scala c3fd8bfb2e16a4c5146d34682d04cb1d4e9bbe72

  samza-core/src/main/scala/org/apache/samza/container/SamzaContainer.scala e0468ee89c89fd720834461771ebb36475475bcb

  samza-core/src/main/scala/org/apache/samza/system/SystemConsumers.scala e2aed5b1c2e77a914268963b21809380972037b6

  samza-core/src/main/scala/org/apache/samza/util/Util.scala c4836f202f7eda1d4e71eac94fd48e46207b0316

  samza-core/src/test/java/org/apache/samza/task/TestAsyncRunLoop.java 6000ffaf2b8723d48a72e58b571f242a42dc8128

  samza-core/src/test/java/org/apache/samza/task/TestAsyncStreamAdapter.java 99e1e18bcfa6bca1e275d8ae030a77ff8d70a4eb

  samza-core/src/test/java/org/apache/samza/task/TestTaskCallbackImpl.java f1dbf35165e6ddfc02e3522887c25d78a4bbfcd7

  samza-core/src/test/java/org/apache/samza/task/TestTaskCallbackManager.java d7110f34a9eae6e9ffc15b4982bfbb180da88b2d

  samza-kv/src/main/scala/org/apache/samza/storage/kv/BaseKeyValueStorageEngineFactory.scala
c975893a42689732c39c39600fecacee843bf9d6 

Diff: https://reviews.apache.org/r/53282/diff/


Testing
-------

./gradlew build

Tested in the yarn hadoop cluster with different kinds of jobs.


Thanks,

Xinyu Liu


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