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Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2019/09/24 13:09:00 UTC
[jira] [Resolved] (SPARK-29205) Pyspark tests failed for suspected
performance problem on ARM
[ https://issues.apache.org/jira/browse/SPARK-29205?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-29205.
----------------------------------
Resolution: Duplicate
Unfortunately, seems it's still flaky (although less flaky after the fix).
> Pyspark tests failed for suspected performance problem on ARM
> -------------------------------------------------------------
>
> Key: SPARK-29205
> URL: https://issues.apache.org/jira/browse/SPARK-29205
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
> Affects Versions: 3.0.0
> Environment: OS: Ubuntu16.04
> Arch: aarch64
> Host: Virtual Machine
> Reporter: zhao bo
> Priority: Major
>
> We test the pyspark on ARM VM. But found some test fails, once we change the source code to extend the wait time for making sure those test tasks had finished, then the test will pass.
>
> The affected test cases including:
> pyspark.mllib.tests.test_streaming_algorithms:StreamingLinearRegressionWithTests.test_parameter_convergence
> pyspark.mllib.tests.test_streaming_algorithms:StreamingLogisticRegressionWithSGDTests.test_convergence
> pyspark.mllib.tests.test_streaming_algorithms:StreamingLogisticRegressionWithSGDTests.test_parameter_accuracy
> pyspark.mllib.tests.test_streaming_algorithms:StreamingLogisticRegressionWithSGDTests.test_training_and_prediction
> The error message about above test fails:
> ======================================================================
> FAIL: test_parameter_convergence (pyspark.mllib.tests.test_streaming_algorithms.StreamingLinearRegressionWithTests)
> Test that the model parameters improve with streaming data.
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> File "/usr/local/src/hth/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 429, in test_parameter_convergen ce
> self._eventually(condition, catch_assertions=True)
> File "/usr/local/src/hth/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 74, in _eventually
> raise lastValue
> File "/usr/local/src/hth/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 65, in _eventually
> lastValue = condition()
> File "/usr/local/src/hth/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 425, in condition
> self.assertEqual(len(model_weights), len(batches))
> AssertionError: 6 != 10
>
>
> ======================================================================
> FAIL: test_convergence (pyspark.mllib.tests.test_streaming_algorithms.StreamingLogisticRegressionWithSGDTests)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> File "/usr/local/src/hth/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 292, in test_convergence
> self._eventually(condition, 60.0, catch_assertions=True)
> File "/usr/local/src/hth/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 74, in _eventually
> raise lastValue
> File "/usr/local/src/hth/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 65, in _eventually
> lastValue = condition()
> File "/usr/local/src/hth/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 288, in condition
> self.assertEqual(len(models), len(input_batches))
> AssertionError: 19 != 20
>
> ======================================================================
> FAIL: test_parameter_accuracy (pyspark.mllib.tests.test_streaming_algorithms.StreamingLogisticRegressionWithSGDTests)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> File "/usr/local/src/hth/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 266, in test_parameter_accuracy
> self._eventually(condition, catch_assertions=True)
> File "/usr/local/src/hth/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 74, in _eventually
> raise lastValue
> File "/usr/local/src/hth/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 65, in _eventually
> lastValue = condition()
> File "/usr/local/src/hth/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 263, in condition
> self.assertAlmostEqual(rel, 0.1, 1)
> AssertionError: 0.21309223935797794 != 0.1 within 1 places
>
> ======================================================================
> FAIL: test_training_and_prediction (pyspark.mllib.tests.test_streaming_algorithms.StreamingLogisticRegressionWithSGDTests)
> Test that the model improves on toy data with no. of batches
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> File "/usr/local/src/hth/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 367, in test_training_and_predic tion
> self._eventually(condition, timeout=60.0)
> File "/usr/local/src/hth/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 78, in _eventually
> % (timeout, lastValue))
> AssertionError: Test failed due to timeout after 60 sec, with last condition returning: Latest errors: 0.67, 0.71, 0.78, 0.7, 0. 75, 0.74, 0.73, 0.69, 0.62, 0.71, 0.69, 0.75, 0.72, 0.77, 0.71, 0.74, 0.76, 0.78, 0.7, 0.78, 0.8
>
>
> Is it possible to expand the job time to make sure the job run finish if the test result is not change?? Any help or advice is welcome. Thanks
>
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