Bump, Just trying to see where I can find what tests are known failing for a particular release, to ensure I’m building upstream correctly following the build docs. I figured this would be the best place to ask as it pertains to building and testing upstream (also more than happy to provide a PR for any docs if required afterwards), however if there would be a more appropriate place, please let me know.
Best, Adam Chhina > On Dec 27, 2022, at 11:37 AM, Adam Chhina <amanschh...@gmail.com> wrote: > > As part of an upgrade I was looking to run upstream PySpark unit tests on > `v3.2.1-rc2` before I applied some downstream patches and tested those. > However, I'm running into some issues with failing unit tests, which I'm not > sure are failing upstream or due to some step I missed in the build. > > The current failing tests (at least so far, since I believe the python script > exits on test failure): > ``` > ====================================================================== > FAIL: test_train_prediction > (pyspark.mllib.tests.test_streaming_algorithms.StreamingLinearRegressionWithTests) > Test that error on test data improves as model is trained. > ---------------------------------------------------------------------- > Traceback (most recent call last): > File > "/Users/adam/OSS/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", > line 474, in test_train_prediction > eventually(condition, timeout=180.0) > File "/Users/adam/OSS/spark/python/pyspark/testing/utils.py", line 86, in > eventually > lastValue = condition() > File > "/Users/adam/OSS/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", > line 469, in condition > self.assertGreater(errors[1] - errors[-1], 2) > AssertionError: 1.8960983527735014 not greater than 2 > > ====================================================================== > FAIL: test_parameter_accuracy > (pyspark.mllib.tests.test_streaming_algorithms.StreamingLogisticRegressionWithSGDTests) > Test that the final value of weights is close to the desired value. > ---------------------------------------------------------------------- > Traceback (most recent call last): > File > "/Users/adam/OSS/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", > line 229, in test_parameter_accuracy > eventually(condition, timeout=60.0, catch_assertions=True) > File "/Users/adam/OSS/spark/python/pyspark/testing/utils.py", line 91, in > eventually > raise lastValue > File "/Users/adam/OSS/spark/python/pyspark/testing/utils.py", line 82, in > eventually > lastValue = condition() > File > "/Users/adam/OSS/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", > line 226, in condition > self.assertAlmostEqual(rel, 0.1, 1) > AssertionError: 0.23052813480829393 != 0.1 within 1 places > (0.13052813480829392 difference) > > ====================================================================== > 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 > "/Users/adam/OSS/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", > line 334, in test_training_and_prediction > eventually(condition, timeout=180.0) > File "/Users/adam/OSS/spark/python/pyspark/testing/utils.py", line 93, in > eventually > raise AssertionError( > AssertionError: Test failed due to timeout after 180 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, > 0.74, 0.77, 0.75, 0.76, 0.76, 0.75, 0.78, 0.74, 0.64, 0.64, 0.71, 0.78, 0.76, > 0.64, 0.68, 0.69, 0.72, 0.77 > > ---------------------------------------------------------------------- > Ran 13 tests in 661.536s > > FAILED (failures=3, skipped=1) > > Had test failures in pyspark.mllib.tests.test_streaming_algorithms with > /usr/local/bin/python3; see logs. > ``` > > Here's how I'm currently building Spark, I was using the > [building-spark](https://spark.apache.org/docs/3..1/building-spark.html) docs > as a reference. > ``` > > git clone g...@github.com:apache/spark.git > > git checkout -b spark-321 v3.2.1 > > ./build/mvn -DskipTests clean package -Phive > > export JAVA_HOME=$(path/to/jdk/11) > > ./python/run-tests > ``` > > Current Java version > ``` > java -version > openjdk version "11.0.17" 2022-10-18 > OpenJDK Runtime Environment Homebrew (build 11.0.17+0) > OpenJDK 64-Bit Server VM Homebrew (build 11.0.17+0, mixed mode) > ``` > > Alternatively, I've also tried simply building Spark and using a python=3.9 > venv and installing the requirements from `pip install -r > dev/requirements.txt` and using that as the interpreter to run tests. > However, I was running into some failing pandas test which to me seemed like > it was coming from a pandas version difference as `requirements.txt` didn't > specify a version. > > I suppose I have a couple of questions in regards to this: > 1. Am I missing a build step to build Spark and run PySpark unit tests? > 2. Where could I find whether an upstream test is failing for a specific > release? > 3. Would it be possible to configure the `run-tests` script to run all tests > regardless of test failures? --------------------------------------------------------------------- To unsubscribe e-mail: dev-unsubscr...@spark.apache.org
