| commit | eee179135ed21dbdd8b342d053c9eda849e2de77 | [log] [tgz] |
|---|---|---|
| author | Hyukjin Kwon <gurwls223@apache.org> | Tue May 07 15:59:20 2024 +0900 |
| committer | Hyukjin Kwon <gurwls223@apache.org> | Tue May 07 15:59:20 2024 +0900 |
| tree | 9c535da71d33f19a3329c48b12c4f971b95c641b | |
| parent | b5e39bedab14a7fd800597ee0114b07448c1b0f9 [diff] |
[SPARK-48090][SS][PYTHON][TESTS] Shorten the traceback in the test checking error message in UDF
### What changes were proposed in this pull request?
This PR reduces traceback so the actual error `ZeroDivisionError` can be tested in `pyspark.sql.tests.connect.streaming.test_parity_streaming.StreamingParityTests.test_stream_exception`
### Why are the changes needed?
So long traceback doesn't affect the test case. It can fail as below:
```
======================================================================
FAIL [1.883s]: test_stream_exception (pyspark.sql.tests.connect.streaming.test_parity_streaming.StreamingParityTests.test_stream_exception)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/runner/work/spark/spark-3.5/python/pyspark/sql/tests/streaming/test_streaming.py", line 287, in test_stream_exception
sq.processAllAvailable()
File "/home/runner/work/spark/spark-3.5/python/pyspark/sql/connect/streaming/query.py", line 129, in processAllAvailable
self._execute_streaming_query_cmd(cmd)
File "/home/runner/work/spark/spark-3.5/python/pyspark/sql/connect/streaming/query.py", line 177, in _execute_streaming_query_cmd
(_, properties) = self._session.client.execute_command(exec_cmd)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/spark/spark-3.5/python/pyspark/sql/connect/client/core.py", line 982, in execute_command
data, _, _, _, properties = self._execute_and_fetch(req)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/spark/spark-3.5/python/pyspark/sql/connect/client/core.py", line 1283, in _execute_and_fetch
for response in self._execute_and_fetch_as_iterator(req):
File "/home/runner/work/spark/spark-3.5/python/pyspark/sql/connect/client/core.py", line 1264, in _execute_and_fetch_as_iterator
self._handle_error(error)
File "/home/runner/work/spark/spark-3.5/python/pyspark/sql/connect/client/core.py", line [150](https://github.com/HyukjinKwon/spark/actions/runs/8978991632/job/24660689666#step:9:151)3, in _handle_error
self._handle_rpc_error(error)
File "/home/runner/work/spark/spark-3.5/python/pyspark/sql/connect/client/core.py", line 1539, in _handle_rpc_error
raise convert_exception(info, status.message) from None
pyspark.errors.exceptions.connect.StreamingQueryException: [STREAM_FAILED] Query [id = 1c0c440d-0b48-41b1-9a03-071e7e13de82, runId = 692ec338-963a-43b1-89cb-2a8b7cb1e21a] terminated with exception: Job aborted due to stage failure: Task 0 in stage 39.0 failed 1 times, most recent failure: Lost task 0.0 in stage 39.0 (TID 58) (fv-az714-234.22nzjvkrszmuhkvqy55p1tioig.phxx.internal.cloudapp.net executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/home/runner/work/spark/spark/python/lib/pyspark.zip/pyspark/worker.py", line 1834, in main
process()
File "/home/runner/work/spark/spark/python/lib/pyspark.zip/pyspark/worker.py", line 1826, in process
serializer.dump_stream(out_iter, outfile)
File "/home/runner/work/spark/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 224, in dump_stream
self.serializer.dump_stream(self._batched(iterator), stream)
File "/home/runner/work/spark/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 145, in dump_stream
for obj in iterator:
File "/home/runner/work/spark/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 213, in _batched
for item in iterator:
File "/home/runner/work/spark/spark/python/lib/pyspark.zip/pyspark/worker.py", line 1734, in mapper
result = tuple(f(*[a[o] for o in arg_offsets]) for arg_offsets, f in udfs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/spark/spark/python/lib/pyspark.zip/pyspark/worker.py", line 1734, in <genexpr>
result = tuple(f(*[a[o] for o in arg_offsets]) for arg_offsets, f in udfs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/spark/spark/python/lib/pyspark.zip/pyspark/worker.py", line 112, in <lambda>
return args_kwargs_offsets, lambda *a: func(*a)
^^^^^^^^
File "/home/runner/work/spark/spark/python/lib/pyspark.zip/pyspark/util.py", line 134, in wrapper
return f(*args, **kwargs)
^^^^^^^^^^^^^^^^^^
File "/home/runner/work/spark/spark-3....
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/runner/work/spark/spark-3.5/python/pyspark/sql/tests/streaming/test_streaming.py", line 291, in test_stream_exception
self._assert_exception_tree_contains_msg(e, "ZeroDivisionError")
File "/home/runner/work/spark/spark-3.5/python/pyspark/sql/tests/streaming/test_streaming.py", line 300, in _assert_exception_tree_contains_msg
self._assert_exception_tree_contains_msg_connect(exception, msg)
File "/home/runner/work/spark/spark-3.5/python/pyspark/sql/tests/streaming/test_streaming.py", line 305, in _assert_exception_tree_contains_msg_connect
self.assertTrue(
AssertionError: False is not true : Exception tree doesn't contain the expected message: ZeroDivisionError
----------------------------------------------------------------------
```
### Does this PR introduce _any_ user-facing change?
No, test-only.
### How was this patch tested?
Tested in my own fork: https://github.com/HyukjinKwon/spark/actions/runs/8978991632
### Was this patch authored or co-authored using generative AI tooling?
No.
Closes #46426 from HyukjinKwon/SPARK-48090.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Spark is built using Apache Maven. To build Spark and its example programs, run:
./build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
More detailed documentation is available from the project site, at “Building Spark”.
For general development tips, including info on developing Spark using an IDE, see “Useful Developer Tools”.
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1,000,000,000:
scala> spark.range(1000 * 1000 * 1000).count()
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1,000,000,000:
>>> spark.range(1000 * 1000 * 1000).count()
Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be spark:// URL, “yarn” to run on YARN, and “local” to run locally with one thread, or “local[N]” to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run tests for a module, or individual tests.
There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at “Specifying the Hadoop Version and Enabling YARN” for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Please review the Contribution to Spark guide for information on how to get started contributing to the project.
