## What changes were proposed in this pull request?
### Background
For the current status, the test script that generates coverage information was merged
into Spark, https://github.com/apache/spark/pull/20204
So, we can generate the coverage report and site by, for example:
```
run-tests-with-coverage --python-executables=python3 --modules=pyspark-sql
```
like `run-tests` script in `./python`.
### Proposed change
The next step is to host this coverage report via `github.io` automatically
by Jenkins (see https://spark-test.github.io/pyspark-coverage-site/).
This uses my testing account for Spark, spark-test, which is shared to Felix and Shivaram a long time ago for testing purpose including AppVeyor.
To cut this short, this PR targets to run the coverage in
[spark-master-test-sbt-hadoop-2.7](https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/)
In the specific job, it will clone the page, and rebase the up-to-date PySpark test coverage from the latest commit. For instance as below:
```bash
# Clone PySpark coverage site.
git clone https://github.com/spark-test/pyspark-coverage-site.git
# Remove existing HTMLs.
rm -fr pyspark-coverage-site/*
# Copy generated coverage HTMLs.
cp -r .../python/test_coverage/htmlcov/* pyspark-coverage-site/
# Check out to a temporary branch.
git symbolic-ref HEAD refs/heads/latest_branch
# Add all the files.
git add -A
# Commit current HTMLs.
git commit -am "Coverage report at latest commit in Apache Spark"
# Delete the old branch.
git branch -D gh-pages
# Rename the temporary branch to master.
git branch -m gh-pages
# Finally, force update to our repository.
git push -f origin gh-pages
```
So, it is a one single up-to-date coverage can be shown in the `github-io` page. The commands above were manually tested.
### TODOs
- [x] Write a draft HyukjinKwon
- [x] `pip install coverage` to all python implementations (pypy, python2, python3) in Jenkins workers - shaneknapp
- [x] Set hidden `SPARK_TEST_KEY` for spark-test's password in Jenkins via Jenkins's feature
This should be set in both PR builder and `spark-master-test-sbt-hadoop-2.7` so that later other PRs can test and fix the bugs - shaneknapp
- [x] Set an environment variable that indicates `spark-master-test-sbt-hadoop-2.7` so that that specific build can report and update the coverage site - shaneknapp
- [x] Make PR builder's test passed HyukjinKwon
- [x] Fix flaky test related with coverage HyukjinKwon
- 6 consecutive passes out of 7 runs
This PR will be co-authored with me and shaneknapp
## How was this patch tested?
It will be tested via Jenkins.
Closes#23117 from HyukjinKwon/SPARK-7721.
Lead-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: shane knapp <incomplete@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
I am investigating flaky tests. I realised that:
```
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/rdd.py", line 2512, in __init__
self.is_barrier = prev._is_barrier() or isFromBarrier
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/rdd.py", line 2412, in _is_barrier
return self._jrdd.rdd().isBarrier()
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/lib/py4j-0.10.8.1-src.zip/py4j/java_gateway.py", line 1286, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/lib/py4j-0.10.8.1-src.zip/py4j/protocol.py", line 342, in get_return_value
return OUTPUT_CONVERTER[type](answer[2:], gateway_client)
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/lib/py4j-0.10.8.1-src.zip/py4j/java_gateway.py", line 2492, in <lambda>
lambda target_id, gateway_client: JavaObject(target_id, gateway_client))
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/lib/py4j-0.10.8.1-src.zip/py4j/java_gateway.py", line 1324, in __init__
ThreadSafeFinalizer.add_finalizer(key, value)
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/lib/py4j-0.10.8.1-src.zip/py4j/finalizer.py", line 43, in add_finalizer
cls.finalizers[id] = weak_ref
File "/usr/lib64/pypy-2.5.1/lib-python/2.7/threading.py", line 216, in __exit__
self.release()
File "/usr/lib64/pypy-2.5.1/lib-python/2.7/threading.py", line 208, in release
self.__block.release()
error: release unlocked lock
```
I assume it might not be directly related with the test itself but I noticed that it `prev._is_barrier()` attempts to access via Py4J.
Accessing via Py4J is expensive. Therefore, this PR proposes to avoid Py4J access when `isFromBarrier` is `True`.
## How was this patch tested?
Unittests should cover this.
Closes#23690 from HyukjinKwon/minor-barrier.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Upgrade Apache Arrow to version 0.12.0. This includes the Java artifacts and fixes to enable usage with pyarrow 0.12.0
Version 0.12.0 includes the following selected fixes/improvements relevant to Spark users:
* Safe cast fails from numpy float64 array with nans to integer, ARROW-4258
* Java, Reduce heap usage for variable width vectors, ARROW-4147
* Binary identity cast not implemented, ARROW-4101
* pyarrow open_stream deprecated, use ipc.open_stream, ARROW-4098
* conversion to date object no longer needed, ARROW-3910
* Error reading IPC file with no record batches, ARROW-3894
* Signed to unsigned integer cast yields incorrect results when type sizes are the same, ARROW-3790
* from_pandas gives incorrect results when converting floating point to bool, ARROW-3428
* Import pyarrow fails if scikit-learn is installed from conda (boost-cpp / libboost issue), ARROW-3048
* Java update to official Flatbuffers version 1.9.0, ARROW-3175
complete list [here](https://issues.apache.org/jira/issues/?jql=project%20%3D%20ARROW%20AND%20status%20in%20(Resolved%2C%20Closed)%20AND%20fixVersion%20%3D%200.12.0)
PySpark requires the following fixes to work with PyArrow 0.12.0
* Encrypted pyspark worker fails due to ChunkedStream missing closed property
* pyarrow now converts dates as objects by default, which causes error because type is assumed datetime64
* ArrowTests fails due to difference in raised error message
* pyarrow.open_stream deprecated
* tests fail because groupby adds index column with duplicate name
## How was this patch tested?
Ran unit tests with pyarrow versions 0.8.0, 0.10.0, 0.11.1, 0.12.0
Closes#23657 from BryanCutler/arrow-upgrade-012.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/21977 added a feature to limit Python worker resource limit.
This PR is kind of a followup of it. It proposes to add a test that checks the actual resource limit set by 'spark.executor.pyspark.memory'.
## How was this patch tested?
Unit tests were added.
Closes#23663 from HyukjinKwon/test_rlimit.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add PMML export support for ML KMeans to PySpark.
## How was this patch tested?
Add tests in ml.tests.PersistenceTest.
Closes#23592 from huaxingao/spark-16838.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This change exposes the `df` (document frequency) as a public val along with the number of documents (`m`) as part of the IDF model.
* The document frequency is returned as an `Array[Long]`
* If the minimum document frequency is set, this is considered in the df calculation. If the count is less than minDocFreq, the df is 0 for such terms
* numDocs is not very required. But it can be useful, if we plan to provide a provision in future for user to give their own idf function, instead of using a default (log((1+m)/(1+df))). In such cases, the user can provide a function taking input of `m` and `df` and returning the idf value
* Pyspark changes
## How was this patch tested?
The existing test case was edited to also check for the document frequency values.
I am not very good with python or pyspark. I have committed and run tests based on my understanding. Kindly let me know if I have missed anything
Reviewer request: mengxr zjffdu yinxusen
Closes#23549 from purijatin/master.
Authored-by: Jatin Puri <purijatin@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Since 0.11.0, PyArrow supports to raise an error for unsafe cast ([PR](https://github.com/apache/arrow/pull/2504)). We should use it to raise a proper error for pandas udf users when such cast is detected.
Added a SQL config `spark.sql.execution.pandas.arrowSafeTypeConversion` to disable Arrow safe type check.
## How was this patch tested?
Added test and manually test.
Closes#22807 from viirya/SPARK-25811.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This particular test is being skipped at PyPy and Python 2.
```
Skipped tests in pyspark.sql.tests.test_context with pypy:
test_unbounded_frames (pyspark.sql.tests.test_context.HiveContextSQLTests) ... skipped "Unittest < 3.3 doesn't support mocking"
Skipped tests in pyspark.sql.tests.test_context with python2.7:
test_unbounded_frames (pyspark.sql.tests.test_context.HiveContextSQLTests) ... skipped "Unittest < 3.3 doesn't support mocking"
```
We don't have to use unittest 3.3 module to mock. And looks the test itself isn't compatible with Python 2.
This PR makes:
- Manually monkey-patch `sys.maxsize` to get rid of unittest 3.3 condition
- Use the built-in `reload` in Python 2, and `importlib.reload` in Python 3
## How was this patch tested?
Manually tested, and unit test is fixed.
Closes#23604 from HyukjinKwon/test-window.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
When parsing datatypes from the json internal representation, PySpark doesn't support decimals with negative scales. Since they are allowed and can actually happen, PySpark should be able to successfully parse them.
## How was this patch tested?
added test
Closes#23575 from mgaido91/SPARK-26645.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The test pyspark.mllib.tests.test_streaming_algorithms StreamingLogisticRegressionWithSGDTests.test_training_and_prediction looks sometimes flaky.
```
======================================================================
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 "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 367, in test_training_and_prediction
self._eventually(condition, timeout=60.0)
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 69, in _eventually
lastValue = condition()
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 362, in condition
self.assertGreater(errors[1] - errors[-1], 0.3)
AssertionError: -0.070000000000000062 not greater than 0.3
----------------------------------------------------------------------
Ran 13 tests in 198.327s
FAILED (failures=1, skipped=1)
Had test failures in pyspark.mllib.tests.test_streaming_algorithms with python3.4; see logs
```
The predict stream can possibly be consumed to the end before the input stream. When it happens, the model improvement is not high as expected and causes test failed. This patch tries to increase number of batches of streams. This won't increase test time because we have a timeout there.
## How was this patch tested?
Manually test.
Closes#23586 from viirya/SPARK-26646.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Misc code cleanup from lgtm.com analysis. See comments below for details.
## How was this patch tested?
Existing tests.
Closes#23571 from srowen/SPARK-26640.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Co-authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fix implementation of unary negation (`__neg__`) in Pyspark DenseVectors
## How was this patch tested?
Existing tests, plus new doctest
Closes#23570 from srowen/SPARK-26638.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR proposes to explicitly document that SparkContext cannot be shared for multiprocessing, and multi-processing execution is not guaranteed in PySpark.
I have seen some cases that users attempt to use multiple processes via `multiprocessing` module time to time. For instance, see the example in the JIRA (https://issues.apache.org/jira/browse/SPARK-25992).
Py4J itself does not support Python's multiprocessing out of the box (sharing the same JavaGateways for instance).
In general, such pattern can cause errors with somewhat arbitrary symptoms difficult to diagnose. For instance, see the error message in JIRA:
```
Traceback (most recent call last):
File "/Users/abdealijk/anaconda3/lib/python3.6/socketserver.py", line 317, in _handle_request_noblock
self.process_request(request, client_address)
File "/Users/abdealijk/anaconda3/lib/python3.6/socketserver.py", line 348, in process_request
self.finish_request(request, client_address)
File "/Users/abdealijk/anaconda3/lib/python3.6/socketserver.py", line 361, in finish_request
self.RequestHandlerClass(request, client_address, self)
File "/Users/abdealijk/anaconda3/lib/python3.6/socketserver.py", line 696, in __init__
self.handle()
File "/usr/local/hadoop/spark2.3.1/python/pyspark/accumulators.py", line 238, in handle
_accumulatorRegistry[aid] += update
KeyError: 0
```
The root cause of this was because global `_accumulatorRegistry` is not shared across processes.
Using thread instead of process is quite easy in Python. See `threading` vs `multiprocessing` in Python - they can be usually direct replacement for each other. For instance, Python also support threadpool as well (`multiprocessing.pool.ThreadPool`) which can be direct replacement of process-based thread pool (`multiprocessing.Pool`).
## How was this patch tested?
Manually tested, and manually built the doc.
Closes#23564 from HyukjinKwon/SPARK-25992.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
It's the follow-up PR for #22962, contains the following works:
- Remove `__init__` in TaskContext and BarrierTaskContext.
- Add more comments to explain the fix.
- Rewrite UT in a new class.
## How was this patch tested?
New UT in test_taskcontext.py
Closes#23435 from xuanyuanking/SPARK-25921-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
During the follow-up work(#23435) for PySpark worker reuse scenario, we found that the worker reuse takes no effect for `sc.parallelize(xrange(...))`. It happened because of the specialize rdd.parallelize logic for xrange(introduced in #3264) generated data by lazy iterable range, which don't need to use the passed-in iterator. But this will break the end of stream checking in python worker and finally cause worker reuse takes no effect. See more details in [SPARK-26549](https://issues.apache.org/jira/browse/SPARK-26549) description.
We fix this by force using the passed-in iterator.
## How was this patch tested?
New UT in test_worker.py.
Closes#23470 from xuanyuanking/SPARK-26549.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
Spark always creates secure py4j connections between java and python,
but it also allows users to pass in their own connection. This ensures
that even passed in connections are secure.
Added test cases verifying the failure with a (mocked) insecure gateway.
This is closely related to SPARK-26019, but this entirely forbids the
insecure connection, rather than creating the "escape-hatch".
Closes#23441 from squito/SPARK-26349.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Due to [API change](https://github.com/numpy/numpy/pull/4257/files#diff-c39521d89f7e61d6c0c445d93b62f7dc) at 1.9, PySpark image doesn't work with numpy version prior to 1.9.
When running image test with numpy version prior to 1.9, we can see error:
```
test_read_images (pyspark.ml.tests.test_image.ImageReaderTest) ... ERROR
test_read_images_multiple_times (pyspark.ml.tests.test_image.ImageReaderTest2) ... ok
======================================================================
ERROR: test_read_images (pyspark.ml.tests.test_image.ImageReaderTest)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/viirya/docker_tmp/repos/spark-1/python/pyspark/ml/tests/test_image.py", line 36, in test_read_images
self.assertEqual(ImageSchema.toImage(array, origin=first_row[0]), first_row)
File "/Users/viirya/docker_tmp/repos/spark-1/python/pyspark/ml/image.py", line 193, in toImage
data = bytearray(array.astype(dtype=np.uint8).ravel().tobytes())
AttributeError: 'numpy.ndarray' object has no attribute 'tobytes'
----------------------------------------------------------------------
Ran 2 tests in 29.040s
FAILED (errors=1)
```
## How was this patch tested?
Manually test with numpy version prior and after 1.9.
Closes#23484 from viirya/fix-pyspark-image.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The PR adds the `trainingCost` value to the `BisectingKMeansSummary`, in order to expose the information retrievable by running `computeCost` on the training dataset. This fills the gap with `KMeans` implementation.
## How was this patch tested?
improved UTs
Closes#22764 from mgaido91/SPARK-25765.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Change aligns argument name with that in Scala version and documentation.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23357 from deepyaman/patch-1.
Authored-by: deepyaman <deepyaman.datta@utexas.edu>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to switch the `DateFormatClass`, `ToUnixTimestamp`, `FromUnixTime`, `UnixTime` on java.time API for parsing/formatting dates and timestamps. The API has been already implemented by the `Timestamp`/`DateFormatter` classes. One of benefit is those classes support parsing timestamps with microsecond precision. Old behaviour can be switched on via SQL config: `spark.sql.legacy.timeParser.enabled` (`false` by default).
## How was this patch tested?
It was tested by existing test suites - `DateFunctionsSuite`, `DateExpressionsSuite`, `JsonSuite`, `CsvSuite`, `SQLQueryTestSuite` as well as PySpark tests.
Closes#23358 from MaxGekk/new-time-cast.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PRs #23150 and #23196 switched JSON and CSV datasources on new formatter for dates/timestamps which is based on `DateTimeFormatter`. In this PR, I replaced `SimpleDateFormat` by `DateTimeFormatter` to reflect the changes.
Closes#23374 from MaxGekk/java-time-docs.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR implements a new feature - window aggregation Pandas UDF for bounded window.
#### Doc:
https://docs.google.com/document/d/14EjeY5z4-NC27-SmIP9CsMPCANeTcvxN44a7SIJtZPc/edit#heading=h.c87w44wcj3wj
#### Example:
```
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.window import Window
df = spark.range(0, 10, 2).toDF('v')
w1 = Window.partitionBy().orderBy('v').rangeBetween(-2, 4)
w2 = Window.partitionBy().orderBy('v').rowsBetween(-2, 2)
pandas_udf('double', PandasUDFType.GROUPED_AGG)
def avg(v):
return v.mean()
df.withColumn('v_mean', avg(df['v']).over(w1)).show()
# +---+------+
# | v|v_mean|
# +---+------+
# | 0| 1.0|
# | 2| 2.0|
# | 4| 4.0|
# | 6| 6.0|
# | 8| 7.0|
# +---+------+
df.withColumn('v_mean', avg(df['v']).over(w2)).show()
# +---+------+
# | v|v_mean|
# +---+------+
# | 0| 2.0|
# | 2| 3.0|
# | 4| 4.0|
# | 6| 5.0|
# | 8| 6.0|
# +---+------+
```
#### High level changes:
This PR modifies the existing WindowInPandasExec physical node to deal with unbounded (growing, shrinking and sliding) windows.
* `WindowInPandasExec` now share the same base class as `WindowExec` and share utility functions. See `WindowExecBase`
* `WindowFunctionFrame` now has two new functions `currentLowerBound` and `currentUpperBound` - to return the lower and upper window bound for the current output row. It is also modified to allow `AggregateProcessor` == null. Null aggregator processor is used for `WindowInPandasExec` where we don't have an aggregator and only uses lower and upper bound functions from `WindowFunctionFrame`
* The biggest change is in `WindowInPandasExec`, where it is modified to take `currentLowerBound` and `currentUpperBound` and write those values together with the input data to the python process for rolling window aggregation. See `WindowInPandasExec` for more details.
#### Discussion
In benchmarking, I found numpy variant of the rolling window UDF is much faster than the pandas version:
Spark SQL window function: 20s
Pandas variant: ~80s
Numpy variant: 10s
Numpy variant with numba: 4s
Allowing numpy variant of the vectorized UDFs is something I want to discuss because of the performance improvement, but doesn't have to be in this PR.
## How was this patch tested?
New tests
Closes#22305 from icexelloss/SPARK-24561-bounded-window-udf.
Authored-by: Li Jin <ice.xelloss@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
If the input parameter 'threshold' to the function approxSimilarityJoin is not a float, we would get an exception. The fix is to convert the 'threshold' into a float before calling the java implementation method.
## How was this patch tested?
Added a new test case. Without this fix, the test will throw an exception as reported in the JIRA. With the fix, the test passes.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23313 from jerryjch/SPARK-26315.
Authored-by: Jing Chen He <jinghe@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Multiple SparkContexts are discouraged and it has been warning for last 4 years, see SPARK-4180. It could cause arbitrary and mysterious error cases, see SPARK-2243.
Honestly, I didn't even know Spark still allows it, which looks never officially supported, see SPARK-2243.
I believe It should be good timing now to remove this configuration.
## How was this patch tested?
Each doc was manually checked and manually tested:
```
$ ./bin/spark-shell --conf=spark.driver.allowMultipleContexts=true
...
scala> new SparkContext()
org.apache.spark.SparkException: Only one SparkContext should be running in this JVM (see SPARK-2243).The currently running SparkContext was created at:
org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:939)
...
org.apache.spark.SparkContext$.$anonfun$assertNoOtherContextIsRunning$2(SparkContext.scala:2435)
at scala.Option.foreach(Option.scala:274)
at org.apache.spark.SparkContext$.assertNoOtherContextIsRunning(SparkContext.scala:2432)
at org.apache.spark.SparkContext$.markPartiallyConstructed(SparkContext.scala:2509)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:80)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:112)
... 49 elided
```
Closes#23311 from HyukjinKwon/SPARK-26362.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Clean up unconditional import statements and move them to the top.
Conditional imports (pandas, numpy, pyarrow) are left as-is.
## How was this patch tested?
Exising tests.
Closes#23314 from icexelloss/clean-up-test-imports.
Authored-by: Li Jin <ice.xelloss@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In PyArrow 0.11, there is a API breaking change.
- [ARROW-1949](https://issues.apache.org/jira/browse/ARROW-1949) - [Python/C++] Add option to Array.from_pandas and pyarrow.array to perform unsafe casts.
This causes test failures in `ScalarPandasUDFTests.test_vectorized_udf_null_(byte|short|int|long)`:
```
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/worker.py", line 377, in main
process()
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/worker.py", line 372, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/serializers.py", line 317, in dump_stream
batch = _create_batch(series, self._timezone)
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/serializers.py", line 286, in _create_batch
arrs = [create_array(s, t) for s, t in series]
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/serializers.py", line 284, in create_array
return pa.Array.from_pandas(s, mask=mask, type=t)
File "pyarrow/array.pxi", line 474, in pyarrow.lib.Array.from_pandas
return array(obj, mask=mask, type=type, safe=safe, from_pandas=True,
File "pyarrow/array.pxi", line 169, in pyarrow.lib.array
return _ndarray_to_array(values, mask, type, from_pandas, safe,
File "pyarrow/array.pxi", line 69, in pyarrow.lib._ndarray_to_array
check_status(NdarrayToArrow(pool, values, mask, from_pandas,
File "pyarrow/error.pxi", line 81, in pyarrow.lib.check_status
raise ArrowInvalid(message)
ArrowInvalid: Floating point value truncated
```
We should add a workaround to support PyArrow 0.11.
## How was this patch tested?
In my local environment.
Closes#23305 from ueshin/issues/SPARK-26355/pyarrow_0.11.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to return partial results from JSON datasource and JSON functions in the PERMISSIVE mode if some of JSON fields are parsed and converted to desired types successfully. The changes are made only for `StructType`. Whole bad JSON records are placed into the corrupt column specified by the `columnNameOfCorruptRecord` option or SQL config.
Partial results are not returned for malformed JSON input.
## How was this patch tested?
Added new UT which checks converting JSON strings with one invalid and one valid field at the end of the string.
Closes#23253 from MaxGekk/json-bad-record.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This is a regression introduced by https://github.com/apache/spark/pull/22104 at Spark 2.4.0.
When we have Python UDF in subquery, we will hit an exception
```
Caused by: java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.AttributeReference cannot be cast to org.apache.spark.sql.catalyst.expressions.PythonUDF
at scala.collection.immutable.Stream.map(Stream.scala:414)
at org.apache.spark.sql.execution.python.EvalPythonExec.$anonfun$doExecute$2(EvalPythonExec.scala:98)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:815)
...
```
https://github.com/apache/spark/pull/22104 turned `ExtractPythonUDFs` from a physical rule to optimizer rule. However, there is a difference between a physical rule and optimizer rule. A physical rule always runs once, an optimizer rule may be applied twice on a query tree even the rule is located in a batch that only runs once.
For a subquery, the `OptimizeSubqueries` rule will execute the entire optimizer on the query plan inside subquery. Later on subquery will be turned to joins, and the optimizer rules will be applied to it again.
Unfortunately, the `ExtractPythonUDFs` rule is not idempotent. When it's applied twice on a query plan inside subquery, it will produce a malformed plan. It extracts Python UDF from Python exec plans.
This PR proposes 2 changes to be double safe:
1. `ExtractPythonUDFs` should skip python exec plans, to make the rule idempotent
2. `ExtractPythonUDFs` should skip subquery
## How was this patch tested?
a new test.
Closes#23248 from cloud-fan/python.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Add PowerIterationCluster (PIC) in R
## How was this patch tested?
Add test case
Closes#23072 from huaxingao/spark-19827.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
… incorrect.
## What changes were proposed in this pull request?
In the reported heartbeat information, the unit of the memory data is bytes, which is converted by the formatBytes() function in the utils.js file before being displayed in the interface. The cardinality of the unit conversion in the formatBytes function is 1000, which should be 1024.
Change the cardinality of the unit conversion in the formatBytes function to 1024.
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22683 from httfighter/SPARK-25696.
Lead-authored-by: 韩田田00222924 <han.tiantian@zte.com.cn>
Co-authored-by: han.tiantian@zte.com.cn <han.tiantian@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add validationIndicatorCol and validationTol to GBT Python.
## How was this patch tested?
Add test in doctest to test the new API.
Closes#21465 from huaxingao/spark-24333.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
When executing `toPandas` with Arrow enabled, partitions that arrive in the JVM out-of-order must be buffered before they can be send to Python. This causes an excess of memory to be used in the driver JVM and increases the time it takes to complete because data must sit in the JVM waiting for preceding partitions to come in.
This change sends un-ordered partitions to Python as soon as they arrive in the JVM, followed by a list of partition indices so that Python can assemble the data in the correct order. This way, data is not buffered at the JVM and there is no waiting on particular partitions so performance will be increased.
Followup to #21546
## How was this patch tested?
Added new test with a large number of batches per partition, and test that forces a small delay in the first partition. These test that partitions are collected out-of-order and then are are put in the correct order in Python.
## Performance Tests - toPandas
Tests run on a 4 node standalone cluster with 32 cores total, 14.04.1-Ubuntu and OpenJDK 8
measured wall clock time to execute `toPandas()` and took the average best time of 5 runs/5 loops each.
Test code
```python
df = spark.range(1 << 25, numPartitions=32).toDF("id").withColumn("x1", rand()).withColumn("x2", rand()).withColumn("x3", rand()).withColumn("x4", rand())
for i in range(5):
start = time.time()
_ = df.toPandas()
elapsed = time.time() - start
```
Spark config
```
spark.driver.memory 5g
spark.executor.memory 5g
spark.driver.maxResultSize 2g
spark.sql.execution.arrow.enabled true
```
Current Master w/ Arrow stream | This PR
---------------------|------------
5.16207 | 4.342533
5.133671 | 4.399408
5.147513 | 4.468471
5.105243 | 4.36524
5.018685 | 4.373791
Avg Master | Avg This PR
------------------|--------------
5.1134364 | 4.3898886
Speedup of **1.164821449**
Closes#22275 from BryanCutler/arrow-toPandas-oo-batches-SPARK-25274.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Looks this test is flaky
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99704/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99569/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99644/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99548/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99454/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99609/console
```
======================================================================
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 "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 367, in test_training_and_prediction
self._eventually(condition)
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 78, in _eventually
% (timeout, lastValue))
AssertionError: Test failed due to timeout after 30 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
----------------------------------------------------------------------
Ran 13 tests in 185.051s
FAILED (failures=1, skipped=1)
```
This looks happening after increasing the parallelism in Jenkins to speed up at https://github.com/apache/spark/pull/23111. I am able to reproduce this manually when the resource usage is heavy (with manual decrease of timeout).
## How was this patch tested?
Manually tested by
```
cd python
./run-tests --testnames 'pyspark.mllib.tests.test_streaming_algorithms StreamingLogisticRegressionWithSGDTests.test_training_and_prediction' --python-executables=python
```
Closes#23236 from HyukjinKwon/SPARK-26275.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This fixes doc of renamed OneHotEncoder in PySpark.
## How was this patch tested?
N/A
Closes#23230 from viirya/remove_one_hot_encoder_followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR is a small follow up that puts some logic and functions into smaller scope and make it localized, and deduplicate.
## How was this patch tested?
Manually tested. Jenkins tests as well.
Closes#23200 from HyukjinKwon/followup-SPARK-26034-SPARK-26033.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
`resource` package is a Unix specific package. See https://docs.python.org/2/library/resource.html and https://docs.python.org/3/library/resource.html.
Note that we document Windows support:
> Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS).
This should be backported into branch-2.4 to restore Windows support in Spark 2.4.1.
## How was this patch tested?
Manually mocking the changed logics.
Closes#23055 from HyukjinKwon/SPARK-26080.
Lead-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: DylanGuedes <djmgguedesgmail.com>
## What changes were proposed in this pull request?
Addition of float, int and list hints for `pyspark.sql` Hint.
## How was this patch tested?
I did manual tests following the same principles used in the Scala version, and also added unit tests.
Closes#20788 from DylanGuedes/jira-21030.
Authored-by: DylanGuedes <djmgguedes@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Python with rpc and disk encryption enabled along with a python broadcast variable and just read the value back on the driver side the job failed with:
Traceback (most recent call last): File "broadcast.py", line 37, in <module> words_new.value File "/pyspark.zip/pyspark/broadcast.py", line 137, in value File "pyspark.zip/pyspark/broadcast.py", line 122, in load_from_path File "pyspark.zip/pyspark/broadcast.py", line 128, in load EOFError: Ran out of input
To reproduce use configs: --conf spark.network.crypto.enabled=true --conf spark.io.encryption.enabled=true
Code:
words_new = sc.broadcast(["scala", "java", "hadoop", "spark", "akka"])
words_new.value
print(words_new.value)
## How was this patch tested?
words_new = sc.broadcast([“scala”, “java”, “hadoop”, “spark”, “akka”])
textFile = sc.textFile(“README.md”)
wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word + words_new.value[1], 1)).reduceByKey(lambda a, b: a+b)
count = wordCounts.count()
print(count)
words_new.value
print(words_new.value)
Closes#23166 from redsanket/SPARK-26201.
Authored-by: schintap <schintap@oath.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
We have deprecated `OneHotEncoder` at Spark 2.3.0 and introduced `OneHotEncoderEstimator`. At 3.0.0, we remove deprecated `OneHotEncoder` and rename `OneHotEncoderEstimator` to `OneHotEncoder`.
TODO: According to ML migration guide, we need to keep `OneHotEncoderEstimator` as an alias after renaming. This is not done at this patch in order to facilitate review.
## How was this patch tested?
Existing tests.
Closes#23100 from viirya/remove_one_hot_encoder.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Currently duplicated map keys are not handled consistently. For example, map look up respects the duplicated key appears first, `Dataset.collect` only keeps the duplicated key appears last, `MapKeys` returns duplicated keys, etc.
This PR proposes to remove duplicated map keys with last wins policy, to follow Java/Scala and Presto. It only applies to built-in functions, as users can create map with duplicated map keys via private APIs anyway.
updated functions: `CreateMap`, `MapFromArrays`, `MapFromEntries`, `StringToMap`, `MapConcat`, `TransformKeys`.
For other places:
1. data source v1 doesn't have this problem, as users need to provide a java/scala map, which can't have duplicated keys.
2. data source v2 may have this problem. I've added a note to `ArrayBasedMapData` to ask the caller to take care of duplicated keys. In the future we should enforce it in the stable data APIs for data source v2.
3. UDF doesn't have this problem, as users need to provide a java/scala map. Same as data source v1.
4. file format. I checked all of them and only parquet does not enforce it. For backward compatibility reasons I change nothing but leave a note saying that the behavior will be undefined if users write map with duplicated keys to parquet files. Maybe we can add a config and fail by default if parquet files have map with duplicated keys. This can be done in followup.
## How was this patch tested?
updated tests and new tests
Closes#23124 from cloud-fan/map.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/22326 made a mistake that, not all python UDFs are unevaluable in join condition. Only python UDFs that refer to attributes from both join side are unevaluable.
This PR fixes this mistake.
## How was this patch tested?
a new test
Closes#23153 from cloud-fan/join.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Implement codegen for `LocalTableScanExec` and `ExistingRDDExec`. Refactor to share code between `LocalTableScanExec`, `ExistingRDDExec`, `InputAdapter` and `RowDataSourceScanExec`.
The difference in `doProduce` between these four was that `ExistingRDDExec` and `RowDataSourceScanExec` triggered adding an `UnsafeProjection`, while `InputAdapter` and `LocalTableScanExec` did not.
In the new trait `InputRDDCodegen` I added a flag `createUnsafeProjection` which the operators set accordingly.
Note: `LocalTableScanExec` explicitly creates its input as `UnsafeRows`, so it was obvious why it doesn't need an `UnsafeProjection`. But if an `InputAdapter` may take input that is `InternalRows` but not `UnsafeRows`, then I think it doesn't need an unsafe projection just because any other operator that is its parent would do that. That assumes that that any parent operator would always result in some `UnsafeProjection` being eventually added, and hence the output of the `WholeStageCodegen` unit would be `UnsafeRows`. If these assumptions hold, I think `createUnsafeProjection` could be set to `(parent == null)`.
Note: Do not codegen `LocalTableScanExec` when it's the only operator. `LocalTableScanExec` has optimized driver-only `executeCollect` and `executeTake` code paths that are used to return `Command` results without starting Spark Jobs. They can no longer be used if the `LocalTableScanExec` gets optimized.
## How was this patch tested?
Covered and used in existing tests.
Closes#23127 from juliuszsompolski/SPARK-26159.
Authored-by: Juliusz Sompolski <julek@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR is to add back `unionAll`, which is widely used. The name is also consistent with our ANSI SQL. We also have the corresponding `intersectAll` and `exceptAll`, which were introduced in Spark 2.4.
## How was this patch tested?
Added a test case in DataFrameSuite
Closes#23131 from gatorsmile/addBackUnionAll.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The DOI foundation recommends [this new resolver](https://www.doi.org/doi_handbook/3_Resolution.html#3.8). Accordingly, this PR re`sed`s all static DOI links ;-)
## How was this patch tested?
It wasn't, since it seems as safe as a "[typo fix](https://spark.apache.org/contributing.html)".
In case any of the files is included from other projects, and should be updated there, please let me know.
Closes#23129 from katrinleinweber/resolve-DOIs-securely.
Authored-by: Katrin Leinweber <9948149+katrinleinweber@users.noreply.github.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose new options for CSV datasource - `lineSep` similar to Text and JSON datasource. The option allows to specify custom line separator of maximum length of 2 characters (because of a restriction in `uniVocity` parser). New option can be used in reading and writing CSV files.
## How was this patch tested?
Added a few tests with custom `lineSep` for enabled/disabled `multiLine` in read as well as tests in write. Also I added roundtrip tests.
Closes#23080 from MaxGekk/csv-line-sep.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The PR removes the deprecated method `computeCost` of `KMeans`.
## How was this patch tested?
NA
Closes#22875 from mgaido91/SPARK-25867.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Following [SPARK-26024](https://issues.apache.org/jira/browse/SPARK-26024), I noticed the number of elements in each partition after repartitioning using `df.repartitionByRange` can vary for the same setup:
```scala
// Shuffle numbers from 0 to 1000, and make a DataFrame
val df = Random.shuffle(0.to(1000)).toDF("val")
// Repartition it using 3 partitions
// Sum up number of elements in each partition, and collect it.
// And do it several times
for (i <- 0 to 9) {
var counts = df.repartitionByRange(3, col("val"))
.mapPartitions{part => Iterator(part.size)}
.collect()
println(counts.toList)
}
// -> the number of elements in each partition varies
```
This is expected as for performance reasons this method uses sampling to estimate the ranges (with default size of 100). Hence, the output may not be consistent, since sampling can return different values. But documentation was not mentioning it at all, leading to misunderstanding.
## What changes were proposed in this pull request?
Update the documentation (Spark & PySpark) to mention the impact of `spark.sql.execution.rangeExchange.sampleSizePerPartition` on the resulting partitioned DataFrame.
Closes#23025 from JulienPeloton/SPARK-26024.
Authored-by: Julien <peloton@lal.in2p3.fr>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The following 5 functions were removed from branch-2.4:
- map_entries
- map_filter
- transform_values
- transform_keys
- map_zip_with
We should update the since version to 3.0.0.
## How was this patch tested?
Existing tests.
Closes#23082 from ueshin/issues/SPARK-26112/since.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently, some of PySpark tests sill assume the tests could be ran in Python 2.6 by importing `unittest2`. For instance:
```python
if sys.version_info[:2] <= (2, 6):
try:
import unittest2 as unittest
except ImportError:
sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
sys.exit(1)
else:
import unittest
```
While I am here, I removed some of unused imports and reordered imports per PEP 8.
We officially dropped Python 2.6 support a while ago and started to discuss about Python 2 drop. It's better to remove them out.
## How was this patch tested?
Manually tests, and existing tests via Jenkins.
Closes#23077 from HyukjinKwon/SPARK-26105.
Lead-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR breaks down the large ml/tests.py file that contains all Python ML unit tests into several smaller test files to be easier to read and maintain.
The tests are broken down as follows:
```
pyspark
├── __init__.py
...
├── ml
│ ├── __init__.py
...
│ ├── tests
│ │ ├── __init__.py
│ │ ├── test_algorithms.py
│ │ ├── test_base.py
│ │ ├── test_evaluation.py
│ │ ├── test_feature.py
│ │ ├── test_image.py
│ │ ├── test_linalg.py
│ │ ├── test_param.py
│ │ ├── test_persistence.py
│ │ ├── test_pipeline.py
│ │ ├── test_stat.py
│ │ ├── test_training_summary.py
│ │ ├── test_tuning.py
│ │ └── test_wrapper.py
...
├── testing
...
│ ├── mlutils.py
...
```
## How was this patch tested?
Ran tests manually by module to ensure test count was the same, and ran `python/run-tests --modules=pyspark-ml` to verify all passing with Python 2.7 and Python 3.6.
Closes#23063 from BryanCutler/python-test-breakup-ml-SPARK-26033.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
