### What changes were proposed in this pull request?
In [SPARK-33139](https://github.com/apache/spark/pull/30042), I was using reflect "Class.forName" in python code to invoke method in SparkSession which is not recommended. using getattr to access "SparkSession$.Module$" instead.
### Why are the changes needed?
Code refine.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#30092 from leanken/leanken-SPARK-33139-followup.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is a sub-task of [SPARK-33138](https://issues.apache.org/jira/browse/SPARK-33138). In order to make SQLConf.get reliable and stable, we need to make sure user can't pollute the SQLConf and SparkSession Context via calling setActiveSession and clearActiveSession.
Change of the PR:
* add legacy config spark.sql.legacy.allowModifyActiveSession to fallback to old behavior if user do need to call these two API.
* by default, if user call these two API, it will throw exception
* add extra two internal and private API setActiveSessionInternal and clearActiveSessionInternal for current internal usage
* change all internal reference to new internal API except for SQLContext.setActive and SQLContext.clearActive
### Why are the changes needed?
Make SQLConf.get reliable and stable.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
* Add UT in SparkSessionBuilderSuite to test the legacy config
* Existing test
Closes#30042 from leanken/leanken-SPARK-33139.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change `df.groupBy.agg()` to `df.groupBy().agg()` in the docstring of `agg()`
### Why are the changes needed?
Fix typo in a docstring
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No
Closes#30060 from ChuliangXiao/patch-1.
Authored-by: Chuliang Xiao <ChuliangX@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Remove unused `typing.Optional` import from `pyspark.resource.profile` stub.
### Why are the changes needed?
Since SPARK-32319 we don't allow unused imports. However, this one slipped both local and CI tests for some reason.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests and mypy check.
Closes#30002 from zero323/SPARK-33086-FOLLOWUP.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
- Annotated return types of `assert_true` and `raise_error` as discussed [here](https://github.com/apache/spark/pull/29947#pullrequestreview-504495801).
- Add `assert_true` and `raise_error` to SparkR NAMESPACE.
- Validating message vector size in SparkR as discussed [here](https://github.com/apache/spark/pull/29947#pullrequestreview-504539004).
### Why are the changes needed?
As discussed in review for #29947.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- Existing tests.
- Validation of annotations using MyPy
Closes#29978 from zero323/SPARK-32793-FOLLOW-UP.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Adds a SQL function `raise_error` which underlies the refactored `assert_true` function. `assert_true` now also (optionally) accepts a custom error message field.
`raise_error` is exposed in SQL, Python, Scala, and R.
`assert_true` was previously only exposed in SQL; it is now also exposed in Python, Scala, and R.
### Why are the changes needed?
Improves usability of `assert_true` by clarifying error messaging, and adds the useful helper function `raise_error`.
### Does this PR introduce _any_ user-facing change?
Yes:
- Adds `raise_error` function to the SQL, Python, Scala, and R APIs.
- Adds `assert_true` function to the SQL, Python and R APIs.
### How was this patch tested?
Adds unit tests in SQL, Python, Scala, and R for `assert_true` and `raise_error`.
Closes#29947 from karenfeng/spark-32793.
Lead-authored-by: Karen Feng <karen.feng@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adds `dropFields` method to:
- PySpark `Column`
- SparkR `Column`
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
No, new API.
### How was this patch tested?
- New unit tests.
- Manual verification of examples / doctests.
- Manual run of MyPy tests
Closes#29967 from zero323/SPARK-32511-FOLLOW-UP-PYSPARK-SPARKR.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR replaces dynamically generated annotations for following modules:
- `pyspark.resource.information`
- `pyspark.resource.profile`
- `pyspark.resource.requests`
### Why are the changes needed?
These modules where not manually annotated in `pyspark-stubs`, but are part of the public API and we should provide more precise annotations.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
MyPy tests:
```
mypy --no-incremental --config python/mypy.ini python/pyspark
```
Closes#29969 from zero323/SPARK-32714-FOLLOW-UP-RESOURCE.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR:
- removes annotations for modules which are not part of the public API.
- removes `__init__.pyi` files, if no annotations, beyond exports, are present.
### Why are the changes needed?
Primarily to reduce maintenance overhead and as requested in the comments to https://github.com/apache/spark/pull/29591
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests and additional MyPy checks:
```
mypy --no-incremental --config python/mypy.ini python/pyspark
MYPYPATH=python/ mypy --no-incremental --config python/mypy.ini examples/src/main/python/ml examples/src/main/python/sql examples/src/main/python/sql/streaming
```
Closes#29879 from zero323/SPARK-33002.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This improves error handling when a failure in conversion from Pandas to Arrow occurs. And fixes tests to be compatible with upcoming Arrow 2.0.0 release.
### Why are the changes needed?
Current tests will fail with Arrow 2.0.0 because of a change in error message when the schema is invalid. For these cases, the current error message also includes information on disabling safe conversion config, which is mainly meant for floating point truncation and overflow. The tests have been updated to use a message that is show for past Arrow versions, and upcoming.
If the user enters an invalid schema, the error produced by pyarrow is not consistent and either `TypeError` or `ArrowInvalid`, with the latter being caught, and raised as a `RuntimeError` with the extra info.
The error handling is improved by:
- narrowing the exception type to `TypeError`s, which `ArrowInvalid` is a subclass and what is raised on safe conversion failures.
- The exception is only raised with additional information on disabling "spark.sql.execution.pandas.convertToArrowArraySafely" if it is enabled in the first place.
- The original exception is chained to better show it to the user.
### Does this PR introduce _any_ user-facing change?
Yes, the error re-raised changes from a RuntimeError to a ValueError, which better categorizes this type of error and in-line with the original Arrow error.
### How was this patch tested?
Existing tests, using pyarrow 1.0.1 and 2.0.0-snapshot
Closes#29951 from BryanCutler/arrow-better-handle-pandas-errors-SPARK-33073.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Adding a method to get the checkpoint directory from the PySpark context to match the Scala API
### Why are the changes needed?
To make the Scala and Python APIs consistent and remove the need to use the JavaObject
### Does this PR introduce _any_ user-facing change?
Yes, there is a new method which makes it easier to get the checkpoint directory directly rather than using the JavaObject
#### Previous behaviour:
```python
>>> spark.sparkContext.setCheckpointDir('/tmp/spark/checkpoint/')
>>> sc._jsc.sc().getCheckpointDir().get()
'file:/tmp/spark/checkpoint/63f7b67c-e5dc-4d11-a70c-33554a71717a'
```
This method returns a confusing Scala error if it has not been set
```python
>>> sc._jsc.sc().getCheckpointDir().get()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/paul/Desktop/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
File "/home/paul/Desktop/spark/python/pyspark/sql/utils.py", line 111, in deco
return f(*a, **kw)
File "/home/paul/Desktop/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o25.get.
: java.util.NoSuchElementException: None.get
at scala.None$.get(Option.scala:529)
at scala.None$.get(Option.scala:527)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
```
#### New method:
```python
>>> spark.sparkContext.setCheckpointDir('/tmp/spark/checkpoint/')
>>> spark.sparkContext.getCheckpointDir()
'file:/tmp/spark/checkpoint/b38aca2e-8ace-44fc-a4c4-f4e36c2da2a7'
```
``getCheckpointDir()`` returns ``None`` if it has not been set
```python
>>> print(spark.sparkContext.getCheckpointDir())
None
```
### How was this patch tested?
Added to existing unit tests. But I'm not sure how to add a test for the case where ``getCheckpointDir()`` should return ``None`` since the existing checkpoint tests set the checkpoint directory in the ``setUp`` method before any tests are run as far as I can tell.
Closes#29918 from reidy-p/SPARK-33017.
Authored-by: reidy-p <paul_reidy@outlook.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Explicitly document that `current_date` and `current_timestamp` are executed at the start of query evaluation. And all calls of `current_date`/`current_timestamp` within the same query return the same value
### Why are the changes needed?
Users could expect that `current_date` and `current_timestamp` return the current date/timestamp at the moment of query execution but in fact the functions are folded by the optimizer at the start of query evaluation:
0df8dd6073/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/finishAnalysis.scala (L71-L91)
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
by running `./dev/scalastyle`.
Closes#29892 from MaxGekk/doc-current_date.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`nth_value` was added at SPARK-27951. This PR adds the corresponding PySpark API.
### Why are the changes needed?
To support the consistent APIs
### Does this PR introduce _any_ user-facing change?
Yes, it introduces a new PySpark function API.
### How was this patch tested?
Unittest was added.
Closes#29899 from HyukjinKwon/SPARK-33020.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Move functions related test cases from `test_context.py` to `test_functions.py`.
### Why are the changes needed?
To group the similar test cases.
### Does this PR introduce _any_ user-facing change?
Nope, test-only.
### How was this patch tested?
Jenkins and GitHub Actions should test.
Closes#29898 from HyukjinKwon/SPARK-33021.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR adds two `type: ignores`, one in `pyspark.install` and one in related tests.
### Why are the changes needed?
To satisfy MyPy type checks. It seems like we originally missed some changes that happened around merge of
31a16fbb40
```
python/pyspark/install.py:30: error: Need type annotation for 'UNSUPPORTED_COMBINATIONS' (hint: "UNSUPPORTED_COMBINATIONS: List[<type>] = ...") [var-annotated]
python/pyspark/tests/test_install_spark.py:105: error: Cannot find implementation or library stub for module named 'xmlrunner' [import]
python/pyspark/tests/test_install_spark.py:105: note: See https://mypy.readthedocs.io/en/latest/running_mypy.html#missing-imports
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- Existing tests.
- MyPy tests
```
mypy --show-error-code --no-incremental --config python/mypy.ini python/pyspark
```
Closes#29878 from zero323/SPARK-32714-FOLLOW-UP.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR removes Hive 1.2 option (and therefore `HIVE_VERSION` environment variable as well).
### Why are the changes needed?
Hive 1.2 is a fork version. We shouldn't promote users to use.
### Does this PR introduce _any_ user-facing change?
Nope, `HIVE_VERSION` and Hive 1.2 are removed but this is new experimental feature in master only.
### How was this patch tested?
Manually tested:
```bash
SPARK_VERSION=3.0.1 HADOOP_VERSION=3.2 pip install pyspark-3.1.0.dev0.tar.gz -v
SPARK_VERSION=3.0.1 HADOOP_VERSION=2.7 pip install pyspark-3.1.0.dev0.tar.gz -v
SPARK_VERSION=3.0.1 HADOOP_VERSION=invalid pip install pyspark-3.1.0.dev0.tar.gz -v
```
Closes#29858 from HyukjinKwon/SPARK-32981.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes migration of [`pyspark-stubs`](https://github.com/zero323/pyspark-stubs) into Spark codebase.
### Why are the changes needed?
### Does this PR introduce _any_ user-facing change?
Yes. This PR adds type annotations directly to Spark source.
This can impact interaction with development tools for users, which haven't used `pyspark-stubs`.
### How was this patch tested?
- [x] MyPy tests of the PySpark source
```
mypy --no-incremental --config python/mypy.ini python/pyspark
```
- [x] MyPy tests of Spark examples
```
MYPYPATH=python/ mypy --no-incremental --config python/mypy.ini examples/src/main/python/ml examples/src/main/python/sql examples/src/main/python/sql/streaming
```
- [x] Existing Flake8 linter
- [x] Existing unit tests
Tested against:
- `mypy==0.790+dev.e959952d9001e9713d329a2f9b196705b028f894`
- `mypy==0.782`
Closes#29591 from zero323/SPARK-32681.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
revert blockify gmm
### Why are the changes needed?
WeichenXu123 and I thought we should use memory size instead of number of rows to blockify instance; then if a buffer's size is large and determined by number of rows, we should discard it.
In GMM, we found that the pre-allocated memory maybe too large and should be discarded:
```
transient private lazy val auxiliaryPDFMat = DenseMatrix.zeros(blockSize, numFeatures)
```
We had some offline discuss and thought it is better to revert blockify GMM.
### Does this PR introduce _any_ user-facing change?
blockSize added in master branch will be removed
### How was this patch tested?
existing testsuites
Closes#29782 from zhengruifeng/unblockify_gmm.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
This PR proposes to add a way to select Hadoop and Hive versions in pip installation.
Users can select Hive or Hadoop versions as below:
```bash
HADOOP_VERSION=3.2 pip install pyspark
HIVE_VERSION=1.2 pip install pyspark
HIVE_VERSION=1.2 HADOOP_VERSION=2.7 pip install pyspark
```
When the environment variables are set, internally it downloads the corresponding Spark version and then sets the Spark home to it. Also this PR exposes a mirror to set as an environment variable, `PYSPARK_RELEASE_MIRROR`.
**Please NOTE that:**
- We cannot currently leverage pip's native installation option, for example:
```bash
pip install pyspark --install-option="hadoop3.2"
```
This is because of a limitation and bug in pip itself. Once they fix this issue, we can switch from the environment variables to the proper installation options, see SPARK-32837.
It IS possible to workaround but very ugly or hacky with a big change. See [this PR](https://github.com/microsoft/nni/pull/139/files) as an example.
- In pip installation, we pack the relevant jars together. This PR _does not touch existing packaging way_ in order to prevent any behaviour changes.
Once this experimental way is proven to be safe, we can avoid packing the relevant jars together (and keep only the relevant Python scripts). And downloads the Spark distribution as this PR proposes.
- This way is sort of consistent with SparkR:
SparkR provides a method `SparkR::install.spark` to support CRAN installation. This is fine because SparkR is provided purely as a R library. For example, `sparkr` script is not packed together.
PySpark cannot take this approach because PySpark packaging ships relevant executable script together, e.g.) `pyspark` shell.
If PySpark has a method such as `pyspark.install_spark`, users cannot call it in `pyspark` because `pyspark` already assumes relevant Spark is installed, JVM is launched, etc.
- There looks no way to release that contains different Hadoop or Hive to PyPI due to [the version semantics](https://www.python.org/dev/peps/pep-0440/). This is not an option.
The usual way looks either `--install-option` above with hacks or environment variables given my investigation.
### Why are the changes needed?
To provide users the options to select Hadoop and Hive versions.
### Does this PR introduce _any_ user-facing change?
Yes, users will be able to select Hive and Hadoop version as below when they install it from `pip`;
```bash
HADOOP_VERSION=3.2 pip install pyspark
HIVE_VERSION=1.2 pip install pyspark
HIVE_VERSION=1.2 HADOOP_VERSION=2.7 pip install pyspark
```
### How was this patch tested?
Unit tests were added. I also manually tested in Mac and Windows (after building Spark with `python/dist/pyspark-3.1.0.dev0.tar.gz`):
```bash
./build/mvn -DskipTests -Phive-thriftserver clean package
```
Mac:
```bash
SPARK_VERSION=3.0.1 HADOOP_VERSION=3.2 pip install pyspark-3.1.0.dev0.tar.gz
```
Windows:
```bash
set HADOOP_VERSION=3.2
set SPARK_VERSION=3.0.1
pip install pyspark-3.1.0.dev0.tar.gz
```
Closes#29703 from HyukjinKwon/SPARK-32017.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adjusts signatures of methods decorated with `keyword_only` to indicate using [Python 3 keyword-only syntax](https://www.python.org/dev/peps/pep-3102/).
__Note__:
For the moment the goal is not to replace `keyword_only`. For justification see https://github.com/apache/spark/pull/29591#discussion_r489402579
### Why are the changes needed?
Right now it is not clear that `keyword_only` methods are indeed keyword only. This proposal addresses that.
In practice we could probably capture `locals` and drop `keyword_only` completel, i.e:
```python
keyword_only
def __init__(self, *, featuresCol="features"):
...
kwargs = self._input_kwargs
self.setParams(**kwargs)
```
could be replaced with
```python
def __init__(self, *, featuresCol="features"):
kwargs = locals()
del kwargs["self"]
...
self.setParams(**kwargs)
```
### Does this PR introduce _any_ user-facing change?
Docstrings and inspect tools will now indicate that `keyword_only` methods expect only keyword arguments.
For example with ` LinearSVC` will change from
```
>>> from pyspark.ml.classification import LinearSVC
>>> ?LinearSVC.__init__
Signature:
LinearSVC.__init__(
self,
featuresCol='features',
labelCol='label',
predictionCol='prediction',
maxIter=100,
regParam=0.0,
tol=1e-06,
rawPredictionCol='rawPrediction',
fitIntercept=True,
standardization=True,
threshold=0.0,
weightCol=None,
aggregationDepth=2,
)
Docstring: __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.0, tol=1e-6, rawPredictionCol="rawPrediction", fitIntercept=True, standardization=True, threshold=0.0, weightCol=None, aggregationDepth=2):
File: /path/to/python/pyspark/ml/classification.py
Type: function
```
to
```
>>> from pyspark.ml.classification import LinearSVC
>>> ?LinearSVC.__init__
Signature:
LinearSVC.__init__ (
self,
*,
featuresCol='features',
labelCol='label',
predictionCol='prediction',
maxIter=100,
regParam=0.0,
tol=1e-06,
rawPredictionCol='rawPrediction',
fitIntercept=True,
standardization=True,
threshold=0.0,
weightCol=None,
aggregationDepth=2,
blockSize=1,
)
Docstring: __init__(self, \*, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.0, tol=1e-6, rawPredictionCol="rawPrediction", fitIntercept=True, standardization=True, threshold=0.0, weightCol=None, aggregationDepth=2, blockSize=1):
File: ~/Workspace/spark/python/pyspark/ml/classification.py
Type: function
```
### How was this patch tested?
Existing tests.
Closes#29799 from zero323/SPARK-32933.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
More precise description of the result of the `percentile_approx()` function and its synonym `approx_percentile()`. The proposed sentence clarifies that the function returns **one of elements** (or array of elements) from the input column.
### Why are the changes needed?
To improve Spark docs and avoid misunderstanding of the function behavior.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
`./dev/scalastyle`
Closes#29835 from MaxGekk/doc-percentile_approx.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Add optional `allowMissingColumns` argument to SparkR `unionByName`.
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
`unionByName` supports `allowMissingColumns`.
### How was this patch tested?
Existing unit tests. New unit tests targeting this feature.
Closes#29813 from zero323/SPARK-32799.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In PySpark shell, if you call `SparkSession.builder.getOrCreate` as below:
```python
import warnings
from pyspark.sql import SparkSession, SQLContext
warnings.simplefilter('always', DeprecationWarning)
spark.stop()
SparkSession.builder.getOrCreate()
```
it shows the deprecation warning as below:
```
/.../spark/python/pyspark/sql/context.py:72: DeprecationWarning: Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.
DeprecationWarning)
```
via d3304268d3/python/pyspark/sql/session.py (L222)
We shouldn't print the deprecation warning from it. This is the only place ^.
### Why are the changes needed?
To prevent to inform users that `SparkSession.builder.getOrCreate` is deprecated mistakenly.
### Does this PR introduce _any_ user-facing change?
Yes, it won't show a deprecation warning to end users for calling `SparkSession.builder.getOrCreate`.
### How was this patch tested?
Manually tested as above.
Closes#29768 from HyukjinKwon/SPARK-32897.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Replace `__metaclass__` fields with `metaclass` keyword in the class statements.
### Why are the changes needed?
`__metaclass__` is no longer supported in Python 3. This means, for example, that types are no longer handled as singletons.
```
>>> from pyspark.sql.types import BooleanType
>>> BooleanType() is BooleanType()
False
```
and classes, which suppose to be abstract, are not
```
>>> import inspect
>>> from pyspark.ml import Estimator
>>> inspect.isabstract(Estimator)
False
```
### Does this PR introduce _any_ user-facing change?
Yes (classes which were no longer abstract or singleton in Python 3, are now), though visible changes should be consider a bug-fix.
### How was this patch tested?
Existing tests.
Closes#29664 from zero323/SPARK-32138-FOLLOW-UP-METACLASS.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adds a `withField` method on the pyspark Column class to call the Scala API method added in https://github.com/apache/spark/pull/27066.
### Why are the changes needed?
To update the Python API to match a new feature in the Scala API.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New unit test
Closes#29699 from Kimahriman/feature/pyspark-with-field.
Authored-by: Adam Binford <adam.binford@radiantsolutions.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This proposes to enhance user document of the API for loading a Dataset of strings storing CSV rows. If the header option is set to true, the API will remove all lines same with the header.
### Why are the changes needed?
This behavior can confuse users. We should explicitly document it.
### Does this PR introduce _any_ user-facing change?
No. Only doc change.
### How was this patch tested?
Only doc change.
Closes#29765 from viirya/SPARK-32888.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Since the data is serialized on the Python side, we should make cache() in PySpark dataframes use StorageLevel.MEMORY_AND_DISK mode which has deserialized=false. This change was done to `pyspark/rdd.py` as part of SPARK-2014 but was missed from `pyspark/dataframe.py`
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Using existing tests
Closes#29242 from abhishekd0907/SPARK-31448.
Authored-by: Abhishek Dixit <abhishekdixit0907@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Upgrade Apache Arrow to version 1.0.1 for the Java dependency and increase minimum version of PyArrow to 1.0.0.
This release marks a transition to binary stability of the columnar format (which was already informally backward-compatible going back to December 2017) and a transition to Semantic Versioning for the Arrow software libraries. Also note that the Java arrow-memory artifact has been split to separate dependence on netty-buffer and allow users to select an allocator. Spark will continue to use `arrow-memory-netty` to maintain performance benefits.
Version 1.0.0 - 1.0.0 include the following selected fixes/improvements relevant to Spark users:
ARROW-9300 - [Java] Separate Netty Memory to its own module
ARROW-9272 - [C++][Python] Reduce complexity in python to arrow conversion
ARROW-9016 - [Java] Remove direct references to Netty/Unsafe Allocators
ARROW-8664 - [Java] Add skip null check to all Vector types
ARROW-8485 - [Integration][Java] Implement extension types integration
ARROW-8434 - [C++] Ipc RecordBatchFileReader deserializes the Schema multiple times
ARROW-8314 - [Python] Provide a method to select a subset of columns of a Table
ARROW-8230 - [Java] Move Netty memory manager into a separate module
ARROW-8229 - [Java] Move ArrowBuf into the Arrow package
ARROW-7955 - [Java] Support large buffer for file/stream IPC
ARROW-7831 - [Java] unnecessary buffer allocation when calling splitAndTransferTo on variable width vectors
ARROW-6111 - [Java] Support LargeVarChar and LargeBinary types and add integration test with C++
ARROW-6110 - [Java] Support LargeList Type and add integration test with C++
ARROW-5760 - [C++] Optimize Take implementation
ARROW-300 - [Format] Add body buffer compression option to IPC message protocol using LZ4 or ZSTD
ARROW-9098 - RecordBatch::ToStructArray cannot handle record batches with 0 column
ARROW-9066 - [Python] Raise correct error in isnull()
ARROW-9223 - [Python] Fix to_pandas() export for timestamps within structs
ARROW-9195 - [Java] Wrong usage of Unsafe.get from bytearray in ByteFunctionsHelper class
ARROW-7610 - [Java] Finish support for 64 bit int allocations
ARROW-8115 - [Python] Conversion when mixing NaT and datetime objects not working
ARROW-8392 - [Java] Fix overflow related corner cases for vector value comparison
ARROW-8537 - [C++] Performance regression from ARROW-8523
ARROW-8803 - [Java] Row count should be set before loading buffers in VectorLoader
ARROW-8911 - [C++] Slicing a ChunkedArray with zero chunks segfaults
View release notes here:
https://arrow.apache.org/release/1.0.1.htmlhttps://arrow.apache.org/release/1.0.0.html
### Why are the changes needed?
Upgrade brings fixes, improvements and stability guarantees.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests with pyarrow 1.0.0 and 1.0.1
Closes#29686 from BryanCutler/arrow-upgrade-100-SPARK-32312.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to add new argument `allowMissingColumns` to `unionByName` for allowing users to specify whether to allow missing columns or not.
### Why are the changes needed?
To expose `allowMissingColumns` argument in Python API also. Currently this is only exposed in Scala/Java APIs.
### Does this PR introduce _any_ user-facing change?
Yes, it adds a new examples with new argument in the docstring.
### How was this patch tested?
Doctest added and manually tested
```
$ python/run-tests --testnames pyspark.sql.dataframe
Running PySpark tests. Output is in /.../spark/python/unit-tests.log
Will test against the following Python executables: ['/.../python3', 'python3.8']
Will test the following Python tests: ['pyspark.sql.dataframe']
/.../python3 python_implementation is CPython
/.../python3 version is: Python 3.8.5
python3.8 python_implementation is CPython
python3.8 version is: Python 3.8.5
Starting test(/.../python3): pyspark.sql.dataframe
Starting test(python3.8): pyspark.sql.dataframe
Finished test(python3.8): pyspark.sql.dataframe (35s)
Finished test(/.../python3): pyspark.sql.dataframe (35s)
Tests passed in 35 seconds
```
Closes#29657 from itholic/SPARK-32798.
Authored-by: itholic <haejoon309@naver.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Change `CreateFunctionCommand` code that add class check before create function.
### Why are the changes needed?
We have different behavior between create permanent function and temporary function when function class is invaild. e.g.,
```
create function f as 'test.non.exists.udf';
-- Time taken: 0.104 seconds
create temporary function f as 'test.non.exists.udf'
-- Error in query: Can not load class 'test.non.exists.udf' when registering the function 'f', please make sure it is on the classpath;
```
And Hive also fails both of them.
### Does this PR introduce _any_ user-facing change?
Yes, user will get exception when create a invalid udf.
### How was this patch tested?
New test.
Closes#29502 from ulysses-you/function.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Removal of branched `StringIO` import.
### Why are the changes needed?
Top level `StringIO` is no longer present in Python 3.x.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#29590 from zero323/SPARK-32138-FOLLOW-UP.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
https://issues.apache.org/jira/browse/SPARK-32719
### What changes were proposed in this pull request?
Add a check to detect missing imports. This makes sure that if we use a specific class, it should be explicitly imported (not using a wildcard).
### Why are the changes needed?
To make sure that the quality of the Python code is up to standard.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing unit-tests and Flake8 static analysis
Closes#29563 from Fokko/fd-add-check-missing-imports.
Authored-by: Fokko Driesprong <fokko@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Fixed `CrossValidatorModel.copy()` so that it correctly calls `.copy()` on the models instead of lists of models.
### Why are the changes needed?
`copy()` was first changed in #29445 . The issue was found in CI of #29524 and fixed. This PR introduces the exact same change so that `CrossValidatorModel.copy()` and its related tests are aligned in branch `master` and branch `branch-3.0`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Updated `test_copy` to make sure `copy()` is called on models instead of lists of models.
Closes#29553 from Louiszr/fix-cv-copy.
Authored-by: Louiszr <zxhst14@gmail.com>
Signed-off-by: Huaxin Gao <huaxing@us.ibm.com>
### What changes were proposed in this pull request?
This PR proposes to move Arrow usage guide from Spark documentation site to PySpark documentation site (at "User Guide").
Here is the demo for reviewing quicker: https://hyukjin-spark.readthedocs.io/en/stable/user_guide/arrow_pandas.html
### Why are the changes needed?
To have a single place for PySpark users, and better documentation.
### Does this PR introduce _any_ user-facing change?
Yes, it will move https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html to our PySpark documentation.
### How was this patch tested?
```bash
cd docs
SKIP_SCALADOC=1 SKIP_RDOC=1 SKIP_SQLDOC=1 jekyll serve --watch
```
and
```bash
cd python/docs
make clean html
```
Closes#29548 from HyukjinKwon/SPARK-32183.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a follow up PR to #29328 to apply the same constraint where `path` option cannot coexist with path parameter to `DataFrameWriter.save()`, `DataStreamReader.load()` and `DataStreamWriter.start()`.
### Why are the changes needed?
The current behavior silently overwrites the `path` option if path parameter is passed to `DataFrameWriter.save()`, `DataStreamReader.load()` and `DataStreamWriter.start()`.
For example,
```
Seq(1).toDF.write.option("path", "/tmp/path1").parquet("/tmp/path2")
```
will write the result to `/tmp/path2`.
### Does this PR introduce _any_ user-facing change?
Yes, if `path` option coexists with path parameter to any of the above methods, it will throw `AnalysisException`:
```
scala> Seq(1).toDF.write.option("path", "/tmp/path1").parquet("/tmp/path2")
org.apache.spark.sql.AnalysisException: There is a 'path' option set and save() is called with a path parameter. Either remove the path option, or call save() without the parameter. To ignore this check, set 'spark.sql.legacy.pathOptionBehavior.enabled' to 'true'.;
```
The user can restore the previous behavior by setting `spark.sql.legacy.pathOptionBehavior.enabled` to `true`.
### How was this patch tested?
Added new tests.
Closes#29543 from imback82/path_option.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Fixes typo in docsting of `toDF`
### Why are the changes needed?
The third argument of `toDF` is actually `sampleRatio`.
related discussion: https://github.com/apache/spark/pull/12746#discussion-diff-62704834
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
This patch doesn't affect any logic, so existing tests should cover it.
Closes#29551 from unirt/minor_fix_docs.
Authored-by: unirt <lunirtc@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR enhances `Catalog.createTable()` to allow users to set the table's description. This corresponds to the following SQL syntax:
```sql
CREATE TABLE ...
COMMENT 'this is a fancy table';
```
### Why are the changes needed?
This brings the Scala/Python catalog APIs a bit closer to what's already possible via SQL.
### Does this PR introduce any user-facing change?
Yes, it adds a new parameter to `Catalog.createTable()`.
### How was this patch tested?
Existing unit tests:
```sh
./python/run-tests \
--python-executables python3.7 \
--testnames 'pyspark.sql.tests.test_catalog,pyspark.sql.tests.test_context'
```
```
$ ./build/sbt
testOnly org.apache.spark.sql.internal.CatalogSuite org.apache.spark.sql.CachedTableSuite org.apache.spark.sql.hive.MetastoreDataSourcesSuite org.apache.spark.sql.hive.execution.HiveDDLSuite
```
Closes#27908 from nchammas/SPARK-31000-table-description.
Authored-by: Nicholas Chammas <nicholas.chammas@liveramp.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
As discussed in https://github.com/apache/spark/pull/29491#discussion_r474451282 and in SPARK-32686, this PR un-deprecates Spark's ability to infer a DataFrame schema from a list of dictionaries. The ability is Pythonic and matches functionality offered by Pandas.
### Why are the changes needed?
This change clarifies to users that this behavior is supported and is not going away in the near future.
### Does this PR introduce _any_ user-facing change?
Yes. There used to be a `UserWarning` for this, but now there isn't.
### How was this patch tested?
I tested this manually.
Before:
```python
>>> spark.createDataFrame(spark.sparkContext.parallelize([{'a': 5}]))
/Users/nchamm/Documents/GitHub/nchammas/spark/python/pyspark/sql/session.py:388: UserWarning: Using RDD of dict to inferSchema is deprecated. Use pyspark.sql.Row instead
warnings.warn("Using RDD of dict to inferSchema is deprecated. "
DataFrame[a: bigint]
>>> spark.createDataFrame([{'a': 5}])
.../python/pyspark/sql/session.py:378: UserWarning: inferring schema from dict is deprecated,please use pyspark.sql.Row instead
warnings.warn("inferring schema from dict is deprecated,"
DataFrame[a: bigint]
```
After:
```python
>>> spark.createDataFrame(spark.sparkContext.parallelize([{'a': 5}]))
DataFrame[a: bigint]
>>> spark.createDataFrame([{'a': 5}])
DataFrame[a: bigint]
```
Closes#29510 from nchammas/SPARK-32686-df-dict-infer-schema.
Authored-by: Nicholas Chammas <nicholas.chammas@liveramp.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
### What changes were proposed in this pull request?
Changed the definitions of `CrossValidatorModel.copy()/_to_java()/_from_java()` so that exposed parameters (i.e. parameters with `get()` methods) are copied in these methods.
### Why are the changes needed?
Parameters are copied in the respective Scala interface for `CrossValidatorModel.copy()`.
It fits the semantics to persist parameters when calling `CrossValidatorModel.save()` and `CrossValidatorModel.load()` so that the user gets the same model by saving and loading it after. Not copying across `numFolds` also causes bugs like Array index out of bound and losing sub-models because this parameters will always default to 3 (as described in the JIRA ticket).
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Tests for `CrossValidatorModel.copy()` and `save()`/`load()` are updated so that they check parameters before and after function calls.
Closes#29445 from Louiszr/master.
Authored-by: Louiszr <zxhst14@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
The class `KMeansSummary` in pyspark is not included in `clustering.py`'s `__all__` declaration. It isn't included in the docs as a result.
`InheritableThread` and `KMeansSummary` should be into corresponding RST files for documentation.
### Why are the changes needed?
It seems like an oversight to not include this as all similar "summary" classes are.
`InheritableThread` should also be documented.
### Does this PR introduce _any_ user-facing change?
I don't believe there are functional changes. It should make this public class appear in docs.
### How was this patch tested?
Existing tests / N/A.
Closes#29470 from srowen/KMeansSummary.
Lead-authored-by: Sean Owen <srowen@gmail.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In the docs concerning the approx_count_distinct I have changed the description of the rsd parameter from **_maximum estimation error allowed_** to _**maximum relative standard deviation allowed**_
### Why are the changes needed?
Maximum estimation error allowed can be misleading. You can set the target relative standard deviation, which affects the estimation error, but on given runs the estimation error can still be above the rsd parameter.
### Does this PR introduce _any_ user-facing change?
This PR should make it easier for users reading the docs to understand that the rsd parameter in approx_count_distinct doesn't cap the estimation error, but just sets the target deviation instead,
### How was this patch tested?
No tests, as no code changes were made.
Closes#29424 from Comonut/fix-approx_count_distinct-rsd-param-description.
Authored-by: alexander-daskalov <alexander.daskalov@adevinta.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR aims to add `StorageLevel.DISK_ONLY_3` as a built-in `StorageLevel`.
### Why are the changes needed?
In a YARN cluster, HDFS uaually provides storages with replication factor 3. So, we can save the result to HDFS to get `StorageLevel.DISK_ONLY_3` technically. However, disaggregate clusters or clusters without storage services are rising. Previously, in that situation, the users were able to use similar `MEMORY_AND_DISK_2` or a user-created `StorageLevel`. This PR aims to support those use cases officially for better UX.
### Does this PR introduce _any_ user-facing change?
Yes. This provides a new built-in option.
### How was this patch tested?
Pass the GitHub Action or Jenkins with the revised test cases.
Closes#29331 from dongjoon-hyun/SPARK-32517.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
add test_createDataFrame_empty_partition in pyspark arrow tests
### Why are the changes needed?
test edge cases.
### Does this PR introduce _any_ user-facing change?
no.
### How was this patch tested?
N/A
Closes#29398 from WeichenXu123/add_one_pyspark_arrow_test.
Authored-by: Weichen Xu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
Disallow the use of unused imports:
- Unnecessary increases the memory footprint of the application
- Removes the imports that are required for the examples in the docstring from the file-scope to the example itself. This keeps the files itself clean, and gives a more complete example as it also includes the imports :)
```
fokkodriesprongFan spark % flake8 python | grep -i "imported but unused"
python/pyspark/cloudpickle.py:46:1: F401 'functools.partial' imported but unused
python/pyspark/cloudpickle.py:55:1: F401 'traceback' imported but unused
python/pyspark/heapq3.py:868:5: F401 '_heapq.*' imported but unused
python/pyspark/__init__.py:61:1: F401 'pyspark.version.__version__' imported but unused
python/pyspark/__init__.py:62:1: F401 'pyspark._globals._NoValue' imported but unused
python/pyspark/__init__.py:115:1: F401 'pyspark.sql.SQLContext' imported but unused
python/pyspark/__init__.py:115:1: F401 'pyspark.sql.HiveContext' imported but unused
python/pyspark/__init__.py:115:1: F401 'pyspark.sql.Row' imported but unused
python/pyspark/rdd.py:21:1: F401 're' imported but unused
python/pyspark/rdd.py:29:1: F401 'tempfile.NamedTemporaryFile' imported but unused
python/pyspark/mllib/regression.py:26:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/clustering.py:28:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/clustering.py:28:1: F401 'pyspark.mllib.linalg.DenseVector' imported but unused
python/pyspark/mllib/classification.py:26:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/feature.py:28:1: F401 'pyspark.mllib.linalg.DenseVector' imported but unused
python/pyspark/mllib/feature.py:28:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/feature.py:30:1: F401 'pyspark.mllib.regression.LabeledPoint' imported but unused
python/pyspark/mllib/tests/test_linalg.py:18:1: F401 'sys' imported but unused
python/pyspark/mllib/tests/test_linalg.py:642:5: F401 'pyspark.mllib.tests.test_linalg.*' imported but unused
python/pyspark/mllib/tests/test_feature.py:21:1: F401 'numpy.random' imported but unused
python/pyspark/mllib/tests/test_feature.py:21:1: F401 'numpy.exp' imported but unused
python/pyspark/mllib/tests/test_feature.py:23:1: F401 'pyspark.mllib.linalg.Vector' imported but unused
python/pyspark/mllib/tests/test_feature.py:23:1: F401 'pyspark.mllib.linalg.VectorUDT' imported but unused
python/pyspark/mllib/tests/test_feature.py:185:5: F401 'pyspark.mllib.tests.test_feature.*' imported but unused
python/pyspark/mllib/tests/test_util.py:97:5: F401 'pyspark.mllib.tests.test_util.*' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.Vector' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.DenseVector' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.VectorUDT' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg._convert_to_vector' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.DenseMatrix' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.SparseMatrix' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.MatrixUDT' imported but unused
python/pyspark/mllib/tests/test_stat.py:181:5: F401 'pyspark.mllib.tests.test_stat.*' imported but unused
python/pyspark/mllib/tests/test_streaming_algorithms.py:18:1: F401 'time.time' imported but unused
python/pyspark/mllib/tests/test_streaming_algorithms.py:18:1: F401 'time.sleep' imported but unused
python/pyspark/mllib/tests/test_streaming_algorithms.py:470:5: F401 'pyspark.mllib.tests.test_streaming_algorithms.*' imported but unused
python/pyspark/mllib/tests/test_algorithms.py:295:5: F401 'pyspark.mllib.tests.test_algorithms.*' imported but unused
python/pyspark/tests/test_serializers.py:90:13: F401 'xmlrunner' imported but unused
python/pyspark/tests/test_rdd.py:21:1: F401 'sys' imported but unused
python/pyspark/tests/test_rdd.py:29:1: F401 'pyspark.resource.ResourceProfile' imported but unused
python/pyspark/tests/test_rdd.py:885:5: F401 'pyspark.tests.test_rdd.*' imported but unused
python/pyspark/tests/test_readwrite.py:19:1: F401 'sys' imported but unused
python/pyspark/tests/test_readwrite.py:22:1: F401 'array.array' imported but unused
python/pyspark/tests/test_readwrite.py:309:5: F401 'pyspark.tests.test_readwrite.*' imported but unused
python/pyspark/tests/test_join.py:62:5: F401 'pyspark.tests.test_join.*' imported but unused
python/pyspark/tests/test_taskcontext.py:19:1: F401 'shutil' imported but unused
python/pyspark/tests/test_taskcontext.py:325:5: F401 'pyspark.tests.test_taskcontext.*' imported but unused
python/pyspark/tests/test_conf.py:36:5: F401 'pyspark.tests.test_conf.*' imported but unused
python/pyspark/tests/test_broadcast.py:148:5: F401 'pyspark.tests.test_broadcast.*' imported but unused
python/pyspark/tests/test_daemon.py:76:5: F401 'pyspark.tests.test_daemon.*' imported but unused
python/pyspark/tests/test_util.py:77:5: F401 'pyspark.tests.test_util.*' imported but unused
python/pyspark/tests/test_pin_thread.py:19:1: F401 'random' imported but unused
python/pyspark/tests/test_pin_thread.py:149:5: F401 'pyspark.tests.test_pin_thread.*' imported but unused
python/pyspark/tests/test_worker.py:19:1: F401 'sys' imported but unused
python/pyspark/tests/test_worker.py:26:5: F401 'resource' imported but unused
python/pyspark/tests/test_worker.py:203:5: F401 'pyspark.tests.test_worker.*' imported but unused
python/pyspark/tests/test_profiler.py:101:5: F401 'pyspark.tests.test_profiler.*' imported but unused
python/pyspark/tests/test_shuffle.py:18:1: F401 'sys' imported but unused
python/pyspark/tests/test_shuffle.py:171:5: F401 'pyspark.tests.test_shuffle.*' imported but unused
python/pyspark/tests/test_rddbarrier.py:43:5: F401 'pyspark.tests.test_rddbarrier.*' imported but unused
python/pyspark/tests/test_context.py:129:13: F401 'userlibrary.UserClass' imported but unused
python/pyspark/tests/test_context.py:140:13: F401 'userlib.UserClass' imported but unused
python/pyspark/tests/test_context.py:310:5: F401 'pyspark.tests.test_context.*' imported but unused
python/pyspark/tests/test_appsubmit.py:241:5: F401 'pyspark.tests.test_appsubmit.*' imported but unused
python/pyspark/streaming/dstream.py:18:1: F401 'sys' imported but unused
python/pyspark/streaming/tests/test_dstream.py:27:1: F401 'pyspark.RDD' imported but unused
python/pyspark/streaming/tests/test_dstream.py:647:5: F401 'pyspark.streaming.tests.test_dstream.*' imported but unused
python/pyspark/streaming/tests/test_kinesis.py:83:5: F401 'pyspark.streaming.tests.test_kinesis.*' imported but unused
python/pyspark/streaming/tests/test_listener.py:152:5: F401 'pyspark.streaming.tests.test_listener.*' imported but unused
python/pyspark/streaming/tests/test_context.py:178:5: F401 'pyspark.streaming.tests.test_context.*' imported but unused
python/pyspark/testing/utils.py:30:5: F401 'scipy.sparse' imported but unused
python/pyspark/testing/utils.py:36:5: F401 'numpy as np' imported but unused
python/pyspark/ml/regression.py:25:1: F401 'pyspark.ml.tree._TreeEnsembleParams' imported but unused
python/pyspark/ml/regression.py:25:1: F401 'pyspark.ml.tree._HasVarianceImpurity' imported but unused
python/pyspark/ml/regression.py:29:1: F401 'pyspark.ml.wrapper.JavaParams' imported but unused
python/pyspark/ml/util.py:19:1: F401 'sys' imported but unused
python/pyspark/ml/__init__.py:25:1: F401 'pyspark.ml.pipeline' imported but unused
python/pyspark/ml/pipeline.py:18:1: F401 'sys' imported but unused
python/pyspark/ml/stat.py:22:1: F401 'pyspark.ml.linalg.DenseMatrix' imported but unused
python/pyspark/ml/stat.py:22:1: F401 'pyspark.ml.linalg.Vectors' imported but unused
python/pyspark/ml/tests/test_training_summary.py:18:1: F401 'sys' imported but unused
python/pyspark/ml/tests/test_training_summary.py:364:5: F401 'pyspark.ml.tests.test_training_summary.*' imported but unused
python/pyspark/ml/tests/test_linalg.py:381:5: F401 'pyspark.ml.tests.test_linalg.*' imported but unused
python/pyspark/ml/tests/test_tuning.py:427:9: F401 'pyspark.sql.functions as F' imported but unused
python/pyspark/ml/tests/test_tuning.py:757:5: F401 'pyspark.ml.tests.test_tuning.*' imported but unused
python/pyspark/ml/tests/test_wrapper.py:120:5: F401 'pyspark.ml.tests.test_wrapper.*' imported but unused
python/pyspark/ml/tests/test_feature.py:19:1: F401 'sys' imported but unused
python/pyspark/ml/tests/test_feature.py:304:5: F401 'pyspark.ml.tests.test_feature.*' imported but unused
python/pyspark/ml/tests/test_image.py:19:1: F401 'py4j' imported but unused
python/pyspark/ml/tests/test_image.py:22:1: F401 'pyspark.testing.mlutils.PySparkTestCase' imported but unused
python/pyspark/ml/tests/test_image.py:71:5: F401 'pyspark.ml.tests.test_image.*' imported but unused
python/pyspark/ml/tests/test_persistence.py:456:5: F401 'pyspark.ml.tests.test_persistence.*' imported but unused
python/pyspark/ml/tests/test_evaluation.py:56:5: F401 'pyspark.ml.tests.test_evaluation.*' imported but unused
python/pyspark/ml/tests/test_stat.py:43:5: F401 'pyspark.ml.tests.test_stat.*' imported but unused
python/pyspark/ml/tests/test_base.py:70:5: F401 'pyspark.ml.tests.test_base.*' imported but unused
python/pyspark/ml/tests/test_param.py:20:1: F401 'sys' imported but unused
python/pyspark/ml/tests/test_param.py:375:5: F401 'pyspark.ml.tests.test_param.*' imported but unused
python/pyspark/ml/tests/test_pipeline.py:62:5: F401 'pyspark.ml.tests.test_pipeline.*' imported but unused
python/pyspark/ml/tests/test_algorithms.py:333:5: F401 'pyspark.ml.tests.test_algorithms.*' imported but unused
python/pyspark/ml/param/__init__.py:18:1: F401 'sys' imported but unused
python/pyspark/resource/tests/test_resources.py:17:1: F401 'random' imported but unused
python/pyspark/resource/tests/test_resources.py:20:1: F401 'pyspark.resource.ResourceProfile' imported but unused
python/pyspark/resource/tests/test_resources.py:75:5: F401 'pyspark.resource.tests.test_resources.*' imported but unused
python/pyspark/sql/functions.py:32:1: F401 'pyspark.sql.udf.UserDefinedFunction' imported but unused
python/pyspark/sql/functions.py:34:1: F401 'pyspark.sql.pandas.functions.pandas_udf' imported but unused
python/pyspark/sql/session.py:30:1: F401 'pyspark.sql.types.Row' imported but unused
python/pyspark/sql/session.py:30:1: F401 'pyspark.sql.types.StringType' imported but unused
python/pyspark/sql/readwriter.py:1084:5: F401 'pyspark.sql.Row' imported but unused
python/pyspark/sql/context.py:26:1: F401 'pyspark.sql.types.IntegerType' imported but unused
python/pyspark/sql/context.py:26:1: F401 'pyspark.sql.types.Row' imported but unused
python/pyspark/sql/context.py:26:1: F401 'pyspark.sql.types.StringType' imported but unused
python/pyspark/sql/context.py:27:1: F401 'pyspark.sql.udf.UDFRegistration' imported but unused
python/pyspark/sql/streaming.py:1212:5: F401 'pyspark.sql.Row' imported but unused
python/pyspark/sql/tests/test_utils.py:55:5: F401 'pyspark.sql.tests.test_utils.*' imported but unused
python/pyspark/sql/tests/test_pandas_map.py:18:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_pandas_map.py:22:1: F401 'pyspark.sql.functions.pandas_udf' imported but unused
python/pyspark/sql/tests/test_pandas_map.py:22:1: F401 'pyspark.sql.functions.PandasUDFType' imported but unused
python/pyspark/sql/tests/test_pandas_map.py:119:5: F401 'pyspark.sql.tests.test_pandas_map.*' imported but unused
python/pyspark/sql/tests/test_catalog.py:193:5: F401 'pyspark.sql.tests.test_catalog.*' imported but unused
python/pyspark/sql/tests/test_group.py:39:5: F401 'pyspark.sql.tests.test_group.*' imported but unused
python/pyspark/sql/tests/test_session.py:361:5: F401 'pyspark.sql.tests.test_session.*' imported but unused
python/pyspark/sql/tests/test_conf.py:49:5: F401 'pyspark.sql.tests.test_conf.*' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:19:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:21:1: F401 'pyspark.sql.functions.sum' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:21:1: F401 'pyspark.sql.functions.PandasUDFType' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:29:5: F401 'pandas.util.testing.assert_series_equal' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:32:5: F401 'pyarrow as pa' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:248:5: F401 'pyspark.sql.tests.test_pandas_cogrouped_map.*' imported but unused
python/pyspark/sql/tests/test_udf.py:24:1: F401 'py4j' imported but unused
python/pyspark/sql/tests/test_pandas_udf_typehints.py:246:5: F401 'pyspark.sql.tests.test_pandas_udf_typehints.*' imported but unused
python/pyspark/sql/tests/test_functions.py:19:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_functions.py:362:9: F401 'pyspark.sql.functions.exists' imported but unused
python/pyspark/sql/tests/test_functions.py:387:5: F401 'pyspark.sql.tests.test_functions.*' imported but unused
python/pyspark/sql/tests/test_pandas_udf_scalar.py:21:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_pandas_udf_scalar.py:45:5: F401 'pyarrow as pa' imported but unused
python/pyspark/sql/tests/test_pandas_udf_window.py:355:5: F401 'pyspark.sql.tests.test_pandas_udf_window.*' imported but unused
python/pyspark/sql/tests/test_arrow.py:38:5: F401 'pyarrow as pa' imported but unused
python/pyspark/sql/tests/test_pandas_grouped_map.py:20:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_pandas_grouped_map.py:38:5: F401 'pyarrow as pa' imported but unused
python/pyspark/sql/tests/test_dataframe.py:382:9: F401 'pyspark.sql.DataFrame' imported but unused
python/pyspark/sql/avro/functions.py:125:5: F401 'pyspark.sql.Row' imported but unused
python/pyspark/sql/pandas/functions.py:19:1: F401 'sys' imported but unused
```
After:
```
fokkodriesprongFan spark % flake8 python | grep -i "imported but unused"
fokkodriesprongFan spark %
```
### What changes were proposed in this pull request?
Removing unused imports from the Python files to keep everything nice and tidy.
### Why are the changes needed?
Cleaning up of the imports that aren't used, and suppressing the imports that are used as references to other modules, preserving backward compatibility.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Adding the rule to the existing Flake8 checks.
Closes#29121 from Fokko/SPARK-32319.
Authored-by: Fokko Driesprong <fokko@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
The current error message from `_infer_type` in `_infer_schema` only includes the unsupported column type but not the column name. This PR adds the column name in the error message to make it easier for users to identify which column should they drop or convert.
### Why are the changes needed?
Improve user experience.
### Does this PR introduce _any_ user-facing change?
Yes. The error message from `_infer_schema` is changed.
Before:
"not supported type: foo"
After:
"Column bar contains not supported type: foo"
### How was this patch tested?
Updated the existing unit test.
Closes#29365 from liangz1/types-error-colname.
Authored-by: Liang Zhang <liang.zhang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The test creates 10 batches of data to train the model and expects to see error on test data improves as model is trained. If the difference between the 2nd error and the 10th error is smaller than 2, the assertion fails:
```
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 "/home/runner/work/spark/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 466, in test_train_prediction
eventually(condition, timeout=180.0)
File "/home/runner/work/spark/spark/python/pyspark/testing/utils.py", line 81, in eventually
lastValue = condition()
File "/home/runner/work/spark/spark/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 461, in condition
self.assertGreater(errors[1] - errors[-1], 2)
AssertionError: 1.672640157855923 not greater than 2
```
I saw this quite a few time on Jenkins but was not able to reproduce this on my local. These are the ten errors I got:
```
4.517395047937127
4.894265404350079
3.0392090466559876
1.8786361640757654
0.8973106042078115
0.3715780507684368
0.20815690742907672
0.17333033743125845
0.15686783249863873
0.12584413600569616
```
I am thinking of having 15 batches of data instead of 10, so the model can be trained for a longer time. Hopefully the 15th error - 2nd error will always be larger than 2 on Jenkins. These are the 15 errors I got on my local:
```
4.517395047937127
4.894265404350079
3.0392090466559876
1.8786361640757658
0.8973106042078115
0.3715780507684368
0.20815690742907672
0.17333033743125845
0.15686783249863873
0.12584413600569616
0.11883853835108477
0.09400261862100823
0.08887491447353497
0.05984929624986607
0.07583948141520978
```
### Why are the changes needed?
Fix flaky test
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manually tested
Closes#29380 from huaxingao/flaky_test.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Huaxin Gao <huaxing@us.ibm.com>
### What changes were proposed in this pull request?
Change casting of map and struct values to strings by using the `{}` brackets instead of `[]`. The behavior is controlled by the SQL config `spark.sql.legacy.castComplexTypesToString.enabled`. When it is `true`, `CAST` wraps maps and structs by `[]` in casting to strings. Otherwise, if this is `false`, which is the default, maps and structs are wrapped by `{}`.
### Why are the changes needed?
- To distinguish structs/maps from arrays.
- To make `show`'s output consistent with Hive and conversions to Hive strings.
- To display dataframe content in the same form by `spark-sql` and `show`
- To be consistent with the `*.sql` tests
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By existing test suite `CastSuite`.
Closes#29308 from MaxGekk/show-struct-map.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a follow-up of #29278.
This PR changes the config name to switch allow/disallow `SparkContext` in executors as per the comment https://github.com/apache/spark/pull/29278#pullrequestreview-460256338.
### Why are the changes needed?
The config name `spark.executor.allowSparkContext` is more reasonable.
### Does this PR introduce _any_ user-facing change?
Yes, the config name is changed.
### How was this patch tested?
Updated tests.
Closes#29340 from ueshin/issues/SPARK-32160/change_config_name.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
