### What changes were proposed in this pull request?
Adapting documentation of `between`, `getField`, `dropFields` and `cast` to the corresponding scala doc
### Why are the changes needed?
Documentation clarity
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Only documentation change
Closes#33369 from dominikgehl/feature/SPARK-36160.
Authored-by: Dominik Gehl <dog@open.ch>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
Introduction: this PR is the last part of SPARK-10816 (EventTime based sessionization (session window)). Please refer #31937 to see the overall view of the code change. (Note that code diff could be diverged a bit.)
### What changes were proposed in this pull request?
This PR proposes to support native session window. Please refer the comments/design doc in SPARK-10816 for more details on the rationalization and design (could be outdated a bit compared to the PR).
The definition of the boundary of "session window" is [the timestamp of start event ~ the timestamp of last event + gap duration). That said, unlike time window, session window is a dynamic window which can expand if new input row is added to the session. To handle expansion of session window, Spark defines session window per input row, and "merge" windows if they can be merged (boundaries are overlapped).
This PR leverages two different approaches on merging session windows:
1. merging session windows with Spark's aggregation logic (a variant of sort aggregation)
2. updating session window for all rows bound to the same session, and applying aggregation logic afterwards
First one is preferable as it outperforms compared to the second one, though it can be only used if merging session window can be applied altogether with aggregation. It is not applicable on all the cases, so second one is used to cover the remaining cases.
This PR also applies the optimization on merging input rows and existing sessions with retaining the order (group keys + start timestamp of session window), leveraging the fact the number of existing sessions per group key won't be huge.
The state format is versioned, so that we can bring a new state format if we find a better one.
### Why are the changes needed?
For now, to deal with sessionization, Spark requires end users to play with (flat)MapGroupsWithState directly which has a couple of major drawbacks:
1. (flat)MapGroupsWithState is lower level API and end users have to code everything in details for defining session window and merging windows
2. built-in aggregate functions cannot be used and end users have to deal with aggregation by themselves
3. (flat)MapGroupsWithState is only available in Scala/Java.
With native support of session window, end users simply use "session_window" like they use "window" for tumbling/sliding window, and leverage built-in aggregate functions as well as UDAFs to simply define aggregations.
Quoting the query example from test suite:
```
val inputData = MemoryStream[(String, Long)]
// Split the lines into words, treat words as sessionId of events
val events = inputData.toDF()
.select($"_1".as("value"), $"_2".as("timestamp"))
.withColumn("eventTime", $"timestamp".cast("timestamp"))
.selectExpr("explode(split(value, ' ')) AS sessionId", "eventTime")
.withWatermark("eventTime", "30 seconds")
val sessionUpdates = events
.groupBy(session_window($"eventTime", "10 seconds") as 'session, 'sessionId)
.agg(count("*").as("numEvents"))
.selectExpr("sessionId", "CAST(session.start AS LONG)", "CAST(session.end AS LONG)",
"CAST(session.end AS LONG) - CAST(session.start AS LONG) AS durationMs",
"numEvents")
```
which is same as StructuredSessionization (native session window is shorter and clearer even ignoring model classes).
39542bb81f/examples/src/main/scala/org/apache/spark/examples/sql/streaming/StructuredSessionization.scala (L66-L105)
(Worth noting that the code in StructuredSessionization only works with processing time. The code doesn't consider old event can update the start time of old session.)
### Does this PR introduce _any_ user-facing change?
Yes. This PR brings the new feature to support session window on both batch and streaming query, which adds a new function "session_window" which usage is similar with "window".
### How was this patch tested?
New test suites. Also tested with benchmark code.
Closes#33081 from HeartSaVioR/SPARK-34893-SPARK-10816-PR-31570-part-5.
Lead-authored-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
Co-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Revisit and manage `InternalField` in more places.
### Why are the changes needed?
There are other places we can manage `InternalField`, and we can keep extension dtypes or `CategoricalDtype`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added some tests.
Closes#33377 from ueshin/issues/SPARK-36167/internal_field.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Updating pyspark months_between documentation to the precision in the scala documentation
### Why are the changes needed?
Documentation clarity
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Only documentation change
Closes#33366 from dominikgehl/feature/SPARK-36158.
Authored-by: Dominik Gehl <dog@open.ch>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR proposes to use Python 3.9 in documentation and linter at GitHub Actions. This PR also contains the fixes for mypy check (introduced by Python 3.9 upgrade)
```
python/pyspark/sql/pandas/_typing/protocols/frame.pyi:64: error: Name "np.ndarray" is not defined
python/pyspark/sql/pandas/_typing/protocols/frame.pyi:91: error: Name "np.recarray" is not defined
python/pyspark/sql/pandas/_typing/protocols/frame.pyi:165: error: Name "np.ndarray" is not defined
python/pyspark/pandas/categorical.py:82: error: Item "dtype[Any]" of "Union[dtype[Any], Any]" has no attribute "categories"
python/pyspark/pandas/categorical.py:109: error: Item "dtype[Any]" of "Union[dtype[Any], Any]" has no attribute "ordered"
python/pyspark/ml/linalg/__init__.pyi:184: error: Return type "ndarray[Any, Any]" of "toArray" incompatible with return type "NoReturn" in supertype "Matrix"
python/pyspark/ml/linalg/__init__.pyi:217: error: Return type "ndarray[Any, Any]" of "toArray" incompatible with return type "NoReturn" in supertype "Matrix"
python/pyspark/pandas/typedef/typehints.py:163: error: Module has no attribute "bool"; maybe "bool_" or "bool8"?
python/pyspark/pandas/typedef/typehints.py:174: error: Module has no attribute "float"; maybe "float_", "cfloat", or "float96"?
python/pyspark/pandas/typedef/typehints.py:180: error: Module has no attribute "int"; maybe "uint", "rint", or "intp"?
python/pyspark/pandas/ml.py:81: error: Value of type variable "_DTypeScalar_co" of "dtype" cannot be "object"
python/pyspark/pandas/indexing.py:1649: error: Module has no attribute "int"; maybe "uint", "rint", or "intp"?
python/pyspark/pandas/indexing.py:1656: error: Module has no attribute "int"; maybe "uint", "rint", or "intp"?
python/pyspark/pandas/frame.py:4969: error: Function "numpy.array" is not valid as a type
python/pyspark/pandas/frame.py:4969: note: Perhaps you need "Callable[...]" or a callback protocol?
python/pyspark/pandas/frame.py:4970: error: Function "numpy.array" is not valid as a type
python/pyspark/pandas/frame.py:4970: note: Perhaps you need "Callable[...]" or a callback protocol?
python/pyspark/pandas/frame.py:7402: error: "List[Any]" has no attribute "tolist"
python/pyspark/pandas/series.py:1030: error: Module has no attribute "_NoValue"
python/pyspark/pandas/series.py:1031: error: Module has no attribute "_NoValue"
python/pyspark/pandas/indexes/category.py:159: error: Item "dtype[Any]" of "Union[dtype[Any], Any]" has no attribute "categories"
python/pyspark/pandas/indexes/category.py:180: error: Item "dtype[Any]" of "Union[dtype[Any], Any]" has no attribute "ordered"
python/pyspark/pandas/namespace.py:2036: error: Argument 1 to "column_name" has incompatible type "float"; expected "str"
python/pyspark/pandas/mlflow.py:59: error: Incompatible types in assignment (expression has type "Type[floating[Any]]", variable has type "str")
python/pyspark/pandas/data_type_ops/categorical_ops.py:43: error: Item "dtype[Any]" of "Union[dtype[Any], Any]" has no attribute "categories"
python/pyspark/pandas/data_type_ops/categorical_ops.py:43: error: Item "dtype[Any]" of "Union[dtype[Any], Any]" has no attribute "ordered"
python/pyspark/pandas/data_type_ops/categorical_ops.py:56: error: Item "dtype[Any]" of "Union[dtype[Any], Any]" has no attribute "categories"
python/pyspark/pandas/tests/test_typedef.py:70: error: Name "np.float" is not defined
python/pyspark/pandas/tests/test_typedef.py:77: error: Name "np.float" is not defined
python/pyspark/pandas/tests/test_typedef.py:85: error: Name "np.float" is not defined
python/pyspark/pandas/tests/test_typedef.py💯 error: Name "np.float" is not defined
python/pyspark/pandas/tests/test_typedef.py:108: error: Name "np.float" is not defined
python/pyspark/mllib/clustering.pyi:152: error: Incompatible types in assignment (expression has type "ndarray[Any, Any]", base class "KMeansModel" defined the type as "List[ndarray[Any, Any]]")
python/pyspark/mllib/classification.pyi:93: error: Signature of "predict" incompatible with supertype "LinearClassificationModel"
Found 32 errors in 15 files (checked 315 source files)
1
```
### Why are the changes needed?
Python 3.6 is deprecated at SPARK-35938
### Does this PR introduce _any_ user-facing change?
No. Maybe static analysis, etc. by some type hints but they are really non-breaking..
### How was this patch tested?
I manually checked by GitHub Actions build in forked repository.
Closes#33356 from HyukjinKwon/SPARK-36146.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Added missing documentation of week and quarter as valid formats to pyspark sql/functions trunc
### Why are the changes needed?
Pyspark documentation and scala documentation didn't mentioned the same supported formats
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Only documentation change
Closes#33359 from dominikgehl/feature/SPARK-36154.
Authored-by: Dominik Gehl <dog@open.ch>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Clearly state which weekday corresponds to which integer
### Why are the changes needed?
Documentation clarity
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
only documentation change
Closes#33345 from dominikgehl/doc/pyspark-dayofweek.
Authored-by: Dominik Gehl <dog@open.ch>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Implement non-equality comparison operators between two Categoricals.
Non-goal: supporting Scalar input will be a follow-up task.
### Why are the changes needed?
pandas supports non-equality comparisons between two Categoricals. We should follow that.
### Does this PR introduce _any_ user-facing change?
Yes. No `NotImplementedError` for `<`, `<=`, `>`, `>=` operators between two Categoricals. An example is shown as below:
From:
```py
>>> import pyspark.pandas as ps
>>> from pandas.api.types import CategoricalDtype
>>> psser = ps.Series([1, 2, 3]).astype(CategoricalDtype([3, 2, 1], ordered=True))
>>> other_psser = ps.Series([2, 1, 3]).astype(CategoricalDtype([3, 2, 1], ordered=True))
>>> with ps.option_context("compute.ops_on_diff_frames", True):
... psser <= other_psser
...
Traceback (most recent call last):
...
NotImplementedError: <= can not be applied to categoricals.
```
To:
```py
>>> import pyspark.pandas as ps
>>> from pandas.api.types import CategoricalDtype
>>> psser = ps.Series([1, 2, 3]).astype(CategoricalDtype([3, 2, 1], ordered=True))
>>> other_psser = ps.Series([2, 1, 3]).astype(CategoricalDtype([3, 2, 1], ordered=True))
>>> with ps.option_context("compute.ops_on_diff_frames", True):
... psser <= other_psser
...
0 False
1 True
2 True
dtype: bool
```
### How was this patch tested?
Unit tests.
Closes#33331 from xinrong-databricks/categorical_compare.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
This is a followup PR for SPARK-36104 (#33307) and removes unused import `typing.cast`.
After that change, Python linter fails.
```
./dev/lint-python
shell: sh -e {0}
env:
LC_ALL: C.UTF-8
LANG: C.UTF-8
pythonLocation: /__t/Python/3.6.13/x64
LD_LIBRARY_PATH: /__t/Python/3.6.13/x64/lib
starting python compilation test...
python compilation succeeded.
starting black test...
black checks passed.
starting pycodestyle test...
pycodestyle checks passed.
starting flake8 test...
flake8 checks failed:
./python/pyspark/pandas/data_type_ops/num_ops.py:19:1: F401 'typing.cast' imported but unused
from typing import cast, Any, Union
^
1 F401 'typing.cast' imported but unused
```
### Why are the changes needed?
To recover CI.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI.
Closes#33315 from sarutak/followup-SPARK-36104.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Manage InternalField for DataTypeOps.neg/abs.
### Why are the changes needed?
The spark data type and nullability must be the same as the original when DataTypeOps.neg/abs.
We should manage InternalField for this case.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit tests.
Closes#33307 from xinrong-databricks/internalField.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Properly set `InternalField` for `DataTypeOps.invert`.
### Why are the changes needed?
The spark data type and nullability must be the same as the original when `DataTypeOps.invert`.
We should manage `InternalField` for this case.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33306 from ueshin/issues/SPARK-36103/invert.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Implement unary operator `invert` of integral ps.Series/Index.
### Why are the changes needed?
Currently, unary operator `invert` of integral ps.Series/Index is not supported. We ought to implement that following pandas' behaviors.
### Does this PR introduce _any_ user-facing change?
Yes.
Before:
```py
>>> import pyspark.pandas as ps
>>> psser = ps.Series([1, 2, 3])
>>> ~psser
Traceback (most recent call last):
...
NotImplementedError: Unary ~ can not be applied to integrals.
```
After:
```py
>>> import pyspark.pandas as ps
>>> psser = ps.Series([1, 2, 3])
>>> ~psser
0 -2
1 -3
2 -4
dtype: int64
```
### How was this patch tested?
Unit tests.
Closes#33285 from xinrong-databricks/numeric_invert.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Properly set `InternalField` more in `DataTypeOps`.
### Why are the changes needed?
There are more places in `DataTypeOps` where we can manage `InternalField`.
We should manage `InternalField` for these cases.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33275 from ueshin/issues/SPARK-36064/fields.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Adjust `test_astype`, `test_neg` for old pandas versions.
### Why are the changes needed?
There are issues in old pandas versions that fail tests in pandas API on Spark. We ought to adjust `test_astype` and `test_neg` for old pandas versions.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit tests. Please refer to https://github.com/apache/spark/pull/33272 for test results with pandas 1.0.1.
Closes#33250 from xinrong-databricks/SPARK-36035.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Merge test_decimal_ops into test_num_ops
- merge test_isnull() into test_num_ops.test_isnull()
- remove test_datatype_ops(), which already covered in 11fcbc73cb/python/pyspark/pandas/tests/data_type_ops/test_base.py (L58-L59)
### Why are the changes needed?
Tests for data-type-based operations of decimal Series are in two places:
- python/pyspark/pandas/tests/data_type_ops/test_decimal_ops.py
- python/pyspark/pandas/tests/data_type_ops/test_num_ops.py
We'd better merge test_decimal_ops into test_num_ops.
See also [SPARK-36002](https://issues.apache.org/jira/browse/SPARK-36002) .
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
unittests passed
Closes#33206 from Yikun/SPARK-36002.
Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
For tests with operations on different Series, sort index of results before comparing them with pandas.
### Why are the changes needed?
We have many tests with operations on different Series in `spark/python/pyspark/pandas/tests/data_type_ops/` that assume the result's index to be sorted and then compare to the pandas' behavior.
The assumption on the result's index ordering is wrong since Spark DataFrame join is used internally and the order is not preserved if the data being in different partitions.
So we should assume the result to be disordered and sort the index of such results before comparing them with pandas.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit tests.
Closes#33274 from xinrong-databricks/datatypeops_testdiffframe.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Try to capture the error message from the `faulthandler` when the Python worker crashes.
### Why are the changes needed?
Currently, we just see an error message saying `"exited unexpectedly (crashed)"` when the UDFs causes the Python worker to crash by like segmentation fault.
We should take advantage of [`faulthandler`](https://docs.python.org/3/library/faulthandler.html) and try to capture the error message from the `faulthandler`.
### Does this PR introduce _any_ user-facing change?
Yes, when a Spark config `spark.python.worker.faulthandler.enabled` is `true`, the stack trace will be seen in the error message when the Python worker crashes.
```py
>>> def f():
... import ctypes
... ctypes.string_at(0)
...
>>> sc.parallelize([1]).map(lambda x: f()).count()
```
```
org.apache.spark.SparkException: Python worker exited unexpectedly (crashed): Fatal Python error: Segmentation fault
Current thread 0x000000010965b5c0 (most recent call first):
File "/.../ctypes/__init__.py", line 525 in string_at
File "<stdin>", line 3 in f
File "<stdin>", line 1 in <lambda>
...
```
### How was this patch tested?
Added some tests, and manually.
Closes#33273 from ueshin/issues/SPARK-36062/faulthandler.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The PR is proposed to standardize TypeError messages for unsupported basic operations by:
- Capitalize the first letter
- Leverage TypeError messages defined in `pyspark/pandas/data_type_ops/base.py`
- Take advantage of the utility `is_valid_operand_for_numeric_arithmetic` to save duplicated TypeError messages
Related unit tests should be adjusted as well.
### Why are the changes needed?
Inconsistent TypeError messages are shown for unsupported data-type-based basic operations.
Take addition's TypeError messages for example:
- addition can not be applied to given types.
- string addition can only be applied to string series or literals.
Standardizing TypeError messages would improve user experience and reduce maintenance costs.
### Does this PR introduce _any_ user-facing change?
No user-facing behavior change. Only TypeError messages are modified.
### How was this patch tested?
Unit tests.
Closes#33237 from xinrong-databricks/datatypeops_err.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Make unary and comparison operators data-type-based. Refactored operators include:
- Unary operators: `__neg__`, `__abs__`, `__invert__`,
- Comparison operators: `>`, `>=`, `<`, `<=`, `==`, `!=`
Non-goal: Tasks below are inspired during the development of this PR.
[[SPARK-35997] Implement comparison operators for CategoricalDtype in pandas API on Spark](https://issues.apache.org/jira/browse/SPARK-35997)
[[SPARK-36000] Support creating a ps.Series/Index with `Decimal('NaN')` with Arrow disabled](https://issues.apache.org/jira/browse/SPARK-36000)
[[SPARK-36001] Assume result's index to be disordered in tests with operations on different Series](https://issues.apache.org/jira/browse/SPARK-36001)
[[SPARK-36002] Consolidate tests for data-type-based operations of decimal Series](https://issues.apache.org/jira/browse/SPARK-36002)
[[SPARK-36003] Implement unary operator `invert` of numeric ps.Series/Index](https://issues.apache.org/jira/browse/SPARK-36003)
### Why are the changes needed?
We have been refactoring basic operators to be data-type-based for readability, flexibility, and extensibility.
Unary and comparison operators are still not data-type-based yet. We should fill the gaps.
### Does this PR introduce _any_ user-facing change?
Yes.
- Better error messages. For example,
Before:
```py
>>> import pyspark.pandas as ps
>>> psser = ps.Series([b"2", b"3", b"4"])
>>> -psser
Traceback (most recent call last):
...
pyspark.sql.utils.AnalysisException: cannot resolve '(- `0`)' due to data type mismatch: ...
```
After:
```py
>>> import pyspark.pandas as ps
>>> psser = ps.Series([b"2", b"3", b"4"])
>>> -psser
Traceback (most recent call last):
...
TypeError: Unary - can not be applied to binaries.
>>>
```
- Support unary `-` of `bool` Series. For example,
Before:
```py
>>> psser = ps.Series([True, False, True])
>>> -psser
Traceback (most recent call last):
...
pyspark.sql.utils.AnalysisException: cannot resolve '(- `0`)' due to data type mismatch: ...
```
After:
```py
>>> psser = ps.Series([True, False, True])
>>> -psser
0 False
1 True
2 False
dtype: bool
```
### How was this patch tested?
Unit tests.
Closes#33162 from xinrong-databricks/datatypeops_refactor.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Currently, inferring nested structs is always using `MapType`.
This behavior causes an issue because it infers the schema with a value type of the first field of the struct as below:
```python
data = [{"inside_struct": {"payment": 100.5, "name": "Lee"}}]
df = spark.createDataFrame(data)
df.show(truncate=False)
+--------------------------------+
|inside_struct |
+--------------------------------+
|{name -> null, payment -> 100.5}|
+--------------------------------+
```
The "name" became `null`, but it should've been `"Lee"`.
In this case, we need to be able to infer the schema with a `StructType` instead of a `MapType`.
Therefore, this PR proposes adding an new configuration `spark.sql.pyspark.inferNestedDictAsStruct.enabled` to handle which type is used for inferring nested structs.
- When `spark.sql.pyspark.inferNestedDictAsStruct.enabled` is `false` (by default), inferring nested structs by `MapType`
- When `spark.sql.pyspark.inferNestedDictAsStruct.enabled` is `true`, inferring nested structs by `StructType`
### Why are the changes needed?
Because always inferring the nested structs by `MapType` doesn't work properly for some cases.
### Does this PR introduce _any_ user-facing change?
New configuration `spark.sql.pyspark.inferNestedDictAsStruct.enabled` is added.
### How was this patch tested?
Added an unit test
Closes#33214 from itholic/SPARK-35929.
Lead-authored-by: itholic <haejoon.lee@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to bump up the mypy version to 0.910 which is the latest.
### Why are the changes needed?
To catch the type hint mistakes better in PySpark.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
GitHub Actions should test it out.
Closes#33223 from HyukjinKwon/SPARK-35684.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Fix the type hint for `pyspark.rdd .RDD.histogram`'s `buckets` argument
### Why are the changes needed?
The current type hint is incomplete.
From `pyspark.rdd .RDD.histogram`'s source:
```python
if isinstance(buckets, int):
...
elif isinstance(buckets, (list, tuple)):
...
else:
raise TypeError("buckets should be a list or tuple or number(int or long)")
```
### Does this PR introduce _any_ user-facing change?
Fixed the warning displayed above.
### How was this patch tested?
Fixed warning above with this change.
Closes#33185 from tpvasconcelos/master.
Authored-by: Tomas Pereira de Vasconcelos <tomasvasconcelos1@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to update `master` branch version to 3.3.0-SNAPSHOT.
### Why are the changes needed?
Start to prepare Apache Spark 3.3.0 and the published snapshot version should not conflict with `branch-3.2`.
### Does this PR introduce _any_ user-facing change?
N/A.
### How was this patch tested?
Pass the CIs.
Closes#33196 from dongjoon-hyun/SPARK-35996.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
We should use `check_exact=False` because the value check in `StatsTest.test_cov_corr_meta` is too strict.
### Why are the changes needed?
In some environment, the precision could be different in pandas' `DataFrame.corr` function and the test `StatsTest.test_cov_corr_meta` fails.
```
AssertionError: DataFrame.iloc[:, 0] (column name="a") are different
DataFrame.iloc[:, 0] (column name="a") values are different (14.28571 %)
[index]: [a, b, c, d, e, f, g]
[left]: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0]
[right]: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.807406715958909e-17]
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Modified tests should still pass.
Closes#33179 from ueshin/issuse/SPARK-35981/corr.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
By default, AQE will set `COALESCE_PARTITIONS_MIN_PARTITION_NUM` to the spark default parallelism, which is usually quite big. This is to keep the parallelism on par with non-AQE, to avoid perf regressions.
However, this usually leads to many small/empty partitions, and hurts performance (although not worse than non-AQE). Users usually blindly set `COALESCE_PARTITIONS_MIN_PARTITION_NUM` to 1, which makes this config quite useless.
This PR adds a new config to set the min partition size, to avoid too small partitions after coalescing. By default, Spark will not respect the target size, and only respect this min partition size, to maximize the parallelism and avoid perf regression in AQE. This PR also adds a bool config to respect the target size when coalescing partitions, and it's recommended to set it to get better overall performance. This PR also deprecates the `COALESCE_PARTITIONS_MIN_PARTITION_NUM` config.
### Why are the changes needed?
AQE is default on now, we should make the perf better in the default case.
### Does this PR introduce _any_ user-facing change?
yes, a new config.
### How was this patch tested?
new tests
Closes#33172 from cloud-fan/aqe2.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Improve unit tests for data-type-based basic operations by:
- removing redundant test cases
- adding `astype` test for ExtensionDtypes
### Why are the changes needed?
Some test cases for basic operations are duplicated after introducing data-type-based basic operations. The PR is proposed to remove redundant test cases.
`astype` is not tested for ExtensionDtypes, which will be adjusted in this PR as well.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit tests.
Closes#33095 from xinrong-databricks/datatypeops_test.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Introduce `Name` and `Label` type aliases to distinguish what is expected instead of `Any` or `Union[Any, Tuple]`.
- `Label`: `Tuple[Any, ...]`
Internal expression for name-like metadata, like `index_names`, `column_labels`, and `column_label_names` in `InternalFrame`, and similar internal structures.
- `Name`: `Union[Any, Label]`
External expression for user-facing names, which can be scalar values or tuples.
### Why are the changes needed?
Currently `Any` or `Union[Any, Tuple]` is used for name-like types, but type aliases should be used to distinguish what is expected clearly.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33159 from ueshin/issues/SPARK-35944/name_and_label.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add deprecation warning for Python 3.6.
### Why are the changes needed?
According to https://endoflife.date/python, Python 3.6 will be EOL on 23 Dec, 2021.
We should prepare for the deprecation of Python 3.6 support in Spark in advance.
### Does this PR introduce _any_ user-facing change?
N/A.
### How was this patch tested?
Manual tests.
Closes#33139 from xinrong-databricks/deprecate3.6_warn.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Currently ,we sets the environment variable `PYSPARK_PIN_THREAD` at the client side of `InhertiableThread` API for Py4J (`python/pyspark/util.py`). If the Py4J gateway is created somewhere else (e.g., Zeppelin, etc), it could introduce a breakage at:
```python
from pyspark import SparkContext
jvm = SparkContext._jvm
thread_connection = jvm._gateway_client.get_thread_connection()
# `AttributeError: 'GatewayClient' object has no attribute 'get_thread_connection'` (non-pinned thread mode)
# `get_thread_connection` is only in 'ClientServer' (pinned thread mode)
```
This PR proposes to check the given gateway created, and do the pinned thread mode behaviour accordingly so we can avoid any breakage when Py4J server/gateway is created separately from somewhere else without a pinned thread mode.
### Why are the changes needed?
To avoid any potential breakage.
### Does this PR introduce _any_ user-facing change?
No, the change happened only in the master (fdd7ca5f4e).
### How was this patch tested?
This is actually a partial revert of fdd7ca5f4e. As long as the existing tests pass, I guess we're all good.
I also manually tested to make doubly sure:
**Before**:
```python
>>> from pyspark import InheritableThread, inheritable_thread_target
>>> InheritableThread(lambda: 1).start()
>>> inheritable_thread_target(lambda: 1)()
Traceback (most recent call last):
File "/.../python3.8/lib/python3.8/threading.py", line 932, in _bootstrap_inner
self.run()
File "/.../python3.8/lib/python3.8/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/.../spark/python/pyspark/util.py", line 361, in copy_local_properties
InheritableThread._clean_py4j_conn_for_current_thread()
File "/.../spark/python/pyspark/util.py", line 381, in _clean_py4j_conn_for_current_thread
thread_connection = jvm._gateway_client.get_thread_connection()
AttributeError: 'GatewayClient' object has no attribute 'get_thread_connection'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/util.py", line 324, in wrapped
InheritableThread._clean_py4j_conn_for_current_thread()
File "/.../spark/python/pyspark/util.py", line 381, in _clean_py4j_conn_for_current_thread
thread_connection = jvm._gateway_client.get_thread_connection()
AttributeError: 'GatewayClient' object has no attribute 'get_thread_connection'
```
**After**:
```python
>>> from pyspark import InheritableThread, inheritable_thread_target
>>> InheritableThread(lambda: 1).start()
>>> inheritable_thread_target(lambda: 1)()
1
```
Closes#33147 from HyukjinKwon/SPARK-35946.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Introduces `Axis` type alias for `axis` argument to be consistent.
### Why are the changes needed?
There are many places to use `axis` argument. We should define `Axis` type alias and reuse it to be consistent.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33144 from ueshin/issues/SPARK-35943/axis.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes removing the legacy Koalas version from pandas API on Spark package.
And also remove the Python version check logic since now pandas-on-Spark should follow the PySpark's Python version.
### Why are the changes needed?
Since Koalas is ported into PySpark, we don't need to keep the version logic for Koalas.
### Does this PR introduce _any_ user-facing change?
Now the legacy Koalas user should follow the version from PySpark.
### How was this patch tested?
Manually built the package and see it's successfully done.
Closes#33128 from itholic/SPARK-35873.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Cleaning up the type hints in pandas-on-Spark.
- Use a single file `_typing.py` for type variables or aliases
- Rename `IndexOpsLike` to `SeriesOrIndex`.
- Rename `T_Frame` and `T_IndexOps` to `FrameLike` and `IndexOpsLike` respectively
- Introduce `DataFrameOrSeries` for `Union[DataFrame, Series]`
### Why are the changes needed?
This is a cleanup for the mypy check stuff series.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33117 from ueshin/issues/SPARK-35859/cleanup.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Add path level discover for python unittests.
### Why are the changes needed?
Now we need to specify the python test cases by manually when we add a new testcase. Sometime, we forgot to add the testcase to module list, the testcase would not be executed.
Such as:
- pyspark-core pyspark.tests.test_pin_thread
Thus we need some auto-discover way to find all testcase rather than specified every case by manually.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Add below code in end of `dev/sparktestsupport/modules.py`
```python
for m in sorted(all_modules):
for g in sorted(m.python_test_goals):
print(m.name, g)
```
Compare the result before and after:
https://www.diffchecker.com/iO3FvhKLCloses#32867 from Yikun/SPARK_DISCOVER_TEST.
Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The PR is proposed to support creating a Column of numpy literal value in pandas-on-Spark. It consists of three changes mainly:
- Enable the `lit` function defined in `pyspark.pandas.spark.functions` to support numpy literals input.
```py
>>> from pyspark.pandas.spark import functions as SF
>>> SF.lit(np.int64(1))
Column<'CAST(1 AS BIGINT)'>
>>> SF.lit(np.int32(1))
Column<'CAST(1 AS INT)'>
>>> SF.lit(np.int8(1))
Column<'CAST(1 AS TINYINT)'>
>>> SF.lit(np.byte(1))
Column<'CAST(1 AS TINYINT)'>
>>> SF.lit(np.float32(1))
Column<'CAST(1.0 AS FLOAT)'>
```
- Substitute `F.lit` by `SF.lit`, that is, use `lit` function defined in `pyspark.pandas.spark.functions` rather than `lit` function defined in `pyspark.sql.functions` to allow creating columns out of numpy literals.
- Enable numpy literals input in `isin` method
Non-goal:
- Some pandas-on-Spark APIs use PySpark column-related APIs internally, and these column-related APIs don't support numpy literals, thus numpy literals are disallowed as input (e.g. `to_replace` parameter in `replace` API). This PR doesn't aim to adjust all of them. This PR adjusts `isin` only, because the PR is inspired by that (as https://github.com/databricks/koalas/issues/2161).
- To complete mappings between all kinds of numpy literals and Spark data types should be a followup task.
### Why are the changes needed?
Spark (`lit` function defined in `pyspark.sql.functions`) doesn't support creating a Column out of numpy literal value.
So `lit` function defined in `pyspark.pandas.spark.functions` is adjusted in order to support that in pandas-on-Spark.
### Does this PR introduce _any_ user-facing change?
Yes.
Before:
```py
>>> a = ps.DataFrame({'source': [1,2,3,4,5]})
>>> a.source.isin([np.int64(1), np.int64(2)])
Traceback (most recent call last):
...
AttributeError: 'numpy.int64' object has no attribute '_get_object_id'
```
After:
```py
>>> a = ps.DataFrame({'source': [1,2,3,4,5]})
>>> a.source.isin([np.int64(1), np.int64(2)])
0 True
1 True
2 False
3 False
4 False
Name: source, dtype: bool
```
### How was this patch tested?
Unit tests.
Closes#32955 from xinrong-databricks/datatypeops_literal.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Refines type hints in `pyspark.pandas.window`.
Also, some refactoring is included to clean up the type hierarchy of `Rolling` and `Expanding`.
### Why are the changes needed?
We can use more strict type hints for functions in pyspark.pandas.window using the generic way.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33097 from ueshin/issues/SPARK-35901/window.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes move `to_pandas_on_spark` function from `pyspark.pandas.frame` to `pyspark.sql.dataframe`, and added the related tests to the PySpark DataFrame tests.
### Why are the changes needed?
Because now the Koalas is ported into PySpark, so we don't need to Spark auto-patch anymore.
And also `to_pandas_on_spark` is belongs to the pandas-on-Spark DataFrame doesn't look make sense.
### Does this PR introduce _any_ user-facing change?
No, it's kinda internal refactoring stuff.
### How was this patch tested?
Added the related tests and manually check they're passed.
Closes#33054 from itholic/SPARK-35605.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Adds more type annotations in the files `python/pyspark/pandas/data_type_ops/*.py` and fixes the mypy check failures.
### Why are the changes needed?
We should enable more disallow_untyped_defs mypy checks.
### Does this PR introduce _any_ user-facing change?
Yes.
This PR adds more type annotations in pandas APIs on Spark module, which can impact interaction with development tools for users.
### How was this patch tested?
The mypy check with a new configuration and existing tests should pass.
Closes#33094 from ueshin/issues/SPARK-35466/disallow_untyped_defs_data_ops.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Adds more type annotations in the file `python/pyspark/pandas/frame.py` and fixes the mypy check failures.
### Why are the changes needed?
We should enable more disallow_untyped_defs mypy checks.
### Does this PR introduce _any_ user-facing change?
Yes.
This PR adds more type annotations in pandas APIs on Spark module, which can impact interaction with development tools for users.
### How was this patch tested?
The mypy check with a new configuration and existing tests should pass.
Closes#33073 from ueshin/issues/SPARK-35471/disallow_untyped_defs_frame.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Adds more type annotations in the file `python/pyspark/pandas/series.py` and fixes the mypy check failures.
### Why are the changes needed?
We should enable more disallow_untyped_defs mypy checks.
### Does this PR introduce _any_ user-facing change?
Yes.
This PR adds more type annotations in pandas APIs on Spark module, which can impact interaction with development tools for users.
### How was this patch tested?
The mypy check with a new configuration and existing tests should pass.
Closes#33045 from ueshin/issues/SPARK-35476/disallow_untyped_defs_series.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to add a migration guide for legacy Koalas users in pandas API on Spark.
### Why are the changes needed?
For easier migration.
### Does this PR introduce _any_ user-facing change?
Yes, this adds a new page for migration from Koalas.
### How was this patch tested?
Manually built the docs and checked manually.
Closes#33050 from HyukjinKwon/SPARK-35301.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR follow-up for SPARK-35696 to fix incorrect underline in the documents to remove warnings.
### Why are the changes needed?
We should build the docs without any incorrect documentation style
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manually build docs and see the warning is removed
Closes#33052 from itholic/SPARK-35696-followup.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to refine the code examples for pandas-on-Spark since some of them still follows the naming for Koalas.
For example,
```python
kdf = ks.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
```
should be refined to
```python
psdf = ps.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
```
Also fixed the several remaining Koalas stuffs in FAQ
### Why are the changes needed?
Because we don't want to use the name "Koalas" in the Apache Spark anymore.
### Does this PR introduce _any_ user-facing change?
Yes, the examples in the documentation will be changed with refined names.
### How was this patch tested?
Manually built the docs and check one by one.
Closes#33017 from itholic/SPARK-35696.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
revert changes related to ANN
### Why are the changes needed?
using the new `softmax` may cause flaky failure
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
reverted testsuite
Closes#33049 from zhengruifeng/revert_softmax_ann.
Authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
softmax support offset and step, then we can use it in ANN and NB
### Why are the changes needed?
to simplify impl
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing testsuite
Closes#32991 from zhengruifeng/softmax_support_offset_step.
Authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Signed-off-by: Huaxin Gao <huaxin_gao@apple.com>
### What changes were proposed in this pull request?
This PR proposes to fix:
- the Binder integration of pandas API on Spark, and merge them together with the existing PySpark one.
- update quickstart of pandas API on Spark, and make it working
The notebooks can be easily reviewed here:
https://mybinder.org/v2/gh/HyukjinKwon/spark/SPARK-35588-3?filepath=python%2Fdocs%2Fsource%2Fgetting_started%2Fquickstart_ps.ipynb
Original page in Koalas: https://koalas.readthedocs.io/en/latest/getting_started/10min.html
### Why are the changes needed?
- To show the working examples of quickstart to end users.
- To allow users to try out the examples without installation easily.
### Does this PR introduce _any_ user-facing change?
No to end users because the existing quickstart of pandas API on Spark is not released yet.
### How was this patch tested?
I manually tested it by uploading built Spark distribution to Binder. See 3bc15310a0Closes#33041 from HyukjinKwon/SPARK-35588-2.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Throw ValueError if version and timestamp are used together in to_delta
### Why are the changes needed?
read_delta has arguments named `version` and `timestamp`, but they cannot be used together.
We should raise the proper error message when they are used together.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
UT
Closes#33023 from Yikun/SPARK-35812.
Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Adds more type annotations in the file `python/pyspark/pandas/groupby.py` and fixes the mypy check failures.
### Why are the changes needed?
We should enable more disallow_untyped_defs mypy checks.
### Does this PR introduce _any_ user-facing change?
Yes.
This PR adds more type annotations in pandas APIs on Spark module, which can impact interaction with development tools for users.
### How was this patch tested?
The mypy check with a new configuration and existing tests should pass.
Closes#33032 from ueshin/issues/SPARK-35473/disallow_untyped_defs_groupby.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Properly set `InternalField` for `DataTypeOps.isnull`.
### Why are the changes needed?
The result of `DataTypeOps.isnull` must always be non-nullable boolean.
We should manage `InternalField` for this case.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added some more tests.
Closes#33005 from ueshin/issues/SPARK-35847/isnull_field.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Make DecimalOps astype data-type-based.
See more in:
https://github.com/apache/spark/pull/32821#issuecomment-861119905
### Why are the changes needed?
Make DecimalOps astype data-type-based.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing test NumOpsTest.test_astype in pyspark/pandas/tests/data_type_ops/test_num_ops.py
Closes#33009 from Yikun/SPARK-35849.
Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
