## What changes were proposed in this pull request?
Not a big deal but it bugged me. This PR removes printing out plans in PySpark UDF tests.
Before:
```
Running tests...
----------------------------------------------------------------------
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
== Parsed Logical Plan ==
GlobalLimit 1
+- LocalLimit 1
+- Project [id#668L, <lambda>(id#668L) AS copy#673]
+- Sort [id#668L ASC NULLS FIRST], true
+- Range (0, 10, step=1, splits=Some(4))
== Analyzed Logical Plan ==
id: bigint, copy: int
GlobalLimit 1
+- LocalLimit 1
+- Project [id#668L, <lambda>(id#668L) AS copy#673]
+- Sort [id#668L ASC NULLS FIRST], true
+- Range (0, 10, step=1, splits=Some(4))
== Optimized Logical Plan ==
GlobalLimit 1
+- LocalLimit 1
+- Project [id#668L, pythonUDF0#676 AS copy#673]
+- BatchEvalPython [<lambda>(id#668L)], [id#668L, pythonUDF0#676]
+- Range (0, 10, step=1, splits=Some(4))
== Physical Plan ==
CollectLimit 1
+- *(2) Project [id#668L, pythonUDF0#676 AS copy#673]
+- BatchEvalPython [<lambda>(id#668L)], [id#668L, pythonUDF0#676]
+- *(1) Range (0, 10, step=1, splits=4)
...........................................
----------------------------------------------------------------------
Ran 43 tests in 19.777s
```
After:
```
Running tests...
----------------------------------------------------------------------
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
...........................................
----------------------------------------------------------------------
Ran 43 tests in 25.201s
```
## How was this patch tested?
N/A
Closes#24661 from HyukjinKwon/remove-explain-in-test.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
We updated our website a long time ago to describe Spark as the unified analytics engine, which is also how Spark is described in the community now. But our README and docs page still use the same description from 2011 ... This patch updates them.
The patch also updates the README example to use more modern APIs, and refer to Structured Streaming rather than Spark Streaming.
Closes#24573 from rxin/consistent-message.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Added method 'meanAveragePrecisionAt' k to RankingMetrics.
This branch is rebased with squashed commits from https://github.com/apache/spark/pull/24458
## How was this patch tested?
Added code in the existing test RankingMetricsSuite.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24543 from qb-tarushg/SPARK-27540-REBASE.
Authored-by: qb-tarushg <tarush.grover@quantumblack.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
### Background:
The data source option `pathGlobFilter` is introduced for Binary file format: https://github.com/apache/spark/pull/24354 , which can be used for filtering file names, e.g. reading `.png` files only while there is `.json` files in the same directory.
### Proposal:
Make the option `pathGlobFilter` as a general option for all file sources. The path filtering should happen in the path globbing on Driver.
### Motivation:
Filtering the file path names in file scan tasks on executors is kind of ugly.
### Impact:
1. The splitting of file partitions will be more balanced.
2. The metrics of file scan will be more accurate.
3. Users can use the option for reading other file sources.
## How was this patch tested?
Unit tests
Closes#24518 from gengliangwang/globFilter.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This fixes an error when a PySpark local iterator, for both RDD and DataFrames, goes out of scope and the connection is closed before fully consuming the iterator. The error occurs on the JVM in the serving thread, when Python closes the local socket while the JVM is writing to it. This usually happens when there is enough data to fill the socket read buffer, causing the write call to block.
Additionally, this fixes a problem when an error occurs in the Python worker and the collect job is cancelled with an exception. Previously, the Python driver was never notified of the error so the user could get a partial result (iteration until the error) and the application will continue. With this change, an error in the worker is sent to the Python iterator and is then raised.
The change here introduces a protocol for PySpark local iterators that work as follows:
1) The local socket connection is made when the iterator is created
2) When iterating, Python first sends a request for partition data as a non-zero integer
3) While the JVM local iterator over partitions has next, it triggers a job to collect the next partition
4) The JVM sends a nonzero response to indicate it has the next partition to send
5) The next partition is sent to Python and read by the PySpark deserializer
6) After sending the entire partition, an `END_OF_DATA_SECTION` is sent to Python which stops the deserializer and allows to make another request
7) When the JVM gets a request from Python but has already consumed it's local iterator, it will send a zero response to Python and both will close the socket cleanly
8) If an error occurs in the worker, a negative response is sent to Python followed by the error message. Python will then raise a RuntimeError with the message, stopping iteration.
9) When the PySpark local iterator is garbage-collected, it will read any remaining data from the current partition (this is data that has already been collected) and send a request of zero to tell the JVM to stop collection jobs and close the connection.
Steps 1, 3, 5, 6 are the same as before. Step 8 was completely missing before because errors in the worker were never communicated back to Python. The other steps add synchronization to allow for a clean closing of the socket, with a small trade-off in performance for each partition. This is mainly because the JVM does not start collecting partition data until it receives a request to do so, where before it would eagerly write all data until the socket receive buffer is full.
## How was this patch tested?
Added new unit tests for DataFrame and RDD `toLocalIterator` and tested not fully consuming the iterator. Manual tests with Python 2.7 and 3.6.
Closes#24070 from BryanCutler/pyspark-toLocalIterator-clean-stop-SPARK-23961.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
This is PR is meant to replace #20503, which lay dormant for a while. The solution in the original PR is still valid, so this is just that patch rebased onto the current master.
Original summary follows.
## What changes were proposed in this pull request?
Fix `__repr__` behaviour for Rows.
Rows `__repr__` assumes data is a string when column name is missing.
Examples,
```
>>> from pyspark.sql.types import Row
>>> Row ("Alice", "11")
<Row(Alice, 11)>
>>> Row (name="Alice", age=11)
Row(age=11, name='Alice')
>>> Row ("Alice", 11)
<snip stack trace>
TypeError: sequence item 1: expected string, int found
```
This is because Row () when called without column names assumes everything is a string.
## How was this patch tested?
Manually tested and a unit test was added to `python/pyspark/sql/tests/test_types.py`.
Closes#24448 from tbcs/SPARK-23299.
Lead-authored-by: Tibor Csögör <tibi@tiborius.net>
Co-authored-by: Shashwat Anand <me@shashwat.me>
Signed-off-by: Holden Karau <holden@pigscanfly.ca>
## What changes were proposed in this pull request?
In SPARK-27612, one correctness issue was reported. When protocol 4 is used to pickle Python objects, we found that unpickled objects were wrong. A temporary fix was proposed by not using highest protocol.
It was found that Opcodes.MEMOIZE was appeared in the opcodes in protocol 4. It is suspect to this issue.
A deeper dive found that Opcodes.MEMOIZE stores objects into internal map of Unpickler object. We use single Unpickler object to unpickle serialized Python bytes. Stored objects intervenes next round of unpickling, if the map is not cleared.
We has two options:
1. Continues to reuse Unpickler, but calls its close after each unpickling.
2. Not to reuse Unpickler and create new Unpickler object in each unpickling.
This patch takes option 1.
## How was this patch tested?
Passing the test added in SPARK-27612 (#24519).
Closes#24521 from viirya/SPARK-27629.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR partially reverts https://github.com/apache/spark/pull/20691
After we changed the Python protocol to highest ones, seems like it introduced a correctness bug. This potentially affects all Python related code paths.
I suspect a bug related to Pryolite (maybe opcodes `MEMOIZE`, `FRAME` and/or our `RowPickler`). I would like to stick to default protocol for now and investigate the issue separately.
I will separately investigate later to bring highest protocol back.
## How was this patch tested?
Unittest was added.
```bash
./run-tests --python-executables=python3.7 --testname "pyspark.sql.tests.test_serde SerdeTests.test_int_array_serialization"
```
Closes#24519 from HyukjinKwon/SPARK-27612.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add a non-intrusive button for python API documentation, which will remove ">>>" prompts and outputs of code - for easier copying of code.
For example: The below code-snippet in the document is difficult to copy due to ">>>" prompts
```
>>> l = [('Alice', 1)]
>>> spark.createDataFrame(l).collect()
[Row(_1='Alice', _2=1)]
```
Becomes this - After the copybutton in the corner of of code-block is pressed - which is easier to copy
```
l = [('Alice', 1)]
spark.createDataFrame(l).collect()
```
## File changes
Made changes to python/docs/conf.py and copybutton.js - thus only modifying sphinx frontend and no changes were made to the documentation itself- Build process for documentation remains the same.
copybutton.js -> This JS snippet was taken from the official python.org documentation site.
## How was this patch tested?
NA
Closes#24456 from sangramga/copybutton.
Authored-by: sangramga <sangramga@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
There is a MemorySink v2 already so v1 can be removed. In this PR I've removed it completely.
What this PR contains:
* V1 memory sink removal
* V2 memory sink renamed to become the only implementation
* Since DSv2 sends exceptions in a chained format (linking them with cause field) I've made python side compliant
* Adapted all the tests
## How was this patch tested?
Existing unit tests.
Closes#24403 from gaborgsomogyi/SPARK-23014.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This PR addresses SPARK-23619: https://issues.apache.org/jira/browse/SPARK-23619
It adds additional comments indicating the default column names for the `explode` and `posexplode`
functions in Spark-SQL.
Functions for which comments have been updated so far:
* stack
* inline
* explode
* posexplode
* explode_outer
* posexplode_outer
## How was this patch tested?
This is just a change in the comments. The package builds and tests successfullly after the change.
Closes#23748 from jashgala/SPARK-23619.
Authored-by: Jash Gala <jashgala@amazon.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Adds the Spark ML Interaction transformer to PySpark
## How was this patch tested?
- Added Python doctest
- Ran the newly added example code
- Manually confirmed that a PipelineModel that contains an Interaction transformer can now be loaded in PySpark
Closes#24426 from Andrew-Crosby/pyspark-interaction-transformer.
Lead-authored-by: Andrew-Crosby <37139900+Andrew-Crosby@users.noreply.github.com>
Co-authored-by: Andrew-Crosby <andrew.crosby@autotrader.co.uk>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
This increases the minimum support version of pyarrow to 0.12.1 and removes workarounds in pyspark to remain compatible with prior versions. This means that users will need to have at least pyarrow 0.12.1 installed and available in the cluster or an `ImportError` will be raised to indicate an upgrade is needed.
## How was this patch tested?
Existing tests using:
Python 2.7.15, pyarrow 0.12.1, pandas 0.24.2
Python 3.6.7, pyarrow 0.12.1, pandas 0.24.0
Closes#24298 from BryanCutler/arrow-bump-min-pyarrow-SPARK-27276.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
have jenkins test against python3.6 (instead of 3.4).
## How was this patch tested?
extensive testing on both the centos and ubuntu jenkins workers.
NOTE: this will need to be backported to all active branches.
Closes#24266 from shaneknapp/updating-python3-executable.
Authored-by: shane knapp <incomplete@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Running PySpark tests with Pandas 0.24.x causes a failure in `test_pandas_udf_grouped_map` test_supported_types:
`ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()`
This is because a column is an ArrayType and the method `sqlutils ReusedSQLTestCase.assertPandasEqual ` does not properly check this.
This PR removes `assertPandasEqual` and replaces it with the built-in `pandas.util.testing.assert_frame_equal` which can properly handle columns of ArrayType and also prints out better diff between the DataFrames when an error occurs.
Additionally, imports of pandas and pyarrow were moved to the top of related test files to avoid duplicating the same import many times.
## How was this patch tested?
Existing tests
Closes#24306 from BryanCutler/python-pandas-assert_frame_equal-SPARK-27387.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
According to current status, `orc` is available even Hive support isn't enabled. This is a minor doc change to reflect it.
## How was this patch tested?
Doc only change.
Closes#24280 from viirya/fix-orc-doc.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In the PR, I propose to deprecate the `from_utc_timestamp()` and `to_utc_timestamp`, and disable them by default. The functions can be enabled back via the SQL config `spark.sql.legacy.utcTimestampFunc.enabled`. By default, any calls of the functions throw an analysis exception.
One of the reason for deprecation is functions violate semantic of `TimestampType` which is number of microseconds since epoch in UTC time zone. Shifting microseconds since epoch by time zone offset doesn't make sense because the result doesn't represent microseconds since epoch in UTC time zone any more, and cannot be considered as `TimestampType`.
## How was this patch tested?
The changes were tested by `DateExpressionsSuite` and `DateFunctionsSuite`.
Closes#24195 from MaxGekk/conv-utc-timestamp-deprecate.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/23285. This PR adds the notes into PySpark and SparkR documentation as well.
While I am here, I revised the doc a bit to make it sound a bit more neutral
## How was this patch tested?
Manually built the doc and verified.
Closes#24272 from HyukjinKwon/SPARK-26224.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
After [SPARK-26856](https://github.com/apache/spark/pull/23797), `Kinesis` Python UT fails with `Found multiple JARs` exception due to a wrong pattern.
- https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/104171/console
```
Exception: Found multiple JARs:
.../spark-streaming-kinesis-asl-assembly-3.0.0-SNAPSHOT.jar,
.../spark-streaming-kinesis-asl-assembly_2.12-3.0.0-SNAPSHOT.jar;
please remove all but one
```
It's because the pattern was changed in a wrong way.
**Original**
```python
kinesis_asl_assembly_dir, "target/scala-*/%s-*.jar" % name_prefix))
kinesis_asl_assembly_dir, "target/%s_*.jar" % name_prefix))
```
**After SPARK-26856**
```python
project_full_path, "target/scala-*/%s*.jar" % jar_name_prefix))
project_full_path, "target/%s*.jar" % jar_name_prefix))
```
The actual kinesis assembly jar files look like the followings.
**SBT Build**
```
-rw-r--r-- 1 dongjoon staff 87459461 Apr 1 19:01 spark-streaming-kinesis-asl-assembly-3.0.0-SNAPSHOT.jar
-rw-r--r-- 1 dongjoon staff 309 Apr 1 18:58 spark-streaming-kinesis-asl-assembly_2.12-3.0.0-SNAPSHOT-tests.jar
-rw-r--r-- 1 dongjoon staff 309 Apr 1 18:58 spark-streaming-kinesis-asl-assembly_2.12-3.0.0-SNAPSHOT.jar
```
**MAVEN Build**
```
-rw-r--r-- 1 dongjoon staff 8.6K Apr 1 18:55 spark-streaming-kinesis-asl-assembly_2.12-3.0.0-SNAPSHOT-sources.jar
-rw-r--r-- 1 dongjoon staff 8.6K Apr 1 18:55 spark-streaming-kinesis-asl-assembly_2.12-3.0.0-SNAPSHOT-test-sources.jar
-rw-r--r-- 1 dongjoon staff 8.7K Apr 1 18:55 spark-streaming-kinesis-asl-assembly_2.12-3.0.0-SNAPSHOT-tests.jar
-rw-r--r-- 1 dongjoon staff 21M Apr 1 18:55 spark-streaming-kinesis-asl-assembly_2.12-3.0.0-SNAPSHOT.jar
```
In addition, after SPARK-26856, the utility function `search_jar` is shared to find `avro` jar files which are identical for both `sbt` and `mvn`. To sum up, The current jar pattern parameter cannot handle both `kinesis` and `avro` jars. This PR splits the single pattern into two patterns.
## How was this patch tested?
Manual. Please note that this will remove only `Found multiple JARs` exception. Kinesis tests need more configurations to run locally.
```
$ build/sbt -Pkinesis-asl test:package streaming-kinesis-asl-assembly/assembly
$ export ENABLE_KINESIS_TESTS=1
$ python/run-tests.py --python-executables python2.7 --module pyspark-streaming
```
Closes#24268 from dongjoon-hyun/SPARK-26856.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to deprecate the `from_utc_timestamp()` and `to_utc_timestamp`, and disable them by default. The functions can be enabled back via the SQL config `spark.sql.legacy.utcTimestampFunc.enabled`. By default, any calls of the functions throw an analysis exception.
One of the reason for deprecation is functions violate semantic of `TimestampType` which is number of microseconds since epoch in UTC time zone. Shifting microseconds since epoch by time zone offset doesn't make sense because the result doesn't represent microseconds since epoch in UTC time zone any more, and cannot be considered as `TimestampType`.
## How was this patch tested?
The changes were tested by `DateExpressionsSuite` and `DateFunctionsSuite`.
Closes#24195 from MaxGekk/conv-utc-timestamp-deprecate.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Before, you could have this code
```
A = SparseMatrix(2, 2, [0, 2, 3], [0], [2])
B = DenseMatrix(2, 2, [2, 0, 0, 0])
B == A # False
A == B # True
```
The second would be `True` as `SparseMatrix` already checks for semantic
equality. This commit changes `DenseMatrix` so that equality is
semantical as well.
## What changes were proposed in this pull request?
Better semantic equality for DenseMatrix
## How was this patch tested?
Unit tests were added, plus manual testing. Note that the code falls back to the old behavior when `other` is not a SparseMatrix.
Closes#17968 from gglanzani/SPARK-9792.
Authored-by: Giovanni Lanzani <giovanni@lanzani.nl>
Signed-off-by: Holden Karau <holden@pigscanfly.ca>
## What changes were proposed in this pull request?
Followup to PR https://github.com/apache/spark/pull/17085
This PR adds the weight column to the pyspark side, which was already added to the scala API.
The PR also undoes a name change in the scala side corresponding to a change in another similar PR as noted here:
https://github.com/apache/spark/pull/17084#discussion_r259648639
## How was this patch tested?
This patch adds python tests for the changes to the pyspark API.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24197 from imatiach-msft/ilmat/regressor-eval-python.
Authored-by: Ilya Matiach <ilmat@microsoft.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Now that we support returning pandas DataFrame for struct type in Scalar Pandas UDF.
If we chain another Pandas UDF after the Scalar Pandas UDF returning pandas DataFrame, the argument of the chained UDF will be pandas DataFrame, but currently we don't support pandas DataFrame as an argument of Scalar Pandas UDF. That means there is an inconsistency between the chained UDF and the single UDF.
We should support taking pandas DataFrame for struct type argument in Scalar Pandas UDF to be consistent.
Currently pyarrow >=0.11 is supported.
## How was this patch tested?
Modified and added some tests.
Closes#24177 from ueshin/issues/SPARK-27240/structtype_argument.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Remove Scala 2.11 support in build files and docs, and in various parts of code that accommodated 2.11. See some targeted comments below.
## How was this patch tested?
Existing tests.
Closes#23098 from srowen/SPARK-26132.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The hashSeed method allocates 64 bytes instead of 8. Other bytes are always zeros (thanks to default behavior of ByteBuffer). And they could be excluded from hash calculation because they don't differentiate inputs.
## How was this patch tested?
By running the existing tests - XORShiftRandomSuite
Closes#20793 from MaxGekk/hash-buff-size.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This change is a cleanup and consolidation of 3 areas related to Pandas UDFs:
1) `ArrowStreamPandasSerializer` now inherits from `ArrowStreamSerializer` and uses the base class `dump_stream`, `load_stream` to create Arrow reader/writer and send Arrow record batches. `ArrowStreamPandasSerializer` makes the conversions to/from Pandas and converts to Arrow record batch iterators. This change removed duplicated creation of Arrow readers/writers.
2) `createDataFrame` with Arrow now uses `ArrowStreamPandasSerializer` instead of doing its own conversions from Pandas to Arrow and sending record batches through `ArrowStreamSerializer`.
3) Grouped Map UDFs now reuse existing logic in `ArrowStreamPandasSerializer` to send Pandas DataFrame results as a `StructType` instead of separating each column from the DataFrame. This makes the code a little more consistent with the Python worker, but does require that the returned StructType column is flattened out in `FlatMapGroupsInPandasExec` in Scala.
## How was this patch tested?
Existing tests and ran tests with pyarrow 0.12.0
Closes#24095 from BryanCutler/arrow-refactor-cleanup-UDFs.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This introduces a new SQL function 'xxhash64' for getting a 64-bit hash of an arbitrary number of columns.
This is designed to exactly mimic the 32-bit `hash`, which uses
MurmurHash3. The name is designed to be more future-proof than the
'hash', by indicating the exact algorithm used, similar to md5 and the
sha hashes.
## How was this patch tested?
The tests for the existing `hash` function were duplicated to run with `xxhash64`.
Closes#24019 from huonw/hash64.
Authored-by: Huon Wilson <Huon.Wilson@data61.csiro.au>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/23882 to handle binary math/string functions. For instance, see the cases below:
**Before:**
```python
>>> from pyspark.sql.functions import lit, ascii
>>> spark.range(1).select(lit('a').alias("value")).select(ascii("value"))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/functions.py", line 51, in _
jc = getattr(sc._jvm.functions, name)(col._jc if isinstance(col, Column) else col)
File "/.../spark/python/lib/py4j-0.10.8.1-src.zip/py4j/java_gateway.py", line 1286, in __call__
File "/.../spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/.../spark/python/lib/py4j-0.10.8.1-src.zip/py4j/protocol.py", line 332, in get_return_value
py4j.protocol.Py4JError: An error occurred while calling z:org.apache.spark.sql.functions.ascii. Trace:
py4j.Py4JException: Method ascii([class java.lang.String]) does not exist
at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:318)
at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:339)
at py4j.Gateway.invoke(Gateway.java:276)
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)
```
```python
>>> from pyspark.sql.functions import atan2
>>> spark.range(1).select(atan2("id", "id"))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/functions.py", line 78, in _
jc = getattr(sc._jvm.functions, name)(col1._jc if isinstance(col1, Column) else float(col1),
ValueError: could not convert string to float: id
```
**After:**
```python
>>> from pyspark.sql.functions import lit, ascii
>>> spark.range(1).select(lit('a').alias("value")).select(ascii("value"))
DataFrame[ascii(value): int]
```
```python
>>> from pyspark.sql.functions import atan2
>>> spark.range(1).select(atan2("id", "id"))
DataFrame[ATAN2(id, id): double]
```
Note that,
- This PR causes a slight behaviour changes for math functions. For instance, numbers as strings (e.g., `"1"`) were supported as arguments of binary math functions before. After this PR, it recognises it as column names.
- I also intentionally didn't document this behaviour changes since we're going ahead for Spark 3.0 and I don't think numbers as strings make much sense in math functions.
- There is another exception `when`, which takes string as literal values as below. This PR doeesn't fix this ambiguity.
```python
>>> spark.range(1).select(when(lit(True), col("id"))).show()
```
```
+--------------------------+
|CASE WHEN true THEN id END|
+--------------------------+
| 0|
+--------------------------+
```
```python
>>> spark.range(1).select(when(lit(True), "id")).show()
```
```
+--------------------------+
|CASE WHEN true THEN id END|
+--------------------------+
| id|
+--------------------------+
```
This PR also fixes as below:
https://github.com/apache/spark/pull/23882 fixed it to:
- Rename `_create_function` to `_create_name_function`
- Define new `_create_function` to take strings as column names.
This PR, I proposes to:
- Revert `_create_name_function` name to `_create_function`.
- Define new `_create_function_over_column` to take strings as column names.
## How was this patch tested?
Some unit tests were added for binary math / string functions.
Closes#24121 from HyukjinKwon/SPARK-26979.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Most SQL functions defined in `spark.sql.functions` have two calling patterns, one with a Column object as input, and another with a string representing a column name, which is then converted into a Column object internally.
There are, however, a few notable exceptions:
- lower()
- upper()
- abs()
- bitwiseNOT()
- ltrim()
- rtrim()
- trim()
- ascii()
- base64()
- unbase64()
While this doesn't break anything, as you can easily create a Column object yourself prior to passing it to one of these functions, it has two undesirable consequences:
1. It is surprising - it breaks coder's expectations when they are first starting with Spark. Every API should be as consistent as possible, so as to make the learning curve smoother and to reduce causes for human error;
2. It gets in the way of stylistic conventions. Most of the time it makes Python code more readable to use literal names, and the API provides ample support for that, but these few exceptions prevent this pattern from being universally applicable.
This patch is meant to fix the aforementioned problem.
### Effect
This patch **enables** support for passing column names as input to those functions mentioned above.
### Side effects
This PR also **fixes** an issue with some functions being defined multiple times by using `_create_function()`.
### How it works
`_create_function()` was redefined to always convert the argument to a Column object. The old implementation has been kept under `_create_name_function()`, and is still being used to generate the following special functions:
- lit()
- col()
- column()
- asc()
- desc()
- asc_nulls_first()
- asc_nulls_last()
- desc_nulls_first()
- desc_nulls_last()
This is because these functions can only take a column name as their argument. This is not a problem, as their semantics require so.
## How was this patch tested?
Ran ./dev/run-tests and tested it manually.
Closes#23882 from asmello/col-name-support-pyspark.
Authored-by: André Sá de Mello <amello@palantir.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This is a minor follow-up PR for SPARK-27096. The original PR reconciled the join types supported between dataset and sql interface. In case of R, we do the join type validation in the R side. In this PR we do the correct validation and adds tests in R to test all the join types along with the error condition. Along with this, i made the necessary doc correction.
## How was this patch tested?
Add R tests.
Closes#24087 from dilipbiswal/joinfix_followup.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
With large partition, pyspark may exceeds executor memory limit and trigger out of memory for python 2.7.
This is because map() is used. Unlike in python3.x, python 2.7 map() will generate a list and need to read all data into memory.
The proposed fix will use imap in python 2.7 and it has been verified.
## How was this patch tested?
Manual test.
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23954 from TigerYang414/patch-1.
Lead-authored-by: TigerYang414 <39265202+TigerYang414@users.noreply.github.com>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
The docs describing RangeBetween & RowsBetween for pySpark & SparkR are not in sync with Spark description.
a. Edited PySpark and SparkR docs and made description same for both RangeBetween and RowsBetween
b. created executable examples in both pySpark and SparkR documentation
c. Locally tested the patch for scala Style checks and UT for checking no testcase failures
Closes#23946 from jagadesh-kiran/master.
Authored-by: Jagadesh Kiran <jagadesh.n@in.verizon.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Avro is built-in but external data source module since Spark 2.4 but `from_avro` and `to_avro` APIs not yet supported in pyspark.
In this PR I've made them available from pyspark.
## How was this patch tested?
Please see the python API examples what I've added.
cd docs/
SKIP_SCALADOC=1 SKIP_RDOC=1 SKIP_SQLDOC=1 jekyll build
Manual webpage check.
Closes#23797 from gaborgsomogyi/SPARK-26856.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Currently, R's Scala codes happened to refer Python's Scala codes for code deduplications. It's a bit odd. For instance, when we face an exception from R, it shows python related code path, which makes confusing to debug. It should rather have one code base and R's and Python's should share.
This PR proposes:
1. Make a `SocketAuthServer` and move `PythonServer` so that `PythonRDD` and `RRDD` can share it.
2. Move `readRDDFromFile` and `readRDDFromInputStream` into `JavaRDD`.
3. Reuse `RAuthHelper` and remove `RSocketAuthHelper` in `RRDD`.
4. Rename `getEncryptionEnabled` to `isEncryptionEnabled` while I am here.
So, now, the places below:
- `sql/core/src/main/scala/org/apache/spark/sql/api/r`
- `core/src/main/scala/org/apache/spark/api/r`
- `mllib/src/main/scala/org/apache/spark/ml/r`
don't refer Python's Scala codes.
## How was this patch tested?
Existing tests should cover this.
Closes#24023 from HyukjinKwon/SPARK-27102.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Cleaning the testcase, drop the database after use
## How was this patch tested?
existing UT
Closes#24021 from sandeep-katta/cleanPythonTest.
Authored-by: sandeep-katta <sandeep.katta2007@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This change adds support for returning StructType from a scalar Pandas UDF, where the return value of the function is a pandas.DataFrame. Nested structs are not supported and an error will be raised, child types can be any other type currently supported.
## How was this patch tested?
Added additional unit tests to `test_pandas_udf_scalar`
Closes#23900 from BryanCutler/pyspark-support-scalar_udf-StructType-SPARK-23836.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
PySpark's Binarizer docstring had two issues:
1) The values did not need to be in the range [0, 1].
2) It can be used for binary classification prediction.
This change corrects both of these issues by making it consistent with Scala's docstring for Binarizer.
## How was this patch tested?
Not applicable because I only changed the docstring. But if I need to do any testing, let me know and I'll do it.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23934 from brookewenig/binarizer-docs-fix.
Authored-by: Brooke Wenig <brookewenig@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add 'Recall_at_k' metric to RankingMetrics
## How was this patch tested?
Add test to RankingMetricsSuite.
Closes#23881 from masa3141/SPARK-26981.
Authored-by: masa3141 <masahiro@kazama.tv>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Clarify that text DataSource read/write, and RDD methods that read text, always use UTF-8 as they use Hadoop's implementation underneath. I think these are all the places that this needs a mention in the user-facing docs.
## How was this patch tested?
Doc tests.
Closes#23962 from srowen/SPARK-26016.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add RawPrediction to OneVsRest in PySpark to make it consistent with scala implementation
## How was this patch tested?
Add doctest
Closes#23910 from huaxingao/spark-27007.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add sample weights to decision trees
## How was this patch tested?
updated testsuites
Closes#23818 from zhengruifeng/py_tree_support_sample_weight.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
After upgrading cloudpickle to 0.6.1 at https://github.com/apache/spark/pull/20691, one regression was found. Cloudpickle had a critical https://github.com/cloudpipe/cloudpickle/pull/240 for that.
Basically, it currently looks existing globals would override globals shipped in a function's, meaning:
**Before:**
```python
>>> def hey():
... return "Hi"
...
>>> spark.range(1).rdd.map(lambda _: hey()).collect()
['Hi']
>>> def hey():
... return "Yeah"
...
>>> spark.range(1).rdd.map(lambda _: hey()).collect()
['Hi']
```
**After:**
```python
>>> def hey():
... return "Hi"
...
>>> spark.range(1).rdd.map(lambda _: hey()).collect()
['Hi']
>>>
>>> def hey():
... return "Yeah"
...
>>> spark.range(1).rdd.map(lambda _: hey()).collect()
['Yeah']
```
Therefore, this PR upgrades cloudpickle to 0.8.0.
Note that cloudpickle's release cycle is quite short.
Between 0.6.1 and 0.7.0, it contains minor bug fixes. I don't see notable changes to double check and/or avoid.
There is virtually only this fix between 0.7.0 and 0.8.1 - other fixes are about testing.
## How was this patch tested?
Manually tested, tests were added. Verified unit tests were added in cloudpickle.
Closes#23904 from HyukjinKwon/SPARK-27000.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Modifies `setup.py` so that `sbin` subdirectory is included in pyspark
## How was this patch tested?
Manually tested with python 2.7 and python 3.7
```sh
$ ./build/mvn -D skipTests -P hive -P hive-thriftserver -P yarn -P mesos clean package
$ cd python
$ python setup.py sdist
$ pip install dist/pyspark-2.1.0.dev0.tar.gz
```
Checked manually that `sbin` is now present in install directory.
srowen holdenk
Closes#23715 from oulenz/pyspark_sbin.
Authored-by: Oliver Urs Lenz <oliver.urs.lenz@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Added .transform() method to Python DataFrame API to be in sync with Scala API.
## How was this patch tested?
Addition has been tested manually.
Closes#23877 from Hellsen83/pyspark-dataframe-transform.
Authored-by: Hellsen83 <erik.christiansen83@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The evaluators BinaryClassificationEvaluator, RegressionEvaluator, and MulticlassClassificationEvaluator and the corresponding metrics classes BinaryClassificationMetrics, RegressionMetrics and MulticlassMetrics should use sample weight data.
I've closed the PR: https://github.com/apache/spark/pull/16557
as recommended in favor of creating three pull requests, one for each of the evaluators (binary/regression/multiclass) to make it easier to review/update.
## How was this patch tested?
I added tests to the metrics and evaluators classes.
Closes#17084 from imatiach-msft/ilmat/binary-evalute.
Authored-by: Ilya Matiach <ilmat@microsoft.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR proposes to make sure processing all available data before stopping and delete the temp directory.
See https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/102518/console
```
ERROR: test_query_manager_await_termination (pyspark.sql.tests.test_streaming.StreamingTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/sql/tests/test_streaming.py", line 259, in test_query_manager_await_termination
shutil.rmtree(tmpPath)
File "/home/anaconda/lib/python2.7/shutil.py", line 256, in rmtree
onerror(os.rmdir, path, sys.exc_info())
File "/home/anaconda/lib/python2.7/shutil.py", line 254, in rmtree
os.rmdir(path)
OSError: [Errno 39] Directory not empty: '/home/jenkins/workspace/SparkPullRequestBuilder/python/target/072153bd-f981-47be-bda2-e2b657a16f65/tmp4WGp7n'
```
See https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/102311/console
```
ERROR: test_stream_await_termination (pyspark.sql.tests.test_streaming.StreamingTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jenkins/workspace/SparkPullRequestBuilder2/python/pyspark/sql/tests/test_streaming.py", line 202, in test_stream_await_termination
shutil.rmtree(tmpPath)
File "/usr/lib64/pypy-2.5.1/lib-python/2.7/shutil.py", line 256, in rmtree
onerror(os.rmdir, path, sys.exc_info())
File "/usr/lib64/pypy-2.5.1/lib-python/2.7/shutil.py", line 254, in rmtree
os.rmdir(path)
OSError: [Errno 39] Directory not empty: '/home/jenkins/workspace/SparkPullRequestBuilder2/python/target/7244f4ff-6b60-4f6c-b787-de4f15922bf5/tmpQbMZSo'
```
## How was this patch tested?
Jenkins tests - I should run multiple times to see if there are other flaky tests + if this PR really fixes it.
Closes#23870 from HyukjinKwon/SPARK-26945.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
We revised the behavior of the param `stringOrderType` of `StringIndexer` in case of equal frequency when under frequencyDesc/Asc. This isn't reflected in PySpark's document. We should do it.
## How was this patch tested?
Only document change.
Closes#23849 from viirya/py-stringindexer-doc.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Holden Karau <holden@pigscanfly.ca>
## What changes were proposed in this pull request?
Add multiple column support to PySpark StringIndexer
## How was this patch tested?
Add doctest
Closes#23741 from huaxingao/spark-22798.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Currently `DataFrame.toPandas()` with arrow enabled or `ArrowStreamPandasSerializer` for pandas UDF with pyarrow<0.12 creates `datetime64[ns]` type series as intermediate data and then convert to `datetime.date` series, but the intermediate `datetime64[ns]` might cause an overflow even if the date is valid.
```
>>> import datetime
>>>
>>> t = [datetime.date(2262, 4, 12), datetime.date(2263, 4, 12)]
>>>
>>> df = spark.createDataFrame(t, 'date')
>>> df.show()
+----------+
| value|
+----------+
|2262-04-12|
|2263-04-12|
+----------+
>>>
>>> spark.conf.set("spark.sql.execution.arrow.enabled", "true")
>>>
>>> df.toPandas()
value
0 1677-09-21
1 1678-09-21
```
We should avoid creating such intermediate data and create `datetime.date` series directly instead.
## How was this patch tested?
Modified some tests to include the date which overflow caused by the intermediate conversion.
Run tests with pyarrow 0.8, 0.10, 0.11, 0.12 in my local environment.
Closes#23795 from ueshin/issues/SPARK-26887/date_as_object.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Allow the caller to customize the py4j JVM subprocess pipes and buffers for programmatic capturing of its output.
https://issues.apache.org/jira/browse/SPARK-21094 has more detail about the use case.
## How was this patch tested?
Tested by running the pyspark unit tests locally.
Closes#18339 from parente/feature/SPARK-21094-popen-args.
Lead-authored-by: Peter Parente <parente@cs.unc.edu>
Co-authored-by: Peter Parente <peter.parente@maxpoint.com>
Signed-off-by: Holden Karau <holden@pigscanfly.ca>
## What changes were proposed in this pull request?
In SPARK-25314, we supported the scenario of having a python UDF that refers to attributes from both legs of a join condition by rewriting the plan to convert an inner join or left semi join to a filter over a cross join. In case of left semi join, this transformation may cause incorrect results when the right leg of join condition produces duplicate rows based on the join condition. This fix disallows the rewrite for left semi join and raises an error in the case like we do for other types of join. In future, we should have separate rule in optimizer to convert left semi join to inner join (I am aware of one case we could do it if we leverage informational constraint i.e when we know the right side does not produce duplicates).
**Python**
```SQL
>>> from pyspark import SparkContext
>>> from pyspark.sql import SparkSession, Column, Row
>>> from pyspark.sql.functions import UserDefinedFunction, udf
>>> from pyspark.sql.types import *
>>> from pyspark.sql.utils import AnalysisException
>>>
>>> spark.conf.set("spark.sql.crossJoin.enabled", "True")
>>> left = spark.createDataFrame([Row(lc1=1, lc2=1), Row(lc1=2, lc2=2)])
>>> right = spark.createDataFrame([Row(rc1=1, rc2=1), Row(rc1=1, rc2=1)])
>>> func = udf(lambda a, b: a == b, BooleanType())
>>> df = left.join(right, func("lc1", "rc1"), "leftsemi").show()
19/02/12 16:07:10 WARN PullOutPythonUDFInJoinCondition: The join condition:<lambda>(lc1#0L, rc1#4L) of the join plan contains PythonUDF only, it will be moved out and the join plan will be turned to cross join.
+---+---+
|lc1|lc2|
+---+---+
| 1| 1|
| 1| 1|
+---+---+
```
**Scala**
```SQL
scala> val left = Seq((1, 1), (2, 2)).toDF("lc1", "lc2")
left: org.apache.spark.sql.DataFrame = [lc1: int, lc2: int]
scala> val right = Seq((1, 1), (1, 1)).toDF("rc1", "rc2")
right: org.apache.spark.sql.DataFrame = [rc1: int, rc2: int]
scala> val equal = udf((p1: Integer, p2: Integer) => {
| p1 == p2
| })
equal: org.apache.spark.sql.expressions.UserDefinedFunction = SparkUserDefinedFunction($Lambda$2141/11016292394666f1b5,BooleanType,List(Some(Schema(IntegerType,true)), Some(Schema(IntegerType,true))),None,false,true)
scala> val df = left.join(right, equal(col("lc1"), col("rc1")), "leftsemi")
df: org.apache.spark.sql.DataFrame = [lc1: int, lc2: int]
scala> df.show()
+---+---+
|lc1|lc2|
+---+---+
| 1| 1|
+---+---+
```
## How was this patch tested?
Modified existing tests.
Closes#23769 from dilipbiswal/dkb_python_udf_in_join.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Follow the [official document](https://docs.python.org/2/library/argparse.html#upgrading-optparse-code) to upgrade the deprecated module 'optparse' to 'argparse'.
## What changes were proposed in this pull request?
This PR proposes to replace 'optparse' module with 'argparse' module.
## How was this patch tested?
Follow the [previous testing](7e3eb3cd20), manually tested and negative tests were also done. My [test results](https://gist.github.com/cchung100m/1661e7df6e8b66940a6e52a20861f61d)
Closes#23730 from cchung100m/solve_deprecated_module_optparse.
Authored-by: cchung100m <cchung100m@cs.ccu.edu.tw>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
add weightCol for python version of MulticlassClassificationEvaluator and MulticlassMetrics
## How was this patch tested?
add doc test
Closes#23157 from huaxingao/spark-26185.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Holden Karau <holden@pigscanfly.ca>
## What changes were proposed in this pull request?
In this PR we've done two things:
1) updated the Spark's copy of cloudpickle to 0.6.1 (current stable)
The main reason Spark stayed with cloudpickle 0.4.x was that the default pickle protocol was changed in later versions.
2) started using pickle.HIGHEST_PROTOCOL for both Python 2 and Python 3 for serializers and broadcast
[Pyrolite](https://github.com/irmen/Pyrolite) has such Pickle protocol version support: reading: 0,1,2,3,4; writing: 2.
## How was this patch tested?
Jenkins tests.
Authors: Sloane Simmons, Boris Shminke
This contribution is original work of Sloane Simmons and Boris Shminke and they licensed it to the project under the project's open source license.
Closes#20691 from inpefess/pickle_protocol_4.
Lead-authored-by: Boris Shminke <boris@shminke.me>
Co-authored-by: singularperturbation <sloanes.k@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Make .unpersist(), .destroy() non-blocking by default and adjust callers to request blocking only where important.
This also adds an optional blocking argument to Pyspark's RDD.unpersist(), which never had one.
## How was this patch tested?
Existing tests.
Closes#23685 from srowen/SPARK-26771.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Python version of https://github.com/apache/spark/pull/17654
## How was this patch tested?
Existing Python unit test
Closes#23676 from huaxingao/spark26754.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
### Background
For the current status, the test script that generates coverage information was merged
into Spark, https://github.com/apache/spark/pull/20204
So, we can generate the coverage report and site by, for example:
```
run-tests-with-coverage --python-executables=python3 --modules=pyspark-sql
```
like `run-tests` script in `./python`.
### Proposed change
The next step is to host this coverage report via `github.io` automatically
by Jenkins (see https://spark-test.github.io/pyspark-coverage-site/).
This uses my testing account for Spark, spark-test, which is shared to Felix and Shivaram a long time ago for testing purpose including AppVeyor.
To cut this short, this PR targets to run the coverage in
[spark-master-test-sbt-hadoop-2.7](https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/)
In the specific job, it will clone the page, and rebase the up-to-date PySpark test coverage from the latest commit. For instance as below:
```bash
# Clone PySpark coverage site.
git clone https://github.com/spark-test/pyspark-coverage-site.git
# Remove existing HTMLs.
rm -fr pyspark-coverage-site/*
# Copy generated coverage HTMLs.
cp -r .../python/test_coverage/htmlcov/* pyspark-coverage-site/
# Check out to a temporary branch.
git symbolic-ref HEAD refs/heads/latest_branch
# Add all the files.
git add -A
# Commit current HTMLs.
git commit -am "Coverage report at latest commit in Apache Spark"
# Delete the old branch.
git branch -D gh-pages
# Rename the temporary branch to master.
git branch -m gh-pages
# Finally, force update to our repository.
git push -f origin gh-pages
```
So, it is a one single up-to-date coverage can be shown in the `github-io` page. The commands above were manually tested.
### TODOs
- [x] Write a draft HyukjinKwon
- [x] `pip install coverage` to all python implementations (pypy, python2, python3) in Jenkins workers - shaneknapp
- [x] Set hidden `SPARK_TEST_KEY` for spark-test's password in Jenkins via Jenkins's feature
This should be set in both PR builder and `spark-master-test-sbt-hadoop-2.7` so that later other PRs can test and fix the bugs - shaneknapp
- [x] Set an environment variable that indicates `spark-master-test-sbt-hadoop-2.7` so that that specific build can report and update the coverage site - shaneknapp
- [x] Make PR builder's test passed HyukjinKwon
- [x] Fix flaky test related with coverage HyukjinKwon
- 6 consecutive passes out of 7 runs
This PR will be co-authored with me and shaneknapp
## How was this patch tested?
It will be tested via Jenkins.
Closes#23117 from HyukjinKwon/SPARK-7721.
Lead-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: shane knapp <incomplete@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
I am investigating flaky tests. I realised that:
```
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/rdd.py", line 2512, in __init__
self.is_barrier = prev._is_barrier() or isFromBarrier
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/rdd.py", line 2412, in _is_barrier
return self._jrdd.rdd().isBarrier()
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/lib/py4j-0.10.8.1-src.zip/py4j/java_gateway.py", line 1286, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/lib/py4j-0.10.8.1-src.zip/py4j/protocol.py", line 342, in get_return_value
return OUTPUT_CONVERTER[type](answer[2:], gateway_client)
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/lib/py4j-0.10.8.1-src.zip/py4j/java_gateway.py", line 2492, in <lambda>
lambda target_id, gateway_client: JavaObject(target_id, gateway_client))
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/lib/py4j-0.10.8.1-src.zip/py4j/java_gateway.py", line 1324, in __init__
ThreadSafeFinalizer.add_finalizer(key, value)
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/lib/py4j-0.10.8.1-src.zip/py4j/finalizer.py", line 43, in add_finalizer
cls.finalizers[id] = weak_ref
File "/usr/lib64/pypy-2.5.1/lib-python/2.7/threading.py", line 216, in __exit__
self.release()
File "/usr/lib64/pypy-2.5.1/lib-python/2.7/threading.py", line 208, in release
self.__block.release()
error: release unlocked lock
```
I assume it might not be directly related with the test itself but I noticed that it `prev._is_barrier()` attempts to access via Py4J.
Accessing via Py4J is expensive. Therefore, this PR proposes to avoid Py4J access when `isFromBarrier` is `True`.
## How was this patch tested?
Unittests should cover this.
Closes#23690 from HyukjinKwon/minor-barrier.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Upgrade Apache Arrow to version 0.12.0. This includes the Java artifacts and fixes to enable usage with pyarrow 0.12.0
Version 0.12.0 includes the following selected fixes/improvements relevant to Spark users:
* Safe cast fails from numpy float64 array with nans to integer, ARROW-4258
* Java, Reduce heap usage for variable width vectors, ARROW-4147
* Binary identity cast not implemented, ARROW-4101
* pyarrow open_stream deprecated, use ipc.open_stream, ARROW-4098
* conversion to date object no longer needed, ARROW-3910
* Error reading IPC file with no record batches, ARROW-3894
* Signed to unsigned integer cast yields incorrect results when type sizes are the same, ARROW-3790
* from_pandas gives incorrect results when converting floating point to bool, ARROW-3428
* Import pyarrow fails if scikit-learn is installed from conda (boost-cpp / libboost issue), ARROW-3048
* Java update to official Flatbuffers version 1.9.0, ARROW-3175
complete list [here](https://issues.apache.org/jira/issues/?jql=project%20%3D%20ARROW%20AND%20status%20in%20(Resolved%2C%20Closed)%20AND%20fixVersion%20%3D%200.12.0)
PySpark requires the following fixes to work with PyArrow 0.12.0
* Encrypted pyspark worker fails due to ChunkedStream missing closed property
* pyarrow now converts dates as objects by default, which causes error because type is assumed datetime64
* ArrowTests fails due to difference in raised error message
* pyarrow.open_stream deprecated
* tests fail because groupby adds index column with duplicate name
## How was this patch tested?
Ran unit tests with pyarrow versions 0.8.0, 0.10.0, 0.11.1, 0.12.0
Closes#23657 from BryanCutler/arrow-upgrade-012.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/21977 added a feature to limit Python worker resource limit.
This PR is kind of a followup of it. It proposes to add a test that checks the actual resource limit set by 'spark.executor.pyspark.memory'.
## How was this patch tested?
Unit tests were added.
Closes#23663 from HyukjinKwon/test_rlimit.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add PMML export support for ML KMeans to PySpark.
## How was this patch tested?
Add tests in ml.tests.PersistenceTest.
Closes#23592 from huaxingao/spark-16838.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This change exposes the `df` (document frequency) as a public val along with the number of documents (`m`) as part of the IDF model.
* The document frequency is returned as an `Array[Long]`
* If the minimum document frequency is set, this is considered in the df calculation. If the count is less than minDocFreq, the df is 0 for such terms
* numDocs is not very required. But it can be useful, if we plan to provide a provision in future for user to give their own idf function, instead of using a default (log((1+m)/(1+df))). In such cases, the user can provide a function taking input of `m` and `df` and returning the idf value
* Pyspark changes
## How was this patch tested?
The existing test case was edited to also check for the document frequency values.
I am not very good with python or pyspark. I have committed and run tests based on my understanding. Kindly let me know if I have missed anything
Reviewer request: mengxr zjffdu yinxusen
Closes#23549 from purijatin/master.
Authored-by: Jatin Puri <purijatin@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Since 0.11.0, PyArrow supports to raise an error for unsafe cast ([PR](https://github.com/apache/arrow/pull/2504)). We should use it to raise a proper error for pandas udf users when such cast is detected.
Added a SQL config `spark.sql.execution.pandas.arrowSafeTypeConversion` to disable Arrow safe type check.
## How was this patch tested?
Added test and manually test.
Closes#22807 from viirya/SPARK-25811.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This particular test is being skipped at PyPy and Python 2.
```
Skipped tests in pyspark.sql.tests.test_context with pypy:
test_unbounded_frames (pyspark.sql.tests.test_context.HiveContextSQLTests) ... skipped "Unittest < 3.3 doesn't support mocking"
Skipped tests in pyspark.sql.tests.test_context with python2.7:
test_unbounded_frames (pyspark.sql.tests.test_context.HiveContextSQLTests) ... skipped "Unittest < 3.3 doesn't support mocking"
```
We don't have to use unittest 3.3 module to mock. And looks the test itself isn't compatible with Python 2.
This PR makes:
- Manually monkey-patch `sys.maxsize` to get rid of unittest 3.3 condition
- Use the built-in `reload` in Python 2, and `importlib.reload` in Python 3
## How was this patch tested?
Manually tested, and unit test is fixed.
Closes#23604 from HyukjinKwon/test-window.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
When parsing datatypes from the json internal representation, PySpark doesn't support decimals with negative scales. Since they are allowed and can actually happen, PySpark should be able to successfully parse them.
## How was this patch tested?
added test
Closes#23575 from mgaido91/SPARK-26645.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The test pyspark.mllib.tests.test_streaming_algorithms StreamingLogisticRegressionWithSGDTests.test_training_and_prediction looks sometimes flaky.
```
======================================================================
FAIL: test_training_and_prediction (pyspark.mllib.tests.test_streaming_algorithms.StreamingLogisticRegressionWithSGDTests)
Test that the model improves on toy data with no. of batches
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 367, in test_training_and_prediction
self._eventually(condition, timeout=60.0)
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 69, in _eventually
lastValue = condition()
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 362, in condition
self.assertGreater(errors[1] - errors[-1], 0.3)
AssertionError: -0.070000000000000062 not greater than 0.3
----------------------------------------------------------------------
Ran 13 tests in 198.327s
FAILED (failures=1, skipped=1)
Had test failures in pyspark.mllib.tests.test_streaming_algorithms with python3.4; see logs
```
The predict stream can possibly be consumed to the end before the input stream. When it happens, the model improvement is not high as expected and causes test failed. This patch tries to increase number of batches of streams. This won't increase test time because we have a timeout there.
## How was this patch tested?
Manually test.
Closes#23586 from viirya/SPARK-26646.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Misc code cleanup from lgtm.com analysis. See comments below for details.
## How was this patch tested?
Existing tests.
Closes#23571 from srowen/SPARK-26640.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Co-authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fix implementation of unary negation (`__neg__`) in Pyspark DenseVectors
## How was this patch tested?
Existing tests, plus new doctest
Closes#23570 from srowen/SPARK-26638.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR proposes to explicitly document that SparkContext cannot be shared for multiprocessing, and multi-processing execution is not guaranteed in PySpark.
I have seen some cases that users attempt to use multiple processes via `multiprocessing` module time to time. For instance, see the example in the JIRA (https://issues.apache.org/jira/browse/SPARK-25992).
Py4J itself does not support Python's multiprocessing out of the box (sharing the same JavaGateways for instance).
In general, such pattern can cause errors with somewhat arbitrary symptoms difficult to diagnose. For instance, see the error message in JIRA:
```
Traceback (most recent call last):
File "/Users/abdealijk/anaconda3/lib/python3.6/socketserver.py", line 317, in _handle_request_noblock
self.process_request(request, client_address)
File "/Users/abdealijk/anaconda3/lib/python3.6/socketserver.py", line 348, in process_request
self.finish_request(request, client_address)
File "/Users/abdealijk/anaconda3/lib/python3.6/socketserver.py", line 361, in finish_request
self.RequestHandlerClass(request, client_address, self)
File "/Users/abdealijk/anaconda3/lib/python3.6/socketserver.py", line 696, in __init__
self.handle()
File "/usr/local/hadoop/spark2.3.1/python/pyspark/accumulators.py", line 238, in handle
_accumulatorRegistry[aid] += update
KeyError: 0
```
The root cause of this was because global `_accumulatorRegistry` is not shared across processes.
Using thread instead of process is quite easy in Python. See `threading` vs `multiprocessing` in Python - they can be usually direct replacement for each other. For instance, Python also support threadpool as well (`multiprocessing.pool.ThreadPool`) which can be direct replacement of process-based thread pool (`multiprocessing.Pool`).
## How was this patch tested?
Manually tested, and manually built the doc.
Closes#23564 from HyukjinKwon/SPARK-25992.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
It's the follow-up PR for #22962, contains the following works:
- Remove `__init__` in TaskContext and BarrierTaskContext.
- Add more comments to explain the fix.
- Rewrite UT in a new class.
## How was this patch tested?
New UT in test_taskcontext.py
Closes#23435 from xuanyuanking/SPARK-25921-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
During the follow-up work(#23435) for PySpark worker reuse scenario, we found that the worker reuse takes no effect for `sc.parallelize(xrange(...))`. It happened because of the specialize rdd.parallelize logic for xrange(introduced in #3264) generated data by lazy iterable range, which don't need to use the passed-in iterator. But this will break the end of stream checking in python worker and finally cause worker reuse takes no effect. See more details in [SPARK-26549](https://issues.apache.org/jira/browse/SPARK-26549) description.
We fix this by force using the passed-in iterator.
## How was this patch tested?
New UT in test_worker.py.
Closes#23470 from xuanyuanking/SPARK-26549.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
Spark always creates secure py4j connections between java and python,
but it also allows users to pass in their own connection. This ensures
that even passed in connections are secure.
Added test cases verifying the failure with a (mocked) insecure gateway.
This is closely related to SPARK-26019, but this entirely forbids the
insecure connection, rather than creating the "escape-hatch".
Closes#23441 from squito/SPARK-26349.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Due to [API change](https://github.com/numpy/numpy/pull/4257/files#diff-c39521d89f7e61d6c0c445d93b62f7dc) at 1.9, PySpark image doesn't work with numpy version prior to 1.9.
When running image test with numpy version prior to 1.9, we can see error:
```
test_read_images (pyspark.ml.tests.test_image.ImageReaderTest) ... ERROR
test_read_images_multiple_times (pyspark.ml.tests.test_image.ImageReaderTest2) ... ok
======================================================================
ERROR: test_read_images (pyspark.ml.tests.test_image.ImageReaderTest)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/viirya/docker_tmp/repos/spark-1/python/pyspark/ml/tests/test_image.py", line 36, in test_read_images
self.assertEqual(ImageSchema.toImage(array, origin=first_row[0]), first_row)
File "/Users/viirya/docker_tmp/repos/spark-1/python/pyspark/ml/image.py", line 193, in toImage
data = bytearray(array.astype(dtype=np.uint8).ravel().tobytes())
AttributeError: 'numpy.ndarray' object has no attribute 'tobytes'
----------------------------------------------------------------------
Ran 2 tests in 29.040s
FAILED (errors=1)
```
## How was this patch tested?
Manually test with numpy version prior and after 1.9.
Closes#23484 from viirya/fix-pyspark-image.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The PR adds the `trainingCost` value to the `BisectingKMeansSummary`, in order to expose the information retrievable by running `computeCost` on the training dataset. This fills the gap with `KMeans` implementation.
## How was this patch tested?
improved UTs
Closes#22764 from mgaido91/SPARK-25765.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Change aligns argument name with that in Scala version and documentation.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23357 from deepyaman/patch-1.
Authored-by: deepyaman <deepyaman.datta@utexas.edu>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to switch the `DateFormatClass`, `ToUnixTimestamp`, `FromUnixTime`, `UnixTime` on java.time API for parsing/formatting dates and timestamps. The API has been already implemented by the `Timestamp`/`DateFormatter` classes. One of benefit is those classes support parsing timestamps with microsecond precision. Old behaviour can be switched on via SQL config: `spark.sql.legacy.timeParser.enabled` (`false` by default).
## How was this patch tested?
It was tested by existing test suites - `DateFunctionsSuite`, `DateExpressionsSuite`, `JsonSuite`, `CsvSuite`, `SQLQueryTestSuite` as well as PySpark tests.
Closes#23358 from MaxGekk/new-time-cast.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PRs #23150 and #23196 switched JSON and CSV datasources on new formatter for dates/timestamps which is based on `DateTimeFormatter`. In this PR, I replaced `SimpleDateFormat` by `DateTimeFormatter` to reflect the changes.
Closes#23374 from MaxGekk/java-time-docs.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR implements a new feature - window aggregation Pandas UDF for bounded window.
#### Doc:
https://docs.google.com/document/d/14EjeY5z4-NC27-SmIP9CsMPCANeTcvxN44a7SIJtZPc/edit#heading=h.c87w44wcj3wj
#### Example:
```
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.window import Window
df = spark.range(0, 10, 2).toDF('v')
w1 = Window.partitionBy().orderBy('v').rangeBetween(-2, 4)
w2 = Window.partitionBy().orderBy('v').rowsBetween(-2, 2)
pandas_udf('double', PandasUDFType.GROUPED_AGG)
def avg(v):
return v.mean()
df.withColumn('v_mean', avg(df['v']).over(w1)).show()
# +---+------+
# | v|v_mean|
# +---+------+
# | 0| 1.0|
# | 2| 2.0|
# | 4| 4.0|
# | 6| 6.0|
# | 8| 7.0|
# +---+------+
df.withColumn('v_mean', avg(df['v']).over(w2)).show()
# +---+------+
# | v|v_mean|
# +---+------+
# | 0| 2.0|
# | 2| 3.0|
# | 4| 4.0|
# | 6| 5.0|
# | 8| 6.0|
# +---+------+
```
#### High level changes:
This PR modifies the existing WindowInPandasExec physical node to deal with unbounded (growing, shrinking and sliding) windows.
* `WindowInPandasExec` now share the same base class as `WindowExec` and share utility functions. See `WindowExecBase`
* `WindowFunctionFrame` now has two new functions `currentLowerBound` and `currentUpperBound` - to return the lower and upper window bound for the current output row. It is also modified to allow `AggregateProcessor` == null. Null aggregator processor is used for `WindowInPandasExec` where we don't have an aggregator and only uses lower and upper bound functions from `WindowFunctionFrame`
* The biggest change is in `WindowInPandasExec`, where it is modified to take `currentLowerBound` and `currentUpperBound` and write those values together with the input data to the python process for rolling window aggregation. See `WindowInPandasExec` for more details.
#### Discussion
In benchmarking, I found numpy variant of the rolling window UDF is much faster than the pandas version:
Spark SQL window function: 20s
Pandas variant: ~80s
Numpy variant: 10s
Numpy variant with numba: 4s
Allowing numpy variant of the vectorized UDFs is something I want to discuss because of the performance improvement, but doesn't have to be in this PR.
## How was this patch tested?
New tests
Closes#22305 from icexelloss/SPARK-24561-bounded-window-udf.
Authored-by: Li Jin <ice.xelloss@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
If the input parameter 'threshold' to the function approxSimilarityJoin is not a float, we would get an exception. The fix is to convert the 'threshold' into a float before calling the java implementation method.
## How was this patch tested?
Added a new test case. Without this fix, the test will throw an exception as reported in the JIRA. With the fix, the test passes.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23313 from jerryjch/SPARK-26315.
Authored-by: Jing Chen He <jinghe@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Multiple SparkContexts are discouraged and it has been warning for last 4 years, see SPARK-4180. It could cause arbitrary and mysterious error cases, see SPARK-2243.
Honestly, I didn't even know Spark still allows it, which looks never officially supported, see SPARK-2243.
I believe It should be good timing now to remove this configuration.
## How was this patch tested?
Each doc was manually checked and manually tested:
```
$ ./bin/spark-shell --conf=spark.driver.allowMultipleContexts=true
...
scala> new SparkContext()
org.apache.spark.SparkException: Only one SparkContext should be running in this JVM (see SPARK-2243).The currently running SparkContext was created at:
org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:939)
...
org.apache.spark.SparkContext$.$anonfun$assertNoOtherContextIsRunning$2(SparkContext.scala:2435)
at scala.Option.foreach(Option.scala:274)
at org.apache.spark.SparkContext$.assertNoOtherContextIsRunning(SparkContext.scala:2432)
at org.apache.spark.SparkContext$.markPartiallyConstructed(SparkContext.scala:2509)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:80)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:112)
... 49 elided
```
Closes#23311 from HyukjinKwon/SPARK-26362.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Clean up unconditional import statements and move them to the top.
Conditional imports (pandas, numpy, pyarrow) are left as-is.
## How was this patch tested?
Exising tests.
Closes#23314 from icexelloss/clean-up-test-imports.
Authored-by: Li Jin <ice.xelloss@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In PyArrow 0.11, there is a API breaking change.
- [ARROW-1949](https://issues.apache.org/jira/browse/ARROW-1949) - [Python/C++] Add option to Array.from_pandas and pyarrow.array to perform unsafe casts.
This causes test failures in `ScalarPandasUDFTests.test_vectorized_udf_null_(byte|short|int|long)`:
```
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/worker.py", line 377, in main
process()
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/worker.py", line 372, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/serializers.py", line 317, in dump_stream
batch = _create_batch(series, self._timezone)
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/serializers.py", line 286, in _create_batch
arrs = [create_array(s, t) for s, t in series]
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/serializers.py", line 284, in create_array
return pa.Array.from_pandas(s, mask=mask, type=t)
File "pyarrow/array.pxi", line 474, in pyarrow.lib.Array.from_pandas
return array(obj, mask=mask, type=type, safe=safe, from_pandas=True,
File "pyarrow/array.pxi", line 169, in pyarrow.lib.array
return _ndarray_to_array(values, mask, type, from_pandas, safe,
File "pyarrow/array.pxi", line 69, in pyarrow.lib._ndarray_to_array
check_status(NdarrayToArrow(pool, values, mask, from_pandas,
File "pyarrow/error.pxi", line 81, in pyarrow.lib.check_status
raise ArrowInvalid(message)
ArrowInvalid: Floating point value truncated
```
We should add a workaround to support PyArrow 0.11.
## How was this patch tested?
In my local environment.
Closes#23305 from ueshin/issues/SPARK-26355/pyarrow_0.11.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to return partial results from JSON datasource and JSON functions in the PERMISSIVE mode if some of JSON fields are parsed and converted to desired types successfully. The changes are made only for `StructType`. Whole bad JSON records are placed into the corrupt column specified by the `columnNameOfCorruptRecord` option or SQL config.
Partial results are not returned for malformed JSON input.
## How was this patch tested?
Added new UT which checks converting JSON strings with one invalid and one valid field at the end of the string.
Closes#23253 from MaxGekk/json-bad-record.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This is a regression introduced by https://github.com/apache/spark/pull/22104 at Spark 2.4.0.
When we have Python UDF in subquery, we will hit an exception
```
Caused by: java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.AttributeReference cannot be cast to org.apache.spark.sql.catalyst.expressions.PythonUDF
at scala.collection.immutable.Stream.map(Stream.scala:414)
at org.apache.spark.sql.execution.python.EvalPythonExec.$anonfun$doExecute$2(EvalPythonExec.scala:98)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:815)
...
```
https://github.com/apache/spark/pull/22104 turned `ExtractPythonUDFs` from a physical rule to optimizer rule. However, there is a difference between a physical rule and optimizer rule. A physical rule always runs once, an optimizer rule may be applied twice on a query tree even the rule is located in a batch that only runs once.
For a subquery, the `OptimizeSubqueries` rule will execute the entire optimizer on the query plan inside subquery. Later on subquery will be turned to joins, and the optimizer rules will be applied to it again.
Unfortunately, the `ExtractPythonUDFs` rule is not idempotent. When it's applied twice on a query plan inside subquery, it will produce a malformed plan. It extracts Python UDF from Python exec plans.
This PR proposes 2 changes to be double safe:
1. `ExtractPythonUDFs` should skip python exec plans, to make the rule idempotent
2. `ExtractPythonUDFs` should skip subquery
## How was this patch tested?
a new test.
Closes#23248 from cloud-fan/python.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Add PowerIterationCluster (PIC) in R
## How was this patch tested?
Add test case
Closes#23072 from huaxingao/spark-19827.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
… incorrect.
## What changes were proposed in this pull request?
In the reported heartbeat information, the unit of the memory data is bytes, which is converted by the formatBytes() function in the utils.js file before being displayed in the interface. The cardinality of the unit conversion in the formatBytes function is 1000, which should be 1024.
Change the cardinality of the unit conversion in the formatBytes function to 1024.
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22683 from httfighter/SPARK-25696.
Lead-authored-by: 韩田田00222924 <han.tiantian@zte.com.cn>
Co-authored-by: han.tiantian@zte.com.cn <han.tiantian@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add validationIndicatorCol and validationTol to GBT Python.
## How was this patch tested?
Add test in doctest to test the new API.
Closes#21465 from huaxingao/spark-24333.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
When executing `toPandas` with Arrow enabled, partitions that arrive in the JVM out-of-order must be buffered before they can be send to Python. This causes an excess of memory to be used in the driver JVM and increases the time it takes to complete because data must sit in the JVM waiting for preceding partitions to come in.
This change sends un-ordered partitions to Python as soon as they arrive in the JVM, followed by a list of partition indices so that Python can assemble the data in the correct order. This way, data is not buffered at the JVM and there is no waiting on particular partitions so performance will be increased.
Followup to #21546
## How was this patch tested?
Added new test with a large number of batches per partition, and test that forces a small delay in the first partition. These test that partitions are collected out-of-order and then are are put in the correct order in Python.
## Performance Tests - toPandas
Tests run on a 4 node standalone cluster with 32 cores total, 14.04.1-Ubuntu and OpenJDK 8
measured wall clock time to execute `toPandas()` and took the average best time of 5 runs/5 loops each.
Test code
```python
df = spark.range(1 << 25, numPartitions=32).toDF("id").withColumn("x1", rand()).withColumn("x2", rand()).withColumn("x3", rand()).withColumn("x4", rand())
for i in range(5):
start = time.time()
_ = df.toPandas()
elapsed = time.time() - start
```
Spark config
```
spark.driver.memory 5g
spark.executor.memory 5g
spark.driver.maxResultSize 2g
spark.sql.execution.arrow.enabled true
```
Current Master w/ Arrow stream | This PR
---------------------|------------
5.16207 | 4.342533
5.133671 | 4.399408
5.147513 | 4.468471
5.105243 | 4.36524
5.018685 | 4.373791
Avg Master | Avg This PR
------------------|--------------
5.1134364 | 4.3898886
Speedup of **1.164821449**
Closes#22275 from BryanCutler/arrow-toPandas-oo-batches-SPARK-25274.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Looks this test is flaky
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99704/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99569/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99644/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99548/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99454/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99609/console
```
======================================================================
FAIL: test_training_and_prediction (pyspark.mllib.tests.test_streaming_algorithms.StreamingLogisticRegressionWithSGDTests)
Test that the model improves on toy data with no. of batches
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 367, in test_training_and_prediction
self._eventually(condition)
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 78, in _eventually
% (timeout, lastValue))
AssertionError: Test failed due to timeout after 30 sec, with last condition returning: Latest errors: 0.67, 0.71, 0.78, 0.7, 0.75, 0.74, 0.73, 0.69, 0.62, 0.71, 0.69, 0.75, 0.72, 0.77, 0.71, 0.74
----------------------------------------------------------------------
Ran 13 tests in 185.051s
FAILED (failures=1, skipped=1)
```
This looks happening after increasing the parallelism in Jenkins to speed up at https://github.com/apache/spark/pull/23111. I am able to reproduce this manually when the resource usage is heavy (with manual decrease of timeout).
## How was this patch tested?
Manually tested by
```
cd python
./run-tests --testnames 'pyspark.mllib.tests.test_streaming_algorithms StreamingLogisticRegressionWithSGDTests.test_training_and_prediction' --python-executables=python
```
Closes#23236 from HyukjinKwon/SPARK-26275.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This fixes doc of renamed OneHotEncoder in PySpark.
## How was this patch tested?
N/A
Closes#23230 from viirya/remove_one_hot_encoder_followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR proposes add a developer option, `--testnames`, to our testing script to allow run specific set of unittests and doctests.
**1. Run unittests in the class**
```bash
./run-tests --testnames 'pyspark.sql.tests.test_arrow ArrowTests'
```
```
Running PySpark tests. Output is in /.../spark/python/unit-tests.log
Will test against the following Python executables: ['python2.7', 'pypy']
Will test the following Python tests: ['pyspark.sql.tests.test_arrow ArrowTests']
Starting test(python2.7): pyspark.sql.tests.test_arrow ArrowTests
Starting test(pypy): pyspark.sql.tests.test_arrow ArrowTests
Finished test(python2.7): pyspark.sql.tests.test_arrow ArrowTests (14s)
Finished test(pypy): pyspark.sql.tests.test_arrow ArrowTests (14s) ... 22 tests were skipped
Tests passed in 14 seconds
Skipped tests in pyspark.sql.tests.test_arrow ArrowTests with pypy:
test_createDataFrame_column_name_encoding (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_fallback_disabled (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_fallback_enabled (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped
...
```
**2. Run single unittest in the class.**
```bash
./run-tests --testnames 'pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion'
```
```
Running PySpark tests. Output is in /.../spark/python/unit-tests.log
Will test against the following Python executables: ['python2.7', 'pypy']
Will test the following Python tests: ['pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion']
Starting test(pypy): pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion
Starting test(python2.7): pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion
Finished test(pypy): pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion (0s) ... 1 tests were skipped
Finished test(python2.7): pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion (8s)
Tests passed in 8 seconds
Skipped tests in pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion with pypy:
test_null_conversion (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
```
**3. Run doctests in single PySpark module.**
```bash
./run-tests --testnames pyspark.sql.dataframe
```
```
Running PySpark tests. Output is in /.../spark/python/unit-tests.log
Will test against the following Python executables: ['python2.7', 'pypy']
Will test the following Python tests: ['pyspark.sql.dataframe']
Starting test(pypy): pyspark.sql.dataframe
Starting test(python2.7): pyspark.sql.dataframe
Finished test(python2.7): pyspark.sql.dataframe (47s)
Finished test(pypy): pyspark.sql.dataframe (48s)
Tests passed in 48 seconds
```
Of course, you can mix them:
```bash
./run-tests --testnames 'pyspark.sql.tests.test_arrow ArrowTests,pyspark.sql.dataframe'
```
```
Running PySpark tests. Output is in /.../spark/python/unit-tests.log
Will test against the following Python executables: ['python2.7', 'pypy']
Will test the following Python tests: ['pyspark.sql.tests.test_arrow ArrowTests', 'pyspark.sql.dataframe']
Starting test(pypy): pyspark.sql.dataframe
Starting test(pypy): pyspark.sql.tests.test_arrow ArrowTests
Starting test(python2.7): pyspark.sql.dataframe
Starting test(python2.7): pyspark.sql.tests.test_arrow ArrowTests
Finished test(pypy): pyspark.sql.tests.test_arrow ArrowTests (0s) ... 22 tests were skipped
Finished test(python2.7): pyspark.sql.tests.test_arrow ArrowTests (18s)
Finished test(python2.7): pyspark.sql.dataframe (50s)
Finished test(pypy): pyspark.sql.dataframe (52s)
Tests passed in 52 seconds
Skipped tests in pyspark.sql.tests.test_arrow ArrowTests with pypy:
test_createDataFrame_column_name_encoding (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_fallback_disabled (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
```
and also you can use all other options (except `--modules`, which will be ignored)
```bash
./run-tests --testnames 'pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion' --python-executables=python
```
```
Running PySpark tests. Output is in /.../spark/python/unit-tests.log
Will test against the following Python executables: ['python']
Will test the following Python tests: ['pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion']
Starting test(python): pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion
Finished test(python): pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion (12s)
Tests passed in 12 seconds
```
See help below:
```bash
./run-tests --help
```
```
Usage: run-tests [options]
Options:
...
Developer Options:
--testnames=TESTNAMES
A comma-separated list of specific modules, classes
and functions of doctest or unittest to test. For
example, 'pyspark.sql.foo' to run the module as
unittests or doctests, 'pyspark.sql.tests FooTests' to
run the specific class of unittests,
'pyspark.sql.tests FooTests.test_foo' to run the
specific unittest in the class. '--modules' option is
ignored if they are given.
```
I intentionally grouped it as a developer option to be more conservative.
## How was this patch tested?
Manually tested. Negative tests were also done.
```bash
./run-tests --testnames 'pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion1' --python-executables=python
```
```
...
AttributeError: type object 'ArrowTests' has no attribute 'test_null_conversion1'
...
```
```bash
./run-tests --testnames 'pyspark.sql.tests.test_arrow ArrowT' --python-executables=python
```
```
...
AttributeError: 'module' object has no attribute 'ArrowT'
...
```
```bash
./run-tests --testnames 'pyspark.sql.tests.test_ar' --python-executables=python
```
```
...
/.../python2.7: No module named pyspark.sql.tests.test_ar
```
Closes#23203 from HyukjinKwon/SPARK-26252.
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 is a small follow up that puts some logic and functions into smaller scope and make it localized, and deduplicate.
## How was this patch tested?
Manually tested. Jenkins tests as well.
Closes#23200 from HyukjinKwon/followup-SPARK-26034-SPARK-26033.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
`resource` package is a Unix specific package. See https://docs.python.org/2/library/resource.html and https://docs.python.org/3/library/resource.html.
Note that we document Windows support:
> Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS).
This should be backported into branch-2.4 to restore Windows support in Spark 2.4.1.
## How was this patch tested?
Manually mocking the changed logics.
Closes#23055 from HyukjinKwon/SPARK-26080.
Lead-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: DylanGuedes <djmgguedesgmail.com>
## What changes were proposed in this pull request?
Addition of float, int and list hints for `pyspark.sql` Hint.
## How was this patch tested?
I did manual tests following the same principles used in the Scala version, and also added unit tests.
Closes#20788 from DylanGuedes/jira-21030.
Authored-by: DylanGuedes <djmgguedes@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Python with rpc and disk encryption enabled along with a python broadcast variable and just read the value back on the driver side the job failed with:
Traceback (most recent call last): File "broadcast.py", line 37, in <module> words_new.value File "/pyspark.zip/pyspark/broadcast.py", line 137, in value File "pyspark.zip/pyspark/broadcast.py", line 122, in load_from_path File "pyspark.zip/pyspark/broadcast.py", line 128, in load EOFError: Ran out of input
To reproduce use configs: --conf spark.network.crypto.enabled=true --conf spark.io.encryption.enabled=true
Code:
words_new = sc.broadcast(["scala", "java", "hadoop", "spark", "akka"])
words_new.value
print(words_new.value)
## How was this patch tested?
words_new = sc.broadcast([“scala”, “java”, “hadoop”, “spark”, “akka”])
textFile = sc.textFile(“README.md”)
wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word + words_new.value[1], 1)).reduceByKey(lambda a, b: a+b)
count = wordCounts.count()
print(count)
words_new.value
print(words_new.value)
Closes#23166 from redsanket/SPARK-26201.
Authored-by: schintap <schintap@oath.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
We have deprecated `OneHotEncoder` at Spark 2.3.0 and introduced `OneHotEncoderEstimator`. At 3.0.0, we remove deprecated `OneHotEncoder` and rename `OneHotEncoderEstimator` to `OneHotEncoder`.
TODO: According to ML migration guide, we need to keep `OneHotEncoderEstimator` as an alias after renaming. This is not done at this patch in order to facilitate review.
## How was this patch tested?
Existing tests.
Closes#23100 from viirya/remove_one_hot_encoder.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Currently duplicated map keys are not handled consistently. For example, map look up respects the duplicated key appears first, `Dataset.collect` only keeps the duplicated key appears last, `MapKeys` returns duplicated keys, etc.
This PR proposes to remove duplicated map keys with last wins policy, to follow Java/Scala and Presto. It only applies to built-in functions, as users can create map with duplicated map keys via private APIs anyway.
updated functions: `CreateMap`, `MapFromArrays`, `MapFromEntries`, `StringToMap`, `MapConcat`, `TransformKeys`.
For other places:
1. data source v1 doesn't have this problem, as users need to provide a java/scala map, which can't have duplicated keys.
2. data source v2 may have this problem. I've added a note to `ArrayBasedMapData` to ask the caller to take care of duplicated keys. In the future we should enforce it in the stable data APIs for data source v2.
3. UDF doesn't have this problem, as users need to provide a java/scala map. Same as data source v1.
4. file format. I checked all of them and only parquet does not enforce it. For backward compatibility reasons I change nothing but leave a note saying that the behavior will be undefined if users write map with duplicated keys to parquet files. Maybe we can add a config and fail by default if parquet files have map with duplicated keys. This can be done in followup.
## How was this patch tested?
updated tests and new tests
Closes#23124 from cloud-fan/map.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/22326 made a mistake that, not all python UDFs are unevaluable in join condition. Only python UDFs that refer to attributes from both join side are unevaluable.
This PR fixes this mistake.
## How was this patch tested?
a new test
Closes#23153 from cloud-fan/join.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
