## What changes were proposed in this pull request?
Change aligns argument name with that in Scala version and documentation.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23357 from deepyaman/patch-1.
Authored-by: deepyaman <deepyaman.datta@utexas.edu>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to switch the `DateFormatClass`, `ToUnixTimestamp`, `FromUnixTime`, `UnixTime` on java.time API for parsing/formatting dates and timestamps. The API has been already implemented by the `Timestamp`/`DateFormatter` classes. One of benefit is those classes support parsing timestamps with microsecond precision. Old behaviour can be switched on via SQL config: `spark.sql.legacy.timeParser.enabled` (`false` by default).
## How was this patch tested?
It was tested by existing test suites - `DateFunctionsSuite`, `DateExpressionsSuite`, `JsonSuite`, `CsvSuite`, `SQLQueryTestSuite` as well as PySpark tests.
Closes#23358 from MaxGekk/new-time-cast.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PRs #23150 and #23196 switched JSON and CSV datasources on new formatter for dates/timestamps which is based on `DateTimeFormatter`. In this PR, I replaced `SimpleDateFormat` by `DateTimeFormatter` to reflect the changes.
Closes#23374 from MaxGekk/java-time-docs.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR implements a new feature - window aggregation Pandas UDF for bounded window.
#### Doc:
https://docs.google.com/document/d/14EjeY5z4-NC27-SmIP9CsMPCANeTcvxN44a7SIJtZPc/edit#heading=h.c87w44wcj3wj
#### Example:
```
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.window import Window
df = spark.range(0, 10, 2).toDF('v')
w1 = Window.partitionBy().orderBy('v').rangeBetween(-2, 4)
w2 = Window.partitionBy().orderBy('v').rowsBetween(-2, 2)
pandas_udf('double', PandasUDFType.GROUPED_AGG)
def avg(v):
return v.mean()
df.withColumn('v_mean', avg(df['v']).over(w1)).show()
# +---+------+
# | v|v_mean|
# +---+------+
# | 0| 1.0|
# | 2| 2.0|
# | 4| 4.0|
# | 6| 6.0|
# | 8| 7.0|
# +---+------+
df.withColumn('v_mean', avg(df['v']).over(w2)).show()
# +---+------+
# | v|v_mean|
# +---+------+
# | 0| 2.0|
# | 2| 3.0|
# | 4| 4.0|
# | 6| 5.0|
# | 8| 6.0|
# +---+------+
```
#### High level changes:
This PR modifies the existing WindowInPandasExec physical node to deal with unbounded (growing, shrinking and sliding) windows.
* `WindowInPandasExec` now share the same base class as `WindowExec` and share utility functions. See `WindowExecBase`
* `WindowFunctionFrame` now has two new functions `currentLowerBound` and `currentUpperBound` - to return the lower and upper window bound for the current output row. It is also modified to allow `AggregateProcessor` == null. Null aggregator processor is used for `WindowInPandasExec` where we don't have an aggregator and only uses lower and upper bound functions from `WindowFunctionFrame`
* The biggest change is in `WindowInPandasExec`, where it is modified to take `currentLowerBound` and `currentUpperBound` and write those values together with the input data to the python process for rolling window aggregation. See `WindowInPandasExec` for more details.
#### Discussion
In benchmarking, I found numpy variant of the rolling window UDF is much faster than the pandas version:
Spark SQL window function: 20s
Pandas variant: ~80s
Numpy variant: 10s
Numpy variant with numba: 4s
Allowing numpy variant of the vectorized UDFs is something I want to discuss because of the performance improvement, but doesn't have to be in this PR.
## How was this patch tested?
New tests
Closes#22305 from icexelloss/SPARK-24561-bounded-window-udf.
Authored-by: Li Jin <ice.xelloss@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
If the input parameter 'threshold' to the function approxSimilarityJoin is not a float, we would get an exception. The fix is to convert the 'threshold' into a float before calling the java implementation method.
## How was this patch tested?
Added a new test case. Without this fix, the test will throw an exception as reported in the JIRA. With the fix, the test passes.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23313 from jerryjch/SPARK-26315.
Authored-by: Jing Chen He <jinghe@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Multiple SparkContexts are discouraged and it has been warning for last 4 years, see SPARK-4180. It could cause arbitrary and mysterious error cases, see SPARK-2243.
Honestly, I didn't even know Spark still allows it, which looks never officially supported, see SPARK-2243.
I believe It should be good timing now to remove this configuration.
## How was this patch tested?
Each doc was manually checked and manually tested:
```
$ ./bin/spark-shell --conf=spark.driver.allowMultipleContexts=true
...
scala> new SparkContext()
org.apache.spark.SparkException: Only one SparkContext should be running in this JVM (see SPARK-2243).The currently running SparkContext was created at:
org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:939)
...
org.apache.spark.SparkContext$.$anonfun$assertNoOtherContextIsRunning$2(SparkContext.scala:2435)
at scala.Option.foreach(Option.scala:274)
at org.apache.spark.SparkContext$.assertNoOtherContextIsRunning(SparkContext.scala:2432)
at org.apache.spark.SparkContext$.markPartiallyConstructed(SparkContext.scala:2509)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:80)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:112)
... 49 elided
```
Closes#23311 from HyukjinKwon/SPARK-26362.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Clean up unconditional import statements and move them to the top.
Conditional imports (pandas, numpy, pyarrow) are left as-is.
## How was this patch tested?
Exising tests.
Closes#23314 from icexelloss/clean-up-test-imports.
Authored-by: Li Jin <ice.xelloss@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In PyArrow 0.11, there is a API breaking change.
- [ARROW-1949](https://issues.apache.org/jira/browse/ARROW-1949) - [Python/C++] Add option to Array.from_pandas and pyarrow.array to perform unsafe casts.
This causes test failures in `ScalarPandasUDFTests.test_vectorized_udf_null_(byte|short|int|long)`:
```
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/worker.py", line 377, in main
process()
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/worker.py", line 372, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/serializers.py", line 317, in dump_stream
batch = _create_batch(series, self._timezone)
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/serializers.py", line 286, in _create_batch
arrs = [create_array(s, t) for s, t in series]
File "/Users/ueshin/workspace/apache-spark/spark/python/pyspark/serializers.py", line 284, in create_array
return pa.Array.from_pandas(s, mask=mask, type=t)
File "pyarrow/array.pxi", line 474, in pyarrow.lib.Array.from_pandas
return array(obj, mask=mask, type=type, safe=safe, from_pandas=True,
File "pyarrow/array.pxi", line 169, in pyarrow.lib.array
return _ndarray_to_array(values, mask, type, from_pandas, safe,
File "pyarrow/array.pxi", line 69, in pyarrow.lib._ndarray_to_array
check_status(NdarrayToArrow(pool, values, mask, from_pandas,
File "pyarrow/error.pxi", line 81, in pyarrow.lib.check_status
raise ArrowInvalid(message)
ArrowInvalid: Floating point value truncated
```
We should add a workaround to support PyArrow 0.11.
## How was this patch tested?
In my local environment.
Closes#23305 from ueshin/issues/SPARK-26355/pyarrow_0.11.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to return partial results from JSON datasource and JSON functions in the PERMISSIVE mode if some of JSON fields are parsed and converted to desired types successfully. The changes are made only for `StructType`. Whole bad JSON records are placed into the corrupt column specified by the `columnNameOfCorruptRecord` option or SQL config.
Partial results are not returned for malformed JSON input.
## How was this patch tested?
Added new UT which checks converting JSON strings with one invalid and one valid field at the end of the string.
Closes#23253 from MaxGekk/json-bad-record.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This is a regression introduced by https://github.com/apache/spark/pull/22104 at Spark 2.4.0.
When we have Python UDF in subquery, we will hit an exception
```
Caused by: java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.AttributeReference cannot be cast to org.apache.spark.sql.catalyst.expressions.PythonUDF
at scala.collection.immutable.Stream.map(Stream.scala:414)
at org.apache.spark.sql.execution.python.EvalPythonExec.$anonfun$doExecute$2(EvalPythonExec.scala:98)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:815)
...
```
https://github.com/apache/spark/pull/22104 turned `ExtractPythonUDFs` from a physical rule to optimizer rule. However, there is a difference between a physical rule and optimizer rule. A physical rule always runs once, an optimizer rule may be applied twice on a query tree even the rule is located in a batch that only runs once.
For a subquery, the `OptimizeSubqueries` rule will execute the entire optimizer on the query plan inside subquery. Later on subquery will be turned to joins, and the optimizer rules will be applied to it again.
Unfortunately, the `ExtractPythonUDFs` rule is not idempotent. When it's applied twice on a query plan inside subquery, it will produce a malformed plan. It extracts Python UDF from Python exec plans.
This PR proposes 2 changes to be double safe:
1. `ExtractPythonUDFs` should skip python exec plans, to make the rule idempotent
2. `ExtractPythonUDFs` should skip subquery
## How was this patch tested?
a new test.
Closes#23248 from cloud-fan/python.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Add PowerIterationCluster (PIC) in R
## How was this patch tested?
Add test case
Closes#23072 from huaxingao/spark-19827.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
… incorrect.
## What changes were proposed in this pull request?
In the reported heartbeat information, the unit of the memory data is bytes, which is converted by the formatBytes() function in the utils.js file before being displayed in the interface. The cardinality of the unit conversion in the formatBytes function is 1000, which should be 1024.
Change the cardinality of the unit conversion in the formatBytes function to 1024.
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22683 from httfighter/SPARK-25696.
Lead-authored-by: 韩田田00222924 <han.tiantian@zte.com.cn>
Co-authored-by: han.tiantian@zte.com.cn <han.tiantian@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add validationIndicatorCol and validationTol to GBT Python.
## How was this patch tested?
Add test in doctest to test the new API.
Closes#21465 from huaxingao/spark-24333.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
When executing `toPandas` with Arrow enabled, partitions that arrive in the JVM out-of-order must be buffered before they can be send to Python. This causes an excess of memory to be used in the driver JVM and increases the time it takes to complete because data must sit in the JVM waiting for preceding partitions to come in.
This change sends un-ordered partitions to Python as soon as they arrive in the JVM, followed by a list of partition indices so that Python can assemble the data in the correct order. This way, data is not buffered at the JVM and there is no waiting on particular partitions so performance will be increased.
Followup to #21546
## How was this patch tested?
Added new test with a large number of batches per partition, and test that forces a small delay in the first partition. These test that partitions are collected out-of-order and then are are put in the correct order in Python.
## Performance Tests - toPandas
Tests run on a 4 node standalone cluster with 32 cores total, 14.04.1-Ubuntu and OpenJDK 8
measured wall clock time to execute `toPandas()` and took the average best time of 5 runs/5 loops each.
Test code
```python
df = spark.range(1 << 25, numPartitions=32).toDF("id").withColumn("x1", rand()).withColumn("x2", rand()).withColumn("x3", rand()).withColumn("x4", rand())
for i in range(5):
start = time.time()
_ = df.toPandas()
elapsed = time.time() - start
```
Spark config
```
spark.driver.memory 5g
spark.executor.memory 5g
spark.driver.maxResultSize 2g
spark.sql.execution.arrow.enabled true
```
Current Master w/ Arrow stream | This PR
---------------------|------------
5.16207 | 4.342533
5.133671 | 4.399408
5.147513 | 4.468471
5.105243 | 4.36524
5.018685 | 4.373791
Avg Master | Avg This PR
------------------|--------------
5.1134364 | 4.3898886
Speedup of **1.164821449**
Closes#22275 from BryanCutler/arrow-toPandas-oo-batches-SPARK-25274.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Looks this test is flaky
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99704/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99569/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99644/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99548/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99454/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99609/console
```
======================================================================
FAIL: test_training_and_prediction (pyspark.mllib.tests.test_streaming_algorithms.StreamingLogisticRegressionWithSGDTests)
Test that the model improves on toy data with no. of batches
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 367, in test_training_and_prediction
self._eventually(condition)
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 78, in _eventually
% (timeout, lastValue))
AssertionError: Test failed due to timeout after 30 sec, with last condition returning: Latest errors: 0.67, 0.71, 0.78, 0.7, 0.75, 0.74, 0.73, 0.69, 0.62, 0.71, 0.69, 0.75, 0.72, 0.77, 0.71, 0.74
----------------------------------------------------------------------
Ran 13 tests in 185.051s
FAILED (failures=1, skipped=1)
```
This looks happening after increasing the parallelism in Jenkins to speed up at https://github.com/apache/spark/pull/23111. I am able to reproduce this manually when the resource usage is heavy (with manual decrease of timeout).
## How was this patch tested?
Manually tested by
```
cd python
./run-tests --testnames 'pyspark.mllib.tests.test_streaming_algorithms StreamingLogisticRegressionWithSGDTests.test_training_and_prediction' --python-executables=python
```
Closes#23236 from HyukjinKwon/SPARK-26275.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This fixes doc of renamed OneHotEncoder in PySpark.
## How was this patch tested?
N/A
Closes#23230 from viirya/remove_one_hot_encoder_followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR is a small follow up that puts some logic and functions into smaller scope and make it localized, and deduplicate.
## How was this patch tested?
Manually tested. Jenkins tests as well.
Closes#23200 from HyukjinKwon/followup-SPARK-26034-SPARK-26033.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
`resource` package is a Unix specific package. See https://docs.python.org/2/library/resource.html and https://docs.python.org/3/library/resource.html.
Note that we document Windows support:
> Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS).
This should be backported into branch-2.4 to restore Windows support in Spark 2.4.1.
## How was this patch tested?
Manually mocking the changed logics.
Closes#23055 from HyukjinKwon/SPARK-26080.
Lead-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: DylanGuedes <djmgguedesgmail.com>
## What changes were proposed in this pull request?
Addition of float, int and list hints for `pyspark.sql` Hint.
## How was this patch tested?
I did manual tests following the same principles used in the Scala version, and also added unit tests.
Closes#20788 from DylanGuedes/jira-21030.
Authored-by: DylanGuedes <djmgguedes@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Python with rpc and disk encryption enabled along with a python broadcast variable and just read the value back on the driver side the job failed with:
Traceback (most recent call last): File "broadcast.py", line 37, in <module> words_new.value File "/pyspark.zip/pyspark/broadcast.py", line 137, in value File "pyspark.zip/pyspark/broadcast.py", line 122, in load_from_path File "pyspark.zip/pyspark/broadcast.py", line 128, in load EOFError: Ran out of input
To reproduce use configs: --conf spark.network.crypto.enabled=true --conf spark.io.encryption.enabled=true
Code:
words_new = sc.broadcast(["scala", "java", "hadoop", "spark", "akka"])
words_new.value
print(words_new.value)
## How was this patch tested?
words_new = sc.broadcast([“scala”, “java”, “hadoop”, “spark”, “akka”])
textFile = sc.textFile(“README.md”)
wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word + words_new.value[1], 1)).reduceByKey(lambda a, b: a+b)
count = wordCounts.count()
print(count)
words_new.value
print(words_new.value)
Closes#23166 from redsanket/SPARK-26201.
Authored-by: schintap <schintap@oath.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
We have deprecated `OneHotEncoder` at Spark 2.3.0 and introduced `OneHotEncoderEstimator`. At 3.0.0, we remove deprecated `OneHotEncoder` and rename `OneHotEncoderEstimator` to `OneHotEncoder`.
TODO: According to ML migration guide, we need to keep `OneHotEncoderEstimator` as an alias after renaming. This is not done at this patch in order to facilitate review.
## How was this patch tested?
Existing tests.
Closes#23100 from viirya/remove_one_hot_encoder.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Currently duplicated map keys are not handled consistently. For example, map look up respects the duplicated key appears first, `Dataset.collect` only keeps the duplicated key appears last, `MapKeys` returns duplicated keys, etc.
This PR proposes to remove duplicated map keys with last wins policy, to follow Java/Scala and Presto. It only applies to built-in functions, as users can create map with duplicated map keys via private APIs anyway.
updated functions: `CreateMap`, `MapFromArrays`, `MapFromEntries`, `StringToMap`, `MapConcat`, `TransformKeys`.
For other places:
1. data source v1 doesn't have this problem, as users need to provide a java/scala map, which can't have duplicated keys.
2. data source v2 may have this problem. I've added a note to `ArrayBasedMapData` to ask the caller to take care of duplicated keys. In the future we should enforce it in the stable data APIs for data source v2.
3. UDF doesn't have this problem, as users need to provide a java/scala map. Same as data source v1.
4. file format. I checked all of them and only parquet does not enforce it. For backward compatibility reasons I change nothing but leave a note saying that the behavior will be undefined if users write map with duplicated keys to parquet files. Maybe we can add a config and fail by default if parquet files have map with duplicated keys. This can be done in followup.
## How was this patch tested?
updated tests and new tests
Closes#23124 from cloud-fan/map.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/22326 made a mistake that, not all python UDFs are unevaluable in join condition. Only python UDFs that refer to attributes from both join side are unevaluable.
This PR fixes this mistake.
## How was this patch tested?
a new test
Closes#23153 from cloud-fan/join.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Implement codegen for `LocalTableScanExec` and `ExistingRDDExec`. Refactor to share code between `LocalTableScanExec`, `ExistingRDDExec`, `InputAdapter` and `RowDataSourceScanExec`.
The difference in `doProduce` between these four was that `ExistingRDDExec` and `RowDataSourceScanExec` triggered adding an `UnsafeProjection`, while `InputAdapter` and `LocalTableScanExec` did not.
In the new trait `InputRDDCodegen` I added a flag `createUnsafeProjection` which the operators set accordingly.
Note: `LocalTableScanExec` explicitly creates its input as `UnsafeRows`, so it was obvious why it doesn't need an `UnsafeProjection`. But if an `InputAdapter` may take input that is `InternalRows` but not `UnsafeRows`, then I think it doesn't need an unsafe projection just because any other operator that is its parent would do that. That assumes that that any parent operator would always result in some `UnsafeProjection` being eventually added, and hence the output of the `WholeStageCodegen` unit would be `UnsafeRows`. If these assumptions hold, I think `createUnsafeProjection` could be set to `(parent == null)`.
Note: Do not codegen `LocalTableScanExec` when it's the only operator. `LocalTableScanExec` has optimized driver-only `executeCollect` and `executeTake` code paths that are used to return `Command` results without starting Spark Jobs. They can no longer be used if the `LocalTableScanExec` gets optimized.
## How was this patch tested?
Covered and used in existing tests.
Closes#23127 from juliuszsompolski/SPARK-26159.
Authored-by: Juliusz Sompolski <julek@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR is to add back `unionAll`, which is widely used. The name is also consistent with our ANSI SQL. We also have the corresponding `intersectAll` and `exceptAll`, which were introduced in Spark 2.4.
## How was this patch tested?
Added a test case in DataFrameSuite
Closes#23131 from gatorsmile/addBackUnionAll.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The DOI foundation recommends [this new resolver](https://www.doi.org/doi_handbook/3_Resolution.html#3.8). Accordingly, this PR re`sed`s all static DOI links ;-)
## How was this patch tested?
It wasn't, since it seems as safe as a "[typo fix](https://spark.apache.org/contributing.html)".
In case any of the files is included from other projects, and should be updated there, please let me know.
Closes#23129 from katrinleinweber/resolve-DOIs-securely.
Authored-by: Katrin Leinweber <9948149+katrinleinweber@users.noreply.github.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose new options for CSV datasource - `lineSep` similar to Text and JSON datasource. The option allows to specify custom line separator of maximum length of 2 characters (because of a restriction in `uniVocity` parser). New option can be used in reading and writing CSV files.
## How was this patch tested?
Added a few tests with custom `lineSep` for enabled/disabled `multiLine` in read as well as tests in write. Also I added roundtrip tests.
Closes#23080 from MaxGekk/csv-line-sep.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The PR removes the deprecated method `computeCost` of `KMeans`.
## How was this patch tested?
NA
Closes#22875 from mgaido91/SPARK-25867.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Following [SPARK-26024](https://issues.apache.org/jira/browse/SPARK-26024), I noticed the number of elements in each partition after repartitioning using `df.repartitionByRange` can vary for the same setup:
```scala
// Shuffle numbers from 0 to 1000, and make a DataFrame
val df = Random.shuffle(0.to(1000)).toDF("val")
// Repartition it using 3 partitions
// Sum up number of elements in each partition, and collect it.
// And do it several times
for (i <- 0 to 9) {
var counts = df.repartitionByRange(3, col("val"))
.mapPartitions{part => Iterator(part.size)}
.collect()
println(counts.toList)
}
// -> the number of elements in each partition varies
```
This is expected as for performance reasons this method uses sampling to estimate the ranges (with default size of 100). Hence, the output may not be consistent, since sampling can return different values. But documentation was not mentioning it at all, leading to misunderstanding.
## What changes were proposed in this pull request?
Update the documentation (Spark & PySpark) to mention the impact of `spark.sql.execution.rangeExchange.sampleSizePerPartition` on the resulting partitioned DataFrame.
Closes#23025 from JulienPeloton/SPARK-26024.
Authored-by: Julien <peloton@lal.in2p3.fr>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The following 5 functions were removed from branch-2.4:
- map_entries
- map_filter
- transform_values
- transform_keys
- map_zip_with
We should update the since version to 3.0.0.
## How was this patch tested?
Existing tests.
Closes#23082 from ueshin/issues/SPARK-26112/since.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently, some of PySpark tests sill assume the tests could be ran in Python 2.6 by importing `unittest2`. For instance:
```python
if sys.version_info[:2] <= (2, 6):
try:
import unittest2 as unittest
except ImportError:
sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
sys.exit(1)
else:
import unittest
```
While I am here, I removed some of unused imports and reordered imports per PEP 8.
We officially dropped Python 2.6 support a while ago and started to discuss about Python 2 drop. It's better to remove them out.
## How was this patch tested?
Manually tests, and existing tests via Jenkins.
Closes#23077 from HyukjinKwon/SPARK-26105.
Lead-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR breaks down the large ml/tests.py file that contains all Python ML unit tests into several smaller test files to be easier to read and maintain.
The tests are broken down as follows:
```
pyspark
├── __init__.py
...
├── ml
│ ├── __init__.py
...
│ ├── tests
│ │ ├── __init__.py
│ │ ├── test_algorithms.py
│ │ ├── test_base.py
│ │ ├── test_evaluation.py
│ │ ├── test_feature.py
│ │ ├── test_image.py
│ │ ├── test_linalg.py
│ │ ├── test_param.py
│ │ ├── test_persistence.py
│ │ ├── test_pipeline.py
│ │ ├── test_stat.py
│ │ ├── test_training_summary.py
│ │ ├── test_tuning.py
│ │ └── test_wrapper.py
...
├── testing
...
│ ├── mlutils.py
...
```
## How was this patch tested?
Ran tests manually by module to ensure test count was the same, and ran `python/run-tests --modules=pyspark-ml` to verify all passing with Python 2.7 and Python 3.6.
Closes#23063 from BryanCutler/python-test-breakup-ml-SPARK-26033.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR breaks down the large mllib/tests.py file that contains all Python MLlib unit tests into several smaller test files to be easier to read and maintain.
The tests are broken down as follows:
```
pyspark
├── __init__.py
...
├── mllib
│ ├── __init__.py
...
│ ├── tests
│ │ ├── __init__.py
│ │ ├── test_algorithms.py
│ │ ├── test_feature.py
│ │ ├── test_linalg.py
│ │ ├── test_stat.py
│ │ ├── test_streaming_algorithms.py
│ │ └── test_util.py
...
├── testing
...
│ ├── mllibutils.py
...
```
## How was this patch tested?
Ran tests manually by module to ensure test count was the same, and ran `python/run-tests --modules=pyspark-mllib` to verify all passing with Python 2.7 and Python 3.6. Also installed scipy to include optional tests in test_linalg.
Closes#23056 from BryanCutler/python-test-breakup-mllib-SPARK-26034.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR continues to break down a big large file into smaller files. See https://github.com/apache/spark/pull/23021. It targets to follow https://github.com/numpy/numpy/tree/master/numpy.
Basically this PR proposes to break down `pyspark/streaming/tests.py` into ...:
```
pyspark
├── __init__.py
...
├── streaming
│ ├── __init__.py
...
│ ├── tests
│ │ ├── __init__.py
│ │ ├── test_context.py
│ │ ├── test_dstream.py
│ │ ├── test_kinesis.py
│ │ └── test_listener.py
...
├── testing
...
│ ├── streamingutils.py
...
```
## How was this patch tested?
Existing tests should cover.
`cd python` and .`/run-tests-with-coverage`. Manually checked they are actually being ran.
Each test (not officially) can be ran via:
```bash
SPARK_TESTING=1 ./bin/pyspark pyspark.tests.test_context
```
Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`.
Closes#23034 from HyukjinKwon/SPARK-26035.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR continues to break down a big large file into smaller files. See https://github.com/apache/spark/pull/23021. It targets to follow https://github.com/numpy/numpy/tree/master/numpy.
Basically this PR proposes to break down `pyspark/tests.py` into ...:
```
pyspark
...
├── testing
...
│ └── utils.py
├── tests
│ ├── __init__.py
│ ├── test_appsubmit.py
│ ├── test_broadcast.py
│ ├── test_conf.py
│ ├── test_context.py
│ ├── test_daemon.py
│ ├── test_join.py
│ ├── test_profiler.py
│ ├── test_rdd.py
│ ├── test_readwrite.py
│ ├── test_serializers.py
│ ├── test_shuffle.py
│ ├── test_taskcontext.py
│ ├── test_util.py
│ └── test_worker.py
...
```
## How was this patch tested?
Existing tests should cover.
`cd python` and .`/run-tests-with-coverage`. Manually checked they are actually being ran.
Each test (not officially) can be ran via:
```bash
SPARK_TESTING=1 ./bin/pyspark pyspark.tests.test_context
```
Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`.
Closes#23033 from HyukjinKwon/SPARK-26036.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Fix fastSquaredDistance to calculate dense-dense situation calculation performance problem and meanwhile enhance the calculation accuracy.
## How was this patch tested?
From different point to test after add this patch, the dense-dense calculation situation performance is enhanced and will do influence other calculation situation like (sparse-sparse, sparse-dense)
**For calculation logic test**
There is my test for sparse-sparse, dense-dense, sparse-dense case
There is test result:
First we need define some branch path logic for sparse-sparse and sparse-dense case
if meet precisionBound1, we define it as LOGIC1
if not meet precisionBound1, and not meet precisionBound2, we define it as LOGIC2
if not meet precisionBound1, but meet precisionBound2, we define it as LOGIC3
(There is a trick, you can manually change the precision value to meet above situation)
sparse- sparse case time cost situation (milliseconds)
LOGIC1
Before add patch: 7786, 7970, 8086
After add patch: 7729, 7653, 7903
LOGIC2
Before add patch: 8412, 9029, 8606
After add patch: 8603, 8724, 9024
LOGIC3
Before add patch: 19365, 19146, 19351
After add patch: 18917, 19007, 19074
sparse-dense case time cost situation (milliseconds)
LOGIC1
Before add patch: 4195, 4014, 4409
After add patch: 4081,3971, 4151
LOGIC2
Before add patch: 4968, 5579, 5080
After add patch: 4980, 5472, 5148
LOGIC3
Before add patch: 11848, 12077, 12168
After add patch: 11718, 11874, 11743
And for dense-dense case like we already discussed in comment, only use sqdist to calculate distance
dense-dense case time cost situation (milliseconds)
Before add patch: 7340, 7816, 7672
After add patch: 5752, 5800, 5753
**For real world data test**
There is my test data situation
I use the data
http://archive.ics.uci.edu/ml/datasets/Condition+monitoring+of+hydraulic+systems
extract file (PS1, PS2, PS3, PS4, PS5, PS6) to form the test data
total instances are 13230
the attributes for line are 6000
Result for sparse-sparse situation time cost (milliseconds)
Before Enhance: 7670, 7704, 7652
After Enhance: 7634, 7729, 7645
Closes#22893 from KyleLi1985/updatekmeanpatch.
Authored-by: 李亮 <liang.li.work@outlook.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This is the official first attempt to break huge single `tests.py` file - I did it locally before few times and gave up for some reasons. Now, currently it really makes the unittests super hard to read and difficult to check. To me, it even bothers me to to scroll down the big file. It's one single 7000 lines file!
This is not only readability issue. Since one big test takes most of tests time, the tests don't run in parallel fully - although it will costs to start and stop the context.
We could pick up one example and follow. Given my investigation, the current style looks closer to NumPy structure and looks easier to follow. Please see https://github.com/numpy/numpy/tree/master/numpy.
Basically this PR proposes to break down `pyspark/sql/tests.py` into ...:
```bash
pyspark
...
├── sql
...
│ ├── tests # Includes all tests broken down from 'pyspark/sql/tests.py'
│ │ │ # Each matchs to module in 'pyspark/sql'. Additionally, some logical group can
│ │ │ # be added. For instance, 'test_arrow.py', 'test_datasources.py' ...
│ │ ├── __init__.py
│ │ ├── test_appsubmit.py
│ │ ├── test_arrow.py
│ │ ├── test_catalog.py
│ │ ├── test_column.py
│ │ ├── test_conf.py
│ │ ├── test_context.py
│ │ ├── test_dataframe.py
│ │ ├── test_datasources.py
│ │ ├── test_functions.py
│ │ ├── test_group.py
│ │ ├── test_pandas_udf.py
│ │ ├── test_pandas_udf_grouped_agg.py
│ │ ├── test_pandas_udf_grouped_map.py
│ │ ├── test_pandas_udf_scalar.py
│ │ ├── test_pandas_udf_window.py
│ │ ├── test_readwriter.py
│ │ ├── test_serde.py
│ │ ├── test_session.py
│ │ ├── test_streaming.py
│ │ ├── test_types.py
│ │ ├── test_udf.py
│ │ └── test_utils.py
...
├── testing # Includes testing utils that can be used in unittests.
│ ├── __init__.py
│ └── sqlutils.py
...
```
## How was this patch tested?
Existing tests should cover.
`cd python` and `./run-tests-with-coverage`. Manually checked they are actually being ran.
Each test (not officially) can be ran via:
```
SPARK_TESTING=1 ./bin/pyspark pyspark.sql.tests.test_pandas_udf_scalar
```
Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`.
Closes#23021 from HyukjinKwon/SPARK-25344.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Running a barrier job after a normal spark job causes the barrier job to run without a BarrierTaskContext. This is because while python worker reuse, BarrierTaskContext._getOrCreate() will still return a TaskContext after firstly submit a normal spark job, we'll get a `AttributeError: 'TaskContext' object has no attribute 'barrier'`. Fix this by adding check logic in BarrierTaskContext._getOrCreate() and make sure it will return BarrierTaskContext in this scenario.
## How was this patch tested?
Add new UT in pyspark-core.
Closes#22962 from xuanyuanking/SPARK-25921.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Clarify Bucketizer handleInvalid docs. Just a resubmit of https://github.com/apache/spark/pull/17169
## How was this patch tested?
N/A
Closes#23003 from srowen/SPARK-19714.
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?
Added JSON options for `json()` in streaming.py that are presented in the similar method in readwriter.py. In particular, missed options are `dropFieldIfAllNull` and `encoding`.
Closes#22973 from MaxGekk/streaming-missed-options.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to add new option `locale` into CSVOptions/JSONOptions to make parsing date/timestamps in local languages possible. Currently the locale is hard coded to `Locale.US`.
## How was this patch tested?
Added two tests for parsing a date from CSV/JSON - `ноя 2018`.
Closes#22951 from MaxGekk/locale.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
- Remove some AccumulableInfo .apply() methods
- Remove non-label-specific multiclass precision/recall/fScore in favor of accuracy
- Remove toDegrees/toRadians in favor of degrees/radians (SparkR: only deprecated)
- Remove approxCountDistinct in favor of approx_count_distinct (SparkR: only deprecated)
- Remove unused Python StorageLevel constants
- Remove Dataset unionAll in favor of union
- Remove unused multiclass option in libsvm parsing
- Remove references to deprecated spark configs like spark.yarn.am.port
- Remove TaskContext.isRunningLocally
- Remove ShuffleMetrics.shuffle* methods
- Remove BaseReadWrite.context in favor of session
- Remove Column.!== in favor of =!=
- Remove Dataset.explode
- Remove Dataset.registerTempTable
- Remove SQLContext.getOrCreate, setActive, clearActive, constructors
Not touched yet
- everything else in MLLib
- HiveContext
- Anything deprecated more recently than 2.0.0, generally
## How was this patch tested?
Existing tests
Closes#22921 from srowen/SPARK-25908.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: hyukjinkwon <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 typos and misspellings, per https://github.com/apache/spark-website/pull/158#issuecomment-435790366
## How was this patch tested?
Existing tests.
Closes#22950 from srowen/Typos.
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 PR proposes to deprecate the `computeCost` method on `BisectingKMeans` in favor of the adoption of `ClusteringEvaluator` in order to evaluate the clustering.
## How was this patch tested?
NA
Closes#22869 from mgaido91/SPARK-25758_3.0.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
New functions takes a struct and converts it to a CSV strings using passed CSV options. It accepts the same CSV options as CSV data source does.
## How was this patch tested?
Added `CsvExpressionsSuite`, `CsvFunctionsSuite` as well as R, Python and SQL tests similar to tests for `to_json()`
Closes#22626 from MaxGekk/to_csv.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to add new function - *schema_of_csv()* which infers schema of CSV string literal. The result of the function is a string containing a schema in DDL format. For example:
```sql
select schema_of_csv('1|abc', map('delimiter', '|'))
```
```
struct<_c0:int,_c1:string>
```
## How was this patch tested?
Added new tests to `CsvFunctionsSuite`, `CsvExpressionsSuite` and SQL tests to `csv-functions.sql`
Closes#22666 from MaxGekk/schema_of_csv-function.
Lead-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
add getActiveSession in session.py
## How was this patch tested?
add doctest
Closes#22295 from huaxingao/spark25255.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Holden Karau <holden@pigscanfly.ca>
## What changes were proposed in this pull request?
The main purpose of `schema_of_json` is the usage of combination with `from_json` (to make up the leak of schema inference) which takes its schema only as literal; however, currently `schema_of_json` allows JSON input as non-literal expressions (e.g, column).
This was mistakenly allowed - we don't have to take other usages rather then the main purpose into account for now.
This PR makes a followup to only allow literals for `schema_of_json`'s JSON input. We can allow non literal expressions later when it's needed or there are some usecase for it.
## How was this patch tested?
Unit tests were added.
Closes#22775 from HyukjinKwon/SPARK-25447-followup.
Lead-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
See the detailed information at https://issues.apache.org/jira/browse/SPARK-25841 on why these APIs should be deprecated and redesigned.
This patch also reverts 8acb51f08b which applies to 2.4.
## How was this patch tested?
Only deprecation and doc changes.
Closes#22841 from rxin/SPARK-25842.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Remove JavaSparkContextVarargsWorkaround
## How was this patch tested?
Existing tests.
Closes#22729 from srowen/SPARK-25737.
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?
In the PR, I propose to switch `from_json` on `FailureSafeParser`, and to make the function compatible to `PERMISSIVE` mode by default, and to support the `FAILFAST` mode as well. The `DROPMALFORMED` mode is not supported by `from_json`.
## How was this patch tested?
It was tested by existing `JsonSuite`/`CSVSuite`, `JsonFunctionsSuite` and `JsonExpressionsSuite` as well as new tests for `from_json` which checks different modes.
Closes#22237 from MaxGekk/from_json-failuresafe.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PR proposes to deprecate the `computeCost` method on `BisectingKMeans` in favor of the adoption of `ClusteringEvaluator` in order to evaluate the clustering.
## How was this patch tested?
NA
Closes#22756 from mgaido91/SPARK-25758.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Currently each test in `SQLTest` in PySpark is not cleaned properly.
We should introduce and use more `contextmanager` to be convenient to clean up the context properly.
## How was this patch tested?
Modified tests.
Closes#22762 from ueshin/issues/SPARK-25763/cleanup_sqltests.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
Master
## What changes were proposed in this pull request?
Previously Pyspark used the private constructor for SparkSession when
building that object. This resulted in a SparkSession without checking
the sql.extensions parameter for additional session extensions. To fix
this we instead use the Session.builder() path as SparkR uses, this
loads the extensions and allows their use in PySpark.
## How was this patch tested?
An integration test was added which mimics the Scala test for the same feature.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#21990 from RussellSpitzer/SPARK-25003-master.
Authored-by: Russell Spitzer <Russell.Spitzer@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The PR adds new function `from_csv()` similar to `from_json()` to parse columns with CSV strings. I added the following methods:
```Scala
def from_csv(e: Column, schema: StructType, options: Map[String, String]): Column
```
and this signature to call it from Python, R and Java:
```Scala
def from_csv(e: Column, schema: String, options: java.util.Map[String, String]): Column
```
## How was this patch tested?
Added new test suites `CsvExpressionsSuite`, `CsvFunctionsSuite` and sql tests.
Closes#22379 from MaxGekk/from_csv.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Remove Kafka 0.8 integration
## How was this patch tested?
Existing tests, build scripts
Closes#22703 from srowen/SPARK-25705.
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?
Removes all vestiges of Flume in the build, for Spark 3.
I don't think this needs Jenkins config changes.
## How was this patch tested?
Existing tests.
Closes#22692 from srowen/SPARK-25598.
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 specify type inference and simple e2e tests. Looks we are not cleanly testing those logics.
For instance, see 08c76b5d39/python/pyspark/sql/types.py (L894-L905)
Looks we intended to support datetime.time and None for type inference too but it does not work:
```
>>> spark.createDataFrame([[datetime.time()]])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/session.py", line 751, in createDataFrame
rdd, schema = self._createFromLocal(map(prepare, data), schema)
File "/.../spark/python/pyspark/sql/session.py", line 432, in _createFromLocal
data = [schema.toInternal(row) for row in data]
File "/.../spark/python/pyspark/sql/types.py", line 604, in toInternal
for f, v, c in zip(self.fields, obj, self._needConversion))
File "/.../spark/python/pyspark/sql/types.py", line 604, in <genexpr>
for f, v, c in zip(self.fields, obj, self._needConversion))
File "/.../spark/python/pyspark/sql/types.py", line 442, in toInternal
return self.dataType.toInternal(obj)
File "/.../spark/python/pyspark/sql/types.py", line 193, in toInternal
else time.mktime(dt.timetuple()))
AttributeError: 'datetime.time' object has no attribute 'timetuple'
>>> spark.createDataFrame([[None]])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/session.py", line 751, in createDataFrame
rdd, schema = self._createFromLocal(map(prepare, data), schema)
File "/.../spark/python/pyspark/sql/session.py", line 419, in _createFromLocal
struct = self._inferSchemaFromList(data, names=schema)
File "/.../python/pyspark/sql/session.py", line 353, in _inferSchemaFromList
raise ValueError("Some of types cannot be determined after inferring")
ValueError: Some of types cannot be determined after inferring
```
## How was this patch tested?
Manual tests and unit tests were added.
Closes#22653 from HyukjinKwon/SPARK-25659.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
We are facing some problems about type conversions between Python data and SQL types in UDFs (Pandas UDFs as well).
It's even difficult to identify the problems (see https://github.com/apache/spark/pull/20163 and https://github.com/apache/spark/pull/22610).
This PR targets to internally document the type conversion table. Some of them looks buggy and we should fix them.
```python
import sys
import array
import datetime
from decimal import Decimal
from pyspark.sql import Row
from pyspark.sql.types import *
from pyspark.sql.functions import udf
if sys.version >= '3':
long = int
data = [
None,
True,
1,
long(1),
"a",
u"a",
datetime.date(1970, 1, 1),
datetime.datetime(1970, 1, 1, 0, 0),
1.0,
array.array("i", [1]),
[1],
(1,),
bytearray([65, 66, 67]),
Decimal(1),
{"a": 1},
Row(kwargs=1),
Row("namedtuple")(1),
]
types = [
BooleanType(),
ByteType(),
ShortType(),
IntegerType(),
LongType(),
StringType(),
DateType(),
TimestampType(),
FloatType(),
DoubleType(),
ArrayType(IntegerType()),
BinaryType(),
DecimalType(10, 0),
MapType(StringType(), IntegerType()),
StructType([StructField("_1", IntegerType())]),
]
df = spark.range(1)
results = []
count = 0
total = len(types) * len(data)
spark.sparkContext.setLogLevel("FATAL")
for t in types:
result = []
for v in data:
try:
row = df.select(udf(lambda: v, t)()).first()
ret_str = repr(row[0])
except Exception:
ret_str = "X"
result.append(ret_str)
progress = "SQL Type: [%s]\n Python Value: [%s(%s)]\n Result Python Value: [%s]" % (
t.simpleString(), str(v), type(v).__name__, ret_str)
count += 1
print("%s/%s:\n %s" % (count, total, progress))
results.append([t.simpleString()] + list(map(str, result)))
schema = ["SQL Type \\ Python Value(Type)"] + list(map(lambda v: "%s(%s)" % (str(v), type(v).__name__), data))
strings = spark.createDataFrame(results, schema=schema)._jdf.showString(20, 20, False)
print("\n".join(map(lambda line: " # %s # noqa" % line, strings.strip().split("\n"))))
```
This table was generated under Python 2 but the code above is Python 3 compatible as well.
## How was this patch tested?
Manually tested and lint check.
Closes#22655 from HyukjinKwon/SPARK-25666.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
If we use accumulators in more than one UDFs, it is possible to overwrite deserialized accumulators and its values. We should check if an accumulator was deserialized before overwriting it in accumulator registry.
## How was this patch tested?
Added test.
Closes#22635 from viirya/SPARK-25591.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
For Pandas UDFs, we get arrow type from defined Catalyst return data type of UDFs. We use this arrow type to do serialization of data. If the defined return data type doesn't match with actual return type of Pandas.Series returned by Pandas UDFs, it has a risk to return incorrect data from Python side.
Currently we don't have reliable approach to check if the data conversion is safe or not. We leave some document to notify this to users for now. When there is next upgrade of PyArrow available we can use to check it, we should add the option to check it.
## How was this patch tested?
Only document change.
Closes#22610 from viirya/SPARK-25461.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Adds support for the setting limit in the sql split function
## How was this patch tested?
1. Updated unit tests
2. Tested using Scala spark shell
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22227 from phegstrom/master.
Authored-by: Parker Hegstrom <phegstrom@palantir.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR proposes to register Grouped aggregate UDF Vectorized UDFs for SQL Statement, for instance:
```python
from pyspark.sql.functions import pandas_udf, PandasUDFType
pandas_udf("integer", PandasUDFType.GROUPED_AGG)
def sum_udf(v):
return v.sum()
spark.udf.register("sum_udf", sum_udf)
q = "SELECT v2, sum_udf(v1) FROM VALUES (3, 0), (2, 0), (1, 1) tbl(v1, v2) GROUP BY v2"
spark.sql(q).show()
```
```
+---+-----------+
| v2|sum_udf(v1)|
+---+-----------+
| 1| 1|
| 0| 5|
+---+-----------+
```
## How was this patch tested?
Manual test and unit test.
Closes#22620 from HyukjinKwon/SPARK-25601.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.0.0-SNAPSHOT.
## How was this patch tested?
N/A
Closes#22606 from gatorsmile/bump3.0.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Add more data types for Pandas UDF Tests for PySpark SQL
## How was this patch tested?
manual tests
Closes#22568 from AlexanderKoryagin/new_types_for_pandas_udf_tests.
Lead-authored-by: Aleksandr Koriagin <aleksandr_koriagin@epam.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Alexander Koryagin <AlexanderKoryagin@users.noreply.github.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to extended the `schema_of_json()` function, and accept JSON options since they can impact on schema inferring. Purpose is to support the same options that `from_json` can use during schema inferring.
## How was this patch tested?
Added SQL, Python and Scala tests (`JsonExpressionsSuite` and `JsonFunctionsSuite`) that checks JSON options are used.
Closes#22442 from MaxGekk/schema_of_json-options.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Thanks for bahchis reporting this. It is more like a follow up work for #16581, this PR fix the scenario of Python UDF accessing attributes from both side of join in join condition.
## How was this patch tested?
Add regression tests in PySpark and `BatchEvalPythonExecSuite`.
Closes#22326 from xuanyuanking/SPARK-25314.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
We have an agreement that the behavior of `from/to_utc_timestamp` is corrected, although the function itself doesn't make much sense in Spark: https://issues.apache.org/jira/browse/SPARK-23715
This PR improves the document.
## How was this patch tested?
N/A
Closes#22543 from cloud-fan/doc.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In [SPARK-20946](https://issues.apache.org/jira/browse/SPARK-20946), we modified `SparkSession.getOrCreate` to not update conf for existing `SparkContext` because `SparkContext` is shared by all sessions.
We should not update it in PySpark side as well.
## How was this patch tested?
Added tests.
Closes#22545 from ueshin/issues/SPARK-25525/not_update_existing_conf.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In Scala, `HiveContext` sets a config `spark.sql.catalogImplementation` of the given `SparkContext` and then passes to `SparkSession.builder`.
The `HiveContext` in PySpark should behave as the same as Scala.
## How was this patch tested?
Existing tests.
Closes#22552 from ueshin/issues/SPARK-25540/hive_context.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PR introduces new JSON option `pretty` which allows to turn on `DefaultPrettyPrinter` of `Jackson`'s Json generator. New option is useful in exploring of deep nested columns and in converting of JSON columns in more readable representation (look at the added test).
## How was this patch tested?
Added rount trip test which convert an JSON string to pretty representation via `from_json()` and `to_json()`.
Closes#22534 from MaxGekk/pretty-json.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add the legacy prefix for spark.sql.execution.pandas.groupedMap.assignColumnsByPosition and rename it to spark.sql.legacy.execution.pandas.groupedMap.assignColumnsByName
## How was this patch tested?
The existing tests.
Closes#22540 from gatorsmile/renameAssignColumnsByPosition.
Lead-authored-by: gatorsmile <gatorsmile@gmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR does not fix the problem itself but just target to add few comments to run PySpark tests on Python 3.6 and macOS High Serria since it actually blocks to run tests on this enviornment.
it does not target to fix the problem yet.
The problem here looks because we fork python workers and the forked workers somehow call Objective-C libraries in some codes at CPython's implementation. After debugging a while, I suspect `pickle` in Python 3.6 has some changes:
58419b9267/python/pyspark/serializers.py (L577)
in particular, it looks also related to which objects are serialized or not as well.
This link (http://sealiesoftware.com/blog/archive/2017/6/5/Objective-C_and_fork_in_macOS_1013.html) and this link (https://blog.phusion.nl/2017/10/13/why-ruby-app-servers-break-on-macos-high-sierra-and-what-can-be-done-about-it/) were helpful for me to understand this.
I am still debugging this but my guts say it's difficult to fix or workaround within Spark side.
## How was this patch tested?
Manually tested:
Before `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`:
```
/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/subprocess.py:766: ResourceWarning: subprocess 27563 is still running
ResourceWarning, source=self)
[Stage 0:> (0 + 1) / 1]objc[27586]: +[__NSPlaceholderDictionary initialize] may have been in progress in another thread when fork() was called.
objc[27586]: +[__NSPlaceholderDictionary initialize] may have been in progress in another thread when fork() was called. We cannot safely call it or ignore it in the fork() child process. Crashing instead. Set a breakpoint on objc_initializeAfterForkError to debug.
ERROR
======================================================================
ERROR: test_streaming_foreach_with_simple_function (pyspark.sql.tests.SQLTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/.../spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/.../spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling o54.processAllAvailable.
: org.apache.spark.sql.streaming.StreamingQueryException: Writing job aborted.
=== Streaming Query ===
Identifier: [id = f508d634-407c-4232-806b-70e54b055c42, runId = 08d1435b-5358-4fb6-b167-811584a3163e]
Current Committed Offsets: {}
Current Available Offsets: {FileStreamSource[file:/var/folders/71/484zt4z10ks1vydt03bhp6hr0000gp/T/tmpolebys1s]: {"logOffset":0}}
Current State: ACTIVE
Thread State: RUNNABLE
Logical Plan:
FileStreamSource[file:/var/folders/71/484zt4z10ks1vydt03bhp6hr0000gp/T/tmpolebys1s]
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:295)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
Caused by: org.apache.spark.SparkException: Writing job aborted.
at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.doExecute(WriteToDataSourceV2Exec.scala:91)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
```
After `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`:
```
test_streaming_foreach_with_simple_function (pyspark.sql.tests.SQLTests) ...
ok
```
Closes#22480 from HyukjinKwon/SPARK-25473.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Always close the tempFile after `serializer.dump_stream(data, tempFile)` in _serialize_to_jvm
## How was this patch tested?
N/A
Closes#22523 from gatorsmile/fixMinor.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to add an overloaded method for `sampleBy` which accepts the first argument of the `Column` type. This will allow to sample by any complex columns as well as sampling by multiple columns. For example:
```Scala
spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17),
("Alice", 10))).toDF("name", "age")
.stat
.sampleBy(struct($"name", $"age"), Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0), 36L)
.show()
+-----+---+
| name|age|
+-----+---+
| Nico| 8|
|Alice| 10|
+-----+---+
```
## How was this patch tested?
Added new test for sampling by multiple columns for Scala and test for Java, Python to check that `sampleBy` is able to sample by `Column` type argument.
Closes#22365 from MaxGekk/sample-by-column.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR proposes to use add a helper in `PythonUtils` instead of direct accessing Scala package.
## How was this patch tested?
Jenkins tests.
Closes#22483 from HyukjinKwon/minor-refactoring.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In ArrayContains, we currently cast the right hand side expression to match the element type of the left hand side Array. This may result in down casting and may return wrong result or questionable result.
Example :
```SQL
spark-sql> select array_contains(array(1), 1.34);
true
```
```SQL
spark-sql> select array_contains(array(1), 'foo');
null
```
We should safely coerce both left and right hand side expressions.
## How was this patch tested?
Added tests in DataFrameFunctionsSuite
Closes#22408 from dilipbiswal/SPARK-25417.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Fix test that constructs a Pandas DataFrame by specifying the column order. Previously this test assumed the columns would be sorted alphabetically, however when using Python 3.6 with Pandas 0.23 or higher, the original column order is maintained. This causes the columns to get mixed up and the test errors.
Manually tested with `python/run-tests` using Python 3.6.6 and Pandas 0.23.4
Closes#22477 from BryanCutler/pyspark-tests-py36-pd23-SPARK-25471.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the dev list, we can still discuss whether the next version is 2.5.0 or 3.0.0. Let us first bump the master branch version to `2.5.0-SNAPSHOT`.
## How was this patch tested?
N/A
Closes#22426 from gatorsmile/bumpVersionMaster.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
(This change is a subset of the changes needed for the JIRA; see https://github.com/apache/spark/pull/22231)
## What changes were proposed in this pull request?
Use raw strings and simpler regex syntax consistently in Python, which also avoids warnings from pycodestyle about accidentally relying Python's non-escaping of non-reserved chars in normal strings. Also, fix a few long lines.
## How was this patch tested?
Existing tests, and some manual double-checking of the behavior of regexes in Python 2/3 to be sure.
Closes#22400 from srowen/SPARK-25238.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose new CSV option `emptyValue` and an update in the SQL Migration Guide which describes how to revert previous behavior when empty strings were not written at all. Since Spark 2.4, empty strings are saved as `""` to distinguish them from saved `null`s.
Closes#22234Closes#22367
## How was this patch tested?
It was tested by `CSVSuite` and new tests added in the PR #22234Closes#22389 from MaxGekk/csv-empty-value-master.
Lead-authored-by: Mario Molina <mmolimar@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Clarify docstring for Scalar functions
## How was this patch tested?
Adds a unit test showing use similar to wordcount, there's existing unit test for array of floats as well.
Closes#20908 from holdenk/SPARK-23672-document-support-for-nested-return-types-in-scalar-with-arrow-udfs.
Authored-by: Holden Karau <holden@pigscanfly.ca>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Deprecate public APIs from ImageSchema.
## How was this patch tested?
N/A
Closes#22349 from WeichenXu123/image_api_deprecate.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
Add value length check in `_create_row`, forbid extra value for custom Row in PySpark.
## How was this patch tested?
New UT in pyspark-sql
Closes#22140 from xuanyuanking/SPARK-25072.
Lead-authored-by: liyuanjian <liyuanjian@baidu.com>
Co-authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
This PR proposes to add another example for multiple grouping key in group aggregate pandas UDF since this feature could make users still confused.
## How was this patch tested?
Manually tested and documentation built.
Closes#22329 from HyukjinKwon/SPARK-25328.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
In the PR, I propose to extended `to_json` and support any types as element types of input arrays. It should allow converting arrays of primitive types and arrays of arrays. For example:
```
select to_json(array('1','2','3'))
> ["1","2","3"]
select to_json(array(array(1,2,3),array(4)))
> [[1,2,3],[4]]
```
## How was this patch tested?
Added a couple sql tests for arrays of primitive type and of arrays. Also I added round trip test `from_json` -> `to_json`.
Closes#22226 from MaxGekk/to_json-array.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Implement an image schema datasource.
This image datasource support:
- partition discovery (loading partitioned images)
- dropImageFailures (the same behavior with `ImageSchema.readImage`)
- path wildcard matching (the same behavior with `ImageSchema.readImage`)
- loading recursively from directory (different from `ImageSchema.readImage`, but use such path: `/path/to/dir/**`)
This datasource **NOT** support:
- specify `numPartitions` (it will be determined by datasource automatically)
- sampling (you can use `df.sample` later but the sampling operator won't be pushdown to datasource)
## How was this patch tested?
Unit tests.
## Benchmark
I benchmark and compare the cost time between old `ImageSchema.read` API and my image datasource.
**cluster**: 4 nodes, each with 64GB memory, 8 cores CPU
**test dataset**: Flickr8k_Dataset (about 8091 images)
**time cost**:
- My image datasource time (automatically generate 258 partitions): 38.04s
- `ImageSchema.read` time (set 16 partitions): 68.4s
- `ImageSchema.read` time (set 258 partitions): 90.6s
**time cost when increase image number by double (clone Flickr8k_Dataset and loads double number images)**:
- My image datasource time (automatically generate 515 partitions): 95.4s
- `ImageSchema.read` (set 32 partitions): 109s
- `ImageSchema.read` (set 515 partitions): 105s
So we can see that my image datasource implementation (this PR) bring some performance improvement compared against old`ImageSchema.read` API.
Closes#22328 from WeichenXu123/image_datasource.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
The PR adds the lift measure to Association rules.
## How was this patch tested?
existing and modified UTs
Closes#22236 from mgaido91/SPARK-10697.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
HyukjinKwon
## What changes were proposed in this pull request?
add __from pyspark.util import \_exception_message__ to python/pyspark/java_gateway.py
## How was this patch tested?
[flake8](http://flake8.pycqa.org) testing of https://github.com/apache/spark on Python 3.7.0
$ __flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics__
```
./python/pyspark/java_gateway.py:172:20: F821 undefined name '_exception_message'
emsg = _exception_message(e)
^
1 F821 undefined name '_exception_message'
1
```
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22265 from cclauss/patch-2.
Authored-by: cclauss <cclauss@bluewin.ch>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
I made one pass over the Python APIs for barrier mode and updated them to match the Scala doc in #22240 . Major changes:
* export the public classes
* expand the docs
* add doc for BarrierTaskInfo.addresss
cc: jiangxb1987
Closes#22261 from mengxr/SPARK-25248.1.
Authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
This changes the calls of `toPandas()` and `createDataFrame()` to use the Arrow stream format, when Arrow is enabled. Previously, Arrow data was written to byte arrays where each chunk is an output of the Arrow file format. This was mainly due to constraints at the time, and caused some overhead by writing the schema/footer on each chunk of data and then having to read multiple Arrow file inputs and concat them together.
Using the Arrow stream format has improved these by increasing performance, lower memory overhead for the average case, and simplified the code. Here are the details of this change:
**toPandas()**
_Before:_
Spark internal rows are converted to Arrow file format, each group of records is a complete Arrow file which contains the schema and other metadata. Next a collect is done and an Array of Arrow files is the result. After that each Arrow file is sent to Python driver which then loads each file and concats them to a single Arrow DataFrame.
_After:_
Spark internal rows are converted to ArrowRecordBatches directly, which is the simplest Arrow component for IPC data transfers. The driver JVM then immediately starts serving data to Python as an Arrow stream, sending the schema first. It then starts a Spark job with a custom handler that sends Arrow RecordBatches to Python. Partitions arriving in order are sent immediately, and out-of-order partitions are buffered until the ones that precede it come in. This improves performance, simplifies memory usage on executors, and improves the average memory usage on the JVM driver. Since the order of partitions must be preserved, the worst case is that the first partition will be the last to arrive all data must be buffered in memory until then. This case is no worse that before when doing a full collect.
**createDataFrame()**
_Before:_
A Pandas DataFrame is split into parts and each part is made into an Arrow file. Then each file is prefixed by the buffer size and written to a temp file. The temp file is read and each Arrow file is parallelized as a byte array.
_After:_
A Pandas DataFrame is split into parts, then an Arrow stream is written to a temp file where each part is an ArrowRecordBatch. The temp file is read as a stream and the Arrow messages are examined. If the message is an ArrowRecordBatch, the data is saved as a byte array. After reading the file, each ArrowRecordBatch is parallelized as a byte array. This has slightly more processing than before because we must look each Arrow message to extract the record batches, but performance ends up a litle better. It is cleaner in the sense that IPC from Python to JVM is done over a single Arrow stream.
## How was this patch tested?
Added new unit tests for the additions to ArrowConverters in Scala, existing tests for 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
```
Current Master | This PR
---------------------|------------
5.803557 | 5.16207
5.409119 | 5.133671
5.493509 | 5.147513
5.433107 | 5.105243
5.488757 | 5.018685
Avg Master | Avg This PR
------------------|--------------
5.5256098 | 5.1134364
Speedup of **1.08060595**
## Performance Tests - createDataFrame
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 `createDataFrame()` and get the first record. Took the average best time of 5 runs/5 loops each.
Test code
```python
def run():
pdf = pd.DataFrame(np.random.rand(10000000, 10))
spark.createDataFrame(pdf).first()
for i in range(6):
start = time.time()
run()
elapsed = time.time() - start
gc.collect()
print("Run %d: %f" % (i, elapsed))
```
Current Master | This PR
--------------------|----------
6.234608 | 5.665641
6.32144 | 5.3475
6.527859 | 5.370803
6.95089 | 5.479151
6.235046 | 5.529167
Avg Master | Avg This PR
---------------|----------------
6.4539686 | 5.4784524
Speedup of **1.178064192**
## Memory Improvements
**toPandas()**
The most significant improvement is reduction of the upper bound space complexity in the JVM driver. Before, the entire dataset was collected in the JVM first before sending it to Python. With this change, as soon as a partition is collected, the result handler immediately sends it to Python, so the upper bound is the size of the largest partition. Also, using the Arrow stream format is more efficient because the schema is written once per stream, followed by record batches. The schema is now only send from driver JVM to Python. Before, multiple Arrow file formats were used that each contained the schema. This duplicated schema was created in the executors, sent to the driver JVM, and then Python where all but the first one received are discarded.
I verified the upper bound limit by running a test that would collect data that would exceed the amount of driver JVM memory available. Using these settings on a standalone cluster:
```
spark.driver.memory 1g
spark.executor.memory 5g
spark.sql.execution.arrow.enabled true
spark.sql.execution.arrow.fallback.enabled false
spark.sql.execution.arrow.maxRecordsPerBatch 0
spark.driver.maxResultSize 2g
```
Test code:
```python
from pyspark.sql.functions import rand
df = spark.range(1 << 25, numPartitions=32).toDF("id").withColumn("x1", rand()).withColumn("x2", rand()).withColumn("x3", rand())
df.toPandas()
```
This makes total data size of 33554432×8×4 = 1073741824
With the current master, it fails with OOM but passes using this PR.
**createDataFrame()**
No significant change in memory except that using the stream format instead of separate file formats avoids duplicated the schema, similar to toPandas above. The process of reading the stream and parallelizing the batches does cause the record batch message metadata to be copied, but it's size is insignificant.
Closes#21546 from BryanCutler/arrow-toPandas-stream-SPARK-23030.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
This eliminates some duplication in the code to connect to a server on localhost to talk directly to the jvm. Also it gives consistent ipv6 and error handling. Two other incidental changes, that shouldn't matter:
1) python barrier tasks perform authentication immediately (rather than waiting for the BARRIER_FUNCTION indicator)
2) for `rdd._load_from_socket`, the timeout is only increased after authentication.
Closes#22247 from squito/py_connection_refactor.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This adds `spark.executor.pyspark.memory` to configure Python's address space limit, [`resource.RLIMIT_AS`](https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS). Limiting Python's address space allows Python to participate in memory management. In practice, we see fewer cases of Python taking too much memory because it doesn't know to run garbage collection. This results in YARN killing fewer containers. This also improves error messages so users know that Python is consuming too much memory:
```
File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in fe_engineer
fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item, []), mat_rec_prep.get(item, []))
File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in leven_list_compare
permutations = sorted(permutations, reverse=True)
MemoryError
```
The new pyspark memory setting is used to increase requested YARN container memory, instead of sharing overhead memory between python and off-heap JVM activity.
## How was this patch tested?
Tested memory limits in our YARN cluster and verified that MemoryError is thrown.
Author: Ryan Blue <blue@apache.org>
Closes#21977 from rdblue/SPARK-25004-add-python-memory-limit.
## What changes were proposed in this pull request?
The PR excludes Python UDFs filters in FileSourceStrategy so that they don't ExtractPythonUDF rule to throw exception. It doesn't make sense to pass Python UDF filters in FileSourceStrategy anyway because they cannot be used as push down filters.
## How was this patch tested?
Add a new regression test
Closes#22104 from icexelloss/SPARK-24721-udf-filter.
Authored-by: Li Jin <ice.xelloss@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In feature.py, VectorSizeHint setSize and getSize don't return value. Add return.
## How was this patch tested?
I tested the changes on my local.
Closes#22136 from huaxingao/spark-25124.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Joseph K. Bradley <joseph@databricks.com>
## What changes were proposed in this pull request?
Include PandasUDFType in the import all of pyspark.sql.functions
## How was this patch tested?
Run the test case from the pyspark shell from the jira [spark-25105](https://jira.apache.org/jira/browse/SPARK-25105?jql=project%20%3D%20SPARK%20AND%20component%20in%20(ML%2C%20PySpark%2C%20SQL%2C%20%22Structured%20Streaming%22))
I manually test on pyspark-shell:
before:
`
>>> from pyspark.sql.functions import *
>>> foo = pandas_udf(lambda x: x, 'v int', PandasUDFType.GROUPED_MAP)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'PandasUDFType' is not defined
>>>
`
after:
`
>>> from pyspark.sql.functions import *
>>> foo = pandas_udf(lambda x: x, 'v int', PandasUDFType.GROUPED_MAP)
>>>
`
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22100 from kevinyu98/spark-25105.
Authored-by: Kevin Yu <qyu@us.ibm.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Fix issues arising from the fact that builtins __file__, __long__, __raw_input()__, __unicode__, __xrange()__, etc. were all removed from Python 3. __Undefined names__ have the potential to raise [NameError](https://docs.python.org/3/library/exceptions.html#NameError) at runtime.
## How was this patch tested?
* $ __python2 -m flake8 . --count --select=E9,F82 --show-source --statistics__
* $ __python3 -m flake8 . --count --select=E9,F82 --show-source --statistics__
holdenk
flake8 testing of https://github.com/apache/spark on Python 3.6.3
$ __python3 -m flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics__
```
./dev/merge_spark_pr.py:98:14: F821 undefined name 'raw_input'
result = raw_input("\n%s (y/n): " % prompt)
^
./dev/merge_spark_pr.py:136:22: F821 undefined name 'raw_input'
primary_author = raw_input(
^
./dev/merge_spark_pr.py:186:16: F821 undefined name 'raw_input'
pick_ref = raw_input("Enter a branch name [%s]: " % default_branch)
^
./dev/merge_spark_pr.py:233:15: F821 undefined name 'raw_input'
jira_id = raw_input("Enter a JIRA id [%s]: " % default_jira_id)
^
./dev/merge_spark_pr.py:278:20: F821 undefined name 'raw_input'
fix_versions = raw_input("Enter comma-separated fix version(s) [%s]: " % default_fix_versions)
^
./dev/merge_spark_pr.py:317:28: F821 undefined name 'raw_input'
raw_assignee = raw_input(
^
./dev/merge_spark_pr.py:430:14: F821 undefined name 'raw_input'
pr_num = raw_input("Which pull request would you like to merge? (e.g. 34): ")
^
./dev/merge_spark_pr.py:442:18: F821 undefined name 'raw_input'
result = raw_input("Would you like to use the modified title? (y/n): ")
^
./dev/merge_spark_pr.py:493:11: F821 undefined name 'raw_input'
while raw_input("\n%s (y/n): " % pick_prompt).lower() == "y":
^
./dev/create-release/releaseutils.py:58:16: F821 undefined name 'raw_input'
response = raw_input("%s [y/n]: " % msg)
^
./dev/create-release/releaseutils.py:152:38: F821 undefined name 'unicode'
author = unidecode.unidecode(unicode(author, "UTF-8")).strip()
^
./python/setup.py:37:11: F821 undefined name '__version__'
VERSION = __version__
^
./python/pyspark/cloudpickle.py:275:18: F821 undefined name 'buffer'
dispatch[buffer] = save_buffer
^
./python/pyspark/cloudpickle.py:807:18: F821 undefined name 'file'
dispatch[file] = save_file
^
./python/pyspark/sql/conf.py:61:61: F821 undefined name 'unicode'
if not isinstance(obj, str) and not isinstance(obj, unicode):
^
./python/pyspark/sql/streaming.py:25:21: F821 undefined name 'long'
intlike = (int, long)
^
./python/pyspark/streaming/dstream.py:405:35: F821 undefined name 'long'
return self._sc._jvm.Time(long(timestamp * 1000))
^
./sql/hive/src/test/resources/data/scripts/dumpdata_script.py:21:10: F821 undefined name 'xrange'
for i in xrange(50):
^
./sql/hive/src/test/resources/data/scripts/dumpdata_script.py:22:14: F821 undefined name 'xrange'
for j in xrange(5):
^
./sql/hive/src/test/resources/data/scripts/dumpdata_script.py:23:18: F821 undefined name 'xrange'
for k in xrange(20022):
^
20 F821 undefined name 'raw_input'
20
```
Closes#20838 from cclauss/fix-undefined-names.
Authored-by: cclauss <cclauss@bluewin.ch>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Add method `barrier()` and `getTaskInfos()` in python TaskContext, these two methods are only allowed for barrier tasks.
## How was this patch tested?
Add new tests in `tests.py`
Closes#22085 from jiangxb1987/python.barrier.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
Sometimes Python worker can't open socket in `_load_from_socket` for some reason, but it's difficult to figure out the reason because the exception doesn't even contain the messages from `socket.error`s.
We should at least add the error messages when raising the exception.
## How was this patch tested?
Manually in my local environment.
Closes#22132 from ueshin/issues/SPARK-25142/socket_error.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Adding `BinaryType` support for Arrow in pyspark, conditional on using pyarrow >= 0.10.0. Earlier versions will continue to raise a TypeError.
## How was this patch tested?
Additional unit tests in pyspark for code paths that use Arrow for createDataFrame, toPandas, and scalar pandas_udfs.
Closes#20725 from BryanCutler/arrow-binary-type-support-SPARK-23555.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Follow up for SPARK-24665, find some others hard code during code review.
## How was this patch tested?
Existing UT.
Closes#22122 from xuanyuanking/SPARK-24665-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Upgrade Apache Arrow to 0.10.0
Version 0.10.0 has a number of bug fixes and improvements with the following pertaining directly to usage in Spark:
* Allow for adding BinaryType support ARROW-2141
* Bug fix related to array serialization ARROW-1973
* Python2 str will be made into an Arrow string instead of bytes ARROW-2101
* Python bytearrays are supported in as input to pyarrow ARROW-2141
* Java has common interface for reset to cleanup complex vectors in Spark ArrowWriter ARROW-1962
* Cleanup pyarrow type equality checks ARROW-2423
* ArrowStreamWriter should not hold references to ArrowBlocks ARROW-2632, ARROW-2645
* Improved low level handling of messages for RecordBatch ARROW-2704
## How was this patch tested?
existing tests
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#21939 from BryanCutler/arrow-upgrade-010.
## What changes were proposed in this pull request?
The PR removes a restriction for element types of array type which exists in `from_json` for the root type. Currently, the function can handle only arrays of structs. Even array of primitive types is disallowed. The PR allows arrays of any types currently supported by JSON datasource. Here is an example of an array of a primitive type:
```
scala> import org.apache.spark.sql.functions._
scala> val df = Seq("[1, 2, 3]").toDF("a")
scala> val schema = new ArrayType(IntegerType, false)
scala> val arr = df.select(from_json($"a", schema))
scala> arr.printSchema
root
|-- jsontostructs(a): array (nullable = true)
| |-- element: integer (containsNull = true)
```
and result of converting of the json string to the `ArrayType`:
```
scala> arr.show
+----------------+
|jsontostructs(a)|
+----------------+
| [1, 2, 3]|
+----------------+
```
## How was this patch tested?
I added a few positive and negative tests:
- array of primitive types
- array of arrays
- array of structs
- array of maps
Closes#21439 from MaxGekk/from_json-array.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When the grid of the parameters is created in `ParamGridBuilder`, the implicit type coercion is not enforced. So using an integer in the list of parameters to set for a parameter accepting a double can cause a class cast exception.
The PR proposes to enforce the type coercion when building the parameters.
## How was this patch tested?
added UT
Closes#22076 from mgaido91/SPARK-25090.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Small typo fixes in Pyspark. These were the only ones I stumbled across after looking around for a while.
## How was this patch tested?
Manually
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22016 from tynan-cr/typo-fix-pyspark.
Authored-by: Tynan CR <tynancr@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
Fixing typos is sometimes very hard. It's not so easy to visually review them. Recently, I discovered a very useful tool for it, [misspell](https://github.com/client9/misspell).
This pull request fixes minor typos detected by [misspell](https://github.com/client9/misspell) except for the false positives. If you would like me to work on other files as well, let me know.
## How was this patch tested?
### before
```
$ misspell . | grep -v '.js'
R/pkg/R/SQLContext.R:354:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:424:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:445:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:495:43: "definiton" is a misspelling of "definition"
NOTICE-binary:454:16: "containd" is a misspelling of "contained"
R/pkg/R/context.R:46:43: "definiton" is a misspelling of "definition"
R/pkg/R/context.R:74:43: "definiton" is a misspelling of "definition"
R/pkg/R/DataFrame.R:591:48: "persistance" is a misspelling of "persistence"
R/pkg/R/streaming.R:166:44: "occured" is a misspelling of "occurred"
R/pkg/inst/worker/worker.R:65:22: "ouput" is a misspelling of "output"
R/pkg/tests/fulltests/test_utils.R:106:25: "environemnt" is a misspelling of "environment"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/InMemoryStoreSuite.java:38:39: "existant" is a misspelling of "existent"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/LevelDBSuite.java:83:39: "existant" is a misspelling of "existent"
common/network-common/src/main/java/org/apache/spark/network/crypto/TransportCipher.java:243:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:234:19: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:238:63: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:244:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:276:39: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
common/unsafe/src/test/scala/org/apache/spark/unsafe/types/UTF8StringPropertyCheckSuite.scala:195:15: "orgin" is a misspelling of "origin"
core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:621:39: "gauranteed" is a misspelling of "guaranteed"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/main/scala/org/apache/spark/storage/DiskStore.scala:282:18: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/util/ListenerBus.scala:64:17: "overriden" is a misspelling of "overridden"
core/src/test/scala/org/apache/spark/ShuffleSuite.scala:211:7: "substracted" is a misspelling of "subtracted"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:2468:84: "truely" is a misspelling of "truly"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:25:18: "persistance" is a misspelling of "persistence"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:26:69: "persistance" is a misspelling of "persistence"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
dev/run-pip-tests:55:28: "enviroments" is a misspelling of "environments"
dev/run-pip-tests:91:37: "virutal" is a misspelling of "virtual"
dev/merge_spark_pr.py:377:72: "accross" is a misspelling of "across"
dev/merge_spark_pr.py:378:66: "accross" is a misspelling of "across"
dev/run-pip-tests:126:25: "enviroments" is a misspelling of "environments"
docs/configuration.md:1830:82: "overriden" is a misspelling of "overridden"
docs/structured-streaming-programming-guide.md:525:45: "processs" is a misspelling of "processes"
docs/structured-streaming-programming-guide.md:1165:61: "BETWEN" is a misspelling of "BETWEEN"
docs/sql-programming-guide.md:1891:810: "behaivor" is a misspelling of "behavior"
examples/src/main/python/sql/arrow.py:98:8: "substract" is a misspelling of "subtract"
examples/src/main/python/sql/arrow.py:103:27: "substract" is a misspelling of "subtract"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala:230:24: "inital" is a misspelling of "initial"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala:237:26: "descripiton" is a misspelling of "descriptions"
python/pyspark/find_spark_home.py:30:13: "enviroment" is a misspelling of "environment"
python/pyspark/context.py:937:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:938:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:939:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:940:12: "supress" is a misspelling of "suppress"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:713:8: "probabilty" is a misspelling of "probability"
python/pyspark/ml/clustering.py:1038:8: "Currenlty" is a misspelling of "Currently"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/ml/regression.py:1378:20: "paramter" is a misspelling of "parameter"
python/pyspark/mllib/stat/_statistics.py:262:8: "probabilty" is a misspelling of "probability"
python/pyspark/rdd.py:1363:32: "paramter" is a misspelling of "parameter"
python/pyspark/streaming/tests.py:825:42: "retuns" is a misspelling of "returns"
python/pyspark/sql/tests.py:768:29: "initalization" is a misspelling of "initialization"
python/pyspark/sql/tests.py:3616:31: "initalize" is a misspelling of "initialize"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala:120:39: "arbitary" is a misspelling of "arbitrary"
resource-managers/mesos/src/test/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArgumentsSuite.scala:26:45: "sucessfully" is a misspelling of "successfully"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala:358:27: "constaints" is a misspelling of "constraints"
resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala:111:24: "senstive" is a misspelling of "sensitive"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala:1063:5: "overwirte" is a misspelling of "overwrite"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala:1348:17: "compatability" is a misspelling of "compatibility"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala:77:36: "paramter" is a misspelling of "parameter"
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:1374:22: "precendence" is a misspelling of "precedence"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:238:27: "unnecassary" is a misspelling of "unnecessary"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala:212:17: "whn" is a misspelling of "when"
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinHelper.scala:147:60: "timestmap" is a misspelling of "timestamp"
sql/core/src/test/scala/org/apache/spark/sql/TPCDSQuerySuite.scala:150:45: "precentage" is a misspelling of "percentage"
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchemaSuite.scala:135:29: "infered" is a misspelling of "inferred"
sql/hive/src/test/resources/golden/udf_instr-1-2e76f819563dbaba4beb51e3a130b922:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_instr-2-32da357fc754badd6e3898dcc8989182:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-1-6e41693c9c6dceea4d7fab4c02884e4e:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-2-d9b5934457931447874d6bb7c13de478:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:9:79: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:13:110: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/annotate_stats_join.q:46:105: "distint" is a misspelling of "distinct"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/auto_sortmerge_join_11.q:29:3: "Currenly" is a misspelling of "Currently"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/avro_partitioned.q:72:15: "existant" is a misspelling of "existent"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/decimal_udf.q:25:3: "substraction" is a misspelling of "subtraction"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby2_map_multi_distinct.q:16:51: "funtion" is a misspelling of "function"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby_sort_8.q:15:30: "issueing" is a misspelling of "issuing"
sql/hive/src/test/scala/org/apache/spark/sql/sources/HadoopFsRelationTest.scala:669:52: "wiht" is a misspelling of "with"
sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSessionImpl.java:474:9: "Refering" is a misspelling of "Referring"
```
### after
```
$ misspell . | grep -v '.js'
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
```
Closes#22070 from seratch/fix-typo.
Authored-by: Kazuhiro Sera <seratch@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
This PR add python support for barrier execution mode, thus enable launch a job containing barrier stage(s) from PySpark.
We just forked the existing `RDDBarrier` and `RDD.barrier()` in Python api.
## How was this patch tested?
Manually tested:
```
>>> rdd = sc.parallelize([1, 2, 3, 4])
>>> def f(iterator): yield sum(iterator)
...
>>> rdd.barrier().mapPartitions(f).isBarrier() == True
True
```
Unit tests will be added in a follow-up PR that implements BarrierTaskContext on python side.
Closes#22011 from jiangxb1987/python.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Parquet file provides six codecs: "snappy", "gzip", "lzo", "lz4", "brotli", "zstd".
This pr add missing compression codec :"lz4", "brotli", "zstd" .
## How was this patch tested?
N/A
Closes#22068 from 10110346/nosupportlz4.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR fixes typo regarding `auxiliary verb + verb[s]`. This is a follow-on of #21956.
## How was this patch tested?
N/A
Closes#22040 from kiszk/spellcheck1.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The PR adds the SQL function `array_intersect`. The behavior of the function is based on Presto's one.
This function returns returns an array of the elements in the intersection of array1 and array2.
Note: The order of elements in the result is not defined.
## How was this patch tested?
Added UTs
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21102 from kiszk/SPARK-23913.
## What changes were proposed in this pull request?
The `dropFieldIfAllNull` parameter of the `json` method wasn't set as an option. This PR fixes that.
## How was this patch tested?
I added a test to `sql/test.py`
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#22002 from MaxGekk/drop-field-if-all-null.
## What changes were proposed in this pull request?
In the PR, I propose column-based API for the `pivot()` function. It allows using of any column expressions as the pivot column. Also this makes it consistent with how groupBy() works.
## How was this patch tested?
I added new tests to `DataFramePivotSuite` and updated PySpark examples for the `pivot()` function.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21699 from MaxGekk/pivot-column.
## What changes were proposed in this pull request?
Fixed little typo for a comment
## How was this patch tested?
Manual test
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Onwuka Gideon <dongidomed@gmail.com>
Closes#21992 from dongido001/patch-1.
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-25011
add prefix to __all__ in fpm.py
## How was this patch tested?
existing unit test.
Author: Yuhao Yang <yuhao.yang@intel.com>
Closes#21981 from hhbyyh/prefixall.
## What changes were proposed in this pull request?
This pr fixes lint-python.
```
./python/pyspark/accumulators.py:231:9: E306 expected 1 blank line before a nested definition, found 0
./python/pyspark/accumulators.py:257:101: E501 line too long (107 > 100 characters)
./python/pyspark/accumulators.py:264:1: E302 expected 2 blank lines, found 1
./python/pyspark/accumulators.py:281:1: E302 expected 2 blank lines, found 1
```
## How was this patch tested?
Executed lint-python manually.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#21973 from ueshin/issues/build/1/fix_lint-python.
## What changes were proposed in this pull request?
The PR adds the SQL function `array_except`. The behavior of the function is based on Presto's one.
This function returns returns an array of the elements in array1 but not in array2.
Note: The order of elements in the result is not defined.
## How was this patch tested?
Added UTs.
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21103 from kiszk/SPARK-23915.
## What changes were proposed in this pull request?
See [ARROW-2432](https://jira.apache.org/jira/browse/ARROW-2432). Seems using `from_pandas` to convert decimals fails if encounters a value of `None`:
```python
import pyarrow as pa
import pandas as pd
from decimal import Decimal
pa.Array.from_pandas(pd.Series([Decimal('3.14'), None]), type=pa.decimal128(3, 2))
```
**Arrow 0.8.0**
```
<pyarrow.lib.Decimal128Array object at 0x10a572c58>
[
Decimal('3.14'),
NA
]
```
**Arrow 0.9.0**
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "array.pxi", line 383, in pyarrow.lib.Array.from_pandas
File "array.pxi", line 177, in pyarrow.lib.array
File "error.pxi", line 77, in pyarrow.lib.check_status
File "error.pxi", line 77, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Error converting from Python objects to Decimal: Got Python object of type NoneType but can only handle these types: decimal.Decimal
```
This PR propose to work around this via Decimal NaN:
```python
pa.Array.from_pandas(pd.Series([Decimal('3.14'), Decimal('NaN')]), type=pa.decimal128(3, 2))
```
```
<pyarrow.lib.Decimal128Array object at 0x10ffd2e68>
[
Decimal('3.14'),
NA
]
```
## How was this patch tested?
Manually tested:
```bash
SPARK_TESTING=1 ./bin/pyspark pyspark.sql.tests ScalarPandasUDFTests
```
**Before**
```
Traceback (most recent call last):
File "/.../spark/python/pyspark/sql/tests.py", line 4672, in test_vectorized_udf_null_decimal
self.assertEquals(df.collect(), res.collect())
File "/.../spark/python/pyspark/sql/dataframe.py", line 533, in collect
sock_info = self._jdf.collectToPython()
File "/.../spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/.../spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/.../spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
format(target_id, ".", name), value)
Py4JJavaError: An error occurred while calling o51.collectToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 3 in stage 1.0 failed 1 times, most recent failure: Lost task 3.0 in stage 1.0 (TID 7, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/.../spark/python/pyspark/worker.py", line 320, in main
process()
File "/.../spark/python/pyspark/worker.py", line 315, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/.../spark/python/pyspark/serializers.py", line 274, in dump_stream
batch = _create_batch(series, self._timezone)
File "/.../spark/python/pyspark/serializers.py", line 243, in _create_batch
arrs = [create_array(s, t) for s, t in series]
File "/.../spark/python/pyspark/serializers.py", line 241, in create_array
return pa.Array.from_pandas(s, mask=mask, type=t)
File "array.pxi", line 383, in pyarrow.lib.Array.from_pandas
File "array.pxi", line 177, in pyarrow.lib.array
File "error.pxi", line 77, in pyarrow.lib.check_status
File "error.pxi", line 77, in pyarrow.lib.check_status
ArrowInvalid: Error converting from Python objects to Decimal: Got Python object of type NoneType but can only handle these types: decimal.Decimal
```
**After**
```
Running tests...
----------------------------------------------------------------------
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
.......S.............................
----------------------------------------------------------------------
Ran 37 tests in 21.980s
```
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21928 from HyukjinKwon/SPARK-24976.
## What changes were proposed in this pull request?
Add numIter to Python version of ClusteringSummary
## How was this patch tested?
Modified existing UT test_multiclass_logistic_regression_summary
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21925 from huaxingao/spark-24973.
## What changes were proposed in this pull request?
Update Pandas UDFs section in sql-programming-guide. Add section for grouped aggregate pandas UDF.
## How was this patch tested?
Author: Li Jin <ice.xelloss@gmail.com>
Closes#21887 from icexelloss/SPARK-23633-sql-programming-guide.
## What changes were proposed in this pull request?
Implements INTERSECT ALL clause through query rewrites using existing operators in Spark. Please refer to [Link](https://drive.google.com/open?id=1nyW0T0b_ajUduQoPgZLAsyHK8s3_dko3ulQuxaLpUXE) for the design.
Input Query
``` SQL
SELECT c1 FROM ut1 INTERSECT ALL SELECT c1 FROM ut2
```
Rewritten Query
```SQL
SELECT c1
FROM (
SELECT replicate_row(min_count, c1)
FROM (
SELECT c1,
IF (vcol1_cnt > vcol2_cnt, vcol2_cnt, vcol1_cnt) AS min_count
FROM (
SELECT c1, count(vcol1) as vcol1_cnt, count(vcol2) as vcol2_cnt
FROM (
SELECT c1, true as vcol1, null as vcol2 FROM ut1
UNION ALL
SELECT c1, null as vcol1, true as vcol2 FROM ut2
) AS union_all
GROUP BY c1
HAVING vcol1_cnt >= 1 AND vcol2_cnt >= 1
)
)
)
```
## How was this patch tested?
Added test cases in SQLQueryTestSuite, DataFrameSuite, SetOperationSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#21886 from dilipbiswal/dkb_intersect_all_final.
## What changes were proposed in this pull request?
Implements EXCEPT ALL clause through query rewrites using existing operators in Spark. In this PR, an internal UDTF (replicate_rows) is added to aid in preserving duplicate rows. Please refer to [Link](https://drive.google.com/open?id=1nyW0T0b_ajUduQoPgZLAsyHK8s3_dko3ulQuxaLpUXE) for the design.
**Note** This proposed UDTF is kept as a internal function that is purely used to aid with this particular rewrite to give us flexibility to change to a more generalized UDTF in future.
Input Query
``` SQL
SELECT c1 FROM ut1 EXCEPT ALL SELECT c1 FROM ut2
```
Rewritten Query
```SQL
SELECT c1
FROM (
SELECT replicate_rows(sum_val, c1)
FROM (
SELECT c1, sum_val
FROM (
SELECT c1, sum(vcol) AS sum_val
FROM (
SELECT 1L as vcol, c1 FROM ut1
UNION ALL
SELECT -1L as vcol, c1 FROM ut2
) AS union_all
GROUP BY union_all.c1
)
WHERE sum_val > 0
)
)
```
## How was this patch tested?
Added test cases in SQLQueryTestSuite, DataFrameSuite and SetOperationSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#21857 from dilipbiswal/dkb_except_all_final.
## What changes were proposed in this pull request?
This PR adds a new collection function: shuffle. It generates a random permutation of the given array. This implementation uses the "inside-out" version of Fisher-Yates algorithm.
## How was this patch tested?
New tests are added to CollectionExpressionsSuite.scala and DataFrameFunctionsSuite.scala.
Author: Takuya UESHIN <ueshin@databricks.com>
Author: pkuwm <ihuizhi.lu@gmail.com>
Closes#21802 from ueshin/issues/SPARK-23928/shuffle.
## What changes were proposed in this pull request?
Add support for custom encoding on csv writer, see https://issues.apache.org/jira/browse/SPARK-19018
## How was this patch tested?
Added two unit tests in CSVSuite
Author: crafty-coder <carlospb86@gmail.com>
Author: Carlos <crafty-coder@users.noreply.github.com>
Closes#20949 from crafty-coder/master.
## What changes were proposed in this pull request?
Fix a typo in pyspark sql tests
Author: William Sheu <william.sheu@databricks.com>
Closes#21833 from PenguinToast/fix-test-typo.
## What changes were proposed in this pull request?
Deprecate `KMeans.computeCost` which was introduced as a temp fix and now it is not needed anymore, since we introduced `ClusteringEvaluator`.
## How was this patch tested?
manual test (deprecation warning displayed)
Scala
```
...
scala> model.computeCost(dataset)
warning: there was one deprecation warning; re-run with -deprecation for details
res1: Double = 0.0
```
Python
```
>>> import warnings
>>> warnings.simplefilter('always', DeprecationWarning)
...
>>> model.computeCost(df)
/Users/mgaido/apache/spark/python/pyspark/ml/clustering.py:330: DeprecationWarning: Deprecated in 2.4.0. It will be removed in 3.0.0. Use ClusteringEvaluator instead.
" instead.", DeprecationWarning)
```
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20629 from mgaido91/SPARK-23451.
## What changes were proposed in this pull request?
Add ```sequence``` in functions.py
## How was this patch tested?
Add doctest.
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21820 from huaxingao/spark-24868.
## What changes were proposed in this pull request?
In the PR, I propose to output an warning if the `addFile()` or `addJar()` methods are callled more than once for the same path. Currently, overwriting of already added files is not supported. New comments and warning are reflected the existing behaviour.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21771 from MaxGekk/warning-on-adding-file.
## What changes were proposed in this pull request?
Use longs in calculating min hash to avoid bias due to int overflow.
## How was this patch tested?
Existing tests.
Author: Sean Owen <srowen@gmail.com>
Closes#21750 from srowen/SPARK-24754.
## What changes were proposed in this pull request?
The PR is a followup to move the test cases introduced by the original PR in their proper location.
## How was this patch tested?
moved UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21751 from mgaido91/SPARK-24208_followup.
## What changes were proposed in this pull request?
The PR adds the SQL function `array_union`. The behavior of the function is based on Presto's one.
This function returns returns an array of the elements in the union of array1 and array2.
Note: The order of elements in the result is not defined.
## How was this patch tested?
Added UTs
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21061 from kiszk/SPARK-23914.
## What changes were proposed in this pull request?
In the PR, I propose to extend `RuntimeConfig` by new method `isModifiable()` which returns `true` if a config parameter can be modified at runtime (for current session state). For static SQL and core parameters, the method returns `false`.
## How was this patch tested?
Added new test to `RuntimeConfigSuite` for checking Spark core and SQL parameters.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21730 from MaxGekk/is-modifiable.
## What changes were proposed in this pull request?
A self-join on a dataset which contains a `FlatMapGroupsInPandas` fails because of duplicate attributes. This happens because we are not dealing with this specific case in our `dedupAttr` rules.
The PR fix the issue by adding the management of the specific case
## How was this patch tested?
added UT + manual tests
Author: Marco Gaido <marcogaido91@gmail.com>
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#21737 from mgaido91/SPARK-24208.
## What changes were proposed in this pull request?
Implement map_concat high order function.
This implementation does not pick a winner when the specified maps have overlapping keys. Therefore, this implementation preserves existing duplicate keys in the maps and potentially introduces new duplicates (After discussion with ueshin, we settled on option 1 from [here](https://issues.apache.org/jira/browse/SPARK-23936?focusedCommentId=16464245&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-16464245)).
## How was this patch tested?
New tests
Manual tests
Run all sbt SQL tests
Run all pyspark sql tests
Author: Bruce Robbins <bersprockets@gmail.com>
Closes#21073 from bersprockets/SPARK-23936.
## What changes were proposed in this pull request?
This pr supported column arguments in timezone of `from_utc_timestamp/to_utc_timestamp` (follow-up of #21693).
## How was this patch tested?
Added tests.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#21723 from maropu/SPARK-24673-FOLLOWUP.
## What changes were proposed in this pull request?
Fixed a small typo in the code that caused 20 random characters to be added to the UID, rather than 12.
Author: mcteo <mc_teo@live.ie>
Closes#21675 from mcteo/SPARK-24698-fix.
## What changes were proposed in this pull request?
In the PR, I propose to add new function - *schema_of_json()* which infers schema of JSON string literal. The result of the function is a string containing a schema in DDL format.
One of the use cases is using of *schema_of_json()* in the combination with *from_json()*. Currently, _from_json()_ requires a schema as a mandatory argument. The *schema_of_json()* function will allow to point out an JSON string as an example which has the same schema as the first argument of _from_json()_. For instance:
```sql
select from_json(json_column, schema_of_json('{"c1": [0], "c2": [{"c3":0}]}'))
from json_table;
```
## How was this patch tested?
Added new test to `JsonFunctionsSuite`, `JsonExpressionsSuite` and SQL tests to `json-functions.sql`
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21686 from MaxGekk/infer_schema_json.
## What changes were proposed in this pull request?
Use SQLConf for PySpark to manage all sql configs, drop all the hard code in config usage.
## How was this patch tested?
Existing UT.
Author: Yuanjian Li <xyliyuanjian@gmail.com>
Closes#21648 from xuanyuanking/SPARK-24665.
## What changes were proposed in this pull request?
add distanceMeasure to BisectingKMeans in Python.
## How was this patch tested?
added doctest and also manually tested it.
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21557 from huaxingao/spark-24439.
## What changes were proposed in this pull request?
Adds basic PMML export support for Spark ML stages to PySpark as was previously done in Scala. Includes LinearRegressionModel as the first stage to implement.
## How was this patch tested?
Doctest, the main testing work for this is on the Scala side. (TODO holden add the unittest once I finish locally).
Author: Holden Karau <holden@pigscanfly.ca>
Closes#21172 from holdenk/SPARK-23120-add-pmml-export-support-to-pyspark.
## What changes were proposed in this pull request?
[SPARK-14712](https://issues.apache.org/jira/browse/SPARK-14712)
spark.mllib LogisticRegressionModel overrides toString to print a little model info. We should do the same in spark.ml and override repr in pyspark.
## How was this patch tested?
LogisticRegressionSuite.scala
Python doctest in pyspark.ml.classification.py
Author: bravo-zhang <mzhang1230@gmail.com>
Closes#18826 from bravo-zhang/spark-14712.
## What changes were proposed in this pull request?
Address comments in #21370 and add more test.
## How was this patch tested?
Enhance test in pyspark/sql/test.py and DataFrameSuite
Author: Yuanjian Li <xyliyuanjian@gmail.com>
Closes#21553 from xuanyuanking/SPARK-24215-follow.
## What changes were proposed in this pull request?
Currently, a `pandas_udf` of type `PandasUDFType.GROUPED_MAP` will assign the resulting columns based on index of the return pandas.DataFrame. If a new DataFrame is returned and constructed using a dict, then the order of the columns could be arbitrary and be different than the defined schema for the UDF. If the schema types still match, then no error will be raised and the user will see column names and column data mixed up.
This change will first try to assign columns using the return type field names. If a KeyError occurs, then the column index is checked if it is string based. If so, then the error is raised as it is most likely a naming mistake, else it will fallback to assign columns by position and raise a TypeError if the field types do not match.
## How was this patch tested?
Added a test that returns a new DataFrame with column order different than the schema.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#21427 from BryanCutler/arrow-grouped-map-mixesup-cols-SPARK-24324.
## What changes were proposed in this pull request?
Fix for SyntaxWarning on tests.py
## How was this patch tested?
./dev/run-tests
Author: Rekha Joshi <rekhajoshm@gmail.com>
Closes#21604 from rekhajoshm/SPARK-24614.
## What changes were proposed in this pull request?
Add array_distinct to remove duplicate value from the array.
## How was this patch tested?
Add unit tests
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21050 from huaxingao/spark-23912.
## What changes were proposed in this pull request?
Currently, the micro-batches in the MicroBatchExecution is not exposed to the user through any public API. This was because we did not want to expose the micro-batches, so that all the APIs we expose, we can eventually support them in the Continuous engine. But now that we have better sense of buiding a ContinuousExecution, I am considering adding APIs which will run only the MicroBatchExecution. I have quite a few use cases where exposing the microbatch output as a dataframe is useful.
- Pass the output rows of each batch to a library that is designed only the batch jobs (example, uses many ML libraries need to collect() while learning).
- Reuse batch data sources for output whose streaming version does not exists (e.g. redshift data source).
- Writer the output rows to multiple places by writing twice for each batch. This is not the most elegant thing to do for multiple-output streaming queries but is likely to be better than running two streaming queries processing the same data twice.
The proposal is to add a method `foreachBatch(f: Dataset[T] => Unit)` to Scala/Java/Python `DataStreamWriter`.
## How was this patch tested?
New unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#21571 from tdas/foreachBatch.
## What changes were proposed in this pull request?
This pr added a new JSON option `dropFieldIfAllNull ` to ignore column of all null values or empty array/struct during JSON schema inference.
## How was this patch tested?
Added tests in `JsonSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Author: Xiangrui Meng <meng@databricks.com>
Closes#20929 from maropu/SPARK-23772.
## What changes were proposed in this pull request?
This PR adds `foreach` for streaming queries in Python. Users will be able to specify their processing logic in two different ways.
- As a function that takes a row as input.
- As an object that has methods `open`, `process`, and `close` methods.
See the python docs in this PR for more details.
## How was this patch tested?
Added java and python unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#21477 from tdas/SPARK-24396.
## What changes were proposed in this pull request?
Fix typo in exception raised in Python serializer
## How was this patch tested?
No code changes
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Ruben Berenguel Montoro <ruben@mostlymaths.net>
Closes#21566 from rberenguel/fix_typo_pyspark_serializers.
## What changes were proposed in this pull request?
In the PR, I propose to support any DataType represented as DDL string for the from_json function. After the changes, it will be possible to specify `MapType` in SQL like:
```sql
select from_json('{"a":1, "b":2}', 'map<string, int>')
```
and in Scala (similar in other languages)
```scala
val in = Seq("""{"a": {"b": 1}}""").toDS()
val schema = "map<string, map<string, int>>"
val out = in.select(from_json($"value", schema, Map.empty[String, String]))
```
## How was this patch tested?
Added a couple sql tests and modified existing tests for Python and Scala. The former tests were modified because it is not imported for them in which format schema for `from_json` is provided.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21550 from MaxGekk/from_json-ddl-schema.
…ark shell
## What changes were proposed in this pull request?
This PR catches TypeError when testing existence of HiveConf when creating pyspark shell
## How was this patch tested?
Manually tested. Here are the manual test cases:
Build with hive:
```
(pyarrow-dev) Lis-MacBook-Pro:spark icexelloss$ bin/pyspark
Python 3.6.5 | packaged by conda-forge | (default, Apr 6 2018, 13:44:09)
[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
18/06/14 14:55:41 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 2.4.0-SNAPSHOT
/_/
Using Python version 3.6.5 (default, Apr 6 2018 13:44:09)
SparkSession available as 'spark'.
>>> spark.conf.get('spark.sql.catalogImplementation')
'hive'
```
Build without hive:
```
(pyarrow-dev) Lis-MacBook-Pro:spark icexelloss$ bin/pyspark
Python 3.6.5 | packaged by conda-forge | (default, Apr 6 2018, 13:44:09)
[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
18/06/14 15:04:52 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 2.4.0-SNAPSHOT
/_/
Using Python version 3.6.5 (default, Apr 6 2018 13:44:09)
SparkSession available as 'spark'.
>>> spark.conf.get('spark.sql.catalogImplementation')
'in-memory'
```
Failed to start shell:
```
(pyarrow-dev) Lis-MacBook-Pro:spark icexelloss$ bin/pyspark
Python 3.6.5 | packaged by conda-forge | (default, Apr 6 2018, 13:44:09)
[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
18/06/14 15:07:53 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
/Users/icexelloss/workspace/spark/python/pyspark/shell.py:45: UserWarning: Failed to initialize Spark session.
warnings.warn("Failed to initialize Spark session.")
Traceback (most recent call last):
File "/Users/icexelloss/workspace/spark/python/pyspark/shell.py", line 41, in <module>
spark = SparkSession._create_shell_session()
File "/Users/icexelloss/workspace/spark/python/pyspark/sql/session.py", line 581, in _create_shell_session
return SparkSession.builder.getOrCreate()
File "/Users/icexelloss/workspace/spark/python/pyspark/sql/session.py", line 168, in getOrCreate
raise py4j.protocol.Py4JError("Fake Py4JError")
py4j.protocol.Py4JError: Fake Py4JError
(pyarrow-dev) Lis-MacBook-Pro:spark icexelloss$
```
Author: Li Jin <ice.xelloss@gmail.com>
Closes#21569 from icexelloss/SPARK-24563-fix-pyspark-shell-without-hive.
## What changes were proposed in this pull request?
This PR enables using a grouped aggregate pandas UDFs as window functions. The semantics is the same as using SQL aggregation function as window functions.
```
>>> from pyspark.sql.functions import pandas_udf, PandasUDFType
>>> from pyspark.sql import Window
>>> df = spark.createDataFrame(
... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
... ("id", "v"))
>>> pandas_udf("double", PandasUDFType.GROUPED_AGG)
... def mean_udf(v):
... return v.mean()
>>> w = Window.partitionBy('id')
>>> df.withColumn('mean_v', mean_udf(df['v']).over(w)).show()
+---+----+------+
| id| v|mean_v|
+---+----+------+
| 1| 1.0| 1.5|
| 1| 2.0| 1.5|
| 2| 3.0| 6.0|
| 2| 5.0| 6.0|
| 2|10.0| 6.0|
+---+----+------+
```
The scope of this PR is somewhat limited in terms of:
(1) Only supports unbounded window, which acts essentially as group by.
(2) Only supports aggregation functions, not "transform" like window functions (n -> n mapping)
Both of these are left as future work. Especially, (1) needs careful thinking w.r.t. how to pass rolling window data to python efficiently. (2) is a bit easier but does require more changes therefore I think it's better to leave it as a separate PR.
## How was this patch tested?
WindowPandasUDFTests
Author: Li Jin <ice.xelloss@gmail.com>
Closes#21082 from icexelloss/SPARK-22239-window-udf.
## What changes were proposed in this pull request?
The PR adds the SQL function `map_from_arrays`. The behavior of the function is based on Presto's `map`. Since SparkSQL already had a `map` function, we prepared the different name for this behavior.
This function returns returns a map from a pair of arrays for keys and values.
## How was this patch tested?
Added UTs
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21258 from kiszk/SPARK-23933.
Signed-off-by: DylanGuedes <djmgguedesgmail.com>
## What changes were proposed in this pull request?
Addition of arrays_zip function to spark sql functions.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Unit tests that checks if the results are correct.
Author: DylanGuedes <djmgguedes@gmail.com>
Closes#21045 from DylanGuedes/SPARK-23931.
## What changes were proposed in this pull request?
Add locale support for `StopWordsRemover`.
## How was this patch tested?
[Scala|Python] unit tests.
Author: Lee Dongjin <dongjin@apache.org>
Closes#21501 from dongjinleekr/feature/SPARK-15064.
## What changes were proposed in this pull request?
add spark.ml Python API for PIC
## How was this patch tested?
add doctest
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21513 from huaxingao/spark--19826.
## What changes were proposed in this pull request?
SPARK-23754 was fixed in #21383 by changing the UDF code to wrap the user function, but this required a hack to save its argspec. This PR reverts this change and fixes the `StopIteration` bug in the worker
## How does this work?
The root of the problem is that when an user-supplied function raises a `StopIteration`, pyspark might stop processing data, if this function is used in a for-loop. The solution is to catch `StopIteration`s exceptions and re-raise them as `RuntimeError`s, so that the execution fails and the error is reported to the user. This is done using the `fail_on_stopiteration` wrapper, in different ways depending on where the function is used:
- In RDDs, the user function is wrapped in the driver, because this function is also called in the driver itself.
- In SQL UDFs, the function is wrapped in the worker, since all processing happens there. Moreover, the worker needs the signature of the user function, which is lost when wrapping it, but passing this signature to the worker requires a not so nice hack.
## How was this patch tested?
Same tests, plus tests for pandas UDFs
Author: edorigatti <emilio.dorigatti@gmail.com>
Closes#21467 from e-dorigatti/fix_udf_hack.
## What changes were proposed in this pull request?
This PR proposes to wrap the transformed rdd within `TransformFunction`. `PythonTransformFunction` looks requiring to return `JavaRDD` in `_jrdd`.
39e2bad6a8/python/pyspark/streaming/util.py (L67)6ee28423ad/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala (L43)
However, this could be `JavaPairRDD` by some APIs, for example, `zip` in PySpark's RDD API.
`_jrdd` could be checked as below:
```python
>>> rdd.zip(rdd)._jrdd.getClass().toString()
u'class org.apache.spark.api.java.JavaPairRDD'
```
So, here, I wrapped it with `map` so that it ensures returning `JavaRDD`.
```python
>>> rdd.zip(rdd).map(lambda x: x)._jrdd.getClass().toString()
u'class org.apache.spark.api.java.JavaRDD'
```
I tried to elaborate some failure cases as below:
```python
from pyspark.streaming import StreamingContext
ssc = StreamingContext(spark.sparkContext, 10)
ssc.queueStream([sc.range(10)]) \
.transform(lambda rdd: rdd.cartesian(rdd)) \
.pprint()
ssc.start()
```
```python
from pyspark.streaming import StreamingContext
ssc = StreamingContext(spark.sparkContext, 10)
ssc.queueStream([sc.range(10)]).foreachRDD(lambda rdd: rdd.cartesian(rdd))
ssc.start()
```
```python
from pyspark.streaming import StreamingContext
ssc = StreamingContext(spark.sparkContext, 10)
ssc.queueStream([sc.range(10)]).foreachRDD(lambda rdd: rdd.zip(rdd))
ssc.start()
```
```python
from pyspark.streaming import StreamingContext
ssc = StreamingContext(spark.sparkContext, 10)
ssc.queueStream([sc.range(10)]).foreachRDD(lambda rdd: rdd.zip(rdd).union(rdd.zip(rdd)))
ssc.start()
```
```python
from pyspark.streaming import StreamingContext
ssc = StreamingContext(spark.sparkContext, 10)
ssc.queueStream([sc.range(10)]).foreachRDD(lambda rdd: rdd.zip(rdd).coalesce(1))
ssc.start()
```
## How was this patch tested?
Unit tests were added in `python/pyspark/streaming/tests.py` and manually tested.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19498 from HyukjinKwon/SPARK-17756.
## What changes were proposed in this pull request?
This PR attaches submodules to ml's `__init__.py` module.
Also, adds `ImageSchema` into `image.py` explicitly.
## How was this patch tested?
Before:
```python
>>> from pyspark import ml
>>> ml.image
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'module' object has no attribute 'image'
>>> ml.image.ImageSchema
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'module' object has no attribute 'image'
```
```python
>>> "image" in globals()
False
>>> from pyspark.ml import *
>>> "image" in globals()
False
>>> image
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'image' is not defined
```
After:
```python
>>> from pyspark import ml
>>> ml.image
<module 'pyspark.ml.image' from '/.../spark/python/pyspark/ml/image.pyc'>
>>> ml.image.ImageSchema
<pyspark.ml.image._ImageSchema object at 0x10d973b10>
```
```python
>>> "image" in globals()
False
>>> from pyspark.ml import *
>>> "image" in globals()
True
>>> image
<module 'pyspark.ml.image' from #'/.../spark/python/pyspark/ml/image.pyc'>
```
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21483 from HyukjinKwon/SPARK-24454.
Currently, in spark-shell, if the session fails to start, the
user sees a bunch of unrelated errors which are caused by code
in the shell initialization that references the "spark" variable,
which does not exist in that case. Things like:
```
<console>:14: error: not found: value spark
import spark.sql
```
The user is also left with a non-working shell (unless they want
to just write non-Spark Scala or Python code, that is).
This change fails the whole shell session at the point where the
failure occurs, so that the last error message is the one with
the actual information about the failure.
For the python error handling, I moved the session initialization code
to session.py, so that traceback.print_exc() only shows the last error.
Otherwise, the printed exception would contain all previous exceptions
with a message "During handling of the above exception, another
exception occurred", making the actual error kinda hard to parse.
Tested with spark-shell, pyspark (with 2.7 and 3.5), by forcing an
error during SparkContext initialization.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#21368 from vanzin/SPARK-16451.
## What changes were proposed in this pull request?
Currently column names of headers in CSV files are not checked against provided schema of CSV data. It could cause errors like showed in the [SPARK-23786](https://issues.apache.org/jira/browse/SPARK-23786) and https://github.com/apache/spark/pull/20894#issuecomment-375957777. I introduced new CSV option - `enforceSchema`. If it is enabled (by default `true`), Spark forcibly applies provided or inferred schema to CSV files. In that case, CSV headers are ignored and not checked against the schema. If `enforceSchema` is set to `false`, additional checks can be performed. For example, if column in CSV header and in the schema have different ordering, the following exception is thrown:
```
java.lang.IllegalArgumentException: CSV file header does not contain the expected fields
Header: depth, temperature
Schema: temperature, depth
CSV file: marina.csv
```
## How was this patch tested?
The changes were tested by existing tests of CSVSuite and by 2 new tests.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Author: Maxim Gekk <max.gekk@gmail.com>
Closes#20894 from MaxGekk/check-column-names.
## What changes were proposed in this pull request?
add array_remove to remove all elements that equal element from array
## How was this patch tested?
add unit tests
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21069 from huaxingao/spark-23920.
## What changes were proposed in this pull request?
Added sections to pandas_udf docs, in the grouped map section, to indicate columns are assigned by position.
## How was this patch tested?
NA
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#21471 from BryanCutler/arrow-doc-pandas_udf-column_by_pos-SPARK-21427.
## What changes were proposed in this pull request?
This adds a new API `TaskContext.getLocalProperty(key)` to the Python TaskContext. It mirrors the Java TaskContext API of returning a string value if the key exists, or None if the key does not exist.
## How was this patch tested?
New test added.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#21437 from tdas/SPARK-24397.
## What changes were proposed in this pull request?
spark.ml parity for sequential pattern mining - PrefixSpan: Python API
## How was this patch tested?
doctests
Author: WeichenXu <weichen.xu@databricks.com>
Closes#21265 from WeichenXu123/prefix_span_py.
## What changes were proposed in this pull request?
Add featureSubsetStrategy in GBTClassifier and GBTRegressor. Also make GBTClassificationModel inherit from JavaClassificationModel instead of prediction model so it will have numClasses.
## How was this patch tested?
Add tests in doctest
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21413 from huaxingao/spark-23161.
## What changes were proposed in this pull request?
Make sure that `StopIteration`s raised in users' code do not silently interrupt processing by spark, but are raised as exceptions to the users. The users' functions are wrapped in `safe_iter` (in `shuffle.py`), which re-raises `StopIteration`s as `RuntimeError`s
## How was this patch tested?
Unit tests, making sure that the exceptions are indeed raised. I am not sure how to check whether a `Py4JJavaError` contains my exception, so I simply looked for the exception message in the java exception's `toString`. Can you propose a better way?
## License
This is my original work, licensed in the same way as spark
Author: e-dorigatti <emilio.dorigatti@gmail.com>
Author: edorigatti <emilio.dorigatti@gmail.com>
Closes#21383 from e-dorigatti/fix_spark_23754.
## What changes were proposed in this pull request?
The pandas_udf functionality was introduced in 2.3.0, but is not completely stable and still evolving. This adds a label to indicate it is still an experimental API.
## How was this patch tested?
NA
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#21435 from BryanCutler/arrow-pandas_udf-experimental-SPARK-24392.
## What changes were proposed in this pull request?
Logical `Range` node has been added with `outputOrdering` recently. It's used to eliminate redundant `Sort` during optimization. However, this `outputOrdering` doesn't not propagate to physical `RangeExec` node.
We also add correct `outputPartitioning` to `RangeExec` node.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#21291 from viirya/SPARK-24242.
## What changes were proposed in this pull request?
cloudpickle 0.4.4 is released - https://github.com/cloudpipe/cloudpickle/releases/tag/v0.4.4
There's no invasive change - the main difference is that we are now able to pickle the root logger, which fix is pretty isolated.
## How was this patch tested?
Jenkins tests.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21350 from HyukjinKwon/SPARK-24303.
## What changes were proposed in this pull request?
The PR adds the function `arrays_overlap`. This function returns `true` if the input arrays contain a non-null common element; if not, it returns `null` if any of the arrays contains a `null` element, `false` otherwise.
## How was this patch tested?
added UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21028 from mgaido91/SPARK-23922.
## What changes were proposed in this pull request?
The PR adds a new collection function, array_repeat. As there already was a function repeat with the same signature, with the only difference being the expected return type (String instead of Array), the new function is called array_repeat to distinguish.
The behaviour of the function is based on Presto's one.
The function creates an array containing a given element repeated the requested number of times.
## How was this patch tested?
New unit tests added into:
- CollectionExpressionsSuite
- DataFrameFunctionsSuite
Author: Florent Pépin <florentpepin.92@gmail.com>
Author: Florent Pépin <florent.pepin14@imperial.ac.uk>
Closes#21208 from pepinoflo/SPARK-23925.
## What changes were proposed in this pull request?
### Problem
When we run _PySpark shell with Yarn client mode_, specified `--py-files` are not recognised in _driver side_.
Here are the steps I took to check:
```bash
$ cat /home/spark/tmp.py
def testtest():
return 1
```
```bash
$ ./bin/pyspark --master yarn --deploy-mode client --py-files /home/spark/tmp.py
```
```python
>>> def test():
... import tmp
... return tmp.testtest()
...
>>> spark.range(1).rdd.map(lambda _: test()).collect() # executor side
[1]
>>> test() # driver side
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 2, in test
ImportError: No module named tmp
```
### How did it happen?
Unlike Yarn cluster and client mode with Spark submit, when Yarn client mode with PySpark shell specifically,
1. It first runs Python shell via:
3cb82047f2/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java (L158) as pointed out by tgravescs in the JIRA.
2. this triggers shell.py and submit another application to launch a py4j gateway:
209b9361ac/python/pyspark/java_gateway.py (L45-L60)
3. it runs a Py4J gateway:
3cb82047f2/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala (L425)
4. it copies (or downloads) --py-files into local temp directory:
3cb82047f2/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala (L365-L376)
and then these files are set up to `spark.submit.pyFiles`
5. Py4J JVM is launched and then the Python paths are set via:
7013eea11c/python/pyspark/context.py (L209-L216)
However, these are not actually set because those files were copied into a tmp directory in 4. whereas this code path looks for `SparkFiles.getRootDirectory` where the files are stored only when `SparkContext.addFile()` is called.
In other cluster mode, `spark.files` are set via:
3cb82047f2/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala (L554-L555)
and those files are explicitly added via:
ecb8b383af/core/src/main/scala/org/apache/spark/SparkContext.scala (L395)
So we are fine in other modes.
In case of Yarn client and cluster with _submit_, these are manually being handled. In particular https://github.com/apache/spark/pull/6360 added most of the logics. In this case, the Python path looks manually set via, for example, `deploy.PythonRunner`. We don't use `spark.files` here.
### How does the PR fix the problem?
I tried to make an isolated approach as possible as I can: simply copy py file or zip files into `SparkFiles.getRootDirectory()` in driver side if not existing. Another possible way is to set `spark.files` but it does unnecessary stuff together and sounds a bit invasive.
**Before**
```python
>>> def test():
... import tmp
... return tmp.testtest()
...
>>> spark.range(1).rdd.map(lambda _: test()).collect()
[1]
>>> test()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 2, in test
ImportError: No module named tmp
```
**After**
```python
>>> def test():
... import tmp
... return tmp.testtest()
...
>>> spark.range(1).rdd.map(lambda _: test()).collect()
[1]
>>> test()
1
```
## How was this patch tested?
I manually tested in standalone and yarn cluster with PySpark shell. .zip and .py files were also tested with the similar steps above. It's difficult to add a test.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21267 from HyukjinKwon/SPARK-21945.
## What changes were proposed in this pull request?
See SPARK-23455 for reference. Now default params in ML are saved separately in metadata file in Scala. We must change it for Python for Spark 2.4.0 as well in order to keep them in sync.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#21153 from viirya/SPARK-24058.
## What changes were proposed in this pull request?
Add evaluateEachIteration for GBTClassification and GBTRegressionModel
## How was this patch tested?
doctest
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lu WANG <lu.wang@databricks.com>
Closes#21335 from ludatabricks/SPARK-14682.
## What changes were proposed in this pull request?
Right now `ArrayWriter` used to output Arrow data for array type, doesn't do `clear` or `reset` after each batch. It produces wrong output.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#21312 from viirya/SPARK-24259.
## What changes were proposed in this pull request?
Currently, the from_json function support StructType or ArrayType as the root type. The PR allows to specify MapType(StringType, DataType) as the root type additionally to mentioned types. For example:
```scala
import org.apache.spark.sql.types._
val schema = MapType(StringType, IntegerType)
val in = Seq("""{"a": 1, "b": 2, "c": 3}""").toDS()
in.select(from_json($"value", schema, Map[String, String]())).collect()
```
```
res1: Array[org.apache.spark.sql.Row] = Array([Map(a -> 1, b -> 2, c -> 3)])
```
## How was this patch tested?
It was checked by new tests for the map type with integer type and struct type as value types. Also roundtrip tests like from_json(to_json) and to_json(from_json) for MapType are added.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Author: Maxim Gekk <max.gekk@gmail.com>
Closes#21108 from MaxGekk/from_json-map-type.
## What changes were proposed in this pull request?
Updates `functon` to `function`. This was called out in holdenk's PyCon 2018 conference talk. Didn't see any existing PR's for this.
holdenk happy to fix the Pandas.Series bug too but will need a bit more guidance.
Author: Kelley Robinson <krobinson@twilio.com>
Closes#21304 from robinske/master.
## What changes were proposed in this pull request?
It's useful to know what relationship between date1 and date2 results in a positive number.
Author: aditkumar <aditkumar@gmail.com>
Author: Adit Kumar <aditkumar@gmail.com>
Closes#20787 from aditkumar/master.
## What changes were proposed in this pull request?
I propose to add a clear statement for functions like `collect_list()` about non-deterministic behavior of such functions. The behavior must be taken into account by user while creating and running queries.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21228 from MaxGekk/deterministic-comments.
## What changes were proposed in this pull request?
More close to Scala API behavior when can't parse input by throwing exception. Add tests.
## How was this patch tested?
Added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#21211 from viirya/SPARK-24131-followup.
## What changes were proposed in this pull request?
Change FPGrowth from private to private[spark]. If no numPartitions is specified, then default value -1 is used. But -1 is only valid in the construction function of FPGrowth, but not in setNumPartitions. So I make this change and use the constructor directly rather than using set method.
## How was this patch tested?
Unit test is added
Author: Jeff Zhang <zjffdu@apache.org>
Closes#13493 from zjffdu/SPARK-15750.
## What changes were proposed in this pull request?
The PR add the `slice` function. The behavior of the function is based on Presto's one.
The function slices an array according to the requested start index and length.
## How was this patch tested?
added UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21040 from mgaido91/SPARK-23930.
## What changes were proposed in this pull request?
The PR adds the SQL function `array_sort`. The behavior of the function is based on Presto's one.
The function sorts the input array in ascending order. The elements of the input array must be orderable. Null elements will be placed at the end of the returned array.
## How was this patch tested?
Added UTs
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21021 from kiszk/SPARK-23921.
This avoids polluting and leaving garbage behind in /tmp, and allows the
usual build tools to clean up any leftover files.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#21198 from vanzin/SPARK-24126.
## What changes were proposed in this pull request?
We need to determine Spark major and minor versions in PySpark. We can add a `majorMinorVersion` API to PySpark which is similar to the Scala API in `VersionUtils.majorMinorVersion`.
## How was this patch tested?
Added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#21203 from viirya/SPARK-24131.
## What changes were proposed in this pull request?
When `PyArrow` or `Pandas` are not available, the corresponding PySpark tests are skipped automatically. Currently, PySpark tests fail when we are not using `-Phive`. This PR aims to skip Hive related PySpark tests when `-Phive` is not given.
**BEFORE**
```bash
$ build/mvn -DskipTests clean package
$ python/run-tests.py --python-executables python2.7 --modules pyspark-sql
File "/Users/dongjoon/spark/python/pyspark/sql/readwriter.py", line 295, in pyspark.sql.readwriter.DataFrameReader.table
...
IllegalArgumentException: u"Error while instantiating 'org.apache.spark.sql.hive.HiveExternalCatalog':"
**********************************************************************
1 of 3 in pyspark.sql.readwriter.DataFrameReader.table
***Test Failed*** 1 failures.
```
**AFTER**
```bash
$ build/mvn -DskipTests clean package
$ python/run-tests.py --python-executables python2.7 --modules pyspark-sql
...
Tests passed in 138 seconds
Skipped tests in pyspark.sql.tests with python2.7:
...
test_hivecontext (pyspark.sql.tests.HiveSparkSubmitTests) ... skipped 'Hive is not available.'
```
## How was this patch tested?
This is a test-only change. First, this should pass the Jenkins. Then, manually do the following.
```bash
build/mvn -DskipTests clean package
python/run-tests.py --python-executables python2.7 --modules pyspark-sql
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#21141 from dongjoon-hyun/SPARK-23853.
## What changes were proposed in this pull request?
I propose to support the `samplingRatio` option for schema inferring of CSV datasource similar to the same option of JSON datasource:
b14993e1fc/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JSONOptions.scala (L49-L50)
## How was this patch tested?
Added 2 tests for json and 2 tests for csv datasources. The tests checks that only subset of input dataset is used for schema inferring.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Author: Maxim Gekk <max.gekk@gmail.com>
Closes#20959 from MaxGekk/csv-sampling.
## What changes were proposed in this pull request?
I propose new option for JSON datasource which allows to specify encoding (charset) of input and output files. Here is an example of using of the option:
```
spark.read.schema(schema)
.option("multiline", "true")
.option("encoding", "UTF-16LE")
.json(fileName)
```
If the option is not specified, charset auto-detection mechanism is used by default.
The option can be used for saving datasets to jsons. Currently Spark is able to save datasets into json files in `UTF-8` charset only. The changes allow to save data in any supported charset. Here is the approximate list of supported charsets by Oracle Java SE: https://docs.oracle.com/javase/8/docs/technotes/guides/intl/encoding.doc.html . An user can specify the charset of output jsons via the charset option like `.option("charset", "UTF-16BE")`. By default the output charset is still `UTF-8` to keep backward compatibility.
The solution has the following restrictions for per-line mode (`multiline = false`):
- If charset is different from UTF-8, the lineSep option must be specified. The option required because Hadoop LineReader cannot detect the line separator correctly. Here is the ticket for solving the issue: https://issues.apache.org/jira/browse/SPARK-23725
- Encoding with [BOM](https://en.wikipedia.org/wiki/Byte_order_mark) are not supported. For example, the `UTF-16` and `UTF-32` encodings are blacklisted. The problem can be solved by https://github.com/MaxGekk/spark-1/pull/2
## How was this patch tested?
I added the following tests:
- reads an json file in `UTF-16LE` encoding with BOM in `multiline` mode
- read json file by using charset auto detection (`UTF-32BE` with BOM)
- read json file using of user's charset (`UTF-16LE`)
- saving in `UTF-32BE` and read the result by standard library (not by Spark)
- checking that default charset is `UTF-8`
- handling wrong (unsupported) charset
Author: Maxim Gekk <maxim.gekk@databricks.com>
Author: Maxim Gekk <max.gekk@gmail.com>
Closes#20937 from MaxGekk/json-encoding-line-sep.
## What changes were proposed in this pull request?
This PR proposes to remove duplicated dependency checking logics and also print out skipped tests from unittests.
For example, as below:
```
Skipped tests in pyspark.sql.tests with pypy:
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
...
Skipped tests in pyspark.sql.tests with python3:
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
...
```
Currently, it's not printed out in the console. I think we should better print out skipped tests in the console.
## How was this patch tested?
Manually tested. Also, fortunately, Jenkins has good environment to test the skipped output.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21107 from HyukjinKwon/skipped-tests-print.
## What changes were proposed in this pull request?
Print out the data type in the AssertionError message to make it more meaningful.
## How was this patch tested?
I manually tested the changed code on my local, but didn't add any test.
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21159 from huaxingao/spark-24057.
## What changes were proposed in this pull request?
The PR adds the SQL function `array_join`. The behavior of the function is based on Presto's one.
The function accepts an `array` of `string` which is to be joined, a `string` which is the delimiter to use between the items of the first argument and optionally a `string` which is used to replace `null` values.
## How was this patch tested?
added UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21011 from mgaido91/SPARK-23916.
## What changes were proposed in this pull request?
HIVE-15511 introduced the `roundOff` flag in order to disable the rounding to 8 digits which is performed in `months_between`. Since this can be a computational intensive operation, skipping it may improve performances when the rounding is not needed.
## How was this patch tested?
modified existing UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21008 from mgaido91/SPARK-23902.
## What changes were proposed in this pull request?
Added the `samplingRatio` option to the `json()` method of PySpark DataFrame Reader. Improving existing tests for Scala API according to review of the PR: https://github.com/apache/spark/pull/20959
## How was this patch tested?
Added new test for PySpark, updated 2 existing tests according to reviews of https://github.com/apache/spark/pull/20959 and added new negative test
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21056 from MaxGekk/json-sampling.
## What changes were proposed in this pull request?
The PR adds the SQL function `element_at`. The behavior of the function is based on Presto's one.
This function returns element of array at given index in value if column is array, or returns value for the given key in value if column is map.
## How was this patch tested?
Added UTs
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21053 from kiszk/SPARK-23924.
## What changes were proposed in this pull request?
The PR adds the SQL function `array_position`. The behavior of the function is based on Presto's one.
The function returns the position of the first occurrence of the element in array x (or 0 if not found) using 1-based index as BigInt.
## How was this patch tested?
Added UTs
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21037 from kiszk/SPARK-23919.
## What changes were proposed in this pull request?
The `StreamingListener` in PySpark side seems to be lack of `onStreamingStarted` method. This patch adds it and a test for it.
This patch also includes a trivial doc improvement for `createDirectStream`.
Original PR is #21057.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#21098 from viirya/SPARK-24014.
## What changes were proposed in this pull request?
Python API for DataFrame-based multivariate summarizer.
## How was this patch tested?
doctest added.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#20695 from WeichenXu123/py_summarizer.
## What changes were proposed in this pull request?
The PR adds the SQL function `array_min`. It takes an array as argument and returns the minimum value in it.
## How was this patch tested?
added UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21025 from mgaido91/SPARK-23918.
## What changes were proposed in this pull request?
Add python API for collecting sub-models during CrossValidator/TrainValidationSplit fitting.
## How was this patch tested?
UT added.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19627 from WeichenXu123/expose-model-list-py.
## What changes were proposed in this pull request?
The PR adds the SQL function `array_max`. It takes an array as argument and returns the maximum value in it.
## How was this patch tested?
added UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21024 from mgaido91/SPARK-23917.
## What changes were proposed in this pull request?
This PR proposes to add `collect` to a query executor as an action.
Seems `collect` / `collect` with Arrow are not recognised via `QueryExecutionListener` as an action. For example, if we have a custom listener as below:
```scala
package org.apache.spark.sql
import org.apache.spark.internal.Logging
import org.apache.spark.sql.execution.QueryExecution
import org.apache.spark.sql.util.QueryExecutionListener
class TestQueryExecutionListener extends QueryExecutionListener with Logging {
override def onSuccess(funcName: String, qe: QueryExecution, durationNs: Long): Unit = {
logError("Look at me! I'm 'onSuccess'")
}
override def onFailure(funcName: String, qe: QueryExecution, exception: Exception): Unit = { }
}
```
and set `spark.sql.queryExecutionListeners` to `org.apache.spark.sql.TestQueryExecutionListener`
Other operations in PySpark or Scala side seems fine:
```python
>>> sql("SELECT * FROM range(1)").show()
```
```
18/04/09 17:02:04 ERROR TestQueryExecutionListener: Look at me! I'm 'onSuccess'
+---+
| id|
+---+
| 0|
+---+
```
```scala
scala> sql("SELECT * FROM range(1)").collect()
```
```
18/04/09 16:58:41 ERROR TestQueryExecutionListener: Look at me! I'm 'onSuccess'
res1: Array[org.apache.spark.sql.Row] = Array([0])
```
but ..
**Before**
```python
>>> sql("SELECT * FROM range(1)").collect()
```
```
[Row(id=0)]
```
```python
>>> spark.conf.set("spark.sql.execution.arrow.enabled", "true")
>>> sql("SELECT * FROM range(1)").toPandas()
```
```
id
0 0
```
**After**
```python
>>> sql("SELECT * FROM range(1)").collect()
```
```
18/04/09 16:57:58 ERROR TestQueryExecutionListener: Look at me! I'm 'onSuccess'
[Row(id=0)]
```
```python
>>> spark.conf.set("spark.sql.execution.arrow.enabled", "true")
>>> sql("SELECT * FROM range(1)").toPandas()
```
```
18/04/09 17:53:26 ERROR TestQueryExecutionListener: Look at me! I'm 'onSuccess'
id
0 0
```
## How was this patch tested?
I have manually tested as described above and unit test was added.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21007 from HyukjinKwon/SPARK-23942.
MultilayerPerceptronClassifier had 4 occurrences
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
## 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.
Author: JBauerKogentix <37910022+JBauerKogentix@users.noreply.github.com>
Closes#21030 from JBauerKogentix/patch-1.
## What changes were proposed in this pull request?
There was a mistake in `tests.py` missing `assertEquals`.
## How was this patch tested?
Fixed tests.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21035 from HyukjinKwon/SPARK-23847.
## What changes were proposed in this pull request?
add python api for VectorAssembler handleInvalid
## How was this patch tested?
Add doctest
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21003 from huaxingao/spark-23871.
## What changes were proposed in this pull request?
Kolmogorov-Smirnoff test Python API in `pyspark.ml`
**Note** API with `CDF` is a little difficult to support in python. We can add it in following PR.
## How was this patch tested?
doctest
Author: WeichenXu <weichen.xu@databricks.com>
Closes#20904 from WeichenXu123/ks-test-py.
## What changes were proposed in this pull request?
Column.scala and Functions.scala have asc_nulls_first, asc_nulls_last, desc_nulls_first and desc_nulls_last. Add the corresponding python APIs in column.py and functions.py
## How was this patch tested?
Add doctest
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#20962 from huaxingao/spark-23847.
## What changes were proposed in this pull request?
The Scala StringIndexerModel has an alternate constructor that will create the model from an array of label strings. Add the corresponding Python API:
model = StringIndexerModel.from_labels(["a", "b", "c"])
## How was this patch tested?
Add doctest and unit test.
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#20968 from huaxingao/spark-23828.
## What changes were proposed in this pull request?
Add docstring to clarify default window frame boundaries with and without orderBy clause
## How was this patch tested?
Manually generate doc and check.
Author: Li Jin <ice.xelloss@gmail.com>
Closes#20978 from icexelloss/SPARK-23861-window-doc.
## What changes were proposed in this pull request?
Adding test for default params for `CountVectorizerModel` constructed from vocabulary. This required that the param `maxDF` be added, which was done in SPARK-23615.
## How was this patch tested?
Added an explicit test for CountVectorizerModel in DefaultValuesTests.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20942 from BryanCutler/pyspark-CountVectorizerModel-default-param-test-SPARK-15009.
## What changes were proposed in this pull request?
This PR proposes to add lineSep option for a configurable line separator in text datasource.
It supports this option by using `LineRecordReader`'s functionality with passing it to the constructor.
The approach is similar with https://github.com/apache/spark/pull/20727; however, one main difference is, it uses text datasource's `lineSep` option to parse line by line in JSON's schema inference.
## How was this patch tested?
Manually tested and unit tests were added.
Author: hyukjinkwon <gurwls223@apache.org>
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20877 from HyukjinKwon/linesep-json.
## What changes were proposed in this pull request?
When using Arrow for createDataFrame or toPandas and an error is encountered with fallback disabled, this will raise the same type of error instead of a RuntimeError. This change also allows for the traceback of the error to be retained and prevents the accidental chaining of exceptions with Python 3.
## How was this patch tested?
Updated existing tests to verify error type.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20839 from BryanCutler/arrow-raise-same-error-SPARK-23699.
## What changes were proposed in this pull request?
Adding r2adj in LinearRegressionSummary for Python API.
## How was this patch tested?
Added unit tests to exercise the api calls for the summary classes in tests.py.
Author: Kevin Yu <qyu@us.ibm.com>
Closes#20842 from kevinyu98/spark-23162.
## What changes were proposed in this pull request?
Add documentation about the limitations of `pandas_udf` with keyword arguments and related concepts, like `functools.partial` fn objects.
NOTE: intermediate commits on this PR show some of the steps that can be taken to fix some (but not all) of these pain points.
### Survey of problems we face today:
(Initialize) Note: python 3.6 and spark 2.4snapshot.
```
from pyspark.sql import SparkSession
import inspect, functools
from pyspark.sql.functions import pandas_udf, PandasUDFType, col, lit, udf
spark = SparkSession.builder.getOrCreate()
print(spark.version)
df = spark.range(1,6).withColumn('b', col('id') * 2)
def ok(a,b): return a+b
```
Using a keyword argument at the call site `b=...` (and yes, *full* stack trace below, haha):
```
---> 14 df.withColumn('ok', pandas_udf(f=ok, returnType='bigint')('id', b='id')).show() # no kwargs
TypeError: wrapper() got an unexpected keyword argument 'b'
```
Using partial with a keyword argument where the kw-arg is the first argument of the fn:
*(Aside: kind of interesting that lines 15,16 work great and then 17 explodes)*
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-9-e9f31b8799c1> in <module>()
15 df.withColumn('ok', pandas_udf(f=functools.partial(ok, 7), returnType='bigint')('id')).show()
16 df.withColumn('ok', pandas_udf(f=functools.partial(ok, b=7), returnType='bigint')('id')).show()
---> 17 df.withColumn('ok', pandas_udf(f=functools.partial(ok, a=7), returnType='bigint')('id')).show()
/Users/stu/ZZ/spark/python/pyspark/sql/functions.py in pandas_udf(f, returnType, functionType)
2378 return functools.partial(_create_udf, returnType=return_type, evalType=eval_type)
2379 else:
-> 2380 return _create_udf(f=f, returnType=return_type, evalType=eval_type)
2381
2382
/Users/stu/ZZ/spark/python/pyspark/sql/udf.py in _create_udf(f, returnType, evalType)
54 argspec.varargs is None:
55 raise ValueError(
---> 56 "Invalid function: 0-arg pandas_udfs are not supported. "
57 "Instead, create a 1-arg pandas_udf and ignore the arg in your function."
58 )
ValueError: Invalid function: 0-arg pandas_udfs are not supported. Instead, create a 1-arg pandas_udf and ignore the arg in your function.
```
Author: Michael (Stu) Stewart <mstewart141@gmail.com>
Closes#20900 from mstewart141/udfkw2.
## What changes were proposed in this pull request?
This cleans up unused imports, mainly from pyspark.sql module. Added a note in function.py that imports `UserDefinedFunction` only to maintain backwards compatibility for using `from pyspark.sql.function import UserDefinedFunction`.
## How was this patch tested?
Existing tests and built docs.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20892 from BryanCutler/pyspark-cleanup-imports-SPARK-23700.
## What changes were proposed in this pull request?
The maxDF parameter is for filtering out frequently occurring terms. This param was recently added to the Scala CountVectorizer and needs to be added to Python also.
## How was this patch tested?
add test
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#20777 from huaxingao/spark-23615.
## What changes were proposed in this pull request?
Currently when a PySpark Model is transformed, default params that have not been explicitly set are then set on the Java side on the call to `wrapper._transfer_values_to_java`. This incorrectly changes the state of the Param as it should still be marked as a default value only.
## How was this patch tested?
Added a new test to verify that when transferring Params to Java, default params have their state preserved.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#18982 from BryanCutler/pyspark-ml-param-to-java-defaults-SPARK-21685.
## What changes were proposed in this pull request?
This PR proposes to remove out unused codes, `_ignore_brackets_split` and `_BRACKETS`.
`_ignore_brackets_split` was introduced in d57daf1f77 to refactor and support `toDF("...")`; however, ebc124d4c4 replaced the logics here. Seems `_ignore_brackets_split` is not referred anymore.
`_BRACKETS` was introduced in 880eabec37; however, all other usages were removed out in 648a8626b8.
This is rather a followup for ebc124d4c4 which I missed in that PR.
## How was this patch tested?
Manually tested. Existing tests should cover this. I also double checked by `grep` in the whole repo.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#20878 from HyukjinKwon/minor-remove-unused.
## What changes were proposed in this pull request?
This PR proposes to add `lineSep` option for a configurable line separator in text datasource.
It supports this option by using `LineRecordReader`'s functionality with passing it to the constructor.
## How was this patch tested?
Manual tests and unit tests were added.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20727 from HyukjinKwon/linesep-text.
## What changes were proposed in this pull request?
d6632d185e added an useful util
```python
contextmanager
def sql_conf(self, pairs):
...
```
to allow configuration set/unset within a block:
```python
with self.sql_conf({"spark.blah.blah.blah", "blah"})
# test codes
```
This PR proposes to use this util where possible in PySpark tests.
Note that there look already few places affecting tests without restoring the original value back in unittest classes.
## How was this patch tested?
Manually tested via:
```
./run-tests --modules=pyspark-sql --python-executables=python2
./run-tests --modules=pyspark-sql --python-executables=python3
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20830 from HyukjinKwon/cleanup-sql-conf.
## What changes were proposed in this pull request?
Added a class method to construct CountVectorizerModel from a list of vocabulary strings, equivalent to the Scala version. Introduced a common param base class `_CountVectorizerParams` to allow the Python model to also own the parameters. This now matches the Scala class hierarchy.
## How was this patch tested?
Added to CountVectorizer doctests to do a transform on a model constructed from vocab, and unit test to verify params and vocab are constructed correctly.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#16770 from BryanCutler/pyspark-CountVectorizerModel-vocab_ctor-SPARK-15009.
## What changes were proposed in this pull request?
Currently, some tests have an assumption that `spark.sql.sources.default=parquet`. In fact, that is a correct assumption, but that assumption makes it difficult to test new data source format.
This PR aims to
- Improve test suites more robust and makes it easy to test new data sources in the future.
- Test new native ORC data source with the full existing Apache Spark test coverage.
As an example, the PR uses `spark.sql.sources.default=orc` during reviews. The value should be `parquet` when this PR is accepted.
## How was this patch tested?
Pass the Jenkins with updated tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20705 from dongjoon-hyun/SPARK-23553.
## What changes were proposed in this pull request?
This PR proposes to fix the error message for Kinesis in PySpark when its jar is missing but explicitly enabled.
```bash
ENABLE_KINESIS_TESTS=1 SPARK_TESTING=1 bin/pyspark pyspark.streaming.tests
```
Before:
```
Skipped test_flume_stream (enable by setting environment variable ENABLE_FLUME_TESTS=1Skipped test_kafka_stream (enable by setting environment variable ENABLE_KAFKA_0_8_TESTS=1Traceback (most recent call last):
File "/usr/local/Cellar/python/2.7.14_3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/runpy.py", line 174, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "/usr/local/Cellar/python/2.7.14_3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/.../spark/python/pyspark/streaming/tests.py", line 1572, in <module>
% kinesis_asl_assembly_dir) +
NameError: name 'kinesis_asl_assembly_dir' is not defined
```
After:
```
Skipped test_flume_stream (enable by setting environment variable ENABLE_FLUME_TESTS=1Skipped test_kafka_stream (enable by setting environment variable ENABLE_KAFKA_0_8_TESTS=1Traceback (most recent call last):
File "/usr/local/Cellar/python/2.7.14_3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/runpy.py", line 174, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "/usr/local/Cellar/python/2.7.14_3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/.../spark/python/pyspark/streaming/tests.py", line 1576, in <module>
"You need to build Spark with 'build/sbt -Pkinesis-asl "
Exception: Failed to find Spark Streaming Kinesis assembly jar in /.../spark/external/kinesis-asl-assembly. You need to build Spark with 'build/sbt -Pkinesis-asl assembly/package streaming-kinesis-asl-assembly/assembly'or 'build/mvn -Pkinesis-asl package' before running this test.
```
## How was this patch tested?
Manually tested.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20834 from HyukjinKwon/minor-variable.
The exit() builtin is only for interactive use. applications should use sys.exit().
## What changes were proposed in this pull request?
All usage of the builtin `exit()` function is replaced by `sys.exit()`.
## How was this patch tested?
I ran `python/run-tests`.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Benjamin Peterson <benjamin@python.org>
Closes#20682 from benjaminp/sys-exit.
## What changes were proposed in this pull request?
This PR proposes to support an alternative function from with group aggregate pandas UDF.
The current form:
```
def foo(pdf):
return ...
```
Takes a single arg that is a pandas DataFrame.
With this PR, an alternative form is supported:
```
def foo(key, pdf):
return ...
```
The alternative form takes two argument - a tuple that presents the grouping key, and a pandas DataFrame represents the data.
## How was this patch tested?
GroupbyApplyTests
Author: Li Jin <ice.xelloss@gmail.com>
Closes#20295 from icexelloss/SPARK-23011-groupby-apply-key.
## What changes were proposed in this pull request?
This PR adds a configuration to control the fallback of Arrow optimization for `toPandas` and `createDataFrame` with Pandas DataFrame.
## How was this patch tested?
Manually tested and unit tests added.
You can test this by:
**`createDataFrame`**
```python
spark.conf.set("spark.sql.execution.arrow.enabled", False)
pdf = spark.createDataFrame([[{'a': 1}]]).toPandas()
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", True)
spark.createDataFrame(pdf, "a: map<string, int>")
```
```python
spark.conf.set("spark.sql.execution.arrow.enabled", False)
pdf = spark.createDataFrame([[{'a': 1}]]).toPandas()
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", False)
spark.createDataFrame(pdf, "a: map<string, int>")
```
**`toPandas`**
```python
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", True)
spark.createDataFrame([[{'a': 1}]]).toPandas()
```
```python
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", False)
spark.createDataFrame([[{'a': 1}]]).toPandas()
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20678 from HyukjinKwon/SPARK-23380-conf.
The `__del__` method that explicitly detaches the object was moved from `JavaParams` to `JavaWrapper` class, this way model summaries could also be garbage collected in Java. A test case was added to make sure that relevant error messages are thrown after the objects are deleted.
I ran pyspark tests agains `pyspark-ml` module
`./python/run-tests --python-executables=$(which python) --modules=pyspark-ml`
Author: Yogesh Garg <yogesh(dot)garg()databricks(dot)com>
Closes#20724 from yogeshg/java_wrapper_memory.
## What changes were proposed in this pull request?
Provide more details in trigonometric function documentations. Referenced `java.lang.Math` for further details in the descriptions.
## How was this patch tested?
Ran full build, checked generated documentation manually
Author: Mihaly Toth <misutoth@gmail.com>
Closes#20618 from misutoth/trigonometric-doc.
## What changes were proposed in this pull request?
Check python version to determine whether to use `inspect.getargspec` or `inspect.getfullargspec` before applying `pandas_udf` core logic to a function. The former is python2.7 (deprecated in python3) and the latter is python3.x. The latter correctly accounts for type annotations, which are syntax errors in python2.x.
## How was this patch tested?
Locally, on python 2.7 and 3.6.
Author: Michael (Stu) Stewart <mstewart141@gmail.com>
Closes#20728 from mstewart141/pandas_udf_fix.
## What changes were proposed in this pull request?
This PR proposes for `pyspark.util._exception_message` to produce the trace from Java side by `Py4JJavaError`.
Currently, in Python 2, it uses `message` attribute which `Py4JJavaError` didn't happen to have:
```python
>>> from pyspark.util import _exception_message
>>> try:
... sc._jvm.java.lang.String(None)
... except Exception as e:
... pass
...
>>> e.message
''
```
Seems we should use `str` instead for now:
aa6c53b590/py4j-python/src/py4j/protocol.py (L412)
but this doesn't address the problem with non-ascii string from Java side -
`https://github.com/bartdag/py4j/issues/306`
So, we could directly call `__str__()`:
```python
>>> e.__str__()
u'An error occurred while calling None.java.lang.String.\n: java.lang.NullPointerException\n\tat java.lang.String.<init>(String.java:588)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:422)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:238)\n\tat py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)\n\tat py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:214)\n\tat java.lang.Thread.run(Thread.java:745)\n'
```
which doesn't type coerce unicodes to `str` in Python 2.
This can be actually a problem:
```python
from pyspark.sql.functions import udf
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.range(1).select(udf(lambda x: [[]])()).toPandas()
```
**Before**
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/dataframe.py", line 2009, in toPandas
raise RuntimeError("%s\n%s" % (_exception_message(e), msg))
RuntimeError:
Note: toPandas attempted Arrow optimization because 'spark.sql.execution.arrow.enabled' is set to true. Please set it to false to disable this.
```
**After**
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/dataframe.py", line 2009, in toPandas
raise RuntimeError("%s\n%s" % (_exception_message(e), msg))
RuntimeError: An error occurred while calling o47.collectAsArrowToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 7 in stage 0.0 failed 1 times, most recent failure: Lost task 7.0 in stage 0.0 (TID 7, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/.../spark/python/pyspark/worker.py", line 245, in main
process()
File "/.../spark/python/pyspark/worker.py", line 240, in process
...
Note: toPandas attempted Arrow optimization because 'spark.sql.execution.arrow.enabled' is set to true. Please set it to false to disable this.
```
## How was this patch tested?
Manually tested and unit tests were added.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20680 from HyukjinKwon/SPARK-23517.
## What changes were proposed in this pull request?
Clarify JSON and CSV reader behavior in document.
JSON doesn't support partial results for corrupted records.
CSV only supports partial results for the records with more or less tokens.
## How was this patch tested?
Pass existing tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20666 from viirya/SPARK-23448-2.
## What changes were proposed in this pull request?
Fix the build instructions supplied by exception messages in python streaming tests.
I also added -DskipTests to the maven instructions to avoid the 170 minutes of scala tests that occurs each time one wants to add a jar to the assembly directory.
## How was this patch tested?
- clone branch
- run build/sbt package
- run python/run-tests --modules "pyspark-streaming" , expect error message
- follow instructions in error message. i.e., run build/sbt assembly/package streaming-kafka-0-8-assembly/assembly
- rerun python tests, expect error message
- follow instructions in error message. i.e run build/sbt -Pflume assembly/package streaming-flume-assembly/assembly
- rerun python tests, see success.
- repeated all of the above for mvn version of the process.
Author: Bruce Robbins <bersprockets@gmail.com>
Closes#20638 from bersprockets/SPARK-23417_propa.
## What changes were proposed in this pull request?
The PR adds the `distanceMeasure` param to ClusteringEvaluator in the Python API. This allows the user to specify `cosine` as distance measure in addition to the default `squaredEuclidean`.
## How was this patch tested?
added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20627 from mgaido91/SPARK-23217_python.
## What changes were proposed in this pull request?
Murmur3 hash generates a different value from the original and other implementations (like Scala standard library and Guava or so) when the length of a bytes array is not multiple of 4.
## How was this patch tested?
Added a unit test.
**Note: When we merge this PR, please give all the credits to Shintaro Murakami.**
Author: Shintaro Murakami <mrkm4ntrgmail.com>
Author: gatorsmile <gatorsmile@gmail.com>
Author: Shintaro Murakami <mrkm4ntr@gmail.com>
Closes#20630 from gatorsmile/pr-20568.
## What changes were proposed in this pull request?
This PR explicitly specifies and checks the types we supported in `toPandas`. This was a hole. For example, we haven't finished the binary type support in Python side yet but now it allows as below:
```python
spark.conf.set("spark.sql.execution.arrow.enabled", "false")
df = spark.createDataFrame([[bytearray("a")]])
df.toPandas()
spark.conf.set("spark.sql.execution.arrow.enabled", "true")
df.toPandas()
```
```
_1
0 [97]
_1
0 a
```
This should be disallowed. I think the same things also apply to nested timestamps too.
I also added some nicer message about `spark.sql.execution.arrow.enabled` in the error message.
## How was this patch tested?
Manually tested and tests added in `python/pyspark/sql/tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20625 from HyukjinKwon/pandas_convertion_supported_type.
## What changes were proposed in this pull request?
Deprecating the field `name` in PySpark is not expected. This PR is to revert the change.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20595 from gatorsmile/removeDeprecate.
## What changes were proposed in this pull request?
This PR targets to explicitly specify supported types in Pandas UDFs.
The main change here is to add a deduplicated and explicit type checking in `returnType` ahead with documenting this; however, it happened to fix multiple things.
1. Currently, we don't support `BinaryType` in Pandas UDFs, for example, see:
```python
from pyspark.sql.functions import pandas_udf
pudf = pandas_udf(lambda x: x, "binary")
df = spark.createDataFrame([[bytearray(1)]])
df.select(pudf("_1")).show()
```
```
...
TypeError: Unsupported type in conversion to Arrow: BinaryType
```
We can document this behaviour for its guide.
2. Also, the grouped aggregate Pandas UDF fails fast on `ArrayType` but seems we can support this case.
```python
from pyspark.sql.functions import pandas_udf, PandasUDFType
foo = pandas_udf(lambda v: v.mean(), 'array<double>', PandasUDFType.GROUPED_AGG)
df = spark.range(100).selectExpr("id", "array(id) as value")
df.groupBy("id").agg(foo("value")).show()
```
```
...
NotImplementedError: ArrayType, StructType and MapType are not supported with PandasUDFType.GROUPED_AGG
```
3. Since we can check the return type ahead, we can fail fast before actual execution.
```python
# we can fail fast at this stage because we know the schema ahead
pandas_udf(lambda x: x, BinaryType())
```
## How was this patch tested?
Manually tested and unit tests for `BinaryType` and `ArrayType(...)` were added.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20531 from HyukjinKwon/pudf-cleanup.
## What changes were proposed in this pull request?
Expose range partitioning shuffle introduced by spark-22614
## How was this patch tested?
Unit test in dataframe.py
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: xubo245 <601450868@qq.com>
Closes#20456 from xubo245/SPARK22624_PysparkRangePartition.
## What changes were proposed in this pull request?
Added unboundedPreceding(), unboundedFollowing() and currentRow() to PySpark, also updated the rangeBetween API
## How was this patch tested?
did unit test on my local. Please let me know if I need to add unit test in tests.py
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#20400 from huaxingao/spark_23084.
## What changes were proposed in this pull request?
When tz_localize a tz-naive timetamp, pandas will throw exception if the timestamp is during daylight saving time period, e.g., `2015-11-01 01:30:00`. This PR fixes this issue by setting `ambiguous=False` when calling tz_localize, which is the same default behavior of pytz.
## How was this patch tested?
Add `test_timestamp_dst`
Author: Li Jin <ice.xelloss@gmail.com>
Closes#20537 from icexelloss/SPARK-23314.
## What changes were proposed in this pull request?
SPARK-22119 introduced a new parameter for KMeans, ie. `distanceMeasure`. The PR adds it also to the Python interface.
## How was this patch tested?
added UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20520 from mgaido91/SPARK-23344.
## What changes were proposed in this pull request?
Currently we use `tzlocal()` to get Python local timezone, but it sometimes causes unexpected behavior.
I changed the way to get Python local timezone to use pytz if the timezone is specified in environment variable, or timezone file via dateutil .
## How was this patch tested?
Added a test and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20559 from ueshin/issues/SPARK-23360/master.
## What changes were proposed in this pull request?
This PR proposes to disallow default value None when 'to_replace' is not a dictionary.
It seems weird we set the default value of `value` to `None` and we ended up allowing the case as below:
```python
>>> df.show()
```
```
+----+------+-----+
| age|height| name|
+----+------+-----+
| 10| 80|Alice|
...
```
```python
>>> df.na.replace('Alice').show()
```
```
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80|null|
...
```
**After**
This PR targets to disallow the case above:
```python
>>> df.na.replace('Alice').show()
```
```
...
TypeError: value is required when to_replace is not a dictionary.
```
while we still allow when `to_replace` is a dictionary:
```python
>>> df.na.replace({'Alice': None}).show()
```
```
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80|null|
...
```
## How was this patch tested?
Manually tested, tests were added in `python/pyspark/sql/tests.py` and doctests were fixed.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20499 from HyukjinKwon/SPARK-19454-followup.
## What changes were proposed in this pull request?
This is a followup pr of #20487.
When importing module but it doesn't exists, the error message is slightly different between Python 2 and 3.
E.g., in Python 2:
```
No module named pandas
```
in Python 3:
```
No module named 'pandas'
```
So, one test to check an import error fails in Python 3 without pandas.
This pr fixes it.
## How was this patch tested?
Tested manually in my local environment.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20538 from ueshin/issues/SPARK-23319/fup1.
## What changes were proposed in this pull request?
This PR proposes to explicitly specify Pandas and PyArrow versions in PySpark tests to skip or test.
We declared the extra dependencies:
b8bfce51ab/python/setup.py (L204)
In case of PyArrow:
Currently we only check if pyarrow is installed or not without checking the version. It already fails to run tests. For example, if PyArrow 0.7.0 is installed:
```
======================================================================
ERROR: test_vectorized_udf_wrong_return_type (pyspark.sql.tests.ScalarPandasUDF)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/.../spark/python/pyspark/sql/tests.py", line 4019, in test_vectorized_udf_wrong_return_type
f = pandas_udf(lambda x: x * 1.0, MapType(LongType(), LongType()))
File "/.../spark/python/pyspark/sql/functions.py", line 2309, in pandas_udf
return _create_udf(f=f, returnType=return_type, evalType=eval_type)
File "/.../spark/python/pyspark/sql/udf.py", line 47, in _create_udf
require_minimum_pyarrow_version()
File "/.../spark/python/pyspark/sql/utils.py", line 132, in require_minimum_pyarrow_version
"however, your version was %s." % pyarrow.__version__)
ImportError: pyarrow >= 0.8.0 must be installed on calling Python process; however, your version was 0.7.0.
----------------------------------------------------------------------
Ran 33 tests in 8.098s
FAILED (errors=33)
```
In case of Pandas:
There are few tests for old Pandas which were tested only when Pandas version was lower, and I rewrote them to be tested when both Pandas version is lower and missing.
## How was this patch tested?
Manually tested by modifying the condition:
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.'
```
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
```
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.'
```
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20487 from HyukjinKwon/pyarrow-pandas-skip.
## What changes were proposed in this pull request?
Replace `registerTempTable` by `createOrReplaceTempView`.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20523 from gatorsmile/updateExamples.
## What changes were proposed in this pull request?
Update the description and tests of three external API or functions `createFunction `, `length` and `repartitionByRange `
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20495 from gatorsmile/updateFunc.
## What changes were proposed in this pull request?
In b2ce17b4c9, I mistakenly renamed `VectorizedUDFTests` to `ScalarPandasUDF`. This PR fixes the mistake.
## How was this patch tested?
Existing tests.
Author: Li Jin <ice.xelloss@gmail.com>
Closes#20489 from icexelloss/fix-scalar-udf-tests.
## What changes were proposed in this pull request?
In Python 2, when `pandas_udf` tries to return string type value created in the udf with `".."`, the execution fails. E.g.,
```python
from pyspark.sql.functions import pandas_udf, col
import pandas as pd
df = spark.range(10)
str_f = pandas_udf(lambda x: pd.Series(["%s" % i for i in x]), "string")
df.select(str_f(col('id'))).show()
```
raises the following exception:
```
...
java.lang.AssertionError: assertion failed: Invalid schema from pandas_udf: expected StringType, got BinaryType
at scala.Predef$.assert(Predef.scala:170)
at org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:93)
...
```
Seems like pyarrow ignores `type` parameter for `pa.Array.from_pandas()` and consider it as binary type when the type is string type and the string values are `str` instead of `unicode` in Python 2.
This pr adds a workaround for the case.
## How was this patch tested?
Added a test and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20507 from ueshin/issues/SPARK-23334.
## What changes were proposed in this pull request?
This PR proposes to add `columnSchema` in Python side too.
```python
>>> from pyspark.ml.image import ImageSchema
>>> ImageSchema.columnSchema.simpleString()
'struct<origin:string,height:int,width:int,nChannels:int,mode:int,data:binary>'
```
## How was this patch tested?
Manually tested and unittest was added in `python/pyspark/ml/tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20475 from HyukjinKwon/SPARK-23256.
## What changes were proposed in this pull request?
This is a follow-up pr of #19872 which uses `assertRaisesRegex` but it doesn't exist in Python 2, so some tests fail when running tests in Python 2 environment.
Unfortunately, we missed it because currently Python 2 environment of the pr builder doesn't have proper versions of pandas or pyarrow, so the tests were skipped.
This pr modifies to use `assertRaisesRegexp` instead of `assertRaisesRegex`.
## How was this patch tested?
Tested manually in my local environment.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20467 from ueshin/issues/SPARK-22274/fup1.
## What changes were proposed in this pull request?
In the current PySpark code, Python created `jsparkSession` doesn't add to JVM's defaultSession, this `SparkSession` object cannot be fetched from Java side, so the below scala code will be failed when loaded in PySpark application.
```scala
class TestSparkSession extends SparkListener with Logging {
override def onOtherEvent(event: SparkListenerEvent): Unit = {
event match {
case CreateTableEvent(db, table) =>
val session = SparkSession.getActiveSession.orElse(SparkSession.getDefaultSession)
assert(session.isDefined)
val tableInfo = session.get.sharedState.externalCatalog.getTable(db, table)
logInfo(s"Table info ${tableInfo}")
case e =>
logInfo(s"event $e")
}
}
}
```
So here propose to add fresh create `jsparkSession` to `defaultSession`.
## How was this patch tested?
Manual verification.
Author: jerryshao <sshao@hortonworks.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Saisai Shao <sai.sai.shao@gmail.com>
Closes#20404 from jerryshao/SPARK-23228.
## What changes were proposed in this pull request?
Rename the public APIs and names of pandas udfs.
- `PANDAS SCALAR UDF` -> `SCALAR PANDAS UDF`
- `PANDAS GROUP MAP UDF` -> `GROUPED MAP PANDAS UDF`
- `PANDAS GROUP AGG UDF` -> `GROUPED AGG PANDAS UDF`
## How was this patch tested?
The existing tests
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20428 from gatorsmile/renamePandasUDFs.
## What changes were proposed in this pull request?
It's not obvious from the comments that any added column must be a
function of the dataset that we are adding it to. Add a comment to
that effect to Scala, Python and R Data* methods.
Author: Henry Robinson <henry@cloudera.com>
Closes#20429 from henryr/SPARK-23157.
## What changes were proposed in this pull request?
Reproducer:
```python
from pyspark.sql.functions import udf
f = udf(lambda x: x)
spark.range(1).select(f("id")) # cache JVM UDF instance.
f = f.asNondeterministic()
spark.range(1).select(f("id"))._jdf.logicalPlan().projectList().head().deterministic()
```
It should return `False` but the current master returns `True`. Seems it's because we cache the JVM UDF instance and then we reuse it even after setting `deterministic` disabled once it's called.
## How was this patch tested?
Manually tested. I am not sure if I should add the test with a lot of JVM accesses with the intetnal stuff .. Let me know if anyone feels so. I will add.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20409 from HyukjinKwon/SPARK-23233.
## What changes were proposed in this pull request?
Bucketizer support multi-column in the python side
## How was this patch tested?
existing tests and added tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#19892 from zhengruifeng/20542_py.
## What changes were proposed in this pull request?
Add colRegex API to PySpark
## How was this patch tested?
add a test in sql/tests.py
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#20390 from huaxingao/spark-23081.
## What changes were proposed in this pull request?
This syncs the ML Python API with Scala for differences found after the 2.3 QA audit.
## How was this patch tested?
NA
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20354 from BryanCutler/pyspark-ml-doc-sync-23163.
## What changes were proposed in this pull request?
We extract Python UDFs in logical aggregate which depends on aggregate expression or grouping key in ExtractPythonUDFFromAggregate rule. But Python UDFs which don't depend on above expressions should also be extracted to avoid the issue reported in the JIRA.
A small code snippet to reproduce that issue looks like:
```python
import pyspark.sql.functions as f
df = spark.createDataFrame([(1,2), (3,4)])
f_udf = f.udf(lambda: str("const_str"))
df2 = df.distinct().withColumn("a", f_udf())
df2.show()
```
Error exception is raised as:
```
: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: pythonUDF0#50
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:91)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:90)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:90)
at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$38.apply(HashAggregateExec.scala:514)
at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$38.apply(HashAggregateExec.scala:513)
```
This exception raises because `HashAggregateExec` tries to bind the aliased Python UDF expression (e.g., `pythonUDF0#50 AS a#44`) to grouping key.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20360 from viirya/SPARK-23177.
## What changes were proposed in this pull request?
Add support for using pandas UDFs with groupby().agg().
This PR introduces a new type of pandas UDF - group aggregate pandas UDF. This type of UDF defines a transformation of multiple pandas Series -> a scalar value. Group aggregate pandas UDFs can be used with groupby().agg(). Note group aggregate pandas UDF doesn't support partial aggregation, i.e., a full shuffle is required.
This PR doesn't support group aggregate pandas UDFs that return ArrayType, StructType or MapType. Support for these types is left for future PR.
## How was this patch tested?
GroupbyAggPandasUDFTests
Author: Li Jin <ice.xelloss@gmail.com>
Closes#19872 from icexelloss/SPARK-22274-groupby-agg.
## What changes were proposed in this pull request?
This PR is to update the docs for UDF registration
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20348 from gatorsmile/testUpdateDoc.
## What changes were proposed in this pull request?
Pipe action convert objects into strings using a way that was affected by the default encoding setting of Python environment.
This patch fixed the problem. The detailed description is added here:
https://issues.apache.org/jira/browse/SPARK-20947
## How was this patch tested?
Run the following statement in pyspark-shell, and it will NOT raise exception if this patch is applied:
```python
sc.parallelize([u'\u6d4b\u8bd5']).pipe('cat').collect()
```
Author: 王晓哲 <wxz@linkdoc.com>
Closes#18277 from chaoslawful/fix_pipe_encoding_error.
## What changes were proposed in this pull request?
This is a follow-up of #20246.
If a UDT in Python doesn't have its corresponding Scala UDT, cast to string will be the raw string of the internal value, e.g. `"org.apache.spark.sql.catalyst.expressions.UnsafeArrayDataxxxxxxxx"` if the internal type is `ArrayType`.
This pr fixes it by using its `sqlType` casting.
## How was this patch tested?
Added a test and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20306 from ueshin/issues/SPARK-23054/fup1.
## What changes were proposed in this pull request?
Self-explanatory.
## How was this patch tested?
New python tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#20309 from tdas/SPARK-23143.
## What changes were proposed in this pull request?
Currently `UDFRegistration.registerJavaFunction` doesn't support data type string as a `returnType` whereas `UDFRegistration.register`, `udf`, or `pandas_udf` does.
We can support it for `UDFRegistration.registerJavaFunction` as well.
## How was this patch tested?
Added a doctest and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20307 from ueshin/issues/SPARK-23141.
## What changes were proposed in this pull request?
This PR proposes to deprecate `register*` for UDFs in `SQLContext` and `Catalog` in Spark 2.3.0.
These are inconsistent with Scala / Java APIs and also these basically do the same things with `spark.udf.register*`.
Also, this PR moves the logcis from `[sqlContext|spark.catalog].register*` to `spark.udf.register*` and reuse the docstring.
This PR also handles minor doc corrections. It also includes https://github.com/apache/spark/pull/20158
## How was this patch tested?
Manually tested, manually checked the API documentation and tests added to check if deprecated APIs call the aliases correctly.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20288 from HyukjinKwon/deprecate-udf.
## What changes were proposed in this pull request?
This PR proposes to actually run the doctests in `ml/image.py`.
## How was this patch tested?
doctests in `python/pyspark/ml/image.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20294 from HyukjinKwon/trigger-image.
## What changes were proposed in this pull request?
Make the default behavior of EXCEPT (i.e. EXCEPT DISTINCT) more
explicit in the documentation, and call out the change in behavior
from 1.x.
Author: Henry Robinson <henry@cloudera.com>
Closes#20254 from henryr/spark-23062.
## What changes were proposed in this pull request?
Register Vectorized UDFs for SQL Statement. For example,
```Python
>>> from pyspark.sql.functions import pandas_udf, PandasUDFType
>>> pandas_udf("integer", PandasUDFType.SCALAR)
... def add_one(x):
... return x + 1
...
>>> _ = spark.udf.register("add_one", add_one)
>>> spark.sql("SELECT add_one(id) FROM range(3)").collect()
[Row(add_one(id)=1), Row(add_one(id)=2), Row(add_one(id)=3)]
```
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20171 from gatorsmile/supportVectorizedUDF.
## What changes were proposed in this pull request?
The current `Datset.showString` prints rows thru `RowEncoder` deserializers like;
```
scala> Seq(Seq(Seq(1, 2), Seq(3), Seq(4, 5, 6))).toDF("a").show(false)
+------------------------------------------------------------+
|a |
+------------------------------------------------------------+
|[WrappedArray(1, 2), WrappedArray(3), WrappedArray(4, 5, 6)]|
+------------------------------------------------------------+
```
This result is incorrect because the correct one is;
```
scala> Seq(Seq(Seq(1, 2), Seq(3), Seq(4, 5, 6))).toDF("a").show(false)
+------------------------+
|a |
+------------------------+
|[[1, 2], [3], [4, 5, 6]]|
+------------------------+
```
So, this pr fixed code in `showString` to cast field data to strings before printing.
## How was this patch tested?
Added tests in `DataFrameSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20214 from maropu/SPARK-23023.
## What changes were proposed in this pull request?
This PR proposes to add a note that saying the length of a scalar Pandas UDF's `Series` is not of the whole input column but of the batch.
We are fine for a group map UDF because the usage is different from our typical UDF but scalar UDFs might cause confusion with the normal UDF.
For example, please consider this example:
```python
from pyspark.sql.functions import pandas_udf, col, lit
df = spark.range(1)
f = pandas_udf(lambda x, y: len(x) + y, LongType())
df.select(f(lit('text'), col('id'))).show()
```
```
+------------------+
|<lambda>(text, id)|
+------------------+
| 1|
+------------------+
```
```python
from pyspark.sql.functions import udf, col, lit
df = spark.range(1)
f = udf(lambda x, y: len(x) + y, "long")
df.select(f(lit('text'), col('id'))).show()
```
```
+------------------+
|<lambda>(text, id)|
+------------------+
| 4|
+------------------+
```
## How was this patch tested?
Manually built the doc and checked the output.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20237 from HyukjinKwon/SPARK-22980.
## What changes were proposed in this pull request?
This patch bumps the master branch version to `2.4.0-SNAPSHOT`.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20222 from gatorsmile/bump24.
## What changes were proposed in this pull request?
mark OneHotEncoder python API deprecated
## How was this patch tested?
N/A
Author: WeichenXu <weichen.xu@databricks.com>
Closes#20241 from WeichenXu123/mark_ohe_deprecated.
## What changes were proposed in this pull request?
OnehotEncoderEstimator python API.
## How was this patch tested?
doctest
Author: WeichenXu <weichen.xu@databricks.com>
Closes#20209 from WeichenXu123/ohe_py.
## What changes were proposed in this pull request?
Add a note to the `HasCheckpointInterval` parameter doc that clarifies that this setting is ignored when no checkpoint directory has been set on the spark context.
## How was this patch tested?
No tests necessary, just a doc update.
Author: sethah <shendrickson@cloudera.com>
Closes#20188 from sethah/als_checkpoint_doc.
## What changes were proposed in this pull request?
This the case when calling `SparkSession.createDataFrame` using a Pandas DataFrame that has non-str column labels.
The column name conversion logic to handle non-string or unicode in python2 is:
```
if column is not any type of string:
name = str(column)
else if column is unicode in Python 2:
name = column.encode('utf-8')
```
## How was this patch tested?
Added a new test with a Pandas DataFrame that has int column labels
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20210 from BryanCutler/python-createDataFrame-int-col-error-SPARK-23009.
## What changes were proposed in this pull request?
This fixes createDataFrame from Pandas to only assign modified timestamp series back to a copied version of the Pandas DataFrame. Previously, if the Pandas DataFrame was only a reference (e.g. a slice of another) each series will still get assigned back to the reference even if it is not a modified timestamp column. This caused the following warning "SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame."
## How was this patch tested?
existing tests
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20213 from BryanCutler/pyspark-createDataFrame-copy-slice-warn-SPARK-23018.
## What changes were proposed in this pull request?
It provides a better error message when doing `spark_session.createDataFrame(pandas_df)` with no schema and an error occurs in the schema inference due to incompatible types.
The Pandas column names are propagated down and the error message mentions which column had the merging error.
https://issues.apache.org/jira/browse/SPARK-22566
## How was this patch tested?
Manually in the `./bin/pyspark` console, and with new tests: `./python/run-tests`
<img width="873" alt="screen shot 2017-11-21 at 13 29 49" src="https://user-images.githubusercontent.com/3977115/33080121-382274e0-cecf-11e7-808f-057a65bb7b00.png">
I state that the contribution is my original work and that I license the work to the Apache Spark project under the project’s open source license.
Author: Guilherme Berger <gberger@palantir.com>
Closes#19792 from gberger/master.
## What changes were proposed in this pull request?
This PR wraps the `asNondeterministic` attribute in the wrapped UDF function to set the docstring properly.
```python
from pyspark.sql.functions import udf
help(udf(lambda x: x).asNondeterministic)
```
Before:
```
Help on function <lambda> in module pyspark.sql.udf:
<lambda> lambda
(END
```
After:
```
Help on function asNondeterministic in module pyspark.sql.udf:
asNondeterministic()
Updates UserDefinedFunction to nondeterministic.
.. versionadded:: 2.3
(END)
```
## How was this patch tested?
Manually tested and a simple test was added.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20173 from HyukjinKwon/SPARK-22901-followup.
## What changes were proposed in this pull request?
Add tests for using non deterministic UDFs in aggregate.
Update pandas_udf docstring w.r.t to determinism.
## How was this patch tested?
test_nondeterministic_udf_in_aggregate
Author: Li Jin <ice.xelloss@gmail.com>
Closes#20142 from icexelloss/SPARK-22930-pandas-udf-deterministic.
## What changes were proposed in this pull request?
```Python
import random
from pyspark.sql.functions import udf
from pyspark.sql.types import IntegerType, StringType
random_udf = udf(lambda: int(random.random() * 100), IntegerType()).asNondeterministic()
spark.catalog.registerFunction("random_udf", random_udf, StringType())
spark.sql("SELECT random_udf()").collect()
```
We will get the following error.
```
Py4JError: An error occurred while calling o29.__getnewargs__. Trace:
py4j.Py4JException: Method __getnewargs__([]) does not exist
at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:318)
at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:326)
at py4j.Gateway.invoke(Gateway.java:274)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:745)
```
This PR is to support it.
## How was this patch tested?
WIP
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20137 from gatorsmile/registerFunction.
## What changes were proposed in this pull request?
R Structured Streaming API for withWatermark, trigger, partitionBy
## How was this patch tested?
manual, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#20129 from felixcheung/rwater.
## What changes were proposed in this pull request?
This change adds `ArrayType` support for working with Arrow in pyspark when creating a DataFrame, calling `toPandas()`, and using vectorized `pandas_udf`.
## How was this patch tested?
Added new Python unit tests using Array data.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20114 from BryanCutler/arrow-ArrayType-support-SPARK-22530.
Previously, `FeatureHasher` always treats numeric type columns as numbers and never as categorical features. It is quite common to have categorical features represented as numbers or codes in data sources.
In order to hash these features as categorical, users must first explicitly convert them to strings which is cumbersome.
Add a new param `categoricalCols` which specifies the numeric columns that should be treated as categorical features.
## How was this patch tested?
New unit tests.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#19991 from MLnick/hasher-num-cat.
## What changes were proposed in this pull request?
This pr modified `concat` to concat binary inputs into a single binary output.
`concat` in the current master always output data as a string. But, in some databases (e.g., PostgreSQL), if all inputs are binary, `concat` also outputs binary.
## How was this patch tested?
Added tests in `SQLQueryTestSuite` and `TypeCoercionSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#19977 from maropu/SPARK-22771.
(Please fill in changes proposed in this fix)
Python API for VectorSizeHint Transformer.
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
doc-tests.
Author: Bago Amirbekian <bago@databricks.com>
Closes#20112 from MrBago/vectorSizeHint-PythonAPI.
## What changes were proposed in this pull request?
Adding fitMultiple API to `Estimator` with default implementation. Also update have ml.tuning meta-estimators use this API.
## How was this patch tested?
Unit tests.
Author: Bago Amirbekian <bago@databricks.com>
Closes#20058 from MrBago/python-fitMultiple.
## What changes were proposed in this pull request?
This is a follow-up pr of #19587.
If `xmlrunner` is installed, `VectorizedUDFTests.test_vectorized_udf_check_config` fails by the following error because the `self` which is a subclass of `unittest.TestCase` in the UDF `check_records_per_batch` can't be pickled anymore.
```
PicklingError: Cannot pickle files that are not opened for reading: w
```
This changes the UDF not to refer the `self`.
## How was this patch tested?
Tested locally.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20115 from ueshin/issues/SPARK-22370_fup1.
## What changes were proposed in this pull request?
This PR explicitly imports the missing `warnings` in `flume.py`.
## How was this patch tested?
Manually tested.
```python
>>> import warnings
>>> warnings.simplefilter('always', DeprecationWarning)
>>> from pyspark.streaming import flume
>>> flume.FlumeUtils.createStream(None, None, None)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/streaming/flume.py", line 60, in createStream
warnings.warn(
NameError: global name 'warnings' is not defined
```
```python
>>> import warnings
>>> warnings.simplefilter('always', DeprecationWarning)
>>> from pyspark.streaming import flume
>>> flume.FlumeUtils.createStream(None, None, None)
/.../spark/python/pyspark/streaming/flume.py:65: DeprecationWarning: Deprecated in 2.3.0. Flume support is deprecated as of Spark 2.3.0. See SPARK-22142.
DeprecationWarning)
...
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20110 from HyukjinKwon/SPARK-22313-followup.
## What changes were proposed in this pull request?
Escape of escape should be considered when using the UniVocity csv encoding/decoding library.
Ref: https://github.com/uniVocity/univocity-parsers#escaping-quote-escape-characters
One option is added for reading and writing CSV: `escapeQuoteEscaping`
## How was this patch tested?
Unit test added.
Author: soonmok-kwon <soonmok.kwon@navercorp.com>
Closes#20004 from ep1804/SPARK-22818.
## What changes were proposed in this pull request?
In SPARK-20586 the flag `deterministic` was added to Scala UDF, but it is not available for python UDF. This flag is useful for cases when the UDF's code can return different result with the same input. Due to optimization, duplicate invocations may be eliminated or the function may even be invoked more times than it is present in the query. This can lead to unexpected behavior.
This PR adds the deterministic flag, via the `asNondeterministic` method, to let the user mark the function as non-deterministic and therefore avoid the optimizations which might lead to strange behaviors.
## How was this patch tested?
Manual tests:
```
>>> from pyspark.sql.functions import *
>>> from pyspark.sql.types import *
>>> df_br = spark.createDataFrame([{'name': 'hello'}])
>>> import random
>>> udf_random_col = udf(lambda: int(100*random.random()), IntegerType()).asNondeterministic()
>>> df_br = df_br.withColumn('RAND', udf_random_col())
>>> random.seed(1234)
>>> udf_add_ten = udf(lambda rand: rand + 10, IntegerType())
>>> df_br.withColumn('RAND_PLUS_TEN', udf_add_ten('RAND')).show()
+-----+----+-------------+
| name|RAND|RAND_PLUS_TEN|
+-----+----+-------------+
|hello| 3| 13|
+-----+----+-------------+
```
Author: Marco Gaido <marcogaido91@gmail.com>
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19929 from mgaido91/SPARK-22629.
## What changes were proposed in this pull request?
Decimal type is not yet supported in `ArrowWriter`.
This is adding the decimal type support.
## How was this patch tested?
Added a test to `ArrowConvertersSuite`.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18754 from ueshin/issues/SPARK-21552.
## What changes were proposed in this pull request?
This is a follow-up pr of #20054 modifying error messages for both pandas and pyarrow to show actual versions.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20074 from ueshin/issues/SPARK-22874_fup1.
## What changes were proposed in this pull request?
Currently we check pandas version by capturing if `ImportError` for the specific imports is raised or not but we can compare `LooseVersion` of the version strings as the same as we're checking pyarrow version.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20054 from ueshin/issues/SPARK-22874.
## What changes were proposed in this pull request?
Upgrade Spark to Arrow 0.8.0 for Java and Python. Also includes an upgrade of Netty to 4.1.17 to resolve dependency requirements.
The highlights that pertain to Spark for the update from Arrow versoin 0.4.1 to 0.8.0 include:
* Java refactoring for more simple API
* Java reduced heap usage and streamlined hot code paths
* Type support for DecimalType, ArrayType
* Improved type casting support in Python
* Simplified type checking in Python
## How was this patch tested?
Existing tests
Author: Bryan Cutler <cutlerb@gmail.com>
Author: Shixiong Zhu <zsxwing@gmail.com>
Closes#19884 from BryanCutler/arrow-upgrade-080-SPARK-22324.
## What changes were proposed in this pull request?
Expose Python API for _LinearRegression_ with _huber_ loss.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#19994 from yanboliang/spark-22810.
## What changes were proposed in this pull request?
Like `Parquet`, users can use `ORC` with Apache Spark structured streaming. This PR adds `orc()` to `DataStreamReader`(Scala/Python) in order to support creating streaming dataset with ORC file format more easily like the other file formats. Also, this adds a test coverage for ORC data source and updates the document.
**BEFORE**
```scala
scala> spark.readStream.schema("a int").orc("/tmp/orc_ss").writeStream.format("console").start()
<console>:24: error: value orc is not a member of org.apache.spark.sql.streaming.DataStreamReader
spark.readStream.schema("a int").orc("/tmp/orc_ss").writeStream.format("console").start()
```
**AFTER**
```scala
scala> spark.readStream.schema("a int").orc("/tmp/orc_ss").writeStream.format("console").start()
res0: org.apache.spark.sql.streaming.StreamingQuery = org.apache.spark.sql.execution.streaming.StreamingQueryWrapper678b3746
scala>
-------------------------------------------
Batch: 0
-------------------------------------------
+---+
| a|
+---+
| 1|
+---+
```
## How was this patch tested?
Pass the newly added test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19975 from dongjoon-hyun/SPARK-22781.
## What changes were proposed in this pull request?
This change adds local checkpoint support to datasets and respective bind from Python Dataframe API.
If reliability requirements can be lowered to favor performance, as in cases of further quick transformations followed by a reliable save, localCheckpoints() fit very well.
Furthermore, at the moment Reliable checkpoints still incur double computation (see #9428)
In general it makes the API more complete as well.
## How was this patch tested?
Python land quick use case:
```python
>>> from time import sleep
>>> from pyspark.sql import types as T
>>> from pyspark.sql import functions as F
>>> def f(x):
sleep(1)
return x*2
...:
>>> df1 = spark.range(30, numPartitions=6)
>>> df2 = df1.select(F.udf(f, T.LongType())("id"))
>>> %time _ = df2.collect()
CPU times: user 7.79 ms, sys: 5.84 ms, total: 13.6 ms
Wall time: 12.2 s
>>> %time df3 = df2.localCheckpoint()
CPU times: user 2.38 ms, sys: 2.3 ms, total: 4.68 ms
Wall time: 10.3 s
>>> %time _ = df3.collect()
CPU times: user 5.09 ms, sys: 410 µs, total: 5.5 ms
Wall time: 148 ms
>>> sc.setCheckpointDir(".")
>>> %time df3 = df2.checkpoint()
CPU times: user 4.04 ms, sys: 1.63 ms, total: 5.67 ms
Wall time: 20.3 s
```
Author: Fernando Pereira <fernando.pereira@epfl.ch>
Closes#19805 from ferdonline/feature_dataset_localCheckpoint.
## What changes were proposed in this pull request?
pyspark.ml.tests is missing a py4j import. I've added the import and fixed the test that uses it. This test was only failing when testing without Hive.
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Bago Amirbekian <bago@databricks.com>
Closes#19997 from MrBago/fix-ImageReaderTest2.
## What changes were proposed in this pull request?
In multiple text analysis problems, it is not often desirable for the rows to be split by "\n". There exists a wholeText reader for RDD API, and this JIRA just adds the same support for Dataset API.
## How was this patch tested?
Added relevant new tests for both scala and Java APIs
Author: Prashant Sharma <prashsh1@in.ibm.com>
Author: Prashant Sharma <prashant@apache.org>
Closes#14151 from ScrapCodes/SPARK-16496/wholetext.
## What changes were proposed in this pull request?
Calling `ImageSchema.readImages` multiple times as below in PySpark shell:
```python
from pyspark.ml.image import ImageSchema
data_path = 'data/mllib/images/kittens'
_ = ImageSchema.readImages(data_path, recursive=True, dropImageFailures=True).collect()
_ = ImageSchema.readImages(data_path, recursive=True, dropImageFailures=True).collect()
```
throws an error as below:
```
...
org.datanucleus.exceptions.NucleusDataStoreException: Unable to open a test connection to the given database. JDBC url = jdbc:derby:;databaseName=metastore_db;create=true, username = APP. Terminating connection pool (set lazyInit to true if you expect to start your database after your app). Original Exception: ------
java.sql.SQLException: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1742f639f, see the next exception for details.
...
at org.apache.derby.jdbc.AutoloadedDriver.connect(Unknown Source)
...
at org.apache.hadoop.hive.metastore.HiveMetaStore.newRetryingHMSHandler(HiveMetaStore.java:5762)
...
at org.apache.spark.sql.hive.client.HiveClientImpl.newState(HiveClientImpl.scala:180)
...
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$databaseExists$1.apply$mcZ$sp(HiveExternalCatalog.scala:195)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$databaseExists$1.apply(HiveExternalCatalog.scala:195)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$databaseExists$1.apply(HiveExternalCatalog.scala:195)
at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:97)
at org.apache.spark.sql.hive.HiveExternalCatalog.databaseExists(HiveExternalCatalog.scala:194)
at org.apache.spark.sql.internal.SharedState.externalCatalog$lzycompute(SharedState.scala:100)
at org.apache.spark.sql.internal.SharedState.externalCatalog(SharedState.scala:88)
at org.apache.spark.sql.hive.HiveSessionStateBuilder.externalCatalog(HiveSessionStateBuilder.scala:39)
at org.apache.spark.sql.hive.HiveSessionStateBuilder.catalog$lzycompute(HiveSessionStateBuilder.scala:54)
at org.apache.spark.sql.hive.HiveSessionStateBuilder.catalog(HiveSessionStateBuilder.scala:52)
at org.apache.spark.sql.hive.HiveSessionStateBuilder$$anon$1.<init>(HiveSessionStateBuilder.scala:69)
at org.apache.spark.sql.hive.HiveSessionStateBuilder.analyzer(HiveSessionStateBuilder.scala:69)
at org.apache.spark.sql.internal.BaseSessionStateBuilder$$anonfun$build$2.apply(BaseSessionStateBuilder.scala:293)
at org.apache.spark.sql.internal.BaseSessionStateBuilder$$anonfun$build$2.apply(BaseSessionStateBuilder.scala:293)
at org.apache.spark.sql.internal.SessionState.analyzer$lzycompute(SessionState.scala:79)
at org.apache.spark.sql.internal.SessionState.analyzer(SessionState.scala:79)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:70)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:68)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:51)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:70)
at org.apache.spark.sql.SparkSession.internalCreateDataFrame(SparkSession.scala:574)
at org.apache.spark.sql.SparkSession.createDataFrame(SparkSession.scala:593)
at org.apache.spark.sql.SparkSession.createDataFrame(SparkSession.scala:348)
at org.apache.spark.sql.SparkSession.createDataFrame(SparkSession.scala:348)
at org.apache.spark.ml.image.ImageSchema$$anonfun$readImages$2$$anonfun$apply$1.apply(ImageSchema.scala:253)
...
Caused by: ERROR XJ040: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$1742f639f, see the next exception for details.
at org.apache.derby.iapi.error.StandardException.newException(Unknown Source)
at org.apache.derby.impl.jdbc.SQLExceptionFactory.wrapArgsForTransportAcrossDRDA(Unknown Source)
... 121 more
Caused by: ERROR XSDB6: Another instance of Derby may have already booted the database /.../spark/metastore_db.
...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/ml/image.py", line 190, in readImages
dropImageFailures, float(sampleRatio), seed)
File "/.../spark/python/lib/py4j-0.10.6-src.zip/py4j/java_gateway.py", line 1160, in __call__
File "/.../spark/python/pyspark/sql/utils.py", line 69, in deco
raise AnalysisException(s.split(': ', 1)[1], stackTrace)
pyspark.sql.utils.AnalysisException: u'java.lang.RuntimeException: java.lang.RuntimeException: Unable to instantiate org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient;'
```
Seems we better stick to `SparkSession.builder.getOrCreate()` like:
51620e288b/python/pyspark/sql/streaming.py (L329)dc5d34d8dc/python/pyspark/sql/column.py (L541)33d43bf1b6/python/pyspark/sql/readwriter.py (L105)
## How was this patch tested?
This was tested as below in PySpark shell:
```python
from pyspark.ml.image import ImageSchema
data_path = 'data/mllib/images/kittens'
_ = ImageSchema.readImages(data_path, recursive=True, dropImageFailures=True).collect()
_ = ImageSchema.readImages(data_path, recursive=True, dropImageFailures=True).collect()
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19845 from HyukjinKwon/SPARK-22651.
## What changes were proposed in this pull request?
Image test seems failed in Python 3.6.0 / NumPy 1.13.3. I manually tested as below:
```
======================================================================
ERROR: test_read_images (pyspark.ml.tests.ImageReaderTest)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/.../spark/python/pyspark/ml/tests.py", line 1831, in test_read_images
self.assertEqual(ImageSchema.toImage(array, origin=first_row[0]), first_row)
File "/.../spark/python/pyspark/ml/image.py", line 149, in toImage
data = bytearray(array.astype(dtype=np.uint8).ravel())
TypeError: only integer scalar arrays can be converted to a scalar index
----------------------------------------------------------------------
Ran 1 test in 7.606s
```
To be clear, I think the error seems from NumPy - 75b2d5d427/numpy/core/src/multiarray/number.c (L947)
For a smaller scope:
```python
>>> import numpy as np
>>> bytearray(np.array([1]).astype(dtype=np.uint8))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: only integer scalar arrays can be converted to a scalar index
```
In Python 2.7 / NumPy 1.13.1, it prints:
```
bytearray(b'\x01')
```
So, here, I simply worked around it by converting it to bytes as below:
```python
>>> bytearray(np.array([1]).astype(dtype=np.uint8).tobytes())
bytearray(b'\x01')
```
Also, while looking into it again, I realised few arguments could be quite confusing, for example, `Row` that needs some specific attributes and `numpy.ndarray`. I added few type checking and added some tests accordingly. So, it shows an error message as below:
```
TypeError: array argument should be numpy.ndarray; however, it got [<class 'str'>].
```
## How was this patch tested?
Manually tested with `./python/run-tests`.
And also:
```
PYSPARK_PYTHON=python3 SPARK_TESTING=1 bin/pyspark pyspark.ml.tests ImageReaderTest
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19835 from HyukjinKwon/SPARK-21866-followup.
## What changes were proposed in this pull request?
When converting Pandas DataFrame/Series from/to Spark DataFrame using `toPandas()` or pandas udfs, timestamp values behave to respect Python system timezone instead of session timezone.
For example, let's say we use `"America/Los_Angeles"` as session timezone and have a timestamp value `"1970-01-01 00:00:01"` in the timezone. Btw, I'm in Japan so Python timezone would be `"Asia/Tokyo"`.
The timestamp value from current `toPandas()` will be the following:
```
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
>>> df = spark.createDataFrame([28801], "long").selectExpr("timestamp(value) as ts")
>>> df.show()
+-------------------+
| ts|
+-------------------+
|1970-01-01 00:00:01|
+-------------------+
>>> df.toPandas()
ts
0 1970-01-01 17:00:01
```
As you can see, the value becomes `"1970-01-01 17:00:01"` because it respects Python timezone.
As we discussed in #18664, we consider this behavior is a bug and the value should be `"1970-01-01 00:00:01"`.
## How was this patch tested?
Added tests and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#19607 from ueshin/issues/SPARK-22395.
## What changes were proposed in this pull request?
In PySpark API Document, DataFrame.write.csv() says that setting the quote parameter to an empty string should turn off quoting. Instead, it uses the [null character](https://en.wikipedia.org/wiki/Null_character) as the quote.
This PR fixes the doc.
## How was this patch tested?
Manual.
```
cd python/docs
make html
open _build/html/pyspark.sql.html
```
Author: gaborgsomogyi <gabor.g.somogyi@gmail.com>
Closes#19814 from gaborgsomogyi/SPARK-22484.
## What changes were proposed in this pull request?
Adding spark image reader, an implementation of schema for representing images in spark DataFrames
The code is taken from the spark package located here:
(https://github.com/Microsoft/spark-images)
Please see the JIRA for more information (https://issues.apache.org/jira/browse/SPARK-21866)
Please see mailing list for SPIP vote and approval information:
(http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-SPIP-SPARK-21866-Image-support-in-Apache-Spark-td22510.html)
# Background and motivation
As Apache Spark is being used more and more in the industry, some new use cases are emerging for different data formats beyond the traditional SQL types or the numerical types (vectors and matrices). Deep Learning applications commonly deal with image processing. A number of projects add some Deep Learning capabilities to Spark (see list below), but they struggle to communicate with each other or with MLlib pipelines because there is no standard way to represent an image in Spark DataFrames. We propose to federate efforts for representing images in Spark by defining a representation that caters to the most common needs of users and library developers.
This SPIP proposes a specification to represent images in Spark DataFrames and Datasets (based on existing industrial standards), and an interface for loading sources of images. It is not meant to be a full-fledged image processing library, but rather the core description that other libraries and users can rely on. Several packages already offer various processing facilities for transforming images or doing more complex operations, and each has various design tradeoffs that make them better as standalone solutions.
This project is a joint collaboration between Microsoft and Databricks, which have been testing this design in two open source packages: MMLSpark and Deep Learning Pipelines.
The proposed image format is an in-memory, decompressed representation that targets low-level applications. It is significantly more liberal in memory usage than compressed image representations such as JPEG, PNG, etc., but it allows easy communication with popular image processing libraries and has no decoding overhead.
## How was this patch tested?
Unit tests in scala ImageSchemaSuite, unit tests in python
Author: Ilya Matiach <ilmat@microsoft.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19439 from imatiach-msft/ilmat/spark-images.
## What changes were proposed in this pull request?
Add python api for VectorIndexerModel support handle unseen categories via handleInvalid.
## How was this patch tested?
doctest added.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19753 from WeichenXu123/vector_indexer_invalid_py.
## What changes were proposed in this pull request?
Besides conditional expressions such as `when` and `if`, users may want to conditionally execute python udfs by short-curcuit evaluation. We should also explicitly note that python udfs don't support this kind of conditional execution too.
## How was this patch tested?
N/A, just document change.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19787 from viirya/SPARK-22541.
## What changes were proposed in this pull request?
* Add a "function type" argument to pandas_udf.
* Add a new public enum class `PandasUdfType` in pyspark.sql.functions
* Refactor udf related code from pyspark.sql.functions to pyspark.sql.udf
* Merge "PythonUdfType" and "PythonEvalType" into a single enum class "PythonEvalType"
Example:
```
from pyspark.sql.functions import pandas_udf, PandasUDFType
pandas_udf('double', PandasUDFType.SCALAR):
def plus_one(v):
return v + 1
```
## Design doc
https://docs.google.com/document/d/1KlLaa-xJ3oz28xlEJqXyCAHU3dwFYkFs_ixcUXrJNTc/edit
## How was this patch tested?
Added PandasUDFTests
## TODO:
* [x] Implement proper enum type for `PandasUDFType`
* [x] Update documentation
* [x] Add more tests in PandasUDFTests
Author: Li Jin <ice.xelloss@gmail.com>
Closes#19630 from icexelloss/spark-22409-pandas-udf-type.
## What changes were proposed in this pull request?
In PySpark API Document, [SparkSession.build](http://spark.apache.org/docs/2.2.0/api/python/pyspark.sql.html) is not documented and shows default value description.
```
SparkSession.builder = <pyspark.sql.session.Builder object ...
```
This PR adds the doc.
The following is the diff of the generated result.
```
$ diff old.html new.html
95a96,101
> <dl class="attribute">
> <dt id="pyspark.sql.SparkSession.builder">
> <code class="descname">builder</code><a class="headerlink" href="#pyspark.sql.SparkSession.builder" title="Permalink to this definition">¶</a></dt>
> <dd><p>A class attribute having a <a class="reference internal" href="#pyspark.sql.SparkSession.Builder" title="pyspark.sql.SparkSession.Builder"><code class="xref py py-class docutils literal"><span class="pre">Builder</span></code></a> to construct <a class="reference internal" href="#pyspark.sql.SparkSession" title="pyspark.sql.SparkSession"><code class="xref py py-class docutils literal"><span class="pre">SparkSession</span></code></a> instances</p>
> </dd></dl>
>
212,216d217
< <dt id="pyspark.sql.SparkSession.builder">
< <code class="descname">builder</code><em class="property"> = <pyspark.sql.session.SparkSession.Builder object></em><a class="headerlink" href="#pyspark.sql.SparkSession.builder" title="Permalink to this definition">¶</a></dt>
< <dd></dd></dl>
<
< <dl class="attribute">
```
## How was this patch tested?
Manual.
```
cd python/docs
make html
open _build/html/pyspark.sql.html
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19726 from dongjoon-hyun/SPARK-22490.
## What changes were proposed in this pull request?
If schema is passed as a list of unicode strings for column names, they should be re-encoded to 'utf-8' to be consistent. This is similar to the #13097 but for creation of DataFrame using Arrow.
## How was this patch tested?
Added new test of using unicode names for schema.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#19738 from BryanCutler/arrow-createDataFrame-followup-unicode-SPARK-20791.
## What changes were proposed in this pull request?
This change uses Arrow to optimize the creation of a Spark DataFrame from a Pandas DataFrame. The input df is sliced according to the default parallelism. The optimization is enabled with the existing conf "spark.sql.execution.arrow.enabled" and is disabled by default.
## How was this patch tested?
Added new unit test to create DataFrame with and without the optimization enabled, then compare results.
Author: Bryan Cutler <cutlerb@gmail.com>
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#19459 from BryanCutler/arrow-createDataFrame-from_pandas-SPARK-20791.
## What changes were proposed in this pull request?
This PR proposes to add `errorifexists` to SparkR API and fix the rest of them describing the mode, mainly, in API documentations as well.
This PR also replaces `convertToJSaveMode` to `setWriteMode` so that string as is is passed to JVM and executes:
b034f2565f/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala (L72-L82)
and remove the duplication here:
3f958a9992/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala (L187-L194)
## How was this patch tested?
Manually checked the built documentation. These were mainly found by `` grep -r `error` `` and `grep -r 'error'`.
Also, unit tests added in `test_sparkSQL.R`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19673 from HyukjinKwon/SPARK-21640-followup.
## What changes were proposed in this pull request?
This PR adds support for a new function called `dayofweek` that returns the day of the week of the given argument as an integer value in the range 1-7, where 1 represents Sunday.
## How was this patch tested?
Unit tests and manual tests.
Author: ptkool <michael.styles@shopify.com>
Closes#19672 from ptkool/day_of_week_function.
## What changes were proposed in this pull request?
Currently, a pandas.DataFrame that contains a timestamp of type 'datetime64[ns]' when converted to a Spark DataFrame with `createDataFrame` will interpret the values as LongType. This fix will check for a timestamp type and convert it to microseconds which will allow Spark to read as TimestampType.
## How was this patch tested?
Added unit test to verify Spark schema is expected for TimestampType and DateType when created from pandas
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#19646 from BryanCutler/pyspark-non-arrow-createDataFrame-ts-fix-SPARK-22417.
## What changes were proposed in this pull request?
When writing using jdbc with python currently we are wrongly assigning by default None as writing mode. This is due to wrongly calling mode on the `_jwrite` object instead of `self` and it causes an exception.
## How was this patch tested?
manual tests
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19654 from mgaido91/SPARK-22437.
## What changes were proposed in this pull request?
Under the current execution mode of Python UDFs, we don't well support Python UDFs as branch values or else value in CaseWhen expression.
Since to fix it might need the change not small (e.g., #19592) and this issue has simpler workaround. We should just notice users in the document about this.
## How was this patch tested?
Only document change.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19617 from viirya/SPARK-22347-3.
## What changes were proposed in this pull request?
Update the url of reference paper.
## How was this patch tested?
It is comments, so nothing tested.
Author: bomeng <bmeng@us.ibm.com>
Closes#19614 from bomeng/22399.
## What changes were proposed in this pull request?
This PR propose to add `ReusedSQLTestCase` which deduplicate `setUpClass` and `tearDownClass` in `sql/tests.py`.
## How was this patch tested?
Jenkins tests and manual tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19595 from HyukjinKwon/reduce-dupe.
## What changes were proposed in this pull request?
`ArrowEvalPythonExec` and `FlatMapGroupsInPandasExec` are refering config values of `SQLConf` in function for `mapPartitions`/`mapPartitionsInternal`, but we should capture them in Driver.
## How was this patch tested?
Added a test and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#19587 from ueshin/issues/SPARK-22370.
## What changes were proposed in this pull request?
Add parallelism support for ML tuning in pyspark.
## How was this patch tested?
Test updated.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19122 from WeichenXu123/par-ml-tuning-py.
## What changes were proposed in this pull request?
Adding date and timestamp support with Arrow for `toPandas()` and `pandas_udf`s. Timestamps are stored in Arrow as UTC and manifested to the user as timezone-naive localized to the Python system timezone.
## How was this patch tested?
Added Scala tests for date and timestamp types under ArrowConverters, ArrowUtils, and ArrowWriter suites. Added Python tests for `toPandas()` and `pandas_udf`s with date and timestamp types.
Author: Bryan Cutler <cutlerb@gmail.com>
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18664 from BryanCutler/arrow-date-timestamp-SPARK-21375.
## What changes were proposed in this pull request?
This PR proposes to mark the existing warnings as `DeprecationWarning` and print out warnings for deprecated functions.
This could be actually useful for Spark app developers. I use (old) PyCharm and this IDE can detect this specific `DeprecationWarning` in some cases:
**Before**
<img src="https://user-images.githubusercontent.com/6477701/31762664-df68d9f8-b4f6-11e7-8773-f0468f70a2cc.png" height="45" />
**After**
<img src="https://user-images.githubusercontent.com/6477701/31762662-de4d6868-b4f6-11e7-98dc-3c8446a0c28a.png" height="70" />
For console usage, `DeprecationWarning` is usually disabled (see https://docs.python.org/2/library/warnings.html#warning-categories and https://docs.python.org/3/library/warnings.html#warning-categories):
```
>>> import warnings
>>> filter(lambda f: f[2] == DeprecationWarning, warnings.filters)
[('ignore', <_sre.SRE_Pattern object at 0x10ba58c00>, <type 'exceptions.DeprecationWarning'>, <_sre.SRE_Pattern object at 0x10bb04138>, 0), ('ignore', None, <type 'exceptions.DeprecationWarning'>, None, 0)]
```
so, it won't actually mess up the terminal much unless it is intended.
If this is intendedly enabled, it'd should as below:
```
>>> import warnings
>>> warnings.simplefilter('always', DeprecationWarning)
>>>
>>> from pyspark.sql import functions
>>> functions.approxCountDistinct("a")
.../spark/python/pyspark/sql/functions.py:232: DeprecationWarning: Deprecated in 2.1, use approx_count_distinct instead.
"Deprecated in 2.1, use approx_count_distinct instead.", DeprecationWarning)
...
```
These instances were found by:
```
cd python/pyspark
grep -r "Deprecated" .
grep -r "deprecated" .
grep -r "deprecate" .
```
## How was this patch tested?
Manually tested.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19535 from HyukjinKwon/deprecated-warning.
## What changes were proposed in this pull request?
This is a follow-up of #18732.
This pr modifies `GroupedData.apply()` method to convert pandas udf to grouped udf implicitly.
## How was this patch tested?
Exisiting tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#19517 from ueshin/issues/SPARK-20396/fup2.
## What changes were proposed in this pull request?
Currently percentile_approx never returns the first element when percentile is in (relativeError, 1/N], where relativeError default 1/10000, and N is the total number of elements. But ideally, percentiles in [0, 1/N] should all return the first element as the answer.
For example, given input data 1 to 10, if a user queries 10% (or even less) percentile, it should return 1, because the first value 1 already reaches 10%. Currently it returns 2.
Based on the paper, targetError is not rounded up, and searching index should start from 0 instead of 1. By following the paper, we should be able to fix the cases mentioned above.
## How was this patch tested?
Added a new test case and fix existing test cases.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#19438 from wzhfy/improve_percentile_approx.
## What changes were proposed in this pull request?
This PR adds an apply() function on df.groupby(). apply() takes a pandas udf that is a transformation on `pandas.DataFrame` -> `pandas.DataFrame`.
Static schema
-------------------
```
schema = df.schema
pandas_udf(schema)
def normalize(df):
df = df.assign(v1 = (df.v1 - df.v1.mean()) / df.v1.std()
return df
df.groupBy('id').apply(normalize)
```
Dynamic schema
-----------------------
**This use case is removed from the PR and we will discuss this as a follow up. See discussion https://github.com/apache/spark/pull/18732#pullrequestreview-66583248**
Another example to use pd.DataFrame dtypes as output schema of the udf:
```
sample_df = df.filter(df.id == 1).toPandas()
def foo(df):
ret = # Some transformation on the input pd.DataFrame
return ret
foo_udf = pandas_udf(foo, foo(sample_df).dtypes)
df.groupBy('id').apply(foo_udf)
```
In interactive use case, user usually have a sample pd.DataFrame to test function `foo` in their notebook. Having been able to use `foo(sample_df).dtypes` frees user from specifying the output schema of `foo`.
Design doc: https://github.com/icexelloss/spark/blob/pandas-udf-doc/docs/pyspark-pandas-udf.md
## How was this patch tested?
* Added GroupbyApplyTest
Author: Li Jin <ice.xelloss@gmail.com>
Author: Takuya UESHIN <ueshin@databricks.com>
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#18732 from icexelloss/groupby-apply-SPARK-20396.
## What changes were proposed in this pull request?
This is a follow-up of #19384.
In the previous pr, only definitions of the config names were modified, but we also need to modify the names in runtime or tests specified as string literal.
## How was this patch tested?
Existing tests but modified the config names.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#19462 from ueshin/issues/SPARK-22159/fup1.
This PR adds methods `recommendForUserSubset` and `recommendForItemSubset` to `ALSModel`. These allow recommending for a specified set of user / item ids rather than for every user / item (as in the `recommendForAllX` methods).
The subset methods take a `DataFrame` as input, containing ids in the column specified by the param `userCol` or `itemCol`. The model will generate recommendations for each _unique_ id in this input dataframe.
## How was this patch tested?
New unit tests in `ALSSuite` and Python doctests in `ALS`. Ran updated examples locally.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#18748 from MLnick/als-recommend-df.
## What changes were proposed in this pull request?
Move flume behind a profile, take 2. See https://github.com/apache/spark/pull/19365 for most of the back-story.
This change should fix the problem by removing the examples module dependency and moving Flume examples to the module itself. It also adds deprecation messages, per a discussion on dev about deprecating for 2.3.0.
## How was this patch tested?
Existing tests, which still enable flume integration.
Author: Sean Owen <sowen@cloudera.com>
Closes#19412 from srowen/SPARK-22142.2.
## What changes were proposed in this pull request?
Add 'flume' profile to enable Flume-related integration modules
## How was this patch tested?
Existing tests; no functional change
Author: Sean Owen <sowen@cloudera.com>
Closes#19365 from srowen/SPARK-22142.
## What changes were proposed in this pull request?
Fixed some minor issues with pandas_udf related docs and formatting.
## How was this patch tested?
NA
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#19375 from BryanCutler/arrow-pandas_udf-cleanup-minor.
## What changes were proposed in this pull request?
Currently we use Arrow File format to communicate with Python worker when invoking vectorized UDF but we can use Arrow Stream format.
This pr replaces the Arrow File format with the Arrow Stream format.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#19349 from ueshin/issues/SPARK-22125.
## What changes were proposed in this pull request?
We added a method to the scala API for creating a `DataFrame` from `DataSet[String]` storing CSV in [SPARK-15463](https://issues.apache.org/jira/browse/SPARK-15463) but PySpark doesn't have `Dataset` to support this feature. Therfore, I add an API to create a `DataFrame` from `RDD[String]` storing csv and it's also consistent with PySpark's `spark.read.json`.
For example as below
```
>>> rdd = sc.textFile('python/test_support/sql/ages.csv')
>>> df2 = spark.read.csv(rdd)
>>> df2.dtypes
[('_c0', 'string'), ('_c1', 'string')]
```
## How was this patch tested?
add unit test cases.
Author: goldmedal <liugs963@gmail.com>
Closes#19339 from goldmedal/SPARK-22112.
## What changes were proposed in this pull request?
This change disables the use of 0-parameter pandas_udfs due to the API being overly complex and awkward, and can easily be worked around by using an index column as an input argument. Also added doctests for pandas_udfs which revealed bugs for handling empty partitions and using the pandas_udf decorator.
## How was this patch tested?
Reworked existing 0-parameter test to verify error is raised, added doctest for pandas_udf, added new tests for empty partition and decorator usage.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#19325 from BryanCutler/arrow-pandas_udf-0-param-remove-SPARK-22106.
## What changes were proposed in this pull request?
The `percentile_approx` function previously accepted numeric type input and output double type results.
But since all numeric types, date and timestamp types are represented as numerics internally, `percentile_approx` can support them easily.
After this PR, it supports date type, timestamp type and numeric types as input types. The result type is also changed to be the same as the input type, which is more reasonable for percentiles.
This change is also required when we generate equi-height histograms for these types.
## How was this patch tested?
Added a new test and modified some existing tests.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#19321 from wzhfy/approx_percentile_support_types.
## What changes were proposed in this pull request?
When calling `DataFrame.toPandas()` (without Arrow enabled), if there is a `IntegralType` column (`IntegerType`, `ShortType`, `ByteType`) that has null values the following exception is thrown:
ValueError: Cannot convert non-finite values (NA or inf) to integer
This is because the null values first get converted to float NaN during the construction of the Pandas DataFrame in `from_records`, and then it is attempted to be converted back to to an integer where it fails.
The fix is going to check if the Pandas DataFrame can cause such failure when converting, if so, we don't do the conversion and use the inferred type by Pandas.
Closes#18945
## How was this patch tested?
Added pyspark test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19319 from viirya/SPARK-21766.
This PR adds vectorized UDFs to the Python API
**Proposed API**
Introduce a flag to turn on vectorization for a defined UDF, for example:
```
pandas_udf(DoubleType())
def plus(a, b)
return a + b
```
or
```
plus = pandas_udf(lambda a, b: a + b, DoubleType())
```
Usage is the same as normal UDFs
0-parameter UDFs
pandas_udf functions can declare an optional `**kwargs` and when evaluated, will contain a key "size" that will give the required length of the output. For example:
```
pandas_udf(LongType())
def f0(**kwargs):
return pd.Series(1).repeat(kwargs["size"])
df.select(f0())
```
Added new unit tests in pyspark.sql that are enabled if pyarrow and Pandas are available.
- [x] Fix support for promoted types with null values
- [ ] Discuss 0-param UDF API (use of kwargs)
- [x] Add tests for chained UDFs
- [ ] Discuss behavior when pyarrow not installed / enabled
- [ ] Cleanup pydoc and add user docs
Author: Bryan Cutler <cutlerb@gmail.com>
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18659 from BryanCutler/arrow-vectorized-udfs-SPARK-21404.
## What changes were proposed in this pull request?
Added Python interface for ClusteringEvaluator
## How was this patch tested?
Manual test, eg. the example Python code in the comments.
cc yanboliang
Author: Marco Gaido <mgaido@hortonworks.com>
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#19204 from mgaido91/SPARK-21981.
## What changes were proposed in this pull request?
Clarify behavior of to_utc_timestamp/from_utc_timestamp with an example
## How was this patch tested?
Doc only change / existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#19276 from srowen/SPARK-22049.
## What changes were proposed in this pull request?
Remove unnecessary default value setting for all evaluators, as we have set them in corresponding _HasXXX_ base classes.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#19262 from yanboliang/evaluation.
## What changes were proposed in this pull request?
This PR proposes to improve error message from:
```
>>> sc.show_profiles()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/context.py", line 1000, in show_profiles
self.profiler_collector.show_profiles()
AttributeError: 'NoneType' object has no attribute 'show_profiles'
>>> sc.dump_profiles("/tmp/abc")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/context.py", line 1005, in dump_profiles
self.profiler_collector.dump_profiles(path)
AttributeError: 'NoneType' object has no attribute 'dump_profiles'
```
to
```
>>> sc.show_profiles()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/context.py", line 1003, in show_profiles
raise RuntimeError("'spark.python.profile' configuration must be set "
RuntimeError: 'spark.python.profile' configuration must be set to 'true' to enable Python profile.
>>> sc.dump_profiles("/tmp/abc")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/context.py", line 1012, in dump_profiles
raise RuntimeError("'spark.python.profile' configuration must be set "
RuntimeError: 'spark.python.profile' configuration must be set to 'true' to enable Python profile.
```
## How was this patch tested?
Unit tests added in `python/pyspark/tests.py` and manual tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19260 from HyukjinKwon/profile-errors.
## What changes were proposed in this pull request?
(edited)
Fixes a bug introduced in #16121
In PairDeserializer convert each batch of keys and values to lists (if they do not have `__len__` already) so that we can check that they are the same size. Normally they already are lists so this should not have a performance impact, but this is needed when repeated `zip`'s are done.
## How was this patch tested?
Additional unit test
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#19226 from aray/SPARK-21985.
## What changes were proposed in this pull request?
StructType.fromInternal is calling f.fromInternal(v) for every field.
We can use precalculated information about type to limit the number of function calls. (its calculated once per StructType and used in per record calculations)
Benchmarks (Python profiler)
```
df = spark.range(10000000).selectExpr("id as id0", "id as id1", "id as id2", "id as id3", "id as id4", "id as id5", "id as id6", "id as id7", "id as id8", "id as id9", "struct(id) as s").cache()
df.count()
df.rdd.map(lambda x: x).count()
```
Before
```
310274584 function calls (300272456 primitive calls) in 1320.684 seconds
Ordered by: internal time, cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
10000000 253.417 0.000 486.991 0.000 types.py:619(<listcomp>)
30000000 192.272 0.000 1009.986 0.000 types.py:612(fromInternal)
100000000 176.140 0.000 176.140 0.000 types.py:88(fromInternal)
20000000 156.832 0.000 328.093 0.000 types.py:1471(_create_row)
14000 107.206 0.008 1237.917 0.088 {built-in method loads}
20000000 80.176 0.000 1090.162 0.000 types.py:1468(<lambda>)
```
After
```
210274584 function calls (200272456 primitive calls) in 1035.974 seconds
Ordered by: internal time, cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
30000000 215.845 0.000 698.748 0.000 types.py:612(fromInternal)
20000000 165.042 0.000 351.572 0.000 types.py:1471(_create_row)
14000 116.834 0.008 946.791 0.068 {built-in method loads}
20000000 87.326 0.000 786.073 0.000 types.py:1468(<lambda>)
20000000 85.477 0.000 134.607 0.000 types.py:1519(__new__)
10000000 65.777 0.000 126.712 0.000 types.py:619(<listcomp>)
```
Main difference is types.py:619(<listcomp>) and types.py:88(fromInternal) (which is removed in After)
The number of function calls is 100 million less. And performance is 20% better.
Benchmark (worst case scenario.)
Test
```
df = spark.range(1000000).selectExpr("current_timestamp as id0", "current_timestamp as id1", "current_timestamp as id2", "current_timestamp as id3", "current_timestamp as id4", "current_timestamp as id5", "current_timestamp as id6", "current_timestamp as id7", "current_timestamp as id8", "current_timestamp as id9").cache()
df.count()
df.rdd.map(lambda x: x).count()
```
Before
```
31166064 function calls (31163984 primitive calls) in 150.882 seconds
```
After
```
31166064 function calls (31163984 primitive calls) in 153.220 seconds
```
IMPORTANT:
The benchmark was done on top of https://github.com/apache/spark/pull/19246.
Without https://github.com/apache/spark/pull/19246 the performance improvement will be even greater.
## How was this patch tested?
Existing tests.
Performance benchmark.
Author: Maciej Bryński <maciek-github@brynski.pl>
Closes#19249 from maver1ck/spark_22032.
## What changes were proposed in this pull request?
In previous work SPARK-21513, we has allowed `MapType` and `ArrayType` of `MapType`s convert to a json string but only for Scala API. In this follow-up PR, we will make SparkSQL support it for PySpark and SparkR, too. We also fix some little bugs and comments of the previous work in this follow-up PR.
### For PySpark
```
>>> data = [(1, {"name": "Alice"})]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(to_json(df.value).alias("json")).collect()
[Row(json=u'{"name":"Alice")']
>>> data = [(1, [{"name": "Alice"}, {"name": "Bob"}])]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(to_json(df.value).alias("json")).collect()
[Row(json=u'[{"name":"Alice"},{"name":"Bob"}]')]
```
### For SparkR
```
# Converts a map into a JSON object
df2 <- sql("SELECT map('name', 'Bob')) as people")
df2 <- mutate(df2, people_json = to_json(df2$people))
# Converts an array of maps into a JSON array
df2 <- sql("SELECT array(map('name', 'Bob'), map('name', 'Alice')) as people")
df2 <- mutate(df2, people_json = to_json(df2$people))
```
## How was this patch tested?
Add unit test cases.
cc viirya HyukjinKwon
Author: goldmedal <liugs963@gmail.com>
Closes#19223 from goldmedal/SPARK-21513-fp-PySaprkAndSparkR.
## What changes were proposed in this pull request?
#19197 fixed double caching for MLlib algorithms, but missed PySpark ```OneVsRest```, this PR fixed it.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#19220 from yanboliang/SPARK-18608.
## What changes were proposed in this pull request?
Added LogisticRegressionTrainingSummary for MultinomialLogisticRegression in Python API
## How was this patch tested?
Added unit test
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Ming Jiang <mjiang@fanatics.com>
Author: Ming Jiang <jmwdpk@gmail.com>
Author: jmwdpk <jmwdpk@gmail.com>
Closes#19185 from jmwdpk/SPARK-21854.
## What changes were proposed in this pull request?
Put Kafka 0.8 support behind a kafka-0-8 profile.
## How was this patch tested?
Existing tests, but, until PR builder and Jenkins configs are updated the effect here is to not build or test Kafka 0.8 support at all.
Author: Sean Owen <sowen@cloudera.com>
Closes#19134 from srowen/SPARK-21893.
# What changes were proposed in this pull request?
Added tunable parallelism to the pyspark implementation of one vs. rest classification. Added a parallelism parameter to the Scala implementation of one vs. rest along with functionality for using the parameter to tune the level of parallelism.
I take this PR #18281 over because the original author is busy but we need merge this PR soon.
After this been merged, we can close#18281 .
## How was this patch tested?
Test suite added.
Author: Ajay Saini <ajays725@gmail.com>
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19110 from WeichenXu123/spark-21027.
Probability and rawPrediction has been added to MultilayerPerceptronClassifier for Python
Add unit test.
Author: Chunsheng Ji <chunsheng.ji@gmail.com>
Closes#19172 from chunshengji/SPARK-21856.
## What changes were proposed in this pull request?
`typeName` classmethod has been fixed by using type -> typeName map.
## How was this patch tested?
local build
Author: Peter Szalai <szalaipeti.vagyok@gmail.com>
Closes#17435 from szalai1/datatype-gettype-fix.
## What changes were proposed in this pull request?
Correct DataFrame doc.
## How was this patch tested?
Only doc change, no tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#19173 from yanboliang/df-doc.
https://issues.apache.org/jira/browse/SPARK-19866
## What changes were proposed in this pull request?
Add Python API for findSynonymsArray matching Scala API.
## How was this patch tested?
Manual test
`./python/run-tests --python-executables=python2.7 --modules=pyspark-ml`
Author: Xin Ren <iamshrek@126.com>
Author: Xin Ren <renxin.ubc@gmail.com>
Author: Xin Ren <keypointt@users.noreply.github.com>
Closes#17451 from keypointt/SPARK-19866.
## What changes were proposed in this pull request?
This PR proposes to support unicodes in Param methods in ML, other missed functions in DataFrame.
For example, this causes a `ValueError` in Python 2.x when param is a unicode string:
```python
>>> from pyspark.ml.classification import LogisticRegression
>>> lr = LogisticRegression()
>>> lr.hasParam("threshold")
True
>>> lr.hasParam(u"threshold")
Traceback (most recent call last):
...
raise TypeError("hasParam(): paramName must be a string")
TypeError: hasParam(): paramName must be a string
```
This PR is based on https://github.com/apache/spark/pull/13036
## How was this patch tested?
Unit tests in `python/pyspark/ml/tests.py` and `python/pyspark/sql/tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Author: sethah <seth.hendrickson16@gmail.com>
Closes#17096 from HyukjinKwon/SPARK-15243.
## What changes were proposed in this pull request?
`pyspark.sql.tests.SQLTests2` doesn't stop newly created spark context in the test and it might affect the following tests.
This pr makes `pyspark.sql.tests.SQLTests2` stop `SparkContext`.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#19158 from ueshin/issues/SPARK-21950.
## What changes were proposed in this pull request?
This PR proposes to add a wrapper for `unionByName` API to R and Python as well.
**Python**
```python
df1 = spark.createDataFrame([[1, 2, 3]], ["col0", "col1", "col2"])
df2 = spark.createDataFrame([[4, 5, 6]], ["col1", "col2", "col0"])
df1.unionByName(df2).show()
```
```
+----+----+----+
|col0|col1|col3|
+----+----+----+
| 1| 2| 3|
| 6| 4| 5|
+----+----+----+
```
**R**
```R
df1 <- select(createDataFrame(mtcars), "carb", "am", "gear")
df2 <- select(createDataFrame(mtcars), "am", "gear", "carb")
head(unionByName(limit(df1, 2), limit(df2, 2)))
```
```
carb am gear
1 4 1 4
2 4 1 4
3 4 1 4
4 4 1 4
```
## How was this patch tested?
Doctests for Python and unit test added in `test_sparkSQL.R` for R.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19105 from HyukjinKwon/unionByName-r-python.
## What changes were proposed in this pull request?
This PR proposes to remove private functions that look not used in the main codes, `_split_schema_abstract`, `_parse_field_abstract`, `_parse_schema_abstract` and `_infer_schema_type`.
## How was this patch tested?
Existing tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18647 from HyukjinKwon/remove-abstract.
## What changes were proposed in this pull request?
This PR make `DataFrame.sample(...)` can omit `withReplacement` defaulting `False`, consistently with equivalent Scala / Java API.
In short, the following examples are allowed:
```python
>>> df = spark.range(10)
>>> df.sample(0.5).count()
7
>>> df.sample(fraction=0.5).count()
3
>>> df.sample(0.5, seed=42).count()
5
>>> df.sample(fraction=0.5, seed=42).count()
5
```
In addition, this PR also adds some type checking logics as below:
```python
>>> df = spark.range(10)
>>> df.sample().count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [].
>>> df.sample(True).count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'bool'>].
>>> df.sample(42).count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'int'>].
>>> df.sample(fraction=False, seed="a").count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'bool'>, <type 'str'>].
>>> df.sample(seed=[1]).count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'list'>].
>>> df.sample(withReplacement="a", fraction=0.5, seed=1)
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'str'>, <type 'float'>, <type 'int'>].
```
## How was this patch tested?
Manually tested, unit tests added in doc tests and manually checked the built documentation for Python.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18999 from HyukjinKwon/SPARK-21779.
## What changes were proposed in this pull request?
`PickleException` is thrown when creating dataframe from python row with empty bytearray
spark.createDataFrame(spark.sql("select unhex('') as xx").rdd.map(lambda x: {"abc": x.xx})).show()
net.razorvine.pickle.PickleException: invalid pickle data for bytearray; expected 1 or 2 args, got 0
at net.razorvine.pickle.objects.ByteArrayConstructor.construct(ByteArrayConstructor.java
...
`ByteArrayConstructor` doesn't deal with empty byte array pickled by Python3.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19085 from viirya/SPARK-21534.
## What changes were proposed in this pull request?
This PR aims to support `spark.sql.orc.compression.codec` like Parquet's `spark.sql.parquet.compression.codec`. Users can use SQLConf to control ORC compression, too.
## How was this patch tested?
Pass the Jenkins with new and updated test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19055 from dongjoon-hyun/SPARK-21839.
## What changes were proposed in this pull request?
This patch adds allowUnquotedControlChars option in JSON data source to allow JSON Strings to contain unquoted control characters (ASCII characters with value less than 32, including tab and line feed characters)
## How was this patch tested?
Add new test cases
Author: vinodkc <vinod.kc.in@gmail.com>
Closes#19008 from vinodkc/br_fix_SPARK-21756.
## What changes were proposed in this pull request?
While preparing to take over https://github.com/apache/spark/pull/16537, I realised a (I think) better approach to make the exception handling in one point.
This PR proposes to fix `_to_java_column` in `pyspark.sql.column`, which most of functions in `functions.py` and some other APIs use. This `_to_java_column` basically looks not working with other types than `pyspark.sql.column.Column` or string (`str` and `unicode`).
If this is not `Column`, then it calls `_create_column_from_name` which calls `functions.col` within JVM:
42b9eda80e/sql/core/src/main/scala/org/apache/spark/sql/functions.scala (L76)
And it looks we only have `String` one with `col`.
So, these should work:
```python
>>> from pyspark.sql.column import _to_java_column, Column
>>> _to_java_column("a")
JavaObject id=o28
>>> _to_java_column(u"a")
JavaObject id=o29
>>> _to_java_column(spark.range(1).id)
JavaObject id=o33
```
whereas these do not:
```python
>>> _to_java_column(1)
```
```
...
py4j.protocol.Py4JError: An error occurred while calling z:org.apache.spark.sql.functions.col. Trace:
py4j.Py4JException: Method col([class java.lang.Integer]) does not exist
...
```
```python
>>> _to_java_column([])
```
```
...
py4j.protocol.Py4JError: An error occurred while calling z:org.apache.spark.sql.functions.col. Trace:
py4j.Py4JException: Method col([class java.util.ArrayList]) does not exist
...
```
```python
>>> class A(): pass
>>> _to_java_column(A())
```
```
...
AttributeError: 'A' object has no attribute '_get_object_id'
```
Meaning most of functions using `_to_java_column` such as `udf` or `to_json` or some other APIs throw an exception as below:
```python
>>> from pyspark.sql.functions import udf
>>> udf(lambda x: x)(None)
```
```
...
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.sql.functions.col.
: java.lang.NullPointerException
...
```
```python
>>> from pyspark.sql.functions import to_json
>>> to_json(None)
```
```
...
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.sql.functions.col.
: java.lang.NullPointerException
...
```
**After this PR**:
```python
>>> from pyspark.sql.functions import udf
>>> udf(lambda x: x)(None)
...
```
```
TypeError: Invalid argument, not a string or column: None of type <type 'NoneType'>. For column literals, use 'lit', 'array', 'struct' or 'create_map' functions.
```
```python
>>> from pyspark.sql.functions import to_json
>>> to_json(None)
```
```
...
TypeError: Invalid argument, not a string or column: None of type <type 'NoneType'>. For column literals, use 'lit', 'array', 'struct' or 'create_map' functions.
```
## How was this patch tested?
Unit tests added in `python/pyspark/sql/tests.py` and manual tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Author: zero323 <zero323@users.noreply.github.com>
Closes#19027 from HyukjinKwon/SPARK-19165.
## What changes were proposed in this pull request?
Modify MLP model to inherit `ProbabilisticClassificationModel` and so that it can expose the probability column when transforming data.
## How was this patch tested?
Test added.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#17373 from WeichenXu123/expose_probability_in_mlp_model.
## What changes were proposed in this pull request?
Added call to copy values of Params from Estimator to Model after fit in PySpark ML. This will copy values for any params that are also defined in the Model. Since currently most Models do not define the same params from the Estimator, also added method to create new Params from looking at the Java object if they do not exist in the Python object. This is a temporary fix that can be removed once the PySpark models properly define the params themselves.
## How was this patch tested?
Refactored the `check_params` test to optionally check if the model params for Python and Java match and added this check to an existing fitted model that shares params between Estimator and Model.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#17849 from BryanCutler/pyspark-models-own-params-SPARK-10931.
## What changes were proposed in this pull request?
Based on https://github.com/apache/spark/pull/18282 by rgbkrk this PR attempts to update to the current released cloudpickle and minimize the difference between Spark cloudpickle and "stock" cloud pickle with the goal of eventually using the stock cloud pickle.
Some notable changes:
* Import submodules accessed by pickled functions (cloudpipe/cloudpickle#80)
* Support recursive functions inside closures (cloudpipe/cloudpickle#89, cloudpipe/cloudpickle#90)
* Fix ResourceWarnings and DeprecationWarnings (cloudpipe/cloudpickle#88)
* Assume modules with __file__ attribute are not dynamic (cloudpipe/cloudpickle#85)
* Make cloudpickle Python 3.6 compatible (cloudpipe/cloudpickle#72)
* Allow pickling of builtin methods (cloudpipe/cloudpickle#57)
* Add ability to pickle dynamically created modules (cloudpipe/cloudpickle#52)
* Support method descriptor (cloudpipe/cloudpickle#46)
* No more pickling of closed files, was broken on Python 3 (cloudpipe/cloudpickle#32)
* ** Remove non-standard __transient__check (cloudpipe/cloudpickle#110)** -- while we don't use this internally, and have no tests or documentation for its use, downstream code may use __transient__, although it has never been part of the API, if we merge this we should include a note about this in the release notes.
* Support for pickling loggers (yay!) (cloudpipe/cloudpickle#96)
* BUG: Fix crash when pickling dynamic class cycles. (cloudpipe/cloudpickle#102)
## How was this patch tested?
Existing PySpark unit tests + the unit tests from the cloudpickle project on their own.
Author: Holden Karau <holden@us.ibm.com>
Author: Kyle Kelley <rgbkrk@gmail.com>
Closes#18734 from holdenk/holden-rgbkrk-cloudpickle-upgrades.
Add Python API for `FeatureHasher` transformer.
## How was this patch tested?
New doc test.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#18970 from MLnick/SPARK-21468-pyspark-hasher.
## What changes were proposed in this pull request?
Adds the recently added `summary` method to the python dataframe interface.
## How was this patch tested?
Additional inline doctests.
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#18762 from aray/summary-py.
Proposed changes:
* Clarify the type error that `Column.substr()` gives.
Test plan:
* Tested this manually.
* Test code:
```python
from pyspark.sql.functions import col, lit
spark.createDataFrame([['nick']], schema=['name']).select(col('name').substr(0, lit(1)))
```
* Before:
```
TypeError: Can not mix the type
```
* After:
```
TypeError: startPos and length must be the same type. Got <class 'int'> and
<class 'pyspark.sql.column.Column'>, respectively.
```
Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Closes#18926 from nchammas/SPARK-21712-substr-type-error.
## What changes were proposed in this pull request?
JIRA issue: https://issues.apache.org/jira/browse/SPARK-21658
Add default None for value in `na.replace` since `Dataframe.replace` and `DataframeNaFunctions.replace` are alias.
The default values are the same now.
```
>>> df = sqlContext.createDataFrame([('Alice', 10, 80.0)])
>>> df.replace({"Alice": "a"}).first()
Row(_1=u'a', _2=10, _3=80.0)
>>> df.na.replace({"Alice": "a"}).first()
Row(_1=u'a', _2=10, _3=80.0)
```
## How was this patch tested?
Existing tests.
cc viirya
Author: byakuinss <grace.chinhanyu@gmail.com>
Closes#18895 from byakuinss/SPARK-21658.
## What changes were proposed in this pull request?
Implemented a Python-only persistence framework for pipelines containing stages that cannot be saved using Java.
## How was this patch tested?
Created a custom Python-only UnaryTransformer, included it in a Pipeline, and saved/loaded the pipeline. The loaded pipeline was compared against the original using _compare_pipelines() in tests.py.
Author: Ajay Saini <ajays725@gmail.com>
Closes#18888 from ajaysaini725/PythonPipelines.
## What changes were proposed in this pull request?
Currently `df.na.replace("*", Map[String, String]("NULL" -> null))` will produce exception.
This PR enables passing null/None as value in the replacement map in DataFrame.replace().
Note that the replacement map keys and values should still be the same type, while the values can have a mix of null/None and that type.
This PR enables following operations for example:
`df.na.replace("*", Map[String, String]("NULL" -> null))`(scala)
`df.na.replace("*", Map[Any, Any](60 -> null, 70 -> 80))`(scala)
`df.na.replace('Alice', None)`(python)
`df.na.replace([10, 20])`(python, replacing with None is by default)
One use case could be: I want to replace all the empty strings with null/None because they were incorrectly generated and then drop all null/None data
`df.na.replace("*", Map("" -> null)).na.drop()`(scala)
`df.replace(u'', None).dropna()`(python)
## How was this patch tested?
Scala unit test.
Python doctest and unit test.
Author: bravo-zhang <mzhang1230@gmail.com>
Closes#18820 from bravo-zhang/spark-14932.
## What changes were proposed in this pull request?
This modification increases the timeout for `serveIterator` (which is not dynamically configurable). This fixes timeout issues in pyspark when using `collect` and similar functions, in cases where Python may take more than a couple seconds to connect.
See https://issues.apache.org/jira/browse/SPARK-21551
## How was this patch tested?
Ran the tests.
cc rxin
Author: peay <peay@protonmail.com>
Closes#18752 from peay/spark-21551.
## What changes were proposed in this pull request?
Update breeze to 0.13.1 for an emergency bugfix in strong wolfe line search
https://github.com/scalanlp/breeze/pull/651
## How was this patch tested?
N/A
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#18797 from WeichenXu123/update-breeze.
## What changes were proposed in this pull request?
PySpark GLR ```model.summary``` should return a printable representation by calling Scala ```toString```.
## How was this patch tested?
```
from pyspark.ml.regression import GeneralizedLinearRegression
dataset = spark.read.format("libsvm").load("data/mllib/sample_linear_regression_data.txt")
glr = GeneralizedLinearRegression(family="gaussian", link="identity", maxIter=10, regParam=0.3)
model = glr.fit(dataset)
model.summary
```
Before this PR:
After this PR:
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18870 from yanboliang/spark-19270.
## What changes were proposed in this pull request?
Added DefaultParamsWriteable, DefaultParamsReadable, DefaultParamsWriter, and DefaultParamsReader to Python to support Python-only persistence of Json-serializable parameters.
## How was this patch tested?
Instantiated an estimator with Json-serializable parameters (ex. LogisticRegression), saved it using the added helper functions, and loaded it back, and compared it to the original instance to make sure it is the same. This test was both done in the Python REPL and implemented in the unit tests.
Note to reviewers: there are a few excess comments that I left in the code for clarity but will remove before the code is merged to master.
Author: Ajay Saini <ajays725@gmail.com>
Closes#18742 from ajaysaini725/PythonPersistenceHelperFunctions.
## What changes were proposed in this pull request?
Enhanced some existing documentation
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Mac <maclockard@gmail.com>
Closes#18710 from maclockard/maclockard-patch-1.
## What changes were proposed in this pull request?
Implemented UnaryTransformer in Python.
## How was this patch tested?
This patch was tested by creating a MockUnaryTransformer class in the unit tests that extends UnaryTransformer and testing that the transform function produced correct output.
Author: Ajay Saini <ajays725@gmail.com>
Closes#18746 from ajaysaini725/AddPythonUnaryTransformer.
## What changes were proposed in this pull request?
Python API for Constrained Logistic Regression based on #17922 , thanks for the original contribution from zero323 .
## How was this patch tested?
Unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18759 from yanboliang/SPARK-20601.
## What changes were proposed in this pull request?
When using PySpark broadcast variables in a multi-threaded environment, `SparkContext._pickled_broadcast_vars` becomes a shared resource. A race condition can occur when broadcast variables that are pickled from one thread get added to the shared ` _pickled_broadcast_vars` and become part of the python command from another thread. This PR introduces a thread-safe pickled registry using thread local storage so that when python command is pickled (causing the broadcast variable to be pickled and added to the registry) each thread will have their own view of the pickle registry to retrieve and clear the broadcast variables used.
## How was this patch tested?
Added a unit test that causes this race condition using another thread.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#18695 from BryanCutler/pyspark-bcast-threadsafe-SPARK-12717.
## What changes were proposed in this pull request?
GBTs inherit from HasStepSize & LInearSVC/Binarizer from HasThreshold
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Author: Ruifeng Zheng <ruifengz@foxmail.com>
Closes#18612 from zhengruifeng/override_HasXXX.
## What changes were proposed in this pull request?
This PR proposes `StructType.fieldNames` that returns a copy of a field name list rather than a (undocumented) `StructType.names`.
There are two points here:
- API consistency with Scala/Java
- Provide a safe way to get the field names. Manipulating these might cause unexpected behaviour as below:
```python
from pyspark.sql.types import *
struct = StructType([StructField("f1", StringType(), True)])
names = struct.names
del names[0]
spark.createDataFrame([{"f1": 1}], struct).show()
```
```
...
java.lang.IllegalStateException: Input row doesn't have expected number of values required by the schema. 1 fields are required while 0 values are provided.
at org.apache.spark.sql.execution.python.EvaluatePython$.fromJava(EvaluatePython.scala:138)
at org.apache.spark.sql.SparkSession$$anonfun$6.apply(SparkSession.scala:741)
at org.apache.spark.sql.SparkSession$$anonfun$6.apply(SparkSession.scala:741)
...
```
## How was this patch tested?
Added tests in `python/pyspark/sql/tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18618 from HyukjinKwon/SPARK-20090.
## What changes were proposed in this pull request?
add `setWeightCol` method for OneVsRest.
`weightCol` is ignored if classifier doesn't inherit HasWeightCol trait.
## How was this patch tested?
+ [x] add an unit test.
Author: Yan Facai (颜发才) <facai.yan@gmail.com>
Closes#18554 from facaiy/BUG/oneVsRest_missing_weightCol.
## What changes were proposed in this pull request?
This is a refactoring of `ArrowConverters` and related classes.
1. Refactor `ColumnWriter` as `ArrowWriter`.
2. Add `ArrayType` and `StructType` support.
3. Refactor `ArrowConverters` to skip intermediate `ArrowRecordBatch` creation.
## How was this patch tested?
Added some tests and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18655 from ueshin/issues/SPARK-21440.
### What changes were proposed in this pull request?
Like [Hive UDFType](https://hive.apache.org/javadocs/r2.0.1/api/org/apache/hadoop/hive/ql/udf/UDFType.html), we should allow users to add the extra flags for ScalaUDF and JavaUDF too. _stateful_/_impliesOrder_ are not applicable to our Scala UDF. Thus, we only add the following two flags.
- deterministic: Certain optimizations should not be applied if UDF is not deterministic. Deterministic UDF returns same result each time it is invoked with a particular input. This determinism just needs to hold within the context of a query.
When the deterministic flag is not correctly set, the results could be wrong.
For ScalaUDF in Dataset APIs, users can call the following extra APIs for `UserDefinedFunction` to make the corresponding changes.
- `nonDeterministic`: Updates UserDefinedFunction to non-deterministic.
Also fixed the Java UDF name loss issue.
Will submit a separate PR for `distinctLike` for UDAF
### How was this patch tested?
Added test cases for both ScalaUDF
Author: gatorsmile <gatorsmile@gmail.com>
Author: Wenchen Fan <cloud0fan@gmail.com>
Closes#17848 from gatorsmile/udfRegister.
## What changes were proposed in this pull request?
After SPARK-12661, I guess we officially dropped Python 2.6 support. It looks there are few places missing this notes.
I grepped "Python 2.6" and "python 2.6" and the results were below:
```
./core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala: // Unpickle array.array generated by Python 2.6
./docs/index.md:Note that support for Java 7, Python 2.6 and old Hadoop versions before 2.6.5 were removed as of Spark 2.2.0.
./docs/rdd-programming-guide.md:Spark {{site.SPARK_VERSION}} works with Python 2.6+ or Python 3.4+. It can use the standard CPython interpreter,
./docs/rdd-programming-guide.md:Note that support for Python 2.6 is deprecated as of Spark 2.0.0, and may be removed in Spark 2.2.0.
./python/pyspark/context.py: warnings.warn("Support for Python 2.6 is deprecated as of Spark 2.0.0")
./python/pyspark/ml/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/mllib/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/serializers.py: # On Python 2.6, we can't write bytearrays to streams, so we need to convert them
./python/pyspark/sql/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/streaming/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/tests.py: # NOTE: dict is used instead of collections.Counter for Python 2.6
./python/pyspark/tests.py: # NOTE: dict is used instead of collections.Counter for Python 2.6
```
This PR only proposes to change visible changes as below:
```
./docs/rdd-programming-guide.md:Spark {{site.SPARK_VERSION}} works with Python 2.6+ or Python 3.4+. It can use the standard CPython interpreter,
./docs/rdd-programming-guide.md:Note that support for Python 2.6 is deprecated as of Spark 2.0.0, and may be removed in Spark 2.2.0.
./python/pyspark/context.py: warnings.warn("Support for Python 2.6 is deprecated as of Spark 2.0.0")
```
This one is already correct:
```
./docs/index.md:Note that support for Java 7, Python 2.6 and old Hadoop versions before 2.6.5 were removed as of Spark 2.2.0.
```
## How was this patch tested?
```bash
grep -r "Python 2.6" .
grep -r "python 2.6" .
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18682 from HyukjinKwon/minor-python.26.
## What changes were proposed in this pull request?
This is the reopen of https://github.com/apache/spark/pull/14198, with merge conflicts resolved.
ueshin Could you please take a look at my code?
Fix bugs about types that result an array of null when creating DataFrame using python.
Python's array.array have richer type than python itself, e.g. we can have `array('f',[1,2,3])` and `array('d',[1,2,3])`. Codes in spark-sql and pyspark didn't take this into consideration which might cause a problem that you get an array of null values when you have `array('f')` in your rows.
A simple code to reproduce this bug is:
```
from pyspark import SparkContext
from pyspark.sql import SQLContext,Row,DataFrame
from array import array
sc = SparkContext()
sqlContext = SQLContext(sc)
row1 = Row(floatarray=array('f',[1,2,3]), doublearray=array('d',[1,2,3]))
rows = sc.parallelize([ row1 ])
df = sqlContext.createDataFrame(rows)
df.show()
```
which have output
```
+---------------+------------------+
| doublearray| floatarray|
+---------------+------------------+
|[1.0, 2.0, 3.0]|[null, null, null]|
+---------------+------------------+
```
## How was this patch tested?
New test case added
Author: Xiang Gao <qasdfgtyuiop@gmail.com>
Author: Gao, Xiang <qasdfgtyuiop@gmail.com>
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18444 from zasdfgbnm/fix_array_infer.
## What changes were proposed in this pull request?
Added functionality for CrossValidator and TrainValidationSplit to persist nested estimators such as OneVsRest. Also added CrossValidator and TrainValidation split persistence to pyspark.
## How was this patch tested?
Performed both cross validation and train validation split with a one vs. rest estimator and tested read/write functionality of the estimator parameter maps required by these meta-algorithms.
Author: Ajay Saini <ajays725@gmail.com>
Closes#18428 from ajaysaini725/MetaAlgorithmPersistNestedEstimators.
## What changes were proposed in this pull request?
This PR proposes to avoid `__name__` in the tuple naming the attributes assigned directly from the wrapped function to the wrapper function, and use `self._name` (`func.__name__` or `obj.__class__.name__`).
After SPARK-19161, we happened to break callable objects as UDFs in Python as below:
```python
from pyspark.sql import functions
class F(object):
def __call__(self, x):
return x
foo = F()
udf = functions.udf(foo)
```
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/sql/functions.py", line 2142, in udf
return _udf(f=f, returnType=returnType)
File ".../spark/python/pyspark/sql/functions.py", line 2133, in _udf
return udf_obj._wrapped()
File ".../spark/python/pyspark/sql/functions.py", line 2090, in _wrapped
functools.wraps(self.func)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/functools.py", line 33, in update_wrapper
setattr(wrapper, attr, getattr(wrapped, attr))
AttributeError: F instance has no attribute '__name__'
```
This worked in Spark 2.1:
```python
from pyspark.sql import functions
class F(object):
def __call__(self, x):
return x
foo = F()
udf = functions.udf(foo)
spark.range(1).select(udf("id")).show()
```
```
+-----+
|F(id)|
+-----+
| 0|
+-----+
```
**After**
```python
from pyspark.sql import functions
class F(object):
def __call__(self, x):
return x
foo = F()
udf = functions.udf(foo)
spark.range(1).select(udf("id")).show()
```
```
+-----+
|F(id)|
+-----+
| 0|
+-----+
```
_In addition, we also happened to break partial functions as below_:
```python
from pyspark.sql import functions
from functools import partial
partial_func = partial(lambda x: x, x=1)
udf = functions.udf(partial_func)
```
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/sql/functions.py", line 2154, in udf
return _udf(f=f, returnType=returnType)
File ".../spark/python/pyspark/sql/functions.py", line 2145, in _udf
return udf_obj._wrapped()
File ".../spark/python/pyspark/sql/functions.py", line 2099, in _wrapped
functools.wraps(self.func, assigned=assignments)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/functools.py", line 33, in update_wrapper
setattr(wrapper, attr, getattr(wrapped, attr))
AttributeError: 'functools.partial' object has no attribute '__module__'
```
This worked in Spark 2.1:
```python
from pyspark.sql import functions
from functools import partial
partial_func = partial(lambda x: x, x=1)
udf = functions.udf(partial_func)
spark.range(1).select(udf()).show()
```
```
+---------+
|partial()|
+---------+
| 1|
+---------+
```
**After**
```python
from pyspark.sql import functions
from functools import partial
partial_func = partial(lambda x: x, x=1)
udf = functions.udf(partial_func)
spark.range(1).select(udf()).show()
```
```
+---------+
|partial()|
+---------+
| 1|
+---------+
```
## How was this patch tested?
Unit tests in `python/pyspark/sql/tests.py` and manual tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18615 from HyukjinKwon/callable-object.
## What changes were proposed in this pull request?
```RFormula``` should handle invalid for both features and label column.
#18496 only handle invalid values in features column. This PR add handling invalid values for label column and test cases.
## How was this patch tested?
Add test cases.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18613 from yanboliang/spark-20307.
## What changes were proposed in this pull request?
1, HasHandleInvaild support override
2, Make QuantileDiscretizer/Bucketizer/StringIndexer/RFormula inherit from HasHandleInvalid
## How was this patch tested?
existing tests
[JIRA](https://issues.apache.org/jira/browse/SPARK-18619)
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#18582 from zhengruifeng/heritate_HasHandleInvalid.
## What changes were proposed in this pull request?
This PR deals with four points as below:
- Reuse existing DDL parser APIs rather than reimplementing within PySpark
- Support DDL formatted string, `field type, field type`.
- Support case-insensitivity for parsing.
- Support nested data types as below:
**Before**
```
>>> spark.createDataFrame([[[1]]], "struct<a: struct<b: int>>").show()
...
ValueError: The strcut field string format is: 'field_name:field_type', but got: a: struct<b: int>
```
```
>>> spark.createDataFrame([[[1]]], "a: struct<b: int>").show()
...
ValueError: The strcut field string format is: 'field_name:field_type', but got: a: struct<b: int>
```
```
>>> spark.createDataFrame([[1]], "a int").show()
...
ValueError: Could not parse datatype: a int
```
**After**
```
>>> spark.createDataFrame([[[1]]], "struct<a: struct<b: int>>").show()
+---+
| a|
+---+
|[1]|
+---+
```
```
>>> spark.createDataFrame([[[1]]], "a: struct<b: int>").show()
+---+
| a|
+---+
|[1]|
+---+
```
```
>>> spark.createDataFrame([[1]], "a int").show()
+---+
| a|
+---+
| 1|
+---+
```
## How was this patch tested?
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18590 from HyukjinKwon/deduplicate-python-ddl.
## What changes were proposed in this pull request?
This PR proposes to simply ignore the results in examples that are timezone-dependent in `unix_timestamp` and `from_unixtime`.
```
Failed example:
time_df.select(unix_timestamp('dt', 'yyyy-MM-dd').alias('unix_time')).collect()
Expected:
[Row(unix_time=1428476400)]
Got:unix_timestamp
[Row(unix_time=1428418800)]
```
```
Failed example:
time_df.select(from_unixtime('unix_time').alias('ts')).collect()
Expected:
[Row(ts=u'2015-04-08 00:00:00')]
Got:
[Row(ts=u'2015-04-08 16:00:00')]
```
## How was this patch tested?
Manually tested and `./run-tests --modules pyspark-sql`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18597 from HyukjinKwon/SPARK-20456.
## What changes were proposed in this pull request?
At example of repartitionAndSortWithinPartitions at rdd.py, third argument should be True or False.
I proposed fix of example code.
## How was this patch tested?
* I rename test_repartitionAndSortWithinPartitions to test_repartitionAndSortWIthinPartitions_asc to specify boolean argument.
* I added test_repartitionAndSortWithinPartitions_desc to test False pattern at third argument.
(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.
Author: chie8842 <chie8842@gmail.com>
Closes#18586 from chie8842/SPARK-21358.
## What changes were proposed in this pull request?
Integrate Apache Arrow with Spark to increase performance of `DataFrame.toPandas`. This has been done by using Arrow to convert data partitions on the executor JVM to Arrow payload byte arrays where they are then served to the Python process. The Python DataFrame can then collect the Arrow payloads where they are combined and converted to a Pandas DataFrame. Data types except complex, date, timestamp, and decimal are currently supported, otherwise an `UnsupportedOperation` exception is thrown.
Additions to Spark include a Scala package private method `Dataset.toArrowPayload` that will convert data partitions in the executor JVM to `ArrowPayload`s as byte arrays so they can be easily served. A package private class/object `ArrowConverters` that provide data type mappings and conversion routines. In Python, a private method `DataFrame._collectAsArrow` is added to collect Arrow payloads and a SQLConf "spark.sql.execution.arrow.enable" can be used in `toPandas()` to enable using Arrow (uses the old conversion by default).
## How was this patch tested?
Added a new test suite `ArrowConvertersSuite` that will run tests on conversion of Datasets to Arrow payloads for supported types. The suite will generate a Dataset and matching Arrow JSON data, then the dataset is converted to an Arrow payload and finally validated against the JSON data. This will ensure that the schema and data has been converted correctly.
Added PySpark tests to verify the `toPandas` method is producing equal DataFrames with and without pyarrow. A roundtrip test to ensure the pandas DataFrame produced by pyspark is equal to a one made directly with pandas.
Author: Bryan Cutler <cutlerb@gmail.com>
Author: Li Jin <ice.xelloss@gmail.com>
Author: Li Jin <li.jin@twosigma.com>
Author: Wes McKinney <wes.mckinney@twosigma.com>
Closes#18459 from BryanCutler/toPandas_with_arrow-SPARK-13534.
## What changes were proposed in this pull request?
This PR supports schema in a DDL formatted string for `from_json` in R/Python and `dapply` and `gapply` in R, which are commonly used and/or consistent with Scala APIs.
Additionally, this PR exposes `structType` in R to allow working around in other possible corner cases.
**Python**
`from_json`
```python
from pyspark.sql.functions import from_json
data = [(1, '''{"a": 1}''')]
df = spark.createDataFrame(data, ("key", "value"))
df.select(from_json(df.value, "a INT").alias("json")).show()
```
**R**
`from_json`
```R
df <- sql("SELECT named_struct('name', 'Bob') as people")
df <- mutate(df, people_json = to_json(df$people))
head(select(df, from_json(df$people_json, "name STRING")))
```
`structType.character`
```R
structType("a STRING, b INT")
```
`dapply`
```R
dapply(createDataFrame(list(list(1.0)), "a"), function(x) {x}, "a DOUBLE")
```
`gapply`
```R
gapply(createDataFrame(list(list(1.0)), "a"), "a", function(key, x) { x }, "a DOUBLE")
```
## How was this patch tested?
Doc tests for `from_json` in Python and unit tests `test_sparkSQL.R` in R.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18498 from HyukjinKwon/SPARK-21266.
## What changes were proposed in this pull request?
This adds documentation to many functions in pyspark.sql.functions.py:
`upper`, `lower`, `reverse`, `unix_timestamp`, `from_unixtime`, `rand`, `randn`, `collect_list`, `collect_set`, `lit`
Add units to the trigonometry functions.
Renames columns in datetime examples to be more informative.
Adds links between some functions.
## How was this patch tested?
`./dev/lint-python`
`python python/pyspark/sql/functions.py`
`./python/run-tests.py --module pyspark-sql`
Author: Michael Patterson <map222@gmail.com>
Closes#17865 from map222/spark-20456.
## What changes were proposed in this pull request?
Currently `ArrayConstructor` handles an array of typecode `'l'` as `int` when converting Python object in Python 2 into Java object, so if the value is larger than `Integer.MAX_VALUE` or smaller than `Integer.MIN_VALUE` then the overflow occurs.
```python
import array
data = [Row(longarray=array.array('l', [-9223372036854775808, 0, 9223372036854775807]))]
df = spark.createDataFrame(data)
df.show(truncate=False)
```
```
+----------+
|longarray |
+----------+
|[0, 0, -1]|
+----------+
```
This should be:
```
+----------------------------------------------+
|longarray |
+----------------------------------------------+
|[-9223372036854775808, 0, 9223372036854775807]|
+----------------------------------------------+
```
## How was this patch tested?
Added a test and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18553 from ueshin/issues/SPARK-21327.
## What changes were proposed in this pull request?
Support register Java UDAFs in PySpark so that user can use Java UDAF in PySpark. Besides that I also add api in `UDFRegistration`
## How was this patch tested?
Unit test is added
Author: Jeff Zhang <zjffdu@apache.org>
Closes#17222 from zjffdu/SPARK-19439.
## What changes were proposed in this pull request?
Add offset to PySpark in GLM as in #16699.
## How was this patch tested?
Python test
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#18534 from actuaryzhang/pythonOffset.
## What changes were proposed in this pull request?
**Context**
While reviewing https://github.com/apache/spark/pull/17227, I realised here we type-dispatch per record. The PR itself is fine in terms of performance as is but this prints a prefix, `"obj"` in exception message as below:
```
from pyspark.sql.types import *
schema = StructType([StructField('s', IntegerType(), nullable=False)])
spark.createDataFrame([["1"]], schema)
...
TypeError: obj.s: IntegerType can not accept object '1' in type <type 'str'>
```
I suggested to get rid of this but during investigating this, I realised my approach might bring a performance regression as it is a hot path.
Only for SPARK-19507 and https://github.com/apache/spark/pull/17227, It needs more changes to cleanly get rid of the prefix and I rather decided to fix both issues together.
**Propersal**
This PR tried to
- get rid of per-record type dispatch as we do in many code paths in Scala so that it improves the performance (roughly ~25% improvement) - SPARK-21296
This was tested with a simple code `spark.createDataFrame(range(1000000), "int")`. However, I am quite sure the actual improvement in practice is larger than this, in particular, when the schema is complicated.
- improve error message in exception describing field information as prose - SPARK-19507
## How was this patch tested?
Manually tested and unit tests were added in `python/pyspark/sql/tests.py`.
Benchmark - codes: https://gist.github.com/HyukjinKwon/c3397469c56cb26c2d7dd521ed0bc5a3
Error message - codes: https://gist.github.com/HyukjinKwon/b1b2c7f65865444c4a8836435100e398
**Before**
Benchmark:
- Results: https://gist.github.com/HyukjinKwon/4a291dab45542106301a0c1abcdca924
Error message
- Results: https://gist.github.com/HyukjinKwon/57b1916395794ce924faa32b14a3fe19
**After**
Benchmark
- Results: https://gist.github.com/HyukjinKwon/21496feecc4a920e50c4e455f836266e
Error message
- Results: https://gist.github.com/HyukjinKwon/7a494e4557fe32a652ce1236e504a395Closes#17227
Author: hyukjinkwon <gurwls223@gmail.com>
Author: David Gingrich <david@textio.com>
Closes#18521 from HyukjinKwon/python-type-dispatch.
## What changes were proposed in this pull request?
Currently, it throws a NPE when missing columns but join type is speicified in join at PySpark as below:
```python
spark.conf.set("spark.sql.crossJoin.enabled", "false")
spark.range(1).join(spark.range(1), how="inner").show()
```
```
Traceback (most recent call last):
...
py4j.protocol.Py4JJavaError: An error occurred while calling o66.join.
: java.lang.NullPointerException
at org.apache.spark.sql.Dataset.join(Dataset.scala:931)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
...
```
```python
spark.conf.set("spark.sql.crossJoin.enabled", "true")
spark.range(1).join(spark.range(1), how="inner").show()
```
```
...
py4j.protocol.Py4JJavaError: An error occurred while calling o84.join.
: java.lang.NullPointerException
at org.apache.spark.sql.Dataset.join(Dataset.scala:931)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
...
```
This PR suggests to follow Scala's one as below:
```scala
scala> spark.conf.set("spark.sql.crossJoin.enabled", "false")
scala> spark.range(1).join(spark.range(1), Seq.empty[String], "inner").show()
```
```
org.apache.spark.sql.AnalysisException: Detected cartesian product for INNER join between logical plans
Range (0, 1, step=1, splits=Some(8))
and
Range (0, 1, step=1, splits=Some(8))
Join condition is missing or trivial.
Use the CROSS JOIN syntax to allow cartesian products between these relations.;
...
```
```scala
scala> spark.conf.set("spark.sql.crossJoin.enabled", "true")
scala> spark.range(1).join(spark.range(1), Seq.empty[String], "inner").show()
```
```
+---+---+
| id| id|
+---+---+
| 0| 0|
+---+---+
```
**After**
```python
spark.conf.set("spark.sql.crossJoin.enabled", "false")
spark.range(1).join(spark.range(1), how="inner").show()
```
```
Traceback (most recent call last):
...
pyspark.sql.utils.AnalysisException: u'Detected cartesian product for INNER join between logical plans\nRange (0, 1, step=1, splits=Some(8))\nand\nRange (0, 1, step=1, splits=Some(8))\nJoin condition is missing or trivial.\nUse the CROSS JOIN syntax to allow cartesian products between these relations.;'
```
```python
spark.conf.set("spark.sql.crossJoin.enabled", "true")
spark.range(1).join(spark.range(1), how="inner").show()
```
```
+---+---+
| id| id|
+---+---+
| 0| 0|
+---+---+
```
## How was this patch tested?
Added tests in `python/pyspark/sql/tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18484 from HyukjinKwon/SPARK-21264.
## What changes were proposed in this pull request?
This PR is to maintain API parity with changes made in SPARK-17498 to support a new option
'keep' in StringIndexer to handle unseen labels or NULL values with PySpark.
Note: This is updated version of #17237 , the primary author of this PR is VinceShieh .
## How was this patch tested?
Unit tests.
Author: VinceShieh <vincent.xie@intel.com>
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18453 from yanboliang/spark-19852.
## What changes were proposed in this pull request?
1, make param support non-final with `finalFields` option
2, generate `HasSolver` with `finalFields = false`
3, override `solver` in LiR, GLR, and make MLPC inherit `HasSolver`
## How was this patch tested?
existing tests
Author: Ruifeng Zheng <ruifengz@foxmail.com>
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#16028 from zhengruifeng/param_non_final.
## What changes were proposed in this pull request?
This pr supported a DDL-formatted string in `DataStreamReader.schema`.
This fix could make users easily define a schema without importing the type classes.
For example,
```scala
scala> spark.readStream.schema("col0 INT, col1 DOUBLE").load("/tmp/abc").printSchema()
root
|-- col0: integer (nullable = true)
|-- col1: double (nullable = true)
```
## How was this patch tested?
Added tests in `DataStreamReaderWriterSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18373 from HyukjinKwon/SPARK-20431.
## What changes were proposed in this pull request?
Integrate Apache Arrow with Spark to increase performance of `DataFrame.toPandas`. This has been done by using Arrow to convert data partitions on the executor JVM to Arrow payload byte arrays where they are then served to the Python process. The Python DataFrame can then collect the Arrow payloads where they are combined and converted to a Pandas DataFrame. All non-complex data types are currently supported, otherwise an `UnsupportedOperation` exception is thrown.
Additions to Spark include a Scala package private method `Dataset.toArrowPayloadBytes` that will convert data partitions in the executor JVM to `ArrowPayload`s as byte arrays so they can be easily served. A package private class/object `ArrowConverters` that provide data type mappings and conversion routines. In Python, a public method `DataFrame.collectAsArrow` is added to collect Arrow payloads and an optional flag in `toPandas(useArrow=False)` to enable using Arrow (uses the old conversion by default).
## How was this patch tested?
Added a new test suite `ArrowConvertersSuite` that will run tests on conversion of Datasets to Arrow payloads for supported types. The suite will generate a Dataset and matching Arrow JSON data, then the dataset is converted to an Arrow payload and finally validated against the JSON data. This will ensure that the schema and data has been converted correctly.
Added PySpark tests to verify the `toPandas` method is producing equal DataFrames with and without pyarrow. A roundtrip test to ensure the pandas DataFrame produced by pyspark is equal to a one made directly with pandas.
Author: Bryan Cutler <cutlerb@gmail.com>
Author: Li Jin <ice.xelloss@gmail.com>
Author: Li Jin <li.jin@twosigma.com>
Author: Wes McKinney <wes.mckinney@twosigma.com>
Closes#15821 from BryanCutler/wip-toPandas_with_arrow-SPARK-13534.
## What changes were proposed in this pull request?
Currently we convert a spark DataFrame to Pandas Dataframe by `pd.DataFrame.from_records`. It infers the data type from the data and doesn't respect the spark DataFrame Schema. This PR fixes it.
## How was this patch tested?
a new regression test
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Wenchen Fan <wenchen@databricks.com>
Author: Wenchen Fan <cloud0fan@gmail.com>
Closes#18378 from cloud-fan/to_pandas.
## What changes were proposed in this pull request?
Add Python wrappers for `o.a.s.sql.functions.explode_outer` and `o.a.s.sql.functions.posexplode_outer`.
## How was this patch tested?
Unit tests, doctests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#18049 from zero323/SPARK-20830.
## What changes were proposed in this pull request?
Extend setJobDescription to PySpark and JavaSpark APIs
SPARK-21125
## How was this patch tested?
Testing was done by running a local Spark shell on the built UI. I originally had added a unit test but the PySpark context cannot easily access the Scala Spark Context's private variable with the Job Description key so I omitted the test, due to the simplicity of this addition.
Also ran the existing tests.
# Misc
This contribution is my original work and that I license the work to the project under the project's open source license.
Author: sjarvie <sjarvie@uber.com>
Closes#18332 from sjarvie/add_python_set_job_description.
## What changes were proposed in this pull request?
LinearSVC should use its own threshold param, rather than the shared one, since it applies to rawPrediction instead of probability. This PR changes the param in the Scala, Python and R APIs.
## How was this patch tested?
New unit test to make sure the threshold can be set to any Double value.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#18151 from jkbradley/ml-2.2-linearsvc-cleanup.
## What changes were proposed in this pull request?
Fix some typo of the document.
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Xianyang Liu <xianyang.liu@intel.com>
Closes#18350 from ConeyLiu/fixtypo.
## What changes were proposed in this pull request?
This fix tries to address the issue in SPARK-19975 where we
have `map_keys` and `map_values` functions in SQL yet there
is no Python equivalent functions.
This fix adds `map_keys` and `map_values` functions to Python.
## How was this patch tested?
This fix is tested manually (See Python docs for examples).
Author: Yong Tang <yong.tang.github@outlook.com>
Closes#17328 from yongtang/SPARK-19975.
### What changes were proposed in this pull request?
The current option name `wholeFile` is misleading for CSV users. Currently, it is not representing a record per file. Actually, one file could have multiple records. Thus, we should rename it. Now, the proposal is `multiLine`.
### How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#18202 from gatorsmile/renameCVSOption.
## What changes were proposed in this pull request?
Document Dataset.union is resolution by position, not by name, since this has been a confusing point for a lot of users.
## How was this patch tested?
N/A - doc only change.
Author: Reynold Xin <rxin@databricks.com>
Closes#18256 from rxin/SPARK-21042.
## What changes were proposed in this pull request?
Allow fill/replace of NAs with booleans, both in Python and Scala
## How was this patch tested?
Unit tests, doctests
This PR is original work from me and I license this work to the Spark project
Author: Ruben Berenguel Montoro <ruben@mostlymaths.net>
Author: Ruben Berenguel <ruben@mostlymaths.net>
Closes#18164 from rberenguel/SPARK-19732-fillna-bools.
### What changes were proposed in this pull request?
This PR does the following tasks:
- Added since
- Added the Python API
- Added test cases
### How was this patch tested?
Added test cases to both Scala and Python
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18147 from gatorsmile/createOrReplaceGlobalTempView.
## What changes were proposed in this pull request?
PySpark supports stringIndexerOrderType in RFormula as in #17967.
## How was this patch tested?
docstring test
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#18122 from actuaryzhang/PythonRFormula.
Now that Structured Streaming has been out for several Spark release and has large production use cases, the `Experimental` label is no longer appropriate. I've left `InterfaceStability.Evolving` however, as I think we may make a few changes to the pluggable Source & Sink API in Spark 2.3.
Author: Michael Armbrust <michael@databricks.com>
Closes#18065 from marmbrus/streamingGA.
## What changes were proposed in this pull request?
Expose numPartitions (expert) param of PySpark FPGrowth.
## How was this patch tested?
+ [x] Pass all unit tests.
Author: Yan Facai (颜发才) <facai.yan@gmail.com>
Closes#18058 from facaiy/ENH/pyspark_fpg_add_num_partition.
## What changes were proposed in this pull request?
Follow-up for #17218, some minor fix for PySpark ```FPGrowth```.
## How was this patch tested?
Existing UT.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18089 from yanboliang/spark-19281.
## What changes were proposed in this pull request?
Fixed TypeError with python3 and numpy 1.12.1. Numpy's `reshape` no longer takes floats as arguments as of 1.12. Also, python3 uses float division for `/`, we should be using `//` to ensure that `_dataWithBiasSize` doesn't get set to a float.
## How was this patch tested?
Existing tests run using python3 and numpy 1.12.
Author: Bago Amirbekian <bago@databricks.com>
Closes#18081 from MrBago/BF-py3floatbug.
## What changes were proposed in this pull request?
- Fix incorrect tests for `_check_thresholds`.
- Move test to `ParamTests`.
## How was this patch tested?
Unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#18085 from zero323/SPARK-20631-FOLLOW-UP.
## What changes were proposed in this pull request?
Add test cases for PR-18062
## How was this patch tested?
The existing UT
Author: Peng <peng.meng@intel.com>
Closes#18068 from mpjlu/moreTest.
Changes:
pyspark.ml Estimators can take either a list of param maps or a dict of params. This change allows the CrossValidator and TrainValidationSplit Estimators to pass through lists of param maps to the underlying estimators so that those estimators can handle parallelization when appropriate (eg distributed hyper parameter tuning).
Testing:
Existing unit tests.
Author: Bago Amirbekian <bago@databricks.com>
Closes#18077 from MrBago/delegate_params.
