### What changes were proposed in this pull request?
The str of `CapaturedException` is now returned by str(self.desc) rather than repr(self.desc), which is more user-friendly. It also handles unicode under python2 specially.
### Why are the changes needed?
This is an improvement, and makes exception more human readable in python side.
### Does this PR introduce any user-facing change?
Before this pr, select `中文字段` throws exception something likes below:
```
Traceback (most recent call last):
File "/Users/advancedxy/code_workspace/github/spark/python/pyspark/sql/tests/test_utils.py", line 34, in test_capture_user_friendly_exception
raise e
AnalysisException: u"cannot resolve '`\u4e2d\u6587\u5b57\u6bb5`' given input columns: []; line 1 pos 7;\n'Project ['\u4e2d\u6587\u5b57\u6bb5]\n+- OneRowRelation\n"
```
after this pr:
```
Traceback (most recent call last):
File "/Users/advancedxy/code_workspace/github/spark/python/pyspark/sql/tests/test_utils.py", line 34, in test_capture_user_friendly_exception
raise e
AnalysisException: cannot resolve '`中文字段`' given input columns: []; line 1 pos 7;
'Project ['中文字段]
+- OneRowRelation
```
### How was this patch
Add a new test to verify unicode are correctly converted and manual checks for thrown exceptions.
This pr's credits should go to uncleGen and is based on https://github.com/apache/spark/pull/17267Closes#25814 from advancedxy/python_exception_19926_and_21045.
Authored-by: Xianjin YE <advancedxy@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
reorganize the packages of DS v2 interfaces/classes:
1. `org.spark.sql.connector.catalog`: put `TableCatalog`, `Table` and other related interfaces/classes
2. `org.spark.sql.connector.expression`: put `Expression`, `Transform` and other related interfaces/classes
3. `org.spark.sql.connector.read`: put `ScanBuilder`, `Scan` and other related interfaces/classes
4. `org.spark.sql.connector.write`: put `WriteBuilder`, `BatchWrite` and other related interfaces/classes
### Why are the changes needed?
Data Source V2 has evolved a lot. It's a bit weird that `Expression` is in `org.spark.sql.catalog.v2` and `Table` is in `org.spark.sql.sources.v2`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#25700 from cloud-fan/package.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to allow `bytes` as an acceptable type for binary type for `createDataFrame`.
### Why are the changes needed?
`bytes` is a standard type for binary in Python. This should be respected in PySpark side.
### Does this PR introduce any user-facing change?
Yes, _when specified type is binary_, we will allow `bytes` as a binary type. Previously this was not allowed in both Python 2 and Python 3 as below:
```python
spark.createDataFrame([[b"abcd"]], "col binary")
```
in Python 3
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/session.py", line 787, in createDataFrame
rdd, schema = self._createFromLocal(map(prepare, data), schema)
File "/.../spark/python/pyspark/sql/session.py", line 442, in _createFromLocal
data = list(data)
File "/.../spark/python/pyspark/sql/session.py", line 769, in prepare
verify_func(obj)
File "/.../forked/spark/python/pyspark/sql/types.py", line 1403, in verify
verify_value(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1384, in verify_struct
verifier(v)
File "/.../spark/python/pyspark/sql/types.py", line 1403, in verify
verify_value(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1397, in verify_default
verify_acceptable_types(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1282, in verify_acceptable_types
% (dataType, obj, type(obj))))
TypeError: field col: BinaryType can not accept object b'abcd' in type <class 'bytes'>
```
in Python 2:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/session.py", line 787, in createDataFrame
rdd, schema = self._createFromLocal(map(prepare, data), schema)
File "/.../spark/python/pyspark/sql/session.py", line 442, in _createFromLocal
data = list(data)
File "/.../spark/python/pyspark/sql/session.py", line 769, in prepare
verify_func(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1403, in verify
verify_value(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1384, in verify_struct
verifier(v)
File "/.../spark/python/pyspark/sql/types.py", line 1403, in verify
verify_value(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1397, in verify_default
verify_acceptable_types(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1282, in verify_acceptable_types
% (dataType, obj, type(obj))))
TypeError: field col: BinaryType can not accept object 'abcd' in type <type 'str'>
```
So, it won't break anything.
### How was this patch tested?
Unittests were added and also manually tested as below.
```bash
./run-tests --python-executables=python2,python3 --testnames "pyspark.sql.tests.test_serde"
```
Closes#25749 from HyukjinKwon/SPARK-29041.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
- Remove SQLContext.createExternalTable and Catalog.createExternalTable, deprecated in favor of createTable since 2.2.0, plus tests of deprecated methods
- Remove HiveContext, deprecated in 2.0.0, in favor of `SparkSession.builder.enableHiveSupport`
- Remove deprecated KinesisUtils.createStream methods, plus tests of deprecated methods, deprecate in 2.2.0
- Remove deprecated MLlib (not Spark ML) linear method support, mostly utility constructors and 'train' methods, and associated docs. This includes methods in LinearRegression, LogisticRegression, Lasso, RidgeRegression. These have been deprecated since 2.0.0
- Remove deprecated Pyspark MLlib linear method support, including LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD
- Remove 'runs' argument in KMeans.train() method, which has been a no-op since 2.0.0
- Remove deprecated ChiSqSelector isSorted protected method
- Remove deprecated 'yarn-cluster' and 'yarn-client' master argument in favor of 'yarn' and deploy mode 'cluster', etc
Notes:
- I was not able to remove deprecated DataFrameReader.json(RDD) in favor of DataFrameReader.json(Dataset); the former was deprecated in 2.2.0, but, it is still needed to support Pyspark's .json() method, which can't use a Dataset.
- Looks like SQLContext.createExternalTable was not actually deprecated in Pyspark, but, almost certainly was meant to be? Catalog.createExternalTable was.
- I afterwards noted that the toDegrees, toRadians functions were almost removed fully in SPARK-25908, but Felix suggested keeping just the R version as they hadn't been technically deprecated. I'd like to revisit that. Do we really want the inconsistency? I'm not against reverting it again, but then that implies leaving SQLContext.createExternalTable just in Pyspark too, which seems weird.
- I *kept* LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD, RidgeRegressionWithSGD in Pyspark, though deprecated, as it is hard to remove them (still used by StreamingLogisticRegressionWithSGD?) and they are not fully removed in Scala. Maybe should not have been deprecated.
### Why are the changes needed?
Deprecated items are easiest to remove in a major release, so we should do so as much as possible for Spark 3. This does not target items deprecated 'recently' as of Spark 2.3, which is still 18 months old.
### Does this PR introduce any user-facing change?
Yes, in that deprecated items are removed from some public APIs.
### How was this patch tested?
Existing tests.
Closes#25684 from srowen/SPARK-28980.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
`DataFrameReader.json()` accepts a partition column that is of numeric, date or timestamp type, according to the implementation in `JDBCRelation.scala`. Update the scaladoc accordingly, to match the documentation in `sql-data-sources-jdbc.md` too.
### Why are the changes needed?
scaladoc is incorrect.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A
Closes#25687 from srowen/SPARK-28977.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The Experimental and Evolving annotations are both (like Unstable) used to express that a an API may change. However there are many things in the code that have been marked that way since even Spark 1.x. Per the dev thread, anything introduced at or before Spark 2.3.0 is pretty much 'stable' in that it would not change without a deprecation cycle. Therefore I'd like to remove most of these annotations. And, remove the `:: Experimental ::` scaladoc tag too. And likewise for Python, R.
The changes below can be summarized as:
- Generally, anything introduced at or before Spark 2.3.0 has been unmarked as neither Evolving nor Experimental
- Obviously experimental items like DSv2, Barrier mode, ExperimentalMethods are untouched
- I _did_ unmark a few MLlib classes introduced in 2.4, as I am quite confident they're not going to change (e.g. KolmogorovSmirnovTest, PowerIterationClustering)
It's a big change to review, so I'd suggest scanning the list of _files_ changed to see if any area seems like it should remain partly experimental and examine those.
### Why are the changes needed?
Many of these annotations are incorrect; the APIs are de facto stable. Leaving them also makes legitimate usages of the annotations less meaningful.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#25558 from srowen/SPARK-28855.
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 match the test with branch-2.4. See https://github.com/apache/spark/pull/25593#discussion_r318109047
Seems using `SparkSession.builder` with Spark conf possibly affects other tests.
### Why are the changes needed?
To match with branch-2.4 and to make easier to backport.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Test was fixed.
Closes#25603 from HyukjinKwon/SPARK-28881-followup.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Make `spark.sql.crossJoin.enabled` default value true
### Why are the changes needed?
For implicit cross join, we can set up a watchdog to cancel it if running for a long time.
When "spark.sql.crossJoin.enabled" is false, because `CheckCartesianProducts` is implemented in logical plan stage, it may generate some mismatching error which may confuse end user:
* it's done in logical phase, so we may fail queries that can be executed via broadcast join, which is very fast.
* if we move the check to the physical phase, then a query may success at the beginning, and begin to fail when the table size gets larger (other people insert data to the table). This can be quite confusing.
* the CROSS JOIN syntax doesn't work well if join reorder happens.
* some non-equi-join will generate plan using cartesian product, but `CheckCartesianProducts` do not detect it and raise error.
So that in order to address this in simpler way, we can turn off showing this cross-join error by default.
For reference, I list some cases raising mismatching error here:
Providing:
```
spark.range(2).createOrReplaceTempView("sm1") // can be broadcast
spark.range(50000000).createOrReplaceTempView("bg1") // cannot be broadcast
spark.range(60000000).createOrReplaceTempView("bg2") // cannot be broadcast
```
1) Some join could be convert to broadcast nested loop join, but CheckCartesianProducts raise error. e.g.
```
select sm1.id, bg1.id from bg1 join sm1 where sm1.id < bg1.id
```
2) Some join will run by CartesianJoin but CheckCartesianProducts DO NOT raise error. e.g.
```
select bg1.id, bg2.id from bg1 join bg2 where bg1.id < bg2.id
```
### Does this PR introduce any user-facing change?
### How was this patch tested?
Closes#25520 from WeichenXu123/SPARK-28621.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to add a test case for:
```bash
./bin/pyspark --conf spark.driver.maxResultSize=1m
spark.conf.set("spark.sql.execution.arrow.enabled",True)
```
```python
spark.range(10000000).toPandas()
```
```
Empty DataFrame
Columns: [id]
Index: []
```
which can result in partial results (see https://github.com/apache/spark/pull/25593#issuecomment-525153808). This regression was found between Spark 2.3 and Spark 2.4, and accidentally fixed.
### Why are the changes needed?
To prevent the same regression in the future.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Test was added.
Closes#25594 from HyukjinKwon/SPARK-28881.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The parameters doc string of the function format_string was changed from _col_, _d_ to _format_, _cols_ which is what the actual function declaration states
### Why are the changes needed?
The parameters stated by the documentation was inaccurate
### Does this PR introduce any user-facing change?
Yes.
**BEFORE**
**AFTER**
### How was this patch tested?
N/A: documentation only
<!--
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If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future.
If tests were not added, please describe why they were not added and/or why it was difficult to add.
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Closes#25506 from darrentirto/SPARK-28777.
Authored-by: darrentirto <darrentirto@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
A GROUPED_AGG pandas python udf can't work, if without group by clause, like `select udf(id) from table`.
This doesn't match with aggregate function like sum, count..., and also dataset API like `df.agg(udf(df['id']))`.
When we parse a udf (or an aggregate function) like that from SQL syntax, it is known as a function in a project. `GlobalAggregates` rule in analysis makes such project as aggregate, by looking for aggregate expressions. At the moment, we should also look for GROUPED_AGG pandas python udf.
## How was this patch tested?
Added tests.
Closes#25352 from viirya/SPARK-28422.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The mapping of Spark schema to Avro schema is many-to-many. (See https://spark.apache.org/docs/latest/sql-data-sources-avro.html#supported-types-for-spark-sql---avro-conversion)
The default schema mapping might not be exactly what users want. For example, by default, a "string" column is always written as "string" Avro type, but users might want to output the column as "enum" Avro type.
With PR https://github.com/apache/spark/pull/21847, Spark supports user-specified schema in the batch writer.
For the function `to_avro`, we should support user-specified output schema as well.
## How was this patch tested?
Unit test.
Closes#25419 from gengliangwang/to_avro.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Right now, batch DataFrame always changes the schema to nullable automatically (See this line: 325bc8e9c6/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala (L399)). But streaming file source is missing this.
This PR updates the streaming file source schema to force it be nullable. I also added a flag `spark.sql.streaming.fileSource.schema.forceNullable` to disable this change since some users may rely on the old behavior.
## How was this patch tested?
The new unit test.
Closes#25382 from zsxwing/SPARK-28651.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR proposes to use `AtomicReference` so that parent and child threads can access to the same file block holder.
Python UDF expressions are turned to a plan and then it launches a separate thread to consume the input iterator. In the separate child thread, the iterator sets `InputFileBlockHolder.set` before the parent does which the parent thread is unable to read later.
1. In this separate child thread, if it happens to call `InputFileBlockHolder.set` first without initialization of the parent's thread local (which is done when the `ThreadLocal.get()` is first called), the child thread seems calling its own `initialValue` to initialize.
2. After that, the parent calls its own `initialValue` to initializes at the first call of `ThreadLocal.get()`.
3. Both now have two different references. Updating at child isn't reflected to parent.
This PR fixes it via initializing parent's thread local with `AtomicReference` for file status so that they can be used in each task, and children thread's update is reflected.
I also tried to explain this a bit more at https://github.com/apache/spark/pull/24958#discussion_r297203041.
## How was this patch tested?
Manually tested and unittest was added.
Closes#24958 from HyukjinKwon/SPARK-28153.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
PySpark worker daemon reads from stdin the worker PIDs to kill. 1bb60ab839/python/pyspark/daemon.py (L127)
However, the worker process is a forked process from the worker daemon process and we didn't close stdin on the child after fork. This means the child and user program can read stdin as well, which blocks daemon from receiving the PID to kill. This can cause issues because the task reaper might detect the task was not terminated and eventually kill the JVM.
This PR fix this by redirecting the standard input of the forked child to devnull.
## How was this patch tested?
Manually test.
In `pyspark`, run:
```
import subprocess
def task(_):
subprocess.check_output(["cat"])
sc.parallelize(range(1), 1).mapPartitions(task).count()
```
Before:
The job will get stuck and press Ctrl+C to exit the job but the python worker process do not exit.
After:
The job finish correctly. The "cat" print nothing (because the dummay stdin is "/dev/null").
The python worker process exit normally.
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#25138 from WeichenXu123/SPARK-26175.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to use `uuuu` for years instead of `yyyy` in date/timestamp patterns without the era pattern `G` (https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html). **Parsing/formatting of positive years (current era) will be the same.** The difference is in formatting negative years belong to previous era - BC (Before Christ).
I replaced the `yyyy` pattern by `uuuu` everywhere except:
1. Test, Suite & Benchmark. Existing tests must work as is.
2. `SimpleDateFormat` because it doesn't support the `uuuu` pattern.
3. Comments and examples (except comments related to already replaced patterns).
Before the changes, the year of common era `100` and the year of BC era `-99`, showed similarly as `100`. After the changes negative years will be formatted with the `-` sign.
Before:
```Scala
scala> Seq(java.time.LocalDate.of(-99, 1, 1)).toDF().show
+----------+
| value|
+----------+
|0100-01-01|
+----------+
```
After:
```Scala
scala> Seq(java.time.LocalDate.of(-99, 1, 1)).toDF().show
+-----------+
| value|
+-----------+
|-0099-01-01|
+-----------+
```
## How was this patch tested?
By existing test suites, and added tests for negative years to `DateFormatterSuite` and `TimestampFormatterSuite`.
Closes#25230 from MaxGekk/year-pattern-uuuu.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This adds simple check for `count` argument:
- If it is a `Column` we apply `_to_java_column` before invoking JVM counterpart
- Otherwise we proceed as before.
## How was this patch tested?
Manual testing.
Closes#25193 from zero323/SPARK-28278.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In the following python code
```
df.write.mode("overwrite").insertInto("table")
```
```insertInto``` ignores ```mode("overwrite")``` and appends by default.
## How was this patch tested?
Add Unit test.
Closes#25175 from huaxingao/spark-28411.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to convert options values to strings by using `to_str()` for the following functions: `from_csv()`, `to_csv()`, `from_json()`, `to_json()`, `schema_of_csv()` and `schema_of_json()`. This will make handling of function options consistent to option handling in `DataFrameReader`/`DataFrameWriter`.
For example:
```Python
df.select(from_csv(df.value, "s string", {'ignoreLeadingWhiteSpace': True})
```
## How was this patch tested?
Added an example for `from_csv()` which was tested by:
```Shell
./python/run-tests --testnames pyspark.sql.functions
```
Closes#25182 from MaxGekk/options_to_str.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
It fixes a flaky test:
```
ERROR [0.164s]: test_query_execution_listener_on_collect (pyspark.sql.tests.test_dataframe.QueryExecutionListenerTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jenkins/python/pyspark/sql/tests/test_dataframe.py", line 758, in test_query_execution_listener_on_collect
"The callback from the query execution listener should be called after 'collect'")
AssertionError: The callback from the query execution listener should be called after 'collect'
```
Seems it can be failed because the event was somehow delayed but checked first.
## How was this patch tested?
Manually.
Closes#25177 from HyukjinKwon/SPARK-28418.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This upgraded to a newer version of Pyrolite. Most updates [1] in the newer version are for dotnot. For java, it includes a bug fix to Unpickler regarding cleaning up Unpickler memo, and support of protocol 5.
After upgrading, we can remove the fix at SPARK-27629 for the bug in Unpickler.
[1] https://github.com/irmen/Pyrolite/compare/pyrolite-4.23...master
## How was this patch tested?
Manually tested on Python 3.6 in local on existing tests.
Closes#25143 from viirya/upgrade-pyrolite.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
There is a bug in `ExtractPythonUDFs` that produces wrong result attributes. It causes a failure when using `PythonUDF`s among multiple child plans, e.g., join. An example is using `PythonUDF`s in join condition.
```python
>>> left = spark.createDataFrame([Row(a=1, a1=1, a2=1), Row(a=2, a1=2, a2=2)])
>>> right = spark.createDataFrame([Row(b=1, b1=1, b2=1), Row(b=1, b1=3, b2=1)])
>>> f = udf(lambda a: a, IntegerType())
>>> df = left.join(right, [f("a") == f("b"), left.a1 == right.b1])
>>> df.collect()
19/07/10 12:20:49 ERROR Executor: Exception in task 5.0 in stage 0.0 (TID 5)
java.lang.ArrayIndexOutOfBoundsException: 1
at org.apache.spark.sql.catalyst.expressions.GenericInternalRow.genericGet(rows.scala:201)
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getAs(rows.scala:35)
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.isNullAt(rows.scala:36)
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.isNullAt$(rows.scala:36)
at org.apache.spark.sql.catalyst.expressions.GenericInternalRow.isNullAt(rows.scala:195)
at org.apache.spark.sql.catalyst.expressions.JoinedRow.isNullAt(JoinedRow.scala:70)
...
```
## How was this patch tested?
Added test.
Closes#25091 from viirya/SPARK-28323.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
This PR adds some tests converted from `pgSQL/case.sql'` to test UDFs. Please see contribution guide of this umbrella ticket - [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
This PR also contains two minor fixes:
1. Change name of Scala UDF from `UDF:name(...)` to `name(...)` to be consistent with Python'
2. Fix Scala UDF at `IntegratedUDFTestUtils.scala ` to handle `null` in strings.
<details><summary>Diff comparing to 'pgSQL/case.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/pgSQL/case.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-case.sql.out
index fa078d16d6d..55bef64338f 100644
--- a/sql/core/src/test/resources/sql-tests/results/pgSQL/case.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-case.sql.out
-115,7 +115,7 struct<>
-- !query 13
SELECT '3' AS `One`,
CASE
- WHEN 1 < 2 THEN 3
+ WHEN CAST(udf(1 < 2) AS boolean) THEN 3
END AS `Simple WHEN`
-- !query 13 schema
struct<One:string,Simple WHEN:int>
-126,10 +126,10 struct<One:string,Simple WHEN:int>
-- !query 14
SELECT '<NULL>' AS `One`,
CASE
- WHEN 1 > 2 THEN 3
+ WHEN 1 > 2 THEN udf(3)
END AS `Simple default`
-- !query 14 schema
-struct<One:string,Simple default:int>
+struct<One:string,Simple default:string>
-- !query 14 output
<NULL> NULL
-137,17 +137,17 struct<One:string,Simple default:int>
-- !query 15
SELECT '3' AS `One`,
CASE
- WHEN 1 < 2 THEN 3
- ELSE 4
+ WHEN udf(1) < 2 THEN udf(3)
+ ELSE udf(4)
END AS `Simple ELSE`
-- !query 15 schema
-struct<One:string,Simple ELSE:int>
+struct<One:string,Simple ELSE:string>
-- !query 15 output
3 3
-- !query 16
-SELECT '4' AS `One`,
+SELECT udf('4') AS `One`,
CASE
WHEN 1 > 2 THEN 3
ELSE 4
-159,10 +159,10 struct<One:string,ELSE default:int>
-- !query 17
-SELECT '6' AS `One`,
+SELECT udf('6') AS `One`,
CASE
- WHEN 1 > 2 THEN 3
- WHEN 4 < 5 THEN 6
+ WHEN CAST(udf(1 > 2) AS boolean) THEN 3
+ WHEN udf(4) < 5 THEN 6
ELSE 7
END AS `Two WHEN with default`
-- !query 17 schema
-173,7 +173,7 struct<One:string,Two WHEN with default:int>
-- !query 18
SELECT '7' AS `None`,
- CASE WHEN rand() < 0 THEN 1
+ CASE WHEN rand() < udf(0) THEN 1
END AS `NULL on no matches`
-- !query 18 schema
struct<None:string,NULL on no matches:int>
-182,36 +182,36 struct<None:string,NULL on no matches:int>
-- !query 19
-SELECT CASE WHEN 1=0 THEN 1/0 WHEN 1=1 THEN 1 ELSE 2/0 END
+SELECT CASE WHEN CAST(udf(1=0) AS boolean) THEN 1/0 WHEN 1=1 THEN 1 ELSE 2/0 END
-- !query 19 schema
-struct<CASE WHEN (1 = 0) THEN (CAST(1 AS DOUBLE) / CAST(0 AS DOUBLE)) WHEN (1 = 1) THEN CAST(1 AS DOUBLE) ELSE (CAST(2 AS DOUBLE) / CAST(0 AS DOUBLE)) END:double>
+struct<CASE WHEN CAST(udf((1 = 0)) AS BOOLEAN) THEN (CAST(1 AS DOUBLE) / CAST(0 AS DOUBLE)) WHEN (1 = 1) THEN CAST(1 AS DOUBLE) ELSE (CAST(2 AS DOUBLE) / CAST(0 AS DOUBLE)) END:double>
-- !query 19 output
1.0
-- !query 20
-SELECT CASE 1 WHEN 0 THEN 1/0 WHEN 1 THEN 1 ELSE 2/0 END
+SELECT CASE 1 WHEN 0 THEN 1/udf(0) WHEN 1 THEN 1 ELSE 2/0 END
-- !query 20 schema
-struct<CASE WHEN (1 = 0) THEN (CAST(1 AS DOUBLE) / CAST(0 AS DOUBLE)) WHEN (1 = 1) THEN CAST(1 AS DOUBLE) ELSE (CAST(2 AS DOUBLE) / CAST(0 AS DOUBLE)) END:double>
+struct<CASE WHEN (1 = 0) THEN (CAST(1 AS DOUBLE) / CAST(CAST(udf(0) AS DOUBLE) AS DOUBLE)) WHEN (1 = 1) THEN CAST(1 AS DOUBLE) ELSE (CAST(2 AS DOUBLE) / CAST(0 AS DOUBLE)) END:double>
-- !query 20 output
1.0
-- !query 21
-SELECT CASE WHEN i > 100 THEN 1/0 ELSE 0 END FROM case_tbl
+SELECT CASE WHEN i > 100 THEN udf(1/0) ELSE udf(0) END FROM case_tbl
-- !query 21 schema
-struct<CASE WHEN (i > 100) THEN (CAST(1 AS DOUBLE) / CAST(0 AS DOUBLE)) ELSE CAST(0 AS DOUBLE) END:double>
+struct<CASE WHEN (i > 100) THEN udf((cast(1 as double) / cast(0 as double))) ELSE udf(0) END:string>
-- !query 21 output
-0.0
-0.0
-0.0
-0.0
+0
+0
+0
+0
-- !query 22
-SELECT CASE 'a' WHEN 'a' THEN 1 ELSE 2 END
+SELECT CASE 'a' WHEN 'a' THEN udf(1) ELSE udf(2) END
-- !query 22 schema
-struct<CASE WHEN (a = a) THEN 1 ELSE 2 END:int>
+struct<CASE WHEN (a = a) THEN udf(1) ELSE udf(2) END:string>
-- !query 22 output
1
-283,7 +283,7 big
-- !query 27
-SELECT * FROM CASE_TBL WHERE COALESCE(f,i) = 4
+SELECT * FROM CASE_TBL WHERE udf(COALESCE(f,i)) = 4
-- !query 27 schema
struct<i:int,f:double>
-- !query 27 output
-291,7 +291,7 struct<i:int,f:double>
-- !query 28
-SELECT * FROM CASE_TBL WHERE NULLIF(f,i) = 2
+SELECT * FROM CASE_TBL WHERE udf(NULLIF(f,i)) = 2
-- !query 28 schema
struct<i:int,f:double>
-- !query 28 output
-299,10 +299,10 struct<i:int,f:double>
-- !query 29
-SELECT COALESCE(a.f, b.i, b.j)
+SELECT udf(COALESCE(a.f, b.i, b.j))
FROM CASE_TBL a, CASE2_TBL b
-- !query 29 schema
-struct<coalesce(f, CAST(i AS DOUBLE), CAST(j AS DOUBLE)):double>
+struct<udf(coalesce(f, cast(i as double), cast(j as double))):string>
-- !query 29 output
-30.3
-30.3
-332,8 +332,8 struct<coalesce(f, CAST(i AS DOUBLE), CAST(j AS DOUBLE)):double>
-- !query 30
SELECT *
- FROM CASE_TBL a, CASE2_TBL b
- WHERE COALESCE(a.f, b.i, b.j) = 2
+ FROM CASE_TBL a, CASE2_TBL b
+ WHERE udf(COALESCE(a.f, b.i, b.j)) = 2
-- !query 30 schema
struct<i:int,f:double,i:int,j:int>
-- !query 30 output
-342,7 +342,7 struct<i:int,f:double,i:int,j:int>
-- !query 31
-SELECT '' AS Five, NULLIF(a.i,b.i) AS `NULLIF(a.i,b.i)`,
+SELECT udf('') AS Five, NULLIF(a.i,b.i) AS `NULLIF(a.i,b.i)`,
NULLIF(b.i, 4) AS `NULLIF(b.i,4)`
FROM CASE_TBL a, CASE2_TBL b
-- !query 31 schema
-377,7 +377,7 struct<Five:string,NULLIF(a.i,b.i):int,NULLIF(b.i,4):int>
-- !query 32
SELECT '' AS `Two`, *
FROM CASE_TBL a, CASE2_TBL b
- WHERE COALESCE(f,b.i) = 2
+ WHERE CAST(udf(COALESCE(f,b.i) = 2) AS boolean)
-- !query 32 schema
struct<Two:string,i:int,f:double,i:int,j:int>
-- !query 32 output
-388,15 +388,15 struct<Two:string,i:int,f:double,i:int,j:int>
-- !query 33
SELECT CASE
(CASE vol('bar')
- WHEN 'foo' THEN 'it was foo!'
- WHEN vol(null) THEN 'null input'
+ WHEN udf('foo') THEN 'it was foo!'
+ WHEN udf(vol(null)) THEN 'null input'
WHEN 'bar' THEN 'it was bar!' END
)
- WHEN 'it was foo!' THEN 'foo recognized'
- WHEN 'it was bar!' THEN 'bar recognized'
- ELSE 'unrecognized' END
+ WHEN udf('it was foo!') THEN 'foo recognized'
+ WHEN 'it was bar!' THEN udf('bar recognized')
+ ELSE 'unrecognized' END AS col
-- !query 33 schema
-struct<CASE WHEN (CASE WHEN (UDF:vol(bar) = foo) THEN it was foo! WHEN (UDF:vol(bar) = UDF:vol(null)) THEN null input WHEN (UDF:vol(bar) = bar) THEN it was bar! END = it was foo!) THEN foo recognized WHEN (CASE WHEN (UDF:vol(bar) = foo) THEN it was foo! WHEN (UDF:vol(bar) = UDF:vol(null)) THEN null input WHEN (UDF:vol(bar) = bar) THEN it was bar! END = it was bar!) THEN bar recognized ELSE unrecognized END:string>
+struct<col:string>
-- !query 33 output
bar recognized
```
</p>
</details>
https://github.com/apache/spark/pull/25069 contains the same minor fixes as it's required to write the tests.
## How was this patch tested?
Tested as guided in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
Closes#25070 from HyukjinKwon/SPARK-28273.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR proposes to rename `mapPartitionsInPandas` to `mapInPandas` with a separate evaluation type .
Had an offline discussion with rxin, mengxr and cloud-fan
The reason is basically:
1. `SCALAR_ITER` doesn't make sense with `mapPartitionsInPandas`.
2. It cannot share the same Pandas UDF, for instance, at `select` and `mapPartitionsInPandas` unlike `GROUPED_AGG` because iterator's return type is different.
3. `mapPartitionsInPandas` -> `mapInPandas` - see https://github.com/apache/spark/pull/25044#issuecomment-508298552 and https://github.com/apache/spark/pull/25044#issuecomment-508299764
Renaming `SCALAR_ITER` as `MAP_ITER` is abandoned due to 2. reason.
For `XXX_ITER`, it might have to have a different interface in the future if we happen to add other versions of them. But this is an orthogonal topic with `mapPartitionsInPandas`.
## How was this patch tested?
Existing tests should cover.
Closes#25044 from HyukjinKwon/SPARK-28198.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In Python 2.7 with latest PyArrow and Pandas, the error message seems a bit different with Python 3. This PR simply fixes the test.
```
======================================================================
FAIL: test_createDataFrame_with_incorrect_schema (pyspark.sql.tests.test_arrow.ArrowTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/.../spark/python/pyspark/sql/tests/test_arrow.py", line 275, in test_createDataFrame_with_incorrect_schema
self.spark.createDataFrame(pdf, schema=wrong_schema)
AssertionError: "integer.*required.*got.*str" does not match "('Exception thrown when converting pandas.Series (object) to Arrow Array (int32). It can be caused by overflows or other unsafe conversions warned by Arrow. Arrow safe type check can be disabled by using SQL config `spark.sql.execution.pandas.arrowSafeTypeConversion`.', ArrowTypeError('an integer is required',))"
======================================================================
FAIL: test_createDataFrame_with_incorrect_schema (pyspark.sql.tests.test_arrow.EncryptionArrowTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/.../spark/python/pyspark/sql/tests/test_arrow.py", line 275, in test_createDataFrame_with_incorrect_schema
self.spark.createDataFrame(pdf, schema=wrong_schema)
AssertionError: "integer.*required.*got.*str" does not match "('Exception thrown when converting pandas.Series (object) to Arrow Array (int32). It can be caused by overflows or other unsafe conversions warned by Arrow. Arrow safe type check can be disabled by using SQL config `spark.sql.execution.pandas.arrowSafeTypeConversion`.', ArrowTypeError('an integer is required',))"
```
## How was this patch tested?
Manually tested.
```
cd python
./run-tests --python-executables=python --modules pyspark-sql
```
Closes#25042 from HyukjinKwon/SPARK-28240.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR proposes to add `mapPartitionsInPandas` API to DataFrame by using existing `SCALAR_ITER` as below:
1. Filtering via setting the column
```python
from pyspark.sql.functions import pandas_udf, PandasUDFType
df = spark.createDataFrame([(1, 21), (2, 30)], ("id", "age"))
pandas_udf(df.schema, PandasUDFType.SCALAR_ITER)
def filter_func(iterator):
for pdf in iterator:
yield pdf[pdf.id == 1]
df.mapPartitionsInPandas(filter_func).show()
```
```
+---+---+
| id|age|
+---+---+
| 1| 21|
+---+---+
```
2. `DataFrame.loc`
```python
from pyspark.sql.functions import pandas_udf, PandasUDFType
import pandas as pd
df = spark.createDataFrame([['aa'], ['bb'], ['cc'], ['aa'], ['aa'], ['aa']], ["value"])
pandas_udf(df.schema, PandasUDFType.SCALAR_ITER)
def filter_func(iterator):
for pdf in iterator:
yield pdf.loc[pdf.value.str.contains('^a'), :]
df.mapPartitionsInPandas(filter_func).show()
```
```
+-----+
|value|
+-----+
| aa|
| aa|
| aa|
| aa|
+-----+
```
3. `pandas.melt`
```python
from pyspark.sql.functions import pandas_udf, PandasUDFType
import pandas as pd
df = spark.createDataFrame(
pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
'B': {0: 1, 1: 3, 2: 5},
'C': {0: 2, 1: 4, 2: 6}}))
pandas_udf("A string, variable string, value long", PandasUDFType.SCALAR_ITER)
def filter_func(iterator):
for pdf in iterator:
import pandas as pd
yield pd.melt(pdf, id_vars=['A'], value_vars=['B', 'C'])
df.mapPartitionsInPandas(filter_func).show()
```
```
+---+--------+-----+
| A|variable|value|
+---+--------+-----+
| a| B| 1|
| a| C| 2|
| b| B| 3|
| b| C| 4|
| c| B| 5|
| c| C| 6|
+---+--------+-----+
```
The current limitation of `SCALAR_ITER` is that it doesn't allow different length of result, which is pretty critical in practice - for instance, we cannot simply filter by using Pandas APIs but we merely just map N to N. This PR allows map N to M like flatMap.
This API mimics the way of `mapPartitions` but keeps API shape of `SCALAR_ITER` by allowing different results.
### How does this PR implement?
This PR adds mimics both `dapply` with Arrow optimization and Grouped Map Pandas UDF. At Python execution side, it reuses existing `SCALAR_ITER` code path.
Therefore, externally, we don't introduce any new type of Pandas UDF but internally we use another evaluation type code `205` (`SQL_MAP_PANDAS_ITER_UDF`).
This approach is similar with Pandas' Windows function implementation with Grouped Aggregation Pandas UDF functions - internally we have `203` (`SQL_WINDOW_AGG_PANDAS_UDF`) but externally we just share the same `GROUPED_AGG`.
## How was this patch tested?
Manually tested and unittests were added.
Closes#24997 from HyukjinKwon/scalar-udf-iter.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add docstring/doctest for `SCALAR_ITER` Pandas UDF. I explicitly mentioned that per-partition execution is an implementation detail, not guaranteed. I will submit another PR to add the same to user guide, just to keep this PR minimal.
I didn't add "doctest: +SKIP" in the first commit so it is easy to test locally.
cc: HyukjinKwon gatorsmile icexelloss BryanCutler WeichenXu123
## How was this patch tested?
doctest
Closes#25005 from mengxr/SPARK-28056.2.
Authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Closes the generator when Python UDFs stop early.
### Manually verification on pandas iterator UDF and mapPartitions
```python
from pyspark.sql import SparkSession
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.functions import col, udf
from pyspark.taskcontext import TaskContext
import time
import os
spark.conf.set('spark.sql.execution.arrow.maxRecordsPerBatch', '1')
spark.conf.set('spark.sql.pandas.udf.buffer.size', '4')
pandas_udf("int", PandasUDFType.SCALAR_ITER)
def fi1(it):
try:
for batch in it:
yield batch + 100
time.sleep(1.0)
except BaseException as be:
print("Debug: exception raised: " + str(type(be)))
raise be
finally:
open("/tmp/000001.tmp", "a").close()
df1 = spark.range(10).select(col('id').alias('a')).repartition(1)
# will see log Debug: exception raised: <class 'GeneratorExit'>
# and file "/tmp/000001.tmp" generated.
df1.select(col('a'), fi1('a')).limit(2).collect()
def mapper(it):
try:
for batch in it:
yield batch
except BaseException as be:
print("Debug: exception raised: " + str(type(be)))
raise be
finally:
open("/tmp/000002.tmp", "a").close()
df2 = spark.range(10000000).repartition(1)
# will see log Debug: exception raised: <class 'GeneratorExit'>
# and file "/tmp/000002.tmp" generated.
df2.rdd.mapPartitions(mapper).take(2)
```
## How was this patch tested?
Unit test added.
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#24986 from WeichenXu123/pandas_iter_udf_limit.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Currently with `toLocalIterator()` and `toPandas()` with Arrow enabled, if the Spark job being run in the background serving thread errors, it will be caught and sent to Python through the PySpark serializer.
This is not the ideal solution because it is only catch a SparkException, it won't handle an error that occurs in the serializer, and each method has to have it's own special handling to propagate the error.
This PR instead returns the Python Server object along with the serving port and authentication info, so that it allows the Python caller to join with the serving thread. During the call to join, the serving thread Future is completed either successfully or with an exception. In the latter case, the exception will be propagated to Python through the Py4j call.
## How was this patch tested?
Existing tests
Closes#24834 from BryanCutler/pyspark-propagate-server-error-SPARK-27992.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
This patch removes `fillna(0)` when creating ArrowBatch from a pandas Series.
With `fillna(0)`, the original code would turn a timestamp type into object type, which pyarrow will complain later:
```
>>> s = pd.Series([pd.NaT, pd.Timestamp('2015-01-01')])
>>> s.dtypes
dtype('<M8[ns]')
>>> s.fillna(0)
0 0
1 2015-01-01 00:00:00
dtype: object
```
## How was this patch tested?
Added `test_timestamp_nat`
Closes#24844 from icexelloss/SPARK-28003-arrow-nat.
Authored-by: Li Jin <ice.xelloss@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Currently, pretty skipped message added by f7435bec6a mechanism seems not working when xmlrunner is installed apparently.
This PR fixes two things:
1. When `xmlrunner` is installed, seems `xmlrunner` does not respect `vervosity` level in unittests (default is level 1).
So the output looks as below
```
Running tests...
----------------------------------------------------------------------
SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS
----------------------------------------------------------------------
```
So it is not caught by our message detection mechanism.
2. If we manually set the `vervocity` level to `xmlrunner`, it prints messages as below:
```
test_mixed_udf (pyspark.sql.tests.test_pandas_udf_scalar.ScalarPandasUDFTests) ... SKIP (0.000s)
test_mixed_udf_and_sql (pyspark.sql.tests.test_pandas_udf_scalar.ScalarPandasUDFTests) ... SKIP (0.000s)
...
```
This is different in our Jenkins machine:
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.23.2 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.23.2 must be installed; however, it was not found.'
...
```
Note that last `SKIP` is different. This PR fixes the regular expression to catch `SKIP` case as well.
## How was this patch tested?
Manually tested.
**Before:**
```
Starting test(python2.7): pyspark....
Finished test(python2.7): pyspark.... (0s)
...
Tests passed in 562 seconds
========================================================================
...
```
**After:**
```
Starting test(python2.7): pyspark....
Finished test(python2.7): pyspark.... (48s) ... 93 tests were skipped
...
Tests passed in 560 seconds
Skipped tests pyspark.... with python2.7:
pyspark...(...) ... SKIP (0.000s)
...
========================================================================
...
```
Closes#24927 from HyukjinKwon/SPARK-28130.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When running FlatMapGroupsInPandasExec or AggregateInPandasExec the shuffle uses a default number of partitions of 200 in "spark.sql.shuffle.partitions". If the data is small, e.g. in testing, many of the partitions will be empty but are treated just the same.
This PR checks the `mapPartitionsInternal` iterator to be non-empty before calling `ArrowPythonRunner` to start computation on the iterator.
## How was this patch tested?
Existing tests. Ran the following benchmarks a simple example where most partitions are empty:
```python
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.types import *
df = spark.createDataFrame(
[(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
("id", "v"))
pandas_udf("id long, v double", PandasUDFType.GROUPED_MAP)
def normalize(pdf):
v = pdf.v
return pdf.assign(v=(v - v.mean()) / v.std())
df.groupby("id").apply(normalize).count()
```
**Before**
```
In [4]: %timeit df.groupby("id").apply(normalize).count()
1.58 s ± 62.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [5]: %timeit df.groupby("id").apply(normalize).count()
1.52 s ± 29.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [6]: %timeit df.groupby("id").apply(normalize).count()
1.52 s ± 37.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
**After this Change**
```
In [2]: %timeit df.groupby("id").apply(normalize).count()
646 ms ± 89.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [3]: %timeit df.groupby("id").apply(normalize).count()
408 ms ± 84.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [4]: %timeit df.groupby("id").apply(normalize).count()
381 ms ± 29.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
Closes#24926 from BryanCutler/pyspark-pandas_udf-map-agg-skip-empty-parts-SPARK-28128.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When using `DROPMALFORMED` mode, corrupted records aren't dropped if malformed columns aren't read. This behavior is due to CSV parser column pruning. Current doc of `DROPMALFORMED` doesn't mention the effect of column pruning. Users will be confused by the fact that `DROPMALFORMED` mode doesn't work as expected.
Column pruning also affects other modes. This is a doc improvement to add a note to doc of `mode` to explain it.
## How was this patch tested?
N/A. This is just doc change.
Closes#24894 from viirya/SPARK-28058.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This increases the minimum supported version of Pandas to 0.23.2. Using a lower version will raise an error `Pandas >= 0.23.2 must be installed; however, your version was 0.XX`. Also, a workaround for using pyarrow with Pandas 0.19.2 was removed.
## How was this patch tested?
Existing Tests
Closes#24867 from BryanCutler/pyspark-increase-min-pandas-SPARK-28041.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Allow Pandas UDF to take an iterator of pd.Series or an iterator of tuple of pd.Series.
Note the UDF input args will be always one iterator:
* if the udf take only column as input, the iterator's element will be pd.Series (corresponding to the column values batch)
* if the udf take multiple columns as inputs, the iterator's element will be a tuple composed of multiple `pd.Series`s, each one corresponding to the multiple columns as inputs (keep the same order). For example:
```
pandas_udf("int", PandasUDFType.SCALAR_ITER)
def the_udf(iterator):
for col1_batch, col2_batch in iterator:
yield col1_batch + col2_batch
df.select(the_udf("col1", "col2"))
```
The udf above will add col1 and col2.
I haven't add unit tests, but manually tests show it works fine. So it is ready for first pass review.
We can test several typical cases:
```
from pyspark.sql import SparkSession
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.functions import udf
from pyspark.taskcontext import TaskContext
df = spark.createDataFrame([(1, 20), (3, 40)], ["a", "b"])
pandas_udf("int", PandasUDFType.SCALAR_ITER)
def fi1(it):
pid = TaskContext.get().partitionId()
print("DBG: fi1: do init stuff, partitionId=" + str(pid))
for batch in it:
yield batch + 100
print("DBG: fi1: do close stuff, partitionId=" + str(pid))
pandas_udf("int", PandasUDFType.SCALAR_ITER)
def fi2(it):
pid = TaskContext.get().partitionId()
print("DBG: fi2: do init stuff, partitionId=" + str(pid))
for batch in it:
yield batch + 10000
print("DBG: fi2: do close stuff, partitionId=" + str(pid))
pandas_udf("int", PandasUDFType.SCALAR_ITER)
def fi3(it):
pid = TaskContext.get().partitionId()
print("DBG: fi3: do init stuff, partitionId=" + str(pid))
for x, y in it:
yield x + y * 10 + 100000
print("DBG: fi3: do close stuff, partitionId=" + str(pid))
pandas_udf("int", PandasUDFType.SCALAR)
def fp1(x):
return x + 1000
udf("int")
def fu1(x):
return x + 10
# test select "pandas iter udf/pandas udf/sql udf" expressions at the same time.
# Note this case the `fi1("a"), fi2("b"), fi3("a", "b")` will generate only one plan,
# and `fu1("a")`, `fp1("a")` will generate another two separate plans.
df.select(fi1("a"), fi2("b"), fi3("a", "b"), fu1("a"), fp1("a")).show()
# test chain two pandas iter udf together
# Note this case `fi2(fi1("a"))` will generate only one plan
# Also note the init stuff/close stuff call order will be like:
# (debug output following)
# DBG: fi2: do init stuff, partitionId=0
# DBG: fi1: do init stuff, partitionId=0
# DBG: fi1: do close stuff, partitionId=0
# DBG: fi2: do close stuff, partitionId=0
df.select(fi2(fi1("a"))).show()
# test more complex chain
# Note this case `fi1("a"), fi2("a")` will generate one plan,
# and `fi3(fi1_output, fi2_output)` will generate another plan
df.select(fi3(fi1("a"), fi2("a"))).show()
```
## How was this patch tested?
To be added.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24643 from WeichenXu123/pandas_udf_iter.
Lead-authored-by: WeichenXu <weichen.xu@databricks.com>
Co-authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
I suspect that the doc of `rowsBetween` methods in Scala and PySpark looks wrong.
Because:
```scala
scala> val df = Seq((1, "a"), (2, "a"), (3, "a"), (4, "a"), (5, "a"), (6, "a")).toDF("id", "category")
df: org.apache.spark.sql.DataFrame = [id: int, category: string]
scala> val byCategoryOrderedById = Window.partitionBy('category).orderBy('id).rowsBetween(-1, 2)
byCategoryOrderedById: org.apache.spark.sql.expressions.WindowSpec = org.apache.spark.sql.expressions.WindowSpec7f04de97
scala> df.withColumn("sum", sum('id) over byCategoryOrderedById).show()
+---+--------+---+
| id|category|sum|
+---+--------+---+
| 1| a| 6| # sum from index 0 to (0 + 2): 1 + 2 + 3 = 6
| 2| a| 10| # sum from index (1 - 1) to (1 + 2): 1 + 2 + 3 + 4 = 10
| 3| a| 14|
| 4| a| 18|
| 5| a| 15|
| 6| a| 11|
+---+--------+---+
```
So the frame (-1, 2) for row with index 5, as described in the doc, should range from index 4 to index 7.
## How was this patch tested?
N/A, just doc change.
Closes#24864 from viirya/window-spec-doc.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Just found the doctest on `over` function of `Column` is commented out. The window spec is also not for the window function used there.
We should either remove the doctest, or improve it.
Because other functions of `Column` have doctest generally, so this PR tries to improve it.
## How was this patch tested?
Added doctest.
Closes#24854 from viirya/column-test-minor.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When Arrow optimization is enabled in Python 2.7,
```python
import pandas
pdf = pandas.DataFrame(["test1", "test2"])
df = spark.createDataFrame(pdf)
df.show()
```
I got the following output:
```
+----------------+
| 0|
+----------------+
|[74 65 73 74 31]|
|[74 65 73 74 32]|
+----------------+
```
This looks because Python's `str` and `byte` are same. it does look right:
```python
>>> str == bytes
True
>>> isinstance("a", bytes)
True
```
To cut it short:
1. Python 2 treats `str` as `bytes`.
2. PySpark added some special codes and hacks to recognizes `str` as string types.
3. PyArrow / Pandas followed Python 2 difference
To fix, we have two options:
1. Fix it to match the behaviour to PySpark's
2. Note the differences
but Python 2 is deprecated anyway. I think it's better to just note it and for go option 2.
## How was this patch tested?
Manually tested.
Doc was checked too:
Closes#24838 from HyukjinKwon/SPARK-27995.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Similar to https://github.com/apache/spark/pull/24070, we now propagate SparkExceptions that are encountered during the collect in the java process to the python process.
Fixes https://jira.apache.org/jira/browse/SPARK-27805
## How was this patch tested?
Added a new unit test
Closes#24677 from dvogelbacher/dv/betterErrorMsgWhenUsingArrow.
Authored-by: David Vogelbacher <dvogelbacher@palantir.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
`spark.sql.execution.arrow.enabled` was added when we add PySpark arrow optimization.
Later, in the current master, SparkR arrow optimization was added and it's controlled by the same configuration `spark.sql.execution.arrow.enabled`.
There look two issues about this:
1. `spark.sql.execution.arrow.enabled` in PySpark was added from 2.3.0 whereas SparkR optimization was added 3.0.0. The stability is different so it's problematic when we change the default value for one of both optimization first.
2. Suppose users want to share some JVM by PySpark and SparkR. They are currently forced to use the optimization for all or none if the configuration is set globally.
This PR proposes two separate configuration groups for PySpark and SparkR about Arrow optimization:
- Deprecate `spark.sql.execution.arrow.enabled`
- Add `spark.sql.execution.arrow.pyspark.enabled` (fallback to `spark.sql.execution.arrow.enabled`)
- Add `spark.sql.execution.arrow.sparkr.enabled`
- Deprecate `spark.sql.execution.arrow.fallback.enabled`
- Add `spark.sql.execution.arrow.pyspark.fallback.enabled ` (fallback to `spark.sql.execution.arrow.fallback.enabled`)
Note that `spark.sql.execution.arrow.maxRecordsPerBatch` is used within JVM side for both.
Note that `spark.sql.execution.arrow.fallback.enabled` was added due to behaviour change. We don't need it in SparkR - SparkR side has the automatic fallback.
## How was this patch tested?
Manually tested and some unittests were added.
Closes#24700 from HyukjinKwon/separate-sparkr-arrow.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Unset InputFileBlockHolder at the end of tasks to stop the file name from leaking over to other tasks in the same thread. This happens in particular in Pyspark because of its complex threading model.
## How was this patch tested?
new pyspark test
Closes#24605 from jose-torres/fix254.
Authored-by: Jose Torres <torres.joseph.f+github@gmail.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/22275 introduced a performance improvement where we send partitions out of order to python and then, as a last step, send the partition order as well.
However, if there are no partitions we will never send the partition order and we will get an "EofError" on the python side.
This PR fixes this by also sending the partition order if there are no partitions present.
## How was this patch tested?
New unit test added.
Closes#24650 from dvogelbacher/dv/fixNoPartitionArrowConversion.
Authored-by: David Vogelbacher <dvogelbacher@palantir.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
