## 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?
Py4J 0.10.8.1 is released on October 21st and is the first release of Py4J to support Python 3.7 officially. We had better have this to get the official support. Also, there are some patches related to garbage collections.
https://www.py4j.org/changelog.html#py4j-0-10-8-and-py4j-0-10-8-1
## How was this patch tested?
Pass the Jenkins.
Closes#22901 from dongjoon-hyun/SPARK-25891.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@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>
