### What changes were proposed in this pull request?
Document the dependency between the config `spark.metrics.staticSources.enabled` and JVMSource registration.
### Why are the changes needed?
This PT just documents the dependency between config `spark.metrics.staticSources.enabled` and JVM source registration.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manually tested.
Closes#29203 from LucaCanali/bugJVMMetricsRegistration.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims to add `StorageLevel.DISK_ONLY_3` as a built-in `StorageLevel`.
### Why are the changes needed?
In a YARN cluster, HDFS uaually provides storages with replication factor 3. So, we can save the result to HDFS to get `StorageLevel.DISK_ONLY_3` technically. However, disaggregate clusters or clusters without storage services are rising. Previously, in that situation, the users were able to use similar `MEMORY_AND_DISK_2` or a user-created `StorageLevel`. This PR aims to support those use cases officially for better UX.
### Does this PR introduce _any_ user-facing change?
Yes. This provides a new built-in option.
### How was this patch tested?
Pass the GitHub Action or Jenkins with the revised test cases.
Closes#29331 from dongjoon-hyun/SPARK-32517.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This proposes to port old PySpark migration guide to new PySpark docs.
### Why are the changes needed?
Better documentation.
### Does this PR introduce _any_ user-facing change?
No. Documentation only.
### How was this patch tested?
Generated document locally.
<img width="1521" alt="Screen Shot 2020-08-07 at 1 53 20 PM" src="https://user-images.githubusercontent.com/68855/89687618-672e7700-d8b5-11ea-8f29-67a9ab271fa8.png">
Closes#29385 from viirya/SPARK-32191.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Convert `NULL` elements of maps, structs and arrays to the `"null"` string while converting maps/struct/array values to strings. The SQL config `spark.sql.legacy.omitNestedNullInCast.enabled` controls the behaviour. When it is `true`, `NULL` elements of structs/maps/arrays will be omitted otherwise, when it is `false`, `NULL` elements will be converted to `"null"`.
### Why are the changes needed?
1. It is impossible to distinguish empty string and null, for instance:
```scala
scala> Seq(Seq(""), Seq(null)).toDF().show
+-----+
|value|
+-----+
| []|
| []|
+-----+
```
2. Inconsistent NULL conversions for top-level values and nested columns, for instance:
```scala
scala> sql("select named_struct('c', null), null").show
+---------------------+----+
|named_struct(c, NULL)|NULL|
+---------------------+----+
| []|null|
+---------------------+----+
```
3. `.show()` is different from conversions to Hive strings, and as a consequence its output is different from `spark-sql` (sql tests):
```sql
spark-sql> select named_struct('c', null) as struct;
{"c":null}
```
```scala
scala> sql("select named_struct('c', null) as struct").show
+------+
|struct|
+------+
| []|
+------+
```
4. It is impossible to distinguish empty struct/array from struct/array with null in the current implementation:
```scala
scala> Seq[Seq[String]](Seq(), Seq(null)).toDF.show()
+-----+
|value|
+-----+
| []|
| []|
+-----+
```
### Does this PR introduce _any_ user-facing change?
Yes, before:
```scala
scala> Seq(Seq(""), Seq(null)).toDF().show
+-----+
|value|
+-----+
| []|
| []|
+-----+
```
After:
```scala
scala> Seq(Seq(""), Seq(null)).toDF().show
+------+
| value|
+------+
| []|
|[null]|
+------+
```
### How was this patch tested?
By existing test suite `CastSuite`.
Closes#29311 from MaxGekk/nested-null-to-string.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to write the main page of PySpark documentation. The base work is finished at https://github.com/apache/spark/pull/29188.
### Why are the changes needed?
For better usability and readability in PySpark documentation.
### Does this PR introduce _any_ user-facing change?
Yes, it creates a new main page as below:
### How was this patch tested?
Manually built the PySpark documentation.
```bash
cd python
make clean html
```
Closes#29320 from HyukjinKwon/SPARK-32507.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Change casting of map and struct values to strings by using the `{}` brackets instead of `[]`. The behavior is controlled by the SQL config `spark.sql.legacy.castComplexTypesToString.enabled`. When it is `true`, `CAST` wraps maps and structs by `[]` in casting to strings. Otherwise, if this is `false`, which is the default, maps and structs are wrapped by `{}`.
### Why are the changes needed?
- To distinguish structs/maps from arrays.
- To make `show`'s output consistent with Hive and conversions to Hive strings.
- To display dataframe content in the same form by `spark-sql` and `show`
- To be consistent with the `*.sql` tests
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By existing test suite `CastSuite`.
Closes#29308 from MaxGekk/show-struct-map.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a follow-up of #29278.
This PR changes the config name to switch allow/disallow `SparkContext` in executors as per the comment https://github.com/apache/spark/pull/29278#pullrequestreview-460256338.
### Why are the changes needed?
The config name `spark.executor.allowSparkContext` is more reasonable.
### Does this PR introduce _any_ user-facing change?
Yes, the config name is changed.
### How was this patch tested?
Updated tests.
Closes#29340 from ueshin/issues/SPARK-32160/change_config_name.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Change _To restore the behavior before Spark **3.0**_ to _To restore the behavior before Spark **3.1**_ in the SQL migration guide while telling about the behaviour before new version 3.1.
### Why are the changes needed?
To have correct info in the SQL migration guide.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes#29336 from MaxGekk/fix-version-in-sql-migration.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a follow-up of #28986.
This PR adds a config to switch allow/disallow to create `SparkContext` in executors.
- `spark.driver.allowSparkContextInExecutors`
### Why are the changes needed?
Some users or libraries actually create `SparkContext` in executors.
We shouldn't break their workloads.
### Does this PR introduce _any_ user-facing change?
Yes, users will be able to create `SparkContext` in executors with the config enabled.
### How was this patch tested?
More tests are added.
Closes#29278 from ueshin/issues/SPARK-32160/add_configs.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to:
1. Fix the error message when the output schema is misbatched with R DataFrame from the given function. For example,
```R
df <- createDataFrame(list(list(a=1L, b="2")))
count(gapply(df, "a", function(key, group) { group }, structType("a int, b int")))
```
**Before:**
```
Error in handleErrors(returnStatus, conn) :
...
java.lang.UnsupportedOperationException
...
```
**After:**
```
Error in handleErrors(returnStatus, conn) :
...
java.lang.AssertionError: assertion failed: Invalid schema from gapply: expected IntegerType, IntegerType, got IntegerType, StringType
...
```
2. Update documentation about the schema matching for `gapply` and `dapply`.
### Why are the changes needed?
To show which schema is not matched, and let users know what's going on.
### Does this PR introduce _any_ user-facing change?
Yes, error message is updated as above, and documentation is updated.
### How was this patch tested?
Manually tested and unitttests were added.
Closes#29283 from HyukjinKwon/r-vectorized-error.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
When `spark.sql.caseSensitive` is `false` (by default), check that there are not duplicate column names on the same level (top level or nested levels) in reading from in-built datasources Parquet, ORC, Avro and JSON. If such duplicate columns exist, throw the exception:
```
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in the data schema:
```
### Why are the changes needed?
To make handling of duplicate nested columns is similar to handling of duplicate top-level columns i. e. output the same error when `spark.sql.caseSensitive` is `false`:
```Scala
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in the data schema: `camelcase`
```
Checking of top-level duplicates was introduced by https://github.com/apache/spark/pull/17758.
### Does this PR introduce _any_ user-facing change?
Yes. For the example from SPARK-32431:
ORC:
```scala
java.io.IOException: Error reading file: file:/private/var/folders/p3/dfs6mf655d7fnjrsjvldh0tc0000gn/T/spark-c02c2f9a-0cdc-4859-94fc-b9c809ca58b1/part-00001-63e8c3f0-7131-4ec9-be02-30b3fdd276f4-c000.snappy.orc
at org.apache.orc.impl.RecordReaderImpl.nextBatch(RecordReaderImpl.java:1329)
at org.apache.orc.mapreduce.OrcMapreduceRecordReader.ensureBatch(OrcMapreduceRecordReader.java:78)
...
Caused by: java.io.EOFException: Read past end of RLE integer from compressed stream Stream for column 3 kind DATA position: 6 length: 6 range: 0 offset: 12 limit: 12 range 0 = 0 to 6 uncompressed: 3 to 3
at org.apache.orc.impl.RunLengthIntegerReaderV2.readValues(RunLengthIntegerReaderV2.java:61)
at org.apache.orc.impl.RunLengthIntegerReaderV2.next(RunLengthIntegerReaderV2.java:323)
```
JSON:
```scala
+------------+
|StructColumn|
+------------+
| [,,]|
+------------+
```
Parquet:
```scala
+------------+
|StructColumn|
+------------+
| [0,, 1]|
+------------+
```
Avro:
```scala
+------------+
|StructColumn|
+------------+
| [,,]|
+------------+
```
After the changes, Parquet, ORC, JSON and Avro output the same error:
```scala
Found duplicate column(s) in the data schema: `camelcase`;
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in the data schema: `camelcase`;
at org.apache.spark.sql.util.SchemaUtils$.checkColumnNameDuplication(SchemaUtils.scala:112)
at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtils.scala:51)
at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtils.scala:67)
```
### How was this patch tested?
Run modified test suites:
```
$ build/sbt "sql/test:testOnly org.apache.spark.sql.FileBasedDataSourceSuite"
$ build/sbt "avro/test:testOnly org.apache.spark.sql.avro.*"
```
and added new UT to `SchemaUtilsSuite`.
Closes#29234 from MaxGekk/nested-case-insensitive-column.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes:
1. To introduce `InheritableThread` class, that works identically with `threading.Thread` but it can inherit the inheritable attributes of a JVM thread such as `InheritableThreadLocal`.
This was a problem from the pinned thread mode, see also https://github.com/apache/spark/pull/24898. Now it works as below:
```python
import pyspark
spark.sparkContext.setLocalProperty("a", "hi")
def print_prop():
print(spark.sparkContext.getLocalProperty("a"))
pyspark.InheritableThread(target=print_prop).start()
```
```
hi
```
2. Also, it adds the resource leak fix into `InheritableThread`. Py4J leaks the thread and does not close the connection from Python to JVM. In `InheritableThread`, it manually closes the connections when PVM garbage collection happens. So, JVM threads finish safely. I manually verified by profiling but there's also another easy way to verify:
```bash
PYSPARK_PIN_THREAD=true ./bin/pyspark
```
```python
>>> from threading import Thread
>>> Thread(target=lambda: spark.range(1000).collect()).start()
>>> Thread(target=lambda: spark.range(1000).collect()).start()
>>> Thread(target=lambda: spark.range(1000).collect()).start()
>>> spark._jvm._gateway_client.deque
deque([<py4j.clientserver.ClientServerConnection object at 0x119f7aba8>, <py4j.clientserver.ClientServerConnection object at 0x119fc9b70>, <py4j.clientserver.ClientServerConnection object at 0x119fc9e10>, <py4j.clientserver.ClientServerConnection object at 0x11a015358>, <py4j.clientserver.ClientServerConnection object at 0x119fc00f0>])
>>> Thread(target=lambda: spark.range(1000).collect()).start()
>>> spark._jvm._gateway_client.deque
deque([<py4j.clientserver.ClientServerConnection object at 0x119f7aba8>, <py4j.clientserver.ClientServerConnection object at 0x119fc9b70>, <py4j.clientserver.ClientServerConnection object at 0x119fc9e10>, <py4j.clientserver.ClientServerConnection object at 0x11a015358>, <py4j.clientserver.ClientServerConnection object at 0x119fc08d0>, <py4j.clientserver.ClientServerConnection object at 0x119fc00f0>])
```
This issue is fixed now.
3. Because now we have a fix for the issue here, it also proposes to deprecate `collectWithJobGroup` which was a temporary workaround added to avoid this leak issue.
### Why are the changes needed?
To support pinned thread mode properly without a resource leak, and a proper inheritable local properties.
### Does this PR introduce _any_ user-facing change?
Yes, it adds an API `InheritableThread` class for pinned thread mode.
### How was this patch tested?
Manually tested as described above, and unit test was added as well.
Closes#28968 from HyukjinKwon/SPARK-32010.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Document the stage level scheduling feature.
### Why are the changes needed?
Document the stage level scheduling feature.
### Does this PR introduce _any_ user-facing change?
Documentation.
### How was this patch tested?
n/a docs only
Closes#29292 from tgravescs/SPARK-30322.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Rewrite a clearer and complete BLAS native acceleration enabling guide.
### Why are the changes needed?
The document of enabling BLAS native acceleration in ML guide (https://spark.apache.org/docs/latest/ml-guide.html#dependencies) is incomplete and unclear to the user.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
N/A
Closes#29139 from xwu99/blas-doc.
Lead-authored-by: Xiaochang Wu <xiaochang.wu@intel.com>
Co-authored-by: Wu, Xiaochang <xiaochang.wu@intel.com>
Signed-off-by: Huaxin Gao <huaxing@us.ibm.com>
### What changes were proposed in this pull request?
return an empty list instead of None when calling `df.head()`
### Why are the changes needed?
`df.head()` and `df.head(1)` are inconsistent when df is empty.
### Does this PR introduce _any_ user-facing change?
Yes. If a user relies on `df.head()` to return None, things like `if df.head() is None:` will be broken.
### How was this patch tested?
Closes#29214 from tianshizz/SPARK-31525.
Authored-by: Tianshi Zhu <zhutianshirea@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
When using `Seconds.toMicros` API to convert epoch seconds to microseconds,
```scala
/**
* Equivalent to
* {link #convert(long, TimeUnit) MICROSECONDS.convert(duration, this)}.
* param duration the duration
* return the converted duration,
* or {code Long.MIN_VALUE} if conversion would negatively
* overflow, or {code Long.MAX_VALUE} if it would positively overflow.
*/
```
This PR change it to `Math.multiplyExact(epochSeconds, MICROS_PER_SECOND)`
### Why are the changes needed?
fix silent data change between 3.x and 2.x
```
~/Downloads/spark/spark-3.1.0-SNAPSHOT-bin-20200722 bin/spark-sql -S -e "select to_timestamp('300000', 'y');"
+294247-01-10 12:00:54.775807
```
```
kentyaohulk ~/Downloads/spark/spark-2.4.5-bin-hadoop2.7 bin/spark-sql -S -e "select to_timestamp('300000', 'y');"
284550-10-19 15:58:1010.448384
```
### Does this PR introduce _any_ user-facing change?
Yes, we will raise `ArithmeticException` instead of giving the wrong answer if overflow.
### How was this patch tested?
add unit test
Closes#29220 from yaooqinn/SPARK-32424.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to redesign the PySpark documentation.
I made a demo site to make it easier to review: https://hyukjin-spark.readthedocs.io/en/stable/reference/index.html.
Here is the initial draft for the final PySpark docs shape: https://hyukjin-spark.readthedocs.io/en/latest/index.html.
In more details, this PR proposes:
1. Use [pydata_sphinx_theme](https://github.com/pandas-dev/pydata-sphinx-theme) theme - [pandas](https://pandas.pydata.org/docs/) and [Koalas](https://koalas.readthedocs.io/en/latest/) use this theme. The CSS overwrite is ported from Koalas. The colours in the CSS were actually chosen by designers to use in Spark.
2. Use the Sphinx option to separate `source` and `build` directories as the documentation pages will likely grow.
3. Port current API documentation into the new style. It mimics Koalas and pandas to use the theme most effectively.
One disadvantage of this approach is that you should list up APIs or classes; however, I think this isn't a big issue in PySpark since we're being conservative on adding APIs. I also intentionally listed classes only instead of functions in ML and MLlib to make it relatively easier to manage.
### Why are the changes needed?
Often I hear the complaints, from the users, that current PySpark documentation is pretty messy to read - https://spark.apache.org/docs/latest/api/python/index.html compared other projects such as [pandas](https://pandas.pydata.org/docs/) and [Koalas](https://koalas.readthedocs.io/en/latest/).
It would be nicer if we can make it more organised instead of just listing all classes, methods and attributes to make it easier to navigate.
Also, the documentation has been there from almost the very first version of PySpark. Maybe it's time to update it.
### Does this PR introduce _any_ user-facing change?
Yes, PySpark API documentation will be redesigned.
### How was this patch tested?
Manually tested, and the demo site was made to show.
Closes#29188 from HyukjinKwon/SPARK-32179.
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 set the minimum Arrow version as 1.0.0 to minimise the maintenance overhead and keep the minimal version up to date.
Other required changes to support 1.0.0 were already made in SPARK-32451.
### Why are the changes needed?
R side, people rather aggressively encourage people to use the latest version, and SparkR vectorization is very experimental that was added from Spark 3.0.
Also, we're technically not testing old Arrow versions in SparkR for now.
### Does this PR introduce _any_ user-facing change?
Yes, users wouldn't be able to use SparkR with old Arrow.
### How was this patch tested?
GitHub Actions and AppVeyor are already testing them.
Closes#29253 from HyukjinKwon/SPARK-32452.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR extends the RESET command to support reset SQL configuration one by one.
### Why are the changes needed?
Currently, the reset command only supports restore all of the runtime configurations to their defaults. In most cases, users do not want this, but just want to restore one or a small group of settings.
The SET command can work as a workaround for this, but you have to keep the defaults in your mind or by temp variables, which turns out not very convenient to use.
Hive supports this:
https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Clients#HiveServer2Clients-BeelineExample
reset <key> | Resets the value of a particular configuration variable (key) to the default value.Note: If you misspell the variable name, Beeline will not show an error.
-- | --
PostgreSQL supports this too
https://www.postgresql.org/docs/9.1/sql-reset.html
### Does this PR introduce _any_ user-facing change?
yes, reset can restore one configuration now
### How was this patch tested?
add new unit tests.
Closes#29202 from yaooqinn/SPARK-32406.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Improve the `SQLKeywordSuite` so that:
1. it checks keywords under default mode as well
2. it checks if there are typos in the doc (found one and fixed in this PR)
### Why are the changes needed?
better test coverage
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
N/A
Closes#29200 from cloud-fan/test.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In Hive mode, permanent functions are shared with Hive metastore so that functions may be modified by other Hive client. With in long-lived spark scene, it's hard to update the change of function.
Here are 2 reasons:
* Spark cache the function in memory using `FunctionRegistry`.
* User may not know the location or classname of udf when using `replace function`.
Note that we use v2 command code path to add new command.
### Why are the changes needed?
Give a easy way to make spark function registry sync with Hive metastore.
Then we can call
```
refresh function functionName
```
### Does this PR introduce _any_ user-facing change?
Yes, new command.
### How was this patch tested?
New UT.
Closes#28840 from ulysses-you/SPARK-31999.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
add link for Debugging your Application in `running-on-yarn.html#launching-spark-on-yar`
### Why are the changes needed?
Currrently on running-on-yarn.html page launching-spark-on-yarn section, it mentions to refer for Debugging your Application. It is better to add a direct link for it to save reader time to find the section
### Does this PR introduce _any_ user-facing change?
Yes.
Docs changes.
1. add link for Debugging your Application in `running-on-yarn.html#launching-spark-on-yarn` section
Updated behavior:
2. update Spark Properties link to anchor link only
### How was this patch tested?
manual test has been performed to test the updated
Closes#29154 from brandonJY/patch-1.
Authored-by: Brandon <brandonJY@users.noreply.github.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adds the SQL standard command - `SET TIME ZONE` to the current default time zone displacement for the current SQL-session, which is the same as the existing `set spark.sql.session.timeZone=xxx'.
All in all, this PR adds syntax as following,
```
SET TIME ZONE LOCAL;
SET TIME ZONE 'valid time zone'; -- zone offset or region
SET TIME ZONE INTERVAL XXXX; -- xxx must in [-18, + 18] hours, * this range is bigger than ansi [-14, + 14]
```
### Why are the changes needed?
ANSI compliance and supply pure SQL users a way to retrieve all supported TimeZones
### Does this PR introduce _any_ user-facing change?
yes, add new syntax.
### How was this patch tested?
add unit tests.
and locally verified reference doc
Closes#29064 from yaooqinn/SPARK-32272.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Support fetching taskList by status as below:
```
/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]/taskList?status=failed
```
### Why are the changes needed?
When there're large number of tasks in one stage, current api is hard to get taskList by status
### Does this PR introduce _any_ user-facing change?
Yes. Updated monitoring doc.
### How was this patch tested?
Added tests in `HistoryServerSuite`
Closes#28942 from warrenzhu25/SPARK-32125.
Authored-by: Warren Zhu <zhonzh@microsoft.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
This PR will remove references to these "blacklist" and "whitelist" terms besides the blacklisting feature as a whole, which can be handled in a separate JIRA/PR.
This touches quite a few files, but the changes are straightforward (variable/method/etc. name changes) and most quite self-contained.
### Why are the changes needed?
As per discussion on the Spark dev list, it will be beneficial to remove references to problematic language that can alienate potential community members. One such reference is "blacklist" and "whitelist". While it seems to me that there is some valid debate as to whether these terms have racist origins, the cultural connotations are inescapable in today's world.
### Does this PR introduce _any_ user-facing change?
In the test file `HiveQueryFileTest`, a developer has the ability to specify the system property `spark.hive.whitelist` to specify a list of Hive query files that should be tested. This system property has been renamed to `spark.hive.includelist`. The old property has been kept for compatibility, but will log a warning if used. I am open to feedback from others on whether keeping a deprecated property here is unnecessary given that this is just for developers running tests.
### How was this patch tested?
Existing tests should be suitable since no behavior changes are expected as a result of this PR.
Closes#28874 from xkrogen/xkrogen-SPARK-32036-rename-blacklists.
Authored-by: Erik Krogen <ekrogen@linkedin.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Add a new class HybridStore to make the history server faster when loading event files. When rebuilding the application state from event logs, HybridStore will write data to InMemoryStore at first and use a background thread to dump data to LevelDB once the writing to InMemoryStore is completed. HybridStore is to make content serving faster by using more memory. It's only safe to enable it when the cluster is not having a heavy load.
### Why are the changes needed?
HybridStore can greatly reduce the event logs loading time, especially for large log files. In general, it has 4x - 6x UI loading speed improvement for large log files. The detailed result is shown in comments.
### Does this PR introduce any user-facing change?
This PR adds new configs `spark.history.store.hybridStore.enabled` and `spark.history.store.hybridStore.maxMemoryUsage`.
### How was this patch tested?
A test suite for HybridStore is added. I also manually tested it on 3.1.0 on mac os.
This is a follow-up for the work done by Hieu Huynh in 2019.
Closes#28412 from baohe-zhang/SPARK-31608.
Authored-by: Baohe Zhang <baohe.zhang@verizonmedia.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This PR aims to drop Python 2.7, 3.4 and 3.5.
Roughly speaking, it removes all the widely known Python 2 compatibility workarounds such as `sys.version` comparison, `__future__`. Also, it removes the Python 2 dedicated codes such as `ArrayConstructor` in Spark.
### Why are the changes needed?
1. Unsupport EOL Python versions
2. Reduce maintenance overhead and remove a bit of legacy codes and hacks for Python 2.
3. PyPy2 has a critical bug that causes a flaky test, SPARK-28358 given my testing and investigation.
4. Users can use Python type hints with Pandas UDFs without thinking about Python version
5. Users can leverage one latest cloudpickle, https://github.com/apache/spark/pull/28950. With Python 3.8+ it can also leverage C pickle.
### Does this PR introduce _any_ user-facing change?
Yes, users cannot use Python 2.7, 3.4 and 3.5 in the upcoming Spark version.
### How was this patch tested?
Manually tested and also tested in Jenkins.
Closes#28957 from HyukjinKwon/SPARK-32138.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This change replaces the world slave with alternatives matching the context.
### Why are the changes needed?
There is no need to call things slave, we might as well use better clearer names.
### Does this PR introduce _any_ user-facing change?
Yes, the ouput JSON does change. To allow backwards compatibility this is an additive change.
The shell scripts for starting & stopping workers are renamed, and for backwards compatibility old scripts are added to call through to the new ones while printing a deprecation message to stderr.
### How was this patch tested?
Existing tests.
Closes#28864 from holdenk/SPARK-32004-drop-references-to-slave.
Lead-authored-by: Holden Karau <hkarau@apple.com>
Co-authored-by: Holden Karau <holden@pigscanfly.ca>
Signed-off-by: Holden Karau <hkarau@apple.com>
### What changes were proposed in this pull request?
This PR changes `true` to `True` in the python code.
### Why are the changes needed?
The previous example is a syntax error.
### Does this PR introduce _any_ user-facing change?
Yes, but this is doc-only typo fix.
### How was this patch tested?
Manually run the example.
Closes#29073 from ChuliangXiao/patch-1.
Authored-by: Chuliang Xiao <ChuliangX@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR improves the test to make sure all the SQL keywords are documented correctly. It fixes several issues:
1. some keywords are not documented
2. some keywords are not ANSI SQL keywords but documented as reserved/non-reserved.
### Why are the changes needed?
To make sure the implementation matches the doc.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
new test
Closes#29055 from cloud-fan/keyword.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
In this PR, I suppose we support 'f'-suffixed float literal, e.g. `select 1.1f`
### Why are the changes needed?
a very common feature across platforms, checked with pg, presto, hive, MySQL...
### Does this PR introduce _any_ user-facing change?
yes,
`select 1.1f` results float value 1.1 instead of throwing AnlaysisExceptiion`Can't extract value from 1: need struct type but got int;`
### How was this patch tested?
add unit tests
Closes#29022 from yaooqinn/SPARK-32207.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
I resolved some of the inconsistencies of AWS env variables. They're fixed in the documentation as well as in the examples. I grep-ed through the repo to try & find any more instances but nothing popped up.
### Why are the changes needed?
As previously mentioned, there is a JIRA request, SPARK-32035, which encapsulates all the issues. But, in summary, the naming of items was inconsistent.
### Does this PR introduce _any_ user-facing change?
Correct names:
AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY
AWS_SESSION_TOKEN
These are the same that AWS uses in their libraries.
However, looking through the Spark documentation and comments, I see that these are not denoted correctly across the board:
docs/cloud-integration.md
106:1. `spark-submit` reads the `AWS_ACCESS_KEY`, `AWS_SECRET_KEY` <-- both different
107:and `AWS_SESSION_TOKEN` environment variables and sets the associated authentication options
docs/streaming-kinesis-integration.md
232:- Set up the environment variables `AWS_ACCESS_KEY_ID` and `AWS_SECRET_KEY` with your AWS credentials. <-- secret key different
external/kinesis-asl/src/main/python/examples/streaming/kinesis_wordcount_asl.py
34: $ export AWS_ACCESS_KEY_ID=<your-access-key>
35: $ export AWS_SECRET_KEY=<your-secret-key> <-- different
48: Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY <-- secret key different
core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
438: val keyId = System.getenv("AWS_ACCESS_KEY_ID")
439: val accessKey = System.getenv("AWS_SECRET_ACCESS_KEY")
448: val sessionToken = System.getenv("AWS_SESSION_TOKEN")
external/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala
53: * $ export AWS_ACCESS_KEY_ID=<your-access-key>
54: * $ export AWS_SECRET_KEY=<your-secret-key> <-- different
65: * Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY <-- secret key different
external/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java
59: * $ export AWS_ACCESS_KEY_ID=[your-access-key]
60: * $ export AWS_SECRET_KEY=<your-secret-key> <-- different
71: * Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY <-- secret key different
These were all fixed to match names listed under the "correct names" heading.
### How was this patch tested?
I built the documentation using jekyll and verified that the changes were present & accurate.
Closes#29058 from Moovlin/SPARK-32035.
Authored-by: moovlin <richjoerger@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
update REGEXP usage and examples in sql-ref-syntx-qry-select-like.cmd
### Why are the changes needed?
make the usage of REGEXP known to more users
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No tests
Closes#29009 from GuoPhilipse/update-migrate-guide.
Lead-authored-by: GuoPhilipse <46367746+GuoPhilipse@users.noreply.github.com>
Co-authored-by: GuoPhilipse <guofei_ok@126.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
1. Set the SQL config `spark.sql.legacy.allowCastNumericToTimestamp` to `true` by default
2. Remove explicit sets of `spark.sql.legacy.allowCastNumericToTimestamp` to `true` in the cast suites.
### Why are the changes needed?
To avoid breaking changes in minor versions (in the upcoming Spark 3.1.0) according to the the semantic versioning guidelines (https://spark.apache.org/versioning-policy.html)
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By `CastSuite`.
Closes#29012 from MaxGekk/allow-cast-numeric-to-timestamp.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
docs/sql-ref-syntax-qry-select-usedb.md -> docs/sql-ref-syntax-ddl-usedb.md
docs/sql-ref-syntax-aux-refresh-table.md -> docs/sql-ref-syntax-aux-cache-refresh-table.md
### Why are the changes needed?
usedb belongs to DDL. Its location should be consistent with other DDL commands file locations
similar reason for refresh table
### Does this PR introduce _any_ user-facing change?
before change, when clicking USE DATABASE, the side bar menu shows select commands
<img width="1200" alt="Screen Shot 2020-07-04 at 9 05 35 AM" src="https://user-images.githubusercontent.com/13592258/86516696-b45f8a80-bdd7-11ea-8dba-3a5cca22aad3.png">
after change, when clicking USE DATABASE, the side bar menu shows DDL commands
<img width="1120" alt="Screen Shot 2020-07-04 at 9 06 06 AM" src="https://user-images.githubusercontent.com/13592258/86516703-bf1a1f80-bdd7-11ea-8a90-ae7eaaafd44c.png">
before change, when clicking refresh table, the side bar menu shows Auxiliary statements
<img width="1200" alt="Screen Shot 2020-07-04 at 9 30 40 AM" src="https://user-images.githubusercontent.com/13592258/86516877-3d2af600-bdd9-11ea-9568-0a6f156f57da.png">
after change, when clicking refresh table, the side bar menu shows Cache statements
<img width="1199" alt="Screen Shot 2020-07-04 at 9 35 21 AM" src="https://user-images.githubusercontent.com/13592258/86516937-b4f92080-bdd9-11ea-8ad1-5f5a7f58d76b.png">
### How was this patch tested?
Manually build and check
Closes#28995 from huaxingao/docs_fix.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Huaxin Gao <huaxing@us.ibm.com>
### What changes were proposed in this pull request?
Set the JSON option `inferTimestamp` to `false` if an user don't pass it as datasource option.
### Why are the changes needed?
To prevent perf regression while inferring schemas from JSON with potential timestamps fields.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
- Modified existing tests in `JsonSuite` and `JsonInferSchemaSuite`.
- Regenerated results of `JsonBenchmark` in the environment:
| Item | Description |
| ---- | ----|
| Region | us-west-2 (Oregon) |
| Instance | r3.xlarge |
| AMI | ubuntu/images/hvm-ssd/ubuntu-bionic-18.04-amd64-server-20190722.1 (ami-06f2f779464715dc5) |
| Java | OpenJDK 64-Bit Server VM 1.8.0_252 and OpenJDK 64-Bit Server VM 11.0.7+10 |
Closes#28966 from MaxGekk/json-inferTimestamps-disable-by-default.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR fixes a typo for a configuration property in the `spark-standalone.md`.
`spark.driver.resourcesfile` should be `spark.driver.resourcesFile`.
I look for similar typo but this is the only typo.
### Why are the changes needed?
The property name is wrong.
### Does this PR introduce _any_ user-facing change?
Yes. The property name is corrected.
### How was this patch tested?
I confirmed the spell of the property name is the correct from the property name defined in o.a.s.internal.config.package.scala.
Closes#28958 from sarutak/fix-resource-typo.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
The 3rd link in `IBM Cloud Object Storage connector for Apache Spark` is broken. The PR removes this link.
### Why are the changes needed?
broken link
### Does this PR introduce _any_ user-facing change?
yes, the broken link is removed from the doc.
### How was this patch tested?
doc generation passes successfully as before
Closes#28927 from guykhazma/spark32099.
Authored-by: Guy Khazma <guykhag@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR is to add a redirect to sql-ref.html.
### Why are the changes needed?
Before Spark 3.0 release, we are using sql-reference.md, which was replaced by sql-ref.md instead. A number of Google searches I’ve done today have turned up https://spark.apache.org/docs/latest/sql-reference.html, which does not exist any more. Thus, we should add a redirect to sql-ref.html.
### Does this PR introduce _any_ user-facing change?
https://spark.apache.org/docs/latest/sql-reference.html will be redirected to https://spark.apache.org/docs/latest/sql-ref.html
### How was this patch tested?
Build it in my local environment. It works well. The sql-reference.html file was generated. The contents are like:
```
<!DOCTYPE html>
<html lang="en-US">
<meta charset="utf-8">
<title>Redirecting…</title>
<link rel="canonical" href="http://localhost:4000/sql-ref.html">
<script>location="http://localhost:4000/sql-ref.html"</script>
<meta http-equiv="refresh" content="0; url=http://localhost:4000/sql-ref.html">
<meta name="robots" content="noindex">
<h1>Redirecting…</h1>
<a href="http://localhost:4000/sql-ref.html">Click here if you are not redirected.</a>
</html>
```
Closes#28914 from gatorsmile/addRedirectSQLRef.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Spark 3.0 accidentally dropped R < 3.5. It is built by R 3.6.3 which not support R < 3.5:
```
Error in readRDS(pfile) : cannot read workspace version 3 written by R 3.6.3; need R 3.5.0 or newer version.
```
In fact, with SPARK-31918, we will have to drop R < 3.5 entirely to support R 4.0.0. This is inevitable to release on CRAN because they require to make the tests pass with the latest R.
### Why are the changes needed?
To show the supported versions correctly, and support R 4.0.0 to unblock the releases.
### Does this PR introduce _any_ user-facing change?
In fact, no because Spark 3.0.0 already does not work with R < 3.5.
Compared to Spark 2.4, yes. R < 3.5 would not work.
### How was this patch tested?
Jenkins should test it out.
Closes#28908 from HyukjinKwon/SPARK-32073.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Spark uses an old and deprecated API named `KafkaConsumer.poll(long)` which never returns and stays in live lock if metadata is not updated (for instance when broker disappears at consumer creation). Please see [Kafka documentation](https://kafka.apache.org/25/javadoc/org/apache/kafka/clients/consumer/KafkaConsumer.html#poll-long-) and [standalone test application](https://github.com/gaborgsomogyi/kafka-get-assignment) for further details.
In this PR I've applied the new `KafkaConsumer.poll(Duration)` API on executor side. Please note driver side still uses the old API which will be fixed in SPARK-32032.
### Why are the changes needed?
Infinite wait in `KafkaConsumer.poll(long)`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Closes#28871 from gaborgsomogyi/SPARK-32033.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Emphasize the Streaming tab is for DStream API.
### Why are the changes needed?
Some users reported that it's a little confusing of the streaming tab and structured streaming tab.
### Does this PR introduce _any_ user-facing change?
Document change.
### How was this patch tested?
N/A
Closes#28854 from xuanyuanking/minor-doc.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Fix executor container name typo. `executor` should be `spark-kubernetes-executor`.
### Why are the changes needed?
The Executor pod container name the users actually get from their Kubernetes clusters is different from that described in the documentation.
For example, below is what a user get from an executor pod.
```
Containers:
spark-kubernetes-executor:
Container ID: docker://aaaabbbbccccddddeeeeffff
Image: <imagename>
Image ID: docker-pullable://0000.dkr.ecr.us-east-0.amazonaws.com/spark
Port: 7079/TCP
Host Port: 0/TCP
Args:
executor
State: Running
Started: Thu, 28 May 2020 05:54:04 -0700
Ready: True
Restart Count: 0
Limits:
memory: 16Gi
```
### Does this PR introduce _any_ user-facing change?
Document change.
### How was this patch tested?
N/A
Closes#28862 from yuj/patch-1.
Authored-by: James Yu <yuj@users.noreply.github.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
If a Spark distribution has built-in hadoop runtime, Spark will not populate the hadoop classpath from `yarn.application.classpath` and `mapreduce.application.classpath` when a job is submitted to Yarn. Users can override this behavior by setting `spark.yarn.populateHadoopClasspath` to `true`.
### Why are the changes needed?
Without this, Spark will populate the hadoop classpath from `yarn.application.classpath` and `mapreduce.application.classpath` even Spark distribution has built-in hadoop. This results jar conflict and many unexpected behaviors in runtime.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manually test with two builds, with-hadoop and no-hadoop builds.
Closes#28788 from dbtsai/yarn-classpath.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
we fail casting from numeric to timestamp by default.
## Why are the changes needed?
casting from numeric to timestamp is not a non-standard,meanwhile it may generate different result between spark and other systems,for example hive
## Does this PR introduce any user-facing change?
Yes,user cannot cast numeric to timestamp directly,user have to use the following function to achieve the same effect:TIMESTAMP_SECONDS/TIMESTAMP_MILLIS/TIMESTAMP_MICROS
## How was this patch tested?
unit test added
Closes#28593 from GuoPhilipse/31710-fix-compatibility.
Lead-authored-by: GuoPhilipse <guofei_ok@126.com>
Co-authored-by: GuoPhilipse <46367746+GuoPhilipse@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR renames the variable from "numLateInputs" to "numRowsDroppedByWatermark" so that it becomes self-explanation.
### Why are the changes needed?
This is originated from post-review, see https://github.com/apache/spark/pull/28607#discussion_r439853232
### Does this PR introduce _any_ user-facing change?
No, as SPARK-24634 is not introduced in any release yet.
### How was this patch tested?
Existing UTs.
Closes#28828 from HeartSaVioR/SPARK-24634-v3-followup.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR intends to move keywords `ANTI`, `SEMI`, and `MINUS` from reserved to non-reserved.
### Why are the changes needed?
To comply with the ANSI/SQL standard.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added tests.
Closes#28807 from maropu/SPARK-26905-2.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR proposes to use "mdc.XXX" as the consistent key for both `sc.setLocalProperty` and `log4j.properties` when setting up configurations for MDC.
### Why are the changes needed?
It's weird that we use "mdc.XXX" as key to set MDC value via `sc.setLocalProperty` while we use "XXX" as key to set MDC pattern in log4j.properties. It could also bring extra burden to the user.
### Does this PR introduce _any_ user-facing change?
No, as MDC feature is added in version 3.1, which hasn't been released.
### How was this patch tested?
Tested manually.
Closes#28801 from Ngone51/consistent-mdc.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Please refer https://issues.apache.org/jira/browse/SPARK-24634 to see rationalization of the issue.
This patch adds a new metric to count the number of inputs arrived later than watermark plus allowed delay. To make changes simpler, this patch doesn't count the exact number of input rows which are later than watermark plus allowed delay. Instead, this patch counts the inputs which are dropped in the logic of operator. The difference of twos are shown in streaming aggregation: to optimize the calculation, streaming aggregation "pre-aggregates" the input rows, and later checks the lateness against "pre-aggregated" inputs, hence the number might be reduced.
The new metric will be provided via two places:
1. On Spark UI: check the metrics in stateful operator nodes in query execution details page in SQL tab
2. On Streaming Query Listener: check "numLateInputs" in "stateOperators" in QueryProcessEvent.
### Why are the changes needed?
Dropping late inputs means that end users might not get expected outputs. Even end users may indicate the fact and tolerate the result (as that's what allowed lateness is for), but they should be able to observe whether the current value of allowed lateness drops inputs or not so that they can adjust the value.
Also, whatever the chance they have multiple of stateful operators in a single query, if Spark drops late inputs "between" these operators, it becomes "correctness" issue. Spark should disallow such possibility, but given we already provided the flexibility, at least we should provide the way to observe the correctness issue and decide whether they should make correction of their query or not.
### Does this PR introduce _any_ user-facing change?
Yes. End users will be able to retrieve the information of late inputs via two ways:
1. SQL tab in Spark UI
2. Streaming Query Listener
### How was this patch tested?
New UTs added & existing UTs are modified to reflect the change.
And ran manual test reproducing SPARK-28094.
I've picked the specific case on "B outer C outer D" which is enough to represent the "intermediate late row" issue due to global watermark.
https://gist.github.com/jammann/b58bfbe0f4374b89ecea63c1e32c8f17
Spark logs warning message on the query which means SPARK-28074 is working correctly,
```
20/05/30 17:52:47 WARN UnsupportedOperationChecker: Detected pattern of possible 'correctness' issue due to global watermark. The query contains stateful operation which can emit rows older than the current watermark plus allowed late record delay, which are "late rows" in downstream stateful operations and these rows can be discarded. Please refer the programming guide doc for more details.;
Join LeftOuter, ((D_FK#28 = D_ID#87) AND (B_LAST_MOD#26-T30000ms = D_LAST_MOD#88-T30000ms))
:- Join LeftOuter, ((C_FK#27 = C_ID#58) AND (B_LAST_MOD#26-T30000ms = C_LAST_MOD#59-T30000ms))
: :- EventTimeWatermark B_LAST_MOD#26: timestamp, 30 seconds
: : +- Project [v#23.B_ID AS B_ID#25, v#23.B_LAST_MOD AS B_LAST_MOD#26, v#23.C_FK AS C_FK#27, v#23.D_FK AS D_FK#28]
: : +- Project [from_json(StructField(B_ID,StringType,false), StructField(B_LAST_MOD,TimestampType,false), StructField(C_FK,StringType,true), StructField(D_FK,StringType,true), value#21, Some(UTC)) AS v#23]
: : +- Project [cast(value#8 as string) AS value#21]
: : +- StreamingRelationV2 org.apache.spark.sql.kafka010.KafkaSourceProvider3a7fd18c, kafka, org.apache.spark.sql.kafka010.KafkaSourceProvider$KafkaTable396d2958, org.apache.spark.sql.util.CaseInsensitiveStringMapa51ee61a, [key#7, value#8, topic#9, partition#10, offset#11L, timestamp#12, timestampType#13], StreamingRelation DataSource(org.apache.spark.sql.SparkSessiond221af8,kafka,List(),None,List(),None,Map(inferSchema -> true, startingOffsets -> earliest, subscribe -> B, kafka.bootstrap.servers -> localhost:9092),None), kafka, [key#0, value#1, topic#2, partition#3, offset#4L, timestamp#5, timestampType#6]
: +- EventTimeWatermark C_LAST_MOD#59: timestamp, 30 seconds
: +- Project [v#56.C_ID AS C_ID#58, v#56.C_LAST_MOD AS C_LAST_MOD#59]
: +- Project [from_json(StructField(C_ID,StringType,false), StructField(C_LAST_MOD,TimestampType,false), value#54, Some(UTC)) AS v#56]
: +- Project [cast(value#41 as string) AS value#54]
: +- StreamingRelationV2 org.apache.spark.sql.kafka010.KafkaSourceProvider3f507373, kafka, org.apache.spark.sql.kafka010.KafkaSourceProvider$KafkaTable7b6736a4, org.apache.spark.sql.util.CaseInsensitiveStringMapa51ee61b, [key#40, value#41, topic#42, partition#43, offset#44L, timestamp#45, timestampType#46], StreamingRelation DataSource(org.apache.spark.sql.SparkSessiond221af8,kafka,List(),None,List(),None,Map(inferSchema -> true, startingOffsets -> earliest, subscribe -> C, kafka.bootstrap.servers -> localhost:9092),None), kafka, [key#33, value#34, topic#35, partition#36, offset#37L, timestamp#38, timestampType#39]
+- EventTimeWatermark D_LAST_MOD#88: timestamp, 30 seconds
+- Project [v#85.D_ID AS D_ID#87, v#85.D_LAST_MOD AS D_LAST_MOD#88]
+- Project [from_json(StructField(D_ID,StringType,false), StructField(D_LAST_MOD,TimestampType,false), value#83, Some(UTC)) AS v#85]
+- Project [cast(value#70 as string) AS value#83]
+- StreamingRelationV2 org.apache.spark.sql.kafka010.KafkaSourceProvider2b90e779, kafka, org.apache.spark.sql.kafka010.KafkaSourceProvider$KafkaTable36f8cd29, org.apache.spark.sql.util.CaseInsensitiveStringMapa51ee620, [key#69, value#70, topic#71, partition#72, offset#73L, timestamp#74, timestampType#75], StreamingRelation DataSource(org.apache.spark.sql.SparkSessiond221af8,kafka,List(),None,List(),None,Map(inferSchema -> true, startingOffsets -> earliest, subscribe -> D, kafka.bootstrap.servers -> localhost:9092),None), kafka, [key#62, value#63, topic#64, partition#65, offset#66L, timestamp#67, timestampType#68]
```
and we can find the late inputs from the batch 4 as follows:
which represents intermediate inputs are being lost, ended up with correctness issue.
Closes#28607 from HeartSaVioR/SPARK-24634-v3.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Currently, `date_format` and `from_unixtime`, `unix_timestamp`,`to_unix_timestamp`, `to_timestamp`, `to_date` have different exception handling behavior for formatting datetime values.
In this PR, we apply the exception handling behavior of `date_format` to `from_unixtime`, `unix_timestamp`,`to_unix_timestamp`, `to_timestamp` and `to_date`.
In the phase of creating the datetime formatted or formating, exceptions will be raised.
e.g.
```java
spark-sql> select date_format(make_timestamp(1, 1 ,1,1,1,1), 'yyyyyyyyyyy-MM-aaa');
20/05/28 15:25:38 ERROR SparkSQLDriver: Failed in [select date_format(make_timestamp(1, 1 ,1,1,1,1), 'yyyyyyyyyyy-MM-aaa')]
org.apache.spark.SparkUpgradeException: You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'yyyyyyyyyyy-MM-aaa' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0. 2) You can form a valid datetime pattern with the guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
```
```java
spark-sql> select date_format(make_timestamp(1, 1 ,1,1,1,1), 'yyyyyyyyyyy-MM-AAA');
20/05/28 15:26:10 ERROR SparkSQLDriver: Failed in [select date_format(make_timestamp(1, 1 ,1,1,1,1), 'yyyyyyyyyyy-MM-AAA')]
java.lang.IllegalArgumentException: Illegal pattern character: A
```
```java
spark-sql> select date_format(make_timestamp(1,1,1,1,1,1), 'yyyyyyyyyyy-MM-dd');
20/05/28 15:23:23 ERROR SparkSQLDriver: Failed in [select date_format(make_timestamp(1,1,1,1,1,1), 'yyyyyyyyyyy-MM-dd')]
java.lang.ArrayIndexOutOfBoundsException: 11
at java.time.format.DateTimeFormatterBuilder$NumberPrinterParser.format(DateTimeFormatterBuilder.java:2568)
```
In the phase of parsing, `DateTimeParseException | DateTimeException | ParseException` will be suppressed, but `SparkUpgradeException` will still be raised
e.g.
```java
spark-sql> set spark.sql.legacy.timeParserPolicy=exception;
spark.sql.legacy.timeParserPolicy exception
spark-sql> select to_timestamp("2020-01-27T20:06:11.847-0800", "yyyy-MM-dd'T'HH:mm:ss.SSSz");
20/05/28 15:31:15 ERROR SparkSQLDriver: Failed in [select to_timestamp("2020-01-27T20:06:11.847-0800", "yyyy-MM-dd'T'HH:mm:ss.SSSz")]
org.apache.spark.SparkUpgradeException: You may get a different result due to the upgrading of Spark 3.0: Fail to parse '2020-01-27T20:06:11.847-0800' in the new parser. You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0, or set to CORRECTED and treat it as an invalid datetime string.
```
```java
spark-sql> set spark.sql.legacy.timeParserPolicy=corrected;
spark.sql.legacy.timeParserPolicy corrected
spark-sql> select to_timestamp("2020-01-27T20:06:11.847-0800", "yyyy-MM-dd'T'HH:mm:ss.SSSz");
NULL
spark-sql> set spark.sql.legacy.timeParserPolicy=legacy;
spark.sql.legacy.timeParserPolicy legacy
spark-sql> select to_timestamp("2020-01-27T20:06:11.847-0800", "yyyy-MM-dd'T'HH:mm:ss.SSSz");
2020-01-28 12:06:11.847
```
### Why are the changes needed?
Consistency
### Does this PR introduce _any_ user-facing change?
Yes, invalid datetime patterns will fail `from_unixtime`, `unix_timestamp`,`to_unix_timestamp`, `to_timestamp` and `to_date` instead of resulting `NULL`
### How was this patch tested?
add more tests
Closes#28650 from yaooqinn/SPARK-31830.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
These changes implement an application wait mechanism which will allow spark-submit to wait until the application finishes in Standalone Spark Mode. This will delay the exit of spark-submit JVM until the job is completed. This implementation will keep monitoring the application until it is either finished, failed or killed. This will be controlled via a flag (spark.submit.waitForCompletion) which will be set to false by default.
### Why are the changes needed?
Currently, Livy API for Standalone Cluster Mode doesn't know when the job has finished. If this flag is enabled, this can be used by Livy API (/batches/{batchId}/state) to find out when the application has finished/failed. This flag is Similar to spark.yarn.submit.waitAppCompletion.
### Does this PR introduce any user-facing change?
Yes, this PR introduces a new flag but it will be disabled by default.
### How was this patch tested?
Couldn't implement unit tests since the pollAndReportStatus method has System.exit() calls. Please provide any suggestions.
Tested spark-submit locally for the following scenarios:
1. With the flag enabled, spark-submit exits once the job is finished.
2. With the flag enabled and job failed, spark-submit exits when the job fails.
3. With the flag disabled, spark-submit exists post submitting the job (existing behavior).
4. Existing behavior is unchanged when the flag is not added explicitly.
Closes#28258 from akshatb1/master.
Lead-authored-by: Akshat Bordia <akshat.bordia31@gmail.com>
Co-authored-by: Akshat Bordia <akshat.bordia@citrix.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
SPARK-28199 (#24996) made the trigger related public API to be exposed only from static methods of Trigger class. This is backward incompatible change, so some users may experience compilation error after upgrading to Spark 3.0.0.
While we plan to mention the change into release note, it's good to mention the change to the migration guide doc as well, since the purpose of the doc is to collect the major changes/incompatibilities between versions and end users would refer the doc.
### Why are the changes needed?
SPARK-28199 is technically backward incompatible change and we should kindly guide the change.
### Does this PR introduce _any_ user-facing change?
Doc change.
### How was this patch tested?
N/A, as it's just a doc change.
Closes#28763 from HeartSaVioR/SPARK-28199-FOLLOWUP-doc.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`fileNameOnly` parameter is split to 2 pieces in [this](dbb8143501) commit. This PR re-unites it.
### Why are the changes needed?
Parameter description split in doc.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
```
cd docs/
SKIP_API=1 jekyll build
```
Manual webpage check.
Closes#28739 from gaborgsomogyi/datasettxtfix.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
# What changes were proposed in this pull request?
After all these attempts https://github.com/apache/spark/pull/28692 and https://github.com/apache/spark/pull/28719 an https://github.com/apache/spark/pull/28727.
they all have limitations as mentioned in their discussions.
Maybe the only way is to forbid them all
### Why are the changes needed?
These week-based fields need Locale to express their semantics, the first day of the week varies from country to country.
From the Java doc of WeekFields
```java
/**
* Gets the first day-of-week.
* <p>
* The first day-of-week varies by culture.
* For example, the US uses Sunday, while France and the ISO-8601 standard use Monday.
* This method returns the first day using the standard {code DayOfWeek} enum.
*
* return the first day-of-week, not null
*/
public DayOfWeek getFirstDayOfWeek() {
return firstDayOfWeek;
}
```
But for the SimpleDateFormat, the day-of-week is not localized
```
u Day number of week (1 = Monday, ..., 7 = Sunday) Number 1
```
Currently, the default locale we use is the US, so the result moved a day or a year or a week backward.
e.g.
For the date `2019-12-29(Sunday)`, in the Sunday Start system(e.g. en-US), it belongs to 2020 of week-based-year, in the Monday Start system(en-GB), it goes to 2019. the week-of-week-based-year(w) will be affected too
```sql
spark-sql> SELECT to_csv(named_struct('time', to_timestamp('2019-12-29', 'yyyy-MM-dd')), map('timestampFormat', 'YYYY', 'locale', 'en-US'));
2020
spark-sql> SELECT to_csv(named_struct('time', to_timestamp('2019-12-29', 'yyyy-MM-dd')), map('timestampFormat', 'YYYY', 'locale', 'en-GB'));
2019
spark-sql> SELECT to_csv(named_struct('time', to_timestamp('2019-12-29', 'yyyy-MM-dd')), map('timestampFormat', 'YYYY-ww-uu', 'locale', 'en-US'));
2020-01-01
spark-sql> SELECT to_csv(named_struct('time', to_timestamp('2019-12-29', 'yyyy-MM-dd')), map('timestampFormat', 'YYYY-ww-uu', 'locale', 'en-GB'));
2019-52-07
spark-sql> SELECT to_csv(named_struct('time', to_timestamp('2020-01-05', 'yyyy-MM-dd')), map('timestampFormat', 'YYYY-ww-uu', 'locale', 'en-US'));
2020-02-01
spark-sql> SELECT to_csv(named_struct('time', to_timestamp('2020-01-05', 'yyyy-MM-dd')), map('timestampFormat', 'YYYY-ww-uu', 'locale', 'en-GB'));
2020-01-07
```
For other countries, please refer to [First Day of the Week in Different Countries](http://chartsbin.com/view/41671)
### Does this PR introduce _any_ user-facing change?
With this change, user can not use 'YwuW', but 'e' for 'u' instead. This can at least turn this not to be a silent data change.
### How was this patch tested?
add unit tests
Closes#28728 from yaooqinn/SPARK-31879-NEW2.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The Pyspark Migration Guide needs to mention a breaking change of the Pyspark ML API.
### Why are the changes needed?
In SPARK-29093, all setters have been removed from `Params` mixins in `pyspark.ml.param.shared`. Those setters had been part of the public pyspark ML API, hence this is a breaking change.
### Does this PR introduce _any_ user-facing change?
Only documentation.
### How was this patch tested?
Visually.
Closes#28663 from EnricoMi/branch-pyspark-migration-guide-setters.
Authored-by: Enrico Minack <github@enrico.minack.dev>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR disables week-based date filed for parsing
closes#28674
### Why are the changes needed?
1. It's an un-fixable behavior change to fill the gap between SimpleDateFormat and DateTimeFormater and backward-compatibility for different JDKs.A lot of effort has been made to prove it at https://github.com/apache/spark/pull/28674
2. The existing behavior itself in 2.4 is confusing, e.g.
```sql
spark-sql> select to_timestamp('1', 'w');
1969-12-28 00:00:00
spark-sql> select to_timestamp('1', 'u');
1970-01-05 00:00:00
```
The 'u' here seems not to go to the Monday of the first week in week-based form or the first day of the year in non-week-based form but go to the Monday of the second week in week-based form.
And, e.g.
```sql
spark-sql> select to_timestamp('2020 2020', 'YYYY yyyy');
2020-01-01 00:00:00
spark-sql> select to_timestamp('2020 2020', 'yyyy YYYY');
2019-12-29 00:00:00
spark-sql> select to_timestamp('2020 2020 1', 'YYYY yyyy w');
NULL
spark-sql> select to_timestamp('2020 2020 1', 'yyyy YYYY w');
2019-12-29 00:00:00
```
I think we don't need to introduce all the weird behavior from Java
3. The current test coverage for week-based date fields is almost 0%, which indicates that we've never imagined using it.
4. Avoiding JDK bugs
https://issues.apache.org/jira/browse/SPARK-31880
### Does this PR introduce _any_ user-facing change?
Yes, the 'Y/W/w/u/F/E' pattern cannot be used datetime parsing functions.
### How was this patch tested?
more tests added
Closes#28706 from yaooqinn/SPARK-31892.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
SQL Rest API exposes query execution details and metrics as Public API. Its documentation will be useful for the end-users.
### Why are the changes needed?
SQL Rest API does not exist under Spark Rest API.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manually build and check
Closes#28354 from erenavsarogullari/SPARK-31566.
Lead-authored-by: Eren Avsarogullari <eren.avsarogullari@gmail.com>
Co-authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Co-authored-by: Eren Avsarogullari <erenavsarogullari@gmail.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
We should use dataType.catalogString to unified the data type mismatch message.
Before:
```sql
spark-sql> create table SPARK_31834(a int) using parquet;
spark-sql> insert into SPARK_31834 select '1';
Error in query: Cannot write incompatible data to table '`default`.`spark_31834`':
- Cannot safely cast 'a': StringType to IntegerType;
```
After:
```sql
spark-sql> create table SPARK_31834(a int) using parquet;
spark-sql> insert into SPARK_31834 select '1';
Error in query: Cannot write incompatible data to table '`default`.`spark_31834`':
- Cannot safely cast 'a': string to int;
```
### How was this patch tested?
UT.
Closes#28654 from lipzhu/SPARK-31834.
Authored-by: lipzhu <lipzhu@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
As mentioned in https://github.com/apache/spark/pull/28673 and suggested via cloud-fan at https://github.com/apache/spark/pull/28673#discussion_r432817075
In this PR, we disable datetime pattern in the form of `y..y` and `Y..Y` whose lengths are greater than 10 to avoid sort of JDK bug as described below
he new datetime formatter introduces silent data change like,
```sql
spark-sql> select from_unixtime(1, 'yyyyyyyyyyy-MM-dd');
NULL
spark-sql> set spark.sql.legacy.timeParserPolicy=legacy;
spark.sql.legacy.timeParserPolicy legacy
spark-sql> select from_unixtime(1, 'yyyyyyyyyyy-MM-dd');
00000001970-01-01
spark-sql>
```
For patterns that support `SignStyle.EXCEEDS_PAD`, e.g. `y..y`(len >=4), when using the `NumberPrinterParser` to format it
```java
switch (signStyle) {
case EXCEEDS_PAD:
if (minWidth < 19 && value >= EXCEED_POINTS[minWidth]) {
buf.append(decimalStyle.getPositiveSign());
}
break;
....
```
the `minWidth` == `len(y..y)`
the `EXCEED_POINTS` is
```java
/**
* Array of 10 to the power of n.
*/
static final long[] EXCEED_POINTS = new long[] {
0L,
10L,
100L,
1000L,
10000L,
100000L,
1000000L,
10000000L,
100000000L,
1000000000L,
10000000000L,
};
```
So when the `len(y..y)` is greater than 10, ` ArrayIndexOutOfBoundsException` will be raised.
And at the caller side, for `from_unixtime`, the exception will be suppressed and silent data change occurs. for `date_format`, the `ArrayIndexOutOfBoundsException` will continue.
### Why are the changes needed?
fix silent data change
### Does this PR introduce _any_ user-facing change?
Yes, SparkUpgradeException will take place of `null` result when the pattern contains 10 or more continuous 'y' or 'Y'
### How was this patch tested?
new tests
Closes#28684 from yaooqinn/SPARK-31867-2.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
If `LLL`/`qqq` is used in the datetime pattern string, and the current JDK in use has a bug for the stand-alone form (see https://bugs.openjdk.java.net/browse/JDK-8114833), throw an exception with a clear error message.
### Why are the changes needed?
to keep backward compatibility with Spark 2.4
### Does this PR introduce _any_ user-facing change?
Yes
Spark 2.4
```
scala> sql("select date_format('1990-1-1', 'LLL')").show
+---------------------------------------------+
|date_format(CAST(1990-1-1 AS TIMESTAMP), LLL)|
+---------------------------------------------+
| Jan|
+---------------------------------------------+
```
Spark 3.0 with Java 11
```
scala> sql("select date_format('1990-1-1', 'LLL')").show
+---------------------------------------------+
|date_format(CAST(1990-1-1 AS TIMESTAMP), LLL)|
+---------------------------------------------+
| Jan|
+---------------------------------------------+
```
Spark 3.0 with Java 8
```
// before this PR
+---------------------------------------------+
|date_format(CAST(1990-1-1 AS TIMESTAMP), LLL)|
+---------------------------------------------+
| 1|
+---------------------------------------------+
// after this PR
scala> sql("select date_format('1990-1-1', 'LLL')").show
org.apache.spark.SparkUpgradeException
```
### How was this patch tested?
manual test with java 8 and 11
Closes#28646 from cloud-fan/format.
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 adds the structured streaming UI introduction to the Web UI doc.
### Why are the changes needed?
The structured streaming web UI introduced before was missing from the Web UI documentation.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N.A.
Closes#28609 from xccui/ss-ui-doc.
Authored-by: Xingcan Cui <xccui@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Five continuous pattern characters with 'G/M/L/E/u/Q/q' means Narrow-Text Style while we turn to use `java.time.DateTimeFormatterBuilder` since 3.0.0, which output the leading single letter of the value, e.g. `December` would be `D`. In Spark 2.4 they mean Full-Text Style.
In this PR, we explicitly disable Narrow-Text Style for these pattern characters.
### Why are the changes needed?
Without this change, there will be a silent data change.
### Does this PR introduce _any_ user-facing change?
Yes, queries with datetime operations using datetime patterns, e.g. `G/M/L/E/u` will fail if the pattern length is 5 and other patterns, e,g. 'k', 'm' also can accept a certain number of letters.
1. datetime patterns that are not supported by the new parser but the legacy will get SparkUpgradeException, e.g. "GGGGG", "MMMMM", "LLLLL", "EEEEE", "uuuuu", "aa", "aaa". 2 options are given to end-users, one is to use legacy mode, and the other is to follow the new online doc for correct datetime patterns
2, datetime patterns that are not supported by both the new parser and the legacy, e.g. "QQQQQ", "qqqqq", will get IllegalArgumentException which is captured by Spark internally and results NULL to end-users.
### How was this patch tested?
add unit tests
Closes#28592 from yaooqinn/SPARK-31771.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
add docs for sql migration-guide
### Why are the changes needed?
let user know more about the cast scenarios in which Hive and Spark generate different results
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
no need to test
Closes#28605 from GuoPhilipse/spark-docs.
Lead-authored-by: GuoPhilipse <guofei_ok@126.com>
Co-authored-by: GuoPhilipse <46367746+GuoPhilipse@users.noreply.github.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Added MDC support in all thread pools.
ThreaddUtils create new pools that pass over MDC.
### Why are the changes needed?
In many cases, it is very hard to understand from which actions the logs in the executor come from.
when you are doing multi-thread work in the driver and send actions in parallel.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
No test added because no new functionality added it is thread pull change and all current tests pass.
Closes#26624 from igreenfield/master.
Authored-by: Izek Greenfield <igreenfield@axiomsl.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Describe standard 'M' and stand-alone 'L' text forms
2. Add examples for all supported number of month letters
<img width="1047" alt="Screenshot 2020-05-18 at 08 57 31" src="https://user-images.githubusercontent.com/1580697/82178856-b16f1000-98e5-11ea-87c0-456ef94dcd43.png">
### Why are the changes needed?
To improve docs and show how to use month patterns.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By building docs and checking by eyes.
Closes#28558 from MaxGekk/describe-L-M-date-pattern.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch effectively reverts SPARK-30098 via below changes:
* Removed the config
* Removed the changes done in parser rule
* Removed the usage of config in tests
* Removed tests which depend on the config
* Rolled back some tests to before SPARK-30098 which were affected by SPARK-30098
* Reflect the change into docs (migration doc, create table syntax)
### Why are the changes needed?
SPARK-30098 brought confusion and frustration on using create table DDL query, and we agreed about the bad effect on the change.
Please go through the [discussion thread](http://apache-spark-developers-list.1001551.n3.nabble.com/DISCUSS-Resolve-ambiguous-parser-rule-between-two-quot-create-table-quot-s-td29051i20.html) to see the details.
### Does this PR introduce _any_ user-facing change?
No, compared to Spark 2.4.x. End users tried to experiment with Spark 3.0.0 previews will see the change that the behavior is going back to Spark 2.4.x, but I believe we won't guarantee compatibility in preview releases.
### How was this patch tested?
Existing UTs.
Closes#28517 from HeartSaVioR/revert-SPARK-30098.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
# What changes were proposed in this pull request?
This PR is a follow-up to fix a version of configuration document.
### Why are the changes needed?
The original PR is backported to branch-3.0.
### Does this PR introduce _any_ user-facing change?
Yes.
### How was this patch tested?
Manual.
Closes#28530 from dongjoon-hyun/SPARK-31696-2.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims to add `spark.kubernetes.driver.service.annotation` like `spark.kubernetes.driver.service.annotation`.
### Why are the changes needed?
Annotations are used in many ways. One example is that Prometheus monitoring system search metric endpoint via annotation.
- https://github.com/helm/charts/tree/master/stable/prometheus#scraping-pod-metrics-via-annotations
### Does this PR introduce _any_ user-facing change?
Yes. The documentation is added.
### How was this patch tested?
Pass Jenkins with the updated unit tests.
Closes#28518 from dongjoon-hyun/SPARK-31696.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR proposes to set the minimum Arrow version as 0.15.1 to be consistent with PySpark side at.
### Why are the changes needed?
It will reduce the maintenance overhead to match the Arrow versions, and minimize the supported range. SparkR Arrow optimization is experimental yet.
### Does this PR introduce _any_ user-facing change?
No, it's the change in unreleased branches only.
### How was this patch tested?
0.15.x was already tested at SPARK-29378, and we're testing the latest version of SparkR currently in AppVeyor. I already manually tested too.
Closes#28520 from HyukjinKwon/SPARK-31701.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This changes the docs to make it clearer that order preservation is not guaranteed when saving a RDD to disk and reading it back ([SPARK-5300](https://issues.apache.org/jira/browse/SPARK-5300)).
I added two sentences about this in the RDD Programming Guide.
The issue was discussed on the dev mailing list:
http://apache-spark-developers-list.1001551.n3.nabble.com/RDD-order-guarantees-td10142.html
### Why are the changes needed?
Because RDDs are order-aware collections, it is natural to expect that if I use `saveAsTextFile` and then load the resulting file with `sparkContext.textFile`, I obtain a RDD in the same order.
This is unfortunately not the case at the moment and there is no agreed upon way to fix this in Spark itself (see PR #4204 which attempted to fix this). Users should be aware of this.
### Does this PR introduce _any_ user-facing change?
Yes, two new sentences in the documentation.
### How was this patch tested?
By checking that the documentation looks good.
Closes#28465 from wetneb/SPARK-5300-docs.
Authored-by: Antonin Delpeuch <antonin@delpeuch.eu>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR aims to new Prometheus-format metric endpoints experimental in Apache Spark 3.0.0.
### Why are the changes needed?
Although the new metrics are disabled by default, we had better make it experimental explicitly in Apache Spark 3.0.0 since the output format is still not fixed. We can finalize it in Apache Spark 3.1.0.
### Does this PR introduce _any_ user-facing change?
Only doc-change is visible to the users.
### How was this patch tested?
Manually check the code since this is a documentation and class annotation change.
Closes#28495 from dongjoon-hyun/SPARK-31674.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Remove the unneeded embedded inline HTML markup by using the basic markdown syntax.
Please see #28414
### Why are the changes needed?
Make the doc cleaner and easily editable by MD editors.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manually build and check
Closes#28451 from huaxingao/html_cleanup.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR adds `spark.yarn.applicationType` to identify the application type
### Why are the changes needed?
The current application defaults to the SPARK type.
In fact, different types of applications have different characteristics and are suitable for different scenarios.For example: SPAKR-SQL, SPARK-STREAMING.
I recommend distinguishing them by the parameter `spark.yarn.applicationType` so that we can more easily manage and maintain different types of applications.
### How was this patch tested?
1.add UT
2.Tested by verifying Yarn-UI `ApplicationType` in the following cases:
- client and cluster mode
Need additional explanation:
limit cannot exceed 20 characters, can be empty or space
The reasons are as follows:
```
// org.apache.hadoop.yarn.server.resourcemanager.submitApplication.
if (submissionContext.getApplicationType() == null) {
submissionContext
.setApplicationType(YarnConfiguration.DEFAULT_APPLICATION_TYPE);
} else {
// APPLICATION_TYPE_LENGTH = 20
if (submissionContext.getApplicationType().length() > YarnConfiguration.APPLICATION_TYPE_LENGTH) {
submissionContext.setApplicationType(submissionContext
.getApplicationType().substring(0,
YarnConfiguration.APPLICATION_TYPE_LENGTH));
}
}
```
Closes#28009 from wang-zhun/SPARK-31235.
Authored-by: wang-zhun <wangzhun6103@gmail.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
This reverts commit 43a73e387c. It sets `INT96` as the timestamp type while saving timestamps to parquet files.
### Why are the changes needed?
To be compatible with Hive and Presto that don't support the `TIMESTAMP_MICROS` type in current stable releases.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By existing test suites.
Closes#28450 from MaxGekk/parquet-int96.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Fixed typo in `docs` directory and in `project/MimaExcludes.scala`
### Why are the changes needed?
Better readability of documents
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No test needed
Closes#28447 from kiszk/typo_20200504.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The "spark.dynamicAllocation.shuffleTimeout" configuration only takes effect if "spark.dynamicAllocation.shuffleTracking.enabled" is true, so we should re-namespace that configuration so that it's nested under the "shuffleTracking" one.
### How was this patch tested?
Covered by current existing test cases.
Closes#28426 from jiangxb1987/confName.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is to clean up the markdown file in SHOW COLUMNS page.
- remove the unneeded embedded inline HTML markup by using the basic markdown syntax.
- use the ``` sql for highlighting the SQL syntax.
### Why are the changes needed?
Make the doc cleaner and easily editable by MD editors.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
**Before**
**After**
Closes#28414 from gatorsmile/cleanupShowColumns.
Lead-authored-by: Xiao Li <gatorsmile@gmail.com>
Co-authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
- Rephrase the API doc for `Column.as`
- Simplify the UTs
### Why are the changes needed?
Address comments in https://github.com/apache/spark/pull/28326
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
New UT added.
Closes#28390 from xuanyuanking/SPARK-27340-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
We are adding a new Spark Yarn configuration, `spark.yarn.populateHadoopClasspath` to not populate Hadoop classpath from `yarn.application.classpath` and `mapreduce.application.classpath`.
### Why are the changes needed?
Spark Yarn client populates extra Hadoop classpath from `yarn.application.classpath` and `mapreduce.application.classpath` when a job is submitted to a Yarn Hadoop cluster.
However, for `with-hadoop` Spark build that embeds Hadoop runtime, it can cause jar conflicts because Spark distribution can contain different version of Hadoop jars.
One case we have is when a user uses an Apache Spark distribution with its-own embedded hadoop, and submits a job to Cloudera or Hortonworks Yarn clusters, because of two different incompatible Hadoop jars in the classpath, it runs into errors.
By not populating the Hadoop classpath from the clusters can address this issue.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
An UT is added, but very hard to add a new integration test since this requires using different incompatible versions of Hadoop.
We also manually tested this PR, and we are able to submit a Spark job using Spark distribution built with Apache Hadoop 2.10 to CDH 5.6 without populating CDH classpath.
Closes#28376 from dbtsai/yarn-classpath.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
This PR addresses two things:
- `SHOW TBLPROPERTIES` should supports view (a regression introduced by #26921)
- `SHOW TBLPROPERTIES` on a temporary view should return empty result (2.4 behavior instead of throwing `AnalysisException`.
### Why are the changes needed?
It's a bug.
### Does this PR introduce any user-facing change?
Yes, now `SHOW TBLPROPERTIES` works on views:
```
scala> sql("CREATE VIEW view TBLPROPERTIES('p1'='v1', 'p2'='v2') AS SELECT 1 AS c1")
scala> sql("SHOW TBLPROPERTIES view").show(truncate=false)
+---------------------------------+-------------+
|key |value |
+---------------------------------+-------------+
|view.catalogAndNamespace.numParts|2 |
|view.query.out.col.0 |c1 |
|view.query.out.numCols |1 |
|p2 |v2 |
|view.catalogAndNamespace.part.0 |spark_catalog|
|p1 |v1 |
|view.catalogAndNamespace.part.1 |default |
+---------------------------------+-------------+
```
And for a temporary view:
```
scala> sql("CREATE TEMPORARY VIEW tview TBLPROPERTIES('p1'='v1', 'p2'='v2') AS SELECT 1 AS c1")
scala> sql("SHOW TBLPROPERTIES tview").show(truncate=false)
+---+-----+
|key|value|
+---+-----+
+---+-----+
```
### How was this patch tested?
Added tests.
Closes#28375 from imback82/show_tblproperties_followup.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds `-Phive` profile to the pre-build phase to build the hive module to dev classpath.
Then reflect the HiveUtils object to dump all configurations in the class.
### Why are the changes needed?
supply SQL configurations from hive module to doc
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
passing Jenkins
add verified locally
Closes#28394 from yaooqinn/SPARK-31596.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR proposes to use a different approach instead of breaking it per Micheal's rubric added at https://spark.apache.org/versioning-policy.html. It deprecates the behaviour for now. It will be gradually removed in the future releases.
After this change,
```python
import warnings
warnings.simplefilter("always")
from pyspark.sql.functions import *
df = spark.range(2)
map_col = create_map(lit(0), lit(100), lit(1), lit(200))
df.withColumn("mapped", map_col.getItem(col('id'))).show()
```
```
/.../python/pyspark/sql/column.py:311: DeprecationWarning: A column as 'key' in getItem is
deprecated as of Spark 3.0, and will not be supported in the future release. Use `column[key]`
or `column.key` syntax instead.
DeprecationWarning)
...
```
```python
import warnings
warnings.simplefilter("always")
from pyspark.sql.functions import *
df = spark.range(2)
struct_col = struct(lit(0), lit(100), lit(1), lit(200))
df.withColumn("struct", struct_col.getField(lit("col1"))).show()
```
```
/.../spark/python/pyspark/sql/column.py:336: DeprecationWarning: A column as 'name'
in getField is deprecated as of Spark 3.0, and will not be supported in the future release. Use
`column[name]` or `column.name` syntax instead.
DeprecationWarning)
```
### Why are the changes needed?
To prevent the radical behaviour change after the amended versioning policy.
### Does this PR introduce any user-facing change?
Yes, it will show the deprecated warning message.
### How was this patch tested?
Manually tested.
Closes#28327 from HyukjinKwon/SPARK-29664.
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 remove the non-existed `hiveClientCalls.count` metric documentation of `CodeGenerator` of the Spark metrics system in the monitoring guide.
There is a duplicated `hiveClientCalls.count` metric in both `namespace=HiveExternalCatalog` and `namespace=CodeGenerator` bullet lists, but there is only one defined inside object `HiveCatalogMetrics`.
Closes#28292 from wezhang/monitoringdoc.
Authored-by: Wei Zhang <wezhang@outlook.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Give more friendly warning message/migration guide of deprecated scala udf to users.
### Why are the changes needed?
User can not distinguish function signature between typed and untyped scala udf. Instead, we shall tell user what to do directly.
### Does this PR introduce any user-facing change?
No, it's newly added in Spark 3.0.
### How was this patch tested?
Pass Jenkins.
Closes#28311 from Ngone51/update_udf_doc.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add migration guide for removed accumulator v1 APIs.
### Why are the changes needed?
Provide better guidance for users' migration.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass Jenkins.
Closes#28309 from Ngone51/SPARK-16775-migration-guide.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Need to address a few more comments
### Why are the changes needed?
Fix a few problems
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
Manually build and check
Closes#28306 from huaxingao/literal-folllowup.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Currently, only the non-static public SQL configurations are dump to public doc, we'd better also add those static public ones as the command `set -v`
This PR force call StaticSQLConf to buildStaticConf.
### Why are the changes needed?
Fix missing SQL configurations in doc
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
add unit test and verify locally to see if public static SQL conf is in `docs/sql-config.html`
Closes#28274 from yaooqinn/SPARK-31498.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
**Hive 2.3.7** fixed these issues:
- HIVE-21508: ClassCastException when initializing HiveMetaStoreClient on JDK10 or newer
- HIVE-21980:Parsing time can be high in case of deeply nested subqueries
- HIVE-22249: Support Parquet through HCatalog
### Why are the changes needed?
Fix CCE during creating HiveMetaStoreClient in JDK11 environment: [SPARK-29245](https://issues.apache.org/jira/browse/SPARK-29245).
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
- [x] Test Jenkins with Hadoop 2.7 (https://github.com/apache/spark/pull/28148#issuecomment-616757840)
- [x] Test Jenkins with Hadoop 3.2 on JDK11 (https://github.com/apache/spark/pull/28148#issuecomment-616294353)
- [x] Manual test with remote hive metastore.
Hive side:
```
export JAVA_HOME=/usr/lib/jdk1.8.0_221
export PATH=$JAVA_HOME/bin:$PATH
cd /usr/lib/hive-2.3.6 # Start Hive metastore with Hive 2.3.6
bin/schematool -dbType derby -initSchema --verbose
bin/hive --service metastore
```
Spark side:
```
export JAVA_HOME=/usr/lib/jdk-11.0.3
export PATH=$JAVA_HOME/bin:$PATH
build/sbt clean package -Phive -Phadoop-3.2 -Phive-thriftserver
export SPARK_PREPEND_CLASSES=true
bin/spark-sql --conf spark.hadoop.hive.metastore.uris=thrift://localhost:9083
```
Closes#28148 from wangyum/SPARK-31381.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR is the follow-up PR of https://github.com/apache/spark/pull/28003
- add a migration guide
- add an end-to-end test case.
### Why are the changes needed?
The original PR made the major behavior change in the user-facing RESET command.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added a new end-to-end test
Closes#28265 from gatorsmile/spark-31234followup.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
This adds newly supported `nfs` volume type description into the document for Apache Spark 3.1.0.
### Why are the changes needed?
To complete the document.
### Does this PR introduce any user-facing change?
Yes. (Doc)
### How was this patch tested?
Manually generate doc and check it.
```
SKIP_API=1 jekyll build
```
Closes#28236 from dongjoon-hyun/SPARK-NFS-DOC.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Update the document and shell script to warn user about the deprecation of multiple workers on the same host support.
### Why are the changes needed?
This is a sub-task of [SPARK-30978](https://issues.apache.org/jira/browse/SPARK-30978), which plans to totally remove support of multiple workers in Spark 3.1. This PR makes the first step to deprecate it firstly in Spark 3.0.
### Does this PR introduce any user-facing change?
Yeah, user see warning when they run start worker script.
### How was this patch tested?
Tested manually.
Closes#27768 from Ngone51/deprecate_spark_worker_instances.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
Add a SQL example for UDAF
### Why are the changes needed?
To make SQL Reference complete
### Does this PR introduce any user-facing change?
Yes.
Add the following page, also change ```Sql``` to ```SQL``` in the example tab for all the sql examples.
<img width="1110" alt="Screen Shot 2020-04-13 at 6 09 24 PM" src="https://user-images.githubusercontent.com/13592258/79175240-06cd7400-7db2-11ea-8f3e-af71a591a64b.png">
### How was this patch tested?
Manually build and check
Closes#28209 from huaxingao/udf_followup.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR intends to drop the built-in function pages from SQL references. We've already had a complete list of built-in functions in the API documents.
See related discussions for more details:
https://github.com/apache/spark/pull/28170#issuecomment-611917191
### Why are the changes needed?
For better SQL documents.
### Does this PR introduce any user-facing change?
### How was this patch tested?
Manually checked.
Closes#28203 from maropu/DropBuiltinFunctionDocs.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR intends to clean up the SQL documents in `doc/sql-ref*`.
Main changes are as follows;
- Fixes wrong syntaxes and capitalize sub-titles
- Adds some DDL queries in `Examples` so that users can run examples there
- Makes query output in `Examples` follows the `Dataset.showString` (right-aligned) format
- Adds/Removes spaces, Indents, or blank lines to follow the format below;
```
---
license...
---
### Description
Writes what's the syntax is.
### Syntax
{% highlight sql %}
SELECT...
WHERE... // 4 indents after the second line
...
{% endhighlight %}
### Parameters
<dl>
<dt><code><em>Param Name</em></code></dt>
<dd>
Param Description
</dd>
...
</dl>
### Examples
{% highlight sql %}
-- It is better that users are able to execute example queries here.
-- So, we prepare test data in the first section if possible.
CREATE TABLE t (key STRING, value DOUBLE);
INSERT INTO t VALUES
('a', 1.0), ('a', 2.0), ('b', 3.0), ('c', 4.0);
-- query output has 2 indents and it follows the `Dataset.showString`
-- format (right-aligned).
SELECT * FROM t;
+---+-----+
|key|value|
+---+-----+
| a| 1.0|
| a| 2.0|
| b| 3.0|
| c| 4.0|
+---+-----+
-- Query statements after the second line have 4 indents.
SELECT key, SUM(value)
FROM t
GROUP BY key;
+---+----------+
|key|sum(value)|
+---+----------+
| c| 4.0|
| b| 3.0|
| a| 3.0|
+---+----------+
...
{% endhighlight %}
### Related Statements
* [XXX](xxx.html)
* ...
```
### Why are the changes needed?
The most changes of this PR are pretty minor, but I think the consistent formats/rules to write documents are important for long-term maintenance in our community
### Does this PR introduce any user-facing change?
Yes.
### How was this patch tested?
Manually checked.
Closes#28151 from maropu/MakeRightAligned.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This pull request adds SparkR wrapper for `FMRegressor`:
- Supporting ` org.apache.spark.ml.r.FMRegressorWrapper`.
- `FMRegressionModel` S4 class.
- Corresponding `spark.fmRegressor`, `predict`, `summary` and `write.ml` generics.
- Corresponding docs and tests.
### Why are the changes needed?
Feature parity.
### Does this PR introduce any user-facing change?
No (new API).
### How was this patch tested?
New unit tests.
Closes#27571 from zero323/SPARK-30819.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Document Spark integration with Hive UDFs/UDAFs/UDTFs
### Why are the changes needed?
To make SQL Reference complete
### Does this PR introduce any user-facing change?
Yes
<img width="1031" alt="Screen Shot 2020-04-02 at 2 22 42 PM" src="https://user-images.githubusercontent.com/13592258/78301971-cc7cf080-74ee-11ea-93c8-7d4c75213b47.png">
### How was this patch tested?
Manually build and check
Closes#28104 from huaxingao/hive-udfs.
Lead-authored-by: Huaxin Gao <huaxing@us.ibm.com>
Co-authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR explicitly mention that the requirement of Iterator of Series to Iterator of Series and Iterator of Multiple Series to Iterator of Series (previously Scalar Iterator pandas UDF).
The actual limitation of this UDF is the same length of the _entire input and output_, instead of each series's length. Namely you can do something as below:
```python
from typing import Iterator, Tuple
import pandas as pd
from pyspark.sql.functions import pandas_udf
pandas_udf("long")
def func(
iterator: Iterator[pd.Series]) -> Iterator[pd.Series]:
return iter([pd.concat(iterator)])
spark.range(100).select(func("id")).show()
```
This characteristic allows you to prefetch the data from the iterator to speed up, compared to the regular Scalar to Scalar (previously Scalar pandas UDF).
### Why are the changes needed?
To document the correct restriction and characteristics of a feature.
### Does this PR introduce any user-facing change?
Yes in the documentation but only in unreleased branches.
### How was this patch tested?
Github Actions should test the documentation build
Closes#28160 from HyukjinKwon/SPARK-30722-followup.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This pull request adds SparkR wrapper for `LinearRegression`
- Supporting `org.apache.spark.ml.rLinearRegressionWrapper`.
- `LinearRegressionModel` S4 class.
- Corresponding `spark.lm` predict, summary and write.ml generics.
- Corresponding docs and tests.
### Why are the changes needed?
Feature parity.
### Does this PR introduce any user-facing change?
No (new API).
### How was this patch tested?
New unit tests.
Closes#27593 from zero323/SPARK-30818.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
HyukjinKwon have ported back all the PR about version to branch-3.0.
I make a double check and found GraphX table lost version head.
This PR will fix the issue.
HyukjinKwon, please help me merge this PR to master and branch-3.0
### Why are the changes needed?
Add version head of GraphX table
### Does this PR introduce any user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#28149 from beliefer/fix-head-of-graphx-table.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Previously, user can issue `SHOW TABLES` to get info of both tables and views.
This PR (SPARK-31113) implements `SHOW VIEWS` SQL command similar to HIVE to get views only.(https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-ShowViews)
**Hive** -- Only show view names
```
hive> SHOW VIEWS;
OK
view_1
view_2
...
```
**Spark(Hive-Compatible)** -- Only show view names, used in tests and `SparkSQLDriver` for CLI applications
```
SHOW VIEWS IN showdb;
view_1
view_2
...
```
**Spark** -- Show more information database/viewName/isTemporary
```
spark-sql> SHOW VIEWS;
userdb view_1 false
userdb view_2 false
...
```
### Why are the changes needed?
`SHOW VIEWS` command provides better granularity to only get information of views.
### Does this PR introduce any user-facing change?
Add new `SHOW VIEWS` SQL command
### How was this patch tested?
Add new test `show-views.sql` and pass existing tests
Closes#27897 from Eric5553/ShowViews.
Authored-by: Eric Wu <492960551@qq.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This pull request adds SparkR wrapper for `FMClassifier`:
- Supporting ` org.apache.spark.ml.r.FMClassifierWrapper`.
- `FMClassificationModel` S4 class.
- Corresponding `spark.fmClassifier`, `predict`, `summary` and `write.ml` generics.
- Corresponding docs and tests.
### Why are the changes needed?
Feature parity.
### Does this PR introduce any user-facing change?
No (new API).
### How was this patch tested?
New unit tests.
Closes#27570 from zero323/SPARK-30820.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Add migration guide for extracting second from datetimes
### Why are the changes needed?
doc the behavior change for extract expression
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A, just passing jenkins
Closes#28140 from yaooqinn/SPARK-29311.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR intends to improve the SQL document of `GROUP BY`; it added the description about FILTER clauses of aggregate functions.
### Why are the changes needed?
To improve the SQL documents
### Does this PR introduce any user-facing change?
Yes.
<img src="https://user-images.githubusercontent.com/692303/78558612-e2234a80-784d-11ea-9353-b3feac4d57a7.png" width="500">
### How was this patch tested?
Manually checked.
Closes#28134 from maropu/SPARK-31358.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR fixes the outdated requirement for `spark.dynamicAllocation.enabled=true`.
### Why are the changes needed?
This is found during 3.0.0 RC1 document review and testing. As described at `spark.dynamicAllocation.shuffleTracking.enabled` in the same table, we can enabled Dynamic Allocation without external shuffle service.
### Does this PR introduce any user-facing change?
Yes. (Doc.)
### How was this patch tested?
Manually generate the doc by `SKIP_API=1 jekyll build`
**BEFORE**
**AFTER**
Closes#28132 from dongjoon-hyun/SPARK-DA-DOC.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR intends to add a new SQL config for controlling a plan explain mode in the events of (e.g., `SparkListenerSQLExecutionStart` and `SparkListenerSQLAdaptiveExecutionUpdate`) SQL listeners. In the current master, the output of `QueryExecution.toString` (this is equivalent to the "extended" explain mode) is stored in these events. I think it is useful to control the content via `SQLConf`. For example, the query "Details" content (TPCDS q66 query) of a SQL tab in a Spark web UI will be changed as follows;
Before this PR:
After this PR:
### Why are the changes needed?
For better usability.
### Does this PR introduce any user-facing change?
Yes; since Spark 3.1, SQL UI data adopts the `formatted` mode for the query plan explain results. To restore the behavior before Spark 3.0, you can set `spark.sql.ui.explainMode` to `extended`.
### How was this patch tested?
Added unit tests.
Closes#28097 from maropu/SPARK-31325.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
For the stage level scheduling feature, add the ability to optionally merged resource profiles if they were specified on multiple RDD within a stage. There is a config to enable this feature, its off by default (spark.scheduler.resourceProfile.mergeConflicts). When the config is set to true, Spark will merge the profiles selecting the max value of each resource (cores, memory, gpu, etc). further documentation will be added with SPARK-30322.
This also added in the ability to check if an equivalent resource profile already exists. This is so that if a user is running stages and combining the same profiles over and over again we don't get an explosion in the number of profiles.
### Why are the changes needed?
To allow users to specify resource on multiple RDD and not worry as much about if they go into the same stage and fail.
### Does this PR introduce any user-facing change?
Yes, when the config is turned on it now merges the profiles instead of errorring out.
### How was this patch tested?
Unit tests
Closes#28053 from tgravescs/SPARK-29153.
Lead-authored-by: Thomas Graves <tgraves@apache.org>
Co-authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
This PR adds description for `Shuffle Write Time` to `web-ui.md`.
### Why are the changes needed?
#27837 added `Shuffle Write Time` metric to task metrics summary but it's not documented yet.
### Does this PR introduce any user-facing change?
Yes.
We can see the description for `Shuffle Write Time` in the new `web-ui.html`.
<img width="956" alt="shuffle-write-time-description" src="https://user-images.githubusercontent.com/4736016/78175342-a9722280-7495-11ea-9cc6-62c6f3619aa3.png">
### How was this patch tested?
Built docs by `SKIP_API=1 jekyll build` in `doc` directory and then confirmed `web-ui.html`.
Closes#28093 from sarutak/SPARK-31073-doc.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Add back the deprecated R APIs removed by https://github.com/apache/spark/pull/22843/ and https://github.com/apache/spark/pull/22815.
These APIs are
- `sparkR.init`
- `sparkRSQL.init`
- `sparkRHive.init`
- `registerTempTable`
- `createExternalTable`
- `dropTempTable`
No need to port the function such as
```r
createExternalTable <- function(x, ...) {
dispatchFunc("createExternalTable(tableName, path = NULL, source = NULL, ...)", x, ...)
}
```
because this was for the backward compatibility when SQLContext exists before assuming from https://github.com/apache/spark/pull/9192, but seems we don't need it anymore since SparkR replaced SQLContext with Spark Session at https://github.com/apache/spark/pull/13635.
### Why are the changes needed?
Amend Spark's Semantic Versioning Policy
### Does this PR introduce any user-facing change?
Yes
The removed R APIs are put back.
### How was this patch tested?
Add back the removed tests
Closes#28058 from huaxingao/r.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR (SPARK-30775) aims to improve the description of the executor metrics in the monitoring documentation.
### Why are the changes needed?
Improve and clarify monitoring documentation by:
- adding reference to the Prometheus end point, as implemented in [SPARK-29064]
- extending the list and descripion of executor metrics, following up from [SPARK-27157]
### Does this PR introduce any user-facing change?
Documentation update.
### How was this patch tested?
n.a.
Closes#27526 from LucaCanali/docPrometheusMetricsFollowupSpark29064.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR (SPARK-31293) fixes wrong command examples, parameter descriptions and help message format for Amazon Kinesis integration with Spark Streaming.
### Why are the changes needed?
To improve usability of those commands.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
I ran the fixed commands manually and confirmed they worked as expected.
Closes#28063 from sekikn/SPARK-31293.
Authored-by: Kengo Seki <sekikn@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Fix a broken link and make the relevant docs reference to the new doc
### Why are the changes needed?
### Does this PR introduce any user-facing change?
Yes, make CACHE TABLE, UNCACHE TABLE, CLEAR CACHE, REFRESH TABLE link to the new doc
### How was this patch tested?
Manually build and check
Closes#28065 from huaxingao/spark-30363-follow-up.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR adds "Pandas Function API" into the menu.
### Why are the changes needed?
To be consistent and to make easier to navigate.
### Does this PR introduce any user-facing change?
No, master only.
### How was this patch tested?
Manually verified by `SKIP_API=1 jekyll build`.
Closes#28054 from HyukjinKwon/followup-spark-30722.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Based on the discussion in the mailing list [[Proposal] Modification to Spark's Semantic Versioning Policy](http://apache-spark-developers-list.1001551.n3.nabble.com/Proposal-Modification-to-Spark-s-Semantic-Versioning-Policy-td28938.html) , this PR is to add back the following APIs whose maintenance cost are relatively small.
- HiveContext
- createExternalTable APIs
### Why are the changes needed?
Avoid breaking the APIs that are commonly used.
### Does this PR introduce any user-facing change?
Adding back the APIs that were removed in 3.0 branch does not introduce the user-facing changes, because Spark 3.0 has not been released.
### How was this patch tested?
add a new test suite for createExternalTable APIs.
Closes#27815 from gatorsmile/addAPIsBack.
Lead-authored-by: gatorsmile <gatorsmile@gmail.com>
Co-authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Skew join handling comes with an overhead: we need to read some data repeatedly. We should treat a partition as skewed if it's large enough so that it's beneficial to do so.
Currently the size threshold is the advisory partition size, which is 64 MB by default. This is not large enough for the skewed partition size threshold.
This PR adds a new config for the threshold and set default value as 256 MB.
### Why are the changes needed?
Avoid skew join handling that may introduce a perf regression.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#27967 from cloud-fan/aqe.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Currently, ResetCommand clear all configurations, including sql configs, static sql configs and spark context level configs.
for example:
```sql
spark-sql> set xyz=abc;
xyz abc
spark-sql> set;
spark.app.id local-1585055396930
spark.app.name SparkSQL::10.242.189.214
spark.driver.host 10.242.189.214
spark.driver.port 65094
spark.executor.id driver
spark.jars
spark.master local[*]
spark.sql.catalogImplementation hive
spark.sql.hive.version 1.2.1
spark.submit.deployMode client
xyz abc
spark-sql> reset;
spark-sql> set;
spark-sql> set spark.sql.hive.version;
spark.sql.hive.version 1.2.1
spark-sql> set spark.app.id;
spark.app.id <undefined>
```
In this PR, we restore spark confs to RuntimeConfig after it is cleared
### Why are the changes needed?
reset command overkills configs which are static.
### Does this PR introduce any user-facing change?
yes, the ResetCommand do not change static configs now
### How was this patch tested?
add ut
Closes#28003 from yaooqinn/SPARK-31234.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Update ml-guide to include ```MulticlassClassificationEvaluator``` weight support in highlights
### Why are the changes needed?
```MulticlassClassificationEvaluator``` weight support is very important, so should include it in highlights
### Does this PR introduce any user-facing change?
Yes
after:
### How was this patch tested?
manually build and check
Closes#28031 from huaxingao/highlights-followup.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Spark introduced CHAR type for hive compatibility but it only works for hive tables. CHAR type is never documented and is treated as STRING type for non-Hive tables.
However, this leads to confusing behaviors
**Apache Spark 3.0.0-preview2**
```
spark-sql> CREATE TABLE t(a CHAR(3));
spark-sql> INSERT INTO TABLE t SELECT 'a ';
spark-sql> SELECT a, length(a) FROM t;
a 2
```
**Apache Spark 2.4.5**
```
spark-sql> CREATE TABLE t(a CHAR(3));
spark-sql> INSERT INTO TABLE t SELECT 'a ';
spark-sql> SELECT a, length(a) FROM t;
a 3
```
According to the SQL standard, `CHAR(3)` should guarantee all the values are of length 3. Since `CHAR(3)` is treated as STRING so Spark doesn't guarantee it.
This PR forbids CHAR type in non-Hive tables as it's not supported correctly.
### Why are the changes needed?
avoid confusing/wrong behavior
### Does this PR introduce any user-facing change?
yes, now users can't create/alter non-Hive tables with CHAR type.
### How was this patch tested?
new tests
Closes#27902 from cloud-fan/char.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
https://github.com/apache/spark/pull/26412 introduced a behavior change that `date_add`/`date_sub` functions can't accept string and double values in the second parameter. This is reasonable as it's error-prone to cast string/double to int at runtime.
However, using string literals as function arguments is very common in SQL databases. To avoid breaking valid use cases that the string literal is indeed an integer, this PR proposes to add ansi_cast for string literal in date_add/date_sub functions. If the string value is not a valid integer, we fail at query compiling time because of constant folding.
### Why are the changes needed?
avoid breaking changes
### Does this PR introduce any user-facing change?
Yes, now 3.0 can run `date_add('2011-11-11', '1')` like 2.4
### How was this patch tested?
new tests.
Closes#27965 from cloud-fan/string.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`INSERT OVERWRITE DIRECTORY` can only use file format (class implements `org.apache.spark.sql.execution.datasources.FileFormat`). This PR fixes it and other minor improvement.
### Why are the changes needed?
### Does this PR introduce any user-facing change?
### How was this patch tested?
Closes#27891 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?
Change BLAS for part of level-1 routines(axpy, dot, scal(double, denseVector)) from java implementation to NativeBLAS when vector size>256
### Why are the changes needed?
In current ML BLAS.scala, all level-1 routines are fixed to use java
implementation. But NativeBLAS(intel MKL, OpenBLAS) can bring up to 11X
performance improvement based on performance test which apply direct
calls against these methods. We should provide a way to allow user take
advantage of NativeBLAS for level-1 routines. Here we do it through
switching to NativeBLAS for these methods from f2jBLAS.
### Does this PR introduce any user-facing change?
Yes, methods axpy, dot, scal in level-1 routines will switch to NativeBLAS when it has more than nativeL1Threshold(fixed value 256) elements and will fallback to f2jBLAS if native BLAS is not properly configured in system.
### How was this patch tested?
Perf test direct calls level-1 routines
Closes#27546 from yma11/SPARK-30773.
Lead-authored-by: yan ma <yan.ma@intel.com>
Co-authored-by: Ma Yan <yan.ma@intel.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Fix errors and missing parts for datetime pattern document
1. The pattern we use is similar to DateTimeFormatter and SimpleDateFormat but not identical. So we shouldn't use any of them in the API docs but use a link to the doc of our own.
2. Some pattern letters are missing
3. Some pattern letters are explicitly banned - Set('A', 'c', 'e', 'n', 'N')
4. the second fraction pattern different logic for parsing and formatting
### Why are the changes needed?
fix and improve doc
### Does this PR introduce any user-facing change?
yes, new and updated doc
### How was this patch tested?
pass Jenkins
viewed locally with `jekyll serve`
Closes#27956 from yaooqinn/SPARK-31189.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
A followup of https://github.com/apache/spark/pull/27936 to update document.
### Why are the changes needed?
correct document
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
N/A
Closes#27950 from cloud-fan/null.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
The meaning of 'u' was day number of the week in SimpleDateFormat, it was changed to year in DateTimeFormatter. Now we keep the old meaning of 'u' by substituting 'u' to 'e' internally and use DateTimeFormatter to parse the pattern string. In DateTimeFormatter, the 'e' and 'c' also represents day-of-week. e.g.
```sql
select date_format(timestamp '2019-10-06', 'yyyy-MM-dd uuuu');
select date_format(timestamp '2019-10-06', 'yyyy-MM-dd uuee');
select date_format(timestamp '2019-10-06', 'yyyy-MM-dd eeee');
```
Because of the substitution, they all goes to `.... eeee` silently. The users may congitive problems of their meanings, so we should mark them as illegal pattern characters to stay the same as before.
This pr move the method `convertIncompatiblePattern` from `DatetimeUtils` to `DateTimeFormatterHelper` object, since it is quite specific for `DateTimeFormatterHelper` class.
And 'e' and 'c' char checking in this method.
Besides,`convertIncompatiblePattern` has a bug that will lose the last `'` if it ends with it, this pr fixes this too. e.g.
```sql
spark-sql> select date_format(timestamp "2019-10-06", "yyyy-MM-dd'S'");
20/03/18 11:19:45 ERROR SparkSQLDriver: Failed in [select date_format(timestamp "2019-10-06", "yyyy-MM-dd'S'")]
java.lang.IllegalArgumentException: Pattern ends with an incomplete string literal: uuuu-MM-dd'S
spark-sql> select to_timestamp("2019-10-06S", "yyyy-MM-dd'S'");
NULL
```
### Why are the changes needed?
avoid vagueness
bug fix
### Does this PR introduce any user-facing change?
no, these are not exposed yet
### How was this patch tested?
add ut
Closes#27939 from yaooqinn/SPARK-31176.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR will add the user guide for AQE and the detailed configurations about the three mainly features in AQE.
### Why are the changes needed?
Add the detailed configurations.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
only add doc no need ut.
Closes#27616 from JkSelf/aqeuserguide.
Authored-by: jiake <ke.a.jia@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The current migration guide of SQL is too long for most readers to find the needed info. This PR is to group the items in the migration guide of Spark SQL based on the corresponding components.
Note. This PR does not change the contents of the migration guides. Attached figure is the screenshot after the change.
### Why are the changes needed?
The current migration guide of SQL is too long for most readers to find the needed info.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#27909 from gatorsmile/migrationGuideReorg.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Since we reverted the original change in https://github.com/apache/spark/pull/27540, this PR is to remove the corresponding migration guide made in the commit https://github.com/apache/spark/pull/24948
### Why are the changes needed?
N/A
### Does this PR introduce any user-facing change?
N/A
### How was this patch tested?
N/A
Closes#27896 from gatorsmile/SPARK-28093Followup.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
When loading DataFrames from JDBC datasource with Kerberos authentication, remote executors (yarn-client/cluster etc. modes) fail to establish a connection due to lack of Kerberos ticket or ability to generate it.
This is a real issue when trying to ingest data from kerberized data sources (SQL Server, Oracle) in enterprise environment where exposing simple authentication access is not an option due to IT policy issues.
In this PR I've added Postgres support (other supported databases will come in later PRs).
What this PR contains:
* Added `keytab` and `principal` JDBC options
* Added `ConnectionProvider` trait and it's impementations:
* `BasicConnectionProvider` => unsecure connection
* `PostgresConnectionProvider` => postgres secure connection
* Added `ConnectionProvider` tests
* Added `PostgresKrbIntegrationSuite` docker integration test
* Created `SecurityUtils` to concentrate re-usable security related functionalities
* Documentation
### Why are the changes needed?
Missing JDBC kerberos support.
### Does this PR introduce any user-facing change?
Yes, 2 additional JDBC options added:
* keytab
* principal
If both provided then Spark does kerberos authentication.
### How was this patch tested?
To demonstrate the functionality with a standalone application I've created this repository: https://github.com/gaborgsomogyi/docker-kerberos
* Additional + existing unit tests
* Additional docker integration test
* Test on cluster manually
* `SKIP_API=1 jekyll build`
Closes#27637 from gaborgsomogyi/SPARK-30874.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@apache.org>
### What changes were proposed in this pull request?
spark.sql.legacy.timeParser.enabled should be removed from SQLConf and the migration guide
spark.sql.legacy.timeParsePolicy is the right one
### Why are the changes needed?
fix doc
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
Pass the jenkins
Closes#27889 from yaooqinn/SPARK-31131.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
1.Add version information to the configuration of `Status`.
2.Update the docs of `Status`.
3.By the way supplementary documentation about https://github.com/apache/spark/pull/27847
I sorted out some information show below.
Item name | Since version | JIRA ID | Commit ID | Note
-- | -- | -- | -- | --
spark.appStateStore.asyncTracking.enable | 2.3.0 | SPARK-20653 | 772e4648d95bda3353723337723543c741ea8476#diff-9ab674b7af7b2097f7d28cb6f5fd1e8c |
spark.ui.liveUpdate.period | 2.3.0 | SPARK-20644 | c7f38e5adb88d43ef60662c5d6ff4e7a95bff580#diff-9ab674b7af7b2097f7d28cb6f5fd1e8c |
spark.ui.liveUpdate.minFlushPeriod | 2.4.2 | SPARK-27394 | a8a2ba11ac10051423e58920062b50f328b06421#diff-9ab674b7af7b2097f7d28cb6f5fd1e8c |
spark.ui.retainedJobs | 1.2.0 | SPARK-2321 | 9530316887612dca060a128fca34dd5a6ab2a9a9#diff-1f32bcb61f51133bd0959a4177a066a5 |
spark.ui.retainedStages | 0.9.0 | None | 112c0a1776bbc866a1026a9579c6f72f293414c4#diff-1f32bcb61f51133bd0959a4177a066a5 | 0.9.0-incubating-SNAPSHOT
spark.ui.retainedTasks | 2.0.1 | SPARK-15083 | 55db26245d69bb02b7d7d5f25029b1a1cd571644#diff-6bdad48cfc34314e89599655442ff210 |
spark.ui.retainedDeadExecutors | 2.0.0 | SPARK-7729 | 9f4263392e492b5bc0acecec2712438ff9a257b7#diff-a0ba36f9b1f9829bf3c4689b05ab6cf2 |
spark.ui.dagGraph.retainedRootRDDs | 2.1.0 | SPARK-17171 | cc87280fcd065b01667ca7a59a1a32c7ab757355#diff-3f492c527ea26679d4307041b28455b8 |
spark.metrics.appStatusSource.enabled | 3.0.0 | SPARK-30060 | 60f20e5ea2000ab8f4a593b5e4217fd5637c5e22#diff-9f796ae06b0272c1f0a012652a5b68d0 |
### Why are the changes needed?
Supplemental configuration version information.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Exists UT
Closes#27848 from beliefer/add-version-to-status-config.
Lead-authored-by: beliefer <beliefer@163.com>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
A few improvements to the sql ref SELECT doc:
1. correct the syntax of SELECT query
2. correct the default of null sort order
3. correct the GROUP BY syntax
4. several minor fixes
### Why are the changes needed?
refine document
### Does this PR introduce any user-facing change?
N/A
### How was this patch tested?
N/A
Closes#27866 from cloud-fan/doc.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR reverts https://github.com/apache/spark/pull/26051 and https://github.com/apache/spark/pull/26066
### Why are the changes needed?
There is no standard requiring that `size(null)` must return null, and returning -1 looks reasonable as well. This is kind of a cosmetic change and we should avoid it if it breaks existing queries. This is similar to reverting TRIM function parameter order change.
### Does this PR introduce any user-facing change?
Yes, change the behavior of `size(null)` back to be the same as 2.4.
### How was this patch tested?
N/A
Closes#27834 from cloud-fan/revert.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
In Spark version 2.4 and earlier, datetime parsing, formatting and conversion are performed by using the hybrid calendar (Julian + Gregorian).
Since the Proleptic Gregorian calendar is de-facto calendar worldwide, as well as the chosen one in ANSI SQL standard, Spark 3.0 switches to it by using Java 8 API classes (the java.time packages that are based on ISO chronology ). The switching job is completed in SPARK-26651.
But after the switching, there are some patterns not compatible between Java 8 and Java 7, Spark needs its own definition on the patterns rather than depends on Java API.
In this PR, we achieve this by writing the document and shadow the incompatible letters. See more details in [SPARK-31030](https://issues.apache.org/jira/browse/SPARK-31030)
### Why are the changes needed?
For backward compatibility.
### Does this PR introduce any user-facing change?
No.
After we define our own datetime parsing and formatting patterns, it's same to old Spark version.
### How was this patch tested?
Existing and new added UT.
Locally document test:
Closes#27830 from xuanyuanking/SPARK-31030.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR makes the following refinements to the workflow for building docs:
* Install Python and Ruby consistently using pyenv and rbenv across both the docs README and the release Dockerfile.
* Pin the Python and Ruby versions we use.
* Pin all direct Python and Ruby dependency versions.
* Eliminate any use of `sudo pip`, which the Python community discourages, or `sudo gem`.
### Why are the changes needed?
This PR should increase the consistency and reproducibility of the doc-building process by managing Python and Ruby in a more consistent way, and by eliminating unused or outdated code.
Here's a possible example of an issue building the docs that would be addressed by the changes in this PR: https://github.com/apache/spark/pull/27459#discussion_r376135719
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manual tests:
* I was able to build the Docker image successfully, minus the final part about `RUN useradd`.
* I am unable to run `do-release-docker.sh` because I am not a committer and don't have the required GPG key.
* I built the docs locally and viewed them in the browser.
I think I need a committer to more fully test out these changes.
Closes#27534 from nchammas/SPARK-30731-building-docs.
Authored-by: Nicholas Chammas <nicholas.chammas@liveramp.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Updating ML docs for 3.0 changes
### Why are the changes needed?
I am auditing 3.0 ML changes, found some docs are missing or not updated. Need to update these.
### Does this PR introduce any user-facing change?
Yes, doc changes
### How was this patch tested?
Manually build and check
Closes#27762 from huaxingao/spark-doc.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This pr intends to support 32 or more grouping attributes for GROUPING_ID. In the current master, an integer overflow can occur to compute grouping IDs;
e75d9afb2f/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala (L613)
For example, the query below generates wrong grouping IDs in the master;
```
scala> val numCols = 32 // or, 31
scala> val cols = (0 until numCols).map { i => s"c$i" }
scala> sql(s"create table test_$numCols (${cols.map(c => s"$c int").mkString(",")}, v int) using parquet")
scala> val insertVals = (0 until numCols).map { _ => 1 }.mkString(",")
scala> sql(s"insert into test_$numCols values ($insertVals,3)")
scala> sql(s"select grouping_id(), sum(v) from test_$numCols group by grouping sets ((${cols.mkString(",")}), (${cols.init.mkString(",")}))").show(10, false)
scala> sql(s"drop table test_$numCols")
// numCols = 32
+-------------+------+
|grouping_id()|sum(v)|
+-------------+------+
|0 |3 |
|0 |3 | // Wrong Grouping ID
+-------------+------+
// numCols = 31
+-------------+------+
|grouping_id()|sum(v)|
+-------------+------+
|0 |3 |
|1 |3 |
+-------------+------+
```
To fix this issue, this pr change code to use long values for `GROUPING_ID` instead of int values.
### Why are the changes needed?
To support more cases in `GROUPING_ID`.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added unit tests.
Closes#26918 from maropu/FixGroupingIdIssue.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR intends to fix typos and phrases in the `/docs` directory. To find them, I run the Intellij typo checker.
### Why are the changes needed?
For better documents.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A
Closes#27819 from maropu/TypoFix-20200306.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
rename the config and make it non-internal.
### Why are the changes needed?
Now we fail the query if duplicated map keys are detected, and provide a legacy config to deduplicate it. However, we must provide a way to get users out of this situation, instead of just rejecting to run the query. This exit strategy should always be there, while legacy config indicates that it may be removed someday.
### Does this PR introduce any user-facing change?
no, just rename a config which was added in 3.0
### How was this patch tested?
add more tests for the fail behavior.
Closes#27772 from cloud-fan/map.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
-c is short for --conf, it was introduced since v1.1.0 but hidden from users until now
### Why are the changes needed?
### Does this PR introduce any user-facing change?
no
expose hidden feature
### How was this patch tested?
Nah
Closes#27802 from yaooqinn/conf.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Fix the migration guide document for `spark.sql.legacy.ctePrecedence.enabled`, which is introduced in #27579.
### Why are the changes needed?
The config value changed.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Document only.
Closes#27782 from xuanyuanking/SPARK-30829-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
ForEachBatch Java example was incorrect
### Why are the changes needed?
Example did not compile
### Does this PR introduce any user-facing change?
Yes, to docs.
### How was this patch tested?
In IDE.
Closes#27740 from roland1982/foreachwriter_java_example_fix.
Authored-by: roland-ondeviceresearch <roland@ondeviceresearch.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Remove automatically resource coordination support from Standalone.
### Why are the changes needed?
Resource coordination is mainly designed for the scenario where multiple workers launched on the same host. However, it's, actually, a non-existed scenario for today's Spark. Because, Spark now can start multiple executors in a single Worker, while it only allow one executor per Worker at very beginning. So, now, it really help nothing for user to launch multiple workers on the same host. Thus, it's not worth for us to bring over complicated implementation and potential high maintain cost for such an impossible scenario.
### Does this PR introduce any user-facing change?
No, it's Spark 3.0 feature.
### How was this patch tested?
Pass Jenkins.
Closes#27722 from Ngone51/abandon_coordination.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
1.Add version information to the configuration of `Kryo`.
2.Update the docs of `Kryo`.
I sorted out some information show below.
Item name | Since version | JIRA ID | Commit ID | Note
-- | -- | -- | -- | --
spark.kryo.registrationRequired | 1.1.0 | SPARK-2102 | efdaeb111917dd0314f1d00ee8524bed1e2e21ca#diff-1f81c62dad0e2dfc387a974bb08c497c |
spark.kryo.registrator | 0.5.0 | None | 91c07a33d90ab0357e8713507134ecef5c14e28a#diff-792ed56b3398163fa14e8578549d0d98 | This is not a release version, do we need to record it?
spark.kryo.classesToRegister | 1.2.0 | SPARK-1813 | 6bb56faea8d238ea22c2de33db93b1b39f492b3a#diff-529fc5c06b9731c1fbda6f3db60b16aa |
spark.kryo.unsafe | 2.1.0 | SPARK-928 | bc167a2a53f5a795d089e8a884569b1b3e2cd439#diff-1f81c62dad0e2dfc387a974bb08c497c |
spark.kryo.pool | 3.0.0 | SPARK-26466 | 38f030725c561979ca98b2a6cc7ca6c02a1f80ed#diff-a3c6b992784f9abeb9f3047d3dcf3ed9 |
spark.kryo.referenceTracking | 0.8.0 | None | 0a8cc309211c62f8824d76618705c817edcf2424#diff-1f81c62dad0e2dfc387a974bb08c497c |
spark.kryoserializer.buffer | 1.4.0 | SPARK-5932 | 2d222fb39dd978e5a33cde6ceb59307cbdf7b171#diff-1f81c62dad0e2dfc387a974bb08c497c |
spark.kryoserializer.buffer.max | 1.4.0 | SPARK-5932 | 2d222fb39dd978e5a33cde6ceb59307cbdf7b171#diff-1f81c62dad0e2dfc387a974bb08c497c |
### Why are the changes needed?
Supplemental configuration version information.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Exists UT
Closes#27734 from beliefer/add-version-to-kryo-config.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
