This commit is published into the public domain.
### What changes were proposed in this pull request?
Some syntax issues in docstrings have been fixed.
### Why are the changes needed?
In some places, the documentation did not render as intended, e.g. parameter documentations were not formatted as such.
### Does this PR introduce any user-facing change?
Slight improvements in documentation.
### How was this patch tested?
Manual testing. No new Sphinx warnings arise due to this change.
Closes#27613 from DavidToneian/SPARK-30859.
Authored-by: David Toneian <david@toneian.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to port Scala's bugfix https://github.com/scala/scala/pull/7693 (Scala 2.13) to address https://github.com/scala/bug/issues/10495 issue.
In short, it is possible for different product instances having the same children to have the same hash. See:
```scala
scala> spark.range(1).selectExpr("id - 1").queryExecution.analyzed.semanticHash()
res0: Int = -565572825
scala> spark.range(1).selectExpr("id + 1").queryExecution.analyzed.semanticHash()
res1: Int = -565572825
```
### Why are the changes needed?
It was found during the review of https://github.com/apache/spark/pull/27565. We should better produce different hash for different objects.
### Does this PR introduce any user-facing change?
No, it's not identified. Possibly performance related issue.
### How was this patch tested?
Manually tested, and unittest was added.
Closes#27601 from HyukjinKwon/SPARK-30847.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In ALTER TABLE, a column in ADD COLUMNS can depend on the position of a column that is just being added. For example, for a table with the following schema:
```
root:
- a: string
- b: long
```
, the following should work:
```
ALTER TABLE t ADD COLUMNS (x int AFTER a, y int AFTER x)
```
Currently, the above statement will throw an exception saying that AFTER x cannot be resolved, because x doesn't exist yet. This PR proposes to fix this issue.
### Why are the changes needed?
To fix a bug described above.
### Does this PR introduce any user-facing change?
Yes, now
```
ALTER TABLE t ADD COLUMNS (x int AFTER a, y int AFTER x)
```
works as expected.
### How was this patch tested?
Added new tests
Closes#27584 from imback82/alter_table_pos_fix.
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 added two DeveloperApis to the Dataset[T] class. Both methods are just exposing lower-level methods to the Dataset[T] class.
### Why are the changes needed?
They are useful for checking whether two dataframes are the same when implementing dataframe caching in python, and also get a unique ID. It's easier to use if we wrap the lower-level APIs.
### Does this PR introduce any user-facing change?
```
scala> val df1 = Seq((1,2),(4,5)).toDF("col1", "col2")
df1: org.apache.spark.sql.DataFrame = [col1: int, col2: int]
scala> val df2 = Seq((1,2),(4,5)).toDF("col1", "col2")
df2: org.apache.spark.sql.DataFrame = [col1: int, col2: int]
scala> val df3 = Seq((0,2),(4,5)).toDF("col1", "col2")
df3: org.apache.spark.sql.DataFrame = [col1: int, col2: int]
scala> val df4 = Seq((0,2),(4,5)).toDF("col0", "col2")
df4: org.apache.spark.sql.DataFrame = [col0: int, col2: int]
scala> df1.semanticHash
res0: Int = 594427822
scala> df2.semanticHash
res1: Int = 594427822
scala> df1.sameSemantics(df2)
res2: Boolean = true
scala> df1.sameSemantics(df3)
res3: Boolean = false
scala> df3.semanticHash
res4: Int = -1592702048
scala> df4.semanticHash
res5: Int = -1592702048
scala> df4.sameSemantics(df3)
res6: Boolean = true
```
### How was this patch tested?
Unit test in scala and doctest in python.
Note: comments are copied from the corresponding lower-level APIs.
Note: There are some issues to be fixed that would improve the hash collision rate: https://github.com/apache/spark/pull/27565#discussion_r379881028Closes#27565 from liangz1/df-same-result.
Authored-by: Liang Zhang <liang.zhang@databricks.com>
Signed-off-by: WeichenXu <weichen.xu@databricks.com>
### Why are the changes needed?
In most of our docs, you can click on a heading to immediately get an anchor link to that specific section of the docs. This is very handy when you are reading the docs and want to share a link to a specific part.
The SQL function docs are lacking this. This PR adds this convenience to the SQL function docs.
Here's the impact on the generated HTML.
Before this PR:
```html
<h3 id="array_join">array_join</h3>
```
After this PR:
```html
<h3 id="array_join"><a class="toclink" href="#array_join">array_join</a></h3>
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
I built the docs manually and reviewed the results in my browser.
Closes#27585 from nchammas/SPARK-30832-sql-doc-headers.
Authored-by: Nicholas Chammas <nicholas.chammas@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
I checked the all the window function and found all of them not add parameter information and version information to the document.
This PR will make a supplement.
### Why are the changes needed?
Documentation is missing and does not meet new standards.
### Does this PR introduce any user-facing change?
Yes. User will face the information of parameters and version.
### How was this patch tested?
Exists UT
Closes#27572 from beliefer/add_since_for_window_function.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
`INSERT OVERWRITE LOCAL DIRECTORY` is supported with ensuring the provided path is always using `file://` as scheme and removing the check which throws exception if we do insert overwrite by mentioning directory with `LOCAL` syntax
### Why are the changes needed?
without the modification in PR, ``` insert overwrite local directory <location> using ```
throws exception
```
Error: org.apache.spark.sql.catalyst.parser.ParseException:
LOCAL is not supported in INSERT OVERWRITE DIRECTORY to data source(line 1, pos 0)
```
which was introduced in https://github.com/apache/spark/pull/18975, but this restriction is not needed, hence dropping the same.
Keep behaviour consistent for local and remote file-system in `INSERT OVERWRITE DIRECTORY`
### Does this PR introduce any user-facing change?
Yes, after this change `INSERT OVERWRITE LOCAL DIRECTORY` will not throw exception
### How was this patch tested?
Added UT
Closes#27039 from ajithme/insertoverwrite2.
Authored-by: Ajith <ajith2489@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In `org.apache.spark.sql.execution.exchange.BroadcastExchangeExec#relationFuture` make a copy of `org.apache.spark.SparkContext#localProperties` and pass it to the broadcast execution thread in `org.apache.spark.sql.execution.exchange.BroadcastExchangeExec#executionContext`
### Why are the changes needed?
When executing `BroadcastExchangeExec`, the relationFuture is evaluated via a separate thread. The threads inherit the `localProperties` from `sparkContext` as they are the child threads.
These threads are created in the executionContext (thread pools). Each Thread pool has a default `keepAliveSeconds` of 60 seconds for idle threads.
Scenarios where the thread pool has threads which are idle and reused for a subsequent new query, the thread local properties will not be inherited from spark context (thread properties are inherited only on thread creation) hence end up having old or no properties set. This will cause taskset properties to be missing when properties are transferred by child thread via `sparkContext.runJob/submitJob`
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added UT
Closes#27266 from ajithme/broadcastlocalprop.
Authored-by: Ajith <ajith2489@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
- Set `spark.sql.datetime.java8API.enabled` to `true` in `hiveResultString()`, and restore it back at the end of the call.
- Convert collected `java.time.Instant` & `java.time.LocalDate` to `java.sql.Timestamp` and `java.sql.Date` for correct formatting.
### Why are the changes needed?
Because of textual representation of timestamps/dates before 1582 year is incorrect:
```shell
$ export TZ="America/Los_Angeles"
$ ./bin/spark-sql -S
```
```sql
spark-sql> set spark.sql.session.timeZone=America/Los_Angeles;
spark.sql.session.timeZone America/Los_Angeles
spark-sql> SELECT DATE_TRUNC('MILLENNIUM', DATE '1970-03-20');
1001-01-01 00:07:02
```
It must be 1001-01-01 00:**00:00**.
### Does this PR introduce any user-facing change?
Yes. After the changes:
```shell
$ export TZ="America/Los_Angeles"
$ ./bin/spark-sql -S
```
```sql
spark-sql> set spark.sql.session.timeZone=America/Los_Angeles;
spark.sql.session.timeZone America/Los_Angeles
spark-sql> SELECT DATE_TRUNC('MILLENNIUM', DATE '1970-03-20');
1001-01-01 00:00:00
```
### How was this patch tested?
By running hive-thiftserver tests. In particular:
```
./build/sbt -Phadoop-2.7 -Phive-2.3 -Phive-thriftserver "hive-thriftserver/test:testOnly *SparkThriftServerProtocolVersionsSuite"
```
Closes#27552 from MaxGekk/hive-thriftserver-java8-time-api.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Let sub optimizer's `postHocOptimizationBatches` also includes super's `postHocOptimizationBatches`.
### Why are the changes needed?
It's necessary according to the design of catalyst optimizer.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass jenkins.
Closes#27607 from Ngone51/spark_15616_followup.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Follow-up work for #25600. In this PR, we move `sql/dynamicpruning` to `sql/execution/dynamicpruning`.
### Why are the changes needed?
Fix the unexpected public APIs in 3.0.0 #27560.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes#27581 from xuanyuanking/SPARK-11150-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
[HIVE-15167](https://issues.apache.org/jira/browse/HIVE-15167) removed the `SerDe` interface. This may break custom `SerDe` builds for Hive 1.2. This PR update the migration guide for this change.
### Why are the changes needed?
Otherwise:
```
2020-01-27 05:11:20.446 - stderr> 20/01/27 05:11:20 INFO DAGScheduler: ResultStage 2 (main at NativeMethodAccessorImpl.java:0) failed in 1.000 s due to Job aborted due to stage failure: Task 0 in stage 2.0 failed 4 times, most recent failure: Lost task 0.3 in stage 2.0 (TID 13, 10.110.21.210, executor 1): java.lang.NoClassDefFoundError: org/apache/hadoop/hive/serde2/SerDe
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.defineClass1(Native Method)
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.defineClass(ClassLoader.java:756)
2020-01-27 05:11:20.446 - stderr> at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader.defineClass(URLClassLoader.java:468)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader.access$100(URLClassLoader.java:74)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader$1.run(URLClassLoader.java:369)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader$1.run(URLClassLoader.java:363)
2020-01-27 05:11:20.446 - stderr> at java.security.AccessController.doPrivileged(Native Method)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader.findClass(URLClassLoader.java:362)
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
2020-01-27 05:11:20.446 - stderr> at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:352)
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.loadClass(ClassLoader.java:405)
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
2020-01-27 05:11:20.446 - stderr> at java.lang.Class.forName0(Native Method)
2020-01-27 05:11:20.446 - stderr> at java.lang.Class.forName(Class.java:348)
2020-01-27 05:11:20.446 - stderr> at org.apache.hadoop.hive.ql.plan.TableDesc.getDeserializerClass(TableDesc.java:76)
.....
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manual test
Closes#27492 from wangyum/SPARK-30755.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
We only need to do aggregate evaluation once per group in `UnboundedWindowFunctionFrame`
### Why are the changes needed?
Currently, in `UnboundedWindowFunctionFrame.write`，it re-evaluate the processor for each row in a group, which is not necessary in fact which I'll address later. It hurts performance when the evaluation is time-consuming (for example, Percentile's eval need to sort its buffer and do some calculation). In our production, there is a percentile with window operation sql, it costs more than 10 hours in SparkSQL while 10min in Hive.
In fact, `UnboundedWindowFunctionFrame` can be treated as `SlidingWindowFunctionFrame` with `lbound = UnboundedPreceding` and `ubound = UnboundedFollowing`, just as its comments. In that case, `SlidingWindowFunctionFrame` also only do evaluation once for each group.
The performance issue can be reproduced by running the follow scripts in local spark-shell
```
spark.range(100*100).map(i => (i, "India")).toDF("uv", "country").createOrReplaceTempView("test")
sql("select uv, country, percentile(uv, 0.95) over (partition by country) as ptc95 from test").collect.foreach(println)
```
Before this patch, the sql costs **128048 ms**.
With this patch, the sql costs **3485 ms**.
If we increase the data size to 1000*1000 for example, then spark cannot even produce result without this patch(I'v waited for several hours).
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
Existing UT
Closes#27558 from WangGuangxin/windows.
Authored-by: wangguangxin.cn <wangguangxin.cn@gmail.com>
Signed-off-by: herman <herman@databricks.com>
### What changes were proposed in this pull request?
Define a new enumeration `LegacyBehaviorPolicy` in SQLConf, it will be used as the common value for result change configs.
### Why are the changes needed?
During API auditing for the 3.0 release, we found several new approaches that will change the results silently. For these features, we need a common three-value config.
### Does this PR introduce any user-facing change?
Yes, original config `spark.sql.legacy.ctePrecedence.enabled` change to `spark.sql.legacy.ctePrecedencePolicy`.
### How was this patch tested?
Existing UT.
Closes#27579 from xuanyuanking/SPARK-30829.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1, distributedly gather matrix `contingency` of each feature
2, distributedly compute the results and then collect them back to the driver
### Why are the changes needed?
existing impl is not efficient:
1, it directly collect matrix `contingency` of partial featues to driver and compute the corresponding result on one pass;
2, a matrix `contingency` of a featues is of size numDistinctValues X numDistinctLabels, so only 1000 matrices can be collected at a time;
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#27461 from zhengruifeng/chisq_opt.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
See JIRA: https://issues.apache.org/jira/browse/SPARK-29089
Mailing List: http://apache-spark-developers-list.1001551.n3.nabble.com/DataFrameReader-bottleneck-in-DataSource-checkAndGlobPathIfNecessary-when-reading-S3-files-td27828.html
When using DataFrameReader#csv to read many files on S3, globbing and fs.exists on DataSource#checkAndGlobPathIfNecessary becomes a bottleneck.
From the mailing list discussions, an improvement that can be made is to parallelize the blocking FS calls:
> - have SparkHadoopUtils differentiate between files returned by globStatus(), and which therefore exist, and those which it didn't glob for -it will only need to check those.
> - add parallel execution to the glob and existence checks
### Why are the changes needed?
Verifying/globbing files happens on the driver, and if this operations take a long time (for example against S3), then the entire cluster has to wait, potentially sitting idle. This change hopes to make this process faster.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
I added a test suite `DataSourceSuite` - open to suggestions for better naming.
See [here](https://github.com/apache/spark/pull/25899#issuecomment-534380034) and [here](https://github.com/apache/spark/pull/25899#issuecomment-534069194) for some measurements
Closes#25899 from cozos/master.
Lead-authored-by: Arwin Tio <Arwin.tio@adroll.com>
Co-authored-by: Arwin Tio <arwin.tio@hotmail.com>
Co-authored-by: Arwin Tio <arwin.tio@adroll.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
In the PR, I propose to replace `%` by `Math.floorMod` in `DateTimeUtils.truncTimestamp` for the `SECOND` and `MINUTE` levels.
### Why are the changes needed?
This fixes the issue of incorrect truncation of timestamps before the epoch `1970-01-01T00:00:00.000000Z` to the `SECOND` and `MINUTE` levels. For example, timestamps after the epoch are truncated by cutting off the rest part of the timestamp:
```sql
spark-sql> select date_trunc('SECOND', '2020-02-11 00:01:02.123');
2020-02-11 00:01:02
```
but seconds in the truncated timestamp before the epoch are increased by 1:
```sql
spark-sql> select date_trunc('SECOND', '1960-02-11 00:01:02.123');
1960-02-11 00:01:03
```
### Does this PR introduce any user-facing change?
Yes. After the changes, the example above outputs correct result:
```sql
spark-sql> select date_trunc('SECOND', '1960-02-11 00:01:02.123');
1960-02-11 00:01:02
```
### How was this patch tested?
Added new tests to `DateFunctionsSuite`.
Closes#27543 from MaxGekk/fix-second-minute-truc.
Authored-by: Maxim 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 for #23124, add a new config `spark.sql.legacy.allowDuplicatedMapKeys` to control the behavior of removing duplicated map keys in build-in functions. With the default value `false`, Spark will throw a RuntimeException while duplicated keys are found.
### Why are the changes needed?
Prevent silent behavior changes.
### Does this PR introduce any user-facing change?
Yes, new config added and the default behavior for duplicated map keys changed to RuntimeException thrown.
### How was this patch tested?
Modify existing UT.
Closes#27478 from xuanyuanking/SPARK-25892-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Make logging events dropping every 60s works fine, the orignal implementaion some times not working due to susequent events comming and updating the DroppedEventCounter
### Why are the changes needed?
Currenly, the logging may be skipped and delayed a long time under high concurrency, that make debugging hard. So This PR will try to fix it.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
NA
Closes#27002 from liupc/Improve-logging-dropped-events-and-logging-threadDump.
Authored-by: Liupengcheng <liupengcheng@xiaomi.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch addresses the post-hoc review comment linked here - https://github.com/apache/spark/pull/25670#discussion_r373304076
### Why are the changes needed?
We would like to explicitly document the direct relationship before we finish up structuring of configurations.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A
Closes#27576 from HeartSaVioR/SPARK-28869-FOLLOWUP-doc.
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 is a follow-up of #27577. This pr intends to add a link to the configuration naming guideline in `.github/PULL_REQUEST_TEMPLATE`.
### Why are the changes needed?
For reminding developers to follow the naming rules.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A
Closes#27602 from maropu/pr27577-FOLLOWUP.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. Rewrite DateTimeUtils methods `getHours()`, `getMinutes()`, `getSeconds()`, `getSecondsWithFraction()`, `getMilliseconds()` and `getMicroseconds()` using Java 8 time APIs. This will automatically switch the `Hour`, `Minute`, `Second` and `DatePart` expressions on Proleptic Gregorian calendar.
2. Remove unused methods and constant of DateTimeUtils - `to2001`, `YearZero `, `toYearZero` and `absoluteMicroSecond()`.
3. Remove unused value `timeZone` from `TimeZoneAwareExpression` since all expressions have been migrated to Java 8 time API, and legacy instance of `TimeZone` is not needed any more.
4. Change signatures of modified DateTimeUtils methods, and pass `ZoneId` instead of `TimeZone`. This will allow to avoid unnecessary conversions `TimeZone` -> `String` -> `ZoneId`.
5. Modify tests in `DateTimeUtilsSuite` and in `DateExpressionsSuite` to pass `ZoneId` instead of `TimeZone`. Correct the tests, to pass tested zone id instead of None.
### Why are the changes needed?
The changes fix the issue of wrong results returned by the `hour()`, `minute()`, `second()`, `date_part('millisecond', ...)` and `date_part('microsecond', ....)`, see example in [SPARK-30843](https://issues.apache.org/jira/browse/SPARK-30843).
### Does this PR introduce any user-facing change?
Yes. After the changes, the results of examples from SPARK-30843:
```sql
spark-sql> select hour(timestamp '0010-01-01 00:00:00');
0
spark-sql> select minute(timestamp '0010-01-01 00:00:00');
0
spark-sql> select second(timestamp '0010-01-01 00:00:00');
0
spark-sql> select date_part('milliseconds', timestamp '0010-01-01 00:00:00');
0.000
spark-sql> select date_part('microseconds', timestamp '0010-01-01 00:00:00');
0
```
### How was this patch tested?
- By existing test suites `DateTimeUtilsSuite`, `DateExpressionsSuite` and `DateFunctionsSuite`.
- Add new tests to `DateExpressionsSuite` and `DateTimeUtilsSuite` for 10 year, like:
```scala
input = date(10, 1, 1, 0, 0, 0, 0, zonePST)
assert(getHours(input, zonePST) === 0)
```
- Re-run `DateTimeBenchmark` using Amazon EC2.
| Item | Description |
| ---- | ----|
| Region | us-west-2 (Oregon) |
| Instance | r3.xlarge |
| AMI | ami-06f2f779464715dc5 (ubuntu/images/hvm-ssd/ubuntu-bionic-18.04-amd64-server-20190722.1) |
| Java | OpenJDK8/11 |
Closes#27596 from MaxGekk/localtimestamp-greg-cal.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Max Gekk <max.gekk@gmail.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-1-30.us-west-2.compute.internal>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add docs to describe the config naming guideline.
### Why are the changes needed?
To encourage contributors to name configs more consistently.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
N/A
Closes#27577 from cloud-fan/config.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
No v2 command supports temp views and the `ResolveCatalogs`/`ResolveSessionCatalog` framework is designed with this assumption.
However, `ResolveSessionCatalog` needs to fallback to v1 commands, which do support temp views (e.g. CACHE TABLE). To work around it, we add a hack in `CatalogAndIdentifier`, which does not expand the given identifier with current namespace if the catalog is session catalog.
This works fine in most cases, as temp views should take precedence over tables during lookup. So if `CatalogAndIdentifier` returns a single name "t", the v1 commands can still resolve it to temp views correctly, or resolve it to table "default.t" if temp view doesn't exist.
However, if users write `spark_catalog.t`, it shouldn't be resolved to temp views as temp views don't belong to any catalog. `CatalogAndIdentifier` can't distinguish between `spark_catalog.t` and `t`, so the caller side may mistakenly resolve `spark_catalog.t` to a temp view.
This PR proposes to fix this issue by
1. remove the hack in `CatalogAndIdentifier`, and clearly document that this shouldn't be used to resolve temp views.
2. update `ResolveSessionCatalog` to explicitly look up temp views first before calling `CatalogAndIdentifier`, for v1 commands that support temp views.
### Why are the changes needed?
To avoid releasing a behavior that we should not support.
Removing the hack also fixes the problem we hit in https://github.com/apache/spark/pull/27532/files#diff-57b3d87be744b7d79a9beacf8e5e5eb2R937
### Does this PR introduce any user-facing change?
yes, now it's not allowed to refer to a temp view with `spark_catalog` prefix.
### How was this patch tested?
new tests
Closes#27550 from cloud-fan/ns.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
fix style issue in the k8s document, please go to http://spark.apache.org/docs/3.0.0-preview2/running-on-kubernetes.html and search the keyword`spark.kubernetes.file.upload.path` to jump to the error context
### Why are the changes needed?
doc correctness
### Does this PR introduce any user-facing change?
Nah
### How was this patch tested?
Nah
Closes#27582 from yaooqinn/k8s-doc.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add doc for recommended pandas and pyarrow versions.
### Why are the changes needed?
The recommended versions are those that have been thoroughly tested by Spark CI. Other versions may be used at the discretion of the user.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
NA
Closes#27587 from BryanCutler/python-doc-rec-pandas-pyarrow-SPARK-30834-3.0.
Lead-authored-by: Bryan Cutler <cutlerb@gmail.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/27489.
It declares the ANSI SQL compliance options as experimental in the documentation.
### Why are the changes needed?
The options are experimental. There can be new features/behaviors in future releases.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Generating doc
Closes#27590 from gengliangwang/ExperimentalAnsi.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
it is said in [LeastSquaresAggregator](12e1bbaddb/mllib/src/main/scala/org/apache/spark/ml/optim/aggregator/LeastSquaresAggregator.scala (L188)) that :
> // do not use tuple assignment above because it will circumvent the transient tag
I then check this issue with Scala 2.13.1 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_241)
### Why are the changes needed?
avoid tuple assignment because it will circumvent the transient tag
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#27523 from zhengruifeng/avoid_tuple_assign_to_transient.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Change the link to the Scala API document.
```
$ git grep "#org.apache.spark.package"
docs/_layouts/global.html: <li><a href="api/scala/index.html#org.apache.spark.package">Scala</a></li>
docs/index.md:* [Spark Scala API (Scaladoc)](api/scala/index.html#org.apache.spark.package)
docs/rdd-programming-guide.md:[Scala](api/scala/#org.apache.spark.package), [Java](api/java/), [Python](api/python/) and [R](api/R/).
```
### Why are the changes needed?
The home page link for Scala API document is incorrect after upgrade to 3.0
### Does this PR introduce any user-facing change?
Document UI change only.
### How was this patch tested?
Local test, attach screenshots below:
Before:
After:
Closes#27549 from xuanyuanking/scala-doc.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Fix typo
def setRest(dstId: VertexId, localDstId: Int, dstAttr: VD, attr: ED)
to
def setDest(dstId: VertexId, localDstId: Int, dstAttr: VD, attr: ED)
### Why are the changes needed?
Typo
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#27594 from xwu99/fix-graphx-setDest.
Authored-by: Wu, Xiaochang <xiaochang.wu@intel.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
In the PR, I propose to convert the attribute name of `StringStartsWith` pushed down to the Parquet datasource to column reference via the `nameToParquetField` map. Similar conversions are performed for other source filters pushed down to parquet.
### Why are the changes needed?
This fixes the bug described in [SPARK-30826](https://issues.apache.org/jira/browse/SPARK-30826). The query from an external table:
```sql
CREATE TABLE t1 (col STRING)
USING parquet
OPTIONS (path '$path')
```
created on top of written parquet files by `Seq("42").toDF("COL").write.parquet(path)` returns wrong empty result:
```scala
spark.sql("SELECT * FROM t1 WHERE col LIKE '4%'").show
+---+
|col|
+---+
+---+
```
### Does this PR introduce any user-facing change?
Yes. After the changes the result is correct for the example above:
```scala
spark.sql("SELECT * FROM t1 WHERE col LIKE '4%'").show
+---+
|col|
+---+
| 42|
+---+
```
### How was this patch tested?
Added a test to `ParquetFilterSuite`
Closes#27574 from MaxGekk/parquet-StringStartsWith-case-sens.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. `InMemoryTable` was flatting the nested columns, and then the flatten columns was used to look up the indices which is not correct.
This PR implements partitioned by nested column for `InMemoryTable`.
### Why are the changes needed?
This PR implements partitioned by nested column for `InMemoryTable`, so we can test this features in DSv2
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing unit tests and new tests.
Closes#26929 from dbtsai/addTests.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
This PR is based on an existing/previou PR - https://github.com/apache/spark/pull/19045
### What changes were proposed in this pull request?
This changes adds a decommissioning state that we can enter when the cloud provider/scheduler lets us know we aren't going to be removed immediately but instead will be removed soon. This concept fits nicely in K8s and also with spot-instances on AWS / preemptible instances all of which we can get a notice that our host is going away. For now we simply stop scheduling jobs, in the future we could perform some kind of migration of data during scale-down, or at least stop accepting new blocks to cache.
There is a design document at https://docs.google.com/document/d/1xVO1b6KAwdUhjEJBolVPl9C6sLj7oOveErwDSYdT-pE/edit?usp=sharing
### Why are the changes needed?
With more move to preemptible multi-tenancy, serverless environments, and spot-instances better handling of node scale down is required.
### Does this PR introduce any user-facing change?
There is no API change, however an additional configuration flag is added to enable/disable this behaviour.
### How was this patch tested?
New integration tests in the Spark K8s integration testing. Extension of the AppClientSuite to test decommissioning seperate from the K8s.
Closes#26440 from holdenk/SPARK-20628-keep-track-of-nodes-which-are-going-to-be-shutdown-r4.
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 address the comment at https://github.com/apache/spark/pull/26496#discussion_r379194091 and improves the migration guide to explicitly note that the legacy environment variable to set in both executor and driver.
### Why are the changes needed?
To clarify this env should be set both in driver and executors.
### Does this PR introduce any user-facing change?
Nope.
### How was this patch tested?
I checked it via md editor.
Closes#27573 from HyukjinKwon/SPARK-29748.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
### What changes were proposed in this pull request?
In the PR, I propose to use Java 8 time API in timestamp truncations to the levels of `HOUR` and `DAY`. The problem is in the usage of `timeZone.getOffset(millis)` in days/hours truncations where the combined calendar (Julian + Gregorian) is used underneath.
### Why are the changes needed?
The change fix wrong truncations. For example, the following truncation to hours should print `0010-01-01 01:00:00` but it outputs wrong timestamp:
```scala
Seq("0010-01-01 01:02:03.123456").toDF()
.select($"value".cast("timestamp").as("ts"))
.select(date_trunc("HOUR", $"ts").cast("string"))
.show(false)
+------------------------------------+
|CAST(date_trunc(HOUR, ts) AS STRING)|
+------------------------------------+
|0010-01-01 01:30:17 |
+------------------------------------+
```
### Does this PR introduce any user-facing change?
Yes. After the changes, the result of the example above is:
```scala
+------------------------------------+
|CAST(date_trunc(HOUR, ts) AS STRING)|
+------------------------------------+
|0010-01-01 01:00:00 |
+------------------------------------+
```
### How was this patch tested?
- Added new test to `DateFunctionsSuite`
- By `DateExpressionsSuite` and `DateTimeUtilsSuite`
Closes#27512 from MaxGekk/fix-trunc-old-timestamp.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fixes `DataFrameReader.csv(dataset: Dataset[String])` API to take a `Dataset[String]` originated from a column name different from `value`. This is a long-standing bug started from the very first place.
`CSVUtils.filterCommentAndEmpty` assumed the `Dataset[String]` to be originated with `value` column. This PR changes to use the first column name in the schema.
### Why are the changes needed?
For `DataFrameReader.csv(dataset: Dataset[String])` to support any `Dataset[String]` as the signature indicates.
### Does this PR introduce any user-facing change?
Yes,
```scala
val ds = spark.range(2).selectExpr("concat('a,b,', id) AS text").as[String]
spark.read.option("header", true).option("inferSchema", true).csv(ds).show()
```
Before:
```
org.apache.spark.sql.AnalysisException: cannot resolve '`value`' given input columns: [text];;
'Filter (length(trim('value, None)) > 0)
+- Project [concat(a,b,, cast(id#0L as string)) AS text#2]
+- Range (0, 2, step=1, splits=Some(2))
```
After:
```
+---+---+---+
| a| b| 0|
+---+---+---+
| a| b| 1|
+---+---+---+
```
### How was this patch tested?
Unittest was added.
Closes#27561 from HyukjinKwon/SPARK-30810.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
We should convert Spark InternalRows to hive data via `HiveInspectors.wrapperFor`.
### Why are the changes needed?
We may hit below exception without this change:
```
[info] org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1, 192.168.1.6, executor driver): java.lang.ClassCastException: org.apache.spark.sql.types.Decimal cannot be cast to org.apache.hadoop.hive.common.type.HiveDecimal
[info] at org.apache.hadoop.hive.serde2.objectinspector.primitive.JavaHiveDecimalObjectInspector.getPrimitiveJavaObject(JavaHiveDecimalObjectInspector.java:55)
[info] at org.apache.hadoop.hive.serde2.lazy.LazyUtils.writePrimitiveUTF8(LazyUtils.java:321)
[info] at org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe.serialize(LazySimpleSerDe.java:292)
[info] at org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe.serializeField(LazySimpleSerDe.java:247)
[info] at org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe.doSerialize(LazySimpleSerDe.java:231)
[info] at org.apache.hadoop.hive.serde2.AbstractEncodingAwareSerDe.serialize(AbstractEncodingAwareSerDe.java:55)
[info] at org.apache.spark.sql.hive.execution.ScriptTransformationWriterThread.$anonfun$run$2(ScriptTransformationExec.scala:300)
[info] at org.apache.spark.sql.hive.execution.ScriptTransformationWriterThread.$anonfun$run$2$adapted(ScriptTransformationExec.scala:281)
[info] at scala.collection.Iterator.foreach(Iterator.scala:941)
[info] at scala.collection.Iterator.foreach$(Iterator.scala:941)
[info] at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
[info] at org.apache.spark.sql.hive.execution.ScriptTransformationWriterThread.$anonfun$run$1(ScriptTransformationExec.scala:281)
[info] at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
[info] at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1932)
[info] at org.apache.spark.sql.hive.execution.ScriptTransformationWriterThread.run(ScriptTransformationExec.scala:270)
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added new test. But please note that this test returns different result between Hive1.2 and Hive2.3 due to `HiveDecimal` or `SerDe` difference(don't know the root cause yet).
Closes#27556 from Ngone51/script_transform.
Lead-authored-by: yi.wu <yi.wu@databricks.com>
Co-authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This commit is published into the public domain.
### What changes were proposed in this pull request?
In analogy to `python/docs/Makefile`, which has
> export PYTHONPATH=$(realpath ..):$(realpath ../lib/py4j-0.10.8.1-src.zip)
on line 10, this PR adds
> set PYTHONPATH=..;..\lib\py4j-0.10.8.1-src.zip
to `make2.bat`.
Since there is no `realpath` in default installations of Windows, I left the relative paths unresolved. Per the instructions on how to build docs, `make.bat` is supposed to be run from `python/docs` as the working directory, so this should probably not cause issues (`%BUILDDIR%` is a relative path as well.)
### Why are the changes needed?
When building the PySpark documentation on Windows, by changing directory to `python/docs` and running `make.bat` (which runs `make2.bat`), the majority of the documentation may not be built if pyspark is not in the default `%PYTHONPATH%`. Sphinx then reports that `pyspark` (and possibly dependencies) cannot be imported.
If `pyspark` is in the default `%PYTHONPATH%`, I suppose it is that version of `pyspark` – as opposed to the version found above the `python/docs` directory – that is considered when building the documentation, which may result in documentation that does not correspond to the development version one is trying to build.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manual tests on my Windows 10 machine. Additional tests with other environments very welcome!
Closes#27569 from DavidToneian/SPARK-30823.
Authored-by: David Toneian <david@toneian.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR makes sure AQE is either enabled or disabled for the entire query, including the main query and all subqueries.
Currently there are unsupported queries by AQE, e.g., queries that contain DPP filters. We need to make sure that if the main query is unsupported, none of the sub-queries should apply AQE, otherwise it can lead to performance regressions due to missed opportunity of sub-query reuse.
### Why are the changes needed?
To get rid of potential perf regressions when AQE is turned on.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Updated DynamicPartitionPruningSuite:
1. Removed the existing workaround `withSQLConf(SQLConf.ADAPTIVE_EXECUTION_ENABLED.key, "false")`
2. Added `DynamicPartitionPruningSuiteAEOn` and `DynamicPartitionPruningSuiteAEOff` to enable testing this suite with AQE on and off options
3. Added a check in `checkPartitionPruningPredicate` to verify that the subqueries are always in sync with the main query in terms of whether AQE is applied.
Closes#27554 from maryannxue/spark-30801.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This commit is published into the public domain.
### What changes were proposed in this pull request?
This PR fixes the documentation of `DataFrameReader`, `DataFrameWriter`, `DataStreamReader`, and `DataStreamWriter`, where attributes of other classes were misrepresented as functions. Additionally, creation of hyperlinks across modules was fixed in these instances.
### Why are the changes needed?
The old state produced documentation that suggested invalid usage of PySpark objects (accessing attributes as though they were callable.)
### Does this PR introduce any user-facing change?
No, except for improved documentation.
### How was this patch tested?
No test added; documentation build runs through.
Closes#27553 from DavidToneian/docfix-DataFrameReader-DataFrameWriter-DataStreamReader-DataStreamWriter.
Authored-by: David Toneian <david@toneian.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The `allGather` method is added to the `BarrierTaskContext`. This method contains the same functionality as the `BarrierTaskContext.barrier` method; it blocks the task until all tasks make the call, at which time they may continue execution. In addition, the `allGather` method takes an input message. Upon returning from the `allGather` the task receives a list of all the messages sent by all the tasks that made the `allGather` call.
### Why are the changes needed?
There are many situations where having the tasks communicate in a synchronized way is useful. One simple example is if each task needs to start a server to serve requests from one another; first the tasks must find a free port (the result of which is undetermined beforehand) and then start making requests, but to do so they each must know the port chosen by the other task. An `allGather` method would allow them to inform each other of the port they will run on.
### Does this PR introduce any user-facing change?
Yes, an `BarrierTaskContext.allGather` method will be available through the Scala, Java, and Python APIs.
### How was this patch tested?
Most of the code path is already covered by tests to the `barrier` method, since this PR includes a refactor so that much code is shared by the `barrier` and `allGather` methods. However, a test is added to assert that an all gather on each tasks partition ID will return a list of every partition ID.
An example through the Python API:
```python
>>> from pyspark import BarrierTaskContext
>>>
>>> def f(iterator):
... context = BarrierTaskContext.get()
... return [context.allGather('{}'.format(context.partitionId()))]
...
>>> sc.parallelize(range(4), 4).barrier().mapPartitions(f).collect()[0]
[u'3', u'1', u'0', u'2']
```
Closes#27395 from sarthfrey/master.
Lead-authored-by: sarthfrey-db <sarth.frey@databricks.com>
Co-authored-by: sarthfrey <sarth.frey@gmail.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
### What changes were proposed in this pull request?
This PR scopes `SparkSession.active` to prevent problems with processing queries with possibly different spark sessions (and different configs). A new method, `withActive` is introduced on `SparkSession` that restores the previous spark session after the block of code is executed.
### Why are the changes needed?
`SparkSession.active` is a thread local variable that points to the current thread's spark session. It is important to note that the `SQLConf.get` method depends on `SparkSession.active`. In the current implementation it is possible that `SparkSession.active` points to a different session which causes various problems. Most of these problems arise because part of the query processing is done using the configurations of a different session. For example, when creating a data frame using a new session, i.e., `session.sql("...")`, part of the data frame is constructed using the currently active spark session, which can be a different session from the one used later for processing the query.
### Does this PR introduce any user-facing change?
The `withActive` method is introduced on `SparkSession`.
### How was this patch tested?
Unit tests (to be added)
Closes#27387 from dbaliafroozeh/UseWithActiveSessionInQueryExecution.
Authored-by: Ali Afroozeh <ali.afroozeh@databricks.com>
Signed-off-by: herman <herman@databricks.com>
### What changes were proposed in this pull request?
This PR aims to fix `dev-run-integration-tests.sh` to ignore empty params correctly.
### Why are the changes needed?
The following script runs `mvn` integration test like the following.
```
$ resource-managers/kubernetes/integration-tests/dev/dev-run-integration-tests.sh
...
build/mvn integration-test
-f /Users/dongjoon/APACHE/spark/pom.xml
-pl resource-managers/kubernetes/integration-tests
-am
-Pscala-2.12
-Pkubernetes
-Pkubernetes-integration-tests
-Djava.version=8
-Dspark.kubernetes.test.sparkTgz=N/A
-Dspark.kubernetes.test.imageTag=N/A
-Dspark.kubernetes.test.imageRepo=docker.io/kubespark
-Dspark.kubernetes.test.deployMode=minikube
-Dtest.include.tags=k8s
-Dspark.kubernetes.test.namespace=
-Dspark.kubernetes.test.serviceAccountName=
-Dspark.kubernetes.test.kubeConfigContext=
-Dspark.kubernetes.test.master=
-Dtest.exclude.tags=
-Dspark.kubernetes.test.jvmImage=spark
-Dspark.kubernetes.test.pythonImage=spark-py
-Dspark.kubernetes.test.rImage=spark-r
```
After this PR, the empty parameters like the followings will be skipped like the original design.
```
-Dspark.kubernetes.test.namespace=
-Dspark.kubernetes.test.serviceAccountName=
-Dspark.kubernetes.test.kubeConfigContext=
-Dspark.kubernetes.test.master=
-Dtest.exclude.tags=
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass the Jenkins K8S integration test.
Closes#27566 from dongjoon-hyun/SPARK-30816.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR aims to support JDK11 test in K8S integration tests.
- This is an update in testing framework instead of individual tests.
- This will enable JDK11 runtime test when you didn't installed JDK11 on your local system.
### Why are the changes needed?
Apache Spark 3.0.0 adds JDK11 support, but K8s integration tests use JDK8 until now.
### Does this PR introduce any user-facing change?
No. This is a dev-only test-related PR.
### How was this patch tested?
This is irrelevant to Jenkins UT, but Jenkins K8S IT (JDK8) should pass.
- https://github.com/apache/spark/pull/27559#issuecomment-585903489 (JDK8 Passed)
And, manually do the following for JDK11 test.
```
$ NO_MANUAL=1 ./dev/make-distribution.sh --r --pip --tgz -Phadoop-3.2 -Pkubernetes
$ resource-managers/kubernetes/integration-tests/dev/dev-run-integration-tests.sh --java-image-tag 11-jre-slim --spark-tgz $PWD/spark-*.tgz
```
```
$ docker run -it --rm kubespark/spark:1318DD8A-2B15-4A00-BC69-D0E90CED235B /usr/local/openjdk-11/bin/java --version | tail -n1
OpenJDK 64-Bit Server VM 18.9 (build 11.0.6+10, mixed mode)
```
Closes#27559 from dongjoon-hyun/SPARK-30807.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
<!--
Thanks for sending a pull request! Here are some tips for you:
1. If this is your first time, please read our contributor guidelines: https://spark.apache.org/contributing.html
2. Ensure you have added or run the appropriate tests for your PR: https://spark.apache.org/developer-tools.html
3. If the PR is unfinished, add '[WIP]' in your PR title, e.g., '[WIP][SPARK-XXXX] Your PR title ...'.
4. Be sure to keep the PR description updated to reflect all changes.
5. Please write your PR title to summarize what this PR proposes.
6. If possible, provide a concise example to reproduce the issue for a faster review.
-->
### What changes were proposed in this pull request?
<!--
Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue.
If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below.
1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers.
2. If you fix some SQL features, you can provide some references of other DBMSes.
3. If there is design documentation, please add the link.
4. If there is a discussion in the mailing list, please add the link.
-->
This is a followup of https://github.com/apache/spark/pull/26434
This PR use one special shuffle reader for skew join, so that we only have one join after optimization. In order to do that, this PR
1. add a very general `CustomShuffledRowRDD` which support all kind of partition arrangement.
2. move the logic of coalescing shuffle partitions to a util function, and call it during skew join optimization, to totally decouple with the `ReduceNumShufflePartitions` rule. It's too complicated to interfere skew join with `ReduceNumShufflePartitions`, as you need to consider the size of split partitions which don't respect target size already.
### Why are the changes needed?
<!--
Please clarify why the changes are needed. For instance,
1. If you propose a new API, clarify the use case for a new API.
2. If you fix a bug, you can clarify why it is a bug.
-->
The current skew join optimization has a serious performance issue: the size of the query plan depends on the number and size of skewed partitions.
### Does this PR introduce any user-facing change?
<!--
If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible.
If no, write 'No'.
-->
no
### How was this patch tested?
<!--
If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible.
If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future.
If tests were not added, please describe why they were not added and/or why it was difficult to add.
-->
existing tests
test UI manually:
explain output
```
AdaptiveSparkPlan(isFinalPlan=true)
+- OverwriteByExpression org.apache.spark.sql.execution.datasources.noop.NoopTable$403a2ed5, [AlwaysTrue()], org.apache.spark.sql.util.CaseInsensitiveStringMap1f
+- *(5) SortMergeJoin(skew=true) [key1#2L], [key2#6L], Inner
:- *(3) Sort [key1#2L ASC NULLS FIRST], false, 0
: +- SkewJoinShuffleReader 2 skewed partitions with size(max=5 KB, min=5 KB, avg=5 KB)
: +- ShuffleQueryStage 0
: +- Exchange hashpartitioning(key1#2L, 200), true, [id=#53]
: +- *(1) Project [(id#0L % 2) AS key1#2L]
: +- *(1) Filter isnotnull((id#0L % 2))
: +- *(1) Range (0, 100000, step=1, splits=6)
+- *(4) Sort [key2#6L ASC NULLS FIRST], false, 0
+- SkewJoinShuffleReader 2 skewed partitions with size(max=5 KB, min=5 KB, avg=5 KB)
+- ShuffleQueryStage 1
+- Exchange hashpartitioning(key2#6L, 200), true, [id=#64]
+- *(2) Project [((id#4L % 2) + 1) AS key2#6L]
+- *(2) Filter isnotnull(((id#4L % 2) + 1))
+- *(2) Range (0, 100000, step=1, splits=6)
```
Closes#27493 from cloud-fan/aqe.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: herman <herman@databricks.com>
### What changes were proposed in this pull request?
In this PR, we add a parameter in the python function vector_to_array(col) that allows converting to a column of arrays of Float (32bits) in scala, which would be mapped to a numpy array of dtype=float32.
### Why are the changes needed?
In the downstream ML training, using float32 instead of float64 (default) would allow a larger batch size, i.e., allow more data to fit in the memory.
### Does this PR introduce any user-facing change?
Yes.
Old: `vector_to_array()` only take one param
```
df.select(vector_to_array("colA"), ...)
```
New: `vector_to_array()` can take an additional optional param: `dtype` = "float32" (or "float64")
```
df.select(vector_to_array("colA", "float32"), ...)
```
### How was this patch tested?
Unit test in scala.
doctest in python.
Closes#27522 from liangz1/udf-float32.
Authored-by: Liang Zhang <liang.zhang@databricks.com>
Signed-off-by: WeichenXu <weichen.xu@databricks.com>
