### What changes were proposed in this pull request?
At the moment we do not have any function to compute length of JSON array directly.
I propose a `json_array_length` function which will return the length of the outermost JSON array.
- This function will return length of the outermost JSON array, if JSON array is valid.
```
scala> spark.sql("select json_array_length('[1,2,3,[33,44],{\"key\":[2,3,4]}]')").show
+--------------------------------------------------+
|json_array_length([1,2,3,[33,44],{"key":[2,3,4]}])|
+--------------------------------------------------+
| 5|
+--------------------------------------------------+
scala> spark.sql("select json_array_length('[[1],[2,3]]')").show
+------------------------------+
|json_array_length([[1],[2,3]])|
+------------------------------+
| 2|
+------------------------------+
```
- In case of any other valid JSON string, invalid JSON string or null array or `NULL` input , `NULL` will be returned.
```
scala> spark.sql("select json_array_length('')").show
+-------------------+
|json_array_length()|
+-------------------+
| null|
+-------------------+
```
### Why are the changes needed?
- As mentioned in JIRA, this function is supported by presto, postgreSQL, redshift, SQLite, MySQL, MariaDB, IBM DB2.
- for better user experience and ease of use.
```
Performance Result for Json array - [1, 2, 3, 4]
Intel(R) Core(TM) i7-9750H CPU 2.60GHz
JSON functions: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
json_array_length 7728 7762 53 1.3 772.8 1.0X
size+from_json 12739 12895 199 0.8 1273.9 0.6X
```
### Does this PR introduce any user-facing change?
Yes, now users can get length of a json array by using `json_array_length`.
### How was this patch tested?
Added UT.
Closes#27759 from iRakson/jsonArrayLength.
Authored-by: iRakson <raksonrakesh@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
I found a bug in the stage level scheduling dynamic allocation code when you have a non default profile and it has an initial number of executors the same as what the number of executors needed for the first job, then we don't properly request the executors. This causes a hang.
The issue is that when a new stage is added and the initial number of executors is set, we set the target to be the initial number. Unfortunately that makes the code in the update and sync function think it has already requested that number. So to fix this, when there is an initial number we just go ahead and request executors at that point. This is basically what happens on startup to handle the case with the default profile.
### Why are the changes needed?
bug
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
unit test and manually test on yarn cluster. Went though multiple scenarios initial numbers, minimum number and number executor required by the first stage.
Closes#28146 from tgravescs/SPARK-31378.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Add a cleanShuffleDependencies as an experimental developer feature to allow folks to clean up shuffle files more aggressively than we currently do.
### Why are the changes needed?
Dynamic scaling on Kubernetes (introduced in Spark 3) depends on only shutting down executors without shuffle files. However Spark does not aggressively clean up shuffle files (see SPARK-5836) and instead depends on JVM GC on the driver to trigger deletes. We already have a mechanism to explicitly clean up shuffle files from the ALS algorithm where we create a lot of quickly orphaned shuffle files. We should expose this as an advanced developer feature to enable people to better clean-up shuffle files improving dynamic scaling of their jobs on Kubernetes.
### Does this PR introduce any user-facing change?
This adds a new experimental API.
### How was this patch tested?
ALS already used a mechanism like this, re-targets the ALS code to the new interface, tested with existing ALS tests.
Closes#28038 from holdenk/SPARK-31208-allow-users-to-cleanup-shuffle-files.
Authored-by: Holden Karau <hkarau@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@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 PR fixes the added version of `spark.sql.execution.pandas.udf.buffer.size` to 3.0.0 (see also SPARK-27870)
### Why are the changes needed?
To show the correct version added.
### Does this PR introduce any user-facing change?
Yes but only in the unreleased branches. It will change the version shown in SQL documentation.
### How was this patch tested?
Not tested. Jenkins will test it out.
Closes#28144 from HyukjinKwon/SPARK-30841-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 `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 change (SPARK-31360) fixes a hung-up issue in StagePage.
StagePage will be hung-up with following operations.
1. Run a job with shuffle.
`scala> sc.parallelize(1 to 10).map(x => (x, x)).reduceByKey(_ + _).collect`
2. Visit StagePage for the stage writing shuffle data and check `Shuffle Write Time`.
<img width="401" alt="check-shuffle-write-time" src="https://user-images.githubusercontent.com/4736016/78557730-4513e200-784c-11ea-8b42-a5053b9489a5.png">
3. Run a job with no shuffle.
`scala> sc.parallelize(1 to 10).collect`
4. Visit StagePage for the last stage.
<img width="956" alt="hungup" src="https://user-images.githubusercontent.com/4736016/78557746-4f35e080-784c-11ea-83e8-5db745b88535.png">
This issue is caused by following reason.
In stagepage.js, an array `optionalColumns` has indices for columns for optional metrics.
If a stage doesn't perform shuffle read or write, the corresponding indices are removed from the array.
StagePage doesn't try to create column for such metrics, even if the state of corresponding optional metrics are preserved as "visible".
But, if a stage doesn't perform both shuffle read and write, the index for `Shuffle Write Time` isn't removed.
In that case, StagePage tries to create a column for `Shuffle Write Time` even though there are no metrics for shuffle write, leading hungup.
### Why are the changes needed?
This is a bug.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
I tested with operations I explained above and confirmed that StagePage won't be hung-up.
Closes#28136 from sarutak/fix-ui-hungup.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Jira: https://issues.apache.org/jira/browse/SPARK-31352
We added this to Apache Airflow project ([PR](https://github.com/apache/airflow/pull/6689)) and I think it would be good to have the Spark's website at the top.
Also, this would allow Spark's PMC Members and committer to control common Github project settings by themselves without having to ask Apache INFRA team.
More info: https://cwiki.apache.org/confluence/display/INFRA/.asf.yaml+features+for+git+repositories
We (Airflow PMC and Committers) used this file to enable Github issues and disable PR Merge button etc
### Why are the changes needed?
Explained above
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
n/a
Closes#28122 from kaxil/patch-1.
Authored-by: Kaxil Naik <kaxilnaik@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.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 is a minor PR used to fix some typo and improve comments mentioned with https://github.com/apache/spark/pull/28081/files#r402874997
### Why are the changes needed?
Fix some typo and improve comments.
### Does this PR introduce any user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#28112 from beliefer/fix-typo-in-codegen.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
After my investigation, SQLQueryTestSuite spent a lot of time to generate java code.
This PR is related to https://github.com/apache/spark/pull/28081.
https://github.com/apache/spark/pull/28081 used to display compile time, but this PR used to display generate time.
This PR will add the following to `SQLQueryTestSuite`'s output.
```
=== Metrics of Whole Codegen ===
Total generate time: 82.640913862 seconds
Total compile time: 98.649663572 seconds
```
### Why are the changes needed?
Display the total generate time for generated java code.
### Does this PR introduce any user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#28105 from beliefer/output-codegen-generation-time.
Lead-authored-by: beliefer <beliefer@163.com>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to add tests to check that code generation doesn't fail if expressions string argument contains escape chars. The PR adds similar tests added by https://github.com/apache/spark/pull/20182 for `from_utc_timestamp` / `to_utc_timestamp`.
### Why are the changes needed?
To prevent regressions in the future.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running the affected tests
Closes#28115 from MaxGekk/tests-arg-escape.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
For now `SHOW CREATE TABLE` command doesn't support views, but `SHOW CREATE TABLE AS SERDE` supports it. Since the views syntax are the same between Hive DDL and Spark DDL, we should be able to support views in both two commands.
This is Hive syntax for creating views:
```
CREATE VIEW [IF NOT EXISTS] [db_name.]view_name [(column_name [COMMENT column_comment], ...) ]
[COMMENT view_comment]
[TBLPROPERTIES (property_name = property_value, ...)]
AS SELECT ...;
```
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-CreateView
This is Spark syntax for creating views:
```
CREATE [OR REPLACE] [[GLOBAL] TEMPORARY] VIEW [IF NOT EXISTS [db_name.]view_name
create_view_clauses
AS query;
```
https://spark.apache.org/docs/3.0.0-preview/sql-ref-syntax-ddl-create-view.html
Looks like it is the same. We could support views in both commands.
This patch proposes to add views support to `SHOW CREATE TABLE`.
### Why are the changes needed?
To extend the view support of `SHOW CREATE TABLE`, so users can use `SHOW CREATE TABLE` to show Spark DDL for views.
### Does this PR introduce any user-facing change?
Yes. `SHOW CREATE TABLE` can be used to show Spark DDL for views.
### How was this patch tested?
Unit tests.
Closes#27984 from viirya/spark-view.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to set the `America/Los_Angeles` time zone in the date-time benchmarks `DateTimeBenchmark` and `DateTimeRebaseBenchmark` via `withDefaultTimeZone(LA)` and `withSQLConf(SQLConf.SESSION_LOCAL_TIMEZONE.key -> LA.getId)`.
The results of affected benchmarks was given on an Amazon EC2 instance w/ the configuration:
| 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 | OpenJDK8/11 |
### Why are the changes needed?
Performance of date-time functions can depend on the system JVM time zone or SQL config `spark.sql.session.timeZone`. The changes allow to avoid any fluctuations of benchmarks results related to time zones, and set a reliable baseline for future optimization.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By regenerating results of DateTimeBenchmark and DateTimeRebaseBenchmark.
Closes#28127 from MaxGekk/set-timezone-in-benchmarks.
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 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 patch proposed to skip predicates on PythonUDFs to be pushdown through Aggregate.
### Why are the changes needed?
The predicates on PythonUDFs cannot be pushdown through Aggregate. Pushed down predicates cannot be evaluate because PythonUDFs cannot be evaluated on Filter and cause error like:
```
Caused by: java.lang.UnsupportedOperationException: Cannot generate code for expression: mean(input[1, struct<bar:bigint>, true].bar)
at org.apache.spark.sql.catalyst.expressions.Unevaluable.doGenCode(Expression.scala:304)
at org.apache.spark.sql.catalyst.expressions.Unevaluable.doGenCode$(Expression.scala:303)
at org.apache.spark.sql.catalyst.expressions.PythonUDF.doGenCode(PythonUDF.scala:52)
at org.apache.spark.sql.catalyst.expressions.Expression.$anonfun$genCode$3(Expression.scala:146)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:141)
at org.apache.spark.sql.catalyst.expressions.CastBase.doGenCode(Cast.scala:821)
at org.apache.spark.sql.catalyst.expressions.Expression.$anonfun$genCode$3(Expression.scala:146)
at scala.Option.getOrElse(Option.scala:189)
```
### Does this PR introduce any user-facing change?
Yes. Previously the predicates on PythonUDFs will be pushdown through Aggregate can cause error. After this change, the query can work.
### How was this patch tested?
Unit test.
Closes#28089 from viirya/SPARK-30921.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The auto-generated alias name of built-in function `version()` is `sparkversion()`. After this PR, it is updated to `version()`.
### Why are the changes needed?
Based on our auto-generated alias name convention for the built-in functions, the alias names should be consistent with the function names.
This built-in function `version` is added in the upcoming Spark 3.0. Thus, we should fix it before the release.
### Does this PR introduce any user-facing change?
Yes. Update the column name in schema if users do not specify the alias.
### How was this patch tested?
Added a test case.
Closes#28131 from gatorsmile/spark-29554followup.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR unsets `setuptools` version in CI. This was fixed in the 0.46.1.2+ `setuptools` - pypa/setuptools#2046. `setuptools` 0.46.1.0 and 0.46.1.1 still have this problem.
### Why are the changes needed?
To test the latest setuptools out to see if users can actually install and use it.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Jenkins will test.
Closes#28111 from HyukjinKwon/SPARK-31231-revert.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Mark `RDD.resourceProfile` as `transient`.
### Why are the changes needed?
`RDD.resourceProfile` should only be used at driver side.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass Jenkins.
Closes#28108 from Ngone51/spark_29154_followup.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Write Spark version into Avro file metadata
### Why are the changes needed?
The version info is very useful for backward compatibility. This is also done in parquet/orc.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
new test
Closes#28102 from cloud-fan/avro.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In `CoarseGrainedSchedulerBackendSuite.RegisterExecutor`, change it to post `SparkListenerExecutorAdded` before `context.reply(true)`.
### Why are the changes needed?
To fix flaky `CoarseGrainedSchedulerBackendSuite.custom log url for Spark UI is applied`.
In this test, though we use `askSync` to register executor but `askSync` could be finished before we posting the 3rd `SparkListenerExecutorAdded` event to the listener bus due to the reason that `context.reply(true)` comes before `listenerBus.post`.
The error can be reproduced if you:
- loop it for 500 times in one turn
- or, insert a `Thread.sleep(1000)` between `post` and `reply`.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Loop the flaky tests for 1000 times without any error.
Closes#28100 from Ngone51/fix_spark_31249.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. Fix the `rebaseGregorianToJulianMicros()` function in `DateTimeUtils` by passing the daylight saving offset associated with the input `micros` to the constructed instance of `GregorianCalendar`. The problem is in `cal.getTimeInMillis` which returns earliest instant in the case of local date-time overlaps, see https://github.com/AdoptOpenJDK/openjdk-jdk8u/blob/master/jdk/src/share/classes/java/util/GregorianCalendar.java#L2783-L2786 . I fixed the issue by keeping the standard zone offset as is, and set the DST offset only. I don't set `ZONE_OFFSET` because time zone resolution works differently in Java 8 and Java 7 time APIs. So, if I would set the standard zone offsets too, this could change the behavior, and rebasing won't give the same result as Spark 2.4.
2. Fix `rebaseJulianToGregorianMicros()` by changing resulted zoned date-time if `DST_OFFSET` is zero which means the input date-time has passed an autumn daylight savings cutover. So, I take the latest local timestamp out of 2 overlapped timestamps. Otherwise I return a zoned date-time w/o any modification because it is equal to calling the `withEarlierOffsetAtOverlap()` method, so, we can optimize the case.
### Why are the changes needed?
This fixes the bug of loosing of DST offset info in rebasing timestamps via local date-time. For example, there are 2 different timestamps in the `America/Los_Angeles` time zone: `2019-11-03T01:00:00-07:00` and `2019-11-03T01:00:00-08:00`, though they are mapped to the same local date-time `2019-11-03T01:00`, see
<img width="456" alt="Screen Shot 2020-04-02 at 10 19 24" src="https://user-images.githubusercontent.com/1580697/78245697-95a7da00-74f0-11ea-9eba-c08138851cb3.png">
Currently, the UTC timestamp `2019-11-03T09:00:00Z` is converted to `2019-11-03T01:00:00-08:00`, and then to `2019-11-03T01:00:00` (in the original calendar, for instance Proleptic Gregorian calendar) and back to the UTC timestamp `2019-11-03T08:00:00Z` (in the hybrid calendar - Gregorian for the timestamp). That's wrong because the local timestamp must be converted to the original timestamp `2019-11-03T09:00:00Z`.
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
- Added a test to `DateTimeUtilsSuite` which checks that rebased micros are the same as the input during DST. The result must be the same if Java 8 and 7 time API functions return the same time zone offsets.
- Run the following code to check that there is no difference between rebased and original micros for modern timestamps:
```scala
test("rebasing differences") {
withDefaultTimeZone(getZoneId("America/Los_Angeles")) {
val start = instantToMicros(LocalDateTime.of(1, 1, 1, 0, 0, 0)
.atZone(getZoneId("America/Los_Angeles"))
.toInstant)
val end = instantToMicros(LocalDateTime.of(2030, 1, 1, 0, 0, 0)
.atZone(getZoneId("America/Los_Angeles"))
.toInstant)
var micros = start
var diff = Long.MaxValue
var counter = 0
while (micros < end) {
val rebased = rebaseGregorianToJulianMicros(micros)
val curDiff = rebased - micros
if (curDiff != diff) {
counter += 1
diff = curDiff
val ldt = microsToInstant(micros).atZone(getZoneId("America/Los_Angeles")).toLocalDateTime
println(s"local date-time = $ldt diff = ${diff / MICROS_PER_MINUTE} minutes")
}
micros += 30 * MICROS_PER_MINUTE
}
println(s"counter = $counter")
}
}
```
```
local date-time = 0001-01-01T00:00 diff = -2872 minutes
local date-time = 0100-03-01T00:00 diff = -1432 minutes
local date-time = 0200-03-01T00:00 diff = 7 minutes
local date-time = 0300-03-01T00:00 diff = 1447 minutes
local date-time = 0500-03-01T00:00 diff = 2887 minutes
local date-time = 0600-03-01T00:00 diff = 4327 minutes
local date-time = 0700-03-01T00:00 diff = 5767 minutes
local date-time = 0900-03-01T00:00 diff = 7207 minutes
local date-time = 1000-03-01T00:00 diff = 8647 minutes
local date-time = 1100-03-01T00:00 diff = 10087 minutes
local date-time = 1300-03-01T00:00 diff = 11527 minutes
local date-time = 1400-03-01T00:00 diff = 12967 minutes
local date-time = 1500-03-01T00:00 diff = 14407 minutes
local date-time = 1582-10-15T00:00 diff = 7 minutes
local date-time = 1883-11-18T12:22:58 diff = 0 minutes
counter = 15
```
The code is not added to `DateTimeUtilsSuite` because it takes > 30 seconds.
- By running the updated benchmark `DateTimeRebaseBenchmark` via the command:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.DateTimeRebaseBenchmark"
```
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 1.8.0_242-8u242/11.0.6+10 |
Closes#28101 from MaxGekk/fix-local-date-overlap.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-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 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>
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### What changes were proposed in this pull request?
<!--
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This is a followup of https://github.com/apache/spark/pull/28022, to address three issues:
1. Add an assert in `CustomShuffleReaderExec` to make sure the partitions specs are all `PartialMapperPartitionSpec` or none.
2. Do not use `lazy val` for `partitionDataSizeMetrics` and `skewedPartitionMetrics`, as they will be merged into `metrics`, and `lazy val` will be serialized.
3. mark `metrics` as `transient`, as it's only used at driver-side
4. move `FileUtils.byteCountToDisplaySize` to `logDebug`, to save some calculation if log level is above debug.
### 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.
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followup improvement
### 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.
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no
### How was this patch tested?
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existing tests
Closes#28103 from cloud-fan/ui.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.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?
For TransportFactory, the requests sent to the same address share a clientPool.
Specially, when the io.numConnectionPerPeer is 1, these requests would share a same client.
When this address is unreachable, the createClient operation would be still timeout.
And these requests would block each other during createClient, because there is a lock for this shared client.
It would cost connectionNum \* connectionTimeOut \* maxRetry to retry, and then fail the task.
It fact, it is expected that this task could fail in connectionTimeOut * maxRetry.
In this PR, I set a fastFail time window for the clientPool, if the last connection failed in this time window, the new connection would fast fail.
## Why are the changes needed?
It can save time for some cases.
## Does this PR introduce any user-facing change?
No.
## How was this patch tested?
Existing UT.
Closes#27943 from turboFei/SPARK-31179-fast-fail-connection.
Authored-by: turbofei <fwang12@ebay.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
After my investigation, `SQLQueryTestSuite` spent a lot of time compiling the generated java code.
Take `group-by.sql` as an example.
At first, I added some debug log into `SQLQueryTestSuite`.
Please reference 92b6af740c/sql/core/src/test/scala/org/apache/spark/sql/SQLQueryTestSuite.scala (L402)
The execution command is as follows:
`build/sbt "~sql/test-only *SQLQueryTestSuite -- -z group-by.sql"`
The output show below:
```
00:56:06.192 WARN org.apache.spark.sql.SQLQueryTestSuite: group-by.sql using configs: spark.sql.codegen.wholeStage=true. run time: 20604
00:56:13.719 WARN org.apache.spark.sql.SQLQueryTestSuite: group-by.sql using configs: spark.sql.codegen.wholeStage=false,spark.sql.codegen.factoryMode=CODEGEN_ONLY. run time: 7526
00:56:18.786 WARN org.apache.spark.sql.SQLQueryTestSuite: group-by.sql using configs: spark.sql.codegen.wholeStage=false,spark.sql.codegen.factoryMode=NO_CODEGEN. run time: 5066
```
According to the log, we know.
Config | Run time(ms)
-- | --
spark.sql.codegen.wholeStage=true | 20604
spark.sql.codegen.wholeStage=false,spark.sql.codegen.factoryMode=CODEGEN_ONLY | 7526
spark.sql.codegen.wholeStage=false,spark.sql.codegen.factoryMode=NO_CODEGEN | 5066
We should display the total compile time for generated java code.
This PR will add the following to `SQLQueryTestSuite`'s output.
```
=== Metrics of Whole Codegen ===
Total compile time: 80.564516529 seconds
```
Note: At first, I wanted to use `CodegenMetrics.METRIC_COMPILATION_TIME` to do this. After many experiments, I found that `CodegenMetrics.METRIC_COMPILATION_TIME` is only effective for a single test case, and cannot play a role in the whole life cycle of `SQLQueryTestSuite`.
I checked the type of ` CodegenMetrics.METRIC_COMPILATION_TIME` is `Histogram` and the latter preserves 1028 elements.` Histogram` is a metric which calculates the distribution of a value.
### Why are the changes needed?
Display the total compile time for generated java code.
### Does this PR introduce any user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#28081 from beliefer/output-codegen-compile-time.
Lead-authored-by: beliefer <beliefer@163.com>
Co-authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The `SaveMode` is resolved before we create `FileWriteBuilder` to build `BatchWrite`.
In https://github.com/apache/spark/pull/25876, we removed save mode for DSV2 from DataFrameWriter. So that the `mode` method is never used which makes `validateInputs` fail determinately without `mode` set.
### Why are the changes needed?
rm dead code.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests.
Closes#28090 from yaooqinn/SPARK-31321.
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 (SPARK-31324) aims to include stream ID in the error thrown when a stream does not stop() in time.
### Why are the changes needed?
https://github.com/apache/spark/pull/26771/ added a conf to set a requested timeout for stopping a stream, after which the stop() method throws. From seeing this in a production use case with several streams running, it's helpful to include which stream failed to stop in the error message.
### Does this PR introduce any user-facing change?
If a stream times out when terminating, the error message now includes the stream ID.
Before:
`Stream Execution thread failed to stop within 2000 milliseconds (specified by spark.sql.streaming.stopTimeout). See the cause on what was being executed in the streaming query thread.`
After:
`Stream Execution thread for stream [id = 8513769d-b9d2-4902-9b36-3668bd022245, runId = 21ed8c35-9bfe-423f-853d-c022d91818bc] failed to stop within 2000 milliseconds (specified by spark.sql.streaming.stopTimeout). See the cause on what was being executed in the streaming query thread.`
### How was this patch tested?
Updated existing unit test
Closes#28095 from mukulmurthy/31324-id.
Authored-by: Mukul Murthy <mukul.murthy@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
rename `QueryPlan.collectInPlanAndSubqueries` to `collectWithSubqueries`
### Why are the changes needed?
The old name is too verbose. `QueryPlan` is internal but it's the core of catalyst and we'd better make the API name clearer before we release it.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
N/A
Closes#28092 from cloud-fan/rename.
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 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?
In `TaskSchedulerImpl`, Spark will upper schedule mode `SchedulingMode.withName(schedulingModeConf.toUpperCase(Locale.ROOT))`.
But at other place, Spark does not. Such as [AllJobsPage](5945d46c11/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala (L304)).
We should have the same behavior and uppercase schema mode string at config.
### Why are the changes needed?
Before this pr, it's ok to set `spark.scheduler.mode=fair` logically.
But Spark will throw warn log
```
java.util.NoSuchElementException: No value found for 'fair'
at scala.Enumeration.withName(Enumeration.scala:124)
at org.apache.spark.ui.jobs.AllJobsPage$$anonfun$22.apply(AllJobsPage.scala:314)
at org.apache.spark.ui.jobs.AllJobsPage$$anonfun$22.apply(AllJobsPage.scala:314)
at scala.Option.map(Option.scala:146)
at org.apache.spark.ui.jobs.AllJobsPage.render(AllJobsPage.scala:314)
at org.apache.spark.ui.WebUI$$anonfun$2.apply(WebUI.scala:90)
at org.apache.spark.ui.WebUI$$anonfun$2.apply(WebUI.scala:90)
at org.apache.spark.ui.JettyUtils$$anon$3.doGet(JettyUtils.scala:90)
```
### Does this PR introduce any user-facing change?
Almost no.
### How was this patch tested?
Exists Test.
Closes#28049 from ulysses-you/SPARK-31285.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
The release script stops working after d5865493ae , as we require `mkdocs 1.0.0`.
This PR upgrades `mkdocs` from 0.1.6.3 to 1.0.4. To do that ruby is also upgraded to 2.5.
This PR also fixes some small issues.
### Why are the changes needed?
to make RC
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
tested by 3.0.0-rc1
Closes#28088 from cloud-fan/rc.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to add new benchmarks to `DateTimeRebaseBenchmark` for saving and loading dates/timestamps to/from ORC files. I extracted common code from the benchmark for Parquet datasource and place it to the methods `caseName()` and `getPath()`. Added benchmarks for ORC save/load dates before and after 1582-10-15 because an implementation may have different performance for dates before the Julian calendar cutover day, see #28067 as an example.
### Why are the changes needed?
To have the base line for future optimizations of `fromJavaDate()`/`toJavaDate()` and `toJavaTimestamp()`/`fromJavaTimestamp()` in `DateTimeUtils`. The methods are used while saving/loading dates/timestamps by ORC datasource.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running the updated benchmark `DateTimeRebaseBenchmark` via the command:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.DateTimeRebaseBenchmark"
```
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 1.8.0_242-8u242/11.0.6+10 |
Closes#28076 from MaxGekk/rebase-benchmark-orc.
Lead-authored-by: Max Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to replace the following SQL configs:
1. `spark.sql.legacy.parquet.rebaseDateTime.enabled` by
- `spark.sql.legacy.parquet.rebaseDateTimeInWrite.enabled` (`false` by default). The config enables rebasing dates/timestamps while saving to Parquet files. If it is set to `true`, dates/timestamps are converted to local date-time in Proleptic Gregorian calendar, date-time fields are extracted, and used in building new local date-time in the hybrid calendar (Julian + Gregorian). The resulted local date-time is converted to days or microseconds since the epoch.
- `spark.sql.legacy.parquet.rebaseDateTimeInRead.enabled` (`false` by default). The config enables rebasing of dates/timestamps in reading from Parquet files.
2. `spark.sql.legacy.avro.rebaseDateTime.enabled` by
- `spark.sql.legacy.avro.rebaseDateTimeInWrite.enabled` (`false` by default). It enables dates/timestamps rebasing from Proleptic Gregorian calendar to the hybrid calendar via local date/timestamps.
- `spark.sql.legacy.avro.rebaseDateTimeInRead.enabled` (`false` by default). It enables rebasing dates/timestamps from the hybrid calendar to Proleptic Gregorian calendar in read. The rebasing is performed by converting micros/millis/days to a local date/timestamp in the source calendar, interpreting the resulted date/timestamp in the target calendar, and getting the number of micros/millis/days since the epoch 1970-01-01 00:00:00Z.
### Why are the changes needed?
This allows to load dates/timestamps saved by Spark 2.4, and save to Parquet/Avro files without rebasing. And the reverse use case - load data saved by Spark 3.0, and save it in the form which is compatible with Spark 2.4.
### Does this PR introduce any user-facing change?
Yes, users have to use new SQL configs. Old SQL configs are removed by the PR.
### How was this patch tested?
By existing test suites `AvroV1Suite`, `AvroV2Suite` and `ParquetIOSuite`.
Closes#28082 from MaxGekk/split-rebase-configs.
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 aims to add `m01` as a node name additionally to `PVTestsSuite`.
### Why are the changes needed?
minikube 1.8.0 ~ 1.8.2 generate a cluster with a nodename `m01` while all the other versions have `minikube`. This causes `PVTestSuite` failure.
```
$ minikube --vm-driver=hyperkit start --memory 6000 --cpus 8
* minikube v1.8.2 on Darwin 10.15.3
- MINIKUBE_ACTIVE_DOCKERD=minikube
* Using the hyperkit driver based on user configuration
* Creating hyperkit VM (CPUs=8, Memory=6000MB, Disk=20000MB) ...
* Preparing Kubernetes v1.18.0 on Docker 19.03.6 ...
* Launching Kubernetes ...
* Enabling addons: default-storageclass, storage-provisioner
* Waiting for cluster to come online ...
* Done! kubectl is now configured to use "minikube"
$ kubectl get nodes
NAME STATUS ROLES AGE VERSION
m01 Ready master 22s v1.17.3
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
This only adds a new node name. So, K8S Jenkins job should passed.
In addition, `K8s` integration test suite should be tested on `minikube 1.8.2` manually.
```
KubernetesSuite:
- Run SparkPi with no resources
- Run SparkPi with a very long application name.
- Use SparkLauncher.NO_RESOURCE
- Run SparkPi with a master URL without a scheme.
- Run SparkPi with an argument.
- Run SparkPi with custom labels, annotations, and environment variables.
- All pods have the same service account by default
- Run extraJVMOptions check on driver
- Run SparkRemoteFileTest using a remote data file
- Run SparkPi with env and mount secrets.
- Run PySpark on simple pi.py example
- Run PySpark with Python2 to test a pyfiles example
- Run PySpark with Python3 to test a pyfiles example
- Run PySpark with memory customization
- Run in client mode.
- Start pod creation from template
- PVs with local storage
- Launcher client dependencies
- Test basic decommissioning
- Run SparkR on simple dataframe.R example
Run completed in 10 minutes, 23 seconds.
Total number of tests run: 20
Suites: completed 2, aborted 0
Tests: succeeded 20, failed 0, canceled 0, ignored 0, pending 0
All tests passed.
```
For the above test, Minikube 1.8.2 and K8s v1.18.0 is used.
```
$ minikube version
minikube version: v1.8.2
commit: eb13446e786c9ef70cb0a9f85a633194e62396a1
$ kubectl version --short
Client Version: v1.18.0
Server Version: v1.18.0
```
Closes#28080 from dongjoon-hyun/SPARK-31313.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### 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-31308) proposed to add python dependencies even it is not Python applications.
### Why are the changes needed?
For now, we add `pyFiles` argument to `files` argument only for Python applications, in SparkSubmit. Like the reason in #21420, "for some Spark applications, though they're a java program, they require not only jar dependencies, but also python dependencies.", we need to add `pyFiles` to `files` even it is not Python applications.
### Does this PR introduce any user-facing change?
Yes. After this change, for non-PySpark applications, the Python files specified by `pyFiles` are also added to `files` like PySpark applications.
### How was this patch tested?
Manually test on jupyter notebook or do `spark-submit` with `--verbose`.
```
Spark config:
...
(spark.files,file:/Users/dongjoon/PRS/SPARK-PR-28077/a.py)
(spark.submit.deployMode,client)
(spark.master,local[*])
```
Closes#28077 from viirya/pyfile.
Lead-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Co-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR (SPARK-31248) uses `ManualClock` to disable `ExecutorAllocationManager.schedule()` in order to avoid unexpected update of target executors.
### Why are the changes needed?
`ExecutorAllocationManager` will call `schedule` periodically, which may update target executors before we checking 496f6ac860/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala (L864)
And fail the check:
```
sbt.ForkMain$ForkError: org.scalatest.exceptions.TestFailedException: 12 did not equal 8
at org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:530)
at org.scalatest.Assertions.newAssertionFailedException$(Assertions.scala:529)
at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1560)
at org.scalatest.Assertions$AssertionsHelper.macroAssert(Assertions.scala:503)
at org.apache.spark.ExecutorAllocationManagerSuite.$anonfun$new$51(ExecutorAllocationManagerSuite.scala:864)
at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:186)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:151)
at org.scalatest.FunSuiteLike.invokeWithFixture$1(FunSuiteLike.scala:184)
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Update test.
Closes#28084 from Ngone51/spark_31248.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Add ANOVATest example for ml.stat.ANOVATest in python/java/scala
### Why are the changes needed?
Improve ML example
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
manually run the example
Closes#28073 from kevinyu98/add-ANOVA-example.
Authored-by: Qianyang Yu <qyu@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
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### What changes were proposed in this pull request?
<!--
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Add SQL metrics to the AQE shuffle reader (`CustomShuffleReaderExec`)
### Why are the changes needed?
<!--
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1. If you propose a new API, clarify the use case for a new API.
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to be more UI friendly
### Does this PR introduce any user-facing change?
<!--
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No
### How was this patch tested?
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new test
Closes#28022 from cloud-fan/metrics.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Added Java UDF suggestion in the in error message of untyped Scala UDF.
### Why are the changes needed?
To help user migrate their use case from deprecate untyped Scala UDF to other supported UDF.
### Does this PR introduce any user-facing change?
No. It haven't been released.
### How was this patch tested?
Pass Jenkins.
Closes#28070 from Ngone51/spark_31010.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch proposes to cache Class instance for the UDF instance in HiveFunctionWrapper to fix the case where Hive simple UDF is somehow transformed (expression is copied) and evaluated later with another classloader (for the case current thread context classloader is somehow changed). In this case, Spark throws CNFE as of now.
It's only occurred for Hive simple UDF, as HiveFunctionWrapper caches the UDF instance whereas it doesn't do for `UDF` type. The comment says Spark has to create instance every time for UDF, so we cannot simply do the same. This patch caches Class instance instead, and switch current thread context classloader to which loads the Class instance.
This patch extends the test boundary as well. We only tested with GenericUDTF for SPARK-26560, and this patch actually requires only UDF. But to avoid regression for other types as well, this patch adds all available types (UDF, GenericUDF, AbstractGenericUDAFResolver, UDAF, GenericUDTF) into the boundary of tests.
Credit to cloud-fan as he discovered the problem and proposed the solution.
### Why are the changes needed?
Above section describes why it's a bug and how it's fixed.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
New UTs added.
Closes#28079 from HeartSaVioR/SPARK-31312.
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?
Create statement plans in `DataFrameWriter(V2)`, like the SQL API.
### Why are the changes needed?
It's better to leave all the resolution work to the analyzer.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#27992 from cloud-fan/statement.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This reverts commit 8cf76f8d61. #25962
### Why are the changes needed?
In SPARK-29285, we change to create shuffle temporary eagerly. This is helpful for not to fail the entire task in the scenario of occasional disk failure. But for the applications that many tasks don't actually create shuffle files, it caused overhead. See the below benchmark:
Env: Spark local-cluster[2, 4, 19968], each queries run 5 round, each round 5 times.
Data: TPC-DS scale=99 generate by spark-tpcds-datagen
Results:
| | Base | Revert |
|-----|---------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|
| Q20 | Vector(4.096865667, 2.76231748, 2.722007606, 2.514433591, 2.400373579) Median 2.722007606 | Vector(3.763185446, 2.586498463, 2.593472842, 2.320522846, 2.224627274) Median 2.586498463 |
| Q33 | Vector(5.872176321, 4.854397586, 4.568787136, 4.393378146, 4.423996818) Median 4.568787136 | Vector(5.38746785, 4.361236877, 4.082311276, 3.867206824, 3.783188024) Median 4.082311276 |
| Q52 | Vector(3.978870321, 3.225437871, 3.282411608, 2.869674887, 2.644490664) Median 3.225437871 | Vector(4.000381522, 3.196025108, 3.248787619, 2.767444508, 2.606163423) Median 3.196025108 |
| Q56 | Vector(6.238045133, 4.820535173, 4.609965579, 4.313509894, 4.221256227) Median 4.609965579 | Vector(6.241611339, 4.225592467, 4.195202502, 3.757085755, 3.657525982) Median 4.195202502 |
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#28072 from xuanyuanking/SPARK-29285-revert.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
