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…sql.hive.caseSensitiveInferenceMode (Link to Jira: https://issues.apache.org/jira/browse/SPARK-20888) ## What changes were proposed in this pull request? Document change of default setting of spark.sql.hive.caseSensitiveInferenceMode configuration key from NEVER_INFO to INFER_AND_SAVE in the Spark SQL 2.1 to 2.2 migration notes. Author: Michael Allman <michael@videoamp.com> Closes #18112 from mallman/spark-20888-document_infer_and_save.
## What changes were proposed in this pull request? from_json function required to take in a java.util.Hashmap. For other functions, a java wrapper is provided which casts a java hashmap to a scala map. Only a java function is provided in this case, forcing scala users to pass in a java.util.Hashmap. Added the missing wrapper. ## How was this patch tested? Added a unit test for passing in a scala map Author: setjet <rubenljanssen@gmail.com> Closes #18094 from setjet/spark-20775.
## What changes were proposed in this pull request? When handling strings, the category dropped by RFormula and R are different: - RFormula drops the least frequent level - R drops the first level after ascending alphabetical ordering This PR supports different string ordering types in StringIndexer #17879 so that RFormula can drop the same level as R when handling strings using`stringOrderType = "alphabetDesc"`. ## How was this patch tested? new tests Author: Wayne Zhang <actuaryzhang@uber.com> Closes #17967 from actuaryzhang/RFormula.
## What changes were proposed in this pull request? It is reported that there is performance downgrade when applying ML pipeline for dataset with many columns but few rows. A big part of the performance downgrade comes from some operations (e.g., `select`) on DataFrame/Dataset which re-create new DataFrame/Dataset with a new `LogicalPlan`. The cost can be ignored in the usage of SQL, normally. However, it's not rare to chain dozens of pipeline stages in ML. When the query plan grows incrementally during running those stages, the total cost spent on re-creation of DataFrame grows too. In particular, the `Analyzer` will go through the big query plan even most part of it is analyzed. By eliminating part of the cost, the time to run the example code locally is reduced from about 1min to about 30 secs. In particular, the time applying the pipeline locally is mostly spent on calling transform of the 137 `Bucketizer`s. Before the change, each call of `Bucketizer`'s transform can cost about 0.4 sec. So the total time spent on all `Bucketizer`s' transform is about 50 secs. After the change, each call only costs about 0.1 sec. <del>We also make `boundEnc` as lazy variable to reduce unnecessary running time.</del> ### Performance improvement The codes and datasets provided by Barry Becker to re-produce this issue and benchmark can be found on the JIRA. Before this patch: about 1 min After this patch: about 20 secs ## How was this patch tested? Existing tests. Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #17770 from viirya/SPARK-20392.
## What changes were proposed in this pull request? 1, add an example for sparkr `decisionTree` 2, document it in user guide ## How was this patch tested? local submit Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #18067 from zhengruifeng/dt_example.
…ter FileChannel.transferTo ## What changes were proposed in this pull request? Long time ago we fixed a [bug](https://issues.apache.org/jira/browse/SPARK-3948) in shuffle writer about `FileChannel.transferTo`. We were not very confident about that fix, so we added a position check after the writing, try to discover the bug earlier. However this checking is missing in the new `UnsafeShuffleWriter`, this PR adds it. https://issues.apache.org/jira/browse/SPARK-18105 maybe related to that `FileChannel.transferTo` bug, hopefully we can find out the root cause after adding this position check. ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #18091 from cloud-fan/shuffle.
## What changes were proposed in this pull request? Include documentation of the fact that the updateFunc is sometimes called with no new values. This is documented in the main documentation here: https://spark.apache.org/docs/latest/streaming-programming-guide.html#updatestatebykey-operation however from the docs included with the code it is not clear that this is the case. ## How was this patch tested? PR only changes comments. Confirmed code still builds. Author: Wil Selwood <wil.selwood@sa.catapult.org.uk> Closes #18088 from wselwood/note-edge-case-in-docs.
## What changes were proposed in this pull request? `ConfigBuilder` builds `ConfigEntry` which can only read value with one key, if we wanna change the config name but still keep the old one, it's hard to do. This PR introduce `ConfigBuilder.withAlternative`, to support reading config value with alternative keys. And also rename `spark.scheduler.listenerbus.eventqueue.size` to `spark.scheduler.listenerbus.eventqueue.capacity` with this feature, according to #14269 (comment) ## How was this patch tested? a new test Author: Wenchen Fan <wenchen@databricks.com> Closes #18110 from cloud-fan/config.
…-cores parameter is setted less than 0 when submit a application ## What changes were proposed in this pull request? In my test, the submitted app running with out an error when the --total-executor-cores less than 0 and given the warnings: "2017-05-22 17:19:36,319 WARN org.apache.spark.scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources"; It should exit directly when the --total-executor-cores parameter is setted less than 0 when submit a application (Please fill in changes proposed in this fix) ## How was this patch tested? Run the ut tests (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Please review http://spark.apache.org/contributing.html before opening a pull request. Author: 10129659 <chen.yanshan@zte.com.cn> Closes #18060 from eatoncys/totalcores.
Now that Structured Streaming has been out for several Spark release and has large production use cases, the `Experimental` label is no longer appropriate. I've left `InterfaceStability.Evolving` however, as I think we may make a few changes to the pluggable Source & Sink API in Spark 2.3. Author: Michael Armbrust <michael@databricks.com> Closes #18065 from marmbrus/streamingGA.
## What changes were proposed in this pull request? When the individual partition size in a spill is small, mergeSpillsWithTransferTo method does many small disk ios which is really inefficient. One way to improve the performance will be to use mergeSpillsWithFileStream method by turning off transfer to and using buffered file read/write to improve the io throughput. However, the current implementation of mergeSpillsWithFileStream does not do a buffer read/write of the files and in addition to that it unnecessarily flushes the output files for each partitions. ## How was this patch tested? Tested this change by running a job on the cluster and the map stage run time was reduced by around 20%. Author: Sital Kedia <skedia@fb.com> Closes #17343 from sitalkedia/upstream_mergeSpillsWithFileStream.
…By and sortBy in SQL guide ## What changes were proposed in this pull request? - Add Scala, Python and Java examples for `partitionBy`, `sortBy` and `bucketBy`. - Add _Bucketing, Sorting and Partitioning_ section to SQL Programming Guide - Remove bucketing from Unsupported Hive Functionalities. ## How was this patch tested? Manual tests, docs build. Author: zero323 <zero323@users.noreply.github.com> Closes #17938 from zero323/DOCS-BUCKETING-AND-PARTITIONING.
## What changes were proposed in this pull request? Upon encountering an invalid columntype, the column type object is printed, rather than the type. This change improves this by outputting its name. ## How was this patch tested? Added a simple unit test to verify the contents of the raised exception Author: setjet <rubenljanssen@gmail.com> Closes #18097 from setjet/spark-20873.
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### What changes were proposed in this pull request? 1. Make more expressions extend `NullIntolerant`. 2. Add a checker(in `ExpressionInfoSuite`) to identify whether the expression is `NullIntolerant`. ### Why are the changes needed? Avoid skew join if the join column has many null values and can improve query performance. For examples: ```sql CREATE TABLE t1(c1 string, c2 string) USING parquet; CREATE TABLE t2(c1 string, c2 string) USING parquet; EXPLAIN SELECT t1.* FROM t1 JOIN t2 ON upper(t1.c1) = upper(t2.c1); ``` Before and after this PR: ```sql == Physical Plan == *(2) Project [c1#5, c2#6] +- *(2) BroadcastHashJoin [upper(c1#5)], [upper(c1#7)], Inner, BuildLeft :- BroadcastExchange HashedRelationBroadcastMode(List(upper(input[0, string, true]))), [id=#41] : +- *(1) ColumnarToRow : +- FileScan parquet default.t1[c1#5,c2#6] +- *(2) ColumnarToRow +- FileScan parquet default.t2[c1#7] == Physical Plan == *(2) Project [c1#5, c2#6] +- *(2) BroadcastHashJoin [upper(c1#5)], [upper(c1#7)], Inner, BuildRight :- *(2) Project [c1#5, c2#6] : +- *(2) Filter isnotnull(c1#5) : +- *(2) ColumnarToRow : +- FileScan parquet default.t1[c1#5,c2#6] +- BroadcastExchange HashedRelationBroadcastMode(List(upper(input[0, string, true]))), [id=#59] +- *(1) Project [c1#7] +- *(1) Filter isnotnull(c1#7) +- *(1) ColumnarToRow +- FileScan parquet default.t2[c1#7] ``` ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Unit test. Closes apache#28626 from wangyum/SPARK-28481. Authored-by: Yuming Wang <yumwang@ebay.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>
GulajavaMinistudio
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…query
### What changes were proposed in this pull request?
Remove redundant aliases after `RewritePredicateSubquery`. For example:
```scala
sql("CREATE TABLE t1 USING parquet AS SELECT id AS a, id AS b, id AS c FROM range(10)")
sql("CREATE TABLE t2 USING parquet AS SELECT id AS x, id AS y FROM range(8)")
sql(
"""
|SELECT *
|FROM t1
|WHERE a IN (SELECT x
| FROM (SELECT x AS x,
| Rank() OVER (partition BY x ORDER BY Sum(y) DESC) AS ranking
| FROM t2
| GROUP BY x) tmp1
| WHERE ranking <= 5)
|""".stripMargin).explain
```
Before this PR:
```
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- BroadcastHashJoin [a#10L], [x#7L], LeftSemi, BuildRight, false
:- FileScan parquet default.t1[a#10L,b#11L,c#12L]
+- BroadcastExchange HashedRelationBroadcastMode(List(input[0, bigint, true]),false), [id=#68]
+- Project [x#7L]
+- Filter (ranking#8 <= 5)
+- Window [rank(_w2#25L) windowspecdefinition(x#15L, _w2#25L DESC NULLS LAST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS ranking#8], [x#15L], [_w2#25L DESC NULLS LAST]
+- Sort [x#15L ASC NULLS FIRST, _w2#25L DESC NULLS LAST], false, 0
+- Exchange hashpartitioning(x#15L, 5), ENSURE_REQUIREMENTS, [id=#62]
+- HashAggregate(keys=[x#15L], functions=[sum(y#16L)])
+- Exchange hashpartitioning(x#15L, 5), ENSURE_REQUIREMENTS, [id=#59]
+- HashAggregate(keys=[x#15L], functions=[partial_sum(y#16L)])
+- FileScan parquet default.t2[x#15L,y#16L]
```
After this PR:
```
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- BroadcastHashJoin [a#10L], [x#15L], LeftSemi, BuildRight, false
:- FileScan parquet default.t1[a#10L,b#11L,c#12L]
+- BroadcastExchange HashedRelationBroadcastMode(List(input[0, bigint, true]),false), [id=#67]
+- Project [x#15L]
+- Filter (ranking#8 <= 5)
+- Window [rank(_w2#25L) windowspecdefinition(x#15L, _w2#25L DESC NULLS LAST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS ranking#8], [x#15L], [_w2#25L DESC NULLS LAST]
+- Sort [x#15L ASC NULLS FIRST, _w2#25L DESC NULLS LAST], false, 0
+- HashAggregate(keys=[x#15L], functions=[sum(y#16L)])
+- Exchange hashpartitioning(x#15L, 5), ENSURE_REQUIREMENTS, [id=#59]
+- HashAggregate(keys=[x#15L], functions=[partial_sum(y#16L)])
+- FileScan parquet default.t2[x#15L,y#16L]
```
### Why are the changes needed?
Reduce shuffle to improve query performance. This change can benefit TPC-DS q70.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes apache#33509 from wangyum/SPARK-36280.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
GulajavaMinistudio
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…HAVING
### What changes were proposed in this pull request?
This PR enhanced the analyzer to handle the following pattern properly.
```
Sort
- Filter
- Aggregate
```
### Why are the changes needed?
```
spark-sql (default)> CREATE TABLE t1 (flag BOOLEAN, dt STRING);
spark-sql (default)> SELECT LENGTH(dt),
> COUNT(t1.flag)
> FROM t1
> GROUP BY LENGTH(dt)
> HAVING COUNT(t1.flag) > 1
> ORDER BY LENGTH(dt);
[UNRESOLVED_COLUMN.WITH_SUGGESTION] A column or function parameter with name `dt` cannot be resolved. Did you mean one of the following? [`length(dt)`, `count(flag)`].; line 6 pos 16;
'Sort ['LENGTH('dt) ASC NULLS FIRST], true
+- Filter (count(flag)#60L > cast(1 as bigint))
+- Aggregate [length(dt#9)], [length(dt#9) AS length(dt)#59, count(flag#8) AS count(flag)#60L]
+- SubqueryAlias spark_catalog.default.t1
+- Relation spark_catalog.default.t1[flag#8,dt#9] parquet
```
The above code demonstrates the failure case, the query failed during the analysis phase when both `HAVING` and `ORDER BY` clauses are present, but successful if only one is present.
### Does this PR introduce _any_ user-facing change?
Yes, maybe we can call it a bugfix.
### How was this patch tested?
New UTs are added
### Was this patch authored or co-authored using generative AI tooling?
No.
Closes apache#44352 from pan3793/SPARK-28386.
Authored-by: Cheng Pan <chengpan@apache.org>
Signed-off-by: Kent Yao <yao@apache.org>
GulajavaMinistudio
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Jan 6, 2025
…ead pool ### What changes were proposed in this pull request? This PR aims to use a meaningful class name prefix for REST Submission API thread pool instead of the default value of Jetty QueuedThreadPool, `"qtp"+super.hashCode()`. https://github.com/dekellum/jetty/blob/3dc0120d573816de7d6a83e2d6a97035288bdd4a/jetty-util/src/main/java/org/eclipse/jetty/util/thread/QueuedThreadPool.java#L64 ### Why are the changes needed? This is helpful during JVM investigation. **BEFORE (4.0.0-preview2)** ``` $ SPARK_MASTER_OPTS='-Dspark.master.rest.enabled=true' sbin/start-master.sh $ jstack 28217 | grep qtp "qtp1925630411-52" #52 daemon prio=5 os_prio=31 cpu=0.07ms elapsed=19.06s tid=0x0000000134906c10 nid=0xde03 runnable [0x0000000314592000] "qtp1925630411-53" #53 daemon prio=5 os_prio=31 cpu=0.05ms elapsed=19.06s tid=0x0000000134ac6810 nid=0xc603 runnable [0x000000031479e000] "qtp1925630411-54" #54 daemon prio=5 os_prio=31 cpu=0.06ms elapsed=19.06s tid=0x000000013491ae10 nid=0xdc03 runnable [0x00000003149aa000] "qtp1925630411-55" #55 daemon prio=5 os_prio=31 cpu=0.08ms elapsed=19.06s tid=0x0000000134ac9810 nid=0xc803 runnable [0x0000000314bb6000] "qtp1925630411-56" #56 daemon prio=5 os_prio=31 cpu=0.04ms elapsed=19.06s tid=0x0000000134ac9e10 nid=0xda03 runnable [0x0000000314dc2000] "qtp1925630411-57" #57 daemon prio=5 os_prio=31 cpu=0.05ms elapsed=19.06s tid=0x0000000134aca410 nid=0xca03 runnable [0x0000000314fce000] "qtp1925630411-58" #58 daemon prio=5 os_prio=31 cpu=0.04ms elapsed=19.06s tid=0x0000000134acaa10 nid=0xcb03 runnable [0x00000003151da000] "qtp1925630411-59" #59 daemon prio=5 os_prio=31 cpu=0.06ms elapsed=19.06s tid=0x0000000134acb010 nid=0xcc03 runnable [0x00000003153e6000] "qtp1925630411-60-acceptor-0108e9815-ServerConnector1e497474{HTTP/1.1, (http/1.1)}{M3-Max.local:6066}" #60 daemon prio=3 os_prio=31 cpu=0.11ms elapsed=19.06s tid=0x00000001317ffa10 nid=0xcd03 runnable [0x00000003155f2000] "qtp1925630411-61-acceptor-11d90f2aa-ServerConnector1e497474{HTTP/1.1, (http/1.1)}{M3-Max.local:6066}" #61 daemon prio=3 os_prio=31 cpu=0.10ms elapsed=19.06s tid=0x00000001314ed610 nid=0xcf03 waiting on condition [0x00000003157fe000] ``` **AFTER** ``` $ SPARK_MASTER_OPTS='-Dspark.master.rest.enabled=true' sbin/start-master.sh $ jstack 28317 | grep StandaloneRestServer "StandaloneRestServer-52" #52 daemon prio=5 os_prio=31 cpu=0.09ms elapsed=60.06s tid=0x00000001284a8e10 nid=0xdb03 runnable [0x000000032cfce000] "StandaloneRestServer-53" #53 daemon prio=5 os_prio=31 cpu=0.06ms elapsed=60.06s tid=0x00000001284acc10 nid=0xda03 runnable [0x000000032d1da000] "StandaloneRestServer-54" #54 daemon prio=5 os_prio=31 cpu=0.05ms elapsed=60.06s tid=0x00000001284ae610 nid=0xd803 runnable [0x000000032d3e6000] "StandaloneRestServer-55" #55 daemon prio=5 os_prio=31 cpu=0.09ms elapsed=60.06s tid=0x00000001284aec10 nid=0xd703 runnable [0x000000032d5f2000] "StandaloneRestServer-56" #56 daemon prio=5 os_prio=31 cpu=0.06ms elapsed=60.06s tid=0x00000001284af210 nid=0xc803 runnable [0x000000032d7fe000] "StandaloneRestServer-57" #57 daemon prio=5 os_prio=31 cpu=0.05ms elapsed=60.06s tid=0x00000001284af810 nid=0xc903 runnable [0x000000032da0a000] "StandaloneRestServer-58" #58 daemon prio=5 os_prio=31 cpu=0.06ms elapsed=60.06s tid=0x00000001284afe10 nid=0xcb03 runnable [0x000000032dc16000] "StandaloneRestServer-59" #59 daemon prio=5 os_prio=31 cpu=0.05ms elapsed=60.06s tid=0x00000001284b0410 nid=0xcc03 runnable [0x000000032de22000] "StandaloneRestServer-60-acceptor-04aefbaa8-ServerConnector44284d85{HTTP/1.1, (http/1.1)}{M3-Max.local:6066}" #60 daemon prio=3 os_prio=31 cpu=0.13ms elapsed=60.05s tid=0x000000015cda1a10 nid=0xcd03 runnable [0x000000032e02e000] "StandaloneRestServer-61-acceptor-148976251-ServerConnector44284d85{HTTP/1.1, (http/1.1)}{M3-Max.local:6066}" #61 daemon prio=3 os_prio=31 cpu=0.12ms elapsed=60.05s tid=0x000000015cd1c810 nid=0xce03 waiting on condition [0x000000032e23a000] ``` ### Does this PR introduce _any_ user-facing change? No, the thread names are accessed during the debugging. ### How was this patch tested? Manual review. ### Was this patch authored or co-authored using generative AI tooling? No. Closes apache#48924 from dongjoon-hyun/SPARK-50385. Authored-by: Dongjoon Hyun <dongjoon@apache.org> Signed-off-by: panbingkun <panbingkun@apache.org>
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Please review http://spark.apache.org/contributing.html before opening a pull request.