## What changes were proposed in this pull request?
Add offset to PySpark in GLM as in #16699.
## How was this patch tested?
Python test
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#18534 from actuaryzhang/pythonOffset.
## What changes were proposed in this pull request?
Add `returnNullable` to `StaticInvoke` the same as #15780 is trying to add to `Invoke` and modify to handle properly.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#16056 from ueshin/issues/SPARK-18623.
## What changes were proposed in this pull request?
For these collection-related encoder expressions, we don't need to create `isNull` variable if the loop element is not nullable.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18529 from cloud-fan/minor.
## What changes were proposed in this pull request?
Add doc for methods that were left out, and fix various style and consistency issues.
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#18493 from actuaryzhang/sparkRDocCleanup.
## What changes were proposed in this pull request?
`ExternalMapToCatalyst` should null-check map key prior to converting to internal value to throw an appropriate Exception instead of something like NPE.
## How was this patch tested?
Added a test and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18524 from ueshin/issues/SPARK-21300.
### What changes were proposed in this pull request?
It is strange to see the following error message. Actually, the column is from another table.
```
cannot resolve '`right.a`' given input columns: [a, c, d];
```
After the PR, the error message looks like
```
cannot resolve '`right.a`' given input columns: [left.a, right.c, right.d];
```
### How was this patch tested?
Added a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18520 from gatorsmile/removeSQLConf.
## What changes were proposed in this pull request?
After running R tests in local build, it outputs Rplots.pdf. This one should be ignored in the git repository.
Author: wangmiao1981 <wm624@hotmail.com>
Closes#18518 from wangmiao1981/ignore.
## What changes were proposed in this pull request?
Grouped documentation for column window methods.
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#18481 from actuaryzhang/sparkRDocWindow.
## What changes were proposed in this pull request?
The scal() and creation of newCenter vector is done in the driver, after a collectAsMap operation while it could be done in the distributed RDD.
This PR moves this code before the collectAsMap for more efficiency
## How was this patch tested?
This was tested manually by running the KMeansExample and verifying that the new code ran without error and gave same output as before.
Author: dardelet <guillaumegorp@gmail.com>
Author: Guillaume Dardelet <dardelet@users.noreply.github.com>
Closes#18491 from dardelet/move-center-calculation-to-distributed-map-kmean.
## What changes were proposed in this pull request?
`SessionState` is designed to be created lazily. However, in reality, it created immediately in `SparkSession.Builder.getOrCreate` ([here](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala#L943)).
This PR aims to recover the lazy behavior by keeping the options into `initialSessionOptions`. The benefit is like the following. Users can start `spark-shell` and use RDD operations without any problems.
**BEFORE**
```scala
$ bin/spark-shell
java.lang.IllegalArgumentException: Error while instantiating 'org.apache.spark.sql.hive.HiveSessionStateBuilder'
...
Caused by: org.apache.spark.sql.AnalysisException:
org.apache.hadoop.hive.ql.metadata.HiveException:
MetaException(message:java.security.AccessControlException:
Permission denied: user=spark, access=READ,
inode="/apps/hive/warehouse":hive:hdfs:drwx------
```
As reported in SPARK-20256, this happens when the warehouse directory is not allowed for this user.
**AFTER**
```scala
$ bin/spark-shell
...
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.3.0-SNAPSHOT
/_/
Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_112)
Type in expressions to have them evaluated.
Type :help for more information.
scala> sc.range(0, 10, 1).count()
res0: Long = 10
```
## How was this patch tested?
Manual.
This closes#18512 .
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#18501 from dongjoon-hyun/SPARK-20256.
## What changes were proposed in this pull request?
when creating table like following:
> create table timestamp_test(id int(11), time_stamp timestamp not null default current_timestamp);
The result of Excuting "insert into timestamp_test values (111, null)" is different between Spark and JDBC.
```
mysql> select * from timestamp_test;
+------+---------------------+
| id | time_stamp |
+------+---------------------+
| 111 | 1970-01-01 00:00:00 | -> spark
| 111 | 2017-06-27 19:32:38 | -> mysql
+------+---------------------+
2 rows in set (0.00 sec)
```
Because in such case ```StructField.nullable``` is false, so the generated codes of ```InvokeLike``` and ```BoundReference``` don't check whether the field is null or not. Instead, they directly use ```CodegenContext.INPUT_ROW.getLong(1)```, however, ```UnsafeRow.setNullAt(1)``` will put 0 in the underlying memory.
The PR will ```always``` set ```StructField.nullable``` true after obtaining metadata from jdbc connection, Since we can insert null to not null timestamp column in MySQL. In this way, spark will propagate null to underlying DB engine, and let DB to choose how to process NULL.
## How was this patch tested?
Added tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: YIHAODIAN\wangshuangshuang <wangshuangshuang@yihaodian.com>
Author: Shuangshuang Wang <wsszone@gmail.com>
Closes#18445 from shuangshuangwang/SPARK-19726.
### What changes were proposed in this pull request?
SQLConf is moved to Catalyst. We are adding more and more test cases for verifying the conf-specific behaviors. It is nice to add a helper function to simplify the test cases.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18469 from gatorsmile/withSQLConf.
## What changes were proposed in this pull request?
**Context**
While reviewing https://github.com/apache/spark/pull/17227, I realised here we type-dispatch per record. The PR itself is fine in terms of performance as is but this prints a prefix, `"obj"` in exception message as below:
```
from pyspark.sql.types import *
schema = StructType([StructField('s', IntegerType(), nullable=False)])
spark.createDataFrame([["1"]], schema)
...
TypeError: obj.s: IntegerType can not accept object '1' in type <type 'str'>
```
I suggested to get rid of this but during investigating this, I realised my approach might bring a performance regression as it is a hot path.
Only for SPARK-19507 and https://github.com/apache/spark/pull/17227, It needs more changes to cleanly get rid of the prefix and I rather decided to fix both issues together.
**Propersal**
This PR tried to
- get rid of per-record type dispatch as we do in many code paths in Scala so that it improves the performance (roughly ~25% improvement) - SPARK-21296
This was tested with a simple code `spark.createDataFrame(range(1000000), "int")`. However, I am quite sure the actual improvement in practice is larger than this, in particular, when the schema is complicated.
- improve error message in exception describing field information as prose - SPARK-19507
## How was this patch tested?
Manually tested and unit tests were added in `python/pyspark/sql/tests.py`.
Benchmark - codes: https://gist.github.com/HyukjinKwon/c3397469c56cb26c2d7dd521ed0bc5a3
Error message - codes: https://gist.github.com/HyukjinKwon/b1b2c7f65865444c4a8836435100e398
**Before**
Benchmark:
- Results: https://gist.github.com/HyukjinKwon/4a291dab45542106301a0c1abcdca924
Error message
- Results: https://gist.github.com/HyukjinKwon/57b1916395794ce924faa32b14a3fe19
**After**
Benchmark
- Results: https://gist.github.com/HyukjinKwon/21496feecc4a920e50c4e455f836266e
Error message
- Results: https://gist.github.com/HyukjinKwon/7a494e4557fe32a652ce1236e504a395Closes#17227
Author: hyukjinkwon <gurwls223@gmail.com>
Author: David Gingrich <david@textio.com>
Closes#18521 from HyukjinKwon/python-type-dispatch.
## What changes were proposed in this pull request?
Currently, running the shell below:
```bash
$ ./bin/spark-submit tmp.R a b c
```
with R file, `tmp.R` as below:
```r
#!/usr/bin/env Rscript
library(SparkR)
sparkRSQL.init(sparkR.init(master = "local"))
collect(createDataFrame(list(list(1))))
print(commandArgs(trailingOnly = TRUE))
```
working fine as below:
```bash
_1
1 1
[1] "a" "b" "c"
```
However, it looks not printed in usage documentation as below:
```bash
$ ./bin/spark-submit
```
```
Usage: spark-submit [options] <app jar | python file> [app arguments]
...
```
For `./bin/sparkR`, it looks fine as below:
```bash
$ ./bin/sparkR tmp.R
```
```
Running R applications through 'sparkR' is not supported as of Spark 2.0.
Use ./bin/spark-submit <R file>
```
Running the script below:
```bash
$ ./bin/spark-submit
```
**Before**
```
Usage: spark-submit [options] <app jar | python file> [app arguments]
...
```
**After**
```
Usage: spark-submit [options] <app jar | python file | R file> [app arguments]
...
```
## How was this patch tested?
Manually tested.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18505 from HyukjinKwon/minor-doc-summit.
## What changes were proposed in this pull request?
Added "les" as french stop word (plurial of le)
Author: Thomas Decaux <ebuildy@gmail.com>
Closes#18514 from ebuildy/patch-1.
## What changes were proposed in this pull request?
Currently, it throws a NPE when missing columns but join type is speicified in join at PySpark as below:
```python
spark.conf.set("spark.sql.crossJoin.enabled", "false")
spark.range(1).join(spark.range(1), how="inner").show()
```
```
Traceback (most recent call last):
...
py4j.protocol.Py4JJavaError: An error occurred while calling o66.join.
: java.lang.NullPointerException
at org.apache.spark.sql.Dataset.join(Dataset.scala:931)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
...
```
```python
spark.conf.set("spark.sql.crossJoin.enabled", "true")
spark.range(1).join(spark.range(1), how="inner").show()
```
```
...
py4j.protocol.Py4JJavaError: An error occurred while calling o84.join.
: java.lang.NullPointerException
at org.apache.spark.sql.Dataset.join(Dataset.scala:931)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
...
```
This PR suggests to follow Scala's one as below:
```scala
scala> spark.conf.set("spark.sql.crossJoin.enabled", "false")
scala> spark.range(1).join(spark.range(1), Seq.empty[String], "inner").show()
```
```
org.apache.spark.sql.AnalysisException: Detected cartesian product for INNER join between logical plans
Range (0, 1, step=1, splits=Some(8))
and
Range (0, 1, step=1, splits=Some(8))
Join condition is missing or trivial.
Use the CROSS JOIN syntax to allow cartesian products between these relations.;
...
```
```scala
scala> spark.conf.set("spark.sql.crossJoin.enabled", "true")
scala> spark.range(1).join(spark.range(1), Seq.empty[String], "inner").show()
```
```
+---+---+
| id| id|
+---+---+
| 0| 0|
+---+---+
```
**After**
```python
spark.conf.set("spark.sql.crossJoin.enabled", "false")
spark.range(1).join(spark.range(1), how="inner").show()
```
```
Traceback (most recent call last):
...
pyspark.sql.utils.AnalysisException: u'Detected cartesian product for INNER join between logical plans\nRange (0, 1, step=1, splits=Some(8))\nand\nRange (0, 1, step=1, splits=Some(8))\nJoin condition is missing or trivial.\nUse the CROSS JOIN syntax to allow cartesian products between these relations.;'
```
```python
spark.conf.set("spark.sql.crossJoin.enabled", "true")
spark.range(1).join(spark.range(1), how="inner").show()
```
```
+---+---+
| id| id|
+---+---+
| 0| 0|
+---+---+
```
## How was this patch tested?
Added tests in `python/pyspark/sql/tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18484 from HyukjinKwon/SPARK-21264.
## What changes were proposed in this pull request?
`FileAppender` is used to write `stderr` and `stdout` files in `ExecutorRunner`, But before writing `ErrorStream` into the the `stderr` file, the header information has been written into ,if FileOutputStream is not created as append mode, the header information will be lost
## How was this patch tested?
unit test case
Author: liuxian <liu.xian3@zte.com.cn>
Closes#18507 from 10110346/wip-lx-0703.
## What changes were proposed in this pull request?
If the created ACTIVE sparkContext is not EXPLICITLY passed through the Builder's API `sparkContext()`, the conf of this sparkContext will also contain the conf set through the API `config()`; otherwise, the conf of this sparkContext will NOT contain the conf set through the API `config()`
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18517 from gatorsmile/fixTestCase2.
## What changes were proposed in this pull request?
Looking at the code in `SessionCatalog.registerFunction`, the parameter `ignoreIfExists` is a wrong name. When `ignoreIfExists` is true, we will override the function if it already exists. So `overrideIfExists` should be the corrected name.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18510 from cloud-fan/minor.
## What changes were proposed in this pull request?
This pr added code to print the same warning messages with `===` cases when using NULL-safe equals (`<=>`).
## How was this patch tested?
Existing tests.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18436 from maropu/SPARK-20073.
### Idea
This PR adds validation to REFRESH sql statements. Currently, users can specify whatever they want as resource path. For example, spark.sql("REFRESH ! $ !") will be executed without any exceptions.
### Implementation
I am not sure that my current implementation is the most optimal, so any feedback is appreciated. My first idea was to make the grammar as strict as possible. Unfortunately, there were some problems. I tried the approach below:
SqlBase.g4
```
...
| REFRESH TABLE tableIdentifier #refreshTable
| REFRESH resourcePath #refreshResource
...
resourcePath
: STRING
| (IDENTIFIER | number | nonReserved | '/' | '-')+ // other symbols can be added if needed
;
```
It is not flexible enough and requires to explicitly mention all possible symbols. Therefore, I came up with the current approach that is implemented in the code.
Let me know your opinion on which one is better.
Author: aokolnychyi <anton.okolnychyi@sap.com>
Closes#18368 from aokolnychyi/spark-21102.
## What changes were proposed in this pull request?
It is strange that we will get "table not found" error if **the first sql** uses upper case table names, when developers write tests with `TestHiveSingleton`, **although case insensitivity**. This is because in `TestHiveQueryExecution`, test tables are loaded based on exact matching instead of case sensitivity.
## How was this patch tested?
Added a new test case.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#18504 from wzhfy/testHive.
## What changes were proposed in this pull request?
Parallelize FileInputFormat.listStatus in Hadoop API via LIST_STATUS_NUM_THREADS to speed up examination of file sizes for wholeTextFiles et al
## How was this patch tested?
Existing tests, which will exercise the key path here: using a local file system.
Author: Sean Owen <sowen@cloudera.com>
Closes#18441 from srowen/SPARK-21137.
## What changes were proposed in this pull request?
Move `compileValue` method in JDBCRDD to JdbcDialect, and override the `compileValue` method in OracleDialect to rewrite the Oracle-specific timestamp and date literals in where clause.
## How was this patch tested?
An integration test has been added.
Author: Rui Zha <zrdt713@gmail.com>
Author: Zharui <zrdt713@gmail.com>
Closes#18451 from SharpRay/extend-compileValue-to-dialects.
## What changes were proposed in this pull request?
This PR is to maintain API parity with changes made in SPARK-17498 to support a new option
'keep' in StringIndexer to handle unseen labels or NULL values with PySpark.
Note: This is updated version of #17237 , the primary author of this PR is VinceShieh .
## How was this patch tested?
Unit tests.
Author: VinceShieh <vincent.xie@intel.com>
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18453 from yanboliang/spark-19852.
## What changes were proposed in this pull request?
OutputFakerExec was added long ago and is not used anywhere now so we should remove it.
## How was this patch tested?
N/A
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#18473 from jiangxb1987/OutputFakerExec.
## What changes were proposed in this pull request?
Not adding the exception to the suppressed if it is the same instance as originalThrowable.
## How was this patch tested?
Added new tests to verify this, these tests fail without source code changes and passes with the change.
Author: Devaraj K <devaraj@apache.org>
Closes#18384 from devaraj-kavali/SPARK-21170.
## What changes were proposed in this pull request?
1, make param support non-final with `finalFields` option
2, generate `HasSolver` with `finalFields = false`
3, override `solver` in LiR, GLR, and make MLPC inherit `HasSolver`
## How was this patch tested?
existing tests
Author: Ruifeng Zheng <ruifengz@foxmail.com>
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#16028 from zhengruifeng/param_non_final.
## What changes were proposed in this pull request?
We currently implement statistics propagation directly in logical plan. Given we already have two different implementations, it'd make sense to actually decouple the two and add stats propagation using mixin. This would reduce the coupling between logical plan and statistics handling.
This can also be a powerful pattern in the future to add additional properties (e.g. constraints).
## How was this patch tested?
Should be covered by existing test cases.
Author: Reynold Xin <rxin@databricks.com>
Closes#18479 from rxin/stats-trait.
## What changes were proposed in this pull request?
Update stats after the following data changing commands:
- InsertIntoHadoopFsRelationCommand
- InsertIntoHiveTable
- LoadDataCommand
- TruncateTableCommand
- AlterTableSetLocationCommand
- AlterTableDropPartitionCommand
## How was this patch tested?
Added new test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#18334 from wzhfy/changeStatsForOperation.
## What changes were proposed in this pull request?
For performance reasons, `UnsafeRow.getString`, `getStruct`, etc. return a "pointer" that points to a memory region of this unsafe row. This makes the unsafe projection a little dangerous, because all of its output rows share one instance.
When we implement SQL operators, we should be careful to not cache the input rows because they may be produced by unsafe projection from child operator and thus its content may change overtime.
However, when we updating values of InternalRow(e.g. in mutable projection and safe projection), we only copy UTF8String, we should also copy InternalRow, ArrayData and MapData. This PR fixes this, and also fixes the copy of vairous InternalRow, ArrayData and MapData implementations.
## How was this patch tested?
new regression tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18483 from cloud-fan/fix-copy.
## What changes were proposed in this pull request?
Remove `numHashCollisions` in `BytesToBytesMap`. And change `getAverageProbesPerLookup()` to `getAverageProbesPerLookup` as suggested.
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18480 from viirya/SPARK-21052-followup.
### What changes were proposed in this pull request?
Function argument should not be named expressions. It could cause two issues:
- Misleading error message
- Unexpected query results when the column name is `distinct`, which is not a reserved word in our parser.
```
spark-sql> select count(distinct c1, distinct c2) from t1;
Error in query: cannot resolve '`distinct`' given input columns: [c1, c2]; line 1 pos 26;
'Project [unresolvedalias('count(c1#30, 'distinct), None)]
+- SubqueryAlias t1
+- CatalogRelation `default`.`t1`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [c1#30, c2#31]
```
After the fix, the error message becomes
```
spark-sql> select count(distinct c1, distinct c2) from t1;
Error in query:
extraneous input 'c2' expecting {')', ',', '.', '[', 'OR', 'AND', 'IN', NOT, 'BETWEEN', 'LIKE', RLIKE, 'IS', EQ, '<=>', '<>', '!=', '<', LTE, '>', GTE, '+', '-', '*', '/', '%', 'DIV', '&', '|', '||', '^'}(line 1, pos 35)
== SQL ==
select count(distinct c1, distinct c2) from t1
-----------------------------------^^^
```
### How was this patch tested?
Added a test case to parser suite.
Author: Xiao Li <gatorsmile@gmail.com>
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18338 from gatorsmile/parserDistinctAggFunc.
# What issue does this PR address ?
Jira:https://issues.apache.org/jira/browse/SPARK-21223
fix the Thread-safety issue in FsHistoryProvider
Currently, Spark HistoryServer use a HashMap named fileToAppInfo in class FsHistoryProvider to store the map of eventlog path and attemptInfo.
When use ThreadPool to Replay the log files in the list and merge the list of old applications with new ones, multi thread may update fileToAppInfo at the same time, which may cause Thread-safety issues, such as falling into an infinite loop because of calling resize func of the hashtable.
Author: 曾林西 <zenglinxi@meituan.com>
Closes#18430 from zenglinxi0615/master.
## What changes were proposed in this pull request?
Fix scala-2.10 build failure of ```GeneralizedLinearRegressionSuite```.
## How was this patch tested?
Build with scala-2.10.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18489 from yanboliang/glr.
## What changes were proposed in this pull request?
This PR makes the following changes:
- Implement a new commit protocol `HadoopMapRedCommitProtocol` which support the old `mapred` package's committer;
- Refactor SparkHadoopWriter and SparkHadoopMapReduceWriter, now they are combined together, thus we can support write through both mapred and mapreduce API by the new SparkHadoopWriter, a lot of duplicated codes are removed.
After this change, it should be pretty easy for us to support the committer from both the new and the old hadoop API at high level.
## How was this patch tested?
No major behavior change, passed the existing test cases.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#18438 from jiangxb1987/SparkHadoopWriter.
## What changes were proposed in this pull request?
Add support for offset in GLM. This is useful for at least two reasons:
1. Account for exposure: e.g., when modeling the number of accidents, we may need to use miles driven as an offset to access factors on frequency.
2. Test incremental effects of new variables: we can use predictions from the existing model as offset and run a much smaller model on only new variables. This avoids re-estimating the large model with all variables (old + new) and can be very important for efficient large-scaled analysis.
## How was this patch tested?
New test.
yanboliang srowen felixcheung sethah
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#16699 from actuaryzhang/offset.
## What changes were proposed in this pull request?
Grouped documentation for column collection methods.
Author: actuaryzhang <actuaryzhang10@gmail.com>
Author: Wayne Zhang <actuaryzhang10@gmail.com>
Closes#18458 from actuaryzhang/sparkRDocCollection.
## What changes were proposed in this pull request?
Grouped documentation for column misc methods.
Author: actuaryzhang <actuaryzhang10@gmail.com>
Author: Wayne Zhang <actuaryzhang10@gmail.com>
Closes#18448 from actuaryzhang/sparkRDocMisc.
## What changes were proposed in this pull request?
`WindowExec` currently improperly stores complex objects (UnsafeRow, UnsafeArrayData, UnsafeMapData, UTF8String) during aggregation by keeping a reference in the buffer used by `GeneratedMutableProjections` to the actual input data. Things go wrong when the input object (or the backing bytes) are reused for other things. This could happen in window functions when it starts spilling to disk. When reading the back the spill files the `UnsafeSorterSpillReader` reuses the buffer to which the `UnsafeRow` points, leading to weird corruption scenario's. Note that this only happens for aggregate functions that preserve (parts of) their input, for example `FIRST`, `LAST`, `MIN` & `MAX`.
This was not seen before, because the spilling logic was not doing actual spills as much and actually used an in-memory page. This page was not cleaned up during window processing and made sure unsafe objects point to their own dedicated memory location. This was changed by https://github.com/apache/spark/pull/16909, after this PR Spark spills more eagerly.
This PR provides a surgical fix because we are close to releasing Spark 2.2. This change just makes sure that there cannot be any object reuse at the expensive of a little bit of performance. We will follow-up with a more subtle solution at a later point.
## How was this patch tested?
Added a regression test to `DataFrameWindowFunctionsSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#18470 from hvanhovell/SPARK-21258.
## What changes were proposed in this pull request?
A follow up PR to fix Scala 2.10 build for #18472
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#18478 from zsxwing/SPARK-21253-2.
## What changes were proposed in this pull request?
Please see also https://issues.apache.org/jira/browse/SPARK-21176
This change limits the number of selector threads that jetty creates to maximum 8 per proxy servlet (Jetty default is number of processors / 2).
The newHttpClient for Jettys ProxyServlet class is overwritten to avoid the Jetty defaults (which are designed for high-performance http servers).
Once https://github.com/eclipse/jetty.project/issues/1643 is available, the code could be cleaned up to avoid the method override.
I really need this on v2.1.1 - what is the best way for a backport automatic merge works fine)? Shall I create another PR?
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
The patch was tested manually on a Spark cluster with a head node that has 88 processors using JMX to verify that the number of selector threads is now limited to 8 per proxy.
gurvindersingh zsxwing can you please review the change?
Author: IngoSchuster <ingo.schuster@de.ibm.com>
Author: Ingo Schuster <ingo.schuster@de.ibm.com>
Closes#18437 from IngoSchuster/master.
## What changes were proposed in this pull request?
Disable spark.reducer.maxReqSizeShuffleToMem because it breaks the old shuffle service.
Credits to wangyum
Closes#18466
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18467 from zsxwing/SPARK-21253.
## What changes were proposed in this pull request?
If a network error happens before processing StreamResponse/StreamFailure events, StreamCallback.onFailure won't be called.
This PR fixes `failOutstandingRequests` to also notify outstanding StreamCallbacks.
## How was this patch tested?
The new unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#18472 from zsxwing/fix-stream-2.
## What changes were proposed in this pull request?
Since the objects `readLocksByTask`, `writeLocksByTask` and `info`s are coupled and supposed to be modified by other threads concurrently, all the read and writes of them in the method `releaseAllLocksForTask` should be protected by a single synchronized block like other similar methods.
## How was this patch tested?
existing tests
Author: Feng Liu <fengliu@databricks.com>
Closes#18400 from liufengdb/synchronize.
## What changes were proposed in this pull request?
This adds the average hash map probe metrics to join operator such as `BroadcastHashJoin` and `ShuffledHashJoin`.
This PR adds the API to `HashedRelation` to get average hash map probe.
## How was this patch tested?
Related test cases are added.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18301 from viirya/SPARK-21052.
JIRA Issue:https://issues.apache.org/jira/browse/SPARK-21225
In the function "resourceOffers", It declare a variable "tasks" for storage the tasks which have allocated a executor. It declared like this:
`val tasks = shuffledOffers.map(o => new ArrayBuffer[TaskDescription](o.cores))`
But, I think this code only conside a situation for that one task per core. If the user set "spark.task.cpus" as 2 or 3, It really don't need so much Mem. I think It can motify as follow:
val tasks = shuffledOffers.map(o => new ArrayBuffer[TaskDescription](o.cores / CPUS_PER_TASK))
to instead.
Motify like this the other earning is that it's more easy to understand the way how the tasks allocate offers.
Author: 杨治国10192065 <yang.zhiguo@zte.com.cn>
Closes#18435 from JackYangzg/motifyTaskCoreDisp.
## What changes were proposed in this pull request?
Hide duration of incompleted applications.
## How was this patch tested?
manual tests
Author: fjh100456 <fu.jinhua6@zte.com.cn>
Closes#18351 from fjh100456/master.
