## What changes were proposed in this pull request?
The current `SelectedField` extractor is somewhat complicated and it seems to be handling cases that should be handled automatically:
- `GetArrayItem(child: GetStructFieldObject())`
- `GetArrayStructFields(child: GetArrayStructFields())`
- `GetMap(value: GetStructFieldObject())`
This PR removes those cases and simplifies the extractor by passing down the data type instead of a field.
## How was this patch tested?
Existing tests.
Closes#23397 from hvanhovell/SPARK-26495.
Authored-by: Herman van Hovell <hvanhovell@databricks.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
As the description in SPARK-26339, spark.read behavior is very confusing when reading files that start with underscore, fix this by throwing exception which message is "Path does not exist".
## How was this patch tested?
manual tests.
Both of codes below throws exception which message is "Path does not exist".
```
spark.read.csv("/home/forcia/work/spark/_test.csv")
spark.read.schema("test STRING, number INT").csv("/home/forcia/work/spark/_test.csv")
```
Closes#23288 from KeiichiHirobe/SPARK-26339.
Authored-by: Hirobe Keiichi <keiichi_hirobe@forcia.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Proposed new class `StringConcat` for converting a sequence of strings to string with one memory allocation in the `toString` method. `StringConcat` replaces `StringBuilderWriter` in methods of dumping of Spark plans and codegen to strings.
All `Writer` arguments are replaced by `String => Unit` in methods related to Spark plans stringification.
## How was this patch tested?
It was tested by existing suites `QueryExecutionSuite`, `DebuggingSuite` as well as new tests for `StringConcat` in `StringUtilsSuite`.
Closes#23406 from MaxGekk/rope-plan.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
#20560/[SPARK-23375](https://issues.apache.org/jira/browse/SPARK-23375) introduced an optimizer rule to eliminate redundant Sort. For a test case named "Sort metrics" in `SQLMetricsSuite`, because range is already sorted, sort is removed by the `RemoveRedundantSorts`, which makes this test case meaningless.
This PR modifies the query for testing Sort metrics and checks Sort exists in the plan.
## How was this patch tested?
Modify the existing test case.
Closes#23258 from seancxmao/sort-metrics.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the method `taskList`(since https://github.com/apache/spark/pull/21688), the executor log value is queried in KV store for every task(method `constructTaskData`).
This PR propose to use a hashmap for reducing duplicated KV store lookups in the method.
## How was this patch tested?
Manual check
Closes#23310 from gengliangwang/removeExecutorLog.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This pr makes hardcoded "spark.history" configs to use `ConfigEntry` and put them in `History` config object.
## How was this patch tested?
Existing tests.
Closes#23384 from ueshin/issues/SPARK-26443/hardcoded_history_configs.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Similar with https://github.com/apache/spark/pull/21446. Looks random string is not quite safe as a directory name.
```scala
scala> val prefix = Random.nextString(10); val dir = new File("/tmp", "del_" + prefix + "-" + UUID.randomUUID.toString); dir.mkdirs()
prefix: String = 窽텘⒘駖ⵚ駢⡞Ρ닋
dir: java.io.File = /tmp/del_窽텘⒘駖ⵚ駢⡞Ρ닋-a3f99855-c429-47a0-a108-47bca6905745
res40: Boolean = false // nope, didn't like this one
```
## How was this patch tested?
Unit test was added, and manually.
Closes#23405 from HyukjinKwon/SPARK-26496.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This PR fixes the codegen bug introduced by #23358 .
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.7-ubuntu-scala-2.11/158/
```
Line 44, Column 93: A method named "apply" is not declared in any enclosing class
nor any supertype, nor through a static import
```
## How was this patch tested?
Manual. `DateExpressionsSuite` should be passed with Scala-2.11.
Closes#23394 from dongjoon-hyun/SPARK-26424.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Add docs to describe how remove policy act while considering the property `spark.dynamicAllocation.cachedExecutorIdleTimeout` in ExecutorAllocationManager
## How was this patch tested?
comment-only PR.
Closes#23386 from TopGunViper/SPARK-26446.
Authored-by: wuqingxin <wuqingxin@baidu.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Change aligns argument name with that in Scala version and documentation.
## How was this patch tested?
(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.
Closes#23357 from deepyaman/patch-1.
Authored-by: deepyaman <deepyaman.datta@utexas.edu>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Currently the `AttributeReference.withMetadata` method have return type `Attribute`, the rest of with methods in the `AttributeReference` return type are `AttributeReference`, as the [spark-25892](https://issues.apache.org/jira/browse/SPARK-25892?jql=project%20%3D%20SPARK%20AND%20component%20in%20(ML%2C%20PySpark%2C%20SQL)) mentioned.
This PR will change `AttributeReference.withMetadata` method's return type from `Attribute` to `AttributeReference`.
## How was this patch tested?
Run all `sql/test,` `catalyst/test` and `org.apache.spark.sql.execution.streaming.*`
Closes#22918 from kevinyu98/spark-25892.
Authored-by: Kevin Yu <qyu@us.ibm.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to add `maxFields` parameter to all functions involved in creation of textual representation of spark plans such as `simpleString` and `verboseString`. New parameter restricts number of fields converted to truncated strings. Any elements beyond the limit will be dropped and replaced by a `"... N more fields"` placeholder. The threshold is bumped up to `Int.MaxValue` for `toFile()`.
## How was this patch tested?
Added a test to `QueryExecutionSuite` which checks `maxFields` impacts on number of truncated fields in `LocalRelation`.
Closes#23159 from MaxGekk/to-file-max-fields.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
Spark SQL doesn't support creating partitioned table using Hive CTAS in SQL syntax. However it is supported by using DataFrameWriter API.
```scala
val df = Seq(("a", 1)).toDF("part", "id")
df.write.format("hive").partitionBy("part").saveAsTable("t")
```
Hive begins to support this syntax in newer version: https://issues.apache.org/jira/browse/HIVE-20241:
```
CREATE TABLE t PARTITIONED BY (part) AS SELECT 1 as id, "a" as part
```
This patch adds this support to SQL syntax.
## How was this patch tested?
Added tests.
Closes#23376 from viirya/hive-ctas-partitioned-table.
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 switch the `DateFormatClass`, `ToUnixTimestamp`, `FromUnixTime`, `UnixTime` on java.time API for parsing/formatting dates and timestamps. The API has been already implemented by the `Timestamp`/`DateFormatter` classes. One of benefit is those classes support parsing timestamps with microsecond precision. Old behaviour can be switched on via SQL config: `spark.sql.legacy.timeParser.enabled` (`false` by default).
## How was this patch tested?
It was tested by existing test suites - `DateFunctionsSuite`, `DateExpressionsSuite`, `JsonSuite`, `CsvSuite`, `SQLQueryTestSuite` as well as PySpark tests.
Closes#23358 from MaxGekk/new-time-cast.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.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?
fix ExpresionInfo assert error in windows operation system, when running unit tests.
## How was this patch tested?
unit tests
Closes#23363 from yanlin-Lynn/unit-test-windows.
Authored-by: wangyanlin01 <wangyanlin01@baidu.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Variation of https://github.com/apache/spark/pull/20500
I cheated by not referencing fields or columns at all as this exception propagates in contexts where both would be applicable.
## How was this patch tested?
Existing tests
Closes#23373 from srowen/SPARK-14023.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
The PRs #23150 and #23196 switched JSON and CSV datasources on new formatter for dates/timestamps which is based on `DateTimeFormatter`. In this PR, I replaced `SimpleDateFormat` by `DateTimeFormatter` to reflect the changes.
Closes#23374 from MaxGekk/java-time-docs.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
GetStructField with different optional names should be semantically equal. We will use this as building block to compare the nested fields used in the plans to be optimized by catalyst optimizer.
This PR also fixes a bug below that accessing nested fields with different cases in case insensitive mode will result `AnalysisException`.
```
sql("create table t (s struct<i: Int>) using json")
sql("select s.I from t group by s.i")
```
which is currently failing
```
org.apache.spark.sql.AnalysisException: expression 'default.t.`s`' is neither present in the group by, nor is it an aggregate function
```
as cloud-fan pointed out.
## How was this patch tested?
New tests are added.
Closes#23353 from dbtsai/nestedEqual.
Lead-authored-by: DB Tsai <d_tsai@apple.com>
Co-authored-by: DB Tsai <dbtsai@dbtsai.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This patch adds explanation of `why "spark.sql.shuffle.partitions" keeps unchanged in structured streaming`, which couple of users already wondered and some of them even thought it as a bug.
This patch would help other end users to know about such behavior before they find by theirselves and being wondered.
## How was this patch tested?
No need to test because this is a simple addition on guide doc with markdown editor.
Closes#22238 from HeartSaVioR/SPARK-25245.
Lead-authored-by: Jungtaek Lim <kabhwan@gmail.com>
Co-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
…leAccumulator
## What changes were proposed in this pull request?
This PR implements metric sources for LongAccumulator and DoubleAccumulator, such that a user can register these accumulators easily and have their values be reported by the driver's metric namespace.
## How was this patch tested?
Unit tests, and manual tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23242 from abellina/SPARK-26285_accumulator_source.
Lead-authored-by: Alessandro Bellina <abellina@yahoo-inc.com>
Co-authored-by: Alessandro Bellina <abellina@oath.com>
Co-authored-by: Alessandro Bellina <abellina@gmail.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
This PR aims to upgrade Maven Surefile plugin for JDK11 support. 3.0.0-M2 is [released Dec. 9th.](https://issues.apache.org/jira/projects/SUREFIRE/versions/12344396)
```
[SUREFIRE-1568] Versions 2.21 and higher doesn't work with junit-platform for Java 9 module
[SUREFIRE-1605] NoClassDefFoundError (RunNotifier) with JDK 11
[SUREFIRE-1600] Surefire Project using surefire:2.12.4 is not fully able to work with JDK 10+ on internal build system. Therefore surefire-shadefire should go with Surefire:3.0.0-M2.
[SUREFIRE-1593] 3.0.0-M1 produces invalid code sources on Windows
[SUREFIRE-1602] Surefire fails loading class ForkedBooter when using a sub-directory pom file and a local maven repo
[SUREFIRE-1606] maven-shared-utils must not be on provider's classpath
[SUREFIRE-1531] Option to switch-off Java 9 modules
[SUREFIRE-1590] Deploy multiple versions of Report XSD
[SUREFIRE-1591] Java 1.7 feature Diamonds replaced Generics
[SUREFIRE-1594] Java 1.7 feature try-catch - multiple exceptions in one catch
[SUREFIRE-1595] Java 1.7 feature System.lineSeparator()
[SUREFIRE-1597] ModularClasspathForkConfiguration with debug logs (args file and its path on file system)
[SUREFIRE-1596] Unnecessary check JAVA_RECENT == JAVA_1_7 in unit tests
[SUREFIRE-1598] Fixed typo in assertion statement in integration test Surefire855AllowFailsafeUseArtifactFileIT
[SUREFIRE-1607] Roadmap on Project Site
```
## How was this patch tested?
Pass the Jenkins.
Closes#23370 from dongjoon-hyun/SPARK-26430.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Use of `ProcessingTime` class was deprecated in favor of `Trigger.ProcessingTime` in Spark 2.2. And, [SPARK-21464](https://issues.apache.org/jira/browse/SPARK-21464) minimized it at 2.2.1. Recently, it grows again in test suites. This PR aims to clean up newly introduced deprecation warnings for Spark 3.0.
## How was this patch tested?
Pass the Jenkins with existing tests and manually check the warnings.
Closes#23367 from dongjoon-hyun/SPARK-26428.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This PR aims to update Apache ORC dependency to the latest version 1.5.4 released at Dec. 20. ([Release Notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12318320&version=12344187]))
```
[ORC-237] OrcFile.mergeFiles Specified block size is less than configured minimum value
[ORC-409] Changes for extending MemoryManagerImpl
[ORC-410] Fix a locale-dependent test in TestCsvReader
[ORC-416] Avoid opening data reader when there is no stripe
[ORC-417] Use dynamic Apache Maven mirror link
[ORC-419] Ensure to call `close` at RecordReaderImpl constructor exception
[ORC-432] openjdk 8 has a bug that prevents surefire from working
[ORC-435] Ability to read stripes that are greater than 2GB
[ORC-437] Make acid schema checks case insensitive
[ORC-411] Update build to work with Java 10.
[ORC-418] Fix broken docker build script
```
## How was this patch tested?
Build and pass Jenkins.
Closes#23364 from dongjoon-hyun/SPARK-26427.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
A followup of https://github.com/apache/spark/pull/23178 , to keep binary compability by using abstract class.
## How was this patch tested?
Manual test. I created a simple app with Spark 2.4
```
object TryUDF {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder().appName("test").master("local[*]").getOrCreate()
import spark.implicits._
val f1 = udf((i: Int) => i + 1)
println(f1.deterministic)
spark.range(10).select(f1.asNonNullable().apply($"id")).show()
spark.stop()
}
}
```
When I run it with current master, it fails with
```
java.lang.IncompatibleClassChangeError: Found interface org.apache.spark.sql.expressions.UserDefinedFunction, but class was expected
```
When I run it with this PR, it works
Closes#23351 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Recently, the ability to expose the metrics for YARN Shuffle Service was added as part of [SPARK-18364](https://github.com/apache/spark/pull/22485). We need to add some metrics to be able to determine the number of active connections as well as open connections to the external shuffle service to benchmark network and connection issues on large cluster environments.
Added two more shuffle server metrics for Spark Yarn shuffle service: numRegisteredConnections which indicate the number of registered connections to the shuffle service and numActiveConnections which indicate the number of active connections to the shuffle service at any given point in time.
If these metrics are outputted to a file, we get something like this:
1533674653489 default.shuffleService: Hostname=server1.abc.com, openBlockRequestLatencyMillis_count=729, openBlockRequestLatencyMillis_rate15=0.7110833548897356, openBlockRequestLatencyMillis_rate5=1.657808981793011, openBlockRequestLatencyMillis_rate1=2.2404486061620474, openBlockRequestLatencyMillis_rateMean=0.9242558551196706,
numRegisteredConnections=35,
blockTransferRateBytes_count=2635880512, blockTransferRateBytes_rate15=2578547.6094160094, blockTransferRateBytes_rate5=6048721.726302424, blockTransferRateBytes_rate1=8548922.518223226, blockTransferRateBytes_rateMean=3341878.633637769, registeredExecutorsSize=5, registerExecutorRequestLatencyMillis_count=5, registerExecutorRequestLatencyMillis_rate15=0.0027973949328659836, registerExecutorRequestLatencyMillis_rate5=0.0021278007987206426, registerExecutorRequestLatencyMillis_rate1=2.8270296777387467E-6, registerExecutorRequestLatencyMillis_rateMean=0.006339206380043053, numActiveConnections=35
Closes#22498 from pgandhi999/SPARK-18364.
Authored-by: pgandhi <pgandhi@oath.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
As I mentioned in jira [SPARK-26269](https://issues.apache.org/jira/browse/SPARK-26269), in order to maxmize the use of cluster resource, this pr try to make `YarnAllocator` have the same blacklist behaviour with YARN.
## How was this patch tested?
Added.
Closes#23223 from Ngone51/dev-YarnAllocator-should-have-same-blacklist-behaviour-with-YARN.
Lead-authored-by: wuyi <ngone_5451@163.com>
Co-authored-by: Ngone51 <ngone_5451@163.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
Due to [KAFKA-7703](https://issues.apache.org/jira/browse/KAFKA-7703), Kafka may return an earliest offset when we are request a latest offset. This will cause Spark to reprocess data.
As per suggestion in KAFKA-7703, we put a position call between poll and seekToEnd to block the fetch request triggered by `poll` before calling `seekToEnd`.
In addition, to avoid other unknown issues, we also use the previous known offsets to audit the latest offsets returned by Kafka. If we find some incorrect offsets (a latest offset is less than an offset in `knownOffsets`), we will retry at most `maxOffsetFetchAttempts` times.
## How was this patch tested?
Jenkins
Closes#23324 from zsxwing/SPARK-26267.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
Currently, even if I explicitly disable Hive support in SparkR session as below:
```r
sparkSession <- sparkR.session("local[4]", "SparkR", Sys.getenv("SPARK_HOME"),
enableHiveSupport = FALSE)
```
produces when the Hadoop version is not supported by our Hive fork:
```
java.lang.reflect.InvocationTargetException
...
Caused by: java.lang.IllegalArgumentException: Unrecognized Hadoop major version number: 3.1.1.3.1.0.0-78
at org.apache.hadoop.hive.shims.ShimLoader.getMajorVersion(ShimLoader.java:174)
at org.apache.hadoop.hive.shims.ShimLoader.loadShims(ShimLoader.java:139)
at org.apache.hadoop.hive.shims.ShimLoader.getHadoopShims(ShimLoader.java:100)
at org.apache.hadoop.hive.conf.HiveConf$ConfVars.<clinit>(HiveConf.java:368)
... 43 more
Error in handleErrors(returnStatus, conn) :
java.lang.ExceptionInInitializerError
at org.apache.hadoop.hive.conf.HiveConf.<clinit>(HiveConf.java:105)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at org.apache.spark.util.Utils$.classForName(Utils.scala:193)
at org.apache.spark.sql.SparkSession$.hiveClassesArePresent(SparkSession.scala:1116)
at org.apache.spark.sql.api.r.SQLUtils$.getOrCreateSparkSession(SQLUtils.scala:52)
at org.apache.spark.sql.api.r.SQLUtils.getOrCreateSparkSession(SQLUtils.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
```
The root cause is that:
```
SparkSession.hiveClassesArePresent
```
check if the class is loadable or not to check if that's in classpath but `org.apache.hadoop.hive.conf.HiveConf` has a check for Hadoop version as static logic which is executed right away. This throws an `IllegalArgumentException` and that's not caught:
36edbac1c8/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala (L1113-L1121)
So, currently, if users have a Hive built-in Spark with unsupported Hadoop version by our fork (namely 3+), there's no way to use SparkR even though it could work.
This PR just propose to change the order of bool comparison so that we can don't execute `SparkSession.hiveClassesArePresent` when:
1. `enableHiveSupport` is explicitly disabled
2. `spark.sql.catalogImplementation` is `in-memory`
so that we **only** check `SparkSession.hiveClassesArePresent` when Hive support is explicitly enabled by short circuiting.
## How was this patch tested?
It's difficult to write a test since we don't run tests against Hadoop 3 yet. See https://github.com/apache/spark/pull/21588. Manually tested.
Closes#23356 from HyukjinKwon/SPARK-26422.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
`HadoopFileWholeTextReader` and `HadoopFileLinesReader` will be eventually called in `FileSourceScanExec`.
In fact, locality has been implemented in `FileScanRDD`, even if we implement it in `HadoopFileWholeTextReader ` and `HadoopFileLinesReader`, it would be useless.
So I think these `TODO` can be removed.
## How was this patch tested?
N/A
Closes#23339 from 10110346/noneededtodo.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Add Instrumentation to PrefixSpan
## How was this patch tested?
existing tests
Closes#22971 from zhengruifeng/log_PrefixSpan.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
Right now, we cancel pending allocate requests by its sending order. I thing we can take
locality preference into account when do this to perfom least impact on task locality preference.
## How was this patch tested?
N.A.
Closes#23344 from Ngone51/dev-cancel-pending-allocate-requests-by-taking-locality-preference-into-account.
Authored-by: Ngone51 <ngone_5451@163.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
`SQLConf` is supposed to be serializable. However, currently it is not serializable in `WithTestConf`. `WithTestConf` uses the method `overrideConfs` in closure, while the classes which implements it (`TestHiveSessionStateBuilder` and `TestSQLSessionStateBuilder`) are not serializable.
This PR is to use a local variable to fix it.
## How was this patch tested?
Add unit test.
Closes#23352 from gengliangwang/serializableSQLConf.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Added docs for running spark jobs with Mesos on SSL
Closes#23342 from jomach/master.
Lead-authored-by: Jorge Machado <jorge.w.machado@hotmail.com>
Co-authored-by: Jorge Machado <dxc.machado@extaccount.com>
Co-authored-by: Jorge Machado <jorge.machado.ext@kiwigrid.com>
Co-authored-by: Jorge Machado <JorgeWilson.Machado@ext.gfk.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
When NoClassDefFoundError thrown,it will cause job hang.
`Exception in thread "dag-scheduler-event-loop" java.lang.NoClassDefFoundError: Lcom/xxx/data/recommend/aggregator/queue/QueueName;
at java.lang.Class.getDeclaredFields0(Native Method)
at java.lang.Class.privateGetDeclaredFields(Class.java:2436)
at java.lang.Class.getDeclaredField(Class.java:1946)
at java.io.ObjectStreamClass.getDeclaredSUID(ObjectStreamClass.java:1659)
at java.io.ObjectStreamClass.access$700(ObjectStreamClass.java:72)
at java.io.ObjectStreamClass$2.run(ObjectStreamClass.java:480)
at java.io.ObjectStreamClass$2.run(ObjectStreamClass.java:468)
at java.security.AccessController.doPrivileged(Native Method)
at java.io.ObjectStreamClass.<init>(ObjectStreamClass.java:468)
at java.io.ObjectStreamClass.lookup(ObjectStreamClass.java:365)
at java.io.ObjectOutputStream.writeClass(ObjectOutputStream.java:1212)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1119)
at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
at java.io.ObjectOutputStream.writeArray(ObjectOutputStream.java:1377)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1173)
at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
at java.io.ObjectOutputStream.writeArray(ObjectOutputStream.java:1377)`
It is caused by NoClassDefFoundError will not catch up during task seriazation.
`var taskBinary: Broadcast[Array[Byte]] = null
try {
// For ShuffleMapTask, serialize and broadcast (rdd, shuffleDep).
// For ResultTask, serialize and broadcast (rdd, func).
val taskBinaryBytes: Array[Byte] = stage match {
case stage: ShuffleMapStage =>
JavaUtils.bufferToArray(
closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef))
case stage: ResultStage =>
JavaUtils.bufferToArray(closureSerializer.serialize((stage.rdd, stage.func): AnyRef))
}
taskBinary = sc.broadcast(taskBinaryBytes)
} catch {
// In the case of a failure during serialization, abort the stage.
case e: NotSerializableException =>
abortStage(stage, "Task not serializable: " + e.toString, Some(e))
runningStages -= stage
// Abort execution
return
case NonFatal(e) =>
abortStage(stage, s"Task serialization failed: $e\n${Utils.exceptionString(e)}", Some(e))
runningStages -= stage
return
}`
image below shows that stage 33 blocked and never be scheduled.
<img width="1273" alt="2018-06-28 4 28 42" src="https://user-images.githubusercontent.com/26762018/42621188-b87becca-85ef-11e8-9a0b-0ddf07504c96.png">
<img width="569" alt="2018-06-28 4 28 49" src="https://user-images.githubusercontent.com/26762018/42621191-b8b260e8-85ef-11e8-9d10-e97a5918baa6.png">
## How was this patch tested?
UT
Closes#21664 from caneGuy/zhoukang/fix-noclassdeferror.
Authored-by: zhoukang <zhoukang199191@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Currently, when we infer the schema for scala/java decimals, we return as data type the `SYSTEM_DEFAULT` implementation, ie. the decimal type with precision 38 and scale 18. But this is not right, as we know nothing about the right precision and scale and these values can be not enough to store the data. This problem arises in particular with UDF, where we cast all the input of type `DecimalType` to a `DecimalType(38, 18)`: in case this is not enough, null is returned as input for the UDF.
The PR defines a custom handling for casting to the expected data types for ScalaUDF: the decimal precision and scale is picked from the input, so no casting to different and maybe wrong percision and scale happens.
## How was this patch tested?
added UTs
Closes#23308 from mgaido91/SPARK-26308.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Deprecate Row.merge
## How was this patch tested?
N/A
Closes#23271 from KyleLi1985/master.
Authored-by: 李亮 <liang.li.work@outlook.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In Spark 2.3.0 and previous versions, Hive CTAS command will convert to use data source to write data into the table when the table is convertible. This behavior is controlled by the configs like HiveUtils.CONVERT_METASTORE_ORC and HiveUtils.CONVERT_METASTORE_PARQUET.
In 2.3.1, we drop this optimization by mistake in the PR [SPARK-22977](https://github.com/apache/spark/pull/20521/files#r217254430). Since that Hive CTAS command only uses Hive Serde to write data.
This patch adds this optimization back to Hive CTAS command. This patch adds OptimizedCreateHiveTableAsSelectCommand which uses data source to write data.
## How was this patch tested?
Added test.
Closes#22514 from viirya/SPARK-25271-2.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
For better test coverage, this pr proposed to use the 4 mixed config sets of `WHOLESTAGE_CODEGEN_ENABLED` and `CODEGEN_FACTORY_MODE` when running `SQLQueryTestSuite`:
1. WHOLESTAGE_CODEGEN_ENABLED=true, CODEGEN_FACTORY_MODE=CODEGEN_ONLY
2. WHOLESTAGE_CODEGEN_ENABLED=false, CODEGEN_FACTORY_MODE=CODEGEN_ONLY
3. WHOLESTAGE_CODEGEN_ENABLED=true, CODEGEN_FACTORY_MODE=NO_CODEGEN
4. WHOLESTAGE_CODEGEN_ENABLED=false, CODEGEN_FACTORY_MODE=NO_CODEGEN
This pr also moved some existing tests into `ExplainSuite` because explain output results are different between codegen and interpreter modes.
## How was this patch tested?
Existing tests.
Closes#23213 from maropu/InterpreterModeTest.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a small clean up.
By design catalyst rules should be orthogonal: each rule should have its own responsibility. However, the `ColumnPruning` rule does not only do column pruning, but also remove no-op project and window.
This PR updates the `RemoveRedundantProject` rule to remove no-op window as well, and clean up the `ColumnPruning` rule to only do column pruning.
## How was this patch tested?
existing tests
Closes#23343 from cloud-fan/column-pruning.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
In `ReplaceExceptWithFilter` we do not consider properly the case in which the condition returns NULL. Indeed, in that case, since negating NULL still returns NULL, so it is not true the assumption that negating the condition returns all the rows which didn't satisfy it, rows returning NULL may not be returned. This happens when constraints inferred by `InferFiltersFromConstraints` are not enough, as it happens with `OR` conditions.
The rule had also problems with non-deterministic conditions: in such a scenario, this rule would change the probability of the output.
The PR fixes these problem by:
- returning False for the condition when it is Null (in this way we do return all the rows which didn't satisfy it);
- avoiding any transformation when the condition is non-deterministic.
## How was this patch tested?
added UTs
Closes#23315 from mgaido91/SPARK-26366.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
This change hooks up the k8s backed to the updated HadoopDelegationTokenManager,
so that delegation tokens are also available in client mode, and keytab-based token
renewal is enabled.
The change re-works the k8s feature steps related to kerberos so
that the driver does all the credential management and provides all
the needed information to executors - so nothing needs to be added
to executor pods. This also makes cluster mode behave a lot more
similarly to client mode, since no driver-related config steps are run
in the latter case.
The main two things that don't need to happen in executors anymore are:
- adding the Hadoop config to the executor pods: this is not needed
since the Spark driver will serialize the Hadoop config and send
it to executors when running tasks.
- mounting the kerberos config file in the executor pods: this is
not needed once you remove the above. The Hadoop conf sent by
the driver with the tasks is already resolved (i.e. has all the
kerberos names properly defined), so executors do not need access
to the kerberos realm information anymore.
The change also avoids creating delegation tokens unnecessarily.
This means that they'll only be created if a secret with tokens
was not provided, and if a keytab is not provided. In either of
those cases, the driver code will handle delegation tokens: in
cluster mode by creating a secret and stashing them, in client
mode by using existing mechanisms to send DTs to executors.
One last feature: the change also allows defining a keytab with
a "local:" URI. This is supported in client mode (although that's
the same as not saying "local:"), and in k8s cluster mode. This
allows the keytab to be mounted onto the image from a pre-existing
secret, for example.
Finally, the new code always sets SPARK_USER in the driver and
executor pods. This is in line with how other resource managers
behave: the submitting user reflects which user will access
Hadoop services in the app. (With kerberos, that's overridden
by the logged in user.) That user is unrelated to the OS user
the app is running as inside the containers.
Tested:
- client and cluster mode with kinit
- cluster mode with keytab
- cluster mode with local: keytab
- YARN cluster with keytab (to make sure it isn't broken)
Closes#22911 from vanzin/SPARK-25815.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Change microseconds to milliseconds in annotation of Utils.timeStringAsMs.
Closes#23346 from stczwd/stczwd.
Authored-by: Jackey Lee <qcsd2011@163.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This is kind of a followup of https://github.com/apache/spark/pull/23239
The `UnsafeProject` will normalize special float/double values(NaN and -0.0), so the sorter doesn't have to handle it.
However, for consistency and future-proof, this PR proposes to normalize `-0.0` in the prefix comparator, so that it's same with the normal ordering. Note that prefix comparator handles NaN as well.
This is not a bug fix, but a safe guard.
## How was this patch tested?
existing tests
Closes#23334 from cloud-fan/sort.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Currently, SQL configs are not propagated to executors while schema inferring in CSV datasource. For example, changing of `spark.sql.legacy.timeParser.enabled` does not impact on inferring timestamp types. In the PR, I propose to fix the issue by wrapping schema inferring action using `SQLExecution.withSQLConfPropagated`.
## How was this patch tested?
Added logging to `TimestampFormatter`:
```patch
-object TimestampFormatter {
+object TimestampFormatter extends Logging {
def apply(format: String, timeZone: TimeZone, locale: Locale): TimestampFormatter = {
if (SQLConf.get.legacyTimeParserEnabled) {
+ logError("LegacyFallbackTimestampFormatter is being used")
new LegacyFallbackTimestampFormatter(format, timeZone, locale)
} else {
+ logError("Iso8601TimestampFormatter is being used")
new Iso8601TimestampFormatter(format, timeZone, locale)
}
}
```
and run the command in `spark-shell`:
```shell
$ ./bin/spark-shell --conf spark.sql.legacy.timeParser.enabled=true
```
```scala
scala> Seq("2010|10|10").toDF.repartition(1).write.mode("overwrite").text("/tmp/foo")
scala> spark.read.option("inferSchema", "true").option("header", "false").option("timestampFormat", "yyyy|MM|dd").csv("/tmp/foo").printSchema()
18/12/18 10:47:27 ERROR TimestampFormatter: LegacyFallbackTimestampFormatter is being used
root
|-- _c0: timestamp (nullable = true)
```
Closes#23345 from MaxGekk/csv-schema-infer-propagate-configs.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
spark.executorEnv.JAVA_HOME does not take effect when a Worker starting an Executor process in Standalone mode.
This PR fixed this.
## How was this patch tested?
Manual tests.
Closes#21663 from stanzhai/fix-executor-env-java-home.
Lead-authored-by: Stan Zhai <zhaishidan@haizhi.com>
Co-authored-by: Stan Zhai <mail@stanzhai.site>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR proposes to use foreach instead of misuse of map (for Unit). This could cause some weird errors potentially and it's not a good practice anyway. See also SPARK-16694
## How was this patch tested?
N/A
Closes#23341 from HyukjinKwon/followup-SPARK-26081.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The `JsonInferSchema` class is extended to support `TimestampType` inferring from string fields in JSON input:
- If the `prefersDecimal` option is set to `true`, it tries to infer decimal type from the string field.
- If decimal type inference fails or `prefersDecimal` is disabled, `JsonInferSchema` tries to infer `TimestampType`.
- If timestamp type inference fails, `StringType` is returned as the inferred type.
## How was this patch tested?
Added new test suite - `JsonInferSchemaSuite` to check date and timestamp types inferring from JSON using `JsonInferSchema` directly. A few tests were added `JsonSuite` to check type merging and roundtrip tests. This changes was tested by `JsonSuite`, `JsonExpressionsSuite` and `JsonFunctionsSuite` as well.
Closes#23201 from MaxGekk/json-infer-time.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR implements a new feature - window aggregation Pandas UDF for bounded window.
#### Doc:
https://docs.google.com/document/d/14EjeY5z4-NC27-SmIP9CsMPCANeTcvxN44a7SIJtZPc/edit#heading=h.c87w44wcj3wj
#### Example:
```
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.window import Window
df = spark.range(0, 10, 2).toDF('v')
w1 = Window.partitionBy().orderBy('v').rangeBetween(-2, 4)
w2 = Window.partitionBy().orderBy('v').rowsBetween(-2, 2)
pandas_udf('double', PandasUDFType.GROUPED_AGG)
def avg(v):
return v.mean()
df.withColumn('v_mean', avg(df['v']).over(w1)).show()
# +---+------+
# | v|v_mean|
# +---+------+
# | 0| 1.0|
# | 2| 2.0|
# | 4| 4.0|
# | 6| 6.0|
# | 8| 7.0|
# +---+------+
df.withColumn('v_mean', avg(df['v']).over(w2)).show()
# +---+------+
# | v|v_mean|
# +---+------+
# | 0| 2.0|
# | 2| 3.0|
# | 4| 4.0|
# | 6| 5.0|
# | 8| 6.0|
# +---+------+
```
#### High level changes:
This PR modifies the existing WindowInPandasExec physical node to deal with unbounded (growing, shrinking and sliding) windows.
* `WindowInPandasExec` now share the same base class as `WindowExec` and share utility functions. See `WindowExecBase`
* `WindowFunctionFrame` now has two new functions `currentLowerBound` and `currentUpperBound` - to return the lower and upper window bound for the current output row. It is also modified to allow `AggregateProcessor` == null. Null aggregator processor is used for `WindowInPandasExec` where we don't have an aggregator and only uses lower and upper bound functions from `WindowFunctionFrame`
* The biggest change is in `WindowInPandasExec`, where it is modified to take `currentLowerBound` and `currentUpperBound` and write those values together with the input data to the python process for rolling window aggregation. See `WindowInPandasExec` for more details.
#### Discussion
In benchmarking, I found numpy variant of the rolling window UDF is much faster than the pandas version:
Spark SQL window function: 20s
Pandas variant: ~80s
Numpy variant: 10s
Numpy variant with numba: 4s
Allowing numpy variant of the vectorized UDFs is something I want to discuss because of the performance improvement, but doesn't have to be in this PR.
## How was this patch tested?
New tests
Closes#22305 from icexelloss/SPARK-24561-bounded-window-udf.
Authored-by: Li Jin <ice.xelloss@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In K8S Cluster mode, the algorithm to generate spark-app-selector/spark.app.id of spark driver is different with spark executor.
This patch makes sure spark driver and executor to use the same spark-app-selector/spark.app.id if spark.app.id is set, otherwise it will use superclass applicationId.
In K8S Client mode, spark-app-selector/spark.app.id for executors will use superclass applicationId.
## How was this patch tested?
Manually run."
Closes#23322 from suxingfate/SPARK-25922.
Lead-authored-by: suxingfate <suxingfate@163.com>
Co-authored-by: xinglwang <xinglwang@ebay.com>
Signed-off-by: Yinan Li <ynli@google.com>
