## What changes were proposed in this pull request?
Checkpoints of Dataframes currently do not show up in SQL UI. This PR fixes that by setting an execution id for the execution of the checkpoint by wrapping the checkpoint code with a `withAction`.
## How was this patch tested?
A unit test was added to DatasetSuite.
Closes#23636 from tomvanbussel/SPARK-26690.
Authored-by: Tom van Bussel <tom.vanbussel@databricks.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
When a user passes a Stream to groupBy, ```CodegenSupport.consume``` ends up lazily generating ```inputVars``` from a Stream, since the field ```output``` will be a Stream. At the time ```output.zipWithIndex.map``` is called, conditions are correct. However, by the time the map operation actually executes, conditions are no longer appropriate. The closure used by the map operation ends up using a reference to the partially created ```inputVars```. As a result, a StackOverflowError occurs.
This PR ensures that ```inputVars``` is eagerly created while conditions are appropriate. It seems this was also an issue with the code path for creating ```inputVars``` from ```outputVars``` (SPARK-25767). I simply extended the solution for that code path to encompass both code paths.
## How was this patch tested?
SQL unit tests
new test
python tests
Closes#23617 from bersprockets/SPARK-26680_opt1.
Authored-by: Bruce Robbins <bersprockets@gmail.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
Updates the attempt ID used by FileFormatWriter. Tasks in stage attempts use the same task attempt number and could conflict. Using Spark's task attempt ID guarantees that Hadoop TaskAttemptID instances are unique.
## How was this patch tested?
Existing tests. Also validated that we no longer detect this failure case in our logs after deployment.
Closes#23608 from rdblue/SPARK-26682-fix-hadoop-task-attempt-id.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Fixed several places in CacheManager where a write lock was being held while running the query optimizer. This could cause a very lock block if the query optimization takes a long time. This builds on changes from [SPARK-26548] that fixed this issue for one specific case in the CacheManager.
gatorsmile This is very similar to the PR you approved last week.
## How was this patch tested?
Has been tested on a live system where the blocking was causing major issues and it is working well.
CacheManager has no explicit unit test but is used in many places internally as part of the SharedState.
Closes#23539 from DaveDeCaprio/cache-manager-locks.
Lead-authored-by: Dave DeCaprio <daved@alum.mit.edu>
Co-authored-by: David DeCaprio <daved@alum.mit.edu>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Implement copy() for ColumnarArray
## How was this patch tested?
Updating test case to existing tests in ColumnVectorSuite
Closes#23569 from ayudovin/copy-for-columnArray.
Authored-by: ayudovin <a.yudovin6695@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR contains a minor change in `Cast$mayTruncate` that fixes its logic for bytes.
Right now, `mayTruncate(ByteType, LongType)` returns `false` while `mayTruncate(ShortType, LongType)` returns `true`. Consequently, `spark.range(1, 3).as[Byte]` and `spark.range(1, 3).as[Short]` behave differently.
Potentially, this bug can silently corrupt someone's data.
```scala
// executes silently even though Long is converted into Byte
spark.range(Long.MaxValue - 10, Long.MaxValue).as[Byte]
.map(b => b - 1)
.show()
+-----+
|value|
+-----+
| -12|
| -11|
| -10|
| -9|
| -8|
| -7|
| -6|
| -5|
| -4|
| -3|
+-----+
// throws an AnalysisException: Cannot up cast `id` from bigint to smallint as it may truncate
spark.range(Long.MaxValue - 10, Long.MaxValue).as[Short]
.map(s => s - 1)
.show()
```
## How was this patch tested?
This PR comes with a set of unit tests.
Closes#23632 from aokolnychyi/cast-fix.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Currently, some ML library may generate large ml model, which may be referenced in the task closure, so driver will broadcasting large task binary, and executor may not able to deserialize it and result in OOM failures(for instance, executor's memory is not enough). This problem not only affects apps using ml library, some user specified closure or function which refers large data may also have this problem.
In order to facilitate the debuging of memory problem caused by large taskBinary broadcast, we can add same warning logs for it.
This PR will add some warning logs on the driver side when broadcasting a large task binary, and it also included some minor log changes in the reading of broadcast.
## How was this patch tested?
NA-Just log changes.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23580 from liupc/Add-warning-logs-for-large-taskBinary-size.
Authored-by: Liupengcheng <liupengcheng@xiaomi.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This adds a new pattern to recognize Ammonite REPL classes and return the correct scope.
## How was this patch tested?
Manually tested with Spark in an Ammonite session.
Closes#23607 from rdblue/SPARK-26681-support-ammonite-scopes.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose using of the `stringToDate` and `stringToTimestamp` methods in parsing JDBC lower/upper bounds of the partition column if it has `DateType` or `TimestampType`. Since those methods have been ported on Proleptic Gregorian calendar by #23512, the PR switches parsing of JDBC bounds of the partition column on the calendar as well.
## How was this patch tested?
This was tested by `JDBCSuite`.
Closes#23597 from MaxGekk/jdbc-parse-timestamp-bounds.
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?
In the master, GetArrayItem nullable is always true;
cf133e6110/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeExtractors.scala (L236)
But, If input array size is constant and ordinal is foldable, we could make GetArrayItem nullability more precise. This pr added code to make `GetArrayItem` nullability more precise.
## How was this patch tested?
Added tests in `ComplexTypeSuite`.
Closes#23566 from maropu/GetArrayItemNullability.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Followup of #20091. We could also use existing partitioner when defaultNumPartitions is equal to the maxPartitioner's numPartitions.
## How was this patch tested?
Existed.
Closes#23581 from Ngone51/dev-use-existing-partitioner-when-defaultNumPartitions-equalTo-MaxPartitioner#-numPartitions.
Authored-by: Ngone51 <ngone_5451@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Avoid memory problems in closure cleaning when handling large Gramians (>= 16K rows/cols) by using null as zeroValue
## How was this patch tested?
Existing tests.
Note that it's hard to test the case that triggers this issue as it would require a large amount of memory and run a while. I confirmed locally that a 16K x 16K Gramian failed with tons of driver memory before, and didn't fail upfront after this change.
Closes#23600 from srowen/SPARK-26228.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Documentation and .gitignore
## How was this patch tested?
Manual test that SBT honors the settings in .jvmopts if present
Closes#23615 from sadhen/impr/gitignore.
Authored-by: Darcy Shen <sadhen@zoho.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The explain output of the Hive CTAS command, regardless of whether it's actually writing via Hive's SerDe or converted into using Spark's data source, would always show that it's using `InsertIntoHiveTable` because it's hardcoded.
e.g.
```
Execute OptimizedCreateHiveTableAsSelectCommand [Database:default, TableName: foo, InsertIntoHiveTable]
```
This CTAS is converted into using Spark's data source, but it still says `InsertIntoHiveTable` in the explain output.
It's better to show the actual class name of the writing command used. For the example above, it'd be:
```
Execute OptimizedCreateHiveTableAsSelectCommand [Database:default, TableName: foo, InsertIntoHadoopFsRelationCommand]
```
## How was this patch tested?
Added test case in `HiveExplainSuite`
Closes#23582 from rednaxelafx/fix-explain-1.
Authored-by: Kris Mok <kris.mok@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
Latest Docker releases are stricter in their enforcement of build argument scope. The location of the `ARG spark_uid` declaration in the Python and R Dockerfiles means the variable is out of scope by the time it is used in a `USER` declaration resulting in a container running as root rather than the default/configured UID.
Also with some of the refactoring of the script that has happened since my PR that introduced the configurable UID it turns out the `-u <uid>` argument is not being properly passed to the Python and R image builds when those are opted into
## What changes were proposed in this pull request?
This commit moves the `ARG` declaration to just before the argument is used such that it is in scope. It also ensures that Python and R image builds receive the build arguments that include the `spark_uid` argument where relevant
## How was this patch tested?
Prior to the patch images are produced where the Python and R images ignore the default/configured UID:
```
> docker run -it --entrypoint /bin/bash rvesse/spark-py:uid456
bash-4.4# whoami
root
bash-4.4# id -u
0
bash-4.4# exit
> docker run -it --entrypoint /bin/bash rvesse/spark:uid456
bash-4.4$ id -u
456
bash-4.4$ exit
```
Note that the Python image is still running as `root` having ignored the configured UID of 456 while the base image has the correct UID because the relevant `ARG` declaration is correctly in scope.
After the patch the correct UID is observed:
```
> docker run -it --entrypoint /bin/bash rvesse/spark-r:uid456
bash-4.4$ id -u
456
bash-4.4$ exit
exit
> docker run -it --entrypoint /bin/bash rvesse/spark-py:uid456
bash-4.4$ id -u
456
bash-4.4$ exit
exit
> docker run -it --entrypoint /bin/bash rvesse/spark:uid456
bash-4.4$ id -u
456
bash-4.4$ exit
```
Closes#23611 from rvesse/SPARK-26685.
Authored-by: Rob Vesse <rvesse@dotnetrdf.org>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
This enhancement allows for specifying the desired context to use for the initial K8S client auto-configuration. This allows users to more easily access alternative K8S contexts without having to first
explicitly change their current context via kubectl.
Explicitly set my K8S context to a context pointing to a non-existent cluster, then launched Spark jobs with explicitly specified contexts via the new `spark.kubernetes.context` configuration property.
Example Output:
```
> kubectl config current-context
minikube
> minikube status
minikube: Stopped
cluster:
kubectl:
> ./spark-submit --master k8s://https://localhost:6443 --deploy-mode cluster --name spark-pi --class org.apache.spark.examples.SparkPi --conf spark.executor.instances=2 --conf spark.kubernetes.context=docker-for-desktop --conf spark.kubernetes.container.image=rvesse/spark:debian local:///opt/spark/examples/jars/spark-examples_2.11-3.0.0-SNAPSHOT.jar 4
18/10/31 11:57:51 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
18/10/31 11:57:51 INFO SparkKubernetesClientFactory: Auto-configuring K8S client using context docker-for-desktop from users K8S config file
18/10/31 11:57:52 INFO LoggingPodStatusWatcherImpl: State changed, new state:
pod name: spark-pi-1540987071845-driver
namespace: default
labels: spark-app-selector -> spark-2c4abc226ed3415986eb602bd13f3582, spark-role -> driver
pod uid: 32462cac-dd04-11e8-b6c6-025000000001
creation time: 2018-10-31T11:57:52Z
service account name: default
volumes: spark-local-dir-1, spark-conf-volume, default-token-glpfv
node name: N/A
start time: N/A
phase: Pending
container status: N/A
18/10/31 11:57:52 INFO LoggingPodStatusWatcherImpl: State changed, new state:
pod name: spark-pi-1540987071845-driver
namespace: default
labels: spark-app-selector -> spark-2c4abc226ed3415986eb602bd13f3582, spark-role -> driver
pod uid: 32462cac-dd04-11e8-b6c6-025000000001
creation time: 2018-10-31T11:57:52Z
service account name: default
volumes: spark-local-dir-1, spark-conf-volume, default-token-glpfv
node name: docker-for-desktop
start time: N/A
phase: Pending
container status: N/A
...
18/10/31 11:58:03 INFO LoggingPodStatusWatcherImpl: State changed, new state:
pod name: spark-pi-1540987071845-driver
namespace: default
labels: spark-app-selector -> spark-2c4abc226ed3415986eb602bd13f3582, spark-role -> driver
pod uid: 32462cac-dd04-11e8-b6c6-025000000001
creation time: 2018-10-31T11:57:52Z
service account name: default
volumes: spark-local-dir-1, spark-conf-volume, default-token-glpfv
node name: docker-for-desktop
start time: 2018-10-31T11:57:52Z
phase: Succeeded
container status:
container name: spark-kubernetes-driver
container image: rvesse/spark:debian
container state: terminated
container started at: 2018-10-31T11:57:54Z
container finished at: 2018-10-31T11:58:02Z
exit code: 0
termination reason: Completed
```
Without the `spark.kubernetes.context` setting this will fail because the current context - `minikube` - is pointing to a non-running cluster e.g.
```
> ./spark-submit --master k8s://https://localhost:6443 --deploy-mode cluster --name spark-pi --class org.apache.spark.examples.SparkPi --conf spark.executor.instances=2 --conf spark.kubernetes.container.image=rvesse/spark:debian local:///opt/spark/examples/jars/spark-examples_2.11-3.0.0-SNAPSHOT.jar 4
18/10/31 12:02:30 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
18/10/31 12:02:30 INFO SparkKubernetesClientFactory: Auto-configuring K8S client using current context from users K8S config file
18/10/31 12:02:31 WARN WatchConnectionManager: Exec Failure
javax.net.ssl.SSLHandshakeException: sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
at sun.security.ssl.Alerts.getSSLException(Alerts.java:192)
at sun.security.ssl.SSLSocketImpl.fatal(SSLSocketImpl.java:1949)
at sun.security.ssl.Handshaker.fatalSE(Handshaker.java:302)
at sun.security.ssl.Handshaker.fatalSE(Handshaker.java:296)
at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1509)
at sun.security.ssl.ClientHandshaker.processMessage(ClientHandshaker.java:216)
at sun.security.ssl.Handshaker.processLoop(Handshaker.java:979)
at sun.security.ssl.Handshaker.process_record(Handshaker.java:914)
at sun.security.ssl.SSLSocketImpl.readRecord(SSLSocketImpl.java:1062)
at sun.security.ssl.SSLSocketImpl.performInitialHandshake(SSLSocketImpl.java:1375)
at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:1403)
at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:1387)
at okhttp3.internal.connection.RealConnection.connectTls(RealConnection.java:281)
at okhttp3.internal.connection.RealConnection.establishProtocol(RealConnection.java:251)
at okhttp3.internal.connection.RealConnection.connect(RealConnection.java:151)
at okhttp3.internal.connection.StreamAllocation.findConnection(StreamAllocation.java:195)
at okhttp3.internal.connection.StreamAllocation.findHealthyConnection(StreamAllocation.java:121)
at okhttp3.internal.connection.StreamAllocation.newStream(StreamAllocation.java:100)
at okhttp3.internal.connection.ConnectInterceptor.intercept(ConnectInterceptor.java:42)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at okhttp3.internal.cache.CacheInterceptor.intercept(CacheInterceptor.java:93)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at okhttp3.internal.http.BridgeInterceptor.intercept(BridgeInterceptor.java:93)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RetryAndFollowUpInterceptor.intercept(RetryAndFollowUpInterceptor.java:120)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at io.fabric8.kubernetes.client.utils.BackwardsCompatibilityInterceptor.intercept(BackwardsCompatibilityInterceptor.java:119)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at io.fabric8.kubernetes.client.utils.ImpersonatorInterceptor.intercept(ImpersonatorInterceptor.java:66)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at io.fabric8.kubernetes.client.utils.HttpClientUtils$2.intercept(HttpClientUtils.java:109)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at okhttp3.RealCall.getResponseWithInterceptorChain(RealCall.java:185)
at okhttp3.RealCall$AsyncCall.execute(RealCall.java:135)
at okhttp3.internal.NamedRunnable.run(NamedRunnable.java:32)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
at sun.security.validator.PKIXValidator.doBuild(PKIXValidator.java:387)
at sun.security.validator.PKIXValidator.engineValidate(PKIXValidator.java:292)
at sun.security.validator.Validator.validate(Validator.java:260)
at sun.security.ssl.X509TrustManagerImpl.validate(X509TrustManagerImpl.java:324)
at sun.security.ssl.X509TrustManagerImpl.checkTrusted(X509TrustManagerImpl.java:229)
at sun.security.ssl.X509TrustManagerImpl.checkServerTrusted(X509TrustManagerImpl.java:124)
at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1491)
... 39 more
Caused by: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
at sun.security.provider.certpath.SunCertPathBuilder.build(SunCertPathBuilder.java:141)
at sun.security.provider.certpath.SunCertPathBuilder.engineBuild(SunCertPathBuilder.java:126)
at java.security.cert.CertPathBuilder.build(CertPathBuilder.java:280)
at sun.security.validator.PKIXValidator.doBuild(PKIXValidator.java:382)
... 45 more
Exception in thread "kubernetes-dispatcher-0" Exception in thread "main" java.util.concurrent.RejectedExecutionException: Task java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask611a9c09 rejected from java.util.concurrent.ScheduledThreadPoolExecutor404819e4[Terminated, pool size = 0, active threads = 0, queued tasks = 0, completed tasks = 0]
at java.util.concurrent.ThreadPoolExecutor$AbortPolicy.rejectedExecution(ThreadPoolExecutor.java:2047)
at java.util.concurrent.ThreadPoolExecutor.reject(ThreadPoolExecutor.java:823)
at java.util.concurrent.ScheduledThreadPoolExecutor.delayedExecute(ScheduledThreadPoolExecutor.java:326)
at java.util.concurrent.ScheduledThreadPoolExecutor.schedule(ScheduledThreadPoolExecutor.java:533)
at java.util.concurrent.ScheduledThreadPoolExecutor.submit(ScheduledThreadPoolExecutor.java:632)
at java.util.concurrent.Executors$DelegatedExecutorService.submit(Executors.java:678)
at io.fabric8.kubernetes.client.dsl.internal.WatchConnectionManager.scheduleReconnect(WatchConnectionManager.java:300)
at io.fabric8.kubernetes.client.dsl.internal.WatchConnectionManager.access$800(WatchConnectionManager.java:48)
at io.fabric8.kubernetes.client.dsl.internal.WatchConnectionManager$2.onFailure(WatchConnectionManager.java:213)
at okhttp3.internal.ws.RealWebSocket.failWebSocket(RealWebSocket.java:543)
at okhttp3.internal.ws.RealWebSocket$2.onFailure(RealWebSocket.java:208)
at okhttp3.RealCall$AsyncCall.execute(RealCall.java:148)
at okhttp3.internal.NamedRunnable.run(NamedRunnable.java:32)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
io.fabric8.kubernetes.client.KubernetesClientException: Failed to start websocket
at io.fabric8.kubernetes.client.dsl.internal.WatchConnectionManager$2.onFailure(WatchConnectionManager.java:204)
at okhttp3.internal.ws.RealWebSocket.failWebSocket(RealWebSocket.java:543)
at okhttp3.internal.ws.RealWebSocket$2.onFailure(RealWebSocket.java:208)
at okhttp3.RealCall$AsyncCall.execute(RealCall.java:148)
at okhttp3.internal.NamedRunnable.run(NamedRunnable.java:32)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: javax.net.ssl.SSLHandshakeException: sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
at sun.security.ssl.Alerts.getSSLException(Alerts.java:192)
at sun.security.ssl.SSLSocketImpl.fatal(SSLSocketImpl.java:1949)
at sun.security.ssl.Handshaker.fatalSE(Handshaker.java:302)
at sun.security.ssl.Handshaker.fatalSE(Handshaker.java:296)
at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1509)
at sun.security.ssl.ClientHandshaker.processMessage(ClientHandshaker.java:216)
at sun.security.ssl.Handshaker.processLoop(Handshaker.java:979)
at sun.security.ssl.Handshaker.process_record(Handshaker.java:914)
at sun.security.ssl.SSLSocketImpl.readRecord(SSLSocketImpl.java:1062)
at sun.security.ssl.SSLSocketImpl.performInitialHandshake(SSLSocketImpl.java:1375)
at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:1403)
at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:1387)
at okhttp3.internal.connection.RealConnection.connectTls(RealConnection.java:281)
at okhttp3.internal.connection.RealConnection.establishProtocol(RealConnection.java:251)
at okhttp3.internal.connection.RealConnection.connect(RealConnection.java:151)
at okhttp3.internal.connection.StreamAllocation.findConnection(StreamAllocation.java:195)
at okhttp3.internal.connection.StreamAllocation.findHealthyConnection(StreamAllocation.java:121)
at okhttp3.internal.connection.StreamAllocation.newStream(StreamAllocation.java:100)
at okhttp3.internal.connection.ConnectInterceptor.intercept(ConnectInterceptor.java:42)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at okhttp3.internal.cache.CacheInterceptor.intercept(CacheInterceptor.java:93)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at okhttp3.internal.http.BridgeInterceptor.intercept(BridgeInterceptor.java:93)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RetryAndFollowUpInterceptor.intercept(RetryAndFollowUpInterceptor.java:120)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at io.fabric8.kubernetes.client.utils.BackwardsCompatibilityInterceptor.intercept(BackwardsCompatibilityInterceptor.java:119)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at io.fabric8.kubernetes.client.utils.ImpersonatorInterceptor.intercept(ImpersonatorInterceptor.java:66)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at io.fabric8.kubernetes.client.utils.HttpClientUtils$2.intercept(HttpClientUtils.java:109)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at okhttp3.RealCall.getResponseWithInterceptorChain(RealCall.java:185)
at okhttp3.RealCall$AsyncCall.execute(RealCall.java:135)
... 4 more
Caused by: sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
at sun.security.validator.PKIXValidator.doBuild(PKIXValidator.java:387)
at sun.security.validator.PKIXValidator.engineValidate(PKIXValidator.java:292)
at sun.security.validator.Validator.validate(Validator.java:260)
at sun.security.ssl.X509TrustManagerImpl.validate(X509TrustManagerImpl.java:324)
at sun.security.ssl.X509TrustManagerImpl.checkTrusted(X509TrustManagerImpl.java:229)
at sun.security.ssl.X509TrustManagerImpl.checkServerTrusted(X509TrustManagerImpl.java:124)
at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1491)
... 39 more
Caused by: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
at sun.security.provider.certpath.SunCertPathBuilder.build(SunCertPathBuilder.java:141)
at sun.security.provider.certpath.SunCertPathBuilder.engineBuild(SunCertPathBuilder.java:126)
at java.security.cert.CertPathBuilder.build(CertPathBuilder.java:280)
at sun.security.validator.PKIXValidator.doBuild(PKIXValidator.java:382)
... 45 more
18/10/31 12:02:31 INFO ShutdownHookManager: Shutdown hook called
18/10/31 12:02:31 INFO ShutdownHookManager: Deleting directory /private/var/folders/6b/y1010qp107j9w2dhhy8csvz0000xq3/T/spark-5e649891-8a0f-4f17-bf3a-33b34082eba8
```
Suggested reviews: mccheah liyinan926 - this is the follow up fix to the bug discovered while working on SPARK-25809 (PR #22805)
Closes#22904 from rvesse/SPARK-25887.
Authored-by: Rob Vesse <rvesse@dotnetrdf.org>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
`ByteBuffer.allocate` may throw `OutOfMemoryError` when the block is large but no enough memory is available. However, when this happens, right now BlockTransferService.fetchBlockSync will just hang forever as its `BlockFetchingListener. onBlockFetchSuccess` doesn't complete `Promise`.
This PR catches `Throwable` and uses the error to complete `Promise`.
## How was this patch tested?
Added a unit test. Since I cannot make `ByteBuffer.allocate` throw `OutOfMemoryError`, I passed a negative size to make `ByteBuffer.allocate` fail. Although the error type is different, it should trigger the same code path.
Closes#23590 from zsxwing/SPARK-26665.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
The expressions `DayWeek`, `DayOfWeek`, `WeekDay` and `WeekOfYear` are changed to use Proleptic Gregorian calendar instead of the hybrid one (Julian+Gregorian). This was achieved by using Java 8 API for date/timestamp manipulation, in particular the `LocalDate` class.
Week of year calculation is performed according to ISO-8601. The first week of a week-based-year is the first Monday-based week of the standard ISO year that has at least 4 days in the new year (see https://docs.oracle.com/javase/8/docs/api/java/time/temporal/IsoFields.html).
## How was this patch tested?
The changes were tested by `DateExpressionsSuite` and `DateFunctionsSuite`.
Closes#23594 from MaxGekk/dayweek-gregorian.
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?
Add PMML export support for ML KMeans to PySpark.
## How was this patch tested?
Add tests in ml.tests.PersistenceTest.
Closes#23592 from huaxingao/spark-16838.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In MessageEncoder.java, the header would always be allocated on onheap memory regardless of whether netty was configured to use/prefer onheap or offheap. By default this made netty allocate 16mb of onheap memory for a tiny header message. It would be more practical to use preallocated buffers.
Using a memory monitor tool on a simple spark application, the following services currently allocate 16 mb of onheap memory:
netty-rpc-client
netty-blockTransfer-client
netty-external-shuffle-client
With this change, the memory monitor tool reports all three of these services as using 0 b of onheap memory. The offheap memory allocation does not increase, but more of the already-allocated space is used.
## How was this patch tested?
Manually tested change using spark-memory-tool https://github.com/squito/spark-memoryCloses#22114 from NiharS/nettybuffer.
Lead-authored-by: Nihar Sheth <niharrsheth@gmail.com>
Co-authored-by: Nihar Sheth <nsheth@cloudera.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The PR makes hardcoded `spark.dynamicAllocation`, `spark.scheduler`, `spark.rpc`, `spark.task`, `spark.speculation`, and `spark.cleaner` configs to use `ConfigEntry`.
## How was this patch tested?
Existing tests
Closes#23416 from kiszk/SPARK-26463.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This change exposes the `df` (document frequency) as a public val along with the number of documents (`m`) as part of the IDF model.
* The document frequency is returned as an `Array[Long]`
* If the minimum document frequency is set, this is considered in the df calculation. If the count is less than minDocFreq, the df is 0 for such terms
* numDocs is not very required. But it can be useful, if we plan to provide a provision in future for user to give their own idf function, instead of using a default (log((1+m)/(1+df))). In such cases, the user can provide a function taking input of `m` and `df` and returning the idf value
* Pyspark changes
## How was this patch tested?
The existing test case was edited to also check for the document frequency values.
I am not very good with python or pyspark. I have committed and run tests based on my understanding. Kindly let me know if I have missed anything
Reviewer request: mengxr zjffdu yinxusen
Closes#23549 from purijatin/master.
Authored-by: Jatin Puri <purijatin@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Since 0.11.0, PyArrow supports to raise an error for unsafe cast ([PR](https://github.com/apache/arrow/pull/2504)). We should use it to raise a proper error for pandas udf users when such cast is detected.
Added a SQL config `spark.sql.execution.pandas.arrowSafeTypeConversion` to disable Arrow safe type check.
## How was this patch tested?
Added test and manually test.
Closes#22807 from viirya/SPARK-25811.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Following https://github.com/apache/spark/pull/23086, this PR does the API refactor for micro-batch read, w.r.t. the [doc](https://docs.google.com/document/d/1uUmKCpWLdh9vHxP7AWJ9EgbwB_U6T3EJYNjhISGmiQg/edit?usp=sharing)
The major changes:
1. rename `XXXMicroBatchReadSupport` to `XXXMicroBatchReadStream`
2. implement `TableProvider`, `Table`, `ScanBuilder` and `Scan` for streaming sources
3. at the beginning of micro-batch streaming execution, convert `StreamingRelationV2` to `StreamingDataSourceV2Relation` directly, instead of `StreamingExecutionRelation`.
followup:
support operator pushdown for stream sources
## How was this patch tested?
existing tests
Closes#23430 from cloud-fan/micro-batch.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This particular test is being skipped at PyPy and Python 2.
```
Skipped tests in pyspark.sql.tests.test_context with pypy:
test_unbounded_frames (pyspark.sql.tests.test_context.HiveContextSQLTests) ... skipped "Unittest < 3.3 doesn't support mocking"
Skipped tests in pyspark.sql.tests.test_context with python2.7:
test_unbounded_frames (pyspark.sql.tests.test_context.HiveContextSQLTests) ... skipped "Unittest < 3.3 doesn't support mocking"
```
We don't have to use unittest 3.3 module to mock. And looks the test itself isn't compatible with Python 2.
This PR makes:
- Manually monkey-patch `sys.maxsize` to get rid of unittest 3.3 condition
- Use the built-in `reload` in Python 2, and `importlib.reload` in Python 3
## How was this patch tested?
Manually tested, and unit test is fixed.
Closes#23604 from HyukjinKwon/test-window.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
The `fromString` and `fromJSON` methods of the `Literal` object are removed because they are not used.
Closes#23596Closes#23603 from MaxGekk/remove-literal-fromstring.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Currently, there are some minor inconsistencies in doc compared to the code. In this PR, I am correcting those inconsistencies.
1) Links related to the evaluation metrics in the docs are not working
2) Minor correction in the evaluation metrics formulas in docs.
## How was this patch tested?
NA
Closes#23589 from shahidki31/docCorrection.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR proposes to extend the spark-submit option --num-executors to be applicable to Spark on K8S too. It is motivated by convenience, for example when migrating jobs written for YARN to run on K8S.
## How was this patch tested?
Manually tested on a K8S cluster.
Author: Luca Canali <luca.canali@cern.ch>
Closes#23573 from LucaCanali/addNumExecutorsToK8s.
## What changes were proposed in this pull request?
When parsing datatypes from the json internal representation, PySpark doesn't support decimals with negative scales. Since they are allowed and can actually happen, PySpark should be able to successfully parse them.
## How was this patch tested?
added test
Closes#23575 from mgaido91/SPARK-26645.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Correct some unit test mistakes.
## How was this patch tested?
N/A
Closes#23583 from 10110346/unused_symbol.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The PR makes hardcoded `spark.unsafe` configs to use ConfigEntry and put them in the `config` package.
## How was this patch tested?
Existing UTs
Closes#23412 from kiszk/SPARK-26477.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
The test pyspark.mllib.tests.test_streaming_algorithms StreamingLogisticRegressionWithSGDTests.test_training_and_prediction looks sometimes flaky.
```
======================================================================
FAIL: test_training_and_prediction (pyspark.mllib.tests.test_streaming_algorithms.StreamingLogisticRegressionWithSGDTests)
Test that the model improves on toy data with no. of batches
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 367, in test_training_and_prediction
self._eventually(condition, timeout=60.0)
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 69, in _eventually
lastValue = condition()
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 362, in condition
self.assertGreater(errors[1] - errors[-1], 0.3)
AssertionError: -0.070000000000000062 not greater than 0.3
----------------------------------------------------------------------
Ran 13 tests in 198.327s
FAILED (failures=1, skipped=1)
Had test failures in pyspark.mllib.tests.test_streaming_algorithms with python3.4; see logs
```
The predict stream can possibly be consumed to the end before the input stream. When it happens, the model improvement is not high as expected and causes test failed. This patch tries to increase number of batches of streams. This won't increase test time because we have a timeout there.
## How was this patch tested?
Manually test.
Closes#23586 from viirya/SPARK-26646.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to make creation of typed Literals `TIMESTAMP` and `DATE` consistent to the `Cast` expression. More precisely, reusing the `Cast` expression in the type constructors. In this way, it allows:
- To use the same calendar in parsing methods
- To support the same set of timestamp/date patterns
For example, creating timestamp literal:
```sql
SELECT TIMESTAMP '2019-01-14 20:54:00.000'
```
behaves similarly as casting the string literal:
```sql
SELECT CAST('2019-01-14 20:54:00.000' AS TIMESTAMP)
```
## How was this patch tested?
This was tested by `SQLQueryTestSuite` as well as `ExpressionParserSuite`.
Closes#23541 from MaxGekk/timestamp-date-constructors.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
`DataWritingCommandExec` generates `cmd.nodeName` twice in its explain output, e.g. when running this query `spark.sql("create table foo stored as parquet as select id, id % 10 as cat1, id % 20 as cat2 from range(10)")`,
```
Execute OptimizedCreateHiveTableAsSelectCommand OptimizedCreateHiveTableAsSelectCommand [Database:default, TableName: foo, InsertIntoHiveTable]
+- *(1) Project [id#2L, (id#2L % 10) AS cat1#0L, (id#2L % 20) AS cat2#1L]
+- *(1) Range (0, 10, step=1, splits=8)
```
After the fix, it'll go back to normal:
```
Execute OptimizedCreateHiveTableAsSelectCommand [Database:default, TableName: foo, InsertIntoHiveTable]
+- *(1) Project [id#2L, (id#2L % 10) AS cat1#0L, (id#2L % 20) AS cat2#1L]
+- *(1) Range (0, 10, step=1, splits=8)
```
This duplication is introduced when this specialized `DataWritingCommandExec` was created in place of `ExecutedCommandExec`.
The former is a `UnaryExecNode` whose `children` include the physical plan of the query, and the `cmd` is picked up via `TreeNode.stringArgs` into the argument string. The duplication comes from: `DataWritingCommandExec.nodeName` is `s"Execute ${cmd.nodeName}"` while the argument string is `cmd.simpleString()` which also includes `cmd.nodeName`.
The latter didn't have that problem because it's a `LeafExecNode` with no children, and it declares the `cmd` as being a part of the `innerChildren` which is excluded from the argument string.
## How was this patch tested?
Manual testing of running the example above in a local Spark Shell.
Also added a new test case in `ExplainSuite`.
Closes#23579 from rednaxelafx/fix-explain.
Authored-by: Kris Mok <kris.mok@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Misc code cleanup from lgtm.com analysis. See comments below for details.
## How was this patch tested?
Existing tests.
Closes#23571 from srowen/SPARK-26640.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Co-authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fix implementation of unary negation (`__neg__`) in Pyspark DenseVectors
## How was this patch tested?
Existing tests, plus new doctest
Closes#23570 from srowen/SPARK-26638.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Try to make labels more obvious
"avg hash probe" avg hash probe bucket iterations
"partition pruning time (ms)" dynamic partition pruning time
"total number of files in the table" file count
"number of files that would be returned by partition pruning alone" file count after partition pruning
"total size of files in the table" file size
"size of files that would be returned by partition pruning alone" file size after partition pruning
"metadata time (ms)" metadata time
"aggregate time" time in aggregation build
"aggregate time" time in aggregation build
"time to construct rdd bc" time to build
"total time to remove rows" time to remove
"total time to update rows" time to update
Add proper metric type to some metrics:
"bytes of written output" written output - createSizeMetric
"metadata time" - createTimingMetric
"dataSize" - createSizeMetric
"collectTime" - createTimingMetric
"buildTime" - createTimingMetric
"broadcastTIme" - createTimingMetric
## How is this patch tested?
Existing tests.
Author: Stacy Kerkela <stacy.kerkeladatabricks.com>
Signed-off-by: Juliusz Sompolski <julekdatabricks.com>
Closes#23551 from juliuszsompolski/SPARK-26622.
Lead-authored-by: Juliusz Sompolski <julek@databricks.com>
Co-authored-by: Stacy Kerkela <stacy.kerkela@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The PR makes hardcoded `spark.shuffle` configs to use ConfigEntry and put them in the config package.
## How was this patch tested?
Existing unit tests
Closes#23550 from 10110346/ConfigEntry_shuffle.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to use *java.time* classes in `stringToDate` and `stringToTimestamp`. This switches the methods from the hybrid calendar (Gregorian+Julian) to Proleptic Gregorian calendar. And it should make the casting consistent to other Spark classes that converts textual representation of dates/timestamps to `DateType`/`TimestampType`.
## How was this patch tested?
The changes were tested by existing suites - `HashExpressionsSuite`, `CastSuite` and `DateTimeUtilsSuite`.
Closes#23512 from MaxGekk/utf8string-timestamp-parsing.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
Create a framework for file source V2 based on data source V2 API.
As a good example for demonstrating the framework, this PR also migrate ORC source. This is because ORC file source supports both row scan and columnar scan, and the implementation is simpler comparing with Parquet.
Note: Currently only read path of V2 API is done, this framework and migration are only for the read path.
Supports the following scan:
- Scan ColumnarBatch
- Scan UnsafeRow
- Push down filters
- Push down required columns
Not supported( due to the limitation of data source V2 API):
- Stats metrics
- Catalog table
- Writes
## How was this patch tested?
Unit test
Closes#23383 from gengliangwang/latest_orcV2.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
I know that yarn provided all hadoop configurations. But I guess it may be fine that the historyserver unify all configuration in it. It will be convenient for us to debug some problems.
## How was this patch tested?
Closes#23486 from deshanxiao/spark-26457.
Lead-authored-by: xiaodeshan <xiaodeshan@xiaomi.com>
Co-authored-by: deshanxiao <42019462+deshanxiao@users.noreply.github.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The call to `translate_component` only supplied 2 out of the 3 required arguments. I added a default empty list for the missing argument to avoid a run-time error.
I work for Semmle, and noticed the bug with our LGTM code analyzer:
0655f1624f/files/dev/create-release/releaseutils.py?sort=name&dir=ASC&mode=heatmap#x1434915b6576fb40:1
## How was this patch tested?
I checked that `./dev/run-tests` pass OK.
Closes#23567 from ipwright/wrong-number-of-arguments-fix.
Authored-by: wright <wright@semmle.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The PR makes hardcoded configs below to use `ConfigEntry`.
* spark.kryo
* spark.kryoserializer
* spark.serializer
* spark.jars
* spark.files
* spark.submit
* spark.deploy
* spark.worker
This patch doesn't change configs which are not relevant to SparkConf (e.g. system properties).
## How was this patch tested?
Existing tests.
Closes#23532 from HeartSaVioR/SPARK-26466-v2.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Spark-submit usage message should be put in sync with recent changes in particular regarding K8S support. These are the proposed changes to the usage message:
--executor-cores NUM -> can be useed for Spark on YARN and K8S
--principal PRINCIPAL and --keytab KEYTAB -> can be used for Spark on YARN and K8S
--total-executor-cores NUM> can be used for Spark standalone, YARN and K8S
In addition this PR proposes to remove certain implementation details from the --keytab argument description as the implementation details vary between YARN and K8S, for example.
## How was this patch tested?
Manually tested
Closes#23518 from LucaCanali/updateSparkSubmitArguments.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add `ExecutorClassLoader.getResourceAsStream`, so that classes dynamically generated by the REPL can be accessed by user code as `InputStream`s for non-class-loading purposes, such as reading the class file for extracting method/constructor parameter names.
Caveat: The convention in Java's `ClassLoader` is that `ClassLoader.getResourceAsStream()` should be considered as a convenience method of `ClassLoader.getResource()`, where the latter provides a `URL` for the resource, and the former invokes `openStream()` on it to serve the resource as an `InputStream`. The former should also catch `IOException` from `openStream()` and convert it to `null`.
This PR breaks this convention by only overriding `ClassLoader.getResourceAsStream()` instead of also overriding `ClassLoader.getResource()`, so after this PR, it would be possible to get a non-null result from the former, but get a null result from the latter. This isn't ideal, but it's sufficient to cover the main use case and practically it shouldn't matter.
To implement the convention properly, we'd need to register a URL protocol handler with Java to allow it to properly handle the `spark://` protocol, etc, which sounds like an overkill for the intent of this PR.
Credit goes to zsxwing for the initial investigation and fix suggestion.
## How was this patch tested?
Added new test case in `ExecutorClassLoaderSuite` and `ReplSuite`.
Closes#23558 from rednaxelafx/executorclassloader-getresourceasstream.
Authored-by: Kris Mok <kris.mok@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The regex (spark.redaction.regex) that is used to decide which config properties or environment settings are sensitive should also include oauthToken to match spark.kubernetes.authenticate.submission.oauthToken
## How was this patch tested?
Simple regex addition - happy to add a test if needed.
Author: Vinoo Ganesh <vganesh@palantir.com>
Closes#23555 from vinooganesh/vinooganesh/SPARK-26625.
## What changes were proposed in this pull request?
`SerializeFromObject` now keeps all serializer expressions for domain object even when only part of output attributes are used by top plan.
We should be able to prune unused serializers from `SerializeFromObject` in such case.
## How was this patch tested?
Added tests.
Closes#23562 from viirya/SPARK-26619.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
In the PR, I propose new built-in datasource with name `noop` which can be used in:
- benchmarking to avoid additional overhead of actions and unnecessary type conversions
- caching of datasets/dataframes
- producing other side effects as a consequence of row materialisations like uploading data to a IO caches.
## How was this patch tested?
Added a test to check that datasource rows are materialised.
Closes#23471 from MaxGekk/none-datasource.
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?
When a streaming query has multiple file streams, and there is a batch where one of the file streams dont have data in that batch, then if the query has to restart from that, it will throw the following error.
```
java.lang.IllegalStateException: batch 1 doesn't exist
at org.apache.spark.sql.execution.streaming.HDFSMetadataLog$.verifyBatchIds(HDFSMetadataLog.scala:300)
at org.apache.spark.sql.execution.streaming.FileStreamSourceLog.get(FileStreamSourceLog.scala:120)
at org.apache.spark.sql.execution.streaming.FileStreamSource.getBatch(FileStreamSource.scala:181)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$populateStartOffsets$2.apply(MicroBatchExecution.scala:294)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$populateStartOffsets$2.apply(MicroBatchExecution.scala:291)
at scala.collection.Iterator$class.foreach(Iterator.scala:891)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1334)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$populateStartOffsets(MicroBatchExecution.scala:291)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:178)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:175)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:175)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:251)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:61)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:175)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:169)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:295)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:205)
```
Existing `HDFSMetadata.verifyBatchIds` threw error whenever the `batchIds` list was empty. In the context of `FileStreamSource.getBatch` (where verify is called) and `FileStreamSourceLog` (subclass of `HDFSMetadata`), this is usually okay because, in a streaming query with one file stream, the `batchIds` can never be empty:
- A batch is planned only when the `FileStreamSourceLog` has seen new offset (that is, there are new data files).
- So `FileStreamSource.getBatch` will be called on X to Y where X will always be > Y. This calls internally`HDFSMetadata.verifyBatchIds (X+1, Y)` with X+1-Y ids.
For example.,`FileStreamSource.getBatch(4, 5)` will call `verify(batchIds = Seq(5), start = 5, end = 5)`. However, the invariant of X > Y is not true when there are two file stream sources, as a batch may be planned even when only one of the file streams has data. So one of the file stream may not have data, which can call `FileStreamSource.getBatch(X, X)` -> `verify(batchIds = Seq.empty, start = X+1, end = X)` -> failure.
Note that `FileStreamSource.getBatch(X, X)` gets called **only when restarting a query in a batch where a file source did not have data**. This is because in normal planning of batches, `MicroBatchExecution` avoids calling `FileStreamSource.getBatch(X, X)` when offset X has not changed. However, when restarting a stream at such a batch, `MicroBatchExecution.populateStartOffsets()` calls `FileStreamSource.getBatch(X, X)` (DataSource V1 hack to initialize the source with last known offsets) thus hitting this issue.
The minimum solution here is to skip verification when `FileStreamSource.getBatch(X, X)`.
## 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#23557 from tdas/SPARK-26629.
Authored-by: Tathagata Das <tathagata.das1565@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
