## What changes were proposed in this pull request?
`HadoopDelegationTokenProvider` has basically the same functionality just like `ServiceCredentialProvider` so the interfaces can be merged.
`YARNHadoopDelegationTokenManager` now loads `ServiceCredentialProvider`s in one step. The drawback of this if one provider fails all others are not loaded. `HadoopDelegationTokenManager` loads `HadoopDelegationTokenProvider`s independently so it provides more robust behaviour.
In this PR I've I've made the following changes:
* Deleted `YARNHadoopDelegationTokenManager` and `ServiceCredentialProvider`
* Made `HadoopDelegationTokenProvider` a `DeveloperApi`
## How was this patch tested?
Existing unit tests.
Closes#23686 from gaborgsomogyi/SPARK-26772.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This patch proposes to change the approach on extracting log urls as well as attributes from YARN executor:
- AS-IS: extract information from `Container` API and include them to container launch context
- TO-BE: let YARN executor self-extracting information
This approach leads us to populate more attributes like nodemanager's IPC port which can let us configure custom log url to JHS log url directly.
## How was this patch tested?
Existing unit tests.
Closes#23706 from HeartSaVioR/SPARK-26790.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
In the PR, I propose to use `System.nanoTime()` instead of `System.currentTimeMillis()` in measurements of time intervals.
`System.currentTimeMillis()` returns current wallclock time and will follow changes to the system clock. Thus, negative wallclock adjustments can cause timeouts to "hang" for a long time (until wallclock time has caught up to its previous value again). This can happen when ntpd does a "step" after the network has been disconnected for some time. The most canonical example is during system bootup when DHCP takes longer than usual. This can lead to failures that are really hard to understand/reproduce. `System.nanoTime()` is guaranteed to be monotonically increasing irrespective of wallclock changes.
## How was this patch tested?
By existing test suites.
Closes#23727 from MaxGekk/system-nanotime.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This patch makes hardcoded configs in "mesos" module to use ConfigEntry, avoiding issues on mistake like SPARK-26082.
Please note that there're some changes on type while migrating to ConfigEntry: specifically "comma-separated list on a string" becomes "sequence of strings". While SparkConf smoothly handles on the change (comma-separated list on a string is still supported so backward compatible), there're some methods in utility class (`mesos` package private) to depend on the type change, so this patch also modifies the method signature for them a bit.
## How was this patch tested?
Existing tests.
Closes#23743 from HeartSaVioR/SPARK-26843.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
#23744 added a UT to prevent a future regression. However, it breaks Scala-2.11 build. This fixes that.
## How was this patch tested?
Manual test with Scala-2.11 profile.
Closes#23755 from HeartSaVioR/SPARK-26082-FOLLOW-UP-V2.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Delegation token providers interface now has a parameter `fileSystems` but this is needed only for `HadoopFSDelegationTokenProvider`.
In this PR I've addressed this issue in the following way:
* Removed `fileSystems` parameter from `HadoopDelegationTokenProvider`
* Moved `YarnSparkHadoopUtil.hadoopFSsToAccess` into `HadoopFSDelegationTokenProvider`
* Moved `spark.yarn.stagingDir` into core
* Moved `spark.yarn.access.namenodes` into core and renamed to `spark.kerberos.access.namenodes`
* Moved `spark.yarn.access.hadoopFileSystems` into core and renamed to `spark.kerberos.access.hadoopFileSystems`
## How was this patch tested?
Existing unit tests.
Closes#23698 from gaborgsomogyi/SPARK-26766.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This patch adds UT on testing SPARK-26082 to avoid regression. While #23743 reduces the possibility to make a similar mistake, the needed lines of code for adding tests are not that huge, so I guess it might be worth to add them.
## How was this patch tested?
Newly added UTs. Test "supports setting fetcher cache" fails when #23743 is not applied and succeeds when #23743 is applied.
Closes#23744 from HeartSaVioR/SPARK-26082-add-unit-test.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
* change MesosClusterScheduler to use correct argument name for Mesos fetch cache (spark.mesos.fetchCache.enable -> spark.mesos.fetcherCache.enable)
## How was this patch tested?
Not sure this requires a test, since it's just a string change.
Closes#23734 from mwlon/SPARK-26082.
Authored-by: mwlon <mloncaric@hmc.edu>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
Merge both case statements, and remove unused variables that
are not set by the Scala code anymore.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#23655 from vanzin/SPARK-26733.
## What changes were proposed in this pull request?
- Covers latest minikube versions.
- keeps the older version support
Note: While I was facing disk pressure issues locally on machine, I noticed minikube status command would report that everything was working fine even if some kube-system pods were not up. I don't think the output is 100% reliable but it is good enough for most cases.
## How was this patch tested?
Run it against latest version of minikube (v0.32.0).
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#23520 from skonto/update-mini-backend.
## What changes were proposed in this pull request?
This patch proposes adding a new configuration on SHS: custom executor log URL pattern. This will enable end users to replace executor logs to other than RM provide, like external log service, which enables to serve executor logs when NodeManager becomes unavailable in case of YARN.
End users can build their own of custom executor log URLs with pre-defined patterns which would be vary on each resource manager. This patch adds some patterns to YARN resource manager. (For others, there's even no executor log url available so cannot define patterns as well.)
Please refer the doc change as well as added UTs in this patch to see how to set up the feature.
## How was this patch tested?
Added UT, as well as manual test with YARN cluster
Closes#23260 from HeartSaVioR/SPARK-26311.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
This change addes a new mode for credential renewal that does not require
a keytab; it uses the local ticket cache instead, so it works while the
user keeps the cache valid.
This can be useful for, e.g., people running long spark-shell sessions where
their kerberos login is kept up-to-date.
The main change to enable this behavior is in HadoopDelegationTokenManager,
with a small change in the HDFS token provider. The other changes are to avoid
creating duplicate tokens when submitting the application to YARN; they allow
the tokens from the scheduler to be sent to the YARN AM, reducing the round trips
to HDFS.
For that, the scheduler initialization code was changed a little bit so that
the tokens are available when the YARN client is initialized. That basically
takes care of a long-standing TODO that was in the code to clean up configuration
propagation to the driver's RPC endpoint (in CoarseGrainedSchedulerBackend).
Tested with an app designed to stress this functionality, with both keytab and
cache-based logins. Some basic kerberos tests on k8s also.
Closes#23525 from vanzin/SPARK-26595.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Providing a new configuration "spark.yarn.un-managed-am" (defaults to false) to enable the Unmanaged AM Application in Yarn Client mode which launches the Application Master service as part of the Client. It utilizes the existing code for communicating between the Application Master <-> Task Scheduler for the container requests/allocations/launch, and eliminates these,
1. Allocating and launching the Application Master container
2. Remote Node/Process communication between Application Master <-> Task Scheduler
## How was this patch tested?
I verified manually running the applications in yarn-client mode with "spark.yarn.un-managed-am" enabled, and also ensured that there is no impact to the existing execution flows.
I would like to hear others feedback/thoughts on this.
Closes#19616 from devaraj-kavali/SPARK-22404.
Authored-by: Devaraj K <devaraj@apache.org>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
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?
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?
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?
Added a new configuration 'spark.mesos.driver.memoryOverhead' for providing the driver memory overhead in mesos cluster mode.
## How was this patch tested?
Verified it manually, Resource Scheduler allocates (drivermemory+ driver memoryOverhead) for driver in mesos cluster mode.
Closes#17726 from devaraj-kavali/SPARK-17928.
Authored-by: Devaraj K <devaraj@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This fixes K8S integration test compilation failure introduced by #23423 .
```scala
$ build/sbt -Pkubernetes-integration-tests test:package
...
[error] /Users/dongjoon/APACHE/spark/resource-managers/kubernetes/integration-tests/src/test/scala/org/apache/spark/deploy/k8s/integrationtest/KubernetesTestComponents.scala:71: type mismatch;
[error] found : org.apache.spark.internal.config.OptionalConfigEntry[Boolean]
[error] required: String
[error] .set(IS_TESTING, false)
[error] ^
[error] /Users/dongjoon/APACHE/spark/resource-managers/kubernetes/integration-tests/src/test/scala/org/apache/spark/deploy/k8s/integrationtest/KubernetesTestComponents.scala:71: type mismatch;
[error] found : Boolean(false)
[error] required: String
[error] .set(IS_TESTING, false)
[error] ^
[error] two errors found
```
## How was this patch tested?
Pass the K8S integration test.
Closes#23527 from dongjoon-hyun/SPARK-26482.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
The PR makes hardcoded configs below to use `ConfigEntry`.
* spark.ui
* spark.ssl
* spark.authenticate
* spark.master.rest
* spark.master.ui
* spark.metrics
* spark.admin
* spark.modify.acl
This patch doesn't change configs which are not relevant to SparkConf (e.g. system properties).
## How was this patch tested?
Existing tests.
Closes#23423 from HeartSaVioR/SPARK-26466.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This fixes the compilation error.
```
$ cd resource-managers/kubernetes/integration-tests
$ mvn test-compile
[ERROR] /Users/dongjoon/APACHE/spark/resource-managers/kubernetes/integration-tests/src/test/scala/org/apache/spark/deploy/k8s/integrationtest/KubernetesTestComponents.scala:71: type mismatch;
found : org.apache.spark.internal.config.OptionalConfigEntry[Boolean]
required: String
[ERROR] .set(IS_TESTING, false)
[ERROR] ^
```
## How was this patch tested?
Pass the Jenkins K8S Integration test or Manual.
Closes#23505 from dongjoon-hyun/SPARK-26491.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Currently, `Client#createConfArchive` do not handle IOException, and some detail info is not provided in logs. Sometimes, this may delay the time of locating the root cause of io error.
This PR will add debug logs for confArchive when preparing local resource.
## How was this patch tested?
unittest
Closes#23444 from liupc/Add-logs-for-IOException-when-preparing-local-resource.
Authored-by: Liupengcheng <liupengcheng@xiaomi.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
Currently there is code scattered in a bunch of places to do different
things related to HTTP security, such as access control, setting
security-related headers, and filtering out bad content. This makes it
really easy to miss these things when writing new UI code.
This change creates a new filter that does all of those things, and
makes sure that all servlet handlers that are attached to the UI get
the new filter and any user-defined filters consistently. The extent
of the actual features should be the same as before.
The new filter is added at the end of the filter chain, because authentication
is done by custom filters and thus needs to happen first. This means that
custom filters see unfiltered HTTP requests - which is actually the current
behavior anyway.
As a side-effect of some of the code refactoring, handlers added after
the initial set also get wrapped with a GzipHandler, which didn't happen
before.
Tested with added unit tests and in a history server with SPNEGO auth
configured.
Closes#23302 from vanzin/SPARK-24522.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
The PR makes hardcoded `spark.test` and `spark.testing` configs to use `ConfigEntry` and put them in the config package.
## How was this patch tested?
existing UTs
Closes#23413 from mgaido91/SPARK-26491.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
This change modifies the behavior of the delegation token code when running
on YARN, so that the driver controls the renewal, in both client and cluster
mode. For that, a few different things were changed:
* The AM code only runs code that needs DTs when DTs are available.
In a way, this restores the AM behavior to what it was pre-SPARK-23361, but
keeping the fix added in that bug. Basically, all the AM code is run in a
"UGI.doAs()" block; but code that needs to talk to HDFS (basically the
distributed cache handling code) was delayed to the point where the driver
is up and running, and thus when valid delegation tokens are available.
* SparkSubmit / ApplicationMaster now handle user login, not the token manager.
The previous AM code was relying on the token manager to keep the user
logged in when keytabs are used. This required some odd APIs in the token
manager and the AM so that the right UGI was exposed and used in the right
places.
After this change, the logged in user is handled separately from the token
manager, so the API was cleaned up, and, as explained above, the whole AM
runs under the logged in user, which also helps with simplifying some more code.
* Distributed cache configs are sent separately to the AM.
Because of the delayed initialization of the cached resources in the AM, it
became easier to write the cache config to a separate properties file instead
of bundling it with the rest of the Spark config. This also avoids having
to modify the SparkConf to hide things from the UI.
* Finally, the AM doesn't manage the token manager anymore.
The above changes allow the token manager to be completely handled by the
driver's scheduler backend code also in YARN mode (whether client or cluster),
making it similar to other RMs. To maintain the fix added in SPARK-23361 also
in client mode, the AM now sends an extra message to the driver on initialization
to fetch delegation tokens; and although it might not really be needed, the
driver also keeps the running AM updated when new tokens are created.
Tested in a kerberized cluster with the same tests used to validate SPARK-23361,
in both client and cluster mode. Also tested with a non-kerberized cluster.
Closes#23338 from vanzin/SPARK-25689.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
This PR upgrades Mockito from 1.10.19 to 2.23.4. The following changes are required.
- Replace `org.mockito.Matchers` with `org.mockito.ArgumentMatchers`
- Replace `anyObject` with `any`
- Replace `getArgumentAt` with `getArgument` and add type annotation.
- Use `isNull` matcher in case of `null` is invoked.
```scala
saslHandler.channelInactive(null);
- verify(handler).channelInactive(any(TransportClient.class));
+ verify(handler).channelInactive(isNull());
```
- Make and use `doReturn` wrapper to avoid [SI-4775](https://issues.scala-lang.org/browse/SI-4775)
```scala
private def doReturn(value: Any) = org.mockito.Mockito.doReturn(value, Seq.empty: _*)
```
## How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#23452 from dongjoon-hyun/SPARK-26536.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Increase test memory to avoid OOM in TimSort-related tests.
## How was this patch tested?
Existing tests.
Closes#23425 from srowen/SPARK-26306.
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?
The PR makes hardcoded spark.driver, spark.executor, and spark.cores.max configs to use `ConfigEntry`.
Note that some config keys are from `SparkLauncher` instead of defining in the config package object because the string is already defined in it and it does not depend on core module.
## How was this patch tested?
Existing tests.
Closes#23415 from ueshin/issues/SPARK-26445/hardcoded_driver_executor_configs.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The PR makes hardcoded configs below to use ConfigEntry.
* spark.pyspark
* spark.python
* spark.r
This patch doesn't change configs which are not relevant to SparkConf (e.g. system properties, python source code)
## How was this patch tested?
Existing tests.
Closes#23428 from HeartSaVioR/SPARK-26489.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.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?
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>
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?
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>
## What changes were proposed in this pull request?
User specified filters are not applied to SQL tab in yarn mode, as it is overridden by the yarn AmIp filter.
So we need to append user provided filters (spark.ui.filters) with yarn filter.
## How was this patch tested?
【Test step】：
1) Launch spark sql with authentication filter as below:
2) spark-sql --master yarn --conf spark.ui.filters=org.apache.hadoop.security.authentication.server.AuthenticationFilter --conf spark.org.apache.hadoop.security.authentication.server.AuthenticationFilter.params="type=simple"
3) Go to Yarn application list UI link
4) Launch the application master for the Spark-SQL app ID and access all the tabs by appending tab name.
5) It will display an error for all tabs including SQL tab.(before able to access SQL tab,as Authentication filter is not applied for SQL tab)
6) Also can be verified with info logs,that Authentication filter applied to SQL tab.(before it is not applied).
I have attached the behaviour below in following order..
1) Command used
2) Before fix (logs and UI)
3) After fix (logs and UI)
**1) COMMAND USED**:
launching spark-sql with authentication filter.
**2) BEFORE FIX:**
**UI result:**
able to access SQL tab.
**logs**:
authentication filter not applied to SQL tab.
**3) AFTER FIX:**
**UI result**:
Not able to access SQL tab.
**in logs**:
Both yarn filter and Authentication filter applied to SQL tab.
Closes#23312 from chakravarthiT/SPARK-26255_ui.
Authored-by: chakravarthi <tcchakra@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
YARN applicationMaster metrics registration introduced in SPARK-24594 causes further registration of static metrics (Codegenerator and HiveExternalCatalog) and of JVM metrics, which I believe do not belong in this context.
This looks like an unintended side effect of using the start method of [[MetricsSystem]].
A possible solution proposed here, is to introduce startNoRegisterSources to avoid these additional registrations of static sources and of JVM sources in the case of YARN applicationMaster metrics (this could be useful for other metrics that may be added in the future).
## How was this patch tested?
Manually tested on a YARN cluster,
Closes#22279 from LucaCanali/YarnMetricsRemoveExtraSourceRegistration.
Lead-authored-by: Luca Canali <luca.canali@cern.ch>
Co-authored-by: LucaCanali <luca.canali@cern.ch>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
This change makes the driver and executor builders a lot simpler
by encapsulating almost all feature logic into the respective
feature classes. The only logic that remains is the creation of
the initial pod, which needs to happen before anything else so
is better to be left in the builder class.
Most feature classes already behave fine when the config has nothing
they should handle, but a few minor tweaks had to be added. Unit
tests were also updated or added to account for these.
The builder suites were simplified a lot and just test the remaining
pod-related code in the builders themselves.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#23220 from vanzin/SPARK-25877.
This proposes an alternative way to load secret keys into a Spark application that is running on Kubernetes. Instead of automatically generating the secret, the secret key can reside in a file that is shared between both the driver and executor containers.
Unit tests.
Closes#23252 from mccheah/auth-secret-with-file.
Authored-by: mcheah <mcheah@palantir.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
… incorrect.
## What changes were proposed in this pull request?
In the reported heartbeat information, the unit of the memory data is bytes, which is converted by the formatBytes() function in the utils.js file before being displayed in the interface. The cardinality of the unit conversion in the formatBytes function is 1000, which should be 1024.
Change the cardinality of the unit conversion in the formatBytes function to 1024.
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22683 from httfighter/SPARK-25696.
Lead-authored-by: 韩田田00222924 <han.tiantian@zte.com.cn>
Co-authored-by: han.tiantian@zte.com.cn <han.tiantian@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
This change modifies the logic in the SecurityManager to do two
things:
- generate unique app secrets also when k8s is being used
- only store the secret in the user's UGI on YARN
The latter is needed so that k8s won't unnecessarily create
k8s secrets for the UGI credentials when only the auth token
is stored there.
On the k8s side, the secret is propagated to executors using
an environment variable instead. This ensures it works in both
client and cluster mode.
Security doc was updated to mention the feature and clarify that
proper access control in k8s should be enabled for it to be secure.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#23174 from vanzin/SPARK-26194.
When I try to run `./bin/pyspark` cmd in a pod in Kubernetes(image built without change from pyspark Dockerfile), I'm getting an error:
```
$SPARK_HOME/bin/pyspark --deploy-mode client --master k8s://https://$KUBERNETES_SERVICE_HOST:$KUBERNETES_SERVICE_PORT_HTTPS ...
Python 2.7.15 (default, Aug 22 2018, 13:24:18)
[GCC 6.4.0] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Could not open PYTHONSTARTUP
IOError: [Errno 2] No such file or directory: '/opt/spark/python/pyspark/shell.py'
```
This is because `pyspark` folder doesn't exist under `/opt/spark/python/`
## What changes were proposed in this pull request?
Added `COPY python/pyspark ${SPARK_HOME}/python/pyspark` to pyspark Dockerfile to resolve issue above.
## How was this patch tested?
Google Kubernetes Engine
Closes#23037 from AzureQ/master.
Authored-by: Qi Shao <qi.shao.nyu@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Adds proper labels when deleting executor pods.
## How was this patch tested?
Manually with tests.
Closes#23209 from skonto/fix-deletion-labels.
Authored-by: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Keeps K8s executor resources present if case of failure or normal termination.
Introduces a new boolean config option: `spark.kubernetes.deleteExecutors`, with default value set to true.
The idea is to update Spark K8s backend structures but leave the resources around.
The assumption is that since entries are not removed from the `removedExecutorsCache` we are immune to updates that refer to the the executor resources previously removed.
The only delete operation not touched is the one in the `doKillExecutors` method.
Reason is right now we dont support [blacklisting](https://issues.apache.org/jira/browse/SPARK-23485) and dynamic allocation with Spark on K8s. In both cases in the future we might want to handle these scenarios although its more complicated.
More tests can be added if approach is approved.
## How was this patch tested?
Manually by running a Spark job and verifying pods are not deleted.
Closes#23136 from skonto/keep_pods.
Authored-by: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Signed-off-by: Yinan Li <ynli@google.com>
There are a few issues with the current configuration types used in
the kubernetes backend:
- they use type parameters for role-specific specialization, which makes
type signatures really noisy throughout the code base.
- they break encapsulation by forcing the code that creates the config
object to remove the configuration from SparkConf before creating the
k8s-specific wrapper.
- they don't provide an easy way for tests to have default values for
fields they do not use.
This change fixes those problems by:
- creating a base config type with role-specific specialization using
inheritance
- encapsulating the logic of parsing SparkConf into k8s-specific views
inside the k8s config classes
- providing some helper code for tests to easily override just the part
of the configs they want.
Most of the change relates to the above, especially cleaning up the
tests. While doing that, I also made some smaller changes elsewhere:
- removed unnecessary type parameters in KubernetesVolumeSpec
- simplified the error detection logic in KubernetesVolumeUtils; all
the call sites would just throw the first exception collected by
that class, since they all called "get" on the "Try" object. Now
the unnecessary wrapping is gone and the exception is just thrown
where it occurs.
- removed a lot of unnecessary mocking from tests.
- changed the kerberos-related code so that less logic needs to live
in the driver builder. In spirit it should be part of the upcoming
work in this series of cleanups, but it made parts of this change
simpler.
Tested with existing unit tests and integration tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#22959 from vanzin/SPARK-25876.
Adds USER directives to the Dockerfiles which is configurable via build argument (`spark_uid`) for easy customisation. A `-u` flag is added to `bin/docker-image-tool.sh` to make it easy to customise this e.g.
```
> bin/docker-image-tool.sh -r rvesse -t uid -u 185 build
> bin/docker-image-tool.sh -r rvesse -t uid push
```
If no UID is explicitly specified it defaults to `185` - this is per skonto's suggestion to align with the OpenShift standard reserved UID for Java apps (
https://lists.openshift.redhat.com/openshift-archives/users/2016-March/msg00283.html)
Notes:
- We have to make the `WORKDIR` writable by the root group or otherwise jobs will fail with `AccessDeniedException`
To Do:
- [x] Debug and resolve issue with client mode test
- [x] Consider whether to always propagate `SPARK_USER_NAME` to environment of driver and executor pods so `entrypoint.sh` can insert that into `/etc/passwd` entry
- [x] Rebase once PR #23013 is merged and update documentation accordingly
Built the Docker images with the new Dockerfiles that include the `USER` directives. Ran the Spark on K8S integration tests against the new images. All pass except client mode which I am currently debugging further.
Also manually dropped myself into the resulting container images via `docker run` and checked `id -u` output to see that UID is as expected.
Tried customising the UID from the default via the new `-u` argument to `docker-image-tool.sh` and again checked the resulting image for the correct runtime UID.
cc felixcheung skonto vanzin
Closes#23017 from rvesse/SPARK-26015.
Authored-by: Rob Vesse <rvesse@dotnetrdf.org>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
The "build context" for a docker image - basically the whole contents of the
current directory where "docker" is invoked - can be huge in a dev build,
easily breaking a couple of gigs.
Doing that copy 3 times during the build of docker images severely slows
down the process.
This patch creates a smaller build context - basically mimicking what the
make-distribution.sh script does, so that when building the docker images,
only the necessary bits are in the current directory. For PySpark and R that
is optimized further, since those images are built based on the previously
built Spark main image.
In my current local clone, the dir size is about 2G, but with this script
the "context" sent to docker is about 250M for the main image, 1M for the
pyspark image and 8M for the R image. That speeds up the image builds
considerably.
I also snuck in a fix to the k8s integration test dependencies in the sbt
build, so that the examples are properly built (without having to do it
manually).
Closes#23019 from vanzin/SPARK-26025.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This PR adds configurations to use subpaths with Spark on k8s. Subpaths (https://kubernetes.io/docs/concepts/storage/volumes/#using-subpath) allow the user to specify a path within a volume to use instead of the volume's root.
## How was this patch tested?
Added unit tests. Ran SparkPi on a cluster with event logging pointed at a subpath-mount and verified the driver host created and used the subpath.
Closes#23026 from NiharS/k8s_subpath.
Authored-by: Nihar Sheth <niharrsheth@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
When I ran spark-shell on JDK11+28(2018-09-25), It failed with the error below.
```
Exception in thread "main" java.lang.ExceptionInInitializerError
at org.apache.hadoop.util.StringUtils.<clinit>(StringUtils.java:80)
at org.apache.hadoop.security.SecurityUtil.getAuthenticationMethod(SecurityUtil.java:611)
at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:273)
at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:261)
at org.apache.hadoop.security.UserGroupInformation.loginUserFromSubject(UserGroupInformation.java:791)
at org.apache.hadoop.security.UserGroupInformation.getLoginUser(UserGroupInformation.java:761)
at org.apache.hadoop.security.UserGroupInformation.getCurrentUser(UserGroupInformation.java:634)
at org.apache.spark.util.Utils$.$anonfun$getCurrentUserName$1(Utils.scala:2427)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.util.Utils$.getCurrentUserName(Utils.scala:2427)
at org.apache.spark.SecurityManager.<init>(SecurityManager.scala:79)
at org.apache.spark.deploy.SparkSubmit.secMgr$lzycompute$1(SparkSubmit.scala:359)
at org.apache.spark.deploy.SparkSubmit.secMgr$1(SparkSubmit.scala:359)
at org.apache.spark.deploy.SparkSubmit.$anonfun$prepareSubmitEnvironment$9(SparkSubmit.scala:367)
at scala.Option.map(Option.scala:146)
at org.apache.spark.deploy.SparkSubmit.prepareSubmitEnvironment(SparkSubmit.scala:367)
at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:143)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:86)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:927)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:936)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.StringIndexOutOfBoundsException: begin 0, end 3, length 2
at java.base/java.lang.String.checkBoundsBeginEnd(String.java:3319)
at java.base/java.lang.String.substring(String.java:1874)
at org.apache.hadoop.util.Shell.<clinit>(Shell.java:52)
```
This is a Hadoop issue that fails to parse some java.version. It has been fixed from Hadoop-2.7.4(see [HADOOP-14586](https://issues.apache.org/jira/browse/HADOOP-14586)).
Note, Hadoop-2.7.5 or upper have another problem with Spark ([SPARK-25330](https://issues.apache.org/jira/browse/SPARK-25330)). So upgrading to 2.7.4 would be fine for now.
## How was this patch tested?
Existing tests.
Closes#23101 from tasanuma/SPARK-26134.
Authored-by: Takanobu Asanuma <tasanuma@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
bin/docker-image-tool.sh tries to build all docker images (JVM, PySpark
and SparkR) by default. But not all spark distributions are built with
SparkR and hence this script will fail on such distros.
With this change, we make building alternate language binding docker images (PySpark and SparkR) optional. User has to specify dockerfile for those language bindings using -p and -R flags accordingly, to build the binding docker images.
## How was this patch tested?
Tested following scenarios.
*bin/docker-image-tool.sh -r <repo> -t <tag> build* --> Builds only JVM docker image (default behavior)
*bin/docker-image-tool.sh -r <repo> -t <tag> -p kubernetes/dockerfiles/spark/bindings/python/Dockerfile build* --> Builds both JVM and PySpark docker images
*bin/docker-image-tool.sh -r <repo> -t <tag> -p kubernetes/dockerfiles/spark/bindings/python/Dockerfile -R kubernetes/dockerfiles/spark/bindings/R/Dockerfile build* --> Builds JVM, PySpark and SparkR docker images.
Author: Nagaram Prasad Addepally <ram@cloudera.com>
Closes#23053 from ramaddepally/SPARK-25957.
## What changes were proposed in this pull request?
Current `memLimitExceededLogMessage`:
<img src="https://user-images.githubusercontent.com/5399861/48467789-ec8e1000-e824-11e8-91fc-280d342e1bf3.png" width="360">
It‘s not very clear, because physical memory exceeds but suggestion contains virtual memory config. This pr makes it more clear and replace deprecated config: ```spark.yarn.executor.memoryOverhead```.
## How was this patch tested?
manual tests
Closes#23030 from wangyum/EXECUTOR_MEMORY_OVERHEAD.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The build has a lot of deprecation warnings. Some are new in Scala 2.12 and Java 11. We've fixed some, but I wanted to take a pass at fixing lots of easy miscellaneous ones here.
They're too numerous and small to list here; see the pull request. Some highlights:
- `BeanInfo` is deprecated in 2.12, and BeanInfo classes are pretty ancient in Java. Instead, case classes can explicitly declare getters
- Eta expansion of zero-arg methods; foo() becomes () => foo() in many cases
- Floating-point Range is inexact and deprecated, like 0.0 to 100.0 by 1.0
- finalize() is finally deprecated (just needs to be suppressed)
- StageInfo.attempId was deprecated and easiest to remove here
I'm not now going to touch some chunks of deprecation warnings:
- Parquet deprecations
- Hive deprecations (particularly serde2 classes)
- Deprecations in generated code (mostly Thriftserver CLI)
- ProcessingTime deprecations (we may need to revive this class as internal)
- many MLlib deprecations because they concern methods that may be removed anyway
- a few Kinesis deprecations I couldn't figure out
- Mesos get/setRole, which I don't know well
- Kafka/ZK deprecations (e.g. poll())
- Kinesis
- a few other ones that will probably resolve by deleting a deprecated method
## How was this patch tested?
Existing tests, including manual testing with the 2.11 build and Java 11.
Closes#23065 from srowen/SPARK-26090.
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?
This PR makes Spark's default Scala version as 2.12, and Scala 2.11 will be the alternative version. This implies that Scala 2.12 will be used by our CI builds including pull request builds.
We'll update the Jenkins to include a new compile-only jobs for Scala 2.11 to ensure the code can be still compiled with Scala 2.11.
## How was this patch tested?
existing tests
Closes#22967 from dbtsai/scala2.12.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Deprecated in Java 11, replace Class.newInstance with Class.getConstructor.getInstance, and primtive wrapper class constructors with valueOf or equivalent
## How was this patch tested?
Existing tests.
Closes#22988 from srowen/SPARK-25984.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
The integration tests can now be run in sbt if the right profile
is enabled, using the "test" task under the respective project.
This avoids having to fall back to maven to run the tests, which
invalidates all your compiled stuff when you go back to sbt, making
development way slower than it should.
There's also a task to run the tests directly without refreshing
the docker images, which is helpful if you just made a change to
the submission code which should not affect the code in the images.
The sbt tasks currently are not very customizable; there's some
very minor things you can set in the sbt shell itself, but otherwise
it's hardcoded to run on minikube.
I also had to make some slight adjustments to the IT code itself,
mostly to remove assumptions about the existing harness.
Tested on sbt and maven.
Closes#22909 from vanzin/SPARK-25897.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Currently definitions of config entries in `core` module are in several files separately. We should move them into `internal/config` to be easy to manage.
## How was this patch tested?
Existing tests.
Closes#22928 from ueshin/issues/SPARK-25926/single_config_file.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
- Issue is described in detail in [SPARK-25930](https://issues.apache.org/jira/browse/SPARK-25930). Since we rely on the std output, pick always the last line which contains the wanted value. Although minor, current implementation breaks tests.
## How was this patch tested?
manually. rm -rf ~/.m2 and then run the tests.
Closes#22931 from skonto/fix_scala_detection.
Authored-by: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Right now there are 3 different classes dealing with building the driver
command to run inside the pod, one for each "binding" supported by Spark.
This has two main shortcomings:
- the code in the 3 classes is very similar; changing things in one place
would probably mean making a similar change in the others.
- it gives the false impression that the step implementation is the only
place where binding-specific logic is needed. That is not true; there
was code in KubernetesConf that was binding-specific, and there's also
code in the executor-specific config step. So the 3 classes weren't really
working as a language-specific abstraction.
On top of that, the current code was propagating command line parameters in
a different way depending on the binding. That doesn't seem necessary, and
in fact using environment variables for command line parameters is in general
a really bad idea, since you can't handle special characters (e.g. spaces)
that way.
This change merges the 3 different code paths for Java, Python and R into
a single step, and also merges the 3 code paths to start the Spark driver
in the k8s entry point script. This increases the amount of shared code,
and also moves more feature logic into the step itself, so it doesn't live
in KubernetesConf.
Note that not all logic related to setting up the driver lives in that
step. For example, the memory overhead calculation still lives separately,
except it now happens in the driver config step instead of outside the
step hierarchy altogether.
Some of the noise in the diff is because of changes to KubernetesConf, which
will be addressed in a separate change.
Tested with new and updated unit tests + integration tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#22897 from vanzin/SPARK-25875.
## What changes were proposed in this pull request?
Currently K8S integration tests are hardcoded to use a `minikube` based backend. `minikube` is VM based so can be resource hungry and also doesn't cope well with certain networking setups (for example using Cisco AnyConnect software VPN `minikube` is unusable as it detects its own IP incorrectly).
This PR Adds a new K8S integration testing backend that allows for using the Kubernetes support in [Docker for Desktop](https://blog.docker.com/2018/07/kubernetes-is-now-available-in-docker-desktop-stable-channel/). It also generalises the framework to be able to run the integration tests against an arbitrary Kubernetes cluster.
To Do:
- [x] General Kubernetes cluster backend
- [x] Documentation on Kubernetes integration testing
- [x] Testing of general K8S backend
- [x] Check whether change from timestamps being `Time` to `String` in Fabric 8 upgrade needs additional fix up
## How was this patch tested?
Ran integration tests with Docker for Desktop and all passed:
Suggested Reviewers: ifilonenko srowen
Author: Rob Vesse <rvesse@dotnetrdf.org>
Closes#22805 from rvesse/SPARK-25809.
This avoids having two classes to deal with tokens; now the above
class is a one-stop shop for dealing with delegation tokens. The
YARN backend extends that class instead of doing composition like
before, resulting in a bit less code there too.
The renewer functionality is basically the same code that used to
be in YARN's AMCredentialRenewer. That is also the reason why the
public API of HadoopDelegationTokenManager is a little bit odd;
the YARN AM has some odd requirements for how this all should be
initialized, and the weirdness is needed currently to support that.
Tested:
- YARN with stress app for DT renewal
- Mesos and K8S with basic kerberos tests (both tgt and keytab)
Closes#22624 from vanzin/SPARK-23781.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
Py4J 0.10.8.1 is released on October 21st and is the first release of Py4J to support Python 3.7 officially. We had better have this to get the official support. Also, there are some patches related to garbage collections.
https://www.py4j.org/changelog.html#py4j-0-10-8-and-py4j-0-10-8-1
## How was this patch tested?
Pass the Jenkins.
Closes#22901 from dongjoon-hyun/SPARK-25891.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
New feature to pass podspec files for driver and executor pods.
## How was this patch tested?
new unit and integration tests
- [x] more overwrites in integration tests
- [ ] invalid template integration test, documentation
Author: Onur Satici <osatici@palantir.com>
Author: Yifei Huang <yifeih@palantir.com>
Author: onursatici <onursatici@gmail.com>
Closes#22146 from onursatici/pod-template.
## What changes were proposed in this pull request?
Changed the `kubernetes-client` version and refactored code that broke as a result
## How was this patch tested?
Unit and Integration tests
Closes#22820 from ifilonenko/SPARK-25828.
Authored-by: Ilan Filonenko <ifilondz@gmail.com>
Signed-off-by: Erik Erlandson <eerlands@redhat.com>
## What changes were proposed in this pull request?
- Fixes the scala version propagation issue.
- Disables the tests under the k8s profile, now we will run them manually. Adds a test specific profile otherwise tests will not run if we just remove the module from the kubernetes profile (quickest solution I can think of).
## How was this patch tested?
Manually by running the tests with different versions of scala.
Closes#22838 from skonto/propagate-scala2.12.
Authored-by: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
As this is targeted for 3.0.0 and Python2 will be deprecated by Jan 1st, 2020, I feel it is appropriate to change the default to Python3. Especially as these projects [found here](https://python3statement.org/) are deprecating their support.
## How was this patch tested?
Unit and Integration tests
Author: Ilan Filonenko <ifilondz@gmail.com>
Closes#22810 from ifilonenko/SPARK-24516.
## What changes were proposed in this pull request?
`removeExecutorFromSpark` tries to fetch the reason the executor exited from Kubernetes, which may be useful if the pod was OOMKilled. However, the code previously deleted the pod from Kubernetes first which made retrieving this status impossible. This fixes the ordering.
On a separate but related note, it would be nice to wait some time before removing the pod - to let the operator examine logs and such.
## How was this patch tested?
Running on my local cluster.
Author: Mike Kaplinskiy <mike.kaplinskiy@gmail.com>
Closes#22720 from mikekap/patch-1.
This way the image generated from both environments has the same layout,
with just a difference in contents that should not affect functionality.
Also added some minor error checking to the image script.
Closes#22681 from vanzin/SPARK-25682.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
The test fix is to allocate a `Resource` object only after the resource
types have been initialized. Otherwise the YARN classes get in a weird
state and throw a different exception than expected, because the resource
has a different view of the registered resources.
I also removed a test for a null resource since that seems unnecessary
and made the fix more complicated.
All the other changes are just cleanup; basically simplify the tests by
defining what is being tested and deriving the resource type registration
and the SparkConf from that data, instead of having redundant definitions
in the tests.
Ran tests with Hadoop 3 (and also without it).
Closes#22751 from vanzin/SPARK-20327.fix.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
This is the work on setting up Secure HDFS interaction with Spark-on-K8S.
The architecture is discussed in this community-wide google [doc](https://docs.google.com/document/d/1RBnXD9jMDjGonOdKJ2bA1lN4AAV_1RwpU_ewFuCNWKg)
This initiative can be broken down into 4 Stages
**STAGE 1**
- [x] Detecting `HADOOP_CONF_DIR` environmental variable and using Config Maps to store all Hadoop config files locally, while also setting `HADOOP_CONF_DIR` locally in the driver / executors
**STAGE 2**
- [x] Grabbing `TGT` from `LTC` or using keytabs+principle and creating a `DT` that will be mounted as a secret or using a pre-populated secret
**STAGE 3**
- [x] Driver
**STAGE 4**
- [x] Executor
## How was this patch tested?
Locally tested on a single-noded, pseudo-distributed Kerberized Hadoop Cluster
- [x] E2E Integration tests https://github.com/apache/spark/pull/22608
- [ ] Unit tests
## Docs and Error Handling?
- [x] Docs
- [x] Error Handling
## Contribution Credit
kimoonkim skonto
Closes#21669 from ifilonenko/secure-hdfs.
Lead-authored-by: Ilan Filonenko <if56@cornell.edu>
Co-authored-by: Ilan Filonenko <ifilondz@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This PR adds CLI support for YARN custom resources, e.g. GPUs and any other resources YARN defines.
The custom resources are defined with Spark properties, no additional CLI arguments were introduced.
The properties can be defined in the following form:
**AM resources, client mode:**
Format: `spark.yarn.am.resource.<resource-name>`
The property name follows the naming convention of YARN AM cores / memory properties: `spark.yarn.am.memory and spark.yarn.am.cores
`
**Driver resources, cluster mode:**
Format: `spark.yarn.driver.resource.<resource-name>`
The property name follows the naming convention of driver cores / memory properties: `spark.driver.memory and spark.driver.cores.`
**Executor resources:**
Format: `spark.yarn.executor.resource.<resource-name>`
The property name follows the naming convention of executor cores / memory properties: `spark.executor.memory / spark.executor.cores`.
For the driver resources (cluster mode) and executor resources properties, we use the `yarn` prefix here as custom resource types are specific to YARN, currently.
**Validation:**
Please note that a validation logic is added to avoid having requested resources defined in 2 ways, for example defining the following configs:
```
"--conf", "spark.driver.memory=2G",
"--conf", "spark.yarn.driver.resource.memory=1G"
```
will not start execution and will print an error message.
## How was this patch tested?
Unit tests + manual execution with Hadoop2 and Hadoop 3 builds.
Testing have been performed on a real cluster with Spark and YARN configured:
Cluster and client mode
Request Resource Types with lowercase and uppercase units
Start Spark job with only requesting standard resources (mem / cpu)
Error handling cases:
- Request unknown resource type
- Request Resource type (either memory / cpu) with duplicate configs at the same time (e.g. with this config:
```
--conf spark.yarn.am.resource.memory=1G \
--conf spark.yarn.driver.resource.memory=2G \
--conf spark.yarn.executor.resource.memory=3G \
```
), ResourceTypeValidator handles these cases well, so it is not permitted
- Request standard resource (memory / cpu) with the new style configs, e.g. --conf spark.yarn.am.resource.memory=1G, this is not permitted and handled well.
An example about how I ran the testcases:
```
cd ~;export HADOOP_CONF_DIR=/opt/hadoop/etc/hadoop/;
./spark-2.4.0-SNAPSHOT-bin-custom-spark/bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master yarn \
--deploy-mode cluster \
--driver-memory 1G \
--driver-cores 1 \
--executor-memory 1G \
--executor-cores 1 \
--conf spark.logConf=true \
--conf spark.yarn.executor.resource.gpu=3G \
--verbose \
./spark-2.4.0-SNAPSHOT-bin-custom-spark/examples/jars/spark-examples_2.11-2.4.0-SNAPSHOT.jar \
10;
```
Closes#20761 from szyszy/SPARK-20327.
Authored-by: Szilard Nemeth <snemeth@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Remove Hadoop 2.6 references and make 2.7 the default.
Obviously, this is for master/3.0.0 only.
After this we can also get rid of the separate test jobs for Hadoop 2.6.
## How was this patch tested?
Existing tests
Closes#22615 from srowen/SPARK-25016.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
The docker file was referencing a path that only existed in the
distribution tarball; it needs to be parameterized so that the
right path can be used in a dev build.
Tested on local dev build.
Closes#22634 from vanzin/SPARK-25646.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.0.0-SNAPSHOT.
## How was this patch tested?
N/A
Closes#22606 from gatorsmile/bump3.0.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR is follow-up of closed https://github.com/apache/spark/pull/17401 which only ended due to of inactivity, but its still nice feature to have.
Given review by jerryshao taken in consideration and edited:
- VisibleForTesting deleted because of dependency conflicts
- removed unnecessary reflection for `MetricsSystemImpl`
- added more available types for gauge
## How was this patch tested?
Manual deploy of new yarn-shuffle jar into a Node Manager and verifying that the metrics appear in the Node Manager-standard location. This is JMX with an query endpoint running on `hostname:port`
Resulting metrics look like this:
```
curl -sk -XGET hostname:port | grep -v '#' | grep 'shuffleService'
hadoop_nodemanager_openblockrequestlatencymillis_rate15{name="shuffleService",} 0.31428910657834713
hadoop_nodemanager_blocktransferratebytes_rate15{name="shuffleService",} 566144.9983653595
hadoop_nodemanager_blocktransferratebytes_ratemean{name="shuffleService",} 2464409.9678099006
hadoop_nodemanager_openblockrequestlatencymillis_rate1{name="shuffleService",} 1.2893844732240272
hadoop_nodemanager_registeredexecutorssize{name="shuffleService",} 2.0
hadoop_nodemanager_openblockrequestlatencymillis_ratemean{name="shuffleService",} 1.255574678369966
hadoop_nodemanager_openblockrequestlatencymillis_count{name="shuffleService",} 315.0
hadoop_nodemanager_openblockrequestlatencymillis_rate5{name="shuffleService",} 0.7661929192569739
hadoop_nodemanager_registerexecutorrequestlatencymillis_ratemean{name="shuffleService",} 0.0
hadoop_nodemanager_registerexecutorrequestlatencymillis_count{name="shuffleService",} 0.0
hadoop_nodemanager_registerexecutorrequestlatencymillis_rate1{name="shuffleService",} 0.0
hadoop_nodemanager_registerexecutorrequestlatencymillis_rate5{name="shuffleService",} 0.0
hadoop_nodemanager_blocktransferratebytes_count{name="shuffleService",} 6.18271213E8
hadoop_nodemanager_registerexecutorrequestlatencymillis_rate15{name="shuffleService",} 0.0
hadoop_nodemanager_blocktransferratebytes_rate5{name="shuffleService",} 1154114.4881816586
hadoop_nodemanager_blocktransferratebytes_rate1{name="shuffleService",} 574745.0749848988
```
Closes#22485 from mareksimunek/SPARK-18364.
Lead-authored-by: marek.simunek <marek.simunek@firma.seznam.cz>
Co-authored-by: Andrew Ash <andrew@andrewash.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
Spurious logs like /sec.
2018-09-26 09:33:57 DEBUG ExecutorPodsLifecycleManager:58 - Removed executors with ids from Spark that were either found to be deleted or non-existent in the cluster.
2018-09-26 09:33:58 DEBUG ExecutorPodsLifecycleManager:58 - Removed executors with ids from Spark that were either found to be deleted or non-existent in the cluster.
2018-09-26 09:33:59 DEBUG ExecutorPodsLifecycleManager:58 - Removed executors with ids from Spark that were either found to be deleted or non-existent in the cluster.
2018-09-26 09:34:00 DEBUG ExecutorPodsLifecycleManager:58 - Removed executors with ids from Spark that were either found to be deleted or non-existent in the cluster.
The fix is easy, first check if there are any removed executors, before producing the log message.
## How was this patch tested?
Tested by manually deploying to a minikube cluster.
Closes#22565 from ScrapCodes/spark-25543/k8s/debug-log-spurious-warning.
Authored-by: Prashant Sharma <prashsh1@in.ibm.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This PR adds a rule to force `.toLowerCase(Locale.ROOT)` or `toUpperCase(Locale.ROOT)`.
It produces an error as below:
```
[error] Are you sure that you want to use toUpperCase or toLowerCase without the root locale? In most cases, you
[error] should use toUpperCase(Locale.ROOT) or toLowerCase(Locale.ROOT) instead.
[error] If you must use toUpperCase or toLowerCase without the root locale, wrap the code block with
[error] // scalastyle:off caselocale
[error] .toUpperCase
[error] .toLowerCase
[error] // scalastyle:on caselocale
```
This PR excludes the cases above for SQL code path for external calls like table name, column name and etc.
For test suites, or when it's clear there's no locale problem like Turkish locale problem, it uses `Locale.ROOT`.
One minor problem is, `UTF8String` has both methods, `toLowerCase` and `toUpperCase`, and the new rule detects them as well. They are ignored.
## How was this patch tested?
Manually tested, and Jenkins tests.
Closes#22581 from HyukjinKwon/SPARK-25565.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Heartbeat shouldn't include accumulators for zero metrics.
Heartbeats sent from executors to the driver every 10 seconds contain metrics and are generally on the order of a few KBs. However, for large jobs with lots of tasks, heartbeats can be on the order of tens of MBs, causing tasks to die with heartbeat failures. We can mitigate this by not sending zero metrics to the driver.
## 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#22473 from mukulmurthy/25449-heartbeat.
Authored-by: Mukul Murthy <mukul.murthy@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
Added fix to flakiness that was present in PySpark tests w.r.t Executors not being tested.
Important fix to executorConf which was failing tests when executors *were* tested
## How was this patch tested?
Unit and Integration tests
Closes#22415 from ifilonenko/SPARK-25291.
Authored-by: Ilan Filonenko <if56@cornell.edu>
Signed-off-by: Yinan Li <ynli@google.com>
## What changes were proposed in this pull request?
In the dev list, we can still discuss whether the next version is 2.5.0 or 3.0.0. Let us first bump the master branch version to `2.5.0-SNAPSHOT`.
## How was this patch tested?
N/A
Closes#22426 from gatorsmile/bumpVersionMaster.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR ensures to call `super.afterAll()` in `override afterAll()` method for test suites.
* Some suites did not call `super.afterAll()`
* Some suites may call `super.afterAll()` only under certain condition
* Others never call `super.afterAll()`.
This PR also ensures to call `super.beforeAll()` in `override beforeAll()` for test suites.
## How was this patch tested?
Existing UTs
Closes#22337 from kiszk/SPARK-25338.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Fixes the collision issue with spark executor names in client mode, see SPARK-25295 for the details.
It follows the cluster name convention as app-name will be used as the prefix and if that is not defined we use "spark" as the default prefix. Eg. `spark-pi-1536781360723-exec-1` where spark-pi is the name of the app passed at the config side or transformed if it contains illegal characters.
Also fixes the issue with spark app name having spaces in cluster mode.
If you run the Spark Pi test in client mode it passes.
The tricky part is the user may set the app name:
3030b82c89/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala (L30)
If i do:
```
./bin/spark-submit
...
--deploy-mode cluster --name "spark pi"
...
```
it will fail as the app name is used for the prefix of driver's pod name and it cannot have spaces (according to k8s conventions).
## How was this patch tested?
Manually by running spark job in client mode.
To reproduce do:
```
kubectl create -f service.yaml
kubectl create -f pod.yaml
```
service.yaml :
```
kind: Service
apiVersion: v1
metadata:
name: spark-test-app-1-svc
spec:
clusterIP: None
selector:
spark-app-selector: spark-test-app-1
ports:
- protocol: TCP
name: driver-port
port: 7077
targetPort: 7077
- protocol: TCP
name: block-manager
port: 10000
targetPort: 10000
```
pod.yaml:
```
apiVersion: v1
kind: Pod
metadata:
name: spark-test-app-1
labels:
spark-app-selector: spark-test-app-1
spec:
containers:
- name: spark-test
image: skonto/spark:k8s-client-fix
imagePullPolicy: Always
command:
- 'sh'
- '-c'
- "/opt/spark/bin/spark-submit
--verbose
--master k8s://https://kubernetes.default.svc
--deploy-mode client
--class org.apache.spark.examples.SparkPi
--conf spark.app.name=spark
--conf spark.executor.instances=1
--conf spark.kubernetes.container.image=skonto/spark:k8s-client-fix
--conf spark.kubernetes.container.image.pullPolicy=Always
--conf spark.kubernetes.authenticate.oauthTokenFile=/var/run/secrets/kubernetes.io/serviceaccount/token
--conf spark.kubernetes.authenticate.caCertFile=/var/run/secrets/kubernetes.io/serviceaccount/ca.crt
--conf spark.executor.memory=500m
--conf spark.executor.cores=1
--conf spark.executor.instances=1
--conf spark.driver.host=spark-test-app-1-svc.default.svc
--conf spark.driver.port=7077
--conf spark.driver.blockManager.port=10000
local:///opt/spark/examples/jars/spark-examples_2.11-2.4.0-SNAPSHOT.jar 1000000"
```
Closes#22405 from skonto/fix-k8s-client-mode-executor-names.
Authored-by: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Signed-off-by: Yinan Li <ynli@google.com>
## What changes were proposed in this pull request?
Correct some comparisons between unrelated types to what they seem to… have been trying to do
## How was this patch tested?
Existing tests.
Closes#22384 from srowen/SPARK-25398.
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?
Add spark.executor.pyspark.memory limit for K8S
## How was this patch tested?
Unit and Integration tests
Closes#22298 from ifilonenko/SPARK-25021.
Authored-by: Ilan Filonenko <if56@cornell.edu>
Signed-off-by: Holden Karau <holden@pigscanfly.ca>
## What changes were proposed in this pull request?
The default behaviour of Spark on K8S currently is to create `emptyDir` volumes to back `SPARK_LOCAL_DIRS`. In some environments e.g. diskless compute nodes this may actually hurt performance because these are backed by the Kubelet's node storage which on a diskless node will typically be some remote network storage.
Even if this is enterprise grade storage connected via a high speed interconnect the way Spark uses these directories as scratch space (lots of relatively small short lived files) has been observed to cause serious performance degradation. Therefore we would like to provide the option to use K8S's ability to instead back these `emptyDir` volumes with `tmpfs`. Therefore this PR adds a configuration option that enables `SPARK_LOCAL_DIRS` to be backed by Memory backed `emptyDir` volumes rather than the default.
Documentation is added to describe both the default behaviour plus this new option and its implications. One of which is that scratch space then counts towards your pods memory limits and therefore users will need to adjust their memory requests accordingly.
*NB* - This is an alternative version of PR #22256 reduced to just the `tmpfs` piece
## How was this patch tested?
Ran with this option in our diskless compute environments to verify functionality
Author: Rob Vesse <rvesse@dotnetrdf.org>
Closes#22323 from rvesse/SPARK-25262-tmpfs.
## What changes were proposed in this pull request?
The configuration parameter "spark.shuffle.service.enabled" has defined in `package.scala`, and it is also used in many place, so we can replace it with `SHUFFLE_SERVICE_ENABLED`.
and unified this configuration parameter "spark.shuffle.service.port" together.
## How was this patch tested?
N/A
Closes#22306 from 10110346/unifiedserviceenable.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Fixes the issue brought up in https://github.com/GoogleCloudPlatform/spark-on-k8s-operator/issues/273 where the arguments were being comma-delineated, which was incorrect wrt to the PythonRunner and RRunner.
## How was this patch tested?
Modified unit test to test this change.
Author: Ilan Filonenko <if56@cornell.edu>
Closes#22257 from ifilonenko/SPARK-25264.
## What changes were proposed in this pull request?
Add a PAM configuration in k8s dockerfile to require authentication into wheel to run as `su`
## How was this patch tested?
Verify against CI that PAM config succeeds & causes no regressions
Closes#22285 from erikerlandson/spark-25275.
Authored-by: Erik Erlandson <eerlands@redhat.com>
Signed-off-by: Erik Erlandson <eerlands@redhat.com>
## What changes were proposed in this pull request?
This adds `spark.executor.pyspark.memory` to configure Python's address space limit, [`resource.RLIMIT_AS`](https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS). Limiting Python's address space allows Python to participate in memory management. In practice, we see fewer cases of Python taking too much memory because it doesn't know to run garbage collection. This results in YARN killing fewer containers. This also improves error messages so users know that Python is consuming too much memory:
```
File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in fe_engineer
fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item, []), mat_rec_prep.get(item, []))
File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in leven_list_compare
permutations = sorted(permutations, reverse=True)
MemoryError
```
The new pyspark memory setting is used to increase requested YARN container memory, instead of sharing overhead memory between python and off-heap JVM activity.
## How was this patch tested?
Tested memory limits in our YARN cluster and verified that MemoryError is thrown.
Author: Ryan Blue <blue@apache.org>
Closes#21977 from rdblue/SPARK-25004-add-python-memory-limit.
## What changes were proposed in this pull request?
YARN `AmIpFilter` adds a new parameter "RM_HA_URLS" to support RM HA, but Spark on YARN doesn't provide a such parameter, so it will be failed to redirect when running on RM HA. The detailed exception can be checked from JIRA. So here fixing this issue by adding "RM_HA_URLS" parameter.
## How was this patch tested?
Local verification.
Closes#22164 from jerryshao/SPARK-23679.
Authored-by: jerryshao <sshao@hortonworks.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Our HDFS cluster configured 5 nameservices: `nameservices1`, `nameservices2`, `nameservices3`, `nameservices-dev1` and `nameservices4`, but `nameservices-dev1` unstable. So sometimes an error occurred and causing the entire job failed since [SPARK-24149](https://issues.apache.org/jira/browse/SPARK-24149):
I think it's best to add a switch here.
## How was this patch tested?
manual tests
Closes#21734 from wangyum/SPARK-24149.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
When using older versions of spark releases, a use case generated a huge code-gen file which hit the limitation `Constant pool has grown past JVM limit of 0xFFFF`. In this situation, it should fail immediately. But the diagnosis message sent to RM is too large, the ApplicationMaster suspended and RM's ZKStateStore was crashed. For 2.3 or later spark releases the limitation of code-gen has been removed, but maybe there are still some uncaught exceptions that contain oversized error message will cause such a problem.
This PR is aim to cut down the diagnosis message size.
## How was this patch tested?
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22180 from yaooqinn/SPARK-25174.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
**## What changes were proposed in this pull request?**
When the yarn.nodemanager.resource.memory-mb or yarn.scheduler.maximum-allocation-mb
memory assignment is insufficient, Spark always reports an error request to adjust
yarn.scheduler.maximum-allocation-mb even though in message it shows the memory value
of yarn.nodemanager.resource.memory-mb parameter,As the error Message is bit misleading to the user we can modify the same, We can keep the error message same as executor memory validation message.
Defintion of **yarn.nodemanager.resource.memory-mb:**
Amount of physical memory, in MB, that can be allocated for containers. It means the amount of memory YARN can utilize on this node and therefore this property should be lower then the total memory of that machine.
**yarn.scheduler.maximum-allocation-mb:**
It defines the maximum memory allocation available for a container in MB
it means RM can only allocate memory to containers in increments of "yarn.scheduler.minimum-allocation-mb" and not exceed "yarn.scheduler.maximum-allocation-mb" and It should not be more than total allocated memory of the Node.
**## How was this patch tested?**
Manually tested in hdfs-Yarn clustaer
Closes#22199 from sujith71955/maste_am_log.
Authored-by: s71955 <sujithchacko.2010@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Introducing R Bindings for Spark R on K8s
- [x] Running SparkR Job
## How was this patch tested?
This patch was tested with
- [x] Unit Tests
- [x] Integration Tests
## Example:
Commands to run example spark job:
1. `dev/make-distribution.sh --pip --r --tgz -Psparkr -Phadoop-2.7 -Pkubernetes`
2. `bin/docker-image-tool.sh -m -t testing build`
3.
```
bin/spark-submit \
--master k8s://https://192.168.64.33:8443 \
--deploy-mode cluster \
--name spark-r \
--conf spark.executor.instances=1 \
--conf spark.kubernetes.container.image=spark-r:testing \
local:///opt/spark/examples/src/main/r/dataframe.R
```
This above spark-submit command works given the distribution. (Will include this integration test in PR once PRB is ready).
Author: Ilan Filonenko <if56@cornell.edu>
Closes#21584 from ifilonenko/spark-r.
## What changes were proposed in this pull request?
Small formatting change to have Python Version be camelCase as per request during PR review.
## How was this patch tested?
Tested with unit and integration tests
Author: Ilan Filonenko <if56@cornell.edu>
Closes#22095 from ifilonenko/spark-py-edits.
## What changes were proposed in this pull request?
We shall check whether the barrier stage requires more slots (to be able to launch all tasks in the barrier stage together) than the total number of active slots currently, and fail fast if trying to submit a barrier stage that requires more slots than current total number.
This PR proposes to add a new method `getNumSlots()` to try to get the total number of currently active slots in `SchedulerBackend`, support of this new method has been added to all the first-class scheduler backends except `MesosFineGrainedSchedulerBackend`.
## How was this patch tested?
Added new test cases in `BarrierStageOnSubmittedSuite`.
Closes#22001 from jiangxb1987/SPARK-24819.
Lead-authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Co-authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
(a) disabled rest submission server by default in standalone mode
(b) fails the standalone master if rest server enabled & authentication secret set
(c) fails the mesos cluster dispatcher if authentication secret set
(d) doc updates
(e) when submitting a standalone app, only try the rest submission first if spark.master.rest.enabled=true
otherwise you'd see a 10 second pause like
18/08/09 08:13:22 INFO RestSubmissionClient: Submitting a request to launch an application in spark://...
18/08/09 08:13:33 WARN RestSubmissionClient: Unable to connect to server spark://...
I also made sure the mesos cluster dispatcher failed with the secret enabled, though I had to do that on slightly different code as I don't have mesos native libs around.
## How was this patch tested?
I ran the tests in the mesos module & in core for org.apache.spark.deploy.*
I ran a test on a cluster with standalone master to make sure I could still start with the right configs, and would fail the right way too.
Closes#22071 from squito/rest_doc_updates.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fixing typos is sometimes very hard. It's not so easy to visually review them. Recently, I discovered a very useful tool for it, [misspell](https://github.com/client9/misspell).
This pull request fixes minor typos detected by [misspell](https://github.com/client9/misspell) except for the false positives. If you would like me to work on other files as well, let me know.
## How was this patch tested?
### before
```
$ misspell . | grep -v '.js'
R/pkg/R/SQLContext.R:354:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:424:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:445:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:495:43: "definiton" is a misspelling of "definition"
NOTICE-binary:454:16: "containd" is a misspelling of "contained"
R/pkg/R/context.R:46:43: "definiton" is a misspelling of "definition"
R/pkg/R/context.R:74:43: "definiton" is a misspelling of "definition"
R/pkg/R/DataFrame.R:591:48: "persistance" is a misspelling of "persistence"
R/pkg/R/streaming.R:166:44: "occured" is a misspelling of "occurred"
R/pkg/inst/worker/worker.R:65:22: "ouput" is a misspelling of "output"
R/pkg/tests/fulltests/test_utils.R:106:25: "environemnt" is a misspelling of "environment"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/InMemoryStoreSuite.java:38:39: "existant" is a misspelling of "existent"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/LevelDBSuite.java:83:39: "existant" is a misspelling of "existent"
common/network-common/src/main/java/org/apache/spark/network/crypto/TransportCipher.java:243:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:234:19: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:238:63: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:244:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:276:39: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
common/unsafe/src/test/scala/org/apache/spark/unsafe/types/UTF8StringPropertyCheckSuite.scala:195:15: "orgin" is a misspelling of "origin"
core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:621:39: "gauranteed" is a misspelling of "guaranteed"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/main/scala/org/apache/spark/storage/DiskStore.scala:282:18: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/util/ListenerBus.scala:64:17: "overriden" is a misspelling of "overridden"
core/src/test/scala/org/apache/spark/ShuffleSuite.scala:211:7: "substracted" is a misspelling of "subtracted"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:2468:84: "truely" is a misspelling of "truly"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:25:18: "persistance" is a misspelling of "persistence"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:26:69: "persistance" is a misspelling of "persistence"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
dev/run-pip-tests:55:28: "enviroments" is a misspelling of "environments"
dev/run-pip-tests:91:37: "virutal" is a misspelling of "virtual"
dev/merge_spark_pr.py:377:72: "accross" is a misspelling of "across"
dev/merge_spark_pr.py:378:66: "accross" is a misspelling of "across"
dev/run-pip-tests:126:25: "enviroments" is a misspelling of "environments"
docs/configuration.md:1830:82: "overriden" is a misspelling of "overridden"
docs/structured-streaming-programming-guide.md:525:45: "processs" is a misspelling of "processes"
docs/structured-streaming-programming-guide.md:1165:61: "BETWEN" is a misspelling of "BETWEEN"
docs/sql-programming-guide.md:1891:810: "behaivor" is a misspelling of "behavior"
examples/src/main/python/sql/arrow.py:98:8: "substract" is a misspelling of "subtract"
examples/src/main/python/sql/arrow.py:103:27: "substract" is a misspelling of "subtract"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala:230:24: "inital" is a misspelling of "initial"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala:237:26: "descripiton" is a misspelling of "descriptions"
python/pyspark/find_spark_home.py:30:13: "enviroment" is a misspelling of "environment"
python/pyspark/context.py:937:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:938:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:939:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:940:12: "supress" is a misspelling of "suppress"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:713:8: "probabilty" is a misspelling of "probability"
python/pyspark/ml/clustering.py:1038:8: "Currenlty" is a misspelling of "Currently"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/ml/regression.py:1378:20: "paramter" is a misspelling of "parameter"
python/pyspark/mllib/stat/_statistics.py:262:8: "probabilty" is a misspelling of "probability"
python/pyspark/rdd.py:1363:32: "paramter" is a misspelling of "parameter"
python/pyspark/streaming/tests.py:825:42: "retuns" is a misspelling of "returns"
python/pyspark/sql/tests.py:768:29: "initalization" is a misspelling of "initialization"
python/pyspark/sql/tests.py:3616:31: "initalize" is a misspelling of "initialize"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala:120:39: "arbitary" is a misspelling of "arbitrary"
resource-managers/mesos/src/test/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArgumentsSuite.scala:26:45: "sucessfully" is a misspelling of "successfully"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala:358:27: "constaints" is a misspelling of "constraints"
resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala:111:24: "senstive" is a misspelling of "sensitive"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala:1063:5: "overwirte" is a misspelling of "overwrite"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala:1348:17: "compatability" is a misspelling of "compatibility"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala:77:36: "paramter" is a misspelling of "parameter"
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:1374:22: "precendence" is a misspelling of "precedence"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:238:27: "unnecassary" is a misspelling of "unnecessary"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala:212:17: "whn" is a misspelling of "when"
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinHelper.scala:147:60: "timestmap" is a misspelling of "timestamp"
sql/core/src/test/scala/org/apache/spark/sql/TPCDSQuerySuite.scala:150:45: "precentage" is a misspelling of "percentage"
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchemaSuite.scala:135:29: "infered" is a misspelling of "inferred"
sql/hive/src/test/resources/golden/udf_instr-1-2e76f819563dbaba4beb51e3a130b922:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_instr-2-32da357fc754badd6e3898dcc8989182:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-1-6e41693c9c6dceea4d7fab4c02884e4e:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-2-d9b5934457931447874d6bb7c13de478:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:9:79: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:13:110: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/annotate_stats_join.q:46:105: "distint" is a misspelling of "distinct"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/auto_sortmerge_join_11.q:29:3: "Currenly" is a misspelling of "Currently"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/avro_partitioned.q:72:15: "existant" is a misspelling of "existent"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/decimal_udf.q:25:3: "substraction" is a misspelling of "subtraction"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby2_map_multi_distinct.q:16:51: "funtion" is a misspelling of "function"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby_sort_8.q:15:30: "issueing" is a misspelling of "issuing"
sql/hive/src/test/scala/org/apache/spark/sql/sources/HadoopFsRelationTest.scala:669:52: "wiht" is a misspelling of "with"
sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSessionImpl.java:474:9: "Refering" is a misspelling of "Referring"
```
### after
```
$ misspell . | grep -v '.js'
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
```
Closes#22070 from seratch/fix-typo.
Authored-by: Kazuhiro Sera <seratch@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
Adds the user-set service account name for the driver pod in the client mode integration test
## How was this patch tested?
Manual test against a custom Kubernetes cluster
Author: mcheah <mcheah@palantir.com>
Closes#21924 from mccheah/fix-service-account.
## What changes were proposed in this pull request?
Don't set service account name for the pod created in client mode
## How was this patch tested?
Test should continue running smoothly in Jenkins.
Author: mcheah <mcheah@palantir.com>
Closes#21900 from mccheah/fix-integration-test-service-account.
## What changes were proposed in this pull request?
Propose new APIs and modify job/task scheduling to support barrier execution mode, which requires all tasks in a same barrier stage start at the same time, and retry all tasks in case some tasks fail in the middle. The barrier execution mode is useful for some ML/DL workloads.
The proposed API changes include:
- `RDDBarrier` that marks an RDD as barrier (Spark must launch all the tasks together for the current stage).
- `BarrierTaskContext` that support global sync of all tasks in a barrier stage, and provide extra `BarrierTaskInfo`s.
In DAGScheduler, we retry all tasks of a barrier stage in case some tasks fail in the middle, this is achieved by unregistering map outputs for a shuffleId (for ShuffleMapStage) or clear the finished partitions in an active job (for ResultStage).
## How was this patch tested?
Add `RDDBarrierSuite` to ensure we convert RDDs correctly;
Add new test cases in `DAGSchedulerSuite` to ensure we do task scheduling correctly;
Add new test cases in `SparkContextSuite` to ensure the barrier execution mode actually works (both under local mode and local cluster mode).
Add new test cases in `TaskSchedulerImplSuite` to ensure we schedule tasks for barrier taskSet together.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#21758 from jiangxb1987/barrier-execution-mode.
## What changes were proposed in this pull request?
Enable client mode integration test after merging from master.
## How was this patch tested?
Check the integration test runs in the build.
Author: mcheah <mcheah@palantir.com>
Closes#21874 from mccheah/enable-client-mode-test.
## What changes were proposed in this pull request?
Support client mode for the Kubernetes scheduler.
Client mode works more or less identically to cluster mode. However, in client mode, the Spark Context needs to be manually bootstrapped with certain properties which would have otherwise been set up by spark-submit in cluster mode. Specifically:
- If the user doesn't provide a driver pod name, we don't add an owner reference. This is for usage when the driver is not running in a pod in the cluster. In such a case, the driver can only provide a best effort to clean up the executors when the driver exits, but cleaning up the resources is not guaranteed. The executor JVMs should exit if the driver JVM exits, but the pods will still remain in the cluster in a COMPLETED or FAILED state.
- The user must provide a host (spark.driver.host) and port (spark.driver.port) that the executors can connect to. When using spark-submit in cluster mode, spark-submit generates the headless service automatically; in client mode, the user is responsible for setting up their own connectivity.
We also change the authentication configuration prefixes for client mode.
## How was this patch tested?
Adding an integration test to exercise client mode support.
Author: mcheah <mcheah@palantir.com>
Closes#21748 from mccheah/k8s-client-mode.
## What changes were proposed in this pull request?
In this PR metrics are introduced for YARN. As up to now there was no metrics in the YARN module a new metric system is created with the name "applicationMaster".
To support both client and cluster mode the metric system lifecycle is bound to the AM.
## How was this patch tested?
Both client and cluster mode was tested manually.
Before the test on one of the YARN node spark-core was removed to cause the allocation failure.
Spark was started as (in case of client mode):
```
spark2-submit \
--class org.apache.spark.examples.SparkPi \
--conf "spark.yarn.blacklist.executor.launch.blacklisting.enabled=true" --conf "spark.blacklist.application.maxFailedExecutorsPerNode=2" --conf "spark.dynamicAllocation.enabled=true" --conf "spark.metrics.conf.*.sink.console.class=org.apache.spark.metrics.sink.ConsoleSink" \
--master yarn \
--deploy-mode client \
original-spark-examples_2.11-2.4.0-SNAPSHOT.jar \
1000
```
In both cases the YARN logs contained the new metrics as:
```
$ yarn logs --applicationId application_1529926424933_0015
...
-- Gauges ----------------------------------------------------------------------
application_1531751594108_0046.applicationMaster.numContainersPendingAllocate
value = 0
application_1531751594108_0046.applicationMaster.numExecutorsFailed
value = 3
application_1531751594108_0046.applicationMaster.numExecutorsRunning
value = 9
application_1531751594108_0046.applicationMaster.numLocalityAwareTasks
value = 0
application_1531751594108_0046.applicationMaster.numReleasedContainers
value = 0
...
```
Author: “attilapiros” <piros.attila.zsolt@gmail.com>
Author: Attila Zsolt Piros <2017933+attilapiros@users.noreply.github.com>
Closes#21635 from attilapiros/SPARK-24594.
## What changes were proposed in this pull request?
[SPARK-24182](https://github.com/apache/spark/pull/21243) changed the `logApplicationReport` from `false` to `true`. This pr revert it to `false`. otherwise `spark-shell` will show noisy log output:
```java
...
18/07/16 04:46:25 INFO Client: Application report for application_1530676576026_54551 (state: RUNNING)
18/07/16 04:46:26 INFO Client: Application report for application_1530676576026_54551 (state: RUNNING)
...
```
Closes https://github.com/apache/spark/pull/21827
## How was this patch tested?
manual tests
Author: Yuming Wang <yumwang@ebay.com>
Closes#21784 from wangyum/SPARK-24182.
## What changes were proposed in this pull request?
- Adds integration tests for env and mount secrets.
## How was this patch tested?
Manually by checking that secrets were added to the containers and by tuning the tests.
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#21652 from skonto/add-secret-its.
…rEnv properly
Running in yarn cluster mode and trying to set pythonpath via spark.yarn.appMasterEnv.PYTHONPATH doesn't work.
the yarn Client code looks at the env variables:
val pythonPathStr = (sys.env.get("PYTHONPATH") ++ pythonPath)
But when you set spark.yarn.appMasterEnv it puts it into the local env.
So the python path set in spark.yarn.appMasterEnv isn't properly set.
You can work around if you are running in cluster mode by setting it on the client like:
PYTHONPATH=./addon/python/ spark-submit
## What changes were proposed in this pull request?
In Client.scala, PYTHONPATH was being overridden, so changed code to append values to PYTHONPATH instead of overriding them.
## How was this patch tested?
Added log statements to ApplicationMaster.scala to check for environment variable PYTHONPATH, ran a spark job in cluster mode before the change and verified the issue. Performed the same test after the change and verified the fix.
Author: pgandhi <pgandhi@oath.com>
Closes#21468 from pgandhi999/SPARK-22151.
## What changes were proposed in this pull request?
Make the integration test script build all modules.
In order to not run all the non-Kubernetes integration tests in the build, support specifying tags and tag all integration tests specifically with "k8s". Supply the k8s tag in the dev/dev-run-integration-tests.sh script.
## How was this patch tested?
The build system will test this.
Author: mcheah <mcheah@palantir.com>
Closes#21800 from mccheah/k8s-integration-tests-maven-fix.
## What changes were proposed in this pull request?
I added integration tests for PySpark ( + checking JVM options + RemoteFileTest) which wasn't properly merged in the initial integration test PR.
## How was this patch tested?
I tested this with integration tests using:
`dev/dev-run-integration-tests.sh --spark-tgz spark-2.4.0-SNAPSHOT-bin-2.7.3.tgz`
Author: Ilan Filonenko <if56@cornell.edu>
Closes#21583 from ifilonenko/master.
## What changes were proposed in this pull request?
This PR enables a Java bytecode check tool [spotbugs](https://spotbugs.github.io/) to avoid possible integer overflow at multiplication. When an violation is detected, the build process is stopped.
Due to the tool limitation, some other checks will be enabled. In this PR, [these patterns](http://spotbugs-in-kengo-toda.readthedocs.io/en/lqc-list-detectors/detectors.html#findpuzzlers) in `FindPuzzlers` can be detected.
This check is enabled at `compile` phase. Thus, `mvn compile` or `mvn package` launches this check.
## How was this patch tested?
Existing UTs
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21542 from kiszk/SPARK-24529.
This PR continues #21095 and intersects with #21238. I've added volume mounts as a separate step and added PersistantVolumeClaim support.
There is a fundamental problem with how we pass the options through spark conf to fabric8. For each volume type and all possible volume options we would have to implement some custom code to map config values to fabric8 calls. This will result in big body of code we would have to support and means that Spark will always be somehow out of sync with k8s.
I think there needs to be a discussion on how to proceed correctly (eg use PodPreset instead)
----
Due to the complications of provisioning and managing actual resources this PR addresses only volume mounting of already present resources.
----
- [x] emptyDir support
- [x] Testing
- [x] Documentation
- [x] KubernetesVolumeUtils tests
Author: Andrew Korzhuev <andrew.korzhuev@klarna.com>
Author: madanadit <adit@alluxio.com>
Closes#21260 from andrusha/k8s-vol.
## What changes were proposed in this pull request?
- Allows to pass more than one app args to tests.
## How was this patch tested?
Manually tested it with a spark test that requires more than on app args.
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#21672 from skonto/fix_itsets-args.
## What changes were proposed in this pull request?
- disables maven surfire plugin to allow tags function properly, doc here: http://www.scalatest.org/user_guide/using_the_scalatest_maven_plugin
## How was this patch tested?
Manually by adding tags.
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#21697 from skonto/fix-tags.
## What changes were proposed in this pull request?
Remove code that is misleading and is a leftover from a previous implementation.
## How was this patch tested?
Manually.
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#21462 from skonto/fix-k8s-docs.
## What changes were proposed in this pull request?
Make SparkSubmit pass in the main class even if `SparkLauncher.NO_RESOURCE` is the primary resource.
## How was this patch tested?
New integration test written to capture this case.
Author: mcheah <mcheah@palantir.com>
Closes#21660 from mccheah/fix-k8s-no-resource.
This PR use spark.network.timeout in place of spark.storage.blockManagerSlaveTimeoutMs when it is not configured, as configuration doc said
manual test
Author: xueyu <278006819@qq.com>
Closes#21575 from xueyumusic/slaveTimeOutConfig.
Because the way YARN executes commands via bash -c, everything needs
to be quoted so that the whole command is fully contained inside a
bash string and is interpreted correctly when the string is read by
bash. This is a bit different than the quoting done when executing
things as if typing in a bash shell.
Tweaked unit tests to exercise the bad behavior, which would cause
existing tests to time out without the fix. Also tested on a real
cluster, verifying the shell script created by YARN to run the
container.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#21476 from vanzin/SPARK-24446.
## What changes were proposed in this pull request?
This change extends YARN resource allocation handling with blacklisting functionality.
This handles cases when node is messed up or misconfigured such that a container won't launch on it. Before this change backlisting only focused on task execution but this change introduces YarnAllocatorBlacklistTracker which tracks allocation failures per host (when enabled via "spark.yarn.blacklist.executor.launch.blacklisting.enabled").
## How was this patch tested?
### With unit tests
Including a new suite: YarnAllocatorBlacklistTrackerSuite.
#### Manually
It was tested on a cluster by deleting the Spark jars on one of the node.
#### Behaviour before these changes
Starting Spark as:
```
spark2-shell --master yarn --deploy-mode client --num-executors 4 --conf spark.executor.memory=4g --conf "spark.yarn.max.executor.failures=6"
```
Log is:
```
18/04/12 06:49:36 INFO yarn.ApplicationMaster: Final app status: FAILED, exitCode: 11, (reason: Max number of executor failures (6) reached)
18/04/12 06:49:39 INFO yarn.ApplicationMaster: Unregistering ApplicationMaster with FAILED (diag message: Max number of executor failures (6) reached)
18/04/12 06:49:39 INFO impl.AMRMClientImpl: Waiting for application to be successfully unregistered.
18/04/12 06:49:39 INFO yarn.ApplicationMaster: Deleting staging directory hdfs://apiros-1.gce.test.com:8020/user/systest/.sparkStaging/application_1523459048274_0016
18/04/12 06:49:39 INFO util.ShutdownHookManager: Shutdown hook called
```
#### Behaviour after these changes
Starting Spark as:
```
spark2-shell --master yarn --deploy-mode client --num-executors 4 --conf spark.executor.memory=4g --conf "spark.yarn.max.executor.failures=6" --conf "spark.yarn.blacklist.executor.launch.blacklisting.enabled=true"
```
And the log is:
```
18/04/13 05:37:43 INFO yarn.YarnAllocator: Will request 1 executor container(s), each with 1 core(s) and 4505 MB memory (including 409 MB of overhead)
18/04/13 05:37:43 INFO yarn.YarnAllocator: Submitted 1 unlocalized container requests.
18/04/13 05:37:43 INFO yarn.YarnAllocator: Launching container container_1523459048274_0025_01_000008 on host apiros-4.gce.test.com for executor with ID 6
18/04/13 05:37:43 INFO yarn.YarnAllocator: Received 1 containers from YARN, launching executors on 1 of them.
18/04/13 05:37:43 INFO yarn.YarnAllocator: Completed container container_1523459048274_0025_01_000007 on host: apiros-4.gce.test.com (state: COMPLETE, exit status: 1)
18/04/13 05:37:43 INFO yarn.YarnAllocatorBlacklistTracker: blacklisting host as YARN allocation failed: apiros-4.gce.test.com
18/04/13 05:37:43 INFO yarn.YarnAllocatorBlacklistTracker: adding nodes to YARN application master's blacklist: List(apiros-4.gce.test.com)
18/04/13 05:37:43 WARN yarn.YarnAllocator: Container marked as failed: container_1523459048274_0025_01_000007 on host: apiros-4.gce.test.com. Exit status: 1. Diagnostics: Exception from container-launch.
Container id: container_1523459048274_0025_01_000007
Exit code: 1
Stack trace: ExitCodeException exitCode=1:
at org.apache.hadoop.util.Shell.runCommand(Shell.java:604)
at org.apache.hadoop.util.Shell.run(Shell.java:507)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:789)
at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:213)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:302)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
```
Where the most important part is:
```
18/04/13 05:37:43 INFO yarn.YarnAllocatorBlacklistTracker: blacklisting host as YARN allocation failed: apiros-4.gce.test.com
18/04/13 05:37:43 INFO yarn.YarnAllocatorBlacklistTracker: adding nodes to YARN application master's blacklist: List(apiros-4.gce.test.com)
```
And execution was continued (no shutdown called).
### Testing the backlisting of the whole cluster
Starting Spark with YARN blacklisting enabled then removing a the Spark core jar one by one from all the cluster nodes. Then executing a simple spark job which fails checking the yarn log the expected exit status is contained:
```
18/06/15 01:07:10 INFO yarn.ApplicationMaster: Final app status: FAILED, exitCode: 11, (reason: Due to executor failures all available nodes are blacklisted)
18/06/15 01:07:13 INFO util.ShutdownHookManager: Shutdown hook called
```
Author: “attilapiros” <piros.attila.zsolt@gmail.com>
Closes#21068 from attilapiros/SPARK-16630.
## What changes were proposed in this pull request?
This PR changes the entrypoint.sh to provide an option to run non spark-on-k8s commands (init, driver, executor) in order to let the user keep with the normal workflow without hacking the image to bypass the entrypoint
## How was this patch tested?
This patch was built manually in my local machine and I ran some tests with a combination of ```docker run``` commands.
Author: rimolive <ricardo.martinelli.oliveira@gmail.com>
Closes#21572 from rimolive/rimolive-spark-24534.
`WebUI` defines `addStaticHandler` that web UIs don't use (and simply introduce duplication). Let's clean them up and remove duplications.
Local build and waiting for Jenkins
Author: Jacek Laskowski <jacek@japila.pl>
Closes#21510 from jaceklaskowski/SPARK-24490-Use-WebUI.addStaticHandler.
## What changes were proposed in this pull request?
Previously, the scheduler backend was maintaining state in many places, not only for reading state but also writing to it. For example, state had to be managed in both the watch and in the executor allocator runnable. Furthermore, one had to keep track of multiple hash tables.
We can do better here by:
1. Consolidating the places where we manage state. Here, we take inspiration from traditional Kubernetes controllers. These controllers tend to follow a level-triggered mechanism. This means that the controller will continuously monitor the API server via watches and polling, and on periodic passes, the controller will reconcile the current state of the cluster with the desired state. We implement this by introducing the concept of a pod snapshot, which is a given state of the executors in the Kubernetes cluster. We operate periodically on snapshots. To prevent overloading the API server with polling requests to get the state of the cluster (particularly for executor allocation where we want to be checking frequently to get executors to launch without unbearably bad latency), we use watches to populate snapshots by applying observed events to a previous snapshot to get a new snapshot. Whenever we do poll the cluster, the polled state replaces any existing snapshot - this ensures eventual consistency and mirroring of the cluster, as is desired in a level triggered architecture.
2. Storing less specialized in-memory state in general. Previously we were creating hash tables to represent the state of executors. Instead, it's easier to represent state solely by the snapshots.
## How was this patch tested?
Integration tests should test there's no regressions end to end. Unit tests to be updated, in particular focusing on different orderings of events, particularly accounting for when events come in unexpected ordering.
Author: mcheah <mcheah@palantir.com>
Closes#21366 from mccheah/event-queue-driven-scheduling.
These tests were developed in the https://github.com/apache-spark-on-k8s/spark-integration repo
by several contributors. This is a copy of the current state into the main apache spark repo.
The only changes from the current spark-integration repo state are:
* Move the files from the repo root into resource-managers/kubernetes/integration-tests
* Add a reference to these tests in the root README.md
* Fix a path reference in dev/dev-run-integration-tests.sh
* Add a TODO in include/util.sh
## What changes were proposed in this pull request?
Incorporation of Kubernetes integration tests.
## How was this patch tested?
This code has its own unit tests, but the main purpose is to provide the integration tests.
I tested this on my laptop by running dev/dev-run-integration-tests.sh --spark-tgz ~/spark-2.4.0-SNAPSHOT-bin--.tgz
The spark-integration tests have already been running for months in AMPLab, here is an example:
https://amplab.cs.berkeley.edu/jenkins/job/testing-k8s-scheduled-spark-integration-master/
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Sean Suchter <sean-github@suchter.com>
Author: Sean Suchter <ssuchter@pepperdata.com>
Closes#20697 from ssuchter/ssuchter-k8s-integration-tests.
## What changes were proposed in this pull request?
Introducing Python Bindings for PySpark.
- [x] Running PySpark Jobs
- [x] Increased Default Memory Overhead value
- [ ] Dependency Management for virtualenv/conda
## How was this patch tested?
This patch was tested with
- [x] Unit Tests
- [x] Integration tests with [this addition](https://github.com/apache-spark-on-k8s/spark-integration/pull/46)
```
KubernetesSuite:
- Run SparkPi with no resources
- Run SparkPi with a very long application name.
- Run SparkPi with a master URL without a scheme.
- Run SparkPi with an argument.
- Run SparkPi with custom labels, annotations, and environment variables.
- Run SparkPi with a test secret mounted into the driver and executor pods
- Run extraJVMOptions check on driver
- Run SparkRemoteFileTest using a remote data file
- Run PySpark on simple pi.py example
- Run PySpark with Python2 to test a pyfiles example
- Run PySpark with Python3 to test a pyfiles example
Run completed in 4 minutes, 28 seconds.
Total number of tests run: 11
Suites: completed 2, aborted 0
Tests: succeeded 11, failed 0, canceled 0, ignored 0, pending 0
All tests passed.
```
Author: Ilan Filonenko <if56@cornell.edu>
Author: Ilan Filonenko <ifilondz@gmail.com>
Closes#21092 from ifilonenko/master.
## What changes were proposed in this pull request?
* Adds support for local:// scheme like in k8s case for image based deployments where the jar is already in the image. Affects cluster mode and the mesos dispatcher. Covers also file:// scheme. Keeps the default case where jar resolution happens on the host.
## How was this patch tested?
Dispatcher image with the patch, use it to start DC/OS Spark service:
skonto/spark-local-disp:test
Test image with my application jar located at the root folder:
skonto/spark-local:test
Dockerfile for that image.
From mesosphere/spark:2.3.0-2.2.1-2-hadoop-2.6
COPY spark-examples_2.11-2.2.1.jar /
WORKDIR /opt/spark/dist
Tests:
The following work as expected:
* local normal example
```
dcos spark run --submit-args="--conf spark.mesos.appJar.local.resolution.mode=container --conf spark.executor.memory=1g --conf spark.mesos.executor.docker.image=skonto/spark-local:test
--conf spark.executor.cores=2 --conf spark.cores.max=8
--class org.apache.spark.examples.SparkPi local:///spark-examples_2.11-2.2.1.jar"
```
* make sure the flag does not affect other uris
```
dcos spark run --submit-args="--conf spark.mesos.appJar.local.resolution.mode=container --conf spark.executor.memory=1g --conf spark.executor.cores=2 --conf spark.cores.max=8
--class org.apache.spark.examples.SparkPi https://s3-eu-west-1.amazonaws.com/fdp-stavros-test/spark-examples_2.11-2.1.1.jar"
```
* normal example no local
```
dcos spark run --submit-args="--conf spark.executor.memory=1g --conf spark.executor.cores=2 --conf spark.cores.max=8
--class org.apache.spark.examples.SparkPi https://s3-eu-west-1.amazonaws.com/fdp-stavros-test/spark-examples_2.11-2.1.1.jar"
```
The following fails
* uses local with no setting, default is host.
```
dcos spark run --submit-args="--conf spark.executor.memory=1g --conf spark.mesos.executor.docker.image=skonto/spark-local:test
--conf spark.executor.cores=2 --conf spark.cores.max=8
--class org.apache.spark.examples.SparkPi local:///spark-examples_2.11-2.2.1.jar"
```
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#21378 from skonto/local-upstream.
This PR adds a debugging log for YARN-specific credential providers which is loaded by service loader mechanism.
It took me a while to debug if it's actually loaded or not. I had to explicitly set the deprecated configuration and check if that's actually being loaded.
The change scope is manually tested. Logs are like:
```
Using the following builtin delegation token providers: hadoopfs, hive, hbase.
Using the following YARN-specific credential providers: yarn-test.
```
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21466 from HyukjinKwon/minor-log.
Change-Id: I18e2fb8eeb3289b148f24c47bb3130a560a881cf
## What changes were proposed in this pull request?
In client side before context initialization specifically, .py file doesn't work in client side before context initialization when the application is a Python file. See below:
```
$ cat /home/spark/tmp.py
def testtest():
return 1
```
This works:
```
$ cat app.py
import pyspark
pyspark.sql.SparkSession.builder.getOrCreate()
import tmp
print("************************%s" % tmp.testtest())
$ ./bin/spark-submit --master yarn --deploy-mode client --py-files /home/spark/tmp.py app.py
...
************************1
```
but this doesn't:
```
$ cat app.py
import pyspark
import tmp
pyspark.sql.SparkSession.builder.getOrCreate()
print("************************%s" % tmp.testtest())
$ ./bin/spark-submit --master yarn --deploy-mode client --py-files /home/spark/tmp.py app.py
Traceback (most recent call last):
File "/home/spark/spark/app.py", line 2, in <module>
import tmp
ImportError: No module named tmp
```
### How did it happen?
In client mode specifically, the paths are being added into PythonRunner as are:
628c7b5179/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala (L430)628c7b5179/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala (L49-L88)
The problem here is, .py file shouldn't be added as are since `PYTHONPATH` expects a directory or an archive like zip or egg.
### How does this PR fix?
We shouldn't simply just add its parent directory because other files in the parent directory could also be added into the `PYTHONPATH` in client mode before context initialization.
Therefore, we copy .py files into a temp directory for .py files and add it to `PYTHONPATH`.
## How was this patch tested?
Unit tests are added and manually tested in both standalond and yarn client modes with submit.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21426 from HyukjinKwon/SPARK-24384.
## What changes were proposed in this pull request?
This PR replaces `getTimeAsMs` with `getTimeAsSeconds` to fix the issue that reading "spark.network.timeout" using a wrong time unit when the user doesn't specify a time out.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <zsxwing@gmail.com>
Closes#21382 from zsxwing/fix-network-timeout-conf.
## What changes were proposed in this pull request?
The PR retrieves the proxyBase automatically from the header `X-Forwarded-Context` (if available). This is the header used by Knox to inform the proxied service about the base path.
This provides 0-configuration support for Knox gateway (instead of having to properly set `spark.ui.proxyBase`) and it allows to access directly SHS when it is proxied by Knox. In the previous scenario, indeed, after setting `spark.ui.proxyBase`, direct access to SHS was not working fine (due to bad link generated).
## How was this patch tested?
added UT + manual tests
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21268 from mgaido91/SPARK-24209.
## What changes were proposed in this pull request?
Hadoop 3 introduces HDFS federation. This means that multiple namespaces are allowed on the same HDFS cluster. In Spark, we need to ask the delegation token for all the namenodes (for each namespace), otherwise accessing any other namespace different from the default one (for which we already fetch the delegation token) fails.
The PR adds the automatic discovery of all the namenodes related to all the namespaces available according to the configs in hdfs-site.xml.
## How was this patch tested?
manual tests in dockerized env
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21216 from mgaido91/SPARK-24149.
Instead of always throwing a generic exception when the AM fails,
print a generic error and throw the exception with the YARN
diagnostics containing the reason for the failure.
There was an issue with YARN sometimes providing a generic diagnostic
message, even though the AM provides a failure reason when
unregistering. That was happening because the AM was registering
too late, and if errors happened before the registration, YARN would
just create a generic "ExitCodeException" which wasn't very helpful.
Since most errors in this path are a result of not being able to
connect to the driver, this change modifies the AM registration
a bit so that the AM is registered before the connection to the
driver is established. That way, errors are properly propagated
through YARN back to the driver.
As part of that, I also removed the code that retried connections
to the driver from the client AM. At that point, the driver should
already be up and waiting for connections, so it's unlikely that
retrying would help - and in case it does, that means a flaky
network, which would mean problems would probably show up again.
The effect of that is that connection-related errors are reported
back to the driver much faster now (through the YARN report).
One thing to note is that there seems to be a race on the YARN
side that causes a report to be sent to the client without the
corresponding diagnostics string from the AM; the diagnostics are
available later from the RM web page. For that reason, the generic
error messages are kept in the Spark scheduler code, to help
guide users to a way of debugging their failure.
Also of note is that if YARN's max attempts configuration is lower
than Spark's, Spark will not unregister the AM with a proper
diagnostics message. Unfortunately there seems to be no way to
unregister the AM and still allow further re-attempts to happen.
Testing:
- existing unit tests
- some of our integration tests
- hardcoded an invalid driver address in the code and verified
the error in the shell. e.g.
```
scala> 18/05/04 15:09:34 ERROR cluster.YarnClientSchedulerBackend: YARN application has exited unexpectedly with state FAILED! Check the YARN application logs for more details.
18/05/04 15:09:34 ERROR cluster.YarnClientSchedulerBackend: Diagnostics message: Uncaught exception: org.apache.spark.SparkException: Exception thrown in awaitResult:
<AM stack trace>
Caused by: java.io.IOException: Failed to connect to localhost/127.0.0.1:1234
<More stack trace>
```
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#21243 from vanzin/SPARK-24182.
## What changes were proposed in this pull request?
Drastically improves performance and won't cause Spark applications to fail because they write too much data to the Docker image's specific file system. The file system's directories that back emptydir volumes are generally larger and more performant.
## How was this patch tested?
Has been in use via the prototype version of Kubernetes support, but lost in the transition to here.
Author: mcheah <mcheah@palantir.com>
Closes#21238 from mccheah/mount-local-dirs.
## What changes were proposed in this pull request?
Shell escaped the name passed to spark-submit and change how conf attributes are shell escaped.
## How was this patch tested?
This test has been tested manually with Hive-on-spark with mesos or with the use case described in the issue with the sparkPi application with a custom name which contains illegal shell characters.
With this PR, hive-on-spark on mesos works like a charm with hive 3.0.0-SNAPSHOT.
I state that this contribution is my original work and that I license the work to the project under the project’s open source license
Author: Bounkong Khamphousone <bounkong.khamphousone@ebiznext.com>
Closes#21014 from tiboun/fix/SPARK-23941.
## What changes were proposed in this pull request?
Added support to specify the 'spark.executor.extraJavaOptions' value in terms of the `{{APP_ID}}` and/or `{{EXECUTOR_ID}}`, `{{APP_ID}}` will be replaced by Application Id and `{{EXECUTOR_ID}}` will be replaced by Executor Id while starting the executor.
## How was this patch tested?
I have verified this by checking the executor process command and gc logs. I verified the same in different deployment modes(Standalone, YARN, Mesos) client and cluster modes.
Author: Devaraj K <devaraj@apache.org>
Closes#21088 from devaraj-kavali/SPARK-24003.
## What changes were proposed in this pull request?
When a user specifies the wrong class -- or, in fact, a class instead of an object -- Spark throws an NPE which is not useful for debugging. This was reported in [SPARK-23830](https://issues.apache.org/jira/browse/SPARK-23830). This PR adds a check to ensure the main method was found and logs a useful error in the event that it's null.
## How was this patch tested?
* Unit tests + Manual testing
* The scope of the changes is very limited
Author: eric-maynard <emaynard@cloudera.com>
Author: Eric Maynard <emaynard@cloudera.com>
Closes#21168 from eric-maynard/feature/SPARK-23830.
## What changes were proposed in this pull request?
Log stacktrace for uncaught exception
## How was this patch tested?
UT and manually test
Author: zhoukang <zhoukang199191@gmail.com>
Closes#21151 from caneGuy/zhoukang/log-stacktrace.
## What changes were proposed in this pull request?
We propose not to hard-code the RPC port in the AM registration.
## How was this patch tested?
Tested application reports from a pseudo-distributed cluster
```
18/04/10 14:56:21 INFO Client:
client token: N/A
diagnostics: N/A
ApplicationMaster host: localhost
ApplicationMaster RPC port: 58338
queue: default
start time: 1523397373659
final status: UNDEFINED
tracking URL: http://localhost:8088/proxy/application_1523370127531_0016/
```
Author: Gera Shegalov <gera@apache.org>
Closes#21047 from gerashegalov/gera/am-to-rm-nmhost.
## What changes were proposed in this pull request?
Breaks down the construction of driver pods and executor pods in a way that uses a common abstraction for both spark-submit creating the driver and KubernetesClusterSchedulerBackend creating the executor. Encourages more code reuse and is more legible than the older approach.
The high-level design is discussed in more detail on the JIRA ticket. This pull request is the implementation of that design with some minor changes in the implementation details.
No user-facing behavior should break as a result of this change.
## How was this patch tested?
Migrated all unit tests from the old submission steps architecture to the new architecture. Integration tests should not have to change and pass given that this shouldn't change any outward behavior.
Author: mcheah <mcheah@palantir.com>
Closes#20910 from mccheah/spark-22839-incremental.
The current in-process launcher implementation just calls the SparkSubmit
object, which, in case of errors, will more often than not exit the JVM.
This is not desirable since this launcher is meant to be used inside other
applications, and that would kill the application.
The change turns SparkSubmit into a class, and abstracts aways some of
the functionality used to print error messages and abort the submission
process. The default implementation uses the logging system for messages,
and throws exceptions for errors. As part of that I also moved some code
that doesn't really belong in SparkSubmit to a better location.
The command line invocation of spark-submit now uses a special implementation
of the SparkSubmit class that overrides those behaviors to do what is expected
from the command line version (print to the terminal, exit the JVM, etc).
A lot of the changes are to replace calls to methods such as "printErrorAndExit"
with the new API.
As part of adding tests for this, I had to fix some small things in the
launcher option parser so that things like "--version" can work when
used in the launcher library.
There is still code that prints directly to the terminal, like all the
Ivy-related code in SparkSubmitUtils, and other areas where some re-factoring
would help, like the CommandLineUtils class, but I chose to leave those
alone to keep this change more focused.
Aside from existing and added unit tests, I ran command line tools with
a bunch of different arguments to make sure messages and errors behave
like before.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20925 from vanzin/SPARK-22941.
## What changes were proposed in this pull request?
`YarnAllocator` uses `numExecutorsRunning` to track the number of running executor. `numExecutorsRunning` is used to check if there're executors missing and need to allocate more.
In current code, `numExecutorsRunning` can be negative when driver asks to kill a same idle executor multiple times.
## How was this patch tested?
UT added
Author: jinxing <jinxing6042@126.com>
Closes#20781 from jinxing64/SPARK-23637.
## What changes were proposed in this pull request?
Pass through the `imagePullSecrets` option to the k8s pod in order to allow user to access private image registries.
See https://kubernetes.io/docs/tasks/configure-pod-container/pull-image-private-registry/
## How was this patch tested?
Unit tests + manual testing.
Manual testing procedure:
1. Have private image registry.
2. Spark-submit application with no `spark.kubernetes.imagePullSecret` set. Do `kubectl describe pod ...`. See the error message:
```
Error syncing pod, skipping: failed to "StartContainer" for "spark-kubernetes-driver" with ErrImagePull: "rpc error: code = 2 desc = Error: Status 400 trying to pull repository ...: \"{\\n \\\"errors\\\" : [ {\\n \\\"status\\\" : 400,\\n \\\"message\\\" : \\\"Unsupported docker v1 repository request for '...'\\\"\\n } ]\\n}\""
```
3. Create secret `kubectl create secret docker-registry ...`
4. Spark-submit with `spark.kubernetes.imagePullSecret` set to the new secret. See that deployment was successful.
Author: Andrew Korzhuev <andrew.korzhuev@klarna.com>
Author: Andrew Korzhuev <korzhuev@andrusha.me>
Closes#20811 from andrusha/spark-23668-image-pull-secrets.
## What changes were proposed in this pull request?
As mentioned in SPARK-23285, this PR introduces a new configuration property `spark.kubernetes.executor.cores` for specifying the physical CPU cores requested for each executor pod. This is to avoid changing the semantics of `spark.executor.cores` and `spark.task.cpus` and their role in task scheduling, task parallelism, dynamic resource allocation, etc. The new configuration property only determines the physical CPU cores available to an executor. An executor can still run multiple tasks simultaneously by using appropriate values for `spark.executor.cores` and `spark.task.cpus`.
## How was this patch tested?
Unit tests.
felixcheung srowen jiangxb1987 jerryshao mccheah foxish
Author: Yinan Li <ynli@google.com>
Author: Yinan Li <liyinan926@gmail.com>
Closes#20553 from liyinan926/master.
## What changes were proposed in this pull request?
Kubernetes driver and executor pods should request `memory + memoryOverhead` as their resources instead of just `memory`, see https://issues.apache.org/jira/browse/SPARK-23825
## How was this patch tested?
Existing unit tests were adapted.
Author: David Vogelbacher <dvogelbacher@palantir.com>
Closes#20943 from dvogelbacher/spark-23825.
Currently, the Spark AM relies on the initial set of tokens created by
the submission client to be able to talk to HDFS and other services that
require delegation tokens. This means that after those tokens expire, a
new AM will fail to start (e.g. when there is an application failure and
re-attempts are enabled).
This PR makes it so that the first thing the AM does when the user provides
a principal and keytab is to create new delegation tokens for use. This
makes sure that the AM can be started irrespective of how old the original
token set is. It also allows all of the token management to be done by the
AM - there is no need for the submission client to set configuration values
to tell the AM when to renew tokens.
Note that even though in this case the AM will not be using the delegation
tokens created by the submission client, those tokens still need to be provided
to YARN, since they are used to do log aggregation.
To be able to re-use the code in the AMCredentialRenewal for the above
purposes, I refactored that class a bit so that it can fetch tokens into
a pre-defined UGI, insted of always logging in.
Another issue with re-attempts is that, after the fix that allows the AM
to restart correctly, new executors would get confused about when to
update credentials, because the credential updater used the update time
initially set up by the submission code. This could make the executor
fail to update credentials in time, since that value would be very out
of date in the situation described in the bug.
To fix that, I changed the YARN code to use the new RPC-based mechanism
for distributing tokens to executors. This allowed the old credential
updater code to be removed, and a lot of code in the renewer to be
simplified.
I also made two currently hardcoded values (the renewal time ratio, and
the retry wait) configurable; while this probably never needs to be set
by anyone in a production environment, it helps with testing; that's also
why they're not documented.
Tested on real cluster with a specially crafted application to test this
functionality: checked proper access to HDFS, Hive and HBase in cluster
mode with token renewal on and AM restarts. Tested things still work in
client mode too.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20657 from vanzin/SPARK-23361.
## What changes were proposed in this pull request?
Yarn throws the following exception in cluster mode when the application is really small:
```
18/03/07 23:34:22 WARN netty.NettyRpcEnv: Ignored failure: java.util.concurrent.RejectedExecutionException: Task java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask7c974942 rejected from java.util.concurrent.ScheduledThreadPoolExecutor1eea9d2d[Terminated, pool size = 0, active threads = 0, queued tasks = 0, completed tasks = 0]
18/03/07 23:34:22 ERROR yarn.ApplicationMaster: Uncaught exception:
org.apache.spark.SparkException: Exception thrown in awaitResult:
at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:205)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
at org.apache.spark.rpc.RpcEndpointRef.askSync(RpcEndpointRef.scala:92)
at org.apache.spark.rpc.RpcEndpointRef.askSync(RpcEndpointRef.scala:76)
at org.apache.spark.deploy.yarn.YarnAllocator.<init>(YarnAllocator.scala:102)
at org.apache.spark.deploy.yarn.YarnRMClient.register(YarnRMClient.scala:77)
at org.apache.spark.deploy.yarn.ApplicationMaster.registerAM(ApplicationMaster.scala:450)
at org.apache.spark.deploy.yarn.ApplicationMaster.runDriver(ApplicationMaster.scala:493)
at org.apache.spark.deploy.yarn.ApplicationMaster.org$apache$spark$deploy$yarn$ApplicationMaster$$runImpl(ApplicationMaster.scala:345)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$run$2.apply$mcV$sp(ApplicationMaster.scala:260)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$run$2.apply(ApplicationMaster.scala:260)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$run$2.apply(ApplicationMaster.scala:260)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$5.run(ApplicationMaster.scala:810)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1920)
at org.apache.spark.deploy.yarn.ApplicationMaster.doAsUser(ApplicationMaster.scala:809)
at org.apache.spark.deploy.yarn.ApplicationMaster.run(ApplicationMaster.scala:259)
at org.apache.spark.deploy.yarn.ApplicationMaster$.main(ApplicationMaster.scala:834)
at org.apache.spark.deploy.yarn.ApplicationMaster.main(ApplicationMaster.scala)
Caused by: org.apache.spark.rpc.RpcEnvStoppedException: RpcEnv already stopped.
at org.apache.spark.rpc.netty.Dispatcher.postMessage(Dispatcher.scala:158)
at org.apache.spark.rpc.netty.Dispatcher.postLocalMessage(Dispatcher.scala:135)
at org.apache.spark.rpc.netty.NettyRpcEnv.ask(NettyRpcEnv.scala:229)
at org.apache.spark.rpc.netty.NettyRpcEndpointRef.ask(NettyRpcEnv.scala:523)
at org.apache.spark.rpc.RpcEndpointRef.askSync(RpcEndpointRef.scala:91)
... 17 more
18/03/07 23:34:22 INFO yarn.ApplicationMaster: Final app status: FAILED, exitCode: 13, (reason: Uncaught exception: org.apache.spark.SparkException: Exception thrown in awaitResult: )
```
Example application:
```
object ExampleApp {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("ExampleApp")
val sc = new SparkContext(conf)
try {
// Do nothing
} finally {
sc.stop()
}
}
```
This PR pauses user class thread after `SparkContext` created and keeps it so until application master initialises properly.
## How was this patch tested?
Automated: Existing unit tests
Manual: Application submitted into small cluster
Author: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Closes#20807 from gaborgsomogyi/SPARK-23660.
## What changes were proposed in this pull request?
Removal of the init-container for downloading remote dependencies. Built off of the work done by vanzin in an attempt to refactor driver/executor configuration elaborated in [this](https://issues.apache.org/jira/browse/SPARK-22839) ticket.
## How was this patch tested?
This patch was tested with unit and integration tests.
Author: Ilan Filonenko <if56@cornell.edu>
Closes#20669 from ifilonenko/remove-init-container.
## What changes were proposed in this pull request?
As described in SPARK-23680, entrypoint.sh returns an error code because of a command pipeline execution where it is expected in case of Openshift environments, where arbitrary UIDs are used to run containers
## How was this patch tested?
This patch was manually tested by using docker-image-toll.sh script to generate a Spark driver image and running an example against an OpenShift cluster.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Ricardo Martinelli de Oliveira <rmartine@rmartine.gru.redhat.com>
Closes#20822 from rimolive/rmartine-spark-23680.
## What changes were proposed in this pull request?
In the current Spark on YARN code, AM always will copy and overwrite its env variables to executors, so we cannot set different values for executors.
To reproduce issue, user could start spark-shell like:
```
./bin/spark-shell --master yarn-client --conf spark.executorEnv.SPARK_ABC=executor_val --conf spark.yarn.appMasterEnv.SPARK_ABC=am_val
```
Then check executor env variables by
```
sc.parallelize(1 to 1).flatMap \{ i => sys.env.toSeq }.collect.foreach(println)
```
We will always get `am_val` instead of `executor_val`. So we should not let AM to overwrite specifically set executor env variables.
## How was this patch tested?
Added UT and tested in local cluster.
Author: jerryshao <sshao@hortonworks.com>
Closes#20799 from jerryshao/SPARK-23635.
This change restores functionality that was inadvertently removed as part
of the fix for SPARK-22372.
Also modified an existing unit test to make sure the feature works as intended.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20776 from vanzin/SPARK-23630.
For some JVM options, like `-XX:+UnlockExperimentalVMOptions` ordering is necessary.
## What changes were proposed in this pull request?
Keep original `extraJavaOptions` ordering, when passing them through environment variables inside the Docker container.
## How was this patch tested?
Ran base branch a couple of times and checked startup command in logs. Ordering differed every time. Added sorting, ordering was consistent to what user had in `extraJavaOptions`.
Author: Andrew Korzhuev <korzhuev@andrusha.me>
Closes#20628 from andrusha/patch-2.
This commit modifies the Mesos submission client to allow the principal
and secret to be provided indirectly via files. The path to these files
can be specified either via Spark configuration or via environment
variable.
Assuming these files are appropriately protected by FS/OS permissions
this means we don't ever leak the actual values in process info like ps
Environment variable specification is useful because it allows you to
interpolate the location of this file when using per-user Mesos
credentials.
For some background as to why we have taken this approach I will briefly describe our set up. On our systems we provide each authorised user account with their own Mesos credentials to provide certain security and audit guarantees to our customers. These credentials are managed by a central Secret management service. In our `spark-env.sh` we determine the appropriate secret and principal files to use depending on the user who is invoking Spark hence the need to inject these via environment variables as well as by configuration properties. So we set these environment variables appropriately and our Spark read in the contents of those files to authenticate itself with Mesos.
This is functionality we have been using it in production across multiple customer sites for some time. This has been in the field for around 18 months with no reported issues. These changes have been sufficient to meet our customer security and audit requirements.
We have been building and deploying custom builds of Apache Spark with various minor tweaks like this which we are now looking to contribute back into the community in order that we can rely upon stock Apache Spark builds and stop maintaining our own internal fork.
Author: Rob Vesse <rvesse@dotnetrdf.org>
Closes#20167 from rvesse/SPARK-16501.
## What changes were proposed in this pull request?
Include stacktrace in the diagnostics message upon abnormal unregister from RM
## How was this patch tested?
Tested with a failing job, and confirmed a stacktrace in the client output and YARN webUI.
Author: Gera Shegalov <gera@apache.org>
Closes#20470 from gerashegalov/gera/stacktrace-diagnostics.
Third time is the charm?
There was still a race that was left in previous attempts. If the handle
closes the connection, the close() implementation would clean up state
that would prevent the thread from waiting on the connection thread to
finish. That could cause the race causing the test flakiness reported
in the bug.
The fix is to move the "wait for connection thread" code to a separate
close method that is used by the handle; that also simplifies the code
a bit and makes it also easier to follow.
I included an unrelated, but correct, change to a YARN test so that
it triggers when the PR is built.
Tested by inserting a sleep in the connection thread to mimic the race;
test failed reliably with the sleep, passes now. (Sleep not included in
the patch.) Also ran YARN tests to make sure.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20388 from vanzin/SPARK-23020.
The race in the code is because the handle might update
its state to the wrong state if the connection handling
thread is still processing incoming data; so the handle
needs to wait for the connection to finish up before
checking the final state.
The race in the test is because when waiting for a handle
to reach a final state, the waitFor() method needs to wait
until all handle state is updated (which also includes
waiting for the connection thread above to finish).
Otherwise, waitFor() may return too early, which would cause
a bunch of different races (like the listener not being yet
notified of the state change, or being in the middle of
being notified, or the handle not being properly disposed
and causing postChecks() to assert).
On top of that I found, by code inspection, a couple of
potential races that could make a handle end up in the
wrong state when being killed.
The original version of this fix introduced the flipped
version of the first race described above; the connection
closing might override the handle state before the
handle might have a chance to do cleanup. The fix there
is to only dispose of the handle from the connection
when there is an error, and let the handle dispose
itself in the normal case.
The fix also caused a bug in YarnClusterSuite to be surfaced;
the code was checking for a file in the classpath that was
not expected to be there in client mode. Because of the above
issues, the error was not propagating correctly and the (buggy)
test was incorrectly passing.
Tested by running the existing unit tests a lot (and not
seeing the errors I was seeing before).
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20297 from vanzin/SPARK-23020.
## What changes were proposed in this pull request?
In the Kubernetes mode, fails fast in the submission process if any submission client local dependencies are used as the use case is not supported yet.
## How was this patch tested?
Unit tests, integration tests, and manual tests.
vanzin foxish
Author: Yinan Li <liyinan926@gmail.com>
Closes#20320 from liyinan926/master.
Pass through spark java options to the executor in context of docker image.
Closes#20296
andrusha: Deployed two version of containers to local k8s, checked that java options were present in the updated image on the running executor.
Manual test
Author: Andrew Korzhuev <korzhuev@andrusha.me>
Closes#20322 from foxish/patch-1.
## What changes were proposed in this pull request?
This patch bumps the master branch version to `2.4.0-SNAPSHOT`.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20222 from gatorsmile/bump24.
This change allows a user to submit a Spark application on kubernetes
having to provide a single image, instead of one image for each type
of container. The image's entry point now takes an extra argument that
identifies the process that is being started.
The configuration still allows the user to provide different images
for each container type if they so desire.
On top of that, the entry point was simplified a bit to share more
code; mainly, the same env variable is used to propagate the user-defined
classpath to the different containers.
Aside from being modified to match the new behavior, the
'build-push-docker-images.sh' script was renamed to 'docker-image-tool.sh'
to more closely match its purpose; the old name was a little awkward
and now also not entirely correct, since there is a single image. It
was also moved to 'bin' since it's not necessarily an admin tool.
Docs have been updated to match the new behavior.
Tested locally with minikube.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20192 from vanzin/SPARK-22994.
## What changes were proposed in this pull request?
Two filesystems comparing does not consider the authority of URI. This is specific for
WASB file storage system, where userInfo is honored to differentiate filesystems.
For example: wasbs://user1xyz.net, wasbs://user2xyz.net would consider as two filesystem.
Therefore, we have to add the authority to compare two filesystem, and two filesystem with different authority can not be the same FS.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Mingjie Tang <mtang@hortonworks.com>
Closes#19885 from merlintang/EAR-7377.
## What changes were proposed in this pull request?
The environment variable `SPARK_MOUNTED_CLASSPATH` is referenced in the executor's Dockerfile, where its value is added to the classpath of the executor. However, the scheduler backend code missed setting it when creating the executor pods. This PR fixes it.
## How was this patch tested?
Unit tested.
vanzin Can you help take a look? Thanks!
foxish
Author: Yinan Li <liyinan926@gmail.com>
Closes#20193 from liyinan926/master.
## What changes were proposed in this pull request?
Remove the use of FQDN to access the driver because it assumes that it's set up in a DNS zone - `cluster.local` which is common but not ubiquitous
Note that we already access the in-cluster API server through `kubernetes.default.svc`, so, by extension, this should work as well.
The alternative is to introduce DNS zones for both of those addresses.
## How was this patch tested?
Unit tests
cc vanzin liyinan926 mridulm mccheah
Author: foxish <ramanathana@google.com>
Closes#20187 from foxish/cluster.local.
## What changes were proposed in this pull request?
This PR reverts the `ARG base_image` before `FROM` in the images of driver, executor, and init-container, introduced in https://github.com/apache/spark/pull/20154. The reason is Docker versions before 17.06 do not support this use (`ARG` before `FROM`).
## How was this patch tested?
Tested manually.
vanzin foxish kimoonkim
Author: Yinan Li <liyinan926@gmail.com>
Closes#20170 from liyinan926/master.
## What changes were proposed in this pull request?
Register spark.history.ui.port as a known spark conf to be used in substitution expressions even if it's not set explicitly.
## How was this patch tested?
Added unit test to demonstrate the issue
Author: Gera Shegalov <gera@apache.org>
Author: Gera Shegalov <gshegalov@salesforce.com>
Closes#20098 from gerashegalov/gera/register-SHS-port-conf.
- Make it possible to build images from a git clone.
- Make it easy to use minikube to test things.
Also fixed what seemed like a bug: the base image wasn't getting the tag
provided in the command line. Adding the tag allows users to use multiple
Spark builds in the same kubernetes cluster.
Tested by deploying images on minikube and running spark-submit from a dev
environment; also by building the images with different tags and verifying
"docker images" in minikube.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20154 from vanzin/SPARK-22960.
## What changes were proposed in this pull request?
User-specified secrets are mounted into both the main container and init-container (when it is used) in a Spark driver/executor pod, using the `MountSecretsBootstrap`. Because `MountSecretsBootstrap` always adds new secret volumes for the secrets to the pod, the same secret volumes get added twice, one when mounting the secrets to the main container, and the other when mounting the secrets to the init-container. This PR fixes the issue by separating `MountSecretsBootstrap.mountSecrets` out into two methods: `addSecretVolumes` for adding secret volumes to a pod and `mountSecrets` for mounting secret volumes to a container, respectively. `addSecretVolumes` is only called once for each pod, whereas `mountSecrets` is called individually for the main container and the init-container (if it is used).
Ref: https://github.com/apache-spark-on-k8s/spark/issues/594.
## How was this patch tested?
Unit tested and manually tested.
vanzin This replaces https://github.com/apache/spark/pull/20148.
hex108 foxish kimoonkim
Author: Yinan Li <liyinan926@gmail.com>
Closes#20159 from liyinan926/master.
This change adds a new launcher that allows applications to be run
in a separate thread in the same process as the calling code. To
achieve that, some code from the child process implementation was
moved to abstract classes that implement the common functionality,
and the new launcher inherits from those.
The new launcher was added as a new class, instead of implemented
as a new option to the existing SparkLauncher, to avoid ambigous
APIs. For example, SparkLauncher has ways to set the child app's
environment, modify SPARK_HOME, or control the logging of the
child process, none of which apply to in-process apps.
The in-process launcher has limitations: it needs Spark in the
context class loader of the calling thread, and it's bound by
Spark's current limitation of a single client-mode application
per JVM. It also relies on the recently added SparkApplication
trait to make sure different apps don't mess up each other's
configuration, so config isolation is currently limited to cluster mode.
I also chose to keep the same socket-based communication for in-process
apps, even though it might be possible to avoid it for in-process
mode. That helps both implementations share more code.
Tested with new and existing unit tests, and with a simple app that
uses the launcher; also made sure the app ran fine with older launcher
jar to check binary compatibility.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19591 from vanzin/SPARK-11035.
## What changes were proposed in this pull request?
This PR expands the Kubernetes mode to be able to use remote dependencies on http/https endpoints, GCS, S3, etc. It adds steps for configuring and appending the Kubernetes init-container into the driver and executor pods for downloading remote dependencies.
[Init-containers](https://kubernetes.io/docs/concepts/workloads/pods/init-containers/), as the name suggests, are containers that are run to completion before the main containers start, and are often used to perform initialization tasks prior to starting the main containers. We use init-containers to localize remote application dependencies before the driver/executors start running. The code that the init-container runs is also included. This PR also adds a step to the driver and executors for mounting user-specified secrets that may store credentials for accessing data storage, e.g., S3 and Google Cloud Storage (GCS), into the driver and executors.
## How was this patch tested?
* The patch contains unit tests which are passing.
* Manual testing: `./build/mvn -Pkubernetes clean package` succeeded.
* Manual testing of the following cases:
* [x] Running SparkPi using container-local spark-example jar.
* [x] Running SparkPi using container-local spark-example jar with user-specific secret mounted.
* [x] Running SparkPi using spark-example jar hosted remotely on an https endpoint.
cc rxin felixcheung mateiz (shepherd)
k8s-big-data SIG members & contributors: mccheah foxish ash211 ssuchter varunkatta kimoonkim erikerlandson tnachen ifilonenko liyinan926
reviewers: vanzin felixcheung jiangxb1987 mridulm
Author: Yinan Li <liyinan926@gmail.com>
Closes#19954 from liyinan926/init-container.
## What changes were proposed in this pull request?
The path was recently changed in https://github.com/apache/spark/pull/19946, but the dockerfile was not updated.
This is a trivial 1 line fix.
## How was this patch tested?
`./sbin/build-push-docker-images.sh -r spark-repo -t latest build`
cc/ vanzin mridulm rxin jiangxb1987 liyinan926
Author: Anirudh Ramanathan <ramanathana@google.com>
Author: foxish <ramanathana@google.com>
Closes#20051 from foxish/patch-1.
## What changes were proposed in this pull request?
Fixing configuration that was taking an int which should take time. Discussion in https://github.com/apache/spark/pull/19946#discussion_r156682354
Made the granularity milliseconds as opposed to seconds since there's a use-case for sub-second reactions to scale-up rapidly especially with dynamic allocation.
## How was this patch tested?
TODO: manual run of integration tests against this PR.
PTAL
cc/ mccheah liyinan926 kimoonkim vanzin mridulm jiangxb1987 ueshin
Author: foxish <ramanathana@google.com>
Closes#20032 from foxish/fix-time-conf.
# What changes were proposed in this pull request?
1. entrypoint.sh for Kubernetes spark-base image is marked as executable (644 -> 755)
2. make-distribution script will now create kubernetes/dockerfiles directory when Kubernetes support is compiled.
## How was this patch tested?
Manual testing
cc/ ueshin jiangxb1987 mridulm vanzin rxin liyinan926
Author: foxish <ramanathana@google.com>
Closes#20007 from foxish/fix-dockerfiles.
## What changes were proposed in this pull request?
Changes discussed in https://github.com/apache/spark/pull/19946#discussion_r157063535
docker -> container, since with CRI, we are not limited to running only docker images.
## How was this patch tested?
Manual testing
Author: foxish <ramanathana@google.com>
Closes#19995 from foxish/make-docker-container.
## What changes were proposed in this pull request?
This PR added the missing service metadata for `KubernetesClusterManager`. Without the metadata, the service loader couldn't load `KubernetesClusterManager`, and caused the driver to fail to create a `ExternalClusterManager`, as being reported in SPARK-22778. The PR also changed the `k8s:` prefix used to `k8s://`, which is what existing Spark on k8s users are familiar and used to.
## How was this patch tested?
Manual testing verified that the fix resolved the issue in SPARK-22778.
/cc vanzin felixcheung jiangxb1987
Author: Yinan Li <liyinan926@gmail.com>
Closes#19972 from liyinan926/fix-22778.
## What changes were proposed in this pull request?
PR closed with all the comments -> https://github.com/apache/spark/pull/19793
It solves the problem when submitting a wrong CreateSubmissionRequest to Spark Dispatcher was causing a bad state of Dispatcher and making it inactive as a Mesos framework.
https://issues.apache.org/jira/browse/SPARK-22574
## How was this patch tested?
All spark test passed successfully.
It was tested sending a wrong request (without appArgs) before and after the change. The point is easy, check if the value is null before being accessed.
This was before the change, leaving the dispatcher inactive:
```
Exception in thread "Thread-22" java.lang.NullPointerException
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.getDriverCommandValue(MesosClusterScheduler.scala:444)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.buildDriverCommand(MesosClusterScheduler.scala:451)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.org$apache$spark$scheduler$cluster$mesos$MesosClusterScheduler$$createTaskInfo(MesosClusterScheduler.scala:538)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler$$anonfun$scheduleTasks$1.apply(MesosClusterScheduler.scala:570)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler$$anonfun$scheduleTasks$1.apply(MesosClusterScheduler.scala:555)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.scheduleTasks(MesosClusterScheduler.scala:555)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.resourceOffers(MesosClusterScheduler.scala:621)
```
And after:
```
"message" : "Malformed request: org.apache.spark.deploy.rest.SubmitRestProtocolException: Validation of message CreateSubmissionRequest failed!\n\torg.apache.spark.deploy.rest.SubmitRestProtocolMessage.validate(SubmitRestProtocolMessage.scala:70)\n\torg.apache.spark.deploy.rest.SubmitRequestServlet.doPost(RestSubmissionServer.scala:272)\n\tjavax.servlet.http.HttpServlet.service(HttpServlet.java:707)\n\tjavax.servlet.http.HttpServlet.service(HttpServlet.java:790)\n\torg.spark_project.jetty.servlet.ServletHolder.handle(ServletHolder.java:845)\n\torg.spark_project.jetty.servlet.ServletHandler.doHandle(ServletHandler.java:583)\n\torg.spark_project.jetty.server.handler.ContextHandler.doHandle(ContextHandler.java:1180)\n\torg.spark_project.jetty.servlet.ServletHandler.doScope(ServletHandler.java:511)\n\torg.spark_project.jetty.server.handler.ContextHandler.doScope(ContextHandler.java:1112)\n\torg.spark_project.jetty.server.handler.ScopedHandler.handle(ScopedHandler.java:141)\n\torg.spark_project.jetty.server.handler.HandlerWrapper.handle(HandlerWrapper.java:134)\n\torg.spark_project.jetty.server.Server.handle(Server.java:524)\n\torg.spark_project.jetty.server.HttpChannel.handle(HttpChannel.java:319)\n\torg.spark_project.jetty.server.HttpConnection.onFillable(HttpConnection.java:253)\n\torg.spark_project.jetty.io.AbstractConnection$ReadCallback.succeeded(AbstractConnection.java:273)\n\torg.spark_project.jetty.io.FillInterest.fillable(FillInterest.java:95)\n\torg.spark_project.jetty.io.SelectChannelEndPoint$2.run(SelectChannelEndPoint.java:93)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.executeProduceConsume(ExecuteProduceConsume.java:303)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.produceConsume(ExecuteProduceConsume.java:148)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.run(ExecuteProduceConsume.java:136)\n\torg.spark_project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:671)\n\torg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:589)\n\tjava.lang.Thread.run(Thread.java:745)"
```
Author: German Schiavon <germanschiavon@gmail.com>
Closes#19966 from Gschiavon/fix-submission-request.
## What changes were proposed in this pull request?
It solves the problem when submitting a wrong CreateSubmissionRequest to Spark Dispatcher was causing a bad state of Dispatcher and making it inactive as a Mesos framework.
https://issues.apache.org/jira/browse/SPARK-22574
## How was this patch tested?
All spark test passed successfully.
It was tested sending a wrong request (without appArgs) before and after the change. The point is easy, check if the value is null before being accessed.
This was before the change, leaving the dispatcher inactive:
```
Exception in thread "Thread-22" java.lang.NullPointerException
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.getDriverCommandValue(MesosClusterScheduler.scala:444)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.buildDriverCommand(MesosClusterScheduler.scala:451)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.org$apache$spark$scheduler$cluster$mesos$MesosClusterScheduler$$createTaskInfo(MesosClusterScheduler.scala:538)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler$$anonfun$scheduleTasks$1.apply(MesosClusterScheduler.scala:570)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler$$anonfun$scheduleTasks$1.apply(MesosClusterScheduler.scala:555)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.scheduleTasks(MesosClusterScheduler.scala:555)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.resourceOffers(MesosClusterScheduler.scala:621)
```
And after:
```
"message" : "Malformed request: org.apache.spark.deploy.rest.SubmitRestProtocolException: Validation of message CreateSubmissionRequest failed!\n\torg.apache.spark.deploy.rest.SubmitRestProtocolMessage.validate(SubmitRestProtocolMessage.scala:70)\n\torg.apache.spark.deploy.rest.SubmitRequestServlet.doPost(RestSubmissionServer.scala:272)\n\tjavax.servlet.http.HttpServlet.service(HttpServlet.java:707)\n\tjavax.servlet.http.HttpServlet.service(HttpServlet.java:790)\n\torg.spark_project.jetty.servlet.ServletHolder.handle(ServletHolder.java:845)\n\torg.spark_project.jetty.servlet.ServletHandler.doHandle(ServletHandler.java:583)\n\torg.spark_project.jetty.server.handler.ContextHandler.doHandle(ContextHandler.java:1180)\n\torg.spark_project.jetty.servlet.ServletHandler.doScope(ServletHandler.java:511)\n\torg.spark_project.jetty.server.handler.ContextHandler.doScope(ContextHandler.java:1112)\n\torg.spark_project.jetty.server.handler.ScopedHandler.handle(ScopedHandler.java:141)\n\torg.spark_project.jetty.server.handler.HandlerWrapper.handle(HandlerWrapper.java:134)\n\torg.spark_project.jetty.server.Server.handle(Server.java:524)\n\torg.spark_project.jetty.server.HttpChannel.handle(HttpChannel.java:319)\n\torg.spark_project.jetty.server.HttpConnection.onFillable(HttpConnection.java:253)\n\torg.spark_project.jetty.io.AbstractConnection$ReadCallback.succeeded(AbstractConnection.java:273)\n\torg.spark_project.jetty.io.FillInterest.fillable(FillInterest.java:95)\n\torg.spark_project.jetty.io.SelectChannelEndPoint$2.run(SelectChannelEndPoint.java:93)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.executeProduceConsume(ExecuteProduceConsume.java:303)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.produceConsume(ExecuteProduceConsume.java:148)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.run(ExecuteProduceConsume.java:136)\n\torg.spark_project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:671)\n\torg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:589)\n\tjava.lang.Thread.run(Thread.java:745)"
```
Author: German Schiavon <germanschiavon@gmail.com>
Closes#19793 from Gschiavon/fix-submission-request.
This PR contains implementation of the basic submission client for the cluster mode of Spark on Kubernetes. It's step 2 from the step-wise plan documented [here](https://github.com/apache-spark-on-k8s/spark/issues/441#issuecomment-330802935).
This addition is covered by the [SPIP](http://apache-spark-developers-list.1001551.n3.nabble.com/SPIP-Spark-on-Kubernetes-td22147.html) vote which passed on Aug 31.
This PR and #19468 together form a MVP of Spark on Kubernetes that allows users to run Spark applications that use resources locally within the driver and executor containers on Kubernetes 1.6 and up. Some changes on pom and build/test setup are copied over from #19468 to make this PR self contained and testable.
The submission client is mainly responsible for creating the Kubernetes pod that runs the Spark driver. It follows a step-based approach to construct the driver pod, as the code under the `submit.steps` package shows. The steps are orchestrated by `DriverConfigurationStepsOrchestrator`. `Client` creates the driver pod and waits for the application to complete if it's configured to do so, which is the case by default.
This PR also contains Dockerfiles of the driver and executor images. They are included because some of the environment variables set in the code would not make sense without referring to the Dockerfiles.
* The patch contains unit tests which are passing.
* Manual testing: ./build/mvn -Pkubernetes clean package succeeded.
* It is a subset of the entire changelist hosted at http://github.com/apache-spark-on-k8s/spark which is in active use in several organizations.
* There is integration testing enabled in the fork currently hosted by PepperData which is being moved over to RiseLAB CI.
* Detailed documentation on trying out the patch in its entirety is in: https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html
cc rxin felixcheung mateiz (shepherd)
k8s-big-data SIG members & contributors: mccheah foxish ash211 ssuchter varunkatta kimoonkim erikerlandson tnachen ifilonenko liyinan926
Author: Yinan Li <liyinan926@gmail.com>
Closes#19717 from liyinan926/spark-kubernetes-4.
## What changes were proposed in this pull request?
I see the two instances where the exception is occurring.
**Instance 1:**
```
17/11/10 15:49:32 ERROR util.Utils: Uncaught exception in thread driver-revive-thread
org.apache.spark.SparkException: Could not find CoarseGrainedScheduler.
at org.apache.spark.rpc.netty.Dispatcher.postMessage(Dispatcher.scala:160)
at org.apache.spark.rpc.netty.Dispatcher.postOneWayMessage(Dispatcher.scala:140)
at org.apache.spark.rpc.netty.NettyRpcEnv.send(NettyRpcEnv.scala:187)
at org.apache.spark.rpc.netty.NettyRpcEndpointRef.send(NettyRpcEnv.scala:521)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(CoarseGrainedSchedulerBackend.scala:125)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(CoarseGrainedSchedulerBackend.scala:125)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint$$anon$1$$anonfun$run$1.apply$mcV$sp(CoarseGrainedSchedulerBackend.scala:125)
at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1344)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint$$anon$1.run(CoarseGrainedSchedulerBackend.scala:124)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:308)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:294)
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)
```
In CoarseGrainedSchedulerBackend.scala, driver-revive-thread starts with DriverEndpoint.onStart() and keeps sending the ReviveOffers messages periodically till it gets shutdown as part DriverEndpoint.onStop(). There is no proper coordination between the driver-revive-thread(shutdown) and the RpcEndpoint unregister, RpcEndpoint unregister happens first and then driver-revive-thread shuts down as part of DriverEndpoint.onStop(), In-between driver-revive-thread may try to send the ReviveOffers message which is leading to the above exception.
To fix this issue, this PR moves the shutting down of driver-revive-thread to CoarseGrainedSchedulerBackend.stop() which executes before the DriverEndpoint unregister.
**Instance 2:**
```
17/11/10 16:31:38 ERROR cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Error requesting driver to remove executor 1 for reason Executor for container container_1508535467865_0226_01_000002 exited because of a YARN event (e.g., pre-emption) and not because of an error in the running job.
org.apache.spark.SparkException: Could not find CoarseGrainedScheduler.
at org.apache.spark.rpc.netty.Dispatcher.postMessage(Dispatcher.scala:160)
at org.apache.spark.rpc.netty.Dispatcher.postLocalMessage(Dispatcher.scala:135)
at org.apache.spark.rpc.netty.NettyRpcEnv.ask(NettyRpcEnv.scala:229)
at org.apache.spark.rpc.netty.NettyRpcEndpointRef.ask(NettyRpcEnv.scala:516)
at org.apache.spark.rpc.RpcEndpointRef.ask(RpcEndpointRef.scala:63)
at org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnSchedulerEndpoint$$anonfun$receive$1.applyOrElse(YarnSchedulerBackend.scala:269)
at org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:117)
at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:205)
at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:101)
at org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:221)
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)
```
Here YarnDriverEndpoint tries to send remove executor messages after the Yarn scheduler backend service stop, which is leading to the above exception. To avoid the above exception,
1) We may add a condition(which checks whether service has stopped or not) before sending executor remove message
2) Add a warn log message in onFailure case when the service is already stopped
In this PR, chosen the 2) option which adds a log message in the case of onFailure without the exception stack trace since the option 1) would need to to go through for every remove executor message.
## How was this patch tested?
I verified it manually, I don't see these exceptions with the PR changes.
Author: Devaraj K <devaraj@apache.org>
Closes#19741 from devaraj-kavali/SPARK-14228.
The main goal of this change is to allow multiple cluster-mode
submissions from the same JVM, without having them end up with
mixed configuration. That is done by extending the SparkApplication
trait, and doing so was reasonably trivial for standalone and
mesos modes.
For YARN mode, there was a complication. YARN used a "SPARK_YARN_MODE"
system property to control behavior indirectly in a whole bunch of
places, mainly in the SparkHadoopUtil / YarnSparkHadoopUtil classes.
Most of the changes here are removing that.
Since we removed support for Hadoop 1.x, some methods that lived in
YarnSparkHadoopUtil can now live in SparkHadoopUtil. The remaining
methods don't need to be part of the class, and can be called directly
from the YarnSparkHadoopUtil object, so now there's a single
implementation of SparkHadoopUtil.
There were two places in the code that relied on SPARK_YARN_MODE to
make decisions about YARN-specific functionality, and now explicitly check
the master from the configuration for that instead:
* fetching the external shuffle service port, which can come from the YARN
configuration.
* propagation of the authentication secret using Hadoop credentials. This also
was cleaned up a little to not need so many methods in `SparkHadoopUtil`.
With those out of the way, actually changing the YARN client
to extend SparkApplication was easy.
Tested with existing unit tests, and also by running YARN apps
with auth and kerberos both on and off in a real cluster.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19631 from vanzin/SPARK-22372.
## What changes were proposed in this pull request?
- Solves the issue described in the ticket by preserving reservation and allocation info in all cases (port handling included).
- upgrades to 1.4
- Adds extra debug level logging to make debugging easier in the future, for example we add reservation info when applicable.
```
17/09/29 14:53:07 DEBUG MesosCoarseGrainedSchedulerBackend: Accepting offer: f20de49b-dee3-45dd-a3c1-73418b7de891-O32 with attributes: Map() allocation info: role: "spark-prive"
reservation info: name: "ports"
type: RANGES
ranges {
range {
begin: 31000
end: 32000
}
}
role: "spark-prive"
reservation {
principal: "test"
}
allocation_info {
role: "spark-prive"
}
```
- Some style cleanup.
## How was this patch tested?
Manually by running the example in the ticket with and without a principal. Specifically I tested it on a dc/os 1.10 cluster with 7 nodes and played with reservations. From the master node in order to reserve resources I executed:
```for i in 0 1 2 3 4 5 6
do
curl -i \
-d slaveId=90ec65ea-1f7b-479f-a824-35d2527d6d26-S$i \
-d resources='[
{
"name": "cpus",
"type": "SCALAR",
"scalar": { "value": 2 },
"role": "spark-role",
"reservation": {
"principal": ""
}
},
{
"name": "mem",
"type": "SCALAR",
"scalar": { "value": 8026 },
"role": "spark-role",
"reservation": {
"principal": ""
}
}
]' \
-X POST http://master.mesos:5050/master/reserve
done
```
Nodes had 4 cpus (m3.xlarge instances) and I reserved either 2 or 4 cpus (all for a role).
I verified it launches tasks on nodes with reserved resources under `spark-role` role only if
a) there are remaining resources for (*) default role and the spark driver has no role assigned to it.
b) the spark driver has a role assigned to it and it is the same role used in reservations.
I also tested this locally on my machine.
Author: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Closes#19390 from skonto/fix_dynamic_reservation.
## What changes were proposed in this pull request?
This is a stripped down version of the `KubernetesClusterSchedulerBackend` for Spark with the following components:
- Static Allocation of Executors
- Executor Pod Factory
- Executor Recovery Semantics
It's step 1 from the step-wise plan documented [here](https://github.com/apache-spark-on-k8s/spark/issues/441#issuecomment-330802935).
This addition is covered by the [SPIP vote](http://apache-spark-developers-list.1001551.n3.nabble.com/SPIP-Spark-on-Kubernetes-td22147.html) which passed on Aug 31 .
## How was this patch tested?
- The patch contains unit tests which are passing.
- Manual testing: `./build/mvn -Pkubernetes clean package` succeeded.
- It is a **subset** of the entire changelist hosted in http://github.com/apache-spark-on-k8s/spark which is in active use in several organizations.
- There is integration testing enabled in the fork currently [hosted by PepperData](spark-k8s-jenkins.pepperdata.org:8080) which is being moved over to RiseLAB CI.
- Detailed documentation on trying out the patch in its entirety is in: https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html
cc rxin felixcheung mateiz (shepherd)
k8s-big-data SIG members & contributors: mccheah ash211 ssuchter varunkatta kimoonkim erikerlandson liyinan926 tnachen ifilonenko
Author: Yinan Li <liyinan926@gmail.com>
Author: foxish <ramanathana@google.com>
Author: mcheah <mcheah@palantir.com>
Closes#19468 from foxish/spark-kubernetes-3.
The first scheduled renewal time is is set to the exact expiration time,
and all subsequent renewal times are 75% of the renewal time. This makes
it so that the inital renewal time is also 75%.
## What changes were proposed in this pull request?
Set the initial renewal time to be 75% of renewal time.
## How was this patch tested?
Tested locally in a test HDFS cluster, checking various renewal times.
Author: Kalvin Chau <kalvin.chau@viasat.com>
Closes#19798 from kalvinnchau/fix-inital-renewal-time.
## What changes were proposed in this pull request?
tl;dr: Add a class, `MesosHadoopDelegationTokenManager` that updates delegation tokens on a schedule on the behalf of Spark Drivers. Broadcast renewed credentials to the executors.
## The problem
We recently added Kerberos support to Mesos-based Spark jobs as well as Secrets support to the Mesos Dispatcher (SPARK-16742, SPARK-20812, respectively). However the delegation tokens have a defined expiration. This poses a problem for long running Spark jobs (e.g. Spark Streaming applications). YARN has a solution for this where a thread is scheduled to renew the tokens they reach 75% of their way to expiration. It then writes the tokens to HDFS for the executors to find (uses a monotonically increasing suffix).
## This solution
We replace the current method in `CoarseGrainedSchedulerBackend` which used to discard the token renewal time with a protected method `fetchHadoopDelegationTokens`. Now the individual cluster backends are responsible for overriding this method to fetch and manage token renewal. The delegation tokens themselves, are still part of the `CoarseGrainedSchedulerBackend` as before.
In the case of Mesos renewed Credentials are broadcasted to the executors. This maintains all transfer of Credentials within Spark (as opposed to Spark-to-HDFS). It also does not require any writing of Credentials to disk. It also does not require any GC of old files.
## How was this patch tested?
Manually against a Kerberized HDFS cluster.
Thank you for the reviews.
Author: ArtRand <arand@soe.ucsc.edu>
Closes#19272 from ArtRand/spark-21842-450-kerberos-ticket-renewal.
## What changes were proposed in this pull request?
Removed the unnecessary stagingDirPath null check in ApplicationMaster.cleanupStagingDir().
## How was this patch tested?
I verified with the existing test cases.
Author: Devaraj K <devaraj@apache.org>
Closes#19749 from devaraj-kavali/SPARK-22519.
## What changes were proposed in this pull request?
A discussed in SPARK-19606, the addition of a new config property named "spark.mesos.constraints.driver" for constraining drivers running on a Mesos cluster
## How was this patch tested?
Corresponding unit test added also tested locally on a Mesos cluster
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Paul Mackles <pmackles@adobe.com>
Closes#19543 from pmackles/SPARK-19606.
## What changes were proposed in this pull request?
When I ran self contained sql apps, such as
```scala
import org.apache.spark.sql.SparkSession
object ShowHiveTables {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder()
.appName("Show Hive Tables")
.enableHiveSupport()
.getOrCreate()
spark.sql("show tables").show()
spark.stop()
}
}
```
with **yarn cluster** mode and `hive-site.xml` correctly within `$SPARK_HOME/conf`,they failed to connect the right hive metestore for not seeing hive-site.xml in AM/Driver's classpath.
Although submitting them with `--files/--jars local/path/to/hive-site.xml` or puting it to `$HADOOP_CONF_DIR/YARN_CONF_DIR` can make these apps works well in cluster mode as client mode, according to the official doc, see http://spark.apache.org/docs/latest/sql-programming-guide.html#hive-tables
> Configuration of Hive is done by placing your hive-site.xml, core-site.xml (for security configuration), and hdfs-site.xml (for HDFS configuration) file in conf/.
We may respect these configuration files too or modify the doc for hive-tables in cluster mode.
## How was this patch tested?
cc cloud-fan gatorsmile
Author: Kent Yao <yaooqinn@hotmail.com>
Closes#19663 from yaooqinn/SPARK-21888.
## Background
In #18837 , ArtRand added Mesos secrets support to the dispatcher. **This PR is to add the same secrets support to the drivers.** This means if the secret configs are set, the driver will launch executors that have access to either env or file-based secrets.
One use case for this is to support TLS in the driver <=> executor communication.
## What changes were proposed in this pull request?
Most of the changes are a refactor of the dispatcher secrets support (#18837) - moving it to a common place that can be used by both the dispatcher and drivers. The same goes for the unit tests.
## How was this patch tested?
There are four config combinations: [env or file-based] x [value or reference secret]. For each combination:
- Added a unit test.
- Tested in DC/OS.
Author: Susan X. Huynh <xhuynh@mesosphere.com>
Closes#19437 from susanxhuynh/sh-mesos-driver-secret.
The bug was introduced in SPARK-22290, which changed how the app's user
is impersonated in the AM. The changed missed an initialization function
that needs to be run as the app owner (who has the right credentials to
read from HDFS).
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19566 from vanzin/SPARK-22341.
Hive delegation tokens are only needed when the Spark driver has no access
to the kerberos TGT. That happens only in two situations:
- when using a proxy user
- when using cluster mode without a keytab
This change modifies the Hive provider so that it only generates delegation
tokens in those situations, and tweaks the YARN AM so that it makes the proper
user visible to the Hive code when running with keytabs, so that the TGT
can be used instead of a delegation token.
The effect of this change is that now it's possible to initialize multiple,
non-concurrent SparkContext instances in the same JVM. Before, the second
invocation would fail to fetch a new Hive delegation token, which then could
make the second (or third or...) application fail once the token expired.
With this change, the TGT will be used to authenticate to the HMS instead.
This change also avoids polluting the current logged in user's credentials
when launching applications. The credentials are copied only when running
applications as a proxy user. This makes it possible to implement SPARK-11035
later, where multiple threads might be launching applications, and each app
should have its own set of credentials.
Tested by verifying HDFS and Hive access in following scenarios:
- client and cluster mode
- client and cluster mode with proxy user
- client and cluster mode with principal / keytab
- long-running cluster app with principal / keytab
- pyspark app that creates (and stops) multiple SparkContext instances
through its lifetime
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19509 from vanzin/SPARK-22290.
## What changes were proposed in this pull request?
Added Launcher support for monitoring Mesos apps in Client mode. SPARK-11033 can handle the support for Mesos/Cluster mode since the Standalone/Cluster and Mesos/Cluster modes use the same code at client side.
## How was this patch tested?
I verified it manually by running launcher application, able to launch, stop and kill the mesos applications and also can invoke other launcher API's.
Author: Devaraj K <devaraj@apache.org>
Closes#19385 from devaraj-kavali/SPARK-11034.
## What changes were proposed in this pull request?
Improve the Spark-Mesos coarse-grained scheduler to consider the preferred locations when dynamic allocation is enabled.
## How was this patch tested?
Added a unittest, and performed manual testing on AWS.
Author: Gene Pang <gene.pang@gmail.com>
Closes#18098 from gpang/mesos_data_locality.
## What changes were proposed in this pull request?
Fix a trivial bug with how metrics are registered in the mesos dispatcher. Bug resulted in creating a new registry each time the metricRegistry() method was called.
## How was this patch tested?
Verified manually on local mesos setup
Author: Paul Mackles <pmackles@adobe.com>
Closes#19358 from pmackles/SPARK-22135.
## What changes were proposed in this pull request?
When the libraries temp directory(i.e. __spark_libs__*.zip dir) file system and staging dir(destination) file systems are the same then the __spark_libs__*.zip is not copying to the staging directory. But after making this decision the libraries zip file is getting deleted immediately and becoming unavailable for the Node Manager's localization.
With this change, client copies the files to remote always when the source scheme is "file".
## How was this patch tested?
I have verified it manually in yarn/cluster and yarn/client modes with hdfs and local file systems.
Author: Devaraj K <devaraj@apache.org>
Closes#19141 from devaraj-kavali/SPARK-21384.
The live listener bus now cleans up after itself and releases listeners
after stopping, so code cannot get references to listeners after the
Spark context is stopped.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19297 from vanzin/SPARK-18838.hotfix.
…build; fix some things that will be warnings or errors in 2.12; restore Scala 2.12 profile infrastructure
## What changes were proposed in this pull request?
This change adds back the infrastructure for a Scala 2.12 build, but does not enable it in the release or Python test scripts.
In order to make that meaningful, it also resolves compile errors that the code hits in 2.12 only, in a way that still works with 2.11.
It also updates dependencies to the earliest minor release of dependencies whose current version does not yet support Scala 2.12. This is in a sense covered by other JIRAs under the main umbrella, but implemented here. The versions below still work with 2.11, and are the _latest_ maintenance release in the _earliest_ viable minor release.
- Scalatest 2.x -> 3.0.3
- Chill 0.8.0 -> 0.8.4
- Clapper 1.0.x -> 1.1.2
- json4s 3.2.x -> 3.4.2
- Jackson 2.6.x -> 2.7.9 (required by json4s)
This change does _not_ fully enable a Scala 2.12 build:
- It will also require dropping support for Kafka before 0.10. Easy enough, just didn't do it yet here
- It will require recreating `SparkILoop` and `Main` for REPL 2.12, which is SPARK-14650. Possible to do here too.
What it does do is make changes that resolve much of the remaining gap without affecting the current 2.11 build.
## How was this patch tested?
Existing tests and build. Manually tested with `./dev/change-scala-version.sh 2.12` to verify it compiles, modulo the exceptions above.
Author: Sean Owen <sowen@cloudera.com>
Closes#18645 from srowen/SPARK-14280.
Mesos has secrets primitives for environment and file-based secrets, this PR adds that functionality to the Spark dispatcher and the appropriate configuration flags.
Unit tested and manually tested against a DC/OS cluster with Mesos 1.4.
Author: ArtRand <arand@soe.ucsc.edu>
Closes#18837 from ArtRand/spark-20812-dispatcher-secrets-and-labels.
In the current code, if NM recovery is not enabled then `YarnShuffleService` will write shuffle metadata to NM local dir-1, if this local dir-1 is on bad disk, then `YarnShuffleService` will be failed to start. So to solve this issue, in Spark side if NM recovery is not enabled, then Spark will not persist data into leveldb, in that case yarn shuffle service can still be served but lose the ability for recovery, (it is fine because the failure of NM will kill the containers as well as applications).
Tested in the local cluster with NM recovery off and on to see if folder is created or not. MiniCluster UT isn't added because in MiniCluster NM will always set port to 0, but NM recovery requires non-ephemeral port.
Author: jerryshao <sshao@hortonworks.com>
Closes#19032 from jerryshao/SPARK-17321.
Change-Id: I8f2fe73d175e2ad2c4e380caede3873e0192d027
JIRA ticket: https://issues.apache.org/jira/browse/SPARK-21694
## What changes were proposed in this pull request?
Spark already supports launching containers attached to a given CNI network by specifying it via the config `spark.mesos.network.name`.
This PR adds support to pass in network labels to CNI plugins via a new config option `spark.mesos.network.labels`. These network labels are key-value pairs that are set in the `NetworkInfo` of both the driver and executor tasks. More details in the related Mesos documentation: http://mesos.apache.org/documentation/latest/cni/#mesos-meta-data-to-cni-plugins
## How was this patch tested?
Unit tests, for both driver and executor tasks.
Manual integration test to submit a job with the `spark.mesos.network.labels` option, hit the mesos/state.json endpoint, and check that the labels are set in the driver and executor tasks.
ArtRand skonto
Author: Susan X. Huynh <xhuynh@mesosphere.com>
Closes#18910 from susanxhuynh/sh-mesos-cni-labels.
## What changes were proposed in this pull request?
Fix typos
## How was this patch tested?
Existing tests
Author: Andrew Ash <andrew@andrewash.com>
Closes#18996 from ash211/patch-2.
## What changes were proposed in this pull request?
Add Kerberos Support to Mesos. This includes kinit and --keytab support, but does not include delegation token renewal.
## How was this patch tested?
Manually against a Secure DC/OS Apache HDFS cluster.
Author: ArtRand <arand@soe.ucsc.edu>
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#18519 from mgummelt/SPARK-16742-kerberos.
This version fixes a few issues in the import order checker; it provides
better error messages, and detects more improper ordering (thus the need
to change a lot of files in this patch). The main fix is that it correctly
complains about the order of packages vs. classes.
As part of the above, I moved some "SparkSession" import in ML examples
inside the "$example on$" blocks; that didn't seem consistent across
different source files to start with, and avoids having to add more on/off blocks
around specific imports.
The new scalastyle also seems to have a better header detector, so a few
license headers had to be updated to match the expected indentation.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18943 from vanzin/SPARK-21731.
## What changes were proposed in this pull request?
Adds a sandbox link per driver in the dispatcher ui with minimal changes after a bug was fixed here:
https://issues.apache.org/jira/browse/MESOS-4992
The sandbox uri has the following format:
http://<proxy_uri>/#/slaves/\<agent-id\>/ frameworks/ \<scheduler-id\>/executors/\<driver-id\>/browse
For dc/os the proxy uri is <dc/os uri>/mesos. For the dc/os deployment scenario and to make things easier I introduced a new config property named `spark.mesos.proxy.baseURL` which should be passed to the dispatcher when launched using --conf. If no such configuration is detected then no sandbox uri is depicted, and there is an empty column with a header (this can be changed so nothing is shown).
Within dc/os the base url must be a property for the dispatcher that we should add in the future here:
9e7c909c3b/repo/packages/S/spark/26/config.json
It is not easy to detect in different environments what is that uri so user should pass it.
## How was this patch tested?
Tested with the mesos test suite here: https://github.com/typesafehub/mesos-spark-integration-tests.
Attached image shows the ui modification where the sandbox header is added.
Tested the uri redirection the way it was suggested here:
https://issues.apache.org/jira/browse/MESOS-4992
Built mesos 1.4 from the master branch and started the mesos dispatcher with the command:
`./sbin/start-mesos-dispatcher.sh --conf spark.mesos.proxy.baseURL=http://localhost:5050 -m mesos://127.0.0.1:5050`
Run a spark example:
`./bin/spark-submit --class org.apache.spark.examples.SparkPi --master mesos://10.10.1.79:7078 --deploy-mode cluster --executor-memory 2G --total-executor-cores 2 http://<path>/spark-examples_2.11-2.1.1.jar 10`
Sandbox uri is shown at the bottom of the page:
Redirection works as expected:
Author: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Closes#18528 from skonto/adds_the_sandbox_uri.
The main goal of this change is to avoid the situation described
in the bug, where an AM restart in the middle of a job may cause
no new executors to be allocated because of faulty logic in the
reset path.
The change does two things:
- fixes the executor alloc manager's reset() so that it does not
stop allocation after a reset() in the middle of a job
- re-orders the initialization of the YarnAllocator class so that
it fetches the current executor ID before triggering the reset()
above.
This ensures both that the new allocator gets new requests for executors,
and that it starts from the correct executor id.
Tested with unit tests and by manually causing AM restarts while
running jobs using spark-shell in YARN mode.
Closes#17882
Author: Marcelo Vanzin <vanzin@cloudera.com>
Author: Guoqiang Li <witgo@qq.com>
Closes#18663 from vanzin/SPARK-20079.
If you run a spark job without creating the SparkSession or SparkContext, the spark job logs says it succeeded but yarn says it fails and retries 3 times. Also, since, Application Master unregisters with Resource Manager and exits successfully, it deletes the spark staging directory, so when yarn makes subsequent retries, it fails to find the staging directory and thus, the retries fail.
Added a flag to check whether user has initialized SparkContext. If it is true, we let Application Master unregister with Resource Manager else, we do not let AM unregister with RM.
## How was this patch tested?
Manually tested the fix.
Before:
<img width="1253" alt="screen shot-before" src="https://user-images.githubusercontent.com/22228190/28647214-69bf81e2-722b-11e7-9ed0-d416d2bf23be.png">
After:
<img width="1319" alt="screen shot-after" src="https://user-images.githubusercontent.com/22228190/28647220-70f9eea2-722b-11e7-85c6-e56276b15614.png">
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: pgandhi <pgandhi@yahoo-inc.com>
Author: pgandhi999 <parthkgandhi9@gmail.com>
Closes#18741 from pgandhi999/SPARK-21541.
There was some code based on the old SASL handler in the new auth client that
was incorrectly using the SASL user as the user to authenticate against the
external shuffle service. This caused the external service to not be able to
find the correct secret to authenticate the connection, failing the connection.
In the course of debugging, I found that some log messages from the YARN shuffle
service were a little noisy, so I silenced some of them, and also added a couple
of new ones that helped find this issue. On top of that, I found that a check
in the code that records app secrets was wrong, causing more log spam and also
using an O(n) operation instead of an O(1) call.
Also added a new integration suite for the YARN shuffle service with auth on,
and verified it failed before, and passes now.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18706 from vanzin/SPARK-21494.
When NodeManagers launching Executors,
the `missing` value will exceed the
real value when the launch is slow, this can lead to YARN allocates more resource.
We add the `numExecutorsRunning` when calculate the `missing` to avoid this.
Test by experiment.
Author: DjvuLee <lihu@bytedance.com>
Closes#18651 from djvulee/YarnAllocate.
## What changes were proposed in this pull request?
With supervise enabled for a driver, re-launching it was failing because the driver had the same framework Id. This patch creates a new driver framework id every time we re-launch a driver, but we keep the driver submission id the same since that is the same with the task id the driver was launched with on mesos and retry state and other info within Dispatcher's data structures uses that as a key.
We append a "-retry-%4d" string as a suffix to the framework id passed by the dispatcher to the driver and the same value to the app_id created by each driver, except the first time where we dont need the retry suffix.
The previous format for the frameworkId was 'DispactherFId-DriverSubmissionId'.
We also detect the case where we have multiple spark contexts started from within the same driver and we do set proper names to their corresponding app-ids. The old practice was to unset the framework id passed from the dispatcher after the driver framework was started for the first time and let mesos decide the framework ID for subsequent spark contexts. The decided fId was passed as an appID.
This patch affects heavily the history server. Btw we dont have the issues of the standalone case where driver id must be different since the dispatcher will re-launch a driver(mesos task) only if it gets an update that it is dead and this is verified by mesos implicitly. We also dont fix the fine grained mode which is deprecated and of no use.
## How was this patch tested?
This task was manually tested on dc/os. Launched a driver, stoped its container and verified the expected behavior.
Initial retry of the driver, driver in pending state:
Driver re-launched:
Another re-try:
The resulted entries in history server at the bottom:
Regarding multiple spark contexts here is the end result regarding the spark history server, for the second spark context we add an increasing number as a suffix:
Author: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Closes#18705 from skonto/fix_supervise_flag.
## What changes were proposed in this pull request?
Current behavior: in Mesos cluster mode, the driver failover_timeout is set to zero. If the driver temporarily loses connectivity with the Mesos master, the framework will be torn down and all executors killed.
Proposed change: make the failover_timeout configurable via a new option, spark.mesos.driver.failoverTimeout. The default value is still zero.
Note: with non-zero failover_timeout, an explicit teardown is needed in some cases. This is captured in https://issues.apache.org/jira/browse/SPARK-21458
## How was this patch tested?
Added a unit test to make sure the config option is set while creating the scheduler driver.
Ran an integration test with mesosphere/spark showing that with a non-zero failover_timeout the Spark job finishes after a driver is disconnected from the master.
Author: Susan X. Huynh <xhuynh@mesosphere.com>
Closes#18674 from susanxhuynh/sh-mesos-failover-timeout.
Instead of using the host's cpu count, use the number of cores allocated
for the Spark process when sizing the RPC dispatch thread pool. This avoids
creating large thread pools on large machines when the number of allocated
cores is small.
Tested by verifying number of threads with spark.executor.cores set
to 1 and 4; same thing for YARN AM.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18639 from vanzin/SPARK-21408.
## What changes were proposed in this pull request?
In the current `YARNHadoopDelegationTokenManager`, `FileSystem` to which to get tokens are created out of KDC logged UGI, using these `FileSystem` to get new tokens will lead to exception. The main thing is that Spark code trying to get new tokens from the FS created with token auth-ed UGI, but Hadoop can only grant new tokens in kerberized UGI. To fix this issue, we should lazily create these FileSystem within KDC logged UGI.
## How was this patch tested?
Manual verification in secure cluster.
CC vanzin mgummelt please help to review, thanks!
Author: jerryshao <sshao@hortonworks.com>
Closes#18633 from jerryshao/SPARK-21411.
## What changes were proposed in this pull request?
Address scapegoat warnings for:
- BigDecimal double constructor
- Catching NPE
- Finalizer without super
- List.size is O(n)
- Prefer Seq.empty
- Prefer Set.empty
- reverse.map instead of reverseMap
- Type shadowing
- Unnecessary if condition.
- Use .log1p
- Var could be val
In some instances like Seq.empty, I avoided making the change even where valid in test code to keep the scope of the change smaller. Those issues are concerned with performance and it won't matter for tests.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#18635 from srowen/Scapegoat1.
## What changes were proposed in this pull request?
In this issue we have a long running Spark application with secure HBase, which requires `HBaseCredentialProvider` to get tokens periodically, we specify HBase related jars with `--packages`, but these dependencies are not added into AM classpath, so when `HBaseCredentialProvider` tries to initialize HBase connections to get tokens, it will be failed.
Currently because jars specified with `--jars` or `--packages` are not added into AM classpath, the only way to extend AM classpath is to use "spark.driver.extraClassPath" which supposed to be used in yarn cluster mode.
So in this fix, we proposed to use/reuse a classloader for `AMCredentialRenewer` to acquire new tokens.
Also in this patch, we fixed AM cannot get tokens from HDFS issue, it is because FileSystem is gotten before kerberos logged, so using this FS to get tokens will throw exception.
## How was this patch tested?
Manual verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#18616 from jerryshao/SPARK-21377.
## What changes were proposed in this pull request?
The current code is very verbose on shutdown.
The changes I propose is to change the log level when the driver is shutting down and the RPC connections are closed (RpcEnvStoppedException).
## How was this patch tested?
Tested with word count(deploy-mode = cluster, master = yarn, num-executors = 4) with 300GB of data.
Author: John Lee <jlee2@yahoo-inc.com>
Closes#18547 from yoonlee95/SPARK-21321.
When localizing the gateway config files in a YARN application, avoid
overwriting final configs by distributing the gateway files to a separate
directory, and explicitly loading them into the Hadoop config, instead
of placing those files before the cluster's files in the classpath.
This is done by saving the gateway's config to a separate XML file
distributed with the rest of the Spark app's config, and loading that
file when creating a new config through `YarnSparkHadoopUtil`.
Tested with existing unit tests, and by verifying the behavior in a YARN
cluster (final values are not overridden, non-final values are).
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18370 from vanzin/SPARK-9825.
## What changes were proposed in this pull request?
This issue happens in long running application with yarn cluster mode, because yarn#client doesn't sync token with AM, so it will always keep the initial token, this token may be expired in the long running scenario, so when yarn#client tries to clean up staging directory after application finished, it will use this expired token and meet token expire issue.
## How was this patch tested?
Manual verification is secure cluster.
Author: jerryshao <sshao@hortonworks.com>
Closes#18617 from jerryshao/SPARK-21376.
## What changes were proposed in this pull request?
Adding the default UncaughtExceptionHandler to the Worker.
## How was this patch tested?
I verified it manually, when any of the worker thread gets uncaught exceptions then the default UncaughtExceptionHandler will handle those exceptions.
Author: Devaraj K <devaraj@apache.org>
Closes#18357 from devaraj-kavali/SPARK-21146.
Currently the code monitoring the launch of the client AM uses the value of
spark.yarn.report.interval as the interval for polling the RM; if someone
has that value to a really large interval, it would take that long to detect
that the client AM has started, which is not expected.
Instead, have a separate config for the interval to use when the client AM is
starting. The other config is still used in cluster mode, and to detect the
status of the client AM after it is already running.
Tested by running client and cluster mode apps with a modified value of
spark.yarn.report.interval, verifying client AM launch is detected before
that interval elapses.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18380 from vanzin/SPARK-16019.
## What changes were proposed in this pull request?
This PR aims to bump Py4J in order to fix the following float/double bug.
Py4J 0.10.5 fixes this (https://github.com/bartdag/py4j/issues/272) and the latest Py4J is 0.10.6.
**BEFORE**
```
>>> df = spark.range(1)
>>> df.select(df['id'] + 17.133574204226083).show()
+--------------------+
|(id + 17.1335742042)|
+--------------------+
| 17.1335742042|
+--------------------+
```
**AFTER**
```
>>> df = spark.range(1)
>>> df.select(df['id'] + 17.133574204226083).show()
+-------------------------+
|(id + 17.133574204226083)|
+-------------------------+
| 17.133574204226083|
+-------------------------+
```
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#18546 from dongjoon-hyun/SPARK-21278.
## What changes were proposed in this pull request?
Currently the shuffle service registration timeout and retry has been hardcoded. This works well for small workloads but under heavy workload when the shuffle service is busy transferring large amount of data we see significant delay in responding to the registration request, as a result we often see the executors fail to register with the shuffle service, eventually failing the job. We need to make these two parameters configurable.
## How was this patch tested?
* Updated `BlockManagerSuite` to test registration timeout and max attempts configuration actually works.
cc sitalkedia
Author: Li Yichao <lyc@zhihu.com>
Closes#18092 from liyichao/SPARK-20640.
## What changes were proposed in this pull request?
When I set different clusters for "spark.hadoop.fs.defaultFS" and "spark.yarn.stagingDir" as follows：
```
spark.hadoop.fs.defaultFS hdfs://tl-nn-tdw.tencent-distribute.com:54310
spark.yarn.stagingDir hdfs://ss-teg-2-v2/tmp/spark
```
The staging dir can not be deleted, it will prompt following message:
```
java.lang.IllegalArgumentException: Wrong FS: hdfs://ss-teg-2-v2/tmp/spark/.sparkStaging/application_1496819138021_77618, expected: hdfs://tl-nn-tdw.tencent-distribute.com:54310
```
## How was this patch tested?
Existing tests
Author: sharkdtu <sharkdtu@tencent.com>
Closes#18352 from sharkdtu/master.
## What changes were proposed in this pull request?
Move Hadoop delegation token code from `spark-yarn` to `spark-core`, so that other schedulers (such as Mesos), may use it. In order to avoid exposing Hadoop interfaces in spark-core, the new Hadoop delegation token classes are kept private. In order to provider backward compatiblity, and to allow YARN users to continue to load their own delegation token providers via Java service loading, the old YARN interfaces, as well as the client code that uses them, have been retained.
Summary:
- Move registered `yarn.security.ServiceCredentialProvider` classes from `spark-yarn` to `spark-core`. Moved them into a new, private hierarchy under `HadoopDelegationTokenProvider`. Client code in `HadoopDelegationTokenManager` now loads credentials from a whitelist of three providers (`HadoopFSDelegationTokenProvider`, `HiveDelegationTokenProvider`, `HBaseDelegationTokenProvider`), instead of service loading, which means that users are not able to implement their own delegation token providers, as they are in the `spark-yarn` module.
- The `yarn.security.ServiceCredentialProvider` interface has been kept for backwards compatibility, and to continue to allow YARN users to implement their own delegation token provider implementations. Client code in YARN now fetches tokens via the new `YARNHadoopDelegationTokenManager` class, which fetches tokens from the core providers through `HadoopDelegationTokenManager`, as well as service loads them from `yarn.security.ServiceCredentialProvider`.
Old Hierarchy:
```
yarn.security.ServiceCredentialProvider (service loaded)
HadoopFSCredentialProvider
HiveCredentialProvider
HBaseCredentialProvider
yarn.security.ConfigurableCredentialManager
```
New Hierarchy:
```
HadoopDelegationTokenManager
HadoopDelegationTokenProvider (not service loaded)
HadoopFSDelegationTokenProvider
HiveDelegationTokenProvider
HBaseDelegationTokenProvider
yarn.security.ServiceCredentialProvider (service loaded)
yarn.security.YARNHadoopDelegationTokenManager
```
## How was this patch tested?
unit tests
Author: Michael Gummelt <mgummelt@mesosphere.io>
Author: Dr. Stefan Schimanski <sttts@mesosphere.io>
Closes#17723 from mgummelt/SPARK-20434-refactor-kerberos.
## What changes were proposed in this pull request?
Add Mesos labels support to the Spark Dispatcher
## How was this patch tested?
unit tests
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#18220 from mgummelt/SPARK-21000-dispatcher-labels.
In Spark on YARN, when configuring "spark.yarn.jars" with local jars (jars started with "local" scheme), we will get inaccurate classpath for AM and containers. This is because we don't remove "local" scheme when concatenating classpath. It is OK to run because classpath is separated with ":" and java treat "local" as a separate jar. But we could improve it to remove the scheme.
Updated `ClientSuite` to check "local" is not in the classpath.
cc jerryshao
Author: Li Yichao <lyc@zhihu.com>
Author: Li Yichao <liyichao.good@gmail.com>
Closes#18129 from liyichao/SPARK-20365.
## What changes were proposed in this pull request?
Deleted generated JARs archive after distribution to HDFS
## How was this patch tested?
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lior Regev <lioregev@gmail.com>
Closes#17986 from liorregev/master.
Restore code that was removed as part of SPARK-17979, but instead of
using the deprecated env variable name to propagate the class path, use
a new one.
Verified by running "./bin/spark-class o.a.s.executor.CoarseGrainedExecutorBackend"
manually.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18037 from vanzin/SPARK-20814.
## What changes were proposed in this pull request?
Add stripXSS and stripXSSMap to Spark Core's UIUtils. Calling these functions at any point that getParameter is called against a HttpServletRequest.
## How was this patch tested?
Unit tests, IBM Security AppScan Standard no longer showing vulnerabilities, manual verification of WebUI pages.
Author: NICHOLAS T. MARION <nmarion@us.ibm.com>
Closes#17686 from n-marion/xss-fix.
## What changes were proposed in this pull request?
After SPARK-10997, client mode Netty RpcEnv doesn't require to start server, so port configurations are not used any more, here propose to remove these two configurations: "spark.executor.port" and "spark.am.port".
## How was this patch tested?
Existing UTs.
Author: jerryshao <sshao@hortonworks.com>
Closes#17866 from jerryshao/SPARK-20605.
## What changes were proposed in this pull request?
Currently, `spark.executor.instances` is deprecated in `spark-env.sh`, because we suggest config it in `spark-defaults.conf` or other config file. And also this parameter is useless even if you set it in `spark-env.sh`, so remove it in this patch.
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Xianyang Liu <xianyang.liu@intel.com>
Closes#17881 from ConeyLiu/deprecatedParam.
Signed-off-by: liuxian <liu.xian3zte.com.cn>
## What changes were proposed in this pull request?
When the input parameter is null, may be a runtime exception occurs
## How was this patch tested?
Existing unit tests
Author: liuxian <liu.xian3@zte.com.cn>
Closes#17796 from 10110346/wip_lx_0428.
## What changes were proposed in this pull request?
Fix build warnings primarily related to Breeze 0.13 operator changes, Java style problems
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#17803 from srowen/SPARK-20523.
## What changes were proposed in this pull request?
Add test case for scenarios where executor.cores is set as a
(non)divisor of spark.cores.max
This tests the change in
#17786
## How was this patch tested?
Ran the existing test suite with the new tests
dbtsai
Author: Davis Shepherd <dshepherd@netflix.com>
Closes#17788 from dgshep/add_mesos_test.
## What changes were proposed in this pull request?
Set maxCores to be a multiple of the smallest executor that can be launched. This ensures that we correctly detect the condition where no more executors will be launched when spark.cores.max is not a multiple of spark.executor.cores
## How was this patch tested?
This was manually tested with other sample frameworks measuring their incoming offers to determine if starvation would occur.
dbtsai mgummelt
Author: Davis Shepherd <dshepherd@netflix.com>
Closes#17786 from dgshep/fix_mesos_max_cores.
This change does a more thorough redaction of sensitive information from logs and UI
Add unit tests that ensure that no regressions happen that leak sensitive information to the logs.
The motivation for this change was appearance of password like so in `SparkListenerEnvironmentUpdate` in event logs under some JVM configurations:
`"sun.java.command":"org.apache.spark.deploy.SparkSubmit ... --conf spark.executorEnv.HADOOP_CREDSTORE_PASSWORD=secret_password ..."
`
Previously redaction logic was only checking if the key matched the secret regex pattern, it'd redact it's value. That worked for most cases. However, in the above case, the key (sun.java.command) doesn't tell much, so the value needs to be searched. This PR expands the check to check for values as well.
## How was this patch tested?
New unit tests added that ensure that no sensitive information is present in the event logs or the yarn logs. Old unit test in UtilsSuite was modified because the test was asserting that a non-sensitive property's value won't be redacted. However, the non-sensitive value had the literal "secret" in it which was causing it to redact. Simply updating the non-sensitive property's value to another arbitrary value (that didn't have "secret" in it) fixed it.
Author: Mark Grover <mark@apache.org>
Closes#17725 from markgrover/spark-20435.
## What changes were proposed in this pull request?
Submitted Time' field, the date format **needs to be formatted**, in running Drivers table or Completed Drivers table in master web ui.
Before fix this problem e.g.
Completed Drivers
Submission ID **Submitted Time** Worker State Cores Memory Main Class
driver-20170419145755-0005 **Wed Apr 19 14:57:55 CST 2017** worker-20170419145250-zdh120-40412 FAILED 1 1024.0 MB cn.zte.HdfsTest
please see the attachment:https://issues.apache.org/jira/secure/attachment/12863977/before_fix.png
After fix this problem e.g.
Completed Drivers
Submission ID **Submitted Time** Worker State Cores Memory Main Class
driver-20170419145755-0006 **2017/04/19 16:01:25** worker-20170419145250-zdh120-40412 FAILED 1 1024.0 MB cn.zte.HdfsTest
please see the attachment:https://issues.apache.org/jira/secure/attachment/12863976/after_fix.png
'Submitted Time' field, the date format **has been formatted**, in running Applications table or Completed Applicationstable in master web ui, **it is correct.**
e.g.
Running Applications
Application ID Name Cores Memory per Executor **Submitted Time** User State Duration
app-20170419160910-0000 (kill) SparkSQL::10.43.183.120 1 5.0 GB **2017/04/19 16:09:10** root RUNNING 53 s
**Format after the time easier to observe, and consistent with the applications table,so I think it's worth fixing.**
## 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.
Author: 郭小龙 10207633 <guo.xiaolong1@zte.com.cn>
Author: guoxiaolong <guo.xiaolong1@zte.com.cn>
Author: guoxiaolongzte <guo.xiaolong1@zte.com.cn>
Closes#17682 from guoxiaolongzte/SPARK-20385.
## What changes were proposed in this pull request?
Typo fix: distitrbuted -> distributed
## How was this patch tested?
Existing tests
Author: Andrew Ash <andrew@andrewash.com>
Closes#17664 from ash211/patch-1.
## What changes were proposed in this pull request?
Allow passing in arbitrary parameters into docker when launching spark executors on mesos with docker containerizer tnachen
## How was this patch tested?
Manually built and tested with passed in parameter
Author: Ji Yan <jiyan@Jis-MacBook-Air.local>
Closes#17109 from yanji84/ji/allow_set_docker_user.
## What changes were proposed in this pull request?
This PR proposes to run Spark unidoc to test Javadoc 8 build as Javadoc 8 is easily re-breakable.
There are several problems with it:
- It introduces little extra bit of time to run the tests. In my case, it took 1.5 mins more (`Elapsed :[94.8746569157]`). How it was tested is described in "How was this patch tested?".
- > One problem that I noticed was that Unidoc appeared to be processing test sources: if we can find a way to exclude those from being processed in the first place then that might significantly speed things up.
(see joshrosen's [comment](https://issues.apache.org/jira/browse/SPARK-18692?focusedCommentId=15947627&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15947627))
To complete this automated build, It also suggests to fix existing Javadoc breaks / ones introduced by test codes as described above.
There fixes are similar instances that previously fixed. Please refer https://github.com/apache/spark/pull/15999 and https://github.com/apache/spark/pull/16013
Note that this only fixes **errors** not **warnings**. Please see my observation https://github.com/apache/spark/pull/17389#issuecomment-288438704 for spurious errors by warnings.
## How was this patch tested?
Manually via `jekyll build` for building tests. Also, tested via running `./dev/run-tests`.
This was tested via manually adding `time.time()` as below:
```diff
profiles_and_goals = build_profiles + sbt_goals
print("[info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments: ",
" ".join(profiles_and_goals))
+ import time
+ st = time.time()
exec_sbt(profiles_and_goals)
+ print("Elapsed :[%s]" % str(time.time() - st))
```
produces
```
...
========================================================================
Building Unidoc API Documentation
========================================================================
...
[info] Main Java API documentation successful.
...
Elapsed :[94.8746569157]
...
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17477 from HyukjinKwon/SPARK-18692.
## What changes were proposed in this pull request?
Add Locale.ROOT to internal calls to String `toLowerCase`, `toUpperCase`, to avoid inadvertent locale-sensitive variation in behavior (aka the "Turkish locale problem").
The change looks large but it is just adding `Locale.ROOT` (the locale with no country or language specified) to every call to these methods.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#17527 from srowen/SPARK-20156.
## What changes were proposed in this pull request?
Add spark.mesos.task.labels configuration option to add mesos key:value labels to the executor.
"k1:v1,k2:v2" as the format, colons separating key-value and commas to list out more than one.
Discussion of labels with mgummelt at #17404
## How was this patch tested?
Added unit tests to verify labels were added correctly, with incorrect labels being ignored and added a test to test the name of the executor.
Tested with: `./build/sbt -Pmesos mesos/test`
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Kalvin Chau <kalvin.chau@viasat.com>
Closes#17413 from kalvinnchau/mesos-labels.
Current test code tries to override the RackResolver used by setting
configuration params, but because YARN libs statically initialize the
resolver the first time it's used, that means that those configs don't
really take effect during Spark tests.
This change adds a wrapper class that easily allows tests to override the
behavior of the resolver for the Spark code that uses it.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#17508 from vanzin/SPARK-20191.
## What changes were proposed in this pull request?
Currently we use system classloader to find HBase jars, if it is specified by `--jars`, then it will be failed with ClassNotFound issue. So here changing to use child classloader.
Also putting added jars and main jar into classpath of submitted application in yarn cluster mode, otherwise HBase jars specified with `--jars` will never be honored in cluster mode, and fetching tokens in client side will always be failed.
## How was this patch tested?
Unit test and local verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#17388 from jerryshao/SPARK-20059.
## What changes were proposed in this pull request?
In the current Spark on YARN code, we will obtain tokens from provided services, but we're not going to add these tokens to the current user's credentials. This will make all the following operations to these services still require TGT rather than delegation tokens. This is unnecessary since we already got the tokens, also this will lead to failure in user impersonation scenario, because the TGT is granted by real user, not proxy user.
So here changing to put all the tokens to the current UGI, so that following operations to these services will honor tokens rather than TGT, and this will further handle the proxy user issue mentioned above.
## How was this patch tested?
Local verified in secure cluster.
vanzin tgravescs mridulm dongjoon-hyun please help to review, thanks a lot.
Author: jerryshao <sshao@hortonworks.com>
Closes#17335 from jerryshao/SPARK-19995.
## What changes were proposed in this pull request?
Adding additional information to existing logging messages:
- YarnAllocator: log the executor ID together with the container id when a container for an executor is launched.
- NettyRpcEnv: log the receiver address when there is a timeout waiting for an answer to a remote call.
- ExecutorAllocationManager: fix a typo in the logging message for the list of executors to be removed.
## How was this patch tested?
Build spark and submit the word count example to a YARN cluster using cluster mode
Author: Juan Rodriguez Hortala <hortala@amazon.com>
Closes#17411 from juanrh/logging-improvements.
## What changes were proposed in this pull request?
Adding configurable mesos executor names and labels using `spark.mesos.task.name` and `spark.mesos.task.labels`.
Labels were defined as `k1:v1,k2:v2`.
mgummelt
## How was this patch tested?
Added unit tests to verify labels were added correctly, with incorrect labels being ignored and added a test to test the name of the executor.
Tested with: `./build/sbt -Pmesos mesos/test`
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Kalvin Chau <kalvin.chau@viasat.com>
Closes#17404 from kalvinnchau/mesos-config.
This commit adds a killTaskAttempt method to SparkContext, to allow users to
kill tasks so that they can be re-scheduled elsewhere.
This also refactors the task kill path to allow specifying a reason for the task kill. The reason is propagated opaquely through events, and will show up in the UI automatically as `(N killed: $reason)` and `TaskKilled: $reason`. Without this change, there is no way to provide the user feedback through the UI.
Currently used reasons are "stage cancelled", "another attempt succeeded", and "killed via SparkContext.killTask". The user can also specify a custom reason through `SparkContext.killTask`.
cc rxin
In the stage overview UI the reasons are summarized:
Within the stage UI you can see individual task kill reasons:
Existing tests, tried killing some stages in the UI and verified the messages are as expected.
Author: Eric Liang <ekl@databricks.com>
Author: Eric Liang <ekl@google.com>
Closes#17166 from ericl/kill-reason.
## What changes were proposed in this pull request?
Fixup typo in comment.
## How was this patch tested?
Don't need.
Author: Ye Yin <eyniy@qq.com>
Closes#17396 from hustcat/fix.
Add parentheses so that both lines form a single statement; also add
a log message so that the issue becomes more explicit if it shows up
again.
Tested manually with integration test that exercises the feature.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#17198 from vanzin/SPARK-19857.
## What changes were proposed in this pull request?
Increase default refuse_seconds timeout, and make it configurable. See JIRA for details on how this reduces the risk of starvation.
## How was this patch tested?
Unit tests, Manual testing, and Mesos/Spark integration test suite
cc susanxhuynh skonto jmlvanre
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#17031 from mgummelt/SPARK-19702-suppress-revive.
## What changes were proposed in this pull request?
See JIRA
## How was this patch tested?
Unit tests, Mesos/Spark integration tests
cc skonto susanxhuynh
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#17045 from mgummelt/SPARK-19373-registered-resources.
## What changes were proposed in this pull request?
Adding the default UncaughtExceptionHandler to the MesosClusterDispatcher.
## How was this patch tested?
I verified it manually, when any of the dispatcher thread gets uncaught exceptions then the default UncaughtExceptionHandler will handle those exceptions.
Author: Devaraj K <devaraj@apache.org>
Closes#13072 from devaraj-kavali/SPARK-15288.
## What changes were proposed in this pull request?
self.environment will be propagated to executor. Should set PYTHONHASHSEED as long as the python version is greater than 3.3
## How was this patch tested?
Manually tested it.
Author: Jeff Zhang <zjffdu@apache.org>
Closes#11211 from zjffdu/SPARK-13330.
## What changes were proposed in this pull request?
Because yarn#client will reset the `spark.yarn.keytab` configuration to point to the location in distributed file, so if user still uses the old `SparkConf` to create `SparkSession` with Hive enabled, it will read keytab from the path in distributed cached. This is OK for yarn cluster mode, but in yarn client mode where driver is running out of container, it will be failed to fetch the keytab.
So here we should avoid reseting this configuration in the `yarn#client` and only overwriting it for AM, so using `spark.yarn.keytab` could get correct keytab path no matter running in client (keytab in local fs) or cluster (keytab in distributed cache) mode.
## How was this patch tested?
Verified in security cluster.
Author: jerryshao <sshao@hortonworks.com>
Closes#16923 from jerryshao/SPARK-19038.
Allow an application to use the History Server URL as the tracking
URL in the YARN RM, so there's still a link to the web UI somewhere
in YARN even if the driver's UI is disabled. This is useful, for
example, if an admin wants to disable the driver UI by default for
applications, since it's harder to secure it (since it involves non
trivial ssl certificate and auth management that admins may not want
to expose to user apps).
This needs to be opt-in, because of the way the YARN proxy works, so
a new configuration was added to enable the option.
The YARN RM will proxy requests to live AMs instead of redirecting
the client, so pages in the SHS UI will not render correctly since
they'll reference invalid paths in the RM UI. The proxy base support
in the SHS cannot be used since that would prevent direct access to
the SHS.
So, to solve this problem, for the feature to work end-to-end, a new
YARN-specific filter was added that detects whether the requests come
from the proxy and redirects the client appropriatly. The SHS admin has
to add this filter manually if they want the feature to work.
Tested with new unit test, and by running with the documented configuration
set in a test cluster. Also verified the driver UI is used when it's
enabled.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#16946 from vanzin/SPARK-19554.
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/14065, we introduced a configurable credential manager for Spark running on YARN. Also two configs `spark.yarn.credentials.renewalTime` and `spark.yarn.credentials.updateTime` were added, one is for the credential renewer and the other updater. But now we just query `spark.yarn.credentials.renewalTime` by mistake during CREDENTIALS UPDATING, where should be actually `spark.yarn.credentials.updateTime` .
This PR fixes this mistake.
## How was this patch tested?
existing test
cc jerryshao vanzin
Author: Kent Yao <yaooqinn@hotmail.com>
Closes#16955 from yaooqinn/cred_update.
## What changes were proposed in this pull request?
`askSync` is already added in `RpcEndpointRef` (see SPARK-19347 and https://github.com/apache/spark/pull/16690#issuecomment-276850068) and `askWithRetry` is marked as deprecated.
As mentioned SPARK-18113(https://github.com/apache/spark/pull/16503#event-927953218):
>askWithRetry is basically an unneeded API, and a leftover from the akka days that doesn't make sense anymore. It's prone to cause deadlocks (exactly because it's blocking), it imposes restrictions on the caller (e.g. idempotency) and other things that people generally don't pay that much attention to when using it.
Since `askWithRetry` is just used inside spark and not in user logic. It might make sense to replace all of them with `askSync`.
## How was this patch tested?
This PR doesn't change code logic, existing unit test can cover.
Author: jinxing <jinxing@meituan.com>
Closes#16790 from jinxing64/SPARK-19450.
- Move external/java8-tests tests into core, streaming, sql and remove
- Remove MaxPermGen and related options
- Fix some reflection / TODOs around Java 8+ methods
- Update doc references to 1.7/1.8 differences
- Remove Java 7/8 related build profiles
- Update some plugins for better Java 8 compatibility
- Fix a few Java-related warnings
For the future:
- Update Java 8 examples to fully use Java 8
- Update Java tests to use lambdas for simplicity
- Update Java internal implementations to use lambdas
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#16871 from srowen/SPARK-19493.
## What changes were proposed in this pull request?
Now handling the spark exception which gets thrown for invalid job configuration, marking that job as failed and continuing to launch the other drivers instead of throwing the exception.
## How was this patch tested?
I verified manually, now the misconfigured jobs move to Finished Drivers section in UI and continue to launch the other jobs.
Author: Devaraj K <devaraj@apache.org>
Closes#13077 from devaraj-kavali/SPARK-10748.
## What changes were proposed in this pull request?
Due to the newly added API in Hadoop 2.6.4+, Spark builds against Hadoop 2.6.0~2.6.3 will meet compile error. So here still reverting back to use reflection to handle this issue.
## How was this patch tested?
Manual verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#16884 from jerryshao/SPARK-19545.
Add back mockito test dep in YARN module, as it ends up being required in a Maven build
## How was this patch tested?
PR builder again, but also a local `mvn` run using the command that the broken Jenkins job uses
Author: Sean Owen <sowen@cloudera.com>
Closes#16853 from srowen/SPARK-19464.2.
## What changes were proposed in this pull request?
After using Apache Parquet 1.8.2, `ParquetAvroCompatibilitySuite` fails on **Maven** test. It is because `org.apache.parquet.avro.AvroParquetWriter` in the test code used new `avro 1.8.0` specific class, `LogicalType`. This PR aims to fix the test dependency of `sql/core` module to use avro 1.8.0.
https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.7/2530/consoleFull
```
ParquetAvroCompatibilitySuite:
*** RUN ABORTED ***
java.lang.NoClassDefFoundError: org/apache/avro/LogicalType
at org.apache.parquet.avro.AvroParquetWriter.writeSupport(AvroParquetWriter.java:144)
```
## How was this patch tested?
Pass the existing test with **Maven**.
```
$ build/mvn -Pyarn -Phadoop-2.7 -Pkinesis-asl -Phive -Phive-thriftserver test
...
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 02:07 h
[INFO] Finished at: 2017-02-04T05:41:43+00:00
[INFO] Final Memory: 77M/987M
[INFO] ------------------------------------------------------------------------
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#16795 from dongjoon-hyun/SPARK-19409-2.
## What changes were proposed in this pull request?
- Remove support for Hadoop 2.5 and earlier
- Remove reflection and code constructs only needed to support multiple versions at once
- Update docs to reflect newer versions
- Remove older versions' builds and profiles.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#16810 from srowen/SPARK-19464.
That method is prone to stack overflows when the input map is really
large; instead, use plain "map". Also includes a unit test that was
tested and caused stack overflows without the fix.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#16667 from vanzin/SPARK-18750.
This change introduces a new auth mechanism to the transport library,
to be used when users enable strong encryption. This auth mechanism
has better security than the currently used DIGEST-MD5.
The new protocol uses symmetric key encryption to mutually authenticate
the endpoints, and is very loosely based on ISO/IEC 9798.
The new protocol falls back to SASL when it thinks the remote end is old.
Because SASL does not support asking the server for multiple auth protocols,
which would mean we could re-use the existing SASL code by just adding a
new SASL provider, the protocol is implemented outside of the SASL API
to avoid the boilerplate of adding a new provider.
Details of the auth protocol are discussed in the included README.md
file.
This change partly undos the changes added in SPARK-13331; AES encryption
is now decoupled from SASL authentication. The encryption code itself,
though, has been re-used as part of this change.
## How was this patch tested?
- Unit tests
- Tested Spark 2.2 against Spark 1.6 shuffle service with SASL enabled
- Tested Spark 2.2 against Spark 2.2 shuffle service with SASL fallback disabled
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#16521 from vanzin/SPARK-19139.
## What changes were proposed in this pull request?
remove ununsed imports and outdated comments, and fix some minor code style issue.
## How was this patch tested?
existing ut
Author: uncleGen <hustyugm@gmail.com>
Closes#16591 from uncleGen/SPARK-19227.
## What changes were proposed in this pull request?
`spark.yarn.access.namenodes` configuration cannot actually reflects the usage of it, inside the code it is the Hadoop filesystems we get tokens, not NNs. So here propose to update the name of this configuration, also change the related code and doc.
## How was this patch tested?
Local verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#16560 from jerryshao/SPARK-19179.
## What changes were proposed in this pull request?
#16092 moves YARN resource manager related code to resource-managers/yarn directory. The test case ```YarnSchedulerBackendSuite``` was added after that but with the wrong place. I move it to correct directory in this PR.
## How was this patch tested?
Existing test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16595 from yanboliang/yarn.
Currently Spark can only get token renewal interval from security HDFS (hdfs://), if Spark runs with other security file systems like webHDFS (webhdfs://), wasb (wasb://), ADLS, it will ignore these tokens and not get token renewal intervals from these tokens. These will make Spark unable to work with these security clusters. So instead of only checking HDFS token, we should generalize to support different DelegationTokenIdentifier.
## How was this patch tested?
Manually verified in security cluster.
Author: jerryshao <sshao@hortonworks.com>
Closes#16432 from jerryshao/SPARK-19021.
In the existing code, there are three layers of serialization
involved in sending a task from the scheduler to an executor:
- A Task object is serialized
- The Task object is copied to a byte buffer that also
contains serialized information about any additional JARs,
files, and Properties needed for the task to execute. This
byte buffer is stored as the member variable serializedTask
in the TaskDescription class.
- The TaskDescription is serialized (in addition to the serialized
task + JARs, the TaskDescription class contains the task ID and
other metadata) and sent in a LaunchTask message.
While it *is* necessary to have two layers of serialization, so that
the JAR, file, and Property info can be deserialized prior to
deserializing the Task object, the third layer of deserialization is
unnecessary. This commit eliminates a layer of serialization by moving
the JARs, files, and Properties into the TaskDescription class.
This commit also serializes the Properties manually (by traversing the map),
as is done with the JARs and files, which reduces the final serialized size.
Unit tests
This is a simpler alternative to the approach proposed in #15505.
shivaram and I did some benchmarking of this and #15505 on a 20-machine m2.4xlarge EC2 machines (160 cores). We ran ~30 trials of code [1] (a very simple job with 10K tasks per stage) and measured the average time per stage:
Before this change: 2490ms
With this change: 2345 ms (so ~6% improvement over the baseline)
With witgo's approach in #15505: 2046 ms (~18% improvement over baseline)
The reason that #15505 has a more significant improvement is that it also moves the serialization from the TaskSchedulerImpl thread to the CoarseGrainedSchedulerBackend thread. I added that functionality on top of this change, and got almost the same improvement [1] as #15505 (average of 2103ms). I think we should decouple these two changes, both so we have some record of the improvement form each individual improvement, and because this change is more about simplifying the code base (the improvement is negligible) while the other is about performance improvement. The plan, currently, is to merge this PR and then merge the remaining part of #15505 that moves serialization.
[1] The reason the improvement wasn't quite as good as with #15505 when we ran the benchmarks is almost certainly because, at the point when we ran the benchmarks, I hadn't updated the code to manually serialize the Properties (instead the code was using Java's default serialization for the Properties object, whereas #15505 manually serialized the Properties). This PR has since been updated to manually serialize the Properties, just like the other maps.
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#16053 from kayousterhout/SPARK-17931.
## What changes were proposed in this pull request?
There are many locations in the Spark repo where the same word occurs consecutively. Sometimes they are appropriately placed, but many times they are not. This PR removes the inappropriately duplicated words.
## How was this patch tested?
N/A since only docs or comments were updated.
Author: Niranjan Padmanabhan <niranjan.padmanabhan@gmail.com>
Closes#16455 from neurons/np.structure_streaming_doc.
## What changes were proposed in this pull request?
LauncherState should be only set to SUBMITTED after the application is submitted.
Currently the state is set before the application is actually submitted.
## How was this patch tested?
no test is added in this patch
Author: mingfei <mingfei.smf@alipay.com>
Closes#16459 from shimingfei/fixLauncher.
## What changes were proposed in this pull request?
Not adding the Killed applications for retry.
## How was this patch tested?
I have verified manually in the Mesos cluster, with the changes the killed applications move to Finished Drivers section and will not retry.
Author: Devaraj K <devaraj@apache.org>
Closes#13323 from devaraj-kavali/SPARK-15555.
## What changes were proposed in this pull request?
The configuration `spark.yarn.security.tokens.{service}.enabled` is deprecated. Now we should use `spark.yarn.security.credentials.{service}.enabled`. Some places in the doc is not updated yet.
## How was this patch tested?
N/A. Just doc change.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16444 from viirya/minor-credential-provider-doc.
Remove spark-tag's compile-scope dependency (and, indirectly, spark-core's compile-scope transitive-dependency) on scalatest by splitting test-oriented tags into spark-tags' test JAR.
Alternative to #16303.
Author: Ryan Williams <ryan.blake.williams@gmail.com>
Closes#16311 from ryan-williams/tt.
## What changes were proposed in this pull request?
93cdb8a7d0 Introduced a compile error under scala 2.10, this fixes that error.
## How was this patch tested?
locally ran
```
dev/change-version-to-2.10.sh
build/sbt -Pyarn -Phadoop-2.4 -Dhadoop.version=2.6.0 -Dscala-2.10 "project yarn" "test-only *YarnAllocatorSuite"
```
(which failed at test compilation before this change)
Author: Imran Rashid <irashid@cloudera.com>
Closes#16298 from squito/blacklist-2.10.
## What changes were proposed in this pull request?
This builds upon the blacklisting introduced in SPARK-17675 to add blacklisting of executors and nodes for an entire Spark application. Resources are blacklisted based on tasks that fail, in tasksets that eventually complete successfully; they are automatically returned to the pool of active resources based on a timeout. Full details are available in a design doc attached to the jira.
## How was this patch tested?
Added unit tests, ran them via Jenkins, also ran a handful of them in a loop to check for flakiness.
The added tests include:
- verifying BlacklistTracker works correctly
- verifying TaskSchedulerImpl interacts with BlacklistTracker correctly (via a mock BlacklistTracker)
- an integration test for the entire scheduler with blacklisting in a few different scenarios
Author: Imran Rashid <irashid@cloudera.com>
Author: mwws <wei.mao@intel.com>
Closes#14079 from squito/blacklist-SPARK-8425.
## What changes were proposed in this pull request?
Fix `java.util.NoSuchElementException` when running Spark in non-hdfs security environment.
In the current code, we assume `HDFS_DELEGATION_KIND` token will be found in Credentials. But in some cloud environments, HDFS is not required, so we should avoid this exception.
## How was this patch tested?
Manually verified in local environment.
Author: jerryshao <sshao@hortonworks.com>
Closes#16265 from jerryshao/SPARK-18840.
