This PR is based on an existing/previou PR - https://github.com/apache/spark/pull/19045
### What changes were proposed in this pull request?
This changes adds a decommissioning state that we can enter when the cloud provider/scheduler lets us know we aren't going to be removed immediately but instead will be removed soon. This concept fits nicely in K8s and also with spot-instances on AWS / preemptible instances all of which we can get a notice that our host is going away. For now we simply stop scheduling jobs, in the future we could perform some kind of migration of data during scale-down, or at least stop accepting new blocks to cache.
There is a design document at https://docs.google.com/document/d/1xVO1b6KAwdUhjEJBolVPl9C6sLj7oOveErwDSYdT-pE/edit?usp=sharing
### Why are the changes needed?
With more move to preemptible multi-tenancy, serverless environments, and spot-instances better handling of node scale down is required.
### Does this PR introduce any user-facing change?
There is no API change, however an additional configuration flag is added to enable/disable this behaviour.
### How was this patch tested?
New integration tests in the Spark K8s integration testing. Extension of the AppClientSuite to test decommissioning seperate from the K8s.
Closes#26440 from holdenk/SPARK-20628-keep-track-of-nodes-which-are-going-to-be-shutdown-r4.
Lead-authored-by: Holden Karau <hkarau@apple.com>
Co-authored-by: Holden Karau <holden@pigscanfly.ca>
Signed-off-by: Holden Karau <hkarau@apple.com>
### What changes were proposed in this pull request?
This PR aims to fix `dev-run-integration-tests.sh` to ignore empty params correctly.
### Why are the changes needed?
The following script runs `mvn` integration test like the following.
```
$ resource-managers/kubernetes/integration-tests/dev/dev-run-integration-tests.sh
...
build/mvn integration-test
-f /Users/dongjoon/APACHE/spark/pom.xml
-pl resource-managers/kubernetes/integration-tests
-am
-Pscala-2.12
-Pkubernetes
-Pkubernetes-integration-tests
-Djava.version=8
-Dspark.kubernetes.test.sparkTgz=N/A
-Dspark.kubernetes.test.imageTag=N/A
-Dspark.kubernetes.test.imageRepo=docker.io/kubespark
-Dspark.kubernetes.test.deployMode=minikube
-Dtest.include.tags=k8s
-Dspark.kubernetes.test.namespace=
-Dspark.kubernetes.test.serviceAccountName=
-Dspark.kubernetes.test.kubeConfigContext=
-Dspark.kubernetes.test.master=
-Dtest.exclude.tags=
-Dspark.kubernetes.test.jvmImage=spark
-Dspark.kubernetes.test.pythonImage=spark-py
-Dspark.kubernetes.test.rImage=spark-r
```
After this PR, the empty parameters like the followings will be skipped like the original design.
```
-Dspark.kubernetes.test.namespace=
-Dspark.kubernetes.test.serviceAccountName=
-Dspark.kubernetes.test.kubeConfigContext=
-Dspark.kubernetes.test.master=
-Dtest.exclude.tags=
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass the Jenkins K8S integration test.
Closes#27566 from dongjoon-hyun/SPARK-30816.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR aims to support JDK11 test in K8S integration tests.
- This is an update in testing framework instead of individual tests.
- This will enable JDK11 runtime test when you didn't installed JDK11 on your local system.
### Why are the changes needed?
Apache Spark 3.0.0 adds JDK11 support, but K8s integration tests use JDK8 until now.
### Does this PR introduce any user-facing change?
No. This is a dev-only test-related PR.
### How was this patch tested?
This is irrelevant to Jenkins UT, but Jenkins K8S IT (JDK8) should pass.
- https://github.com/apache/spark/pull/27559#issuecomment-585903489 (JDK8 Passed)
And, manually do the following for JDK11 test.
```
$ NO_MANUAL=1 ./dev/make-distribution.sh --r --pip --tgz -Phadoop-3.2 -Pkubernetes
$ resource-managers/kubernetes/integration-tests/dev/dev-run-integration-tests.sh --java-image-tag 11-jre-slim --spark-tgz $PWD/spark-*.tgz
```
```
$ docker run -it --rm kubespark/spark:1318DD8A-2B15-4A00-BC69-D0E90CED235B /usr/local/openjdk-11/bin/java --version | tail -n1
OpenJDK 64-Bit Server VM 18.9 (build 11.0.6+10, mixed mode)
```
Closes#27559 from dongjoon-hyun/SPARK-30807.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This is another PR for stage level scheduling. In particular this adds changes to the dynamic allocation manager and the scheduler backend to be able to track what executors are needed per ResourceProfile. Note the api is still private to Spark until the entire feature gets in, so this functionality will be there but only usable by tests for profiles other then the DefaultProfile.
The main changes here are simply tracking things on a ResourceProfile basis as well as sending the executor requests to the scheduler backend for all ResourceProfiles.
I introduce a ResourceProfileManager in this PR that will track all the actual ResourceProfile objects so that we can keep them all in a single place and just pass around and use in datastructures the resource profile id. The resource profile id can be used with the ResourceProfileManager to get the actual ResourceProfile contents.
There are various places in the code that use executor "slots" for things. The ResourceProfile adds functionality to keep that calculation in it. This logic is more complex then it should due to standalone mode and mesos coarse grained not setting the executor cores config. They default to all cores on the worker, so calculating slots is harder there.
This PR keeps the functionality to make the cores the limiting resource because the scheduler still uses that for "slots" for a few things.
This PR does also add the resource profile id to the Stage and stage info classes to be able to test things easier. That full set of changes will come with the scheduler PR that will be after this one.
The PR stops at the scheduler backend pieces for the cluster manager and the real YARN support hasn't been added in this PR, that again will be in a separate PR, so this has a few of the API changes up to the cluster manager and then just uses the default profile requests to continue.
The code for the entire feature is here for reference: https://github.com/apache/spark/pull/27053/files although it needs to be upmerged again as well.
### Why are the changes needed?
Needed for stage level scheduling feature.
### Does this PR introduce any user-facing change?
No user facing api changes added here.
### How was this patch tested?
Lots of unit tests and manually testing. I tested on yarn, k8s, standalone, local modes. Ran both failure and success cases.
Closes#27313 from tgravescs/SPARK-29148.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Currently, it doesn't seem to be possible to have Spark Driver set the serviceAccountName for executor pods it launches.
### Why are the changes needed?
it will allow settings serviceAccountName for executors pods.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
It was covered by unit test.
Closes#27034 from ayudovin/srevice-account-name-for-executor-pods.
Authored-by: yudovin <artsiom.yudovin@profitero.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR use a specific version of docker image instead of `latest`. As of today, when I run K8s integration test locally, this test case fails always.
Also, in this PR, I shows two consecutive failures with a dummy change.
- https://github.com/apache/spark/pull/27465#issuecomment-582326614
- https://github.com/apache/spark/pull/27465#issuecomment-582329114
```
- Launcher client dependencies *** FAILED ***
```
After that, I added the patch and K8s Integration test passed.
- https://github.com/apache/spark/pull/27465#issuecomment-582361696
### Why are the changes needed?
[SPARK-28465](https://github.com/apache/spark/pull/25222) switched from `v4.0.0-stable-4.0-master-centos-7-x86_64` to `latest` to catch up the API change. However, the API change seems to occur again. We had better use a specific version to prevent accidental failures.
```scala
- .withImage("ceph/daemon:v4.0.0-stable-4.0-master-centos-7-x86_64")
+ .withImage("ceph/daemon:latest")
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass `Launcher client dependencies` test in Jenkins K8s Integration Suite.
Or, run K8s Integration test locally.
Closes#27465 from dongjoon-hyun/SPARK-K8S-IT.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Bump fabric8 kubernetes-client to 4.7.1
### Why are the changes needed?
New fabric8 version brings support for Kubernetes 1.17 clusters.
Full release notes:
- https://github.com/fabric8io/kubernetes-client/releases/tag/v4.7.0
- https://github.com/fabric8io/kubernetes-client/releases/tag/v4.7.1
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing unit and integration tests cover creation of K8S objects. Adjusted them to work with the new fabric8 version
Closes#27443 from onursatici/os/bump-fabric8.
Authored-by: Onur Satici <onursatici@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This change is to allow custom resource scheduler (GPUs,FPGAs,etc) resource discovery to be more flexible. Users are asking for it to work with hadoop 2.x versions that do not support resource scheduling in YARN and/or also they may not run in an isolated environment.
This change creates a plugin api that users can write their own resource discovery class that allows a lot more flexibility. The user can chain plugins for different resource types. The user specified plugins execute in the order specified and will fall back to use the discovery script plugin if they don't return information for a particular resource.
I had to open up a few of the classes to be public and change them to not be case classes and make them developer api in order for the the plugin to get enough information it needs.
I also relaxed the yarn side so that if yarn isn't configured for resource scheduling we just warn and go on. This helps users that have yarn 3.1 but haven't configured the resource scheduling side on their cluster yet, or aren't running in isolated environment.
The user would configured this like:
--conf spark.resources.discovery.plugin="org.apache.spark.resource.ResourceDiscoveryFPGAPlugin, org.apache.spark.resource.ResourceDiscoveryGPUPlugin"
Note the executor side had to be wrapped with a classloader to make sure we include the user classpath for jars they specified on submission.
Note this is more flexible because the discovery script has limitations such as spawning it in a separate process. This means if you are trying to allocate resources in that process they might be released when the script returns. Other things are the class makes it more flexible to be able to integrate with existing systems and solutions for assigning resources.
### Why are the changes needed?
to more easily use spark resource scheduling with older versions of hadoop or in non-isolated enivronments.
### Does this PR introduce any user-facing change?
Yes a plugin api
### How was this patch tested?
Unit tests added and manual testing done on yarn and standalone modes.
Closes#27410 from tgravescs/hadoop27spark3.
Lead-authored-by: Thomas Graves <tgraves@nvidia.com>
Co-authored-by: Thomas Graves <tgraves@apache.org>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Add the allocated resources to parameters to the PluginContext so that any plugins in driver or executor could use this information to initialize devices or use this information in a useful manner.
### Why are the changes needed?
To allow users to initialize/track devices once at the executor level before each task runs to use them.
### Does this PR introduce any user-facing change?
Yes to the people using the Executor/Driver plugin interface.
### How was this patch tested?
Unit tests and manually by writing a plugin that initialized GPU's using this interface.
Closes#27367 from tgravescs/pluginWithResources.
Lead-authored-by: Thomas Graves <tgraves@nvidia.com>
Co-authored-by: Thomas Graves <tgraves@apache.org>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Add SPARK_APPLICATION_ID environment when spark configure driver pod.
### Why are the changes needed?
Currently, driver doesn't have this in environments and it's no convenient to retrieve spark id.
The use case is we want to look up spark application id and create application folder and redirect driver logs to application folder.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
unit tested. I also build new distribution and container image to kick off a job in Kubernetes and I do see SPARK_APPLICATION_ID added there. .
Closes#27347 from Jeffwan/SPARK-30626.
Authored-by: Jiaxin Shan <seedjeffwan@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
make KUBERNETES_MASTER_INTERNAL_URL configurable
### Why are the changes needed?
we do not always use the default port number 443 to access our kube-apiserver, and even in some mulit-tenant cluster, people do not use the service `kubernetes.default.svc` to access the kube-apiserver, so make the internal master configurable is necessary。
### Does this PR introduce any user-facing change?
user can configure the internal master url by
```
--conf spark.kubernetes.internal.master=https://kubernetes.default.svc:6443
```
### How was this patch tested?
run in multi-cluster that do not use the https://kubernetes.default.svc to access the kube-apiserver
Closes#27029 from wackxu/internalmaster.
Authored-by: xushiwei 00425595 <xushiwei5@huawei.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This is the second PR for the Stage Level Scheduling. This is adding in the necessary executor side changes:
1) executors to know what ResourceProfile they should be using
2) handle parsing the resource profile settings - these are not in the global configs
3) then reporting back to the driver what resource profile it was started with.
This PR adds all the piping for YARN to pass the information all the way to executors, but it just uses the default ResourceProfile (which is the global applicatino level configs).
At a high level these changes include:
1) adding a new --resourceProfileId option to the CoarseGrainedExecutorBackend
2) Add the ResourceProfile settings to new internal confs that gets passed into the Executor
3) Executor changes that use the resource profile id passed in to read the corresponding ResourceProfile confs and then parse those requests and discover resources as necessary
4) Executor registers to Driver with the Resource profile id so that the ExecutorMonitor can track how many executor with each profile are running
5) YARN side changes to show that passing the resource profile id and confs actually works. Just uses the DefaultResourceProfile for now.
I also removed a check from the CoarseGrainedExecutorBackend that used to check to make sure there were task requirements before parsing any custom resource executor requests. With the resource profiles this becomes much more expensive because we would then have to pass the task requests to each executor and the check was just a short cut and not really needed. It was much cleaner just to remove it.
Note there were some changes to the ResourceProfile, ExecutorResourceRequests, and TaskResourceRequests in this PR as well because I discovered some issues with things not being immutable. That api now look like:
val rpBuilder = new ResourceProfileBuilder()
val ereq = new ExecutorResourceRequests()
val treq = new TaskResourceRequests()
ereq.cores(2).memory("6g").memoryOverhead("2g").pysparkMemory("2g").resource("gpu", 2, "/home/tgraves/getGpus")
treq.cpus(2).resource("gpu", 2)
val resourceProfile = rpBuilder.require(ereq).require(treq).build
This makes is so that ResourceProfile is immutable and Spark can use it directly without worrying about the user changing it.
### Why are the changes needed?
These changes are needed for the executor to report which ResourceProfile they are using so that ultimately the dynamic allocation manager can use that information to know how many with a profile are running and how many more it needs to request. Its also needed to get the resource profile confs to the executor so that it can run the appropriate discovery script if needed.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Unit tests and manually on YARN.
Closes#26682 from tgravescs/SPARK-29306.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
The issue here is that when Spark is downscaling the application and deletes
a few pod requests that aren't needed anymore, it may actually race with the
K8S scheduler, who may be bringing up those executors. So they may have enough
time to connect back to the driver, register, to just be deleted soon after.
This wastes resources and causes misleading entries in the driver log.
The change (ab)uses the blacklisting mechanism to consider the deleted excess
pods as blacklisted, so that if they try to connect back, the driver will deny
it.
It also changes the executor registration slightly, since even with the above
change there were misleading logs. That was because the executor registration
message was an RPC that always succeeded (bar network issues), so the executor
would always try to send an unregistration message to the driver, which would
then log several messages about not knowing anything about the executor. The
change makes the registration RPC succeed or fail directly, instead of using
the separate failure message that would lead to this issue.
Note the last change required some changes in a standalone test suite related
to dynamic allocation, since it relied on the driver not throwing exceptions
when a duplicate executor registration happened.
Tested with existing unit tests, and with live cluster with dyn alloc on.
Closes#26586 from vanzin/SPARK-29950.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
Remove `executorsPendingToRemove.clear()` from `CoarseGrainedSchedulerBackend.reset()`.
### Why are the changes needed?
Clear `executorsPendingToRemove` before remove executors will cause all tasks running on those "pending to remove" executors to count failures. But that's not true for the case of `executorsPendingToRemove(execId)=true`.
Besides, `executorsPendingToRemove` will be cleaned up within `removeExecutor()` at the end just as same as `executorsPendingLossReason`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added a new test in `TaskSetManagerSuite`.
Closes#27017 from Ngone51/dont-clear-eptr-in-reset.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
ShutdownHook of YarnClientSchedulerBackend prints just "Stopped" which can be improved to "YarnClientSchedulerBackend Stopped" for better understanding.
### Why are the changes needed?
While stopping or gracefully exiting the spark-shell/spark-sql --master yarn, only printing `stopped` is useless.
### Does this PR introduce any user-facing change?
Yes. Log info message change.
### How was this patch tested?
Manually
Closes#27049 from jobitmathew/imp_stop_message.
Authored-by: Jobit Mathew <jobit.mathew@huawei.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
1. Revert "Preparing development version 3.0.1-SNAPSHOT": 56dcd79
2. Revert "Preparing Spark release v3.0.0-preview2-rc2": c216ef1
### Why are the changes needed?
Shouldn't change master.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
manual test:
https://github.com/apache/spark/compare/5de5e46..wangyum:revert-masterCloses#26915 from wangyum/revert-master.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
Include `$SPARK_DIST_CLASSPATH` in class path when launching `CoarseGrainedExecutorBackend` on Kubernetes executors using the provided `entrypoint.sh`
### Why are the changes needed?
For user provided Hadoop, `$SPARK_DIST_CLASSPATH` contains the required jars.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
Kubernetes 1.14, Spark 2.4.4, Hadoop 3.2.1. Adding $SPARK_DIST_CLASSPATH to `-cp ` param of entrypoint.sh enables launching the executors correctly.
Closes#26493 from sshakeri/master.
Authored-by: Shahin Shakeri <shahin.shakeri@pwc.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
Added apt-get update as per [docker best-practices](https://docs.docker.com/develop/develop-images/dockerfile_best-practices/#apt-get)
### Why are the changes needed?
Builder is failing because:
Without doing apt-get update, the APT lists get outdated and begins referring to package versions that no longer exist, hence the 404 trying to download them (Debian does not keep old versions in the archive when a package is updated).
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
k8s builder
Closes#26753 from ifilonenko/SPARK-30111.
Authored-by: Ilan Filonenko <ifilonenko@bloomberg.net>
Signed-off-by: shane knapp <incomplete@gmail.com>
### What changes were proposed in this pull request?
Use JUnit assertions in tests uniformly, not JVM assert() statements.
### Why are the changes needed?
assert() statements do not produce as useful errors when they fail, and, if they were somehow disabled, would fail to test anything.
### Does this PR introduce any user-facing change?
No. The assertion logic should be identical.
### How was this patch tested?
Existing tests.
Closes#26581 from srowen/assertToJUnit.
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?
When set `spark.yarn.jars=/xxx/xxx` which is just a no schema path, spark will throw a NullPointerException.
The reason is hdfs will return null if pathFs.globStatus(path) is not exist, and spark just use `pathFs.globStatus(path).filter(_.isFile())` without check it.
### Why are the changes needed?
Avoid NullPointerException.
### Does this PR introduce any user-facing change?
Yes. User will get a FileNotFoundException instead NullPointerException when `spark.yarn.jars` does not have schema and not exists.
### How was this patch tested?
Add UT.
Closes#26462 from ulysses-you/check-yarn-jars-path-exist.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
This basically does what BasicDriverFeatureStep already does to achieve the
same thing in cluster mode; but since that class (or any other feature) is
not invoked in client mode, it needs to be done elsewhere.
I also modified the client mode integration test to check the executor name
prefix; while there I had to fix the minikube backend to parse the output
from newer minikube versions (I have 1.5.2).
Closes#26488 from vanzin/SPARK-29865.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Erik Erlandson <eerlands@redhat.com>
### What changes were proposed in this pull request?
With this change, executor's bindAddress is passed as an input parameter for RPCEnv.create.
A previous PR https://github.com/apache/spark/pull/21261 which addressed the same, was using a Spark Conf property to get the bindAddress which wouldn't have worked for multiple executors.
This PR is to enable anyone overriding CoarseGrainedExecutorBackend with their custom one to be able to invoke CoarseGrainedExecutorBackend.main() along with the option to configure bindAddress.
### Why are the changes needed?
This is required when Kernel-based Virtual Machine (KVM)'s are used inside Linux container where the hostname is not the same as container hostname.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Tested by running jobs with executors on KVMs inside a linux container.
Closes#26331 from nishchalv/SPARK-29670.
Lead-authored-by: Nishchal Venkataramana <nishchal@apple.com>
Co-authored-by: nishchal <nishchal@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
Priority for YARN to define pending applications ordering policy, those with higher priority have a better opportunity to be activated. YARN CapacityScheduler only.
### Why are the changes needed?
Ordering pending spark apps
### Does this PR introduce any user-facing change?
add a conf
### How was this patch tested?
add ut
Closes#26255 from yaooqinn/SPARK-29603.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
To push the built jars to maven release repository, we need to remove the 'SNAPSHOT' tag from the version name.
Made the following changes in this PR:
* Update all the `3.0.0-SNAPSHOT` version name to `3.0.0-preview`
* Update the sparkR version number check logic to allow jvm version like `3.0.0-preview`
**Please note those changes were generated by the release script in the past, but this time since we manually add tags on master branch, we need to manually apply those changes too.**
We shall revert the changes after 3.0.0-preview release passed.
### Why are the changes needed?
To make the maven release repository to accept the built jars.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
### What changes were proposed in this pull request?
To push the built jars to maven release repository, we need to remove the 'SNAPSHOT' tag from the version name.
Made the following changes in this PR:
* Update all the `3.0.0-SNAPSHOT` version name to `3.0.0-preview`
* Update the PySpark version from `3.0.0.dev0` to `3.0.0`
**Please note those changes were generated by the release script in the past, but this time since we manually add tags on master branch, we need to manually apply those changes too.**
We shall revert the changes after 3.0.0-preview release passed.
### Why are the changes needed?
To make the maven release repository to accept the built jars.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26243 from jiangxb1987/3.0.0-preview-prepare.
Lead-authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
Updated kubernetes client.
### Why are the changes needed?
https://issues.apache.org/jira/browse/SPARK-27812https://issues.apache.org/jira/browse/SPARK-27927
We need this fix https://github.com/fabric8io/kubernetes-client/pull/1768 that was released on version 4.6 of the client. The root cause of the problem is better explained in https://github.com/apache/spark/pull/25785
### Does this PR introduce any user-facing change?
Nope, it should be transparent to users
### How was this patch tested?
This patch was tested manually using a simple pyspark job
```python
from pyspark.sql import SparkSession
if __name__ == '__main__':
spark = SparkSession.builder.getOrCreate()
```
The expected behaviour of this "job" is that both python's and jvm's process exit automatically after the main runs. This is the case for spark versions <= 2.4. On version 2.4.3, the jvm process hangs because there's a non daemon thread running
```
"OkHttp WebSocket https://10.96.0.1/..." #121 prio=5 os_prio=0 tid=0x00007fb27c005800 nid=0x24b waiting on condition [0x00007fb300847000]
"OkHttp WebSocket https://10.96.0.1/..." #117 prio=5 os_prio=0 tid=0x00007fb28c004000 nid=0x247 waiting on condition [0x00007fb300e4b000]
```
This is caused by a bug on `kubernetes-client` library, which is fixed on the version that we are upgrading to.
When the mentioned job is run with this patch applied, the behaviour from spark <= 2.4.3 is restored and both processes terminate successfully
Closes#26093 from igorcalabria/k8s-client-update.
Authored-by: igor.calabria <igor.calabria@ubee.in>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Support executor for selecting scheduler through scheduler name in the case of k8s multi-scheduler scenario.
### Why are the changes needed?
If there is no such function, spark can not support the case of k8s multi-scheduler scenario.
### Does this PR introduce any user-facing change?
Yes, users can add scheduler name through configuration.
### How was this patch tested?
Manually tested with spark + k8s cluster
Closes#26088 from merrily01/SPARK-29436.
Authored-by: maruilei <maruilei@jd.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
This pr invoke the start method of `LoggingPodStatusWatcherImpl` for status logging at intervals.
### Why are the changes needed?
This pr invoke the start method of `LoggingPodStatusWatcherImpl` is declared but never called
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
manually test
Closes#25648 from yaooqinn/SPARK-28947.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Re-introduced SparkR integration tests as part of the SparkR on K8S release. This PR awaits Jenkins availability.
## How was this patch tested?
This patch was tested with unit tests and integration tests.
Closes#22145 from ifilonenko/spark-r-with-tests.
Authored-by: Ilan Filonenko <if56@cornell.edu>
Signed-off-by: shane knapp <incomplete@gmail.com>
### What changes were proposed in this pull request?
Use `.sameElements` to compare (non-nested) arrays, as `Arrays.deep` is removed in 2.13 and wasn't the best way to do this in the first place.
### Why are the changes needed?
To compile with 2.13.
### Does this PR introduce any user-facing change?
None.
### How was this patch tested?
Existing tests.
Closes#26073 from srowen/SPARK-29416.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
The current docker image used by Kubernetes is `openjdk:8-alpine`. It was not supported and was removed with the commit 3eb0351b20 (diff-f95ffa3d1377774732c33f7b8368e099).
This PR proposes to move to a supported docker image.
### Why are the changes needed?
I think there are at least two reasons:
1. According to the commit, Alpine/musl is not officially supported by the OpenJDK project.
2. As no more OpenJDK 8 Alpine images, new JDK updates including security fixes
, are not applied to it. See below:
```
docker run -it --rm openjdk:8-alpine java -version
openjdk version "1.8.0_212"
OpenJDK Runtime Environment (IcedTea 3.12.0) (Alpine 8.212.04-r0)
OpenJDK 64-Bit Server VM (build 25.212-b04, mixed mode)
```
```
docker run -it --rm openjdk:8-jdk-slim java -version
openjdk version "1.8.0_222"
OpenJDK Runtime Environment (build 1.8.0_222-b10)
OpenJDK 64-Bit Server VM (build 25.222-b10, mixed mode)
```
### Does this PR introduce any user-facing change?
Yes. This changes the base docker image of Spark.
### How was this patch tested?
Existing tests.
Closes#26037 from viirya/SPARK-28938.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In kubernetes, there are some naming regular expression requirements and restrictions on environment variable names, such as:
- In kubernetes version release-1.7 and earlier, the naming rules of pod environment variable names should meet the requirements of regular expressions: [[A-Za-z_] [A-Za-z0-9_]*](https://github.com/kubernetes/kubernetes/blob/release-1.7/staging/src/k8s.io/apimachinery/pkg/util/validation/validation.go#L169)
- In kubernetes version release-1.8 and later, the naming rules of pod environment variable names should meet the requirements of regular expressions: [[-. _ A-ZA-Z][-. _ A-ZA-Z0-9].*](https://github.com/kubernetes/kubernetes/blob/release-1.8/staging/src/k8s.io/apimachinery/pkg/util/validation/validation.go#L305)
However, in spark on k8s mode, spark should add restrictions on environmental variable names when creating executorEnv.
In addition, we need to use regular expressions adapted to the high version of k8s to increase the restrictions on the names of environmental variables.
Otherwise, the pod will not be created properly and the spark application will be suspended.
To solve the problem above, a regular validation to executorEnv is added and committed.
### Why are the changes needed?
If no validation rules are added, the environment variable names that don't meet the requirements will cause the pod to not be created properly and the application will be suspended.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Add unit tests and manually run.
Closes#25920 from merrily01/SPARK-29233.
Authored-by: maruilei <maruilei@jd.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to remove `scalatest` deprecation warnings with the following changes.
- `org.scalatest.mockito.MockitoSugar` -> `org.scalatestplus.mockito.MockitoSugar`
- `org.scalatest.selenium.WebBrowser` -> `org.scalatestplus.selenium.WebBrowser`
- `org.scalatest.prop.Checkers` -> `org.scalatestplus.scalacheck.Checkers`
- `org.scalatest.prop.GeneratorDrivenPropertyChecks` -> `org.scalatestplus.scalacheck.ScalaCheckDrivenPropertyChecks`
### Why are the changes needed?
According to the Jenkins logs, there are 118 warnings about this.
```
grep "is deprecated" ~/consoleText | grep scalatest | wc -l
118
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
After Jenkins passes, we need to check the Jenkins log.
Closes#25982 from dongjoon-hyun/SPARK-29307.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Scala 2.13 emits a deprecation warning for procedure-like declarations:
```
def foo() {
...
```
This is equivalent to the following, so should be changed to avoid a warning:
```
def foo(): Unit = {
...
```
### Why are the changes needed?
It will avoid about a thousand compiler warnings when we start to support Scala 2.13. I wanted to make the change in 3.0 as there are less likely to be back-ports from 3.0 to 2.4 than 3.1 to 3.0, for example, minimizing that downside to touching so many files.
Unfortunately, that makes this quite a big change.
### Does this PR introduce any user-facing change?
No behavior change at all.
### How was this patch tested?
Existing tests.
Closes#25968 from srowen/SPARK-29291.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Switch from using a Thread sleep for waiting for commands to finish to just waiting for the command to finish with a watcher & improve the error messages in the SecretsTestsSuite.
### Why are the changes needed?
Currently some of the Spark Kubernetes tests have race conditions with command execution, and the frequent use of eventually makes debugging test failures difficult.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing tests pass after removal of thread.sleep
Closes#25765 from holdenk/SPARK-28937SPARK-28936-improve-kubernetes-integration-tests.
Authored-by: Holden Karau <hkarau@apple.com>
Signed-off-by: Holden Karau <hkarau@apple.com>
### What changes were proposed in this pull request?
This PR aims to increase the JVM CodeCacheSize from 0.5G to 1G.
### Why are the changes needed?
After upgrading to `Scala 2.12.10`, the following is observed during building.
```
2019-09-18T20:49:23.5030586Z OpenJDK 64-Bit Server VM warning: CodeCache is full. Compiler has been disabled.
2019-09-18T20:49:23.5032920Z OpenJDK 64-Bit Server VM warning: Try increasing the code cache size using -XX:ReservedCodeCacheSize=
2019-09-18T20:49:23.5034959Z CodeCache: size=524288Kb used=521399Kb max_used=521423Kb free=2888Kb
2019-09-18T20:49:23.5035472Z bounds [0x00007fa62c000000, 0x00007fa64c000000, 0x00007fa64c000000]
2019-09-18T20:49:23.5035781Z total_blobs=156549 nmethods=155863 adapters=592
2019-09-18T20:49:23.5036090Z compilation: disabled (not enough contiguous free space left)
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually check the Jenkins or GitHub Action build log (which should not have the above).
Closes#25836 from dongjoon-hyun/SPARK-CODE-CACHE-1G.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In `ApplicationMaster.Reporter` thread, fatal exception information is swallowed. It's better to expose it.
We found our thrift server was shutdown due to a fatal exception but no useful information from log.
> 19/09/16 06:59:54,498 INFO [Reporter] yarn.ApplicationMaster:54 : Final app status: FAILED, exitCode: 12, (reason: Exception was thrown 1 time(s) from Reporter thread.)
19/09/16 06:59:54,500 ERROR [Driver] thriftserver.HiveThriftServer2:91 : Error starting HiveThriftServer2
java.lang.InterruptedException: sleep interrupted
at java.lang.Thread.sleep(Native Method)
at org.apache.spark.sql.hive.thriftserver.HiveThriftServer2$.main(HiveThriftServer2.scala:160)
at org.apache.spark.sql.hive.thriftserver.HiveThriftServer2.main(HiveThriftServer2.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$4.run(ApplicationMaster.scala:708)
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manual test
Closes#25810 from LantaoJin/SPARK-29112.
Authored-by: LantaoJin <jinlantao@gmail.com>
Signed-off-by: jerryshao <jerryshao@tencent.com>
### What changes were proposed in this pull request?
In this PR, I fix some annotation errors and remove meaningless annotations in project.
### Why are the changes needed?
There are some annotation errors and meaningless annotations in project.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Verified manually.
Closes#25809 from turboFei/SPARK-29113.
Authored-by: turbofei <fwang12@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Make r file extension check case insensitive for spark-submit.
### Why are the changes needed?
spark-submit does not accept `.r` files as R scripts. Some codebases have r files that end with lowercase file extensions. It is inconvenient to use spark-submit with lowercase extension R files. The error is not very clear (https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala#L232).
```
$ ./bin/spark-submit examples/src/main/r/dataframe.r
Exception in thread "main" org.apache.spark.SparkException: Cannot load main class from JAR file:/Users/dongjoon/APACHE/spark-release/spark-2.4.4-bin-hadoop2.7/examples/src/main/r/dataframe.r
```
### Does this PR introduce any user-facing change?
Yes. spark-submit can now be used to run R scripts with `.r` file extension.
### How was this patch tested?
Manual.
```
$ mv examples/src/main/r/dataframe.R examples/src/main/r/dataframe.r
$ ./bin/spark-submit examples/src/main/r/dataframe.r
```
Closes#25778 from Loquats/r-case.
Authored-by: Andy Zhang <yue.zhang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
- Remove SQLContext.createExternalTable and Catalog.createExternalTable, deprecated in favor of createTable since 2.2.0, plus tests of deprecated methods
- Remove HiveContext, deprecated in 2.0.0, in favor of `SparkSession.builder.enableHiveSupport`
- Remove deprecated KinesisUtils.createStream methods, plus tests of deprecated methods, deprecate in 2.2.0
- Remove deprecated MLlib (not Spark ML) linear method support, mostly utility constructors and 'train' methods, and associated docs. This includes methods in LinearRegression, LogisticRegression, Lasso, RidgeRegression. These have been deprecated since 2.0.0
- Remove deprecated Pyspark MLlib linear method support, including LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD
- Remove 'runs' argument in KMeans.train() method, which has been a no-op since 2.0.0
- Remove deprecated ChiSqSelector isSorted protected method
- Remove deprecated 'yarn-cluster' and 'yarn-client' master argument in favor of 'yarn' and deploy mode 'cluster', etc
Notes:
- I was not able to remove deprecated DataFrameReader.json(RDD) in favor of DataFrameReader.json(Dataset); the former was deprecated in 2.2.0, but, it is still needed to support Pyspark's .json() method, which can't use a Dataset.
- Looks like SQLContext.createExternalTable was not actually deprecated in Pyspark, but, almost certainly was meant to be? Catalog.createExternalTable was.
- I afterwards noted that the toDegrees, toRadians functions were almost removed fully in SPARK-25908, but Felix suggested keeping just the R version as they hadn't been technically deprecated. I'd like to revisit that. Do we really want the inconsistency? I'm not against reverting it again, but then that implies leaving SQLContext.createExternalTable just in Pyspark too, which seems weird.
- I *kept* LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD, RidgeRegressionWithSGD in Pyspark, though deprecated, as it is hard to remove them (still used by StreamingLogisticRegressionWithSGD?) and they are not fully removed in Scala. Maybe should not have been deprecated.
### Why are the changes needed?
Deprecated items are easiest to remove in a major release, so we should do so as much as possible for Spark 3. This does not target items deprecated 'recently' as of Spark 2.3, which is still 18 months old.
### Does this PR introduce any user-facing change?
Yes, in that deprecated items are removed from some public APIs.
### How was this patch tested?
Existing tests.
Closes#25684 from srowen/SPARK-28980.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Matches the response from minikube service against a regex to extract the URL
### Why are the changes needed?
minikube 1.3.1 on OSX has different formatting than expected
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Ran the existing integration test run on OSX with minikube 1.3.1
Closes#25599 from holdenk/SPARK-28886-fix-deps-tests-with-minikube-1.3.1.
Authored-by: Holden Karau <hkarau@apple.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Per https://github.com/apache/spark/pull/25640#issuecomment-527397689 also bump K8S client version in integration-tests module.
### Why are the changes needed?
Harmonize the version as intended.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#25664 from srowen/SPARK-28921.2.
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?
If MEMORY_OFFHEAP_ENABLED is true, add MEMORY_OFFHEAP_SIZE to resource requested for executor to ensure instance has enough memory to use.
In this pr add a helper method `executorOffHeapMemorySizeAsMb` in `YarnSparkHadoopUtil`.
## How was this patch tested?
Add 3 new test suite to test `YarnSparkHadoopUtil#executorOffHeapMemorySizeAsMb`
Closes#25309 from LuciferYang/spark-28577.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Upgrade kubernetes client from 4.1.2 to 4.4.2
### Why are the changes needed?
To fix compatibility issue with EKS since Amazon rolled out some security patches over the past week; 1.15.3, 1.14.6, 1.13.10, 1.12.10, and 1.11.10.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Pass the Jenkins and manually test on EKS.
Closes#25640 from andygrove/SPARK-28921.
Authored-by: Andy Grove <andygrove73@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This fixes issues that can arise when the jars for different hadoop versions mix, and short-circuits the case where we are running with a spark that was not built for yarn 3 (resource support).
## How was this patch tested?
I tested it manually.
Closes#25403 from abellina/SPARK-28679.
Authored-by: Alessandro Bellina <abellina@nvidia.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
we need to add the ability to test PRBs against java11.
see comments here: https://github.com/apache/spark/pull/25405
## How was this patch tested?
the build system will test this.
Closes#25423 from shaneknapp/spark-prb-java11.
Authored-by: shane knapp <incomplete@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Resolves [SPARK-28778: Shuffle jobs fail due to incorrect advertised address when running in a virtual network on Mesos](https://issues.apache.org/jira/browse/SPARK-28778).
This patch fixes a bug which occurs when shuffle jobs are launched by Mesos in a virtual network. Mesos scheduler sets executor `--hostname` parameter to `0.0.0.0` in the case when `spark.mesos.network.name` is provided. This makes executors use `0.0.0.0` as their advertised address and, in the presence of shuffle, executors fail to fetch shuffle blocks from each other using `0.0.0.0` as the origin. When a virtual network is used the hostname or IP address is not known upfront and assigned to a container at its start time so the executor process needs to advertise the correct dynamically assigned address to be reachable by other executors.
Changes:
- added a fallback to `Utils.localHostName()` in Spark Executors when `--hostname` is not provided
- removed setting executor address to `0.0.0.0` from Mesos scheduler
- refactored the code related to building executor command in Mesos scheduler
- added network configuration support to Docker containerizer
- added unit tests
### Why are the changes needed?
The bug described above prevents Mesos users from running any jobs which involve shuffle due to the inability of executors to fetch shuffle blocks because of incorrect advertised address when virtual network is used.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
- added unit test to `MesosCoarseGrainedSchedulerBackendSuite` which verifies the absence of `--hostname` parameter when `spark.mesos.network.name` is provided and its presence otherwise
- added unit test to `MesosSchedulerBackendUtilSuite` which verifies that `MesosSchedulerBackendUtil.buildContainerInfo` sets network-related properties for Docker containerizer
- unit tests from this repo launched with profiles: `./build/mvn test -Pmesos -Pnetlib-lgpl -Psparkr -Phive -Phive-thriftserver`, build log attached: [mvn.test.log](https://github.com/apache/spark/files/3516891/mvn.test.log)
- integration tests from [DCOS Spark repo](https://github.com/mesosphere/spark-build), more specifically - [test_spark_cni.py](https://github.com/mesosphere/spark-build/blob/master/tests/test_spark_cni.py) which runs a specific [shuffle job](https://github.com/mesosphere/spark-build/blob/master/tests/jobs/scala/src/main/scala/ShuffleApp.scala) and verifies its successful completion, Mesos task network configuration, and IP addresses for both Mesos and Docker containerizers
Closes#25500 from akirillov/DCOS-45840-fix-advertised-ip-in-virtual-networks.
Authored-by: Anton Kirillov <akirillov@mesosophere.io>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
This change makes the client mode AM ignore any login configuration,
which is now always handled by the driver. The previous code tried
to achieve that by modifying the configuration visible to the AM, but
that missed the case where old configuration names were being used.
Tested in real cluster with reproduction provided in the bug.
Closes#25467 from vanzin/SPARK-28634.
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 explicit exclusions to avoid Maven `JDK11` dependency issues.
### Why are the changes needed?
Maven/Ivy seems to be confused during dependency generation on `JDK11` environment.
This is not only wrong, but also causes a Jenkins failure during dependency manifest check on `JDK11` environment.
**JDK8**
```
$ cd core
$ mvn -X dependency:tree -Dincludes=jakarta.activation:jakarta.activation-api
...
[DEBUG] org.glassfish.jersey.core:jersey-server:jar:2.29:compile (version managed from 2.22.2)
[DEBUG] org.glassfish.jersey.media:jersey-media-jaxb:jar:2.29:compile
[DEBUG] javax.validation:validation-api:jar:2.0.1.Final:compile
```
**JDK11**
```
[DEBUG] org.glassfish.jersey.core:jersey-server:jar:2.29:compile (version managed from 2.22.2)
[DEBUG] org.glassfish.jersey.media:jersey-media-jaxb:jar:2.29:compile
[DEBUG] javax.validation:validation-api:jar:2.0.1.Final:compile
[DEBUG] jakarta.xml.bind:jakarta.xml.bind-api🫙2.3.2:compile
[DEBUG] jakarta.activation:jakarta.activation-api🫙1.2.1:compile
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Do the following in both `JDK8` and `JDK11` environment. The dependency manifest should not be changed. In the current `master` branch, `JDK11` changes the dependency manifest.
```
$ dev/test-dependencies.sh --replace-manifest
```
Closes#25481 from dongjoon-hyun/SPARK-28765.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
This change implements a few changes to the k8s pod allocator so
that it behaves a little better when dynamic allocation is on.
(i) Allow the application to ramp up immediately when there's a
change in the target number of executors. Without this change,
scaling would only trigger when a change happened in the state of
the cluster, e.g. an executor going down, or when the periodical
snapshot was taken (default every 30s).
(ii) Get rid of pending pod requests, both acknowledged (i.e. Spark
knows that a pod is pending resource allocation) and unacknowledged
(i.e. Spark has requested the pod but the API server hasn't created it
yet), when they're not needed anymore. This avoids starting those
executors to just remove them after the idle timeout, wasting resources
in the meantime.
(iii) Re-work some of the code to avoid unnecessary logging. While not
bad without dynamic allocation, the existing logging was very chatty
when dynamic allocation was on. With the changes, all the useful
information is still there, but only when interesting changes happen.
(iv) Gracefully shut down executors when they become idle. Just deleting
the pod causes a lot of ugly logs to show up, so it's better to ask pods
to exit nicely. That also allows Spark to respect the "don't delete
pods" option when dynamic allocation is on.
Tested on a small k8s cluster running different TPC-DS workloads.
Closes#25236 from vanzin/SPARK-28487.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Current two PySpark version tests in PythonTestsSuite, just test against Python version at driver side. Because the test script doesn't run any spark job requiring python worker, it doesn't actually do version check at worker side. This patch adds pieces of code to the test script, to run a simple job to verify Python version.
## How was this patch tested?
Unit test. Locally manual test.
Closes#25411 from viirya/SPARK-28652.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This patch tries to keep consistency whenever UTF-8 charset is needed, as using `StandardCharsets.UTF_8` instead of using "UTF-8". If the String type is needed, `StandardCharsets.UTF_8.name()` is used.
This change also brings the benefit of getting rid of `UnsupportedEncodingException`, as we're providing `Charset` instead of `String` whenever possible.
This also changes some private Catalyst helper methods to operate on encodings as `Charset` objects rather than strings.
## How was this patch tested?
Existing unit tests.
Closes#25335 from HeartSaVioR/SPARK-28601.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
After SPARK-27677, the shuffle client not only handles the shuffle block but also responsible for local persist RDD blocks. For better code scalability and precise semantics(as the [discussion](https://github.com/apache/spark/pull/24892#discussion_r300173331)), here we did several changes:
- Rename ShuffleClient to BlockStoreClient.
- Correspondingly rename the ExternalShuffleClient to ExternalBlockStoreClient, also change the server-side class from ExternalShuffleBlockHandler to ExternalBlockHandler.
- Move MesosExternalBlockStoreClient to Mesos package.
Note, we still keep the name of BlockTransferService, because the `Service` contains both client and server, also the name of BlockTransferService is not referencing shuffle client only.
## How was this patch tested?
Existing UT.
Closes#25327 from xuanyuanking/SPARK-28593.
Lead-authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Co-authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently, if we run the Kubernetes integration tests with `SPARK_HOME` already set, it refers the `SPARK_HOME` even when `--spark-tgz` is specified.
This PR proposes to unset `SPARK_HOME` to let the docker-image-tool script detect `SPARK_HOME`. Otherwise, it cannot indicate the unpacked directory as its home.
## How was this patch tested?
```bash
export SPARK_HOME=`pwd`
dev/make-distribution.sh --pip --tgz -Phadoop-2.7 -Pkubernetes
resource-managers/kubernetes/integration-tests/dev/dev-run-integration-tests.sh --deploy-mode docker-for-desktop --spark-tgz $PWD/spark-*.tgz
```
**Before:**
```
+ /.../spark/resource-managers/kubernetes/integration-tests/target/spark-dist-unpacked/bin/docker-image-tool.sh -r docker.io/kubespark -t 650B51C8-BBED-47C9-AEAB-E66FC9A0E64E -p /.../spark/resource-managers/kubernetes/integration-tests/target/spark-dist-unpacked/kubernetes/dockerfiles/spark/bindings/python/Dockerfile build
cp: resource-managers/kubernetes/docker/src/main/dockerfiles: No such file or directory
cp: assembly/target/scala-2.12/jars: No such file or directory
cp: resource-managers/kubernetes/integration-tests/tests: No such file or directory
cp: examples/target/scala-2.12/jars/*: No such file or directory
cp: resource-managers/kubernetes/docker/src/main/dockerfiles: No such file or directory
cp: resource-managers/kubernetes/docker/src/main/dockerfiles: No such file or directory
Cannot find docker image. This script must be run from a runnable distribution of Apache Spark.
...
[INFO] Spark Project Kubernetes Integration Tests ......... FAILURE [ 4.870 s]
[INFO] ------------------------------------------------------------------------
[INFO] BUILD FAILURE
```
**After:**
```
+ /.../spark/resource-managers/kubernetes/integration-tests/target/spark-dist-unpacked/bin/docker-image-tool.sh -r docker.io/kubespark -t 2BA5883A-A0AC-4D2B-8D00-702D31B59B23 -p /.../spark/resource-managers/kubernetes/integration-tests/target/spark-dist-unpacked/kubernetes/dockerfiles/spark/bindings/python/Dockerfile build
Sending build context to Docker daemon 250.2MB
Step 1/15 : FROM openjdk:8-alpine
---> a3562aa0b991
...
Successfully built 8614fb5ac279
Successfully tagged kubespark/spark:2BA5883A-A0AC-4D2B-8D00-702D31B59B23
```
Closes#25283 from HyukjinKwon/SPARK-28550.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This pr is used to support using hostpath/PV volume mounts as local storage. In KubernetesExecutorBuilder.scala, the LocalDrisFeatureStep is built before MountVolumesFeatureStep which means we cannot use any volumes mount later. This pr adjust the order of feature building steps which moves localDirsFeature at last so that we can check if directories in SPARK_LOCAL_DIRS are set to volumes mounted such as hostPath, PV, or others.
## How was this patch tested?
Unit tests
Closes#24879 from chenjunjiedada/SPARK-28042.
Lead-authored-by: Junjie Chen <jimmyjchen@tencent.com>
Co-authored-by: Junjie Chen <cjjnjust@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This PR aims to recover our K8s integration test suite by extending node affinity in order to pass `PVTestsSuite` in `DockerForDesktop` environment, too. Previously, `PVTestsSuite` fails at `--deploy-mode docker-for-desktop` option because the node affinity requires `minibase` node.
For `Docker Desktop`, there are two node names like the following. Note that Spark testing needs K8s v1.13 and above. So, this PR should be verified with `Docker Desktop (Edge)` version. This PR adds both because next stable `Docker Desktop` will have K8s v1.14.3.
**Docker Desktop (Stable, K8s v1.10.11)**
```
$ kubectl get node
NAME STATUS ROLES AGE VERSION
docker-for-desktop Ready master 52s v1.10.11
```
**Docker Desktop 2.1.0.0 (Edge, K8s v1.14.3, Released 2019-07-26)**
```
$ kubectl get node
NAME STATUS ROLES AGE VERSION
docker-desktop Ready master 16h v1.14.3
```
## How was this patch tested?
Pass the Jenkins K8s integration test (`minibase`) and install `Docker Desktop 2.1.0.0 (Edge)` and run the integration test in `DockerForDesktop`. Note that this fixes only `PVTestsSuite`.
```
$ dev/make-distribution.sh --pip --tgz -Phadoop-2.7 -Pkubernetes
$ resource-managers/kubernetes/integration-tests/dev/dev-run-integration-tests.sh --deploy-mode docker-for-desktop --spark-tgz $PWD/spark-*.tgz
...
KubernetesSuite:
...
- PVs with local storage
...
```
Closes#25269 from dongjoon-hyun/SPARK-28534.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Fixes the tests. Follows instructions here: https://github.com/ceph/cn/issues/115#issuecomment-497384369
## How was this patch tested?
Manually by running the tests with minikube.
Closes#25222 from skonto/fix-ceph.
Authored-by: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Change the format of the build command in the README to start with a `./` prefix
./build/mvn -DskipTests clean package
This increases stylistic consistency across the README- all the other commands have a `./` prefix. Having a visible `./` prefix also makes it clear to the user that the shell command requires the current working directory to be at the repository root.
## How was this patch tested?
README.md was reviewed both in raw markdown and in the Github rendered landing page for stylistic consistency.
Closes#25231 from Mister-Meeseeks/master.
Lead-authored-by: Douglas R Colkitt <douglas.colkitt@gmail.com>
Co-authored-by: Mister-Meeseeks <douglas.colkitt@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
we are adding in generic resource support into spark where we have suffix for the amount of the resource so that we could support other configs.
Spark on yarn already had added configs to request resources via the configs spark.yarn.{executor/driver/am}.resource=<some amount>, where the <some amount> is value and unit together. We should change those configs to have a `.amount` suffix on them to match the spark configs and to allow future configs to be more easily added. YARN itself already supports tags and attributes so if we want the user to be able to pass those from spark at some point having a suffix makes sense. it would allow for a spark.yarn.{executor/driver/am}.resource.{resource}.tag= type config.
## How was this patch tested?
Tested via unit tests and manually on a yarn 3.x cluster with GPU resources configured on.
Closes#24989 from tgravescs/SPARK-27959-yarn-resourceconfigs.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This PR adds two new config properties: `spark.driver.defaultJavaOptions` and `spark.executor.defaultJavaOptions`. These are intended to be set by administrators in a file of defaults for options like JVM garbage collection algorithm. Users will still set `extraJavaOptions` properties, and both sets of JVM options will be added to start a JVM (default options are prepended to extra options).
## How was this patch tested?
Existing + additional unit tests.
```
cd docs/
SKIP_API=1 jekyll build
```
Manual webpage check.
Closes#24804 from gaborgsomogyi/SPARK-23472.
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?
Add error handling to `ExecutorPodsPollingSnapshotSource`
Closes#24952 from onursatici/os/polling-source.
Authored-by: Onur Satici <onursatici@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
SparkRackResolver generates an INFO message every time is called with 0 arguments.
In this PR I've deleted it because it's too verbose.
## How was this patch tested?
Existing unit tests + spark-shell.
Closes#24935 from gaborgsomogyi/SPARK-28005.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
Continue the work from https://github.com/apache/spark/pull/24821. Refactor resource handling code to make the code more readable. Major changes:
* Moved resource-related classes to `spark.resource` from `spark`.
* Added ResourceUtils and helper classes so we don't need to directly deal with Spark conf.
* ResourceID: resource identifier and it provides conf keys
* ResourceRequest/Allocation: abstraction for requested and allocated resources
* Added `TestResourceIDs` to reference commonly used resource IDs in tests like `spark.executor.resource.gpu`.
cc: tgravescs jiangxb1987 Ngone51
## How was this patch tested?
Unit tests for added utils and existing unit tests.
Closes#24856 from mengxr/SPARK-27823.
Lead-authored-by: Xiangrui Meng <meng@databricks.com>
Co-authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
## What changes were proposed in this pull request?
Fixes the service account inconsistency that breaks pull secrets. It gives the option to the user to setup a specific service account for the executors if he has to
(via `spark.kubernetes.authenticate.executor.serviceAccountName`). Defaults to the driver's one.
We are not supporting special authentication credentials for the executors with this PR.
## How was this patch tested?
Tested manually by launching a Spark job exercising the introduced settings.
Added a new integration tests for this fix.
Closes#24748 from skonto/fix_executor_sa.
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?
Change the resource config spark.{executor/driver}.resource.{resourceName}.count to .amount to allow future usage of containing both a count and a unit. Right now we only support counts - # of gpus for instance, but in the future we may want to support units for things like memory - 25G. I think making the user only have to specify a single config .amount is better then making them specify 2 separate configs of a .count and then a .unit. Change it now since its a user facing config.
Amount also matches how the spark on yarn configs are setup.
## How was this patch tested?
Unit tests and manually verified on yarn and local cluster mode
Closes#24810 from tgravescs/SPARK-27760-amount.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
This PR adds support to schedule tasks with extra resource requirements (eg. GPUs) on executors with available resources. It also introduce a new method `TaskContext.resources()` so tasks can access available resource addresses allocated to them.
## How was this patch tested?
* Added new end-to-end test cases in `SparkContextSuite`;
* Added new test case in `CoarseGrainedSchedulerBackendSuite`;
* Added new test case in `CoarseGrainedExecutorBackendSuite`;
* Added new test case in `TaskSchedulerImplSuite`;
* Added new test case in `TaskSetManagerSuite`;
* Updated existing tests.
Closes#24374 from jiangxb1987/gpu.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
This PR targets to deduplicate hardcoded `py4j-0.10.8.1-src.zip` in order to make py4j upgrade easier.
## How was this patch tested?
N/A
Closes#24770 from HyukjinKwon/minor-py4j-dedup.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Add ability to map the spark resource configs spark.{executor/driver}.resource.{resourceName} to kubernetes Container builder so that we request resources (gpu,s/fpgas/etc) from kubernetes.
Note that the spark configs will overwrite any resource configs users put into a pod template.
I added a generic vendor config which is only used by kubernetes right now. I intentionally didn't put it into the kubernetes config namespace just to avoid adding more config prefixes.
I will add more documentation for this under jira SPARK-27492. I think it will be easier to do all at once to get cohesive story.
## How was this patch tested?
Unit tests and manually testing on k8s cluster.
Closes#24703 from tgravescs/SPARK-27362.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
Add a metric for number of exceptions caught in the `ExternalShuffleBlockHandler`, the idea being that spikes in this metric over some time window (or more desirably, the lack thereof) can be used as an indicator of the health of an external shuffle service. (Where "health" refers to its ability to successfully respond to client requests.)
## How was this patch tested?
Deployed a build of this PR to a YARN cluster, and confirmed that the NodeManagers' JMX metrics include `numCaughtExceptions`.
Closes#24645 from sjrand/SPARK-27773.
Authored-by: Steven Rand <srand@palantir.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Add yarn support for GPU-aware scheduling. Since SPARK-20327 already added yarn custom resource support, this jira is really just making sure the spark resource configs get mapped into the yarn resource configs and user doesn't specify both yarn and spark config for the known types of resources (gpu and fpga are the known types on yarn).
You can find more details on the design and requirements documented: https://issues.apache.org/jira/browse/SPARK-27376
Note that the running on yarn docs already state to use it, it must be yarn 3.0+. We will add any further documentation under SPARK-20327
## How was this patch tested?
Unit tests and manually testing on yarn cluster
Closes#24634 from tgravescs/SPARK-27361.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This pr wrap all `PrintWriter` with `Utils.tryWithResource` to prevent resource leak.
## How was this patch tested?
Existing test
Closes#24739 from wangyum/SPARK-27875.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
- solves the current issue with --packages in cluster mode (there is no ticket for it). Also note of some [issues](https://issues.apache.org/jira/browse/SPARK-22657) of the past here when hadoop libs are used at the spark submit side.
- supports spark.jars, spark.files, app jar.
It works as follows:
Spark submit uploads the deps to the HCFS. Then the driver serves the deps via the Spark file server.
No hcfs uris are propagated.
The related design document is [here](https://docs.google.com/document/d/1peg_qVhLaAl4weo5C51jQicPwLclApBsdR1To2fgc48/edit). the next option to add is the RSS but has to be improved given the discussion in the past about it (Spark 2.3).
## How was this patch tested?
- Run integration test suite.
- Run an example using S3:
```
./bin/spark-submit \
...
--packages com.amazonaws:aws-java-sdk:1.7.4,org.apache.hadoop:hadoop-aws:2.7.6 \
--deploy-mode cluster \
--name spark-pi \
--class org.apache.spark.examples.SparkPi \
--conf spark.executor.memory=1G \
--conf spark.kubernetes.namespace=spark \
--conf spark.kubernetes.authenticate.driver.serviceAccountName=spark-sa \
--conf spark.driver.memory=1G \
--conf spark.executor.instances=2 \
--conf spark.sql.streaming.metricsEnabled=true \
--conf "spark.driver.extraJavaOptions=-Divy.cache.dir=/tmp -Divy.home=/tmp" \
--conf spark.kubernetes.container.image.pullPolicy=Always \
--conf spark.kubernetes.container.image=skonto/spark:k8s-3.0.0 \
--conf spark.kubernetes.file.upload.path=s3a://fdp-stavros-test \
--conf spark.hadoop.fs.s3a.access.key=... \
--conf spark.hadoop.fs.s3a.impl=org.apache.hadoop.fs.s3a.S3AFileSystem \
--conf spark.hadoop.fs.s3a.fast.upload=true \
--conf spark.kubernetes.executor.deleteOnTermination=false \
--conf spark.hadoop.fs.s3a.secret.key=... \
--conf spark.files=client:///...resolv.conf \
file:///my.jar **
```
Added integration tests based on [Ceph nano](https://github.com/ceph/cn). Looks very [active](http://www.sebastien-han.fr/blog/2019/02/24/Ceph-nano-is-getting-better-and-better/).
Unfortunately minio needs hadoop >= 2.8.
Closes#23546 from skonto/support-client-deps.
Authored-by: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Signed-off-by: Erik Erlandson <eerlands@redhat.com>
## What changes were proposed in this pull request?
avoid hardcoded configs in `SparkConf` and `SparkSubmit` and test
## How was this patch tested?
N/A
Closes#24631 from wenxuanguan/minor-fix.
Authored-by: wenxuanguan <choose_home@126.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Spark on k8s supports config for specifying the executor cpu requests
(spark.kubernetes.executor.request.cores) but a similar config is missing
for the driver. Instead, currently `spark.driver.cores` value is used for integer value.
Although `pod spec` can have `cpu` for the fine-grained control like the following, this PR proposes additional configuration `spark.kubernetes.driver.request.cores` for driver request cores.
```
resources:
requests:
memory: "64Mi"
cpu: "250m"
```
## How was this patch tested?
Unit tests
Closes#24630 from arunmahadevan/SPARK-27754.
Authored-by: Arun Mahadevan <arunm@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This replaces use of collection classes like `MutableList` and `ArrayStack` with workalikes that are available in 2.12, as they will be removed in 2.13. It also removes use of `.to[Collection]` as its uses was superfluous anyway. Removing `collection.breakOut` will have to wait until 2.13
## How was this patch tested?
Existing tests
Closes#24586 from srowen/SPARK-27682.
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 in GPU and generic resource type allocation to the executors.
Note this is part of a bigger feature for gpu-aware scheduling and is just how the executor find the resources. The general flow :
- users ask for a certain set of resources, for instance number of gpus - each cluster manager has a specific way to do this.
- cluster manager allocates a container or set of resources (standalone mode)
- When spark launches the executor in that container, the executor either has to be told what resources it has or it has to auto discover them.
- Executor has to register with Driver and tell the driver the set of resources it has so the scheduler can use that to schedule tasks that requires a certain amount of each of those resources
In this pr I added configs and arguments to the executor to be able discover resources. The argument to the executor is intended to be used by standalone mode or other cluster managers that don't have isolation so that it can assign specific resources to specific executors in case there are multiple executors on a node. The argument is a file contains JSON Array of ResourceInformation objects.
The discovery script is meant to be used in an isolated environment where the executor only sees the resources it should use.
Note that there will be follow on PRs to add other parts like the scheduler part. See the epic high level jira: https://issues.apache.org/jira/browse/SPARK-24615
## How was this patch tested?
Added unit tests and manually tested.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24406 from tgravescs/gpu-sched-executor-clean.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
This patch fixes a bug where `--supervised` Spark jobs would retry multiple times whenever an agent would crash, come back, and re-register even when those jobs had already relaunched on a different agent.
That is:
```
- supervised driver is running on agent1
- agent1 crashes
- driver is relaunched on another agent as `<task-id>-retry-1`
- agent1 comes back online and re-registers with scheduler
- spark relaunches the same job as `<task-id>-retry-2`
- now there are two jobs running simultaneously
```
This is because when an agent would come back and re-register it would send a status update `TASK_FAILED` for its old driver-task. Previous logic would indiscriminately remove the `submissionId` from Zookeeper's `launchedDrivers` node and add it to `retryList` node. Then, when a new offer came in, it would relaunch another `-retry-` task even though one was previously running.
For example logs, scroll to bottom
## How was this patch tested?
- Added a unit test to simulate behavior described above
- Tested manually on a DC/OS cluster by
```
- launching a --supervised spark job
- dcos node ssh <to the agent with the running spark-driver>
- systemctl stop dcos-mesos-slave
- docker kill <driver-container-id>
- [ wait until spark job is relaunched ]
- systemctl start dcos-mesos-slave
- [ observe spark driver is not relaunched as `-retry-2` ]
```
Log snippets included below. Notice the `-retry-1` task is running when status update for the old task comes in afterward:
```
19/01/15 19:21:38 TRACE MesosClusterScheduler: Received offers from Mesos:
... [offers] ...
19/01/15 19:21:39 TRACE MesosClusterScheduler: Using offer 5d421001-0630-4214-9ecb-d5838a2ec149-O2532 to launch driver driver-20190115192138-0001 with taskId: value: "driver-20190115192138-0001"
...
19/01/15 19:21:42 INFO MesosClusterScheduler: Received status update: taskId=driver-20190115192138-0001 state=TASK_STARTING message=''
19/01/15 19:21:43 INFO MesosClusterScheduler: Received status update: taskId=driver-20190115192138-0001 state=TASK_RUNNING message=''
...
19/01/15 19:29:12 INFO MesosClusterScheduler: Received status update: taskId=driver-20190115192138-0001 state=TASK_LOST message='health check timed out' reason=REASON_SLAVE_REMOVED
...
19/01/15 19:31:12 TRACE MesosClusterScheduler: Using offer 5d421001-0630-4214-9ecb-d5838a2ec149-O2681 to launch driver driver-20190115192138-0001 with taskId: value: "driver-20190115192138-0001-retry-1"
...
19/01/15 19:31:15 INFO MesosClusterScheduler: Received status update: taskId=driver-20190115192138-0001-retry-1 state=TASK_STARTING message=''
19/01/15 19:31:16 INFO MesosClusterScheduler: Received status update: taskId=driver-20190115192138-0001-retry-1 state=TASK_RUNNING message=''
...
19/01/15 19:33:45 INFO MesosClusterScheduler: Received status update: taskId=driver-20190115192138-0001 state=TASK_FAILED message='Unreachable agent re-reregistered'
...
19/01/15 19:33:45 INFO MesosClusterScheduler: Received status update: taskId=driver-20190115192138-0001 state=TASK_FAILED message='Abnormal executor termination: unknown container' reason=REASON_EXECUTOR_TERMINATED
19/01/15 19:33:45 ERROR MesosClusterScheduler: Unable to find driver with driver-20190115192138-0001 in status update
...
19/01/15 19:33:47 TRACE MesosClusterScheduler: Using offer 5d421001-0630-4214-9ecb-d5838a2ec149-O2729 to launch driver driver-20190115192138-0001 with taskId: value: "driver-20190115192138-0001-retry-2"
...
19/01/15 19:33:50 INFO MesosClusterScheduler: Received status update: taskId=driver-20190115192138-0001-retry-2 state=TASK_STARTING message=''
19/01/15 19:33:51 INFO MesosClusterScheduler: Received status update: taskId=driver-20190115192138-0001-retry-2 state=TASK_RUNNING message=''
```
Closes#24276 from samvantran/SPARK-27347-duplicate-retries.
Authored-by: Sam Tran <stran@mesosphere.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
For the below three thread configuration items applied to both driver and executor,
spark.rpc.io.serverThreads
spark.rpc.io.clientThreads
spark.rpc.netty.dispatcher.numThreads,
we separate them to driver specifics and executor specifics.
spark.driver.rpc.io.serverThreads < - > spark.executor.rpc.io.serverThreads
spark.driver.rpc.io.clientThreads < - > spark.executor.rpc.io.clientThreads
spark.driver.rpc.netty.dispatcher.numThreads < - > spark.executor.rpc.netty.dispatcher.numThreads
Spark reads these specifics first and fall back to the common configurations.
## How was this patch tested?
We ran the SimpleMap app without shuffle for benchmark purpose to test Spark's scalability in HPC with omini-path NIC which has higher bandwidth than normal ethernet NIC.
Spark's base version is 2.4.0.
Spark ran in the Standalone mode. Driver was in a standalone node.
After the separation, the performance is improved a lot in 256 nodes and 512 nodes. see below test results of SimpleMapTask before and after the enhancement. You can view the tables in the [JIRA](https://issues.apache.org/jira/browse/SPARK-26632) too.
ds: spark.driver.rpc.io.serverThreads
dc: spark.driver.rpc.io.clientThreads
dd: spark.driver.rpc.netty.dispatcher.numThreads
ed: spark.executor.rpc.netty.dispatcher.numThreads
time: Overall Time (s)
old time: Overall Time without Separation (s)
**Before:**
nodes | ds | dc | dd | ed | time
-- |-- | -- | -- | -- | --
128 nodes | 8 | 8 | 8 | 8 | 108
256 nodes | 8 | 8 | 8 | 8 | 196
512 nodes | 8 | 8 | 8 | 8 | 377
**After:**
nodes | ds | dc | dd | ed | time | improvement
-- | -- | -- | -- | -- | -- | --
128 nodes | 15 | 15 | 10 | 30 | 107 | 0.9%
256 nodes | 12 | 15 | 10 | 30 | 159 | 18.8%
512 nodes | 12 | 15 | 10 | 30 | 283 | 24.9%
Closes#23560 from zjf2012/thread_conf_separation.
Authored-by: jiafu.zhang@intel.com <jiafu.zhang@intel.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Fixed the `spark-<version>-yarn-shuffle.jar` artifact packaging to shade the native netty libraries:
- shade the `META-INF/native/libnetty_*` native libraries when packagin
the yarn shuffle service jar. This is required as netty library loader
derives that based on shaded package name.
- updated the `org/spark_project` shade package prefix to `org/sparkproject`
(i.e. removed underscore) as the former breaks the netty native lib loading.
This was causing the yarn external shuffle service to fail
when spark.shuffle.io.mode=EPOLL
## How was this patch tested?
Manual tests
Closes#24502 from amuraru/SPARK-27610_master.
Authored-by: Adi Muraru <amuraru@adobe.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This PR avoids usage of reflective calls in Scala. It removes the import that suppresses the warnings and rewrites code in small ways to avoid accessing methods that aren't technically accessible.
## How was this patch tested?
Existing tests.
Closes#24463 from srowen/SPARK-27571.
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?
As discovered by users making use of this feature, there is a bug in the
declaration of the `R_IMAGE_NAME` variable that causes the default name to
not be properly set to `spark-r` but rather to just `-r`
## How was this patch tested?
Verified that the image name for the R image is now appropriately populated in the integration test script via Bash debug output.
NB - The fact that this wasn't spotted earlier highlights the fact that currently the K8S integration test suite does not have any tests for the R image as if it had this would have failed integration testing in the original PR #23846Closes#24449 from rvesse/SPARK-26729.
Authored-by: Rob Vesse <rvesse@dotnetrdf.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This patch makes several test flakiness fixes.
## How was this patch tested?
N/A
Closes#24434 from gatorsmile/fixFlakyTest.
Lead-authored-by: gatorsmile <gatorsmile@gmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Fix build warnings -- see some details below.
But mostly, remove use of postfix syntax where it causes warnings without the `scala.language.postfixOps` import. This is mostly in expressions like "120000 milliseconds". Which, I'd like to simplify to things like "2.minutes" anyway.
## How was this patch tested?
Existing tests.
Closes#24314 from srowen/SPARK-27404.
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?
When you submit a stage on a large cluster, rack resolving takes a long time when initializing TaskSetManager because a script is invoked to resolve the rack of each host, one by one.
Based on current implementation, it takes 30~40 seconds to resolve the racks in our 5000 nodes' cluster. After applied the patch, it decreased to less than 15 seconds.
YARN-9332 has added an interface to handle multiple hosts in one invocation to save time. But before upgrading to the newest Hadoop, we could construct the same tool in Spark to resolve this issue.
## How was this patch tested?
UT and manually testing on a 5000 node cluster.
Closes#24245 from squito/SPARK-13704_update.
Lead-authored-by: LantaoJin <jinlantao@gmail.com>
Co-authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
Fix testing issues with `yarn` module in Hadoop-3:
1. Upgrade jersey-1 to `1.19` to fix ```Cause: java.lang.NoClassDefFoundError: com/sun/jersey/spi/container/servlet/ServletContainer```.
2. Copy `ServerSocketUtil` from hadoop-common-project/hadoop-common/src/test/java/org/apache/hadoop/net/ServerSocketUtil.java to fix ```java.lang.NoClassDefFoundError: org/apache/hadoop/net/ServerSocketUtil```.
3. Adapte `SessionHandler` from jetty-9.3.25.v20180904/jetty-server/src/main/java/org/eclipse/jetty/server/session/SessionHandler.java to fix ```java.lang.NoSuchMethodError: org.eclipse.jetty.server.session.SessionHandler.getSessionManager()Lorg/eclipse/jetty/server/SessionManager```.
## How was this patch tested?
manual tests:
```shell
build/sbt yarn/test -Pyarn
build/sbt yarn/test -Phadoop-3.2 -Pyarn
build/mvn -Dtest=none -DwildcardSuites=org.apache.spark.deploy.yarn.YarnClusterSuite -pl resource-managers/yarn test -Pyarn
build/mvn -Dtest=none -DwildcardSuites=org.apache.spark.deploy.yarn.YarnClusterSuite -pl resource-managers/yarn test -Pyarn -Phadoop-3.2
```
Closes#24115 from wangyum/hadoop3-yarn.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Use Single Abstract Method syntax where possible (and minor related cleanup). Comments below. No logic should change here.
## How was this patch tested?
Existing tests.
Closes#24241 from srowen/SPARK-27323.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
```
[WARNING] Some problems were encountered while building the effective model for org.apache.spark:spark-kubernetes_2.12🫙3.0.0-SNAPSHOT
[WARNING] 'dependencies.dependency.(groupId:artifactId:type:classifier)' must be unique: org.mockito:mockito-core:jar -> duplicate declaration of version (?) org.apache.spark:spark-kubernetes_2.12:[unknown-version], /Users/yumwang/spark/resource-managers/kubernetes/core/pom.xml, line 98, column 17
```
This pr remove duplicate declaration of `mockito-core`.
## How was this patch tested?
N/A
Closes#24256 from wangyum/SPARK-24793-FOLLOW-UP.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
- Adds persistent volume integration tests
- Adds a custom tag to the test to exclude it if it is run against a cloud backend.
- Assumes default fs type for the host, AFAIK that is ext4.
## How was this patch tested?
Manually run the tests against minikube as usual:
```
[INFO] --- scalatest-maven-plugin:1.0:test (integration-test) spark-kubernetes-integration-tests_2.12 ---
Discovery starting.
Discovery completed in 192 milliseconds.
Run starting. Expected test count is: 16
KubernetesSuite:
- Run SparkPi with no resources
- Run SparkPi with a very long application name.
- Use SparkLauncher.NO_RESOURCE
- Run SparkPi with a master URL without a scheme.
- Run SparkPi with an argument.
- Run SparkPi with custom labels, annotations, and environment variables.
- Run extraJVMOptions check on driver
- Run SparkRemoteFileTest using a remote data file
- Run SparkPi with env and mount secrets.
- 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 PySpark with memory customization
- Run in client mode.
- Start pod creation from template
- Test PVs with local storage
```
Closes#23514 from skonto/pvctests.
Authored-by: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Signed-off-by: shane knapp <incomplete@gmail.com>
## What changes were proposed in this pull request?
Remove Scala 2.11 support in build files and docs, and in various parts of code that accommodated 2.11. See some targeted comments below.
## How was this patch tested?
Existing tests.
Closes#23098 from srowen/SPARK-26132.
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?
There is some hardcode configs in code, I think it best to modify。
## How was this patch tested?
Existing tests
Closes#24103 from wangjiaochun/yarnHardCode.
Authored-by: 10087686 <wang.jiaochun@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Allow specifying system properties to customise the image names for the images used in the integration testing. Useful if your CI/CD pipeline or policy requires using a different naming format.
This is one part of addressing SPARK-26729, I plan to have a follow up patch that will also make the names configurable when using `docker-image-tool.sh`
## How was this patch tested?
Ran integration tests against custom images generated by our CI/CD pipeline that do not follow Spark's existing hardcoded naming conventions using the new system properties to override the image names appropriately:
```
mvn clean integration-test -pl :spark-kubernetes-integration-tests_${SCALA_VERSION} \
-Pkubernetes -Pkubernetes-integration-tests \
-P${SPARK_HADOOP_PROFILE} -Dhadoop.version=${HADOOP_VERSION} \
-Dspark.kubernetes.test.sparkTgz=${TARBALL} \
-Dspark.kubernetes.test.imageTag=${TAG} \
-Dspark.kubernetes.test.imageRepo=${REPO} \
-Dspark.kubernetes.test.namespace=${K8S_NAMESPACE} \
-Dspark.kubernetes.test.kubeConfigContext=${K8S_CONTEXT} \
-Dspark.kubernetes.test.deployMode=${K8S_TEST_DEPLOY_MODE} \
-Dspark.kubernetes.test.jvmImage=apache-spark \
-Dspark.kubernetes.test.pythonImage=apache-spark-py \
-Dspark.kubernetes.test.rImage=apache-spark-r \
-Dtest.include.tags=k8s
...
[INFO] --- scalatest-maven-plugin:1.0:test (integration-test) spark-kubernetes-integration-tests_2.12 ---
Discovery starting.
Discovery completed in 230 milliseconds.
Run starting. Expected test count is: 15
KubernetesSuite:
- Run SparkPi with no resources
- Run SparkPi with a very long application name.
- Use SparkLauncher.NO_RESOURCE
- Run SparkPi with a master URL without a scheme.
- Run SparkPi with an argument.
- Run SparkPi with custom labels, annotations, and environment variables.
- Run extraJVMOptions check on driver
- Run SparkRemoteFileTest using a remote data file
- Run SparkPi with env and mount secrets.
- 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 PySpark with memory customization
- Run in client mode.
- Start pod creation from template
Run completed in 8 minutes, 33 seconds.
Total number of tests run: 15
Suites: completed 2, aborted 0
Tests: succeeded 15, failed 0, canceled 0, ignored 0, pending 0
All tests passed.
```
Closes#23846 from rvesse/SPARK-26729.
Authored-by: Rob Vesse <rvesse@dotnetrdf.org>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
To avoid the case where the YARN libraries would swallow the exception and
prevent YarnAllocator from shutting down, call the offending code in a
separate thread, so that the parent thread can respond appropriately to
the shut down.
As a safeguard, also explicitly stop the executor launch thread pool when
shutting down the application, to prevent new executors from coming up
after the application started its shutdown.
Tested with unit tests + some internal tests on real cluster.
Closes#24017 from vanzin/SPARK-27094.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
while performing some tests on our existing minikube and k8s infrastructure, i noticed that the integration tests were failing. i dug in and discovered the following message buried at the end of the stacktrace:
```
Caused by: java.io.FileNotFoundException: /usr/lib/libnss3.so
at sun.security.pkcs11.Secmod.initialize(Secmod.java:193)
at sun.security.pkcs11.SunPKCS11.<init>(SunPKCS11.java:218)
... 81 more
```
after i added the `nss` package to `resource-managers/kubernetes/docker/src/main/dockerfiles/spark/Dockerfile`, everything worked.
this is also impacting current builds. see: https://amplab.cs.berkeley.edu/jenkins/job/testing-k8s-prb-make-spark-distribution-unified/8959/console
## How was this patch tested?
i tested locally before pushing, and the build system will test the rest.
Closes#24111 from shaneknapp/add-nss-package-to-dockerfile.
Authored-by: shane knapp <incomplete@gmail.com>
Signed-off-by: shane knapp <incomplete@gmail.com>
## What changes were proposed in this pull request?
When we run YarnSchedulerBackendSuite, the class path seems to be made from the classes folder(resource-managers/yarn/target/scala-2.12/classes) instead of jar (resource-managers/yarn/target/spark-yarn_2.12-3.0.0-SNAPSHOT.jar) . ui.getHandlers is in spark-core and its loaded from spark-core.jar which is shaded and hence refers to org.spark_project.jetty.servlet.ServletContextHandler
Here in org.apache.spark.scheduler.cluster.YarnSchedulerBackend, as its not shaded, it expects org.eclipse.jetty.servlet.ServletContextHandler
Refer discussion https://issues.apache.org/jira/browse/SPARK-27122?focusedCommentId=16792318&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-16792318
Hence as a fix, org.apache.spark.ui.WebUI must only return a wrapper class instance or references so that Jetty classes can be avoided in getters which are accessed outside spark-core
## How was this patch tested?
Existing UT can pass
Closes#24088 from ajithme/shadebug.
Authored-by: Ajith <ajith2489@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Currently, when enabled streaming dynamic allocation for streaming applications, the maxNumExecutorFailures in ApplicationMaster is still computed with `spark.dynamicAllocation.maxExecutors`.
Actually, we should consider `spark.streaming.dynamicAllocation.maxExecutors` instead.
Related codes:
f87153a3ac/resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala (L101)
## How was this patch tested?
NA
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23845 from liupc/Fix-incorrect-maxNumExecutorFailures-for-streaming.
Lead-authored-by: Liupengcheng <liupengcheng@xiaomi.com>
Co-authored-by: liupengcheng <liupengcheng@xiaomi.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Speed up running k8s integration tests locally by allowing folks to skip the tgz dist build and extraction
Run tests locally without a distribution of Spark, just a local build
Closes#23380 from holdenk/SPARK-26343-Speed-up-running-the-kubernetes-integration-tests-locally.
Authored-by: Holden Karau <holden@pigscanfly.ca>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Expose Spark UI port on driver service to access logs from service.
## How was this patch tested?
The patch was tested using unit tests being contributed as a part of the PR
Closes#23990 from chandulal/SPARK-27061.
Authored-by: chandulal.kavar <cckavar@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This pr adds 2 maven properties to help us upgrade the built-in Hive.
| Property Name | Default | In future |
| ------ | ------ | ------ |
| hive.classifier | (none) | core |
| hive.parquet.group | com.twitter | org.apache.parquet |
## How was this patch tested?
existing tests
Closes#23996 from wangyum/add_2_maven_properties.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Make k8s client timeouts configurable. No test suite exists for the client factory class, happy to add one if needed
Closes#23928 from onursatici/os/k8s-client-timeouts.
Lead-authored-by: Onur Satici <osatici@palantir.com>
Co-authored-by: Onur Satici <onursatici@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Escape arguments for submissions sent to a Mesos dispatcher; analogous change to https://issues.apache.org/jira/browse/SPARK-24380 for confs.
Since this changes behavior than some users are undoubtedly already working around, probably best to only only merge into master.
## How was this patch tested?
Added a new unit test, covering some existing behavior as well.
Closes#23967 from mwlon/SPARK-27015.
Authored-by: mwlon <mloncaric@hmc.edu>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Introducing new config for initially blacklisted YARN nodes.
## How was this patch tested?
With existing and a new unit test.
Closes#23616 from attilapiros/SPARK-26688.
Lead-authored-by: “attilapiros” <piros.attila.zsolt@gmail.com>
Co-authored-by: Attila Zsolt Piros <2017933+attilapiros@users.noreply.github.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
Retrieve enableFetcherCache option from submission conf rather than dispatcher conf. This resolves some confusing behavior where Spark drivers currently get this conf from the dispatcher, whereas Spark executors get this conf from the submission. After this change, the conf will only need to be specified once.
## How was this patch tested?
With (updated) existing tests.
Closes#23924 from mwlon/SPARK-26192.
Authored-by: mwlon <mloncaric@hmc.edu>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
Running Spark in Docker image with Alpine Linux 3.9.0 throws errors when using snappy.
The issue can be reproduced for example as follows: `Seq(1,2).toDF("id").write.format("parquet").save("DELETEME1")`
The key part of the error stack is as follows `SparkException: Task failed while writing rows. .... Caused by: java.lang.UnsatisfiedLinkError: /tmp/snappy-1.1.7-2b4872f1-7c41-4b84-bda1-dbcb8dd0ce4c-libsnappyjava.so: Error loading shared library ld-linux-x86-64.so.2: Noded by /tmp/snappy-1.1.7-2b4872f1-7c41-4b84-bda1-dbcb8dd0ce4c-libsnappyjava.so)`
The source of the error appears to be that libsnappyjava.so needs ld-linux-x86-64.so.2 and looks for it in /lib, while in Alpine Linux 3.9.0 with libc6-compat version 1.1.20-r3 ld-linux-x86-64.so.2 is located in /lib64.
Note: this issue is not present with Alpine Linux 3.8 and libc6-compat version 1.1.19-r10
## What changes were proposed in this pull request?
A possible workaround proposed with this PR is to modify the Dockerfile by adding a symbolic link between /lib and /lib64 so that linux-x86-64.so.2 can be found in /lib. This is probably not the cleanest solution, but I have observed that this is what happened/happens already when using Alpine Linux 3.8.1 (a version of Alpine Linux which was not affected by the issue reported here).
## How was this patch tested?
Manually tested by running a simple workload with spark-shell, using docker on a client machine and using Spark on a Kubernetes cluster. The test workload is: `Seq(1,2).toDF("id").write.format("parquet").save("DELETEME1")`
Added a test to the KubernetesSuite / BasicTestsSuite
Closes#23898 from LucaCanali/dockerfileUpdateSPARK26995.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
`MetricsSystem` instance creations have a scattered distribution in the project code. So do their names. It may cause some inconvenience for browsing and management.
This PR tries to put them together. In this way, we can have a uniform location for adding or removing them, and have a overall view of `MetircsSystem `instances in current project.
It's also helpful for maintaining user documents by avoiding missing something.
## How was this patch tested?
Existing unit tests.
Closes#23869 from SongYadong/metrics_system_inst_manage.
Lead-authored-by: SongYadong <song.yadong1@zte.com.cn>
Co-authored-by: walter2001 <ydsong2007@163.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Using the current time as an ID is more prone to clashes than people generally
realize, so try to make things a bit more unique without necessarily using a
UUID, which would eat too much space in the names otherwise.
The implemented approach uses some bits from the current time, plus some random
bits, which should be more resistant to clashes.
Closes#23805 from vanzin/SPARK-26420.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
Before this change, there was some code in the k8s backend to deal
with how to resolve dependencies and make them available to the
Spark application. It turns out that none of that code is necessary,
since spark-submit already handles all that for applications started
in client mode - like the k8s driver that is run inside a Spark-created
pod.
For that reason, specifically for pyspark, there's no need for the
k8s backend to deal with PYTHONPATH; or, in general, to change the URIs
provided by the user at all. spark-submit takes care of that.
For testing, I created a pyspark script that depends on another module
that is shipped with --py-files. Then I used:
- --py-files http://.../dep.pyhttp://.../test.py
- --py-files http://.../dep.ziphttp://.../test.py
- --py-files local:/.../dep.py local:/.../test.py
- --py-files local:/.../dep.zip local:/.../test.py
Without this change, all of the above commands fail. With the change, the
driver is able to see the dependencies in all the above cases; but executors
don't see the dependencies in the last two. That's a bug in shared Spark code
that deals with local: dependencies in pyspark (SPARK-26934).
I also tested a Scala app using the main jar from an http server.
Closes#23793 from vanzin/SPARK-24736.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Comparing whether Boolean expression is equal to true is redundant
For example:
The datatype of `a` is boolean.
Before:
if (a == true)
After:
if (a)
## How was this patch tested?
N/A
Closes#23884 from 10110346/simplifyboolean.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This patch applies redaction to command line arguments before logging them. This applies to two resource managers: standalone cluster and YARN.
This patch only concerns about arguments starting with `-D` since Spark is likely passing the Spark configuration to command line arguments as `-Dspark.blabla=blabla`. More change is necessary if we also want to handle the case of `--conf spark.blabla=blabla`.
## How was this patch tested?
Added UT for redact logic. This patch only touches how to log so not easy to add UT regarding it.
Closes#23820 from HeartSaVioR/MINOR-redact-command-line-args-for-running-driver-executor.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
Since the yarn module is actually private to Spark, this interface was never
actually "public". Since it has no use inside of Spark, let's avoid adding
a yarn-specific extension that isn't public, and point any potential users
are more general solutions (like using a SparkListener).
Closes#23839 from vanzin/SPARK-26788.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Changed the `kubernetes-client` version to 4.1.2. Latest version fix error with exec credentials (used by aws eks) and this will be used to talk with kubernetes API server. Users can submit spark job to EKS api endpoint now with this patch.
## How was this patch tested?
unit tests and manual tests.
Closes#23814 from Jeffwan/update_k8s_sdk.
Authored-by: Jiaxin Shan <seedjeffwan@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR aims to fix some outdated comments about task schedulers.
1. Change "ClusterScheduler" to "YarnScheduler" in comments of `YarnClusterScheduler`
According to [SPARK-1140 Remove references to ClusterScheduler](https://issues.apache.org/jira/browse/SPARK-1140), ClusterScheduler is not used anymore.
I also searched "ClusterScheduler" within the whole project, no other occurrences are found in comments or test cases. Note classes like `YarnClusterSchedulerBackend` or `MesosClusterScheduler` are not relevant.
2. Update comments about `statusUpdate` from `TaskSetManager`
`statusUpdate` has been moved to `TaskSchedulerImpl`. StatusUpdate event handling is delegated to `handleSuccessfulTask`/`handleFailedTask`.
## How was this patch tested?
N/A. Fix comments only.
Closes#23844 from seancxmao/taskscheduler-comments.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Currently, when running applications on yarn mode, the app staging directory of is controlled by `spark.yarn.stagingDir` config if specified, and this directory cannot separate different users, sometimes, it's inconvenient for file and quota management for users.
Sometimes, there might be an unexpected increasing of the staging files, two possible reasons are:
1. The `spark.yarn.preserve.staging.files` provided can be misused by users
2. cron task constantly starting new applications on non-existent yarn queue(wrong configuration).
But now, we are not easy to find out the which user obtains the most HDFS files or spaces.
what's more, even we want set HDFS name quota or space quota for each user to limit the increase is impossible.
So I propose to add user sub directories under this app staging directory which is more clear.
existing UT
Closes#23786 from liupc/Support-user-level-app-staging-dir.
Authored-by: Liupengcheng <liupengcheng@xiaomi.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
Since the host name is derived from the app name, which can contain arbitrary
characters, it needs to be sanitized so that only valid characters are allowed.
On top of that, take extra care that truncation doesn't leave characters that
are valid except at the start of a host name.
Closes#23781 from vanzin/SPARK-24894.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Add the kubernetes integration tests to the scalastyle profiles.
## How was this patch tested?
Run ./dev/scalastyle with a bad change manually
## Follow on work
See SPARK-26898 to add scalastyle for k8s integration to the CI
Closes#23792 from holdenk/SPARK-26882-check-k8s-integration-tests-when-linting.
Authored-by: Holden Karau <holden@pigscanfly.ca>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
The test "RequestExecutors reflects node blacklist and is serializable" is flaky because of multi threaded access of the mock task scheduler. For details check [Mockito FAQ (occasional exceptions like: WrongTypeOfReturnValue)](https://github.com/mockito/mockito/wiki/FAQ#is-mockito-thread-safe). So instead of mocking the task scheduler in the test TaskSchedulerImpl is simply subclassed.
This multithreaded access of the `nodeBlacklist()` method is coming from:
1) the unit test thread via calling of the method `prepareRequestExecutors()`
2) the `DriverEndpoint.onStart` which runs a periodic task that ends up calling this method
Existing unittest.
Closes#23801 from attilapiros/SPARK-26891.
Authored-by: “attilapiros” <piros.attila.zsolt@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## 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.
