## 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>
