## What changes were proposed in this pull request?
Spark still use a few years old version 3.2.11. This change is to upgrade json4s to 3.5.3.
Note that this change does not include the Jackson update because the Jackson version referenced in json4s 3.5.3 is 2.8.4, which has a security vulnerability ([see](https://issues.apache.org/jira/browse/SPARK-20433)).
## How was this patch tested?
Existing unit tests and build.
Author: shimamoto <chibochibo@gmail.com>
Closes#20233 from shimamoto/upgrade-json4s.
When resources happen to be constrained on an executor the first time a broadcast variable is instantiated it is persisted to disk by the BlockManager. Consequently, every subsequent call to TorrentBroadcast::readBroadcastBlock from other instances of that broadcast variable spawns another instance of the underlying value. That is, broadcast variables are spawned once per executor **unless** memory is constrained, in which case every instance of a broadcast variable is provided with a unique copy of the underlying value.
This patch fixes the above by explicitly caching the underlying values using weak references in a ReferenceMap.
Author: ho3rexqj <ho3rexqj@gmail.com>
Closes#20183 from ho3rexqj/fix/cache-broadcast-values.
There are two main changes to speed up rendering of the tasks list
when rendering the stage page.
The first one makes the code only load the tasks being shown in the
current page of the tasks table, and information related to only
those tasks. One side-effect of this change is that the graph that
shows task-related events now only shows events for the tasks in
the current page, instead of the previously hardcoded limit of "events
for the first 1000 tasks". That ends up helping with readability,
though.
To make sorting efficient when using a disk store, the task wrapper
was extended to include many new indices, one for each of the sortable
columns in the UI, and metrics for which quantiles are calculated.
The second changes the way metric quantiles are calculated for stages.
Instead of using the "Distribution" class to process data for all task
metrics, which requires scanning all tasks of a stage, the code now
uses the KVStore "skip()" functionality to only read tasks that contain
interesting information for the quantiles that are desired.
This is still not cheap; because there are many metrics that the UI
and API track, the code needs to scan the index for each metric to
gather the information. Savings come mainly from skipping deserialization
when using the disk store, but the in-memory code also seems to be
faster than before (most probably because of other changes in this
patch).
To make subsequent calls faster, some quantiles are cached in the
status store. This makes UIs much faster after the first time a stage
has been loaded.
With the above changes, a lot of code in the UI layer could be simplified.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20013 from vanzin/SPARK-20657.
## What changes were proposed in this pull request?
This patch modifies Spark's `MemoryAllocator` implementations so that `free(MemoryBlock)` mutates the passed block to clear pointers (in the off-heap case) or null out references to backing `long[]` arrays (in the on-heap case). The goal of this change is to add an extra layer of defense against use-after-free bugs because currently it's hard to detect corruption caused by blind writes to freed memory blocks.
## How was this patch tested?
New unit tests in `PlatformSuite`, including new tests for existing functionality because we did not have sufficient mutation coverage of the on-heap memory allocator's pooling logic.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#20191 from JoshRosen/SPARK-22997-add-defenses-against-use-after-free-bugs-in-memory-allocator.
## What changes were proposed in this pull request?
1. Deprecate attemptId in StageInfo and add `def attemptNumber() = attemptId`
2. Replace usage of stageAttemptId with stageAttemptNumber
## How was this patch tested?
I manually checked the compiler warning info
Author: Xianjin YE <advancedxy@gmail.com>
Closes#20178 from advancedxy/SPARK-22952.
The code in LiveListenerBus was queueing events before start in the
queues themselves; so in situations like the following:
bus.post(someEvent)
bus.addToEventLogQueue(listener)
bus.start()
"someEvent" would not be delivered to "listener" if that was the first
listener in the queue, because the queue wouldn't exist when the
event was posted.
This change buffers the events before starting the bus in the bus itself,
so that they can be delivered to all registered queues when the bus is
started.
Also tweaked the unit tests to cover the behavior above.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20039 from vanzin/SPARK-22850.
## What changes were proposed in this pull request?
stageAttemptId added in TaskContext and corresponding construction modification
## How was this patch tested?
Added a new test in TaskContextSuite, two cases are tested:
1. Normal case without failure
2. Exception case with resubmitted stages
Link to [SPARK-22897](https://issues.apache.org/jira/browse/SPARK-22897)
Author: Xianjin YE <advancedxy@gmail.com>
Closes#20082 from advancedxy/SPARK-22897.
The scheduled task was racing with the test code and could influence
the values returned to the test, triggering assertions. The change adds
a new config that is only used during testing, and overrides it
on the affected test suite.
The issue in the bug can be reliably reproduced by reducing the interval
in the test (e.g. to 10ms).
While there, fixed an exception that shows up in the logs while these
tests run, and simplified some code (which was also causing misleading
log messages in the log output of the test).
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20050 from vanzin/SPARK-22864.
This change adds a new configuration option and support code that limits
how much disk space the SHS will use. The default value is pretty generous
so that applications will, hopefully, only rarely need to be replayed
because of their disk stored being evicted.
This works by keeping track of how much data each application is using.
Also, because it's not possible to know, before replaying, how much space
will be needed, it's possible that usage will exceed the configured limit
temporarily. The code uses the concept of a "lease" to try to limit how
much the SHS will exceed the limit in those cases.
Active UIs are also tracked, so they're never deleted. This works in
tandem with the existing option of how many active UIs are loaded; because
unused UIs will be unloaded, their disk stores will also become candidates
for deletion. If the data is not deleted, though, re-loading the UI is
pretty quick.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20011 from vanzin/SPARK-20654.
This change adds a new launcher that allows applications to be run
in a separate thread in the same process as the calling code. To
achieve that, some code from the child process implementation was
moved to abstract classes that implement the common functionality,
and the new launcher inherits from those.
The new launcher was added as a new class, instead of implemented
as a new option to the existing SparkLauncher, to avoid ambigous
APIs. For example, SparkLauncher has ways to set the child app's
environment, modify SPARK_HOME, or control the logging of the
child process, none of which apply to in-process apps.
The in-process launcher has limitations: it needs Spark in the
context class loader of the calling thread, and it's bound by
Spark's current limitation of a single client-mode application
per JVM. It also relies on the recently added SparkApplication
trait to make sure different apps don't mess up each other's
configuration, so config isolation is currently limited to cluster mode.
I also chose to keep the same socket-based communication for in-process
apps, even though it might be possible to avoid it for in-process
mode. That helps both implementations share more code.
Tested with new and existing unit tests, and with a simple app that
uses the launcher; also made sure the app ran fine with older launcher
jar to check binary compatibility.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19591 from vanzin/SPARK-11035.
Port code from the old executors listener to the new one, so that
the driver logs present in the application start event are kept.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20038 from vanzin/SPARK-22836.
## What changes were proposed in this pull request?
In choosing a Partitioner to use for a cogroup-like operation between a number of RDDs, the default behaviour was if some of the RDDs already have a partitioner, we choose the one amongst them with the maximum number of partitions.
This behaviour, in some cases, could hit the 2G limit (SPARK-6235). To illustrate one such scenario, consider two RDDs:
rDD1: with smaller data and smaller number of partitions, alongwith a Partitioner.
rDD2: with much larger data and a larger number of partitions, without a Partitioner.
The cogroup of these two RDDs could hit the 2G limit, as a larger amount of data is shuffled into a smaller number of partitions.
This PR introduces a safety-check wherein the Partitioner is chosen only if either of the following conditions are met:
1. if the number of partitions of the RDD associated with the Partitioner is greater than or equal to the max number of upstream partitions; or
2. if the number of partitions of the RDD associated with the Partitioner is less than and within a single order of magnitude of the max number of upstream partitions.
## How was this patch tested?
Unit tests in PartitioningSuite and PairRDDFunctionsSuite
Author: sujithjay <sujith@logistimo.com>
Closes#20002 from sujithjay/SPARK-22465.
The code was ignoring SparkListenerLogStart, which was added
somewhat recently to record the Spark version used to generate
an event log.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20049 from vanzin/SPARK-22854.
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/19681 we introduced a new interface called `AppStatusPlugin`, to register listeners and set up the UI for both live and history UI.
However I think it's an overkill for live UI. For example, we should not register `SQLListener` if users are not using SQL functions. Previously we register the `SQLListener` and set up SQL tab when `SparkSession` is firstly created, which indicates users are going to use SQL functions. But in #19681 , we register the SQL functions during `SparkContext` creation. The same thing should apply to streaming too.
I think we should keep the previous behavior, and only use this new interface for history server.
To reflect this change, I also rename the new interface to `SparkHistoryUIPlugin`
This PR also refines the tests for sql listener.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19981 from cloud-fan/listener.
This change restores the functionality that keeps a limited number of
different types (jobs, stages, etc) depending on configuration, to avoid
the store growing indefinitely over time.
The feature is implemented by creating a new type (ElementTrackingStore)
that wraps a KVStore and allows triggers to be set up for when elements
of a certain type meet a certain threshold. Triggers don't need to
necessarily only delete elements, but the current API is set up in a way
that makes that use case easier.
The new store also has a trigger for the "close" call, which makes it
easier for listeners to register code for cleaning things up and flushing
partial state to the store.
The old configurations for cleaning up the stored elements from the core
and SQL UIs are now active again, and the old unit tests are re-enabled.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19751 from vanzin/SPARK-20653.
## What changes were proposed in this pull request?
Changes discussed in https://github.com/apache/spark/pull/19946#discussion_r157063535
docker -> container, since with CRI, we are not limited to running only docker images.
## How was this patch tested?
Manual testing
Author: foxish <ramanathana@google.com>
Closes#19995 from foxish/make-docker-container.
## What changes were proposed in this pull request?
This PR added the missing service metadata for `KubernetesClusterManager`. Without the metadata, the service loader couldn't load `KubernetesClusterManager`, and caused the driver to fail to create a `ExternalClusterManager`, as being reported in SPARK-22778. The PR also changed the `k8s:` prefix used to `k8s://`, which is what existing Spark on k8s users are familiar and used to.
## How was this patch tested?
Manual testing verified that the fix resolved the issue in SPARK-22778.
/cc vanzin felixcheung jiangxb1987
Author: Yinan Li <liyinan926@gmail.com>
Closes#19972 from liyinan926/fix-22778.
Use a semaphore to synchronize the tasks with the listener code
that is trying to cancel the job or stage, so that the listener
won't try to cancel a job or stage that has already finished.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19956 from vanzin/SPARK-22764.
## What changes were proposed in this pull request?
PR closed with all the comments -> https://github.com/apache/spark/pull/19793
It solves the problem when submitting a wrong CreateSubmissionRequest to Spark Dispatcher was causing a bad state of Dispatcher and making it inactive as a Mesos framework.
https://issues.apache.org/jira/browse/SPARK-22574
## How was this patch tested?
All spark test passed successfully.
It was tested sending a wrong request (without appArgs) before and after the change. The point is easy, check if the value is null before being accessed.
This was before the change, leaving the dispatcher inactive:
```
Exception in thread "Thread-22" java.lang.NullPointerException
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.getDriverCommandValue(MesosClusterScheduler.scala:444)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.buildDriverCommand(MesosClusterScheduler.scala:451)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.org$apache$spark$scheduler$cluster$mesos$MesosClusterScheduler$$createTaskInfo(MesosClusterScheduler.scala:538)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler$$anonfun$scheduleTasks$1.apply(MesosClusterScheduler.scala:570)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler$$anonfun$scheduleTasks$1.apply(MesosClusterScheduler.scala:555)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.scheduleTasks(MesosClusterScheduler.scala:555)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.resourceOffers(MesosClusterScheduler.scala:621)
```
And after:
```
"message" : "Malformed request: org.apache.spark.deploy.rest.SubmitRestProtocolException: Validation of message CreateSubmissionRequest failed!\n\torg.apache.spark.deploy.rest.SubmitRestProtocolMessage.validate(SubmitRestProtocolMessage.scala:70)\n\torg.apache.spark.deploy.rest.SubmitRequestServlet.doPost(RestSubmissionServer.scala:272)\n\tjavax.servlet.http.HttpServlet.service(HttpServlet.java:707)\n\tjavax.servlet.http.HttpServlet.service(HttpServlet.java:790)\n\torg.spark_project.jetty.servlet.ServletHolder.handle(ServletHolder.java:845)\n\torg.spark_project.jetty.servlet.ServletHandler.doHandle(ServletHandler.java:583)\n\torg.spark_project.jetty.server.handler.ContextHandler.doHandle(ContextHandler.java:1180)\n\torg.spark_project.jetty.servlet.ServletHandler.doScope(ServletHandler.java:511)\n\torg.spark_project.jetty.server.handler.ContextHandler.doScope(ContextHandler.java:1112)\n\torg.spark_project.jetty.server.handler.ScopedHandler.handle(ScopedHandler.java:141)\n\torg.spark_project.jetty.server.handler.HandlerWrapper.handle(HandlerWrapper.java:134)\n\torg.spark_project.jetty.server.Server.handle(Server.java:524)\n\torg.spark_project.jetty.server.HttpChannel.handle(HttpChannel.java:319)\n\torg.spark_project.jetty.server.HttpConnection.onFillable(HttpConnection.java:253)\n\torg.spark_project.jetty.io.AbstractConnection$ReadCallback.succeeded(AbstractConnection.java:273)\n\torg.spark_project.jetty.io.FillInterest.fillable(FillInterest.java:95)\n\torg.spark_project.jetty.io.SelectChannelEndPoint$2.run(SelectChannelEndPoint.java:93)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.executeProduceConsume(ExecuteProduceConsume.java:303)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.produceConsume(ExecuteProduceConsume.java:148)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.run(ExecuteProduceConsume.java:136)\n\torg.spark_project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:671)\n\torg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:589)\n\tjava.lang.Thread.run(Thread.java:745)"
```
Author: German Schiavon <germanschiavon@gmail.com>
Closes#19966 from Gschiavon/fix-submission-request.
## What changes were proposed in this pull request?
While spark code changes, there are new events in event log: #19649
And we used to maintain a whitelist to avoid exceptions: #15663
Currently Spark history server will stop parsing on unknown events or unrecognized properties. We may still see part of the UI data.
For better compatibility, we can ignore unknown events and parse through the log file.
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#19953 from gengliangwang/ReplayListenerBus.
… than spark.network.timeout or not
## What changes were proposed in this pull request?
If spark.executor.heartbeatInterval bigger than spark.network.timeout,it will almost always cause exception below.
`Job aborted due to stage failure: Task 4763 in stage 3.0 failed 4 times, most recent failure: Lost task 4763.3 in stage 3.0 (TID 22383, executor id: 4761, host: xxx): ExecutorLostFailure (executor 4761 exited caused by one of the running tasks) Reason: Executor heartbeat timed out after 154022 ms`
Since many users do not get that point.He will set spark.executor.heartbeatInterval incorrectly.
This patch check this case when submit applications.
## How was this patch tested?
Test in cluster
Author: zhoukang <zhoukang199191@gmail.com>
Closes#19942 from caneGuy/zhoukang/check-heartbeat.
## What changes were proposed in this pull request?
It solves the problem when submitting a wrong CreateSubmissionRequest to Spark Dispatcher was causing a bad state of Dispatcher and making it inactive as a Mesos framework.
https://issues.apache.org/jira/browse/SPARK-22574
## How was this patch tested?
All spark test passed successfully.
It was tested sending a wrong request (without appArgs) before and after the change. The point is easy, check if the value is null before being accessed.
This was before the change, leaving the dispatcher inactive:
```
Exception in thread "Thread-22" java.lang.NullPointerException
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.getDriverCommandValue(MesosClusterScheduler.scala:444)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.buildDriverCommand(MesosClusterScheduler.scala:451)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.org$apache$spark$scheduler$cluster$mesos$MesosClusterScheduler$$createTaskInfo(MesosClusterScheduler.scala:538)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler$$anonfun$scheduleTasks$1.apply(MesosClusterScheduler.scala:570)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler$$anonfun$scheduleTasks$1.apply(MesosClusterScheduler.scala:555)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.scheduleTasks(MesosClusterScheduler.scala:555)
at org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler.resourceOffers(MesosClusterScheduler.scala:621)
```
And after:
```
"message" : "Malformed request: org.apache.spark.deploy.rest.SubmitRestProtocolException: Validation of message CreateSubmissionRequest failed!\n\torg.apache.spark.deploy.rest.SubmitRestProtocolMessage.validate(SubmitRestProtocolMessage.scala:70)\n\torg.apache.spark.deploy.rest.SubmitRequestServlet.doPost(RestSubmissionServer.scala:272)\n\tjavax.servlet.http.HttpServlet.service(HttpServlet.java:707)\n\tjavax.servlet.http.HttpServlet.service(HttpServlet.java:790)\n\torg.spark_project.jetty.servlet.ServletHolder.handle(ServletHolder.java:845)\n\torg.spark_project.jetty.servlet.ServletHandler.doHandle(ServletHandler.java:583)\n\torg.spark_project.jetty.server.handler.ContextHandler.doHandle(ContextHandler.java:1180)\n\torg.spark_project.jetty.servlet.ServletHandler.doScope(ServletHandler.java:511)\n\torg.spark_project.jetty.server.handler.ContextHandler.doScope(ContextHandler.java:1112)\n\torg.spark_project.jetty.server.handler.ScopedHandler.handle(ScopedHandler.java:141)\n\torg.spark_project.jetty.server.handler.HandlerWrapper.handle(HandlerWrapper.java:134)\n\torg.spark_project.jetty.server.Server.handle(Server.java:524)\n\torg.spark_project.jetty.server.HttpChannel.handle(HttpChannel.java:319)\n\torg.spark_project.jetty.server.HttpConnection.onFillable(HttpConnection.java:253)\n\torg.spark_project.jetty.io.AbstractConnection$ReadCallback.succeeded(AbstractConnection.java:273)\n\torg.spark_project.jetty.io.FillInterest.fillable(FillInterest.java:95)\n\torg.spark_project.jetty.io.SelectChannelEndPoint$2.run(SelectChannelEndPoint.java:93)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.executeProduceConsume(ExecuteProduceConsume.java:303)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.produceConsume(ExecuteProduceConsume.java:148)\n\torg.spark_project.jetty.util.thread.strategy.ExecuteProduceConsume.run(ExecuteProduceConsume.java:136)\n\torg.spark_project.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:671)\n\torg.spark_project.jetty.util.thread.QueuedThreadPool$2.run(QueuedThreadPool.java:589)\n\tjava.lang.Thread.run(Thread.java:745)"
```
Author: German Schiavon <germanschiavon@gmail.com>
Closes#19793 from Gschiavon/fix-submission-request.
This PR contains implementation of the basic submission client for the cluster mode of Spark on Kubernetes. It's step 2 from the step-wise plan documented [here](https://github.com/apache-spark-on-k8s/spark/issues/441#issuecomment-330802935).
This addition is covered by the [SPIP](http://apache-spark-developers-list.1001551.n3.nabble.com/SPIP-Spark-on-Kubernetes-td22147.html) vote which passed on Aug 31.
This PR and #19468 together form a MVP of Spark on Kubernetes that allows users to run Spark applications that use resources locally within the driver and executor containers on Kubernetes 1.6 and up. Some changes on pom and build/test setup are copied over from #19468 to make this PR self contained and testable.
The submission client is mainly responsible for creating the Kubernetes pod that runs the Spark driver. It follows a step-based approach to construct the driver pod, as the code under the `submit.steps` package shows. The steps are orchestrated by `DriverConfigurationStepsOrchestrator`. `Client` creates the driver pod and waits for the application to complete if it's configured to do so, which is the case by default.
This PR also contains Dockerfiles of the driver and executor images. They are included because some of the environment variables set in the code would not make sense without referring to the Dockerfiles.
* The patch contains unit tests which are passing.
* Manual testing: ./build/mvn -Pkubernetes clean package succeeded.
* It is a subset of the entire changelist hosted at http://github.com/apache-spark-on-k8s/spark which is in active use in several organizations.
* There is integration testing enabled in the fork currently hosted by PepperData which is being moved over to RiseLAB CI.
* Detailed documentation on trying out the patch in its entirety is in: https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html
cc rxin felixcheung mateiz (shepherd)
k8s-big-data SIG members & contributors: mccheah foxish ash211 ssuchter varunkatta kimoonkim erikerlandson tnachen ifilonenko liyinan926
Author: Yinan Li <liyinan926@gmail.com>
Closes#19717 from liyinan926/spark-kubernetes-4.
…structures
## What changes were proposed in this pull request?
In general, the SHS pages now use the public API types to represent applications. Some internal code paths still used its own view of what applications and attempts look like (`ApplicationHistoryInfo` and `ApplicationAttemptInfo`), declared in ApplicationHistoryProvider.scala.
This pull request removes these classes and updates the rest of the code to use `status.api.v1.ApplicationInfo` and `status.api.v1.ApplicationAttemptInfo` instead.
Furthermore `status.api.v1.ApplicationInfo` and `status.api.v1.ApplicationAttemptInfo` were changed to case class to
- facilitate copying instances
- equality checking in test code
- nicer toString()
To simplify the code a bit `v1.` prefixes were also removed from occurrences of v1.ApplicationInfo and v1.ApplicationAttemptInfo as there is no more ambiguity between classes in history and status.api.v1.
## How was this patch tested?
By running existing automated tests.
Author: Sandor Murakozi <smurakozi@gmail.com>
Closes#19920 from smurakozi/SPARK-21672.
…a-2.12 and JDK9
## What changes were proposed in this pull request?
Some compile error after upgrading to scala-2.12
```javascript
spark_source/core/src/main/scala/org/apache/spark/executor/Executor.scala:455: ambiguous reference to overloaded definition, method limit in class ByteBuffer of type (x$1: Int)java.nio.ByteBuffer
method limit in class Buffer of type ()Int
match expected type ?
val resultSize = serializedDirectResult.limit
error
```
The limit method was moved from ByteBuffer to the superclass Buffer and it can no longer be called without (). The same reason for position method.
```javascript
/home/zly/prj/oss/jdk9_HOS_SOURCE/spark_source/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformationExec.scala:427: ambiguous reference to overloaded definition, [error] both method putAll in class Properties of type (x$1: java.util.Map[_, _])Unit [error] and method putAll in class Hashtable of type (x$1: java.util.Map[_ <: Object, _ <: Object])Unit [error] match argument types (java.util.Map[String,String])
[error] props.putAll(outputSerdeProps.toMap.asJava)
[error] ^
```
This is because the key type is Object instead of String which is unsafe.
## How was this patch tested?
running tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: kellyzly <kellyzly@126.com>
Closes#19854 from kellyzly/SPARK-22660.
## What changes were proposed in this pull request?
I was looking at the config code today and found that configs defined using ConfigBuilder.fallbackConf didn't trigger onCreate function. This patch fixes it.
This doesn't require backporting since we currently have no configs that use it.
## How was this patch tested?
Added a test case for all the config final creator functions in ConfigEntrySuite.
Author: Reynold Xin <rxin@databricks.com>
Closes#19905 from rxin/SPARK-22710.
## What changes were proposed in this pull request?
As the doc says "For accumulator updates performed inside actions only, Spark guarantees that each task’s update to the accumulator will only be applied once, i.e. restarted tasks will not update the value."
But currently the code doesn't guarantee this.
## How was this patch tested?
New added tests.
Author: Carson Wang <carson.wang@intel.com>
Closes#19877 from carsonwang/fixAccum.
The main goal of this change is to allow multiple cluster-mode
submissions from the same JVM, without having them end up with
mixed configuration. That is done by extending the SparkApplication
trait, and doing so was reasonably trivial for standalone and
mesos modes.
For YARN mode, there was a complication. YARN used a "SPARK_YARN_MODE"
system property to control behavior indirectly in a whole bunch of
places, mainly in the SparkHadoopUtil / YarnSparkHadoopUtil classes.
Most of the changes here are removing that.
Since we removed support for Hadoop 1.x, some methods that lived in
YarnSparkHadoopUtil can now live in SparkHadoopUtil. The remaining
methods don't need to be part of the class, and can be called directly
from the YarnSparkHadoopUtil object, so now there's a single
implementation of SparkHadoopUtil.
There were two places in the code that relied on SPARK_YARN_MODE to
make decisions about YARN-specific functionality, and now explicitly check
the master from the configuration for that instead:
* fetching the external shuffle service port, which can come from the YARN
configuration.
* propagation of the authentication secret using Hadoop credentials. This also
was cleaned up a little to not need so many methods in `SparkHadoopUtil`.
With those out of the way, actually changing the YARN client
to extend SparkApplication was easy.
Tested with existing unit tests, and also by running YARN apps
with auth and kerberos both on and off in a real cluster.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19631 from vanzin/SPARK-22372.
I have modified SparkHadoopWriter so that executors and the driver always use consistent JobIds during the hadoop commit. Before SPARK-18191, spark always used the rddId, it just incorrectly named the variable stageId. After SPARK-18191, it used the rddId as the jobId on the driver's side, and the stageId as the jobId on the executors' side. With this change executors and the driver will consistently uses rddId as the jobId. Also with this change, during the hadoop commit protocol spark uses actual stageId to check whether a stage can be committed unlike before that it was using executors' jobId to do this check.
In addition to the existing unit tests, a test has been added to check whether executors and the driver are using the same JobId. The test failed before this change and passed after applying this fix.
Author: Reza Safi <rezasafi@cloudera.com>
Closes#19848 from rezasafi/stagerddsimple.
## What changes were proposed in this pull request?
This updates a behavior of `addJar` method of `sparkContext` class. If path without any scheme is passed as input it is used literally without url encoding/decoding it.
## How was this patch tested?
A unit test is added for this.
Author: Jakub Dubovsky <jakub.dubovsky@seznam.cz>
Closes#19834 from james64/SPARK-22585-encode-add-jar.
The only remaining use of this class was the SparkStatusTracker, which
was modified to use the new status store. The test code to wait for
executors was moved to TestUtils and now uses the SparkStatusTracker API.
Indirectly, ConsoleProgressBar also uses this data. Because it has
some lower latency requirements, a shortcut to efficiently get the
active stages from the active listener was added to the AppStateStore.
Now that all UI code goes through the status store to get its data,
the FsHistoryProvider can be cleaned up to only replay event logs
when needed - that is, when there is no pre-existing disk store for
the application.
As part of this change I also modified the streaming UI to read the needed
data from the store, which was missed in the previous patch that made
JobProgressListener redundant.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19750 from vanzin/SPARK-20650.
## What changes were proposed in this pull request?
In adaptive execution, the map output statistics of all mappers will be aggregated after previous stage is successfully executed. Driver takes the aggregation job while it will get slow when the number of `mapper * shuffle partitions` is large, since it only uses single thread to compute. This PR uses multi-thread to deal with this single point bottleneck.
## How was this patch tested?
Test cases are in `MapOutputTrackerSuite.scala`
Author: GuoChenzhao <chenzhao.guo@intel.com>
Author: gczsjdy <gczsjdy1994@gmail.com>
Closes#19763 from gczsjdy/single_point_mapstatistics.
This change hooks up the config reader to `SparkConf.getDeprecatedConfig`,
so that config constants with deprecated names generate the proper warnings.
It also changes two deprecated configs from the new "alternatives" system to
the old deprecation system, since they're not yet hooked up to each other.
Added a few unit tests to verify the desired behavior.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19760 from vanzin/SPARK-22533.
This PR enables to use ``OffHeapColumnVector`` when ``spark.sql.columnVector.offheap.enable`` is set to ``true``. While ``ColumnVector`` has two implementations ``OnHeapColumnVector`` and ``OffHeapColumnVector``, only ``OnHeapColumnVector`` is always used.
This PR implements the followings
- Pass ``OffHeapColumnVector`` to ``ColumnarBatch.allocate()`` when ``spark.sql.columnVector.offheap.enable`` is set to ``true``
- Free all of off-heap memory regions by ``OffHeapColumnVector.close()``
- Ensure to call ``OffHeapColumnVector.close()``
Use existing tests
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#17436 from kiszk/SPARK-20101.
## What changes were proposed in this pull request?
ScalaTest 3.0 uses an implicit `Signaler`. This PR makes it sure all Spark tests uses `ThreadSignaler` explicitly which has the same default behavior of interrupting a thread on the JVM like ScalaTest 2.2.x. This will reduce potential flakiness.
## How was this patch tested?
This is testsuite-only update. This should passes the Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19784 from dongjoon-hyun/use_thread_signaler.
## What changes were proposed in this pull request?
Ensure HighlyCompressedMapStatus calculates correct avgSize
## How was this patch tested?
New unit test added.
Author: yucai <yucai.yu@intel.com>
Closes#19765 from yucai/avgsize.
This change is a little larger because there's a whole lot of logic
behind these pages, all really tied to internal types and listeners,
and some of that logic had to be implemented in the new listener and
the needed data exposed through the API types.
- Added missing StageData and ExecutorStageSummary fields which are
used by the UI. Some json golden files needed to be updated to account
for new fields.
- Save RDD graph data in the store. This tries to re-use existing types as
much as possible, so that the code doesn't need to be re-written. So it's
probably not very optimal.
- Some old classes (e.g. JobProgressListener) still remain, since they're used
in other parts of the code; they're not used by the UI anymore, though, and
will be cleaned up in a separate change.
- Save information about active pools in the store. This data is not really used
in the SHS, but it's not a lot of data so it's still recorded when replaying
applications.
- Because the new store sorts things slightly differently from the previous
code, some json golden files had some elements within them shuffled around.
- The retention unit test in UISeleniumSuite was disabled because the code
to throw away old stages / tasks hasn't been added yet.
- The job description field in the API tries to follow the old behavior, which
makes it be empty most of the time, even though there's information to fill it
in. For stages, a new field was added to hold the description (which is basically
the job description), so that the UI can be rendered in the old way.
- A new stage status ("SKIPPED") was added to account for the fact that the API
couldn't represent that state before. Without this, the stage would show up as
"PENDING" in the UI, which is now based on API types.
- The API used to expose "executorRunTime" as the value of the task's duration,
which wasn't really correct (also because that value was easily available
from the metrics object); this change fixes that by storing the correct duration,
which also means a few expectation files needed to be updated to account for
the new durations and sorting differences due to the changed values.
- Added changes to implement SPARK-20713 and SPARK-21922 in the new code.
Tested with existing unit tests (and by using the UI a lot).
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19698 from vanzin/SPARK-20648.
Continuation of PR#19528 (https://github.com/apache/spark/pull/19529#issuecomment-340252119)
The problem with the maven build in the previous PR was the new tests.... the creation of a spark session outside the tests meant there was more than one spark session around at a time.
I was using the spark session outside the tests so that the tests could share data; I've changed it so that each test creates the data anew.
Author: Nathan Kronenfeld <nicole.oresme@gmail.com>
Author: Nathan Kronenfeld <nkronenfeld@uncharted.software>
Closes#19705 from nkronenfeld/alternative-style-tests-2.
This required adding information about StreamBlockId to the store,
which is not available yet via the API. So an internal type was added
until there's a need to expose that information in the API.
The UI only lists RDDs that have cached partitions, and that information
wasn't being correctly captured in the listener, so that's also fixed,
along with some minor (internal) API adjustments so that the UI can
get the correct data.
Because of the way partitions are cached, some optimizations w.r.t. how
often the data is flushed to the store could not be applied to this code;
because of that, some different ways to make the code more performant
were added to the data structures tracking RDD blocks, with the goal of
avoiding expensive copies when lots of blocks are being updated.
Tested with existing and updated unit tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19679 from vanzin/SPARK-20647.
## What changes were proposed in this pull request?
Fixing nits in MetricsSystemSuite file
1) Using Sink instead of Source while casting
2) Using meaningful naming for variables, which reflect their usage
## How was this patch tested?
Ran the tests locally and all of them passing
Author: Srinivasa Reddy Vundela <vsr@cloudera.com>
Closes#19699 from vundela/master.
## What changes were proposed in this pull request?
Preliminary changes to get ClosureCleaner to work with Scala 2.12. Makes many usages just work, but not all. This does _not_ resolve the JIRA.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#19675 from srowen/SPARK-14540.0.
The executors page is built on top of the REST API, so the page itself
was easy to hook up to the new code.
Some other pages depend on the `ExecutorListener` class that is being
removed, though, so they needed to be modified to use data from the
new store. Fortunately, all they seemed to need is the map of executor
logs, so that was somewhat easy too.
The executor timeline graph required adding some properties to the
ExecutorSummary API type. Instead of following the previous code,
which stored all the listener events in memory, the timeline is
now created based on the data available from the API.
I had to change some of the test golden files because the old code would
return executors in "random" order (since it used a mutable Map instead
of something that returns a sorted list), and the new code returns executors
in id order.
Tested with existing unit tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19678 from vanzin/SPARK-20646.
This change modifies the status listener to collect the information
needed to render the envionment page, and populates that page and the
API with information collected by the listener.
Tested with existing and added unit tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19677 from vanzin/SPARK-20645.
…alization.
## What changes were proposed in this pull request?
Use non-linear containsKey operation for serialized maps, lookup into underlying map.
## How was this patch tested?
unit tests
Author: Alexander Istomin <istomin@rutarget.ru>
Closes#19553 from Whoosh/SPARK-22330.
There are two somewhat unrelated things going on in this patch, but
both are meant to make integration of individual UI pages later on
much easier.
The first part is some tweaking of the code in the listener so that
it does less updates of the kvstore for data that changes fast; for
example, it avoids writing changes down to the store for every
task-related event, since those can arrive very quickly at times.
Instead, for these kinds of events, it chooses to only flush things
if a certain interval has passed. The interval is based on how often
the current spark-shell code updates the progress bar for jobs, so
that users can get reasonably accurate data.
The code also delays as much as possible hitting the underlying kvstore
when replaying apps in the history server. This is to avoid unnecessary
writes to disk.
The second set of changes prepare the history server and SparkUI for
integrating with the kvstore. A new class, AppStatusStore, is used
for translating between the stored data and the types used in the
UI / API. The SHS now populates a kvstore with data loaded from
event logs when an application UI is requested.
Because this store can hold references to disk-based resources, the
code was modified to retrieve data from the store under a read lock.
This allows the SHS to detect when the store is still being used, and
only update it (e.g. because an updated event log was detected) when
there is no other thread using the store.
This change ended up creating a lot of churn in the ApplicationCache
code, which was cleaned up a lot in the process. I also removed some
metrics which don't make too much sense with the new code.
Tested with existing and added unit tests, and by making sure the SHS
still works on a real cluster.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19582 from vanzin/SPARK-20644.
