## What changes were proposed in this pull request?
The behavior of `SparkContext.addFile()` changed slightly with the introduction of the Netty-RPC-based file server, which was introduced in Spark 1.6 (where it was disabled by default) and became the default / only file server in Spark 2.0.0.
Prior to 2.0, calling `SparkContext.addFile()` with files that have the same name and identical contents would succeed. This behavior was never explicitly documented but Spark has behaved this way since very early 1.x versions.
In 2.0 (or 1.6 with the Netty file server enabled), the second `addFile()` call will fail with a requirement error because NettyStreamManager tries to guard against duplicate file registration.
This problem also affects `addJar()` in a more subtle way: the `fileServer.addJar()` call will also fail with an exception but that exception is logged and ignored; I believe that the problematic exception-catching path was mistakenly copied from some old code which was only relevant to very old versions of Spark and YARN mode.
I believe that this change of behavior was unintentional, so this patch weakens the `require` check so that adding the same filename at the same path will succeed.
At file download time, Spark tasks will fail with exceptions if an executor already has a local copy of a file and that file's contents do not match the contents of the file being downloaded / added. As a result, it's important that we prevent files with the same name and different contents from being served because allowing that can effectively brick an executor by preventing it from successfully launching any new tasks. Before this patch's change, this was prevented by forbidding `addFile()` from being called twice on files with the same name. Because Spark does not defensively copy local files that are passed to `addFile` it is vulnerable to files' contents changing, so I think it's okay to rely on an implicit assumption that these files are intended to be immutable (since if they _are_ mutable then this can lead to either explicit task failures or implicit incorrectness (in case new executors silently get newer copies of the file while old executors continue to use an older version)). To guard against this, I have decided to only update the file addition timestamps on the first call to `addFile()`; duplicate calls will succeed but will not update the timestamp. This behavior is fine as long as we assume files are immutable, which seems reasonable given the behaviors described above.
As part of this change, I also improved the thread-safety of the `addedJars` and `addedFiles` maps; this is important because these maps may be concurrently read by a task launching thread and written by a driver thread in case the user's driver code is multi-threaded.
## How was this patch tested?
I added regression tests in `SparkContextSuite`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#14396 from JoshRosen/SPARK-16787.
## What changes were proposed in this pull request?
Casting ConcurrentHashMap to ConcurrentMap allows to run code compiled with Java 8 on Java 7
## How was this patch tested?
Compilation. Existing automatic tests
Author: Maciej Brynski <maciej.brynski@adpilot.pl>
Closes#14459 from maver1ck/spark-15541-master.
## What changes were proposed in this pull request?
Use foreach/for instead of map where operation requires execution of body, not actually defining a transformation
## How was this patch tested?
Jenkins
Author: Sean Owen <sowen@cloudera.com>
Closes#14332 from srowen/SPARK-16694.
## What changes were proposed in this pull request?
New config var: spark.mesos.docker.containerizer={"mesos","docker" (default)}
This adds support for running docker containers via the Mesos unified containerizer: http://mesos.apache.org/documentation/latest/container-image/
The benefit is losing the dependency on `dockerd`, and all the costs which it incurs.
I've also updated the supported Mesos version to 0.28.2 for support of the required protobufs.
This is blocked on: https://github.com/apache/spark/pull/14167
## How was this patch tested?
- manually testing jobs submitted with both "mesos" and "docker" settings for the new config var.
- spark/mesos integration test suite
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#14275 from mgummelt/unified-containerizer.
## What changes were proposed in this pull request?
Adding a new property to SparkConf called spark.metrics.namespace that allows users to
set a custom namespace for executor and driver metrics in the metrics systems.
By default, the root namespace used for driver or executor metrics is
the value of `spark.app.id`. However, often times, users want to be able to track the metrics
across apps for driver and executor metrics, which is hard to do with application ID
(i.e. `spark.app.id`) since it changes with every invocation of the app. For such use cases,
users can set the `spark.metrics.namespace` property to another spark configuration key like
`spark.app.name` which is then used to populate the root namespace of the metrics system
(with the app name in our example). `spark.metrics.namespace` property can be set to any
arbitrary spark property key, whose value would be used to set the root namespace of the
metrics system. Non driver and executor metrics are never prefixed with `spark.app.id`, nor
does the `spark.metrics.namespace` property have any such affect on such metrics.
## How was this patch tested?
Added new unit tests, modified existing unit tests.
Author: Mark Grover <mark@apache.org>
Closes#14270 from markgrover/spark-5847.
## What changes were proposed in this pull request?
This change adds a new configuration entry to specify the size of the spark listener bus event queue. The value for this config ("spark.scheduler.listenerbus.eventqueue.size") is set to a default to 10000.
Note:
I haven't currently documented the configuration entry. We can decide whether it would be appropriate to make it a public configuration or keep it as an undocumented one. Refer JIRA for more details.
## How was this patch tested?
Ran existing jobs and verified the event queue size with debug logs and from the Spark WebUI Environment tab.
Author: Dhruve Ashar <dhruveashar@gmail.com>
Closes#14269 from dhruve/bug/SPARK-15703.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Mesos agents by default will not pull docker images which are cached
locally already. In order to run Spark executors from mutable tags like
`:latest` this commit introduces a Spark setting
(`spark.mesos.executor.docker.forcePullImage`). Setting this flag to
true will tell the Mesos agent to force pull the docker image (default is `false` which is consistent with the previous
implementation and Mesos' default
behaviour).
Author: Philipp Hoffmann <mail@philipphoffmann.de>
Closes#14348 from philipphoffmann/force-pull-image.
## What changes were proposed in this pull request?
This patch adds pagination support for the Job Tables in the Jobs tab. Pagination is provided for all of the three Job Tables (active, completed, and failed). Interactions (jumping, sorting, and setting page size) for paged tables are also included.
The diff didn't keep track of some lines based on the original ones. The function `makeRow`of the original `AllJobsPage.scala` is reused. They are separated at the beginning of the function `jobRow` (L427-439) and the function `row`(L594-618) in the new `AllJobsPage.scala`.
## How was this patch tested?
Tested manually by using checking the Web UI after completing and failing hundreds of jobs.
Generate completed jobs by:
```scala
val d = sc.parallelize(Array(1,2,3,4,5))
for(i <- 1 to 255){ var b = d.collect() }
```
Generate failed jobs by calling the following code multiple times:
```scala
var b = d.map(_/0).collect()
```
Interactions like jumping, sorting, and setting page size are all tested.
This shows the pagination for completed jobs:
This shows the sorting works in job tables:
This shows the pagination for failed jobs and the effect of jumping and setting page size:
Author: Tao Lin <nblintao@gmail.com>
Closes#13620 from nblintao/dev.
## What changes were proposed in this pull request?
Currently in the log and UI display, only on-heap storage memory is calculated and displayed,
```
16/06/27 13:41:52 INFO MemoryStore: Block rdd_5_0 stored as values in memory (estimated size 17.8 KB, free 665.9 MB)
```
<img width="1232" alt="untitled" src="https://cloud.githubusercontent.com/assets/850797/16369960/53fb614e-3c6e-11e6-8fa3-7ffe65abcb49.png">
With [SPARK-13992](https://issues.apache.org/jira/browse/SPARK-13992) off-heap memory is supported for data persistence, so here change to also take off-heap storage memory into consideration.
## How was this patch tested?
Unit test and local verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#13920 from jerryshao/SPARK-16166.
## What changes were proposed in this pull request?
Mesos agents by default will not pull docker images which are cached
locally already. In order to run Spark executors from mutable tags like
`:latest` this commit introduces a Spark setting
`spark.mesos.executor.docker.forcePullImage`. Setting this flag to
true will tell the Mesos agent to force pull the docker image (default is `false` which is consistent with the previous
implementation and Mesos' default
behaviour).
## How was this patch tested?
I ran a sample application including this change on a Mesos cluster and verified the correct behaviour for both, with and without, force pulling the executor image. As expected the image is being force pulled if the flag is set.
Author: Philipp Hoffmann <mail@philipphoffmann.de>
Closes#13051 from philipphoffmann/force-pull-image.
…close between each partition
## What changes were proposed in this pull request?
Replace commitAndClose with separate commit and close to avoid opening and closing
the file between partitions.
## How was this patch tested?
Run existing unit tests, add a few unit tests regarding reverts.
Observed a ~20% reduction in total time in tasks on stages with shuffle
writes to many partitions.
JoshRosen
Author: Brian Cho <bcho@fb.com>
Closes#13382 from dafrista/separatecommit-master.
## What changes were proposed in this pull request?
Force eager creation of loggers to avoid shutdown hook conflicts.
## How was this patch tested?
Manually tested with a project using Log4j 2, verified that the shutdown hook conflict issue was solved.
Author: Mikael Ståldal <mikael.staldal@magine.com>
Closes#14320 from mikaelstaldal/shutdown-hook-logging.
## What changes were proposed in this pull request?
Added new configuration namespace: spark.mesos.env.*
This allows a user submitting a job in cluster mode to set arbitrary environment variables on the driver.
spark.mesos.driverEnv.KEY=VAL will result in the env var "KEY" being set to "VAL"
I've also refactored the tests a bit so we can re-use code in MesosClusterScheduler.
And I've refactored the command building logic in `buildDriverCommand`. Command builder values were very intertwined before, and now it's easier to determine exactly how each variable is set.
## How was this patch tested?
unit tests
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#14167 from mgummelt/driver-env-vars.
This allows configuration to be more flexible, for example, when the cluster does
not have a homogeneous configuration (e.g. packages are installed on different
paths in different nodes). By allowing one to reference the environment from
the conf, it becomes possible to work around those in certain cases.
As part of the implementation, ConfigEntry now keeps track of all "known" configs
(i.e. those created through the use of ConfigBuilder), since that list is used
by the resolution code. This duplicates some code in SQLConf, which could potentially
be merged with this now. It will also make it simpler to implement some missing
features such as filtering which configs show up in the UI or in event logs - which
are not part of this change.
Another change is in the way ConfigEntry reads config data; it now takes a string
map and a function that reads env variables, so that it can be called both from
SparkConf and SQLConf. This makes it so both places follow the same read path,
instead of having to replicate certain logic in SQLConf. There are still a
couple of methods in SQLConf that peek into fields of ConfigEntry directly,
though.
Tested via unit tests, and by using the new variable expansion functionality
in a shell session with a custom spark.sql.hive.metastore.jars value.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#14022 from vanzin/SPARK-16272.
1. Create the executorspage-template.html for displaying application information in datables.
2. Added REST API endpoint "allexecutors" to be able to see all executors created for particular job.
3. The executorspage.js uses jQuery to access the data from /api/v1/applications/appid/allexecutors REST API, and use DataTable to display executors for the application. It also, generates summary of dead/live and total executors created during life of the application.
4. Similar changes applicable to Executors Page on history server for a given application.
Snapshots for how it looks like now:
<img width="938" alt="screen shot 2016-06-14 at 2 45 44 pm" src="https://cloud.githubusercontent.com/assets/6090397/16060092/ad1de03a-324b-11e6-8469-9eaa3f2548b5.png">
New Executors Page screenshot looks like this:
<img width="1436" alt="screen shot 2016-06-15 at 10 12 01 am" src="https://cloud.githubusercontent.com/assets/6090397/16085514/ee7004f0-32e1-11e6-9340-33d91e407f2b.png">
Author: Kishor Patil <kpatil@yahoo-inc.com>
Closes#13670 from kishorvpatil/execTemplates.
## What changes were proposed in this pull request?
Document RDD.pipe semantics; don't execute process for empty input partitions.
Note this includes the fix in https://github.com/apache/spark/pull/14256 because it's necessary to even test this. One or the other will merge the fix.
## How was this patch tested?
Jenkins tests including new test.
Author: Sean Owen <sowen@cloudera.com>
Closes#14260 from srowen/SPARK-16613.
## What changes were proposed in this pull request?
This change moves the include jar test from R to SparkSubmitSuite and uses a dynamically compiled jar. This helps us remove the binary jar from the R package and solves both the CRAN warnings and the lack of source being available for this jar.
## How was this patch tested?
SparkR unit tests, SparkSubmitSuite, check-cran.sh
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#14243 from shivaram/sparkr-jar-move.
## What changes were proposed in this pull request?
Adds a few public methods to `SparkLauncher` to allow configuring some extra features of the `ProcessBuilder`, including the working directory, output and error stream redirection.
## How was this patch tested?
Unit testing + simple Spark driver programs
Author: Andrew Duffy <root@aduffy.org>
Closes#14201 from andreweduffy/feature/launcher.
## What changes were proposed in this pull request?
Currently `RDD.pipe(command: String)`:
- works only when the command is specified without any options, such as `RDD.pipe("wc")`
- does NOT work when the command is specified with some options, such as `RDD.pipe("wc -l")`
This is a regression from Spark 1.6.
This patch adds back the tokenization process in `RDD.pipe(command: String)` to fix this regression.
## How was this patch tested?
Added a test which:
- would pass in `1.6`
- _[prior to this patch]_ would fail in `master`
- _[after this patch]_ would pass in `master`
Author: Liwei Lin <lwlin7@gmail.com>
Closes#14256 from lw-lin/rdd-pipe.
https://issues.apache.org/jira/browse/SPARK-16535
## What changes were proposed in this pull request?
When I scan through the pom.xml of sub projects, I found this warning as below and attached screenshot
```
Definition of groupId is redundant, because it's inherited from the parent
```
I've tried to remove some of the lines with groupId definition, and the build on my local machine is still ok.
```
<groupId>org.apache.spark</groupId>
```
As I just find now `<maven.version>3.3.9</maven.version>` is being used in Spark 2.x, and Maven-3 supports versionless parent elements: Maven 3 will remove the need to specify the parent version in sub modules. THIS is great (in Maven 3.1).
ref: http://stackoverflow.com/questions/3157240/maven-3-worth-it/3166762#3166762
## How was this patch tested?
I've tested by re-building the project, and build succeeded.
Author: Xin Ren <iamshrek@126.com>
Closes#14189 from keypointt/SPARK-16535.
## What changes were proposed in this pull request?
With the fix from SPARK-13112, I see that `LaunchTask` is always processed after `RegisteredExecutor` is done and so it gets chance to do all retries to startup an executor. There is still a problem that if `Executor` creation itself fails and there is some exception, it gets unnoticed and the executor is killed when it tries to process the `LaunchTask` as `executor` is null : https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala#L88 So if one looks at the logs, it does not tell that there was problem during `Executor` creation and thats why it was killed.
This PR explicitly catches exception in `Executor` creation, logs a proper message and then exits the JVM. Also, I have changed the `exitExecutor` method to accept `reason` so that backends can use that reason and do stuff like logging to a DB to get an aggregate of such exits at a cluster level
## How was this patch tested?
I am relying on existing tests
Author: Tejas Patil <tejasp@fb.com>
Closes#14202 from tejasapatil/exit_executor_failure.
## What changes were proposed in this pull request?
Currently when running spark on yarn, jars specified with --jars, --packages will be added twice, one is Spark's own file server, another is yarn's distributed cache, this can be seen from log:
for example:
```
./bin/spark-shell --master yarn-client --jars examples/target/scala-2.11/jars/scopt_2.11-3.3.0.jar
```
If specified the jar to be added is scopt jar, it will added twice:
```
...
16/07/14 15:06:48 INFO Server: Started 5603ms
16/07/14 15:06:48 INFO Utils: Successfully started service 'SparkUI' on port 4040.
16/07/14 15:06:48 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://192.168.0.102:4040
16/07/14 15:06:48 INFO SparkContext: Added JAR file:/Users/sshao/projects/apache-spark/examples/target/scala-2.11/jars/scopt_2.11-3.3.0.jar at spark://192.168.0.102:63996/jars/scopt_2.11-3.3.0.jar with timestamp 1468480008637
16/07/14 15:06:49 INFO RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
16/07/14 15:06:49 INFO Client: Requesting a new application from cluster with 1 NodeManagers
16/07/14 15:06:49 INFO Client: Verifying our application has not requested more than the maximum memory capability of the cluster (8192 MB per container)
16/07/14 15:06:49 INFO Client: Will allocate AM container, with 896 MB memory including 384 MB overhead
16/07/14 15:06:49 INFO Client: Setting up container launch context for our AM
16/07/14 15:06:49 INFO Client: Setting up the launch environment for our AM container
16/07/14 15:06:49 INFO Client: Preparing resources for our AM container
16/07/14 15:06:49 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
16/07/14 15:06:50 INFO Client: Uploading resource file:/private/var/folders/tb/8pw1511s2q78mj7plnq8p9g40000gn/T/spark-a446300b-84bf-43ff-bfb1-3adfb0571a42/__spark_libs__6486179704064718817.zip -> hdfs://localhost:8020/user/sshao/.sparkStaging/application_1468468348998_0009/__spark_libs__6486179704064718817.zip
16/07/14 15:06:51 INFO Client: Uploading resource file:/Users/sshao/projects/apache-spark/examples/target/scala-2.11/jars/scopt_2.11-3.3.0.jar -> hdfs://localhost:8020/user/sshao/.sparkStaging/application_1468468348998_0009/scopt_2.11-3.3.0.jar
16/07/14 15:06:51 INFO Client: Uploading resource file:/private/var/folders/tb/8pw1511s2q78mj7plnq8p9g40000gn/T/spark-a446300b-84bf-43ff-bfb1-3adfb0571a42/__spark_conf__326416236462420861.zip -> hdfs://localhost:8020/user/sshao/.sparkStaging/application_1468468348998_0009/__spark_conf__.zip
...
```
So here try to avoid adding jars to Spark's fileserver unnecessarily.
## How was this patch tested?
Manually verified both in yarn client and cluster mode, also in standalone mode.
Author: jerryshao <sshao@hortonworks.com>
Closes#14196 from jerryshao/SPARK-16540.
## What changes were proposed in this pull request?
Currently if `spark.dynamicAllocation.initialExecutors` is less than `spark.dynamicAllocation.minExecutors`, Spark will automatically pick the minExecutors without any warning. While in 1.6 Spark will throw exception if configured like this. So here propose to add warning log if these parameters are configured invalidly.
## How was this patch tested?
Unit test added to verify the scenario.
Author: jerryshao <sshao@hortonworks.com>
Closes#14149 from jerryshao/SPARK-16435.
## What changes were proposed in this pull request?
I fixed a misassigned var, numCompletedTasks was assigned to numSkippedTasks in the convertJobData method
## How was this patch tested?
dev/run-tests
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#14141 from ajbozarth/spark16375.
## What changes were proposed in this pull request?
Since externalShuffleService is essential for spark, better monitoring for shuffle service is necessary. In order to do so, we added various metrics in shuffle service and imported into ExternalShuffleServiceSource for metric system.
Metrics added in shuffle service:
* registeredExecutorsSize
* openBlockRequestLatencyMillis
* registerExecutorRequestLatencyMillis
* blockTransferRateBytes
JIRA Issue: https://issues.apache.org/jira/browse/SPARK-16405
## How was this patch tested?
Some test cases are added to verify metrics as expected in metric system. Those unit test cases are shown in `ExternalShuffleBlockHandlerSuite `
Author: Yangyang Liu <yangyangliu@fb.com>
Closes#14080 from lovexi/yangyang-metrics.
## What changes were proposed in this pull request?
After SPARK-16476 (committed earlier today as #14128), we can finally bump the version number.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#14130 from rxin/SPARK-16477.
## What changes were proposed in this pull request?
It's possible to also change the callers to not pass in empty chunks, but it seems cleaner to just allow `ChunkedByteBuffer` to handle empty arrays. cc JoshRosen
## How was this patch tested?
Unit tests, also checked that the original reproduction case in https://github.com/apache/spark/pull/11748#issuecomment-230760283 is resolved.
Author: Eric Liang <ekl@databricks.com>
Closes#14099 from ericl/spark-16432.
## What changes were proposed in this pull request?
Avoid error finding max of empty Seq when stageIds is empty. It does fix the immediate problem; I don't know if it results in meaningful output, but not an error at least.
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#14105 from srowen/SPARK-16376.
## What changes were proposed in this pull request?
This uses the try/finally pattern to ensure streams are closed after use. `UnsafeShuffleWriter` wasn't closing compression streams, causing them to leak resources until garbage collected. This was causing a problem with codecs that use off-heap memory.
## How was this patch tested?
Current tests are sufficient. This should not change behavior.
Author: Ryan Blue <blue@apache.org>
Closes#14093 from rdblue/SPARK-16420-unsafe-shuffle-writer-leak.
## What changes were proposed in this pull request?
This moves over old PR https://github.com/apache/spark/pull/13664 to target master rather than branch-1.6.
Added links to logs (or an indication that there are no logs) for entries which list an executor in the stage details page of the UI.
This helps streamline the workflow where a user views a stage details page and determines that they would like to see the associated executor log for further examination. Previously, a user would have to cross reference the executor id listed on the stage details page with the corresponding entry on the executors tab.
Link to the JIRA: https://issues.apache.org/jira/browse/SPARK-15885
## How was this patch tested?
Ran existing unit tests.
Ran test queries on a platform which did not record executor logs and again on a platform which did record executor logs and verified that the new table column was empty and links to the logs (which were verified as linking to the appropriate files), respectively.
Attached is a screenshot of the UI page with no links, with the new columns highlighted. Additional screenshot of these columns with the populated links.
Without links:
With links:
This contribution is my original work and I license the work to the project under the Apache Spark project's open source license.
Author: Tom Magrino <tmagrino@fb.com>
Closes#13861 from tmagrino/uilogstweak.
## What changes were proposed in this pull request?
Make SparkContext `cancelJob` and `cancelStage` APIs public. This allows applications to use `SparkListener` to do their own management of jobs via events, but without using the REST API.
## How was this patch tested?
Existing tests (dev/run-tests)
Author: MasterDDT <miteshp@live.com>
Closes#14072 from MasterDDT/SPARK-16398.
## What changes were proposed in this pull request?
The commit 044971eca0 introduced a lazy val to simplify code in Logging. Simple enough, though one side effect is that accessing log now means grabbing the instance's lock. This in turn turned up a form of deadlock in the Mesos code. It was arguably a bit of a problem in how this code is structured, but, in any event the safest thing to do seems to be to revert the commit, and that's 90% of the change here; it's just not worth the risk of similar more subtle issues.
What I didn't revert here was the removal of this odd override of log in the Mesos code. In retrospect it might have been put in place at some stage as a defense against this type of problem. After all the Logging code still involved a lock at initialization before the change in question.
Even after the revert, it doesn't seem like it does anything, given how Logging works now, so I left it removed. However, I also removed the particular log message that ended up playing a part in this problem anyway, maybe being paranoid, to make sure this type of problem can't happen even with how the current locking works in logging initialization.
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#14069 from srowen/SPARK-16379.
## What changes were proposed in this pull request?
This patch updates the failure handling logic so Spark executor does not crash when seeing LinkageError.
## How was this patch tested?
Added an end-to-end test in FailureSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#13982 from petermaxlee/SPARK-16304.
## What changes were proposed in this pull request?
This patch adds pagination support for the Stage Tables in the Stage tab. Pagination is provided for all of the four Job Tables (active, pending, completed, and failed). Besides, the paged stage tables are also used in JobPage (the detail page for one job) and PoolPage.
Interactions (jumping, sorting, and setting page size) for paged tables are also included.
## How was this patch tested?
Tested manually by using checking the Web UI after completing and failing hundreds of jobs. Same as the testings for [Paginate Job Table in Jobs tab](https://github.com/apache/spark/pull/13620).
This shows the pagination for completed stages:
Author: Tao Lin <nblintao@gmail.com>
Closes#13708 from nblintao/stageTable.
Using "Method.invoke" causes an exception to be thrown, not an error, so
Utils.waitForProcess() was always throwing an exception when run on Java 7.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#14056 from vanzin/SPARK-16385.
## What changes were proposed in this pull request?
Coincidentally, I discovered that a couple images were unused in `docs/`, and then searched and found more, and then realized some PNGs were pretty big and could be crushed, and before I knew it, had done the same for the ASF site (not committed yet).
No functional change at all, just less superfluous image data.
## How was this patch tested?
`jekyll serve`
Author: Sean Owen <sowen@cloudera.com>
Closes#14029 from srowen/RemoveCompressImages.
## What changes were proposed in this pull request?
This PR fixes the minor Java linter errors like the following.
```
- public int read(char cbuf[], int off, int len) throws IOException {
+ public int read(char[] cbuf, int off, int len) throws IOException {
```
## How was this patch tested?
Manual.
```
$ build/mvn -T 4 -q -DskipTests -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install
$ dev/lint-java
Using `mvn` from path: /usr/local/bin/mvn
Checkstyle checks passed.
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14017 from dongjoon-hyun/minor_build_java_linter_error.
## What changes were proposed in this pull request?
In structured streaming, Spark does not report errors when the specified directory does not exist. This is a behavior different from the batch mode. This patch changes the behavior to fail if the directory does not exist (when the path is not a glob pattern).
## How was this patch tested?
Updated unit tests to reflect the new behavior.
Author: Reynold Xin <rxin@databricks.com>
Closes#14002 from rxin/SPARK-16335.
## What changes were proposed in this pull request?
Utils.terminateProcess should `destroy()` first and only fall back to `destroyForcibly()` if it fails. It's kind of bad that we're force-killing executors -- and only in Java 8. See JIRA for an example of the impact: no shutdown
While here: `Utils.waitForProcess` should use the Java 8 method if available instead of a custom implementation.
## How was this patch tested?
Existing tests, which cover the force-kill case, and Amplab tests, which will cover both Java 7 and Java 8 eventually. However I tested locally on Java 8 and the PR builder will try Java 7 here.
Author: Sean Owen <sowen@cloudera.com>
Closes#13973 from srowen/SPARK-16182.
## What changes were proposed in this pull request?
Before this change, when you turn on blacklisting with `spark.scheduler.executorTaskBlacklistTime`, but you have fewer than `spark.task.maxFailures` executors, you can end with a job "hung" after some task failures.
Whenever a taskset is unable to schedule anything on resourceOfferSingleTaskSet, we check whether the last pending task can be scheduled on *any* known executor. If not, the taskset (and any corresponding jobs) are failed.
* Worst case, this is O(maxTaskFailures + numTasks). But unless many executors are bad, this should be small
* This does not fail as fast as possible -- when a task becomes unschedulable, we keep scheduling other tasks. This is to avoid an O(numPendingTasks * numExecutors) operation
* Also, it is conceivable this fails too quickly. You may be 1 millisecond away from unblacklisting a place for a task to run, or acquiring a new executor.
## How was this patch tested?
Added unit test which failed before the change, ran new test 5k times manually, ran all scheduler tests manually, and the full suite via jenkins.
Author: Imran Rashid <irashid@cloudera.com>
Closes#13603 from squito/progress_w_few_execs_and_blacklist.
## What changes were proposed in this pull request?
Force the sorter to Spill when number of elements in the pointer array reach a certain size. This is to workaround the issue of timSort failing on large buffer size.
## How was this patch tested?
Tested by running a job which was failing without this change due to TimSort bug.
Author: Sital Kedia <skedia@fb.com>
Closes#13107 from sitalkedia/fix_TimSort.
## What changes were proposed in this pull request?
This extends SPARK-15860 to include metrics for the actual bytecode size of janino-generated methods. They can be accessed in the same way as any other codahale metric, e.g.
```
scala> org.apache.spark.metrics.source.CodegenMetrics.METRIC_GENERATED_CLASS_BYTECODE_SIZE.getSnapshot().getValues()
res7: Array[Long] = Array(532, 532, 532, 542, 1479, 2670, 3585, 3585)
scala> org.apache.spark.metrics.source.CodegenMetrics.METRIC_GENERATED_METHOD_BYTECODE_SIZE.getSnapshot().getValues()
res8: Array[Long] = Array(5, 5, 5, 5, 10, 10, 10, 10, 15, 15, 15, 38, 63, 79, 88, 94, 94, 94, 132, 132, 165, 165, 220, 220)
```
## How was this patch tested?
Small unit test, also verified manually that the performance impact is minimal (<10%). hvanhovell
Author: Eric Liang <ekl@databricks.com>
Closes#13934 from ericl/spark-16238.
## What changes were proposed in this pull request?
Previously, the TaskLocation implementation would not allow for executor ids which include underscores. This tweaks the string split used to get the hostname and executor id, allowing for underscores in the executor id.
This addresses the JIRA found here: https://issues.apache.org/jira/browse/SPARK-16148
This is moved over from a previous PR against branch-1.6: https://github.com/apache/spark/pull/13857
## How was this patch tested?
Ran existing unit tests for core and streaming. Manually ran a simple streaming job with an executor whose id contained underscores and confirmed that the job ran successfully.
This is my original work and I license the work to the project under the project's open source license.
Author: Tom Magrino <tmagrino@fb.com>
Closes#13858 from tmagrino/fixtasklocation.
## What changes were proposed in this pull request?
TaskSchedulerImpl used to only set `newExecAvailable` when a new *host* was added, not when a new executor was added to an existing host. It also didn't update some internal state tracking live executors until a task was scheduled on the executor. This patch changes it to properly update as soon as it knows about a new executor.
## How was this patch tested?
added a unit test, ran everything via jenkins.
Author: Imran Rashid <irashid@cloudera.com>
Closes#13826 from squito/SPARK-16106_executorByHosts.
## What changes were proposed in this pull request?
TaskManagerSuite "Kill other task attempts when one attempt belonging to the same task succeeds" was flaky. When checking whether a task is speculatable, at least one millisecond must pass since the task was submitted. Use a manual clock to avoid the problem.
I noticed these tests were leaving lots of threads lying around as well (which prevented me from running the test repeatedly), so I fixed that too.
## How was this patch tested?
Ran the test 1k times on my laptop, passed every time (it failed about 20% of the time before this).
Author: Imran Rashid <irashid@cloudera.com>
Closes#13848 from squito/fix_flaky_taskmanagersuite.
## What changes were proposed in this pull request?
Free memory size displayed in the log is wrong (used memory), fix to make it correct.
## How was this patch tested?
N/A
Author: jerryshao <sshao@hortonworks.com>
Closes#13804 from jerryshao/memory-log-fix.
## What changes were proposed in this pull request?
Make spill tests wait until job has completed before returning the number of stages that spilled
## How was this patch tested?
Existing Jenkins tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#13896 from srowen/SPARK-16193.
## What changes were proposed in this pull request?
Currently the initial buffer size in the sorter is hard coded inside the code and is too small for large workload. As a result, the sorter spends significant time expanding the buffer size and copying the data. It would be useful to have it configurable.
## How was this patch tested?
Tested by running a job on the cluster.
Author: Sital Kedia <skedia@fb.com>
Closes#13699 from sitalkedia/config_sort_buffer_upstream.
## The problem
Before this change, if either of the following cases happened to a task , the task would be marked as `FAILED` instead of `KILLED`:
- the task was killed before it was deserialized
- `executor.kill()` marked `taskRunner.killed`, but before calling `task.killed()` the worker thread threw the `TaskKilledException`
The reason is, in the `catch` block of the current [Executor.TaskRunner](https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/executor/Executor.scala#L362)'s implementation, we are mistakenly catching:
```scala
case _: TaskKilledException | _: InterruptedException if task.killed => ...
```
the semantics of which is:
- **(**`TaskKilledException` **OR** `InterruptedException`**)** **AND** `task.killed`
Then when `TaskKilledException` is thrown but `task.killed` is not marked, we would mark the task as `FAILED` (which should really be `KILLED`).
## What changes were proposed in this pull request?
This patch alters the catch condition's semantics from:
- **(**`TaskKilledException` **OR** `InterruptedException`**)** **AND** `task.killed`
to
- `TaskKilledException` **OR** **(**`InterruptedException` **AND** `task.killed`**)**
so that we can catch `TaskKilledException` correctly and mark the task as `KILLED` correctly.
## How was this patch tested?
Added unit test which failed before the change, ran new test 1000 times manually
Author: Liwei Lin <lwlin7@gmail.com>
Closes#13685 from lw-lin/fix-task-killed.
## What changes were proposed in this pull request?
Replace use of `commons-lang` in favor of `commons-lang3` and forbid the former via scalastyle; remove `NotImplementedException` from `comons-lang` in favor of JDK `UnsupportedOperationException`
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#13843 from srowen/SPARK-16129.
## What changes were proposed in this pull request?
Since SPARK-13220(Deprecate "yarn-client" and "yarn-cluster"), YarnClusterSuite doesn't test "yarn cluster" mode correctly.
This pull request fixes it.
## How was this patch tested?
Unit test
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: peng.zhang <peng.zhang@xiaomi.com>
Closes#13836 from renozhang/SPARK-16125-test-yarn-cluster-mode.
## What changes were proposed in this pull request?
This changes the behavior of --num-executors and spark.executor.instances when using dynamic allocation. Instead of turning dynamic allocation off, it uses the value for the initial number of executors.
This changes was discussed on [SPARK-13723](https://issues.apache.org/jira/browse/SPARK-13723). I highly recommend using it while we can change the behavior for 2.0.0. In practice, the 1.x behavior causes unexpected behavior for users (it is not clear that it disables dynamic allocation) and wastes cluster resources because users rarely notice the log message.
## How was this patch tested?
This patch updates tests and adds a test for Utils.getDynamicAllocationInitialExecutors.
Author: Ryan Blue <blue@apache.org>
Closes#13338 from rdblue/SPARK-13723-num-executors-with-dynamic-allocation.
## What changes were proposed in this pull request?
Initialize logger instance lazily in Scala preferred way
## How was this patch tested?
By running `./build/mvn clean test` locally
Author: Prajwal Tuladhar <praj@infynyxx.com>
Closes#13842 from infynyxx/spark_internal_logger.
## What changes were proposed in this pull request?
This fixes SerializationDebugger to not recurse forever when `writeReplace` returns an object of the same class, which is the case for at least the `SQLMetrics` class.
See also the OpenJDK unit tests on the behavior of recursive `writeReplace()`:
f4d80957e8/test/java/io/Serializable/nestedReplace/NestedReplace.java
cc davies cloud-fan
## How was this patch tested?
Unit tests for SerializationDebugger.
Author: Eric Liang <ekl@databricks.com>
Closes#13814 from ericl/spark-16003.
## What changes were proposed in this pull request?
Three changes here -- first two were causing failures w/ BlacklistIntegrationSuite
1. The testing framework didn't include the reviveOffers thread, so the test which involved delay scheduling might never submit offers late enough for the delay scheduling to kick in. So added in the periodic revive offers, just like the real scheduler.
2. `assertEmptyDataStructures` would occasionally fail, because it appeared there was still an active job. This is because in DAGScheduler, the jobWaiter is notified of the job completion before the data structures are cleaned up. Most of the time the test code that is waiting on the jobWaiter won't become active until after the data structures are cleared, but occasionally the race goes the other way, and the assertions fail.
3. `DAGSchedulerSuite` was not stopping all the inner parts it was setting up, so each test was leaking a number of threads. So we stop those parts too.
4. Turns out that `assertMapOutputAvailable` is not terribly useful in this framework -- most of the places I was trying to use it suffer from some race.
5. When there is an exception in the backend, try to improve the error msg a little bit. Before the exception was printed to the console, but the test would fail w/ a timeout, and the logs wouldn't show anything.
## How was this patch tested?
I ran all the tests in `BlacklistIntegrationSuite` 5k times and everything in `DAGSchedulerSuite` 1k times on my laptop. Also I ran a full jenkins build with `BlacklistIntegrationSuite` 500 times and `DAGSchedulerSuite` 50 times, see https://github.com/apache/spark/pull/13548. (I tried more times but jenkins timed out.)
To check for more leaked threads, I added some code to dump the list of all threads at the end of each test in DAGSchedulerSuite, which is how I discovered the mapOutputTracker and eventLoop were leaking threads. (I removed that code from the final pr, just part of the testing.)
And I'll run Jenkins on this a couple of times to do one more check.
Author: Imran Rashid <irashid@cloudera.com>
Closes#13565 from squito/blacklist_extra_tests.
## What changes were proposed in this pull request?
Add a configuration to allow people to set a minimum polling delay when no new data arrives (default is 10ms). This PR also cleans up some INFO logs.
## How was this patch tested?
Existing unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13718 from zsxwing/SPARK-16002.
## What changes were proposed in this pull request?
This PR makes `input_file_name()` function return the file paths not empty strings for external data sources based on `NewHadoopRDD`, such as [spark-redshift](cba5eee1ab/src/main/scala/com/databricks/spark/redshift/RedshiftRelation.scala (L149)) and [spark-xml](https://github.com/databricks/spark-xml/blob/master/src/main/scala/com/databricks/spark/xml/util/XmlFile.scala#L39-L47).
The codes with the external data sources below:
```scala
df.select(input_file_name).show()
```
will produce
- **Before**
```
+-----------------+
|input_file_name()|
+-----------------+
| |
+-----------------+
```
- **After**
```
+--------------------+
| input_file_name()|
+--------------------+
|file:/private/var...|
+--------------------+
```
## How was this patch tested?
Unit tests in `ColumnExpressionSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#13759 from HyukjinKwon/SPARK-16044.
## What changes were proposed in this pull request?
[SPARK-15395](https://issues.apache.org/jira/browse/SPARK-15395) changes the behavior that how the driver gets the executor host and the driver will get the executor IP address instead of the host name. This PR just sends the hostname from executors to driver so that driver can pass it to TaskScheduler.
## How was this patch tested?
Existing unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13741 from zsxwing/SPARK-16017.
## What changes were proposed in this pull request?
This pull request refactors parts of the DAGScheduler to improve readability, focusing on the code around stage creation. One goal of this change it to make it clearer which functions may create new stages (as opposed to looking up stages that already exist). There are no functionality changes in this pull request. In more detail:
* shuffleToMapStage was renamed to shuffleIdToMapStage (when reading the existing code I have sometimes struggled to remember what the key is -- is it a stage? A stage id? This change is intended to avoid that confusion)
* Cleaned up the code to create shuffle map stages. Previously, creating a shuffle map stage involved 3 different functions (newOrUsedShuffleStage, newShuffleMapStage, and getShuffleMapStage), and it wasn't clear what the purpose of each function was. With the new code, a single function (getOrCreateShuffleMapStage) is responsible for getting a stage (if it already exists) or creating new shuffle map stages and any missing ancestor stages, and it delegates to createShuffleMapStage when new stages need to be created. There's some remaining confusion here because the getOrCreateParentStages call in createShuffleMapStage may recursively create ancestor stages; this is an issue I plan to fix in a future pull request, because it's trickier to fix and involves a slight functionality change.
* newResultStage was renamed to createResultStage, for consistency with naming around shuffle map stages.
* getParentStages has been renamed to getOrCreateParentStages, to make it clear that this function will sometimes create missing ancestor stages.
* The only *slight* functionality change is that on line 478, updateJobIdStageIdMaps now uses a stage's parents instance variable rather than re-calculating them (I couldn't see any reason why they'd need to be re-calculated, and suspect this is just leftover from older code).
* getAncestorShuffleDependencies was renamed to getMissingAncestorShuffleDependencies, to make it clear that this only returns dependencies that have not yet been run.
cc squito markhamstra JoshRosen (who requested more DAG scheduler commenting long ago -- an issue this pull request tries, in part, to address)
FYI rxin
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#13677 from kayousterhout/SPARK-15926.
When `--packages` is specified with spark-shell the classes from those packages cannot be found, which I think is due to some of the changes in SPARK-12343.
Tested manually with both scala 2.10 and 2.11 repls.
vanzin davies can you guys please review?
Author: Marcelo Vanzin <vanzin@cloudera.com>
Author: Nezih Yigitbasi <nyigitbasi@netflix.com>
Closes#13709 from nezihyigitbasi/SPARK-15782.
## What changes were proposed in this pull request?
Reduce `spark.memory.fraction` default to 0.6 in order to make it fit within default JVM old generation size (2/3 heap). See JIRA discussion. This means a full cache doesn't spill into the new gen. CC andrewor14
## How was this patch tested?
Jenkins tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#13618 from srowen/SPARK-15796.
## What changes were proposed in this pull request?
gapply() applies an R function on groups grouped by one or more columns of a DataFrame, and returns a DataFrame. It is like GroupedDataSet.flatMapGroups() in the Dataset API.
Please, let me know what do you think and if you have any ideas to improve it.
Thank you!
## How was this patch tested?
Unit tests.
1. Primitive test with different column types
2. Add a boolean column
3. Compute average by a group
Author: Narine Kokhlikyan <narine.kokhlikyan@gmail.com>
Author: NarineK <narine.kokhlikyan@us.ibm.com>
Closes#12836 from NarineK/gapply2.
## What changes were proposed in this pull request?
The way bash script `build/spark-build-info` is called from core/pom.xml prevents Spark building on Windows. Instead of calling the script directly we call bash and pass the script as an argument. This enables running it on Windows with bash installed which typically comes with Git.
This brings https://github.com/apache/spark/pull/13612 up-to-date and also addresses comments from the code review.
Closes#13612
## How was this patch tested?
I built manually (on a Mac) to verify it didn't break Mac compilation.
Author: Reynold Xin <rxin@databricks.com>
Author: avulanov <nashb@yandex.ru>
Closes#13691 from rxin/SPARK-15851.
## What changes were proposed in this pull request?
SPARK-15927 exacerbated a race in BasicSchedulerIntegrationTest, so it went from very unlikely to fairly frequent. The issue is that stage numbering is not completely deterministic, but these tests treated it like it was. So turn off the tests.
## How was this patch tested?
on my laptop the test failed abotu 10% of the time before this change, and didn't fail in 500 runs after the change.
Author: Imran Rashid <irashid@cloudera.com>
Closes#13688 from squito/hotfix_basic_scheduler.
## What changes were proposed in this pull request?
When `--packages` is specified with `spark-shell` the classes from those packages cannot be found, which I think is due to some of the changes in `SPARK-12343`. In particular `SPARK-12343` removes a line that sets the `spark.jars` system property in client mode, which is used by the repl main class to set the classpath.
## How was this patch tested?
Tested manually.
This system property is used by the repl to populate its classpath. If
this is not set properly the classes for external packages cannot be
found.
tgravescs vanzin as you may be familiar with this part of the code.
Author: Nezih Yigitbasi <nyigitbasi@netflix.com>
Closes#13527 from nezihyigitbasi/repl-fix.
## What changes were proposed in this pull request?
Link to jira which describes the problem: https://issues.apache.org/jira/browse/SPARK-15826
The fix in this PR is to allow users specify encoding in the pipe() operation. For backward compatibility,
keeping the default value to be system default.
## How was this patch tested?
Ran existing unit tests
Author: Tejas Patil <tejasp@fb.com>
Closes#13563 from tejasapatil/pipedrdd_utf8.
## What changes were proposed in this pull request?
This patch is a follow-up to https://github.com/apache/spark/pull/13288 completing the renaming:
- LocalScheduler -> LocalSchedulerBackend~~Endpoint~~
## How was this patch tested?
Updated test cases to reflect the name change.
Author: Liwei Lin <lwlin7@gmail.com>
Closes#13683 from lw-lin/rename-backend.
Use the config variable definition both to set and parse the value,
avoiding issues with code expecting the value in a different format.
Tested by running spark-submit with --principal / --keytab.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#13669 from vanzin/SPARK-15046.
To try to eliminate redundant code to traverse the RDD dependency graph,
this PR creates a new function getShuffleDependencies that returns
shuffle dependencies that are immediate parents of a given RDD. This
new function is used by getParentStages and
getAncestorShuffleDependencies.
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#13646 from kayousterhout/SPARK-15927.
## What changes were proposed in this pull request?
Another PR to clean up recent build warnings. This particularly cleans up several instances of the old accumulator API usage in tests that are straightforward to update. I think this qualifies as "minor".
## How was this patch tested?
Jenkins
Author: Sean Owen <sowen@cloudera.com>
Closes#13642 from srowen/BuildWarnings.
## What changes were proposed in this pull request?
`Dispatcher.stopped` is guarded by `this`, but it is used without synchronization in `postMessage` function. This PR fixes this and also the exception message became more accurate.
## How was this patch tested?
Pass the existing Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13634 from dongjoon-hyun/SPARK-15913.
## What changes were proposed in this pull request?
Remove deprecated support for `zk://` master (`mesos://zk//` remains supported)
## How was this patch tested?
Jenkins
Author: Sean Owen <sowen@cloudera.com>
Closes#13625 from srowen/SPARK-15876.
## What changes were proposed in this pull request?
- Deprecate old Java accumulator API; should use Scala now
- Update Java tests and examples
- Don't bother testing old accumulator API in Java 8 (too)
- (fix a misspelling too)
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#13606 from srowen/SPARK-15086.
## What changes were proposed in this pull request?
SPARK_MASTER_IP is a deprecated environment variable. It is replaced by SPARK_MASTER_HOST according to MasterArguments.scala.
## How was this patch tested?
Manually verified.
Author: bomeng <bmeng@us.ibm.com>
Closes#13543 from bomeng/SPARK-15806.
## What changes were proposed in this pull request?
These tests weren't properly using `LocalSparkContext` so weren't cleaning up correctly when tests failed.
## How was this patch tested?
Jenkins.
Author: Imran Rashid <irashid@cloudera.com>
Closes#13602 from squito/SPARK-15878_cleanup_replaylistener.
## What changes were proposed in this pull request?
Adds codahale metrics for the codegen source text size and how long it takes to compile. The size is particularly interesting, since the JVM does have hard limits on how large methods can get.
To simplify, I added the metrics under a statically-initialized source that is always registered with SparkEnv.
## How was this patch tested?
Unit tests
Author: Eric Liang <ekl@databricks.com>
Closes#13586 from ericl/spark-15860.
## What changes were proposed in this pull request?
This adds support for radix sort of nullable long fields. When a sort field is null and radix sort is enabled, we keep nulls in a separate region of the sort buffer so that radix sort does not need to deal with them. This also has performance benefits when sorting smaller integer types, since the current representation of nulls in two's complement (Long.MIN_VALUE) otherwise forces a full-width radix sort.
This strategy for nulls does mean the sort is no longer stable. cc davies
## How was this patch tested?
Existing randomized sort tests for correctness. I also tested some TPCDS queries and there does not seem to be any significant regression for non-null sorts.
Some test queries (best of 5 runs each).
Before change:
scala> val start = System.nanoTime; spark.range(5000000).selectExpr("if(id > 5, cast(hash(id) as long), NULL) as h").coalesce(1).orderBy("h").collect(); (System.nanoTime - start) / 1e6
start: Long = 3190437233227987
res3: Double = 4716.471091
After change:
scala> val start = System.nanoTime; spark.range(5000000).selectExpr("if(id > 5, cast(hash(id) as long), NULL) as h").coalesce(1).orderBy("h").collect(); (System.nanoTime - start) / 1e6
start: Long = 3190367870952791
res4: Double = 2981.143045
Author: Eric Liang <ekl@databricks.com>
Closes#13161 from ericl/sc-2998.
## What changes were proposed in this pull request?
Use new Spark logo including "Apache" (now, with crushed PNGs). Remove old unreferenced logo files.
## How was this patch tested?
Manual check of generated HTML site and Spark UI. I searched for references to the deleted files to make sure they were not used.
Author: Sean Owen <sowen@cloudera.com>
Closes#13609 from srowen/SPARK-15879.
## What changes were proposed in this pull request?
In scala, immutable.List.length is an expensive operation so we should
avoid using Seq.length == 0 or Seq.lenth > 0, and use Seq.isEmpty and Seq.nonEmpty instead.
## How was this patch tested?
existing tests
Author: wangyang <wangyang@haizhi.com>
Closes#13601 from yangw1234/isEmpty.
This reverts commit 695dbc816a.
This change is being reverted because it hurts performance of some jobs, and
only helps in a narrow set of cases. For more discussion, refer to the JIRA.
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#13580 from kayousterhout/revert-SPARK-14485.
## What changes were proposed in this pull request?
SparkContext.listAccumulator, by Spark's convention, makes it sound like "list" is a verb and the method should return a list of accumulators. This patch renames the method and the class collection accumulator.
## How was this patch tested?
Updated test case to reflect the names.
Author: Reynold Xin <rxin@databricks.com>
Closes#13594 from rxin/SPARK-15866.
## What changes were proposed in this pull request?
With very wide tables, e.g. thousands of fields, the plan output is unreadable and often causes OOMs due to inefficient string processing. This truncates all struct and operator field lists to a user configurable threshold to limit performance impact.
It would also be nice to optimize string generation to avoid these sort of O(n^2) slowdowns entirely (i.e. use StringBuilder everywhere including expressions), but this is probably too large of a change for 2.0 at this point, and truncation has other benefits for usability.
## How was this patch tested?
Added a microbenchmark that covers this case particularly well. I also ran the microbenchmark while varying the truncation threshold.
```
numFields = 5
wide shallowly nested struct field r/w: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
2000 wide x 50 rows (write in-mem) 2336 / 2558 0.0 23364.4 0.1X
numFields = 25
wide shallowly nested struct field r/w: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
2000 wide x 50 rows (write in-mem) 4237 / 4465 0.0 42367.9 0.1X
numFields = 100
wide shallowly nested struct field r/w: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
2000 wide x 50 rows (write in-mem) 10458 / 11223 0.0 104582.0 0.0X
numFields = Infinity
wide shallowly nested struct field r/w: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
[info] java.lang.OutOfMemoryError: Java heap space
```
Author: Eric Liang <ekl@databricks.com>
Author: Eric Liang <ekhliang@gmail.com>
Closes#13537 from ericl/truncated-string.
## What changes were proposed in this pull request?
This makes microbenchmarks run for at least 2 seconds by default, to allow some time for jit compilation to kick in.
## How was this patch tested?
Tested manually with existing microbenchmarks. This change is backwards compatible in that existing microbenchmarks which specified numIters per-case will still run exactly that number of iterations. Microbenchmarks which previously overrode defaultNumIters now override minNumIters.
cc hvanhovell
Author: Eric Liang <ekl@databricks.com>
Author: Eric Liang <ekhliang@gmail.com>
Closes#13472 from ericl/spark-15735.
Now, when executor is removed by driver with heartbeats timeout, driver will re-queue the task on this executor and send a kill command to cluster to kill this executor.
But, in a situation, the running task of this executor is finished and return result to driver before this executor killed by kill command sent by driver. At this situation, driver will accept the task finished event and ignore speculative task and re-queued task.
But, as we know, this executor has removed by driver, the result of this finished task can not save in driver because the BlockManagerId has also removed from BlockManagerMaster by driver. So, the result data of this stage is not complete, and then, it will cause fetch failure. For more details, [link to jira issues SPARK-14485](https://issues.apache.org/jira/browse/SPARK-14485)
This PR introduce a mechanism to ignore this kind of task finished.
N/A
Author: zhonghaihua <793507405@qq.com>
Closes#12258 from zhonghaihua/ignoreTaskFinishForExecutorLostAndRemovedByDriver.
## What changes were proposed in this pull request?
There is still some flakiness in BlacklistIntegrationSuite, so turning it off for the moment to avoid breaking more builds -- will turn it back with more fixes.
## How was this patch tested?
jenkins.
Author: Imran Rashid <irashid@cloudera.com>
Closes#13528 from squito/ignore_blacklist.
## What changes were proposed in this pull request?
Change the way spark picks up version information. Also embed the build information to better identify the spark version running.
More context can be found here : https://github.com/apache/spark/pull/12152
## How was this patch tested?
Ran the mvn and sbt builds to verify the version information was being displayed correctly on executing <code>spark-submit --version </code>
Author: Dhruve Ashar <dhruveashar@gmail.com>
Closes#13061 from dhruve/impr/SPARK-14279.
## What changes were proposed in this pull request?
`an -> a`
Use cmds like `find . -name '*.R' | xargs -i sh -c "grep -in ' an [^aeiou]' {} && echo {}"` to generate candidates, and review them one by one.
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13515 from zhengruifeng/an_a.
## What changes were proposed in this pull request?
Stop using the abbreviated and ambiguous timezone "EST" in a test, since it is machine-local default timezone dependent, and fails in different timezones. Fixed [SPARK-15723](https://issues.apache.org/jira/browse/SPARK-15723).
## How was this patch tested?
Note that to reproduce this problem in any locale/timezone, you can modify the scalatest-maven-plugin argLine to add a timezone:
<argLine>-ea -Xmx3g -XX:MaxPermSize=${MaxPermGen} -XX:ReservedCodeCacheSize=${CodeCacheSize} -Duser.timezone="Australia/Sydney"</argLine>
and run
$ mvn test -DwildcardSuites=org.apache.spark.status.api.v1.SimpleDateParamSuite -Dtest=none. Equally this will fix it in an effected timezone:
<argLine>-ea -Xmx3g -XX:MaxPermSize=${MaxPermGen} -XX:ReservedCodeCacheSize=${CodeCacheSize} -Duser.timezone="America/New_York"</argLine>
To test the fix, apply the above change to `pom.xml` to set test TZ to `Australia/Sydney`, and confirm the test now passes.
Author: Brett Randall <javabrett@gmail.com>
Closes#13462 from javabrett/SPARK-15723-SimpleDateParamSuite.
## What changes were proposed in this pull request?
Currently, the memory for temporary buffer used by TimSort is always allocated as on-heap without bookkeeping, it could cause OOM both in on-heap and off-heap mode.
This PR will try to manage that by preallocate it together with the pointer array, same with RadixSort. It both works for on-heap and off-heap mode.
This PR also change the loadFactor of BytesToBytesMap to 0.5 (it was 0.70), it enables use to radix sort also makes sure that we have enough memory for timsort.
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#13318 from davies/fix_timsort.
## What changes were proposed in this pull request?
Currently `SparkContext API setLogLevel(level: String) `can not handle lower case or mixed case input string. But `org.apache.log4j.Level.toLevel` can take lowercase or mixed case.
This PR is to allow case-insensitive user input for the log level.
## How was this patch tested?
A unit testcase is added.
Author: Xin Wu <xinwu@us.ibm.com>
Closes#13422 from xwu0226/reset_loglevel.
## What changes were proposed in this pull request?
After upgrading Jetty to 9.2, we always see "WARN org.eclipse.jetty.server.handler.AbstractHandler: No Server set for org.eclipse.jetty.server.handler.ErrorHandler" while running any test cases.
This PR will fix it.
## How was this patch tested?
The existing test cases will cover it.
Author: bomeng <bmeng@us.ibm.com>
Closes#13475 from bomeng/SPARK-15737.
## What changes were proposed in this pull request?
BlacklistIntegrationSuite (introduced by SPARK-10372) is a bit flaky because of some race conditions:
1. Failed jobs might have non-empty results, because the resultHandler will be invoked for successful tasks (if there are task successes before failures)
2. taskScheduler.taskIdToTaskSetManager must be protected by a lock on taskScheduler
(1) has failed a handful of jenkins builds recently. I don't think I've seen (2) in jenkins, but I've run into with some uncommitted tests I'm working on where there are lots more tasks.
While I was in there, I also made an unrelated fix to `runningTasks`in the test framework -- there was a pointless `O(n)` operation to remove completed tasks, could be `O(1)`.
## How was this patch tested?
I modified the o.a.s.scheduler.BlacklistIntegrationSuite to have it run the tests 1k times on my laptop. It failed 11 times before this change, and none with it. (Pretty sure all the failures were problem (1), though I didn't check all of them).
Also the full suite of tests via jenkins.
Author: Imran Rashid <irashid@cloudera.com>
Closes#13454 from squito/SPARK-15714.
If an RDD partition is cached on disk and the DiskStore file is lost, then reads of that cached partition will fail and the missing partition is supposed to be recomputed by a new task attempt. In the current BlockManager implementation, however, the missing file does not trigger any metadata updates / does not invalidate the cache, so subsequent task attempts will be scheduled on the same executor and the doomed read will be repeatedly retried, leading to repeated task failures and eventually a total job failure.
In order to fix this problem, the executor with the missing file needs to properly mark the corresponding block as missing so that it stops advertising itself as a cache location for that block.
This patch fixes this bug and adds an end-to-end regression test (in `FailureSuite`) and a set of unit tests (`in BlockManagerSuite`).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#13473 from JoshRosen/handle-missing-cache-files.
## What changes were proposed in this pull request?
Set minimum number of dispatcher threads to 3 to avoid deadlocks on machines with only 2 cores
## How was this patch tested?
Spark test builds
Author: Pete Robbins <robbinspg@gmail.com>
Closes#13355 from robbinspg/SPARK-13906.
## What changes were proposed in this pull request?
This PR corrects the remaining cases for using old accumulators.
This does not change some old accumulator usages below:
- `ImplicitSuite.scala` - Tests dedicated to old accumulator, for implicits with `AccumulatorParam`
- `AccumulatorSuite.scala` - Tests dedicated to old accumulator
- `JavaSparkContext.scala` - For supporting old accumulators for Java API.
- `debug.package.scala` - Usage with `HashSet[String]`. Currently, it seems no implementation for this. I might be able to write an anonymous class for this but I didn't because I think it is not worth writing a lot of codes only for this.
- `SQLMetricsSuite.scala` - This uses the old accumulator for checking type boxing. It seems new accumulator does not require type boxing for this case whereas the old one requires (due to the use of generic).
## How was this patch tested?
Existing tests cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#13434 from HyukjinKwon/accum.
