## What changes were proposed in this pull request?
This PR tries to fix all typos in all markdown files under `docs` module,
and fixes similar typos in other comments, too.
## How was the this patch tested?
manual tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11300 from dongjoon-hyun/minor_fix_typos.
## What changes were proposed in this pull request?
This PR removes the support of SIMR, since SIMR is not actively used and maintained for a long time, also is not supported from `SparkSubmit`, so here propose to remove it.
## How was the this patch tested?
This patch is tested locally by running unit tests.
Author: jerryshao <sshao@hortonworks.com>
Closes#11296 from jerryshao/SPARK-13426.
## What changes were proposed in this pull request?
`JobWaiter.taskSucceeded` will be called for each task. When `resultHandler` throws an exception, `taskSucceeded` will also throw it for each task. DAGScheduler just catches it and reports it like this:
```Scala
try {
job.listener.taskSucceeded(rt.outputId, event.result)
} catch {
case e: Exception =>
// TODO: Perhaps we want to mark the resultStage as failed?
job.listener.jobFailed(new SparkDriverExecutionException(e))
}
```
Therefore `JobWaiter.jobFailed` may be called multiple times.
So `JobWaiter.jobFailed` should use `Promise.tryFailure` instead of `Promise.failure` because the latter one doesn't support calling multiple times.
## How was the this patch tested?
Jenkins tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11280 from zsxwing/SPARK-13408.
`TaskMetrics.fromAccumulatorUpdates()` can fail if accumulators have been garbage-collected on the driver. To guard against this, this patch introduces `ListenerTaskMetrics`, a subclass of `TaskMetrics` which is used only in `TaskMetrics.fromAccumulatorUpdates()` and which eliminates the need to access the original accumulators on the driver.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11276 from JoshRosen/accum-updates-fix.
Clarify that reduce functions need to be commutative, and fold functions do not
See https://github.com/apache/spark/pull/11091
Author: Sean Owen <sowen@cloudera.com>
Closes#11217 from srowen/SPARK-13339.
## What changes were proposed in this pull request?
Fix some comparisons between unequal types that cause IJ warnings and in at least one case a likely bug (TaskSetManager)
## How was the this patch tested?
Running Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#11253 from srowen/SPARK-13371.
This commit removes an unnecessary duplicate check in addPendingTask that meant
that scheduling a task set took time proportional to (# tasks)^2.
Author: Sital Kedia <skedia@fb.com>
Closes#11175 from sitalkedia/fix_stuck_driver.
See http://openjdk.java.net/jeps/223 for more information about the JDK 9 version string scheme.
Author: Claes Redestad <claes.redestad@gmail.com>
Closes#11160 from cl4es/master.
Due to being on a Windows platform I have been unable to run the tests as described in the "Contributing to Spark" instructions. As the change is only to two lines of code in the Web UI, which I have manually built and tested, I am submitting this pull request anyway. I hope this is OK.
Is it worth considering also including this fix in any future 1.5.x releases (if any)?
I confirm this is my own original work and license it to the Spark project under its open source license.
Author: markpavey <mark.pavey@thefilter.com>
Closes#11135 from markpavey/JIRA_SPARK-13142_WindowsWebUILogFix.
Overrode the start() method, which was previously starting a thread causing a race condition. I believe this should fix the flaky test.
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#11164 from mgummelt/fix_mesos_tests.
This JIRA is related to
https://github.com/apache/spark/pull/5852
Had to do some minor rework and test to make sure it
works with current version of spark.
Author: Sanket <schintap@untilservice-lm>
Closes#10838 from redsanket/limit-outbound-connections.
When the HistoryServer is showing an incomplete app, it needs to check if there is a newer version of the app available. It does this by checking if a version of the app has been loaded with a larger *filesize*. If so, it detaches the current UI, attaches the new one, and redirects back to the same URL to show the new UI.
https://issues.apache.org/jira/browse/SPARK-7889
Author: Steve Loughran <stevel@hortonworks.com>
Author: Imran Rashid <irashid@cloudera.com>
Closes#11118 from squito/SPARK-7889-alternate.
This commit removes an unnecessary duplicate check in addPendingTask that meant
that scheduling a task set took time proportional to (# tasks)^2.
Author: Sital Kedia <skedia@fb.com>
Closes#11167 from sitalkedia/fix_stuck_driver and squashes the following commits:
3fe1af8 [Sital Kedia] [SPARK-13279] Remove unnecessary duplicate check in addPendingTask function
Made sure the old tables continue to use the old css and the new DataTables use the new css. Also fixed it so the Safari Web Inspector doesn't throw errors when on the new DataTables pages.
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#11038 from ajbozarth/spark13124.
The "getPersistentRDDs()" is a useful API of SparkContext to get cached RDDs. However, the JavaSparkContext does not have this API.
Add a simple getPersistentRDDs() to get java.util.Map<Integer, JavaRDD> for Java users.
Author: Junyang <fly.shenjy@gmail.com>
Closes#10978 from flyjy/master.
Remove spark.closure.serializer option and use JavaSerializer always
CC andrewor14 rxin I see there's a discussion in the JIRA but just thought I'd offer this for a look at what the change would be.
Author: Sean Owen <sowen@cloudera.com>
Closes#11150 from srowen/SPARK-12414.
The right margin of the history page is little bit off. A simple fix for that issue.
Author: zhuol <zhuol@yahoo-inc.com>
Closes#11029 from zhuoliu/13126.
The column width for the new DataTables now adjusts for the current page rather than being hard-coded for the entire table's data.
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#11057 from ajbozarth/spark13163.
This is the next iteration of tnachen's previous PR: https://github.com/apache/spark/pull/4027
In that PR, we resolved with andrewor14 and pwendell to implement the Mesos scheduler's support of `spark.executor.cores` to be consistent with YARN and Standalone. This PR implements that resolution.
This PR implements two high-level features. These two features are co-dependent, so they're implemented both here:
- Mesos support for spark.executor.cores
- Multiple executors per slave
We at Mesosphere have been working with Typesafe on a Spark/Mesos integration test suite: https://github.com/typesafehub/mesos-spark-integration-tests, which passes for this PR.
The contribution is my original work and I license the work to the project under the project's open source license.
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#10993 from mgummelt/executor_sizing.
This PR improve the lookup of BytesToBytesMap by:
1. Generate code for calculate the hash code of grouping keys.
2. Do not use MemoryLocation, fetch the baseObject and offset for key and value directly (remove the indirection).
Author: Davies Liu <davies@databricks.com>
Closes#11010 from davies/gen_map.
Call shuffleMetrics's incRemoteBytesRead and incRemoteBlocksFetched when polling FetchResult from `results` so as to always use shuffleMetrics in one thread.
Also fix a race condition that could cause memory leak.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11138 from zsxwing/SPARK-13245.
Adds the benchmark results as comments.
The codegen version is slower than the interpreted version for `simple` case becasue of 3 reasons:
1. codegen version use a more complex hash algorithm than interpreted version, i.e. `Murmur3_x86_32.hashInt` vs [simple multiplication and addition](https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala#L153).
2. codegen version will write the hash value to a row first and then read it out. I tried to create a `GenerateHasher` that can generate code to return hash value directly and got about 60% speed up for the `simple` case, does it worth?
3. the row in `simple` case only has one int field, so the runtime reflection may be removed because of branch prediction, which makes the interpreted version faster.
The `array` case is also slow for similar reasons, e.g. array elements are of same type, so interpreted version can probably get rid of runtime reflection by branch prediction.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10917 from cloud-fan/hash-benchmark.
Since Spark requires at least JRE 1.7, it is safe to use built-in java.nio.Files.
Author: Jakob Odersky <jakob@odersky.com>
Closes#11098 from jodersky/SPARK-13176.
Additional changes to #10835, mainly related to style and visibility. This patch also adds back a few deprecated methods for backward compatibility.
Author: Andrew Or <andrew@databricks.com>
Closes#10958 from andrewor14/task-metrics-to-accums-followups.
There is a bug when we try to grow the buffer, OOM is ignore wrongly (the assert also skipped by JVM), then we try grow the array again, this one will trigger spilling free the current page, the current record we inserted will be invalid.
The root cause is that JVM has less free memory than MemoryManager thought, it will OOM when allocate a page without trigger spilling. We should catch the OOM, and acquire memory again to trigger spilling.
And also, we could not grow the array in `insertRecord` of `InMemorySorter` (it was there just for easy testing).
Author: Davies Liu <davies@databricks.com>
Closes#11095 from davies/fix_expand.
rxin srowen
I work out note message for rdd.take function, please help to review.
If it's fine, I can apply to all other function later.
Author: Tommy YU <tummyyu@163.com>
Closes#10874 from Wenpei/spark-5865-add-warning-for-localdatastructure.
Trivial search-and-replace to eliminate deprecation warnings in Scala 2.11.
Also works with 2.10
Author: Jakob Odersky <jakob@odersky.com>
Closes#11085 from jodersky/SPARK-13171.
Fix for [SPARK-13002](https://issues.apache.org/jira/browse/SPARK-13002) about the initial number of executors when running with dynamic allocation on Mesos.
Instead of fixing it just for the Mesos case, made the change in `ExecutorAllocationManager`. It is already driving the number of executors running on Mesos, only no the initial value.
The `None` and `Some(0)` are internal details on the computation of resources to reserved, in the Mesos backend scheduler. `executorLimitOption` has to be initialized correctly, otherwise the Mesos backend scheduler will, either, create to many executors at launch, or not create any executors and not be able to recover from this state.
Removed the 'special case' description in the doc. It was not totally accurate, and is not needed anymore.
This doesn't fix the same problem visible with Spark standalone. There is no straightforward way to send the initial value in standalone mode.
Somebody knowing this part of the yarn support should review this change.
Author: Luc Bourlier <luc.bourlier@typesafe.com>
Closes#11047 from skyluc/issue/initial-dyn-alloc-2.
The config already describes time and accepts a general format
that is not restricted to ms. This commit renames the internal
config to use a format that's consistent in Spark.
These were ignored because they are incorrectly written; they don't actually trigger stage retries, which is what the tests are testing. These tests are now rewritten to induce stage retries through fetch failures.
Note: there were 2 tests before and now there's only 1. What happened? It turns out that the case where we only resubmit a subset of of the original missing partitions is very difficult to simulate in tests without potentially introducing flakiness. This is because the `DAGScheduler` removes all map outputs associated with a given executor when this happens, and we will need multiple executors to trigger this case, and sometimes the scheduler still removes map outputs from all executors.
Author: Andrew Or <andrew@databricks.com>
Closes#10969 from andrewor14/unignore-accum-test.
Currently the Master would always set an application's initial executor limit to infinity. If the user specified `spark.dynamicAllocation.initialExecutors`, the config would not take effect. This is similar to #11047 but for standalone mode.
Author: Andrew Or <andrew@databricks.com>
Closes#11054 from andrewor14/standalone-da-initial.
Building with scala 2.11 results in the warning trait SynchronizedBuffer in package mutable is deprecated: Synchronization via traits is deprecated as it is inherently unreliable. Consider java.util.concurrent.ConcurrentLinkedQueue as an alternative. Investigation shows we are already using ConcurrentLinkedQueue in other locations so switch our uses of SynchronizedBuffer to ConcurrentLinkedQueue.
Author: Holden Karau <holden@us.ibm.com>
Closes#11059 from holdenk/SPARK-13164-replace-deprecated-synchronized-buffer-in-core.
In the current implementation the mesos coarse scheduler does not wait for the mesos tasks to complete before ending the driver. This causes a race where the task has to finish cleaning up before the mesos driver terminates it with a SIGINT (and SIGKILL after 3 seconds if the SIGINT doesn't work).
This PR causes the mesos coarse scheduler to wait for the mesos tasks to finish (with a timeout defined by `spark.mesos.coarse.shutdown.ms`)
This PR also fixes a regression caused by [SPARK-10987] whereby submitting a shutdown causes a race between the local shutdown procedure and the notification of the scheduler driver disconnection. If the scheduler driver disconnection wins the race, the coarse executor incorrectly exits with status 1 (instead of the proper status 0)
With this patch the mesos coarse scheduler terminates properly, the executors clean up, and the tasks are reported as `FINISHED` in the Mesos console (as opposed to `KILLED` in < 1.6 or `FAILED` in 1.6 and later)
Author: Charles Allen <charles@allen-net.com>
Closes#10319 from drcrallen/SPARK-12330.
JIRA: https://issues.apache.org/jira/browse/SPARK-13113
As we shift bits right, looks like the bitwise AND operation is unnecessary.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11002 from viirya/improve-decodepagenumber.
Make an internal non-deprecated version of incBytesRead and incRecordsRead so we don't have unecessary deprecation warnings in our build.
Right now incBytesRead and incRecordsRead are marked as deprecated and for internal use only. We should make private[spark] versions which are not deprecated and switch to those internally so as to not clutter up the warning messages when building.
cc andrewor14 who did the initial deprecation
Author: Holden Karau <holden@us.ibm.com>
Closes#11056 from holdenk/SPARK-13152-fix-task-metrics-deprecation-warnings.
Best time is stabler than average time, also added a column for nano seconds per row (which could be used to estimate contributions of each components in a query).
Having best time and average time together for more information (we can see kind of variance).
rate, time per row and relative are all calculated using best time.
The result looks like this:
```
Intel(R) Core(TM) i7-4558U CPU 2.80GHz
rang/filter/sum: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
rang/filter/sum codegen=false 14332 / 16646 36.0 27.8 1.0X
rang/filter/sum codegen=true 845 / 940 620.0 1.6 17.0X
```
Author: Davies Liu <davies@databricks.com>
Closes#11018 from davies/gen_bench.
`rpcEnv.awaitTermination()` was not added in #10854 because some Streaming Python tests hung forever.
This patch fixed the hung issue and added rpcEnv.awaitTermination() back to SparkEnv.
Previously, Streaming Kafka Python tests shutdowns the zookeeper server before stopping StreamingContext. Then when stopping StreamingContext, KafkaReceiver may be hung due to https://issues.apache.org/jira/browse/KAFKA-601, hence, some thread of RpcEnv's Dispatcher cannot exit and rpcEnv.awaitTermination is hung.The patch just changed the shutdown order to fix it.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11031 from zsxwing/awaitTermination.
https://issues.apache.org/jira/browse/SPARK-13122
A race condition can occur in MemoryStore's unrollSafely() method if two threads that
return the same value for currentTaskAttemptId() execute this method concurrently. This
change makes the operation of reading the initial amount of unroll memory used, performing
the unroll, and updating the associated memory maps atomic in order to avoid this race
condition.
Initial proposed fix wraps all of unrollSafely() in a memoryManager.synchronized { } block. A cleaner approach might be introduce a mechanism that synchronizes based on task attempt ID. An alternative option might be to track unroll/pending unroll memory based on block ID rather than task attempt ID.
Author: Adam Budde <budde@amazon.com>
Closes#11012 from budde/master.
