## What changes were proposed in this pull request?
Fix reservoir sampling bias for small k. An off-by-one error meant that the probability of replacement was slightly too high -- k/(l-1) after l element instead of k/l, which matters for small k.
## How was this patch tested?
Existing test plus new test case.
Author: Sean Owen <sowen@cloudera.com>
Closes#16129 from srowen/SPARK-18678.
## What changes were proposed in this pull request?
`spark.sql.unsafe.enabled` is deprecated since 1.6. There still are codes in UI to check it. We should remove it and clean the codes.
## How was this patch tested?
Changes to related existing unit test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16095 from viirya/remove-deprecated-config-code.
The problem exists because it's not possible to just concatenate encrypted
partition data from different spill files; currently each partition would
have its own initial vector to set up encryption, and the final merged file
should contain a single initial vector for each merged partiton, otherwise
iterating over each record becomes really hard.
To fix that, UnsafeShuffleWriter now decrypts the partitions when merging,
so that the merged file contains a single initial vector at the start of
the partition data.
Because it's not possible to do that using the fast transferTo path, when
encryption is enabled UnsafeShuffleWriter will revert back to using file
streams when merging. It may be possible to use a hybrid approach when
using encryption, using an intermediate direct buffer when reading from
files and encrypting the data, but that's better left for a separate patch.
As part of the change I made DiskBlockObjectWriter take a SerializerManager
instead of a "wrap stream" closure, since that makes it easier to test the
code without having to mock SerializerManager functionality.
Tested with newly added unit tests (UnsafeShuffleWriterSuite for the write
side and ExternalAppendOnlyMapSuite for integration), and by running some
apps that failed without the fix.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#15982 from vanzin/SPARK-18546.
## What changes were proposed in this pull request?
The method `TaskSchedulerImpl.runningTasksByExecutors()` accesses the mutable `executorIdToRunningTaskIds` map without proper synchronization. In addition, as markhamstra pointed out in #15986, the signature's use of parentheses is a little odd given that this is a pure getter method.
This patch fixes both issues.
## How was this patch tested?
Covered by existing tests.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#16073 from JoshRosen/runningTasksByExecutors-thread-safety.
## What changes were proposed in this pull request?
#15992 provided a solution to fix the bug, i.e. **receiver data can not be deserialized properly**. As zsxwing said, it is a critical bug, but we should not break APIs between maintenance releases. It may be a rational choice to close auto pick kryo serializer for Spark Streaming in the first step. I will continue #15992 to optimize the solution.
## How was this patch tested?
existing ut
Author: uncleGen <hustyugm@gmail.com>
Closes#16052 from uncleGen/SPARK-18617.
## What changes were proposed in this pull request?
_This is the master branch version of #15986; the original description follows:_
This patch fixes a critical resource leak in the TaskScheduler which could cause RDDs and ShuffleDependencies to be kept alive indefinitely if an executor with running tasks is permanently lost and the associated stage fails.
This problem was originally identified by analyzing the heap dump of a driver belonging to a cluster that had run out of shuffle space. This dump contained several `ShuffleDependency` instances that were retained by `TaskSetManager`s inside the scheduler but were not otherwise referenced. Each of these `TaskSetManager`s was considered a "zombie" but had no running tasks and therefore should have been cleaned up. However, these zombie task sets were still referenced by the `TaskSchedulerImpl.taskIdToTaskSetManager` map.
Entries are added to the `taskIdToTaskSetManager` map when tasks are launched and are removed inside of `TaskScheduler.statusUpdate()`, which is invoked by the scheduler backend while processing `StatusUpdate` messages from executors. The problem with this design is that a completely dead executor will never send a `StatusUpdate`. There is [some code](072f4c518c/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala (L338)) in `statusUpdate` which handles tasks that exit with the `TaskState.LOST` state (which is supposed to correspond to a task failure triggered by total executor loss), but this state only seems to be used in Mesos fine-grained mode. There doesn't seem to be any code which performs per-task state cleanup for tasks that were running on an executor that completely disappears without sending any sort of final death message. The `executorLost` and [`removeExecutor`](072f4c518c/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala (L527)) methods don't appear to perform any cleanup of the `taskId -> *` mappings, causing the leaks observed here.
This patch's fix is to maintain a `executorId -> running task id` mapping so that these `taskId -> *` maps can be properly cleaned up following an executor loss.
There are some potential corner-case interactions that I'm concerned about here, especially some details in [the comment](072f4c518c/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala (L523)) in `removeExecutor`, so I'd appreciate a very careful review of these changes.
## How was this patch tested?
I added a new unit test to `TaskSchedulerImplSuite`.
/cc kayousterhout and markhamstra, who reviewed #15986.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#16045 from JoshRosen/fix-leak-following-total-executor-loss-master.
This change modifies the method used to propagate encryption keys used during
shuffle. Instead of relying on YARN's UserGroupInformation credential propagation,
this change explicitly distributes the key using the messages exchanged between
driver and executor during registration. When RPC encryption is enabled, this means
key propagation is also secure.
This allows shuffle encryption to work in non-YARN mode, which means that it's
easier to write unit tests for areas of the code that are affected by the feature.
The key is stored in the SecurityManager; because there are many instances of
that class used in the code, the key is only guaranteed to exist in the instance
managed by the SparkEnv. This path was chosen to avoid storing the key in the
SparkConf, which would risk having the key being written to disk as part of the
configuration (as, for example, is done when starting YARN applications).
Tested by new and existing unit tests (which were moved from the YARN module to
core), and by running apps with shuffle encryption enabled.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#15981 from vanzin/SPARK-18547.
## What changes were proposed in this pull request?
This adds tests to verify the interaction between TaskSetBlacklist and
TaskSchedulerImpl. TaskSetBlacklist was introduced by SPARK-17675 but
it neglected to add these tests.
This change does not fix any bugs -- it is just for increasing test
coverage.
## How was this patch tested?
Jenkins
Author: Imran Rashid <irashid@cloudera.com>
Closes#15644 from squito/taskset_blacklist_test_update.
## What changes were proposed in this pull request?
This patch adds a new property called `spark.secret.redactionPattern` that
allows users to specify a scala regex to decide which Spark configuration
properties and environment variables in driver and executor environments
contain sensitive information. When this regex matches the property or
environment variable name, its value is redacted from the environment UI and
various logs like YARN and event logs.
This change uses this property to redact information from event logs and YARN
logs. It also, updates the UI code to adhere to this property instead of
hardcoding the logic to decipher which properties are sensitive.
Here's an image of the UI post-redaction:
Here's the text in the YARN logs, post-redaction:
``HADOOP_CREDSTORE_PASSWORD -> *********(redacted)``
Here's the text in the event logs, post-redaction:
``...,"spark.executorEnv.HADOOP_CREDSTORE_PASSWORD":"*********(redacted)","spark.yarn.appMasterEnv.HADOOP_CREDSTORE_PASSWORD":"*********(redacted)",...``
## How was this patch tested?
1. Unit tests are added to ensure that redaction works.
2. A YARN job reading data off of S3 with confidential information
(hadoop credential provider password) being provided in the environment
variables of driver and executor. And, afterwards, logs were grepped to make
sure that no mention of secret password was present. It was also ensure that
the job was able to read the data off of S3 correctly, thereby ensuring that
the sensitive information was being trickled down to the right places to read
the data.
3. The event logs were checked to make sure no mention of secret password was
present.
4. UI environment tab was checked to make sure there was no secret information
being displayed.
Author: Mark Grover <mark@apache.org>
Closes#15971 from markgrover/master_redaction.
## What changes were proposed in this pull request?
This PR avoids that a result of an expression is negative due to signed integer overflow (e.g. 0x10?????? * 8 < 0). This PR casts each operand to `long` before executing a calculation. Since the result is interpreted as long, the result of the expression is positive.
## How was this patch tested?
Manually executed query82 of TPC-DS with 100TB
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#15907 from kiszk/SPARK-18458.
Adds --conf option to set spark configuration properties in mesos dispacther.
Properties provided with --conf take precedence over properties within the properties file.
The reason for this PR is that for simple configuration or testing purposes we need to provide a property file (ideally a shared one for a cluster) even if we just provide a single property.
Manually tested.
Author: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#14650 from skonto/dipatcher_conf.
## What changes were proposed in this pull request?
This is a follow-up work of #15618.
Close file source;
For any newly created streaming context outside the withContext, explicitly close the context.
## How was this patch tested?
Existing unit tests.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#15818 from wangmiao1981/rtest.
## What changes were proposed in this pull request?
We should call `setConf` if `OutputFormat` is `Configurable`, this should be done before we create `OutputCommitter` and `RecordWriter`.
This is follow up of #15769, see discussion [here](https://github.com/apache/spark/pull/15769/files#r87064229)
## How was this patch tested?
Add test of this case in `PairRDDFunctionsSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15823 from jiangxb1987/config-format.
## What changes were proposed in this pull request?
Application links generated on the history server UI no longer (regression from 1.6) contain the configured spark.ui.proxyBase in the links. To address this, made the uiRoot available globally to all javascripts for Web UI. Updated the mustache template (historypage-template.html) to include the uiroot for rendering links to the applications.
The existing test was not sufficient to verify the scenario where ajax call is used to populate the application listing template, so added a new selenium test case to cover this scenario.
## How was this patch tested?
Existing tests and a new unit test.
No visual changes to the UI.
Author: Vinayak <vijoshi5@in.ibm.com>
Closes#15742 from vijoshi/SPARK-16808_master.
## What changes were proposed in this pull request?
"StandaloneSchedulerBackend.dead" is called in a RPC thread, so it should not call "SparkContext.stop" in the same thread. "SparkContext.stop" will block until all RPC threads exit, if it's called inside a RPC thread, it will be dead-lock.
This PR add a thread local flag inside RPC threads. `SparkContext.stop` uses it to decide if launching a new thread to stop the SparkContext.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15775 from zsxwing/SPARK-18280.
## What changes were proposed in this pull request?
This PR port RDD API to use commit protocol, the changes made here:
1. Add new internal helper class that saves an RDD using a Hadoop OutputFormat named `SparkNewHadoopWriter`, it's similar with `SparkHadoopWriter` but uses commit protocol. This class supports the newer `mapreduce` API, instead of the old `mapred` API which is supported by `SparkHadoopWriter`;
2. Rewrite `PairRDDFunctions.saveAsNewAPIHadoopDataset` function, so it uses commit protocol now.
## How was this patch tested?
Exsiting test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15769 from jiangxb1987/rdd-commit.
## What changes were proposed in this pull request?
When profiling heap dumps from the HistoryServer and live Spark web UIs, I found a large amount of memory being wasted on duplicated objects and strings. This patch's changes remove most of this duplication, resulting in over 40% memory savings for some benchmarks.
- **Task metrics** (6441f0624dfcda9c7193a64bfb416a145b5aabdf): previously, every `TaskUIData` object would have its own instances of `InputMetricsUIData`, `OutputMetricsUIData`, `ShuffleReadMetrics`, and `ShuffleWriteMetrics`, but for many tasks these metrics are irrelevant because they're all zero. This patch changes how we construct these metrics in order to re-use a single immutable "empty" value for the cases where these metrics are empty.
- **TaskInfo.accumulables** (ade86db901127bf13c0e0bdc3f09c933a093bb76): Previously, every `TaskInfo` object had its own empty `ListBuffer` for holding updates from named accumulators. Tasks which didn't use named accumulators still paid for the cost of allocating and storing this empty buffer. To avoid this overhead, I changed the `val` with a mutable buffer into a `var` which holds an immutable Scala list, allowing tasks which do not have named accumulator updates to share the same singleton `Nil` object.
- **String.intern() in JSONProtocol** (7e05630e9a78c455db8c8c499f0590c864624e05): in the HistoryServer, executor hostnames and ids are deserialized from JSON, leading to massive duplication of these string objects. By calling `String.intern()` on the deserialized values we can remove all of this duplication. Since Spark now requires Java 7+ we don't have to worry about string interning exhausting the permgen (see http://java-performance.info/string-intern-in-java-6-7-8/).
## How was this patch tested?
I ran
```
sc.parallelize(1 to 100000, 100000).count()
```
in `spark-shell` with event logging enabled, then loaded that event log in the HistoryServer, performed a full GC, and took a heap dump. According to YourKit, the changes in this patch reduced memory consumption by roughly 28 megabytes (or 770k Java objects):
Here's a table illustrating the drop in objects due to deduplication (the drop is <100k for some objects because some events were dropped from the listener bus; this is a separate, existing bug that I'll address separately after CPU-profiling):
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15743 from JoshRosen/spark-ui-memory-usage.
## What changes were proposed in this pull request?
Close `FileStreams`, `ZipFiles` etc to release the resources after using. Not closing the resources will cause IO Exception to be raised while deleting temp files.
## How was this patch tested?
Existing tests
Author: U-FAREAST\tl <tl@microsoft.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Tao LI <tl@microsoft.com>
Closes#15618 from HyukjinKwon/SPARK-14914-1.
## What changes were proposed in this pull request?
[SPARK-18200](https://issues.apache.org/jira/browse/SPARK-18200) reports Apache Spark 2.x raises `java.lang.IllegalArgumentException: requirement failed: Invalid initial capacity` while running `triangleCount`. The root cause is that `VertexSet`, a type alias of `OpenHashSet`, does not allow zero as a initial size. This PR loosens the restriction to allow zero.
## How was this patch tested?
Pass the Jenkins test with a new test case in `OpenHashSetSuite`.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15741 from dongjoon-hyun/SPARK-18200.
## What changes were proposed in this pull request?
Fix `Locale.US` for all usages of `DateFormat`, `NumberFormat`
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15610 from srowen/SPARK-18076.
## What changes were proposed in this pull request?
Removing `appUIAddress` attribute since it is no longer in use.
## How was this patch tested?
Local build
Author: Jacek Laskowski <jacek@japila.pl>
Closes#15603 from jaceklaskowski/sparkui-fixes.
## What changes were proposed in this pull request?
While learning core scheduler code, I found two lines of wrong comments. This PR simply corrects the comments.
## How was this patch tested?
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#15631 from wangmiao1981/Rbug.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
`Utils.getIteratorZipWithIndex` was added to deal with number of records > 2147483647 in one partition.
method `getIteratorZipWithIndex` accepts `startIndex` < 0, which leads to negative index.
This PR just adds a defensive check on `startIndex` to make sure it is >= 0.
## How was this patch tested?
Add a new unit test.
Author: Miao Wang <miaowang@Miaos-MacBook-Pro.local>
Closes#15639 from wangmiao1981/zip.
## What changes were proposed in this pull request?
[SPARK-16757](https://issues.apache.org/jira/browse/SPARK-16757) sets the hadoop `CallerContext` when calling hadoop/hdfs apis to make spark applications more diagnosable in hadoop/hdfs logs. However, the `org.apache.hadoop.ipc.CallerContext` class is only added since [hadoop 2.8](https://issues.apache.org/jira/browse/HDFS-9184), which is not officially releaed yet. So each time `utils.CallerContext.setCurrentContext()` is called (e.g [when a task is created](https://github.com/apache/spark/blob/b678e46/core/src/main/scala/org/apache/spark/scheduler/Task.scala#L95-L96)), a "java.lang.ClassNotFoundException: org.apache.hadoop.ipc.CallerContext"
error is logged, which pollutes the spark logs when there are lots of tasks.
This patch improves this behaviour by only logging the `ClassNotFoundException` once.
## How was this patch tested?
Existing tests.
Author: Shuai Lin <linshuai2012@gmail.com>
Closes#15377 from lins05/spark-17802-improve-callercontext-logging.
## What changes were proposed in this pull request?
Currently users can kill stages via the web ui but not jobs directly (jobs are killed if one of their stages is). I've added the ability to kill jobs via the web ui. This code change is based on #4823 by lianhuiwang and updated to work with the latest code matching how stages are currently killed. In general I've copied the kill stage code warning and note comments and all. I also updated applicable tests and documentation.
## How was this patch tested?
Manually tested and dev/run-tests
Author: Alex Bozarth <ajbozart@us.ibm.com>
Author: Lianhui Wang <lianhuiwang09@gmail.com>
Closes#15441 from ajbozarth/spark4411.
spark history server log needs to be fixed to show https url when ssl is enabled
Author: chie8842 <chie@chie-no-Mac-mini.local>
Closes#15611 from hayashidac/SPARK-16988.
The named parameter in an overridden class isn't supported in Scala 2.10 so was breaking the build.
cc zsxwing
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#15617 from kayousterhout/hotfix.
`TaskSetManager` should have unique name to avoid adding duplicate ones to parent `Pool` via `SchedulableBuilder`. This problem has been surfaced with following discussion: [[PR: Avoid adding duplicate schedulables]](https://github.com/apache/spark/pull/15326)
**Proposal** :
There is 1x1 relationship between `stageAttemptId` and `TaskSetManager` so `taskSet.Id` covering both `stageId` and `stageAttemptId` looks to be used for uniqueness of `TaskSetManager` name instead of just `stageId`.
**Current TaskSetManager Name** :
`var name = "TaskSet_" + taskSet.stageId.toString`
**Sample**: TaskSet_0
**Proposed TaskSetManager Name** :
`val name = "TaskSet_" + taskSet.Id ` `// taskSet.Id = (stageId + "." + stageAttemptId)`
**Sample** : TaskSet_0.0
Added new Unit Test.
Author: erenavsarogullari <erenavsarogullari@gmail.com>
Closes#15463 from erenavsarogullari/SPARK-17894.
## What changes were proposed in this pull request?
`Array[T]()` -> `Array.empty[T]` to avoid allocating 0-length arrays.
Use regex `find . -name '*.scala' | xargs -i bash -c 'egrep "Array\[[A-Za-z]+\]\(\)" -n {} && echo {}'` to find modification candidates.
cc srowen
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15564 from zhengruifeng/avoid_0_length_array.
## What changes were proposed in this pull request?
- Fix bug of RDD `zipWithIndex` generating wrong result when one partition contains more than 2147483647 records.
- Fix bug of RDD `zipWithUniqueId` generating wrong result when one partition contains more than 2147483647 records.
## How was this patch tested?
test added.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#15550 from WeichenXu123/fix_rdd_zipWithIndex_overflow.
## What changes were proposed in this pull request?
This PR adds support for executor log compression.
## How was this patch tested?
Unit tests
cc: yhuai tdas mengxr
Author: Yu Peng <loneknightpy@gmail.com>
Closes#15285 from loneknightpy/compress-executor-log.
## What changes were proposed in this pull request?
Currently we use BufferedInputStream to read the shuffle file which copies the file content from os buffer cache to the user buffer. This adds additional latency in reading the spill files. We made a change to use java nio's direct buffer to read the spill files and for certain pipelines spilling significant amount of data, we see up to 7% speedup for the entire pipeline.
## How was this patch tested?
Tested by running the job in the cluster and observed up to 7% speedup.
Author: Sital Kedia <skedia@fb.com>
Closes#15408 from sitalkedia/skedia/nio_spill_read.
This reverts commit ed14633414.
The patch merged had obvious quality and documentation issue. The idea is useful, and we should work towards improving its quality and merging it in again.
## What changes were proposed in this pull request?
Restructure the code and implement two new task assigner.
PackedAssigner: try to allocate tasks to the executors with least available cores, so that spark can release reserved executors when dynamic allocation is enabled.
BalancedAssigner: try to allocate tasks to the executors with more available cores in order to balance the workload across all executors.
By default, the original round robin assigner is used.
We test a pipeline, and new PackedAssigner save around 45% regarding the reserved cpu and memory with dynamic allocation enabled.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Both unit test in TaskSchedulerImplSuite and manual tests in production pipeline.
Author: Zhan Zhang <zhanzhang@fb.com>
Closes#15218 from zhzhan/packed-scheduler.
## What changes were proposed in this pull request?
This is a step along the way to SPARK-8425.
To enable incremental review, the first step proposed here is to expand the blacklisting within tasksets. In particular, this will enable blacklisting for
* (task, executor) pairs (this already exists via an undocumented config)
* (task, node)
* (taskset, executor)
* (taskset, node)
Adding (task, node) is critical to making spark fault-tolerant of one-bad disk in a cluster, without requiring careful tuning of "spark.task.maxFailures". The other additions are also important to avoid many misleading task failures and long scheduling delays when there is one bad node on a large cluster.
Note that some of the code changes here aren't really required for just this -- they put pieces in place for SPARK-8425 even though they are not used yet (eg. the `BlacklistTracker` helper is a little out of place, `TaskSetBlacklist` holds onto a little more info than it needs to for just this change, and `ExecutorFailuresInTaskSet` is more complex than it needs to be).
## How was this patch tested?
Added unit tests, run tests via jenkins.
Author: Imran Rashid <irashid@cloudera.com>
Author: mwws <wei.mao@intel.com>
Closes#15249 from squito/taskset_blacklist_only.
## What changes were proposed in this pull request?
Add a flag to ignore corrupt files. For Spark core, the configuration is `spark.files.ignoreCorruptFiles`. For Spark SQL, it's `spark.sql.files.ignoreCorruptFiles`.
## How was this patch tested?
The added unit tests
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15422 from zsxwing/SPARK-17850.
## What changes were proposed in this pull request?
This PR modified the test case `test("script")` to use resource path for `test_script.sh`. Make the test case portable (even in IntelliJ).
## How was this patch tested?
Passed the test case.
Before:
Run `test("script")` in IntelliJ:
```
Caused by: org.apache.spark.SparkException: Subprocess exited with status 127. Error: bash: src/test/resources/test_script.sh: No such file or directory
```
After:
Test passed.
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes#15246 from weiqingy/hivetest.
## What changes were proposed in this pull request?
Fix:
- GroupedMeanEvaluator and GroupedSumEvaluator are unused, as is the StudentTCacher support class
- CountEvaluator can return a lower bound < 0, when counts can't be negative
- MeanEvaluator will actually fail on exactly 1 datum (yields t-test with 0 DOF)
- CountEvaluator uses a normal distribution, which may be an inappropriate approximation (leading to above)
- Test for SumEvaluator asserts incorrect expected sums – e.g. after observing 10% of data has sum of 2, expectation should be 20, not 38
- CountEvaluator, MeanEvaluator have no unit tests to catch these
- Duplication of distribution code across CountEvaluator, GroupedCountEvaluator
- The stats in each could use a bit of documentation as I had to guess at them
- (Code could use a few cleanups and optimizations too)
## How was this patch tested?
Existing and new tests
Author: Sean Owen <sowen@cloudera.com>
Closes#15341 from srowen/SPARK-17768.
## What changes were proposed in this pull request?
Mock SparkContext to reduce memory usage of BlockManagerSuite
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15350 from zsxwing/SPARK-17778.
## What changes were proposed in this pull request?
Return Iterator of applications internally in history server, for consistency and performance. See https://github.com/apache/spark/pull/15248 for some back-story.
The code called by and calling HistoryServer.getApplicationList wants an Iterator, but this method materializes an Iterable, which potentially causes a performance problem. It's simpler too to make this internal method also pass through an Iterator.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15321 from srowen/SPARK-17671.
## What changes were proposed in this pull request?
This PR proposes to fix/skip some tests failed on Windows. This PR takes over https://github.com/apache/spark/pull/12696.
**Before**
- **SparkSubmitSuite**
```
[info] - launch simple application with spark-submit *** FAILED *** (202 milliseconds)
[info] java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specifie
[info] - includes jars passed in through --jars *** FAILED *** (1 second, 625 milliseconds)
[info] java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
```
- **DiskStoreSuite**
```
[info] - reads of memory-mapped and non memory-mapped files are equivalent *** FAILED *** (1 second, 78 milliseconds)
[info] diskStoreMapped.remove(blockId) was false (DiskStoreSuite.scala:41)
```
**After**
- **SparkSubmitSuite**
```
[info] - launch simple application with spark-submit (578 milliseconds)
[info] - includes jars passed in through --jars (1 second, 875 milliseconds)
```
- **DiskStoreSuite**
```
[info] DiskStoreSuite:
[info] - reads of memory-mapped and non memory-mapped files are equivalent !!! CANCELED !!! (766 milliseconds
```
For `CreateTableAsSelectSuite` and `FsHistoryProviderSuite`, I could not reproduce as the Java version seems higher than the one that has the bugs about `setReadable(..)` and `setWritable(...)` but as they are bugs reported clearly, it'd be sensible to skip those. We should revert the changes for both back as soon as we drop the support of Java 7.
## How was this patch tested?
Manually tested via AppVeyor.
Closes#12696
Author: Tao LI <tl@microsoft.com>
Author: U-FAREAST\tl <tl@microsoft.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15320 from HyukjinKwon/SPARK-14914.
## What changes were proposed in this pull request?
As a followup to SPARK-17666, ensure filesystem connections are not leaked at least in unit tests. This is done here by intercepting filesystem calls as suggested by JoshRosen . At the end of each test, we assert no filesystem streams are left open.
This applies to all tests using SharedSQLContext or SharedSparkContext.
## How was this patch tested?
I verified that tests in sql and core are indeed using the filesystem backend, and fixed the detected leaks. I also checked that reverting https://github.com/apache/spark/pull/15245 causes many actual test failures due to connection leaks.
Author: Eric Liang <ekl@databricks.com>
Author: Eric Liang <ekhliang@gmail.com>
Closes#15306 from ericl/sc-4672.
## What changes were proposed in this pull request?
This PR makes block replication strategies pluggable. It provides two trait that can be implemented, one that maps a host to its topology and is used in the master, and the second that helps prioritize a list of peers for block replication and would run in the executors.
This patch contains default implementations of these traits that make sure current Spark behavior is unchanged.
## How was this patch tested?
This patch should not change Spark behavior in any way, and was tested with unit tests for storage.
Author: Shubham Chopra <schopra31@bloomberg.net>
Closes#13152 from shubhamchopra/RackAwareBlockReplication.
## What changes were proposed in this pull request?
FsHistoryProvider was writing a hidden file (to check the fs's clock).
Even though it deleted the file immediately, sometimes another thread
would try to scan the files on the fs in-between, and then there would
be an error msg logged which was very misleading for the end-user.
(The logged error was harmless, though.)
## How was this patch tested?
I added one unit test, but to be clear, that test was passing before. The actual change in behavior in that test is just logging (after the change, there is no more logged error), which I just manually verified.
Author: Imran Rashid <irashid@cloudera.com>
Closes#15250 from squito/SPARK-17676.
## What changes were proposed in this pull request?
The Seq[WorkerOffer] is accessed by index, so it really should be an
IndexedSeq, otherwise an O(n) operation becomes O(n^2). In practice
this hasn't been an issue b/c where these offers are generated, the call
to `.toSeq` just happens to create an IndexedSeq anyway.I got bitten by
this in performance tests I was doing, and its better for the types to be
more precise so eg. a change in Scala doesn't destroy performance.
## How was this patch tested?
Unit tests via jenkins.
Author: Imran Rashid <irashid@cloudera.com>
Closes#15221 from squito/SPARK-17648.
## What changes were proposed in this pull request?
| Time |Thread 1 , Job1 | Thread 2 , Job2 |
|:-------------:|:-------------:|:-----:|
| 1 | abort stage due to FetchFailed | |
| 2 | failedStages += failedStage | |
| 3 | | task failed due to FetchFailed |
| 4 | | can not post ResubmitFailedStages because failedStages is not empty |
Then job2 of thread2 never resubmit the failed stage and hang.
We should not add the failedStages when abortStage for fetch failure
## How was this patch tested?
added unit test
Author: w00228970 <wangfei1@huawei.com>
Author: wangfei <wangfei_hello@126.com>
Closes#15213 from scwf/dag-resubmit.
## What changes were proposed in this pull request?
1. Pass `jobId` to Task.
2. Invoke Hadoop APIs.
* A new function `setCallerContext` is added in `Utils`. `setCallerContext` function invokes APIs of `org.apache.hadoop.ipc.CallerContext` to set up spark caller contexts, which will be written into `hdfs-audit.log` and Yarn RM audit log.
* For HDFS: Spark sets up its caller context by invoking`org.apache.hadoop.ipc.CallerContext` in `Task` and Yarn `Client` and `ApplicationMaster`.
* For Yarn: Spark sets up its caller context by invoking `org.apache.hadoop.ipc.CallerContext` in Yarn `Client`.
## How was this patch tested?
Manual Tests against some Spark applications in Yarn client mode and Yarn cluster mode. Need to check if spark caller contexts are written into HDFS hdfs-audit.log and Yarn RM audit log successfully.
For example, run SparkKmeans in Yarn client mode:
```
./bin/spark-submit --verbose --executor-cores 3 --num-executors 1 --master yarn --deploy-mode client --class org.apache.spark.examples.SparkKMeans examples/target/original-spark-examples_2.11-2.1.0-SNAPSHOT.jar hdfs://localhost:9000/lr_big.txt 2 5
```
**Before**:
There will be no Spark caller context in records of `hdfs-audit.log` and Yarn RM audit log.
**After**:
Spark caller contexts will be written in records of `hdfs-audit.log` and Yarn RM audit log.
These are records in `hdfs-audit.log`:
```
2016-09-20 11:54:24,116 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_CLIENT_AppId_application_1474394339641_0005
2016-09-20 11:54:28,164 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_TASK_AppId_application_1474394339641_0005_JobId_0_StageId_0_AttemptId_0_TaskId_2_AttemptNum_0
2016-09-20 11:54:28,164 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_TASK_AppId_application_1474394339641_0005_JobId_0_StageId_0_AttemptId_0_TaskId_1_AttemptNum_0
2016-09-20 11:54:28,164 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_TASK_AppId_application_1474394339641_0005_JobId_0_StageId_0_AttemptId_0_TaskId_0_AttemptNum_0
```
```
2016-09-20 11:59:33,868 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=mkdirs src=/private/tmp/hadoop-wyang/nm-local-dir/usercache/wyang/appcache/application_1474394339641_0006/container_1474394339641_0006_01_000001/spark-warehouse dst=null perm=wyang:supergroup:rwxr-xr-x proto=rpc callerContext=SPARK_APPLICATION_MASTER_AppId_application_1474394339641_0006_AttemptId_1
2016-09-20 11:59:37,214 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_TASK_AppId_application_1474394339641_0006_AttemptId_1_JobId_0_StageId_0_AttemptId_0_TaskId_1_AttemptNum_0
2016-09-20 11:59:37,215 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_TASK_AppId_application_1474394339641_0006_AttemptId_1_JobId_0_StageId_0_AttemptId_0_TaskId_2_AttemptNum_0
2016-09-20 11:59:37,215 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_TASK_AppId_application_1474394339641_0006_AttemptId_1_JobId_0_StageId_0_AttemptId_0_TaskId_0_AttemptNum_0
2016-09-20 11:59:42,391 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_TASK_AppId_application_1474394339641_0006_AttemptId_1_JobId_0_StageId_0_AttemptId_0_TaskId_3_AttemptNum_0
```
This is a record in Yarn RM log:
```
2016-09-20 11:59:24,050 INFO org.apache.hadoop.yarn.server.resourcemanager.RMAuditLogger: USER=wyang IP=127.0.0.1 OPERATION=Submit Application Request TARGET=ClientRMService RESULT=SUCCESS APPID=application_1474394339641_0006 CALLERCONTEXT=SPARK_CLIENT_AppId_application_1474394339641_0006
```
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes#14659 from Sherry302/callercontextSubmit.
## What changes were proposed in this pull request?
Fix two comments since Actor is not used anymore.
Author: Ding Fei <danis@danix>
Closes#15251 from danix800/comment-fixing.
## What changes were proposed in this pull request?
When a malformed URL was sent to Executors through `sc.addJar` and `sc.addFile`, the executors become unusable, because they constantly throw `MalformedURLException`s and can never acknowledge that the file or jar is just bad input.
This PR tries to fix that problem by making sure MalformedURLs can never be submitted through `sc.addJar` and `sc.addFile`. Another solution would be to blacklist bad files and jars on Executors. Maybe fail the first time, and then ignore the second time (but print a warning message).
## How was this patch tested?
Unit tests in SparkContextSuite
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15224 from brkyvz/SPARK-17650.
Currently task metrics don't support executor CPU time, so there's no way to calculate how much CPU time a stage/task took from History Server metrics. This PR enables reporting CPU time.
Author: jisookim <jisookim0513@gmail.com>
Closes#10212 from jisookim0513/add-cpu-time-metric.
## What changes were proposed in this pull request?
We are killing multiple executors together instead of iterating over expensive RPC calls to kill single executor.
## How was this patch tested?
Executed sample spark job to observe executors being killed/removed with dynamic allocation enabled.
Author: Dhruve Ashar <dashar@yahoo-inc.com>
Author: Dhruve Ashar <dhruveashar@gmail.com>
Closes#15152 from dhruve/impr/SPARK-17365.
## What changes were proposed in this pull request?
Yarn and mesos cluster mode support remote python path (HDFS/S3 scheme) by their own mechanism, it is not necessary to check and format the python when running on these modes. This is a potential regression compared to 1.6, so here propose to fix it.
## How was this patch tested?
Unit test to verify SparkSubmit arguments, also with local cluster verification. Because of lack of `MiniDFSCluster` support in Spark unit test, there's no integration test added.
Author: jerryshao <sshao@hortonworks.com>
Closes#15137 from jerryshao/SPARK-17512.
## What changes were proposed in this pull request?
In TaskResultGetter, enqueueFailedTask currently deserializes the result
as a TaskEndReason. But the type is actually more specific, its a
TaskFailedReason. This just leads to more blind casting later on – it
would be more clear if the msg was cast to the right type immediately,
so method parameter types could be tightened.
## How was this patch tested?
Existing unit tests via jenkins. Note that the code was already performing a blind-cast to a TaskFailedReason before in any case, just in a different spot, so there shouldn't be any behavior change.
Author: Imran Rashid <irashid@cloudera.com>
Closes#15181 from squito/SPARK-17623.
The goal of this feature is to allow the Spark driver to run in an
isolated environment, such as a docker container, and be able to use
the host's port forwarding mechanism to be able to accept connections
from the outside world.
The change is restricted to the driver: there is no support for achieving
the same thing on executors (or the YARN AM for that matter). Those still
need full access to the outside world so that, for example, connections
can be made to an executor's block manager.
The core of the change is simple: add a new configuration that tells what's
the address the driver should bind to, which can be different than the address
it advertises to executors (spark.driver.host). Everything else is plumbing
the new configuration where it's needed.
To use the feature, the host starting the container needs to set up the
driver's port range to fall into a range that is being forwarded; this
required the block manager port to need a special configuration just for
the driver, which falls back to the existing spark.blockManager.port when
not set. This way, users can modify the driver settings without affecting
the executors; it would theoretically be nice to also have different
retry counts for driver and executors, but given that docker (at least)
allows forwarding port ranges, we can probably live without that for now.
Because of the nature of the feature it's kinda hard to add unit tests;
I just added a simple one to make sure the configuration works.
This was tested with a docker image running spark-shell with the following
command:
docker blah blah blah \
-p 38000-38100:38000-38100 \
[image] \
spark-shell \
--num-executors 3 \
--conf spark.shuffle.service.enabled=false \
--conf spark.dynamicAllocation.enabled=false \
--conf spark.driver.host=[host's address] \
--conf spark.driver.port=38000 \
--conf spark.driver.blockManager.port=38020 \
--conf spark.ui.port=38040
Running on YARN; verified the driver works, executors start up and listen
on ephemeral ports (instead of using the driver's config), and that caching
and shuffling (without the shuffle service) works. Clicked through the UI
to make sure all pages (including executor thread dumps) worked. Also tested
apps without docker, and ran unit tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#15120 from vanzin/SPARK-4563.
## What changes were proposed in this pull request?
`MemoryStore.putIteratorAsBytes()` may silently lose values when used with `KryoSerializer` because it does not properly close the serialization stream before attempting to deserialize the already-serialized values, which may cause values buffered in Kryo's internal buffers to not be read.
This is the root cause behind a user-reported "wrong answer" bug in PySpark caching reported by bennoleslie on the Spark user mailing list in a thread titled "pyspark persist MEMORY_ONLY vs MEMORY_AND_DISK". Due to Spark 2.0's automatic use of KryoSerializer for "safe" types (such as byte arrays, primitives, etc.) this misuse of serializers manifested itself as silent data corruption rather than a StreamCorrupted error (which you might get from JavaSerializer).
The minimal fix, implemented here, is to close the serialization stream before attempting to deserialize written values. In addition, this patch adds several additional assertions / precondition checks to prevent misuse of `PartiallySerializedBlock` and `ChunkedByteBufferOutputStream`.
## How was this patch tested?
The original bug was masked by an invalid assert in the memory store test cases: the old assert compared two results record-by-record with `zip` but didn't first check that the lengths of the two collections were equal, causing missing records to go unnoticed. The updated test case reproduced this bug.
In addition, I added a new `PartiallySerializedBlockSuite` to unit test that component.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15043 from JoshRosen/partially-serialized-block-values-iterator-bugfix.
## What changes were proposed in this pull request?
Add a clearUntil() method on BitSet (adapted from the pre-existing setUntil() method).
Use this method to clear the subset of the BitSet which needs to be used during merge joins.
## How was this patch tested?
dev/run-tests, as well as performance tests on skewed data as described in jira.
I expect there to be a small local performance hit using BitSet.clearUntil rather than BitSet.clear for normally shaped (unskewed) joins (additional read on the last long). This is expected to be de-minimis and was not specifically tested.
Author: David Navas <davidn@clearstorydata.com>
Closes#15084 from davidnavas/bitSet.
## What changes were proposed in this pull request?
If a BlockManager `put()` call failed after the BlockManagerMaster was notified of a block's availability then incomplete cleanup logic in a `finally` block would never send a second block status method to inform the master of the block's unavailability. This, in turn, leads to fetch failures and used to be capable of causing complete job failures before #15037 was fixed.
This patch addresses this issue via multiple small changes:
- The `finally` block now calls `removeBlockInternal` when cleaning up from a failed `put()`; in addition to removing the `BlockInfo` entry (which was _all_ that the old cleanup logic did), this code (redundantly) tries to remove the block from the memory and disk stores (as an added layer of defense against bugs lower down in the stack) and optionally notifies the master of block removal (which now happens during exception-triggered cleanup).
- When a BlockManager receives a request for a block that it does not have it will now notify the master to update its block locations. This ensures that bad metadata pointing to non-existent blocks will eventually be fixed. Note that I could have implemented this logic in the block manager client (rather than in the remote server), but that would introduce the problem of distinguishing between transient and permanent failures; on the server, however, we know definitively that the block isn't present.
- Catch `NonFatal` instead of `Exception` to avoid swallowing `InterruptedException`s thrown from synchronous block replication calls.
This patch depends upon the refactorings in #15036, so that other patch will also have to be backported when backporting this fix.
For more background on this issue, including example logs from a real production failure, see [SPARK-17484](https://issues.apache.org/jira/browse/SPARK-17484).
## How was this patch tested?
Two new regression tests in BlockManagerSuite.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15085 from JoshRosen/SPARK-17484.
## What changes were proposed in this pull request?
Make CollectionAccumulator and SetAccumulator's value can be read thread-safely to fix the ConcurrentModificationException reported in [JIRA](https://issues.apache.org/jira/browse/SPARK-17463).
## How was this patch tested?
Existing tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15063 from zsxwing/SPARK-17463.
## What changes were proposed in this pull request?
Currently, ORDER BY clause returns nulls value according to sorting order (ASC|DESC), considering null value is always smaller than non-null values.
However, SQL2003 standard support NULLS FIRST or NULLS LAST to allow users to specify whether null values should be returned first or last, regardless of sorting order (ASC|DESC).
This PR is to support this new feature.
## How was this patch tested?
New test cases are added to test NULLS FIRST|LAST for regular select queries and windowing queries.
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Xin Wu <xinwu@us.ibm.com>
Closes#14842 from xwu0226/SPARK-10747.
## What changes were proposed in this pull request?
In Spark's `RDD.getOrCompute` we first try to read a local copy of a cached RDD block, then a remote copy, and only fall back to recomputing the block if no cached copy (local or remote) can be read. This logic works correctly in the case where no remote copies of the block exist, but if there _are_ remote copies and reads of those copies fail (due to network issues or internal Spark bugs) then the BlockManager will throw a `BlockFetchException` that will fail the task (and which could possibly fail the whole job if the read failures keep occurring).
In the cases of TorrentBroadcast and task result fetching we really do want to fail the entire job in case no remote blocks can be fetched, but this logic is inappropriate for reads of cached RDD blocks because those can/should be recomputed in case cached blocks are unavailable.
Therefore, I think that the `BlockManager.getRemoteBytes()` method should never throw on remote fetch errors and, instead, should handle failures by returning `None`.
## How was this patch tested?
Block manager changes should be covered by modified tests in `BlockManagerSuite`: the old tests expected exceptions to be thrown on failed remote reads, while the modified tests now expect `None` to be returned from the `getRemote*` method.
I also manually inspected all usages of `BlockManager.getRemoteValues()`, `getRemoteBytes()`, and `get()` to verify that they correctly pattern-match on the result and handle `None`. Note that these `None` branches are already exercised because the old `getRemoteBytes` returned `None` when no remote locations for the block could be found (which could occur if an executor died and its block manager de-registered with the master).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15037 from JoshRosen/SPARK-17485.
## What changes were proposed in this pull request?
MemoryStore may throw OutOfMemoryError when trying to cache a super big RDD that cannot fit in memory.
```
scala> sc.parallelize(1 to 1000000000, 100).map(x => new Array[Long](1000)).cache().count()
java.lang.OutOfMemoryError: Java heap space
at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:24)
at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:23)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$JoinIterator.next(Iterator.scala:232)
at org.apache.spark.storage.memory.PartiallyUnrolledIterator.next(MemoryStore.scala:683)
at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1684)
at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
```
Spark MemoryStore uses SizeTrackingVector as a temporary unrolling buffer to store all input values that it has read so far before transferring the values to storage memory cache. The problem is that when the input RDD is too big for caching in memory, the temporary unrolling memory SizeTrackingVector is not garbage collected in time. As SizeTrackingVector can occupy all available storage memory, it may cause the executor JVM to run out of memory quickly.
More info can be found at https://issues.apache.org/jira/browse/SPARK-17503
## How was this patch tested?
Unit test and manual test.
### Before change
Heap memory consumption
<img width="702" alt="screen shot 2016-09-12 at 4 16 15 pm" src="https://cloud.githubusercontent.com/assets/2595532/18429524/60d73a26-7906-11e6-9768-6f286f5c58c8.png">
Heap dump
<img width="1402" alt="screen shot 2016-09-12 at 4 34 19 pm" src="https://cloud.githubusercontent.com/assets/2595532/18429577/cbc1ef20-7906-11e6-847b-b5903f450b3b.png">
### After change
Heap memory consumption
<img width="706" alt="screen shot 2016-09-12 at 4 29 10 pm" src="https://cloud.githubusercontent.com/assets/2595532/18429503/4abe9342-7906-11e6-844a-b2f815072624.png">
Author: Sean Zhong <seanzhong@databricks.com>
Closes#15056 from clockfly/memory_store_leak.
## What changes were proposed in this pull request?
This patch adds methods for extracting major and minor versions as Int types in Scala from a Spark version string.
Motivation: There are many hacks within Spark's codebase to identify and compare Spark versions. We should add a simple utility to standardize these code paths, especially since there have been mistakes made in the past. This will let us add unit tests as well. Currently, I want this functionality to check Spark versions to provide backwards compatibility for ML model persistence.
## How was this patch tested?
Unit tests
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#15017 from jkbradley/version-parsing.
## What changes were proposed in this pull request?
This pull request adds the functionality to enable accessing worker and application UI through master UI itself. Thus helps in accessing SparkUI when running spark cluster in closed networks e.g. Kubernetes. Cluster admin needs to expose only spark master UI and rest of the UIs can be in the private network, master UI will reverse proxy the connection request to corresponding resource. It adds the path for workers/application UIs as
WorkerUI: <http/https>://master-publicIP:<port>/target/workerID/
ApplicationUI: <http/https>://master-publicIP:<port>/target/appID/
This makes it easy for users to easily protect the Spark master cluster access by putting some reverse proxy e.g. https://github.com/bitly/oauth2_proxy
## How was this patch tested?
The functionality has been tested manually and there is a unit test too for testing access to worker UI with reverse proxy address.
pwendell bomeng BryanCutler can you please review it, thanks.
Author: Gurvinder Singh <gurvinder.singh@uninett.no>
Closes#13950 from gurvindersingh/rproxy.
## What changes were proposed in this pull request?
DAGScheduler invalidates shuffle files when an executor loss event occurs, but not when the external shuffle service is enabled. This is because when shuffle service is on, the shuffle file lifetime can exceed the executor lifetime.
However, it also doesn't invalidate shuffle files when the shuffle service itself is lost (due to whole slave loss). This can cause long hangs when slaves are lost since the file loss is not detected until a subsequent stage attempts to read the shuffle files.
The proposed fix is to also invalidate shuffle files when an executor is lost due to a `SlaveLost` event.
## How was this patch tested?
Unit tests, also verified on an actual cluster that slave loss invalidates shuffle files immediately as expected.
cc mateiz
Author: Eric Liang <ekl@databricks.com>
Closes#14931 from ericl/sc-4439.
## What changes were proposed in this pull request?
We should generally use `ArrayBuffer.+=(A)` rather than `ArrayBuffer.append(A)`, because `append(A)` would involve extra boxing / unboxing.
## How was this patch tested?
N/A
Author: Liwei Lin <lwlin7@gmail.com>
Closes#14914 from lw-lin/append_to_plus_eq_v2.
## What changes were proposed in this pull request?
This patch fixes a `java.io.StreamCorruptedException` error affecting remote reads of cached values when certain data types are used. The problem stems from #11801 / SPARK-13990, a patch to have Spark automatically pick the "best" serializer when caching RDDs. If PySpark cached a PythonRDD, then this would be cached as an `RDD[Array[Byte]]` and the automatic serializer selection would pick KryoSerializer for replication and block transfer. However, the `getRemoteValues()` / `getRemoteBytes()` code path did not pass proper class tags in order to enable the same serializer to be used during deserialization, causing Java to be inappropriately used instead of Kryo, leading to the StreamCorruptedException.
We already fixed a similar bug in #14311, which dealt with similar issues in block replication. Prior to that patch, it seems that we had no tests to ensure that block replication actually succeeded. Similarly, prior to this bug fix patch it looks like we had no tests to perform remote reads of cached data, which is why this bug was able to remain latent for so long.
This patch addresses the bug by modifying `BlockManager`'s `get()` and `getRemoteValues()` methods to accept ClassTags, allowing the proper class tag to be threaded in the `getOrElseUpdate` code path (which is used by `rdd.iterator`)
## How was this patch tested?
Extended the caching tests in `DistributedSuite` to exercise the `getRemoteValues` path, plus manual testing to verify that the PySpark bug reproduction in SPARK-17110 is fixed.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#14952 from JoshRosen/SPARK-17110.
## What changes were proposed in this pull request?
This pull request reverts the changes made as a part of #14605, which simply side-steps the deadlock issue. Instead, I propose the following approach:
* Use `scheduleWithFixedDelay` when calling `ExecutorAllocationManager.schedule` for scheduling executor requests. The intent of this is that if invocations are delayed beyond the default schedule interval on account of lock contention, then we avoid a situation where calls to `schedule` are made back-to-back, potentially releasing and then immediately reacquiring these locks - further exacerbating contention.
* Replace a number of calls to `askWithRetry` with `ask` inside of message handling code in `CoarseGrainedSchedulerBackend` and its ilk. This allows us queue messages with the relevant endpoints, release whatever locks we might be holding, and then block whilst awaiting the response. This change is made at the cost of being able to retry should sending the message fail, as retrying outside of the lock could easily cause race conditions if other conflicting messages have been sent whilst awaiting a response. I believe this to be the lesser of two evils, as in many cases these RPC calls are to process local components, and so failures are more likely to be deterministic, and timeouts are more likely to be caused by lock contention.
## How was this patch tested?
Existing tests, and manual tests under yarn-client mode.
Author: Angus Gerry <angolon@gmail.com>
Closes#14710 from angolon/SPARK-16533.
## What changes were proposed in this pull request?
With the new History Server the summary page loads the application list via the the REST API, this makes it very slow to impossible to load with large (10K+) application history. This pr fixes this by adding the `spark.history.ui.maxApplications` conf to limit the number of applications the History Server displays. This is accomplished using a new optional `limit` param for the `applications` api. (Note this only applies to what the summary page displays, all the Application UI's are still accessible if the user knows the App ID and goes to the Application UI directly.)
I've also added a new test for the `limit` param in `HistoryServerSuite.scala`
## How was this patch tested?
Manual testing and dev/run-tests
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#14835 from ajbozarth/spark17243.
This patch is using Apache Commons Crypto library to enable shuffle encryption support.
Author: Ferdinand Xu <cheng.a.xu@intel.com>
Author: kellyzly <kellyzly@126.com>
Closes#8880 from winningsix/SPARK-10771.
## What changes were proposed in this pull request?
Clean up unused variables and unused import statements, unnecessary `return` and `toArray`, and some more style improvement, when I walk through the code examples.
## How was this patch tested?
Testet manually on local laptop.
Author: Xin Ren <iamshrek@126.com>
Closes#14836 from keypointt/codeWalkThroughML.
## What changes were proposed in this pull request?
Move Mesos code into a mvn module
## How was this patch tested?
unit tests
manually submitting a client mode and cluster mode job
spark/mesos integration test suite
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#14637 from mgummelt/mesos-module.
Make the config reader transient, and initialize it lazily so that
serialization works with both java and kryo (and hopefully any other
custom serializer).
Added unit test to make sure SparkConf remains serializable and the
reader works with both built-in serializers.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#14813 from vanzin/SPARK-17240.
## What changes were proposed in this pull request?
Based on #12990 by tankkyo
Since the History Server currently loads all application's data it can OOM if too many applications have a significant task count. `spark.ui.trimTasks` (default: false) can be set to true to trim tasks by `spark.ui.retainedTasks` (default: 10000)
(This is a "quick fix" to help those running into the problem until a update of how the history server loads app data can be done)
## How was this patch tested?
Manual testing and dev/run-tests
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#14673 from ajbozarth/spark15083.
## What changes were proposed in this pull request?
This is a straightforward clone of JoshRosen 's original patch. I have follow-up changes to fix block replication for repl-defined classes as well, but those appear to be flaking tests so I'm going to leave that for SPARK-17042
## How was this patch tested?
End-to-end test in ReplSuite (also more tests in DistributedSuite from the original patch).
Author: Eric Liang <ekl@databricks.com>
Closes#14311 from ericl/spark-16550.
JIRA issue link:
https://issues.apache.org/jira/browse/SPARK-16961
Changed one line of Utils.randomizeInPlace to allow elements to stay in place.
Created a unit test that runs a Pearson's chi squared test to determine whether the output diverges significantly from a uniform distribution.
Author: Nick Lavers <nick.lavers@videoamp.com>
Closes#14551 from nicklavers/SPARK-16961-randomizeInPlace.
Both core and sql have slightly different code that does variable substitution
of config values. This change refactors that code and encapsulates the logic
of reading config values and expading variables in a new helper class, which
can be configured so that both core and sql can use it without losing existing
functionality, and allows for easier testing and makes it easier to add more
features in the future.
Tested with existing and new unit tests, and by running spark-shell with
some configs referencing variables and making sure it behaved as expected.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#14468 from vanzin/SPARK-16671.
- Make mesos coarse grained scheduler accept port offers and pre-assign ports
Previous attempt was for fine grained: https://github.com/apache/spark/pull/10808
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Author: Stavros Kontopoulos <stavros.kontopoulos@typesafe.com>
Closes#11157 from skonto/honour_ports_coarse.
## What changes were proposed in this pull request?
Fixed warnings below after scanning through warnings during build:
```
[warn] /home/jenkins/workspace/SparkPullRequestBuilder/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterSchedulerSuite.scala:34: imported `Utils' is permanently hidden by definition of object Utils in package mesos
[warn] import org.apache.spark.scheduler.cluster.mesos.Utils
[warn] ^
```
and
```
[warn] /home/jenkins/workspace/SparkPullRequestBuilder/core/src/test/scala/org/apache/spark/storage/DiskBlockObjectWriterSuite.scala:113: method shuffleBytesWritten in class ShuffleWriteMetrics is deprecated: use bytesWritten instead
[warn] assert(writeMetrics.shuffleBytesWritten === file.length())
[warn] ^
[warn] /home/jenkins/workspace/SparkPullRequestBuilder/core/src/test/scala/org/apache/spark/storage/DiskBlockObjectWriterSuite.scala:119: method shuffleBytesWritten in class ShuffleWriteMetrics is deprecated: use bytesWritten instead
[warn] assert(writeMetrics.shuffleBytesWritten === file.length())
[warn] ^
[warn] /home/jenkins/workspace/SparkPullRequestBuilder/core/src/test/scala/org/apache/spark/storage/DiskBlockObjectWriterSuite.scala:131: method shuffleBytesWritten in class ShuffleWriteMetrics is deprecated: use bytesWritten instead
[warn] assert(writeMetrics.shuffleBytesWritten === file.length())
[warn] ^
[warn] /home/jenkins/workspace/SparkPullRequestBuilder/core/src/test/scala/org/apache/spark/storage/DiskBlockObjectWriterSuite.scala:135: method shuffleBytesWritten in class ShuffleWriteMetrics is deprecated: use bytesWritten instead
[warn] assert(writeMetrics.shuffleBytesWritten === file.length())
[warn] ^
```
## How was this patch tested?
Tested manually on local laptop.
Author: Xin Ren <iamshrek@126.com>
Closes#14609 from keypointt/suiteWarnings.
Before this PR, user have to export environment variable to specify the python of driver & executor which is not so convenient for users. This PR is trying to allow user to specify python through configuration "--pyspark-driver-python" & "--pyspark-executor-python"
Manually test in local & yarn mode for pyspark-shell and pyspark batch mode.
Author: Jeff Zhang <zjffdu@apache.org>
Closes#13146 from zjffdu/SPARK-13081.
## What changes were proposed in this pull request?
Added shutdown hook to DriverRunner to kill the driver process in case the Worker JVM exits suddenly and the `WorkerWatcher` was unable to properly catch this. Did some cleanup to consolidate driver state management and setting of finalized vars within the running thread.
## How was this patch tested?
Added unit tests to verify that final state and exception variables are set accordingly for successfull, failed, and errors in the driver process. Retrofitted existing test to verify killing of mocked process ends with the correct state and stops properly
Manually tested (with deploy-mode=cluster) that the shutdown hook is called by forcibly exiting the `Worker` and various points in the code with the `WorkerWatcher` both disabled and enabled. Also, manually killed the driver through the ui and verified that the `DriverRunner` interrupted, killed the process and exited properly.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#11746 from BryanCutler/DriverRunner-shutdown-hook-SPARK-13602.
## What changes were proposed in this pull request?
remove requirement to set spark.mesos.executor.home when spark.executor.uri is used
## How was this patch tested?
unit tests
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#14552 from mgummelt/fix-spark-home.
When large number of jobs are run concurrently with Spark thrift server, thrift server starts running at high CPU due to GC pressure. Job UI retention causes memory pressure with large jobs. https://issues.apache.org/jira/secure/attachment/12783302/SPARK-12920.profiler_job_progress_listner.png has the profiler snapshot. This PR honors `spark.ui.retainedStages` strictly to reduce memory pressure.
Manual and unit tests
Author: Rajesh Balamohan <rbalamohan@apache.org>
Closes#10846 from rajeshbalamohan/SPARK-12920.
## What changes were proposed in this pull request?
Spark applications running on Mesos throw exception upon exit. For details, refer to https://issues.apache.org/jira/browse/SPARK-16522.
I am not sure if there is any better fix, so wait for review comments.
## How was this patch tested?
Manual test. Observed that the exception is gone upon application exit.
Author: Sun Rui <sunrui2016@gmail.com>
Closes#14175 from sun-rui/SPARK-16522.
## What changes were proposed in this pull request?
Avoid using postfix operation for command execution in SQLQuerySuite where it wasn't whitelisted and audit existing whitelistings removing postfix operators from most places. Some notable places where postfix operation remains is in the XML parsing & time units (seconds, millis, etc.) where it arguably can improve readability.
## How was this patch tested?
Existing tests.
Author: Holden Karau <holden@us.ibm.com>
Closes#14407 from holdenk/SPARK-16779.
## What changes were proposed in this pull request?
This patch fixes a bug in Spark's standalone Master which could cause applications to hang if tasks cause executors to exit with zero exit codes.
As an example of the bug, run
```
sc.parallelize(1 to 1, 1).foreachPartition { _ => System.exit(0) }
```
on a standalone cluster which has a single Spark application. This will cause all executors to die but those executors won't be replaced unless another Spark application or worker joins or leaves the cluster (or if an executor exits with a non-zero exit code). This behavior is caused by a bug in how the Master handles the `ExecutorStateChanged` event: the current implementation calls `schedule()` only if the executor exited with a non-zero exit code, so a task which causes a JVM to unexpectedly exit "cleanly" will skip the `schedule()` call.
This patch addresses this by modifying the `ExecutorStateChanged` to always unconditionally call `schedule()`. This should be safe because it should always be safe to call `schedule()`; adding extra `schedule()` calls can only affect performance and should not introduce correctness bugs.
## How was this patch tested?
I added a regression test in `DistributedSuite`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#14510 from JoshRosen/SPARK-16925.
## 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?
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?
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.
## 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.
## 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?
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?
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?
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?
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?
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?
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?
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?
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?
[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?
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.
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?
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?
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?
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?
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?
`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?
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?
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.
## What changes were proposed in this pull request?
1. The class allocated 4x space than needed as it was using `Int` to store the `Byte` values
2. If CircularBuffer isn't full, currently toString() will print some garbage chars along with the content written as is tries to print the entire array allocated for the buffer. The fix is to keep track of buffer getting full and don't print the tail of the buffer if it isn't full (suggestion by sameeragarwal over https://github.com/apache/spark/pull/12194#discussion_r64495331)
3. Simplified `toString()`
## How was this patch tested?
Added new test case
Author: Tejas Patil <tejasp@fb.com>
Closes#13351 from tejasapatil/circular_buffer.
## What changes were proposed in this pull request?
With this patch, TaskSetManager kills other running attempts when any one of the attempt succeeds for the same task. Also killed tasks will not be considered as failed tasks and they get listed separately in the UI and also shows the task state as KILLED instead of FAILED.
## How was this patch tested?
core\src\test\scala\org\apache\spark\ui\jobs\JobProgressListenerSuite.scala
core\src\test\scala\org\apache\spark\util\JsonProtocolSuite.scala
I have verified this patch manually by enabling spark.speculation as true, when any attempt gets succeeded then other running attempts are getting killed for the same task and other pending tasks are getting assigned in those. And also when any attempt gets killed then they are considered as KILLED tasks and not considered as FAILED tasks. Please find the attached screen shots for the reference.
Ref : https://github.com/apache/spark/pull/11916
Author: Devaraj K <devaraj@apache.org>
Closes#11996 from devaraj-kavali/SPARK-10530.
## What changes were proposed in this pull request?
This is a simple patch that makes package names for Java 8 test suites consistent. I moved everything to test.org.apache.spark to we can test package private APIs properly. Also added "java8" as the package name so we can easily run all the tests related to Java 8.
## How was this patch tested?
This is a test only change.
Author: Reynold Xin <rxin@databricks.com>
Closes#13364 from rxin/SPARK-15633.
## What changes were proposed in this pull request?
Temp directory used to save records is not deleted after program exit in DataFrameExample. Although it called deleteOnExit, it doesn't work as the directory is not empty. Similar things happend in ContextCleanerSuite. Update the code to make sure temp directory is deleted after program exit.
## How was this patch tested?
unit tests and local build.
Author: dding3 <ding.ding@intel.com>
Closes#13328 from dding3/master.
## What changes were proposed in this pull request?
Profiling a Spark job spilling large amount of intermediate data we found that significant portion of time is being spent in DiskObjectWriter.updateBytesWritten function. Looking at the code, we see that the function is being called too frequently to update the number of bytes written to disk. We should reduce the frequency to avoid this.
## How was this patch tested?
Tested by running the job on cluster and saw 20% CPU gain by this change.
Author: Sital Kedia <skedia@fb.com>
Closes#13332 from sitalkedia/DiskObjectWriter.
## What changes were proposed in this pull request?
This patch fixes a few integer overflows in `UnsafeSortDataFormat.copyRange()` and `ShuffleSortDataFormat copyRange()` that seems to be the most likely cause behind a number of `TimSort` contract violation errors seen in Spark 2.0 and Spark 1.6 while sorting large datasets.
## How was this patch tested?
Added a test in `ExternalSorterSuite` that instantiates a large array of the form of [150000000, 150000001, 150000002, ...., 300000000, 0, 1, 2, ..., 149999999] that triggers a `copyRange` in `TimSort.mergeLo` or `TimSort.mergeHi`. Note that the input dataset should contain at least 268.43 million rows with a certain data distribution for an overflow to occur.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#13336 from sameeragarwal/timsort-bug.
This is a basic framework for testing the entire scheduler. The tests this adds aren't very interesting -- the point of this PR is just to setup the framework, to keep the initial change small, but it can be built upon to test more features (eg., speculation, killing tasks, blacklisting, etc.).
Author: Imran Rashid <irashid@cloudera.com>
Closes#8559 from squito/SPARK-10372-scheduler-integs.
## What changes were proposed in this pull request?
Currently a method `submitStage()` for waiting stages is called on every iteration of the event loop in `DAGScheduler` to submit all waiting stages, but most of them are not necessary because they are not related to Stage status.
The case we should try to submit waiting stages is only when their parent stages are successfully completed.
This elimination can improve `DAGScheduler` performance.
## How was this patch tested?
Added some checks and other existing tests, and our projects.
We have a project bottle-necked by `DAGScheduler`, having about 2000 stages.
Before this patch the almost all execution time in `Driver` process was spent to process `submitStage()` of `dag-scheduler-event-loop` thread but after this patch the performance was improved as follows:
| | total execution time | `dag-scheduler-event-loop` thread time | `submitStage()` |
|--------|---------------------:|---------------------------------------:|----------------:|
| Before | 760 sec | 710 sec | 667 sec |
| After | 440 sec | 14 sec | 10 sec |
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#12060 from ueshin/issues/SPARK-14269.
## What changes were proposed in this pull request?
1. Making 'name' field of RDDInfo mutable.
2. In StorageListener: catching the fact that RDD's name was changed and updating it in RDDInfo.
## How was this patch tested?
1. Manual verification - the 'Storage' tab now behaves as expected.
2. The commit also contains a new unit test which verifies this.
Author: Lukasz <lgieron@gmail.com>
Closes#13264 from lgieron/SPARK-9044.
## What changes were proposed in this pull request?
This patch renames various scheduler backends to make them consistent:
- LocalScheduler -> LocalSchedulerBackend
- AppClient -> StandaloneAppClient
- AppClientListener -> StandaloneAppClientListener
- SparkDeploySchedulerBackend -> StandaloneSchedulerBackend
- CoarseMesosSchedulerBackend -> MesosCoarseGrainedSchedulerBackend
- MesosSchedulerBackend -> MesosFineGrainedSchedulerBackend
## How was this patch tested?
Updated test cases to reflect the name change.
Author: Reynold Xin <rxin@databricks.com>
Closes#13288 from rxin/SPARK-15518.
## What changes were proposed in this pull request?
Previously, SPARK-8893 added the constraints on positive number of partitions for repartition/coalesce operations in general. This PR adds one missing part for that and adds explicit two testcases.
**Before**
```scala
scala> sc.parallelize(1 to 5).coalesce(0)
java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive.
...
scala> sc.parallelize(1 to 5).repartition(0).collect()
res1: Array[Int] = Array() // empty
scala> spark.sql("select 1").coalesce(0)
res2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [1: int]
scala> spark.sql("select 1").coalesce(0).collect()
java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive.
scala> spark.sql("select 1").repartition(0)
res3: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [1: int]
scala> spark.sql("select 1").repartition(0).collect()
res4: Array[org.apache.spark.sql.Row] = Array() // empty
```
**After**
```scala
scala> sc.parallelize(1 to 5).coalesce(0)
java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive.
...
scala> sc.parallelize(1 to 5).repartition(0)
java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive.
...
scala> spark.sql("select 1").coalesce(0)
java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive.
...
scala> spark.sql("select 1").repartition(0)
java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive.
...
```
## How was this patch tested?
Pass the Jenkins tests with new testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13282 from dongjoon-hyun/SPARK-15512.
## What changes were proposed in this pull request?
This PR fixes some obsolete comments and assertion in `takeSample` testcase of `RDDSuite.scala`.
## How was this patch tested?
This fixes the testcase only.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13260 from dongjoon-hyun/SPARK-15481.
## What changes were proposed in this pull request?
Currently command `ADD FILE|JAR <filepath | jarpath>` is supported natively in SparkSQL. However, when this command is run, the file/jar is added to the resources that can not be looked up by `LIST FILE(s)|JAR(s)` command because the `LIST` command is passed to Hive command processor in Spark-SQL or simply not supported in Spark-shell. There is no way users can find out what files/jars are added to the spark context.
Refer to [Hive commands](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Cli)
This PR is to support following commands:
`LIST (FILE[s] [filepath ...] | JAR[s] [jarfile ...])`
### For example:
##### LIST FILE(s)
```
scala> spark.sql("add file hdfs://bdavm009.svl.ibm.com:8020/tmp/test.txt")
res1: org.apache.spark.sql.DataFrame = []
scala> spark.sql("add file hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt")
res2: org.apache.spark.sql.DataFrame = []
scala> spark.sql("list file hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt").show(false)
+----------------------------------------------+
|result |
+----------------------------------------------+
|hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt|
+----------------------------------------------+
scala> spark.sql("list files").show(false)
+----------------------------------------------+
|result |
+----------------------------------------------+
|hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt|
|hdfs://bdavm009.svl.ibm.com:8020/tmp/test.txt |
+----------------------------------------------+
```
##### LIST JAR(s)
```
scala> spark.sql("add jar /Users/xinwu/spark/core/src/test/resources/TestUDTF.jar")
res9: org.apache.spark.sql.DataFrame = [result: int]
scala> spark.sql("list jar TestUDTF.jar").show(false)
+---------------------------------------------+
|result |
+---------------------------------------------+
|spark://192.168.1.234:50131/jars/TestUDTF.jar|
+---------------------------------------------+
scala> spark.sql("list jars").show(false)
+---------------------------------------------+
|result |
+---------------------------------------------+
|spark://192.168.1.234:50131/jars/TestUDTF.jar|
+---------------------------------------------+
```
## How was this patch tested?
New test cases are added for Spark-SQL, Spark-Shell and SparkContext API code path.
Author: Xin Wu <xinwu@us.ibm.com>
Author: xin Wu <xinwu@us.ibm.com>
Closes#13212 from xwu0226/list_command.
## What changes were proposed in this pull request?
In general, the Web UI doesn't need to store the Accumulator/AccumulableInfo for every task. It only needs the Accumulator values.
In this PR, it creates new UIData classes to store the necessary fields and make `JobProgressListener` store only these new classes, so that `JobProgressListener` won't store Accumulator/AccumulableInfo and the size of `JobProgressListener` becomes pretty small. I also eliminates `AccumulableInfo` from `SQLListener` so that we don't keep any references for those unused `AccumulableInfo`s.
## How was this patch tested?
I ran two tests reported in JIRA locally:
The first one is:
```
val data = spark.range(0, 10000, 1, 10000)
data.cache().count()
```
The retained size of JobProgressListener decreases from 60.7M to 6.9M.
The second one is:
```
import org.apache.spark.ml.CC
import org.apache.spark.sql.SQLContext
val sqlContext = SQLContext.getOrCreate(sc)
CC.runTest(sqlContext)
```
This test won't cause OOM after applying this patch.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13153 from zsxwing/memory.
## What changes were proposed in this pull request?
A writer lock could be acquired when 1) create a new block 2) remove a block 3) evict a block to disk. 1) and 3) could happen in the same time within the same task, all of them could happen in the same time outside a task. It's OK that when someone try to grab the write block for a block, but the block is acquired by another one that has the same task attempt id.
This PR remove the check.
## How was this patch tested?
Updated existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#13082 from davies/write_lock_conflict.
## What changes were proposed in this pull request?
Remove redundant set master in OutputCommitCoordinatorIntegrationSuite, as we are already setting it in SparkContext below on line 43.
## How was this patch tested?
existing tests
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13168 from techaddict/minor-1.
## What changes were proposed in this pull request?
Since we support forced spilling for Spillable, which only works in OnHeap mode, different from other SQL operators (could be OnHeap or OffHeap), we should considering the mode of consumer before calling trigger forced spilling.
## How was this patch tested?
Add new test.
Author: Davies Liu <davies@databricks.com>
Closes#13151 from davies/fix_mode.
## What changes were proposed in this pull request?
I use Intellj-IDEA to search usage of deprecate SparkContext.accumulator in the whole spark project, and update the code.(except those test code for accumulator method itself)
## How was this patch tested?
Exisiting unit tests
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#13112 from WeichenXu123/update_accuV2_in_mllib.
## What changes were proposed in this pull request?
`DAGScheduler`sometimes generate incorrect stage graph.
Suppose you have the following DAG:
```
[A] <--(s_A)-- [B] <--(s_B)-- [C] <--(s_C)-- [D]
\ /
<-------------
```
Note: [] means an RDD, () means a shuffle dependency.
Here, RDD `B` has a shuffle dependency on RDD `A`, and RDD `C` has shuffle dependency on both `B` and `A`. The shuffle dependency IDs are numbers in the `DAGScheduler`, but to make the example easier to understand, let's call the shuffled data from `A` shuffle dependency ID `s_A` and the shuffled data from `B` shuffle dependency ID `s_B`.
The `getAncestorShuffleDependencies` method in `DAGScheduler` (incorrectly) does not check for duplicates when it's adding ShuffleDependencies to the parents data structure, so for this DAG, when `getAncestorShuffleDependencies` gets called on `C` (previous of the final RDD), `getAncestorShuffleDependencies` will return `s_A`, `s_B`, `s_A` (`s_A` gets added twice: once when the method "visit"s RDD `C`, and once when the method "visit"s RDD `B`). This is problematic because this line of code: https://github.com/apache/spark/blob/8ef3399/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L289 then generates a new shuffle stage for each dependency returned by `getAncestorShuffleDependencies`, resulting in duplicate map stages that compute the map output from RDD `A`.
As a result, `DAGScheduler` generates the following stages and their parents for each shuffle:
| | stage | parents |
|----|----|----|
| s_A | ShuffleMapStage 2 | List() |
| s_B | ShuffleMapStage 1 | List(ShuffleMapStage 0) |
| s_C | ShuffleMapStage 3 | List(ShuffleMapStage 1, ShuffleMapStage 2) |
| - | ResultStage 4 | List(ShuffleMapStage 3) |
The stage for s_A should be `ShuffleMapStage 0`, but the stage for `s_A` is generated twice as `ShuffleMapStage 2` and `ShuffleMapStage 0` is overwritten by `ShuffleMapStage 2`, and the stage `ShuffleMap Stage1` keeps referring the old stage `ShuffleMapStage 0`.
This patch is fixing it.
## How was this patch tested?
I added the sample RDD graph to show the illegal stage graph to `DAGSchedulerSuite`.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#12655 from ueshin/issues/SPARK-13902.
## What changes were proposed in this pull request?
Break copyAndReset into two methods copy and reset instead of just one.
## How was this patch tested?
Existing Tests
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#12936 from techaddict/SPARK-15080.
## What changes were proposed in this pull request?
When we acquire execution memory, we do a lot of things between shrinking the storage memory pool and enlarging the execution memory pool. In particular, we call `memoryStore.evictBlocksToFreeSpace`, which may do a lot of I/O and can throw exceptions. If an exception is thrown, the pool sizes on that executor will be in a bad state.
This patch minimizes the things we do between the two calls to make the resizing more atomic.
## How was this patch tested?
Jenkins.
Author: Andrew Or <andrew@databricks.com>
Closes#13039 from andrewor14/safer-pool.
## What changes were proposed in this pull request?
After SPARK-14669 it seems the sort time metric includes both spill and record insertion time. This makes it not very useful since the metric becomes close to the total execution time of the node.
We should track just the time spent for in-memory sort, as before.
## How was this patch tested?
Verified metric in the UI, also unit test on UnsafeExternalRowSorter.
cc davies
Author: Eric Liang <ekl@databricks.com>
Author: Eric Liang <ekhliang@gmail.com>
Closes#13035 from ericl/fix-metrics.
Sending un-updated accumulators back to driver makes no sense, as merging a zero value accumulator is a no-op. We should only send back updated accumulators, to save network IO.
new test in `TaskContextSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12899 from cloud-fan/acc.
Without this, the code would build an invalid spark-submit command line,
and a more cryptic error would be presented to the user. Also, expose
a constant that allows users to set a dummy resource in cases where
they don't need an actual resource file; for backwards compatibility,
that uses the same "spark-internal" resource that Spark itself uses.
Tested via unit tests, run-example, spark-shell, and running the
thrift server with mixed spark and hive command line arguments.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#12909 from vanzin/SPARK-11249.
## What changes were proposed in this pull request?
Currently PipedRDD internally uses PrintWriter to write data to the stdin of the piped process, which by default uses a BufferedWriter of buffer size 8k. In our experiment, we have seen that 8k buffer size is too small and the job spends significant amount of CPU time in system calls to copy the data. We should have a way to configure the buffer size for the writer.
## How was this patch tested?
Ran PipedRDDSuite tests.
Author: Sital Kedia <skedia@fb.com>
Closes#12309 from sitalkedia/bufferedPipedRDD.
## What changes were proposed in this pull request?
Removed blockTransferService and sparkFilesDir from SparkEnv since they're rarely used and don't need to be in stored in the env. Edited their few usages to accommodate the change.
## How was this patch tested?
ran dev/run-tests locally
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#12970 from ajbozarth/spark10653.
The main issue we are trying to solve is the memory bloat of the Driver when tasks request the map output statuses. This means with a large number of tasks you either need a huge amount of memory on Driver or you have to repartition to smaller number. This makes it really difficult to run over say 50000 tasks.
The main issues that cause the memory bloat are:
1) no flow control on sending the map output status responses. We serialize the map status output and then hand off to netty to send. netty is sending asynchronously and it can't send them fast enough to keep up with incoming requests so we end up with lots of copies of the serialized map output statuses sitting there and this causes huge bloat when you have 10's of thousands of tasks and map output status is in the 10's of MB.
2) When initial reduce tasks are started up, they all request the map output statuses from the Driver. These requests are handled by multiple threads in parallel so even though we check to see if we have a cached version, initially when we don't have a cached version yet, many of initial requests can all end up serializing the exact same map output statuses.
This patch does a couple of things:
- When the map output status size is over a threshold (default 512K) then it uses broadcast to send the map statuses. This means we no longer serialize a large map output status and thus we don't have issues with memory bloat. the messages sizes are now in the 300-400 byte range and the map status output are broadcast. If its under the threadshold it sends it as before, the message contains the DIRECT indicator now.
- synchronize the incoming requests to allow one thread to cache the serialized output and broadcast the map output status that can then be used by everyone else. This ensures we don't create multiple broadcast variables when we don't need to. To ensure this happens I added a second thread pool which the Dispatcher hands the requests to so that those threads can block without blocking the main dispatcher threads (which would cause things like heartbeats and such not to come through)
Note that some of design and code was contributed by mridulm
## How was this patch tested?
Unit tests and a lot of manually testing.
Ran with akka and netty rpc. Ran with both dynamic allocation on and off.
one of the large jobs I used to test this was a join of 15TB of data. it had 200,000 map tasks, and 20,000 reduce tasks. Executors ranged from 200 to 2000. This job ran successfully with 5GB of memory on the driver with these changes. Without these changes I was using 20GB and only had 500 reduce tasks. The job has 50mb of serialized map output statuses and took roughly the same amount of time for the executors to get the map output statuses as before.
Ran a variety of other jobs, from large wordcounts to small ones not using broadcasts.
Author: Thomas Graves <tgraves@staydecay.corp.gq1.yahoo.com>
Closes#12113 from tgravescs/SPARK-1239.
This patch has the new logic from #8512 that uses a parallel collection to compute partitions in UnionRDD. The rest of #8512 added an alternative code path for calculating splits in S3, but that isn't necessary to get the same speedup. The underlying problem wasn't that bulk listing wasn't used, it was that an extra FileStatus was retrieved for each file. The fix was just committed as [HADOOP-12810](https://issues.apache.org/jira/browse/HADOOP-12810). (I think the original commit also used a single prefix to enumerate all paths, but that isn't always helpful and it was removed in later versions so there is no need for SparkS3Utils.)
I tested this using the same table that piapiaozhexiu was using. Calculating splits for a 10-day period took 25 seconds with this change and HADOOP-12810, which is on par with the results from #8512.
Author: Ryan Blue <blue@apache.org>
Author: Cheolsoo Park <cheolsoop@netflix.com>
Closes#11242 from rdblue/SPARK-9926-parallelize-union-rdd.
Similar to https://github.com/apache/spark/pull/8639
This change rejects offers for 120s when reached `spark.cores.max` in coarse-grained mode to mitigate offer starvation. This prevents Mesos to send us offers again and again, starving other frameworks. This is especially problematic when running many small frameworks on the same Mesos cluster, e.g. many small Sparks streaming jobs, and cause the bigger spark jobs to stop receiving offers. By rejecting the offers for a long period of time, they become available to those other frameworks.
Author: Sebastien Rainville <sebastien@hopper.com>
Closes#10924 from sebastienrainville/master.
Remove history server functionality from standalone Master. Previously, the Master process rebuilt a SparkUI once the application was completed which sometimes caused problems, such as OOM, when the application event log is large (see SPARK-6270). Keeping this functionality out of the Master will help to simplify the process and increase stability.
Testing for this change included running core unit tests and manually running an application on a standalone cluster to verify that it completed successfully and that the Master UI functioned correctly. Also added 2 unit tests to verify killing an application and driver from MasterWebUI makes the correct request to the Master.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#10991 from BryanCutler/remove-history-master-SPARK-12299.
## What changes were proposed in this pull request?
We currently have a single suite that is very large, making it difficult to maintain and play with specific primitives. This patch reorganizes the file by creating multiple benchmark suites in a single package.
Most of the changes are straightforward move of code. On top of the code moving, I did:
1. Use SparkSession instead of SQLContext.
2. Turned most benchmark scenarios into a their own test cases, rather than having multiple scenarios in a single test case, which takes forever to run.
## How was this patch tested?
This is a test only change.
Author: Reynold Xin <rxin@databricks.com>
Closes#12891 from rxin/SPARK-15115.
## What changes were proposed in this pull request?
Currently only a list of users can be specified for view and modify acls. This change enables a group of admins/devs/users to be provisioned for viewing and modifying Spark jobs.
**Changes Proposed in the fix**
Three new corresponding config entries have been added where the user can specify the groups to be given access.
```
spark.admin.acls.groups
spark.modify.acls.groups
spark.ui.view.acls.groups
```
New config entries were added because specifying the users and groups explicitly is a better and cleaner way compared to specifying them in the existing config entry using a delimiter.
A generic trait has been introduced to provide the user to group mapping which makes it pluggable to support a variety of mapping protocols - similar to the one used in hadoop. A default unix shell based implementation has been provided.
Custom user to group mapping protocol can be specified and configured by the entry ```spark.user.groups.mapping```
**How the patch was Tested**
We ran different spark jobs setting the config entries in combinations of admin, modify and ui acls. For modify acls we tried killing the job stages from the ui and using yarn commands. For view acls we tried accessing the UI tabs and the logs. Headless accounts were used to launch these jobs and different users tried to modify and view the jobs to ensure that the groups mapping applied correctly.
Additional Unit tests have been added without modifying the existing ones. These test for different ways of setting the acls through configuration and/or API and validate the expected behavior.
Author: Dhruve Ashar <dhruveashar@gmail.com>
Closes#12760 from dhruve/impr/SPARK-4224.
## What changes were proposed in this pull request?
This patch changes our micro-benchmark util to allow setting different iteration numbers for different test cases. For some of our benchmarks, turning off whole-stage codegen can make the runtime 20X slower, making it very difficult to run a large number of times without substantially shortening the input cardinality.
With this change, I set the default num iterations to 2 for whole stage codegen off, and 5 for whole stage codegen on. I also updated some results.
## How was this patch tested?
N/A - this is a test util.
Author: Reynold Xin <rxin@databricks.com>
Closes#12884 from rxin/SPARK-15107.
## What changes were proposed in this pull request?
coalesce doesn't handle UnionRDD with partial locality properly. I had a user who had a UnionRDD that was made up of mapPartitionRDD without preferred locations and a checkpointedRDD with preferred locations (getting from hdfs). It took the driver over 20 minutes to setup the groups and put the partitions into those groups before it even started any tasks. Even perhaps worse is it didn't end up with the number of partitions he was asking for because it didn't put a partition in each of the groups properly.
The changes in this patch get rid of a n^2 while loop that was causing the 20 minutes, it properly distributes the partitions to have at least one per group, and it changes from using the rotation iterator which got the preferred locations many times to get all the preferred locations once up front.
Note that the n^2 while loop that I removed in setupGroups took so long because all of the partitions with preferred locations were already assigned to group, so it basically looped through every single one and wasn't ever able to assign it. At the time I had 960 partitions with preferred locations and 1020 without and did the outer while loop 319 times because that is the # of groups left to create. Note that each of those times through the inner while loop is going off to hdfs to get the block locations, so this is extremely inefficient.
## How was the this patch tested?
Added unit tests for this case and ran existing ones that applied to make sure no regressions.
Also manually tested on the users production job to make sure it fixed their issue. It created the proper number of partitions and now it takes about 6 seconds rather then 20 minutes.
I did also run some basic manual tests with spark-shell doing coalesced to smaller number, same number, and then greater with shuffle.
Author: Thomas Graves <tgraves@prevailsail.corp.gq1.yahoo.com>
Closes#11327 from tgravescs/SPARK-11316.
## What changes were proposed in this pull request?
Added tests for ListAccumulator and LegacyAccumulatorWrapper, test for ListAccumulator is one similar to old Collection Accumulators
## How was this patch tested?
Ran tests locally.
cc rxin
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#12862 from techaddict/SPARK-15082.
## What changes were proposed in this pull request?
Remove AccumulatorV2.localValue and keep only value
## How was this patch tested?
existing tests
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#12865 from techaddict/SPARK-15087.
## What changes were proposed in this pull request?
This patch moves AccumulatorV2 and subclasses into util package.
## How was this patch tested?
Updated relevant tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12863 from rxin/SPARK-15081.
## What changes were proposed in this pull request?
This patch removes AverageAccumulator and adds the ability to compute average to LongAccumulator and DoubleAccumulator. The patch also improves documentation for the two accumulators.
## How was this patch tested?
Added unit tests for this.
Author: Reynold Xin <rxin@databricks.com>
Closes#12858 from rxin/SPARK-15079.
## What changes were proposed in this pull request?
This updates the java-/scala- doc for setLocalProperty to document heritability of localProperties. This also adds tests for that behaviour.
## How was this patch tested?
Tests pass. New tests were added.
Author: Marcin Tustin <marcin.tustin@gmail.com>
Closes#12455 from marcintustin/SPARK-14685.
## What changes were proposed in this pull request?
NewAccumulator isn't the best name if we ever come up with v3 of the API.
## How was this patch tested?
Updated tests to reflect the change.
Author: Reynold Xin <rxin@databricks.com>
Closes#12827 from rxin/SPARK-15049.
## What changes were proposed in this pull request?
This PR proposes to use ConcurrentHashMap in place of HashMap for NewAccumulator.originals
This should result in better performance.
## How was this patch tested?
Existing unit test suite
cloud-fan
Author: tedyu <yuzhihong@gmail.com>
Closes#12776 from tedyu/master.
## What changes were proposed in this pull request?
`AccumulatorContext` is not thread-safe, that's why all of its methods are synchronized. However, there is one exception: the `AccumulatorContext.originals`. `NewAccumulator` use it to check if it's registered, which is wrong as it's not synchronized.
This PR mark `AccumulatorContext.originals` as `private` and now all access to `AccumulatorContext` is synchronized.
## How was this patch tested?
I verified it locally. To be safe, we can let jenkins test it many times to make sure this problem is gone.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12773 from cloud-fan/debug.
https://issues.apache.org/jira/browse/SPARK-14935
In DistributedSuite, the "local-cluster format" test actually launches a bunch of clusters, but this doesn't seem necessary for what should just be a unit test of a regex. We should clean up the code so that this is testable without actually launching a cluster, which should buy us about 20 seconds per build.
Passed unit test on my local machine
Author: Xin Ren <iamshrek@126.com>
Closes#12744 from keypointt/SPARK-14935.
## What changes were proposed in this pull request?
This PR introduces a new accumulator API which is much simpler than before:
1. the type hierarchy is simplified, now we only have an `Accumulator` class
2. Combine `initialValue` and `zeroValue` concepts into just one concept: `zeroValue`
3. there in only one `register` method, the accumulator registration and cleanup registration are combined.
4. the `id`,`name` and `countFailedValues` are combined into an `AccumulatorMetadata`, and is provided during registration.
`SQLMetric` is a good example to show the simplicity of this new API.
What we break:
1. no `setValue` anymore. In the new API, the intermedia type can be different from the result type, it's very hard to implement a general `setValue`
2. accumulator can't be serialized before registered.
Problems need to be addressed in follow-ups:
1. with this new API, `AccumulatorInfo` doesn't make a lot of sense, the partial output is not partial updates, we need to expose the intermediate value.
2. `ExceptionFailure` should not carry the accumulator updates. Why do users care about accumulator updates for failed cases? It looks like we only use this feature to update the internal metrics, how about we sending a heartbeat to update internal metrics after the failure event?
3. the public event `SparkListenerTaskEnd` carries a `TaskMetrics`. Ideally this `TaskMetrics` don't need to carry external accumulators, as the only method of `TaskMetrics` that can access external accumulators is `private[spark]`. However, `SQLListener` use it to retrieve sql metrics.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12612 from cloud-fan/acc.
## What changes were proposed in this pull request?
With the addition of ExternalClusterManager(ECM) interface in PR #11723, any cluster manager can now be integrated with Spark. It was suggested in ExternalClusterManager PR that one of the existing cluster managers should start using the new interface to ensure that the API is correct. Ideally, all the existing cluster managers should eventually use the ECM interface but as a first step yarn will now use the ECM interface. This PR refactors YARN code from SparkContext.createTaskScheduler function into YarnClusterManager that implements ECM interface.
## How was this patch tested?
Since this is refactoring, no new tests has been added. Existing tests have been run. Basic manual testing with YARN was done too.
Author: Hemant Bhanawat <hemant@snappydata.io>
Closes#12641 from hbhanawat/yarnClusterMgr.
## What changes were proposed in this pull request?
Java `sampleByKey` methods should accept `Map` with `java.lang.Double` values
## How was this patch tested?
Existing (updated) Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#12637 from srowen/SPARK-14873.
## What changes were proposed in this pull request?
Implement some `hashCode` and `equals` together in order to enable the scalastyle.
This is a first batch, I will continue to implement them but I wanted to know your thoughts.
Author: Joan <joan@goyeau.com>
Closes#12157 from joan38/SPARK-6429-HashCode-Equals.
## What changes were proposed in this pull request?
Spark currently uses TimSort for all in-memory sorts, including sorts done for shuffle. One low-hanging fruit is to use radix sort when possible (e.g. sorting by integer keys). This PR adds a radix sort implementation to the unsafe sort package and switches shuffles and sorts to use it when possible.
The current implementation does not have special support for null values, so we cannot radix-sort `LongType`. I will address this in a follow-up PR.
## How was this patch tested?
Unit tests, enabling radix sort on existing tests. Microbenchmark results:
```
Running benchmark: radix sort 25000000
Java HotSpot(TM) 64-Bit Server VM 1.8.0_66-b17 on Linux 3.13.0-44-generic
Intel(R) Core(TM) i7-4600U CPU 2.10GHz
radix sort 25000000: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
reference TimSort key prefix array 15546 / 15859 1.6 621.9 1.0X
reference Arrays.sort 2416 / 2446 10.3 96.6 6.4X
radix sort one byte 133 / 137 188.4 5.3 117.2X
radix sort two bytes 255 / 258 98.2 10.2 61.1X
radix sort eight bytes 991 / 997 25.2 39.6 15.7X
radix sort key prefix array 1540 / 1563 16.2 61.6 10.1X
```
I also ran a mix of the supported TPCDS queries and compared TimSort vs RadixSort metrics. The overall benchmark ran ~10% faster with radix sort on. In the breakdown below, the radix-enabled sort phases averaged about 20x faster than TimSort, however sorting is only a small fraction of the overall runtime. About half of the TPCDS queries were able to take advantage of radix sort.
```
TPCDS on master: 2499s real time, 8185s executor
- 1171s in TimSort, avg 267 MB/s
(note the /s accounting is weird here since dataSize counts the record sizes too)
TPCDS with radix enabled: 2294s real time, 7391s executor
- 596s in TimSort, avg 254 MB/s
- 26s in radix sort, avg 4.2 GB/s
```
cc davies rxin
Author: Eric Liang <ekl@databricks.com>
Closes#12490 from ericl/sort-benchmark.
## What changes were proposed in this pull request?
In general, `onDisconnected` is for dealing with unexpected network disconnections. When RpcEnv.shutdown is called, the disconnections are expected so RpcEnv should not fire these events.
This PR moves `dispatcher.stop()` above closing the connections so that when stopping RpcEnv, the endpoints won't receive `onDisconnected` events.
In addition, Outbox should not close the client since it will be reused by others. This PR fixes it as well.
## How was this patch tested?
test("SPARK-14699: RpcEnv.shutdown should not fire onDisconnected events")
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#12481 from zsxwing/SPARK-14699.
## What changes were proposed in this pull request?
In #9241 It implemented a mechanism to call spill() on those SQL operators that support spilling if there is not enough memory for execution.
But ExternalSorter and AppendOnlyMap in Spark core are not worked. So this PR make them benefit from #9241. Now when there is not enough memory for execution, it can get memory by spilling ExternalSorter and AppendOnlyMap in Spark core.
## How was this patch tested?
add two unit tests for it.
Author: Lianhui Wang <lianhuiwang09@gmail.com>
Closes#10024 from lianhuiwang/SPARK-4452-2.
## What changes were proposed in this pull request?
the `Accumulable.internal` flag is only used to avoid registering internal accumulators for 2 certain cases:
1. `TaskMetrics.createTempShuffleReadMetrics`: the accumulators in the temp shuffle read metrics should not be registered.
2. `TaskMetrics.fromAccumulatorUpdates`: the created task metrics is only used to post event, accumulators inside it should not be registered.
For 1, we can create a `TempShuffleReadMetrics` that don't create accumulators, just keep the data and merge it at last.
For 2, we can un-register these accumulators immediately.
TODO: remove `internal` flag in `AccumulableInfo` with followup PR
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12525 from cloud-fan/acc.
## What changes were proposed in this pull request?
The DAG visualization can cause an OOM when generating the DOT file.
This happens because clusters are not correctly deduped by a contains
check because they use the default equals implementation. This adds a
working equals implementation.
## How was this patch tested?
This adds a test suite that checks the new equals implementation.
Author: Ryan Blue <blue@apache.org>
Closes#12437 from rdblue/SPARK-14679-fix-ui-oom.
## What changes were proposed in this pull request?
Before this PR, we create accumulators at driver side(and register them) and send them to executor side, then we create `TaskMetrics` with these accumulators at executor side.
After this PR, we will create `TaskMetrics` at driver side and send it to executor side, so that we can create accumulators inside `TaskMetrics` directly, which is cleaner.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12472 from cloud-fan/acc.
## What changes were proposed in this pull request?
This PR adds support for specifying an optional custom coalescer to the `coalesce()` method. Currently I have only added this feature to the `RDD` interface, and once we sort out the details we can proceed with adding this feature to the other APIs (`Dataset` etc.)
## How was this patch tested?
Added a unit test for this functionality.
/cc rxin (per our discussion on the mailing list)
Author: Nezih Yigitbasi <nyigitbasi@netflix.com>
Closes#11865 from nezihyigitbasi/custom_coalesce_policy.
When `Await.result` throws an exception which originated from a different thread, the resulting stacktrace doesn't include the path leading to the `Await.result` call itself, making it difficult to identify the impact of these exceptions. For example, I've seen cases where broadcast cleaning errors propagate to the main thread and crash it but the resulting stacktrace doesn't include any of the main thread's code, making it difficult to pinpoint which exception crashed that thread.
This patch addresses this issue by explicitly catching, wrapping, and re-throwing exceptions that are thrown by `Await.result`.
I tested this manually using 16b31c8251, a patch which reproduces an issue where an RPC exception which occurs while unpersisting RDDs manages to crash the main thread without any useful stacktrace, and verified that informative, full stacktraces were generated after applying the fix in this PR.
/cc rxin nongli yhuai anabranch
Author: Josh Rosen <joshrosen@databricks.com>
Closes#12433 from JoshRosen/wrap-and-rethrow-await-exceptions.
## What changes were proposed in this pull request?
The sort shuffle manager has been the default since Spark 1.2. It is time to remove the old hash shuffle manager.
## How was this patch tested?
Removed some tests related to the old manager.
Author: Reynold Xin <rxin@databricks.com>
Closes#12423 from rxin/SPARK-14667.
## What changes were proposed in this pull request?
This PR is a follow up for https://github.com/apache/spark/pull/12417, now we always track input/output/shuffle metrics in spark JSON protocol and status API.
Most of the line changes are because of re-generating the gold answer for `HistoryServerSuite`, and we add a lot of 0 values for read/write metrics.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12462 from cloud-fan/follow.
## What changes were proposed in this pull request?
When there are multiple tests running, "NettyBlockTransferServiceSuite.can bind to a specific port twice and the second increments" may fail.
E.g., assume there are 2 tests running. Here are the execution order to reproduce the test failure.
| Execution Order | Test 1 | Test 2 |
| ------------- | ------------- | ------------- |
| 1 | service0 binds to 17634 | |
| 2 | | service0 binds to 17635 (17634 is occupied) |
| 3 | service1 binds to 17636 | |
| 4 | pass test | |
| 5 | service0.close (release 17634) | |
| 6 | | service1 binds to 17634 |
| 7 | | `service1.port should be (service0.port + 1)` fails (17634 != 17635 + 1) |
Here is an example in Jenkins: https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test/job/spark-master-test-maven-hadoop-2.2/786/testReport/junit/org.apache.spark.network.netty/NettyBlockTransferServiceSuite/can_bind_to_a_specific_port_twice_and_the_second_increments/
This PR makes two changes:
- Use a random port between 17634 and 27634 to reduce the possibility of port conflicts.
- Make `service1` use `service0.port` to bind to avoid the above race condition.
## How was this patch tested?
Jenkins unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#12477 from zsxwing/SPARK-14713.
## What changes were proposed in this pull request?
This commit adds support for pluggable cluster manager. And also allows a cluster manager to clean up tasks without taking the parent process down.
To plug a new external cluster manager, ExternalClusterManager trait should be implemented. It returns task scheduler and backend scheduler that will be used by SparkContext to schedule tasks. An external cluster manager is registered using the java.util.ServiceLoader mechanism (This mechanism is also being used to register data sources like parquet, json, jdbc etc.). This allows auto-loading implementations of ExternalClusterManager interface.
Currently, when a driver fails, executors exit using system.exit. This does not bode well for cluster managers that would like to reuse the parent process of an executor. Hence,
1. Moving system.exit to a function that can be overriden in subclasses of CoarseGrainedExecutorBackend.
2. Added functionality of killing all the running tasks in an executor.
## How was this patch tested?
ExternalClusterManagerSuite.scala was added to test this patch.
Author: Hemant Bhanawat <hemant@snappydata.io>
Closes#11723 from hbhanawat/pluggableScheduler.
## What changes were proposed in this pull request?
Part of the reason why TaskMetrics and its callers are complicated are due to the optional metrics we collect, including input, output, shuffle read, and shuffle write. I think we can always track them and just assign 0 as the initial values. It is usually very obvious whether a task is supposed to read any data or not. By always tracking them, we can remove a lot of map, foreach, flatMap, getOrElse(0L) calls throughout Spark.
This patch also changes a few behaviors.
1. Removed the distinction of data read/write methods (e.g. Hadoop, Memory, Network, etc).
2. Accumulate all data reads and writes, rather than only the first method. (Fixes SPARK-5225)
## How was this patch tested?
existing tests.
This is bases on https://github.com/apache/spark/pull/12388, with more test fixes.
Author: Reynold Xin <rxin@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12417 from cloud-fan/metrics-refactor.
## What changes were proposed in this pull request?
This patch removes some of the deprecated APIs in TaskMetrics. This is part of my bigger effort to simplify accumulators and task metrics.
## How was this patch tested?
N/A - only removals
Author: Reynold Xin <rxin@databricks.com>
Closes#12375 from rxin/SPARK-14617.
## What changes were proposed in this pull request?
When there are multiple attempts for a stage, we currently only reset internal accumulator values if all the tasks are resubmitted. It would make more sense to reset the accumulator values for each stage attempt. This will allow us to eventually get rid of the internal flag in the Accumulator class. This is part of my bigger effort to simplify accumulators and task metrics.
## How was this patch tested?
Covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12378 from rxin/SPARK-14619.
## What changes were proposed in this pull request?
I was trying to understand the accumulator and metrics update source code and these two classes don't really need to be case classes. It would also be more consistent with other UI classes if they are not case classes. This is part of my bigger effort to simplify accumulators and task metrics.
## How was this patch tested?
This is a straightforward refactoring without behavior change.
Author: Reynold Xin <rxin@databricks.com>
Closes#12386 from rxin/SPARK-14625.
## What changes were proposed in this pull request?
According to the [Spark Code Style Guide](https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide) and [Scala Style Guide](http://docs.scala-lang.org/style/control-structures.html#curlybraces), we had better enforce the following rule.
```
case: Always omit braces in case clauses.
```
This PR makes a new ScalaStyle rule, 'OmitBracesInCase', and enforces it to the code.
## How was this patch tested?
Pass the Jenkins tests (including Scala style checking)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12280 from dongjoon-hyun/SPARK-14508.
## What changes were proposed in this pull request?
This adds a new API call `TaskContext.getLocalProperty` for getting properties set in the driver from executors. These local properties are automatically propagated from the driver to executors. For streaming, the context for streaming tasks will be the initial driver context when ssc.start() is called.
## How was this patch tested?
Unit tests.
cc JoshRosen
Author: Eric Liang <ekl@databricks.com>
Closes#12248 from ericl/sc-2813.
## What changes were proposed in this pull request?
When deciding whether a CommitDeniedException caused a task to fail, consider the root cause of the Exception.
## How was this patch tested?
Added a test suite for the component that extracts the root cause of the error.
Made a distribution after cherry-picking this commit to branch-1.6 and used to run our Spark application that would quite often fail due to the CommitDeniedException.
Author: Jason Moore <jasonmoore2k@outlook.com>
Closes#12228 from jasonmoore2k/SPARK-14357.
## What changes were proposed in this pull request?
Here is why SPARK-14437 happens:
BlockManagerId is created using NettyBlockTransferService.hostName which comes from `customHostname`. And `Executor` will set `customHostname` to the hostname which is detected by the driver. However, the driver may not be able to detect the correct address in some complicated network (Netty's Channel.remoteAddress doesn't always return a connectable address). In such case, `BlockManagerId` will be created using a wrong hostname.
To fix this issue, this PR uses `hostname` provided by `SparkEnv.create` to create `NettyBlockTransferService` and set `NettyBlockTransferService.hostname` to this one directly. A bonus of this approach is NettyBlockTransferService won't bound to `0.0.0.0` which is much safer.
## How was this patch tested?
Manually checked the bound address using local-cluster.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#12240 from zsxwing/SPARK-14437.
Currently all `SparkFirehoseListener` implementations are broken since we expect listeners to extend `SparkListener`, while the fire hose only extends `SparkListenerInterface`. This changes the addListener function and the config based injection to use the interface instead.
The existing tests in SparkListenerSuite are improved such that they would have caught this.
Follow-up to #12142
Author: Michael Armbrust <michael@databricks.com>
Closes#12227 from marmbrus/fixListener.
## What changes were proposed in this pull request?
`OutputCommitCoordinator` was introduced to deal with concurrent task attempts racing to write output, leading to data loss or corruption. For more detail, read the [JIRA description](https://issues.apache.org/jira/browse/SPARK-14468).
Before: `OutputCommitCoordinator` is enabled only if speculation is enabled.
After: `OutputCommitCoordinator` is always enabled.
Users may still disable this through `spark.hadoop.outputCommitCoordination.enabled`, but they really shouldn't...
## How was this patch tested?
`OutputCommitCoordinator*Suite`
Author: Andrew Or <andrew@databricks.com>
Closes#12244 from andrewor14/always-occ.
The current package name uses a dash, which is a little weird but seemed
to work. That is, until a new test tried to mock a class that references
one of those shaded types, and then things started failing.
Most changes are just noise to fix the logging configs.
For reference, SPARK-8815 also raised this issue, although at the time it
did not cause any issues in Spark, so it was not addressed.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#11941 from vanzin/SPARK-14134.
## What changes were proposed in this pull request?
Send `RegisterExecutorResponse` using `executorRef` in order to make sure RegisterExecutorResponse and LaunchTask are both sent using the same channel. Then RegisterExecutorResponse will always arrive before LaunchTask
## How was this patch tested?
Existing unit tests
Closes#12078
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#12211 from zsxwing/SPARK-13112.
## What changes were proposed in this pull request?
As mentioned in the ticket this was because one get path in the refactored `BlockManager` did not check for remote storage.
## How was this patch tested?
Unit test, also verified manually with reproduction in the ticket.
cc JoshRosen
Author: Eric Liang <ekl@databricks.com>
Closes#12193 from ericl/spark-14252.
Because SQL keeps track of all known configs, some customization was
needed in SQLConf to allow that, since the core API does not have that
feature.
Tested via existing (and slightly updated) unit tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#11570 from vanzin/SPARK-529-sql.
This change modifies the "assembly/" module to just copy needed
dependencies to its build directory, and modifies the packaging
script to pick those up (and remove duplicate jars packages in the
examples module).
I also made some minor adjustments to dependencies to remove some
test jars from the final packaging, and remove jars that conflict with each
other when packaged separately (e.g. servlet api).
Also note that this change restores guava in applications' classpaths, even
though it's still shaded inside Spark. This is now needed for the Hadoop
libraries that are packaged with Spark, which now are not processed by
the shade plugin.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#11796 from vanzin/SPARK-13579.
## What changes were proposed in this pull request?
This PR contains the following 5 types of maintenance fix over 59 files (+94 lines, -93 lines).
- Fix typos(exception/log strings, testcase name, comments) in 44 lines.
- Fix lint-java errors (MaxLineLength) in 6 lines. (New codes after SPARK-14011)
- Use diamond operators in 40 lines. (New codes after SPARK-13702)
- Fix redundant semicolon in 5 lines.
- Rename class `InferSchemaSuite` to `CSVInferSchemaSuite` in CSVInferSchemaSuite.scala.
## How was this patch tested?
Manual and pass the Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12139 from dongjoon-hyun/SPARK-14355.
## What changes were proposed in this pull request?
This special cases 0 and 1 counts to avoid passing 0 degrees of freedom.
## How was this patch tested?
Tests run successfully. New test added.
## Note:
This recreates #11982 which was closed to due to non-updated diff. rxin srowen Commented there.
This also adds tests, reworks the code to perform the special casing (based on srowen's comments), and adds equality machinery for BoundedDouble, as well as changing how it is transformed to string.
Author: Marcin Tustin <mtustin@handybook.com>
Author: Marcin Tustin <mtustin@handy.com>
Closes#12016 from mtustin-handy/SPARK-14163.
## What changes were proposed in this pull request?
Straggler references to Tachyon were removed:
- for docs, `tachyon` has been generalized as `off-heap memory`;
- for Mesos test suits, the key-value `tachyon:true`/`tachyon:false` has been changed to `os:centos`/`os:ubuntu`, since `os` is an example constrain used by the [Mesos official docs](http://mesos.apache.org/documentation/attributes-resources/).
## How was this patch tested?
Existing test suites.
Author: Liwei Lin <lwlin7@gmail.com>
Closes#12129 from lw-lin/tachyon-cleanup.
## What changes were proposed in this pull request?
This PR aims to fix all Scala-Style multiline comments into Java-Style multiline comments in Scala codes.
(All comment-only changes over 77 files: +786 lines, −747 lines)
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12130 from dongjoon-hyun/use_multiine_javadoc_comments.
## What changes were proposed in this pull request?
Adding long values for each Date in the ApplicationAttemptInfo API for easier use in code
## How was the this patch tested?
Tested with dev/run-tests
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#11326 from ajbozarth/spark13241.
This patch adds support for caching blocks in the executor processes using direct / off-heap memory.
## User-facing changes
**Updated semantics of `OFF_HEAP` storage level**: In Spark 1.x, the `OFF_HEAP` storage level indicated that an RDD should be cached in Tachyon. Spark 2.x removed the external block store API that Tachyon caching was based on (see #10752 / SPARK-12667), so `OFF_HEAP` became an alias for `MEMORY_ONLY_SER`. As of this patch, `OFF_HEAP` means "serialized and cached in off-heap memory or on disk". Via the `StorageLevel` constructor, `useOffHeap` can be set if `serialized == true` and can be used to construct custom storage levels which support replication.
**Storage UI reporting**: the storage UI will now report whether in-memory blocks are stored on- or off-heap.
**Only supported by UnifiedMemoryManager**: for simplicity, this feature is only supported when the default UnifiedMemoryManager is used; applications which use the legacy memory manager (`spark.memory.useLegacyMode=true`) are not currently able to allocate off-heap storage memory, so using off-heap caching will fail with an error when legacy memory management is enabled. Given that we plan to eventually remove the legacy memory manager, this is not a significant restriction.
**Memory management policies:** the policies for dividing available memory between execution and storage are the same for both on- and off-heap memory. For off-heap memory, the total amount of memory available for use by Spark is controlled by `spark.memory.offHeap.size`, which is an absolute size. Off-heap storage memory obeys `spark.memory.storageFraction` in order to control the amount of unevictable storage memory. For example, if `spark.memory.offHeap.size` is 1 gigabyte and Spark uses the default `storageFraction` of 0.5, then up to 500 megabytes of off-heap cached blocks will be protected from eviction due to execution memory pressure. If necessary, we can split `spark.memory.storageFraction` into separate on- and off-heap configurations, but this doesn't seem necessary now and can be done later without any breaking changes.
**Use of off-heap memory does not imply use of off-heap execution (or vice-versa)**: for now, the settings controlling the use of off-heap execution memory (`spark.memory.offHeap.enabled`) and off-heap caching are completely independent, so Spark SQL can be configured to use off-heap memory for execution while continuing to cache blocks on-heap. If desired, we can change this in a followup patch so that `spark.memory.offHeap.enabled` affect the default storage level for cached SQL tables.
## Internal changes
- Rename `ByteArrayChunkOutputStream` to `ChunkedByteBufferOutputStream`
- It now returns a `ChunkedByteBuffer` instead of an array of byte arrays.
- Its constructor now accept an `allocator` function which is called to allocate `ByteBuffer`s. This allows us to control whether it allocates regular ByteBuffers or off-heap DirectByteBuffers.
- Because block serialization is now performed during the unroll process, a `ChunkedByteBufferOutputStream` which is configured with a `DirectByteBuffer` allocator will use off-heap memory for both unroll and storage memory.
- The `MemoryStore`'s MemoryEntries now tracks whether blocks are stored on- or off-heap.
- `evictBlocksToFreeSpace()` now accepts a `MemoryMode` parameter so that we don't try to evict off-heap blocks in response to on-heap memory pressure (or vice-versa).
- Make sure that off-heap buffers are properly de-allocated during MemoryStore eviction.
- The JVM limits the total size of allocated direct byte buffers using the `-XX:MaxDirectMemorySize` flag and the default tends to be fairly low (< 512 megabytes in some JVMs). To work around this limitation, this patch adds a custom DirectByteBuffer allocator which ignores this memory limit.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11805 from JoshRosen/off-heap-caching.
## What changes were proposed in this pull request?
Currently in Spark on YARN, configurations can be passed through SparkConf, env and command arguments, some parts are duplicated, like client argument and SparkConf. So here propose to simplify the command arguments.
## How was this patch tested?
This patch is tested manually with unit test.
CC vanzin tgravescs , please help to suggest this proposal. The original purpose of this JIRA is to remove `ClientArguments`, through refactoring some arguments like `--class`, `--arg` are not so easy to replace, so here I remove the most part of command line arguments, only keep the minimal set.
Author: jerryshao <sshao@hortonworks.com>
Closes#11603 from jerryshao/SPARK-12343.
## What changes were proposed in this pull request?
This PR try to use `incUpdatedBlockStatuses ` to update the `updatedBlockStatuses ` when removing blocks, making sure `BlockManager` correctly updates `updatedBlockStatuses`
## How was this patch tested?
test("updated block statuses") in BlockManagerSuite.scala
Author: jeanlyn <jeanlyn92@gmail.com>
Closes#12091 from jeanlyn/updateBlock.
## What changes were proposed in this pull request?
We have a streaming job using `FlumePollInputStream` always driver OOM after few days, here is some driver heap dump before OOM
```
num #instances #bytes class name
----------------------------------------------
1: 13845916 553836640 org.apache.spark.storage.BlockStatus
2: 14020324 336487776 org.apache.spark.storage.StreamBlockId
3: 13883881 333213144 scala.collection.mutable.DefaultEntry
4: 8907 89043952 [Lscala.collection.mutable.HashEntry;
5: 62360 65107352 [B
6: 163368 24453904 [Ljava.lang.Object;
7: 293651 20342664 [C
...
```
`BlockStatus` and `StreamBlockId` keep on growing, and the driver OOM in the end.
After investigated, i found the `executorIdToStorageStatus` in `StorageStatusListener` seems never remove the blocks from `StorageStatus`.
In order to fix the issue, i try to use `onBlockUpdated` replace `onTaskEnd ` , so we can update the block informations(add blocks, drop the block from memory to disk and delete the blocks) in time.
## How was this patch tested?
Existing unit tests and manual tests
Author: jeanlyn <jeanlyn92@gmail.com>
Closes#11779 from jeanlyn/fix_driver_oom.
## What changes were proposed in this pull request?
Extract the workaround for HADOOP-10622 introduced by #11940 into UninterruptibleThread so that we can test and reuse it.
## How was this patch tested?
Unit tests
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11971 from zsxwing/uninterrupt.
## What changes were proposed in this pull request?
Currently, for the key that can not fit within a long, we build a hash map for UnsafeHashedRelation, it's converted to BytesToBytesMap after serialization and deserialization. We should build a BytesToBytesMap directly to have better memory efficiency.
In order to do that, BytesToBytesMap should support multiple (K,V) pair with the same K, Location.putNewKey() is renamed to Location.append(), which could append multiple values for the same key (same Location). `Location.newValue()` is added to find the next value for the same key.
## How was this patch tested?
Existing tests. Added benchmark for broadcast hash join with duplicated keys.
Author: Davies Liu <davies@databricks.com>
Closes#11870 from davies/map2.
JIRA: https://issues.apache.org/jira/browse/SPARK-13742
## What changes were proposed in this pull request?
`RandomSampler.sample` currently accepts iterator as input and output another iterator. This makes it inappropriate to use in wholestage codegen of `Sampler` operator #11517. This change is to add non-iterator interface to `RandomSampler`.
This change adds a new method `def sample(): Int` to the trait `RandomSampler`. As we don't need to know the actual values of the sampling items, so this new method takes no arguments.
This method will decide whether to sample the next item or not. It returns how many times the next item will be sampled.
For `BernoulliSampler` and `BernoulliCellSampler`, the returned sampling times can only be 0 or 1. It simply means whether to sample the next item or not.
For `PoissonSampler`, the returned value can be more than 1, meaning the next item will be sampled multiple times.
## How was this patch tested?
Tests are added into `RandomSamplerSuite`.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11578 from viirya/random-sampler-no-iterator.
This patch extends Spark's `UnifiedMemoryManager` to add bookkeeping support for off-heap storage memory, an requirement for enabling off-heap caching (which will be done by #11805). The `MemoryManager`'s `storageMemoryPool` has been split into separate on- and off-heap pools and the storage and unroll memory allocation methods have been updated to accept a `memoryMode` parameter to specify whether allocations should be performed on- or off-heap.
In order to reduce the testing surface, the `StaticMemoryManager` does not support off-heap caching (we plan to eventually remove the `StaticMemoryManager`, so this isn't a significant limitation).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11942 from JoshRosen/off-heap-storage-memory-bookkeeping.
When a block is persisted in the MemoryStore at a serialized storage level, the current MemoryStore.putIterator() code will unroll the entire iterator as Java objects in memory, then will turn around and serialize an iterator obtained from the unrolled array. This is inefficient and doubles our peak memory requirements.
Instead, I think that we should incrementally serialize blocks while unrolling them.
A downside to incremental serialization is the fact that we will need to deserialize the partially-unrolled data in case there is not enough space to unroll the block and the block cannot be dropped to disk. However, I'm hoping that the memory efficiency improvements will outweigh any performance losses as a result of extra serialization in that hopefully-rare case.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11791 from JoshRosen/serialize-incrementally.
## What changes were proposed in this pull request?
A fix for local metrics tests that can fail on fast machines.
This is probably what is suggested here #3380 by aarondav?
## How was this patch tested?
CI Tests
Cheers
Author: Joan <joan@goyeau.com>
Closes#11747 from joan38/SPARK-2208-Local-metrics-tests.
## What changes were proposed in this pull request?
In case of failure in subprocess launched in PipedRDD, the failure exception reads “Subprocess exited with status XXX”. Debugging this is not easy for users especially if there are multiple pipe() operations in the Spark application.
Changes done:
- Changed the exception message when non-zero exit code is seen
- If the reader and writer threads see exception, simply logging the command ran. The current model is to propagate the exception "as is" so that upstream Spark logic will take the right action based on what the exception was (eg. for fetch failure, it needs to retry; but for some fatal exception, it will decide to fail the stage / job). So wrapping the exception with a generic exception will not work. Altering the exception message will keep that guarantee but that is ugly (plus not all exceptions might have a constructor for a string message)
## How was this patch tested?
- Added a new test case
- Ran all existing tests for PipedRDD
Author: Tejas Patil <tejasp@fb.com>
Closes#11927 from tejasapatil/SPARK-14110-piperdd-failure.
This patch refactors the `MemoryStore` so that it can be tested without needing to construct / mock an entire `BlockManager`.
- The block manager's serialization- and compression-related methods have been moved from `BlockManager` to `SerializerManager`.
- `BlockInfoManager `is now passed directly to classes that need it, rather than being passed via the `BlockManager`.
- The `MemoryStore` now calls `dropFromMemory` via a new `BlockEvictionHandler` interface rather than directly calling the `BlockManager`. This change helps to enforce a narrow interface between the `MemoryStore` and `BlockManager` functionality and makes this interface easier to mock in tests.
- Several of the block unrolling tests have been moved from `BlockManagerSuite` into a new `MemoryStoreSuite`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11899 from JoshRosen/reduce-memorystore-blockmanager-coupling.
Building on the `SerializerManager` introduced in SPARK-13926/ #11755, this patch Spark modifies Spark's BlockManager to use RDD's ClassTags in order to select the best serializer to use when caching RDD blocks.
When storing a local block, the BlockManager `put()` methods use implicits to record ClassTags and stores those tags in the blocks' BlockInfo records. When reading a local block, the stored ClassTag is used to pick the appropriate serializer. When a block is stored with replication, the class tag is written into the block transfer metadata and will also be stored in the remote BlockManager.
There are two or three places where we don't properly pass ClassTags, including TorrentBroadcast and BlockRDD. I think this happens to work because the missing ClassTag always happens to be `ClassTag.Any`, but it might be worth looking more carefully at those places to see whether we should be more explicit.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11801 from JoshRosen/pick-best-serializer-for-caching.
## What changes were proposed in this pull request?
[Spark Coding Style Guide](https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide) has 100-character limit on lines, but it's disabled for Java since 11/09/15. This PR enables **LineLength** checkstyle again. To help that, this also introduces **RedundantImport** and **RedundantModifier**, too. The following is the diff on `checkstyle.xml`.
```xml
- <!-- TODO: 11/09/15 disabled - the lengths are currently > 100 in many places -->
- <!--
<module name="LineLength">
<property name="max" value="100"/>
<property name="ignorePattern" value="^package.*|^import.*|a href|href|http://|https://|ftp://"/>
</module>
- -->
<module name="NoLineWrap"/>
<module name="EmptyBlock">
<property name="option" value="TEXT"/>
-167,5 +164,7
</module>
<module name="CommentsIndentation"/>
<module name="UnusedImports"/>
+ <module name="RedundantImport"/>
+ <module name="RedundantModifier"/>
```
## How was this patch tested?
Currently, `lint-java` is disabled in Jenkins. It needs a manual test.
After passing the Jenkins tests, `dev/lint-java` should passes locally.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11831 from dongjoon-hyun/SPARK-14011.
This patch modifies the BlockManager, MemoryStore, and several other storage components so that serialized cached blocks are stored as multiple small chunks rather than as a single contiguous ByteBuffer.
This change will help to improve the efficiency of memory allocation and the accuracy of memory accounting when serializing blocks. Our current serialization code uses a ByteBufferOutputStream, which doubles and re-allocates its backing byte array; this increases the peak memory requirements during serialization (since we need to hold extra memory while expanding the array). In addition, we currently don't account for the extra wasted space at the end of the ByteBuffer's backing array, so a 129 megabyte serialized block may actually consume 256 megabytes of memory. After switching to storing blocks in multiple chunks, we'll be able to efficiently trim the backing buffers so that no space is wasted.
This change is also a prerequisite to being able to cache blocks which are larger than 2GB (although full support for that depends on several other changes which have not bee implemented yet).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11748 from JoshRosen/chunked-block-serialization.
## What changes were proposed in this pull request?
Logging was made private in Spark 2.0. If we move it, then users would be able to create a Logging trait themselves to avoid changing their own code.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11764 from cloud-fan/logger.
Because ClassTags are available when constructing ShuffledRDD we can use them to automatically use Kryo for shuffle serialization when the RDD's types are known to be compatible with Kryo.
This patch introduces `SerializerManager`, a component which picks the "best" serializer for a shuffle given the elements' ClassTags. It will automatically pick a Kryo serializer for ShuffledRDDs whose key, value, and/or combiner types are primitives, arrays of primitives, or strings. In the future we can use this class as a narrow extension point to integrate specialized serializers for other types, such as ByteBuffers.
In a planned followup patch, I will extend the BlockManager APIs so that we're able to use similar automatic serializer selection when caching RDDs (this is a little trickier because the ClassTags need to be threaded through many more places).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11755 from JoshRosen/automatically-pick-best-serializer.
## What changes were proposed in this pull request?
In SparkContext, throw Illegalargumentexception when trying to broadcast rdd directly, instead of logging the warning.
## How was this patch tested?
mvn clean install
Add UT in BroadcastSuite
Author: Wesley Tang <tangmingjun@mininglamp.com>
Closes#11735 from breakdawn/master.
## What changes were proposed in this pull request?
PipedRDD creates a child thread to read output of the parent stage and feed it to the pipe process. Used a variable to save the exception thrown in the child thread and then propagating the exception in the main thread if the variable was set.
## How was this patch tested?
- Added a unit test
- Ran all the existing tests in PipedRDDSuite and they all pass with the change
- Tested the patch with a real pipe() job, bounced the executor node which ran the parent stage to simulate a fetch failure and observed that the parent stage was re-ran.
Author: Tejas Patil <tejasp@fb.com>
Closes#11628 from tejasapatil/pipe_rdd.
## What changes were proposed in this pull request?
Follow up to https://github.com/apache/spark/pull/11657
- Also update `String.getBytes("UTF-8")` to use `StandardCharsets.UTF_8`
- And fix one last new Coverity warning that turned up (use of unguarded `wait()` replaced by simpler/more robust `java.util.concurrent` classes in tests)
- And while we're here cleaning up Coverity warnings, just fix about 15 more build warnings
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#11725 from srowen/SPARK-13823.2.
This patch refactors the MemoryStore to remove the concept of `pendingUnrollMemory`. It also fixes fixes SPARK-6157: "Unrolling with MEMORY_AND_DISK should always release memory".
Key changes:
- Inline `MemoryStore.tryToPut` at its three call sites in the `MemoryStore`.
- Inline `Memory.unrollSafely` at its only call site (in `MemoryStore.putIterator`).
- Inline `MemoryManager.acquireStorageMemory` at its call sites.
- Simplify the code as a result of this inlining (some parameters have fixed values after inlining, so lots of branches can be removed).
- Remove the `pendingUnrollMemory` map by returning the amount of unrollMemory allocated when returning an iterator after a failed `putIterator` call.
- Change `putIterator` to return an instance of `PartiallyUnrolledIterator`, a special iterator subclass which will automatically free the unroll memory of its partially-unrolled elements when the iterator is consumed. To handle cases where the iterator is not consumed (e.g. when a MEMORY_ONLY put fails), `PartiallyUnrolledIterator` exposes a `close()` method which may be called to discard the unrolled values and free their memory.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11613 from JoshRosen/cleanup-unroll-memory.
I am using dynamic container allocation and speculation and am seeing issues with the active task accounting. The Executor UI still shows active tasks on the an executor but the job/stage is all completed. I think its also affecting the dynamic allocation being able to release containers because it thinks there are still tasks.
There are multiple issues with this:
- If the task end for tasks (in this case probably because of speculation) comes in after the stage is finished, then the DAGScheduler.handleTaskCompletion will skip the task completion event
Author: Thomas Graves <tgraves@prevailsail.corp.gq1.yahoo.com>
Author: Thomas Graves <tgraves@staydecay.corp.gq1.yahoo.com>
Author: Tom Graves <tgraves@yahoo-inc.com>
Closes#10951 from tgravescs/SPARK-11701.
## Problem description:
Mesos shuffle service is completely unusable since Spark 1.6.0 . The problem seems to occur since the move from akka to netty in the networking layer. Until now, a connection from the driver to each shuffle service was used as a signal for the shuffle service to determine, whether the driver is still running. Since 1.6.0, this connection is closed after spark.shuffle.io.connectionTimeout (or spark.network.timeout if the former is not set) due to it being idle. The shuffle service interprets this as a signal that the driver has stopped, despite the driver still being alive. Thus, shuffle files are deleted before the application has stopped.
### Context and analysis:
spark shuffle fails with mesos after 2mins: https://issues.apache.org/jira/browse/SPARK-12583
External shuffle service broken w/ Mesos: https://issues.apache.org/jira/browse/SPARK-13159
This is a follow up on #11207 .
## What changes were proposed in this pull request?
This PR adds a heartbeat signal from the Driver (in MesosExternalShuffleClient) to all registered external mesos shuffle service instances. In MesosExternalShuffleBlockHandler, a thread periodically checks whether a driver has timed out and cleans an application's shuffle files if this is the case.
## How was the this patch tested?
This patch has been tested on a small mesos test cluster using the spark-shell. Log output from mesos shuffle service:
```
16/02/19 15:13:45 INFO mesos.MesosExternalShuffleBlockHandler: Received registration request from app 294def07-3249-4e0f-8d71-bf8c83c58a50-0018 (remote address /xxx.xxx.xxx.xxx:52391, heartbeat timeout 120000 ms).
16/02/19 15:13:47 INFO shuffle.ExternalShuffleBlockResolver: Registered executor AppExecId{appId=294def07-3249-4e0f-8d71-bf8c83c58a50-0018, execId=3} with ExecutorShuffleInfo{localDirs=[/foo/blockmgr-c84c0697-a3f9-4f61-9c64-4d3ee227c047], subDirsPerLocalDir=64, shuffleManager=sort}
16/02/19 15:13:47 INFO shuffle.ExternalShuffleBlockResolver: Registered executor AppExecId{appId=294def07-3249-4e0f-8d71-bf8c83c58a50-0018, execId=7} with ExecutorShuffleInfo{localDirs=[/foo/blockmgr-bf46497a-de80-47b9-88f9-563123b59e03], subDirsPerLocalDir=64, shuffleManager=sort}
16/02/19 15:16:02 INFO mesos.MesosExternalShuffleBlockHandler: Application 294def07-3249-4e0f-8d71-bf8c83c58a50-0018 timed out. Removing shuffle files.
16/02/19 15:16:02 INFO shuffle.ExternalShuffleBlockResolver: Application 294def07-3249-4e0f-8d71-bf8c83c58a50-0018 removed, cleanupLocalDirs = true
16/02/19 15:16:02 INFO shuffle.ExternalShuffleBlockResolver: Cleaning up executor AppExecId{appId=294def07-3249-4e0f-8d71-bf8c83c58a50-0018, execId=3}'s 1 local dirs
16/02/19 15:16:02 INFO shuffle.ExternalShuffleBlockResolver: Cleaning up executor AppExecId{appId=294def07-3249-4e0f-8d71-bf8c83c58a50-0018, execId=7}'s 1 local dirs
```
Note: there are 2 executors running on this slave.
Author: Bertrand Bossy <bertrand.bossy@teralytics.net>
Closes#11272 from bbossy/SPARK-12583-mesos-shuffle-service-heartbeat.
trait SynchronizedSet in package mutable is deprecated
Author: Wilson Wu <wilson888888888@gmail.com>
Closes#11580 from wilson888888888/spark-synchronizedset.
## What changes were proposed in this pull request?
This PR fixes 135 typos over 107 files:
* 121 typos in comments
* 11 typos in testcase name
* 3 typos in log messages
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11689 from dongjoon-hyun/fix_more_typos.
## What changes were proposed in this pull request?
- Fixes calls to `new String(byte[])` or `String.getBytes()` that rely on platform default encoding, to use UTF-8
- Same for `InputStreamReader` and `OutputStreamWriter` constructors
- Standardizes on UTF-8 everywhere
- Standardizes specifying the encoding with `StandardCharsets.UTF-8`, not the Guava constant or "UTF-8" (which means handling `UnuspportedEncodingException`)
- (also addresses the other remaining Coverity scan issues, which are pretty trivial; these are separated into commit 1deecd8d9c )
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#11657 from srowen/SPARK-13823.
When dynamic resource allocation is enabled fetching broadcast variables from removed executors were causing job failures and SPARK-9591 fixed this problem by trying all locations of a block before giving up. However, the locations of a block is retrieved only once from the driver in this process and the locations in this list can be stale due to dynamic resource allocation. This situation gets worse when running on a large cluster as the size of this location list can be in the order of several hundreds out of which there may be tens of stale entries. What we have observed is with the default settings of 3 max retries and 5s between retries (that's 15s per location) the time it takes to read a broadcast variable can be as high as ~17m (70 failed attempts * 15s/attempt)
Author: Nezih Yigitbasi <nyigitbasi@netflix.com>
Closes#11241 from nezihyigitbasi/SPARK-13328.
## What changes were proposed in this pull request?
Here lists all cases that Master cannot talk with Worker for a while and then network is back.
1. Master doesn't know the network issue (not yet timeout)
a. Worker doesn't know the network issue (onDisconnected is not called)
- Worker keeps sending Heartbeat. Both Worker and Master don't know the network issue. Nothing to do. (Finally, Master will notice the heartbeat timeout if network is not recovered)
b. Worker knows the network issue (onDisconnected is called)
- Worker stops sending Heartbeat and sends `RegisterWorker` to master. Master will reply `RegisterWorkerFailed("Duplicate worker ID")`. Worker calls "System.exit(1)" (Finally, Master will notice the heartbeat timeout if network is not recovered) (May leak driver processes. See [SPARK-13602](https://issues.apache.org/jira/browse/SPARK-13602))
2. Worker timeout (Master knows the network issue). In such case, master removes Worker and its executors and drivers.
a. Worker doesn't know the network issue (onDisconnected is not called)
- Worker keeps sending Heartbeat.
- If the network is back, say Master receives Heartbeat, Master sends `ReconnectWorker` to Worker
- Worker send `RegisterWorker` to master.
- Master accepts `RegisterWorker` but doesn't know executors and drivers in Worker. (may leak executors)
b. Worker knows the network issue (onDisconnected is called)
- Worker stop sending `Heartbeat`. Worker will send "RegisterWorker" to master.
- Master accepts `RegisterWorker` but doesn't know executors and drivers in Worker. (may leak executors)
This PR fixes executors and drivers leak in 2.a and 2.b when Worker reregisters with Master. The approach is making Worker send `WorkerLatestState` to sync the state after registering with master successfully. Then Master will ask Worker to kill unknown executors and drivers.
Note: Worker cannot just kill executors after registering with master because in the worker, `LaunchExecutor` and `RegisteredWorker` are processed in two threads. If `LaunchExecutor` happens before `RegisteredWorker`, Worker's executor list will contain new executors after Master accepts `RegisterWorker`. We should not kill these executors. So sending the list to Master and let Master tell Worker which executors should be killed.
## How was this patch tested?
test("SPARK-13604: Master should ask Worker kill unknown executors and drivers")
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11455 from zsxwing/orphan-executors.
## What changes were proposed in this pull request?
Since the opening curly brace, '{', has many usages as discussed in [SPARK-3854](https://issues.apache.org/jira/browse/SPARK-3854), this PR adds a ScalaStyle rule to prevent '){' pattern for the following majority pattern and fixes the code accordingly. If we enforce this in ScalaStyle from now, it will improve the Scala code quality and reduce review time.
```
// Correct:
if (true) {
println("Wow!")
}
// Incorrect:
if (true){
println("Wow!")
}
```
IntelliJ also shows new warnings based on this.
## How was this patch tested?
Pass the Jenkins ScalaStyle test.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11637 from dongjoon-hyun/SPARK-3854.
Today, both the MemoryStore and DiskStore implement a common `BlockStore` API, but I feel that this API is inappropriate because it abstracts away important distinctions between the behavior of these two stores.
For instance, the disk store doesn't have a notion of storing deserialized objects, so it's confusing for it to expose object-based APIs like putIterator() and getValues() instead of only exposing binary APIs and pushing the responsibilities of serialization and deserialization to the client. Similarly, the DiskStore put() methods accepted a `StorageLevel` parameter even though the disk store can only store blocks in one form.
As part of a larger BlockManager interface cleanup, this patch remove the BlockStore interface and refines the MemoryStore and DiskStore interfaces to reflect more narrow sets of responsibilities for those components. Some of the benefits of this interface cleanup are reflected in simplifications to several unit tests to eliminate now-unnecessary mocking, significant simplification of the BlockManager's `getLocal()` and `doPut()` methods, and a narrower API between the MemoryStore and DiskStore.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11534 from JoshRosen/remove-blockstore-interface.
The contains() method does not return consistently with get() if the key is deprecated. For example,
import org.apache.spark.SparkConf
val conf = new SparkConf()
conf.set("spark.io.compression.lz4.block.size", "12345") # display some deprecated warning message
conf.get("spark.io.compression.lz4.block.size") # return 12345
conf.get("spark.io.compression.lz4.blockSize") # return 12345
conf.contains("spark.io.compression.lz4.block.size") # return true
conf.contains("spark.io.compression.lz4.blockSize") # return false
The fix will make the contains() and get() more consistent.
I've added a test case for this.
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Unit tests should be sufficient.
Author: bomeng <bmeng@us.ibm.com>
Closes#11568 from bomeng/SPARK-13727.
## What changes were proposed in this pull request?
Previously the Mesos framework webui URL was being derived only from the Spark UI address leaving no possibility to configure it. This commit makes it configurable. If unset it falls back to the previous behavior.
Motivation:
This change is necessary in order to be able to install Spark on DCOS and to be able to give it a custom service link. The configured `webui_url` is configured to point to a reverse proxy in the DCOS environment.
## How was this patch tested?
Locally, using unit tests and on DCOS testing and stable revision.
Author: Sergiusz Urbaniak <sur@mesosphere.io>
Closes#11369 from s-urbaniak/sur-webui-url.
## What changes were proposed in this pull request?
In order to make `docs/examples` (and other related code) more simple/readable/user-friendly, this PR replaces existing codes like the followings by using `diamond` operator.
```
- final ArrayList<Product2<Object, Object>> dataToWrite =
- new ArrayList<Product2<Object, Object>>();
+ final ArrayList<Product2<Object, Object>> dataToWrite = new ArrayList<>();
```
Java 7 or higher supports **diamond** operator which replaces the type arguments required to invoke the constructor of a generic class with an empty set of type parameters (<>). Currently, Spark Java code use mixed usage of this.
## How was this patch tested?
Manual.
Pass the existing tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11541 from dongjoon-hyun/SPARK-13702.
## What changes were proposed in this pull request?
This issue fixes the following potential bugs and Java coding style detected by Coverity and Checkstyle.
- Implement both null and type checking in equals functions.
- Fix wrong type casting logic in SimpleJavaBean2.equals.
- Add `implement Cloneable` to `UTF8String` and `SortedIterator`.
- Remove dereferencing before null check in `AbstractBytesToBytesMapSuite`.
- Fix coding style: Add '{}' to single `for` statement in mllib examples.
- Remove unused imports in `ColumnarBatch` and `JavaKinesisStreamSuite`.
- Remove unused fields in `ChunkFetchIntegrationSuite`.
- Add `stop()` to prevent resource leak.
Please note that the last two checkstyle errors exist on newly added commits after [SPARK-13583](https://issues.apache.org/jira/browse/SPARK-13583).
## How was this patch tested?
manual via `./dev/lint-java` and Coverity site.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11530 from dongjoon-hyun/SPARK-13692.
When a cached block is spilled to disk and read back in serialized form (i.e. as bytes), the current BlockManager implementation will attempt to re-insert the serialized block into the MemoryStore even if the block's storage level requests deserialized caching.
This behavior adds some complexity to the MemoryStore but I don't think it offers many performance benefits and I'd like to remove it in order to simplify a larger refactoring patch. Therefore, this patch changes the behavior so that disk store reads will only cache bytes in the memory store for blocks with serialized storage levels.
There are two places where we request serialized bytes from the BlockStore:
1. getLocalBytes(), which is only called when reading local copies of TorrentBroadcast pieces. Broadcast pieces are always cached using a serialized storage level, so this won't lead to a mismatch in serialization forms if spilled bytes read from disk are cached as bytes in the memory store.
2. the non-shuffle-block branch in getBlockData(), which is only called by the NettyBlockRpcServer when responding to requests to read remote blocks. Caching the serialized bytes in memory will only benefit us if those cached bytes are read before they're evicted and the likelihood of that happening seems low since the frequency of remote reads of non-broadcast cached blocks seems very low. Caching these bytes when they have a low probability of being read is bad if it risks the eviction of blocks which are cached in their expected serialized/deserialized forms, since those blocks seem more likely to be read in local computation.
Given the argument above, I think this change is unlikely to cause performance regressions.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11533 from JoshRosen/remove-memorystore-level-mismatch.
In preparation for larger refactoring, this patch removes the confusing `returnValues` option from the BlockStore put() APIs: returning the value is only useful in one place (caching) and in other situations, such as block replication, it's simpler to put() and then get().
As part of this change, I needed to refactor `BlockManager.doPut()`'s block replication code. I also changed `doPut()` to access the memory and disk stores directly rather than calling them through the BlockStore interface; this is in anticipation of a followup patch to remove the BlockStore interface so that the disk store can expose a binary-data-oriented API which is not concerned with Java objects or serialization.
These changes should be covered by the existing storage unit tests. The best way to review this patch is probably to look at the individual commits, all of which are small and have useful descriptions to guide the review.
/cc davies for review.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11502 from JoshRosen/remove-returnvalues.
This is, in a way, the basics to enable SPARK-529 (which was closed as
won't fix but I think is still valuable). In fact, Spark SQL created
something for that, and this change basically factors out that code
and inserts it into SparkConf, with some extra bells and whistles.
To showcase the usage of this pattern, I modified the YARN backend
to use the new config keys (defined in the new `config` package object
under `o.a.s.deploy.yarn`). Most of the changes are mechanic, although
logic had to be slightly modified in a handful of places.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#10205 from vanzin/conf-opts.
## What changes were proposed in this pull request?
This PR fixes typos in comments and testcase name of code.
## How was this patch tested?
manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11481 from dongjoon-hyun/minor_fix_typos_in_code.
## What changes were proposed in this pull request?
Fixes (another) compile problem due to inadvertent use of Option.contains, only in Scala 2.11
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#11496 from srowen/SPARK-13423.3.
## What changes were proposed in this pull request?
After SPARK-6990, `dev/lint-java` keeps Java code healthy and helps PR review by saving much time.
This issue aims remove unused imports from Java/Scala code and add `UnusedImports` checkstyle rule to help developers.
## How was this patch tested?
```
./dev/lint-java
./build/sbt compile
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11438 from dongjoon-hyun/SPARK-13583.
## What changes were proposed in this pull request?
Make some cross-cutting code improvements according to static analysis. These are individually up for discussion since they exist in separate commits that can be reverted. The changes are broadly:
- Inner class should be static
- Mismatched hashCode/equals
- Overflow in compareTo
- Unchecked warnings
- Misuse of assert, vs junit.assert
- get(a) + getOrElse(b) -> getOrElse(a,b)
- Array/String .size -> .length (occasionally, -> .isEmpty / .nonEmpty) to avoid implicit conversions
- Dead code
- tailrec
- exists(_ == ) -> contains find + nonEmpty -> exists filter + size -> count
- reduce(_+_) -> sum map + flatten -> map
The most controversial may be .size -> .length simply because of its size. It is intended to avoid implicits that might be expensive in some places.
## How was the this patch tested?
Existing Jenkins unit tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#11292 from srowen/SPARK-13423.
Moved TestExecutor.scala from src to test package and removed the unused file TestClient.scala.
Author: Devaraj K <devaraj@apache.org>
Closes#11474 from devaraj-kavali/SPARK-13621.
## What changes were proposed in this pull request?
In order to tell OutputStream that the task has failed or not, we should call the failure callbacks BEFORE calling writer.close().
## How was this patch tested?
Added new unit tests.
Author: Davies Liu <davies@databricks.com>
Closes#11450 from davies/callback.
CacheManager directly calls MemoryStore.unrollSafely() and has its own logic for handling graceful fallback to disk when cached data does not fit in memory. However, this logic also exists inside of the MemoryStore itself, so this appears to be unnecessary duplication.
Thanks to the addition of block-level read/write locks in #10705, we can refactor the code to remove the CacheManager and replace it with an atomic `BlockManager.getOrElseUpdate()` method.
This pull request replaces / subsumes #10748.
/cc andrewor14 and nongli for review. Note that this changes the locking semantics of a couple of internal BlockManager methods (`doPut()` and `lockNewBlockForWriting`), so please pay attention to the Scaladoc changes and new test cases for those methods.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11436 from JoshRosen/remove-cachemanager.
## What changes were proposed in this pull request?
TaskContext supports task completion callback, which gets called regardless of task failures. However, there is no way for the listener to know if there is an error. This patch adds a new listener that gets called when a task fails.
## How was the this patch tested?
New unit test case and integration test case covering the code path
Author: Reynold Xin <rxin@databricks.com>
Closes#11340 from rxin/SPARK-13465.
## Motivation
As a pre-requisite to off-heap caching of blocks, we need a mechanism to prevent pages / blocks from being evicted while they are being read. With on-heap objects, evicting a block while it is being read merely leads to memory-accounting problems (because we assume that an evicted block is a candidate for garbage-collection, which will not be true during a read), but with off-heap memory this will lead to either data corruption or segmentation faults.
## Changes
### BlockInfoManager and reader/writer locks
This patch adds block-level read/write locks to the BlockManager. It introduces a new `BlockInfoManager` component, which is contained within the `BlockManager`, holds the `BlockInfo` objects that the `BlockManager` uses for tracking block metadata, and exposes APIs for locking blocks in either shared read or exclusive write modes.
`BlockManager`'s `get*()` and `put*()` methods now implicitly acquire the necessary locks. After a `get()` call successfully retrieves a block, that block is locked in a shared read mode. A `put()` call will block until it acquires an exclusive write lock. If the write succeeds, the write lock will be downgraded to a shared read lock before returning to the caller. This `put()` locking behavior allows us store a block and then immediately turn around and read it without having to worry about it having been evicted between the write and the read, which will allow us to significantly simplify `CacheManager` in the future (see #10748).
See `BlockInfoManagerSuite`'s test cases for a more detailed specification of the locking semantics.
### Auto-release of locks at the end of tasks
Our locking APIs support explicit release of locks (by calling `unlock()`), but it's not always possible to guarantee that locks will be released prior to the end of the task. One reason for this is our iterator interface: since our iterators don't support an explicit `close()` operator to signal that no more records will be consumed, operations like `take()` or `limit()` don't have a good means to release locks on their input iterators' blocks. Another example is broadcast variables, whose block locks can only be released at the end of the task.
To address this, `BlockInfoManager` uses a pair of maps to track the set of locks acquired by each task. Lock acquisitions automatically record the current task attempt id by obtaining it from `TaskContext`. When a task finishes, code in `Executor` calls `BlockInfoManager.unlockAllLocksForTask(taskAttemptId)` to free locks.
### Locking and the MemoryStore
In order to prevent in-memory blocks from being evicted while they are being read, the `MemoryStore`'s `evictBlocksToFreeSpace()` method acquires write locks on blocks which it is considering as candidates for eviction. These lock acquisitions are non-blocking, so a block which is being read will not be evicted. By holding write locks until the eviction is performed or skipped (in case evicting the blocks would not free enough memory), we avoid a race where a new reader starts to read a block after the block has been marked as an eviction candidate but before it has been removed.
### Locking and remote block transfer
This patch makes small changes to to block transfer and network layer code so that locks acquired by the BlockTransferService are released as soon as block transfer messages are consumed and released by Netty. This builds on top of #11193, a bug fix related to freeing of network layer ManagedBuffers.
## FAQ
- **Why not use Java's built-in [`ReadWriteLock`](https://docs.oracle.com/javase/7/docs/api/java/util/concurrent/locks/ReadWriteLock.html)?**
Our locks operate on a per-task rather than per-thread level. Under certain circumstances a task may consist of multiple threads, so using `ReadWriteLock` would mean that we might call `unlock()` from a thread which didn't hold the lock in question, an operation which has undefined semantics. If we could rely on Java 8 classes, we might be able to use [`StampedLock`](https://docs.oracle.com/javase/8/docs/api/java/util/concurrent/locks/StampedLock.html) to work around this issue.
- **Why not detect "leaked" locks in tests?**:
See above notes about `take()` and `limit`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10705 from JoshRosen/pin-pages.
Fixed the HTTP Server Host Name/IP issue i.e. HTTP Server to take the
configured host name/IP and not '0.0.0.0' always.
Author: Devaraj K <devaraj@apache.org>
Closes#11133 from devaraj-kavali/SPARK-13117.
Added an exception to be thrown in UnifiedMemoryManager.scala if the configuration given for executor memory is too low. Also modified the exception message thrown when driver memory is too low.
This patch was tested manually by passing in config options to Spark shell. I also added a test in UnifiedMemoryManagerSuite.scala
Author: Daniel Jalova <djalova@us.ibm.com>
Closes#11255 from djalova/SPARK-12759.
Currently the Mesos cluster dispatcher is not using offers from multiple roles correctly, as it simply aggregates all the offers resource values into one, but doesn't apply them correctly before calling the driver as Mesos needs the resources from the offers to be specified which role it originally belongs to. Multiple roles is already supported with fine/coarse grain scheduler, so porting that logic here to the cluster scheduler.
https://issues.apache.org/jira/browse/SPARK-10749
Author: Timothy Chen <tnachen@gmail.com>
Closes#8872 from tnachen/cluster_multi_roles.
## 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.
## 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.
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.
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.
