## What changes were proposed in this pull request?
Use Long.parseLong which returns a primative.
Use a series of appends() reduces the creation of an extra StringBuilder type
## How was this patch tested?
Unit tests
Author: Azeem Jiva <azeemj@gmail.com>
Closes#12520 from javawithjiva/minor.
## What changes were proposed in this pull request?
Handling exception for the below mentioned issue
```
➜ spark git:(master) ✗ ./bin/spark-shell -c spark.scheduler.mode=NONE
16/04/25 09:15:00 ERROR SparkContext: Error initializing SparkContext.
scala.MatchError: NONE (of class scala.Enumeration$Val)
at org.apache.spark.scheduler.Pool.<init>(Pool.scala:53)
at org.apache.spark.scheduler.TaskSchedulerImpl.initialize(TaskSchedulerImpl.scala:131)
at org.apache.spark.SparkContext$.org$apache$spark$SparkContext$$createTaskScheduler(SparkContext.scala:2352)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:492)
```
The exception now looks like
```
java.lang.RuntimeException: The scheduler mode NONE is not supported by Spark.
```
## How was this patch tested?
manual tests
Author: Subhobrata Dey <sbcd90@gmail.com>
Closes#12666 from sbcd90/schedulerModeIssue.
## What changes were proposed in this pull request?
SPARK-12130 make 2.0 shuffle service incompatible with 1.x. So from discussion: [http://apache-spark-developers-list.1001551.n3.nabble.com/YARN-Shuffle-service-and-its-compatibility-td17222.html](url) we should maintain compatibility between Spark 1.x and Spark 2.x's shuffle service.
I put string comparison into executor's register at first avoid string comparison in getBlockData every time.
## How was this patch tested?
N/A
Author: Lianhui Wang <lianhuiwang09@gmail.com>
Closes#12568 from lianhuiwang/SPARK-14731.
## What changes were proposed in this pull request?
Implement the same memory size validations for the StaticMemoryManager (Legacy) as the UnifiedMemoryManager has.
## How was this patch tested?
Manual tests were done in CDH cluster.
Test with small executor memory:
`
spark-submit --class org.apache.spark.examples.SparkPi --deploy-mode client --master yarn --executor-memory 15m --conf spark.memory.useLegacyMode=true /opt/cloudera/parcels/CDH/lib/spark/examples/lib/spark-examples*.jar 10
`
Exception thrown:
```
ERROR spark.SparkContext: Error initializing SparkContext.
java.lang.IllegalArgumentException: Executor memory 15728640 must be at least 471859200. Please increase executor memory using the --executor-memory option or spark.executor.memory in Spark configuration.
at org.apache.spark.memory.StaticMemoryManager$.org$apache$spark$memory$StaticMemoryManager$$getMaxExecutionMemory(StaticMemoryManager.scala:127)
at org.apache.spark.memory.StaticMemoryManager.<init>(StaticMemoryManager.scala:46)
at org.apache.spark.SparkEnv$.create(SparkEnv.scala:352)
at org.apache.spark.SparkEnv$.createDriverEnv(SparkEnv.scala:193)
at org.apache.spark.SparkContext.createSparkEnv(SparkContext.scala:289)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:462)
at org.apache.spark.examples.SparkPi$.main(SparkPi.scala:29)
at org.apache.spark.examples.SparkPi.main(SparkPi.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
```
Author: Peter Ableda <peter.ableda@cloudera.com>
Closes#12395 from peterableda/SPARK-14636.
## What changes were proposed in this pull request?
Change default logging to WARN for pyspark shell and sparkR shell for a much cleaner environment.
## How was this patch tested?
Manually running pyspark and sparkR shell
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#12648 from felixcheung/pylogging.
## What changes were proposed in this pull request?
Spark uses `NewLineAtEofChecker` rule in Scala by ScalaStyle. And, most Java code also comply with the rule. This PR aims to enforce the same rule `NewlineAtEndOfFile` by CheckStyle explicitly. Also, this fixes lint-java errors since SPARK-14465. The followings are the items.
- Adds a new line at the end of the files (19 files)
- Fixes 25 lint-java errors (12 RedundantModifier, 6 **ArrayTypeStyle**, 2 LineLength, 2 UnusedImports, 2 RegexpSingleline, 1 ModifierOrder)
## How was this patch tested?
After the Jenkins test succeeds, `dev/lint-java` should pass. (Currently, Jenkins dose not run lint-java.)
```bash
$ dev/lint-java
Using `mvn` from path: /usr/local/bin/mvn
Checkstyle checks passed.
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12632 from dongjoon-hyun/SPARK-14868.
## 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?
1. Fix the "spill size" of TungstenAggregate and Sort
2. Rename "data size" to "peak memory" to match the actual meaning (also consistent with task metrics)
3. Added "data size" for ShuffleExchange and BroadcastExchange
4. Added some timing for Sort, Aggregate and BroadcastExchange (this requires another patch to work)
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#12425 from davies/fix_metrics.
## What changes were proposed in this pull request?
This is a follow-up to #12557, with the following changes:
1. Fixes some of the style issues.
2. Merges Signaling and SignalLogger into a new class called SignalUtils. It was pretty confusing to have Signaling and Signal in one file, and it was also confusing to have two classes named Signaling and one called the other.
3. Made logging registration idempotent.
## How was this patch tested?
N/A.
Author: Reynold Xin <rxin@databricks.com>
Closes#12605 from rxin/SPARK-10001.
## 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?
Improve signal handling to allow interrupting running tasks from the REPL (with Ctrl+C).
If no tasks are running or Ctrl+C is pressed twice, the signal is forwarded to the default handler resulting in the usual termination of the application.
This PR is a rewrite of -- and therefore closes#8216 -- as per piaozhexiu's request
## How was this patch tested?
Signal handling is not easily testable therefore no unit tests were added. Nevertheless, the new functionality is implemented in a best-effort approach, soft-failing in case signals aren't available on a specific OS.
Author: Jakob Odersky <jakob@odersky.com>
Closes#12557 from jodersky/SPARK-10001-sigint.
## 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 patch makes event log processing multi threaded.
## How was this patch tested?
Existing tests pass, there is no new tests needed to test the functionality as this is a perf improvement. I tested the patch locally by generating one big event log (big1), one small event log(small1) and again a big event log(big2). Without this patch UI does not render any app for almost 30 seconds and then big2 and small1 appears. another 30 second delay and finally big1 also shows up in UI. With this change small1 shows up immediately and big1 and big2 comes up in 30 seconds. Locally it also displays them in the correct order in the UI.
Author: Parth Brahmbhatt <pbrahmbhatt@netflix.com>
Closes#11800 from Parth-Brahmbhatt/SPARK-13988.
In o.a.s.deploy.worker.Worker.scala, when receiving a KillExecutor message from an invalid Master, fixed typo by changing the log message to read "..attemped to kill executor.."
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#12546 from BryanCutler/worker-killexecutor-log-message.
## 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.
This change avoids using the environment to pass this information, since
with many jars it's easy to hit limits on certain OSes. Instead, it encodes
the information into the Spark configuration propagated to the AM.
The first problem that needed to be solved is a chicken & egg issue: the
config file is distributed using the cache, and it needs to contain information
about the files that are being distributed. To solve that, the code now treats
the config archive especially, and uses slightly different code to distribute
it, so that only its cache path needs to be saved to the config file.
The second problem is that the extra information would show up in the Web UI,
which made the environment tab even more noisy than it already is when lots
of jars are listed. This is solved by two changes: the list of cached files
is now read only once in the AM, and propagated down to the ExecutorRunnable
code (which actually sends the list to the NMs when starting containers). The
second change is to unset those config entries after the list is read, so that
the SparkContext never sees them.
Tested with both client and cluster mode by running "run-example SparkPi". This
uploads a whole lot of files when run from a build dir (instead of a distribution,
where the list is cleaned up), and I verified that the configs do not show
up in the UI.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#12487 from vanzin/SPARK-14602.
## What changes were proposed in this pull request?
This PR has two main changes.
1. Move Hive-specific methods from HiveContext to HiveSessionState, which help the work of removing HiveContext.
2. Create a SparkSession Class, which will later be the entry point of Spark SQL users.
## How was this patch tested?
Existing tests
This PR is trying to fix test failures of https://github.com/apache/spark/pull/12485.
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12522 from yhuai/spark-session.
## What changes were proposed in this pull request?
This proposal removes the class `HttpServer`, with the changing of internal file/jar/class transmission to RPC layer, currently there's no code using this `HttpServer`, so here propose to remove it.
## How was this patch tested?
Unit test is verified locally.
Author: jerryshao <sshao@hortonworks.com>
Closes#12526 from jerryshao/SPARK-14725.
Updated the log page by replacing the current pagination with a javascript-based infinite scroll solution
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#10910 from ajbozarth/spark8171.
## What changes were proposed in this pull request?
- replaced `FileSystem.get(conf)` calls with `path.getFileSystem(conf)`
## How was this patch tested?
N/A
Author: Liwei Lin <lwlin7@gmail.com>
Closes#12450 from lw-lin/fix-fs-get.
## 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.
Add R API for `read.jdbc`, `write.jdbc`.
Tested this quite a bit manually with different combinations of parameters. It's not clear if we could have automated tests in R for this - Scala `JDBCSuite` depends on Java H2 in-memory database.
Refactored some code into util so they could be tested.
Core's R SerDe code needs to be updated to allow access to java.util.Properties as `jobj` handle which is required by DataFrameReader/Writer's `jdbc` method. It would be possible, though more code to add a `sql/r/SQLUtils` helper function.
Tested:
```
# with postgresql
../bin/sparkR --driver-class-path /usr/share/java/postgresql-9.4.1207.jre7.jar
# read.jdbc
df <- read.jdbc(sqlContext, "jdbc:postgresql://localhost/db", "films2", user = "user", password = "12345")
df <- read.jdbc(sqlContext, "jdbc:postgresql://localhost/db", "films2", user = "user", password = 12345)
# partitionColumn and numPartitions test
df <- read.jdbc(sqlContext, "jdbc:postgresql://localhost/db", "films2", partitionColumn = "did", lowerBound = 0, upperBound = 200, numPartitions = 4, user = "user", password = 12345)
a <- SparkR:::toRDD(df)
SparkR:::getNumPartitions(a)
[1] 4
SparkR:::collectPartition(a, 2L)
# defaultParallelism test
df <- read.jdbc(sqlContext, "jdbc:postgresql://localhost/db", "films2", partitionColumn = "did", lowerBound = 0, upperBound = 200, user = "user", password = 12345)
SparkR:::getNumPartitions(a)
[1] 2
# predicates test
df <- read.jdbc(sqlContext, "jdbc:postgresql://localhost/db", "films2", predicates = list("did<=105"), user = "user", password = 12345)
count(df) == 1
# write.jdbc, default save mode "error"
irisDf <- as.DataFrame(sqlContext, iris)
write.jdbc(irisDf, "jdbc:postgresql://localhost/db", "films2", user = "user", password = "12345")
"error, already exists"
write.jdbc(irisDf, "jdbc:postgresql://localhost/db", "iris", user = "user", password = "12345")
```
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#10480 from felixcheung/rreadjdbc.
## What changes were proposed in this pull request?
The current benchmark framework runs a code block for several iterations and reports statistics. However there is no way to exclude per-iteration setup time from the overall results. This PR adds a timer control object passed into the closure that can be used for this purpose.
## How was this patch tested?
Existing benchmark code. Also see https://github.com/apache/spark/pull/12490
Author: Eric Liang <ekl@databricks.com>
Closes#12502 from ericl/spark-14733.
## 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.
## What changes were proposed in this pull request?
This PR returns correct processor name in ```/proc/cpuinfo``` on Linux from ```Benchmark.getPorcessorName()```. Now, this return ```Unknown processor```.
Since ```Utils.executeAndGetOutput(Seq("which", "grep"))``` return ```/bin/grep\n```, it is failed to execute ```/bin/grep\n```. This PR strips ```\n``` at the end of the line of a result of ```Utils.executeAndGetOutput()```
Before applying this PR
````
Java HotSpot(TM) 64-Bit Server VM 1.8.0_66-b17 on Linux 2.6.32-504.el6.x86_64
Unknown processor
back-to-back filter: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
Dataset 472 / 503 21.2 47.2 1.0X
DataFrame 51 / 58 198.0 5.1 9.3X
RDD 189 / 211 52.8 18.9 2.5X
````
After applying this PR
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_66-b17 on Linux 2.6.32-504.el6.x86_64
Intel(R) Xeon(R) CPU E5-2667 v2 3.30GHz
back-to-back filter: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
Dataset 490 / 502 20.4 49.0 1.0X
DataFrame 55 / 61 183.4 5.5 9.0X
RDD 210 / 237 47.7 21.0 2.3X
```
## How was this patch tested?
Run Benchmark programs on Linux by hand
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#12411 from kiszk/SPARK-14656.
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?
This PR adds exit code parameter to exitExecutor() so that caller can specify different exit code.
## How was this patch tested?
Existing test
rxin hbhanawat
Author: tedyu <yuzhihong@gmail.com>
Closes#12457 from tedyu/master.
## 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.
https://issues.apache.org/jira/browse/SPARK-13227
It might confuse the future developers when they use OpenHashMap.apply() with a numeric value type.
null.asInstance[Int], null.asInstance[Long], null.asInstace[Float] and null.asInstance[Double] will return 0/0.0/0L, which might confuse the developer if the value set contains 0/0.0/0L with an existing key
The current patch only adds the comments describing the issue, with the respect to apply the minimum changes to the code base
The more direct, yet more aggressive, approach is use Option as the return type
andrewor14 JoshRosen any thoughts about how to avoid the potential issue?
Author: CodingCat <zhunansjtu@gmail.com>
Closes#11107 from CodingCat/SPARK-13227.
## 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?
This PR removes
- Inappropriate type notations
For example, from
```scala
words.foreachRDD { (rdd: RDD[String], time: Time) =>
...
```
to
```scala
words.foreachRDD { (rdd, time) =>
...
```
- Extra anonymous closure within functional transformations.
For example,
```scala
.map(item => {
...
})
```
which can be just simply as below:
```scala
.map { item =>
...
}
```
and corrects some obvious style nits.
## How was this patch tested?
This was tested after adding rules in `scalastyle-config.xml`, which ended up with not finding all perfectly.
The rules applied were below:
- For the first correction,
```xml
<check customId="NoExtraClosure" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">(?m)\.[a-zA-Z_][a-zA-Z0-9]*\(\s*[^,]+s*=>\s*\{[^\}]+\}\s*\)</parameter></parameters>
</check>
```
```xml
<check customId="NoExtraClosure" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">\.[a-zA-Z_][a-zA-Z0-9]*\s*[\{|\(]([^\n>,]+=>)?\s*\{([^()]|(?R))*\}^[,]</parameter></parameters>
</check>
```
- For the second correction
```xml
<check customId="TypeNotation" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">\.[a-zA-Z_][a-zA-Z0-9]*\s*[\{|\(]\s*\([^):]*:R))*\}^[,]</parameter></parameters>
</check>
```
**Those rules were not added**
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12413 from HyukjinKwon/SPARK-style.
## 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?
Round memory bytes and convert it to Long to it’s original type. This change fixes the formatting issue in the Exception message.
## How was this patch tested?
Manual tests were done in CDH cluster.
Author: Peter Ableda <peter.ableda@cloudera.com>
Closes#12392 from peterableda/SPARK-14633.
## What changes were proposed in this pull request?
Removing references to assembly jar in documentation.
Adding an additional (previously undocumented) usage of spark-submit to run examples.
## How was this patch tested?
Ran spark-submit usage to ensure formatting was fine. Ran examples using SparkSubmit.
Author: Mark Grover <mark@apache.org>
Closes#12365 from markgrover/spark-14601.
## What changes were proposed in this pull request?
When we clean a closure, if its outermost parent is not a closure, we won't clone and clean it as cloning user's objects is dangerous. However, if it's a REPL line object, which may carry a lot of unnecessary references(like hadoop conf, spark conf, etc.), we should clean it as it's not a user object.
This PR improves the check for user's objects to exclude REPL line object.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12327 from cloud-fan/closure.
## 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?
Move json4s, breeze dependency declaration into parent
## How was this patch tested?
Should be no functional change, but Jenkins tests will test that.
Author: Sean Owen <sowen@cloudera.com>
Closes#12390 from srowen/SPARK-14612.
## What changes were proposed in this pull request?
Currently many public abstract methods (in abstract classes as well as traits) don't declare return types explicitly, such as in [o.a.s.streaming.dstream.InputDStream](https://github.com/apache/spark/blob/master/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala#L110):
```scala
def start() // should be: def start(): Unit
def stop() // should be: def stop(): Unit
```
These methods exist in core, sql, streaming; this PR fixes them.
## How was this patch tested?
N/A
## Which piece of scala style rule led to the changes?
the rule was added separately in https://github.com/apache/spark/pull/12396
Author: Liwei Lin <lwlin7@gmail.com>
Closes#12389 from lw-lin/public-abstract-methods.
## 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?
This PR removes extra anonymous closure within functional transformations.
For example,
```scala
.map(item => {
...
})
```
which can be just simply as below:
```scala
.map { item =>
...
}
```
## How was this patch tested?
Related unit tests and `sbt scalastyle`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12382 from HyukjinKwon/minor-extra-closers.
## What changes were proposed in this pull request?
Old `HadoopFsRelation` API includes `buildInternalScan()` which uses `SqlNewHadoopRDD` in `ParquetRelation`.
Because now the old API is removed, `SqlNewHadoopRDD` is not used anymore.
So, this PR removes `SqlNewHadoopRDD` and several unused imports.
This was discussed in https://github.com/apache/spark/pull/12326.
## How was this patch tested?
Several related existing unit tests and `sbt scalastyle`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12354 from HyukjinKwon/SPARK-14596.
This patch makes the postStartHook throw an IllegalStateException if the SparkContext is shutdown while it is waiting for the backend to be ready
Author: Charles Allen <charles@allen-net.com>
Closes#12301 from drcrallen/SPARK-14537.
