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* [SPARK-4193][BUILD] Disable doclint in Java 8 to prevent from build error.Takuya UESHIN2014-11-287-6/+35
| | | | | | | | | | | | | | Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #3058 from ueshin/issues/SPARK-4193 and squashes the following commits: e096bb1 [Takuya UESHIN] Add a plugin declaration to pluginManagement. 6762ec2 [Takuya UESHIN] Fix usage of -Xdoclint javadoc option. fdb280a [Takuya UESHIN] Fix Javadoc errors. 4745f3c [Takuya UESHIN] Merge branch 'master' into issues/SPARK-4193 923e2f0 [Takuya UESHIN] Use doclint option `-missing` instead of `none`. 30d6718 [Takuya UESHIN] Fix Javadoc errors. b548017 [Takuya UESHIN] Disable doclint in Java 8 to prevent from build error.
* [SPARK-4643] [Build] Remove unneeded staging repositories from buildDaoyuan Wang2014-11-281-24/+0
| | | | | | | | | | | The old location will return a 404. Author: Daoyuan Wang <daoyuan.wang@intel.com> Closes #3504 from adrian-wang/repo and squashes the following commits: f604e05 [Daoyuan Wang] already in maven central, remove at all f494fac [Daoyuan Wang] spark staging repo outdated
* Delete unnecessary functionKaiXinXiaoLei2014-11-281-7/+0
| | | | | | | | | | | | | | when building spark by sbt, the function “runAlternateBoot" in sbt/sbt-launch-lib.bash is not used. And this function is not used by spark code. So I think this function is not necessary. And the option of "sbt.boot.properties" can be configured in the command line when building spark, eg: sbt/sbt assembly -Dsbt.boot.properties=$bootpropsfile. The file from https://github.com/sbt/sbt-launcher-package is changed. And the function “runAlternateBoot" is deleted in upstream project. I think spark project should delete this function in file sbt/sbt-launch-lib.bash. Thanks. Author: KaiXinXiaoLei <huleilei1@huawei.com> Closes #3224 from KaiXinXiaoLei/deleteFunction and squashes the following commits: e8eac49 [KaiXinXiaoLei] Delete blank lines. efe36d4 [KaiXinXiaoLei] Delete unnecessary function
* [SPARK-4645][SQL] Disables asynchronous execution in Hive 0.13.1 ↵Cheng Lian2014-11-281-100/+39
| | | | | | | | | | | | | | | | HiveThriftServer2 This PR disables HiveThriftServer2 asynchronous execution by setting `runInBackground` argument in `ExecuteStatementOperation` to `false`, and reverting `SparkExecuteStatementOperation.run` in Hive 13 shim to Hive 12 version. This change makes Simba ODBC driver v1.0.0.1000 work. <!-- Reviewable:start --> [<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/3506) <!-- Reviewable:end --> Author: Cheng Lian <lian@databricks.com> Closes #3506 from liancheng/disable-async-exec and squashes the following commits: 593804d [Cheng Lian] Disables asynchronous execution in Hive 0.13.1 HiveThriftServer2
* [SPARK-4619][Storage]delete redundant time suffixmaji20142014-11-281-1/+1
| | | | | | | | | | Time suffix exists in Utils.getUsedTimeMs(startTime), no need to append again, delete that Author: maji2014 <maji3@asiainfo.com> Closes #3475 from maji2014/SPARK-4619 and squashes the following commits: df0da4e [maji2014] delete redundant time suffix
* [SPARK-4613][Core] Java API for JdbcRDDCheng Lian2014-11-273-5/+204
| | | | | | | | | | | | | | | | | | | This PR introduces a set of Java APIs for using `JdbcRDD`: 1. Trait (interface) `JdbcRDD.ConnectionFactory`: equivalent to the `getConnection: () => Connection` parameter in `JdbcRDD` constructor. 2. Two overloaded versions of `Jdbc.create`: used to create `JavaRDD` that wraps a `JdbcRDD`. <!-- Reviewable:start --> [<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/3478) <!-- Reviewable:end --> Author: Cheng Lian <lian@databricks.com> Closes #3478 from liancheng/japi-jdbc-rdd and squashes the following commits: 9a54625 [Cheng Lian] Only shutdowns a single DB rather than the whole Derby driver d4cedc5 [Cheng Lian] Moves Java JdbcRDD test case to a separate test suite ffcdf2e [Cheng Lian] Java API for JdbcRDD
* [SPARK-4626] Kill a task only if the executorId is (still) registered with ↵roxchkplusony2014-11-271-1/+7
| | | | | | | | | | | the scheduler Author: roxchkplusony <roxchkplusony@gmail.com> Closes #3483 from roxchkplusony/bugfix/4626 and squashes the following commits: aba9184 [roxchkplusony] replace warning message per review 5e7fdea [roxchkplusony] [SPARK-4626] Kill a task only if the executorId is (still) registered with the scheduler
* SPARK-4170 [CORE] Closure problems when running Scala app that "extends App"Sean Owen2014-11-273-34/+44
| | | | | | | | | | | Warn against subclassing scala.App, and remove one instance of this in examples Author: Sean Owen <sowen@cloudera.com> Closes #3497 from srowen/SPARK-4170 and squashes the following commits: 4a6131f [Sean Owen] Restore multiline string formatting a8ca895 [Sean Owen] Warn against subclassing scala.App, and remove one instance of this in examples
* [Release] Automate generation of contributors listAndrew Or2014-11-262-0/+330
| | | | | | This commit provides a script that computes the contributors list by linking the github commits with JIRA issues. Automatically translating github usernames remains a TODO at this point.
* [SPARK-732][SPARK-3628][CORE][RESUBMIT] eliminate duplicate update on accmulatorCodingCat2014-11-264-30/+67
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-3628 In current implementation, the accumulator will be updated for every successfully finished task, even the task is from a resubmitted stage, which makes the accumulator counter-intuitive In this patch, I changed the way for the DAGScheduler to update the accumulator, DAGScheduler maintains a HashTable, mapping the stage id to the received <accumulator_id , value> pairs. Only when the stage becomes independent, (no job needs it any more), we accumulate the values of the <accumulator_id , value> pairs, when a task finished, we check if the HashTable has contained such stageId, it saves the accumulator_id, value only when the task is the first finished task of a new stage or the stage is running for the first attempt... Author: CodingCat <zhunansjtu@gmail.com> Closes #2524 from CodingCat/SPARK-732-1 and squashes the following commits: 701a1e8 [CodingCat] roll back change on Accumulator.scala 1433e6f [CodingCat] make MIMA happy b233737 [CodingCat] address Matei's comments 02261b8 [CodingCat] rollback some changes 6b0aff9 [CodingCat] update document 2b2e8cf [CodingCat] updateAccumulator 83b75f8 [CodingCat] style fix 84570d2 [CodingCat] re-enable the bad accumulator guard 1e9e14d [CodingCat] add NPE guard 21b6840 [CodingCat] simplify the patch 88d1f03 [CodingCat] fix rebase error f74266b [CodingCat] add test case for resubmitted result stage 5cf586f [CodingCat] de-duplicate on task level 138f9b3 [CodingCat] make MIMA happy 67593d2 [CodingCat] make if allowing duplicate update as an option of accumulator
* [SPARK-4614][MLLIB] Slight API changes in Matrix and MatricesXiangrui Meng2014-11-263-11/+65
| | | | | | | | | | | | | Before we have a full picture of the operators we want to add, it might be safer to hide `Matrix.transposeMultiply` in 1.2.0. Another update we want to change is `Matrix.randn` and `Matrix.rand`, both of which should take a `Random` implementation. Otherwise, it is very likely to produce inconsistent RDDs. I also added some unit tests for matrix factory methods. All APIs are new in 1.2, so there is no incompatible changes. brkyvz Author: Xiangrui Meng <meng@databricks.com> Closes #3468 from mengxr/SPARK-4614 and squashes the following commits: 3b0e4e2 [Xiangrui Meng] add mima excludes 6bfd8a4 [Xiangrui Meng] hide transposeMultiply; add rng to rand and randn; add unit tests
* Removing confusing TripletFieldsJoseph E. Gonzalez2014-11-264-33/+8
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | After additional discussion with rxin, I think having all the possible `TripletField` options is confusing. This pull request reduces the triplet fields to: ```java /** * None of the triplet fields are exposed. */ public static final TripletFields None = new TripletFields(false, false, false); /** * Expose only the edge field and not the source or destination field. */ public static final TripletFields EdgeOnly = new TripletFields(false, false, true); /** * Expose the source and edge fields but not the destination field. (Same as Src) */ public static final TripletFields Src = new TripletFields(true, false, true); /** * Expose the destination and edge fields but not the source field. (Same as Dst) */ public static final TripletFields Dst = new TripletFields(false, true, true); /** * Expose all the fields (source, edge, and destination). */ public static final TripletFields All = new TripletFields(true, true, true); ``` Author: Joseph E. Gonzalez <joseph.e.gonzalez@gmail.com> Closes #3472 from jegonzal/SimplifyTripletFields and squashes the following commits: 91796b5 [Joseph E. Gonzalez] removing confusing triplet fields
* [SPARK-4612] Reduce task latency and increase scheduling throughput by ↵Tathagata Das2014-11-251-1/+1
| | | | | | | | | | | | | | | making configuration initialization lazy https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/executor/Executor.scala#L337 creates a configuration object for every task that is launched, even if there is no new dependent file/JAR to update. This is a heavy-weight creation that should be avoided if there is no new file/JAR to update. This PR makes that creation lazy. Quick local test in spark-perf scheduling throughput tests gives the following numbers in a local standalone scheduler mode. 1 job with 10000 tasks: before 7.8395 seconds, after 2.6415 seconds = 3x increase in task scheduling throughput pwendell JoshRosen Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #3463 from tdas/lazy-config and squashes the following commits: c791c1e [Tathagata Das] Reduce task latency by making configuration initialization lazy
* [SPARK-4516] Avoid allocating Netty PooledByteBufAllocators unnecessarilyAaron Davidson2014-11-262-10/+8
| | | | | | | | | | | | Turns out we are allocating an allocator pool for every TransportClient (which means that the number increases with the number of nodes in the cluster), when really we should just reuse one for all clients. This patch, as expected, greatly decreases off-heap memory allocation, and appears to make allocation only proportional to the number of cores. Author: Aaron Davidson <aaron@databricks.com> Closes #3465 from aarondav/fewer-pools and squashes the following commits: 36c49da [Aaron Davidson] [SPARK-4516] Avoid allocating unnecessarily Netty PooledByteBufAllocators
* [SPARK-4516] Cap default number of Netty threads at 8Aaron Davidson2014-11-251-7/+37
| | | | | | | | | | | | In practice, only 2-4 cores should be required to transfer roughly 10 Gb/s, and each core that we use will have an initial overhead of roughly 32 MB of off-heap memory, which comes at a premium. Thus, this value should still retain maximum throughput and reduce wasted off-heap memory allocation. It can be overridden by setting the number of serverThreads and clientThreads manually in Spark's configuration. Author: Aaron Davidson <aaron@databricks.com> Closes #3469 from aarondav/fewer-pools2 and squashes the following commits: 087c59f [Aaron Davidson] [SPARK-4516] Cap default number of Netty threads at 8
* [SPARK-4604][MLLIB] make MatrixFactorizationModel publicXiangrui Meng2014-11-252-3/+81
| | | | | | | | | | | | User could construct an MF model directly. I added a note about the performance. Author: Xiangrui Meng <meng@databricks.com> Closes #3459 from mengxr/SPARK-4604 and squashes the following commits: f64bcd3 [Xiangrui Meng] organize imports ed08214 [Xiangrui Meng] check preconditions and unit tests a624c12 [Xiangrui Meng] make MatrixFactorizationModel public
* [HOTFIX]: Adding back without-hive distPatrick Wendell2014-11-251-0/+1
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* [SPARK-4583] [mllib] LogLoss for GradientBoostedTrees fix + doc updatesJoseph K. Bradley2014-11-256-72/+146
| | | | | | | | | | | | | | | | | | | | | | | Currently, the LogLoss used by GradientBoostedTrees has 2 issues: * the gradient (and therefore loss) does not match that used by Friedman (1999) * the error computation uses 0/1 accuracy, not log loss This PR updates LogLoss. It also adds some doc for boosting and forests. I tested it on sample data and made sure the log loss is monotonically decreasing with each boosting iteration. CC: mengxr manishamde codedeft Author: Joseph K. Bradley <joseph@databricks.com> Closes #3439 from jkbradley/gbt-loss-fix and squashes the following commits: cfec17e [Joseph K. Bradley] removed forgotten temp comments a27eb6d [Joseph K. Bradley] corrections to last log loss commit ed5da2c [Joseph K. Bradley] updated LogLoss (boosting) for numerical stability 5e52bff [Joseph K. Bradley] * Removed the 1/2 from SquaredError. This also required updating the test suite since it effectively doubles the gradient and loss. * Added doc for developers within RandomForest. * Small cleanup in test suite (generating data only once) e57897a [Joseph K. Bradley] Fixed LogLoss for GradientBoostedTrees, and updated doc for losses, forests, and boosting
* [Spark-4509] Revert EC2 tag-based cluster membership patchXiangrui Meng2014-11-252-69/+28
| | | | | | | | | | | | | | | | | | | | | | This PR reverts changes related to tag-based cluster membership. As discussed in SPARK-3332, we didn't figure out a safe strategy to use tags to determine cluster membership, because tagging is not atomic. The following changes are reverted: SPARK-2333: 94053a7b766788bb62e2dbbf352ccbcc75f71fc0 SPARK-3213: 7faf755ae4f0cf510048e432340260a6e609066d SPARK-3608: 78d4220fa0bf2f9ee663e34bbf3544a5313b02f0. I tested launch, login, and destroy. It is easy to check the diff by comparing it to Josh's patch for branch-1.1: https://github.com/apache/spark/pull/2225/files JoshRosen I sent the PR to master. It might be easier for us to keep master and branch-1.2 the same at this time. We can always re-apply the patch once we figure out a stable solution. Author: Xiangrui Meng <meng@databricks.com> Closes #3453 from mengxr/SPARK-4509 and squashes the following commits: f0b708b [Xiangrui Meng] revert 94053a7b766788bb62e2dbbf352ccbcc75f71fc0 4298ea5 [Xiangrui Meng] revert 7faf755ae4f0cf510048e432340260a6e609066d 35963a1 [Xiangrui Meng] Revert "SPARK-3608 Break if the instance tag naming succeeds"
* Fix SPARK-4471: blockManagerIdFromJson function throws exception while B...hushan[胡珊]2014-11-252-3/+16
| | | | | | | | | | | | Fix [SPARK-4471](https://issues.apache.org/jira/browse/SPARK-4471): blockManagerIdFromJson function throws exception while BlockManagerId be null in MetadataFetchFailedException Author: hushan[胡珊] <hushan@xiaomi.com> Closes #3340 from suyanNone/fix-blockmanagerId-jnothing-2 and squashes the following commits: 159f9a3 [hushan[胡珊]] Refine test code for blockmanager is null 4380d73 [hushan[胡珊]] remove useless blank line 3ccf651 [hushan[胡珊]] Fix SPARK-4471: blockManagerIdFromJson function throws exception while metadata fetch failed
* [SPARK-4546] Improve HistoryServer first time user experienceAndrew Or2014-11-254-22/+40
| | | | | | | | | | | | | | | | | | | The documentation points the user to run the following ``` sbin/start-history-server.sh ``` The first thing this does is throw an exception that complains a log directory is not specified. The exception message itself does not say anything about what to set. Instead we should have a default and a landing page with a better message. The new default log directory is `file:/tmp/spark-events`. This is what it looks like as of this PR: ![after](https://issues.apache.org/jira/secure/attachment/12682985/after.png) Author: Andrew Or <andrew@databricks.com> Closes #3411 from andrewor14/minor-history-improvements and squashes the following commits: f33d6b3 [Andrew Or] Point user to set config if default log dir does not exist fc4c17a [Andrew Or] Improve HistoryServer UX
* [SPARK-4592] Avoid duplicate worker registrations in standalone modeAndrew Or2014-11-252-7/+47
| | | | | | | | | | | | | | | | | | | | | | | **Summary.** On failover, the Master may receive duplicate registrations from the same worker, causing the worker to exit. This is caused by this commit https://github.com/apache/spark/commit/4afe9a4852ebeb4cc77322a14225cd3dec165f3f, which adds logic for the worker to re-register with the master in case of failures. However, the following race condition may occur: (1) Master A fails and Worker attempts to reconnect to all masters (2) Master B takes over and notifies Worker (3) Worker responds by registering with Master B (4) Meanwhile, Worker's previous reconnection attempt reaches Master B, causing the same Worker to register with Master B twice **Fix.** Instead of attempting to register with all known masters, the worker should re-register with only the one that it has been communicating with. This is safe because the fact that a failover has occurred means the old master must have died. Then, when the worker is finally notified of a new master, it gives up on the old one in favor of the new one. **Caveat.** Even this fix is subject to more obscure race conditions. For instance, if Master B fails and Master A recovers immediately, then Master A may still observe duplicate worker registrations. However, this and other potential race conditions summarized in [SPARK-4592](https://issues.apache.org/jira/browse/SPARK-4592), are much, much less likely than the one described above, which is deterministically reproducible. Author: Andrew Or <andrew@databricks.com> Closes #3447 from andrewor14/standalone-failover and squashes the following commits: 0d9716c [Andrew Or] Move re-registration logic to actor for thread-safety 79286dc [Andrew Or] Preserve old behavior for initial retries 83b321c [Andrew Or] Tweak wording 1fce6a9 [Andrew Or] Active master actor could be null in the beginning b6f269e [Andrew Or] Avoid duplicate worker registrations
* [SPARK-4196][SPARK-4602][Streaming] Fix serialization issue in ↵Tathagata Das2014-11-252-16/+70
| | | | | | | | | | | | | | | PairDStreamFunctions.saveAsNewAPIHadoopFiles Solves two JIRAs in one shot - Makes the ForechDStream created by saveAsNewAPIHadoopFiles serializable for checkpoints - Makes the default configuration object used saveAsNewAPIHadoopFiles be the Spark's hadoop configuration Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #3457 from tdas/savefiles-fix and squashes the following commits: bb4729a [Tathagata Das] Same treatment for saveAsHadoopFiles b382ea9 [Tathagata Das] Fix serialization issue in PairDStreamFunctions.saveAsNewAPIHadoopFiles.
* [SPARK-4581][MLlib] Refactorize StandardScaler to improve the transformation ↵DB Tsai2014-11-251-20/+50
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | performance The following optimizations are done to improve the StandardScaler model transformation performance. 1) Covert Breeze dense vector to primitive vector to reduce the overhead. 2) Since mean can be potentially a sparse vector, we explicitly convert it to dense primitive vector. 3) Have a local reference to `shift` and `factor` array so JVM can locate the value with one operation call. 4) In pattern matching part, we use the mllib SparseVector/DenseVector instead of breeze's vector to make the codebase cleaner. Benchmark with mnist8m dataset: Before, DenseVector withMean and withStd: 50.97secs DenseVector withMean and withoutStd: 42.11secs DenseVector withoutMean and withStd: 8.75secs SparseVector withoutMean and withStd: 5.437secs With this PR, DenseVector withMean and withStd: 5.76secs DenseVector withMean and withoutStd: 5.28secs DenseVector withoutMean and withStd: 5.30secs SparseVector withoutMean and withStd: 1.27secs Note that without the local reference copy of `factor` and `shift` arrays, the runtime is almost three time slower. DenseVector withMean and withStd: 18.15secs DenseVector withMean and withoutStd: 18.05secs DenseVector withoutMean and withStd: 18.54secs SparseVector withoutMean and withStd: 2.01secs The following code, ```scala while (i < size) { values(i) = (values(i) - shift(i)) * factor(i) i += 1 } ``` will generate the bytecode ``` L13 LINENUMBER 106 L13 FRAME FULL [org/apache/spark/mllib/feature/StandardScalerModel org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/DenseVector T [D I I] [] ILOAD 7 ILOAD 6 IF_ICMPGE L14 L15 LINENUMBER 107 L15 ALOAD 5 ILOAD 7 ALOAD 5 ILOAD 7 DALOAD ALOAD 0 INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.shift ()[D ILOAD 7 DALOAD DSUB ALOAD 0 INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.factor ()[D ILOAD 7 DALOAD DMUL DASTORE L16 LINENUMBER 108 L16 ILOAD 7 ICONST_1 IADD ISTORE 7 GOTO L13 ``` , while with the local reference of the `shift` and `factor` arrays, the bytecode will be ``` L14 LINENUMBER 107 L14 ALOAD 0 INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.factor ()[D ASTORE 9 L15 LINENUMBER 108 L15 FRAME FULL [org/apache/spark/mllib/feature/StandardScalerModel org/apache/spark/mllib/linalg/Vector [D org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/DenseVector T [D I I [D] [] ILOAD 8 ILOAD 7 IF_ICMPGE L16 L17 LINENUMBER 109 L17 ALOAD 6 ILOAD 8 ALOAD 6 ILOAD 8 DALOAD ALOAD 2 ILOAD 8 DALOAD DSUB ALOAD 9 ILOAD 8 DALOAD DMUL DASTORE L18 LINENUMBER 110 L18 ILOAD 8 ICONST_1 IADD ISTORE 8 GOTO L15 ``` You can see that with local reference, the both of the arrays will be in the stack, so JVM can access the value without calling `INVOKESPECIAL`. Author: DB Tsai <dbtsai@alpinenow.com> Closes #3435 from dbtsai/standardscaler and squashes the following commits: 85885a9 [DB Tsai] revert to have lazy in shift array. daf2b06 [DB Tsai] Address the feedback cdb5cef [DB Tsai] small change 9c51eef [DB Tsai] style fc795e4 [DB Tsai] update 5bffd3d [DB Tsai] first commit
* [SPARK-4601][Streaming] Set correct call site for streaming jobs so that it ↵Tathagata Das2014-11-252-1/+6
| | | | | | | | | | | | is displayed correctly on the Spark UI When running the NetworkWordCount, the description of the word count jobs are set as "getCallsite at DStream:xxx" . This should be set to the line number of the streaming application that has the output operation that led to the job being created. This is because the callsite is incorrectly set in the thread launching the jobs. This PR fixes that. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #3455 from tdas/streaming-callsite-fix and squashes the following commits: 69fc26f [Tathagata Das] Set correct call site for streaming jobs so that it is displayed correctly on the Spark UI
* [SPARK-4344][DOCS] adding documentation on spark.yarn.user.classpath.firstarahuja2014-11-251-0/+1
| | | | | | | | | | The documentation for the two parameters is the same with a pointer from the standalone parameter to the yarn parameter Author: arahuja <aahuja11@gmail.com> Closes #3209 from arahuja/yarn-classpath-first-param and squashes the following commits: 51cb9b2 [arahuja] [SPARK-4344][DOCS] adding documentation for YARN on userClassPathFirst
* [SPARK-4381][Streaming]Add warning log when user set spark.master to local ↵jerryshao2014-11-251-0/+5
| | | | | | | | | | | | in Spark Streaming and there's no job executed Author: jerryshao <saisai.shao@intel.com> Closes #3244 from jerryshao/SPARK-4381 and squashes the following commits: d2486c7 [jerryshao] Improve the warning log d726e85 [jerryshao] Add local[1] to the filter condition eca428b [jerryshao] Add warning log
* [SPARK-4535][Streaming] Fix the error in commentsq002515982014-11-256-9/+9
| | | | | | | | | | | change `NetworkInputDStream` to `ReceiverInputDStream` change `ReceiverInputTracker` to `ReceiverTracker` Author: q00251598 <qiyadong@huawei.com> Closes #3400 from watermen/fix-comments and squashes the following commits: 75d795c [q00251598] change 'NetworkInputDStream' to 'ReceiverInputDStream' && change 'ReceiverInputTracker' to 'ReceiverTracker'
* [SPARK-4526][MLLIB]GradientDescent get a wrong gradient value according to ↵GuoQiang Li2014-11-251-19/+26
| | | | | | | | | | | | | | | | | the gradient formula. This is caused by the miniBatchSize parameter.The number of `RDD.sample` returns is not fixed. cc mengxr Author: GuoQiang Li <witgo@qq.com> Closes #3399 from witgo/GradientDescent and squashes the following commits: 13cb228 [GuoQiang Li] review commit 668ab66 [GuoQiang Li] Double to Long b6aa11a [GuoQiang Li] Check miniBatchSize is greater than 0 0b5c3e3 [GuoQiang Li] Minor fix 12e7424 [GuoQiang Li] GradientDescent get a wrong gradient value according to the gradient formula, which is caused by the miniBatchSize parameter.
* [SPARK-4596][MLLib] Refactorize Normalizer to make code cleanerDB Tsai2014-11-251-10/+19
| | | | | | | | | | | | | | | In this refactoring, the performance will be slightly increased due to removing the overhead from breeze vector. The bottleneck is still in breeze norm which is implemented by activeIterator. This inefficiency of breeze norm will be addressed in next PR. At least, this PR makes the code more consistent in the codebase. Author: DB Tsai <dbtsai@alpinenow.com> Closes #3446 from dbtsai/normalizer and squashes the following commits: e20a2b9 [DB Tsai] first commit
* [DOC][Build] Wrong cmd for build spark with apache hadoop 2.4.X and hive 12wangfei2014-11-241-1/+1
| | | | | | | | | Author: wangfei <wangfei1@huawei.com> Closes #3335 from scwf/patch-10 and squashes the following commits: d343113 [wangfei] add '-Phive' 60d595e [wangfei] [DOC] Wrong cmd for build spark with apache hadoop 2.4.X and Hive 12 support
* [SQL] Compute timeTaken correctlyw002289702014-11-241-7/+4
| | | | | | | | | | ```timeTaken``` should not count the time of printing result. Author: w00228970 <wangfei1@huawei.com> Closes #3423 from scwf/time-taken-bug and squashes the following commits: da7e102 [w00228970] compute time taken correctly
* [SPARK-4582][MLLIB] get raw vectors for further processing in Word2Vectkaessmann2014-11-241-0/+7
| | | | | | | | | | | | | | | | | | | | | This is #3309 for the master branch. e.g. clustering Author: tkaessmann <tobias.kaessmanns24.com> Closes #3309 from tkaessmann/branch-1.2 and squashes the following commits: e3a3142 [tkaessmann] changes the comment for getVectors 58d3d83 [tkaessmann] removes sign from comment a5be213 [tkaessmann] fixes getVectors to fit code guidelines 3782fa9 [tkaessmann] get raw vectors for further processing Author: tkaessmann <tobias.kaessmann@s24.com> Closes #3437 from mengxr/SPARK-4582 and squashes the following commits: 6c666b4 [tkaessmann] get raw vectors for further processing in Word2Vec
* [SPARK-4525] Mesos should decline unused offersJongyoul Lee2014-11-242-21/+65
| | | | | | | | | | | | | | | Functionally, this is just a small change on top of #3393 (by jongyoul). The issue being addressed is discussed in the comments there. I have not yet added a test for the bug there. I will add one shortly. I've also done some minor renaming/clean-up of variables in this class and tests. Author: Patrick Wendell <pwendell@gmail.com> Author: Jongyoul Lee <jongyoul@gmail.com> Closes #3436 from pwendell/mesos-issue and squashes the following commits: 58c35b5 [Patrick Wendell] Adding unit test for this situation c4f0697 [Patrick Wendell] Additional clean-up and fixes on top of existing fix f20f1b3 [Jongyoul Lee] [SPARK-4525] MesosSchedulerBackend.resourceOffers cannot decline unused offers from acceptedOffers - Added code for declining unused offers among acceptedOffers - Edited testCase for checking declining unused offers
* Revert "[SPARK-4525] Mesos should decline unused offers"Patrick Wendell2014-11-242-65/+21
| | | | | | | This reverts commit b043c27424d05e3200e7ba99a1a65656b57fa2f0. I accidentally committed this using my own authorship credential. However, I should have given authoriship to the original author: Jongyoul Lee.
* [SPARK-4525] Mesos should decline unused offersPatrick Wendell2014-11-242-21/+65
| | | | | | | | | | | | | | | Functionally, this is just a small change on top of #3393 (by jongyoul). The issue being addressed is discussed in the comments there. I have not yet added a test for the bug there. I will add one shortly. I've also done some minor renaming/clean-up of variables in this class and tests. Author: Patrick Wendell <pwendell@gmail.com> Author: Jongyoul Lee <jongyoul@gmail.com> Closes #3436 from pwendell/mesos-issue and squashes the following commits: 58c35b5 [Patrick Wendell] Adding unit test for this situation c4f0697 [Patrick Wendell] Additional clean-up and fixes on top of existing fix f20f1b3 [Jongyoul Lee] [SPARK-4525] MesosSchedulerBackend.resourceOffers cannot decline unused offers from acceptedOffers - Added code for declining unused offers among acceptedOffers - Edited testCase for checking declining unused offers
* [SPARK-4266] [Web-UI] Reduce stage page load time.Kay Ousterhout2014-11-248-27/+39
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The commit changes the java script used to show/hide additional metrics in order to reduce page load time. SPARK-4016 significantly increased page load time for the stage page when stages had a lot (thousands or tens of thousands) of tasks, due to the additional Javascript to hide some metrics by default and stripe the tables. This commit reduces page load time in two ways: (1) Now, all of the metrics that are hidden by default are hidden by setting "display: none;" using CSS for the page, rather than hiding them using javascript after the page loads. Without this change, for stages with thousands of tasks, there was a few second delay after page load, where first the additional metrics were shown, and then after a delay were hidden once the relevant JS finished running. (2) CSS is used to stripe all of the tables except for the summary table. The summary table needs javascript to do the striping because some rows are hidden, but the javascript striping is slower, which again resulted in a delay when it was used for the task table (where for a few seconds after page load, all of the rows in the task table would be white, while the browser finished running the JS to stripe the table). cc pwendell This change is intended to be backported to 1.2 to avoid a regression in UI performance when users run large jobs. Author: Kay Ousterhout <kayousterhout@gmail.com> Closes #3328 from kayousterhout/SPARK-4266 and squashes the following commits: f964091 [Kay Ousterhout] [SPARK-4266] [Web-UI] Reduce stage page load time.
* [SPARK-4548] []SPARK-4517] improve performance of python broadcastDavies Liu2014-11-248-258/+135
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Re-implement the Python broadcast using file: 1) serialize the python object using cPickle, write into disks. 2) Create a wrapper in JVM (for the dumped file), it read data from during serialization 3) Using TorrentBroadcast or HttpBroadcast to transfer the data (compressed) into executors 4) During deserialization, writing the data into disk. 5) Passing the path into Python worker, read data from disk and unpickle it into python object, until the first access. It fixes the performance regression introduced in #2659, has similar performance as 1.1, but support object larger than 2G, also improve the memory efficiency (only one compressed copy in driver and executor). Testing with a 500M broadcast and 4 tasks (excluding the benefit from reused worker in 1.2): name | 1.1 | 1.2 with this patch | improvement ---------|--------|---------|-------- python-broadcast-w-bytes | 25.20 | 9.33 | 170.13% | python-broadcast-w-set | 4.13 | 4.50 | -8.35% | Testing with 100 tasks (16 CPUs): name | 1.1 | 1.2 with this patch | improvement ---------|--------|---------|-------- python-broadcast-w-bytes | 38.16 | 8.40 | 353.98% python-broadcast-w-set | 23.29 | 9.59 | 142.80% Author: Davies Liu <davies@databricks.com> Closes #3417 from davies/pybroadcast and squashes the following commits: 50a58e0 [Davies Liu] address comments b98de1d [Davies Liu] disable gc while unpickle e5ee6b9 [Davies Liu] support large string 09303b8 [Davies Liu] read all data into memory dde02dd [Davies Liu] improve performance of python broadcast
* [SPARK-4578] fix asDict() with nested Row()Davies Liu2014-11-242-4/+5
| | | | | | | | | | The Row object is created on the fly once the field is accessed, so we should access them by getattr() in asDict(0 Author: Davies Liu <davies@databricks.com> Closes #3434 from davies/fix_asDict and squashes the following commits: b20f1e7 [Davies Liu] fix asDict() with nested Row()
* [SPARK-4562] [MLlib] speedup vectorDavies Liu2014-11-243-34/+118
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR change the underline array of DenseVector to numpy.ndarray to avoid the conversion, because most of the users will using numpy.array. It also improve the serialization of DenseVector. Before this change: trial | trainingTime | testTime -------|--------|-------- 0 | 5.126 | 1.786 1 |2.698 |1.693 After the change: trial | trainingTime | testTime -------|--------|-------- 0 |4.692 |0.554 1 |2.307 |0.525 This could partially fix the performance regression during test. Author: Davies Liu <davies@databricks.com> Closes #3420 from davies/ser2 and squashes the following commits: 0e1e6f3 [Davies Liu] fix tests 426f5db [Davies Liu] impove toArray() 44707ec [Davies Liu] add name for ISO-8859-1 fa7d791 [Davies Liu] address comments 1cfb137 [Davies Liu] handle zero sparse vector 2548ee2 [Davies Liu] fix tests 9e6389d [Davies Liu] bugfix 470f702 [Davies Liu] speed up DenseMatrix f0d3c40 [Davies Liu] speedup SparseVector ef6ce70 [Davies Liu] speed up dense vector
* [SPARK-4518][SPARK-4519][Streaming] Refactored file stream to prevent files ↵Tathagata Das2014-11-244-156/+245
| | | | | | | | | | | | | | | | | | | | | from being processed multiple times Because of a corner case, a file already selected for batch t can get considered again for batch t+2. This refactoring fixes it by remembering all the files selected in the last 1 minute, so that this corner case does not arise. Also uses spark context's hadoop configuration to access the file system API for listing directories. pwendell Please take look. I still have not run long-running integration tests, so I cannot say for sure whether this has indeed solved the issue. You could do a first pass on this in the meantime. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #3419 from tdas/filestream-fix2 and squashes the following commits: c19dd8a [Tathagata Das] Addressed PR comments. 513b608 [Tathagata Das] Updated docs. d364faf [Tathagata Das] Added the current time condition back 5526222 [Tathagata Das] Removed unnecessary imports. 38bb736 [Tathagata Das] Fix long line. 203bbc7 [Tathagata Das] Un-ignore tests. eaef4e1 [Tathagata Das] Fixed SPARK-4519 9dbd40a [Tathagata Das] Refactored FileInputDStream to remember last few batches.
* [SPARK-4145] Web UI job pagesJosh Rosen2014-11-2421-75/+1054
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR adds two new pages to the Spark Web UI: - A jobs overview page, which shows details on running / completed / failed jobs. - A job details page, which displays information on an individual job's stages. The jobs overview page is now the default UI homepage; the old homepage is still accessible at `/stages`. ### Screenshots #### New UI homepage ![image](https://cloud.githubusercontent.com/assets/50748/5119035/fd0a69e6-701f-11e4-89cb-db7e9705714f.png) #### Job details page (This is effectively a per-job version of the stages page that can be extended later with other things, such as DAG visualizations) ![image](https://cloud.githubusercontent.com/assets/50748/5134910/50b340d4-70c7-11e4-88e1-6b73237ea7c8.png) ### Key changes in this PR - Rename `JobProgressPage` to `AllStagesPage` - Expose `StageInfo` objects in the ``SparkListenerJobStart` event; add backwards-compatibility tests to JsonProtocol. - Add additional data structures to `JobProgressListener` to map from stages to jobs. - Add several fields to `JobUIData`. I also added ~150 lines of Selenium tests as I uncovered UI issues while developing this patch. ### Limitations If a job contains stages that aren't run, then its overall job progress bar may be an underestimate of the total job progress; in other words, a completed job may appear to have a progress bar that's not at 100%. If stages or tasks fail, then the progress bar will not go backwards to reflect the true amount of remaining work. Author: Josh Rosen <joshrosen@databricks.com> Closes #3009 from JoshRosen/job-page and squashes the following commits: eb05e90 [Josh Rosen] Disable kill button in completed stages tables. f00c851 [Josh Rosen] Fix JsonProtocol compatibility b89c258 [Josh Rosen] More JSON protocol backwards-compatibility fixes. ff804cd [Josh Rosen] Don't write "Stage Ids" field in JobStartEvent JSON. 6f17f3f [Josh Rosen] Only store StageInfos in SparkListenerJobStart event. 2bbf41a [Josh Rosen] Update job progress bar to reflect skipped tasks/stages. 61c265a [Josh Rosen] Add “skipped stages” table; only display non-empty tables. 1f45d44 [Josh Rosen] Incorporate a bunch of minor review feedback. 0b77e3e [Josh Rosen] More bug fixes for phantom stages. 034aa8d [Josh Rosen] Use `.max()` to find result stage for job. eebdc2c [Josh Rosen] Don’t display pending stages for completed jobs. 67080ba [Josh Rosen] Ensure that "phantom stages" don't cause memory leaks. 7d10b97 [Josh Rosen] Merge remote-tracking branch 'apache/master' into job-page d69c775 [Josh Rosen] Fix table sorting on all jobs page. 5eb39dc [Josh Rosen] Add pending stages table to job page. f2a15da [Josh Rosen] Add status field to job details page. 171b53c [Josh Rosen] Move `startTime` to the start of SparkContext. e2f2c43 [Josh Rosen] Fix sorting of stages in job details page. 8955f4c [Josh Rosen] Display information for pending stages on jobs page. 8ab6c28 [Josh Rosen] Compute numTasks from job start stage infos. 5884f91 [Josh Rosen] Add StageInfos to SparkListenerJobStart event. 79793cd [Josh Rosen] Track indices of completed stage to avoid overcounting when failures occur. d62ea7b [Josh Rosen] Add failing Selenium test for stage overcounting issue. 1145c60 [Josh Rosen] Display text instead of progress bar for stages. 3d0a007 [Josh Rosen] Merge remote-tracking branch 'origin/master' into job-page 8a2351b [Josh Rosen] Add help tooltip to Spark Jobs page. b7bf30e [Josh Rosen] Add stages progress bar; fix bug where active stages show as completed. 4846ce4 [Josh Rosen] Hide "(Job Group") if no jobs were submitted in job groups. 4d58e55 [Josh Rosen] Change label to "Tasks (for all stages)" 85e9c85 [Josh Rosen] Extract startTime into separate variable. 1cf4987 [Josh Rosen] Fix broken kill links; add Selenium test to avoid future regressions. 56701fa [Josh Rosen] Move last stage name / description logic out of markup. a475ea1 [Josh Rosen] Add progress bars to jobs page. 45343b8 [Josh Rosen] More comments 4b206fb [Josh Rosen] Merge remote-tracking branch 'origin/master' into job-page bfce2b9 [Josh Rosen] Address review comments, except for progress bar. 4487dcb [Josh Rosen] [SPARK-4145] Web UI job pages 2568a6c [Josh Rosen] Rename JobProgressPage to AllStagesPage:
* [SPARK-4487][SQL] Fix attribute reference resolution error when using ORDER BY.Kousuke Saruta2014-11-242-1/+8
| | | | | | | | | | | | | | | | | | | | | When we use ORDER BY clause, at first, attributes referenced by projection are resolved (1). And then, attributes referenced at ORDER BY clause are resolved (2). But when resolving attributes referenced at ORDER BY clause, the resolution result generated in (1) is discarded so for example, following query fails. SELECT c1 + c2 FROM mytable ORDER BY c1; The query above fails because when resolving the attribute reference 'c1', the resolution result of 'c2' is discarded. Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #3363 from sarutak/SPARK-4487 and squashes the following commits: fd314f3 [Kousuke Saruta] Fixed attribute resolution logic in Analyzer 6e60c20 [Kousuke Saruta] Fixed conflicts cb5b7e9 [Kousuke Saruta] Added test case for SPARK-4487 282d529 [Kousuke Saruta] Fixed attributes reference resolution error b6123e6 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into concat-feature 317b7fb [Kousuke Saruta] WIP
* [SQL] Fix path in HiveFromSparkscwf2014-11-241-1/+2
| | | | | | | | | | | | It require us to run ```HiveFromSpark``` in specified dir because ```HiveFromSpark``` use relative path, this leads to ```run-example``` error(http://apache-spark-developers-list.1001551.n3.nabble.com/src-main-resources-kv1-txt-not-found-in-example-of-HiveFromSpark-td9100.html). Author: scwf <wangfei1@huawei.com> Closes #3415 from scwf/HiveFromSpark and squashes the following commits: ed3d6c9 [scwf] revert no need change b00e20c [scwf] fix path usring spark_home dbd321b [scwf] fix path in hivefromspark
* [SQL] Fix comment in HiveShimDaniel Darabos2014-11-241-1/+1
| | | | | | | | | | This file is for Hive 0.13.1 I think. Author: Daniel Darabos <darabos.daniel@gmail.com> Closes #3432 from darabos/patch-2 and squashes the following commits: 4fd22ed [Daniel Darabos] Fix comment. This file is for Hive 0.13.1.
* [SPARK-4479][SQL] Avoids unnecessary defensive copies when sort based ↵Cheng Lian2014-11-243-10/+41
| | | | | | | | | | | | | | | | | | | | | | shuffle is on This PR is a workaround for SPARK-4479. Two changes are introduced: when merge sort is bypassed in `ExternalSorter`, 1. also bypass RDD elements buffering as buffering is the reason that `MutableRow` backed row objects must be copied, and 2. avoids defensive copies in `Exchange` operator <!-- Reviewable:start --> [<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/3422) <!-- Reviewable:end --> Author: Cheng Lian <lian@databricks.com> Closes #3422 from liancheng/avoids-defensive-copies and squashes the following commits: 591f2e9 [Cheng Lian] Passes all shuffle suites 0c3c91e [Cheng Lian] Fixes shuffle write metrics when merge sort is bypassed ed5df3c [Cheng Lian] Fixes styling changes f75089b [Cheng Lian] Avoids unnecessary defensive copies when sort based shuffle is on
* SPARK-4457. Document how to build for Hadoop versions greater than 2.4Sandy Ryza2014-11-241-2/+5
| | | | | | | | | | Author: Sandy Ryza <sandy@cloudera.com> Closes #3322 from sryza/sandy-spark-4457 and squashes the following commits: 5e72b77 [Sandy Ryza] Feedback 0cf05c1 [Sandy Ryza] Caveat be8084b [Sandy Ryza] SPARK-4457. Document how to build for Hadoop versions greater than 2.4
* [SPARK-4377] Fixed serialization issue by switching to akka provided serializer.Prashant Sharma2014-11-224-35/+42
| | | | | | | | | | | | ... - there is no way around this for deserializing actorRef(s). Author: Prashant Sharma <prashant.s@imaginea.com> Closes #3402 from ScrapCodes/SPARK-4377/troubleDeserializing and squashes the following commits: 77233fd [Prashant Sharma] Style fixes 9b35c6e [Prashant Sharma] Scalastyle fixes 29880da [Prashant Sharma] [SPARK-4377] Fixed serialization issue by switching to akka provided serializer - there is no way around this for deserializing actorRef(s).
* [SPARK-4431][MLlib] Implement efficient foreachActive for dense and sparse ↵DB Tsai2014-11-213-72/+105
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | vector Previously, we were using Breeze's activeIterator to access the non-zero elements in dense/sparse vector. Due to the overhead, we switched back to native `while loop` in #SPARK-4129. However, #SPARK-4129 requires de-reference the dv.values/sv.values in each access to the value, which is very expensive. Also, in MultivariateOnlineSummarizer, we're using Breeze's dense vector to store the partial stats, and this is very expensive compared with using primitive scala array. In this PR, efficient foreachActive is implemented to unify the code path for dense and sparse vector operation which makes codebase easier to maintain. Breeze dense vector is replaced by primitive array to reduce the overhead further. Benchmarking with mnist8m dataset on single JVM with first 200 samples loaded in memory, and repeating 5000 times. Before change: Sparse Vector - 30.02 Dense Vector - 38.27 With this PR: Sparse Vector - 6.29 Dense Vector - 11.72 Author: DB Tsai <dbtsai@alpinenow.com> Closes #3288 from dbtsai/activeIterator and squashes the following commits: 844b0e6 [DB Tsai] formating 03dd693 [DB Tsai] futher performance tunning. 1907ae1 [DB Tsai] address feedback 98448bb [DB Tsai] Made the override final, and had a local copy of variables which made the accessing a single step operation. c0cbd5a [DB Tsai] fix a bug 6441f92 [DB Tsai] Finished SPARK-4431
* [SPARK-4531] [MLlib] cache serialized java objectDavies Liu2014-11-217-93/+64
| | | | | | | | | | | | | | | | | | | The Pyrolite is pretty slow (comparing to the adhoc serializer in 1.1), it cause much performance regression in 1.2, because we cache the serialized Python object in JVM, deserialize them into Java object in each step. This PR change to cache the deserialized JavaRDD instead of PythonRDD to avoid the deserialization of Pyrolite. It should have similar memory usage as before, but much faster. Author: Davies Liu <davies@databricks.com> Closes #3397 from davies/cache and squashes the following commits: 7f6e6ce [Davies Liu] Update -> Updater 4b52edd [Davies Liu] using named argument 63b984e [Davies Liu] fix 7da0332 [Davies Liu] add unpersist() dff33e1 [Davies Liu] address comments c2bdfc2 [Davies Liu] refactor d572f00 [Davies Liu] Merge branch 'master' into cache f1063e1 [Davies Liu] cache serialized java object
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