- Nov 11, 2015
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Reynold Xin authored
This is a followup for #9317 to replace volatile fields with AtomicBoolean and AtomicReference. Author: Reynold Xin <rxin@databricks.com> Closes #9611 from rxin/SPARK-10827.
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Josh Rosen authored
This patch aims to reduce the test time and flakiness of HiveSparkSubmitSuite, SparkSubmitSuite, and CliSuite. Key changes: - Disable IO synchronization calls for Derby writes, since durability doesn't matter for tests. This was done for HiveCompatibilitySuite in #6651 and resulted in huge test speedups. - Add a few missing `--conf`s to disable various Spark UIs. The CliSuite, in particular, never disabled these UIs, leaving it prone to port-contention-related flakiness. - Fix two instances where tests defined `beforeAll()` methods which were never called because the appropriate traits were not mixed in. I updated these tests suites to extend `BeforeAndAfterEach` so that they play nicely with our `ResetSystemProperties` trait. Author: Josh Rosen <joshrosen@databricks.com> Closes #9623 from JoshRosen/SPARK-11647.
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Nick Evans authored
[SPARK-11335][STREAMING] update kafka direct python docs on how to get the offset ranges for a KafkaRDD tdas koeninger This updates the Spark Streaming + Kafka Integration Guide doc with a working method to access the offsets of a `KafkaRDD` through Python. Author: Nick Evans <me@nicolasevans.org> Closes #9289 from manygrams/update_kafka_direct_python_docs.
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Reynold Xin authored
Author: Reynold Xin <rxin@databricks.com> Closes #9621 from rxin/SPARK-11645.
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Reynold Xin authored
Author: Reynold Xin <rxin@databricks.com> Closes #9618 from rxin/SPARK-11644.
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Burak Yavuz authored
[SPARK-11639][STREAMING][FLAKY-TEST] Implement BlockingWriteAheadLog for testing the BatchedWriteAheadLog Several elements could be drained if the main thread is not fast enough. zsxwing warned me about a similar problem, but missed it here :( Submitting the fix using a waiter. cc tdas Author: Burak Yavuz <brkyvz@gmail.com> Closes #9605 from brkyvz/fix-flaky-test.
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Josh Rosen authored
This patch modifies Spark's closure cleaner (and a few other places) to use ASM 5, which is necessary in order to support cleaning of closures that were compiled by Java 8. In order to avoid ASM dependency conflicts, Spark excludes ASM from all of its dependencies and uses a shaded version of ASM 4 that comes from `reflectasm` (see [SPARK-782](https://issues.apache.org/jira/browse/SPARK-782) and #232). This patch updates Spark to use a shaded version of ASM 5.0.4 that was published by the Apache XBean project; the POM used to create the shaded artifact can be found at https://github.com/apache/geronimo-xbean/blob/xbean-4.4/xbean-asm5-shaded/pom.xml. http://movingfulcrum.tumblr.com/post/80826553604/asm-framework-50-the-missing-migration-guide was a useful resource while upgrading the code to use the new ASM5 opcodes. I also added a new regression tests in the `java8-tests` subproject; the existing tests were insufficient to catch this bug, which only affected Scala 2.11 user code which was compiled targeting Java 8. Author: Josh Rosen <joshrosen@databricks.com> Closes #9512 from JoshRosen/SPARK-6152.
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Wenchen Fan authored
it may shadows the one from implicits in some case. Author: Wenchen Fan <wenchen@databricks.com> Closes #9629 from cloud-fan/minor.
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Wenchen Fan authored
We need to support custom classes like java beans and combine them into tuple, and it's very hard to do it with the TypeTag-based approach. We should keep only the compose-based way to create tuple encoder. This PR also move `Encoder` to `org.apache.spark.sql` Author: Wenchen Fan <wenchen@databricks.com> Closes #9567 from cloud-fan/java.
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Wenchen Fan authored
insert `aEncoder` like we do in `agg` Author: Wenchen Fan <wenchen@databricks.com> Closes #9630 from cloud-fan/select.
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Wenchen Fan authored
Author: Wenchen Fan <wenchen@databricks.com> Closes #9617 from cloud-fan/tmp.
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Reynold Xin authored
If it returns Text, we can reuse this in Spark SQL to provide a WholeTextFile data source and directly convert the Text into UTF8String without extra string decoding and encoding. Author: Reynold Xin <rxin@databricks.com> Closes #9622 from rxin/SPARK-11646.
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Yuming Wang authored
org.apache.spark.ml.feature.Word2Vec.transform() very slow. we should not read broadcast every sentence. Author: Yuming Wang <q79969786@gmail.com> Author: yuming.wang <q79969786@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #9592 from 979969786/master.
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Wenchen Fan authored
Author: Wenchen Fan <wenchen@databricks.com> Closes #9627 from cloud-fan/follow.
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hyukjinkwon authored
https://issues.apache.org/jira/browse/SPARK-11500 As filed in SPARK-11500, if merging schemas is enabled, the order of files to touch is a matter which might affect the ordering of the output columns. This was mostly because of the use of `Set` and `Map` so I replaced them to `LinkedHashSet` and `LinkedHashMap` to keep the insertion order. Also, I changed `reduceOption` to `reduceLeftOption`, and replaced the order of `filesToTouch` from `metadataStatuses ++ commonMetadataStatuses ++ needMerged` to `needMerged ++ metadataStatuses ++ commonMetadataStatuses` in order to touch the part-files first which always have the schema in footers whereas the others might not exist. One nit is, If merging schemas is not enabled, but when multiple files are given, there is no guarantee of the output order, since there might not be a summary file for the first file, which ends up putting ahead the columns of the other files. However, I thought this should be okay since disabling merging schemas means (assumes) all the files have the same schemas. In addition, in the test code for this, I only checked the names of fields. Author: hyukjinkwon <gurwls223@gmail.com> Closes #9517 from HyukjinKwon/SPARK-11500.
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Tathagata Das authored
Current updateStateByKey provides stateful processing in Spark Streaming. It allows the user to maintain per-key state and manage that state using an updateFunction. The updateFunction is called for each key, and it uses new data and existing state of the key, to generate an updated state. However, based on community feedback, we have learnt the following lessons. * Need for more optimized state management that does not scan every key * Need to make it easier to implement common use cases - (a) timeout of idle data, (b) returning items other than state The high level idea that of this PR * Introduce a new API trackStateByKey that, allows the user to update per-key state, and emit arbitrary records. The new API is necessary as this will have significantly different semantics than the existing updateStateByKey API. This API will have direct support for timeouts. * Internally, the system will keep the state data as a map/list within the partitions of the state RDDs. The new data RDDs will be partitioned appropriately, and for all the key-value data, it will lookup the map/list in the state RDD partition and create a new list/map of updated state data. The new state RDD partition will be created based on the update data and if necessary, with old data. Here is the detailed design doc. Please take a look and provide feedback as comments. https://docs.google.com/document/d/1NoALLyd83zGs1hNGMm0Pc5YOVgiPpMHugGMk6COqxxE/edit#heading=h.ph3w0clkd4em This is still WIP. Major things left to be done. - [x] Implement basic functionality of state tracking, with initial RDD and timeouts - [x] Unit tests for state tracking - [x] Unit tests for initial RDD and timeout - [ ] Unit tests for TrackStateRDD - [x] state creating, updating, removing - [ ] emitting - [ ] checkpointing - [x] Misc unit tests for State, TrackStateSpec, etc. - [x] Update docs and experimental tags Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #9256 from tdas/trackStateByKey.
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Davies Liu authored
Only install signal in main thread, or it will fail to create context in not-main thread. Author: Davies Liu <davies@databricks.com> Closes #9574 from davies/python_signal.
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felixcheung authored
Checked names, none of them should conflict with anything in base shivaram davies rxin Author: felixcheung <felixcheung_m@hotmail.com> Closes #9489 from felixcheung/rstddev.
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Josh Rosen authored
This fixes an NPE introduced in SPARK-10192 / #8402. Author: Josh Rosen <joshrosen@databricks.com> Closes #9620 from JoshRosen/SPARK-10192-hotfix.
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- Nov 10, 2015
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Joseph K. Bradley authored
This PR adds model save/load for spark.ml's LogisticRegressionModel. It also does minor refactoring of the default save/load classes to reuse code. CC: mengxr Author: Joseph K. Bradley <joseph@databricks.com> Closes #9606 from jkbradley/logreg-io2.
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Marc Prud'hommeaux authored
The header wasn't indented properly. Author: Marc Prud'hommeaux <mwp1@cornell.edu> Closes #9312 from mprudhom/patch-1.
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Forest Fang authored
Author: Forest Fang <saurfang@users.noreply.github.com> Closes #9357 from saurfang/patch-1.
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Tathagata Das authored
[SPARK-11361][STREAMING] Show scopes of RDD operations inside DStream.foreachRDD and DStream.transform in DAG viz Currently, when a DStream sets the scope for RDD generated by it, that scope is not allowed to be overridden by the RDD operations. So in case of `DStream.foreachRDD`, all the RDDs generated inside the foreachRDD get the same scope - `foreachRDD <time>`, as set by the `ForeachDStream`. So it is hard to debug generated RDDs in the RDD DAG viz in the Spark UI. This patch allows the RDD operations inside `DStream.transform` and `DStream.foreachRDD` to append their own scopes to the earlier DStream scope. I have also slightly tweaked how callsites are set such that the short callsite reflects the RDD operation name and line number. This tweak is necessary as callsites are not managed through scopes (which support nesting and overriding) and I didnt want to add another local property to control nesting and overriding of callsites. ## Before:  ## After:  The code that was used to generate this is: ``` val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_AND_DISK_SER) val words = lines.flatMap(_.split(" ")) val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _) wordCounts.foreachRDD { rdd => val temp = rdd.map { _ -> 1 }.reduceByKey( _ + _) val temp2 = temp.map { _ -> 1}.reduceByKey(_ + _) val count = temp2.count println(count) } ``` Note - The inner scopes of the RDD operations map/reduceByKey inside foreachRDD is visible - The short callsites of stages refers to the line number of the RDD ops rather than the same line number of foreachRDD in all three cases. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #9315 from tdas/SPARK-11361.
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tedyu authored
See http://search-hadoop.com/m/q3RTtjpe8r1iRbTj2 for discussion. Summary: addition of VisibleForTesting annotation resulted in spark-shell malfunctioning. Author: tedyu <yuzhihong@gmail.com> Closes #9585 from tedyu/master.
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tedyu authored
As vonnagy reported in the following thread: http://search-hadoop.com/m/q3RTtk982kvIow22 Attempts to join the thread in AsynchronousListenerBus resulted in lock up because AsynchronousListenerBus thread was still getting messages `SparkListenerExecutorMetricsUpdate` from the DAGScheduler Author: tedyu <yuzhihong@gmail.com> Closes #9546 from ted-yu/master.
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Imran Rashid authored
just trying to increase test coverage in the scheduler, this already works. It includes a regression test for SPARK-9809 copied some test utils from https://github.com/apache/spark/pull/5636, we can wait till that is merged first Author: Imran Rashid <irashid@cloudera.com> Closes #8402 from squito/test_retry_in_shared_shuffle_dep.
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Yu ISHIKAWA authored
cc jkbradley Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #9534 from yu-iskw/SPARK-11566.
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Bryan Cutler authored
Changed AppClient to be non-blocking in `receiveAndReply` by using a separate thread to wait for response and reply to the context. The threads are managed by a thread pool. Also added unit tests for the AppClient interface. Author: Bryan Cutler <bjcutler@us.ibm.com> Closes #9317 from BryanCutler/appClient-receiveAndReply-SPARK-10827.
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Herman van Hovell authored
This PR is a 2nd follow-up for [SPARK-9241](https://issues.apache.org/jira/browse/SPARK-9241). It contains the following improvements: * Fix for a potential bug in distinct child expression and attribute alignment. * Improved handling of duplicate distinct child expressions. * Added test for distinct UDAF with multiple children. cc yhuai Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #9566 from hvanhovell/SPARK-9241-followup-2.
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Yin Huai authored
[SPARK-9830][SPARK-11641][SQL][FOLLOW-UP] Remove AggregateExpression1 and update toString of Exchange https://issues.apache.org/jira/browse/SPARK-9830 This is the follow-up pr for https://github.com/apache/spark/pull/9556 to address davies' comments. Author: Yin Huai <yhuai@databricks.com> Closes #9607 from yhuai/removeAgg1-followup.
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Joseph K. Bradley authored
This adds LDA to spark.ml, the Pipelines API. It follows the design doc in the JIRA: [https://issues.apache.org/jira/browse/SPARK-5565], with one major change: * I eliminated doc IDs. These are not necessary with DataFrames since the user can add an ID column as needed. Note: This will conflict with [https://github.com/apache/spark/pull/9484], but I'll try to merge [https://github.com/apache/spark/pull/9484] first and then rebase this PR. CC: hhbyyh feynmanliang If you have a chance to make a pass, that'd be really helpful--thanks! Now that I'm done traveling & this PR is almost ready, I'll see about reviewing other PRs critical for 1.6. CC: mengxr Author: Joseph K. Bradley <joseph@databricks.com> Closes #9513 from jkbradley/lda-pipelines.
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Josh Rosen authored
This patch re-enables tests for the Docker JDBC data source. These tests were reverted in #4872 due to transitive dependency conflicts introduced by the `docker-client` library. This patch should avoid those problems by using a version of `docker-client` which shades its transitive dependencies and by performing some build-magic to work around problems with that shaded JAR. In addition, I significantly refactored the tests to simplify the setup and teardown code and to fix several Docker networking issues which caused problems when running in `boot2docker`. Closes #8101. Author: Josh Rosen <joshrosen@databricks.com> Author: Yijie Shen <henry.yijieshen@gmail.com> Closes #9503 from JoshRosen/docker-jdbc-tests.
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felixcheung authored
like `df.agg(corr("col1", "col2")` davies Author: felixcheung <felixcheung_m@hotmail.com> Closes #9536 from felixcheung/pyfunc. -
Pravin Gadakh authored
Author: Pravin Gadakh <pravingadakh177@gmail.com> Closes #9516 from pravingadakh/SPARK-11550.
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Michael Armbrust authored
Author: Michael Armbrust <michael@databricks.com> Closes #9586 from marmbrus/dataset-toString.
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unknown authored
Implementation of step capability for sliding window function in MLlib's RDD. Though one can use current sliding window with step 1 and then filter every Nth window, it will take more time and space (N*data.count times more than needed). For example, below are the results for various windows and steps on 10M data points: Window | Step | Time | Windows produced ------------ | ------------- | ---------- | ---------- 128 | 1 | 6.38 | 9999873 128 | 10 | 0.9 | 999988 128 | 100 | 0.41 | 99999 1024 | 1 | 44.67 | 9998977 1024 | 10 | 4.74 | 999898 1024 | 100 | 0.78 | 99990 ``` import org.apache.spark.mllib.rdd.RDDFunctions._ val rdd = sc.parallelize(1 to 10000000, 10) rdd.count val window = 1024 val step = 1 val t = System.nanoTime(); val windows = rdd.sliding(window, step); println(windows.count); println((System.nanoTime() - t) / 1e9) ``` Author: unknown <ulanov@ULANOV3.americas.hpqcorp.net> Author: Alexander Ulanov <nashb@yandex.ru> Author: Xiangrui Meng <meng@databricks.com> Closes #5855 from avulanov/SPARK-7316-sliding.
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Joseph K. Bradley authored
Refactoring * separated overwrite and param save logic in DefaultParamsWriter * added sparkVersion to DefaultParamsWriter CC: mengxr Author: Joseph K. Bradley <joseph@databricks.com> Closes #9587 from jkbradley/logreg-io.
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Yanbo Liang authored
Follow up #9561. Due to [SPARK-11587](https://issues.apache.org/jira/browse/SPARK-11587) has been fixed, we should compare SparkR::glm summary result with native R output rather than hard-code one. mengxr Author: Yanbo Liang <ybliang8@gmail.com> Closes #9590 from yanboliang/glm-r-test.
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Nong Li authored
This patch adds the building blocks for codegening subexpr elimination and implements it end to end for UnsafeProjection. The building blocks can be used to do the same thing for other operators. It introduces some utilities to compute common sub expressions. Expressions can be added to this data structure. The expr and its children will be recursively matched against existing expressions (ones previously added) and grouped into common groups. This is built using the existing `semanticEquals`. It does not understand things like commutative or associative expressions. This can be done as future work. After building this data structure, the codegen process takes advantage of it by: 1. Generating a helper function in the generated class that computes the common subexpression. This is done for all common subexpressions that have at least two occurrences and the expression tree is sufficiently complex. 2. When generating the apply() function, if the helper function exists, call that instead of regenerating the expression tree. Repeated calls to the helper function shortcircuit the evaluation logic. Author: Nong Li <nong@databricks.com> Author: Nong Li <nongli@gmail.com> This patch had conflicts when merged, resolved by Committer: Michael Armbrust <michael@databricks.com> Closes #9480 from nongli/spark-10371. -
Wenchen Fan authored
Author: Wenchen Fan <wenchen@databricks.com> Closes #9562 from cloud-fan/json-tuple.
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