- 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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Wenchen Fan authored
Currently the user facing api for typed aggregation has some limitations: * the customized typed aggregation must be the first of aggregation list * the customized typed aggregation can only use long as buffer type * the customized typed aggregation can only use flat type as result type This PR tries to remove these limitations. Author: Wenchen Fan <wenchen@databricks.com> Closes #9599 from cloud-fan/agg.
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Oscar D. Lara Yejas authored
This is a follow up on PR #8984, as the corresponding branch for such PR was damaged. Author: Oscar D. Lara Yejas <olarayej@mail.usf.edu> Closes #9579 from olarayej/SPARK-10863_NEW14.
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Yin Huai authored
[SPARK-9830][SQL] Remove AggregateExpression1 and Aggregate Operator used to evaluate AggregateExpression1s https://issues.apache.org/jira/browse/SPARK-9830 This PR contains the following main changes. * Removing `AggregateExpression1`. * Removing `Aggregate` operator, which is used to evaluate `AggregateExpression1`. * Removing planner rule used to plan `Aggregate`. * Linking `MultipleDistinctRewriter` to analyzer. * Renaming `AggregateExpression2` to `AggregateExpression` and `AggregateFunction2` to `AggregateFunction`. * Updating places where we create aggregate expression. The way to create aggregate expressions is `AggregateExpression(aggregateFunction, mode, isDistinct)`. * Changing `val`s in `DeclarativeAggregate`s that touch children of this function to `lazy val`s (when we create aggregate expression in DataFrame API, children of an aggregate function can be unresolved). Author: Yin Huai <yhuai@databricks.com> Closes #9556 from yhuai/removeAgg1.
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Lianhui Wang authored
[SPARK-11252][NETWORK] ShuffleClient should release connection after fetching blocks had been completed for external shuffle with yarn's external shuffle, ExternalShuffleClient of executors reserve its connections for yarn's NodeManager until application has been completed. so it will make NodeManager and executors have many socket connections. in order to reduce network pressure of NodeManager's shuffleService, after registerWithShuffleServer or fetchBlocks have been completed in ExternalShuffleClient, connection for NM's shuffleService needs to be closed.andrewor14 rxin vanzin Author: Lianhui Wang <lianhuiwang09@gmail.com> Closes #9227 from lianhuiwang/spark-11252.
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Josh Rosen authored
This patch modifies Spark's SBT build so that it no longer uses `retrieveManaged` / `lib_managed` to store its dependencies. The motivations for this change are nicely described on the JIRA ticket ([SPARK-7841](https://issues.apache.org/jira/browse/SPARK-7841)); my personal interest in doing this stems from the fact that `lib_managed` has caused me some pain while debugging dependency issues in another PR of mine. Removing our use of `lib_managed` would be trivial except for one snag: the Datanucleus JARs, required by Spark SQL's Hive integration, cannot be included in assembly JARs due to problems with merging OSGI `plugin.xml` files. As a result, several places in the packaging and deployment pipeline assume that these Datanucleus JARs are copied to `lib_managed/jars`. In the interest of maintaining compatibility, I have chosen to retain the `lib_managed/jars` directory _only_ for these Datanucleus JARs and have added custom code to `SparkBuild.scala` to automatically copy those JARs to that folder as part of the `assembly` task. `dev/mima` also depended on `lib_managed` in a hacky way in order to set classpaths when generating MiMa excludes; I've updated this to obtain the classpaths directly from SBT instead. /cc dragos marmbrus pwendell srowen Author: Josh Rosen <joshrosen@databricks.com> Closes #9575 from JoshRosen/SPARK-7841.
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Xusen Yin authored
https://issues.apache.org/jira/browse/SPARK-11382 B.T.W. I fix an error in naive_bayes_example.py. Author: Xusen Yin <yinxusen@gmail.com> Closes #9596 from yinxusen/SPARK-11382.
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Paul Chandler authored
"Comamnd property" => "Command property" Author: Paul Chandler <pestilence669@users.noreply.github.com> Closes #9578 from pestilence669/fix_spelling.
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Davies Liu authored
Author: Davies Liu <davies@databricks.com> Closes #9573 from davies/join_condition.
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Davies Liu authored
The DataFrame APIs that takes a SQL expression always use SQLParser, then the HiveFunctionRegistry will called outside of Hive state, cause NPE if there is not a active Session State for current thread (in PySpark). cc rxin yhuai Author: Davies Liu <davies@databricks.com> Closes #9576 from davies/hive_udf.
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- Nov 09, 2015
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Shivaram Venkataraman authored
The signature is summary(object, ...) as defined in https://stat.ethz.ch/R-manual/R-devel/library/base/html/summary.html Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu> Closes #9582 from shivaram/summary-fix.
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Burak Yavuz authored
While sbt successfully compiles as it properly pulls the mockito dependency, maven builds have broken. We need this in ASAP. tdas Author: Burak Yavuz <brkyvz@gmail.com> Closes #9584 from brkyvz/fix-master.
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Shixiong Zhu authored
Expose executorId to `ReceiverInfo` and UI since it's helpful when there are multiple executors running in the same host. Screenshot: <img width="1058" alt="screen shot 2015-11-02 at 10 52 19 am" src="https://cloud.githubusercontent.com/assets/1000778/10890968/2e2f5512-8150-11e5-8d9d-746e826b69e8.png"> Author: Shixiong Zhu <shixiong@databricks.com> Author: zsxwing <zsxwing@gmail.com> Closes #9418 from zsxwing/SPARK-11333.
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zsxwing authored
Currently, StreamingListener is not Java friendly because it exposes some Scala collections to Java users directly, such as Option, Map. This PR added a Java version of StreamingListener and a bunch of Java friendly classes for Java users. Author: zsxwing <zsxwing@gmail.com> Author: Shixiong Zhu <shixiong@databricks.com> Closes #9420 from zsxwing/java-streaming-listener.
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Burak Yavuz authored
When using S3 as a directory for WALs, the writes take too long. The driver gets very easily bottlenecked when multiple receivers send AddBlock events to the ReceiverTracker. This PR adds batching of events in the ReceivedBlockTracker so that receivers don't get blocked by the driver for too long. cc zsxwing tdas Author: Burak Yavuz <brkyvz@gmail.com> Closes #9143 from brkyvz/batch-wal-writes.
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Burak Yavuz authored
While the KCL handles de-aggregation during the regular operation, during recovery we use the lower level api, and therefore need to de-aggregate the records. tdas Testing is an issue, we need protobuf magic to do the aggregated records. Maybe we could depend on KPL for tests? Author: Burak Yavuz <brkyvz@gmail.com> Closes #9403 from brkyvz/kinesis-deaggregation.
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Yuhao Yang authored
jira: https://issues.apache.org/jira/browse/SPARK-11069 quotes from jira: Tokenizer converts strings to lowercase automatically, but RegexTokenizer does not. It would be nice to add an option to RegexTokenizer to convert to lowercase. Proposal: call the Boolean Param "toLowercase" set default to false (so behavior does not change) Actually sklearn converts to lowercase before tokenizing too Author: Yuhao Yang <hhbyyh@gmail.com> Closes #9092 from hhbyyh/tokenLower.
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Yu ISHIKAWA authored
cc jkbradley Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #9577 from yu-iskw/SPARK-11610.
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Reynold Xin authored
Author: Reynold Xin <rxin@databricks.com> Closes #9557 from rxin/SPARK-11564-1.
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Michael Armbrust authored
This PR adds a new interface for user-defined aggregations, that can be used in `DataFrame` and `Dataset` operations to take all of the elements of a group and reduce them to a single value. For example, the following aggregator extracts an `int` from a specific class and adds them up: ```scala case class Data(i: Int) val customSummer = new Aggregator[Data, Int, Int] { def prepare(d: Data) = d.i def reduce(l: Int, r: Int) = l + r def present(r: Int) = r }.toColumn() val ds: Dataset[Data] = ... val aggregated = ds.select(customSummer) ``` By using helper functions, users can make a generic `Aggregator` that works on any input type: ```scala /** An `Aggregator` that adds up any numeric type returned by the given function. */ class SumOf[I, N : Numeric](f: I => N) extends Aggregator[I, N, N] with Serializable { val numeric = implicitly[Numeric[N]] override def zero: N = numeric.zero override def reduce(b: N, a: I): N = numeric.plus(b, f(a)) override def present(reduction: N): N = reduction } def sum[I, N : Numeric : Encoder](f: I => N): TypedColumn[I, N] = new SumOf(f).toColumn ``` These aggregators can then be used alongside other built-in SQL aggregations. ```scala val ds = Seq(("a", 10), ("a", 20), ("b", 1), ("b", 2), ("c", 1)).toDS() ds .groupBy(_._1) .agg( sum(_._2), // The aggregator defined above. expr("sum(_2)").as[Int], // A built-in dynatically typed aggregation. count("*")) // A built-in statically typed aggregation. .collect() res0: ("a", 30, 30, 2L), ("b", 3, 3, 2L), ("c", 1, 1, 1L) ``` The current implementation focuses on integrating this into the typed API, but currently only supports running aggregations that return a single long value as explained in `TypedAggregateExpression`. This will be improved in a followup PR. Author: Michael Armbrust <michael@databricks.com> Closes #9555 from marmbrus/dataset-useragg.
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