aboutsummaryrefslogtreecommitdiff
Commit message (Collapse)AuthorAgeFilesLines
* MAINTENANCE: Automated closing of pull requests.Patrick Wendell2014-08-020-0/+0
| | | | | | | | | | | | | | This commit exists to close the following pull requests on Github: Closes #706 (close requested by 'pwendell') Closes #453 (close requested by 'pwendell') Closes #557 (close requested by 'tdas') Closes #495 (close requested by 'tdas') Closes #1232 (close requested by 'pwendell') Closes #82 (close requested by 'pwendell') Closes #600 (close requested by 'pwendell') Closes #473 (close requested by 'pwendell') Closes #351 (close requested by 'pwendell')
* HOTFIX: Fix concurrency issue in FlumePollingStreamSuite.Patrick Wendell2014-08-021-1/+6
| | | | | | | | This has been failing on master. One possible cause is that the port gets contended if multiple test runs happen concurrently and they hit this test at the same time. Since this test takes a long time (60 seconds) that's very plausible. This patch randomizes the port used in this test to avoid contention.
* HOTFIX: Fixing test error in maven for flume-sink.Patrick Wendell2014-08-021-1/+4
| | | | | We needed to add an explicit dependency on scalatest since this module will not get it from spark core like others do.
* [SPARK-1812] sql/catalyst - Provide explicit type informationAnand Avati2014-08-021-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | For Scala 2.11 compatibility. Without the explicit type specification, withNullability return type is inferred to be Attribute, and thus calling at() on the returned object fails in these tests: [ERROR] /Users/avati/work/spark/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala:370: value at is not a [ERROR] val c4_notNull = 'a.boolean.notNull.at(3) [ERROR] ^ [ERROR] /Users/avati/work/spark/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala:371: value at is not a [ERROR] val c5_notNull = 'a.boolean.notNull.at(4) [ERROR] ^ [ERROR] /Users/avati/work/spark/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala:372: value at is not a [ERROR] val c6_notNull = 'a.boolean.notNull.at(5) [ERROR] ^ [ERROR] /Users/avati/work/spark/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala:558: value at is not a [ERROR] val s_notNull = 'a.string.notNull.at(0) Signed-off-by: Anand Avati <avatiredhat.com> Author: Anand Avati <avati@redhat.com> Closes #1709 from avati/SPARK-1812-notnull and squashes the following commits: 0470eb3 [Anand Avati] SPARK-1812: sql/catalyst - Provide explicit type information
* [SPARK-2454] Do not ship spark home to WorkersAndrew Or2014-08-0213-26/+15
| | | | | | | | | | | | | | | | | When standalone Workers launch executors, they inherit the Spark home set by the driver. This means if the worker machines do not share the same directory structure as the driver node, the Workers will attempt to run scripts (e.g. bin/compute-classpath.sh) that do not exist locally and fail. This is a common scenario if the driver is launched from outside of the cluster. The solution is to simply not pass the driver's Spark home to the Workers. This PR further makes an attempt to avoid overloading the usages of `spark.home`, which is now only used for setting executor Spark home on Mesos and in python. This is based on top of #1392 and originally reported by YanTangZhai. Tested on standalone cluster. Author: Andrew Or <andrewor14@gmail.com> Closes #1734 from andrewor14/spark-home-reprise and squashes the following commits: f71f391 [Andrew Or] Revert changes in python 1c2532c [Andrew Or] Merge branch 'master' of github.com:apache/spark into spark-home-reprise 188fc5d [Andrew Or] Avoid using spark.home where possible 09272b7 [Andrew Or] Always use Worker's working directory as spark home
* [SPARK-2316] Avoid O(blocks) operations in listenersAndrew Or2014-08-0113-176/+843
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The existing code in `StorageUtils` is not the most efficient. Every time we want to update an `RDDInfo` we end up iterating through all blocks on all block managers just to discard most of them. The symptoms manifest themselves in the bountiful UI bugs observed in the wild. Many of these bugs are caused by the slow consumption of events in `LiveListenerBus`, which frequently leads to the event queue overflowing and `SparkListenerEvent`s being dropped on the floor. The changes made in this PR avoid this by first filtering out only the blocks relevant to us before computing storage information from them. It's worth a mention that this corner of the Spark code is also not very well-tested at all. The bulk of the changes in this PR (more than 60%) is actually test cases for the various logic in `StorageUtils.scala` as well as `StorageTab.scala`. These will eventually be extended to cover the various listeners that constitute the `SparkUI`. Author: Andrew Or <andrewor14@gmail.com> Closes #1679 from andrewor14/fix-drop-events and squashes the following commits: f80c1fa [Andrew Or] Rewrite fold and reduceOption as sum e132d69 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-drop-events 14fa1c3 [Andrew Or] Simplify some code + update a few comments a91be46 [Andrew Or] Make ExecutorsPage blazingly fast bf6f09b [Andrew Or] Minor changes 8981de1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-drop-events af19bc0 [Andrew Or] *UsedByRDD -> *UsedByRdd (minor) 6970bc8 [Andrew Or] Add extensive tests for StorageListener and the new code in StorageUtils e080b9e [Andrew Or] Reduce run time of StorageUtils.updateRddInfo to near constant 2c3ef6a [Andrew Or] Actually filter out only the relevant RDDs 6fef86a [Andrew Or] Add extensive tests for new code in StorageStatus b66b6b0 [Andrew Or] Use more efficient underlying data structures for blocks 6a7b7c0 [Andrew Or] Avoid chained operations on TraversableLike a9ec384 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-drop-events b12fcd7 [Andrew Or] Fix tests + simplify sc.getRDDStorageInfo da8e322 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-drop-events 8e91921 [Andrew Or] Iterate through a filtered set of blocks when updating RDDInfo 7b2c4aa [Andrew Or] Rewrite blockLocationsFromStorageStatus + clean up method signatures 41fa50d [Andrew Or] Add a legacy constructor for StorageStatus 53af15d [Andrew Or] Refactor StorageStatus + add a bunch of tests
* Revert "[SPARK-1470][SPARK-1842] Use the scala-logging wrapper instead of ↵Patrick Wendell2014-08-0135-203/+97
| | | | | | the directly sfl4j api" This reverts commit adc8303294e26efb4ed15e5f5ba1062f7988625d.
* [SPARK-1470][SPARK-1842] Use the scala-logging wrapper instead of the ↵GuoQiang Li2014-08-0135-97/+203
| | | | | | | | | | | directly sfl4j api Author: GuoQiang Li <witgo@qq.com> Closes #1369 from witgo/SPARK-1470_new and squashes the following commits: 66a1641 [GuoQiang Li] IncompatibleResultTypeProblem 73a89ba [GuoQiang Li] Use the scala-logging wrapper instead of the directly sfl4j api.
* StatCounter on NumPy arrays [PYSPARK][SPARK-2012]Jeremy Freeman2014-08-012-8/+37
| | | | | | | | | | | | | | | | | | | These changes allow StatCounters to work properly on NumPy arrays, to fix the issue reported here (https://issues.apache.org/jira/browse/SPARK-2012). If NumPy is installed, the NumPy functions ``maximum``, ``minimum``, and ``sqrt``, which work on arrays, are used to merge statistics. If not, we fall back on scalar operators, so it will work on arrays with NumPy, but will also work without NumPy. New unit tests added, along with a check for NumPy in the tests. Author: Jeremy Freeman <the.freeman.lab@gmail.com> Closes #1725 from freeman-lab/numpy-max-statcounter and squashes the following commits: fe973b1 [Jeremy Freeman] Avoid duplicate array import in tests 7f0e397 [Jeremy Freeman] Refactored check for numpy 8e764dd [Jeremy Freeman] Explicit numpy imports 875414c [Jeremy Freeman] Fixed indents 1c8a832 [Jeremy Freeman] Unit tests for StatCounter with NumPy arrays 176a127 [Jeremy Freeman] Use numpy arrays in StatCounter
* [SPARK-2801][MLlib]: DistributionGenerator renamed to RandomDataGenerator. ↵Burak2014-08-015-46/+52
| | | | | | | | | | | | | | RandomRDD is now of generic type The RandomRDDGenerators used to only output RDD[Double]. Now RandomRDDGenerators.randomRDD can be used to generate a random RDD[T] via a class that extends RandomDataGenerator, by supplying a type T and overriding the nextValue() function as they wish. Author: Burak <brkyvz@gmail.com> Closes #1732 from brkyvz/SPARK-2801 and squashes the following commits: c94a694 [Burak] [SPARK-2801][MLlib] Missing ClassTags added 22d96fe [Burak] [SPARK-2801][MLlib]: DistributionGenerator renamed to RandomDataGenerator, generic types added for RandomRDD instead of Double
* [SPARK-1580][MLLIB] Estimate ALS communication and computation costs.Tor Myklebust2014-08-012-8/+144
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Continue the work from #493. Closes #493 and Closes #593 Author: Tor Myklebust <tmyklebu@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #1731 from mengxr/tmyklebu-alscost and squashes the following commits: 9b56a8b [Xiangrui Meng] updated API and added a simple test 68a3229 [Xiangrui Meng] merge master 217bd1d [Tor Myklebust] Documentation and choleskies -> subproblems. 8cbb718 [Tor Myklebust] Braces get spaces. 0455cd4 [Tor Myklebust] Parens for collectAsMap. 2b2febe [Tor Myklebust] Use `makeLinkRDDs` when estimating costs. 2ab7a5d [Tor Myklebust] Reindent estimateCost's declaration and make it return Seqs. 8b21e6d [Tor Myklebust] Fix overlong lines. 8cbebf1 [Tor Myklebust] Rename and clean up the return format of cost estimator. 6615ed5 [Tor Myklebust] It's more useful to give per-partition estimates. Do that. 5530678 [Tor Myklebust] Merge branch 'master' of https://github.com/apache/spark into alscost 6c31324 [Tor Myklebust] Make it actually build... a1184d1 [Tor Myklebust] Mark ALS.evaluatePartitioner DeveloperApi. 657a71b [Tor Myklebust] Simple-minded estimates of computation and communication costs in ALS. dcf583a [Tor Myklebust] Remove the partitioner member variable; instead, thread that needle everywhere it needs to go. 23d6f91 [Tor Myklebust] Stop making the partitioner configurable. 495784f [Tor Myklebust] Merge branch 'master' of https://github.com/apache/spark 674933a [Tor Myklebust] Fix style. 40edc23 [Tor Myklebust] Fix missing space. f841345 [Tor Myklebust] Fix daft bug creating 'pairs', also for -> foreach. 5ec9e6c [Tor Myklebust] Clean a couple of things up using 'map'. 36a0f43 [Tor Myklebust] Make the partitioner private. d872b09 [Tor Myklebust] Add negative id ALS test. df27697 [Tor Myklebust] Support custom partitioners. Currently we use the same partitioner for users and products. c90b6d8 [Tor Myklebust] Scramble user and product ids before bucketing. c774d7d [Tor Myklebust] Make the partitioner a member variable and use it instead of modding directly.
* [SPARK-2550][MLLIB][APACHE SPARK] Support regularization and intercept in ↵Michael Giannakopoulos2014-08-012-11/+49
| | | | | | | | | | | | | | | | | | | | | pyspark's linear methods. Related to issue: [SPARK-2550](https://issues.apache.org/jira/browse/SPARK-2550?jql=project%20%3D%20SPARK%20AND%20resolution%20%3D%20Unresolved%20AND%20priority%20%3D%20Major%20ORDER%20BY%20key%20DESC). Author: Michael Giannakopoulos <miccagiann@gmail.com> Closes #1624 from miccagiann/new-branch and squashes the following commits: c02e5f5 [Michael Giannakopoulos] Merge cleanly with upstream/master. 8dcb888 [Michael Giannakopoulos] Putting the if/else if statements in brackets. fed8eaa [Michael Giannakopoulos] Adding a space in the message related to the IllegalArgumentException. 44e6ff0 [Michael Giannakopoulos] Adding a blank line before python class LinearRegressionWithSGD. 8eba9c5 [Michael Giannakopoulos] Change function signatures. Exception is thrown from the scala component and not from the python one. 638be47 [Michael Giannakopoulos] Modified code to comply with code standards. ec50ee9 [Michael Giannakopoulos] Shorten the if-elif-else statement in regression.py file b962744 [Michael Giannakopoulos] Replaced the enum classes, with strings-keywords for defining the values of 'regType' parameter. 78853ec [Michael Giannakopoulos] Providing intercept and regualizer functionallity for linear methods in only one function. 3ac8874 [Michael Giannakopoulos] Added support for regularizer and intercection parameters for linear regression method.
* Streaming mllib [SPARK-2438][MLLIB]Jeremy Freeman2014-08-018-2/+433
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR implements a streaming linear regression analysis, in which a linear regression model is trained online as new data arrive. The design is based on discussions with tdas and mengxr, in which we determined how to add this functionality in a general way, with minimal changes to existing libraries. __Summary of additions:__ _StreamingLinearAlgorithm_ - An abstract class for fitting generalized linear models online to streaming data, including training on (and updating) a model, and making predictions. _StreamingLinearRegressionWithSGD_ - Class and companion object for running streaming linear regression _StreamingLinearRegressionTestSuite_ - Unit tests _StreamingLinearRegression_ - Example use case: fitting a model online to data from one stream, and making predictions on other data __Notes__ - If this looks good, I can use the StreamingLinearAlgorithm class to easily implement other analyses that follow the same logic (Ridge, Lasso, Logistic, SVM). Author: Jeremy Freeman <the.freeman.lab@gmail.com> Author: freeman <the.freeman.lab@gmail.com> Closes #1361 from freeman-lab/streaming-mllib and squashes the following commits: 775ea29 [Jeremy Freeman] Throw error if user doesn't initialize weights 4086fee [Jeremy Freeman] Fixed current weight formatting 8b95b27 [Jeremy Freeman] Restored broadcasting 29f27ec [Jeremy Freeman] Formatting 8711c41 [Jeremy Freeman] Used return to avoid indentation 777b596 [Jeremy Freeman] Restored treeAggregate 74cf440 [Jeremy Freeman] Removed static methods d28cf9a [Jeremy Freeman] Added usage notes c3326e7 [Jeremy Freeman] Improved documentation 9541a41 [Jeremy Freeman] Merge remote-tracking branch 'upstream/master' into streaming-mllib 66eba5e [Jeremy Freeman] Fixed line lengths 2fe0720 [Jeremy Freeman] Minor cleanup 7d51378 [Jeremy Freeman] Moved streaming loader to MLUtils b9b69f6 [Jeremy Freeman] Added setter methods c3f8b5a [Jeremy Freeman] Modified logging 00aafdc [Jeremy Freeman] Add modifiers 14b801e [Jeremy Freeman] Name changes c7d38a3 [Jeremy Freeman] Move check for empty data to GradientDescent 4b0a5d3 [Jeremy Freeman] Cleaned up tests 74188d6 [Jeremy Freeman] Eliminate dependency on commons 50dd237 [Jeremy Freeman] Removed experimental tag 6bfe1e6 [Jeremy Freeman] Fixed imports a2a63ad [freeman] Makes convergence test more robust 86220bc [freeman] Streaming linear regression unit tests fb4683a [freeman] Minor changes for scalastyle consistency fd31e03 [freeman] Changed logging behavior 453974e [freeman] Fixed indentation c4b1143 [freeman] Streaming linear regression 604f4d7 [freeman] Expanded private class to include mllib d99aa85 [freeman] Helper methods for streaming MLlib apps 0898add [freeman] Added dependency on streaming
* [SPARK-2764] Simplify daemon.py process structureJosh Rosen2014-08-012-110/+79
| | | | | | | | | | | | | | | | | | | | Curently, daemon.py forks a pool of numProcessors subprocesses, and those processes fork themselves again to create the actual Python worker processes that handle data. I think that this extra layer of indirection is unnecessary and adds a lot of complexity. This commit attempts to remove this middle layer of subprocesses by launching the workers directly from daemon.py. See https://github.com/mesos/spark/pull/563 for the original PR that added daemon.py, where I raise some issues with the current design. Author: Josh Rosen <joshrosen@apache.org> Closes #1680 from JoshRosen/pyspark-daemon and squashes the following commits: 5abbcb9 [Josh Rosen] Replace magic number: 4 -> EINTR 5495dff [Josh Rosen] Throw IllegalStateException if worker launch fails. b79254d [Josh Rosen] Detect failed fork() calls; improve error logging. 282c2c4 [Josh Rosen] Remove daemon.py exit logging, since it caused problems: 8554536 [Josh Rosen] Fix daemon’s shutdown(); log shutdown reason. 4e0fab8 [Josh Rosen] Remove shared-memory exit_flag; don't die on worker death. e9892b4 [Josh Rosen] [WIP] [SPARK-2764] Simplify daemon.py process structure.
* [SPARK-2800]: Exclude scalastyle-output.xml Apache RAT checksGuoQiang Li2014-08-011-0/+1
| | | | | | | | Author: GuoQiang Li <witgo@qq.com> Closes #1729 from witgo/SPARK-2800 and squashes the following commits: 13ca966 [GuoQiang Li] Add scalastyle-output.xml to .rat-excludes file
* [SPARK-2116] Load spark-defaults.conf from SPARK_CONF_DIR if setAlbert Chu2014-08-011-0/+11
| | | | | | | | | | If SPARK_CONF_DIR environment variable is set, search it for spark-defaults.conf. Author: Albert Chu <chu11@llnl.gov> Closes #1059 from chu11/SPARK-2116 and squashes the following commits: 9f3ac94 [Albert Chu] SPARK-2116: If SPARK_CONF_DIR environment variable is set, search it for spark-defaults.conf.
* [SPARK-2212][SQL] Hash Outer Join (follow-up bug fix).Yin Huai2014-08-013-2/+114
| | | | | | | | | | We need to carefully set the ouputPartitioning of the HashOuterJoin Operator. Otherwise, we may not correctly handle nulls. Author: Yin Huai <huai@cse.ohio-state.edu> Closes #1721 from yhuai/SPARK-2212-BugFix and squashes the following commits: ed5eef7 [Yin Huai] Correctly choosing outputPartitioning for the HashOuterJoin operator.
* [SPARK-2010] [PySpark] [SQL] support nested structure in SchemaRDDDavies Liu2014-08-015-444/+996
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Convert Row in JavaSchemaRDD into Array[Any] and unpickle them as tuple in Python, then convert them into namedtuple, so use can access fields just like attributes. This will let nested structure can be accessed as object, also it will reduce the size of serialized data and better performance. root |-- field1: integer (nullable = true) |-- field2: string (nullable = true) |-- field3: struct (nullable = true) | |-- field4: integer (nullable = true) | |-- field5: array (nullable = true) | | |-- element: integer (containsNull = false) |-- field6: array (nullable = true) | |-- element: struct (containsNull = false) | | |-- field7: string (nullable = true) Then we can access them by row.field3.field5[0] or row.field6[5].field7 It also will infer the schema in Python, convert Row/dict/namedtuple/objects into tuple before serialization, then call applySchema in JVM. During inferSchema(), the top level of dict in row will be StructType, but any nested dictionary will be MapType. You can use pyspark.sql.Row to convert unnamed structure into Row object, make the RDD can be inferable. Such as: ctx.inferSchema(rdd.map(lambda x: Row(a=x[0], b=x[1])) Or you could use Row to create a class just like namedtuple, for example: Person = Row("name", "age") ctx.inferSchema(rdd.map(lambda x: Person(*x))) Also, you can call applySchema to apply an schema to a RDD of tuple/list and turn it into a SchemaRDD. The `schema` should be StructType, see the API docs for details. schema = StructType([StructField("name, StringType, True), StructType("age", IntegerType, True)]) ctx.applySchema(rdd, schema) PS: In order to use namedtuple to inferSchema, you should make namedtuple picklable. Author: Davies Liu <davies.liu@gmail.com> Closes #1598 from davies/nested and squashes the following commits: f1d15b6 [Davies Liu] verify schema with the first few rows 8852aaf [Davies Liu] check type of schema abe9e6e [Davies Liu] address comments 61b2292 [Davies Liu] add @deprecated to pythonToJavaMap 1e5b801 [Davies Liu] improve cache of classes 51aa135 [Davies Liu] use Row to infer schema e9c0d5c [Davies Liu] remove string typed schema 353a3f2 [Davies Liu] fix code style 63de8f8 [Davies Liu] fix typo c79ca67 [Davies Liu] fix serialization of nested data 6b258b5 [Davies Liu] fix pep8 9d8447c [Davies Liu] apply schema provided by string of names f5df97f [Davies Liu] refactor, address comments 9d9af55 [Davies Liu] use arrry to applySchema and infer schema in Python 84679b3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into nested 0eaaf56 [Davies Liu] fix doc tests b3559b4 [Davies Liu] use generated Row instead of namedtuple c4ddc30 [Davies Liu] fix conflict between name of fields and variables 7f6f251 [Davies Liu] address all comments d69d397 [Davies Liu] refactor 2cc2d45 [Davies Liu] refactor 182fb46 [Davies Liu] refactor bc6e9e1 [Davies Liu] switch to new Schema API 547bf3e [Davies Liu] Merge branch 'master' into nested a435b5a [Davies Liu] add docs and code refactor 2c8debc [Davies Liu] Merge branch 'master' into nested 644665a [Davies Liu] use tuple and namedtuple for schemardd
* [SPARK-2796] [mllib] DecisionTree bug fix: ordered categorical featuresJoseph K. Bradley2014-08-012-18/+56
| | | | | | | | | | | | | | | | | | Bug: In DecisionTree, the method sequentialBinSearchForOrderedCategoricalFeatureInClassification() indexed bins from 0 to (math.pow(2, featureCategories.toInt - 1) - 1). This upper bound is the bound for unordered categorical features, not ordered ones. The upper bound should be the arity (i.e., max value) of the feature. Added new test to DecisionTreeSuite to catch this: "regression stump with categorical variables of arity 2" Bug fix: Modified upper bound discussed above. Also: Small improvements to coding style in DecisionTree. CC mengxr manishamde Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #1720 from jkbradley/decisiontree-bugfix2 and squashes the following commits: 225822f [Joseph K. Bradley] Bug: In DecisionTree, the method sequentialBinSearchForOrderedCategoricalFeatureInClassification() indexed bins from 0 to (math.pow(2, featureCategories.toInt - 1) - 1). This upper bound is the bound for unordered categorical features, not ordered ones. The upper bound should be the arity (i.e., max value) of the feature.
* [SPARK-2786][mllib] Python correlationsDoris Xin2014-08-016-30/+199
| | | | | | | | | | | | | | | | Author: Doris Xin <doris.s.xin@gmail.com> Closes #1713 from dorx/pythonCorrelation and squashes the following commits: 5f1e60c [Doris Xin] reviewer comments. 46ff6eb [Doris Xin] reviewer comments. ad44085 [Doris Xin] style fix e69d446 [Doris Xin] fixed missed conflicts. eb5bf56 [Doris Xin] merge master cc9f725 [Doris Xin] units passed. 9141a63 [Doris Xin] WIP2 d199f1f [Doris Xin] Moved correlation names into a public object cd163d6 [Doris Xin] WIP
* SPARK-2791: Fix committing, reverting and state tracking in shuffle file ↵Aaron Davidson2014-08-018-52/+146
| | | | | | | | | | | | | | | | | | consolidation All changes from this PR are by mridulm and are drawn from his work in #1609. This patch is intended to fix all major issues related to shuffle file consolidation that mridulm found, while minimizing changes to the code, with the hope that it may be more easily merged into 1.1. This patch is **not** intended as a replacement for #1609, which provides many additional benefits, including fixes to ExternalAppendOnlyMap, improvements to DiskBlockObjectWriter's API, and several new unit tests. If it is feasible to merge #1609 for the 1.1 deadline, that is a preferable option. Author: Aaron Davidson <aaron@databricks.com> Closes #1678 from aarondav/consol and squashes the following commits: 53b3f6d [Aaron Davidson] Correct behavior when writing unopened file 701d045 [Aaron Davidson] Rebase with sort-based shuffle 9160149 [Aaron Davidson] SPARK-2532: Minimal shuffle consolidation fixes
* [SPARK-2379] Fix the bug that streaming's receiver may fall into a dead loopjoyyoj2014-08-011-1/+1
| | | | | | | | | | | Author: joyyoj <sunshch@gmail.com> Closes #1694 from joyyoj/SPARK-2379 and squashes the following commits: d73790d [joyyoj] SPARK-2379 Fix the bug that streaming's receiver may fall into a dead loop 22e7821 [joyyoj] Merge remote-tracking branch 'apache/master' 3f4a602 [joyyoj] Merge remote-tracking branch 'remotes/apache/master' f4660c5 [joyyoj] [SPARK-1998] SparkFlumeEvent with body bigger than 1020 bytes are not read properly
* SPARK-1612: Fix potential resource leakszsxwing2014-08-011-13/+22
| | | | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-1612 Move the "close" statements into a "finally" block. Author: zsxwing <zsxwing@gmail.com> Closes #535 from zsxwing/SPARK-1612 and squashes the following commits: ae52f50 [zsxwing] Update to follow the code style 549ba13 [zsxwing] SPARK-1612: Fix potential resource leaks
* [SPARK-2490] Change recursive visiting on RDD dependencies to iterative approachLiang-Chi Hsieh2014-08-011-8/+75
| | | | | | | | | | | | | | | | | | | | | | | When performing some transformations on RDDs after many iterations, the dependencies of RDDs could be very long. It can easily cause StackOverflowError when recursively visiting these dependencies in Spark core. For example: var rdd = sc.makeRDD(Array(1)) for (i <- 1 to 1000) { rdd = rdd.coalesce(1).cache() rdd.collect() } This PR changes recursive visiting on rdd's dependencies to iterative approach to avoid StackOverflowError. In addition to the recursive visiting, since the Java serializer has a known [bug](http://bugs.java.com/bugdatabase/view_bug.do?bug_id=4152790) that causes StackOverflowError too when serializing/deserializing a large graph of objects. So applying this PR only solves part of the problem. Using KryoSerializer to replace Java serializer might be helpful. However, since KryoSerializer is not supported for `spark.closure.serializer` now, I can not test if KryoSerializer can solve Java serializer's problem completely. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #1418 from viirya/remove_recursive_visit and squashes the following commits: 6b2c615 [Liang-Chi Hsieh] change function name; comply with code style. 5f072a7 [Liang-Chi Hsieh] add comments to explain Stack usage. 8742dbb [Liang-Chi Hsieh] comply with code style. 900538b [Liang-Chi Hsieh] change recursive visiting on rdd's dependencies to iterative approach to avoid stackoverflowerror.
* [SPARK-695] In DAGScheduler's getPreferredLocs, track set of visited partitions.Aaron Staple2014-08-015-7/+37
| | | | | | | | | | | | | | | | | | | | | getPreferredLocs traverses a dependency graph of partitions using depth first search. Given a complex dependency graph, the old implementation may explore a set of paths in the graph that is exponential in the number of nodes. By maintaining a set of visited nodes the new implementation avoids revisiting nodes, preventing exponential blowup. Some comment and whitespace cleanups are also included. Author: Aaron Staple <aaron.staple@gmail.com> Closes #1362 from staple/SPARK-695 and squashes the following commits: ecea0f3 [Aaron Staple] address review comments 751c661 [Aaron Staple] [SPARK-695] Add a unit test. 5adf326 [Aaron Staple] Replace getPreferredLocsInternal's HashMap argument with a simpler HashSet. 58e37d0 [Aaron Staple] Replace comment documenting NarrowDependency. 6751ced [Aaron Staple] Revert "Remove unused variable." 04c7097 [Aaron Staple] Fix indentation. 0030884 [Aaron Staple] Remove unused variable. 33f67c6 [Aaron Staple] Clarify comment. 4e42b46 [Aaron Staple] Remove apparently incorrect comment describing NarrowDependency. 65c2d3d [Aaron Staple] [SPARK-695] In DAGScheduler's getPreferredLocs, track set of visited partitions.
* [SQL] Documentation: Explain cacheTable commandCrazyJvm2014-08-011-0/+10
| | | | | | | | | | | | | add the `cacheTable` specification Author: CrazyJvm <crazyjvm@gmail.com> Closes #1681 from CrazyJvm/sql-programming-guide-cache and squashes the following commits: 0a231e0 [CrazyJvm] grammar fixes a04020e [CrazyJvm] modify title to Cached tables 18b6594 [CrazyJvm] fix format 2cbbf58 [CrazyJvm] add cacheTable guide
* [SPARK-2767] [SQL] SparkSQL CLI doens't output error message if query failed.Cheng Hao2014-08-013-6/+8
| | | | | | | | | Author: Cheng Hao <hao.cheng@intel.com> Closes #1686 from chenghao-intel/spark_sql_cli and squashes the following commits: eb664cc [Cheng Hao] Output detailed failure message in console 93b0382 [Cheng Hao] Fix Bug of no output in cli if exception thrown internally
* [SPARK-2729] [SQL] Forgot to match Timestamp type in ColumnBuilderchutium2014-08-013-2/+3
| | | | | | | | | | | | just a match forgot, found after SPARK-2710 , TimestampType can be used by a SchemaRDD generated from JDBC ResultSet Author: chutium <teng.qiu@gmail.com> Closes #1636 from chutium/SPARK-2729 and squashes the following commits: 71af77a [chutium] [SPARK-2729] [SQL] added Timestamp in NullableColumnAccessorSuite 39cf9f8 [chutium] [SPARK-2729] add Timestamp Type into ColumnBuilder TestSuite, ref. #1636 ab6ff97 [chutium] [SPARK-2729] Forgot to match Timestamp type in ColumnBuilder
* [SQL][SPARK-2212]Hash Outer JoinCheng Hao2014-08-013-6/+319
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This patch is to support the hash based outer join. Currently, outer join for big relations are resort to `BoradcastNestedLoopJoin`, which is super slow. This PR will create 2 hash tables for both relations in the same partition, which greatly reduce the table scans. Here is the testing code that I used: ``` package org.apache.spark.sql.hive import org.apache.spark.SparkContext import org.apache.spark.SparkConf import org.apache.spark.sql._ case class Record(key: String, value: String) object JoinTablePrepare extends App { import TestHive2._ val rdd = sparkContext.parallelize((1 to 3000000).map(i => Record(s"${i % 828193}", s"val_$i"))) runSqlHive("SHOW TABLES") runSqlHive("DROP TABLE if exists a") runSqlHive("DROP TABLE if exists b") runSqlHive("DROP TABLE if exists result") rdd.registerAsTable("records") runSqlHive("""CREATE TABLE a (key STRING, value STRING) | ROW FORMAT SERDE | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe' | STORED AS RCFILE """.stripMargin) runSqlHive("""CREATE TABLE b (key STRING, value STRING) | ROW FORMAT SERDE | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe' | STORED AS RCFILE """.stripMargin) runSqlHive("""CREATE TABLE result (key STRING, value STRING) | ROW FORMAT SERDE | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe' | STORED AS RCFILE """.stripMargin) hql(s"""from records | insert into table a | select key, value """.stripMargin) hql(s"""from records | insert into table b select key + 100000, value """.stripMargin) } object JoinTablePerformanceTest extends App { import TestHive2._ hql("SHOW TABLES") hql("set spark.sql.shuffle.partitions=20") val leftOuterJoin = "insert overwrite table result select a.key, b.value from a left outer join b on a.key=b.key" val rightOuterJoin = "insert overwrite table result select a.key, b.value from a right outer join b on a.key=b.key" val fullOuterJoin = "insert overwrite table result select a.key, b.value from a full outer join b on a.key=b.key" val results = ("LeftOuterJoin", benchmark(leftOuterJoin)) :: ("LeftOuterJoin", benchmark(leftOuterJoin)) :: ("RightOuterJoin", benchmark(rightOuterJoin)) :: ("RightOuterJoin", benchmark(rightOuterJoin)) :: ("FullOuterJoin", benchmark(fullOuterJoin)) :: ("FullOuterJoin", benchmark(fullOuterJoin)) :: Nil val explains = hql(s"explain $leftOuterJoin").collect ++ hql(s"explain $rightOuterJoin").collect ++ hql(s"explain $fullOuterJoin").collect println(explains.mkString(",\n")) results.foreach { case (prompt, result) => { println(s"$prompt: took ${result._1} ms (${result._2} records)") } } def benchmark(cmd: String) = { val begin = System.currentTimeMillis() val result = hql(cmd) val end = System.currentTimeMillis() val count = hql("select count(1) from result").collect.mkString("") ((end - begin), count) } } ``` And the result as shown below: ``` [Physical execution plan:], [InsertIntoHiveTable (MetastoreRelation default, result, None), Map(), true], [ Project [key#95,value#98]], [ HashOuterJoin [key#95], [key#97], LeftOuter, None], [ Exchange (HashPartitioning [key#95], 20)], [ HiveTableScan [key#95], (MetastoreRelation default, a, None), None], [ Exchange (HashPartitioning [key#97], 20)], [ HiveTableScan [key#97,value#98], (MetastoreRelation default, b, None), None], [Physical execution plan:], [InsertIntoHiveTable (MetastoreRelation default, result, None), Map(), true], [ Project [key#102,value#105]], [ HashOuterJoin [key#102], [key#104], RightOuter, None], [ Exchange (HashPartitioning [key#102], 20)], [ HiveTableScan [key#102], (MetastoreRelation default, a, None), None], [ Exchange (HashPartitioning [key#104], 20)], [ HiveTableScan [key#104,value#105], (MetastoreRelation default, b, None), None], [Physical execution plan:], [InsertIntoHiveTable (MetastoreRelation default, result, None), Map(), true], [ Project [key#109,value#112]], [ HashOuterJoin [key#109], [key#111], FullOuter, None], [ Exchange (HashPartitioning [key#109], 20)], [ HiveTableScan [key#109], (MetastoreRelation default, a, None), None], [ Exchange (HashPartitioning [key#111], 20)], [ HiveTableScan [key#111,value#112], (MetastoreRelation default, b, None), None] LeftOuterJoin: took 16072 ms ([3000000] records) LeftOuterJoin: took 14394 ms ([3000000] records) RightOuterJoin: took 14802 ms ([3000000] records) RightOuterJoin: took 14747 ms ([3000000] records) FullOuterJoin: took 17715 ms ([6000000] records) FullOuterJoin: took 17629 ms ([6000000] records) ``` Without this PR, the benchmark will run seems never end. Author: Cheng Hao <hao.cheng@intel.com> Closes #1147 from chenghao-intel/hash_based_outer_join and squashes the following commits: 65c599e [Cheng Hao] Fix issues with the community comments 72b1394 [Cheng Hao] Fix bug of stale value in joinedRow 55baef7 [Cheng Hao] Add HashOuterJoin
* [SPARK-2179][SQL] A minor refactoring Java data type APIs (2179 follow-up).Yin Huai2014-08-0124-118/+108
| | | | | | | | | | | It is a follow-up PR of SPARK-2179 (https://issues.apache.org/jira/browse/SPARK-2179). It makes package names of data type APIs more consistent across languages (Scala: `org.apache.spark.sql`, Java: `org.apache.spark.sql.api.java`, Python: `pyspark.sql`). Author: Yin Huai <huai@cse.ohio-state.edu> Closes #1712 from yhuai/javaDataType and squashes the following commits: 62eb705 [Yin Huai] Move package-info. add4bcb [Yin Huai] Make the package names of data type classes consistent across languages by moving all Java data type classes to package sql.api.java.
* SPARK-2099. Report progress while task is running.Sandy Ryza2014-08-0124-157/+467
| | | | | | | | | | | | | | | | | | This is a sketch of a patch that allows the UI to show metrics for tasks that have not yet completed. It adds a heartbeat every 2 seconds from the executors to the driver, reporting metrics for all of the executor's tasks. It still needs unit tests, polish, and cluster testing, but I wanted to put it up to get feedback on the approach. Author: Sandy Ryza <sandy@cloudera.com> Closes #1056 from sryza/sandy-spark-2099 and squashes the following commits: 93b9fdb [Sandy Ryza] Up heartbeat interval to 10 seconds and other tidying 132aec7 [Sandy Ryza] Heartbeat and HeartbeatResponse are already Serializable as case classes 38dffde [Sandy Ryza] Additional review feedback and restore test that was removed in BlockManagerSuite 51fa396 [Sandy Ryza] Remove hostname race, add better comments about threading, and some stylistic improvements 3084f10 [Sandy Ryza] Make TaskUIData a case class again 3bda974 [Sandy Ryza] Stylistic fixes 0dae734 [Sandy Ryza] SPARK-2099. Report progress while task is running.
* [HOTFIX] downgrade breeze version to 0.7Xiangrui Meng2014-08-011-1/+1
| | | | | | | | | | breeze-0.8.1 causes dependency issues, as discussed in #940 . Author: Xiangrui Meng <meng@databricks.com> Closes #1718 from mengxr/revert-breeze and squashes the following commits: 99c4681 [Xiangrui Meng] downgrade breeze version to 0.7
* [SPARK-1997] update breeze to version 0.8.1witgo2014-08-011-1/+5
| | | | | | | | | | `breeze 0.8.1` dependent on `scala-logging-slf4j 2.1.1` The relevant code on #1369 Author: witgo <witgo@qq.com> Closes #940 from witgo/breeze-8.0.1 and squashes the following commits: 65cc65e [witgo] update breeze to version 0.8.1
* SPARK-2768 [MLLIB] Add product, user recommend method to ↵Sean Owen2014-08-012-19/+100
| | | | | | | | | | | | | | | | | | MatrixFactorizationModel Right now, `MatrixFactorizationModel` can only predict a score for one or more `(user,product)` tuples. As a comment in the file notes, it would be more useful to expose a recommend method, that computes top N scoring products for a user (or vice versa – users for a product). (This also corrects some long lines in the Java ALS test suite.) As you can see, it's a little messy to access the class from Java. Should there be a Java-friendly wrapper for it? with a pointer about where that should go, I could add that. Author: Sean Owen <srowen@gmail.com> Closes #1687 from srowen/SPARK-2768 and squashes the following commits: b349675 [Sean Owen] Additional review changes c9edb04 [Sean Owen] Updates from code review 7bc35f9 [Sean Owen] Add recommend methods to MatrixFactorizationModel
* [SPARK-2103][Streaming] Change to ClassTag for KafkaInputDStream and fix ↵jerryshao2014-08-013-19/+18
| | | | | | | | | | | | | | | | | | | reflection issue This PR updates previous Manifest for KafkaInputDStream's Decoder to ClassTag, also fix the problem addressed in [SPARK-2103](https://issues.apache.org/jira/browse/SPARK-2103). Previous Java interface cannot actually get the type of Decoder, so when using this Manifest to reconstruct the decode object will meet reflection exception. Also for other two Java interfaces, ClassTag[String] is useless because calling Scala API will get the right implicit ClassTag. Current Kafka unit test cannot actually verify the interface. I've tested these interfaces in my local and distribute settings. Author: jerryshao <saisai.shao@intel.com> Closes #1508 from jerryshao/SPARK-2103 and squashes the following commits: e90c37b [jerryshao] Add Mima excludes 7529810 [jerryshao] Change Manifest to ClassTag for KafkaInputDStream's Decoder and fix Decoder construct issue when using Java API
* [Spark 2557] fix LOCAL_N_REGEX in createTaskScheduler and make local-n and ↵Ye Xianjin2014-08-012-3/+30
| | | | | | | | | | | | | local-n-failures consistent [SPARK-2557](https://issues.apache.org/jira/browse/SPARK-2557) Author: Ye Xianjin <advancedxy@gmail.com> Closes #1464 from advancedxy/SPARK-2557 and squashes the following commits: d844d67 [Ye Xianjin] add local-*-n-failures, bad-local-n, bad-local-n-failures test case 3bbc668 [Ye Xianjin] fix LOCAL_N_REGEX regular expression and make local_n_failures accept * as all cores on the computer
* SPARK-2134: Report metrics before application finishesRahul Singhal2014-08-0113-0/+34
| | | | | | | | Author: Rahul Singhal <rahul.singhal@guavus.com> Closes #1076 from rahulsinghaliitd/SPARK-2134 and squashes the following commits: 15f18b6 [Rahul Singhal] SPARK-2134: Report metrics before application finishes
* SPARK-983. Support external sorting in sortByKey()Matei Zaharia2014-08-012-12/+20
| | | | | | | | | | | | This patch simply uses the ExternalSorter class from sort-based shuffle. Closes #931 and Closes #1090 Author: Matei Zaharia <matei@databricks.com> Closes #1677 from mateiz/spark-983 and squashes the following commits: 96b3fda [Matei Zaharia] SPARK-983. Support external sorting in sortByKey()
* [SPARK-2670] FetchFailedException should be thrown when local fetch has failedKousuke Saruta2014-08-012-8/+151
| | | | | | | | | | | | | | | | | Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #1578 from sarutak/SPARK-2670 and squashes the following commits: 85c8938 [Kousuke Saruta] Removed useless results.put for fail fast e8713cc [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2670 d353984 [Kousuke Saruta] Refined assertion messages in BlockFetcherIteratorSuite.scala 03bcb02 [Kousuke Saruta] Merge branch 'SPARK-2670' of github.com:sarutak/spark into SPARK-2670 5d05855 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2670 4fca130 [Kousuke Saruta] Added test cases for BasicBlockFetcherIterator b7b8250 [Kousuke Saruta] Modified BasicBlockFetchIterator to fail fast when local fetch error has been occurred a3a9be1 [Kousuke Saruta] Modified BlockFetcherIterator for SPARK-2670 460dc01 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2670 e310c0b [Kousuke Saruta] Modified BlockFetcherIterator to handle local fetch failure as fatch fail
* SPARK-2738. Remove redundant imports in BlockManagerSuiteSandy Ryza2014-07-311-3/+0
| | | | | | | | Author: Sandy Ryza <sandy@cloudera.com> Closes #1642 from sryza/sandy-spark-2738 and squashes the following commits: a923e4e [Sandy Ryza] SPARK-2738. Remove redundant imports in BlockManagerSuite
* SPARK-2632, SPARK-2576. Fixed by only importing what is necessary during ↵Prashant Sharma2014-07-315-5/+67
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | class definition. Without this patch, it imports everything available in the scope. ```scala scala> val a = 10l val a = 10l a: Long = 10 scala> import a._ import a._ import a._ scala> case class A(a: Int) // show case class A(a: Int) // show class $read extends Serializable { def <init>() = { super.<init>; () }; class $iwC extends Serializable { def <init>() = { super.<init>; () }; class $iwC extends Serializable { def <init>() = { super.<init>; () }; import org.apache.spark.SparkContext._; class $iwC extends Serializable { def <init>() = { super.<init>; () }; val $VAL5 = $line5.$read.INSTANCE; import $VAL5.$iw.$iw.$iw.$iw.a; class $iwC extends Serializable { def <init>() = { super.<init>; () }; import a._; class $iwC extends Serializable { def <init>() = { super.<init>; () }; class $iwC extends Serializable { def <init>() = { super.<init>; () }; case class A extends scala.Product with scala.Serializable { <caseaccessor> <paramaccessor> val a: Int = _; def <init>(a: Int) = { super.<init>; () } } }; val $iw = new $iwC.<init> }; val $iw = new $iwC.<init> }; val $iw = new $iwC.<init> }; val $iw = new $iwC.<init> }; val $iw = new $iwC.<init> }; val $iw = new $iwC.<init> } object $read extends scala.AnyRef { def <init>() = { super.<init>; () }; val INSTANCE = new $read.<init> } defined class A ``` With this patch, it just imports only the necessary. ```scala scala> val a = 10l val a = 10l a: Long = 10 scala> import a._ import a._ import a._ scala> case class A(a: Int) // show case class A(a: Int) // show class $read extends Serializable { def <init>() = { super.<init>; () }; class $iwC extends Serializable { def <init>() = { super.<init>; () }; class $iwC extends Serializable { def <init>() = { super.<init>; () }; case class A extends scala.Product with scala.Serializable { <caseaccessor> <paramaccessor> val a: Int = _; def <init>(a: Int) = { super.<init>; () } } }; val $iw = new $iwC.<init> }; val $iw = new $iwC.<init> } object $read extends scala.AnyRef { def <init>() = { super.<init>; () }; val INSTANCE = new $read.<init> } defined class A scala> ``` This patch also adds a `:fallback` mode on being enabled it will restore the spark-shell's 1.0.0 behaviour. Author: Prashant Sharma <scrapcodes@gmail.com> Author: Yin Huai <huai@cse.ohio-state.edu> Author: Prashant Sharma <prashant.s@imaginea.com> Closes #1635 from ScrapCodes/repl-fix-necessary-imports and squashes the following commits: b1968d2 [Prashant Sharma] Added toschemaRDD to test case. 0b712bb [Yin Huai] Add a REPL test to test importing a method. 02ad8ff [Yin Huai] Add a REPL test for importing SQLContext.createSchemaRDD. ed6d0c7 [Prashant Sharma] Added a fallback mode, incase users run into issues while using repl. b63d3b2 [Prashant Sharma] SPARK-2632, SPARK-2576. Fixed by only importing what is necessary during class definition.
* [SPARK-2702][Core] Upgrade Tachyon dependency to 0.5.0Haoyuan Li2014-07-312-5/+5
| | | | | | | | Author: Haoyuan Li <haoyuan@cs.berkeley.edu> Closes #1651 from haoyuan/upgrade-tachyon and squashes the following commits: 6f3f98f [Haoyuan Li] upgrade tachyon to 0.5.0
* [SPARK-2782][mllib] Bug fix for getRanks in SpearmanCorrelationDoris Xin2014-07-313-18/+47
| | | | | | | | | | | | getRanks computes the wrong rank when numPartition >= size in the input RDDs before this patch. added units to address this bug. Author: Doris Xin <doris.s.xin@gmail.com> Closes #1710 from dorx/correlationBug and squashes the following commits: 733def4 [Doris Xin] bugs and reviewer comments. 31db920 [Doris Xin] revert unnecessary change 043ff83 [Doris Xin] bug fix for spearman corner case
* [SPARK-2777][MLLIB] change ALS factors storage level to MEMORY_AND_DISKXiangrui Meng2014-07-311-2/+2
| | | | | | | | | | | | Now the factors are persisted in memory only. If they get kicked off by later jobs, we might have to start the computation from very beginning. A better solution is changing the storage level to `MEMORY_AND_DISK`. srowen Author: Xiangrui Meng <meng@databricks.com> Closes #1700 from mengxr/als-level and squashes the following commits: c103d76 [Xiangrui Meng] change ALS factors storage level to MEMORY_AND_DISK
* SPARK-2766: ScalaReflectionSuite throw an llegalArgumentException in JDK 6GuoQiang Li2014-07-311-1/+1
| | | | | | | | Author: GuoQiang Li <witgo@qq.com> Closes #1683 from witgo/SPARK-2766 and squashes the following commits: d0db00c [GuoQiang Li] ScalaReflectionSuite throw an llegalArgumentException in JDK 6
* [SPARK-2779] [SQL] asInstanceOf[Map[...]] should use scala.collection.Map ↵Yin Huai2014-07-314-0/+24
| | | | | | | | | | | | | | instead of scala.collection.immutable.Map Since we let users create Rows. It makes sense to accept mutable Maps as values of MapType columns. JIRA: https://issues.apache.org/jira/browse/SPARK-2779 Author: Yin Huai <huai@cse.ohio-state.edu> Closes #1705 from yhuai/SPARK-2779 and squashes the following commits: 00d72fd [Yin Huai] Use scala.collection.Map.
* [SPARK-2756] [mllib] Decision tree bug fixesJoseph K. Bradley2014-07-3110-193/+538
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | (1) Inconsistent aggregate (agg) indexing for unordered features. (2) Fixed gain calculations for edge cases. (3) One-off error in choosing thresholds for continuous features for small datasets. (4) (not a bug) Changed meaning of tree depth by 1 to fit scikit-learn and rpart. (Depth 1 used to mean 1 leaf node; depth 0 now means 1 leaf node.) Other updates, to help with tests: * Updated DecisionTreeRunner to print more info. * Added utility functions to DecisionTreeModel: toString, depth, numNodes * Improved internal DecisionTree documentation Bug fix details: (1) Indexing was inconsistent for aggregate calculations for unordered features (in multiclass classification with categorical features, where the features had few enough values such that they could be considered unordered, i.e., isSpaceSufficientForAllCategoricalSplits=true). * updateBinForUnorderedFeature indexed agg as (node, feature, featureValue, binIndex), where ** featureValue was from arr (so it was a feature value) ** binIndex was in [0,…, 2^(maxFeatureValue-1)-1) * The rest of the code indexed agg as (node, feature, binIndex, label). * Corrected this bug by changing updateBinForUnorderedFeature to use the second indexing pattern. Unit tests in DecisionTreeSuite * Updated a few tests to train a model and test its training accuracy, which catches the indexing bug from updateBinForUnorderedFeature() discussed above. * Added new test (“stump with categorical variables for multiclass classification, with just enough bins”) to test bin extremes. (2) Bug fix: calculateGainForSplit (for classification): * It used to return dummy prediction values when either the right or left children had 0 weight. These were incorrect for multiclass classification. It has been corrected. Updated impurities to allow for count = 0. This was related to the above bug fix for calculateGainForSplit (for classification). Small updates to documentation and coding style. (3) Bug fix: Off-by-1 when finding thresholds for splits for continuous features. * Exhibited bug in new test in DecisionTreeSuite: “stump with 1 continuous variable for binary classification, to check off-by-1 error” * Description: When finding thresholds for possible splits for continuous features in DecisionTree.findSplitsBins, the thresholds were set according to individual training examples’ feature values. * Fix: The threshold is set to be the average of 2 consecutive (sorted) examples’ feature values. E.g.: If the old code set the threshold using example i, the new code sets the threshold using exam * Note: In 4 DecisionTreeSuite tests with all labels identical, removed check of threshold since it is somewhat arbitrary. CC: mengxr manishamde Please let me know if I missed something! Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #1673 from jkbradley/decisiontree-bugfix and squashes the following commits: 2b20c61 [Joseph K. Bradley] Small doc and style updates dab0b67 [Joseph K. Bradley] Added documentation for DecisionTree internals 8bb8aa0 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix 978cfcf [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix 6eed482 [Joseph K. Bradley] In DecisionTree: Changed from using procedural syntax for functions returning Unit to explicitly writing Unit return type. 376dca2 [Joseph K. Bradley] Updated meaning of maxDepth by 1 to fit scikit-learn and rpart. * In code, replaced usages of maxDepth <-- maxDepth + 1 * In params, replace settings of maxDepth <-- maxDepth - 1 59750f8 [Joseph K. Bradley] * Updated Strategy to check numClassesForClassification only if algo=Classification. * Updates based on comments: ** DecisionTreeRunner *** Made dataFormat arg default to libsvm ** Small cleanups ** tree.Node: Made recursive helper methods private, and renamed them. 52e17c5 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix da50db7 [Joseph K. Bradley] Added one more test to DecisionTreeSuite: stump with 2 continuous variables for binary classification. Caused problems in past, but fixed now. 8ea8750 [Joseph K. Bradley] Bug fix: Off-by-1 when finding thresholds for splits for continuous features. 2283df8 [Joseph K. Bradley] 2 bug fixes. 73fbea2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix 5f920a1 [Joseph K. Bradley] Demonstration of bug before submitting fix: Updated DecisionTreeSuite so that 3 tests fail. Will describe bug in next commit.
* [SPARK-2724] Python version of RandomRDDGeneratorsDoris Xin2014-07-316-36/+348
| | | | | | | | | | | | | | | | | | | | | | RandomRDDGenerators but without support for randomRDD and randomVectorRDD, which take in arbitrary DistributionGenerator. `randomRDD.py` is named to avoid collision with the built-in Python `random` package. Author: Doris Xin <doris.s.xin@gmail.com> Closes #1628 from dorx/pythonRDD and squashes the following commits: 55c6de8 [Doris Xin] review comments. all python units passed. f831d9b [Doris Xin] moved default args logic into PythonMLLibAPI 2d73917 [Doris Xin] fix for linalg.py 8663e6a [Doris Xin] reverting back to a single python file for random f47c481 [Doris Xin] docs update 687aac0 [Doris Xin] add RandomRDDGenerators.py to run-tests 4338f40 [Doris Xin] renamed randomRDD to rand and import as random 29d205e [Doris Xin] created mllib.random package bd2df13 [Doris Xin] typos 07ddff2 [Doris Xin] units passed. 23b2ecd [Doris Xin] WIP
* [SPARK-2531 & SPARK-2436] [SQL] Optimize the BuildSide when planning ↵Zongheng Yang2014-07-312-28/+55
| | | | | | | | | | | | | | | | | | BroadcastNestedLoopJoin. This PR resolves the following two tickets: - [SPARK-2531](https://issues.apache.org/jira/browse/SPARK-2531): BNLJ currently assumes the build side is the right relation. This patch refactors some of its logic to take into account a BuildSide properly. - [SPARK-2436](https://issues.apache.org/jira/browse/SPARK-2436): building on top of the above, we simply use the physical size statistics (if available) of both relations, and make the smaller relation the build side in the planner. Author: Zongheng Yang <zongheng.y@gmail.com> Closes #1448 from concretevitamin/bnlj-buildSide and squashes the following commits: 1780351 [Zongheng Yang] Use size estimation to decide optimal build side of BNLJ. 68e6c5b [Zongheng Yang] Consolidate two adjacent pattern matchings. 96d312a [Zongheng Yang] Use a while loop instead of collection methods chaining. 4bc525e [Zongheng Yang] Make BroadcastNestedLoopJoin take a BuildSide.
* SPARK-2282: Reuse Socket for sending accumulator updates to PysparkAaron Davidson2014-07-312-12/+42
| | | | | | | | | | | | | | | | Prior to this change, every PySpark task completion opened a new socket to the accumulator server, passed its updates through, and then quit. I'm not entirely sure why PySpark always sends accumulator updates, but regardless this causes a very rapid buildup of ephemeral TCP connections that remain in the TCP_WAIT state for around a minute before being cleaned up. Rather than trying to allow these sockets to be cleaned up faster, this patch simply reuses the connection between tasks completions (since they're fed updates in a single-threaded manner by the DAGScheduler anyway). The only tricky part here was making sure that the AccumulatorServer was able to shutdown in a timely manner (i.e., stop polling for new data), and this was accomplished via minor feats of magic. I have confirmed that this patch eliminates the buildup of ephemeral sockets due to the accumulator updates. However, I did note that there were still significant sockets being created against the PySpark daemon port, but my machine was not able to create enough sockets fast enough to fail. This may not be the last time we've seen this issue, though. Author: Aaron Davidson <aaron@databricks.com> Closes #1503 from aarondav/accum and squashes the following commits: b3e12f7 [Aaron Davidson] SPARK-2282: Reuse Socket for sending accumulator updates to Pyspark
generated by cgit v1.2.3 (git 2.39.1) at 2024-08-31 21:39:54 +0000