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* [SPARK-2734][SQL] Remove tables from cache when DROP TABLE is run.Michael Armbrust2014-07-304-1/+74
| | | | | | | | | Author: Michael Armbrust <michael@databricks.com> Closes #1650 from marmbrus/dropCached and squashes the following commits: e6ab80b [Michael Armbrust] Support if exists. 83426c6 [Michael Armbrust] Remove tables from cache when DROP TABLE is run.
* SPARK-2741 - Publish version of spark assembly which does not contain HiveBrock Noland2014-07-301-0/+2
| | | | | | | | | | Provide a version of the Spark tarball which does not package Hive. This is meant for HIve + Spark users. Author: Brock Noland <brock@apache.org> Closes #1667 from brockn/master and squashes the following commits: 5beafb2 [Brock Noland] SPARK-2741 - Publish version of spark assembly which does not contain Hive
* SPARK-2749 [BUILD]. Spark SQL Java tests aren't compiling in Jenkins' Maven ↵Sean Owen2014-07-3010-0/+50
| | | | | | | | | | | | | | | | | | | | | | | | | | | builds; missing junit:junit dep The Maven-based builds in the build matrix have been failing for a few days: https://amplab.cs.berkeley.edu/jenkins/view/Spark/ On inspection, it looks like the Spark SQL Java tests don't compile: https://amplab.cs.berkeley.edu/jenkins/view/Spark/job/Spark-Master-Maven-pre-YARN/hadoop.version=1.0.4,label=centos/244/consoleFull I confirmed it by repeating the command vs master: `mvn -Dhadoop.version=1.0.4 -Dlabel=centos -DskipTests clean package` The problem is that this module doesn't depend on JUnit. In fact, none of the modules do, but `com.novocode:junit-interface` (the SBT-JUnit bridge) pulls it in, in most places. However this module doesn't depend on `com.novocode:junit-interface` Adding the `junit:junit` dependency fixes the compile problem. In fact, the other modules with Java tests should probably depend on it explicitly instead of happening to get it via `com.novocode:junit-interface`, since that is a bit SBT/Scala-specific (and I am not even sure it's needed). Author: Sean Owen <srowen@gmail.com> Closes #1660 from srowen/SPARK-2749 and squashes the following commits: 858ff7c [Sean Owen] Add explicit junit dep to other modules with Java tests for robustness 9636794 [Sean Owen] Add junit dep so that Spark SQL Java tests compile
* Properly pass SBT_MAVEN_PROFILES into sbt.Reynold Xin2014-07-301-14/+13
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* Set AMPLAB_JENKINS_BUILD_PROFILE.Reynold Xin2014-07-301-0/+12
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* Wrap JAR_DL in dev/check-license.Reynold Xin2014-07-301-2/+2
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* [SPARK-2024] Add saveAsSequenceFile to PySparkKan Zhang2014-07-3014-186/+1085
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | JIRA issue: https://issues.apache.org/jira/browse/SPARK-2024 This PR is a followup to #455 and adds capabilities for saving PySpark RDDs using SequenceFile or any Hadoop OutputFormats. * Added RDD methods ```saveAsSequenceFile```, ```saveAsHadoopFile``` and ```saveAsHadoopDataset```, for both old and new MapReduce APIs. * Default converter for converting common data types to Writables. Users may specify custom converters to convert to desired data types. * No out-of-box support for reading/writing arrays, since ArrayWritable itself doesn't have a no-arg constructor for creating an empty instance upon reading. Users need to provide ArrayWritable subtypes. Custom converters for converting arrays to suitable ArrayWritable subtypes are also needed when writing. When reading, the default converter will convert any custom ArrayWritable subtypes to ```Object[]``` and they get pickled to Python tuples. * Added HBase and Cassandra output examples to show how custom output formats and converters can be used. cc MLnick mateiz ahirreddy pwendell Author: Kan Zhang <kzhang@apache.org> Closes #1338 from kanzhang/SPARK-2024 and squashes the following commits: c01e3ef [Kan Zhang] [SPARK-2024] code formatting 6591e37 [Kan Zhang] [SPARK-2024] renaming pickled -> pickledRDD d998ad6 [Kan Zhang] [SPARK-2024] refectoring to get method params below 10 57a7a5e [Kan Zhang] [SPARK-2024] correcting typo 75ca5bd [Kan Zhang] [SPARK-2024] Better type checking for batch serialized RDD 0bdec55 [Kan Zhang] [SPARK-2024] Refactoring newly added tests 9f39ff4 [Kan Zhang] [SPARK-2024] Adding 2 saveAsHadoopDataset tests 0c134f3 [Kan Zhang] [SPARK-2024] Test refactoring and adding couple unbatched cases 7a176df [Kan Zhang] [SPARK-2024] Add saveAsSequenceFile to PySpark
* dev/check-license wrap folders in quotes.Reynold Xin2014-07-301-5/+5
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* [SQL] Fix compiling of catalyst docs.Michael Armbrust2014-07-301-1/+4
| | | | | | | | Author: Michael Armbrust <michael@databricks.com> Closes #1653 from marmbrus/fixDocs and squashes the following commits: 0aa1feb [Michael Armbrust] Fix compiling of catalyst docs.
* More wrapping FWDIR in quotes.Reynold Xin2014-07-303-3/+3
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* Wrap FWDIR in quotes in dev/check-license.Reynold Xin2014-07-301-1/+1
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* Wrap FWDIR in quotes.Reynold Xin2014-07-301-1/+1
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* [SPARK-2746] Set SBT_MAVEN_PROFILES only when it is not set explicitly by ↵Reynold Xin2014-07-301-1/+4
| | | | | | | | | | the user. Author: Reynold Xin <rxin@apache.org> Closes #1655 from rxin/SBT_MAVEN_PROFILES and squashes the following commits: b268c4b [Reynold Xin] [SPARK-2746] Set SBT_MAVEN_PROFILES only when it is not set explicitly by the user.
* [SPARK-2544][MLLIB] Improve ALS algorithm resource usageGuoQiang Li2014-07-301-0/+6
| | | | | | | | | | | Author: GuoQiang Li <witgo@qq.com> Author: witgo <witgo@qq.com> Closes #929 from witgo/improve_als and squashes the following commits: ea25033 [GuoQiang Li] checkpoint products 3,6,9 ... 154dccf [GuoQiang Li] checkpoint products only c5779ff [witgo] Improve ALS algorithm resource usage
* Avoid numerical instabilityNaftali Harris2014-07-301-1/+2
| | | | | | | | | | | | | | | | | | | | This avoids basically doing 1 - 1, for example: ```python >>> from math import exp >>> margin = -40 >>> 1 - 1 / (1 + exp(margin)) 0.0 >>> exp(margin) / (1 + exp(margin)) 4.248354255291589e-18 >>> ``` Author: Naftali Harris <naftaliharris@gmail.com> Closes #1652 from naftaliharris/patch-2 and squashes the following commits: 0d55a9f [Naftali Harris] Avoid numerical instability
* [SPARK-2747] git diff --dirstat can miss sql changes and not run Hive testsReynold Xin2014-07-301-1/+1
| | | | | | | | | | | | dev/run-tests use "git diff --dirstat master" to check whether sql is changed. However, --dirstat won't show sql if sql's change is negligible (e.g. 1k loc change in core, and only 1 loc change in hive). We should use "git diff --name-only master" instead. Author: Reynold Xin <rxin@apache.org> Closes #1656 from rxin/hiveTest and squashes the following commits: f5eab9f [Reynold Xin] [SPARK-2747] git diff --dirstat can miss sql changes and not run Hive tests.
* [SPARK-2521] Broadcast RDD object (instead of sending it along with every task)Reynold Xin2014-07-3012-302/+198
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This is a resubmission of #1452. It was reverted because it broke the build. Currently (as of Spark 1.0.1), Spark sends RDD object (which contains closures) using Akka along with the task itself to the executors. This is inefficient because all tasks in the same stage use the same RDD object, but we have to send RDD object multiple times to the executors. This is especially bad when a closure references some variable that is very large. The current design led to users having to explicitly broadcast large variables. The patch uses broadcast to send RDD objects and the closures to executors, and use Akka to only send a reference to the broadcast RDD/closure along with the partition specific information for the task. For those of you who know more about the internals, Spark already relies on broadcast to send the Hadoop JobConf every time it uses the Hadoop input, because the JobConf is large. The user-facing impact of the change include: 1. Users won't need to decide what to broadcast anymore, unless they would want to use a large object multiple times in different operations 2. Task size will get smaller, resulting in faster scheduling and higher task dispatch throughput. In addition, the change will simplify some internals of Spark, eliminating the need to maintain task caches and the complex logic to broadcast JobConf (which also led to a deadlock recently). A simple way to test this: ```scala val a = new Array[Byte](1000*1000); scala.util.Random.nextBytes(a); sc.parallelize(1 to 1000, 1000).map { x => a; x }.groupBy { x => a; x }.count ``` Numbers on 3 r3.8xlarge instances on EC2 ``` master branch: 5.648436068 s, 4.715361895 s, 5.360161877 s with this change: 3.416348793 s, 1.477846558 s, 1.553432156 s ``` Author: Reynold Xin <rxin@apache.org> Closes #1498 from rxin/broadcast-task and squashes the following commits: f7364db [Reynold Xin] Code review feedback. f8535dc [Reynold Xin] Fixed the style violation. 252238d [Reynold Xin] Serialize the final task closure as well as ShuffleDependency in taskBinary. 111007d [Reynold Xin] Fix broadcast tests. 797c247 [Reynold Xin] Properly send SparkListenerStageSubmitted and SparkListenerStageCompleted. bab1d8b [Reynold Xin] Check for NotSerializableException in submitMissingTasks. cf38450 [Reynold Xin] Use TorrentBroadcastFactory. 991c002 [Reynold Xin] Use HttpBroadcast. de779f8 [Reynold Xin] Fix TaskContextSuite. cc152fc [Reynold Xin] Don't cache the RDD broadcast variable. d256b45 [Reynold Xin] Fixed unit test failures. One more to go. cae0af3 [Reynold Xin] [SPARK-2521] Broadcast RDD object (instead of sending it along with every task).
* SPARK-2748 [MLLIB] [GRAPHX] Loss of precision for small arguments to ↵Sean Owen2014-07-302-6/+8
| | | | | | | | | | | | | | | | | | Math.exp, Math.log In a few places in MLlib, an expression of the form `log(1.0 + p)` is evaluated. When p is so small that `1.0 + p == 1.0`, the result is 0.0. However the correct answer is very near `p`. This is why `Math.log1p` exists. Similarly for one instance of `exp(m) - 1` in GraphX; there's a special `Math.expm1` method. While the errors occur only for very small arguments, given their use in machine learning algorithms, this is entirely possible. Also note the related PR for Python: https://github.com/apache/spark/pull/1652 Author: Sean Owen <srowen@gmail.com> Closes #1659 from srowen/SPARK-2748 and squashes the following commits: c5926d4 [Sean Owen] Use log1p, expm1 for better precision for tiny arguments
* SPARK-2543: Allow user to set maximum Kryo buffer sizeKoert Kuipers2014-07-303-5/+44
| | | | | | | | | | | | Author: Koert Kuipers <koert@tresata.com> Closes #735 from koertkuipers/feat-kryo-max-buffersize and squashes the following commits: 15f6d81 [Koert Kuipers] change default for spark.kryoserializer.buffer.max.mb to 64mb and add some documentation 1bcc22c [Koert Kuipers] Merge branch 'master' into feat-kryo-max-buffersize 0c9f8eb [Koert Kuipers] make default for kryo max buffer size 16MB 143ec4d [Koert Kuipers] test resizable buffer in kryo Output 0732445 [Koert Kuipers] support setting maxCapacity to something different than capacity in kryo Output
* [SPARK-2179][SQL] Public API for DataTypes and SchemaYin Huai2014-07-3061-386/+3442
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The current PR contains the following changes: * Expose `DataType`s in the sql package (internal details are private to sql). * Users can create Rows. * Introduce `applySchema` to create a `SchemaRDD` by applying a `schema: StructType` to an `RDD[Row]`. * Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`. * `ScalaReflection.typeOfObject` provides a way to infer the Catalyst data type based on an object. Also, we can compose `typeOfObject` with some custom logics to form a new function to infer the data type (for different use cases). * `JsonRDD` has been refactored to use changes introduced by this PR. * Add a field `containsNull` to `ArrayType`. So, we can explicitly mark if an `ArrayType` can contain null values. The default value of `containsNull` is `false`. New APIs are introduced in the sql package object and SQLContext. You can find the scaladoc at [sql package object](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.package) and [SQLContext](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.SQLContext). An example of using `applySchema` is shown below. ```scala import org.apache.spark.sql._ val sqlContext = new org.apache.spark.sql.SQLContext(sc) val schema = StructType( StructField("name", StringType, false) :: StructField("age", IntegerType, true) :: Nil) val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Row(p(0), p(1).trim.toInt)) val peopleSchemaRDD = sqlContext. applySchema(people, schema) peopleSchemaRDD.printSchema // root // |-- name: string (nullable = false) // |-- age: integer (nullable = true) peopleSchemaRDD.registerAsTable("people") sqlContext.sql("select name from people").collect.foreach(println) ``` I will add new contents to the SQL programming guide later. JIRA: https://issues.apache.org/jira/browse/SPARK-2179 Author: Yin Huai <huai@cse.ohio-state.edu> Closes #1346 from yhuai/dataTypeAndSchema and squashes the following commits: 1d45977 [Yin Huai] Clean up. a6e08b4 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema c712fbf [Yin Huai] Converts types of values based on defined schema. 4ceeb66 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema e5f8df5 [Yin Huai] Scaladoc. 122d1e7 [Yin Huai] Address comments. 03bfd95 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema 2476ed0 [Yin Huai] Minor updates. ab71f21 [Yin Huai] Format. fc2bed1 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema bd40a33 [Yin Huai] Address comments. 991f860 [Yin Huai] Move "asJavaDataType" and "asScalaDataType" to DataTypeConversions.scala. 1cb35fe [Yin Huai] Add "valueContainsNull" to MapType. 3edb3ae [Yin Huai] Python doc. 692c0b9 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema 1d93395 [Yin Huai] Python APIs. 246da96 [Yin Huai] Add java data type APIs to javadoc index. 1db9531 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema d48fc7b [Yin Huai] Minor updates. 33c4fec [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema b9f3071 [Yin Huai] Java API for applySchema. 1c9f33c [Yin Huai] Java APIs for DataTypes and Row. 624765c [Yin Huai] Tests for applySchema. aa92e84 [Yin Huai] Update data type tests. 8da1a17 [Yin Huai] Add Row.fromSeq. 9c99bc0 [Yin Huai] Several minor updates. 1d9c13a [Yin Huai] Update applySchema API. 85e9b51 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema e495e4e [Yin Huai] More comments. 42d47a3 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema c3f4a02 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema 2e58dbd [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema b8b7db4 [Yin Huai] 1. Move sql package object and package-info to sql-core. 2. Minor updates on APIs. 3. Update scala doc. 68525a2 [Yin Huai] Update JSON unit test. 3209108 [Yin Huai] Add unit tests. dcaf22f [Yin Huai] Add a field containsNull to ArrayType to indicate if an array can contain null values or not. If an ArrayType is constructed by "ArrayType(elementType)" (the existing constructor), the value of containsNull is false. 9168b83 [Yin Huai] Update comments. fc649d7 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema eca7d04 [Yin Huai] Add two apply methods which will be used to extract StructField(s) from a StructType. 949d6bb [Yin Huai] When creating a SchemaRDD for a JSON dataset, users can apply an existing schema. 7a6a7e5 [Yin Huai] Fix bug introduced by the change made on SQLContext.inferSchema. 43a45e1 [Yin Huai] Remove sql.util.package introduced in a previous commit. 0266761 [Yin Huai] Format 03eec4c [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema 90460ac [Yin Huai] Infer the Catalyst data type from an object and cast a data value to the expected type. 3fa0df5 [Yin Huai] Provide easier ways to construct a StructType. 16be3e5 [Yin Huai] This commit contains three changes: * Expose `DataType`s in the sql package (internal details are private to sql). * Introduce `createSchemaRDD` to create a `SchemaRDD` from an `RDD` with a provided schema (represented by a `StructType`) and a provided function to construct `Row`, * Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`.
* [SPARK-2260] Fix standalone-cluster mode, which was brokenAndrew Or2014-07-2916-51/+93
| | | | | | | | | | | | | | | | | | | | | | | | | | | | The main thing was that spark configs were not propagated to the driver, and so applications that do not specify `master` or `appName` automatically failed. This PR fixes that and a couple of miscellaneous things that are related. One thing that may or may not be an issue is that the jars must be available on the driver node. In `standalone-cluster` mode, this effectively means these jars must be available on all the worker machines, since the driver is launched on one of them. The semantics here are not the same as `yarn-cluster` mode, where all the relevant jars are uploaded to a distributed cache automatically and shipped to the containers. This is probably not a concern, but still worth a mention. Author: Andrew Or <andrewor14@gmail.com> Closes #1538 from andrewor14/standalone-cluster and squashes the following commits: 8c11a0d [Andrew Or] Clean up imports / comments (minor) 2678d13 [Andrew Or] Handle extraJavaOpts properly 7660547 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-cluster 6f64a9b [Andrew Or] Revert changes in YARN 2f2908b [Andrew Or] Fix tests ed01491 [Andrew Or] Don't go overboard with escaping 8e105e1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-cluster b890949 [Andrew Or] Abstract usages of converting spark opts to java opts 79f63a3 [Andrew Or] Move sparkProps into javaOpts 78752f8 [Andrew Or] Fix tests 5a9c6c7 [Andrew Or] Fix line too long c141a00 [Andrew Or] Don't display "unknown app" on driver log pages d7e2728 [Andrew Or] Avoid deprecation warning in standalone Client 6ceb14f [Andrew Or] Allow relevant configs to propagate to standalone Driver 7f854bc [Andrew Or] Fix test 855256e [Andrew Or] Fix standalone-cluster mode fd9da51 [Andrew Or] Formatting changes (minor)
* [SQL] Handle null values in debug()Michael Armbrust2014-07-291-1/+3
| | | | | | | | Author: Michael Armbrust <michael@databricks.com> Closes #1646 from marmbrus/nullDebug and squashes the following commits: 49050a8 [Michael Armbrust] Handle null values in debug()
* [SPARK-2568] RangePartitioner should run only one job if data is balancedXiangrui Meng2014-07-293-19/+171
| | | | | | | | | | | | | | | | | | | | | | | | | | | As of Spark 1.0, RangePartitioner goes through data twice: once to compute the count and once to do sampling. As a result, to do sortByKey, Spark goes through data 3 times (once to count, once to sample, and once to sort). `RangePartitioner` should go through data only once, collecting samples from input partitions as well as counting. If the data is balanced, this should give us a good sketch. If we see big partitions, we re-sample from them in order to collect enough items. The downside is that we need to collect more from each partition in the first pass. An alternative solution is caching the intermediate result and decide whether to fetch the data after. Author: Xiangrui Meng <meng@databricks.com> Author: Reynold Xin <rxin@apache.org> Closes #1562 from mengxr/range-partitioner and squashes the following commits: 6cc2551 [Xiangrui Meng] change foreach to for eb39b08 [Xiangrui Meng] Merge branch 'master' into range-partitioner eb95dd8 [Xiangrui Meng] separate sketching and determining bounds impl c436d30 [Xiangrui Meng] fix binary metrics unit tests db58a55 [Xiangrui Meng] add unit tests a6e35d6 [Xiangrui Meng] minor update 60be09e [Xiangrui Meng] remove importance sampler 9ee9992 [Xiangrui Meng] update range partitioner to run only one job on roughly balanced data cc12f47 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into range-part 06ac2ec [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into range-part 17bcbf3 [Reynold Xin] Added seed. badf20d [Reynold Xin] Renamed the method. 6940010 [Reynold Xin] Reservoir sampling implementation.
* [SPARK-2054][SQL] Code Generation for Expression EvaluationMichael Armbrust2014-07-2953-297/+1889
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Adds a new method for evaluating expressions using code that is generated though Scala reflection. This functionality is configured by the SQLConf option `spark.sql.codegen` and is currently turned off by default. Evaluation can be done in several specialized ways: - *Projection* - Given an input row, produce a new row from a set of expressions that define each column in terms of the input row. This can either produce a new Row object or perform the projection in-place on an existing Row (MutableProjection). - *Ordering* - Compares two rows based on a list of `SortOrder` expressions - *Condition* - Returns `true` or `false` given an input row. For each of the above operations there is both a Generated and Interpreted version. When generation for a given expression type is undefined, the code generator falls back on calling the `eval` function of the expression class. Even without custom code, there is still a potential speed up, as loops are unrolled and code can still be inlined by JIT. This PR also contains a new type of Aggregation operator, `GeneratedAggregate`, that performs aggregation by using generated `Projection` code. Currently the required expression rewriting only works for simple aggregations like `SUM` and `COUNT`. This functionality will be extended in a future PR. This PR also performs several clean ups that simplified the implementation: - The notion of `Binding` all expressions in a tree automatically before query execution has been removed. Instead it is the responsibly of an operator to provide the input schema when creating one of the specialized evaluators defined above. In cases when the standard eval method is going to be called, binding can still be done manually using `BindReferences`. There are a few reasons for this change: First, there were many operators where it just didn't work before. For example, operators with more than one child, and operators like aggregation that do significant rewriting of the expression. Second, the semantics of equality with `BoundReferences` are broken. Specifically, we have had a few bugs where partitioning breaks because of the binding. - A copy of the current `SQLContext` is automatically propagated to all `SparkPlan` nodes by the query planner. Before this was done ad-hoc for the nodes that needed this. However, this required a lot of boilerplate as one had to always remember to make it `transient` and also had to modify the `otherCopyArgs`. Author: Michael Armbrust <michael@databricks.com> Closes #993 from marmbrus/newCodeGen and squashes the following commits: 96ef82c [Michael Armbrust] Merge remote-tracking branch 'apache/master' into newCodeGen f34122d [Michael Armbrust] Merge remote-tracking branch 'apache/master' into newCodeGen 67b1c48 [Michael Armbrust] Use conf variable in SQLConf object 4bdc42c [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen 41a40c9 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen de22aac [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen fed3634 [Michael Armbrust] Inspectors are not serializable. ef8d42b [Michael Armbrust] comments 533fdfd [Michael Armbrust] More logging of expression rewriting for GeneratedAggregate. 3cd773e [Michael Armbrust] Allow codegen for Generate. 64b2ee1 [Michael Armbrust] Implement copy 3587460 [Michael Armbrust] Drop unused string builder function. 9cce346 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen 1a61293 [Michael Armbrust] Address review comments. 0672e8a [Michael Armbrust] Address comments. 1ec2d6e [Michael Armbrust] Address comments 033abc6 [Michael Armbrust] off by default 4771fab [Michael Armbrust] Docs, more test coverage. d30fee2 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen d2ad5c5 [Michael Armbrust] Refactor putting SQLContext into SparkPlan. Fix ordering, other test cases. be2cd6b [Michael Armbrust] WIP: Remove old method for reference binding, more work on configuration. bc88ecd [Michael Armbrust] Style 6cc97ca [Michael Armbrust] Merge remote-tracking branch 'origin/master' into newCodeGen 4220f1e [Michael Armbrust] Better config, docs, etc. ca6cc6b [Michael Armbrust] WIP 9d67d85 [Michael Armbrust] Fix hive planner fc522d5 [Michael Armbrust] Hook generated aggregation in to the planner. e742640 [Michael Armbrust] Remove unneeded changes and code. 675e679 [Michael Armbrust] Upgrade paradise. 0093376 [Michael Armbrust] Comment / indenting cleanup. d81f998 [Michael Armbrust] include schema for binding. 0e889e8 [Michael Armbrust] Use typeOf instead tq f623ffd [Michael Armbrust] Quiet logging from test suite. efad14f [Michael Armbrust] Remove some half finished functions. 92e74a4 [Michael Armbrust] add overrides a2b5408 [Michael Armbrust] WIP: Code generation with scala reflection.
* [SPARK-2305] [PySpark] Update Py4J to version 0.8.2.1Josh Rosen2014-07-299-8/+8
| | | | | | | | Author: Josh Rosen <joshrosen@apache.org> Closes #1626 from JoshRosen/SPARK-2305 and squashes the following commits: 03fb283 [Josh Rosen] Update Py4J to version 0.8.2.1.
* [SPARK-2631][SQL] Use SQLConf to configure in-memory columnar cachingMichael Armbrust2014-07-292-2/+4
| | | | | | | | Author: Michael Armbrust <michael@databricks.com> Closes #1638 from marmbrus/cachedConfig and squashes the following commits: 2362082 [Michael Armbrust] Use SQLConf to configure in-memory columnar caching
* [SPARK-2716][SQL] Don't check resolved for having filters.Michael Armbrust2014-07-293-1/+140
| | | | | | | | | | For queries like `... HAVING COUNT(*) > 9` the expression is always resolved since it contains no attributes. This was causing us to avoid doing the Having clause aggregation rewrite. Author: Michael Armbrust <michael@databricks.com> Closes #1640 from marmbrus/havingNoRef and squashes the following commits: 92d3901 [Michael Armbrust] Don't check resolved for having filters.
* MAINTENANCE: Automated closing of pull requests.Patrick Wendell2014-07-290-0/+0
| | | | | | | | | | | This commit exists to close the following pull requests on Github: Closes #740 (close requested by 'rxin') Closes #647 (close requested by 'rxin') Closes #1383 (close requested by 'rxin') Closes #1485 (close requested by 'pwendell') Closes #693 (close requested by 'rxin') Closes #478 (close requested by 'JoshRosen')
* [SPARK-2393][SQL] Cost estimation optimization framework for Catalyst ↵Zongheng Yang2014-07-2918-124/+256
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | logical plans & sample usage. The idea is that every Catalyst logical plan gets hold of a Statistics class, the usage of which provides useful estimations on various statistics. See the implementations of `MetastoreRelation`. This patch also includes several usages of the estimation interface in the planner. For instance, we now use physical table sizes from the estimate interface to convert an equi-join to a broadcast join (when doing so is beneficial, as determined by a size threshold). Finally, there are a couple minor accompanying changes including: - Remove the not-in-use `BaseRelation`. - Make SparkLogicalPlan take a `SQLContext` in the second param list. Author: Zongheng Yang <zongheng.y@gmail.com> Closes #1238 from concretevitamin/estimates and squashes the following commits: 329071d [Zongheng Yang] Address review comments; turn config name from string to field in SQLConf. 8663e84 [Zongheng Yang] Use BigInt for stat; for logical leaves, by default throw an exception. 2f2fb89 [Zongheng Yang] Fix statistics for SparkLogicalPlan. 9951305 [Zongheng Yang] Remove childrenStats. 16fc60a [Zongheng Yang] Avoid calling statistics on plans if auto join conversion is disabled. 8bd2816 [Zongheng Yang] Add a note on performance of statistics. 6e594b8 [Zongheng Yang] Get size info from metastore for MetastoreRelation. 01b7a3e [Zongheng Yang] Update scaladoc for a field and move it to @param section. 549061c [Zongheng Yang] Remove numTuples in Statistics for now. 729a8e2 [Zongheng Yang] Update docs to be more explicit. 573e644 [Zongheng Yang] Remove singleton SQLConf and move back `settings` to the trait. 2d99eb5 [Zongheng Yang] {Cleanup, use synchronized in, enrich} StatisticsSuite. ca5b825 [Zongheng Yang] Inject SQLContext into SparkLogicalPlan, removing SQLConf mixin from it. 43d38a6 [Zongheng Yang] Revert optimization for BroadcastNestedLoopJoin (this fixes tests). 0ef9e5b [Zongheng Yang] Use multiplication instead of sum for default estimates. 4ef0d26 [Zongheng Yang] Make Statistics a case class. 3ba8f3e [Zongheng Yang] Add comment. e5bcf5b [Zongheng Yang] Fix optimization conditions & update scala docs to explain. 7d9216a [Zongheng Yang] Apply estimation to planning ShuffleHashJoin & BroadcastNestedLoopJoin. 73cde01 [Zongheng Yang] Move SQLConf back. Assign default sizeInBytes to SparkLogicalPlan. 73412be [Zongheng Yang] Move SQLConf to Catalyst & add default val for sizeInBytes. 7a60ab7 [Zongheng Yang] s/Estimates/Statistics, s/cardinality/numTuples. de3ae13 [Zongheng Yang] Add parquetAfter() properly in test. dcff9bd [Zongheng Yang] Cleanups. 84301a4 [Zongheng Yang] Refactors. 5bf5586 [Zongheng Yang] Typo. 56a8e6e [Zongheng Yang] Prototype impl of estimations for Catalyst logical plans.
* [SPARK-2082] stratified sampling in PairRDDFunctions that guarantees exact ↵Doris Xin2014-07-297-16/+656
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | sample size Implemented stratified sampling that guarantees exact sample size using ScaRSR with two passes over the RDD for sampling without replacement and three passes for sampling with replacement. Author: Doris Xin <doris.s.xin@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #1025 from dorx/stratified and squashes the following commits: 245439e [Doris Xin] moved minSamplingRate to getUpperBound eaf5771 [Doris Xin] bug fixes. 17a381b [Doris Xin] fixed a merge issue and a failed unit ea7d27f [Doris Xin] merge master b223529 [Xiangrui Meng] use approx bounds for poisson fix poisson mean for waitlisting add unit tests for Java b3013a4 [Xiangrui Meng] move math3 back to test scope eecee5f [Doris Xin] Merge branch 'master' into stratified f4c21f3 [Doris Xin] Reviewer comments a10e68d [Doris Xin] style fix a2bf756 [Doris Xin] Merge branch 'master' into stratified 680b677 [Doris Xin] use mapPartitionWithIndex instead 9884a9f [Doris Xin] style fix bbfb8c9 [Doris Xin] Merge branch 'master' into stratified ee9d260 [Doris Xin] addressed reviewer comments 6b5b10b [Doris Xin] Merge branch 'master' into stratified 254e03c [Doris Xin] minor fixes and Java API. 4ad516b [Doris Xin] remove unused imports from PairRDDFunctions bd9dc6e [Doris Xin] unit bug and style violation fixed 1fe1cff [Doris Xin] Changed fractionByKey to a map to enable arg check 944a10c [Doris Xin] [SPARK-2145] Add lower bound on sampling rate 0214a76 [Doris Xin] cleanUp 90d94c0 [Doris Xin] merge master 9e74ab5 [Doris Xin] Separated out most of the logic in sampleByKey 7327611 [Doris Xin] merge master 50581fc [Doris Xin] added a TODO for logging in python 46f6c8c [Doris Xin] fixed the NPE caused by closures being cleaned before being passed into the aggregate function 7e1a481 [Doris Xin] changed the permission on SamplingUtil 1d413ce [Doris Xin] fixed checkstyle issues 9ee94ee [Doris Xin] [SPARK-2082] stratified sampling in PairRDDFunctions that guarantees exact sample size e3fd6a6 [Doris Xin] Merge branch 'master' into takeSample 7cab53a [Doris Xin] fixed import bug in rdd.py ffea61a [Doris Xin] SPARK-1939: Refactor takeSample method in RDD 1441977 [Doris Xin] SPARK-1939 Refactor takeSample method in RDD to use ScaSRS
* [SPARK-2674] [SQL] [PySpark] support datetime type for SchemaRDDDavies Liu2014-07-294-44/+68
| | | | | | | | | | | | | | | Datetime and time in Python will be converted into java.util.Calendar after serialization, it will be converted into java.sql.Timestamp during inferSchema(). In javaToPython(), Timestamp will be converted into Calendar, then be converted into datetime in Python after pickling. Author: Davies Liu <davies.liu@gmail.com> Closes #1601 from davies/date and squashes the following commits: f0599b0 [Davies Liu] remove tests for sets and tuple in sql, fix list of list c9d607a [Davies Liu] convert datetype for runtime 709d40d [Davies Liu] remove brackets 96db384 [Davies Liu] support datetime type for SchemaRDD
* [SPARK-2730][SQL] When retrieving a value from a Map, GetItem evaluates key ↵Yin Huai2014-07-291-1/+0
| | | | | | | | | | | | twice JIRA: https://issues.apache.org/jira/browse/SPARK-2730 Author: Yin Huai <huai@cse.ohio-state.edu> Closes #1637 from yhuai/SPARK-2730 and squashes the following commits: 1a9f24e [Yin Huai] Remove unnecessary key evaluation.
* [SQL]change some test listsDaoyuan2014-07-291-5/+1
| | | | | | | | | | | | | 1. there's no `hook_context.q` but a `hook_context_cs.q` in query folder 2. there's no `compute_stats_table.q` in query folder 3. there's no `having1.q` in query folder 4. `udf_E` and `udf_PI` appear twice in white list Author: Daoyuan <daoyuan.wang@intel.com> Closes #1634 from adrian-wang/testcases and squashes the following commits: d7482ce [Daoyuan] change some test lists
* [STREAMING] SPARK-1729. Make Flume pull data from source, rather than the ↵Hari Shreedharan2014-07-2918-13/+1524
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | current pu... ...sh model Currently Spark uses Flume's internal Avro Protocol to ingest data from Flume. If the executor running the receiver fails, it currently has to be restarted on the same node to be able to receive data. This commit adds a new Sink which can be deployed to a Flume agent. This sink can be polled by a new DStream that is also included in this commit. This model ensures that data can be pulled into Spark from Flume even if the receiver is restarted on a new node. This also allows the receiver to receive data on multiple threads for better performance. Author: Hari Shreedharan <harishreedharan@gmail.com> Author: Hari Shreedharan <hshreedharan@apache.org> Author: Tathagata Das <tathagata.das1565@gmail.com> Author: harishreedharan <hshreedharan@cloudera.com> Closes #807 from harishreedharan/master and squashes the following commits: e7f70a3 [Hari Shreedharan] Merge remote-tracking branch 'asf-git/master' 96cfb6f [Hari Shreedharan] Merge remote-tracking branch 'asf/master' e48d785 [Hari Shreedharan] Documenting flume-sink being ignored for Mima checks. 5f212ce [Hari Shreedharan] Ignore Spark Sink from mima. 981bf62 [Hari Shreedharan] Merge remote-tracking branch 'asf/master' 7a1bc6e [Hari Shreedharan] Fix SparkBuild.scala a082eb3 [Hari Shreedharan] Merge remote-tracking branch 'asf/master' 1f47364 [Hari Shreedharan] Minor fixes. 73d6f6d [Hari Shreedharan] Cleaned up tests a bit. Added some docs in multiple places. 65b76b4 [Hari Shreedharan] Fixing the unit test. e59cc20 [Hari Shreedharan] Use SparkFlumeEvent instead of the new type. Also, Flume Polling Receiver now uses the store(ArrayBuffer) method. f3c99d1 [Hari Shreedharan] Merge remote-tracking branch 'asf/master' 3572180 [Hari Shreedharan] Adding a license header, making Jenkins happy. 799509f [Hari Shreedharan] Fix a compile issue. 3c5194c [Hari Shreedharan] Merge remote-tracking branch 'asf/master' d248d22 [harishreedharan] Merge pull request #1 from tdas/flume-polling 10b6214 [Tathagata Das] Changed public API, changed sink package, and added java unit test to make sure Java API is callable from Java. 1edc806 [Hari Shreedharan] SPARK-1729. Update logging in Spark Sink. 8c00289 [Hari Shreedharan] More debug messages 393bd94 [Hari Shreedharan] SPARK-1729. Use LinkedBlockingQueue instead of ArrayBuffer to keep track of connections. 120e2a1 [Hari Shreedharan] SPARK-1729. Some test changes and changes to utils classes. 9fd0da7 [Hari Shreedharan] SPARK-1729. Use foreach instead of map for all Options. 8136aa6 [Hari Shreedharan] Adding TransactionProcessor to map on returning batch of data 86aa274 [Hari Shreedharan] Merge remote-tracking branch 'asf/master' 205034d [Hari Shreedharan] Merging master in 4b0c7fc [Hari Shreedharan] FLUME-1729. New Flume-Spark integration. bda01fc [Hari Shreedharan] FLUME-1729. Flume-Spark integration. 0d69604 [Hari Shreedharan] FLUME-1729. Better Flume-Spark integration. 3c23c18 [Hari Shreedharan] SPARK-1729. New Spark-Flume integration. 70bcc2a [Hari Shreedharan] SPARK-1729. New Flume-Spark integration. d6fa3aa [Hari Shreedharan] SPARK-1729. New Flume-Spark integration. e7da512 [Hari Shreedharan] SPARK-1729. Fixing import order 9741683 [Hari Shreedharan] SPARK-1729. Fixes based on review. c604a3c [Hari Shreedharan] SPARK-1729. Optimize imports. 0f10788 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model 87775aa [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model 8df37e4 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model 03d6c1c [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model 08176ad [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model d24d9d4 [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model 6d6776a [Hari Shreedharan] SPARK-1729. Make Flume pull data from source, rather than the current push model
* Minor indentation and comment typo fixes.Aaron Staple2014-07-292-13/+13
| | | | | | | | | Author: Aaron Staple <astaple@gmail.com> Closes #1630 from staple/minor and squashes the following commits: 6f295a2 [Aaron Staple] Fix typos in comment about ExprId. 8566467 [Aaron Staple] Fix off by one column indentation in SqlParser.
* [SPARK-2174][MLLIB] treeReduce and treeAggregateXiangrui Meng2014-07-295-15/+98
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | In `reduce` and `aggregate`, the driver node spends linear time on the number of partitions. It becomes a bottleneck when there are many partitions and the data from each partition is big. SPARK-1485 (#506) tracks the progress of implementing AllReduce on Spark. I did several implementations including butterfly, reduce + broadcast, and treeReduce + broadcast. treeReduce + BT broadcast seems to be right way to go for Spark. Using binary tree may introduce some overhead in communication, because the driver still need to coordinate on data shuffling. In my experiments, n -> sqrt(n) -> 1 gives the best performance in general, which is why I set "depth = 2" in MLlib algorithms. But it certainly needs more testing. I left `treeReduce` and `treeAggregate` public for easy testing. Some numbers from a test on 32-node m3.2xlarge cluster. code: ~~~ import breeze.linalg._ import org.apache.log4j._ Logger.getRootLogger.setLevel(Level.OFF) for (n <- Seq(1, 10, 100, 1000, 10000, 100000, 1000000)) { val vv = sc.parallelize(0 until 1024, 1024).map(i => DenseVector.zeros[Double](n)) var start = System.nanoTime(); vv.treeReduce(_ + _, 2); println((System.nanoTime() - start) / 1e9) start = System.nanoTime(); vv.reduce(_ + _); println((System.nanoTime() - start) / 1e9) } ~~~ out: | n | treeReduce(,2) | reduce | |---|---------------------|-----------| | 10 | 0.215538731 | 0.204206899 | | 100 | 0.278405907 | 0.205732582 | | 1000 | 0.208972182 | 0.214298272 | | 10000 | 0.194792071 | 0.349353687 | | 100000 | 0.347683285 | 6.086671892 | | 1000000 | 2.589350682 | 66.572906702 | CC: @pwendell This is clearly more scalable than the default implementation. My question is whether we should use this implementation in `reduce` and `aggregate` or put them as separate methods. The concern is that users may use `reduce` and `aggregate` as collect, where having multiple stages doesn't reduce the data size. However, in this case, `collect` is more appropriate. Author: Xiangrui Meng <meng@databricks.com> Closes #1110 from mengxr/tree and squashes the following commits: c6cd267 [Xiangrui Meng] make depth default to 2 b04b96a [Xiangrui Meng] address comments 9bcc5d3 [Xiangrui Meng] add depth for readability 7495681 [Xiangrui Meng] fix compile error 142a857 [Xiangrui Meng] merge master d58a087 [Xiangrui Meng] move treeReduce and treeAggregate to mllib 8a2a59c [Xiangrui Meng] Merge branch 'master' into tree be6a88a [Xiangrui Meng] use treeAggregate in mllib 0f94490 [Xiangrui Meng] add docs eb71c33 [Xiangrui Meng] add treeReduce fe42a5e [Xiangrui Meng] add treeAggregate
* [SPARK-2726] and [SPARK-2727] Remove SortOrder and do in-place sort.Reynold Xin2014-07-294-31/+18
| | | | | | | | | | | | | | The pull request includes two changes: 1. Removes SortOrder introduced by SPARK-2125. The key ordering already includes the SortOrder information since an Ordering can be reverse. This is similar to Java's Comparator interface. Rarely does an API accept both a Comparator as well as a SortOrder. 2. Replaces the sortWith call in HashShuffleReader with an in-place quick sort. Author: Reynold Xin <rxin@apache.org> Closes #1631 from rxin/sortOrder and squashes the following commits: c9d37e1 [Reynold Xin] [SPARK-2726] and [SPARK-2727] Remove SortOrder and do in-place sort.
* [SPARK-791] [PySpark] fix pickle itemgetter with cloudpickleDavies Liu2014-07-292-2/+9
| | | | | | | | | | fix the problem with pickle operator.itemgetter with multiple index. Author: Davies Liu <davies.liu@gmail.com> Closes #1627 from davies/itemgetter and squashes the following commits: aabd7fa [Davies Liu] fix pickle itemgetter with cloudpickle
* [SPARK-2580] [PySpark] keep silent in worker if JVM close the socketDavies Liu2014-07-292-8/+19
| | | | | | | | | | | | | During rdd.take(n), JVM will close the socket if it had got enough data, the Python worker should keep silent in this case. In the same time, the worker should not print the trackback into stderr if it send the traceback to JVM successfully. Author: Davies Liu <davies.liu@gmail.com> Closes #1625 from davies/error and squashes the following commits: 4fbcc6d [Davies Liu] disable log4j during testing when exception is expected. cc14202 [Davies Liu] keep silent in worker if JVM close the socket
* Excess judgmentYadong Qi2014-07-281-1/+1
| | | | | | | | Author: Yadong Qi <qiyadong2010@gmail.com> Closes #1629 from watermen/bug-fix2 and squashes the following commits: 59b7237 [Yadong Qi] Update HiveQl.scala
* Use commons-lang3 in SignalLogger rather than commons-langAaron Davidson2014-07-281-1/+1
| | | | | | | | | | Spark only transitively depends on the latter, based on the Hadoop version. Author: Aaron Davidson <aaron@databricks.com> Closes #1621 from aarondav/lang3 and squashes the following commits: 93c93bf [Aaron Davidson] Use commons-lang3 in SignalLogger rather than commons-lang
* [SPARK-2410][SQL] Merging Hive Thrift/JDBC server (with Maven profile fix)Cheng Lian2014-07-2854-96/+1790
| | | | | | | | | | | | | | | JIRA issue: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410) Another try for #1399 & #1600. Those two PR breaks Jenkins builds because we made a separate profile `hive-thriftserver` in sub-project `assembly`, but the `hive-thriftserver` module is defined outside the `hive-thriftserver` profile. Thus every time a pull request that doesn't touch SQL code will also execute test suites defined in `hive-thriftserver`, but tests fail because related .class files are not included in the assembly jar. In the most recent commit, module `hive-thriftserver` is moved into its own profile to fix this problem. All previous commits are squashed for clarity. Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #1620 from liancheng/jdbc-with-maven-fix and squashes the following commits: 629988e [Cheng Lian] Moved hive-thriftserver module definition into its own profile ec3c7a7 [Cheng Lian] Cherry picked the Hive Thrift server
* [SPARK-2479][MLlib] Comparing floating-point numbers using relative error in ↵DB Tsai2014-07-2810-130/+438
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | UnitTests Floating point math is not exact, and most floating-point numbers end up being slightly imprecise due to rounding errors. Simple values like 0.1 cannot be precisely represented using binary floating point numbers, and the limited precision of floating point numbers means that slight changes in the order of operations or the precision of intermediates can change the result. That means that comparing two floats to see if they are equal is usually not what we want. As long as this imprecision stays small, it can usually be ignored. Based on discussion in the community, we have implemented two different APIs for relative tolerance, and absolute tolerance. It makes sense that test writers should know which one they need depending on their circumstances. Developers also need to explicitly specify the eps, and there is no default value which will sometimes cause confusion. When comparing against zero using relative tolerance, a exception will be raised to warn users that it's meaningless. For relative tolerance, users can now write assert(23.1 ~== 23.52 relTol 0.02) assert(23.1 ~== 22.74 relTol 0.02) assert(23.1 ~= 23.52 relTol 0.02) assert(23.1 ~= 22.74 relTol 0.02) assert(!(23.1 !~= 23.52 relTol 0.02)) assert(!(23.1 !~= 22.74 relTol 0.02)) // This will throw exception with the following message. // "Did not expect 23.1 and 23.52 to be within 0.02 using relative tolerance." assert(23.1 !~== 23.52 relTol 0.02) // "Expected 23.1 and 22.34 to be within 0.02 using relative tolerance." assert(23.1 ~== 22.34 relTol 0.02) For absolute error, assert(17.8 ~== 17.99 absTol 0.2) assert(17.8 ~== 17.61 absTol 0.2) assert(17.8 ~= 17.99 absTol 0.2) assert(17.8 ~= 17.61 absTol 0.2) assert(!(17.8 !~= 17.99 absTol 0.2)) assert(!(17.8 !~= 17.61 absTol 0.2)) // This will throw exception with the following message. // "Did not expect 17.8 and 17.99 to be within 0.2 using absolute error." assert(17.8 !~== 17.99 absTol 0.2) // "Expected 17.8 and 17.59 to be within 0.2 using absolute error." assert(17.8 ~== 17.59 absTol 0.2) Authors: DB Tsai <dbtsaialpinenow.com> Marek Kolodziej <marekalpinenow.com> Author: DB Tsai <dbtsai@alpinenow.com> Closes #1425 from dbtsai/SPARK-2479_comparing_floating_point and squashes the following commits: 8c7cbcc [DB Tsai] Alpine Data Labs
* [SPARK-2523] [SQL] Hadoop table scan bug fixingCheng Hao2014-07-284-115/+138
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | In HiveTableScan.scala, ObjectInspector was created for all of the partition based records, which probably causes ClassCastException if the object inspector is not identical among table & partitions. This is the follow up with: https://github.com/apache/spark/pull/1408 https://github.com/apache/spark/pull/1390 I've run a micro benchmark in my local with 15000000 records totally, and got the result as below: With This Patch | Partition-Based Table | Non-Partition-Based Table ------------ | ------------- | ------------- No | 1927 ms | 1885 ms Yes | 1541 ms | 1524 ms It showed this patch will also improve the performance. PS: the benchmark code is also attached. (thanks liancheng ) ``` package org.apache.spark.sql.hive import org.apache.spark.SparkContext import org.apache.spark.SparkConf import org.apache.spark.sql._ object HiveTableScanPrepare extends App { case class Record(key: String, value: String) val sparkContext = new SparkContext( new SparkConf() .setMaster("local") .setAppName(getClass.getSimpleName.stripSuffix("$"))) val hiveContext = new LocalHiveContext(sparkContext) val rdd = sparkContext.parallelize((1 to 3000000).map(i => Record(s"$i", s"val_$i"))) import hiveContext._ hql("SHOW TABLES") hql("DROP TABLE if exists part_scan_test") hql("DROP TABLE if exists scan_test") hql("DROP TABLE if exists records") rdd.registerAsTable("records") hql("""CREATE TABLE part_scan_test (key STRING, value STRING) PARTITIONED BY (part1 string, part2 STRING) | ROW FORMAT SERDE | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe' | STORED AS RCFILE """.stripMargin) hql("""CREATE TABLE scan_test (key STRING, value STRING) | ROW FORMAT SERDE | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe' | STORED AS RCFILE """.stripMargin) for (part1 <- 2000 until 2001) { for (part2 <- 1 to 5) { hql(s"""from records | insert into table part_scan_test PARTITION (part1='$part1', part2='2010-01-$part2') | select key, value """.stripMargin) hql(s"""from records | insert into table scan_test select key, value """.stripMargin) } } } object HiveTableScanTest extends App { val sparkContext = new SparkContext( new SparkConf() .setMaster("local") .setAppName(getClass.getSimpleName.stripSuffix("$"))) val hiveContext = new LocalHiveContext(sparkContext) import hiveContext._ hql("SHOW TABLES") val part_scan_test = hql("select key, value from part_scan_test") val scan_test = hql("select key, value from scan_test") val r_part_scan_test = (0 to 5).map(i => benchmark(part_scan_test)) val r_scan_test = (0 to 5).map(i => benchmark(scan_test)) println("Scanning Partition-Based Table") r_part_scan_test.foreach(printResult) println("Scanning Non-Partition-Based Table") r_scan_test.foreach(printResult) def printResult(result: (Long, Long)) { println(s"Duration: ${result._1} ms Result: ${result._2}") } def benchmark(srdd: SchemaRDD) = { val begin = System.currentTimeMillis() val result = srdd.count() val end = System.currentTimeMillis() ((end - begin), result) } } ``` Author: Cheng Hao <hao.cheng@intel.com> Closes #1439 from chenghao-intel/hadoop_table_scan and squashes the following commits: 888968f [Cheng Hao] Fix issues in code style 27540ba [Cheng Hao] Fix the TableScan Bug while partition serde differs 40a24a7 [Cheng Hao] Add Unit Test
* [SPARK-1550] [PySpark] Allow SparkContext creation after failed attemptsJosh Rosen2014-07-272-6/+18
| | | | | | | | | | This addresses a PySpark issue where a failed attempt to construct SparkContext would prevent any future SparkContext creation. Author: Josh Rosen <joshrosen@apache.org> Closes #1606 from JoshRosen/SPARK-1550 and squashes the following commits: ec7fadc [Josh Rosen] [SPARK-1550] [PySpark] Allow SparkContext creation after failed attempts
* SPARK-2651: Add maven scalastyle pluginRahul Singhal2014-07-272-0/+25
| | | | | | | | | | Can be run as: "mvn scalastyle:check" Author: Rahul Singhal <rahul.singhal@guavus.com> Closes #1550 from rahulsinghaliitd/SPARK-2651 and squashes the following commits: 53748dd [Rahul Singhal] SPARK-2651: Add maven scalastyle plugin
* Revert "[SPARK-2410][SQL] Merging Hive Thrift/JDBC server"Patrick Wendell2014-07-2754-1781/+96
| | | | This reverts commit f6ff2a61d00d12481bfb211ae13d6992daacdcc2.
* [SPARK-2514] [mllib] Random RDD generatorDoris Xin2014-07-275-0/+940
| | | | | | | | | | | | | | | | | | | | | | | | | | Utilities for generating random RDDs. RandomRDD and RandomVectorRDD are created instead of using `sc.parallelize(range:Range)` because `Range` objects in Scala can only have `size <= Int.MaxValue`. The object `RandomRDDGenerators` can be transformed into a generator class to reduce the number of auxiliary methods for optional arguments. Author: Doris Xin <doris.s.xin@gmail.com> Closes #1520 from dorx/randomRDD and squashes the following commits: 01121ac [Doris Xin] reviewer comments 6bf27d8 [Doris Xin] Merge branch 'master' into randomRDD a8ea92d [Doris Xin] Reviewer comments 063ea0b [Doris Xin] Merge branch 'master' into randomRDD aec68eb [Doris Xin] newline bc90234 [Doris Xin] units passed. d56cacb [Doris Xin] impl with RandomRDD 92d6f1c [Doris Xin] solution for Cloneable df5bcff [Doris Xin] Merge branch 'generator' into randomRDD f46d928 [Doris Xin] WIP 49ed20d [Doris Xin] alternative poisson distribution generator 7cb0e40 [Doris Xin] fix for data inconsistency 8881444 [Doris Xin] RandomRDDGenerator: initial design
* [SPARK-1777] Prevent OOMs from single partitionsAndrew Or2014-07-2721-517/+1165
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | **Problem.** When caching, we currently unroll the entire RDD partition before making sure we have enough free memory. This is a common cause for OOMs especially when (1) the BlockManager has little free space left in memory, and (2) the partition is large. **Solution.** We maintain a global memory pool of `M` bytes shared across all threads, similar to the way we currently manage memory for shuffle aggregation. Then, while we unroll each partition, periodically check if there is enough space to continue. If not, drop enough RDD blocks to ensure we have at least `M` bytes to work with, then try again. If we still don't have enough space to unroll the partition, give up and drop the block to disk directly if applicable. **New configurations.** - `spark.storage.bufferFraction` - the value of `M` as a fraction of the storage memory. (default: 0.2) - `spark.storage.safetyFraction` - a margin of safety in case size estimation is slightly off. This is the equivalent of the existing `spark.shuffle.safetyFraction`. (default 0.9) For more detail, see the [design document](https://issues.apache.org/jira/secure/attachment/12651793/spark-1777-design-doc.pdf). Tests pending for performance and memory usage patterns. Author: Andrew Or <andrewor14@gmail.com> Closes #1165 from andrewor14/them-rdd-memories and squashes the following commits: e77f451 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories c7c8832 [Andrew Or] Simplify logic + update a few comments 269d07b [Andrew Or] Very minor changes to tests 6645a8a [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories b7e165c [Andrew Or] Add new tests for unrolling blocks f12916d [Andrew Or] Slightly clean up tests 71672a7 [Andrew Or] Update unrollSafely tests 369ad07 [Andrew Or] Correct ensureFreeSpace and requestMemory behavior f4d035c [Andrew Or] Allow one thread to unroll multiple blocks a66fbd2 [Andrew Or] Rename a few things + update comments 68730b3 [Andrew Or] Fix weird scalatest behavior e40c60d [Andrew Or] Fix MIMA excludes ff77aa1 [Andrew Or] Fix tests 1a43c06 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories b9a6eee [Andrew Or] Simplify locking behavior on unrollMemoryMap ed6cda4 [Andrew Or] Formatting fix (super minor) f9ff82e [Andrew Or] putValues -> putIterator + putArray beb368f [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 8448c9b [Andrew Or] Fix tests a49ba4d [Andrew Or] Do not expose unroll memory check period 69bc0a5 [Andrew Or] Always synchronize on putLock before unrollMemoryMap 3f5a083 [Andrew Or] Simplify signature of ensureFreeSpace dce55c8 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 8288228 [Andrew Or] Synchronize put and unroll properly 4f18a3d [Andrew Or] bufferFraction -> unrollFraction 28edfa3 [Andrew Or] Update a few comments / log messages 728323b [Andrew Or] Do not synchronize every 1000 elements 5ab2329 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 129c441 [Andrew Or] Fix bug: Use toArray rather than array 9a65245 [Andrew Or] Update a few comments + minor control flow changes 57f8d85 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories abeae4f [Andrew Or] Add comment clarifying the MEMORY_AND_DISK case 3dd96aa [Andrew Or] AppendOnlyBuffer -> Vector (+ a few small changes) f920531 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 0871835 [Andrew Or] Add an effective storage level interface to BlockManager 64e7d4c [Andrew Or] Add/modify a few comments (minor) 8af2f35 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 4f4834e [Andrew Or] Use original storage level for blocks dropped to disk ecc8c2d [Andrew Or] Fix binary incompatibility 24185ea [Andrew Or] Avoid dropping a block back to disk if reading from disk 2b7ee66 [Andrew Or] Fix bug in SizeTracking* 9b9a273 [Andrew Or] Fix tests 20eb3e5 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 649bdb3 [Andrew Or] Document spark.storage.bufferFraction a10b0e7 [Andrew Or] Add initial memory request threshold + rename a few things e9c3cb0 [Andrew Or] cacheMemoryMap -> unrollMemoryMap 198e374 [Andrew Or] Unfold -> unroll 0d50155 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories d9d02a8 [Andrew Or] Remove unused param in unfoldSafely ec728d8 [Andrew Or] Add tests for safe unfolding of blocks 22b2209 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 078eb83 [Andrew Or] Add check for hasNext in PrimitiveVector.iterator 0871535 [Andrew Or] Fix tests in BlockManagerSuite d68f31e [Andrew Or] Safely unfold blocks for all memory puts 5961f50 [Andrew Or] Fix tests 195abd7 [Andrew Or] Refactor: move unfold logic to MemoryStore 1e82d00 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 3ce413e [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories d5dd3b4 [Andrew Or] Free buffer memory in finally ea02eec [Andrew Or] Fix tests b8e1d9c [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories a8704c1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories e1b8b25 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories 87aa75c [Andrew Or] Fix mima excludes again (typo) 11eb921 [Andrew Or] Clarify comment (minor) 50cae44 [Andrew Or] Remove now duplicate mima exclude 7de5ef9 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories df47265 [Andrew Or] Fix binary incompatibility 6d05a81 [Andrew Or] Merge branch 'master' of github.com:apache/spark into them-rdd-memories f94f5af [Andrew Or] Update a few comments (minor) 776aec9 [Andrew Or] Prevent OOM if a single RDD partition is too large bbd3eea [Andrew Or] Fix CacheManagerSuite to use Array 97ea499 [Andrew Or] Change BlockManager interface to use Arrays c12f093 [Andrew Or] Add SizeTrackingAppendOnlyBuffer and tests
* [SPARK-2410][SQL] Merging Hive Thrift/JDBC serverCheng Lian2014-07-2754-96/+1781
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | (This is a replacement of #1399, trying to fix potential `HiveThriftServer2` port collision between parallel builds. Please refer to [these comments](https://github.com/apache/spark/pull/1399#issuecomment-50212572) for details.) JIRA issue: [SPARK-2410](https://issues.apache.org/jira/browse/SPARK-2410) Merging the Hive Thrift/JDBC server from [branch-1.0-jdbc](https://github.com/apache/spark/tree/branch-1.0-jdbc). Thanks chenghao-intel for his initial contribution of the Spark SQL CLI. Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #1600 from liancheng/jdbc and squashes the following commits: ac4618b [Cheng Lian] Uses random port for HiveThriftServer2 to avoid collision with parallel builds 090beea [Cheng Lian] Revert changes related to SPARK-2678, decided to move them to another PR 21c6cf4 [Cheng Lian] Updated Spark SQL programming guide docs fe0af31 [Cheng Lian] Reordered spark-submit options in spark-shell[.cmd] 199e3fb [Cheng Lian] Disabled MIMA for hive-thriftserver 1083e9d [Cheng Lian] Fixed failed test suites 7db82a1 [Cheng Lian] Fixed spark-submit application options handling logic 9cc0f06 [Cheng Lian] Starts beeline with spark-submit cfcf461 [Cheng Lian] Updated documents and build scripts for the newly added hive-thriftserver profile 061880f [Cheng Lian] Addressed all comments by @pwendell 7755062 [Cheng Lian] Adapts test suites to spark-submit settings 40bafef [Cheng Lian] Fixed more license header issues e214aab [Cheng Lian] Added missing license headers b8905ba [Cheng Lian] Fixed minor issues in spark-sql and start-thriftserver.sh f975d22 [Cheng Lian] Updated docs for Hive compatibility and Shark migration guide draft 3ad4e75 [Cheng Lian] Starts spark-sql shell with spark-submit a5310d1 [Cheng Lian] Make HiveThriftServer2 play well with spark-submit 61f39f4 [Cheng Lian] Starts Hive Thrift server via spark-submit 2c4c539 [Cheng Lian] Cherry picked the Hive Thrift server
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