Hive supports the `explain` the CTAS, which was supported by Spark SQL previously, however, seems it was reverted after the code refactoring in HiveQL.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3357 from chenghao-intel/explain and squashes the following commits:
7aace63 [Cheng Hao] Support the CTAS in EXPLAIN command
Executing sum distinct for empty table throws `java.lang.UnsupportedOperationException: empty.reduceLeft`.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#3184 from ueshin/issues/SPARK-4318 and squashes the following commits:
8168c42 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-4318
66fdb0a [Takuya UESHIN] Re-refine aggregate functions.
6186eb4 [Takuya UESHIN] Fix Sum of GeneratedAggregate.
d2975f6 [Takuya UESHIN] Refine Sum and Average of GeneratedAggregate.
1bba675 [Takuya UESHIN] Refine Sum, SumDistinct and Average functions.
917e533 [Takuya UESHIN] Use aggregate instead of groupBy().
1a5f874 [Takuya UESHIN] Add tests to be executed as non-partial aggregation.
a5a57d2 [Takuya UESHIN] Fix empty Average.
22799dc [Takuya UESHIN] Fix empty Sum and SumDistinct.
65b7dd2 [Takuya UESHIN] Fix empty sum distinct.
The relational operator '<=>' is not working in Spark SQL. Same works in Spark HiveQL
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#3387 from ravipesala/<=> and squashes the following commits:
7198e90 [ravipesala] Supporting relational operator '<=>' in Spark SQL
```
class RandomForestModel
| A model trained by RandomForest
|
| numTrees(self)
| Get number of trees in forest.
|
| predict(self, x)
| Predict values for a single data point or an RDD of points using the model trained.
|
| toDebugString(self)
| Full model
|
| totalNumNodes(self)
| Get total number of nodes, summed over all trees in the forest.
|
class RandomForest
| trainClassifier(cls, data, numClassesForClassification, categoricalFeaturesInfo, numTrees, featureSubsetStrategy='auto', impurity='gini', maxDepth=4, maxBins=32, seed=None):
| Method to train a decision tree model for binary or multiclass classification.
|
| :param data: Training dataset: RDD of LabeledPoint.
| Labels should take values {0, 1, ..., numClasses-1}.
| :param numClassesForClassification: number of classes for classification.
| :param categoricalFeaturesInfo: Map storing arity of categorical features.
| E.g., an entry (n -> k) indicates that feature n is categorical
| with k categories indexed from 0: {0, 1, ..., k-1}.
| :param numTrees: Number of trees in the random forest.
| :param featureSubsetStrategy: Number of features to consider for splits at each node.
| Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
| If "auto" is set, this parameter is set based on numTrees:
| if numTrees == 1, set to "all";
| if numTrees > 1 (forest) set to "sqrt".
| :param impurity: Criterion used for information gain calculation.
| Supported values: "gini" (recommended) or "entropy".
| :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means
| 1 internal node + 2 leaf nodes. (default: 4)
| :param maxBins: maximum number of bins used for splitting features (default: 100)
| :param seed: Random seed for bootstrapping and choosing feature subsets.
| :return: RandomForestModel that can be used for prediction
|
| trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetStrategy='auto', impurity='variance', maxDepth=4, maxBins=32, seed=None):
| Method to train a decision tree model for regression.
|
| :param data: Training dataset: RDD of LabeledPoint.
| Labels are real numbers.
| :param categoricalFeaturesInfo: Map storing arity of categorical features.
| E.g., an entry (n -> k) indicates that feature n is categorical
| with k categories indexed from 0: {0, 1, ..., k-1}.
| :param numTrees: Number of trees in the random forest.
| :param featureSubsetStrategy: Number of features to consider for splits at each node.
| Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
| If "auto" is set, this parameter is set based on numTrees:
| if numTrees == 1, set to "all";
| if numTrees > 1 (forest) set to "onethird".
| :param impurity: Criterion used for information gain calculation.
| Supported values: "variance".
| :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means
| 1 internal node + 2 leaf nodes.(default: 4)
| :param maxBins: maximum number of bins used for splitting features (default: 100)
| :param seed: Random seed for bootstrapping and choosing feature subsets.
| :return: RandomForestModel that can be used for prediction
|
```
Author: Davies Liu <davies@databricks.com>
Closes#3320 from davies/forest and squashes the following commits:
8003dfc [Davies Liu] reorder
53cf510 [Davies Liu] fix docs
4ca593d [Davies Liu] fix docs
e0df852 [Davies Liu] fix docs
0431746 [Davies Liu] rebased
2b6f239 [Davies Liu] Merge branch 'master' of github.com:apache/spark into forest
885abee [Davies Liu] address comments
dae7fc0 [Davies Liu] address comments
89a000f [Davies Liu] fix docs
565d476 [Davies Liu] add python api for random forest
This PR enables the Web UI storage tab to show the in-memory table name instead of the mysterious query plan string as the name of the in-memory columnar RDD.
Note that after #2501, a single columnar RDD can be shared by multiple in-memory tables, as long as their query results are the same. In this case, only the first cached table name is shown. For example:
```sql
CACHE TABLE first AS SELECT * FROM src;
CACHE TABLE second AS SELECT * FROM src;
```
The Web UI only shows "In-memory table first".
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Author: Cheng Lian <lian@databricks.com>
Closes#3383 from liancheng/columnar-rdd-name and squashes the following commits:
071907f [Cheng Lian] Fixes tests
12ddfa6 [Cheng Lian] Names in-memory columnar RDD with corresponding table name
There are some inconsistencies in the gradient boosting APIs. The target is a general boosting meta-algorithm, but the implementation is attached to trees. This was partially due to the delay of SPARK-1856. But for the 1.2 release, we should make the APIs consistent.
1. WeightedEnsembleModel -> private[tree] TreeEnsembleModel and renamed members accordingly.
1. GradientBoosting -> GradientBoostedTrees
1. Add RandomForestModel and GradientBoostedTreesModel and hide CombiningStrategy
1. Slightly refactored TreeEnsembleModel (Vote takes weights into consideration.)
1. Remove `trainClassifier` and `trainRegressor` from `GradientBoostedTrees` because they are the same as `train`
1. Rename class `train` method to `run` because it hides the static methods with the same name in Java. Deprecated `DecisionTree.train` class method.
1. Simplify BoostingStrategy and make sure the input strategy is not modified. Users should put algo and numClasses in treeStrategy. We create ensembleStrategy inside boosting.
1. Fix a bug in GradientBoostedTreesSuite with AbsoluteError
1. doc updates
manishamde jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#3374 from mengxr/SPARK-4486 and squashes the following commits:
7097251 [Xiangrui Meng] address joseph's comments
98dea09 [Xiangrui Meng] address manish's comments
4aae3b7 [Xiangrui Meng] add RandomForestModel and GradientBoostedTreesModel, hide CombiningStrategy
ea4c467 [Xiangrui Meng] fix unit tests
751da4e [Xiangrui Meng] rename class method train -> run
19030a5 [Xiangrui Meng] update boosting public APIs
MetadataCleaner schedule task with a wrong param for delay time .
Author: Leolh <leosandylh@gmail.com>
Closes#3306 from Leolh/master and squashes the following commits:
4a21f4e [Leolh] Update MetadataCleaner.scala
**Summary.** Currently, we may spill many small files in `ExternalAppendOnlyMap` and `ExternalSorter`. The underlying root cause of this is summarized in [SPARK-4452](https://issues.apache.org/jira/browse/SPARK-4452). This PR does not address this root cause, but simply provides the guarantee that we never spill the in-memory data structure if its size is less than a configurable threshold of 5MB. This config is not documented because we don't want users to set it themselves, and it is not hard-coded because we need to change it in tests.
**Symptom.** Each spill is orders of magnitude smaller than 1MB, and there are many spills. In environments where the ulimit is set, this frequently causes "too many open file" exceptions observed in [SPARK-3633](https://issues.apache.org/jira/browse/SPARK-3633).
```
14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4792 B to disk (292769 spills so far)
14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4760 B to disk (292770 spills so far)
14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4520 B to disk (292771 spills so far)
14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4560 B to disk (292772 spills so far)
14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4792 B to disk (292773 spills so far)
14/11/13 19:20:43 INFO collection.ExternalSorter: Thread 60 spilling in-memory batch of 4784 B to disk (292774 spills so far)
```
**Reproduction.** I ran the following on a small 4-node cluster with 512MB executors. Note that the back-to-back shuffle here is necessary for reasons described in [SPARK-4522](https://issues.apache.org/jira/browse/SPARK-4452). The second shuffle is a `reduceByKey` because it performs a map-side combine.
```
sc.parallelize(1 to 100000000, 100)
.map { i => (i, i) }
.groupByKey()
.reduceByKey(_ ++ _)
.count()
```
Before the change, I notice that each thread may spill up to 1000 times, and the size of each spill is on the order of 10KB. After the change, each thread spills only up to 20 times in the worst case, and the size of each spill is on the order of 1MB.
Author: Andrew Or <andrew@databricks.com>
Closes#3353 from andrewor14/avoid-small-spills and squashes the following commits:
49f380f [Andrew Or] Merge branch 'master' of https://git-wip-us.apache.org/repos/asf/spark into avoid-small-spills
27d6966 [Andrew Or] Merge branch 'master' of github.com:apache/spark into avoid-small-spills
f4736e3 [Andrew Or] Fix tests
a919776 [Andrew Or] Avoid many small spills
The check for maxResultSize > 0 is missing, results in failures. Also, error message needs to be improved so the developers know that there is a new parameter to be configured
Author: Nishkam Ravi <nravi@cloudera.com>
Author: nravi <nravi@c1704.halxg.cloudera.com>
Author: nishkamravi2 <nishkamravi@gmail.com>
Closes#3360 from nishkamravi2/master_nravi and squashes the following commits:
5c9a4cb [nishkamravi2] Update TaskSetManagerSuite.scala
535295a [nishkamravi2] Update TaskSetManager.scala
3e1b616 [Nishkam Ravi] Modify test for maxResultSize
9f6583e [Nishkam Ravi] Changes to maxResultSize code (improve error message and add condition to check if maxResultSize > 0)
5f8f9ed [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi
636a9ff [nishkamravi2] Update YarnAllocator.scala
8f76c8b [Nishkam Ravi] Doc change for yarn memory overhead
35daa64 [Nishkam Ravi] Slight change in the doc for yarn memory overhead
5ac2ec1 [Nishkam Ravi] Remove out
dac1047 [Nishkam Ravi] Additional documentation for yarn memory overhead issue
42c2c3d [Nishkam Ravi] Additional changes for yarn memory overhead issue
362da5e [Nishkam Ravi] Additional changes for yarn memory overhead
c726bd9 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi
f00fa31 [Nishkam Ravi] Improving logging for AM memoryOverhead
1cf2d1e [nishkamravi2] Update YarnAllocator.scala
ebcde10 [Nishkam Ravi] Modify default YARN memory_overhead-- from an additive constant to a multiplier (redone to resolve merge conflicts)
2e69f11 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi
efd688a [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark
2b630f9 [nravi] Accept memory input as "30g", "512M" instead of an int value, to be consistent with rest of Spark
3bf8fad [nravi] Merge branch 'master' of https://github.com/apache/spark
5423a03 [nravi] Merge branch 'master' of https://github.com/apache/spark
eb663ca [nravi] Merge branch 'master' of https://github.com/apache/spark
df2aeb1 [nravi] Improved fix for ConcurrentModificationIssue (Spark-1097, Hadoop-10456)
6b840f0 [nravi] Undo the fix for SPARK-1758 (the problem is fixed)
5108700 [nravi] Fix in Spark for the Concurrent thread modification issue (SPARK-1097, HADOOP-10456)
681b36f [nravi] Fix for SPARK-1758: failing test org.apache.spark.JavaAPISuite.wholeTextFiles
This rebases PR 3368.
This commit fixes totalRegisteredExecutors update [SPARK-4478], so that we can correctly keep track of number of registered executors.
Author: Akshat Aranya <aaranya@quantcast.com>
Closes#3373 from coolfrood/topic/SPARK-4478 and squashes the following commits:
8a4d1e4 [Akshat Aranya] Added comment
150ae93 [Akshat Aranya] [SPARK-4478] Keep totalRegisteredExecutors up-to-date
This pull request revises the programming guide to reflect changes in the GraphX API as well as the deprecated mapReduceTriplets operator.
Author: Joseph E. Gonzalez <joseph.e.gonzalez@gmail.com>
Closes#3359 from jegonzal/GraphXProgrammingGuide and squashes the following commits:
4421964 [Joseph E. Gonzalez] updating documentation for graphx
This commit fixes a memory leak in JobProgressListener that I introduced in SPARK-2321 and adds a testing framework to ensure that it’s very difficult to inadvertently introduce new memory leaks.
This solution might be overkill, but the main idea is to partition JobProgressListener's state into three buckets: collections that should be empty once Spark is idle, collections that must obey some hard size limit, and collections that have a soft size limit (they can grow arbitrarily large when Spark is active but must shrink to fit within some bound after Spark becomes idle).
Based on this, we can write fairly generic tests that run workloads that submit more than `spark.ui.retainedStages` stages and `spark.ui.retainedJobs` jobs then check that these various collections' sizes obey their contracts.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#3372 from JoshRosen/SPARK-4495 and squashes the following commits:
c73fab5 [Josh Rosen] "data structures" -> collections
be72e81 [Josh Rosen] [SPARK-4495] Fix memory leaks in JobProgressListener
In class TransformedDStream:
```scala
require(parents.length > 0, "List of DStreams to transform is empty")
require(parents.map(.ssc).distinct.size == 1, "Some of the DStreams have different contexts")
require(parents.map(.slideDuration).distinct.size == 1,
"Some of the DStreams have different slide durations")
```
In class UnionDStream:
```scala
if (parents.length == 0)
{ throw new IllegalArgumentException("Empty array of parents") }
if (parents.map(.ssc).distinct.size > 1)
{ throw new IllegalArgumentException("Array of parents have different StreamingContexts") }
if (parents.map(.slideDuration).distinct.size > 1)
{ throw new IllegalArgumentException("Array of parents have different slide times") }
```
The function is the same, but the realization is not. I think they shoule be the same.
Author: Yadong Qi <qiyadong2010@gmail.com>
Closes#3152 from watermen/bug-fix1 and squashes the following commits:
ed66db6 [Yadong Qi] Change transform to union
b6b3b8b [Yadong Qi] The same function should have the same realization.
If there some big broadcasts (or other object) in Python worker, the free memory could be used for sorting will be too small, then it will keep spilling small files into disks, finally failed with too many open files.
This PR try to delay the spilling until the used memory goes over limit and start to increase since last spilling, it will increase the size of spilling files, improve the stability and performance in this cases. (We also do this in ExternalAggregator).
Author: Davies Liu <davies@databricks.com>
Closes#3252 from davies/sort and squashes the following commits:
711fb6c [Davies Liu] improve sort spilling
I tried to build for Scala 2.11 using sbt with the following command:
```
$ sbt/sbt -Dscala-2.11 assembly
```
but it ends with the following error messages:
```
[error] (streaming-kafka/*:update) sbt.ResolveException: unresolved dependency: org.apache.kafka#kafka_2.11;0.8.0: not found
[error] (catalyst/*:update) sbt.ResolveException: unresolved dependency: org.scalamacros#quasiquotes_2.11;2.0.1: not found
```
The reason is:
If system property `-Dscala-2.11` (without value) was set, `SparkBuild.scala` adds `scala-2.11` profile, but also `sbt-pom-reader` activates `scala-2.10` profile instead of `scala-2.11` profile because the activator `PropertyProfileActivator` used by `sbt-pom-reader` internally checks if the property value is empty or not.
The value is set to non-empty value, then no need to add profiles in `SparkBuild.scala` because `sbt-pom-reader` can handle as expected.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#3342 from ueshin/issues/SPARK-4429 and squashes the following commits:
14d86e8 [Takuya UESHIN] Add a comment.
4eef52b [Takuya UESHIN] Remove unneeded condition.
ce98d0f [Takuya UESHIN] Set non-empty value to system property "scala-2.11" if the property exists instead of adding profile.
This commit should be merged for 1.2 release.
cc tdas
Author: Ken Takagiwa <ugw.gi.world@gmail.com>
Closes#3311 from giwa/patch-3 and squashes the following commits:
ab474a8 [Ken Takagiwa] [DOC][PySpark][Streaming] Fix docstring for sphinx
Somehow maven shade plugin is set in infinite loop of creating effective pom.
Author: Prashant Sharma <prashant.s@imaginea.com>
Author: Prashant Sharma <scrapcodes@gmail.com>
Closes#2959 from ScrapCodes/SPARK-3962/scope-provided and squashes the following commits:
994d1d3 [Prashant Sharma] Fixed failing flume tests
270b4fb [Prashant Sharma] Removed most of the unused code.
bb3bbfd [Prashant Sharma] SPARK-3962 Marked scope as provided for external.
This is blocking #3353 and other patches.
Author: Andrew Or <andrew@databricks.com>
Closes#3371 from andrewor14/mima-hot-fix and squashes the following commits:
842d059 [Andrew Or] Move excludes to the right section
c4d4f4e [Andrew Or] MIMA hot fix
Removed `If `this` function returns None, then corresponding state key-value pair will be eliminated.` for the description of `updateFunc: (Iterator[(K, Seq[V], Option[S])]) => Iterator[(K, S)]`
Author: zsxwing <zsxwing@gmail.com>
Closes#3356 from zsxwing/SPARK-4481 and squashes the following commits:
76a9891 [zsxwing] Add a note that keys may be added or removed
0ebc42a [zsxwing] Fix the wrong description of updateFunc
The write ahead log of ReceivedBlockTracker gets enabled as soon as checkpoint directory is set. This should not happen, as the WAL should be enabled only if the WAL is enabled in the Spark configuration.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#3358 from tdas/SPARK-4482 and squashes the following commits:
b740136 [Tathagata Das] Fixed bug in ReceivedBlockTracker
When running Spark locally, if number of threads is specified as 0 (e.g., `spark-submit --master local[0] ...`), the job got stuck and does not run at all.
I think it's better to validate the parameter.
Fix for [SPARK-4470](https://issues.apache.org/jira/browse/SPARK-4470).
Author: Kenichi Maehashi <webmaster@kenichimaehashi.com>
Closes#3337 from kmaehashi/spark-4470 and squashes the following commits:
3ad76f3 [Kenichi Maehashi] fix code style
7716734 [Kenichi Maehashi] SPARK-4470: Validate number of threads in local mode
the elementsRead variable should be reset to 0 after each spilling
Author: Tianshuo Deng <tdeng@twitter.com>
Closes#3302 from tsdeng/fix_external_sorter_record_count and squashes the following commits:
7b56ca0 [Tianshuo Deng] fix method signature
782c7de [Tianshuo Deng] make elementsRead private, fix comment
bb7ff28 [Tianshuo Deng] update elemetsRead through addElementsRead method
74ca246 [Tianshuo Deng] fix elements read count
pwendell
Please take a look
Author: tedyu <yuzhihong@gmail.com>
Closes#3286 from tedyu/master and squashes the following commits:
e61e610 [tedyu] SPARK-4455 Exclude dependency on hbase-annotations module
7e3a57a [tedyu] Merge branch 'master' of https://git-wip-us.apache.org/repos/asf/spark
2f28b08 [tedyu] Exclude dependency on hbase-annotations module
This commit exists to close the following pull requests on Github:
Closes#2777 (close requested by 'ankurdave')
Closes#2947 (close requested by 'nchammas')
Closes#3141 (close requested by 'tdas')
Closes#2989 (close requested by 'pwendell')
InStream is not closed after data is read from Tachyon. which makes the blocks in Tachyon locked after accessed.
Author: Mingfei <mingfei.shi@intel.com>
Closes#3290 from shimingfei/lockFix and squashes the following commits:
fffe345 [Mingfei] close InStream after the block is accessed
Currently Tachyon client is not closed when TachyonBlockManager is shut down. which causes some resources in Tachyon not reclaimed
Author: Mingfei <mingfei.shi@intel.com>
Closes#3299 from shimingfei/closeClient and squashes the following commits:
0913fbd [Mingfei] close Tachyon client when TachyonBlockManager is shutdown
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#3277 from vanzin/version-1.3 and squashes the following commits:
7c3c396 [Marcelo Vanzin] Added temp repo to sbt build.
5f404ff [Marcelo Vanzin] Add another exclusion.
19457e7 [Marcelo Vanzin] Update old version to 1.2, add temporary 1.2 repo.
3c8d705 [Marcelo Vanzin] Workaround for MIMA checks.
e940810 [Marcelo Vanzin] Bumping version to 1.3.0-SNAPSHOT.
For expressions like `10 < someVar`, we should create an `Operators.Gt` filter, but right now an `Operators.Lt` is created. This issue affects all inequality predicates with literals on the left hand side.
(This bug existed before #3317 and affects branch-1.1. #3338 was opened to backport this to branch-1.1.)
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Author: Cheng Lian <lian@databricks.com>
Closes#3334 from liancheng/fix-parquet-comp-filter and squashes the following commits:
0130897 [Cheng Lian] Fixes Parquet comparison filter generation
```
pyspark.RDD.randomSplit(self, weights, seed=None)
Randomly splits this RDD with the provided weights.
:param weights: weights for splits, will be normalized if they don't sum to 1
:param seed: random seed
:return: split RDDs in an list
>>> rdd = sc.parallelize(range(10), 1)
>>> rdd1, rdd2, rdd3 = rdd.randomSplit([0.4, 0.6, 1.0], 11)
>>> rdd1.collect()
[3, 6]
>>> rdd2.collect()
[0, 5, 7]
>>> rdd3.collect()
[1, 2, 4, 8, 9]
```
Author: Davies Liu <davies@databricks.com>
Closes#3193 from davies/randomSplit and squashes the following commits:
78bf997 [Davies Liu] fix tests, do not use numpy in randomSplit, no performance gain
f5fdf63 [Davies Liu] fix bug with int in weights
4dfa2cd [Davies Liu] refactor
f866bcf [Davies Liu] remove unneeded change
c7a2007 [Davies Liu] switch to python implementation
95a48ac [Davies Liu] Merge branch 'master' of github.com:apache/spark into randomSplit
0d9b256 [Davies Liu] refactor
1715ee3 [Davies Liu] address comments
41fce54 [Davies Liu] randomSplit()
Spark hangs with the following code:
~~~
sc.parallelize(1 to 10).zipWithIndex.repartition(10).count()
~~~
This is because ZippedWithIndexRDD triggers a job in getPartitions and it causes a deadlock in DAGScheduler.getPreferredLocs (synced). The fix is to compute `startIndices` during construction.
This should be applied to branch-1.0, branch-1.1, and branch-1.2.
pwendell
Author: Xiangrui Meng <meng@databricks.com>
Closes#3291 from mengxr/SPARK-4433 and squashes the following commits:
c284d9f [Xiangrui Meng] fix a racing condition in zipWithIndex
This patch will bring support for broadcasting objects larger than 2G.
pickle, zlib, FrameSerializer and Array[Byte] all can not support objects larger than 2G, so this patch introduce LargeObjectSerializer to serialize broadcast objects, the object will be serialized and compressed into small chunks, it also change the type of Broadcast[Array[Byte]]] into Broadcast[Array[Array[Byte]]]].
Testing for support broadcast objects larger than 2G is slow and memory hungry, so this is tested manually, could be added into SparkPerf.
Author: Davies Liu <davies@databricks.com>
Author: Davies Liu <davies.liu@gmail.com>
Closes#2659 from davies/huge and squashes the following commits:
7b57a14 [Davies Liu] add more tests for broadcast
28acff9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
a2f6a02 [Davies Liu] bug fix
4820613 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
5875c73 [Davies Liu] address comments
10a349b [Davies Liu] address comments
0c33016 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
6182c8f [Davies Liu] Merge branch 'master' into huge
d94b68f [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
2514848 [Davies Liu] address comments
fda395b [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
1c2d928 [Davies Liu] fix scala style
091b107 [Davies Liu] broadcast objects larger than 2G
```
class LogisticRegressionWithLBFGS
| train(cls, data, iterations=100, initialWeights=None, corrections=10, tolerance=0.0001, regParam=0.01, intercept=False)
| Train a logistic regression model on the given data.
|
| :param data: The training data, an RDD of LabeledPoint.
| :param iterations: The number of iterations (default: 100).
| :param initialWeights: The initial weights (default: None).
| :param regParam: The regularizer parameter (default: 0.01).
| :param regType: The type of regularizer used for training
| our model.
| :Allowed values:
| - "l1" for using L1 regularization
| - "l2" for using L2 regularization
| - None for no regularization
| (default: "l2")
| :param intercept: Boolean parameter which indicates the use
| or not of the augmented representation for
| training data (i.e. whether bias features
| are activated or not).
| :param corrections: The number of corrections used in the LBFGS update (default: 10).
| :param tolerance: The convergence tolerance of iterations for L-BFGS (default: 1e-4).
|
| >>> data = [
| ... LabeledPoint(0.0, [0.0, 1.0]),
| ... LabeledPoint(1.0, [1.0, 0.0]),
| ... ]
| >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data))
| >>> lrm.predict([1.0, 0.0])
| 1
| >>> lrm.predict([0.0, 1.0])
| 0
| >>> lrm.predict(sc.parallelize([[1.0, 0.0], [0.0, 1.0]])).collect()
| [1, 0]
```
Author: Davies Liu <davies@databricks.com>
Closes#3307 from davies/lbfgs and squashes the following commits:
34bd986 [Davies Liu] Merge branch 'master' of http://git-wip-us.apache.org/repos/asf/spark into lbfgs
5a945a6 [Davies Liu] address comments
941061b [Davies Liu] Merge branch 'master' of github.com:apache/spark into lbfgs
03e5543 [Davies Liu] add it to docs
ed2f9a8 [Davies Liu] add regType
76cd1b6 [Davies Liu] reorder arguments
4429a74 [Davies Liu] Update classification.py
9252783 [Davies Liu] python api for LogisticRegressionWithLBFGS
This commit removes the behavior where when a user clicks
"Show additional metrics" on the stage page, all of the additional
metrics are automatically selected; now, collapsing and expanding
the additional metrics has no effect on which options are selected.
Instead, there's a "(De)select All" box at the top; checking this box
checks all additional metrics (and similarly, unchecking it unchecks
all additional metrics).
This commit is intended to be backported to 1.2, so that the additional
metrics behavior is not confusing to users.
Now when a user clicks the "Show additional metrics" menu, this is what
it looks like:
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#3331 from kayousterhout/SPARK-4463 and squashes the following commits:
9e17cea [Kay Ousterhout] Added italics
b731230 [Kay Ousterhout] [SPARK-4463] Add (de)select all button for add'l metrics.
The progress bar will look like this:
In the right corner, the numbers are: finished tasks, running tasks, total tasks.
After the stage has finished, it will disappear.
The progress bar is only showed if logging level is WARN or higher (but progress in title is still showed), it can be turned off by spark.driver.showConsoleProgress.
Author: Davies Liu <davies@databricks.com>
Closes#3029 from davies/progress and squashes the following commits:
95336d5 [Davies Liu] Merge branch 'master' of github.com:apache/spark into progress
fc49ac8 [Davies Liu] address commentse
2e90f75 [Davies Liu] show multiple stages in same time
0081bcc [Davies Liu] address comments
38c42f1 [Davies Liu] fix tests
ab87958 [Davies Liu] disable progress bar during tests
30ac852 [Davies Liu] re-implement progress bar
b3f34e5 [Davies Liu] Merge branch 'master' of github.com:apache/spark into progress
6fd30ff [Davies Liu] show progress bar if no task finished in 500ms
e4e7344 [Davies Liu] refactor
e1f524d [Davies Liu] revert unnecessary change
a60477c [Davies Liu] Merge branch 'master' of github.com:apache/spark into progress
5cae3f2 [Davies Liu] fix style
ea49fe0 [Davies Liu] address comments
bc53d99 [Davies Liu] refactor
e6bb189 [Davies Liu] fix logging in sparkshell
7e7d4e7 [Davies Liu] address commments
5df26bb [Davies Liu] fix style
9e42208 [Davies Liu] show progress bar in console and title
If SparkSubmit die first, then bootstrapper will be blocked by shutdown hook. sys.exit() in a shutdown hook will cause some kind of dead lock.
cc andrewor14
Author: Davies Liu <davies@databricks.com>
Closes#3289 from davies/fix_bootstraper and squashes the following commits:
ea5cdd1 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_bootstraper
e04b690 [Davies Liu] remove sys.exit in hook
4d11366 [Davies Liu] remove shutdown hook if subprocess die fist
This PR adds a regression test for SPARK-4434.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#3326 from sarutak/add-triple-slash-testcase and squashes the following commits:
82bc9cc [Kousuke Saruta] Fixed wrong grammar in comment
9149027 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into add-triple-slash-testcase
c1c80ca [Kousuke Saruta] Fixed style
4f30210 [Kousuke Saruta] Modified comments
9e09da2 [Kousuke Saruta] Fixed URI validation for jar file
d4b99ef [Kousuke Saruta] [SPARK-4075] [Deploy] Jar url validation is not enough for Jar file
ac79906 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into add-triple-slash-testcase
6d4f47e [Kousuke Saruta] Added a test case as a regression check for SPARK-4434
Author: Michael Armbrust <michael@databricks.com>
Closes#3272 from marmbrus/keyInPartitionedTable and squashes the following commits:
447f08c [Michael Armbrust] Support partitioned parquet tables that have the key in both the directory and the file
In PySpark, ALS can take an RDD of (user, product, rating) tuples as input. However, model.predict outputs an RDD of Rating. So on the input side, users can use r[0], r[1], r[2], while on the output side, users have to use r.user, r.product, r.rating. We should allow lookup by index in Rating by making Rating a namedtuple.
davies
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Author: Xiangrui Meng <meng@databricks.com>
Closes#3261 from mengxr/SPARK-4396 and squashes the following commits:
543aef0 [Xiangrui Meng] use named tuple to implement ALS
0b61bae [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-4396
d3bd7d4 [Xiangrui Meng] allow lookup by index in Python's Rating
This PR add setThrehold() and clearThreshold() for LogisticRegressionModel and SVMModel, also support RDD of vector in LogisticRegressionModel.predict(), SVNModel.predict() and NaiveBayes.predict()
Author: Davies Liu <davies@databricks.com>
Closes#3305 from davies/setThreshold and squashes the following commits:
d0b835f [Davies Liu] Merge branch 'master' of github.com:apache/spark into setThreshold
e4acd76 [Davies Liu] address comments
2231a5f [Davies Liu] bugfix
7bd9009 [Davies Liu] address comments
0b0a8a7 [Davies Liu] address comments
c1e5573 [Davies Liu] improve classification
Author: Felix Maximilian Möller <felixmaximilian.moeller@immobilienscout24.de>
Closes#3343 from felixmaximilian/fix-documentation and squashes the following commits:
43dcdfb [Felix Maximilian Möller] Removed the information about the switch implicitPrefs. The parameter implicitPrefs cannot be set in this context because it is inherent true when calling the trainImplicit method.
7d172ba [Felix Maximilian Möller] added missing parameter alpha in doc string.
The maven release plug-in does not have support for publishing two separate sets of artifacts for a single release. Because of the way that Scala 2.11 support in Spark works, we have to write some customized code to do this. The good news is that the Maven release API is just a thin wrapper on doing git commits and pushing artifacts to the HTTP API of Apache's Sonatype server and this might overall make our deployment easier to understand.
This was already used for the 1.2 snapshot, so I think it is working well. One other nice thing is this could be pretty easily extended to publish nightly snapshots.
Author: Patrick Wendell <pwendell@gmail.com>
Closes#3332 from pwendell/releases and squashes the following commits:
2fedaed [Patrick Wendell] Automate the opening and closing of Sonatype repos
e2a24bb [Patrick Wendell] Fixing issue where we overrode non-spark version numbers
9df3a50 [Patrick Wendell] Adding TODO
1cc1749 [Patrick Wendell] Don't build the thriftserver for 2.11
933201a [Patrick Wendell] Make tagging of release commit eager
d0388a6 [Patrick Wendell] Support Scala 2.11 build
4f4dc62 [Patrick Wendell] Change to 2.11 should not be included when committing new patch
bf742e1 [Patrick Wendell] Minor fixes
ffa1df2 [Patrick Wendell] Adding a Scala 2.11 package to test it
9ac4381 [Patrick Wendell] Addressing TODO
b3105ff [Patrick Wendell] Removing commented out code
d906803 [Patrick Wendell] Small fix
3f4d985 [Patrick Wendell] More work
fcd54c2 [Patrick Wendell] Consolidating use of keys
df2af30 [Patrick Wendell] Changes to release stuff
While reviewing PR #3083 and #3161, I noticed that Parquet record filter generation code can be simplified significantly according to the clue stated in [SPARK-4453](https://issues.apache.org/jira/browse/SPARK-4213). This PR addresses both SPARK-4453 and SPARK-4213 with this simplification.
While generating `ParquetTableScan` operator, we need to remove all Catalyst predicates that have already been pushed down to Parquet. Originally, we first generate the record filter, and then call `findExpression` to traverse the generated filter to find out all pushed down predicates [[1](64c6b9bad5/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala (L213-L228))]. In this way, we have to introduce the `CatalystFilter` class hierarchy to bind the Catalyst predicates together with their generated Parquet filter, and complicate the code base a lot.
The basic idea of this PR is that, we don't need `findExpression` after filter generation, because we already know a predicate can be pushed down if we can successfully generate its corresponding Parquet filter. SPARK-4213 is fixed by returning `None` for any unsupported predicate type.
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Author: Cheng Lian <lian@databricks.com>
Closes#3317 from liancheng/simplify-parquet-filters and squashes the following commits:
d6a9499 [Cheng Lian] Fixes import styling issue
43760e8 [Cheng Lian] Simplifies Parquet filter generation logic
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3308 from chenghao-intel/unwrap_constant_oi and squashes the following commits:
156b500 [Cheng Hao] rebase the master
c5b20ab [Cheng Hao] unwrap for the ConstantObjectInspector
The `totalSize` of external table is always zero, which will influence join strategy(always use broadcast join for external table).
Author: w00228970 <wangfei1@huawei.com>
Closes#3304 from scwf/statistics and squashes the following commits:
568f321 [w00228970] fix statistics for external table
This PR is exactly the same as #3178 except it reverts the `FileStatus.isDir` to `FileStatus.isDirectory` change, since it doesn't compile with Hadoop 1.
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Author: Cheng Lian <lian@databricks.com>
Closes#3298 from liancheng/date-for-thriftserver and squashes the following commits:
866037e [Cheng Lian] Revers isDirectory to isDir (it breaks Hadoop 1 profile)
6f71d0b [Cheng Lian] Makes toHiveString static
26fa955 [Cheng Lian] Fixes complex type support in Hive 0.13.1 shim
a92882a [Cheng Lian] Updates HiveShim for 0.13.1
73f442b [Cheng Lian] Adds Date support for HiveThriftServer2 (Hive 0.12.0)
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3217 from chenghao-intel/mutablerow and squashes the following commits:
e8a10bd [Cheng Hao] revert the change of Row object
4681aea [Cheng Hao] Add toMutableRow method in object Row
a751838 [Cheng Hao] Construct the MutableRow from an existed row
`Cast` from `NaN` or `Infinity` of `Double` or `Float` to `TimestampType` throws `NumberFormatException`.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#3283 from ueshin/issues/SPARK-4425 and squashes the following commits:
14def0c [Takuya UESHIN] Fix Cast to be able to handle NaN or Infinity to TimestampType.
This is follow-up of [SPARK-4390](https://issues.apache.org/jira/browse/SPARK-4390) (#3256).
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#3278 from ueshin/issues/SPARK-4420 and squashes the following commits:
7fea558 [Takuya UESHIN] Add some tests.
cb2301a [Takuya UESHIN] Fix tests.
133bad5 [Takuya UESHIN] Change nullability of Cast from DoubleType/FloatType to DecimalType.
