aboutsummaryrefslogtreecommitdiff
path: root/python
Commit message (Collapse)AuthorAgeFilesLines
* [SPARK-5089][PYSPARK][MLLIB] Fix vector convertfreeman2015-01-052-1/+11
| | | | | | | | | | | | | | | This is a small change addressing a potentially significant bug in how PySpark + MLlib handles non-float64 numpy arrays. The automatic conversion to `DenseVector` that occurs when passing RDDs to MLlib algorithms in PySpark should automatically upcast to float64s, but currently this wasn't actually happening. As a result, non-float64 would be silently parsed inappropriately during SerDe, yielding erroneous results when running, for example, KMeans. The PR includes the fix, as well as a new test for the correct conversion behavior. davies Author: freeman <the.freeman.lab@gmail.com> Closes #3902 from freeman-lab/fix-vector-convert and squashes the following commits: 764db47 [freeman] Add a test for proper conversion behavior 704f97e [freeman] Return array after changing type
* [SPARK-3325][Streaming] Add a parameter to the method print in class DStreamYadong Qi2015-01-021-5/+7
| | | | | | | | | | | | | | | | | | | | | | | This PR is a fixed version of the original PR #3237 by watermen and scwf. This adds the ability to specify how many elements to print in `DStream.print`. Author: Yadong Qi <qiyadong2010@gmail.com> Author: q00251598 <qiyadong@huawei.com> Author: Tathagata Das <tathagata.das1565@gmail.com> Author: wangfei <wangfei1@huawei.com> Closes #3865 from tdas/print-num and squashes the following commits: cd34e9e [Tathagata Das] Fix bug 7c09f16 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into HEAD bb35d1a [Yadong Qi] Update MimaExcludes.scala f8098ca [Yadong Qi] Update MimaExcludes.scala f6ac3cb [Yadong Qi] Update MimaExcludes.scala e4ed897 [Yadong Qi] Update MimaExcludes.scala 3b9d5cf [wangfei] fix conflicts ec8a3af [q00251598] move to Spark 1.3 26a70c0 [q00251598] extend the Python DStream's print b589a4b [q00251598] add another print function
* [SPARK-4501][Core] - Create build/mvn to automatically download ↵Brennon York2014-12-271-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | maven/zinc/scalac Creates a top level directory script (as `build/mvn`) to automatically download zinc and the specific version of scala used to easily build spark. This will also download and install maven if the user doesn't already have it and all packages are hosted under the `build/` directory. Tested on both Linux and OSX OS's and both work. All commands pass through to the maven binary so it acts exactly as a traditional maven call would. Author: Brennon York <brennon.york@capitalone.com> Closes #3707 from brennonyork/SPARK-4501 and squashes the following commits: 0e5a0e4 [Brennon York] minor incorrect doc verbage (with -> this) 9b79e38 [Brennon York] fixed merge conflicts with dev/run-tests, properly quoted args in sbt/sbt, fixed bug where relative paths would fail if passed in from build/mvn d2d41b6 [Brennon York] added blurb about leverging zinc with build/mvn b979c58 [Brennon York] updated the merge conflict c5634de [Brennon York] updated documentation to overview build/mvn, updated all points where sbt/sbt was referenced with build/sbt b8437ba [Brennon York] set progress bars for curl and wget when not run on jenkins, no progress bar when run on jenkins, moved sbt script to build/sbt, wrote stub and warning under sbt/sbt which calls build/sbt, modified build/sbt to use the correct directory, fixed bug in build/sbt-launch-lib.bash to correctly pull the sbt version be11317 [Brennon York] added switch to silence download progress only if AMPLAB_JENKINS is set 28d0a99 [Brennon York] updated to remove the python dependency, uses grep instead 7e785a6 [Brennon York] added silent and quiet flags to curl and wget respectively, added single echo output to denote start of a download if download is needed 14a5da0 [Brennon York] removed unnecessary zinc output on startup 1af4a94 [Brennon York] fixed bug with uppercase vs lowercase variable 3e8b9b3 [Brennon York] updated to properly only restart zinc if it was freshly installed a680d12 [Brennon York] Added comments to functions and tested various mvn calls bb8cc9d [Brennon York] removed package files ef017e6 [Brennon York] removed OS complexities, setup generic install_app call, removed extra file complexities, removed help, removed forced install (defaults now), removed double-dash from cli 07bf018 [Brennon York] Updated to specifically handle pulling down the correct scala version f914dea [Brennon York] Beginning final portions of localized scala home 69c4e44 [Brennon York] working linux and osx installers for purely local mvn build 4a1609c [Brennon York] finalizing working linux install for maven to local ./build/apache-maven folder cbfcc68 [Brennon York] Changed the default sbt/sbt to build/sbt and added a build/mvn which will automatically download, install, and execute maven with zinc for easier build capability
* [SPARK-4860][pyspark][sql] speeding up `sample()` and `takeSample()`jbencook2014-12-231-0/+28
| | | | | | | | | | | | | | | | | This PR modifies the python `SchemaRDD` to use `sample()` and `takeSample()` from Scala instead of the slower python implementations from `rdd.py`. This is worthwhile because the `Row`'s are already serialized as Java objects. In order to use the faster `takeSample()`, a `takeSampleToPython()` method was implemented in `SchemaRDD.scala` following the pattern of `collectToPython()`. Author: jbencook <jbenjamincook@gmail.com> Author: J. Benjamin Cook <jbenjamincook@gmail.com> Closes #3764 from jbencook/master and squashes the following commits: 6fbc769 [J. Benjamin Cook] [SPARK-4860][pyspark][sql] fixing sloppy indentation for takeSampleToPython() arguments 5170da2 [J. Benjamin Cook] [SPARK-4860][pyspark][sql] fixing typo: from RDD to SchemaRDD de22f70 [jbencook] [SPARK-4860][pyspark][sql] using sample() method from JavaSchemaRDD b916442 [jbencook] [SPARK-4860][pyspark][sql] adding sample() to JavaSchemaRDD 020cbdf [jbencook] [SPARK-4860][pyspark][sql] using Scala implementations of `sample()` and `takeSample()`
* [SPARK-4822] Use sphinx tags for Python doc annotationslewuathe2014-12-176-17/+17
| | | | | | | | | | | | Modify python annotations for sphinx. There is no change to build process from. https://github.com/apache/spark/blob/master/docs/README.md Author: lewuathe <lewuathe@me.com> Closes #3685 from Lewuathe/sphinx-tag-for-pydoc and squashes the following commits: 88a0fd9 [lewuathe] [SPARK-4822] Fix DevelopApi and WARN tags 3d7a398 [lewuathe] [SPARK-4822] Use sphinx tags for Python doc annotations
* [SPARK-4821] [mllib] [python] [docs] Fix for pyspark.mllib.rand docJoseph K. Bradley2014-12-173-30/+5
| | | | | | | | | | | | | | | | | + small doc edit + include edit to make IntelliJ happy CC: davies mengxr Note to davies -- this does not fix the "WARNING: Literal block expected; none found." warnings since that seems to involve spacing which IntelliJ does not like. (Those warnings occur when generating the Python docs.) Author: Joseph K. Bradley <joseph@databricks.com> Closes #3669 from jkbradley/python-warnings and squashes the following commits: 4587868 [Joseph K. Bradley] fixed warning 8cb073c [Joseph K. Bradley] Updated based on davies recommendation c51eca4 [Joseph K. Bradley] Updated rst file for pyspark.mllib.rand doc. Small doc edit. Small include edit to make IntelliJ happy.
* [SPARK-4866] support StructType as key in MapTypeDavies Liu2014-12-162-7/+18
| | | | | | | | | | | This PR brings support of using StructType(and other hashable types) as key in MapType. Author: Davies Liu <davies@databricks.com> Closes #3714 from davies/fix_struct_in_map and squashes the following commits: 68585d7 [Davies Liu] fix primitive types in MapType 9601534 [Davies Liu] support StructType as key in MapType
* [SPARK-4855][mllib] testing the Chi-squared hypothesis testjbencook2014-12-161-1/+99
| | | | | | | | | | | | | | | | | | This PR tests the pyspark Chi-squared hypothesis test from this commit: c8abddc5164d8cf11cdede6ab3d5d1ea08028708 and moves some of the error messaging in to python. It is a port of the Scala tests here: [HypothesisTestSuite.scala](https://github.com/apache/spark/blob/master/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala) Hopefully, SPARK-2980 can be closed. Author: jbencook <jbenjamincook@gmail.com> Closes #3679 from jbencook/master and squashes the following commits: 44078e0 [jbencook] checking that bad input throws the correct exceptions f12ee10 [jbencook] removing checks for ValueError since input tests are on the Scala side 7536cf1 [jbencook] removing python checks for invalid input a17ee84 [jbencook] [SPARK-2980][mllib] adding unit tests for the pyspark chi-squared test 3aeb0d9 [jbencook] [SPARK-2980][mllib] bringing Chi-squared error messages to the python side
* [SPARK-4841] fix zip with textFile()Davies Liu2014-12-153-14/+26
| | | | | | | | | | | | | | UTF8Deserializer can not be used in BatchedSerializer, so always use PickleSerializer() when change batchSize in zip(). Also, if two RDD have the same batch size already, they did not need re-serialize any more. Author: Davies Liu <davies@databricks.com> Closes #3706 from davies/fix_4841 and squashes the following commits: 20ce3a3 [Davies Liu] fix bug in _reserialize() e3ebf7c [Davies Liu] add comment 379d2c8 [Davies Liu] fix zip with textFile()
* [SPARK-4494][mllib] IDFModel.transform() add support for single vectorYuu ISHIKAWA2014-12-151-7/+15
| | | | | | | | | | | | | | I improved `IDFModel.transform` to allow using a single vector. [[SPARK-4494] IDFModel.transform() add support for single vector - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-4494) Author: Yuu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #3603 from yu-iskw/idf and squashes the following commits: 256ff3d [Yuu ISHIKAWA] Fix typo a3bf566 [Yuu ISHIKAWA] - Fix typo - Optimize import order - Aggregate the assertion tests - Modify `IDFModel.transform` API for pyspark d25e49b [Yuu ISHIKAWA] Add the implementation of `IDFModel.transform` for a term frequency vector
* [SPARK-4580] [SPARK-4610] [mllib] [docs] Documentation for tree ensembles + ↵Joseph K. Bradley2014-12-041-3/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | DecisionTree API fix Major changes: * Added programming guide sections for tree ensembles * Added examples for tree ensembles * Updated DecisionTree programming guide with more info on parameters * **API change**: Standardized the tree parameter for the number of classes (for classification) Minor changes: * Updated decision tree documentation * Updated existing tree and tree ensemble examples * Use train/test split, and compute test error instead of training error. * Fixed decision_tree_runner.py to actually use the number of classes it computes from data. (small bug fix) Note: I know this is a lot of lines, but most is covered by: * Programming guide sections for gradient boosting and random forests. (The changes are probably best viewed by generating the docs locally.) * New examples (which were copied from the programming guide) * The "numClasses" renaming I have run all examples and relevant unit tests. CC: mengxr manishamde codedeft Author: Joseph K. Bradley <joseph@databricks.com> Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #3461 from jkbradley/ensemble-docs and squashes the following commits: 70a75f3 [Joseph K. Bradley] updated forest vs boosting comparison d1de753 [Joseph K. Bradley] Added note about toString and toDebugString for DecisionTree to migration guide 8e87f8f [Joseph K. Bradley] Combined GBT and RandomForest guides into one ensembles guide 6fab846 [Joseph K. Bradley] small fixes based on review b9f8576 [Joseph K. Bradley] updated decision tree doc 375204c [Joseph K. Bradley] fixed python style 2b60b6e [Joseph K. Bradley] merged Java RandomForest examples into 1 file. added header. Fixed small bug in same example in the programming guide. 706d332 [Joseph K. Bradley] updated python DT runner to print full model if it is small c76c823 [Joseph K. Bradley] added migration guide for mllib abe5ed7 [Joseph K. Bradley] added examples for random forest in Java and Python to examples folder 07fc11d [Joseph K. Bradley] Renamed numClassesForClassification to numClasses everywhere in trees and ensembles. This is a breaking API change, but it was necessary to correct an API inconsistency in Spark 1.1 (where Python DecisionTree used numClasses but Scala used numClassesForClassification). cdfdfbc [Joseph K. Bradley] added examples for GBT 6372a2b [Joseph K. Bradley] updated decision tree examples to use random split. tested all of them. ad3e695 [Joseph K. Bradley] added gbt and random forest to programming guide. still need to update their examples
* [SPARK-4548] []SPARK-4517] improve performance of python broadcastDavies Liu2014-11-245-233/+80
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Re-implement the Python broadcast using file: 1) serialize the python object using cPickle, write into disks. 2) Create a wrapper in JVM (for the dumped file), it read data from during serialization 3) Using TorrentBroadcast or HttpBroadcast to transfer the data (compressed) into executors 4) During deserialization, writing the data into disk. 5) Passing the path into Python worker, read data from disk and unpickle it into python object, until the first access. It fixes the performance regression introduced in #2659, has similar performance as 1.1, but support object larger than 2G, also improve the memory efficiency (only one compressed copy in driver and executor). Testing with a 500M broadcast and 4 tasks (excluding the benefit from reused worker in 1.2): name | 1.1 | 1.2 with this patch | improvement ---------|--------|---------|-------- python-broadcast-w-bytes | 25.20 | 9.33 | 170.13% | python-broadcast-w-set | 4.13 | 4.50 | -8.35% | Testing with 100 tasks (16 CPUs): name | 1.1 | 1.2 with this patch | improvement ---------|--------|---------|-------- python-broadcast-w-bytes | 38.16 | 8.40 | 353.98% python-broadcast-w-set | 23.29 | 9.59 | 142.80% Author: Davies Liu <davies@databricks.com> Closes #3417 from davies/pybroadcast and squashes the following commits: 50a58e0 [Davies Liu] address comments b98de1d [Davies Liu] disable gc while unpickle e5ee6b9 [Davies Liu] support large string 09303b8 [Davies Liu] read all data into memory dde02dd [Davies Liu] improve performance of python broadcast
* [SPARK-4578] fix asDict() with nested Row()Davies Liu2014-11-242-4/+5
| | | | | | | | | | The Row object is created on the fly once the field is accessed, so we should access them by getattr() in asDict(0 Author: Davies Liu <davies@databricks.com> Closes #3434 from davies/fix_asDict and squashes the following commits: b20f1e7 [Davies Liu] fix asDict() with nested Row()
* [SPARK-4562] [MLlib] speedup vectorDavies Liu2014-11-242-26/+53
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR change the underline array of DenseVector to numpy.ndarray to avoid the conversion, because most of the users will using numpy.array. It also improve the serialization of DenseVector. Before this change: trial | trainingTime | testTime -------|--------|-------- 0 | 5.126 | 1.786 1 |2.698 |1.693 After the change: trial | trainingTime | testTime -------|--------|-------- 0 |4.692 |0.554 1 |2.307 |0.525 This could partially fix the performance regression during test. Author: Davies Liu <davies@databricks.com> Closes #3420 from davies/ser2 and squashes the following commits: 0e1e6f3 [Davies Liu] fix tests 426f5db [Davies Liu] impove toArray() 44707ec [Davies Liu] add name for ISO-8859-1 fa7d791 [Davies Liu] address comments 1cfb137 [Davies Liu] handle zero sparse vector 2548ee2 [Davies Liu] fix tests 9e6389d [Davies Liu] bugfix 470f702 [Davies Liu] speed up DenseMatrix f0d3c40 [Davies Liu] speedup SparseVector ef6ce70 [Davies Liu] speed up dense vector
* [SPARK-4531] [MLlib] cache serialized java objectDavies Liu2014-11-214-13/+8
| | | | | | | | | | | | | | | | | | | The Pyrolite is pretty slow (comparing to the adhoc serializer in 1.1), it cause much performance regression in 1.2, because we cache the serialized Python object in JVM, deserialize them into Java object in each step. This PR change to cache the deserialized JavaRDD instead of PythonRDD to avoid the deserialization of Pyrolite. It should have similar memory usage as before, but much faster. Author: Davies Liu <davies@databricks.com> Closes #3397 from davies/cache and squashes the following commits: 7f6e6ce [Davies Liu] Update -> Updater 4b52edd [Davies Liu] using named argument 63b984e [Davies Liu] fix 7da0332 [Davies Liu] add unpersist() dff33e1 [Davies Liu] address comments c2bdfc2 [Davies Liu] refactor d572f00 [Davies Liu] Merge branch 'master' into cache f1063e1 [Davies Liu] cache serialized java object
* [SPARK-4477] [PySpark] remove numpy from RDDSamplerDavies Liu2014-11-202-69/+40
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | In RDDSampler, it try use numpy to gain better performance for possion(), but the number of call of random() is only (1+faction) * N in the pure python implementation of possion(), so there is no much performance gain from numpy. numpy is not a dependent of pyspark, so it maybe introduce some problem, such as there is no numpy installed in slaves, but only installed master, as reported in SPARK-927. It also complicate the code a lot, so we may should remove numpy from RDDSampler. I also did some benchmark to verify that: ``` >>> from pyspark.mllib.random import RandomRDDs >>> rdd = RandomRDDs.uniformRDD(sc, 1 << 20, 1).cache() >>> rdd.count() # cache it >>> rdd.sample(True, 0.9).count() # measure this line ``` the results: |withReplacement | random | numpy.random | ------- | ------------ | ------- |True | 1.5 s| 1.4 s| |False| 0.6 s | 0.8 s| closes #2313 Note: this patch including some commits that not mirrored to github, it will be OK after it catches up. Author: Davies Liu <davies@databricks.com> Author: Xiangrui Meng <meng@databricks.com> Closes #3351 from davies/numpy and squashes the following commits: 5c438d7 [Davies Liu] fix comment c5b9252 [Davies Liu] Merge pull request #1 from mengxr/SPARK-4477 98eb31b [Xiangrui Meng] make poisson sampling slightly faster ee17d78 [Davies Liu] remove = for float 13f7b05 [Davies Liu] Merge branch 'master' of http://git-wip-us.apache.org/repos/asf/spark into numpy f583023 [Davies Liu] fix tests 51649f5 [Davies Liu] remove numpy in RDDSampler 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-4439] [MLlib] add python api for random forestDavies Liu2014-11-202-23/+221
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ``` 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
* [SPARK-4228][SQL] SchemaRDD to JSONDan McClary2014-11-201-1/+16
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Here's a simple fix for SchemaRDD to JSON. Author: Dan McClary <dan.mcclary@gmail.com> Closes #3213 from dwmclary/SPARK-4228 and squashes the following commits: d714e1d [Dan McClary] fixed PEP 8 error cac2879 [Dan McClary] move pyspark comment and doctest to correct location f9471d3 [Dan McClary] added pyspark doc and doctest 6598cee [Dan McClary] adding complex type queries 1a5fd30 [Dan McClary] removing SPARK-4228 from SQLQuerySuite 4a651f0 [Dan McClary] cleaned PEP and Scala style failures. Moved tests to JsonSuite 47ceff6 [Dan McClary] cleaned up scala style issues 2ee1e70 [Dan McClary] moved rowToJSON to JsonRDD 4387dd5 [Dan McClary] Added UserDefinedType, cleaned up case formatting 8f7bfb6 [Dan McClary] Map type added to SchemaRDD.toJSON 1b11980 [Dan McClary] Map and UserDefinedTypes partially done 11d2016 [Dan McClary] formatting and unicode deserialization default fixed 6af72d1 [Dan McClary] deleted extaneous comment 4d11c0c [Dan McClary] JsonFactory rewrite of toJSON for SchemaRDD 149dafd [Dan McClary] wrapped scala toJSON in sql.py 5e5eb1b [Dan McClary] switched to Jackson for JSON processing 6c94a54 [Dan McClary] added toJSON to pyspark SchemaRDD aaeba58 [Dan McClary] added toJSON to pyspark SchemaRDD 1d171aa [Dan McClary] upated missing brace on if statement 319e3ba [Dan McClary] updated to upstream master with merged SPARK-4228 424f130 [Dan McClary] tests pass, ready for pull and PR 626a5b1 [Dan McClary] added toJSON to SchemaRDD f7d166a [Dan McClary] added toJSON method 5d34e37 [Dan McClary] merge resolved d6d19e9 [Dan McClary] pr example
* [SPARK-4384] [PySpark] improve sort spillingDavies Liu2014-11-191-1/+10
| | | | | | | | | | | | 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
* [DOC][PySpark][Streaming] Fix docstring for sphinxKen Takagiwa2014-11-191-2/+2
| | | | | | | | | | | 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
* [SPARK-4327] [PySpark] Python API for RDD.randomSplit()Davies Liu2014-11-182-3/+41
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ``` 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-3721] [PySpark] broadcast objects larger than 2GDavies Liu2014-11-186-17/+239
| | | | | | | | | | | | | | | | | | | | | | | | | | | 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
* [SPARK-4306] [MLlib] Python API for LogisticRegressionWithLBFGSDavies Liu2014-11-181-4/+53
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ``` 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
* [SPARK-4396] allow lookup by index in Python's RatingXiangrui Meng2014-11-181-11/+15
| | | | | | | | | | | | | | | | | | 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 <!-- Reviewable:start --> [<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/3261) <!-- Reviewable:end --> 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
* [SPARK-4435] [MLlib] [PySpark] improve classificationDavies Liu2014-11-181-29/+106
| | | | | | | | | | | | | | | 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
* [SPARK-4415] [PySpark] JVM should exit after Python exitDavies Liu2014-11-141-1/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | When JVM is started in a Python process, it should exit once the stdin is closed. test: add spark.driver.memory in conf/spark-defaults.conf ``` daviesdm:~/work/spark$ cat conf/spark-defaults.conf spark.driver.memory 8g daviesdm:~/work/spark$ bin/pyspark >>> quit daviesdm:~/work/spark$ jps 4931 Jps 286 daviesdm:~/work/spark$ python wc.py 943738 0.719928026199 daviesdm:~/work/spark$ jps 286 4990 Jps ``` Author: Davies Liu <davies@databricks.com> Closes #3274 from davies/exit and squashes the following commits: df0e524 [Davies Liu] address comments ce8599c [Davies Liu] address comments 050651f [Davies Liu] JVM should exit after Python exit
* [SPARK-4398][PySpark] specialize sc.parallelize(xrange)Xiangrui Meng2014-11-141-4/+21
| | | | | | | | | | | | | `sc.parallelize(range(1 << 20), 1).count()` may take 15 seconds to finish and the rdd object stores the entire list, making task size very large. This PR adds a specialized version for xrange. JoshRosen davies Author: Xiangrui Meng <meng@databricks.com> Closes #3264 from mengxr/SPARK-4398 and squashes the following commits: 8953c41 [Xiangrui Meng] follow davies' suggestion cbd58e3 [Xiangrui Meng] specialize sc.parallelize(xrange)
* [SPARK-4372][MLLIB] Make LR and SVM's default parameters consistent in Scala ↵Xiangrui Meng2014-11-132-35/+37
| | | | | | | | | | | | | | | | | | and Python The current default regParam is 1.0 and regType is claimed to be none in Python (but actually it is l2), while regParam = 0.0 and regType is L2 in Scala. We should make the default values consistent. This PR sets the default regType to L2 and regParam to 0.01. Note that the default regParam value in LIBLINEAR (and hence scikit-learn) is 1.0. However, we use average loss instead of total loss in our formulation. Hence regParam=1.0 is definitely too heavy. In LinearRegression, we set regParam=0.0 and regType=None, because we have separate classes for Lasso and Ridge, both of which use regParam=0.01 as the default. davies atalwalkar Author: Xiangrui Meng <meng@databricks.com> Closes #3232 from mengxr/SPARK-4372 and squashes the following commits: 9979837 [Xiangrui Meng] update Ridge/Lasso to use default regParam 0.01 cast input arguments d3ba096 [Xiangrui Meng] change 'none' back to None 1909a6e [Xiangrui Meng] change default regParam to 0.01 and regType to L2 in LR and SVM
* [SPARK-4348] [PySpark] [MLlib] rename random.py to rand.pyDavies Liu2014-11-136-20/+38
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR rename random.py to rand.py to avoid the side affects of conflict with random module, but still keep the same interface as before. ``` >>> from pyspark.mllib.random import RandomRDDs ``` ``` $ pydoc pyspark.mllib.random Help on module random in pyspark.mllib: NAME random - Python package for random data generation. FILE /Users/davies/work/spark/python/pyspark/mllib/rand.py CLASSES __builtin__.object pyspark.mllib.random.RandomRDDs class RandomRDDs(__builtin__.object) | Generator methods for creating RDDs comprised of i.i.d samples from | some distribution. | | Static methods defined here: | | normalRDD(sc, size, numPartitions=None, seed=None) ``` cc mengxr reference link: http://xion.org.pl/2012/05/06/hacking-python-imports/ Author: Davies Liu <davies@databricks.com> Closes #3216 from davies/random and squashes the following commits: 7ac4e8b [Davies Liu] rename random.py to rand.py
* [SPARK-4369] [MLLib] fix TreeModel.predict() with RDDDavies Liu2014-11-121-12/+14
| | | | | | | | | | | | Fix TreeModel.predict() with RDD, added tests for it. (Also checked that other models don't have this issue) Author: Davies Liu <davies@databricks.com> Closes #3230 from davies/predict and squashes the following commits: 81172aa [Davies Liu] fix predict
* [SPARK-4324] [PySpark] [MLlib] support numpy.array for all MLlib APIDavies Liu2014-11-107-32/+105
| | | | | | | | | | | | | | | This PR check all of the existing Python MLlib API to make sure that numpy.array is supported as Vector (also RDD of numpy.array). It also improve some docstring and doctest. cc mateiz mengxr Author: Davies Liu <davies@databricks.com> Closes #3189 from davies/numpy and squashes the following commits: d5057c4 [Davies Liu] fix tests 6987611 [Davies Liu] support numpy.array for all MLlib API
* [MLLIB] [PYTHON] SPARK-4221: Expose nonnegative ALS in the python APIMichelangelo D'Agostino2014-11-071-15/+25
| | | | | | | | | | | | | | | SPARK-1553 added alternating nonnegative least squares to MLLib, however it's not possible to access it via the python API. This pull request resolves that. Author: Michelangelo D'Agostino <mdagostino@civisanalytics.com> Closes #3095 from mdagost/python_nmf and squashes the following commits: a6743ad [Michelangelo D'Agostino] Use setters instead of static methods in PythonMLLibAPI. Remove the new static methods I added. Set seed in tests. Change ratings to ratingsRDD in both train and trainImplicit for consistency. 7cffd39 [Michelangelo D'Agostino] Swapped nonnegative and seed in a few more places. 3fdc851 [Michelangelo D'Agostino] Moved seed to the end of the python parameter list. bdcc154 [Michelangelo D'Agostino] Change seed type to java.lang.Long so that it can handle null. cedf043 [Michelangelo D'Agostino] Added in ability to set the seed from python and made that play nice with the nonnegative changes. Also made the python ALS tests more exact. a72fdc9 [Michelangelo D'Agostino] Expose nonnegative ALS in the python API.
* [SPARK-4304] [PySpark] Fix sort on empty RDDDavies Liu2014-11-072-0/+5
| | | | | | | | | | | | | This PR fix sortBy()/sortByKey() on empty RDD. This should be back ported into 1.1/1.2 Author: Davies Liu <davies@databricks.com> Closes #3162 from davies/fix_sort and squashes the following commits: 84f64b7 [Davies Liu] add tests 52995b5 [Davies Liu] fix sortByKey() on empty RDD
* [SPARK-4186] add binaryFiles and binaryRecords in PythonDavies Liu2014-11-062-1/+50
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | add binaryFiles() and binaryRecords() in Python ``` binaryFiles(self, path, minPartitions=None): :: Developer API :: Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array. Each file is read as a single record and returned in a key-value pair, where the key is the path of each file, the value is the content of each file. Note: Small files are preferred, large file is also allowable, but may cause bad performance. binaryRecords(self, path, recordLength): Load data from a flat binary file, assuming each record is a set of numbers with the specified numerical format (see ByteBuffer), and the number of bytes per record is constant. :param path: Directory to the input data files :param recordLength: The length at which to split the records ``` Author: Davies Liu <davies@databricks.com> Closes #3078 from davies/binary and squashes the following commits: cd0bdbd [Davies Liu] Merge branch 'master' of github.com:apache/spark into binary 3aa349b [Davies Liu] add experimental notes 24e84b6 [Davies Liu] Merge branch 'master' of github.com:apache/spark into binary 5ceaa8a [Davies Liu] Merge branch 'master' of github.com:apache/spark into binary 1900085 [Davies Liu] bugfix bb22442 [Davies Liu] add binaryFiles and binaryRecords in Python
* [SPARK-3964] [MLlib] [PySpark] add Hypothesis test Python APIDavies Liu2014-11-043-4/+153
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ``` pyspark.mllib.stat.StatisticschiSqTest(observed, expected=None) :: Experimental :: If `observed` is Vector, conduct Pearson's chi-squared goodness of fit test of the observed data against the expected distribution, or againt the uniform distribution (by default), with each category having an expected frequency of `1 / len(observed)`. (Note: `observed` cannot contain negative values) If `observed` is matrix, conduct Pearson's independence test on the input contingency matrix, which cannot contain negative entries or columns or rows that sum up to 0. If `observed` is an RDD of LabeledPoint, conduct Pearson's independence test for every feature against the label across the input RDD. For each feature, the (feature, label) pairs are converted into a contingency matrix for which the chi-squared statistic is computed. All label and feature values must be categorical. :param observed: it could be a vector containing the observed categorical counts/relative frequencies, or the contingency matrix (containing either counts or relative frequencies), or an RDD of LabeledPoint containing the labeled dataset with categorical features. Real-valued features will be treated as categorical for each distinct value. :param expected: Vector containing the expected categorical counts/relative frequencies. `expected` is rescaled if the `expected` sum differs from the `observed` sum. :return: ChiSquaredTest object containing the test statistic, degrees of freedom, p-value, the method used, and the null hypothesis. ``` Author: Davies Liu <davies@databricks.com> Closes #3091 from davies/his and squashes the following commits: 145d16c [Davies Liu] address comments 0ab0764 [Davies Liu] fix float 5097d54 [Davies Liu] add Hypothesis test Python API
* [SPARK-3886] [PySpark] simplify serializer, use AutoBatchedSerializer by ↵Davies Liu2014-11-038-186/+94
| | | | | | | | | | | | | | | | | | | | | | | | | | | default. This PR simplify serializer, always use batched serializer (AutoBatchedSerializer as default), even batch size is 1. Author: Davies Liu <davies@databricks.com> This patch had conflicts when merged, resolved by Committer: Josh Rosen <joshrosen@databricks.com> Closes #2920 from davies/fix_autobatch and squashes the following commits: e544ef9 [Davies Liu] revert unrelated change 6880b14 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch 1d557fc [Davies Liu] fix tests 8180907 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch 76abdce [Davies Liu] clean up 53fa60b [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch d7ac751 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch 2cc2497 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch b4292ce [Davies Liu] fix bug in master d79744c [Davies Liu] recover hive tests be37ece [Davies Liu] refactor eb3938d [Davies Liu] refactor serializer in scala 8d77ef2 [Davies Liu] simplify serializer, use AutoBatchedSerializer by default.
* [SPARK-3573][MLLIB] Make MLlib's Vector compatible with SQL's SchemaRDDXiangrui Meng2014-11-032-3/+86
| | | | | | | | | | | | | | | Register MLlib's Vector as a SQL user-defined type (UDT) in both Scala and Python. With this PR, we can easily map a RDD[LabeledPoint] to a SchemaRDD, and then select columns or save to a Parquet file. Examples in Scala/Python are attached. The Scala code was copied from jkbradley. ~~This PR contains the changes from #3068 . I will rebase after #3068 is merged.~~ marmbrus jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #3070 from mengxr/SPARK-3573 and squashes the following commits: 3a0b6e5 [Xiangrui Meng] organize imports 236f0a0 [Xiangrui Meng] register vector as UDT and provide dataset examples
* [SPARK-4192][SQL] Internal API for Python UDTXiangrui Meng2014-11-032-3/+296
| | | | | | | | | | | | | | | | | | | | | Following #2919, this PR adds Python UDT (for internal use only) with tests under "pyspark.tests". Before `SQLContext.applySchema`, we check whether we need to convert user-type instances into SQL recognizable data. In the current implementation, a Python UDT must be paired with a Scala UDT for serialization on the JVM side. A following PR will add VectorUDT in MLlib for both Scala and Python. marmbrus jkbradley davies Author: Xiangrui Meng <meng@databricks.com> Closes #3068 from mengxr/SPARK-4192-sql and squashes the following commits: acff637 [Xiangrui Meng] merge master dba5ea7 [Xiangrui Meng] only use pyClass for Python UDT output sqlType as well 2c9d7e4 [Xiangrui Meng] move import to global setup; update needsConversion 7c4a6a9 [Xiangrui Meng] address comments 75223db [Xiangrui Meng] minor update f740379 [Xiangrui Meng] remove UDT from default imports e98d9d0 [Xiangrui Meng] fix py style 4e84fce [Xiangrui Meng] remove local hive tests and add more tests 39f19e0 [Xiangrui Meng] add tests b7f666d [Xiangrui Meng] add Python UDT
* [SPARK-3594] [PySpark] [SQL] take more rows to infer schema or samplingDavies Liu2014-11-032-68/+147
| | | | | | | | | | | | | | | | | | | | | | | | This patch will try to infer schema for RDD which has empty value (None, [], {}) in the first row. It will try first 100 rows and merge the types into schema, also merge fields of StructType together. If there is still NullType in schema, then it will show an warning, tell user to try with sampling. If sampling is presented, it will infer schema from all the rows after sampling. Also, add samplingRatio for jsonFile() and jsonRDD() Author: Davies Liu <davies.liu@gmail.com> Author: Davies Liu <davies@databricks.com> Closes #2716 from davies/infer and squashes the following commits: e678f6d [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer 34b5c63 [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer 567dc60 [Davies Liu] update docs 9767b27 [Davies Liu] Merge branch 'master' into infer e48d7fb [Davies Liu] fix tests 29e94d5 [Davies Liu] let NullType inherit from PrimitiveType ee5d524 [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer 540d1d5 [Davies Liu] merge fields for StructType f93fd84 [Davies Liu] add more tests 3603e00 [Davies Liu] take more rows to infer schema, or infer the schema by sampling the RDD
* [SPARK-4148][PySpark] fix seed distribution and add some tests for rdd.sampleXiangrui Meng2014-11-033-9/+20
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The current way of seed distribution makes the random sequences from partition i and i+1 offset by 1. ~~~ In [14]: import random In [15]: r1 = random.Random(10) In [16]: r1.randint(0, 1) Out[16]: 1 In [17]: r1.random() Out[17]: 0.4288890546751146 In [18]: r1.random() Out[18]: 0.5780913011344704 In [19]: r2 = random.Random(10) In [20]: r2.randint(0, 1) Out[20]: 1 In [21]: r2.randint(0, 1) Out[21]: 0 In [22]: r2.random() Out[22]: 0.5780913011344704 ~~~ Note: The new tests are not for this bug fix. Author: Xiangrui Meng <meng@databricks.com> Closes #3010 from mengxr/SPARK-4148 and squashes the following commits: 869ae4b [Xiangrui Meng] move tests tests.py c1bacd9 [Xiangrui Meng] fix seed distribution and add some tests for rdd.sample
* [SPARK-3930] [SPARK-3933] Support fixed-precision decimal in SQL, and some ↵Matei Zaharia2014-11-011-3/+32
| | | | | | | | | | | | | | | | | | | | | | | | | optimizations - Adds optional precision and scale to Spark SQL's decimal type, which behave similarly to those in Hive 13 (https://cwiki.apache.org/confluence/download/attachments/27362075/Hive_Decimal_Precision_Scale_Support.pdf) - Replaces our internal representation of decimals with a Decimal class that can store small values in a mutable Long, saving memory in this situation and letting some operations happen directly on Longs This is still marked WIP because there are a few TODOs, but I'll remove that tag when done. Author: Matei Zaharia <matei@databricks.com> Closes #2983 from mateiz/decimal-1 and squashes the following commits: 35e6b02 [Matei Zaharia] Fix issues after merge 227f24a [Matei Zaharia] Review comments 31f915e [Matei Zaharia] Implement Davies's suggestions in Python eb84820 [Matei Zaharia] Support reading/writing decimals as fixed-length binary in Parquet 4dc6bae [Matei Zaharia] Fix decimal support in PySpark d1d9d68 [Matei Zaharia] Fix compile error and test issues after rebase b28933d [Matei Zaharia] Support decimal precision/scale in Hive metastore 2118c0d [Matei Zaharia] Some test and bug fixes 81db9cb [Matei Zaharia] Added mutable Decimal that will be more efficient for small precisions 7af0c3b [Matei Zaharia] Add optional precision and scale to DecimalType, but use Unlimited for now ec0a947 [Matei Zaharia] Make the result of AVG on Decimals be Decimal, not Double
* [SPARK-3569][SQL] Add metadata field to StructFieldXiangrui Meng2014-11-011-4/+11
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Add `metadata: Metadata` to `StructField` to store extra information of columns. `Metadata` is a simple wrapper over `Map[String, Any]` with value types restricted to Boolean, Long, Double, String, Metadata, and arrays of those types. SerDe is via JSON. Metadata is preserved through simple operations like `SELECT`. marmbrus liancheng Author: Xiangrui Meng <meng@databricks.com> Author: Michael Armbrust <michael@databricks.com> Closes #2701 from mengxr/structfield-metadata and squashes the following commits: dedda56 [Xiangrui Meng] merge remote 5ef930a [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata c35203f [Xiangrui Meng] Merge pull request #1 from marmbrus/pr/2701 886b85c [Michael Armbrust] Expose Metadata and MetadataBuilder through the public scala and java packages. 589f314 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata 1e2abcf [Xiangrui Meng] change default value of metadata to None in python 611d3c2 [Xiangrui Meng] move metadata from Expr to NamedExpr ddfcfad [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata a438440 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata 4266f4d [Xiangrui Meng] add StructField.toString back for backward compatibility 3f49aab [Xiangrui Meng] remove StructField.toString 24a9f80 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata 473a7c5 [Xiangrui Meng] merge master c9d7301 [Xiangrui Meng] organize imports 1fcbf13 [Xiangrui Meng] change metadata type in StructField for Scala/Java 60cc131 [Xiangrui Meng] add doc and header 60614c7 [Xiangrui Meng] add metadata e42c452 [Xiangrui Meng] merge master 93518fb [Xiangrui Meng] support metadata in python 905bb89 [Xiangrui Meng] java conversions 618e349 [Xiangrui Meng] make tests work in scala 61b8e0f [Xiangrui Meng] merge master 7e5a322 [Xiangrui Meng] do not output metadata in StructField.toString c41a664 [Xiangrui Meng] merge master d8af0ed [Xiangrui Meng] move tests to SQLQuerySuite 67fdebb [Xiangrui Meng] add test on join d65072e [Xiangrui Meng] remove Map.empty 367d237 [Xiangrui Meng] add test c194d5e [Xiangrui Meng] add metadata field to StructField and Attribute
* [SPARK-3870] EOL character enforcementKousuke Saruta2014-10-312-249/+249
| | | | | | | | | | | | | | | | | | | We have shell scripts and Windows batch files, so we should enforce proper EOL character. Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #2726 from sarutak/eol-enforcement and squashes the following commits: 9748c3f [Kousuke Saruta] Fixed make.bat 252de89 [Kousuke Saruta] Removed extra characters from make.bat 5b81c00 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into eol-enforcement 8633ed2 [Kousuke Saruta] merge branch 'master' of git://git.apache.org/spark into eol-enforcement 5d630d8 [Kousuke Saruta] Merged ba10797 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into eol-enforcement 7407515 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into eol-enforcement 772fd4e [Kousuke Saruta] Normized EOL character in make.bat and compute-classpath.cmd ac7f873 [Kousuke Saruta] Added an entry for .gitattributes to .rat-excludes 1570e77 [Kousuke Saruta] Added .gitattributes
* [SPARK-4150][PySpark] return self in rdd.setNameXiangrui Meng2014-10-311-2/+2
| | | | | | | | | | | Then we can do `rdd.setName('abc').cache().count()`. Author: Xiangrui Meng <meng@databricks.com> Closes #3011 from mengxr/rdd-setname and squashes the following commits: 10d0d60 [Xiangrui Meng] update test 4ac3bbd [Xiangrui Meng] return self in rdd.setName
* [SPARK-3826][SQL]enable hive-thriftserver to support hive-0.13.1wangfei2014-10-311-27/+0
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | In #2241 hive-thriftserver is not enabled. This patch enable hive-thriftserver to support hive-0.13.1 by using a shim layer refer to #2241. 1 A light shim layer(code in sql/hive-thriftserver/hive-version) for each different hive version to handle api compatibility 2 New pom profiles "hive-default" and "hive-versions"(copy from #2241) to activate different hive version 3 SBT cmd for different version as follows: hive-0.12.0 --- sbt/sbt -Phive,hadoop-2.3 -Phive-0.12.0 assembly hive-0.13.1 --- sbt/sbt -Phive,hadoop-2.3 -Phive-0.13.1 assembly 4 Since hive-thriftserver depend on hive subproject, this patch should be merged with #2241 to enable hive-0.13.1 for hive-thriftserver Author: wangfei <wangfei1@huawei.com> Author: scwf <wangfei1@huawei.com> Closes #2685 from scwf/shim-thriftserver1 and squashes the following commits: f26f3be [wangfei] remove clean to save time f5cac74 [wangfei] remove local hivecontext test 578234d [wangfei] use new shaded hive 18fb1ff [wangfei] exclude kryo in hive pom fa21d09 [wangfei] clean package assembly/assembly 8a4daf2 [wangfei] minor fix 0d7f6cf [wangfei] address comments f7c93ae [wangfei] adding build with hive 0.13 before running tests bcf943f [wangfei] Merge branch 'master' of https://github.com/apache/spark into shim-thriftserver1 c359822 [wangfei] reuse getCommandProcessor in hiveshim 52674a4 [scwf] sql/hive included since examples depend on it 3529e98 [scwf] move hive module to hive profile f51ff4e [wangfei] update and fix conflicts f48d3a5 [scwf] Merge branch 'master' of https://github.com/apache/spark into shim-thriftserver1 41f727b [scwf] revert pom changes 13afde0 [scwf] fix small bug 4b681f4 [scwf] enable thriftserver in profile hive-0.13.1 0bc53aa [scwf] fixed when result filed is null dfd1c63 [scwf] update run-tests to run hive-0.12.0 default now c6da3ce [scwf] Merge branch 'master' of https://github.com/apache/spark into shim-thriftserver 7c66b8e [scwf] update pom according spark-2706 ae47489 [scwf] update and fix conflicts
* [SPARK-4124] [MLlib] [PySpark] simplify serialization in MLlib Python APIDavies Liu2014-10-3011-347/+242
| | | | | | | | | | | | | | | | | Create several helper functions to call MLlib Java API, convert the arguments to Java type and convert return value to Python object automatically, this simplify serialization in MLlib Python API very much. After this, the MLlib Python API does not need to deal with serialization details anymore, it's easier to add new API. cc mengxr Author: Davies Liu <davies@databricks.com> Closes #2995 from davies/cleanup and squashes the following commits: 8fa6ec6 [Davies Liu] address comments 16b85a0 [Davies Liu] Merge branch 'master' of github.com:apache/spark into cleanup 43743e5 [Davies Liu] bugfix 731331f [Davies Liu] simplify serialization in MLlib Python API
* [SPARK-4133] [SQL] [PySpark] type conversionfor python udfDavies Liu2014-10-281-3/+13
| | | | | | | | | | | | | | | | Call Python UDF on ArrayType/MapType/PrimitiveType, the returnType can also be ArrayType/MapType/PrimitiveType. For StructType, it will act as tuple (without attributes). If returnType is StructType, it also should be tuple. Author: Davies Liu <davies@databricks.com> Closes #2973 from davies/udf_array and squashes the following commits: 306956e [Davies Liu] Merge branch 'master' of github.com:apache/spark into udf_array 2c00e43 [Davies Liu] fix merge 11395fa [Davies Liu] Merge branch 'master' of github.com:apache/spark into udf_array 9df50a2 [Davies Liu] address comments 79afb4e [Davies Liu] type conversionfor python udf
* [SPARK-3988][SQL] add public API for date typeDaoyuan Wang2014-10-281-18/+39
| | | | | | | | | | | | | | | | Add json and python api for date type. By using Pickle, `java.sql.Date` was serialized as calendar, and recognized in python as `datetime.datetime`. Author: Daoyuan Wang <daoyuan.wang@intel.com> Closes #2901 from adrian-wang/spark3988 and squashes the following commits: c51a24d [Daoyuan Wang] convert datetime to date 5670626 [Daoyuan Wang] minor line combine f760d8e [Daoyuan Wang] fix indent 444f100 [Daoyuan Wang] fix a typo 1d74448 [Daoyuan Wang] fix scala style 8d7dd22 [Daoyuan Wang] add json and python api for date type
* [SPARK-4058] [PySpark] Log file name is hard coded even though there is a ↵Kousuke Saruta2014-10-281-2/+2
| | | | | | | | | | | | variable '$LOG_FILE ' In a script 'python/run-tests', log file name is represented by a variable 'LOG_FILE' and it is used in run-tests. But, there are some hard-coded log file name in the script. Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #2905 from sarutak/SPARK-4058 and squashes the following commits: 7710490 [Kousuke Saruta] Fixed python/run-tests not to use hard-coded log file name
* [SPARK-3961] [MLlib] [PySpark] Python API for mllib.featureDavies Liu2014-10-282-57/+354
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Added completed Python API for MLlib.feature Normalizer StandardScalerModel StandardScaler HashTF IDFModel IDF cc mengxr Author: Davies Liu <davies@databricks.com> Author: Davies Liu <davies.liu@gmail.com> Closes #2819 from davies/feature and squashes the following commits: 4f48f48 [Davies Liu] add a note for HashingTF 67f6d21 [Davies Liu] address comments b628693 [Davies Liu] rollback changes in Word2Vec efb4f4f [Davies Liu] Merge branch 'master' into feature 806c7c2 [Davies Liu] address comments 3abb8c2 [Davies Liu] address comments 59781b9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into feature a405ae7 [Davies Liu] fix tests 7a1891a [Davies Liu] fix tests 486795f [Davies Liu] update programming guide, HashTF -> HashingTF 8a50584 [Davies Liu] Python API for mllib.feature
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-06 05:46:17 +0000