**This PR introduces an API + simple implementation for Latent Dirichlet Allocation (LDA).**
The [design doc for this PR](https://docs.google.com/document/d/1kSsDqTeZMEB94Bs4GTd0mvdAmduvZSSkpoSfn-seAzo) has been updated since I initially posted it. In particular, see the API and Planning for the Future sections.
* Settle on a public API which may eventually include:
* more inference algorithms
* more options / functionality
* Have an initial easy-to-understand implementation which others may improve.
* This is NOT intended to support every topic model out there. However, if there are suggestions for making this extensible or pluggable in the future, that could be nice, as long as it does not complicate the API or implementation too much.
* This may not be very scalable currently. It will be important to check and improve accuracy. For correctness of the implementation, please check against the Asuncion et al. (2009) paper in the design doc.
**Dependency: This makes MLlib depend on GraphX.**
Files and classes:
* LDA.scala (441 lines):
* class LDA (main estimator class)
* LDA.Document (text + document ID)
* LDAModel.scala (266 lines)
* abstract class LDAModel
* class LocalLDAModel
* class DistributedLDAModel
* LDAExample.scala (245 lines): script to run LDA + a simple (private) Tokenizer
* LDASuite.scala (144 lines)
Data/model representation and algorithm:
* Data/model: Uses GraphX, with term vertices + document vertices
* Algorithm: EM, following [Asuncion, Welling, Smyth, and Teh. "On Smoothing and Inference for Topic Models." UAI, 2009.](http://arxiv-web3.library.cornell.edu/abs/1205.2662v1)
* For more details, please see the description in the “DEVELOPERS NOTE” in LDA.scala
Please refer to the JIRA for more discussion + the [design doc for this PR](https://docs.google.com/document/d/1kSsDqTeZMEB94Bs4GTd0mvdAmduvZSSkpoSfn-seAzo)
Here, I list the main changes AFTER the design doc was posted.
Design decisions:
* logLikelihood() computes the log likelihood of the data and the current point estimate of parameters. This is different from the likelihood of the data given the hyperparameters, which would be harder to compute. I’d describe the current approach as more frequentist, whereas the harder approach would be more Bayesian.
* The current API takes Documents as token count vectors. I believe there should be an extended API taking RDD[String] or RDD[Array[String]] in a future PR. I have sketched this out in the design doc (as well as handier versions of getTopics returning Strings).
* Hyperparameters should be set differently for different inference/learning algorithms. See Asuncion et al. (2009) in the design doc for a good demonstration. I encourage good behavior via defaults and warning messages.
Items planned for future PRs:
* perplexity
* API taking Strings
* Should LDA be called LatentDirichletAllocation (and LDAModel be LatentDirichletAllocationModel)?
* Pro: We may someday want LinearDiscriminantAnalysis.
* Con: Very long names
* Should LDA reside in clustering? Or do we want a sub-package?
* mllib.topicmodel
* mllib.clustering.topicmodel
* Does the API seem reasonable and extensible?
* Unit tests:
* Should there be a test which checks a clustering results? E.g., train on a small, fake dataset with 2 very distinct topics/clusters, and ensure LDA finds those 2 topics/clusters. Does that sound useful or too flaky?
This has not been tested much for scaling. I have run it on a laptop for 200 iterations on a 5MB dataset with 1000 terms and 5 topics. Running it for 500 iterations made it fail because of GC problems. I'm running larger scale tests & will put results here, but future PRs may need to improve the scaling.
* dlwh for the initial implementation
* + jegonzal for some code in the initial implementation
* The many contributors towards topic model implementations in Spark which were referenced as a basis for this PR: akopich witgo yinxusen dlwh EntilZha jegonzal IlyaKozlov
* Note: The plan is to include this full list in the authors if this PR gets merged. Please notify me if you prefer otherwise.
CC: mengxr
Authors:
Joseph K. Bradley <joseph@databricks.com>
Joseph Gonzalez <joseph.e.gonzalez@gmail.com>
David Hall <david.lw.hall@gmail.com>
Guoqiang Li <witgo@qq.com>
Xiangrui Meng <meng@databricks.com>
Pedro Rodriguez <pedro@snowgeek.org>
Avanesov Valeriy <acopich@gmail.com>
Xusen Yin <yinxusen@gmail.com>
Closes#2388Closes#4047 from jkbradley/davidhall-lda and squashes the following commits:
77e8814 [Joseph K. Bradley] small doc fix
5c74345 [Joseph K. Bradley] cleaned up doc based on code review
589728b [Joseph K. Bradley] Updates per code review. Main change was in LDAExample for faster vocab computation. Also updated PeriodicGraphCheckpointerSuite.scala to clean up checkpoint files at end
e3980d2 [Joseph K. Bradley] cleaned up PeriodicGraphCheckpointerSuite.scala
74487e5 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into davidhall-lda
4ae2a7d [Joseph K. Bradley] removed duplicate graphx dependency in mllib/pom.xml
e391474 [Joseph K. Bradley] Removed LDATiming. Added PeriodicGraphCheckpointerSuite.scala. Small LDA cleanups.
e8d8acf [Joseph K. Bradley] Added catch for BreakIterator exception. Improved preprocessing to reduce passes over data
1a231b4 [Joseph K. Bradley] fixed scalastyle
91aadfe [Joseph K. Bradley] Added Java-friendly run method to LDA. Added Java test suite for LDA. Changed LDAModel.describeTopics to return Java-friendly type
b75472d [Joseph K. Bradley] merged improvements from LDATiming into LDAExample. Will remove LDATiming after done testing
993ca56 [Joseph K. Bradley] * Removed Document type in favor of (Long, Vector) * Changed doc ID restriction to be: id must be nonnegative and unique in the doc (instead of 0,1,2,...) * Add checks for valid ranges of eta, alpha * Rename “LearningState” to “EMOptimizer” * Renamed params: termSmoothing -> topicConcentration, topicSmoothing -> docConcentration * Also added aliases alpha, beta
cb5a319 [Joseph K. Bradley] Added checkpointing to LDA * new class PeriodicGraphCheckpointer * params checkpointDir, checkpointInterval to LDA
43c1c40 [Joseph K. Bradley] small cleanup
0b90393 [Joseph K. Bradley] renamed LDA LearningState.collectTopicTotals to globalTopicTotals
77a2c85 [Joseph K. Bradley] Moved auto term,topic smoothing computation to get*Smoothing methods. Changed word to term in some places. Updated LDAExample to use default smoothing amounts.
fb1e7b5 [Xiangrui Meng] minor
08d59a3 [Xiangrui Meng] reset spacing
9fe0b95 [Xiangrui Meng] optimize aggregateMessages
cec0a9c [Xiangrui Meng] * -> *=
6cb11b0 [Xiangrui Meng] optimize computePTopic
9eb3d02 [Xiangrui Meng] + -> +=
892530c [Xiangrui Meng] use axpy
45cc7f2 [Xiangrui Meng] mapPart -> flatMap
ce53be9 [Joseph K. Bradley] fixed example name
75749e7 [Joseph K. Bradley] scala style fix
9f2a492 [Joseph K. Bradley] Unit tests and fixes for LDA, now ready for PR
377ebd9 [Joseph K. Bradley] separated LDA models into own file. more cleanups before PR
2d40006 [Joseph K. Bradley] cleanups before PR
2891e89 [Joseph K. Bradley] Prepped LDA main class for PR, but some cleanups remain
0cb7187 [Joseph K. Bradley] Added 3 files from dlwh LDA implementation
The only issue is that `analyzeBlock` is removed, which was marked as a developer API. I didn't change other tests in the ALSSuite under `spark.mllib` to ensure that the implementation is correct.
CC: srowen coderxiang
Author: Xiangrui Meng <meng@databricks.com>
Closes#4321 from mengxr/SPARK-5536 and squashes the following commits:
5a3cee8 [Xiangrui Meng] update python tests that are too strict
e840acf [Xiangrui Meng] ignore scala style check for ALS.train
e9a721c [Xiangrui Meng] update mima excludes
9ee6a36 [Xiangrui Meng] merge master
9a8aeac [Xiangrui Meng] update tests
d8c3271 [Xiangrui Meng] remove analyzeBlocks
d68eee7 [Xiangrui Meng] add checkpoint to new ALS
22a56f8 [Xiangrui Meng] wrap old ALS
c387dff [Xiangrui Meng] support random seed
3bdf24b [Xiangrui Meng] make storage level configurable in the new ALS
There isn't a good way to write a SparkListener that receives all SparkListener events and which will be future-compatible (e.g. it will receive events introduced in newer versions of Spark without having to override new methods to process those events).
To address this, this patch adds `SparkFirehoseListener`, a SparkListener implementation that receives all events and dispatches them to a single `onEvent` method (which can be overridden by users).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#4210 from JoshRosen/firehose-listener and squashes the following commits:
223f579 [Josh Rosen] Expand comment to explain rationale for this being a Java class.
ecdfaed [Josh Rosen] Add SparkFirehoseListener class for consuming all SparkListener events.
This PR aims to support `INSERT INTO/OVERWRITE TABLE tableName` and `CREATE TABLE tableName AS SELECT` for the data source API (partitioned tables are not supported).
In this PR, I am also adding the support of `IF NOT EXISTS` for our ddl parser. The current semantic of `IF NOT EXISTS` is explained as follows.
* For a `CREATE TEMPORARY TABLE` statement, it does not `IF NOT EXISTS` for now.
* For a `CREATE TABLE` statement (we are creating a metastore table), if there is an existing table having the same name ...
* when `IF NOT EXISTS` clause is used, we will do nothing.
* when `IF NOT EXISTS` clause is not used, the user will see an exception saying the table already exists.
TODOs:
- [x] CTAS support
- [x] Programmatic APIs
- [ ] Python API (another PR)
- [x] More unit tests
- [ ] Documents (another PR)
marmbrus liancheng rxin
Author: Yin Huai <yhuai@databricks.com>
Closes#4294 from yhuai/writeSupport and squashes the following commits:
3db1539 [Yin Huai] save does not take overwrite.
1c98881 [Yin Huai] Fix test.
142372a [Yin Huai] Merge remote-tracking branch 'upstream/master' into writeSupport
34e1bfb [Yin Huai] Address comments.
1682ca6 [Yin Huai] Better support for CTAS statements.
e789d64 [Yin Huai] For the Scala API, let users to use tuples to provide options.
0128065 [Yin Huai] Short hand versions of save and load.
66ebd74 [Yin Huai] Formatting.
9203ec2 [Yin Huai] Merge remote-tracking branch 'upstream/master' into writeSupport
e5d29f2 [Yin Huai] Programmatic APIs.
1a719a5 [Yin Huai] CREATE TEMPORARY TABLE with IF NOT EXISTS is not allowed for now.
909924f [Yin Huai] Add saveAsTable for the data source API to DataFrame.
95a7c71 [Yin Huai] Fix bug when handling IF NOT EXISTS clause in a CREATE TEMPORARY TABLE statement.
d37b19c [Yin Huai] Cheng's comments.
fd6758c [Yin Huai] Use BeforeAndAfterAll.
7880891 [Yin Huai] Support CREATE TABLE AS SELECT STATEMENT and the IF NOT EXISTS clause.
cb85b05 [Yin Huai] Initial write support.
2f91354 [Yin Huai] Make INSERT OVERWRITE/INTO statements consistent between HiveQL and SqlParser.
.this was https://github.com/apache/spark/pull/2676https://issues.apache.org/jira/browse/SPARK-3778
This affects if someone is trying to access secure hdfs something like:
val lines = {
val hconf = new Configuration()
hconf.set("mapred.input.dir", "mydir")
hconf.set("textinputformat.record.delimiter","\003432\n")
sc.newAPIHadoopRDD(hconf, classOf[TextInputFormat], classOf[LongWritable], classOf[Text])
}
Author: Thomas Graves <tgraves@apache.org>
Closes#4292 from tgravescs/SPARK-3788 and squashes the following commits:
cf3b453 [Thomas Graves] newAPIHadoopRDD doesn't properly pass credentials for secure hdfs on yarn
This adds support for streaming logistic regression with stochastic gradient descent, in the same manner as the existing implementation of streaming linear regression. It is a relatively simple addition because most of the work is already done by the abstract class `StreamingLinearAlgorithm` and existing algorithms and models from MLlib.
The PR includes
- Streaming Logistic Regression algorithm
- Unit tests for accuracy, streaming convergence, and streaming prediction
- An example use
cc mengxr tdas
Author: freeman <the.freeman.lab@gmail.com>
Closes#4306 from freeman-lab/streaming-logisitic-regression and squashes the following commits:
5c2c70b [freeman] Use Option on model
5cca2bc [freeman] Merge remote-tracking branch 'upstream/master' into streaming-logisitic-regression
275f8bd [freeman] Make private to mllib
3926e4e [freeman] Line formatting
5ee8694 [freeman] Experimental tag for docs
2fc68ac [freeman] Fix example formatting
85320b1 [freeman] Fixed line length
d88f717 [freeman] Remove stray comment
59d7ecb [freeman] Add streaming logistic regression
e78fe28 [freeman] Add streaming logistic regression example
321cc66 [freeman] Set private and protected within mllib
Author: zsxwing <zsxwing@gmail.com>
Closes#4019 from zsxwing/SPARK-5219 and squashes the following commits:
36a8b4e [zsxwing] Add locks to avoid race conditions
These are needed transitively from the other Jetty libraries
we include. It was not picked up by unit tests because we
disable the UI.
Author: Patrick Wendell <patrick@databricks.com>
Closes#4323 from pwendell/jetty and squashes the following commits:
d8669da [Patrick Wendell] SPARK-3996: Add jetty servlet and continuations.
These are some changes to the build script to allow parts of it to be run independently. This has already been tested during the 1.2.1 release cycle.
Author: Patrick Wendell <patrick@databricks.com>
Author: Patrick Wendell <pwendell@gmail.com>
Closes#4319 from pwendell/release-updates and squashes the following commits:
dfe7ed9 [Patrick Wendell] Merge remote-tracking branch 'apache/master' into release-updates
478b072 [Patrick Wendell] Merge remote-tracking branch 'apache/master' into release-updates
126dd0c [Patrick Wendell] Allow decoupling Maven publishing from cutting release
Simple PR to Remove unused import JsonUtil from from org.apache.spark.util.JsonProtocol.scala which fails builds with older versions of hadoop-core
This import is unused. It was introduced in PR #4029https://github.com/apache/spark/pull/4029 as a part of JIRA SPARK-5231
Author: nemccarthy <nathan@nemccarthy.me>
Closes#4320 from nemccarthy/master and squashes the following commits:
8e34a11 [nemccarthy] [SPARK-5543][WebUI] Remove unused import JsonUtil from from org.apache.spark.util.JsonProtocol.scala which fails builds with older versions of hadoop-core
This pull request contains a Spark SQL data source that can pull data from, and can put data into, a JDBC database.
I have tested both read and write support with H2, MySQL, and Postgres. It would surprise me if both read and write support worked flawlessly out-of-the-box for any other database; different databases have different names for different JDBC data types and different meanings for SQL types with the same name. However, this code is designed (see `DriverQuirks.scala`) to make it *relatively* painless to add support for another database by augmenting the type mapping contained in this PR.
Author: Tor Myklebust <tmyklebu@gmail.com>
Closes#4261 from tmyklebu/master and squashes the following commits:
cf167ce [Tor Myklebust] Work around other Java tests ruining TestSQLContext.
67893bf [Tor Myklebust] Move the jdbcRDD methods into SQLContext itself.
585f95b [Tor Myklebust] Dependencies go into the project's pom.xml.
829d5ba [Tor Myklebust] Merge branch 'master' of https://github.com/apache/spark
41647ef [Tor Myklebust] Hide a couple things that don't need to be public.
7318aea [Tor Myklebust] Fix scalastyle warnings.
a09eeac [Tor Myklebust] JDBC data source for Spark SQL.
176bb98 [Tor Myklebust] Add test deps for JDBC support.
As suggested by the paper of Power Iteration Clustering, it is useful to set the initial vector v0 as the degree vector d. This pr tries to add a running method for that.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#4301 from viirya/pic_degreevector and squashes the following commits:
7db28fb [Liang-Chi Hsieh] Refactor it to address comments.
19cf94e [Liang-Chi Hsieh] Add an option to select initialization method.
ec88567 [Liang-Chi Hsieh] Run the PIC algorithm with degree vector d as suggected by the PIC paper.
This PR brings the Python API for Spark Streaming Kafka data source.
```
class KafkaUtils(__builtin__.object)
| Static methods defined here:
|
| createStream(ssc, zkQuorum, groupId, topics, storageLevel=StorageLevel(True, True, False, False,
2), keyDecoder=<function utf8_decoder>, valueDecoder=<function utf8_decoder>)
| Create an input stream that pulls messages from a Kafka Broker.
|
| :param ssc: StreamingContext object
| :param zkQuorum: Zookeeper quorum (hostname:port,hostname:port,..).
| :param groupId: The group id for this consumer.
| :param topics: Dict of (topic_name -> numPartitions) to consume.
| Each partition is consumed in its own thread.
| :param storageLevel: RDD storage level.
| :param keyDecoder: A function used to decode key
| :param valueDecoder: A function used to decode value
| :return: A DStream object
```
run the example:
```
bin/spark-submit --driver-class-path external/kafka-assembly/target/scala-*/spark-streaming-kafka-assembly-*.jar examples/src/main/python/streaming/kafka_wordcount.py localhost:2181 test
```
Author: Davies Liu <davies@databricks.com>
Author: Tathagata Das <tdas@databricks.com>
Closes#3715 from davies/kafka and squashes the following commits:
d93bfe0 [Davies Liu] Update make-distribution.sh
4280d04 [Davies Liu] address comments
e6d0427 [Davies Liu] Merge branch 'master' of github.com:apache/spark into kafka
f257071 [Davies Liu] add tests for null in RDD
23b039a [Davies Liu] address comments
9af51c4 [Davies Liu] Merge branch 'kafka' of github.com:davies/spark into kafka
a74da87 [Davies Liu] address comments
dc1eed0 [Davies Liu] Update kafka_wordcount.py
31e2317 [Davies Liu] Update kafka_wordcount.py
370ba61 [Davies Liu] Update kafka.py
97386b3 [Davies Liu] address comment
2c567a5 [Davies Liu] update logging and comment
33730d1 [Davies Liu] Merge branch 'master' of github.com:apache/spark into kafka
adeeb38 [Davies Liu] Merge pull request #3 from tdas/kafka-python-api
aea8953 [Tathagata Das] Kafka-assembly for Python API
eea16a7 [Davies Liu] refactor
f6ce899 [Davies Liu] add example and fix bugs
98c8d17 [Davies Liu] fix python style
5697a01 [Davies Liu] bypass decoder in scala
048dbe6 [Davies Liu] fix python style
75d485e [Davies Liu] add mqtt
07923c4 [Davies Liu] support kafka in Python
1. Throw UnsupportedOperationException if a Column is not computable.
2. Perform eager analysis on DataFrame so we can catch errors when they happen (not when an action is run).
Author: Reynold Xin <rxin@databricks.com>
Author: Davies Liu <davies@databricks.com>
Closes#4296 from rxin/col-computability and squashes the following commits:
6527b86 [Reynold Xin] Merge pull request #8 from davies/col-computability
fd92bc7 [Reynold Xin] Merge branch 'master' into col-computability
f79034c [Davies Liu] fix python tests
5afe1ff [Reynold Xin] Fix scala test.
17f6bae [Reynold Xin] Various fixes.
b932e86 [Reynold Xin] Added eager analysis for error reporting.
e6f00b8 [Reynold Xin] [SQL][API] ComputableColumn vs IncomputableColumn
SPARK-3883: SSL support for Akka connections and Jetty based file servers.
This story introduced the following changes:
- Introduced SSLOptions object which holds the SSL configuration and can build the appropriate configuration for Akka or Jetty. SSLOptions can be created by parsing SparkConf entries at a specified namespace.
- SSLOptions is created and kept by SecurityManager
- All Akka actor address creation snippets based on interpolated strings were replaced by a dedicated methods from AkkaUtils. Those methods select the proper Akka protocol - whether akka.tcp or akka.ssl.tcp
- Added tests cases for AkkaUtils, FileServer, SSLOptions and SecurityManager
- Added a way to use node local SSL configuration by executors and driver in standalone mode. It can be done by specifying spark.ssl.useNodeLocalConf in SparkConf.
- Made CoarseGrainedExecutorBackend not overwrite the settings which are executor startup configuration - they are passed anyway from Worker
Refer to https://github.com/apache/spark/pull/3571 for discussion and details
Author: Jacek Lewandowski <lewandowski.jacek@gmail.com>
Author: Jacek Lewandowski <jacek.lewandowski@datastax.com>
Closes#3571 from jacek-lewandowski/SPARK-3883-master and squashes the following commits:
9ef4ed1 [Jacek Lewandowski] Merge pull request #2 from jacek-lewandowski/SPARK-3883-docs2
fb31b49 [Jacek Lewandowski] SPARK-3883: Added SSL setup documentation
2532668 [Jacek Lewandowski] SPARK-3883: Refactored AkkaUtils.protocol method to not use Try
90a8762 [Jacek Lewandowski] SPARK-3883: Refactored methods to resolve Akka address and made it possible to easily configure multiple communication layers for SSL
72b2541 [Jacek Lewandowski] SPARK-3883: A reference to the fallback SSLOptions can be provided when constructing SSLOptions
93050f4 [Jacek Lewandowski] SPARK-3883: SSL support for HttpServer and Akka
This method survived the code review and it has been there since v1.1.0. It exposes jblas types. Let's remove it from the public API. I think no one calls it directly.
Author: Xiangrui Meng <meng@databricks.com>
Closes#4318 from mengxr/SPARK-5540 and squashes the following commits:
586ade6 [Xiangrui Meng] hide ALS.solveLeastSquares
This fixes getStorageLevel for EdgeRDDImpl and VertexRDDImpl (and therefore for Graph).
See code example on JIRA which failed before but works with this patch: [https://issues.apache.org/jira/browse/SPARK-5534]
(The added unit tests also failed before but work with this fix.)
Note: I used partitionsRDD, assuming that getStorageLevel will only be called on the driver.
CC: mengxr (related to LDA PR), rxin ankurdave Thanks in advance!
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#4317 from jkbradley/graphx-storagelevel and squashes the following commits:
1c21e49 [Joseph K. Bradley] made graph getStorageLevel test more robust
18d64ca [Joseph K. Bradley] Added tests for getStorageLevel in VertexRDDSuite, EdgeRDDSuite, GraphSuite
17b488b [Joseph K. Bradley] overrode getStorageLevel in Vertex/EdgeRDDImpl to use partitionsRDD
Author: Reynold Xin <rxin@databricks.com>
Closes#4313 from rxin/SPARK-5514 and squashes the following commits:
e34e91b [Reynold Xin] [SPARK-5514] DataFrame.collect should call executeCollect
override the MetastoreRelation's sameresult method only compare databasename and table name
because in previous :
cache table t1;
select count(*) from t1;
it will read data from memory but the sql below will not,instead it read from hdfs:
select count(*) from t1 t;
because cache data is keyed by logical plan and compare with sameResult ,so when table with alias the same table 's logicalplan is not the same logical plan with out alias so modify the sameresult method only compare databasename and table name
Author: seayi <405078363@qq.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#3898 from seayi/branch-1.2 and squashes the following commits:
8f0c7d2 [seayi] Update CachedTableSuite.scala
a277120 [seayi] Update HiveMetastoreCatalog.scala
8d910aa [seayi] Update HiveMetastoreCatalog.scala
#1379 is automatically closed by asfgit, and github can not reopen it once it's closed, so this will be the new PR.
Binary Logistic Regression can be extended to Multinomial Logistic Regression by running K-1 independent Binary Logistic Regression models. The following formula is implemented.
http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297/25
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#3833 from dbtsai/mlor and squashes the following commits:
4e2f354 [DB Tsai] triger jenkins
697b7c9 [DB Tsai] address some feedback
4ce4d33 [DB Tsai] refactoring
ff843b3 [DB Tsai] rebase
f114135 [DB Tsai] refactoring
4348426 [DB Tsai] Addressed feedback from Sean Owen
a252197 [DB Tsai] first commit
This PR ports the NNLS solver to the new ALS implementation.
CC: coderxiang
Author: Xiangrui Meng <meng@databricks.com>
Closes#4302 from mengxr/SPARK-5513 and squashes the following commits:
4cbdab0 [Xiangrui Meng] fix serialization
88de634 [Xiangrui Meng] add NNLS to ml's ALS
Store daysSinceEpoch as an Int value(4 bytes) to represent DateType, instead of using java.sql.Date(8 bytes as Long) in catalyst row. This ensures the same comparison behavior of Hive and Catalyst.
Subsumes #3381
I thinks there are already some tests in JavaSQLSuite, and for python it will not affect python's datetime class.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#3732 from adrian-wang/datenative and squashes the following commits:
0ed0fdc [Daoyuan Wang] fix test data
a2fdd4e [Daoyuan Wang] getDate
c37832b [Daoyuan Wang] row to catalyst
f0005b1 [Daoyuan Wang] add date in sql parser and java type conversion
024c9a6 [Daoyuan Wang] clean some import order
d6715fc [Daoyuan Wang] refactoring Date as Primitive Int internally
374abd5 [Daoyuan Wang] spark native date type support
...ll cause problems
Author: Sandy Ryza <sandy@cloudera.com>
Closes#4293 from sryza/sandy-spark-5500 and squashes the following commits:
e9ce742 [Sandy Ryza] Change to warning
cc46e52 [Sandy Ryza] Add instructions and extend to NewHadoopRDD
6e1932a [Sandy Ryza] Throw exception on cache
0f6c4eb [Sandy Ryza] SPARK-5500. Document that feeding hadoopFile into a shuffle operation will cause problems
Added the 2 methods to Graph and GraphImpl. Both make calls to the underlying vertex and edge RDDs.
This is needed for another PR (for LDA): [https://github.com/apache/spark/pull/4047]
Notes:
* getCheckpointedFiles is plural and returns a Seq[String] instead of an Option[String].
* I attempted to test to make sure the methods returned the correct values after checkpointing. It did not work; I guess that checkpointing does not occur quickly enough? I noticed that there are not checkpointing tests for RDDs; is it just hard to test well?
CC: rxin
CC: mengxr (since related to LDA)
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#4253 from jkbradley/graphx-checkpoint and squashes the following commits:
b680148 [Joseph K. Bradley] added class tag to firstParent call in VertexRDDImpl.isCheckpointed, though not needed to compile
250810e [Joseph K. Bradley] In EdgeRDDImple, VertexRDDImpl, added transient back to partitionsRDD, and made isCheckpointed check firstParent instead of partitionsRDD
695b7a3 [Joseph K. Bradley] changed partitionsRDD in EdgeRDDImpl, VertexRDDImpl to be non-transient
cc00767 [Joseph K. Bradley] added overrides for isCheckpointed, getCheckpointFile in EdgeRDDImpl, VertexRDDImpl. The corresponding Graph methods now work.
188665f [Joseph K. Bradley] improved documentation
235738c [Joseph K. Bradley] Added isCheckpointed and getCheckpointFiles to Graph, GraphImpl
SPARK-5425: Fixed usages of system properties
This patch fixes few problems caused by the fact that the Scala wrapper over system properties is not thread-safe and is basically invalid because it doesn't take into account the default values which could have been set in the properties object. The problem is fixed by modifying `Utils.getSystemProperties` method so that it uses `stringPropertyNames` method of the `Properties` class, which is thread-safe (internally it creates a defensive copy in a synchronized method) and returns keys of the properties which were set explicitly and which are defined as defaults.
The other related problem, which is fixed here. was in `ResetSystemProperties` mix-in. It created a copy of the system properties in the wrong way.
This patch also introduces a test case for thread-safeness of SparkConf creation.
Refer to the discussion in https://github.com/apache/spark/pull/4220 for more details.
Author: Jacek Lewandowski <lewandowski.jacek@gmail.com>
Closes#4222 from jacek-lewandowski/SPARK-5425-1.3 and squashes the following commits:
03da61b [Jacek Lewandowski] SPARK-5425: Modified Utils.getSystemProperties to return a map of all system properties - explicit + defaults
8faf2ea [Jacek Lewandowski] SPARK-5425: Use SerializationUtils to save properties in ResetSystemProperties trait
71aa572 [Jacek Lewandowski] SPARK-5425: Use synchronised methods in system properties to create SparkConf
This patch makes Spark 1.2.1rc2 work again on Windows.
Without it you get following log output on creating a Spark context:
INFO org.apache.spark.SparkEnv:59 - Registering BlockManagerMaster
ERROR org.apache.spark.util.Utils:75 - Failed to create local root dir in .... Ignoring this directory.
ERROR org.apache.spark.storage.DiskBlockManager:75 - Failed to create any local dir.
Author: Martin Weindel <martin.weindel@gmail.com>
Author: mweindel <m.weindel@usu-software.de>
Closes#4299 from MartinWeindel/branch-1.2 and squashes the following commits:
535cb7f [Martin Weindel] fixed last commit
f17072e [Martin Weindel] moved condition to caller to avoid confusion on chmod700() return value
4de5e91 [Martin Weindel] reverted to unix line ends
fe2740b [mweindel] moved comment
ac4749c [mweindel] fixed chmod700 for Windows
Whenever a directory is created by the utility method, immediately restrict
its permissions so that only the owner has access to its contents.
Signed-off-by: Josh Rosen <joshrosen@databricks.com>
This fixes two sources of non-deterministic failures in this test:
- wait for a receiver to be up before pushing data through MQTT
- gracefully handle the case where the MQTT client is overloaded. There’s
a hard-coded limit of 10 in-flight messages, and this test may hit it.
Instead of crashing, we retry sending the message.
Both of these are needed to make the test pass reliably on my machine.
Author: Iulian Dragos <jaguarul@gmail.com>
Closes#4270 from dragos/issue/fix-flaky-test-SPARK-4631 and squashes the following commits:
f66c482 [Iulian Dragos] [SPARK-4631][streaming] Wait for a receiver to start before publishing test data.
d408a8e [Iulian Dragos] Install callback before connecting to MQTT broker.
This pr adds the support of schema-less syntax, custom field delimiter and SerDe for HiveQL's transform.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#4014 from viirya/schema_less_trans and squashes the following commits:
ac2d1fe [Liang-Chi Hsieh] Refactor codes for comments.
a137933 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into schema_less_trans
aa10fbd [Liang-Chi Hsieh] Add Hive golden answer files again.
575f695 [Liang-Chi Hsieh] Add Hive golden answer files for new unit tests.
a422562 [Liang-Chi Hsieh] Use createQueryTest for unit tests and remove unnecessary imports.
ccb71e3 [Liang-Chi Hsieh] Refactor codes for comments.
37bd391 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into schema_less_trans
6000889 [Liang-Chi Hsieh] Wrap input and output schema into ScriptInputOutputSchema.
21727f7 [Liang-Chi Hsieh] Move schema-less output to proper place. Use multilines instead of a long line SQL.
9a6dc04 [Liang-Chi Hsieh] setRecordReaderID is introduced in 0.13.1, use reflection API to call it.
7a14f31 [Liang-Chi Hsieh] Fix bug.
799b5e1 [Liang-Chi Hsieh] Call getSerializedClass instead of using Text.
be2c3fc [Liang-Chi Hsieh] Fix style.
32d3046 [Liang-Chi Hsieh] Add SerDe support.
ab22f7b [Liang-Chi Hsieh] Fix style.
7a48e42 [Liang-Chi Hsieh] Add support of custom field delimiter.
b1729d9 [Liang-Chi Hsieh] Fix style.
ccee49e [Liang-Chi Hsieh] Add unit test.
f561c37 [Liang-Chi Hsieh] Add support of schema-less script transformation.
when call killExecutor method, it will only go to the else branch, because the variable executorIdToContainer never be put any value.
Author: Xutingjun <1039320815@qq.com>
Closes#4309 from XuTingjun/dynamicAllocator and squashes the following commits:
c823418 [Xutingjun] fix bugwq
Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Closes#4312 from nchammas/patch-2 and squashes the following commits:
9d943aa [Nicholas Chammas] [Docs] Fix Building Spark link text
now when we run python application on yarn cluster mode through spark-submit, spark-submit does not support python application on yarn cluster mode. so i modify code of submit and yarn's AM in order to support it.
through specifying .py file or primaryResource file via spark-submit, we can make pyspark run in yarn-cluster mode.
example:spark-submit --master yarn-master --num-executors 1 --driver-memory 1g --executor-memory 1g xx.py --primaryResource yy.conf
this config is same as pyspark on yarn-client mode.
firstly,we put local path of .py or primaryResource to yarn's dist.files.that can be distributed on slave nodes.and then in spark-submit we transfer --py-files and --primaryResource to yarn.Client and use "org.apache.spark.deploy.PythonRunner" to user class that can run .py files on ApplicationMaster.
in yarn.Client we transfer --py-files and --primaryResource to ApplicationMaster.
in ApplicationMaster, user's class is org.apache.spark.deploy.PythonRunner, and user's args is primaryResource and -py-files. so that can make pyspark run on ApplicationMaster.
JoshRosen tgravescs sryza
Author: lianhuiwang <lianhuiwang09@gmail.com>
Author: Wang Lianhui <lianhuiwang09@gmail.com>
Closes#3976 from lianhuiwang/SPARK-5173 and squashes the following commits:
28a8a58 [lianhuiwang] fix variable name
67f8cee [lianhuiwang] update with andrewor's comments
0319ae3 [lianhuiwang] address with sryza's comments
2385ef6 [lianhuiwang] address with sryza's comments
03640ab [lianhuiwang] add sparkHome to env
47d2fc3 [lianhuiwang] fix test
2adc8f5 [lianhuiwang] add spark.test.home
d60bc60 [lianhuiwang] fix test
5b30064 [lianhuiwang] add test
097a5ec [lianhuiwang] fix line length exceeds 100
905a106 [lianhuiwang] update with sryza and andrewor 's comments
f1f55b6 [lianhuiwang] when yarn-cluster, all python files can be non-local
172eec1 [Wang Lianhui] fix a min submit's bug
9c941bc [lianhuiwang] support python application running on yarn cluster mode
... initial number
Author: Sandy Ryza <sandy@cloudera.com>
Closes#4051 from sryza/sandy-spark-4585 and squashes the following commits:
d1dd039 [Sandy Ryza] Add spark.dynamicAllocation.initialNumExecutors and make min and max not required
b7c59dc [Sandy Ryza] SPARK-4585. Spark dynamic executor allocation should use minExecutors as initial number
The following is implemented:
1) generic traits for feature selection and filtering
2) trait for feature selection of LabeledPoint with discrete data
3) traits for calculation of contingency table and chi squared
4) class for chi-squared feature selection
5) tests for the above
Needs some optimization in matrix operations.
This request is a try to implement feature selection for MLLIB, the previous work by the issue author izendejas was not finished (https://issues.apache.org/jira/browse/SPARK-1473). This request is also related to data discretization issues: https://issues.apache.org/jira/browse/SPARK-1303 and https://issues.apache.org/jira/browse/SPARK-1216 that weren't merged.
Author: Alexander Ulanov <nashb@yandex.ru>
Closes#1484 from avulanov/featureselection and squashes the following commits:
755d358 [Alexander Ulanov] Addressing reviewers comments @mengxr
a6ad82a [Alexander Ulanov] Addressing reviewers comments @mengxr
714b878 [Alexander Ulanov] Addressing reviewers comments @mengxr
010acff [Alexander Ulanov] Rebase
427ca4e [Alexander Ulanov] Addressing reviewers comments: implement VectorTransformer interface, use Statistics.chiSqTest
f9b070a [Alexander Ulanov] Adding Apache header in tests...
80363ca [Alexander Ulanov] Tests, comments, apache headers and scala style
150a3e0 [Alexander Ulanov] Scala style fix
f356365 [Alexander Ulanov] Chi Squared by contingency table. Refactoring
2bacdc7 [Alexander Ulanov] Combinations and chi-squared values test
66e0333 [Alexander Ulanov] Feature selector, fix of lazyness
aab9b73 [Alexander Ulanov] Feature selection redesign with vigdorchik
e24eee4 [Alexander Ulanov] Traits for FeatureSelection, CombinationsCalculator and FeatureFilter
ca49e80 [Alexander Ulanov] Feature selection filter
2ade254 [Alexander Ulanov] Code style
0bd8434 [Alexander Ulanov] Chi Squared feature selection: initial version
Author: Sandy Ryza <sandy@cloudera.com>
Closes#4305 from sryza/sandy-spark-5492 and squashes the following commits:
b7d4497 [Sandy Ryza] SPARK-5492. Thread statistics can break with older Hadoop versions
Author: Tobias Schlatter <tobias@meisch.ch>
Closes#4130 from gzm0/log-repl-loading and squashes the following commits:
4fa0582 [Tobias Schlatter] Log failures in REPL class loading
(v2 of this patch with a fix that was only relevant for the maven build).
This patch piggy-back's on vanzin's work to simplify the Guava shading,
and adds Jetty as a shaded library in Spark. Other than adding Jetty,
it consilidates the <artifactSet>'s into the root pom. I found it was
a bit easier to follow that way, since you don't need to look into
child pom's to find out specific artifact sets included in shading.
Author: Patrick Wendell <patrick@databricks.com>
Closes#4285 from pwendell/jetty and squashes the following commits:
d3e7f4e [Patrick Wendell] Fix for shaded deps causing compile errors
19f0710 [Patrick Wendell] More code review feedback
961452d [Patrick Wendell] Responding to feedback from Marcello
6df25ca [Patrick Wendell] [WIP] [SPARK-3996]: Shade Jetty in Spark deliverables
Apriori is the classic algorithm for frequent item set mining in a transactional data set. It will be useful if Apriori algorithm is added to MLLib in Spark. This PR add an implementation for it.
There is a point I am not sure wether it is most efficient. In order to filter out the eligible frequent item set, currently I am using a cartesian operation on two RDDs to calculate the degree of support of each item set, not sure wether it is better to use broadcast variable to achieve the same.
I will add an example to use this algorithm if requires
Author: Jacky Li <jacky.likun@huawei.com>
Author: Jacky Li <jackylk@users.noreply.github.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#2847 from jackylk/apriori and squashes the following commits:
bee3093 [Jacky Li] Merge pull request #1 from mengxr/SPARK-4001
7e69725 [Xiangrui Meng] simplify FPTree and update FPGrowth
ec21f7d [Jacky Li] fix scalastyle
93f3280 [Jacky Li] create FPTree class
d110ab2 [Jacky Li] change test case to use MLlibTestSparkContext
a6c5081 [Jacky Li] Add Parallel FPGrowth algorithm
eb3e4ca [Jacky Li] add FPGrowth
03df2b6 [Jacky Li] refactory according to comments
7b77ad7 [Jacky Li] fix scalastyle check
f68a0bd [Jacky Li] add 2 apriori implemenation and fp-growth implementation
889b33f [Jacky Li] modify per scalastyle check
da2cba7 [Jacky Li] adding apriori algorithm for frequent item set mining in Spark
JIRA link: https://issues.apache.org/jira/browse/SPARK-5406
The code in breeze svd imposes the upper bound for LocalLAPACK in RowMatrix.computeSVD
code from breeze svd (https://github.com/scalanlp/breeze/blob/master/math/src/main/scala/breeze/linalg/functions/svd.scala)
val workSize = ( 3
* scala.math.min(m, n)
* scala.math.min(m, n)
+ scala.math.max(scala.math.max(m, n), 4 * scala.math.min(m, n)
* scala.math.min(m, n) + 4 * scala.math.min(m, n))
)
val work = new Array[Double](workSize)
As a result, 7 * n * n + 4 * n < Int.MaxValue at least (depends on JVM)
In some worse cases, like n = 25000, work size will become positive again (80032704) and bring wired behavior.
The PR is only the beginning, to support Genbase ( an important biological benchmark that would help promote Spark to genetic applications, http://www.paradigm4.com/wp-content/uploads/2014/06/Genomics-Benchmark-Technical-Report.pdf),
which needs to compute svd for matrix up to 60K * 70K. I found many potential issues and would like to know if there's any plan undergoing that would expand the range of matrix computation based on Spark.
Thanks.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#4200 from hhbyyh/rowMatrix and squashes the following commits:
f7864d0 [Yuhao Yang] update auto logic for rowMatrix svd
23860e4 [Yuhao Yang] fix comment style
e48a6e4 [Yuhao Yang] make latent svd computation constraint clear
Not all Catalyst filter expressions can be converted to Parquet filter predicates. We should try to convert each individual predicate and then collect those convertible ones.
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Author: Cheng Lian <lian@databricks.com>
Closes#4255 from liancheng/spark-5465 and squashes the following commits:
14ccd37 [Cheng Lian] Fixes filter push-down for Parquet data source
I'll add test case in #4040
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#4057 from adrian-wang/coal and squashes the following commits:
4d0111a [Daoyuan Wang] address Yin's comments
c393e18 [Daoyuan Wang] fix rebase conflicts
e47c03a [Daoyuan Wang] add coalesce in parser
c74828d [Daoyuan Wang] cast types for coalesce
Support `comment` in create a table field.
__CREATE TEMPORARY TABLE people(name string `comment` "the name of a person")__
Author: OopsOutOfMemory <victorshengli@126.com>
Closes#3999 from OopsOutOfMemory/meta_comment and squashes the following commits:
39150d4 [OopsOutOfMemory] add comment and refine test suite
Modified environment strings and path separators to platform-independent style if possible.
Author: Masayoshi TSUZUKI <tsudukim@oss.nttdata.co.jp>
Closes#3943 from tsudukim/feature/SPARK-1825 and squashes the following commits:
ec4b865 [Masayoshi TSUZUKI] Rebased and modified as comments.
f8a1d5a [Masayoshi TSUZUKI] Merge branch 'master' of github.com:tsudukim/spark into feature/SPARK-1825
3d03d35 [Masayoshi TSUZUKI] [SPARK-1825] Make Windows Spark client work fine with Linux YARN cluster
Output an error message if the thrift server is started in cluster mode.
Author: Tom Panning <tom.panning@nextcentury.com>
Closes#4137 from tpanningnextcen/spark-5176-thrift-cluster-mode-error and squashes the following commits:
f5c0509 [Tom Panning] [SPARK-5176] The thrift server does not support cluster mode
Build fails with spark-ganglia-lgpl profile at the moment. This is because pom.xml for spark-ganglia-lgpl is not updated.
This PR is related to #4218, #4209 and #3812.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#4303 from sarutak/fix-ganglia-pom-for-metric and squashes the following commits:
5cf455f [Kousuke Saruta] Fixed pom.xml for ganglia in order to use io.dropwizard.metrics
