This is to support JSON serialization of Param[Vector] in the pipeline API. It could be used for other purposes too. The schema is the same as `VectorUDT`. jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#9751 from mengxr/SPARK-11766.
Remove some old yarn related building codes, please review, thanks a lot.
Author: jerryshao <sshao@hortonworks.com>
Closes#9625 from jerryshao/remove-old-module.
This patch modifies Spark's closure cleaner (and a few other places) to use ASM 5, which is necessary in order to support cleaning of closures that were compiled by Java 8.
In order to avoid ASM dependency conflicts, Spark excludes ASM from all of its dependencies and uses a shaded version of ASM 4 that comes from `reflectasm` (see [SPARK-782](https://issues.apache.org/jira/browse/SPARK-782) and #232). This patch updates Spark to use a shaded version of ASM 5.0.4 that was published by the Apache XBean project; the POM used to create the shaded artifact can be found at https://github.com/apache/geronimo-xbean/blob/xbean-4.4/xbean-asm5-shaded/pom.xml.
http://movingfulcrum.tumblr.com/post/80826553604/asm-framework-50-the-missing-migration-guide was a useful resource while upgrading the code to use the new ASM5 opcodes.
I also added a new regression tests in the `java8-tests` subproject; the existing tests were insufficient to catch this bug, which only affected Scala 2.11 user code which was compiled targeting Java 8.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9512 from JoshRosen/SPARK-6152.
This patch re-enables tests for the Docker JDBC data source. These tests were reverted in #4872 due to transitive dependency conflicts introduced by the `docker-client` library. This patch should avoid those problems by using a version of `docker-client` which shades its transitive dependencies and by performing some build-magic to work around problems with that shaded JAR.
In addition, I significantly refactored the tests to simplify the setup and teardown code and to fix several Docker networking issues which caused problems when running in `boot2docker`.
Closes#8101.
Author: Josh Rosen <joshrosen@databricks.com>
Author: Yijie Shen <henry.yijieshen@gmail.com>
Closes#9503 from JoshRosen/docker-jdbc-tests.
This patch modifies Spark's SBT build so that it no longer uses `retrieveManaged` / `lib_managed` to store its dependencies. The motivations for this change are nicely described on the JIRA ticket ([SPARK-7841](https://issues.apache.org/jira/browse/SPARK-7841)); my personal interest in doing this stems from the fact that `lib_managed` has caused me some pain while debugging dependency issues in another PR of mine.
Removing our use of `lib_managed` would be trivial except for one snag: the Datanucleus JARs, required by Spark SQL's Hive integration, cannot be included in assembly JARs due to problems with merging OSGI `plugin.xml` files. As a result, several places in the packaging and deployment pipeline assume that these Datanucleus JARs are copied to `lib_managed/jars`. In the interest of maintaining compatibility, I have chosen to retain the `lib_managed/jars` directory _only_ for these Datanucleus JARs and have added custom code to `SparkBuild.scala` to automatically copy those JARs to that folder as part of the `assembly` task.
`dev/mima` also depended on `lib_managed` in a hacky way in order to set classpaths when generating MiMa excludes; I've updated this to obtain the classpaths directly from SBT instead.
/cc dragos marmbrus pwendell srowen
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9575 from JoshRosen/SPARK-7841.
sbt's version resolution code always picks the most recent version, and we
don't want that for guava.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#9508 from vanzin/SPARK-11538.
Spark should build against Scala 2.10.5, since that includes a fix for Scaladoc that will fix doc snapshot publishing: https://issues.scala-lang.org/browse/SI-8479
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9450 from JoshRosen/upgrade-to-scala-2.10.5.
Like ml ```LinearRegression```, ```LogisticRegression``` should provide a training summary including feature names and their coefficients.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9303 from yanboliang/spark-9492.
Since we do not need to preserve a page before calling compute(), MapPartitionsWithPreparationRDD is not needed anymore.
This PR basically revert #8543, #8511, #8038, #8011
Author: Davies Liu <davies@databricks.com>
Closes#9381 from davies/remove_prepare2.
There's a lot of duplication between SortShuffleManager and UnsafeShuffleManager. Given that these now provide the same set of functionality, now that UnsafeShuffleManager supports large records, I think that we should replace SortShuffleManager's serialized shuffle implementation with UnsafeShuffleManager's and should merge the two managers together.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8829 from JoshRosen/consolidate-sort-shuffle-implementations.
Modify the SBT build script to include GitHub source links for generated Scaladocs, on releases only (no snapshots).
Author: Jakob Odersky <jodersky@gmail.com>
Closes#9110 from jodersky/unidoc.
This PR improve the sessions management by replacing the thread-local based to one SQLContext per session approach, introduce separated temporary tables and UDFs/UDAFs for each session.
A new session of SQLContext could be created by:
1) create an new SQLContext
2) call newSession() on existing SQLContext
For HiveContext, in order to reduce the cost for each session, the classloader and Hive client are shared across multiple sessions (created by newSession).
CacheManager is also shared by multiple sessions, so cache a table multiple times in different sessions will not cause multiple copies of in-memory cache.
Added jars are still shared by all the sessions, because SparkContext does not support sessions.
cc marmbrus yhuai rxin
Author: Davies Liu <davies@databricks.com>
Closes#8909 from davies/sessions.
This PR remove the typeId in columnar cache, it's not needed anymore, it also remove DATE and TIMESTAMP (use INT/LONG instead).
Author: Davies Liu <davies@databricks.com>
Closes#8989 from davies/refactor_cache.
In many modeling application, data points are not necessarily sampled with equal probabilities. Linear regression should support weighting which account the over or under sampling.
work in progress.
Author: Meihua Wu <meihuawu@umich.edu>
Closes#8631 from rotationsymmetry/SPARK-9642.
When speculative execution is enabled, consider a scenario where the authorized committer of a particular output partition fails during the OutputCommitter.commitTask() call. In this case, the OutputCommitCoordinator is supposed to release that committer's exclusive lock on committing once that task fails. However, due to a unit mismatch (we used task attempt number in one place and task attempt id in another) the lock will not be released, causing Spark to go into an infinite retry loop.
This bug was masked by the fact that the OutputCommitCoordinator does not have enough end-to-end tests (the current tests use many mocks). Other factors contributing to this bug are the fact that we have many similarly-named identifiers that have different semantics but the same data types (e.g. attemptNumber and taskAttemptId, with inconsistent variable naming which makes them difficult to distinguish).
This patch adds a regression test and fixes this bug by always using task attempt numbers throughout this code.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8544 from JoshRosen/SPARK-10381.
In fraud detection dataset, almost all the samples are negative while only couple of them are positive. This type of high imbalanced data will bias the models toward negative resulting poor performance. In python-scikit, they provide a correction allowing users to Over-/undersample the samples of each class according to the given weights. In auto mode, selects weights inversely proportional to class frequencies in the training set. This can be done in a more efficient way by multiplying the weights into loss and gradient instead of doing actual over/undersampling in the training dataset which is very expensive.
http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
On the other hand, some of the training data maybe more important like the training samples from tenure users while the training samples from new users maybe less important. We should be able to provide another "weight: Double" information in the LabeledPoint to weight them differently in the learning algorithm.
Author: DB Tsai <dbt@netflix.com>
Author: DB Tsai <dbt@dbs-mac-pro.corp.netflix.com>
Closes#7884 from dbtsai/SPARK-7685.
This change does two things:
- tag a few tests and adds the mechanism in the build to be able to disable those tags,
both in maven and sbt, for both junit and scalatest suites.
- add some logic to run-tests.py to disable some tags depending on what files have
changed; that's used to disable expensive tests when a module hasn't explicitly
been changed, to speed up testing for changes that don't directly affect those
modules.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#8437 from vanzin/test-tags.
Previously, project/plugins.sbt explicitly set scalaVersion to 2.10.4. This can cause issues when using a version of sbt that is compiled against a different version of Scala (for example sbt 0.13.9 uses 2.10.5). Removing this explicit setting will cause build files to be compiled and run against the same version of Scala that sbt is compiled against.
Note that this only applies to the project build files (items in project/), it is distinct from the version of Scala we target for the actual spark compilation.
Author: Ahir Reddy <ahirreddy@gmail.com>
Closes#8709 from ahirreddy/sbt-scala-version-fix.
We introduced the Netty network module for shuffle in Spark 1.2, and has turned it on by default for 3 releases. The old ConnectionManager is difficult to maintain. If we merge the patch now, by the time it is released, it would be 1 yr for which ConnectionManager is off by default. It's time to remove it.
Author: Reynold Xin <rxin@databricks.com>
Closes#8161 from rxin/SPARK-9767.
To correctly isolate applications, when requests to read shuffle data
arrive at the shuffle service, proper authorization checks need to
be performed. This change makes sure that only the application that
created the shuffle data can read from it.
Such checks are only enabled when "spark.authenticate" is enabled,
otherwise there's no secure way to make sure that the client is really
who it says it is.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#8218 from vanzin/SPARK-10004.
This change aims at speeding up the dev cycle a little bit, by making
sure that all tests behave the same w.r.t. where the code to be tested
is loaded from. Namely, that means that tests don't rely on the assembly
anymore, rather loading all needed classes from the build directories.
The main change is to make sure all build directories (classes and test-classes)
are added to the classpath of child processes when running tests.
YarnClusterSuite required some custom code since the executors are run
differently (i.e. not through the launcher library, like standalone and
Mesos do).
I also found a couple of tests that could leak a SparkContext on failure,
and added code to handle those.
With this patch, it's possible to run the following command from a clean
source directory and have all tests pass:
mvn -Pyarn -Phadoop-2.4 -Phive-thriftserver install
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#7629 from vanzin/SPARK-9284.
Replace `JavaConversions` implicits with `JavaConverters`
Most occurrences I've seen so far are necessary conversions; a few have been avoidable. None are in critical code as far as I see, yet.
Author: Sean Owen <sowen@cloudera.com>
Closes#8033 from srowen/SPARK-9613.
A fundamental limitation of the existing SQL tests is that *there is simply no way to create your own `SparkContext`*. This is a serious limitation because the user may wish to use a different master or config. As a case in point, `BroadcastJoinSuite` is entirely commented out because there is no way to make it pass with the existing infrastructure.
This patch removes the singletons `TestSQLContext` and `TestData`, and instead introduces a `SharedSQLContext` that starts a context per suite. Unfortunately the singletons were so ingrained in the SQL tests that this patch necessarily needed to touch *all* the SQL test files.
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Author: Andrew Or <andrew@databricks.com>
Closes#8111 from andrewor14/sql-tests-refactor.
Made ProbabilisticClassifier, Identifiable, VectorUDT public. All are annotated as DeveloperApi.
CC: mengxr EronWright
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#8004 from jkbradley/ml-api-public-items and squashes the following commits:
7ebefda [Joseph K. Bradley] update per code review
7ff0768 [Joseph K. Bradley] attepting to add mima fix
756d84c [Joseph K. Bradley] VectorUDT annotated as AlphaComponent
ae7767d [Joseph K. Bradley] added another warning
94fd553 [Joseph K. Bradley] Made ProbabilisticClassifier, Identifiable, VectorUDT public APIs
The REST server is not actually used in most tests and so we can disable it. It is a source of flakiness because it tries to bind to a specific port in vain. There was also some code that avoided the shuffle service in tests. This is actually not necessary because the shuffle service is already off by default.
Author: Andrew Or <andrew@databricks.com>
Closes#8084 from andrewor14/fix-master-suite-again.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#8092 from tdas/SPARK-9727 and squashes the following commits:
b1b01fd [Tathagata Das] Updated streaming kinesis project name
This PR is based on #4229, thanks prabeesh.
Closes#4229
Author: Prabeesh K <prabsmails@gmail.com>
Author: zsxwing <zsxwing@gmail.com>
Author: prabs <prabsmails@gmail.com>
Author: Prabeesh K <prabeesh.k@namshi.com>
Closes#7833 from zsxwing/pr4229 and squashes the following commits:
9570bec [zsxwing] Fix the variable name and check null in finally
4a9c79e [zsxwing] Fix pom.xml indentation
abf5f18 [zsxwing] Merge branch 'master' into pr4229
935615c [zsxwing] Fix the flaky MQTT tests
47278c5 [zsxwing] Include the project class files
478f844 [zsxwing] Add unpack
5f8a1d4 [zsxwing] Make the maven build generate the test jar for Python MQTT tests
734db99 [zsxwing] Merge branch 'master' into pr4229
126608a [Prabeesh K] address the comments
b90b709 [Prabeesh K] Merge pull request #1 from zsxwing/pr4229
d07f454 [zsxwing] Register StreamingListerner before starting StreamingContext; Revert unncessary changes; fix the python unit test
a6747cb [Prabeesh K] wait for starting the receiver before publishing data
87fc677 [Prabeesh K] address the comments:
97244ec [zsxwing] Make sbt build the assembly test jar for streaming mqtt
80474d1 [Prabeesh K] fix
1f0cfe9 [Prabeesh K] python style fix
e1ee016 [Prabeesh K] scala style fix
a5a8f9f [Prabeesh K] added Python test
9767d82 [Prabeesh K] implemented Python-friendly class
a11968b [Prabeesh K] fixed python style
795ec27 [Prabeesh K] address comments
ee387ae [Prabeesh K] Fix assembly jar location of mqtt-assembly
3f4df12 [Prabeesh K] updated version
b34c3c1 [prabs] adress comments
3aa7fff [prabs] Added Python streaming mqtt word count example
b7d42ff [prabs] Mqtt streaming support in Python
There are a few changes in this pull request:
1. Moved all data sources to execution.datasources, except the public JDBC APIs.
2. In order to maintain backward compatibility from 1, added a backward compatibility translation map in data source resolution.
3. Moved ui and metric package into execution.
4. Added more documentation on some internal classes.
5. Renamed DataSourceRegister.format -> shortName.
6. Added "override" modifier on shortName.
7. Removed IntSQLMetric.
Author: Reynold Xin <rxin@databricks.com>
Closes#8056 from rxin/SPARK-9763 and squashes the following commits:
9df4801 [Reynold Xin] Removed hardcoded name in test cases.
d9babc6 [Reynold Xin] Shorten.
e484419 [Reynold Xin] Removed VisibleForTesting.
171b812 [Reynold Xin] MimaExcludes.
2041389 [Reynold Xin] Compile ...
79dda42 [Reynold Xin] Compile.
0818ba3 [Reynold Xin] Removed IntSQLMetric.
c46884f [Reynold Xin] Two more fixes.
f9aa88d [Reynold Xin] [SPARK-9763][SQL] Minimize exposure of internal SQL classes.
https://issues.apache.org/jira/browse/SPARK-9602
Although we have hidden Akka behind RPC interface, I found that the Akka/Actor-related comments are still spreading everywhere. To make it consistent, we shall remove "actor"/"akka" words from the comments...
Author: CodingCat <zhunansjtu@gmail.com>
Closes#7936 from CodingCat/SPARK-9602 and squashes the following commits:
e8296a3 [CodingCat] remove actor words from comments
Certain use cases of Spark involve RDDs with long lineages that must be truncated periodically (e.g. GraphX). The existing way of doing it is through `rdd.checkpoint()`, which is expensive because it writes to HDFS. This patch provides an alternative to truncate lineages cheaply *without providing the same level of fault tolerance*.
**Local checkpointing** writes checkpointed data to the local file system through the block manager. It is much faster than replicating to a reliable storage and provides the same semantics as long as executors do not fail. It is accessible through a new operator `rdd.localCheckpoint()` and leaves the old one unchanged. Users may even decide to combine the two and call the reliable one less frequently.
The bulk of this patch involves refactoring the checkpointing interface to accept custom implementations of checkpointing. [Design doc](https://issues.apache.org/jira/secure/attachment/12741708/SPARK-7292-design.pdf).
Author: Andrew Or <andrew@databricks.com>
Closes#7279 from andrewor14/local-checkpoint and squashes the following commits:
729600f [Andrew Or] Oops, fix tests
34bc059 [Andrew Or] Avoid computing all partitions in local checkpoint
e43bbb6 [Andrew Or] Merge branch 'master' of github.com:apache/spark into local-checkpoint
3be5aea [Andrew Or] Address comments
bf846a6 [Andrew Or] Merge branch 'master' of github.com:apache/spark into local-checkpoint
ab003a3 [Andrew Or] Fix compile
c2e111b [Andrew Or] Address comments
33f167a [Andrew Or] Merge branch 'master' of github.com:apache/spark into local-checkpoint
e908a42 [Andrew Or] Fix tests
f5be0f3 [Andrew Or] Use MEMORY_AND_DISK as the default local checkpoint level
a92657d [Andrew Or] Update a few comments
e58e3e3 [Andrew Or] Merge branch 'master' of github.com:apache/spark into local-checkpoint
4eb6eb1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into local-checkpoint
1bbe154 [Andrew Or] Simplify LocalCheckpointRDD
48a9996 [Andrew Or] Avoid traversing dependency tree + rewrite tests
62aba3f [Andrew Or] Merge branch 'master' of github.com:apache/spark into local-checkpoint
db70dc2 [Andrew Or] Express local checkpointing through caching the original RDD
87d43c6 [Andrew Or] Merge branch 'master' of github.com:apache/spark into local-checkpoint
c449b38 [Andrew Or] Fix style
4a182f3 [Andrew Or] Add fine-grained tests for local checkpointing
53b363b [Andrew Or] Rename a few more awkwardly named methods (minor)
e4cf071 [Andrew Or] Simplify LocalCheckpointRDD + docs + clean ups
4880deb [Andrew Or] Fix style
d096c67 [Andrew Or] Fix mima
172cb66 [Andrew Or] Fix mima?
e53d964 [Andrew Or] Fix style
56831c5 [Andrew Or] Add a few warnings and clear exception messages
2e59646 [Andrew Or] Add local checkpoint clean up tests
4dbbab1 [Andrew Or] Refactor CheckpointSuite to test local checkpointing
4514dc9 [Andrew Or] Clean local checkpoint files through RDD cleanups
0477eec [Andrew Or] Rename a few methods with awkward names (minor)
2e902e5 [Andrew Or] First implementation of local checkpointing
8447454 [Andrew Or] Fix tests
4ac1896 [Andrew Or] Refactor checkpoint interface for modularity
Dynamic allocation is a feature that allows a Spark application to scale the number of executors up and down dynamically based on the workload. Support was first introduced in YARN since 1.2, and then extended to Mesos coarse-grained mode recently. Today, it is finally supported in standalone mode as well!
I tested this locally and it works as expected. This is WIP because unit tests are coming.
Author: Andrew Or <andrew@databricks.com>
Closes#7532 from andrewor14/standalone-da and squashes the following commits:
b3c1736 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-da
879e928 [Andrew Or] Add end-to-end tests for standalone dynamic allocation
accc8f6 [Andrew Or] Address comments
ee686a8 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-da
c0a2c02 [Andrew Or] Fix build after merge conflict
24149eb [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-da
2e762d6 [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-da
6832bd7 [Andrew Or] Add tests for scheduling with executor limit
a82e907 [Andrew Or] Fix comments
0a8be79 [Andrew Or] Simplify logic by removing the worker blacklist
b7742af [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-da
2eb5f3f [Andrew Or] Merge branch 'master' of github.com:apache/spark into standalone-da
1334e9a [Andrew Or] Fix MiMa
32abe44 [Andrew Or] Fix style
58cb06f [Andrew Or] Privatize worker blacklist for cleanliness
42ac215 [Andrew Or] Clean up comments and rewrite code for readability
49702d1 [Andrew Or] Clean up shuffle files after application exits
80047aa [Andrew Or] First working implementation
This PR adds the Python API for Kinesis, including a Python example and a simple unit test.
Author: zsxwing <zsxwing@gmail.com>
Closes#6955 from zsxwing/kinesis-python and squashes the following commits:
e42e471 [zsxwing] Merge branch 'master' into kinesis-python
455f7ea [zsxwing] Remove streaming_kinesis_asl_assembly module and simply add the source folder to streaming_kinesis_asl module
32e6451 [zsxwing] Merge remote-tracking branch 'origin/master' into kinesis-python
5082d28 [zsxwing] Fix the syntax error for Python 2.6
fca416b [zsxwing] Fix wrong comparison
96670ff [zsxwing] Fix the compilation error after merging master
756a128 [zsxwing] Merge branch 'master' into kinesis-python
6c37395 [zsxwing] Print stack trace for debug
7c5cfb0 [zsxwing] RUN_KINESIS_TESTS -> ENABLE_KINESIS_TESTS
cc9d071 [zsxwing] Fix the python test errors
466b425 [zsxwing] Add python tests for Kinesis
e33d505 [zsxwing] Merge remote-tracking branch 'origin/master' into kinesis-python
3da2601 [zsxwing] Fix the kinesis folder
687446b [zsxwing] Fix the error message and the maven output path
add2beb [zsxwing] Merge branch 'master' into kinesis-python
4957c0b [zsxwing] Add the Python API for Kinesis
This was turned off accidentally in #7591.
Author: Reynold Xin <rxin@databricks.com>
Closes#7637 from rxin/enable-mem-leak-detect and squashes the following commits:
34bc3ef [Reynold Xin] Enable memory leak detection for Tungsten.
Reverts ObjectPool. As it stands, it has a few problems:
1. ObjectPool doesn't work with spilling and memory accounting.
2. I don't think in the long run the idea of an object pool is what we want to support, since it essentially goes back to unmanaged memory, and creates pressure on GC, and is hard to account for the total in memory size.
3. The ObjectPool patch removed the specialized getters for strings and binary, and as a result, actually introduced branches when reading non primitive data types.
If we do want to support arbitrary user defined types in the future, I think we can just add an object array in UnsafeRow, rather than relying on indirect memory addressing through a pool. We also need to pick execution strategies that are optimized for those, rather than keeping a lot of unserialized JVM objects in memory during aggregation.
This is probably the hardest thing I had to revert in Spark, due to recent patches that also change the same part of the code. Would be great to get a careful look.
Author: Reynold Xin <rxin@databricks.com>
Closes#7591 from rxin/revert-object-pool and squashes the following commits:
01db0bc [Reynold Xin] Scala style.
eda89fc [Reynold Xin] Fixed describe.
2967118 [Reynold Xin] Fixed accessor for JoinedRow.
e3294eb [Reynold Xin] Merge branch 'master' into revert-object-pool
657855f [Reynold Xin] Temp commit.
c20f2c8 [Reynold Xin] Style fix.
fe37079 [Reynold Xin] Revert "[SPARK-8579] [SQL] support arbitrary object in UnsafeRow"
I've seen a few cases in the past few weeks that the compiler is throwing warnings that are caused by legitimate bugs. This patch upgrades warnings to errors, except deprecation warnings.
Note that ideally we should be able to mark deprecation warnings as errors as well. However, due to the lack of ability to suppress individual warning messages in the Scala compiler, we cannot do that (since we do need to access deprecated APIs in Hadoop).
Most of the work are done by ericl.
Author: Reynold Xin <rxin@databricks.com>
Author: Eric Liang <ekl@databricks.com>
Closes#7598 from rxin/warnings and squashes the following commits:
beb311b [Reynold Xin] Fixed tests.
542c031 [Reynold Xin] Fixed one more warning.
87c354a [Reynold Xin] Fixed all non-deprecation warnings.
78660ac [Eric Liang] first effort to fix warnings
This way, the sources package contains only public facing interfaces.
Author: Reynold Xin <rxin@databricks.com>
Closes#7565 from rxin/move-ds and squashes the following commits:
7661aff [Reynold Xin] Mima
9d5196a [Reynold Xin] Rearranged imports.
3dd7174 [Reynold Xin] [SPARK-8906][SQL] Move all internal data source classes into execution.datasources.
This PR also remove the duplicated code between registerFunction and UserDefinedFunction.
cc JoshRosen
Author: Davies Liu <davies@databricks.com>
Closes#7450 from davies/fix_return_type and squashes the following commits:
e80bf9f [Davies Liu] remove debugging code
f94b1f6 [Davies Liu] fix mima
8f9c58b [Davies Liu] convert returned object from UDF into internal type
Modifying Vector, DenseVector, and SparseVector to implement argmax functionality. This work is to set the stage for changes to be done in Spark-7423.
Author: George Dittmar <georgedittmar@gmail.com>
Author: George <dittmar@Georges-MacBook-Pro.local>
Author: dittmarg <george.dittmar@webtrends.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#6112 from GeorgeDittmar/SPARK-7422 and squashes the following commits:
3e0a939 [George Dittmar] Merge pull request #1 from mengxr/SPARK-7422
127dec5 [Xiangrui Meng] update argmax impl
2ea6a55 [George Dittmar] Added MimaExcludes for Vectors.argmax
98058f4 [George Dittmar] Merge branch 'master' of github.com:apache/spark into SPARK-7422
5fd9380 [George Dittmar] fixing style check error
42341fb [George Dittmar] refactoring arg max check to better handle zero values
b22af46 [George Dittmar] Fixing spaces between commas in unit test
f2eba2f [George Dittmar] Cleaning up unit tests to be fewer lines
aa330e3 [George Dittmar] Fixing some last if else spacing issues
ac53c55 [George Dittmar] changing dense vector argmax unit test to be one line call vs 2
d5b5423 [George Dittmar] Fixing code style and updating if logic on when to check for zero values
ee1a85a [George Dittmar] Cleaning up unit tests a bit and modifying a few cases
3ee8711 [George Dittmar] Fixing corner case issue with zeros in the active values of the sparse vector. Updated unit tests
b1f059f [George Dittmar] Added comment before we start arg max calculation. Updated unit tests to cover corner cases
f21dcce [George Dittmar] commit
af17981 [dittmarg] Initial work fixing bug that was made clear in pr
eeda560 [George] Fixing SparseVector argmax function to ignore zero values while doing the calculation.
4526acc [George] Merge branch 'master' of github.com:apache/spark into SPARK-7422
df9538a [George] Added argmax to sparse vector and added unit test
3cffed4 [George] Adding unit tests for argmax functions for Dense and Sparse vectors
04677af [George] initial work on adding argmax to Vector and SparseVector
I Implemented the KMeans API for spark.ml Pipelines. But it doesn't include clustering abstractions for spark.ml (SPARK-7610). It would fit for another issues. And I'll try it later, since we are trying to add the hierarchical clustering algorithms in another issue. Thanks.
[SPARK-7879] KMeans API for spark.ml Pipelines - ASF JIRA https://issues.apache.org/jira/browse/SPARK-7879
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes#6756 from yu-iskw/SPARK-7879 and squashes the following commits:
be752de [Yu ISHIKAWA] Add assertions
a14939b [Yu ISHIKAWA] Fix the dashed line's length in pyspark.ml.rst
4c61693 [Yu ISHIKAWA] Remove the test about whether "features" and "prediction" columns exist or not in Python
fb2417c [Yu ISHIKAWA] Use getInt, instead of get
f397be4 [Yu ISHIKAWA] Switch the comparisons.
ca78b7d [Yu ISHIKAWA] Add the Scala docs about the constraints of each parameter.
effc650 [Yu ISHIKAWA] Using expertSetParam and expertGetParam
c8dc6e6 [Yu ISHIKAWA] Remove an unnecessary test
19a9d63 [Yu ISHIKAWA] Include spark.ml.clustering to python tests
1abb19c [Yu ISHIKAWA] Add the statements about spark.ml.clustering into pyspark.ml.rst
f8338bc [Yu ISHIKAWA] Add the placeholders in Python
4a03003 [Yu ISHIKAWA] Test for contains in Python
6566c8b [Yu ISHIKAWA] Use `get`, instead of `apply`
288e8d5 [Yu ISHIKAWA] Using `contains` to check the column names
5a7d574 [Yu ISHIKAWA] Renamce `validateInitializationMode` to `validateInitMode` and remove throwing exception
97cfae3 [Yu ISHIKAWA] Fix the type of return value of `KMeans.copy`
e933723 [Yu ISHIKAWA] Remove the default value of seed from the Model class
978ee2c [Yu ISHIKAWA] Modify the docs of KMeans, according to mllib's KMeans
2ec80bc [Yu ISHIKAWA] Fit on 1 line
e186be1 [Yu ISHIKAWA] Make a few variables, setters and getters be expert ones
b2c205c [Yu ISHIKAWA] Rename the method `getInitializationSteps` to `getInitSteps` and `setInitializationSteps` to `setInitSteps` in Scala and Python
f43f5b4 [Yu ISHIKAWA] Rename the method `getInitializationMode` to `getInitMode` and `setInitializationMode` to `setInitMode` in Scala and Python
3cb5ba4 [Yu ISHIKAWA] Modify the description about epsilon and the validation
4fa409b [Yu ISHIKAWA] Add a comment about the default value of epsilon
2f392e1 [Yu ISHIKAWA] Make some variables `final` and Use `IntParam` and `DoubleParam`
19326f8 [Yu ISHIKAWA] Use `udf`, instead of callUDF
4d2ad1e [Yu ISHIKAWA] Modify the indentations
0ae422f [Yu ISHIKAWA] Add a test for `setParams`
4ff7913 [Yu ISHIKAWA] Add "ml.clustering" to `javacOptions` in SparkBuild.scala
11ffdf1 [Yu ISHIKAWA] Use `===` and the variable
220a176 [Yu ISHIKAWA] Set a random seed in the unit testing
92c3efc [Yu ISHIKAWA] Make the points for a test be fewer
c758692 [Yu ISHIKAWA] Modify the parameters of KMeans in Python
6aca147 [Yu ISHIKAWA] Add some unit testings to validate the setter methods
687cacc [Yu ISHIKAWA] Alias mllib.KMeans as MLlibKMeans in KMeansSuite.scala
a4dfbef [Yu ISHIKAWA] Modify the last brace and indentations
5bedc51 [Yu ISHIKAWA] Remve an extra new line
444c289 [Yu ISHIKAWA] Add the validation for `runs`
e41989c [Yu ISHIKAWA] Modify how to validate `initStep`
7ea133a [Yu ISHIKAWA] Change how to validate `initMode`
7991e15 [Yu ISHIKAWA] Add a validation for `k`
c2df35d [Yu ISHIKAWA] Make `predict` private
93aa2ff [Yu ISHIKAWA] Use `withColumn` in `transform`
d3a79f7 [Yu ISHIKAWA] Remove the inhefited docs
e9532e1 [Yu ISHIKAWA] make `parentModel` of KMeansModel private
8559772 [Yu ISHIKAWA] Remove the `paramMap` parameter of KMeans
6684850 [Yu ISHIKAWA] Rename `initializationSteps` to `initSteps`
99b1b96 [Yu ISHIKAWA] Rename `initializationMode` to `initMode`
79ea82b [Yu ISHIKAWA] Modify the parameters of KMeans docs
6569bcd [Yu ISHIKAWA] Change how to set the default values with `setDefault`
20a795a [Yu ISHIKAWA] Change how to set the default values with `setDefault`
11c2a12 [Yu ISHIKAWA] Limit the imports
badb481 [Yu ISHIKAWA] Alias spark.mllib.{KMeans, KMeansModel}
f80319a [Yu ISHIKAWA] Rebase mater branch and add copy methods
85d92b1 [Yu ISHIKAWA] Add `KMeans.setPredictionCol`
aa9469d [Yu ISHIKAWA] Fix a python test suite error caused by python 3.x
c2d6bcb [Yu ISHIKAWA] ADD Java test suites of the KMeans API for spark.ml Pipeline
598ed2e [Yu ISHIKAWA] Implement the KMeans API for spark.ml Pipelines in Python
63ad785 [Yu ISHIKAWA] Implement the KMeans API for spark.ml Pipelines in Scala
This PR enables SparkR to dynamically ship the SparkR binary package to the AM node in YARN cluster mode, thus it is no longer required that the SparkR package be installed on each worker node.
This PR uses the JDK jar tool to package the SparkR package, because jar is thought to be available on both Linux/Windows platforms where JDK has been installed.
This PR does not address the R worker involved in RDD API. Will address it in a separate JIRA issue.
This PR does not address SBT build. SparkR installation and packaging by SBT will be addressed in a separate JIRA issue.
R/install-dev.bat is not tested. shivaram , Could you help to test it?
Author: Sun Rui <rui.sun@intel.com>
Closes#6743 from sun-rui/SPARK-6797 and squashes the following commits:
ca63c86 [Sun Rui] Adjust MimaExcludes after rebase.
7313374 [Sun Rui] Fix unit test errors.
72695fb [Sun Rui] Fix unit test failures.
193882f [Sun Rui] Fix Mima test error.
fe25a33 [Sun Rui] Fix Mima test error.
35ecfa3 [Sun Rui] Fix comments.
c38a005 [Sun Rui] Unzipped SparkR binary package is still required for standalone and Mesos modes.
b05340c [Sun Rui] Fix scala style.
2ca5048 [Sun Rui] Fix comments.
1acefd1 [Sun Rui] Fix scala style.
0aa1e97 [Sun Rui] Fix scala style.
41d4f17 [Sun Rui] Add support for locating SparkR package for R workers required by RDD APIs.
49ff948 [Sun Rui] Invoke jar.exe with full path in install-dev.bat.
7b916c5 [Sun Rui] Use 'rem' consistently.
3bed438 [Sun Rui] Add a comment.
681afb0 [Sun Rui] Fix a bug that RRunner does not handle client deployment modes.
cedfbe2 [Sun Rui] [SPARK-6797][SPARKR] Add support for YARN cluster mode.
Author: Jonathan Alter <jonalter@users.noreply.github.com>
Closes#7093 from jonalter/SPARK-7977 and squashes the following commits:
ccd44cc [Jonathan Alter] Changed println to log in ThreadingSuite
7fcac3e [Jonathan Alter] Reverting to println in ThreadingSuite
10724b6 [Jonathan Alter] Changing some printlns to logs in tests
eeec1e7 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
0b1dcb4 [Jonathan Alter] More println cleanup
aedaf80 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
925fd98 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
0c16fa3 [Jonathan Alter] Replacing some printlns with logs
45c7e05 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
5c8e283 [Jonathan Alter] Allowing println in audit-release examples
5b50da1 [Jonathan Alter] Allowing printlns in example files
ca4b477 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
83ab635 [Jonathan Alter] Fixing new printlns
54b131f [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
1cd8a81 [Jonathan Alter] Removing some unnecessary comments and printlns
b837c3a [Jonathan Alter] Disallowing println
This PR adds `metadata` to `InputInfo`. `InputDStream` can report its metadata for a batch and it will be shown in the batch page.
For example,
FileInputDStream will display the new files for a batch, and DirectKafkaInputDStream will display its offset ranges.
Author: zsxwing <zsxwing@gmail.com>
Closes#7081 from zsxwing/input-metadata and squashes the following commits:
f7abd9b [zsxwing] Revert the space changes in project/MimaExcludes.scala
d906209 [zsxwing] Merge branch 'master' into input-metadata
74762da [zsxwing] Fix MiMa tests
7903e33 [zsxwing] Merge branch 'master' into input-metadata
450a46c [zsxwing] Address comments
1d94582 [zsxwing] Raname InputInfo to StreamInputInfo and change "metadata" to Map[String, Any]
d496ae9 [zsxwing] Add input metadata in the batch page
This PR fixes the converter for Python DataFrame, especially for DecimalType
Closes#7106
Author: Davies Liu <davies@databricks.com>
Closes#7131 from davies/decimal_python and squashes the following commits:
4d3c234 [Davies Liu] Merge branch 'master' of github.com:apache/spark into decimal_python
20531d6 [Davies Liu] Merge branch 'master' of github.com:apache/spark into decimal_python
7d73168 [Davies Liu] fix conflit
6cdd86a [Davies Liu] Merge branch 'master' of github.com:apache/spark into decimal_python
7104e97 [Davies Liu] improve type infer
9cd5a21 [Davies Liu] run python tests with SPARK_PREPEND_CLASSES
829a05b [Davies Liu] fix UDT in python
c99e8c5 [Davies Liu] fix mima
c46814a [Davies Liu] convert decimal for Python DataFrames
As rxin suggested in #7298 , we should consider to remove `RDDApi`.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#7302 from sarutak/remove-rddapi and squashes the following commits:
e495d35 [Kousuke Saruta] Fixed mima
cb7ebb9 [Kousuke Saruta] Removed overriding RDDApi
This PR is a follow-up of #6617 and is part of [SPARK-6774] [2], which aims to ensure interoperability and backwards-compatibility for Spark SQL Parquet support. And this one fixes the read path. Now Spark SQL is expected to be able to read legacy Parquet data files generated by most (if not all) common libraries/tools like parquet-thrift, parquet-avro, and parquet-hive. However, we still need to refactor the write path to write standard Parquet LISTs and MAPs ([SPARK-8848] [4]).
### Major changes
1. `CatalystConverter` class hierarchy refactoring
- Replaces `CatalystConverter` trait with a much simpler `ParentContainerUpdater`.
Now instead of extending the original `CatalystConverter` trait, every converter class accepts an updater which is responsible for propagating the converted value to some parent container. For example, appending array elements to a parent array buffer, appending a key-value pairs to a parent mutable map, or setting a converted value to some specific field of a parent row. Root converter doesn't have a parent and thus uses a `NoopUpdater`.
This simplifies the design since converters don't need to care about details of their parent converters anymore.
- Unifies `CatalystRootConverter`, `CatalystGroupConverter` and `CatalystPrimitiveRowConverter` into `CatalystRowConverter`
Specifically, now all row objects are represented by `SpecificMutableRow` during conversion.
- Refactors `CatalystArrayConverter`, and removes `CatalystArrayContainsNullConverter` and `CatalystNativeArrayConverter`
`CatalystNativeArrayConverter` was probably designed with the intention of avoiding boxing costs. However, the way it uses Scala generics actually doesn't achieve this goal.
The new `CatalystArrayConverter` handles both nullable and non-nullable array elements in a consistent way.
- Implements backwards-compatibility rules in `CatalystArrayConverter`
When Parquet records are being converted, schema of Parquet files should have already been verified. So we only need to care about the structure rather than field names in the Parquet schema. Since all map objects represented in legacy systems have the same structure as the standard one (see [backwards-compatibility rules for MAP] [1]), we only need to deal with LIST (namely array) in `CatalystArrayConverter`.
2. Requested columns handling
When specifying requested columns in `RowReadSupport`, we used to use a Parquet `MessageType` converted from a Catalyst `StructType` which contains all requested columns. This is not preferable when taking compatibility and interoperability into consideration. Because the actual Parquet file may have different physical structure from the converted schema.
In this PR, the schema for requested columns is constructed using the following method:
- For a column that exists in the target Parquet file, we extract the column type by name from the full file schema, and construct a single-field `MessageType` for that column.
- For a column that doesn't exist in the target Parquet file, we create a single-field `StructType` and convert it to a `MessageType` using `CatalystSchemaConverter`.
- Unions all single-field `MessageType`s into a full schema containing all requested fields
With this change, we also fix [SPARK-6123] [3] by validating the global schema against each individual Parquet part-files.
### Testing
This PR also adds compatibility tests for parquet-avro, parquet-thrift, and parquet-hive. Please refer to `README.md` under `sql/core/src/test` for more information about these tests. To avoid build time code generation and adding extra complexity to the build system, Java code generated from testing Thrift schema and Avro IDL is also checked in.
[1]: https://github.com/apache/incubator-parquet-format/blob/master/LogicalTypes.md#backward-compatibility-rules-1
[2]: https://issues.apache.org/jira/browse/SPARK-6774
[3]: https://issues.apache.org/jira/browse/SPARK-6123
[4]: https://issues.apache.org/jira/browse/SPARK-8848
Author: Cheng Lian <lian@databricks.com>
Closes#7231 from liancheng/spark-6776 and squashes the following commits:
360fe18 [Cheng Lian] Adds ParquetHiveCompatibilitySuite
c6fbc06 [Cheng Lian] Removes WIP file committed by mistake
b8c1295 [Cheng Lian] Excludes the whole parquet package from MiMa
598c3e8 [Cheng Lian] Adds extra Maven repo for hadoop-lzo, which is a transitive dependency of parquet-thrift
926af87 [Cheng Lian] Simplifies Parquet compatibility test suites
7946ee1 [Cheng Lian] Fixes Scala styling issues
3d7ab36 [Cheng Lian] Fixes .rat-excludes
a8f13bb [Cheng Lian] Using Parquet writer API to do compatibility tests
f2208cd [Cheng Lian] Adds README.md for Thrift/Avro code generation
1d390aa [Cheng Lian] Adds parquet-thrift compatibility test
440f7b3 [Cheng Lian] Adds generated files to .rat-excludes
13b9121 [Cheng Lian] Adds ParquetAvroCompatibilitySuite
06cfe9d [Cheng Lian] Adds comments about TimestampType handling
a099d3e [Cheng Lian] More comments
0cc1b37 [Cheng Lian] Fixes MiMa checks
884d3e6 [Cheng Lian] Fixes styling issue and reverts unnecessary changes
802cbd7 [Cheng Lian] Fixes bugs related to schema merging and empty requested columns
38fe1e7 [Cheng Lian] Adds explicit return type
7fb21f1 [Cheng Lian] Reverts an unnecessary debugging change
1781dff [Cheng Lian] Adds test case for SPARK-8811
6437d4b [Cheng Lian] Assembles requested schema from Parquet file schema
bcac49f [Cheng Lian] Removes the 16-byte restriction of decimals
a74fb2c [Cheng Lian] More comments
0525346 [Cheng Lian] Removes old Parquet record converters
03c3bd9 [Cheng Lian] Refactors Parquet read path to implement backwards-compatibility rules
All compressed sensing applications, and some of the regression use-cases will have better result by turning the feature scaling off. However, if we implement this naively by training the dataset without doing any standardization, the rate of convergency will not be good. This can be implemented by still standardizing the training dataset but we penalize each component differently to get effectively the same objective function but a better numerical problem. As a result, for those columns with high variances, they will be penalized less, and vice versa. Without this, since all the features are standardized, so they will be penalized the same.
In R, there is an option for this.
`standardize`
Logical flag for x variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is standardize=TRUE. If variables are in the same units already, you might not wish to standardize. See details below for y standardization with family="gaussian".
+cc holdenk mengxr jkbradley
Author: DB Tsai <dbt@netflix.com>
Closes#7080 from dbtsai/lors and squashes the following commits:
877e6c7 [DB Tsai] repahse the doc
7cf45f2 [DB Tsai] address feedback
78d75c9 [DB Tsai] small change
c2c9e60 [DB Tsai] style
6e1a8e0 [DB Tsai] first commit
I am increasing the perm gen size to 256m.
https://issues.apache.org/jira/browse/SPARK-8776
Author: Yin Huai <yhuai@databricks.com>
Closes#7196 from yhuai/SPARK-8776 and squashes the following commits:
60901b4 [Yin Huai] Fix test.
d44b713 [Yin Huai] Make sparkShell and hiveConsole use 256m PermGen size.
30aaf8e [Yin Huai] Increase the default PermGen size to 256m.
Matrices allow zeros to be stored in values. Sometimes a method is handy to check if the numNonZeros are same as number of Active values.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#6904 from MechCoder/nnz_matrix and squashes the following commits:
252c6b7 [MechCoder] Add to MiMa excludes
e2390f5 [MechCoder] Use count instead of foreach
2f62b2f [MechCoder] Add to MiMa excludes
d6e96ef [MechCoder] [SPARK-8479] Add numNonzeros and numActives to linalg.Matrices
Author: jerryshao <saisai.shao@intel.com>
Closes#7120 from jerryshao/SPARK-7820 and squashes the following commits:
6902439 [jerryshao] fix Java8-tests suite compile error under sbt
Author: zsxwing <zsxwing@gmail.com>
Closes#6830 from zsxwing/flume-python and squashes the following commits:
78dfdac [zsxwing] Fix the compile error in the test code
f1bf3c0 [zsxwing] Address TD's comments
0449723 [zsxwing] Add sbt goal streaming-flume-assembly/assembly
e93736b [zsxwing] Fix the test case for determine_modules_to_test
9d5821e [zsxwing] Fix pyspark_core dependencies
f9ee681 [zsxwing] Merge branch 'master' into flume-python
7a55837 [zsxwing] Add streaming_flume_assembly to run-tests.py
b96b0de [zsxwing] Merge branch 'master' into flume-python
ce85e83 [zsxwing] Fix incompatible issues for Python 3
01cbb3d [zsxwing] Add import sys
152364c [zsxwing] Fix the issue that StringIO doesn't work in Python 3
14ba0ff [zsxwing] Add flume-assembly for sbt building
b8d5551 [zsxwing] Merge branch 'master' into flume-python
4762c34 [zsxwing] Fix the doc
0336579 [zsxwing] Refactor Flume unit tests and also add tests for Python API
9f33873 [zsxwing] Add the Python API for Flume
This PR introduces `CatalystSchemaConverter` for converting Parquet schema to Spark SQL schema and vice versa. Original conversion code in `ParquetTypesConverter` is removed. Benefits of the new version are:
1. When converting Spark SQL schemas, it generates standard Parquet schemas conforming to [the most updated Parquet format spec] [1]. Converting to old style Parquet schemas is also supported via feature flag `spark.sql.parquet.followParquetFormatSpec` (which is set to `false` for now, and should be set to `true` after both read and write paths are fixed).
Note that although this version of Parquet format spec hasn't been officially release yet, Parquet MR 1.7.0 already sticks to it. So it should be safe to follow.
1. It implements backwards-compatibility rules described in the most updated Parquet format spec. Thus can recognize more schema patterns generated by other/legacy systems/tools.
1. Code organization follows convention used in [parquet-mr] [2], which is easier to follow. (Structure of `CatalystSchemaConverter` is similar to `AvroSchemaConverter`).
To fully implement backwards-compatibility rules in both read and write path, we also need to update `CatalystRowConverter` (which is responsible for converting Parquet records to `Row`s), `RowReadSupport`, and `RowWriteSupport`. These would be done in follow-up PRs.
TODO
- [x] More schema conversion test cases for legacy schema patterns.
[1]: ea09522659/LogicalTypes.md
[2]: https://github.com/apache/parquet-mr/
Author: Cheng Lian <lian@databricks.com>
Closes#6617 from liancheng/spark-6777 and squashes the following commits:
2a2062d [Cheng Lian] Don't convert decimals without precision information
b60979b [Cheng Lian] Adds a constructor which accepts a Configuration, and fixes default value of assumeBinaryIsString
743730f [Cheng Lian] Decimal scale shouldn't be larger than precision
a104a9e [Cheng Lian] Fixes Scala style issue
1f71d8d [Cheng Lian] Adds feature flag to allow falling back to old style Parquet schema conversion
ba84f4b [Cheng Lian] Fixes MapType schema conversion bug
13cb8d5 [Cheng Lian] Fixes MiMa failure
81de5b0 [Cheng Lian] Fixes UDT, workaround read path, and add tests
28ef95b [Cheng Lian] More AnalysisExceptions
b10c322 [Cheng Lian] Replaces require() with analysisRequire() which throws AnalysisException
cceaf3f [Cheng Lian] Implements backwards compatibility rules in CatalystSchemaConverter
This commit changes the MiMa tests to test against the released 1.4.0 artifacts rather than 1.4.0-rc4; this change is necessary to fix a Jenkins build break since it seems that the RC4 snapshot is no longer available via Maven.
I also enabled MiMa checks for the `launcher` subproject, which we should have done right after 1.4.0 was released.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#6974 from JoshRosen/mima-hotfix and squashes the following commits:
4b4175a [Josh Rosen] [HOTFIX] [BUILD] Fix MiMa checks in master branch; enable MiMa for launcher project
Author: Holden Karau <holden@pigscanfly.ca>
Closes#6927 from holdenk/SPARK-7888-Be-able-to-disable-intercept-in-Linear-Regression-in-ML-package and squashes the following commits:
0ad384c [Holden Karau] Add MiMa excludes
4016fac [Holden Karau] Switch to wild card import, remove extra blank lines
ae5baa8 [Holden Karau] CR feedback, move the fitIntercept down rather than changing ymean and etc above
f34971c [Holden Karau] Fix some more long lines
319bd3f [Holden Karau] Fix long lines
3bb9ee1 [Holden Karau] Update the regression suite tests
7015b9f [Holden Karau] Our code performs the same with R, except we need more than one data point but that seems reasonable
0b0c8c0 [Holden Karau] fix the issue with the sample R code
e2140ba [Holden Karau] Add a test, it fails!
5e84a0b [Holden Karau] Write out thoughts and use the correct trait
91ffc0a [Holden Karau] more murh
006246c [Holden Karau] murp?
…ed KafkaRDD methods. Possible fix for [SPARK-7122], but probably a worthwhile optimization regardless.
Author: cody koeninger <cody@koeninger.org>
Closes#6632 from koeninger/kafka-rdd-count and squashes the following commits:
321340d [cody koeninger] [SPARK-8127][Streaming][Kafka] additional test of ordering of take()
5a05d0f [cody koeninger] [SPARK-8127][Streaming][Kafka] additional test of isEmpty
f68bd32 [cody koeninger] [Streaming][Kafka][SPARK-8127] code cleanup
9555b73 [cody koeninger] Merge branch 'master' into kafka-rdd-count
253031d [cody koeninger] [Streaming][Kafka][SPARK-8127] mima exclusion for change to private method
8974b9e [cody koeninger] [Streaming][Kafka][SPARK-8127] check offset ranges before constructing KafkaRDD
c3768c5 [cody koeninger] [Streaming][Kafka] Take advantage of offset range info for size-related KafkaRDD methods. Possible fix for [SPARK-7122], but probably a worthwhile optimization regardless.
to make it easier to start & stop http servers in sbt
https://issues.apache.org/jira/browse/SPARK-6782
Author: Imran Rashid <irashid@cloudera.com>
Closes#5426 from squito/SPARK-6782 and squashes the following commits:
dc4fb19 [Imran Rashid] add sbt-revolved plugin, to make it easier to start & stop http servers in sbt
If you ran "clean" at the top-level sbt project, the temp dir would
go away, so running "test" without restarting sbt would fail. This
fixes that by making sure the temp dir exists before running tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#6805 from vanzin/SPARK-8126-fix and squashes the following commits:
12d7768 [Marcelo Vanzin] [SPARK-8126] [build] Make sure temp dir exists when running tests.
Unit test is still in Scala.
Author: Reynold Xin <rxin@databricks.com>
Closes#6738 from rxin/utf8string-java and squashes the following commits:
562dc6e [Reynold Xin] Flag...
98e600b [Reynold Xin] Another try with encoding setting ..
cfa6bdf [Reynold Xin] Merge branch 'master' into utf8string-java
a3b124d [Reynold Xin] Try different UTF-8 encoded characters.
1ff7c82 [Reynold Xin] Enable UTF-8 encoding.
82d58cc [Reynold Xin] Reset run-tests.
2cb3c69 [Reynold Xin] Use utf-8 encoding in set bytes.
53f8ef4 [Reynold Xin] Hack Jenkins to run one test.
9a48e8d [Reynold Xin] Fixed runtime compilation error.
911c450 [Reynold Xin] Moved unit test also to Java.
4eff7bd [Reynold Xin] Improved unit test coverage.
8e89a3c [Reynold Xin] Fixed tests.
77c64bd [Reynold Xin] Fixed string type codegen.
ffedb62 [Reynold Xin] Code review feedback.
0967ce6 [Reynold Xin] Fixed import ordering.
45a123d [Reynold Xin] [SPARK-8286] Rewrite UTF8String in Java and move it into unsafe package.
This change is a simple one and specifies a stack size of 4096k instead of the vendor default for Java tests (the defaults vary between Java vendors). This remedies test failures observed with JavaALSSuite with IBM and Oracle Java owing to a lower default size in comparison to the size with OpenJDK. 4096k is a suitable default where the tests pass with each Java vendor tested. The alternative is to reduce the number of iterations in the test (no observed failures with 5 iterations instead of 15).
-Xss works with Oracle's HotSpot VM, IBM's J9 VM and OpenJDK (IcedTea).
I have ensured this does not have any negative implications for other tests.
Author: Adam Roberts <aroberts@uk.ibm.com>
Author: a-roberts <aroberts@uk.ibm.com>
Closes#6727 from a-roberts/IncJavaStackSize and squashes the following commits:
ab40aea [Adam Roberts] Specify stack size for SBT builds
5032d8d [a-roberts] Update pom.xml
Even with all the efforts to cleanup the temp directories created by
unit tests, Spark leaves a lot of garbage in /tmp after a test run.
This change overrides java.io.tmpdir to place those files under the
build directory instead.
After an sbt full unit test run, I was left with > 400 MB of temp
files. Since they're now under the build dir, it's much easier to
clean them up.
Also make a slight change to a unit test to make it not pollute the
source directory with test data.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#6674 from vanzin/SPARK-8126 and squashes the following commits:
0f8ad41 [Marcelo Vanzin] Make sure tmp dir exists when tests run.
643e916 [Marcelo Vanzin] [MINOR] [BUILD] Use custom temp directory during build.
see if requiring producer acks eliminates the need for waitUntilLeaderOffset calls in tests
Author: cody koeninger <cody@koeninger.org>
Closes#5921 from koeninger/kafka-0.8.2-test-cleanup and squashes the following commits:
1e89dc8 [cody koeninger] Merge branch 'master' into kafka-0.8.2-test-cleanup
4662828 [cody koeninger] [Streaming][Kafka] filter mima issue for removal of method from private test class
af1e083 [cody koeninger] Merge branch 'master' into kafka-0.8.2-test-cleanup
4298ac2 [cody koeninger] [Streaming][Kafka] update comment to trigger jenkins attempt
1274afb [cody koeninger] [Streaming][Kafka] see if requiring producer acks eliminates the need for waitUntilLeaderOffset calls in tests
Even with all the efforts to cleanup the temp directories created by
unit tests, Spark leaves a lot of garbage in /tmp after a test run.
This change overrides java.io.tmpdir to place those files under the
build directory instead.
After an sbt full unit test run, I was left with > 400 MB of temp
files. Since they're now under the build dir, it's much easier to
clean them up.
Also make a slight change to a unit test to make it not pollute the
source directory with test data.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#6653 from vanzin/unit-test-tmp and squashes the following commits:
31e2dd5 [Marcelo Vanzin] Fix tests that depend on each other.
aa92944 [Marcelo Vanzin] [minor] [build] Use custom temp directory during build.
Derby has a `derby.system.durability` configuration property that can be used to disable I/O synchronization calls for writes. This sacrifices durability but can result in large performance gains, which is appropriate for tests.
We should enable this in our test system properties in order to speed up the Hive compatibility tests. I saw 2-3x speedups locally with this change.
See https://db.apache.org/derby/docs/10.8/ref/rrefproperdurability.html for more documentation of this property.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#6651 from JoshRosen/hive-compat-suite-speedup and squashes the following commits:
b7a08a2 [Josh Rosen] Set derby.system.durability=test in our unit tests.
This patch replaces Distinct with Aggregate in the optimizer, so Distinct will become
more efficient over time as we optimize Aggregate (via Tungsten).
Author: Reynold Xin <rxin@databricks.com>
Closes#6637 from rxin/replace-distinct and squashes the following commits:
b3cc50e [Reynold Xin] Mima excludes.
93d6117 [Reynold Xin] Code review feedback.
87e4741 [Reynold Xin] [SPARK-7440][SQL] Remove physical Distinct operator in favor of Aggregate.
In order to reduce the overhead of codegen, this PR switch to use Janino to compile SQL expressions into bytecode.
After this, the time used to compile a SQL expression is decreased from 100ms to 5ms, which is necessary to turn on codegen for general workload, also tests.
cc rxin
Author: Davies Liu <davies@databricks.com>
Closes#6479 from davies/janino and squashes the following commits:
cc689f5 [Davies Liu] remove globalLock
262d848 [Davies Liu] Merge branch 'master' of github.com:apache/spark into janino
eec3a33 [Davies Liu] address comments from Josh
f37c8c3 [Davies Liu] fix DecimalType and cast to String
202298b [Davies Liu] Merge branch 'master' of github.com:apache/spark into janino
a21e968 [Davies Liu] fix style
0ed3dc6 [Davies Liu] Merge branch 'master' of github.com:apache/spark into janino
551a851 [Davies Liu] fix tests
c3bdffa [Davies Liu] remove print
6089ce5 [Davies Liu] change logging level
7e46ac3 [Davies Liu] fix style
d8f0f6c [Davies Liu] Merge branch 'master' of github.com:apache/spark into janino
da4926a [Davies Liu] fix tests
03660f3 [Davies Liu] WIP: use Janino to compile Java source
f2629cd [Davies Liu] Merge branch 'master' of github.com:apache/spark into janino
f7d66cf [Davies Liu] use template based string for codegen
Author: Patrick Wendell <patrick@databricks.com>
Closes#6328 from pwendell/spark-1.5-update and squashes the following commits:
2f42d02 [Patrick Wendell] A few more excludes
4bebcf0 [Patrick Wendell] Update to RC4
61aaf46 [Patrick Wendell] Using new release candidate
55f1610 [Patrick Wendell] Another exclude
04b4f04 [Patrick Wendell] More issues with transient 1.4 changes
36f549b [Patrick Wendell] [SPARK-7801] [BUILD] Updating versions to SPARK 1.5.0
This PR updates `genjavadocSettings` to use a patched version of `genjavadoc-plugin` that hides package private classes/methods/interfaces in the generated Java API doc. The patch can be found at: https://github.com/typesafehub/genjavadoc/compare/master...mengxr:spark-1.4.
It wasn't merged into the main repo because there exist corner cases where a package private Scala class has to be a Java public class in order to compile. This doesn't seem to apply to the Spark codebase. So we release a patched version under `org.spark-project` and use it in the Spark build. brkyvz is publishing the artifacts to Maven Central.
Need more people audit the generated APIs and make sure we don't have false negatives.
Current listed classes under `org.apache.spark.rdd`:
After this PR:
cc: pwendell rxin srowen
Author: Xiangrui Meng <meng@databricks.com>
Closes#6506 from mengxr/SPARK-5610 and squashes the following commits:
489c785 [Xiangrui Meng] update genjavadocSettings to use the patched version of genjavadoc
Author: Holden Karau <holden@pigscanfly.ca>
Closes#6464 from holdenk/SPARK-7910-expose-partitioner-information-in-javardd and squashes the following commits:
de1e644 [Holden Karau] Fix the test to get the partitioner
bdb31cc [Holden Karau] Add Mima exclude for the new method
347ef4c [Holden Karau] Add a quick little test for the partitioner JavaAPI
f49dca9 [Holden Karau] Add partitoner information to JavaRDDLike and fix some whitespace
https://issues.apache.org/jira/browse/SPARK-7805
Because `sql/hive`'s tests depend on the test jar of `sql/core`, we do not need to store `SQLTestUtils` and `ParquetTest` in `src/main`. We should only add stuff that will be needed by `sql/console` or Python tests (for Python, we need it in `src/main`, right? davies).
Author: Yin Huai <yhuai@databricks.com>
Closes#6334 from yhuai/SPARK-7805 and squashes the following commits:
af6d0c9 [Yin Huai] mima
b86746a [Yin Huai] Move SQLTestUtils.scala and ParquetTest.scala to src/test.
Author: Michael Armbrust <michael@databricks.com>
Closes#6165 from marmbrus/wrongColumn and squashes the following commits:
4fad158 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into wrongColumn
aad7eab [Michael Armbrust] rxins comments
f1e8df1 [Michael Armbrust] [SPARK-6743][SQL] Fix empty projections of cached data
There excludes are unnecessary for 1.3 because the changes were made in 1.4.x.
Author: Xiangrui Meng <meng@databricks.com>
Closes#6254 from mengxr/SPARK-7681-mima and squashes the following commits:
7f0cea0 [Xiangrui Meng] remove mima excludes for 1.3
JIRA: https://issues.apache.org/jira/browse/SPARK-7681
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#6209 from viirya/sparsevector_gemv and squashes the following commits:
ce0bb8b [Liang-Chi Hsieh] Still need to scal y when beta is 0.0 because it clears out y.
b890e63 [Liang-Chi Hsieh] Do not delete multiply for DenseVector.
57a8c1e [Liang-Chi Hsieh] Add MimaExcludes for v1.4.
458d1ae [Liang-Chi Hsieh] List DenseMatrix.multiply and SparseMatrix.multiply to MimaExcludes too.
054f05d [Liang-Chi Hsieh] Fix scala style.
410381a [Liang-Chi Hsieh] Address comments. Make Matrix.multiply more generalized.
4616696 [Liang-Chi Hsieh] Add support for SparseVector with SparseMatrix.
5d6d07a [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into sparsevector_gemv
c069507 [Liang-Chi Hsieh] Add SparseVector support for gemv with DenseMatrix.
Replace the DriverQuirks with JdbcDialect(s) (and MySQLDialect/PostgresDialect)
and allow developers to change the dialects on the fly (for new JDBCRRDs only).
Some types (like an unsigned 64bit number) can be trivially mapped to java.
The status quo is that the RRD will fail to load.
This patch makes it possible to overwrite the type mapping to read e.g.
64Bit numbers as strings and handle them afterwards in software.
JDBCSuite has an example that maps all types to String, which should always
work (at the cost of extra code afterwards).
As a side effect it should now be possible to develop simple dialects
out-of-tree and even with spark-shell.
Author: Rene Treffer <treffer@measite.de>
Closes#5555 from rtreffer/jdbc-dialects and squashes the following commits:
3cbafd7 [Rene Treffer] [SPARK-6888] ignore classes belonging to changed API in MIMA report
fe7e2e8 [Rene Treffer] [SPARK-6888] Make the jdbc driver handling user-definable
This patch introduces a new shuffle manager that enhances the existing sort-based shuffle with a new cache-friendly sort algorithm that operates directly on binary data. The goals of this patch are to lower memory usage and Java object overheads during shuffle and to speed up sorting. It also lays groundwork for follow-up patches that will enable end-to-end processing of serialized records.
The new shuffle manager, `UnsafeShuffleManager`, can be enabled by setting `spark.shuffle.manager=tungsten-sort` in SparkConf.
The new shuffle manager uses directly-managed memory to implement several performance optimizations for certain types of shuffles. In cases where the new performance optimizations cannot be applied, the new shuffle manager delegates to SortShuffleManager to handle those shuffles.
UnsafeShuffleManager's optimizations will apply when _all_ of the following conditions hold:
- The shuffle dependency specifies no aggregation or output ordering.
- The shuffle serializer supports relocation of serialized values (this is currently supported
by KryoSerializer and Spark SQL's custom serializers).
- The shuffle produces fewer than 16777216 output partitions.
- No individual record is larger than 128 MB when serialized.
In addition, extra spill-merging optimizations are automatically applied when the shuffle compression codec supports concatenation of serialized streams. This is currently supported by Spark's LZF serializer.
At a high-level, UnsafeShuffleManager's design is similar to Spark's existing SortShuffleManager. In sort-based shuffle, incoming records are sorted according to their target partition ids, then written to a single map output file. Reducers fetch contiguous regions of this file in order to read their portion of the map output. In cases where the map output data is too large to fit in memory, sorted subsets of the output can are spilled to disk and those on-disk files are merged to produce the final output file.
UnsafeShuffleManager optimizes this process in several ways:
- Its sort operates on serialized binary data rather than Java objects, which reduces memory consumption and GC overheads. This optimization requires the record serializer to have certain properties to allow serialized records to be re-ordered without requiring deserialization. See SPARK-4550, where this optimization was first proposed and implemented, for more details.
- It uses a specialized cache-efficient sorter (UnsafeShuffleExternalSorter) that sorts arrays of compressed record pointers and partition ids. By using only 8 bytes of space per record in the sorting array, this fits more of the array into cache.
- The spill merging procedure operates on blocks of serialized records that belong to the same partition and does not need to deserialize records during the merge.
- When the spill compression codec supports concatenation of compressed data, the spill merge simply concatenates the serialized and compressed spill partitions to produce the final output partition. This allows efficient data copying methods, like NIO's `transferTo`, to be used and avoids the need to allocate decompression or copying buffers during the merge.
The shuffle read path is unchanged.
This patch is similar to [SPARK-4550](http://issues.apache.org/jira/browse/SPARK-4550) / #4450 but uses a slightly different implementation. The `unsafe`-based implementation featured in this patch lays the groundwork for followup patches that will enable sorting to operate on serialized data pages that will be prepared by Spark SQL's new `unsafe` operators (such as the new aggregation operator introduced in #5725).
### Future work
There are several tasks that build upon this patch, which will be left to future work:
- [SPARK-7271](https://issues.apache.org/jira/browse/SPARK-7271) Redesign / extend the shuffle interfaces to accept binary data as input. The goal here is to let us bypass serialization steps in cases where the sort input is produced by an operator that operates directly on binary data.
- Extension / redesign of the `Serializer` API. We can add new methods which allow serializers to determine the size requirements for serializing objects and for serializing objects directly to a specified memory address (similar to how `UnsafeRowConverter` works in Spark SQL).
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Author: Josh Rosen <joshrosen@databricks.com>
Closes#5868 from JoshRosen/unsafe-sort and squashes the following commits:
ef0a86e [Josh Rosen] Fix scalastyle errors
7610f2f [Josh Rosen] Add tests for proper cleanup of shuffle data.
d494ffe [Josh Rosen] Fix deserialization of JavaSerializer instances.
52a9981 [Josh Rosen] Fix some bugs in the address packing code.
51812a7 [Josh Rosen] Change shuffle manager sort name to tungsten-sort
4023fa4 [Josh Rosen] Add @Private annotation to some Java classes.
de40b9d [Josh Rosen] More comments to try to explain metrics code
df07699 [Josh Rosen] Attempt to clarify confusing metrics update code
5e189c6 [Josh Rosen] Track time spend closing / flushing files; split TimeTrackingOutputStream into separate file.
d5779c6 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-sort
c2ce78e [Josh Rosen] Fix a missed usage of MAX_PARTITION_ID
e3b8855 [Josh Rosen] Cleanup in UnsafeShuffleWriter
4a2c785 [Josh Rosen] rename 'sort buffer' to 'pointer array'
6276168 [Josh Rosen] Remove ability to disable spilling in UnsafeShuffleExternalSorter.
57312c9 [Josh Rosen] Clarify fileBufferSize units
2d4e4f4 [Josh Rosen] Address some minor comments in UnsafeShuffleExternalSorter.
fdcac08 [Josh Rosen] Guard against overflow when expanding sort buffer.
85da63f [Josh Rosen] Cleanup in UnsafeShuffleSorterIterator.
0ad34da [Josh Rosen] Fix off-by-one in nextInt() call
56781a1 [Josh Rosen] Rename UnsafeShuffleSorter to UnsafeShuffleInMemorySorter
e995d1a [Josh Rosen] Introduce MAX_SHUFFLE_OUTPUT_PARTITIONS.
e58a6b4 [Josh Rosen] Add more tests for PackedRecordPointer encoding.
4f0b770 [Josh Rosen] Attempt to implement proper shuffle write metrics.
d4e6d89 [Josh Rosen] Update to bit shifting constants
69d5899 [Josh Rosen] Remove some unnecessary override vals
8531286 [Josh Rosen] Add tests that automatically trigger spills.
7c953f9 [Josh Rosen] Add test that covers UnsafeShuffleSortDataFormat.swap().
e1855e5 [Josh Rosen] Fix a handful of misc. IntelliJ inspections
39434f9 [Josh Rosen] Avoid integer multiplication overflow in getMemoryUsage (thanks FindBugs!)
1e3ad52 [Josh Rosen] Delete unused ByteBufferOutputStream class.
ea4f85f [Josh Rosen] Roll back an unnecessary change in Spillable.
ae538dc [Josh Rosen] Document UnsafeShuffleManager.
ec6d626 [Josh Rosen] Add notes on maximum # of supported shuffle partitions.
0d4d199 [Josh Rosen] Bump up shuffle.memoryFraction to make tests pass.
b3b1924 [Josh Rosen] Properly implement close() and flush() in DummySerializerInstance.
1ef56c7 [Josh Rosen] Revise compression codec support in merger; test cross product of configurations.
b57c17f [Josh Rosen] Disable some overly-verbose logs that rendered DEBUG useless.
f780fb1 [Josh Rosen] Add test demonstrating which compression codecs support concatenation.
4a01c45 [Josh Rosen] Remove unnecessary log message
27b18b0 [Josh Rosen] That for inserting records AT the max record size.
fcd9a3c [Josh Rosen] Add notes + tests for maximum record / page sizes.
9d1ee7c [Josh Rosen] Fix MiMa excludes for ShuffleWriter change
fd4bb9e [Josh Rosen] Use own ByteBufferOutputStream rather than Kryo's
67d25ba [Josh Rosen] Update Exchange operator's copying logic to account for new shuffle manager
8f5061a [Josh Rosen] Strengthen assertion to check partitioning
01afc74 [Josh Rosen] Actually read data in UnsafeShuffleWriterSuite
1929a74 [Josh Rosen] Update to reflect upstream ShuffleBlockManager -> ShuffleBlockResolver rename.
e8718dd [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-sort
9b7ebed [Josh Rosen] More defensive programming RE: cleaning up spill files and memory after errors
7cd013b [Josh Rosen] Begin refactoring to enable proper tests for spilling.
722849b [Josh Rosen] Add workaround for transferTo() bug in merging code; refactor tests.
9883e30 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-sort
b95e642 [Josh Rosen] Refactor and document logic that decides when to spill.
1ce1300 [Josh Rosen] More minor cleanup
5e8cf75 [Josh Rosen] More minor cleanup
e67f1ea [Josh Rosen] Remove upper type bound in ShuffleWriter interface.
cfe0ec4 [Josh Rosen] Address a number of minor review comments:
8a6fe52 [Josh Rosen] Rename UnsafeShuffleSpillWriter to UnsafeShuffleExternalSorter
11feeb6 [Josh Rosen] Update TODOs related to shuffle write metrics.
b674412 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-sort
aaea17b [Josh Rosen] Add comments to UnsafeShuffleSpillWriter.
4f70141 [Josh Rosen] Fix merging; now passes UnsafeShuffleSuite tests.
133c8c9 [Josh Rosen] WIP towards testing UnsafeShuffleWriter.
f480fb2 [Josh Rosen] WIP in mega-refactoring towards shuffle-specific sort.
57f1ec0 [Josh Rosen] WIP towards packed record pointers for use in optimized shuffle sort.
69232fd [Josh Rosen] Enable compressible address encoding for off-heap mode.
7ee918e [Josh Rosen] Re-order imports in tests
3aeaff7 [Josh Rosen] More refactoring and cleanup; begin cleaning iterator interfaces
3490512 [Josh Rosen] Misc. cleanup
f156a8f [Josh Rosen] Hacky metrics integration; refactor some interfaces.
2776aca [Josh Rosen] First passing test for ExternalSorter.
5e100b2 [Josh Rosen] Super-messy WIP on external sort
595923a [Josh Rosen] Remove some unused variables.
8958584 [Josh Rosen] Fix bug in calculating free space in current page.
f17fa8f [Josh Rosen] Add missing newline
c2fca17 [Josh Rosen] Small refactoring of SerializerPropertiesSuite to enable test re-use:
b8a09fe [Josh Rosen] Back out accidental log4j.properties change
bfc12d3 [Josh Rosen] Add tests for serializer relocation property.
240864c [Josh Rosen] Remove PrefixComputer and require prefix to be specified as part of insert()
1433b42 [Josh Rosen] Store record length as int instead of long.
026b497 [Josh Rosen] Re-use a buffer in UnsafeShuffleWriter
0748458 [Josh Rosen] Port UnsafeShuffleWriter to Java.
87e721b [Josh Rosen] Renaming and comments
d3cc310 [Josh Rosen] Flag that SparkSqlSerializer2 supports relocation
e2d96ca [Josh Rosen] Expand serializer API and use new function to help control when new UnsafeShuffle path is used.
e267cee [Josh Rosen] Fix compilation of UnsafeSorterSuite
9c6cf58 [Josh Rosen] Refactor to use DiskBlockObjectWriter.
253f13e [Josh Rosen] More cleanup
8e3ec20 [Josh Rosen] Begin code cleanup.
4d2f5e1 [Josh Rosen] WIP
3db12de [Josh Rosen] Minor simplification and sanity checks in UnsafeSorter
767d3ca [Josh Rosen] Fix invalid range in UnsafeSorter.
e900152 [Josh Rosen] Add test for empty iterator in UnsafeSorter
57a4ea0 [Josh Rosen] Make initialSize configurable in UnsafeSorter
abf7bfe [Josh Rosen] Add basic test case.
81d52c5 [Josh Rosen] WIP on UnsafeSorter
JavaTypeInference into catalyst
types.DateUtils into catalyst
CacheManager into execution
DefaultParserDialect into catalyst
Author: Reynold Xin <rxin@databricks.com>
Closes#6108 from rxin/sql-rename and squashes the following commits:
3fc9613 [Reynold Xin] Fixed import ordering.
83d9ff4 [Reynold Xin] Fixed codegen tests.
e271e86 [Reynold Xin] mima
f4e24a6 [Reynold Xin] [SQL] Move some classes into packages that are more appropriate.
This PR migrates Parquet data source to the newly introduced `FSBasedRelation`. `FSBasedParquetRelation` is created to replace `ParquetRelation2`. Major differences are:
1. Partition discovery code has been factored out to `FSBasedRelation`
1. `AppendingParquetOutputFormat` is not used now. Instead, an anonymous subclass of `ParquetOutputFormat` is used to handle appending and writing dynamic partitions
1. When scanning partitioned tables, `FSBasedParquetRelation.buildScan` only builds an `RDD[Row]` for a single selected partition
1. `FSBasedParquetRelation` doesn't rely on Catalyst expressions for filter push down, thus it doesn't extend `CatalystScan` anymore
After migrating `JSONRelation` (which extends `CatalystScan`), we can remove `CatalystScan`.
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Author: Cheng Lian <lian@databricks.com>
Closes#6090 from liancheng/parquet-migration and squashes the following commits:
6063f87 [Cheng Lian] Casts to OutputCommitter rather than FileOutputCommtter
bfd1cf0 [Cheng Lian] Fixes compilation error introduced while rebasing
f9ea56e [Cheng Lian] Adds ParquetRelation2 related classes to MiMa check whitelist
261d8c1 [Cheng Lian] Minor bug fix and more tests
db65660 [Cheng Lian] Migrates Parquet data source to FSBasedRelation
This PR adds partitioning support for the external data sources API. It aims to simplify development of file system based data sources, and provide first class partitioning support for both read path and write path. Existing data sources like JSON and Parquet can be simplified with this work.
## New features provided
1. Hive compatible partition discovery
This actually generalizes the partition discovery strategy used in Parquet data source in Spark 1.3.0.
1. Generalized partition pruning optimization
Now partition pruning is handled during physical planning phase. Specific data sources don't need to worry about this harness anymore.
(This also implies that we can remove `CatalystScan` after migrating the Parquet data source, since now we don't need to pass Catalyst expressions to data source implementations.)
1. Insertion with dynamic partitions
When inserting data to a `FSBasedRelation`, data can be partitioned dynamically by specified partition columns.
## New structures provided
### Developer API
1. `FSBasedRelation`
Base abstract class for file system based data sources.
1. `OutputWriter`
Base abstract class for output row writers, responsible for writing a single row object.
1. `FSBasedRelationProvider`
A new relation provider for `FSBasedRelation` subclasses. Note that data sources extending `FSBasedRelation` don't need to extend `RelationProvider` and `SchemaRelationProvider`.
### User API
New overloaded versions of
1. `DataFrame.save()`
1. `DataFrame.saveAsTable()`
1. `SQLContext.load()`
are provided to allow users to save/load DataFrames with user defined dynamic partition columns.
### Spark SQL query planning
1. `InsertIntoFSBasedRelation`
Used to implement write path for `FSBasedRelation`s.
1. New rules for `FSBasedRelation` in `DataSourceStrategy`
These are added to hook `FSBasedRelation` into physical query plan in read path, and perform partition pruning.
## TODO
- [ ] Use scratch directories when overwriting a table with data selected from itself.
Currently, this is not supported, because the table been overwritten is always deleted before writing any data to it.
- [ ] When inserting with dynamic partition columns, use external sorter to group the data first.
This ensures that we only need to open a single `OutputWriter` at a time. For data sources like Parquet, `OutputWriter`s can be quite memory consuming. One issue is that, this approach breaks the row distribution in the original DataFrame. However, we did't promise to preserve data distribution when writing a DataFrame.
- [x] More tests. Specifically, test cases for
- [x] Self-join
- [x] Loading partitioned relations with a subset of partition columns stored in data files.
- [x] `SQLContext.load()` with user defined dynamic partition columns.
## Parquet data source migration
Parquet data source migration is covered in PR https://github.com/liancheng/spark/pull/6, which is against this PR branch and for preview only. A formal PR need to be made after this one is merged.
Author: Cheng Lian <lian@databricks.com>
Closes#5526 from liancheng/partitioning-support and squashes the following commits:
5351a1b [Cheng Lian] Fixes compilation error introduced while rebasing
1f9b1a5 [Cheng Lian] Tweaks data schema passed to FSBasedRelations
43ba50e [Cheng Lian] Avoids serializing generated projection code
edf49e7 [Cheng Lian] Removed commented stale code block
348a922 [Cheng Lian] Adds projection in FSBasedRelation.buildScan(requiredColumns, inputPaths)
ad4d4de [Cheng Lian] Enables HDFS style globbing
8d12e69 [Cheng Lian] Fixes compilation error
c71ac6c [Cheng Lian] Addresses comments from @marmbrus
7552168 [Cheng Lian] Fixes typo in MimaExclude.scala
0349e09 [Cheng Lian] Fixes compilation error introduced while rebasing
52b0c9b [Cheng Lian] Adjusts project/MimaExclude.scala
c466de6 [Cheng Lian] Addresses comments
bc3f9b4 [Cheng Lian] Uses projection to separate partition columns and data columns while inserting rows
795920a [Cheng Lian] Fixes compilation error after rebasing
0b8cd70 [Cheng Lian] Adds Scala/Catalyst row conversion when writing non-partitioned tables
fa543f3 [Cheng Lian] Addresses comments
5849dd0 [Cheng Lian] Fixes doc typos. Fixes partition discovery refresh.
51be443 [Cheng Lian] Replaces FSBasedRelation.outputCommitterClass with FSBasedRelation.prepareForWrite
c4ed4fe [Cheng Lian] Bug fixes and a new test suite
a29e663 [Cheng Lian] Bug fix: should only pass actuall data files to FSBaseRelation.buildScan
5f423d3 [Cheng Lian] Bug fixes. Lets data source to customize OutputCommitter rather than OutputFormat
54c3d7b [Cheng Lian] Enforces that FileOutputFormat must be used
be0c268 [Cheng Lian] Uses TaskAttempContext rather than Configuration in OutputWriter.init
0bc6ad1 [Cheng Lian] Resorts to new Hadoop API, and now FSBasedRelation can customize output format class
f320766 [Cheng Lian] Adds prepareForWrite() hook, refactored writer containers
422ff4a [Cheng Lian] Fixes style issue
ce52353 [Cheng Lian] Adds new SQLContext.load() overload with user defined dynamic partition columns
8d2ff71 [Cheng Lian] Merges partition columns when reading partitioned relations
ca1805b [Cheng Lian] Removes duplicated partition discovery code in new Parquet
f18dec2 [Cheng Lian] More strict schema checking
b746ab5 [Cheng Lian] More tests
9b487bf [Cheng Lian] Fixes compilation errors introduced while rebasing
ea6c8dd [Cheng Lian] Removes remote debugging stuff
327bb1d [Cheng Lian] Implements partitioning support for data sources API
3c5073a [Cheng Lian] Fixes SaveModes used in test cases
fb5a607 [Cheng Lian] Fixes compilation error
9d17607 [Cheng Lian] Adds the contract that OutputWriter should have zero-arg constructor
5de194a [Cheng Lian] Forgot Apache licence header
95d0b4d [Cheng Lian] Renames PartitionedSchemaRelationProvider to FSBasedRelationProvider
770b5ba [Cheng Lian] Adds tests for FSBasedRelation
3ba9bbf [Cheng Lian] Adds DataFrame.saveAsTable() overrides which support partitioning
1b8231f [Cheng Lian] Renames FSBasedPrunedFilteredScan to FSBasedRelation
aa8ba9a [Cheng Lian] Javadoc fix
012ed2d [Cheng Lian] Adds PartitioningOptions
7dd8dd5 [Cheng Lian] Adds new interfaces and stub methods for data sources API partitioning support
The sbt part of the build is hacky; it basically tricks sbt
into generating the zip by using a generator, but returns
an empty list for the generated files so that nothing is
actually added to the assembly.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#6022 from vanzin/SPARK-7485 and squashes the following commits:
22c1e04 [Marcelo Vanzin] Remove unneeded code.
4893622 [Marcelo Vanzin] [SPARK-7485] [build] Remove pyspark files from assembly.
Based on https://github.com/apache/spark/pull/5478 that provide a PYSPARK_ARCHIVES_PATH env. within this PR, we just should export PYSPARK_ARCHIVES_PATH=/user/spark/pyspark.zip,/user/spark/python/lib/py4j-0.8.2.1-src.zip in conf/spark-env.sh when we don't install PySpark on each node of Yarn. i run python application successfully on yarn-client and yarn-cluster with this PR.
andrewor14 sryza Sephiroth-Lin Can you take a look at this?thanks.
Author: Lianhui Wang <lianhuiwang09@gmail.com>
Closes#5580 from lianhuiwang/SPARK-6869 and squashes the following commits:
66ffa43 [Lianhui Wang] Update Client.scala
c2ad0f9 [Lianhui Wang] Update Client.scala
1c8f664 [Lianhui Wang] Merge remote-tracking branch 'remotes/apache/master' into SPARK-6869
008850a [Lianhui Wang] Merge remote-tracking branch 'remotes/apache/master' into SPARK-6869
f0b4ed8 [Lianhui Wang] Merge remote-tracking branch 'remotes/apache/master' into SPARK-6869
150907b [Lianhui Wang] Merge remote-tracking branch 'remotes/apache/master' into SPARK-6869
20402cd [Lianhui Wang] use ZipEntry
9d87c3f [Lianhui Wang] update scala style
e7bd971 [Lianhui Wang] address vanzin's comments
4b8a3ed [Lianhui Wang] use pyArchivesEnvOpt
e6b573b [Lianhui Wang] address vanzin's comments
f11f84a [Lianhui Wang] zip pyspark archives
5192cca [Lianhui Wang] update import path
3b1e4c8 [Lianhui Wang] address tgravescs's comments
9396346 [Lianhui Wang] put zip to make-distribution.sh
0d2baf7 [Lianhui Wang] update import paths
e0179be [Lianhui Wang] add zip pyspark archives in build or sparksubmit
31e8e06 [Lianhui Wang] update code style
9f31dac [Lianhui Wang] update code and add comments
f72987c [Lianhui Wang] add archives path to PYTHONPATH
This PR switches Spark SQL's Hive support to use the isolated hive client interface introduced by #5851, instead of directly interacting with the client. By using this isolated client we can now allow users to dynamically configure the version of Hive that they are connecting to by setting `spark.sql.hive.metastore.version` without the need recompile. This also greatly reduces the surface area for our interaction with the hive libraries, hopefully making it easier to support other versions in the future.
Jars for the desired hive version can be configured using `spark.sql.hive.metastore.jars`, which accepts the following options:
- a colon-separated list of jar files or directories for hive and hadoop.
- `builtin` - attempt to discover the jars that were used to load Spark SQL and use those. This
option is only valid when using the execution version of Hive.
- `maven` - download the correct version of hive on demand from maven.
By default, `builtin` is used for Hive 13.
This PR also removes the test step for building against Hive 12, as this will no longer be required to talk to Hive 12 metastores. However, the full removal of the Shim is deferred until a later PR.
Remaining TODOs:
- Remove the Hive Shims and inline code for Hive 13.
- Several HiveCompatibility tests are not yet passing.
- `nullformatCTAS` - As detailed below, we now are handling CTAS parsing ourselves instead of hacking into the Hive semantic analyzer. However, we currently only handle the common cases and not things like CTAS where the null format is specified.
- `combine1` now leaks state about compression somehow, breaking all subsequent tests. As such we currently add it to the blacklist
- `part_inherit_tbl_props` and `part_inherit_tbl_props_with_star` do not work anymore. We are correctly propagating the information
- "load_dyn_part14.*" - These tests pass when run on their own, but fail when run with all other tests. It seems our `RESET` mechanism may not be as robust as it used to be?
Other required changes:
- `CreateTableAsSelect` no longer carries parts of the HiveQL AST with it through the query execution pipeline. Instead, we parse CTAS during the HiveQL conversion and construct a `HiveTable`. The full parsing here is not yet complete as detailed above in the remaining TODOs. Since the operator is Hive specific, it is moved to the hive package.
- `Command` is simplified to be a trait that simply acts as a marker for a LogicalPlan that should be eagerly evaluated.
Author: Michael Armbrust <michael@databricks.com>
Closes#5876 from marmbrus/useIsolatedClient and squashes the following commits:
258d000 [Michael Armbrust] really really correct path handling
e56fd4a [Michael Armbrust] getAbsolutePath
5a259f5 [Michael Armbrust] fix typos
81bb366 [Michael Armbrust] comments from vanzin
5f3945e [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
4b5cd41 [Michael Armbrust] yin's comments
f5de7de [Michael Armbrust] cleanup
11e9c72 [Michael Armbrust] better coverage in versions suite
7e8f010 [Michael Armbrust] better error messages and jar handling
e7b3941 [Michael Armbrust] more permisive checking for function registration
da91ba7 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
5fe5894 [Michael Armbrust] fix serialization suite
81711c4 [Michael Armbrust] Initial support for running without maven
1d8ae44 [Michael Armbrust] fix final tests?
1c50813 [Michael Armbrust] more comments
a3bee70 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
a6f5df1 [Michael Armbrust] style
ab07f7e [Michael Armbrust] WIP
4d8bf02 [Michael Armbrust] Remove hive 12 compilation
8843a25 [Michael Armbrust] [SPARK-6908] [SQL] Use isolated Hive client
Now that 1.3 has been released, we should enable MiMa checks for the `sql` subproject.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#5727 from JoshRosen/enable-more-mima-checks and squashes the following commits:
3ad302b [Josh Rosen] Merge remote-tracking branch 'origin/master' into enable-more-mima-checks
0c48e4d [Josh Rosen] Merge remote-tracking branch 'origin/master' into enable-more-mima-checks
e276cee [Josh Rosen] Fix SQL MiMa checks via excludes and private[sql]
44d0d01 [Josh Rosen] Add back 'launcher' exclude
1aae027 [Josh Rosen] Enable MiMa checks for launcher and sql projects.
This patch suppresses compiler warnings due to our use of `sun.misc.Unsafe` (introduced in #5725). These warnings can only be suppressed via the `-XDignore.symbol.file` javac flag; the `SuppressWarnings` annotation won't work for these.
In order to restrict uses of this compiler flag to the `unsafe` module, I placed a facade in front of `Unsafe` so that other modules won't call it directly. This facade also will also help us to avoid accidental usage of deprecated Unsafe methods or methods that aren't supported in Java 6.
I also removed an unnecessary use of `Unsafe.setMemory`, which isn't present in certain versions of Java 6, and excluded the new `unsafe` module from Javadoc.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#5814 from JoshRosen/unsafe-compiler-warnings-fixes and squashes the following commits:
9e8c483 [Josh Rosen] Exclude new unsafe module from Javadoc
ba75ecf [Josh Rosen] Only apply -XDignore.symbol.file flag in unsafe project.
7403345 [Josh Rosen] Put facade in front of Unsafe.
50230c0 [Josh Rosen] Remove usage of Unsafe.setMemory
96d41c9 [Josh Rosen] Use -XDignore.symbol.file to suppress warnings about sun.misc.Unsafe usage
Added ml.recommendation, ml.regression to SparkBuild
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#5758 from jkbradley/SPARK-7207 and squashes the following commits:
a28158a [Joseph K. Bradley] Added ml.recommendation, ml.regression to SparkBuild
This patch adds managed-memory-based aggregation to Spark SQL / DataFrames. Instead of working with Java objects, this new aggregation path uses `sun.misc.Unsafe` to manipulate raw memory. This reduces the memory footprint for aggregations, resulting in fewer spills, OutOfMemoryErrors, and garbage collection pauses. As a result, this allows for higher memory utilization. It can also result in better cache locality since objects will be stored closer together in memory.
This feature can be eanbled by setting `spark.sql.unsafe.enabled=true`. For now, this feature is only supported when codegen is enabled and only supports aggregations for which the grouping columns are primitive numeric types or strings and aggregated values are numeric.
### Managing memory with sun.misc.Unsafe
This patch supports both on- and off-heap managed memory.
- In on-heap mode, memory addresses are identified by the combination of a base Object and an offset within that object.
- In off-heap mode, memory is addressed directly with 64-bit long addresses.
To support both modes, functions that manipulate memory accept both `baseObject` and `baseOffset` fields. In off-heap mode, we simply pass `null` as `baseObject`.
We allocate memory in large chunks, so memory fragmentation and allocation speed are not significant bottlenecks.
By default, we use on-heap mode. To enable off-heap mode, set `spark.unsafe.offHeap=true`.
To track allocated memory, this patch extends `SparkEnv` with an `ExecutorMemoryManager` and supplies each `TaskContext` with a `TaskMemoryManager`. These classes work together to track allocations and detect memory leaks.
### Compact tuple format
This patch introduces `UnsafeRow`, a compact row layout. In this format, each tuple has three parts: a null bit set, fixed length values, and variable-length values:
- Rows are always 8-byte word aligned (so their sizes will always be a multiple of 8 bytes)
- The bit set is used for null tracking:
- Position _i_ is set if and only if field _i_ is null
- The bit set is aligned to an 8-byte word boundary.
- Every field appears as an 8-byte word in the fixed-length values part:
- If a field is null, we zero out the values.
- If a field is variable-length, the word stores a relative offset (w.r.t. the base of the tuple) that points to the beginning of the field's data in the variable-length part.
- Each variable-length data type can have its own encoding:
- For strings, the first word stores the length of the string and is followed by UTF-8 encoded bytes. If necessary, the end of the string is padded with empty bytes in order to ensure word-alignment.
For example, a tuple that consists 3 fields of type (int, string, string), with value (null, “data”, “bricks”) would look like this:
This format allows us to compare tuples for equality by directly comparing their raw bytes. This also enables fast hashing of tuples.
### Hash map for performing aggregations
This patch introduces `UnsafeFixedWidthAggregationMap`, a hash map for performing aggregations where the aggregation result columns are fixed-with. This map's keys and values are `Row` objects. `UnsafeFixedWidthAggregationMap` is implemented on top of `BytesToBytesMap`, an append-only map which supports byte-array keys and values.
`BytesToBytesMap` stores pointers to key and value tuples. For each record with a new key, we copy the key and create the aggregation value buffer for that key and put them in a buffer. The hash table then simply stores pointers to the key and value. For each record with an existing key, we simply run the aggregation function to update the values in place.
This map is implemented using open hashing with triangular sequence probing. Each entry stores two words in a long array: the first word stores the address of the key and the second word stores the relative offset from the key tuple to the value tuple, as well as the key's 32-bit hashcode. By storing the full hashcode, we reduce the number of equality checks that need to be performed to handle position collisions ()since the chance of hashcode collision is much lower than position collision).
`UnsafeFixedWidthAggregationMap` allows regular Spark SQL `Row` objects to be used when probing the map. Internally, it encodes these rows into `UnsafeRow` format using `UnsafeRowConverter`. This conversion has a small overhead that can be eliminated in the future once we use UnsafeRows in other operators.
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Author: Josh Rosen <joshrosen@databricks.com>
Closes#5725 from JoshRosen/unsafe and squashes the following commits:
eeee512 [Josh Rosen] Add converters for Null, Boolean, Byte, and Short columns.
81f34f8 [Josh Rosen] Follow 'place children last' convention for GeneratedAggregate
1bc36cc [Josh Rosen] Refactor UnsafeRowConverter to avoid unnecessary boxing.
017b2dc [Josh Rosen] Remove BytesToBytesMap.finalize()
50e9671 [Josh Rosen] Throw memory leak warning even in case of error; add warning about code duplication
70a39e4 [Josh Rosen] Split MemoryManager into ExecutorMemoryManager and TaskMemoryManager:
6e4b192 [Josh Rosen] Remove an unused method from ByteArrayMethods.
de5e001 [Josh Rosen] Fix debug vs. trace in logging message.
a19e066 [Josh Rosen] Rename unsafe Java test suites to match Scala test naming convention.
78a5b84 [Josh Rosen] Add logging to MemoryManager
ce3c565 [Josh Rosen] More comments, formatting, and code cleanup.
529e571 [Josh Rosen] Measure timeSpentResizing in nanoseconds instead of milliseconds.
3ca84b2 [Josh Rosen] Only zero the used portion of groupingKeyConversionScratchSpace
162caf7 [Josh Rosen] Fix test compilation
b45f070 [Josh Rosen] Don't redundantly store the offset from key to value, since we can compute this from the key size.
a8e4a3f [Josh Rosen] Introduce MemoryManager interface; add to SparkEnv.
0925847 [Josh Rosen] Disable MiMa checks for new unsafe module
cde4132 [Josh Rosen] Add missing pom.xml
9c19fc0 [Josh Rosen] Add configuration options for heap vs. offheap
6ffdaa1 [Josh Rosen] Null handling improvements in UnsafeRow.
31eaabc [Josh Rosen] Lots of TODO and doc cleanup.
a95291e [Josh Rosen] Cleanups to string handling code
afe8dca [Josh Rosen] Some Javadoc cleanup
f3dcbfe [Josh Rosen] More mod replacement
854201a [Josh Rosen] Import and comment cleanup
06e929d [Josh Rosen] More warning cleanup
ef6b3d3 [Josh Rosen] Fix a bunch of FindBugs and IntelliJ inspections
29a7575 [Josh Rosen] Remove debug logging
49aed30 [Josh Rosen] More long -> int conversion.
b26f1d3 [Josh Rosen] Fix bug in murmur hash implementation.
765243d [Josh Rosen] Enable optional performance metrics for hash map.
23a440a [Josh Rosen] Bump up default hash map size
628f936 [Josh Rosen] Use ints intead of longs for indexing.
92d5a06 [Josh Rosen] Address a number of minor code review comments.
1f4b716 [Josh Rosen] Merge Unsafe code into the regular GeneratedAggregate, guarded by a configuration flag; integrate planner support and re-enable all tests.
d85eeff [Josh Rosen] Add basic sanity test for UnsafeFixedWidthAggregationMap
bade966 [Josh Rosen] Comment update (bumping to refresh GitHub cache...)
b3eaccd [Josh Rosen] Extract aggregation map into its own class.
d2bb986 [Josh Rosen] Update to implement new Row methods added upstream
58ac393 [Josh Rosen] Use UNSAFE allocator in GeneratedAggregate (TODO: make this configurable)
7df6008 [Josh Rosen] Optimizations related to zeroing out memory:
c1b3813 [Josh Rosen] Fix bug in UnsafeMemoryAllocator.free():
738fa33 [Josh Rosen] Add feature flag to guard UnsafeGeneratedAggregate
c55bf66 [Josh Rosen] Free buffer once iterator has been fully consumed.
62ab054 [Josh Rosen] Optimize for fact that get() is only called on String columns.
c7f0b56 [Josh Rosen] Reuse UnsafeRow pointer in UnsafeRowConverter
ae39694 [Josh Rosen] Add finalizer as "cleanup method of last resort"
c754ae1 [Josh Rosen] Now that the store*() contract has been stregthened, we can remove an extra lookup
f764d13 [Josh Rosen] Simplify address + length calculation in Location.
079f1bf [Josh Rosen] Some clarification of the BytesToBytesMap.lookup() / set() contract.
1a483c5 [Josh Rosen] First version that passes some aggregation tests:
fc4c3a8 [Josh Rosen] Sketch how the converters will be used in UnsafeGeneratedAggregate
53ba9b7 [Josh Rosen] Start prototyping Java Row -> UnsafeRow converters
1ff814d [Josh Rosen] Add reminder to free memory on iterator completion
8a8f9df [Josh Rosen] Add skeleton for GeneratedAggregate integration.
5d55cef [Josh Rosen] Add skeleton for Row implementation.
f03e9c1 [Josh Rosen] Play around with Unsafe implementations of more string methods.
ab68e08 [Josh Rosen] Begin merging the UTF8String implementations.
480a74a [Josh Rosen] Initial import of code from Databricks unsafe utils repo.
Add `compressed` to `Vector` with some other methods: `numActives`, `numNonzeros`, `toSparse`, and `toDense`. jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#5756 from mengxr/SPARK-6756 and squashes the following commits:
8d4ecbd [Xiangrui Meng] address comment and add mima excludes
da54179 [Xiangrui Meng] add toSparse, toDense, numActives, numNonzeros, and compressed to Vector
jira: https://issues.apache.org/jira/browse/SPARK-7090
LDA was implemented with extensibility in mind. And with the development of OnlineLDA and Gibbs Sampling, we are collecting more detailed requirements from different algorithms.
As Joseph Bradley jkbradley proposed in https://github.com/apache/spark/pull/4807 and with some further discussion, we'd like to adjust the code structure a little to present the common interface and extension point clearly.
Basically class LDA would be a common entrance for LDA computing. And each LDA object will refer to a LDAOptimizer for the concrete algorithm implementation. Users can customize LDAOptimizer with specific parameters and assign it to LDA.
Concrete changes:
1. Add a trait `LDAOptimizer`, which defines the common iterface for concrete implementations. Each subClass is a wrapper for a specific LDA algorithm.
2. Move EMOptimizer to file LDAOptimizer and inherits from LDAOptimizer, rename to EMLDAOptimizer. (in case a more generic EMOptimizer comes in the future)
-adjust the constructor of EMOptimizer, since all the parameters should be passed in through initialState method. This can avoid unwanted confusion or overwrite.
-move the code from LDA.initalState to initalState of EMLDAOptimizer
3. Add property ldaOptimizer to LDA and its getter/setter, and EMLDAOptimizer is the default Optimizer.
4. Change the return type of LDA.run from DistributedLDAModel to LDAModel.
Further work:
add OnlineLDAOptimizer and other possible Optimizers once ready.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#5661 from hhbyyh/ldaRefactor and squashes the following commits:
0e2e006 [Yuhao Yang] respond to review comments
08a45da [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaRefactor
e756ce4 [Yuhao Yang] solve mima exception
d74fd8f [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaRefactor
0bb8400 [Yuhao Yang] refactor LDA with Optimizer
ec2f857 [Yuhao Yang] protoptype for discussion
