Fix the style violation (space before , and :).
This PR is a followup for #10643.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#10685 from sarutak/SPARK-12692-followup-streaming.
Replace Guava `Optional` with (an API clone of) Java 8 `java.util.Optional` (edit: and a clone of Guava `Optional`)
See also https://github.com/apache/spark/pull/10512
Author: Sean Owen <sowen@cloudera.com>
Closes#10513 from srowen/SPARK-4819.
The default serializer in Kryo is FieldSerializer and it ignores transient fields and never calls `writeObject` or `readObject`. So we should register OpenHashMapBasedStateMap using `DefaultSerializer` to make it work with Kryo.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10609 from zsxwing/SPARK-12591.
This PR includes the following changes:
1. Rename `ActorReceiver` to `ActorReceiverSupervisor`
2. Remove `ActorHelper`
3. Add a new `ActorReceiver` for Scala and `JavaActorReceiver` for Java
4. Add `JavaActorWordCount` example
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10457 from zsxwing/java-actor-stream.
Whole code of Vector.scala, VectorSuite.scala and GraphKryoRegistrator.scala are no longer used so it's time to remove them in Spark 2.0.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#10613 from sarutak/SPARK-12665.
Cartesian product use UnsafeExternalSorter without comparator to do spilling, it will NPE if spilling happens.
This bug also hitted by #10605
cc JoshRosen
Author: Davies Liu <davies@databricks.com>
Closes#10606 from davies/fix_spilling.
I looked at each case individually and it looks like they can all be removed. The only one that I had to think twice was toArray (I even thought about un-deprecating it, until I realized it was a problem in Java to have toArray returning java.util.List).
Author: Reynold Xin <rxin@databricks.com>
Closes#10569 from rxin/SPARK-12615.
### Remove AkkaRpcEnv
Keep `SparkEnv.actorSystem` because Streaming still uses it. Will remove it and AkkaUtils after refactoring Streaming actorStream API.
### Remove systemName
There are 2 places using `systemName`:
* `RpcEnvConfig.name`. Actually, although it's used as `systemName` in `AkkaRpcEnv`, `NettyRpcEnv` uses it as the service name to output the log `Successfully started service *** on port ***`. Since the service name in log is useful, I keep `RpcEnvConfig.name`.
* `def setupEndpointRef(systemName: String, address: RpcAddress, endpointName: String)`. Each `ActorSystem` has a `systemName`. Akka requires `systemName` in its URI and will refuse a connection if `systemName` is not matched. However, `NettyRpcEnv` doesn't use it. So we can remove `systemName` from `setupEndpointRef` since we are removing `AkkaRpcEnv`.
### Remove RpcEnv.uriOf
`uriOf` exists because Akka uses different URI formats for with and without authentication, e.g., `akka.ssl.tcp...` and `akka.tcp://...`. But `NettyRpcEnv` uses the same format. So it's not necessary after removing `AkkaRpcEnv`.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10459 from zsxwing/remove-akka-rpc-env.
We switched to TorrentBroadcast in Spark 1.1, and HttpBroadcast has been undocumented since then. It's time to remove it in Spark 2.0.
Author: Reynold Xin <rxin@databricks.com>
Closes#10531 from rxin/SPARK-12588.
Add `computePrincipalComponentsAndVariance` to also compute PCA's explained variance.
CC mengxr
Author: Sean Owen <sowen@cloudera.com>
Closes#9736 from srowen/SPARK-11530.
The json endpoint for stages doesn't include information on the stage duration that is present in the UI. This looks like a simple oversight, they should be included. eg., the metrics should be included at api/v1/applications/<appId>/stages.
Metrics I've added are: submissionTime, firstTaskLaunchedTime and completionTime
Author: Xin Ren <iamshrek@126.com>
Closes#10107 from keypointt/SPARK-11155.
I have tried to address all the comments in pull request https://github.com/apache/spark/pull/2447.
Note that the second commit (using the new method in all internal code of all components) is quite intrusive and could be omitted.
Author: Jeroen Schot <jeroen.schot@surfsara.nl>
Closes#9767 from schot/master.
In the previous implementation, the driver needs to know the executor listening address to send the thread dump request. However, in Netty RPC, the executor doesn't listen to any port, so the executor thread dump feature is broken.
This patch makes the driver use the endpointRef stored in BlockManagerMasterEndpoint to send the thread dump request to fix it.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#9976 from zsxwing/executor-thread-dump.
Currently streaming foreachRDD Java API uses a function prototype requiring a return value of null. This PR deprecates the old method and uses VoidFunction to allow for more concise declaration. Also added VoidFunction2 to Java API in order to use in Streaming methods. Unit test is added for using foreachRDD with VoidFunction, and changes have been tested with Java 7 and Java 8 using lambdas.
Author: Bryan Cutler <bjcutler@us.ibm.com>
Closes#9488 from BryanCutler/foreachRDD-VoidFunction-SPARK-4557.
This adds an extra filter for private or protected classes. We only filter for package private right now.
Author: Timothy Hunter <timhunter@databricks.com>
Closes#9697 from thunterdb/spark-11732.
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.
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.
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.
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.
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
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 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
