## What changes were proposed in this pull request?
Add links to API docs for ML algos
## How was this patch tested?
Manual checking for the API links
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15890 from zhengruifeng/algo_link.
## What changes were proposed in this pull request?
Fix typos in the 'configuration', 'monitoring' and 'sql-programming-guide' documentation.
## How was this patch tested?
Manually.
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes#15886 from weiqingy/fixTypo.
## What changes were proposed in this pull request?
1,Remove `runs` from docs of mllib.KMeans
2,Add notes for `k` according to comments in sources
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15873 from zhengruifeng/update_doc_mllib_kmeans.
## What changes were proposed in this pull request?
Adds support for CNI-isolated containers
## How was this patch tested?
I launched SparkPi both with and without `spark.mesos.network.name`, and verified the job completed successfully.
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#15740 from mgummelt/spark-342-cni.
## What changes were proposed in this pull request?
1, Add link of `VertexRDD` and `EdgeRDD`
2, Notify in `Vertex and Edge RDDs` that not all methods are listed
3, `VertexID` -> `VertexId`
## How was this patch tested?
No tests, only docs is modified
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15875 from zhengruifeng/update_graphop_doc.
## What changes were proposed in this pull request?
Update the python section of the Structured Streaming Guide from .builder() to .builder
## How was this patch tested?
Validated documentation and successfully running the test example.
Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.
'Builder' object is not callable object hence changed .builder() to
.builder
Author: Denny Lee <dennylee@gallifrey.local>
Closes#15872 from dennyglee/master.
## What changes were proposed in this pull request?
Many applications take Spark as a computing engine and run on it. This PR adds a configuration property `spark.log.callerContext` that can be used by Spark's upstream applications (e.g. Oozie) to set up their caller contexts into Spark. In the end, Spark will combine its own caller context with the caller contexts of its upstream applications, and write them into Yarn RM log and HDFS audit log.
The audit log has a config to truncate the caller contexts passed in (default 128). The caller contexts will be sent over rpc, so it should be concise. The call context written into HDFS log and Yarn log consists of two parts: the information `A` specified by Spark itself and the value `B` of `spark.log.callerContext` property. Currently `A` typically takes 64 to 74 characters, so `B` can have up to 50 characters (mentioned in the doc `running-on-yarn.md`)
## How was this patch tested?
Manual tests. I have run some Spark applications with `spark.log.callerContext` configuration in Yarn client/cluster mode, and verified that the caller contexts were written into Yarn RM log and HDFS audit log correctly.
The ways to configure `spark.log.callerContext` property:
- In spark-defaults.conf:
```
spark.log.callerContext infoSpecifiedByUpstreamApp
```
- In app's source code:
```
val spark = SparkSession
.builder
.appName("SparkKMeans")
.config("spark.log.callerContext", "infoSpecifiedByUpstreamApp")
.getOrCreate()
```
When running on Spark Yarn cluster mode, the driver is unable to pass 'spark.log.callerContext' to Yarn client and AM since Yarn client and AM have already started before the driver performs `.config("spark.log.callerContext", "infoSpecifiedByUpstreamApp")`.
The following example shows the command line used to submit a SparkKMeans application and the corresponding records in Yarn RM log and HDFS audit log.
Command:
```
./bin/spark-submit --verbose --executor-cores 3 --num-executors 1 --master yarn --deploy-mode client --class org.apache.spark.examples.SparkKMeans examples/target/original-spark-examples_2.11-2.1.0-SNAPSHOT.jar hdfs://localhost:9000/lr_big.txt 2 5
```
Yarn RM log:
<img width="1440" alt="screen shot 2016-10-19 at 9 12 03 pm" src="https://cloud.githubusercontent.com/assets/8546874/19547050/7d2f278c-9649-11e6-9df8-8d5ff12609f0.png">
HDFS audit log:
<img width="1400" alt="screen shot 2016-10-19 at 10 18 14 pm" src="https://cloud.githubusercontent.com/assets/8546874/19547102/096060ae-964a-11e6-981a-cb28efd5a058.png">
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes#15563 from weiqingy/SPARK-16759.
## What changes were proposed in this pull request?
DIGEST-MD5 mechanism is used for SASL authentication and secure communication. DIGEST-MD5 mechanism supports 3DES, DES, and RC4 ciphers. However, 3DES, DES and RC4 are slow relatively.
AES provide better performance and security by design and is a replacement for 3DES according to NIST. Apache Common Crypto is a cryptographic library optimized with AES-NI, this patch employ Apache Common Crypto as enc/dec backend for SASL authentication and secure channel to improve spark RPC.
## How was this patch tested?
Unit tests and Integration test.
Author: Junjie Chen <junjie.j.chen@intel.com>
Closes#15172 from cjjnjust/shuffle_rpc_encrypt.
## What changes were proposed in this pull request?
1, `**Example**` => `**Examples**`, because more algos use `**Examples**`.
2, delete `### Examples` in `Isotonic regression`, because it's not that special in http://spark.apache.org/docs/latest/ml-classification-regression.html
3, add missing marks for `LDA` and other algos.
## How was this patch tested?
No tests for it only modify doc
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15783 from zhengruifeng/doc_fix.
## What changes were proposed in this pull request?
This Pull request comprises of the critical bug SPARK-16575 changes. This change rectifies the issue with BinaryFileRDD partition calculations as upon creating an RDD with sc.binaryFiles, the resulting RDD always just consisted of two partitions only.
## How was this patch tested?
The original issue ie. getNumPartitions on binary Files RDD (always having two partitions) was first replicated and then tested upon the changes. Also the unit tests have been checked and passed.
This contribution is my original work and I licence the work to the project under the project's open source license
srowen hvanhovell rxin vanzin skyluc kmader zsxwing datafarmer Please have a look .
Author: fidato <fidato.july13@gmail.com>
Closes#15327 from fidato13/SPARK-16575.
## What changes were proposed in this pull request?
Document that Java 7, Python 2.6, Scala 2.10, Hadoop < 2.6 are deprecated in Spark 2.1.0. This does not actually implement any of the change in SPARK-18138, just peppers the documentation with notices about it.
## How was this patch tested?
Doc build
Author: Sean Owen <sowen@cloudera.com>
Closes#15733 from srowen/SPARK-18138.
## What changes were proposed in this pull request?
This patch uses `{% highlight lang %}...{% endhighlight %}` to highlight code snippets in the `Structured Streaming Kafka010 integration doc` and the `Spark Streaming Kafka010 integration doc`.
This patch consists of two commits:
- the first commit fixes only the leading spaces -- this is large
- the second commit adds the highlight instructions -- this is much simpler and easier to review
## How was this patch tested?
SKIP_API=1 jekyll build
## Screenshots
**Before**
**After**
Author: Liwei Lin <lwlin7@gmail.com>
Closes#15715 from lw-lin/doc-highlight-code-snippet.
## What changes were proposed in this pull request?
- Renamed kbest to numTopFeatures
- Renamed alpha to fpr
- Added missing Since annotations
- Doc cleanups
## How was this patch tested?
Added new standardized unit tests for spark.ml.
Improved existing unit test coverage a bit.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#15647 from jkbradley/chisqselector-follow-ups.
In SPARK-4761 / #3621 (December 2014) we enabled Kryo serialization by default in the Spark Thrift Server. However, I don't think that the original rationale for doing this still holds now that most Spark SQL serialization is now performed via encoders and our UnsafeRow format.
In addition, the use of Kryo as the default serializer can introduce performance problems because the creation of new KryoSerializer instances is expensive and we haven't performed instance-reuse optimizations in several code paths (including DirectTaskResult deserialization).
Given all of this, I propose to revert back to using JavaSerializer as the default serializer in the Thrift Server.
/cc liancheng
Author: Josh Rosen <joshrosen@databricks.com>
Closes#14906 from JoshRosen/disable-kryo-in-thriftserver.
Mesos 0.23.0 introduces a Fetch Cache feature http://mesos.apache.org/documentation/latest/fetcher/ which allows caching of resources specified in command URIs.
This patch:
- Updates the Mesos shaded protobuf dependency to 0.23.0
- Allows setting `spark.mesos.fetcherCache.enable` to enable the fetch cache for all specified URIs. (URIs must be specified for the setting to have any affect)
- Updates documentation for Mesos configuration with the new setting.
This patch does NOT:
- Allow for per-URI caching configuration. The cache setting is global to ALL URIs for the command.
Author: Charles Allen <charles@allen-net.com>
Closes#13713 from drcrallen/SPARK15994.
## What changes were proposed in this pull request?
This PR merges multiple lines enumerating items in order to remove the redundant spaces following slashes in [Structured Streaming Programming Guide in 2.0.2-rc1](http://people.apache.org/~pwendell/spark-releases/spark-2.0.2-rc1-docs/structured-streaming-programming-guide.html).
- Before: `Scala/ Java/ Python`
- After: `Scala/Java/Python`
## How was this patch tested?
Manual by the followings because this is documentation update.
```
cd docs
SKIP_API=1 jekyll build
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15686 from dongjoon-hyun/minor_doc_space.
## What changes were proposed in this pull request?
This patch makes RBackend connection timeout configurable by user.
## How was this patch tested?
N/A
Author: Hossein <hossein@databricks.com>
Closes#15471 from falaki/SPARK-17919.
## What changes were proposed in this pull request?
This PR is an enhancement of PR with commit ID:57dc326bd00cf0a49da971e9c573c48ae28acaa2.
NaN is a special type of value which is commonly seen as invalid. But We find that there are certain cases where NaN are also valuable, thus need special handling. We provided user when dealing NaN values with 3 options, to either reserve an extra bucket for NaN values, or remove the NaN values, or report an error, by setting handleNaN "keep", "skip", or "error"(default) respectively.
'''Before:
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
'''After:
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
.setHandleNaN("keep")
## How was this patch tested?
Tests added in QuantileDiscretizerSuite, BucketizerSuite and DataFrameStatSuite
Signed-off-by: VinceShieh <vincent.xieintel.com>
Author: VinceShieh <vincent.xie@intel.com>
Author: Vincent Xie <vincent.xie@intel.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#15428 from VinceShieh/spark-17219_followup.
## What changes were proposed in this pull request?
maxOffsetsPerTrigger option for rate limiting, proportionally based on volume of different topicpartitions.
## How was this patch tested?
Added unit test
Author: cody koeninger <cody@koeninger.org>
Closes#15527 from koeninger/SPARK-17813.
## What changes were proposed in this pull request?
API and programming guide doc changes for Scala, Python and R.
## How was this patch tested?
manual test
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15629 from felixcheung/jsondoc.
## What changes were proposed in this pull request?
Currently users can kill stages via the web ui but not jobs directly (jobs are killed if one of their stages is). I've added the ability to kill jobs via the web ui. This code change is based on #4823 by lianhuiwang and updated to work with the latest code matching how stages are currently killed. In general I've copied the kill stage code warning and note comments and all. I also updated applicable tests and documentation.
## How was this patch tested?
Manually tested and dev/run-tests
Author: Alex Bozarth <ajbozart@us.ibm.com>
Author: Lianhui Wang <lianhuiwang09@gmail.com>
Closes#15441 from ajbozarth/spark4411.
## What changes were proposed in this pull request?
Always resolve spark.sql.warehouse.dir as a local path, and as relative to working dir not home dir
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15382 from srowen/SPARK-17810.
## What changes were proposed in this pull request?
Document `user:password` syntax as possible means of specifying credentials for password-protected `--repositories`
## How was this patch tested?
Doc build
Author: Sean Owen <sowen@cloudera.com>
Closes#15584 from srowen/SPARK-17898.
## What changes were proposed in this pull request?
Minor doc change to mention kafka configuration for larger spark batches.
## How was this patch tested?
Doc change only, confirmed via jekyll.
The configuration issue was discussed / confirmed with users on the mailing list.
Author: cody koeninger <cody@koeninger.org>
Closes#15570 from koeninger/kafka-doc-heartbeat.
## What changes were proposed in this pull request?
startingOffsets takes specific per-topicpartition offsets as a json argument, usable with any consumer strategy
assign with specific topicpartitions as a consumer strategy
## How was this patch tested?
Unit tests
Author: cody koeninger <cody@koeninger.org>
Closes#15504 from koeninger/SPARK-17812.
## What changes were proposed in this pull request?
Add crossJoin and do not default to cross join if joinExpr is left out
## How was this patch tested?
unit test
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15559 from felixcheung/rcrossjoin.
## What changes were proposed in this pull request?
Update docs to not suggest to package Spark before running tests.
## How was this patch tested?
Not creating a JIRA since this pretty small. We haven't had the need to run mvn package before mvn test since 1.6 at least, or so I am told. So, updating the docs to not be misguiding.
Author: Mark Grover <mark@apache.org>
Closes#15572 from markgrover/doc_update.
## What changes were proposed in this pull request?
`SerializationUtils.clone()` of commons-lang3 (<3.5) has a bug that breaks thread safety, which gets stack sometimes caused by race condition of initializing hash map.
See https://issues.apache.org/jira/browse/LANG-1251.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#15548 from ueshin/issues/SPARK-17985.
## What changes were proposed in this pull request?
In http://spark.apache.org/docs/latest/sql-programming-guide.html, Section "Untyped Dataset Operations (aka DataFrame Operations)"
Link to R DataFrame doesn't work that return
The requested URL /docs/latest/api/R/DataFrame.html was not found on this server.
Correct link is SparkDataFrame.html for spark 2.0
## How was this patch tested?
Manual checked.
Author: Tommy YU <tummyyu@163.com>
Closes#15543 from Wenpei/spark-18001.
This reverts commit bfe7885aee.
The commit caused build failures on Hadoop 2.2 profile:
```
[error] /scratch/rxin/spark/core/src/main/scala/org/apache/spark/util/Utils.scala:1489: value read is not a member of object org.apache.commons.io.IOUtils
[error] var numBytes = IOUtils.read(gzInputStream, buf)
[error] ^
[error] /scratch/rxin/spark/core/src/main/scala/org/apache/spark/util/Utils.scala:1492: value read is not a member of object org.apache.commons.io.IOUtils
[error] numBytes = IOUtils.read(gzInputStream, buf)
[error] ^
```
## What changes were proposed in this pull request?
Add more built-in sources in sql-programming-guide.md.
## How was this patch tested?
Manually.
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes#15522 from weiqingy/dsDoc.
## What changes were proposed in this pull request?
`SerializationUtils.clone()` of commons-lang3 (<3.5) has a bug that breaks thread safety, which gets stack sometimes caused by race condition of initializing hash map.
See https://issues.apache.org/jira/browse/LANG-1251.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#15525 from ueshin/issues/SPARK-17985.
## What changes were proposed in this pull request?
This PR adds support for executor log compression.
## How was this patch tested?
Unit tests
cc: yhuai tdas mengxr
Author: Yu Peng <loneknightpy@gmail.com>
Closes#15285 from loneknightpy/compress-executor-log.
This reverts commit ed14633414.
The patch merged had obvious quality and documentation issue. The idea is useful, and we should work towards improving its quality and merging it in again.
## What changes were proposed in this pull request?
Restructure the code and implement two new task assigner.
PackedAssigner: try to allocate tasks to the executors with least available cores, so that spark can release reserved executors when dynamic allocation is enabled.
BalancedAssigner: try to allocate tasks to the executors with more available cores in order to balance the workload across all executors.
By default, the original round robin assigner is used.
We test a pipeline, and new PackedAssigner save around 45% regarding the reserved cpu and memory with dynamic allocation enabled.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Both unit test in TaskSchedulerImplSuite and manual tests in production pipeline.
Author: Zhan Zhang <zhanzhang@fb.com>
Closes#15218 from zhzhan/packed-scheduler.
## What changes were proposed in this pull request?
This is a step along the way to SPARK-8425.
To enable incremental review, the first step proposed here is to expand the blacklisting within tasksets. In particular, this will enable blacklisting for
* (task, executor) pairs (this already exists via an undocumented config)
* (task, node)
* (taskset, executor)
* (taskset, node)
Adding (task, node) is critical to making spark fault-tolerant of one-bad disk in a cluster, without requiring careful tuning of "spark.task.maxFailures". The other additions are also important to avoid many misleading task failures and long scheduling delays when there is one bad node on a large cluster.
Note that some of the code changes here aren't really required for just this -- they put pieces in place for SPARK-8425 even though they are not used yet (eg. the `BlacklistTracker` helper is a little out of place, `TaskSetBlacklist` holds onto a little more info than it needs to for just this change, and `ExecutorFailuresInTaskSet` is more complex than it needs to be).
## How was this patch tested?
Added unit tests, run tests via jenkins.
Author: Imran Rashid <irashid@cloudera.com>
Author: mwws <wei.mao@intel.com>
Closes#15249 from squito/taskset_blacklist_only.
## What changes were proposed in this pull request?
Documentation fix to make it clear that reusing group id for different streams is super duper bad, just like it is with the underlying Kafka consumer.
## How was this patch tested?
I built jekyll doc and made sure it looked ok.
Author: cody koeninger <cody@koeninger.org>
Closes#15442 from koeninger/SPARK-17853.
## What changes were proposed in this pull request?
In `programming-guide.md`, the url which links to `AccumulatorV2` says `api/scala/index.html#org.apache.spark.AccumulatorV2` but `api/scala/index.html#org.apache.spark.util.AccumulatorV2` is correct.
## How was this patch tested?
manual test.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#15439 from sarutak/SPARK-17880.
Couple of mvn build examples use `-Dhadoop.version=VERSION` instead of actual version number
Author: Alexander Pivovarov <apivovarov@gmail.com>
Closes#15440 from apivovarov/patch-1.
## What changes were proposed in this pull request?
This PR proposes to fix arbitrary usages among `Map[String, String]`, `Properties` and `JDBCOptions` instances for options in `execution/jdbc` package and make the connection properties exclude Spark-only options.
This PR includes some changes as below:
- Unify `Map[String, String]`, `Properties` and `JDBCOptions` in `execution/jdbc` package to `JDBCOptions`.
- Move `batchsize`, `fetchszie`, `driver` and `isolationlevel` options into `JDBCOptions` instance.
- Document `batchSize` and `isolationlevel` with marking both read-only options and write-only options. Also, this includes minor types and detailed explanation for some statements such as url.
- Throw exceptions fast by checking arguments first rather than in execution time (e.g. for `fetchsize`).
- Exclude Spark-only options in connection properties.
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15292 from HyukjinKwon/SPARK-17719.
## What changes were proposed in this pull request?
Enable GPU resources to be used when running coarse grain mode with Mesos.
## How was this patch tested?
Manual test with GPU.
Author: Timothy Chen <tnachen@gmail.com>
Closes#14644 from tnachen/gpu_mesos.
## What changes were proposed in this pull request?
Global temporary view is a cross-session temporary view, which means it's shared among all sessions. Its lifetime is the lifetime of the Spark application, i.e. it will be automatically dropped when the application terminates. It's tied to a system preserved database `global_temp`(configurable via SparkConf), and we must use the qualified name to refer a global temp view, e.g. SELECT * FROM global_temp.view1.
changes for `SessionCatalog`:
1. add a new field `gloabalTempViews: GlobalTempViewManager`, to access the shared global temp views, and the global temp db name.
2. `createDatabase` will fail if users wanna create `global_temp`, which is system preserved.
3. `setCurrentDatabase` will fail if users wanna set `global_temp`, which is system preserved.
4. add `createGlobalTempView`, which is used in `CreateViewCommand` to create global temp views.
5. add `dropGlobalTempView`, which is used in `CatalogImpl` to drop global temp view.
6. add `alterTempViewDefinition`, which is used in `AlterViewAsCommand` to update the view definition for local/global temp views.
7. `renameTable`/`dropTable`/`isTemporaryTable`/`lookupRelation`/`getTempViewOrPermanentTableMetadata`/`refreshTable` will handle global temp views.
changes for SQL commands:
1. `CreateViewCommand`/`AlterViewAsCommand` is updated to support global temp views
2. `ShowTablesCommand` outputs a new column `database`, which is used to distinguish global and local temp views.
3. other commands can also handle global temp views if they call `SessionCatalog` APIs which accepts global temp views, e.g. `DropTableCommand`, `AlterTableRenameCommand`, `ShowColumnsCommand`, etc.
changes for other public API
1. add a new method `dropGlobalTempView` in `Catalog`
2. `Catalog.findTable` can find global temp view
3. add a new method `createGlobalTempView` in `Dataset`
## How was this patch tested?
new tests in `SQLViewSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14897 from cloud-fan/global-temp-view.
## What changes were proposed in this pull request?
This expands calls to Jetty's simple `ServerConnector` constructor to explicitly specify a `ScheduledExecutorScheduler` that makes daemon threads. It should otherwise result in exactly the same configuration, because the other args are copied from the constructor that is currently called.
(I'm not sure we should change the Hive Thriftserver impl, but I did anyway.)
This also adds `sc.stop()` to the quick start guide example.
## How was this patch tested?
Existing tests; _pending_ at least manual verification of the fix.
Author: Sean Owen <sowen@cloudera.com>
Closes#15381 from srowen/SPARK-17707.
## What changes were proposed in this pull request?
This PR adds a new project ` external/kafka-0-10-sql` for Structured Streaming Kafka source.
It's based on the design doc: https://docs.google.com/document/d/19t2rWe51x7tq2e5AOfrsM9qb8_m7BRuv9fel9i0PqR8/edit?usp=sharing
tdas did most of work and part of them was inspired by koeninger's work.
### Introduction
The Kafka source is a structured streaming data source to poll data from Kafka. The schema of reading data is as follows:
Column | Type
---- | ----
key | binary
value | binary
topic | string
partition | int
offset | long
timestamp | long
timestampType | int
The source can deal with deleting topics. However, the user should make sure there is no Spark job processing the data when deleting a topic.
### Configuration
The user can use `DataStreamReader.option` to set the following configurations.
Kafka Source's options | value | default | meaning
------ | ------- | ------ | -----
startingOffset | ["earliest", "latest"] | "latest" | The start point when a query is started, either "earliest" which is from the earliest offset, or "latest" which is just from the latest offset. Note: This only applies when a new Streaming query is started, and that resuming will always pick up from where the query left off.
failOnDataLost | [true, false] | true | Whether to fail the query when it's possible that data is lost (e.g., topics are deleted, or offsets are out of range). This may be a false alarm. You can disable it when it doesn't work as you expected.
subscribe | A comma-separated list of topics | (none) | The topic list to subscribe. Only one of "subscribe" and "subscribeParttern" options can be specified for Kafka source.
subscribePattern | Java regex string | (none) | The pattern used to subscribe the topic. Only one of "subscribe" and "subscribeParttern" options can be specified for Kafka source.
kafka.consumer.poll.timeoutMs | long | 512 | The timeout in milliseconds to poll data from Kafka in executors
fetchOffset.numRetries | int | 3 | Number of times to retry before giving up fatch Kafka latest offsets.
fetchOffset.retryIntervalMs | long | 10 | milliseconds to wait before retrying to fetch Kafka offsets
Kafka's own configurations can be set via `DataStreamReader.option` with `kafka.` prefix, e.g, `stream.option("kafka.bootstrap.servers", "host:port")`
### Usage
* Subscribe to 1 topic
```Scala
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host:port")
.option("subscribe", "topic1")
.load()
```
* Subscribe to multiple topics
```Scala
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host:port")
.option("subscribe", "topic1,topic2")
.load()
```
* Subscribe to a pattern
```Scala
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host:port")
.option("subscribePattern", "topic.*")
.load()
```
## How was this patch tested?
The new unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Shixiong Zhu <zsxwing@gmail.com>
Author: cody koeninger <cody@koeninger.org>
Closes#15102 from zsxwing/kafka-source.
## What changes were proposed in this pull request?
Updates user guide to reflect that LogisticRegression now supports multiclass. Also adds new examples to show multiclass training.
## How was this patch tested?
Ran locally using spark-submit, run-example, and copy/paste from user guide into shells. Generated docs and verified correct output.
Author: sethah <seth.hendrickson16@gmail.com>
Closes#15349 from sethah/SPARK-17239.
## What changes were proposed in this pull request?
Move note about labels being +1/-1 in formulation only to be just under the table of formulations.
## How was this patch tested?
Doc build
Author: Sean Owen <sowen@cloudera.com>
Closes#15330 from srowen/SPARK-17718.
## What changes were proposed in this pull request?
To build R docs (which are built when R tests are run), users need to install pandoc and rmarkdown. This was done for Jenkins in ~~[SPARK-17420](https://issues.apache.org/jira/browse/SPARK-17420)~~
… pandoc]
Author: Jagadeesan <as2@us.ibm.com>
Closes#15309 from jagadeesanas2/SPARK-17736.
## What changes were proposed in this pull request?
This PR aims to make the doc up-to-date. The documentation is generally correct, but after https://issues.apache.org/jira/browse/SPARK-13926, Spark starts to choose Kyro as a default serialization library during shuffling of simple types, arrays of simple types, or string type.
## How was this patch tested?
This is a documentation update.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15315 from dongjoon-hyun/SPARK-DOC-SERIALIZER.
## What changes were proposed in this pull request?
`FsHistoryProviderSuite` fails if `root` user runs it. The test case **SPARK-3697: ignore directories that cannot be read** depends on `setReadable(false, false)` to make test data files and expects the number of accessible files is 1. But, `root` can access all files, so it returns 2.
This PR adds the assumption explicitly on doc. `building-spark.md`.
## How was this patch tested?
This is a documentation change.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15291 from dongjoon-hyun/SPARK-17412.
## What changes were proposed in this pull request?
The discussion of the interaction of Accumulators and Broadcast Variables should logically follow the discussion on Checkpointing. As currently written, this section discusses Checkpointing before it is formally introduced. To remedy this:
- Rename this section to "Accumulators, Broadcast Variables, and Checkpoints", and
- Move this section after "Checkpointing".
## How was this patch tested?
Testing: ran
$ SKIP_API=1 jekyll build
, and verified changes in a Web browser pointed at docs/_site/index.html.
Author: José Hiram Soltren <jose@cloudera.com>
Closes#15281 from jsoltren/doc-changes.
## What changes were proposed in this pull request?
This pr is just to fix the document of `spark-kinesis-integration`.
Since `SPARK-17418` prevented all the kinesis stuffs (including kinesis example code)
from publishing, `bin/run-example streaming.KinesisWordCountASL` and `bin/run-example streaming.JavaKinesisWordCountASL` does not work.
Instead, it fetches the kinesis jar from the Spark Package.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#15260 from maropu/DocFixKinesis.
Corrected a link to the configuration.html page, it was pointing to a page that does not exist (configurations.html).
Documentation change, verified in preview.
Author: Andrew Mills <ammills01@users.noreply.github.com>
Closes#15244 from ammills01/master.
## What changes were proposed in this pull request?
When reading file stream with non-globbing path, the results return data with all `null`s for the
partitioned columns. E.g.,
case class A(id: Int, value: Int)
val data = spark.createDataset(Seq(
A(1, 1),
A(2, 2),
A(2, 3))
)
val url = "/tmp/test"
data.write.partitionBy("id").parquet(url)
spark.read.parquet(url).show
+-----+---+
|value| id|
+-----+---+
| 2| 2|
| 3| 2|
| 1| 1|
+-----+---+
val s = spark.readStream.schema(spark.read.load(url).schema).parquet(url)
s.writeStream.queryName("test").format("memory").start()
sql("SELECT * FROM test").show
+-----+----+
|value| id|
+-----+----+
| 2|null|
| 3|null|
| 1|null|
+-----+----+
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#14803 from viirya/filestreamsource-option.
## What changes were proposed in this pull request?
This change modifies the implementation of DataFrameWriter.save such that it works with jdbc, and the call to jdbc merely delegates to save.
## How was this patch tested?
This was tested via unit tests in the JDBCWriteSuite, of which I added one new test to cover this scenario.
## Additional details
rxin This seems to have been most recently touched by you and was also commented on in the JIRA.
This contribution is my original work and I license the work to the project under the project's open source license.
Author: Justin Pihony <justin.pihony@gmail.com>
Author: Justin Pihony <justin.pihony@typesafe.com>
Closes#12601 from JustinPihony/jdbc_reconciliation.
## What changes were proposed in this pull request?
Spark will add sparkr.zip to archive only when it is yarn mode (SparkSubmit.scala).
```
if (args.isR && clusterManager == YARN) {
val sparkRPackagePath = RUtils.localSparkRPackagePath
if (sparkRPackagePath.isEmpty) {
printErrorAndExit("SPARK_HOME does not exist for R application in YARN mode.")
}
val sparkRPackageFile = new File(sparkRPackagePath.get, SPARKR_PACKAGE_ARCHIVE)
if (!sparkRPackageFile.exists()) {
printErrorAndExit(s"$SPARKR_PACKAGE_ARCHIVE does not exist for R application in YARN mode.")
}
val sparkRPackageURI = Utils.resolveURI(sparkRPackageFile.getAbsolutePath).toString
// Distribute the SparkR package.
// Assigns a symbol link name "sparkr" to the shipped package.
args.archives = mergeFileLists(args.archives, sparkRPackageURI + "#sparkr")
// Distribute the R package archive containing all the built R packages.
if (!RUtils.rPackages.isEmpty) {
val rPackageFile =
RPackageUtils.zipRLibraries(new File(RUtils.rPackages.get), R_PACKAGE_ARCHIVE)
if (!rPackageFile.exists()) {
printErrorAndExit("Failed to zip all the built R packages.")
}
val rPackageURI = Utils.resolveURI(rPackageFile.getAbsolutePath).toString
// Assigns a symbol link name "rpkg" to the shipped package.
args.archives = mergeFileLists(args.archives, rPackageURI + "#rpkg")
}
}
```
So it is necessary to pass spark.master from R process to JVM. Otherwise sparkr.zip won't be distributed to executor. Besides that I also pass spark.yarn.keytab/spark.yarn.principal to spark side, because JVM process need them to access secured cluster.
## How was this patch tested?
Verify it manually in R Studio using the following code.
```
Sys.setenv(SPARK_HOME="/Users/jzhang/github/spark")
.libPaths(c(file.path(Sys.getenv(), "R", "lib"), .libPaths()))
library(SparkR)
sparkR.session(master="yarn-client", sparkConfig = list(spark.executor.instances="1"))
df <- as.DataFrame(mtcars)
head(df)
```
…
Author: Jeff Zhang <zjffdu@apache.org>
Closes#14784 from zjffdu/SPARK-17210.
## What changes were proposed in this pull request?
Modified the documentation to clarify that `build/mvn` and `pom.xml` always add Java 7-specific parameters to `MAVEN_OPTS`, and that developers can safely ignore warnings about `-XX:MaxPermSize` that may result from compiling or running tests with Java 8.
## How was this patch tested?
Rebuilt HTML documentation, made sure that building-spark.html displays correctly in a browser.
Author: frreiss <frreiss@us.ibm.com>
Closes#15005 from frreiss/fred-17421a.
The goal of this feature is to allow the Spark driver to run in an
isolated environment, such as a docker container, and be able to use
the host's port forwarding mechanism to be able to accept connections
from the outside world.
The change is restricted to the driver: there is no support for achieving
the same thing on executors (or the YARN AM for that matter). Those still
need full access to the outside world so that, for example, connections
can be made to an executor's block manager.
The core of the change is simple: add a new configuration that tells what's
the address the driver should bind to, which can be different than the address
it advertises to executors (spark.driver.host). Everything else is plumbing
the new configuration where it's needed.
To use the feature, the host starting the container needs to set up the
driver's port range to fall into a range that is being forwarded; this
required the block manager port to need a special configuration just for
the driver, which falls back to the existing spark.blockManager.port when
not set. This way, users can modify the driver settings without affecting
the executors; it would theoretically be nice to also have different
retry counts for driver and executors, but given that docker (at least)
allows forwarding port ranges, we can probably live without that for now.
Because of the nature of the feature it's kinda hard to add unit tests;
I just added a simple one to make sure the configuration works.
This was tested with a docker image running spark-shell with the following
command:
docker blah blah blah \
-p 38000-38100:38000-38100 \
[image] \
spark-shell \
--num-executors 3 \
--conf spark.shuffle.service.enabled=false \
--conf spark.dynamicAllocation.enabled=false \
--conf spark.driver.host=[host's address] \
--conf spark.driver.port=38000 \
--conf spark.driver.blockManager.port=38020 \
--conf spark.ui.port=38040
Running on YARN; verified the driver works, executors start up and listen
on ephemeral ports (instead of using the driver's config), and that caching
and shuffling (without the shuffle service) works. Clicked through the UI
to make sure all pages (including executor thread dumps) worked. Also tested
apps without docker, and ran unit tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#15120 from vanzin/SPARK-4563.
## What changes were proposed in this pull request?
This PR fixes an issue when Bucketizer is called to handle a dataset containing NaN value.
Sometimes, null value might also be useful to users, so in these cases, Bucketizer should
reserve one extra bucket for NaN values, instead of throwing an illegal exception.
Before:
```
Bucketizer.transform on NaN value threw an illegal exception.
```
After:
```
NaN values will be grouped in an extra bucket.
```
## How was this patch tested?
New test cases added in `BucketizerSuite`.
Signed-off-by: VinceShieh <vincent.xieintel.com>
Author: VinceShieh <vincent.xie@intel.com>
Closes#14858 from VinceShieh/spark-17219.
## What changes were proposed in this pull request?
Point references to spark-packages.org to https://cwiki.apache.org/confluence/display/SPARK/Third+Party+Projects
This will be accompanied by a parallel change to the spark-website repo, and additional changes to this wiki.
## How was this patch tested?
Jenkins tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15075 from srowen/SPARK-17445.
## What changes were proposed in this pull request?
The relation between spark.network.timeout and spark.executor.heartbeatInterval should be mentioned in the document.
… network timeout]
Author: Jagadeesan <as2@us.ibm.com>
Closes#15042 from jagadeesanas2/SPARK-17449.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
Streaming doc correction.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Satendra Kumar <satendra@knoldus.com>
Closes#14996 from satendrakumar06/patch-1.
## What changes were proposed in this pull request?
This pull request adds the functionality to enable accessing worker and application UI through master UI itself. Thus helps in accessing SparkUI when running spark cluster in closed networks e.g. Kubernetes. Cluster admin needs to expose only spark master UI and rest of the UIs can be in the private network, master UI will reverse proxy the connection request to corresponding resource. It adds the path for workers/application UIs as
WorkerUI: <http/https>://master-publicIP:<port>/target/workerID/
ApplicationUI: <http/https>://master-publicIP:<port>/target/appID/
This makes it easy for users to easily protect the Spark master cluster access by putting some reverse proxy e.g. https://github.com/bitly/oauth2_proxy
## How was this patch tested?
The functionality has been tested manually and there is a unit test too for testing access to worker UI with reverse proxy address.
pwendell bomeng BryanCutler can you please review it, thanks.
Author: Gurvinder Singh <gurvinder.singh@uninett.no>
Closes#13950 from gurvindersingh/rproxy.
After change [SPARK-16405](https://github.com/apache/spark/pull/14080), we need to update docs by adding shuffle service metrics entry in currently supporting metrics list.
Author: Yangyang Liu <yangyangliu@fb.com>
Closes#14254 from lovexi/yangyang-monitoring-doc.
## What changes were proposed in this pull request?
Avoid allocating some 0-length arrays, esp. in UTF8String, and by using Array.empty in Scala over Array[T]()
## How was this patch tested?
Jenkins
Author: Sean Owen <sowen@cloudera.com>
Closes#14895 from srowen/SPARK-17331.
## What changes were proposed in this pull request?
Allow user to set sparkr shell command through --conf spark.r.shell.command
## How was this patch tested?
Unit test is added and also verify it manually through
```
bin/sparkr --master yarn-client --conf spark.r.shell.command=/usr/local/bin/R
```
Author: Jeff Zhang <zjffdu@apache.org>
Closes#14744 from zjffdu/SPARK-17178.
## What changes were proposed in this pull request?
With the new History Server the summary page loads the application list via the the REST API, this makes it very slow to impossible to load with large (10K+) application history. This pr fixes this by adding the `spark.history.ui.maxApplications` conf to limit the number of applications the History Server displays. This is accomplished using a new optional `limit` param for the `applications` api. (Note this only applies to what the summary page displays, all the Application UI's are still accessible if the user knows the App ID and goes to the Application UI directly.)
I've also added a new test for the `limit` param in `HistoryServerSuite.scala`
## How was this patch tested?
Manual testing and dev/run-tests
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#14835 from ajbozarth/spark17243.
This patch is using Apache Commons Crypto library to enable shuffle encryption support.
Author: Ferdinand Xu <cheng.a.xu@intel.com>
Author: kellyzly <kellyzly@126.com>
Closes#8880 from winningsix/SPARK-10771.
## What changes were proposed in this pull request?
Fix minor typos python example code in streaming programming guide
## How was this patch tested?
N/A
Author: Dmitriy Sokolov <silentsokolov@gmail.com>
Closes#14805 from silentsokolov/fix-typos.
## What changes were proposed in this pull request?
Allow centering / mean scaling of sparse vectors in StandardScaler, if requested. This is for compatibility with `VectorAssembler` in common usages.
## How was this patch tested?
Jenkins tests, including new caes to reflect the new behavior.
Author: Sean Owen <sowen@cloudera.com>
Closes#14663 from srowen/SPARK-17001.
## What changes were proposed in this pull request?
Move Mesos code into a mvn module
## How was this patch tested?
unit tests
manually submitting a client mode and cluster mode job
spark/mesos integration test suite
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#14637 from mgummelt/mesos-module.
## What changes were proposed in this pull request?
Updated links of external dstream projects.
## How was this patch tested?
Just document changes.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#14814 from zsxwing/dstream-link.
## What changes were proposed in this pull request?
Based on #12990 by tankkyo
Since the History Server currently loads all application's data it can OOM if too many applications have a significant task count. `spark.ui.trimTasks` (default: false) can be set to true to trim tasks by `spark.ui.retainedTasks` (default: 10000)
(This is a "quick fix" to help those running into the problem until a update of how the history server loads app data can be done)
## How was this patch tested?
Manual testing and dev/run-tests
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#14673 from ajbozarth/spark15083.
## What changes were proposed in this pull request?
Collect GC discussion in one section, and documenting findings about G1 GC heap region size.
## How was this patch tested?
Jekyll doc build
Author: Sean Owen <sowen@cloudera.com>
Closes#14732 from srowen/SPARK-16320.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
This is the document for previous JDBC Writer options.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Unit test has been added in previous PR.
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: GraceH <jhuang1@paypal.com>
Closes#14683 from GraceH/jdbc_options.
## What changes were proposed in this pull request?
`spark.ssl.enabled`=true, but failing to set `spark.ssl.protocol` will fail and throw meaningless exception. `spark.ssl.protocol` is required when `spark.ssl.enabled`.
Improvement: require `spark.ssl.protocol` when initializing SSLContext, otherwise throws an exception to indicate that.
Remove the OrElse("default").
Document this requirement in configure.md
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Manual tests:
Build document and check document
Configure `spark.ssl.enabled` only, it throws exception below:
6/08/16 16:04:37 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(mwang); groups with view permissions: Set(); users with modify permissions: Set(mwang); groups with modify permissions: Set()
Exception in thread "main" java.lang.IllegalArgumentException: requirement failed: spark.ssl.protocol is required when enabling SSL connections.
at scala.Predef$.require(Predef.scala:224)
at org.apache.spark.SecurityManager.<init>(SecurityManager.scala:285)
at org.apache.spark.deploy.master.Master$.startRpcEnvAndEndpoint(Master.scala:1026)
at org.apache.spark.deploy.master.Master$.main(Master.scala:1011)
at org.apache.spark.deploy.master.Master.main(Master.scala)
Configure `spark.ssl.protocol` and `spark.ssl.protocol`
It works fine.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#14674 from wangmiao1981/ssl.
## What changes were proposed in this pull request?
- adds documentation for https://issues.apache.org/jira/browse/SPARK-11714
## How was this patch tested?
Doc no test needed.
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#14667 from skonto/add_doc.
## What changes were proposed in this pull request?
Remove the api doc link for mapReduceTriplets operator because in latest api they are remove so when user link to that api they will not get mapReduceTriplets there so its more good to remove than confuse the user.
## How was this patch tested?
Run all the test cases
Author: sandy <phalodi@gmail.com>
Closes#14669 from phalodi/SPARK-17089.
## What changes were proposed in this pull request?
As README.md file is updated over time. Some code snippet outputs are not correct based on new README.md file. For example:
```
scala> textFile.count()
res0: Long = 126
```
should be
```
scala> textFile.count()
res0: Long = 99
```
This pr is to add comments to point out this problem so that new spark learners have a correct reference.
Also, fixed a samll bug, inside current documentation, the outputs of linesWithSpark.count() without and with cache are different (one is 15 and the other is 19)
```
scala> val linesWithSpark = textFile.filter(line => line.contains("Spark"))
linesWithSpark: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[2] at filter at <console>:27
scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"?
res3: Long = 15
...
scala> linesWithSpark.cache()
res7: linesWithSpark.type = MapPartitionsRDD[2] at filter at <console>:27
scala> linesWithSpark.count()
res8: Long = 19
```
## How was this patch tested?
manual test: run `$ SKIP_API=1 jekyll serve --watch`
Author: linbojin <linbojin203@gmail.com>
Closes#14645 from linbojin/quick-start-documentation.
## What changes were proposed in this pull request?
When documentation is built is should reference examples from the same build. There are times when the docs have links that point to files in the GitHub head which may not be valid on the current release. Changed that in URLs to make them point to the right tag in git using ```SPARK_VERSION_SHORT```
…from its own release version] [Streaming programming guide]
Author: Jagadeesan <as2@us.ibm.com>
Closes#14596 from jagadeesanas2/SPARK-12370.
## What changes were proposed in this pull request?
The configuration doc lost the config option `spark.ui.enabled` (default value is `true`)
I think this option is important because many cases we would like to turn it off.
so I add it.
## How was this patch tested?
N/A
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#14604 from WeichenXu123/add_doc_param_spark_ui_enabled.
Before this PR, user have to export environment variable to specify the python of driver & executor which is not so convenient for users. This PR is trying to allow user to specify python through configuration "--pyspark-driver-python" & "--pyspark-executor-python"
Manually test in local & yarn mode for pyspark-shell and pyspark batch mode.
Author: Jeff Zhang <zjffdu@apache.org>
Closes#13146 from zjffdu/SPARK-13081.
## What changes were proposed in this pull request?
Originally this PR was based on #14491 but I realised that fixing examples are more sensible rather than comments.
This PR fixes three things below:
- Fix two wrong examples in `structured-streaming-programming-guide.md`. Loading via `read.load(..)` without `as` will be `Dataset<Row>` not `Dataset<String>` in Java.
- Fix indentation across `structured-streaming-programming-guide.md`. Python has 4 spaces and Scala and Java have double spaces. These are inconsistent across the examples.
- Fix `StructuredNetworkWordCountWindowed` and `StructuredNetworkWordCount` in Java and Scala to initially load `DataFrame` and `Dataset<Row>` to be consistent with the comments and some examples in `structured-streaming-programming-guide.md` and to match Scala and Java to Python one (Python one loads it as `DataFrame` initially).
## How was this patch tested?
N/A
Closes https://github.com/apache/spark/pull/14491
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Ganesh Chand <ganeshchand@Ganeshs-MacBook-Pro-2.local>
Closes#14564 from HyukjinKwon/SPARK-16886.
Docs adjustment to:
- link to other relevant section of docs
- correct statement about the only value when actually other values are supported
Author: Andrew Ash <andrew@andrewash.com>
Closes#14581 from ash211/patch-10.
## What changes were proposed in this pull request?
change the remain percent to right one.
## How was this patch tested?
Manual review
Author: Tao Wang <wangtao111@huawei.com>
Closes#14591 from WangTaoTheTonic/patch-1.
## What changes were proposed in this pull request?
Add a configurable token manager for Spark on running on yarn.
### Current Problems ###
1. Supported token provider is hard-coded, currently only hdfs, hbase and hive are supported and it is impossible for user to add new token provider without code changes.
2. Also this problem exits in timely token renewer and updater.
### Changes In This Proposal ###
In this proposal, to address the problems mentioned above and make the current code more cleaner and easier to understand, mainly has 3 changes:
1. Abstract a `ServiceTokenProvider` as well as `ServiceTokenRenewable` interface for token provider. Each service wants to communicate with Spark through token way needs to implement this interface.
2. Provide a `ConfigurableTokenManager` to manage all the register token providers, also token renewer and updater. Also this class offers the API for other modules to obtain tokens, get renewal interval and so on.
3. Implement 3 built-in token providers `HDFSTokenProvider`, `HiveTokenProvider` and `HBaseTokenProvider` to keep the same semantics as supported today. Whether to load in these built-in token providers is controlled by configuration "spark.yarn.security.tokens.${service}.enabled", by default for all the built-in token providers are loaded.
### Behavior Changes ###
For the end user there's no behavior change, we still use the same configuration `spark.yarn.security.tokens.${service}.enabled` to decide which token provider is enabled (hbase or hive).
For user implemented token provider (assume the name of token provider is "test") needs to add into this class should have two configurations:
1. `spark.yarn.security.tokens.test.enabled` to true
2. `spark.yarn.security.tokens.test.class` to the full qualified class name.
So we still keep the same semantics as current code while add one new configuration.
### Current Status ###
- [x] token provider interface and management framework.
- [x] implement built-in token providers (hdfs, hbase, hive).
- [x] Coverage of unit test.
- [x] Integrated test with security cluster.
## How was this patch tested?
Unit test and integrated test.
Please suggest and review, any comment is greatly appreciated.
Author: jerryshao <sshao@hortonworks.com>
Closes#14065 from jerryshao/SPARK-16342.
