## What changes were proposed in this pull request?
This pr adds some test cases for statistics: case sensitive column names, non ascii column names, refresh table, and also improves some documentation.
## How was this patch tested?
add test cases
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#15360 from wzhfy/colStats2.
## What changes were proposed in this pull request?
Spark-submit support jar url with http protocol. However, if the url contains any query strings, `worker.DriverRunner.downloadUserJar()` method will throw "Did not see expected jar" exception. This is because this method checks the existance of a downloaded jar whose name contains query strings. This is a problem when your jar is located on some web service which requires some additional information to retrieve the file.
This pr just removes query strings before checking jar existance on worker.
## How was this patch tested?
For now, you can only test this patch by manual test.
* Deploy a spark cluster locally
* Make sure apache httpd service is on
* Save an uber jar, e.g spark-job.jar under `/var/www/html/`
* Use http://localhost/spark-job.jar?param=1 as jar url when running `spark-submit`
* Job should be launched
Author: invkrh <invkrh@gmail.com>
Closes#15420 from invkrh/spark-17855.
## What changes were proposed in this pull request?
For feature selection method ChiSquareSelector, it is based on the ChiSquareTestResult.statistic (ChiSqure value) to select the features. It select the features with the largest ChiSqure value. But the Degree of Freedom (df) of ChiSqure value is different in Statistics.chiSqTest(RDD), and for different df, you cannot base on ChiSqure value to select features.
So we change statistic to pValue for SelectKBest and SelectPercentile
## How was this patch tested?
change existing test
Author: Peng <peng.meng@intel.com>
Closes#15444 from mpjlu/chisqure-bug.
## What changes were proposed in this pull request?
Add BisectingKMeansSummary
## How was this patch tested?
unit test
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#12394 from zhengruifeng/biKMSummary.
## What changes were proposed in this pull request?
Since ```ml.evaluation``` has supported save/load at Scala side, supporting it at Python side is very straightforward and easy.
## How was this patch tested?
Add python doctest.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#13194 from yanboliang/spark-15402.
## What changes were proposed in this pull request?
`HiveExternalCatalog` should be the only interface to talk to the hive metastore. In `MetastoreRelation` we can just use `ExternalCatalog` instead of `HiveClient` to interact with hive metastore, and add missing API in `ExternalCatalog`.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15460 from cloud-fan/relation.
## What changes were proposed in this pull request?
This patch does a few changes to the file structure of data sources:
- Break fileSourceInterfaces.scala into multiple pieces (HadoopFsRelation, FileFormat, OutputWriter)
- Move ParquetOutputWriter into its own file
I created this as a separate patch so it'd be easier to review my future PRs that focus on refactoring this internal logic. This patch only moves code around, and has no logic changes.
## How was this patch tested?
N/A - should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#15473 from rxin/SPARK-17925.
## What changes were proposed in this pull request?
speculationEnabled and DATASOURCE_OUTPUTPATH seem like just dead code.
## How was this patch tested?
Tests should fail if they are not dead code.
Author: Reynold Xin <rxin@databricks.com>
Closes#15477 from rxin/SPARK-17927.
## What changes were proposed in this pull request?
Follow-up work of #13675, add Python API for ```RFormula forceIndexLabel```.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15430 from yanboliang/spark-15957-python.
## What changes were proposed in this pull request?
Value classes were unsupported because catalyst data types were
obtained through reflection on erased types, which would resolve to a
value class' wrapped type and hence lead to unavailable methods during
code generation.
E.g. the following class
```scala
case class Foo(x: Int) extends AnyVal
```
would be seen as an `int` in catalyst and will cause instance cast failures when generated java code tries to treat it as a `Foo`.
This patch simply removes the erasure step when getting data types for
catalyst.
## How was this patch tested?
Additional tests in `ExpressionEncoderSuite`.
Author: Jakob Odersky <jakob@odersky.com>
Closes#15284 from jodersky/value-classes.
## What changes were proposed in this pull request?
There are 4 listLeafFiles-related functions in Spark:
- ListingFileCatalog.listLeafFiles (which calls HadoopFsRelation.listLeafFilesInParallel if the number of paths passed in is greater than a threshold; if it is lower, then it has its own serial version implemented)
- HadoopFsRelation.listLeafFiles (called only by HadoopFsRelation.listLeafFilesInParallel)
- HadoopFsRelation.listLeafFilesInParallel (called only by ListingFileCatalog.listLeafFiles)
It is actually very confusing and error prone because there are effectively two distinct implementations for the serial version of listing leaf files. As an example, SPARK-17599 updated only one of the code path and ignored the other one.
This code can be improved by:
- Move all file listing code into ListingFileCatalog, since it is the only class that needs this.
- Keep only one function for listing files in serial.
## How was this patch tested?
This change should be covered by existing unit and integration tests. I also moved a test case for HadoopFsRelation.shouldFilterOut from HadoopFsRelationSuite to ListingFileCatalogSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#15235 from petermaxlee/SPARK-17661.
## What changes were proposed in this pull request?
Metrics are needed for monitoring structured streaming apps. Here is the design doc for implementing the necessary metrics.
https://docs.google.com/document/d/1NIdcGuR1B3WIe8t7VxLrt58TJB4DtipWEbj5I_mzJys/edit?usp=sharing
Specifically, this PR adds the following public APIs changes.
### New APIs
- `StreamingQuery.status` returns a `StreamingQueryStatus` object (renamed from `StreamingQueryInfo`, see later)
- `StreamingQueryStatus` has the following important fields
- inputRate - Current rate (rows/sec) at which data is being generated by all the sources
- processingRate - Current rate (rows/sec) at which the query is processing data from
all the sources
- ~~outputRate~~ - *Does not work with wholestage codegen*
- latency - Current average latency between the data being available in source and the sink writing the corresponding output
- sourceStatuses: Array[SourceStatus] - Current statuses of the sources
- sinkStatus: SinkStatus - Current status of the sink
- triggerStatus - Low-level detailed status of the last completed/currently active trigger
- latencies - getOffset, getBatch, full trigger, wal writes
- timestamps - trigger start, finish, after getOffset, after getBatch
- numRows - input, output, state total/updated rows for aggregations
- `SourceStatus` has the following important fields
- inputRate - Current rate (rows/sec) at which data is being generated by the source
- processingRate - Current rate (rows/sec) at which the query is processing data from the source
- triggerStatus - Low-level detailed status of the last completed/currently active trigger
- Python API for `StreamingQuery.status()`
### Breaking changes to existing APIs
**Existing direct public facing APIs**
- Deprecated direct public-facing APIs `StreamingQuery.sourceStatuses` and `StreamingQuery.sinkStatus` in favour of `StreamingQuery.status.sourceStatuses/sinkStatus`.
- Branch 2.0 should have it deprecated, master should have it removed.
**Existing advanced listener APIs**
- `StreamingQueryInfo` renamed to `StreamingQueryStatus` for consistency with `SourceStatus`, `SinkStatus`
- Earlier StreamingQueryInfo was used only in the advanced listener API, but now it is used in direct public-facing API (StreamingQuery.status)
- Field `queryInfo` in listener events `QueryStarted`, `QueryProgress`, `QueryTerminated` changed have name `queryStatus` and return type `StreamingQueryStatus`.
- Field `offsetDesc` in `SourceStatus` was Option[String], converted it to `String`.
- For `SourceStatus` and `SinkStatus` made constructor private instead of private[sql] to make them more java-safe. Instead added `private[sql] object SourceStatus/SinkStatus.apply()` which are harder to accidentally use in Java.
## How was this patch tested?
Old and new unit tests.
- Rate calculation and other internal logic of StreamMetrics tested by StreamMetricsSuite.
- New info in statuses returned through StreamingQueryListener is tested in StreamingQueryListenerSuite.
- New and old info returned through StreamingQuery.status is tested in StreamingQuerySuite.
- Source-specific tests for making sure input rows are counted are is source-specific test suites.
- Additional tests to test minor additions in LocalTableScanExec, StateStore, etc.
Metrics also manually tested using Ganglia sink
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15307 from tdas/SPARK-17731.
## What changes were proposed in this pull request?
Because `KafkaConsumer.poll(0)` may update the partition offsets, this PR just calls `seekToBeginning` to manually set the earliest offsets for the KafkaSource initial offsets.
## How was this patch tested?
Existing tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15397 from zsxwing/SPARK-17834.
## What changes were proposed in this pull request?
correct the expected type from Length function to be Int
## How was this patch tested?
Test runs on little endian and big endian platforms
Author: Pete Robbins <robbinspg@gmail.com>
Closes#15464 from robbinspg/SPARK-17827.
## What changes were proposed in this pull request?
This patch annotates all the remaining APIs in SQL (excluding streaming) with InterfaceStability.
## How was this patch tested?
N/A - just annotation change.
Author: Reynold Xin <rxin@databricks.com>
Closes#15457 from rxin/SPARK-17830-2.
### What changes were proposed in this pull request?
Hive allows users to change the table type from `Managed` to `External` or from `External` to `Managed` by altering table's property `EXTERNAL`. See the JIRA: https://issues.apache.org/jira/browse/HIVE-1329
So far, Spark SQL does not correctly support it, although users can do it. Many assumptions are broken in the implementation. Thus, this PR is to disallow users to change it.
In addition, we also do not allow users to set the property `EXTERNAL` when creating a table.
### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15230 from gatorsmile/alterTableSetExternal.
## What changes were proposed in this pull request?
In our universal gateway service we need to specify different jars to Spark according to scala version. For now only after launching Spark application can we know which version of Scala it depends on. It makes hard for us to support different Scala + Spark versions to pick the right jars.
So here propose to print out Scala version according to Spark version in "spark-submit --version", so that user could leverage this output to make the choice without needing to launching application.
## How was this patch tested?
Manually verified in local environment.
Author: jerryshao <sshao@hortonworks.com>
Closes#15456 from jerryshao/SPARK-17686.
## What changes were proposed in this pull request?
Currently `HiveExternalCatalog` will filter out the Spark SQL internal table properties, e.g. `spark.sql.sources.provider`, `spark.sql.sources.schema`, etc. This is reasonable for external users as they don't want to see these internal properties in `DESC TABLE`.
However, as a Spark developer, sometimes we do wanna see the raw table properties. This PR adds a new internal SQL conf, `spark.sql.debug`, to enable debug mode and keep these raw table properties.
This config can also be used in similar places where we wanna retain debug information in the future.
## How was this patch tested?
new test in MetastoreDataSourcesSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15458 from cloud-fan/debug.
## What changes were proposed in this pull request?
This is a reworked PR based on feedback in #9238 after it was closed and not reopened. As suggested in that PR I've only added the download feature. This functionality already exists in the api and this allows easier access to download event logs to share with others.
I've attached a screenshot of the committed version, but I will also include alternate options with screen shots in the comments below. I'm personally not sure which option is best.
## How was this patch tested?
Manual testing
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#15400 from ajbozarth/spark11272.
## What changes were proposed in this pull request?
minor doc fix for "getAnyValAs" in class Row
## How was this patch tested?
None.
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: buzhihuojie <ren.weiluo@gmail.com>
Closes#15452 from david-weiluo-ren/minorDocFixForRow.
## What changes were proposed in this pull request?
Two issues regarding Dataset.dropduplicates:
1. Dataset.dropDuplicates should consider the columns with same column name
We find and get the first resolved attribute from output with the given column name in `Dataset.dropDuplicates`. When we have the more than one columns with the same name. Other columns are put into aggregation columns, instead of grouping columns.
2. Dataset.dropDuplicates should not change the output of child plan
We create new `Alias` with new exprId in `Dataset.dropDuplicates` now. However it causes problem when we want to select the columns as follows:
val ds = Seq(("a", 1), ("a", 2), ("b", 1), ("a", 1)).toDS()
// ds("_2") will cause analysis exception
ds.dropDuplicates("_1").select(ds("_1").as[String], ds("_2").as[Int])
Because the two issues are both related to `Dataset.dropduplicates` and the code changes are not big, so submitting them together as one PR.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#15427 from viirya/fix-dropduplicates.
## What changes were proposed in this pull request?
The CompactibleFileStreamLog materializes the whole metadata log in memory as a String. This can cause issues when there are lots of files that are being committed, especially during a compaction batch.
You may come across stacktraces that look like:
```
java.lang.OutOfMemoryError: Requested array size exceeds VM limit
at java.lang.StringCoding.encode(StringCoding.java:350)
at java.lang.String.getBytes(String.java:941)
at org.apache.spark.sql.execution.streaming.FileStreamSinkLog.serialize(FileStreamSinkLog.scala:127)
```
The safer way is to write to an output stream so that we don't have to materialize a huge string.
## How was this patch tested?
Existing unit tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15437 from brkyvz/ser-to-stream.
## What changes were proposed in this pull request?
[SPARK-14077](https://issues.apache.org/jira/browse/SPARK-14077) copied the ```NaiveBayes``` implementation from mllib to ml and left mllib as a wrapper. However, there are some difference between mllib and ml to handle labels:
* mllib allow input labels as {-1, +1}, however, ml assumes the input labels in range [0, numClasses).
* mllib ```NaiveBayesModel``` expose ```labels``` but ml did not due to the assumption mention above.
During the copy in [SPARK-14077](https://issues.apache.org/jira/browse/SPARK-14077), we use
```val labels = data.map(_.label).distinct().collect().sorted```
to get the distinct labels firstly, and then encode the labels for training. It involves extra Spark job compared with the original implementation. Since ```NaiveBayes``` only do one pass aggregation during training, adding another one seems less efficient. We can get the labels in a single pass along with ```NaiveBayes``` training and send them to MLlib side.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15402 from yanboliang/spark-17835.
## What changes were proposed in this pull request?
update python api for NaiveBayes: add weight col parameter.
## How was this patch tested?
doctests added.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#15406 from WeichenXu123/nb_python_update.
## What changes were proposed in this pull request?
This patch improves the window function frame boundary API to make it more obvious to read and to use. The two high level changes are:
1. Create Window.currentRow, Window.unboundedPreceding, Window.unboundedFollowing to indicate the special values in frame boundaries. These methods map to the special integral values so we are not breaking backward compatibility here. This change makes the frame boundaries more self-evident (instead of Long.MinValue, it becomes Window.unboundedPreceding).
2. In Python, for any value less than or equal to JVM's Long.MinValue, treat it as Window.unboundedPreceding. For any value larger than or equal to JVM's Long.MaxValue, treat it as Window.unboundedFollowing. Before this change, if the user specifies any value that is less than Long.MinValue but not -sys.maxsize (e.g. -sys.maxsize + 1), the number we pass over to the JVM would overflow, resulting in a frame that does not make sense.
Code example required to specify a frame before this patch:
```
Window.rowsBetween(-Long.MinValue, 0)
```
While the above code should still work, the new way is more obvious to read:
```
Window.rowsBetween(Window.unboundedPreceding, Window.currentRow)
```
## How was this patch tested?
- Updated DataFrameWindowSuite (for Scala/Java)
- Updated test_window_functions_cumulative_sum (for Python)
- Renamed DataFrameWindowSuite DataFrameWindowFunctionsSuite to better reflect its purpose
Author: Reynold Xin <rxin@databricks.com>
Closes#15438 from rxin/SPARK-17845.
## What changes were proposed in this pull request?
Alternative approach to https://github.com/apache/spark/pull/15387
Author: cody koeninger <cody@koeninger.org>
Closes#15401 from koeninger/SPARK-17782-alt.
## 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?
Add a flag to ignore corrupt files. For Spark core, the configuration is `spark.files.ignoreCorruptFiles`. For Spark SQL, it's `spark.sql.files.ignoreCorruptFiles`.
## How was this patch tested?
The added unit tests
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15422 from zsxwing/SPARK-17850.
## What changes were proposed in this pull request?
Link to contributing wiki in PR template, README.md
## How was this patch tested?
Doc-only change, tested by Jekyll
Author: Sean Owen <sowen@cloudera.com>
Closes#15429 from srowen/SPARK-17840.
## What changes were proposed in this pull request?
If the R data structure that is being parallelized is larger than `INT_MAX` we use files to transfer data to JVM. The serialization protocol mimics Python pickling. This allows us to simply call `PythonRDD.readRDDFromFile` to create the RDD.
I tested this on my MacBook. Following code works with this patch:
```R
intMax <- .Machine$integer.max
largeVec <- 1:intMax
rdd <- SparkR:::parallelize(sc, largeVec, 2)
```
## How was this patch tested?
* [x] Unit tests
Author: Hossein <hossein@databricks.com>
Closes#15375 from falaki/SPARK-17790.
## What changes were proposed in this pull request?
This change adds a check in castToInterval method of Cast expression , such that if converted value is null , then isNull variable should be set to true.
Earlier, the expression Cast(Literal(), CalendarIntervalType) was throwing NullPointerException because of the above mentioned reason.
## How was this patch tested?
Added test case in CastSuite.scala
jira entry for detail: https://issues.apache.org/jira/browse/SPARK-17884
Author: prigarg <prigarg@adobe.com>
Closes#15449 from priyankagargnitk/SPARK-17884.
## What changes were proposed in this pull request?
In PySpark, the invalid join type will not throw error for the following join:
```df1.join(df2, how='not-a-valid-join-type')```
The signature of the join is:
```def join(self, other, on=None, how=None):```
The existing code completely ignores the `how` parameter when `on` is `None`. This patch will process the arguments passed to join and pass in to JVM Spark SQL Analyzer, which will validate the join type passed.
## How was this patch tested?
Used manual and existing test suites.
Author: Bijay Pathak <bkpathak@mtu.edu>
Closes#15409 from bkpathak/SPARK-14761.
## What changes were proposed in this pull request?
This is a revival of https://github.com/apache/spark/pull/14948 and related to https://github.com/apache/spark/pull/14937. This removes the 'runs' parameter, which has already been disabled, from the K-means implementation and further deprecates API methods that involve it.
This also happens to resolve the issue that K-means should not return duplicate centers, meaning that it may return less than k centroids if not enough data is available.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#15342 from srowen/SPARK-11560.
## 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?
SQLConf is session-scoped and mutable. However, we do have the requirement for a static SQL conf, which is global and immutable, e.g. the `schemaStringThreshold` in `HiveExternalCatalog`, the flag to enable/disable hive support, the global temp view database in https://github.com/apache/spark/pull/14897.
Actually we've already implemented static SQL conf implicitly via `SparkConf`, this PR just make it explicit and expose it to users, so that they can see the config value via SQL command or `SparkSession.conf`, and forbid users to set/unset static SQL conf.
## How was this patch tested?
new tests in SQLConfSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15295 from cloud-fan/global-conf.
## What changes were proposed in this pull request?
The root cause that we would ignore SparkConf when launching JVM is that SparkConf require JVM to be created first. https://github.com/apache/spark/blob/master/python/pyspark/conf.py#L106
In this PR, I would defer the launching of JVM until SparkContext is created so that we can pass SparkConf to JVM correctly.
## How was this patch tested?
Use the example code in the description of SPARK-17387,
```
$ SPARK_HOME=$PWD PYTHONPATH=python:python/lib/py4j-0.10.3-src.zip python
Python 2.7.12 (default, Jul 1 2016, 15:12:24)
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> from pyspark import SparkContext
>>> from pyspark import SparkConf
>>> conf = SparkConf().set("spark.driver.memory", "4g")
>>> sc = SparkContext(conf=conf)
```
And verify the spark.driver.memory is correctly picked up.
```
...op/ -Xmx4g org.apache.spark.deploy.SparkSubmit --conf spark.driver.memory=4g pyspark-shell
```
Author: Jeff Zhang <zjffdu@apache.org>
Closes#14959 from zjffdu/SPARK-17387.
## What changes were proposed in this pull request?
Fix SparkR ```spark.naiveBayes``` error when response variable of dataset is numeric type.
See details and how to reproduce this bug at [SPARK-15153](https://issues.apache.org/jira/browse/SPARK-15153).
## How was this patch tested?
Add unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15431 from yanboliang/spark-15153-2.
## What changes were proposed in this pull request?
Quoted from JIRA description:
Calling repartition on a PySpark RDD to increase the number of partitions results in highly skewed partition sizes, with most having 0 rows. The repartition method should evenly spread out the rows across the partitions, and this behavior is correctly seen on the Scala side.
Please reference the following code for a reproducible example of this issue:
num_partitions = 20000
a = sc.parallelize(range(int(1e6)), 2) # start with 2 even partitions
l = a.repartition(num_partitions).glom().map(len).collect() # get length of each partition
min(l), max(l), sum(l)/len(l), len(l) # skewed!
In Scala's `repartition` code, we will distribute elements evenly across output partitions. However, the RDD from Python is serialized as a single binary data, so the distribution fails. We need to convert the RDD in Python to java object before repartitioning.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#15389 from viirya/pyspark-rdd-repartition.
## What changes were proposed in this pull request?
Currently `Canonicalize` object doesn't support `And` and `Or`. So we can compare canonicalized form of predicates consistently. We should add the support.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#15388 from viirya/canonicalize-and-or.
## What changes were proposed in this pull request?
The data type API has not been changed since Spark 1.3.0, and is ready for graduation. This patch marks them as stable APIs using the new InterfaceStability annotation.
This patch also looks at the various files in the catalyst module (not the "package") and marks the remaining few classes appropriately as well.
## How was this patch tested?
This is an annotation change. No functional changes.
Author: Reynold Xin <rxin@databricks.com>
Closes#15426 from rxin/SPARK-17864.
## What changes were proposed in this pull request?
address post hoc review comments for https://github.com/apache/spark/pull/14897
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15424 from cloud-fan/global-temp-view.
## What changes were proposed in this pull request?
Upgraded to a newer version of Pyrolite which supports serialization of a BinaryType StructField for PySpark.SQL
## How was this patch tested?
Added a unit test which fails with a raised ValueError when using the previous version of Pyrolite 4.9 and Python3
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#15386 from BryanCutler/pyrolite-upgrade-SPARK-17808.
## What changes were proposed in this pull request?
```RFormula``` will index label only when it is string type currently. If the label is numeric type and we use ```RFormula``` to present a classification model, there is no label attributes in label column metadata. The label attributes are useful when making prediction for classification, so we can force to index label by ```StringIndexer``` whether it is numeric or string type for classification. Then SparkR wrappers can extract label attributes from label column metadata successfully. This feature can help us to fix bug similar with [SPARK-15153](https://issues.apache.org/jira/browse/SPARK-15153).
For regression, we will still to keep label as numeric type.
In this PR, we add a param ```indexLabel``` to control whether to force to index label for ```RFormula```.
## How was this patch tested?
Unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#13675 from yanboliang/spark-15957.
## What changes were proposed in this pull request?
When I was creating the example code for SPARK-10496, I realized it was pretty convoluted to define the frame boundaries for window functions when there is no partition column or ordering column. The reason is that we don't provide a way to create a WindowSpec directly with the frame boundaries. We can trivially improve this by adding rowsBetween and rangeBetween to Window object.
As an example, to compute cumulative sum using the natural ordering, before this pr:
```
df.select('key, sum("value").over(Window.partitionBy(lit(1)).rowsBetween(Long.MinValue, 0)))
```
After this pr:
```
df.select('key, sum("value").over(Window.rowsBetween(Long.MinValue, 0)))
```
Note that you could argue there is no point specifying a window frame without partitionBy/orderBy -- but it is strange that only rowsBetween and rangeBetween are not the only two APIs not available.
This also fixes https://issues.apache.org/jira/browse/SPARK-17656 (removing _root_.scala).
## How was this patch tested?
Added test cases to compute cumulative sum in DataFrameWindowSuite for Scala/Java and tests.py for Python.
Author: Reynold Xin <rxin@databricks.com>
Closes#15412 from rxin/SPARK-17844.
## 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.
