## What changes were proposed in this pull request?
This pr added `withName` in `UserDefinedFunction` for printing UDF names in EXPLAIN
## How was this patch tested?
Added tests in `UDFSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17712 from maropu/SPARK-20416.
## What changes were proposed in this pull request?
This pr added parsing rules to support aliases in table value functions.
The previous pr (#17666) has been reverted because of the regression. This new pr fixed the regression and add tests in `SQLQueryTestSuite`.
## How was this patch tested?
Added tests in `PlanParserSuite` and `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17928 from maropu/SPARK-20311-3.
## What changes were proposed in this pull request?
`RuntimeReplaceable` always has a constructor with the expression to replace with, and this constructor should not be the function builder.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17876 from cloud-fan/minor.
## What changes were proposed in this pull request?
Don't convert toRdd when doing toJSON
## How was this patch tested?
Existing unit tests
Author: Robert Kruszewski <robertk@palantir.com>
Closes#14615 from robert3005/robertk/correct-tojson.
## What changes were proposed in this pull request?
There's a latent corner-case bug in PySpark UDF evaluation where executing a `BatchPythonEvaluation` with a single multi-argument UDF where _at least one argument value is repeated_ will crash at execution with a confusing error.
This problem was introduced in #12057: the code there has a fast path for handling a "batch UDF evaluation consisting of a single Python UDF", but that branch incorrectly assumes that a single UDF won't have repeated arguments and therefore skips the code for unpacking arguments from the input row (whose schema may not necessarily match the UDF inputs due to de-duplication of repeated arguments which occurred in the JVM before sending UDF inputs to Python).
This fix here is simply to remove this special-casing: it turns out that the code in the "multiple UDFs" branch just so happens to work for the single-UDF case because Python treats `(x)` as equivalent to `x`, not as a single-argument tuple.
## How was this patch tested?
New regression test in `pyspark.python.sql.tests` module (tested and confirmed that it fails before my fix).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#17927 from JoshRosen/SPARK-20685.
## What changes were proposed in this pull request?
It turns out pyspark doctest is calling saveAsTable without ever dropping them. Since we have separate python tests for bucketed table, and there is no checking of results, there is really no need to run the doctest, other than leaving it as an example in the generated doc
## How was this patch tested?
Jenkins
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17932 from felixcheung/pytablecleanup.
## What changes were proposed in this pull request?
b486ffc86d left behind references to "number of generated rows" metrics, that should have been removed.
## How was this patch tested?
Existing unit tests.
Author: Ala Luszczak <ala@databricks.com>
Closes#17939 from ala/SPARK-19447-fix.
## What changes were proposed in this pull request?
This PR proposes to fix the lint-breaks as below:
```
[ERROR] src/main/java/org/apache/spark/unsafe/Platform.java:[51] (regexp) RegexpSingleline: No trailing whitespace allowed.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[45,25] (naming) MethodName: Method name 'ProcessingTime' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[62,25] (naming) MethodName: Method name 'ProcessingTime' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[78,25] (naming) MethodName: Method name 'ProcessingTime' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[92,25] (naming) MethodName: Method name 'ProcessingTime' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[102,25] (naming) MethodName: Method name 'Once' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/test/java/org/apache/spark/streaming/kinesis/JavaKinesisInputDStreamBuilderSuite.java:[28,8] (imports) UnusedImports: Unused import - org.apache.spark.streaming.api.java.JavaDStream.
```
after:
```
dev/lint-java
Checkstyle checks passed.
```
[Test Result](https://travis-ci.org/ConeyLiu/spark/jobs/229666169)
## How was this patch tested?
Travis CI
Author: Xianyang Liu <xianyang.liu@intel.com>
Closes#17890 from ConeyLiu/codestyle.
## What changes were proposed in this pull request?
In filter estimation, we update column stats for those columns in filter condition. However, if the number of rows decreases after the filter (i.e. the overall selectivity is less than 1), we need to update (scale down) the number of distinct values (NDV) for all columns, no matter they are in filter conditions or not.
This pr also fixes the inconsistency of rounding mode for ndv and rowCount.
## How was this patch tested?
Added new tests.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17918 from wzhfy/scaleDownNdvAfterFilter.
## What changes were proposed in this pull request?
In `CheckAnalysis`, we should call `checkAnalysis` for `ScalarSubquery` at the beginning, as later we will call `plan.output` which is invalid if `plan` is not resolved.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17930 from cloud-fan/tmp.
## What changes were proposed in this pull request?
Add stripXSS and stripXSSMap to Spark Core's UIUtils. Calling these functions at any point that getParameter is called against a HttpServletRequest.
## How was this patch tested?
Unit tests, IBM Security AppScan Standard no longer showing vulnerabilities, manual verification of WebUI pages.
Author: NICHOLAS T. MARION <nmarion@us.ibm.com>
Closes#17686 from n-marion/xss-fix.
## What changes were proposed in this pull request?
A few comments around the code mention RDD classes that do not exist anymore. I'm not sure of the best way to replace these, so I've just removed them here.
## How was this patch tested?
Only changes code comments, no testing required
Author: Michael Mior <mmior@uwaterloo.ca>
Closes#17900 from michaelmior/remove-old-rdds.
## What changes were proposed in this pull request?
#14617 added new columns to the executor table causing the visibility checks for the logs and threadDump columns to toggle the wrong columns since they used hard-coded column numbers.
I've updated the checks to use column names instead of numbers so future updates don't accidentally break this again.
Note: This will also need to be back ported into 2.2 since #14617 was merged there
## How was this patch tested?
Manually tested
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#17904 from ajbozarth/spark20630.
## What changes were proposed in this pull request?
- Replace `getParam` calls with `getOrDefault` calls.
- Fix exception message to avoid unintended `TypeError`.
- Add unit tests
## How was this patch tested?
New unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17891 from zero323/SPARK-20631.
## What changes were proposed in this pull request?
When registering Scala UDF, we can know if the udf will return nullable value or not. `ScalaUDF` and related classes should handle the nullability.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#17911 from ueshin/issues/SPARK-20668.
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-20670
As suggested by Sean Owen in https://github.com/apache/spark/pull/17130, the transform code in FPGrowthModel can be simplified.
As I tested on some public dataset http://fimi.ua.ac.be/data/, the performance of the new transform code is even or better than the old implementation.
## How was this patch tested?
Existing unit test.
Author: Yuhao Yang <yuhao.yang@intel.com>
Closes#17912 from hhbyyh/fpgrowthTransform.
## What changes were proposed in this pull request?
The query
```
SELECT 1 FROM (SELECT COUNT(*) WHERE FALSE) t1
```
should return a single row of output because the subquery is an aggregate without a group-by and thus should return a single row. However, Spark incorrectly returns zero rows.
This is caused by SPARK-16208 / #13906, a patch which added an optimizer rule to propagate EmptyRelation through operators. The logic for handling aggregates is wrong: it checks whether aggregate expressions are non-empty for deciding whether the output should be empty, whereas it should be checking grouping expressions instead:
An aggregate with non-empty grouping expression will return one output row per group. If the input to the grouped aggregate is empty then all groups will be empty and thus the output will be empty. It doesn't matter whether the aggregation output columns include aggregate expressions since that won't affect the number of output rows.
If the grouping expressions are empty, however, then the aggregate will always produce a single output row and thus we cannot propagate the EmptyRelation.
The current implementation is incorrect and also misses an optimization opportunity by not propagating EmptyRelation in the case where a grouped aggregate has aggregate expressions (in other words, `SELECT COUNT(*) from emptyRelation GROUP BY x` would _not_ be optimized to `EmptyRelation` in the old code, even though it safely could be).
This patch resolves this issue by modifying `PropagateEmptyRelation` to consider only the presence/absence of grouping expressions, not the aggregate functions themselves, when deciding whether to propagate EmptyRelation.
## How was this patch tested?
- Added end-to-end regression tests in `SQLQueryTest`'s `group-by.sql` file.
- Updated unit tests in `PropagateEmptyRelationSuite`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#17929 from JoshRosen/fix-PropagateEmptyRelation.
## What changes were proposed in this pull request?
One of the common usability problems around reading data in spark (particularly CSV) is that there can often be a conflict between different readers in the classpath.
As an example, if someone launches a 2.x spark shell with the spark-csv package in the classpath, Spark currently fails in an extremely unfriendly way (see databricks/spark-csv#367):
```bash
./bin/spark-shell --packages com.databricks:spark-csv_2.11:1.5.0
scala> val df = spark.read.csv("/foo/bar.csv")
java.lang.RuntimeException: Multiple sources found for csv (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat, com.databricks.spark.csv.DefaultSource15), please specify the fully qualified class name.
at scala.sys.package$.error(package.scala:27)
at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:574)
at org.apache.spark.sql.execution.datasources.DataSource.providingClass$lzycompute(DataSource.scala:85)
at org.apache.spark.sql.execution.datasources.DataSource.providingClass(DataSource.scala:85)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:295)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:533)
at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:412)
... 48 elided
```
This PR proposes a simple way of fixing this error by picking up the internal datasource if there is single (the datasource that has "org.apache.spark" prefix).
```scala
scala> spark.range(1).write.format("csv").mode("overwrite").save("/tmp/abc")
17/05/10 09:47:44 WARN DataSource: Multiple sources found for csv (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat,
com.databricks.spark.csv.DefaultSource15), defaulting to the internal datasource (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat).
```
```scala
scala> spark.range(1).write.format("Csv").mode("overwrite").save("/tmp/abc")
17/05/10 09:47:52 WARN DataSource: Multiple sources found for Csv (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat,
com.databricks.spark.csv.DefaultSource15), defaulting to the internal datasource (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat).
```
## How was this patch tested?
Manually tested as below:
```bash
./bin/spark-shell --packages com.databricks:spark-csv_2.11:1.5.0
```
```scala
spark.sparkContext.setLogLevel("WARN")
```
**positive cases**:
```scala
scala> spark.range(1).write.format("csv").mode("overwrite").save("/tmp/abc")
17/05/10 09:47:44 WARN DataSource: Multiple sources found for csv (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat,
com.databricks.spark.csv.DefaultSource15), defaulting to the internal datasource (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat).
```
```scala
scala> spark.range(1).write.format("Csv").mode("overwrite").save("/tmp/abc")
17/05/10 09:47:52 WARN DataSource: Multiple sources found for Csv (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat,
com.databricks.spark.csv.DefaultSource15), defaulting to the internal datasource (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat).
```
(newlines were inserted for readability).
```scala
scala> spark.range(1).write.format("com.databricks.spark.csv").mode("overwrite").save("/tmp/abc")
```
```scala
scala> spark.range(1).write.format("org.apache.spark.sql.execution.datasources.csv.CSVFileFormat").mode("overwrite").save("/tmp/abc")
```
**negative cases**:
```scala
scala> spark.range(1).write.format("com.databricks.spark.csv.CsvRelation").save("/tmp/abc")
java.lang.InstantiationException: com.databricks.spark.csv.CsvRelation
...
```
```scala
scala> spark.range(1).write.format("com.databricks.spark.csv.CsvRelatio").save("/tmp/abc")
java.lang.ClassNotFoundException: Failed to find data source: com.databricks.spark.csv.CsvRelatio. Please find packages at http://spark.apache.org/third-party-projects.html
...
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17916 from HyukjinKwon/datasource-detect.
## What changes were proposed in this pull request?
The following SQL query cause `IndexOutOfBoundsException` issue when `LIMIT > 1310720`:
```sql
CREATE TABLE tab1(int int, int2 int, str string);
CREATE TABLE tab2(int int, int2 int, str string);
INSERT INTO tab1 values(1,1,'str');
INSERT INTO tab1 values(2,2,'str');
INSERT INTO tab2 values(1,1,'str');
INSERT INTO tab2 values(2,3,'str');
SELECT
count(*)
FROM
(
SELECT t1.int, t2.int2
FROM (SELECT * FROM tab1 LIMIT 1310721) t1
INNER JOIN (SELECT * FROM tab2 LIMIT 1310721) t2
ON (t1.int = t2.int AND t1.int2 = t2.int2)
) t;
```
This pull request fix this issue.
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#17920 from wangyum/SPARK-17685.
## What changes were proposed in this pull request?
Any Dataset/DataFrame batch query with the operation `withWatermark` does not execute because the batch planner does not have any rule to explicitly handle the EventTimeWatermark logical plan.
The right solution is to simply remove the plan node, as the watermark should not affect any batch query in any way.
Changes:
- In this PR, we add a new rule `EliminateEventTimeWatermark` to check if we need to ignore the event time watermark. We will ignore watermark in any batch query.
Depends upon:
- [SPARK-20672](https://issues.apache.org/jira/browse/SPARK-20672). We can not add this rule into analyzer directly, because streaming query will be copied to `triggerLogicalPlan ` in every trigger, and the rule will be applied to `triggerLogicalPlan` mistakenly.
Others:
- A typo fix in example.
## How was this patch tested?
add new unit test.
Author: uncleGen <hustyugm@gmail.com>
Closes#17896 from uncleGen/SPARK-20373.
## What changes were proposed in this pull request?
Drop the hadoop distirbution name from the Python version (PEP440 - https://www.python.org/dev/peps/pep-0440/). We've been using the local version string to disambiguate between different hadoop versions packaged with PySpark, but PEP0440 states that local versions should not be used when publishing up-stream. Since we no longer make PySpark pip packages for different hadoop versions, we can simply drop the hadoop information. If at a later point we need to start publishing different hadoop versions we can look at make different packages or similar.
## How was this patch tested?
Ran `make-distribution` locally
Author: Holden Karau <holden@us.ibm.com>
Closes#17885 from holdenk/SPARK-20627-remove-pip-local-version-string.
## What changes were proposed in this pull request?
Simply moves `Trigger.java` to `src/main/java` from `src/main/scala`
See https://github.com/apache/spark/pull/17219
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#17921 from srowen/SPARK-19876.2.
## What changes were proposed in this pull request?
For some reason we don't have an API to register UserDefinedFunction as named UDF. It is a no brainer to add one, in addition to the existing register functions we have.
## How was this patch tested?
Added a test case in UDFSuite for the new API.
Author: Reynold Xin <rxin@databricks.com>
Closes#17915 from rxin/SPARK-20674.
## What changes were proposed in this pull request?
`ReplSuite.newProductSeqEncoder with REPL defined class` was flaky and throws OOM exception frequently. By analyzing the heap dump, we found the reason is that, in each test case of `ReplSuite`, we create a REPL instance, which creates a classloader and loads a lot of classes related to `SparkContext`. More details please see https://github.com/apache/spark/pull/17833#issuecomment-298711435.
In this PR, we create a new test suite, `SingletonReplSuite`, which shares one REPL instances among all the test cases. Then we move most of the tests from `ReplSuite` to `SingletonReplSuite`, to avoid creating a lot of REPL instances and reduce memory footprint.
## How was this patch tested?
test only change
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17844 from cloud-fan/flaky-test.
## What changes were proposed in this pull request?
Spark Version for a specific application is not displayed on the history page now. It should be nice to switch the spark version on the UI when we click on the specific application.
Currently there seems to be way as SparkListenerLogStart records the application version. So, it should be trivial to listen to this event and provision this change on the UI.
For Example
<img width="1439" alt="screen shot 2017-04-06 at 3 23 41 pm" src="https://cloud.githubusercontent.com/assets/8295799/25092650/41f3970a-2354-11e7-9b0d-4646d0adeb61.png">
<img width="1399" alt="screen shot 2017-04-17 at 9 59 33 am" src="https://cloud.githubusercontent.com/assets/8295799/25092743/9f9e2f28-2354-11e7-9605-f2f1c63f21fe.png">
{"Event":"SparkListenerLogStart","Spark Version":"2.0.0"}
(Please fill in changes proposed in this fix)
Modified the SparkUI for History server to listen to SparkLogListenerStart event and extract the version and print it.
## How was this patch tested?
Manual testing of UI page. Attaching the UI screenshot changes here
(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)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Sanket <schintap@untilservice-lm>
Closes#17658 from redsanket/SPARK-20355.
## What changes were proposed in this pull request?
This pr added parsing rules to support aliases in table value functions.
## How was this patch tested?
Added tests in `PlanParserSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17666 from maropu/SPARK-20311.
## What changes were proposed in this pull request?
So far, we do not drop all the cataloged objects after each package. Sometimes, we might hit strange test case errors because the previous test suite did not drop the cataloged/temporary objects (tables/functions/database). At least, we can first clean up the environment when completing the package of `sql/core` and `sql/hive`.
## How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17908 from gatorsmile/reset.
## What changes were proposed in this pull request?
Remove ML methods we deprecated in 2.1.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17867 from yanboliang/spark-20606.
## What changes were proposed in this pull request?
Added a check for for the number of defined values. Previously the argmax function assumed that at least one value was defined if the vector size was greater than zero.
## How was this patch tested?
Tests were added to the existing VectorsSuite to cover this case.
Author: Jon McLean <jon.mclean@atsid.com>
Closes#17877 from jonmclean/vectorArgmaxIndexBug.
This PR is a `DataFrame` version of #17742 for [SPARK-11968](https://issues.apache.org/jira/browse/SPARK-11968), for improving the performance of `recommendAll` methods.
## How was this patch tested?
Existing unit tests.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#17845 from MLnick/ml-als-perf.
The recommendForAll of MLLIB ALS is very slow.
GC is a key problem of the current method.
The task use the following code to keep temp result:
val output = new Array[(Int, (Int, Double))](m*n)
m = n = 4096 (default value, no method to set)
so output is about 4k * 4k * (4 + 4 + 8) = 256M. This is a large memory and cause serious GC problem, and it is frequently OOM.
Actually, we don't need to save all the temp result. Support we recommend topK (topK is about 10, or 20) product for each user, we only need 4k * topK * (4 + 4 + 8) memory to save the temp result.
The Test Environment:
3 workers: each work 10 core, each work 30G memory, each work 1 executor.
The Data: User 480,000, and Item 17,000
BlockSize: 1024 2048 4096 8192
Old method: 245s 332s 488s OOM
This solution: 121s 118s 117s 120s
The existing UT.
Author: Peng <peng.meng@intel.com>
Author: Peng Meng <peng.meng@intel.com>
Closes#17742 from mpjlu/OptimizeAls.
## What changes were proposed in this pull request?
Change it to check for relative count like in this test https://github.com/apache/spark/blame/master/R/pkg/inst/tests/testthat/test_sparkSQL.R#L3355 for catalog APIs
## How was this patch tested?
unit tests, this needs to combine with another commit with SQL change to check
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17905 from felixcheung/rtabletests.
## What changes were proposed in this pull request?
Cleaning existing temp tables before running tableNames tests
## How was this patch tested?
SparkR Unit tests
Author: Hossein <hossein@databricks.com>
Closes#17903 from falaki/SPARK-20661.
## What changes were proposed in this pull request?
After SPARK-10997, client mode Netty RpcEnv doesn't require to start server, so port configurations are not used any more, here propose to remove these two configurations: "spark.executor.port" and "spark.am.port".
## How was this patch tested?
Existing UTs.
Author: jerryshao <sshao@hortonworks.com>
Closes#17866 from jerryshao/SPARK-20605.
## What changes were proposed in this pull request?
Currently, `spark.executor.instances` is deprecated in `spark-env.sh`, because we suggest config it in `spark-defaults.conf` or other config file. And also this parameter is useless even if you set it in `spark-env.sh`, so remove it in this patch.
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Xianyang Liu <xianyang.liu@intel.com>
Closes#17881 from ConeyLiu/deprecatedParam.
Existing test cases for `recommendForAllX` methods (added in [SPARK-19535](https://issues.apache.org/jira/browse/SPARK-19535)) test `k < num items` and `k = num items`. Technically we should also test that `k > num items` returns the same results as `k = num items`.
## How was this patch tested?
Updated existing unit tests.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#17860 from MLnick/SPARK-20596-als-rec-tests.
## What changes were proposed in this pull request?
When call the method getLocations of BlockManager, we only compare the data block host. Random selection for non-local data blocks, this may cause the selected data block to be in a different rack. So in this patch to increase the sort of the rack.
## How was this patch tested?
New test case.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Xianyang Liu <xianyang.liu@intel.com>
Closes#17300 from ConeyLiu/blockmanager.
Signed-off-by: liuxian <liu.xian3zte.com.cn>
## What changes were proposed in this pull request?
When the input parameter is null, may be a runtime exception occurs
## How was this patch tested?
Existing unit tests
Author: liuxian <liu.xian3@zte.com.cn>
Closes#17796 from 10110346/wip_lx_0428.
## What changes were proposed in this pull request?
Fix typo in vignettes
Author: Wayne Zhang <actuaryzhang@uber.com>
Closes#17884 from actuaryzhang/typo.
### What changes were proposed in this pull request?
Table comment was not getting set/unset using **ALTER TABLE SET/UNSET TBLPROPERTIES** query
eg: ALTER TABLE table_with_comment SET TBLPROPERTIES("comment"= "modified comment)
when user alter the table properties and adds/updates table comment,table comment which is a field of **CatalogTable** instance is not getting updated and old table comment if exists was shown to user, inorder to handle this issue, update the comment field value in **CatalogTable** with the newly added/modified comment along with other table level properties when user executes **ALTER TABLE SET TBLPROPERTIES** query.
This pr has also taken care of unsetting the table comment when user executes query **ALTER TABLE UNSET TBLPROPERTIES** inorder to unset or remove table comment.
eg: ALTER TABLE table_comment UNSET TBLPROPERTIES IF EXISTS ('comment')
### How was this patch tested?
Added test cases as part of **SQLQueryTestSuite** for verifying table comment using desc formatted table query after adding/modifying table comment as part of **AlterTableSetPropertiesCommand** and unsetting the table comment using **AlterTableUnsetPropertiesCommand**.
Author: sujith71955 <sujithchacko.2010@gmail.com>
Closes#17649 from sujith71955/alter_table_comment.
## What changes were proposed in this pull request?
set timezone on windows
## How was this patch tested?
unit test, AppVeyor
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17892 from felixcheung/rtimestamptest.
## What changes were proposed in this pull request?
This change allows timestamps in parquet-based hive table to behave as a "floating time", without a timezone, as timestamps are for other file formats. If the storage timezone is the same as the session timezone, this conversion is a no-op. When data is read from a hive table, the table property is *always* respected. This allows spark to not change behavior when reading old data, but read newly written data correctly (whatever the source of the data is).
Spark inherited the original behavior from Hive, but Hive is also updating behavior to use the same scheme in HIVE-12767 / HIVE-16231.
The default for Spark remains unchanged; created tables do not include the new table property.
This will only apply to hive tables; nothing is added to parquet metadata to indicate the timezone, so data that is read or written directly from parquet files will never have any conversions applied.
## How was this patch tested?
Added a unit test which creates tables, reads and writes data, under a variety of permutations (different storage timezones, different session timezones, vectorized reading on and off).
Author: Imran Rashid <irashid@cloudera.com>
Closes#16781 from squito/SPARK-12297.
## What changes were proposed in this pull request?
Adds Python wrappers for `DataFrameWriter.bucketBy` and `DataFrameWriter.sortBy` ([SPARK-16931](https://issues.apache.org/jira/browse/SPARK-16931))
## How was this patch tested?
Unit tests covering new feature.
__Note__: Based on work of GregBowyer (f49b9a23468f7af32cb53d2b654272757c151725)
CC HyukjinKwon
Author: zero323 <zero323@users.noreply.github.com>
Author: Greg Bowyer <gbowyer@fastmail.co.uk>
Closes#17077 from zero323/SPARK-16931.
## What changes were proposed in this pull request?
- Add SparkR wrapper for `Dataset.alias`.
- Adjust roxygen annotations for `functions.alias` (including example usage).
## How was this patch tested?
Unit tests, `check_cran.sh`.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17825 from zero323/SPARK-20550.
## What changes were proposed in this pull request?
* Docs are consistent (across different `unix_timestamp` variants and their internal expressions)
* typo hunting
## How was this patch tested?
local build
Author: Jacek Laskowski <jacek@japila.pl>
Closes#17801 from jaceklaskowski/unix_timestamp.
## What changes were proposed in this pull request?
add environment
## How was this patch tested?
wait for appveyor run
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17878 from felixcheung/appveyorrcran.
## What changes were proposed in this pull request?
Add a new `spark-hadoop-cloud` module and maven profile to pull in object store support from `hadoop-openstack`, `hadoop-aws` and `hadoop-azure` (Hadoop 2.7+) JARs, along with their dependencies, fixing up the dependencies so that everything works, in particular Jackson.
It restores `s3n://` access to S3, adds its `s3a://` replacement, OpenStack `swift://` and azure `wasb://`.
There's a documentation page, `cloud_integration.md`, which covers the basic details of using Spark with object stores, referring the reader to the supplier's own documentation, with specific warnings on security and the possible mismatch between a store's behavior and that of a filesystem. In particular, users are advised be very cautious when trying to use an object store as the destination of data, and to consult the documentation of the storage supplier and the connector.
(this is the successor to #12004; I can't re-open it)
## How was this patch tested?
Downstream tests exist in [https://github.com/steveloughran/spark-cloud-examples/tree/master/cloud-examples](https://github.com/steveloughran/spark-cloud-examples/tree/master/cloud-examples)
Those verify that the dependencies are sufficient to allow downstream applications to work with s3a, azure wasb and swift storage connectors, and perform basic IO & dataframe operations thereon. All seems well.
Manually clean build & verify that assembly contains the relevant aws-* hadoop-* artifacts on Hadoop 2.6; azure on a hadoop-2.7 profile.
SBT build: `build/sbt -Phadoop-cloud -Phadoop-2.7 package`
maven build `mvn install -Phadoop-cloud -Phadoop-2.7`
This PR *does not* update `dev/deps/spark-deps-hadoop-2.7` or `dev/deps/spark-deps-hadoop-2.6`, because unless the hadoop-cloud profile is enabled, no extra JARs show up in the dependency list. The dependency check in Jenkins isn't setting the property, so the new JARs aren't visible.
Author: Steve Loughran <stevel@apache.org>
Author: Steve Loughran <stevel@hortonworks.com>
Closes#17834 from steveloughran/cloud/SPARK-7481-current.
