## What changes were proposed in this pull request?
Fix build warnings primarily related to Breeze 0.13 operator changes, Java style problems
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#17803 from srowen/SPARK-20523.
## What changes were proposed in this pull request?
A fix for the same problem was made in #17693 but ignored `JsonToStructs`. This PR uses the same fix for `JsonToStructs`.
## How was this patch tested?
Regression test
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#17826 from brkyvz/SPARK-20549.
## What changes were proposed in this pull request?
Add support for the SQL standard distinct predicate to SPARK SQL.
```
<expression> IS [NOT] DISTINCT FROM <expression>
```
## How was this patch tested?
Tested using unit tests, integration tests, manual tests.
Author: ptkool <michael.styles@shopify.com>
Closes#17764 from ptkool/is_not_distinct_from.
## What changes were proposed in this pull request?
Generate exec does not produce `null` values if the generator for the input row is empty and the generate operates in outer mode without join. This is caused by the fact that the `join=false` code path is different from the `join=true` code path, and that the `join=false` code path did deal with outer properly. This PR addresses this issue.
## How was this patch tested?
Updated `outer*` tests in `GeneratorFunctionSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17810 from hvanhovell/SPARK-20534.
## What changes were proposed in this pull request?
Currently, when the type string is invalid, it looks printing empty parentheses. This PR proposes a small improvement in an error message by removing it in the parse as below:
```scala
spark.range(1).select($"col".cast("aa"))
```
**Before**
```
org.apache.spark.sql.catalyst.parser.ParseException:
DataType aa() is not supported.(line 1, pos 0)
== SQL ==
aa
^^^
```
**After**
```
org.apache.spark.sql.catalyst.parser.ParseException:
DataType aa is not supported.(line 1, pos 0)
== SQL ==
aa
^^^
```
## How was this patch tested?
Unit tests in `DataTypeParserSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17784 from HyukjinKwon/SPARK-20492.
## What changes were proposed in this pull request?
This PR proposes to fill up the documentation with examples for `bitwiseOR`, `bitwiseAND`, `bitwiseXOR`. `contains`, `asc` and `desc` in `Column` API.
Also, this PR fixes minor typos in the documentation and matches some of the contents between Scala doc and Python doc.
Lastly, this PR suggests to use `spark` rather than `sc` in doc tests in `Column` for Python documentation.
## How was this patch tested?
Doc tests were added and manually tested with the commands below:
`./python/run-tests.py --module pyspark-sql`
`./python/run-tests.py --module pyspark-sql --python-executable python3`
`./dev/lint-python`
Output was checked via `make html` under `./python/docs`. The snapshots will be left on the codes with comments.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17737 from HyukjinKwon/SPARK-20442.
## What changes were proposed in this pull request?
This pr added a new rule in `Analyzer` to resolve aliases in `GROUP BY`.
The current master throws an exception if `GROUP BY` clauses have aliases in `SELECT`;
```
scala> spark.sql("select a a1, a1 + 1 as b, count(1) from t group by a1")
org.apache.spark.sql.AnalysisException: cannot resolve '`a1`' given input columns: [a]; line 1 pos 51;
'Aggregate ['a1], [a#83L AS a1#87L, ('a1 + 1) AS b#88, count(1) AS count(1)#90L]
+- SubqueryAlias t
+- Project [id#80L AS a#83L]
+- Range (0, 10, step=1, splits=Some(8))
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:77)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)
```
## How was this patch tested?
Added tests in `SQLQuerySuite` and `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17191 from maropu/SPARK-14471.
## What changes were proposed in this pull request?
Relax the requirement that a `TimeZoneAwareExpression` has to have its `timeZoneId` set to be considered resolved.
With this change, a `Cast` (which is a `TimeZoneAwareExpression`) can be considered resolved if the `(fromType, toType)` combination doesn't require time zone information.
Also de-relaxed test cases in `CastSuite` so Casts in that test suite don't get a default`timeZoneId = Option("GMT")`.
## How was this patch tested?
Ran the de-relaxed`CastSuite` and it's passing. Also ran the SQL unit tests and they're passing too.
Author: Kris Mok <kris.mok@databricks.com>
Closes#17777 from rednaxelafx/fix-catalyst-cast-timezone.
## What changes were proposed in this pull request?
change to using Jackson's `com.fasterxml.jackson.core.JsonFactory`
public JsonParser createParser(String content)
## How was this patch tested?
existing unit tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Eric Wasserman <ericw@sgn.com>
Closes#17693 from ewasserman/SPARK-20314.
## What changes were proposed in this pull request?
This patch adds support for customizing the spark session by injecting user-defined custom extensions. This allows a user to add custom analyzer rules/checks, optimizer rules, planning strategies or even a customized parser.
## How was this patch tested?
Unit Tests in SparkSessionExtensionSuite
Author: Sameer Agarwal <sameerag@cs.berkeley.edu>
Closes#17724 from sameeragarwal/session-extensions.
## What changes were proposed in this pull request?
This PR avoids an exception in the case where `scala.math.BigInt` has a value that does not fit into long value range (e.g. `Long.MAX_VALUE+1`). When we run the following code by using the current Spark, the following exception is thrown.
This PR keeps the value using `BigDecimal` if we detect such an overflow case by catching `ArithmeticException`.
Sample program:
```
case class BigIntWrapper(value:scala.math.BigInt)```
spark.createDataset(BigIntWrapper(scala.math.BigInt("10000000000000000002"))::Nil).show
```
Exception:
```
Error while encoding: java.lang.ArithmeticException: BigInteger out of long range
staticinvoke(class org.apache.spark.sql.types.Decimal$, DecimalType(38,0), apply, assertnotnull(assertnotnull(input[0, org.apache.spark.sql.BigIntWrapper, true])).value, true) AS value#0
java.lang.RuntimeException: Error while encoding: java.lang.ArithmeticException: BigInteger out of long range
staticinvoke(class org.apache.spark.sql.types.Decimal$, DecimalType(38,0), apply, assertnotnull(assertnotnull(input[0, org.apache.spark.sql.BigIntWrapper, true])).value, true) AS value#0
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:290)
at org.apache.spark.sql.SparkSession$$anonfun$2.apply(SparkSession.scala:454)
at org.apache.spark.sql.SparkSession$$anonfun$2.apply(SparkSession.scala:454)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.immutable.List.map(List.scala:285)
at org.apache.spark.sql.SparkSession.createDataset(SparkSession.scala:454)
at org.apache.spark.sql.Agg$$anonfun$18.apply$mcV$sp(MySuite.scala:192)
at org.apache.spark.sql.Agg$$anonfun$18.apply(MySuite.scala:192)
at org.apache.spark.sql.Agg$$anonfun$18.apply(MySuite.scala:192)
at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:68)
at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163)
at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175)
...
Caused by: java.lang.ArithmeticException: BigInteger out of long range
at java.math.BigInteger.longValueExact(BigInteger.java:4531)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:140)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:434)
at org.apache.spark.sql.types.Decimal.apply(Decimal.scala)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:287)
... 59 more
```
## How was this patch tested?
Add new test suite into `DecimalSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#17684 from kiszk/SPARK-20341.
## What changes were proposed in this pull request?
If a partitionSpec is supposed to not contain optional values, a ParseException should be thrown, and not nulls returned.
The nulls can later cause NullPointerExceptions in places not expecting them.
## How was this patch tested?
A query like "SHOW PARTITIONS tbl PARTITION(col1='val1', col2)" used to throw a NullPointerException.
Now it throws a ParseException.
Author: Juliusz Sompolski <julek@databricks.com>
Closes#17707 from juliuszsompolski/SPARK-20412.
## What changes were proposed in this pull request?
It is often useful to be able to track changes to the `ExternalCatalog`. This PR makes the `ExternalCatalog` emit events when a catalog object is changed. Events are fired before and after the change.
The following events are fired per object:
- Database
- CreateDatabasePreEvent: event fired before the database is created.
- CreateDatabaseEvent: event fired after the database has been created.
- DropDatabasePreEvent: event fired before the database is dropped.
- DropDatabaseEvent: event fired after the database has been dropped.
- Table
- CreateTablePreEvent: event fired before the table is created.
- CreateTableEvent: event fired after the table has been created.
- RenameTablePreEvent: event fired before the table is renamed.
- RenameTableEvent: event fired after the table has been renamed.
- DropTablePreEvent: event fired before the table is dropped.
- DropTableEvent: event fired after the table has been dropped.
- Function
- CreateFunctionPreEvent: event fired before the function is created.
- CreateFunctionEvent: event fired after the function has been created.
- RenameFunctionPreEvent: event fired before the function is renamed.
- RenameFunctionEvent: event fired after the function has been renamed.
- DropFunctionPreEvent: event fired before the function is dropped.
- DropFunctionPreEvent: event fired after the function has been dropped.
The current events currently only contain the names of the object modified. We add more events, and more details at a later point.
A user can monitor changes to the external catalog by adding a listener to the Spark listener bus checking for `ExternalCatalogEvent`s using the `SparkListener.onOtherEvent` hook. A more direct approach is add listener directly to the `ExternalCatalog`.
## How was this patch tested?
Added the `ExternalCatalogEventSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17710 from hvanhovell/SPARK-20420.
## What changes were proposed in this pull request?
A cast expression with a resolved time zone is not equal to a cast expression without a resolved time zone. The `ResolveAggregateFunction` assumed that these expression were the same, and would fail to resolve `HAVING` clauses which contain a `Cast` expression.
This is in essence caused by the fact that a `TimeZoneAwareExpression` can be resolved without a set time zone. This PR fixes this, and makes a `TimeZoneAwareExpression` unresolved as long as it has no TimeZone set.
## How was this patch tested?
Added a regression test to the `SQLQueryTestSuite.having` file.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17641 from hvanhovell/SPARK-20329.
## What changes were proposed in this pull request?
Address a follow up in [comment](https://github.com/apache/spark/pull/16954#discussion_r105718880)
Currently subqueries with correlated predicates containing aggregate expression having mixture of outer references and local references generate a codegen error like following :
```SQL
SELECT t1a
FROM t1
GROUP BY 1
HAVING EXISTS (SELECT 1
FROM t2
WHERE t2a < min(t1a + t2a));
```
Exception snippet.
```
Cannot evaluate expression: min((input[0, int, false] + input[4, int, false]))
at org.apache.spark.sql.catalyst.expressions.Unevaluable$class.doGenCode(Expression.scala:226)
at org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression.doGenCode(interfaces.scala:87)
at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:106)
at org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:103)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:103)
```
After this PR, a better error message is issued.
```
org.apache.spark.sql.AnalysisException
Error in query: Found an aggregate expression in a correlated
predicate that has both outer and local references, which is not supported yet.
Aggregate expression: min((t1.`t1a` + t2.`t2a`)),
Outer references: t1.`t1a`,
Local references: t2.`t2a`.;
```
## How was this patch tested?
Added tests in SQLQueryTestSuite.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#17636 from dilipbiswal/subquery_followup1.
## What changes were proposed in this pull request?
It's illegal to have aggregate function in GROUP BY, and we should fail at analysis phase, if this happens.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17704 from cloud-fan/minor.
### What changes were proposed in this pull request?
Database and Table names conform the Hive standard ("[a-zA-z_0-9]+"), i.e. if this name only contains characters, numbers, and _.
When calling `toLowerCase` on the names, we should add `Locale.ROOT` to the `toLowerCase`for avoiding inadvertent locale-sensitive variation in behavior (aka the "Turkish locale problem").
### How was this patch tested?
Added a test case
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17655 from gatorsmile/locale.
## What changes were proposed in this pull request?
Also went through the same file to ensure other string concatenation are correct.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17691 from zsxwing/fix-error-message.
## What changes were proposed in this pull request?
Apply Complementation Laws during boolean expression simplification.
## How was this patch tested?
Tested using unit tests, integration tests, and manual tests.
Author: ptkool <michael.styles@shopify.com>
Author: Michael Styles <michael.styles@shopify.com>
Closes#17650 from ptkool/apply_complementation_laws.
Avoid necessary execution that can lead to NPE in EliminateOuterJoin and add test in DataFrameSuite to confirm NPE is no longer thrown
## What changes were proposed in this pull request?
Change leftHasNonNullPredicate and rightHasNonNullPredicate to lazy so they are only executed when needed.
## How was this patch tested?
Added test in DataFrameSuite that failed before this fix and now succeeds. Note that a test in catalyst project would be better but i am unsure how to do this.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Koert Kuipers <koert@tresata.com>
Closes#17660 from koertkuipers/feat-catch-npe-in-eliminate-outer-join.
## What changes were proposed in this pull request?
If a plan has multi-level successive joins, e.g.:
```
Join
/ \
Union t5
/ \
Join t4
/ \
Join t3
/ \
t1 t2
```
Currently we fail to reorder the inside joins, i.e. t1, t2, t3.
In join reorder, we use `OrderedJoin` to indicate a join has been ordered, such that when transforming down the plan, these joins don't need to be rerodered again.
But there's a problem in the definition of `OrderedJoin`:
The real join node is a parameter, but not a child. This breaks the transform procedure because `mapChildren` applies transform function on parameters which should be children.
In this patch, we change `OrderedJoin` to a class having the same structure as a join node.
## How was this patch tested?
Add a corresponding test case.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17668 from wzhfy/recursiveReorder.
## What changes were proposed in this pull request?
fix typo
## How was this patch tested?
manual
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17663 from felixcheung/likedoctypo.
## What changes were proposed in this pull request?
Replace non-existent `repartitionBy` with `distribute` in `CollapseRepartitionSuite`.
## How was this patch tested?
local build and `catalyst/testOnly *CollapseRepartitionSuite`
Author: Jacek Laskowski <jacek@japila.pl>
Closes#17657 from jaceklaskowski/CollapseRepartitionSuite.
## What changes were proposed in this pull request?
This patch fixes a bug in the way LIKE patterns are translated to Java regexes. The bug causes any character following an escaped backslash to be escaped, i.e. there is double-escaping.
A concrete example is the following pattern:`'%\\%'`. The expected Java regex that this pattern should correspond to (according to the behavior described below) is `'.*\\.*'`, however the current situation leads to `'.*\\%'` instead.
---
Update: in light of the discussion that ensued, we should explicitly define the expected behaviour of LIKE expressions, especially in certain edge cases. With the help of gatorsmile, we put together a list of different RDBMS and their variations wrt to certain standard features.
| RDBMS\Features | Wildcards | Default escape [1] | Case sensitivity |
| --- | --- | --- | --- |
| [MS SQL Server](https://msdn.microsoft.com/en-us/library/ms179859.aspx) | _, %, [], [^] | none | no |
| [Oracle](https://docs.oracle.com/cd/B12037_01/server.101/b10759/conditions016.htm) | _, % | none | yes |
| [DB2 z/OS](http://www.ibm.com/support/knowledgecenter/SSEPEK_11.0.0/sqlref/src/tpc/db2z_likepredicate.html) | _, % | none | yes |
| [MySQL](http://dev.mysql.com/doc/refman/5.7/en/string-comparison-functions.html) | _, % | none | no |
| [PostreSQL](https://www.postgresql.org/docs/9.0/static/functions-matching.html) | _, % | \ | yes |
| [Hive](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF) | _, % | none | yes |
| Current Spark | _, % | \ | yes |
[1] Default escape character: most systems do not have a default escape character, instead the user can specify one by calling a like expression with an escape argument [A] LIKE [B] ESCAPE [C]. This syntax is currently not supported by Spark, however I would volunteer to implement this feature in a separate ticket.
The specifications are often quite terse and certain scenarios are undocumented, so here is a list of scenarios that I am uncertain about and would appreciate any input. Specifically I am looking for feedback on whether or not Spark's current behavior should be changed.
1. [x] Ending a pattern with the escape sequence, e.g. `like 'a\'`.
PostreSQL gives an error: 'LIKE pattern must not end with escape character', which I personally find logical. Currently, Spark allows "non-terminated" escapes and simply ignores them as part of the pattern.
According to [DB2's documentation](http://www.ibm.com/support/knowledgecenter/SSEPGG_9.7.0/com.ibm.db2.luw.messages.sql.doc/doc/msql00130n.html), ending a pattern in an escape character is invalid.
_Proposed new behaviour in Spark: throw AnalysisException_
2. [x] Empty input, e.g. `'' like ''`
Postgres and DB2 will match empty input only if the pattern is empty as well, any other combination of empty input will not match. Spark currently follows this rule.
3. [x] Escape before a non-special character, e.g. `'a' like '\a'`.
Escaping a non-wildcard character is not really documented but PostgreSQL just treats it verbatim, which I also find the least surprising behavior. Spark does the same.
According to [DB2's documentation](http://www.ibm.com/support/knowledgecenter/SSEPGG_9.7.0/com.ibm.db2.luw.messages.sql.doc/doc/msql00130n.html), it is invalid to follow an escape character with anything other than an escape character, an underscore or a percent sign.
_Proposed new behaviour in Spark: throw AnalysisException_
The current specification is also described in the operator's source code in this patch.
## How was this patch tested?
Extra case in regex unit tests.
Author: Jakob Odersky <jakob@odersky.com>
This patch had conflicts when merged, resolved by
Committer: Reynold Xin <rxin@databricks.com>
Closes#15398 from jodersky/SPARK-17647.
### What changes were proposed in this pull request?
The session catalog caches some persistent functions in the `FunctionRegistry`, so there can be duplicates. Our Catalog API `listFunctions` does not handle it.
It would be better if `SessionCatalog` API can de-duplciate the records, instead of doing it by each API caller. In `FunctionRegistry`, our functions are identified by the unquoted string. Thus, this PR is try to parse it using our parser interface and then de-duplicate the names.
### How was this patch tested?
Added test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17646 from gatorsmile/showFunctions.
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/17398 we introduced `UnresolvedMapObjects` as a placeholder of `MapObjects`. Unfortunately `UnresolvedMapObjects` is not serializable as its `function` may reference Scala `Type` which is not serializable.
Ideally this is fine, as we will never serialize and send unresolved expressions to executors. However users may accidentally do this, e.g. mistakenly reference an encoder instance when implementing `Aggregator`, we should fix it so that it's just a performance issue(more network traffic) and should not fail the query.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17639 from cloud-fan/minor.
## What changes were proposed in this pull request?
Currently when estimating predicates like col > literal or col = literal, we will update min or max in column stats based on literal value. However, literal value is of Catalyst type (internal type), while min/max is of external type. Then for the next predicate, we again need to do type conversion to compare and update column stats. This is awkward and causes many unnecessary conversions in estimation.
To solve this, we use Catalyst type for min/max in `ColumnStat`. Note that the persistent format in metastore is still of external type, so there's no inconsistency for statistics in metastore.
This pr also fixes a bug for boolean type in `IN` condition.
## How was this patch tested?
The changes for ColumnStat are covered by existing tests.
For bug fix, a new test for boolean type in IN condition is added
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17630 from wzhfy/refactorColumnStat.
## What changes were proposed in this pull request?
AssertNotNull's toString/simpleString dumps the entire walkedTypePath. walkedTypePath is used for error message reporting and shouldn't be part of the output.
## How was this patch tested?
Manually tested.
Author: Reynold Xin <rxin@databricks.com>
Closes#17616 from rxin/SPARK-20304.
### What changes were proposed in this pull request?
Session catalog API `createTempFunction` is being used by Hive build-in functions, persistent functions, and temporary functions. Thus, the name is confusing. This PR is to rename it by `registerFunction`. Also we can move construction of `FunctionBuilder` and `ExpressionInfo` into the new `registerFunction`, instead of duplicating the logics everywhere.
In the next PRs, the remaining Function-related APIs also need cleanups.
### How was this patch tested?
Existing test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17615 from gatorsmile/cleanupCreateTempFunction.
## What changes were proposed in this pull request?
This PR proposes to run Spark unidoc to test Javadoc 8 build as Javadoc 8 is easily re-breakable.
There are several problems with it:
- It introduces little extra bit of time to run the tests. In my case, it took 1.5 mins more (`Elapsed :[94.8746569157]`). How it was tested is described in "How was this patch tested?".
- > One problem that I noticed was that Unidoc appeared to be processing test sources: if we can find a way to exclude those from being processed in the first place then that might significantly speed things up.
(see joshrosen's [comment](https://issues.apache.org/jira/browse/SPARK-18692?focusedCommentId=15947627&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15947627))
To complete this automated build, It also suggests to fix existing Javadoc breaks / ones introduced by test codes as described above.
There fixes are similar instances that previously fixed. Please refer https://github.com/apache/spark/pull/15999 and https://github.com/apache/spark/pull/16013
Note that this only fixes **errors** not **warnings**. Please see my observation https://github.com/apache/spark/pull/17389#issuecomment-288438704 for spurious errors by warnings.
## How was this patch tested?
Manually via `jekyll build` for building tests. Also, tested via running `./dev/run-tests`.
This was tested via manually adding `time.time()` as below:
```diff
profiles_and_goals = build_profiles + sbt_goals
print("[info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments: ",
" ".join(profiles_and_goals))
+ import time
+ st = time.time()
exec_sbt(profiles_and_goals)
+ print("Elapsed :[%s]" % str(time.time() - st))
```
produces
```
...
========================================================================
Building Unidoc API Documentation
========================================================================
...
[info] Main Java API documentation successful.
...
Elapsed :[94.8746569157]
...
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17477 from HyukjinKwon/SPARK-18692.
## What changes were proposed in this pull request?
Update count distinct error message for streaming datasets/dataframes to match current behavior. These aggregations are not yet supported, regardless of whether the dataset/dataframe is aggregated.
Author: jtoka <jason.tokayer@gmail.com>
Closes#17609 from jtoka/master.
## What changes were proposed in this pull request?
When we perform a cast expression and the from and to types are structurally the same (having the same structure but different field names), we should be able to skip the actual cast.
## How was this patch tested?
Added unit tests for the newly introduced functions.
Author: Reynold Xin <rxin@databricks.com>
Closes#17614 from rxin/SPARK-20302.
## What changes were proposed in this pull request?
`NaNvl(float value, null)` will be converted into `NaNvl(float value, Cast(null, DoubleType))` and finally `NaNvl(Cast(float value, DoubleType), Cast(null, DoubleType))`.
This will cause mismatching in the output type when the input type is float.
By adding extra rule in TypeCoercion can resolve this issue.
## How was this patch tested?
unite tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: DB Tsai <dbt@netflix.com>
Closes#17606 from dbtsai/fixNaNvl.
## What changes were proposed in this pull request?
Dataset typed API currently uses NewInstance to box primitive types (i.e. calling the constructor). Instead, it'd be slightly more idiomatic in Java to use PrimitiveType.valueOf, which can be invoked using StaticInvoke expression.
## How was this patch tested?
The change should be covered by existing tests for Dataset encoders.
Author: Reynold Xin <rxin@databricks.com>
Closes#17604 from rxin/SPARK-20289.
## What changes were proposed in this pull request?
Similar to `ListQuery`, `Exists` should not be evaluated in `Join` operator too.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17491 from viirya/dont-push-exists-to-join.
## What changes were proposed in this pull request?
This is a regression caused by SPARK-19716.
Before SPARK-19716, we will cast an array field to the expected array type. However, after SPARK-19716, the cast is removed, but we forgot to push the cast to the element level.
## How was this patch tested?
new regression tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17587 from cloud-fan/array.
## What changes were proposed in this pull request?
Add Locale.ROOT to internal calls to String `toLowerCase`, `toUpperCase`, to avoid inadvertent locale-sensitive variation in behavior (aka the "Turkish locale problem").
The change looks large but it is just adding `Locale.ROOT` (the locale with no country or language specified) to every call to these methods.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#17527 from srowen/SPARK-20156.
## What changes were proposed in this pull request?
```
sql("SELECT t1.b, rand(0) as r FROM cachedData, cachedData t1 GROUP BY t1.b having r > 0.5").show()
```
We will get the following error:
```
Job aborted due to stage failure: Task 1 in stage 4.0 failed 1 times, most recent failure: Lost task 1.0 in stage 4.0 (TID 8, localhost, executor driver): java.lang.NullPointerException
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate.eval(Unknown Source)
at org.apache.spark.sql.execution.joins.BroadcastNestedLoopJoinExec$$anonfun$org$apache$spark$sql$execution$joins$BroadcastNestedLoopJoinExec$$boundCondition$1.apply(BroadcastNestedLoopJoinExec.scala:87)
at org.apache.spark.sql.execution.joins.BroadcastNestedLoopJoinExec$$anonfun$org$apache$spark$sql$execution$joins$BroadcastNestedLoopJoinExec$$boundCondition$1.apply(BroadcastNestedLoopJoinExec.scala:87)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:463)
```
Filters could be pushed down to the join conditions by the optimizer rule `PushPredicateThroughJoin`. However, Analyzer [blocks users to add non-deterministics conditions](https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala#L386-L395) (For details, see the PR https://github.com/apache/spark/pull/7535).
We should not push down non-deterministic conditions; otherwise, we need to explicitly initialize the non-deterministic expressions. This PR is to simply block it.
### How was this patch tested?
Added a test case
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17585 from gatorsmile/joinRandCondition.
## What changes were proposed in this pull request?
This PR proposes to add `IGNORE NULLS` keyword in `first`/`last` in Spark's parser likewise http://docs.oracle.com/cd/B19306_01/server.102/b14200/functions057.htm. This simply maps the keywords to existing `ignoreNullsExpr`.
**Before**
```scala
scala> sql("select first('a' IGNORE NULLS)").show()
```
```
org.apache.spark.sql.catalyst.parser.ParseException:
extraneous input 'NULLS' expecting {')', ','}(line 1, pos 24)
== SQL ==
select first('a' IGNORE NULLS)
------------------------^^^
at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:210)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:112)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:46)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:66)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:622)
... 48 elided
```
**After**
```scala
scala> sql("select first('a' IGNORE NULLS)").show()
```
```
+--------------+
|first(a, true)|
+--------------+
| a|
+--------------+
```
## How was this patch tested?
Unit tests in `ExpressionParserSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17566 from HyukjinKwon/SPARK-19518.
## What changes were proposed in this pull request?
Like `Expression`, `QueryPlan` should also have a `semanticHash` method, then we can put plans to a hash map and look it up fast. This PR refactors `QueryPlan` to follow `Expression` and put all the normalization logic in `QueryPlan.canonicalized`, so that it's very natural to implement `semanticHash`.
follow-up: improve `CacheManager` to leverage this `semanticHash` and speed up plan lookup, instead of iterating all cached plans.
## How was this patch tested?
existing tests. Note that we don't need to test the `semanticHash` method, once the existing tests prove `sameResult` is correct, we are good.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17541 from cloud-fan/plan-semantic.
## What changes were proposed in this pull request?
AssertNotNull currently throws RuntimeException. It should throw NullPointerException, which is more specific.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#17573 from rxin/SPARK-20262.
## What changes were proposed in this pull request?
Similar to `Project`, when `Aggregate` has non-deterministic expressions, we should not push predicate down through it, as it will change the number of input rows and thus change the evaluation result of non-deterministic expressions in `Aggregate`.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17562 from cloud-fan/filter.
## What changes were proposed in this pull request?
Currently `LogicalRelation` has a `expectedOutputAttributes` parameter, which makes it hard to reason about what the actual output is. Like other leaf nodes, `LogicalRelation` should also take `output` as a parameter, to simplify the logic
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17552 from cloud-fan/minor.
## What changes were proposed in this pull request?
This is a tiny addendum to SPARK-19495 to remove the private visibility for copy, which is the only package private method in the entire file.
## How was this patch tested?
N/A - no semantic change.
Author: Reynold Xin <rxin@databricks.com>
Closes#17555 from rxin/SPARK-19495-2.
## What changes were proposed in this pull request?
This commit moves star schema code from ```join.scala``` to ```StarSchemaDetection.scala```. It also applies some minor fixes in ```StarJoinReorderSuite.scala```.
## How was this patch tested?
Run existing ```StarJoinReorderSuite.scala```.
Author: Ioana Delaney <ioanamdelaney@gmail.com>
Closes#17544 from ioana-delaney/starSchemaCBOv2.
## What changes were proposed in this pull request?
Make sure SESSION_LOCAL_TIMEZONE reflects the change in JVM's default timezone setting. Currently several timezone related tests fail as the change to default timezone is not picked up by SQLConf.
## How was this patch tested?
Added an unit test in ConfigEntrySuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#17537 from dilipbiswal/timezone_debug.
## What changes were proposed in this pull request?
Previously when we construct deserializer expression for array type, we will first cast the corresponding field to expected array type and then apply `MapObjects`.
However, by doing that, we lose the opportunity to do by-name resolution for struct type inside array type. In this PR, I introduce a `UnresolvedMapObjects` to hold the lambda function and the input array expression. Then during analysis, after the input array expression is resolved, we get the actual array element type and apply by-name resolution. Then we don't need to add `Cast` for array type when constructing the deserializer expression, as the element type is determined later at analyzer.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17398 from cloud-fan/dataset.
## What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/17285 .
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17521 from cloud-fan/conf.
### What changes were proposed in this pull request?
Observed by felixcheung , in `SparkSession`.`Catalog` APIs, we have different conventions/rules for table/function identifiers/names. Most APIs accept the qualified name (i.e., `databaseName`.`tableName` or `databaseName`.`functionName`). However, the following five APIs do not accept it.
- def listColumns(tableName: String): Dataset[Column]
- def getTable(tableName: String): Table
- def getFunction(functionName: String): Function
- def tableExists(tableName: String): Boolean
- def functionExists(functionName: String): Boolean
To make them consistent with the other Catalog APIs, this PR does the changes, updates the function/API comments and adds the `params` to clarify the inputs we allow.
### How was this patch tested?
Added the test cases .
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17518 from gatorsmile/tableIdentifier.
### What changes were proposed in this pull request?
This PR is to unify and clean up the outputs of `DESC EXTENDED/FORMATTED` and `SHOW TABLE EXTENDED` by moving the logics into the Catalog interface. The output formats are improved. We also add the missing attributes. It impacts the DDL commands like `SHOW TABLE EXTENDED`, `DESC EXTENDED` and `DESC FORMATTED`.
In addition, by following what we did in Dataset API `printSchema`, we can use `treeString` to show the schema in the more readable way.
Below is the current way:
```
Schema: STRUCT<`a`: STRING (nullable = true), `b`: INT (nullable = true), `c`: STRING (nullable = true), `d`: STRING (nullable = true)>
```
After the change, it should look like
```
Schema: root
|-- a: string (nullable = true)
|-- b: integer (nullable = true)
|-- c: string (nullable = true)
|-- d: string (nullable = true)
```
### How was this patch tested?
`describe.sql` and `show-tables.sql`
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17394 from gatorsmile/descFollowUp.
## What changes were proposed in this pull request?
**Description** from JIRA
The TimestampType in Spark SQL is of microsecond precision. Ideally, we should convert Spark SQL timestamp values into Parquet TIMESTAMP_MICROS. But unfortunately parquet-mr hasn't supported it yet.
For the read path, we should be able to read TIMESTAMP_MILLIS Parquet values and pad a 0 microsecond part to read values.
For the write path, currently we are writing timestamps as INT96, similar to Impala and Hive. One alternative is that, we can have a separate SQL option to let users be able to write Spark SQL timestamp values as TIMESTAMP_MILLIS. Of course, in this way the microsecond part will be truncated.
## How was this patch tested?
Added new tests in ParquetQuerySuite and ParquetIOSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#15332 from dilipbiswal/parquet-time-millis.
## What changes were proposed in this pull request?
In SQL queries, we also see predicate expressions involving two columns such as "column-1 (op) column-2" where column-1 and column-2 belong to same table. Note that, if column-1 and column-2 belong to different tables, then it is a join operator's work, NOT a filter operator's work.
This PR estimates filter selectivity on two columns of same table. For example, multiple tpc-h queries have this predicate "WHERE l_commitdate < l_receiptdate"
## How was this patch tested?
We added 6 new test cases to test various logical predicates involving two columns of same table.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Ron Hu <ron.hu@huawei.com>
Author: U-CHINA\r00754707 <r00754707@R00754707-SC04.china.huawei.com>
Closes#17415 from ron8hu/filterTwoColumns.
## What changes were proposed in this pull request?
Range in SQL should be case insensitive
## How was this patch tested?
unit test
Author: samelamin <hussam.elamin@gmail.com>
Author: samelamin <sam_elamin@discovery.com>
Closes#17487 from samelamin/SPARK-20145.
## What changes were proposed in this pull request?
This patch implements `listPartitionsByFilter()` for `InMemoryCatalog` and thus resolves an outstanding TODO causing the `PruneFileSourcePartitions` optimizer rule not to apply when "spark.sql.catalogImplementation" is set to "in-memory" (which is the default).
The change is straightforward: it extracts the code for further filtering of the list of partitions returned by the metastore's `getPartitionsByFilter()` out from `HiveExternalCatalog` into `ExternalCatalogUtils` and calls this new function from `InMemoryCatalog` on the whole list of partitions.
Now that this method is implemented we can always pass the `CatalogTable` to the `DataSource` in `FindDataSourceTable`, so that the latter is resolved to a relation with a `CatalogFileIndex`, which is what the `PruneFileSourcePartitions` rule matches for.
## How was this patch tested?
Ran existing tests and added new test for `listPartitionsByFilter` in `ExternalCatalogSuite`, which is subclassed by both `InMemoryCatalogSuite` and `HiveExternalCatalogSuite`.
Author: Adrian Ionescu <adrian@databricks.com>
Closes#17510 from adrian-ionescu/InMemoryCatalog.
## What changes were proposed in this pull request?
This PR proposes to use `XXX` format instead of `ZZ`. `ZZ` seems a `FastDateFormat` specific.
`ZZ` supports "ISO 8601 extended format time zones" but it seems `FastDateFormat` specific option.
I misunderstood this is compatible format with `SimpleDateFormat` when this change is introduced.
Please see [SimpleDateFormat documentation]( https://docs.oracle.com/javase/7/docs/api/java/text/SimpleDateFormat.html#iso8601timezone) and [FastDateFormat documentation](https://commons.apache.org/proper/commons-lang/apidocs/org/apache/commons/lang3/time/FastDateFormat.html).
It seems we better replace `ZZ` to `XXX` because they look using the same strategy - [FastDateParser.java#L930](8767cd4f1a/src/main/java/org/apache/commons/lang3/time/FastDateParser.java (L930)), [FastDateParser.java#L932-L951 ](8767cd4f1a/src/main/java/org/apache/commons/lang3/time/FastDateParser.java (L932-L951)) and [FastDateParser.java#L596-L601](8767cd4f1a/src/main/java/org/apache/commons/lang3/time/FastDateParser.java (L596-L601)).
I also checked the codes and manually debugged it for sure. It seems both cases use the same pattern `( Z|(?:[+-]\\d{2}(?::)\\d{2}))`.
_Note that this should be rather a fix about documentation and not the behaviour change because `ZZ` seems invalid date format in `SimpleDateFormat` as documented in `DataFrameReader` and etc, and both `ZZ` and `XXX` look identically working with `FastDateFormat`_
Current documentation is as below:
```
* <li>`timestampFormat` (default `yyyy-MM-dd'T'HH:mm:ss.SSSZZ`): sets the string that
* indicates a timestamp format. Custom date formats follow the formats at
* `java.text.SimpleDateFormat`. This applies to timestamp type.</li>
```
## How was this patch tested?
Existing tests should cover this. Also, manually tested as below (BTW, I don't think these are worth being added as tests within Spark):
**Parse**
```scala
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000-11:00")
res4: java.util.Date = Tue Mar 21 20:00:00 KST 2017
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000Z")
res10: java.util.Date = Tue Mar 21 09:00:00 KST 2017
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000-11:00")
java.text.ParseException: Unparseable date: "2017-03-21T00:00:00.000-11:00"
at java.text.DateFormat.parse(DateFormat.java:366)
... 48 elided
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000Z")
java.text.ParseException: Unparseable date: "2017-03-21T00:00:00.000Z"
at java.text.DateFormat.parse(DateFormat.java:366)
... 48 elided
```
```scala
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000-11:00")
res7: java.util.Date = Tue Mar 21 20:00:00 KST 2017
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000Z")
res1: java.util.Date = Tue Mar 21 09:00:00 KST 2017
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000-11:00")
res8: java.util.Date = Tue Mar 21 20:00:00 KST 2017
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000Z")
res2: java.util.Date = Tue Mar 21 09:00:00 KST 2017
```
**Format**
```scala
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").format(new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000-11:00"))
res6: String = 2017-03-21T20:00:00.000+09:00
```
```scala
scala> val fd = org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSZZ")
fd: org.apache.commons.lang3.time.FastDateFormat = FastDateFormat[yyyy-MM-dd'T'HH:mm:ss.SSSZZ,ko_KR,Asia/Seoul]
scala> fd.format(fd.parse("2017-03-21T00:00:00.000-11:00"))
res1: String = 2017-03-21T20:00:00.000+09:00
scala> val fd = org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSXXX")
fd: org.apache.commons.lang3.time.FastDateFormat = FastDateFormat[yyyy-MM-dd'T'HH:mm:ss.SSSXXX,ko_KR,Asia/Seoul]
scala> fd.format(fd.parse("2017-03-21T00:00:00.000-11:00"))
res2: String = 2017-03-21T20:00:00.000+09:00
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17489 from HyukjinKwon/SPARK-20166.
## What changes were proposed in this pull request?
Currently, `DataType.fromJson` throws `scala.MatchError` or `java.util.NoSuchElementException` in some cases when the JSON input is invalid as below:
```scala
DataType.fromJson(""""abcd"""")
```
```
java.util.NoSuchElementException: key not found: abcd
at ...
```
```scala
DataType.fromJson("""{"abcd":"a"}""")
```
```
scala.MatchError: JObject(List((abcd,JString(a)))) (of class org.json4s.JsonAST$JObject)
at ...
```
```scala
DataType.fromJson("""{"fields": [{"a":123}], "type": "struct"}""")
```
```
scala.MatchError: JObject(List((a,JInt(123)))) (of class org.json4s.JsonAST$JObject)
at ...
```
After this PR,
```scala
DataType.fromJson(""""abcd"""")
```
```
java.lang.IllegalArgumentException: Failed to convert the JSON string 'abcd' to a data type.
at ...
```
```scala
DataType.fromJson("""{"abcd":"a"}""")
```
```
java.lang.IllegalArgumentException: Failed to convert the JSON string '{"abcd":"a"}' to a data type.
at ...
```
```scala
DataType.fromJson("""{"fields": [{"a":123}], "type": "struct"}""")
at ...
```
```
java.lang.IllegalArgumentException: Failed to convert the JSON string '{"a":123}' to a field.
```
## How was this patch tested?
Unit test added in `DataTypeSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17468 from HyukjinKwon/fromjson_exception.
## What changes were proposed in this pull request?
`BroadcastHint` should use child's statistics and set `isBroadcastable` to true.
## How was this patch tested?
Added a new stats estimation test for `BroadcastHint`.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17504 from wzhfy/broadcastHintEstimation.
## What changes were proposed in this pull request?
The query plan in an `AnalysisException` may be `null` when an `AnalysisException` object is serialized and then deserialized, since `plan` is marked `transient`. Or when someone throws an `AnalysisException` with a null query plan (which should not happen).
`def getMessage` is not tolerant of this and throws a `NullPointerException`, leading to loss of information about the original exception.
The fix is to add a `null` check in `getMessage`.
## How was this patch tested?
- Unit test
Author: Kunal Khamar <kkhamar@outlook.com>
Closes#17486 from kunalkhamar/spark-20164.
## What changes were proposed in this pull request?
Instead of iterating all expressions that can return null for null inputs, we can just check `NullIntolerant`.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17450 from cloud-fan/null.
…adoc
## What changes were proposed in this pull request?
Use recommended values for row boundaries in Window's scaladoc, i.e. `Window.unboundedPreceding`, `Window.unboundedFollowing`, and `Window.currentRow` (that were introduced in 2.1.0).
## How was this patch tested?
Local build
Author: Jacek Laskowski <jacek@japila.pl>
Closes#17417 from jaceklaskowski/window-expression-scaladoc.
## What changes were proposed in this pull request?
Commit 6c70a38 broke the build for scala 2.10. The commit uses some reflections which are not available in Scala 2.10. This PR fixes them.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#17473 from ueshin/issues/SPARK-19088.
### What changes were proposed in this pull request?
`FalseLiteral` and `TrueLiteral` should have been eliminated by optimizer rule `BooleanSimplification`, but null literals might be added by optimizer rule `NullPropagation`. For safety, our filter estimation should handle all the eligible literal cases.
Our optimizer rule BooleanSimplification is unable to remove the null literal in many cases. For example, `a < 0 or null`. Thus, we need to handle null literal in filter estimation.
`Not` can be pushed down below `And` and `Or`. Then, we could see two consecutive `Not`, which need to be collapsed into one. Because of the limited expression support for filter estimation, we just need to handle the case `Not(null)` for avoiding incorrect error due to the boolean operation on null. For details, see below matrix.
```
not NULL = NULL
NULL or false = NULL
NULL or true = true
NULL or NULL = NULL
NULL and false = false
NULL and true = NULL
NULL and NULL = NULL
```
### How was this patch tested?
Added the test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17446 from gatorsmile/constantFilterEstimation.
## What changes were proposed in this pull request?
This pr added `StructType.fromDDL` to convert a DDL format string into `StructType` for defining schemas in `functions.from_json`.
## How was this patch tested?
Added tests in `JsonFunctionsSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17406 from maropu/SPARK-20009.
## What changes were proposed in this pull request?
When we build the deserializer expression for map type, we will use `StaticInvoke` to call `ArrayBasedMapData.toScalaMap`, and declare the return type as `scala.collection.immutable.Map`. If the map is inside an Option, we will wrap this `StaticInvoke` with `WrapOption`, which requires the input to be `scala.collect.Map`. Ideally this should be fine, as `scala.collection.immutable.Map` extends `scala.collect.Map`, but our `ObjectType` is too strict about this, this PR fixes it.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17454 from cloud-fan/map.
## What changes were proposed in this pull request?
Join reorder algorithm should keep exactly the same order of output attributes in the top project.
For example, if user want to select a, b, c, after reordering, we should output a, b, c in the same order as specified by user, instead of b, a, c or other orders.
## How was this patch tested?
A new test case is added in `JoinReorderSuite`.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17453 from wzhfy/keepOrderInProject.
## What changes were proposed in this pull request?
TPCDS q45 fails becuase:
`ReorderJoin` collects all predicates and try to put them into join condition when creating ordered join. If a predicate with an IN subquery (`ListQuery`) is in a join condition instead of a filter condition, `RewritePredicateSubquery.rewriteExistentialExpr` would fail to convert the subquery to an `ExistenceJoin`, and thus result in error.
We should prevent push down of IN subquery to Join operator.
## How was this patch tested?
Add a new test case in `FilterPushdownSuite`.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17428 from wzhfy/noSubqueryInJoinCond.
## What changes were proposed in this pull request?
The current SessionState initialization code path is quite complex. A part of the creation is done in the SessionState companion objects, a part of the creation is one inside the SessionState class, and a part is done by passing functions.
This PR refactors this code path, and consolidates SessionState initialization into a builder class. This SessionState will not do any initialization and just becomes a place holder for the various Spark SQL internals. This also lays the ground work for two future improvements:
1. This provides us with a start for removing the `HiveSessionState`. Removing the `HiveSessionState` would also require us to move resource loading into a separate class, and to (re)move metadata hive.
2. This makes it easier to customize the Spark Session. Currently you will need to create a custom version of the builder. I have added hooks to facilitate this. A future step will be to create a semi stable API on top of this.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17433 from hvanhovell/SPARK-20100.
## What changes were proposed in this pull request?
In current stage, we don't have advanced statistics such as sketches or histograms. As a result, some operator can't estimate `nullCount` accurately. E.g. left outer join estimation does not accurately update `nullCount` currently. So for `IsNull` and `IsNotNull` predicates, we only estimate them when the child is a leaf node, whose `nullCount` is accurate.
## How was this patch tested?
A new test case is added in `FilterEstimationSuite`.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17438 from wzhfy/nullEstimation.
## What changes were proposed in this pull request?
The `CollapseWindow` is currently to aggressive when collapsing adjacent windows. It also collapses windows in the which the parent produces a column that is consumed by the child; this creates an invalid window which will fail at runtime.
This PR fixes this by adding a check for dependent adjacent windows to the `CollapseWindow` rule.
## How was this patch tested?
Added a new test case to `CollapseWindowSuite`
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17432 from hvanhovell/SPARK-20086.
## What changes were proposed in this pull request?
The `FailureSafeParser` is only used in sql core, it doesn't make sense to put it in catalyst module.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17408 from cloud-fan/minor.
## What changes were proposed in this pull request?
Constraint propagation can be computation expensive and block the driver execution for long time. For example, the below benchmark needs 30mins.
Compared with previous PRs #16998, #16785, this is a much simpler option: add a flag to disable constraint propagation.
### Benchmark
Run the following codes locally.
import org.apache.spark.ml.{Pipeline, PipelineStage}
import org.apache.spark.ml.feature.{OneHotEncoder, StringIndexer, VectorAssembler}
import org.apache.spark.sql.internal.SQLConf
spark.conf.set(SQLConf.CONSTRAINT_PROPAGATION_ENABLED.key, false)
val df = (1 to 40).foldLeft(Seq((1, "foo"), (2, "bar"), (3, "baz")).toDF("id", "x0"))((df, i) => df.withColumn(s"x$i", $"x0"))
val indexers = df.columns.tail.map(c => new StringIndexer()
.setInputCol(c)
.setOutputCol(s"${c}_indexed")
.setHandleInvalid("skip"))
val encoders = indexers.map(indexer => new OneHotEncoder()
.setInputCol(indexer.getOutputCol)
.setOutputCol(s"${indexer.getOutputCol}_encoded")
.setDropLast(true))
val stages: Array[PipelineStage] = indexers ++ encoders
val pipeline = new Pipeline().setStages(stages)
val startTime = System.nanoTime
pipeline.fit(df).transform(df).show
val runningTime = System.nanoTime - startTime
Before this patch: 1786001 ms ~= 30 mins
After this patch: 26392 ms = less than half of a minute
Related PRs: #16998, #16785.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17186 from viirya/add-flag-disable-constraint-propagation.
## What changes were proposed in this pull request?
Fix for typo in Analyzer
## How was this patch tested?
local build
Author: Jacek Laskowski <jacek@japila.pl>
Closes#17409 from jaceklaskowski/analyzer-typo.
## What changes were proposed in this pull request?
Since the state is tied a "group" in the "mapGroupsWithState" operations, its better to call the state "GroupState" instead of a key. This would make it more general if you extends this operation to RelationGroupedDataset and python APIs.
## How was this patch tested?
Existing unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#17385 from tdas/SPARK-20057.
## What changes were proposed in this pull request?
Currently, when we perform count with timestamp types, it prints the internal representation as the column name as below:
```scala
Seq(new java.sql.Timestamp(1)).toDF("a").groupBy("a").pivot("a").count().show()
```
```
+--------------------+----+
| a|1000|
+--------------------+----+
|1969-12-31 16:00:...| 1|
+--------------------+----+
```
This PR proposes to use external Scala value instead of the internal representation in the column names as below:
```
+--------------------+-----------------------+
| a|1969-12-31 16:00:00.001|
+--------------------+-----------------------+
|1969-12-31 16:00:...| 1|
+--------------------+-----------------------+
```
## How was this patch tested?
Unit test in `DataFramePivotSuite` and manual tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17348 from HyukjinKwon/SPARK-20018.
## What changes were proposed in this pull request?
This PR proposes to make `mode` options in both CSV and JSON to use `cass object` and fix some related comments related previous fix.
Also, this PR modifies some tests related parse modes.
## How was this patch tested?
Modified unit tests in both `CSVSuite.scala` and `JsonSuite.scala`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17377 from HyukjinKwon/SPARK-19949.
## What changes were proposed in this pull request?
Adding event time based timeout. The user sets the timeout timestamp directly using `KeyedState.setTimeoutTimestamp`. The keys times out when the watermark crosses the timeout timestamp.
## How was this patch tested?
Unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#17361 from tdas/SPARK-20030.
## What changes were proposed in this pull request?
Change the nullability of function `StringToMap` from `false` to `true`.
Author: zhaorongsheng <334362872@qq.com>
Closes#17350 from zhaorongsheng/bug-fix_strToMap_NPE.
## What changes were proposed in this pull request?
Support` ALTER TABLE ADD COLUMNS (...) `syntax for Hive serde and some datasource tables.
In this PR, we consider a few aspects:
1. View is not supported for `ALTER ADD COLUMNS`
2. Since tables created in SparkSQL with Hive DDL syntax will populate table properties with schema information, we need make sure the consistency of the schema before and after ALTER operation in order for future use.
3. For embedded-schema type of format, such as `parquet`, we need to make sure that the predicate on the newly-added columns can be evaluated properly, or pushed down properly. In case of the data file does not have the columns for the newly-added columns, such predicates should return as if the column values are NULLs.
4. For datasource table, this feature does not support the following:
4.1 TEXT format, since there is only one default column `value` is inferred for text format data.
4.2 ORC format, since SparkSQL native ORC reader does not support the difference between user-specified-schema and inferred schema from ORC files.
4.3 Third party datasource types that implements RelationProvider, including the built-in JDBC format, since different implementations by the vendors may have different ways to dealing with schema.
4.4 Other datasource types, such as `parquet`, `json`, `csv`, `hive` are supported.
5. Column names being added can not be duplicate of any existing data column or partition column names. Case sensitivity is taken into consideration according to the sql configuration.
6. This feature also supports In-Memory catalog, while Hive support is turned off.
## How was this patch tested?
Add new test cases
Author: Xin Wu <xinwu@us.ibm.com>
Closes#16626 from xwu0226/alter_add_columns.
## What changes were proposed in this pull request?
1. Improve documentation for class `Cost` and `JoinReorderDP` and method `buildJoin()`.
2. Change code structure of `buildJoin()` to make the logic clearer.
3. Add a debug-level log to record information for join reordering, including time cost, the number of items and the number of plans in memo.
## How was this patch tested?
Not related.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17353 from wzhfy/reorderFollow.
### What changes were proposed in this pull request?
SessionCatalog API setCurrentDatabase does not set the current database of the underlying ExternalCatalog. Thus, weird errors could come in the test suites after we call reset. We need to fix it.
So far, have not found the direct impact in the other code paths because we expect all the SessionCatalog APIs should always use the current database value we managed, unless some of code paths skip it. Thus, we fix it in the test-only function reset().
### How was this patch tested?
Multiple test case failures are observed in mvn and add a test case in SessionCatalogSuite.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17354 from gatorsmile/useDB.
## What changes were proposed in this pull request?
Currently JSON and CSV have exactly the same logic about handling bad records, this PR tries to abstract it and put it in a upper level to reduce code duplication.
The overall idea is, we make the JSON and CSV parser to throw a BadRecordException, then the upper level, FailureSafeParser, handles bad records according to the parse mode.
Behavior changes:
1. with PERMISSIVE mode, if the number of tokens doesn't match the schema, previously CSV parser will treat it as a legal record and parse as many tokens as possible. After this PR, we treat it as an illegal record, and put the raw record string in a special column, but we still parse as many tokens as possible.
2. all logging is removed as they are not very useful in practice.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Wenchen Fan <cloud0fan@gmail.com>
Closes#17315 from cloud-fan/bad-record2.
## What changes were proposed in this pull request?
A Bean serializer in `ExpressionEncoder` could change values when Beans having NULL. A concrete example is as follows;
```
scala> :paste
class Outer extends Serializable {
private var cls: Inner = _
def setCls(c: Inner): Unit = cls = c
def getCls(): Inner = cls
}
class Inner extends Serializable {
private var str: String = _
def setStr(s: String): Unit = str = str
def getStr(): String = str
}
scala> Seq("""{"cls":null}""", """{"cls": {"str":null}}""").toDF().write.text("data")
scala> val encoder = Encoders.bean(classOf[Outer])
scala> val schema = encoder.schema
scala> val df = spark.read.schema(schema).json("data").as[Outer](encoder)
scala> df.show
+------+
| cls|
+------+
|[null]|
| null|
+------+
scala> df.map(x => x)(encoder).show()
+------+
| cls|
+------+
|[null]|
|[null]| // <-- Value changed
+------+
```
This is because the Bean serializer does not have the NULL-check expressions that the serializer of Scala's product types has. Actually, this value change does not happen in Scala's product types;
```
scala> :paste
case class Outer(cls: Inner)
case class Inner(str: String)
scala> val encoder = Encoders.product[Outer]
scala> val schema = encoder.schema
scala> val df = spark.read.schema(schema).json("data").as[Outer](encoder)
scala> df.show
+------+
| cls|
+------+
|[null]|
| null|
+------+
scala> df.map(x => x)(encoder).show()
+------+
| cls|
+------+
|[null]|
| null|
+------+
```
This pr added the NULL-check expressions in Bean serializer along with the serializer of Scala's product types.
## How was this patch tested?
Added tests in `JavaDatasetSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17347 from maropu/SPARK-19980.
## What changes were proposed in this pull request?
After sort merge join for inner join, now we only keep left key ordering. However, after inner join, right key has the same value and order as left key. So if we need another smj on right key, we will unnecessarily add a sort which causes additional cost.
As a more complicated example, A join B on A.key = B.key join C on B.key = C.key join D on A.key = D.key. We will unnecessarily add a sort on B.key when join {A, B} and C, and add a sort on A.key when join {A, B, C} and D.
To fix this, we need to propagate all sorted information (equivalent expressions) from bottom up through `outputOrdering` and `SortOrder`.
## How was this patch tested?
Test cases are added.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17339 from wzhfy/sortEnhance.
## What changes were proposed in this pull request?
update `StatFunctions.multipleApproxQuantiles` to handle NaN/null
## How was this patch tested?
existing tests and added tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#16971 from zhengruifeng/quantiles_nan.
## What changes were proposed in this pull request?
Star schema consists of one or more fact tables referencing a number of dimension tables. In general, queries against star schema are expected to run fast because of the established RI constraints among the tables. This design proposes a join reordering based on natural, generally accepted heuristics for star schema queries:
- Finds the star join with the largest fact table and places it on the driving arm of the left-deep join. This plan avoids large tables on the inner, and thus favors hash joins.
- Applies the most selective dimensions early in the plan to reduce the amount of data flow.
The design document was included in SPARK-17791.
Link to the google doc: [StarSchemaDetection](https://docs.google.com/document/d/1UAfwbm_A6wo7goHlVZfYK99pqDMEZUumi7pubJXETEA/edit?usp=sharing)
## How was this patch tested?
A new test suite StarJoinSuite.scala was implemented.
Author: Ioana Delaney <ioanamdelaney@gmail.com>
Closes#15363 from ioana-delaney/starJoinReord2.
## What changes were proposed in this pull request?
This PR proposes to support an array of struct type in `to_json` as below:
```scala
import org.apache.spark.sql.functions._
val df = Seq(Tuple1(Tuple1(1) :: Nil)).toDF("a")
df.select(to_json($"a").as("json")).show()
```
```
+----------+
| json|
+----------+
|[{"_1":1}]|
+----------+
```
Currently, it throws an exception as below (a newline manually inserted for readability):
```
org.apache.spark.sql.AnalysisException: cannot resolve 'structtojson(`array`)' due to data type
mismatch: structtojson requires that the expression is a struct expression.;;
```
This allows the roundtrip with `from_json` as below:
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = ArrayType(StructType(StructField("a", IntegerType) :: Nil))
val df = Seq("""[{"a":1}, {"a":2}]""").toDF("json").select(from_json($"json", schema).as("array"))
df.show()
// Read back.
df.select(to_json($"array").as("json")).show()
```
```
+----------+
| array|
+----------+
|[[1], [2]]|
+----------+
+-----------------+
| json|
+-----------------+
|[{"a":1},{"a":2}]|
+-----------------+
```
Also, this PR proposes to rename from `StructToJson` to `StructsToJson ` and `JsonToStruct` to `JsonToStructs`.
## How was this patch tested?
Unit tests in `JsonFunctionsSuite` and `JsonExpressionsSuite` for Scala, doctest for Python and test in `test_sparkSQL.R` for R.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17192 from HyukjinKwon/SPARK-19849.
## What changes were proposed in this pull request?
When a key does not get any new data in `mapGroupsWithState`, the mapping function is never called on it. So we need a timeout feature that calls the function again in such cases, so that the user can decide whether to continue waiting or clean up (remove state, save stuff externally, etc.).
Timeouts can be either based on processing time or event time. This JIRA is for processing time, but defines the high level API design for both. The usage would look like this.
```
def stateFunction(key: K, value: Iterator[V], state: KeyedState[S]): U = {
...
state.setTimeoutDuration(10000)
...
}
dataset // type is Dataset[T]
.groupByKey[K](keyingFunc) // generates KeyValueGroupedDataset[K, T]
.mapGroupsWithState[S, U](
func = stateFunction,
timeout = KeyedStateTimeout.withProcessingTime) // returns Dataset[U]
```
Note the following design aspects.
- The timeout type is provided as a param in mapGroupsWithState as a parameter global to all the keys. This is so that the planner knows this at planning time, and accordingly optimize the execution based on whether to saves extra info in state or not (e.g. timeout durations or timestamps).
- The exact timeout duration is provided inside the function call so that it can be customized on a per key basis.
- When the timeout occurs for a key, the function is called with no values, and KeyedState.isTimingOut() set to true.
- The timeout is reset for key every time the function is called on the key, that is, when the key has new data, or the key has timed out. So the user has to set the timeout duration everytime the function is called, otherwise there will not be any timeout set.
Guarantees provided on timeout of key, when timeout duration is D ms:
- Timeout will never be called before real clock time has advanced by D ms
- Timeout will be called eventually when there is a trigger with any data in it (i.e. after D ms). So there is a no strict upper bound on when the timeout would occur. For example, if there is no data in the stream (for any key) for a while, then the timeout will not be hit.
Implementation details:
- Added new param to `mapGroupsWithState` for timeout
- Added new method to `StateStore` to filter data based on timeout timestamp
- Changed the internal map type of `HDFSBackedStateStore` from Java's `HashMap` to `ConcurrentHashMap` as the latter allows weakly-consistent fail-safe iterators on the map data. See comments in code for more details.
- Refactored logic of `MapGroupsWithStateExec` to
- Save timeout info to state store for each key that has data.
- Then, filter states that should be timed out based on the current batch processing timestamp.
- Moved KeyedState for `o.a.s.sql` to `o.a.s.sql.streaming`. I remember that this was a feedback in the MapGroupsWithState PR that I had forgotten to address.
## How was this patch tested?
New unit tests in
- MapGroupsWithStateSuite for timeouts.
- StateStoreSuite for new APIs in StateStore.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#17179 from tdas/mapgroupwithstate-timeout.
## What changes were proposed in this pull request?
If case classes have circular references below, it throws StackOverflowError;
```
scala> :pasge
case class classA(i: Int, cls: classB)
case class classB(cls: classA)
scala> Seq(classA(0, null)).toDS()
java.lang.StackOverflowError
at scala.reflect.internal.Symbols$Symbol.info(Symbols.scala:1494)
at scala.reflect.runtime.JavaMirrors$JavaMirror$$anon$1.scala$reflect$runtime$SynchronizedSymbols$SynchronizedSymbol$$super$info(JavaMirrors.scala:66)
at scala.reflect.runtime.SynchronizedSymbols$SynchronizedSymbol$$anonfun$info$1.apply(SynchronizedSymbols.scala:127)
at scala.reflect.runtime.SynchronizedSymbols$SynchronizedSymbol$$anonfun$info$1.apply(SynchronizedSymbols.scala:127)
at scala.reflect.runtime.Gil$class.gilSynchronized(Gil.scala:19)
at scala.reflect.runtime.JavaUniverse.gilSynchronized(JavaUniverse.scala:16)
at scala.reflect.runtime.SynchronizedSymbols$SynchronizedSymbol$class.gilSynchronizedIfNotThreadsafe(SynchronizedSymbols.scala:123)
at scala.reflect.runtime.JavaMirrors$JavaMirror$$anon$1.gilSynchronizedIfNotThreadsafe(JavaMirrors.scala:66)
at scala.reflect.runtime.SynchronizedSymbols$SynchronizedSymbol$class.info(SynchronizedSymbols.scala:127)
at scala.reflect.runtime.JavaMirrors$JavaMirror$$anon$1.info(JavaMirrors.scala:66)
at scala.reflect.internal.Mirrors$RootsBase.getModuleOrClass(Mirrors.scala:48)
at scala.reflect.internal.Mirrors$RootsBase.getModuleOrClass(Mirrors.scala:45)
at scala.reflect.internal.Mirrors$RootsBase.getModuleOrClass(Mirrors.scala:45)
at scala.reflect.internal.Mirrors$RootsBase.getModuleOrClass(Mirrors.scala:45)
at scala.reflect.internal.Mirrors$RootsBase.getModuleOrClass(Mirrors.scala:45)
```
This pr added code to throw UnsupportedOperationException in that case as follows;
```
scala> :paste
case class A(cls: B)
case class B(cls: A)
scala> Seq(A(null)).toDS()
java.lang.UnsupportedOperationException: cannot have circular references in class, but got the circular reference of class B
at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor(ScalaReflection.scala:627)
at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$9.apply(ScalaReflection.scala:644)
at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$9.apply(ScalaReflection.scala:632)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
```
## How was this patch tested?
Added tests in `DatasetSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17318 from maropu/SPARK-19896.
## What changes were proposed in this pull request?
We have some concerns about removing size in the cost model [in the previous pr](https://github.com/apache/spark/pull/17240). It's a tradeoff between code structure and algorithm completeness. I tend to keep the size and thus create this new pr without changing cost model.
What this pr does:
1. We only consider consecutive inner joinable items, thus excluding cartesian products in reordering procedure. This significantly reduces the search space and memory overhead of memo. Otherwise every combination of items will exist in the memo.
2. This pr also includes a bug fix: if a leaf item is a project(_, child), current solution will miss the project.
## How was this patch tested?
Added test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17286 from wzhfy/joinReorder3.
## What changes were proposed in this pull request?
This pr added entries in `FunctionRegistry` and supported `from_json` in SQL.
## How was this patch tested?
Added tests in `JsonFunctionsSuite` and `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17320 from maropu/SPARK-19967.
## What changes were proposed in this pull request?
Allows null values of the pivot column to be included in the pivot values list without throwing NPE
Note this PR was made as an alternative to #17224 but preserves the two phase aggregate operation that is needed for good performance.
## How was this patch tested?
Additional unit test
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#17226 from aray/pivot-null.
## What changes were proposed in this pull request?
Currently `SessionCatalogSuite` is only for `InMemoryCatalog`, there is no suite for `HiveExternalCatalog`.
And there are some ddl function is not proper to test in `ExternalCatalogSuite`, because some logic are not full implement in `ExternalCatalog`, these ddl functions are full implement in `SessionCatalog`(e.g. merge the same logic from `ExternalCatalog` up to `SessionCatalog` ).
It is better to test it in `SessionCatalogSuite` for this situation.
So we should add a test suite for `SessionCatalog` with `HiveExternalCatalog`
The main change is that in `SessionCatalogSuite` add two functions:
`withBasicCatalog` and `withEmptyCatalog`
And replace the code like `val catalog = new SessionCatalog(newBasicCatalog)` with above two functions
## How was this patch tested?
add `HiveExternalSessionCatalogSuite`
Author: windpiger <songjun@outlook.com>
Closes#17287 from windpiger/sessioncatalogsuit.
### What changes were proposed in this pull request?
Specifying the table schema in DDL formats is needed for different scenarios. For example,
- [specifying the schema in SQL function `from_json` using DDL formats](https://issues.apache.org/jira/browse/SPARK-19637), which is suggested by marmbrus ,
- [specifying the customized JDBC data types](https://github.com/apache/spark/pull/16209).
These two PRs need users to use the JSON format to specify the table schema. This is not user friendly.
This PR is to provide a `parseTableSchema` API in `ParserInterface`.
### How was this patch tested?
Added a test suite `TableSchemaParserSuite`
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17171 from gatorsmile/parseDDLStmt.
## What changes were proposed in this pull request?
The current master throws `StackOverflowError` in `createDataFrame`/`createDataset` if bean has one's own class in fields;
```
public class SelfClassInFieldBean implements Serializable {
private SelfClassInFieldBean child;
...
}
```
This pr added code to throw `UnsupportedOperationException` in that case as soon as possible.
## How was this patch tested?
Added tests in `JavaDataFrameSuite` and `JavaDatasetSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17188 from maropu/SPARK-19751.
## What changes were proposed in this pull request?
unify a exception erro msg for dropdatabase when the database still have some tables for HiveExternalCatalog and InMemoryCatalog
## How was this patch tested?
N/A
Author: windpiger <songjun@outlook.com>
Closes#17305 from windpiger/unifyErromsg.
## What issue does this PR address ?
Jira: https://issues.apache.org/jira/browse/SPARK-13450
In `SortMergeJoinExec`, rows of the right relation having the same value for a join key are buffered in-memory. In case of skew, this causes OOMs (see comments in SPARK-13450 for more details). Heap dump from a failed job confirms this : https://issues.apache.org/jira/secure/attachment/12846382/heap-dump-analysis.png . While its possible to increase the heap size to workaround, Spark should be resilient to such issues as skews can happen arbitrarily.
## Change proposed in this pull request
- Introduces `ExternalAppendOnlyUnsafeRowArray`
- It holds `UnsafeRow`s in-memory upto a certain threshold.
- After the threshold is hit, it switches to `UnsafeExternalSorter` which enables spilling of the rows to disk. It does NOT sort the data.
- Allows iterating the array multiple times. However, any alteration to the array (using `add` or `clear`) will invalidate the existing iterator(s)
- `WindowExec` was already using `UnsafeExternalSorter` to support spilling. Changed it to use the new array
- Changed `SortMergeJoinExec` to use the new array implementation
- NOTE: I have not changed FULL OUTER JOIN to use this new array implementation. Changing that will need more surgery and I will rather put up a separate PR for that once this gets in.
- Changed `CartesianProductExec` to use the new array implementation
#### Note for reviewers
The diff can be divided into 3 parts. My motive behind having all the changes in a single PR was to demonstrate that the API is sane and supports 2 use cases. If reviewing as 3 separate PRs would help, I am happy to make the split.
## How was this patch tested ?
#### Unit testing
- Added unit tests `ExternalAppendOnlyUnsafeRowArray` to validate all its APIs and access patterns
- Added unit test for `SortMergeExec`
- with and without spill for inner join, left outer join, right outer join to confirm that the spill threshold config behaves as expected and output is as expected.
- This PR touches the scanning logic in `SortMergeExec` for _all_ joins (except FULL OUTER JOIN). However, I expect existing test cases to cover that there is no regression in correctness.
- Added unit test for `WindowExec` to check behavior of spilling and correctness of results.
#### Stress testing
- Confirmed that OOM is gone by running against a production job which used to OOM
- Since I cannot share details about prod workload externally, created synthetic data to mimic the issue. Ran before and after the fix to demonstrate the issue and query success with this PR
Generating the synthetic data
```
./bin/spark-shell --driver-memory=6G
import org.apache.spark.sql._
val hc = SparkSession.builder.master("local").getOrCreate()
hc.sql("DROP TABLE IF EXISTS spark_13450_large_table").collect
hc.sql("DROP TABLE IF EXISTS spark_13450_one_row_table").collect
val df1 = (0 until 1).map(i => ("10", "100", i.toString, (i * 2).toString)).toDF("i", "j", "str1", "str2")
df1.write.format("org.apache.spark.sql.hive.orc.OrcFileFormat").bucketBy(100, "i", "j").sortBy("i", "j").saveAsTable("spark_13450_one_row_table")
val df2 = (0 until 3000000).map(i => ("10", "100", i.toString, (i * 2).toString)).toDF("i", "j", "str1", "str2")
df2.write.format("org.apache.spark.sql.hive.orc.OrcFileFormat").bucketBy(100, "i", "j").sortBy("i", "j").saveAsTable("spark_13450_large_table")
```
Ran this against trunk VS local build with this PR. OOM repros with trunk and with the fix this query runs fine.
```
./bin/spark-shell --driver-java-options="-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/tmp/spark.driver.heapdump.hprof"
import org.apache.spark.sql._
val hc = SparkSession.builder.master("local").getOrCreate()
hc.sql("SET spark.sql.autoBroadcastJoinThreshold=1")
hc.sql("SET spark.sql.sortMergeJoinExec.buffer.spill.threshold=10000")
hc.sql("DROP TABLE IF EXISTS spark_13450_result").collect
hc.sql("""
CREATE TABLE spark_13450_result
AS
SELECT
a.i AS a_i, a.j AS a_j, a.str1 AS a_str1, a.str2 AS a_str2,
b.i AS b_i, b.j AS b_j, b.str1 AS b_str1, b.str2 AS b_str2
FROM
spark_13450_one_row_table a
JOIN
spark_13450_large_table b
ON
a.i=b.i AND
a.j=b.j
""")
```
## Performance comparison
### Macro-benchmark
I ran a SMB join query over two real world tables (2 trillion rows (40 TB) and 6 million rows (120 GB)). Note that this dataset does not have skew so no spill happened. I saw improvement in CPU time by 2-4% over version without this PR. This did not add up as I was expected some regression. I think allocating array of capacity of 128 at the start (instead of starting with default size 16) is the sole reason for the perf. gain : https://github.com/tejasapatil/spark/blob/SPARK-13450_smb_buffer_oom/sql/core/src/main/scala/org/apache/spark/sql/execution/ExternalAppendOnlyUnsafeRowArray.scala#L43 . I could remove that and rerun, but effectively the change will be deployed in this form and I wanted to see the effect of it over large workload.
### Micro-benchmark
Two types of benchmarking can be found in `ExternalAppendOnlyUnsafeRowArrayBenchmark`:
[A] Comparing `ExternalAppendOnlyUnsafeRowArray` against raw `ArrayBuffer` when all rows fit in-memory and there is no spill
```
Array with 1000 rows: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
ArrayBuffer 7821 / 7941 33.5 29.8 1.0X
ExternalAppendOnlyUnsafeRowArray 8798 / 8819 29.8 33.6 0.9X
Array with 30000 rows: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
ArrayBuffer 19200 / 19206 25.6 39.1 1.0X
ExternalAppendOnlyUnsafeRowArray 19558 / 19562 25.1 39.8 1.0X
Array with 100000 rows: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
ArrayBuffer 5949 / 6028 17.2 58.1 1.0X
ExternalAppendOnlyUnsafeRowArray 6078 / 6138 16.8 59.4 1.0X
```
[B] Comparing `ExternalAppendOnlyUnsafeRowArray` against raw `UnsafeExternalSorter` when there is spilling of data
```
Spilling with 1000 rows: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
UnsafeExternalSorter 9239 / 9470 28.4 35.2 1.0X
ExternalAppendOnlyUnsafeRowArray 8857 / 8909 29.6 33.8 1.0X
Spilling with 10000 rows: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
UnsafeExternalSorter 4 / 5 39.3 25.5 1.0X
ExternalAppendOnlyUnsafeRowArray 5 / 6 29.8 33.5 0.8X
```
Author: Tejas Patil <tejasp@fb.com>
Closes#16909 from tejasapatil/SPARK-13450_smb_buffer_oom.
## What changes were proposed in this pull request?
We should restrict the nested level of a view, to avoid stack overflow exception during the view resolution.
## How was this patch tested?
Add new test case in `SQLViewSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#17241 from jiangxb1987/view-depth.
## What changes were proposed in this pull request?
When dynamic partition value is null or empty string, we should write the data to a directory like `a=__HIVE_DEFAULT_PARTITION__`, when we read the data back, we should respect this special directory name and treat it as null.
This is the same behavior of impala, see https://issues.apache.org/jira/browse/IMPALA-252
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17277 from cloud-fan/partition.
## What changes were proposed in this pull request?
As timezone setting can also affect partition values, it works for all formats, we should make it clear.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#17281 from ueshin/issues/SPARK-19817.
## What changes were proposed in this pull request?
This PR fixes the following problem:
````
Seq((1, 2)).toDF("a1", "a2").createOrReplaceTempView("a")
Seq[(java.lang.Integer, java.lang.Integer)]((1, null)).toDF("b1", "b2").createOrReplaceTempView("b")
// The expected result is 1 row of (1,2) as shown in the next statement.
sql("select * from a where a1 not in (select b1 from b where b2 = a2)").show
+---+---+
| a1| a2|
+---+---+
+---+---+
sql("select * from a where a1 not in (select b1 from b where b2 = 2)").show
+---+---+
| a1| a2|
+---+---+
| 1| 2|
+---+---+
````
There are a number of scenarios to consider:
1. When the correlated predicate yields a match (i.e., B.B2 = A.A2)
1.1. When the NOT IN expression yields a match (i.e., A.A1 = B.B1)
1.2. When the NOT IN expression yields no match (i.e., A.A1 = B.B1 returns false)
1.3. When A.A1 is null
1.4. When B.B1 is null
1.4.1. When A.A1 is not null
1.4.2. When A.A1 is null
2. When the correlated predicate yields no match (i.e.,B.B2 = A.A2 is false or unknown)
2.1. When B.B2 is null and A.A2 is null
2.2. When B.B2 is null and A.A2 is not null
2.3. When the value of A.A2 does not match any of B.B2
````
A.A1 A.A2 B.B1 B.B2
----- ----- ----- -----
1 1 1 1 (1.1)
2 1 (1.2)
null 1 (1.3)
1 3 null 3 (1.4.1)
null 3 (1.4.2)
1 null 1 null (2.1)
null 2 (2.2 & 2.3)
````
We can divide the evaluation of the above correlated NOT IN subquery into 2 groups:-
Group 1: The rows in A when there is a match from the correlated predicate (A.A1 = B.B1)
In this case, the result of the subquery is not empty and the semantics of the NOT IN depends solely on the evaluation of the equality comparison of the columns of NOT IN, i.e., A1 = B1, which says
- If A.A1 is null, the row is filtered (1.3 and 1.4.2)
- If A.A1 = B.B1, the row is filtered (1.1)
- If B.B1 is null, any rows of A in the same group (A.A2 = B.B2) is filtered (1.4.1 & 1.4.2)
- Otherwise, the row is qualified.
Hence, in this group, the result is the row from (1.2).
Group 2: The rows in A when there is no match from the correlated predicate (A.A2 = B.B2)
In this case, all the rows in A, including the rows where A.A1, are qualified because the subquery returns an empty set and by the semantics of the NOT IN, all rows from the parent side qualifies as the result set, that is, the rows from (2.1, 2.2 and 2.3).
In conclusion, the correct result set of the above query is
````
A.A1 A.A2
----- -----
2 1 (1.2)
1 null (2.1)
null 2 (2.2 & 2.3)
````
## How was this patch tested?
unit tests, regression tests, and new test cases focusing on the problem being fixed.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#17294 from nsyca/18966.
## What changes were proposed in this pull request?
The `RemoveRedundantAlias` rule can change the output attributes (the expression id's to be precise) of a query by eliminating the redundant alias producing them. This is no problem for a regular query, but can cause problems for correlated subqueries: The attributes produced by the subquery are used in the parent plan; changing them will break the parent plan.
This PR fixes this by wrapping a subquery in a `Subquery` top level node when it gets optimized. The `RemoveRedundantAlias` rule now recognizes `Subquery` and makes sure that the output attributes of the `Subquery` node are retained.
## How was this patch tested?
Added a test case to `RemoveRedundantAliasAndProjectSuite` and added a regression test to `SubquerySuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17278 from hvanhovell/SPARK-19933.
## What changes were proposed in this pull request?
Commit 4ce970d714 in accidentally broke the 2.10 build for Spark. This PR fixes this by simplifying the offending pattern match.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17288 from hvanhovell/SPARK-18874.
## What changes were proposed in this pull request?
We currently cannot use aliases in SQL function calls. This is inconvenient when you try to create a struct. This SQL query for example `select struct(1, 2) st`, will create a struct with column names `col1` and `col2`. This is even more problematic when we want to append a field to an existing struct. For example if we want to a field to struct `st` we would issue the following SQL query `select struct(st.*, 1) as st from src`, the result will be struct `st` with an a column with a non descriptive name `col3` (if `st` itself has 2 fields).
This PR proposes to change this by allowing the use of aliased expression in function parameters. For example `select struct(1 as a, 2 as b) st`, will create a struct with columns `a` & `b`.
## How was this patch tested?
Added a test to `ExpressionParserSuite` and added a test file for `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17245 from hvanhovell/SPARK-19850.
## What changes were proposed in this pull request?
This patch moves SQLConf from sql/core to sql/catalyst. To minimize the changes, the patch used type alias to still keep CatalystConf (as a type alias) and SimpleCatalystConf (as a concrete class that extends SQLConf).
Motivation for the change is that it is pretty weird to have SQLConf only in sql/core and then we have to duplicate config options that impact optimizer/analyzer in sql/catalyst using CatalystConf.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#17285 from rxin/SPARK-19944.
## What changes were proposed in this pull request?
- Timestamp hashing is done as per [TimestampWritable.hashCode()](ff67cdda1c/serde/src/java/org/apache/hadoop/hive/serde2/io/TimestampWritable.java (L406)) in Hive
- Interval hashing is done as per [HiveIntervalDayTime.hashCode()](ff67cdda1c/storage-api/src/java/org/apache/hadoop/hive/common/type/HiveIntervalDayTime.java (L178)). Note that there are inherent differences in how Hive and Spark store intervals under the hood which limits the ability to be in completely sync with hive's hashing function. I have explained this in the method doc.
- Date type was already supported. This PR adds test for that.
## How was this patch tested?
Added unit tests
Author: Tejas Patil <tejasp@fb.com>
Closes#17062 from tejasapatil/SPARK-17495_time_related_types.
## What changes were proposed in this pull request?
In spark SQL, map type can't be used in equality test/comparison, and `Intersect`/`Except`/`Distinct` do need equality test for all columns, we should not allow map type in `Intersect`/`Except`/`Distinct`.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17236 from cloud-fan/map.
## Summary of changes
Add a new configuration option that allows Spark SQL to infer a case-sensitive schema from a Hive Metastore table's data files when a case-sensitive schema can't be read from the table properties.
- Add spark.sql.hive.caseSensitiveInferenceMode param to SQLConf
- Add schemaPreservesCase field to CatalogTable (set to false when schema can't
successfully be read from Hive table props)
- Perform schema inference in HiveMetastoreCatalog if schemaPreservesCase is
false, depending on spark.sql.hive.caseSensitiveInferenceMode
- Add alterTableSchema() method to the ExternalCatalog interface
- Add HiveSchemaInferenceSuite tests
- Refactor and move ParquetFileForamt.meregeMetastoreParquetSchema() as
HiveMetastoreCatalog.mergeWithMetastoreSchema
- Move schema merging tests from ParquetSchemaSuite to HiveSchemaInferenceSuite
[JIRA for this change](https://issues.apache.org/jira/browse/SPARK-19611)
## How was this patch tested?
The tests in ```HiveSchemaInferenceSuite``` should verify that schema inference is working as expected. ```ExternalCatalogSuite``` has also been extended to cover the new ```alterTableSchema()``` API.
Author: Budde <budde@amazon.com>
Closes#16944 from budde/SPARK-19611.
## What changes were proposed in this pull request?
If we create a external datasource table with a non-qualified location , we should qualified it to store in catalog.
```
CREATE TABLE t(a string)
USING parquet
LOCATION '/path/xx'
CREATE TABLE t1(a string, b string)
USING parquet
PARTITIONED BY(b)
LOCATION '/path/xx'
```
when we get the table from catalog, the location should be qualified, e.g.'file:/path/xxx'
## How was this patch tested?
unit test added
Author: windpiger <songjun@outlook.com>
Closes#17095 from windpiger/tablepathQualified.
## What changes were proposed in this pull request?
A follow up to SPARK-19859:
- extract the calculation of `delayMs` and reuse it.
- update EventTimeWatermarkExec
- use the correct `delayMs` in EventTimeWatermark
## How was this patch tested?
Jenkins.
Author: uncleGen <hustyugm@gmail.com>
Closes#17221 from uncleGen/SPARK-19859.
Forking a newSession() from SparkSession currently makes a new SparkSession that does not retain SessionState (i.e. temporary tables, SQL config, registered functions etc.) This change adds a method cloneSession() which creates a new SparkSession with a copy of the parent's SessionState.
Subsequent changes to base session are not propagated to cloned session, clone is independent after creation.
If the base is changed after clone has been created, say user registers new UDF, then the new UDF will not be available inside the clone. Same goes for configs and temp tables.
Unit tests
Author: Kunal Khamar <kkhamar@outlook.com>
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16826 from kunalkhamar/fork-sparksession.
## What changes were proposed in this pull request?
Add a output mode parameter to `flatMapGroupsWithState` and just define `mapGroupsWithState` as `flatMapGroupsWithState(Update)`.
`UnsupportedOperationChecker` is modified to disallow unsupported cases.
- Batch mapGroupsWithState or flatMapGroupsWithState is always allowed.
- For streaming (map/flatMap)GroupsWithState, see the following table:
| Operators | Supported Query Output Mode |
| ------------- | ------------- |
| flatMapGroupsWithState(Update) without aggregation | Update |
| flatMapGroupsWithState(Update) with aggregation | None |
| flatMapGroupsWithState(Append) without aggregation | Append |
| flatMapGroupsWithState(Append) before aggregation | Append, Update, Complete |
| flatMapGroupsWithState(Append) after aggregation | None |
| Multiple flatMapGroupsWithState(Append)s | Append |
| Multiple mapGroupsWithStates | None |
| Mxing mapGroupsWithStates and flatMapGroupsWithStates | None |
| Other cases of multiple flatMapGroupsWithState | None |
## How was this patch tested?
The added unit tests. Here are the tests related to (map/flatMap)GroupsWithState:
```
[info] - batch plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on batch relation: supported (1 millisecond)
[info] - batch plan - flatMapGroupsWithState - multiple flatMapGroupsWithState(Append)s on batch relation: supported (0 milliseconds)
[info] - batch plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on batch relation: supported (0 milliseconds)
[info] - batch plan - flatMapGroupsWithState - multiple flatMapGroupsWithState(Update)s on batch relation: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation without aggregation in update mode: supported (2 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation without aggregation in append mode: not supported (7 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation without aggregation in complete mode: not supported (5 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation with aggregation in Append mode: not supported (11 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation with aggregation in Update mode: not supported (5 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation with aggregation in Complete mode: not supported (5 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation without aggregation in append mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation without aggregation in update mode: not supported (6 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation before aggregation in Append mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation before aggregation in Update mode: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation before aggregation in Complete mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation after aggregation in Append mode: not supported (6 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation after aggregation in Update mode: not supported (4 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation in complete mode: not supported (2 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on batch relation inside streaming relation in Append output mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on batch relation inside streaming relation in Update output mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on batch relation inside streaming relation in Append output mode: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on batch relation inside streaming relation in Update output mode: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - multiple flatMapGroupsWithStates on streaming relation and all are in append mode: supported (2 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - multiple flatMapGroupsWithStates on s streaming relation but some are not in append mode: not supported (7 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation without aggregation in append mode: not supported (3 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation without aggregation in complete mode: not supported (3 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation with aggregation in Append mode: not supported (6 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation with aggregation in Update mode: not supported (3 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation with aggregation in Complete mode: not supported (4 milliseconds)
[info] - streaming plan - mapGroupsWithState - multiple mapGroupsWithStates on streaming relation and all are in append mode: not supported (4 milliseconds)
[info] - streaming plan - mapGroupsWithState - mixing mapGroupsWithStates and flatMapGroupsWithStates on streaming relation: not supported (4 milliseconds)
```
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17197 from zsxwing/mapgroups-check.
## What changes were proposed in this pull request?
Fix for SQL round function that modifies original column when underlying data frame is created from a local product.
import org.apache.spark.sql.functions._
case class NumericRow(value: BigDecimal)
val df = spark.createDataFrame(Seq(NumericRow(BigDecimal("1.23456789"))))
df.show()
+--------------------+
| value|
+--------------------+
|1.234567890000000000|
+--------------------+
df.withColumn("value_rounded", round('value)).show()
// before
+--------------------+-------------+
| value|value_rounded|
+--------------------+-------------+
|1.000000000000000000| 1|
+--------------------+-------------+
// after
+--------------------+-------------+
| value|value_rounded|
+--------------------+-------------+
|1.234567890000000000| 1|
+--------------------+-------------+
## How was this patch tested?
New unit test added to existing suite `org.apache.spark.sql.MathFunctionsSuite`
Author: Wojtek Szymanski <wk.szymanski@gmail.com>
Closes#17075 from wojtek-szymanski/SPARK-19727.
### What changes were proposed in this pull request?
Observed by felixcheung in https://github.com/apache/spark/pull/16739, when users use the shuffle-enabled `repartition` API, they expect the partition they got should be the exact number they provided, even if they call shuffle-disabled `coalesce` later.
Currently, `CollapseRepartition` rule does not consider whether shuffle is enabled or not. Thus, we got the following unexpected result.
```Scala
val df = spark.range(0, 10000, 1, 5)
val df2 = df.repartition(10)
assert(df2.coalesce(13).rdd.getNumPartitions == 5)
assert(df2.coalesce(7).rdd.getNumPartitions == 5)
assert(df2.coalesce(3).rdd.getNumPartitions == 3)
```
This PR is to fix the issue. We preserve shuffle-enabled Repartition.
### How was this patch tested?
Added a test case
Author: Xiao Li <gatorsmile@gmail.com>
Closes#16933 from gatorsmile/CollapseRepartition.
## What changes were proposed in this pull request?
Since we have a `View` node now, we can remove the view identifier in `SubqueryAlias`, which was used to indicate a view node before.
## How was this patch tested?
Update the related test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#17210 from jiangxb1987/SubqueryAlias.
## What changes were proposed in this pull request?
Reorder the joins using a dynamic programming algorithm (Selinger paper):
First we put all items (basic joined nodes) into level 1, then we build all two-way joins at level 2 from plans at level 1 (single items), then build all 3-way joins from plans at previous levels (two-way joins and single items), then 4-way joins ... etc, until we build all n-way joins and pick the best plan among them.
When building m-way joins, we only keep the best plan (with the lowest cost) for the same set of m items. E.g., for 3-way joins, we keep only the best plan for items {A, B, C} among plans (A J B) J C, (A J C) J B and (B J C) J A. Thus, the plans maintained for each level when reordering four items A, B, C, D are as follows:
```
level 1: p({A}), p({B}), p({C}), p({D})
level 2: p({A, B}), p({A, C}), p({A, D}), p({B, C}), p({B, D}), p({C, D})
level 3: p({A, B, C}), p({A, B, D}), p({A, C, D}), p({B, C, D})
level 4: p({A, B, C, D})
```
where p({A, B, C, D}) is the final output plan.
For cost evaluation, since physical costs for operators are not available currently, we use cardinalities and sizes to compute costs.
## How was this patch tested?
add test cases
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#17138 from wzhfy/joinReorder.
Previously, we were using the mirror of passed in `TypeTag` when reflecting to build an encoder. This fails when the outer class is built in (i.e. `Seq`'s default mirror is based on root classloader) but inner classes (i.e. `A` in `Seq[A]`) are defined in the REPL or a library.
This patch changes us to always reflect based on a mirror created using the context classloader.
Author: Michael Armbrust <michael@databricks.com>
Closes#17201 from marmbrus/replSeqEncoder.
## What changes were proposed in this pull request?
The new watermark should override the old one. Otherwise, we just pick up the first column which has a watermark, it may be unexpected.
## How was this patch tested?
The new test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17199 from zsxwing/SPARK-19859.
## What changes were proposed in this pull request?
Jira : https://issues.apache.org/jira/browse/SPARK-19843
Created wrapper classes (`IntWrapper`, `LongWrapper`) to wrap the result of parsing (which are primitive types). In case of problem in parsing, the method would return a boolean.
## How was this patch tested?
- Added new unit tests
- Ran a prod job which had conversion from string -> int and verified the outputs
## Performance
Tiny regression when all strings are valid integers
```
conversion to int: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
--------------------------------------------------------------------------------
trunk 502 / 522 33.4 29.9 1.0X
SPARK-19843 493 / 503 34.0 29.4 1.0X
```
Huge gain when all strings are invalid integers
```
conversion to int: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------
trunk 33913 / 34219 0.5 2021.4 1.0X
SPARK-19843 154 / 162 108.8 9.2 220.0X
```
Author: Tejas Patil <tejasp@fb.com>
Closes#17184 from tejasapatil/SPARK-19843_is_numeric_maybe.
## What changes were proposed in this pull request?
This pr added entries in `FunctionRegistry` and supported `to_json` in SQL.
## How was this patch tested?
Added tests in `JsonFunctionsSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#16981 from maropu/SPARK-19637.
## What changes were proposed in this pull request?
1. support boolean type in binary expression estimation.
2. deal with compound Not conditions.
3. avoid convert BigInt/BigDecimal directly to double unless it's within range (0, 1).
4. reorganize test code.
## How was this patch tested?
modify related test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#17148 from wzhfy/fixFilter.
## What changes were proposed in this pull request?
Before this pr, LocalLimit/GlobalLimit/Sample propagates the same row count and column stats from its child, which is incorrect.
We can get the correct rowCount in Statistics for GlobalLimit/Sample whether cbo is enabled or not.
We don't know the rowCount for LocalLimit because we don't know the partition number at that time. Column stats should not be propagated because we don't know the distribution of columns after Limit or Sample.
## How was this patch tested?
Added test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16696 from wzhfy/limitEstimation.
## What changes were proposed in this pull request?
Currently we don't explicitly forbid the following behaviors:
1. The statement CREATE VIEW AS INSERT INTO throws the following exception:
```
scala> spark.sql("CREATE VIEW testView AS INSERT INTO tab VALUES (1, \"a\")")
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: org.apache.hadoop.hive.ql.metadata.HiveException: at least one column must be specified for the table;
scala> spark.sql("CREATE VIEW testView(a, b) AS INSERT INTO tab VALUES (1, \"a\")")
org.apache.spark.sql.AnalysisException: The number of columns produced by the SELECT clause (num: `0`) does not match the number of column names specified by CREATE VIEW (num: `2`).;
```
2. The statement INSERT INTO view VALUES throws the following exception from checkAnalysis:
```
scala> spark.sql("INSERT INTO testView VALUES (1, \"a\")")
org.apache.spark.sql.AnalysisException: Inserting into an RDD-based table is not allowed.;;
'InsertIntoTable View (`default`.`testView`, [a#16,b#17]), false, false
+- LocalRelation [col1#14, col2#15]
```
After this PR, the behavior changes to:
```
scala> spark.sql("CREATE VIEW testView AS INSERT INTO tab VALUES (1, \"a\")")
org.apache.spark.sql.catalyst.parser.ParseException: Operation not allowed: CREATE VIEW ... AS INSERT INTO;
scala> spark.sql("CREATE VIEW testView(a, b) AS INSERT INTO tab VALUES (1, \"a\")")
org.apache.spark.sql.catalyst.parser.ParseException: Operation not allowed: CREATE VIEW ... AS INSERT INTO;
scala> spark.sql("INSERT INTO testView VALUES (1, \"a\")")
org.apache.spark.sql.AnalysisException: `default`.`testView` is a view, inserting into a view is not allowed;
```
## How was this patch tested?
Add a new test case in `SparkSqlParserSuite`;
Update the corresponding test case in `SQLViewSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#17125 from jiangxb1987/insert-with-view.
## What changes were proposed in this pull request?
Currently we treat the location of table/partition/database as URI string.
It will be safer if we can make the type of location as java.net.URI.
In this PR, there are following classes changes:
**1. CatalogDatabase**
```
case class CatalogDatabase(
name: String,
description: String,
locationUri: String,
properties: Map[String, String])
--->
case class CatalogDatabase(
name: String,
description: String,
locationUri: URI,
properties: Map[String, String])
```
**2. CatalogStorageFormat**
```
case class CatalogStorageFormat(
locationUri: Option[String],
inputFormat: Option[String],
outputFormat: Option[String],
serde: Option[String],
compressed: Boolean,
properties: Map[String, String])
---->
case class CatalogStorageFormat(
locationUri: Option[URI],
inputFormat: Option[String],
outputFormat: Option[String],
serde: Option[String],
compressed: Boolean,
properties: Map[String, String])
```
Before and After this PR, it is transparent for user, there is no change that the user should concern. The `String` to `URI` just happened in SparkSQL internally.
Here list some operation related location:
**1. whitespace in the location**
e.g. `/a/b c/d`
For both table location and partition location,
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b c/d'` ,
then `DESC EXTENDED t ` show the location is `/a/b c/d`,
and the real path in the FileSystem also show `/a/b c/d`
**2. colon(:) in the location**
e.g. `/a/b:c/d`
For both table location and partition location,
when `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b:c/d'` ,
**In linux file system**
`DESC EXTENDED t ` show the location is `/a/b:c/d`,
and the real path in the FileSystem also show `/a/b:c/d`
**in HDFS** throw exception:
`java.lang.IllegalArgumentException: Pathname /a/b:c/d from hdfs://iZbp1151s8hbnnwriekxdeZ:9000/a/b:c/d is not a valid DFS filename.`
**while** After `INSERT INTO TABLE t PARTITION(a="a:b") SELECT 1`
then `DESC EXTENDED t ` show the location is `/xxx/a=a%3Ab`,
and the real path in the FileSystem also show `/xxx/a=a%3Ab`
**3. percent sign(%) in the location**
e.g. `/a/b%c/d`
For both table location and partition location,
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b%c/d'` ,
then `DESC EXTENDED t ` show the location is `/a/b%c/d`,
and the real path in the FileSystem also show `/a/b%c/d`
**4. encoded(%25) in the location**
e.g. `/a/b%25c/d`
For both table location and partition location,
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b%25c/d'` ,
then `DESC EXTENDED t ` show the location is `/a/b%25c/d`,
and the real path in the FileSystem also show `/a/b%25c/d`
**while** After `INSERT INTO TABLE t PARTITION(a="%25") SELECT 1`
then `DESC EXTENDED t ` show the location is `/xxx/a=%2525`,
and the real path in the FileSystem also show `/xxx/a=%2525`
**Additionally**, except the location, there are two other factors will affect the location of the table/partition. one is the table name which does not allowed to have special characters, and the other is `partition name` which have the same actions with `partition value`, and `partition name` with special character situation has add some testcase and resolve a bug in [PR](https://github.com/apache/spark/pull/17173)
### Summary:
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION path`,
the path which we get from `DESC TABLE` and `real path in FileSystem` are all the same with the `CREATE TABLE` command(different filesystem has different action that allow what kind of special character to create the path, e.g. HDFS does not allow colon, but linux filesystem allow it ).
`DataBase` also have the same logic with `CREATE TABLE`
while if the `partition value` has some special character like `%` `:` `#` etc, then we will get the path with encoded `partition value` like `/xxx/a=A%25B` from `DESC TABLE` and `real path in FileSystem`
In this PR, the core change code is using `new Path(str).toUri` and `new Path(uri).toString`
which transfrom `str to uri `or `uri to str`.
for example:
```
val str = '/a/b c/d'
val uri = new Path(str).toUri --> '/a/b%20c/d'
val strFromUri = new Path(uri).toString -> '/a/b c/d'
```
when we restore table/partition from metastore, or get the location from `CREATE TABLE` command, we can use it as above to change string to uri `new Path(str).toUri `
## How was this patch tested?
unit test added.
The `current master branch` also `passed all the test cases` added in this PR by a litter change.
https://github.com/apache/spark/pull/17149/files#diff-b7094baa12601424a5d19cb930e3402fR1764
here `toURI` -> `toString` when test in master branch.
This can show that this PR is transparent for user.
Author: windpiger <songjun@outlook.com>
Closes#17149 from windpiger/changeStringToURI.
## What changes were proposed in this pull request?
This PR adds a new `Once` analysis rule batch consists of a single analysis rule `LookupFunctions` that performs simple existence check over `UnresolvedFunctions` without actually resolving them.
The benefit of this rule is that it doesn't require function arguments to be resolved first and therefore doesn't rely on relation resolution, which may incur potentially expensive partition/schema discovery cost.
Please refer to [SPARK-19737][1] for more details about the motivation.
## How was this patch tested?
New test case added in `AnalysisErrorSuite`.
[1]: https://issues.apache.org/jira/browse/SPARK-19737
Author: Cheng Lian <lian@databricks.com>
Closes#17168 from liancheng/spark-19737-lookup-functions.
## What changes were proposed in this pull request?
Hive hash to support Decimal datatype. [Hive internally normalises decimals](4ba713ccd8/storage-api/src/java/org/apache/hadoop/hive/common/type/HiveDecimalV1.java (L307)) and I have ported that logic as-is to HiveHash.
## How was this patch tested?
Added unit tests
Author: Tejas Patil <tejasp@fb.com>
Closes#17056 from tejasapatil/SPARK-17495_decimal.
## What changes were proposed in this pull request?
This PR proposes to both,
**Do not allow json arrays with multiple elements and return null in `from_json` with `StructType` as the schema.**
Currently, it only reads the single row when the input is a json array. So, the codes below:
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = StructType(StructField("a", IntegerType) :: Nil)
Seq(("""[{"a": 1}, {"a": 2}]""")).toDF("struct").select(from_json(col("struct"), schema)).show()
```
prints
```
+--------------------+
|jsontostruct(struct)|
+--------------------+
| [1]|
+--------------------+
```
This PR simply suggests to print this as `null` if the schema is `StructType` and input is json array.with multiple elements
```
+--------------------+
|jsontostruct(struct)|
+--------------------+
| null|
+--------------------+
```
**Support json arrays in `from_json` with `ArrayType` as the schema.**
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = ArrayType(StructType(StructField("a", IntegerType) :: Nil))
Seq(("""[{"a": 1}, {"a": 2}]""")).toDF("array").select(from_json(col("array"), schema)).show()
```
prints
```
+-------------------+
|jsontostruct(array)|
+-------------------+
| [[1], [2]]|
+-------------------+
```
## How was this patch tested?
Unit test in `JsonExpressionsSuite`, `JsonFunctionsSuite`, Python doctests and manual test.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16929 from HyukjinKwon/disallow-array.
## What changes were proposed in this pull request?
This pr is to support Seq, Map, and Struct in functions.lit; it adds a new IF named `lit2` with `TypeTag` for avoiding type erasure.
## How was this patch tested?
Added tests in `LiteralExpressionSuite`
Author: Takeshi Yamamuro <yamamuro@apache.org>
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#16610 from maropu/SPARK-19254.
## What changes were proposed in this pull request?
This is a follow-up pr of #16308 and #16750.
This pr enables timezone support in partition values.
We should use `timeZone` option introduced at #16750 to parse/format partition values of the `TimestampType`.
For example, if you have timestamp `"2016-01-01 00:00:00"` in `GMT` which will be used for partition values, the values written by the default timezone option, which is `"GMT"` because the session local timezone is `"GMT"` here, are:
```scala
scala> spark.conf.set("spark.sql.session.timeZone", "GMT")
scala> val df = Seq((1, new java.sql.Timestamp(1451606400000L))).toDF("i", "ts")
df: org.apache.spark.sql.DataFrame = [i: int, ts: timestamp]
scala> df.show()
+---+-------------------+
| i| ts|
+---+-------------------+
| 1|2016-01-01 00:00:00|
+---+-------------------+
scala> df.write.partitionBy("ts").save("/path/to/gmtpartition")
```
```sh
$ ls /path/to/gmtpartition/
_SUCCESS ts=2016-01-01 00%3A00%3A00
```
whereas setting the option to `"PST"`, they are:
```scala
scala> df.write.option("timeZone", "PST").partitionBy("ts").save("/path/to/pstpartition")
```
```sh
$ ls /path/to/pstpartition/
_SUCCESS ts=2015-12-31 16%3A00%3A00
```
We can properly read the partition values if the session local timezone and the timezone of the partition values are the same:
```scala
scala> spark.read.load("/path/to/gmtpartition").show()
+---+-------------------+
| i| ts|
+---+-------------------+
| 1|2016-01-01 00:00:00|
+---+-------------------+
```
And even if the timezones are different, we can properly read the values with setting corrent timezone option:
```scala
// wrong result
scala> spark.read.load("/path/to/pstpartition").show()
+---+-------------------+
| i| ts|
+---+-------------------+
| 1|2015-12-31 16:00:00|
+---+-------------------+
// correct result
scala> spark.read.option("timeZone", "PST").load("/path/to/pstpartition").show()
+---+-------------------+
| i| ts|
+---+-------------------+
| 1|2016-01-01 00:00:00|
+---+-------------------+
```
## How was this patch tested?
Existing tests and added some tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#17053 from ueshin/issues/SPARK-18939.
## What changes were proposed in this pull request?
When we resolve inline tables in analyzer, we will evaluate the expressions of inline tables.
When it evaluates a `TimeZoneAwareExpression` expression, an error will happen because the `TimeZoneAwareExpression` is not associated with timezone yet.
So we need to resolve these `TimeZoneAwareExpression`s with time zone when resolving inline tables.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17114 from viirya/resolve-timeawareexpr-inline-table.
## What changes were proposed in this pull request?
`MetastoreRelation` is used to represent table relation for hive tables, and provides some hive related information. We will resolve `SimpleCatalogRelation` to `MetastoreRelation` for hive tables, which is unnecessary as these 2 are the same essentially. This PR merges `SimpleCatalogRelation` and `MetastoreRelation`
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17015 from cloud-fan/table-relation.
## What changes were proposed in this pull request?
This PR proposes to fix the lint-breaks as below:
```
[ERROR] src/test/java/org/apache/spark/network/TransportResponseHandlerSuite.java:[29,8] (imports) UnusedImports: Unused import - org.apache.spark.network.buffer.ManagedBuffer.
[ERROR] src/main/java/org/apache/spark/unsafe/types/UTF8String.java:[156,10] (modifier) ModifierOrder: 'Nonnull' annotation modifier does not precede non-annotation modifiers.
[ERROR] src/main/java/org/apache/spark/SparkFirehoseListener.java:[122] (sizes) LineLength: Line is longer than 100 characters (found 105).
[ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java:[164,78] (coding) OneStatementPerLine: Only one statement per line allowed.
[ERROR] src/test/java/test/org/apache/spark/JavaAPISuite.java:[1157] (sizes) LineLength: Line is longer than 100 characters (found 121).
[ERROR] src/test/java/org/apache/spark/streaming/JavaMapWithStateSuite.java:[149] (sizes) LineLength: Line is longer than 100 characters (found 113).
[ERROR] src/test/java/test/org/apache/spark/streaming/Java8APISuite.java:[146] (sizes) LineLength: Line is longer than 100 characters (found 122).
[ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[32,8] (imports) UnusedImports: Unused import - org.apache.spark.streaming.Time.
[ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[611] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[1317] (sizes) LineLength: Line is longer than 100 characters (found 102).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetAggregatorSuite.java:[91] (sizes) LineLength: Line is longer than 100 characters (found 102).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[113] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[164] (sizes) LineLength: Line is longer than 100 characters (found 110).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[212] (sizes) LineLength: Line is longer than 100 characters (found 114).
[ERROR] src/test/java/org/apache/spark/mllib/tree/JavaDecisionTreeSuite.java:[36] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java:[26,8] (imports) UnusedImports: Unused import - com.amazonaws.regions.RegionUtils.
[ERROR] src/test/java/org/apache/spark/streaming/kinesis/JavaKinesisStreamSuite.java:[20,8] (imports) UnusedImports: Unused import - com.amazonaws.regions.RegionUtils.
[ERROR] src/test/java/org/apache/spark/streaming/kinesis/JavaKinesisStreamSuite.java:[94] (sizes) LineLength: Line is longer than 100 characters (found 103).
[ERROR] src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java:[30,8] (imports) UnusedImports: Unused import - org.apache.spark.sql.api.java.UDF1.
[ERROR] src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java:[72] (sizes) LineLength: Line is longer than 100 characters (found 104).
[ERROR] src/main/java/org/apache/spark/examples/mllib/JavaRankingMetricsExample.java:[121] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java:[28,8] (imports) UnusedImports: Unused import - org.apache.spark.api.java.JavaRDD.
[ERROR] src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java:[29,8] (imports) UnusedImports: Unused import - org.apache.spark.api.java.JavaSparkContext.
```
## How was this patch tested?
Manually via
```bash
./dev/lint-java
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17072 from HyukjinKwon/java-lint.
## What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/16395. It fixes some code style issues, naming issues, some missing cases in pattern match, etc.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17065 from cloud-fan/follow-up.
### What changes were proposed in this pull request?
As explained in Hive JIRA https://issues.apache.org/jira/browse/HIVE-12224, HOLD_DDLTIME was broken as soon as it landed. Hive 2.0 removes HOLD_DDLTIME from the API. In Spark SQL, we always set it to FALSE. Like Hive, we should also remove it from our Catalog APIs.
### How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17063 from gatorsmile/removalHoldDDLTime.
## What changes were proposed in this pull request?
Currently we can only check the estimated stats in logical plans by debugging. We need to provide an easier and more efficient way for developers/users.
In this pr, we add EXPLAIN COST command to show stats in the optimized logical plan.
E.g.
```
spark-sql> EXPLAIN COST select count(1) from store_returns;
...
== Optimized Logical Plan ==
Aggregate [count(1) AS count(1)#24L], Statistics(sizeInBytes=16.0 B, rowCount=1, isBroadcastable=false)
+- Project, Statistics(sizeInBytes=4.3 GB, rowCount=5.76E+8, isBroadcastable=false)
+- Relation[sr_returned_date_sk#3,sr_return_time_sk#4,sr_item_sk#5,sr_customer_sk#6,sr_cdemo_sk#7,sr_hdemo_sk#8,sr_addr_sk#9,sr_store_sk#10,sr_reason_sk#11,sr_ticket_number#12,sr_return_quantity#13,sr_return_amt#14,sr_return_tax#15,sr_return_amt_inc_tax#16,sr_fee#17,sr_return_ship_cost#18,sr_refunded_cash#19,sr_reversed_charge#20,sr_store_credit#21,sr_net_loss#22] parquet, Statistics(sizeInBytes=28.6 GB, rowCount=5.76E+8, isBroadcastable=false)
...
```
## How was this patch tested?
Add test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#16594 from wzhfy/showStats.
## What changes were proposed in this pull request?
Fixed the line ending of `FilterEstimation.scala` (It's still using `\n\r`). Also improved the tests to cover the cases where the literals are on the left side of a binary operator.
## How was this patch tested?
Existing unit tests.
Author: Shuai Lin <linshuai2012@gmail.com>
Closes#17051 from lins05/fix-cbo-filter-file-encoding.
## What changes were proposed in this pull request?
This PR adds tests hive-hash by comparing the outputs generated against Hive 1.2.1. Following datatypes are covered by this PR:
- null
- boolean
- byte
- short
- int
- long
- float
- double
- string
- array
- map
- struct
Datatypes that I have _NOT_ covered but I will work on separately are:
- Decimal (handled separately in https://github.com/apache/spark/pull/17056)
- TimestampType
- DateType
- CalendarIntervalType
## How was this patch tested?
NA
Author: Tejas Patil <tejasp@fb.com>
Closes#17049 from tejasapatil/SPARK-17495_remaining_types.
## What changes were proposed in this pull request?
We traverse predicate and evaluate the logical expressions to compute the selectivity of a FILTER operator.
## How was this patch tested?
We add a new test suite to test various logical operators.
Author: Ron Hu <ron.hu@huawei.com>
Closes#16395 from ron8hu/filterSelectivity.
## What changes were proposed in this pull request?
This PR adds a special streaming deduplication operator to support `dropDuplicates` with `aggregation` and watermark. It reuses the `dropDuplicates` API but creates new logical plan `Deduplication` and new physical plan `DeduplicationExec`.
The following cases are supported:
- one or multiple `dropDuplicates()` without aggregation (with or without watermark)
- `dropDuplicates` before aggregation
Not supported cases:
- `dropDuplicates` after aggregation
Breaking changes:
- `dropDuplicates` without aggregation doesn't work with `complete` or `update` mode.
## How was this patch tested?
The new unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16970 from zsxwing/dedup.
