### What changes were proposed in this pull request?
For full outer shuffled hash join with building hash map on left side, and having non-equal condition, the join can produce wrong result.
The root cause is `boundCondition` in `HashJoin.scala` always assumes the left side row is `streamedPlan` and right side row is `buildPlan` ([streamedPlan.output ++ buildPlan.output](https://github.com/apache/spark/blob/branch-3.1/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala#L141)). This is valid assumption, except for full outer + build left case.
The fix is to correct `boundCondition` in `HashJoin.scala` to handle full outer + build left case properly. See reproduce in https://issues.apache.org/jira/browse/SPARK-32399?focusedCommentId=17298414&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-17298414 .
### Why are the changes needed?
Fix data correctness bug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Changed the test in `OuterJoinSuite.scala` to cover full outer shuffled hash join.
Before this change, the unit test `basic full outer join using ShuffledHashJoin` in `OuterJoinSuite.scala` is failed.
Closes#31792 from c21/join-bugfix.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Fixes a mistake in `TableCapabilityCheckSuite`, which runs some tests repeatedly.
### Why are the changes needed?
code cleanup
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
N/A
Closes#31788 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR adds `ADD ARCHIVE` and `LIST ARCHIVES` commands to SQL and updates relevant documents.
SPARK-33530 added `addArchive` and `listArchives` to `SparkContext` but it's not supported yet to add/list archives with SQL.
### Why are the changes needed?
To complement features.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added new test and confirmed the generated HTML from the updated documents.
Closes#31721 from sarutak/sql-archive.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposed to fix a bug introduced in #31273 (https://github.com/apache/spark/pull/31273/files#r589494855).
### Why are the changes needed?
This fixes a bug where global temp view's database name was not passed correctly.
### Does this PR introduce _any_ user-facing change?
Yes, now the global temp view's database is correctly stored.
### How was this patch tested?
Added a new test that catches the bug.
Closes#31783 from imback82/SPARK-34152-bug-fix.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Use resolved attributes instead of data-frame fields for replacing values.
### Why are the changes needed?
dataframe.na.replace() does not work for column having a dot in the name
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
Added unit tests for the same
Closes#31769 from amandeep-sharma/master.
Authored-by: Amandeep Sharma <happyaman91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR intends to remove unnecessary `SQLConf.withExistingConf` in `CastSuite`; since we've remove `ParVector ` in #31775, we no longer need to copy SQL configs into each thread env.
### Why are the changes needed?
Clean up the code.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Run the existing tests.
Closes#31785 from maropu/UpdateCastSuite.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Added new tests to `UDFSuite` to check `java.time.Period`/`java.time.Duration` in UDF as input parameters as well as UDF results.
### Why are the changes needed?
To improve test coverage.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running new tests:
```
$ build/sbt "test:testOnly *UDFSuite"
```
Closes#31779 from MaxGekk/interval-udf.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add `DayTimeIntervalType` and `YearMonthIntervalType` to `DataTypeTestUtils.ordered`/`atomicTypes`, and implement values generation of those types in `LiteralGenerator`/`RandomDataGenerator`. In this way, the types will be tested automatically in:
1. ArithmeticExpressionSuite:
- "function least"
- "function greatest"
2. PredicateSuite
- "BinaryComparison consistency check"
- "AND, OR, EqualTo, EqualNullSafe consistency check"
3. ConditionalExpressionSuite
- "if"
4. RandomDataGeneratorSuite
- "Basic types"
5. CastSuite
- "null cast"
- "up-cast"
- "SPARK-27671: cast from nested null type in struct"
6. OrderingSuite
- "GenerateOrdering with DayTimeIntervalType"
- "GenerateOrdering with YearMonthIntervalType"
7. PredicateSuite
- "IN with different types"
8. UnsafeRowSuite
- "calling get(ordinal, datatype) on null columns"
9. SortSuite
- "sorting on YearMonthIntervalType ..."
- "sorting on DayTimeIntervalType ..."
### Why are the changes needed?
To improve test coverage.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the affected test suites.
Closes#31782 from MaxGekk/test-interval-as-atomic.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR updates UnresolvedTableValuedFunction's name to be a FunctionIdentifier instead of a string.
### Why are the changes needed?
To make UnresolvedTableValuedFunction consistent with UnresolvedFunction that uses FunctionIdentifier as the function name.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes#31749 from allisonwang-db/spark-34627.
Authored-by: allisonwang-db <66282705+allisonwang-db@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
RewritePredicateSubquery Optimizer Rule must not update Filters without subqueries.
Following is one such example.
```
=== Applying Rule org.apache.spark.sql.catalyst.optimizer.RewritePredicateSubquery ===
Project [a#0] Project [a#0]
!+- Filter (((a#0 > 1) OR (b#1 > 2)) AND ((c#2 > 1) AND (d#3 > 2))) +- Filter ((((a#0 > 1) OR (b#1 > 2)) AND (c#2 > 1)) AND (d#3 > 2))
+- LocalRelation <empty>, [a#0, b#1, c#2, d#3] +- LocalRelation <empty>, [a#0, b#1, c#2, d#3]
```
### Why are the changes needed?
minor change.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UTs pass.
Closes#31712 from Swinky/rewritePredicateFix.
Authored-by: Swinky <mannswinky@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to extend Spark SQL API to accept [`java.time.Period`](https://docs.oracle.com/javase/8/docs/api/java/time/Period.html) as an external type of recently added new Catalyst type - `YearMonthIntervalType` (see #31614). The Java class `java.time.Period` has similar semantic to ANSI SQL year-month interval type, and it is the most suitable to be an external type for `YearMonthIntervalType`. In more details:
1. Added `PeriodConverter` which converts `java.time.Period` instances to/from internal representation of the Catalyst type `YearMonthIntervalType` (to `Int` type). The `PeriodConverter` object uses new methods of `IntervalUtils`:
- `periodToMonths()` converts the input period to the total length in months. If this period is too large to fit `Int`, the method throws the exception `ArithmeticException`. **Note:** _the input period has "days" precision, the method just ignores the days unit._
- `monthToPeriod()` obtains a `java.time.Period` representing a number of months.
2. Support new type `YearMonthIntervalType` in `RowEncoder` via the methods `createDeserializerForPeriod()` and `createSerializerForJavaPeriod()`.
3. Extended the Literal API to construct literals from `java.time.Period` instances.
### Why are the changes needed?
1. To allow users parallelization of `java.time.Period` collections, and construct year-month interval columns. Also to collect such columns back to the driver side.
2. This will allow to write tests in other sub-tasks of SPARK-27790.
### Does this PR introduce _any_ user-facing change?
The PR extends existing functionality. So, users can parallelize instances of the `java.time.Duration` class and collect them back:
```scala
scala> val ds = Seq(java.time.Period.ofYears(10).withMonths(2)).toDS
ds: org.apache.spark.sql.Dataset[java.time.Period] = [value: yearmonthinterval]
scala> ds.collect
res0: Array[java.time.Period] = Array(P10Y2M)
```
### How was this patch tested?
- Added a few tests to `CatalystTypeConvertersSuite` to check conversion from/to `java.time.Period`.
- Checking row encoding by new tests in `RowEncoderSuite`.
- Making literals of `YearMonthIntervalType` are tested in `LiteralExpressionSuite`.
- Check collecting by `DatasetSuite` and `JavaDatasetSuite`.
- New tests in `IntervalUtilsSuites` to check conversions `java.time.Period` <-> months.
Closes#31765 from MaxGekk/java-time-period.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`ParquetFileReader.readFooter` related methods has been identified as `Deprecated` and `Apache Parquet` suggests replace it with the combination of `ParquetFileReader.open() and getFooter()` methods.
This PR introduces the `ParquetFooterReader` utility class due to some repetitive code patterns when read parquet file footer.
### Why are the changes needed?
Cleanup deprecated API usage.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#31711 from LuciferYang/parquet-read-footer.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
pyrolite 4.21 introduced and enabled value comparison by default (`valueCompare=true`) during object memoization and serialization: https://github.com/irmen/Pyrolite/blob/pyrolite-4.21/java/src/main/java/net/razorvine/pickle/Pickler.java#L112-L122
This change has undesired effect when we serialize a row (actually `GenericRowWithSchema`) to be passed to python: https://github.com/apache/spark/blob/branch-3.0/sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvaluatePython.scala#L60. A simple example is that
```
new GenericRowWithSchema(Array(1.0, 1.0), StructType(Seq(StructField("_1", DoubleType), StructField("_2", DoubleType))))
```
and
```
new GenericRowWithSchema(Array(1, 1), StructType(Seq(StructField("_1", IntegerType), StructField("_2", IntegerType))))
```
are currently equal and the second instance is replaced to the short code of the first one during serialization.
### Why are the changes needed?
The above can cause nasty issues like the one in https://issues.apache.org/jira/browse/SPARK-34545 description:
```
>>> from pyspark.sql.functions import udf
>>> from pyspark.sql.types import *
>>>
>>> def udf1(data_type):
def u1(e):
return e[0]
return udf(u1, data_type)
>>>
>>> df = spark.createDataFrame([((1.0, 1.0), (1, 1))], ['c1', 'c2'])
>>>
>>> df = df.withColumn("c3", udf1(DoubleType())("c1"))
>>> df = df.withColumn("c4", udf1(IntegerType())("c2"))
>>>
>>> df.select("c3").show()
+---+
| c3|
+---+
|1.0|
+---+
>>> df.select("c4").show()
+---+
| c4|
+---+
| 1|
+---+
>>> df.select("c3", "c4").show()
+---+----+
| c3| c4|
+---+----+
|1.0|null|
+---+----+
```
This is because during serialization from JVM to Python `GenericRowWithSchema(1.0, 1.0)` (`c1`) is memoized first and when `GenericRowWithSchema(1, 1)` (`c2`) comes next, it is replaced to some short code of the `c1` (instead of serializing `c2` out) as they are `equal()`. The python functions then runs but the return type of `c4` is expected to be `IntegerType` and if a different type (`DoubleType`) comes back from python then it is discarded: https://github.com/apache/spark/blob/branch-3.0/sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvaluatePython.scala#L108-L113
After this PR:
```
>>> df.select("c3", "c4").show()
+---+---+
| c3| c4|
+---+---+
|1.0| 1|
+---+---+
```
### Does this PR introduce _any_ user-facing change?
Yes, fixes a correctness issue.
### How was this patch tested?
Added new UT + manual tests.
Closes#31682 from peter-toth/SPARK-34545-fix-row-comparison.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
When we do self join with transform in a CTE, spark will throw AnalysisException.
A simple way to reproduce is
```
create temporary view t as select * from values 0, 1, 2 as t(a);
WITH temp AS (
SELECT TRANSFORM(a) USING 'cat' AS (b string) FROM t
)
SELECT t1.b FROM temp t1 JOIN temp t2 ON t1.b = t2.b
```
before this patch, it throws
```
org.apache.spark.sql.AnalysisException: cannot resolve '`t1.b`' given input columns: [t1.b]; line 6 pos 41;
'Project ['t1.b]
+- 'Join Inner, ('t1.b = 't2.b)
:- SubqueryAlias t1
: +- SubqueryAlias temp
: +- ScriptTransformation [a#1], cat, [b#2], ScriptInputOutputSchema(List(),List(),Some(org.apache.hadoop.hive.serde2.DelimitedJSONSerDe),Some(org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe),List((field.delim, )),List((field.delim, )),Some(org.apache.hadoop.hive.ql.exec.TextRecordReader),Some(org.apache.hadoop.hive.ql.exec.TextRecordWriter),false)
: +- SubqueryAlias t
: +- Project [a#1]
: +- SubqueryAlias t
: +- LocalRelation [a#1]
+- SubqueryAlias t2
+- SubqueryAlias temp
+- ScriptTransformation [a#1], cat, [b#2], ScriptInputOutputSchema(List(),List(),Some(org.apache.hadoop.hive.serde2.DelimitedJSONSerDe),Some(org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe),List((field.delim, )),List((field.delim, )),Some(org.apache.hadoop.hive.ql.exec.TextRecordReader),Some(org.apache.hadoop.hive.ql.exec.TextRecordWriter),false)
+- SubqueryAlias t
+- Project [a#1]
+- SubqueryAlias t
+- LocalRelation [a#1]
```
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
Add a UT
Closes#31752 from WangGuangxin/selfjoin-with-transform.
Authored-by: wangguangxin.cn <wangguangxin.cn@bytedance.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This pr remove `GlobalLimit` operator if its child max rows not larger than limit number. For example:
```
val testRelation = LocalRelation.fromExternalRows(Seq("a".attr.int, "b".attr.int, "c".attr.int), 1.to(10).map(_ => Row(1, 2, 3)) )
val query = GlobalLimit(100, testRelation)
```
We can remove this `GlobalLimit`.
### Why are the changes needed?
Further optimize the query.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#31750 from wangyum/SPARK-34628.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This pr make DPP support RLIKE expression:
```sql
SELECT date_id, product_id FROM fact_sk f
JOIN dim_store s
ON f.store_id = s.store_id WHERE s.country RLIKE '[DE|US]'
```
### Why are the changes needed?
Improve query performance.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#31722 from chaojun-zhang/SPARK-34595.
Authored-by: helloman <zcj23085@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Unify output of ShowCreateTableAsSerdeCommand and ShowCreateTableCommand
### Why are the changes needed?
Unify output of ShowCreateTableAsSerdeCommand and ShowCreateTableCommand
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Closes#31737 from AngersZhuuuu/SPARK-34621.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR intends to refactor the logic to resolve `__grouping_id` in the `Analyzer`; it moves the logic from `ResolveFunctions` to `ResolveReferences` (`resolveLiteralFunction`).
The original author of this PR is sqlwindspeaker (#30781).
Closes#30781.
### Why are the changes needed?
Code refactoring.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added tests in `AnalysisSuite`.
Closes#31751 from maropu/SPARK-22748.
Authored-by: suqilong <suqilong@qiyi.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
It's a bit confusing to see `resolveExpressionBottomUp` and `resolveExpressionTopDown`, which provide similar functionalities but with different tree traverse order. It turns out that the real difference between these 2 methods is: which attributes should the columns be resolved to? `resolveExpressionTopDown` resolves columns using output attributes of the plan children, `resolveExpressionBottomUp` resolves columns using output attributes of the plan itself.
This PR unifies `resolveExpressionBottomUp` and `resolveExpressionTopDown` and put the common logic in a new method, and let `resolveExpressionBottomUp` and `resolveExpressionTopDown` just call the new method. This PR also renames `resolveExpressionBottomUp` and `resolveExpressionTopDown` to make the difference clear.
### Why are the changes needed?
code cleanup
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests
Closes#31728 from cloud-fan/resolve.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Remove unused output of AddJarCommand, keep consistence and clean
### Why are the changes needed?
Keep consistence and clean
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Not need
Closes#31725 from AngersZhuuuu/SPARK-34608.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add allow list to capture sql config for view.
### Why are the changes needed?
Spark use origin text sql to store view then capture and store sql config into view metadata.
Capture config will skip some config with some prefix, e.g. `spark.sql.optimizer.` but unfortunately `spark.sql.optimizer.disableHints` is start with `spark.sql.optimizer.`.
We need a allow list to help capture the config.
### Does this PR introduce _any_ user-facing change?
Yes bug fix.
### How was this patch tested?
Add test.
Closes#31732 from ulysses-you/SPARK-34613.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
We have equality in `SqlBase.g4` for `RLIKE: 'RLIKE' | 'REGEXP';`
We seemed to miss adding` REGEXP` as a SQL function just like` RLIKE`
### Why are the changes needed?
symmetry and beauty
This is also a builtin function in Hive, we can reduce the migration pain for those users
### Does this PR introduce _any_ user-facing change?
yes new regexp function as an alias as rlike
### How was this patch tested?
new tests
Closes#31488 from yaooqinn/SPARK-34376.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR intends to fix a bug of `objects.NewInstance` if a user runs Spark on jdk8u and a given `cls` in `NewInstance` is a deeply-nested inner class, e.g.,.
```
object OuterLevelWithVeryVeryVeryLongClassName1 {
object OuterLevelWithVeryVeryVeryLongClassName2 {
object OuterLevelWithVeryVeryVeryLongClassName3 {
object OuterLevelWithVeryVeryVeryLongClassName4 {
object OuterLevelWithVeryVeryVeryLongClassName5 {
object OuterLevelWithVeryVeryVeryLongClassName6 {
object OuterLevelWithVeryVeryVeryLongClassName7 {
object OuterLevelWithVeryVeryVeryLongClassName8 {
object OuterLevelWithVeryVeryVeryLongClassName9 {
object OuterLevelWithVeryVeryVeryLongClassName10 {
object OuterLevelWithVeryVeryVeryLongClassName11 {
object OuterLevelWithVeryVeryVeryLongClassName12 {
object OuterLevelWithVeryVeryVeryLongClassName13 {
object OuterLevelWithVeryVeryVeryLongClassName14 {
object OuterLevelWithVeryVeryVeryLongClassName15 {
object OuterLevelWithVeryVeryVeryLongClassName16 {
object OuterLevelWithVeryVeryVeryLongClassName17 {
object OuterLevelWithVeryVeryVeryLongClassName18 {
object OuterLevelWithVeryVeryVeryLongClassName19 {
object OuterLevelWithVeryVeryVeryLongClassName20 {
case class MalformedNameExample2(x: Int)
}}}}}}}}}}}}}}}}}}}}
```
The root cause that Kris (rednaxelafx) investigated is as follows (Kudos to Kris);
The reason why the test case above is so convoluted is in the way Scala generates the class name for nested classes. In general, Scala generates a class name for a nested class by inserting the dollar-sign ( `$` ) in between each level of class nesting. The problem is that this format can concatenate into a very long string that goes beyond certain limits, so Scala will change the class name format beyond certain length threshold.
For the example above, we can see that the first two levels of class nesting have class names that look like this:
```
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassName1$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassName1$OuterLevelWithVeryVeryVeryLongClassName2$
```
If we leave out the fact that Scala uses a dollar-sign ( `$` ) suffix for the class name of the companion object, `OuterLevelWithVeryVeryVeryLongClassName1`'s full name is a prefix (substring) of `OuterLevelWithVeryVeryVeryLongClassName2`.
But if we keep going deeper into the levels of nesting, you'll find names that look like:
```
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$2a1321b953c615695d7442b2adb1$$$$ryVeryLongClassName8$OuterLevelWithVeryVeryVeryLongClassName9$OuterLevelWithVeryVeryVeryLongClassName10$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$2a1321b953c615695d7442b2adb1$$$$ryVeryLongClassName8$OuterLevelWithVeryVeryVeryLongClassName9$OuterLevelWithVeryVeryVeryLongClassName10$OuterLevelWithVeryVeryVeryLongClassName11$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$85f068777e7ecf112afcbe997d461b$$$$VeryLongClassName11$OuterLevelWithVeryVeryVeryLongClassName12$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$85f068777e7ecf112afcbe997d461b$$$$VeryLongClassName11$OuterLevelWithVeryVeryVeryLongClassName12$OuterLevelWithVeryVeryVeryLongClassName13$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$85f068777e7ecf112afcbe997d461b$$$$VeryLongClassName11$OuterLevelWithVeryVeryVeryLongClassName12$OuterLevelWithVeryVeryVeryLongClassName13$OuterLevelWithVeryVeryVeryLongClassName14$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$5f7ad51804cb1be53938ea804699fa$$$$VeryLongClassName14$OuterLevelWithVeryVeryVeryLongClassName15$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$5f7ad51804cb1be53938ea804699fa$$$$VeryLongClassName14$OuterLevelWithVeryVeryVeryLongClassName15$OuterLevelWithVeryVeryVeryLongClassName16$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$5f7ad51804cb1be53938ea804699fa$$$$VeryLongClassName14$OuterLevelWithVeryVeryVeryLongClassName15$OuterLevelWithVeryVeryVeryLongClassName16$OuterLevelWithVeryVeryVeryLongClassName17$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$69b54f16b1965a31e88968df1a58d8$$$$VeryLongClassName17$OuterLevelWithVeryVeryVeryLongClassName18$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$69b54f16b1965a31e88968df1a58d8$$$$VeryLongClassName17$OuterLevelWithVeryVeryVeryLongClassName18$OuterLevelWithVeryVeryVeryLongClassName19$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$69b54f16b1965a31e88968df1a58d8$$$$VeryLongClassName17$OuterLevelWithVeryVeryVeryLongClassName18$OuterLevelWithVeryVeryVeryLongClassName19$OuterLevelWithVeryVeryVeryLongClassName20$
```
with a hash code in the middle and various levels of nesting omitted.
The `java.lang.Class.isMemberClass` method is implemented in JDK8u as:
http://hg.openjdk.java.net/jdk8u/jdk8u/jdk/file/tip/src/share/classes/java/lang/Class.java#l1425
```
/**
* Returns {code true} if and only if the underlying class
* is a member class.
*
* return {code true} if and only if this class is a member class.
* since 1.5
*/
public boolean isMemberClass() {
return getSimpleBinaryName() != null && !isLocalOrAnonymousClass();
}
/**
* Returns the "simple binary name" of the underlying class, i.e.,
* the binary name without the leading enclosing class name.
* Returns {code null} if the underlying class is a top level
* class.
*/
private String getSimpleBinaryName() {
Class<?> enclosingClass = getEnclosingClass();
if (enclosingClass == null) // top level class
return null;
// Otherwise, strip the enclosing class' name
try {
return getName().substring(enclosingClass.getName().length());
} catch (IndexOutOfBoundsException ex) {
throw new InternalError("Malformed class name", ex);
}
}
```
and the problematic code is `getName().substring(enclosingClass.getName().length())` -- if a class's enclosing class's full name is *longer* than the nested class's full name, this logic would end up going out of bounds.
The bug has been fixed in JDK9 by https://bugs.java.com/bugdatabase/view_bug.do?bug_id=8057919 , but still exists in the latest JDK8u release. So from the Spark side we'd need to do something to avoid hitting this problem.
### Why are the changes needed?
Bugfix on jdk8u.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added tests.
Closes#31733 from maropu/SPARK-34607.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to extend Spark SQL API to accept [`java.time.Duration`](https://docs.oracle.com/javase/8/docs/api/java/time/Duration.html) as an external type of recently added new Catalyst type - `DayTimeIntervalType` (see #31614). The Java class `java.time.Duration` has similar semantic to ANSI SQL day-time interval type, and it is the most suitable to be an external type for `DayTimeIntervalType`. In more details:
1. Added `DurationConverter` which converts `java.time.Duration` instances to/from internal representation of the Catalyst type `DayTimeIntervalType` (to `Long` type). The `DurationConverter` object uses new methods of `IntervalUtils`:
- `durationToMicros()` converts the input duration to the total length in microseconds. If this duration is too large to fit `Long`, the method throws the exception `ArithmeticException`. **Note:** _the input duration has nanosecond precision, the method casts the nanos part to microseconds by dividing by 1000._
- `microsToDuration()` obtains a `java.time.Duration` representing a number of microseconds.
2. Support new type `DayTimeIntervalType` in `RowEncoder` via the methods `createDeserializerForDuration()` and `createSerializerForJavaDuration()`.
3. Extended the Literal API to construct literals from `java.time.Duration` instances.
### Why are the changes needed?
1. To allow users parallelization of `java.time.Duration` collections, and construct day-time interval columns. Also to collect such columns back to the driver side.
2. This will allow to write tests in other sub-tasks of SPARK-27790.
### Does this PR introduce _any_ user-facing change?
The PR extends existing functionality. So, users can parallelize instances of the `java.time.Duration` class and collect them back:
```Scala
scala> val ds = Seq(java.time.Duration.ofDays(10)).toDS
ds: org.apache.spark.sql.Dataset[java.time.Duration] = [value: daytimeinterval]
scala> ds.collect
res0: Array[java.time.Duration] = Array(PT240H)
```
### How was this patch tested?
- Added a few tests to `CatalystTypeConvertersSuite` to check conversion from/to `java.time.Duration`.
- Checking row encoding by new tests in `RowEncoderSuite`.
- Making literals of `DayTimeIntervalType` are tested in `LiteralExpressionSuite`
- Check collecting by `DatasetSuite` and `JavaDatasetSuite`.
Closes#31729 from MaxGekk/java-time-duration.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Set the active SparkSession to `sparkSessionForStream` and diable AQE & CBO before initializing the `StreamExecution.logicalPlan`.
### Why are the changes needed?
The active session should be `sparkSessionForStream`. Otherwise, settings like
6b34745cb9/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala (L332-L335)
wouldn't take effect if callers access them from the active SQLConf, e.g., the rule of `InsertAdaptiveSparkPlan`. Besides, unlike `InsertAdaptiveSparkPlan` (which skips streaming plan), `CostBasedJoinReorder` seems to have the chance to take effect theoretically.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Tested manually. Before the fix, `InsertAdaptiveSparkPlan` would try to apply AQE on the plan(wouldn't take effect though). After this fix, the rule returns directly.
Closes#31600 from Ngone51/active-session-for-stream.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In ANSI mode, casting String to Boolean should throw an exception on parse error, instead of returning null
### Why are the changes needed?
For better ANSI compliance
### Does this PR introduce _any_ user-facing change?
Yes, in ANSI mode there will be an exception on parse failure of casting String value to Boolean type.
### How was this patch tested?
Unit tests.
Closes#31734 from gengliangwang/ansiCastToBoolean.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
`ResolveInsertInto.staticDeleteExpression` should use `UnresolvedAttribute.quoted` to create the delete expression so that we will treat the entire `attr.name` as a column name.
### Why are the changes needed?
When users use `dot` in a partition column name, queries like ```INSERT OVERWRITE $t1 PARTITION (`a.b` = 'a') (`c.d`) VALUES('b')``` is not working.
### Does this PR introduce _any_ user-facing change?
Without this test, the above query will throw
```
[info] org.apache.spark.sql.AnalysisException: cannot resolve '`a.b`' given input columns: [a.b, c.d];
[info] 'OverwriteByExpression RelationV2[a.b#17, c.d#18] default.tbl, ('a.b <=> cast(a as string)), false
[info] +- Project [a.b#19, ansi_cast(col1#16 as string) AS c.d#20]
[info] +- Project [cast(a as string) AS a.b#19, col1#16]
[info] +- LocalRelation [col1#16]
```
With the fix, the query will run correctly.
### How was this patch tested?
The new added test.
Closes#31713 from zsxwing/SPARK-34599.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to generate "stable" output attributes per the logical node of the DESCRIBE NAMESPACE command.
### Why are the changes needed?
This fixes the issue demonstrated by the example:
```
sql(s"CREATE NAMESPACE ns")
val description = sql(s"DESCRIBE NAMESPACE ns")
description.drop("name")
```
```
[info] org.apache.spark.sql.AnalysisException: Resolved attribute(s) name#74 missing from name#25,value#26 in operator !Project [name#74]. Attribute(s) with the same name appear in the operation: name. Please check if the right attribute(s) are used.;
[info] !Project [name#74]
[info] +- LocalRelation [name#25, value#26]
```
### Does this PR introduce _any_ user-facing change?
After this change user `drop()/add()` works well.
### How was this patch tested?
Added UT
Closes#31705 from AngersZhuuuu/SPARK-34577.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/27597 and simply apply the fix in the v2 table insertion code path.
### Why are the changes needed?
bug fix
### Does this PR introduce _any_ user-facing change?
yes, now v2 table insertion with static partitions also follow StoreAssignmentPolicy.
### How was this patch tested?
moved the test from https://github.com/apache/spark/pull/27597 to the general test suite `SQLInsertTestSuite`, which covers DS v2, file source, and hive tables.
Closes#31726 from cloud-fan/insert.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add the `ResetSystemProperties` trait to `SQLQuerySuite` so that system property changes made by any of the tests will not affect other suites/tests. Specifically, the system property changes made by `SPARK-33084: Add jar support Ivy URI in SQL -- jar contains udf class` are targeted here (which sets and then clears `ivy.home`).
### Why are the changes needed?
PR #29966 added a new test case that adjusts the `ivy.home` system property to force Ivy to resolve an artifact from a custom location. At the end of the test, the value is cleared. Clearing the value meant that, if a custom value of `ivy.home` was configured externally, it would not apply for tests run after this test case.
### Does this PR introduce _any_ user-facing change?
No, this is only in tests.
### How was this patch tested?
Existing unit tests continue to pass, whether or not `spark.jars.ivySettings` is configured (which adjusts the behavior of Ivy w.r.t. handling of `ivy.home` and `ivy.default.ivy.user.dir` properties).
Closes#31694 from xkrogen/xkrogen-SPARK-33084-ivyhome-sysprop-followon.
Authored-by: Erik Krogen <xkrogen@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Use a non-recursive implementation for the function buildBalancedPredicate
### Why are the changes needed?
For better performance.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing unit tests.
Also, a quick benchmark:
```
test("buildBalancedPredicate") {
val expressions = (1 to 1000).map(_ => Literal(true))
val start = System.currentTimeMillis()
buildBalancedPredicate(expressions, And)
println(System.currentTimeMillis() - start)
}
```
Before: 47ms
After: 4ms
Closes#31724 from gengliangwang/nonrecursive.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
Add metadataOutput as a fallback to resolution.
Builds off https://github.com/apache/spark/pull/31654.
### Why are the changes needed?
The metadata columns could not be resolved via `df.col("metadataColName")` from the DataFrame API.
### Does this PR introduce _any_ user-facing change?
Yes, the metadata columns can now be resolved as described above.
### How was this patch tested?
Scala unit test.
Closes#31668 from karenfeng/spark-34555.
Authored-by: Karen Feng <karen.feng@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Hive support type constructed value as partition spec value, spark should support too.
### Why are the changes needed?
Support TypeConstructed partition spec value keep same with hive
### Does this PR introduce _any_ user-facing change?
Yes, user can use TypeConstruct value as partition spec value such as
```
CREATE TABLE t1(name STRING) PARTITIONED BY (part DATE)
INSERT INTO t1 PARTITION(part = date'2019-01-02') VALUES('a')
CREATE TABLE t2(name STRING) PARTITIONED BY (part TIMESTAMP)
INSERT INTO t2 PARTITION(part = timestamp'2019-01-02 11:11:11') VALUES('a')
CREATE TABLE t4(name STRING) PARTITIONED BY (part BINARY)
INSERT INTO t4 PARTITION(part = X'537061726B2053514C') VALUES('a')
```
### How was this patch tested?
Added UT
Closes#30421 from AngersZhuuuu/SPARK-33474.
Lead-authored-by: angerszhu <angers.zhu@gmail.com>
Co-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
spark.sql.adaptive.coalescePartitions.initialPartitionNum 200 -> (none)
spark.sql.adaptive.skewJoin.skewedPartitionFactor is 10 -> 5
### Why are the changes needed?
the wrong doc misguide people
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
passing doc
Closes#31717 from yaooqinn/minordoc0.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Enhance boolean simplification rule by handling following scenarios:
(((a && b) && a && (a && c))) => a && b && c)
(((a || b) || a || (a || c))) => a || b || c
### Why are the changes needed?
Minor improvement
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UTs
Closes#31318 from Swinky/booleansimplification.
Authored-by: Swinky <mannswinky@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to generate "stable" output attributes per the logical node of the DESCRIBE COLUMN command.
### Why are the changes needed?
This fixes the issue demonstrated by the example:
```
val tbl = "testcat.ns1.ns2.tbl"
sql(s"CREATE TABLE $tbl (c0 INT) USING _")
val description = sql(s"DESCRIBE TABLE $tbl c0")
description.drop("info_name")
```
```
[info] org.apache.spark.sql.AnalysisException: Resolved attribute(s) info_name#74 missing from info_name#25,info_value#26 in operator !Project [info_name#74]. Attribute(s) with the same name appear in the operation: info_name. Please check if the right attribute(s) are used.;
[info] !Project [info_name#74]
[info] +- LocalRelation [info_name#25, info_value#26]
```
### Does this PR introduce _any_ user-facing change?
After this change user `drop()/add()` works well.
### How was this patch tested?
Added UT
Closes#31696 from AngersZhuuuu/SPARK-34576.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to extend Catalyst's type system by two new types that conform to the SQL standard (see SQL:2016, section 4.6.3):
- `DayTimeIntervalType` represents the day-time interval type,
- `YearMonthIntervalType` for SQL year-month interval type.
This PR only adds the two new DataType implementations, and there will be more PRs as sub-tasks of SPARK-27790 to completely support the new ANSI interval types.
### Why are the changes needed?
Spark as it is today supports an INTERVAL datatype. However this type is of very limited use. Existing interval values cannot be compared with any other interval values, or persisted to storage. Spark users request to either implement new or expand existing built-in functions which produce some sort of measures for elapsed time, such as `DATEDIFF()`. Rather than work around the edges to fill the potholes of the existing INTERVAL data type, I would like to propose to deliver a proper ANSI compliant INTERVAL type that can be introduced with minimal incompatibility, is comparable and thus sortable, and can be persisted in tables.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
1. By checking coding style via:
```
$ ./dev/scalastyle
$ ./dev/lint-java
```
2. Run the test for the default sizes:
```
$ build/sbt "test:testOnly *DataTypeSuite"
```
Closes#31614 from MaxGekk/day-time-interval-type.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`BroadcastNestedLoopJoinExec` does not preserve `outputPartitioning` and `outputOrdering` right now. But it can preserve the streamed side partitioning and ordering when possible. This can help avoid shuffle and sort in later stage, if there's join and aggregation in the query. See example queries in added unit test in `JoinSuite.scala`.
In addition, fix a bunch of minor places in `BroadcastNestedLoopJoinExec.scala` for better style and readability.
### Why are the changes needed?
Avoid shuffle and sort for certain complicated query shape. Better query performance can be achieved.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit test in `JoinSuite.scala`.
Closes#31708 from c21/nested-join.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Use `Utils.getSimpleName` to avoid hitting `Malformed class name` error in `NewInstance.doGenCode`.
### Why are the changes needed?
On older JDK versions (e.g. JDK8u), nested Scala classes may trigger `java.lang.Class.getSimpleName` to throw an `java.lang.InternalError: Malformed class name` error.
In this particular case, creating an `ExpressionEncoder` on such a nested Scala class would create a `NewInstance` expression under the hood, which will trigger the problem during codegen.
Similar to https://github.com/apache/spark/pull/29050, we should use Spark's `Utils.getSimpleName` utility function in place of `Class.getSimpleName` to avoid hitting the issue.
There are two other occurrences of `java.lang.Class.getSimpleName` in the same file, but they're safe because they're only guaranteed to be only used on Java classes, which don't have this problem, e.g.:
```scala
// Make a copy of the data if it's unsafe-backed
def makeCopyIfInstanceOf(clazz: Class[_ <: Any], value: String) =
s"$value instanceof ${clazz.getSimpleName}? ${value}.copy() : $value"
val genFunctionValue: String = lambdaFunction.dataType match {
case StructType(_) => makeCopyIfInstanceOf(classOf[UnsafeRow], genFunction.value)
case ArrayType(_, _) => makeCopyIfInstanceOf(classOf[UnsafeArrayData], genFunction.value)
case MapType(_, _, _) => makeCopyIfInstanceOf(classOf[UnsafeMapData], genFunction.value)
case _ => genFunction.value
}
```
The Unsafe-* family of types are all Java types, so they're okay.
### Does this PR introduce _any_ user-facing change?
Fixes a bug that throws an error when using `ExpressionEncoder` on some nested Scala types, otherwise no changes.
### How was this patch tested?
Added a test case to `org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite`. It'll fail on JDK8u before the fix, and pass after the fix.
Closes#31709 from rednaxelafx/spark-34596-master.
Authored-by: Kris Mok <kris.mok@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Call `toSeq` to fix Scala 2.13 build error.
### Why are the changes needed?
It is needed to fix 2.13 build error.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#31716 from viirya/SPARK-34548-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch proposes to remove unnecessary children from Union under Distince and Deduplicate
### Why are the changes needed?
If there are any duplicate child of `Union` under `Distinct` and `Deduplicate`, it can be removed to simplify query plan.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test
Closes#31656 from viirya/SPARK-34548.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Currently, the warehouse path gets fully qualified in the caller side for creating a database, table, partition, etc. An unqualified path is populated into Spark and Hadoop confs, which leads to inconsistent API behaviors. We should make it qualified ahead.
When the value is a relative path `spark.sql.warehouse.dir=lakehouse`, some behaviors become inconsistent, for example.
If the default database is absent at runtime, the app fails with
```java
Caused by: java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: file:./lakehouse
at org.apache.hadoop.fs.Path.initialize(Path.java:263)
at org.apache.hadoop.fs.Path.<init>(Path.java:254)
at org.apache.hadoop.hive.metastore.Warehouse.getDnsPath(Warehouse.java:133)
at org.apache.hadoop.hive.metastore.Warehouse.getDnsPath(Warehouse.java:137)
at org.apache.hadoop.hive.metastore.Warehouse.getWhRoot(Warehouse.java:150)
at org.apache.hadoop.hive.metastore.Warehouse.getDefaultDatabasePath(Warehouse.java:163)
at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB_core(HiveMetaStore.java:636)
at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:655)
at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.init(HiveMetaStore.java:431)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.hive.metastore.RetryingHMSHandler.invokeInternal(RetryingHMSHandler.java:148)
at org.apache.hadoop.hive.metastore.RetryingHMSHandler.invoke(RetryingHMSHandler.java:107)
at org.apache.hadoop.hive.metastore.RetryingHMSHandler.<init>(RetryingHMSHandler.java:79)
... 73 more
```
If the default database is present at runtime, the app can work with it, and if we create a database, it gets fully qualified, for example
```sql
spark-sql> create database test;
Time taken: 0.052 seconds
spark-sql> desc database test;
Database Name test
Comment
Location file:/Users/kentyao/Downloads/spark/spark-3.2.0-SNAPSHOT-bin-20210226/lakehouse/test.db
Owner kentyao
Time taken: 0.023 seconds, Fetched 4 row(s)
```
Another thing is that the log becomes nubilous, for example.
```logtalk
21/02/27 13:54:17 INFO SharedState: Setting hive.metastore.warehouse.dir ('null') to the value of spark.sql.warehouse.dir ('datalake').
21/02/27 13:54:17 INFO SharedState: Warehouse path is 'lakehouse'.
```
### Why are the changes needed?
fix bug and ambiguity
### Does this PR introduce _any_ user-facing change?
yes, the path now resolved with proper order - `warehouse->database->table->partition`
### How was this patch tested?
w/ ut added
Closes#31671 from yaooqinn/SPARK-34558.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a followup of #31560,
In #31560, we added `JavaSimpleWritableDataSource ` and left some little problems like unused interface `SessionConfigSupport` 、 inconsistent schema between `JavaSimpleWritableDataSource ` and `SimpleWritableDataSource`.
This PR fixes the remaining problems in #31560.
### Why are the changes needed?
1. `SessionConfigSupport` in `JavaSimpleWritableDataSource ` and `SimpleWritableDataSource` is never used, so we don't need to implement it.
2. change the schema of `SimpleWritableDataSource`, to match `TestingV2Source`
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
existing testsuites
Closes#31621 from kevincmchen/SPARK-34498.
Authored-by: kevincmchen <kevincmchen@tencent.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Today, child expressions may be resolved based on "real" or metadata output attributes. We should prefer the real attribute during resolution if one exists.
### Why are the changes needed?
Today, attempting to resolve an expression when there is a "real" output attribute and a metadata attribute with the same name results in resolution failure. This is likely unexpected, as the user may not know about the metadata attribute.
### Does this PR introduce _any_ user-facing change?
Yes. Previously, the user would see an error message when resolving a column with the same name as a "real" output attribute and a metadata attribute as below:
```
org.apache.spark.sql.AnalysisException: Reference 'index' is ambiguous, could be: testcat.ns1.ns2.tableTwo.index, testcat.ns1.ns2.tableOne.index.; line 1 pos 71
at org.apache.spark.sql.catalyst.expressions.package$AttributeSeq.resolve(package.scala:363)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveChildren(LogicalPlan.scala:107)
```
Now, resolution succeeds and provides the "real" output attribute.
### How was this patch tested?
Added a unit test.
Closes#31654 from karenfeng/fallback-resolve-metadata.
Authored-by: Karen Feng <karen.feng@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
**What changes were proposed in this pull request?**
This PR fixes dataframe.na.fillMap() for column having a dot in the name as mentioned in [SPARK-34417](https://issues.apache.org/jira/browse/SPARK-34417).
Use resolved attributes of a column for replacing null values.
**Why are the changes needed?**
dataframe.na.fillMap() does not work for column having a dot in the name
**Does this PR introduce any user-facing change?**
None
**How was this patch tested?**
Added unit test for the same
Closes#31545 from amandeep-sharma/master.
Lead-authored-by: Amandeep Sharma <happyaman91@gmail.com>
Co-authored-by: Amandeep Sharma <amandeep.sharma@oracle.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
As a follow-up to SPARK-34456, this PR removes `BatchWriteHelper` completely.
### Why are the changes needed?
These changes remove no longer used code.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#31699 from aokolnychyi/spark-34585.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. This PR aims to ignore ORC encryption tests when ORC shim is loaded by old Hadoop library by some other tests. The test coverage is preserved by Jenkins SBT runs and GitHub Action jobs. This PR only aims to recover Maven Jenkins jobs.
2. In addition, this PR simplifies SBT testing by refactor the test config to `SparkBuild.scala/pom.xml` and remove `DedicatedJVMTest`. This will remove one GitHub Action job which was recently added for `DedicatedJVMTest` tag.
### Why are the changes needed?
Currently, Maven test fails when it runs in a batch mode because `HadoopShimsPre2_3$NullKeyProvider` is loaded.
**MVN COMMAND**
```
$ mvn test -pl sql/core --am -Dtest=none -DwildcardSuites=org.apache.spark.sql.execution.datasources.orc.OrcV1QuerySuite,org.apache.spark.sql.execution.datasources.orc.OrcEncryptionSuite
```
**BEFORE**
```
- Write and read an encrypted table *** FAILED ***
...
Cause: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1) (localhost executor driver): java.lang.IllegalArgumentException: Unknown key pii
at org.apache.orc.impl.HadoopShimsPre2_3$NullKeyProvider.getCurrentKeyVersion(HadoopShimsPre2_3.java:71)
at org.apache.orc.impl.WriterImpl.getKey(WriterImpl.java:871)
```
**AFTER**
```
OrcV1QuerySuite
...
OrcEncryptionSuite:
- Write and read an encrypted file !!! CANCELED !!!
[] was empty org.apache.orc.impl.HadoopShimsPre2_3$NullKeyProvider1b705f65 doesn't has the test keys. ORC shim is created with old Hadoop libraries (OrcEncryptionSuite.scala:39)
- Write and read an encrypted table !!! CANCELED !!!
[] was empty org.apache.orc.impl.HadoopShimsPre2_3$NullKeyProvider22adeee1 doesn't has the test keys. ORC shim is created with old Hadoop libraries (OrcEncryptionSuite.scala:67)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the Jenkins Maven tests.
For SBT command,
- the test suite required a dedicated JVM (Before)
- the test suite doesn't require a dedicated JVM (After)
```
$ build/sbt "sql/testOnly *.OrcV1QuerySuite *.OrcEncryptionSuite"
...
[info] OrcV1QuerySuite
...
[info] - SPARK-20728 Make ORCFileFormat configurable between sql/hive and sql/core (26 milliseconds)
[info] OrcEncryptionSuite:
[info] - Write and read an encrypted file (431 milliseconds)
[info] - Write and read an encrypted table (359 milliseconds)
[info] All tests passed.
[info] Passed: Total 35, Failed 0, Errors 0, Passed 35
```
Closes#31697 from dongjoon-hyun/SPARK-34578-TEST.
Lead-authored-by: Dongjoon Hyun <dhyun@apple.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Currently in `SpecificParquetRecordReaderBase` we use deprecated APIs in a few places from Parquet, such as `readFooter`, `ParquetInputSplit`, `new ParquetFileReader`, `filterRowGroups`, etc. This replaces these with the newer APIs. In specific this:
- Replaces `ParquetInputSplit` with `FileSplit`. We never use specific things in the former such as `rowGroupOffsets` so the swap is pretty simple.
- Removes `readFooter` calls by using `ParquetFileReader.open`
- Replace deprecated `ParquetFileReader` ctor with the newer API which takes `ParquetReadOptions`.
- Removes the unnecessary handling of case when `rowGroupOffsets` is not null. It seems this never happens.
### Why are the changes needed?
The aforementioned APIs were deprecated and is going to be removed at some point in future. This is to ensure better supportability.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
This is a cleanup and relies on existing tests on the relevant code paths.
Closes#31667 from sunchao/SPARK-32703.
Lead-authored-by: Chao Sun <sunchao@apache.org>
Co-authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
