### What changes were proposed in this pull request?
A query below failed in the master;
```
scala> sql("select array(array(1, 2), array(3)) ar").select(explode(explode($"ar"))).show()
20/03/01 13:51:56 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)/ 1]
java.lang.ClassCastException: scala.collection.mutable.ArrayOps$ofRef cannot be cast to org.apache.spark.sql.catalyst.util.ArrayData
at org.apache.spark.sql.catalyst.expressions.ExplodeBase.eval(generators.scala:313)
at org.apache.spark.sql.execution.GenerateExec.$anonfun$doExecute$8(GenerateExec.scala:108)
at scala.collection.Iterator$$anon$11.nextCur(Iterator.scala:484)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:490)
at scala.collection.Iterator$ConcatIterator.hasNext(Iterator.scala:222)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
...
```
This pr modified the `hasNestedGenerator` code in `ExtractGenerator` for correctly catching nested inner generators.
### Why are the changes needed?
A bug fix.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added tests.
Closes#27750 from maropu/HandleNestedGenerators.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
This reverts commit afaeb29599.
### What changes were proposed in this pull request?
Based on the result and comment from https://github.com/apache/spark/pull/27552#discussion_r385531744
In the hive module, server-side provides datetime values simply use `value.toSting`, and the client-side regenerates the results back in `HiveBaseResultSet` with `java.sql.Date(Timestamp).valueOf`.
there will be inconsistency between client and server if we use java8 APIs
### Why are the changes needed?
the change is still unclear enough
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
Nah
Closes#27733 from yaooqinn/SPARK-30808.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
To address the concern pointed out in https://github.com/apache/spark/pull/22227. This will make `split` source-compatible by removing minimal cosmetic changes.
### Why are the changes needed?
For source compatibility.
### Does this PR introduce any user-facing change?
No (it will prevent potential user-facing change from the original PR)
### How was this patch tested?
Unittest was changed (in order for us to detect that source compatibility easily).
Closes#27756 from HyukjinKwon/SPARK-25202.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR combines `CustomShuffledRowRDD` and `LocalShuffledRowRDD` into `ShuffledRowRDD`, and creates `CustomShuffleReaderExec` to unify and replace all existing AQE readers: `CoalescedShuffleReaderExec`, `LocalShuffleReaderExec` and `SkewJoinShuffleReaderExec`.
### Why are the changes needed?
To reduce code redundancy.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Passed existing UTs.
Closes#27742 from maryannxue/aqe-readers.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
This patch fixes several incorrect uses of `assume()` in our tests.
If a call to `assume(condition)` fails then it will cause the test to be marked as skipped instead of failed: this feature allows test cases to be skipped if certain prerequisites are missing. For example, we use this to skip certain tests when running on Windows (or when Python dependencies are unavailable).
In contrast, `assert(condition)` will fail the test if the condition doesn't hold.
If `assume()` is accidentally substituted for `assert()`then the resulting test will be marked as skipped in cases where it should have failed, undermining the purpose of the test.
This patch fixes several such cases, replacing certain `assume()` calls with `assert()`.
Credit to ahirreddy for spotting this problem.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#27754 from JoshRosen/fix-assume-vs-assert.
Lead-authored-by: Josh Rosen <rosenville@gmail.com>
Co-authored-by: Josh Rosen <joshrosen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch fixes the bug of UnsafeRow which misses to handle the UDT specifically, in `isFixedLength` and `isMutable`. These methods don't check its SQL type for UDT, always treating UDT as variable-length, and non-mutable.
It doesn't bring any issue if UDT is used to represent complicated type, but when UDT is used to represent some type which is matched with fixed length of SQL type, it exposes the chance of correctness issues, as these informations sometimes decide how the value should be handled.
We got report from user mailing list which suspected as mapGroupsWithState looks like handling UDT incorrectly, but after some investigation it was from GenerateUnsafeRowJoiner in shuffle phase.
0e2ca11d80/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala (L32-L43)
Here updating position should not happen on fixed-length column, but due to this bug, the value of UDT having fixed-length as sql type would be modified, which actually corrupts the value.
### Why are the changes needed?
Misclassifying of the type of length for UDT can corrupt the value when the row is presented to the input of GenerateUnsafeRowJoiner, which brings correctness issue.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
New UT added.
Closes#27747 from HeartSaVioR/SPARK-30993.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Added more test cases to `StatisticsCollectionTestBase`.
### Why are the changes needed?
To improve test coverage.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By `StatisticsSuite` and `StatisticsCollectionSuite`.
Closes#27741 from MaxGekk/stat-collect-tests.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR fixes a thread-safety bug in `SparkSession.createDataset(Seq)`: if the caller-supplied `Encoder` is used in multiple threads then createDataset's usage of the encoder may lead to incorrect / corrupt results because the Encoder's internal mutable state will be updated from multiple threads.
Here is an example demonstrating the problem:
```scala
import org.apache.spark.sql._
val enc = implicitly[Encoder[(Int, Int)]]
val datasets = (1 to 100).par.map { _ =>
val pairs = (1 to 100).map(x => (x, x))
spark.createDataset(pairs)(enc)
}
datasets.reduce(_ union _).collect().foreach {
pair => require(pair._1 == pair._2, s"Pair elements are mismatched: $pair")
}
```
Before this PR's change, the above example fails because Spark produces corrupted records where different input records' fields have been co-mingled.
This bug is similar to SPARK-22355 / #19577, a similar problem in `Dataset.collect()`.
The fix implemented here is based on #24735's updated version of the `Datataset.collect()` bugfix: use `.copy()`. For consistency, I used same [code comment](d841b33ba3/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala (L3414)) / explanation as that PR.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Tested manually using the example listed above.
Thanks to smcnamara-stripe for identifying this bug.
Closes#26076 from JoshRosen/SPARK-29419.
Authored-by: Josh Rosen <rosenville@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Rename `spark.sql.legacy.addDirectory.recursive.enabled` to `spark.sql.legacy.addSingleFileInAddFile`
### Why are the changes needed?
To follow the naming convention
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing UTs.
Closes#27725 from iRakson/SPARK-30234_CONFIG.
Authored-by: iRakson <raksonrakesh@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Renamed configuration from `spark.sql.legacy.useHashOnMapType` to `spark.sql.legacy.allowHashOnMapType`.
### Why are the changes needed?
Better readability of configuration.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing UTs.
Closes#27719 from iRakson/SPARK-27619_FOLLOWUP.
Authored-by: iRakson <raksonrakesh@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently the join operators are not well abstracted, since there are lot of common logic. A trait can be created for easier pattern matching and other future handiness. This is a follow-up PR based on comment
https://github.com/apache/spark/pull/27509#discussion_r379613391 .
This PR refined from the following aspects:
1. Refined structure of all physical join operators
2. Add missing joinType field for CartesianProductExec operator
3. Refined codes related to Explain Formatted
The EXPLAIN FORMATTED changes are
1. Converge all join operator `verboseStringWithOperatorId` implementations to `BaseJoinExec`. Join condition displayed, and join keys displayed if it’s not empty.
2. `#1` will add Join condition to `BroadcastNestedLoopJoinExec`.
3. `#1` will **NOT** affect `CartesianProductExec`,`SortMergeJoin` and `HashJoin`s, since they already got there override implementation before.
4. Converge all join operator `simpleStringWithNodeId` to `BaseJoinExec`, which will enhance the one line description for `CartesianProductExec` with `JoinType` added.
5. Override `simpleStringWithNodeId` in `BroadcastNestedLoopJoinExec` to show `BuildSide`, which was only done for `HashJoin`s before.
### Why are the changes needed?
Make the code consistent with other operators and for future handiness of join operators.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing tests
Closes#27595 from Eric5553/RefineJoin.
Authored-by: Eric Wu <492960551@qq.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When `CREATE TABLE` SQL statement does not specify the provider, leave it to the catalog implementations to decide.
### Why are the changes needed?
It's super weird if we set the default provider to parquet when creating a table in a JDBC catalog.
### Does this PR introduce any user-facing change?
Yes, v2 catalog will not see a "provider" property in table properties if it's not specified in `CREATE TABLE` SQL statement. V2 catalog is new in 3.0.
### How was this patch tested?
new tests
Closes#27650 from cloud-fan/create_table.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Make rule `PruneHiveTablePartitions` to execute as `earlyScanPushDownRules`.
### Why are the changes needed?
Similar to rule `PruneFileSourcePartitions`, `PruneHiveTablePartitions` should also be executed as earlyScanPushDownRules to eliminate the impact on statistic computation later.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass Jenkins.
Closes#27723 from Ngone51/early_hive_prune.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When aliasing in nested column pruning in Project on top of Generate, we should exclude Generate outputs.
### Why are the changes needed?
Right now we would prune nested columns in Project on top of Generate. It is possible that referred nested columns are from Generate's outputs, not from its child. To address that case, we should exclude Generate outputs when aliasing in nested column pruning.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#27702 from viirya/fix-nested-pruning.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This pr intends to remove non-used trait, `GivenWhenThen`, from `HiveComparisonTest`.
### Why are the changes needed?
For better code.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#27726 from maropu/MINOR-20200228.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
In this PR, I addressed the comment from https://github.com/apache/spark/pull/27672#discussion_r383719562 to use `intercept` instead of `try-catch` block to assert failures in the IntervalUtilsSuite
### Why are the changes needed?
improve tests
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
Nah
Closes#27700 from yaooqinn/intervaltest.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR groups all hive upgrade related migration guides inside Spark 3.0 together.
Also add another behavior change of `ScriptTransform` in the new Hive section.
### Why are the changes needed?
Make the doc more clearly to user.
### Does this PR introduce any user-facing change?
No, new doc for Spark 3.0.
### How was this patch tested?
N/A.
Closes#27670 from Ngone51/hive_migration.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When doing base operator abstraction work, we found there are still some code snippet is inconsistent with other abstraction code style. This PR addressed following two code refactor cases.
**Case 1** Override keyword missed for some fields in derived classes. The compiler will not capture it if we rename some fields in the future.
**Case 2** Inconsistent abstract class field definition. The updated style will simplify derived class definition, e.g. `EvalPythonExec` `WindowExecBase`
### Why are the changes needed?
Improve the code style consistency and code quality
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing tests
Closes#27511 from Eric5553/BaseClassAbstraction.
Authored-by: Eric Wu <492960551@qq.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch proposes to throw clear analysis exception if untyped `Dataset.select` takes typed column expression that needs input type.
### Why are the changes needed?
`Dataset` provides few typed `select` helper functions to select typed column expressions. The maximum number of typed columns supported is 5. If wanting to select more than 5 typed columns, it silently calls untyped `Dataset.select` and can causes weird unresolved error, like:
```
org.apache.spark.sql.AnalysisException: unresolved operator 'Aggregate [fooagg(FooAgg(1), None, None, None, input[0, int, false] AS value#114, assertnotnull(cast(value#114 as int)), input[0, int, false] AS value#113, IntegerType, IntegerType, false) AS foo_agg_1#116, fooagg(FooAgg(2), None, None, None, input[0, int, false] AS value#119, assertnotnull(cast(value#119 as int)), input[0, int, false] AS value#118, IntegerType, IntegerType, false) AS foo_agg_2#121, fooagg(FooAgg(3), None, None, None, input[0, int, false] AS value#124, assertnotnull(cast(value#124 as int)), input[0, int, false] AS value#123, IntegerType, IntegerType, false) AS foo_agg_3#126, fooagg(FooAgg(4), None, None, None, input[0, int, false] AS value#129, assertnotnull(cast(value#129 as int)), input[0, int, false] AS value#128, IntegerType, IntegerType, false) AS foo_agg_4#131, fooagg(FooAgg(5), None, None, None, input[0, int, false] AS value#134, assertnotnull(cast(value#134 as int)), input[0, int, false] AS value#133, IntegerType, IntegerType, false) AS foo_agg_5#136, fooagg(FooAgg(6), None, None, None, input[0, int, false] AS value#139, assertnotnull(cast(value#139 as int)), input[0, int, false] AS value#138, IntegerType, IntegerType, false) AS foo_agg_6#141];;
'Aggregate [fooagg(FooAgg(1), None, None, None, input[0, int, false] AS value#114, assertnotnull(cast(value#114 as int)), input[0, int, false] AS value#113, IntegerType, IntegerType, false) AS foo_agg_1#116, fooagg(FooAgg(2), None, None, None, input[0, int, false] AS value#119, assertnotnull(cast(value#119 as int)), input[0, int, false] AS value#118, IntegerType, IntegerType, false) AS foo_agg_2#121, fooagg(FooAgg(3), None, None, None, input[0, int, false] AS value#124, assertnotnull(cast(value#124 as int)), input[0, int, false] AS value#123, IntegerType, IntegerType, false) AS foo_agg_3#126, fooagg(FooAgg(4), None, None, None, input[0, int, false] AS value#129, assertnotnull(cast(value#129 as int)), input[0, int, false] AS value#128, IntegerType, IntegerType, false) AS foo_agg_4#131, fooagg(FooAgg(5), None, None, None, input[0, int, false] AS value#134, assertnotnull(cast(value#134 as int)), input[0, int, false] AS value#133, IntegerType, IntegerType, false) AS foo_agg_5#136, fooagg(FooAgg(6), None, None, None, input[0, int, false] AS value#139, assertnotnull(cast(value#139 as int)), input[0, int, false] AS value#138, IntegerType, IntegerType, false) AS foo_agg_6#141]
+- Project [_1#6 AS a#13, _2#7 AS b#14, _3#8 AS c#15, _4#9 AS d#16, _5#10 AS e#17, _6#11 AS F#18]
+- LocalRelation [_1#6, _2#7, _3#8, _4#9, _5#10, _6#11]
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:43)
at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:95)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$3.apply(CheckAnalysis.scala:431)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$3.apply(CheckAnalysis.scala:430)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:430)
```
However, to fully disallow typed columns as input to untyped `select` API will break current usage like `count` that is a `TypedColumn` in `functions`. In order to keep compatibility, we should allow current usage of certain `TypedColumn`s as input to untyped `select` API. For the `TypedColumn`s that will cause unresolved exception, we should explicitly let users know that they are incorrectly calling untyped `select` with typed columns which need input type.
### Does this PR introduce any user-facing change?
Yes, but this PR only refines the error message.
When users call `Dataset.select` API with typed column that needs input type, an analysis exception will be thrown. Previously an unresolved error will be thrown.
### How was this patch tested?
Unit tests.
Closes#27499 from viirya/SPARK-30590.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR follows https://github.com/apache/spark/pull/25074 and improves the implement.
### Why are the changes needed?
Improve code.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Exists UT
Closes#27699 from beliefer/improve-boolean-test.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
After https://github.com/apache/spark/pull/27659 (see https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7-hive-2.3/253/), the tests below fail consistently, specifically in one job https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7-hive-2.3/ in Jenkins
```
org.apache.spark.sql.hive.execution.HiveSerDeSuite.Test the default fileformat for Hive-serde tables
```
The profile is same as PR builder but seems it fails specifically in this machine.
Several configurations used in `HiveSerDeSuite` are not being set presumably due to the inconsistency between `SQLConf.get` and the active Spark session described in the https://github.com/apache/spark/pull/27387, and as a side effect of the cloned session at https://github.com/apache/spark/pull/27659.
This PR proposes to explicitly set the configuration against `TestHive` by using `withExistingConf` at `withSQLConf`
### Why are the changes needed?
To make `spark-master-test-sbt-hadoop-2.7-hive-2.3` job pass.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Cannot reproduce in my local. Presumably it cannot be reproduced in the PR builder. We should see if the tests pass at `spark-master-test-sbt-hadoop-2.7-hive-2.3` job after this PR is merged.
Closes#27705 from HyukjinKwon/SPARK-30906.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
This is a follow up of #27669 in order to fix a typo.
### Why are the changes needed?
N/A
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
N/A
Closes#27714 from cloud-fan/typo.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
`hash()` and `xxhash64()` cannot be used on elements of `Maptype`. A new configuration `spark.sql.legacy.useHashOnMapType` is introduced to allow users to restore the previous behaviour.
When `spark.sql.legacy.useHashOnMapType` is set to false:
```
scala> spark.sql("select hash(map())");
org.apache.spark.sql.AnalysisException: cannot resolve 'hash(map())' due to data type mismatch: input to function hash cannot contain elements of MapType; line 1 pos 7;
'Project [unresolvedalias(hash(map(), 42), None)]
+- OneRowRelation
```
when `spark.sql.legacy.useHashOnMapType` is set to true :
```
scala> spark.sql("set spark.sql.legacy.useHashOnMapType=true");
res3: org.apache.spark.sql.DataFrame = [key: string, value: string]
scala> spark.sql("select hash(map())").first()
res4: org.apache.spark.sql.Row = [42]
```
### Why are the changes needed?
As discussed in Jira, SparkSql's map hashcodes depends on their order of insertion which is not consistent with the normal scala behaviour which might confuse users.
Code snippet from JIRA :
```
val a = spark.createDataset(Map(1->1, 2->2) :: Nil)
val b = spark.createDataset(Map(2->2, 1->1) :: Nil)
// Demonstration of how Scala Map equality is unaffected by insertion order:
assert(Map(1->1, 2->2).hashCode() == Map(2->2, 1->1).hashCode())
assert(Map(1->1, 2->2) == Map(2->2, 1->1))
assert(a.first() == b.first())
// In contrast, this will print two different hashcodes:
println(Seq(a, b).map(_.selectExpr("hash(*)").first()))
```
Also `MapType` is prohibited for aggregation / joins / equality comparisons #7819 and set operations #17236.
### Does this PR introduce any user-facing change?
Yes. Now users cannot use hash functions on elements of `mapType`. To restore the previous behaviour set `spark.sql.legacy.useHashOnMapType` to true.
### How was this patch tested?
UT added.
Closes#27580 from iRakson/SPARK-27619.
Authored-by: iRakson <raksonrakesh@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to fix an issue where qualified columns are not matched for v2 tables if current catalog/namespace are used.
For v1 tables, you can currently perform the following:
```SQL
SELECT default.t.id FROM t;
```
For v2 tables, the following fails:
```SQL
USE testcat.ns1.ns2;
SELECT testcat.ns1.ns2.t.id FROM t;
org.apache.spark.sql.AnalysisException: cannot resolve '`testcat.ns1.ns2.t.id`' given input columns: [t.id, t.point]; line 1 pos 7;
```
### Why are the changes needed?
It is a bug since qualified column names cannot match if current catalog/namespace are used.
### Does this PR introduce any user-facing change?
Yes, now the following works:
```SQL
USE testcat.ns1.ns2;
SELECT testcat.ns1.ns2.t.id FROM t;
```
### How was this patch tested?
Added new tests
Closes#27532 from imback82/qualifed_col_respect_current.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
After https://github.com/apache/spark/pull/27387 (see https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7-hive-2.3/202/), the tests below fail consistently, specifically in one job https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7-hive-2.3/ in Jenkins
```
org.apache.spark.sql.hive.HiveShowCreateTableSuite.simple hive table
org.apache.spark.sql.hive.HiveShowCreateTableSuite.simple external hive table
org.apache.spark.sql.hive.HiveShowCreateTableSuite.hive bucketing is supported
```
The profile is same as PR builder but seems it fails specifically in this machine. Seems the legacy configuration `spark.sql.legacy.createHiveTableByDefault.enabled` is not being set due to the inconsistency between `SQLConf.get` and the active Spark session as described in the https://github.com/apache/spark/pull/27387.
This PR proposes to explicitly set the configuration against the session used instead of `SQLConf.get`.
### Why are the changes needed?
To make `spark-master-test-sbt-hadoop-2.7-hive-2.3` job pass.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Cannot reproduce in my local. Presumably it cannot be reproduced in the PR builder. We should see if the tests pass at `spark-master-test-sbt-hadoop-2.7-hive-2.3` job after this PR is merged
Closes#27703 from HyukjinKwon/SPARK-30798-followup.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.1.0-SNAPSHOT.
### Why are the changes needed?
N/A
### Does this PR introduce any user-facing change?
N/A
### How was this patch tested?
N/A
Closes#27698 from gatorsmile/updateVersion.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Use the average size of the non-skewed partitions as the target size when splitting skewed partitions, instead of ADAPTIVE_EXECUTION_SKEWED_PARTITION_SIZE_THRESHOLD
### Why are the changes needed?
The goal of skew join optimization is to make the data distribution move even. So it makes more sense the use the average size of the non-skewed partitions as the target size.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#27669 from cloud-fan/aqe.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
In the PR, I propose to replace:
1. `millisToDays()` by `microsToDays()` which accepts microseconds since the epoch and returns days since the epoch in the specified time zone. The last one is the internal representation of Catalyst's DateType.
2. `daysToMillis()` by `daysToMicros()` which accepts days since the epoch in some time zone and returns the number of microseconds since the epoch. The last one is internal representation of Catalyst's TimestampType.
3. `fromMillis()` by `millisToMicros()`
4. `toMillis()` by `microsToMillis()`
### Why are the changes needed?
Spark stores timestamps in microseconds precision, so, there is no actual need to convert dates to milliseconds, and then to microseconds. As examples, look at DateTimeUtils functions `monthsBetween()` and `truncTimestamp()`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By existing test suites UnivocityParserSuite, DateExpressionsSuite, ComputeCurrentTimeSuite, DateTimeUtilsSuite, DateFunctionsSuite, JsonSuite, StreamSuite.
Closes#27618 from MaxGekk/replace-millis-by-micros.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The current behavior of interval multiply and divide follows the ANSI SQL standard when overflow, it is compatible with other operations when `spark.sql.ansi.enabled` is true, but not compatible when `spark.sql.ansi.enabled` is false.
When `spark.sql.ansi.enabled` is false, as the factor is a double value, so it should use java's rounding or truncation behavior for casting double to integrals. when divided by zero, it returns `null`. we also follow the natural rules for intervals as defined in the Gregorian calendar, so we do not add the month fraction to days but add days fraction to microseconds.
### Why are the changes needed?
Make interval multiply and divide's overflow behavior consistent with other interval operations
### Does this PR introduce any user-facing change?
no, these are new features in 3.0
### How was this patch tested?
add uts
Closes#27672 from yaooqinn/SPARK-30919.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Split the nested CTE cases into a single file `cte-nested.sql`, which will be reused in cte-legacy.sql and cte-nonlegacy.sql.
### Why are the changes needed?
Make the cases easy to maintain.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes#27667 from xuanyuanking/SPARK-28228-test.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
For the following:
```
CREATE TABLE t USING json AS SELECT 1 AS i
SELECT * FROM spark_catalog.t
```
`spark_catalog.t` is resolved to `spark_catalog.default.t` assuming the current namespace is `default`. However, this is not consistent with V2 behavior where the namespace must be specified if the catalog name is provided. This PR proposes to fix this inconsistency.
### Why are the changes needed?
To be consistent with V2 table naming scheme in SQL commands.
### Does this PR introduce any user-facing change?
Yes, now the user has to specify the namespace if the catalog name is provided. For example,
```
SELECT * FROM spark_catalog.t # Will throw AnalysisException with 'Session catalog cannot have an empty namespace: spark_catalog.t'
SELECT * FROM spark_catalog.default.t # OK
```
### How was this patch tested?
Added new tests
Closes#27642 from imback82/disallow_spark_catalog_wihtout_db.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Set `FAIL_ON_UNKNOWN_PROPERTIES` to `false` in `JsonProtocol` to allow ignore unknown fields in a Spark event. After this change, if we add new fields to a Spark event parsed by `ObjectMapper`, the event json string generated by a new Spark version can still be read by an old Spark History Server.
Since Spark History Server is an extra service, it usually takes time to upgrade, and it's possible that a Spark application is upgraded before SHS. Forwards-compatibility will allow an old SHS to support new Spark applications (may lose some new features but most of functions should still work).
### Why are the changes needed?
`JsonProtocol` is supposed to provide strong backwards-compatibility and forwards-compatibility guarantees: any version of Spark should be able to read JSON output written by any other version, including newer versions.
However, the forwards-compatibility guarantee is broken for events parsed by `ObjectMapper`. If a new field is added to an event parsed by `ObjectMapper` (e.g., 6dc5921e66 (diff-dc5c7a41fbb7479cef48b67eb41ad254R33)), the event json string generated by a new Spark version cannot be parsed by an old version of SHS right now.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
The new added tests.
Closes#27680 from zsxwing/SPARK-30936.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fixes a bug in nested column aliasing by taking the data type of the referenced nested fields into account when calculating the number of extracted columns. After this PR this query runs without issues:
```
SELECT explodedvalue.*
FROM VALUES array(named_struct('nested', named_struct('a', 1, 'b', 2))) AS (value)
LATERAL VIEW explode(value) AS explodedvalue
```
This is a regression from Spark 2.4.
### Why are the changes needed?
To fix a bug.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added new UT.
Closes#27675 from peter-toth/SPARK-30870.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Right now `StreamingQueryManager` will keep the last terminated query until `resetTerminated` is called. When the last terminated query has lots of states (a large sql plan, cached RDDs, etc.), it will keep a lot of memory unnecessarily. Actually, what `StreamingQueryManager` really needs is just the exception of the last failed query.
This PR changes the internal field `lastTerminatedQuery` in `StreamingQueryManager` to remember the last exception rather than the query to save the memory.
### Why are the changes needed?
Avoid keeping memory unnecessarily.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
This PR doesn't change any public behaviors. The existing tests have covered the touched codes.
Closes#27678 from zsxwing/SPARK-30927.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adds a new `nullOnOverflow` parameter to `MakeDecimal` so as to avoid its value depending on `SQLConf.get` and change during planning.
### Why are the changes needed?
This allows to avoid the issue when the configuration change between different phases of planning, and this can silently break a query plan which can lead to crashes or data corruption.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UTs.
Closes#27656 from peter-toth/SPARK-30898.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adds a new `followThreeValuedLogic` parameter to `ArrayExists` so as to avoid its value depending on `SQLConf.get` and change during planning.
### Why are the changes needed?
This allows to avoid the issue when the configuration change between different phases of planning, and this can silently break a query plan which can lead to crashes or data corruption.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UTs.
Closes#27655 from peter-toth/SPARK-30897.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### Why are the changes needed?
At present, HiveClientImpl.runHive will not throw an exception when it runs incorrectly, which will cause it to fail to feedback error information normally.
Example
```scala
spark.sql("add jar file:///tmp/not_exists.jar")
spark.sql("show databases").show()
```
/tmp/not_exists.jar doesn't exist, thus add jar is failed. However this code will run completely without causing application failure.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
add new suite tests
Closes#27644 from stczwd/SPARK-30868.
Authored-by: lijunqing <lijunqing@baidu.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Merge Into is currently missing additional validation around:
1. The lack of any WHEN statements
2. The first WHEN MATCHED statement needs to have a condition if there are two WHEN MATCHED statements.
3. Single use of UPDATE/DELETE
This PR introduces these validations.
(1) is required, because otherwise the MERGE statement is useless.
(2) is required, because otherwise the second WHEN MATCHED condition becomes dead code
(3) is up for debate, but the idea there is that a single expression should be sufficient to specify when you would like to update or delete your records. We restrict it for now to reduce surface area and ambiguity.
### Why are the changes needed?
To ease DataSource developers when building implementations for MERGE
### Does this PR introduce any user-facing change?
Adds additional validation checks
### How was this patch tested?
Unit tests
Closes#27677 from brkyvz/mergeChecks.
Authored-by: Burak Yavuz <brkyvz@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When skewed join optimization split more skewed readers, the plan may be very large and can not be shown in ui quickly. The config `spark.sql.adaptive.skewedJoinOptimization.skewedPartitionMaxSplits` is to resolve the above ui shown issue. And after [PR#27493](https://github.com/apache/spark/pull/27493) combined the skewed readers into one, we not need this config.
### Why are the changes needed?
remove the unnecessary config
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing test
Closes#27673 from JkSelf/removeMaxSplitNum.
Authored-by: jiake <ke.a.jia@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
- Use `Math.multiplyExact()` in `DateTimeUtils.fromMillis()` to prevent silent overflow in conversion milliseconds to microseconds.
- Use `DateTimeUtils.fromMillis()` in all places where milliseconds are converted to microseconds
- Use `DateTimeUtils.toMillis()` in all places where microseconds are converted to milliseconds
### Why are the changes needed?
1. To prevent silent arithmetic overflow while multiplying by 1000 in `fromMillis()`. Instead of it, `new ArithmeticException("long overflow")` will be thrown, and handled accordantly.
2. To correctly round microseconds in conversion to milliseconds. For example, `1965-01-01 10:11:12.123456` is represented as `-157700927876544` in micro precision. In milliseconds precision the above needs to be represented as `-157700927877` or `1965-01-01 10:11:12.123`.
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
By `TimestampFormatterSuite`, `CastSuite`, `DateExpressionsSuite`, `IntervalExpressionsSuite`, `ExpressionParserSuite`, `ExpressionParserSuite`, `DateTimeUtilsSuite`, `IntervalUtilsSuite`
Closes#27676 from MaxGekk/millis-2-micros-overflow.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Make static partition also follows `StoreAssignmentPolicy` when insert into table:
if `StoreAssignmentPolicy=LEGACY`, using `Cast`;
if `StoreAssignmentPolicy=ANSI | STRIC`, using `AnsiCast`;
E.g., for the table `t` created by:
```
create table t(a int, b string) using parquet partitioned by (a)
```
and insert values with `StoreAssignmentPolicy=ANSI` using:
```
insert into t partition(a='ansi') values('ansi')
```
Before this PR:
```
+----+----+
| b| a|
+----+----+
|ansi|null|
+----+----+
```
After this PR, insert will fail by:
```
java.lang.NumberFormatException: invalid input syntax for type numeric: ansi
```
(It should be better if we could use `TableOutputResolver.checkField` to fully follow `StoreAssignmentPolicy`. But since we lost the data type of static partition's value at first place, it's hard to use `TableOutputResolver.checkField`.)
### Why are the changes needed?
I think we should follow `StoreAssignmentPolicy` when insert into table for any columns, including static partition.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added new test.
Closes#27597 from Ngone51/fix-static-partition.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
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### What changes were proposed in this pull request?
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Add new `CommandCheck` rule and fail fast when detects duplicate columns in `AnalyzeColumnCommand`.
### Why are the changes needed?
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To avoid duplicate statistics computation for the same column in `AnalyzeColumnCommand`.
### Does this PR introduce any user-facing change?
<!--
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Yes. User now get exception when input duplicate columns.
### How was this patch tested?
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Added new test.
Closes#27651 from Ngone51/fail_on_dup_cols.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR fixes SPARK-30904 by adding a null check.
### Why are the changes needed?
For HIVE_CLI_SERVICE_PROTOCOL_V5 and below, serialization fails on NULL-containing decimal columns, caused by a call to `value.toPlainString()`, where `value` might be null. This null check fixes it.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
A test was added for serialization of NULL decimals for all HIVE_CLI_SERVICE_PROTOCOL versions.
Closes#27654 from CJStuart/SPARK-30904.
Authored-by: Christian Stuart <christian.stuart@databricks.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
In the PR, I propose to explain in the description of `removedSQLConfigs` when removed SQL configs should NOT be placed to the map.
### Why are the changes needed?
To make the cases when SQL configs should be added to `removedSQLConfigs` more clear. Recently, `spark.sql.variable.substitute.depth` was removed from the map by #27646 because it contradicts to the condition described by the PR.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By `./dev/scalastyle`
Closes#27653 from MaxGekk/removedSQLConfigs-comment.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Spark `ConfigEntry` and `ConfigBuilder` missing Spark version information of each configuration at release. This is not good for Spark user when they visiting the page of spark configuration.
http://spark.apache.org/docs/latest/configuration.html
The new Spark SQL config docs looks like:
```
> SET -v
spark.sql.adaptive.enabled false When true, enable adaptive query execution.
spark.sql.adaptive.nonEmptyPartitionRatioForBroadcastJoin 0.2 The relation with a non-empty partition ratio lower than this config will not be considered as the build side of a broadcast-hash join in adaptive execution regardless of its size.This configuration only has an effect when 'spark.sql.adaptive.enabled' is enabled.
spark.sql.adaptive.optimizeSkewedJoin.enabled true When true and adaptive execution is enabled, a skewed join is automatically handled at runtime.
spark.sql.adaptive.optimizeSkewedJoin.skewedPartitionFactor 10 A partition is considered as a skewed partition if its size is larger than this factor multiple the median partition size and also larger than spark.sql.adaptive.optimizeSkewedJoin.skewedPartitionSizeThreshold
spark.sql.adaptive.optimizeSkewedJoin.skewedPartitionMaxSplits 5 Configures the maximum number of task to handle a skewed partition in adaptive skewedjoin.
spark.sql.adaptive.optimizeSkewedJoin.skewedPartitionSizeThreshold 64MB Configures the minimum size in bytes for a partition that is considered as a skewed partition in adaptive skewed join.
spark.sql.adaptive.shuffle.fetchShuffleBlocksInBatch.enabled true Whether to fetch the continuous shuffle blocks in batch. Instead of fetching blocks one by one, fetching continuous shuffle blocks for the same map task in batch can reduce IO and improve performance. Note, multiple continuous blocks exist in single fetch request only happen when 'spark.sql.adaptive.enabled' and 'spark.sql.adaptive.shuffle.reducePostShufflePartitions.enabled' is enabled, this feature also depends on a relocatable serializer, the concatenation support codec in use and the new version shuffle fetch protocol.
spark.sql.adaptive.shuffle.localShuffleReader.enabled true When true and 'spark.sql.adaptive.enabled' is enabled, this enables the optimization of converting the shuffle reader to local shuffle reader for the shuffle exchange of the broadcast hash join in probe side.
spark.sql.adaptive.shuffle.maxNumPostShufflePartitions <undefined> The advisory maximum number of post-shuffle partitions used in adaptive execution. This is used as the initial number of pre-shuffle partitions. By default it equals to spark.sql.shuffle.partitions. This configuration only has an effect when 'spark.sql.adaptive.enabled' and 'spark.sql.adaptive.shuffle.reducePostShufflePartitions.enabled' is enabled.
```
**Note**: Because there are so many configuration items that are exposed and require a lot of finishing, I will add the version numbers of these configuration items in another PR.
### Why are the changes needed?
Supplemental configuration version information.
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
Exists UT
Closes#27592 from beliefer/add-version-to-config.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The style of `EXPLAIN FORMATTED` output needs to be improved. We’ve already got some observations/ideas in
https://github.com/apache/spark/pull/27368#discussion_r376694496https://github.com/apache/spark/pull/27368#discussion_r376927143
Observations/Ideas:
1. Using comma as the separator is not clear, especially commas are used inside the expressions too.
2. Show the column counts first? For example, `Results [4]: …`
3. Currently the attribute names are automatically generated, this need to refined.
4. Add arguments field in common implementations as `EXPLAIN EXTENDED` did by calling `argString` in `TreeNode.simpleString`. This will eliminate most existing minor differences between
`EXPLAIN EXTENDED` and `EXPLAIN FORMATTED`.
5. Another improvement we can do is: the generated alias shouldn't include attribute id. collect_set(val, 0, 0)#123 looks clearer than collect_set(val#456, 0, 0)#123
This PR is currently addressing comments 2 & 4, and open for more discussions on improving readability.
### Why are the changes needed?
The readability of `EXPLAIN FORMATTED` need to be improved, which will help user better understand the query plan.
### Does this PR introduce any user-facing change?
Yes, `EXPLAIN FORMATTED` output style changed.
### How was this patch tested?
Update expect results of test cases in explain.sql
Closes#27509 from Eric5553/ExplainFormattedRefine.
Authored-by: Eric Wu <492960551@qq.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to fix the thread-safety issue in turning off AQE for CacheManager by cloning the current session and changing the AQE conf on the cloned session.
This PR also adds a utility function for cloning the session with AQE disabled conf value, which can be shared by another caller.
### Why are the changes needed?
To fix the potential thread-unsafe problem.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually tested CachedTableSuite with AQE settings enabled.
Closes#27659 from maryannxue/spark-30906.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
- Add missing `since` annotation.
- Don't show classes under `org.apache.spark.sql.dynamicpruning` package in API docs.
- Fix the scope of `xxxExactNumeric` to remove it from the API docs.
### Why are the changes needed?
Avoid leaking APIs unintentionally in Spark 3.0.0.
### Does this PR introduce any user-facing change?
No. All these changes are to avoid leaking APIs unintentionally in Spark 3.0.0.
### How was this patch tested?
Manually generated the API docs and verified the above issues have been fixed.
Closes#27560 from xuanyuanking/SPARK-30809.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to add the `legacySizeOfNull ` parameter to the `Size` expression, and pass the value of `spark.sql.legacy.sizeOfNull` if `legacySizeOfNull` is not provided on creation of `Size`.
### Why are the changes needed?
This allows to avoid the issue when the configuration change between different phases of planning, and this can silently break a query plan which can lead to crashes or data corruption.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By `CollectionExpressionsSuite`.
Closes#27658 from MaxGekk/Size-SQLConf-get-deps.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to throw exception by default when user use untyped UDF(a.k.a `org.apache.spark.sql.functions.udf(AnyRef, DataType)`).
And user could still use it by setting `spark.sql.legacy.useUnTypedUdf.enabled` to `true`.
### Why are the changes needed?
According to #23498, since Spark 3.0, the untyped UDF will return the default value of the Java type if the input value is null. For example, `val f = udf((x: Int) => x, IntegerType)`, `f($"x")` will return 0 in Spark 3.0 but null in Spark 2.4. And the behavior change is introduced due to Spark3.0 is built with Scala 2.12 by default.
As a result, this might change data silently and may cause correctness issue if user still expect `null` in some cases. Thus, we'd better to encourage user to use typed UDF to avoid this problem.
### Does this PR introduce any user-facing change?
Yeah. User will hit exception now when use untyped UDF.
### How was this patch tested?
Added test and updated some tests.
Closes#27488 from Ngone51/spark_26580_followup.
Lead-authored-by: yi.wu <yi.wu@databricks.com>
Co-authored-by: wuyi <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This reverts commit bef5d9d6c3.
### Why are the changes needed?
Revert it according to https://github.com/apache/spark/pull/24902#issuecomment-584511167.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass Jenkins.
Closes#27540 from Ngone51/revert_spark_28093.
Lead-authored-by: wuyi <yi.wu@databricks.com>
Co-authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Exclude the SQL config `spark.sql.variable.substitute.depth` from `SQLConf.removedSQLConfigs`
### Why are the changes needed?
By the #27169, the config was placed to `SQLConf.removedSQLConfigs`. And as a consequence of that when an user set it non-default value (1 for example), he/she will get an exception. It is acceptable for SQL configs that could impact on the behavior but not for this particular config. Raising of such exception will just make migration to Spark 3.0 more difficult.
### Does this PR introduce any user-facing change?
Yes, before the changes users get an exception when he/she set `spark.sql.variable.substitute.depth` to a value different from `40`.
### How was this patch tested?
Run `spark.conf.set("spark.sql.variable.substitute.depth", 1)` in `spark-shell`.
Closes#27646 from MaxGekk/remove-substitute-depth-conf.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
- Output warnings for deprecated SQL configs in `SQLConf. setConfWithCheck()` and in `SQLConf. unsetConf()`
- Throw an exception for removed SQL configs in `SQLConf. setConfWithCheck()` when they set to non-default values
- Remove checking of deprecated and removed SQL configs from RuntimeConfig
### Why are the changes needed?
Currently, warnings/exceptions are printed only when a SQL config is set dynamically, for instance via `spark.conf.set()`. After the changes, removed/deprecated SQL configs will be checked when they set statically. For example:
```
$ bin/spark-shell --conf spark.sql.fromJsonForceNullableSchema=false
scala> spark.emptyDataFrame
java.lang.IllegalArgumentException: Error while instantiating 'org.apache.spark.sql.hive.HiveSessionStateBuilder':
...
Caused by: org.apache.spark.sql.AnalysisException: The SQL config 'spark.sql.fromJsonForceNullableSchema' was removed in the version 3.0.0. It was removed to prevent errors like SPARK-23173 for non-default value.
```
```
$ bin/spark-shell --conf spark.sql.hive.verifyPartitionPath=false
scala> spark.emptyDataFrame
20/02/20 02:10:26 WARN SQLConf: The SQL config 'spark.sql.hive.verifyPartitionPath' has been deprecated in Spark v3.0 and may be removed in the future. This config is replaced by 'spark.files.ignoreMissingFiles'.
```
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
By `SQLConfSuite`
Closes#27645 from MaxGekk/remove-sql-configs-followup-2.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
mention the workaround if users do want to use map type as key, and add a test to demonstrate it.
### Why are the changes needed?
it's better to provide an alternative when we ban something.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
N/A
Closes#27621 from cloud-fan/map.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to add the `returnLong` parameter to `IntegralDivide`, and pass the value of `spark.sql.legacy.integralDivide.returnBigint` if `returnLong` is not provided on creation of `IntegralDivide`.
### Why are the changes needed?
This allows to avoid the issue when the configuration change between different phases of planning, and this can silently break a query plan which can lead to crashes or data corruption.
OptionsAttachments
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By `ArithmeticExpressionSuite`.
Closes#27628 from MaxGekk/integral-divide-conf.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Revise the doc of SQL configuration `spark.sql.sources.parallelPartitionDiscovery.threshold`.
### Why are the changes needed?
The doc of configuration "spark.sql.sources.parallelPartitionDiscovery.threshold" is not accurate on the part "This applies to Parquet, ORC, CSV, JSON and LibSVM data sources".
We should revise it as effective on all the file-based data sources.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
None. It's just doc.
Closes#27639 from gengliangwang/reviseParallelPartitionDiscovery.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### Why are the changes needed?
This ports the tests introduced in 7285eea683 to master to avoid future regressions.
### Background
A query with Common Table Expressions can cause a stack overflow when it contains a CTE that refers a non-existing table with the same name. The name of the table need to have a database qualifier. This is caused by a couple of things:
- CTESubstitution runs analysis on the CTE, but this does not throw an exception because the table has a database qualifier. The reason is that we don't fail is because we re-attempt to resolve the relation in a later rule;
- CTESubstitution replace logic does not check if the table it is replacing has a database, it shouldn't replace the relation if it does. So now we will happily replace nonexist.t with t;
Note that this not an issue for master or the spark-3.0 branch.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added regression test to `AnalysisErrorSuite` and `DataFrameSuite`.
Closes#27635 from hvanhovell/SPARK-30811-master.
Authored-by: herman <herman@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
Split from #27534.
### What changes were proposed in this pull request?
This PR updates a deprecated Mkdocs option to use the new name.
### Why are the changes needed?
This change will prevent the docs from failing to build when we update to a version of Mkdocs that no longer supports the deprecated option.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
I built the docs locally and reviewed them in my browser.
Closes#27626 from nchammas/SPARK-30731-mkdocs-dep-opt.
Authored-by: Nicholas Chammas <nicholas.chammas@liveramp.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a follow up in [PR#27563](https://github.com/apache/spark/pull/27563).
This PR adds the prefix of "skewedJoinOptimization" in the skew join related configs.
### Why are the changes needed?
address remaining address
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
only update config and no need new ut.
Closes#27630 from JkSelf/renameskewjoinconfig.
Authored-by: jiake <ke.a.jia@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Table generated by `CREATE TABLE LIKE` a partitioned table is a partitioned table. But when run `ALTER TABLE ADD PARTITION`, it will throw `AnalysisException: ALTER TABLE ADD PARTITION is not allowed`. That's because the default value of `tracksPartitionsInCatalog` from `CREATE TABLE LIKE` always is false.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Add a unit test.
Closes#27538 from LantaoJin/SPARK-30785.
Authored-by: LantaoJin <jinlantao@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
I read the comments of `WindowExec` and found some comment will cause confusion and another need to improve.
### Why are the changes needed?
This PR will enhance the readability and let developer works more easy
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
No need
Closes#27431 from beliefer/improve-window-readability.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR reverts https://github.com/apache/spark/pull/26325 and https://github.com/apache/spark/pull/26347
### Why are the changes needed?
When we do sum/avg, we need a wider type of input to hold the sum value, to reduce the possibility of overflow. For example, we use long to hold the sum of integral inputs, use double to hold the sum of float/double.
However, we don't have a wider type of interval. Also the semantic is unclear: what if the days field overflows but the months field doesn't? Currently the avg of `1 month` and `2 month` is `1 month 15 days`, which assumes 1 month has 30 days and we should avoid this assumption.
### Does this PR introduce any user-facing change?
yes, remove 2 features added in 3.0
### How was this patch tested?
N/A
Closes#27619 from cloud-fan/revert.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: herman <herman@databricks.com>
### What changes were proposed in this pull request?
Prefix by `ansi_` in `toString` if it's a `AnsiCast` or ansi enabled `Cast`.
E.g. run `spark.sql("select cast('51' as int)").queryExecution.analyzed` under ansi mode.
Before this PR:
```
Project [cast(51 as int) AS CAST(51 AS INT)#0]
+- OneRowRelation
```
After this PR:
```
Project [ansi_cast(51 as int) AS CAST(51 AS INT)#0]
+- OneRowRelation
```
### Why are the changes needed?
This is useful while comparing `LogicalPlan`s literally.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass Jenkins.
Closes#27608 from Ngone51/ansi_cast_tostring.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to port Scala's bugfix https://github.com/scala/scala/pull/7693 (Scala 2.13) to address https://github.com/scala/bug/issues/10495 issue.
In short, it is possible for different product instances having the same children to have the same hash. See:
```scala
scala> spark.range(1).selectExpr("id - 1").queryExecution.analyzed.semanticHash()
res0: Int = -565572825
scala> spark.range(1).selectExpr("id + 1").queryExecution.analyzed.semanticHash()
res1: Int = -565572825
```
### Why are the changes needed?
It was found during the review of https://github.com/apache/spark/pull/27565. We should better produce different hash for different objects.
### Does this PR introduce any user-facing change?
No, it's not identified. Possibly performance related issue.
### How was this patch tested?
Manually tested, and unittest was added.
Closes#27601 from HyukjinKwon/SPARK-30847.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In ALTER TABLE, a column in ADD COLUMNS can depend on the position of a column that is just being added. For example, for a table with the following schema:
```
root:
- a: string
- b: long
```
, the following should work:
```
ALTER TABLE t ADD COLUMNS (x int AFTER a, y int AFTER x)
```
Currently, the above statement will throw an exception saying that AFTER x cannot be resolved, because x doesn't exist yet. This PR proposes to fix this issue.
### Why are the changes needed?
To fix a bug described above.
### Does this PR introduce any user-facing change?
Yes, now
```
ALTER TABLE t ADD COLUMNS (x int AFTER a, y int AFTER x)
```
works as expected.
### How was this patch tested?
Added new tests
Closes#27584 from imback82/alter_table_pos_fix.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR added two DeveloperApis to the Dataset[T] class. Both methods are just exposing lower-level methods to the Dataset[T] class.
### Why are the changes needed?
They are useful for checking whether two dataframes are the same when implementing dataframe caching in python, and also get a unique ID. It's easier to use if we wrap the lower-level APIs.
### Does this PR introduce any user-facing change?
```
scala> val df1 = Seq((1,2),(4,5)).toDF("col1", "col2")
df1: org.apache.spark.sql.DataFrame = [col1: int, col2: int]
scala> val df2 = Seq((1,2),(4,5)).toDF("col1", "col2")
df2: org.apache.spark.sql.DataFrame = [col1: int, col2: int]
scala> val df3 = Seq((0,2),(4,5)).toDF("col1", "col2")
df3: org.apache.spark.sql.DataFrame = [col1: int, col2: int]
scala> val df4 = Seq((0,2),(4,5)).toDF("col0", "col2")
df4: org.apache.spark.sql.DataFrame = [col0: int, col2: int]
scala> df1.semanticHash
res0: Int = 594427822
scala> df2.semanticHash
res1: Int = 594427822
scala> df1.sameSemantics(df2)
res2: Boolean = true
scala> df1.sameSemantics(df3)
res3: Boolean = false
scala> df3.semanticHash
res4: Int = -1592702048
scala> df4.semanticHash
res5: Int = -1592702048
scala> df4.sameSemantics(df3)
res6: Boolean = true
```
### How was this patch tested?
Unit test in scala and doctest in python.
Note: comments are copied from the corresponding lower-level APIs.
Note: There are some issues to be fixed that would improve the hash collision rate: https://github.com/apache/spark/pull/27565#discussion_r379881028Closes#27565 from liangz1/df-same-result.
Authored-by: Liang Zhang <liang.zhang@databricks.com>
Signed-off-by: WeichenXu <weichen.xu@databricks.com>
### Why are the changes needed?
In most of our docs, you can click on a heading to immediately get an anchor link to that specific section of the docs. This is very handy when you are reading the docs and want to share a link to a specific part.
The SQL function docs are lacking this. This PR adds this convenience to the SQL function docs.
Here's the impact on the generated HTML.
Before this PR:
```html
<h3 id="array_join">array_join</h3>
```
After this PR:
```html
<h3 id="array_join"><a class="toclink" href="#array_join">array_join</a></h3>
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
I built the docs manually and reviewed the results in my browser.
Closes#27585 from nchammas/SPARK-30832-sql-doc-headers.
Authored-by: Nicholas Chammas <nicholas.chammas@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
I checked the all the window function and found all of them not add parameter information and version information to the document.
This PR will make a supplement.
### Why are the changes needed?
Documentation is missing and does not meet new standards.
### Does this PR introduce any user-facing change?
Yes. User will face the information of parameters and version.
### How was this patch tested?
Exists UT
Closes#27572 from beliefer/add_since_for_window_function.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
`INSERT OVERWRITE LOCAL DIRECTORY` is supported with ensuring the provided path is always using `file://` as scheme and removing the check which throws exception if we do insert overwrite by mentioning directory with `LOCAL` syntax
### Why are the changes needed?
without the modification in PR, ``` insert overwrite local directory <location> using ```
throws exception
```
Error: org.apache.spark.sql.catalyst.parser.ParseException:
LOCAL is not supported in INSERT OVERWRITE DIRECTORY to data source(line 1, pos 0)
```
which was introduced in https://github.com/apache/spark/pull/18975, but this restriction is not needed, hence dropping the same.
Keep behaviour consistent for local and remote file-system in `INSERT OVERWRITE DIRECTORY`
### Does this PR introduce any user-facing change?
Yes, after this change `INSERT OVERWRITE LOCAL DIRECTORY` will not throw exception
### How was this patch tested?
Added UT
Closes#27039 from ajithme/insertoverwrite2.
Authored-by: Ajith <ajith2489@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In `org.apache.spark.sql.execution.exchange.BroadcastExchangeExec#relationFuture` make a copy of `org.apache.spark.SparkContext#localProperties` and pass it to the broadcast execution thread in `org.apache.spark.sql.execution.exchange.BroadcastExchangeExec#executionContext`
### Why are the changes needed?
When executing `BroadcastExchangeExec`, the relationFuture is evaluated via a separate thread. The threads inherit the `localProperties` from `sparkContext` as they are the child threads.
These threads are created in the executionContext (thread pools). Each Thread pool has a default `keepAliveSeconds` of 60 seconds for idle threads.
Scenarios where the thread pool has threads which are idle and reused for a subsequent new query, the thread local properties will not be inherited from spark context (thread properties are inherited only on thread creation) hence end up having old or no properties set. This will cause taskset properties to be missing when properties are transferred by child thread via `sparkContext.runJob/submitJob`
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added UT
Closes#27266 from ajithme/broadcastlocalprop.
Authored-by: Ajith <ajith2489@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
- Set `spark.sql.datetime.java8API.enabled` to `true` in `hiveResultString()`, and restore it back at the end of the call.
- Convert collected `java.time.Instant` & `java.time.LocalDate` to `java.sql.Timestamp` and `java.sql.Date` for correct formatting.
### Why are the changes needed?
Because of textual representation of timestamps/dates before 1582 year is incorrect:
```shell
$ export TZ="America/Los_Angeles"
$ ./bin/spark-sql -S
```
```sql
spark-sql> set spark.sql.session.timeZone=America/Los_Angeles;
spark.sql.session.timeZone America/Los_Angeles
spark-sql> SELECT DATE_TRUNC('MILLENNIUM', DATE '1970-03-20');
1001-01-01 00:07:02
```
It must be 1001-01-01 00:**00:00**.
### Does this PR introduce any user-facing change?
Yes. After the changes:
```shell
$ export TZ="America/Los_Angeles"
$ ./bin/spark-sql -S
```
```sql
spark-sql> set spark.sql.session.timeZone=America/Los_Angeles;
spark.sql.session.timeZone America/Los_Angeles
spark-sql> SELECT DATE_TRUNC('MILLENNIUM', DATE '1970-03-20');
1001-01-01 00:00:00
```
### How was this patch tested?
By running hive-thiftserver tests. In particular:
```
./build/sbt -Phadoop-2.7 -Phive-2.3 -Phive-thriftserver "hive-thriftserver/test:testOnly *SparkThriftServerProtocolVersionsSuite"
```
Closes#27552 from MaxGekk/hive-thriftserver-java8-time-api.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Let sub optimizer's `postHocOptimizationBatches` also includes super's `postHocOptimizationBatches`.
### Why are the changes needed?
It's necessary according to the design of catalyst optimizer.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass jenkins.
Closes#27607 from Ngone51/spark_15616_followup.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Follow-up work for #25600. In this PR, we move `sql/dynamicpruning` to `sql/execution/dynamicpruning`.
### Why are the changes needed?
Fix the unexpected public APIs in 3.0.0 #27560.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes#27581 from xuanyuanking/SPARK-11150-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
[HIVE-15167](https://issues.apache.org/jira/browse/HIVE-15167) removed the `SerDe` interface. This may break custom `SerDe` builds for Hive 1.2. This PR update the migration guide for this change.
### Why are the changes needed?
Otherwise:
```
2020-01-27 05:11:20.446 - stderr> 20/01/27 05:11:20 INFO DAGScheduler: ResultStage 2 (main at NativeMethodAccessorImpl.java:0) failed in 1.000 s due to Job aborted due to stage failure: Task 0 in stage 2.0 failed 4 times, most recent failure: Lost task 0.3 in stage 2.0 (TID 13, 10.110.21.210, executor 1): java.lang.NoClassDefFoundError: org/apache/hadoop/hive/serde2/SerDe
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.defineClass1(Native Method)
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.defineClass(ClassLoader.java:756)
2020-01-27 05:11:20.446 - stderr> at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader.defineClass(URLClassLoader.java:468)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader.access$100(URLClassLoader.java:74)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader$1.run(URLClassLoader.java:369)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader$1.run(URLClassLoader.java:363)
2020-01-27 05:11:20.446 - stderr> at java.security.AccessController.doPrivileged(Native Method)
2020-01-27 05:11:20.446 - stderr> at java.net.URLClassLoader.findClass(URLClassLoader.java:362)
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
2020-01-27 05:11:20.446 - stderr> at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:352)
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.loadClass(ClassLoader.java:405)
2020-01-27 05:11:20.446 - stderr> at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
2020-01-27 05:11:20.446 - stderr> at java.lang.Class.forName0(Native Method)
2020-01-27 05:11:20.446 - stderr> at java.lang.Class.forName(Class.java:348)
2020-01-27 05:11:20.446 - stderr> at org.apache.hadoop.hive.ql.plan.TableDesc.getDeserializerClass(TableDesc.java:76)
.....
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manual test
Closes#27492 from wangyum/SPARK-30755.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
We only need to do aggregate evaluation once per group in `UnboundedWindowFunctionFrame`
### Why are the changes needed?
Currently, in `UnboundedWindowFunctionFrame.write`，it re-evaluate the processor for each row in a group, which is not necessary in fact which I'll address later. It hurts performance when the evaluation is time-consuming (for example, Percentile's eval need to sort its buffer and do some calculation). In our production, there is a percentile with window operation sql, it costs more than 10 hours in SparkSQL while 10min in Hive.
In fact, `UnboundedWindowFunctionFrame` can be treated as `SlidingWindowFunctionFrame` with `lbound = UnboundedPreceding` and `ubound = UnboundedFollowing`, just as its comments. In that case, `SlidingWindowFunctionFrame` also only do evaluation once for each group.
The performance issue can be reproduced by running the follow scripts in local spark-shell
```
spark.range(100*100).map(i => (i, "India")).toDF("uv", "country").createOrReplaceTempView("test")
sql("select uv, country, percentile(uv, 0.95) over (partition by country) as ptc95 from test").collect.foreach(println)
```
Before this patch, the sql costs **128048 ms**.
With this patch, the sql costs **3485 ms**.
If we increase the data size to 1000*1000 for example, then spark cannot even produce result without this patch(I'v waited for several hours).
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
Existing UT
Closes#27558 from WangGuangxin/windows.
Authored-by: wangguangxin.cn <wangguangxin.cn@gmail.com>
Signed-off-by: herman <herman@databricks.com>
### What changes were proposed in this pull request?
Define a new enumeration `LegacyBehaviorPolicy` in SQLConf, it will be used as the common value for result change configs.
### Why are the changes needed?
During API auditing for the 3.0 release, we found several new approaches that will change the results silently. For these features, we need a common three-value config.
### Does this PR introduce any user-facing change?
Yes, original config `spark.sql.legacy.ctePrecedence.enabled` change to `spark.sql.legacy.ctePrecedencePolicy`.
### How was this patch tested?
Existing UT.
Closes#27579 from xuanyuanking/SPARK-30829.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
See JIRA: https://issues.apache.org/jira/browse/SPARK-29089
Mailing List: http://apache-spark-developers-list.1001551.n3.nabble.com/DataFrameReader-bottleneck-in-DataSource-checkAndGlobPathIfNecessary-when-reading-S3-files-td27828.html
When using DataFrameReader#csv to read many files on S3, globbing and fs.exists on DataSource#checkAndGlobPathIfNecessary becomes a bottleneck.
From the mailing list discussions, an improvement that can be made is to parallelize the blocking FS calls:
> - have SparkHadoopUtils differentiate between files returned by globStatus(), and which therefore exist, and those which it didn't glob for -it will only need to check those.
> - add parallel execution to the glob and existence checks
### Why are the changes needed?
Verifying/globbing files happens on the driver, and if this operations take a long time (for example against S3), then the entire cluster has to wait, potentially sitting idle. This change hopes to make this process faster.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
I added a test suite `DataSourceSuite` - open to suggestions for better naming.
See [here](https://github.com/apache/spark/pull/25899#issuecomment-534380034) and [here](https://github.com/apache/spark/pull/25899#issuecomment-534069194) for some measurements
Closes#25899 from cozos/master.
Lead-authored-by: Arwin Tio <Arwin.tio@adroll.com>
Co-authored-by: Arwin Tio <arwin.tio@hotmail.com>
Co-authored-by: Arwin Tio <arwin.tio@adroll.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
In the PR, I propose to replace `%` by `Math.floorMod` in `DateTimeUtils.truncTimestamp` for the `SECOND` and `MINUTE` levels.
### Why are the changes needed?
This fixes the issue of incorrect truncation of timestamps before the epoch `1970-01-01T00:00:00.000000Z` to the `SECOND` and `MINUTE` levels. For example, timestamps after the epoch are truncated by cutting off the rest part of the timestamp:
```sql
spark-sql> select date_trunc('SECOND', '2020-02-11 00:01:02.123');
2020-02-11 00:01:02
```
but seconds in the truncated timestamp before the epoch are increased by 1:
```sql
spark-sql> select date_trunc('SECOND', '1960-02-11 00:01:02.123');
1960-02-11 00:01:03
```
### Does this PR introduce any user-facing change?
Yes. After the changes, the example above outputs correct result:
```sql
spark-sql> select date_trunc('SECOND', '1960-02-11 00:01:02.123');
1960-02-11 00:01:02
```
### How was this patch tested?
Added new tests to `DateFunctionsSuite`.
Closes#27543 from MaxGekk/fix-second-minute-truc.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a follow-up for #23124, add a new config `spark.sql.legacy.allowDuplicatedMapKeys` to control the behavior of removing duplicated map keys in build-in functions. With the default value `false`, Spark will throw a RuntimeException while duplicated keys are found.
### Why are the changes needed?
Prevent silent behavior changes.
### Does this PR introduce any user-facing change?
Yes, new config added and the default behavior for duplicated map keys changed to RuntimeException thrown.
### How was this patch tested?
Modify existing UT.
Closes#27478 from xuanyuanking/SPARK-25892-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Rewrite DateTimeUtils methods `getHours()`, `getMinutes()`, `getSeconds()`, `getSecondsWithFraction()`, `getMilliseconds()` and `getMicroseconds()` using Java 8 time APIs. This will automatically switch the `Hour`, `Minute`, `Second` and `DatePart` expressions on Proleptic Gregorian calendar.
2. Remove unused methods and constant of DateTimeUtils - `to2001`, `YearZero `, `toYearZero` and `absoluteMicroSecond()`.
3. Remove unused value `timeZone` from `TimeZoneAwareExpression` since all expressions have been migrated to Java 8 time API, and legacy instance of `TimeZone` is not needed any more.
4. Change signatures of modified DateTimeUtils methods, and pass `ZoneId` instead of `TimeZone`. This will allow to avoid unnecessary conversions `TimeZone` -> `String` -> `ZoneId`.
5. Modify tests in `DateTimeUtilsSuite` and in `DateExpressionsSuite` to pass `ZoneId` instead of `TimeZone`. Correct the tests, to pass tested zone id instead of None.
### Why are the changes needed?
The changes fix the issue of wrong results returned by the `hour()`, `minute()`, `second()`, `date_part('millisecond', ...)` and `date_part('microsecond', ....)`, see example in [SPARK-30843](https://issues.apache.org/jira/browse/SPARK-30843).
### Does this PR introduce any user-facing change?
Yes. After the changes, the results of examples from SPARK-30843:
```sql
spark-sql> select hour(timestamp '0010-01-01 00:00:00');
0
spark-sql> select minute(timestamp '0010-01-01 00:00:00');
0
spark-sql> select second(timestamp '0010-01-01 00:00:00');
0
spark-sql> select date_part('milliseconds', timestamp '0010-01-01 00:00:00');
0.000
spark-sql> select date_part('microseconds', timestamp '0010-01-01 00:00:00');
0
```
### How was this patch tested?
- By existing test suites `DateTimeUtilsSuite`, `DateExpressionsSuite` and `DateFunctionsSuite`.
- Add new tests to `DateExpressionsSuite` and `DateTimeUtilsSuite` for 10 year, like:
```scala
input = date(10, 1, 1, 0, 0, 0, 0, zonePST)
assert(getHours(input, zonePST) === 0)
```
- Re-run `DateTimeBenchmark` using Amazon EC2.
| Item | Description |
| ---- | ----|
| Region | us-west-2 (Oregon) |
| Instance | r3.xlarge |
| AMI | ami-06f2f779464715dc5 (ubuntu/images/hvm-ssd/ubuntu-bionic-18.04-amd64-server-20190722.1) |
| Java | OpenJDK8/11 |
Closes#27596 from MaxGekk/localtimestamp-greg-cal.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Max Gekk <max.gekk@gmail.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-1-30.us-west-2.compute.internal>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
No v2 command supports temp views and the `ResolveCatalogs`/`ResolveSessionCatalog` framework is designed with this assumption.
However, `ResolveSessionCatalog` needs to fallback to v1 commands, which do support temp views (e.g. CACHE TABLE). To work around it, we add a hack in `CatalogAndIdentifier`, which does not expand the given identifier with current namespace if the catalog is session catalog.
This works fine in most cases, as temp views should take precedence over tables during lookup. So if `CatalogAndIdentifier` returns a single name "t", the v1 commands can still resolve it to temp views correctly, or resolve it to table "default.t" if temp view doesn't exist.
However, if users write `spark_catalog.t`, it shouldn't be resolved to temp views as temp views don't belong to any catalog. `CatalogAndIdentifier` can't distinguish between `spark_catalog.t` and `t`, so the caller side may mistakenly resolve `spark_catalog.t` to a temp view.
This PR proposes to fix this issue by
1. remove the hack in `CatalogAndIdentifier`, and clearly document that this shouldn't be used to resolve temp views.
2. update `ResolveSessionCatalog` to explicitly look up temp views first before calling `CatalogAndIdentifier`, for v1 commands that support temp views.
### Why are the changes needed?
To avoid releasing a behavior that we should not support.
Removing the hack also fixes the problem we hit in https://github.com/apache/spark/pull/27532/files#diff-57b3d87be744b7d79a9beacf8e5e5eb2R937
### Does this PR introduce any user-facing change?
yes, now it's not allowed to refer to a temp view with `spark_catalog` prefix.
### How was this patch tested?
new tests
Closes#27550 from cloud-fan/ns.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to convert the attribute name of `StringStartsWith` pushed down to the Parquet datasource to column reference via the `nameToParquetField` map. Similar conversions are performed for other source filters pushed down to parquet.
### Why are the changes needed?
This fixes the bug described in [SPARK-30826](https://issues.apache.org/jira/browse/SPARK-30826). The query from an external table:
```sql
CREATE TABLE t1 (col STRING)
USING parquet
OPTIONS (path '$path')
```
created on top of written parquet files by `Seq("42").toDF("COL").write.parquet(path)` returns wrong empty result:
```scala
spark.sql("SELECT * FROM t1 WHERE col LIKE '4%'").show
+---+
|col|
+---+
+---+
```
### Does this PR introduce any user-facing change?
Yes. After the changes the result is correct for the example above:
```scala
spark.sql("SELECT * FROM t1 WHERE col LIKE '4%'").show
+---+
|col|
+---+
| 42|
+---+
```
### How was this patch tested?
Added a test to `ParquetFilterSuite`
Closes#27574 from MaxGekk/parquet-StringStartsWith-case-sens.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. `InMemoryTable` was flatting the nested columns, and then the flatten columns was used to look up the indices which is not correct.
This PR implements partitioned by nested column for `InMemoryTable`.
### Why are the changes needed?
This PR implements partitioned by nested column for `InMemoryTable`, so we can test this features in DSv2
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing unit tests and new tests.
Closes#26929 from dbtsai/addTests.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
In the PR, I propose to use Java 8 time API in timestamp truncations to the levels of `HOUR` and `DAY`. The problem is in the usage of `timeZone.getOffset(millis)` in days/hours truncations where the combined calendar (Julian + Gregorian) is used underneath.
### Why are the changes needed?
The change fix wrong truncations. For example, the following truncation to hours should print `0010-01-01 01:00:00` but it outputs wrong timestamp:
```scala
Seq("0010-01-01 01:02:03.123456").toDF()
.select($"value".cast("timestamp").as("ts"))
.select(date_trunc("HOUR", $"ts").cast("string"))
.show(false)
+------------------------------------+
|CAST(date_trunc(HOUR, ts) AS STRING)|
+------------------------------------+
|0010-01-01 01:30:17 |
+------------------------------------+
```
### Does this PR introduce any user-facing change?
Yes. After the changes, the result of the example above is:
```scala
+------------------------------------+
|CAST(date_trunc(HOUR, ts) AS STRING)|
+------------------------------------+
|0010-01-01 01:00:00 |
+------------------------------------+
```
### How was this patch tested?
- Added new test to `DateFunctionsSuite`
- By `DateExpressionsSuite` and `DateTimeUtilsSuite`
Closes#27512 from MaxGekk/fix-trunc-old-timestamp.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fixes `DataFrameReader.csv(dataset: Dataset[String])` API to take a `Dataset[String]` originated from a column name different from `value`. This is a long-standing bug started from the very first place.
`CSVUtils.filterCommentAndEmpty` assumed the `Dataset[String]` to be originated with `value` column. This PR changes to use the first column name in the schema.
### Why are the changes needed?
For `DataFrameReader.csv(dataset: Dataset[String])` to support any `Dataset[String]` as the signature indicates.
### Does this PR introduce any user-facing change?
Yes,
```scala
val ds = spark.range(2).selectExpr("concat('a,b,', id) AS text").as[String]
spark.read.option("header", true).option("inferSchema", true).csv(ds).show()
```
Before:
```
org.apache.spark.sql.AnalysisException: cannot resolve '`value`' given input columns: [text];;
'Filter (length(trim('value, None)) > 0)
+- Project [concat(a,b,, cast(id#0L as string)) AS text#2]
+- Range (0, 2, step=1, splits=Some(2))
```
After:
```
+---+---+---+
| a| b| 0|
+---+---+---+
| a| b| 1|
+---+---+---+
```
### How was this patch tested?
Unittest was added.
Closes#27561 from HyukjinKwon/SPARK-30810.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
We should convert Spark InternalRows to hive data via `HiveInspectors.wrapperFor`.
### Why are the changes needed?
We may hit below exception without this change:
```
[info] 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, 192.168.1.6, executor driver): java.lang.ClassCastException: org.apache.spark.sql.types.Decimal cannot be cast to org.apache.hadoop.hive.common.type.HiveDecimal
[info] at org.apache.hadoop.hive.serde2.objectinspector.primitive.JavaHiveDecimalObjectInspector.getPrimitiveJavaObject(JavaHiveDecimalObjectInspector.java:55)
[info] at org.apache.hadoop.hive.serde2.lazy.LazyUtils.writePrimitiveUTF8(LazyUtils.java:321)
[info] at org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe.serialize(LazySimpleSerDe.java:292)
[info] at org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe.serializeField(LazySimpleSerDe.java:247)
[info] at org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe.doSerialize(LazySimpleSerDe.java:231)
[info] at org.apache.hadoop.hive.serde2.AbstractEncodingAwareSerDe.serialize(AbstractEncodingAwareSerDe.java:55)
[info] at org.apache.spark.sql.hive.execution.ScriptTransformationWriterThread.$anonfun$run$2(ScriptTransformationExec.scala:300)
[info] at org.apache.spark.sql.hive.execution.ScriptTransformationWriterThread.$anonfun$run$2$adapted(ScriptTransformationExec.scala:281)
[info] at scala.collection.Iterator.foreach(Iterator.scala:941)
[info] at scala.collection.Iterator.foreach$(Iterator.scala:941)
[info] at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
[info] at org.apache.spark.sql.hive.execution.ScriptTransformationWriterThread.$anonfun$run$1(ScriptTransformationExec.scala:281)
[info] at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
[info] at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1932)
[info] at org.apache.spark.sql.hive.execution.ScriptTransformationWriterThread.run(ScriptTransformationExec.scala:270)
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added new test. But please note that this test returns different result between Hive1.2 and Hive2.3 due to `HiveDecimal` or `SerDe` difference(don't know the root cause yet).
Closes#27556 from Ngone51/script_transform.
Lead-authored-by: yi.wu <yi.wu@databricks.com>
Co-authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR makes sure AQE is either enabled or disabled for the entire query, including the main query and all subqueries.
Currently there are unsupported queries by AQE, e.g., queries that contain DPP filters. We need to make sure that if the main query is unsupported, none of the sub-queries should apply AQE, otherwise it can lead to performance regressions due to missed opportunity of sub-query reuse.
### Why are the changes needed?
To get rid of potential perf regressions when AQE is turned on.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Updated DynamicPartitionPruningSuite:
1. Removed the existing workaround `withSQLConf(SQLConf.ADAPTIVE_EXECUTION_ENABLED.key, "false")`
2. Added `DynamicPartitionPruningSuiteAEOn` and `DynamicPartitionPruningSuiteAEOff` to enable testing this suite with AQE on and off options
3. Added a check in `checkPartitionPruningPredicate` to verify that the subqueries are always in sync with the main query in terms of whether AQE is applied.
Closes#27554 from maryannxue/spark-30801.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR scopes `SparkSession.active` to prevent problems with processing queries with possibly different spark sessions (and different configs). A new method, `withActive` is introduced on `SparkSession` that restores the previous spark session after the block of code is executed.
### Why are the changes needed?
`SparkSession.active` is a thread local variable that points to the current thread's spark session. It is important to note that the `SQLConf.get` method depends on `SparkSession.active`. In the current implementation it is possible that `SparkSession.active` points to a different session which causes various problems. Most of these problems arise because part of the query processing is done using the configurations of a different session. For example, when creating a data frame using a new session, i.e., `session.sql("...")`, part of the data frame is constructed using the currently active spark session, which can be a different session from the one used later for processing the query.
### Does this PR introduce any user-facing change?
The `withActive` method is introduced on `SparkSession`.
### How was this patch tested?
Unit tests (to be added)
Closes#27387 from dbaliafroozeh/UseWithActiveSessionInQueryExecution.
Authored-by: Ali Afroozeh <ali.afroozeh@databricks.com>
Signed-off-by: herman <herman@databricks.com>
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### What changes were proposed in this pull request?
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This is a followup of https://github.com/apache/spark/pull/26434
This PR use one special shuffle reader for skew join, so that we only have one join after optimization. In order to do that, this PR
1. add a very general `CustomShuffledRowRDD` which support all kind of partition arrangement.
2. move the logic of coalescing shuffle partitions to a util function, and call it during skew join optimization, to totally decouple with the `ReduceNumShufflePartitions` rule. It's too complicated to interfere skew join with `ReduceNumShufflePartitions`, as you need to consider the size of split partitions which don't respect target size already.
### Why are the changes needed?
<!--
Please clarify why the changes are needed. For instance,
1. If you propose a new API, clarify the use case for a new API.
2. If you fix a bug, you can clarify why it is a bug.
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The current skew join optimization has a serious performance issue: the size of the query plan depends on the number and size of skewed partitions.
### Does this PR introduce any user-facing change?
<!--
If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible.
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no
### How was this patch tested?
<!--
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existing tests
test UI manually:
explain output
```
AdaptiveSparkPlan(isFinalPlan=true)
+- OverwriteByExpression org.apache.spark.sql.execution.datasources.noop.NoopTable$403a2ed5, [AlwaysTrue()], org.apache.spark.sql.util.CaseInsensitiveStringMap1f
+- *(5) SortMergeJoin(skew=true) [key1#2L], [key2#6L], Inner
:- *(3) Sort [key1#2L ASC NULLS FIRST], false, 0
: +- SkewJoinShuffleReader 2 skewed partitions with size(max=5 KB, min=5 KB, avg=5 KB)
: +- ShuffleQueryStage 0
: +- Exchange hashpartitioning(key1#2L, 200), true, [id=#53]
: +- *(1) Project [(id#0L % 2) AS key1#2L]
: +- *(1) Filter isnotnull((id#0L % 2))
: +- *(1) Range (0, 100000, step=1, splits=6)
+- *(4) Sort [key2#6L ASC NULLS FIRST], false, 0
+- SkewJoinShuffleReader 2 skewed partitions with size(max=5 KB, min=5 KB, avg=5 KB)
+- ShuffleQueryStage 1
+- Exchange hashpartitioning(key2#6L, 200), true, [id=#64]
+- *(2) Project [((id#4L % 2) + 1) AS key2#6L]
+- *(2) Filter isnotnull(((id#4L % 2) + 1))
+- *(2) Range (0, 100000, step=1, splits=6)
```
Closes#27493 from cloud-fan/aqe.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: herman <herman@databricks.com>
### What changes were proposed in this pull request?
Although Spark SQL support bracketed comments, but `SQLQueryTestSuite` can't treat bracketed comments well and lead to generated golden files can't display bracketed comments well.
This PR will improve the treatment of bracketed comments and add three test case in `PlanParserSuite`.
Spark SQL can't support nested bracketed comments and https://github.com/apache/spark/pull/27495 used to support it.
### Why are the changes needed?
Golden files can't display well.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
New UT.
Closes#27481 from beliefer/ansi-brancket-comments.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to support Hive-style `ALTER TABLE ... REPLACE COLUMNS ...` as described in https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-Add/ReplaceColumns
The user now can do the following:
```SQL
CREATE TABLE t (col1 int, col2 int) USING Foo;
ALTER TABLE t REPLACE COLUMNS (col2 string COMMENT 'comment2', col3 int COMMENT 'comment3');
```
, which drops the existing columns `col1` and `col2`, and add new columns `col2` and `col3`.
### Why are the changes needed?
This is a new DDL statement. Spark currently supports the Hive-style `ALTER TABLE ... CHANGE COLUMN ...`, so this new addition can be useful.
### Does this PR introduce any user-facing change?
Yes, adding a new DDL statement.
### How was this patch tested?
More tests to be added.
Closes#27482 from imback82/replace_cols.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds a config for Dynamic Partition Pruning subquery duplication and turns it off by default due to its potential performance regression.
When planning a DPP filter, it seeks to reuse the broadcast exchange relation if the corresponding join is a BHJ with the filter relation being on the build side, otherwise it will either opt out or plan the filter as an un-reusable subquery duplication based on the cost estimate. However, the cost estimate is not accurate and only takes into account the table scan overhead, thus adding an un-reusable subquery duplication DPP filter can sometimes cause perf regression.
This PR turns off the subquery duplication DPP filter by:
1. adding a config `spark.sql.optimizer.dynamicPartitionPruning.reuseBroadcastOnly` and setting it `true` by default.
2. removing the existing meaningless config `spark.sql.optimizer.dynamicPartitionPruning.reuseBroadcast` since we always want to reuse broadcast results if possible.
### Why are the changes needed?
This is to fix a potential performance regression caused by DPP.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Updated DynamicPartitionPruningSuite to test the new configuration.
Closes#27551 from maryannxue/spark-30528.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
