### What changes were proposed in this pull request?
Postgres and MSSQL connection providers are not able to get custom `appEntry` because under some circumstances the driver is wrapped with `DriverWrapper`. Such case is not handled in the mentioned providers. In this PR I've added this edge case handling by passing unwrapped `Driver` from `JdbcUtils`.
### Why are the changes needed?
`DriverWrapper` is not considered.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing + additional unit tests.
Closes#30024 from gaborgsomogyi/SPARK-32229.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
1. Add the SQL config `spark.sql.legacy.parquet.int96RebaseModeInWrite` to control timestamps rebasing in saving them as INT96. It supports the same set of values as `spark.sql.legacy.parquet.datetimeRebaseModeInWrite` but the default value is `LEGACY` to preserve backward compatibility with Spark <= 3.0.
2. Write the metadata key `org.apache.spark.int96NoRebase` to parquet files if the files are saved with `spark.sql.legacy.parquet.int96RebaseModeInWrite` isn't set to `LEGACY`.
3. Add the SQL config `spark.sql.legacy.parquet.datetimeRebaseModeInRead` to control loading INT96 timestamps when parquet metadata doesn't have enough info (the `org.apache.spark.int96NoRebase` tag) about parquet writer - either INT96 was written by Proleptic Gregorian system or some Julian one.
4. Modified Vectorized and Parquet-mr Readers to support loading/saving INT96 timestamps w/o rebasing depending on SQL config and the metadata tag:
- **No rebasing** in testing when the SQL config `spark.test.forceNoRebase` is set to `true`
- **No rebasing** if parquet metadata contains the tag `org.apache.spark.int96NoRebase`. This is the case when parquet files are saved by Spark >= 3.1 with `spark.sql.legacy.parquet.datetimeRebaseModeInWrite` is set to `CORRECTED`, or saved by other systems with the tag `org.apache.spark.int96NoRebase`.
- **With rebasing** if parquet files saved by Spark (any versions) without the metadata tag `org.apache.spark.int96NoRebase`.
- Rebasing depend on the SQL config `spark.sql.legacy.parquet.datetimeRebaseModeInRead` if there are no metadata tags `org.apache.spark.version` and `org.apache.spark.int96NoRebase`.
New SQL configs are added instead of re-using existing `spark.sql.legacy.parquet.datetimeRebaseModeInWrite` and `spark.sql.legacy.parquet.datetimeRebaseModeInRead` because of:
- To allow users have different modes for INT96 and for TIMESTAMP_MICROS (MILLIS). For example, users might want to save INT96 as LEGACY but TIMESTAMP_MICROS as CORRECTED.
- To have different modes for INT96 and DATE in load (or in save).
- To be backward compatible with Spark 2.4. For now, `spark.sql.legacy.parquet.datetimeRebaseModeInWrite/Read` are set to `EXCEPTION` by default.
### Why are the changes needed?
1. Parquet spec says that INT96 must be stored as Julian days (see https://github.com/apache/parquet-format/pull/49). This doesn't mean that a reader ( or a writer) is based on the Julian calendar. So, rebasing from Proleptic Gregorian to Julian calendar can be not needed.
2. Rebasing from/to Julian calendar can loose information because dates in one calendar don't exist in another one. Like 1582-10-04..1582-10-15 exist in Proleptic Gregorian calendar but not in the hybrid calendar (Julian + Gregorian), and visa versa, Julian date 1000-02-29 doesn't exist in Proleptic Gregorian calendar. We should allow users to save timestamps without loosing such dates (rebasing shifts such dates to the next valid date).
3. It would also make Spark compatible with other systems such as Impala and newer versions of Hive that write proleptic Gregorian based INT96 timestamps.
### Does this PR introduce _any_ user-facing change?
It can when `spark.sql.legacy.parquet.int96RebaseModeInWrite` is set non-default value `LEGACY`.
### How was this patch tested?
- Added a test to check the metadata key `org.apache.spark.int96NoRebase`
- By `ParquetIOSuite`
Closes#30056 from MaxGekk/parquet-rebase-int96.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Currently, actual non-dynamic partition pruning is executed in the optimizer phase (PruneFileSourcePartitions) if an input relation has a catalog file index. The current code assumes the same partition filters are generated again in FileSourceStrategy and passed into FileSourceScanExec. FileSourceScanExec uses the partition filters when listing files, but these non-dynamic partition filters do nothing because unnecessary partitions are already pruned in advance, so the filters are mainly used for explain output in this case. If a WHERE clause has DNF-ed predicates, FileSourceStrategy cannot extract the same filters with PruneFileSourcePartitions and then PartitionFilters is not shown in explain output.
This patch proposes to extract partition filters in FileSourceStrategy and HiveStrategy with `extractPredicatesWithinOutputSet` added in https://github.com/apache/spark/pull/29101/files#diff-6be42cfa3c62a7536b1eb1d6447c073c again, then It will show the partially pushed down partition filter in explain().
### Why are the changes needed?
without the patch, the explained plan is inconsistent with what is actually executed
<b>without the change </b> the explained plan of `"SELECT * FROM t WHERE p = '1' OR (p = '2' AND i = 1)"` for datasource and hive tables are like the following respectively (missing pushed down partition filters)
```
== Physical Plan ==
*(1) Filter ((p#21 = 1) OR ((p#21 = 2) AND (i#20 = 1)))
+- *(1) ColumnarToRow
+- FileScan parquet default.t[i#20,p#21] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/Users/nanzhu/code/spark/sql/hive/target/tmp/hive_execution_test_group/war..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<i:int>
```
```
== Physical Plan ==
*(1) Filter ((p#33 = 1) OR ((p#33 = 2) AND (i#32 = 1)))
+- Scan hive default.t [i#32, p#33], HiveTableRelation [`default`.`t`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Data Cols: [i#32], Partition Cols: [p#33], Pruned Partitions: [(p=1), (p=2)]]
```
<b> with change </b> the plan looks like (the actually executed partition filters are exhibited)
```
== Physical Plan ==
*(1) Filter ((p#21 = 1) OR ((p#21 = 2) AND (i#20 = 1)))
+- *(1) ColumnarToRow
+- FileScan parquet default.t[i#20,p#21] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/Users/nanzhu/code/spark/sql/hive/target/tmp/hive_execution_test_group/war..., PartitionFilters: [((p#21 = 1) OR (p#21 = 2))], PushedFilters: [], ReadSchema: struct<i:int>
```
```
== Physical Plan ==
*(1) Filter ((p#37 = 1) OR ((p#37 = 2) AND (i#36 = 1)))
+- Scan hive default.t [i#36, p#37], HiveTableRelation [`default`.`t`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Data Cols: [i#36], Partition Cols: [p#37], Pruned Partitions: [(p=1), (p=2)]], [((p#37 = 1) OR (p#37 = 2))]
```
### Does this PR introduce _any_ user-facing change
no
### How was this patch tested?
Unit test.
Closes#29831 from CodingCat/SPARK-32351.
Lead-authored-by: Nan Zhu <nanzhu@uber.com>
Co-authored-by: Nan Zhu <CodingCat@users.noreply.github.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. Add the common trait `CommonFileDataSourceSuite` with tests that can be executed for all built-in file-based datasources.
2. Add a test `CommonFileDataSourceSuite` to check that datasource options are propagated to underlying file systems as Hadoop configs.
3. Mix `CommonFileDataSourceSuite` to `AvroSuite`, `OrcSourceSuite`, `TextSuite`, `JsonSuite`, CSVSuite` and to `ParquetFileFormatSuite`.
4. Remove duplicated tests from `AvroSuite` and from `OrcSourceSuite`.
### Why are the changes needed?
To improve test coverage and test all built-in file-based datasources.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the affected test suites.
Closes#30067 from MaxGekk/ds-options-common-test.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch proposes to add a config we can control fast-fail behavior in FileFormatWriter and set it false by default.
### Why are the changes needed?
In SPARK-29649, we catch `FileAlreadyExistsException` in `FileFormatWriter` and fail fast for the task set to prevent task retry.
Due to latest discussion, it is important to be able to keep original behavior that is to retry tasks even `FileAlreadyExistsException` is thrown, because `FileAlreadyExistsException` could be recoverable in some cases.
We are going to add a config we can control this behavior and set it false for fast-fail by default.
### Does this PR introduce _any_ user-facing change?
Yes. By default the task in FileFormatWriter will retry even if `FileAlreadyExistsException` is thrown. This is the behavior before Spark 3.0. User can control fast-fail behavior by enabling it.
### How was this patch tested?
Unit test.
Closes#30073 from viirya/SPARK-33170.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
SPARK-29358 added support for `unionByName` to work when the two datasets didn't necessarily have the same schema, but it does not work with nested columns like structs. This patch adds the support to work with struct columns.
The behavior before this PR:
```scala
scala> val df1 = spark.range(1).selectExpr("id c0", "named_struct('c', id + 1, 'b', id + 2, 'a', id + 3) c1")
scala> val df2 = spark.range(1).selectExpr("id c0", "named_struct('c', id + 1, 'b', id + 2) c1")
scala> df1.unionByName(df2, true).printSchema
org.apache.spark.sql.AnalysisException: Union can only be performed on tables with the compatible column types. struct<c:bigint,b:bigint> <> struct<c:bigint,b:bigint,a:bigint> at the second column of the second table;;
'Union false, false
:- Project [id#0L AS c0#2L, named_struct(c, (id#0L + cast(1 as bigint)), b, (id#0L + cast(2 as bigint)), a, (id#0L + cast(3 as bigint))) AS c1#3]
: +- Range (0, 1, step=1, splits=Some(12))
+- Project [c0#8L, c1#9]
+- Project [id#6L AS c0#8L, named_struct(c, (id#6L + cast(1 as bigint)), b, (id#6L + cast(2 as bigint))) AS c1#9]
+- Range (0, 1, step=1, splits=Some(12))
```
The behavior after this PR:
```scala
scala> df1.unionByName(df2, true).printSchema
root
|-- c0: long (nullable = false)
|-- c1: struct (nullable = false)
| |-- a: long (nullable = true)
| |-- b: long (nullable = false)
| |-- c: long (nullable = false)
scala> df1.unionByName(df2, true).show()
+---+-------------+
| c0| c1|
+---+-------------+
| 0| {3, 2, 1}|
| 0|{ null, 2, 1}|
+---+-------------+
```
### Why are the changes needed?
The `allowMissingColumns` of `unionByName` is a feature allowing merging different schema from two datasets when unioning them together. Nested column support makes the feature more general and flexible for usage.
### Does this PR introduce _any_ user-facing change?
Yes, after this change users can union two datasets with different schema with different structs.
### How was this patch tested?
Unit tests.
Closes#29587 from viirya/SPARK-32376.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
The function `binaryToSQLTimestamp()` is used by Parquet Vectorized reader. Parquet MR reader has similar code for de-serialization of INT96 timestamps. In this PR, I propose to de-duplicate code and re-use `binaryToSQLTimestamp()`.
### Why are the changes needed?
This should improve maintenance, and should allow to avoid errors while changing Vectorized and regular parquet readers.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By existing test suites, for instance `ParquetIOSuite`.
Closes#30069 from MaxGekk/int96-common-serde.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR aims to mark ParquetV1FilterSuite and ParquetV2FilterSuite as `ExtendedSQLTest`.
- ParquetV1FilterSuite/ParquetV2FilterSuite
- ParquetV1SchemaPruningSuite/ParquetV2SchemaPruningSuite
### Why are the changes needed?
Currently, `sql - other tests` is the longest job. This PR will move the above tests to `sql - slow tests` job.
**BEFORE**
- https://github.com/apache/spark/runs/1264150802 (1 hour 37 minutes)
**AFTER**
- https://github.com/apache/spark/pull/30068/checks?check_run_id=1265879896 (1 hour 21 minutes)
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the Github Action with the reduced time.
Closes#30068 from dongjoon-hyun/MOVE3.
Lead-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In Execution web page, when `Succeeded Job`(or Failed Jobs) has many child url elements,they will extend over the edge of the page.
### Why are the changes needed?
To make the page more friendly.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Munual test result shows as below:
Closes#30035 from akiyamaneko/sql_execution_job_overflow.
Authored-by: neko <echohlne@gmail.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
Correct the resolution of having clause.
### Why are the changes needed?
Grouping sets construct new aggregate lost the qualified name of grouping expression. Here is a example:
```
-- Works resolved by `ResolveReferences`
select c1 from values (1) as t1(c1) group by grouping sets(t1.c1) having c1 = 1
-- Works because of the extra expression c1
select c1 as c2 from values (1) as t1(c1) group by grouping sets(t1.c1) having t1.c1 = 1
-- Failed
select c1 from values (1) as t1(c1) group by grouping sets(t1.c1) having t1.c1 = 1
```
It wroks with `Aggregate` without grouping sets through `ResolveReferences`, but Grouping sets not works since the exprId has been changed.
### Does this PR introduce _any_ user-facing change?
Yes, bug fix.
### How was this patch tested?
add test.
Closes#30029 from ulysses-you/SPARK-33131.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR is a sub-task of [SPARK-33138](https://issues.apache.org/jira/browse/SPARK-33138). In order to make SQLConf.get reliable and stable, we need to make sure user can't pollute the SQLConf and SparkSession Context via calling setActiveSession and clearActiveSession.
Change of the PR:
* add legacy config spark.sql.legacy.allowModifyActiveSession to fallback to old behavior if user do need to call these two API.
* by default, if user call these two API, it will throw exception
* add extra two internal and private API setActiveSessionInternal and clearActiveSessionInternal for current internal usage
* change all internal reference to new internal API except for SQLContext.setActive and SQLContext.clearActive
### Why are the changes needed?
Make SQLConf.get reliable and stable.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
* Add UT in SparkSessionBuilderSuite to test the legacy config
* Existing test
Closes#30042 from leanken/leanken-SPARK-33139.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to remove `assert` from `Benchmark` for making it easier to run benchmark codes via `spark-submit`.
### Why are the changes needed?
Since the current `Benchmark` (`master` and `branch-3.0`) has `assert`, we need to pass the proper jars of `scalatest` and `scalactic`;
- scalatest-core_2.12-3.2.0.jar
- scalatest-compatible-3.2.0.jar
- scalactic_2.12-3.0.jar
```
./bin/spark-submit --jars scalatest-core_2.12-3.2.0.jar,scalatest-compatible-3.2.0.jar,scalactic_2.12-3.0.jar,./sql/catalyst/target/spark-catalyst_2.12-3.1.0-SNAPSHOT-tests.jar,./core/target/spark-core_2.12-3.1.0-SNAPSHOT-tests.jar --class org.apache.spark.sql.execution.benchmark.TPCDSQueryBenchmark ./sql/core/target/spark-sql_2.12-3.1.0-SNAPSHOT-tests.jar --data-location /tmp/tpcds-sf1
```
This update can make developers submit benchmark codes without these dependencies;
```
./bin/spark-submit --jars ./sql/catalyst/target/spark-catalyst_2.12-3.1.0-SNAPSHOT-tests.jar,./core/target/spark-core_2.12-3.1.0-SNAPSHOT-tests.jar --class org.apache.spark.sql.execution.benchmark.TPCDSQueryBenchmark ./sql/core/target/spark-sql_2.12-3.1.0-SNAPSHOT-tests.jar --data-location /tmp/tpcds-sf1
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manually checked.
Closes#30064 from maropu/RemoveDepInBenchmark.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add case sensitivity tests for column resolution in ALTER TABLE
### Why are the changes needed?
To make sure `spark.sql.caseSensitive` works for `ResolveAlterTableChanges`
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
new test
Closes#30063 from huaxingao/caseSensitivity.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Added a couple tests to `AvroSuite` and to `ParquetIOSuite` to check that the metadata key 'org.apache.spark.legacyDateTime' is written correctly depending on the SQL configs:
- spark.sql.legacy.avro.datetimeRebaseModeInWrite
- spark.sql.legacy.parquet.datetimeRebaseModeInWrite
This is a follow up https://github.com/apache/spark/pull/28137.
### Why are the changes needed?
1. To improve test coverage
2. To make sure that the metadata key is actually saved to Avro/Parquet files
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the added tests:
```
$ build/sbt "testOnly org.apache.spark.sql.execution.datasources.parquet.ParquetIOSuite"
$ build/sbt "avro/test:testOnly org.apache.spark.sql.avro.AvroV1Suite"
$ build/sbt "avro/test:testOnly org.apache.spark.sql.avro.AvroV2Suite"
```
Closes#30061 from MaxGekk/parquet-test-metakey.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Current solution in build file to enable build failure on compilation warnings with exclusion of deprecation ones is not portable after SBT version 1.3.13 (build import fails with compilation error with SBT 1.4) and could be replaced with more robust and maintainable, especially since Scala 2.13.2 with similar built-in functionality.
Additionally, warnings were fixed to pass the build, with as few changes as possible:
warnings in 2.12 compilation fixed in code,
warnings in 2.13 compilation covered by configuration to be addressed separately
### Why are the changes needed?
Unblocks upgrade to SBT after 1.3.13.
Enhances build file maintainability.
Allows fine tune of warnings configuration in scope of Scala 2.13 compilation.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
`build/sbt`'s `compile` and `Test/compile` for both Scala 2.12 and 2.13 profiles.
Closes#29995 from gemelen/feature/warnings-reporter.
Authored-by: Denis Pyshev <git@gemelen.net>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
I currently have unquoted column names in alter table, e.g. ```ALTER TABLE "test"."alt_table" DROP COLUMN c1```
should change to quoted column name ```ALTER TABLE "test"."alt_table" DROP COLUMN "c1"```
### Why are the changes needed?
We should always use quoted identifiers in JDBC SQLs, e.g. ```CREATE TABLE "test"."abc" ("col" INTEGER ) ``` or ```INSERT INTO "test"."abc" ("col") VALUES (?)```. Using unquoted column name in alterTable causes problems, for example:
```
sql("CREATE TABLE h2.test.alt_table (c1 INTEGER, c2 INTEGER) USING _")
sql("ALTER TABLE h2.test.alt_table DROP COLUMN c1")
org.apache.spark.sql.AnalysisException: Failed table altering: test.alt_table;
......
Caused by: org.h2.jdbc.JdbcSQLException: Column "C1" not found; SQL statement:
ALTER TABLE "test"."alt_table" DROP COLUMN c1 [42122-195]
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests
Closes#30041 from huaxingao/alter_table_followup.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Do not use local shuffle reader at final stage if the root node is write command.
### Why are the changes needed?
Users usually repartition with partition column on dynamic partition overwrite. AQE could break it by removing physical shuffle with local shuffle reader. That could lead to a large number of output files, even exceeding the file system limit.
### Does this PR introduce _any_ user-facing change?
Yes.
### How was this patch tested?
Add test.
Closes#29797 from manuzhang/spark-32932.
Authored-by: manuzhang <owenzhang1990@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Follow the convention and rename the metrics `numRows` to `numOutputRows`
### Why are the changes needed?
`FilterExec`, `HashAggregateExec`, etc. all use `numOutputRows`
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests
Closes#30039 from cloud-fan/minor.
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 proposes to fix a bug on calling `DataType.equalsIgnoreCompatibleNullability` with mistakenly swapped parameters in `V2WriteCommand.outputResolved`. The order of parameters for `DataType.equalsIgnoreCompatibleNullability` are `from` and `to`, which says that the right order of matching variables are `inAttr` and `outAttr`.
### Why are the changes needed?
Spark throws AnalysisException due to unresolved operator in v2 write, while the operator is unresolved due to a bug that parameters to call `DataType.equalsIgnoreCompatibleNullability` in `outputResolved` have been swapped.
### Does this PR introduce _any_ user-facing change?
Yes, end users no longer suffer on unresolved operator in v2 write if they're trying to write dataframe containing non-nullable complex types against table matching complex types as nullable.
### How was this patch tested?
New UT added.
Closes#30033 from HeartSaVioR/SPARK-33136.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
This patch is a small extension to change-request SPARK-28133, which added inverse hyperbolic functions to the SQL interpreter, but did not include those methods within the Scala `sql.functions._` API. This patch makes `acosh`, `asinh` and `atanh` functions available through the Scala API.
Unit-tests have been added to `sql/core/src/test/scala/org/apache/spark/sql/MathFunctionsSuite.scala`. Manual testing has been done via `spark-shell`, using the following recipe:
```
val df = spark.range(0, 11)
.toDF("x")
.withColumn("x", ($"x" - 5) / 2.0)
val hyps = df.withColumn("tanh", tanh($"x"))
.withColumn("sinh", sinh($"x"))
.withColumn("cosh", cosh($"x"))
val invhyps = hyps.withColumn("atanh", atanh($"tanh"))
.withColumn("asinh", asinh($"sinh"))
.withColumn("acosh", acosh($"cosh"))
invhyps.show
```
which produces the following output:
```
+----+--------------------+-------------------+------------------+-------------------+-------------------+------------------+
| x| tanh| sinh| cosh| atanh| asinh| acosh|
+----+--------------------+-------------------+------------------+-------------------+-------------------+------------------+
|-2.5| -0.9866142981514303|-6.0502044810397875| 6.132289479663686| -2.500000000000001|-2.4999999999999956| 2.5|
|-2.0| -0.9640275800758169| -3.626860407847019|3.7621956910836314|-2.0000000000000004|-1.9999999999999991| 2.0|
|-1.5| -0.9051482536448664|-2.1292794550948173| 2.352409615243247|-1.4999999999999998|-1.4999999999999998| 1.5|
|-1.0| -0.7615941559557649|-1.1752011936438014| 1.543080634815244| -1.0| -1.0| 1.0|
|-0.5|-0.46211715726000974|-0.5210953054937474|1.1276259652063807| -0.5|-0.5000000000000002|0.4999999999999998|
| 0.0| 0.0| 0.0| 1.0| 0.0| 0.0| 0.0|
| 0.5| 0.46211715726000974| 0.5210953054937474|1.1276259652063807| 0.5| 0.5|0.4999999999999998|
| 1.0| 0.7615941559557649| 1.1752011936438014| 1.543080634815244| 1.0| 1.0| 1.0|
| 1.5| 0.9051482536448664| 2.1292794550948173| 2.352409615243247| 1.4999999999999998| 1.5| 1.5|
| 2.0| 0.9640275800758169| 3.626860407847019|3.7621956910836314| 2.0000000000000004| 2.0| 2.0|
| 2.5| 0.9866142981514303| 6.0502044810397875| 6.132289479663686| 2.500000000000001| 2.5| 2.5|
+----+--------------------+-------------------+------------------+-------------------+-------------------+------------------+
```
Closes#29938 from rwpenney/fix/inverse-hyperbolics.
Authored-by: Richard Penney <rwp@rwpenney.uk>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
In the PR, I propose to restrict the partial result feature only by root JSON objects. JSON datasource as well as `from_json()` will return `null` for malformed nested JSON objects.
### Why are the changes needed?
1. To not raise exception to users in the PERMISSIVE mode
2. To fix a regression and to have the same behavior as Spark 2.4.x has
3. Current implementation of partial result is supposed to work only for root (top-level) JSON objects, and not tested for bad nested complex JSON fields.
### Does this PR introduce _any_ user-facing change?
Yes. Before the changes, the code below:
```scala
val pokerhand_raw = Seq("""[{"cards": [19], "playerId": 123456}]""").toDF("events")
val event = new StructType().add("playerId", LongType).add("cards", ArrayType(new StructType().add("id", LongType).add("rank", StringType)))
val pokerhand_events = pokerhand_raw.select(from_json($"events", ArrayType(event)).as("event"))
pokerhand_events.show
```
throws the exception even in the default **PERMISSIVE** mode:
```java
java.lang.ClassCastException: java.lang.Long cannot be cast to org.apache.spark.sql.catalyst.util.ArrayData
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getArray(rows.scala:48)
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getArray$(rows.scala:48)
at org.apache.spark.sql.catalyst.expressions.GenericInternalRow.getArray(rows.scala:195)
```
After the changes:
```
+-----+
|event|
+-----+
| null|
+-----+
```
### How was this patch tested?
Added a test to `JsonFunctionsSuite`.
Closes#30031 from MaxGekk/json-skip-row-wrong-schema.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
test-only - > testOnly in docs across the project.
### Why are the changes needed?
Since the sbt version is updated, the older way or running i.e. `test-only` is no longer valid.
### Does this PR introduce _any_ user-facing change?
docs update.
### How was this patch tested?
Manually.
Closes#30028 from ScrapCodes/fix-build/sbt-sample.
Authored-by: Prashant Sharma <prashsh1@in.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
As [SPARK-13860](https://issues.apache.org/jira/browse/SPARK-13860) stated, TPCDS Query 39 returns wrong results using SparkSQL. The root cause is that when stddev_samp is applied to a single element set, with TPCDS answer, it return null; as in SparkSQL, it return Double.NaN which caused the wrong result.
Add an extra legacy config to fallback into the NaN logical, and return null by default to align with TPCDS standard.
### Why are the changes needed?
SQL correctness issue.
### Does this PR introduce any user-facing change?
Yes. See sql-migration-guide
In Spark 3.1, statistical aggregation function includes `std`, `stddev`, `stddev_samp`, `variance`, `var_samp`, `skewness`, `kurtosis`, `covar_samp`, `corr` will return `NULL` instead of `Double.NaN` when `DivideByZero` occurs during expression evaluation, for example, when `stddev_samp` applied on a single element set. In Spark version 3.0 and earlier, it will return `Double.NaN` in such case. To restore the behavior before Spark 3.1, you can set `spark.sql.legacy.statisticalAggregate` to `true`.
### How was this patch tested?
Updated DataFrameAggregateSuite/DataFrameWindowFunctionsSuite to test both default and legacy behavior.
Adjust DataFrameWindowFunctionsSuite/SQLQueryTestSuite and some R case to update to the default return null behavior.
Closes#29983 from leanken/leanken-SPARK-13860.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
- Override the default SQL strings in the DB2 Dialect for:
* ALTER TABLE UPDATE COLUMN TYPE
* ALTER TABLE UPDATE COLUMN NULLABILITY
- Add new docker integration test suite jdbc/v2/DB2IntegrationSuite.scala
### Why are the changes needed?
In SPARK-24907, we implemented JDBC v2 Table Catalog but it doesn't support some ALTER TABLE at the moment. This PR supports DB2 specific ALTER TABLE.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By running new integration test suite:
$ ./build/sbt -Pdocker-integration-tests "test-only *.DB2IntegrationSuite"
Closes#29972 from huaxingao/db2_docker.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In SPARK-24994 we implemented unwrapping cast for **integral types**. This extends it to support **numeric types** such as float/double/decimal, so that filters involving these types can be better pushed down to data sources.
Unlike the cases of integral types, conversions between numeric types can result to rounding up or downs. Consider the following case:
```sql
cast(e as double) < 1.9
```
assume type of `e` is short, since 1.9 is not representable in the type, the casting will either truncate or round. Now suppose the literal is truncated, we cannot convert the expression to:
```sql
e < cast(1.9 as short)
```
as in the previous implementation, since if `e` is 1, the original expression evaluates to true, but converted expression will evaluate to false.
To resolve the above, this PR first finds out whether casting from the wider type to the narrower type will result to truncate or round, by comparing a _roundtrip value_ derived from **converting the literal first to the narrower type, and then to the wider type**, versus the original literal value. For instance, in the above, we'll first obtain a roundtrip value via the conversion (double) 1.9 -> (short) 1 -> (double) 1.0, and then compare it against 1.9.
<img width="1153" alt="Screen Shot 2020-09-28 at 3 30 27 PM" src="https://user-images.githubusercontent.com/506679/94492719-bd29e780-019f-11eb-9111-71d6e3d157f7.png">
Now in the case of truncate, we'd convert the original expression to:
```sql
e <= cast(1.9 as short)
```
instead, so that the conversion also is valid when `e` is 1.
For more details, please check [this blog post](https://prestosql.io/blog/2019/05/21/optimizing-the-casts-away.html) by Presto which offers a very good explanation on how it works.
### Why are the changes needed?
For queries such as:
```sql
SELECT * FROM tbl WHERE short_col < 100.5
```
The predicate `short_col < 100.5` can't be pushed down to data sources because it involves casts. This eliminates the cast so these queries can run more efficiently.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit tests
Closes#29792 from sunchao/SPARK-32858.
Lead-authored-by: Chao Sun <sunchao@apple.com>
Co-authored-by: Chao Sun <sunchao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`ScalarSubquery` should returns the first two rows.
### Why are the changes needed?
To avoid Driver OOM.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing test: d6f3138352/sql/core/src/test/scala/org/apache/spark/sql/SubquerySuite.scala (L147-L154)Closes#30016 from wangyum/SPARK-33119.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
We have a problem when you use CREATE TEMPORARY TABLE with LOCATION
```scala
spark.range(3).write.parquet("/tmp/testspark1")
sql("CREATE TEMPORARY TABLE t USING parquet OPTIONS (path '/tmp/testspark1')")
sql("CREATE TEMPORARY TABLE t USING parquet LOCATION '/tmp/testspark1'")
```
```scala
org.apache.spark.sql.AnalysisException: Unable to infer schema for Parquet. It must be specified manually.;
at org.apache.spark.sql.execution.datasources.DataSource.$anonfun$getOrInferFileFormatSchema$12(DataSource.scala:200)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:200)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:408)
at org.apache.spark.sql.execution.datasources.CreateTempViewUsing.run(ddl.scala:94)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:79)
at org.apache.spark.sql.Dataset.$anonfun$logicalPlan$1(Dataset.scala:229)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3618)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3616)
at org.apache.spark.sql.Dataset.<init>(Dataset.scala:229)
at org.apache.spark.sql.Dataset$.$anonfun$ofRows$2(Dataset.scala:100)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:97)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:607)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:602)
```
This bug was introduced by SPARK-30507.
sparksqlparser --> visitCreateTable --> visitCreateTableClauses --> cleanTableOptions extract the path from the options but in this case CreateTempViewUsing need the path in the options map.
### Why are the changes needed?
To fix the problem
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit testing and manual testing
Closes#30014 from planga82/bugfix/SPARK-33118_create_temp_table_location.
Authored-by: Pablo <pablo.langa@stratio.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
With following scenario when AQE is on, SQLMetrics could be incorrect.
1. Stage A and B are created, and UI updated thru event onAdaptiveExecutionUpdate.
2. Stage A and B are running. Subquery in stage A keep updating metrics thru event onAdaptiveSQLMetricUpdate.
3. Stage B completes, while stage A's subquery is still running, updating metrics.
4. Completion of stage B triggers new stage creation and UI update thru event onAdaptiveExecutionUpdate again (just like step 1).
So decided to make a trade off of keeping more duplicate SQLMetrics without deleting them when AQE with newPlan updated.
### Why are the changes needed?
Make SQLMetrics behavior 100% correct.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Updated SQLAppStatusListenerSuite.
Closes#29965 from leanken/leanken-SPARK-33016.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This pr removes `com.twitter:parquet-hadoop-bundle:1.6.0` and `orc.classifier`.
### Why are the changes needed?
To make code more clear and readable.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing test.
Closes#30005 from wangyum/SPARK-33107.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
At the moment there is no possibility to turn off JDBC authentication providers which exists on the classpath. This can be problematic because service providers are loaded with service loader. In this PR I've added `spark.sql.sources.disabledJdbcConnProviderList` configuration possibility (default: empty).
### Why are the changes needed?
No possibility to turn off JDBC authentication providers.
### Does this PR introduce _any_ user-facing change?
Yes, it introduces new configuration option.
### How was this patch tested?
* Existing + newly added unit tests.
* Existing integration tests.
Closes#29964 from gaborgsomogyi/SPARK-32047.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
While I've implemented JDBC provider disable functionality it has been popped up [here](https://github.com/apache/spark/pull/29964#discussion_r501786746) that `Utils.stringToSeq` must be used when String list type SQL parameter handled. In this PR I've fixed the problematic parameters.
### Why are the changes needed?
`Utils.stringToSeq` must be used when String list type SQL parameter handled.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Closes#29989 from gaborgsomogyi/SPARK-33102.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR removes the leftover of Hive 1.2 workarounds and Hive 1.2 profile in Jenkins script.
- `test-hive1.2` title is not used anymore in Jenkins
- Remove some comments related to Hive 1.2
- Remove unused codes in `OrcFilters.scala` Hive
- Test `spark.sql.hive.convertMetastoreOrc` disabled case for the tests added at SPARK-19809 and SPARK-22267
### Why are the changes needed?
To remove unused codes & improve test coverage
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
Manually ran the unit tests. Also It will be tested in CI in this PR.
Closes#29973 from HyukjinKwon/SPARK-33082-SPARK-20202.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR proposes to add `DataStreamWriter.table` to specify the output "table" to write from the streaming query.
### Why are the changes needed?
For now, there's no way to write to the table (especially catalog table) even the table is capable to handle streaming write, so even with Spark 3, writing to the catalog table via SS should go through the `DataStreamWriter.format(provider)` and wish the provider can handle it as same as we do with catalog table.
With the new API, we can directly point to the catalog table which supports streaming write. Some of usages are covered with tests - simply saying, end users can do the following:
```scala
// assuming `testcat` is a custom catalog, and `ns` is a namespace in the catalog
spark.sql("CREATE TABLE testcat.ns.table1 (id bigint, data string) USING foo")
val query = inputDF
.writeStream
.table("testcat.ns.table1")
.option(...)
.start()
```
### Does this PR introduce _any_ user-facing change?
Yes, as this adds a new public API in DataStreamWriter. This doesn't bring backward incompatible change.
### How was this patch tested?
New unit tests.
Closes#29767 from HeartSaVioR/SPARK-32896.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add distinct info at `UnresolvedFunction.toString`.
### Why are the changes needed?
Make `UnresolvedFunction` info complete.
```
create table test (c1 int, c2 int);
explain extended select sum(distinct c1) from test;
-- before this pr
== Parsed Logical Plan ==
'Project [unresolvedalias('sum('c1), None)]
+- 'UnresolvedRelation [test]
-- after this pr
== Parsed Logical Plan ==
'Project [unresolvedalias('sum(distinct 'c1), None)]
+- 'UnresolvedRelation [test]
```
### Does this PR introduce _any_ user-facing change?
Yes, get distinct info during sql parse.
### How was this patch tested?
manual test.
Closes#29586 from ulysses-you/SPARK-32743.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Propagate ORC options to Hadoop configs in Hive `OrcFileFormat` and in the regular ORC datasource.
### Why are the changes needed?
There is a bug that when running:
```scala
spark.read.format("orc").options(conf).load(path)
```
The underlying file system will not receive the conf options.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
Added UT to `OrcSourceSuite`.
Closes#29976 from MaxGekk/orc-option-propagation.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This is a kind of a followup of SPARK-32646. New JIRA was filed to control the fixed versions properly.
When you use `map`, it might be lazily evaluated and not executed. To avoid this, we should better use `foreach`. See also SPARK-16694. Current codes look not causing any bug for now but it should be best to fix to avoid potential issues.
### Why are the changes needed?
To avoid potential issues from `map` being lazy and not executed.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Ran related tests. CI in this PR should verify.
Closes#29974 from HyukjinKwon/SPARK-32646.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
1. Add new method to the `JdbcDialect` class - `classifyException()`. It converts dialect specific exception to Spark's `AnalysisException` or its sub-classes.
2. Replace H2 exception `org.h2.jdbc.JdbcSQLException` in `JDBCTableCatalogSuite` by `AnalysisException`.
3. Add `H2Dialect`
### Why are the changes needed?
Currently JDBC v2 Table Catalog implementation throws dialect specific exception and ignores exceptions defined in the `TableCatalog` interface. This PR adds new method for converting dialect specific exception, and assumes that follow up PRs will implement `classifyException()`.
### Does this PR introduce _any_ user-facing change?
Yes.
### How was this patch tested?
By running existing test suites `JDBCTableCatalogSuite` and `JDBCV2Suite`.
Closes#29952 from MaxGekk/jdbcv2-classify-exception.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Adds a SQL function `raise_error` which underlies the refactored `assert_true` function. `assert_true` now also (optionally) accepts a custom error message field.
`raise_error` is exposed in SQL, Python, Scala, and R.
`assert_true` was previously only exposed in SQL; it is now also exposed in Python, Scala, and R.
### Why are the changes needed?
Improves usability of `assert_true` by clarifying error messaging, and adds the useful helper function `raise_error`.
### Does this PR introduce _any_ user-facing change?
Yes:
- Adds `raise_error` function to the SQL, Python, Scala, and R APIs.
- Adds `assert_true` function to the SQL, Python and R APIs.
### How was this patch tested?
Adds unit tests in SQL, Python, Scala, and R for `assert_true` and `raise_error`.
Closes#29947 from karenfeng/spark-32793.
Lead-authored-by: Karen Feng <karen.feng@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. Don't print the exception in the error message while loading a built-in provider.
2. Print the exception starting from the INFO level.
Up to the INFO level, the output is:
```
17:48:32.342 ERROR org.apache.spark.sql.execution.datasources.jdbc.connection.ConnectionProvider: Failed to load built in provider.
```
and starting from the INFO level:
```
17:48:32.342 ERROR org.apache.spark.sql.execution.datasources.jdbc.connection.ConnectionProvider: Failed to load built in provider.
17:48:32.342 INFO org.apache.spark.sql.execution.datasources.jdbc.connection.ConnectionProvider: Loading of the provider failed with the exception:
java.util.ServiceConfigurationError: org.apache.spark.sql.jdbc.JdbcConnectionProvider: Provider org.apache.spark.sql.execution.datasources.jdbc.connection.IntentionallyFaultyConnectionProvider could not be instantiated
at java.util.ServiceLoader.fail(ServiceLoader.java:232)
at java.util.ServiceLoader.access$100(ServiceLoader.java:185)
at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:384)
at java.util.ServiceLoader$LazyIterator.next(ServiceLoader.java:404)
at java.util.ServiceLoader$1.next(ServiceLoader.java:480)
at org.apache.spark.sql.execution.datasources.jdbc.connection.ConnectionProvider$.loadProviders(ConnectionProvider.scala:41)
```
### Why are the changes needed?
To avoid "noise" in logs while running tests. Currently, logs are blown up:
```
org.apache.spark.sql.execution.datasources.jdbc.connection.ConnectionProvider: Loading of the provider failed with the exception:
java.util.ServiceConfigurationError: org.apache.spark.sql.jdbc.JdbcConnectionProvider: Provider org.apache.spark.sql.execution.datasources.jdbc.connection.IntentionallyFaultyConnectionProvider could not be instantiated
at java.util.ServiceLoader.fail(ServiceLoader.java:232)
at java.util.ServiceLoader.access$100(ServiceLoader.java:185)
at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:384)
at java.util.ServiceLoader$LazyIterator.next(ServiceLoader.java:404)
at java.util.ServiceLoader$1.next(ServiceLoader.java:480)
at org.apache.spark.sql.execution.datasources.jdbc.connection.ConnectionProvider$.loadProviders(ConnectionProvider.scala:41)
...
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.IllegalArgumentException: Intentional Exception
at org.apache.spark.sql.execution.datasources.jdbc.connection.IntentionallyFaultyConnectionProvider.<init>(IntentionallyFaultyConnectionProvider.scala:26)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at java.lang.Class.newInstance(Class.java:442)
at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:380)
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running:
```
$ build/sbt "sql/test:testOnly org.apache.spark.sql.execution.datasources.v2.jdbc.JDBCTableCatalogSuite"
```
Closes#29968 from MaxGekk/gaborgsomogyi-SPARK-32001-followup.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR removes old Hive-1.2 profile related workaround code.
### Why are the changes needed?
To simply the code.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes#29961 from dongjoon-hyun/SPARK-HIVE12.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR proposes to migrate `DESCRIBE tbl colname` to use `UnresolvedTableOrView` to resolve the table/view identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
### Why are the changes needed?
The current behavior is not consistent between v1 and v2 commands when resolving a temp view.
In v2, the `t` in the following example is resolved to a table:
```scala
sql("CREATE TABLE testcat.ns.t (id bigint) USING foo")
sql("CREATE TEMPORARY VIEW t AS SELECT 2 as i")
sql("USE testcat.ns")
sql("DESCRIBE t i") // 't' is resolved to testcat.ns.t
Describing columns is not supported for v2 tables.;
org.apache.spark.sql.AnalysisException: Describing columns is not supported for v2 tables.;
```
whereas in v1, the `t` is resolved to a temp view:
```scala
sql("CREATE DATABASE test")
sql("CREATE TABLE spark_catalog.test.t (id bigint) USING csv")
sql("CREATE TEMPORARY VIEW t AS SELECT 2 as i")
sql("USE spark_catalog.test")
sql("DESCRIBE t i").show // 't' is resolved to a temp view
+---------+----------+
|info_name|info_value|
+---------+----------+
| col_name| i|
|data_type| int|
| comment| NULL|
+---------+----------+
```
### Does this PR introduce _any_ user-facing change?
After this PR, `DESCRIBE t i` is resolved to a temp view `t` instead of `testcat.ns.t`.
### How was this patch tested?
Added a new test
Closes#29880 from imback82/describe_column_consistent.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Override the default SQL strings in the Oracle Dialect for:
- ALTER TABLE ADD COLUMN
- ALTER TABLE UPDATE COLUMN TYPE
- ALTER TABLE UPDATE COLUMN NULLABILITY
2. Add new docker integration test suite `jdbc/v2/OracleIntegrationSuite.scala`
### Why are the changes needed?
In SPARK-24907, we implemented JDBC v2 Table Catalog but it doesn't support some `ALTER TABLE` at the moment. This PR supports Oracle specific `ALTER TABLE`.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By running new integration test suite:
```
$ ./build/sbt -Pdocker-integration-tests "test-only *.OracleIntegrationSuite"
```
Closes#29912 from MaxGekk/jdbcv2-oracle-alter-table.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Get error message from the expected exception, and check that they are reasonable.
### Why are the changes needed?
To improve tests by expecting particular error messages.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running `JDBCTableCatalogSuite`.
Closes#29957 from MaxGekk/jdbcv2-negative-tests-followup.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. Refactored `WithFields` Expression to make it more extensible (now `UpdateFields`).
2. Added a new `dropFields` method to the `Column` class. This method should allow users to drop a `StructField` in a `StructType` column (with similar semantics to the `drop` method on `Dataset`).
### Why are the changes needed?
Often Spark users have to work with deeply nested data e.g. to fix a data quality issue with an existing `StructField`. To do this with the existing Spark APIs, users have to rebuild the entire struct column.
For example, let's say you have the following deeply nested data structure which has a data quality issue (`5` is missing):
```
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val data = spark.createDataFrame(sc.parallelize(
Seq(Row(Row(Row(1, 2, 3), Row(Row(4, null, 6), Row(7, 8, 9), Row(10, 11, 12)), Row(13, 14, 15))))),
StructType(Seq(
StructField("a", StructType(Seq(
StructField("a", StructType(Seq(
StructField("a", IntegerType),
StructField("b", IntegerType),
StructField("c", IntegerType)))),
StructField("b", StructType(Seq(
StructField("a", StructType(Seq(
StructField("a", IntegerType),
StructField("b", IntegerType),
StructField("c", IntegerType)))),
StructField("b", StructType(Seq(
StructField("a", IntegerType),
StructField("b", IntegerType),
StructField("c", IntegerType)))),
StructField("c", StructType(Seq(
StructField("a", IntegerType),
StructField("b", IntegerType),
StructField("c", IntegerType))))
))),
StructField("c", StructType(Seq(
StructField("a", IntegerType),
StructField("b", IntegerType),
StructField("c", IntegerType))))
)))))).cache
data.show(false)
+---------------------------------+
|a |
+---------------------------------+
|[[1, 2, 3], [[4,, 6], [7, 8, 9]]]|
+---------------------------------+
```
Currently, to drop the missing value users would have to do something like this:
```
val result = data.withColumn("a",
struct(
$"a.a",
struct(
struct(
$"a.b.a.a",
$"a.b.a.c"
).as("a"),
$"a.b.b",
$"a.b.c"
).as("b"),
$"a.c"
))
result.show(false)
+---------------------------------------------------------------+
|a |
+---------------------------------------------------------------+
|[[1, 2, 3], [[4, 6], [7, 8, 9], [10, 11, 12]], [13, 14, 15]]|
+---------------------------------------------------------------+
```
As you can see above, with the existing methods users must call the `struct` function and list all fields, including fields they don't want to change. This is not ideal as:
>this leads to complex, fragile code that cannot survive schema evolution.
[SPARK-16483](https://issues.apache.org/jira/browse/SPARK-16483)
In contrast, with the method added in this PR, a user could simply do something like this to get the same result:
```
val result = data.withColumn("a", 'a.dropFields("b.a.b"))
result.show(false)
+---------------------------------------------------------------+
|a |
+---------------------------------------------------------------+
|[[1, 2, 3], [[4, 6], [7, 8, 9], [10, 11, 12]], [13, 14, 15]]|
+---------------------------------------------------------------+
```
This is the second of maybe 3 methods that could be added to the `Column` class to make it easier to manipulate nested data.
Other methods under discussion in [SPARK-22231](https://issues.apache.org/jira/browse/SPARK-22231) include `withFieldRenamed`.
However, this should be added in a separate PR.
### Does this PR introduce _any_ user-facing change?
The documentation for `Column.withField` method has changed to include an additional note about how to write optimized queries when adding multiple nested Column directly.
### How was this patch tested?
New unit tests were added. Jenkins must pass them.
### Related JIRAs:
More discussion on this topic can be found here:
- https://issues.apache.org/jira/browse/SPARK-22231
- https://issues.apache.org/jira/browse/SPARK-16483Closes#29795 from fqaiser94/SPARK-32511-dropFields-second-try.
Authored-by: fqaiser94@gmail.com <fqaiser94@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add checks for the cases when JDBC v2 Table Catalog commands fail.
### Why are the changes needed?
To improve test coverage.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running `JDBCTableCatalogSuite`.
Closes#29945 from MaxGekk/jdbcv2-negative-tests.
Lead-authored-by: Max Gekk <max.gekk@gmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
As of today,
- SPARK-30034 Apache Spark 3.0.0 switched its default Hive execution engine from Hive 1.2 to Hive 2.3. This removes the direct dependency to the forked Hive 1.2.1 in maven repository.
- SPARK-32981 Apache Spark 3.1.0(`master` branch) removed Hive 1.2 related artifacts from Apache Spark binary distributions.
This PR(SPARK-20202) aims to remove the following usage of unofficial Apache Hive fork completely from Apache Spark master for Apache Spark 3.1.0.
```
<hive.group>org.spark-project.hive</hive.group>
<hive.version>1.2.1.spark2</hive.version>
```
For the forked Hive 1.2.1.spark2 users, Apache Spark 2.4(LTS) and 3.0 (~ 2021.12) will provide it.
### Why are the changes needed?
- First, Apache Spark community should not use the unofficial forked release of another Apache project.
- Second, Apache Hive 1.2.1 was released at 2015-06-26 and the forked Hive `1.2.1.spark2` exposed many unfixable bugs in Apache because the forked `1.2.1.spark2` is not maintained at all. Apache Hive 2.3.0 was released at 2017-07-19 and it has been used with less number of bugs compared with `1.2.1.spark2`. Many bugs still exist in `hive-1.2` profile and new Apache Spark unit tests are added with `HiveUtils.isHive23` condition so far.
### Does this PR introduce _any_ user-facing change?
No. This is a dev-only change. PRBuilder will not accept `[test-hive1.2]` on master and `branch-3.1`.
### How was this patch tested?
1. SBT/Hadoop 3.2/Hive 2.3 (https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/129366)
2. SBT/Hadoop 2.7/Hive 2.3 (https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/129382)
3. SBT/Hadoop 3.2/Hive 1.2 (This has not been supported already due to Hive 1.2 doesn't work with Hadoop 3.2.)
4. SBT/Hadoop 2.7/Hive 1.2 (https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/129383, This is rejected)
Closes#29936 from dongjoon-hyun/SPARK-REMOVE-HIVE1.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR combines the current plan and the initial plan in the AQE query plan string when the two plans are the same. It also removes the `== Current Plan ==` and `== Initial Plan ==` headers:
Before
```scala
AdaptiveSparkPlan isFinalPlan=false
+- == Current Plan ==
SortMergeJoin [key#13], [a#23], Inner
:- Sort [key#13 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(key#13, 5), true, [id=#94]
...
+- == Initial Plan ==
SortMergeJoin [key#13], [a#23], Inner
:- Sort [key#13 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(key#13, 5), true, [id=#94]
...
```
After
```scala
AdaptiveSparkPlan isFinalPlan=false
+- SortMergeJoin [key#13], [a#23], Inner
:- Sort [key#13 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(key#13, 5), true, [id=#94]
...
```
For SQL `EXPLAIN` output:
Before
```scala
AdaptiveSparkPlan (8)
+- == Current Plan ==
Sort (7)
+- Exchange (6)
...
+- == Initial Plan ==
Sort (7)
+- Exchange (6)
...
```
After
```scala
AdaptiveSparkPlan (8)
+- Sort (7)
+- Exchange (6)
...
```
### Why are the changes needed?
To simplify the AQE plan string by removing the redundant plan information.
### Does this PR introduce _any_ user-facing change?
Yes.
### How was this patch tested?
Modified the existing unit test.
Closes#29915 from allisonwang-db/aqe-explain.
Authored-by: allisonwang-db <66282705+allisonwang-db@users.noreply.github.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
Gabor Somogyi
Max Gekk
Nan Zhu
Liang-Chi Hsieh
Dongjoon Hyun
neko
ulysses
xuewei.linxuewei
Takeshi Yamamuro
Huaxin Gao
Denis Pyshev
manuzhang
Wenchen Fan
Jungtaek Lim (HeartSaVioR)
Richard Penney
Prashant Sharma
Chao Sun
Yuming Wang
Pablo
HyukjinKwon
Terry Kim
Karen Feng
Dongjoon Hyun
fqaiser94@gmail.com
allisonwang-db
