## What changes were proposed in this pull request?
In adaptive query processing (AQE), query plans are optimized on the fly during execution. However, a few `Once` rules can be problematic for such optimization since they can either generate wrong plan/unnecessary intermediate plan nodes.
This PR enforces idempotence for "Once" batches that are supposed to run once. This is a key enabler for AQE re-optimization and can improve robustness for existing optimizer rules.
Once batches that are currently not idempotent are marked in a blacklist. We will submit followup PRs to fix idempotence of these rules.
## How was this patch tested?
Existing UTs. Failing Once rules are temporarily blacklisted.
Closes#25249 from yeshengm/idempotence-checker.
Authored-by: Yesheng Ma <kimi.ysma@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Support multiple catalogs in the following InsertTable use cases:
- INSERT INTO [TABLE] catalog.db.tbl
- INSERT OVERWRITE TABLE catalog.db.tbl
Support matrix:
Overwrite|Partitioned Table|Partition Clause |Partition Overwrite Mode|Action
---------|-----------------|-----------------|------------------------|-----
false|*|*|*|AppendData
true|no|(empty)|*|OverwriteByExpression(true)
true|yes|p1,p2 or p1 or p2 or (empty)|STATIC|OverwriteByExpression(true)
true|yes|p2,p2 or p1 or p2 or (empty)|DYNAMIC|OverwritePartitionsDynamic
true|yes|p1=23,p2=3|*|OverwriteByExpression(p1=23 and p2=3)
true|yes|p1=23,p2 or p1=23|STATIC|OverwriteByExpression(p1=23)
true|yes|p1=23,p2 or p1=23|DYNAMIC|OverwritePartitionsDynamic
Notes:
- Assume the partitioned table has 2 partitions: p1 and p2.
- `STATIC` is the default Partition Overwrite Mode for data source tables.
- DSv2 tables currently do not support `IfPartitionNotExists`.
## How was this patch tested?
New tests.
All existing catalyst and sql/core tests.
Closes#24832 from jzhuge/SPARK-27845-pr.
Lead-authored-by: Ryan Blue <blue@apache.org>
Co-authored-by: John Zhuge <jzhuge@apache.org>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
## What changes were proposed in this pull request?
In the current implementation. complex types like Array/Map/StructType are allowed to upcast as StringType.
This is not safe casting. We should disallow it.
## How was this patch tested?
Update the existing test case
Closes#25242 from gengliangwang/fixUpCastStringType.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/7355 add support casting between IntervalType and StringType for scala interface:
```scala
import org.apache.spark.sql.types._
import org.apache.spark.sql.catalyst.expressions._
Cast(Literal("interval 3 month 1 hours"), CalendarIntervalType).eval()
res0: Any = interval 3 months 1 hours
```
But SQL interface does not support it:
```sql
scala> spark.sql("SELECT CAST('interval 3 month 1 hour' AS interval)").show
org.apache.spark.sql.catalyst.parser.ParseException:
DataType interval is not supported.(line 1, pos 41)
== SQL ==
SELECT CAST('interval 3 month 1 hour' AS interval)
-----------------------------------------^^^
at org.apache.spark.sql.catalyst.parser.AstBuilder.$anonfun$visitPrimitiveDataType$1(AstBuilder.scala:1931)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:108)
at org.apache.spark.sql.catalyst.parser.AstBuilder.visitPrimitiveDataType(AstBuilder.scala:1909)
at org.apache.spark.sql.catalyst.parser.AstBuilder.visitPrimitiveDataType(AstBuilder.scala:52)
...
```
This PR add supports accepting the `interval` keyword in the schema string. So that SQL interface can support this feature.
## How was this patch tested?
unit tests
Closes#25189 from wangyum/SPARK-28435.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
query plan was designed to be immutable, but sometimes we do allow it to carry mutable states, because of the complexity of the SQL system. One example is `TreeNodeTag`. It's a state of `TreeNode` and can be carried over during copy and transform. The adaptive execution framework relies on it to link the logical and physical plans.
This leads to a problem: when we get `QueryExecution#analyzed`, the plan can be changed unexpectedly because it's mutable. I hit a real issue in https://github.com/apache/spark/pull/25107 : I use `TreeNodeTag` to carry dataset id in logical plans. However, the analyzed plan ends up with many duplicated dataset id tags in different nodes. It turns out that, the optimizer transforms the logical plan and add the tag to more nodes.
For example, the logical plan is `SubqueryAlias(Filter(...))`, and I expect only the `SubqueryAlais` has the dataset id tag. However, the optimizer removes `SubqueryAlias` and carries over the dataset id tag to `Filter`. When I go back to the analyzed plan, both `SubqueryAlias` and `Filter` has the dataset id tag, which breaks my assumption.
Since now query plan is mutable, I think it's better to limit the life cycle of a query plan instance. We can clone the query plan between analyzer, optimizer and planner, so that the life cycle is limited in one stage.
## How was this patch tested?
new test
Closes#25111 from cloud-fan/clone.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR change `CalendarIntervalType`'s readable string representation from `calendarinterval` to `interval`.
## How was this patch tested?
Existing UT
Closes#25225 from wangyum/SPARK-28469.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Fix CSV datasource to throw `com.univocity.parsers.common.TextParsingException` with large size message, which will make log output consume large disk space.
This issue is troublesome when sometimes we need parse CSV with large size column.
This PR proposes to set CSV parser/writer settings by `setErrorContentLength(1000)` to limit the error message length.
## How was this patch tested?
Manually.
```
val s = "a" * 40 * 1000000
Seq(s).toDF.write.mode("overwrite").csv("/tmp/bogdan/es4196.csv")
spark.read .option("maxCharsPerColumn", 30000000) .csv("/tmp/bogdan/es4196.csv").count
```
**Before:**
The thrown message will include error content of about 30MB size (The column size exceed the max value 30MB, so the error content include the whole parsed content, so it is 30MB).
**After:**
The thrown message will include error content like "...aaa...aa" (the number of 'a' is 1024), i.e. limit the content size to be 1024.
Closes#25184 from WeichenXu123/limit_csv_exception_size.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
New function `make_date()` takes 3 columns `year`, `month` and `day`, and makes new column of the `DATE` type. If values in the input columns are `null` or out of valid ranges, the function returns `null`. Valid ranges are:
- `year` - `[1, 9999]`
- `month` - `[1, 12]`
- `day` - `[1, 31]`
Also constructed date must be valid otherwise `make_date` returns `null`.
The function is implemented similarly to `make_date` in PostgreSQL: https://www.postgresql.org/docs/11/functions-datetime.html to maintain feature parity with it.
Here is an example:
```sql
select make_date(2013, 7, 15);
2013-07-15
```
## How was this patch tested?
Added new tests to `DateExpressionsSuite`.
Closes#25210 from MaxGekk/make_date-timestamp.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Because `Encoder` is not thread safe, the user cannot reuse an `Encoder` in multiple `Dataset`s. However, creating an `Encoder` for a complicated class is slow due to Scala Reflection. To eliminate the cost of Scala Reflection, right now I usually use the private API `ExpressionEncoder.copy` as follows:
```scala
object FooEncoder {
private lazy val _encoder: ExpressionEncoder[Foo] = ExpressionEncoder[Foo]()
implicit def encoder: ExpressionEncoder[Foo] = _encoder.copy()
}
```
This PR proposes a new method `makeCopy` in `Encoder` so that the above codes can be rewritten using public APIs.
```scala
object FooEncoder {
private lazy val _encoder: Encoder[Foo] = Encoders.product[Foo]()
implicit def encoder: Encoder[Foo] = _encoder.makeCopy
}
```
The method name is consistent with `TreeNode.makeCopy`.
## How was this patch tested?
Jenkins
Closes#25209 from zsxwing/encoder-copy.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Implements the `REPLACE TABLE` and `REPLACE TABLE AS SELECT` logical plans. `REPLACE TABLE` is now a valid operation in spark-sql provided that the tables being modified are managed by V2 catalogs.
This also introduces an atomic mix-in that table catalogs can choose to implement. Table catalogs can now implement `TransactionalTableCatalog`. The semantics of this API are that table creation and replacement can be "staged" and then "committed".
On the execution of `REPLACE TABLE AS SELECT`, `REPLACE TABLE`, and `CREATE TABLE AS SELECT`, if the catalog implements transactional operations, the physical plan will use said functionality. Otherwise, these operations fall back on non-atomic variants. For `REPLACE TABLE` in particular, the usage of non-atomic operations can unfortunately lead to inconsistent state.
## How was this patch tested?
Unit tests - multiple additions to `DataSourceV2SQLSuite`.
Closes#24798 from mccheah/spark-27724.
Authored-by: mcheah <mcheah@palantir.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
When a `ScalaUDF` returns a value which overflows, currently it returns null regardless of the value of the config `spark.sql.decimalOperations.nullOnOverflow`.
The PR makes it respect the above-mentioned config and behave accordingly.
## How was this patch tested?
added UT
Closes#25144 from mgaido91/SPARK-28369.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pr proposes to add a prefix '*' to non-nullable attribute names in PlanTestBase.comparePlans failures. In the current master, nullability mismatches might generate the same error message for left/right logical plans like this;
```
// This failure message was extracted from #24765
- constraints should be inferred from aliased literals *** FAILED ***
== FAIL: Plans do not match ===
!'Join Inner, (two#0 = a#0) 'Join Inner, (two#0 = a#0)
:- Filter (isnotnull(a#0) AND (2 <=> a#0)) :- Filter (isnotnull(a#0) AND (2 <=> a#0))
: +- LocalRelation <empty>, [a#0, b#0, c#0] : +- LocalRelation <empty>, [a#0, b#0, c#0]
+- Project [2 AS two#0] +- Project [2 AS two#0]
+- LocalRelation <empty>, [a#0, b#0, c#0] +- LocalRelation <empty>, [a#0, b#0, c#0] (PlanTest.scala:145)
```
With this pr, this error message is changed to one below;
```
- constraints should be inferred from aliased literals *** FAILED ***
== FAIL: Plans do not match ===
!'Join Inner, (*two#0 = a#0) 'Join Inner, (*two#0 = *a#0)
:- Filter (isnotnull(a#0) AND (2 <=> a#0)) :- Filter (isnotnull(a#0) AND (2 <=> a#0))
: +- LocalRelation <empty>, [a#0, b#0, c#0] : +- LocalRelation <empty>, [a#0, b#0, c#0]
+- Project [2 AS two#0] +- Project [2 AS two#0]
+- LocalRelation <empty>, [a#0, b#0, c#0] +- LocalRelation <empty>, [a#0, b#0, c#0] (PlanTest.scala:145)
```
## How was this patch tested?
N/A
Closes#25213 from maropu/MarkForNullability.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
The optimize rule `PushDownPredicate` has been combined into `PushDownPredicates`, update the comment that references the old rule.
## How was this patch tested?
N/A
Closes#25207 from jiangxb1987/comment.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Performance issue using explode was found when a complex field contains huge array is to get duplicated as the number of exploded array elements. Given example:
```scala
val df = spark.sparkContext.parallelize(Seq(("1",
Array.fill(M)({
val i = math.random
(i.toString, (i + 1).toString, (i + 2).toString, (i + 3).toString)
})))).toDF("col", "arr")
.selectExpr("col", "struct(col, arr) as st")
.selectExpr("col", "st.col as col1", "explode(st.arr) as arr_col")
```
The explode causes `st` to be duplicated as many as the exploded elements.
Benchmarks it:
```
[info] Java HotSpot(TM) 64-Bit Server VM 1.8.0_202-b08 on Mac OS X 10.14.4
[info] Intel(R) Core(TM) i7-8750H CPU 2.20GHz
[info] generate big nested struct array: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] generate big nested struct array wholestage off 52668 53162 699 0.0 877803.4 1.0X
[info] generate big nested struct array wholestage on 47261 49093 1125 0.0 787690.2 1.1X
[info]
```
The query plan:
```
== Physical Plan ==
Project [col#508, st#512.col AS col1#515, arr_col#519]
+- Generate explode(st#512.arr), [col#508, st#512], false, [arr_col#519]
+- Project [_1#503 AS col#508, named_struct(col, _1#503, arr, _2#504) AS st#512]
+- SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(assertnotnull(input[0, scala.Tuple2, true]))._1, true, false) AS _1#503, mapobjects(MapObjects_loopValue84, MapObjects_loopIsNull84, ObjectType(class scala.Tuple4), if (isnull(lambdavariable(MapObjects_loopValue84, MapObjects_loopIsNull84, ObjectType(class scala.Tuple4), true))) null else named_struct(_1, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue84, MapObjects_loopIsNull84, ObjectType(class scala.Tuple4), true))._1, true, false), _2, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue84, MapObjects_loopIsNull84, ObjectType(class scala.Tuple4), true))._2, true, false), _3, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue84, MapObjects_loopIsNull84, ObjectType(class scala.Tuple4), true))._3, true, false), _4, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue84, MapObjects_loopIsNull84, ObjectType(class scala.Tuple4), true))._4, true, false)), knownnotnull(assertnotnull(input[0, scala.Tuple2, true]))._2, None) AS _2#504]
+- Scan[obj#534]
```
This patch takes nested column pruning approach to prune unnecessary nested fields. It adds a projection of the needed nested fields as aliases on the child of `Generate`, and substitutes them by alias attributes on the projection on top of `Generate`.
Benchmarks it after the change:
```
[info] Java HotSpot(TM) 64-Bit Server VM 1.8.0_202-b08 on Mac OS X 10.14.4
[info] Intel(R) Core(TM) i7-8750H CPU 2.20GHz
[info] generate big nested struct array: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] generate big nested struct array wholestage off 311 331 28 0.2 5188.6 1.0X
[info] generate big nested struct array wholestage on 297 312 15 0.2 4947.3 1.0X
[info]
```
The query plan:
```
== Physical Plan ==
Project [col#592, _gen_alias_608#608 AS col1#599, arr_col#603]
+- Generate explode(st#596.arr), [col#592, _gen_alias_608#608], false, [arr_col#603]
+- Project [_1#587 AS col#592, named_struct(col, _1#587, arr, _2#588) AS st#596, _1#587 AS _gen_alias_608#608]
+- SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(assertnotnull(in
put[0, scala.Tuple2, true]))._1, true, false) AS _1#587, mapobjects(MapObjects_loopValue102, MapObjects_loopIsNull102, ObjectType(class scala.Tuple4),
if (isnull(lambdavariable(MapObjects_loopValue102, MapObjects_loopIsNull102, ObjectType(class scala.Tuple4), true))) null else named_struct(_1, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue102, MapObjects_loopIsNull102, ObjectType(class scala.Tuple4), true))._1, true, false), _2, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue102, MapObjects_loopIsNull102, ObjectType(class scala.Tuple4), true))._2, true, false), _3, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue102, MapObjects_loopIsNull102, ObjectType(class scala.Tuple4), true))._3, true, false), _4, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue102, MapObjects_loopIsNull102, ObjectType(class scala.Tuple4), true))._4, true, false)), knownnotnull(assertnotnull(input[0, scala.Tuple2, true]))._2, None) AS _2#588]
+- Scan[obj#586]
```
This behavior is controlled by a SQL config `spark.sql.optimizer.expression.nestedPruning.enabled`.
## How was this patch tested?
Added benchmark.
Closes#24637 from viirya/SPARK-27707.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
The `DateTimeUtils.timestampAddInterval` method was rewritten by using Java 8 time API. To add months and microseconds, I used the `plusMonths()` and `plus()` methods of `ZonedDateTime`. Also the signature of `timestampAddInterval()` was changed to accept an `ZoneId` instance instead of `TimeZone`. Using `ZoneId` allows to avoid the conversion `TimeZone` -> `ZoneId` on every invoke of `timestampAddInterval()`.
## How was this patch tested?
By existing test suites `DateExpressionsSuite`, `TypeCoercionSuite` and `CollectionExpressionsSuite`.
Closes#25173 from MaxGekk/timestamp-add-interval.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
A `Filter` predicate using `PythonUDF` can't be push down into join condition, currently. A predicate like that should be able to push down to join condition. For `PythonUDF`s that can't be evaluated in join condition, `PullOutPythonUDFInJoinCondition` will pull them out later.
An example like:
```scala
val pythonTestUDF = TestPythonUDF(name = "udf")
val left = Seq((1, 2), (2, 3)).toDF("a", "b")
val right = Seq((1, 2), (3, 4)).toDF("c", "d")
val df = left.crossJoin(right).where(pythonTestUDF($"a") === pythonTestUDF($"c"))
```
Query plan before the PR:
```
== Physical Plan ==
*(3) Project [a#2121, b#2122, c#2132, d#2133]
+- *(3) Filter (pythonUDF0#2142 = pythonUDF1#2143)
+- BatchEvalPython [udf(a#2121), udf(c#2132)], [pythonUDF0#2142, pythonUDF1#2143]
+- BroadcastNestedLoopJoin BuildRight, Cross
:- *(1) Project [_1#2116 AS a#2121, _2#2117 AS b#2122]
: +- LocalTableScan [_1#2116, _2#2117]
+- BroadcastExchange IdentityBroadcastMode
+- *(2) Project [_1#2127 AS c#2132, _2#2128 AS d#2133]
+- LocalTableScan [_1#2127, _2#2128]
```
Query plan after the PR:
```
== Physical Plan ==
*(3) Project [a#2121, b#2122, c#2132, d#2133]
+- *(3) BroadcastHashJoin [pythonUDF0#2142], [pythonUDF0#2143], Cross, BuildRight
:- BatchEvalPython [udf(a#2121)], [pythonUDF0#2142]
: +- *(1) Project [_1#2116 AS a#2121, _2#2117 AS b#2122]
: +- LocalTableScan [_1#2116, _2#2117]
+- BroadcastExchange HashedRelationBroadcastMode(List(input[2, string, true]))
+- BatchEvalPython [udf(c#2132)], [pythonUDF0#2143]
+- *(2) Project [_1#2127 AS c#2132, _2#2128 AS d#2133]
+- LocalTableScan [_1#2127, _2#2128]
```
After this PR, the join can use `BroadcastHashJoin`, instead of `BroadcastNestedLoopJoin`.
## How was this patch tested?
Added tests.
Closes#25106 from viirya/pythonudf-join-condition.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Existing random generators in tests produce wide ranges of values that can be out of supported ranges for:
- `DateType`, the valid range is `[0001-01-01, 9999-12-31]`
- `TimestampType` supports values in `[0001-01-01T00:00:00.000000Z, 9999-12-31T23:59:59.999999Z]`
- `CalendarIntervalType` should define intervals for the ranges above.
Dates and timestamps produced by random literal generators are usually out of valid ranges for those types. And tests just check invalid values or values caused by arithmetic overflow.
In the PR, I propose to restrict tested pseudo-random values by valid ranges of `DateType`, `TimestampType` and `CalendarIntervalType`. This should allow to check valid values in test, and avoid wasting time on a priori invalid inputs.
## How was this patch tested?
The changes were checked by `DateExpressionsSuite` and modified `DateTimeUtils.dateAddMonths`:
```Scala
def dateAddMonths(days: SQLDate, months: Int): SQLDate = {
localDateToDays(LocalDate.ofEpochDay(days).plusMonths(months))
}
```
Closes#25166 from MaxGekk/datetime-lit-random-gen.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Idempotence of the `NormalizeFloatingNumbers` rule was broken due to the implementation of `ExtractEquiJoinKeys`. There is no reason that we don't remove `EqualNullSafe` join keys from an equi-join's `otherPredicates`.
## How was this patch tested?
A new UT.
Closes#25126 from yeshengm/spark-28306.
Authored-by: Yesheng Ma <kimi.ysma@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In the PR, I propose to use the `plusMonths()` method of `LocalDate` to add months to a date. This method adds the specified amount to the months field of `LocalDate` in three steps:
1. Add the input months to the month-of-year field
2. Check if the resulting date would be invalid
3. Adjust the day-of-month to the last valid day if necessary
The difference between current behavior and propose one is in handling the last day of month in the original date. For example, adding 1 month to `2019-02-28` will produce `2019-03-28` comparing to the current implementation where the result is `2019-03-31`.
The proposed behavior is implemented in MySQL and PostgreSQL.
## How was this patch tested?
By existing test suites `DateExpressionsSuite`, `DateFunctionsSuite` and `DateTimeUtilsSuite`.
Closes#25153 from MaxGekk/add-months.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Adding support to hyperbolic functions like asinh\acosh\atanh in spark SQL.
Feature parity: https://www.postgresql.org/docs/12/functions-math.html#FUNCTIONS-MATH-HYP-TABLE
The followings are the diffence from PostgreSQL.
```
spark-sql> SELECT acosh(0); (PostgreSQL returns `ERROR: input is out of range`)
NaN
spark-sql> SELECT atanh(2); (PostgreSQL returns `ERROR: input is out of range`)
NaN
```
Teradata has similar behavior as PostgreSQL with out of range input float values - It outputs **Invalid Input: numeric value within range only.**
These newly added asinh/acosh/atanh handles special input(NaN, +-Infinity) in the same way as existing cos/sin/tan/acos/asin/atan in spark. For which input value range is not (-∞, ∞)):
out of range float values: Spark returns NaN and PostgreSQL shows input is out of range
NaN: Spark returns NaN, PostgreSQL also returns NaN
Infinity: Spark return NaN, PostgreSQL shows input is out of range
## How was this patch tested?
```
spark.sql("select asinh(xx)")
spark.sql("select acosh(xx)")
spark.sql("select atanh(xx)")
./build/sbt "testOnly org.apache.spark.sql.MathFunctionsSuite"
./build/sbt "testOnly org.apache.spark.sql.catalyst.expressions.MathExpressionsSuite"
```
Closes#25041 from Tonix517/SPARK-28133.
Authored-by: Tony Zhang <tony.zhang@uber.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR adds compatibility of handling a `WITH` clause within another `WITH` cause. Before this PR these queries retuned `1` while after this PR they return `2` as PostgreSQL does:
```
WITH
t AS (SELECT 1),
t2 AS (
WITH t AS (SELECT 2)
SELECT * FROM t
)
SELECT * FROM t2
```
```
WITH t AS (SELECT 1)
SELECT (
WITH t AS (SELECT 2)
SELECT * FROM t
)
```
As this is an incompatible change, the PR introduces the `spark.sql.legacy.cte.substitution.enabled` flag as an option to restore old behaviour.
## How was this patch tested?
Added new UTs.
Closes#25029 from peter-toth/SPARK-28228.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
There are some hardcoded configs, using config entry to replace them.
## How was this patch tested?
Existing UT
Closes#25059 from WangGuangxin/ConfigEntry.
Authored-by: wangguangxin.cn <wangguangxin.cn@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Implement `ALTER TABLE` for v2 tables:
* Add `AlterTable` logical plan and `AlterTableExec` physical plan
* Convert `ALTER TABLE` parsed plans to `AlterTable` when a v2 catalog is responsible for an identifier
* Validate that columns to alter exist in analyzer checks
* Fix nested type handling in `CatalogV2Util`
## How was this patch tested?
* Add extensive tests in `DataSourceV2SQLSuite`
Closes#24937 from rdblue/SPARK-28139-add-v2-alter-table.
Lead-authored-by: Ryan Blue <blue@apache.org>
Co-authored-by: Ryan Blue <rdblue@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The optimizer rule `NormalizeFloatingNumbers` is not idempotent. It will generate multiple `NormalizeNaNAndZero` and `ArrayTransform` expression nodes for multiple runs. This patch fixed this non-idempotence by adding a marking tag above normalized expressions. It also adds missing UTs for `NormalizeFloatingNumbers`.
## How was this patch tested?
New UTs.
Closes#25080 from yeshengm/spark-28306.
Authored-by: Yesheng Ma <kimi.ysma@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The new adaptive execution framework introduced configuration `spark.sql.runtime.reoptimization.enabled`. We now rename it back to `spark.sql.adaptive.enabled` as the umbrella configuration for adaptive execution.
## How was this patch tested?
Existing tests.
Closes#25102 from carsonwang/renameAE.
Authored-by: Carson Wang <carson.wang@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Fix `stringToDate()` for the formats `yyyy` and `yyyy-[m]m` that assumes there are no additional chars after the last components `yyyy` and `[m]m`. In the PR, I propose to check that entire input was consumed for the formats.
After the fix, the input `1999 08 01` will be invalid because it matches to the pattern `yyyy` but the strings contains additional chars ` 08 01`.
Since Spark 1.6.3 ~ 2.4.3, the behavior is the same.
```
spark-sql> SELECT CAST('1999 08 01' AS DATE);
1999-01-01
```
This PR makes it return NULL like Hive.
```
spark-sql> SELECT CAST('1999 08 01' AS DATE);
NULL
```
## How was this patch tested?
Added new checks to `DateTimeUtilsSuite` for the `1999 08 01` and `1999 08` inputs.
Closes#25097 from MaxGekk/spark-28015-invalid-date-format.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This fixes a problem where it is possible to create a v2 table using the default catalog that cannot be loaded with the session catalog. A session catalog should be used when the v1 catalog is responsible for tables with no catalog in the table identifier.
* Adds a v2 catalog implementation that delegates to the analyzer's SessionCatalog
* Uses the v2 session catalog for CTAS and CreateTable when the provider is a v2 provider and no v2 catalog is in the table identifier
* Updates catalog lookup to always provide the default if it is set for consistent behavior
## How was this patch tested?
* Adds a new test suite for the v2 session catalog that validates the TableCatalog API
* Adds test cases in PlanResolutionSuite to validate the v2 session catalog is used
* Adds test suite for LookupCatalog with a default catalog
Closes#24768 from rdblue/SPARK-27919-add-v2-session-catalog.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The interval conversion behavior is same with the PostgreSQL.
https://github.com/postgres/postgres/blob/REL_12_BETA2/src/test/regress/sql/interval.sql#L180-L203
## How was this patch tested?
UT.
Closes#25000 from lipzhu/SPARK-28107.
Lead-authored-by: Zhu, Lipeng <lipzhu@ebay.com>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Co-authored-by: Lipeng Zhu <lipzhu@icloud.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This is a followup of the discussion in https://github.com/apache/spark/pull/24675#discussion_r286786053
`QueryPlan#references` is an important property. The `ColumnPrunning` rule relies on it.
Some query plan nodes have `Seq[Attribute]` parameter, which is used as its output attributes. For example, leaf nodes, `Generate`, `MapPartitionsInPandas`, etc. These nodes override `producedAttributes` to make `missingInputs` correct.
However, these nodes also need to override `references` to make column pruning work. This PR proposes to exclude `producedAttributes` from the default implementation of `QueryPlan#references`, so that we don't need to override `references` in all these nodes.
Note that, technically we can remove `producedAttributes` and always ask query plan nodes to override `references`. But I do find the code can be simpler with `producedAttributes` in some places, where there is a base class for some specific query plan nodes.
## How was this patch tested?
existing tests
Closes#25052 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR adds some tests converted from `pgSQL/case.sql'` to test UDFs. Please see contribution guide of this umbrella ticket - [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
This PR also contains two minor fixes:
1. Change name of Scala UDF from `UDF:name(...)` to `name(...)` to be consistent with Python'
2. Fix Scala UDF at `IntegratedUDFTestUtils.scala ` to handle `null` in strings.
<details><summary>Diff comparing to 'pgSQL/case.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/pgSQL/case.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-case.sql.out
index fa078d16d6d..55bef64338f 100644
--- a/sql/core/src/test/resources/sql-tests/results/pgSQL/case.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-case.sql.out
-115,7 +115,7 struct<>
-- !query 13
SELECT '3' AS `One`,
CASE
- WHEN 1 < 2 THEN 3
+ WHEN CAST(udf(1 < 2) AS boolean) THEN 3
END AS `Simple WHEN`
-- !query 13 schema
struct<One:string,Simple WHEN:int>
-126,10 +126,10 struct<One:string,Simple WHEN:int>
-- !query 14
SELECT '<NULL>' AS `One`,
CASE
- WHEN 1 > 2 THEN 3
+ WHEN 1 > 2 THEN udf(3)
END AS `Simple default`
-- !query 14 schema
-struct<One:string,Simple default:int>
+struct<One:string,Simple default:string>
-- !query 14 output
<NULL> NULL
-137,17 +137,17 struct<One:string,Simple default:int>
-- !query 15
SELECT '3' AS `One`,
CASE
- WHEN 1 < 2 THEN 3
- ELSE 4
+ WHEN udf(1) < 2 THEN udf(3)
+ ELSE udf(4)
END AS `Simple ELSE`
-- !query 15 schema
-struct<One:string,Simple ELSE:int>
+struct<One:string,Simple ELSE:string>
-- !query 15 output
3 3
-- !query 16
-SELECT '4' AS `One`,
+SELECT udf('4') AS `One`,
CASE
WHEN 1 > 2 THEN 3
ELSE 4
-159,10 +159,10 struct<One:string,ELSE default:int>
-- !query 17
-SELECT '6' AS `One`,
+SELECT udf('6') AS `One`,
CASE
- WHEN 1 > 2 THEN 3
- WHEN 4 < 5 THEN 6
+ WHEN CAST(udf(1 > 2) AS boolean) THEN 3
+ WHEN udf(4) < 5 THEN 6
ELSE 7
END AS `Two WHEN with default`
-- !query 17 schema
-173,7 +173,7 struct<One:string,Two WHEN with default:int>
-- !query 18
SELECT '7' AS `None`,
- CASE WHEN rand() < 0 THEN 1
+ CASE WHEN rand() < udf(0) THEN 1
END AS `NULL on no matches`
-- !query 18 schema
struct<None:string,NULL on no matches:int>
-182,36 +182,36 struct<None:string,NULL on no matches:int>
-- !query 19
-SELECT CASE WHEN 1=0 THEN 1/0 WHEN 1=1 THEN 1 ELSE 2/0 END
+SELECT CASE WHEN CAST(udf(1=0) AS boolean) THEN 1/0 WHEN 1=1 THEN 1 ELSE 2/0 END
-- !query 19 schema
-struct<CASE WHEN (1 = 0) THEN (CAST(1 AS DOUBLE) / CAST(0 AS DOUBLE)) WHEN (1 = 1) THEN CAST(1 AS DOUBLE) ELSE (CAST(2 AS DOUBLE) / CAST(0 AS DOUBLE)) END:double>
+struct<CASE WHEN CAST(udf((1 = 0)) AS BOOLEAN) THEN (CAST(1 AS DOUBLE) / CAST(0 AS DOUBLE)) WHEN (1 = 1) THEN CAST(1 AS DOUBLE) ELSE (CAST(2 AS DOUBLE) / CAST(0 AS DOUBLE)) END:double>
-- !query 19 output
1.0
-- !query 20
-SELECT CASE 1 WHEN 0 THEN 1/0 WHEN 1 THEN 1 ELSE 2/0 END
+SELECT CASE 1 WHEN 0 THEN 1/udf(0) WHEN 1 THEN 1 ELSE 2/0 END
-- !query 20 schema
-struct<CASE WHEN (1 = 0) THEN (CAST(1 AS DOUBLE) / CAST(0 AS DOUBLE)) WHEN (1 = 1) THEN CAST(1 AS DOUBLE) ELSE (CAST(2 AS DOUBLE) / CAST(0 AS DOUBLE)) END:double>
+struct<CASE WHEN (1 = 0) THEN (CAST(1 AS DOUBLE) / CAST(CAST(udf(0) AS DOUBLE) AS DOUBLE)) WHEN (1 = 1) THEN CAST(1 AS DOUBLE) ELSE (CAST(2 AS DOUBLE) / CAST(0 AS DOUBLE)) END:double>
-- !query 20 output
1.0
-- !query 21
-SELECT CASE WHEN i > 100 THEN 1/0 ELSE 0 END FROM case_tbl
+SELECT CASE WHEN i > 100 THEN udf(1/0) ELSE udf(0) END FROM case_tbl
-- !query 21 schema
-struct<CASE WHEN (i > 100) THEN (CAST(1 AS DOUBLE) / CAST(0 AS DOUBLE)) ELSE CAST(0 AS DOUBLE) END:double>
+struct<CASE WHEN (i > 100) THEN udf((cast(1 as double) / cast(0 as double))) ELSE udf(0) END:string>
-- !query 21 output
-0.0
-0.0
-0.0
-0.0
+0
+0
+0
+0
-- !query 22
-SELECT CASE 'a' WHEN 'a' THEN 1 ELSE 2 END
+SELECT CASE 'a' WHEN 'a' THEN udf(1) ELSE udf(2) END
-- !query 22 schema
-struct<CASE WHEN (a = a) THEN 1 ELSE 2 END:int>
+struct<CASE WHEN (a = a) THEN udf(1) ELSE udf(2) END:string>
-- !query 22 output
1
-283,7 +283,7 big
-- !query 27
-SELECT * FROM CASE_TBL WHERE COALESCE(f,i) = 4
+SELECT * FROM CASE_TBL WHERE udf(COALESCE(f,i)) = 4
-- !query 27 schema
struct<i:int,f:double>
-- !query 27 output
-291,7 +291,7 struct<i:int,f:double>
-- !query 28
-SELECT * FROM CASE_TBL WHERE NULLIF(f,i) = 2
+SELECT * FROM CASE_TBL WHERE udf(NULLIF(f,i)) = 2
-- !query 28 schema
struct<i:int,f:double>
-- !query 28 output
-299,10 +299,10 struct<i:int,f:double>
-- !query 29
-SELECT COALESCE(a.f, b.i, b.j)
+SELECT udf(COALESCE(a.f, b.i, b.j))
FROM CASE_TBL a, CASE2_TBL b
-- !query 29 schema
-struct<coalesce(f, CAST(i AS DOUBLE), CAST(j AS DOUBLE)):double>
+struct<udf(coalesce(f, cast(i as double), cast(j as double))):string>
-- !query 29 output
-30.3
-30.3
-332,8 +332,8 struct<coalesce(f, CAST(i AS DOUBLE), CAST(j AS DOUBLE)):double>
-- !query 30
SELECT *
- FROM CASE_TBL a, CASE2_TBL b
- WHERE COALESCE(a.f, b.i, b.j) = 2
+ FROM CASE_TBL a, CASE2_TBL b
+ WHERE udf(COALESCE(a.f, b.i, b.j)) = 2
-- !query 30 schema
struct<i:int,f:double,i:int,j:int>
-- !query 30 output
-342,7 +342,7 struct<i:int,f:double,i:int,j:int>
-- !query 31
-SELECT '' AS Five, NULLIF(a.i,b.i) AS `NULLIF(a.i,b.i)`,
+SELECT udf('') AS Five, NULLIF(a.i,b.i) AS `NULLIF(a.i,b.i)`,
NULLIF(b.i, 4) AS `NULLIF(b.i,4)`
FROM CASE_TBL a, CASE2_TBL b
-- !query 31 schema
-377,7 +377,7 struct<Five:string,NULLIF(a.i,b.i):int,NULLIF(b.i,4):int>
-- !query 32
SELECT '' AS `Two`, *
FROM CASE_TBL a, CASE2_TBL b
- WHERE COALESCE(f,b.i) = 2
+ WHERE CAST(udf(COALESCE(f,b.i) = 2) AS boolean)
-- !query 32 schema
struct<Two:string,i:int,f:double,i:int,j:int>
-- !query 32 output
-388,15 +388,15 struct<Two:string,i:int,f:double,i:int,j:int>
-- !query 33
SELECT CASE
(CASE vol('bar')
- WHEN 'foo' THEN 'it was foo!'
- WHEN vol(null) THEN 'null input'
+ WHEN udf('foo') THEN 'it was foo!'
+ WHEN udf(vol(null)) THEN 'null input'
WHEN 'bar' THEN 'it was bar!' END
)
- WHEN 'it was foo!' THEN 'foo recognized'
- WHEN 'it was bar!' THEN 'bar recognized'
- ELSE 'unrecognized' END
+ WHEN udf('it was foo!') THEN 'foo recognized'
+ WHEN 'it was bar!' THEN udf('bar recognized')
+ ELSE 'unrecognized' END AS col
-- !query 33 schema
-struct<CASE WHEN (CASE WHEN (UDF:vol(bar) = foo) THEN it was foo! WHEN (UDF:vol(bar) = UDF:vol(null)) THEN null input WHEN (UDF:vol(bar) = bar) THEN it was bar! END = it was foo!) THEN foo recognized WHEN (CASE WHEN (UDF:vol(bar) = foo) THEN it was foo! WHEN (UDF:vol(bar) = UDF:vol(null)) THEN null input WHEN (UDF:vol(bar) = bar) THEN it was bar! END = it was bar!) THEN bar recognized ELSE unrecognized END:string>
+struct<col:string>
-- !query 33 output
bar recognized
```
</p>
</details>
https://github.com/apache/spark/pull/25069 contains the same minor fixes as it's required to write the tests.
## How was this patch tested?
Tested as guided in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
Closes#25070 from HyukjinKwon/SPARK-28273.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
We changed our non-standard syntax for `trim` function in #24902 from `TRIM(trimStr, str)` to `TRIM(str, trimStr)` to be compatible with other databases. This pr update the migration guide.
I checked various databases(PostgreSQL, Teradata, Vertica, Oracle, DB2, SQL Server 2019, MySQL, Hive, Presto) and it seems that only PostgreSQL and Presto support this non-standard syntax.
**PostgreSQL**:
```sql
postgres=# select substr(version(), 0, 16), trim('yxTomxx', 'x');
substr | btrim
-----------------+-------
PostgreSQL 11.3 | yxTom
(1 row)
```
**Presto**:
```sql
presto> select trim('yxTomxx', 'x');
_col0
-------
yxTom
(1 row)
```
## How was this patch tested?
manual tests
Closes#24948 from wangyum/SPARK-28093-FOLLOW-UP-DOCS.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR adds some more WITH test cases as a follow-up to https://github.com/apache/spark/pull/24842
## How was this patch tested?
Add new UTs.
Closes#24949 from peter-toth/SPARK-28002-follow-up.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
- Currently, `ExpressionEncoder` does not handle bigdecimal overflow. Round-tripping overflowing java/scala BigDecimal/BigInteger returns null.
- The serializer encode java/scala BigDecimal to to sql Decimal, which still has the underlying data to the former.
- When writing out to UnsafeRow, `changePrecision` will be false and row has null value.
24e1e41648/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/codegen/UnsafeRowWriter.java (L202-L206)
- In [SPARK-23179](https://github.com/apache/spark/pull/20350), an option to throw exception on decimal overflow was introduced.
- This PR adds the option in `ExpressionEncoder` to throw when detecting overflowing BigDecimal/BigInteger before its corresponding Decimal gets written to Row. This gives a consistent behavior between decimal arithmetic on sql expression (DecimalPrecision), and getting decimal from dataframe (RowEncoder)
Thanks to mgaido91 for the very first PR `SPARK-23179` and follow-up discussion on this change.
Thanks to JoshRosen for working with me on this.
## How was this patch tested?
added unit tests
Closes#25016 from mickjermsurawong-stripe/SPARK-28200.
Authored-by: Mick Jermsurawong <mickjermsurawong@stripe.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR proposes to rename `mapPartitionsInPandas` to `mapInPandas` with a separate evaluation type .
Had an offline discussion with rxin, mengxr and cloud-fan
The reason is basically:
1. `SCALAR_ITER` doesn't make sense with `mapPartitionsInPandas`.
2. It cannot share the same Pandas UDF, for instance, at `select` and `mapPartitionsInPandas` unlike `GROUPED_AGG` because iterator's return type is different.
3. `mapPartitionsInPandas` -> `mapInPandas` - see https://github.com/apache/spark/pull/25044#issuecomment-508298552 and https://github.com/apache/spark/pull/25044#issuecomment-508299764
Renaming `SCALAR_ITER` as `MAP_ITER` is abandoned due to 2. reason.
For `XXX_ITER`, it might have to have a different interface in the future if we happen to add other versions of them. But this is an orthogonal topic with `mapPartitionsInPandas`.
## How was this patch tested?
Existing tests should cover.
Closes#25044 from HyukjinKwon/SPARK-28198.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Before this PR inserting into a non-existing table returned a weird error message:
```
sql("INSERT INTO test VALUES (1)").show
org.apache.spark.sql.AnalysisException: unresolved operator 'InsertIntoTable 'UnresolvedRelation [test], false, false;;
'InsertIntoTable 'UnresolvedRelation [test], false, false
+- LocalRelation [col1#4]
```
after this PR the error message becomes:
```
org.apache.spark.sql.AnalysisException: Table not found: test;;
'InsertIntoTable 'UnresolvedRelation [test], false, false
+- LocalRelation [col1#0]
```
## How was this patch tested?
Added a new UT.
Closes#25054 from peter-toth/SPARK-28251.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR adds support of `WITH` clause within a subquery so this query becomes valid:
```
SELECT max(c) FROM (
WITH t AS (SELECT 1 AS c)
SELECT * FROM t
)
```
## How was this patch tested?
Added new UTs.
Closes#24831 from peter-toth/SPARK-19799-2.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This is to implement a ReduceNumShufflePartitions rule in the new adaptive execution framework introduced in #24706. This rule is used to adjust the post shuffle partitions based on the map output statistics.
## How was this patch tested?
Added ReduceNumShufflePartitionsSuite
Closes#24978 from carsonwang/reduceNumShufflePartitions.
Authored-by: Carson Wang <carson.wang@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR makes the predicate pushdown logic in catalyst optimizer more efficient by unifying two existing rules `PushdownPredicates` and `PushPredicateThroughJoin`. Previously pushing down a predicate for queries such as `Filter(Join(Join(Join)))` requires n steps. This patch essentially reduces this to a single pass.
To make this actually work, we need to unify a few rules such as `CombineFilters`, `PushDownPredicate` and `PushDownPrdicateThroughJoin`. Otherwise cases such as `Filter(Join(Filter(Join)))` still requires several passes to fully push down predicates. This unification is done by composing several partial functions, which makes a minimal code change and can reuse existing UTs.
Results show that this optimization can improve the catalyst optimization time by 16.5%. For queries with more joins, the performance is even better. E.g., for TPC-DS q64, the performance boost is 49.2%.
## How was this patch tested?
Existing UTs + new a UT for the new rule.
Closes#24956 from yeshengm/fixed-point-opt.
Authored-by: Yesheng Ma <kimi.ysma@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This pr add `PLACING` to `ansiNonReserved` and add `overlay` and `placing` to `TableIdentifierParserSuite`.
## How was this patch tested?
N/A
Closes#25013 from wangyum/SPARK-28077.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Right now they fail only for inner joins, because we implemented the check when that was the only supported type.
## How was this patch tested?
new unit test
Closes#25023 from jose-torres/changevalidation.
Authored-by: Jose Torres <torres.joseph.f+github@gmail.com>
Signed-off-by: Jose Torres <torres.joseph.f+github@gmail.com>
## What changes were proposed in this pull request?
This PR proposes to add `mapPartitionsInPandas` API to DataFrame by using existing `SCALAR_ITER` as below:
1. Filtering via setting the column
```python
from pyspark.sql.functions import pandas_udf, PandasUDFType
df = spark.createDataFrame([(1, 21), (2, 30)], ("id", "age"))
pandas_udf(df.schema, PandasUDFType.SCALAR_ITER)
def filter_func(iterator):
for pdf in iterator:
yield pdf[pdf.id == 1]
df.mapPartitionsInPandas(filter_func).show()
```
```
+---+---+
| id|age|
+---+---+
| 1| 21|
+---+---+
```
2. `DataFrame.loc`
```python
from pyspark.sql.functions import pandas_udf, PandasUDFType
import pandas as pd
df = spark.createDataFrame([['aa'], ['bb'], ['cc'], ['aa'], ['aa'], ['aa']], ["value"])
pandas_udf(df.schema, PandasUDFType.SCALAR_ITER)
def filter_func(iterator):
for pdf in iterator:
yield pdf.loc[pdf.value.str.contains('^a'), :]
df.mapPartitionsInPandas(filter_func).show()
```
```
+-----+
|value|
+-----+
| aa|
| aa|
| aa|
| aa|
+-----+
```
3. `pandas.melt`
```python
from pyspark.sql.functions import pandas_udf, PandasUDFType
import pandas as pd
df = spark.createDataFrame(
pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
'B': {0: 1, 1: 3, 2: 5},
'C': {0: 2, 1: 4, 2: 6}}))
pandas_udf("A string, variable string, value long", PandasUDFType.SCALAR_ITER)
def filter_func(iterator):
for pdf in iterator:
import pandas as pd
yield pd.melt(pdf, id_vars=['A'], value_vars=['B', 'C'])
df.mapPartitionsInPandas(filter_func).show()
```
```
+---+--------+-----+
| A|variable|value|
+---+--------+-----+
| a| B| 1|
| a| C| 2|
| b| B| 3|
| b| C| 4|
| c| B| 5|
| c| C| 6|
+---+--------+-----+
```
The current limitation of `SCALAR_ITER` is that it doesn't allow different length of result, which is pretty critical in practice - for instance, we cannot simply filter by using Pandas APIs but we merely just map N to N. This PR allows map N to M like flatMap.
This API mimics the way of `mapPartitions` but keeps API shape of `SCALAR_ITER` by allowing different results.
### How does this PR implement?
This PR adds mimics both `dapply` with Arrow optimization and Grouped Map Pandas UDF. At Python execution side, it reuses existing `SCALAR_ITER` code path.
Therefore, externally, we don't introduce any new type of Pandas UDF but internally we use another evaluation type code `205` (`SQL_MAP_PANDAS_ITER_UDF`).
This approach is similar with Pandas' Windows function implementation with Grouped Aggregation Pandas UDF functions - internally we have `203` (`SQL_WINDOW_AGG_PANDAS_UDF`) but externally we just share the same `GROUPED_AGG`.
## How was this patch tested?
Manually tested and unittests were added.
Closes#24997 from HyukjinKwon/scalar-udf-iter.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In SPARK-23179, it has been introduced a flag to control the behavior in case of overflow on decimals. The behavior is: returning `null` when `spark.sql.decimalOperations.nullOnOverflow` (default and traditional Spark behavior); throwing an `ArithmeticException` if that conf is false (according to SQL standards, other DBs behavior).
`MakeDecimal` so far had an ambiguous behavior. In case of codegen mode, it returned `null` as the other operators, but in interpreted mode, it was throwing an `IllegalArgumentException`.
The PR aligns `MakeDecimal`'s behavior with the one of other operators as defined in SPARK-23179. So now both modes return `null` or throw `ArithmeticException` according to `spark.sql.decimalOperations.nullOnOverflow`'s value.
Credits for this PR to mickjermsurawong-stripe who pointed out the wrong behavior in #20350.
## How was this patch tested?
improved UTs
Closes#25010 from mgaido91/SPARK-28201.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pr add two API for [SessionCatalog](df4cb471c9/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala):
```scala
def listTables(db: String, pattern: String, includeLocalTempViews: Boolean): Seq[TableIdentifier]
def listLocalTempViews(pattern: String): Seq[TableIdentifier]
```
Because in some cases `listTables` does not need local temporary view and sometimes only need list local temporary view.
## How was this patch tested?
unit tests
Closes#24995 from wangyum/SPARK-28196.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Currently, ORC's `inferSchema` is implemented as randomly choosing one ORC file and reading its schema.
This PR follows the behavior of Parquet, it implements merge schemas logic by reading all ORC files in parallel through a spark job.
Users can enable merge schema by `spark.read.orc("xxx").option("mergeSchema", "true")` or by setting `spark.sql.orc.mergeSchema` to `true`, the prior one has higher priority.
## How was this patch tested?
tested by UT OrcUtilsSuite.scala
Closes#24043 from WangGuangxin/SPARK-11412.
Lead-authored-by: wangguangxin.cn <wangguangxin.cn@gmail.com>
Co-authored-by: wangguangxin.cn <wangguangxin.cn@bytedance.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This is the first part of [SPARK-27396](https://issues.apache.org/jira/browse/SPARK-27396). This is the minimum set of changes necessary to support a pluggable back end for columnar processing. Follow on JIRAs would cover removing some of the duplication between functionality in this patch and functionality currently covered by things like ColumnarBatchScan.
## How was this patch tested?
I added in a new unit test to cover new code not really covered in other places.
I also did manual testing by implementing two plugins/extensions that take advantage of the new APIs to allow for columnar processing for some simple queries. One version runs on the [CPU](https://gist.github.com/revans2/c3cad77075c4fa5d9d271308ee2f1b1d). The other version run on a GPU, but because it has unreleased dependencies I will not include a link to it yet.
The CPU version I would expect to add in as an example with other documentation in a follow on JIRA
This is contributed on behalf of NVIDIA Corporation.
Closes#24795 from revans2/columnar-basic.
Authored-by: Robert (Bobby) Evans <bobby@apache.org>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
The `OVERLAY` function is a `ANSI` `SQL`.
For example:
```
SELECT OVERLAY('abcdef' PLACING '45' FROM 4);
SELECT OVERLAY('yabadoo' PLACING 'daba' FROM 5);
SELECT OVERLAY('yabadoo' PLACING 'daba' FROM 5 FOR 0);
SELECT OVERLAY('babosa' PLACING 'ubb' FROM 2 FOR 4);
```
The results of the above four `SQL` are:
```
abc45f
yabadaba
yabadabadoo
bubba
```
Note: If the input string is null, then the result is null too.
There are some mainstream database support the syntax.
**PostgreSQL:**
https://www.postgresql.org/docs/11/functions-string.html
**Vertica:** https://www.vertica.com/docs/9.2.x/HTML/Content/Authoring/SQLReferenceManual/Functions/String/OVERLAY.htm?zoom_highlight=overlay
**Oracle:**
https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/UTL_RAW.html#GUID-342E37E7-FE43-4CE1-A0E9-7DAABD000369
**DB2:**
https://www.ibm.com/support/knowledgecenter/SSGMCP_5.3.0/com.ibm.cics.rexx.doc/rexx/overlay.html
There are some show of the PR on my production environment.
```
spark-sql> SELECT OVERLAY('abcdef' PLACING '45' FROM 4);
abc45f
Time taken: 6.385 seconds, Fetched 1 row(s)
spark-sql> SELECT OVERLAY('yabadoo' PLACING 'daba' FROM 5);
yabadaba
Time taken: 0.191 seconds, Fetched 1 row(s)
spark-sql> SELECT OVERLAY('yabadoo' PLACING 'daba' FROM 5 FOR 0);
yabadabadoo
Time taken: 0.186 seconds, Fetched 1 row(s)
spark-sql> SELECT OVERLAY('babosa' PLACING 'ubb' FROM 2 FOR 4);
bubba
Time taken: 0.151 seconds, Fetched 1 row(s)
spark-sql> SELECT OVERLAY(null PLACING '45' FROM 4);
NULL
Time taken: 0.22 seconds, Fetched 1 row(s)
spark-sql> SELECT OVERLAY(null PLACING 'daba' FROM 5);
NULL
Time taken: 0.157 seconds, Fetched 1 row(s)
spark-sql> SELECT OVERLAY(null PLACING 'daba' FROM 5 FOR 0);
NULL
Time taken: 0.254 seconds, Fetched 1 row(s)
spark-sql> SELECT OVERLAY(null PLACING 'ubb' FROM 2 FOR 4);
NULL
Time taken: 0.159 seconds, Fetched 1 row(s)
```
## How was this patch tested?
Exists UT and new UT.
Closes#24918 from beliefer/ansi-sql-overlay.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
Avoid hard-coded config: `spark.sql.globalTempDatabase`.
## How was this patch tested?
N/A
Closes#24979 from wangyum/SPARK-28179.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
For simplicity, all `LambdaVariable`s are globally unique, to avoid any potential conflicts. However, this causes a perf problem: we can never hit codegen cache for encoder expressions that deal with collections (which means they contain `LambdaVariable`).
To overcome this problem, `LambdaVariable` should have per-query unique IDs. This PR does 2 things:
1. refactor `LambdaVariable` to carry an ID, so that it's easier to change the ID.
2. add an optimizer rule to reassign `LambdaVariable` IDs, which are per-query unique.
## How was this patch tested?
new tests
Closes#24735 from cloud-fan/dataset.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
SQL ANSI 2011 states that in case of overflow during arithmetic operations, an exception should be thrown. This is what most of the SQL DBs do (eg. SQLServer, DB2). Hive currently returns NULL (as Spark does) but HIVE-18291 is open to be SQL compliant.
The PR introduce an option to decide which behavior Spark should follow, ie. returning NULL on overflow or throwing an exception.
## How was this patch tested?
added UTs
Closes#20350 from mgaido91/SPARK-23179.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
[PostgreSQL](7c850320d8/src/test/regress/sql/strings.sql (L624)) support another trim pattern: `TRIM(trimStr FROM str)`:
Function | Return Type | Description | Example | Result
--- | --- | --- | --- | ---
trim([leading \| trailing \| both] [characters] from string) | text | Remove the longest string containing only characters from characters (a space by default) from the start, end, or both ends (both is the default) of string | trim(both 'xyz' from 'yxTomxx') | Tom
This pr add support this trim pattern. After this pr. We can support all standard syntax except `TRIM(FROM str)` because it conflicts with our Literals:
```sql
Literals of type 'FROM' are currently not supported.(line 1, pos 12)
== SQL ==
SELECT TRIM(FROM ' SPARK SQL ')
```
PostgreSQL, Vertica and MySQL support this pattern. Teradata, Oracle, DB2, SQL Server, Hive and Presto
**PostgreSQL**:
```
postgres=# SELECT substr(version(), 0, 16), trim('xyz' FROM 'yxTomxx');
substr | btrim
-----------------+-------
PostgreSQL 11.3 | Tom
(1 row)
```
**Vertica**:
```
dbadmin=> SELECT version(), trim('xyz' FROM 'yxTomxx');
version | btrim
------------------------------------+-------
Vertica Analytic Database v9.1.1-0 | Tom
(1 row)
```
**MySQL**:
```
mysql> SELECT version(), trim('xyz' FROM 'yxTomxx');
+-----------+----------------------------+
| version() | trim('xyz' FROM 'yxTomxx') |
+-----------+----------------------------+
| 5.7.26 | yxTomxx |
+-----------+----------------------------+
1 row in set (0.00 sec)
```
More details:
https://www.postgresql.org/docs/11/functions-string.html
## How was this patch tested?
unit tests
Closes#24924 from wangyum/SPARK-28075-2.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The `mapChildren` method in the TreeNode class is commonly used across the whole Spark SQL codebase. In this method, there's a if statement that checks non-empty children. However, there's a cached lazy val `containsChild`, which can avoid unnecessary computation since `containsChild` is used in other methods and therefore constructed anyway.
Benchmark showed that this optimization can improve the whole TPC-DS planning time by 6.8%. There is no regression on any TPC-DS query.
## How was this patch tested?
Existing UTs.
Closes#24925 from yeshengm/treenode-children.
Authored-by: Yesheng Ma <kimi.ysma@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR significantly improves the performance of `UTF8String.replace()` by performing direct replacement over UTF8 bytes instead of decoding those bytes into Java Strings.
In cases where the search string is not found (i.e. no replacements are performed, a case which I expect to be common) this new implementation performs no object allocation or memory copying.
My implementation is modeled after `commons-lang3`'s `StringUtils.replace()` method. As part of my implementation, I needed a StringBuilder / resizable buffer, so I moved `UTF8StringBuilder` from the `catalyst` package to `unsafe`.
## How was this patch tested?
Copied tests from `StringExpressionSuite` to `UTF8StringSuite` and added a couple of new cases.
To evaluate performance, I did some quick local benchmarking by running the following code in `spark-shell` (with Java 1.8.0_191):
```scala
import org.apache.spark.unsafe.types.UTF8String
def benchmark(text: String, search: String, replace: String) {
val utf8Text = UTF8String.fromString(text)
val utf8Search = UTF8String.fromString(search)
val utf8Replace = UTF8String.fromString(replace)
val start = System.currentTimeMillis
var i = 0
while (i < 1000 * 1000 * 100) {
utf8Text.replace(utf8Search, utf8Replace)
i += 1
}
val end = System.currentTimeMillis
println(end - start)
}
benchmark("ABCDEFGH", "DEF", "ZZZZ") // replacement occurs
benchmark("ABCDEFGH", "Z", "") // no replacement occurs
```
On my laptop this took ~54 / ~40 seconds seconds before this patch's changes and ~6.5 / ~3.8 seconds afterwards.
Closes#24707 from JoshRosen/faster-string-replace.
Authored-by: Josh Rosen <rosenville@gmail.com>
Signed-off-by: Josh Rosen <rosenville@gmail.com>
## What changes were proposed in this pull request?
[SPARK-28093](https://issues.apache.org/jira/browse/SPARK-28093) fixed `TRIM/LTRIM/RTRIM('str', 'trimStr')` returns an incorrect value, but that fix introduced a new bug, `TRIM(type trimStr FROM str)` returns an incorrect value. This pr fix this issue.
## How was this patch tested?
unit tests and manual tests:
Before this PR:
```sql
spark-sql> SELECT trim('yxTomxx', 'xyz'), trim(BOTH 'xyz' FROM 'yxTomxx');
Tom z
spark-sql> SELECT trim('xxxbarxxx', 'x'), trim(BOTH 'x' FROM 'xxxbarxxx');
bar
spark-sql> SELECT ltrim('zzzytest', 'xyz'), trim(LEADING 'xyz' FROM 'zzzytest');
test xyz
spark-sql> SELECT ltrim('zzzytestxyz', 'xyz'), trim(LEADING 'xyz' FROM 'zzzytestxyz');
testxyz
spark-sql> SELECT ltrim('xyxXxyLAST WORD', 'xy'), trim(LEADING 'xy' FROM 'xyxXxyLAST WORD');
XxyLAST WORD
spark-sql> SELECT rtrim('testxxzx', 'xyz'), trim(TRAILING 'xyz' FROM 'testxxzx');
test xy
spark-sql> SELECT rtrim('xyztestxxzx', 'xyz'), trim(TRAILING 'xyz' FROM 'xyztestxxzx');
xyztest
spark-sql> SELECT rtrim('TURNERyxXxy', 'xy'), trim(TRAILING 'xy' FROM 'TURNERyxXxy');
TURNERyxX
```
After this PR:
```sql
spark-sql> SELECT trim('yxTomxx', 'xyz'), trim(BOTH 'xyz' FROM 'yxTomxx');
Tom Tom
spark-sql> SELECT trim('xxxbarxxx', 'x'), trim(BOTH 'x' FROM 'xxxbarxxx');
bar bar
spark-sql> SELECT ltrim('zzzytest', 'xyz'), trim(LEADING 'xyz' FROM 'zzzytest');
test test
spark-sql> SELECT ltrim('zzzytestxyz', 'xyz'), trim(LEADING 'xyz' FROM 'zzzytestxyz');
testxyz testxyz
spark-sql> SELECT ltrim('xyxXxyLAST WORD', 'xy'), trim(LEADING 'xy' FROM 'xyxXxyLAST WORD');
XxyLAST WORD XxyLAST WORD
spark-sql> SELECT rtrim('testxxzx', 'xyz'), trim(TRAILING 'xyz' FROM 'testxxzx');
test test
spark-sql> SELECT rtrim('xyztestxxzx', 'xyz'), trim(TRAILING 'xyz' FROM 'xyztestxxzx');
xyztest xyztest
spark-sql> SELECT rtrim('TURNERyxXxy', 'xy'), trim(TRAILING 'xy' FROM 'TURNERyxXxy');
TURNERyxX TURNERyxX
```
And PostgreSQL:
```sql
postgres=# SELECT trim('yxTomxx', 'xyz'), trim(BOTH 'xyz' FROM 'yxTomxx');
btrim | btrim
-------+-------
Tom | Tom
(1 row)
postgres=# SELECT trim('xxxbarxxx', 'x'), trim(BOTH 'x' FROM 'xxxbarxxx');
btrim | btrim
-------+-------
bar | bar
(1 row)
postgres=# SELECT ltrim('zzzytest', 'xyz'), trim(LEADING 'xyz' FROM 'zzzytest');
ltrim | ltrim
-------+-------
test | test
(1 row)
postgres=# SELECT ltrim('zzzytestxyz', 'xyz'), trim(LEADING 'xyz' FROM 'zzzytestxyz');
ltrim | ltrim
---------+---------
testxyz | testxyz
(1 row)
postgres=# SELECT ltrim('xyxXxyLAST WORD', 'xy'), trim(LEADING 'xy' FROM 'xyxXxyLAST WORD');
ltrim | ltrim
--------------+--------------
XxyLAST WORD | XxyLAST WORD
(1 row)
postgres=# SELECT rtrim('testxxzx', 'xyz'), trim(TRAILING 'xyz' FROM 'testxxzx');
rtrim | rtrim
-------+-------
test | test
(1 row)
postgres=# SELECT rtrim('xyztestxxzx', 'xyz'), trim(TRAILING 'xyz' FROM 'xyztestxxzx');
rtrim | rtrim
---------+---------
xyztest | xyztest
(1 row)
postgres=# SELECT rtrim('TURNERyxXxy', 'xy'), trim(TRAILING 'xy' FROM 'TURNERyxXxy');
rtrim | rtrim
-----------+-----------
TURNERyxX | TURNERyxX
(1 row)
```
Closes#24911 from wangyum/SPARK-28109.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Current SQL parser's error message for hyphen-connected identifiers without surrounding backquotes(e.g. hyphen-table) is confusing for end users. A possible approach to tackle this is to explicitly capture these wrong usages in the SQL parser. In this way, the end users can fix these errors more quickly.
For example, for a simple query such as `SELECT * FROM test-table`, the original error message is
```
Error in SQL statement: ParseException:
mismatched input '-' expecting <EOF>(line 1, pos 18)
```
which can be confusing in a large query.
After the fix, the error message is:
```
Error in query:
Possibly unquoted identifier test-table detected. Please consider quoting it with back-quotes as `test-table`(line 1, pos 14)
== SQL ==
SELECT * FROM test-table
--------------^^^
```
which is easier for end users to identify the issue and fix.
We safely augmented the current grammar rule to explicitly capture these error cases. The error handling logic is implemented in the SQL parsing listener `PostProcessor`.
However, note that for cases such as `a - my-func(b)`, the parser can't actually tell whether this should be ``a -`my-func`(b) `` or `a - my - func(b)`. Therefore for these cases, we leave the parser as is. Also, in this patch we only provide better error messages for character-only identifiers.
## How was this patch tested?
Adding new unit tests.
Closes#24749 from yeshengm/hyphen-ident.
Authored-by: Yesheng Ma <kimi.ysma@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The original `references` and `validConstraints` implementations in a few `QueryPlan` and `Expression` classes are methods, which means unnecessary re-computation can happen at times. This PR resolves this problem by making these method `lazy val`s.
As shown in the following chart, the planning time(without cost-based optimization) was dramatically reduced after this optimization.
- The average planning time of TPC-DS queries was reduced by 19.63%.
- The planning time of the most time-consuming TPC-DS query (q64) was reduced by 43.03%.
- The running time for rule-based reordering joins(not cost-based join reordering) optimization, which are common in real-world OLAP queries, was largely reduced.
Detailed stats are listed in the following spreadsheet (we warmed up the queries 5 iterations and then took average of the next 5 iterations).
[Lazy val benchmark.xlsx](https://github.com/apache/spark/files/3303530/Lazy.val.benchmark.xlsx)
## How was this patch tested?
Existing UTs.
Closes#24866 from yeshengm/plannode-micro-opt.
Authored-by: Yesheng Ma <kimi.ysma@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Added an interface for handling hint errors, with a default implementation class that logs warnings in the callbacks.
## How was this patch tested?
Passed existing tests.
Closes#24653 from maryannxue/hint-handler.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
With JDK9+, the generate **bytecode** of `FromUnixTime` raise `java.lang.IncompatibleClassChangeError` due to [JDK-8145148](https://bugs.openjdk.java.net/browse/JDK-8145148) . This is a blocker in [Apache Spark JDK11 Jenkins job](https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.7-jdk-11-ubuntu-testing/). Locally, this is reproducible by the following unit test suite with JDK9+.
```
$ build/sbt "catalyst/testOnly *.DateExpressionsSuite"
...
[info] org.apache.spark.sql.catalyst.expressions.DateExpressionsSuite *** ABORTED *** (23 seconds, 75 milliseconds)
[info] java.lang.IncompatibleClassChangeError: Method org.apache.spark.sql.catalyst.util.TimestampFormatter.apply(Ljava/lang/String;Ljava/time/ZoneId;Ljava/util/Locale;)Lorg/apache/spark/sql/catalyst/util/TimestampFormatter; must be InterfaceMeth
```
This bytecode issue is generated by `Janino` , so we replace `.apply` to `.MODULE$$.apply` and adds test coverage for similar codes.
## How was this patch tested?
Manually with the existing UTs by doing the following with JDK9+.
```
build/sbt "catalyst/testOnly *.DateExpressionsSuite"
```
Actually, this is the last JDK11 error in `catalyst` module. So, we can verify with the following, too.
```
$ build/sbt "project catalyst" test
...
[info] Total number of tests run: 3552
[info] Suites: completed 210, aborted 0
[info] Tests: succeeded 3552, failed 0, canceled 0, ignored 2, pending 0
[info] All tests passed.
[info] Passed: Total 3583, Failed 0, Errors 0, Passed 3583, Ignored 2
[success] Total time: 294 s, completed Jun 16, 2019, 10:15:08 PM
```
Closes#24889 from dongjoon-hyun/SPARK-28072.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
The `TRIM` function accept these patterns:
```sql
TRIM(str)
TRIM(trimStr, str)
TRIM(BOTH trimStr FROM str)
TRIM(LEADING trimStr FROM str)
TRIM(TRAILING trimStr FROM str)
```
This pr add support other three patterns:
```sql
TRIM(BOTH FROM str)
TRIM(LEADING FROM str)
TRIM(TRAILING FROM str)
```
PostgreSQL, Vertica, MySQL, Teradata, Oracle and DB2 support these patterns. Hive, Presto and SQL Server does not support this feature.
**PostgreSQL**:
```sql
postgres=# select substr(version(), 0, 16), trim(BOTH from ' SparkSQL '), trim(LEADING FROM ' SparkSQL '), trim(TRAILING FROM ' SparkSQL ');
substr | btrim | ltrim | rtrim
-----------------+----------+-------------+--------------
PostgreSQL 11.3 | SparkSQL | SparkSQL | SparkSQL
(1 row)
```
**Vertica**:
```
dbadmin=> select version(), trim(BOTH from ' SparkSQL '), trim(LEADING FROM ' SparkSQL '), trim(TRAILING FROM ' SparkSQL ');
version | btrim | ltrim | rtrim
------------------------------------+----------+-------------+--------------
Vertica Analytic Database v9.1.1-0 | SparkSQL | SparkSQL | SparkSQL
(1 row)
```
**MySQL**:
```
mysql> select version(), trim(BOTH from ' SparkSQL '), trim(LEADING FROM ' SparkSQL '), trim(TRAILING FROM ' SparkSQL ');
+-----------+-----------------------------------+--------------------------------------+---------------------------------------+
| version() | trim(BOTH from ' SparkSQL ') | trim(LEADING FROM ' SparkSQL ') | trim(TRAILING FROM ' SparkSQL ') |
+-----------+-----------------------------------+--------------------------------------+---------------------------------------+
| 5.7.26 | SparkSQL | SparkSQL | SparkSQL |
+-----------+-----------------------------------+--------------------------------------+---------------------------------------+
1 row in set (0.01 sec)
```
**Teradata**:
**Oracle**:
**DB2**:
## How was this patch tested?
unit tests
Closes#24891 from wangyum/SPARK-28075.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR aims to replace deprecated `.newInstance()` in DSv2 `Catalogs` and distinguish the plugin class errors more. According to the JDK11 build log, there is no other new instance.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.7-jdk-11-ubuntu-testing/978/consoleFull
SPARK-25984 removes all instances of the deprecated `.newInstance()` usages at Nov 10, 2018, but this was added at SPARK-24252 on March 8, 2019.
## How was this patch tested?
Pass the Jenkins with the updated test case.
Closes#24882 from dongjoon-hyun/SPARK-28063.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Currently `ArrayExists` always returns boolean values (if the arguments are not `null`), but it should follow the three-valued boolean logic:
- `true` if the predicate holds at least one `true`
- otherwise, `null` if the predicate holds `null`
- otherwise, `false`
This behavior change is made to match Postgres' equivalent function `ANY/SOME (array)`'s behavior: https://www.postgresql.org/docs/9.6/functions-comparisons.html#AEN21174
## How was this patch tested?
Modified tests and existing tests.
Closes#24873 from ueshin/issues/SPARK-28052/fix_exists.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Allow Pandas UDF to take an iterator of pd.Series or an iterator of tuple of pd.Series.
Note the UDF input args will be always one iterator:
* if the udf take only column as input, the iterator's element will be pd.Series (corresponding to the column values batch)
* if the udf take multiple columns as inputs, the iterator's element will be a tuple composed of multiple `pd.Series`s, each one corresponding to the multiple columns as inputs (keep the same order). For example:
```
pandas_udf("int", PandasUDFType.SCALAR_ITER)
def the_udf(iterator):
for col1_batch, col2_batch in iterator:
yield col1_batch + col2_batch
df.select(the_udf("col1", "col2"))
```
The udf above will add col1 and col2.
I haven't add unit tests, but manually tests show it works fine. So it is ready for first pass review.
We can test several typical cases:
```
from pyspark.sql import SparkSession
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.functions import udf
from pyspark.taskcontext import TaskContext
df = spark.createDataFrame([(1, 20), (3, 40)], ["a", "b"])
pandas_udf("int", PandasUDFType.SCALAR_ITER)
def fi1(it):
pid = TaskContext.get().partitionId()
print("DBG: fi1: do init stuff, partitionId=" + str(pid))
for batch in it:
yield batch + 100
print("DBG: fi1: do close stuff, partitionId=" + str(pid))
pandas_udf("int", PandasUDFType.SCALAR_ITER)
def fi2(it):
pid = TaskContext.get().partitionId()
print("DBG: fi2: do init stuff, partitionId=" + str(pid))
for batch in it:
yield batch + 10000
print("DBG: fi2: do close stuff, partitionId=" + str(pid))
pandas_udf("int", PandasUDFType.SCALAR_ITER)
def fi3(it):
pid = TaskContext.get().partitionId()
print("DBG: fi3: do init stuff, partitionId=" + str(pid))
for x, y in it:
yield x + y * 10 + 100000
print("DBG: fi3: do close stuff, partitionId=" + str(pid))
pandas_udf("int", PandasUDFType.SCALAR)
def fp1(x):
return x + 1000
udf("int")
def fu1(x):
return x + 10
# test select "pandas iter udf/pandas udf/sql udf" expressions at the same time.
# Note this case the `fi1("a"), fi2("b"), fi3("a", "b")` will generate only one plan,
# and `fu1("a")`, `fp1("a")` will generate another two separate plans.
df.select(fi1("a"), fi2("b"), fi3("a", "b"), fu1("a"), fp1("a")).show()
# test chain two pandas iter udf together
# Note this case `fi2(fi1("a"))` will generate only one plan
# Also note the init stuff/close stuff call order will be like:
# (debug output following)
# DBG: fi2: do init stuff, partitionId=0
# DBG: fi1: do init stuff, partitionId=0
# DBG: fi1: do close stuff, partitionId=0
# DBG: fi2: do close stuff, partitionId=0
df.select(fi2(fi1("a"))).show()
# test more complex chain
# Note this case `fi1("a"), fi2("a")` will generate one plan,
# and `fi3(fi1_output, fi2_output)` will generate another plan
df.select(fi3(fi1("a"), fi2("a"))).show()
```
## How was this patch tested?
To be added.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24643 from WeichenXu123/pandas_udf_iter.
Lead-authored-by: WeichenXu <weichen.xu@databricks.com>
Co-authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
Migrate Parquet to File Data Source V2
## How was this patch tested?
Unit test
Closes#24327 from gengliangwang/parquetV2.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Implemented a new SparkPlan that executes the query adaptively. It splits the query plan into independent stages and executes them in order according to their dependencies. The query stage materializes its output at the end. When one stage completes, the data statistics of the materialized output will be used to optimize the remainder of the query.
The adaptive mode is off by default, when turned on, user can see "AdaptiveSparkPlan" as the top node of a query or sub-query. The inner plan of "AdaptiveSparkPlan" is subject to change during query execution but becomes final once the execution is complete. Whether the inner plan is final is included in the EXPLAIN string. Below is an example of the EXPLAIN plan before and after execution:
Query:
```
SELECT * FROM testData JOIN testData2 ON key = a WHERE value = '1'
```
Before execution:
```
== Physical Plan ==
AdaptiveSparkPlan(isFinalPlan=false)
+- SortMergeJoin [key#13], [a#23], Inner
:- Sort [key#13 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(key#13, 5)
: +- Filter (isnotnull(value#14) AND (value#14 = 1))
: +- SerializeFromObject [knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData, true])).key AS key#13, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData, true])).value, true, false) AS value#14]
: +- Scan[obj#12]
+- Sort [a#23 ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(a#23, 5)
+- SerializeFromObject [knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData2, true])).a AS a#23, knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData2, true])).b AS b#24]
+- Scan[obj#22]
```
After execution:
```
== Physical Plan ==
AdaptiveSparkPlan(isFinalPlan=true)
+- *(1) BroadcastHashJoin [key#13], [a#23], Inner, BuildLeft
:- BroadcastQueryStage 2
: +- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)))
: +- ShuffleQueryStage 0
: +- Exchange hashpartitioning(key#13, 5)
: +- *(1) Filter (isnotnull(value#14) AND (value#14 = 1))
: +- *(1) SerializeFromObject [knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData, true])).key AS key#13, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData, true])).value, true, false) AS value#14]
: +- Scan[obj#12]
+- ShuffleQueryStage 1
+- Exchange hashpartitioning(a#23, 5)
+- *(1) SerializeFromObject [knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData2, true])).a AS a#23, knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData2, true])).b AS b#24]
+- Scan[obj#22]
```
Credit also goes to carsonwang and cloud-fan
## How was this patch tested?
Added new UT.
Closes#24706 from maryannxue/aqe.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: herman <herman@databricks.com>
## What changes were proposed in this pull request?
This PR adds support of column aliasing in a CTE so this query becomes valid:
```
WITH t(x) AS (SELECT 1)
SELECT * FROM t WHERE x = 1
```
## How was this patch tested?
Added new UTs.
Closes#24842 from peter-toth/SPARK-28002.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Implemented the `clone` method for `TreeNode` based on `mapChildren`.
## How was this patch tested?
Added new UT.
Closes#24876 from maryannxue/treenode-clone.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: herman <herman@databricks.com>
## What changes were proposed in this pull request?
Currently, SparkSQL can support interval format like this.
```sql
SELECT INTERVAL '0 23:59:59.155' DAY TO SECOND
```
Like Presto/Teradata, this PR aims to support grammar like below.
```sql
SELECT INTERVAL '23:59:59.155' HOUR TO SECOND
```
Although we can add a new function for this pattern, we had better extend the existing code to handle a missing day case. So, the following is also supported.
```sql
SELECT INTERVAL '23:59:59.155' DAY TO SECOND
SELECT INTERVAL '1 23:59:59.155' HOUR TO SECOND
```
Currently Vertica/Teradata/Postgresql/SQL Server have fully support of below interval functions.
- interval ... year to month
- interval ... day to hour
- interval ... day to minute
- interval ... day to second
- interval ... hour to minute
- interval ... hour to second
- interval ... minute to second
https://www.vertica.com/docs/9.2.x/HTML/Content/Authoring/SQLReferenceManual/LanguageElements/Literals/interval-qualifier.htmdf1a699e5b/src/test/regress/sql/interval.sql (L180-L203)https://docs.teradata.com/reader/S0Fw2AVH8ff3MDA0wDOHlQ/KdCtT3pYFo~_enc8~kGKVwhttps://docs.microsoft.com/en-us/sql/odbc/reference/appendixes/interval-literals?view=sql-server-2017
## How was this patch tested?
Pass the Jenkins with the updated test cases.
Closes#24472 from lipzhu/SPARK-27578.
Lead-authored-by: Zhu, Lipeng <lipzhu@ebay.com>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Co-authored-by: Lipeng Zhu <lipzhu@icloud.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Support multi-catalog in the following SELECT code paths:
- SELECT * FROM catalog.db.tbl
- TABLE catalog.db.tbl
- JOIN or UNION tables from different catalogs
- SparkSession.table("catalog.db.tbl")
- CTE relation
- View text
## How was this patch tested?
New unit tests.
All existing unit tests in catalyst and sql core.
Closes#24741 from jzhuge/SPARK-27322-pr.
Authored-by: John Zhuge <jzhuge@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
`NestedColumnAliasing` rule covers `GetStructField` only, currently. It means that some nested field extraction expressions aren't pruned. For example, if only accessing a nested field in an array of struct (`GetArrayStructFields`), this column isn't pruned.
This patch extends the rule to cover general nested field cases, including `GetArrayStructFields`.
## How was this patch tested?
Added tests.
Closes#24599 from viirya/nested-pruning-extract-value.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
Current Spark SQL parser can have pretty confusing error messages when parsing an incorrect SELECT SQL statement. The proposed fix has the following effect.
BEFORE:
```
spark-sql> SELECT * FROM test WHERE x NOT NULL;
Error in query:
mismatched input 'FROM' expecting {<EOF>, 'CLUSTER', 'DISTRIBUTE', 'EXCEPT', 'GROUP', 'HAVING', 'INTERSECT', 'LATERAL', 'LIMIT', 'ORDER', 'MINUS', 'SORT', 'UNION', 'WHERE', 'WINDOW'}(line 1, pos 9)
== SQL ==
SELECT * FROM test WHERE x NOT NULL
---------^^^
```
where in fact the error message should be hinted to be near `NOT NULL`.
AFTER:
```
spark-sql> SELECT * FROM test WHERE x NOT NULL;
Error in query:
mismatched input 'NOT' expecting {<EOF>, 'AND', 'CLUSTER', 'DISTRIBUTE', 'EXCEPT', 'GROUP', 'HAVING', 'INTERSECT', 'LIMIT', 'OR', 'ORDER', 'MINUS', 'SORT', 'UNION', 'WINDOW'}(line 1, pos 27)
== SQL ==
SELECT * FROM test WHERE x NOT NULL
---------------------------^^^
```
In fact, this problem is brought by some problematic Spark SQL grammar. There are two kinds of SELECT statements that are supported by Hive (and thereby supported in SparkSQL):
* `FROM table SELECT blahblah SELECT blahblah`
* `SELECT blah FROM table`
*Reference* [HiveQL single-from stmt grammar](https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/parse/HiveParser.g)
It is fine when these two SELECT syntaxes are supported separately. However, since we are currently supporting these two kinds of syntaxes in a single ANTLR rule, this can be problematic and therefore leading to confusing parser errors. This is because when a SELECT clause was parsed, it can't tell whether the following FROM clause actually belongs to it or is just the beginning of a new `FROM table SELECT *` statement.
## What changes were proposed in this pull request?
1. Modify ANTLR grammar to fix the above-mentioned problem. This fix is important because the previous problematic grammar does affect a lot of real-world queries. Due to the previous problematic and messy grammar, we refactored the grammar related to `querySpecification`.
2. Modify `AstBuilder` to have separate visitors for `SELECT ... FROM ...` and `FROM ... SELECT ...` statements.
3. Drop the `FROM table` statement, which is supported by accident and is actually parsed in the wrong code path. Both Hive and Presto do not support this syntax.
## How was this patch tested?
Existing UTs and new UTs.
Closes#24809 from yeshengm/parser-refactor.
Authored-by: Yesheng Ma <kimi.ysma@gmail.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
## What changes were proposed in this pull request?
The new Spark ThriftServer SparkGetTablesOperation implemented in https://github.com/apache/spark/pull/22794 does a catalog.getTableMetadata request for every table. This can get very slow for large schemas (~50ms per table with an external Hive metastore).
Hive ThriftServer GetTablesOperation uses HiveMetastoreClient.getTableObjectsByName to get table information in bulk, but we don't expose that through our APIs that go through Hive -> HiveClientImpl (HiveClient) -> HiveExternalCatalog (ExternalCatalog) -> SessionCatalog.
If we added and exposed getTableObjectsByName through our catalog APIs, we could resolve that performance problem in SparkGetTablesOperation.
## How was this patch tested?
Add UT
Closes#24774 from LantaoJin/SPARK-27899.
Authored-by: LantaoJin <jinlantao@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Handle the case when ParsedStatement subclass has a Map field but not of type Map[String, String].
In ParsedStatement.productIterator, `case mapArg: Map[_, _]` can match any Map type due to type erasure, thus causing `asInstanceOf[Map[String, String]]` to throw ClassCastException.
The following test reproduces the issue:
```
case class TestStatement(p: Map[String, Int]) extends ParsedStatement {
override def output: Seq[Attribute] = Nil
override def children: Seq[LogicalPlan] = Nil
}
TestStatement(Map("abc" -> 1)).toString
```
Changing the code to `case mapArg: Map[String, String]` will not help due to type erasure. As a matter of fact, compiler gives this warning:
```
Warning:(41, 18) non-variable type argument String in type pattern
scala.collection.immutable.Map[String,String] (the underlying of Map[String,String])
is unchecked since it is eliminated by erasure
case mapArg: Map[String, String] =>
```
## How was this patch tested?
Add 2 unit tests.
Closes#24800 from jzhuge/SPARK-27947.
Authored-by: John Zhuge <jzhuge@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
For caseWhen Object canonicalized is not handled
for e.g let's consider below CaseWhen Object
val attrRef = AttributeReference("ACCESS_CHECK", StringType)()
val caseWhenObj1 = CaseWhen(Seq((attrRef, Literal("A"))))
caseWhenObj1.canonicalized **ouput** is as below
CASE WHEN ACCESS_CHECK#0 THEN A END (**Before Fix)**
**After Fix** : CASE WHEN none#0 THEN A END
So when there will be aliasref like below statements, semantic equals will fail. Sematic equals returns true if the canonicalized form of both the expressions are same.
val attrRef = AttributeReference("ACCESS_CHECK", StringType)()
val aliasAttrRef = attrRef.withName("access_check")
val caseWhenObj1 = CaseWhen(Seq((attrRef, Literal("A"))))
val caseWhenObj2 = CaseWhen(Seq((aliasAttrRef, Literal("A"))))
**assert(caseWhenObj2.semanticEquals(caseWhenObj1.semanticEquals) fails**
**caseWhenObj1.canonicalized**
Before Fix:CASE WHEN ACCESS_CHECK#0 THEN A END
After Fix: CASE WHEN none#0 THEN A END
**caseWhenObj2.canonicalized**
Before Fix:CASE WHEN access_check#0 THEN A END
After Fix: CASE WHEN none#0 THEN A END
## How was this patch tested?
Added UT
Closes#24766 from sandeep-katta/caseWhenIssue.
Authored-by: sandeep katta <sandeep.katta2007@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
When using `from_avro` to deserialize avro data to catalyst StructType format, if `ConvertToLocalRelation` is applied at the time, `from_avro` produces only the last value (overriding previous values).
The cause is `AvroDeserializer` reuses output row for StructType. Normally, it should be fine in Spark SQL. But `ConvertToLocalRelation` just uses `InterpretedProjection` to project local rows. `InterpretedProjection` creates new row for each output thro, it includes the same nested row object from `AvroDeserializer`. By the end, converted local relation has only last value.
I think there're two possible options:
1. Make `AvroDeserializer` output new row for StructType.
2. Use `InterpretedMutableProjection` in `ConvertToLocalRelation` and call `copy()` on output rows.
Option 2 is chose because previously `ConvertToLocalRelation` also creates new rows, this `InterpretedMutableProjection` + `copy()` shoudn't bring too much performance penalty. `ConvertToLocalRelation` should be arguably less critical, compared with `AvroDeserializer`.
## How was this patch tested?
Added test.
Closes#24805 from viirya/SPARK-27798.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Add extractors for v2 catalog transforms.
These extractors are used to match transforms that are equivalent to Spark's internal case classes. This makes it easier to work with v2 transforms.
## How was this patch tested?
Added test suite for the new extractors.
Closes#24812 from rdblue/SPARK-27965-add-transform-extractors.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In PR https://github.com/apache/spark/pull/24365, we pass in the partitionBy columns as options in `DataFrameWriter`. To make this change less intrusive for a patch release, we added a feature flag `LEGACY_PASS_PARTITION_BY_AS_OPTIONS` with the default to be false.
For 3.0, we should just do the correct behavior for DSV1, i.e., always passing partitionBy as options, and remove this legacy feature flag.
## How was this patch tested?
Existing tests.
Closes#24784 from liwensun/SPARK-27453-default.
Authored-by: liwensun <liwen.sun@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Move methods that implement v2 catalog operations to CatalogV2Util so they can be used in #24768.
## How was this patch tested?
Behavior is validated by existing tests.
Closes#24813 from rdblue/SPARK-27964-add-catalog-v2-util.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This moves parsing logic for `ALTER TABLE` into Catalyst and adds parsed logical plans for alter table changes that use multi-part identifiers. This PR is similar to SPARK-27108, PR #24029, that created parsed logical plans for create and CTAS.
* Create parsed logical plans
* Move parsing logic into Catalyst's AstBuilder
* Convert to DataSource plans in DataSourceResolution
* Parse `ALTER TABLE ... SET LOCATION ...` separately from the partition variant
* Parse `ALTER TABLE ... ALTER COLUMN ... [TYPE dataType] [COMMENT comment]` [as discussed on the dev list](http://apache-spark-developers-list.1001551.n3.nabble.com/DISCUSS-Syntax-for-table-DDL-td25197.html#a25270)
* Parse `ALTER TABLE ... RENAME COLUMN ... TO ...`
* Parse `ALTER TABLE ... DROP COLUMNS ...`
## How was this patch tested?
* Added new tests in Catalyst's `DDLParserSuite`
* Moved converted plan tests from SQL `DDLParserSuite` to `PlanResolutionSuite`
* Existing tests for regressions
Closes#24723 from rdblue/SPARK-27857-add-alter-table-statements-in-catalyst.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Currently we are in a strange status that, some data source v2 interfaces(catalog related) are in sql/catalyst, some data source v2 interfaces(Table, ScanBuilder, DataReader, etc.) are in sql/core.
I don't see a reason to keep data source v2 API in 2 modules. If we should pick one module, I think sql/catalyst is the one to go.
Catalyst module already has some user-facing stuff like DataType, Row, etc. And we have to update `Analyzer` and `SessionCatalog` to support the new catalog plugin, which needs to be in the catalyst module.
This PR can solve the problem we have in https://github.com/apache/spark/pull/24246
## How was this patch tested?
existing tests
Closes#24416 from cloud-fan/move.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This updates CTE substitution to avoid needing to run all resolution rules on each substituted expression. Running resolution rules was previously used to avoid infinite recursion. In the updated rule, CTE plans are substituted as sub-queries from right to left. Using this scope-based order, it is not necessary to replace multiple CTEs at the same time using `resolveOperatorsDown`. Instead, `resolveOperatorsUp` is used to replace each CTE individually.
By resolving using `resolveOperatorsUp`, this no longer needs to run all analyzer rules on each substituted expression. Previously, this was done to apply `ResolveRelations`, which would throw an `AnalysisException` for all unresolved relations so that unresolved relations that may cause recursive substitutions were not left in the plan. Because this is no longer needed, `ResolveRelations` no longer needs to throw `AnalysisException` and resolution can be done in multiple rules.
## How was this patch tested?
Existing tests in `SQLQueryTestSuite`, `cte.sql`.
Closes#24763 from rdblue/SPARK-27909-fix-cte-substitution.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
I was quite surprised by the following behavior:
`SELECT str_to_map('1:2|3:4', '|')`
vs
`SELECT str_to_map(replace('1:2|3:4', '|', ','))`
The documentation does not make clear at all what's going on here, but a [dive into the source code shows](fa0d4bf699/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala (L461-L466)) that `split` is being used and in turn the interpretation of `split`'s arguments as RegEx is clearly documented.
## What changes were proposed in this pull request?
Documentation clarification
## How was this patch tested?
N/A
Closes#23888 from MichaelChirico/patch-2.
Authored-by: Michael Chirico <michaelchirico4@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR aims to add `interceptParseException` test utility function to `AnalysisTest` to reduce the duplications of `intercept` functions.
## How was this patch tested?
Pass the Jenkins with the updated test suites.
Closes#24769 from dongjoon-hyun/SPARK-27920.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
`spark.sql.execution.arrow.enabled` was added when we add PySpark arrow optimization.
Later, in the current master, SparkR arrow optimization was added and it's controlled by the same configuration `spark.sql.execution.arrow.enabled`.
There look two issues about this:
1. `spark.sql.execution.arrow.enabled` in PySpark was added from 2.3.0 whereas SparkR optimization was added 3.0.0. The stability is different so it's problematic when we change the default value for one of both optimization first.
2. Suppose users want to share some JVM by PySpark and SparkR. They are currently forced to use the optimization for all or none if the configuration is set globally.
This PR proposes two separate configuration groups for PySpark and SparkR about Arrow optimization:
- Deprecate `spark.sql.execution.arrow.enabled`
- Add `spark.sql.execution.arrow.pyspark.enabled` (fallback to `spark.sql.execution.arrow.enabled`)
- Add `spark.sql.execution.arrow.sparkr.enabled`
- Deprecate `spark.sql.execution.arrow.fallback.enabled`
- Add `spark.sql.execution.arrow.pyspark.fallback.enabled ` (fallback to `spark.sql.execution.arrow.fallback.enabled`)
Note that `spark.sql.execution.arrow.maxRecordsPerBatch` is used within JVM side for both.
Note that `spark.sql.execution.arrow.fallback.enabled` was added due to behaviour change. We don't need it in SparkR - SparkR side has the automatic fallback.
## How was this patch tested?
Manually tested and some unittests were added.
Closes#24700 from HyukjinKwon/separate-sparkr-arrow.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
As outlined in the JIRA by JoshRosen, our conversion mechanism from catalyst types to scala ones is pretty inefficient for primitive data types. Indeed, in these cases, most of the times we are adding useless calls to `identity` function or anyway to functions which return the same value. Using the information we have when we generate the code, we can avoid most of these overheads.
## How was this patch tested?
Here is a simple test which shows the benefit that this PR can bring:
```
test("SPARK-27684: perf evaluation") {
val intLongUdf = ScalaUDF(
(a: Int, b: Long) => a + b, LongType,
Literal(1) :: Literal(1L) :: Nil,
true :: true :: Nil,
nullable = false)
val plan = generateProject(
MutableProjection.create(Alias(intLongUdf, s"udf")() :: Nil),
intLongUdf)
plan.initialize(0)
var i = 0
val N = 100000000
val t0 = System.nanoTime()
while(i < N) {
plan(EmptyRow).get(0, intLongUdf.dataType)
plan(EmptyRow).get(0, intLongUdf.dataType)
plan(EmptyRow).get(0, intLongUdf.dataType)
plan(EmptyRow).get(0, intLongUdf.dataType)
plan(EmptyRow).get(0, intLongUdf.dataType)
plan(EmptyRow).get(0, intLongUdf.dataType)
plan(EmptyRow).get(0, intLongUdf.dataType)
plan(EmptyRow).get(0, intLongUdf.dataType)
plan(EmptyRow).get(0, intLongUdf.dataType)
plan(EmptyRow).get(0, intLongUdf.dataType)
i += 1
}
val t1 = System.nanoTime()
println(s"Avg time: ${(t1 - t0).toDouble / N} ns")
}
```
The output before the patch is:
```
Avg time: 51.27083294 ns
```
after, we get:
```
Avg time: 11.85874227 ns
```
which is ~5X faster.
Moreover a benchmark has been added for Scala UDF. The output after the patch can be seen in this PR, before the patch, the output was:
```
================================================================================================
UDF with mixed input types
================================================================================================
Java HotSpot(TM) 64-Bit Server VM 1.8.0_152-b16 on Mac OS X 10.13.6
Intel(R) Core(TM) i7-4558U CPU 2.80GHz
long/nullable int/string to string: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
long/nullable int/string to string wholestage off 257 287 42 0,4 2569,5 1,0X
long/nullable int/string to string wholestage on 158 172 18 0,6 1579,0 1,6X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_152-b16 on Mac OS X 10.13.6
Intel(R) Core(TM) i7-4558U CPU 2.80GHz
long/nullable int/string to option: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
long/nullable int/string to option wholestage off 104 107 5 1,0 1037,9 1,0X
long/nullable int/string to option wholestage on 80 92 12 1,2 804,0 1,3X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_152-b16 on Mac OS X 10.13.6
Intel(R) Core(TM) i7-4558U CPU 2.80GHz
long/nullable int to primitive: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
long/nullable int to primitive wholestage off 71 76 7 1,4 712,1 1,0X
long/nullable int to primitive wholestage on 64 71 6 1,6 636,2 1,1X
================================================================================================
UDF with primitive types
================================================================================================
Java HotSpot(TM) 64-Bit Server VM 1.8.0_152-b16 on Mac OS X 10.13.6
Intel(R) Core(TM) i7-4558U CPU 2.80GHz
long/nullable int to string: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
long/nullable int to string wholestage off 60 60 0 1,7 600,3 1,0X
long/nullable int to string wholestage on 55 64 8 1,8 551,2 1,1X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_152-b16 on Mac OS X 10.13.6
Intel(R) Core(TM) i7-4558U CPU 2.80GHz
long/nullable int to option: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
long/nullable int to option wholestage off 66 73 9 1,5 663,0 1,0X
long/nullable int to option wholestage on 30 32 2 3,3 300,7 2,2X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_152-b16 on Mac OS X 10.13.6
Intel(R) Core(TM) i7-4558U CPU 2.80GHz
long/nullable int/string to primitive: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
long/nullable int/string to primitive wholestage off 32 35 5 3,2 316,7 1,0X
long/nullable int/string to primitive wholestage on 41 68 17 2,4 414,0 0,8X
```
The improvements are particularly visible in the second case, ie. when only primitive types are used as inputs.
Closes#24636 from mgaido91/SPARK-27684.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Josh Rosen <rosenville@gmail.com>
## What changes were proposed in this pull request?
Support DROP TABLE from V2 catalogs.
Move DROP TABLE into catalyst.
Move parsing tests for DROP TABLE/VIEW to PlanResolutionSuite to validate existing behavior.
Add new tests fo catalyst parser suite.
Separate DROP VIEW into different code path from DROP TABLE.
Move DROP VIEW into catalyst as a new operator.
Add a meaningful exception to indicate view is not currently supported in v2 catalog.
## How was this patch tested?
New unit tests.
Existing unit tests in catalyst and sql core.
Closes#24686 from jzhuge/SPARK-27813-pr.
Authored-by: John Zhuge <jzhuge@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pr wrap all `PrintWriter` with `Utils.tryWithResource` to prevent resource leak.
## How was this patch tested?
Existing test
Closes#24739 from wangyum/SPARK-27875.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Require the lookup function with interface LookupCatalog. Rationale is in the review comments below.
Make `Analyzer` abstract. BaseSessionStateBuilder and HiveSessionStateBuilder implements lookupCatalog with a call to SparkSession.catalog().
Existing test cases and those that don't need catalog lookup will use a newly added `TestAnalyzer` with a default lookup function that throws` CatalogNotFoundException("No catalog lookup function")`.
Rewrote the unit test for LookupCatalog to demonstrate the interface can be used anywhere, not just Analyzer.
Removed Analyzer parameter `lookupCatalog` because we can override in the following manner:
```
new Analyzer() {
override def lookupCatalog(name: String): CatalogPlugin = ???
}
```
## How was this patch tested?
Existing unit tests.
Closes#24689 from jzhuge/SPARK-26946-follow.
Authored-by: John Zhuge <jzhuge@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/22104 , we create the python-eval nodes at the end of the optimization phase, which causes a problem.
After the main optimization batch, Filter and Project nodes are usually pushed to the bottom, near the scan node. However, if we extract Python UDFs from Filter/Project, and create a python-eval node under Filter/Project, it will break column pruning/filter pushdown of the scan node.
There are some hacks in the `ExtractPythonUDFs` rule, to duplicate the column pruning and filter pushdown logic. However, it has some bugs as demonstrated in the new test case(only column pruning is broken). This PR removes the hacks and re-apply the column pruning and filter pushdown rules explicitly.
**Before:**
```
...
== Analyzed Logical Plan ==
a: bigint
Project [a#168L]
+- Filter dummyUDF(a#168L)
+- Relation[a#168L,b#169L] parquet
== Optimized Logical Plan ==
Project [a#168L]
+- Project [a#168L, b#169L]
+- Filter pythonUDF0#174: boolean
+- BatchEvalPython [dummyUDF(a#168L)], [a#168L, b#169L, pythonUDF0#174]
+- Relation[a#168L,b#169L] parquet
== Physical Plan ==
*(2) Project [a#168L]
+- *(2) Project [a#168L, b#169L]
+- *(2) Filter pythonUDF0#174: boolean
+- BatchEvalPython [dummyUDF(a#168L)], [a#168L, b#169L, pythonUDF0#174]
+- *(1) FileScan parquet [a#168L,b#169L] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/private/var/folders/_1/bzcp960d0hlb988k90654z2w0000gp/T/spark-798bae3c-a2..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<a:bigint,b:bigint>
```
**After:**
```
...
== Analyzed Logical Plan ==
a: bigint
Project [a#168L]
+- Filter dummyUDF(a#168L)
+- Relation[a#168L,b#169L] parquet
== Optimized Logical Plan ==
Project [a#168L]
+- Filter pythonUDF0#174: boolean
+- BatchEvalPython [dummyUDF(a#168L)], [pythonUDF0#174]
+- Project [a#168L]
+- Relation[a#168L,b#169L] parquet
== Physical Plan ==
*(2) Project [a#168L]
+- *(2) Filter pythonUDF0#174: boolean
+- BatchEvalPython [dummyUDF(a#168L)], [pythonUDF0#174]
+- *(1) FileScan parquet [a#168L] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/private/var/folders/_1/bzcp960d0hlb988k90654z2w0000gp/T/spark-9500cafb-78..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<a:bigint>
```
## How was this patch tested?
new test
Closes#24675 from cloud-fan/python.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Each time, when I write a complex CREATE DATABASE/VIEW statements, I have to open the .g4 file to find the EXACT order of clauses in CREATE TABLE statement. When the order is not right, I will get A strange confusing error message generated from ANTLR4.
The original g4 grammar for CREATE VIEW is
```
CREATE [OR REPLACE] [[GLOBAL] TEMPORARY] VIEW [db_name.]view_name
[(col_name1 [COMMENT col_comment1], ...)]
[COMMENT table_comment]
[TBLPROPERTIES (key1=val1, key2=val2, ...)]
AS select_statement
```
The proposal is to make the following clauses order insensitive.
```
[COMMENT table_comment]
[TBLPROPERTIES (key1=val1, key2=val2, ...)]
```
–
The original g4 grammar for CREATE DATABASE is
```
CREATE (DATABASE|SCHEMA) [IF NOT EXISTS] db_name
[COMMENT comment_text]
[LOCATION path]
[WITH DBPROPERTIES (key1=val1, key2=val2, ...)]
```
The proposal is to make the following clauses order insensitive.
```
[COMMENT comment_text]
[LOCATION path]
[WITH DBPROPERTIES (key1=val1, key2=val2, ...)]
```
## How was this patch tested?
By adding new unit tests to test duplicate clauses and modifying some existing unit tests to test whether those clauses are actually order insensitive
Closes#24681 from yeshengm/create-view-parser.
Authored-by: Yesheng Ma <kimi.ysma@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This fix prevents the rule EliminateResolvedHint from being applied again if it's already applied.
## How was this patch tested?
Added new UT.
Closes#24692 from maryannxue/eliminatehint-bug.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
