## What changes were proposed in this pull request?
This patch fixes a null handling bug in EqualNullSafe's code generation.
## How was this patch tested?
Updated unit test so they would fail without the fix.
Closes#12628.
Author: Reynold Xin <rxin@databricks.com>
Author: Arash Nabili <arash@levyx.com>
Closes#12799 from rxin/equalnullsafe.
The previous subquery PRs did not include support for pushing subqueries used in filters (`WHERE`/`HAVING`) down. This PR adds this support. For example :
```scala
range(0, 10).registerTempTable("a")
range(5, 15).registerTempTable("b")
range(7, 25).registerTempTable("c")
range(3, 12).registerTempTable("d")
val plan = sql("select * from a join b on a.id = b.id left join c on c.id = b.id where a.id in (select id from d)")
plan.explain(true)
```
Leads to the following Analyzed & Optimized plans:
```
== Parsed Logical Plan ==
...
== Analyzed Logical Plan ==
id: bigint, id: bigint, id: bigint
Project [id#0L,id#4L,id#8L]
+- Filter predicate-subquery#16 [(id#0L = id#12L)]
: +- SubqueryAlias predicate-subquery#16 [(id#0L = id#12L)]
: +- Project [id#12L]
: +- SubqueryAlias d
: +- Range 3, 12, 1, 8, [id#12L]
+- Join LeftOuter, Some((id#8L = id#4L))
:- Join Inner, Some((id#0L = id#4L))
: :- SubqueryAlias a
: : +- Range 0, 10, 1, 8, [id#0L]
: +- SubqueryAlias b
: +- Range 5, 15, 1, 8, [id#4L]
+- SubqueryAlias c
+- Range 7, 25, 1, 8, [id#8L]
== Optimized Logical Plan ==
Join LeftOuter, Some((id#8L = id#4L))
:- Join Inner, Some((id#0L = id#4L))
: :- Join LeftSemi, Some((id#0L = id#12L))
: : :- Range 0, 10, 1, 8, [id#0L]
: : +- Range 3, 12, 1, 8, [id#12L]
: +- Range 5, 15, 1, 8, [id#4L]
+- Range 7, 25, 1, 8, [id#8L]
== Physical Plan ==
...
```
I have also taken the opportunity to move quite a bit of code around:
- Rewriting subqueris and pulling out correlated predicated from subqueries has been moved into the analyzer. The analyzer transforms `Exists` and `InSubQuery` into `PredicateSubquery` expressions. A PredicateSubquery exposes the 'join' expressions and the proper references. This makes things like type coercion, optimization and planning easier to do.
- I have added support for `Aggregate` plans in subqueries. Any correlated expressions will be added to the grouping expressions. I have removed support for `Union` plans, since pulling in an outer reference from beneath a Union has no value (a filtered value could easily be part of another Union child).
- Resolution of subqueries is now done using `OuterReference`s. These are used to wrap any outer reference; this makes the identification of these references easier, and also makes dealing with duplicate attributes in the outer and inner plans easier. The resolution of subqueries initially used a resolution loop which would alternate between calling the analyzer and trying to resolve the outer references. We now use a dedicated analyzer which uses a special rule for outer reference resolution.
These changes are a stepping stone for enabling correlated scalar subqueries, enabling all Hive tests & allowing us to use predicate subqueries anywhere.
Current tests and added test cases in FilterPushdownSuite.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12720 from hvanhovell/SPARK-14858.
#### What changes were proposed in this pull request?
Replaces a logical `Except` operator with a `Left-anti Join` operator. This way, we can take advantage of all the benefits of join implementations (e.g. managed memory, code generation, broadcast joins).
```SQL
SELECT a1, a2 FROM Tab1 EXCEPT SELECT b1, b2 FROM Tab2
==> SELECT DISTINCT a1, a2 FROM Tab1 LEFT ANTI JOIN Tab2 ON a1<=>b1 AND a2<=>b2
```
Note:
1. This rule is only applicable to EXCEPT DISTINCT. Do not use it for EXCEPT ALL.
2. This rule has to be done after de-duplicating the attributes; otherwise, the enerated
join conditions will be incorrect.
This PR also corrects the existing behavior in Spark. Before this PR, the behavior is like
```SQL
test("except") {
val df_left = Seq(1, 2, 2, 3, 3, 4).toDF("id")
val df_right = Seq(1, 3).toDF("id")
checkAnswer(
df_left.except(df_right),
Row(2) :: Row(2) :: Row(4) :: Nil
)
}
```
After this PR, the result is corrected. We strictly follow the SQL compliance of `Except Distinct`.
#### How was this patch tested?
Modified and added a few test cases to verify the optimization rule and the results of operators.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12736 from gatorsmile/exceptByAntiJoin.
## What changes were proposed in this pull request?
This PR aims to implement decimal aggregation optimization for window queries by improving existing `DecimalAggregates`. Historically, `DecimalAggregates` optimizer is designed to transform general `sum/avg(decimal)`, but it breaks recently added windows queries like the followings. The following queries work well without the current `DecimalAggregates` optimizer.
**Sum**
```scala
scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").head
java.lang.RuntimeException: Unsupported window function: MakeDecimal((sum(UnscaledValue(a#31)),mode=Complete,isDistinct=false),12,1)
scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#23]
: +- INPUT
+- Window [MakeDecimal((sum(UnscaledValue(a#21)),mode=Complete,isDistinct=false),12,1) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#23]
+- Exchange SinglePartition, None
+- Generate explode([1.0,2.0]), false, false, [a#21]
+- Scan OneRowRelation[]
```
**Average**
```scala
scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").head
java.lang.RuntimeException: Unsupported window function: cast(((avg(UnscaledValue(a#40)),mode=Complete,isDistinct=false) / 10.0) as decimal(6,5))
scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#44]
: +- INPUT
+- Window [cast(((avg(UnscaledValue(a#42)),mode=Complete,isDistinct=false) / 10.0) as decimal(6,5)) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#44]
+- Exchange SinglePartition, None
+- Generate explode([1.0,2.0]), false, false, [a#42]
+- Scan OneRowRelation[]
```
After this PR, those queries work fine and new optimized physical plans look like the followings.
**Sum**
```scala
scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#35]
: +- INPUT
+- Window [MakeDecimal((sum(UnscaledValue(a#33)),mode=Complete,isDistinct=false) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING),12,1) AS sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#35]
+- Exchange SinglePartition, None
+- Generate explode([1.0,2.0]), false, false, [a#33]
+- Scan OneRowRelation[]
```
**Average**
```scala
scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#47]
: +- INPUT
+- Window [cast(((avg(UnscaledValue(a#45)),mode=Complete,isDistinct=false) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) / 10.0) as decimal(6,5)) AS avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#47]
+- Exchange SinglePartition, None
+- Generate explode([1.0,2.0]), false, false, [a#45]
+- Scan OneRowRelation[]
```
In this PR, *SUM over window* pattern matching is based on the code of hvanhovell ; he should be credited for the work he did.
## How was this patch tested?
Pass the Jenkins tests (with newly added testcases)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12421 from dongjoon-hyun/SPARK-14664.
## What changes were proposed in this pull request?
#12625 exposed a new user-facing conf interface in `SparkSession`. This patch adds a catalog interface.
## How was this patch tested?
See `CatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#12713 from andrewor14/user-facing-catalog.
## What changes were proposed in this pull request?
This patch changes UnresolvedFunction and UnresolvedGenerator to use a FunctionIdentifier rather than just a String for function name. Also changed SessionCatalog to accept FunctionIdentifier in lookupFunction.
## How was this patch tested?
Updated related unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12659 from rxin/SPARK-14888.
#### What changes were proposed in this pull request?
For performance, predicates can be pushed through Window if and only if the following conditions are satisfied:
1. All the expressions are part of window partitioning key. The expressions can be compound.
2. Deterministic
#### How was this patch tested?
TODO:
- [X] DSL needs to be modified for window
- [X] more tests will be added.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#11635 from gatorsmile/pushPredicateThroughWindow.
!< means not less than which is equivalent to >=
!> means not greater than which is equivalent to <=
I'd to create a PR to support these two operators.
I've added new test cases in: DataFrameSuite, ExpressionParserSuite, JDBCSuite, PlanParserSuite, SQLQuerySuite
dilipbiswal viirya gatorsmile
Author: jliwork <jiali@us.ibm.com>
Closes#12316 from jliwork/SPARK-14548.
## What changes were proposed in this pull request?
Currently, `OptimizeIn` optimizer replaces `In` expression into `InSet` expression if the size of set is greater than a constant, 10.
This issue aims to make a configuration `spark.sql.optimizer.inSetConversionThreshold` for that.
After this PR, `OptimizerIn` is configurable.
```scala
scala> sql("select a in (1,2,3) from (select explode(array(1,2)) a) T").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [a#7 IN (1,2,3) AS (a IN (1, 2, 3))#8]
: +- INPUT
+- Generate explode([1,2]), false, false, [a#7]
+- Scan OneRowRelation[]
scala> sqlContext.setConf("spark.sql.optimizer.inSetConversionThreshold", "2")
scala> sql("select a in (1,2,3) from (select explode(array(1,2)) a) T").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [a#16 INSET (1,2,3) AS (a IN (1, 2, 3))#17]
: +- INPUT
+- Generate explode([1,2]), false, false, [a#16]
+- Scan OneRowRelation[]
```
## How was this patch tested?
Pass the Jenkins tests (with a new testcase)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12562 from dongjoon-hyun/SPARK-14796.
## What changes were proposed in this pull request?
Currently, a column could be resolved wrongly if there are columns from both outer table and subquery have the same name, we should only resolve the attributes that can't be resolved within subquery. They may have same exprId than other attributes in subquery, so we should create alias for them.
Also, the column in IN subquery could have same exprId, we should create alias for them.
## How was this patch tested?
Added regression tests. Manually tests TPCDS Q70 and Q95, work well after this patch.
Author: Davies Liu <davies@databricks.com>
Closes#12539 from davies/fix_subquery.
### What changes were proposed in this pull request?
TPCDS Q90 fails to parse because it uses a reserved keyword as an Identifier; `AT` was used as an alias for one of the subqueries. `AT` is not a reserved keyword and should have been registerd as a in the `nonReserved` rule.
In order to prevent this from happening again I have added tests for all keywords that are non-reserved in Hive. See the `nonReserved`, `sql11ReservedKeywordsUsedAsCastFunctionName` & `sql11ReservedKeywordsUsedAsIdentifier` rules in https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/parse/IdentifiersParser.g.
### How was this patch tested?
Added tests to for all Hive non reserved keywords to `TableIdentifierParserSuite`.
cc davies
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12537 from hvanhovell/SPARK-14762.
## What changes were proposed in this pull request?
Implement some `hashCode` and `equals` together in order to enable the scalastyle.
This is a first batch, I will continue to implement them but I wanted to know your thoughts.
Author: Joan <joan@goyeau.com>
Closes#12157 from joan38/SPARK-6429-HashCode-Equals.
## What changes were proposed in this pull request?
Code generation for complex type, `CreateArray`, `CreateMap`, `CreateStruct`, `CreateNamedStruct`, exceeds JVM size limit for large elements.
We should split generated code into multiple `apply` functions if the complex types have large elements, like `UnsafeProjection` or others for large expressions.
## How was this patch tested?
I added some tests to check if the generated codes for the expressions exceed or not.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#12559 from ueshin/issues/SPARK-14793.
## What changes were proposed in this pull request?
This patch moves analyze table parsing into SparkSqlAstBuilder and removes HiveSqlAstBuilder.
In order to avoid extensive refactoring, I created a common trait for CatalogRelation and MetastoreRelation, and match on that. In the future we should probably just consolidate the two into a single thing so we don't need this common trait.
## How was this patch tested?
Updated unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12584 from rxin/SPARK-14821.
`MutableProjection` is not thread-safe and we won't use it in multiple threads. I think the reason that we return `() => MutableProjection` is not about thread safety, but to save the costs of generating code when we need same but individual mutable projections.
However, I only found one place that use this [feature](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala#L122-L123), and comparing to the troubles it brings, I think we should generate `MutableProjection` directly instead of return a function.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#7373 from cloud-fan/project.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-14600
This PR makes `Expand.output` have different attributes from the grouping attributes produced by the underlying `Project`, as they have different meaning, so that we can safely push down filter through `Expand`
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12496 from cloud-fan/expand.
## What changes were proposed in this pull request?
Enable ScalaReflection and User Defined Types for plain Scala classes.
This involves the move of `schemaFor` from `ScalaReflection` trait (which is Runtime and Compile time (macros) reflection) to the `ScalaReflection` object (runtime reflection only) as I believe this code wouldn't work at compile time anyway as it manipulates `Class`'s that are not compiled yet.
## How was this patch tested?
Unit test
Author: Joan <joan@goyeau.com>
Closes#12149 from joan38/SPARK-13929-Scala-reflection.
### What changes were proposed in this pull request?
This PR adds support for in/exists predicate subqueries to Spark. Predicate sub-queries are used as a filtering condition in a query (this is the only supported use case). A predicate sub-query comes in two forms:
- `[NOT] EXISTS(subquery)`
- `[NOT] IN (subquery)`
This PR is (loosely) based on the work of davies (https://github.com/apache/spark/pull/10706) and chenghao-intel (https://github.com/apache/spark/pull/9055). They should be credited for the work they did.
### How was this patch tested?
Modified parsing unit tests.
Added tests to `org.apache.spark.sql.SQLQuerySuite`
cc rxin, davies & chenghao-intel
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12306 from hvanhovell/SPARK-4226.
When `Await.result` throws an exception which originated from a different thread, the resulting stacktrace doesn't include the path leading to the `Await.result` call itself, making it difficult to identify the impact of these exceptions. For example, I've seen cases where broadcast cleaning errors propagate to the main thread and crash it but the resulting stacktrace doesn't include any of the main thread's code, making it difficult to pinpoint which exception crashed that thread.
This patch addresses this issue by explicitly catching, wrapping, and re-throwing exceptions that are thrown by `Await.result`.
I tested this manually using 16b31c8251, a patch which reproduces an issue where an RPC exception which occurs while unpersisting RDDs manages to crash the main thread without any useful stacktrace, and verified that informative, full stacktraces were generated after applying the fix in this PR.
/cc rxin nongli yhuai anabranch
Author: Josh Rosen <joshrosen@databricks.com>
Closes#12433 from JoshRosen/wrap-and-rethrow-await-exceptions.
## What changes were proposed in this pull request?
This PR tries to separate the serialization and deserialization logic from object operators, so that it's easier to eliminate unnecessary serializations in optimizer.
Typed aggregate related operators are special, they will deserialize the input row to multiple objects and it's difficult to simply use a deserializer operator to abstract it, so we still mix the deserialization logic there.
## How was this patch tested?
existing tests and new test in `EliminateSerializationSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12260 from cloud-fan/encoder.
## What changes were proposed in this pull request?
We currently disable codegen for `CaseWhen` if the number of branches is greater than 20 (in CaseWhen.MAX_NUM_CASES_FOR_CODEGEN). It would be better if this value is a non-public config defined in SQLConf.
## How was this patch tested?
Pass the Jenkins tests (including a new testcase `Support spark.sql.codegen.maxCaseBranches option`)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12353 from dongjoon-hyun/SPARK-14577.
## What changes were proposed in this pull request?
The `doGenCode` method currently takes in an `ExprCode`, mutates it and returns the java code to evaluate the given expression. It should instead just return a new `ExprCode` to avoid passing around mutable objects during code generation.
## How was this patch tested?
Existing Tests
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12483 from sameeragarwal/new-exprcode-2.
## What changes were proposed in this pull request?
Per rxin's suggestions, this patch renames `s/gen/genCode` and `s/genCode/doGenCode` to better reflect the semantics of these 2 function calls.
## How was this patch tested?
N/A (refactoring only)
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12475 from sameeragarwal/gencode.
## What changes were proposed in this pull request?
Currently, `HiveTypeCoercion.IfCoercion` removes all predicates whose return-type are null. However, some UDFs need evaluations because they are designed to throw exceptions. This PR fixes that to preserve the predicates. Also, `assert_true` is implemented as Spark SQL function.
**Before**
```
scala> sql("select if(assert_true(false),2,3)").head
res2: org.apache.spark.sql.Row = [3]
```
**After**
```
scala> sql("select if(assert_true(false),2,3)").head
... ASSERT_TRUE ...
```
**Hive**
```
hive> select if(assert_true(false),2,3);
OK
Failed with exception java.io.IOException:org.apache.hadoop.hive.ql.metadata.HiveException: ASSERT_TRUE(): assertion failed.
```
## How was this patch tested?
Pass the Jenkins tests (including a new testcase in `HivePlanTest`)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12340 from dongjoon-hyun/SPARK-14580.
## What changes were proposed in this pull request?
There are many operations that are currently not supported in the streaming execution. For example:
- joining two streams
- unioning a stream and a batch source
- sorting
- window functions (not time windows)
- distinct aggregates
Furthermore, executing a query with a stream source as a batch query should also fail.
This patch add an additional step after analysis in the QueryExecution which will check that all the operations in the analyzed logical plan is supported or not.
## How was this patch tested?
unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#12246 from tdas/SPARK-14473.
## What changes were proposed in this pull request?
This PR aims to add `bound` function (aka Banker's round) by extending current `round` implementation. [Hive supports `bround` since 1.3.0.](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF)
**Hive (1.3 ~ 2.0)**
```
hive> select round(2.5), bround(2.5);
OK
3.0 2.0
```
**After this PR**
```scala
scala> sql("select round(2.5), bround(2.5)").head
res0: org.apache.spark.sql.Row = [3,2]
```
## How was this patch tested?
Pass the Jenkins tests (with extended tests).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12376 from dongjoon-hyun/SPARK-14614.
## What changes were proposed in this pull request?
We currently hard code the max number of optimizer/analyzer iterations to 100. This patch makes it configurable. While I'm at it, I also added the SessionCatalog to the optimizer, so we can use information there in optimization.
## How was this patch tested?
Updated unit tests to reflect the change.
Author: Reynold Xin <rxin@databricks.com>
Closes#12434 from rxin/SPARK-14677.
## What changes were proposed in this pull request?
This PR moves `CurrentDatabase` from sql/hive package to sql/catalyst. It also adds the function description, which looks like the following.
```
scala> sqlContext.sql("describe function extended current_database").collect.foreach(println)
[Function: current_database]
[Class: org.apache.spark.sql.execution.command.CurrentDatabase]
[Usage: current_database() - Returns the current database.]
[Extended Usage:
> SELECT current_database()]
```
## How was this patch tested?
Existing tests
Author: Yin Huai <yhuai@databricks.com>
Closes#12424 from yhuai/SPARK-14668.
## What changes were proposed in this pull request?
Current `LikeSimplification` handles the following four rules.
- 'a%' => expr.StartsWith("a")
- '%b' => expr.EndsWith("b")
- '%a%' => expr.Contains("a")
- 'a' => EqualTo("a")
This PR adds the following rule.
- 'a%b' => expr.Length() >= 2 && expr.StartsWith("a") && expr.EndsWith("b")
Here, 2 is statically calculated from "a".size + "b".size.
**Before**
```
scala> sql("select a from (select explode(array('abc','adc')) a) T where a like 'a%c'").explain()
== Physical Plan ==
WholeStageCodegen
: +- Filter a#5 LIKE a%c
: +- INPUT
+- Generate explode([abc,adc]), false, false, [a#5]
+- Scan OneRowRelation[]
```
**After**
```
scala> sql("select a from (select explode(array('abc','adc')) a) T where a like 'a%c'").explain()
== Physical Plan ==
WholeStageCodegen
: +- Filter ((length(a#5) >= 2) && (StartsWith(a#5, a) && EndsWith(a#5, c)))
: +- INPUT
+- Generate explode([abc,adc]), false, false, [a#5]
+- Scan OneRowRelation[]
```
## How was this patch tested?
Pass the Jenkins tests (including new testcase).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12312 from dongjoon-hyun/SPARK-14545.
## What changes were proposed in this pull request?
Right now, filter push down only works with Project, Aggregate, Generate and Join, they can't be pushed through many other plans.
This PR added support for Union, Intersect, Except and all unary plans.
## How was this patch tested?
Added tests.
Author: Davies Liu <davies@databricks.com>
Closes#12342 from davies/filter_hint.
## What changes were proposed in this pull request?
This patch implements the `CREATE TABLE` command using the `SessionCatalog`. Previously we handled only `CTAS` and `CREATE TABLE ... USING`. This requires us to refactor `CatalogTable` to accept various fields (e.g. bucket and skew columns) and pass them to Hive.
WIP: Note that I haven't verified whether this actually works yet! But I believe it does.
## How was this patch tested?
Tests will come in a future commit.
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12271 from andrewor14/create-table-ddl.
## What changes were proposed in this pull request?
Currently, Union only takes intersect of the constraints from it's children, all others are dropped, we should try to merge them together.
This PR try to merge the constraints that have the same reference but came from different children, for example: `a > 10` and `a < 100` could be merged as `a > 10 || a < 100`.
## How was this patch tested?
Added more cases in existing test.
Author: Davies Liu <davies@databricks.com>
Closes#12328 from davies/union_const.
## What changes were proposed in this pull request?
According to the [Spark Code Style Guide](https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide) and [Scala Style Guide](http://docs.scala-lang.org/style/control-structures.html#curlybraces), we had better enforce the following rule.
```
case: Always omit braces in case clauses.
```
This PR makes a new ScalaStyle rule, 'OmitBracesInCase', and enforces it to the code.
## How was this patch tested?
Pass the Jenkins tests (including Scala style checking)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12280 from dongjoon-hyun/SPARK-14508.
## What changes were proposed in this pull request?
This implements a few alter table partition commands using the `SessionCatalog`. In particular:
```
ALTER TABLE ... ADD PARTITION ...
ALTER TABLE ... DROP PARTITION ...
ALTER TABLE ... RENAME PARTITION ... TO ...
```
The following operations are not supported, and an `AnalysisException` with a helpful error message will be thrown if the user tries to use them:
```
ALTER TABLE ... EXCHANGE PARTITION ...
ALTER TABLE ... ARCHIVE PARTITION ...
ALTER TABLE ... UNARCHIVE PARTITION ...
ALTER TABLE ... TOUCH ...
ALTER TABLE ... COMPACT ...
ALTER TABLE ... CONCATENATE
MSCK REPAIR TABLE ...
```
## How was this patch tested?
`DDLSuite`, `DDLCommandSuite` and `HiveDDLCommandSuite`
Author: Andrew Or <andrew@databricks.com>
Closes#12220 from andrewor14/alter-partition-ddl.
## What changes were proposed in this pull request?
We can simplifies binary comparisons with semantically-equal operands:
1. Replace '<=>' with 'true' literal.
2. Replace '=', '<=', and '>=' with 'true' literal if both operands are non-nullable.
3. Replace '<' and '>' with 'false' literal if both operands are non-nullable.
For example, the following example plan
```
scala> sql("SELECT * FROM (SELECT explode(array(1,2,3)) a) T WHERE a BETWEEN a AND a+7").explain()
...
: +- Filter ((a#59 >= a#59) && (a#59 <= (a#59 + 7)))
...
```
will be optimized into the following.
```
: +- Filter (a#47 <= (a#47 + 7))
```
## How was this patch tested?
Pass the Jenkins tests including new `BinaryComparisonSimplificationSuite`.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12267 from dongjoon-hyun/SPARK-14502.
## What changes were proposed in this pull request?
This PR fix resultResult() for Union.
## How was this patch tested?
Added regression test.
Author: Davies Liu <davies@databricks.com>
Closes#12295 from davies/fix_sameResult.
## What changes were proposed in this pull request?
For an external table's metadata (in Hive's representation), its table type needs to be EXTERNAL_TABLE. Also, there needs to be a field called EXTERNAL set in the table property with a value of TRUE (for a MANAGED_TABLE it will be FALSE) based on https://github.com/apache/hive/blob/release-1.2.1/metastore/src/java/org/apache/hadoop/hive/metastore/ObjectStore.java#L1095-L1105. HiveClientImpl's toHiveTable misses to set this table property.
## How was this patch tested?
Added a new test.
Author: Yin Huai <yhuai@databricks.com>
Closes#12275 from yhuai/SPARK-14506.
#### What changes were proposed in this pull request?
This PR is to provide a native support for DDL `DROP VIEW` and `DROP TABLE`. The PR includes native parsing and native analysis.
Based on the HIVE DDL document for [DROP_VIEW_WEB_LINK](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-
DropView
), `DROP VIEW` is defined as,
**Syntax:**
```SQL
DROP VIEW [IF EXISTS] [db_name.]view_name;
```
- to remove metadata for the specified view.
- illegal to use DROP TABLE on a view.
- illegal to use DROP VIEW on a table.
- this command only works in `HiveContext`. In `SQLContext`, we will get an exception.
This PR also handles `DROP TABLE`.
**Syntax:**
```SQL
DROP TABLE [IF EXISTS] table_name [PURGE];
```
- Previously, the `DROP TABLE` command only can drop Hive tables in `HiveContext`. Now, after this PR, this command also can drop temporary table, external table, external data source table in `SQLContext`.
- In `HiveContext`, we will not issue an exception if the to-be-dropped table does not exist and users did not specify `IF EXISTS`. Instead, we just log an error message. If `IF EXISTS` is specified, we will not issue any error message/exception.
- In `SQLContext`, we will issue an exception if the to-be-dropped table does not exist, unless `IF EXISTS` is specified.
- Data will not be deleted if the tables are `external`, unless table type is `managed_table`.
#### How was this patch tested?
For verifying command parsing, added test cases in `spark/sql/hive/HiveDDLCommandSuite.scala`
For verifying command analysis, added test cases in `spark/sql/hive/execution/HiveDDLSuite.scala`
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12146 from gatorsmile/dropView.
## What changes were proposed in this pull request?
We implement typed filter by `MapPartitions`, which doesn't work well with whole stage codegen. This PR use `Filter` to implement typed filter and we can get the whole stage codegen support for free.
This PR also introduced `DeserializeToObject` and `SerializeFromObject`, to seperate serialization logic from object operator, so that it's eaiser to write optimization rules for adjacent object operators.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12061 from cloud-fan/whole-stage-codegen.
## What changes were proposed in this pull request?
This is a followup to #12117 and addresses some of the TODOs introduced there. In particular, the resolution of database is now pushed into session catalog, which knows about the current database. Further, the logic for checking whether a function exists is pushed into the external catalog.
No change in functionality is expected.
## How was this patch tested?
`SessionCatalogSuite`, `DDLSuite`
Author: Andrew Or <andrew@databricks.com>
Closes#12198 from andrewor14/function-exists.
The current package name uses a dash, which is a little weird but seemed
to work. That is, until a new test tried to mock a class that references
one of those shaded types, and then things started failing.
Most changes are just noise to fix the logging configs.
For reference, SPARK-8815 also raised this issue, although at the time it
did not cause any issues in Spark, so it was not addressed.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#11941 from vanzin/SPARK-14134.
### What changes were proposed in this pull request?
This PR adds support for `LEFT ANTI JOIN` to Spark SQL. A `LEFT ANTI JOIN` is the exact opposite of a `LEFT SEMI JOIN` and can be used to identify rows in one dataset that are not in another dataset. Note that `nulls` on the left side of the join cannot match a row on the right hand side of the join; the result is that left anti join will always select a row with a `null` in one or more of its keys.
We currently add support for the following SQL join syntax:
SELECT *
FROM tbl1 A
LEFT ANTI JOIN tbl2 B
ON A.Id = B.Id
Or using a dataframe:
tbl1.as("a").join(tbl2.as("b"), $"a.id" === $"b.id", "left_anti)
This PR provides serves as the basis for implementing `NOT EXISTS` and `NOT IN (...)` correlated sub-queries. It would also serve as good basis for implementing an more efficient `EXCEPT` operator.
The PR has been (losely) based on PR's by both davies (https://github.com/apache/spark/pull/10706) and chenghao-intel (https://github.com/apache/spark/pull/10563); credit should be given where credit is due.
This PR adds supports for `LEFT ANTI JOIN` to `BroadcastHashJoin` (including codegeneration), `ShuffledHashJoin` and `BroadcastNestedLoopJoin`.
### How was this patch tested?
Added tests to `JoinSuite` and ported `ExistenceJoinSuite` from https://github.com/apache/spark/pull/10563.
cc davies chenghao-intel rxin
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12214 from hvanhovell/SPARK-12610.
LIKE <pattern> is commonly used in SHOW TABLES / FUNCTIONS etc DDL. In the pattern, user can use `|` or `*` as wildcards.
1. Currently, we used `replaceAll()` to replace `*` with `.*`, but the replacement was scattered in several places; I have created an utility method and use it in all the places;
2. Consistency with Hive: the pattern is case insensitive in Hive and white spaces will be trimmed, but current pattern matching does not do that. For example, suppose we have tables (t1, t2, t3), `SHOW TABLES LIKE ' T* ' ` will list all the t-tables. Please use Hive to verify it.
3. Combined with `|`, the result will be sorted. For pattern like `' B*|a* '`, it will list the result in a-b order.
I've made some changes to the utility method to make sure we will get the same result as Hive does.
A new method was created in StringUtil and test cases were added.
andrewor14
Author: bomeng <bmeng@us.ibm.com>
Closes#12206 from bomeng/SPARK-14429.
## What changes were proposed in this pull request?
We have ParserUtils and ParseUtils which are both utility collections for use during the parsing process.
Those names and what they are used for is very similar so I think we can merge them.
Also, the original unescapeSQLString method may have a fault. When "\u0061" style character literals are passed to the method, it's not unescaped successfully.
This patch fix the bug.
## How was this patch tested?
Added a new test case.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#12199 from sarutak/merge-ParseUtils-and-ParserUtils.
## What changes were proposed in this pull request?
Current, SparkSQL `initCap` is using `toTitleCase` function. However, `UTF8String.toTitleCase` implementation changes only the first letter and just copy the other letters: e.g. sParK --> SParK. This is the correct implementation `toTitleCase`.
```
hive> select initcap('sParK');
Spark
```
```
scala> sql("select initcap('sParK')").head
res0: org.apache.spark.sql.Row = [SParK]
```
This PR updates the implementation of `initcap` using `toLowerCase` and `toTitleCase`.
## How was this patch tested?
Pass the Jenkins tests (including new testcase).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12175 from dongjoon-hyun/SPARK-14402.
## What changes were proposed in this pull request?
The `window` function was added to Dataset with [this PR](https://github.com/apache/spark/pull/12008).
This PR adds the Python, and SQL, API for this function.
With this PR, SQL, Java, and Scala will share the same APIs as in users can use:
- `window(timeColumn, windowDuration)`
- `window(timeColumn, windowDuration, slideDuration)`
- `window(timeColumn, windowDuration, slideDuration, startTime)`
In Python, users can access all APIs above, but in addition they can do
- In Python:
`window(timeColumn, windowDuration, startTime=...)`
that is, they can provide the startTime without providing the `slideDuration`. In this case, we will generate tumbling windows.
## How was this patch tested?
Unit tests + manual tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#12136 from brkyvz/python-windows.
## What changes were proposed in this pull request?
This PR implements CreateFunction and DropFunction commands. Besides implementing these two commands, we also change how to manage functions. Here are the main changes.
* `FunctionRegistry` will be a container to store all functions builders and it will not actively load any functions. Because of this change, we do not need to maintain a separate registry for HiveContext. So, `HiveFunctionRegistry` is deleted.
* SessionCatalog takes care the job of loading a function if this function is not in the `FunctionRegistry` but its metadata is stored in the external catalog. For this case, SessionCatalog will (1) load the metadata from the external catalog, (2) load all needed resources (i.e. jars and files), (3) create a function builder based on the function definition, (4) register the function builder in the `FunctionRegistry`.
* A `UnresolvedGenerator` is created. So, the parser will not need to call `FunctionRegistry` directly during parsing, which is not a good time to create a Hive UDTF. In the analysis phase, we will resolve `UnresolvedGenerator`.
This PR is based on viirya's https://github.com/apache/spark/pull/12036/
## How was this patch tested?
Existing tests and new tests.
## TODOs
[x] Self-review
[x] Cleanup
[x] More tests for create/drop functions (we need to more tests for permanent functions).
[ ] File JIRAs for all TODOs
[x] Standardize the error message when a function does not exist.
Author: Yin Huai <yhuai@databricks.com>
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#12117 from yhuai/function.
#### What changes were proposed in this pull request?
Currently, the weird error messages are issued if we use Hive Context-only operations in SQL Context.
For example,
- When calling `Drop Table` in SQL Context, we got the following message:
```
Expected exception org.apache.spark.sql.catalyst.parser.ParseException to be thrown, but java.lang.ClassCastException was thrown.
```
- When calling `Script Transform` in SQL Context, we got the message:
```
assertion failed: No plan for ScriptTransformation [key#9,value#10], cat, [tKey#155,tValue#156], null
+- LogicalRDD [key#9,value#10], MapPartitionsRDD[3] at beforeAll at BeforeAndAfterAll.scala:187
```
Updates:
Based on the investigation from hvanhovell , the root cause is `visitChildren`, which is the default implementation. It always returns the result of the last defined context child. After merging the code changes from hvanhovell , it works! Thank you hvanhovell !
#### How was this patch tested?
A few test cases are added.
Not sure if the same issue exist for the other operators/DDL/DML. hvanhovell
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Herman van Hovell <hvanhovell@questtec.nl>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12134 from gatorsmile/hiveParserCommand.
## What changes were proposed in this pull request?
This PR contains the following 5 types of maintenance fix over 59 files (+94 lines, -93 lines).
- Fix typos(exception/log strings, testcase name, comments) in 44 lines.
- Fix lint-java errors (MaxLineLength) in 6 lines. (New codes after SPARK-14011)
- Use diamond operators in 40 lines. (New codes after SPARK-13702)
- Fix redundant semicolon in 5 lines.
- Rename class `InferSchemaSuite` to `CSVInferSchemaSuite` in CSVInferSchemaSuite.scala.
## How was this patch tested?
Manual and pass the Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12139 from dongjoon-hyun/SPARK-14355.
## What changes were proposed in this pull request?
This PR aims to fix all Scala-Style multiline comments into Java-Style multiline comments in Scala codes.
(All comment-only changes over 77 files: +786 lines, −747 lines)
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12130 from dongjoon-hyun/use_multiine_javadoc_comments.
## What changes were proposed in this pull request?
Currently, `SimplifyConditionals` handles `true` and `false` to optimize branches. This PR improves `SimplifyConditionals` to take advantage of `null` conditions for `if` and `CaseWhen` expressions, too.
**Before**
```
scala> sql("SELECT IF(null, 1, 0)").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [if (null) 1 else 0 AS (IF(CAST(NULL AS BOOLEAN), 1, 0))#4]
: +- INPUT
+- Scan OneRowRelation[]
scala> sql("select case when cast(null as boolean) then 1 else 2 end").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [CASE WHEN null THEN 1 ELSE 2 END AS CASE WHEN CAST(NULL AS BOOLEAN) THEN 1 ELSE 2 END#14]
: +- INPUT
+- Scan OneRowRelation[]
```
**After**
```
scala> sql("SELECT IF(null, 1, 0)").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [0 AS (IF(CAST(NULL AS BOOLEAN), 1, 0))#4]
: +- INPUT
+- Scan OneRowRelation[]
scala> sql("select case when cast(null as boolean) then 1 else 2 end").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [2 AS CASE WHEN CAST(NULL AS BOOLEAN) THEN 1 ELSE 2 END#4]
: +- INPUT
+- Scan OneRowRelation[]
```
**Hive**
```
hive> select if(null,1,2);
OK
2
hive> select case when cast(null as boolean) then 1 else 2 end;
OK
2
```
## How was this patch tested?
Pass the Jenkins tests (including new extended test cases).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12122 from dongjoon-hyun/SPARK-14338.
This PR adds the ability to perform aggregations inside of a `ContinuousQuery`. In order to implement this feature, the planning of aggregation has augmented with a new `StatefulAggregationStrategy`. Unlike batch aggregation, stateful-aggregation uses the `StateStore` (introduced in #11645) to persist the results of partial aggregation across different invocations. The resulting physical plan performs the aggregation using the following progression:
- Partial Aggregation
- Shuffle
- Partial Merge (now there is at most 1 tuple per group)
- StateStoreRestore (now there is 1 tuple from this batch + optionally one from the previous)
- Partial Merge (now there is at most 1 tuple per group)
- StateStoreSave (saves the tuple for the next batch)
- Complete (output the current result of the aggregation)
The following refactoring was also performed to allow us to plug into existing code:
- The get/put implementation is taken from #12013
- The logic for breaking down and de-duping the physical execution of aggregation has been move into a new pattern `PhysicalAggregation`
- The `AttributeReference` used to identify the result of an `AggregateFunction` as been moved into the `AggregateExpression` container. This change moves the reference into the same object as the other intermediate references used in aggregation and eliminates the need to pass around a `Map[(AggregateFunction, Boolean), Attribute]`. Further clean up (using a different aggregation container for logical/physical plans) is deferred to a followup.
- Some planning logic is moved from the `SessionState` into the `QueryExecution` to make it easier to override in the streaming case.
- The ability to write a `StreamTest` that checks only the output of the last batch has been added to simulate the future addition of output modes.
Author: Michael Armbrust <michael@databricks.com>
Closes#12048 from marmbrus/statefulAgg.
## What changes were proposed in this pull request?
This PR adds the function `window` as a column expression.
`window` can be used to bucket rows into time windows given a time column. With this expression, performing time series analysis on batch data, as well as streaming data should become much more simpler.
### Usage
Assume the following schema:
`sensor_id, measurement, timestamp`
To average 5 minute data every 1 minute (window length of 5 minutes, slide duration of 1 minute), we will use:
```scala
df.groupBy(window("timestamp", “5 minutes”, “1 minute”), "sensor_id")
.agg(mean("measurement").as("avg_meas"))
```
This will generate windows such as:
```
09:00:00-09:05:00
09:01:00-09:06:00
09:02:00-09:07:00 ...
```
Intervals will start at every `slideDuration` starting at the unix epoch (1970-01-01 00:00:00 UTC).
To start intervals at a different point of time, e.g. 30 seconds after a minute, the `startTime` parameter can be used.
```scala
df.groupBy(window("timestamp", “5 minutes”, “1 minute”, "30 second"), "sensor_id")
.agg(mean("measurement").as("avg_meas"))
```
This will generate windows such as:
```
09:00:30-09:05:30
09:01:30-09:06:30
09:02:30-09:07:30 ...
```
Support for Python will be made in a follow up PR after this.
## How was this patch tested?
This patch has some basic unit tests for the `TimeWindow` expression testing that the parameters pass validation, and it also has some unit/integration tests testing the correctness of the windowing and usability in complex operations (multi-column grouping, multi-column projections, joins).
Author: Burak Yavuz <brkyvz@gmail.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#12008 from brkyvz/df-time-window.
`Expand` operator now uses its child plan's constraints as its valid constraints (i.e., the base of constraints). This is not correct because `Expand` will set its group by attributes to null values. So the nullability of these attributes should be true.
E.g., for an `Expand` operator like:
val input = LocalRelation('a.int, 'b.int, 'c.int).where('c.attr > 10 && 'a.attr < 5 && 'b.attr > 2)
Expand(
Seq(
Seq('c, Literal.create(null, StringType), 1),
Seq('c, 'a, 2)),
Seq('c, 'a, 'gid.int),
Project(Seq('a, 'c), input))
The `Project` operator has the constraints `IsNotNull('a)`, `IsNotNull('b)` and `IsNotNull('c)`. But the `Expand` should not have `IsNotNull('a)` in its constraints.
This PR is the first step for this issue and remove invalid constraints of `Expand` operator.
A test is added to `ConstraintPropagationSuite`.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#11995 from viirya/fix-expand-constraints.
## What changes were proposed in this pull request?
JIRA: https://issues.apache.org/jira/browse/SPARK-13995
We infer relative `IsNotNull` constraints from logical plan's expressions in `constructIsNotNullConstraints` now. However, we don't consider the case of (nested) `Cast`.
For example:
val tr = LocalRelation('a.int, 'b.long)
val plan = tr.where('a.attr === 'b.attr).analyze
Then, the plan's constraints will have `IsNotNull(Cast(resolveColumn(tr, "a"), LongType))`, instead of `IsNotNull(resolveColumn(tr, "a"))`. This PR fixes it.
Besides, as `IsNotNull` constraints are most useful for `Attribute`, we should do recursing through any `Expression` that is null intolerant and construct `IsNotNull` constraints for all `Attribute`s under these Expressions.
For example, consider the following constraints:
val df = Seq((1,2,3)).toDF("a", "b", "c")
df.where("a + b = c").queryExecution.analyzed.constraints
The inferred isnotnull constraints should be isnotnull(a), isnotnull(b), isnotnull(c), instead of isnotnull(a + c) and isnotnull(c).
## How was this patch tested?
Test is added into `ConstraintPropagationSuite`.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#11809 from viirya/constraint-cast.
### What changes were proposed in this pull request?
This PR removes the ANTLR3 based parser, and moves the new ANTLR4 based parser into the `org.apache.spark.sql.catalyst.parser package`.
### How was this patch tested?
Existing unit tests.
cc rxin andrewor14 yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12071 from hvanhovell/SPARK-14211.
## What changes were proposed in this pull request?
In `ExpressionEncoder`, we use `constructorFor` to build `fromRowExpression` as the `deserializer` in `ObjectOperator`. It's kind of confusing, we should make the name consistent.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12058 from cloud-fan/rename.
## What changes were proposed in this pull request?
Builds on https://github.com/apache/spark/pull/12022 and (a) appends "..." to truncated comment strings and (b) fixes indentation in lines after the commented strings if they happen to have a `(`, `{`, `)` or `}`
## How was this patch tested?
Manually examined the generated code.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12044 from sameeragarwal/comment.
## What changes were proposed in this pull request?
Session catalog was added in #11750. However, it doesn't really support temporary functions properly; right now we only store the metadata in the form of `CatalogFunction`, but this doesn't make sense for temporary functions because there is no class name.
This patch moves the `FunctionRegistry` into the `SessionCatalog`. With this, the user can call `catalog.createTempFunction` and `catalog.lookupFunction` to use the function they registered previously. This is currently still dead code, however.
## How was this patch tested?
`SessionCatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#11972 from andrewor14/temp-functions.
## What changes were proposed in this pull request?
This patch addresses the remaining comments left in #11750 and #11918 after they are merged. For a full list of changes in this patch, just trace the commits.
## How was this patch tested?
`SessionCatalogSuite` and `CatalogTestCases`
Author: Andrew Or <andrew@databricks.com>
Closes#12006 from andrewor14/session-catalog-followup.
### What changes were proposed in this pull request?
The current ANTLR3 parser is quite complex to maintain and suffers from code blow-ups. This PR introduces a new parser that is based on ANTLR4.
This parser is based on the [Presto's SQL parser](https://github.com/facebook/presto/blob/master/presto-parser/src/main/antlr4/com/facebook/presto/sql/parser/SqlBase.g4). The current implementation can parse and create Catalyst and SQL plans. Large parts of the HiveQl DDL and some of the DML functionality is currently missing, the plan is to add this in follow-up PRs.
This PR is a work in progress, and work needs to be done in the following area's:
- [x] Error handling should be improved.
- [x] Documentation should be improved.
- [x] Multi-Insert needs to be tested.
- [ ] Naming and package locations.
### How was this patch tested?
Catalyst and SQL unit tests.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#11557 from hvanhovell/ngParser.
## What changes were proposed in this pull request?
This PR adds support for automatically inferring `IsNotNull` constraints from any non-nullable attributes that are part of an operator's output. This also fixes the issue that causes the optimizer to hit the maximum number of iterations for certain queries in https://github.com/apache/spark/pull/11828.
## How was this patch tested?
Unit test in `ConstraintPropagationSuite`
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11953 from sameeragarwal/infer-isnotnull.
## What changes were proposed in this pull request?
JIRA: https://issues.apache.org/jira/browse/SPARK-12443
`constructorFor` will call `dataTypeFor` to determine if a type is `ObjectType` or not. If there is not case for `Decimal`, it will be recognized as `ObjectType` and causes the bug.
## How was this patch tested?
Test is added into `ExpressionEncoderSuite`.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#10399 from viirya/fix-encoder-decimal.
## What changes were proposed in this pull request?
As we have `CreateArray` and `CreateStruct`, we should also have `CreateMap`. This PR adds the `CreateMap` expression, and the DataFrame API, and python API.
## How was this patch tested?
various new tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11879 from cloud-fan/create_map.
## What changes were proposed in this pull request?
This PR fix the conflict between ColumnPruning and PushPredicatesThroughProject, because ColumnPruning will try to insert a Project before Filter, but PushPredicatesThroughProject will move the Filter before Project.This is fixed by remove the Project before Filter, if the Project only do column pruning.
The RuleExecutor will fail the test if reached max iterations.
Closes#11745
## How was this patch tested?
Existing tests.
This is a test case still failing, disabled for now, will be fixed by https://issues.apache.org/jira/browse/SPARK-14137
Author: Davies Liu <davies@databricks.com>
Closes#11828 from davies/fail_rule.
## What changes were proposed in this pull request?
This reopens#11836, which was merged but promptly reverted because it introduced flaky Hive tests.
## How was this patch tested?
See `CatalogTestCases`, `SessionCatalogSuite` and `HiveContextSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#11938 from andrewor14/session-catalog-again.
## What changes were proposed in this pull request?
unionAll has been deprecated in SPARK-14088.
## How was this patch tested?
Should be covered by all existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#11946 from rxin/SPARK-14142.
## What changes were proposed in this pull request?
`SessionCatalog`, introduced in #11750, is a catalog that keeps track of temporary functions and tables, and delegates metastore operations to `ExternalCatalog`. This functionality overlaps a lot with the existing `analysis.Catalog`.
As of this commit, `SessionCatalog` and `ExternalCatalog` will no longer be dead code. There are still things that need to be done after this patch, namely:
- SPARK-14013: Properly implement temporary functions in `SessionCatalog`
- SPARK-13879: Decide which DDL/DML commands to support natively in Spark
- SPARK-?????: Implement the ones we do want to support through `SessionCatalog`.
- SPARK-?????: Merge SQL/HiveContext
## How was this patch tested?
This is largely a refactoring task so there are no new tests introduced. The particularly relevant tests are `SessionCatalogSuite` and `ExternalCatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#11836 from andrewor14/use-session-catalog.
This PR introduces a 64-bit hashcode expression. Such an expression is especially usefull for HyperLogLog++ and other probabilistic datastructures.
I have implemented xxHash64 which is a 64-bit hashing algorithm created by Yann Colet and Mathias Westerdahl. This is a high speed (C implementation runs at memory bandwidth) and high quality hashcode. It exploits both Instruction Level Parralellism (for speed) and the multiplication and rotation techniques (for quality) like MurMurHash does.
The initial results are promising. I have added a CG'ed test to the `HashBenchmark`, and this results in the following results (running from SBT):
Running benchmark: Hash For simple
Running case: interpreted version
Running case: codegen version
Running case: codegen version 64-bit
Intel(R) Core(TM) i7-4750HQ CPU 2.00GHz
Hash For simple: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
interpreted version 1011 / 1016 132.8 7.5 1.0X
codegen version 1864 / 1869 72.0 13.9 0.5X
codegen version 64-bit 1614 / 1644 83.2 12.0 0.6X
Running benchmark: Hash For normal
Running case: interpreted version
Running case: codegen version
Running case: codegen version 64-bit
Intel(R) Core(TM) i7-4750HQ CPU 2.00GHz
Hash For normal: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
interpreted version 2467 / 2475 0.9 1176.1 1.0X
codegen version 2008 / 2115 1.0 957.5 1.2X
codegen version 64-bit 728 / 758 2.9 347.0 3.4X
Running benchmark: Hash For array
Running case: interpreted version
Running case: codegen version
Running case: codegen version 64-bit
Intel(R) Core(TM) i7-4750HQ CPU 2.00GHz
Hash For array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
interpreted version 1544 / 1707 0.1 11779.6 1.0X
codegen version 2728 / 2745 0.0 20815.5 0.6X
codegen version 64-bit 2508 / 2549 0.1 19132.8 0.6X
Running benchmark: Hash For map
Running case: interpreted version
Running case: codegen version
Running case: codegen version 64-bit
Intel(R) Core(TM) i7-4750HQ CPU 2.00GHz
Hash For map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
interpreted version 1819 / 1826 0.0 444014.3 1.0X
codegen version 183 / 194 0.0 44642.9 9.9X
codegen version 64-bit 173 / 174 0.0 42120.9 10.5X
This shows that algorithm is consistently faster than MurMurHash32 in all cases and up to 3x (!) in the normal case.
I have also added this to HyperLogLog++ and it cuts the processing time of the following code in half:
val df = sqlContext.range(1<<25).agg(approxCountDistinct("id"))
df.explain()
val t = System.nanoTime()
df.show()
val ns = System.nanoTime() - t
// Before
ns: Long = 5821524302
// After
ns: Long = 2836418963
cc cloud-fan (you have been working on hashcodes) / rxin
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#11209 from hvanhovell/xxHash.
#### What changes were proposed in this pull request?
The PR https://github.com/apache/spark/pull/10541 changed the rule `CollapseProject` by enabling collapsing `Project` into `Aggregate`. It leaves a to-do item to remove the duplicate code. This PR is to finish this to-do item. Also added a test case for covering this change.
#### How was this patch tested?
Added a new test case.
liancheng Could you check if the code refactoring is fine? Thanks!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11427 from gatorsmile/collapseProjectRefactor.
## What changes were proposed in this pull request?
Round() in database usually round the number up (away from zero), it's different than Math.round() in Java.
For example:
```
scala> java.lang.Math.round(-3.5)
res3: Long = -3
```
In Database, we should return -4.0 in this cases.
This PR remove the buggy special case for scale=0.
## How was this patch tested?
Add tests for negative values with tie.
Author: Davies Liu <davies@databricks.com>
Closes#11894 from davies/fix_round.
## What changes were proposed in this pull request?
Currently, **BooleanSimplification** optimization can handle the following cases.
* a && (!a || b ) ==> a && b
* a && (b || !a ) ==> a && b
However, it can not handle the followings cases since those equations fail at the comparisons between their canonicalized forms.
* a < 1 && (!(a < 1) || b) ==> (a < 1) && b
* a <= 1 && (!(a <= 1) || b) ==> (a <= 1) && b
* a > 1 && (!(a > 1) || b) ==> (a > 1) && b
* a >= 1 && (!(a >= 1) || b) ==> (a >= 1) && b
This PR implements the above cases and also the followings, too.
* a < 1 && ((a >= 1) || b ) ==> (a < 1) && b
* a <= 1 && ((a > 1) || b ) ==> (a <= 1) && b
* a > 1 && ((a <= 1) || b) ==> (a > 1) && b
* a >= 1 && ((a < 1) || b) ==> (a >= 1) && b
## How was this patch tested?
Pass the Jenkins tests including new test cases in BooleanSimplicationSuite.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11851 from dongjoon-hyun/SPARK-14029.
This PR resolves two issues:
First, expanding * inside aggregate functions of structs when using Dataframe/Dataset APIs. For example,
```scala
structDf.groupBy($"a").agg(min(struct($"record.*")))
```
Second, it improves the error messages when having invalid star usage when using Dataframe/Dataset APIs. For example,
```scala
pagecounts4PartitionsDS
.map(line => (line._1, line._3))
.toDF()
.groupBy($"_1")
.agg(sum("*") as "sumOccurances")
```
Before the fix, the invalid usage will issue a confusing error message, like:
```
org.apache.spark.sql.AnalysisException: cannot resolve '_1' given input columns _1, _2;
```
After the fix, the message is like:
```
org.apache.spark.sql.AnalysisException: Invalid usage of '*' in function 'sum'
```
cc: rxin nongli cloud-fan
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11208 from gatorsmile/sumDataSetResolution.
## What changes were proposed in this pull request?
This is a more aggressive version of PR #11820, which not only fixes the original problem, but also does the following updates to enforce the at-most-one-qualifier constraint:
- Renames `NamedExpression.qualifiers` to `NamedExpression.qualifier`
- Uses `Option[String]` rather than `Seq[String]` for `NamedExpression.qualifier`
Quoted PR description of #11820 here:
> Current implementations of `AttributeReference.sql` and `Alias.sql` joins all available qualifiers, which is logically wrong. But this implementation mistake doesn't cause any real SQL generation bugs though, since there is always at most one qualifier for any given `AttributeReference` or `Alias`.
## How was this patch tested?
Existing tests should be enough.
Author: Cheng Lian <lian@databricks.com>
Closes#11822 from liancheng/spark-14004-aggressive.
#### What changes were proposed in this pull request?
This PR is to support order by position in SQL, e.g.
```SQL
select c1, c2, c3 from tbl order by 1 desc, 3
```
should be equivalent to
```SQL
select c1, c2, c3 from tbl order by c1 desc, c3 asc
```
This is controlled by config option `spark.sql.orderByOrdinal`.
- When true, the ordinal numbers are treated as the position in the select list.
- When false, the ordinal number in order/sort By clause are ignored.
- Only convert integer literals (not foldable expressions). If found foldable expressions, ignore them
- This also works with select *.
**Question**: Do we still need sort by columns that contain zero reference? In this case, it will have no impact on the sorting results. IMO, we should not allow users do it. rxin cloud-fan marmbrus yhuai hvanhovell
-- Update: In these cases, they are ignored in this case.
**Note**: This PR is taken from https://github.com/apache/spark/pull/10731. When merging this PR, please give the credit to zhichao-li
Also cc all the people who are involved in the previous discussion: adrian-wang chenghao-intel tejasapatil
#### How was this patch tested?
Added a few test cases for both positive and negative test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11815 from gatorsmile/orderByPosition.
#### What changes were proposed in this pull request?
This PR is to add a new Optimizer rule for pruning Sort if its SortOrder is no-op. In the phase of **Optimizer**, if a specific `SortOrder` does not have any reference, it has no effect on the sorting results. If `Sort` is empty, remove the whole `Sort`.
For example, in the following SQL query
```SQL
SELECT * FROM t ORDER BY NULL + 5
```
Before the fix, the plan is like
```
== Analyzed Logical Plan ==
a: int, b: int
Sort [(cast(null as int) + 5) ASC], true
+- Project [a#92,b#93]
+- SubqueryAlias t
+- Project [_1#89 AS a#92,_2#90 AS b#93]
+- LocalRelation [_1#89,_2#90], [[1,2],[1,2]]
== Optimized Logical Plan ==
Sort [null ASC], true
+- LocalRelation [a#92,b#93], [[1,2],[1,2]]
== Physical Plan ==
WholeStageCodegen
: +- Sort [null ASC], true, 0
: +- INPUT
+- Exchange rangepartitioning(null ASC, 5), None
+- LocalTableScan [a#92,b#93], [[1,2],[1,2]]
```
After the fix, the plan is like
```
== Analyzed Logical Plan ==
a: int, b: int
Sort [(cast(null as int) + 5) ASC], true
+- Project [a#92,b#93]
+- SubqueryAlias t
+- Project [_1#89 AS a#92,_2#90 AS b#93]
+- LocalRelation [_1#89,_2#90], [[1,2],[1,2]]
== Optimized Logical Plan ==
LocalRelation [a#92,b#93], [[1,2],[1,2]]
== Physical Plan ==
LocalTableScan [a#92,b#93], [[1,2],[1,2]]
```
cc rxin cloud-fan marmbrus Thanks!
#### How was this patch tested?
Added a test suite for covering this rule
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11840 from gatorsmile/sortElimination.
## What changes were proposed in this pull request?
Support queries that JOIN tables with USING clause.
SELECT * from table1 JOIN table2 USING <column_list>
USING clause can be used as a means to simplify the join condition
when :
1) Equijoin semantics is desired and
2) The column names in the equijoin have the same name.
We already have the support for Natural Join in Spark. This PR makes
use of the already existing infrastructure for natural join to
form the join condition and also the projection list.
## How was the this patch tested?
Have added unit tests in SQLQuerySuite, CatalystQlSuite, ResolveNaturalJoinSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#11297 from dilipbiswal/spark-13427.
## What changes were proposed in this pull request?
Fix expression generation for optional types.
Standard Java reflection causes issues when dealing with synthetic Scala objects (things that do not map to Java and thus contain a dollar sign in their name). This patch introduces Scala reflection in such cases.
This patch also adds a regression test for Dataset's handling of classes defined in package objects (which was the initial purpose of this PR).
## How was this patch tested?
A new test in ExpressionEncoderSuite that tests optional inner classes and a regression test for Dataset's handling of package objects.
Author: Jakob Odersky <jakob@odersky.com>
Closes#11708 from jodersky/SPARK-13118-package-objects.
## What changes were proposed in this pull request?
As part of the effort to merge `SQLContext` and `HiveContext`, this patch implements an internal catalog called `SessionCatalog` that handles temporary functions and tables and delegates metastore operations to `ExternalCatalog`. Currently, this is still dead code, but in the future it will be part of `SessionState` and will replace `o.a.s.sql.catalyst.analysis.Catalog`.
A recent patch #11573 parses Hive commands ourselves in Spark, but still passes the entire query text to Hive. In a future patch, we will use `SessionCatalog` to implement the parsed commands.
## How was this patch tested?
800+ lines of tests in `SessionCatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#11750 from andrewor14/temp-catalog.
## What changes were proposed in this pull request?
Narrow down the parameter type of `UserDefinedType#serialize()`. Currently, the parameter type is `Any`, however it would logically make more sense to narrow it down to the type of the actual user defined type.
## How was this patch tested?
Existing tests were successfully run on local machine.
Author: Jakob Odersky <jakob@odersky.com>
Closes#11379 from jodersky/SPARK-11011-udt-types.
## What changes were proposed in this pull request?
**[I'll link it to the JIRA once ASF JIRA is back online]**
This PR modifies the existing `CombineFilters` rule to remove redundant conditions while combining individual filter predicates. For instance, queries of the form `table.where('a === 1 && 'b === 1).where('a === 1 && 'c === 1)` will now be optimized to ` table.where('a === 1 && 'b === 1 && 'c === 1)` (instead of ` table.where('a === 1 && 'a === 1 && 'b === 1 && 'c === 1)`)
## How was this patch tested?
Unit test in `FilterPushdownSuite`
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11670 from sameeragarwal/combine-filters.
## What changes were proposed in this pull request?
This PR generalizes the `NullFiltering` optimizer rule in catalyst to `InferFiltersFromConstraints` that can automatically infer all relevant filters based on an operator's constraints while making sure of 2 things:
(a) no redundant filters are generated, and
(b) filters that do not contribute to any further optimizations are not generated.
## How was this patch tested?
Extended all tests in `InferFiltersFromConstraintsSuite` (that were initially based on `NullFilteringSuite` to test filter inference in `Filter` and `Join` operators.
In particular the 2 tests ( `single inner join with pre-existing filters: filter out values on either side` and `multiple inner joins: filter out values on all sides on equi-join keys` attempts to highlight/test the real potential of this rule for join optimization.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11665 from sameeragarwal/infer-filters.
## What changes were proposed in this pull request?
Follow up to https://github.com/apache/spark/pull/11657
- Also update `String.getBytes("UTF-8")` to use `StandardCharsets.UTF_8`
- And fix one last new Coverity warning that turned up (use of unguarded `wait()` replaced by simpler/more robust `java.util.concurrent` classes in tests)
- And while we're here cleaning up Coverity warnings, just fix about 15 more build warnings
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#11725 from srowen/SPARK-13823.2.
## What changes were proposed in this pull request?
Remove the wrong "expected" parameter in MathFunctionsSuite.scala's checkNaNWithoutCodegen.
This function is to check NaN value, so the "expected" parameter is useless. The Callers do not pass "expected" value and the similar function like checkNaNWithGeneratedProjection and checkNaNWithOptimization do not use it also.
Author: Yucai Yu <yucai.yu@intel.com>
Closes#11718 from yucai/unused_expected.
#### What changes were proposed in this pull request?
Before this PR, two Optimizer rules `ColumnPruning` and `PushPredicateThroughProject` reverse each other's effects. Optimizer always reaches the max iteration when optimizing some queries. Extra `Project` are found in the plan. For example, below is the optimized plan after reaching 100 iterations:
```
Join Inner, Some((cast(id1#16 as bigint) = id1#18L))
:- Project [id1#16]
: +- Filter isnotnull(cast(id1#16 as bigint))
: +- Project [id1#16]
: +- Relation[id1#16,newCol#17] JSON part: struct<>, data: struct<id1:int,newCol:int>
+- Filter isnotnull(id1#18L)
+- Relation[id1#18L] JSON part: struct<>, data: struct<id1:bigint>
```
This PR splits the optimizer rule `ColumnPruning` to `ColumnPruning` and `EliminateOperators`
The issue becomes worse when having another rule `NullFiltering`, which could add extra Filters for `IsNotNull`. We have to be careful when introducing extra `Filter` if the benefit is not large enough. Another PR will be submitted by sameeragarwal to handle this issue.
cc sameeragarwal marmbrus
In addition, `ColumnPruning` should not push `Project` through non-deterministic `Filter`. This could cause wrong results. This will be put in a separate PR.
cc davies cloud-fan yhuai
#### How was this patch tested?
Modified the existing test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11682 from gatorsmile/viewDuplicateNames.
## What changes were proposed in this pull request?
This PR fixes 135 typos over 107 files:
* 121 typos in comments
* 11 typos in testcase name
* 3 typos in log messages
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11689 from dongjoon-hyun/fix_more_typos.
## What changes were proposed in this pull request?
- Fixes calls to `new String(byte[])` or `String.getBytes()` that rely on platform default encoding, to use UTF-8
- Same for `InputStreamReader` and `OutputStreamWriter` constructors
- Standardizes on UTF-8 everywhere
- Standardizes specifying the encoding with `StandardCharsets.UTF-8`, not the Guava constant or "UTF-8" (which means handling `UnuspportedEncodingException`)
- (also addresses the other remaining Coverity scan issues, which are pretty trivial; these are separated into commit 1deecd8d9c )
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#11657 from srowen/SPARK-13823.
#### What changes were proposed in this pull request?
`projectList` is useless. Its value is always the same as the child.output. Remove it from the class `Window`. Removal can simplify the codes in Analyzer and Optimizer.
This PR is based on the discussion started by cloud-fan in a separate PR:
https://github.com/apache/spark/pull/5604#discussion_r55140466
This PR also eliminates useless `Window`.
cloud-fan yhuai
#### How was this patch tested?
Existing test cases cover it.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#11565 from gatorsmile/removeProjListWindow.
## What changes were proposed in this pull request?
This PR adds support for inferring an additional set of data constraints based on attribute equality. For e.g., if an operator has constraints of the form (`a = 5`, `a = b`), we can now automatically infer an additional constraint of the form `b = 5`
## How was this patch tested?
Tested that new constraints are properly inferred for filters (by adding a new test) and equi-joins (by modifying an existing test)
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11618 from sameeragarwal/infer-isequal-constraints.
## What changes were proposed in this pull request?
Since the opening curly brace, '{', has many usages as discussed in [SPARK-3854](https://issues.apache.org/jira/browse/SPARK-3854), this PR adds a ScalaStyle rule to prevent '){' pattern for the following majority pattern and fixes the code accordingly. If we enforce this in ScalaStyle from now, it will improve the Scala code quality and reduce review time.
```
// Correct:
if (true) {
println("Wow!")
}
// Incorrect:
if (true){
println("Wow!")
}
```
IntelliJ also shows new warnings based on this.
## How was this patch tested?
Pass the Jenkins ScalaStyle test.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11637 from dongjoon-hyun/SPARK-3854.
## What changes were proposed in this pull request?
This PR adds support for inferring `IsNotNull` constraints from expressions with an `!==`. More specifically, if an operator has a condition on `a !== b`, we know that both `a` and `b` in the operator output can no longer be null.
## How was this patch tested?
1. Modified a test in `ConstraintPropagationSuite` to test for expressions with an inequality.
2. Added a test in `NullFilteringSuite` for making sure an Inner join with a "non-equal" condition appropriately filters out null from their input.
cc nongli
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11594 from sameeragarwal/isnotequal-constraints.
## What changes were proposed in this pull request?
This PR is a small follow up on https://github.com/apache/spark/pull/11338 (https://issues.apache.org/jira/browse/SPARK-13092) to use `ExpressionSet` as part of the verification logic in `ConstraintPropagationSuite`.
## How was this patch tested?
No new tests added. Just changes the verification logic in `ConstraintPropagationSuite`.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11611 from sameeragarwal/expression-set.
#### What changes were proposed in this pull request?
Remove all the deterministic conditions in a [[Filter]] that are contained in the Child's Constraints.
For example, the first query can be simplified to the second one.
```scala
val queryWithUselessFilter = tr1
.where("tr1.a".attr > 10 || "tr1.c".attr < 10)
.join(tr2.where('d.attr < 100), Inner, Some("tr1.a".attr === "tr2.a".attr))
.where(
("tr1.a".attr > 10 || "tr1.c".attr < 10) &&
'd.attr < 100 &&
"tr2.a".attr === "tr1.a".attr)
```
```scala
val query = tr1
.where("tr1.a".attr > 10 || "tr1.c".attr < 10)
.join(tr2.where('d.attr < 100), Inner, Some("tr1.a".attr === "tr2.a".attr))
```
#### How was this patch tested?
Six test cases are added.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11406 from gatorsmile/FilterRemoval.
#### What changes were proposed in this pull request?
As shown in another PR: https://github.com/apache/spark/pull/11596, we are using `SELECT 1` as a dummy table, when the table is used for SQL statements in which a table reference is required, but the contents of the table are not important. For example,
```SQL
SELECT value FROM (select 1) dummyTable Lateral View explode(array(1,2,3)) adTable as value
```
Before the PR, the optimized plan contains a useless `Project` after Optimizer executing the `ColumnPruning` rule, as shown below:
```
== Analyzed Logical Plan ==
value: int
Project [value#22]
+- Generate explode(array(1, 2, 3)), true, false, Some(adtable), [value#22]
+- SubqueryAlias dummyTable
+- Project [1 AS 1#21]
+- OneRowRelation$
== Optimized Logical Plan ==
Generate explode([1,2,3]), false, false, Some(adtable), [value#22]
+- Project
+- OneRowRelation$
```
After the fix, the optimized plan removed the useless `Project`, as shown below:
```
== Optimized Logical Plan ==
Generate explode([1,2,3]), false, false, Some(adtable), [value#22]
+- OneRowRelation$
```
This PR is to remove `Project` when its Child's output is Nil
#### How was this patch tested?
Added a new unit test case into the suite `ColumnPruningSuite.scala`
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11599 from gatorsmile/projectOneRowRelation.
## What changes were proposed in this pull request?
If there are many branches in a CaseWhen expression, the generated code could go above the 64K limit for single java method, will fail to compile. This PR change it to fallback to interpret mode if there are more than 20 branches.
This PR is based on #11243 and #11221, thanks to joehalliwell
Closes#11243Closes#11221
## How was this patch tested?
Add a test with 50 branches.
Author: Davies Liu <davies@databricks.com>
Closes#11592 from davies/fix_when.
## What changes were proposed in this pull request?
`ScalaReflection.mirror` method should be synchronized when scala version is `2.10` because `universe.runtimeMirror` is not thread safe.
## How was this patch tested?
I added a test to check thread safety of `ScalaRefection.mirror` method in `ScalaReflectionSuite`, which will throw the following Exception in Scala `2.10` without this patch:
```
[info] - thread safety of mirror *** FAILED *** (49 milliseconds)
[info] java.lang.UnsupportedOperationException: tail of empty list
[info] at scala.collection.immutable.Nil$.tail(List.scala:339)
[info] at scala.collection.immutable.Nil$.tail(List.scala:334)
[info] at scala.reflect.internal.SymbolTable.popPhase(SymbolTable.scala:172)
[info] at scala.reflect.internal.Symbols$Symbol.unsafeTypeParams(Symbols.scala:1477)
[info] at scala.reflect.internal.Symbols$TypeSymbol.tpe(Symbols.scala:2777)
[info] at scala.reflect.internal.Mirrors$RootsBase.init(Mirrors.scala:235)
[info] at scala.reflect.runtime.JavaMirrors$class.createMirror(JavaMirrors.scala:34)
[info] at scala.reflect.runtime.JavaMirrors$class.runtimeMirror(JavaMirrors.scala:61)
[info] at scala.reflect.runtime.JavaUniverse.runtimeMirror(JavaUniverse.scala:12)
[info] at scala.reflect.runtime.JavaUniverse.runtimeMirror(JavaUniverse.scala:12)
[info] at org.apache.spark.sql.catalyst.ScalaReflection$.mirror(ScalaReflection.scala:36)
[info] at org.apache.spark.sql.catalyst.ScalaReflectionSuite$$anonfun$12$$anonfun$apply$mcV$sp$1$$anonfun$apply$1$$anonfun$apply$2.apply(ScalaReflectionSuite.scala:256)
[info] at org.apache.spark.sql.catalyst.ScalaReflectionSuite$$anonfun$12$$anonfun$apply$mcV$sp$1$$anonfun$apply$1$$anonfun$apply$2.apply(ScalaReflectionSuite.scala:252)
[info] at scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
[info] at scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
[info] at scala.concurrent.impl.ExecutionContextImpl$$anon$3.exec(ExecutionContextImpl.scala:107)
[info] at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
[info] at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
[info] at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
[info] at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
```
Notice that the test will pass when Scala version is `2.11`.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#11487 from ueshin/issues/SPARK-13640.
## What changes were proposed in this pull request?
This removes the remaining deprecated Octal escape literals. The followings are the warnings on those two lines.
```
LiteralExpressionSuite.scala:99: Octal escape literals are deprecated, use \u0000 instead.
HiveQlSuite.scala:74: Octal escape literals are deprecated, use \u002c instead.
```
## How was this patch tested?
Manual.
During building, there should be no warning on `Octal escape literals`.
```
mvn -DskipTests clean install
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11584 from dongjoon-hyun/SPARK-13400.
## What changes were proposed in this pull request?
In order to avoid StackOverflow when parse a expression with hundreds of ORs, we should use loop instead of recursive functions to flatten the tree as list. This PR also build a balanced tree to reduce the depth of generated And/Or expression, to avoid StackOverflow in analyzer/optimizer.
## How was this patch tested?
Add new unit tests. Manually tested with TPCDS Q3 with hundreds predicates in it [1]. These predicates help to reduce the number of partitions, then the query time went from 60 seconds to 8 seconds.
[1] https://github.com/cloudera/impala-tpcds-kit/blob/master/queries/q3.sql
Author: Davies Liu <davies@databricks.com>
Closes#11501 from davies/long_or.
#### What changes were proposed in this pull request?
Non-deterministic predicates should not be pushed through Generate.
#### How was this patch tested?
Added a test case in `FilterPushdownSuite.scala`
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11562 from gatorsmile/pushPredicateDownWindow.
## What changes were proposed in this pull request?
This PR adds an optimizer rule to eliminate reading (unnecessary) NULL values if they are not required for correctness by inserting `isNotNull` filters is the query plan. These filters are currently inserted beneath existing `Filter` and `Join` operators and are inferred based on their data constraints.
Note: While this optimization is applicable to all types of join, it primarily benefits `Inner` and `LeftSemi` joins.
## How was this patch tested?
1. Added a new `NullFilteringSuite` that tests for `IsNotNull` filters in the query plan for joins and filters. Also, tests interaction with the `CombineFilters` optimizer rules.
2. Test generated ExpressionTrees via `OrcFilterSuite`
3. Test filter source pushdown logic via `SimpleTextHadoopFsRelationSuite`
cc yhuai nongli
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11372 from sameeragarwal/gen-isnotnull.
## What changes were proposed in this pull request?
Today we have `analysis.Catalog` and `catalog.Catalog`. In the future the former will call the latter. When that happens, if both of them are still called `Catalog` it will be very confusing. This patch renames the latter `ExternalCatalog` because it is expected to talk to external systems.
## How was this patch tested?
Jenkins.
Author: Andrew Or <andrew@databricks.com>
Closes#11526 from andrewor14/rename-catalog.
## What changes were proposed in this pull request?
This patch simply moves things to existing package `o.a.s.sql.catalyst.parser` in an effort to reduce the size of the diff in #11048. This is conceptually the same as a recently merged patch #11482.
## How was this patch tested?
Jenkins.
Author: Andrew Or <andrew@databricks.com>
Closes#11506 from andrewor14/parser-package.
## What changes were proposed in this pull request?
After SPARK-6990, `dev/lint-java` keeps Java code healthy and helps PR review by saving much time.
This issue aims remove unused imports from Java/Scala code and add `UnusedImports` checkstyle rule to help developers.
## How was this patch tested?
```
./dev/lint-java
./build/sbt compile
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11438 from dongjoon-hyun/SPARK-13583.
## What changes were proposed in this pull request?
Make some cross-cutting code improvements according to static analysis. These are individually up for discussion since they exist in separate commits that can be reverted. The changes are broadly:
- Inner class should be static
- Mismatched hashCode/equals
- Overflow in compareTo
- Unchecked warnings
- Misuse of assert, vs junit.assert
- get(a) + getOrElse(b) -> getOrElse(a,b)
- Array/String .size -> .length (occasionally, -> .isEmpty / .nonEmpty) to avoid implicit conversions
- Dead code
- tailrec
- exists(_ == ) -> contains find + nonEmpty -> exists filter + size -> count
- reduce(_+_) -> sum map + flatten -> map
The most controversial may be .size -> .length simply because of its size. It is intended to avoid implicits that might be expensive in some places.
## How was the this patch tested?
Existing Jenkins unit tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#11292 from srowen/SPARK-13423.
JIRA: https://issues.apache.org/jira/browse/SPARK-13466
## What changes were proposed in this pull request?
With column pruning rule in optimizer, some Project operators will become redundant. We should remove these redundant Projects.
For an example query:
val input = LocalRelation('key.int, 'value.string)
val query =
Project(Seq($"x.key", $"y.key"),
Join(
SubqueryAlias("x", input),
BroadcastHint(SubqueryAlias("y", input)), Inner, None))
After the first run of column pruning, it would like:
Project(Seq($"x.key", $"y.key"),
Join(
Project(Seq($"x.key"), SubqueryAlias("x", input)),
Project(Seq($"y.key"), <-- inserted by the rule
BroadcastHint(SubqueryAlias("y", input))),
Inner, None))
Actually we don't need the outside Project now. This patch will remove it:
Join(
Project(Seq($"x.key"), SubqueryAlias("x", input)),
Project(Seq($"y.key"),
BroadcastHint(SubqueryAlias("y", input))),
Inner, None)
## How was the this patch tested?
Unit test is added into ColumnPruningSuite.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11341 from viirya/remove-redundant-project.
#### What changes were proposed in this pull request?
This PR is to prune unnecessary columns when the operator is `MapPartitions`. The solution is to add an extra `Project` in the child node.
For the other two operators `AppendColumns` and `MapGroups`, it sounds doable. More discussions are required. The major reason is the current implementation of the `inputPlan` of `groupBy` is based on the child of `AppendColumns`. It might be a bug? Thus, will submit a separate PR.
#### How was this patch tested?
Added a test case in ColumnPruningSuite to verify the rule. Added another test case in DatasetSuite.scala to verify the data.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11460 from gatorsmile/datasetPruningNew.
#### What changes were proposed in this pull request?
After analysis by Analyzer, two operators could have alias. They are `Project` and `Aggregate`. So far, we only rewrite and propagate constraints if `Alias` is defined in `Project`. This PR is to resolve this issue in `Aggregate`.
#### How was this patch tested?
Added a test case for `Aggregate` in `ConstraintPropagationSuite`.
marmbrus sameeragarwal
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11422 from gatorsmile/validConstraintsInUnaryNodes.
## What changes were proposed in this pull request?
Predicates shouldn't be pushed through project with nondeterministic field(s).
See https://github.com/graphframes/graphframes/pull/23 and SPARK-13473 for more details.
This PR targets master, branch-1.6, and branch-1.5.
## How was this patch tested?
A test case is added in `FilterPushdownSuite`. It constructs a query plan where a filter is over a project with a nondeterministic field. Optimized query plan shouldn't change in this case.
Author: Cheng Lian <lian@databricks.com>
Closes#11348 from liancheng/spark-13473-no-ppd-through-nondeterministic-project-field.
## What changes were proposed in this pull request?
This PR mostly rewrite the ColumnPruning rule to support most of the SQL logical plans (except those for Dataset).
This PR also fix a bug in Generate, it should always output UnsafeRow, added an regression test for that.
## How was this patch tested?
This is test by unit tests, also manually test with TPCDS Q78, which could prune all unused columns successfully, improved the performance by 78% (from 22s to 12s).
Author: Davies Liu <davies@databricks.com>
Closes#11354 from davies/fix_column_pruning.
This PR adds a new abstraction called an `ExpressionSet` which attempts to canonicalize expressions to remove cosmetic differences. Deterministic expressions that are in the set after canonicalization will always return the same answer given the same input (i.e. false positives should not be possible). However, it is possible that two canonical expressions that are not equal will in fact return the same answer given any input (i.e. false negatives are possible).
```scala
val set = AttributeSet('a + 1 :: 1 + 'a :: Nil)
set.iterator => Iterator('a + 1)
set.contains('a + 1) => true
set.contains(1 + 'a) => true
set.contains('a + 2) => false
```
Other relevant changes include:
- Since this concept overlaps with the existing `semanticEquals` and `semanticHash`, those functions are also ported to this new infrastructure.
- A memoized `canonicalized` version of the expression is added as a `lazy val` to `Expression` and is used by both `semanticEquals` and `ExpressionSet`.
- A set of unit tests for `ExpressionSet` are added
- Tests which expect `semanticEquals` to be less intelligent than it now is are updated.
As a followup, we should consider auditing the places where we do `O(n)` `semanticEquals` operations and replace them with `ExpressionSet`. We should also consider consolidating `AttributeSet` as a specialized factory for an `ExpressionSet.`
Author: Michael Armbrust <michael@databricks.com>
Closes#11338 from marmbrus/expressionSet.
## What changes were proposed in this pull request?
Reverting SPARK-13376 (d563c8fa01) affects the test added by SPARK-13383. So, I am fixing the test.
Author: Yin Huai <yhuai@databricks.com>
Closes#11355 from yhuai/SPARK-13383-fix-test.
JIRA: https://issues.apache.org/jira/browse/SPARK-13383
## What changes were proposed in this pull request?
When we do column pruning in Optimizer, we put additional Project on top of a logical plan. However, when we already wrap a BroadcastHint on a logical plan, the added Project will hide BroadcastHint after later execution.
We should take care of BroadcastHint when we do column pruning.
## How was the this patch tested?
Unit test is added.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11260 from viirya/keep-broadcasthint.
## What changes were proposed in this pull request?
This PR mostly rewrite the ColumnPruning rule to support most of the SQL logical plans (except those for Dataset).
## How was the this patch tested?
This is test by unit tests, also manually test with TPCDS Q78, which could prune all unused columns successfully, improved the performance by 78% (from 22s to 12s).
Author: Davies Liu <davies@databricks.com>
Closes#11256 from davies/fix_column_pruning.
The current implementation of statistics of UnaryNode does not considering output (for example, Project may product much less columns than it's child), we should considering it to have a better guess.
We usually only join with few columns from a parquet table, the size of projected plan could be much smaller than the original parquet files. Having a better guess of size help we choose between broadcast join or sort merge join.
After this PR, I saw a few queries choose broadcast join other than sort merge join without turning spark.sql.autoBroadcastJoinThreshold for every query, ended up with about 6-8X improvements on end-to-end time.
We use `defaultSize` of DataType to estimate the size of a column, currently For DecimalType/StringType/BinaryType and UDT, we are over-estimate too much (4096 Bytes), so this PR change them to some more reasonable values. Here are the new defaultSize for them:
DecimalType: 8 or 16 bytes, based on the precision
StringType: 20 bytes
BinaryType: 100 bytes
UDF: default size of SQL type
These numbers are not perfect (hard to have a perfect number for them), but should be better than 4096.
Author: Davies Liu <davies@databricks.com>
Closes#11210 from davies/statics.
The type checking functions of `If` and `UnwrapOption` are fixed to eliminate spurious failures. `UnwrapOption` was checking for an input of `ObjectType` but `ObjectType`'s accept function was hard coded to return `false`. `If`'s type check was returning a false negative in the case that the two options differed only by nullability.
Tests added:
- an end-to-end regression test is added to `DatasetSuite` for the reported failure.
- all the unit tests in `ExpressionEncoderSuite` are augmented to also confirm successful analysis. These tests are actually what pointed out the additional issues with `If` resolution.
Author: Michael Armbrust <michael@databricks.com>
Closes#11316 from marmbrus/datasetOptions.
## What changes were proposed in this pull request?
This PR tries to fix all typos in all markdown files under `docs` module,
and fixes similar typos in other comments, too.
## How was the this patch tested?
manual tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11300 from dongjoon-hyun/minor_fix_typos.
JIRA: https://issues.apache.org/jira/browse/SPARK-13321
The following SQL can not be parsed with current parser:
SELECT `u_1`.`id` FROM (((SELECT `t0`.`id` FROM `default`.`t0`) UNION ALL (SELECT `t0`.`id` FROM `default`.`t0`)) UNION ALL (SELECT `t0`.`id` FROM `default`.`t0`)) AS u_1
We should fix it.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11204 from viirya/nested-union.
## What changes were proposed in this pull request?
This is a step towards merging `SQLContext` and `HiveContext`. A new internal Catalog API was introduced in #10982 and extended in #11069. This patch introduces an implementation of this API using `HiveClient`, an existing interface to Hive. It also extends `HiveClient` with additional calls to Hive that are needed to complete the catalog implementation.
*Where should I start reviewing?* The new catalog introduced is `HiveCatalog`. This class is relatively simple because it just calls `HiveClientImpl`, where most of the new logic is. I would not start with `HiveClient`, `HiveQl`, or `HiveMetastoreCatalog`, which are modified mainly because of a refactor.
*Why is this patch so big?* I had to refactor HiveClient to remove an intermediate representation of databases, tables, partitions etc. After this refactor `CatalogTable` convert directly to and from `HiveTable` (etc.). Otherwise we would have to first convert `CatalogTable` to the intermediate representation and then convert that to HiveTable, which is messy.
The new class hierarchy is as follows:
```
org.apache.spark.sql.catalyst.catalog.Catalog
- org.apache.spark.sql.catalyst.catalog.InMemoryCatalog
- org.apache.spark.sql.hive.HiveCatalog
```
Note that, as of this patch, none of these classes are currently used anywhere yet. This will come in the future before the Spark 2.0 release.
## How was the this patch tested?
All existing unit tests, and HiveCatalogSuite that extends CatalogTestCases.
Author: Andrew Or <andrew@databricks.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#11293 from rxin/hive-catalog.
## What changes were proposed in this pull request?
This patch renames logical.Subquery to logical.SubqueryAlias, which is a more appropriate name for this operator (versus subqueries as expressions).
## How was the this patch tested?
Unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#11288 from rxin/SPARK-13420.
This PR introduces several major changes:
1. Replacing `Expression.prettyString` with `Expression.sql`
The `prettyString` method is mostly an internal, developer faced facility for debugging purposes, and shouldn't be exposed to users.
1. Using SQL-like representation as column names for selected fields that are not named expression (back-ticks and double quotes should be removed)
Before, we were using `prettyString` as column names when possible, and sometimes the result column names can be weird. Here are several examples:
Expression | `prettyString` | `sql` | Note
------------------ | -------------- | ---------- | ---------------
`a && b` | `a && b` | `a AND b` |
`a.getField("f")` | `a[f]` | `a.f` | `a` is a struct
1. Adding trait `NonSQLExpression` extending from `Expression` for expressions that don't have a SQL representation (e.g. Scala UDF/UDAF and Java/Scala object expressions used for encoders)
`NonSQLExpression.sql` may return an arbitrary user facing string representation of the expression.
Author: Cheng Lian <lian@databricks.com>
Closes#10757 from liancheng/spark-12799.simplify-expression-string-methods.
```scala
// case 1: missing sort columns are resolvable if join is true
sql("SELECT explode(a) AS val, b FROM data WHERE b < 2 order by val, c")
// case 2: missing sort columns are not resolvable if join is false. Thus, issue an error message in this case
sql("SELECT explode(a) AS val FROM data order by val, c")
```
When sort columns are not in `Generate`, we can resolve them when `join` is equal to `true`. Still trying to add more test cases for the other `UnaryNode` types.
Could you review the changes? davies cloud-fan Thanks!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11198 from gatorsmile/missingInSort.
Conversion of outer joins, if the predicates in filter conditions can restrict the result sets so that all null-supplying rows are eliminated.
- `full outer` -> `inner` if both sides have such predicates
- `left outer` -> `inner` if the right side has such predicates
- `right outer` -> `inner` if the left side has such predicates
- `full outer` -> `left outer` if only the left side has such predicates
- `full outer` -> `right outer` if only the right side has such predicates
If applicable, this can greatly improve the performance, since outer join is much slower than inner join, full outer join is much slower than left/right outer join.
The original PR is https://github.com/apache/spark/pull/10542
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10567 from gatorsmile/outerJoinEliminationByFilterCond.
This PR adds support for rewriting constraints if there are aliases in the query plan. For e.g., if there is a query of form `SELECT a, a AS b`, any constraints on `a` now also apply to `b`.
JIRA: https://issues.apache.org/jira/browse/SPARK-13091
cc marmbrus
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11144 from sameeragarwal/alias.
JIRA: https://issues.apache.org/jira/browse/SPARK-13384
## What changes were proposed in this pull request?
When we de-duplicate attributes in Analyzer, we create new attributes. However, we don't keep original qualifiers. Some plans will be failed to analysed. We should keep original qualifiers in new attributes.
## How was the this patch tested?
Unit test is added.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11261 from viirya/keep-attr-qualifiers.
Currently, the columns in projects of Expand that are not used by Aggregate are not pruned, this PR fix that.
Author: Davies Liu <davies@databricks.com>
Closes#11225 from davies/fix_pruning_expand.
This patch adds a new optimizer rule for performing limit pushdown. Limits will now be pushed down in two cases:
- If a limit is on top of a `UNION ALL` operator, then a partition-local limit operator will be pushed to each of the union operator's children.
- If a limit is on top of an `OUTER JOIN` then a partition-local limit will be pushed to one side of the join. For `LEFT OUTER` and `RIGHT OUTER` joins, the limit will be pushed to the left and right side, respectively. For `FULL OUTER` join, we will only push limits when at most one of the inputs is already limited: if one input is limited we will push a smaller limit on top of it and if neither input is limited then we will limit the input which is estimated to be larger.
These optimizations were proposed previously by gatorsmile in #10451 and #10454, but those earlier PRs were closed and deferred for later because at that time Spark's physical `Limit` operator would trigger a full shuffle to perform global limits so there was a chance that pushdowns could actually harm performance by causing additional shuffles/stages. In #7334, we split the `Limit` operator into separate `LocalLimit` and `GlobalLimit` operators, so we can now push down only local limits (which don't require extra shuffles). This patch is based on both of gatorsmile's patches, with changes and simplifications due to partition-local-limiting.
When we push down the limit, we still keep the original limit in place, so we need a mechanism to ensure that the optimizer rule doesn't keep pattern-matching once the limit has been pushed down. In order to handle this, this patch adds a `maxRows` method to `SparkPlan` which returns the maximum number of rows that the plan can compute, then defines the pushdown rules to only push limits to children if the children's maxRows are greater than the limit's maxRows. This idea is carried over from #10451; see that patch for additional discussion.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11121 from JoshRosen/limit-pushdown-2.
The current implementation of ResolveSortReferences can only push one missing attributes into it's child, it failed to analyze TPCDS Q98, because of there are two missing attributes in that (one from Window, another from Aggregate).
Author: Davies Liu <davies@databricks.com>
Closes#11153 from davies/resolve_sort.
The parser currently parses the following strings without a hitch:
* Table Identifier:
* `a.b.c` should fail, but results in the following table identifier `a.b`
* `table!#` should fail, but results in the following table identifier `table`
* Expression
* `1+2 r+e` should fail, but results in the following expression `1 + 2`
This PR fixes this by adding terminated rules for both expression parsing and table identifier parsing.
cc cloud-fan (we discussed this in https://github.com/apache/spark/pull/10649) jayadevanmurali (this causes your PR https://github.com/apache/spark/pull/11051 to fail)
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#11159 from hvanhovell/SPARK-13276.
Some analysis rules generate aliases or auxiliary attribute references with the same name but different expression IDs. For example, `ResolveAggregateFunctions` introduces `havingCondition` and `aggOrder`, and `DistinctAggregationRewriter` introduces `gid`.
This is OK for normal query execution since these attribute references get expression IDs. However, it's troublesome when converting resolved query plans back to SQL query strings since expression IDs are erased.
Here's an example Spark 1.6.0 snippet for illustration:
```scala
sqlContext.range(10).select('id as 'a, 'id as 'b).registerTempTable("t")
sqlContext.sql("SELECT SUM(a) FROM t GROUP BY a, b ORDER BY COUNT(a), COUNT(b)").explain(true)
```
The above code produces the following resolved plan:
```
== Analyzed Logical Plan ==
_c0: bigint
Project [_c0#101L]
+- Sort [aggOrder#102L ASC,aggOrder#103L ASC], true
+- Aggregate [a#47L,b#48L], [(sum(a#47L),mode=Complete,isDistinct=false) AS _c0#101L,(count(a#47L),mode=Complete,isDistinct=false) AS aggOrder#102L,(count(b#48L),mode=Complete,isDistinct=false) AS aggOrder#103L]
+- Subquery t
+- Project [id#46L AS a#47L,id#46L AS b#48L]
+- LogicalRDD [id#46L], MapPartitionsRDD[44] at range at <console>:26
```
Here we can see that both aggregate expressions in `ORDER BY` are extracted into an `Aggregate` operator, and both of them are named `aggOrder` with different expression IDs.
The solution is to automatically add the expression IDs into the attribute name for the Alias and AttributeReferences that are generated by Analyzer in SQL Generation.
In this PR, it also resolves another issue. Users could use the same name as the internally generated names. The duplicate names should not cause name ambiguity. When resolving the column, Catalyst should not pick the column that is internally generated.
Could you review the solution? marmbrus liancheng
I did not set the newly added flag for all the alias and attribute reference generated by Analyzers. Please let me know if I should do it? Thank you!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11050 from gatorsmile/namingConflicts.
Adds the benchmark results as comments.
The codegen version is slower than the interpreted version for `simple` case becasue of 3 reasons:
1. codegen version use a more complex hash algorithm than interpreted version, i.e. `Murmur3_x86_32.hashInt` vs [simple multiplication and addition](https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala#L153).
2. codegen version will write the hash value to a row first and then read it out. I tried to create a `GenerateHasher` that can generate code to return hash value directly and got about 60% speed up for the `simple` case, does it worth?
3. the row in `simple` case only has one int field, so the runtime reflection may be removed because of branch prediction, which makes the interpreted version faster.
The `array` case is also slow for similar reasons, e.g. array elements are of same type, so interpreted version can probably get rid of runtime reflection by branch prediction.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10917 from cloud-fan/hash-benchmark.
nullability should only be considered as an optimization rather than part of the type system, so instead of failing analysis for mismatch nullability, we should pass analysis and add runtime null check.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11035 from cloud-fan/ignore-nullability.
Trivial search-and-replace to eliminate deprecation warnings in Scala 2.11.
Also works with 2.10
Author: Jakob Odersky <jakob@odersky.com>
Closes#11085 from jodersky/SPARK-13171.
https://issues.apache.org/jira/browse/SPARK-12939
Now we will catch `ObjectOperator` in `Analyzer` and resolve the `fromRowExpression/deserializer` inside it. Also update the `MapGroups` and `CoGroup` to pass in `dataAttributes`, so that we can correctly resolve value deserializer(the `child.output` contains both groupking key and values, which may mess things up if they have same-name attribtues). End-to-end tests are added.
follow-ups:
* remove encoders from typed aggregate expression.
* completely remove resolve/bind in `ExpressionEncoder`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10852 from cloud-fan/bug.
This patch incorporates review feedback from #11069, which is already merged.
Author: Andrew Or <andrew@databricks.com>
Closes#11080 from andrewor14/catalog-follow-ups.
Spark SQL should collapse adjacent `Repartition` operators and only keep the last one.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11064 from JoshRosen/collapse-repartition.
This is a small addendum to #10762 to make the code more robust again future changes.
Author: Reynold Xin <rxin@databricks.com>
Closes#11070 from rxin/SPARK-12828-natural-join.
This is a step towards consolidating `SQLContext` and `HiveContext`.
This patch extends the existing Catalog API added in #10982 to include methods for handling table partitions. In particular, a partition is identified by `PartitionSpec`, which is just a `Map[String, String]`. The Catalog is still not used by anything yet, but its API is now more or less complete and an implementation is fully tested.
About 200 lines are test code.
Author: Andrew Or <andrew@databricks.com>
Closes#11069 from andrewor14/catalog.
The ```SparkSqlLexer``` currently swallows characters which have not been defined in the grammar. This causes problems with SQL commands, such as: ```add jar file:///tmp/ab/TestUDTF.jar```. In this example the `````` is swallowed.
This PR adds an extra Lexer rule to handle such input, and makes a tiny modification to the ```ASTNode```.
cc davies liancheng
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#11052 from hvanhovell/SPARK-13157.
Based on the semantics of a query, we can derive a number of data constraints on output of each (logical or physical) operator. For instance, if a filter defines `‘a > 10`, we know that the output data of this filter satisfies 2 constraints:
1. `‘a > 10`
2. `isNotNull(‘a)`
This PR proposes a possible way of keeping track of these constraints and propagating them in the logical plan, which can then help us build more advanced optimizations (such as pruning redundant filters, optimizing joins, among others). We define constraints as a set of (implicitly conjunctive) expressions. For e.g., if a filter operator has constraints = `Set(‘a > 10, ‘b < 100)`, it’s implied that the outputs satisfy both individual constraints (i.e., `‘a > 10` AND `‘b < 100`).
Design Document: https://docs.google.com/a/databricks.com/document/d/1WQRgDurUBV9Y6CWOBS75PQIqJwT-6WftVa18xzm7nCo/edit?usp=sharing
Author: Sameer Agarwal <sameer@databricks.com>
Closes#10844 from sameeragarwal/constraints.
when we generate map, we first randomly pick a length, then create a seq of key value pair with the expected length, and finally call `toMap`. However, `toMap` will remove all duplicated keys, which makes the actual map size much less than we expected.
This PR fixes this problem by put keys in a set first, to guarantee we have enough keys to build a map with expected length.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10930 from cloud-fan/random-generator.
This pull request creates an internal catalog API. The creation of this API is the first step towards consolidating SQLContext and HiveContext. I envision we will have two different implementations in Spark 2.0: (1) a simple in-memory implementation, and (2) an implementation based on the current HiveClient (ClientWrapper).
I took a look at what Hive's internal metastore interface/implementation, and then created this API based on it. I believe this is the minimal set needed in order to achieve all the needed functionality.
Author: Reynold Xin <rxin@databricks.com>
Closes#10982 from rxin/SPARK-13078.
JIRA: https://issues.apache.org/jira/browse/SPARK-12705
**Scope:**
This PR is a general fix for sorting reference resolution when the child's `outputSet` does not have the order-by attributes (called, *missing attributes*):
- UnaryNode support is limited to `Project`, `Window`, `Aggregate`, `Distinct`, `Filter`, `RepartitionByExpression`.
- We will not try to resolve the missing references inside a subquery, unless the outputSet of this subquery contains it.
**General Reference Resolution Rules:**
- Jump over the nodes with the following types: `Distinct`, `Filter`, `RepartitionByExpression`. Do not need to add missing attributes. The reason is their `outputSet` is decided by their `inputSet`, which is the `outputSet` of their children.
- Group-by expressions in `Aggregate`: missing order-by attributes are not allowed to be added into group-by expressions since it will change the query result. Thus, in RDBMS, it is not allowed.
- Aggregate expressions in `Aggregate`: if the group-by expressions in `Aggregate` contains the missing attributes but aggregate expressions do not have it, just add them into the aggregate expressions. This can resolve the analysisExceptions thrown by the three TCPDS queries.
- `Project` and `Window` are special. We just need to add the missing attributes to their `projectList`.
**Implementation:**
1. Traverse the whole tree in a pre-order manner to find all the resolvable missing order-by attributes.
2. Traverse the whole tree in a post-order manner to add the found missing order-by attributes to the node if their `inputSet` contains the attributes.
3. If the origins of the missing order-by attributes are different nodes, each pass only resolves the missing attributes that are from the same node.
**Risk:**
Low. This rule will be trigger iff ```!s.resolved && child.resolved``` is true. Thus, very few cases are affected.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10678 from gatorsmile/sortWindows.
Our current Intersect physical operator simply delegates to RDD.intersect. We should remove the Intersect physical operator and simply transform a logical intersect into a semi-join with distinct. This way, we can take advantage of all the benefits of join implementations (e.g. managed memory, code generation, broadcast joins).
After a search, I found one of the mainstream RDBMS did the same. In their query explain, Intersect is replaced by Left-semi Join. Left-semi Join could help outer-join elimination in Optimizer, as shown in the PR: https://github.com/apache/spark/pull/10566
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10630 from gatorsmile/IntersectBySemiJoin.
JIRA: https://issues.apache.org/jira/browse/SPARK-11955
Currently we simply skip pushdowning filters in parquet if we enable schema merging.
However, we can actually mark particular fields in merging schema for safely pushdowning filters in parquet.
Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#9940 from viirya/safe-pushdown-parquet-filters.
This patch adds support for complex types for ColumnarBatch. ColumnarBatch supports structs
and arrays. There is a simple mapping between the richer catalyst types to these two. Strings
are treated as an array of bytes.
ColumnarBatch will contain a column for each node of the schema. Non-complex schemas consists
of just leaf nodes. Structs represent an internal node with one child for each field. Arrays
are internal nodes with one child. Structs just contain nullability. Arrays contain offsets
and lengths into the child array. This structure is able to handle arbitrary nesting. It has
the key property that we maintain columnar throughout and that primitive types are only stored
in the leaf nodes and contiguous across rows. For example, if the schema is
```
array<array<int>>
```
There are three columns in the schema. The internal nodes each have one children. The leaf node contains all the int data stored consecutively.
As part of this, this patch adds append APIs in addition to the Put APIs (e.g. putLong(rowid, v)
vs appendLong(v)). These APIs are necessary when the batch contains variable length elements.
The vectors are not fixed length and will grow as necessary. This should make the usage a lot
simpler for the writer.
Author: Nong Li <nong@databricks.com>
Closes#10820 from nongli/spark-12854.
As we begin to use unsafe row writing framework(`BufferHolder` and `UnsafeRowWriter`) in more and more places(`UnsafeProjection`, `UnsafeRowParquetRecordReader`, `GenerateColumnAccessor`, etc.), we should add more doc to it and make it easier to use.
This PR abstract the technique used in `UnsafeRowParquetRecordReader`: avoid unnecessary operatition as more as possible. For example, do not always point the row to the buffer at the end, we only need to update the size of row. If all fields are of primitive type, we can even save the row size updating. Then we can apply this technique to more places easily.
a local benchmark shows `UnsafeProjection` is up to 1.7x faster after this PR:
**old version**
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
unsafe projection: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
single long 2616.04 102.61 1.00 X
single nullable long 3032.54 88.52 0.86 X
primitive types 9121.05 29.43 0.29 X
nullable primitive types 12410.60 21.63 0.21 X
```
**new version**
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
unsafe projection: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
single long 1533.34 175.07 1.00 X
single nullable long 2306.73 116.37 0.66 X
primitive types 8403.93 31.94 0.18 X
nullable primitive types 12448.39 21.56 0.12 X
```
For single non-nullable long(the best case), we can have about 1.7x speed up. Even it's nullable, we can still have 1.3x speed up. For other cases, it's not such a boost as the saved operations only take a little proportion of the whole process. The benchmark code is included in this PR.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10809 from cloud-fan/unsafe-projection.
This pull request implements strength reduction for comparing integral expressions and decimal literals, which is more common now because we switch to parsing fractional literals as decimal types (rather than doubles). I added the rules to the existing DecimalPrecision rule with some refactoring to simplify the control flow. I also moved DecimalPrecision rule into its own file due to the growing size.
Author: Reynold Xin <rxin@databricks.com>
Closes#10882 from rxin/SPARK-12904-1.
Benchmark it on 4 different schemas, the result:
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Hash For simple: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
interpreted version 31.47 266.54 1.00 X
codegen version 64.52 130.01 0.49 X
```
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Hash For normal: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
interpreted version 4068.11 0.26 1.00 X
codegen version 1175.92 0.89 3.46 X
```
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Hash For array: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
interpreted version 9276.70 0.06 1.00 X
codegen version 14762.23 0.04 0.63 X
```
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Hash For map: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
interpreted version 58869.79 0.01 1.00 X
codegen version 9285.36 0.06 6.34 X
```
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10816 from cloud-fan/hash-benchmark.
The existing `Union` logical operator only supports two children. Thus, adding a new logical operator `Unions` which can have arbitrary number of children to replace the existing one.
`Union` logical plan is a binary node. However, a typical use case for union is to union a very large number of input sources (DataFrames, RDDs, or files). It is not uncommon to union hundreds of thousands of files. In this case, our optimizer can become very slow due to the large number of logical unions. We should change the Union logical plan to support an arbitrary number of children, and add a single rule in the optimizer to collapse all adjacent `Unions` into a single `Unions`. Note that this problem doesn't exist in physical plan, because the physical `Unions` already supports arbitrary number of children.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10577 from gatorsmile/unionAllMultiChildren.
Also updated documentation to explain why ComputeCurrentTime and EliminateSubQueries are in the optimizer rather than analyzer.
Author: Reynold Xin <rxin@databricks.com>
Closes#10837 from rxin/optimizer-analyzer-comment.
The three optimization cases are:
1. If the first branch's condition is a true literal, remove the CaseWhen and use the value from that branch.
2. If a branch's condition is a false or null literal, remove that branch.
3. If only the else branch is left, remove the CaseWhen and use the value from the else branch.
Author: Reynold Xin <rxin@databricks.com>
Closes#10827 from rxin/SPARK-12770.
Call `dealias` on local types to fix schema generation for abstract type members, such as
```scala
type KeyValue = (Int, String)
```
Add simple test
Author: Jakob Odersky <jodersky@gmail.com>
Closes#10749 from jodersky/aliased-schema.
I was reading this part of the analyzer code again and got confused by the difference between findWiderTypeForTwo and findTightestCommonTypeOfTwo.
I also simplified WidenSetOperationTypes to make it a lot simpler. The easiest way to review this one is to just read the original code, and the new code. The logic is super simple.
Author: Reynold Xin <rxin@databricks.com>
Closes#10802 from rxin/SPARK-12873.
In this PR the new CatalystQl parser stack reaches grammar parity with the old Parser-Combinator based SQL Parser. This PR also replaces all uses of the old Parser, and removes it from the code base.
Although the existing Hive and SQL parser dialects were mostly the same, some kinks had to be worked out:
- The SQL Parser allowed syntax like ```APPROXIMATE(0.01) COUNT(DISTINCT a)```. In order to make this work we needed to hardcode approximate operators in the parser, or we would have to create an approximate expression. ```APPROXIMATE_COUNT_DISTINCT(a, 0.01)``` would also do the job and is much easier to maintain. So, this PR **removes** this keyword.
- The old SQL Parser supports ```LIMIT``` clauses in nested queries. This is **not supported** anymore. See https://github.com/apache/spark/pull/10689 for the rationale for this.
- Hive has a charset name char set literal combination it supports, for instance the following expression ```_ISO-8859-1 0x4341464562616265``` would yield this string: ```CAFEbabe```. Hive will only allow charset names to start with an underscore. This is quite annoying in spark because as soon as you use a tuple names will start with an underscore. In this PR we **remove** this feature from the parser. It would be quite easy to implement such a feature as an Expression later on.
- Hive and the SQL Parser treat decimal literals differently. Hive will turn any decimal into a ```Double``` whereas the SQL Parser would convert a non-scientific decimal into a ```BigDecimal```, and would turn a scientific decimal into a Double. We follow Hive's behavior here. The new parser supports a big decimal literal, for instance: ```81923801.42BD```, which can be used when a big decimal is needed.
cc rxin viirya marmbrus yhuai cloud-fan
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10745 from hvanhovell/SPARK-12575-2.
The goal of this PR is to eliminate unnecessary translations when there are back-to-back `MapPartitions` operations. In order to achieve this I also made the following simplifications:
- Operators no longer have hold encoders, instead they have only the expressions that they need. The benefits here are twofold: the expressions are visible to transformations so go through the normal resolution/binding process. now that they are visible we can change them on a case by case basis.
- Operators no longer have type parameters. Since the engine is responsible for its own type checking, having the types visible to the complier was an unnecessary complication. We still leverage the scala compiler in the companion factory when constructing a new operator, but after this the types are discarded.
Deferred to a follow up PR:
- Remove as much of the resolution/binding from Dataset/GroupedDataset as possible. We should still eagerly check resolution and throw an error though in the case of mismatches for an `as` operation.
- Eliminate serializations in more cases by adding more cases to `EliminateSerialization`
Author: Michael Armbrust <michael@databricks.com>
Closes#10747 from marmbrus/encoderExpressions.
This pull request rewrites CaseWhen expression to break the single, monolithic "branches" field into a sequence of tuples (Seq[(condition, value)]) and an explicit optional elseValue field.
Prior to this pull request, each even position in "branches" represents the condition for each branch, and each odd position represents the value for each branch. The use of them have been pretty confusing with a lot sliding windows or grouped(2) calls.
Author: Reynold Xin <rxin@databricks.com>
Closes#10734 from rxin/simplify-case.
Fix the style violation (space before , and :).
This PR is a followup for #10643 and rework of #10685 .
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#10732 from sarutak/SPARK-12692-followup-sql.
This patch removes CaseKeyWhen expression and replaces it with a factory method that generates the equivalent CaseWhen. This reduces the amount of code we'd need to maintain in the future for both code generation and optimizer.
Note that we introduced CaseKeyWhen to avoid duplicate evaluations of the key. This is no longer a problem because we now have common subexpression elimination.
Author: Reynold Xin <rxin@databricks.com>
Closes#10722 from rxin/SPARK-12768.
This pull request does a few small things:
1. Separated if simplification from BooleanSimplification and created a new rule SimplifyConditionals. In the future we can also simplify other conditional expressions here.
2. Added unit test for SimplifyConditionals.
3. Renamed SimplifyCaseConversionExpressionsSuite to SimplifyStringCaseConversionSuite
Author: Reynold Xin <rxin@databricks.com>
Closes#10716 from rxin/SPARK-12762.
Fix the style violation (space before , and :).
This PR is a followup for #10643.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#10718 from sarutak/SPARK-12692-followup-sql.
The PR allows us to use the new SQL parser to parse SQL expressions such as: ```1 + sin(x*x)```
We enable this functionality in this PR, but we will not start using this actively yet. This will be done as soon as we have reached grammar parity with the existing parser stack.
cc rxin
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10649 from hvanhovell/SPARK-12576.
This PR tries to enable Spark SQL to convert resolved logical plans back to SQL query strings. For now, the major use case is to canonicalize Spark SQL native view support. The major entry point is `SQLBuilder.toSQL`, which returns an `Option[String]` if the logical plan is recognized.
The current version is still in WIP status, and is quite limited. Known limitations include:
1. The logical plan must be analyzed but not optimized
The optimizer erases `Subquery` operators, which contain necessary scope information for SQL generation. Future versions should be able to recover erased scope information by inserting subqueries when necessary.
1. The logical plan must be created using HiveQL query string
Query plans generated by composing arbitrary DataFrame API combinations are not supported yet. Operators within these query plans need to be rearranged into a canonical form that is more suitable for direct SQL generation. For example, the following query plan
```
Filter (a#1 < 10)
+- MetastoreRelation default, src, None
```
need to be canonicalized into the following form before SQL generation:
```
Project [a#1, b#2, c#3]
+- Filter (a#1 < 10)
+- MetastoreRelation default, src, None
```
Otherwise, the SQL generation process will have to handle a large number of special cases.
1. Only a fraction of expressions and basic logical plan operators are supported in this PR
Currently, 95.7% (1720 out of 1798) query plans in `HiveCompatibilitySuite` can be successfully converted to SQL query strings.
Known unsupported components are:
- Expressions
- Part of math expressions
- Part of string expressions (buggy?)
- Null expressions
- Calendar interval literal
- Part of date time expressions
- Complex type creators
- Special `NOT` expressions, e.g. `NOT LIKE` and `NOT IN`
- Logical plan operators/patterns
- Cube, rollup, and grouping set
- Script transformation
- Generator
- Distinct aggregation patterns that fit `DistinctAggregationRewriter` analysis rule
- Window functions
Support for window functions, generators, and cubes etc. will be added in follow-up PRs.
This PR leverages `HiveCompatibilitySuite` for testing SQL generation in a "round-trip" manner:
* For all select queries, we try to convert it back to SQL
* If the query plan is convertible, we parse the generated SQL into a new logical plan
* Run the new logical plan instead of the original one
If the query plan is inconvertible, the test case simply falls back to the original logic.
TODO
- [x] Fix failed test cases
- [x] Support for more basic expressions and logical plan operators (e.g. distinct aggregation etc.)
- [x] Comments and documentation
Author: Cheng Lian <lian@databricks.com>
Closes#10541 from liancheng/sql-generation.
JIRA: https://issues.apache.org/jira/browse/SPARK-12687
Some queries such as `(select 1 as a) union (select 2 as a)` can't work. This patch fixes it.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#10660 from viirya/fix-union.
Fix most build warnings: mostly deprecated API usages. I'll annotate some of the changes below. CC rxin who is leading the charge to remove the deprecated APIs.
Author: Sean Owen <sowen@cloudera.com>
Closes#10570 from srowen/SPARK-12618.
JIRA: https://issues.apache.org/jira/browse/SPARK-12439
In toCatalystArray, we should look at the data type returned by dataTypeFor instead of silentSchemaFor, to determine if the element is native type. An obvious problem is when the element is Option[Int] class, catalsilentSchemaFor will return Int, then we will wrongly recognize the element is native type.
There is another problem when using Option as array element. When we encode data like Seq(Some(1), Some(2), None) with encoder, we will use MapObjects to construct an array for it later. But in MapObjects, we don't check if the return value of lambdaFunction is null or not. That causes a bug that the decoded data for Seq(Some(1), Some(2), None) would be Seq(1, 2, -1), instead of Seq(1, 2, null).
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#10391 from viirya/fix-catalystarray.
address comments in #10435
This makes the API easier to use if user programmatically generate the call to hash, and they will get analysis exception if the arguments of hash is empty.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10588 from cloud-fan/hash.
just write the arguments into unsafe row and use murmur3 to calculate hash code
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10435 from cloud-fan/hash-expr.
It is currently possible to change the values of the supposedly immutable ```GenericRow``` and ```GenericInternalRow``` classes. This is caused by the fact that scala's ArrayOps ```toArray``` (returned by calling ```toSeq```) will return the backing array instead of a copy. This PR fixes this problem.
This PR was inspired by https://github.com/apache/spark/pull/10374 by apo1.
cc apo1 sarutak marmbrus cloud-fan nongli (everyone in the previous conversation).
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10553 from hvanhovell/SPARK-12421.
Right now, numFields will be passed in by pointTo(), then bitSetWidthInBytes is calculated, making pointTo() a little bit heavy.
It should be part of constructor of UnsafeRow.
Author: Davies Liu <davies@databricks.com>
Closes#10528 from davies/numFields.
Most of cases we should propagate null when call `NewInstance`, and so far there is only one case we should stop null propagation: create product/java bean. So I think it makes more sense to propagate null by dafault.
This also fixes a bug when encode null array/map, which is firstly discovered in https://github.com/apache/spark/pull/10401
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10443 from cloud-fan/encoder.
Moved (case) classes Strategy, Once, FixedPoint and Batch to the companion object. This is necessary if we want to have the Optimizer easily extendable in the following sense: Usually a user wants to add additional rules, and just take the ones that are already there. However, inner classes made that impossible since the code did not compile
This allows easy extension of existing Optimizers see the DefaultOptimizerExtendableSuite for a corresponding test case.
Author: Stephan Kessler <stephan.kessler@sap.com>
Closes#10174 from stephankessler/SPARK-7727.
When the filter is ```"b in ('1', '2')"```, the filter is not pushed down to Parquet. Thanks!
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10278 from gatorsmile/parquetFilterNot.
