## What changes were proposed in this pull request?
This PR split the the single `createPartitions()` call into smaller batches, which could prevent Hive metastore from OOM (caused by millions of partitions).
It will also try to gather all the fast stats (number of files and total size of all files) in parallel to avoid the bottle neck of listing the files in metastore sequential, which is controlled by spark.sql.gatherFastStats (enabled by default).
## How was this patch tested?
Tested locally with 10000 partitions and 100 files with embedded metastore, without gathering fast stats in parallel, adding partitions took 153 seconds, after enable that, gathering the fast stats took about 34 seconds, adding these partitions took 25 seconds (most of the time spent in object store), 59 seconds in total, 2.5X faster (with larger cluster, gathering will much faster).
Author: Davies Liu <davies@databricks.com>
Closes#14607 from davies/repair_batch.
## What changes were proposed in this pull request?
Jira : https://issues.apache.org/jira/browse/SPARK-17271
Planner is adding un-needed SORT operation due to bug in the way comparison for `SortOrder` is done at https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/EnsureRequirements.scala#L253
`SortOrder` needs to be compared semantically because `Expression` within two `SortOrder` can be "semantically equal" but not literally equal objects.
eg. In case of `sql("SELECT * FROM table1 a JOIN table2 b ON a.col1=b.col1")`
Expression in required SortOrder:
```
AttributeReference(
name = "col1",
dataType = LongType,
nullable = false
) (exprId = exprId,
qualifier = Some("a")
)
```
Expression in child SortOrder:
```
AttributeReference(
name = "col1",
dataType = LongType,
nullable = false
) (exprId = exprId)
```
Notice that the output column has a qualifier but the child attribute does not but the inherent expression is the same and hence in this case we can say that the child satisfies the required sort order.
This PR includes following changes:
- Added a `semanticEquals` method to `SortOrder` so that it can compare underlying child expressions semantically (and not using default Object.equals)
- Fixed `EnsureRequirements` to use semantic comparison of SortOrder
## How was this patch tested?
- Added a test case to `PlannerSuite`. Ran rest tests in `PlannerSuite`
Author: Tejas Patil <tejasp@fb.com>
Closes#14841 from tejasapatil/SPARK-17271_sort_order_equals_bug.
## What changes were proposed in this pull request?
As part of breaking Optimizer.scala apart, this patch moves various join rules into a single file.
## How was this patch tested?
This should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#14846 from rxin/SPARK-17274.
## What changes were proposed in this pull request?
As part of breaking Optimizer.scala apart, this patch moves various expression optimization rules into a single file.
## How was this patch tested?
This should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#14845 from rxin/SPARK-17273.
## What changes were proposed in this pull request?
As part of breaking Optimizer.scala apart, this patch moves various subquery rules into a single file.
## How was this patch tested?
This should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#14844 from rxin/SPARK-17272.
## What changes were proposed in this pull request?
As part of breaking Optimizer.scala apart, this patch moves various finish analysis optimization stage rules into a single file. I'm submitting separate pull requests so we can more easily merge this in branch-2.0 to simplify optimizer backports.
## How was this patch tested?
This should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#14838 from rxin/SPARK-17269.
## What changes were proposed in this pull request?
As part of breaking Optimizer.scala apart, this patch moves various Dataset object optimization rules into a single file. I'm submitting separate pull requests so we can more easily merge this in branch-2.0 to simplify optimizer backports.
## How was this patch tested?
This should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#14839 from rxin/SPARK-17270.
## What changes were proposed in this pull request?
Given that non-deterministic expressions can be stateful, pushing them down the query plan during the optimization phase can cause incorrect behavior. This patch fixes that issue by explicitly disabling that.
## How was this patch tested?
A new test in `FilterPushdownSuite` that checks catalyst behavior for both deterministic and non-deterministic join conditions.
Author: Sameer Agarwal <sameerag@cs.berkeley.edu>
Closes#14815 from sameeragarwal/constraint-inputfile.
## What changes were proposed in this pull request?
This PR adds parser support for `BigDecimal` literals. If you append the suffix `BD` to a valid number then this will be interpreted as a `BigDecimal`, for example `12.0E10BD` will interpreted into a BigDecimal with scale -9 and precision 3. This is useful in situations where you need exact values.
## How was this patch tested?
Added tests to `ExpressionParserSuite`, `ExpressionSQLBuilderSuite` and `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14819 from hvanhovell/SPARK-17246.
## What changes were proposed in this pull request?
improve the document to make it easier to understand and also mention window operator.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14822 from cloud-fan/object-agg.
## What changes were proposed in this pull request?
Currently, type-widening does not work between `TimestampType` and `DateType`.
This applies to `SetOperation`, `Union`, `In`, `CaseWhen`, `Greatest`, `Leatest`, `CreateArray`, `CreateMap`, `Coalesce`, `NullIf`, `IfNull`, `Nvl` and `Nvl2`, .
This PR adds the support for widening `DateType` to `TimestampType` for them.
For a simple example,
**Before**
```scala
Seq(Tuple2(new Timestamp(0), new Date(0))).toDF("a", "b").selectExpr("greatest(a, b)").show()
```
shows below:
```
cannot resolve 'greatest(`a`, `b`)' due to data type mismatch: The expressions should all have the same type, got GREATEST(timestamp, date)
```
or union as below:
```scala
val a = Seq(Tuple1(new Timestamp(0))).toDF()
val b = Seq(Tuple1(new Date(0))).toDF()
a.union(b).show()
```
shows below:
```
Union can only be performed on tables with the compatible column types. DateType <> TimestampType at the first column of the second table;
```
**After**
```scala
Seq(Tuple2(new Timestamp(0), new Date(0))).toDF("a", "b").selectExpr("greatest(a, b)").show()
```
shows below:
```
+----------------------------------------------------+
|greatest(CAST(a AS TIMESTAMP), CAST(b AS TIMESTAMP))|
+----------------------------------------------------+
| 1969-12-31 16:00:...|
+----------------------------------------------------+
```
or union as below:
```scala
val a = Seq(Tuple1(new Timestamp(0))).toDF()
val b = Seq(Tuple1(new Date(0))).toDF()
a.union(b).show()
```
shows below:
```
+--------------------+
| _1|
+--------------------+
|1969-12-31 16:00:...|
|1969-12-31 00:00:...|
+--------------------+
```
## How was this patch tested?
Unit tests in `TypeCoercionSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Author: HyukjinKwon <gurwls223@gmail.com>
Closes#14786 from HyukjinKwon/SPARK-17212.
## What changes were proposed in this pull request?
This PR introduces an abstract class `TypedImperativeAggregate` so that an aggregation function of TypedImperativeAggregate can use **arbitrary** user-defined Java object as intermediate aggregation buffer object.
**This has advantages like:**
1. It now can support larger category of aggregation functions. For example, it will be much easier to implement aggregation function `percentile_approx`, which has a complex aggregation buffer definition.
2. It can be used to avoid doing serialization/de-serialization for every call of `update` or `merge` when converting domain specific aggregation object to internal Spark-Sql storage format.
3. It is easier to integrate with other existing monoid libraries like algebird, and supports more aggregation functions with high performance.
Please see `org.apache.spark.sql.TypedImperativeAggregateSuite.TypedMaxAggregate` to find an example of how to defined a `TypedImperativeAggregate` aggregation function.
Please see Java doc of `TypedImperativeAggregate` and Jira ticket SPARK-17187 for more information.
## How was this patch tested?
Unit tests.
Author: Sean Zhong <seanzhong@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#14753 from clockfly/object_aggregation_buffer_try_2.
This patch updates `Literal.sql` to properly generate SQL for `NaN` and `Infinity` float and double literals: these special values need to be handled differently from regular values, since simply appending a suffix to the value's `toString()` representation will not work for these values.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#14777 from JoshRosen/SPARK-17205.
### What changes were proposed in this pull request?
This PR is to fix an incorrect outer join elimination when filter's `isNotNull` constraints is unable to filter out all null-supplying rows. For example, `isnotnull(coalesce(b#227, c#238))`.
Users can hit this error when they try to use `using/natural outer join`, which is converted to a normal outer join with a `coalesce` expression on the `using columns`. For example,
```Scala
val a = Seq((1, 2), (2, 3)).toDF("a", "b")
val b = Seq((2, 5), (3, 4)).toDF("a", "c")
val c = Seq((3, 1)).toDF("a", "d")
val ab = a.join(b, Seq("a"), "fullouter")
ab.join(c, "a").explain(true)
```
The dataframe `ab` is doing `using full-outer join`, which is converted to a normal outer join with a `coalesce` expression. Constraints inference generates a `Filter` with constraints `isnotnull(coalesce(b#227, c#238))`. Then, it triggers a wrong outer join elimination and generates a wrong result.
```
Project [a#251, b#227, c#237, d#247]
+- Join Inner, (a#251 = a#246)
:- Project [coalesce(a#226, a#236) AS a#251, b#227, c#237]
: +- Join FullOuter, (a#226 = a#236)
: :- Project [_1#223 AS a#226, _2#224 AS b#227]
: : +- LocalRelation [_1#223, _2#224]
: +- Project [_1#233 AS a#236, _2#234 AS c#237]
: +- LocalRelation [_1#233, _2#234]
+- Project [_1#243 AS a#246, _2#244 AS d#247]
+- LocalRelation [_1#243, _2#244]
== Optimized Logical Plan ==
Project [a#251, b#227, c#237, d#247]
+- Join Inner, (a#251 = a#246)
:- Project [coalesce(a#226, a#236) AS a#251, b#227, c#237]
: +- Filter isnotnull(coalesce(a#226, a#236))
: +- Join FullOuter, (a#226 = a#236)
: :- LocalRelation [a#226, b#227]
: +- LocalRelation [a#236, c#237]
+- LocalRelation [a#246, d#247]
```
**A note to the `Committer`**, please also give the credit to dongjoon-hyun who submitted another PR for fixing this issue. https://github.com/apache/spark/pull/14580
### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14661 from gatorsmile/fixOuterJoinElimination.
## What changes were proposed in this pull request?
Currently `MapObjects` does not make copies of unsafe-backed data, leading to problems like [SPARK-17061](https://issues.apache.org/jira/browse/SPARK-17061) [SPARK-17093](https://issues.apache.org/jira/browse/SPARK-17093).
This patch makes `MapObjects` make copies of unsafe-backed data.
Generated code - prior to this patch:
```java
...
/* 295 */ if (isNull12) {
/* 296 */ convertedArray1[loopIndex1] = null;
/* 297 */ } else {
/* 298 */ convertedArray1[loopIndex1] = value12;
/* 299 */ }
...
```
Generated code - after this patch:
```java
...
/* 295 */ if (isNull12) {
/* 296 */ convertedArray1[loopIndex1] = null;
/* 297 */ } else {
/* 298 */ convertedArray1[loopIndex1] = value12 instanceof UnsafeRow? value12.copy() : value12;
/* 299 */ }
...
```
## How was this patch tested?
Add a new test case which would fail without this patch.
Author: Liwei Lin <lwlin7@gmail.com>
Closes#14698 from lw-lin/mapobjects-copy.
### What changes were proposed in this pull request?
Since `HiveClient` is used to interact with the Hive metastore, it should be hidden in `HiveExternalCatalog`. After moving `HiveClient` into `HiveExternalCatalog`, `HiveSharedState` becomes a wrapper of `HiveExternalCatalog`. Thus, removal of `HiveSharedState` becomes straightforward. After removal of `HiveSharedState`, the reflection logic is directly applied on the choice of `ExternalCatalog` types, based on the configuration of `CATALOG_IMPLEMENTATION`.
~~`HiveClient` is also used/invoked by the other entities besides HiveExternalCatalog, we defines the following two APIs: getClient and getNewClient~~
### How was this patch tested?
The existing test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14757 from gatorsmile/removeHiveClient.
## What changes were proposed in this pull request?
Given that filters based on non-deterministic constraints shouldn't be pushed down in the query plan, unnecessarily inferring them is confusing and a source of potential bugs. This patch simplifies the inferring logic by simply ignoring them.
## How was this patch tested?
Added a new test in `ConstraintPropagationSuite`.
Author: Sameer Agarwal <sameerag@cs.berkeley.edu>
Closes#14795 from sameeragarwal/deterministic-constraints.
## What changes were proposed in this pull request?
Currently, two-word window functions like `row_number`, `dense_rank`, `percent_rank`, and `cume_dist` are expressed without `_` in error messages. We had better show the correct names.
**Before**
```scala
scala> sql("select row_number()").show
java.lang.UnsupportedOperationException: Cannot evaluate expression: rownumber()
```
**After**
```scala
scala> sql("select row_number()").show
java.lang.UnsupportedOperationException: Cannot evaluate expression: row_number()
```
## How was this patch tested?
Pass the Jenkins and manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14571 from dongjoon-hyun/SPARK-16983.
## What changes were proposed in this pull request?
Actually Spark SQL doesn't support index, the catalog table type `INDEX` is from Hive. However, most operations in Spark SQL can't handle index table, e.g. create table, alter table, etc.
Logically index table should be invisible to end users, and Hive also generates special table name for index table to avoid users accessing it directly. Hive has special SQL syntax to create/show/drop index tables.
At Spark SQL side, although we can describe index table directly, but the result is unreadable, we should use the dedicated SQL syntax to do it(e.g. `SHOW INDEX ON tbl`). Spark SQL can also read index table directly, but the result is always empty.(Can hive read index table directly?)
This PR remove the table type `INDEX`, to make it clear that Spark SQL doesn't support index currently.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14752 from cloud-fan/minor2.
When Spark emits SQL for a string literal, it should wrap the string in single quotes, not double quotes. Databases which adhere more strictly to the ANSI SQL standards, such as Postgres, allow only single-quotes to be used for denoting string literals (see http://stackoverflow.com/a/1992331/590203).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#14763 from JoshRosen/SPARK-17194.
## What changes were proposed in this pull request?
Use `CatalystConf.resolver` consistently for case-sensitivity comparison (removed dups).
## How was this patch tested?
Local build. Waiting for Jenkins to ensure clean build and test.
Author: Jacek Laskowski <jacek@japila.pl>
Closes#14771 from jaceklaskowski/17199-catalystconf-resolver.
## What changes were proposed in this pull request?
This is a sub-task of [SPARK-16283](https://issues.apache.org/jira/browse/SPARK-16283) (Implement percentile_approx SQL function), which moves class QuantileSummaries to project catalyst so that it can be reused when implementing aggregation function `percentile_approx`.
## How was this patch tested?
This PR only does class relocation, class implementation is not changed.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14754 from clockfly/move_QuantileSummaries_to_catalyst.
## What changes were proposed in this pull request?
This PR marks the abstract class `Collect` as non-deterministic since the results of `CollectList` and `CollectSet` depend on the actual order of input rows.
## How was this patch tested?
Existing test cases should be enough.
Author: Cheng Lian <lian@databricks.com>
Closes#14749 from liancheng/spark-17182-non-deterministic-collect.
## What changes were proposed in this pull request?
The range operator previously didn't support SQL generation, which made it not possible to use in views.
## How was this patch tested?
Unit tests.
cc hvanhovell
Author: Eric Liang <ekl@databricks.com>
Closes#14724 from ericl/spark-17162.
## What changes were proposed in this pull request?
In 2.0, we change the threshold of splitting expressions from 16K to 64K, which cause very bad performance on wide table, because the generated method can't be JIT compiled by default (above the limit of 8K bytecode).
This PR will decrease it to 1K, based on the benchmark results for a wide table with 400 columns of LongType.
It also fix a bug around splitting expression in whole-stage codegen (it should not split them).
## How was this patch tested?
Added benchmark suite.
Author: Davies Liu <davies@databricks.com>
Closes#14692 from davies/split_exprs.
## What changes were proposed in this pull request?
Currently, `NullPropagation` optimizer replaces `COUNT` on null literals in a bottom-up fashion. During that, `WindowExpression` is not covered properly. This PR adds the missing propagation logic.
**Before**
```scala
scala> sql("SELECT COUNT(1 + NULL) OVER ()").show
java.lang.UnsupportedOperationException: Cannot evaluate expression: cast(0 as bigint) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
```
**After**
```scala
scala> sql("SELECT COUNT(1 + NULL) OVER ()").show
+----------------------------------------------------------------------------------------------+
|count((1 + CAST(NULL AS INT))) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)|
+----------------------------------------------------------------------------------------------+
| 0|
+----------------------------------------------------------------------------------------------+
```
## How was this patch tested?
Pass the Jenkins test with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14689 from dongjoon-hyun/SPARK-17098.
## What changes were proposed in this pull request?
This patch adds support for SQL generation for inline tables. With this, it would be possible to create a view that depends on inline tables.
## How was this patch tested?
Added a test case in LogicalPlanToSQLSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14709 from petermaxlee/SPARK-17150.
## What changes were proposed in this pull request?
Modifies error message for numeric literals to
Numeric literal <literal> does not fit in range [min, max] for type <T>
## How was this patch tested?
Fixed up the error messages for literals.sql in SqlQueryTestSuite and re-ran via sbt. Also fixed up error messages in ExpressionParserSuite
Author: Srinath Shankar <srinath@databricks.com>
Closes#14721 from srinathshankar/sc4296.
## What changes were proposed in this pull request?
This patch creates array.sql in SQLQueryTestSuite for testing array related functions, including:
- indexing
- array creation
- size
- array_contains
- sort_array
## How was this patch tested?
The patch itself is about adding tests.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14708 from petermaxlee/SPARK-17149.
## What changes were proposed in this pull request?
This patch changes predicate pushdown optimization rule (PushDownPredicate) from using a blacklist to a whitelist. That is to say, operators must be explicitly allowed. This approach is more future-proof: previously it was possible for us to introduce a new operator and then render the optimization rule incorrect.
This also fixes the bug that previously we allowed pushing filter beneath limit, which was incorrect. That is to say, before this patch, the optimizer would rewrite
```
select * from (select * from range(10) limit 5) where id > 3
to
select * from range(10) where id > 3 limit 5
```
## How was this patch tested?
- a unit test case in FilterPushdownSuite
- an end-to-end test in limit.sql
Author: Reynold Xin <rxin@databricks.com>
Closes#14713 from rxin/SPARK-16994.
## What changes were proposed in this pull request?
This patch improves inline table support with the following:
1. Support type coercion.
2. Support using foldable expressions. Previously only literals were supported.
3. Improve error message handling.
4. Improve test coverage.
## How was this patch tested?
Added a new unit test suite ResolveInlineTablesSuite and a new file-based end-to-end test inline-table.sql.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14676 from petermaxlee/SPARK-16947.
## What changes were proposed in this pull request?
This patch fixes the problem described in SPARK-17117, i.e. "SELECT 1 / NULL" throws an analysis exception:
```
org.apache.spark.sql.AnalysisException: cannot resolve '(1 / NULL)' due to data type mismatch: differing types in '(1 / NULL)' (int and null).
```
The problem is that division type coercion did not take null type into account.
## How was this patch tested?
A unit test for the type coercion, and a few end-to-end test cases using SQLQueryTestSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14695 from petermaxlee/SPARK-17117.
## What changes were proposed in this pull request?
This adds analyzer rules for resolving table-valued functions, and adds one builtin implementation for range(). The arguments for range() are the same as those of `spark.range()`.
## How was this patch tested?
Unit tests.
cc hvanhovell
Author: Eric Liang <ekl@databricks.com>
Closes#14656 from ericl/sc-4309.
## What changes were proposed in this pull request?
The `Optimizer` rules `PushThroughSetOperations` and `PushDownPredicate` have a redundant rule to push down `Filter` through `Union`. We should remove it.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#14687 from viirya/remove-extra-pushdown.
## What changes were proposed in this pull request?
I was looking at the code for UnresolvedOrdinal and made a few small changes to make it slightly more clear:
1. Rename the rule to SubstituteUnresolvedOrdinals which is more consistent with other rules that start with verbs. Note that this is still inconsistent with CTESubstitution and WindowsSubstitution.
2. Broke the test suite down from a single test case to three test cases.
## How was this patch tested?
This is a minor cleanup.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14672 from petermaxlee/SPARK-17034.
## What changes were proposed in this pull request?
A TreeNodeException is thrown when executing the following minimal example in Spark 2.0.
import spark.implicits._
case class test (x: Int, q: Int)
val d = Seq(1).toDF("x")
d.withColumn("q", lit(0)).as[test].groupByKey(_.x).flatMapGroups{case (x, iter) => List[Int]()}.show
d.withColumn("q", expr("0")).as[test].groupByKey(_.x).flatMapGroups{case (x, iter) => List[Int]()}.show
The problem is at `FoldablePropagation`. The rule will do `transformExpressions` on `LogicalPlan`. The query above contains a `MapGroups` which has a parameter `dataAttributes:Seq[Attribute]`. One attributes in `dataAttributes` will be transformed to an `Alias(literal(0), _)` in `FoldablePropagation`. `Alias` is not an `Attribute` and causes the error.
We can't easily detect such type inconsistency during transforming expressions. A direct approach to this problem is to skip doing `FoldablePropagation` on object operators as they should not contain such expressions.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#14648 from viirya/flat-mapping.
## What changes were proposed in this pull request?
The current subquery expression interface contains a little bit of technical debt in the form of a few different access paths to get and set the query contained by the expression. This is confusing to anyone who goes over this code.
This PR unifies these access paths.
## How was this patch tested?
(Existing tests)
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14685 from hvanhovell/SPARK-17106.
## What changes were proposed in this pull request?
This PR splits the generated code for ```SafeProjection.apply``` by using ```ctx.splitExpressions()```. This is because the large code body for ```NewInstance``` may grow beyond 64KB bytecode size for ```apply()``` method.
Here is [the original PR](https://github.com/apache/spark/pull/13243) for SPARK-15285. However, it breaks a build with Scala 2.10 since Scala 2.10 does not a case class with large number of members. Thus, it was reverted by [this commit](fa244e5a90).
## How was this patch tested?
Added new tests by using `DefinedByConstructorParams` instead of case class for scala-2.10
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#14670 from kiszk/SPARK-15285-2.
## What changes were proposed in this pull request?
Currently methods in `ParserUtils` are tested indirectly, we should add test cases in `ParserUtilsSuite` to verify their integrity directly.
## How was this patch tested?
New test cases in `ParserUtilsSuite`
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#14620 from jiangxb1987/parserUtils.
## What changes were proposed in this pull request?
This PR adds a field to subquery alias in order to make the usage of views in a resolved `LogicalPlan` more visible (and more understandable).
For example, the following view and query:
```sql
create view constants as select 1 as id union all select 1 union all select 42
select * from constants;
```
...now yields the following analyzed plan:
```
Project [id#39]
+- SubqueryAlias c, `default`.`constants`
+- Project [gen_attr_0#36 AS id#39]
+- SubqueryAlias gen_subquery_0
+- Union
:- Union
: :- Project [1 AS gen_attr_0#36]
: : +- OneRowRelation$
: +- Project [1 AS gen_attr_1#37]
: +- OneRowRelation$
+- Project [42 AS gen_attr_2#38]
+- OneRowRelation$
```
## How was this patch tested?
Added tests for the two code paths in `SessionCatalogSuite` (sql/core) and `HiveMetastoreCatalogSuite` (sql/hive)
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14657 from hvanhovell/SPARK-17068.
## What changes were proposed in this pull request?
This PR renames `ParserUtils.assert` to `ParserUtils.validate`. This is done because this method is used to check requirements, and not to check if the program is in an invalid state.
## How was this patch tested?
Simple rename. Compilation should do.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14665 from hvanhovell/SPARK-17084.
## What changes were proposed in this pull request?
This PR adds expression `UnresolvedOrdinal` to represent the ordinal in GROUP BY or ORDER BY, and fixes the rules when resolving ordinals.
Ordinals in GROUP BY or ORDER BY like `1` in `order by 1` or `group by 1` should be considered as unresolved before analysis. But in current code, it uses `Literal` expression to store the ordinal. This is inappropriate as `Literal` itself is a resolved expression, it gives the user a wrong message that the ordinals has already been resolved.
### Before this change
Ordinal is stored as `Literal` expression
```
scala> sc.setLogLevel("TRACE")
scala> sql("select a from t group by 1 order by 1")
...
'Sort [1 ASC], true
+- 'Aggregate [1], ['a]
+- 'UnresolvedRelation `t
```
For query:
```
scala> Seq(1).toDF("a").createOrReplaceTempView("t")
scala> sql("select count(a), a from t group by 2 having a > 0").show
```
During analysis, the intermediate plan before applying rule `ResolveAggregateFunctions` is:
```
'Filter ('a > 0)
+- Aggregate [2], [count(1) AS count(1)#83L, a#81]
+- LocalRelation [value#7 AS a#9]
```
Before this PR, rule `ResolveAggregateFunctions` believes all expressions of `Aggregate` have already been resolved, and tries to resolve the expressions in `Filter` directly. But this is wrong, as ordinal `2` in Aggregate is not really resolved!
### After this change
Ordinals are stored as `UnresolvedOrdinal`.
```
scala> sc.setLogLevel("TRACE")
scala> sql("select a from t group by 1 order by 1")
...
'Sort [unresolvedordinal(1) ASC], true
+- 'Aggregate [unresolvedordinal(1)], ['a]
+- 'UnresolvedRelation `t`
```
## How was this patch tested?
Unit tests.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14616 from clockfly/spark-16955.
## What changes were proposed in this pull request?
This PR changes the CTE resolving rule to use only **forward-declared** tables in order to prevent infinite loops. More specifically, new logic is like the following.
* Resolve CTEs in `WITH` clauses first before replacing the main SQL body.
* When resolving CTEs, only forward-declared CTEs or base tables are referenced.
- Self-referencing is not allowed any more.
- Cross-referencing is not allowed any more.
**Reported Error Scenarios**
```scala
scala> sql("WITH t AS (SELECT 1 FROM t) SELECT * FROM t")
java.lang.StackOverflowError
...
scala> sql("WITH t1 AS (SELECT * FROM t2), t2 AS (SELECT 2 FROM t1) SELECT * FROM t1, t2")
java.lang.StackOverflowError
...
```
Note that `t`, `t1`, and `t2` are not declared in database. Spark falls into infinite loops before resolving table names.
## How was this patch tested?
Pass the Jenkins tests with new two testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14397 from dongjoon-hyun/SPARK-16771-TREENODE.
#### What changes were proposed in this pull request?
So far, the test cases of `TableIdentifierParserSuite` do not cover the quoted cases. We should add one for avoiding regression.
#### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14244 from gatorsmile/quotedIdentifiers.
## What changes were proposed in this pull request?
This patch updates the SQL parser to parse negative numeric literals as numeric literals, instead of unary minus of positive literals.
This allows the parser to parse the minimal value for each data type, e.g. "-32768S".
## How was this patch tested?
Updated test cases.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14608 from petermaxlee/SPARK-17013.
## What changes were proposed in this pull request?
There could be multiple subqueries that generate same results, we could re-use the result instead of running it multiple times.
This PR also cleanup up how we run subqueries.
For SQL query
```sql
select id,(select avg(id) from t) from t where id > (select avg(id) from t)
```
The explain is
```
== Physical Plan ==
*Project [id#15L, Subquery subquery29 AS scalarsubquery()#35]
: +- Subquery subquery29
: +- *HashAggregate(keys=[], functions=[avg(id#15L)])
: +- Exchange SinglePartition
: +- *HashAggregate(keys=[], functions=[partial_avg(id#15L)])
: +- *Range (0, 1000, splits=4)
+- *Filter (cast(id#15L as double) > Subquery subquery29)
: +- Subquery subquery29
: +- *HashAggregate(keys=[], functions=[avg(id#15L)])
: +- Exchange SinglePartition
: +- *HashAggregate(keys=[], functions=[partial_avg(id#15L)])
: +- *Range (0, 1000, splits=4)
+- *Range (0, 1000, splits=4)
```
The visualized plan:
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#14548 from davies/subq.
## What changes were proposed in this pull request?
This patch adds three test files:
1. arithmetic.sql.out
2. order-by-ordinal.sql
3. group-by-ordinal.sql
This includes https://github.com/apache/spark/pull/14594.
## How was this patch tested?
This is a test case change.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14595 from petermaxlee/SPARK-17015.
## What changes were proposed in this pull request?
This PR adds `MINUS` set operator which is equivalent `EXCEPT DISTINCT`. This will slightly improve the compatibility with Oracle.
## How was this patch tested?
Pass the Jenkins with newly added testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14570 from dongjoon-hyun/SPARK-10601.
## What changes were proposed in this pull request?
Fixed small typo - "value ... ~~in~~ is null"
## How was this patch tested?
Still compiles!
Author: Michał Kiełbowicz <jupblb@users.noreply.github.com>
Closes#14569 from jupblb/typo-fix.
