## 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.
Compare both left and right side of the case expression ignoring nullablity when checking for type equality.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#10156 from dilipbiswal/spark-12102.
This PR adds a new expression `AssertNotNull` to ensure non-nullable fields of products and case classes don't receive null values at runtime.
Author: Cheng Lian <lian@databricks.com>
Closes#10331 from liancheng/dataset-nullability-check.
This could simplify the generated code for expressions that is not nullable.
This PR fix lots of bugs about nullability.
Author: Davies Liu <davies@databricks.com>
Closes#10333 from davies/skip_nullable.
This PR removes Hive windows functions from Spark and replaces them with (native) Spark ones. The PR is on par with Hive in terms of features.
This has the following advantages:
* Better memory management.
* The ability to use spark UDAFs in Window functions.
cc rxin / yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#9819 from hvanhovell/SPARK-8641-2.
in https://github.com/apache/spark/pull/10133 we found that, we shoud ensure the children of `TreeNode` are all accessible in the `productIterator`, or the behavior will be very confusing.
In this PR, I try to fix this problem by expsing the `loopVar`.
This also fixes SPARK-12131 which is caused by the hacky `MapObjects`.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10239 from cloud-fan/map-objects.
checked with hive, greatest/least should cast their children to a tightest common type,
i.e. `(int, long) => long`, `(int, string) => error`, `(decimal(10,5), decimal(5, 10)) => error`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10196 from cloud-fan/type-coercion.
Currently, the order of joins is exactly the same as SQL query, some conditions may not pushed down to the correct join, then those join will become cross product and is extremely slow.
This patch try to re-order the inner joins (which are common in SQL query), pick the joins that have self-contain conditions first, delay those that does not have conditions.
After this patch, the TPCDS query Q64/65 can run hundreds times faster.
cc marmbrus nongli
Author: Davies Liu <davies@databricks.com>
Closes#10073 from davies/reorder_joins.
When \u appears in a comment block (i.e. in /**/), code gen will break. So, in Expression and CodegenFallback, we escape \u to \\u.
yhuai Please review it. I did reproduce it and it works after the fix. Thanks!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10155 from gatorsmile/escapeU.
We should upgrade to SBT 0.13.9, since this is a requirement in order to use SBT's new Maven-style resolution features (which will be done in a separate patch, because it's blocked by some binary compatibility issues in the POM reader plugin).
I also upgraded Scalastyle to version 0.8.0, which was necessary in order to fix a Scala 2.10.5 compatibility issue (see https://github.com/scalastyle/scalastyle/issues/156). The newer Scalastyle is slightly stricter about whitespace surrounding tokens, so I fixed the new style violations.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10112 from JoshRosen/upgrade-to-sbt-0.13.9.
create java version of `constructorFor` and `extractorFor` in `JavaTypeInference`
Author: Wenchen Fan <wenchen@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Michael Armbrust <michael@databricks.com>
Closes#9937 from cloud-fan/pojo.
When we build the `fromRowExpression` for an encoder, we set up a lot of "unresolved" stuff and lost the required data type, which may lead to runtime error if the real type doesn't match the encoder's schema.
For example, we build an encoder for `case class Data(a: Int, b: String)` and the real type is `[a: int, b: long]`, then we will hit runtime error and say that we can't construct class `Data` with int and long, because we lost the information that `b` should be a string.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9840 from cloud-fan/err-msg.
In https://github.com/apache/spark/pull/9409 we enabled multi-column counting. The approach taken in that PR introduces a bit of overhead by first creating a row only to check if all of the columns are non-null.
This PR fixes that technical debt. Count now takes multiple columns as its input. In order to make this work I have also added support for multiple columns in the single distinct code path.
cc yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10015 from hvanhovell/SPARK-12024.
This is a followup for https://github.com/apache/spark/pull/9959.
I added more documentation and rewrote some monadic code into simpler ifs.
Author: Reynold Xin <rxin@databricks.com>
Closes#9995 from rxin/SPARK-11973.
this is based on https://github.com/apache/spark/pull/9844, with some bug fix and clean up.
The problems is that, normal operator should be resolved based on its child, but `Sort` operator can also be resolved based on its grandchild. So we have 3 rules that can resolve `Sort`: `ResolveReferences`, `ResolveSortReferences`(if grandchild is `Project`) and `ResolveAggregateFunctions`(if grandchild is `Aggregate`).
For example, `select c1 as a , c2 as b from tab group by c1, c2 order by a, c2`, we need to resolve `a` and `c2` for `Sort`. Firstly `a` will be resolved in `ResolveReferences` based on its child, and when we reach `ResolveAggregateFunctions`, we will try to resolve both `a` and `c2` based on its grandchild, but failed because `a` is not a legal aggregate expression.
whoever merge this PR, please give the credit to dilipbiswal
Author: Dilip Biswal <dbiswal@us.ibm.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9961 from cloud-fan/sort.
Currently, filter can't be pushed through aggregation with alias or literals, this patch fix that.
After this patch, the time of TPC-DS query 4 go down to 13 seconds from 141 seconds (10x improvements).
cc nongli yhuai
Author: Davies Liu <davies@databricks.com>
Closes#9959 from davies/push_filter2.
Can someone review my code to make sure I'm not missing anything? Thanks!
Author: Xiu Guo <xguo27@gmail.com>
Author: Xiu Guo <guoxi@us.ibm.com>
Closes#9612 from xguo27/SPARK-11628.
Hive has since changed this behavior as well. https://issues.apache.org/jira/browse/HIVE-3454
Author: Nong Li <nong@databricks.com>
Author: Nong Li <nongli@gmail.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#9685 from nongli/spark-11724.
before this PR, when users try to get an encoder for an un-supported class, they will only get a very simple error message like `Encoder for type xxx is not supported`.
After this PR, the error message become more friendly, for example:
```
No Encoder found for abc.xyz.NonEncodable
- array element class: "abc.xyz.NonEncodable"
- field (class: "scala.Array", name: "arrayField")
- root class: "abc.xyz.AnotherClass"
```
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9810 from cloud-fan/error-message.
JIRA: https://issues.apache.org/jira/browse/SPARK-11817
Instead of return None, we should truncate the fractional seconds to prevent inserting NULL.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9834 from viirya/truncate-fractional-sec.
After some experiment, I found it's not convenient to have separate encoder builders: `FlatEncoder` and `ProductEncoder`. For example, when create encoders for `ScalaUDF`, we have no idea if the type `T` is flat or not. So I revert the splitting change in https://github.com/apache/spark/pull/9693, while still keeping the bug fixes and tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9726 from cloud-fan/follow.
We currently rely on the optimizer's constant folding to replace current_timestamp and current_date. However, this can still result in different values for different instances of current_timestamp/current_date if the optimizer is not running fast enough.
A better solution is to replace these functions in the analyzer in one shot.
Author: Reynold Xin <rxin@databricks.com>
Closes#9833 from rxin/SPARK-11849.
Also added some nicer error messages for incompatible types (private types and primitive types) for Kryo/Java encoder.
Author: Reynold Xin <rxin@databricks.com>
Closes#9823 from rxin/SPARK-11833.
Before this PR there were two things that would blow up if you called `df.as[MyClass]` if `MyClass` was defined in the REPL:
- [x] Because `classForName` doesn't work on the munged names returned by `tpe.erasure.typeSymbol.asClass.fullName`
- [x] Because we don't have anything to pass into the constructor for the `$outer` pointer.
Note that this PR is just adding the infrastructure for working with inner classes in encoder and is not yet sufficient to make them work in the REPL. Currently, the implementation show in 95cec7d413 is causing a bug that breaks code gen due to some interaction between janino and the `ExecutorClassLoader`. This will be addressed in a follow-up PR.
Author: Michael Armbrust <michael@databricks.com>
Closes#9602 from marmbrus/dataset-replClasses.
This patch refactors the existing Kryo encoder expressions and adds support for Java serialization.
Author: Reynold Xin <rxin@databricks.com>
Closes#9802 from rxin/SPARK-11810.
If user use primitive parameters in UDF, there is no way for him to do the null-check for primitive inputs, so we are assuming the primitive input is null-propagatable for this case and return null if the input is null.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9770 from cloud-fan/udf.
I also found a bug with self-joins returning incorrect results in the Dataset API. Two test cases attached and filed SPARK-11803.
Author: Reynold Xin <rxin@databricks.com>
Closes#9789 from rxin/SPARK-11802.
Based on the comment of cloud-fan in https://github.com/apache/spark/pull/9216, update the AttributeReference's hashCode function by including the hashCode of the other attributes including name, nullable and qualifiers.
Here, I am not 100% sure if we should include name in the hashCode calculation, since the original hashCode calculation does not include it.
marmbrus cloud-fan Please review if the changes are good.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#9761 from gatorsmile/hashCodeNamedExpression.
The randomly generated ArrayData used for the UDT `ExamplePoint` in `RowEncoderSuite` sometimes doesn't have enough elements. In this case, this test will fail. This patch is to fix it.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9757 from viirya/fix-randomgenerated-udt.
Invocation of getters for type extending AnyVal returns default value (if field value is null) instead of throwing NPE. Please check comments for SPARK-11553 issue for more details.
Author: Bartlomiej Alberski <bartlomiej.alberski@allegrogroup.com>
Closes#9642 from alberskib/bugfix/SPARK-11553.
These 2 are very similar, we can consolidate them into one.
Also add tests for it and fix a bug.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9729 from cloud-fan/tuple.
JIRA: https://issues.apache.org/jira/browse/SPARK-11743
RowEncoder doesn't support UserDefinedType now. We should add the support for it.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9712 from viirya/rowencoder-udt.
code snippet to reproduce it:
```
TimeZone.setDefault(TimeZone.getTimeZone("Asia/Shanghai"))
val t = Timestamp.valueOf("1900-06-11 12:14:50.789")
val us = fromJavaTimestamp(t)
assert(getSeconds(us) === t.getSeconds)
```
it will be good to add a regression test for it, but the reproducing code need to change the default timezone, and even we change it back, the `lazy val defaultTimeZone` in `DataTimeUtils` is fixed.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9728 from cloud-fan/seconds.
also add more tests for encoders, and fix bugs that I found:
* when convert array to catalyst array, we can only skip element conversion for native types(e.g. int, long, boolean), not `AtomicType`(String is AtomicType but we need to convert it)
* we should also handle scala `BigDecimal` when convert from catalyst `Decimal`.
* complex map type should be supported
other issues that still in investigation:
* encode java `BigDecimal` and decode it back, seems we will loss precision info.
* when encode case class that defined inside a object, `ClassNotFound` exception will be thrown.
I'll remove unused code in a follow-up PR.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9693 from cloud-fan/split.
`to_unix_timestamp` is the deterministic version of `unix_timestamp`, as it accepts at least one parameters.
Since the behavior here is quite similar to `unix_timestamp`, I think the dataframe API is not necessary here.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#9347 from adrian-wang/to_unix_timestamp.
This patch adds the building blocks for codegening subexpr elimination and implements
it end to end for UnsafeProjection. The building blocks can be used to do the same thing
for other operators.
It introduces some utilities to compute common sub expressions. Expressions can be added to
this data structure. The expr and its children will be recursively matched against existing
expressions (ones previously added) and grouped into common groups. This is built using
the existing `semanticEquals`. It does not understand things like commutative or associative
expressions. This can be done as future work.
After building this data structure, the codegen process takes advantage of it by:
1. Generating a helper function in the generated class that computes the common
subexpression. This is done for all common subexpressions that have at least
two occurrences and the expression tree is sufficiently complex.
2. When generating the apply() function, if the helper function exists, call that
instead of regenerating the expression tree. Repeated calls to the helper function
shortcircuit the evaluation logic.
Author: Nong Li <nong@databricks.com>
Author: Nong Li <nongli@gmail.com>
This patch had conflicts when merged, resolved by
Committer: Michael Armbrust <michael@databricks.com>
Closes#9480 from nongli/spark-10371.
https://issues.apache.org/jira/browse/SPARK-9830
This PR contains the following main changes.
* Removing `AggregateExpression1`.
* Removing `Aggregate` operator, which is used to evaluate `AggregateExpression1`.
* Removing planner rule used to plan `Aggregate`.
* Linking `MultipleDistinctRewriter` to analyzer.
* Renaming `AggregateExpression2` to `AggregateExpression` and `AggregateFunction2` to `AggregateFunction`.
* Updating places where we create aggregate expression. The way to create aggregate expressions is `AggregateExpression(aggregateFunction, mode, isDistinct)`.
* Changing `val`s in `DeclarativeAggregate`s that touch children of this function to `lazy val`s (when we create aggregate expression in DataFrame API, children of an aggregate function can be unresolved).
Author: Yin Huai <yhuai@databricks.com>
Closes#9556 from yhuai/removeAgg1.
This PR adds support for multiple column in a single count distinct aggregate to the new aggregation path.
cc yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#9409 from hvanhovell/SPARK-11451.
https://issues.apache.org/jira/browse/SPARK-10116
This is really trivial, just happened to notice it -- if `XORShiftRandom.hashSeed` is really supposed to have random bits throughout (as the comment implies), it needs to do something for the conversion to `long`.
mengxr mkolod
Author: Imran Rashid <irashid@cloudera.com>
Closes#8314 from squito/SPARK-10116.
A cleanup for https://github.com/apache/spark/pull/9085.
The `DecimalLit` is very similar to `FloatLit`, we can just keep one of them.
Also added low level unit test at `SqlParserSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9482 from cloud-fan/parser.
Currently, if the Timestamp is before epoch (1970/01/01), the hours, minutes and seconds will be negative (also rounding up).
Author: Davies Liu <davies@databricks.com>
Closes#9502 from davies/neg_hour.
Add a rule in optimizer to convert NULL [NOT] IN (expr1,...,expr2) to
Literal(null).
This is a follow up defect to SPARK-8654
cloud-fan Can you please take a look ?
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#9348 from dilipbiswal/spark_11024.
This PR adds a new operation `joinWith` to a `Dataset`, which returns a `Tuple` for each pair where a given `condition` evaluates to true.
```scala
case class ClassData(a: String, b: Int)
val ds1 = Seq(ClassData("a", 1), ClassData("b", 2)).toDS()
val ds2 = Seq(("a", 1), ("b", 2)).toDS()
> ds1.joinWith(ds2, $"_1" === $"a").collect()
res0: Array((ClassData("a", 1), ("a", 1)), (ClassData("b", 2), ("b", 2)))
```
This operation is similar to the relation `join` function with one important difference in the result schema. Since `joinWith` preserves objects present on either side of the join, the result schema is similarly nested into a tuple under the column names `_1` and `_2`.
This type of join can be useful both for preserving type-safety with the original object types as well as working with relational data where either side of the join has column names in common.
## Required Changes to Encoders
In the process of working on this patch, several deficiencies to the way that we were handling encoders were discovered. Specifically, it turned out to be very difficult to `rebind` the non-expression based encoders to extract the nested objects from the results of joins (and also typed selects that return tuples).
As a result the following changes were made.
- `ClassEncoder` has been renamed to `ExpressionEncoder` and has been improved to also handle primitive types. Additionally, it is now possible to take arbitrary expression encoders and rewrite them into a single encoder that returns a tuple.
- All internal operations on `Dataset`s now require an `ExpressionEncoder`. If the users tries to pass a non-`ExpressionEncoder` in, an error will be thrown. We can relax this requirement in the future by constructing a wrapper class that uses expressions to project the row to the expected schema, shielding the users code from the required remapping. This will give us a nice balance where we don't force user encoders to understand attribute references and binding, but still allow our native encoder to leverage runtime code generation to construct specific encoders for a given schema that avoid an extra remapping step.
- Additionally, the semantics for different types of objects are now better defined. As stated in the `ExpressionEncoder` scaladoc:
- Classes will have their sub fields extracted by name using `UnresolvedAttribute` expressions
and `UnresolvedExtractValue` expressions.
- Tuples will have their subfields extracted by position using `BoundReference` expressions.
- Primitives will have their values extracted from the first ordinal with a schema that defaults
to the name `value`.
- Finally, the binding lifecycle for `Encoders` has now been unified across the codebase. Encoders are now `resolved` to the appropriate schema in the constructor of `Dataset`. This process replaces an unresolved expressions with concrete `AttributeReference` expressions. Binding then happens on demand, when an encoder is going to be used to construct an object. This closely mirrors the lifecycle for standard expressions when executing normal SQL or `DataFrame` queries.
Author: Michael Armbrust <michael@databricks.com>
Closes#9300 from marmbrus/datasets-tuples.
I'm new to spark. I was trying out the sort_array function then hit this exception. I looked into the spark source code. I found the root cause is that sort_array does not check for an array of NULLs. It's not meaningful to sort an array of entirely NULLs anyway.
I'm adding a check on the input array type to SortArray. If the array consists of NULLs entirely, there is no need to sort such array. I have also added a test case for this.
Please help to review my fix. Thanks!
Author: Jia Li <jiali@us.ibm.com>
Closes#9247 from jliwork/SPARK-11277.
For nested StructType, the underline buffer could be used for others before, we should zero out the padding bytes for those primitive types that have less than 8 bytes.
cc cloud-fan
Author: Davies Liu <davies@databricks.com>
Closes#9217 from davies/zero_out.
*This PR adds a new experimental API to Spark, tentitively named Datasets.*
A `Dataset` is a strongly-typed collection of objects that can be transformed in parallel using functional or relational operations. Example usage is as follows:
### Functional
```scala
> val ds: Dataset[Int] = Seq(1, 2, 3).toDS()
> ds.filter(_ % 1 == 0).collect()
res1: Array[Int] = Array(1, 2, 3)
```
### Relational
```scala
scala> ds.toDF().show()
+-----+
|value|
+-----+
| 1|
| 2|
| 3|
+-----+
> ds.select(expr("value + 1").as[Int]).collect()
res11: Array[Int] = Array(2, 3, 4)
```
## Comparison to RDDs
A `Dataset` differs from an `RDD` in the following ways:
- The creation of a `Dataset` requires the presence of an explicit `Encoder` that can be
used to serialize the object into a binary format. Encoders are also capable of mapping the
schema of a given object to the Spark SQL type system. In contrast, RDDs rely on runtime
reflection based serialization.
- Internally, a `Dataset` is represented by a Catalyst logical plan and the data is stored
in the encoded form. This representation allows for additional logical operations and
enables many operations (sorting, shuffling, etc.) to be performed without deserializing to
an object.
A `Dataset` can be converted to an `RDD` by calling the `.rdd` method.
## Comparison to DataFrames
A `Dataset` can be thought of as a specialized DataFrame, where the elements map to a specific
JVM object type, instead of to a generic `Row` container. A DataFrame can be transformed into
specific Dataset by calling `df.as[ElementType]`. Similarly you can transform a strongly-typed
`Dataset` to a generic DataFrame by calling `ds.toDF()`.
## Implementation Status and TODOs
This is a rough cut at the least controversial parts of the API. The primary purpose here is to get something committed so that we can better parallelize further work and get early feedback on the API. The following is being deferred to future PRs:
- Joins and Aggregations (prototype here f11f91e6f0)
- Support for Java
Additionally, the responsibility for binding an encoder to a given schema is currently done in a fairly ad-hoc fashion. This is an internal detail, and what we are doing today works for the cases we care about. However, as we add more APIs we'll probably need to do this in a more principled way (i.e. separate resolution from binding as we do in DataFrames).
## COMPATIBILITY NOTE
Long term we plan to make `DataFrame` extend `Dataset[Row]`. However,
making this change to che class hierarchy would break the function signatures for the existing
function operations (map, flatMap, etc). As such, this class should be considered a preview
of the final API. Changes will be made to the interface after Spark 1.6.
Author: Michael Armbrust <michael@databricks.com>
Closes#9190 from marmbrus/dataset-infra.
In the analysis phase , while processing the rules for IN predicate, we
compare the in-list types to the lhs expression type and generate
cast operation if necessary. In the case of NULL [NOT] IN expr1 , we end up
generating cast between in list types to NULL like cast (1 as NULL) which
is not a valid cast.
The fix is to find a common type between LHS and RHS expressions and cast
all the expression to the common type.
Author: Dilip Biswal <dbiswal@us.ibm.com>
This patch had conflicts when merged, resolved by
Committer: Michael Armbrust <michael@databricks.com>
Closes#9036 from dilipbiswal/spark_8654_new.
This PR introduce a new feature to run SQL directly on files without create a table, for example:
```
select id from json.`path/to/json/files` as j
```
Author: Davies Liu <davies@databricks.com>
Closes#9173 from davies/source.
Find out the missing attributes by recursively looking
at the sort order expression and rest of the code
takes care of projecting them out.
Added description from cloud-fan
I wanna explain a bit more about this bug.
When we resolve sort ordering, we will use a special method, which only resolves UnresolvedAttributes and UnresolvedExtractValue. However, for something like Floor('a), even the 'a is resolved, the floor expression may still being unresolved as data type mismatch(for example, 'a is string type and Floor need double type), thus can't pass this filter, and we can't push down this missing attribute 'a
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#9123 from dilipbiswal/SPARK-10534.
Implement encode/decode for external row based on `ClassEncoder`.
TODO:
* code cleanup
* ~~fix corner cases~~
* refactor the encoder interface
* improve test for product codegen, to cover more corner cases.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9184 from cloud-fan/encoder.
Push conjunctive predicates though Aggregate operators when their references are a subset of the groupingExpressions.
Query plan before optimisation :-
Filter ((c#138L = 2) && (a#0 = 3))
Aggregate [a#0], [a#0,count(b#1) AS c#138L]
Project [a#0,b#1]
LocalRelation [a#0,b#1,c#2]
Query plan after optimisation :-
Filter (c#138L = 2)
Aggregate [a#0], [a#0,count(b#1) AS c#138L]
Filter (a#0 = 3)
Project [a#0,b#1]
LocalRelation [a#0,b#1,c#2]
Author: nitin goyal <nitin.goyal@guavus.com>
Author: nitin.goyal <nitin.goyal@guavus.com>
Closes#9167 from nitin2goyal/master.
This PR improve the performance by:
1) Generate an Iterator that take Iterator[CachedBatch] as input, and call accessors (unroll the loop for columns), avoid the expensive Iterator.flatMap.
2) Use Unsafe.getInt/getLong/getFloat/getDouble instead of ByteBuffer.getInt/getLong/getFloat/getDouble, the later one actually read byte by byte.
3) Remove the unnecessary copy() in Coalesce(), which is not related to memory cache, found during benchmark.
The following benchmark showed that we can speedup the columnar cache of int by 2x.
```
path = '/opt/tpcds/store_sales/'
int_cols = ['ss_sold_date_sk', 'ss_sold_time_sk', 'ss_item_sk','ss_customer_sk']
df = sqlContext.read.parquet(path).select(int_cols).cache()
df.count()
t = time.time()
print df.select("*")._jdf.queryExecution().toRdd().count()
print time.time() - t
```
Author: Davies Liu <davies@databricks.com>
Closes#9145 from davies/byte_buffer.
Currently, we use CartesianProduct for join with null-safe-equal condition.
```
scala> sqlContext.sql("select * from t a join t b on (a.i <=> b.i)").explain
== Physical Plan ==
TungstenProject [i#2,j#3,i#7,j#8]
Filter (i#2 <=> i#7)
CartesianProduct
LocalTableScan [i#2,j#3], [[1,1]]
LocalTableScan [i#7,j#8], [[1,1]]
```
Actually, we can have an equal-join condition as `coalesce(i, default) = coalesce(b.i, default)`, then an partitioned join algorithm could be used.
After this PR, the plan will become:
```
>>> sqlContext.sql("select * from a join b ON a.id <=> b.id").explain()
TungstenProject [id#0L,id#1L]
Filter (id#0L <=> id#1L)
SortMergeJoin [coalesce(id#0L,0)], [coalesce(id#1L,0)]
TungstenSort [coalesce(id#0L,0) ASC], false, 0
TungstenExchange hashpartitioning(coalesce(id#0L,0),200)
ConvertToUnsafe
Scan PhysicalRDD[id#0L]
TungstenSort [coalesce(id#1L,0) ASC], false, 0
TungstenExchange hashpartitioning(coalesce(id#1L,0),200)
ConvertToUnsafe
Scan PhysicalRDD[id#1L]
```
Author: Davies Liu <davies@databricks.com>
Closes#9120 from davies/null_safe.
We can't parse `NOT` operator with comparison operations like `SELECT NOT TRUE > TRUE`, this PR fixed it.
Takes over https://github.com/apache/spark/pull/6326.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8617 from cloud-fan/not.
The purpose of this PR is to keep the unsafe format detail only inside the unsafe class itself, so when we use them(like use unsafe array in unsafe map, use unsafe array and map in columnar cache), we don't need to understand the format before use them.
change list:
* unsafe array's 4-bytes numElements header is now required(was optional), and become a part of unsafe array format.
* w.r.t the previous changing, the `sizeInBytes` of unsafe array now counts the 4-bytes header.
* unsafe map's format was `[numElements] [key array numBytes] [key array content(without numElements header)] [value array content(without numElements header)]` before, which is a little hacky as it makes unsafe array's header optional. I think saving 4 bytes is not a big deal, so the format is now: `[key array numBytes] [unsafe key array] [unsafe value array]`.
* w.r.t the previous changing, the `sizeInBytes` of unsafe map now counts both map's header and array's header.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9131 from cloud-fan/unsafe.
Actually all of the `UnaryMathExpression` doens't support the Decimal, will create follow ups for supporing it. This is the first PR which will be good to review the approach I am taking.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#9086 from chenghao-intel/ceiling.
Right now, we have QualifiedTableName, TableIdentifier, and Seq[String] to represent table identifiers. We should only have one form and TableIdentifier is the best one because it provides methods to get table name, database name, return unquoted string, and return quoted string.
Author: Wenchen Fan <wenchen@databricks.com>
Author: Wenchen Fan <cloud0fan@163.com>
Closes#8453 from cloud-fan/table-name.
This is a first draft of the ability to construct expressions that will take a catalyst internal row and construct a Product (case class or tuple) that has fields with the correct names. Support include:
- Nested classes
- Maps
- Efficiently handling of arrays of primitive types
Not yet supported:
- Case classes that require custom collection types (i.e. List instead of Seq).
Author: Michael Armbrust <michael@databricks.com>
Closes#9100 from marmbrus/productContructor.
This PR is a first cut at code generating an encoder that takes a Scala `Product` type and converts it directly into the tungsten binary format. This is done through the addition of a new set of expression that can be used to invoke methods on raw JVM objects, extracting fields and converting the result into the required format. These can then be used directly in an `UnsafeProjection` allowing us to leverage the existing encoding logic.
According to some simple benchmarks, this can significantly speed up conversion (~4x). However, replacing CatalystConverters is deferred to a later PR to keep this PR at a reasonable size.
```scala
case class SomeInts(a: Int, b: Int, c: Int, d: Int, e: Int)
val data = SomeInts(1, 2, 3, 4, 5)
val encoder = ProductEncoder[SomeInts]
val converter = CatalystTypeConverters.createToCatalystConverter(ScalaReflection.schemaFor[SomeInts].dataType)
(1 to 5).foreach {iter =>
benchmark(s"converter $iter") {
var i = 100000000
while (i > 0) {
val res = converter(data).asInstanceOf[InternalRow]
assert(res.getInt(0) == 1)
assert(res.getInt(1) == 2)
i -= 1
}
}
benchmark(s"encoder $iter") {
var i = 100000000
while (i > 0) {
val res = encoder.toRow(data)
assert(res.getInt(0) == 1)
assert(res.getInt(1) == 2)
i -= 1
}
}
}
```
Results:
```
[info] converter 1: 7170ms
[info] encoder 1: 1888ms
[info] converter 2: 6763ms
[info] encoder 2: 1824ms
[info] converter 3: 6912ms
[info] encoder 3: 1802ms
[info] converter 4: 7131ms
[info] encoder 4: 1798ms
[info] converter 5: 7350ms
[info] encoder 5: 1912ms
```
Author: Michael Armbrust <michael@databricks.com>
Closes#9019 from marmbrus/productEncoder.
In the analysis phase , while processing the rules for IN predicate, we
compare the in-list types to the lhs expression type and generate
cast operation if necessary. In the case of NULL [NOT] IN expr1 , we end up
generating cast between in list types to NULL like cast (1 as NULL) which
is not a valid cast.
The fix is to not generate such a cast if the lhs type is a NullType instead
we translate the expression to Literal(Null).
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#8983 from dilipbiswal/spark_8654.
The created decimal is wrong if using `Decimal(unscaled, precision, scale)` with unscaled > 1e18 and and precision > 18 and scale > 0.
This bug exists since the beginning.
Author: Davies Liu <davies@databricks.com>
Closes#9014 from davies/fix_decimal.
This patch refactors several of the Aggregate2 interfaces in order to improve code clarity.
The biggest change is a refactoring of the `AggregateFunction2` class hierarchy. In the old code, we had a class named `AlgebraicAggregate` that inherited from `AggregateFunction2`, added a new set of methods, then banned the use of the inherited methods. I found this to be fairly confusing because.
If you look carefully at the existing code, you'll see that subclasses of `AggregateFunction2` fall into two disjoint categories: imperative aggregation functions which directly extended `AggregateFunction2` and declarative, expression-based aggregate functions which extended `AlgebraicAggregate`. In order to make this more explicit, this patch refactors things so that `AggregateFunction2` is a sealed abstract class with two subclasses, `ImperativeAggregateFunction` and `ExpressionAggregateFunction`. The superclass, `AggregateFunction2`, now only contains methods and fields that are common to both subclasses.
After making this change, I updated the various AggregationIterator classes to comply with this new naming scheme. I also performed several small renamings in the aggregate interfaces themselves in order to improve clarity and rewrote or expanded a number of comments.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8973 from JoshRosen/tungsten-agg-comments.
This PR is a completely rewritten of GenerateUnsafeProjection, to accomplish the goal of copying data only once. The old code of GenerateUnsafeProjection is still there to reduce review difficulty.
Instead of creating unsafe conversion code for struct, array and map, we create code of writing the content to the global row buffer.
Author: Wenchen Fan <cloud0fan@163.com>
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8747 from cloud-fan/copy-once.
Floor & Ceiling function should returns Long type, rather than Double.
Verified with MySQL & Hive.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#8933 from chenghao-intel/ceiling.
This is an implementation of Hive's `json_tuple` function using Jackson Streaming.
Author: Nathan Howell <nhowell@godaddy.com>
Closes#7946 from NathanHowell/SPARK-9617.
This PR implements a HyperLogLog based Approximate Count Distinct function using the new UDAF interface.
The implementation is inspired by the ClearSpring HyperLogLog implementation and should produce the same results.
There is still some documentation and testing left to do.
cc yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#8362 from hvanhovell/SPARK-9741.
Intersect and Except are both set operators and they use the all the columns to compare equality between rows. When pushing their Project parent down, the relations they based on would change, therefore not an equivalent transformation.
JIRA: https://issues.apache.org/jira/browse/SPARK-10539
I added some comments based on the fix of https://github.com/apache/spark/pull/8742.
Author: Yijie Shen <henry.yijieshen@gmail.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#8823 from yhuai/fix_set_optimization.
This fixes https://issues.apache.org/jira/browse/SPARK-9794 by using a real ISO8601 parser. (courtesy of the xml component of the standard java library)
cc: angelini
Author: Kevin Cox <kevincox@kevincox.ca>
Closes#8396 from kevincox/kevincox-sql-time-parsing.
Sometimes we can't push down the whole `Project` though `Sort`, but we still have a chance to push down part of it.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8644 from cloud-fan/column-prune.
Use these in the optimizer as well:
A and (not(A) or B) => A and B
not(A and B) => not(A) or not(B)
not(A or B) => not(A) and not(B)
Author: Yash Datta <Yash.Datta@guavus.com>
Closes#5700 from saucam/bool_simp.