## 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?
fix typo in DataSourceRegister
## How was this patch tested?
found when going through latest code
Author: Jacky Li <jacky.likun@huawei.com>
Closes#11686 from jackylk/patch-12.
## What changes were proposed in this pull request?
This PR removes two methods, `collectRows()` and `takeRows()`, from `Dataset[T]`. These methods were added in PR #11443, and were later considered not useful.
## How was this patch tested?
Existing tests should do the work.
Author: Cheng Lian <lian@databricks.com>
Closes#11678 from liancheng/remove-collect-rows-and-take-rows.
PR #11443 added an extra `plan: Option[LogicalPlan]` argument to `AnalysisException` and attached partially analyzed plan to thrown `AnalysisException` in `QueryExecution.assertAnalyzed()`. However, the original stack trace wasn't properly inherited. This PR fixes this issue by inheriting the stack trace.
A test case is added to verify that the first entry of `AnalysisException` stack trace isn't from `QueryExecution`.
Author: Cheng Lian <lian@databricks.com>
Closes#11677 from liancheng/analysis-exception-stacktrace.
## What changes were proposed in this pull request?
This PR split the PhysicalRDD into two classes, PhysicalRDD and PhysicalScan. PhysicalRDD is used for DataFrames that is created from existing RDD. PhysicalScan is used for DataFrame that is created from data sources. This enable use to apply different optimization on both of them.
Also fix the problem for sameResult() on two DataSourceScan.
Also fix the equality check to toString for `In`. It's better to use Seq there, but we can't break this public API (sad).
## How was this patch tested?
Existing tests. Manually tested with TPCDS query Q59 and Q64, all those duplicated exchanges can be re-used now, also saw there are 40+% performance improvement (saving half of the scan).
Author: Davies Liu <davies@databricks.com>
Closes#11514 from davies/existing_rdd.
## What changes were proposed in this pull request?
This patch is ported over from viirya's changes in #11048. Currently for most DDLs we just pass the query text directly to Hive. Instead, we should parse these commands ourselves and in the future (not part of this patch) use the `HiveCatalog` to process these DDLs. This is a pretext to merging `SQLContext` and `HiveContext`.
Note: As of this patch we still pass the query text to Hive. The difference is that we now parse the commands ourselves so in the future we can just use our own catalog.
## How was this patch tested?
Jenkins, new `DDLCommandSuite`, which comprises of about 40% of the changes here.
Author: Andrew Or <andrew@databricks.com>
Closes#11573 from andrewor14/parser-plus-plus.
This is needed to avoid odd compiler errors when building just the
sql package with maven, because of odd interactions between scalac
and shaded classes.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#11640 from vanzin/SPARK-13780.
## What changes were proposed in this pull request?
PR #11443 temporarily disabled MiMA check, this PR re-enables it.
One extra change is that `object DataFrame` is also removed. The only purpose of introducing `object DataFrame` was to use it as an internal factory for creating `Dataset[Row]`. By replacing this internal factory with `Dataset.newDataFrame`, both `DataFrame` and `DataFrame$` are entirely removed from the API, so that we can simply put a `MissingClassProblem` filter in `MimaExcludes.scala` for most DataFrame API changes.
## How was this patch tested?
Tested by MiMA check triggered by Jenkins.
Author: Cheng Lian <lian@databricks.com>
Closes#11656 from liancheng/re-enable-mima.
#### 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 unifies DataFrame and Dataset by migrating existing DataFrame operations to Dataset and make `DataFrame` a type alias of `Dataset[Row]`.
Most Scala code changes are source compatible, but Java API is broken as Java knows nothing about Scala type alias (mostly replacing `DataFrame` with `Dataset<Row>`).
There are several noticeable API changes related to those returning arrays:
1. `collect`/`take`
- Old APIs in class `DataFrame`:
```scala
def collect(): Array[Row]
def take(n: Int): Array[Row]
```
- New APIs in class `Dataset[T]`:
```scala
def collect(): Array[T]
def take(n: Int): Array[T]
def collectRows(): Array[Row]
def takeRows(n: Int): Array[Row]
```
Two specialized methods `collectRows` and `takeRows` are added because Java doesn't support returning generic arrays. Thus, for example, `DataFrame.collect(): Array[T]` actually returns `Object` instead of `Array<T>` from Java side.
Normally, Java users may fall back to `collectAsList` and `takeAsList`. The two new specialized versions are added to avoid performance regression in ML related code (but maybe I'm wrong and they are not necessary here).
1. `randomSplit`
- Old APIs in class `DataFrame`:
```scala
def randomSplit(weights: Array[Double], seed: Long): Array[DataFrame]
def randomSplit(weights: Array[Double]): Array[DataFrame]
```
- New APIs in class `Dataset[T]`:
```scala
def randomSplit(weights: Array[Double], seed: Long): Array[Dataset[T]]
def randomSplit(weights: Array[Double]): Array[Dataset[T]]
```
Similar problem as above, but hasn't been addressed for Java API yet. We can probably add `randomSplitAsList` to fix this one.
1. `groupBy`
Some original `DataFrame.groupBy` methods have conflicting signature with original `Dataset.groupBy` methods. To distinguish these two, typed `Dataset.groupBy` methods are renamed to `groupByKey`.
Other noticeable changes:
1. Dataset always do eager analysis now
We used to support disabling DataFrame eager analysis to help reporting partially analyzed malformed logical plan on analysis failure. However, Dataset encoders requires eager analysi during Dataset construction. To preserve the error reporting feature, `AnalysisException` now takes an extra `Option[LogicalPlan]` argument to hold the partially analyzed plan, so that we can check the plan tree when reporting test failures. This plan is passed by `QueryExecution.assertAnalyzed`.
## How was this patch tested?
Existing tests do the work.
## TODO
- [ ] Fix all tests
- [ ] Re-enable MiMA check
- [ ] Update ScalaDoc (`since`, `group`, and example code)
Author: Cheng Lian <lian@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Author: Cheng Lian <liancheng@users.noreply.github.com>
Closes#11443 from liancheng/ds-to-df.
## 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?
ContinuousQueryManager is sometimes flaky on Jenkins. I could not reproduce it on my machine, so I guess it about the waiting times which causes problems if Jenkins is loaded. I have increased the wait time in the hope that it will be less flaky.
## How was this patch tested?
I reran the unit test many times on a loop in my machine. I am going to run it a few time in Jenkins, that's the real test.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#11638 from tdas/cqm-flaky-test.
## What changes were proposed in this pull request?
We should reuse an object similar to the other non-primitive type getters. For
a query that computes averages over decimal columns, this shows a 10% speedup
on overall query times.
## How was this patch tested?
Existing tests and this benchmark
```
TPCDS Snappy: Best/Avg Time(ms) Rate(M/s) Per Row(ns)
--------------------------------------------------------------------------------
q27-agg (master) 10627 / 11057 10.8 92.3
q27-agg (this patch) 9722 / 9832 11.8 84.4
```
Author: Nong Li <nong@databricks.com>
Closes#11624 from nongli/spark-13790.
JIRA: https://issues.apache.org/jira/browse/SPARK-13636
## What changes were proposed in this pull request?
As shown in the wholestage codegen verion of Sort operator, when Sort is top of Exchange (or other operator that produce UnsafeRow), we will create variables from UnsafeRow, than create another UnsafeRow using these variables. We should avoid the unnecessary unpack and pack variables from UnsafeRows.
## How was this patch tested?
All existing wholestage codegen tests should be passed.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11484 from viirya/direct-consume-unsaferow.
## What changes were proposed in this pull request?
According to #11627 , this PR replace `DataFrameWriter.stream()` with `startStream()` in comments of `ContinuousQueryListener.java`.
## How was this patch tested?
Manual. (It changes on comments.)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11629 from dongjoon-hyun/minor_rename.
## What changes were proposed in this pull request?
The new name makes it more obvious with the verb "start" that we are actually starting some execution.
## How was this patch tested?
This is just a rename. Existing unit tests should cover it.
Author: Reynold Xin <rxin@databricks.com>
Closes#11627 from rxin/SPARK-13794.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-13766
This PR makes the file extensions (written by internal datasource) consistent.
**Before**
- TEXT, CSV and JSON
```
[.COMPRESSION_CODEC_NAME]
```
- Parquet
```
[.COMPRESSION_CODEC_NAME].parquet
```
- ORC
```
.orc
```
**After**
- TEXT, CSV and JSON
```
.txt[.COMPRESSION_CODEC_NAME]
.csv[.COMPRESSION_CODEC_NAME]
.json[.COMPRESSION_CODEC_NAME]
```
- Parquet
```
[.COMPRESSION_CODEC_NAME].parquet
```
- ORC
```
[.COMPRESSION_CODEC_NAME].orc
```
When the compression codec is set,
- For Parquet and ORC, each still stays in Parquet and ORC format but just have compressed data internally. So, I think it is okay to name `.parquet` and `.orc` at the end.
- For Text, CSV and JSON, each does not stays in each format but it has different data format according to compression codec. So, each has the names `.json`, `.csv` and `.txt` before the compression extension.
## How was this patch tested?
Unit tests are used and `./dev/run_tests` for coding style tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#11604 from HyukjinKwon/SPARK-13766.
## What changes were proposed in this pull request?
Fix this use case, which was already fixed in SPARK-10548 in 1.6 but was broken in master due to #9264:
```
(1 to 100).par.foreach { _ => sc.parallelize(1 to 5).map { i => (i, i) }.toDF("a", "b").count() }
```
This threw `IllegalArgumentException` consistently before this patch. For more detail, see the JIRA.
## How was this patch tested?
New test in `SQLExecutionSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#11586 from andrewor14/fix-concurrent-sql.
#### 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?
It’s possible to have common parts in a query, for example, self join, it will be good to avoid the duplicated part to same CPUs and memory (Broadcast or cache).
Exchange will materialize the underlying RDD by shuffle or collect, it’s a great point to check duplicates and reuse them. Duplicated exchanges means they generate exactly the same result inside a query.
In order to find out the duplicated exchanges, we should be able to compare SparkPlan to check that they have same results or not. We already have that for LogicalPlan, so we should move that into QueryPlan to make it available for SparkPlan.
Once we can find the duplicated exchanges, we should replace all of them with same SparkPlan object (could be wrapped by ReusedExchage for explain), then the plan tree become a DAG. Since all the planner only work with tree, so this rule should be the last one for the entire planning.
After the rule, the plan will looks like:
```
WholeStageCodegen
: +- Project [id#0L]
: +- BroadcastHashJoin [id#0L], [id#2L], Inner, BuildRight, None
: :- Project [id#0L]
: : +- BroadcastHashJoin [id#0L], [id#1L], Inner, BuildRight, None
: : :- Range 0, 1, 4, 1024, [id#0L]
: : +- INPUT
: +- INPUT
:- BroadcastExchange HashedRelationBroadcastMode(true,List(id#1L),List(id#1L))
: +- WholeStageCodegen
: : +- Range 0, 1, 4, 1024, [id#1L]
+- ReusedExchange [id#2L], BroadcastExchange HashedRelationBroadcastMode(true,List(id#1L),List(id#1L))
```
For three ways SortMergeJoin,
```
== Physical Plan ==
WholeStageCodegen
: +- Project [id#0L]
: +- SortMergeJoin [id#0L], [id#4L], None
: :- INPUT
: +- INPUT
:- WholeStageCodegen
: : +- Project [id#0L]
: : +- SortMergeJoin [id#0L], [id#3L], None
: : :- INPUT
: : +- INPUT
: :- WholeStageCodegen
: : : +- Sort [id#0L ASC], false, 0
: : : +- INPUT
: : +- Exchange hashpartitioning(id#0L, 200), None
: : +- WholeStageCodegen
: : : +- Range 0, 1, 4, 33554432, [id#0L]
: +- WholeStageCodegen
: : +- Sort [id#3L ASC], false, 0
: : +- INPUT
: +- ReusedExchange [id#3L], Exchange hashpartitioning(id#0L, 200), None
+- WholeStageCodegen
: +- Sort [id#4L ASC], false, 0
: +- INPUT
+- ReusedExchange [id#4L], Exchange hashpartitioning(id#0L, 200), None
```
If the same ShuffleExchange or BroadcastExchange, execute()/executeBroadcast() will be called by different parents, they should cached the RDD/Broadcast, return the same one for all the parents.
## How was this patch tested?
Added some unit tests for this. Had done some manual tests on TPCDS query Q59 and Q64, we can see some exchanges are re-used (this requires a change in PhysicalRDD to for sameResult, is be done in #11514 ).
Author: Davies Liu <davies@databricks.com>
Closes#11403 from davies/dedup.
## 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?
In order to make `docs/examples` (and other related code) more simple/readable/user-friendly, this PR replaces existing codes like the followings by using `diamond` operator.
```
- final ArrayList<Product2<Object, Object>> dataToWrite =
- new ArrayList<Product2<Object, Object>>();
+ final ArrayList<Product2<Object, Object>> dataToWrite = new ArrayList<>();
```
Java 7 or higher supports **diamond** operator which replaces the type arguments required to invoke the constructor of a generic class with an empty set of type parameters (<>). Currently, Spark Java code use mixed usage of this.
## How was this patch tested?
Manual.
Pass the existing tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11541 from dongjoon-hyun/SPARK-13702.
## What changes were proposed in this pull request?
This issue fixes the following potential bugs and Java coding style detected by Coverity and Checkstyle.
- Implement both null and type checking in equals functions.
- Fix wrong type casting logic in SimpleJavaBean2.equals.
- Add `implement Cloneable` to `UTF8String` and `SortedIterator`.
- Remove dereferencing before null check in `AbstractBytesToBytesMapSuite`.
- Fix coding style: Add '{}' to single `for` statement in mllib examples.
- Remove unused imports in `ColumnarBatch` and `JavaKinesisStreamSuite`.
- Remove unused fields in `ChunkFetchIntegrationSuite`.
- Add `stop()` to prevent resource leak.
Please note that the last two checkstyle errors exist on newly added commits after [SPARK-13583](https://issues.apache.org/jira/browse/SPARK-13583).
## How was this patch tested?
manual via `./dev/lint-java` and Coverity site.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11530 from dongjoon-hyun/SPARK-13692.
This PR replaces #9925 which had issues with CI. **Please see the original PR for any previous discussions.**
## What changes were proposed in this pull request?
Deprecate the SparkSQL column operator !== and use =!= as an alternative.
Fixes subtle issues related to operator precedence (basically, !== does not have the same priority as its logical negation, ===).
## How was this patch tested?
All currently existing tests.
Author: Jakob Odersky <jodersky@gmail.com>
Closes#11588 from jodersky/SPARK-7286.
## Motivation
CSV data source was contributed by Databricks. It is the inlined version of https://github.com/databricks/spark-csv. The data source name was `com.databricks.spark.csv`. As a result there are many tables created on older versions of spark with that name as the source. For backwards compatibility we should keep the old name.
## Proposed changes
`com.databricks.spark.csv` was added to list of `backwardCompatibilityMap` in `ResolvedDataSource.scala`
## Tests
A unit test was added to `CSVSuite` to parse a csv file using the old name.
Author: Hossein <hossein@databricks.com>
Closes#11589 from falaki/SPARK-13754.
## What changes were proposed in this pull request?
This PR fix the sizeInBytes of HadoopFsRelation.
## How was this patch tested?
Added regression test for that.
Author: Davies Liu <davies@databricks.com>
Closes#11590 from davies/fix_sizeInBytes.
When generating Graphviz DOT files in the SQL query visualization we need to escape double-quotes inside node labels. This is a followup to #11309, which fixed a similar graph in Spark Core's DAG visualization.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11587 from JoshRosen/graphviz-escaping.
## What changes were proposed in this pull request?
If a filter predicate or a join condition consists of `IsNotNull` checks, we should reorder these checks such that these non-nullability checks are evaluated before the rest of the predicates.
For e.g., if a filter predicate is of the form `a > 5 && isNotNull(b)`, we should rewrite this as `isNotNull(b) && a > 5` during physical plan generation.
## How was this patch tested?
new unit tests that verify the physical plan for both filters and joins in `ReorderedPredicateSuite`
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11511 from sameeragarwal/reorder-isnotnull.
Follow-up to #11509, that simply refactors the interface that we use when resolving a pluggable `DataSource`.
- Multiple functions share the same set of arguments so we make this a case class, called `DataSource`. Actual resolution is now done by calling a function on this class.
- Instead of having multiple methods named `apply` (some of which do writing some of which do reading) we now explicitly have `resolveRelation()` and `write(mode, df)`.
- Get rid of `Array[String]` since this is an internal API and was forcing us to awkwardly call `toArray` in a bunch of places.
Author: Michael Armbrust <michael@databricks.com>
Closes#11572 from marmbrus/dataSourceResolution.
## What changes were proposed in this pull request?
This PR change the way how we generate the code for the output variables passing from a plan to it's parent.
Right now, they are generated before call consume() of it's parent. It's not efficient, if the parent is a Filter or Join, which could filter out most the rows, the time to access some of the columns that are not used by the Filter or Join are wasted.
This PR try to improve this by defering the access of columns until they are actually used by a plan. After this PR, a plan does not need to generate code to evaluate the variables for output, just passing the ExprCode to its parent by `consume()`. In `parent.consumeChild()`, it will check the output from child and `usedInputs`, generate the code for those columns that is part of `usedInputs` before calling `doConsume()`.
This PR also change the `if` from
```
if (cond) {
xxx
}
```
to
```
if (!cond) continue;
xxx
```
The new one could help to reduce the nested indents for multiple levels of Filter and BroadcastHashJoin.
It also added some comments for operators.
## How was the this patch tested?
Unit tests. Manually ran TPCDS Q55, this PR improve the performance about 30% (scale=10, from 2.56s to 1.96s)
Author: Davies Liu <davies@databricks.com>
Closes#11274 from davies/gen_defer.
## What changes were proposed in this pull request?
When we add more DDL parsing logic in the future, SparkQl will become very big. To keep it smaller, we'll introduce helper "parser objects", e.g. one to parse alter table commands. However, these parser objects will need to access some helper methods that exist in CatalystQl. The proposal is to move those methods to an isolated ParserUtils object.
This is based on viirya's changes in #11048. It prefaces the bigger fix for SPARK-13139 to make the diff of that patch smaller.
## How was this patch tested?
No change in functionality, so just Jenkins.
Author: Andrew Or <andrew@databricks.com>
Closes#11529 from andrewor14/parser-utils.
`HadoopFsRelation` is used for reading most files into Spark SQL. However today this class mixes the concerns of file management, schema reconciliation, scan building, bucketing, partitioning, and writing data. As a result, many data sources are forced to reimplement the same functionality and the various layers have accumulated a fair bit of inefficiency. This PR is a first cut at separating this into several components / interfaces that are each described below. Additionally, all implementations inside of Spark (parquet, csv, json, text, orc, svmlib) have been ported to the new API `FileFormat`. External libraries, such as spark-avro will also need to be ported to work with Spark 2.0.
### HadoopFsRelation
A simple `case class` that acts as a container for all of the metadata required to read from a datasource. All discovery, resolution and merging logic for schemas and partitions has been removed. This an internal representation that no longer needs to be exposed to developers.
```scala
case class HadoopFsRelation(
sqlContext: SQLContext,
location: FileCatalog,
partitionSchema: StructType,
dataSchema: StructType,
bucketSpec: Option[BucketSpec],
fileFormat: FileFormat,
options: Map[String, String]) extends BaseRelation
```
### FileFormat
The primary interface that will be implemented by each different format including external libraries. Implementors are responsible for reading a given format and converting it into `InternalRow` as well as writing out an `InternalRow`. A format can optionally return a schema that is inferred from a set of files.
```scala
trait FileFormat {
def inferSchema(
sqlContext: SQLContext,
options: Map[String, String],
files: Seq[FileStatus]): Option[StructType]
def prepareWrite(
sqlContext: SQLContext,
job: Job,
options: Map[String, String],
dataSchema: StructType): OutputWriterFactory
def buildInternalScan(
sqlContext: SQLContext,
dataSchema: StructType,
requiredColumns: Array[String],
filters: Array[Filter],
bucketSet: Option[BitSet],
inputFiles: Array[FileStatus],
broadcastedConf: Broadcast[SerializableConfiguration],
options: Map[String, String]): RDD[InternalRow]
}
```
The current interface is based on what was required to get all the tests passing again, but still mixes a couple of concerns (i.e. `bucketSet` is passed down to the scan instead of being resolved by the planner). Additionally, scans are still returning `RDD`s instead of iterators for single files. In a future PR, bucketing should be removed from this interface and the scan should be isolated to a single file.
### FileCatalog
This interface is used to list the files that make up a given relation, as well as handle directory based partitioning.
```scala
trait FileCatalog {
def paths: Seq[Path]
def partitionSpec(schema: Option[StructType]): PartitionSpec
def allFiles(): Seq[FileStatus]
def getStatus(path: Path): Array[FileStatus]
def refresh(): Unit
}
```
Currently there are two implementations:
- `HDFSFileCatalog` - based on code from the old `HadoopFsRelation`. Infers partitioning by recursive listing and caches this data for performance
- `HiveFileCatalog` - based on the above, but it uses the partition spec from the Hive Metastore.
### ResolvedDataSource
Produces a logical plan given the following description of a Data Source (which can come from DataFrameReader or a metastore):
- `paths: Seq[String] = Nil`
- `userSpecifiedSchema: Option[StructType] = None`
- `partitionColumns: Array[String] = Array.empty`
- `bucketSpec: Option[BucketSpec] = None`
- `provider: String`
- `options: Map[String, String]`
This class is responsible for deciding which of the Data Source APIs a given provider is using (including the non-file based ones). All reconciliation of partitions, buckets, schema from metastores or inference is done here.
### DataSourceAnalysis / DataSourceStrategy
Responsible for analyzing and planning reading/writing of data using any of the Data Source APIs, including:
- pruning the files from partitions that will be read based on filters.
- appending partition columns*
- applying additional filters when a data source can not evaluate them internally.
- constructing an RDD that is bucketed correctly when required*
- sanity checking schema match-up and other analysis when writing.
*In the future we should do that following:
- Break out file handling into its own Strategy as its sufficiently complex / isolated.
- Push the appending of partition columns down in to `FileFormat` to avoid an extra copy / unvectorization.
- Use a custom RDD for scans instead of `SQLNewNewHadoopRDD2`
Author: Michael Armbrust <michael@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11509 from marmbrus/fileDataSource.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-13442
This PR adds the support for inferring `BooleanType` for schema.
It supports to infer case-insensitive `true` / `false` as `BooleanType`.
Unittests were added for `CSVInferSchemaSuite` and `CSVSuite` for end-to-end test.
## How was the this patch tested?
This was tested with unittests and with `dev/run_tests` for coding style
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#11315 from HyukjinKwon/SPARK-13442.
## 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?
It's weird that expressions don't always have all the expressions in it. This PR marks `QueryPlan.expressions` final to forbid sub classes overriding it to exclude some expressions. Currently only `Generate` override it, we can use `producedAttributes` to fix the unresolved attribute problem for it.
Note that this PR doesn't fix the problem in #11497
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11532 from cloud-fan/generate.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
Currently, the parquet reader returns rows one by one which is bad for performance. This patch
updates the reader to directly return ColumnarBatches. This is only enabled with whole stage
codegen, which is the only operator currently that is able to consume ColumnarBatches (instead
of rows). The current implementation is a bit of a hack to get this to work and we should do
more refactoring of these low level interfaces to make this work better.
## How was this patch tested?
```
Results:
TPCDS: Best/Avg Time(ms) Rate(M/s) Per Row(ns)
---------------------------------------------------------------------------------
q55 (before) 8897 / 9265 12.9 77.2
q55 5486 / 5753 21.0 47.6
```
Author: Nong Li <nong@databricks.com>
Closes#11435 from nongli/spark-13255.
## 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?
A test suite added for the bug fix -SPARK 12941; for the mapping of the StringType to corresponding in Oracle
## How was this patch tested?
manual tests done
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: thomastechs <thomas.sebastian@tcs.com>
Author: THOMAS SEBASTIAN <thomas.sebastian@tcs.com>
Closes#11489 from thomastechs/thomastechs-12941-master-new.
## What changes were proposed in this pull request?
This PR support visualization for subquery in SQL web UI, also improve the explain of subquery, especially when it's used together with whole stage codegen.
For example:
```python
>>> sqlContext.range(100).registerTempTable("range")
>>> sqlContext.sql("select id / (select sum(id) from range) from range where id > (select id from range limit 1)").explain(True)
== Parsed Logical Plan ==
'Project [unresolvedalias(('id / subquery#9), None)]
: +- 'SubqueryAlias subquery#9
: +- 'Project [unresolvedalias('sum('id), None)]
: +- 'UnresolvedRelation `range`, None
+- 'Filter ('id > subquery#8)
: +- 'SubqueryAlias subquery#8
: +- 'GlobalLimit 1
: +- 'LocalLimit 1
: +- 'Project [unresolvedalias('id, None)]
: +- 'UnresolvedRelation `range`, None
+- 'UnresolvedRelation `range`, None
== Analyzed Logical Plan ==
(id / scalarsubquery()): double
Project [(cast(id#0L as double) / cast(subquery#9 as double)) AS (id / scalarsubquery())#11]
: +- SubqueryAlias subquery#9
: +- Aggregate [(sum(id#0L),mode=Complete,isDistinct=false) AS sum(id)#10L]
: +- SubqueryAlias range
: +- Range 0, 100, 1, 4, [id#0L]
+- Filter (id#0L > subquery#8)
: +- SubqueryAlias subquery#8
: +- GlobalLimit 1
: +- LocalLimit 1
: +- Project [id#0L]
: +- SubqueryAlias range
: +- Range 0, 100, 1, 4, [id#0L]
+- SubqueryAlias range
+- Range 0, 100, 1, 4, [id#0L]
== Optimized Logical Plan ==
Project [(cast(id#0L as double) / cast(subquery#9 as double)) AS (id / scalarsubquery())#11]
: +- SubqueryAlias subquery#9
: +- Aggregate [(sum(id#0L),mode=Complete,isDistinct=false) AS sum(id)#10L]
: +- Range 0, 100, 1, 4, [id#0L]
+- Filter (id#0L > subquery#8)
: +- SubqueryAlias subquery#8
: +- GlobalLimit 1
: +- LocalLimit 1
: +- Project [id#0L]
: +- Range 0, 100, 1, 4, [id#0L]
+- Range 0, 100, 1, 4, [id#0L]
== Physical Plan ==
WholeStageCodegen
: +- Project [(cast(id#0L as double) / cast(subquery#9 as double)) AS (id / scalarsubquery())#11]
: : +- Subquery subquery#9
: : +- WholeStageCodegen
: : : +- TungstenAggregate(key=[], functions=[(sum(id#0L),mode=Final,isDistinct=false)], output=[sum(id)#10L])
: : : +- INPUT
: : +- Exchange SinglePartition, None
: : +- WholeStageCodegen
: : : +- TungstenAggregate(key=[], functions=[(sum(id#0L),mode=Partial,isDistinct=false)], output=[sum#14L])
: : : +- Range 0, 1, 4, 100, [id#0L]
: +- Filter (id#0L > subquery#8)
: : +- Subquery subquery#8
: : +- CollectLimit 1
: : +- WholeStageCodegen
: : : +- Project [id#0L]
: : : +- Range 0, 1, 4, 100, [id#0L]
: +- Range 0, 1, 4, 100, [id#0L]
```
The web UI looks like:
This PR also change the tree structure of WholeStageCodegen to make it consistent than others. Before this change, Both WholeStageCodegen and InputAdapter hold a references to the same plans, those could be updated without notify another, causing problems, this is discovered by #11403 .
## How was this patch tested?
Existing tests, also manual tests with the example query, check the explain and web UI.
Author: Davies Liu <davies@databricks.com>
Closes#11417 from davies/viz_subquery.
## What changes were proposed in this pull request?
Make ContinuousQueryManagerSuite not output logs to the console. The logs will still output to `unit-tests.log`.
I also updated `SQLListenerMemoryLeakSuite` to use `quietly` to avoid changing the log level which won't output logs to `unit-tests.log`.
## How was this patch tested?
Just check Jenkins output.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11439 from zsxwing/quietly-ContinuousQueryManagerSuite.
## What changes were proposed in this pull request?
This patch simply moves things to a new package in an effort to reduce the size of the diff in #11048. Currently the new package only has one file, but in the future we'll add many new commands in SPARK-13139.
## How was this patch tested?
Jenkins.
Author: Andrew Or <andrew@databricks.com>
Closes#11482 from andrewor14/commands-package.
## What changes were proposed in this pull request?
This PR fixes typos in comments and testcase name of code.
## How was this patch tested?
manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11481 from dongjoon-hyun/minor_fix_typos_in_code.
## What changes were proposed in this pull request?
This PR adds the support to specify compression codecs for both ORC and Parquet.
## How was this patch tested?
unittests within IDE and code style tests with `dev/run_tests`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#11464 from HyukjinKwon/SPARK-13543.
## What changes were proposed in this pull request?
Fixes compile problem due to inadvertent use of `Option.contains`, only in Scala 2.11. The change should have been to replace `Option.exists(_ == x)` with `== Some(x)`. Replacing exists with contains only makes sense for collections. Replacing use of `Option.exists` still makes sense though as it's misleading.
## How was this patch tested?
Jenkins tests / compilation
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Sean Owen <sowen@cloudera.com>
Closes#11493 from srowen/SPARK-13423.2.
## 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 to make the short names of compression codecs in `ParquetRelation` consistent against other ones. This pr comes from #11324.
## How was this patch tested?
Add more tests in `TextSuite`.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#11408 from maropu/SPARK-13528.
## What changes were proposed in this pull request?
Also updated the other benchmarks when the default to use vectorized decode was flipped.
Author: Nong Li <nong@databricks.com>
Closes#11454 from nongli/benchmark.
## What changes were proposed in this pull request?
In order to tell OutputStream that the task has failed or not, we should call the failure callbacks BEFORE calling writer.close().
## How was this patch tested?
Added new unit tests.
Author: Davies Liu <davies@databricks.com>
Closes#11450 from davies/callback.
#### 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.
Rows with null values in partition column are not included in the results because none of the partition
where clause specify is null predicate on the partition column. This fix adds is null predicate on the partition column to the first JDBC partition where clause.
Example:
JDBCPartition(THEID < 1 or THEID is null, 0),JDBCPartition(THEID >= 1 AND THEID < 2,1),
JDBCPartition(THEID >= 2, 2)
Author: sureshthalamati <suresh.thalamati@gmail.com>
Closes#11063 from sureshthalamati/nullable_jdbc_part_col_spark-13167.
## What changes were proposed in this pull request?
Broadcast left semi join without joining keys is already supported in BroadcastNestedLoopJoin, it has the same implementation as LeftSemiJoinBNL, we should remove that.
## How was this patch tested?
Updated unit tests.
Author: Davies Liu <davies@databricks.com>
Closes#11448 from davies/remove_bnl.
## What changes were proposed in this pull request?
This PR defer the resolution from a id of dictionary to value until the column is actually accessed (inside getInt/getLong), this is very useful for those columns and rows that are filtered out. It's also useful for binary type, we will not need to copy all the byte arrays.
This PR also change the underlying type for small decimal that could be fit within a Int, in order to use getInt() to lookup the value from IntDictionary.
## How was this patch tested?
Manually test TPCDS Q7 with scale factor 10, saw about 30% improvements (after PR #11274).
Author: Davies Liu <davies@databricks.com>
Closes#11437 from davies/decode_dict.
JIRA: https://issues.apache.org/jira/browse/SPARK-13511
## What changes were proposed in this pull request?
Current limit operator doesn't support wholestage codegen. This is open to add support for it.
In the `doConsume` of `GlobalLimit` and `LocalLimit`, we use a count term to count the processed rows. Once the row numbers catches the limit number, we set the variable `stopEarly` of `BufferedRowIterator` newly added in this pr to `true` that indicates we want to stop processing remaining rows. Then when the wholestage codegen framework checks `shouldStop()`, it will stop the processing of the row iterator.
Before this, the executed plan for a query `sqlContext.range(N).limit(100).groupBy().sum()` is:
TungstenAggregate(key=[], functions=[(sum(id#5L),mode=Final,isDistinct=false)], output=[sum(id)#6L])
+- TungstenAggregate(key=[], functions=[(sum(id#5L),mode=Partial,isDistinct=false)], output=[sum#9L])
+- GlobalLimit 100
+- Exchange SinglePartition, None
+- LocalLimit 100
+- Range 0, 1, 1, 524288000, [id#5L]
After add wholestage codegen support:
WholeStageCodegen
: +- TungstenAggregate(key=[], functions=[(sum(id#40L),mode=Final,isDistinct=false)], output=[sum(id)#41L])
: +- TungstenAggregate(key=[], functions=[(sum(id#40L),mode=Partial,isDistinct=false)], output=[sum#44L])
: +- GlobalLimit 100
: +- INPUT
+- Exchange SinglePartition, None
+- WholeStageCodegen
: +- LocalLimit 100
: +- Range 0, 1, 1, 524288000, [id#40L]
## How was this patch tested?
A test is added into BenchmarkWholeStageCodegen.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11391 from viirya/wholestage-limit.
## What changes were proposed in this pull request?
This PR adds support for implementing whole state codegen for sort. Builds heaving on nongli 's PR: https://github.com/apache/spark/pull/11008 (which actually implements the feature), and adds the following changes on top:
- [x] Generated code updates peak execution memory metrics
- [x] Unit tests in `WholeStageCodegenSuite` and `SQLMetricsSuite`
## How was this patch tested?
New unit tests in `WholeStageCodegenSuite` and `SQLMetricsSuite`. Further, all existing sort tests should pass.
Author: Sameer Agarwal <sameer@databricks.com>
Author: Nong Li <nong@databricks.com>
Closes#11359 from sameeragarwal/sort-codegen.
https://issues.apache.org/jira/browse/SPARK-13507https://issues.apache.org/jira/browse/SPARK-13509
## What changes were proposed in this pull request?
This PR adds the support to write CSV data directly by a single call to the given path.
Several unitests were added for each functionality.
## How was this patch tested?
This was tested with unittests and with `dev/run_tests` for coding style
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Closes#11389 from HyukjinKwon/SPARK-13507-13509.
## What changes were proposed in this pull request?
Nested classes defined within Scala objects are translated into Java static nested classes. Unlike inner classes, they don't need outer scopes. But the analyzer still thinks that an outer scope is required.
This PR fixes this issue simply by checking whether a nested class is static before looking up its outer scope.
## How was this patch tested?
A test case is added to `DatasetSuite`. It checks contents of a Dataset whose element type is a nested class declared in a Scala object.
Author: Cheng Lian <lian@databricks.com>
Closes#11421 from liancheng/spark-13540-object-as-outer-scope.
JIRA: https://issues.apache.org/jira/browse/SPARK-13537
## What changes were proposed in this pull request?
In readBytes of VectorizedPlainValuesReader, we use buffer[offset] to access bytes in buffer. It is incorrect because offset is added with Platform.BYTE_ARRAY_OFFSET when initialization. We should fix it.
## How was this patch tested?
`ParquetHadoopFsRelationSuite` sometimes (depending on the randomly generated data) will be [failed](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/52136/consoleFull) by this bug. After applying this, the test can be passed.
I added a test to `ParquetHadoopFsRelationSuite` with the data which will fail without this patch.
The error exception:
[info] ParquetHadoopFsRelationSuite:
[info] - test all data types - StringType (440 milliseconds)
[info] - test all data types - BinaryType (434 milliseconds)
[info] - test all data types - BooleanType (406 milliseconds)
20:59:38.618 ERROR org.apache.spark.executor.Executor: Exception in task 0.0 in stage 2597.0 (TID 67966)
java.lang.ArrayIndexOutOfBoundsException: 46
at org.apache.spark.sql.execution.datasources.parquet.VectorizedPlainValuesReader.readBytes(VectorizedPlainValuesReader.java:88)
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11418 from viirya/fix-readbytes.
## What changes were proposed in this pull request?
This creates a `SessionState`, which groups a few fields that existed in `SQLContext`. Because `HiveContext` extends `SQLContext` we also need to make changes there. This is mainly a cleanup task that will soon pave the way for merging the two contexts.
## How was this patch tested?
Existing unit tests; this patch introduces no change in behavior.
Author: Andrew Or <andrew@databricks.com>
Closes#11405 from andrewor14/refactor-session.
## What changes were proposed in this pull request?
Fix readBytes in VectorizedPlainValuesReader. This fixes a copy and paste issue.
## How was this patch tested?
Ran ParquetHadoopFsRelationSuite which failed before this.
Author: Nong Li <nong@databricks.com>
Closes#11414 from nongli/spark-13533.
JIRA: https://issues.apache.org/jira/browse/SPARK-13530
## What changes were proposed in this pull request?
By enabling vectorized parquet scanner by default, the unit test `ParquetHadoopFsRelationSuite` based on `HadoopFsRelationTest` will be failed due to the lack of short type support in `UnsafeRowParquetRecordReader`. We should fix it.
The error exception:
[info] ParquetHadoopFsRelationSuite:
[info] - test all data types - StringType (499 milliseconds)
[info] - test all data types - BinaryType (447 milliseconds)
[info] - test all data types - BooleanType (520 milliseconds)
[info] - test all data types - ByteType (418 milliseconds)
00:22:58.920 ERROR org.apache.spark.executor.Executor: Exception in task 0.0 in stage 124.0 (TID 1949)
org.apache.commons.lang.NotImplementedException: Unimplemented type: ShortType
at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader$ColumnReader.readIntBatch(UnsafeRowParquetRecordReader.java:769)
at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader$ColumnReader.readBatch(UnsafeRowParquetRecordReader.java:640)
at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader$ColumnReader.access$000(UnsafeRowParquetRecordReader.java:461)
at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader.nextBatch(UnsafeRowParquetRecordReader.java:224)
## How was this patch tested?
The unit test `ParquetHadoopFsRelationSuite` based on `HadoopFsRelationTest` will be [failed](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/52110/consoleFull) due to the lack of short type support in UnsafeRowParquetRecordReader. By adding this support, the test can be passed.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11412 from viirya/add-shorttype-support.
## What changes were proposed in this pull request?
Change the default of the flag to enable this feature now that the implementation is complete.
## How was this patch tested?
The new parquet reader should be a drop in, so will be exercised by the existing tests.
Author: Nong Li <nong@databricks.com>
Closes#11397 from nongli/spark-13518.
## What changes were proposed in this pull request?
This patch includes these performance fixes:
- Remove unnecessary setNotNull() calls. The NULL bits are cleared already.
- Speed up RLE group decoding
- Speed up dictionary decoding by decoding NULLs directly into the result.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
In addition to the updated benchmarks, on TPCDS, the result of these changes
running Q55 (sf40) is:
```
TPCDS: Best/Avg Time(ms) Rate(M/s) Per Row(ns)
---------------------------------------------------------------------------------
q55 (Before) 6398 / 6616 18.0 55.5
q55 (After) 4983 / 5189 23.1 43.3
```
Author: Nong Li <nong@databricks.com>
Closes#11375 from nongli/spark-13499.
## What changes were proposed in this pull request?
Currently, BroadcastNestedLoopJoin is implemented for worst case, it's too slow, very easy to hang forever. This PR will create fast path for some joinType and buildSide, also improve the worst case (will use much less memory than before).
Before this PR, one task requires O(N*K) + O(K) in worst cases, N is number of rows from one partition of streamed table, it could hang the job (because of GC).
In order to workaround this for InnerJoin, we have to disable auto-broadcast, switch to CartesianProduct: This could be workaround for InnerJoin, see https://forums.databricks.com/questions/6747/how-do-i-get-a-cartesian-product-of-a-huge-dataset.html
In this PR, we will have fast path for these joins :
InnerJoin with BuildLeft or BuildRight
LeftOuterJoin with BuildRight
RightOuterJoin with BuildLeft
LeftSemi with BuildRight
These fast paths are all stream based (take one pass on streamed table), required O(1) memory.
All other join types and build types will take two pass on streamed table, one pass to find the matched rows that includes streamed part, which require O(1) memory, another pass to find the rows from build table that does not have a matched row from streamed table, which required O(K) memory, K is the number rows from build side, one bit per row, should be much smaller than the memory for broadcast. The following join types work in this way:
LeftOuterJoin with BuildLeft
RightOuterJoin with BuildRight
FullOuterJoin with BuildLeft or BuildRight
LeftSemi with BuildLeft
This PR also added tests for all the join types for BroadcastNestedLoopJoin.
After this PR, for InnerJoin with one small table, BroadcastNestedLoopJoin should be faster than CartesianProduct, we don't need that workaround anymore.
## How was the this patch tested?
Added unit tests.
Author: Davies Liu <davies@databricks.com>
Closes#11328 from davies/nested_loop.
## What changes were proposed in this pull request?
This is another try of PR #11323.
This PR removes DataFrame RDD operations except for `foreach` and `foreachPartitions` (they are actions rather than transformations). Original calls are now replaced by calls to methods of `DataFrame.rdd`.
PR #11323 was reverted because it introduced a regression: both `DataFrame.foreach` and `DataFrame.foreachPartitions` wrap underlying RDD operations with `withNewExecutionId` to track Spark jobs. But they are removed in #11323.
## How was the this patch tested?
No extra tests are added. Existing tests should do the work.
Author: Cheng Lian <lian@databricks.com>
Closes#11388 from liancheng/remove-df-rdd-ops.
## What changes were proposed in this pull request?
This patch creates the public API for runtime configuration and an implementation for it. The public runtime configuration includes configs for existing SQL, as well as Hadoop Configuration.
This new interface is currently dead code. It will be added to SQLContext and a session entry point to Spark when we add that.
## How was this patch tested?
a new unit test suite
Author: Reynold Xin <rxin@databricks.com>
Closes#11378 from rxin/SPARK-13487.
## What changes were proposed in this pull request?
This Pull request is used for the fix SPARK-12941, creating a data type mapping to Oracle for the corresponding data type"Stringtype" from dataframe. This PR is for the master branch fix, where as another PR is already tested with the branch 1.4
## How was the this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
This patch was tested using the Oracle docker .Created a new integration suite for the same.The oracle.jdbc jar was to be downloaded from the maven repository.Since there was no jdbc jar available in the maven repository, the jar was downloaded from oracle site manually and installed in the local; thus tested. So, for SparkQA test case run, the ojdbc jar might be manually placed in the local maven repository(com/oracle/ojdbc6/11.2.0.2.0) while Spark QA test run.
Author: thomastechs <thomas.sebastian@tcs.com>
Closes#11306 from thomastechs/master.
This pr added benchmark codes for Encoder#compress().
Also, it replaced the benchmark results with new ones because the output format of `Benchmark` changed.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#11236 from maropu/CompressionSpike.
## Motivation
As a pre-requisite to off-heap caching of blocks, we need a mechanism to prevent pages / blocks from being evicted while they are being read. With on-heap objects, evicting a block while it is being read merely leads to memory-accounting problems (because we assume that an evicted block is a candidate for garbage-collection, which will not be true during a read), but with off-heap memory this will lead to either data corruption or segmentation faults.
## Changes
### BlockInfoManager and reader/writer locks
This patch adds block-level read/write locks to the BlockManager. It introduces a new `BlockInfoManager` component, which is contained within the `BlockManager`, holds the `BlockInfo` objects that the `BlockManager` uses for tracking block metadata, and exposes APIs for locking blocks in either shared read or exclusive write modes.
`BlockManager`'s `get*()` and `put*()` methods now implicitly acquire the necessary locks. After a `get()` call successfully retrieves a block, that block is locked in a shared read mode. A `put()` call will block until it acquires an exclusive write lock. If the write succeeds, the write lock will be downgraded to a shared read lock before returning to the caller. This `put()` locking behavior allows us store a block and then immediately turn around and read it without having to worry about it having been evicted between the write and the read, which will allow us to significantly simplify `CacheManager` in the future (see #10748).
See `BlockInfoManagerSuite`'s test cases for a more detailed specification of the locking semantics.
### Auto-release of locks at the end of tasks
Our locking APIs support explicit release of locks (by calling `unlock()`), but it's not always possible to guarantee that locks will be released prior to the end of the task. One reason for this is our iterator interface: since our iterators don't support an explicit `close()` operator to signal that no more records will be consumed, operations like `take()` or `limit()` don't have a good means to release locks on their input iterators' blocks. Another example is broadcast variables, whose block locks can only be released at the end of the task.
To address this, `BlockInfoManager` uses a pair of maps to track the set of locks acquired by each task. Lock acquisitions automatically record the current task attempt id by obtaining it from `TaskContext`. When a task finishes, code in `Executor` calls `BlockInfoManager.unlockAllLocksForTask(taskAttemptId)` to free locks.
### Locking and the MemoryStore
In order to prevent in-memory blocks from being evicted while they are being read, the `MemoryStore`'s `evictBlocksToFreeSpace()` method acquires write locks on blocks which it is considering as candidates for eviction. These lock acquisitions are non-blocking, so a block which is being read will not be evicted. By holding write locks until the eviction is performed or skipped (in case evicting the blocks would not free enough memory), we avoid a race where a new reader starts to read a block after the block has been marked as an eviction candidate but before it has been removed.
### Locking and remote block transfer
This patch makes small changes to to block transfer and network layer code so that locks acquired by the BlockTransferService are released as soon as block transfer messages are consumed and released by Netty. This builds on top of #11193, a bug fix related to freeing of network layer ManagedBuffers.
## FAQ
- **Why not use Java's built-in [`ReadWriteLock`](https://docs.oracle.com/javase/7/docs/api/java/util/concurrent/locks/ReadWriteLock.html)?**
Our locks operate on a per-task rather than per-thread level. Under certain circumstances a task may consist of multiple threads, so using `ReadWriteLock` would mean that we might call `unlock()` from a thread which didn't hold the lock in question, an operation which has undefined semantics. If we could rely on Java 8 classes, we might be able to use [`StampedLock`](https://docs.oracle.com/javase/8/docs/api/java/util/concurrent/locks/StampedLock.html) to work around this issue.
- **Why not detect "leaked" locks in tests?**:
See above notes about `take()` and `limit`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10705 from JoshRosen/pin-pages.
## What changes were proposed in this pull request?
This PR removes DataFrame RDD operations. Original calls are now replaced by calls to methods of `DataFrame.rdd`.
## How was the this patch tested?
No extra tests are added. Existing tests should do the work.
Author: Cheng Lian <lian@databricks.com>
Closes#11323 from liancheng/remove-df-rdd-ops.
## What changes were proposed in this pull request?
This patch moves SQLConf into org.apache.spark.sql.internal package to make it very explicit that it is internal. Soon I will also submit more API work that creates implementations of interfaces in this internal package.
## How was this patch tested?
If it compiles, then the refactoring should work.
Author: Reynold Xin <rxin@databricks.com>
Closes#11363 from rxin/SPARK-13486.
## 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.
## What changes were proposed in this pull request?
* Scala DataFrameStatFunctions: Added version of approxQuantile taking a List instead of an Array, for Python compatbility
* Python DataFrame and DataFrameStatFunctions: Added approxQuantile
## How was this patch tested?
* unit test in sql/tests.py
Documentation was copied from the existing approxQuantile exactly.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#11356 from jkbradley/approx-quantile-python.
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.
Some parts of the engine rely on UnsafeRow which the vectorized parquet scanner does not want
to produce. This add a conversion in Physical RDD. In the case where codegen is used (and the
scan is the start of the pipeline), there is no requirement to use UnsafeRow. This patch adds
update PhysicallRDD to support codegen, which eliminates the need for the UnsafeRow conversion
in all cases.
The result of these changes for TPCDS-Q19 at the 10gb sf reduces the query time from 9.5 seconds
to 6.5 seconds.
Author: Nong Li <nong@databricks.com>
Closes#11141 from nongli/spark-13250.
## What changes were proposed in this pull request?
When we pass a Python function to JVM side, we also need to send its context, e.g. `envVars`, `pythonIncludes`, `pythonExec`, etc. However, it's annoying to pass around so many parameters at many places. This PR abstract python function along with its context, to simplify some pyspark code and make the logic more clear.
## How was the this patch tested?
by existing unit tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11342 from cloud-fan/python-clean.
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.
## What changes were proposed in this pull request?
This continues thunterdb 's work on `approxQuantile` API. It changes the signature of `approxQuantile` from `(col: String, quantile: Double, epsilon: Double): Double` to `(col: String, probabilities: Array[Double], relativeError: Double): Array[Double]` and update API doc. It also improves the error message in tests and simplifies the merge algorithm for summaries.
## How was the this patch tested?
Use the same unit tests as before.
Closes#11325
Author: Timothy Hunter <timhunter@databricks.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#11332 from mengxr/SPARK-6761.
## What changes were proposed in this pull request?
Generates code for SortMergeJoin.
## How was the this patch tested?
Unit tests and manually tested with TPCDS Q72, which showed 70% performance improvements (from 42s to 25s), but micro benchmark only show minor improvements, it may depends the distribution of data and number of columns.
Author: Davies Liu <davies@databricks.com>
Closes#11248 from davies/gen_smj.
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.
JIRA: https://issues.apache.org/jira/browse/SPARK-6761
Compute approximate quantile based on the paper Greenwald, Michael and Khanna, Sanjeev, "Space-efficient Online Computation of Quantile Summaries," SIGMOD '01.
Author: Timothy Hunter <timhunter@databricks.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#6042 from viirya/approximate_quantile.
#### What changes were proposed in this pull request?
Ensure that all built-in expressions can be mapped to its SQL representation if there is one (e.g. ScalaUDF doesn't have a SQL representation). The function lists are from the expression list in `FunctionRegistry`.
window functions, grouping sets functions (`cube`, `rollup`, `grouping`, `grouping_id`), generator functions (`explode` and `json_tuple`) are covered by separate JIRA and PRs. Thus, this PR does not cover them. Except these functions, all the built-in expressions are covered. For details, see the list in `ExpressionToSQLSuite`.
Fixed a few issues. For example, the `prettyName` of `approx_count_distinct` is not right. The `sql` of `hash` function is not right, since the `hash` function does not accept `seed`.
Additionally, also correct the order of expressions in `FunctionRegistry` so that people are easier to find which functions are missing.
cc liancheng
#### How was the this patch tested?
Added two test cases in LogicalPlanToSQLSuite for covering `not like` and `not in`.
Added a new test suite `ExpressionToSQLSuite` to cover the functions:
1. misc non-aggregate functions + complex type creators + null expressions
2. math functions
3. aggregate functions
4. string functions
5. date time functions + calendar interval
6. collection functions
7. misc functions
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11314 from gatorsmile/expressionToSQL.
Use the HashedRelation which is a more optimized datastructure and reduce code complexity
Author: Xiu Guo <xguo27@gmail.com>
Closes#11291 from xguo27/SPARK-13422.
A common problem that users encounter with Spark 1.6.0 is that writing to a partitioned parquet table OOMs. The root cause is that parquet allocates a significant amount of memory that is not accounted for by our own mechanisms. As a workaround, we can ensure that only a single file is open per task unless the user explicitly asks for more.
Author: Michael Armbrust <michael@databricks.com>
Closes#11308 from marmbrus/parquetWriteOOM.
## 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.
https://issues.apache.org/jira/browse/SPARK-13381
This PR adds the support to load CSV data directly by a single call with given paths.
Also, I corrected this to refer all paths rather than the first path in schema inference, which JSON datasource dose.
Several unitests were added for each functionality.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#11262 from HyukjinKwon/SPARK-13381.
## What changes were proposed in this pull request?
This PR adds equality operators to UDT classes so that they can be correctly tested for dataType equality during union operations.
This was previously causing `"AnalysisException: u"unresolved operator 'Union;""` when trying to unionAll two dataframes with UDT columns as below.
```
from pyspark.sql.tests import PythonOnlyPoint, PythonOnlyUDT
from pyspark.sql import types
schema = types.StructType([types.StructField("point", PythonOnlyUDT(), True)])
a = sqlCtx.createDataFrame([[PythonOnlyPoint(1.0, 2.0)]], schema)
b = sqlCtx.createDataFrame([[PythonOnlyPoint(3.0, 4.0)]], schema)
c = a.unionAll(b)
```
## How was the this patch tested?
Tested using two unit tests in sql/test.py and the DataFrameSuite.
Additional information here : https://issues.apache.org/jira/browse/SPARK-13410
Author: Franklyn D'souza <franklynd@gmail.com>
Closes#11279 from damnMeddlingKid/udt-union-all.
