https://issues.apache.org/jira/browse/SPARK-12901
This PR refactors the options in JSON and CSV datasources.
In more details,
1. `JSONOptions` uses the same format as `CSVOptions`.
2. Not case classes.
3. `CSVRelation` that does not have to be serializable (it was `with Serializable` but I removed)
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#10895 from HyukjinKwon/SPARK-12901.
When actual row length doesn't conform to specified schema field length, we should give a better error message instead of throwing an unintuitive `ArrayOutOfBoundsException`.
Author: Cheng Lian <lian@databricks.com>
Closes#10886 from liancheng/spark-12624.
ErrorPositionSuite and one of the HiveComparisonTest tests have been consistently failing on the Hadoop 2.3 SBT build (but on no other builds). I believe that this is due to test isolation issues (e.g. tests sharing state via the sets of temporary tables that are registered to TestHive).
This patch attempts to improve the isolation of these tests in order to address this issue.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10884 from JoshRosen/fix-failing-hadoop-2.3-hive-tests.
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.
https://issues.apache.org/jira/browse/SPARK-12872
This PR makes the JSON datasource can compress output by option instead of manually setting Hadoop configurations.
For reflecting codec by names, it is similar with https://github.com/apache/spark/pull/10805.
As `CSVCompressionCodecs` can be shared with other datasources, it became a separate class to share as `CompressionCodecs`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#10858 from HyukjinKwon/SPARK-12872.
When users turn off bucketing in SQLConf, we should issue some messages to tell users these operations will be converted to normal way.
Also added a test case for this scenario and fixed the helper function.
Do you think this PR is helpful when using bucket tables? cloud-fan Thank you!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10870 from gatorsmile/bucketTableWritingTestcases.
The current parser turns a decimal literal, for example ```12.1```, into a Double. The problem with this approach is that we convert an exact literal into a non-exact ```Double```. The PR changes this behavior, a Decimal literal is now converted into an extact ```BigDecimal```.
The behavior for scientific decimals, for example ```12.1e01```, is unchanged. This will be converted into a Double.
This PR replaces the ```BigDecimal``` literal by a ```Double``` literal, because the ```BigDecimal``` is the default now. You can use the double literal by appending a 'D' to the value, for instance: ```3.141527D```
cc davies rxin
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10796 from hvanhovell/SPARK-12848.
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.
Currently, HiveTableScan runs with getCallSite which is really expensive and shows up when scanning through large table with partitions (e.g TPC-DS) which slows down the overall runtime of the job. It would be good to consider having dummyCallSite in HiveTableScan.
Author: Rajesh Balamohan <rbalamohan@apache.org>
Closes#10825 from rajeshbalamohan/SPARK-12898.
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.
https://issues.apache.org/jira/browse/SPARK-12871
This PR added an option to support to specify compression codec.
This adds the option `codec` as an alias `compression` as filed in [SPARK-12668 ](https://issues.apache.org/jira/browse/SPARK-12668).
Note that I did not add configurations for Hadoop 1.x as this `CsvRelation` is using Hadoop 2.x API and I guess it is going to drop Hadoop 1.x support.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#10805 from HyukjinKwon/SPARK-12420.
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.
See https://issues.apache.org/jira/browse/SPARK-12560
This isn't causing any problems currently because the tests for string predicate pushdown are currently disabled. I ran into this while trying to turn them back on with a different version of parquet. Figure it was good to fix now in any case.
Author: Imran Rashid <irashid@cloudera.com>
Closes#10510 from squito/SPARK-12560.
JIRA: https://issues.apache.org/jira/browse/SPARK-12867
When intersecting one nullable column with one non-nullable column, the result will not contain any null. Thus, we can make nullability of `intersect` stricter.
liancheng Could you please check if the code changes are appropriate? Also added test cases to verify the results. Thanks!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10812 from gatorsmile/nullabilityIntersect.
This is a step in implementing SPARK-10620, which migrates TaskMetrics to accumulators.
TaskMetrics has a bunch of var's, some are fully public, some are `private[spark]`. This is bad coding style that makes it easy to accidentally overwrite previously set metrics. This has happened a few times in the past and caused bugs that were difficult to debug.
Instead, we should have get-or-create semantics, which are more readily understandable. This makes sense in the case of TaskMetrics because these are just aggregated metrics that we want to collect throughout the task, so it doesn't matter who's incrementing them.
Parent PR: #10717
Author: Andrew Or <andrew@databricks.com>
Author: Josh Rosen <joshrosen@databricks.com>
Author: andrewor14 <andrew@databricks.com>
Closes#10815 from andrewor14/get-or-create-metrics.
for normal parquet file without bucket, it's file name ends with a jobUUID which maybe all numbers and mistakeny regarded as bucket id. This PR improves the format of bucket id in file name by using a different seperator, `_`, so that the regex is more robust.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10799 from cloud-fan/fix-bucket.
Currently SortMergeJoin and BroadcastHashJoin do not support condition, the need a followed Filter for that, the result projection to generate UnsafeRow could be very expensive if they generate lots of rows and could be filtered mostly by condition.
This PR brings the support of condition for SortMergeJoin and BroadcastHashJoin, just like other outer joins do.
This could improve the performance of Q72 by 7x (from 120s to 16.5s).
Author: Davies Liu <davies@databricks.com>
Closes#10653 from davies/filter_join.
Based on discussions in #10801, I'm submitting a pull request to rename ParserDialect to ParserInterface.
Author: Reynold Xin <rxin@databricks.com>
Closes#10817 from rxin/SPARK-12889.
Right now, the bucket tests are kind of hard to understand, this PR simplifies them and add more commetns.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10813 from cloud-fan/bucket-comment.
In SPARK-10743 we wrap cast with `UnresolvedAlias` to give `Cast` a better alias if possible. However, for cases like `filter`, the `UnresolvedAlias` can't be resolved and actually we don't need a better alias for this case. This PR move the cast wrapping logic to `Column.named` so that we will only do it when we need a alias name.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10781 from cloud-fan/bug.
This pull request removes the public developer parser API for external parsers. Given everything a parser depends on (e.g. logical plans and expressions) are internal and not stable, external parsers will break with every release of Spark. It is a bad idea to create the illusion that Spark actually supports pluggable parsers. In addition, this also reduces incentives for 3rd party projects to contribute parse improvements back to Spark.
Author: Reynold Xin <rxin@databricks.com>
Closes#10801 from rxin/SPARK-12855.
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.
This is the initial work for whole stage codegen, it support Projection/Filter/Range, we will continue work on this to support more physical operators.
A micro benchmark show that a query with range, filter and projection could be 3X faster then before.
It's turned on by default. For a tree that have at least two chained plans, a WholeStageCodegen will be inserted into it, for example, the following plan
```
Limit 10
+- Project [(id#5L + 1) AS (id + 1)#6L]
+- Filter ((id#5L & 1) = 1)
+- Range 0, 1, 4, 10, [id#5L]
```
will be translated into
```
Limit 10
+- WholeStageCodegen
+- Project [(id#1L + 1) AS (id + 1)#2L]
+- Filter ((id#1L & 1) = 1)
+- Range 0, 1, 4, 10, [id#1L]
```
Here is the call graph to generate Java source for A and B (A support codegen, but B does not):
```
* WholeStageCodegen Plan A FakeInput Plan B
* =========================================================================
*
* -> execute()
* |
* doExecute() --------> produce()
* |
* doProduce() -------> produce()
* |
* doProduce() ---> execute()
* |
* consume()
* doConsume() ------------|
* |
* doConsume() <----- consume()
```
A SparkPlan that support codegen need to implement doProduce() and doConsume():
```
def doProduce(ctx: CodegenContext): (RDD[InternalRow], String)
def doConsume(ctx: CodegenContext, child: SparkPlan, input: Seq[ExprCode]): String
```
Author: Davies Liu <davies@databricks.com>
Closes#10735 from davies/whole2.
We iterate the bytes to calculate hashCode before, but now we have `Murmur3_x86_32.hashUnsafeBytes` that don't require the bytes to be word algned, we should use that instead.
A simple benchmark shows it's about 3 X faster, benchmark code: https://gist.github.com/cloud-fan/fa77713ccebf0823b2ab#file-arrayhashbenchmark-scala
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10784 from cloud-fan/array-hashcode.
This inlines a few of the Parquet decoders and adds vectorized APIs to support decoding in batch.
There are a few particulars in the Parquet encodings that make this much more efficient. In
particular, RLE encodings are very well suited for batch decoding. The Parquet 2.0 encodings are
also very suited for this.
This is a work in progress and does not affect the current execution. In subsequent patches, we will
support more encodings and types before enabling this.
Simple benchmarks indicate this can decode single ints about > 3x faster.
Author: Nong Li <nong@databricks.com>
Author: Nong <nongli@gmail.com>
Closes#10593 from nongli/spark-12644.
This PR adds the support to read bucketed tables, and correctly populate `outputPartitioning`, so that we can avoid shuffle for some cases.
TODO(follow-up PRs):
* bucket pruning
* avoid shuffle for bucketed table join when use any super-set of the bucketing key.
(we should re-visit it after https://issues.apache.org/jira/browse/SPARK-12704 is fixed)
* recognize hive bucketed table
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10604 from cloud-fan/bucket-read.
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.
We made it a `NamedExpression` to workaroud some hacky cases long time ago, and now seems it's safe to remove it.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10765 from cloud-fan/minor.
CSV is the most common data format in the "small data" world. It is often the first format people want to try when they see Spark on a single node. Having to rely on a 3rd party component for this leads to poor user experience for new users. This PR merges the popular spark-csv data source package (https://github.com/databricks/spark-csv) with SparkSQL.
This is a first PR to bring the functionality to spark 2.0 master. We will complete items outlines in the design document (see JIRA attachment) in follow up pull requests.
Author: Hossein <hossein@databricks.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#10766 from rxin/csv.
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.
