## What changes were proposed in this pull request?
Appends s3 specific configurations and spark.hadoop configurations to hive configuration.
## How was this patch tested?
Tested by running a job on cluster.
…figurations to hive configuration.
Author: Sital Kedia <skedia@fb.com>
Closes#11876 from sitalkedia/hiveConf.
## What changes were proposed in this pull request?
This PR aims to fix all Scala-Style multiline comments into Java-Style multiline comments in Scala codes.
(All comment-only changes over 77 files: +786 lines, −747 lines)
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12130 from dongjoon-hyun/use_multiine_javadoc_comments.
## What changes were proposed in this pull request?
Currently, `SimplifyConditionals` handles `true` and `false` to optimize branches. This PR improves `SimplifyConditionals` to take advantage of `null` conditions for `if` and `CaseWhen` expressions, too.
**Before**
```
scala> sql("SELECT IF(null, 1, 0)").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [if (null) 1 else 0 AS (IF(CAST(NULL AS BOOLEAN), 1, 0))#4]
: +- INPUT
+- Scan OneRowRelation[]
scala> sql("select case when cast(null as boolean) then 1 else 2 end").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [CASE WHEN null THEN 1 ELSE 2 END AS CASE WHEN CAST(NULL AS BOOLEAN) THEN 1 ELSE 2 END#14]
: +- INPUT
+- Scan OneRowRelation[]
```
**After**
```
scala> sql("SELECT IF(null, 1, 0)").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [0 AS (IF(CAST(NULL AS BOOLEAN), 1, 0))#4]
: +- INPUT
+- Scan OneRowRelation[]
scala> sql("select case when cast(null as boolean) then 1 else 2 end").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [2 AS CASE WHEN CAST(NULL AS BOOLEAN) THEN 1 ELSE 2 END#4]
: +- INPUT
+- Scan OneRowRelation[]
```
**Hive**
```
hive> select if(null,1,2);
OK
2
hive> select case when cast(null as boolean) then 1 else 2 end;
OK
2
```
## How was this patch tested?
Pass the Jenkins tests (including new extended test cases).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12122 from dongjoon-hyun/SPARK-14338.
## What changes were proposed in this pull request?
Typo fixes. No functional changes.
## How was this patch tested?
Built the sources and ran with samples.
Author: Jacek Laskowski <jacek@japila.pl>
Closes#11802 from jaceklaskowski/typo-fixes.
## What changes were proposed in this pull request?
While trying to create a PR (which was not an issue at the end), I just corrected some style nits.
So, I removed the changes except for some coding style corrections.
- According to the [scala-style-guide#documentation-style](https://github.com/databricks/scala-style-guide#documentation-style), Scala style comments are discouraged.
>```scala
>/** This is a correct one-liner, short description. */
>
>/**
> * This is correct multi-line JavaDoc comment. And
> * this is my second line, and if I keep typing, this would be
> * my third line.
> */
>
>/** In Spark, we don't use the ScalaDoc style so this
> * is not correct.
> */
>```
- Double newlines between consecutive methods was removed. According to [scala-style-guide#blank-lines-vertical-whitespace](https://github.com/databricks/scala-style-guide#blank-lines-vertical-whitespace), single newline appears when
>Between consecutive members (or initializers) of a class: fields, constructors, methods, nested classes, static initializers, instance initializers.
- Remove uesless parentheses in tests
- Use `mapPartitions` instead of `mapPartitionsWithIndex()`.
## How was this patch tested?
Unit tests were used and `dev/run_tests` for style tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12109 from HyukjinKwon/SPARK-14271.
## What changes were proposed in this pull request?
In the Dataset API, it is fairly difficult for users to perform simple aggregations in a type-safe way at the moment because there are no aggregators that have been implemented. This pull request adds a few common aggregate functions in expressions.scala.typed package, and also creates the expressions.java.typed package without implementation. The java implementation should probably come as a separate pull request. One challenge there is to resolve the type difference between Scala primitive types and Java boxed types.
## How was this patch tested?
Added unit tests for them.
Author: Reynold Xin <rxin@databricks.com>
Closes#12077 from rxin/SPARK-14285.
## What changes were proposed in this pull request?
This PR implements `EXPLAIN CODEGEN` SQL command which returns generated codes like `debugCodegen`. In `spark-shell`, we don't need to `import debug` module. In `spark-sql`, we can use this SQL command now.
**Before**
```
scala> import org.apache.spark.sql.execution.debug._
scala> sql("select 'a' as a group by 1").debugCodegen()
Found 2 WholeStageCodegen subtrees.
== Subtree 1 / 2 ==
...
Generated code:
...
== Subtree 2 / 2 ==
...
Generated code:
...
```
**After**
```
scala> sql("explain extended codegen select 'a' as a group by 1").collect().foreach(println)
[Found 2 WholeStageCodegen subtrees.]
[== Subtree 1 / 2 ==]
...
[]
[Generated code:]
...
[]
[== Subtree 2 / 2 ==]
...
[]
[Generated code:]
...
```
## How was this patch tested?
Pass the Jenkins tests (including new testcases)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12099 from dongjoon-hyun/SPARK-14251.
## What changes were proposed in this pull request?
This PR reduces Java byte code size of method in ```SpecificColumnarIterator``` by using a approach to make a group for lot of ```ColumnAccessor``` instantiations or method calls (more than 200) into a method
## How was this patch tested?
Added a new unit test, which includes large instantiations and method calls, to ```InMemoryColumnarQuerySuite```
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#12108 from kiszk/SPARK-14138-master.
## What changes were proposed in this pull request?
`SizeBasedWindowFunction.n` is a global singleton attribute created for evaluating size based aggregate window functions like `CUME_DIST`. However, this attribute gets different expression IDs when created on both driver side and executor side. This PR adds `withPartitionSize` method to `SizeBasedWindowFunction` so that we can easily rewrite `SizeBasedWindowFunction.n` on executor side.
## How was this patch tested?
A test case is added in `HiveSparkSubmitSuite`, which supports launching multi-process clusters.
Author: Cheng Lian <lian@databricks.com>
Closes#12040 from liancheng/spark-14244-fix-sized-window-function.
This PR adds the ability to perform aggregations inside of a `ContinuousQuery`. In order to implement this feature, the planning of aggregation has augmented with a new `StatefulAggregationStrategy`. Unlike batch aggregation, stateful-aggregation uses the `StateStore` (introduced in #11645) to persist the results of partial aggregation across different invocations. The resulting physical plan performs the aggregation using the following progression:
- Partial Aggregation
- Shuffle
- Partial Merge (now there is at most 1 tuple per group)
- StateStoreRestore (now there is 1 tuple from this batch + optionally one from the previous)
- Partial Merge (now there is at most 1 tuple per group)
- StateStoreSave (saves the tuple for the next batch)
- Complete (output the current result of the aggregation)
The following refactoring was also performed to allow us to plug into existing code:
- The get/put implementation is taken from #12013
- The logic for breaking down and de-duping the physical execution of aggregation has been move into a new pattern `PhysicalAggregation`
- The `AttributeReference` used to identify the result of an `AggregateFunction` as been moved into the `AggregateExpression` container. This change moves the reference into the same object as the other intermediate references used in aggregation and eliminates the need to pass around a `Map[(AggregateFunction, Boolean), Attribute]`. Further clean up (using a different aggregation container for logical/physical plans) is deferred to a followup.
- Some planning logic is moved from the `SessionState` into the `QueryExecution` to make it easier to override in the streaming case.
- The ability to write a `StreamTest` that checks only the output of the last batch has been added to simulate the future addition of output modes.
Author: Michael Armbrust <michael@databricks.com>
Closes#12048 from marmbrus/statefulAgg.
## What changes were proposed in this pull request?
RpcEndpoint is not thread safe and allows multiple messages to be processed at the same time. StateStoreCoordinator should use ThreadSafeRpcEndpoint.
## How was this patch tested?
Existing unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#12100 from zsxwing/fix-StateStoreCoordinator.
JIRA: https://issues.apache.org/jira/browse/SPARK-13674
## What changes were proposed in this pull request?
Sample operator doesn't support wholestage codegen now. This pr is to add support to it.
## How was this patch tested?
A test is added into `BenchmarkWholeStageCodegen`. Besides, all tests should be passed.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11517 from viirya/add-wholestage-sample.
## What changes were proposed in this pull request?
This PR adds the function `window` as a column expression.
`window` can be used to bucket rows into time windows given a time column. With this expression, performing time series analysis on batch data, as well as streaming data should become much more simpler.
### Usage
Assume the following schema:
`sensor_id, measurement, timestamp`
To average 5 minute data every 1 minute (window length of 5 minutes, slide duration of 1 minute), we will use:
```scala
df.groupBy(window("timestamp", “5 minutes”, “1 minute”), "sensor_id")
.agg(mean("measurement").as("avg_meas"))
```
This will generate windows such as:
```
09:00:00-09:05:00
09:01:00-09:06:00
09:02:00-09:07:00 ...
```
Intervals will start at every `slideDuration` starting at the unix epoch (1970-01-01 00:00:00 UTC).
To start intervals at a different point of time, e.g. 30 seconds after a minute, the `startTime` parameter can be used.
```scala
df.groupBy(window("timestamp", “5 minutes”, “1 minute”, "30 second"), "sensor_id")
.agg(mean("measurement").as("avg_meas"))
```
This will generate windows such as:
```
09:00:30-09:05:30
09:01:30-09:06:30
09:02:30-09:07:30 ...
```
Support for Python will be made in a follow up PR after this.
## How was this patch tested?
This patch has some basic unit tests for the `TimeWindow` expression testing that the parameters pass validation, and it also has some unit/integration tests testing the correctness of the windowing and usability in complex operations (multi-column grouping, multi-column projections, joins).
Author: Burak Yavuz <brkyvz@gmail.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#12008 from brkyvz/df-time-window.
## What changes were proposed in this pull request?
This patch enables use of OrcRelation for SQL queries which read data from Hive tables. Changes in this patch:
- Added a new rule `OrcConversions` which would alter the plan to use `OrcRelation`. In this diff, the conversion is done only for reads.
- Added a new config `spark.sql.hive.convertMetastoreOrc` to control the conversion
BEFORE
```
scala> hqlContext.sql("SELECT * FROM orc_table").explain(true)
== Parsed Logical Plan ==
'Project [unresolvedalias(*, None)]
+- 'UnresolvedRelation `orc_table`, None
== Analyzed Logical Plan ==
key: string, value: string
Project [key#171,value#172]
+- MetastoreRelation default, orc_table, None
== Optimized Logical Plan ==
MetastoreRelation default, orc_table, None
== Physical Plan ==
HiveTableScan [key#171,value#172], MetastoreRelation default, orc_table, None
```
AFTER
```
scala> hqlContext.sql("SELECT * FROM orc_table").explain(true)
== Parsed Logical Plan ==
'Project [unresolvedalias(*, None)]
+- 'UnresolvedRelation `orc_table`, None
== Analyzed Logical Plan ==
key: string, value: string
Project [key#76,value#77]
+- SubqueryAlias orc_table
+- Relation[key#76,value#77] ORC part: struct<>, data: struct<key:string,value:string>
== Optimized Logical Plan ==
Relation[key#76,value#77] ORC part: struct<>, data: struct<key:string,value:string>
== Physical Plan ==
WholeStageCodegen
: +- Scan ORC part: struct<>, data: struct<key:string,value:string>[key#76,value#77] InputPaths: file:/user/hive/warehouse/orc_table
```
## How was this patch tested?
- Added a new unit test. Ran existing unit tests
- Ran with production like data
## Performance gains
Ran on a production table in Facebook (note that the data was in DWRF file format which is similar to ORC)
Best case : when there was no matching rows for the predicate in the query (everything is filtered out)
```
CPU time Wall time Total wall time across all tasks
================================================================
Without the change 541_515 sec 25.0 mins 165.8 hours
With change 407 sec 1.5 mins 15 mins
```
Average case: A subset of rows in the data match the query predicate
```
CPU time Wall time Total wall time across all tasks
================================================================
Without the change 624_630 sec 31.0 mins 199.0 h
With change 14_769 sec 5.3 mins 7.7 h
```
Author: Tejas Patil <tejasp@fb.com>
Closes#11891 from tejasapatil/orc_ppd.
`Expand` operator now uses its child plan's constraints as its valid constraints (i.e., the base of constraints). This is not correct because `Expand` will set its group by attributes to null values. So the nullability of these attributes should be true.
E.g., for an `Expand` operator like:
val input = LocalRelation('a.int, 'b.int, 'c.int).where('c.attr > 10 && 'a.attr < 5 && 'b.attr > 2)
Expand(
Seq(
Seq('c, Literal.create(null, StringType), 1),
Seq('c, 'a, 2)),
Seq('c, 'a, 'gid.int),
Project(Seq('a, 'c), input))
The `Project` operator has the constraints `IsNotNull('a)`, `IsNotNull('b)` and `IsNotNull('c)`. But the `Expand` should not have `IsNotNull('a)` in its constraints.
This PR is the first step for this issue and remove invalid constraints of `Expand` operator.
A test is added to `ConstraintPropagationSuite`.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#11995 from viirya/fix-expand-constraints.
## What changes were proposed in this pull request?
JIRA: https://issues.apache.org/jira/browse/SPARK-13995
We infer relative `IsNotNull` constraints from logical plan's expressions in `constructIsNotNullConstraints` now. However, we don't consider the case of (nested) `Cast`.
For example:
val tr = LocalRelation('a.int, 'b.long)
val plan = tr.where('a.attr === 'b.attr).analyze
Then, the plan's constraints will have `IsNotNull(Cast(resolveColumn(tr, "a"), LongType))`, instead of `IsNotNull(resolveColumn(tr, "a"))`. This PR fixes it.
Besides, as `IsNotNull` constraints are most useful for `Attribute`, we should do recursing through any `Expression` that is null intolerant and construct `IsNotNull` constraints for all `Attribute`s under these Expressions.
For example, consider the following constraints:
val df = Seq((1,2,3)).toDF("a", "b", "c")
df.where("a + b = c").queryExecution.analyzed.constraints
The inferred isnotnull constraints should be isnotnull(a), isnotnull(b), isnotnull(c), instead of isnotnull(a + c) and isnotnull(c).
## How was this patch tested?
Test is added into `ConstraintPropagationSuite`.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#11809 from viirya/constraint-cast.
## What changes were proposed in this pull request?
This PR updates the usage comments of `debug` according to the following commits.
- [SPARK-9754](https://issues.apache.org/jira/browse/SPARK-9754) removed `typeCheck`.
- [SPARK-14227](https://issues.apache.org/jira/browse/SPARK-14227) added `debugCodegen`.
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12094 from dongjoon-hyun/minor_fix_debug_usage.
## What changes were proposed in this pull request?
This PR throws Unsupported Operation exception for create index, drop index, alter index , lock table , lock database, unlock table, and unlock database operations that are not supported in Spark SQL. Currently these operations are executed executed by Hive.
Error:
spark-sql> drop index my_index on my_table;
Error in query:
Unsupported operation: drop index(line 1, pos 0)
## How was this patch tested?
Added test cases to HiveQuerySuite
yhuai hvanhovell andrewor14
Author: sureshthalamati <suresh.thalamati@gmail.com>
Closes#12069 from sureshthalamati/unsupported_ddl_spark-14133.
## What changes were proposed in this pull request?
This PR addresses the following
1. Supports native execution of SHOW DATABASES command
2. Fixes SHOW TABLES to apply the identifier_with_wildcards pattern if supplied.
SHOW TABLE syntax
```
SHOW TABLES [IN database_name] ['identifier_with_wildcards'];
```
SHOW DATABASES syntax
```
SHOW (DATABASES|SCHEMAS) [LIKE 'identifier_with_wildcards'];
```
## How was this patch tested?
Tests added in SQLQuerySuite (both hive and sql contexts) and DDLCommandSuite
Note: Since the table name pattern was not working , tests are added in both SQLQuerySuite to
verify the application of the table pattern.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#11991 from dilipbiswal/dkb_show_database.
## What changes were proposed in this pull request?
This PR implements `FileFormat.buildReader()` for the LibSVM data source. Besides that, a new interface method `prepareRead()` is added to `FileFormat`:
```scala
def prepareRead(
sqlContext: SQLContext,
options: Map[String, String],
files: Seq[FileStatus]): Map[String, String] = options
```
After migrating from `buildInternalScan()` to `buildReader()`, we lost the opportunity to collect necessary global information, since `buildReader()` works in a per-partition manner. For example, LibSVM needs to infer the total number of features if the `numFeatures` data source option is not set. Any necessary collected global information should be returned using the data source options map. By default, this method just returns the original options untouched.
An alternative approach is to absorb `inferSchema()` into `prepareRead()`, since schema inference is also some kind of global information gathering. However, this approach wasn't chosen because schema inference is optional, while `prepareRead()` must be called whenever a `HadoopFsRelation` based data source relation is instantiated.
One unaddressed problem is that, when `numFeatures` is absent, now the input data will be scanned twice. The `buildInternalScan()` code path doesn't need to do this because it caches the raw parsed RDD in memory before computing the total number of features. However, with `FileScanRDD`, the raw parsed RDD is created in a different way (e.g. partitioning) from the final RDD.
## How was this patch tested?
Tested using existing test suites.
Author: Cheng Lian <lian@databricks.com>
Closes#12088 from liancheng/spark-14295-libsvm-build-reader.
## What changes were proposed in this pull request?
This PR support multiple Python UDFs within single batch, also improve the performance.
```python
>>> from pyspark.sql.types import IntegerType
>>> sqlContext.registerFunction("double", lambda x: x * 2, IntegerType())
>>> sqlContext.registerFunction("add", lambda x, y: x + y, IntegerType())
>>> sqlContext.sql("SELECT double(add(1, 2)), add(double(2), 1)").explain(True)
== Parsed Logical Plan ==
'Project [unresolvedalias('double('add(1, 2)), None),unresolvedalias('add('double(2), 1), None)]
+- OneRowRelation$
== Analyzed Logical Plan ==
double(add(1, 2)): int, add(double(2), 1): int
Project [double(add(1, 2))#14,add(double(2), 1)#15]
+- Project [double(add(1, 2))#14,add(double(2), 1)#15]
+- Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15]
+- EvaluatePython [add(pythonUDF1#17, 1)], [pythonUDF0#18]
+- EvaluatePython [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17]
+- OneRowRelation$
== Optimized Logical Plan ==
Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15]
+- EvaluatePython [add(pythonUDF1#17, 1)], [pythonUDF0#18]
+- EvaluatePython [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17]
+- OneRowRelation$
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15]
: +- INPUT
+- !BatchPythonEvaluation [add(pythonUDF1#17, 1)], [pythonUDF0#16,pythonUDF1#17,pythonUDF0#18]
+- !BatchPythonEvaluation [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17]
+- Scan OneRowRelation[]
```
## How was this patch tested?
Added new tests.
Using the following script to benchmark 1, 2 and 3 udfs,
```
df = sqlContext.range(1, 1 << 23, 1, 4)
double = F.udf(lambda x: x * 2, LongType())
print df.select(double(df.id)).count()
print df.select(double(df.id), double(df.id + 1)).count()
print df.select(double(df.id), double(df.id + 1), double(df.id + 2)).count()
```
Here is the results:
N | Before | After | speed up
---- |------------ | -------------|------
1 | 22 s | 7 s | 3.1X
2 | 38 s | 13 s | 2.9X
3 | 58 s | 16 s | 3.6X
This benchmark ran locally with 4 CPUs. For 3 UDFs, it launched 12 Python before before this patch, 4 process after this patch. After this patch, it will use less memory for multiple UDFs than before (less buffering).
Author: Davies Liu <davies@databricks.com>
Closes#12057 from davies/multi_udfs.
## What changes were proposed in this pull request?
If I press `CTRL-C` when running these tests, the temp files will be left in `sql/core` folder and I need to delete them manually. It's annoying. This PR just moves the temp files to the `java.io.tmpdir` folder and add a name prefix for them.
## How was this patch tested?
Existing Jenkins tests
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#12093 from zsxwing/temp-file.
This PR is to provide native parsing support for DDL commands: `Alter View`. Since its AST trees are highly similar to `Alter Table`. Thus, both implementation are integrated into the same one.
Based on the Hive DDL document:
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL and https://cwiki.apache.org/confluence/display/Hive/PartitionedViews
**Syntax:**
```SQL
ALTER VIEW view_name RENAME TO new_view_name
```
- to change the name of a view to a different name
**Syntax:**
```SQL
ALTER VIEW view_name SET TBLPROPERTIES ('comment' = new_comment);
```
- to add metadata to a view
**Syntax:**
```SQL
ALTER VIEW view_name UNSET TBLPROPERTIES [IF EXISTS] ('comment', 'key')
```
- to remove metadata from a view
**Syntax:**
```SQL
ALTER VIEW view_name ADD [IF NOT EXISTS] PARTITION spec1[, PARTITION spec2, ...]
```
- to add the partitioning metadata for a view.
- the syntax of partition spec in `ALTER VIEW` is identical to `ALTER TABLE`, **EXCEPT** that it is **ILLEGAL** to specify a `LOCATION` clause.
**Syntax:**
```SQL
ALTER VIEW view_name DROP [IF EXISTS] PARTITION spec1[, PARTITION spec2, ...]
```
- to drop the related partition metadata for a view.
Added the related test cases to `DDLCommandSuite`
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#11987 from gatorsmile/parseAlterView.
## What changes were proposed in this pull request?
Fixes a minor bug in the record reader constructor that was possibly introduced during refactoring.
## How was this patch tested?
N/A
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12070 from sameeragarwal/vectorized-rr.
## What changes were proposed in this pull request?
This PR proposes a new data-structure based on a vectorized hashmap that can be potentially _codegened_ in `TungstenAggregate` to speed up aggregates with group by. Micro-benchmarks show a 10x improvement over the current `BytesToBytes` aggregation map.
## How was this patch tested?
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
BytesToBytesMap: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
hash 108 / 119 96.9 10.3 1.0X
fast hash 63 / 70 166.2 6.0 1.7X
arrayEqual 70 / 73 150.8 6.6 1.6X
Java HashMap (Long) 141 / 200 74.3 13.5 0.8X
Java HashMap (two ints) 145 / 185 72.3 13.8 0.7X
Java HashMap (UnsafeRow) 499 / 524 21.0 47.6 0.2X
BytesToBytesMap (off Heap) 483 / 548 21.7 46.0 0.2X
BytesToBytesMap (on Heap) 485 / 562 21.6 46.2 0.2X
Vectorized Hashmap 54 / 60 193.7 5.2 2.0X
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12055 from sameeragarwal/vectorized-hashmap.
### What changes were proposed in this pull request?
This PR removes the ANTLR3 based parser, and moves the new ANTLR4 based parser into the `org.apache.spark.sql.catalyst.parser package`.
### How was this patch tested?
Existing unit tests.
cc rxin andrewor14 yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12071 from hvanhovell/SPARK-14211.
## What changes were proposed in this pull request?
Major changes:
1. Implement `FileFormat.buildReader()` for the CSV data source.
1. Add an extra argument to `FileFormat.buildReader()`, `physicalSchema`, which is basically the result of `FileFormat.inferSchema` or user specified schema.
This argument is necessary because the CSV data source needs to know all the columns of the underlying files to read the file.
## How was this patch tested?
Existing tests should do the work.
Author: Cheng Lian <lian@databricks.com>
Closes#12002 from liancheng/spark-14206-csv-build-reader.
## What changes were proposed in this pull request?
This change resolves an issue where `DataFrameNaFunctions.fill` changes a `FloatType` column to a `DoubleType`. We also clarify the contract that replacement values will be cast to the column data type, which may change the replacement value when casting to a lower precision type.
## How was this patch tested?
This patch has associated unit tests.
Author: Travis Crawford <travis@medium.com>
Closes#11967 from traviscrawford/SPARK-14081-dataframena.
## What changes were proposed in this pull request?
This pr is to add a config to control the maximum number of files as even small files have a non-trivial fixed cost. The current packing can put a lot of small files together which cases straggler tasks.
## How was this patch tested?
I added tests to check if many files get split into partitions in FileSourceStrategySuite.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#12068 from maropu/SPARK-14259.
## What changes were proposed in this pull request?
In `ExpressionEncoder`, we use `constructorFor` to build `fromRowExpression` as the `deserializer` in `ObjectOperator`. It's kind of confusing, we should make the name consistent.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12058 from cloud-fan/rename.
## What changes were proposed in this pull request?
This PR implements buildReader for text data source and enable it in the new data source code path.
## How was this patch tested?
Existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11934 from cloud-fan/text.
#### What changes were proposed in this pull request?
This PR is to implement the following four Database-related DDL commands:
- `CREATE DATABASE|SCHEMA [IF NOT EXISTS] database_name`
- `DROP DATABASE [IF EXISTS] database_name [RESTRICT|CASCADE]`
- `DESCRIBE DATABASE [EXTENDED] db_name`
- `ALTER (DATABASE|SCHEMA) database_name SET DBPROPERTIES (property_name=property_value, ...)`
Another PR will be submitted to handle the unsupported commands. In the Database-related DDL commands, we will issue an error exception for `ALTER (DATABASE|SCHEMA) database_name SET OWNER [USER|ROLE] user_or_role`.
cc yhuai andrewor14 rxin Could you review the changes? Is it in the right direction? Thanks!
#### How was this patch tested?
Added a few test cases in `command/DDLSuite.scala` for testing DDL command execution in `SQLContext`. Since `HiveContext` also shares the same implementation, the existing test cases in `\hive` also verifies the correctness of these commands.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12009 from gatorsmile/dbDDL.
## What changes were proposed in this pull request?
Builds on https://github.com/apache/spark/pull/12022 and (a) appends "..." to truncated comment strings and (b) fixes indentation in lines after the commented strings if they happen to have a `(`, `{`, `)` or `}`
## How was this patch tested?
Manually examined the generated code.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12044 from sameeragarwal/comment.
## What changes were proposed in this pull request?
This PR brings the support for chained Python UDFs, for example
```sql
select udf1(udf2(a))
select udf1(udf2(a) + 3)
select udf1(udf2(a) + udf3(b))
```
Also directly chained unary Python UDFs are put in single batch of Python UDFs, others may require multiple batches.
For example,
```python
>>> sqlContext.sql("select double(double(1))").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF#10 AS double(double(1))#9]
: +- INPUT
+- !BatchPythonEvaluation double(double(1)), [pythonUDF#10]
+- Scan OneRowRelation[]
>>> sqlContext.sql("select double(double(1) + double(2))").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF#19 AS double((double(1) + double(2)))#16]
: +- INPUT
+- !BatchPythonEvaluation double((pythonUDF#17 + pythonUDF#18)), [pythonUDF#17,pythonUDF#18,pythonUDF#19]
+- !BatchPythonEvaluation double(2), [pythonUDF#17,pythonUDF#18]
+- !BatchPythonEvaluation double(1), [pythonUDF#17]
+- Scan OneRowRelation[]
```
TODO: will support multiple unrelated Python UDFs in one batch (another PR).
## How was this patch tested?
Added new unit tests for chained UDFs.
Author: Davies Liu <davies@databricks.com>
Closes#12014 from davies/py_udfs.
## What changes were proposed in this pull request?
This PR is a simple fix for an exception message to print `string[]` content correctly.
```java
String[] colPath = requestedSchema.getPaths().get(i);
...
- throw new IOException("Required column is missing in data file. Col: " + colPath);
+ throw new IOException("Required column is missing in data file. Col: " + Arrays.toString(colPath));
```
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12041 from dongjoon-hyun/fix_exception_message_with_string_array.
## What changes were proposed in this pull request?
This PR fixes two trivial typos: 'does not **much**' --> 'does not **match**'.
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12042 from dongjoon-hyun/fix_typo_by_replacing_much_with_match.
## What changes were proposed in this pull request?
Renames SQL option `spark.sql.parquet.fileScan` since now all `HadoopFsRelation` based data sources are being migrated to `FileScanRDD` code path.
## How was this patch tested?
None.
Author: Cheng Lian <lian@databricks.com>
Closes#12003 from liancheng/spark-14208-option-renaming.
## What changes were proposed in this pull request?
This PR implements buildReader for json data source and enable it in the new data source code path.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11960 from cloud-fan/json.
## What changes were proposed in this pull request?
This adds a metric to parquet scans that measures the time in just the scan phase. This is
only possible when the scan returns ColumnarBatches, otherwise the overhead is too high.
This combined with the pipeline metric lets us easily see what percent of the time was
in the scan.
Author: Nong Li <nong@databricks.com>
Closes#12007 from nongli/spark-14210.
## What changes were proposed in this pull request?
This improves the Filter codegen for NULLs by deferring loading the values for IsNotNull.
Instead of generating code like:
boolean isNull = ...
int value = ...
if (isNull) continue;
we will generate:
boolean isNull = ...
if (isNull) continue;
int value = ...
This is useful since retrieving the values can be non-trivial (they can be dictionary encoded
among other things). This currently only works when the attribute comes from the column batch
but could be extended to other cases in the future.
## How was this patch tested?
On tpcds q55, this fixes the regression from introducing the IsNotNull predicates.
```
TPCDS Snappy: Best/Avg Time(ms) Rate(M/s) Per Row(ns)
--------------------------------------------------------------------------------
q55 4564 / 5036 25.2 39.6
q55 4064 / 4340 28.3 35.3
```
Author: Nong Li <nong@databricks.com>
Closes#11792 from nongli/spark-13981.
### What changes were proposed in this pull request?
This PR migrates all HiveQl parsing to the new ANTLR4 parser. This PR is build on top of https://github.com/apache/spark/pull/12011, and we should wait with merging until that one is in (hence the WIP tag).
As soon as this PR is merged we can start removing much of the old parser infrastructure.
### How was this patch tested?
Exisiting Hive unit tests.
cc rxin andrewor14 yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12015 from hvanhovell/SPARK-14213.
## What changes were proposed in this pull request?
After DataFrame and Dataset are merged, the trait `Queryable` becomes unnecessary as it has only one implementation. We should remove it.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12001 from cloud-fan/df-ds.
## What changes were proposed in this pull request?
Before: We just pass all role commands to Hive even though it doesn't work.
After: We throw an `AnalysisException` that looks like this:
```
scala> sql("CREATE ROLE x")
org.apache.spark.sql.AnalysisException: Unsupported Hive operation: CREATE ROLE;
at org.apache.spark.sql.hive.HiveQl$$anonfun$parsePlan$1.apply(HiveQl.scala:213)
at org.apache.spark.sql.hive.HiveQl$$anonfun$parsePlan$1.apply(HiveQl.scala:208)
at org.apache.spark.sql.catalyst.parser.CatalystQl.safeParse(CatalystQl.scala:49)
at org.apache.spark.sql.hive.HiveQl.parsePlan(HiveQl.scala:208)
at org.apache.spark.sql.SQLContext.parseSql(SQLContext.scala:198)
```
## How was this patch tested?
`HiveQuerySuite`
Author: Andrew Or <andrew@databricks.com>
Closes#11948 from andrewor14/ddl-role-management.
## What changes were proposed in this pull request?
Session catalog was added in #11750. However, it doesn't really support temporary functions properly; right now we only store the metadata in the form of `CatalogFunction`, but this doesn't make sense for temporary functions because there is no class name.
This patch moves the `FunctionRegistry` into the `SessionCatalog`. With this, the user can call `catalog.createTempFunction` and `catalog.lookupFunction` to use the function they registered previously. This is currently still dead code, however.
## How was this patch tested?
`SessionCatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#11972 from andrewor14/temp-functions.
## What changes were proposed in this pull request?
Extract the workaround for HADOOP-10622 introduced by #11940 into UninterruptibleThread so that we can test and reuse it.
## How was this patch tested?
Unit tests
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11971 from zsxwing/uninterrupt.
## What changes were proposed in this pull request?
UserDefinedType is a developer API in Spark 1.x. With very high probability we will create a new API for user-defined type that also works well with column batches as well as encoders (datasets). In Spark 2.0, let's make `UserDefinedType` `private[spark]` first.
## How was this patch tested?
Existing unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#11955 from rxin/SPARK-14155.
