## What changes were proposed in this pull request?
del unused imports in ML/MLLIB
## How was this patch tested?
unit tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#12497 from zhengruifeng/del_unused_imports.
## What changes were proposed in this pull request?
When FileSourceStrategy is used, record reader is created which incurs a NN call internally. Later in OrcRelation.unwrapOrcStructs, it ends ups reading the file information to get the ObjectInspector. This incurs additional NN call. It would be good to avoid this additional NN call (specifically for partitioned datasets).
Added OrcRecordReader which is very similar to OrcNewInputFormat.OrcRecordReader with an option of exposing the ObjectInspector. This eliminates the need to look up the file later for generating the object inspector. This would be specifically be useful for partitioned tables/datasets.
## How was this patch tested?
Ran tpc-ds queries manually and also verified by running org.apache.spark.sql.hive.orc.OrcSuite,org.apache.spark.sql.hive.orc.OrcQuerySuite,org.apache.spark.sql.hive.orc.OrcPartitionDiscoverySuite,OrcPartitionDiscoverySuite.OrcHadoopFsRelationSuite,org.apache.spark.sql.hive.execution.HiveCompatibilitySuite
…SourceStrategy mode
Author: Rajesh Balamohan <rbalamohan@apache.org>
Closes#12319 from rajeshbalamohan/SPARK-14551.
## What changes were proposed in this pull request?
This patch breaks SQLQuerySuite out into smaller test suites. It was a little bit too large for debugging.
## How was this patch tested?
This is a test only change.
Author: Reynold Xin <rxin@databricks.com>
Closes#12630 from rxin/SPARK-14866.
Caching TreeNode's `hashCode` can lead to orders-of-magnitude performance improvement in certain optimizer rules when operating on huge/complex schemas.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#12626 from JoshRosen/cache-treenode-hashcode.
## What changes were proposed in this pull request?
Currently, the Parquet reader decide whether to return batch based on required schema or full schema, it's not consistent, this PR fix that.
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#12619 from davies/fix_return_batch.
## What changes were proposed in this pull request?
This patch re-implements view creation command in sql/core, based on the pre-existing view creation command in the Hive module. This consolidates the view creation logical command and physical command into a single one, called CreateViewCommand.
## How was this patch tested?
All the code should've been tested by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12615 from rxin/SPARK-14842-2.
## What changes were proposed in this pull request?
This PR creates a compatibility module in sql (called `hive-1-x-compatibility`), which will host HiveContext in Spark 2.0 (moving HiveContext to here will be done separately). This module is not included in assembly because only users who still want to access HiveContext need it.
## How was this patch tested?
I manually tested `sbt/sbt -Phive package` and `mvn -Phive package -DskipTests`.
Author: Yin Huai <yhuai@databricks.com>
Closes#12580 from yhuai/compatibility.
## What changes were proposed in this pull request?
This patch adds "Exec" suffix to all physical operators. Before this patch, Spark's physical operators and logical operators are named the same (e.g. Project could be logical.Project or execution.Project), which caused small issues in code review and bigger issues in code refactoring.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#12617 from rxin/exec-node.
## What changes were proposed in this pull request?
When creating a file stream using sqlContext.write.stream(), existing files are scanned twice for finding the schema
- Once, when creating a DataSource + StreamingRelation in the DataFrameReader.stream()
- Again, when creating streaming Source from the DataSource, in DataSource.createSource()
Instead, the schema should be generated only once, at the time of creating the dataframe, and when the streaming source is created, it should just reuse that schema
The solution proposed in this PR is to add a lazy field in DataSource that caches the schema. Then streaming Source created by the DataSource can just reuse the schema.
## How was this patch tested?
Refactored unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#12591 from tdas/SPARK-14832.
## What changes were proposed in this pull request?
This PR try to increase the parallelism for small table (a few of big files) to reduce the query time, by decrease the maxSplitBytes, the goal is to have at least one task per CPU in the cluster, if the total size of all files is bigger than openCostInBytes * 2 * nCPU.
For example, a small/medium table could be used as dimension table in huge query, this will be useful to reduce the time waiting for broadcast.
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#12344 from davies/more_partition.
## What changes were proposed in this pull request?
Currently, `OptimizeIn` optimizer replaces `In` expression into `InSet` expression if the size of set is greater than a constant, 10.
This issue aims to make a configuration `spark.sql.optimizer.inSetConversionThreshold` for that.
After this PR, `OptimizerIn` is configurable.
```scala
scala> sql("select a in (1,2,3) from (select explode(array(1,2)) a) T").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [a#7 IN (1,2,3) AS (a IN (1, 2, 3))#8]
: +- INPUT
+- Generate explode([1,2]), false, false, [a#7]
+- Scan OneRowRelation[]
scala> sqlContext.setConf("spark.sql.optimizer.inSetConversionThreshold", "2")
scala> sql("select a in (1,2,3) from (select explode(array(1,2)) a) T").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [a#16 INSET (1,2,3) AS (a IN (1, 2, 3))#17]
: +- INPUT
+- Generate explode([1,2]), false, false, [a#16]
+- Scan OneRowRelation[]
```
## How was this patch tested?
Pass the Jenkins tests (with a new testcase)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12562 from dongjoon-hyun/SPARK-14796.
## What changes were proposed in this pull request?
1. Fix the "spill size" of TungstenAggregate and Sort
2. Rename "data size" to "peak memory" to match the actual meaning (also consistent with task metrics)
3. Added "data size" for ShuffleExchange and BroadcastExchange
4. Added some timing for Sort, Aggregate and BroadcastExchange (this requires another patch to work)
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#12425 from davies/fix_metrics.
## What changes were proposed in this pull request?
SparkPlan.prepare() could be called in different threads (BroadcastExchange will call it in a thread pool), it only make sure that doPrepare() will only be called once, the second call to prepare() may return earlier before all the children had finished prepare(). Then some operator may call doProduce() before prepareSubqueries(), `null` will be used as the result of subquery, which is wrong. This cause TPCDS Q23B returns wrong answer sometimes.
This PR added synchronization for prepare(), make sure all the children had finished prepare() before return. Also call prepare() in produce() (similar to execute()).
Added checking for ScalarSubquery to make sure that the subquery has finished before using the result.
## How was this patch tested?
Manually tested with Q23B, no wrong answer anymore.
Author: Davies Liu <davies@databricks.com>
Closes#12600 from davies/fix_risk.
## What changes were proposed in this pull request?
Currently, a column could be resolved wrongly if there are columns from both outer table and subquery have the same name, we should only resolve the attributes that can't be resolved within subquery. They may have same exprId than other attributes in subquery, so we should create alias for them.
Also, the column in IN subquery could have same exprId, we should create alias for them.
## How was this patch tested?
Added regression tests. Manually tests TPCDS Q70 and Q95, work well after this patch.
Author: Davies Liu <davies@databricks.com>
Closes#12539 from davies/fix_subquery.
### What changes were proposed in this pull request?
TPCDS Q90 fails to parse because it uses a reserved keyword as an Identifier; `AT` was used as an alias for one of the subqueries. `AT` is not a reserved keyword and should have been registerd as a in the `nonReserved` rule.
In order to prevent this from happening again I have added tests for all keywords that are non-reserved in Hive. See the `nonReserved`, `sql11ReservedKeywordsUsedAsCastFunctionName` & `sql11ReservedKeywordsUsedAsIdentifier` rules in https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/parse/IdentifiersParser.g.
### How was this patch tested?
Added tests to for all Hive non reserved keywords to `TableIdentifierParserSuite`.
cc davies
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12537 from hvanhovell/SPARK-14762.
## What changes were proposed in this pull request?
This patch moves SQLBuilder into sql/core so we can in the future move view generation also into sql/core.
## How was this patch tested?
Also moved unit tests.
Author: Reynold Xin <rxin@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12602 from rxin/SPARK-14841.
## What changes were proposed in this pull request?
In Python, the `option` and `options` method of `DataFrameReader` and `DataFrameWriter` were sending the string "None" instead of `null` when passed `None`, therefore making it impossible to send an actual `null`. This fixes that problem.
This is based on #11305 from mathieulongtin.
## How was this patch tested?
Added test to readwriter.py.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: mathieu longtin <mathieu.longtin@nuance.com>
Closes#12494 from viirya/py-df-none-option.
## What changes were proposed in this pull request?
Change test to compare sets rather than sequence
## How was this patch tested?
Full test runs on little endian and big endian platforms
Author: Pete Robbins <robbinspg@gmail.com>
Closes#12610 from robbinspg/DatasetSuiteFix.
## What changes were proposed in this pull request?
Implement some `hashCode` and `equals` together in order to enable the scalastyle.
This is a first batch, I will continue to implement them but I wanted to know your thoughts.
Author: Joan <joan@goyeau.com>
Closes#12157 from joan38/SPARK-6429-HashCode-Equals.
## What changes were proposed in this pull request?
Add the native support for LOAD DATA DDL command that loads data into Hive table/partition.
## How was this patch tested?
`HiveDDLCommandSuite` and `HiveQuerySuite`. Besides, few Hive tests (`WindowQuerySuite`, `HiveTableScanSuite` and `HiveSerDeSuite`) also use `LOAD DATA` command.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#12412 from viirya/ddl-load-data.
## What changes were proposed in this pull request?
This patch removes HiveQueryExecution. As part of this, I consolidated all the describe commands into DescribeTableCommand.
## How was this patch tested?
Should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12588 from rxin/SPARK-14826.
## What changes were proposed in this pull request?
This patch removes SQLBuilder's dependency on MetastoreRelation. We should be able to move SQLBuilder into the sql/core package after this change.
## How was this patch tested?
N/A - covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12594 from rxin/SPARK-14835.
(This PR is a rebased version of PR #12153.)
## What changes were proposed in this pull request?
This PR adds preliminary locality support for `FileFormat` data sources by overriding `FileScanRDD.preferredLocations()`. The strategy can be divided into two parts:
1. Block location lookup
Unlike `HadoopRDD` or `NewHadoopRDD`, `FileScanRDD` doesn't have access to the underlying `InputFormat` or `InputSplit`, and thus can't rely on `InputSplit.getLocations()` to gather locality information. Instead, this PR queries block locations using `FileSystem.getBlockLocations()` after listing all `FileStatus`es in `HDFSFileCatalog` and convert all `FileStatus`es into `LocatedFileStatus`es.
Note that although S3/S3A/S3N file systems don't provide valid locality information, their `getLocatedStatus()` implementations don't actually issue remote calls either. So there's no need to special case these file systems.
2. Selecting preferred locations
For each `FilePartition`, we pick up top 3 locations that containing the most data to be retrieved. This isn't necessarily the best algorithm out there. Further improvements may be brought up in follow-up PRs.
## How was this patch tested?
Tested by overriding default `FileSystem` implementation for `file:///` with a mocked one, which returns mocked block locations.
Author: Cheng Lian <lian@databricks.com>
Closes#12527 from liancheng/spark-14369-locality-rebased.
## What changes were proposed in this pull request?
This PR adds support for all primitive datatypes, decimal types and stringtypes in the VectorizedHashmap during aggregation.
## How was this patch tested?
Existing tests for group-by aggregates should already test for all these datatypes. Additionally, manually inspected the generated code for all supported datatypes (details below).
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12440 from sameeragarwal/all-datatypes.
## What changes were proposed in this pull request?
Code generation for complex type, `CreateArray`, `CreateMap`, `CreateStruct`, `CreateNamedStruct`, exceeds JVM size limit for large elements.
We should split generated code into multiple `apply` functions if the complex types have large elements, like `UnsafeProjection` or others for large expressions.
## How was this patch tested?
I added some tests to check if the generated codes for the expressions exceed or not.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#12559 from ueshin/issues/SPARK-14793.
## What changes were proposed in this pull request?
Just a rename so we can get rid of `HiveContext.scala`. Note that this will conflict with #12585.
## How was this patch tested?
No change in functionality.
Author: Andrew Or <andrew@databricks.com>
Closes#12586 from andrewor14/rename-hc-object.
## What changes were proposed in this pull request?
This patch moves analyze table parsing into SparkSqlAstBuilder and removes HiveSqlAstBuilder.
In order to avoid extensive refactoring, I created a common trait for CatalogRelation and MetastoreRelation, and match on that. In the future we should probably just consolidate the two into a single thing so we don't need this common trait.
## How was this patch tested?
Updated unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12584 from rxin/SPARK-14821.
## What changes were proposed in this pull request?
Spark currently uses TimSort for all in-memory sorts, including sorts done for shuffle. One low-hanging fruit is to use radix sort when possible (e.g. sorting by integer keys). This PR adds a radix sort implementation to the unsafe sort package and switches shuffles and sorts to use it when possible.
The current implementation does not have special support for null values, so we cannot radix-sort `LongType`. I will address this in a follow-up PR.
## How was this patch tested?
Unit tests, enabling radix sort on existing tests. Microbenchmark results:
```
Running benchmark: radix sort 25000000
Java HotSpot(TM) 64-Bit Server VM 1.8.0_66-b17 on Linux 3.13.0-44-generic
Intel(R) Core(TM) i7-4600U CPU 2.10GHz
radix sort 25000000: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
reference TimSort key prefix array 15546 / 15859 1.6 621.9 1.0X
reference Arrays.sort 2416 / 2446 10.3 96.6 6.4X
radix sort one byte 133 / 137 188.4 5.3 117.2X
radix sort two bytes 255 / 258 98.2 10.2 61.1X
radix sort eight bytes 991 / 997 25.2 39.6 15.7X
radix sort key prefix array 1540 / 1563 16.2 61.6 10.1X
```
I also ran a mix of the supported TPCDS queries and compared TimSort vs RadixSort metrics. The overall benchmark ran ~10% faster with radix sort on. In the breakdown below, the radix-enabled sort phases averaged about 20x faster than TimSort, however sorting is only a small fraction of the overall runtime. About half of the TPCDS queries were able to take advantage of radix sort.
```
TPCDS on master: 2499s real time, 8185s executor
- 1171s in TimSort, avg 267 MB/s
(note the /s accounting is weird here since dataSize counts the record sizes too)
TPCDS with radix enabled: 2294s real time, 7391s executor
- 596s in TimSort, avg 254 MB/s
- 26s in radix sort, avg 4.2 GB/s
```
cc davies rxin
Author: Eric Liang <ekl@databricks.com>
Closes#12490 from ericl/sort-benchmark.
## What changes were proposed in this pull request?
We recently made `ColumnarBatch.row` mutable and added a new `ColumnVector.putDecimal` method to support putting `Decimal` values in the `ColumnarBatch`. This unfortunately introduced a bug wherein we were not updating the vector with the proper unscaled values.
## How was this patch tested?
This codepath is hit only when the vectorized aggregate hashmap is enabled. https://github.com/apache/spark/pull/12440 makes sure that a number of regression tests/benchmarks test this bugfix.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12541 from sameeragarwal/fix-bigdecimal.
## What changes were proposed in this pull request?
This patch moves native command and script transformation into SparkSqlAstBuilder. This builds on #12561. See the last commit for diff.
## How was this patch tested?
Updated test cases to reflect this.
Author: Reynold Xin <rxin@databricks.com>
Closes#12564 from rxin/SPARK-14798.
## What changes were proposed in this pull request?
After removing most of `HiveContext` in 8fc267ab33 we can now move existing functionality in `SQLContext` to `SparkSession`. As of this PR `SQLContext` becomes a simple wrapper that has a `SparkSession` and delegates all functionality to it.
## How was this patch tested?
Jenkins.
Author: Andrew Or <andrew@databricks.com>
Closes#12553 from andrewor14/implement-spark-session.
## What changes were proposed in this pull request?
This class is currently in HiveMetastoreCatalog.scala, which is a large file that makes refactoring and searching of usage difficult. Moving it out so I can then do SPARK-14799 and make the review of that simpler.
## How was this patch tested?
N/A - this is a straightforward move and should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12567 from rxin/SPARK-14801.
## What changes were proposed in this pull request?
This patch builds on #12556 and completely removes the use of Hive's variable substitution.
## How was this patch tested?
Covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12561 from rxin/SPARK-14795.
## What changes were proposed in this pull request?
This patch isolates AnalyzeTable's dependency on MetastoreRelation into a single line. After this we can work on converging MetastoreRelation and CatalogTable.
## How was this patch tested?
Covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12566 from rxin/SPARK-14799.
In IsolatedClientLoader, we have a`catch` block which throws an exception without wrapping the original exception, causing the full exception stacktrace and any nested exceptions to be lost. This patch fixes this, improving the usefulness of classloading error messages.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#12548 from JoshRosen/improve-logging-for-hive-classloader-issues.
Spark SQL's POM hardcodes a dependency on `spark-sketch_2.11`, which causes Scala 2.10 builds to include the `_2.11` dependency. This is harmless since `spark-sketch` is a pure-Java module (see #12334 for a discussion of dropping the Scala version suffixes from these modules' artifactIds), but it's confusing to people looking at the published POMs.
This patch fixes this by using `${scala.binary.version}` to substitute the correct suffix, and also adds a set of Maven Enforcer rules to ensure that `_2.11` artifacts are not used in 2.10 builds (and vice-versa).
/cc ahirreddy, who spotted this issue.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#12563 from JoshRosen/fix-sketch-scala-version.
## What changes were proposed in this pull request?
As we moved most parsing rules to `SparkSqlParser`, some tests expected to throw exception are not correct anymore.
## How was this patch tested?
`DDLCommandSuite`
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#12572 from viirya/hotfix-ddl.
## What changes were proposed in this pull request?
the `Accumulable.internal` flag is only used to avoid registering internal accumulators for 2 certain cases:
1. `TaskMetrics.createTempShuffleReadMetrics`: the accumulators in the temp shuffle read metrics should not be registered.
2. `TaskMetrics.fromAccumulatorUpdates`: the created task metrics is only used to post event, accumulators inside it should not be registered.
For 1, we can create a `TempShuffleReadMetrics` that don't create accumulators, just keep the data and merge it at last.
For 2, we can un-register these accumulators immediately.
TODO: remove `internal` flag in `AccumulableInfo` with followup PR
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12525 from cloud-fan/acc.
## What changes were proposed in this pull request?
We shouldn't pass analyze command to Hive because some of those would require running MapReduce jobs. For now, let's just always run the no scan analyze.
## How was this patch tested?
Updated test case to reflect this change.
Author: Reynold Xin <rxin@databricks.com>
Closes#12558 from rxin/parser-analyze.
## What changes were proposed in this pull request?
This patch moves as many parsing rules as possible into SQL parser. There are only three more left after this patch: (1) run native command, (2) analyze, and (3) script IO. These 3 will be dealt with in a follow-up PR.
## How was this patch tested?
No test change. This simply moves code around.
Author: Reynold Xin <rxin@databricks.com>
Closes#12556 from rxin/SPARK-14792.
The `hive` subproject currently depends on `hive-cli` in order to perform a check to see whether a `SessionState` is an instance of `org.apache.hadoop.hive.cli.CliSessionState` (see #9589). The introduction of this `hive-cli` dependency has caused problems for users whose Hive metastore JAR classpaths don't include the `hive-cli` classes (such as in #11495).
This patch removes this dependency on `hive-cli` and replaces the `isInstanceOf` check by reflection. I added a Maven Enforcer rule to ban `hive-cli` from the `hive` subproject in order to make sure that this dependency is not accidentally reintroduced.
/cc rxin yhuai adrian-wang preecet
Author: Josh Rosen <joshrosen@databricks.com>
Closes#12551 from JoshRosen/remove-hive-cli-dep-from-hive-subproject.
## What changes were proposed in this pull request?
The patch removes HiveConf dependency from HiveSqlAstBuilder. This is required in order to merge HiveSqlParser and SparkSqlAstBuilder, which would require getting rid of the Hive specific dependencies in HiveSqlParser.
This patch also accomplishes [SPARK-14778] Remove HiveSessionState.substitutor.
## How was this patch tested?
This should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12550 from rxin/SPARK-14782.
## What changes were proposed in this pull request?
The path rewrite in TestHiveSparkSession is pretty hacky. I think we can remove those complexity and just do a string replacement when we read the query files in. This would remove the overloading of runNativeSql in TestHive, which will simplify the removal of Hive specific variable substitution.
## How was this patch tested?
This is a small test refactoring to simplify test infrastructure.
Author: Reynold Xin <rxin@databricks.com>
Closes#12543 from rxin/SPARK-14775.
## What changes were proposed in this pull request?
In order to fully merge the Hive parser and the SQL parser, we'd need to support variable substitution in Spark. The implementation of the substitute algorithm is mostly copied from Hive, but I simplified the overall structure quite a bit and added more comprehensive test coverage.
Note that this pull request does not yet use this functionality anywhere.
## How was this patch tested?
Added VariableSubstitutionSuite for unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12538 from rxin/SPARK-14769.
## What changes were proposed in this pull request?
We currently have five folders in queries: clientcompare, clientnegative, clientpositive, negative, and positive. Only clientpositive is used. We can remove the rest.
## How was this patch tested?
N/A - removing unused test resources.
Author: Reynold Xin <rxin@databricks.com>
Closes#12540 from rxin/SPARK-14770.
## What changes were proposed in this pull request?
3 testcases namely,
```
"count is partially aggregated"
"count distinct is partially aggregated"
"mixed aggregates are partially aggregated"
```
were failing when running PlannerSuite individually.
The PR provides a fix for this.
## How was this patch tested?
unit tests
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Subhobrata Dey <sbcd90@gmail.com>
Closes#12532 from sbcd90/plannersuitetestsfix.
## What changes were proposed in this pull request?
This PR adds a special log for FileStreamSink for two purposes:
- Versioning. A future Spark version should be able to read the metadata of an old FileStreamSink.
- Compaction. As reading from many small files is usually pretty slow, we should compact small metadata files into big files.
FileStreamSinkLog has a new log format instead of Java serialization format. It will write one log file for each batch. The first line of the log file is the version number, and there are multiple JSON lines following. Each JSON line is a JSON format of FileLog.
FileStreamSinkLog will compact log files every "spark.sql.sink.file.log.compactLen" batches into a big file. When doing a compact, it will read all history logs and merge them with the new batch. During the compaction, it will also delete the files that are deleted (marked by FileLog.action). When the reader uses allLogs to list all files, this method only returns the visible files (drops the deleted files).
## How was this patch tested?
FileStreamSinkLogSuite
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#12435 from zsxwing/sink-log.
## What changes were proposed in this pull request?
This PR has two main changes.
1. Move Hive-specific methods from HiveContext to HiveSessionState, which help the work of removing HiveContext.
2. Create a SparkSession Class, which will later be the entry point of Spark SQL users.
## How was this patch tested?
Existing tests
This PR is trying to fix test failures of https://github.com/apache/spark/pull/12485.
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12522 from yhuai/spark-session.
## What changes were proposed in this pull request?
Consider the following directory structure
dir/col=X/some-files
If we create a text format streaming dataframe on `dir/col=X/` then it should not consider as partitioning in columns. Even though the streaming dataframe does not do so, the generated batch dataframes pick up col as a partitioning columns, causing mismatch streaming source schema and generated df schema. This leads to runtime failure:
```
18:55:11.262 ERROR org.apache.spark.sql.execution.streaming.StreamExecution: Query query-0 terminated with error
java.lang.AssertionError: assertion failed: Invalid batch: c#2 != c#7,type#8
```
The reason is that the partition inferring code has no idea of a base path, above which it should not search of partitions. This PR makes sure that the batch DF is generated with the basePath set as the original path on which the file stream source is defined.
## How was this patch tested?
New unit test
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#12517 from tdas/SPARK-14741.
## What changes were proposed in this pull request?
This patch provides a first cut of python APIs for structured streaming. This PR provides the new classes:
- ContinuousQuery
- Trigger
- ProcessingTime
in pyspark under `pyspark.sql.streaming`.
In addition, it contains the new methods added under:
- `DataFrameWriter`
a) `startStream`
b) `trigger`
c) `queryName`
- `DataFrameReader`
a) `stream`
- `DataFrame`
a) `isStreaming`
This PR doesn't contain all methods exposed for `ContinuousQuery`, for example:
- `exception`
- `sourceStatuses`
- `sinkStatus`
They may be added in a follow up.
This PR also contains some very minor doc fixes in the Scala side.
## How was this patch tested?
Python doc tests
TODO:
- [ ] verify Python docs look good
Author: Burak Yavuz <brkyvz@gmail.com>
Author: Burak Yavuz <burak@databricks.com>
Closes#12320 from brkyvz/stream-python.
## What changes were proposed in this pull request?
- replaced `FileSystem.get(conf)` calls with `path.getFileSystem(conf)`
## How was this patch tested?
N/A
Author: Liwei Lin <lwlin7@gmail.com>
Closes#12450 from lw-lin/fix-fs-get.
`MutableProjection` is not thread-safe and we won't use it in multiple threads. I think the reason that we return `() => MutableProjection` is not about thread safety, but to save the costs of generating code when we need same but individual mutable projections.
However, I only found one place that use this [feature](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala#L122-L123), and comparing to the troubles it brings, I think we should generate `MutableProjection` directly instead of return a function.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#7373 from cloud-fan/project.
## What changes were proposed in this pull request?
Since [SPARK-12719: SQL Generation supports for generators](https://issues.apache.org/jira/browse/SPARK-12719) was resolved, this PR enables the related testcases: `explode()` and `json_tuple()`.
## How was this patch tested?
Pass the Jenkins tests (with re-enabled test cases).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12329 from dongjoon-hyun/minor_enable_testcases.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-14600
This PR makes `Expand.output` have different attributes from the grouping attributes produced by the underlying `Project`, as they have different meaning, so that we can safely push down filter through `Expand`
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12496 from cloud-fan/expand.
## What changes were proposed in this pull request?
Before this PR, we create accumulators at driver side(and register them) and send them to executor side, then we create `TaskMetrics` with these accumulators at executor side.
After this PR, we will create `TaskMetrics` at driver side and send it to executor side, so that we can create accumulators inside `TaskMetrics` directly, which is cleaner.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12472 from cloud-fan/acc.
## What changes were proposed in this pull request?
Change SubquerySuite to validate test results utilizing checkAnswer helper method
## How was this patch tested?
Existing tests
Author: Luciano Resende <lresende@apache.org>
Closes#12269 from lresende/SPARK-13419.
## What changes were proposed in this pull request?
Enable ScalaReflection and User Defined Types for plain Scala classes.
This involves the move of `schemaFor` from `ScalaReflection` trait (which is Runtime and Compile time (macros) reflection) to the `ScalaReflection` object (runtime reflection only) as I believe this code wouldn't work at compile time anyway as it manipulates `Class`'s that are not compiled yet.
## How was this patch tested?
Unit test
Author: Joan <joan@goyeau.com>
Closes#12149 from joan38/SPARK-13929-Scala-reflection.
## What changes were proposed in this pull request?
This PR moves `HadoopFsRelation` related data source API into `execution/datasources` package.
Note that to avoid conflicts, this PR is based on #12153. Effective changes for this PR only consist of the last three commits. Will rebase after merging #12153.
## How was this patch tested?
Existing tests.
Author: Yin Huai <yhuai@databricks.com>
Author: Cheng Lian <lian@databricks.com>
Closes#12361 from liancheng/spark-14407-hide-hadoop-fs-relation.
### What changes were proposed in this pull request?
This PR adds support for in/exists predicate subqueries to Spark. Predicate sub-queries are used as a filtering condition in a query (this is the only supported use case). A predicate sub-query comes in two forms:
- `[NOT] EXISTS(subquery)`
- `[NOT] IN (subquery)`
This PR is (loosely) based on the work of davies (https://github.com/apache/spark/pull/10706) and chenghao-intel (https://github.com/apache/spark/pull/9055). They should be credited for the work they did.
### How was this patch tested?
Modified parsing unit tests.
Added tests to `org.apache.spark.sql.SQLQuerySuite`
cc rxin, davies & chenghao-intel
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12306 from hvanhovell/SPARK-4226.
## What changes were proposed in this pull request?
After https://github.com/apache/spark/pull/12067, we now use expressions to do the aggregation in `TypedAggregateExpression`. To implement buffer merge, we produce a new buffer deserializer expression by replacing `AttributeReference` with right-side buffer attribute, like other `DeclarativeAggregate`s do, and finally combine the left and right buffer deserializer with `Invoke`.
However, after https://github.com/apache/spark/pull/12338, we will add loop variable to class members when codegen `MapObjects`. If the `Aggregator` buffer type is `Seq`, which is implemented by `MapObjects` expression, we will add the same loop variable to class members twice(by left and right buffer deserializer), which cause the `ClassFormatError`.
This PR fixes this issue by calling `distinct` before declare the class menbers.
## How was this patch tested?
new regression test in `DatasetAggregatorSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12468 from cloud-fan/bug.
When `Await.result` throws an exception which originated from a different thread, the resulting stacktrace doesn't include the path leading to the `Await.result` call itself, making it difficult to identify the impact of these exceptions. For example, I've seen cases where broadcast cleaning errors propagate to the main thread and crash it but the resulting stacktrace doesn't include any of the main thread's code, making it difficult to pinpoint which exception crashed that thread.
This patch addresses this issue by explicitly catching, wrapping, and re-throwing exceptions that are thrown by `Await.result`.
I tested this manually using 16b31c8251, a patch which reproduces an issue where an RPC exception which occurs while unpersisting RDDs manages to crash the main thread without any useful stacktrace, and verified that informative, full stacktraces were generated after applying the fix in this PR.
/cc rxin nongli yhuai anabranch
Author: Josh Rosen <joshrosen@databricks.com>
Closes#12433 from JoshRosen/wrap-and-rethrow-await-exceptions.
#### What changes were proposed in this pull request?
https://github.com/apache/spark/pull/12185 contains the original PR I submitted in https://github.com/apache/spark/pull/10418
However, it misses one of the extended example, a wrong description and a few typos for collection functions. This PR is fix all these issues.
#### How was this patch tested?
The existing test cases already cover it.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12492 from gatorsmile/expressionUpdate.
## What changes were proposed in this pull request?
This PR tries to separate the serialization and deserialization logic from object operators, so that it's easier to eliminate unnecessary serializations in optimizer.
Typed aggregate related operators are special, they will deserialize the input row to multiple objects and it's difficult to simply use a deserializer operator to abstract it, so we still mix the deserialization logic there.
## How was this patch tested?
existing tests and new test in `EliminateSerializationSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12260 from cloud-fan/encoder.
## What changes were proposed in this pull request?
These test suites were removed while refactoring `HadoopFsRelation` related API. This PR brings them back.
This PR also fixes two regressions:
- SPARK-14458, which causes runtime error when saving partitioned tables using `FileFormat` data sources that are not able to infer their own schemata. This bug wasn't detected by any built-in data sources because all of them happen to have schema inference feature.
- SPARK-14566, which happens to be covered by SPARK-14458 and causes wrong query result or runtime error when
- appending a Dataset `ds` to a persisted partitioned data source relation `t`, and
- partition columns in `ds` don't all appear after data columns
## How was this patch tested?
`CommitFailureTestRelationSuite` uses a testing relation that always fails when committing write tasks to test write job cleanup.
`SimpleTextHadoopFsRelationSuite` uses a testing relation to test general `HadoopFsRelation` and `FileFormat` interfaces.
The two regressions are both covered by existing test cases.
Author: Cheng Lian <lian@databricks.com>
Closes#12179 from liancheng/spark-13681-commit-failure-test.
## What changes were proposed in this pull request?
We currently disable codegen for `CaseWhen` if the number of branches is greater than 20 (in CaseWhen.MAX_NUM_CASES_FOR_CODEGEN). It would be better if this value is a non-public config defined in SQLConf.
## How was this patch tested?
Pass the Jenkins tests (including a new testcase `Support spark.sql.codegen.maxCaseBranches option`)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12353 from dongjoon-hyun/SPARK-14577.
## What changes were proposed in this pull request?
I have compared non-reserved list in Antlr3 and Antlr4 one by one as well as all the existing keywords defined in Antlr4, added the missing keywords to the non-reserved keywords list. If we need to support more syntax, we can add more keywords by then.
Any recommendation for the above is welcome.
## How was this patch tested?
I manually checked the keywords one by one. Please let me know if there is a better way to test.
Another thought: I suggest to put all the keywords definition and non-reserved list in order, that will be much easier to check in the future.
Author: bomeng <bmeng@us.ibm.com>
Closes#12191 from bomeng/SPARK-14398.
## What changes were proposed in this pull request?
This is roughly based on the input metrics logic in `SqlNewHadoopRDD`
## How was this patch tested?
Not sure how to write a test, I manually verified it in Spark UI.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12352 from cloud-fan/metrics.
## What changes were proposed in this pull request?
Per rxin's suggestions, this patch renames `upstreams()` to `inputRDDs()` in `WholeStageCodegen` for better implied semantics
## How was this patch tested?
N/A
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12486 from sameeragarwal/codegen-cleanup.
## What changes were proposed in this pull request?
The `doGenCode` method currently takes in an `ExprCode`, mutates it and returns the java code to evaluate the given expression. It should instead just return a new `ExprCode` to avoid passing around mutable objects during code generation.
## How was this patch tested?
Existing Tests
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12483 from sameeragarwal/new-exprcode-2.
## What changes were proposed in this pull request?
The sort shuffle manager has been the default since Spark 1.2. It is time to remove the old hash shuffle manager.
## How was this patch tested?
Removed some tests related to the old manager.
Author: Reynold Xin <rxin@databricks.com>
Closes#12423 from rxin/SPARK-14667.
## What changes were proposed in this pull request?
This is just cleanup. This allows us to remove HiveContext later without inflating the diff too much. This PR fixes the conflicts of https://github.com/apache/spark/pull/12431. It also removes the `def hiveConf` from `HiveSqlParser`. So, we will pass the HiveConf associated with a session explicitly instead of relying on Hive's `SessionState` to pass `HiveConf`.
## How was this patch tested?
Existing tests.
Closes#12431
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12449 from yhuai/hiveconf.
## What changes were proposed in this pull request?
Per rxin's suggestions, this patch renames `s/gen/genCode` and `s/genCode/doGenCode` to better reflect the semantics of these 2 function calls.
## How was this patch tested?
N/A (refactoring only)
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12475 from sameeragarwal/gencode.
## What changes were proposed in this pull request?
This patch adds a SharedState that groups state shared across multiple SQLContexts. This is analogous to the SessionState added in SPARK-13526 that groups session-specific state. This cleanup makes the constructors of the contexts simpler and ultimately allows us to remove HiveContext in the near future.
## How was this patch tested?
Existing tests.
Author: Yin Huai <yhuai@databricks.com>
Closes#12463 from yhuai/sharedState.
## What changes were proposed in this pull request?
Currently, `HiveTypeCoercion.IfCoercion` removes all predicates whose return-type are null. However, some UDFs need evaluations because they are designed to throw exceptions. This PR fixes that to preserve the predicates. Also, `assert_true` is implemented as Spark SQL function.
**Before**
```
scala> sql("select if(assert_true(false),2,3)").head
res2: org.apache.spark.sql.Row = [3]
```
**After**
```
scala> sql("select if(assert_true(false),2,3)").head
... ASSERT_TRUE ...
```
**Hive**
```
hive> select if(assert_true(false),2,3);
OK
Failed with exception java.io.IOException:org.apache.hadoop.hive.ql.metadata.HiveException: ASSERT_TRUE(): assertion failed.
```
## How was this patch tested?
Pass the Jenkins tests (including a new testcase in `HivePlanTest`)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12340 from dongjoon-hyun/SPARK-14580.
## What changes were proposed in this pull request?
There are many operations that are currently not supported in the streaming execution. For example:
- joining two streams
- unioning a stream and a batch source
- sorting
- window functions (not time windows)
- distinct aggregates
Furthermore, executing a query with a stream source as a batch query should also fail.
This patch add an additional step after analysis in the QueryExecution which will check that all the operations in the analyzed logical plan is supported or not.
## How was this patch tested?
unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#12246 from tdas/SPARK-14473.
## What changes were proposed in this pull request?
This PR aims to add `bound` function (aka Banker's round) by extending current `round` implementation. [Hive supports `bround` since 1.3.0.](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF)
**Hive (1.3 ~ 2.0)**
```
hive> select round(2.5), bround(2.5);
OK
3.0 2.0
```
**After this PR**
```scala
scala> sql("select round(2.5), bround(2.5)").head
res0: org.apache.spark.sql.Row = [3,2]
```
## How was this patch tested?
Pass the Jenkins tests (with extended tests).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12376 from dongjoon-hyun/SPARK-14614.
## What changes were proposed in this pull request?
We currently only have implicit encoders for scala primitive types. We should also add implicit encoders for boxed primitives. Otherwise, the following code would not have an encoder:
```scala
sqlContext.range(1000).map { i => i }
```
## How was this patch tested?
Added a unit test case for this.
Author: Reynold Xin <rxin@databricks.com>
Closes#12466 from rxin/SPARK-14696.
## What changes were proposed in this pull request?
set the input encoder for `TypedColumn` in `RelationalGroupedDataset.agg`.
## How was this patch tested?
new tests in `DatasetAggregatorSuite`
close https://github.com/apache/spark/pull/11269
This PR brings https://github.com/apache/spark/pull/12359 up to date and fix the compile.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12451 from cloud-fan/agg.
## What changes were proposed in this pull request?
The patch fixes the issue with the randomSplit method which is not able to split dataframes which has maps in schema. The bug was introduced in spark 1.6.1.
## How was this patch tested?
Tested with unit tests.
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Subhobrata Dey <sbcd90@gmail.com>
Closes#12438 from sbcd90/randomSplitIssue.
## What changes were proposed in this pull request?
Move the implementation of `hiveContext.analyze` to the command of `AnalyzeTable`.
## How was this patch tested?
Existing tests.
Closes#12429
Author: Yin Huai <yhuai@databricks.com>
Author: Andrew Or <andrew@databricks.com>
Closes#12448 from yhuai/analyzeTable.
## What changes were proposed in this pull request?
This patch adds a SharedState that groups state shared across multiple SQLContexts. This is analogous to the SessionState added in SPARK-13526 that groups session-specific state. This cleanup makes the constructors of the contexts simpler and ultimately allows us to remove HiveContext in the near future.
## How was this patch tested?
Existing tests.
Closes#12405
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12447 from yhuai/sharedState.
## What changes were proposed in this pull request?
This is a follow-up to make the max iteration number an internal config.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#12441 from rxin/maxIterConfInternal.
## What changes were proposed in this pull request?
This PR removes
- Inappropriate type notations
For example, from
```scala
words.foreachRDD { (rdd: RDD[String], time: Time) =>
...
```
to
```scala
words.foreachRDD { (rdd, time) =>
...
```
- Extra anonymous closure within functional transformations.
For example,
```scala
.map(item => {
...
})
```
which can be just simply as below:
```scala
.map { item =>
...
}
```
and corrects some obvious style nits.
## How was this patch tested?
This was tested after adding rules in `scalastyle-config.xml`, which ended up with not finding all perfectly.
The rules applied were below:
- For the first correction,
```xml
<check customId="NoExtraClosure" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">(?m)\.[a-zA-Z_][a-zA-Z0-9]*\(\s*[^,]+s*=>\s*\{[^\}]+\}\s*\)</parameter></parameters>
</check>
```
```xml
<check customId="NoExtraClosure" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">\.[a-zA-Z_][a-zA-Z0-9]*\s*[\{|\(]([^\n>,]+=>)?\s*\{([^()]|(?R))*\}^[,]</parameter></parameters>
</check>
```
- For the second correction
```xml
<check customId="TypeNotation" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">\.[a-zA-Z_][a-zA-Z0-9]*\s*[\{|\(]\s*\([^):]*:R))*\}^[,]</parameter></parameters>
</check>
```
**Those rules were not added**
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12413 from HyukjinKwon/SPARK-style.
## What changes were proposed in this pull request?
set the input encoder for `TypedColumn` in `RelationalGroupedDataset.agg`.
## How was this patch tested?
new tests in `DatasetAggregatorSuite`
close https://github.com/apache/spark/pull/11269
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12359 from cloud-fan/agg.
## What changes were proposed in this pull request?
We currently hard code the max number of optimizer/analyzer iterations to 100. This patch makes it configurable. While I'm at it, I also added the SessionCatalog to the optimizer, so we can use information there in optimization.
## How was this patch tested?
Updated unit tests to reflect the change.
Author: Reynold Xin <rxin@databricks.com>
Closes#12434 from rxin/SPARK-14677.
## What changes were proposed in this pull request?
This PR moves `CurrentDatabase` from sql/hive package to sql/catalyst. It also adds the function description, which looks like the following.
```
scala> sqlContext.sql("describe function extended current_database").collect.foreach(println)
[Function: current_database]
[Class: org.apache.spark.sql.execution.command.CurrentDatabase]
[Usage: current_database() - Returns the current database.]
[Extended Usage:
> SELECT current_database()]
```
## How was this patch tested?
Existing tests
Author: Yin Huai <yhuai@databricks.com>
Closes#12424 from yhuai/SPARK-14668.
## What changes were proposed in this pull request?
This PR uses a better hashing algorithm while probing the AggregateHashMap:
```java
long h = 0
h = (h ^ (0x9e3779b9)) + key_1 + (h << 6) + (h >>> 2);
h = (h ^ (0x9e3779b9)) + key_2 + (h << 6) + (h >>> 2);
h = (h ^ (0x9e3779b9)) + key_3 + (h << 6) + (h >>> 2);
...
h = (h ^ (0x9e3779b9)) + key_n + (h << 6) + (h >>> 2);
return h
```
Depends on: https://github.com/apache/spark/pull/12345
## How was this patch tested?
Java HotSpot(TM) 64-Bit Server VM 1.8.0_73-b02 on Mac OS X 10.11.4
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Aggregate w keys: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
codegen = F 2417 / 2457 8.7 115.2 1.0X
codegen = T hashmap = F 1554 / 1581 13.5 74.1 1.6X
codegen = T hashmap = T 877 / 929 23.9 41.8 2.8X
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12379 from sameeragarwal/hash.
## What changes were proposed in this pull request?
`ExpressionEncoder` is just a container for serialization and deserialization expressions, we can use these expressions to build `TypedAggregateExpression` directly, so that it can fit in `DeclarativeAggregate`, which is more efficient.
One trick is, for each buffer serializer expression, it will reference to the result object of serialization and function call. To avoid re-calculating this result object, we can serialize the buffer object to a single struct field, so that we can use a special `Expression` to only evaluate result object once.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12067 from cloud-fan/typed_udaf.
## What changes were proposed in this pull request?
This patch speeds up group-by aggregates by around 3-5x by leveraging an in-memory `AggregateHashMap` (please see https://github.com/apache/spark/pull/12161), an append-only aggregate hash map that can act as a 'cache' for extremely fast key-value lookups while evaluating aggregates (and fall back to the `BytesToBytesMap` if a given key isn't found).
Architecturally, it is backed by a power-of-2-sized array for index lookups and a columnar batch that stores the key-value pairs. The index lookups in the array rely on linear probing (with a small number of maximum tries) and use an inexpensive hash function which makes it really efficient for a majority of lookups. However, using linear probing and an inexpensive hash function also makes it less robust as compared to the `BytesToBytesMap` (especially for a large number of keys or even for certain distribution of keys) and requires us to fall back on the latter for correctness.
## How was this patch tested?
Java HotSpot(TM) 64-Bit Server VM 1.8.0_73-b02 on Mac OS X 10.11.4
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Aggregate w keys: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
codegen = F 2124 / 2204 9.9 101.3 1.0X
codegen = T hashmap = F 1198 / 1364 17.5 57.1 1.8X
codegen = T hashmap = T 369 / 600 56.8 17.6 5.8X
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12345 from sameeragarwal/tungsten-aggregate-integration.
## What changes were proposed in this pull request?
Removing references to assembly jar in documentation.
Adding an additional (previously undocumented) usage of spark-submit to run examples.
## How was this patch tested?
Ran spark-submit usage to ensure formatting was fine. Ran examples using SparkSubmit.
Author: Mark Grover <mark@apache.org>
Closes#12365 from markgrover/spark-14601.
## What changes were proposed in this pull request?
Current `LikeSimplification` handles the following four rules.
- 'a%' => expr.StartsWith("a")
- '%b' => expr.EndsWith("b")
- '%a%' => expr.Contains("a")
- 'a' => EqualTo("a")
This PR adds the following rule.
- 'a%b' => expr.Length() >= 2 && expr.StartsWith("a") && expr.EndsWith("b")
Here, 2 is statically calculated from "a".size + "b".size.
**Before**
```
scala> sql("select a from (select explode(array('abc','adc')) a) T where a like 'a%c'").explain()
== Physical Plan ==
WholeStageCodegen
: +- Filter a#5 LIKE a%c
: +- INPUT
+- Generate explode([abc,adc]), false, false, [a#5]
+- Scan OneRowRelation[]
```
**After**
```
scala> sql("select a from (select explode(array('abc','adc')) a) T where a like 'a%c'").explain()
== Physical Plan ==
WholeStageCodegen
: +- Filter ((length(a#5) >= 2) && (StartsWith(a#5, a) && EndsWith(a#5, c)))
: +- INPUT
+- Generate explode([abc,adc]), false, false, [a#5]
+- Scan OneRowRelation[]
```
## How was this patch tested?
Pass the Jenkins tests (including new testcase).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12312 from dongjoon-hyun/SPARK-14545.
## What changes were proposed in this pull request?
JIRA: https://issues.apache.org/jira/browse/SPARK-14592
This patch adds native support for DDL command `CREATE TABLE LIKE`.
The SQL syntax is like:
CREATE TABLE table_name LIKE existing_table
CREATE TABLE IF NOT EXISTS table_name LIKE existing_table
## How was this patch tested?
`HiveDDLCommandSuite`. `HiveQuerySuite` already tests `CREATE TABLE LIKE`.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
This patch had conflicts when merged, resolved by
Committer: Andrew Or <andrew@databricks.com>
Closes#12362 from viirya/create-table-like.
#### What changes were proposed in this pull request?
This PR is to add a test to ensure drop partitions of an external table will not delete data.
cc yhuai andrewor14
#### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
This patch had conflicts when merged, resolved by
Committer: Andrew Or <andrew@databricks.com>
Closes#12350 from gatorsmile/testDropPartition.
## What changes were proposed in this pull request?
When there are multiple attempts for a stage, we currently only reset internal accumulator values if all the tasks are resubmitted. It would make more sense to reset the accumulator values for each stage attempt. This will allow us to eventually get rid of the internal flag in the Accumulator class. This is part of my bigger effort to simplify accumulators and task metrics.
## How was this patch tested?
Covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12378 from rxin/SPARK-14619.
