## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/18600 we removed the `metadata` field from `SparkPlanInfo`. This causes a problem when we replay event logs that are generated by older Spark versions.
## How was this patch tested?
a regression test.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19237 from cloud-fan/event.
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/18266 add a new feature to support read JDBC table use custom schema, but we must specify all the fields. For simplicity, this PR support specify partial fields.
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#19231 from wangyum/SPARK-22002.
## What changes were proposed in this pull request?
In previous work SPARK-21513, we has allowed `MapType` and `ArrayType` of `MapType`s convert to a json string but only for Scala API. In this follow-up PR, we will make SparkSQL support it for PySpark and SparkR, too. We also fix some little bugs and comments of the previous work in this follow-up PR.
### For PySpark
```
>>> data = [(1, {"name": "Alice"})]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(to_json(df.value).alias("json")).collect()
[Row(json=u'{"name":"Alice")']
>>> data = [(1, [{"name": "Alice"}, {"name": "Bob"}])]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(to_json(df.value).alias("json")).collect()
[Row(json=u'[{"name":"Alice"},{"name":"Bob"}]')]
```
### For SparkR
```
# Converts a map into a JSON object
df2 <- sql("SELECT map('name', 'Bob')) as people")
df2 <- mutate(df2, people_json = to_json(df2$people))
# Converts an array of maps into a JSON array
df2 <- sql("SELECT array(map('name', 'Bob'), map('name', 'Alice')) as people")
df2 <- mutate(df2, people_json = to_json(df2$people))
```
## How was this patch tested?
Add unit test cases.
cc viirya HyukjinKwon
Author: goldmedal <liugs963@gmail.com>
Closes#19223 from goldmedal/SPARK-21513-fp-PySaprkAndSparkR.
## What changes were proposed in this pull request?
Add default stats to StreamingExecutionRelation.
## How was this patch tested?
existing unit tests and an explain() test to be sure
Author: Jose Torres <jose@databricks.com>
Closes#19212 from joseph-torres/SPARK-21988.
## What changes were proposed in this pull request?
Drop test tables and improve comments.
## How was this patch tested?
Modified existing test.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#19213 from wzhfy/useless_comment.
## What changes were proposed in this pull request?
This PR is clean the codes in https://github.com/apache/spark/pull/18975
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19225 from gatorsmile/refactorSPARK-4131.
## What changes were proposed in this pull request?
This pr added a new option to filter TPC-DS queries to run in `TPCDSQueryBenchmark`.
By default, `TPCDSQueryBenchmark` runs all the TPC-DS queries.
This change could enable developers to run some of the TPC-DS queries by this option,
e.g., to run q2, q4, and q6 only:
```
spark-submit --class <this class> --conf spark.sql.tpcds.queryFilter="q2,q4,q6" --jars <spark sql test jar>
```
## How was this patch tested?
Manually checked.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#19188 from maropu/RunPartialQueriesInTPCDS.
## What changes were proposed in this pull request?
Auto generated Oracle schema some times not we expect:
- `number(1)` auto mapped to BooleanType, some times it's not we expect, per [SPARK-20921](https://issues.apache.org/jira/browse/SPARK-20921).
- `number` auto mapped to Decimal(38,10), It can't read big data, per [SPARK-20427](https://issues.apache.org/jira/browse/SPARK-20427).
This PR fix this issue by custom schema as follows:
```scala
val props = new Properties()
props.put("customSchema", "ID decimal(38, 0), N1 int, N2 boolean")
val dfRead = spark.read.schema(schema).jdbc(jdbcUrl, "tableWithCustomSchema", props)
dfRead.show()
```
or
```sql
CREATE TEMPORARY VIEW tableWithCustomSchema
USING org.apache.spark.sql.jdbc
OPTIONS (url '$jdbcUrl', dbTable 'tableWithCustomSchema', customSchema'ID decimal(38, 0), N1 int, N2 boolean')
```
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18266 from wangyum/SPARK-20427.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-21980
This PR fixes the issue in ResolveGroupingAnalytics rule, which indexes the column references in grouping functions without considering case sensitive configurations.
The problem can be reproduced by:
`val df = spark.createDataFrame(Seq((1, 1), (2, 1), (2, 2))).toDF("a", "b")
df.cube("a").agg(grouping("A")).show()`
## How was this patch tested?
unit tests
Author: donnyzone <wellfengzhu@gmail.com>
Closes#19202 from DonnyZone/ResolveGroupingAnalytics.
## What changes were proposed in this pull request?
1. Removing all redundant throws declarations from Java codebase.
2. Removing dead code made visible by this from `ShuffleExternalSorter#closeAndGetSpills`
## How was this patch tested?
Build still passes.
Author: Armin <me@obrown.io>
Closes#19182 from original-brownbear/SPARK-21970.
# What changes were proposed in this pull request?
UDF to_json only supports converting `StructType` or `ArrayType` of `StructType`s to a json output string now.
According to the discussion of JIRA SPARK-21513, I allow to `to_json` support converting `MapType` and `ArrayType` of `MapType`s to a json output string.
This PR is for SQL and Scala API only.
# How was this patch tested?
Adding unit test case.
cc viirya HyukjinKwon
Author: goldmedal <liugs963@gmail.com>
Author: Jia-Xuan Liu <liugs963@gmail.com>
Closes#18875 from goldmedal/SPARK-21513.
## What changes were proposed in this pull request?
TPCDSQueryBenchmark packaged into a jar doesn't work with spark-submit.
It's because of the failure of reference query files in the jar file.
## How was this patch tested?
Ran the benchmark.
Author: sarutak <sarutak@oss.nttdata.co.jp>
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#18592 from sarutak/fix-tpcds-benchmark.
## What changes were proposed in this pull request?
Support DESC (EXTENDED | FORMATTED) ? TABLE COLUMN command.
Support DESC EXTENDED | FORMATTED TABLE COLUMN command to show column-level statistics.
Do NOT support describe nested columns.
## How was this patch tested?
Added test cases.
Author: Zhenhua Wang <wzh_zju@163.com>
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16422 from wzhfy/descColumn.
## What changes were proposed in this pull request?
When the `requiredSchema` only contains `_corrupt_record`, the derived `actualSchema` is empty and the `_corrupt_record` are all null for all rows. This PR captures above situation and raise an exception with a reasonable workaround messag so that users can know what happened and how to fix the query.
## How was this patch tested?
Added unit test in `CSVSuite`.
Author: Jen-Ming Chung <jenmingisme@gmail.com>
Closes#19199 from jmchung/SPARK-21610-FOLLOWUP.
## What changes were proposed in this pull request?
this PR describe remove the import class that are unused.
## How was this patch tested?
N/A
Author: caoxuewen <cao.xuewen@zte.com.cn>
Closes#19131 from heary-cao/unuse_import.
## What changes were proposed in this pull request?
```
echo '{"field": 1}
{"field": 2}
{"field": "3"}' >/tmp/sample.json
```
```scala
import org.apache.spark.sql.types._
val schema = new StructType()
.add("field", ByteType)
.add("_corrupt_record", StringType)
val file = "/tmp/sample.json"
val dfFromFile = spark.read.schema(schema).json(file)
scala> dfFromFile.show(false)
+-----+---------------+
|field|_corrupt_record|
+-----+---------------+
|1 |null |
|2 |null |
|null |{"field": "3"} |
+-----+---------------+
scala> dfFromFile.filter($"_corrupt_record".isNotNull).count()
res1: Long = 0
scala> dfFromFile.filter($"_corrupt_record".isNull).count()
res2: Long = 3
```
When the `requiredSchema` only contains `_corrupt_record`, the derived `actualSchema` is empty and the `_corrupt_record` are all null for all rows. This PR captures above situation and raise an exception with a reasonable workaround messag so that users can know what happened and how to fix the query.
## How was this patch tested?
Added test case.
Author: Jen-Ming Chung <jenmingisme@gmail.com>
Closes#18865 from jmchung/SPARK-21610.
## What changes were proposed in this pull request?
This PR implements the sql feature:
INSERT OVERWRITE [LOCAL] DIRECTORY directory1
[ROW FORMAT row_format] [STORED AS file_format]
SELECT ... FROM ...
## How was this patch tested?
Added new unittests and also pulled the code to fb-spark so that we could test writing to hdfs directory.
Author: Jane Wang <janewang@fb.com>
Closes#18975 from janewangfb/port_local_directory.
## What changes were proposed in this pull request?
Correct DataFrame doc.
## How was this patch tested?
Only doc change, no tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#19173 from yanboliang/df-doc.
## What changes were proposed in this pull request?
`JacksonUtils.verifySchema` verifies if a data type can be converted to JSON. For `MapType`, it now verifies the key type. However, in `JacksonGenerator`, when converting a map to JSON, we only care about its values and create a writer for the values. The keys in a map are treated as strings by calling `toString` on the keys.
Thus, we should change `JacksonUtils.verifySchema` to verify the value type of `MapType`.
## How was this patch tested?
Added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19167 from viirya/test-jacksonutils.
## What changes were proposed in this pull request?
In a driver heap dump containing 390,105 instances of SQLTaskMetrics this
would have saved me approximately 3.2MB of memory.
Since we're not getting any benefit from storing this unused value, let's
eliminate it until a future PR makes use of it.
## How was this patch tested?
Existing unit tests
Author: Andrew Ash <andrew@andrewash.com>
Closes#19153 from ash211/aash/trim-sql-listener.
## What changes were proposed in this pull request?
This PR fixes flaky test `InMemoryCatalogedDDLSuite "alter table: rename cached table"`.
Since this test validates distributed DataFrame, the result should be checked by using `checkAnswer`. The original version used `df.collect().Seq` method that does not guaranty an order of each element of the result.
## How was this patch tested?
Use existing test case
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19159 from kiszk/SPARK-21946.
Since ScalaTest 3.0.0, `org.scalatest.concurrent.Timeouts` is deprecated.
This PR replaces the deprecated one with `org.scalatest.concurrent.TimeLimits`.
```scala
-import org.scalatest.concurrent.Timeouts._
+import org.scalatest.concurrent.TimeLimits._
```
Pass the existing test suites.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19150 from dongjoon-hyun/SPARK-21939.
Change-Id: I1a1b07f1b97e51e2263dfb34b7eaaa099b2ded5e
## What changes were proposed in this pull request?
Currently, users meet job abortions while creating or altering ORC/Parquet tables with invalid column names. We had better prevent this by raising **AnalysisException** with a guide to use aliases instead like Paquet data source tables.
**BEFORE**
```scala
scala> sql("CREATE TABLE orc1 USING ORC AS SELECT 1 `a b`")
17/09/04 13:28:21 ERROR Utils: Aborting task
java.lang.IllegalArgumentException: Error: : expected at the position 8 of 'struct<a b:int>' but ' ' is found.
17/09/04 13:28:21 ERROR FileFormatWriter: Job job_20170904132821_0001 aborted.
17/09/04 13:28:21 ERROR Executor: Exception in task 0.0 in stage 1.0 (TID 1)
org.apache.spark.SparkException: Task failed while writing rows.
```
**AFTER**
```scala
scala> sql("CREATE TABLE orc1 USING ORC AS SELECT 1 `a b`")
17/09/04 13:27:40 ERROR CreateDataSourceTableAsSelectCommand: Failed to write to table orc1
org.apache.spark.sql.AnalysisException: Attribute name "a b" contains invalid character(s) among " ,;{}()\n\t=". Please use alias to rename it.;
```
## How was this patch tested?
Pass the Jenkins with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19124 from dongjoon-hyun/SPARK-21912.
## What changes were proposed in this pull request?
This is a follow-up of #19050 to deal with `ExistenceJoin` case.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19151 from viirya/SPARK-21835-followup.
## What changes were proposed in this pull request?
Just `StateOperatorProgress.toString` + few formatting fixes
## How was this patch tested?
Local build. Waiting for OK from Jenkins.
Author: Jacek Laskowski <jacek@japila.pl>
Closes#19112 from jaceklaskowski/SPARK-21901-StateOperatorProgress-toString.
## What changes were proposed in this pull request?
Add an assert in logical plan optimization that the isStreaming bit stays the same, and fix empty relation rules where that wasn't happening.
## How was this patch tested?
new and existing unit tests
Author: Jose Torres <joseph.torres@databricks.com>
Author: Jose Torres <joseph-torres@databricks.com>
Closes#19056 from joseph-torres/SPARK-21765-followup.
## What changes were proposed in this pull request?
Correlated predicate subqueries are rewritten into `Join` by the rule `RewritePredicateSubquery` during optimization.
It is possibly that the two sides of the `Join` have conflicting attributes. The query plans produced by `RewritePredicateSubquery` become unresolved and break structural integrity.
We should check if there are conflicting attributes in the `Join` and de-duplicate them by adding a `Project`.
## How was this patch tested?
Added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19050 from viirya/SPARK-21835.
## What changes were proposed in this pull request?
For the given example below, the predicate added by `InferFiltersFromConstraints` is folded by `ConstantPropagation` later, this leads to unconverged optimize iteration:
```
Seq((1, 1)).toDF("col1", "col2").createOrReplaceTempView("t1")
Seq(1, 2).toDF("col").createOrReplaceTempView("t2")
sql("SELECT * FROM t1, t2 WHERE t1.col1 = 1 AND 1 = t1.col2 AND t1.col1 = t2.col AND t1.col2 = t2.col")
```
We can fix this by adjusting the indent of the optimize rules.
## How was this patch tested?
Add test case that would have failed in `SQLQuerySuite`.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#19099 from jiangxb1987/unconverge-optimization.
## What changes were proposed in this pull request?
We should make codegen fallback of expressions configurable. So far, it is always on. We might hide it when our codegen have compilation bugs. Thus, we should also disable the codegen fallback when running test cases.
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19119 from gatorsmile/fallbackCodegen.
## What changes were proposed in this pull request?
There was a bug in Univocity Parser that causes the issue in SPARK-20978. This was fixed as below:
```scala
val df = spark.read.schema("a string, b string, unparsed string").option("columnNameOfCorruptRecord", "unparsed").csv(Seq("a").toDS())
df.show()
```
**Before**
```
java.lang.NullPointerException
at scala.collection.immutable.StringLike$class.stripLineEnd(StringLike.scala:89)
at scala.collection.immutable.StringOps.stripLineEnd(StringOps.scala:29)
at org.apache.spark.sql.execution.datasources.csv.UnivocityParser.org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$getCurrentInput(UnivocityParser.scala:56)
at org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$convert$1.apply(UnivocityParser.scala:207)
at org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$convert$1.apply(UnivocityParser.scala:207)
...
```
**After**
```
+---+----+--------+
| a| b|unparsed|
+---+----+--------+
| a|null| a|
+---+----+--------+
```
It was fixed in 2.5.0 and 2.5.4 was released. I guess it'd be safe to upgrade this.
## How was this patch tested?
Unit test added in `CSVSuite.scala`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19113 from HyukjinKwon/bump-up-univocity.
## What changes were proposed in this pull request?
Currently, `withDatabase` fails if the database is not empty. It would be great if we drop cleanly with CASCADE.
## How was this patch tested?
This is a change on test util. Pass the existing Jenkins.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19125 from dongjoon-hyun/SPARK-21913.
## What changes were proposed in this pull request?
If no SparkConf is available to Utils.redact, simply don't redact.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#19123 from srowen/SPARK-21418.
## What changes were proposed in this pull request?
SQL predicates don't have complete expression description. This patch goes to complement the description by adding arguments, examples.
This change also adds related test cases for the SQL predicate expressions.
## How was this patch tested?
Existing tests. And added predicate test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18869 from viirya/SPARK-21654.
## What changes were proposed in this pull request?
Add `TBLPROPERTIES` to the DDL statement `CREATE TABLE USING`.
After this change, the DDL becomes
```
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db_name.]table_name
USING table_provider
[OPTIONS table_property_list]
[PARTITIONED BY (col_name, col_name, ...)]
[CLUSTERED BY (col_name, col_name, ...)
[SORTED BY (col_name [ASC|DESC], ...)]
INTO num_buckets BUCKETS
]
[LOCATION path]
[COMMENT table_comment]
[TBLPROPERTIES (property_name=property_value, ...)]
[[AS] select_statement];
```
## How was this patch tested?
Add a few tests
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19100 from gatorsmile/addTablePropsToCreateTableUsing.
…build; fix some things that will be warnings or errors in 2.12; restore Scala 2.12 profile infrastructure
## What changes were proposed in this pull request?
This change adds back the infrastructure for a Scala 2.12 build, but does not enable it in the release or Python test scripts.
In order to make that meaningful, it also resolves compile errors that the code hits in 2.12 only, in a way that still works with 2.11.
It also updates dependencies to the earliest minor release of dependencies whose current version does not yet support Scala 2.12. This is in a sense covered by other JIRAs under the main umbrella, but implemented here. The versions below still work with 2.11, and are the _latest_ maintenance release in the _earliest_ viable minor release.
- Scalatest 2.x -> 3.0.3
- Chill 0.8.0 -> 0.8.4
- Clapper 1.0.x -> 1.1.2
- json4s 3.2.x -> 3.4.2
- Jackson 2.6.x -> 2.7.9 (required by json4s)
This change does _not_ fully enable a Scala 2.12 build:
- It will also require dropping support for Kafka before 0.10. Easy enough, just didn't do it yet here
- It will require recreating `SparkILoop` and `Main` for REPL 2.12, which is SPARK-14650. Possible to do here too.
What it does do is make changes that resolve much of the remaining gap without affecting the current 2.11 build.
## How was this patch tested?
Existing tests and build. Manually tested with `./dev/change-scala-version.sh 2.12` to verify it compiles, modulo the exceptions above.
Author: Sean Owen <sowen@cloudera.com>
Closes#18645 from srowen/SPARK-14280.
## What changes were proposed in this pull request?
As shown below, for example, When the job 5 is running, It was a mistake to think that five jobs were running, So I think it would be more appropriate to change jobs to job id.
## How was this patch tested?
no need
Author: he.qiao <he.qiao17@zte.com.cn>
Closes#19093 from Geek-He/08_31_sqltable.
## What changes were proposed in this pull request?
This PR make `DataFrame.sample(...)` can omit `withReplacement` defaulting `False`, consistently with equivalent Scala / Java API.
In short, the following examples are allowed:
```python
>>> df = spark.range(10)
>>> df.sample(0.5).count()
7
>>> df.sample(fraction=0.5).count()
3
>>> df.sample(0.5, seed=42).count()
5
>>> df.sample(fraction=0.5, seed=42).count()
5
```
In addition, this PR also adds some type checking logics as below:
```python
>>> df = spark.range(10)
>>> df.sample().count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [].
>>> df.sample(True).count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'bool'>].
>>> df.sample(42).count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'int'>].
>>> df.sample(fraction=False, seed="a").count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'bool'>, <type 'str'>].
>>> df.sample(seed=[1]).count()
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'list'>].
>>> df.sample(withReplacement="a", fraction=0.5, seed=1)
...
TypeError: withReplacement (optional), fraction (required) and seed (optional) should be a bool, float and number; however, got [<type 'str'>, <type 'float'>, <type 'int'>].
```
## How was this patch tested?
Manually tested, unit tests added in doc tests and manually checked the built documentation for Python.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18999 from HyukjinKwon/SPARK-21779.
Removing a check in the ColumnarBatchSuite that depended on a Java assertion. This assertion is being compiled out in the Maven builds causing the test to fail. This part of the test is not specifically from to the functionality that is being tested here.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#19098 from BryanCutler/hotfix-ColumnarBatchSuite-assertion.
… Dataset with LogicalRDD logical operator
## What changes were proposed in this pull request?
Reusing `SparkSession.internalCreateDataFrame` wherever possible (to cut dups)
## How was this patch tested?
Local build and waiting for Jenkins
Author: Jacek Laskowski <jacek@japila.pl>
Closes#19095 from jaceklaskowski/SPARK-21886-internalCreateDataFrame.
## What changes were proposed in this pull request?
Creates `SQLMetricsTestUtils` for the utility functions of both Hive-specific and the other SQLMetrics test cases.
Also, move two SQLMetrics test cases from sql/hive to sql/core.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19092 from gatorsmile/rewriteSQLMetrics.
## What changes were proposed in this pull request?
This PR allows the creation of a `ColumnarBatch` from `ReadOnlyColumnVectors` where previously a columnar batch could only allocate vectors internally. This is useful for using `ArrowColumnVectors` in a batch form to do row-based iteration. Also added `ArrowConverter.fromPayloadIterator` which converts `ArrowPayload` iterator to `InternalRow` iterator and uses a `ColumnarBatch` internally.
## How was this patch tested?
Added a new unit test for creating a `ColumnarBatch` with `ReadOnlyColumnVectors` and a test to verify the roundtrip of rows -> ArrowPayload -> rows, using `toPayloadIterator` and `fromPayloadIterator`.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#18787 from BryanCutler/arrow-ColumnarBatch-support-SPARK-21583.
## What changes were proposed in this pull request?
Fix Java code style so `./dev/lint-java` succeeds
## How was this patch tested?
Run `./dev/lint-java`
Author: Andrew Ash <andrew@andrewash.com>
Closes#19088 from ash211/spark-21875-lint-java.
## What changes were proposed in this pull request?
This PR aims to support `spark.sql.orc.compression.codec` like Parquet's `spark.sql.parquet.compression.codec`. Users can use SQLConf to control ORC compression, too.
## How was this patch tested?
Pass the Jenkins with new and updated test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19055 from dongjoon-hyun/SPARK-21839.
## What changes were proposed in this pull request?
igore("shuffle hash join") is to shuffle hash join to test _case class ShuffledHashJoinExec_.
But when you 'ignore' -> 'test', the test is _case class BroadcastHashJoinExec_.
Before modified, as a result of：canBroadcast is true.
Print information in _canBroadcast(plan: LogicalPlan)_
```
canBroadcast plan.stats.sizeInBytes:6710880
canBroadcast conf.autoBroadcastJoinThreshold:10000000
```
After modified, plan.stats.sizeInBytes is 11184808.
Print information in _canBuildLocalHashMap(plan: LogicalPlan)_
and _muchSmaller(a: LogicalPlan, b: LogicalPlan)_ :
```
canBuildLocalHashMap plan.stats.sizeInBytes:11184808
canBuildLocalHashMap conf.autoBroadcastJoinThreshold:10000000
canBuildLocalHashMap conf.numShufflePartitions:2
```
```
muchSmaller a.stats.sizeInBytes * 3:33554424
muchSmaller b.stats.sizeInBytes:33554432
```
## How was this patch tested?
existing test case.
Author: caoxuewen <cao.xuewen@zte.com.cn>
Closes#19069 from heary-cao/shuffle_hash_join.
## What changes were proposed in this pull request?
We should make codegen fallback of expressions configurable. So far, it is always on. We might hide it when our codegen have compilation bugs. Thus, we should also disable the codegen fallback when running test cases.
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19062 from gatorsmile/fallbackCodegen.
## What changes were proposed in this pull request?
This is a follow-up for https://github.com/apache/spark/pull/18488, to simplify the code.
The major change is, we should map java enum to string type, instead of a struct type with a single string field.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19066 from cloud-fan/fix.
## What changes were proposed in this pull request?
Add trait UserDefinedExpression to identify user-defined functions.
UDF can be expensive. In optimizer we may need to avoid executing UDF multiple times.
E.g.
```scala
table.select(UDF as 'a).select('a, ('a + 1) as 'b)
```
If UDF is expensive in this case, optimizer should not collapse the project to
```scala
table.select(UDF as 'a, (UDF+1) as 'b)
```
Currently UDF classes like PythonUDF, HiveGenericUDF are not defined in catalyst.
This PR is to add a new trait to make it easier to identify user-defined functions.
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#19064 from gengliangwang/UDFType.
## What changes were proposed in this pull request?
As mentioned at https://github.com/apache/spark/pull/18680#issuecomment-316820409, when we have more `ColumnVector` implementations, it might (or might not) have huge performance implications because it might disable inlining, or force virtual dispatches.
As for read path, one of the major paths is the one generated by `ColumnBatchScan`. Currently it refers `ColumnVector` so the penalty will be bigger as we have more classes, but we can know the concrete type from its usage, e.g. vectorized Parquet reader uses `OnHeapColumnVector`. We can use the concrete type in the generated code directly to avoid the penalty.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18989 from ueshin/issues/SPARK-21781.
Signed-off-by: iamhumanbeing <iamhumanbeinggmail.com>
## What changes were proposed in this pull request?
testNameNote = "(minNumPostShufflePartitions: 3) is not correct.
it should be "(minNumPostShufflePartitions: " + numPartitions + ")" in ExchangeCoordinatorSuite
## How was this patch tested?
unit tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: iamhumanbeing <iamhumanbeing@gmail.com>
Closes#19058 from iamhumanbeing/testnote.
