## What changes were proposed in this pull request?
This PR fixes `pivot(Column)` can accepts `collection.mutable.WrappedArray`.
Note that we return `collection.mutable.WrappedArray` from `ArrayType`, and `Literal.apply` doesn't support this.
We can unwrap the array and use it for type dispatch.
```scala
val df = Seq(
(2, Seq.empty[String]),
(2, Seq("a", "x")),
(3, Seq.empty[String]),
(3, Seq("a", "x"))).toDF("x", "s")
df.groupBy("x").pivot("s").count().show()
```
Before:
```
Unsupported literal type class scala.collection.mutable.WrappedArray$ofRef WrappedArray()
java.lang.RuntimeException: Unsupported literal type class scala.collection.mutable.WrappedArray$ofRef WrappedArray()
at org.apache.spark.sql.catalyst.expressions.Literal$.apply(literals.scala:80)
at org.apache.spark.sql.RelationalGroupedDataset.$anonfun$pivot$2(RelationalGroupedDataset.scala:427)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:237)
at scala.collection.IndexedSeqOptimized.foreach(IndexedSeqOptimized.scala:36)
at scala.collection.IndexedSeqOptimized.foreach$(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:39)
at scala.collection.TraversableLike.map(TraversableLike.scala:237)
at scala.collection.TraversableLike.map$(TraversableLike.scala:230)
at scala.collection.AbstractTraversable.map(Traversable.scala:108)
at org.apache.spark.sql.RelationalGroupedDataset.pivot(RelationalGroupedDataset.scala:425)
at org.apache.spark.sql.RelationalGroupedDataset.pivot(RelationalGroupedDataset.scala:406)
at org.apache.spark.sql.RelationalGroupedDataset.pivot(RelationalGroupedDataset.scala:317)
at org.apache.spark.sql.DataFramePivotSuite.$anonfun$new$1(DataFramePivotSuite.scala:341)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
```
After:
```
+---+---+------+
| x| []|[a, x]|
+---+---+------+
| 3| 1| 1|
| 2| 1| 1|
+---+---+------+
```
## How was this patch tested?
Manually tested and unittests were added.
Closes#23349 from HyukjinKwon/SPARK-26403.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
`DataSourceScanExec` overrides "wrong" `treeString` method without `append`. In the PR, I propose to make `treeString`s **final** to prevent such mistakes in the future. And removed the `treeString` and `verboseString` since they both use `simpleString` with reduction.
## How was this patch tested?
It was tested by `DataSourceScanExecRedactionSuite`
Closes#23431 from MaxGekk/datasource-scan-exec-followup.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In `org.apache.spark.sql.execution.metric.SQLMetricsSuite`, there's a test case named "WholeStageCodegen metrics". However, it is executed with whole-stage codegen disabled. This PR fixes this by enable whole-stage codegen for this test case.
## How was this patch tested?
Tested locally using exiting test cases.
Closes#23224 from seancxmao/codegen-metrics.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
…Type
## What changes were proposed in this pull request?
Modifed JdbcUtils.makeGetter to handle ByteType.
## How was this patch tested?
Added a new test to JDBCSuite that maps ```TINYINT``` to ```ByteType```.
Closes#23400 from twdsilva/tiny_int_support.
Authored-by: Thomas D'Silva <tdsilva@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The current `SelectedField` extractor is somewhat complicated and it seems to be handling cases that should be handled automatically:
- `GetArrayItem(child: GetStructFieldObject())`
- `GetArrayStructFields(child: GetArrayStructFields())`
- `GetMap(value: GetStructFieldObject())`
This PR removes those cases and simplifies the extractor by passing down the data type instead of a field.
## How was this patch tested?
Existing tests.
Closes#23397 from hvanhovell/SPARK-26495.
Authored-by: Herman van Hovell <hvanhovell@databricks.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
As the description in SPARK-26339, spark.read behavior is very confusing when reading files that start with underscore, fix this by throwing exception which message is "Path does not exist".
## How was this patch tested?
manual tests.
Both of codes below throws exception which message is "Path does not exist".
```
spark.read.csv("/home/forcia/work/spark/_test.csv")
spark.read.schema("test STRING, number INT").csv("/home/forcia/work/spark/_test.csv")
```
Closes#23288 from KeiichiHirobe/SPARK-26339.
Authored-by: Hirobe Keiichi <keiichi_hirobe@forcia.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Proposed new class `StringConcat` for converting a sequence of strings to string with one memory allocation in the `toString` method. `StringConcat` replaces `StringBuilderWriter` in methods of dumping of Spark plans and codegen to strings.
All `Writer` arguments are replaced by `String => Unit` in methods related to Spark plans stringification.
## How was this patch tested?
It was tested by existing suites `QueryExecutionSuite`, `DebuggingSuite` as well as new tests for `StringConcat` in `StringUtilsSuite`.
Closes#23406 from MaxGekk/rope-plan.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
#20560/[SPARK-23375](https://issues.apache.org/jira/browse/SPARK-23375) introduced an optimizer rule to eliminate redundant Sort. For a test case named "Sort metrics" in `SQLMetricsSuite`, because range is already sorted, sort is removed by the `RemoveRedundantSorts`, which makes this test case meaningless.
This PR modifies the query for testing Sort metrics and checks Sort exists in the plan.
## How was this patch tested?
Modify the existing test case.
Closes#23258 from seancxmao/sort-metrics.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Similar with https://github.com/apache/spark/pull/21446. Looks random string is not quite safe as a directory name.
```scala
scala> val prefix = Random.nextString(10); val dir = new File("/tmp", "del_" + prefix + "-" + UUID.randomUUID.toString); dir.mkdirs()
prefix: String = 窽텘⒘駖ⵚ駢⡞Ρ닋
dir: java.io.File = /tmp/del_窽텘⒘駖ⵚ駢⡞Ρ닋-a3f99855-c429-47a0-a108-47bca6905745
res40: Boolean = false // nope, didn't like this one
```
## How was this patch tested?
Unit test was added, and manually.
Closes#23405 from HyukjinKwon/SPARK-26496.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to add `maxFields` parameter to all functions involved in creation of textual representation of spark plans such as `simpleString` and `verboseString`. New parameter restricts number of fields converted to truncated strings. Any elements beyond the limit will be dropped and replaced by a `"... N more fields"` placeholder. The threshold is bumped up to `Int.MaxValue` for `toFile()`.
## How was this patch tested?
Added a test to `QueryExecutionSuite` which checks `maxFields` impacts on number of truncated fields in `LocalRelation`.
Closes#23159 from MaxGekk/to-file-max-fields.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
Spark SQL doesn't support creating partitioned table using Hive CTAS in SQL syntax. However it is supported by using DataFrameWriter API.
```scala
val df = Seq(("a", 1)).toDF("part", "id")
df.write.format("hive").partitionBy("part").saveAsTable("t")
```
Hive begins to support this syntax in newer version: https://issues.apache.org/jira/browse/HIVE-20241:
```
CREATE TABLE t PARTITIONED BY (part) AS SELECT 1 as id, "a" as part
```
This patch adds this support to SQL syntax.
## How was this patch tested?
Added tests.
Closes#23376 from viirya/hive-ctas-partitioned-table.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to switch the `DateFormatClass`, `ToUnixTimestamp`, `FromUnixTime`, `UnixTime` on java.time API for parsing/formatting dates and timestamps. The API has been already implemented by the `Timestamp`/`DateFormatter` classes. One of benefit is those classes support parsing timestamps with microsecond precision. Old behaviour can be switched on via SQL config: `spark.sql.legacy.timeParser.enabled` (`false` by default).
## How was this patch tested?
It was tested by existing test suites - `DateFunctionsSuite`, `DateExpressionsSuite`, `JsonSuite`, `CsvSuite`, `SQLQueryTestSuite` as well as PySpark tests.
Closes#23358 from MaxGekk/new-time-cast.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PRs #23150 and #23196 switched JSON and CSV datasources on new formatter for dates/timestamps which is based on `DateTimeFormatter`. In this PR, I replaced `SimpleDateFormat` by `DateTimeFormatter` to reflect the changes.
Closes#23374 from MaxGekk/java-time-docs.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
GetStructField with different optional names should be semantically equal. We will use this as building block to compare the nested fields used in the plans to be optimized by catalyst optimizer.
This PR also fixes a bug below that accessing nested fields with different cases in case insensitive mode will result `AnalysisException`.
```
sql("create table t (s struct<i: Int>) using json")
sql("select s.I from t group by s.i")
```
which is currently failing
```
org.apache.spark.sql.AnalysisException: expression 'default.t.`s`' is neither present in the group by, nor is it an aggregate function
```
as cloud-fan pointed out.
## How was this patch tested?
New tests are added.
Closes#23353 from dbtsai/nestedEqual.
Lead-authored-by: DB Tsai <d_tsai@apple.com>
Co-authored-by: DB Tsai <dbtsai@dbtsai.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Use of `ProcessingTime` class was deprecated in favor of `Trigger.ProcessingTime` in Spark 2.2. And, [SPARK-21464](https://issues.apache.org/jira/browse/SPARK-21464) minimized it at 2.2.1. Recently, it grows again in test suites. This PR aims to clean up newly introduced deprecation warnings for Spark 3.0.
## How was this patch tested?
Pass the Jenkins with existing tests and manually check the warnings.
Closes#23367 from dongjoon-hyun/SPARK-26428.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
A followup of https://github.com/apache/spark/pull/23178 , to keep binary compability by using abstract class.
## How was this patch tested?
Manual test. I created a simple app with Spark 2.4
```
object TryUDF {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder().appName("test").master("local[*]").getOrCreate()
import spark.implicits._
val f1 = udf((i: Int) => i + 1)
println(f1.deterministic)
spark.range(10).select(f1.asNonNullable().apply($"id")).show()
spark.stop()
}
}
```
When I run it with current master, it fails with
```
java.lang.IncompatibleClassChangeError: Found interface org.apache.spark.sql.expressions.UserDefinedFunction, but class was expected
```
When I run it with this PR, it works
Closes#23351 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently, even if I explicitly disable Hive support in SparkR session as below:
```r
sparkSession <- sparkR.session("local[4]", "SparkR", Sys.getenv("SPARK_HOME"),
enableHiveSupport = FALSE)
```
produces when the Hadoop version is not supported by our Hive fork:
```
java.lang.reflect.InvocationTargetException
...
Caused by: java.lang.IllegalArgumentException: Unrecognized Hadoop major version number: 3.1.1.3.1.0.0-78
at org.apache.hadoop.hive.shims.ShimLoader.getMajorVersion(ShimLoader.java:174)
at org.apache.hadoop.hive.shims.ShimLoader.loadShims(ShimLoader.java:139)
at org.apache.hadoop.hive.shims.ShimLoader.getHadoopShims(ShimLoader.java:100)
at org.apache.hadoop.hive.conf.HiveConf$ConfVars.<clinit>(HiveConf.java:368)
... 43 more
Error in handleErrors(returnStatus, conn) :
java.lang.ExceptionInInitializerError
at org.apache.hadoop.hive.conf.HiveConf.<clinit>(HiveConf.java:105)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at org.apache.spark.util.Utils$.classForName(Utils.scala:193)
at org.apache.spark.sql.SparkSession$.hiveClassesArePresent(SparkSession.scala:1116)
at org.apache.spark.sql.api.r.SQLUtils$.getOrCreateSparkSession(SQLUtils.scala:52)
at org.apache.spark.sql.api.r.SQLUtils.getOrCreateSparkSession(SQLUtils.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
```
The root cause is that:
```
SparkSession.hiveClassesArePresent
```
check if the class is loadable or not to check if that's in classpath but `org.apache.hadoop.hive.conf.HiveConf` has a check for Hadoop version as static logic which is executed right away. This throws an `IllegalArgumentException` and that's not caught:
36edbac1c8/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala (L1113-L1121)
So, currently, if users have a Hive built-in Spark with unsupported Hadoop version by our fork (namely 3+), there's no way to use SparkR even though it could work.
This PR just propose to change the order of bool comparison so that we can don't execute `SparkSession.hiveClassesArePresent` when:
1. `enableHiveSupport` is explicitly disabled
2. `spark.sql.catalogImplementation` is `in-memory`
so that we **only** check `SparkSession.hiveClassesArePresent` when Hive support is explicitly enabled by short circuiting.
## How was this patch tested?
It's difficult to write a test since we don't run tests against Hadoop 3 yet. See https://github.com/apache/spark/pull/21588. Manually tested.
Closes#23356 from HyukjinKwon/SPARK-26422.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
`HadoopFileWholeTextReader` and `HadoopFileLinesReader` will be eventually called in `FileSourceScanExec`.
In fact, locality has been implemented in `FileScanRDD`, even if we implement it in `HadoopFileWholeTextReader ` and `HadoopFileLinesReader`, it would be useless.
So I think these `TODO` can be removed.
## How was this patch tested?
N/A
Closes#23339 from 10110346/noneededtodo.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
`SQLConf` is supposed to be serializable. However, currently it is not serializable in `WithTestConf`. `WithTestConf` uses the method `overrideConfs` in closure, while the classes which implements it (`TestHiveSessionStateBuilder` and `TestSQLSessionStateBuilder`) are not serializable.
This PR is to use a local variable to fix it.
## How was this patch tested?
Add unit test.
Closes#23352 from gengliangwang/serializableSQLConf.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Currently, when we infer the schema for scala/java decimals, we return as data type the `SYSTEM_DEFAULT` implementation, ie. the decimal type with precision 38 and scale 18. But this is not right, as we know nothing about the right precision and scale and these values can be not enough to store the data. This problem arises in particular with UDF, where we cast all the input of type `DecimalType` to a `DecimalType(38, 18)`: in case this is not enough, null is returned as input for the UDF.
The PR defines a custom handling for casting to the expected data types for ScalaUDF: the decimal precision and scale is picked from the input, so no casting to different and maybe wrong percision and scale happens.
## How was this patch tested?
added UTs
Closes#23308 from mgaido91/SPARK-26308.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In Spark 2.3.0 and previous versions, Hive CTAS command will convert to use data source to write data into the table when the table is convertible. This behavior is controlled by the configs like HiveUtils.CONVERT_METASTORE_ORC and HiveUtils.CONVERT_METASTORE_PARQUET.
In 2.3.1, we drop this optimization by mistake in the PR [SPARK-22977](https://github.com/apache/spark/pull/20521/files#r217254430). Since that Hive CTAS command only uses Hive Serde to write data.
This patch adds this optimization back to Hive CTAS command. This patch adds OptimizedCreateHiveTableAsSelectCommand which uses data source to write data.
## How was this patch tested?
Added test.
Closes#22514 from viirya/SPARK-25271-2.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
For better test coverage, this pr proposed to use the 4 mixed config sets of `WHOLESTAGE_CODEGEN_ENABLED` and `CODEGEN_FACTORY_MODE` when running `SQLQueryTestSuite`:
1. WHOLESTAGE_CODEGEN_ENABLED=true, CODEGEN_FACTORY_MODE=CODEGEN_ONLY
2. WHOLESTAGE_CODEGEN_ENABLED=false, CODEGEN_FACTORY_MODE=CODEGEN_ONLY
3. WHOLESTAGE_CODEGEN_ENABLED=true, CODEGEN_FACTORY_MODE=NO_CODEGEN
4. WHOLESTAGE_CODEGEN_ENABLED=false, CODEGEN_FACTORY_MODE=NO_CODEGEN
This pr also moved some existing tests into `ExplainSuite` because explain output results are different between codegen and interpreter modes.
## How was this patch tested?
Existing tests.
Closes#23213 from maropu/InterpreterModeTest.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In `ReplaceExceptWithFilter` we do not consider properly the case in which the condition returns NULL. Indeed, in that case, since negating NULL still returns NULL, so it is not true the assumption that negating the condition returns all the rows which didn't satisfy it, rows returning NULL may not be returned. This happens when constraints inferred by `InferFiltersFromConstraints` are not enough, as it happens with `OR` conditions.
The rule had also problems with non-deterministic conditions: in such a scenario, this rule would change the probability of the output.
The PR fixes these problem by:
- returning False for the condition when it is Null (in this way we do return all the rows which didn't satisfy it);
- avoiding any transformation when the condition is non-deterministic.
## How was this patch tested?
added UTs
Closes#23315 from mgaido91/SPARK-26366.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Currently, SQL configs are not propagated to executors while schema inferring in CSV datasource. For example, changing of `spark.sql.legacy.timeParser.enabled` does not impact on inferring timestamp types. In the PR, I propose to fix the issue by wrapping schema inferring action using `SQLExecution.withSQLConfPropagated`.
## How was this patch tested?
Added logging to `TimestampFormatter`:
```patch
-object TimestampFormatter {
+object TimestampFormatter extends Logging {
def apply(format: String, timeZone: TimeZone, locale: Locale): TimestampFormatter = {
if (SQLConf.get.legacyTimeParserEnabled) {
+ logError("LegacyFallbackTimestampFormatter is being used")
new LegacyFallbackTimestampFormatter(format, timeZone, locale)
} else {
+ logError("Iso8601TimestampFormatter is being used")
new Iso8601TimestampFormatter(format, timeZone, locale)
}
}
```
and run the command in `spark-shell`:
```shell
$ ./bin/spark-shell --conf spark.sql.legacy.timeParser.enabled=true
```
```scala
scala> Seq("2010|10|10").toDF.repartition(1).write.mode("overwrite").text("/tmp/foo")
scala> spark.read.option("inferSchema", "true").option("header", "false").option("timestampFormat", "yyyy|MM|dd").csv("/tmp/foo").printSchema()
18/12/18 10:47:27 ERROR TimestampFormatter: LegacyFallbackTimestampFormatter is being used
root
|-- _c0: timestamp (nullable = true)
```
Closes#23345 from MaxGekk/csv-schema-infer-propagate-configs.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR proposes to use foreach instead of misuse of map (for Unit). This could cause some weird errors potentially and it's not a good practice anyway. See also SPARK-16694
## How was this patch tested?
N/A
Closes#23341 from HyukjinKwon/followup-SPARK-26081.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The `JsonInferSchema` class is extended to support `TimestampType` inferring from string fields in JSON input:
- If the `prefersDecimal` option is set to `true`, it tries to infer decimal type from the string field.
- If decimal type inference fails or `prefersDecimal` is disabled, `JsonInferSchema` tries to infer `TimestampType`.
- If timestamp type inference fails, `StringType` is returned as the inferred type.
## How was this patch tested?
Added new test suite - `JsonInferSchemaSuite` to check date and timestamp types inferring from JSON using `JsonInferSchema` directly. A few tests were added `JsonSuite` to check type merging and roundtrip tests. This changes was tested by `JsonSuite`, `JsonExpressionsSuite` and `JsonFunctionsSuite` as well.
Closes#23201 from MaxGekk/json-infer-time.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR implements a new feature - window aggregation Pandas UDF for bounded window.
#### Doc:
https://docs.google.com/document/d/14EjeY5z4-NC27-SmIP9CsMPCANeTcvxN44a7SIJtZPc/edit#heading=h.c87w44wcj3wj
#### Example:
```
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.window import Window
df = spark.range(0, 10, 2).toDF('v')
w1 = Window.partitionBy().orderBy('v').rangeBetween(-2, 4)
w2 = Window.partitionBy().orderBy('v').rowsBetween(-2, 2)
pandas_udf('double', PandasUDFType.GROUPED_AGG)
def avg(v):
return v.mean()
df.withColumn('v_mean', avg(df['v']).over(w1)).show()
# +---+------+
# | v|v_mean|
# +---+------+
# | 0| 1.0|
# | 2| 2.0|
# | 4| 4.0|
# | 6| 6.0|
# | 8| 7.0|
# +---+------+
df.withColumn('v_mean', avg(df['v']).over(w2)).show()
# +---+------+
# | v|v_mean|
# +---+------+
# | 0| 2.0|
# | 2| 3.0|
# | 4| 4.0|
# | 6| 5.0|
# | 8| 6.0|
# +---+------+
```
#### High level changes:
This PR modifies the existing WindowInPandasExec physical node to deal with unbounded (growing, shrinking and sliding) windows.
* `WindowInPandasExec` now share the same base class as `WindowExec` and share utility functions. See `WindowExecBase`
* `WindowFunctionFrame` now has two new functions `currentLowerBound` and `currentUpperBound` - to return the lower and upper window bound for the current output row. It is also modified to allow `AggregateProcessor` == null. Null aggregator processor is used for `WindowInPandasExec` where we don't have an aggregator and only uses lower and upper bound functions from `WindowFunctionFrame`
* The biggest change is in `WindowInPandasExec`, where it is modified to take `currentLowerBound` and `currentUpperBound` and write those values together with the input data to the python process for rolling window aggregation. See `WindowInPandasExec` for more details.
#### Discussion
In benchmarking, I found numpy variant of the rolling window UDF is much faster than the pandas version:
Spark SQL window function: 20s
Pandas variant: ~80s
Numpy variant: 10s
Numpy variant with numba: 4s
Allowing numpy variant of the vectorized UDFs is something I want to discuss because of the performance improvement, but doesn't have to be in this PR.
## How was this patch tested?
New tests
Closes#22305 from icexelloss/SPARK-24561-bounded-window-udf.
Authored-by: Li Jin <ice.xelloss@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
SinkProgress should report similar properties like SourceProgress as long as they are available for given Sink. Count of written rows is metric availble for all Sinks. Since relevant progress information is with respect to commited rows, ideal object to carry this info is WriterCommitMessage. For brevity the implementation will focus only on Sinks with API V2 and on Micro Batch mode. Implemention for Continuous mode will be provided at later date.
### Before
```
{"description":"org.apache.spark.sql.kafka010.KafkaSourceProvider3c0bd317"}
```
### After
```
{"description":"org.apache.spark.sql.kafka010.KafkaSourceProvider3c0bd317","numOutputRows":5000}
```
### This PR is related to:
- https://issues.apache.org/jira/browse/SPARK-24647
- https://issues.apache.org/jira/browse/SPARK-21313
## How was this patch tested?
Existing and new unit tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#21919 from vackosar/feature/SPARK-24933-numOutputRows.
Lead-authored-by: Vaclav Kosar <admin@vaclavkosar.com>
Co-authored-by: Kosar, Vaclav: Functions Transformation <Vaclav.Kosar@barclayscapital.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR is a follow-up of the PR https://github.com/apache/spark/pull/17899. It is to add the rule TransposeWindow the optimizer batch.
## How was this patch tested?
The existing tests.
Closes#23222 from gatorsmile/followupSPARK-20636.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
- The original comment about `updateDriverMetrics` is not right.
- Refactor the code to ensure `selectedPartitions ` has been set before sending the driver-side metrics.
- Restore the original name, which is more general and extendable.
## How was this patch tested?
The existing tests.
Closes#23328 from gatorsmile/followupSpark-26142.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The optimizer rule `org.apache.spark.sql.catalyst.optimizer.ReorderJoin` performs join reordering on inner joins. This was introduced from SPARK-12032 (https://github.com/apache/spark/pull/10073) in 2015-12.
After it had reordered the joins, though, it didn't check whether or not the output attribute order is still the same as before. Thus, it's possible to have a mismatch between the reordered output attributes order vs the schema that a DataFrame thinks it has.
The same problem exists in the CBO version of join reordering (`CostBasedJoinReorder`) too.
This can be demonstrated with the example:
```scala
spark.sql("create table table_a (x int, y int) using parquet")
spark.sql("create table table_b (i int, j int) using parquet")
spark.sql("create table table_c (a int, b int) using parquet")
val df = spark.sql("""
with df1 as (select * from table_a cross join table_b)
select * from df1 join table_c on a = x and b = i
""")
```
here's what the DataFrame thinks:
```
scala> df.printSchema
root
|-- x: integer (nullable = true)
|-- y: integer (nullable = true)
|-- i: integer (nullable = true)
|-- j: integer (nullable = true)
|-- a: integer (nullable = true)
|-- b: integer (nullable = true)
```
here's what the optimized plan thinks, after join reordering:
```
scala> df.queryExecution.optimizedPlan.output.foreach(a => println(s"|-- ${a.name}: ${a.dataType.typeName}"))
|-- x: integer
|-- y: integer
|-- a: integer
|-- b: integer
|-- i: integer
|-- j: integer
```
If we exclude the `ReorderJoin` rule (using Spark 2.4's optimizer rule exclusion feature), it's back to normal:
```
scala> spark.conf.set("spark.sql.optimizer.excludedRules", "org.apache.spark.sql.catalyst.optimizer.ReorderJoin")
scala> val df = spark.sql("with df1 as (select * from table_a cross join table_b) select * from df1 join table_c on a = x and b = i")
df: org.apache.spark.sql.DataFrame = [x: int, y: int ... 4 more fields]
scala> df.queryExecution.optimizedPlan.output.foreach(a => println(s"|-- ${a.name}: ${a.dataType.typeName}"))
|-- x: integer
|-- y: integer
|-- i: integer
|-- j: integer
|-- a: integer
|-- b: integer
```
Note that this output attribute ordering problem leads to data corruption, and can manifest itself in various symptoms:
* Silently corrupting data, if the reordered columns happen to either have matching types or have sufficiently-compatible types (e.g. all fixed length primitive types are considered as "sufficiently compatible" in an `UnsafeRow`), then only the resulting data is going to be wrong but it might not trigger any alarms immediately. Or
* Weird Java-level exceptions like `java.lang.NegativeArraySizeException`, or even SIGSEGVs.
## How was this patch tested?
Added new unit test in `JoinReorderSuite` and new end-to-end test in `JoinSuite`.
Also made `JoinReorderSuite` and `StarJoinReorderSuite` assert more strongly on maintaining output attribute order.
Closes#23303 from rednaxelafx/fix-join-reorder.
Authored-by: Kris Mok <rednaxelafx@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
CSV parsing accidentally uses the previous good value for a bad input field. See example in Jira.
This PR ensures that the associated column is set to null when an input field cannot be converted.
## How was this patch tested?
Added new test.
Ran all SQL unit tests (testOnly org.apache.spark.sql.*).
Ran pyspark tests for pyspark-sql
Closes#23323 from bersprockets/csv-bad-field.
Authored-by: Bruce Robbins <bersprockets@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When there is a self-join as result of a IN subquery, the join condition may be invalid, resulting in trivially true predicates and return wrong results.
The PR deduplicates the subquery output in order to avoid the issue.
## How was this patch tested?
added UT
Closes#23057 from mgaido91/SPARK-26078.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to switch on **java.time API** for parsing timestamps and dates from JSON inputs with microseconds precision. The SQL config `spark.sql.legacy.timeParser.enabled` allow to switch back to previous behavior with using `java.text.SimpleDateFormat`/`FastDateFormat` for parsing/generating timestamps/dates.
## How was this patch tested?
It was tested by `JsonExpressionsSuite`, `JsonFunctionsSuite` and `JsonSuite`.
Closes#23196 from MaxGekk/json-time-parser.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Multiple SparkContexts are discouraged and it has been warning for last 4 years, see SPARK-4180. It could cause arbitrary and mysterious error cases, see SPARK-2243.
Honestly, I didn't even know Spark still allows it, which looks never officially supported, see SPARK-2243.
I believe It should be good timing now to remove this configuration.
## How was this patch tested?
Each doc was manually checked and manually tested:
```
$ ./bin/spark-shell --conf=spark.driver.allowMultipleContexts=true
...
scala> new SparkContext()
org.apache.spark.SparkException: Only one SparkContext should be running in this JVM (see SPARK-2243).The currently running SparkContext was created at:
org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:939)
...
org.apache.spark.SparkContext$.$anonfun$assertNoOtherContextIsRunning$2(SparkContext.scala:2435)
at scala.Option.foreach(Option.scala:274)
at org.apache.spark.SparkContext$.assertNoOtherContextIsRunning(SparkContext.scala:2432)
at org.apache.spark.SparkContext$.markPartiallyConstructed(SparkContext.scala:2509)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:80)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:112)
... 49 elided
```
Closes#23311 from HyukjinKwon/SPARK-26362.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When using a higher-order function with the same variable name as the existing columns in `Filter` or something which uses `Analyzer.resolveExpressionBottomUp` during the resolution, e.g.,:
```scala
val df = Seq(
(Seq(1, 9, 8, 7), 1, 2),
(Seq(5, 9, 7), 2, 2),
(Seq.empty, 3, 2),
(null, 4, 2)
).toDF("i", "x", "d")
checkAnswer(df.filter("exists(i, x -> x % d == 0)"),
Seq(Row(Seq(1, 9, 8, 7), 1, 2)))
checkAnswer(df.select("x").filter("exists(i, x -> x % d == 0)"),
Seq(Row(1)))
```
the following exception happens:
```
java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.BoundReference cannot be cast to org.apache.spark.sql.catalyst.expressions.NamedExpression
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:237)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at scala.collection.TraversableLike.map(TraversableLike.scala:237)
at scala.collection.TraversableLike.map$(TraversableLike.scala:230)
at scala.collection.AbstractTraversable.map(Traversable.scala:108)
at org.apache.spark.sql.catalyst.expressions.HigherOrderFunction.$anonfun$functionsForEval$1(higherOrderFunctions.scala:147)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:237)
at scala.collection.immutable.List.foreach(List.scala:392)
at scala.collection.TraversableLike.map(TraversableLike.scala:237)
at scala.collection.TraversableLike.map$(TraversableLike.scala:230)
at scala.collection.immutable.List.map(List.scala:298)
at org.apache.spark.sql.catalyst.expressions.HigherOrderFunction.functionsForEval(higherOrderFunctions.scala:145)
at org.apache.spark.sql.catalyst.expressions.HigherOrderFunction.functionsForEval$(higherOrderFunctions.scala:145)
at org.apache.spark.sql.catalyst.expressions.ArrayExists.functionsForEval$lzycompute(higherOrderFunctions.scala:369)
at org.apache.spark.sql.catalyst.expressions.ArrayExists.functionsForEval(higherOrderFunctions.scala:369)
at org.apache.spark.sql.catalyst.expressions.SimpleHigherOrderFunction.functionForEval(higherOrderFunctions.scala:176)
at org.apache.spark.sql.catalyst.expressions.SimpleHigherOrderFunction.functionForEval$(higherOrderFunctions.scala:176)
at org.apache.spark.sql.catalyst.expressions.ArrayExists.functionForEval(higherOrderFunctions.scala:369)
at org.apache.spark.sql.catalyst.expressions.ArrayExists.nullSafeEval(higherOrderFunctions.scala:387)
at org.apache.spark.sql.catalyst.expressions.SimpleHigherOrderFunction.eval(higherOrderFunctions.scala:190)
at org.apache.spark.sql.catalyst.expressions.SimpleHigherOrderFunction.eval$(higherOrderFunctions.scala:185)
at org.apache.spark.sql.catalyst.expressions.ArrayExists.eval(higherOrderFunctions.scala:369)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate.eval(Unknown Source)
at org.apache.spark.sql.execution.FilterExec.$anonfun$doExecute$3(basicPhysicalOperators.scala:216)
at org.apache.spark.sql.execution.FilterExec.$anonfun$doExecute$3$adapted(basicPhysicalOperators.scala:215)
...
```
because the `UnresolvedAttribute`s in `LambdaFunction` are unexpectedly resolved by the rule.
This pr modified to use a placeholder `UnresolvedNamedLambdaVariable` to prevent unexpected resolution.
## How was this patch tested?
Added a test and modified some tests.
Closes#23320 from ueshin/issues/SPARK-26370/hof_resolution.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
I was looking at the code and it was a bit difficult to see the life cycle of InMemoryFileIndex passed into getOrInferFileFormatSchema, because once it is passed in, and another time it was created in getOrInferFileFormatSchema. It'd be easier to understand the life cycle if we move the creation of it out.
## How was this patch tested?
This is a simple code move and should be covered by existing tests.
Closes#23317 from rxin/SPARK-26368.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Regarding the performance issue of SPARK-26155, it reports the issue on TPC-DS. I think it is better to add a benchmark for `LongToUnsafeRowMap` which is the root cause of performance regression.
It can be easier to show performance difference between different metric implementations in `LongToUnsafeRowMap`.
## How was this patch tested?
Manually run added benchmark.
Closes#23284 from viirya/SPARK-26337.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Added query status updates to ContinuousExecution.
## How was this patch tested?
Existing unit tests + added ContinuousQueryStatusAndProgressSuite.
Closes#23095 from gaborgsomogyi/SPARK-23886.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
As discussed in https://github.com/apache/spark/pull/23208/files#r239684490 , we should put `newScanBuilder` in read related mix-in traits like `SupportsBatchRead`, to support write-only table.
In the `Append` operator, we should skip schema validation if not necessary. In the future we would introduce a capability API, so that data source can tell Spark that it doesn't want to do validation.
## How was this patch tested?
existing tests.
Closes#23266 from cloud-fan/ds-read.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Follow up pr for #23207, include following changes:
- Rename `SQLShuffleMetricsReporter` to `SQLShuffleReadMetricsReporter` to make it match with write side naming.
- Display text changes for read side for naming consistent.
- Rename function in `ShuffleWriteProcessor`.
- Delete `private[spark]` in execution package.
## How was this patch tested?
Existing tests.
Closes#23286 from xuanyuanking/SPARK-26193-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
If `checkForContinuous` is called ( `checkForStreaming` is called in `checkForContinuous` ), the `checkForStreaming` mothod will be called twice in `createQuery` , this is not necessary, and the `checkForStreaming` method has a lot of statements, so it's better to remove one of them.
## How was this patch tested?
Existing unit tests in `StreamingQueryManagerSuite` and `ContinuousAggregationSuite`
Closes#23251 from 10110346/isUnsupportedOperationCheckEnabled.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to return partial results from JSON datasource and JSON functions in the PERMISSIVE mode if some of JSON fields are parsed and converted to desired types successfully. The changes are made only for `StructType`. Whole bad JSON records are placed into the corrupt column specified by the `columnNameOfCorruptRecord` option or SQL config.
Partial results are not returned for malformed JSON input.
## How was this patch tested?
Added new UT which checks converting JSON strings with one invalid and one valid field at the end of the string.
Closes#23253 from MaxGekk/json-bad-record.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This is a regression introduced by https://github.com/apache/spark/pull/22104 at Spark 2.4.0.
When we have Python UDF in subquery, we will hit an exception
```
Caused by: java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.AttributeReference cannot be cast to org.apache.spark.sql.catalyst.expressions.PythonUDF
at scala.collection.immutable.Stream.map(Stream.scala:414)
at org.apache.spark.sql.execution.python.EvalPythonExec.$anonfun$doExecute$2(EvalPythonExec.scala:98)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:815)
...
```
https://github.com/apache/spark/pull/22104 turned `ExtractPythonUDFs` from a physical rule to optimizer rule. However, there is a difference between a physical rule and optimizer rule. A physical rule always runs once, an optimizer rule may be applied twice on a query tree even the rule is located in a batch that only runs once.
For a subquery, the `OptimizeSubqueries` rule will execute the entire optimizer on the query plan inside subquery. Later on subquery will be turned to joins, and the optimizer rules will be applied to it again.
Unfortunately, the `ExtractPythonUDFs` rule is not idempotent. When it's applied twice on a query plan inside subquery, it will produce a malformed plan. It extracts Python UDF from Python exec plans.
This PR proposes 2 changes to be double safe:
1. `ExtractPythonUDFs` should skip python exec plans, to make the rule idempotent
2. `ExtractPythonUDFs` should skip subquery
## How was this patch tested?
a new test.
Closes#23248 from cloud-fan/python.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
`RDDConversions` would get disproportionately slower as the number of columns in the query increased,
for the type of `converters` before is `scala.collection.immutable.::` which is a subtype of list.
This PR removing `RDDConversions` and using `RowEncoder` to convert the Row to InternalRow.
The test of `PrunedScanSuite` for 2000 columns and 20k rows takes 409 seconds before this PR, and 361 seconds after.
## How was this patch tested?
Test case of `PrunedScanSuite`
Closes#23262 from eatoncys/toarray.
Authored-by: 10129659 <chen.yanshan@zte.com.cn>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
… incorrect.
## What changes were proposed in this pull request?
In the reported heartbeat information, the unit of the memory data is bytes, which is converted by the formatBytes() function in the utils.js file before being displayed in the interface. The cardinality of the unit conversion in the formatBytes function is 1000, which should be 1024.
Change the cardinality of the unit conversion in the formatBytes function to 1024.
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22683 from httfighter/SPARK-25696.
Lead-authored-by: 韩田田00222924 <han.tiantian@zte.com.cn>
Co-authored-by: han.tiantian@zte.com.cn <han.tiantian@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
1. Implement `SQLShuffleWriteMetricsReporter` on the SQL side as the customized `ShuffleWriteMetricsReporter`.
2. Add shuffle write metrics to `ShuffleExchangeExec`, and use these metrics to create corresponding `SQLShuffleWriteMetricsReporter` in shuffle dependency.
3. Rework on `ShuffleMapTask` to add new class named `ShuffleWriteProcessor` which control shuffle write process, we use sql shuffle write metrics by customizing a ShuffleWriteProcessor on SQL side.
## How was this patch tested?
Add UT in SQLMetricsSuite.
Manually test locally, update screen shot to document attached in JIRA.
Closes#23207 from xuanyuanking/SPARK-26193.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
A followup of https://github.com/apache/spark/pull/23043
There are 4 places we need to deal with NaN and -0.0:
1. comparison expressions. `-0.0` and `0.0` should be treated as same. Different NaNs should be treated as same.
2. Join keys. `-0.0` and `0.0` should be treated as same. Different NaNs should be treated as same.
3. grouping keys. `-0.0` and `0.0` should be assigned to the same group. Different NaNs should be assigned to the same group.
4. window partition keys. `-0.0` and `0.0` should be treated as same. Different NaNs should be treated as same.
The case 1 is OK. Our comparison already handles NaN and -0.0, and for struct/array/map, we will recursively compare the fields/elements.
Case 2, 3 and 4 are problematic, as they compare `UnsafeRow` binary directly, and different NaNs have different binary representation, and the same thing happens for -0.0 and 0.0.
To fix it, a simple solution is: normalize float/double when building unsafe data (`UnsafeRow`, `UnsafeArrayData`, `UnsafeMapData`). Then we don't need to worry about it anymore.
Following this direction, this PR moves the handling of NaN and -0.0 from `Platform` to `UnsafeWriter`, so that places like `UnsafeRow.setFloat` will not handle them, which reduces the perf overhead. It's also easier to add comments explaining why we do it in `UnsafeWriter`.
## How was this patch tested?
existing tests
Closes#23239 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Parquet files appear to have nullability info when being written, not being read.
## How was this patch tested?
Some test code: (running spark 2.3, but the relevant code in DataSource looks identical on master)
case class NullTest(bo: Boolean, opbol: Option[Boolean])
val testDf = spark.createDataFrame(Seq(NullTest(true, Some(false))))
defined class NullTest
testDf: org.apache.spark.sql.DataFrame = [bo: boolean, opbol: boolean]
testDf.write.parquet("s3://asana-stats/tmp_dima/parquet_check_schema")
spark.read.parquet("s3://asana-stats/tmp_dima/parquet_check_schema/part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet4").printSchema()
root
|-- bo: boolean (nullable = true)
|-- opbol: boolean (nullable = true)
Meanwhile, the parquet file formed does have nullable info:
[]batchprod-report000:/tmp/dimakamalov-batch$ aws s3 ls s3://asana-stats/tmp_dima/parquet_check_schema/
2018-10-17 21:03:52 0 _SUCCESS
2018-10-17 21:03:50 504 part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet
[]batchprod-report000:/tmp/dimakamalov-batch$ aws s3 cp s3://asana-stats/tmp_dima/parquet_check_schema/part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet .
download: s3://asana-stats/tmp_dima/parquet_check_schema/part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet to ./part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet
[]batchprod-report000:/tmp/dimakamalov-batch$ java -jar parquet-tools-1.8.2.jar schema part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet
message spark_schema {
required boolean bo;
optional boolean opbol;
}
Closes#22759 from dima-asana/dima-asana-nullable-parquet-doc.
Authored-by: dima-asana <42555784+dima-asana@users.noreply.github.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up of #23031 to rename the config name to `spark.sql.legacy.setCommandRejectsSparkCoreConfs`.
## How was this patch tested?
Existing tests.
Closes#23245 from ueshin/issues/SPARK-26060/rename_config.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Currently if user provides data schema, partition column values are converted as per it. But if the conversion failed, e.g. converting string to int, the column value is null.
This PR proposes to throw exception in such case, instead of converting into null value silently:
1. These null partition column values doesn't make sense to users in most cases. It is better to show the conversion failure, and then users can adjust the schema or ETL jobs to fix it.
2. There are always exceptions on such conversion failure for non-partition data columns. Partition columns should have the same behavior.
We can reproduce the case above as following:
```
/tmp/testDir
├── p=bar
└── p=foo
```
If we run:
```
val schema = StructType(Seq(StructField("p", IntegerType, false)))
spark.read.schema(schema).csv("/tmp/testDir/").show()
```
We will get:
```
+----+
| p|
+----+
|null|
|null|
+----+
```
## How was this patch tested?
Unit test
Closes#23215 from gengliangwang/SPARK-26263.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
`enablePerfMetrics `was originally designed in `BytesToBytesMap `to control `getNumHashCollisions getTimeSpentResizingNs getAverageProbesPerLookup`.
However, as the Spark version gradual progress. this parameter is only used for `getAverageProbesPerLookup ` and always given to true when using `BytesToBytesMap`.
it is also dangerous to determine whether `getAverageProbesPerLookup `opens and throws an `IllegalStateException `exception.
So this pr will be remove `enablePerfMetrics `parameter from `BytesToBytesMap`. thanks.
## How was this patch tested?
the existed test cases.
Closes#23244 from heary-cao/enablePerfMetrics.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
When executing `toPandas` with Arrow enabled, partitions that arrive in the JVM out-of-order must be buffered before they can be send to Python. This causes an excess of memory to be used in the driver JVM and increases the time it takes to complete because data must sit in the JVM waiting for preceding partitions to come in.
This change sends un-ordered partitions to Python as soon as they arrive in the JVM, followed by a list of partition indices so that Python can assemble the data in the correct order. This way, data is not buffered at the JVM and there is no waiting on particular partitions so performance will be increased.
Followup to #21546
## How was this patch tested?
Added new test with a large number of batches per partition, and test that forces a small delay in the first partition. These test that partitions are collected out-of-order and then are are put in the correct order in Python.
## Performance Tests - toPandas
Tests run on a 4 node standalone cluster with 32 cores total, 14.04.1-Ubuntu and OpenJDK 8
measured wall clock time to execute `toPandas()` and took the average best time of 5 runs/5 loops each.
Test code
```python
df = spark.range(1 << 25, numPartitions=32).toDF("id").withColumn("x1", rand()).withColumn("x2", rand()).withColumn("x3", rand()).withColumn("x4", rand())
for i in range(5):
start = time.time()
_ = df.toPandas()
elapsed = time.time() - start
```
Spark config
```
spark.driver.memory 5g
spark.executor.memory 5g
spark.driver.maxResultSize 2g
spark.sql.execution.arrow.enabled true
```
Current Master w/ Arrow stream | This PR
---------------------|------------
5.16207 | 4.342533
5.133671 | 4.399408
5.147513 | 4.468471
5.105243 | 4.36524
5.018685 | 4.373791
Avg Master | Avg This PR
------------------|--------------
5.1134364 | 4.3898886
Speedup of **1.164821449**
Closes#22275 from BryanCutler/arrow-toPandas-oo-batches-SPARK-25274.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
this code come from PR: https://github.com/apache/spark/pull/11190,
but this code has never been used, only since PR: https://github.com/apache/spark/pull/14548,
Let's continue fix it. thanks.
## How was this patch tested?
N / A
Closes#23227 from heary-cao/unuseSparkPlan.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
When we encode a Decimal from external source we don't check for overflow. That method is useful not only in order to enforce that we can represent the correct value in the specified range, but it also changes the underlying data to the right precision/scale. Since in our code generation we assume that a decimal has exactly the same precision and scale of its data type, missing to enforce it can lead to corrupted output/results when there are subsequent transformations.
## How was this patch tested?
added UT
Closes#23210 from mgaido91/SPARK-26233.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to use **java.time API** for parsing timestamps and dates from CSV content with microseconds precision. The SQL config `spark.sql.legacy.timeParser.enabled` allow to switch back to previous behaviour with using `java.text.SimpleDateFormat`/`FastDateFormat` for parsing/generating timestamps/dates.
## How was this patch tested?
It was tested by `UnivocityParserSuite`, `CsvExpressionsSuite`, `CsvFunctionsSuite` and `CsvSuite`.
Closes#23150 from MaxGekk/time-parser.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
It will throw:
```
requirement failed: When reading JDBC data sources, users need to specify all or none for the following options: 'partitionColumn', 'lowerBound', 'upperBound', and 'numPartitions'
```
and
```
User-defined partition column subq.c1 not found in the JDBC relation ...
```
This PR fix this error example.
## How was this patch tested?
manual tests
Closes#23170 from wangyum/SPARK-24499.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the DDLSuit, there are four test cases have the same codes , writing a function can combine the same code.
## How was this patch tested?
existing tests.
Closes#23194 from CarolinePeng/Update_temp.
Authored-by: 彭灿00244106 <00244106@zte.intra>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
## What changes were proposed in this pull request?
In SPARK-23711, we have implemented the expression fallback logic to an interpreted mode. So, this pr fixed code to support the same fallback mode in `SafeProjection` based on `CodeGeneratorWithInterpretedFallback`.
## How was this patch tested?
Add tests in `CodeGeneratorWithInterpretedFallbackSuite` and `UnsafeRowConverterSuite`.
Closes#22468 from maropu/SPARK-25374-3.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pr is to return an empty config set when regenerating the golden files in `SQLQueryTestSuite`.
This is the follow-up of #22512.
## How was this patch tested?
N/A
Closes#23212 from maropu/SPARK-25498-FOLLOWUP.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Since `AggregationIterator` uses `MutableProjection` for `UnsafeRow`, `InterpretedMutableProjection` needs to handle `UnsafeRow` as buffer internally for fixed-length types only.
## How was this patch tested?
Run 'SQLQueryTestSuite' with the interpreted mode.
Closes#22512 from maropu/InterpreterTest.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Partition column name is required to be unique under the same directory. The following paths are invalid partitioned directory:
```
hdfs://host:9000/path/a=1
hdfs://host:9000/path/b=2
```
If case sensitive, the following paths should be invalid too:
```
hdfs://host:9000/path/a=1
hdfs://host:9000/path/A=2
```
Since column 'a' and 'A' are different, and it is wrong to use either one as the column name in partition schema.
Also, there is a `TODO` comment in the code. Currently the Spark doesn't validate such case when `CASE_SENSITIVE` enabled.
This PR is to resolve the problem.
## How was this patch tested?
Add unit test
Closes#23186 from gengliangwang/SPARK-26230.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to change behaviour of `UnivocityParser` and `FailureSafeParser`, and return all fields that were parsed and converted to expected types successfully instead of just returning a row with all `null`s for a bad input in the `PERMISSIVE` mode. For example, for CSV line `0,2013-111-11 12:13:14` and DDL schema `a int, b timestamp`, new result is `Row(0, null)`.
## How was this patch tested?
It was checked by existing tests from `CsvSuite` and `CsvFunctionsSuite`.
Closes#23120 from MaxGekk/failuresafe-partial-result.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
When build hash Map with one row of data and run out of memory, we should throw a SparkOutOfMemoryError exception, which is more accurate than SparkException. this PR fix it.
## How was this patch tested?
N / A
Closes#23190 from heary-cao/throwUnsafeHashedRelation.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Add headers to empty csv files when header=true, because otherwise these files are invalid when reading.
## How was this patch tested?
Added test for roundtrip of empty dataframe to csv file with headers and back in CSVSuite
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23173 from koertkuipers/feat-empty-csv-with-header.
Authored-by: Koert Kuipers <koert@tresata.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
It's a bad idea to use case class as public API, as it has a very wide surface. For example, the `copy` method, its fields, the companion object, etc.
For a particular case, `UserDefinedFunction`. It has a private constructor, and I believe we only want users to access a few methods:`apply`, `nullable`, `asNonNullable`, etc.
However, all its fields, and `copy` method, and the companion object are public unexpectedly. As a result, we made many tricks to work around the binary compatibility issues.
This PR proposes to only make interfaces public, and hide implementations behind with a private class. Now `UserDefinedFunction` is a pure trait, and the concrete implementation is `SparkUserDefinedFunction`, which is private.
Changing class to interface is not binary compatible(but source compatible), so 3.0 is a good chance to do it.
This is the first PR to go with this direction. If it's accepted, I'll create a umbrella JIRA and fix all the public case classes.
## How was this patch tested?
existing tests.
Closes#23178 from cloud-fan/udf.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose filtering out all empty files inside of `FileSourceScanExec` and exclude them from file splits. It should reduce overhead of opening and reading files without any data, and as consequence datasources will not produce empty partitions for such files.
## How was this patch tested?
Added a test which creates an empty and non-empty files. If empty files are ignored in load, Text datasource in the `wholetext` mode must create only one partition for non-empty file.
Closes#23130 from MaxGekk/ignore-empty-files.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a small change for better debugging: to pass query uuid in IncrementalExecution, when we look at the QueryExecution in isolation to trace back the query.
## How was this patch tested?
N/A - just add some field for better debugging.
Closes#23192 from rxin/SPARK-26241.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In an earlier PR, we missed measuring the optimization phase time for streaming queries. This patch adds it.
## How was this patch tested?
Given this is a debugging feature, and it is very convoluted to add tests to verify the phase is set properly, I am not introducing a streaming specific test.
Closes#23193 from rxin/SPARK-26226-1.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Currently, the common `withTempDir` function is used in Spark SQL test cases. To handle `val dir = Utils. createTempDir()` and `Utils. deleteRecursively (dir)`. Unfortunately, the `withTempDir` function cannot be used in the Spark Core test case. This PR Sharing `withTempDir` function in Spark Sql and SparkCore to clean up SparkCore test cases. thanks.
## How was this patch tested?
N / A
Closes#23151 from heary-cao/withCreateTempDir.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This patch changes the query plan tracker added earlier to report phase timeline, rather than just a duration for each phase. This way, we can easily find time that's unaccounted for.
## How was this patch tested?
Updated test cases to reflect that.
Closes#23183 from rxin/SPARK-26226.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This is the first step of the data source v2 API refactor [proposal](https://docs.google.com/document/d/1uUmKCpWLdh9vHxP7AWJ9EgbwB_U6T3EJYNjhISGmiQg/edit?usp=sharing)
It adds the new API for batch read, without removing the old APIs, as they are still needed for streaming sources.
More concretely, it adds
1. `TableProvider`, works like an anonymous catalog
2. `Table`, represents a structured data set.
3. `ScanBuilder` and `Scan`, a logical represents of data source scan
4. `Batch`, a physical representation of data source batch scan.
## How was this patch tested?
existing tests
Closes#23086 from cloud-fan/refactor-batch.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR is to fix a regression introduced in: https://github.com/apache/spark/pull/21004/files#r236998030
If user specifies schema, Spark don't need to infer data type for of partition columns, otherwise the data type might not match with the one user provided.
E.g. for partition directory `p=4d`, after data type inference the column value will be `4.0`.
See https://issues.apache.org/jira/browse/SPARK-26188 for more details.
Note that user specified schema **might not cover all the data columns**:
```
val schema = new StructType()
.add("id", StringType)
.add("ex", ArrayType(StringType))
val df = spark.read
.schema(schema)
.format("parquet")
.load(src.toString)
assert(df.schema.toList === List(
StructField("ex", ArrayType(StringType)),
StructField("part", IntegerType), // inferred partitionColumn dataType
StructField("id", StringType))) // used user provided partitionColumn dataType
```
For the missing columns in user specified schema, Spark still need to infer their data types if `partitionColumnTypeInferenceEnabled` is enabled.
To implement the partially inference, refactor `PartitioningUtils.parsePartitions` and pass the user specified schema as parameter to cast partition values.
## How was this patch tested?
Add unit test.
Closes#23165 from gengliangwang/fixFileIndex.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently the `SET` command works without any warnings even if the specified key is for `SparkConf` entries and it has no effect because the command does not update `SparkConf`, but the behavior might confuse users. We should track `SparkConf` entries and make the command reject for such entries.
## How was this patch tested?
Added a test and existing tests.
Closes#23031 from ueshin/issues/SPARK-26060/set_command.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose using of the locale option to parse decimals from CSV input. After the changes, `UnivocityParser` converts input string to `BigDecimal` and to Spark's Decimal by using `java.text.DecimalFormat`.
## How was this patch tested?
Added a test for the `en-US`, `ko-KR`, `ru-RU`, `de-DE` locales.
Closes#22979 from MaxGekk/decimal-parsing-locale.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Follow up for https://github.com/apache/spark/pull/23128, move sql read metrics relatives to `SQLShuffleMetricsReporter`, in order to put sql shuffle read metrics relatives closer and avoid possible problem about forgetting update SQLShuffleMetricsReporter while new metrics added by others.
## How was this patch tested?
Existing tests.
Closes#23175 from xuanyuanking/SPARK-26142-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to postpone creation of `OutputStream`/`Univocity`/`JacksonGenerator` till the first row should be written. This prevents creation of empty files for empty partitions. So, no need to open and to read such files back while loading data from the location.
## How was this patch tested?
Added tests for Text, JSON and CSV datasource where empty dataset is written but should not produce any files.
Closes#23052 from MaxGekk/text-empty-files.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose using of the locale option to parse (and infer) decimals from JSON input. After the changes, `JacksonParser` converts input string to `BigDecimal` and to Spark's Decimal by using `java.text.DecimalFormat`. New behaviour can be switched off via SQL config `spark.sql.legacy.decimalParsing.enabled`.
## How was this patch tested?
Added 2 tests to `JsonExpressionsSuite` for the `en-US`, `ko-KR`, `ru-RU`, `de-DE` locales:
- Inferring decimal type using locale from JSON field values
- Converting JSON field values to specified decimal type using the locales.
Closes#23132 from MaxGekk/json-decimal-parsing-locale.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently duplicated map keys are not handled consistently. For example, map look up respects the duplicated key appears first, `Dataset.collect` only keeps the duplicated key appears last, `MapKeys` returns duplicated keys, etc.
This PR proposes to remove duplicated map keys with last wins policy, to follow Java/Scala and Presto. It only applies to built-in functions, as users can create map with duplicated map keys via private APIs anyway.
updated functions: `CreateMap`, `MapFromArrays`, `MapFromEntries`, `StringToMap`, `MapConcat`, `TransformKeys`.
For other places:
1. data source v1 doesn't have this problem, as users need to provide a java/scala map, which can't have duplicated keys.
2. data source v2 may have this problem. I've added a note to `ArrayBasedMapData` to ask the caller to take care of duplicated keys. In the future we should enforce it in the stable data APIs for data source v2.
3. UDF doesn't have this problem, as users need to provide a java/scala map. Same as data source v1.
4. file format. I checked all of them and only parquet does not enforce it. For backward compatibility reasons I change nothing but leave a note saying that the behavior will be undefined if users write map with duplicated keys to parquet files. Maybe we can add a config and fail by default if parquet files have map with duplicated keys. This can be done in followup.
## How was this patch tested?
updated tests and new tests
Closes#23124 from cloud-fan/map.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
a followup of https://github.com/apache/spark/pull/23043 . Add a test to show the minor behavior change introduced by #23043 , and add migration guide.
## How was this patch tested?
a new test
Closes#23141 from cloud-fan/follow.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Implement codegen for `LocalTableScanExec` and `ExistingRDDExec`. Refactor to share code between `LocalTableScanExec`, `ExistingRDDExec`, `InputAdapter` and `RowDataSourceScanExec`.
The difference in `doProduce` between these four was that `ExistingRDDExec` and `RowDataSourceScanExec` triggered adding an `UnsafeProjection`, while `InputAdapter` and `LocalTableScanExec` did not.
In the new trait `InputRDDCodegen` I added a flag `createUnsafeProjection` which the operators set accordingly.
Note: `LocalTableScanExec` explicitly creates its input as `UnsafeRows`, so it was obvious why it doesn't need an `UnsafeProjection`. But if an `InputAdapter` may take input that is `InternalRows` but not `UnsafeRows`, then I think it doesn't need an unsafe projection just because any other operator that is its parent would do that. That assumes that that any parent operator would always result in some `UnsafeProjection` being eventually added, and hence the output of the `WholeStageCodegen` unit would be `UnsafeRows`. If these assumptions hold, I think `createUnsafeProjection` could be set to `(parent == null)`.
Note: Do not codegen `LocalTableScanExec` when it's the only operator. `LocalTableScanExec` has optimized driver-only `executeCollect` and `executeTake` code paths that are used to return `Command` results without starting Spark Jobs. They can no longer be used if the `LocalTableScanExec` gets optimized.
## How was this patch tested?
Covered and used in existing tests.
Closes#23127 from juliuszsompolski/SPARK-26159.
Authored-by: Juliusz Sompolski <julek@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Create a new suite DataFrameSetOperationsSuite for the test cases of DataFrame/Dataset's set operations.
Also, add test cases of NULL handling for Array Except and Array Intersect.
## How was this patch tested?
N/A
Closes#23137 from gatorsmile/setOpsTest.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
SparkSQL doesn't support to encode `Option[Product]` as a top-level row now, because in SparkSQL entire top-level row can't be null.
However for use cases like Aggregator, it is reasonable to use `Option[Product]` as buffer and output column types. Due to above limitation, we don't do it for now.
This patch proposes to encode `Option[Product]` at top-level as single struct column. So we can work around the issue that entire top-level row can't be null.
To summarize encoding of `Product` and `Option[Product]`.
For `Product`, 1. at root level, the schema is all fields are flatten it into multiple columns. The `Product ` can't be null, otherwise it throws an exception.
```scala
val df = Seq((1 -> "a"), (2 -> "b")).toDF()
df.printSchema()
root
|-- _1: integer (nullable = false)
|-- _2: string (nullable = true)
```
2. At non-root level, `Product` is a struct type column.
```scala
val df = Seq((1, (1 -> "a")), (2, (2 -> "b")), (3, null)).toDF()
df.printSchema()
root
|-- _1: integer (nullable = false)
|-- _2: struct (nullable = true)
| |-- _1: integer (nullable = false)
| |-- _2: string (nullable = true)
```
For `Option[Product]`, 1. it was not supported at root level. After this change, it is a struct type column.
```scala
val df = Seq(Some(1 -> "a"), Some(2 -> "b"), None).toDF()
df.printSchema
root
|-- value: struct (nullable = true)
| |-- _1: integer (nullable = false)
| |-- _2: string (nullable = true)
```
2. At non-root level, it is also a struct type column.
```scala
val df = Seq((1, Some(1 -> "a")), (2, Some(2 -> "b")), (3, None)).toDF()
df.printSchema
root
|-- _1: integer (nullable = false)
|-- _2: struct (nullable = true)
| |-- _1: integer (nullable = false)
| |-- _2: string (nullable = true)
```
3. For use case like Aggregator, it was not supported too. After this change, we support to use `Option[Product]` as buffer/output column type.
```scala
val df = Seq(
OptionBooleanIntData("bob", Some((true, 1))),
OptionBooleanIntData("bob", Some((false, 2))),
OptionBooleanIntData("bob", None)).toDF()
val group = df
.groupBy("name")
.agg(OptionBooleanIntAggregator("isGood").toColumn.alias("isGood"))
group.printSchema
root
|-- name: string (nullable = true)
|-- isGood: struct (nullable = true)
| |-- _1: boolean (nullable = false)
| |-- _2: integer (nullable = false)
```
The buffer and output type of `OptionBooleanIntAggregator` is both `Option[(Boolean, Int)`.
## How was this patch tested?
Added test.
Closes#21732 from viirya/SPARK-24762.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR is to add back `unionAll`, which is widely used. The name is also consistent with our ANSI SQL. We also have the corresponding `intersectAll` and `exceptAll`, which were introduced in Spark 2.4.
## How was this patch tested?
Added a test case in DataFrameSuite
Closes#23131 from gatorsmile/addBackUnionAll.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The DOI foundation recommends [this new resolver](https://www.doi.org/doi_handbook/3_Resolution.html#3.8). Accordingly, this PR re`sed`s all static DOI links ;-)
## How was this patch tested?
It wasn't, since it seems as safe as a "[typo fix](https://spark.apache.org/contributing.html)".
In case any of the files is included from other projects, and should be updated there, please let me know.
Closes#23129 from katrinleinweber/resolve-DOIs-securely.
Authored-by: Katrin Leinweber <9948149+katrinleinweber@users.noreply.github.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This patch defines an internal Spark interface for reporting shuffle metrics and uses that in shuffle reader. Before this patch, shuffle metrics is tied to a specific implementation (using a thread local temporary data structure and accumulators). After this patch, callers that define their own shuffle RDDs can create a custom metrics implementation.
With this patch, we would be able to create a better metrics for the SQL layer, e.g. reporting shuffle metrics in the SQL UI, for each exchange operator.
Note that I'm separating read side and write side implementations, as they are very different, to simplify code review. Write side change is at https://github.com/apache/spark/pull/23106
## How was this patch tested?
No behavior change expected, as it is a straightforward refactoring. Updated all existing test cases.
Closes#23105 from rxin/SPARK-26140.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In the PR, I propose new options for CSV datasource - `lineSep` similar to Text and JSON datasource. The option allows to specify custom line separator of maximum length of 2 characters (because of a restriction in `uniVocity` parser). New option can be used in reading and writing CSV files.
## How was this patch tested?
Added a few tests with custom `lineSep` for enabled/disabled `multiLine` in read as well as tests in write. Also I added roundtrip tests.
Closes#23080 from MaxGekk/csv-line-sep.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
the pr #20014 which introduced `SparkOutOfMemoryError` to avoid killing the entire executor when an `OutOfMemoryError `is thrown.
so apply for memory using `MemoryConsumer. allocatePage `when catch exception, use `SparkOutOfMemoryError `instead of `OutOfMemoryError`
## How was this patch tested?
N / A
Closes#23084 from heary-cao/SparkOutOfMemoryError.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
GROUP BY treats -0.0 and 0.0 as different values which is unlike hive's behavior.
In addition current behavior with codegen is unpredictable (see example in JIRA ticket).
## What changes were proposed in this pull request?
In Platform.putDouble/Float() checking if the value is -0.0, and if so replacing with 0.0.
This is used by UnsafeRow so it won't have -0.0 values.
## How was this patch tested?
Added tests
Closes#23043 from adoron/adoron-spark-26021-replace-minus-zero-with-zero.
Authored-by: Alon Doron <adoron@palantir.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The corrupt column specified via JSON/CSV option *columnNameOfCorruptRecord* must have the `string` type and be `nullable`. This has been already checked in `DataFrameReader`.`csv`/`json` and in `Json`/`CsvFileFormat` but not in `from_json`/`from_csv`. The PR adds such checks inside functions as well.
## How was this patch tested?
Added tests to `Json`/`CsvExpressionSuite` for checking type of the corrupt column. They don't check the `nullable` property because `schema` is forcibly casted to nullable.
Closes#23070 from MaxGekk/verify-corrupt-column-csv-json.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When doing typed aggregation on a Dataset, for struct key type, the key attribute is named as "key". But for non-struct type, the key attribute is named as "value". This key attribute should also be named as "key" for non-struct type.
## How was this patch tested?
Added test.
Closes#23054 from viirya/SPARK-26085.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
An input without valid JSON tokens on the root level will be treated as a bad record, and handled according to `mode`. Previously such input was converted to `null`. After the changes, the input is converted to a row with `null`s in the `PERMISSIVE` mode according the schema. This allows to remove a code in the `from_json` function which can produce `null` as result rows.
## How was this patch tested?
It was tested by existing test suites. Some of them I have to modify (`JsonSuite` for example) because previously bad input was just silently ignored. For now such input is handled according to specified `mode`.
Closes#22938 from MaxGekk/json-nulls.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose:
- new SQL config `spark.sql.debug.maxToStringFields` to control maximum number fields up to which `truncatedString` cuts its input sequences.
- Moving `truncatedString` out of `core` to `sql/catalyst` because it is used only in the `sql/catalyst` packages for restricting number of fields converted to strings from `TreeNode` and expressions of`StructType`.
## How was this patch tested?
Added a test to `QueryExecutionSuite` to check that `spark.sql.debug.maxToStringFields` impacts to behavior of `truncatedString`.
Closes#23039 from MaxGekk/truncated-string-catalyst.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
We currently don't have good visibility into query planning time (analysis vs optimization vs physical planning). This patch adds a simple utility to track the runtime of various rules and various planning phases.
## How was this patch tested?
Added unit tests and end-to-end integration tests.
Closes#23096 from rxin/SPARK-26129.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request?
This change fixes a particular scenario where default spark SQL can't encode (thrift) types that are generated by twitter scrooge. These types are a trait that extends `scala.ProductX` with a constructor defined only in a companion object, rather than a actual case class. The actual case class used is child class, but that type is almost never referred to in code. The type has no corresponding constructor symbol and causes an exception. For all other purposes, these classes act just like case classes, so it is unfortunate that spark SQL can't serialize them nicely as it can actual case classes. For an full example of a scrooge codegen class, see https://gist.github.com/anonymous/ba13d4b612396ca72725eaa989900314.
This change catches the case where the type has no constructor but does have an `apply` method on the type's companion object. This allows for thrift types to be serialized/deserialized with implicit encoders the same way as normal case classes. This fix had to be done in three places where the constructor is assumed to be an actual constructor:
1) In serializing, determining the schema for the dataframe relies on inspecting its constructor (`ScalaReflection.constructParams`). Here we fall back to using the companion constructor arguments.
2) In deserializing or evaluating, in the java codegen ( `NewInstance.doGenCode`), the type couldn't be constructed with the new keyword. If there is no constructor, we change the constructor call to try the companion constructor.
3) In deserializing or evaluating, without codegen, the constructor is directly invoked (`NewInstance.constructor`). This was fixed with scala reflection to get the actual companion apply method.
The return type of `findConstructor` was changed because the companion apply method constructor can't be represented as a `java.lang.reflect.Constructor`.
There might be situations in which this approach would also fail in a new way, but it does at a minimum work for the specific scrooge example and will not impact cases that were already succeeding prior to this change
Note: this fix does not enable using scrooge thrift enums, additional work for this is necessary. With this patch, it seems like you could patch `com.twitter.scrooge.ThriftEnum` to extend `_root_.scala.Product1[Int]` with `def _1 = value` to get spark's implicit encoders to handle enums, but I've yet to use this method myself.
Note: I previously opened a PR for this issue, but only was able to fix case 1) there: https://github.com/apache/spark/pull/18766
## How was this patch tested?
I've fixed all 3 cases and added two tests that use a case class that is similar to scrooge generated one. The test in ScalaReflectionSuite checks 1), and the additional asserting in ObjectExpressionsSuite checks 2) and 3).
Closes#23062 from drewrobb/SPARK-8288.
Authored-by: Drew Robb <drewrobb@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to pass the CSV option `encoding`/`charset` to `uniVocity` parser to allow parsing CSV files in different encodings when `multiLine` is enabled. The value of the option is passed to the `beginParsing` method of `CSVParser`.
## How was this patch tested?
Added new test to `CSVSuite` for different encodings and enabled/disabled header.
Closes#23091 from MaxGekk/csv-miltiline-encoding.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Extend the `ReplaceNullWithFalse` optimizer rule introduced in SPARK-25860 (https://github.com/apache/spark/pull/22857) to also support optimizing predicates in higher-order functions of `ArrayExists`, `ArrayFilter`, `MapFilter`.
Also rename the rule to `ReplaceNullWithFalseInPredicate` to better reflect its intent.
Example:
```sql
select filter(a, e -> if(e is null, null, true)) as b from (
select array(null, 1, null, 3) as a)
```
The optimized logical plan:
**Before**:
```
== Optimized Logical Plan ==
Project [filter([null,1,null,3], lambdafunction(if (isnull(lambda e#13)) null else true, lambda e#13, false)) AS b#9]
+- OneRowRelation
```
**After**:
```
== Optimized Logical Plan ==
Project [filter([null,1,null,3], lambdafunction(if (isnull(lambda e#13)) false else true, lambda e#13, false)) AS b#9]
+- OneRowRelation
```
## How was this patch tested?
Added new unit test cases to the `ReplaceNullWithFalseInPredicateSuite` (renamed from `ReplaceNullWithFalseSuite`).
Closes#23079 from rednaxelafx/catalyst-master.
Authored-by: Kris Mok <kris.mok@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The build has a lot of deprecation warnings. Some are new in Scala 2.12 and Java 11. We've fixed some, but I wanted to take a pass at fixing lots of easy miscellaneous ones here.
They're too numerous and small to list here; see the pull request. Some highlights:
- `BeanInfo` is deprecated in 2.12, and BeanInfo classes are pretty ancient in Java. Instead, case classes can explicitly declare getters
- Eta expansion of zero-arg methods; foo() becomes () => foo() in many cases
- Floating-point Range is inexact and deprecated, like 0.0 to 100.0 by 1.0
- finalize() is finally deprecated (just needs to be suppressed)
- StageInfo.attempId was deprecated and easiest to remove here
I'm not now going to touch some chunks of deprecation warnings:
- Parquet deprecations
- Hive deprecations (particularly serde2 classes)
- Deprecations in generated code (mostly Thriftserver CLI)
- ProcessingTime deprecations (we may need to revive this class as internal)
- many MLlib deprecations because they concern methods that may be removed anyway
- a few Kinesis deprecations I couldn't figure out
- Mesos get/setRole, which I don't know well
- Kafka/ZK deprecations (e.g. poll())
- Kinesis
- a few other ones that will probably resolve by deleting a deprecated method
## How was this patch tested?
Existing tests, including manual testing with the 2.11 build and Java 11.
Closes#23065 from srowen/SPARK-26090.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Due to implementation limitation, currently Spark can't compare or do equality check between map types. As a result, map values can't appear in EQUAL or comparison expressions, can't be grouping key, etc.
The more important thing is, map loop up needs to do equality check of the map key, and thus can't support map as map key when looking up values from a map. Thus it's not useful to have map as map key.
This PR proposes to stop users from creating maps using map type as key. The list of expressions that are updated: `CreateMap`, `MapFromArrays`, `MapFromEntries`, `MapConcat`, `TransformKeys`. I manually checked all the places that create `MapType`, and came up with this list.
Note that, maps with map type key still exist, via reading from parquet files, converting from scala/java map, etc. This PR is not to completely forbid map as map key, but to avoid creating it by Spark itself.
Motivation: when I was trying to fix the duplicate key problem, I found it's impossible to do it with map type map key. I think it's reasonable to avoid map type map key for builtin functions.
## How was this patch tested?
updated test
Closes#23045 from cloud-fan/map-key.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Following [SPARK-26024](https://issues.apache.org/jira/browse/SPARK-26024), I noticed the number of elements in each partition after repartitioning using `df.repartitionByRange` can vary for the same setup:
```scala
// Shuffle numbers from 0 to 1000, and make a DataFrame
val df = Random.shuffle(0.to(1000)).toDF("val")
// Repartition it using 3 partitions
// Sum up number of elements in each partition, and collect it.
// And do it several times
for (i <- 0 to 9) {
var counts = df.repartitionByRange(3, col("val"))
.mapPartitions{part => Iterator(part.size)}
.collect()
println(counts.toList)
}
// -> the number of elements in each partition varies
```
This is expected as for performance reasons this method uses sampling to estimate the ranges (with default size of 100). Hence, the output may not be consistent, since sampling can return different values. But documentation was not mentioning it at all, leading to misunderstanding.
## What changes were proposed in this pull request?
Update the documentation (Spark & PySpark) to mention the impact of `spark.sql.execution.rangeExchange.sampleSizePerPartition` on the resulting partitioned DataFrame.
Closes#23025 from JulienPeloton/SPARK-26024.
Authored-by: Julien <peloton@lal.in2p3.fr>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The following 5 functions were removed from branch-2.4:
- map_entries
- map_filter
- transform_values
- transform_keys
- map_zip_with
We should update the since version to 3.0.0.
## How was this patch tested?
Existing tests.
Closes#23082 from ueshin/issues/SPARK-26112/since.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This restores scaladoc artifact generation, which got dropped with the Scala 2.12 update. The change looks large, but is almost all due to needing to make the InterfaceStability annotations top-level classes (i.e. `InterfaceStability.Stable` -> `Stable`), unfortunately. A few inner class references had to be qualified too.
Lots of scaladoc warnings now reappear. We can choose to disable generation by default and enable for releases, later.
## How was this patch tested?
N/A; build runs scaladoc now.
Closes#23069 from srowen/SPARK-26026.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Events are dispatched on a separate thread, so need to wait for them to be
actually delivered before checking that the listener got them.
Closes#23050 from vanzin/SPARK-26079.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Use CheckpointFileManager to write the streaming `metadata` file so that the `metadata` file will never be a partial file.
## How was this patch tested?
Jenkins
Closes#23060 from zsxwing/SPARK-26092.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
This is a follow-up of #20393.
We should read the conf `"spark.sql.sort.enableRadixSort"` from `SQLConf` instead of `SparkConf`, i.e., use `SQLConf.get.enableRadixSort` instead of `SparkEnv.get.conf.get(SQLConf.RADIX_SORT_ENABLED)`, otherwise the config is never read.
## How was this patch tested?
Existing tests.
Closes#23046 from ueshin/issues/SPARK-23207/conf.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In SPARK-24865 `AnalysisBarrier` was removed and in order to improve resolution speed, the `analyzed` flag was (re-)introduced in order to process only plans which are not yet analyzed. This should not be the case when performing attribute deduplication as in that case we need to transform also the plans which were already analyzed, otherwise we can miss to rewrite some attributes leading to invalid plans.
## How was this patch tested?
added UT
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23035 from mgaido91/SPARK-26057.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR makes Spark's default Scala version as 2.12, and Scala 2.11 will be the alternative version. This implies that Scala 2.12 will be used by our CI builds including pull request builds.
We'll update the Jenkins to include a new compile-only jobs for Scala 2.11 to ensure the code can be still compiled with Scala 2.11.
## How was this patch tested?
existing tests
Closes#22967 from dbtsai/scala2.12.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Add scala and java lint check rules to ban the usage of `throw new xxxErrors` and fix up all exists instance followed by https://github.com/apache/spark/pull/22989#issuecomment-437939830. See more details in https://github.com/apache/spark/pull/22969.
## How was this patch tested?
Local test with lint-scala and lint-java.
Closes#22989 from xuanyuanking/SPARK-25986.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
As initializing lazy vals shares the same lock, a thread is trying to initialize `executedPlan` when `isRDD` is running, this thread will hang forever.
This PR just materializes `executedPlan` so that accessing it when `toRdd` is running doesn't need to wait for a lock
## How was this patch tested?
Jenkins
Closes#23023 from zsxwing/SPARK-26042.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
This is the official first attempt to break huge single `tests.py` file - I did it locally before few times and gave up for some reasons. Now, currently it really makes the unittests super hard to read and difficult to check. To me, it even bothers me to to scroll down the big file. It's one single 7000 lines file!
This is not only readability issue. Since one big test takes most of tests time, the tests don't run in parallel fully - although it will costs to start and stop the context.
We could pick up one example and follow. Given my investigation, the current style looks closer to NumPy structure and looks easier to follow. Please see https://github.com/numpy/numpy/tree/master/numpy.
Basically this PR proposes to break down `pyspark/sql/tests.py` into ...:
```bash
pyspark
...
├── sql
...
│ ├── tests # Includes all tests broken down from 'pyspark/sql/tests.py'
│ │ │ # Each matchs to module in 'pyspark/sql'. Additionally, some logical group can
│ │ │ # be added. For instance, 'test_arrow.py', 'test_datasources.py' ...
│ │ ├── __init__.py
│ │ ├── test_appsubmit.py
│ │ ├── test_arrow.py
│ │ ├── test_catalog.py
│ │ ├── test_column.py
│ │ ├── test_conf.py
│ │ ├── test_context.py
│ │ ├── test_dataframe.py
│ │ ├── test_datasources.py
│ │ ├── test_functions.py
│ │ ├── test_group.py
│ │ ├── test_pandas_udf.py
│ │ ├── test_pandas_udf_grouped_agg.py
│ │ ├── test_pandas_udf_grouped_map.py
│ │ ├── test_pandas_udf_scalar.py
│ │ ├── test_pandas_udf_window.py
│ │ ├── test_readwriter.py
│ │ ├── test_serde.py
│ │ ├── test_session.py
│ │ ├── test_streaming.py
│ │ ├── test_types.py
│ │ ├── test_udf.py
│ │ └── test_utils.py
...
├── testing # Includes testing utils that can be used in unittests.
│ ├── __init__.py
│ └── sqlutils.py
...
```
## How was this patch tested?
Existing tests should cover.
`cd python` and `./run-tests-with-coverage`. Manually checked they are actually being ran.
Each test (not officially) can be ran via:
```
SPARK_TESTING=1 ./bin/pyspark pyspark.sql.tests.test_pandas_udf_scalar
```
Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`.
Closes#23021 from HyukjinKwon/SPARK-25344.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
`Dataset.groupByKey` will bring in new attributes from serializer. If key type is the same as original Dataset's object type, they have same serializer output and so the attribute names will conflict.
This won't be a problem at most of cases, if we don't refer conflict attributes:
```scala
val ds: Dataset[(ClassData, Long)] = Seq(ClassData("one", 1), ClassData("two", 2)).toDS()
.map(c => ClassData(c.a, c.b + 1))
.groupByKey(p => p).count()
```
But if we use conflict attributes, `Analyzer` will complain about ambiguous references:
```scala
val ds = Seq(1, 2, 3).toDS()
val agg = ds.groupByKey(_ >= 2).agg(sum("value").as[Long], sum($"value" + 1).as[Long])
```
We have discussed two fixes https://github.com/apache/spark/pull/22944#discussion_r230977212:
1. Implicitly add alias to key attribute:
Works for primitive type. But for product type, we can't implicitly add aliases to key attributes because we might need to access key attributes by names in methods like `mapGroups`.
2. Detect conflict from key attributes and warn users to add alias manually
This might work, but needs to add some hacks to Analyzer or AttributeSeq.resolve.
This patch applies another simpler fix. We resolve aggregate expressions with `AppendColumns`'s children, instead of `AppendColumns`. `AppendColumns`'s output contains its children's output and serializer output, aggregate expressions shouldn't use serializer output.
## How was this patch tested?
Added test.
Closes#22944 from viirya/dataset_agg.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
An expressions with a subquery can be pushed down as a data source filter. Despite the filter is not actively used, this causes anyway a re-execution of the subquery becuase the `ReuseSubquery` optimization rule is ineffective in this case.
The PR avoids this problem by forbidding the push down of filters containing a subquery.
## How was this patch tested?
added UT
Closes#22518 from mgaido91/SPARK-25482.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
When sorting rows, ShuffleExchangeExec uses the entire row instead of just the columns references in SortOrder to create the RangePartitioner. This causes the RangePartitioner to sample entire rows to create rangeBounds and can cause OOM issues on the driver when rows contain large fields.
This change creates a projection and only use columns involved in the SortOrder for the RangePartitioner
## How was this patch tested?
Existing tests in spark-sql.
Plus
Started a local spark-shell with a small spark.driver.maxResultSize:
```
spark-shell --master 'local[16]' --conf spark.driver.maxResultSize=128M --driver-memory 4g
```
and ran the following script:
```
import com.google.common.io.Files
import org.apache.spark.SparkContext
import org.apache.spark.sql.SparkSession
import scala.util.Random
transient val sc = SparkContext.getOrCreate()
transient val spark = SparkSession.builder().getOrCreate()
import spark.implicits._
val path = Files.createTempDir().toString
// this creates a dataset with 1024 entries, each 1MB in size, across 16 partitions
sc.parallelize(0 until (1 << 10), sc.defaultParallelism).
map(_ => Array.fill(1 << 18)(Random.nextInt)).
toDS.
write.mode("overwrite").parquet(path)
spark.read.parquet(path).
orderBy('value (0)).
write.mode("overwrite").parquet(s"$path-sorted")
spark.read.parquet(s"$path-sorted").show
```
execution would fail when initializing RangePartitioner without this change.
execution succeeds and generates a correctly sorted dataset with this change.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22961 from mu5358271/sort-improvement.
Authored-by: mu5358271 <shuheng.dai@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose new method for debugging queries by dumping info about their execution to a file. It saves logical, optimized and physical plan similar to the `explain()` method + generated code. One of the advantages of the method over `explain` is it does not materializes full output as one string in memory which can cause OOMs.
## How was this patch tested?
Added a few tests to `QueryExecutionSuite` to check positive and negative scenarios.
Closes#23018 from MaxGekk/truncated-plan-to-file.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
In `SQLAppStatusListener.aggregateMetrics`, we use the `metricIds` only to filter the relevant metrics. And this is a Seq which is also sorted. When there are many metrics involved, this can be pretty inefficient. The PR proposes to use a Set for it.
## How was this patch tested?
NA
Closes#23002 from mgaido91/SPARK-26003.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Passing current value of SQL config `spark.sql.columnNameOfCorruptRecord` to `CSVOptions` inside of `DataFrameReader`.`csv()`.
## How was this patch tested?
Added a test where default value of `spark.sql.columnNameOfCorruptRecord` is changed.
Closes#23006 from MaxGekk/csv-corrupt-sql-config.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Deprecated in Java 11, replace Class.newInstance with Class.getConstructor.getInstance, and primtive wrapper class constructors with valueOf or equivalent
## How was this patch tested?
Existing tests.
Closes#22988 from srowen/SPARK-25984.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Currently, Spark writes Spark version number into Hive Table properties with `spark.sql.create.version`.
```
parameters:{
spark.sql.sources.schema.part.0={
"type":"struct",
"fields":[{"name":"a","type":"integer","nullable":true,"metadata":{}}]
},
transient_lastDdlTime=1541142761,
spark.sql.sources.schema.numParts=1,
spark.sql.create.version=2.4.0
}
```
This PR aims to write Spark versions to ORC/Parquet file metadata with `org.apache.spark.sql.create.version` because we used `org.apache.` prefix in Parquet metadata already. It's different from Hive Table property key `spark.sql.create.version`, but it seems that we cannot change Hive Table property for backward compatibility.
After this PR, ORC and Parquet file generated by Spark will have the following metadata.
**ORC (`native` and `hive` implmentation)**
```
$ orc-tools meta /tmp/o
File Version: 0.12 with ...
...
User Metadata:
org.apache.spark.sql.create.version=3.0.0
```
**PARQUET**
```
$ parquet-tools meta /tmp/p
...
creator: parquet-mr version 1.10.0 (build 031a6654009e3b82020012a18434c582bd74c73a)
extra: org.apache.spark.sql.create.version = 3.0.0
extra: org.apache.spark.sql.parquet.row.metadata = {"type":"struct","fields":[{"name":"id","type":"long","nullable":false,"metadata":{}}]}
```
## How was this patch tested?
Pass the Jenkins with newly added test cases.
This closes#22255.
Closes#22932 from dongjoon-hyun/SPARK-25102.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Very minor parser bug, but possibly problematic for code-generated queries:
Consider the following two queries:
```
SELECT avg(k) OVER (w) FROM kv WINDOW w AS (PARTITION BY v ORDER BY w) ORDER BY 1
```
and
```
SELECT avg(k) OVER w FROM kv WINDOW w AS (PARTITION BY v ORDER BY w) ORDER BY 1
```
The former, with parens around the OVER condition, fails to parse while the latter, without parens, succeeds:
```
Error in SQL statement: ParseException:
mismatched input '(' expecting {<EOF>, ',', 'FROM', 'WHERE', 'GROUP', 'ORDER', 'HAVING', 'LIMIT', 'LATERAL', 'WINDOW', 'UNION', 'EXCEPT', 'MINUS', 'INTERSECT', 'SORT', 'CLUSTER', 'DISTRIBUTE'}(line 1, pos 19)
== SQL ==
SELECT avg(k) OVER (w) FROM kv WINDOW w AS (PARTITION BY v ORDER BY w) ORDER BY 1
-------------------^^^
```
This was found when running the cockroach DB tests.
I tried PostgreSQL, The SQL with parentheses is also workable.
## How was this patch tested?
Unit test
Closes#22987 from gengliangwang/windowParentheses.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
When the queries do not use the column names with the same case, users might hit various errors. Below is a typical test failure they can hit.
```
Expected only partition pruning predicates: ArrayBuffer(isnotnull(tdate#237), (cast(tdate#237 as string) >= 2017-08-15));
org.apache.spark.sql.AnalysisException: Expected only partition pruning predicates: ArrayBuffer(isnotnull(tdate#237), (cast(tdate#237 as string) >= 2017-08-15));
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogUtils$.prunePartitionsByFilter(ExternalCatalogUtils.scala:146)
at org.apache.spark.sql.catalyst.catalog.InMemoryCatalog.listPartitionsByFilter(InMemoryCatalog.scala:560)
at org.apache.spark.sql.catalyst.catalog.SessionCatalog.listPartitionsByFilter(SessionCatalog.scala:925)
```
## How was this patch tested?
Added two test cases.
Closes#22990 from gatorsmile/fix1283.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Remove `BenchmarkWithCodegen` as we don't use it anymore.
More details: https://github.com/apache/spark/pull/22484#discussion_r221397904
## How was this patch tested?
N/A
Closes#22985 from wangyum/SPARK-25510.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to add new option `locale` into CSVOptions/JSONOptions to make parsing date/timestamps in local languages possible. Currently the locale is hard coded to `Locale.US`.
## How was this patch tested?
Added two tests for parsing a date from CSV/JSON - `ноя 2018`.
Closes#22951 from MaxGekk/locale.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
1. OrcReadBenchmark is under hive module, so the way to run it should be
```
build/sbt "hive/test:runMain <this class>"
```
2. The benchmark "String with Nulls Scan" should be with case "String with Nulls Scan(5%/50%/95%)", not "(0.05%/0.5%/0.95%)"
3. Add the null value percentages in the test case names of DataSourceReadBenchmark, for the benchmark "String with Nulls Scan" .
## How was this patch tested?
Re-run benchmarks
Closes#22965 from gengliangwang/fixHiveOrcReadBenchmark.
Lead-authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Co-authored-by: Gengliang Wang <ltnwgl@gmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
#22970 fixed Scala 2.12 build error, and this PR updates the function according to the review comments.
## How was this patch tested?
This is also manually tested with Scala 2.12 build.
Closes#22978 from dongjoon-hyun/SPARK-25676-3.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up pr of #20014 which introduced `SparkOutOfMemoryError` to avoid killing the entire executor when an `OutOfMemoryError` is thrown.
We should throw `SparkOutOfMemoryError` in `HashAggregateExec`, too.
## How was this patch tested?
Existing tests.
Closes#22969 from ueshin/issues/SPARK-22827/oome.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Currently, `SQLQueryTestSuite` is sensitive in terms of the bytes of parquet files in table partitions. If we change the default file format (from Parquet to ORC) or update the metadata of them, the test case should be changed accordingly. This PR aims to make `SQLQueryTestSuite` more robust by ignoring the partition byte statistics.
```
-Partition Statistics 1144 bytes, 2 rows
+Partition Statistics [not included in comparison] bytes, 2 rows
```
## How was this patch tested?
Pass the Jenkins with the newly updated test cases.
Closes#22972 from dongjoon-hyun/SPARK-25971.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to port existing JSON tests from `JsonFunctionsSuite` that are applicable for CSV, and put them to `CsvFunctionsSuite`. In particular:
- roundtrip `from_csv` to `to_csv`, and `to_csv` to `from_csv`
- using `schema_of_csv` in `from_csv`
- Java API `from_csv`
- using `from_csv` and `to_csv` in exprs.
Closes#22960 from MaxGekk/csv-additional-tests.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The PR fixes an issue when the corrupt record column specified via `spark.sql.columnNameOfCorruptRecord` or JSON options `columnNameOfCorruptRecord` is propagated to JacksonParser, and returned row breaks an assumption in `FailureSafeParser` that the row must contain only actual data. The issue is fixed by passing actual schema without the corrupt record field into `JacksonParser`.
## How was this patch tested?
Added a test with the corrupt record column in the middle of user's schema.
Closes#22958 from MaxGekk/from_json-corrupt-record-schema.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR fixes the Scala-2.12 build.
## How was this patch tested?
Manual build with Scala-2.12 profile.
Closes#22970 from dongjoon-hyun/SPARK-25676-2.12.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
- Remove some AccumulableInfo .apply() methods
- Remove non-label-specific multiclass precision/recall/fScore in favor of accuracy
- Remove toDegrees/toRadians in favor of degrees/radians (SparkR: only deprecated)
- Remove approxCountDistinct in favor of approx_count_distinct (SparkR: only deprecated)
- Remove unused Python StorageLevel constants
- Remove Dataset unionAll in favor of union
- Remove unused multiclass option in libsvm parsing
- Remove references to deprecated spark configs like spark.yarn.am.port
- Remove TaskContext.isRunningLocally
- Remove ShuffleMetrics.shuffle* methods
- Remove BaseReadWrite.context in favor of session
- Remove Column.!== in favor of =!=
- Remove Dataset.explode
- Remove Dataset.registerTempTable
- Remove SQLContext.getOrCreate, setActive, clearActive, constructors
Not touched yet
- everything else in MLLib
- HiveContext
- Anything deprecated more recently than 2.0.0, generally
## How was this patch tested?
Existing tests
Closes#22921 from srowen/SPARK-25908.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
JVMs can't allocate arrays of length exactly Int.MaxValue, so ensure we never try to allocate an array that big. This commit changes some defaults & configs to gracefully fallover to something that doesn't require one large array in some cases; in other cases it simply improves an error message for cases which will still fail.
Closes#22818 from squito/SPARK-25827.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
Fix for `CsvToStructs` to take into account SQL config `spark.sql.columnNameOfCorruptRecord` similar to `from_json`.
## How was this patch tested?
Added new test where `spark.sql.columnNameOfCorruptRecord` is set to corrupt column name different from default.
Closes#22956 from MaxGekk/csv-tests.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Refactor BenchmarkWideTable to use main method.
Generate benchmark result:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.WideTableBenchmark"
```
## How was this patch tested?
manual tests
Closes#22823 from yucai/BenchmarkWideTable.
Lead-authored-by: yucai <yyu1@ebay.com>
Co-authored-by: Yucai Yu <yucai.yu@foxmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Upgrade ASM to 7.x to support JDK11
## How was this patch tested?
Existing tests.
Closes#22953 from dbtsai/asm7.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Currently definitions of config entries in `core` module are in several files separately. We should move them into `internal/config` to be easy to manage.
## How was this patch tested?
Existing tests.
Closes#22928 from ueshin/issues/SPARK-25926/single_config_file.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to extend `UnaryExecNode` instead of `SparkPlan` by unary nodes.
Closes#22925 from MaxGekk/unary-exec-node.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
New functions takes a struct and converts it to a CSV strings using passed CSV options. It accepts the same CSV options as CSV data source does.
## How was this patch tested?
Added `CsvExpressionsSuite`, `CsvFunctionsSuite` as well as R, Python and SQL tests similar to tests for `to_json()`
Closes#22626 from MaxGekk/to_csv.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Added new benchmark which forcibly invokes Jackson parser to check overhead of its creation for short and wide JSON strings. Existing benchmarks do not allow to check that due to an optimisation introduced by #21909 for empty schema pushed down to JSON datasource. The `count()` action passes empty schema as required schema to the datasource, and Jackson parser is not created at all in that case.
Besides of new benchmark I also refactored existing benchmarks:
- Added `numIters` to control number of iteration in each benchmark
- Renamed `JSON per-line parsing` -> `count a short column`, `JSON parsing of wide lines` -> `count a wide column`, and `Count a dataset with 10 columns` -> `Select a subset of 10 columns`.
Closes#22920 from MaxGekk/json-benchmark-follow-up.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
'refreshInterval' is not used any where in the headerSparkPage method. So, we don't need to pass the parameter while calling the 'headerSparkPage' method.
## How was this patch tested?
Existing tests
Closes#22864 from shahidki31/unusedCode.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Unfortunately, it seems that we missed this in 2.4.0. In Spark 2.4, if the default file system is not the local file system, `LOAD DATA LOCAL INPATH` only works in case of absolute paths. This PR aims to fix it to support relative paths. This is a regression in 2.4.0.
```scala
$ ls kv1.txt
kv1.txt
scala> spark.sql("LOAD DATA LOCAL INPATH 'kv1.txt' INTO TABLE t")
org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: kv1.txt;
```
## How was this patch tested?
Pass the Jenkins
Closes#22927 from dongjoon-hyun/SPARK-LOAD.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
When `SHOW CREATE TABLE` for Datasource tables, we are missing `TBLPROPERTIES` and `COMMENT`, and we should use `LOCATION` instead of path in `OPTION`.
## How was this patch tested?
Splitted `ShowCreateTableSuite` to confirm to work with both `InMemoryCatalog` and `HiveExternalCatalog`, and added some tests.
Closes#22892 from ueshin/issues/SPARK-25884/show_create_table.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to add new function - *schema_of_csv()* which infers schema of CSV string literal. The result of the function is a string containing a schema in DDL format. For example:
```sql
select schema_of_csv('1|abc', map('delimiter', '|'))
```
```
struct<_c0:int,_c1:string>
```
## How was this patch tested?
Added new tests to `CsvFunctionsSuite`, `CsvExpressionsSuite` and SQL tests to `csv-functions.sql`
Closes#22666 from MaxGekk/schema_of_csv-function.
Lead-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR proposes a new optimization rule that replaces `Literal(null, _)` with `FalseLiteral` in conditions in `Join` and `Filter`, predicates in `If`, conditions in `CaseWhen`.
The idea is that some expressions evaluate to `false` if the underlying expression is `null` (as an example see `GeneratePredicate$create` or `doGenCode` and `eval` methods in `If` and `CaseWhen`). Therefore, we can replace `Literal(null, _)` with `FalseLiteral`, which can lead to more optimizations later on.
Let’s consider a few examples.
```
val df = spark.range(1, 100).select($"id".as("l"), ($"id" > 50).as("b"))
df.createOrReplaceTempView("t")
df.createOrReplaceTempView("p")
```
**Case 1**
```
spark.sql("SELECT * FROM t WHERE if(l > 10, false, NULL)").explain(true)
// without the new rule
…
== Optimized Logical Plan ==
Project [id#0L AS l#2L, cast(id#0L as string) AS s#3]
+- Filter if ((id#0L > 10)) false else null
+- Range (1, 100, step=1, splits=Some(12))
== Physical Plan ==
*(1) Project [id#0L AS l#2L, cast(id#0L as string) AS s#3]
+- *(1) Filter if ((id#0L > 10)) false else null
+- *(1) Range (1, 100, step=1, splits=12)
// with the new rule
…
== Optimized Logical Plan ==
LocalRelation <empty>, [l#2L, s#3]
== Physical Plan ==
LocalTableScan <empty>, [l#2L, s#3]
```
**Case 2**
```
spark.sql("SELECT * FROM t WHERE CASE WHEN l < 10 THEN null WHEN l > 40 THEN false ELSE null END”).explain(true)
// without the new rule
...
== Optimized Logical Plan ==
Project [id#0L AS l#2L, cast(id#0L as string) AS s#3]
+- Filter CASE WHEN (id#0L < 10) THEN null WHEN (id#0L > 40) THEN false ELSE null END
+- Range (1, 100, step=1, splits=Some(12))
== Physical Plan ==
*(1) Project [id#0L AS l#2L, cast(id#0L as string) AS s#3]
+- *(1) Filter CASE WHEN (id#0L < 10) THEN null WHEN (id#0L > 40) THEN false ELSE null END
+- *(1) Range (1, 100, step=1, splits=12)
// with the new rule
...
== Optimized Logical Plan ==
LocalRelation <empty>, [l#2L, s#3]
== Physical Plan ==
LocalTableScan <empty>, [l#2L, s#3]
```
**Case 3**
```
spark.sql("SELECT * FROM t JOIN p ON IF(t.l > p.l, null, false)").explain(true)
// without the new rule
...
== Optimized Logical Plan ==
Join Inner, if ((l#2L > l#37L)) null else false
:- Project [id#0L AS l#2L, cast(id#0L as string) AS s#3]
: +- Range (1, 100, step=1, splits=Some(12))
+- Project [id#0L AS l#37L, cast(id#0L as string) AS s#38]
+- Range (1, 100, step=1, splits=Some(12))
== Physical Plan ==
BroadcastNestedLoopJoin BuildRight, Inner, if ((l#2L > l#37L)) null else false
:- *(1) Project [id#0L AS l#2L, cast(id#0L as string) AS s#3]
: +- *(1) Range (1, 100, step=1, splits=12)
+- BroadcastExchange IdentityBroadcastMode
+- *(2) Project [id#0L AS l#37L, cast(id#0L as string) AS s#38]
+- *(2) Range (1, 100, step=1, splits=12)
// with the new rule
...
== Optimized Logical Plan ==
LocalRelation <empty>, [l#2L, s#3, l#37L, s#38]
```
## How was this patch tested?
This PR comes with a set of dedicated tests.
Closes#22857 from aokolnychyi/spark-25860.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Refactor BuiltInDataSourceWriteBenchmark, DataSourceWriteBenchmark and AvroWriteBenchmark to use main method.
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.BuiltInDataSourceWriteBenchmark"
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "avro/test:runMain org.apache.spark.sql.execution.benchmark.AvroWriteBenchmark"
```
## How was this patch tested?
manual tests
Closes#22861 from yucai/BuiltInDataSourceWriteBenchmark.
Lead-authored-by: yucai <yyu1@ebay.com>
Co-authored-by: Yucai Yu <yucai.yu@foxmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This patch removes the rangeBetween functions introduced in SPARK-21608. As explained in SPARK-25841, these functions are confusing and don't quite work. We will redesign them and introduce better ones in SPARK-25843.
## How was this patch tested?
Removed relevant test cases as well. These test cases will need to be added back in SPARK-25843.
Closes#22870 from rxin/SPARK-25862.
Lead-authored-by: Reynold Xin <rxin@databricks.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Refactor JSONBenchmark to use main method
use spark-submit:
`bin/spark-submit --class org.apache.spark.sql.execution.datasources.json.JSONBenchmark --jars ./core/target/spark-core_2.11-3.0.0-SNAPSHOT-tests.jar,./sql/catalyst/target/spark-catalyst_2.11-3.0.0-SNAPSHOT-tests.jar ./sql/core/target/spark-sql_2.11-3.0.0-SNAPSHOT-tests.jar`
Generate benchmark result:
`SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.datasources.json.JSONBenchmark"`
## How was this patch tested?
manual tests
Closes#22844 from heary-cao/JSONBenchmarks.
Lead-authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Co-authored-by: heary <cao.xuewen@zte.com.cn>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
use spark-submit:
`bin/spark-submit --class org.apache.spark.sql.execution.datasources.csv.CSVBenchmark --jars ./core/target/spark-core_2.11-3.0.0-SNAPSHOT-tests.jar,./sql/catalyst/target/spark-catalyst_2.11-3.0.0-SNAPSHOT-tests.jar ./sql/core/target/spark-sql_2.11-3.0.0-SNAPSHOT-tests.jar`
Generate benchmark result:
`SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.datasources.csv.CSVBenchmark"`
## How was this patch tested?
manual tests
Closes#22845 from heary-cao/CSVBenchmarks.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Currently, the BroadcastHashJoinExec physical plan supports CodeGen and non-codegen, but only CodeGen code is tested in the unit tests of InnerJoinSuite、OuterJoinSuite、ExistenceJoinSuite, and non-codegen code is not tested. This PR supplements this part of the test.
## How was this patch tested?
add new unit tested.
Closes#22755 from heary-cao/AddTestToBroadcastHashJoinExec.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Code generation is incorrect if `outputVars` parameter of `consume` method in `CodegenSupport` contains a lazily evaluated stream of expressions.
This PR fixes the issue by forcing the evaluation of `inputVars` before generating the code for UnsafeRow.
## How was this patch tested?
Tested with the sample program provided in https://issues.apache.org/jira/browse/SPARK-25767Closes#22789 from peter-toth/SPARK-25767.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
Set main args correctly in BenchmarkBase, to make it accessible for its subclass.
It will benefit:
- BuiltInDataSourceWriteBenchmark
- AvroWriteBenchmark
## How was this patch tested?
manual tests
Closes#22872 from yucai/main_args.
Authored-by: yucai <yyu1@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Extractors are made of 2 expressions, one of them defines the the value to be extract from (called `child`) and the other defines the way of extraction (called `extraction`). In this term extractors have 2 children so they shouldn't be `UnaryExpression`s.
`ResolveReferences` was changed in this commit: 36b826f5d1 which resulted a regression with nested extractors. An extractor need to define its children as the set of both `child` and `extraction`; and should try to resolve both in `ResolveReferences`.
This PR changes `UnresolvedExtractValue` to a `BinaryExpression`.
## How was this patch tested?
added UT
Closes#22817 from peter-toth/SPARK-25816.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The instance of `FileSplit` is redundant for `ParquetFileFormat` and `hive\orc\OrcFileFormat` class.
## How was this patch tested?
Existing unit tests in `ParquetQuerySuite.scala` and `HiveOrcQuerySuite.scala`
Closes#22802 from 10110346/FileSplitnotneed.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
WindowSpecDefinition checks start < last, but CalendarIntervalType is not comparable, so it would throw the following exception at runtime:
```
scala.MatchError: CalendarIntervalType (of class org.apache.spark.sql.types.CalendarIntervalType$) at
org.apache.spark.sql.catalyst.util.TypeUtils$.getInterpretedOrdering(TypeUtils.scala:58) at
org.apache.spark.sql.catalyst.expressions.BinaryComparison.ordering$lzycompute(predicates.scala:592) at
org.apache.spark.sql.catalyst.expressions.BinaryComparison.ordering(predicates.scala:592) at
org.apache.spark.sql.catalyst.expressions.GreaterThan.nullSafeEval(predicates.scala:797) at org.apache.spark.sql.catalyst.expressions.BinaryExpression.eval(Expression.scala:496) at org.apache.spark.sql.catalyst.expressions.SpecifiedWindowFrame.isGreaterThan(windowExpressions.scala:245) at
org.apache.spark.sql.catalyst.expressions.SpecifiedWindowFrame.checkInputDataTypes(windowExpressions.scala:216) at
org.apache.spark.sql.catalyst.expressions.Expression.resolved$lzycompute(Expression.scala:171) at
org.apache.spark.sql.catalyst.expressions.Expression.resolved(Expression.scala:171) at
org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$childrenResolved$1.apply(Expression.scala:183) at
org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$childrenResolved$1.apply(Expression.scala:183) at
scala.collection.IndexedSeqOptimized$class.prefixLengthImpl(IndexedSeqOptimized.scala:38) at scala.collection.IndexedSeqOptimized$class.forall(IndexedSeqOptimized.scala:43) at scala.collection.mutable.ArrayBuffer.forall(ArrayBuffer.scala:48) at
org.apache.spark.sql.catalyst.expressions.Expression.childrenResolved(Expression.scala:183) at
org.apache.spark.sql.catalyst.expressions.WindowSpecDefinition.resolved$lzycompute(windowExpressions.scala:48) at
org.apache.spark.sql.catalyst.expressions.WindowSpecDefinition.resolved(windowExpressions.scala:48) at
org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$childrenResolved$1.apply(Expression.scala:183) at
org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$childrenResolved$1.apply(Expression.scala:183) at
scala.collection.LinearSeqOptimized$class.forall(LinearSeqOptimized.scala:83)
```
We fix the issue by only perform the check on boundary expressions that are AtomicType.
## How was this patch tested?
Add new test case in `DataFrameWindowFramesSuite`
Closes#22853 from jiangxb1987/windowBoundary.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
## What changes were proposed in this pull request?
After https://github.com/apache/spark/pull/22745 , Dataset encoder supports the combination of java bean and map type. This PR is to fix the Scala side.
The reason why it didn't work before is, `CatalystToExternalMap` tries to get the data type of the input map expression, while it can be unresolved and its data type is known. To fix it, we can follow `UnresolvedMapObjects`, to create a `UnresolvedCatalystToExternalMap`, and only create `CatalystToExternalMap` when the input map expression is resolved and the data type is known.
## How was this patch tested?
enable a old test case
Closes#22812 from cloud-fan/map.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Implements Every, Some, Any aggregates in SQL. These new aggregate expressions are analyzed in normal way and rewritten to equivalent existing aggregate expressions in the optimizer.
Every(x) => Min(x) where x is boolean.
Some(x) => Max(x) where x is boolean.
Any is a synonym for Some.
SQL
```
explain extended select every(v) from test_agg group by k;
```
Plan :
```
== Parsed Logical Plan ==
'Aggregate ['k], [unresolvedalias('every('v), None)]
+- 'UnresolvedRelation `test_agg`
== Analyzed Logical Plan ==
every(v): boolean
Aggregate [k#0], [every(v#1) AS every(v)#5]
+- SubqueryAlias `test_agg`
+- Project [k#0, v#1]
+- SubqueryAlias `test_agg`
+- LocalRelation [k#0, v#1]
== Optimized Logical Plan ==
Aggregate [k#0], [min(v#1) AS every(v)#5]
+- LocalRelation [k#0, v#1]
== Physical Plan ==
*(2) HashAggregate(keys=[k#0], functions=[min(v#1)], output=[every(v)#5])
+- Exchange hashpartitioning(k#0, 200)
+- *(1) HashAggregate(keys=[k#0], functions=[partial_min(v#1)], output=[k#0, min#7])
+- LocalTableScan [k#0, v#1]
Time taken: 0.512 seconds, Fetched 1 row(s)
```
## How was this patch tested?
Added tests in SQLQueryTestSuite, DataframeAggregateSuite
Closes#22809 from dilipbiswal/SPARK-19851-specific-rewrite.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Remove SQLContext methods deprecated in 1.4
## How was this patch tested?
Existing tests.
Closes#22815 from srowen/SPARK-25821.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/21596, Jackson is upgraded to 2.9.6.
There are some deprecated API warnings in SQLListener.
Create a trivial PR to fix them.
```
[warn] SQLListener.scala:92: method uncheckedSimpleType in class TypeFactory is deprecated: see corresponding Javadoc for more information.
[warn] val objectType = typeFactory.uncheckedSimpleType(classOf[Object])
[warn]
[warn] SQLListener.scala:93: method constructSimpleType in class TypeFactory is deprecated: see corresponding Javadoc for more information.
[warn] typeFactory.constructSimpleType(classOf[(_, _)], classOf[(_, _)], Array(objectType, objectType))
[warn]
[warn] SQLListener.scala:97: method uncheckedSimpleType in class TypeFactory is deprecated: see corresponding Javadoc for more information.
[warn] val longType = typeFactory.uncheckedSimpleType(classOf[Long])
[warn]
[warn] SQLListener.scala:98: method constructSimpleType in class TypeFactory is deprecated: see corresponding Javadoc for more information.
[warn] typeFactory.constructSimpleType(classOf[(_, _)], classOf[(_, _)], Array(longType, longType))
```
## How was this patch tested?
Existing unit tests.
Closes#22848 from gengliangwang/fixSQLListenerWarning.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The main purpose of `schema_of_json` is the usage of combination with `from_json` (to make up the leak of schema inference) which takes its schema only as literal; however, currently `schema_of_json` allows JSON input as non-literal expressions (e.g, column).
This was mistakenly allowed - we don't have to take other usages rather then the main purpose into account for now.
This PR makes a followup to only allow literals for `schema_of_json`'s JSON input. We can allow non literal expressions later when it's needed or there are some usecase for it.
## How was this patch tested?
Unit tests were added.
Closes#22775 from HyukjinKwon/SPARK-25447-followup.
Lead-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
See the detailed information at https://issues.apache.org/jira/browse/SPARK-25841 on why these APIs should be deprecated and redesigned.
This patch also reverts 8acb51f08b which applies to 2.4.
## How was this patch tested?
Only deprecation and doc changes.
Closes#22841 from rxin/SPARK-25842.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
There is a race condition when releasing a Python worker. If `ReaderIterator.handleEndOfDataSection` is not running in the task thread, when a task is early terminated (such as `take(N)`), the task completion listener may close the worker but "handleEndOfDataSection" can still put the worker into the worker pool to reuse.
0e07b483d2 is a patch to reproduce this issue.
I also found a user reported this in the mail list: http://mail-archives.apache.org/mod_mbox/spark-user/201610.mbox/%3CCAAUq=H+YLUEpd23nwvq13Ms5hOStkhX3ao4f4zQV6sgO5zM-xAmail.gmail.com%3E
This PR fixes the issue by using `compareAndSet` to make sure we will never return a closed worker to the work pool.
## How was this patch tested?
Jenkins.
Closes#22816 from zsxwing/fix-socket-closed.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
This is inspired during implementing #21732. For now `ScalaReflection` needs to consider how `ExpressionEncoder` uses generated serializers and deserializers. And `ExpressionEncoder` has a weird `flat` flag. After discussion with cloud-fan, it seems to be better to refactor `ExpressionEncoder`. It should make SPARK-24762 easier to do.
To summarize the proposed changes:
1. `serializerFor` and `deserializerFor` return expressions for serializing/deserializing an input expression for a given type. They are private and should not be called directly.
2. `serializerForType` and `deserializerForType` returns an expression for serializing/deserializing for an object of type T to/from Spark SQL representation. It assumes the input object/Spark SQL representation is located at ordinal 0 of a row.
So in other words, `serializerForType` and `deserializerForType` return expressions for atomically serializing/deserializing JVM object to/from Spark SQL value.
A serializer returned by `serializerForType` will serialize an object at `row(0)` to a corresponding Spark SQL representation, e.g. primitive type, array, map, struct.
A deserializer returned by `deserializerForType` will deserialize an input field at `row(0)` to an object with given type.
3. The construction of `ExpressionEncoder` takes a pair of serializer and deserializer for type `T`. It uses them to create serializer and deserializer for T <-> row serialization. Now `ExpressionEncoder` dones't need to remember if serializer is flat or not. When we need to construct new `ExpressionEncoder` based on existing ones, we only need to change input location in the atomic serializer and deserializer.
## How was this patch tested?
Existing tests.
Closes#22749 from viirya/SPARK-24762-refactor.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to switch `from_json` on `FailureSafeParser`, and to make the function compatible to `PERMISSIVE` mode by default, and to support the `FAILFAST` mode as well. The `DROPMALFORMED` mode is not supported by `from_json`.
## How was this patch tested?
It was tested by existing `JsonSuite`/`CSVSuite`, `JsonFunctionsSuite` and `JsonExpressionsSuite` as well as new tests for `from_json` which checks different modes.
Closes#22237 from MaxGekk/from_json-failuresafe.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This is a follow-up PR for #22708. It considers another case of java beans deserialization: java maps with struct keys/values.
When deserializing values of MapType with struct keys/values in java beans, fields of structs get mixed up. I suggest using struct data types retrieved from resolved input data instead of inferring them from java beans.
## What changes were proposed in this pull request?
Invocations of "keyArray" and "valueArray" functions are used to extract arrays of keys and values. Struct type of keys or values is also inferred from java bean structure and ends up with mixed up field order.
I created a new UnresolvedInvoke expression as a temporary substitution of Invoke expression while no actual data is available. It allows to provide the resulting data type during analysis based on the resolved input data, not on the java bean (similar to UnresolvedMapObjects).
Key and value arrays are then fed to MapObjects expression which I replaced with UnresolvedMapObjects, just like in case of ArrayType.
Finally I added resolution of UnresolvedInvoke expressions in Analyzer.resolveExpression method as an additional pattern matching case.
## How was this patch tested?
Added a test case.
Built complete project on travis.
viirya kiszk cloud-fan michalsenkyr marmbrus liancheng
Closes#22745 from vofque/SPARK-21402-FOLLOWUP.
Lead-authored-by: Vladimir Kuriatkov <vofque@gmail.com>
Co-authored-by: Vladimir Kuriatkov <Vladimir_Kuriatkov@epam.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
The original test would sometimes fail if the listener bus did not keep
up, so just wait till the listener bus is empty. Tested by adding a
sleep in the listener, which made the test consistently fail without the
fix, but pass consistently after the fix.
Closes#22799 from squito/SPARK-25805.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This takes over original PR at #22019. The original proposal is to have null for float and double types. Later a more reasonable proposal is to disallow empty strings. This patch adds logic to throw exception when finding empty strings for non string types.
## How was this patch tested?
Added test.
Closes#22787 from viirya/SPARK-25040.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This goes to reduce test time for ContinuousStressSuite - from 8 mins 13 sec to 43 seconds.
The approach taken by this is to reduce the triggers and epochs to wait and to reduce the expected rows accordingly.
## How was this patch tested?
Existing tests.
Closes#22662 from viirya/SPARK-25627.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Currently, there are some tests testing function descriptions:
```bash
$ grep -ir "describe function" sql/core/src/test/resources/sql-tests/inputs
sql/core/src/test/resources/sql-tests/inputs/json-functions.sql:describe function to_json;
sql/core/src/test/resources/sql-tests/inputs/json-functions.sql:describe function extended to_json;
sql/core/src/test/resources/sql-tests/inputs/json-functions.sql:describe function from_json;
sql/core/src/test/resources/sql-tests/inputs/json-functions.sql:describe function extended from_json;
```
Looks there are not quite good points about testing them since we're not going to test documentation itself.
For `DESCRIBE FCUNTION` functionality itself, they are already being tested here and there.
See the test failures in https://github.com/apache/spark/pull/18749 (where I added examples to function descriptions)
We better remove those tests so that people don't add such tests in the SQL tests.
## How was this patch tested?
Manual.
Closes#22776 from HyukjinKwon/SPARK-25779.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
`needsUnsafeRowConversion` is used in 2 places:
1. `ColumnarBatchScan.produceRows`
2. `FileSourceScanExec.doExecute`
When we hit `ColumnarBatchScan.produceRows`, it means whole stage codegen is on but the vectorized reader is off. The vectorized reader can be off for several reasons:
1. the file format doesn't have a vectorized reader(json, csv, etc.)
2. the vectorized reader config is off
3. the schema is not supported
Anyway when the vectorized reader is off, file format reader will always return unsafe rows, and other `ColumnarBatchScan` implementations also always return unsafe rows, so `ColumnarBatchScan.needsUnsafeRowConversion` is not needed.
When we hit `FileSourceScanExec.doExecute`, it means whole stage codegen is off. For this case, we need the `needsUnsafeRowConversion` to convert `ColumnarRow` to `UnsafeRow`, if the file format reader returns batch.
This PR removes `ColumnarBatchScan.needsUnsafeRowConversion`, and keep this flag only in `FileSourceScanExec`
## How was this patch tested?
existing tests
Closes#22750 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Refactor `WideSchemaBenchmark` to use main method.
1. use `spark-submit`:
```console
bin/spark-submit --class org.apache.spark.sql.execution.benchmark.WideSchemaBenchmark --jars ./core/target/spark-core_2.11-3.0.0-SNAPSHOT-tests.jar ./sql/core/target/spark-sql_2.11-3.0.0-SNAPSHOT-tests.jar
```
2. Generate benchmark result:
```console
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.WideSchemaBenchmark"
```
## How was this patch tested?
manual tests
Closes#22501 from wangyum/SPARK-25492.
Lead-authored-by: Yuming Wang <yumwang@ebay.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This PR adds `prettyNames` for `from_json`, `to_json`, `from_csv`, and `schema_of_json` so that appropriate names are used.
## How was this patch tested?
Unit tests
Closes#22773 from HyukjinKwon/minor-prettyNames.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
SQL interface support specify `StorageLevel` when cache table. The semantic is:
```sql
CACHE TABLE tableName OPTIONS('storageLevel' 'DISK_ONLY');
```
All supported `StorageLevel` are:
eefdf9f9dd/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala (L172-L183)
## How was this patch tested?
unit tests and manual tests.
manual tests configuration:
```
--executor-memory 15G --executor-cores 5 --num-executors 50
```
Data:
Input Size / Records: 1037.7 GB / 11732805788
Result:
Closes#22263 from wangyum/SPARK-25269.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This is a follow-up PR for #22259. The extra field added in `ScalaUDF` with the original PR was declared optional, but should be indeed required, otherwise callers of `ScalaUDF`'s constructor could ignore this new field and cause the result to be incorrect. This PR makes the new field required and changes its name to `handleNullForInputs`.
#22259 breaks the previous behavior for null-handling of primitive-type input parameters. For example, for `val f = udf({(x: Int, y: Any) => x})`, `f(null, "str")` should return `null` but would return `0` after #22259. In this PR, all UDF methods except `def udf(f: AnyRef, dataType: DataType): UserDefinedFunction` have been restored with the original behavior. The only exception is documented in the Spark SQL migration guide.
In addition, now that we have this extra field indicating if a null-test should be applied on the corresponding input value, we can also make use of this flag to avoid the rule `HandleNullInputsForUDF` being applied infinitely.
## How was this patch tested?
Added UT in UDFSuite
Passed affected existing UTs:
AnalysisSuite
UDFSuite
Closes#22732 from maryannxue/spark-25044-followup.
Lead-authored-by: maryannxue <maryannxue@apache.org>
Co-authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This allows an implementer of Spark Session Extensions to utilize a
method "injectFunction" which will add a new function to the default
Spark Session Catalogue.
## What changes were proposed in this pull request?
Adds a new function to SparkSessionExtensions
def injectFunction(functionDescription: FunctionDescription)
Where function description is a new type
type FunctionDescription = (FunctionIdentifier, FunctionBuilder)
The functions are loaded in BaseSessionBuilder when the function registry does not have a parent
function registry to get loaded from.
## How was this patch tested?
New unit tests are added for the extension in SparkSessionExtensionSuite
Closes#22576 from RussellSpitzer/SPARK-25560.
Authored-by: Russell Spitzer <Russell.Spitzer@gmail.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
CSVs with windows style crlf ('\r\n') don't work in multiline mode. They work fine in single line mode because the line separation is done by Hadoop, which can handle all the different types of line separators. This PR fixes it by enabling Univocity's line separator detection in multiline mode, which will detect '\r\n', '\r', or '\n' automatically as it is done by hadoop in single line mode.
## How was this patch tested?
Unit test with a file with crlf line endings.
Closes#22503 from justinuang/fix-clrf-multiline.
Authored-by: Justin Uang <juang@palantir.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Only `AddJarCommand` return `0`, the user will be confused about what it means. This PR sets it to empty.
```sql
spark-sql> add jar /Users/yumwang/spark/sql/hive/src/test/resources/TestUDTF.jar;
ADD JAR /Users/yumwang/spark/sql/hive/src/test/resources/TestUDTF.jar
0
spark-sql>
```
## How was this patch tested?
manual tests
```sql
spark-sql> add jar /Users/yumwang/spark/sql/hive/src/test/resources/TestUDTF.jar;
ADD JAR /Users/yumwang/spark/sql/hive/src/test/resources/TestUDTF.jar
spark-sql>
```
Closes#22747 from wangyum/AddJarCommand.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
Master
## What changes were proposed in this pull request?
Previously Pyspark used the private constructor for SparkSession when
building that object. This resulted in a SparkSession without checking
the sql.extensions parameter for additional session extensions. To fix
this we instead use the Session.builder() path as SparkR uses, this
loads the extensions and allows their use in PySpark.
## How was this patch tested?
An integration test was added which mimics the Scala test for the same feature.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#21990 from RussellSpitzer/SPARK-25003-master.
Authored-by: Russell Spitzer <Russell.Spitzer@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
When deserializing values of ArrayType with struct elements in java beans, fields of structs get mixed up.
I suggest using struct data types retrieved from resolved input data instead of inferring them from java beans.
## What changes were proposed in this pull request?
MapObjects expression is used to map array elements to java beans. Struct type of elements is inferred from java bean structure and ends up with mixed up field order.
I used UnresolvedMapObjects instead of MapObjects, which allows to provide element type for MapObjects during analysis based on the resolved input data, not on the java bean.
## How was this patch tested?
Added a test case.
Built complete project on travis.
michalsenkyr cloud-fan marmbrus liancheng
Closes#22708 from vofque/SPARK-21402.
Lead-authored-by: Vladimir Kuriatkov <vofque@gmail.com>
Co-authored-by: Vladimir Kuriatkov <Vladimir_Kuriatkov@epam.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The SQL execution listener framework was created from scratch(see https://github.com/apache/spark/pull/9078). It didn't leverage what we already have in the spark listener framework, and one major problem is, the listener runs on the spark execution thread, which means a bad listener can block spark's query processing.
This PR re-implements the SQL execution listener framework. Now `ExecutionListenerManager` is just a normal spark listener, which watches the `SparkListenerSQLExecutionEnd` events and post events to the
user-provided SQL execution listeners.
## How was this patch tested?
existing tests.
Closes#22674 from cloud-fan/listener.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
`Literal.value` should have a value a value corresponding to `dataType`. This pr added code to verify it and fixed the existing tests to do so.
## How was this patch tested?
Modified the existing tests.
Closes#22724 from maropu/SPARK-25734.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PR adds new function `from_csv()` similar to `from_json()` to parse columns with CSV strings. I added the following methods:
```Scala
def from_csv(e: Column, schema: StructType, options: Map[String, String]): Column
```
and this signature to call it from Python, R and Java:
```Scala
def from_csv(e: Column, schema: String, options: java.util.Map[String, String]): Column
```
## How was this patch tested?
Added new test suites `CsvExpressionsSuite`, `CsvFunctionsSuite` and sql tests.
Closes#22379 from MaxGekk/from_csv.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
AFAIK multi-column count is not widely supported by the mainstream databases(postgres doesn't support), and the SQL standard doesn't define it clearly, as near as I can tell.
Since Spark supports it, we should clearly document the current behavior and add tests to verify it.
## How was this patch tested?
N/A
Closes#22728 from cloud-fan/doc.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Only test these 4 cases is enough:
be2238fb50/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetWriteSupport.scala (L269-L279)
## How was this patch tested?
Manual tests on my local machine.
before:
```
- filter pushdown - decimal (13 seconds, 683 milliseconds)
```
after:
```
- filter pushdown - decimal (9 seconds, 713 milliseconds)
```
Closes#22636 from wangyum/SPARK-25629.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
LOAD DATA INPATH didn't work if the defaultFS included a port for hdfs.
Handling this just requires a small change to use the correct URI
constructor.
## How was this patch tested?
Added a unit test, ran all tests via jenkins
Closes#22733 from squito/SPARK-25738.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This PR is a follow-up of https://github.com/apache/spark/pull/22594 . This alternative can avoid the unneeded computation in the hot code path.
- For row-based scan, we keep the original way.
- For the columnar scan, we just need to update the stats after each batch.
## How was this patch tested?
N/A
Closes#22731 from gatorsmile/udpateStatsFileScanRDD.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PR addresses [the comment](https://github.com/apache/spark/pull/22715#discussion_r225024084) in the previous one. `outputOrdering` becomes a field of `InMemoryRelation`.
## How was this patch tested?
existing UTs
Closes#22726 from mgaido91/SPARK-25727_followup.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Add `outputOrdering ` to `otherCopyArgs` in InMemoryRelation so that this field will be copied when we doing the tree transformation.
```
val data = Seq(100).toDF("count").cache()
data.queryExecution.optimizedPlan.toJSON
```
The above code can generate the following error:
```
assertion failed: InMemoryRelation fields: output, cacheBuilder, statsOfPlanToCache, outputOrdering, values: List(count#178), CachedRDDBuilder(true,10000,StorageLevel(disk, memory, deserialized, 1 replicas),*(1) Project [value#176 AS count#178]
+- LocalTableScan [value#176]
,None), Statistics(sizeInBytes=12.0 B, hints=none)
java.lang.AssertionError: assertion failed: InMemoryRelation fields: output, cacheBuilder, statsOfPlanToCache, outputOrdering, values: List(count#178), CachedRDDBuilder(true,10000,StorageLevel(disk, memory, deserialized, 1 replicas),*(1) Project [value#176 AS count#178]
+- LocalTableScan [value#176]
,None), Statistics(sizeInBytes=12.0 B, hints=none)
at scala.Predef$.assert(Predef.scala:170)
at org.apache.spark.sql.catalyst.trees.TreeNode.jsonFields(TreeNode.scala:611)
at org.apache.spark.sql.catalyst.trees.TreeNode.org$apache$spark$sql$catalyst$trees$TreeNode$$collectJsonValue$1(TreeNode.scala:599)
at org.apache.spark.sql.catalyst.trees.TreeNode.jsonValue(TreeNode.scala:604)
at org.apache.spark.sql.catalyst.trees.TreeNode.toJSON(TreeNode.scala:590)
```
## How was this patch tested?
Added a test
Closes#22715 from gatorsmile/copyArgs1.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
[SPARK-22479](https://github.com/apache/spark/pull/19708/files#diff-5c22ac5160d3c9d81225c5dd86265d27R31) adds a test case which sometimes fails because the used password string `123` matches `41230802`. This PR aims to fix the flakiness.
- https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/97343/consoleFull
```scala
SaveIntoDataSourceCommandSuite:
- simpleString is redacted *** FAILED ***
"SaveIntoDataSourceCommand .org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider41230802, Map(password -> *********(redacted), url -> *********(redacted), driver -> mydriver), ErrorIfExists
+- Range (0, 1, step=1, splits=Some(2))
" contained "123" (SaveIntoDataSourceCommandSuite.scala:42)
```
## How was this patch tested?
Pass the Jenkins with the updated test case
Closes#22716 from dongjoon-hyun/SPARK-25726.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Currently `Range` reports metrics in batch granularity. This is acceptable, but it's better if we can make it row granularity without performance penalty.
Before this PR, the metrics are updated when preparing the batch, which is before we actually consume data. In this PR, the metrics are updated after the data are consumed. There are 2 different cases:
1. The data processing loop has a stop check. The metrics are updated when we need to stop.
2. no stop check. The metrics are updated after the loop.
## How was this patch tested?
existing tests and a new benchmark
Closes#22698 from cloud-fan/range.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
```Scala
val df1 = Seq(("abc", 1), (null, 3)).toDF("col1", "col2")
df1.write.mode(SaveMode.Overwrite).parquet("/tmp/test1")
val df2 = spark.read.parquet("/tmp/test1")
df2.filter("col1 = 'abc' OR (col1 != 'abc' AND col2 == 3)").show()
```
Before the PR, it returns both rows. After the fix, it returns `Row ("abc", 1))`. This is to fix the bug in NULL handling in BooleanSimplification. This is a bug introduced in Spark 1.6 release.
## How was this patch tested?
Added test cases
Closes#22702 from gatorsmile/fixBooleanSimplify2.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Refactor `JoinBenchmark` to use main method.
1. use `spark-submit`:
```console
bin/spark-submit --class org.apache.spark.sql.execution.benchmark.JoinBenchmark --jars ./core/target/spark-core_2.11-3.0.0-SNAPSHOT-tests.jar ./sql/catalyst/target/spark-sql_2.11-3.0.0-SNAPSHOT-tests.jar
```
2. Generate benchmark result:
```console
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.JoinBenchmark"
```
## How was this patch tested?
manual tests
Closes#22661 from wangyum/SPARK-25664.
Lead-authored-by: Yuming Wang <yumwang@ebay.com>
Co-authored-by: Yuming Wang <wgyumg@gmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
The PR addresses the exception raised on accessing chars out of delimiter string. In particular, the backward slash `\` as the CSV fields delimiter causes the following exception on reading `abc\1`:
```Scala
String index out of range: 1
java.lang.StringIndexOutOfBoundsException: String index out of range: 1
at java.lang.String.charAt(String.java:658)
```
because `str.charAt(1)` tries to access a char out of `str` in `CSVUtils.toChar`
## How was this patch tested?
Added tests for empty string and string containing the backward slash to `CSVUtilsSuite`. Besides of that I added an end-to-end test to check how the backward slash is handled in reading CSV string with it.
Closes#22654 from MaxGekk/csv-slash-delim.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Currently SQL tab in the WEBUI doesn't support pagination. Because of that following issues are happening.
1) For large number of executions, SQL page is throwing OOM exception (around 40,000)
2) For large number of executions, loading SQL page is taking time.
3) Difficult to analyse the execution table for large number of execution.
[Note: spark.sql.ui.retainedExecutions = 50000]
All the tabs, Jobs, Stages etc. supports pagination. So, to make it consistent with other tabs
SQL tab also should support pagination.
I have followed the similar flow of the pagination code in the Jobs and Stages page for SQL page.
Also, this patch doesn't make any behavior change for the SQL tab except the pagination support.
## How was this patch tested?
bin/spark-shell --conf spark.sql.ui.retainedExecutions=50000
Run 50,000 sql queries.
**Before this PR**
**After this PR**
Loading of the page is faster, and OOM issue doesn't happen.
Closes#22645 from shahidki31/SPARK-25566.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
According to the SQL standard, when a query contains `HAVING`, it indicates an aggregate operator. For more details please refer to https://blog.jooq.org/2014/12/04/do-you-really-understand-sqls-group-by-and-having-clauses/
However, in Spark SQL parser, we treat HAVING as a normal filter when there is no GROUP BY, which breaks SQL semantic and lead to wrong result. This PR fixes the parser.
## How was this patch tested?
new test
Closes#22696 from cloud-fan/having.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
1. Move `CSVDataSource.makeSafeHeader` to `CSVUtils.makeSafeHeader` (as is).
- Historically and at the first place of refactoring (which I did), I intended to put all CSV specific handling (like options), filtering, extracting header, etc.
- See `JsonDataSource`. Now `CSVDataSource` is quite consistent with `JsonDataSource`. Since CSV's code path is quite complicated, we might better match them as possible as we can.
2. Create `CSVHeaderChecker` and put `enforceSchema` logics into that.
- The checking header and column pruning stuff were added (per https://github.com/apache/spark/pull/20894 and https://github.com/apache/spark/pull/21296) but some of codes such as https://github.com/apache/spark/pull/22123 are duplicated
- Also, checking header code is basically here and there. We better put them in a single place, which was quite error-prone. See (https://github.com/apache/spark/pull/22656).
3. Move `CSVDataSource.checkHeaderColumnNames` to `CSVHeaderChecker.checkHeaderColumnNames` (as is).
- Similar reasons above with 1.
## How was this patch tested?
Existing tests should cover this.
Closes#22676 from HyukjinKwon/refactoring-csv.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
If the records are incremented by more than 1 at a time,the number of bytes might rarely ever get updated，because it might skip over the count that is an exact multiple of UPDATE_INPUT_METRICS_INTERVAL_RECORDS.
This PR just checks whether the increment causes the value to exceed a higher multiple of UPDATE_INPUT_METRICS_INTERVAL_RECORDS.
## How was this patch tested?
existed unit tests
Closes#22594 from 10110346/inputMetrics.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
remove Redundant semicolons in SortMergeJoinExec， thanks.
## How was this patch tested?
N/A
Closes#22695 from heary-cao/RedundantSemicolons.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
1. Refactor DataSourceReadBenchmark
## How was this patch tested?
Manually tested and regenerated results.
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.DataSourceReadBenchmark"
```
Closes#22664 from peter-toth/SPARK-25662.
Lead-authored-by: Peter Toth <peter.toth@gmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Inspired by https://github.com/apache/spark/pull/22574 .
We can partially push down top level conjunctive predicates to Orc.
This PR improves Orc predicate push down in both SQL and Hive module.
## How was this patch tested?
New unit test.
Closes#22684 from gengliangwang/pushOrcFilters.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
This is a follow up of https://github.com/apache/spark/pull/22574. Renamed the parameter and added comments.
## How was this patch tested?
N/A
Closes#22679 from gatorsmile/followupSPARK-25559.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
This PR is inspired by https://github.com/apache/spark/pull/22524, but proposes a safer fix.
The current limit whole stage codegen has 2 problems:
1. It's only applied to `InputAdapter`, many leaf nodes can't stop earlier w.r.t. limit.
2. It needs to override a method, which will break if we have more than one limit in the whole-stage.
The first problem is easy to fix, just figure out which nodes can stop earlier w.r.t. limit, and update them. This PR updates `RangeExec`, `ColumnarBatchScan`, `SortExec`, `HashAggregateExec`.
The second problem is hard to fix. This PR proposes to propagate the limit counter variable name upstream, so that the upstream leaf/blocking nodes can check the limit counter and quit the loop earlier.
For better performance, the implementation here follows `CodegenSupport.needStopCheck`, so that we only codegen the check only if there is limit in the query. For columnar node like range, we check the limit counter per-batch instead of per-row, to make the inner loop tight and fast.
Why this is safer?
1. the leaf/blocking nodes don't have to check the limit counter and stop earlier. It's only for performance. (this is same as before)
2. The blocking operators can stop propagating the limit counter name, because the counter of limit after blocking operators will never increase, before blocking operators consume all the data from upstream operators. So the upstream operators don't care about limit after blocking operators. This is also for performance only, it's OK if we forget to do it for some new blocking operators.
## How was this patch tested?
a new test
Closes#22630 from cloud-fan/limit.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
## What changes were proposed in this pull request?
Currently the first row of dataset of CSV strings is compared to field names of user specified or inferred schema independently of presence of CSV header. It causes false-positive error messages. For example, parsing `"1,2"` outputs the error:
```java
java.lang.IllegalArgumentException: CSV header does not conform to the schema.
Header: 1, 2
Schema: _c0, _c1
Expected: _c0 but found: 1
```
In the PR, I propose:
- Checking CSV header only when it exists
- Filter header from the input dataset only if it exists
## How was this patch tested?
Added a test to `CSVSuite` which reproduces the issue.
Closes#22656 from MaxGekk/inferred-header-check.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
`InMemoryFileIndex` contains a cache of `LocatedFileStatus` objects. Each `LocatedFileStatus` object can contain several `BlockLocation`s or some subclass of it. Filling up this cache by listing files happens recursively either on the driver or on the executors, depending on the parallel discovery threshold (`spark.sql.sources.parallelPartitionDiscovery.threshold`). If the listing happens on the executors block location objects are converted to simple `BlockLocation` objects to ensure serialization requirements. If it happens on the driver then there is no conversion and depending on the file system a `BlockLocation` object can be a subclass like `HdfsBlockLocation` and consume more memory. This PR adds the conversion to the latter case and decreases memory consumption.
## How was this patch tested?
Added unit test.
Closes#22603 from peter-toth/SPARK-25062.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This PR fixes the Scala-2.12 build error due to ambiguity in `foreachBatch` test cases.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.7-ubuntu-scala-2.12/428/console
```scala
[error] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7-ubuntu-scala-2.12/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/sources/ForeachBatchSinkSuite.scala:102: ambiguous reference to overloaded definition,
[error] both method foreachBatch in class DataStreamWriter of type (function: org.apache.spark.api.java.function.VoidFunction2[org.apache.spark.sql.Dataset[Int],Long])org.apache.spark.sql.streaming.DataStreamWriter[Int]
[error] and method foreachBatch in class DataStreamWriter of type (function: (org.apache.spark.sql.Dataset[Int], Long) => Unit)org.apache.spark.sql.streaming.DataStreamWriter[Int]
[error] match argument types ((org.apache.spark.sql.Dataset[Int], Any) => Unit)
[error] ds.writeStream.foreachBatch((_, _) => {}).trigger(Trigger.Continuous("1 second")).start()
[error] ^
[error] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7-ubuntu-scala-2.12/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/sources/ForeachBatchSinkSuite.scala:106: ambiguous reference to overloaded definition,
[error] both method foreachBatch in class DataStreamWriter of type (function: org.apache.spark.api.java.function.VoidFunction2[org.apache.spark.sql.Dataset[Int],Long])org.apache.spark.sql.streaming.DataStreamWriter[Int]
[error] and method foreachBatch in class DataStreamWriter of type (function: (org.apache.spark.sql.Dataset[Int], Long) => Unit)org.apache.spark.sql.streaming.DataStreamWriter[Int]
[error] match argument types ((org.apache.spark.sql.Dataset[Int], Any) => Unit)
[error] ds.writeStream.foreachBatch((_, _) => {}).partitionBy("value").start()
[error] ^
```
## How was this patch tested?
Manual.
Since this failure occurs in Scala-2.12 profile and test cases, Jenkins will not test this. We need to build with Scala-2.12 and run the tests.
Closes#22649 from dongjoon-hyun/SPARK-SCALA212.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Refactor `MiscBenchmark ` to use main method.
Generate benchmark result:
```sh
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.MiscBenchmark"
```
## How was this patch tested?
manual tests
Closes#22500 from wangyum/SPARK-25488.
Lead-authored-by: Yuming Wang <yumwang@ebay.com>
Co-authored-by: Yuming Wang <wgyumg@gmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
By replacing loops with random possible value.
- `read partitioning bucketed tables with bucket pruning filters` reduce from 55s to 7s
- `read partitioning bucketed tables having composite filters` reduce from 54s to 8s
- total time: reduce from 288s to 192s
## How was this patch tested?
Unit test
Closes#22640 from gengliangwang/fastenBucketedReadSuite.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Current the CSV's infer schema code inlines `TypeCoercion.findTightestCommonType`. This is a minor refactor to make use of the common type coercion code when applicable. This way we can take advantage of any improvement to the base method.
Thanks to MaxGekk for finding this while reviewing another PR.
## How was this patch tested?
This is a minor refactor. Existing tests are used to verify the change.
Closes#22619 from dilipbiswal/csv_minor.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Adds support for the setting limit in the sql split function
## How was this patch tested?
1. Updated unit tests
2. Tested using Scala spark shell
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22227 from phegstrom/master.
Authored-by: Parker Hegstrom <phegstrom@palantir.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In this test case, we are verifying that the result of an UDF is cached when the underlying data frame is cached and that the udf is not evaluated again when the cached data frame is used.
To reduce the runtime we do :
1) Use a single partition dataframe, so the total execution time of UDF is more deterministic.
2) Cut down the size of the dataframe from 10 to 2.
3) Reduce the sleep time in the UDF from 5secs to 2secs.
4) Reduce the failafter condition from 3 to 2.
With the above change, it takes about 4 secs to cache the first dataframe. And subsequent check takes a few hundred milliseconds.
The new runtime for 5 consecutive runs of this test is as follows :
```
[info] - cache UDF result correctly (4 seconds, 906 milliseconds)
[info] - cache UDF result correctly (4 seconds, 281 milliseconds)
[info] - cache UDF result correctly (4 seconds, 288 milliseconds)
[info] - cache UDF result correctly (4 seconds, 355 milliseconds)
[info] - cache UDF result correctly (4 seconds, 280 milliseconds)
```
## How was this patch tested?
This is s test fix.
Closes#22638 from dilipbiswal/SPARK-25610.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Before ORC 1.5.3, `orc.dictionary.key.threshold` and `hive.exec.orc.dictionary.key.size.threshold` are applied for all columns. This has been a big huddle to enable dictionary encoding. From ORC 1.5.3, `orc.column.encoding.direct` is added to enforce direct encoding selectively in a column-wise manner. This PR aims to add that feature by upgrading ORC from 1.5.2 to 1.5.3.
The followings are the patches in ORC 1.5.3 and this feature is the only one related to Spark directly.
```
ORC-406: ORC: Char(n) and Varchar(n) writers truncate to n bytes & corrupts multi-byte data (gopalv)
ORC-403: [C++] Add checks to avoid invalid offsets in InputStream
ORC-405: Remove calcite as a dependency from the benchmarks.
ORC-375: Fix libhdfs on gcc7 by adding #include <functional> two places.
ORC-383: Parallel builds fails with ConcurrentModificationException
ORC-382: Apache rat exclusions + add rat check to travis
ORC-401: Fix incorrect quoting in specification.
ORC-385: Change RecordReader to extend Closeable.
ORC-384: [C++] fix memory leak when loading non-ORC files
ORC-391: [c++] parseType does not accept underscore in the field name
ORC-397: Allow selective disabling of dictionary encoding. Original patch was by Mithun Radhakrishnan.
ORC-389: Add ability to not decode Acid metadata columns
```
## How was this patch tested?
Pass the Jenkins with newly added test cases.
Closes#22622 from dongjoon-hyun/SPARK-25635.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The java `foreachBatch` API in `DataStreamWriter` should accept `java.lang.Long` rather `scala.Long`.
## How was this patch tested?
New java test.
Closes#22633 from zsxwing/fix-java-foreachbatch.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
When constructing a DataFrame from a Java bean, using nested beans throws an error despite [documentation](http://spark.apache.org/docs/latest/sql-programming-guide.html#inferring-the-schema-using-reflection) stating otherwise. This PR aims to add that support.
This PR does not yet add nested beans support in array or List fields. This can be added later or in another PR.
## How was this patch tested?
Nested bean was added to the appropriate unit test.
Also manually tested in Spark shell on code emulating the referenced JIRA:
```
scala> import scala.beans.BeanProperty
import scala.beans.BeanProperty
scala> class SubCategory(BeanProperty var id: String, BeanProperty var name: String) extends Serializable
defined class SubCategory
scala> class Category(BeanProperty var id: String, BeanProperty var subCategory: SubCategory) extends Serializable
defined class Category
scala> import scala.collection.JavaConverters._
import scala.collection.JavaConverters._
scala> spark.createDataFrame(Seq(new Category("s-111", new SubCategory("sc-111", "Sub-1"))).asJava, classOf[Category])
java.lang.IllegalArgumentException: The value (SubCategory65130cf2) of the type (SubCategory) cannot be converted to struct<id:string,name:string>
at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:262)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:238)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:103)
at org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:396)
at org.apache.spark.sql.SQLContext$$anonfun$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1108)
at org.apache.spark.sql.SQLContext$$anonfun$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1108)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
at org.apache.spark.sql.SQLContext$$anonfun$beansToRows$1.apply(SQLContext.scala:1108)
at org.apache.spark.sql.SQLContext$$anonfun$beansToRows$1.apply(SQLContext.scala:1106)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
at scala.collection.Iterator$class.toStream(Iterator.scala:1320)
at scala.collection.AbstractIterator.toStream(Iterator.scala:1334)
at scala.collection.TraversableOnce$class.toSeq(TraversableOnce.scala:298)
at scala.collection.AbstractIterator.toSeq(Iterator.scala:1334)
at org.apache.spark.sql.SparkSession.createDataFrame(SparkSession.scala:423)
... 51 elided
```
New behavior:
```
scala> spark.createDataFrame(Seq(new Category("s-111", new SubCategory("sc-111", "Sub-1"))).asJava, classOf[Category])
res0: org.apache.spark.sql.DataFrame = [id: string, subCategory: struct<id: string, name: string>]
scala> res0.show()
+-----+---------------+
| id| subCategory|
+-----+---------------+
|s-111|[sc-111, Sub-1]|
+-----+---------------+
```
Closes#22527 from michalsenkyr/SPARK-17952.
Authored-by: Michal Senkyr <mike.senkyr@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
Hi all,
Jackson is incompatible with upstream versions, therefore bump the Jackson version to a more recent one. I bumped into some issues with Azure CosmosDB that is using a more recent version of Jackson. This can be fixed by adding exclusions and then it works without any issues. So no breaking changes in the API's.
I would also consider bumping the version of Jackson in Spark. I would suggest to keep up to date with the dependencies, since in the future this issue will pop up more frequently.
## What changes were proposed in this pull request?
Bump Jackson to 2.9.6
## How was this patch tested?
Compiled and tested it locally to see if anything broke.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#21596 from Fokko/fd-bump-jackson.
Authored-by: Fokko Driesprong <fokkodriesprong@godatadriven.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
``As part of insert command in FileFormatWriter, a job context is created for handling the write operation , While initializing the job context using setupJob() API
in HadoopMapReduceCommitProtocol , we set the jobid in the Jobcontext configuration.In FileFormatWriter since we are directly getting the jobId from the map reduce JobContext the job id will come as null while adding the log. As a solution we shall get the jobID from the configuration of the map reduce Jobcontext.``
## How was this patch tested?
Manually, verified the logs after the changes.
Closes#22572 from sujith71955/master_log_issue.
Authored-by: s71955 <sujithchacko.2010@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PR changes the test introduced for SPARK-22226, so that we don't run analysis and optimization on the plan. The scope of the test is code generation and running the above mentioned operation is expensive and useless for the test.
The UT was also moved to the `CodeGenerationSuite` which is a better place given the scope of the test.
## How was this patch tested?
running the UT before SPARK-22226 fails, after it passes. The execution time is about 50% the original one. On my laptop this means that the test now runs in about 23 seconds (instead of 50 seconds).
Closes#22629 from mgaido91/SPARK-25609.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Refactor `DatasetBenchmark` to use main method.
Generate benchmark result:
```sh
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.DatasetBenchmark"
```
## How was this patch tested?
manual tests
Closes#22488 from wangyum/SPARK-25479.
Lead-authored-by: Yuming Wang <yumwang@ebay.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
In `SparkPlan.getByteArrayRdd`, we should only call `it.hasNext` when the limit is not hit, as `iter.hasNext` may produce one row and buffer it, and cause wrong metrics.
## How was this patch tested?
new tests
Closes#22621 from cloud-fan/range.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Refactor `UnsafeArrayDataBenchmark` to use main method.
Generate benchmark result:
```sh
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.UnsafeArrayDataBenchmark"
```
## How was this patch tested?
manual tests
Closes#22491 from wangyum/SPARK-25483.
Lead-authored-by: Yuming Wang <yumwang@ebay.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This PR aims to add `BloomFilterBenchmark`. For ORC data source, Apache Spark has been supporting for a long time. For Parquet data source, it's expected to be added with next Parquet release update.
## How was this patch tested?
Manual.
```scala
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.BloomFilterBenchmark"
```
Closes#22605 from dongjoon-hyun/SPARK-25589.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Rename method `benchmark` in `BenchmarkBase` as `runBenchmarkSuite `. Also add comments.
Currently the method name `benchmark` is a bit confusing. Also the name is the same as instances of `Benchmark`:
f246813afb/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcReadBenchmark.scala (L330-L339)
## How was this patch tested?
Unit test.
Closes#22599 from gengliangwang/renameBenchmarkSuite.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.0.0-SNAPSHOT.
## How was this patch tested?
N/A
Closes#22606 from gatorsmile/bump3.0.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Currently, SQL tab in the WEBUI doesn't support hiding table. Other tabs in the web ui like, Jobs, stages etc supports hiding table (refer SPARK-23024 https://github.com/apache/spark/pull/20216).
In this PR, added the support for hide table in the sql tab also.
## How was this patch tested?
bin/spark-shell
```
sql("create table a (id int)")
for(i <- 1 to 100) sql(s"insert into a values ($i)")
```
Open SQL tab in the web UI
**Before fix:**
**After fix:** Consistent with the other tabs.
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22592 from shahidki31/SPARK-25575.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR does 2 things:
1. Add a new trait(`SqlBasedBenchmark`) to better support Dataset and DataFrame API.
2. Refactor `AggregateBenchmark` to use main method. Generate benchmark result:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.AggregateBenchmark"
```
## How was this patch tested?
manual tests
Closes#22484 from wangyum/SPARK-25476.
Lead-authored-by: Yuming Wang <yumwang@ebay.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
#22519 introduced a bug when the attributes in the pivot clause are cosmetically different from the output ones (eg. different case). In particular, the problem is that the PR used a `Set[Attribute]` instead of an `AttributeSet`.
## How was this patch tested?
added UT
Closes#22582 from mgaido91/SPARK-25505_followup.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR adds a rule to force `.toLowerCase(Locale.ROOT)` or `toUpperCase(Locale.ROOT)`.
It produces an error as below:
```
[error] Are you sure that you want to use toUpperCase or toLowerCase without the root locale? In most cases, you
[error] should use toUpperCase(Locale.ROOT) or toLowerCase(Locale.ROOT) instead.
[error] If you must use toUpperCase or toLowerCase without the root locale, wrap the code block with
[error] // scalastyle:off caselocale
[error] .toUpperCase
[error] .toLowerCase
[error] // scalastyle:on caselocale
```
This PR excludes the cases above for SQL code path for external calls like table name, column name and etc.
For test suites, or when it's clear there's no locale problem like Turkish locale problem, it uses `Locale.ROOT`.
One minor problem is, `UTF8String` has both methods, `toLowerCase` and `toUpperCase`, and the new rule detects them as well. They are ignored.
## How was this patch tested?
Manually tested, and Jenkins tests.
Closes#22581 from HyukjinKwon/SPARK-25565.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to extend implementation of existing method:
```
def pivot(pivotColumn: Column, values: Seq[Any]): RelationalGroupedDataset
```
to support values of the struct type. This allows pivoting by multiple columns combined by `struct`:
```
trainingSales
.groupBy($"sales.year")
.pivot(
pivotColumn = struct(lower($"sales.course"), $"training"),
values = Seq(
struct(lit("dotnet"), lit("Experts")),
struct(lit("java"), lit("Dummies")))
).agg(sum($"sales.earnings"))
```
## How was this patch tested?
Added a test for values specified via `struct` in Java and Scala.
Closes#22316 from MaxGekk/pivoting-by-multiple-columns2.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to extended the `schema_of_json()` function, and accept JSON options since they can impact on schema inferring. Purpose is to support the same options that `from_json` can use during schema inferring.
## How was this patch tested?
Added SQL, Python and Scala tests (`JsonExpressionsSuite` and `JsonFunctionsSuite`) that checks JSON options are used.
Closes#22442 from MaxGekk/schema_of_json-options.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Currently, in `ParquetFilters`, if one of the children predicates is not supported by Parquet, the entire predicates will be thrown away. In fact, if the unsupported predicate is in the top level `And` condition or in the child before hitting `Not` or `Or` condition, it can be safely removed.
## How was this patch tested?
Tests are added.
Closes#22574 from dbtsai/removeUnsupportedPredicatesInParquet.
Lead-authored-by: DB Tsai <d_tsai@apple.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Co-authored-by: DB Tsai <dbtsai@dbtsai.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Use `Set` instead of `Array` to improve `accumulatorIds.contains(acc.id)` performance.
This PR close https://github.com/apache/spark/pull/22420
## How was this patch tested?
manual tests.
Benchmark code:
```scala
def benchmark(func: () => Unit): Long = {
val start = System.currentTimeMillis()
func()
val end = System.currentTimeMillis()
end - start
}
val range = Range(1, 1000000)
val set = range.toSet
val array = range.toArray
for (i <- 0 until 5) {
val setExecutionTime =
benchmark(() => for (i <- 0 until 500) { set.contains(scala.util.Random.nextInt()) })
val arrayExecutionTime =
benchmark(() => for (i <- 0 until 500) { array.contains(scala.util.Random.nextInt()) })
println(s"set execution time: $setExecutionTime, array execution time: $arrayExecutionTime")
}
```
Benchmark result:
```
set execution time: 4, array execution time: 2760
set execution time: 1, array execution time: 1911
set execution time: 3, array execution time: 2043
set execution time: 12, array execution time: 2214
set execution time: 6, array execution time: 1770
```
Closes#22579 from wangyum/SPARK-25429.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
**Description from the JIRA :**
Currently, to collect the statistics of all the columns, users need to specify the names of all the columns when calling the command "ANALYZE TABLE ... FOR COLUMNS...". This is not user friendly. Instead, we can introduce the following SQL command to achieve it without specifying the column names.
```
ANALYZE TABLE [db_name.]tablename COMPUTE STATISTICS FOR ALL COLUMNS;
```
## How was this patch tested?
Added new tests in SparkSqlParserSuite and StatisticsSuite
Closes#22566 from dilipbiswal/SPARK-25458.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The grouping columns from a Pivot query are inferred as "input columns - pivot columns - pivot aggregate columns", where input columns are the output of the child relation of Pivot. The grouping columns will be the leading columns in the pivot output and they should preserve the same order as specified by the input. For example,
```
SELECT * FROM (
SELECT course, earnings, "a" as a, "z" as z, "b" as b, "y" as y, "c" as c, "x" as x, "d" as d, "w" as w
FROM courseSales
)
PIVOT (
sum(earnings)
FOR course IN ('dotNET', 'Java')
)
```
The output columns should be "a, z, b, y, c, x, d, w, ..." but now it is "a, b, c, d, w, x, y, z, ..."
The fix is to use the child plan's `output` instead of `outputSet` so that the order can be preserved.
## How was this patch tested?
Added UT.
Closes#22519 from maryannxue/spark-25505.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The `show create table` will show a lot of generated attributes for views that created by older Spark version. This PR will basically revert https://issues.apache.org/jira/browse/SPARK-19272 back, so when you `DESC [FORMATTED|EXTENDED] view` will show the original view DDL text.
## How was this patch tested?
Unit test.
Closes#22458 from zheyuan28/testbranch.
Lead-authored-by: Chris Zhao <chris.zhao@databricks.com>
Co-authored-by: Christopher Zhao <chris.zhao@databricks.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
There are 2 places we check for problematic `InSubquery`: the rule `ResolveSubquery` and `InSubquery.checkInputDataTypes`. We should unify them.
## How was this patch tested?
existing tests
Closes#22563 from cloud-fan/followup.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
As per the discussion in https://github.com/apache/spark/pull/22553#pullrequestreview-159192221,
override `filterKeys` violates the documented semantics.
This PR is to remove it and add documentation.
Also fix one potential non-serializable map in `FileStreamOptions`.
The only one call of `CaseInsensitiveMap`'s `filterKeys` left is
c3c45cbd76/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveOptions.scala (L88-L90)
But this one is OK.
## How was this patch tested?
Existing unit tests.
Closes#22562 from gengliangwang/SPARK-25541-FOLLOWUP.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PR removes the `InSubquery` expression which was introduced a long time ago and its only usage was removed in 4ce970d714. Hence it is not used anymore.
## How was this patch tested?
existing UTs
Closes#22556 from mgaido91/minor_insubq.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In ElementAt, when first argument is MapType, we should coerce the key type and the second argument based on findTightestCommonType. This is not happening currently. We may produce wrong output as we will incorrectly downcast the right hand side double expression to int.
```SQL
spark-sql> select element_at(map(1,"one", 2, "two"), 2.2);
two
```
Also, when the first argument is ArrayType, the second argument should be an integer type or a smaller integral type that can be safely casted to an integer type. Currently we may do an unsafe cast. In the following case, we should fail with an error as 2.2 is not a integer index. But instead we down cast it to int currently and return a result instead.
```SQL
spark-sql> select element_at(array(1,2), 1.24D);
1
```
This PR also supports implicit cast between two MapTypes. I have followed similar logic that exists today to do implicit casts between two array types.
## How was this patch tested?
Added new tests in DataFrameFunctionSuite, TypeCoercionSuite.
Closes#22544 from dilipbiswal/SPARK-25522.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Refactor `ColumnarBatchBenchmark` to use main method.
Generate benchmark result:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.vectorized.ColumnarBatchBenchmark"
```
## How was this patch tested?
manual tests
Closes#22490 from yucai/SPARK-25481.
Lead-authored-by: yucai <yyu1@ebay.com>
Co-authored-by: Yucai Yu <yucai.yu@foxmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/20023 proposed to allow precision lose during decimal operations, to reduce the possibilities of overflow. This is a behavior change and is protected by the DECIMAL_OPERATIONS_ALLOW_PREC_LOSS config. However, that PR introduced another behavior change: pick a minimum precision for integral literals, which is not protected by a config. This PR add a new config for it: `spark.sql.literal.pickMinimumPrecision`.
This can allow users to work around issue in SPARK-25454, which is caused by a long-standing bug of negative scale.
## How was this patch tested?
a new test
Closes#22494 from cloud-fan/decimal.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Currently, Spark has 7 `withTempPath` and 6 `withSQLConf` functions. This PR aims to remove duplicated and inconsistent code and reduce them to the following meaningful implementations.
**withTempPath**
- `SQLHelper.withTempPath`: The one which was used in `SQLTestUtils`.
**withSQLConf**
- `SQLHelper.withSQLConf`: The one which was used in `PlanTest`.
- `ExecutorSideSQLConfSuite.withSQLConf`: The one which doesn't throw `AnalysisException` on StaticConf changes.
- `SQLTestUtils.withSQLConf`: The one which overrides intentionally to change the active session.
```scala
protected override def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = {
SparkSession.setActiveSession(spark)
super.withSQLConf(pairs: _*)(f)
}
```
## How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#22548 from dongjoon-hyun/SPARK-25534.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
The PR introduces new JSON option `pretty` which allows to turn on `DefaultPrettyPrinter` of `Jackson`'s Json generator. New option is useful in exploring of deep nested columns and in converting of JSON columns in more readable representation (look at the added test).
## How was this patch tested?
Added rount trip test which convert an JSON string to pretty representation via `from_json()` and `to_json()`.
Closes#22534 from MaxGekk/pretty-json.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
