## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/15704 will fail if we use int literal in `DROP PARTITION`, and we have reverted it in branch-2.1.
This PR reverts it in master branch, and add a regression test for it, to make sure the master branch is healthy.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16036 from cloud-fan/revert.
## What changes were proposed in this pull request?
In Spark 2.1, we did a lot of refactor for `HiveExternalCatalog` and related code path. These refactor may introduce external behavior changes and break backward compatibility. e.g. http://issues.apache.org/jira/browse/SPARK-18464
To avoid future compatibility problems of `HiveExternalCatalog`, this PR dumps some typical table metadata from tables created by 2.0, and test if they can recognized by current version of Spark.
## How was this patch tested?
test only change
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16003 from cloud-fan/test.
### What changes were proposed in this pull request?
Currently, the name validation checks are limited to table creation. It is enfored by Analyzer rule: `PreWriteCheck`.
However, table renaming and database creation have the same issues. It makes more sense to do the checks in `SessionCatalog`. This PR is to add it into `SessionCatalog`.
### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16018 from gatorsmile/nameValidate.
## What changes were proposed in this pull request?
This PR only tries to fix things that looks pretty straightforward and were fixed in other previous PRs before.
This PR roughly fixes several things as below:
- Fix unrecognisable class and method links in javadoc by changing it from `[[..]]` to `` `...` ``
```
[error] .../spark/sql/core/target/java/org/apache/spark/sql/streaming/DataStreamReader.java:226: error: reference not found
[error] * Loads text files and returns a {link DataFrame} whose schema starts with a string column named
```
- Fix an exception annotation and remove code backticks in `throws` annotation
Currently, sbt unidoc with Java 8 complains as below:
```
[error] .../java/org/apache/spark/sql/streaming/StreamingQuery.java:72: error: unexpected text
[error] * throws StreamingQueryException, if <code>this</code> query has terminated with an exception.
```
`throws` should specify the correct class name from `StreamingQueryException,` to `StreamingQueryException` without backticks. (see [JDK-8007644](https://bugs.openjdk.java.net/browse/JDK-8007644)).
- Fix `[[http..]]` to `<a href="http..."></a>`.
```diff
- * [[https://blogs.oracle.com/java-platform-group/entry/diagnosing_tls_ssl_and_https Oracle
- * blog page]].
+ * <a href="https://blogs.oracle.com/java-platform-group/entry/diagnosing_tls_ssl_and_https">
+ * Oracle blog page</a>.
```
`[[http...]]` link markdown in scaladoc is unrecognisable in javadoc.
- It seems class can't have `return` annotation. So, two cases of this were removed.
```
[error] .../java/org/apache/spark/mllib/regression/IsotonicRegression.java:27: error: invalid use of return
[error] * return New instance of IsotonicRegression.
```
- Fix < to `<` and > to `>` according to HTML rules.
- Fix `</p>` complaint
- Exclude unrecognisable in javadoc, `constructor`, `todo` and `groupname`.
## How was this patch tested?
Manually tested by `jekyll build` with Java 7 and 8
```
java version "1.7.0_80"
Java(TM) SE Runtime Environment (build 1.7.0_80-b15)
Java HotSpot(TM) 64-Bit Server VM (build 24.80-b11, mixed mode)
```
```
java version "1.8.0_45"
Java(TM) SE Runtime Environment (build 1.8.0_45-b14)
Java HotSpot(TM) 64-Bit Server VM (build 25.45-b02, mixed mode)
```
Note: this does not yet make sbt unidoc suceed with Java 8 yet but it reduces the number of errors with Java 8.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15999 from HyukjinKwon/SPARK-3359-errors.
## What changes were proposed in this pull request?
The current implementation of column stats uses the base64 encoding of the internal UnsafeRow format to persist statistics (in table properties in Hive metastore). This is an internal format that is not stable across different versions of Spark and should NOT be used for persistence. In addition, it would be better if statistics stored in the catalog is human readable.
This pull request introduces the following changes:
1. Created a single ColumnStat class to for all data types. All data types track the same set of statistics.
2. Updated the implementation for stats collection to get rid of the dependency on internal data structures (e.g. InternalRow, or storing DateType as an int32). For example, previously dates were stored as a single integer, but are now stored as java.sql.Date. When we implement the next steps of CBO, we can add code to convert those back into internal types again.
3. Documented clearly what JVM data types are being used to store what data.
4. Defined a simple Map[String, String] interface for serializing and deserializing column stats into/from the catalog.
5. Rearranged the method/function structure so it is more clear what the supported data types are, and also moved how stats are generated into ColumnStat class so they are easy to find.
## How was this patch tested?
Removed most of the original test cases created for column statistics, and added three very simple ones to cover all the cases. The three test cases validate:
1. Roundtrip serialization works.
2. Behavior when analyzing non-existent column or unsupported data type column.
3. Result for stats collection for all valid data types.
Also moved parser related tests into a parser test suite and added an explicit serialization test for the Hive external catalog.
Author: Reynold Xin <rxin@databricks.com>
Closes#15959 from rxin/SPARK-18522.
### What changes were proposed in this pull request?
In Spark 2.0, `SaveAsTable` does not work when the target table is a Hive serde table, but Spark 1.6 works.
**Spark 1.6**
``` Scala
scala> sql("create table sample.sample stored as SEQUENCEFILE as select 1 as key, 'abc' as value")
res2: org.apache.spark.sql.DataFrame = []
scala> val df = sql("select key, value as value from sample.sample")
df: org.apache.spark.sql.DataFrame = [key: int, value: string]
scala> df.write.mode("append").saveAsTable("sample.sample")
scala> sql("select * from sample.sample").show()
+---+-----+
|key|value|
+---+-----+
| 1| abc|
| 1| abc|
+---+-----+
```
**Spark 2.0**
``` Scala
scala> df.write.mode("append").saveAsTable("sample.sample")
org.apache.spark.sql.AnalysisException: Saving data in MetastoreRelation sample, sample
is not supported.;
```
So far, we do not plan to support it in Spark 2.1 due to the risk. Spark 1.6 works because it internally uses insertInto. But, if we change it back it will break the semantic of saveAsTable (this method uses by-name resolution instead of using by-position resolution used by insertInto). More extra changes are needed to support `hive` as a `format` in DataFrameWriter.
Instead, users should use insertInto API. This PR corrects the error messages. Users can understand how to bypass it before we support it in a separate PR.
### How was this patch tested?
Test cases are added
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15926 from gatorsmile/saveAsTableFix5.
## What changes were proposed in this pull request?
While this behavior is debatable, consider the following use case:
```sql
UNCACHE TABLE foo;
CACHE TABLE foo AS
SELECT * FROM bar
```
The command above fails the first time you run it. But I want to run the command above over and over again, and I don't want to change my code just for the first run of it.
The issue is that subsequent `CACHE TABLE` commands do not overwrite the existing table.
Now we can do:
```sql
UNCACHE TABLE IF EXISTS foo;
CACHE TABLE foo AS
SELECT * FROM bar
```
## How was this patch tested?
Unit tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15896 from brkyvz/uncache.
## What changes were proposed in this pull request?
HiveExternalCatalog.listPartitions should only call `getTable` once, instead of calling it for every partitions.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15978 from cloud-fan/perf.
## What changes were proposed in this pull request?
This PR adds the support for `UserDefinedType` when writing out instead of throwing `ClassCastException` in ORC data source.
In more details, `OrcStruct` is being created based on string from`DataType.catalogString`. For user-defined type, it seems it returns `sqlType.simpleString` for `catalogString` by default[1]. However, during type-dispatching to match the output with the schema, it tries to cast to, for example, `StructType`[2].
So, running the codes below (`MyDenseVector` was borrowed[3]) :
``` scala
val data = Seq((1, new UDT.MyDenseVector(Array(0.25, 2.25, 4.25))))
val udtDF = data.toDF("id", "vectors")
udtDF.write.orc("/tmp/test.orc")
```
ends up throwing an exception as below:
```
java.lang.ClassCastException: org.apache.spark.sql.UDT$MyDenseVectorUDT cannot be cast to org.apache.spark.sql.types.ArrayType
at org.apache.spark.sql.hive.HiveInspectors$class.wrapperFor(HiveInspectors.scala:381)
at org.apache.spark.sql.hive.orc.OrcSerializer.wrapperFor(OrcFileFormat.scala:164)
...
```
So, this PR uses `UserDefinedType.sqlType` during finding the correct converter when writing out in ORC data source.
[1]dfdcab00c7/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala (L95)
[2]d2dc8c4a16/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala (L326)
[3]2bfed1a0c5/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala (L38-L70)
## How was this patch tested?
Unit tests in `OrcQuerySuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15361 from HyukjinKwon/SPARK-17765.
## What changes were proposed in this pull request?
I'm spending more time at the design & code level for cost-based optimizer now, and have found a number of issues related to maintainability and compatibility that I will like to address.
This is a small pull request to clean up AnalyzeColumnCommand:
1. Removed warning on duplicated columns. Warnings in log messages are useless since most users that run SQL don't see them.
2. Removed the nested updateStats function, by just inlining the function.
3. Renamed a few functions to better reflect what they do.
4. Removed the factory apply method for ColumnStatStruct. It is a bad pattern to use a apply method that returns an instantiation of a class that is not of the same type (ColumnStatStruct.apply used to return CreateNamedStruct).
5. Renamed ColumnStatStruct to just AnalyzeColumnCommand.
6. Added more documentation explaining some of the non-obvious return types and code blocks.
In follow-up pull requests, I'd like to address the following:
1. Get rid of the Map[String, ColumnStat] map, since internally we should be using Attribute to reference columns, rather than strings.
2. Decouple the fields exposed by ColumnStat and internals of Spark SQL's execution path. Currently the two are coupled because ColumnStat takes in an InternalRow.
3. Correctness: Remove code path that stores statistics in the catalog using the base64 encoding of the UnsafeRow format, which is not stable across Spark versions.
4. Clearly document the data representation stored in the catalog for statistics.
## How was this patch tested?
Affected test cases have been updated.
Author: Reynold Xin <rxin@databricks.com>
Closes#15933 from rxin/SPARK-18505.
## What changes were proposed in this pull request?
When reading zero columns (e.g., count(*)) from ORC or any other format that uses HiveShim, actually set the read column list to empty for Hive to use.
## How was this patch tested?
Query correctness is handled by existing unit tests. I'm happy to add more if anyone can point out some case that is not covered.
Reduction in data read can be verified in the UI when built with a recent version of Hadoop say:
```
build/mvn -Pyarn -Phadoop-2.7 -Dhadoop.version=2.7.0 -Phive -DskipTests clean package
```
However the default Hadoop 2.2 that is used for unit tests does not report actual bytes read and instead just full file sizes (see FileScanRDD.scala line 80). Therefore I don't think there is a good way to add a unit test for this.
I tested with the following setup using above build options
```
case class OrcData(intField: Long, stringField: String)
spark.range(1,1000000).map(i => OrcData(i, s"part-$i")).toDF().write.format("orc").save("orc_test")
sql(
s"""CREATE EXTERNAL TABLE orc_test(
| intField LONG,
| stringField STRING
|)
|STORED AS ORC
|LOCATION '${System.getProperty("user.dir") + "/orc_test"}'
""".stripMargin)
```
## Results
query | Spark 2.0.2 | this PR
---|---|---
`sql("select count(*) from orc_test").collect`|4.4 MB|199.4 KB
`sql("select intField from orc_test").collect`|743.4 KB|743.4 KB
`sql("select * from orc_test").collect`|4.4 MB|4.4 MB
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#15898 from aray/sql-orc-no-col.
## What changes were proposed in this pull request?
The current semantic of the warehouse config:
1. it's a static config, which means you can't change it once your spark application is launched.
2. Once a database is created, its location won't change even the warehouse path config is changed.
3. default database is a special case, although its location is fixed, but the locations of tables created in it are not. If a Spark app starts with warehouse path B(while the location of default database is A), then users create a table `tbl` in default database, its location will be `B/tbl` instead of `A/tbl`. If uses change the warehouse path config to C, and create another table `tbl2`, its location will still be `B/tbl2` instead of `C/tbl2`.
rule 3 doesn't make sense and I think we made it by mistake, not intentionally. Data source tables don't follow rule 3 and treat default database like normal ones.
This PR fixes hive serde tables to make it consistent with data source tables.
## How was this patch tested?
HiveSparkSubmitSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15812 from cloud-fan/default-db.
## What changes were proposed in this pull request?
Before Spark 2.1, users can create an external data source table without schema, and we will infer the table schema at runtime. In Spark 2.1, we decided to infer the schema when the table was created, so that we don't need to infer it again and again at runtime.
This is a good improvement, but we should still respect and support old tables which doesn't store table schema in metastore.
## How was this patch tested?
regression test.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15900 from cloud-fan/hive-catalog.
## What changes were proposed in this pull request?
While being evaluated in Spark SQL, Hive UDAFs don't support partial aggregation. This PR migrates `HiveUDAFFunction`s to `TypedImperativeAggregate`, which already provides partial aggregation support for aggregate functions that may use arbitrary Java objects as aggregation states.
The following snippet shows the effect of this PR:
```scala
import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax
sql(s"CREATE FUNCTION hive_max AS '${classOf[GenericUDAFMax].getName}'")
spark.range(100).createOrReplaceTempView("t")
// A query using both Spark SQL native `max` and Hive `max`
sql(s"SELECT max(id), hive_max(id) FROM t").explain()
```
Before this PR:
```
== Physical Plan ==
SortAggregate(key=[], functions=[max(id#1L), default.hive_max(default.hive_max, HiveFunctionWrapper(org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax,org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax7475f57e), id#1L, false, 0, 0)])
+- Exchange SinglePartition
+- *Range (0, 100, step=1, splits=Some(1))
```
After this PR:
```
== Physical Plan ==
SortAggregate(key=[], functions=[max(id#1L), default.hive_max(default.hive_max, HiveFunctionWrapper(org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax,org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax5e18a6a7), id#1L, false, 0, 0)])
+- Exchange SinglePartition
+- SortAggregate(key=[], functions=[partial_max(id#1L), partial_default.hive_max(default.hive_max, HiveFunctionWrapper(org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax,org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax5e18a6a7), id#1L, false, 0, 0)])
+- *Range (0, 100, step=1, splits=Some(1))
```
The tricky part of the PR is mostly about updating and passing around aggregation states of `HiveUDAFFunction`s since the aggregation state of a Hive UDAF may appear in three different forms. Let's take a look at the testing `MockUDAF` added in this PR as an example. This UDAF computes the count of non-null values together with the count of nulls of a given column. Its aggregation state may appear as the following forms at different time:
1. A `MockUDAFBuffer`, which is a concrete subclass of `GenericUDAFEvaluator.AggregationBuffer`
The form used by Hive UDAF API. This form is required by the following scenarios:
- Calling `GenericUDAFEvaluator.iterate()` to update an existing aggregation state with new input values.
- Calling `GenericUDAFEvaluator.terminate()` to get the final aggregated value from an existing aggregation state.
- Calling `GenericUDAFEvaluator.merge()` to merge other aggregation states into an existing aggregation state.
The existing aggregation state to be updated must be in this form.
Conversions:
- To form 2:
`GenericUDAFEvaluator.terminatePartial()`
- To form 3:
Convert to form 2 first, and then to 3.
2. An `Object[]` array containing two `java.lang.Long` values.
The form used to interact with Hive's `ObjectInspector`s. This form is required by the following scenarios:
- Calling `GenericUDAFEvaluator.terminatePartial()` to convert an existing aggregation state in form 1 to form 2.
- Calling `GenericUDAFEvaluator.merge()` to merge other aggregation states into an existing aggregation state.
The input aggregation state must be in this form.
Conversions:
- To form 1:
No direct method. Have to create an empty `AggregationBuffer` and merge it into the empty buffer.
- To form 3:
`unwrapperFor()`/`unwrap()` method of `HiveInspectors`
3. The byte array that holds data of an `UnsafeRow` with two `LongType` fields.
The form used by Spark SQL to shuffle partial aggregation results. This form is required because `TypedImperativeAggregate` always asks its subclasses to serialize their aggregation states into a byte array.
Conversions:
- To form 1:
Convert to form 2 first, and then to 1.
- To form 2:
`wrapperFor()`/`wrap()` method of `HiveInspectors`
Here're some micro-benchmark results produced by the most recent master and this PR branch.
Master:
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
hive udaf vs spark af: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
w/o groupBy 339 / 372 3.1 323.2 1.0X
w/ groupBy 503 / 529 2.1 479.7 0.7X
```
This PR:
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
hive udaf vs spark af: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
w/o groupBy 116 / 126 9.0 110.8 1.0X
w/ groupBy 151 / 159 6.9 144.0 0.8X
```
Benchmark code snippet:
```scala
test("Hive UDAF benchmark") {
val N = 1 << 20
sparkSession.sql(s"CREATE TEMPORARY FUNCTION hive_max AS '${classOf[GenericUDAFMax].getName}'")
val benchmark = new Benchmark(
name = "hive udaf vs spark af",
valuesPerIteration = N,
minNumIters = 5,
warmupTime = 5.seconds,
minTime = 5.seconds,
outputPerIteration = true
)
benchmark.addCase("w/o groupBy") { _ =>
sparkSession.range(N).agg("id" -> "hive_max").collect()
}
benchmark.addCase("w/ groupBy") { _ =>
sparkSession.range(N).groupBy($"id" % 10).agg("id" -> "hive_max").collect()
}
benchmark.run()
sparkSession.sql(s"DROP TEMPORARY FUNCTION IF EXISTS hive_max")
}
```
## How was this patch tested?
New test suite `HiveUDAFSuite` is added.
Author: Cheng Lian <lian@databricks.com>
Closes#15703 from liancheng/partial-agg-hive-udaf.
## What changes were proposed in this pull request?
This PR aims to improve DataSource option keys to be more case-insensitive
DataSource partially use CaseInsensitiveMap in code-path. For example, the following fails to find url.
```scala
val df = spark.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
df.write.format("jdbc")
.option("UrL", url1)
.option("dbtable", "TEST.SAVETEST")
.options(properties.asScala)
.save()
```
This PR makes DataSource options to use CaseInsensitiveMap internally and also makes DataSource to use CaseInsensitiveMap generally except `InMemoryFileIndex` and `InsertIntoHadoopFsRelationCommand`. We can not pass them CaseInsensitiveMap because they creates new case-sensitive HadoopConfs by calling newHadoopConfWithOptions(options) inside.
## How was this patch tested?
Pass the Jenkins test with newly added test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15884 from dongjoon-hyun/SPARK-18433.
## What changes were proposed in this pull request?
it's weird that every session can set its own warehouse path at runtime, we should forbid it and make it a static conf.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15825 from cloud-fan/warehouse.
## What changes were proposed in this pull request?
Currently, `SQLBuilder` handles `LIMIT` by always adding `LIMIT` at the end of the generated subSQL. It makes `RuntimeException`s like the following. This PR adds a parenthesis always except `SubqueryAlias` is used together with `LIMIT`.
**Before**
``` scala
scala> sql("CREATE TABLE tbl(id INT)")
scala> sql("CREATE VIEW v1(id2) AS SELECT id FROM tbl LIMIT 2")
java.lang.RuntimeException: Failed to analyze the canonicalized SQL: ...
```
**After**
``` scala
scala> sql("CREATE TABLE tbl(id INT)")
scala> sql("CREATE VIEW v1(id2) AS SELECT id FROM tbl LIMIT 2")
scala> sql("SELECT id2 FROM v1")
res4: org.apache.spark.sql.DataFrame = [id2: int]
```
**Fixed cases in this PR**
The following two cases are the detail query plans having problematic SQL generations.
1. `SELECT * FROM (SELECT id FROM tbl LIMIT 2)`
Please note that **FROM SELECT** part of the generated SQL in the below. When we don't use '()' for limit, this fails.
```scala
# Original logical plan:
Project [id#1]
+- GlobalLimit 2
+- LocalLimit 2
+- Project [id#1]
+- MetastoreRelation default, tbl
# Canonicalized logical plan:
Project [gen_attr_0#1 AS id#4]
+- SubqueryAlias tbl
+- Project [gen_attr_0#1]
+- GlobalLimit 2
+- LocalLimit 2
+- Project [gen_attr_0#1]
+- SubqueryAlias gen_subquery_0
+- Project [id#1 AS gen_attr_0#1]
+- SQLTable default, tbl, [id#1]
# Generated SQL:
SELECT `gen_attr_0` AS `id` FROM (SELECT `gen_attr_0` FROM SELECT `gen_attr_0` FROM (SELECT `id` AS `gen_attr_0` FROM `default`.`tbl`) AS gen_subquery_0 LIMIT 2) AS tbl
```
2. `SELECT * FROM (SELECT id FROM tbl TABLESAMPLE (2 ROWS))`
Please note that **((~~~) AS gen_subquery_0 LIMIT 2)** in the below. When we use '()' for limit on `SubqueryAlias`, this fails.
```scala
# Original logical plan:
Project [id#1]
+- Project [id#1]
+- GlobalLimit 2
+- LocalLimit 2
+- MetastoreRelation default, tbl
# Canonicalized logical plan:
Project [gen_attr_0#1 AS id#4]
+- SubqueryAlias tbl
+- Project [gen_attr_0#1]
+- GlobalLimit 2
+- LocalLimit 2
+- SubqueryAlias gen_subquery_0
+- Project [id#1 AS gen_attr_0#1]
+- SQLTable default, tbl, [id#1]
# Generated SQL:
SELECT `gen_attr_0` AS `id` FROM (SELECT `gen_attr_0` FROM ((SELECT `id` AS `gen_attr_0` FROM `default`.`tbl`) AS gen_subquery_0 LIMIT 2)) AS tbl
```
## How was this patch tested?
Pass the Jenkins test with a newly added test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15546 from dongjoon-hyun/SPARK-17982.
## What changes were proposed in this pull request?
As of current 2.1, INSERT OVERWRITE with dynamic partitions against a Datasource table will overwrite the entire table instead of only the partitions matching the static keys, as in Hive. It also doesn't respect custom partition locations.
This PR adds support for all these operations to Datasource tables managed by the Hive metastore. It is implemented as follows
- During planning time, the full set of partitions affected by an INSERT or OVERWRITE command is read from the Hive metastore.
- The planner identifies any partitions with custom locations and includes this in the write task metadata.
- FileFormatWriter tasks refer to this custom locations map when determining where to write for dynamic partition output.
- When the write job finishes, the set of written partitions is compared against the initial set of matched partitions, and the Hive metastore is updated to reflect the newly added / removed partitions.
It was necessary to introduce a method for staging files with absolute output paths to `FileCommitProtocol`. These files are not handled by the Hadoop output committer but are moved to their final locations when the job commits.
The overwrite behavior of legacy Datasource tables is also changed: no longer will the entire table be overwritten if a partial partition spec is present.
cc cloud-fan yhuai
## How was this patch tested?
Unit tests, existing tests.
Author: Eric Liang <ekl@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15814 from ericl/sc-5027.
## What changes were proposed in this pull request?
Randomized tests in `ObjectHashAggregateSuite` is being flaky and breaks PR builds. This PR disables them temporarily to bring back the PR build.
## How was this patch tested?
N/A
Author: Cheng Lian <lian@databricks.com>
Closes#15845 from liancheng/ignore-flaky-object-hash-agg-suite.
## What changes were proposed in this pull request?
This PR corrects several partition related behaviors of `ExternalCatalog`:
1. default partition location should not always lower case the partition column names in path string(fix `HiveExternalCatalog`)
2. rename partition should not always lower case the partition column names in updated partition path string(fix `HiveExternalCatalog`)
3. rename partition should update the partition location only for managed table(fix `InMemoryCatalog`)
4. create partition with existing directory should be fine(fix `InMemoryCatalog`)
5. create partition with non-existing directory should create that directory(fix `InMemoryCatalog`)
6. drop partition from external table should not delete the directory(fix `InMemoryCatalog`)
## How was this patch tested?
new tests in `ExternalCatalogSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15797 from cloud-fan/partition.
(Link to Jira issue: https://issues.apache.org/jira/browse/SPARK-17993)
## What changes were proposed in this pull request?
PR #14690 broke parquet log output redirection for converted partitioned Hive tables. For example, when querying parquet files written by Parquet-mr 1.6.0 Spark prints a torrent of (harmless) warning messages from the Parquet reader:
```
Oct 18, 2016 7:42:18 PM WARNING: org.apache.parquet.CorruptStatistics: Ignoring statistics because created_by could not be parsed (see PARQUET-251): parquet-mr version 1.6.0
org.apache.parquet.VersionParser$VersionParseException: Could not parse created_by: parquet-mr version 1.6.0 using format: (.+) version ((.*) )?\(build ?(.*)\)
at org.apache.parquet.VersionParser.parse(VersionParser.java:112)
at org.apache.parquet.CorruptStatistics.shouldIgnoreStatistics(CorruptStatistics.java:60)
at org.apache.parquet.format.converter.ParquetMetadataConverter.fromParquetStatistics(ParquetMetadataConverter.java:263)
at org.apache.parquet.hadoop.ParquetFileReader$Chunk.readAllPages(ParquetFileReader.java:583)
at org.apache.parquet.hadoop.ParquetFileReader.readNextRowGroup(ParquetFileReader.java:513)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.checkEndOfRowGroup(VectorizedParquetRecordReader.java:270)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:225)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:137)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:102)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:162)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:102)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.scan_nextBatch$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:372)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:231)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:225)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
```
This only happens during execution, not planning, and it doesn't matter what log level the `SparkContext` is set to. That's because Parquet (versions < 1.9) doesn't use slf4j for logging. Note, you can tell that log redirection is not working here because the log message format does not conform to the default Spark log message format.
This is a regression I noted as something we needed to fix as a follow up.
It appears that the problem arose because we removed the call to `inferSchema` during Hive table conversion. That call is what triggered the output redirection.
## How was this patch tested?
I tested this manually in four ways:
1. Executing `spark.sqlContext.range(10).selectExpr("id as a").write.mode("overwrite").parquet("test")`.
2. Executing `spark.read.format("parquet").load(legacyParquetFile).show` for a Parquet file `legacyParquetFile` written using Parquet-mr 1.6.0.
3. Executing `select * from legacy_parquet_table limit 1` for some unpartitioned Parquet-based Hive table written using Parquet-mr 1.6.0.
4. Executing `select * from legacy_partitioned_parquet_table where partcol=x limit 1` for some partitioned Parquet-based Hive table written using Parquet-mr 1.6.0.
I ran each test with a new instance of `spark-shell` or `spark-sql`.
Incidentally, I found that test case 3 was not a regression—redirection was not occurring in the master codebase prior to #14690.
I spent some time working on a unit test, but based on my experience working on this ticket I feel that automated testing here is far from feasible.
cc ericl dongjoon-hyun
Author: Michael Allman <michael@videoamp.com>
Closes#15538 from mallman/spark-17993-fix_parquet_log_redirection.
## What changes were proposed in this pull request?
`InsertIntoHadoopFsRelationCommand` does not keep track if it inserts into a table and what table it inserts to. This can make debugging these statements problematic. This PR adds table information the `InsertIntoHadoopFsRelationCommand`. Explaining this SQL command `insert into prq select * from range(0, 100000)` now yields the following executed plan:
```
== Physical Plan ==
ExecutedCommand
+- InsertIntoHadoopFsRelationCommand file:/dev/assembly/spark-warehouse/prq, ParquetFormat, <function1>, Map(serialization.format -> 1, path -> file:/dev/assembly/spark-warehouse/prq), Append, CatalogTable(
Table: `default`.`prq`
Owner: hvanhovell
Created: Wed Nov 09 17:42:30 CET 2016
Last Access: Thu Jan 01 01:00:00 CET 1970
Type: MANAGED
Schema: [StructField(id,LongType,true)]
Provider: parquet
Properties: [transient_lastDdlTime=1478709750]
Storage(Location: file:/dev/assembly/spark-warehouse/prq, InputFormat: org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat, OutputFormat: org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat, Serde: org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe, Properties: [serialization.format=1]))
+- Project [id#7L]
+- Range (0, 100000, step=1, splits=None)
```
## How was this patch tested?
Added extra checks to the `ParquetMetastoreSuite`
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15832 from hvanhovell/SPARK-18370.
## What changes were proposed in this pull request?
Test case initialization order under Maven and SBT are different. Maven always creates instances of all test cases and then run them all together.
This fails `ObjectHashAggregateSuite` because the randomized test cases there register a temporary Hive function right before creating a test case, and can be cleared while initializing other successive test cases. In SBT, this is fine since the created test case is executed immediately after creating the temporary function.
To fix this issue, we should put initialization/destruction code into `beforeAll()` and `afterAll()`.
## How was this patch tested?
Existing tests.
Author: Cheng Lian <lian@databricks.com>
Closes#15802 from liancheng/fix-flaky-object-hash-agg-suite.
## What changes were proposed in this pull request?
`LogicalPlanToSQLSuite` uses the following command to update the existing answer files.
```bash
SPARK_GENERATE_GOLDEN_FILES=1 build/sbt "hive/test-only *LogicalPlanToSQLSuite"
```
However, after introducing `getTestResourcePath`, it fails to update the previous golden answer files in the predefined directory. This issue aims to fix that.
## How was this patch tested?
It's a testsuite update. Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15789 from dongjoon-hyun/SPARK-18292.
### What changes were proposed in this pull request?
`Partitioned View` is not supported by SPARK SQL. For Hive partitioned view, SHOW CREATE TABLE is unable to generate the right DDL. Thus, SHOW CREATE TABLE should not support it like the other Hive-only features. This PR is to issue an exception when detecting the view is a partitioned view.
### How was this patch tested?
Added a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15233 from gatorsmile/partitionedView.
## What changes were proposed in this pull request?
These are no longer needed after https://issues.apache.org/jira/browse/SPARK-17183
cc cloud-fan
## How was this patch tested?
Existing parquet and orc tests.
Author: Eric Liang <ekl@databricks.com>
Closes#15799 from ericl/sc-4929.
## What changes were proposed in this pull request?
This PR port RDD API to use commit protocol, the changes made here:
1. Add new internal helper class that saves an RDD using a Hadoop OutputFormat named `SparkNewHadoopWriter`, it's similar with `SparkHadoopWriter` but uses commit protocol. This class supports the newer `mapreduce` API, instead of the old `mapred` API which is supported by `SparkHadoopWriter`;
2. Rewrite `PairRDDFunctions.saveAsNewAPIHadoopDataset` function, so it uses commit protocol now.
## How was this patch tested?
Exsiting test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15769 from jiangxb1987/rdd-commit.
## What changes were proposed in this pull request?
a follow up of https://github.com/apache/spark/pull/15688
## How was this patch tested?
updated test in `DDLSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15805 from cloud-fan/truncate.
## What changes were proposed in this pull request?
In RewriteDistinctAggregates rewrite funtion,after the UDAF's childs are mapped to AttributeRefference, If the UDAF(such as ApproximatePercentile) has a foldable TypeCheck for the input, It will failed because the AttributeRefference is not foldable,then the UDAF is not resolved, and then nullify on the unresolved object will throw a Exception.
In this PR, only map Unfoldable child to AttributeRefference, this can avoid the UDAF's foldable TypeCheck. and then only Expand Unfoldable child, there is no need to Expand a static value(foldable value).
**Before sql result**
> select percentile_approxy(key,0.99999),count(distinct key),sume(distinc key) from src limit 1
> org.apache.spark.sql.catalyst.analysis.UnresolvedException: Invalid call to dataType on unresolved object, tree: 'percentile_approx(CAST(src.`key` AS DOUBLE), CAST(0.99999BD AS DOUBLE), 10000)
> at org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute.dataType(unresolved.scala:92)
> at org.apache.spark.sql.catalyst.optimizer.RewriteDistinctAggregates$.org$apache$spark$sql$catalyst$optimizer$RewriteDistinctAggregates$$nullify(RewriteDistinctAggregates.scala:261)
**After sql result**
> select percentile_approxy(key,0.99999),count(distinct key),sume(distinc key) from src limit 1
> [498.0,309,79136]
## How was this patch tested?
Add a test case in HiveUDFSuit.
Author: root <root@iZbp1gsnrlfzjxh82cz80vZ.(none)>
Closes#15668 from windpiger/RewriteDistinctUDAFUnresolveExcep.
### What changes were proposed in this pull request?
Based on the discussion in [SPARK-18209](https://issues.apache.org/jira/browse/SPARK-18209). It doesn't really make sense to create permanent views based on temporary views or temporary UDFs.
To disallow the supports and issue the exceptions, this PR needs to detect whether a temporary view/UDF is being used when defining a permanent view. Basically, this PR can be split to two sub-tasks:
**Task 1:** detecting a temporary view from the query plan of view definition.
When finding an unresolved temporary view, Analyzer replaces it by a `SubqueryAlias` with the corresponding logical plan, which is stored in an in-memory HashMap. After replacement, it is impossible to detect whether the `SubqueryAlias` is added/generated from a temporary view. Thus, to detect the usage of a temporary view in view definition, this PR traverses the unresolved logical plan and uses the name of an `UnresolvedRelation` to detect whether it is a (global) temporary view.
**Task 2:** detecting a temporary UDF from the query plan of view definition.
Detecting usage of a temporary UDF in view definition is not straightfoward.
First, in the analyzed plan, we are having different forms to represent the functions. More importantly, some classes (e.g., `HiveGenericUDF`) are not accessible from `CreateViewCommand`, which is part of `sql/core`. Thus, we used the unanalyzed plan `child` of `CreateViewCommand` to detect the usage of a temporary UDF. Because the plan has already been successfully analyzed, we can assume the functions have been defined/registered.
Second, in Spark, the functions have four forms: Spark built-in functions, built-in hash functions, permanent UDFs and temporary UDFs. We do not have any direct way to determine whether a function is temporary or not. Thus, we introduced a function `isTemporaryFunction` in `SessionCatalog`. This function contains the detailed logics to determine whether a function is temporary or not.
### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15764 from gatorsmile/blockTempFromPermViewCreation.
## What changes were proposed in this pull request?
This adds support for Hive variables:
* Makes values set via `spark-sql --hivevar name=value` accessible
* Adds `getHiveVar` and `setHiveVar` to the `HiveClient` interface
* Adds a SessionVariables trait for sessions like Hive that support variables (including Hive vars)
* Adds SessionVariables support to variable substitution
* Adds SessionVariables support to the SET command
## How was this patch tested?
* Adds a test to all supported Hive versions for accessing Hive variables
* Adds HiveVariableSubstitutionSuite
Author: Ryan Blue <blue@apache.org>
Closes#15738 from rdblue/SPARK-18086-add-hivevar-support.
## What changes were proposed in this pull request?
Add a function to check if two integers are compatible when invoking `acceptsType()` in `DataType`.
## How was this patch tested?
Manually.
E.g.
```
spark.sql("create table t3(a map<bigint, array<string>>)")
spark.sql("select * from t3 where a[1] is not null")
```
Before:
```
cannot resolve 't.`a`[1]' due to data type mismatch: argument 2 requires bigint type, however, '1' is of int type.; line 1 pos 22
org.apache.spark.sql.AnalysisException: cannot resolve 't.`a`[1]' due to data type mismatch: argument 2 requires bigint type, however, '1' is of int type.; line 1 pos 22
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:82)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:307)
```
After:
Run the sql queries above. No errors.
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes#15448 from weiqingy/SPARK_17108.
### What changes were proposed in this pull request?
Currently, the Hive built-in `hash` function is not being used in Spark since Spark 2.0. The public interface does not allow users to unregister the Spark built-in functions. Thus, users will never use Hive's built-in `hash` function.
The only exception here is `TestHiveFunctionRegistry`, which allows users to unregister the built-in functions. Thus, we can load Hive's hash function in the test cases. If we disable it, 10+ test cases will fail because the results are different from the Hive golden answer files.
This PR is to remove `hash` from the list of `hiveFunctions` in `HiveSessionCatalog`. It will also remove `TestHiveFunctionRegistry`. This removal makes us easier to remove `TestHiveSessionState` in the future.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14498 from gatorsmile/removeHash.
## What changes were proposed in this pull request?
Previously `TRUNCATE TABLE ... PARTITION` will always truncate the whole table for data source tables, this PR fixes it and improve `InMemoryCatalog` to make this command work with it.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15688 from cloud-fan/truncate.
## What changes were proposed in this pull request?
For data source tables, we will put its table schema, partition columns, etc. to table properties, to work around some hive metastore issues, e.g. not case-preserving, bad decimal type support, etc.
We should also do this for hive serde tables, to reduce the difference between hive serde tables and data source tables, e.g. column names should be case preserving.
## How was this patch tested?
existing tests, and a new test in `HiveExternalCatalog`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14750 from cloud-fan/minor1.
## What changes were proposed in this pull request?
It seems the proximate cause of the test failures is that `cast(str as decimal)` in derby will raise an exception instead of returning NULL. This is a problem since Hive sometimes inserts `__HIVE_DEFAULT_PARTITION__` entries into the partition table as documented here: https://github.com/apache/hive/blob/trunk/metastore/src/java/org/apache/hadoop/hive/metastore/MetaStoreDirectSql.java#L1034
Basically, when these special default partitions are present, partition pruning pushdown using the SQL-direct mode will fail due this cast exception. As commented on in `MetaStoreDirectSql.java` above, this is normally fine since Hive falls back to JDO pruning, however when the pruning predicate contains an unsupported operator such as `>`, that will fail as well.
The only remaining question is why this behavior is nondeterministic. We know that when the test flakes, retries do not help, therefore the cause must be environmental. The current best hypothesis is that some config is different between different jenkins runs, which is why this PR prints out the Spark SQL and Hive confs for the test. The hope is that by comparing the config state for failure vs success we can isolate the root cause of the flakiness.
**Update:** we could not isolate the issue. It does not seem to be due to configuration differences. As such, I'm going to enable the non-flaky parts of the test since we are fairly confident these issues only occur with Derby (which is not used in production).
## How was this patch tested?
N/A
Author: Eric Liang <ekl@databricks.com>
Closes#15725 from ericl/print-confs-out.
hive.exec.stagingdir have no effect in spark2.0.1，
Hive confs in hive-site.xml will be loaded in `hadoopConf`, so we should use `hadoopConf` in `InsertIntoHiveTable` instead of `SessionState.conf`
Author: 福星 <fuxing@wacai.com>
Closes#15744 from ClassNotFoundExp/master.
## What changes were proposed in this pull request?
This patch renames partitionProviderIsHive to tracksPartitionsInCatalog, as the old name was too Hive specific.
## How was this patch tested?
Should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#15750 from rxin/SPARK-18244.
## What changes were proposed in this pull request?
This PR adds a new hash-based aggregate operator named `ObjectHashAggregateExec` that supports `TypedImperativeAggregate`, which may use arbitrary Java objects as aggregation states. Please refer to the [design doc](https://issues.apache.org/jira/secure/attachment/12834260/%5BDesign%20Doc%5D%20Support%20for%20Arbitrary%20Aggregation%20States.pdf) attached in [SPARK-17949](https://issues.apache.org/jira/browse/SPARK-17949) for more details about it.
The major benefit of this operator is better performance when evaluating `TypedImperativeAggregate` functions, especially when there are relatively few distinct groups. Functions like Hive UDAFs, `collect_list`, and `collect_set` may also benefit from this after being migrated to `TypedImperativeAggregate`.
The following feature flag is introduced to enable or disable the new aggregate operator:
- Name: `spark.sql.execution.useObjectHashAggregateExec`
- Default value: `true`
We can also configure the fallback threshold using the following SQL operation:
- Name: `spark.sql.objectHashAggregate.sortBased.fallbackThreshold`
- Default value: 128
Fallback to sort-based aggregation when more than 128 distinct groups are accumulated in the aggregation hash map. This number is intentionally made small to avoid GC problems since aggregation buffers of this operator may contain arbitrary Java objects.
This may be improved by implementing size tracking for this operator, but that can be done in a separate PR.
Code generation and size tracking are planned to be implemented in follow-up PRs.
## Benchmark results
### `ObjectHashAggregateExec` vs `SortAggregateExec`
The first benchmark compares `ObjectHashAggregateExec` and `SortAggregateExec` by evaluating `typed_count`, a testing `TypedImperativeAggregate` version of the SQL `count` function.
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
object agg v.s. sort agg: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
sort agg w/ group by 31251 / 31908 3.4 298.0 1.0X
object agg w/ group by w/o fallback 6903 / 7141 15.2 65.8 4.5X
object agg w/ group by w/ fallback 20945 / 21613 5.0 199.7 1.5X
sort agg w/o group by 4734 / 5463 22.1 45.2 6.6X
object agg w/o group by w/o fallback 4310 / 4529 24.3 41.1 7.3X
```
The next benchmark compares `ObjectHashAggregateExec` and `SortAggregateExec` by evaluating the Spark native version of `percentile_approx`.
Note that `percentile_approx` is so heavy an aggregate function that the bottleneck of the benchmark is evaluating the aggregate function itself rather than the aggregate operator since I couldn't run a large scale benchmark on my laptop. That's why the results are so close and looks counter-intuitive (aggregation with grouping is even faster than that aggregation without grouping).
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
object agg v.s. sort agg: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
sort agg w/ group by 3418 / 3530 0.6 1630.0 1.0X
object agg w/ group by w/o fallback 3210 / 3314 0.7 1530.7 1.1X
object agg w/ group by w/ fallback 3419 / 3511 0.6 1630.1 1.0X
sort agg w/o group by 4336 / 4499 0.5 2067.3 0.8X
object agg w/o group by w/o fallback 4271 / 4372 0.5 2036.7 0.8X
```
### Hive UDAF vs Spark AF
This benchmark compares the following two kinds of aggregate functions:
- "hive udaf": Hive implementation of `percentile_approx`, without partial aggregation supports, evaluated using `SortAggregateExec`.
- "spark af": Spark native implementation of `percentile_approx`, with partial aggregation support, evaluated using `ObjectHashAggregateExec`
The performance differences are mostly due to faster implementation and partial aggregation support in the Spark native version of `percentile_approx`.
This benchmark basically shows the performance differences between the worst case, where an aggregate function without partial aggregation support is evaluated using `SortAggregateExec`, and the best case, where a `TypedImperativeAggregate` with partial aggregation support is evaluated using `ObjectHashAggregateExec`.
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
hive udaf vs spark af: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
hive udaf w/o group by 5326 / 5408 0.0 81264.2 1.0X
spark af w/o group by 93 / 111 0.7 1415.6 57.4X
hive udaf w/ group by 3804 / 3946 0.0 58050.1 1.4X
spark af w/ group by w/o fallback 71 / 90 0.9 1085.7 74.8X
spark af w/ group by w/ fallback 98 / 111 0.7 1501.6 54.1X
```
### Real world benchmark
We also did a relatively large benchmark using a real world query involving `percentile_approx`:
- Hive UDAF implementation, sort-based aggregation, w/o partial aggregation support
24.77 minutes
- Native implementation, sort-based aggregation, w/ partial aggregation support
4.64 minutes
- Native implementation, object hash aggregator, w/ partial aggregation support
1.80 minutes
## How was this patch tested?
New unit tests and randomized test cases are added in `ObjectAggregateFunctionSuite`.
Author: Cheng Lian <lian@databricks.com>
Closes#15590 from liancheng/obj-hash-agg.
## What changes were proposed in this pull request?
I was reading this part of the code and was really confused by the "partition" parameter. This patch adds some documentation for it to reduce confusion in the future.
I also looked around other logical plans but most of them are either already documented, or pretty self-evident to people that know Spark SQL.
## How was this patch tested?
N/A - doc change only.
Author: Reynold Xin <rxin@databricks.com>
Closes#15749 from rxin/doc-improvement.
## What changes were proposed in this pull request?
This PR proposes to change the documentation for functions. Please refer the discussion from https://github.com/apache/spark/pull/15513
The changes include
- Re-indent the documentation
- Add examples/arguments in `extended` where the arguments are multiple or specific format (e.g. xml/ json).
For examples, the documentation was updated as below:
### Functions with single line usage
**Before**
- `pow`
``` sql
Usage: pow(x1, x2) - Raise x1 to the power of x2.
Extended Usage:
> SELECT pow(2, 3);
8.0
```
- `current_timestamp`
``` sql
Usage: current_timestamp() - Returns the current timestamp at the start of query evaluation.
Extended Usage:
No example for current_timestamp.
```
**After**
- `pow`
``` sql
Usage: pow(expr1, expr2) - Raises `expr1` to the power of `expr2`.
Extended Usage:
Examples:
> SELECT pow(2, 3);
8.0
```
- `current_timestamp`
``` sql
Usage: current_timestamp() - Returns the current timestamp at the start of query evaluation.
Extended Usage:
No example/argument for current_timestamp.
```
### Functions with (already) multiple line usage
**Before**
- `approx_count_distinct`
``` sql
Usage: approx_count_distinct(expr) - Returns the estimated cardinality by HyperLogLog++.
approx_count_distinct(expr, relativeSD=0.05) - Returns the estimated cardinality by HyperLogLog++
with relativeSD, the maximum estimation error allowed.
Extended Usage:
No example for approx_count_distinct.
```
- `percentile_approx`
``` sql
Usage:
percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric
column `col` at the given percentage. The value of percentage must be between 0.0
and 1.0. The `accuracy` parameter (default: 10000) is a positive integer literal which
controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields
better accuracy, `1.0/accuracy` is the relative error of the approximation.
percentile_approx(col, array(percentage1 [, percentage2]...) [, accuracy]) - Returns the approximate
percentile array of column `col` at the given percentage array. Each value of the
percentage array must be between 0.0 and 1.0. The `accuracy` parameter (default: 10000) is
a positive integer literal which controls approximation accuracy at the cost of memory.
Higher value of `accuracy` yields better accuracy, `1.0/accuracy` is the relative error of
the approximation.
Extended Usage:
No example for percentile_approx.
```
**After**
- `approx_count_distinct`
``` sql
Usage:
approx_count_distinct(expr[, relativeSD]) - Returns the estimated cardinality by HyperLogLog++.
`relativeSD` defines the maximum estimation error allowed.
Extended Usage:
No example/argument for approx_count_distinct.
```
- `percentile_approx`
``` sql
Usage:
percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric
column `col` at the given percentage. The value of percentage must be between 0.0
and 1.0. The `accuracy` parameter (default: 10000) is a positive numeric literal which
controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields
better accuracy, `1.0/accuracy` is the relative error of the approximation.
When `percentage` is an array, each value of the percentage array must be between 0.0 and 1.0.
In this case, returns the approximate percentile array of column `col` at the given
percentage array.
Extended Usage:
Examples:
> SELECT percentile_approx(10.0, array(0.5, 0.4, 0.1), 100);
[10.0,10.0,10.0]
> SELECT percentile_approx(10.0, 0.5, 100);
10.0
```
## How was this patch tested?
Manually tested
**When examples are multiple**
``` sql
spark-sql> describe function extended reflect;
Function: reflect
Class: org.apache.spark.sql.catalyst.expressions.CallMethodViaReflection
Usage: reflect(class, method[, arg1[, arg2 ..]]) - Calls a method with reflection.
Extended Usage:
Examples:
> SELECT reflect('java.util.UUID', 'randomUUID');
c33fb387-8500-4bfa-81d2-6e0e3e930df2
> SELECT reflect('java.util.UUID', 'fromString', 'a5cf6c42-0c85-418f-af6c-3e4e5b1328f2');
a5cf6c42-0c85-418f-af6c-3e4e5b1328f2
```
**When `Usage` is in single line**
``` sql
spark-sql> describe function extended min;
Function: min
Class: org.apache.spark.sql.catalyst.expressions.aggregate.Min
Usage: min(expr) - Returns the minimum value of `expr`.
Extended Usage:
No example/argument for min.
```
**When `Usage` is already in multiple lines**
``` sql
spark-sql> describe function extended percentile_approx;
Function: percentile_approx
Class: org.apache.spark.sql.catalyst.expressions.aggregate.ApproximatePercentile
Usage:
percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric
column `col` at the given percentage. The value of percentage must be between 0.0
and 1.0. The `accuracy` parameter (default: 10000) is a positive numeric literal which
controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields
better accuracy, `1.0/accuracy` is the relative error of the approximation.
When `percentage` is an array, each value of the percentage array must be between 0.0 and 1.0.
In this case, returns the approximate percentile array of column `col` at the given
percentage array.
Extended Usage:
Examples:
> SELECT percentile_approx(10.0, array(0.5, 0.4, 0.1), 100);
[10.0,10.0,10.0]
> SELECT percentile_approx(10.0, 0.5, 100);
10.0
```
**When example/argument is missing**
``` sql
spark-sql> describe function extended rank;
Function: rank
Class: org.apache.spark.sql.catalyst.expressions.Rank
Usage:
rank() - Computes the rank of a value in a group of values. The result is one plus the number
of rows preceding or equal to the current row in the ordering of the partition. The values
will produce gaps in the sequence.
Extended Usage:
No example/argument for rank.
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15677 from HyukjinKwon/SPARK-17963-1.
## What changes were proposed in this pull request?
Due to a limitation of hive metastore(table location must be directory path, not file path), we always store `path` for data source table in storage properties, instead of the `locationUri` field. However, we should not expose this difference to `CatalogTable` level, but just treat it as a hack in `HiveExternalCatalog`, like we store table schema of data source table in table properties.
This PR unifies `path` and `locationUri` outside of `HiveExternalCatalog`, both data source table and hive serde table should use the `locationUri` field.
This PR also unifies the way we handle default table location for managed table. Previously, the default table location of hive serde managed table is set by external catalog, but the one of data source table is set by command. After this PR, we follow the hive way and the default table location is always set by external catalog.
For managed non-file-based tables, we will assign a default table location and create an empty directory for it, the table location will be removed when the table is dropped. This is reasonable as metastore doesn't care about whether a table is file-based or not, and an empty table directory has no harm.
For external non-file-based tables, ideally we can omit the table location, but due to a hive metastore issue, we will assign a random location to it, and remove it right after the table is created. See SPARK-15269 for more details. This is fine as it's well isolated in `HiveExternalCatalog`.
To keep the existing behaviour of the `path` option, in this PR we always add the `locationUri` to storage properties using key `path`, before passing storage properties to `DataSource` as data source options.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15024 from cloud-fan/path.
## What changes were proposed in this pull request?
When a user appended a column using a "nondeterministic" function to a DataFrame, e.g., `rand`, `randn`, and `monotonically_increasing_id`, the expected semantic is the following:
- The value in each row should remain unchanged, as if we materialize the column immediately, regardless of later DataFrame operations.
However, since we use `TaskContext.getPartitionId` to get the partition index from the current thread, the values from nondeterministic columns might change if we call `union` or `coalesce` after. `TaskContext.getPartitionId` returns the partition index of the current Spark task, which might not be the corresponding partition index of the DataFrame where we defined the column.
See the unit tests below or JIRA for examples.
This PR uses the partition index from `RDD.mapPartitionWithIndex` instead of `TaskContext` and fixes the partition initialization logic in whole-stage codegen, normal codegen, and codegen fallback. `initializeStatesForPartition(partitionIndex: Int)` was added to `Projection`, `Nondeterministic`, and `Predicate` (codegen) and initialized right after object creation in `mapPartitionWithIndex`. `newPredicate` now returns a `Predicate` instance rather than a function for proper initialization.
## How was this patch tested?
Unit tests. (Actually I'm not very confident that this PR fixed all issues without introducing new ones ...)
cc: rxin davies
Author: Xiangrui Meng <meng@databricks.com>
Closes#15567 from mengxr/SPARK-14393.
## What changes were proposed in this pull request?
Simplify struct creation, especially the aspect of `CleanupAliases` which missed some aliases when handling trees created by `CreateStruct`.
This PR includes:
1. A failing test (create struct with nested aliases, some of the aliases survive `CleanupAliases`).
2. A fix that transforms `CreateStruct` into a `CreateNamedStruct` constructor, effectively eliminating `CreateStruct` from all expression trees.
3. A `NamePlaceHolder` used by `CreateStruct` when column names cannot be extracted from unresolved `NamedExpression`.
4. A new Analyzer rule that resolves `NamePlaceHolder` into a string literal once the `NamedExpression` is resolved.
5. `CleanupAliases` code was simplified as it no longer has to deal with `CreateStruct`'s top level columns.
## How was this patch tested?
Running all tests-suits in package org.apache.spark.sql, especially including the analysis suite, making sure added test initially fails, after applying suggested fix rerun the entire analysis package successfully.
Modified few tests that expected `CreateStruct` which is now transformed into `CreateNamedStruct`.
Author: eyal farago <eyal farago>
Author: Herman van Hovell <hvanhovell@databricks.com>
Author: eyal farago <eyal.farago@gmail.com>
Author: Eyal Farago <eyal.farago@actimize.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Author: eyalfa <eyal.farago@gmail.com>
Closes#15718 from hvanhovell/SPARK-16839-2.
## What changes were proposed in this pull request?
Fix `Locale.US` for all usages of `DateFormat`, `NumberFormat`
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15610 from srowen/SPARK-18076.
## What changes were proposed in this pull request?
There are a couple issues with the current 2.1 behavior when inserting into Datasource tables with partitions managed by Hive.
(1) OVERWRITE TABLE ... PARTITION will actually overwrite the entire table instead of just the specified partition.
(2) INSERT|OVERWRITE does not work with partitions that have custom locations.
This PR fixes both of these issues for Datasource tables managed by Hive. The behavior for legacy tables or when `manageFilesourcePartitions = false` is unchanged.
There is one other issue in that INSERT OVERWRITE with dynamic partitions will overwrite the entire table instead of just the updated partitions, but this behavior is pretty complicated to implement for Datasource tables. We should address that in a future release.
## How was this patch tested?
Unit tests.
Author: Eric Liang <ekl@databricks.com>
Closes#15705 from ericl/sc-4942.
(Link to Jira issue: https://issues.apache.org/jira/browse/SPARK-17992)
## What changes were proposed in this pull request?
We recently added table partition pruning for partitioned Hive tables converted to using `TableFileCatalog`. When the Hive configuration option `hive.metastore.try.direct.sql` is set to `false`, Hive will throw an exception for unsupported filter expressions. For example, attempting to filter on an integer partition column will throw a `org.apache.hadoop.hive.metastore.api.MetaException`.
I discovered this behavior because VideoAmp uses the CDH version of Hive with a Postgresql metastore DB. In this configuration, CDH sets `hive.metastore.try.direct.sql` to `false` by default, and queries that filter on a non-string partition column will fail.
Rather than throw an exception in query planning, this patch catches this exception, logs a warning and returns all table partitions instead. Clients of this method are already expected to handle the possibility that the filters will not be honored.
## How was this patch tested?
A unit test was added.
Author: Michael Allman <michael@videoamp.com>
Closes#15673 from mallman/spark-17992-catch_hive_partition_filter_exception.
