## 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?
- Raise Analysis exception when correlated predicates exist in the descendant operators of either operand of a Full outer join in a subquery as well as in a FOJ operator itself
- Raise Analysis exception when correlated predicates exists in a Window operator (a side effect inadvertently introduced by SPARK-17348)
## How was this patch tested?
Run sql/test catalyst/test and new test cases, added to SubquerySuite, showing the reported incorrect results.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#16005 from nsyca/FOJ-incorrect.1.
## What changes were proposed in this pull request?
This PR addressed the rest comments in #15951.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15997 from zsxwing/SPARK-18510-follow-up.
## What changes were proposed in this pull request?
### The Issue
If I specify my schema when doing
```scala
spark.read
.schema(someSchemaWherePartitionColumnsAreStrings)
```
but if the partition inference can infer it as IntegerType or I assume LongType or DoubleType (basically fixed size types), then once UnsafeRows are generated, your data will be corrupted.
### Proposed solution
The partition handling code path is kind of a mess. In my fix I'm probably adding to the mess, but at least trying to standardize the code path.
The real issue is that a user that uses the `spark.read` code path can never clearly specify what the partition columns are. If you try to specify the fields in `schema`, we practically ignore what the user provides, and fall back to our inferred data types. What happens in the end is data corruption.
My solution tries to fix this by always trying to infer partition columns the first time you specify the table. Once we find what the partition columns are, we try to find them in the user specified schema and use the dataType provided there, or fall back to the smallest common data type.
We will ALWAYS append partition columns to the user's schema, even if they didn't ask for it. We will only use the data type they provided if they specified it. While this is confusing, this has been the behavior since Spark 1.6, and I didn't want to change this behavior in the QA period of Spark 2.1. We may revisit this decision later.
A side effect of this PR is that we won't need https://github.com/apache/spark/pull/15942 if this PR goes in.
## How was this patch tested?
Regression tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15951 from brkyvz/partition-corruption.
## What changes were proposed in this pull request?
When we try to create the default database, we ask hive to do nothing if it already exists. However, Hive will log an error message instead of doing nothing, and the error message is quite annoying and confusing.
In this PR, we only create default database if it doesn't exist.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15993 from cloud-fan/default-db.
## 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 SQL, some expression may output safe format values, e.g. `CreateArray`, `CreateStruct`, `Cast`, etc. When we compare 2 values, we should be able to compare safe and unsafe formats.
The `GreaterThan`, `LessThan`, etc. in Spark SQL already handles it, but the `EqualTo` doesn't. This PR fixes it.
## How was this patch tested?
new unit test and regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15929 from cloud-fan/type-aware.
## What changes were proposed in this pull request?
Updates links to the wiki to links to the new location of content on spark.apache.org.
## How was this patch tested?
Doc builds
Author: Sean Owen <sowen@cloudera.com>
Closes#15967 from srowen/SPARK-18073.1.
## What changes were proposed in this pull request?
Fixes the inconsistency of error raised between data source and hive serde
tables when schema is specified in CTAS scenario. In the process the grammar for
create table (datasource) is simplified.
**before:**
``` SQL
spark-sql> create table t2 (c1 int, c2 int) using parquet as select * from t1;
Error in query:
mismatched input 'as' expecting {<EOF>, '.', 'OPTIONS', 'CLUSTERED', 'PARTITIONED'}(line 1, pos 64)
== SQL ==
create table t2 (c1 int, c2 int) using parquet as select * from t1
----------------------------------------------------------------^^^
```
**After:**
```SQL
spark-sql> create table t2 (c1 int, c2 int) using parquet as select * from t1
> ;
Error in query:
Operation not allowed: Schema may not be specified in a Create Table As Select (CTAS) statement(line 1, pos 0)
== SQL ==
create table t2 (c1 int, c2 int) using parquet as select * from t1
^^^
```
## How was this patch tested?
Added a new test in CreateTableAsSelectSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#15968 from dilipbiswal/ctas.
### 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?
This PR blocks an incorrect result scenario in scalar subquery where there are GROUP BY column(s)
that are not part of the correlated predicate(s).
Example:
// Incorrect result
Seq(1).toDF("c1").createOrReplaceTempView("t1")
Seq((1,1),(1,2)).toDF("c1","c2").createOrReplaceTempView("t2")
sql("select (select sum(-1) from t2 where t1.c1=t2.c1 group by t2.c2) from t1").show
// How can selecting a scalar subquery from a 1-row table return 2 rows?
## How was this patch tested?
sql/test, catalyst/test
new test case covering the reported problem is added to SubquerySuite.scala
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#15936 from nsyca/scalarSubqueryIncorrect-1.
## What changes were proposed in this pull request?
Right now we are testing the most of `CompactibleFileStreamLog` in `FileStreamSinkLogSuite` (because `FileStreamSinkLog` once was the only subclass of `CompactibleFileStreamLog`, but now it's not the case any more).
Let's refactor the tests so that `CompactibleFileStreamLog` is directly tested, making future changes (like https://github.com/apache/spark/pull/15828, https://github.com/apache/spark/pull/15827) to `CompactibleFileStreamLog` much easier to test and much easier to review.
## How was this patch tested?
the PR itself is about tests
Author: Liwei Lin <lwlin7@gmail.com>
Closes#15870 from lw-lin/test-compact-1113.
## What changes were proposed in this pull request?
This PR adds two of the newly added methods of `Dataset`s to Python:
`withWatermark` and `checkpoint`
## How was this patch tested?
Doc tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15921 from brkyvz/py-watermark.
## What changes were proposed in this pull request?
Currently, `DROP TABLE IF EXISTS` shows warning for non-existing tables. However, it had better be quiet for this case by definition of the command.
**BEFORE**
```scala
scala> sql("DROP TABLE IF EXISTS nonexist")
16/11/20 20:48:26 WARN DropTableCommand: org.apache.spark.sql.catalyst.analysis.NoSuchTableException: Table or view 'nonexist' not found in database 'default';
```
**AFTER**
```scala
scala> sql("DROP TABLE IF EXISTS nonexist")
res0: org.apache.spark.sql.DataFrame = []
```
## How was this patch tested?
Manual because this is related to the warning messages instead of exceptions.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15953 from dongjoon-hyun/SPARK-18517.
## What changes were proposed in this pull request?
This PR adds a new JDBCOption `maxConnections` which means the maximum number of simultaneous JDBC connections allowed. This option applies only to writing with coalesce operation if needed. It defaults to the number of partitions of RDD. Previously, SQL users cannot cannot control this while Scala/Java/Python users can use `coalesce` (or `repartition`) API.
**Reported Scenario**
For the following cases, the number of connections becomes 200 and database cannot handle all of them.
```sql
CREATE OR REPLACE TEMPORARY VIEW resultview
USING org.apache.spark.sql.jdbc
OPTIONS (
url "jdbc:oracle:thin:10.129.10.111:1521:BKDB",
dbtable "result",
user "HIVE",
password "HIVE"
);
-- set spark.sql.shuffle.partitions=200
INSERT OVERWRITE TABLE resultview SELECT g, count(1) AS COUNT FROM tnet.DT_LIVE_INFO GROUP BY g
```
## How was this patch tested?
Manual. Do the followings and see Spark UI.
**Step 1 (MySQL)**
```
CREATE TABLE t1 (a INT);
CREATE TABLE data (a INT);
INSERT INTO data VALUES (1);
INSERT INTO data VALUES (2);
INSERT INTO data VALUES (3);
```
**Step 2 (Spark)**
```scala
SPARK_HOME=$PWD bin/spark-shell --driver-memory 4G --driver-class-path mysql-connector-java-5.1.40-bin.jar
scala> sql("SET spark.sql.shuffle.partitions=3")
scala> sql("CREATE OR REPLACE TEMPORARY VIEW data USING org.apache.spark.sql.jdbc OPTIONS (url 'jdbc:mysql://localhost:3306/t', dbtable 'data', user 'root', password '')")
scala> sql("CREATE OR REPLACE TEMPORARY VIEW t1 USING org.apache.spark.sql.jdbc OPTIONS (url 'jdbc:mysql://localhost:3306/t', dbtable 't1', user 'root', password '', maxConnections '1')")
scala> sql("INSERT OVERWRITE TABLE t1 SELECT a FROM data GROUP BY a")
scala> sql("CREATE OR REPLACE TEMPORARY VIEW t1 USING org.apache.spark.sql.jdbc OPTIONS (url 'jdbc:mysql://localhost:3306/t', dbtable 't1', user 'root', password '', maxConnections '2')")
scala> sql("INSERT OVERWRITE TABLE t1 SELECT a FROM data GROUP BY a")
scala> sql("CREATE OR REPLACE TEMPORARY VIEW t1 USING org.apache.spark.sql.jdbc OPTIONS (url 'jdbc:mysql://localhost:3306/t', dbtable 't1', user 'root', password '', maxConnections '3')")
scala> sql("INSERT OVERWRITE TABLE t1 SELECT a FROM data GROUP BY a")
scala> sql("CREATE OR REPLACE TEMPORARY VIEW t1 USING org.apache.spark.sql.jdbc OPTIONS (url 'jdbc:mysql://localhost:3306/t', dbtable 't1', user 'root', password '', maxConnections '4')")
scala> sql("INSERT OVERWRITE TABLE t1 SELECT a FROM data GROUP BY a")
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15868 from dongjoon-hyun/SPARK-18413.
## What changes were proposed in this pull request?
This PR adds code generation to `Generate`. It supports two code paths:
- General `TraversableOnce` based iteration. This used for regular `Generator` (code generation supporting) expressions. This code path expects the expression to return a `TraversableOnce[InternalRow]` and it will iterate over the returned collection. This PR adds code generation for the `stack` generator.
- Specialized `ArrayData/MapData` based iteration. This is used for the `explode`, `posexplode` & `inline` functions and operates directly on the `ArrayData`/`MapData` result that the child of the generator returns.
### Benchmarks
I have added some benchmarks and it seems we can create a nice speedup for explode:
#### Environment
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.11.6
Intel(R) Core(TM) i7-4980HQ CPU 2.80GHz
```
#### Explode Array
##### Before
```
generate explode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate explode array wholestage off 7377 / 7607 2.3 439.7 1.0X
generate explode array wholestage on 6055 / 6086 2.8 360.9 1.2X
```
##### After
```
generate explode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate explode array wholestage off 7432 / 7696 2.3 443.0 1.0X
generate explode array wholestage on 631 / 646 26.6 37.6 11.8X
```
#### Explode Map
##### Before
```
generate explode map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate explode map wholestage off 12792 / 12848 1.3 762.5 1.0X
generate explode map wholestage on 11181 / 11237 1.5 666.5 1.1X
```
##### After
```
generate explode map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate explode map wholestage off 10949 / 10972 1.5 652.6 1.0X
generate explode map wholestage on 870 / 913 19.3 51.9 12.6X
```
#### Posexplode
##### Before
```
generate posexplode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate posexplode array wholestage off 7547 / 7580 2.2 449.8 1.0X
generate posexplode array wholestage on 5786 / 5838 2.9 344.9 1.3X
```
##### After
```
generate posexplode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate posexplode array wholestage off 7535 / 7548 2.2 449.1 1.0X
generate posexplode array wholestage on 620 / 624 27.1 37.0 12.1X
```
#### Inline
##### Before
```
generate inline array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate inline array wholestage off 6935 / 6978 2.4 413.3 1.0X
generate inline array wholestage on 6360 / 6400 2.6 379.1 1.1X
```
##### After
```
generate inline array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate inline array wholestage off 6940 / 6966 2.4 413.6 1.0X
generate inline array wholestage on 1002 / 1012 16.7 59.7 6.9X
```
#### Stack
##### Before
```
generate stack: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate stack wholestage off 12980 / 13104 1.3 773.7 1.0X
generate stack wholestage on 11566 / 11580 1.5 689.4 1.1X
```
##### After
```
generate stack: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate stack wholestage off 12875 / 12949 1.3 767.4 1.0X
generate stack wholestage on 840 / 845 20.0 50.0 15.3X
```
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#13065 from hvanhovell/SPARK-15214.
## What changes were proposed in this pull request?
Fix since 2.1.0 on new SparkSession.close() method. I goofed in https://github.com/apache/spark/pull/15932 because it was back-ported to 2.1 instead of just master as originally planned.
Author: Sean Owen <sowen@cloudera.com>
Closes#15938 from srowen/SPARK-18448.2.
## What changes were proposed in this pull request?
Just adds `close()` + `Closeable` as a synonym for `stop()`. This makes it usable in Java in try-with-resources, as suggested by ash211 (`Closeable` extends `AutoCloseable` BTW)
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#15932 from srowen/SPARK-18448.
## What changes were proposed in this pull request?
The issue in ForeachSink is the new created DataSet still uses the old QueryExecution. When `foreachPartition` is called, `QueryExecution.toString` will be called and then fail because it doesn't know how to plan EventTimeWatermark.
This PR just replaces the QueryExecution with IncrementalExecution to fix the issue.
## How was this patch tested?
`test("foreach with watermark")`.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15934 from zsxwing/SPARK-18497.
## 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?
HDFS `write` may just hang until timeout if some network error happens. It's better to enable interrupts to allow stopping the query fast on HDFS.
This PR just changes the logic to only disable interrupts for local file system, as HADOOP-10622 only happens for local file system.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15911 from zsxwing/interrupt-on-dfs.
## What changes were proposed in this pull request?
CompactibleFileStreamLog relys on "compactInterval" to detect a compaction batch. If the "compactInterval" is reset by user, CompactibleFileStreamLog will return wrong answer, resulting data loss. This PR procides a way to check the validity of 'compactInterval', and calculate an appropriate value.
## How was this patch tested?
When restart a stream, we change the 'spark.sql.streaming.fileSource.log.compactInterval' different with the former one.
The primary solution to this issue was given by uncleGen
Added extensions include an additional metadata field in OffsetSeq and CompactibleFileStreamLog APIs. zsxwing
Author: Tyson Condie <tcondie@gmail.com>
Author: genmao.ygm <genmao.ygm@genmaoygmdeMacBook-Air.local>
Closes#15852 from tcondie/spark-18187.
## What changes were proposed in this pull request?
This patch fixes a `ClassCastException: java.lang.Integer cannot be cast to java.lang.Long` error which could occur in the HistoryServer while trying to process a deserialized `SparkListenerDriverAccumUpdates` event.
The problem stems from how `jackson-module-scala` handles primitive type parameters (see https://github.com/FasterXML/jackson-module-scala/wiki/FAQ#deserializing-optionint-and-other-primitive-challenges for more details). This was causing a problem where our code expected a field to be deserialized as a `(Long, Long)` tuple but we got an `(Int, Int)` tuple instead.
This patch hacks around this issue by registering a custom `Converter` with Jackson in order to deserialize the tuples as `(Object, Object)` and perform the appropriate casting.
## How was this patch tested?
New regression tests in `SQLListenerSuite`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15922 from JoshRosen/SPARK-18462.
## What changes were proposed in this pull request?
In ShuffleExchange, the nodename's extraInfo are the same when exchangeCoordinator.isEstimated
is true or false.
Merge the two situation in the PR.
Author: root <root@iZbp1gsnrlfzjxh82cz80vZ.(none)>
Closes#15920 from windpiger/DupNodeNameShuffleExchange.
## What changes were proposed in this pull request?
I found the documentation for the sample method to be confusing, this adds more clarification across all languages.
- [x] Scala
- [x] Python
- [x] R
- [x] RDD Scala
- [ ] RDD Python with SEED
- [X] RDD Java
- [x] RDD Java with SEED
- [x] RDD Python
## How was this patch tested?
NA
Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.
Author: anabranch <wac.chambers@gmail.com>
Author: Bill Chambers <bill@databricks.com>
Closes#15815 from anabranch/SPARK-18365.
## 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?
SPARK-18459: triggerId seems like a number that should be increasing with each trigger, whether or not there is data in it. However, actually, triggerId increases only where there is a batch of data in a trigger. So its better to rename it to batchId.
SPARK-18460: triggerDetails was missing from json representation. Fixed it.
## How was this patch tested?
Updated existing unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15895 from tdas/SPARK-18459.
### What changes were proposed in this pull request?
Currently, when CTE is used in RunnableCommand, the Analyzer does not replace the logical node `With`. The child plan of RunnableCommand is not resolved. Thus, the output of the `With` plan node looks very confusing.
For example,
```
sql(
"""
|CREATE VIEW cte_view AS
|WITH w AS (SELECT 1 AS n), cte1 (select 2), cte2 as (select 3)
|SELECT n FROM w
""".stripMargin).explain()
```
The output is like
```
ExecutedCommand
+- CreateViewCommand `cte_view`, WITH w AS (SELECT 1 AS n), cte1 (select 2), cte2 as (select 3)
SELECT n FROM w, false, false, PersistedView
+- 'With [(w,SubqueryAlias w
+- Project [1 AS n#16]
+- OneRowRelation$
), (cte1,'SubqueryAlias cte1
+- 'Project [unresolvedalias(2, None)]
+- OneRowRelation$
), (cte2,'SubqueryAlias cte2
+- 'Project [unresolvedalias(3, None)]
+- OneRowRelation$
)]
+- 'Project ['n]
+- 'UnresolvedRelation `w`
```
After the fix, the output is as shown below.
```
ExecutedCommand
+- CreateViewCommand `cte_view`, WITH w AS (SELECT 1 AS n), cte1 (select 2), cte2 as (select 3)
SELECT n FROM w, false, false, PersistedView
+- CTE [w, cte1, cte2]
: :- SubqueryAlias w
: : +- Project [1 AS n#16]
: : +- OneRowRelation$
: :- 'SubqueryAlias cte1
: : +- 'Project [unresolvedalias(2, None)]
: : +- OneRowRelation$
: +- 'SubqueryAlias cte2
: +- 'Project [unresolvedalias(3, None)]
: +- OneRowRelation$
+- 'Project ['n]
+- 'UnresolvedRelation `w`
```
BTW, this PR also fixes the output of the view type.
### How was this patch tested?
Manual
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15854 from gatorsmile/cteName.
## 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?
SPARK-18012 refactored the file write path in FileStreamSink using FileFormatWriter which always uses the default non-streaming QueryExecution to perform the writes. This is wrong for FileStreamSink, because the streaming QueryExecution (i.e. IncrementalExecution) should be used for correctly incrementalizing aggregation. The addition of watermarks in SPARK-18124, file stream sink should logically supports aggregation + watermark + append mode. But actually it fails with
```
16:23:07.389 ERROR org.apache.spark.sql.execution.streaming.StreamExecution: Query query-0 terminated with error
java.lang.AssertionError: assertion failed: No plan for EventTimeWatermark timestamp#7: timestamp, interval 10 seconds
+- LocalRelation [timestamp#7]
at scala.Predef$.assert(Predef.scala:170)
at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:92)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:77)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:74)
at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:157)
at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1336)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:74)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:66)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:92)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:77)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:74)
```
This PR fixes it by passing the correct query execution.
## How was this patch tested?
New unit test
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15885 from tdas/SPARK-18440.
## What changes were proposed in this pull request?
It would be nice if memory sinks can also recover from checkpoints. For correctness reasons, the only time we should support it is in `Complete` OutputMode. We can support this in CompleteMode, because the output of the StateStore is already persisted in the checkpoint directory.
## How was this patch tested?
Unit test
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15801 from brkyvz/mem-stream.
## What changes were proposed in this pull request?
The largest parallelism in PartitioningAwareFileIndex #listLeafFilesInParallel() is 10000 in hard code. We may need to make this number configurable. And in PR, I reduce it to 100.
## How was this patch tested?
Existing ut.
Author: genmao.ygm <genmao.ygm@genmaoygmdeMacBook-Air.local>
Author: dylon <hustyugm@gmail.com>
Closes#15829 from uncleGen/SPARK-18379.
## What changes were proposed in this pull request?
The `FoldablePropagation` optimizer rule, pulls foldable values out from under an `Expand`. This breaks the `Expand` in two ways:
- It rewrites the output attributes of the `Expand`. We explicitly define output attributes for `Expand`, these are (unfortunately) considered as part of the expressions of the `Expand` and can be rewritten.
- Expand can actually change the column (it will typically re-use the attributes or the underlying plan). This means that we cannot safely propagate the expressions from under an `Expand`.
This PR fixes this and (hopefully) other issues by explicitly whitelisting allowed operators.
## How was this patch tested?
Added tests to `FoldablePropagationSuite` and to `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15857 from hvanhovell/SPARK-18300.
### What changes were proposed in this pull request?
When the exception is an invocation exception during function lookup, we return a useless/confusing error message:
For example,
```Scala
df.selectExpr("concat_ws()")
```
Below is the error message we got:
```
null; line 1 pos 0
org.apache.spark.sql.AnalysisException: null; line 1 pos 0
```
To get the meaningful error message, we need to get the cause. The fix is exactly the same as what we did in https://github.com/apache/spark/pull/12136. After the fix, the message we got is the exception issued in the constuctor of function implementation:
```
requirement failed: concat_ws requires at least one argument.; line 1 pos 0
org.apache.spark.sql.AnalysisException: requirement failed: concat_ws requires at least one argument.; line 1 pos 0
```
### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15878 from gatorsmile/functionNotFound.
This PR adds a new method `withWatermark` to the `Dataset` API, which can be used specify an _event time watermark_. An event time watermark allows the streaming engine to reason about the point in time after which we no longer expect to see late data. This PR also has augmented `StreamExecution` to use this watermark for several purposes:
- To know when a given time window aggregation is finalized and thus results can be emitted when using output modes that do not allow updates (e.g. `Append` mode).
- To minimize the amount of state that we need to keep for on-going aggregations, by evicting state for groups that are no longer expected to change. Although, we do still maintain all state if the query requires (i.e. if the event time is not present in the `groupBy` or when running in `Complete` mode).
An example that emits windowed counts of records, waiting up to 5 minutes for late data to arrive.
```scala
df.withWatermark("eventTime", "5 minutes")
.groupBy(window($"eventTime", "1 minute") as 'window)
.count()
.writeStream
.format("console")
.mode("append") // In append mode, we only output finalized aggregations.
.start()
```
### Calculating the watermark.
The current event time is computed by looking at the `MAX(eventTime)` seen this epoch across all of the partitions in the query minus some user defined _delayThreshold_. An additional constraint is that the watermark must increase monotonically.
Note that since we must coordinate this value across partitions occasionally, the actual watermark used is only guaranteed to be at least `delay` behind the actual event time. In some cases we may still process records that arrive more than delay late.
This mechanism was chosen for the initial implementation over processing time for two reasons:
- it is robust to downtime that could affect processing delay
- it does not require syncing of time or timezones between the producer and the processing engine.
### Other notable implementation details
- A new trigger metric `eventTimeWatermark` outputs the current value of the watermark.
- We mark the event time column in the `Attribute` metadata using the key `spark.watermarkDelay`. This allows downstream operations to know which column holds the event time. Operations like `window` propagate this metadata.
- `explain()` marks the watermark with a suffix of `-T${delayMs}` to ease debugging of how this information is propagated.
- Currently, we don't filter out late records, but instead rely on the state store to avoid emitting records that are both added and filtered in the same epoch.
### Remaining in this PR
- [ ] The test for recovery is currently failing as we don't record the watermark used in the offset log. We will need to do so to ensure determinism, but this is deferred until #15626 is merged.
### Other follow-ups
There are some natural additional features that we should consider for future work:
- Ability to write records that arrive too late to some external store in case any out-of-band remediation is required.
- `Update` mode so you can get partial results before a group is evicted.
- Other mechanisms for calculating the watermark. In particular a watermark based on quantiles would be more robust to outliers.
Author: Michael Armbrust <michael@databricks.com>
Closes#15702 from marmbrus/watermarks.
## What changes were proposed in this pull request?
Return an Analysis exception when there is a correlated non-equality predicate in a subquery and the correlated column from the outer reference is not from the immediate parent operator of the subquery. This PR prevents incorrect results from subquery transformation in such case.
Test cases, both positive and negative tests, are added.
## How was this patch tested?
sql/test, catalyst/test, hive/test, and scenarios that will produce incorrect results without this PR and product correct results when subquery transformation does happen.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#15763 from nsyca/spark-17348.
## What changes were proposed in this pull request?
StateStore.get() causes temporary files to be created immediately, even if the store is not used to make updates for new version. The temp file is not closed as store.commit() is not called in those cases, thus keeping the output stream to temp file open forever.
This PR fixes it by opening the temp file only when there are updates being made.
## How was this patch tested?
New unit test
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15859 from tdas/SPARK-18416.
## 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?
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?
~In `TypedAggregateExpression.evaluateExpression`, we may create `ReferenceToExpressions` with `CreateStruct`, and `CreateStruct` may generate too many codes and split them into several methods. `ReferenceToExpressions` will replace `BoundReference` in `CreateStruct` with `LambdaVariable`, which can only be used as local variables and doesn't work if we split the generated code.~
It's already fixed by #15693 , this pr adds regression test
## How was this patch tested?
new test in `DatasetAggregatorSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15807 from cloud-fan/typed-agg.
## What changes were proposed in this pull request?
Currently we use java serialization for the WAL that stores the offsets contained in each batch. This has two main issues:
It can break across spark releases (though this is not the only thing preventing us from upgrading a running query)
It is unnecessarily opaque to the user.
I'd propose we require offsets to provide a user readable serialization and use that instead. JSON is probably a good option.
## How was this patch tested?
Tests were added for KafkaSourceOffset in [KafkaSourceOffsetSuite](external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSourceOffsetSuite.scala) and for LongOffset in [OffsetSuite](sql/core/src/test/scala/org/apache/spark/sql/streaming/OffsetSuite.scala)
Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.
zsxwing marmbrus
Author: Tyson Condie <tcondie@gmail.com>
Author: Tyson Condie <tcondie@clash.local>
Closes#15626 from tcondie/spark-8360.
## 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?
`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?
If the rename operation in the state store fails (`fs.rename` returns `false`), the StateStore should throw an exception and have the task retry. Currently if renames fail, nothing happens during execution immediately. However, you will observe that snapshot operations will fail, and then any attempt at recovery (executor failure / checkpoint recovery) also fails.
## How was this patch tested?
Unit test
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15804 from brkyvz/rename-state.
## 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?
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?
Right now, there is no way to join the output of a memory sink with any table:
> UnsupportedOperationException: LeafNode MemoryPlan must implement statistics
This patch adds statistics to MemorySink, making joining snapshots of memory streams with tables possible.
## How was this patch tested?
Added a test case.
Author: Liwei Lin <lwlin7@gmail.com>
Closes#15786 from lw-lin/memory-sink-stat.
## 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?
This PR proposes to match up the behaviour of `to_json` to `from_json` function for null-safety.
Currently, it throws `NullPointException` but this PR fixes this to produce `null` instead.
with the data below:
```scala
import spark.implicits._
val df = Seq(Some(Tuple1(Tuple1(1))), None).toDF("a")
df.show()
```
```
+----+
| a|
+----+
| [1]|
|null|
+----+
```
the codes below
```scala
import org.apache.spark.sql.functions._
df.select(to_json($"a")).show()
```
produces..
**Before**
throws `NullPointException` as below:
```
java.lang.NullPointerException
at org.apache.spark.sql.catalyst.json.JacksonGenerator.org$apache$spark$sql$catalyst$json$JacksonGenerator$$writeFields(JacksonGenerator.scala:138)
at org.apache.spark.sql.catalyst.json.JacksonGenerator$$anonfun$write$1.apply$mcV$sp(JacksonGenerator.scala:194)
at org.apache.spark.sql.catalyst.json.JacksonGenerator.org$apache$spark$sql$catalyst$json$JacksonGenerator$$writeObject(JacksonGenerator.scala:131)
at org.apache.spark.sql.catalyst.json.JacksonGenerator.write(JacksonGenerator.scala:193)
at org.apache.spark.sql.catalyst.expressions.StructToJson.eval(jsonExpressions.scala:544)
at org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:142)
at org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:48)
at org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:30)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
```
**After**
```
+---------------+
|structtojson(a)|
+---------------+
| {"_1":1}|
| null|
+---------------+
```
## How was this patch tested?
Unit test in `JsonExpressionsSuite.scala` and `JsonFunctionsSuite.scala`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15792 from HyukjinKwon/SPARK-18295.
## What changes were proposed in this pull request?
When profiling heap dumps from the HistoryServer and live Spark web UIs, I found a large amount of memory being wasted on duplicated objects and strings. This patch's changes remove most of this duplication, resulting in over 40% memory savings for some benchmarks.
- **Task metrics** (6441f0624dfcda9c7193a64bfb416a145b5aabdf): previously, every `TaskUIData` object would have its own instances of `InputMetricsUIData`, `OutputMetricsUIData`, `ShuffleReadMetrics`, and `ShuffleWriteMetrics`, but for many tasks these metrics are irrelevant because they're all zero. This patch changes how we construct these metrics in order to re-use a single immutable "empty" value for the cases where these metrics are empty.
- **TaskInfo.accumulables** (ade86db901127bf13c0e0bdc3f09c933a093bb76): Previously, every `TaskInfo` object had its own empty `ListBuffer` for holding updates from named accumulators. Tasks which didn't use named accumulators still paid for the cost of allocating and storing this empty buffer. To avoid this overhead, I changed the `val` with a mutable buffer into a `var` which holds an immutable Scala list, allowing tasks which do not have named accumulator updates to share the same singleton `Nil` object.
- **String.intern() in JSONProtocol** (7e05630e9a78c455db8c8c499f0590c864624e05): in the HistoryServer, executor hostnames and ids are deserialized from JSON, leading to massive duplication of these string objects. By calling `String.intern()` on the deserialized values we can remove all of this duplication. Since Spark now requires Java 7+ we don't have to worry about string interning exhausting the permgen (see http://java-performance.info/string-intern-in-java-6-7-8/).
## How was this patch tested?
I ran
```
sc.parallelize(1 to 100000, 100000).count()
```
in `spark-shell` with event logging enabled, then loaded that event log in the HistoryServer, performed a full GC, and took a heap dump. According to YourKit, the changes in this patch reduced memory consumption by roughly 28 megabytes (or 770k Java objects):
Here's a table illustrating the drop in objects due to deduplication (the drop is <100k for some objects because some events were dropped from the listener bus; this is a separate, existing bug that I'll address separately after CPU-profiling):
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15743 from JoshRosen/spark-ui-memory-usage.
## What changes were proposed in this pull request?
As reported in the jira, sometimes the generated java code in codegen will cause compilation error.
Code snippet to test it:
case class Route(src: String, dest: String, cost: Int)
case class GroupedRoutes(src: String, dest: String, routes: Seq[Route])
val ds = sc.parallelize(Array(
Route("a", "b", 1),
Route("a", "b", 2),
Route("a", "c", 2),
Route("a", "d", 10),
Route("b", "a", 1),
Route("b", "a", 5),
Route("b", "c", 6))
).toDF.as[Route]
val grped = ds.map(r => GroupedRoutes(r.src, r.dest, Seq(r)))
.groupByKey(r => (r.src, r.dest))
.reduceGroups { (g1: GroupedRoutes, g2: GroupedRoutes) =>
GroupedRoutes(g1.src, g1.dest, g1.routes ++ g2.routes)
}.map(_._2)
The problem here is, in `ReferenceToExpressions` we evaluate the children vars to local variables. Then the result expression is evaluated to use those children variables. In the above case, the result expression code is too long and will be split by `CodegenContext.splitExpression`. So those local variables cannot be accessed and cause compilation error.
## How was this patch tested?
Jenkins tests.
Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#15693 from viirya/fix-codege-compilation-error.
## What changes were proposed in this pull request?
We have an undocumented naming convention to call expression unit tests ExpressionsSuite, and the end-to-end tests FunctionsSuite. It'd be great to make all test suites consistent with this naming convention.
## How was this patch tested?
This is a test-only naming change.
Author: Reynold Xin <rxin@databricks.com>
Closes#15793 from rxin/SPARK-18296.
## 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?
Currently, there are the three cases when reading CSV by datasource when it is `PERMISSIVE` parse mode.
- schema == parsed tokens (from each line)
No problem to cast the value in the tokens to the field in the schema as they are equal.
- schema < parsed tokens (from each line)
It slices the tokens into the number of fields in schema.
- schema > parsed tokens (from each line)
It appends `null` into parsed tokens so that safely values can be casted with the schema.
However, when `null` is appended in the third case, we should take `null` into account when casting the values.
In case of `StringType`, it is fine as `UTF8String.fromString(datum)` produces `null` when the input is `null`. Therefore, this case will happen only when schema is explicitly given and schema includes data types that are not `StringType`.
The codes below:
```scala
val path = "/tmp/a"
Seq("1").toDF().write.text(path.getAbsolutePath)
val schema = StructType(
StructField("a", IntegerType, true) ::
StructField("b", IntegerType, true) :: Nil)
spark.read.schema(schema).option("header", "false").csv(path).show()
```
prints
**Before**
```
java.lang.NumberFormatException: null
at java.lang.Integer.parseInt(Integer.java:542)
at java.lang.Integer.parseInt(Integer.java:615)
at scala.collection.immutable.StringLike$class.toInt(StringLike.scala:272)
at scala.collection.immutable.StringOps.toInt(StringOps.scala:29)
at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$.castTo(CSVInferSchema.scala:24)
```
**After**
```
+---+----+
| a| b|
+---+----+
| 1|null|
+---+----+
```
## How was this patch tested?
Unit test in `CSVSuite.scala` and `CSVTypeCastSuite.scala`
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15767 from HyukjinKwon/SPARK-18269.
## What changes were proposed in this pull request?
This PR proposes `rand`/`randn` accept `null` as input in Scala/SQL and `LongType` as input in SQL. In this case, it treats the values as `0`.
So, this PR includes both changes below:
- `null` support
It seems MySQL also accepts this.
``` sql
mysql> select rand(0);
+---------------------+
| rand(0) |
+---------------------+
| 0.15522042769493574 |
+---------------------+
1 row in set (0.00 sec)
mysql> select rand(NULL);
+---------------------+
| rand(NULL) |
+---------------------+
| 0.15522042769493574 |
+---------------------+
1 row in set (0.00 sec)
```
and also Hive does according to [HIVE-14694](https://issues.apache.org/jira/browse/HIVE-14694)
So the codes below:
``` scala
spark.range(1).selectExpr("rand(null)").show()
```
prints..
**Before**
```
Input argument to rand must be an integer literal.;; line 1 pos 0
org.apache.spark.sql.AnalysisException: Input argument to rand must be an integer literal.;; line 1 pos 0
at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:465)
at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:444)
```
**After**
```
+-----------------------+
|rand(CAST(NULL AS INT))|
+-----------------------+
| 0.13385709732307427|
+-----------------------+
```
- `LongType` support in SQL.
In addition, it make the function allows to take `LongType` consistently within Scala/SQL.
In more details, the codes below:
``` scala
spark.range(1).select(rand(1), rand(1L)).show()
spark.range(1).selectExpr("rand(1)", "rand(1L)").show()
```
prints..
**Before**
```
+------------------+------------------+
| rand(1)| rand(1)|
+------------------+------------------+
|0.2630967864682161|0.2630967864682161|
+------------------+------------------+
Input argument to rand must be an integer literal.;; line 1 pos 0
org.apache.spark.sql.AnalysisException: Input argument to rand must be an integer literal.;; line 1 pos 0
at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:465)
at
```
**After**
```
+------------------+------------------+
| rand(1)| rand(1)|
+------------------+------------------+
|0.2630967864682161|0.2630967864682161|
+------------------+------------------+
+------------------+------------------+
| rand(1)| rand(1)|
+------------------+------------------+
|0.2630967864682161|0.2630967864682161|
+------------------+------------------+
```
## How was this patch tested?
Unit tests in `DataFrameSuite.scala` and `RandomSuite.scala`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15432 from HyukjinKwon/SPARK-17854.
## What changes were proposed in this pull request?
Prior this pr, the following code would cause an NPE:
`case class point(a:String, b:String, c:String, d: Int)`
`val data = Seq(
point("1","2","3", 1),
point("4","5","6", 1),
point("7","8","9", 1)
)`
`sc.parallelize(data).toDF().registerTempTable("table")`
`spark.sql("select a, b, c, count(d) from table group by a, b, c GROUPING SETS ((a)) ").show()`
The reason is that when the grouping_id() behavior was changed in #10677, some code (which should be changed) was left out.
Take the above code for example, prior #10677, the bit mask for set "(a)" was `001`, while after #10677 the bit mask was changed to `011`. However, the `nonNullBitmask` was not changed accordingly.
This pr will fix this problem.
## How was this patch tested?
add integration tests
Author: wangyang <wangyang@haizhi.com>
Closes#15416 from yangw1234/groupingid.
## What changes were proposed in this pull request?
This PR proposes to fix
```diff
test("FileStreamSink - json") {
- testFormat(Some("text"))
+ testFormat(Some("json"))
}
```
`text` is being tested above
```
test("FileStreamSink - text") {
testFormat(Some("text"))
}
```
## How was this patch tested?
Fixed test in `FileStreamSinkSuite.scala`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15785 from HyukjinKwon/SPARK-18192.
## 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?
The `PushDownPredicate` rule can create a wrong result if we try to push a filter containing a predicate subquery through a project when the subquery and the project share attributes (have the same source).
The current PR fixes this by making sure that we do not push down when there is a predicate subquery that outputs the same attributes as the filters new child plan.
## How was this patch tested?
Added a test to `SubquerySuite`. nsyca has done previous work this. I have taken test from his initial PR.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15761 from hvanhovell/SPARK-17337.
## What changes were proposed in this pull request?
`QueryExecution.toString` currently captures `java.lang.Throwable`s; this is far from a best practice and can lead to confusing situation or invalid application states. This PR fixes this by only capturing `AnalysisException`s.
## How was this patch tested?
Added a `QueryExecutionSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15760 from hvanhovell/SPARK-18259.
## What changes were proposed in this pull request?
This patch improves error reporting for FileStressSuite, when there is an error in Spark itself (not user code). This works by simply tightening the exception verification, and gets rid of the unnecessary thread for starting the stream.
Also renamed the class FileStreamStressSuite to make it more obvious it is a streaming suite.
## How was this patch tested?
This is a test only change and I manually verified error reporting by injecting some bug in the addBatch code for FileStreamSink.
Author: Reynold Xin <rxin@databricks.com>
Closes#15757 from rxin/SPARK-18257.
## 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?
When `FilterExec` contains `isNotNull`, which could be inferred and pushed down or users specified, we convert the nullability of the involved columns if the top-layer expression is null-intolerant. However, this is not correct, if the top-layer expression is not a leaf expression, it could still tolerate the null when it has null-tolerant child expressions.
For example, `cast(coalesce(a#5, a#15) as double)`. Although `cast` is a null-intolerant expression, but obviously`coalesce` is null-tolerant. Thus, it could eat null.
When the nullability is wrong, we could generate incorrect results in different cases. For example,
``` Scala
val df1 = Seq((1, 2), (2, 3)).toDF("a", "b")
val df2 = Seq((2, 5), (3, 4)).toDF("a", "c")
val joinedDf = df1.join(df2, Seq("a"), "outer").na.fill(0)
val df3 = Seq((3, 1)).toDF("a", "d")
joinedDf.join(df3, "a").show
```
The optimized plan is like
```
Project [a#29, b#30, c#31, d#42]
+- Join Inner, (a#29 = a#41)
:- Project [cast(coalesce(cast(coalesce(a#5, a#15) as double), 0.0) as int) AS a#29, cast(coalesce(cast(b#6 as double), 0.0) as int) AS b#30, cast(coalesce(cast(c#16 as double), 0.0) as int) AS c#31]
: +- Filter isnotnull(cast(coalesce(cast(coalesce(a#5, a#15) as double), 0.0) as int))
: +- Join FullOuter, (a#5 = a#15)
: :- LocalRelation [a#5, b#6]
: +- LocalRelation [a#15, c#16]
+- LocalRelation [a#41, d#42]
```
Without the fix, it returns an empty result. With the fix, it can return a correct answer:
```
+---+---+---+---+
| a| b| c| d|
+---+---+---+---+
| 3| 0| 4| 1|
+---+---+---+---+
```
### How was this patch tested?
Added test cases to verify the nullability changes in FilterExec. Also added a test case for verifying the reported incorrect result.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15523 from gatorsmile/nullabilityFilterExec.
## What changes were proposed in this pull request?
This patch moves the new commit protocol API from sql/core to core module, so we can use it in the future in the RDD API.
As part of this patch, I also moved the speficiation of the random uuid for the write path out of the commit protocol, and instead pass in a job id.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#15731 from rxin/SPARK-18219.
## What changes were proposed in this pull request?
In Spark 1.6 and earlier, we can drop the database we are using. In Spark 2.0, native implementation prevent us from dropping current database, which may break some old queries. This PR would re-enable the feature.
## How was this patch tested?
one new unit test in `SessionCatalogSuite`.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#15011 from adrian-wang/dropcurrent.
## 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?
RuntimeReplaceable is used to create aliases for expressions, but the way it deals with type coercion is pretty weird (each expression is responsible for how to handle type coercion, which does not obey the normal implicit type cast rules).
This patch simplifies its handling by allowing the analyzer to traverse into the actual expression of a RuntimeReplaceable.
## How was this patch tested?
- Correctness should be guaranteed by existing unit tests already
- Removed SQLCompatibilityFunctionSuite and moved it sql-compatibility-functions.sql
- Added a new test case in sql-compatibility-functions.sql for verifying explain behavior.
Author: Reynold Xin <rxin@databricks.com>
Closes#15723 from rxin/SPARK-18214.
## 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?
The PR fixes the bug that the QueryStartedEvent is not logged
the postToAll() in the original code is actually calling StreamingQueryListenerBus.postToAll() which has no listener at all....we shall post by sparkListenerBus.postToAll(s) and this.postToAll() to trigger local listeners as well as the listeners registered in LiveListenerBus
zsxwing
## How was this patch tested?
The following snapshot shows that QueryStartedEvent has been logged correctly
Author: CodingCat <zhunansjtu@gmail.com>
Closes#15675 from CodingCat/SPARK-18144.
## What changes were proposed in this pull request?
This patch adds support for all file formats in structured streaming sinks. This is actually a very small change thanks to all the previous refactoring done using the new internal commit protocol API.
## How was this patch tested?
Updated FileStreamSinkSuite to add test cases for json, text, and parquet.
Author: Reynold Xin <rxin@databricks.com>
Closes#15711 from rxin/SPARK-18192.
## 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.
## What changes were proposed in this pull request?
When the metadata logs for various parts of Structured Streaming are stored on non-HDFS filesystems such as NFS or ext4, the HDFSMetadataLog class leaves hidden HDFS-style checksum (CRC) files in the log directory, one file per batch. This PR modifies HDFSMetadataLog so that it detects the use of a filesystem that doesn't use CRC files and removes the CRC files.
## How was this patch tested?
Modified an existing test case in HDFSMetadataLogSuite to check whether HDFSMetadataLog correctly removes CRC files on the local POSIX filesystem. Ran the entire regression suite.
Author: frreiss <frreiss@us.ibm.com>
Closes#15027 from frreiss/fred-17475.
## What changes were proposed in this pull request?
Column.expr is private[sql], but it's an actually really useful field to have for debugging. We should open it up, similar to how we use QueryExecution.
## How was this patch tested?
N/A - this is a simple visibility change.
Author: Reynold Xin <rxin@databricks.com>
Closes#15724 from rxin/SPARK-18216.
## What changes were proposed in this pull request?
This patch adds a new commit protocol implementation ManifestFileCommitProtocol that follows the existing streaming flow, and uses it in FileStreamSink to consolidate the write path in structured streaming with the batch mode write path.
This deletes a lot of code, and would make it trivial to support other functionalities that are currently available in batch but not in streaming, including all file formats and bucketing.
## How was this patch tested?
Should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#15710 from rxin/SPARK-18025.
## What changes were proposed in this pull request?
This PR proposes to add `to_json` function in contrast with `from_json` in Scala, Java and Python.
It'd be useful if we can convert a same column from/to json. Also, some datasources do not support nested types. If we are forced to save a dataframe into those data sources, we might be able to work around by this function.
The usage is as below:
``` scala
val df = Seq(Tuple1(Tuple1(1))).toDF("a")
df.select(to_json($"a").as("json")).show()
```
``` bash
+--------+
| json|
+--------+
|{"_1":1}|
+--------+
```
## How was this patch tested?
Unit tests in `JsonFunctionsSuite` and `JsonExpressionsSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15354 from HyukjinKwon/SPARK-17764.
## What changes were proposed in this pull request?
Aggregation Without Window/GroupBy expressions will fail in `checkAnalysis`, the error message is a bit misleading, we should generate a more specific error message for this case.
For example,
```
spark.read.load("/some-data")
.withColumn("date_dt", to_date($"date"))
.withColumn("year", year($"date_dt"))
.withColumn("week", weekofyear($"date_dt"))
.withColumn("user_count", count($"userId"))
.withColumn("daily_max_in_week", max($"user_count").over(weeklyWindow))
)
```
creates the following output:
```
org.apache.spark.sql.AnalysisException: expression '`randomColumn`' is neither present in the group by, nor is it an aggregate function. Add to group by or wrap in first() (or first_value) if you don't care which value you get.;
```
In the error message above, `randomColumn` doesn't appear in the query(acturally it's added by function `withColumn`), so the message is not enough for the user to address the problem.
## How was this patch tested?
Manually test
Before:
```
scala> spark.sql("select col, count(col) from tbl")
org.apache.spark.sql.AnalysisException: expression 'tbl.`col`' is neither present in the group by, nor is it an aggregate function. Add to group by or wrap in first() (or first_value) if you don't care which value you get.;;
```
After:
```
scala> spark.sql("select col, count(col) from tbl")
org.apache.spark.sql.AnalysisException: grouping expressions sequence is empty, and 'tbl.`col`' is not an aggregate function. Wrap '(count(col#231L) AS count(col)#239L)' in windowing function(s) or wrap 'tbl.`col`' in first() (or first_value) if you don't care which value you get.;;
```
Also add new test sqls in `group-by.sql`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15672 from jiangxb1987/groupBy-empty.
## What changes were proposed in this pull request?
Likewise [DataSet.scala](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala#L156) KeyValueGroupedDataset should mark the queryExecution as transient.
As mentioned in the Jira ticket, without transient we saw serialization issues like
```
Caused by: java.io.NotSerializableException: org.apache.spark.sql.execution.QueryExecution
Serialization stack:
- object not serializable (class: org.apache.spark.sql.execution.QueryExecution, value: ==
```
## How was this patch tested?
Run the query which is specified in the Jira ticket before and after:
```
val a = spark.createDataFrame(sc.parallelize(Seq((1,2),(3,4)))).as[(Int,Int)]
val grouped = a.groupByKey(
{x:(Int,Int)=>x._1}
)
val mappedGroups = grouped.mapGroups((k,x)=>
{(k,1)}
)
val yyy = sc.broadcast(1)
val last = mappedGroups.rdd.map(xx=>
{ val simpley = yyy.value 1 }
)
```
Author: Ergin Seyfe <eseyfe@fb.com>
Closes#15706 from seyfe/keyvaluegrouped_serialization.
## What changes were proposed in this pull request?
This is a follow-up to https://github.com/apache/spark/pull/15634.
## How was this patch tested?
N/A
Author: Liwei Lin <lwlin7@gmail.com>
Closes#15712 from lw-lin/18103.
## 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`.
Credit goes to hvanhovell for assisting with this PR.
Author: eyal farago <eyal farago>
Author: eyal farago <eyal.farago@gmail.com>
Author: Herman van Hovell <hvanhovell@databricks.com>
Author: Eyal Farago <eyal.farago@actimize.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Author: eyalfa <eyal.farago@gmail.com>
Closes#14444 from eyalfa/SPARK-16839_redundant_aliases_after_cleanupAliases.
## What changes were proposed in this pull request?
Currently an unqualified `getFunction(..)`call returns a wrong result; the returned function is shown as temporary function without a database. For example:
```
scala> sql("create function fn1 as 'org.apache.hadoop.hive.ql.udf.generic.GenericUDFAbs'")
res0: org.apache.spark.sql.DataFrame = []
scala> spark.catalog.getFunction("fn1")
res1: org.apache.spark.sql.catalog.Function = Function[name='fn1', className='org.apache.hadoop.hive.ql.udf.generic.GenericUDFAbs', isTemporary='true']
```
This PR fixes this by adding database information to ExpressionInfo (which is used to store the function information).
## How was this patch tested?
Added more thorough tests to `CatalogSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15542 from hvanhovell/SPARK-17996.
## What changes were proposed in this pull request?
When multiple records have the minimum value, the answer of ApproximatePercentile is wrong.
## How was this patch tested?
add a test case
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#15641 from wzhfy/percentile.
## What changes were proposed in this pull request?
This patch introduces an internal commit protocol API that is used by the batch data source to do write commits. It currently has only one implementation that uses Hadoop MapReduce's OutputCommitter API. In the future, this commit API can be used to unify streaming and batch commits.
## How was this patch tested?
Should be covered by existing write tests.
Author: Reynold Xin <rxin@databricks.com>
Author: Eric Liang <ekl@databricks.com>
Closes#15707 from rxin/SPARK-18024-2.
## What changes were proposed in this pull request?
When inserting into datasource tables with partitions managed by the hive metastore, we need to notify the metastore of newly added partitions. Previously this was implemented via `msck repair table`, but this is more expensive than needed.
This optimizes the insertion path to add only the updated partitions.
## How was this patch tested?
Existing tests (I verified manually that tests fail if the repair operation is omitted).
Author: Eric Liang <ekl@databricks.com>
Closes#15633 from ericl/spark-18087.
## What changes were proposed in this pull request?
The test `when schema inference is turned on, should read partition data` should not delete files because the source maybe is listing files. This PR just removes the delete actions since they are not necessary.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15699 from zsxwing/SPARK-18030.
## What changes were proposed in this pull request?
### Problem
Iterative ML code may easily create query plans that grow exponentially. We found that query planning time also increases exponentially even when all the sub-plan trees are cached.
The following snippet illustrates the problem:
``` scala
(0 until 6).foldLeft(Seq(1, 2, 3).toDS) { (plan, iteration) =>
println(s"== Iteration $iteration ==")
val time0 = System.currentTimeMillis()
val joined = plan.join(plan, "value").join(plan, "value").join(plan, "value").join(plan, "value")
joined.cache()
println(s"Query planning takes ${System.currentTimeMillis() - time0} ms")
joined.as[Int]
}
// == Iteration 0 ==
// Query planning takes 9 ms
// == Iteration 1 ==
// Query planning takes 26 ms
// == Iteration 2 ==
// Query planning takes 53 ms
// == Iteration 3 ==
// Query planning takes 163 ms
// == Iteration 4 ==
// Query planning takes 700 ms
// == Iteration 5 ==
// Query planning takes 3418 ms
```
This is because when building a new Dataset, the new plan is always built upon `QueryExecution.analyzed`, which doesn't leverage existing cached plans.
On the other hand, usually, doing caching every a few iterations may not be the right direction for this problem since caching is too memory consuming (imaging computing connected components over a graph with 50 billion nodes). What we really need here is to truncate both the query plan (to minimize query planning time) and the lineage of the underlying RDD (to avoid stack overflow).
### Changes introduced in this PR
This PR tries to fix this issue by introducing a `checkpoint()` method into `Dataset[T]`, which does exactly the things described above. The following snippet, which is essentially the same as the one above but invokes `checkpoint()` instead of `cache()`, shows the micro benchmark result of this PR:
One key point is that the checkpointed Dataset should preserve the origianl partitioning and ordering information of the original Dataset, so that we can avoid unnecessary shuffling (similar to reading from a pre-bucketed table). This is done by adding `outputPartitioning` and `outputOrdering` to `LogicalRDD` and `RDDScanExec`.
### Micro benchmark
``` scala
spark.sparkContext.setCheckpointDir("/tmp/cp")
(0 until 100).foldLeft(Seq(1, 2, 3).toDS) { (plan, iteration) =>
println(s"== Iteration $iteration ==")
val time0 = System.currentTimeMillis()
val cp = plan.checkpoint()
cp.count()
System.out.println(s"Checkpointing takes ${System.currentTimeMillis() - time0} ms")
val time1 = System.currentTimeMillis()
val joined = cp.join(cp, "value").join(cp, "value").join(cp, "value").join(cp, "value")
val result = joined.as[Int]
println(s"Query planning takes ${System.currentTimeMillis() - time1} ms")
result
}
// == Iteration 0 ==
// Checkpointing takes 591 ms
// Query planning takes 13 ms
// == Iteration 1 ==
// Checkpointing takes 1605 ms
// Query planning takes 16 ms
// == Iteration 2 ==
// Checkpointing takes 782 ms
// Query planning takes 8 ms
// == Iteration 3 ==
// Checkpointing takes 729 ms
// Query planning takes 10 ms
// == Iteration 4 ==
// Checkpointing takes 734 ms
// Query planning takes 9 ms
// == Iteration 5 ==
// ...
// == Iteration 50 ==
// Checkpointing takes 571 ms
// Query planning takes 7 ms
// == Iteration 51 ==
// Checkpointing takes 548 ms
// Query planning takes 7 ms
// == Iteration 52 ==
// Checkpointing takes 596 ms
// Query planning takes 8 ms
// == Iteration 53 ==
// Checkpointing takes 568 ms
// Query planning takes 7 ms
// ...
```
You may see that although checkpointing is more heavy weight an operation, it always takes roughly the same amount of time to perform both checkpointing and query planning.
### Open question
mengxr mentioned that it would be more convenient if we can make `Dataset.checkpoint()` eager, i.e., always performs a `RDD.count()` after calling `RDD.checkpoint()`. Not quite sure whether this is a universal requirement. Maybe we can add a `eager: Boolean` argument for `Dataset.checkpoint()` to support that.
## How was this patch tested?
Unit test added in `DatasetSuite`.
Author: Cheng Lian <lian@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#15651 from liancheng/ds-checkpoint.
## What changes were proposed in this pull request?
Because of the refactoring work in Structured Streaming, the event logs generated by Strucutred Streaming in Spark 2.0.0 and 2.0.1 cannot be parsed.
This PR just ignores these logs in ReplayListenerBus because no places use them.
## How was this patch tested?
- Generated events logs using Spark 2.0.0 and 2.0.1, and saved them as `structured-streaming-query-event-logs-2.0.0.txt` and `structured-streaming-query-event-logs-2.0.1.txt`
- The new added test makes sure ReplayListenerBus will skip these bad jsons.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15663 from zsxwing/fix-event-log.
## What changes were proposed in this pull request?
Currently, `ANALYZE TABLE` command accepts `identifier` for option `NOSCAN`. This PR raises a ParseException for unknown option.
**Before**
```scala
scala> sql("create table test(a int)")
res0: org.apache.spark.sql.DataFrame = []
scala> sql("analyze table test compute statistics blah")
res1: org.apache.spark.sql.DataFrame = []
```
**After**
```scala
scala> sql("create table test(a int)")
res0: org.apache.spark.sql.DataFrame = []
scala> sql("analyze table test compute statistics blah")
org.apache.spark.sql.catalyst.parser.ParseException:
Expected `NOSCAN` instead of `blah`(line 1, pos 0)
```
## How was this patch tested?
Pass the Jenkins test with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15640 from dongjoon-hyun/SPARK-18106.
## What changes were proposed in this pull request?
To reduce the number of components in SQL named *Catalog, rename *FileCatalog to *FileIndex. A FileIndex is responsible for returning the list of partitions / files to scan given a filtering expression.
```
TableFileCatalog => CatalogFileIndex
FileCatalog => FileIndex
ListingFileCatalog => InMemoryFileIndex
MetadataLogFileCatalog => MetadataLogFileIndex
PrunedTableFileCatalog => PrunedInMemoryFileIndex
```
cc yhuai marmbrus
## How was this patch tested?
N/A
Author: Eric Liang <ekl@databricks.com>
Author: Eric Liang <ekhliang@gmail.com>
Closes#15634 from ericl/rename-file-provider.
## What changes were proposed in this pull request?
The behavior of union is not well defined here. It is safer to explicitly execute these commands in order. The other use of `Union` in this way will be removed by https://github.com/apache/spark/pull/15633
## How was this patch tested?
Existing tests.
cc yhuai cloud-fan
Author: Eric Liang <ekhliang@gmail.com>
Author: Eric Liang <ekl@databricks.com>
Closes#15665 from ericl/spark-18146.
## What changes were proposed in this pull request?
Fixed the issue that ForeachSink didn't rethrow the exception.
## How was this patch tested?
The fixed unit test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15674 from zsxwing/foreach-sink-error.
## What changes were proposed in this pull request?
We should follow hive table and also store partition spec in metastore for data source table.
This brings 2 benefits:
1. It's more flexible to manage the table data files, as users can use `ADD PARTITION`, `DROP PARTITION` and `RENAME PARTITION`
2. We don't need to cache all file status for data source table anymore.
## How was this patch tested?
existing tests.
Author: Eric Liang <ekl@databricks.com>
Author: Michael Allman <michael@videoamp.com>
Author: Eric Liang <ekhliang@gmail.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15515 from cloud-fan/partition.
## What changes were proposed in this pull request?
A follow up PR for #14553 to fix the flaky test. It's flaky because the file list API doesn't guarantee any order of the return list.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15661 from zsxwing/fix-StreamingQuerySuite.
## What changes were proposed in this pull request?
This PR is an enhancement of PR with commit ID:57dc326bd00cf0a49da971e9c573c48ae28acaa2.
NaN is a special type of value which is commonly seen as invalid. But We find that there are certain cases where NaN are also valuable, thus need special handling. We provided user when dealing NaN values with 3 options, to either reserve an extra bucket for NaN values, or remove the NaN values, or report an error, by setting handleNaN "keep", "skip", or "error"(default) respectively.
'''Before:
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
'''After:
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
.setHandleNaN("keep")
## How was this patch tested?
Tests added in QuantileDiscretizerSuite, BucketizerSuite and DataFrameStatSuite
Signed-off-by: VinceShieh <vincent.xieintel.com>
Author: VinceShieh <vincent.xie@intel.com>
Author: Vincent Xie <vincent.xie@intel.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#15428 from VinceShieh/spark-17219_followup.
## What changes were proposed in this pull request?
API and programming guide doc changes for Scala, Python and R.
## How was this patch tested?
manual test
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15629 from felixcheung/jsondoc.
## What changes were proposed in this pull request?
A short code snippet that uses toLocalIterator() on a dataframe produced by a RunnableCommand
reproduces the problem. toLocalIterator() is called by thriftserver when
`spark.sql.thriftServer.incrementalCollect`is set to handle queries producing large result
set.
**Before**
```SQL
scala> spark.sql("show databases")
res0: org.apache.spark.sql.DataFrame = [databaseName: string]
scala> res0.toLocalIterator()
16/10/26 03:00:24 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.GenericInternalRow cannot be cast to org.apache.spark.sql.catalyst.expressions.UnsafeRow
```
**After**
```SQL
scala> spark.sql("drop database databases")
res30: org.apache.spark.sql.DataFrame = []
scala> spark.sql("show databases")
res31: org.apache.spark.sql.DataFrame = [databaseName: string]
scala> res31.toLocalIterator().asScala foreach println
[default]
[parquet]
```
## How was this patch tested?
Added a test in DDLSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#15642 from dilipbiswal/SPARK-18009.
## What changes were proposed in this pull request?
This PR contains changes to the Source trait such that the scheduler can notify data sources when it is safe to discard buffered data. Summary of changes:
* Added a method `commit(end: Offset)` that tells the Source that is OK to discard all offsets up `end`, inclusive.
* Changed the semantics of a `None` value for the `getBatch` method to mean "from the very beginning of the stream"; as opposed to "all data present in the Source's buffer".
* Added notes that the upper layers of the system will never call `getBatch` with a start value less than the last value passed to `commit`.
* Added a `lastCommittedOffset` method to allow the scheduler to query the status of each Source on restart. This addition is not strictly necessary, but it seemed like a good idea -- Sources will be maintaining their own persistent state, and there may be bugs in the checkpointing code.
* The scheduler in `StreamExecution.scala` now calls `commit` on its stream sources after marking each batch as complete in its checkpoint.
* `MemoryStream` now cleans committed batches out of its internal buffer.
* `TextSocketSource` now cleans committed batches from its internal buffer.
## How was this patch tested?
Existing regression tests already exercise the new code.
Author: frreiss <frreiss@us.ibm.com>
Closes#14553 from frreiss/fred-16963.
## What changes were proposed in this pull request?
Currently we have several test cases for group analytics(ROLLUP/CUBE/GROUPING SETS) in `SQLQuerySuite`, should better move them into a query file test.
The following test cases are moved to `group-analytics.sql`:
```
test("rollup")
test("grouping sets when aggregate functions containing groupBy columns")
test("cube")
test("grouping sets")
test("grouping and grouping_id")
test("grouping and grouping_id in having")
test("grouping and grouping_id in sort")
```
This is followup work of #15582
## How was this patch tested?
Modified query file `group-analytics.sql`, which will be tested by `SQLQueryTestSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15624 from jiangxb1987/group-analytics-test.
## What changes were proposed in this pull request?
Calling `Await.result` will allow other tasks to be run on the same thread when using ForkJoinPool. However, SQL uses a `ThreadLocal` execution id to trace Spark jobs launched by a query, which doesn't work perfectly in ForkJoinPool.
This PR just uses `Awaitable.result` instead to prevent ForkJoinPool from running other tasks in the current waiting thread.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15520 from zsxwing/SPARK-13747.
…rdless of warehouse dir's existence
## What changes were proposed in this pull request?
Appending a trailing slash, if there already isn't one for the
sake comparison of the two paths. It doesn't take away from
the essence of the check, but removes any potential mismatch
due to lack of trailing slash.
## How was this patch tested?
Ran unit tests and they passed.
Author: Mark Grover <mark@apache.org>
Closes#15623 from markgrover/spark-18093.
## What changes were proposed in this pull request?
The function `QueryPlan.inferAdditionalConstraints` and `UnaryNode.getAliasedConstraints` can produce a non-converging set of constraints for recursive functions. For instance, if we have two constraints of the form(where a is an alias):
`a = b, a = f(b, c)`
Applying both these rules in the next iteration would infer:
`f(b, c) = f(f(b, c), c)`
This process repeated, the iteration won't converge and the set of constraints will grow larger and larger until OOM.
~~To fix this problem, we collect alias from expressions and skip infer constraints if we are to transform an `Expression` to another which contains it.~~
To fix this problem, we apply additional check in `inferAdditionalConstraints`, when it's possible to generate recursive constraints, we skip generate that.
## How was this patch tested?
Add new testcase in `SQLQuerySuite`/`InferFiltersFromConstraintsSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15319 from jiangxb1987/constraints.
## What changes were proposed in this pull request?
On null next exception in JDBC, don't init it as cause or suppressed
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#15599 from srowen/SPARK-18022.
### What changes were proposed in this pull request?
```SQL
CREATE TABLE tab1(col1 int COMMENT 'a', col2 int) USING parquet
INSERT INTO TABLE tab1 SELECT 1, 2
```
The insert attempt will fail if the target table has a column with comments. The error is strange to the external users:
```
assertion failed: No plan for InsertIntoTable Relation[col1#15,col2#16] parquet, false, false
+- Project [1 AS col1#19, 2 AS col2#20]
+- OneRowRelation$
```
This PR is to fix the above bug by checking the metadata when comparing the schema between the table and the query. If not matched, we also copy the metadata. This is an alternative to https://github.com/apache/spark/pull/15266
### How was this patch tested?
Added a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15615 from gatorsmile/insertDataSourceTableWithCommentSolution2.
## What changes were proposed in this pull request?
Binary operator requires its inputs to be of same type, but it should not consider nullability, e.g. `EqualTo` should be able to compare an element-nullable array and an element-non-nullable array.
## How was this patch tested?
a regression test in `DataFrameSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15606 from cloud-fan/type-bug.
## What changes were proposed in this pull request?
Currently we always lowercase the partition columns of partition spec in parser, with the assumption that table partition columns are always lowercased.
However, this is not true for data source tables, which are case preserving. It's safe for now because data source tables don't store partition spec in metastore and don't support `ADD PARTITION`, `DROP PARTITION`, `RENAME PARTITION`, but we should make our code future-proof.
This PR makes partition spec case preserving at parser, and improve the `PreprocessTableInsertion` analyzer rule to normalize the partition columns in partition spec, w.r.t. the table partition columns.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15566 from cloud-fan/partition-spec.
### What changes were proposed in this pull request?
This follow-up PR is for addressing the [comment](https://github.com/apache/spark/pull/15048).
We added two test cases based on the suggestion from yhuai . One is a new test case using the `saveAsTable` API to create a data source table. Another is for CTAS on Hive serde table.
Note: No need to backport this PR to 2.0. Will submit a new PR to backport the whole fix with new test cases to Spark 2.0
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15459 from gatorsmile/ctasOptimizedTestCases.
## What changes were proposed in this pull request?
Simplify/cleanup TableFileCatalog:
1. pass a `CatalogTable` instead of `databaseName` and `tableName` into `TableFileCatalog`, so that we don't need to fetch table metadata from metastore again
2. In `TableFileCatalog.filterPartitions0`, DO NOT set `PartitioningAwareFileCatalog.BASE_PATH_PARAM`. According to the [classdoc](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningAwareFileCatalog.scala#L189-L209), the default value of `basePath` already satisfies our need. What's more, if we set this parameter, we may break the case 2 which is metioned in the classdoc.
3. add `equals` and `hashCode` to `TableFileCatalog`
4. add `SessionCatalog.listPartitionsByFilter` which handles case sensitivity.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15568 from cloud-fan/table-file-catalog.
## What changes were proposed in this pull request?
The reason for the flakiness was follows. The test starts the maintenance background thread, and then writes 20 versions of the state store. The maintenance thread is expected to create snapshots in the middle, and clean up old files that are not needed any more. The earliest delta file (1.delta) is expected to be deleted as snapshots will ensure that the earliest delta would not be needed.
However, the default configuration for the maintenance thread is to retain files such that last 2 versions can be recovered, and delete the rest. Now while generating the versions, the maintenance thread can kick in and create snapshots anywhere between version 10 and 20 (at least 10 deltas needed for snapshot). Then later it will choose to retain only version 20 and 19 (last 2). There are two cases.
- Common case: One of the version between 10 and 19 gets snapshotted. Then recovering versions 19 and 20 just needs 19.snapshot and 20.delta, so 1.delta gets deleted.
- Uncommon case (reason for flakiness): Only version 20 gets snapshotted. Then recovering versoin 20 requires 20.snapshot, and recovering version 19 all the previous 19...1.delta. So 1.delta does not get deleted.
This PR rearranges the checks such that it create 20 versions, and then waits that there is at least one snapshot, then creates another 20. This will ensure that the latest 2 versions cannot require anything older than the first snapshot generated, and therefore will 1.delta will be deleted.
In addition, I have added more logs, and comments that I felt would help future debugging and understanding what is going on.
## How was this patch tested?
Ran the StateStoreSuite > 6K times in a heavily loaded machine (10 instances of tests running in parallel). No failures.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15592 from tdas/SPARK-17624.
## What changes were proposed in this pull request?
Always resolve spark.sql.warehouse.dir as a local path, and as relative to working dir not home dir
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15382 from srowen/SPARK-17810.
## What changes were proposed in this pull request?
The testsuite `HiveDataFrameAnalyticsSuite` has nothing to do with HIVE, we should move it to package `sql`.
The original test cases in that suite are splited into two existing testsuites: `DataFrameAggregateSuite` tests for the functions and ~~`SQLQuerySuite`~~`SQLQueryTestSuite` tests for the SQL statements.
## How was this patch tested?
~~Modified `SQLQuerySuite` in package `sql`.~~
Add query file for `SQLQueryTestSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15582 from jiangxb1987/group-analytics-test.
## What changes were proposed in this pull request?
Jira : https://issues.apache.org/jira/browse/SPARK-18038
This was a suggestion by rxin over one of the dev list discussion : http://apache-spark-developers-list.1001551.n3.nabble.com/Project-not-preserving-child-partitioning-td19417.html
His words:
>> It would be better (safer) to move the output partitioning definition into each of the operator and remove it from UnaryExecNode.
With this PR, following is the output partitioning and ordering for all the impls of `UnaryExecNode`.
UnaryExecNode's impl | outputPartitioning | outputOrdering | comment
------------ | ------------- | ------------ | ------------
AppendColumnsExec | child's | Nil | child's ordering can be used
AppendColumnsWithObjectExec | child's | Nil | child's ordering can be used
BroadcastExchangeExec | BroadcastPartitioning | Nil | -
CoalesceExec | UnknownPartitioning | Nil | -
CollectLimitExec | SinglePartition | Nil | -
DebugExec | child's | Nil | child's ordering can be used
DeserializeToObjectExec | child's | Nil | child's ordering can be used
ExpandExec | UnknownPartitioning | Nil | -
FilterExec | child's | child's | -
FlatMapGroupsInRExec | child's | Nil | child's ordering can be used
GenerateExec | child's | Nil | need to dig more
GlobalLimitExec | child's | child's | -
HashAggregateExec | child's | Nil | -
InputAdapter | child's | child's | -
InsertIntoHiveTable | child's | Nil | terminal node, doesn't need partitioning
LocalLimitExec | child's | child's | -
MapElementsExec | child's | child's | -
MapGroupsExec | child's | Nil | child's ordering can be used
MapPartitionsExec | child's | Nil | child's ordering can be used
ProjectExec | child's | child's | -
SampleExec | child's | Nil | child's ordering can be used
ScriptTransformation | child's | Nil | child's ordering can be used
SerializeFromObjectExec | child's | Nil | child's ordering can be used
ShuffleExchange | custom | Nil | -
SortAggregateExec | child's | sort over grouped exprs | -
SortExec | child's | custom | -
StateStoreRestoreExec | child's | Nil | child's ordering can be used
StateStoreSaveExec | child's | Nil | child's ordering can be used
SubqueryExec | child's | child's | -
TakeOrderedAndProjectExec | SinglePartition | custom | -
WholeStageCodegenExec | child's | child's | -
WindowExec | child's | child's | -
## How was this patch tested?
This does NOT change any existing functionality so relying on existing tests
Author: Tejas Patil <tejasp@fb.com>
Closes#15575 from tejasapatil/SPARK-18038_UnaryNodeExec_output_partitioning.
## What changes were proposed in this pull request?
Jira: https://issues.apache.org/jira/browse/SPARK-18035
In HiveInspectors, I saw that converting Java map to Spark's `ArrayBasedMapData` spent quite sometime in buffer copying : https://github.com/apache/spark/blob/master/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala#L658
The reason being `map.toSeq` allocates a new buffer and copies the map entries to it: https://github.com/scala/scala/blob/2.11.x/src/library/scala/collection/MapLike.scala#L323
This copy is not needed as we get rid of it once we extract the key and value arrays.
Here is the call trace:
```
org.apache.spark.sql.hive.HiveInspectors$$anonfun$unwrapperFor$41.apply(HiveInspectors.scala:664)
scala.collection.AbstractMap.toSeq(Map.scala:59)
scala.collection.MapLike$class.toSeq(MapLike.scala:323)
scala.collection.AbstractMap.toBuffer(Map.scala:59)
scala.collection.MapLike$class.toBuffer(MapLike.scala:326)
scala.collection.AbstractTraversable.copyToBuffer(Traversable.scala:104)
scala.collection.TraversableOnce$class.copyToBuffer(TraversableOnce.scala:275)
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
scala.collection.AbstractIterable.foreach(Iterable.scala:54)
scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
scala.collection.Iterator$class.foreach(Iterator.scala:893)
scala.collection.generic.Growable$$anonfun$$plus$plus$eq$1.apply(Growable.scala:59)
scala.collection.generic.Growable$$anonfun$$plus$plus$eq$1.apply(Growable.scala:59)
```
Also, earlier code was populating keys and values arrays separately by iterating twice. The PR avoids double iteration of the map and does it in one iteration.
EDIT: During code review, there were several more places in the code which were found to do similar thing. The PR dedupes those instances and introduces convenient APIs which are performant and memory efficient
## Performance gains
The number is subjective and depends on how many map columns are accessed in the query and average entries per map. For one the queries that I tried out, I saw 3% CPU savings (end-to-end) for the query.
## How was this patch tested?
This does not change the end result produced so relying on existing tests.
Author: Tejas Patil <tejasp@fb.com>
Closes#15573 from tejasapatil/SPARK-18035_avoid_toSeq.
## What changes were proposed in this pull request?
In SPARK-16980, we removed the full in-memory cache of table partitions in favor of loading only needed partitions from the metastore. This greatly improves the initial latency of queries that only read a small fraction of table partitions.
However, since the metastore does not store file statistics, we need to discover those from remote storage. With the loss of the in-memory file status cache this has to happen on each query, increasing the latency of repeated queries over the same partitions.
The proposal is to add back a per-table cache of partition contents, i.e. Map[Path, Array[FileStatus]]. This cache would be retained per-table, and can be invalidated through refreshTable() and refreshByPath(). Unlike the prior cache, it can be incrementally updated as new partitions are read.
## How was this patch tested?
Existing tests and new tests in `HiveTablePerfStatsSuite`.
cc mallman
Author: Eric Liang <ekl@databricks.com>
Author: Michael Allman <michael@videoamp.com>
Author: Eric Liang <ekhliang@gmail.com>
Closes#15539 from ericl/meta-cache.
## What changes were proposed in this pull request?
Tiny follow-up to SPARK-16606 / https://github.com/apache/spark/pull/14533 , to correct more instances of the same log message typo
## How was this patch tested?
Existing tests (no functional change anyway)
Author: Sean Owen <sowen@cloudera.com>
Closes#15586 from srowen/SPARK-16606.2.
## What changes were proposed in this pull request?
This patch adds a new "path" method on OutputWriter that returns the path of the file written by the OutputWriter. This is part of the necessary work to consolidate structured streaming and batch write paths.
The batch write path has a nice feature that each data source can define the extension of the files, and allow Spark to specify the staging directory and the prefix for the files. However, in the streaming path we need to collect the list of files written, and there is no interface right now to do that.
## How was this patch tested?
N/A - there is no behavior change and this should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#15580 from rxin/SPARK-18042.
## What changes were proposed in this pull request?
In `FileStreamSource.getBatch`, we will create a `DataSource` with specified schema, to avoid inferring the schema again and again. However, we don't pass the partition columns, and will infer the partition again and again.
This PR fixes it by keeping the partition columns in `FileStreamSource`, like schema.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15581 from cloud-fan/stream.
## What changes were proposed in this pull request?
StreamingQueryStatus exposed through StreamingQueryListener often needs to be recorded (similar to SparkListener events). This PR adds `.json` and `.prettyJson` to `StreamingQueryStatus`, `SourceStatus` and `SinkStatus`.
## How was this patch tested?
New unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15476 from tdas/SPARK-17926.
## What changes were proposed in this pull request?
`Array[T]()` -> `Array.empty[T]` to avoid allocating 0-length arrays.
Use regex `find . -name '*.scala' | xargs -i bash -c 'egrep "Array\[[A-Za-z]+\]\(\)" -n {} && echo {}'` to find modification candidates.
cc srowen
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15564 from zhengruifeng/avoid_0_length_array.
## What changes were proposed in this pull request?
In `PruneFileSourcePartitions`, we will replace the `LogicalRelation` with a pruned one. However, this replacement may change the output of the `LogicalRelation` if it doesn't have `expectedOutputAttributes`. This PR fixes it.
## How was this patch tested?
the new `PruneFileSourcePartitionsSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15569 from cloud-fan/partition-bug.
## What changes were proposed in this pull request?
My hunch is `mkdirs` fails. Just add more checks to collect more info.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15577 from zsxwing/SPARK-18030-debug.
## What changes were proposed in this pull request?
Currently each data source OutputWriter is responsible for specifying the entire file name for each file output. This, however, does not make any sense because we rely on file naming schemes for certain behaviors in Spark SQL, e.g. bucket id. The current approach allows individual data sources to break the implementation of bucketing.
On the flip side, we also don't want to move file naming entirely out of data sources, because different data sources do want to specify different extensions.
This patch divides file name specification into two parts: the first part is a prefix specified by the caller of OutputWriter (in WriteOutput), and the second part is the suffix that can be specified by the OutputWriter itself. Note that a side effect of this change is that now all file based data sources also support bucketing automatically.
There are also some other minor cleanups:
- Removed the UUID passed through generic Configuration string
- Some minor rewrites for better clarity
- Renamed "path" in multiple places to "stagingDir", to more accurately reflect its meaning
## How was this patch tested?
This should be covered by existing data source tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#15562 from rxin/SPARK-18021.
## What changes were proposed in this pull request?
Add mapValues to KeyValueGroupedDataset
## How was this patch tested?
New test in DatasetSuite for groupBy function, mapValues, flatMap
Author: Koert Kuipers <koert@tresata.com>
Closes#13526 from koertkuipers/feat-keyvaluegroupeddataset-mapvalues.
## What changes were proposed in this pull request?
SHOW COLUMNS command validates the user supplied database
name with database name from qualified table name name to make
sure both of them are consistent. This comparison should respect
case sensitivity.
## How was this patch tested?
Added tests in DDLSuite and existing tests were moved to use new sql based test infrastructure.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#15423 from dilipbiswal/dkb_show_column_fix.
## What changes were proposed in this pull request?
Currently, Spark 2.0 raises an `input path does not exist` AnalysisException if the file name contains '*'. It is misleading since it occurs when there exist some matched files. Also, it was a supported feature in Spark 1.6.2. This PR aims to support wildcard characters in filename for `LOAD DATA LOCAL INPATH` SQL command like Spark 1.6.2.
**Reported Error Scenario**
```scala
scala> sql("CREATE TABLE t(a string)")
res0: org.apache.spark.sql.DataFrame = []
scala> sql("LOAD DATA LOCAL INPATH '/tmp/x*' INTO TABLE t")
org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: /tmp/x*;
```
## How was this patch tested?
Pass the Jenkins test with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15376 from dongjoon-hyun/SPARK-17796.
## What changes were proposed in this pull request?
This should apply to non-converted metastore relations. WIP to see if this causes any test failures.
## How was this patch tested?
Existing tests.
Author: Eric Liang <ekl@databricks.com>
Closes#15475 from ericl/try-enabling-pruning.
## What changes were proposed in this pull request?
This patch refactors WriterContainer to simplify the logic and make control flow more obvious.The previous code setup made it pretty difficult to track the actual dependencies on variables and setups because the driver side and the executor side were using the same set of variables.
## How was this patch tested?
N/A - this should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#15551 from rxin/writercontainer-refactor.
## What changes were proposed in this pull request?
This PR proposes to check the second argument, `ascendingOrder` rather than throwing `ClassCastException` exception message.
```sql
select sort_array(array('b', 'd'), '1');
```
**Before**
```
16/10/19 13:16:08 ERROR SparkSQLDriver: Failed in [select sort_array(array('b', 'd'), '1')]
java.lang.ClassCastException: org.apache.spark.unsafe.types.UTF8String cannot be cast to java.lang.Boolean
at scala.runtime.BoxesRunTime.unboxToBoolean(BoxesRunTime.java:85)
at org.apache.spark.sql.catalyst.expressions.SortArray.nullSafeEval(collectionOperations.scala:185)
at org.apache.spark.sql.catalyst.expressions.BinaryExpression.eval(Expression.scala:416)
at org.apache.spark.sql.catalyst.optimizer.ConstantFolding$$anonfun$apply$1$$anonfun$applyOrElse$1.applyOrElse(expressions.scala:50)
at org.apache.spark.sql.catalyst.optimizer.ConstantFolding$$anonfun$apply$1$$anonfun$applyOrElse$1.applyOrElse(expressions.scala:43)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:292)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:292)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:74)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:291)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:297)
```
**After**
```
Error in query: cannot resolve 'sort_array(array('b', 'd'), '1')' due to data type mismatch: Sort order in second argument requires a boolean literal.; line 1 pos 7;
```
## How was this patch tested?
Unit test in `DataFrameFunctionsSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15532 from HyukjinKwon/SPARK-17989.
## What changes were proposed in this pull request?
Unlike Hive, in Spark SQL, ALTER TABLE RENAME TO cannot move a table from one database to another(e.g. `ALTER TABLE db1.tbl RENAME TO db2.tbl2`), and will report error if the database in source table and destination table is different. So in #14955 , we forbid users to specify database of destination table in ALTER TABLE RENAME TO, to be consistent with other database systems and also make it easier to rename tables in non-current database, e.g. users can write `ALTER TABLE db1.tbl RENAME TO tbl2`, instead of `ALTER TABLE db1.tbl RENAME TO db1.tbl2`.
However, this is a breaking change. Users may already have queries that specify database of destination table in ALTER TABLE RENAME TO.
This PR reverts most of #14955 , and simplify the usage of ALTER TABLE RENAME TO by making database of source table the default database of destination table, instead of current database, so that users can still write `ALTER TABLE db1.tbl RENAME TO tbl2`, which is consistent with other databases like MySQL, Postgres, etc.
## How was this patch tested?
The added back tests and some new tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15434 from cloud-fan/revert.
## What changes were proposed in this pull request?
There was a bug introduced in https://github.com/apache/spark/pull/14690 which broke refreshByPath with converted hive tables (though, it turns out it was very difficult to refresh converted hive tables anyways, since you had to specify the exact path of one of the partitions).
This changes refreshByPath to invalidate by prefix instead of exact match, and fixes the issue.
cc sameeragarwal for refreshByPath changes
mallman
## How was this patch tested?
Extended unit test.
Author: Eric Liang <ekl@databricks.com>
Closes#15521 from ericl/fix-caching.
## What changes were proposed in this pull request?
As per rxin request, here are further API changes
- Changed `Stream(Started/Progress/Terminated)` events to `Stream*Event`
- Changed the fields in `StreamingQueryListener.on***` from `query*` to `event`
## How was this patch tested?
Existing unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15530 from tdas/SPARK-17731-1.
## What changes were proposed in this pull request?
This PR proposes to make `DataFrameReader.jdbc` call `DataFrameReader.format("jdbc").load` consistently with other APIs in `DataFrameReader`/`DataFrameWriter` and avoid calling `sparkSession.baseRelationToDataFrame(..)` here and there.
The changes were mostly copied from `DataFrameWriter.jdbc()` which was recently updated.
```diff
- val params = extraOptions.toMap ++ connectionProperties.asScala.toMap
- val options = new JDBCOptions(url, table, params)
- val relation = JDBCRelation(parts, options)(sparkSession)
- sparkSession.baseRelationToDataFrame(relation)
+ this.extraOptions = this.extraOptions ++ connectionProperties.asScala
+ // explicit url and dbtable should override all
+ this.extraOptions += ("url" -> url, "dbtable" -> table)
+ format("jdbc").load()
```
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15499 from HyukjinKwon/SPARK-17955.
## What changes were proposed in this pull request?
This renames `BasicFileCatalog => FileCatalog`, combines `SessionFileCatalog` with `PartitioningAwareFileCatalog`, and removes the old `FileCatalog` trait.
In summary,
```
MetadataLogFileCatalog extends PartitioningAwareFileCatalog
ListingFileCatalog extends PartitioningAwareFileCatalog
PartitioningAwareFileCatalog extends FileCatalog
TableFileCatalog extends FileCatalog
```
(note that this is a re-submission of https://github.com/apache/spark/pull/15518 which got reverted)
## How was this patch tested?
Existing tests
Author: Eric Liang <ekl@databricks.com>
Closes#15533 from ericl/fix-scalastyle-revert.
## What changes were proposed in this pull request?
Currently, Spark only supports to infer `IntegerType`, `LongType`, `DoubleType` and `StringType`.
`DecimalType` is being tried but it seems it never infers type as `DecimalType` as `DoubleType` is being tried first. Also, it seems `DateType` and `TimestampType` could be inferred.
As far as I know, it is pretty common to use both for a partition column.
This PR fixes the incorrect `DecimalType` try and also adds the support for both `DateType` and `TimestampType` for inferring partition column type.
## How was this patch tested?
Unit tests in `ParquetPartitionDiscoverySuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14947 from HyukjinKwon/SPARK-17388.
## What changes were proposed in this pull request?
Scala 2.10 does not have Option.contains, which broke Scala 2.10 build.
## How was this patch tested?
Locally compiled and ran sql/core unit tests in 2.10
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15531 from tdas/metrics-flaky-test-fix-1.
This work has largely been done by lw-lin in his PR #15497. This is a slight refactoring of it.
## What changes were proposed in this pull request?
There were two sources of flakiness in StreamingQueryListener test.
- When testing with manual clock, consecutive attempts to advance the clock can occur without the stream execution thread being unblocked and doing some work between the two attempts. Hence the following can happen with the current ManualClock.
```
+-----------------------------------+--------------------------------+
| StreamExecution thread | testing thread |
+-----------------------------------+--------------------------------+
| ManualClock.waitTillTime(100) { | |
| _isWaiting = true | |
| wait(10) | |
| still in wait(10) | if (_isWaiting) advance(100) |
| still in wait(10) | if (_isWaiting) advance(200) | <- this should be disallowed !
| still in wait(10) | if (_isWaiting) advance(300) | <- this should be disallowed !
| wake up from wait(10) | |
| current time is 600 | |
| _isWaiting = false | |
| } | |
+-----------------------------------+--------------------------------+
```
- Second source of flakiness is that the adding data to memory stream may get processing in any trigger, not just the first trigger.
My fix is to make the manual clock wait for the other stream execution thread to start waiting for the clock at the right wait start time. That is, `advance(200)` (see above) will wait for stream execution thread to complete the wait that started at time 0, and start a new wait at time 200 (i.e. time stamp after the previous `advance(100)`).
In addition, since this is a feature that is solely used by StreamExecution, I removed all the non-generic code from ManualClock and put them in StreamManualClock inside StreamTest.
## How was this patch tested?
Ran existing unit test MANY TIME in Jenkins
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Liwei Lin <lwlin7@gmail.com>
Closes#15519 from tdas/metrics-flaky-test-fix.
## What changes were proposed in this pull request?
This renames `BasicFileCatalog => FileCatalog`, combines `SessionFileCatalog` with `PartitioningAwareFileCatalog`, and removes the old `FileCatalog` trait.
In summary,
```
MetadataLogFileCatalog extends PartitioningAwareFileCatalog
ListingFileCatalog extends PartitioningAwareFileCatalog
PartitioningAwareFileCatalog extends FileCatalog
TableFileCatalog extends FileCatalog
```
cc cloud-fan mallman
## How was this patch tested?
Existing tests
Author: Eric Liang <ekl@databricks.com>
Closes#15518 from ericl/refactor-session-file-catalog.
## What changes were proposed in this pull request?
Reopens the closed PR https://github.com/apache/spark/pull/15190
(Please refer to the above link for review comments on the PR)
Make sure the hive.default.fileformat is used to when creating the storage format metadata.
Output
``` SQL
scala> spark.sql("SET hive.default.fileformat=orc")
res1: org.apache.spark.sql.DataFrame = [key: string, value: string]
scala> spark.sql("CREATE TABLE tmp_default(id INT)")
res2: org.apache.spark.sql.DataFrame = []
```
Before
```SQL
scala> spark.sql("DESC FORMATTED tmp_default").collect.foreach(println)
..
[# Storage Information,,]
[SerDe Library:,org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe,]
[InputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcInputFormat,]
[OutputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat,]
[Compressed:,No,]
[Storage Desc Parameters:,,]
[ serialization.format,1,]
```
After
```SQL
scala> spark.sql("DESC FORMATTED tmp_default").collect.foreach(println)
..
[# Storage Information,,]
[SerDe Library:,org.apache.hadoop.hive.ql.io.orc.OrcSerde,]
[InputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcInputFormat,]
[OutputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat,]
[Compressed:,No,]
[Storage Desc Parameters:,,]
[ serialization.format,1,]
```
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Added new tests to HiveDDLCommandSuite, SQLQuerySuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#15495 from dilipbiswal/orc2.
### What changes were proposed in this pull request?
Dataset always does eager analysis now. Thus, `spark.sql.eagerAnalysis` is not used any more. Thus, we need to remove it.
This PR also outputs the plan. Without the fix, the analysis error is like
```
cannot resolve '`k1`' given input columns: [k, v]; line 1 pos 12
```
After the fix, the analysis error becomes:
```
org.apache.spark.sql.AnalysisException: cannot resolve '`k1`' given input columns: [k, v]; line 1 pos 12;
'Project [unresolvedalias(CASE WHEN ('k1 = 2) THEN 22 WHEN ('k1 = 4) THEN 44 ELSE 0 END, None), v#6]
+- SubqueryAlias t
+- Project [_1#2 AS k#5, _2#3 AS v#6]
+- LocalRelation [_1#2, _2#3]
```
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15316 from gatorsmile/eagerAnalysis.
## What changes were proposed in this pull request?
Currently we use BufferedInputStream to read the shuffle file which copies the file content from os buffer cache to the user buffer. This adds additional latency in reading the spill files. We made a change to use java nio's direct buffer to read the spill files and for certain pipelines spilling significant amount of data, we see up to 7% speedup for the entire pipeline.
## How was this patch tested?
Tested by running the job in the cluster and observed up to 7% speedup.
Author: Sital Kedia <skedia@fb.com>
Closes#15408 from sitalkedia/skedia/nio_spill_read.
### What changes were proposed in this pull request?
Just document the impact of `spark.sql.debug`:
When enabling the debug, Spark SQL internal table properties are not filtered out; however, some related DDL commands (e.g., Analyze Table and CREATE TABLE LIKE) might not work properly.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15494 from gatorsmile/addDocForSQLDebug.
## What changes were proposed in this pull request?
### Before:
```scala
SparkSession.builder()
.master("local")
.appName("Word Count")
.config("spark.some.config.option", "some-value").
.getOrCreate()
```
### After:
```scala
SparkSession.builder()
.master("local")
.appName("Word Count")
.config("spark.some.config.option", "some-value")
.getOrCreate()
```
There was one unexpected dot!
Author: Jun Kim <i2r.jun@gmail.com>
Closes#15498 from tae-jun/SPARK-17953.
(This PR addresses https://issues.apache.org/jira/browse/SPARK-16980.)
## What changes were proposed in this pull request?
In a new Spark session, when a partitioned Hive table is converted to use Spark's `HadoopFsRelation` in `HiveMetastoreCatalog`, metadata for every partition of that table are retrieved from the metastore and loaded into driver memory. In addition, every partition's metadata files are read from the filesystem to perform schema inference.
If a user queries such a table with predicates which prune that table's partitions, we would like to be able to answer that query without consulting partition metadata which are not involved in the query. When querying a table with a large number of partitions for some data from a small number of partitions (maybe even a single partition), the current conversion strategy is highly inefficient. I suspect this scenario is not uncommon in the wild.
In addition to being inefficient in running time, the current strategy is inefficient in its use of driver memory. When the sum of the number of partitions of all tables loaded in a driver reaches a certain level (somewhere in the tens of thousands), their cached data exhaust all driver heap memory in the default configuration. I suspect this scenario is less common (in that not too many deployments work with tables with tens of thousands of partitions), however this does illustrate how large the memory footprint of this metadata can be. With tables with hundreds or thousands of partitions, I would expect the `HiveMetastoreCatalog` table cache to represent a significant portion of the driver's heap space.
This PR proposes an alternative approach. Basically, it makes four changes:
1. It adds a new method, `listPartitionsByFilter` to the Catalyst `ExternalCatalog` trait which returns the partition metadata for a given sequence of partition pruning predicates.
1. It refactors the `FileCatalog` type hierarchy to include a new `TableFileCatalog` to efficiently return files only for partitions matching a sequence of partition pruning predicates.
1. It removes partition loading and caching from `HiveMetastoreCatalog`.
1. It adds a new Catalyst optimizer rule, `PruneFileSourcePartitions`, which applies a plan's partition-pruning predicates to prune out unnecessary partition files from a `HadoopFsRelation`'s underlying file catalog.
The net effect is that when a query over a partitioned Hive table is planned, the analyzer retrieves the table metadata from `HiveMetastoreCatalog`. As part of this operation, the `HiveMetastoreCatalog` builds a `HadoopFsRelation` with a `TableFileCatalog`. It does not load any partition metadata or scan any files. The optimizer prunes-away unnecessary table partitions by sending the partition-pruning predicates to the relation's `TableFileCatalog `. The `TableFileCatalog` in turn calls the `listPartitionsByFilter` method on its external catalog. This queries the Hive metastore, passing along those filters.
As a bonus, performing partition pruning during optimization leads to a more accurate relation size estimate. This, along with c481bdf, can lead to automatic, safe application of the broadcast optimization in a join where it might previously have been omitted.
## Open Issues
1. This PR omits partition metadata caching. I can add this once the overall strategy for the cold path is established, perhaps in a future PR.
1. This PR removes and omits partitioned Hive table schema reconciliation. As a result, it fails to find Parquet schema columns with upper case letters because of the Hive metastore's case-insensitivity. This issue may be fixed by #14750, but that PR appears to have stalled. ericl has contributed to this PR a workaround for Parquet wherein schema reconciliation occurs at query execution time instead of planning. Whether ORC requires a similar patch is an open issue.
1. This PR omits an implementation of `listPartitionsByFilter` for the `InMemoryCatalog`.
1. This PR breaks parquet log output redirection during query execution. I can work around this by running `Class.forName("org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$")` first thing in a Spark shell session, but I haven't figured out how to fix this properly.
## How was this patch tested?
The current Spark unit tests were run, and some ad-hoc tests were performed to validate that only the necessary partition metadata is loaded.
Author: Michael Allman <michael@videoamp.com>
Author: Eric Liang <ekl@databricks.com>
Author: Eric Liang <ekhliang@gmail.com>
Closes#14690 from mallman/spark-16980-lazy_partition_fetching.
## What changes were proposed in this pull request?
Add a crossJoin function to the DataFrame API similar to that in Scala. Joins with no condition (cartesian products) must be specified with the crossJoin API
## How was this patch tested?
Added python tests to ensure that an AnalysisException if a cartesian product is specified without crossJoin(), and that cartesian products can execute if specified via crossJoin()
(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 https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.
Author: Srinath Shankar <srinath@databricks.com>
Closes#15493 from srinathshankar/crosspython.
## What changes were proposed in this pull request?
This patch graduates a list of Spark SQL APIs and mark them stable.
The following are marked stable:
Dataset/DataFrame
- functions, since 1.3
- ColumnName, since 1.3
- DataFrameNaFunctions, since 1.3.1
- DataFrameStatFunctions, since 1.4
- UserDefinedFunction, since 1.3
- UserDefinedAggregateFunction, since 1.5
- Window and WindowSpec, since 1.4
Data sources:
- DataSourceRegister, since 1.5
- RelationProvider, since 1.3
- SchemaRelationProvider, since 1.3
- CreatableRelationProvider, since 1.3
- BaseRelation, since 1.3
- TableScan, since 1.3
- PrunedScan, since 1.3
- PrunedFilteredScan, since 1.3
- InsertableRelation, since 1.3
The following are kept experimental / evolving:
Data sources:
- CatalystScan (tied to internal logical plans so it is not stable by definition)
Structured streaming:
- all classes (introduced new in 2.0 and will likely change)
Dataset typed operations (introduced in 1.6 and 2.0 and might change, although probability is low)
- all typed methods on Dataset
- KeyValueGroupedDataset
- o.a.s.sql.expressions.javalang.typed
- o.a.s.sql.expressions.scalalang.typed
- methods that return typed Dataset in SparkSession
We should discuss more whether we want to mark Dataset typed operations stable in 2.1.
## How was this patch tested?
N/A - just annotation changes.
Author: Reynold Xin <rxin@databricks.com>
Closes#15469 from rxin/SPARK-17900.
Currently pyspark can only call the builtin java UDF, but can not call custom java UDF. It would be better to allow that. 2 benefits:
* Leverage the power of rich third party java library
* Improve the performance. Because if we use python UDF, python daemons will be started on worker which will affect the performance.
Author: Jeff Zhang <zjffdu@apache.org>
Closes#9766 from zjffdu/SPARK-11775.
[SPARK-11905](https://issues.apache.org/jira/browse/SPARK-11905) added support for `persist`/`cache` for `Dataset`. However, there is no user-facing API to check if a `Dataset` is cached and if so what the storage level is. This PR adds `getStorageLevel` to `Dataset`, analogous to `RDD.getStorageLevel`.
Updated `DatasetCacheSuite`.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#13780 from MLnick/ds-storagelevel.
Signed-off-by: Michael Armbrust <michael@databricks.com>
## What changes were proposed in this pull request?
We are trying to resolve the attribute in sort by pulling up some column for grandchild into child, but that's wrong when the child is Distinct, because the added column will change the behavior of Distinct, we should not do that.
## How was this patch tested?
Added regression test.
Author: Davies Liu <davies@databricks.com>
Closes#15489 from davies/order_distinct.
## What changes were proposed in this pull request?
Minor typo fix
## How was this patch tested?
Existing unit tests on Jenkins
Author: Andrew Ash <andrew@andrewash.com>
Closes#15486 from ash211/patch-8.
## What changes were proposed in this pull request?
This pr adds some test cases for statistics: case sensitive column names, non ascii column names, refresh table, and also improves some documentation.
## How was this patch tested?
add test cases
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#15360 from wzhfy/colStats2.
## What changes were proposed in this pull request?
This patch does a few changes to the file structure of data sources:
- Break fileSourceInterfaces.scala into multiple pieces (HadoopFsRelation, FileFormat, OutputWriter)
- Move ParquetOutputWriter into its own file
I created this as a separate patch so it'd be easier to review my future PRs that focus on refactoring this internal logic. This patch only moves code around, and has no logic changes.
## How was this patch tested?
N/A - should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#15473 from rxin/SPARK-17925.
## What changes were proposed in this pull request?
speculationEnabled and DATASOURCE_OUTPUTPATH seem like just dead code.
## How was this patch tested?
Tests should fail if they are not dead code.
Author: Reynold Xin <rxin@databricks.com>
Closes#15477 from rxin/SPARK-17927.
## What changes were proposed in this pull request?
There are 4 listLeafFiles-related functions in Spark:
- ListingFileCatalog.listLeafFiles (which calls HadoopFsRelation.listLeafFilesInParallel if the number of paths passed in is greater than a threshold; if it is lower, then it has its own serial version implemented)
- HadoopFsRelation.listLeafFiles (called only by HadoopFsRelation.listLeafFilesInParallel)
- HadoopFsRelation.listLeafFilesInParallel (called only by ListingFileCatalog.listLeafFiles)
It is actually very confusing and error prone because there are effectively two distinct implementations for the serial version of listing leaf files. As an example, SPARK-17599 updated only one of the code path and ignored the other one.
This code can be improved by:
- Move all file listing code into ListingFileCatalog, since it is the only class that needs this.
- Keep only one function for listing files in serial.
## How was this patch tested?
This change should be covered by existing unit and integration tests. I also moved a test case for HadoopFsRelation.shouldFilterOut from HadoopFsRelationSuite to ListingFileCatalogSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#15235 from petermaxlee/SPARK-17661.
## What changes were proposed in this pull request?
Metrics are needed for monitoring structured streaming apps. Here is the design doc for implementing the necessary metrics.
https://docs.google.com/document/d/1NIdcGuR1B3WIe8t7VxLrt58TJB4DtipWEbj5I_mzJys/edit?usp=sharing
Specifically, this PR adds the following public APIs changes.
### New APIs
- `StreamingQuery.status` returns a `StreamingQueryStatus` object (renamed from `StreamingQueryInfo`, see later)
- `StreamingQueryStatus` has the following important fields
- inputRate - Current rate (rows/sec) at which data is being generated by all the sources
- processingRate - Current rate (rows/sec) at which the query is processing data from
all the sources
- ~~outputRate~~ - *Does not work with wholestage codegen*
- latency - Current average latency between the data being available in source and the sink writing the corresponding output
- sourceStatuses: Array[SourceStatus] - Current statuses of the sources
- sinkStatus: SinkStatus - Current status of the sink
- triggerStatus - Low-level detailed status of the last completed/currently active trigger
- latencies - getOffset, getBatch, full trigger, wal writes
- timestamps - trigger start, finish, after getOffset, after getBatch
- numRows - input, output, state total/updated rows for aggregations
- `SourceStatus` has the following important fields
- inputRate - Current rate (rows/sec) at which data is being generated by the source
- processingRate - Current rate (rows/sec) at which the query is processing data from the source
- triggerStatus - Low-level detailed status of the last completed/currently active trigger
- Python API for `StreamingQuery.status()`
### Breaking changes to existing APIs
**Existing direct public facing APIs**
- Deprecated direct public-facing APIs `StreamingQuery.sourceStatuses` and `StreamingQuery.sinkStatus` in favour of `StreamingQuery.status.sourceStatuses/sinkStatus`.
- Branch 2.0 should have it deprecated, master should have it removed.
**Existing advanced listener APIs**
- `StreamingQueryInfo` renamed to `StreamingQueryStatus` for consistency with `SourceStatus`, `SinkStatus`
- Earlier StreamingQueryInfo was used only in the advanced listener API, but now it is used in direct public-facing API (StreamingQuery.status)
- Field `queryInfo` in listener events `QueryStarted`, `QueryProgress`, `QueryTerminated` changed have name `queryStatus` and return type `StreamingQueryStatus`.
- Field `offsetDesc` in `SourceStatus` was Option[String], converted it to `String`.
- For `SourceStatus` and `SinkStatus` made constructor private instead of private[sql] to make them more java-safe. Instead added `private[sql] object SourceStatus/SinkStatus.apply()` which are harder to accidentally use in Java.
## How was this patch tested?
Old and new unit tests.
- Rate calculation and other internal logic of StreamMetrics tested by StreamMetricsSuite.
- New info in statuses returned through StreamingQueryListener is tested in StreamingQueryListenerSuite.
- New and old info returned through StreamingQuery.status is tested in StreamingQuerySuite.
- Source-specific tests for making sure input rows are counted are is source-specific test suites.
- Additional tests to test minor additions in LocalTableScanExec, StateStore, etc.
Metrics also manually tested using Ganglia sink
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15307 from tdas/SPARK-17731.
## What changes were proposed in this pull request?
correct the expected type from Length function to be Int
## How was this patch tested?
Test runs on little endian and big endian platforms
Author: Pete Robbins <robbinspg@gmail.com>
Closes#15464 from robbinspg/SPARK-17827.
## What changes were proposed in this pull request?
This patch annotates all the remaining APIs in SQL (excluding streaming) with InterfaceStability.
## How was this patch tested?
N/A - just annotation change.
Author: Reynold Xin <rxin@databricks.com>
Closes#15457 from rxin/SPARK-17830-2.
## What changes were proposed in this pull request?
Currently `HiveExternalCatalog` will filter out the Spark SQL internal table properties, e.g. `spark.sql.sources.provider`, `spark.sql.sources.schema`, etc. This is reasonable for external users as they don't want to see these internal properties in `DESC TABLE`.
However, as a Spark developer, sometimes we do wanna see the raw table properties. This PR adds a new internal SQL conf, `spark.sql.debug`, to enable debug mode and keep these raw table properties.
This config can also be used in similar places where we wanna retain debug information in the future.
## How was this patch tested?
new test in MetastoreDataSourcesSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15458 from cloud-fan/debug.
## What changes were proposed in this pull request?
Two issues regarding Dataset.dropduplicates:
1. Dataset.dropDuplicates should consider the columns with same column name
We find and get the first resolved attribute from output with the given column name in `Dataset.dropDuplicates`. When we have the more than one columns with the same name. Other columns are put into aggregation columns, instead of grouping columns.
2. Dataset.dropDuplicates should not change the output of child plan
We create new `Alias` with new exprId in `Dataset.dropDuplicates` now. However it causes problem when we want to select the columns as follows:
val ds = Seq(("a", 1), ("a", 2), ("b", 1), ("a", 1)).toDS()
// ds("_2") will cause analysis exception
ds.dropDuplicates("_1").select(ds("_1").as[String], ds("_2").as[Int])
Because the two issues are both related to `Dataset.dropduplicates` and the code changes are not big, so submitting them together as one PR.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#15427 from viirya/fix-dropduplicates.
## What changes were proposed in this pull request?
The CompactibleFileStreamLog materializes the whole metadata log in memory as a String. This can cause issues when there are lots of files that are being committed, especially during a compaction batch.
You may come across stacktraces that look like:
```
java.lang.OutOfMemoryError: Requested array size exceeds VM limit
at java.lang.StringCoding.encode(StringCoding.java:350)
at java.lang.String.getBytes(String.java:941)
at org.apache.spark.sql.execution.streaming.FileStreamSinkLog.serialize(FileStreamSinkLog.scala:127)
```
The safer way is to write to an output stream so that we don't have to materialize a huge string.
## How was this patch tested?
Existing unit tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15437 from brkyvz/ser-to-stream.
## What changes were proposed in this pull request?
This patch improves the window function frame boundary API to make it more obvious to read and to use. The two high level changes are:
1. Create Window.currentRow, Window.unboundedPreceding, Window.unboundedFollowing to indicate the special values in frame boundaries. These methods map to the special integral values so we are not breaking backward compatibility here. This change makes the frame boundaries more self-evident (instead of Long.MinValue, it becomes Window.unboundedPreceding).
2. In Python, for any value less than or equal to JVM's Long.MinValue, treat it as Window.unboundedPreceding. For any value larger than or equal to JVM's Long.MaxValue, treat it as Window.unboundedFollowing. Before this change, if the user specifies any value that is less than Long.MinValue but not -sys.maxsize (e.g. -sys.maxsize + 1), the number we pass over to the JVM would overflow, resulting in a frame that does not make sense.
Code example required to specify a frame before this patch:
```
Window.rowsBetween(-Long.MinValue, 0)
```
While the above code should still work, the new way is more obvious to read:
```
Window.rowsBetween(Window.unboundedPreceding, Window.currentRow)
```
## How was this patch tested?
- Updated DataFrameWindowSuite (for Scala/Java)
- Updated test_window_functions_cumulative_sum (for Python)
- Renamed DataFrameWindowSuite DataFrameWindowFunctionsSuite to better reflect its purpose
Author: Reynold Xin <rxin@databricks.com>
Closes#15438 from rxin/SPARK-17845.
## What changes were proposed in this pull request?
This is a step along the way to SPARK-8425.
To enable incremental review, the first step proposed here is to expand the blacklisting within tasksets. In particular, this will enable blacklisting for
* (task, executor) pairs (this already exists via an undocumented config)
* (task, node)
* (taskset, executor)
* (taskset, node)
Adding (task, node) is critical to making spark fault-tolerant of one-bad disk in a cluster, without requiring careful tuning of "spark.task.maxFailures". The other additions are also important to avoid many misleading task failures and long scheduling delays when there is one bad node on a large cluster.
Note that some of the code changes here aren't really required for just this -- they put pieces in place for SPARK-8425 even though they are not used yet (eg. the `BlacklistTracker` helper is a little out of place, `TaskSetBlacklist` holds onto a little more info than it needs to for just this change, and `ExecutorFailuresInTaskSet` is more complex than it needs to be).
## How was this patch tested?
Added unit tests, run tests via jenkins.
Author: Imran Rashid <irashid@cloudera.com>
Author: mwws <wei.mao@intel.com>
Closes#15249 from squito/taskset_blacklist_only.
## What changes were proposed in this pull request?
Add a flag to ignore corrupt files. For Spark core, the configuration is `spark.files.ignoreCorruptFiles`. For Spark SQL, it's `spark.sql.files.ignoreCorruptFiles`.
## How was this patch tested?
The added unit tests
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15422 from zsxwing/SPARK-17850.
## What changes were proposed in this pull request?
SQLConf is session-scoped and mutable. However, we do have the requirement for a static SQL conf, which is global and immutable, e.g. the `schemaStringThreshold` in `HiveExternalCatalog`, the flag to enable/disable hive support, the global temp view database in https://github.com/apache/spark/pull/14897.
Actually we've already implemented static SQL conf implicitly via `SparkConf`, this PR just make it explicit and expose it to users, so that they can see the config value via SQL command or `SparkSession.conf`, and forbid users to set/unset static SQL conf.
## How was this patch tested?
new tests in SQLConfSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15295 from cloud-fan/global-conf.
## What changes were proposed in this pull request?
address post hoc review comments for https://github.com/apache/spark/pull/14897
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15424 from cloud-fan/global-temp-view.
## What changes were proposed in this pull request?
When I was creating the example code for SPARK-10496, I realized it was pretty convoluted to define the frame boundaries for window functions when there is no partition column or ordering column. The reason is that we don't provide a way to create a WindowSpec directly with the frame boundaries. We can trivially improve this by adding rowsBetween and rangeBetween to Window object.
As an example, to compute cumulative sum using the natural ordering, before this pr:
```
df.select('key, sum("value").over(Window.partitionBy(lit(1)).rowsBetween(Long.MinValue, 0)))
```
After this pr:
```
df.select('key, sum("value").over(Window.rowsBetween(Long.MinValue, 0)))
```
Note that you could argue there is no point specifying a window frame without partitionBy/orderBy -- but it is strange that only rowsBetween and rangeBetween are not the only two APIs not available.
This also fixes https://issues.apache.org/jira/browse/SPARK-17656 (removing _root_.scala).
## How was this patch tested?
Added test cases to compute cumulative sum in DataFrameWindowSuite for Scala/Java and tests.py for Python.
Author: Reynold Xin <rxin@databricks.com>
Closes#15412 from rxin/SPARK-17844.
## What changes were proposed in this pull request?
This PR proposes to fix arbitrary usages among `Map[String, String]`, `Properties` and `JDBCOptions` instances for options in `execution/jdbc` package and make the connection properties exclude Spark-only options.
This PR includes some changes as below:
- Unify `Map[String, String]`, `Properties` and `JDBCOptions` in `execution/jdbc` package to `JDBCOptions`.
- Move `batchsize`, `fetchszie`, `driver` and `isolationlevel` options into `JDBCOptions` instance.
- Document `batchSize` and `isolationlevel` with marking both read-only options and write-only options. Also, this includes minor types and detailed explanation for some statements such as url.
- Throw exceptions fast by checking arguments first rather than in execution time (e.g. for `fetchsize`).
- Exclude Spark-only options in connection properties.
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15292 from HyukjinKwon/SPARK-17719.
## What changes were proposed in this pull request?
Currently, CSV datasource allows to load duplicated empty string fields or fields having `nullValue` in the header. It'd be great if this can deal with normal fields as well.
This PR proposes handling the duplicates consistently with the existing behaviour with considering case-sensitivity (`spark.sql.caseSensitive`) as below:
data below:
```
fieldA,fieldB,,FIELDA,fielda,,
1,2,3,4,5,6,7
```
is parsed as below:
```scala
spark.read.format("csv").option("header", "true").load("test.csv").show()
```
- when `spark.sql.caseSensitive` is `false` (by default).
```
+-------+------+---+-------+-------+---+---+
|fieldA0|fieldB|_c2|FIELDA3|fieldA4|_c5|_c6|
+-------+------+---+-------+-------+---+---+
| 1| 2| 3| 4| 5| 6| 7|
+-------+------+---+-------+-------+---+---+
```
- when `spark.sql.caseSensitive` is `true`.
```
+-------+------+---+-------+-------+---+---+
|fieldA0|fieldB|_c2| FIELDA|fieldA4|_c5|_c6|
+-------+------+---+-------+-------+---+---+
| 1| 2| 3| 4| 5| 6| 7|
+-------+------+---+-------+-------+---+---+
```
**In more details**,
There is a good reference about this problem, `read.csv()` in R. So, I initially wanted to propose the similar behaviour.
In case of R, the CSV data below:
```
fieldA,fieldB,,fieldA,fieldA,,
1,2,3,4,5,6,7
```
is parsed as below:
```r
test <- read.csv(file="test.csv",header=TRUE,sep=",")
> test
fieldA fieldB X fieldA.1 fieldA.2 X.1 X.2
1 1 2 3 4 5 6 7
```
However, Spark CSV datasource already is handling duplicated empty strings and `nullValue` as field names. So the data below:
```
,,,fieldA,,fieldB,
1,2,3,4,5,6,7
```
is parsed as below:
```scala
spark.read.format("csv").option("header", "true").load("test.csv").show()
```
```
+---+---+---+------+---+------+---+
|_c0|_c1|_c2|fieldA|_c4|fieldB|_c6|
+---+---+---+------+---+------+---+
| 1| 2| 3| 4| 5| 6| 7|
+---+---+---+------+---+------+---+
```
R starts the number for each duplicate but Spark adds the number for its position for all fields for `nullValue` and empty strings.
In terms of case-sensitivity, it seems R is case-sensitive as below: (it seems it is not configurable).
```
a,a,a,A,A
1,2,3,4,5
```
is parsed as below:
```r
test <- read.csv(file="test.csv",header=TRUE,sep=",")
> test
a a.1 a.2 A A.1
1 1 2 3 4 5
```
## How was this patch tested?
Unit test in `CSVSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14745 from HyukjinKwon/SPARK-16896.
## What changes were proposed in this pull request?
The default buffer size is not big enough for randomly generated MapType.
## How was this patch tested?
Ran the tests in 100 times, it never fail (it fail 8 times before the patch).
Author: Davies Liu <davies@databricks.com>
Closes#15395 from davies/flaky_map.
## What changes were proposed in this pull request?
This patch annotates the InterfaceStability level for top level classes in o.a.spark.sql and o.a.spark.sql.util packages, to experiment with this new annotation.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#15392 from rxin/SPARK-17830.
## What changes were proposed in this pull request?
The function `SparkSqlParserSuite.createTempViewUsing` is not used for now and causes build failure, this PR simply removes it.
## How was this patch tested?
N/A
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15418 from jiangxb1987/parserSuite.
## What changes were proposed in this pull request?
Global temporary view is a cross-session temporary view, which means it's shared among all sessions. Its lifetime is the lifetime of the Spark application, i.e. it will be automatically dropped when the application terminates. It's tied to a system preserved database `global_temp`(configurable via SparkConf), and we must use the qualified name to refer a global temp view, e.g. SELECT * FROM global_temp.view1.
changes for `SessionCatalog`:
1. add a new field `gloabalTempViews: GlobalTempViewManager`, to access the shared global temp views, and the global temp db name.
2. `createDatabase` will fail if users wanna create `global_temp`, which is system preserved.
3. `setCurrentDatabase` will fail if users wanna set `global_temp`, which is system preserved.
4. add `createGlobalTempView`, which is used in `CreateViewCommand` to create global temp views.
5. add `dropGlobalTempView`, which is used in `CatalogImpl` to drop global temp view.
6. add `alterTempViewDefinition`, which is used in `AlterViewAsCommand` to update the view definition for local/global temp views.
7. `renameTable`/`dropTable`/`isTemporaryTable`/`lookupRelation`/`getTempViewOrPermanentTableMetadata`/`refreshTable` will handle global temp views.
changes for SQL commands:
1. `CreateViewCommand`/`AlterViewAsCommand` is updated to support global temp views
2. `ShowTablesCommand` outputs a new column `database`, which is used to distinguish global and local temp views.
3. other commands can also handle global temp views if they call `SessionCatalog` APIs which accepts global temp views, e.g. `DropTableCommand`, `AlterTableRenameCommand`, `ShowColumnsCommand`, etc.
changes for other public API
1. add a new method `dropGlobalTempView` in `Catalog`
2. `Catalog.findTable` can find global temp view
3. add a new method `createGlobalTempView` in `Dataset`
## How was this patch tested?
new tests in `SQLViewSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14897 from cloud-fan/global-temp-view.
## What changes were proposed in this pull request?
Currently we use the same rule to parse top level and nested data fields. For example:
```
create table tbl_x(
id bigint,
nested struct<col1:string,col2:string>
)
```
Shows both syntaxes. In this PR we split this rule in a top-level and nested rule.
Before this PR,
```
sql("CREATE TABLE my_tab(column1: INT)")
```
works fine.
After this PR, it will throw a `ParseException`:
```
scala> sql("CREATE TABLE my_tab(column1: INT)")
org.apache.spark.sql.catalyst.parser.ParseException:
no viable alternative at input 'CREATE TABLE my_tab(column1:'(line 1, pos 27)
```
## How was this patch tested?
Add new testcases in `SparkSqlParserSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15346 from jiangxb1987/cdt.
## What changes were proposed in this pull request?
This PR proposes the fix the use of `contains` API which only exists from Scala 2.11.
## How was this patch tested?
Manually checked:
```scala
scala> val o: Option[Boolean] = None
o: Option[Boolean] = None
scala> o == Some(false)
res17: Boolean = false
scala> val o: Option[Boolean] = Some(true)
o: Option[Boolean] = Some(true)
scala> o == Some(false)
res18: Boolean = false
scala> val o: Option[Boolean] = Some(false)
o: Option[Boolean] = Some(false)
scala> o == Some(false)
res19: Boolean = true
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15393 from HyukjinKwon/hotfix.
## What changes were proposed in this pull request?
In HashJoin, we try to rewrite the join key as Long to improve the performance of finding a match. The rewriting part is not well tested, has a bug that could cause wrong result when there are at least three integral columns in the joining key also the total length of the key exceed 8 bytes.
## How was this patch tested?
Added unit test to covering the rewriting with different number of columns and different data types. Manually test the reported case and confirmed that this PR fix the bug.
Author: Davies Liu <davies@databricks.com>
Closes#15390 from davies/rewrite_key.
## What changes were proposed in this pull request?
In practice we cannot guarantee that an `InternalRow` is immutable. This makes the `MutableRow` almost redundant. This PR folds `MutableRow` into `InternalRow`.
The code below illustrates the immutability issue with InternalRow:
```scala
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
val struct = new GenericMutableRow(1)
val row = InternalRow(struct, 1)
println(row)
scala> [[null], 1]
struct.setInt(0, 42)
println(row)
scala> [[42], 1]
```
This might be somewhat controversial, so feedback is appreciated.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15333 from hvanhovell/SPARK-17761.
## What changes were proposed in this pull request?
When execute a Python UDF, we buffer the input row into as queue, then pull them out to join with the result from Python UDF. In the case that Python UDF is slow or the input row is too wide, we could ran out of memory because of the queue. Since we can't flush all the buffers (sockets) between JVM and Python process from JVM side, we can't limit the rows in the queue, otherwise it could deadlock.
This PR will manage the memory used by the queue, spill that into disk when there is no enough memory (also release the memory and disk space as soon as possible).
## How was this patch tested?
Added unit tests. Also manually ran a workload with large input row and slow python UDF (with large broadcast) like this:
```
b = range(1<<24)
add = udf(lambda x: x + len(b), IntegerType())
df = sqlContext.range(1, 1<<26, 1, 4)
print df.select(df.id, lit("adf"*10000).alias("s"), add(df.id).alias("add")).groupBy(length("s")).sum().collect()
```
It ran out of memory (hang because of full GC) before the patch, ran smoothly after the patch.
Author: Davies Liu <davies@databricks.com>
Closes#15089 from davies/spill_udf.
## What changes were proposed in this pull request?
Adds the textFile API which exists in DataFrameReader and serves same purpose.
## How was this patch tested?
Added corresponding testcase.
Author: Prashant Sharma <prashsh1@in.ibm.com>
Closes#14087 from ScrapCodes/textFile.
## What changes were proposed in this pull request?
This PR proposes cleaning up the confusing part in `createRelation` as discussed in https://github.com/apache/spark/pull/12601/files#r80627940
Also, this PR proposes the changes below:
- Add documentation for `batchsize` and `isolationLevel`.
- Move property names into `JDBCOptions` so that they can be managed in a single place. which were, `fetchsize`, `batchsize`, `isolationLevel` and `driver`.
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15263 from HyukjinKwon/SPARK-14525.
## What changes were proposed in this pull request?
When using an incompatible source for structured streaming, it may throw NoClassDefFoundError. It's better to just catch Throwable and report it to the user since the streaming thread is dying.
## How was this patch tested?
`test("NoClassDefFoundError from an incompatible source")`
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15352 from zsxwing/SPARK-17780.
## What changes were proposed in this pull request?
I was looking through API annotations to catch mislabeled APIs, and realized DataStreamReader and DataStreamWriter classes are already annotated as Experimental, and as a result there is no need to annotate each method within them.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#15373 from rxin/SPARK-17798.
## What changes were proposed in this pull request?
Generate the sql test jar to fix the maven build
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15368 from zsxwing/sql-test-jar.
## What changes were proposed in this pull request?
This PR adds a new project ` external/kafka-0-10-sql` for Structured Streaming Kafka source.
It's based on the design doc: https://docs.google.com/document/d/19t2rWe51x7tq2e5AOfrsM9qb8_m7BRuv9fel9i0PqR8/edit?usp=sharing
tdas did most of work and part of them was inspired by koeninger's work.
### Introduction
The Kafka source is a structured streaming data source to poll data from Kafka. The schema of reading data is as follows:
Column | Type
---- | ----
key | binary
value | binary
topic | string
partition | int
offset | long
timestamp | long
timestampType | int
The source can deal with deleting topics. However, the user should make sure there is no Spark job processing the data when deleting a topic.
### Configuration
The user can use `DataStreamReader.option` to set the following configurations.
Kafka Source's options | value | default | meaning
------ | ------- | ------ | -----
startingOffset | ["earliest", "latest"] | "latest" | The start point when a query is started, either "earliest" which is from the earliest offset, or "latest" which is just from the latest offset. Note: This only applies when a new Streaming query is started, and that resuming will always pick up from where the query left off.
failOnDataLost | [true, false] | true | Whether to fail the query when it's possible that data is lost (e.g., topics are deleted, or offsets are out of range). This may be a false alarm. You can disable it when it doesn't work as you expected.
subscribe | A comma-separated list of topics | (none) | The topic list to subscribe. Only one of "subscribe" and "subscribeParttern" options can be specified for Kafka source.
subscribePattern | Java regex string | (none) | The pattern used to subscribe the topic. Only one of "subscribe" and "subscribeParttern" options can be specified for Kafka source.
kafka.consumer.poll.timeoutMs | long | 512 | The timeout in milliseconds to poll data from Kafka in executors
fetchOffset.numRetries | int | 3 | Number of times to retry before giving up fatch Kafka latest offsets.
fetchOffset.retryIntervalMs | long | 10 | milliseconds to wait before retrying to fetch Kafka offsets
Kafka's own configurations can be set via `DataStreamReader.option` with `kafka.` prefix, e.g, `stream.option("kafka.bootstrap.servers", "host:port")`
### Usage
* Subscribe to 1 topic
```Scala
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host:port")
.option("subscribe", "topic1")
.load()
```
* Subscribe to multiple topics
```Scala
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host:port")
.option("subscribe", "topic1,topic2")
.load()
```
* Subscribe to a pattern
```Scala
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host:port")
.option("subscribePattern", "topic.*")
.load()
```
## How was this patch tested?
The new unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Shixiong Zhu <zsxwing@gmail.com>
Author: cody koeninger <cody@koeninger.org>
Closes#15102 from zsxwing/kafka-source.
## What changes were proposed in this pull request?
This PR fixes the following NPE scenario in two ways.
**Reported Error Scenario**
```scala
scala> sql("EXPLAIN DESCRIBE TABLE x").show(truncate = false)
INFO SparkSqlParser: Parsing command: EXPLAIN DESCRIBE TABLE x
java.lang.NullPointerException
```
- **DESCRIBE**: Extend `DESCRIBE` syntax to accept `TABLE`.
- **EXPLAIN**: Prevent NPE in case of the parsing failure of target statement, e.g., `EXPLAIN DESCRIBE TABLES x`.
## How was this patch tested?
Pass the Jenkins test with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15357 from dongjoon-hyun/SPARK-17328.
## What changes were proposed in this pull request?
Currently Spark SQL parses regular decimal literals (e.g. `10.00`) as decimals and scientific decimal literals (e.g. `10.0e10`) as doubles. The difference between the two confuses most users. This PR unifies the parsing behavior and also parses scientific decimal literals as decimals.
This implications in tests are limited to a single Hive compatibility test.
## How was this patch tested?
Updated tests in `ExpressionParserSuite` and `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14828 from hvanhovell/SPARK-17258.
This reverts commit 9ac68dbc57. Turns out
the original fix was correct.
Original change description:
The existing code caches all stats for all columns for each partition
in the driver; for a large relation, this causes extreme memory usage,
which leads to gc hell and application failures.
It seems that only the size in bytes of the data is actually used in the
driver, so instead just colllect that. In executors, the full stats are
still kept, but that's not a big problem; we expect the data to be distributed
and thus not really incur in too much memory pressure in each individual
executor.
There are also potential improvements on the executor side, since the data
being stored currently is very wasteful (e.g. storing boxed types vs.
primitive types for stats). But that's a separate issue.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#15304 from vanzin/SPARK-17549.2.
## What changes were proposed in this pull request?
Made changes to record length offsets to make them uniform throughout various areas of Spark core and unsafe
## How was this patch tested?
This change affects only SPARC architectures and was tested on X86 architectures as well for regression.
Author: sumansomasundar <suman.somasundar@oracle.com>
Closes#14762 from sumansomasundar/master.
## What changes were proposed in this pull request?
Code generation including too many mutable states exceeds JVM size limit to extract values from `references` into fields in the constructor.
We should split the generated extractions in the constructor into smaller functions.
## How was this patch tested?
I added some tests to check if the generated codes for the expressions exceed or not.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#15275 from ueshin/issues/SPARK-17702.
## What changes were proposed in this pull request?
Generate basic column statistics for all the atomic types:
- numeric types: max, min, num of nulls, ndv (number of distinct values)
- date/timestamp types: they are also represented as numbers internally, so they have the same stats as above.
- string: avg length, max length, num of nulls, ndv
- binary: avg length, max length, num of nulls
- boolean: num of nulls, num of trues, num of falsies
Also support storing and loading these statistics.
One thing to notice:
We support analyzing columns independently, e.g.:
sql1: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS key;`
sql2: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS value;`
when running sql2 to collect column stats for `value`, we don’t remove stats of columns `key` which are analyzed in sql1 and not in sql2. As a result, **users need to guarantee consistency** between sql1 and sql2. If the table has been changed before sql2, users should re-analyze column `key` when they want to analyze column `value`:
`ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS key, value;`
## How was this patch tested?
add unit tests
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#15090 from wzhfy/colStats.
## What changes were proposed in this pull request?
This PR proposes to fix/skip some tests failed on Windows. This PR takes over https://github.com/apache/spark/pull/12696.
**Before**
- **SparkSubmitSuite**
```
[info] - launch simple application with spark-submit *** FAILED *** (202 milliseconds)
[info] java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specifie
[info] - includes jars passed in through --jars *** FAILED *** (1 second, 625 milliseconds)
[info] java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified
```
- **DiskStoreSuite**
```
[info] - reads of memory-mapped and non memory-mapped files are equivalent *** FAILED *** (1 second, 78 milliseconds)
[info] diskStoreMapped.remove(blockId) was false (DiskStoreSuite.scala:41)
```
**After**
- **SparkSubmitSuite**
```
[info] - launch simple application with spark-submit (578 milliseconds)
[info] - includes jars passed in through --jars (1 second, 875 milliseconds)
```
- **DiskStoreSuite**
```
[info] DiskStoreSuite:
[info] - reads of memory-mapped and non memory-mapped files are equivalent !!! CANCELED !!! (766 milliseconds
```
For `CreateTableAsSelectSuite` and `FsHistoryProviderSuite`, I could not reproduce as the Java version seems higher than the one that has the bugs about `setReadable(..)` and `setWritable(...)` but as they are bugs reported clearly, it'd be sensible to skip those. We should revert the changes for both back as soon as we drop the support of Java 7.
## How was this patch tested?
Manually tested via AppVeyor.
Closes#12696
Author: Tao LI <tl@microsoft.com>
Author: U-FAREAST\tl <tl@microsoft.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15320 from HyukjinKwon/SPARK-14914.
## What changes were proposed in this pull request?
We added find and exists methods for Databases, Tables and Functions to the user facing Catalog in PR https://github.com/apache/spark/pull/15301. However, it was brought up that the semantics of the `find` methods are more in line a `get` method (get an object or else fail). So we rename these in this PR.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15308 from hvanhovell/SPARK-17717-2.
## What changes were proposed in this pull request?
As a followup to SPARK-17666, ensure filesystem connections are not leaked at least in unit tests. This is done here by intercepting filesystem calls as suggested by JoshRosen . At the end of each test, we assert no filesystem streams are left open.
This applies to all tests using SharedSQLContext or SharedSparkContext.
## How was this patch tested?
I verified that tests in sql and core are indeed using the filesystem backend, and fixed the detected leaks. I also checked that reverting https://github.com/apache/spark/pull/15245 causes many actual test failures due to connection leaks.
Author: Eric Liang <ekl@databricks.com>
Author: Eric Liang <ekhliang@gmail.com>
Closes#15306 from ericl/sc-4672.
## What changes were proposed in this pull request?
The actualSize() of array and map is different from the actual size, the header is Int, rather than Long.
## How was this patch tested?
The flaky test should be fixed.
Author: Davies Liu <davies@databricks.com>
Closes#15305 from davies/fix_MAP.
## What changes were proposed in this pull request?
The current user facing catalog does not implement methods for checking object existence or finding objects. You could theoretically do this using the `list*` commands, but this is rather cumbersome and can actually be costly when there are many objects. This PR adds `exists*` and `find*` methods for Databases, Table and Functions.
## How was this patch tested?
Added tests to `org.apache.spark.sql.internal.CatalogSuite`
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15301 from hvanhovell/SPARK-17717.
Spark SQL has great support for reading text files that contain JSON data. However, in many cases the JSON data is just one column amongst others. This is particularly true when reading from sources such as Kafka. This PR adds a new functions `from_json` that converts a string column into a nested `StructType` with a user specified schema.
Example usage:
```scala
val df = Seq("""{"a": 1}""").toDS()
val schema = new StructType().add("a", IntegerType)
df.select(from_json($"value", schema) as 'json) // => [json: <a: int>]
```
This PR adds support for java, scala and python. I leveraged our existing JSON parsing support by moving it into catalyst (so that we could define expressions using it). I left SQL out for now, because I'm not sure how users would specify a schema.
Author: Michael Armbrust <michael@databricks.com>
Closes#15274 from marmbrus/jsonParser.
## What changes were proposed in this pull request?
Use dialect's table-exists query rather than hard-coded WHERE 1=0 query
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15196 from srowen/SPARK-17614.
## What changes were proposed in this pull request?
We added native versions of `collect_set` and `collect_list` in Spark 2.0. These currently also (try to) collect null values, this is different from the original Hive implementation. This PR fixes this by adding a null check to the `Collect.update` method.
## How was this patch tested?
Added a regression test to `DataFrameAggregateSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15208 from hvanhovell/SPARK-17641.
## What changes were proposed in this pull request?
As a followup for https://github.com/apache/spark/pull/15273 we should move non-JDBC specific tests out of that suite.
## How was this patch tested?
Ran the test.
Author: Eric Liang <ekl@databricks.com>
Closes#15287 from ericl/spark-17713.
## What changes were proposed in this pull request?
It seems the equality check for reuse of `RowDataSourceScanExec` nodes doesn't respect the output schema. This can cause self-joins or unions over the same underlying data source to return incorrect results if they select different fields.
## How was this patch tested?
New unit test passes after the fix.
Author: Eric Liang <ekl@databricks.com>
Closes#15273 from ericl/spark-17673.
## What changes were proposed in this pull request?
This patch addresses a potential cause of resource leaks in data source file scans. As reported in [SPARK-17666](https://issues.apache.org/jira/browse/SPARK-17666), tasks which do not fully-consume their input may cause file handles / network connections (e.g. S3 connections) to be leaked. Spark's `NewHadoopRDD` uses a TaskContext callback to [close its record readers](https://github.com/apache/spark/blame/master/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala#L208), but the new data source file scans will only close record readers once their iterators are fully-consumed.
This patch modifies `RecordReaderIterator` and `HadoopFileLinesReader` to add `close()` methods and modifies all six implementations of `FileFormat.buildReader()` to register TaskContext task completion callbacks to guarantee that cleanup is eventually performed.
## How was this patch tested?
Tested manually for now.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15245 from JoshRosen/SPARK-17666-close-recordreader.
## What changes were proposed in this pull request?
As of Spark 2.0, all the window function execution code are in WindowExec.scala. This file is pretty large (over 1k loc) and has a lot of different abstractions in them. This patch creates a new package sql.execution.window, moves WindowExec.scala in it, and breaks WindowExec.scala into multiple, more maintainable pieces:
- AggregateProcessor.scala
- BoundOrdering.scala
- RowBuffer.scala
- WindowExec
- WindowFunctionFrame.scala
## How was this patch tested?
This patch mostly moves code around, and should not change any existing test coverage.
Author: Reynold Xin <rxin@databricks.com>
Closes#15252 from rxin/SPARK-17677.
## What changes were proposed in this pull request?
This PR removes build waning as below.
```scala
[WARNING] .../spark/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaConverter.scala:448: method listType in object ConversionPatterns is deprecated: see corresponding Javadoc for more information.
[WARNING] ConversionPatterns.listType(
[WARNING] ^
[WARNING] .../spark/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaConverter.scala:464: method listType in object ConversionPatterns is deprecated: see corresponding Javadoc for more information.
[WARNING] ConversionPatterns.listType(
[WARNING] ^
```
This should not use `listOfElements` (recommended to be replaced from `listType`) instead because the new method checks if the name of elements in Parquet's `LIST` is `element` in Parquet schema and throws an exception if not. However, It seems Spark prior to 1.4.x writes `ArrayType` with Parquet's `LIST` but with `array` as its element name.
Therefore, this PR avoids to use both `listOfElements` and `listType` but just use the existing schema builder to construct the same `GroupType`.
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14399 from HyukjinKwon/SPARK-16777.
## What changes were proposed in this pull request?
This PR introduces more compact representation for ```UnsafeArrayData```.
```UnsafeArrayData``` needs to accept ```null``` value in each entry of an array. In the current version, it has three parts
```
[numElements] [offsets] [values]
```
`Offsets` has the number of `numElements`, and represents `null` if its value is negative. It may increase memory footprint, and introduces an indirection for accessing each of `values`.
This PR uses bitvectors to represent nullability for each element like `UnsafeRow`, and eliminates an indirection for accessing each element. The new ```UnsafeArrayData``` has four parts.
```
[numElements][null bits][values or offset&length][variable length portion]
```
In the `null bits` region, we store 1 bit per element, represents whether an element is null. Its total size is ceil(numElements / 8) bytes, and it is aligned to 8-byte boundaries.
In the `values or offset&length` region, we store the content of elements. For fields that hold fixed-length primitive types, such as long, double, or int, we store the value directly in the field. For fields with non-primitive or variable-length values, we store a relative offset (w.r.t. the base address of the array) that points to the beginning of the variable-length field and length (they are combined into a long). Each is word-aligned. For `variable length portion`, each is aligned to 8-byte boundaries.
The new format can reduce memory footprint and improve performance of accessing each element. An example of memory foot comparison:
1024x1024 elements integer array
Size of ```baseObject``` for ```UnsafeArrayData```: 8 + 1024x1024 + 1024x1024 = 2M bytes
Size of ```baseObject``` for ```UnsafeArrayData```: 8 + 1024x1024/8 + 1024x1024 = 1.25M bytes
In summary, we got 1.0-2.6x performance improvements over the code before applying this PR.
Here are performance results of [benchmark programs](04d2e4b6db/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/UnsafeArrayDataBenchmark.scala):
**Read UnsafeArrayData**: 1.7x and 1.6x performance improvements over the code before applying this PR
````
OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64
Intel Xeon E3-12xx v2 (Ivy Bridge)
Without SPARK-15962
Read UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 430 / 436 390.0 2.6 1.0X
Double 456 / 485 367.8 2.7 0.9X
With SPARK-15962
Read UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 252 / 260 666.1 1.5 1.0X
Double 281 / 292 597.7 1.7 0.9X
````
**Write UnsafeArrayData**: 1.0x and 1.1x performance improvements over the code before applying this PR
````
OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64
Intel Xeon E3-12xx v2 (Ivy Bridge)
Without SPARK-15962
Write UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 203 / 273 103.4 9.7 1.0X
Double 239 / 356 87.9 11.4 0.8X
With SPARK-15962
Write UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 196 / 249 107.0 9.3 1.0X
Double 227 / 367 92.3 10.8 0.9X
````
**Get primitive array from UnsafeArrayData**: 2.6x and 1.6x performance improvements over the code before applying this PR
````
OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64
Intel Xeon E3-12xx v2 (Ivy Bridge)
Without SPARK-15962
Get primitive array from UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 207 / 217 304.2 3.3 1.0X
Double 257 / 363 245.2 4.1 0.8X
With SPARK-15962
Get primitive array from UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 151 / 198 415.8 2.4 1.0X
Double 214 / 394 293.6 3.4 0.7X
````
**Create UnsafeArrayData from primitive array**: 1.7x and 2.1x performance improvements over the code before applying this PR
````
OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64
Intel Xeon E3-12xx v2 (Ivy Bridge)
Without SPARK-15962
Create UnsafeArrayData from primitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 340 / 385 185.1 5.4 1.0X
Double 479 / 705 131.3 7.6 0.7X
With SPARK-15962
Create UnsafeArrayData from primitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 206 / 211 306.0 3.3 1.0X
Double 232 / 406 271.6 3.7 0.9X
````
1.7x and 1.4x performance improvements in [```UDTSerializationBenchmark```](https://github.com/apache/spark/blob/master/mllib/src/test/scala/org/apache/spark/mllib/linalg/UDTSerializationBenchmark.scala) over the code before applying this PR
````
OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64
Intel Xeon E3-12xx v2 (Ivy Bridge)
Without SPARK-15962
VectorUDT de/serialization: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
serialize 442 / 533 0.0 441927.1 1.0X
deserialize 217 / 274 0.0 217087.6 2.0X
With SPARK-15962
VectorUDT de/serialization: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
serialize 265 / 318 0.0 265138.5 1.0X
deserialize 155 / 197 0.0 154611.4 1.7X
````
## How was this patch tested?
Added unit tests into ```UnsafeArraySuite```
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#13680 from kiszk/SPARK-15962.
## What changes were proposed in this pull request?
This minor patch fixes a confusing exception message while reserving additional capacity in the vectorized parquet reader.
## How was this patch tested?
Exisiting Unit Tests
Author: Sameer Agarwal <sameerag@cs.berkeley.edu>
Closes#15225 from sameeragarwal/error-msg.
## What changes were proposed in this pull request?
When reading file stream with non-globbing path, the results return data with all `null`s for the
partitioned columns. E.g.,
case class A(id: Int, value: Int)
val data = spark.createDataset(Seq(
A(1, 1),
A(2, 2),
A(2, 3))
)
val url = "/tmp/test"
data.write.partitionBy("id").parquet(url)
spark.read.parquet(url).show
+-----+---+
|value| id|
+-----+---+
| 2| 2|
| 3| 2|
| 1| 1|
+-----+---+
val s = spark.readStream.schema(spark.read.load(url).schema).parquet(url)
s.writeStream.queryName("test").format("memory").start()
sql("SELECT * FROM test").show
+-----+----+
|value| id|
+-----+----+
| 2|null|
| 3|null|
| 1|null|
+-----+----+
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#14803 from viirya/filestreamsource-option.
## What changes were proposed in this pull request?
This change modifies the implementation of DataFrameWriter.save such that it works with jdbc, and the call to jdbc merely delegates to save.
## How was this patch tested?
This was tested via unit tests in the JDBCWriteSuite, of which I added one new test to cover this scenario.
## Additional details
rxin This seems to have been most recently touched by you and was also commented on in the JIRA.
This contribution is my original work and I license the work to the project under the project's open source license.
Author: Justin Pihony <justin.pihony@gmail.com>
Author: Justin Pihony <justin.pihony@typesafe.com>
Closes#12601 from JustinPihony/jdbc_reconciliation.
## What changes were proposed in this pull request?
This pull request adds Scala/Java DataFrame API for null ordering (NULLS FIRST | LAST).
Also did some minor clean up for related code (e.g. incorrect indentation), and renamed "orderby-nulls-ordering.sql" to be consistent with existing test files.
## How was this patch tested?
Added a new test case in DataFrameSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Author: Xin Wu <xinwu@us.ibm.com>
Closes#15123 from petermaxlee/SPARK-17551.
For some sources, it is difficult to provide a global ordering based only on the data in the offset. Since we don't use comparison for correctness, lets remove it.
Author: Michael Armbrust <michael@databricks.com>
Closes#15207 from marmbrus/removeComparable.
## What changes were proposed in this pull request?
Avoid using -1 as the default batchId for FileStreamSource.FileEntry so that we can make sure not writing any FileEntry(..., batchId = -1) into the log. This also avoids people misusing it in future (#15203 is an example).
## How was this patch tested?
Jenkins.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15206 from zsxwing/cleanup.
## What changes were proposed in this pull request?
"agg_plan" are hardcoded in HashAggregateExec, which have potential issue, so removing them.
## How was this patch tested?
existing tests.
Author: Yucai Yu <yucai.yu@intel.com>
Closes#15199 from yucai/agg_plan.
## What changes were proposed in this pull request?
We currently cannot execute an aggregate that contains a single distinct aggregate function and an one or more non-partially plannable aggregate functions, for example:
```sql
select grp,
collect_list(col1),
count(distinct col2)
from tbl_a
group by 1
```
This is a regression from Spark 1.6. This is caused by the fact that the single distinct aggregation code path assumes that all aggregates can be planned in two phases (is partially aggregatable). This PR works around this issue by triggering the `RewriteDistinctAggregates` in such cases (this is similar to the approach taken in 1.6).
## How was this patch tested?
Created `RewriteDistinctAggregatesSuite` which checks if the aggregates with distinct aggregate functions get rewritten into two `Aggregates` and an `Expand`. Added a regression test to `DataFrameAggregateSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15187 from hvanhovell/SPARK-17616.
## What changes were proposed in this pull request?
Consider you have a bucket as `s3a://some-bucket`
and under it you have files:
```
s3a://some-bucket/file1.parquet
s3a://some-bucket/file2.parquet
```
Getting the parent path of `s3a://some-bucket/file1.parquet` yields
`s3a://some-bucket/` and the ListingFileCatalog uses this as the key in the hash map.
When catalog.allFiles is called, we use `s3a://some-bucket` (no slash at the end) to get the list of files, and we're left with an empty list!
This PR fixes this by adding a `/` at the end of the `URI` iff the given `Path` doesn't have a parent, i.e. is the root. This is a no-op if the path already had a `/` at the end, and is handled through the Hadoop Path, path merging semantics.
## How was this patch tested?
Unit test in `FileCatalogSuite`.
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15169 from brkyvz/SPARK-17613.
## What changes were proposed in this pull request?
This comment went stale long time ago, this PR fixes it according to my understanding.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15095 from cloud-fan/update-comment.
## What changes were proposed in this pull request?
We should set expectedOutputAttributes when converting SimpleCatalogRelation to LogicalRelation, otherwise the outputs of LogicalRelation are different from outputs of SimpleCatalogRelation - they have different exprId's.
## How was this patch tested?
add a test case
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#15182 from wzhfy/expectedAttributes.
### What changes were proposed in this pull request?
For data sources without extending `SchemaRelationProvider`, we expect users to not specify schemas when they creating tables. If the schema is input from users, an exception is issued.
Since Spark 2.1, for any data source, to avoid infer the schema every time, we store the schema in the metastore catalog. Thus, when reading a cataloged data source table, the schema could be read from metastore catalog. In this case, we also got an exception. For example,
```Scala
sql(
s"""
|CREATE TABLE relationProvierWithSchema
|USING org.apache.spark.sql.sources.SimpleScanSource
|OPTIONS (
| From '1',
| To '10'
|)
""".stripMargin)
spark.table(tableName).show()
```
```
org.apache.spark.sql.sources.SimpleScanSource does not allow user-specified schemas.;
```
This PR is to fix the above issue. When building a data source, we introduce a flag `isSchemaFromUsers` to indicate whether the schema is really input from users. If true, we issue an exception. Otherwise, we will call the `createRelation` of `RelationProvider` to generate the `BaseRelation`, in which it contains the actual schema.
### How was this patch tested?
Added a few cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15046 from gatorsmile/tempViewCases.
## What changes were proposed in this pull request?
After #15054 , there is no place in Spark SQL that need `SessionCatalog.tableExists` to check temp views, so this PR makes `SessionCatalog.tableExists` only check permanent table/view and removes some hacks.
This PR also improves the `getTempViewOrPermanentTableMetadata` that is introduced in #15054 , to make the code simpler.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15160 from cloud-fan/exists.
All of structured streaming is experimental in its first release. We missed the annotation on two of the APIs.
Author: Michael Armbrust <michael@databricks.com>
Closes#15188 from marmbrus/experimentalApi.
## What changes were proposed in this pull request?
While getting the batch for a `FileStreamSource` in StructuredStreaming, we know which files we must take specifically. We already have verified that they exist, and have committed them to a metadata log. When creating the FileSourceRelation however for an incremental execution, the code checks the existence of every single file once again!
When you have 100,000s of files in a folder, creating the first batch takes 2 hours+ when working with S3! This PR disables that check
## How was this patch tested?
Added a unit test to `FileStreamSource`.
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15122 from brkyvz/SPARK-17569.
## What changes were proposed in this pull request?
We substitute logical plan with CTE definitions in the analyzer rule CTESubstitution. A CTE definition can be used in the logical plan for multiple times, and its analyzed logical plan should be the same. We should not analyze CTE definitions multiple times when they are reused in the query.
By analyzing CTE definitions before substitution, we can support defining CTE in subquery.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#15146 from viirya/cte-analysis-once.
## What changes were proposed in this pull request?
This PR includes the changes below:
1. Upgrade Univocity library from 2.1.1 to 2.2.1
This includes some performance improvement and also enabling auto-extending buffer in `maxCharsPerColumn` option in CSV. Please refer the [release notes](https://github.com/uniVocity/univocity-parsers/releases).
2. Remove useless `rowSeparator` variable existing in `CSVOptions`
We have this unused variable in [CSVOptions.scala#L127](29952ed096/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala (L127)) but it seems possibly causing confusion that it actually does not care of `\r\n`. For example, we have an issue open about this, [SPARK-17227](https://issues.apache.org/jira/browse/SPARK-17227), describing this variable.
This variable is virtually not being used because we rely on `LineRecordReader` in Hadoop which deals with only both `\n` and `\r\n`.
3. Set the default value of `maxCharsPerColumn` to auto-expending.
We are setting 1000000 for the length of each column. It'd be more sensible we allow auto-expending rather than fixed length by default.
To make sure, using `-1` is being described in the release note, [2.2.0](https://github.com/uniVocity/univocity-parsers/releases/tag/v2.2.0).
## How was this patch tested?
N/A
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15138 from HyukjinKwon/SPARK-17583.
## What changes were proposed in this pull request?
This PR fixes an issue when Bucketizer is called to handle a dataset containing NaN value.
Sometimes, null value might also be useful to users, so in these cases, Bucketizer should
reserve one extra bucket for NaN values, instead of throwing an illegal exception.
Before:
```
Bucketizer.transform on NaN value threw an illegal exception.
```
After:
```
NaN values will be grouped in an extra bucket.
```
## How was this patch tested?
New test cases added in `BucketizerSuite`.
Signed-off-by: VinceShieh <vincent.xieintel.com>
Author: VinceShieh <vincent.xie@intel.com>
Closes#14858 from VinceShieh/spark-17219.
## What changes were proposed in this pull request?
The `ListingFileCatalog` lists files given a set of resolved paths. If a folder is deleted at any time between the paths were resolved and the file catalog can check for the folder, the Spark job fails. This may abruptly stop long running StructuredStreaming jobs for example.
Folders may be deleted by users or automatically by retention policies. These cases should not prevent jobs from successfully completing.
## How was this patch tested?
Unit test in `FileCatalogSuite`
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15153 from brkyvz/SPARK-17599.
## What changes were proposed in this pull request?
While reading source code of CORE and SQL core, I found some minor errors in comments such as extra space, missing blank line and grammar error.
I fixed these minor errors and might find more during my source code study.
## How was this patch tested?
Manually build
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#15151 from wangmiao1981/mem.
## What changes were proposed in this pull request?
This issue was introduced in the previous commit of SPARK-15698. Mistakenly change the way to get configuration back to original one, so here with the follow up PR to revert them up.
## How was this patch tested?
N/A
Ping zsxwing , please review again, sorry to bring the inconvenience. Thanks a lot.
Author: jerryshao <sshao@hortonworks.com>
Closes#15173 from jerryshao/SPARK-15698-follow.
## What changes were proposed in this pull request?
This PR modifies StreamExecution such that it discards metadata for batches that have already been fully processed. I used the purge method that was added as part of SPARK-17235.
This is a resubmission of 15126, which was based on work by frreiss in #15067, but fixed the test case along with some typos.
## How was this patch tested?
A new test case in StreamingQuerySuite. The test case would fail without the changes in this pull request.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#15166 from petermaxlee/SPARK-17513-2.
## What changes were proposed in this pull request?
Current `metadataLog` in `FileStreamSource` will add a checkpoint file in each batch but do not have the ability to remove/compact, which will lead to large number of small files when running for a long time. So here propose to compact the old logs into one file. This method is quite similar to `FileStreamSinkLog` but simpler.
## How was this patch tested?
Unit test added.
Author: jerryshao <sshao@hortonworks.com>
Closes#13513 from jerryshao/SPARK-15698.
### What changes were proposed in this pull request?
- When the permanent tables/views do not exist but the temporary view exists, the expected error should be `NoSuchTableException` for partition-related ALTER TABLE commands. However, it always reports a confusing error message. For example,
```
Partition spec is invalid. The spec (a, b) must match the partition spec () defined in table '`testview`';
```
- When the permanent tables/views do not exist but the temporary view exists, the expected error should be `NoSuchTableException` for `ALTER TABLE ... UNSET TBLPROPERTIES`. However, it reports a missing table property. For example,
```
Attempted to unset non-existent property 'p' in table '`testView`';
```
- When `ANALYZE TABLE` is called on a view or a temporary view, we should issue an error message. However, it reports a strange error:
```
ANALYZE TABLE is not supported for Project
```
- When inserting into a temporary view that is generated from `Range`, we will get the following error message:
```
assertion failed: No plan for 'InsertIntoTable Range (0, 10, step=1, splits=Some(1)), false, false
+- Project [1 AS 1#20]
+- OneRowRelation$
```
This PR is to fix the above four issues.
### How was this patch tested?
Added multiple test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15054 from gatorsmile/tempViewDDL.
## What changes were proposed in this pull request?
This PR modifies StreamExecution such that it discards metadata for batches that have already been fully processed. I used the purge method that was added as part of SPARK-17235.
This is based on work by frreiss in #15067, but fixed the test case along with some typos.
## How was this patch tested?
A new test case in StreamingQuerySuite. The test case would fail without the changes in this pull request.
Author: petermaxlee <petermaxlee@gmail.com>
Author: frreiss <frreiss@us.ibm.com>
Closes#15126 from petermaxlee/SPARK-17513.
## What changes were proposed in this pull request?
Currently, the SQL metrics looks like `number of rows: 111111111111`, it's very hard to read how large the number is. So a separator was added by #12425, but removed by #14142, because the separator is weird in some locales (for example, pl_PL), this PR will add that back, but always use "," as the separator, since the SQL UI are all in English.
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#15106 from davies/metric_sep.
## What changes were proposed in this pull request?
Clarify that slide and window duration are absolute, and not relative to a calendar.
## How was this patch tested?
Doc build (no functional change)
Author: Sean Owen <sowen@cloudera.com>
Closes#15142 from srowen/SPARK-17297.
## What changes were proposed in this pull request?
AssertOnQuery has two apply constructor: one that accepts a closure that returns boolean, and another that accepts a closure that returns Unit. This is actually very confusing because developers could mistakenly think that AssertOnQuery always require a boolean return type and verifies the return result, when indeed the value of the last statement is ignored in one of the constructors.
This pull request makes the two constructor consistent and always require boolean value. It will overall make the test suites more robust against developer errors.
As an evidence for the confusing behavior, this change also identified a bug with an existing test case due to file system time granularity. This pull request fixes that test case as well.
## How was this patch tested?
This is a test only change.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#15127 from petermaxlee/SPARK-17571.
## Problem
CSV in Spark 2.0.0:
- does not read null values back correctly for certain data types such as `Boolean`, `TimestampType`, `DateType` -- this is a regression comparing to 1.6;
- does not read empty values (specified by `options.nullValue`) as `null`s for `StringType` -- this is compatible with 1.6 but leads to problems like SPARK-16903.
## What changes were proposed in this pull request?
This patch makes changes to read all empty values back as `null`s.
## How was this patch tested?
New test cases.
Author: Liwei Lin <lwlin7@gmail.com>
Closes#14118 from lw-lin/csv-cast-null.
## What changes were proposed in this pull request?
In `SessionCatalog`, we have several operations(`tableExists`, `dropTable`, `loopupRelation`, etc) that handle both temp views and metastore tables/views. This brings some bugs to DDL commands that want to handle temp view only or metastore table/view only. These bugs are:
1. `CREATE TABLE USING` will fail if a same-name temp view exists
2. `Catalog.dropTempView`will un-cache and drop metastore table if a same-name table exists
3. `saveAsTable` will fail or have unexpected behaviour if a same-name temp view exists.
These bug fixes are pulled out from https://github.com/apache/spark/pull/14962 and targets both master and 2.0 branch
## How was this patch tested?
new regression tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15099 from cloud-fan/fix-view.
### What changes were proposed in this pull request?
In Spark 2.1, we introduced a new internal provider `hive` for telling Hive serde tables from data source tables. This PR is to block users to specify this in `DataFrameWriter` and SQL APIs.
### How was this patch tested?
Added a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15073 from gatorsmile/formatHive.
## What changes were proposed in this pull request?
This PR fixes all the instances which was fixed in the previous PR.
To make sure, I manually debugged and also checked the Scala source. `length` in [LinearSeqOptimized.scala#L49-L57](https://github.com/scala/scala/blob/2.11.x/src/library/scala/collection/LinearSeqOptimized.scala#L49-L57) is O(n). Also, `size` calls `length` via [SeqLike.scala#L106](https://github.com/scala/scala/blob/2.11.x/src/library/scala/collection/SeqLike.scala#L106).
For debugging, I have created these as below:
```scala
ArrayBuffer(1, 2, 3)
Array(1, 2, 3)
List(1, 2, 3)
Seq(1, 2, 3)
```
and then called `size` and `length` for each to debug.
## How was this patch tested?
I ran the bash as below on Mac
```bash
find . -name *.scala -type f -exec grep -il "while (.*\\.length)" {} \; | grep "src/main"
find . -name *.scala -type f -exec grep -il "while (.*\\.size)" {} \; | grep "src/main"
```
and then checked each.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15093 from HyukjinKwon/SPARK-17480-followup.
## What changes were proposed in this pull request?
Add a clearUntil() method on BitSet (adapted from the pre-existing setUntil() method).
Use this method to clear the subset of the BitSet which needs to be used during merge joins.
## How was this patch tested?
dev/run-tests, as well as performance tests on skewed data as described in jira.
I expect there to be a small local performance hit using BitSet.clearUntil rather than BitSet.clear for normally shaped (unskewed) joins (additional read on the last long). This is expected to be de-minimis and was not specifically tested.
Author: David Navas <davidn@clearstorydata.com>
Closes#15084 from davidnavas/bitSet.
The existing code caches all stats for all columns for each partition
in the driver; for a large relation, this causes extreme memory usage,
which leads to gc hell and application failures.
It seems that only the size in bytes of the data is actually used in the
driver, so instead just colllect that. In executors, the full stats are
still kept, but that's not a big problem; we expect the data to be distributed
and thus not really incur in too much memory pressure in each individual
executor.
There are also potential improvements on the executor side, since the data
being stored currently is very wasteful (e.g. storing boxed types vs.
primitive types for stats). But that's a separate issue.
On a mildly related change, I'm also adding code to catch exceptions in the
code generator since Janino was breaking with the test data I tried this
patch on.
Tested with unit tests and by doing a count a very wide table (20k columns)
with many partitions.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#15112 from vanzin/SPARK-17549.
## What changes were proposed in this pull request?
Fix `<ul> / <li>` problems in SQL scaladoc.
## How was this patch tested?
Scaladoc build and manual verification of generated HTML.
Author: Sean Owen <sowen@cloudera.com>
Closes#15117 from srowen/SPARK-17561.
## What changes were proposed in this pull request?
This PR is a follow up of SPARK-17356. Current implementation of `TreeNode.toJSON` recursively converts all fields of TreeNode to JSON, even if the field is of type `Seq` or type Map. This may trigger out of memory exception in cases like:
1. the Seq or Map can be very big. Converting them to JSON may take huge memory, which may trigger out of memory error.
2. Some user space input may also be propagated to the Plan. The user space input can be of arbitrary type, and may also be self-referencing. Trying to print user space input to JSON may trigger out of memory error or stack overflow error.
For a code example, please check the Jira description of SPARK-17426.
In this PR, we refactor the `TreeNode.toJSON` so that we only convert a field to JSON string if the field is a safe type.
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14990 from clockfly/json_oom2.
## What changes were proposed in this pull request?
This change preserves aliases that are given for pivot aggregations
## How was this patch tested?
New unit test
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#15111 from aray/SPARK-17458.
## What changes were proposed in this pull request?
select length(11);
select length(2.0);
these sql will return errors, but hive is ok.
this PR will support casting input types implicitly for function length
the correct result is:
select length(11) return 2
select length(2.0) return 3
Author: 岑玉海 <261810726@qq.com>
Author: cenyuhai <cenyuhai@didichuxing.com>
Closes#15014 from cenyuhai/SPARK-17429.
## What changes were proposed in this pull request?
This PR fixes an issue with aggregates that have an empty input, and use a literals as their grouping keys. These aggregates are currently interpreted as aggregates **without** grouping keys, this triggers the ungrouped code path (which aways returns a single row).
This PR fixes the `RemoveLiteralFromGroupExpressions` optimizer rule, which changes the semantics of the Aggregate by eliminating all literal grouping keys.
## How was this patch tested?
Added tests to `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15101 from hvanhovell/SPARK-17114-3.
## What changes were proposed in this pull request?
Optimize a while loop during batch inserts
## How was this patch tested?
Unit tests were done, specifically "mvn test" for sql
Author: John Muller <jmuller@us.imshealth.com>
Closes#15098 from blue666man/SPARK-17536.
### What changes were proposed in this pull request?
For the following `ALTER TABLE` DDL, we should issue an exception when the target table is a `VIEW`:
```SQL
ALTER TABLE viewName SET LOCATION '/path/to/your/lovely/heart'
ALTER TABLE viewName SET SERDE 'whatever'
ALTER TABLE viewName SET SERDEPROPERTIES ('x' = 'y')
ALTER TABLE viewName PARTITION (a=1, b=2) SET SERDEPROPERTIES ('x' = 'y')
ALTER TABLE viewName ADD IF NOT EXISTS PARTITION (a='4', b='8')
ALTER TABLE viewName DROP IF EXISTS PARTITION (a='2')
ALTER TABLE viewName RECOVER PARTITIONS
ALTER TABLE viewName PARTITION (a='1', b='q') RENAME TO PARTITION (a='100', b='p')
```
In addition, `ALTER TABLE RENAME PARTITION` is unable to handle data source tables, just like the other `ALTER PARTITION` commands. We should issue an exception instead.
### How was this patch tested?
Added a few test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15004 from gatorsmile/altertable.
## What changes were proposed in this pull request?
Make CollectionAccumulator and SetAccumulator's value can be read thread-safely to fix the ConcurrentModificationException reported in [JIRA](https://issues.apache.org/jira/browse/SPARK-17463).
## How was this patch tested?
Existing tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15063 from zsxwing/SPARK-17463.
## What changes were proposed in this pull request?
Currently, ORDER BY clause returns nulls value according to sorting order (ASC|DESC), considering null value is always smaller than non-null values.
However, SQL2003 standard support NULLS FIRST or NULLS LAST to allow users to specify whether null values should be returned first or last, regardless of sorting order (ASC|DESC).
This PR is to support this new feature.
## How was this patch tested?
New test cases are added to test NULLS FIRST|LAST for regular select queries and windowing queries.
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Xin Wu <xinwu@us.ibm.com>
Closes#14842 from xwu0226/SPARK-10747.
## What changes were proposed in this pull request?
I first thought they are missing because they are kind of hidden options but it seems they are just missing.
For example, `spark.sql.parquet.mergeSchema` is documented in [sql-programming-guide.md](https://github.com/apache/spark/blob/master/docs/sql-programming-guide.md) but this function is missing whereas many options such as `spark.sql.join.preferSortMergeJoin` are not documented but have its own function individually.
So, this PR suggests making them consistent by adding the missing functions for some options in `SQLConf` and use them where applicable, in order to make them more readable.
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14678 from HyukjinKwon/sqlconf-cleanup.
## What changes were proposed in this pull request?
In PySpark, `df.take(1)` runs a single-stage job which computes only one partition of the DataFrame, while `df.limit(1).collect()` computes all partitions and runs a two-stage job. This difference in performance is confusing.
The reason why `limit(1).collect()` is so much slower is that `collect()` internally maps to `df.rdd.<some-pyspark-conversions>.toLocalIterator`, which causes Spark SQL to build a query where a global limit appears in the middle of the plan; this, in turn, ends up being executed inefficiently because limits in the middle of plans are now implemented by repartitioning to a single task rather than by running a `take()` job on the driver (this was done in #7334, a patch which was a prerequisite to allowing partition-local limits to be pushed beneath unions, etc.).
In order to fix this performance problem I think that we should generalize the fix from SPARK-10731 / #8876 so that `DataFrame.collect()` also delegates to the Scala implementation and shares the same performance properties. This patch modifies `DataFrame.collect()` to first collect all results to the driver and then pass them to Python, allowing this query to be planned using Spark's `CollectLimit` optimizations.
## How was this patch tested?
Added a regression test in `sql/tests.py` which asserts that the expected number of jobs, stages, and tasks are run for both queries.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15068 from JoshRosen/pyspark-collect-limit.
### What changes were proposed in this pull request?
As explained in https://github.com/apache/spark/pull/14797:
>Some analyzer rules have assumptions on logical plans, optimizer may break these assumption, we should not pass an optimized query plan into QueryExecution (will be analyzed again), otherwise we may some weird bugs.
For example, we have a rule for decimal calculation to promote the precision before binary operations, use PromotePrecision as placeholder to indicate that this rule should not apply twice. But a Optimizer rule will remove this placeholder, that break the assumption, then the rule applied twice, cause wrong result.
We should not optimize the query in CTAS more than once. For example,
```Scala
spark.range(99, 101).createOrReplaceTempView("tab1")
val sqlStmt = "SELECT id, cast(id as long) * cast('1.0' as decimal(38, 18)) as num FROM tab1"
sql(s"CREATE TABLE tab2 USING PARQUET AS $sqlStmt")
checkAnswer(spark.table("tab2"), sql(sqlStmt))
```
Before this PR, the results do not match
```
== Results ==
!== Correct Answer - 2 == == Spark Answer - 2 ==
![100,100.000000000000000000] [100,null]
[99,99.000000000000000000] [99,99.000000000000000000]
```
After this PR, the results match.
```
+---+----------------------+
|id |num |
+---+----------------------+
|99 |99.000000000000000000 |
|100|100.000000000000000000|
+---+----------------------+
```
In this PR, we do not treat the `query` in CTAS as a child. Thus, the `query` will not be optimized when optimizing CTAS statement. However, we still need to analyze it for normalizing and verifying the CTAS in the Analyzer. Thus, we do it in the analyzer rule `PreprocessDDL`, because so far only this rule needs the analyzed plan of the `query`.
### How was this patch tested?
Added a test
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15048 from gatorsmile/ctasOptimized.
## What changes were proposed in this pull request?
Point references to spark-packages.org to https://cwiki.apache.org/confluence/display/SPARK/Third+Party+Projects
This will be accompanied by a parallel change to the spark-website repo, and additional changes to this wiki.
## How was this patch tested?
Jenkins tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15075 from srowen/SPARK-17445.
## What changes were proposed in this pull request?
Scala's List.length method is O(N) and it makes the gatherCompressibilityStats function O(N^2). Eliminate the List.length calls by writing it in Scala way.
https://github.com/scala/scala/blob/2.10.x/src/library/scala/collection/LinearSeqOptimized.scala#L36
As suggested. Extended the fix to HiveInspectors and AggregationIterator classes as well.
## How was this patch tested?
Profiled a Spark job and found that CompressibleColumnBuilder is using 39% of the CPU. Out of this 39% CompressibleColumnBuilder->gatherCompressibilityStats is using 23% of it. 6.24% of the CPU is spend on List.length which is called inside gatherCompressibilityStats.
After this change we started to save 6.24% of the CPU.
Author: Ergin Seyfe <eseyfe@fb.com>
Closes#15032 from seyfe/gatherCompressibilityStats.
## What changes were proposed in this pull request?
CollectLimit.execute() incorrectly omits per-partition limits, leading to performance regressions in case this case is hit (which should not happen in normal operation, but can occur in some cases (see #15068 for one example).
## How was this patch tested?
Regression test in SQLQuerySuite that asserts the number of records scanned from the input RDD.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15070 from JoshRosen/SPARK-17515.
## What changes were proposed in this pull request?
When there is any Python UDF in the Project between Sort and Limit, it will be collected into TakeOrderedAndProjectExec, ExtractPythonUDFs failed to pull the Python UDFs out because QueryPlan.expressions does not include the expression inside Option[Seq[Expression]].
Ideally, we should fix the `QueryPlan.expressions`, but tried with no luck (it always run into infinite loop). In PR, I changed the TakeOrderedAndProjectExec to no use Option[Seq[Expression]] to workaround it. cc JoshRosen
## How was this patch tested?
Added regression test.
Author: Davies Liu <davies@databricks.com>
Closes#15030 from davies/all_expr.
## What changes were proposed in this pull request?
This is a trivial patch that catches all `OutOfMemoryError` while building the broadcast hash relation and rethrows it by wrapping it in a nice error message.
## How was this patch tested?
Existing Tests
Author: Sameer Agarwal <sameerag@cs.berkeley.edu>
Closes#14979 from sameeragarwal/broadcast-join-error.
## What changes were proposed in this pull request?
Check the database warehouse used in Spark UT, and remove the existing database file before run the UT (SPARK-8368).
## How was this patch tested?
Run Spark UT with the command for several times:
./build/sbt -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver "test-only *HiveSparkSubmitSuit*"
Without the patch, the test case can be passed only at the first time, and always failed from the second time.
With the patch the test case always can be passed correctly.
Author: tone-zhang <tone.zhang@linaro.org>
Closes#14894 from tone-zhang/issue1.
## What changes were proposed in this pull request?
This PR fixes `ColumnVectorUtils.populate` so that Parquet vectorized reader can read partitioned table with dates/timestamps. This works fine with Parquet normal reader.
This is being only called within [VectorizedParquetRecordReader.java#L185](https://github.com/apache/spark/blob/master/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedParquetRecordReader.java#L185).
When partition column types are explicitly given to `DateType` or `TimestampType` (rather than inferring the type of partition column), this fails with the exception below:
```
16/09/01 10:30:07 ERROR Executor: Exception in task 0.0 in stage 5.0 (TID 6)
java.lang.ClassCastException: java.lang.Integer cannot be cast to java.sql.Date
at org.apache.spark.sql.execution.vectorized.ColumnVectorUtils.populate(ColumnVectorUtils.java:89)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.initBatch(VectorizedParquetRecordReader.java:185)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.initBatch(VectorizedParquetRecordReader.java:204)
at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anonfun$buildReader$1.apply(ParquetFileFormat.scala:362)
...
```
## How was this patch tested?
Unit tests in `SQLQuerySuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14919 from HyukjinKwon/SPARK-17354.
## What changes were proposed in this pull request?
In `PreprocessDDL` we will check if table columns are duplicated. However, this checking ignores case sensitivity config(it's always case-sensitive) and lead to different result between `HiveExternalCatalog` and `InMemoryCatalog`. `HiveExternalCatalog` will throw exception because hive metastore is always case-nonsensitive, and `InMemoryCatalog` is fine.
This PR fixes it.
## How was this patch tested?
a new test in DDLSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14994 from cloud-fan/check-dup.
## What changes were proposed in this pull request?
`select size(null)` returns -1 in Hive. In order to be compatible, we should return `-1`.
## How was this patch tested?
unit test in `CollectionFunctionsSuite` and `DataFrameFunctionsSuite`.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#14991 from adrian-wang/size.
## What changes were proposed in this pull request?
We should generally use `ArrayBuffer.+=(A)` rather than `ArrayBuffer.append(A)`, because `append(A)` would involve extra boxing / unboxing.
## How was this patch tested?
N/A
Author: Liwei Lin <lwlin7@gmail.com>
Closes#14914 from lw-lin/append_to_plus_eq_v2.
## What changes were proposed in this pull request?
When we create a filestream on a directory that has partitioned subdirs (i.e. dir/x=y/), then ListingFileCatalog.allFiles returns the files in the dir as Seq[String] which internally is a Stream[String]. This is because of this [line](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningAwareFileCatalog.scala#L93), where a LinkedHashSet.values.toSeq returns Stream. Then when the [FileStreamSource](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSource.scala#L79) filters this Stream[String] to remove the seen files, it creates a new Stream[String], which has a filter function that has a $outer reference to the FileStreamSource (in Scala 2.10). Trying to serialize this Stream[String] causes NotSerializableException. This will happened even if there is just one file in the dir.
Its important to note that this behavior is different in Scala 2.11. There is no $outer reference to FileStreamSource, so it does not throw NotSerializableException. However, with a large sequence of files (tested with 10000 files), it throws StackOverflowError. This is because how Stream class is implemented. Its basically like a linked list, and attempting to serialize a long Stream requires *recursively* going through linked list, thus resulting in StackOverflowError.
In short, across both Scala 2.10 and 2.11, serialization fails when both the following conditions are true.
- file stream defined on a partitioned directory
- directory has 10k+ files
The right solution is to convert the seq to an array before writing to the log. This PR implements this fix in two ways.
- Changing all uses for HDFSMetadataLog to ensure Array is used instead of Seq
- Added a `require` in HDFSMetadataLog such that it is never used with type Seq
## How was this patch tested?
Added unit test that test that ensures the file stream source can handle with 10000 files. This tests fails in both Scala 2.10 and 2.11 with different failures as indicated above.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#14987 from tdas/SPARK-17372.
## What changes were proposed in this pull request?
In LongToUnsafeRowMap, we use offset of a value as pointer, stored in a array also in the page for chained values. The offset is not portable, because Platform.LONG_ARRAY_OFFSET will be different with different JVM Heap size, then the deserialized LongToUnsafeRowMap will be corrupt.
This PR will change to use portable address (without Platform.LONG_ARRAY_OFFSET).
## How was this patch tested?
Added a test case with random generated keys, to improve the coverage. But this test is not a regression test, that could require a Spark cluster that have at least 32G heap in driver or executor.
Author: Davies Liu <davies@databricks.com>
Closes#14927 from davies/longmap.
## What changes were proposed in this pull request?
This PR adds better error messages for malformed record when reading a JSON file using DataFrameReader.
For example, for query:
```
import org.apache.spark.sql.types._
val corruptRecords = spark.sparkContext.parallelize("""{"a":{, b:3}""" :: Nil)
val schema = StructType(StructField("a", StringType, true) :: Nil)
val jsonDF = spark.read.schema(schema).json(corruptRecords)
```
**Before change:**
We silently replace corrupted line with null
```
scala> jsonDF.show
+----+
| a|
+----+
|null|
+----+
```
**After change:**
Add an explicit warning message:
```
scala> jsonDF.show
16/09/02 14:43:16 WARN JacksonParser: Found at least one malformed records (sample: {"a":{, b:3}). The JSON reader will replace
all malformed records with placeholder null in current PERMISSIVE parser mode.
To find out which corrupted records have been replaced with null, please use the
default inferred schema instead of providing a custom schema.
Code example to print all malformed records (scala):
===================================================
// The corrupted record exists in column _corrupt_record.
val parsedJson = spark.read.json("/path/to/json/file/test.json")
+----+
| a|
+----+
|null|
+----+
```
###
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14929 from clockfly/logwarning_if_schema_not_contain_corrupted_record.
## What changes were proposed in this pull request?
class `org.apache.spark.sql.types.Metadata` is widely used in mllib to store some ml attributes. `Metadata` is commonly stored in `Alias` expression.
```
case class Alias(child: Expression, name: String)(
val exprId: ExprId = NamedExpression.newExprId,
val qualifier: Option[String] = None,
val explicitMetadata: Option[Metadata] = None,
override val isGenerated: java.lang.Boolean = false)
```
The `Metadata` can take a big memory footprint since the number of attributes is big ( in scale of million). When `toJSON` is called on `Alias` expression, the `Metadata` will also be converted to a big JSON string.
If a plan contains many such kind of `Alias` expressions, it may trigger out of memory error when `toJSON` is called, since converting all `Metadata` references to JSON will take huge memory.
With this PR, we will skip scanning Metadata when doing JSON conversion. For a reproducer of the OOM, and analysis, please look at jira https://issues.apache.org/jira/browse/SPARK-17356.
## How was this patch tested?
Existing tests.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14915 from clockfly/json_oom.
## What changes were proposed in this pull request?
Using the public `Catalog` API, users can create a file-based data source table, without giving the path options. For this case, currently we can create the table successfully, but fail when we read it. Ideally we should fail during creation.
This is because when we create data source table, we resolve the data source relation without validating path: `resolveRelation(checkPathExist = false)`.
Looking back to why we add this trick(`checkPathExist`), it's because when we call `resolveRelation` for managed table, we add the path to data source options but the path is not created yet. So why we add this not-yet-created path to data source options? This PR fix the problem by adding path to options after we call `resolveRelation`. Then we can remove the `checkPathExist` parameter in `DataSource.resolveRelation` and do some related cleanups.
## How was this patch tested?
existing tests and new test in `CatalogSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14921 from cloud-fan/check-path.
## What changes were proposed in this pull request?
1. Support generation table-level statistics for
- hive tables in HiveExternalCatalog
- data source tables in HiveExternalCatalog
- data source tables in InMemoryCatalog.
2. Add a property "catalogStats" in CatalogTable to hold statistics in Spark side.
3. Put logics of statistics transformation between Spark and Hive in HiveClientImpl.
4. Extend Statistics class by adding rowCount (will add estimatedSize when we have column stats).
## How was this patch tested?
add unit tests
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#14712 from wzhfy/tableStats.
## What changes were proposed in this pull request?
It's really weird that we allow users to specify database in both from table name and to table name
in `ALTER TABLE RENAME TO`, while logically we can't support rename a table to a different database.
Both postgres and MySQL disallow this syntax, it's reasonable to follow them and simply our code.
## How was this patch tested?
new test in `DDLCommandSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14955 from cloud-fan/rename.
### What changes were proposed in this pull request?
When we trying to read a table and then write to the same table using the `Overwrite` save mode, we got a very confusing error message:
For example,
```Scala
Seq((1, 2)).toDF("i", "j").write.saveAsTable("tab1")
table("tab1").write.mode(SaveMode.Overwrite).saveAsTable("tab1")
```
```
Job aborted.
org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1.apply$mcV$sp
...
Caused by: org.apache.spark.SparkException: Task failed while writing rows
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:266)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(InsertIntoHadoopFsRelationCommand.scala:143)
at org.apache.spark.sql.execution.datasources
```
After the PR, we will issue an `AnalysisException`:
```
Cannot overwrite table `tab1` that is also being read from
```
### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14954 from gatorsmile/ctasQueryAnalyze.
### What changes were proposed in this pull request?
This is another step to get rid of HiveClient from `HiveSessionState`. All the metastore interactions should be through `ExternalCatalog` interface. However, the existing implementation of `InsertIntoHiveTable ` still requires Hive clients. This PR is to remove HiveClient by moving the metastore interactions into `ExternalCatalog`.
### How was this patch tested?
Existing test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14888 from gatorsmile/removeClientFromInsertIntoHiveTable.
## What changes were proposed in this pull request?
the `catalogString` for `ArrayType` and `MapType` currently calls the `simpleString` method on its children. This is a problem when the child is a struct, the `struct.simpleString` implementation truncates the number of fields it shows (25 at max). This breaks the generation of a proper `catalogString`, and has shown to cause errors while writing to Hive.
This PR fixes this by providing proper `catalogString` implementations for `ArrayData` or `MapData`.
## How was this patch tested?
Added testing for `catalogString` to `DataTypeSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14938 from hvanhovell/SPARK-17335.
## What changes were proposed in this pull request?
Require the use of CROSS join syntax in SQL (and a new crossJoin
DataFrame API) to specify explicit cartesian products between relations.
By cartesian product we mean a join between relations R and S where
there is no join condition involving columns from both R and S.
If a cartesian product is detected in the absence of an explicit CROSS
join, an error must be thrown. Turning on the
"spark.sql.crossJoin.enabled" configuration flag will disable this check
and allow cartesian products without an explicit CROSS join.
The new crossJoin DataFrame API must be used to specify explicit cross
joins. The existing join(DataFrame) method will produce a INNER join
that will require a subsequent join condition.
That is df1.join(df2) is equivalent to select * from df1, df2.
## How was this patch tested?
Added cross-join.sql to the SQLQueryTestSuite to test the check for cartesian products. Added a couple of tests to the DataFrameJoinSuite to test the crossJoin API. Modified various other test suites to explicitly specify a cross join where an INNER join or a comma-separated list was previously used.
Author: Srinath Shankar <srinath@databricks.com>
Closes#14866 from srinathshankar/crossjoin.
## What changes were proposed in this pull request?
This patch fixes a bug in the vectorized parquet reader that's caused by re-using the same dictionary column vector while reading consecutive row groups. Specifically, this issue manifests for a certain distribution of dictionary/plain encoded data while we read/populate the underlying bit packed dictionary data into a column-vector based data structure.
## How was this patch tested?
Manually tested on datasets provided by the community. Thanks to Chris Perluss and Keith Kraus for their invaluable help in tracking down this issue!
Author: Sameer Agarwal <sameerag@cs.berkeley.edu>
Closes#14941 from sameeragarwal/parquet-exception-2.
## What changes were proposed in this pull request?
Some analyzer rules have assumptions on logical plans, optimizer may break these assumption, we should not pass an optimized query plan into QueryExecution (will be analyzed again), otherwise we may some weird bugs.
For example, we have a rule for decimal calculation to promote the precision before binary operations, use PromotePrecision as placeholder to indicate that this rule should not apply twice. But a Optimizer rule will remove this placeholder, that break the assumption, then the rule applied twice, cause wrong result.
Ideally, we should make all the analyzer rules all idempotent, that may require lots of effort to double checking them one by one (may be not easy).
An easier approach could be never feed a optimized plan into Analyzer, this PR fix the case for RunnableComand, they will be optimized, during execution, the passed `query` will also be passed into QueryExecution again. This PR make these `query` not part of the children, so they will not be optimized and analyzed again.
Right now, we did not know a logical plan is optimized or not, we could introduce a flag for that, and make sure a optimized logical plan will not be analyzed again.
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#14797 from davies/fix_writer.
This patch refactors the internals of the JDBC data source in order to allow some of its code to be re-used in an automated comparison testing harness. Here are the key changes:
- Move the JDBC `ResultSetMetadata` to `StructType` conversion logic from `JDBCRDD.resolveTable()` to the `JdbcUtils` object (as a new `getSchema(ResultSet, JdbcDialect)` method), allowing it to be applied on `ResultSet`s that are created elsewhere.
- Move the `ResultSet` to `InternalRow` conversion methods from `JDBCRDD` to `JdbcUtils`:
- It makes sense to move the `JDBCValueGetter` type and `makeGetter` functions here given that their write-path counterparts (`JDBCValueSetter`) are already in `JdbcUtils`.
- Add an internal `resultSetToSparkInternalRows` method which takes a `ResultSet` and schema and returns an `Iterator[InternalRow]`. This effectively extracts the main loop of `JDBCRDD` into its own method.
- Add a public `resultSetToRows` method to `JdbcUtils`, which wraps the minimal machinery around `resultSetToSparkInternalRows` in order to allow it to be called from outside of a Spark job.
- Make `JdbcDialect.get` into a `DeveloperApi` (`JdbcDialect` itself is already a `DeveloperApi`).
Put together, these changes enable the following testing pattern:
```scala
val jdbResultSet: ResultSet = conn.prepareStatement(query).executeQuery()
val resultSchema: StructType = JdbcUtils.getSchema(jdbResultSet, JdbcDialects.get("jdbc:postgresql"))
val jdbcRows: Seq[Row] = JdbcUtils.resultSetToRows(jdbResultSet, schema).toSeq
checkAnswer(sparkResult, jdbcRows) // in a test case
```
Author: Josh Rosen <joshrosen@databricks.com>
Closes#14907 from JoshRosen/modularize-jdbc-internals.
## What changes were proposed in this pull request?
Try increase number of partitions to try so we don't revert to all.
## How was this patch tested?
Empirically. This is common case optimization.
Author: Robert Kruszewski <robertk@palantir.com>
Closes#14573 from robert3005/robertk/execute-take-backoff.
## What changes were proposed in this pull request?
Adds (Scala-specific) and (Java-specific) to Scaladoc.
## How was this patch tested?
local build
Author: Jacek Laskowski <jacek@japila.pl>
Closes#14891 from jaceklaskowski/scala-specifics.
follow #13137 This pr sets the right number of partitions when reading data from a local collection.
Query 'val df = Seq((1, 2)).toDF("key", "value").count' always use defaultParallelism tasks. So it causes run many empty or small tasks.
Manually tested and checked.
Author: Lianhui Wang <lianhuiwang09@gmail.com>
Closes#13979 from lianhuiwang/localTable-Parallel.
## What changes were proposed in this pull request?
This PR is the second step for the following feature:
For hash aggregation in Spark SQL, we use a fast aggregation hashmap to act as a "cache" in order to boost aggregation performance. Previously, the hashmap is backed by a `ColumnarBatch`. This has performance issues when we have wide schema for the aggregation table (large number of key fields or value fields).
In this JIRA, we support another implementation of fast hashmap, which is backed by a `RowBatch`. We then automatically pick between the two implementations based on certain knobs.
In this second-step PR, we enable `RowBasedHashMapGenerator` in `HashAggregateExec`.
## How was this patch tested?
Added tests: `RowBasedAggregateHashMapSuite` and ` VectorizedAggregateHashMapSuite`
Additional micro-benchmarks tests and TPCDS results will be added in a separate PR in the series.
Author: Qifan Pu <qifan.pu@gmail.com>
Author: ooq <qifan.pu@gmail.com>
Closes#14176 from ooq/rowbasedfastaggmap-pr2.
## What changes were proposed in this pull request?
Attempting to use Spark SQL's JDBC data source against the Hive ThriftServer results in a `java.sql.SQLException: Method` not supported exception from `org.apache.hive.jdbc.HiveResultSetMetaData.isSigned`. Here are two user reports of this issue:
- https://stackoverflow.com/questions/34067686/spark-1-5-1-not-working-with-hive-jdbc-1-2-0
- https://stackoverflow.com/questions/32195946/method-not-supported-in-spark
I have filed [HIVE-14684](https://issues.apache.org/jira/browse/HIVE-14684) to attempt to fix this in Hive by implementing the isSigned method, but in the meantime / for compatibility with older JDBC drivers I think we should add special-case error handling to work around this bug.
This patch updates `JDBCRDD`'s `ResultSetMetadata` to schema conversion to catch the "Method not supported" exception from Hive and return `isSigned = true`. I believe that this is safe because, as far as I know, Hive does not support unsigned numeric types.
## How was this patch tested?
Tested manually against a Spark Thrift Server.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#14911 from JoshRosen/hive-jdbc-workaround.
## What changes were proposed in this pull request?
It seems `EqualNullSafe` filter was missed for batch pruneing partitions in cached tables.
It seems supporting this improves the performance roughly 5 times faster.
Running the codes below:
```scala
test("Null-safe equal comparison") {
val N = 20000000
val df = spark.range(N).repartition(20)
val benchmark = new Benchmark("Null-safe equal comparison", N)
df.createOrReplaceTempView("t")
spark.catalog.cacheTable("t")
sql("select id from t where id <=> 1").collect()
benchmark.addCase("Null-safe equal comparison", 10) { _ =>
sql("select id from t where id <=> 1").collect()
}
benchmark.run()
}
```
produces the results below:
**Before:**
```
Running benchmark: Null-safe equal comparison
Running case: Null-safe equal comparison
Stopped after 10 iterations, 2098 ms
Java HotSpot(TM) 64-Bit Server VM 1.8.0_45-b14 on Mac OS X 10.11.5
Intel(R) Core(TM) i7-4850HQ CPU 2.30GHz
Null-safe equal comparison: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Null-safe equal comparison 204 / 210 98.1 10.2 1.0X
```
**After:**
```
Running benchmark: Null-safe equal comparison
Running case: Null-safe equal comparison
Stopped after 10 iterations, 478 ms
Java HotSpot(TM) 64-Bit Server VM 1.8.0_45-b14 on Mac OS X 10.11.5
Intel(R) Core(TM) i7-4850HQ CPU 2.30GHz
Null-safe equal comparison: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Null-safe equal comparison 42 / 48 474.1 2.1 1.0X
```
## How was this patch tested?
Unit tests in `PartitionBatchPruningSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14117 from HyukjinKwon/SPARK-16461.
## What changes were proposed in this pull request?
Avoid allocating some 0-length arrays, esp. in UTF8String, and by using Array.empty in Scala over Array[T]()
## How was this patch tested?
Jenkins
Author: Sean Owen <sowen@cloudera.com>
Closes#14895 from srowen/SPARK-17331.
## What changes were proposed in this pull request?
This PR adds the ability to parse SQL (hexadecimal) binary literals (AKA bit strings). It follows the following syntax `X'[Hexadecimal Characters]+'`, for example: `X'01AB'` would create a binary the following binary array `0x01AB`.
If an uneven number of hexadecimal characters is passed, then the upper 4 bits of the initial byte are kept empty, and the lower 4 bits are filled using the first character. For example `X'1C7'` would create the following binary array `0x01C7`.
Binary data (Array[Byte]) does not have a proper `hashCode` and `equals` functions. This meant that comparing `Literal`s containing binary data was a pain. I have updated Literal.hashCode and Literal.equals to deal properly with binary data.
## How was this patch tested?
Added tests to the `ExpressionParserSuite`, `SQLQueryTestSuite` and `ExpressionSQLBuilderSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14832 from hvanhovell/SPARK-17263.
## What changes were proposed in this pull request?
This is kind of a follow-up of https://github.com/apache/spark/pull/14482 . As we put `CatalogTable` in the logical plan directly, it makes sense to let physical plans take `CatalogTable` directly, instead of extracting some fields of `CatalogTable` in planner and then construct a new `CatalogTable` in physical plan.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14823 from cloud-fan/create-table.
### What changes were proposed in this pull request?
The existing `CREATE TABLE LIKE` command has multiple issues:
- The generated table is non-empty when the source table is a data source table. The major reason is the data source table is using the table property `path` to store the location of table contents. Currently, we keep it unchanged. Thus, we still create the same table with the same location.
- The table type of the generated table is `EXTERNAL` when the source table is an external Hive Serde table. Currently, we explicitly set it to `MANAGED`, but Hive is checking the table property `EXTERNAL` to decide whether the table is `EXTERNAL` or not. (See https://github.com/apache/hive/blob/master/metastore/src/java/org/apache/hadoop/hive/metastore/ObjectStore.java#L1407-L1408) Thus, the created table is still `EXTERNAL`.
- When the source table is a `VIEW`, the metadata of the generated table contains the original view text and view original text. So far, this does not break anything, but it could cause something wrong in Hive. (For example, https://github.com/apache/hive/blob/master/metastore/src/java/org/apache/hadoop/hive/metastore/ObjectStore.java#L1405-L1406)
- The issue regarding the table `comment`. To follow what Hive does, the table comment should be cleaned, but the column comments should be still kept.
- The `INDEX` table is not supported. Thus, we should throw an exception in this case.
- `owner` should not be retained. `ToHiveTable` set it [here](e679bc3c1c/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala (L793)) no matter which value we set in `CatalogTable`. We set it to an empty string for avoiding the confusing output in Explain.
- Add a support for temp tables
- Like Hive, we should not copy the table properties from the source table to the created table, especially for the statistics-related properties, which could be wrong in the created table.
- `unsupportedFeatures` should not be copied from the source table. The created table does not have these unsupported features.
- When the type of source table is a view, the target table is using the default format of data source tables: `spark.sql.sources.default`.
This PR is to fix the above issues.
### How was this patch tested?
Improve the test coverage by adding more test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14531 from gatorsmile/createTableLike.
## What changes were proposed in this pull request?
This PR implements aggregation function `percentile_approx`. Function `percentile_approx` returns the approximate percentile(s) of a column at the given percentage(s). A percentile is a watermark value below which a given percentage of the column values fall. For example, the percentile of column `col` at percentage 50% is the median value of column `col`.
### Syntax:
```
# Returns percentile at a given percentage value. The approximation error can be reduced by increasing parameter accuracy, at the cost of memory.
percentile_approx(col, percentage [, accuracy])
# Returns percentile value array at given percentage value array
percentile_approx(col, array(percentage1 [, percentage2]...) [, accuracy])
```
### Features:
1. This function supports partial aggregation.
2. The memory consumption is bounded. The larger `accuracy` parameter we choose, we smaller error we get. The default accuracy value is 10000, to match with Hive default setting. Choose a smaller value for smaller memory footprint.
3. This function supports window function aggregation.
### Example usages:
```
## Returns the 25th percentile value, with default accuracy
SELECT percentile_approx(col, 0.25) FROM table
## Returns an array of percentile value (25th, 50th, 75th), with default accuracy
SELECT percentile_approx(col, array(0.25, 0.5, 0.75)) FROM table
## Returns 25th percentile value, with custom accuracy value 100, larger accuracy parameter yields smaller approximation error
SELECT percentile_approx(col, 0.25, 100) FROM table
## Returns the 25th, and 50th percentile values, with custom accuracy value 100
SELECT percentile_approx(col, array(0.25, 0.5), 100) FROM table
```
### NOTE:
1. The `percentile_approx` implementation is different from Hive, so the result returned on same query maybe slightly different with Hive. This implementation uses `QuantileSummaries` as the underlying probabilistic data structure, and mainly follows paper `Space-efficient Online Computation of Quantile Summaries` by Greenwald, Michael and Khanna, Sanjeev. (http://dx.doi.org/10.1145/375663.375670)`
2. The current implementation of `QuantileSummaries` doesn't support automatic compression. This PR has a rule to do compression automatically at the caller side, but it may not be optimal.
## How was this patch tested?
Unit test, and Sql query test.
## Acknowledgement
1. This PR's work in based on lw-lin's PR https://github.com/apache/spark/pull/14298, with improvements like supporting partial aggregation, fixing out of memory issue.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14868 from clockfly/appro_percentile_try_2.
## What changes were proposed in this pull request?
according to the discussion in the original PR #10896 and the new approach PR #14876 , we decided to revert these 2 PRs and go with the new approach.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14909 from cloud-fan/revert.
## What changes were proposed in this pull request?
Currently we use `CreateViewCommand` to implement ALTER VIEW AS, which has 3 bugs:
1. SPARK-17180: ALTER VIEW AS should alter temp view if view name has no database part and temp view exists
2. SPARK-17309: ALTER VIEW AS should issue exception if view does not exist.
3. SPARK-17323: ALTER VIEW AS should keep the previous table properties, comment, create_time, etc.
The root cause is, ALTER VIEW AS is quite different from CREATE VIEW, we need different code path to handle them. However, in `CreateViewCommand`, there is no way to distinguish ALTER VIEW AS and CREATE VIEW, we have to introduce extra flag. But instead of doing this, I think a more natural way is to separate the ALTER VIEW AS logic into a new command.
## How was this patch tested?
new tests in SQLViewSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14874 from cloud-fan/minor4.
## What changes were proposed in this pull request?
Clean up unused variables and unused import statements, unnecessary `return` and `toArray`, and some more style improvement, when I walk through the code examples.
## How was this patch tested?
Testet manually on local laptop.
Author: Xin Ren <iamshrek@126.com>
Closes#14836 from keypointt/codeWalkThroughML.
## What changes were proposed in this pull request?
Clarify that only parquet files are supported by DataStreamWriter now
## How was this patch tested?
(Doc build -- no functional changes to test)
Author: Sean Owen <sowen@cloudera.com>
Closes#14860 from srowen/SPARK-17264.
## What changes were proposed in this pull request?
Partial aggregations are generated in `EnsureRequirements`, but the planner fails to
check if partial aggregation satisfies sort requirements.
For the following query:
```
val df2 = (0 to 1000).map(x => (x % 2, x.toString)).toDF("a", "b").createOrReplaceTempView("t2")
spark.sql("select max(b) from t2 group by a").explain(true)
```
Now, the SortAggregator won't insert Sort operator before partial aggregation, this will break sort-based partial aggregation.
```
== Physical Plan ==
SortAggregate(key=[a#5], functions=[max(b#6)], output=[max(b)#17])
+- *Sort [a#5 ASC], false, 0
+- Exchange hashpartitioning(a#5, 200)
+- SortAggregate(key=[a#5], functions=[partial_max(b#6)], output=[a#5, max#19])
+- LocalTableScan [a#5, b#6]
```
Actually, a correct plan is:
```
== Physical Plan ==
SortAggregate(key=[a#5], functions=[max(b#6)], output=[max(b)#17])
+- *Sort [a#5 ASC], false, 0
+- Exchange hashpartitioning(a#5, 200)
+- SortAggregate(key=[a#5], functions=[partial_max(b#6)], output=[a#5, max#19])
+- *Sort [a#5 ASC], false, 0
+- LocalTableScan [a#5, b#6]
```
## How was this patch tested?
Added tests in `PlannerSuite`.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#14865 from maropu/SPARK-17289.
## What changes were proposed in this pull request?
This PR split the the single `createPartitions()` call into smaller batches, which could prevent Hive metastore from OOM (caused by millions of partitions).
It will also try to gather all the fast stats (number of files and total size of all files) in parallel to avoid the bottle neck of listing the files in metastore sequential, which is controlled by spark.sql.gatherFastStats (enabled by default).
## How was this patch tested?
Tested locally with 10000 partitions and 100 files with embedded metastore, without gathering fast stats in parallel, adding partitions took 153 seconds, after enable that, gathering the fast stats took about 34 seconds, adding these partitions took 25 seconds (most of the time spent in object store), 59 seconds in total, 2.5X faster (with larger cluster, gathering will much faster).
Author: Davies Liu <davies@databricks.com>
Closes#14607 from davies/repair_batch.
## What changes were proposed in this pull request?
Jira : https://issues.apache.org/jira/browse/SPARK-17271
Planner is adding un-needed SORT operation due to bug in the way comparison for `SortOrder` is done at https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/EnsureRequirements.scala#L253
`SortOrder` needs to be compared semantically because `Expression` within two `SortOrder` can be "semantically equal" but not literally equal objects.
eg. In case of `sql("SELECT * FROM table1 a JOIN table2 b ON a.col1=b.col1")`
Expression in required SortOrder:
```
AttributeReference(
name = "col1",
dataType = LongType,
nullable = false
) (exprId = exprId,
qualifier = Some("a")
)
```
Expression in child SortOrder:
```
AttributeReference(
name = "col1",
dataType = LongType,
nullable = false
) (exprId = exprId)
```
Notice that the output column has a qualifier but the child attribute does not but the inherent expression is the same and hence in this case we can say that the child satisfies the required sort order.
This PR includes following changes:
- Added a `semanticEquals` method to `SortOrder` so that it can compare underlying child expressions semantically (and not using default Object.equals)
- Fixed `EnsureRequirements` to use semantic comparison of SortOrder
## How was this patch tested?
- Added a test case to `PlannerSuite`. Ran rest tests in `PlannerSuite`
Author: Tejas Patil <tejasp@fb.com>
Closes#14841 from tejasapatil/SPARK-17271_sort_order_equals_bug.
## What changes were proposed in this pull request?
This pr to fix a bug below in sampling with replacement
```
val df = Seq((1, 0), (2, 0), (3, 0)).toDF("a", "b")
df.sample(true, 2.0).withColumn("c", monotonically_increasing_id).select($"c").show
+---+
| c|
+---+
| 0|
| 1|
| 1|
| 1|
| 2|
+---+
```
## How was this patch tested?
Added a test in `DataFrameSuite`.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#14800 from maropu/FixSampleBug.
## What changes were proposed in this pull request?
This patch adds a purge interface to MetadataLog, and an implementation in HDFSMetadataLog. The purge function is currently unused, but I will use it to purge old execution and file source logs in follow-up patches. These changes are required in a production structured streaming job that runs for a long period of time.
## How was this patch tested?
Added a unit test case in HDFSMetadataLogSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14802 from petermaxlee/SPARK-17235.
## What changes were proposed in this pull request?
This PR adds parser support for `BigDecimal` literals. If you append the suffix `BD` to a valid number then this will be interpreted as a `BigDecimal`, for example `12.0E10BD` will interpreted into a BigDecimal with scale -9 and precision 3. This is useful in situations where you need exact values.
## How was this patch tested?
Added tests to `ExpressionParserSuite`, `ExpressionSQLBuilderSuite` and `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14819 from hvanhovell/SPARK-17246.
## What changes were proposed in this pull request?
Before this change, FileStreamSource uses an in-memory hash set to track the list of files processed by the engine. The list can grow indefinitely, leading to OOM or overflow of the hash set.
This patch introduces a new user-defined option called "maxFileAge", default to 24 hours. If a file is older than this age, FileStreamSource will purge it from the in-memory map that was used to track the list of files that have been processed.
## How was this patch tested?
Added unit tests for the underlying utility, and also added an end-to-end test to validate the purge in FileStreamSourceSuite. Also verified the new test cases would fail when the timeout was set to a very large number.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14728 from petermaxlee/SPARK-17165.
### What changes were proposed in this pull request?
Address the comments by yhuai in the original PR: https://github.com/apache/spark/pull/14207
First, issue an exception instead of logging a warning when users specify the partitioning columns without a given schema.
Second, refactor the codes a little.
### How was this patch tested?
Fixed the test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14572 from gatorsmile/followup16552.
## What changes were proposed in this pull request?
This PR enables the tests for `TimestampType` for JSON and unifies the logics for verifying schema when writing in CSV.
In more details, this PR,
- Enables the tests for `TimestampType` for JSON and
This was disabled due to an issue in `DatatypeConverter.parseDateTime` which parses dates incorrectly, for example as below:
```scala
val d = javax.xml.bind.DatatypeConverter.parseDateTime("0900-01-01T00:00:00.000").getTime
println(d.toString)
```
```
Fri Dec 28 00:00:00 KST 899
```
However, since we use `FastDateFormat`, it seems we are safe now.
```scala
val d = FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSS").parse("0900-01-01T00:00:00.000")
println(d)
```
```
Tue Jan 01 00:00:00 PST 900
```
- Verifies all unsupported types in CSV
There is a separate logics to verify the schemas in `CSVFileFormat`. This is actually not quite correct enough because we don't support `NullType` and `CalanderIntervalType` as well `StructType`, `ArrayType`, `MapType`. So, this PR adds both types.
## How was this patch tested?
Tests in `JsonHadoopFsRelation` and `CSVSuite`
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14829 from HyukjinKwon/SPARK-16216-followup.
## What changes were proposed in this pull request?
This PR introduces an abstract class `TypedImperativeAggregate` so that an aggregation function of TypedImperativeAggregate can use **arbitrary** user-defined Java object as intermediate aggregation buffer object.
**This has advantages like:**
1. It now can support larger category of aggregation functions. For example, it will be much easier to implement aggregation function `percentile_approx`, which has a complex aggregation buffer definition.
2. It can be used to avoid doing serialization/de-serialization for every call of `update` or `merge` when converting domain specific aggregation object to internal Spark-Sql storage format.
3. It is easier to integrate with other existing monoid libraries like algebird, and supports more aggregation functions with high performance.
Please see `org.apache.spark.sql.TypedImperativeAggregateSuite.TypedMaxAggregate` to find an example of how to defined a `TypedImperativeAggregate` aggregation function.
Please see Java doc of `TypedImperativeAggregate` and Jira ticket SPARK-17187 for more information.
## How was this patch tested?
Unit tests.
Author: Sean Zhong <seanzhong@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#14753 from clockfly/object_aggregation_buffer_try_2.
## What changes were proposed in this pull request?
When reading float4 and smallint columns from PostgreSQL, Spark's `PostgresDialect` widens these types to Decimal and Integer rather than using the narrower Float and Short types. According to https://www.postgresql.org/docs/7.1/static/datatype.html#DATATYPE-TABLE, Postgres maps the `smallint` type to a signed two-byte integer and the `real` / `float4` types to single precision floating point numbers.
This patch fixes this by adding more special-cases to `getCatalystType`, similar to what was done for the Derby JDBC dialect. I also fixed a similar problem in the write path which causes Spark to create integer columns in Postgres for what should have been ShortType columns.
## How was this patch tested?
New test cases in `PostgresIntegrationSuite` (which I ran manually because Jenkins can't run it right now).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#14796 from JoshRosen/postgres-jdbc-type-fixes.
### What changes were proposed in this pull request?
This PR is to fix an incorrect outer join elimination when filter's `isNotNull` constraints is unable to filter out all null-supplying rows. For example, `isnotnull(coalesce(b#227, c#238))`.
Users can hit this error when they try to use `using/natural outer join`, which is converted to a normal outer join with a `coalesce` expression on the `using columns`. For example,
```Scala
val a = Seq((1, 2), (2, 3)).toDF("a", "b")
val b = Seq((2, 5), (3, 4)).toDF("a", "c")
val c = Seq((3, 1)).toDF("a", "d")
val ab = a.join(b, Seq("a"), "fullouter")
ab.join(c, "a").explain(true)
```
The dataframe `ab` is doing `using full-outer join`, which is converted to a normal outer join with a `coalesce` expression. Constraints inference generates a `Filter` with constraints `isnotnull(coalesce(b#227, c#238))`. Then, it triggers a wrong outer join elimination and generates a wrong result.
```
Project [a#251, b#227, c#237, d#247]
+- Join Inner, (a#251 = a#246)
:- Project [coalesce(a#226, a#236) AS a#251, b#227, c#237]
: +- Join FullOuter, (a#226 = a#236)
: :- Project [_1#223 AS a#226, _2#224 AS b#227]
: : +- LocalRelation [_1#223, _2#224]
: +- Project [_1#233 AS a#236, _2#234 AS c#237]
: +- LocalRelation [_1#233, _2#234]
+- Project [_1#243 AS a#246, _2#244 AS d#247]
+- LocalRelation [_1#243, _2#244]
== Optimized Logical Plan ==
Project [a#251, b#227, c#237, d#247]
+- Join Inner, (a#251 = a#246)
:- Project [coalesce(a#226, a#236) AS a#251, b#227, c#237]
: +- Filter isnotnull(coalesce(a#226, a#236))
: +- Join FullOuter, (a#226 = a#236)
: :- LocalRelation [a#226, b#227]
: +- LocalRelation [a#236, c#237]
+- LocalRelation [a#246, d#247]
```
**A note to the `Committer`**, please also give the credit to dongjoon-hyun who submitted another PR for fixing this issue. https://github.com/apache/spark/pull/14580
### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14661 from gatorsmile/fixOuterJoinElimination.
## What changes were proposed in this pull request?
Method `SQLContext.parseDataType(dataTypeString: String)` could be removed, we should use `SparkSession.parseDataType(dataTypeString: String)` instead.
This require updating PySpark.
## How was this patch tested?
Existing test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#14790 from jiangxb1987/parseDataType.
### What changes were proposed in this pull request?
Since `HiveClient` is used to interact with the Hive metastore, it should be hidden in `HiveExternalCatalog`. After moving `HiveClient` into `HiveExternalCatalog`, `HiveSharedState` becomes a wrapper of `HiveExternalCatalog`. Thus, removal of `HiveSharedState` becomes straightforward. After removal of `HiveSharedState`, the reflection logic is directly applied on the choice of `ExternalCatalog` types, based on the configuration of `CATALOG_IMPLEMENTATION`.
~~`HiveClient` is also used/invoked by the other entities besides HiveExternalCatalog, we defines the following two APIs: getClient and getNewClient~~
### How was this patch tested?
The existing test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14757 from gatorsmile/removeHiveClient.
## What changes were proposed in this pull request?
### Default - ISO 8601
Currently, CSV datasource is writing `Timestamp` and `Date` as numeric form and JSON datasource is writing both as below:
- CSV
```
// TimestampType
1414459800000000
// DateType
16673
```
- Json
```
// TimestampType
1970-01-01 11:46:40.0
// DateType
1970-01-01
```
So, for CSV we can't read back what we write and for JSON it becomes ambiguous because the timezone is being missed.
So, this PR make both **write** `Timestamp` and `Date` in ISO 8601 formatted string (please refer the [ISO 8601 specification](https://www.w3.org/TR/NOTE-datetime)).
- For `Timestamp` it becomes as below: (`yyyy-MM-dd'T'HH:mm:ss.SSSZZ`)
```
1970-01-01T02:00:01.000-01:00
```
- For `Date` it becomes as below (`yyyy-MM-dd`)
```
1970-01-01
```
### Custom date format option - `dateFormat`
This PR also adds the support to write and read dates and timestamps in a formatted string as below:
- **DateType**
- With `dateFormat` option (e.g. `yyyy/MM/dd`)
```
+----------+
| date|
+----------+
|2015/08/26|
|2014/10/27|
|2016/01/28|
+----------+
```
### Custom date format option - `timestampFormat`
- **TimestampType**
- With `dateFormat` option (e.g. `dd/MM/yyyy HH:mm`)
```
+----------------+
| date|
+----------------+
|2015/08/26 18:00|
|2014/10/27 18:30|
|2016/01/28 20:00|
+----------------+
```
## How was this patch tested?
Unit tests were added in `CSVSuite` and `JsonSuite`. For JSON, existing tests cover the default cases.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14279 from HyukjinKwon/SPARK-16216-json-csv.
## What changes were proposed in this pull request?
Actually Spark SQL doesn't support index, the catalog table type `INDEX` is from Hive. However, most operations in Spark SQL can't handle index table, e.g. create table, alter table, etc.
Logically index table should be invisible to end users, and Hive also generates special table name for index table to avoid users accessing it directly. Hive has special SQL syntax to create/show/drop index tables.
At Spark SQL side, although we can describe index table directly, but the result is unreadable, we should use the dedicated SQL syntax to do it(e.g. `SHOW INDEX ON tbl`). Spark SQL can also read index table directly, but the result is always empty.(Can hive read index table directly?)
This PR remove the table type `INDEX`, to make it clear that Spark SQL doesn't support index currently.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14752 from cloud-fan/minor2.
## What changes were proposed in this pull request?
Some JDBC driver (for example PostgreSQL) does not use the underlying exception as cause, but have another APIs (getNextException) to access that, so it it's included in the error logging, making us hard to find the root cause, especially in batch mode.
This PR will pull out the next exception and add it as cause (if it's different) or suppressed (if there is another different cause).
## How was this patch tested?
Can't reproduce this on the default JDBC driver, so did not add a regression test.
Author: Davies Liu <davies@databricks.com>
Closes#14722 from davies/keep_cause.
## What changes were proposed in this pull request?
Use `CatalystConf.resolver` consistently for case-sensitivity comparison (removed dups).
## How was this patch tested?
Local build. Waiting for Jenkins to ensure clean build and test.
Author: Jacek Laskowski <jacek@japila.pl>
Closes#14771 from jaceklaskowski/17199-catalystconf-resolver.
## What changes were proposed in this pull request?
This is a sub-task of [SPARK-16283](https://issues.apache.org/jira/browse/SPARK-16283) (Implement percentile_approx SQL function), which moves class QuantileSummaries to project catalyst so that it can be reused when implementing aggregation function `percentile_approx`.
## How was this patch tested?
This PR only does class relocation, class implementation is not changed.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14754 from clockfly/move_QuantileSummaries_to_catalyst.
## What changes were proposed in this pull request?
`CreateHiveTableAsSelectLogicalPlan` is a dead code after refactoring.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14707 from gatorsmile/removeCreateHiveTable.
## What changes were proposed in this pull request?
The range operator previously didn't support SQL generation, which made it not possible to use in views.
## How was this patch tested?
Unit tests.
cc hvanhovell
Author: Eric Liang <ekl@databricks.com>
Closes#14724 from ericl/spark-17162.
## What changes were proposed in this pull request?
Fix some typos in comments and test hints
## How was this patch tested?
N/A.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14755 from clockfly/fix_minor_typo.
## What changes were proposed in this pull request?
In 2.0, we change the threshold of splitting expressions from 16K to 64K, which cause very bad performance on wide table, because the generated method can't be JIT compiled by default (above the limit of 8K bytecode).
This PR will decrease it to 1K, based on the benchmark results for a wide table with 400 columns of LongType.
It also fix a bug around splitting expression in whole-stage codegen (it should not split them).
## How was this patch tested?
Added benchmark suite.
Author: Davies Liu <davies@databricks.com>
Closes#14692 from davies/split_exprs.
## What changes were proposed in this pull request?
Spark SQL doesn't have its own meta store yet, and use hive's currently. However, hive's meta store has some limitations(e.g. columns can't be too many, not case-preserving, bad decimal type support, etc.), so we have some hacks to successfully store data source table metadata into hive meta store, i.e. put all the information in table properties.
This PR moves these hacks to `HiveExternalCatalog`, tries to isolate hive specific logic in one place.
changes overview:
1. **before this PR**: we need to put metadata(schema, partition columns, etc.) of data source tables to table properties before saving it to external catalog, even the external catalog doesn't use hive metastore(e.g. `InMemoryCatalog`)
**after this PR**: the table properties tricks are only in `HiveExternalCatalog`, the caller side doesn't need to take care of it anymore.
2. **before this PR**: because the table properties tricks are done outside of external catalog, so we also need to revert these tricks when we read the table metadata from external catalog and use it. e.g. in `DescribeTableCommand` we will read schema and partition columns from table properties.
**after this PR**: The table metadata read from external catalog is exactly the same with what we saved to it.
bonus: now we can create data source table using `SessionCatalog`, if schema is specified.
breaks: `schemaStringLengthThreshold` is not configurable anymore. `hive.default.rcfile.serde` is not configurable anymore.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14155 from cloud-fan/catalog-table.
## What changes were proposed in this pull request?
Currently, `NullPropagation` optimizer replaces `COUNT` on null literals in a bottom-up fashion. During that, `WindowExpression` is not covered properly. This PR adds the missing propagation logic.
**Before**
```scala
scala> sql("SELECT COUNT(1 + NULL) OVER ()").show
java.lang.UnsupportedOperationException: Cannot evaluate expression: cast(0 as bigint) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
```
**After**
```scala
scala> sql("SELECT COUNT(1 + NULL) OVER ()").show
+----------------------------------------------------------------------------------------------+
|count((1 + CAST(NULL AS INT))) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)|
+----------------------------------------------------------------------------------------------+
| 0|
+----------------------------------------------------------------------------------------------+
```
## How was this patch tested?
Pass the Jenkins test with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14689 from dongjoon-hyun/SPARK-17098.
## What changes were proposed in this pull request?
This patch fixes a longstanding issue with one of the RelationalGroupedDataset.agg function. Even though the signature accepts vararg of pairs, the underlying implementation turns the seq into a map, and thus not order preserving nor allowing multiple aggregates per column.
This change also allows users to use this function to run multiple different aggregations for a single column, e.g.
```
agg("age" -> "max", "age" -> "count")
```
## How was this patch tested?
Added a test case in DataFrameAggregateSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14697 from petermaxlee/SPARK-17124.
## What changes were proposed in this pull request?
Currently `LogicalRelation.newInstance()` simply creates another `LogicalRelation` object with the same parameters. However, the `newInstance()` method inherited from `MultiInstanceRelation` should return a copy of object with unique expression ids. Current `LogicalRelation.newInstance()` can cause failure when doing self-join.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#14682 from viirya/fix-localrelation.
## What changes were proposed in this pull request?
This patch adds support for SQL generation for inline tables. With this, it would be possible to create a view that depends on inline tables.
## How was this patch tested?
Added a test case in LogicalPlanToSQLSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14709 from petermaxlee/SPARK-17150.
## What changes were proposed in this pull request?
Modifies error message for numeric literals to
Numeric literal <literal> does not fit in range [min, max] for type <T>
## How was this patch tested?
Fixed up the error messages for literals.sql in SqlQueryTestSuite and re-ran via sbt. Also fixed up error messages in ExpressionParserSuite
Author: Srinath Shankar <srinath@databricks.com>
Closes#14721 from srinathshankar/sc4296.
## What changes were proposed in this pull request?
This patch creates array.sql in SQLQueryTestSuite for testing array related functions, including:
- indexing
- array creation
- size
- array_contains
- sort_array
## How was this patch tested?
The patch itself is about adding tests.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14708 from petermaxlee/SPARK-17149.
## What changes were proposed in this pull request?
This patch changes predicate pushdown optimization rule (PushDownPredicate) from using a blacklist to a whitelist. That is to say, operators must be explicitly allowed. This approach is more future-proof: previously it was possible for us to introduce a new operator and then render the optimization rule incorrect.
This also fixes the bug that previously we allowed pushing filter beneath limit, which was incorrect. That is to say, before this patch, the optimizer would rewrite
```
select * from (select * from range(10) limit 5) where id > 3
to
select * from range(10) where id > 3 limit 5
```
## How was this patch tested?
- a unit test case in FilterPushdownSuite
- an end-to-end test in limit.sql
Author: Reynold Xin <rxin@databricks.com>
Closes#14713 from rxin/SPARK-16994.
## What changes were proposed in this pull request?
This patch improves inline table support with the following:
1. Support type coercion.
2. Support using foldable expressions. Previously only literals were supported.
3. Improve error message handling.
4. Improve test coverage.
## How was this patch tested?
Added a new unit test suite ResolveInlineTablesSuite and a new file-based end-to-end test inline-table.sql.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14676 from petermaxlee/SPARK-16947.
## What changes were proposed in this pull request?
This patch fixes the problem described in SPARK-17117, i.e. "SELECT 1 / NULL" throws an analysis exception:
```
org.apache.spark.sql.AnalysisException: cannot resolve '(1 / NULL)' due to data type mismatch: differing types in '(1 / NULL)' (int and null).
```
The problem is that division type coercion did not take null type into account.
## How was this patch tested?
A unit test for the type coercion, and a few end-to-end test cases using SQLQueryTestSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14695 from petermaxlee/SPARK-17117.
## What changes were proposed in this pull request?
This adds analyzer rules for resolving table-valued functions, and adds one builtin implementation for range(). The arguments for range() are the same as those of `spark.range()`.
## How was this patch tested?
Unit tests.
cc hvanhovell
Author: Eric Liang <ekl@databricks.com>
Closes#14656 from ericl/sc-4309.
## What changes were proposed in this pull request?
This patch introduces a new private ReduceAggregator interface that is a subclass of Aggregator. ReduceAggregator only requires a single associative and commutative reduce function. ReduceAggregator is also used to implement KeyValueGroupedDataset.reduceGroups in order to support partial aggregation.
Note that the pull request was initially done by viirya.
## How was this patch tested?
Covered by original tests for reduceGroups, as well as a new test suite for ReduceAggregator.
Author: Reynold Xin <rxin@databricks.com>
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#14576 from rxin/reduceAggregator.
## What changes were proposed in this pull request?
A TreeNodeException is thrown when executing the following minimal example in Spark 2.0.
import spark.implicits._
case class test (x: Int, q: Int)
val d = Seq(1).toDF("x")
d.withColumn("q", lit(0)).as[test].groupByKey(_.x).flatMapGroups{case (x, iter) => List[Int]()}.show
d.withColumn("q", expr("0")).as[test].groupByKey(_.x).flatMapGroups{case (x, iter) => List[Int]()}.show
The problem is at `FoldablePropagation`. The rule will do `transformExpressions` on `LogicalPlan`. The query above contains a `MapGroups` which has a parameter `dataAttributes:Seq[Attribute]`. One attributes in `dataAttributes` will be transformed to an `Alias(literal(0), _)` in `FoldablePropagation`. `Alias` is not an `Attribute` and causes the error.
We can't easily detect such type inconsistency during transforming expressions. A direct approach to this problem is to skip doing `FoldablePropagation` on object operators as they should not contain such expressions.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#14648 from viirya/flat-mapping.
## What changes were proposed in this pull request?
Currently, the stackTrace (as `Array[StackTraceElements]`) reported through StreamingQueryListener.onQueryTerminated is useless as it has the stack trace of where StreamingQueryException is defined, not the stack trace of underlying exception. For example, if a streaming query fails because of a / by zero exception in a task, the `QueryTerminated.stackTrace` will have
```
org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:211)
org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:124)
```
This is basically useless, as it is location where the StreamingQueryException was defined. What we want is
Here is the right way to reason about what should be posted as through StreamingQueryListener.onQueryTerminated
- The actual exception could either be a SparkException, or an arbitrary exception.
- SparkException reports the relevant executor stack trace of a failed task as a string in the the exception message. The `Array[StackTraceElements]` returned by `SparkException.stackTrace()` is mostly irrelevant.
- For any arbitrary exception, the `Array[StackTraceElements]` returned by `exception.stackTrace()` may be relevant.
- When there is an error in a streaming query, it's hard to reason whether the `Array[StackTraceElements]` is useful or not. In fact, it is not clear whether it is even useful to report the stack trace as this array of Java objects. It may be sufficient to report the strack trace as a string, along with the message. This is how Spark reported executor stra
- Hence, this PR simplifies the API by removing the array `stackTrace` from `QueryTerminated`. Instead the `exception` returns a string containing the message and the stack trace of the actual underlying exception that failed the streaming query (i.e. not that of the StreamingQueryException). If anyone is interested in the actual stack trace as an array, can always access them through `streamingQuery.exception` which returns the exception object.
With this change, if a streaming query fails because of a / by zero exception in a task, the `QueryTerminated.exception` will be
```
org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 0.0 failed 1 times, most recent failure: Lost task 1.0 in stage 0.0 (TID 1, localhost): java.lang.ArithmeticException: / by zero
at org.apache.spark.sql.streaming.StreamingQueryListenerSuite$$anonfun$5$$anonfun$apply$mcV$sp$4$$anonfun$apply$mcV$sp$5.apply$mcII$sp(StreamingQueryListenerSuite.scala:153)
at org.apache.spark.sql.streaming.StreamingQueryListenerSuite$$anonfun$5$$anonfun$apply$mcV$sp$4$$anonfun$apply$mcV$sp$5.apply(StreamingQueryListenerSuite.scala:153)
at org.apache.spark.sql.streaming.StreamingQueryListenerSuite$$anonfun$5$$anonfun$apply$mcV$sp$4$$anonfun$apply$mcV$sp$5.apply(StreamingQueryListenerSuite.scala:153)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$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:370)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:232)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:226)
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$24.apply(RDD.scala:803)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:744)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1429)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1417)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1416)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1416)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
...
```
It contains the relevant executor stack trace. In a case non-SparkException, if the streaming source MemoryStream throws an exception, exception message will have the relevant stack trace.
```
java.lang.RuntimeException: this is the exception message
at org.apache.spark.sql.execution.streaming.MemoryStream.getBatch(memory.scala:103)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$5.apply(StreamExecution.scala:316)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$5.apply(StreamExecution.scala:313)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at org.apache.spark.sql.execution.streaming.StreamProgress.flatMap(StreamProgress.scala:25)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch(StreamExecution.scala:313)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1.apply$mcZ$sp(StreamExecution.scala:197)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:43)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:187)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:124)
```
Note that this change in the public `QueryTerminated` class is okay as the APIs are still experimental.
## How was this patch tested?
Unit tests that test whether the right information is present in the exception message reported through QueryTerminated object.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#14675 from tdas/SPARK-17096.
A review of the code, working back from Hadoop's `FileSystem.exists()` and `FileSystem.isDirectory()` code, then removing uses of the calls when superfluous.
1. delete is harmless if called on a nonexistent path, so don't do any checks before deletes
1. any `FileSystem.exists()` check before `getFileStatus()` or `open()` is superfluous as the operation itself does the check. Instead the `FileNotFoundException` is caught and triggers the downgraded path. When a `FileNotFoundException` was thrown before, the code still creates a new FNFE with the error messages. Though now the inner exceptions are nested, for easier diagnostics.
Initially, relying on Jenkins test runs.
One troublespot here is that some of the codepaths are clearly error situations; it's not clear that they have coverage anyway. Trying to create the failure conditions in tests would be ideal, but it will also be hard.
Author: Steve Loughran <stevel@apache.org>
Closes#14371 from steveloughran/cloud/SPARK-16736-superfluous-fs-calls.
## What changes were proposed in this pull request?
We use reflection to convert `TreeNode` to json string, and currently don't support arbitrary object. `UserDefinedGenerator` takes a function object, so we should skip json format test for it, or the tests can be flacky, e.g. `DataFrameSuite.simple explode`, this test always fail with scala 2.10(branch 1.6 builds with scala 2.10 by default), but pass with scala 2.11(master branch builds with scala 2.11 by default).
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14679 from cloud-fan/json.
## What changes were proposed in this pull request?
The current subquery expression interface contains a little bit of technical debt in the form of a few different access paths to get and set the query contained by the expression. This is confusing to anyone who goes over this code.
This PR unifies these access paths.
## How was this patch tested?
(Existing tests)
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14685 from hvanhovell/SPARK-17106.
## What changes were proposed in this pull request?
This PR splits the generated code for ```SafeProjection.apply``` by using ```ctx.splitExpressions()```. This is because the large code body for ```NewInstance``` may grow beyond 64KB bytecode size for ```apply()``` method.
Here is [the original PR](https://github.com/apache/spark/pull/13243) for SPARK-15285. However, it breaks a build with Scala 2.10 since Scala 2.10 does not a case class with large number of members. Thus, it was reverted by [this commit](fa244e5a90).
## How was this patch tested?
Added new tests by using `DefinedByConstructorParams` instead of case class for scala-2.10
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#14670 from kiszk/SPARK-15285-2.
## What changes were proposed in this pull request?
This PR adds a field to subquery alias in order to make the usage of views in a resolved `LogicalPlan` more visible (and more understandable).
For example, the following view and query:
```sql
create view constants as select 1 as id union all select 1 union all select 42
select * from constants;
```
...now yields the following analyzed plan:
```
Project [id#39]
+- SubqueryAlias c, `default`.`constants`
+- Project [gen_attr_0#36 AS id#39]
+- SubqueryAlias gen_subquery_0
+- Union
:- Union
: :- Project [1 AS gen_attr_0#36]
: : +- OneRowRelation$
: +- Project [1 AS gen_attr_1#37]
: +- OneRowRelation$
+- Project [42 AS gen_attr_2#38]
+- OneRowRelation$
```
## How was this patch tested?
Added tests for the two code paths in `SessionCatalogSuite` (sql/core) and `HiveMetastoreCatalogSuite` (sql/hive)
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14657 from hvanhovell/SPARK-17068.
## What changes were proposed in this pull request?
This PR renames `ParserUtils.assert` to `ParserUtils.validate`. This is done because this method is used to check requirements, and not to check if the program is in an invalid state.
## How was this patch tested?
Simple rename. Compilation should do.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14665 from hvanhovell/SPARK-17084.
## What changes were proposed in this pull request?
This PR adds expression `UnresolvedOrdinal` to represent the ordinal in GROUP BY or ORDER BY, and fixes the rules when resolving ordinals.
Ordinals in GROUP BY or ORDER BY like `1` in `order by 1` or `group by 1` should be considered as unresolved before analysis. But in current code, it uses `Literal` expression to store the ordinal. This is inappropriate as `Literal` itself is a resolved expression, it gives the user a wrong message that the ordinals has already been resolved.
### Before this change
Ordinal is stored as `Literal` expression
```
scala> sc.setLogLevel("TRACE")
scala> sql("select a from t group by 1 order by 1")
...
'Sort [1 ASC], true
+- 'Aggregate [1], ['a]
+- 'UnresolvedRelation `t
```
For query:
```
scala> Seq(1).toDF("a").createOrReplaceTempView("t")
scala> sql("select count(a), a from t group by 2 having a > 0").show
```
During analysis, the intermediate plan before applying rule `ResolveAggregateFunctions` is:
```
'Filter ('a > 0)
+- Aggregate [2], [count(1) AS count(1)#83L, a#81]
+- LocalRelation [value#7 AS a#9]
```
Before this PR, rule `ResolveAggregateFunctions` believes all expressions of `Aggregate` have already been resolved, and tries to resolve the expressions in `Filter` directly. But this is wrong, as ordinal `2` in Aggregate is not really resolved!
### After this change
Ordinals are stored as `UnresolvedOrdinal`.
```
scala> sc.setLogLevel("TRACE")
scala> sql("select a from t group by 1 order by 1")
...
'Sort [unresolvedordinal(1) ASC], true
+- 'Aggregate [unresolvedordinal(1)], ['a]
+- 'UnresolvedRelation `t`
```
## How was this patch tested?
Unit tests.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14616 from clockfly/spark-16955.
## What changes were proposed in this pull request?
`CatalogStorageFormat.properties` can be used in 2 ways:
1. for hive tables, it stores the serde properties.
2. for data source tables, it stores the data source options, e.g. `path`, `skipHiveMetadata`, etc.
however, both of them have nothing to do with data source properties, e.g. `spark.sql.sources.provider`, so they should not have limitations about data source properties.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14506 from cloud-fan/table-prop.
## What changes were proposed in this pull request?
Add an instruction to ask the user to remove or upgrade the incompatible DataSourceRegister in the error message.
## How was this patch tested?
Test command:
```
build/sbt -Dscala-2.10 package
SPARK_SCALA_VERSION=2.10 bin/spark-shell --packages ai.h2o:sparkling-water-core_2.10:1.6.5
scala> Seq(1).toDS().write.format("parquet").save("foo")
```
Before:
```
java.util.ServiceConfigurationError: org.apache.spark.sql.sources.DataSourceRegister: Provider org.apache.spark.h2o.DefaultSource could not be instantiated
at java.util.ServiceLoader.fail(ServiceLoader.java:232)
at java.util.ServiceLoader.access$100(ServiceLoader.java:185)
at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:384)
at java.util.ServiceLoader$LazyIterator.next(ServiceLoader.java:404)
at java.util.ServiceLoader$1.next(ServiceLoader.java:480)
...
Caused by: java.lang.NoClassDefFoundError: org/apache/spark/Logging
at java.lang.ClassLoader.defineClass1(Native Method)
at java.lang.ClassLoader.defineClass(ClassLoader.java:760)
at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
at java.net.URLClassLoader.defineClass(URLClassLoader.java:467)
at java.net.URLClassLoader.access$100(URLClassLoader.java:73)
at java.net.URLClassLoader$1.run(URLClassLoader.java:368)
at java.net.URLClassLoader$1.run(URLClassLoader.java:362)
at java.security.AccessController.doPrivileged(Native Method)
...
```
After:
```
java.lang.ClassNotFoundException: Detected an incompatible DataSourceRegister. Please remove the incompatible library from classpath or upgrade it. Error: org.apache.spark.sql.sources.DataSourceRegister: Provider org.apache.spark.h2o.DefaultSource could not be instantiated
at org.apache.spark.sql.execution.datasources.DataSource.lookupDataSource(DataSource.scala:178)
at org.apache.spark.sql.execution.datasources.DataSource.providingClass$lzycompute(DataSource.scala:79)
at org.apache.spark.sql.execution.datasources.DataSource.providingClass(DataSource.scala:79)
at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:441)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:213)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:196)
...
```
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#14651 from zsxwing/SPARK-17065.
Both core and sql have slightly different code that does variable substitution
of config values. This change refactors that code and encapsulates the logic
of reading config values and expading variables in a new helper class, which
can be configured so that both core and sql can use it without losing existing
functionality, and allows for easier testing and makes it easier to add more
features in the future.
Tested with existing and new unit tests, and by running spark-shell with
some configs referencing variables and making sure it behaved as expected.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#14468 from vanzin/SPARK-16671.
## What changes were proposed in this pull request?
Don't override app name specified in `SparkConf` with a random app name. Only set it if the conf has no app name even after options have been applied.
See also https://github.com/apache/spark/pull/14602
This is similar to Sherry302 's original proposal in https://github.com/apache/spark/pull/14556
## How was this patch tested?
Jenkins test, with new case reproducing the bug
Author: Sean Owen <sowen@cloudera.com>
Closes#14630 from srowen/SPARK-16966.2.
## What changes were proposed in this pull request?
In the PR, we just allow the user to add additional options when create a new table in JDBC writer.
The options can be table_options or partition_options.
E.g., "CREATE TABLE t (name string) ENGINE=InnoDB DEFAULT CHARSET=utf8"
Here is the usage example:
```
df.write.option("createTableOptions", "ENGINE=InnoDB DEFAULT CHARSET=utf8").jdbc(...)
```
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
will apply test result soon.
Author: GraceH <93113783@qq.com>
Closes#14559 from GraceH/jdbc_options.
## What changes were proposed in this pull request?
This PR changes the CTE resolving rule to use only **forward-declared** tables in order to prevent infinite loops. More specifically, new logic is like the following.
* Resolve CTEs in `WITH` clauses first before replacing the main SQL body.
* When resolving CTEs, only forward-declared CTEs or base tables are referenced.
- Self-referencing is not allowed any more.
- Cross-referencing is not allowed any more.
**Reported Error Scenarios**
```scala
scala> sql("WITH t AS (SELECT 1 FROM t) SELECT * FROM t")
java.lang.StackOverflowError
...
scala> sql("WITH t1 AS (SELECT * FROM t2), t2 AS (SELECT 2 FROM t1) SELECT * FROM t1, t2")
java.lang.StackOverflowError
...
```
Note that `t`, `t1`, and `t2` are not declared in database. Spark falls into infinite loops before resolving table names.
## How was this patch tested?
Pass the Jenkins tests with new two testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14397 from dongjoon-hyun/SPARK-16771-TREENODE.
## What changes were proposed in this pull request?
This patch updates the SQL parser to parse negative numeric literals as numeric literals, instead of unary minus of positive literals.
This allows the parser to parse the minimal value for each data type, e.g. "-32768S".
## How was this patch tested?
Updated test cases.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14608 from petermaxlee/SPARK-17013.
## What changes were proposed in this pull request?
Currently, Spark ignores path names starting with underscore `_` and `.`. This causes read-failures for the column-partitioned file data sources whose partition column names starts from '_', e.g. `_col`.
**Before**
```scala
scala> spark.range(10).withColumn("_locality_code", $"id").write.partitionBy("_locality_code").save("/tmp/parquet")
scala> spark.read.parquet("/tmp/parquet")
org.apache.spark.sql.AnalysisException: Unable to infer schema for ParquetFormat at /tmp/parquet20. It must be specified manually;
```
**After**
```scala
scala> spark.range(10).withColumn("_locality_code", $"id").write.partitionBy("_locality_code").save("/tmp/parquet")
scala> spark.read.parquet("/tmp/parquet")
res2: org.apache.spark.sql.DataFrame = [id: bigint, _locality_code: int]
```
## How was this patch tested?
Pass the Jenkins with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14585 from dongjoon-hyun/SPARK-16975-PARQUET.
## What changes were proposed in this pull request?
This patch adds literals.sql for testing literal parsing end-to-end in SQL.
## How was this patch tested?
The patch itself is only about adding test cases.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14598 from petermaxlee/SPARK-17018-2.
## What changes were proposed in this pull request?
1. `sampled` doesn't need to be `ArrayBuffer`, we never update it, but assign new value
2. `count` doesn't need to be `var`, we never mutate it.
3. `headSampled` doesn't need to be in constructor, we never pass a non-empty `headSampled` to constructor
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14603 from cloud-fan/simply.
## What changes were proposed in this pull request?
There could be multiple subqueries that generate same results, we could re-use the result instead of running it multiple times.
This PR also cleanup up how we run subqueries.
For SQL query
```sql
select id,(select avg(id) from t) from t where id > (select avg(id) from t)
```
The explain is
```
== Physical Plan ==
*Project [id#15L, Subquery subquery29 AS scalarsubquery()#35]
: +- Subquery subquery29
: +- *HashAggregate(keys=[], functions=[avg(id#15L)])
: +- Exchange SinglePartition
: +- *HashAggregate(keys=[], functions=[partial_avg(id#15L)])
: +- *Range (0, 1000, splits=4)
+- *Filter (cast(id#15L as double) > Subquery subquery29)
: +- Subquery subquery29
: +- *HashAggregate(keys=[], functions=[avg(id#15L)])
: +- Exchange SinglePartition
: +- *HashAggregate(keys=[], functions=[partial_avg(id#15L)])
: +- *Range (0, 1000, splits=4)
+- *Range (0, 1000, splits=4)
```
The visualized plan:
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#14548 from davies/subq.
## What changes were proposed in this pull request?
This patch adds three test files:
1. arithmetic.sql.out
2. order-by-ordinal.sql
3. group-by-ordinal.sql
This includes https://github.com/apache/spark/pull/14594.
## How was this patch tested?
This is a test case change.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14595 from petermaxlee/SPARK-17015.
## What changes were proposed in this pull request?
This patch adds exception testing to SQLQueryTestSuite. When there is an exception in query execution, the query result contains the the exception class along with the exception message.
As part of this, I moved some additional test cases for limit from SQLQuerySuite over to SQLQueryTestSuite.
## How was this patch tested?
This is a test harness change.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14592 from petermaxlee/SPARK-17011.
## What changes were proposed in this pull request?
This patch moves all the test data files in sql/core/src/test/resources to sql/core/src/test/resources/test-data, so we don't clutter the top level sql/core/src/test/resources. Also deleted sql/core/src/test/resources/old-repeated.parquet since it is no longer used.
The change will make it easier to spot sql-tests directory.
## How was this patch tested?
This is a test-only change.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14589 from petermaxlee/SPARK-17007.
## What changes were proposed in this pull request?
This patch enhances SQLQueryTestSuite in two ways:
1. SPARK-17009: Use a new SparkSession for each test case to provide stronger isolation (e.g. config changes in one test case does not impact another). That said, we do not currently isolate catalog changes.
2. SPARK-17008: Normalize query output using sorting, inspired by HiveComparisonTest.
I also ported a few new test cases over from SQLQuerySuite.
## How was this patch tested?
This is a test harness update.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14590 from petermaxlee/SPARK-17008.
## What changes were proposed in this pull request?
In both `OnHeapColumnVector` and `OffHeapColumnVector`, we implemented `getInt()` with the following code pattern:
```
public int getInt(int rowId) {
if (dictionary == null)
{ return intData[rowId]; }
else
{ return dictionary.decodeToInt(dictionaryIds.getInt(rowId)); }
}
```
As `dictionaryIds` is also a `ColumnVector`, this results in a recursive call of `getInt()` and breaks JIT inlining. As a result, `getInt()` will not get inlined.
We fix this by adding a separate method `getDictId()` specific for `dictionaryIds` to use.
## How was this patch tested?
We tested the difference with the following aggregate query on a TPCDS dataset (with scale factor = 5):
```
select
max(ss_sold_date_sk) as max_ss_sold_date_sk,
from store_sales
```
The query runtime is improved, from 202ms (before) to 159ms (after).
Author: Qifan Pu <qifan.pu@gmail.com>
Closes#14513 from ooq/SPARK-16928.
## What changes were proposed in this pull request?
The base class `SpecificParquetRecordReaderBase` used for vectorized parquet reader will try to get pushed-down filters from the given configuration. This pushed-down filters are used for RowGroups-level filtering. However, we don't set up the filters to push down into the configuration. In other words, the filters are not actually pushed down to do RowGroups-level filtering. This patch is to fix this and tries to set up the filters for pushing down to configuration for the reader.
The benchmark that excludes the time of writing Parquet file:
test("Benchmark for Parquet") {
val N = 500 << 12
withParquetTable((0 until N).map(i => (101, i)), "t") {
val benchmark = new Benchmark("Parquet reader", N)
benchmark.addCase("reading Parquet file", 10) { iter =>
sql("SELECT _1 FROM t where t._1 < 100").collect()
}
benchmark.run()
}
}
`withParquetTable` in default will run tests for vectorized reader non-vectorized readers. I only let it run vectorized reader.
When we set the block size of parquet as 1024 to have multiple row groups. The benchmark is:
Before this patch:
The retrieved row groups: 8063
Java HotSpot(TM) 64-Bit Server VM 1.8.0_71-b15 on Linux 3.19.0-25-generic
Intel(R) Core(TM) i7-5557U CPU 3.10GHz
Parquet reader: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
reading Parquet file 825 / 1233 2.5 402.6 1.0X
After this patch:
The retrieved row groups: 0
Java HotSpot(TM) 64-Bit Server VM 1.8.0_71-b15 on Linux 3.19.0-25-generic
Intel(R) Core(TM) i7-5557U CPU 3.10GHz
Parquet reader: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
reading Parquet file 306 / 503 6.7 149.6 1.0X
Next, I run the benchmark for non-pushdown case using the same benchmark code but with disabled pushdown configuration. This time the parquet block size is default value.
Before this patch:
Java HotSpot(TM) 64-Bit Server VM 1.8.0_71-b15 on Linux 3.19.0-25-generic
Intel(R) Core(TM) i7-5557U CPU 3.10GHz
Parquet reader: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
reading Parquet file 136 / 238 15.0 66.5 1.0X
After this patch:
Java HotSpot(TM) 64-Bit Server VM 1.8.0_71-b15 on Linux 3.19.0-25-generic
Intel(R) Core(TM) i7-5557U CPU 3.10GHz
Parquet reader: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
reading Parquet file 124 / 193 16.5 60.7 1.0X
For non-pushdown case, from the results, I think this patch doesn't affect normal code path.
I've manually output the `totalRowCount` in `SpecificParquetRecordReaderBase` to see if this patch actually filter the row-groups. When running the above benchmark:
After this patch:
`totalRowCount = 0`
Before this patch:
`totalRowCount = 1024000`
## How was this patch tested?
Existing tests should be passed.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#13701 from viirya/vectorized-reader-push-down-filter2.
## What changes were proposed in this pull request?
Fix the construction of the file path. Previous way of construction caused the creation of incorrect path on Windows.
## How was this patch tested?
Run SQL unit tests on Windows
Author: avulanov <nashb@yandex.ru>
Closes#13868 from avulanov/SPARK-15899-file.
## What changes were proposed in this pull request?
This patch introduces SQLQueryTestSuite, a basic framework for end-to-end SQL test cases defined in spark/sql/core/src/test/resources/sql-tests. This is a more standard way to test SQL queries end-to-end in different open source database systems, because it is more manageable to work with files.
This is inspired by HiveCompatibilitySuite, but simplified for general Spark SQL tests. Once this is merged, I can work towards porting SQLQuerySuite over, and eventually also move the existing HiveCompatibilitySuite to use this framework.
Unlike HiveCompatibilitySuite, SQLQueryTestSuite compares both the output schema and the output data (in string form).
When there is a mismatch, the error message looks like the following:
```
[info] - blacklist.sql !!! IGNORED !!!
[info] - number-format.sql *** FAILED *** (2 seconds, 405 milliseconds)
[info] Expected "...147483648 -214748364[8]", but got "...147483648 -214748364[9]" Result should match for query #1 (SQLQueryTestSuite.scala:171)
[info] org.scalatest.exceptions.TestFailedException:
[info] at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:495)
[info] at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555)
[info] at org.scalatest.Assertions$class.assertResult(Assertions.scala:1171)
```
## How was this patch tested?
This is a test infrastructure change.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14472 from petermaxlee/SPARK-16866.
## What changes were proposed in this pull request?
Doc that regexp_extract returns empty string when regex or group does not match
## How was this patch tested?
Jenkins test, with a few new test cases
Author: Sean Owen <sowen@cloudera.com>
Closes#14525 from srowen/SPARK-16324.
#### What changes were proposed in this pull request?
When we do not turn on the Hive Support, the following query generates a confusing error message by Planner:
```Scala
sql("CREATE TABLE t2 SELECT a, b from t1")
```
```
assertion failed: No plan for CreateTable CatalogTable(
Table: `t2`
Created: Tue Aug 09 23:45:32 PDT 2016
Last Access: Wed Dec 31 15:59:59 PST 1969
Type: MANAGED
Provider: hive
Storage(InputFormat: org.apache.hadoop.mapred.TextInputFormat, OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat)), ErrorIfExists
+- Relation[a#19L,b#20L] parquet
java.lang.AssertionError: assertion failed: No plan for CreateTable CatalogTable(
Table: `t2`
Created: Tue Aug 09 23:45:32 PDT 2016
Last Access: Wed Dec 31 15:59:59 PST 1969
Type: MANAGED
Provider: hive
Storage(InputFormat: org.apache.hadoop.mapred.TextInputFormat, OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat)), ErrorIfExists
+- Relation[a#19L,b#20L] parquet
```
This PR is to issue a better error message:
```
Hive support is required to use CREATE Hive TABLE AS SELECT
```
#### How was this patch tested?
Added test cases in `DDLSuite.scala`
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13886 from gatorsmile/createCatalogedTableAsSelect.
## What changes were proposed in this pull request?
This PR adds `MINUS` set operator which is equivalent `EXCEPT DISTINCT`. This will slightly improve the compatibility with Oracle.
## How was this patch tested?
Pass the Jenkins with newly added testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14570 from dongjoon-hyun/SPARK-10601.
## What changes were proposed in this pull request?
MSCK REPAIR TABLE could be used to recover the partitions in external catalog based on partitions in file system.
Another syntax is: ALTER TABLE table RECOVER PARTITIONS
The implementation in this PR will only list partitions (not the files with a partition) in driver (in parallel if needed).
## How was this patch tested?
Added unit tests for it and Hive compatibility test suite.
Author: Davies Liu <davies@databricks.com>
Closes#14500 from davies/repair_table.
## What changes were proposed in this pull request?
This package is meant to be internal, and as a result it does not make sense to mark things as private[sql] or private[spark]. It simply makes debugging harder when Spark developers need to inspect the plans at runtime.
This patch removes all private[sql] and private[spark] visibility modifiers in org.apache.spark.sql.execution.
## How was this patch tested?
N/A - just visibility changes.
Author: Reynold Xin <rxin@databricks.com>
Closes#14554 from rxin/remote-private.
## What changes were proposed in this pull request?
This PR fixes the following to make `checkAnswer` raise `TestFailedException` again instead of `java.util.NoSuchElementException: key not found: TZ` in the environments without `TZ` variable. Also, this PR adds `QueryTestSuite` class for testing `QueryTest` itself.
```scala
- |Timezone Env: ${sys.env("TZ")}
+ |Timezone Env: ${sys.env.getOrElse("TZ", "")}
```
## How was this patch tested?
Pass the Jenkins tests with a new test suite.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14528 from dongjoon-hyun/SPARK-16940.
## What changes were proposed in this pull request?
This PR adds argument type information for typed logical plan like MapElements, TypedFilter, and AppendColumn, so that we can use these info in customized optimizer rule.
## How was this patch tested?
Existing test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14494 from clockfly/add_more_info_for_typed_operator.
## What changes were proposed in this pull request?
The logic for LEAD/LAG processing is more complex that it needs to be. This PR fixes that.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14376 from hvanhovell/SPARK-16749.
## What changes were proposed in this pull request?
Avoid using postfix operation for command execution in SQLQuerySuite where it wasn't whitelisted and audit existing whitelistings removing postfix operators from most places. Some notable places where postfix operation remains is in the XML parsing & time units (seconds, millis, etc.) where it arguably can improve readability.
## How was this patch tested?
Existing tests.
Author: Holden Karau <holden@us.ibm.com>
Closes#14407 from holdenk/SPARK-16779.
### What changes were proposed in this pull request?
Currently, the `refreshTable` API is always case sensitive.
When users use the view name without the exact case match, the API silently ignores the call. Users might expect the command has been successfully completed. However, when users run the subsequent SQL commands, they might still get the exception, like
```
Job aborted due to stage failure:
Task 1 in stage 4.0 failed 1 times, most recent failure: Lost task 1.0 in stage 4.0 (TID 7, localhost):
java.io.FileNotFoundException:
File file:/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-bd4b9ea6-9aec-49c5-8f05-01cff426211e/part-r-00000-0c84b915-c032-4f2e-abf5-1d48fdbddf38.snappy.parquet does not exist
```
This PR is to fix the issue.
### How was this patch tested?
Added a test case.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14523 from gatorsmile/refreshTempTable.
#### What changes were proposed in this pull request?
When doing a CTAS with a Partition By clause, we got a wrong error message.
For example,
```SQL
CREATE TABLE gen__tmp
PARTITIONED BY (key string)
AS SELECT key, value FROM mytable1
```
The error message we get now is like
```
Operation not allowed: Schema may not be specified in a Create Table As Select (CTAS) statement(line 2, pos 0)
```
However, based on the code, the message we should get is like
```
Operation not allowed: A Create Table As Select (CTAS) statement is not allowed to create a partitioned table using Hive's file formats. Please use the syntax of "CREATE TABLE tableName USING dataSource OPTIONS (...) PARTITIONED BY ...\" to create a partitioned table through a CTAS statement.(line 2, pos 0)
```
Currently, partitioning columns is part of the schema. This PR fixes the bug by changing the detection orders.
#### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14113 from gatorsmile/ctas.
## What changes were proposed in this pull request?
This PR adds auxiliary info like input class and input schema in TypedAggregateExpression
## How was this patch tested?
Manual test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14501 from clockfly/typed_aggregation.
## What changes were proposed in this pull request?
This patch fixes the incorrect results in the rule ResolveSubquery in Catalyst's Analysis phase by returning an error message when the LIMIT is found in the path from the parent table to the correlated predicate in the subquery.
## How was this patch tested?
./dev/run-tests
a new unit test on the problematic pattern.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#14411 from nsyca/master.
## What changes were proposed in this pull request?
This PR aims to fix a build error on branch 1.6 at 8d87252087, but I think we had better have this consistently in master branch, too. It's because there exist other ongoing PR (https://github.com/apache/spark/pull/14525) about this.
https://amplab.cs.berkeley.edu/jenkins/job/spark-branch-1.6-compile-maven-with-yarn-2.3/286/console
```scala
[error] /home/jenkins/workspace/spark-branch-1.6-compile-maven-with-yarn-2.3/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala:82: value toDF is not a member of Seq[String]
[error] val df = Seq("aaaac").toDF("s")
[error] ^
```
## How was this patch tested?
After passing Jenkins, run compilation test on branch 1.6.
```
build/mvn -DskipTests -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14526 from dongjoon-hyun/SPARK-16939.
## What changes were proposed in this pull request?
regexp_extract actually returns null when it shouldn't when a regex matches but the requested optional group did not. This makes it return an empty string, as apparently designed.
## How was this patch tested?
Additional unit test
Author: Sean Owen <sowen@cloudera.com>
Closes#14504 from srowen/SPARK-16409.
