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## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-13568
It is quite common to encounter missing values in data sets. It would be useful to implement a Transformer that can impute missing data points, similar to e.g. Imputer in scikit-learn.
Initially, options for imputation could include mean, median and most frequent, but we could add various other approaches, where possible existing DataFrame code can be used (e.g. for approximate quantiles etc).
Currently this PR supports imputation for Double and Vector (null and NaN in Vector).
## How was this patch tested?
new unit tests and manual test
Author: Yuhao Yang <hhbyyh@gmail.com>
Author: Yuhao Yang <yuhao.yang@intel.com>
Author: Yuhao <yuhao.yang@intel.com>
Closes #11601 from hhbyyh/imputer.
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### What changes were proposed in this pull request?
Specifying the table schema in DDL formats is needed for different scenarios. For example,
- [specifying the schema in SQL function `from_json` using DDL formats](https://issues.apache.org/jira/browse/SPARK-19637), which is suggested by marmbrus ,
- [specifying the customized JDBC data types](https://github.com/apache/spark/pull/16209).
These two PRs need users to use the JSON format to specify the table schema. This is not user friendly.
This PR is to provide a `parseTableSchema` API in `ParserInterface`.
### How was this patch tested?
Added a test suite `TableSchemaParserSuite`
Author: Xiao Li <gatorsmile@gmail.com>
Closes #17171 from gatorsmile/parseDDLStmt.
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fields
## What changes were proposed in this pull request?
The current master throws `StackOverflowError` in `createDataFrame`/`createDataset` if bean has one's own class in fields;
```
public class SelfClassInFieldBean implements Serializable {
private SelfClassInFieldBean child;
...
}
```
This pr added code to throw `UnsupportedOperationException` in that case as soon as possible.
## How was this patch tested?
Added tests in `JavaDataFrameSuite` and `JavaDatasetSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes #17188 from maropu/SPARK-19751.
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HiveExternalCatalog and InMemoryCatalog
## What changes were proposed in this pull request?
unify a exception erro msg for dropdatabase when the database still have some tables for HiveExternalCatalog and InMemoryCatalog
## How was this patch tested?
N/A
Author: windpiger <songjun@outlook.com>
Closes #17305 from windpiger/unifyErromsg.
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table existence.
It is quirky behaviour that saveAsTable to e.g. a JDBC source with SaveMode other
than Overwrite will nevertheless overwrite the table in the external source,
if that table was not a catalog table.
Author: Juliusz Sompolski <julek@databricks.com>
Closes #17289 from juliuszsompolski/saveAsTableDoc.
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and ordering when aliasing output attributes
## What changes were proposed in this pull request?
Now `InMemoryTableScanExec` simply takes the `outputPartitioning` and `outputOrdering` from the associated `InMemoryRelation`'s `child.outputPartitioning` and `outputOrdering`.
However, `InMemoryTableScanExec` can alias the output attributes. In this case, its `outputPartitioning` and `outputOrdering` are not correct and its parent operators can't correctly determine its data distribution.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes #17175 from viirya/ensure-no-unnecessary-shuffle.
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FAIR Schedulers
## What changes were proposed in this pull request?
The following FIFO & FAIR Schedulers Pool usage cases need to have unit test coverage :
- FIFO Scheduler just uses **root pool** so even if `spark.scheduler.pool` property is set, related pool is not created and `TaskSetManagers` are added to **root pool**.
- FAIR Scheduler uses `default pool` when `spark.scheduler.pool` property is not set. This can be happened when
- `Properties` object is **null**,
- `Properties` object is **empty**(`new Properties()`),
- **default pool** is set(`spark.scheduler.pool=default`).
- FAIR Scheduler creates a **new pool** with **default values** when `spark.scheduler.pool` property points a **non-existent** pool. This can be happened when **scheduler allocation file** is not set or it does not contain related pool.
## How was this patch tested?
New Unit tests are added.
Author: erenavsarogullari <erenavsarogullari@gmail.com>
Closes #15604 from erenavsarogullari/SPARK-18066.
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## What changes were proposed in this pull request?
`PrunedInMemoryFileIndex.prunePartitions` shows `pruned NaN% partitions` for the following case.
```scala
scala> Seq.empty[(String, String)].toDF("a", "p").write.partitionBy("p").saveAsTable("t1")
scala> sc.setLogLevel("INFO")
scala> spark.table("t1").filter($"p" === "1").select($"a").show
...
17/03/13 00:33:04 INFO PrunedInMemoryFileIndex: Selected 0 partitions out of 0, pruned NaN% partitions.
```
After this PR, the message looks like this.
```scala
17/03/15 10:39:48 INFO PrunedInMemoryFileIndex: Selected 0 partitions out of 0, pruned 0 partitions.
```
## How was this patch tested?
Pass the Jenkins with the existing tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #17273 from dongjoon-hyun/SPARK-EMPTY-PARTITION.
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## What changes were proposed in this pull request?
This PR introduces the following changes:
1. Move `SparkHadoopWriter` to `core/internal/io/`, so that it's in the same directory with `SparkHadoopMapReduceWriter`;
2. Move `SparkHadoopWriterUtils` to a separated file.
After this PR is merged, we may consolidate `SparkHadoopWriter` and `SparkHadoopMapReduceWriter`, and make the new commit protocol support the old `mapred` package's committer;
## How was this patch tested?
Tested by existing test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes #17304 from jiangxb1987/writer.
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CartesianProductExec, SortMergeJoin, WindowExec to use it
## What issue does this PR address ?
Jira: https://issues.apache.org/jira/browse/SPARK-13450
In `SortMergeJoinExec`, rows of the right relation having the same value for a join key are buffered in-memory. In case of skew, this causes OOMs (see comments in SPARK-13450 for more details). Heap dump from a failed job confirms this : https://issues.apache.org/jira/secure/attachment/12846382/heap-dump-analysis.png . While its possible to increase the heap size to workaround, Spark should be resilient to such issues as skews can happen arbitrarily.
## Change proposed in this pull request
- Introduces `ExternalAppendOnlyUnsafeRowArray`
- It holds `UnsafeRow`s in-memory upto a certain threshold.
- After the threshold is hit, it switches to `UnsafeExternalSorter` which enables spilling of the rows to disk. It does NOT sort the data.
- Allows iterating the array multiple times. However, any alteration to the array (using `add` or `clear`) will invalidate the existing iterator(s)
- `WindowExec` was already using `UnsafeExternalSorter` to support spilling. Changed it to use the new array
- Changed `SortMergeJoinExec` to use the new array implementation
- NOTE: I have not changed FULL OUTER JOIN to use this new array implementation. Changing that will need more surgery and I will rather put up a separate PR for that once this gets in.
- Changed `CartesianProductExec` to use the new array implementation
#### Note for reviewers
The diff can be divided into 3 parts. My motive behind having all the changes in a single PR was to demonstrate that the API is sane and supports 2 use cases. If reviewing as 3 separate PRs would help, I am happy to make the split.
## How was this patch tested ?
#### Unit testing
- Added unit tests `ExternalAppendOnlyUnsafeRowArray` to validate all its APIs and access patterns
- Added unit test for `SortMergeExec`
- with and without spill for inner join, left outer join, right outer join to confirm that the spill threshold config behaves as expected and output is as expected.
- This PR touches the scanning logic in `SortMergeExec` for _all_ joins (except FULL OUTER JOIN). However, I expect existing test cases to cover that there is no regression in correctness.
- Added unit test for `WindowExec` to check behavior of spilling and correctness of results.
#### Stress testing
- Confirmed that OOM is gone by running against a production job which used to OOM
- Since I cannot share details about prod workload externally, created synthetic data to mimic the issue. Ran before and after the fix to demonstrate the issue and query success with this PR
Generating the synthetic data
```
./bin/spark-shell --driver-memory=6G
import org.apache.spark.sql._
val hc = SparkSession.builder.master("local").getOrCreate()
hc.sql("DROP TABLE IF EXISTS spark_13450_large_table").collect
hc.sql("DROP TABLE IF EXISTS spark_13450_one_row_table").collect
val df1 = (0 until 1).map(i => ("10", "100", i.toString, (i * 2).toString)).toDF("i", "j", "str1", "str2")
df1.write.format("org.apache.spark.sql.hive.orc.OrcFileFormat").bucketBy(100, "i", "j").sortBy("i", "j").saveAsTable("spark_13450_one_row_table")
val df2 = (0 until 3000000).map(i => ("10", "100", i.toString, (i * 2).toString)).toDF("i", "j", "str1", "str2")
df2.write.format("org.apache.spark.sql.hive.orc.OrcFileFormat").bucketBy(100, "i", "j").sortBy("i", "j").saveAsTable("spark_13450_large_table")
```
Ran this against trunk VS local build with this PR. OOM repros with trunk and with the fix this query runs fine.
```
./bin/spark-shell --driver-java-options="-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/tmp/spark.driver.heapdump.hprof"
import org.apache.spark.sql._
val hc = SparkSession.builder.master("local").getOrCreate()
hc.sql("SET spark.sql.autoBroadcastJoinThreshold=1")
hc.sql("SET spark.sql.sortMergeJoinExec.buffer.spill.threshold=10000")
hc.sql("DROP TABLE IF EXISTS spark_13450_result").collect
hc.sql("""
CREATE TABLE spark_13450_result
AS
SELECT
a.i AS a_i, a.j AS a_j, a.str1 AS a_str1, a.str2 AS a_str2,
b.i AS b_i, b.j AS b_j, b.str1 AS b_str1, b.str2 AS b_str2
FROM
spark_13450_one_row_table a
JOIN
spark_13450_large_table b
ON
a.i=b.i AND
a.j=b.j
""")
```
## Performance comparison
### Macro-benchmark
I ran a SMB join query over two real world tables (2 trillion rows (40 TB) and 6 million rows (120 GB)). Note that this dataset does not have skew so no spill happened. I saw improvement in CPU time by 2-4% over version without this PR. This did not add up as I was expected some regression. I think allocating array of capacity of 128 at the start (instead of starting with default size 16) is the sole reason for the perf. gain : https://github.com/tejasapatil/spark/blob/SPARK-13450_smb_buffer_oom/sql/core/src/main/scala/org/apache/spark/sql/execution/ExternalAppendOnlyUnsafeRowArray.scala#L43 . I could remove that and rerun, but effectively the change will be deployed in this form and I wanted to see the effect of it over large workload.
### Micro-benchmark
Two types of benchmarking can be found in `ExternalAppendOnlyUnsafeRowArrayBenchmark`:
[A] Comparing `ExternalAppendOnlyUnsafeRowArray` against raw `ArrayBuffer` when all rows fit in-memory and there is no spill
```
Array with 1000 rows: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
ArrayBuffer 7821 / 7941 33.5 29.8 1.0X
ExternalAppendOnlyUnsafeRowArray 8798 / 8819 29.8 33.6 0.9X
Array with 30000 rows: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
ArrayBuffer 19200 / 19206 25.6 39.1 1.0X
ExternalAppendOnlyUnsafeRowArray 19558 / 19562 25.1 39.8 1.0X
Array with 100000 rows: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
ArrayBuffer 5949 / 6028 17.2 58.1 1.0X
ExternalAppendOnlyUnsafeRowArray 6078 / 6138 16.8 59.4 1.0X
```
[B] Comparing `ExternalAppendOnlyUnsafeRowArray` against raw `UnsafeExternalSorter` when there is spilling of data
```
Spilling with 1000 rows: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
UnsafeExternalSorter 9239 / 9470 28.4 35.2 1.0X
ExternalAppendOnlyUnsafeRowArray 8857 / 8909 29.6 33.8 1.0X
Spilling with 10000 rows: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
UnsafeExternalSorter 4 / 5 39.3 25.5 1.0X
ExternalAppendOnlyUnsafeRowArray 5 / 6 29.8 33.5 0.8X
```
Author: Tejas Patil <tejasp@fb.com>
Closes #16909 from tejasapatil/SPARK-13450_smb_buffer_oom.
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coalesce/repartition
## What changes were proposed in this pull request?
This PR proposes to use the correct deserializer, `BatchedSerializer` for RDD construction for coalesce/repartition when the shuffle is enabled. Currently, it is passing `UTF8Deserializer` as is not `BatchedSerializer` from the copied one.
with the file, `text.txt` below:
```
a
b
d
e
f
g
h
i
j
k
l
```
- Before
```python
>>> sc.textFile('text.txt').repartition(1).collect()
```
```
UTF8Deserializer(True)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/rdd.py", line 811, in collect
return list(_load_from_socket(port, self._jrdd_deserializer))
File ".../spark/python/pyspark/serializers.py", line 549, in load_stream
yield self.loads(stream)
File ".../spark/python/pyspark/serializers.py", line 544, in loads
return s.decode("utf-8") if self.use_unicode else s
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/encodings/utf_8.py", line 16, in decode
return codecs.utf_8_decode(input, errors, True)
UnicodeDecodeError: 'utf8' codec can't decode byte 0x80 in position 0: invalid start byte
```
- After
```python
>>> sc.textFile('text.txt').repartition(1).collect()
```
```
[u'a', u'b', u'', u'd', u'e', u'f', u'g', u'h', u'i', u'j', u'k', u'l', u'']
```
## How was this patch tested?
Unit test in `python/pyspark/tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #17282 from HyukjinKwon/SPARK-19872.
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## What changes were proposed in this pull request?
It is sometimes useful to use multiple threads in a task to parallelize tasks. These threads might register some completion/failure listeners to clean up when the task completes or fails. We currently cannot register such a callback and be sure that it will get called, because the context might be in the process of invoking its callbacks, when the the callback gets registered.
This PR improves this by making sure that you cannot add a completion/failure listener from a different thread when the context is being marked as completed/failed in another thread. This is done by synchronizing these methods on the task context itself.
Failure listeners were called only once. Completion listeners now follow the same pattern; this lifts the idempotency requirement for completion listeners and makes it easier to implement them. In some cases we can (accidentally) add a completion/failure listener after the fact, these listeners will be called immediately in order make sure we can safely clean-up after a task.
As a result of this change we could make the `failure` and `completed` flags non-volatile. The `isCompleted()` method now uses synchronization to ensure that updates are visible across threads.
## How was this patch tested?
Adding tests to `TaskContestSuite` to test adding listeners to a completed/failed context.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes #17244 from hvanhovell/SPARK-19889.
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## What changes were proposed in this pull request?
We should restrict the nested level of a view, to avoid stack overflow exception during the view resolution.
## How was this patch tested?
Add new test case in `SQLViewSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes #17241 from jiangxb1987/view-depth.
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DataStreamReader/Writer
## What changes were proposed in this pull request?
As timezone setting can also affect partition values, it works for all formats, we should make it clear.
## How was this patch tested?
N/A
Author: Liwei Lin <lwlin7@gmail.com>
Closes #17299 from lw-lin/timezone.
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### What changes were proposed in this pull request?
This PR is to support reading data from Hive 2.1 metastore. Need to update shim class because of the Hive API changes caused by the following three Hive JIRAs:
- [HIVE-12730 MetadataUpdater: provide a mechanism to edit the basic statistics of a table (or a partition)](https://issues.apache.org/jira/browse/HIVE-12730)
- [Hive-13341 Stats state is not captured correctly: differentiate load table and create table](https://issues.apache.org/jira/browse/HIVE-13341)
- [HIVE-13622 WriteSet tracking optimizations](https://issues.apache.org/jira/browse/HIVE-13622)
There are three new fields added to Hive APIs.
- `boolean hasFollowingStatsTask`. We always set it to `false`. This is to keep the existing behavior unchanged (starting from 0.13), no matter which Hive metastore client version users choose. If we set it to `true`, the basic table statistics is not collected by Hive. For example,
```SQL
CREATE TABLE tbl AS SELECT 1 AS a
```
When setting `hasFollowingStatsTask ` to `false`, the table properties is like
```
Properties: [numFiles=1, transient_lastDdlTime=1489513927, totalSize=2]
```
When setting `hasFollowingStatsTask ` to `true`, the table properties is like
```
Properties: [transient_lastDdlTime=1489513563]
```
- `AcidUtils.Operation operation`. Obviously, we do not support ACID. Thus, we set it to `AcidUtils.Operation.NOT_ACID`.
- `EnvironmentContext environmentContext`. So far, this is always set to `null`. This was introduced for supporting DDL `alter table s update statistics set ('numRows'='NaN')`. Using this DDL, users can specify the statistics. So far, our Spark SQL does not need it, because we use different table properties to store our generated statistics values. However, when Spark SQL issues ALTER TABLE DDL statements, Hive metastore always automatically invalidate the Hive-generated statistics.
In the follow-up PR, we can fix it by explicitly adding a property to `environmentContext`.
```JAVA
putToProperties(StatsSetupConst.STATS_GENERATED, StatsSetupConst.USER)
```
Another alternative is to set `DO_NOT_UPDATE_STATS`to `TRUE`. See the Hive JIRA: https://issues.apache.org/jira/browse/HIVE-15653. We will not address it in this PR.
### How was this patch tested?
Added test cases to VersionsSuite.scala
Author: Xiao Li <gatorsmile@gmail.com>
Closes #17232 from gatorsmile/Hive21.
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## What changes were proposed in this pull request?
Since we could not directly define the array type in R, this PR proposes to support array types in R as string types that are used in `structField` as below:
```R
jsonArr <- "[{\"name\":\"Bob\"}, {\"name\":\"Alice\"}]"
df <- as.DataFrame(list(list("people" = jsonArr)))
collect(select(df, alias(from_json(df$people, "array<struct<name:string>>"), "arrcol")))
```
prints
```R
arrcol
1 Bob, Alice
```
## How was this patch tested?
Unit tests in `test_sparkSQL.R`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #17178 from HyukjinKwon/SPARK-19828.
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TextInputJsonDataSource
## What changes were proposed in this pull request?
This PR proposes to use text datasource when Json schema inference.
This basically proposes the similar approach in https://github.com/apache/spark/pull/15813 If we use Dataset for initial loading when inferring the schema, there are advantages. Please refer SPARK-18362
It seems JSON one was supposed to be fixed together but taken out according to https://github.com/apache/spark/pull/15813
> A similar problem also affects the JSON file format and this patch originally fixed that as well, but I've decided to split that change into a separate patch so as not to conflict with changes in another JSON PR.
Also, this seems affecting some functionalities because it does not use `FileScanRDD`. This problem is described in SPARK-19885 (but it was CSV's case).
## How was this patch tested?
Existing tests should cover this and manual test by `spark.read.json(path)` and check the UI.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #17255 from HyukjinKwon/json-filescanrdd.
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## What changes were proposed in this pull request?
When dynamic partition value is null or empty string, we should write the data to a directory like `a=__HIVE_DEFAULT_PARTITION__`, when we read the data back, we should respect this special directory name and treat it as null.
This is the same behavior of impala, see https://issues.apache.org/jira/browse/IMPALA-252
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes #17277 from cloud-fan/partition.
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in DataFrameReader/Writer.
## What changes were proposed in this pull request?
As timezone setting can also affect partition values, it works for all formats, we should make it clear.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes #17281 from ueshin/issues/SPARK-19817.
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incorrect result
## What changes were proposed in this pull request?
This PR fixes the following problem:
````
Seq((1, 2)).toDF("a1", "a2").createOrReplaceTempView("a")
Seq[(java.lang.Integer, java.lang.Integer)]((1, null)).toDF("b1", "b2").createOrReplaceTempView("b")
// The expected result is 1 row of (1,2) as shown in the next statement.
sql("select * from a where a1 not in (select b1 from b where b2 = a2)").show
+---+---+
| a1| a2|
+---+---+
+---+---+
sql("select * from a where a1 not in (select b1 from b where b2 = 2)").show
+---+---+
| a1| a2|
+---+---+
| 1| 2|
+---+---+
````
There are a number of scenarios to consider:
1. When the correlated predicate yields a match (i.e., B.B2 = A.A2)
1.1. When the NOT IN expression yields a match (i.e., A.A1 = B.B1)
1.2. When the NOT IN expression yields no match (i.e., A.A1 = B.B1 returns false)
1.3. When A.A1 is null
1.4. When B.B1 is null
1.4.1. When A.A1 is not null
1.4.2. When A.A1 is null
2. When the correlated predicate yields no match (i.e.,B.B2 = A.A2 is false or unknown)
2.1. When B.B2 is null and A.A2 is null
2.2. When B.B2 is null and A.A2 is not null
2.3. When the value of A.A2 does not match any of B.B2
````
A.A1 A.A2 B.B1 B.B2
----- ----- ----- -----
1 1 1 1 (1.1)
2 1 (1.2)
null 1 (1.3)
1 3 null 3 (1.4.1)
null 3 (1.4.2)
1 null 1 null (2.1)
null 2 (2.2 & 2.3)
````
We can divide the evaluation of the above correlated NOT IN subquery into 2 groups:-
Group 1: The rows in A when there is a match from the correlated predicate (A.A1 = B.B1)
In this case, the result of the subquery is not empty and the semantics of the NOT IN depends solely on the evaluation of the equality comparison of the columns of NOT IN, i.e., A1 = B1, which says
- If A.A1 is null, the row is filtered (1.3 and 1.4.2)
- If A.A1 = B.B1, the row is filtered (1.1)
- If B.B1 is null, any rows of A in the same group (A.A2 = B.B2) is filtered (1.4.1 & 1.4.2)
- Otherwise, the row is qualified.
Hence, in this group, the result is the row from (1.2).
Group 2: The rows in A when there is no match from the correlated predicate (A.A2 = B.B2)
In this case, all the rows in A, including the rows where A.A1, are qualified because the subquery returns an empty set and by the semantics of the NOT IN, all rows from the parent side qualifies as the result set, that is, the rows from (2.1, 2.2 and 2.3).
In conclusion, the correct result set of the above query is
````
A.A1 A.A2
----- -----
2 1 (1.2)
1 null (2.1)
null 2 (2.2 & 2.3)
````
## How was this patch tested?
unit tests, regression tests, and new test cases focusing on the problem being fixed.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes #17294 from nsyca/18966.
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## What changes were proposed in this pull request?
The `RemoveRedundantAlias` rule can change the output attributes (the expression id's to be precise) of a query by eliminating the redundant alias producing them. This is no problem for a regular query, but can cause problems for correlated subqueries: The attributes produced by the subquery are used in the parent plan; changing them will break the parent plan.
This PR fixes this by wrapping a subquery in a `Subquery` top level node when it gets optimized. The `RemoveRedundantAlias` rule now recognizes `Subquery` and makes sure that the output attributes of the `Subquery` node are retained.
## How was this patch tested?
Added a test case to `RemoveRedundantAliasAndProjectSuite` and added a regression test to `SubquerySuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes #17278 from hvanhovell/SPARK-19933.
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## What changes were proposed in this pull request?
This pr removed unnecessary type conversions per call in Hive: https://github.com/apache/spark/blob/master/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala#L116
## How was this patch tested?
Existing tests
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes #17264 from maropu/SPARK-19923.
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## What changes were proposed in this pull request?
We should support the statement `SHOW TABLE EXTENDED LIKE 'table_identifier' PARTITION(partition_spec)`, just like that HIVE does.
When partition is specified, the `SHOW TABLE EXTENDED` command should output the information of the partitions instead of the tables.
Note that in this statement, we require exact matched partition spec. For example:
```
CREATE TABLE show_t1(a String, b Int) PARTITIONED BY (c String, d String);
ALTER TABLE show_t1 ADD PARTITION (c='Us', d=1) PARTITION (c='Us', d=22);
-- Output the extended information of Partition(c='Us', d=1)
SHOW TABLE EXTENDED LIKE 'show_t1' PARTITION(c='Us', d=1);
-- Throw an AnalysisException
SHOW TABLE EXTENDED LIKE 'show_t1' PARTITION(c='Us');
```
## How was this patch tested?
Add new test sqls in file `show-tables.sql`.
Add new test case in `DDLSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes #16373 from jiangxb1987/show-partition-extended.
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## What changes were proposed in this pull request?
This PR is to enhance StringIndexer with NULL values handling.
Before the PR, StringIndexer will throw an exception when encounters NULL values.
With this PR:
- handleInvalid=error: Throw an exception as before
- handleInvalid=skip: Skip null values as well as unseen labels
- handleInvalid=keep: Give null values an additional index as well as unseen labels
BTW, I noticed someone was trying to solve the same problem ( #9920 ) but seems getting no progress or response for a long time. Would you mind to give me a chance to solve it ? I'm eager to help. :-)
## How was this patch tested?
new unit tests
Author: Menglong TAN <tanmenglong@renrenche.com>
Author: Menglong TAN <tanmenglong@gmail.com>
Closes #17233 from crackcell/11569_StringIndexer_NULL.
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## What changes were proposed in this pull request?
This commit moved `distinct` in its intended place to avoid duplicated predictions and adds unit test covering the issue.
## How was this patch tested?
Unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Closes #17283 from zero323/SPARK-19940.
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Currently generating synonyms using a large model (I've tested with 3m words) is very slow. These efficiencies have sped things up for us by ~17%
I wasn't sure if such small changes were worthy of a jira, but the guidelines seemed to suggest that that is the preferred approach
## What changes were proposed in this pull request?
Address a few small issues in the findSynonyms logic:
1) remove usage of ``Array.fill`` to zero out the ``cosineVec`` array. The default float value in Scala and Java is 0.0f, so explicitly setting the values to zero is not needed
2) use Floats throughout. The conversion to Doubles before doing the ``priorityQueue`` is totally superfluous, since all the similarity computations are done using Floats anyway. Creating a second large array just serves to put extra strain on the GC
3) convert the slow ``for(i <- cosVec.indices)`` to an ugly, but faster, ``while`` loop
These efficiencies are really only apparent when working with a large model
## How was this patch tested?
Existing unit tests + some in-house tests to time the difference
cc jkbradley MLNick srowen
Author: Asher Krim <krim.asher@gmail.com>
Author: Asher Krim <krim.asher@gmail>
Closes #17263 from Krimit/fasterFindSynonyms.
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optimization
## What changes were proposed in this pull request?
Commit https://github.com/apache/spark/commit/4ce970d71488c7de6025ef925f75b8b92a5a6a79 in accidentally broke the 2.10 build for Spark. This PR fixes this by simplifying the offending pattern match.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes #17288 from hvanhovell/SPARK-18874.
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## What changes were proposed in this pull request?
We currently cannot use aliases in SQL function calls. This is inconvenient when you try to create a struct. This SQL query for example `select struct(1, 2) st`, will create a struct with column names `col1` and `col2`. This is even more problematic when we want to append a field to an existing struct. For example if we want to a field to struct `st` we would issue the following SQL query `select struct(st.*, 1) as st from src`, the result will be struct `st` with an a column with a non descriptive name `col3` (if `st` itself has 2 fields).
This PR proposes to change this by allowing the use of aliased expression in function parameters. For example `select struct(1 as a, 2 as b) st`, will create a struct with columns `a` & `b`.
## How was this patch tested?
Added a test to `ExpressionParserSuite` and added a test file for `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes #17245 from hvanhovell/SPARK-19850.
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## What changes were proposed in this pull request?
This patch moves SQLConf from sql/core to sql/catalyst. To minimize the changes, the patch used type alias to still keep CatalystConf (as a type alias) and SimpleCatalystConf (as a concrete class that extends SQLConf).
Motivation for the change is that it is pretty weird to have SQLConf only in sql/core and then we have to duplicate config options that impact optimizer/analyzer in sql/catalyst using CatalystConf.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes #17285 from rxin/SPARK-19944.
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to Optimizer phase
## What changes were proposed in this pull request?
Currently Analyzer as part of ResolveSubquery, pulls up the correlated predicates to its
originating SubqueryExpression. The subquery plan is then transformed to remove the correlated
predicates after they are moved up to the outer plan. In this PR, the task of pulling up
correlated predicates is deferred to Optimizer. This is the initial work that will allow us to
support the form of correlated subqueries that we don't support today. The design document
from nsyca can be found in the following link :
[DesignDoc](https://docs.google.com/document/d/1QDZ8JwU63RwGFS6KVF54Rjj9ZJyK33d49ZWbjFBaIgU/edit#)
The brief description of code changes (hopefully to aid with code review) can be be found in the
following link:
[CodeChanges](https://docs.google.com/document/d/18mqjhL9V1An-tNta7aVE13HkALRZ5GZ24AATA-Vqqf0/edit#)
## How was this patch tested?
The test case PRs were submitted earlier using.
[16337](https://github.com/apache/spark/pull/16337) [16759](https://github.com/apache/spark/pull/16759) [16841](https://github.com/apache/spark/pull/16841) [16915](https://github.com/apache/spark/pull/16915) [16798](https://github.com/apache/spark/pull/16798) [16712](https://github.com/apache/spark/pull/16712) [16710](https://github.com/apache/spark/pull/16710) [16760](https://github.com/apache/spark/pull/16760) [16802](https://github.com/apache/spark/pull/16802)
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes #16954 from dilipbiswal/SPARK-18874.
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## What changes were proposed in this pull request?
Port Tweedie GLM #16344 to SparkR
felixcheung yanboliang
## How was this patch tested?
new test in SparkR
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes #16729 from actuaryzhang/sparkRTweedie.
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metastore versions.
### What changes were proposed in this pull request?
To improve the quality of our Spark SQL in different Hive metastore versions, this PR is to enable end-to-end testing using different versions. This PR allows the test cases in sql/hive to pass the existing Hive client to create a SparkSession.
- Since Derby does not allow concurrent connections, the pre-built Hive clients use different database from the TestHive's built-in 1.2.1 client.
- Since our test cases in sql/hive only can create a single Spark context in the same JVM, the newly created SparkSession share the same spark context with the existing TestHive's corresponding SparkSession.
### How was this patch tested?
Fixed the existing test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes #17260 from gatorsmile/versionSuite.
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### What changes were proposed in this pull request?
Since we are using shim for most Hive metastore APIs, the exceptions thrown by the underlying method of Method.invoke() are wrapped by `InvocationTargetException`. Instead of doing it one by one, we should handle all of them in the `withClient`. If any of them is missing, the error message could looks unfriendly. For example, below is an example for dropping tables.
```
Expected exception org.apache.spark.sql.AnalysisException to be thrown, but java.lang.reflect.InvocationTargetException was thrown.
ScalaTestFailureLocation: org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14 at (ExternalCatalogSuite.scala:193)
org.scalatest.exceptions.TestFailedException: Expected exception org.apache.spark.sql.AnalysisException to be thrown, but java.lang.reflect.InvocationTargetException was thrown.
at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:496)
at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555)
at org.scalatest.Assertions$class.intercept(Assertions.scala:1004)
at org.scalatest.FunSuite.intercept(FunSuite.scala:1555)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply$mcV$sp(ExternalCatalogSuite.scala:193)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply(ExternalCatalogSuite.scala:183)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply(ExternalCatalogSuite.scala:183)
at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:68)
at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163)
at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite.org$scalatest$BeforeAndAfterEach$$super$runTest(ExternalCatalogSuite.scala:40)
at org.scalatest.BeforeAndAfterEach$class.runTest(BeforeAndAfterEach.scala:255)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite.runTest(ExternalCatalogSuite.scala:40)
at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208)
at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208)
at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:413)
at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:401)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401)
at org.scalatest.SuperEngine.org$scalatest$SuperEngine$$runTestsInBranch(Engine.scala:396)
at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:483)
at org.scalatest.FunSuiteLike$class.runTests(FunSuiteLike.scala:208)
at org.scalatest.FunSuite.runTests(FunSuite.scala:1555)
at org.scalatest.Suite$class.run(Suite.scala:1424)
at org.scalatest.FunSuite.org$scalatest$FunSuiteLike$$super$run(FunSuite.scala:1555)
at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212)
at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212)
at org.scalatest.SuperEngine.runImpl(Engine.scala:545)
at org.scalatest.FunSuiteLike$class.run(FunSuiteLike.scala:212)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:31)
at org.scalatest.BeforeAndAfterAll$class.liftedTree1$1(BeforeAndAfterAll.scala:257)
at org.scalatest.BeforeAndAfterAll$class.run(BeforeAndAfterAll.scala:256)
at org.apache.spark.SparkFunSuite.run(SparkFunSuite.scala:31)
at org.scalatest.tools.SuiteRunner.run(SuiteRunner.scala:55)
at org.scalatest.tools.Runner$$anonfun$doRunRunRunDaDoRunRun$3.apply(Runner.scala:2563)
at org.scalatest.tools.Runner$$anonfun$doRunRunRunDaDoRunRun$3.apply(Runner.scala:2557)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.scalatest.tools.Runner$.doRunRunRunDaDoRunRun(Runner.scala:2557)
at org.scalatest.tools.Runner$$anonfun$runOptionallyWithPassFailReporter$2.apply(Runner.scala:1044)
at org.scalatest.tools.Runner$$anonfun$runOptionallyWithPassFailReporter$2.apply(Runner.scala:1043)
at org.scalatest.tools.Runner$.withClassLoaderAndDispatchReporter(Runner.scala:2722)
at org.scalatest.tools.Runner$.runOptionallyWithPassFailReporter(Runner.scala:1043)
at org.scalatest.tools.Runner$.run(Runner.scala:883)
at org.scalatest.tools.Runner.run(Runner.scala)
at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.runScalaTest2(ScalaTestRunner.java:138)
at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.main(ScalaTestRunner.java:28)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:147)
Caused by: java.lang.reflect.InvocationTargetException
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.sql.hive.client.Shim_v0_14.dropTable(HiveShim.scala:736)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$dropTable$1.apply$mcV$sp(HiveClientImpl.scala:451)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$dropTable$1.apply(HiveClientImpl.scala:451)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$dropTable$1.apply(HiveClientImpl.scala:451)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$withHiveState$1.apply(HiveClientImpl.scala:287)
at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:228)
at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:227)
at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:270)
at org.apache.spark.sql.hive.client.HiveClientImpl.dropTable(HiveClientImpl.scala:450)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$dropTable$1.apply$mcV$sp(HiveExternalCatalog.scala:456)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$dropTable$1.apply(HiveExternalCatalog.scala:454)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$dropTable$1.apply(HiveExternalCatalog.scala:454)
at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:94)
at org.apache.spark.sql.hive.HiveExternalCatalog.dropTable(HiveExternalCatalog.scala:454)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14$$anonfun$apply$mcV$sp$8.apply$mcV$sp(ExternalCatalogSuite.scala:194)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14$$anonfun$apply$mcV$sp$8.apply(ExternalCatalogSuite.scala:194)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14$$anonfun$apply$mcV$sp$8.apply(ExternalCatalogSuite.scala:194)
at org.scalatest.Assertions$class.intercept(Assertions.scala:997)
... 57 more
Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: NoSuchObjectException(message:db2.unknown_table table not found)
at org.apache.hadoop.hive.ql.metadata.Hive.dropTable(Hive.java:1038)
... 79 more
Caused by: NoSuchObjectException(message:db2.unknown_table table not found)
at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.get_table_core(HiveMetaStore.java:1808)
at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.get_table(HiveMetaStore.java:1778)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.hive.metastore.RetryingHMSHandler.invoke(RetryingHMSHandler.java:107)
at com.sun.proxy.$Proxy10.get_table(Unknown Source)
at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.getTable(HiveMetaStoreClient.java:1208)
at org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.getTable(SessionHiveMetaStoreClient.java:131)
at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.dropTable(HiveMetaStoreClient.java:952)
at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.dropTable(HiveMetaStoreClient.java:904)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.invoke(RetryingMetaStoreClient.java:156)
at com.sun.proxy.$Proxy11.dropTable(Unknown Source)
at org.apache.hadoop.hive.ql.metadata.Hive.dropTable(Hive.java:1035)
... 79 more
```
After unwrapping the exception, the message is like
```
org.apache.hadoop.hive.ql.metadata.HiveException: NoSuchObjectException(message:db2.unknown_table table not found);
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: NoSuchObjectException(message:db2.unknown_table table not found);
at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:100)
at org.apache.spark.sql.hive.HiveExternalCatalog.dropTable(HiveExternalCatalog.scala:460)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply$mcV$sp(ExternalCatalogSuite.scala:193)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply(ExternalCatalogSuite.scala:183)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogSuite$$anonfun$14.apply(ExternalCatalogSuite.scala:183)
at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
...
```
### How was this patch tested?
Covered by the existing test case in `test("drop table when database/table does not exist")` in `ExternalCatalogSuite`.
Author: Xiao Li <gatorsmile@gmail.com>
Closes #17265 from gatorsmile/InvocationTargetException.
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## What changes were proposed in this pull request?
Give proper syntax for Java and Python in addition to Scala.
## How was this patch tested?
Manually.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes #17215 from jkbradley/write-err-msg.
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## What changes were proposed in this pull request?
We should only have one centre place to try catch the exception for corrupted files.
## How was this patch tested?
existing test
Author: Wenchen Fan <wenchen@databricks.com>
Closes #17253 from cloud-fan/bad-file.
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## What changes were proposed in this pull request?
- Timestamp hashing is done as per [TimestampWritable.hashCode()](https://github.com/apache/hive/blob/ff67cdda1c538dc65087878eeba3e165cf3230f4/serde/src/java/org/apache/hadoop/hive/serde2/io/TimestampWritable.java#L406) in Hive
- Interval hashing is done as per [HiveIntervalDayTime.hashCode()](https://github.com/apache/hive/blob/ff67cdda1c538dc65087878eeba3e165cf3230f4/storage-api/src/java/org/apache/hadoop/hive/common/type/HiveIntervalDayTime.java#L178). Note that there are inherent differences in how Hive and Spark store intervals under the hood which limits the ability to be in completely sync with hive's hashing function. I have explained this in the method doc.
- Date type was already supported. This PR adds test for that.
## How was this patch tested?
Added unit tests
Author: Tejas Patil <tejasp@fb.com>
Closes #17062 from tejasapatil/SPARK-17495_time_related_types.
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SpecificOffsets
When using the KafkaSource with Structured Streaming, consumer assignments are not what the user expects if startingOffsets is set to an explicit set of topics/partitions in JSON where the topic(s) happen to have uppercase characters. When StartingOffsets is constructed, the original string value from options is transformed toLowerCase to make matching on "earliest" and "latest" case insensitive. However, the toLowerCase JSON is passed to SpecificOffsets for the terminal condition, so topic names may not be what the user intended by the time assignments are made with the underlying KafkaConsumer.
KafkaSourceProvider.scala:
```
val startingOffsets = caseInsensitiveParams.get(STARTING_OFFSETS_OPTION_KEY).map(_.trim.toLowerCase) match {
case Some("latest") => LatestOffsets
case Some("earliest") => EarliestOffsets
case Some(json) => SpecificOffsets(JsonUtils.partitionOffsets(json))
case None => LatestOffsets
}
```
Thank cbowden for reporting.
Jenkins
Author: uncleGen <hustyugm@gmail.com>
Closes #17209 from uncleGen/SPARK-19853.
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"maxDepth" to R models
## What changes were proposed in this pull request?
RandomForest R Wrapper and GBT R Wrapper return param `maxDepth` to R models.
Below 4 R wrappers are changed:
* `RandomForestClassificationWrapper`
* `RandomForestRegressionWrapper`
* `GBTClassificationWrapper`
* `GBTRegressionWrapper`
## How was this patch tested?
Test manually on my local machine.
Author: Xin Ren <iamshrek@126.com>
Closes #17207 from keypointt/SPARK-19282.
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directories of finished applications to avoid the block
Cleaning the application may cost much time at worker, then it will block that the worker send heartbeats master because the worker is extend ThreadSafeRpcEndpoint. If the heartbeat from a worker is blocked by the message ApplicationFinished, master will think the worker is dead. If the worker has a driver, the driver will be scheduled by master again.
It had better reuse the existing cleanupThreadExecutor to clean up the directories of finished applications to avoid the block.
Author: xiaojian.fxj <xiaojian.fxj@alibaba-inc.com>
Closes #17189 from hustfxj/worker-hearbeat.
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## What changes were proposed in this pull request?
- SS python example: `TypeError: 'xxx' object is not callable`
- some other doc issue.
## How was this patch tested?
Jenkins.
Author: uncleGen <hustyugm@gmail.com>
Closes #17257 from uncleGen/docs-ss-python.
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should work
## What changes were proposed in this pull request?
This JIRA is a follow up work after [SPARK-19583](https://issues.apache.org/jira/browse/SPARK-19583)
As we discussed in that [PR](https://github.com/apache/spark/pull/16938)
The following DDL for datasource table with an non-existent location should work:
```
CREATE TABLE ... (PARTITIONED BY ...) LOCATION path
```
Currently it will throw exception that path not exists for datasource table for datasource table
## How was this patch tested?
unit test added
Author: windpiger <songjun@outlook.com>
Closes #17055 from windpiger/CTDataSourcePathNotExists.
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## What changes were proposed in this pull request?
In spark SQL, map type can't be used in equality test/comparison, and `Intersect`/`Except`/`Distinct` do need equality test for all columns, we should not allow map type in `Intersect`/`Except`/`Distinct`.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes #17236 from cloud-fan/map.
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## What changes were proposed in this pull request?
`Dataset.inputFiles` works by matching `FileRelation`s in the query plan. In Spark 2.1, Hive SerDe tables are represented by `MetastoreRelation`, which inherits from `FileRelation`. However, in Spark 2.2, Hive SerDe tables are now represented by `CatalogRelation`, which doesn't inherit from `FileRelation` anymore, due to the unification of Hive SerDe tables and data source tables. This change breaks `Dataset.inputFiles` for Hive SerDe tables.
This PR tries to fix this issue by explicitly matching `CatalogRelation`s that are Hive SerDe tables in `Dataset.inputFiles`. Note that we can't make `CatalogRelation` inherit from `FileRelation` since not all `CatalogRelation`s are file based (e.g., JDBC data source tables).
## How was this patch tested?
New test case added in `HiveDDLSuite`.
Author: Cheng Lian <lian@databricks.com>
Closes #17247 from liancheng/spark-19905-hive-table-input-files.
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## What changes were proposed in this pull request?
The ```HiveMetastoreCatalog.mergeWithMetastoreSchema()``` method added in #16944 may
not preserve the same field order as the metastore schema in some cases, which can cause
queries to fail. This change ensures that the metastore field order is preserved.
## How was this patch tested?
A test for ensuring that metastore order is preserved was added to ```HiveSchemaInferenceSuite.```
The particular failure usecase from #16944 was tested manually as well.
Author: Budde <budde@amazon.com>
Closes #17249 from budde/PreserveMetastoreFieldOrder.
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SPARK_JAVA_OPTS
This fix removes deprecated support for config `SPARK_YARN_USER_ENV`, as is mentioned in SPARK-17979.
This fix also removes deprecated support for the following:
```
SPARK_YARN_USER_ENV
SPARK_JAVA_OPTS
SPARK_CLASSPATH
SPARK_WORKER_INSTANCES
```
Related JIRA:
[SPARK-14453]: https://issues.apache.org/jira/browse/SPARK-14453
[SPARK-12344]: https://issues.apache.org/jira/browse/SPARK-12344
[SPARK-15781]: https://issues.apache.org/jira/browse/SPARK-15781
Existing tests should pass.
Author: Yong Tang <yong.tang.github@outlook.com>
Closes #17212 from yongtang/SPARK-17979.
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## What changes were proposed in this pull request?
When adaptive execution is enabled, an exchange coordinator is used in the Exchange operators. For Join, the same exchange coordinator is used for its two Exchanges. But the physical plan shows two different coordinator Ids which is confusing.
This PR is to fix the incorrect exchange coordinator id in the physical plan. The coordinator object instead of the `Option[ExchangeCoordinator]` should be used to generate the identity hash code of the same coordinator.
## How was this patch tested?
Before the patch, the physical plan shows two different exchange coordinator id for Join.
```
== Physical Plan ==
*Project [key1#3L, value2#12L]
+- *SortMergeJoin [key1#3L], [key2#11L], Inner
:- *Sort [key1#3L ASC NULLS FIRST], false, 0
: +- Exchange(coordinator id: 1804587700) hashpartitioning(key1#3L, 10), coordinator[target post-shuffle partition size: 67108864]
: +- *Project [(id#0L % 500) AS key1#3L]
: +- *Filter isnotnull((id#0L % 500))
: +- *Range (0, 1000, step=1, splits=Some(10))
+- *Sort [key2#11L ASC NULLS FIRST], false, 0
+- Exchange(coordinator id: 793927319) hashpartitioning(key2#11L, 10), coordinator[target post-shuffle partition size: 67108864]
+- *Project [(id#8L % 500) AS key2#11L, id#8L AS value2#12L]
+- *Filter isnotnull((id#8L % 500))
+- *Range (0, 1000, step=1, splits=Some(10))
```
After the patch, two exchange coordinator id are the same.
Author: Carson Wang <carson.wang@intel.com>
Closes #16952 from carsonwang/FixCoordinatorId.
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range()
## What changes were proposed in this pull request?
This PR improves performance of operations with `range()` by changing Java code generated by Catalyst. This PR is inspired by the [blog article](https://databricks.com/blog/2017/02/16/processing-trillion-rows-per-second-single-machine-can-nested-loop-joins-fast.html).
This PR changes generated code in the following two points.
1. Replace a while-loop with long instance variables a for-loop with int local varibles
2. Suppress generation of `shouldStop()` method if this method is unnecessary (e.g. `append()` is not generated).
These points facilitates compiler optimizations in a JIT compiler by feeding the simplified Java code into the JIT compiler. The performance is improved by 7.6x.
Benchmark program:
```java
val N = 1 << 29
val iters = 2
val benchmark = new Benchmark("range.count", N * iters)
benchmark.addCase(s"with this PR") { i =>
var n = 0
var len = 0
while (n < iters) {
len += sparkSession.range(N).selectExpr("count(id)").collect.length
n += 1
}
}
benchmark.run
```
Performance result without this PR
```
OpenJDK 64-Bit Server VM 1.8.0_111-8u111-b14-2ubuntu0.16.04.2-b14 on Linux 4.4.0-47-generic
Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz
range.count: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
w/o this PR 1349 / 1356 796.2 1.3 1.0X
```
Performance result with this PR
```
OpenJDK 64-Bit Server VM 1.8.0_111-8u111-b14-2ubuntu0.16.04.2-b14 on Linux 4.4.0-47-generic
Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz
range.count: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
with this PR 177 / 271 6065.3 0.2 1.0X
```
Here is a comparison between generated code w/o and with this PR. Only the method ```agg_doAggregateWithoutKey``` is changed.
Generated code without this PR
```java
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */ private Object[] references;
/* 007 */ private scala.collection.Iterator[] inputs;
/* 008 */ private boolean agg_initAgg;
/* 009 */ private boolean agg_bufIsNull;
/* 010 */ private long agg_bufValue;
/* 011 */ private org.apache.spark.sql.execution.metric.SQLMetric range_numOutputRows;
/* 012 */ private org.apache.spark.sql.execution.metric.SQLMetric range_numGeneratedRows;
/* 013 */ private boolean range_initRange;
/* 014 */ private long range_number;
/* 015 */ private TaskContext range_taskContext;
/* 016 */ private InputMetrics range_inputMetrics;
/* 017 */ private long range_batchEnd;
/* 018 */ private long range_numElementsTodo;
/* 019 */ private scala.collection.Iterator range_input;
/* 020 */ private UnsafeRow range_result;
/* 021 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder range_holder;
/* 022 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter range_rowWriter;
/* 023 */ private org.apache.spark.sql.execution.metric.SQLMetric agg_numOutputRows;
/* 024 */ private org.apache.spark.sql.execution.metric.SQLMetric agg_aggTime;
/* 025 */ private UnsafeRow agg_result;
/* 026 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder agg_holder;
/* 027 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter agg_rowWriter;
/* 028 */
/* 029 */ public GeneratedIterator(Object[] references) {
/* 030 */ this.references = references;
/* 031 */ }
/* 032 */
/* 033 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 034 */ partitionIndex = index;
/* 035 */ this.inputs = inputs;
/* 036 */ agg_initAgg = false;
/* 037 */
/* 038 */ this.range_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[0];
/* 039 */ this.range_numGeneratedRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[1];
/* 040 */ range_initRange = false;
/* 041 */ range_number = 0L;
/* 042 */ range_taskContext = TaskContext.get();
/* 043 */ range_inputMetrics = range_taskContext.taskMetrics().inputMetrics();
/* 044 */ range_batchEnd = 0;
/* 045 */ range_numElementsTodo = 0L;
/* 046 */ range_input = inputs[0];
/* 047 */ range_result = new UnsafeRow(1);
/* 048 */ this.range_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(range_result, 0);
/* 049 */ this.range_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(range_holder, 1);
/* 050 */ this.agg_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[2];
/* 051 */ this.agg_aggTime = (org.apache.spark.sql.execution.metric.SQLMetric) references[3];
/* 052 */ agg_result = new UnsafeRow(1);
/* 053 */ this.agg_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(agg_result, 0);
/* 054 */ this.agg_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(agg_holder, 1);
/* 055 */
/* 056 */ }
/* 057 */
/* 058 */ private void agg_doAggregateWithoutKey() throws java.io.IOException {
/* 059 */ // initialize aggregation buffer
/* 060 */ agg_bufIsNull = false;
/* 061 */ agg_bufValue = 0L;
/* 062 */
/* 063 */ // initialize Range
/* 064 */ if (!range_initRange) {
/* 065 */ range_initRange = true;
/* 066 */ initRange(partitionIndex);
/* 067 */ }
/* 068 */
/* 069 */ while (true) {
/* 070 */ while (range_number != range_batchEnd) {
/* 071 */ long range_value = range_number;
/* 072 */ range_number += 1L;
/* 073 */
/* 074 */ // do aggregate
/* 075 */ // common sub-expressions
/* 076 */
/* 077 */ // evaluate aggregate function
/* 078 */ boolean agg_isNull1 = false;
/* 079 */
/* 080 */ long agg_value1 = -1L;
/* 081 */ agg_value1 = agg_bufValue + 1L;
/* 082 */ // update aggregation buffer
/* 083 */ agg_bufIsNull = false;
/* 084 */ agg_bufValue = agg_value1;
/* 085 */
/* 086 */ if (shouldStop()) return;
/* 087 */ }
/* 088 */
/* 089 */ if (range_taskContext.isInterrupted()) {
/* 090 */ throw new TaskKilledException();
/* 091 */ }
/* 092 */
/* 093 */ long range_nextBatchTodo;
/* 094 */ if (range_numElementsTodo > 1000L) {
/* 095 */ range_nextBatchTodo = 1000L;
/* 096 */ range_numElementsTodo -= 1000L;
/* 097 */ } else {
/* 098 */ range_nextBatchTodo = range_numElementsTodo;
/* 099 */ range_numElementsTodo = 0;
/* 100 */ if (range_nextBatchTodo == 0) break;
/* 101 */ }
/* 102 */ range_numOutputRows.add(range_nextBatchTodo);
/* 103 */ range_inputMetrics.incRecordsRead(range_nextBatchTodo);
/* 104 */
/* 105 */ range_batchEnd += range_nextBatchTodo * 1L;
/* 106 */ }
/* 107 */
/* 108 */ }
/* 109 */
/* 110 */ private void initRange(int idx) {
/* 111 */ java.math.BigInteger index = java.math.BigInteger.valueOf(idx);
/* 112 */ java.math.BigInteger numSlice = java.math.BigInteger.valueOf(2L);
/* 113 */ java.math.BigInteger numElement = java.math.BigInteger.valueOf(10000L);
/* 114 */ java.math.BigInteger step = java.math.BigInteger.valueOf(1L);
/* 115 */ java.math.BigInteger start = java.math.BigInteger.valueOf(0L);
/* 117 */
/* 118 */ java.math.BigInteger st = index.multiply(numElement).divide(numSlice).multiply(step).add(start);
/* 119 */ if (st.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) {
/* 120 */ range_number = Long.MAX_VALUE;
/* 121 */ } else if (st.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) {
/* 122 */ range_number = Long.MIN_VALUE;
/* 123 */ } else {
/* 124 */ range_number = st.longValue();
/* 125 */ }
/* 126 */ range_batchEnd = range_number;
/* 127 */
/* 128 */ java.math.BigInteger end = index.add(java.math.BigInteger.ONE).multiply(numElement).divide(numSlice)
/* 129 */ .multiply(step).add(start);
/* 130 */ if (end.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) {
/* 131 */ partitionEnd = Long.MAX_VALUE;
/* 132 */ } else if (end.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) {
/* 133 */ partitionEnd = Long.MIN_VALUE;
/* 134 */ } else {
/* 135 */ partitionEnd = end.longValue();
/* 136 */ }
/* 137 */
/* 138 */ java.math.BigInteger startToEnd = java.math.BigInteger.valueOf(partitionEnd).subtract(
/* 139 */ java.math.BigInteger.valueOf(range_number));
/* 140 */ range_numElementsTodo = startToEnd.divide(step).longValue();
/* 141 */ if (range_numElementsTodo < 0) {
/* 142 */ range_numElementsTodo = 0;
/* 143 */ } else if (startToEnd.remainder(step).compareTo(java.math.BigInteger.valueOf(0L)) != 0) {
/* 144 */ range_numElementsTodo++;
/* 145 */ }
/* 146 */ }
/* 147 */
/* 148 */ protected void processNext() throws java.io.IOException {
/* 149 */ while (!agg_initAgg) {
/* 150 */ agg_initAgg = true;
/* 151 */ long agg_beforeAgg = System.nanoTime();
/* 152 */ agg_doAggregateWithoutKey();
/* 153 */ agg_aggTime.add((System.nanoTime() - agg_beforeAgg) / 1000000);
/* 154 */
/* 155 */ // output the result
/* 156 */
/* 157 */ agg_numOutputRows.add(1);
/* 158 */ agg_rowWriter.zeroOutNullBytes();
/* 159 */
/* 160 */ if (agg_bufIsNull) {
/* 161 */ agg_rowWriter.setNullAt(0);
/* 162 */ } else {
/* 163 */ agg_rowWriter.write(0, agg_bufValue);
/* 164 */ }
/* 165 */ append(agg_result);
/* 166 */ }
/* 167 */ }
/* 168 */ }
```
Generated code with this PR
```java
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */ private Object[] references;
/* 007 */ private scala.collection.Iterator[] inputs;
/* 008 */ private boolean agg_initAgg;
/* 009 */ private boolean agg_bufIsNull;
/* 010 */ private long agg_bufValue;
/* 011 */ private org.apache.spark.sql.execution.metric.SQLMetric range_numOutputRows;
/* 012 */ private org.apache.spark.sql.execution.metric.SQLMetric range_numGeneratedRows;
/* 013 */ private boolean range_initRange;
/* 014 */ private long range_number;
/* 015 */ private TaskContext range_taskContext;
/* 016 */ private InputMetrics range_inputMetrics;
/* 017 */ private long range_batchEnd;
/* 018 */ private long range_numElementsTodo;
/* 019 */ private scala.collection.Iterator range_input;
/* 020 */ private UnsafeRow range_result;
/* 021 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder range_holder;
/* 022 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter range_rowWriter;
/* 023 */ private org.apache.spark.sql.execution.metric.SQLMetric agg_numOutputRows;
/* 024 */ private org.apache.spark.sql.execution.metric.SQLMetric agg_aggTime;
/* 025 */ private UnsafeRow agg_result;
/* 026 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder agg_holder;
/* 027 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter agg_rowWriter;
/* 028 */
/* 029 */ public GeneratedIterator(Object[] references) {
/* 030 */ this.references = references;
/* 031 */ }
/* 032 */
/* 033 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 034 */ partitionIndex = index;
/* 035 */ this.inputs = inputs;
/* 036 */ agg_initAgg = false;
/* 037 */
/* 038 */ this.range_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[0];
/* 039 */ this.range_numGeneratedRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[1];
/* 040 */ range_initRange = false;
/* 041 */ range_number = 0L;
/* 042 */ range_taskContext = TaskContext.get();
/* 043 */ range_inputMetrics = range_taskContext.taskMetrics().inputMetrics();
/* 044 */ range_batchEnd = 0;
/* 045 */ range_numElementsTodo = 0L;
/* 046 */ range_input = inputs[0];
/* 047 */ range_result = new UnsafeRow(1);
/* 048 */ this.range_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(range_result, 0);
/* 049 */ this.range_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(range_holder, 1);
/* 050 */ this.agg_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[2];
/* 051 */ this.agg_aggTime = (org.apache.spark.sql.execution.metric.SQLMetric) references[3];
/* 052 */ agg_result = new UnsafeRow(1);
/* 053 */ this.agg_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(agg_result, 0);
/* 054 */ this.agg_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(agg_holder, 1);
/* 055 */
/* 056 */ }
/* 057 */
/* 058 */ private void agg_doAggregateWithoutKey() throws java.io.IOException {
/* 059 */ // initialize aggregation buffer
/* 060 */ agg_bufIsNull = false;
/* 061 */ agg_bufValue = 0L;
/* 062 */
/* 063 */ // initialize Range
/* 064 */ if (!range_initRange) {
/* 065 */ range_initRange = true;
/* 066 */ initRange(partitionIndex);
/* 067 */ }
/* 068 */
/* 069 */ while (true) {
/* 070 */ long range_range = range_batchEnd - range_number;
/* 071 */ if (range_range != 0L) {
/* 072 */ int range_localEnd = (int)(range_range / 1L);
/* 073 */ for (int range_localIdx = 0; range_localIdx < range_localEnd; range_localIdx++) {
/* 074 */ long range_value = ((long)range_localIdx * 1L) + range_number;
/* 075 */
/* 076 */ // do aggregate
/* 077 */ // common sub-expressions
/* 078 */
/* 079 */ // evaluate aggregate function
/* 080 */ boolean agg_isNull1 = false;
/* 081 */
/* 082 */ long agg_value1 = -1L;
/* 083 */ agg_value1 = agg_bufValue + 1L;
/* 084 */ // update aggregation buffer
/* 085 */ agg_bufIsNull = false;
/* 086 */ agg_bufValue = agg_value1;
/* 087 */
/* 088 */ // shouldStop check is eliminated
/* 089 */ }
/* 090 */ range_number = range_batchEnd;
/* 091 */ }
/* 092 */
/* 093 */ if (range_taskContext.isInterrupted()) {
/* 094 */ throw new TaskKilledException();
/* 095 */ }
/* 096 */
/* 097 */ long range_nextBatchTodo;
/* 098 */ if (range_numElementsTodo > 1000L) {
/* 099 */ range_nextBatchTodo = 1000L;
/* 100 */ range_numElementsTodo -= 1000L;
/* 101 */ } else {
/* 102 */ range_nextBatchTodo = range_numElementsTodo;
/* 103 */ range_numElementsTodo = 0;
/* 104 */ if (range_nextBatchTodo == 0) break;
/* 105 */ }
/* 106 */ range_numOutputRows.add(range_nextBatchTodo);
/* 107 */ range_inputMetrics.incRecordsRead(range_nextBatchTodo);
/* 108 */
/* 109 */ range_batchEnd += range_nextBatchTodo * 1L;
/* 110 */ }
/* 111 */
/* 112 */ }
/* 113 */
/* 114 */ private void initRange(int idx) {
/* 115 */ java.math.BigInteger index = java.math.BigInteger.valueOf(idx);
/* 116 */ java.math.BigInteger numSlice = java.math.BigInteger.valueOf(2L);
/* 117 */ java.math.BigInteger numElement = java.math.BigInteger.valueOf(10000L);
/* 118 */ java.math.BigInteger step = java.math.BigInteger.valueOf(1L);
/* 119 */ java.math.BigInteger start = java.math.BigInteger.valueOf(0L);
/* 120 */ long partitionEnd;
/* 121 */
/* 122 */ java.math.BigInteger st = index.multiply(numElement).divide(numSlice).multiply(step).add(start);
/* 123 */ if (st.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) {
/* 124 */ range_number = Long.MAX_VALUE;
/* 125 */ } else if (st.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) {
/* 126 */ range_number = Long.MIN_VALUE;
/* 127 */ } else {
/* 128 */ range_number = st.longValue();
/* 129 */ }
/* 130 */ range_batchEnd = range_number;
/* 131 */
/* 132 */ java.math.BigInteger end = index.add(java.math.BigInteger.ONE).multiply(numElement).divide(numSlice)
/* 133 */ .multiply(step).add(start);
/* 134 */ if (end.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) {
/* 135 */ partitionEnd = Long.MAX_VALUE;
/* 136 */ } else if (end.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) {
/* 137 */ partitionEnd = Long.MIN_VALUE;
/* 138 */ } else {
/* 139 */ partitionEnd = end.longValue();
/* 140 */ }
/* 141 */
/* 142 */ java.math.BigInteger startToEnd = java.math.BigInteger.valueOf(partitionEnd).subtract(
/* 143 */ java.math.BigInteger.valueOf(range_number));
/* 144 */ range_numElementsTodo = startToEnd.divide(step).longValue();
/* 145 */ if (range_numElementsTodo < 0) {
/* 146 */ range_numElementsTodo = 0;
/* 147 */ } else if (startToEnd.remainder(step).compareTo(java.math.BigInteger.valueOf(0L)) != 0) {
/* 148 */ range_numElementsTodo++;
/* 149 */ }
/* 150 */ }
/* 151 */
/* 152 */ protected void processNext() throws java.io.IOException {
/* 153 */ while (!agg_initAgg) {
/* 154 */ agg_initAgg = true;
/* 155 */ long agg_beforeAgg = System.nanoTime();
/* 156 */ agg_doAggregateWithoutKey();
/* 157 */ agg_aggTime.add((System.nanoTime() - agg_beforeAgg) / 1000000);
/* 158 */
/* 159 */ // output the result
/* 160 */
/* 161 */ agg_numOutputRows.add(1);
/* 162 */ agg_rowWriter.zeroOutNullBytes();
/* 163 */
/* 164 */ if (agg_bufIsNull) {
/* 165 */ agg_rowWriter.setNullAt(0);
/* 166 */ } else {
/* 167 */ agg_rowWriter.write(0, agg_bufValue);
/* 168 */ }
/* 169 */ append(agg_result);
/* 170 */ }
/* 171 */ }
/* 172 */ }
```
A part of suppressing `shouldStop()` was originally developed by inouehrs
## How was this patch tested?
Add new tests into `DataFrameRangeSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes #17122 from kiszk/SPARK-19786.
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## What changes were proposed in this pull request?
We need to notify the await batch lock when the stream exits early e.g., when an exception has been thrown.
## How was this patch tested?
Current tests that throw exceptions at runtime will finish faster as a result of this update.
zsxwing
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Tyson Condie <tcondie@gmail.com>
Closes #17231 from tcondie/kafka-writer.
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boxing/unboxing
## What changes were proposed in this pull request?
This PR improve performance of Dataset.map() for primitive types by removing boxing/unbox operations. This is based on [the discussion](https://github.com/apache/spark/pull/16391#discussion_r93788919) with cloud-fan.
Current Catalyst generates a method call to a `apply()` method of an anonymous function written in Scala. The types of an argument and return value are `java.lang.Object`. As a result, each method call for a primitive value involves a pair of unboxing and boxing for calling this `apply()` method and a pair of boxing and unboxing for returning from this `apply()` method.
This PR directly calls a specialized version of a `apply()` method without boxing and unboxing. For example, if types of an arguments ant return value is `int`, this PR generates a method call to `apply$mcII$sp`. This PR supports any combination of `Int`, `Long`, `Float`, and `Double`.
The following is a benchmark result using [this program](https://github.com/apache/spark/pull/16391/files) with 4.7x. Here is a Dataset part of this program.
Without this PR
```
OpenJDK 64-Bit Server VM 1.8.0_111-8u111-b14-2ubuntu0.16.04.2-b14 on Linux 4.4.0-47-generic
Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz
back-to-back map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
RDD 1923 / 1952 52.0 19.2 1.0X
DataFrame 526 / 548 190.2 5.3 3.7X
Dataset 3094 / 3154 32.3 30.9 0.6X
```
With this PR
```
OpenJDK 64-Bit Server VM 1.8.0_111-8u111-b14-2ubuntu0.16.04.2-b14 on Linux 4.4.0-47-generic
Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz
back-to-back map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
RDD 1883 / 1892 53.1 18.8 1.0X
DataFrame 502 / 642 199.1 5.0 3.7X
Dataset 657 / 784 152.2 6.6 2.9X
```
```java
def backToBackMap(spark: SparkSession, numRows: Long, numChains: Int): Benchmark = {
import spark.implicits._
val rdd = spark.sparkContext.range(0, numRows)
val ds = spark.range(0, numRows)
val func = (l: Long) => l + 1
val benchmark = new Benchmark("back-to-back map", numRows)
...
benchmark.addCase("Dataset") { iter =>
var res = ds.as[Long]
var i = 0
while (i < numChains) {
res = res.map(func)
i += 1
}
res.queryExecution.toRdd.foreach(_ => Unit)
}
benchmark
}
```
A motivating example
```java
Seq(1, 2, 3).toDS.map(i => i * 7).show
```
Generated code without this PR
```java
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */ private Object[] references;
/* 007 */ private scala.collection.Iterator[] inputs;
/* 008 */ private scala.collection.Iterator inputadapter_input;
/* 009 */ private UnsafeRow deserializetoobject_result;
/* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder deserializetoobject_holder;
/* 011 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter deserializetoobject_rowWriter;
/* 012 */ private int mapelements_argValue;
/* 013 */ private UnsafeRow mapelements_result;
/* 014 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder mapelements_holder;
/* 015 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter mapelements_rowWriter;
/* 016 */ private UnsafeRow serializefromobject_result;
/* 017 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder serializefromobject_holder;
/* 018 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter serializefromobject_rowWriter;
/* 019 */
/* 020 */ public GeneratedIterator(Object[] references) {
/* 021 */ this.references = references;
/* 022 */ }
/* 023 */
/* 024 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 025 */ partitionIndex = index;
/* 026 */ this.inputs = inputs;
/* 027 */ inputadapter_input = inputs[0];
/* 028 */ deserializetoobject_result = new UnsafeRow(1);
/* 029 */ this.deserializetoobject_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(deserializetoobject_result, 0);
/* 030 */ this.deserializetoobject_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(deserializetoobject_holder, 1);
/* 031 */
/* 032 */ mapelements_result = new UnsafeRow(1);
/* 033 */ this.mapelements_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(mapelements_result, 0);
/* 034 */ this.mapelements_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(mapelements_holder, 1);
/* 035 */ serializefromobject_result = new UnsafeRow(1);
/* 036 */ this.serializefromobject_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(serializefromobject_result, 0);
/* 037 */ this.serializefromobject_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(serializefromobject_holder, 1);
/* 038 */
/* 039 */ }
/* 040 */
/* 041 */ protected void processNext() throws java.io.IOException {
/* 042 */ while (inputadapter_input.hasNext() && !stopEarly()) {
/* 043 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 044 */ int inputadapter_value = inputadapter_row.getInt(0);
/* 045 */
/* 046 */ boolean mapelements_isNull = true;
/* 047 */ int mapelements_value = -1;
/* 048 */ if (!false) {
/* 049 */ mapelements_argValue = inputadapter_value;
/* 050 */
/* 051 */ mapelements_isNull = false;
/* 052 */ if (!mapelements_isNull) {
/* 053 */ Object mapelements_funcResult = null;
/* 054 */ mapelements_funcResult = ((scala.Function1) references[0]).apply(mapelements_argValue);
/* 055 */ if (mapelements_funcResult == null) {
/* 056 */ mapelements_isNull = true;
/* 057 */ } else {
/* 058 */ mapelements_value = (Integer) mapelements_funcResult;
/* 059 */ }
/* 060 */
/* 061 */ }
/* 062 */
/* 063 */ }
/* 064 */
/* 065 */ serializefromobject_rowWriter.zeroOutNullBytes();
/* 066 */
/* 067 */ if (mapelements_isNull) {
/* 068 */ serializefromobject_rowWriter.setNullAt(0);
/* 069 */ } else {
/* 070 */ serializefromobject_rowWriter.write(0, mapelements_value);
/* 071 */ }
/* 072 */ append(serializefromobject_result);
/* 073 */ if (shouldStop()) return;
/* 074 */ }
/* 075 */ }
/* 076 */ }
```
Generated code with this PR (lines 48-56 are changed)
```java
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */ private Object[] references;
/* 007 */ private scala.collection.Iterator[] inputs;
/* 008 */ private scala.collection.Iterator inputadapter_input;
/* 009 */ private UnsafeRow deserializetoobject_result;
/* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder deserializetoobject_holder;
/* 011 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter deserializetoobject_rowWriter;
/* 012 */ private int mapelements_argValue;
/* 013 */ private UnsafeRow mapelements_result;
/* 014 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder mapelements_holder;
/* 015 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter mapelements_rowWriter;
/* 016 */ private UnsafeRow serializefromobject_result;
/* 017 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder serializefromobject_holder;
/* 018 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter serializefromobject_rowWriter;
/* 019 */
/* 020 */ public GeneratedIterator(Object[] references) {
/* 021 */ this.references = references;
/* 022 */ }
/* 023 */
/* 024 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 025 */ partitionIndex = index;
/* 026 */ this.inputs = inputs;
/* 027 */ inputadapter_input = inputs[0];
/* 028 */ deserializetoobject_result = new UnsafeRow(1);
/* 029 */ this.deserializetoobject_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(deserializetoobject_result, 0);
/* 030 */ this.deserializetoobject_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(deserializetoobject_holder, 1);
/* 031 */
/* 032 */ mapelements_result = new UnsafeRow(1);
/* 033 */ this.mapelements_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(mapelements_result, 0);
/* 034 */ this.mapelements_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(mapelements_holder, 1);
/* 035 */ serializefromobject_result = new UnsafeRow(1);
/* 036 */ this.serializefromobject_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(serializefromobject_result, 0);
/* 037 */ this.serializefromobject_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(serializefromobject_holder, 1);
/* 038 */
/* 039 */ }
/* 040 */
/* 041 */ protected void processNext() throws java.io.IOException {
/* 042 */ while (inputadapter_input.hasNext() && !stopEarly()) {
/* 043 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 044 */ int inputadapter_value = inputadapter_row.getInt(0);
/* 045 */
/* 046 */ boolean mapelements_isNull = true;
/* 047 */ int mapelements_value = -1;
/* 048 */ if (!false) {
/* 049 */ mapelements_argValue = inputadapter_value;
/* 050 */
/* 051 */ mapelements_isNull = false;
/* 052 */ if (!mapelements_isNull) {
/* 053 */ mapelements_value = ((scala.Function1) references[0]).apply$mcII$sp(mapelements_argValue);
/* 054 */ }
/* 055 */
/* 056 */ }
/* 057 */
/* 058 */ serializefromobject_rowWriter.zeroOutNullBytes();
/* 059 */
/* 060 */ if (mapelements_isNull) {
/* 061 */ serializefromobject_rowWriter.setNullAt(0);
/* 062 */ } else {
/* 063 */ serializefromobject_rowWriter.write(0, mapelements_value);
/* 064 */ }
/* 065 */ append(serializefromobject_result);
/* 066 */ if (shouldStop()) return;
/* 067 */ }
/* 068 */ }
/* 069 */ }
```
Java bytecode for methods for `i => i * 7`
```java
$ javap -c Test\$\$anonfun\$5\$\$anonfun\$apply\$mcV\$sp\$1.class
Compiled from "Test.scala"
public final class org.apache.spark.sql.Test$$anonfun$5$$anonfun$apply$mcV$sp$1 extends scala.runtime.AbstractFunction1$mcII$sp implements scala.Serializable {
public static final long serialVersionUID;
public final int apply(int);
Code:
0: aload_0
1: iload_1
2: invokevirtual #18 // Method apply$mcII$sp:(I)I
5: ireturn
public int apply$mcII$sp(int);
Code:
0: iload_1
1: bipush 7
3: imul
4: ireturn
public final java.lang.Object apply(java.lang.Object);
Code:
0: aload_0
1: aload_1
2: invokestatic #29 // Method scala/runtime/BoxesRunTime.unboxToInt:(Ljava/lang/Object;)I
5: invokevirtual #31 // Method apply:(I)I
8: invokestatic #35 // Method scala/runtime/BoxesRunTime.boxToInteger:(I)Ljava/lang/Integer;
11: areturn
public org.apache.spark.sql.Test$$anonfun$5$$anonfun$apply$mcV$sp$1(org.apache.spark.sql.Test$$anonfun$5);
Code:
0: aload_0
1: invokespecial #42 // Method scala/runtime/AbstractFunction1$mcII$sp."<init>":()V
4: return
}
```
## How was this patch tested?
Added new test suites to `DatasetPrimitiveSuite`.
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes #17172 from kiszk/SPARK-19008.
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## What changes were proposed in this pull request?
Fix the `throw new IllegalStateException` if statement part.
## How is this patch tested
Regression test
Author: Burak Yavuz <brkyvz@gmail.com>
Closes #17228 from brkyvz/kafka-cause-fix.
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