## What changes were proposed in this pull request?
In SPARK-27612, one correctness issue was reported. When protocol 4 is used to pickle Python objects, we found that unpickled objects were wrong. A temporary fix was proposed by not using highest protocol.
It was found that Opcodes.MEMOIZE was appeared in the opcodes in protocol 4. It is suspect to this issue.
A deeper dive found that Opcodes.MEMOIZE stores objects into internal map of Unpickler object. We use single Unpickler object to unpickle serialized Python bytes. Stored objects intervenes next round of unpickling, if the map is not cleared.
We has two options:
1. Continues to reuse Unpickler, but calls its close after each unpickling.
2. Not to reuse Unpickler and create new Unpickler object in each unpickling.
This patch takes option 1.
## How was this patch tested?
Passing the test added in SPARK-27612 (#24519).
Closes#24521 from viirya/SPARK-27629.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
`date_trunc` argument order was flipped, phrasing was awkward.
## How was this patch tested?
Documentation-only.
Closes#24522 from mojodna/patch-2.
Authored-by: Seth Fitzsimmons <seth@mojodna.net>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
SQLConf does not load hive-site.xml.So HiveSerDe should fall back to hadoopconf if hive.default.fileformat is not found in SQLConf
## How was this patch tested?
Tested manually.
Added UT
Closes#24489 from sandeep-katta/spark-27555.
Authored-by: sandeep katta <sandeep.katta2007@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR replaces for-comprehension if statement with while loop to gain better performance in `TypeUtils.compareBinary`.
## How was this patch tested?
Add UT to test old version and new version comparison result
Closes#24494 from woudygao/opt_binary_compare.
Authored-by: gaoweikang <gaoweikang@bytedance.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
`Row.toString` is currently causing the useless creation of an `Array` containing all the values in the row before generating the string containing it. This operation adds a considerable overhead.
The PR proposes to avoid this operation in order to get a faster implementation.
## How was this patch tested?
Run
```scala
test("Row toString perf test") {
val n = 100000
val rows = (1 to n).map { i =>
Row(i, i.toDouble, i.toString, i.toShort, true, null)
}
// warmup
(1 to 10).foreach { _ => rows.foreach(_.toString) }
val times = (1 to 100).map { _ =>
val t0 = System.nanoTime()
rows.foreach(_.toString)
val t1 = System.nanoTime()
t1 - t0
}
// scalastyle:off println
println(s"Avg time on ${times.length} iterations for $n toString:" +
s" ${times.sum.toDouble / times.length / 1e6} ms")
// scalastyle:on println
}
```
Before the PR:
```
Avg time on 100 iterations for 100000 toString: 61.08408419 ms
```
After the PR:
```
Avg time on 100 iterations for 100000 toString: 38.16539432 ms
```
This means the new implementation is about 1.60X faster than the original one.
Closes#24505 from mgaido91/SPARK-27607.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
After we added other fields, `arguments`, `examples`, `note` and `since` at SPARK-21485 and `deprecated` at SPARK-27328, we have nicer way to separately describe extended usages.
`extended` field and method at `ExpressionDescription`/`ExpressionInfo` is now pretty useless - it's not used in Spark side and only exists to keep backward compatibility.
This PR proposes to deprecate it.
## How was this patch tested?
Manually checked the deprecation waring is properly shown.
Closes#24500 from HyukjinKwon/SPARK-27606.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR is to add test cases for ensuring that we do not have unnecessary access to externalCatalog.
In the future, we can follow these examples to improve our test coverage in this area.
## How was this patch tested?
N/A
Closes#24511 from gatorsmile/addTestcaseSpark-27618.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR adds SparkR with Arrow optimization documentation.
Note that looks CRAN issue in Arrow side won't look likely fixed soon, IMHO, even after Spark 3.0.
If it happen to be fixed, I will fix this doc too later.
Another note is that Arrow R package itself requires R 3.5+. So, I intentionally didn't note this.
## How was this patch tested?
Manually built and checked.
Closes#24506 from HyukjinKwon/SPARK-26924.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Fix bug in UnivocityParser. makeConverter method didn't work correctly for UsedDefinedType
## How was this patch tested?
A test suite for UnivocityParser has been extended.
Closes#24496 from kalkolab/spark-27591.
Authored-by: Artem Kalchenko <artem.kalchenko@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
On a second look in comments, seems like the JobConf isn't needed anymore here. It was used inconsistently before, it seems, and I don't see any reason a Hadoop Job config is required here anyway.
## How was this patch tested?
Existing tests.
Closes#24491 from srowen/SPARK-26936.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/24144 . #24144 missed one case: when hash aggregate fallback to sort aggregate, the life cycle of UDAF is: INIT -> UPDATE -> MERGE -> FINISH.
However, not all Hive UDAF can support it. Hive UDAF knows the aggregation mode when creating the aggregation buffer, so that it can create different buffers for different inputs: the original data or the aggregation buffer. Please see an example in the [sketches library](7f9e76e9e0/src/main/java/com/yahoo/sketches/hive/cpc/DataToSketchUDAF.java (L107)). The buffer for UPDATE may not support MERGE.
This PR updates the Hive UDAF adapter in Spark to support INIT -> UPDATE -> MERGE -> FINISH, by turning it to INIT -> UPDATE -> FINISH + IINIT -> MERGE -> FINISH.
## How was this patch tested?
a new test case
Closes#24459 from cloud-fan/hive-udaf.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
If a file is too big (>2GB), we should fail fast and do not try to read the file.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24483 from mengxr/SPARK-27588.
Authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
There is a MemorySink v2 already so v1 can be removed. In this PR I've removed it completely.
What this PR contains:
* V1 memory sink removal
* V2 memory sink renamed to become the only implementation
* Since DSv2 sends exceptions in a chained format (linking them with cause field) I've made python side compliant
* Adapted all the tests
## How was this patch tested?
Existing unit tests.
Closes#24403 from gaborgsomogyi/SPARK-23014.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This PR avoids usage of reflective calls in Scala. It removes the import that suppresses the warnings and rewrites code in small ways to avoid accessing methods that aren't technically accessible.
## How was this patch tested?
Existing tests.
Closes#24463 from srowen/SPARK-27571.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
I want to get rid of as much use of `scala.language.existentials` as possible for 3.0. It's a complicated language feature that generates warnings unless this value is imported. It might even be on the way out of Scala: https://contributors.scala-lang.org/t/proposal-to-remove-existential-types-from-the-language/2785
For Spark, it comes up mostly where the code plays fast and loose with generic types, not the advanced situations you'll often see referenced where this feature is explained. For example, it comes up in cases where a function returns something like `(String, Class[_])`. Scala doesn't like matching this to any other instance of `(String, Class[_])` because doing so requires inferring the existence of some type that satisfies both. Seems obvious if the generic type is a wildcard, but, not technically something Scala likes to let you get away with.
This is a large PR, and it only gets rid of _most_ instances of `scala.language.existentials`. The change should be all compile-time and shouldn't affect APIs or logic.
Many of the changes simply touch up sloppiness about generic types, making the known correct value explicit in the code.
Some fixes involve being more explicit about the existence of generic types in methods. For instance, `def foo(arg: Class[_])` seems innocent enough but should really be declared `def foo[T](arg: Class[T])` to let Scala select and fix a single type when evaluating calls to `foo`.
For kind of surprising reasons, this comes up in places where code evaluates a tuple of things that involve a generic type, but is OK if the two parts of the tuple are evaluated separately.
One key change was altering `Utils.classForName(...): Class[_]` to the more correct `Utils.classForName[T](...): Class[T]`. This caused a number of small but positive changes to callers that otherwise had to cast the result.
In several tests, `Dataset[_]` was used where `DataFrame` seems to be the clear intent.
Finally, in a few cases in MLlib, the return type `this.type` was used where there are no subclasses of the class that uses it. This really isn't needed and causes issues for Scala reasoning about the return type. These are just changed to be concrete classes as return types.
After this change, we have only a few classes that still import `scala.language.existentials` (because modifying them would require extensive rewrites to fix) and no build warnings.
## How was this patch tested?
Existing tests.
Closes#24431 from srowen/SPARK-27536.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Currently `countDistinct("*")` doesn't work. An analysis exception is thrown:
```scala
val df = sql("select id % 100 from range(100000)")
df.select(countDistinct("*")).first()
org.apache.spark.sql.AnalysisException: Invalid usage of '*' in expression 'count';
```
Users need to use `expr`.
```scala
df.select(expr("count(distinct(*))")).first()
```
This limits some API usage like `df.select(count("*"), countDistinct("*))`.
The PR takes the simplest fix that lets analyzer expand star and resolve `count` function.
## How was this patch tested?
Added unit test.
Closes#24482 from viirya/SPARK-27581.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
```
========================================================================
Running Scala style checks
========================================================================
[info] Checking Scala style using SBT with these profiles: -Phadoop-2.7 -Pkubernetes -Phive-thriftserver -Pkinesis-asl -Pyarn -Pspark-ganglia-lgpl -Phive -Pmesos
Scalastyle checks failed at following occurrences:
[error] /home/jenkins/workspace/SparkPullRequestBuilder/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/FileScan.scala:29:0: org.apache.spark.sql.sources.Filter is in wrong order relative to org.apache.spark.sql.sources.v2.reader..
[error] Total time: 17 s, completed Apr 29, 2019 3:09:43 AM
```
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/104987/console
## How was this patch tested?
manual tests:
```
dev/scalastyle
```
Closes#24487 from wangyum/SPARK-27580.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The method `QueryPlan.sameResult` is used for comparing logical plans in order to:
1. cache data in CacheManager
2. uncache data in CacheManager
3. Reuse subqueries
4. etc...
Currently the method `sameReuslt` always return false for `BatchScanExec`. We should fix it by implementing `doCanonicalize` for the node.
## How was this patch tested?
Unit test
Closes#24475 from gengliangwang/sameResultForV2.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
A follow-up task from SPARK-25348. To save I/O cost, Spark shouldn't attempt to read the file if users didn't request the `content` column. For example:
```
spark.read.format("binaryFile").load(path).filter($"length" < 1000000).count()
```
## How was this patch tested?
Unit test added.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24473 from WeichenXu123/SPARK-27534.
Lead-authored-by: Xiangrui Meng <meng@databricks.com>
Co-authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
This PR addresses SPARK-23619: https://issues.apache.org/jira/browse/SPARK-23619
It adds additional comments indicating the default column names for the `explode` and `posexplode`
functions in Spark-SQL.
Functions for which comments have been updated so far:
* stack
* inline
* explode
* posexplode
* explode_outer
* posexplode_outer
## How was this patch tested?
This is just a change in the comments. The package builds and tests successfullly after the change.
Closes#23748 from jashgala/SPARK-23619.
Authored-by: Jash Gala <jashgala@amazon.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
some minor updates:
- `Execute` action miss `name` message
- typo in SS document
- typo in SQLConf
## How was this patch tested?
N/A
Closes#24466 from uncleGen/minor-fix.
Authored-by: uncleGen <hustyugm@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/24012 , to add the corresponding capabilities for streaming.
## How was this patch tested?
existing tests
Closes#24129 from cloud-fan/capability.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
We can get the latest downloadable Spark versions from https://dist.apache.org/repos/dist/release/spark/
## How was this patch tested?
manually.
Closes#24454 from cloud-fan/test.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When there are mismatched types among cases or else values in case when expression, current error message is hard to read to figure out what and where the mismatch is.
This patch simply improves the error message for mismatched types for case when.
Before:
```scala
scala> spark.range(100).select(when('id === 1, array(struct('id * 123456789 + 123456789 as "x"))).otherwise(array(struct('id * 987654321 + 987654321 as
"y"))))
org.apache.spark.sql.AnalysisException: cannot resolve 'CASE WHEN (`id` = CAST(1 AS BIGINT)) THEN array(named_struct('x', ((`id` * CAST(123456789 AS BI
GINT)) + CAST(123456789 AS BIGINT)))) ELSE array(named_struct('y', ((`id` * CAST(987654321 AS BIGINT)) + CAST(987654321 AS BIGINT)))) END' due to data
type mismatch: THEN and ELSE expressions should all be same type or coercible to a common type;;
```
After:
```scala
scala> spark.range(100).select(when('id === 1, array(struct('id * 123456789 + 123456789 as "x"))).otherwise(array(struct('id * 987654321 + 987654321 as
"y"))))
org.apache.spark.sql.AnalysisException: cannot resolve 'CASE WHEN (`id` = CAST(1 AS BIGINT)) THEN array(named_struct('x', ((`id` * CAST(123456789 AS BI
GINT)) + CAST(123456789 AS BIGINT)))) ELSE array(named_struct('y', ((`id` * CAST(987654321 AS BIGINT)) + CAST(987654321 AS BIGINT)))) END' due to data
type mismatch: THEN and ELSE expressions should all be same type or coercible to a common type, got CASE WHEN ... THEN array<struct<x:bigint>> ELSE arr
ay<struct<y:bigint>> END;;
```
## How was this patch tested?
Added unit test.
Closes#24453 from viirya/SPARK-27551.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
update `HiveExternalCatalogVersionsSuite` to test 2.4.2, as 2.4.1 will be removed from Mirror Network soon.
## How was this patch tested?
N/A
Closes#24452 from cloud-fan/release.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This patch makes several test flakiness fixes.
## How was this patch tested?
N/A
Closes#24434 from gatorsmile/fixFlakyTest.
Lead-authored-by: gatorsmile <gatorsmile@gmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Kind of related to https://github.com/gatorsmile/spark/pull/5 - let's update genjavadoc to see if it generates fewer spurious javadoc errors to begin with.
## How was this patch tested?
Existing docs build
Closes#24443 from srowen/genjavadoc013.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Migrate JSON to File Data Source V2
## How was this patch tested?
Unit test
Closes#24058 from gengliangwang/jsonV2.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR is follow up of https://github.com/apache/spark/pull/24286. As gatorsmile pointed out that column with null value is inaccurate as well.
```
> select key from test;
2
NULL
1
spark-sql> desc extended test key;
col_name key
data_type int
comment NULL
min 1
max 2
num_nulls 1
distinct_count 2
```
The distinct count should be distinct_count + 1 when column contains null value.
## How was this patch tested?
Existing tests & new UT added.
Closes#24436 from pengbo/aggregation_estimation.
Authored-by: pengbo <bo.peng1019@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Added new JSON benchmarks related to date and timestamps operations:
- Write date/timestamp to JSON files
- `to_json()` and `from_json()` for dates and timestamps
- Read date/timestamps from JSON files, and infer schemas
- Parse and infer schemas from `Dataset[String]`
Also existing JSON benchmarks are ported on `NoOp` datasource.
Closes#24430 from MaxGekk/json-datetime-benchmark.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Added new CSV benchmarks related to date and timestamps operations:
- Write date/timestamp to CSV files
- `to_csv()` and `from_csv()` for dates and timestamps
- Read date/timestamps from CSV files, and infer schemas
- Parse and infer schemas from `Dataset[String]`
Also existing CSV benchmarks are ported on `NoOp` datasource.
Closes#24429 from MaxGekk/csv-timestamp-benchmark.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to use the `TIMESTAMP_MICROS` logical type for timestamps written to parquet files. The type matches semantically to Catalyst's `TimestampType`, and stores microseconds since epoch in UTC time zone. This will allow to avoid conversions of microseconds to nanoseconds and to Julian calendar. Also this will reduce sizes of written parquet files.
## How was this patch tested?
By existing test suites.
Closes#24425 from MaxGekk/parquet-timestamp_micros.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Currently "EXPLAIN DESC TABLE" is special cased and outputs a single row relation as following.
Current output:
```sql
spark-sql> EXPLAIN DESCRIBE TABLE t;
== Physical Plan ==
*(1) Scan OneRowRelation[]
```
This is not consistent with how we handle explain processing for other commands. In this PR, the inconsistency is handled by removing the special handling for "describe table".
After change:
```sql
spark-sql> EXPLAIN DESC EXTENDED t
== Physical Plan ==
Execute DescribeTableCommand
+- DescribeTableCommand `t`, true
```
## How was this patch tested?
Added new tests in SQLQueryTestSuite.
Closes#24427 from dilipbiswal/describe_table_explain2.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Otherwise, tests that use tables from multiple sessions will run into issues if they access the same table. The correct location is in shared state.
A couple other minor test improvements.
cc gatorsmile srinathshankar
## How was this patch tested?
Existing unit tests.
Closes#24302 from ericl/test-conflicts.
Lead-authored-by: Eric Liang <ekl@databricks.com>
Co-authored-by: Eric Liang <ekhliang@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to parse strings to timestamps in microsecond precision by the ` to_timestamp()` function if the specified pattern contains a sub-pattern for seconds fractions.
Closes#24342
## How was this patch tested?
By `DateFunctionsSuite` and `DateExpressionsSuite`
Closes#24420 from MaxGekk/to_timestamp-microseconds3.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Added tests to check migration from `INT96` to `TIMESTAMP_MICROS` (`INT64`) for timestamps in parquet files. In particular:
- Append `TIMESTAMP_MICROS` timestamps to **existing parquet** files with `INT96` timestamps
- Append `TIMESTAMP_MICROS` timestamps to a table with `INT96` timestamps
- Append `INT96` to `TIMESTAMP_MICROS` timestamps in **parquet files**
- Append `INT96` to `TIMESTAMP_MICROS` timestamps in a **table**
Closes#24417 from MaxGekk/parquet-timestamp-int64-tests.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
Currently running explain on describe query gives a little confusing output. This is a minor pr that improves the output of explain.
Before
```
1.EXPLAIN DESCRIBE WITH s AS (SELECT 'hello' as col1) SELECT * FROM s;
== Physical Plan ==
Execute DescribeQueryCommand
+- DescribeQueryCommand CTE [s]
2.EXPLAIN EXTENDED DESCRIBE SELECT * from s1 where c1 > 0;
== Physical Plan ==
Execute DescribeQueryCommand
+- DescribeQueryCommand 'Project [*]
```
After
```
1. EXPLAIN DESCRIBE WITH s AS (SELECT 'hello' as col1) SELECT * FROM s;
== Physical Plan ==
Execute DescribeQueryCommand
+- DescribeQueryCommand WITH s AS (SELECT 'hello' as col1) SELECT * FROM s
2. EXPLAIN DESCRIBE SELECT * from s1 where c1 > 0;
== Physical Plan ==
Execute DescribeQueryCommand
+- DescribeQueryCommand SELECT * from s1 where c1 > 0
```
Added a couple of tests in describe-query.sql under SQLQueryTestSuite.
Closes#24385 from dilipbiswal/describe_query_explain.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Support 4 kinds of filters:
- LessThan
- LessThanOrEqual
- GreatThan
- GreatThanOrEqual
Support filters applied on 2 columns:
- modificationTime
- length
Note:
In order to support datasource filter push-down, I flatten schema to be:
```
val schema = StructType(
StructField("path", StringType, false) ::
StructField("modificationTime", TimestampType, false) ::
StructField("length", LongType, false) ::
StructField("content", BinaryType, true) :: Nil)
```
## How was this patch tested?
To be added.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24387 from WeichenXu123/binary_ds_filter.
Lead-authored-by: WeichenXu <weichen.xu@databricks.com>
Co-authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
Because a review is resolved during analysis when we create a dataset, the content of the view is determined when the dataset is created, not when it is evaluated. Now the explain result of a dataset is not correctly consistent with the collected result of it, because we use pre-analyzed logical plan of the dataset in explain command. The explain command will analyzed the logical plan passed in. So if a view is changed after the dataset was created, the plans shown by explain command aren't the same with the plan of the dataset.
```scala
scala> spark.range(10).createOrReplaceTempView("test")
scala> spark.range(5).createOrReplaceTempView("test2")
scala> spark.sql("select * from test").createOrReplaceTempView("tmp001")
scala> val df = spark.sql("select * from tmp001")
scala> spark.sql("select * from test2").createOrReplaceTempView("tmp001")
scala> df.show
+---+
| id|
+---+
| 0|
| 1|
| 2|
| 3|
| 4|
| 5|
| 6|
| 7|
| 8|
| 9|
+---+
scala> df.explain
```
Before:
```scala
== Physical Plan ==
*(1) Range (0, 5, step=1, splits=12)
```
After:
```scala
== Physical Plan ==
*(1) Range (0, 10, step=1, splits=12)
```
## How was this patch tested?
Manually test and unit test.
Closes#24415 from viirya/SPARK-27439.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In the PR, I propose more precise description of `TimestampType` and `DateType`, how they store timestamps and dates internally.
Closes#24424 from MaxGekk/timestamp-date-type-doc.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In file source V1, if some file is deleted manually, reading the DataFrame/Table will throws an exception with suggestion message
```
It is possible the underlying files have been updated. You can explicitly invalidate the cache in Spark by running 'REFRESH TABLE tableName' command in SQL or by recreating the Dataset/DataFrame involved.
```
After refreshing the table/DataFrame, the reads should return correct results.
We should follow it in file source V2 as well.
## How was this patch tested?
Unit test
Closes#24401 from gengliangwang/refreshFileTable.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
A previous change moved the removal of empty window expressions to the RemoveNoopOperations rule, which comes after the CollapseWindow rule. Therefore, by the time we get to CollapseWindow, we aren't guaranteed that empty windows have been removed. This change checks that the window expressions are not empty, and only collapses the windows if both windows are non-empty.
A lengthier description and repro steps here: https://issues.apache.org/jira/browse/SPARK-27514
## How was this patch tested?
A unit test, plus I reran the breaking case mentioned in the Jira ticket.
Closes#24411 from yifeih/yh/spark-27514.
Authored-by: Yifei Huang <yifeih@palantir.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR add test for [HIVE-13083](https://issues.apache.org/jira/browse/HIVE-13083): Writing HiveDecimal to ORC can wrongly suppress present stream.
## How was this patch tested?
manual tests:
```
build/sbt "hive/testOnly *HiveOrcQuerySuite" -Phive -Phadoop-3.2
```
Closes#24397 from wangyum/SPARK-26437.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The test time of `HiveClientVersions` is around 3.5 minutes.
This PR is to add it into the parallel test suite list. To make sure there is no colliding warehouse location, we can change the warehouse path to a temporary directory.
## How was this patch tested?
Unit test
Closes#24404 from gengliangwang/parallelTestFollowUp.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
We added nested schema pruning support to Orc V2 recently. The benchmark result should be updated. The benchmark numbers are obtained by running benchmark on r3.xlarge machine.
## How was this patch tested?
Test only change.
Closes#24399 from viirya/update-orcv2-benchmark.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This patch modifies SparkBuild so that the largest / slowest test suites (or collections of suites) can run in their own forked JVMs, allowing them to be run in parallel with each other. This opt-in / whitelisting approach allows us to increase parallelism without having to fix a long-tail of flakiness / brittleness issues in tests which aren't performance bottlenecks.
See comments in SparkBuild.scala for information on the details, including a summary of why we sometimes opt to run entire groups of tests in a single forked JVM .
The time of full new pull request test in Jenkins is reduced by around 53%:
before changes: 4hr 40min
after changes: 2hr 13min
## How was this patch tested?
Unit test
Closes#24373 from gengliangwang/parallelTest.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently, a `Dateset` with file source V2 always return empty results for method `Dataset.inputFiles()`.
We should fix it.
## How was this patch tested?
Unit test
Closes#24393 from gengliangwang/inputFiles.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/24332 introduced an unnecessary `import` statement and two slight issues in the codegen templates in `Cast` for `Date` <-> `Timestamp`.
This PR removes the unused import statement and fixes the slight codegen issue.
The issue in those two codegen templates is this pattern:
```scala
val zid = JavaCode.global(
ctx.addReferenceObj("zoneId", zoneId, "java.time.ZoneId"),
zoneId.getClass)
```
`zoneId` can refer to an instance of a non-public class, e.g. `java.time.ZoneRegion`, and while this code correctly puts in the 3rd argument to `ctx.addReferenceObj()`, it's still passing `zoneId.getClass` to `JavaCode.global()` which is not desirable, but doesn't cause any immediate bugs in this particular case, because `zid` is used in an expression immediately afterwards.
If this `zid` ever needs to spill to any explicitly typed variables, e.g. a local variable, and if the spill handling uses the `javaType` on this `GlobalVariable`, it'd generate code that looks like:
```java
java.time.ZoneRegion value1 = ((java.time.ZoneId) references[2] /* literal */);
```
which would then be a real bug:
- a non-accessible type `java.time.ZoneRegion` is referenced in the generated code, and
- `ZoneId` -> `ZoneRegion` requires an explicit downcast.
## How was this patch tested?
Existing tests. This PR does not change behavior, and the original PR won't cause any real behavior bug to begin with.
Closes#24392 from rednaxelafx/spark-27423-followup.
Authored-by: Kris Mok <kris.mok@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR adds support for pushing down LeftSemi and LeftAnti joins below the Join operator.
This is a prerequisite work thats needed for the subsequent task of moving the subquery rewrites to the beginning of optimization phase.
The larger PR is [here](https://github.com/apache/spark/pull/23211) . This PR addresses the comment at [link](https://github.com/apache/spark/pull/23211#issuecomment-445705922).
## How was this patch tested?
Added tests under LeftSemiAntiJoinPushDownSuite.
Closes#24331 from dilipbiswal/SPARK-19712-pushleftsemi-belowjoin.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
A followup of https://github.com/apache/spark/pull/24164
broadcast hint should be respected for broadcast nested loop join. This PR also refactors the related code a little bit, to save duplicated code.
## How was this patch tested?
new tests
Closes#24376 from cloud-fan/join.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Finish the rest work of https://github.com/apache/spark/pull/24317, https://github.com/apache/spark/pull/9030
a. Implement Kryo serialization for UnsafeArrayData
b. fix UnsafeMapData Java/Kryo Serialization issue when two machines have different Oops size
c. Move the duplicate code "getBytes()" to Utils.
## How was this patch tested?
According Units has been added & tested
Closes#24357 from pengbo/SPARK-27416_new.
Authored-by: pengbo <bo.peng1019@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Implement binary file data source in Spark.
Format name: "binaryFile" (case-insensitive)
Schema:
- content: BinaryType
- status: StructType
- path: StringType
- modificationTime: TimestampType
- length: LongType
Options:
* pathGlobFilter (instead of pathFilterRegex) to reply on GlobFilter behavior
* maxBytesPerPartition is not implemented since it is controlled by two SQL confs: maxPartitionBytes and openCostInBytes.
## How was this patch tested?
Unit test added.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24354 from WeichenXu123/binary_file_datasource.
Lead-authored-by: WeichenXu <weichen.xu@databricks.com>
Co-authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
Pass partitionBy columns as options and feature-flag this behavior.
## How was this patch tested?
A new unit test.
Closes#24365 from liwensun/partitionby.
Authored-by: liwensun <liwen.sun@databricks.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## What changes were proposed in this pull request?
In SchemaPruning rule, there is duplicate code for data source v1 and v2. Their logic is the same and we can refactor the rule to remove duplicate code.
## How was this patch tested?
Existing tests.
Closes#24383 from viirya/SPARK-27476.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Added Constant instead of referring the same String literal "spark.buffer.pageSize" from many places
## How was this patch tested?
Run the corresponding Unit Test Cases manually.
Closes#24368 from shivusondur/Constant.
Authored-by: shivusondur <shivusondur@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the rule `PreprocessTableCreation`, if an existing table is appended with a different provider, the action will fail.
Currently, there are two implementations for file sources and creating a table with file source V2 will always fall back to V1 FileFormat. We should consider the following cases as valid:
1. Appending a table with file source V2 provider using the v1 file format
2. Appending a table with v1 file format provider using file source V2 format
## How was this patch tested?
Unit test
Closes#24356 from gengliangwang/fixTableProvider.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The upper bound of group-by columns row number is to multiply distinct counts of group-by columns. However, column with only null value will cause the output row number to be 0 which is incorrect.
Ex:
col1 (distinct: 2, rowCount 2)
col2 (distinct: 0, rowCount 2)
=> group by col1, col2
Actual: output rows: 0
Expected: output rows: 2
## How was this patch tested?
According unit test has been added, plus manual test has been done in our tpcds benchmark environement.
Closes#24286 from pengbo/master.
Lead-authored-by: pengbo <bo.peng1019@gmail.com>
Co-authored-by: mingbo_pb <mingbo.pb@alibaba-inc.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This is a minor pr to add a test to describe a multi select query.
## How was this patch tested?
Added a test in describe-query.sql
Closes#24370 from dilipbiswal/describe-query-multiselect-test.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR moves the checks done in `WholeStageCodegen.limitNotReachedCond` into a separate protected method. This makes it easier to introduce new leaf or blocking nodes.
## How was this patch tested?
Existing tests.
Closes#24358 from hvanhovell/SPARK-27449.
Authored-by: herman <herman@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a regression caused by https://github.com/apache/spark/pull/24150
`select * from (from a select * select *)` is supported in 2.4, and we should keep supporting it.
This PR merges the parser rule for single and multi select statements, as they are very similar.
## How was this patch tested?
a new test case
Closes#24348 from cloud-fan/parser.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently, if we select the UDF `input_file_name` as a column in file source V2, the results are empty.
We should support it in file source V2.
## How was this patch tested?
Unit test
Closes#24347 from gengliangwang/input_file_name.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
SPARK-27199 introduced the use of `ZoneId` instead of `TimeZone` in a few date/time expressions.
There were 3 occurrences of `ctx.addReferenceObj("zoneId", zoneId)` in that PR, which had a bug because while the `java.time.ZoneId` base type is public, the actual concrete implementation classes are not public, so using the 2-arg version of `CodegenContext.addReferenceObj` would incorrectly generate code that reference non-public types (`java.time.ZoneRegion`, to be specific). The 3-arg version should be used, with the class name of the referenced object explicitly specified to the public base type.
One of such occurrences was caught in testing in the main PR of SPARK-27199 (https://github.com/apache/spark/pull/24141), for `DateFormatClass`. But the other 2 occurrences slipped through because there were no test cases that covered them.
Example of this bug in the current Apache Spark master, in a Spark Shell:
```
scala> Seq(("2016-04-08", "yyyy-MM-dd")).toDF("s", "f").repartition(1).selectExpr("to_unix_timestamp(s, f)").show
...
java.lang.IllegalAccessError: tried to access class java.time.ZoneRegion from class org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1
```
This PR fixes the codegen issues and adds the corresponding unit tests.
## How was this patch tested?
Enhanced tests in `DateExpressionsSuite` for `to_unix_timestamp` and `from_unixtime`.
Closes#24352 from rednaxelafx/fix-spark-27199.
Authored-by: Kris Mok <kris.mok@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
While using vi or vim to edit the test files the .swp or .swo files are created and attempt to run the test suite in the presence of these files causes errors like below :
```
nfo] - subquery/exists-subquery/.exists-basic.sql.swp *** FAILED *** (117 milliseconds)
[info] java.io.FileNotFoundException: /Users/dbiswal/mygit/apache/spark/sql/core/target/scala-2.12/test-classes/sql-tests/results/subquery/exists-subquery/.exists-basic.sql.swp.out (No such file or directory)
[info] at java.io.FileInputStream.open0(Native Method)
[info] at java.io.FileInputStream.open(FileInputStream.java:195)
[info] at java.io.FileInputStream.<init>(FileInputStream.java:138)
[info] at org.apache.spark.sql.catalyst.util.package$.fileToString(package.scala:49)
[info] at org.apache.spark.sql.SQLQueryTestSuite.runQueries(SQLQueryTestSuite.scala:247)
[info] at org.apache.spark.sql.SQLQueryTestSuite.$anonfun$runTest$11(SQLQueryTestSuite.scala:192)
```
~~This minor pr adds these temp files in the ignore list.~~
While computing the list of test files to process, only consider files with `.sql` extension. This makes sure the unwanted temp files created from various editors are ignored from processing.
## How was this patch tested?
Verified manually.
Closes#24333 from dilipbiswal/dkb_sqlquerytest.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Fix build warnings -- see some details below.
But mostly, remove use of postfix syntax where it causes warnings without the `scala.language.postfixOps` import. This is mostly in expressions like "120000 milliseconds". Which, I'd like to simplify to things like "2.minutes" anyway.
## How was this patch tested?
Existing tests.
Closes#24314 from srowen/SPARK-27404.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR extends the existing BROADCAST join hint (for both broadcast-hash join and broadcast-nested-loop join) by implementing other join strategy hints corresponding to the rest of Spark's existing join strategies: shuffle-hash, sort-merge, cartesian-product. The hint names: SHUFFLE_MERGE, SHUFFLE_HASH, SHUFFLE_REPLICATE_NL are partly different from the code names in order to make them clearer to users and reflect the actual algorithms better.
The hinted strategy will be used for the join with which it is associated if it is applicable/doable.
Conflict resolving rules in case of multiple hints:
1. Conflicts within either side of the join: take the first strategy hint specified in the query, or the top hint node in Dataset. For example, in "select /*+ merge(t1) */ /*+ broadcast(t1) */ k1, v2 from t1 join t2 on t1.k1 = t2.k2", take "merge(t1)"; in ```df1.hint("merge").hint("shuffle_hash").join(df2)```, take "shuffle_hash". This is a general hint conflict resolving strategy, not specific to join strategy hint.
2. Conflicts between two sides of the join:
a) In case of different strategy hints, hints are prioritized as ```BROADCAST``` over ```SHUFFLE_MERGE``` over ```SHUFFLE_HASH``` over ```SHUFFLE_REPLICATE_NL```.
b) In case of same strategy hints but conflicts in build side, choose the build side based on join type and size.
## How was this patch tested?
Added new UTs.
Closes#24164 from maryannxue/join-hints.
Lead-authored-by: maryannxue <maryannxue@apache.org>
Co-authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
System shall update the table stats automatically if user set spark.sql.statistics.size.autoUpdate.enabled as true, currently this property is not having any significance even if it is enabled or disabled. This feature is similar to Hives auto-gather feature where statistics are automatically computed by default if this feature is enabled.
Reference:
https://cwiki.apache.org/confluence/display/Hive/StatsDev
As part of fix , autoSizeUpdateEnabled validation is been done initially so that system will calculate the table size for the user automatically and record it in metastore as per user expectation.
## How was this patch tested?
UT is written and manually verified in cluster.
Tested with unit tests + some internal tests on real cluster.
Before fix:
After fix
Closes#24315 from sujith71955/master_autoupdate.
Authored-by: s71955 <sujithchacko.2010@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Currently, the optimization rule `SchemaPruning` only works for Parquet/Orc V1.
We should have the same optimization in ORC V2.
## How was this patch tested?
Unit test
Closes#24338 from gengliangwang/schemaPruningForV2.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Similar to #22406 , which has made log level for plan changes by each rule configurable ,this PR is to make log level for plan changes by each batch configurable,and I have reused the same configuration: "spark.sql.optimizer.planChangeLog.level".
Config proposed in this PR ,
spark.sql.optimizer.planChangeLog.batches - enable plan change logging only for a set of specified batches, separated by commas.
## How was this patch tested?
Added UT , also tested manually and attached screenshots below.
1)Setting spark.sql.optimizer.planChangeLog.leve to warn.
2)setting spark.sql.optimizer.planChangeLog.batches to Resolution and Subquery.
3) plan change logging enabled only for a set of specified batches(Resolution and Subquery)
Closes#24136 from chakravarthiT/logBatches.
Lead-authored-by: chakravarthiT <45845595+chakravarthiT@users.noreply.github.com>
Co-authored-by: chakravarthiT <tcchakra@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
Necessarily access the external catalog without having to do it
## What changes were proposed in this pull request?
The existsFunction function has been changed because it unnecessarily accessed the externalCatalog to find if the database exists in cases where the function is in the functionRegistry
## How was this patch tested?
It has been tested through spark-shell and accessing the metastore logs of hive.
Inside spark-shell we use spark.table (% tableA%). SelectExpr ("trim (% columnA%)") in the current version and it appears every time:
org.apache.hadoop.hive.metastore.HiveMetaStore.audit: cmd = get_database: default
Once the change is made, no record appears
Closes#24312 from OCaballero/master.
Authored-by: ocaballero <oliver.caballero.alvarez@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
According to SQL standard, value of `DATE` type is union of year, month, dayInMonth, and it is independent from any time zones. To convert it to Catalyst's `TIMESTAMP`, `DATE` value should be "extended" by the time at midnight - `00:00:00`. The resulted local date+time should be considered as a timestamp in the session time zone, and casted to microseconds since epoch in `UTC` accordingly.
The reverse casting from `TIMESTAMP` to `DATE` should be performed in the similar way. `TIMESTAMP` values should be represented as a local date+time in the session time zone. And the time component should be just removed. For example, `TIMESTAMP 2019-04-10 00:10:12` -> `DATE 2019-04-10`. The resulted date is converted to days since epoch `1970-01-01`.
## How was this patch tested?
The changes were tested by existing test suites - `DateFunctionsSuite`, `DateExpressionsSuite` and `CastSuite`.
Closes#24332 from MaxGekk/cast-timestamp-to-date2.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to revert 2 commits 06abd06112 and 61561c1c2d, and take current date via `LocalDate.now` in the session time zone. The result is stored as days since epoch `1970-01-01`.
## How was this patch tested?
It was tested by `DateExpressionsSuite`, `DateFunctionsSuite`, `DateTimeUtilsSuite`, and `ComputeCurrentTimeSuite`.
Closes#24330 from MaxGekk/current-date2.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Dynamic partition will fail when both '' and null values are taken as dynamic partition values simultaneously.
For example, the test bellow will fail before this PR:
test("Null and '' values should not cause dynamic partition failure of string types") {
withTable("t1", "t2") {
spark.range(3).write.saveAsTable("t1")
spark.sql("select id, cast(case when id = 1 then '' else null end as string) as p" +
" from t1").write.partitionBy("p").saveAsTable("t2")
checkAnswer(spark.table("t2").sort("id"), Seq(Row(0, null), Row(1, null), Row(2, null)))
}
}
The error is: 'org.apache.hadoop.fs.FileAlreadyExistsException: File already exists'.
This PR convert the empty strings to null for partition values.
This is another way for PR(https://github.com/apache/spark/pull/23010)
(Please fill in changes proposed in this fix)
How was this patch tested?
New added test.
Closes#24334 from eatoncys/FileFormatWriter.
Authored-by: 10129659 <chen.yanshan@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
For BinaryOperator's toString method, it's better to use `$sqlOperator` instead of `$symbol`.
## How was this patch tested?
We can test this patch with unit tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#21826 from httfighter/SPARK-24872.
Authored-by: 韩田田00222924 <han.tiantian@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In SQL standard, date type is a union of the `year`, `month` and `day` fields. It's timezone independent, which means it does not represent a specific point in the timeline.
Spark SQL follows the SQL standard, this PR is to make it clear that date type is timezone independent
1. improve the doc to highlight that date is timezone independent.
2. when converting string to date, uses the java time API that can directly parse a `LocalDate` from a string, instead of converting `LocalDate` to a `Instant` at UTC first.
3. when converting date to string, uses the java time API that can directly format a `LocalDate` to a string, instead of converting `LocalDate` to a `Instant` at UTC first.
2 and 3 should not introduce any behavior changes.
## How was this patch tested?
existing tests
Closes#24325 from cloud-fan/doc.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This adds a public Expression API that can be used to pass partition transformations to data sources.
## How was this patch tested?
Existing tests to validate no regressions. Added transform cases to DDL suite and v1 conversions suite.
Closes#24117 from rdblue/add-public-transform-api.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PR #24189 changes the behavior of merging SparkConf. The existing doc is not updated for it. This is a followup of it to update the doc.
## How was this patch tested?
Doc only change.
Closes#24326 from viirya/SPARK-27253-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose simpler implementation of `toJavaTimestamp()`/`fromJavaTimestamp()` by reusing existing functions of `DateTimeUtils`. This will allow to:
- Simply implementation of `toJavaTimestamp()`, and handle properly negative inputs.
- Detect `Long` overflow in conversion of milliseconds (`java.sql.Timestamp`) to microseconds (Catalyst's Timestamp).
## How was this patch tested?
By existing test suites `DateTimeUtilsSuite`, `DateFunctionsSuite`, `DateExpressionsSuite` and `CastSuite`. And by new benchmark for export/import timestamps added to `DateTimeBenchmark`:
Before:
```
To/from java.sql.Timestamp: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
From java.sql.Timestamp 290 335 49 17.2 58.0 1.0X
Collect longs 1234 1681 487 4.1 246.8 0.2X
Collect timestamps 1718 1755 63 2.9 343.7 0.2X
```
After:
```
To/from java.sql.Timestamp: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
From java.sql.Timestamp 283 301 19 17.7 56.6 1.0X
Collect longs 1048 1087 36 4.8 209.6 0.3X
Collect timestamps 1425 1479 56 3.5 285.1 0.2X
```
Closes#24311 from MaxGekk/conv-java-sql-date-timestamp.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
File source V2 currently incorrectly continues to use cached data even if the underlying data is overwritten.
We should follow https://github.com/apache/spark/pull/13566 and fix it by invalidating and refreshes all the cached data (and the associated metadata) for any Dataframe that contains the given data source path.
## How was this patch tested?
Unit test
Closes#24318 from gengliangwang/invalidCache.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In DataSourceV2Strategy, it seems we eliminate the subqueries by mistake after normalizing filters.
We have a sql with a scalar subquery:
``` scala
val plan = spark.sql("select * from t2 where t2a > (select max(t1a) from t1)")
plan.explain(true)
```
And we get the log info of DataSourceV2Strategy:
```
Pushing operators to csv:examples/src/main/resources/t2.txt
Pushed Filters:
Post-Scan Filters: isnotnull(t2a#30)
Output: t2a#30, t2b#31
```
The `Post-Scan Filters` should contain the scalar subquery, but we eliminate it by mistake.
```
== Parsed Logical Plan ==
'Project [*]
+- 'Filter ('t2a > scalar-subquery#56 [])
: +- 'Project [unresolvedalias('max('t1a), None)]
: +- 'UnresolvedRelation `t1`
+- 'UnresolvedRelation `t2`
== Analyzed Logical Plan ==
t2a: string, t2b: string
Project [t2a#30, t2b#31]
+- Filter (t2a#30 > scalar-subquery#56 [])
: +- Aggregate [max(t1a#13) AS max(t1a)#63]
: +- SubqueryAlias `t1`
: +- RelationV2[t1a#13, t1b#14] csv:examples/src/main/resources/t1.txt
+- SubqueryAlias `t2`
+- RelationV2[t2a#30, t2b#31] csv:examples/src/main/resources/t2.txt
== Optimized Logical Plan ==
Filter (isnotnull(t2a#30) && (t2a#30 > scalar-subquery#56 []))
: +- Aggregate [max(t1a#13) AS max(t1a)#63]
: +- Project [t1a#13]
: +- RelationV2[t1a#13, t1b#14] csv:examples/src/main/resources/t1.txt
+- RelationV2[t2a#30, t2b#31] csv:examples/src/main/resources/t2.txt
== Physical Plan ==
*(1) Project [t2a#30, t2b#31]
+- *(1) Filter isnotnull(t2a#30)
+- *(1) BatchScan[t2a#30, t2b#31] class org.apache.spark.sql.execution.datasources.v2.csv.CSVScan
```
## How was this patch tested?
ut
Closes#24321 from francis0407/SPARK-27411.
Authored-by: francis0407 <hanmingcong123@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
ApproxCountDistinctForIntervals holds the UnsafeArrayData data to initialize endpoints. When the UnsafeArrayData is serialized with Java serialization, the BYTE_ARRAY_OFFSET in memory can change if two machines have different pointer width (Oops in JVM).
This PR fixes this issue by using the same way in https://github.com/apache/spark/pull/9030
## How was this patch tested?
Manual test has been done in our tpcds environment and regarding unit test case has been added as well
Closes#24317 from pengbo/SPARK-27406.
Authored-by: mingbo_pb <mingbo.pb@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR proposes to two things:
1. Add `deprecated` field to `ExpressionDescription` so that it can be shown in our SQL function documentation (https://spark.apache.org/docs/latest/api/sql/), and it can be shown via `DESCRIBE FUNCTION EXTENDED`.
2. While I am here, add some more restrictions for `note()` and `since()`. Looks some documentations are broken due to malformed `note`:
It should start with 4 spaces and end with a newline. I added some asserts, and fixed the instances together while I am here. This is technically a breaking change but I think it's too trivial to note somewhere (and we're in Spark 3.0.0).
This PR adds `deprecated` property into `from_utc_timestamp` and `to_utc_timestamp` (it's deprecated as of #24195) as examples of using this field.
Now it shows the deprecation information as below:
- **SQL documentation is shown as below:**
- **`DESCRIBE FUNCTION EXTENDED from_utc_timestamp;`**:
```
Function: from_utc_timestamp
Class: org.apache.spark.sql.catalyst.expressions.FromUTCTimestamp
Usage: from_utc_timestamp(timestamp, timezone) - Given a timestamp like '2017-07-14 02:40:00.0', interprets it as a time in UTC, and renders that time as a timestamp in the given time zone. For example, 'GMT+1' would yield '2017-07-14 03:40:00.0'.
Extended Usage:
Examples:
> SELECT from_utc_timestamp('2016-08-31', 'Asia/Seoul');
2016-08-31 09:00:00
Since: 1.5.0
Deprecated:
Deprecated since 3.0.0. See SPARK-25496.
```
## How was this patch tested?
Manually tested via:
- For documentation verification:
```
$ cd sql
$ sh create-docs.sh
```
- For checking description:
```
$ ./bin/spark-sql
```
```
spark-sql> DESCRIBE FUNCTION EXTENDED from_utc_timestamp;
spark-sql> DESCRIBE FUNCTION EXTENDED to_utc_timestamp;
```
Closes#24259 from HyukjinKwon/SPARK-27328.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
in https://github.com/apache/spark/pull/24195 , we deprecate `from/to_utc_timestamp`.
This PR removes unnecessary use of `to_utc_timestamp` in the test.
## How was this patch tested?
test only PR
Closes#24319 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Migrate Text source to File Data Source V2
## How was this patch tested?
Unit test
Closes#24207 from gengliangwang/textV2.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In the PR, I propose to restrict the range of random timestamp literals generated in `LiteralGenerator. timestampLiteralGen`. The generator creates instances of `java.sql.Timestamp` by passing milliseconds since epoch as `Long` type. Converting the milliseconds to microseconds can cause arithmetic overflow of Long type because Catalyst's Timestamp type stores microseconds since epoch in `Long` type internally as well. Proposed interval of random milliseconds is `[Long.MinValue / 1000, Long.MaxValue / 1000]`.
For example, generated timestamp `new java.sql.Timestamp(-3948373668011580000)` causes `Long` overflow at the method:
```scala
def fromJavaTimestamp(t: Timestamp): SQLTimestamp = {
...
MILLISECONDS.toMicros(t.getTime()) + NANOSECONDS.toMicros(t.getNanos()) % NANOS_PER_MICROS
...
}
```
because `t.getTime()` returns `-3948373668011580000` which is multiplied by `1000` at `MILLISECONDS.toMicros`, and the result `-3948373668011580000000` is less than `Long.MinValue`.
## How was this patch tested?
By `DateExpressionsSuite` in the PR https://github.com/apache/spark/pull/24311Closes#24316 from MaxGekk/random-timestamps-gen.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR mainly contains:
1. Upgrade hadoop-3's built-in Hive maven dependencies to 2.3.4.
2. Resolve compatibility issues between Hive 1.2.1 and Hive 2.3.4 in the `sql/hive` module.
## How was this patch tested?
jenkins test hadoop-2.7
manual test hadoop-3:
```shell
build/sbt clean package -Phadoop-3.2 -Phive
export SPARK_PREPEND_CLASSES=true
# rm -rf metastore_db
cat <<EOF > test_hadoop3.scala
spark.range(10).write.saveAsTable("test_hadoop3")
spark.table("test_hadoop3").show
EOF
bin/spark-shell --conf spark.hadoop.hive.metastore.schema.verification=false --conf spark.hadoop.datanucleus.schema.autoCreateAll=true -i test_hadoop3.scala
```
Closes#23788 from wangyum/SPARK-23710-hadoop3.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
As part of schema clipping in `ParquetReadSupport.scala`, we add fields in the Catalyst requested schema which are missing from the Parquet file schema to the Parquet clipped schema. However, nested schema pruning requires we ignore unrequested field data when reading from a Parquet file. Therefore we pass two schema to `ParquetRecordMaterializer`: the schema of the file data we want to read and the schema of the rows we want to return. The reader is responsible for reconciling the differences between the two.
Aside from checking whether schema pruning is enabled, there is an additional complication to constructing the Parquet requested schema. The manner in which Spark's two Parquet readers reconcile the differences between the Parquet requested schema and the Catalyst requested schema differ. Spark's vectorized reader does not (currently) support reading Parquet files with complex types in their schema. Further, it assumes that the Parquet requested schema includes all fields requested in the Catalyst requested schema. It includes logic in its read path to skip fields in the Parquet requested schema which are not present in the file.
Spark's parquet-mr based reader supports reading Parquet files of any kind of complex schema, and it supports nested schema pruning as well. Unlike the vectorized reader, the parquet-mr reader requires that the Parquet requested schema include only those fields present in the underlying Parquet file's schema. Therefore, in the case where we use the parquet-mr reader we intersect the Parquet clipped schema with the Parquet file's schema to construct the Parquet requested schema that's set in the `ReadContext`.
_Additional description (by HyukjinKwon):_
Let's suppose that we have a Parquet schema as below:
```
message spark_schema {
required int32 id;
optional group name {
optional binary first (UTF8);
optional binary last (UTF8);
}
optional binary address (UTF8);
}
```
Currently, the clipped schema as follows:
```
message spark_schema {
optional group name {
optional binary middle (UTF8);
}
optional binary address (UTF8);
}
```
Parquet MR does not support access to the nested non-existent field (`name.middle`).
To workaround this, this PR removes `name.middle` request at all to Parquet reader as below:
```
Parquet requested schema:
message spark_schema {
optional binary address (UTF8);
}
```
and produces the record (`name.middle`) properly as the requested Catalyst schema.
```
root
-- name: struct (nullable = true)
|-- middle: string (nullable = true)
-- address: string (nullable = true)
```
I think technically this is what Parquet library should support since Parquet library made a design decision to produce `null` for non-existent fields IIRC. This PR targets to work around it.
## How was this patch tested?
A previously ignored test case which exercises the failure scenario this PR addresses has been enabled.
This closes#22880Closes#24307 from dongjoon-hyun/SPARK-25407.
Lead-authored-by: Michael Allman <msa@allman.ms>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When scanning file sources, we can prune unnecessary partition columns on constructing input partitions, so that:
1. Reduce the data transformation from Driver to Executors
2. Make it easier to implement columnar batch readers, since the partition columns are already pruned.
## How was this patch tested?
Existing unit tests.
Closes#24296 from gengliangwang/prunePartitionValue.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pr avoid using hard-coded jar names(`hive-contrib-0.13.1.jar` and `hive-hcatalog-core-0.13.1.jar`) in Hive tests. This change makes it easy to change when upgrading the built-in Hive to 2.3.4.
## How was this patch tested?
Existing test
Closes#24294 from wangyum/SPARK-27383.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The user sets the value of spark.sql.thriftserver.scheduler.pool.
Spark thrift server saves this value in the LocalProperty of threadlocal type, but does not clean up after running, causing other sessions to run in the previously set pool name.
## How was this patch tested?
manual tests
Closes#23895 from cxzl25/thrift_server_scheduler_pool_pollute.
Lead-authored-by: cxzl25 <cxzl25@users.noreply.github.com>
Co-authored-by: sychen <sychen@ctrip.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fix a potential deadlock in ContinuousExecution by not initializing the toRDD lazy val.
Closes#24301 from jose-torres/deadlock.
Authored-by: Jose Torres <torres.joseph.f+github@gmail.com>
Signed-off-by: Jose Torres <torres.joseph.f+github@gmail.com>
## What changes were proposed in this pull request?
Th environment of my cluster as follows:
```
OS:Linux version 2.6.32-220.7.1.el6.x86_64 (mockbuildc6b18n3.bsys.dev.centos.org) (gcc version 4.4.6 20110731 (Red Hat 4.4.6-3) (GCC) ) #1 SMP Wed Mar 7 00:52:02 GMT 2012
Hadoop: 2.7.2
Spark: 2.3.0 or 3.0.0(master branch)
Hive: 1.2.1
```
My spark run on deploy mode yarn-client.
If I execute the SQL `insert overwrite local directory '/home/test/call_center/' select * from call_center`, a HiveException will appear as follows:
`Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: java.io.IOException: Mkdirs failed to create file:/home/xitong/hive/stagingdir_hive_2019-02-19_17-31-00_678_1816816774691551856-1/-ext-10000/_temporary/0/_temporary/attempt_20190219173233_0002_m_000000_3 (exists=false, cwd=file:/data10/yarn/nm-local-dir/usercache/xitong/appcache/application_1543893582405_6126857/container_e124_1543893582405_6126857_01_000011)
at org.apache.hadoop.hive.ql.io.HiveFileFormatUtils.getHiveRecordWriter(HiveFileFormatUtils.java:249)`
Current spark sql generate a local temporary path in local staging directory.The schema of local temporary path start with `file`, so the HiveException appears.
This PR change the local temporary path to HDFS temporary path, and use DistributedFileSystem instance copy the data from HDFS temporary path to local directory.
If Spark run on local deploy mode, 'insert overwrite local directory' works fine.
## How was this patch tested?
UT cannot support yarn-client mode.The test is in my product environment.
Closes#23841 from beliefer/fix-bug-of-insert-overwrite-local-dir.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR aims to clean up package name mismatches.
## How was this patch tested?
Pass the Jenkins.
Closes#24300 from dongjoon-hyun/SPARK-27390.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In the current file source V2 framework, the schema of `FileScan` is not returned correctly if there are overlap columns between `dataSchema` and `partitionSchema`. The actual schema should be
`dataSchema - overlapSchema + partitionSchema`, which might have different column order from the pushed down `requiredSchema` in `SupportsPushDownRequiredColumns.pruneColumns`.
For example, if the data schema is `[a: String, b: String, c: String]` and the partition schema is `[b: Int, d: Int]`, the result schema is `[a: String, b: Int, c: String, d: Int]` in current `FileTable` and `HadoopFsRelation`. while the actual scan schema is `[a: String, c: String, b: Int, d: Int]` in `FileScan`.
To fix the corner case, this PR proposes that the output schema of `FileTable` should be `dataSchema - overlapSchema + partitionSchema`, so that the column order is consistent with `FileScan`.
Putting all the partition columns to the end of table schema is more reasonable.
## How was this patch tested?
Unit test.
Closes#24284 from gengliangwang/FixReadSchema.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
With this change, unnecessary file scans are avoided in case of Limit 0 queries.
I added a case (rule) to `PropagateEmptyRelation` to replace `GlobalLimit 0` and `LocalLimit 0` nodes with an empty `LocalRelation`. This prunes the subtree under the Limit 0 node and further allows other rules of `PropagateEmptyRelation` to optimize the Logical Plan - while remaining semantically consistent with the Limit 0 query.
For instance:
**Query:**
`SELECT * FROM table1 INNER JOIN (SELECT * FROM table2 LIMIT 0) AS table2 ON table1.id = table2.id`
**Optimized Plan without fix:**
```
Join Inner, (id#79 = id#87)
:- Filter isnotnull(id#79)
: +- Relation[id#79,num1#80] parquet
+- Filter isnotnull(id#87)
+- GlobalLimit 0
+- LocalLimit 0
+- Relation[id#87,num2#88] parquet
```
**Optimized Plan with fix:**
`LocalRelation <empty>, [id#75, num1#76, id#77, num2#78]`
## How was this patch tested?
Added unit tests to verify Limit 0 optimization for:
- Simple query containing Limit 0
- Inner Join, Left Outer Join, Right Outer Join, Full Outer Join queries containing Limit 0 as one of their children
- Nested Inner Joins between 3 tables with one of them having a Limit 0 clause.
- Intersect query wherein one of the subqueries was a Limit 0 query.
Closes#24271 from aayushmaanjain/optimize-limit0.
Authored-by: Aayushmaan Jain <aayushmaan.jain42@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
In the case where the offending symbol is a CommonToken, this PR increases the accuracy of the start and stop origin by leveraging the start and stop index information from CommonToken.
Closes#21334 from rubenfiszel/patch-1.
Lead-authored-by: Ruben Fiszel <rubenfiszel@gmail.com>
Co-authored-by: rubenfiszel <rfiszel@palantir.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Since Apache Spark 2.4.1 vote passed and is distributed into mirrors, we need to test 2.4.1. This should land on both `master` and `branch-2.4`.
## How was this patch tested?
Pass the Jenkins.
Closes#24292 from dongjoon-hyun/SPARK-27382.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
