## What changes were proposed in this pull request?
AssertNotNull's toString/simpleString dumps the entire walkedTypePath. walkedTypePath is used for error message reporting and shouldn't be part of the output.
## How was this patch tested?
Manually tested.
Author: Reynold Xin <rxin@databricks.com>
Closes#17616 from rxin/SPARK-20304.
### What changes were proposed in this pull request?
Session catalog API `createTempFunction` is being used by Hive build-in functions, persistent functions, and temporary functions. Thus, the name is confusing. This PR is to rename it by `registerFunction`. Also we can move construction of `FunctionBuilder` and `ExpressionInfo` into the new `registerFunction`, instead of duplicating the logics everywhere.
In the next PRs, the remaining Function-related APIs also need cleanups.
### How was this patch tested?
Existing test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17615 from gatorsmile/cleanupCreateTempFunction.
## What changes were proposed in this pull request?
Update count distinct error message for streaming datasets/dataframes to match current behavior. These aggregations are not yet supported, regardless of whether the dataset/dataframe is aggregated.
Author: jtoka <jason.tokayer@gmail.com>
Closes#17609 from jtoka/master.
## What changes were proposed in this pull request?
When we perform a cast expression and the from and to types are structurally the same (having the same structure but different field names), we should be able to skip the actual cast.
## How was this patch tested?
Added unit tests for the newly introduced functions.
Author: Reynold Xin <rxin@databricks.com>
Closes#17614 from rxin/SPARK-20302.
## What changes were proposed in this pull request?
`NaNvl(float value, null)` will be converted into `NaNvl(float value, Cast(null, DoubleType))` and finally `NaNvl(Cast(float value, DoubleType), Cast(null, DoubleType))`.
This will cause mismatching in the output type when the input type is float.
By adding extra rule in TypeCoercion can resolve this issue.
## How was this patch tested?
unite tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: DB Tsai <dbt@netflix.com>
Closes#17606 from dbtsai/fixNaNvl.
## What changes were proposed in this pull request?
Dataset typed API currently uses NewInstance to box primitive types (i.e. calling the constructor). Instead, it'd be slightly more idiomatic in Java to use PrimitiveType.valueOf, which can be invoked using StaticInvoke expression.
## How was this patch tested?
The change should be covered by existing tests for Dataset encoders.
Author: Reynold Xin <rxin@databricks.com>
Closes#17604 from rxin/SPARK-20289.
## What changes were proposed in this pull request?
Similar to `ListQuery`, `Exists` should not be evaluated in `Join` operator too.
## 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#17491 from viirya/dont-push-exists-to-join.
## What changes were proposed in this pull request?
This is a regression caused by SPARK-19716.
Before SPARK-19716, we will cast an array field to the expected array type. However, after SPARK-19716, the cast is removed, but we forgot to push the cast to the element level.
## How was this patch tested?
new regression tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17587 from cloud-fan/array.
## What changes were proposed in this pull request?
Add Locale.ROOT to internal calls to String `toLowerCase`, `toUpperCase`, to avoid inadvertent locale-sensitive variation in behavior (aka the "Turkish locale problem").
The change looks large but it is just adding `Locale.ROOT` (the locale with no country or language specified) to every call to these methods.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#17527 from srowen/SPARK-20156.
## What changes were proposed in this pull request?
```
sql("SELECT t1.b, rand(0) as r FROM cachedData, cachedData t1 GROUP BY t1.b having r > 0.5").show()
```
We will get the following error:
```
Job aborted due to stage failure: Task 1 in stage 4.0 failed 1 times, most recent failure: Lost task 1.0 in stage 4.0 (TID 8, localhost, executor driver): java.lang.NullPointerException
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate.eval(Unknown Source)
at org.apache.spark.sql.execution.joins.BroadcastNestedLoopJoinExec$$anonfun$org$apache$spark$sql$execution$joins$BroadcastNestedLoopJoinExec$$boundCondition$1.apply(BroadcastNestedLoopJoinExec.scala:87)
at org.apache.spark.sql.execution.joins.BroadcastNestedLoopJoinExec$$anonfun$org$apache$spark$sql$execution$joins$BroadcastNestedLoopJoinExec$$boundCondition$1.apply(BroadcastNestedLoopJoinExec.scala:87)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:463)
```
Filters could be pushed down to the join conditions by the optimizer rule `PushPredicateThroughJoin`. However, Analyzer [blocks users to add non-deterministics conditions](https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala#L386-L395) (For details, see the PR https://github.com/apache/spark/pull/7535).
We should not push down non-deterministic conditions; otherwise, we need to explicitly initialize the non-deterministic expressions. This PR is to simply block it.
### How was this patch tested?
Added a test case
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17585 from gatorsmile/joinRandCondition.
## What changes were proposed in this pull request?
This PR proposes to add `IGNORE NULLS` keyword in `first`/`last` in Spark's parser likewise http://docs.oracle.com/cd/B19306_01/server.102/b14200/functions057.htm. This simply maps the keywords to existing `ignoreNullsExpr`.
**Before**
```scala
scala> sql("select first('a' IGNORE NULLS)").show()
```
```
org.apache.spark.sql.catalyst.parser.ParseException:
extraneous input 'NULLS' expecting {')', ','}(line 1, pos 24)
== SQL ==
select first('a' IGNORE NULLS)
------------------------^^^
at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:210)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:112)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:46)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:66)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:622)
... 48 elided
```
**After**
```scala
scala> sql("select first('a' IGNORE NULLS)").show()
```
```
+--------------+
|first(a, true)|
+--------------+
| a|
+--------------+
```
## How was this patch tested?
Unit tests in `ExpressionParserSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17566 from HyukjinKwon/SPARK-19518.
## What changes were proposed in this pull request?
Like `Expression`, `QueryPlan` should also have a `semanticHash` method, then we can put plans to a hash map and look it up fast. This PR refactors `QueryPlan` to follow `Expression` and put all the normalization logic in `QueryPlan.canonicalized`, so that it's very natural to implement `semanticHash`.
follow-up: improve `CacheManager` to leverage this `semanticHash` and speed up plan lookup, instead of iterating all cached plans.
## How was this patch tested?
existing tests. Note that we don't need to test the `semanticHash` method, once the existing tests prove `sameResult` is correct, we are good.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17541 from cloud-fan/plan-semantic.
## What changes were proposed in this pull request?
AssertNotNull currently throws RuntimeException. It should throw NullPointerException, which is more specific.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#17573 from rxin/SPARK-20262.
## What changes were proposed in this pull request?
Similar to `Project`, when `Aggregate` has non-deterministic expressions, we should not push predicate down through it, as it will change the number of input rows and thus change the evaluation result of non-deterministic expressions in `Aggregate`.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17562 from cloud-fan/filter.
## What changes were proposed in this pull request?
Currently `LogicalRelation` has a `expectedOutputAttributes` parameter, which makes it hard to reason about what the actual output is. Like other leaf nodes, `LogicalRelation` should also take `output` as a parameter, to simplify the logic
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17552 from cloud-fan/minor.
## What changes were proposed in this pull request?
This is a tiny addendum to SPARK-19495 to remove the private visibility for copy, which is the only package private method in the entire file.
## How was this patch tested?
N/A - no semantic change.
Author: Reynold Xin <rxin@databricks.com>
Closes#17555 from rxin/SPARK-19495-2.
## What changes were proposed in this pull request?
This commit moves star schema code from ```join.scala``` to ```StarSchemaDetection.scala```. It also applies some minor fixes in ```StarJoinReorderSuite.scala```.
## How was this patch tested?
Run existing ```StarJoinReorderSuite.scala```.
Author: Ioana Delaney <ioanamdelaney@gmail.com>
Closes#17544 from ioana-delaney/starSchemaCBOv2.
## What changes were proposed in this pull request?
Make sure SESSION_LOCAL_TIMEZONE reflects the change in JVM's default timezone setting. Currently several timezone related tests fail as the change to default timezone is not picked up by SQLConf.
## How was this patch tested?
Added an unit test in ConfigEntrySuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#17537 from dilipbiswal/timezone_debug.
## What changes were proposed in this pull request?
Previously when we construct deserializer expression for array type, we will first cast the corresponding field to expected array type and then apply `MapObjects`.
However, by doing that, we lose the opportunity to do by-name resolution for struct type inside array type. In this PR, I introduce a `UnresolvedMapObjects` to hold the lambda function and the input array expression. Then during analysis, after the input array expression is resolved, we get the actual array element type and apply by-name resolution. Then we don't need to add `Cast` for array type when constructing the deserializer expression, as the element type is determined later at analyzer.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17398 from cloud-fan/dataset.
## What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/17285 .
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17521 from cloud-fan/conf.
### What changes were proposed in this pull request?
Observed by felixcheung , in `SparkSession`.`Catalog` APIs, we have different conventions/rules for table/function identifiers/names. Most APIs accept the qualified name (i.e., `databaseName`.`tableName` or `databaseName`.`functionName`). However, the following five APIs do not accept it.
- def listColumns(tableName: String): Dataset[Column]
- def getTable(tableName: String): Table
- def getFunction(functionName: String): Function
- def tableExists(tableName: String): Boolean
- def functionExists(functionName: String): Boolean
To make them consistent with the other Catalog APIs, this PR does the changes, updates the function/API comments and adds the `params` to clarify the inputs we allow.
### How was this patch tested?
Added the test cases .
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17518 from gatorsmile/tableIdentifier.
### What changes were proposed in this pull request?
This PR is to unify and clean up the outputs of `DESC EXTENDED/FORMATTED` and `SHOW TABLE EXTENDED` by moving the logics into the Catalog interface. The output formats are improved. We also add the missing attributes. It impacts the DDL commands like `SHOW TABLE EXTENDED`, `DESC EXTENDED` and `DESC FORMATTED`.
In addition, by following what we did in Dataset API `printSchema`, we can use `treeString` to show the schema in the more readable way.
Below is the current way:
```
Schema: STRUCT<`a`: STRING (nullable = true), `b`: INT (nullable = true), `c`: STRING (nullable = true), `d`: STRING (nullable = true)>
```
After the change, it should look like
```
Schema: root
|-- a: string (nullable = true)
|-- b: integer (nullable = true)
|-- c: string (nullable = true)
|-- d: string (nullable = true)
```
### How was this patch tested?
`describe.sql` and `show-tables.sql`
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17394 from gatorsmile/descFollowUp.
## What changes were proposed in this pull request?
**Description** from JIRA
The TimestampType in Spark SQL is of microsecond precision. Ideally, we should convert Spark SQL timestamp values into Parquet TIMESTAMP_MICROS. But unfortunately parquet-mr hasn't supported it yet.
For the read path, we should be able to read TIMESTAMP_MILLIS Parquet values and pad a 0 microsecond part to read values.
For the write path, currently we are writing timestamps as INT96, similar to Impala and Hive. One alternative is that, we can have a separate SQL option to let users be able to write Spark SQL timestamp values as TIMESTAMP_MILLIS. Of course, in this way the microsecond part will be truncated.
## How was this patch tested?
Added new tests in ParquetQuerySuite and ParquetIOSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#15332 from dilipbiswal/parquet-time-millis.
## What changes were proposed in this pull request?
In SQL queries, we also see predicate expressions involving two columns such as "column-1 (op) column-2" where column-1 and column-2 belong to same table. Note that, if column-1 and column-2 belong to different tables, then it is a join operator's work, NOT a filter operator's work.
This PR estimates filter selectivity on two columns of same table. For example, multiple tpc-h queries have this predicate "WHERE l_commitdate < l_receiptdate"
## How was this patch tested?
We added 6 new test cases to test various logical predicates involving two columns of same table.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Ron Hu <ron.hu@huawei.com>
Author: U-CHINA\r00754707 <r00754707@R00754707-SC04.china.huawei.com>
Closes#17415 from ron8hu/filterTwoColumns.
## What changes were proposed in this pull request?
Range in SQL should be case insensitive
## How was this patch tested?
unit test
Author: samelamin <hussam.elamin@gmail.com>
Author: samelamin <sam_elamin@discovery.com>
Closes#17487 from samelamin/SPARK-20145.
## What changes were proposed in this pull request?
This patch implements `listPartitionsByFilter()` for `InMemoryCatalog` and thus resolves an outstanding TODO causing the `PruneFileSourcePartitions` optimizer rule not to apply when "spark.sql.catalogImplementation" is set to "in-memory" (which is the default).
The change is straightforward: it extracts the code for further filtering of the list of partitions returned by the metastore's `getPartitionsByFilter()` out from `HiveExternalCatalog` into `ExternalCatalogUtils` and calls this new function from `InMemoryCatalog` on the whole list of partitions.
Now that this method is implemented we can always pass the `CatalogTable` to the `DataSource` in `FindDataSourceTable`, so that the latter is resolved to a relation with a `CatalogFileIndex`, which is what the `PruneFileSourcePartitions` rule matches for.
## How was this patch tested?
Ran existing tests and added new test for `listPartitionsByFilter` in `ExternalCatalogSuite`, which is subclassed by both `InMemoryCatalogSuite` and `HiveExternalCatalogSuite`.
Author: Adrian Ionescu <adrian@databricks.com>
Closes#17510 from adrian-ionescu/InMemoryCatalog.
## What changes were proposed in this pull request?
This PR proposes to use `XXX` format instead of `ZZ`. `ZZ` seems a `FastDateFormat` specific.
`ZZ` supports "ISO 8601 extended format time zones" but it seems `FastDateFormat` specific option.
I misunderstood this is compatible format with `SimpleDateFormat` when this change is introduced.
Please see [SimpleDateFormat documentation]( https://docs.oracle.com/javase/7/docs/api/java/text/SimpleDateFormat.html#iso8601timezone) and [FastDateFormat documentation](https://commons.apache.org/proper/commons-lang/apidocs/org/apache/commons/lang3/time/FastDateFormat.html).
It seems we better replace `ZZ` to `XXX` because they look using the same strategy - [FastDateParser.java#L930](8767cd4f1a/src/main/java/org/apache/commons/lang3/time/FastDateParser.java (L930)), [FastDateParser.java#L932-L951 ](8767cd4f1a/src/main/java/org/apache/commons/lang3/time/FastDateParser.java (L932-L951)) and [FastDateParser.java#L596-L601](8767cd4f1a/src/main/java/org/apache/commons/lang3/time/FastDateParser.java (L596-L601)).
I also checked the codes and manually debugged it for sure. It seems both cases use the same pattern `( Z|(?:[+-]\\d{2}(?::)\\d{2}))`.
_Note that this should be rather a fix about documentation and not the behaviour change because `ZZ` seems invalid date format in `SimpleDateFormat` as documented in `DataFrameReader` and etc, and both `ZZ` and `XXX` look identically working with `FastDateFormat`_
Current documentation is as below:
```
* <li>`timestampFormat` (default `yyyy-MM-dd'T'HH:mm:ss.SSSZZ`): sets the string that
* indicates a timestamp format. Custom date formats follow the formats at
* `java.text.SimpleDateFormat`. This applies to timestamp type.</li>
```
## How was this patch tested?
Existing tests should cover this. Also, manually tested as below (BTW, I don't think these are worth being added as tests within Spark):
**Parse**
```scala
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000-11:00")
res4: java.util.Date = Tue Mar 21 20:00:00 KST 2017
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000Z")
res10: java.util.Date = Tue Mar 21 09:00:00 KST 2017
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000-11:00")
java.text.ParseException: Unparseable date: "2017-03-21T00:00:00.000-11:00"
at java.text.DateFormat.parse(DateFormat.java:366)
... 48 elided
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000Z")
java.text.ParseException: Unparseable date: "2017-03-21T00:00:00.000Z"
at java.text.DateFormat.parse(DateFormat.java:366)
... 48 elided
```
```scala
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000-11:00")
res7: java.util.Date = Tue Mar 21 20:00:00 KST 2017
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000Z")
res1: java.util.Date = Tue Mar 21 09:00:00 KST 2017
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000-11:00")
res8: java.util.Date = Tue Mar 21 20:00:00 KST 2017
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000Z")
res2: java.util.Date = Tue Mar 21 09:00:00 KST 2017
```
**Format**
```scala
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").format(new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000-11:00"))
res6: String = 2017-03-21T20:00:00.000+09:00
```
```scala
scala> val fd = org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSZZ")
fd: org.apache.commons.lang3.time.FastDateFormat = FastDateFormat[yyyy-MM-dd'T'HH:mm:ss.SSSZZ,ko_KR,Asia/Seoul]
scala> fd.format(fd.parse("2017-03-21T00:00:00.000-11:00"))
res1: String = 2017-03-21T20:00:00.000+09:00
scala> val fd = org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSXXX")
fd: org.apache.commons.lang3.time.FastDateFormat = FastDateFormat[yyyy-MM-dd'T'HH:mm:ss.SSSXXX,ko_KR,Asia/Seoul]
scala> fd.format(fd.parse("2017-03-21T00:00:00.000-11:00"))
res2: String = 2017-03-21T20:00:00.000+09:00
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17489 from HyukjinKwon/SPARK-20166.
## What changes were proposed in this pull request?
Currently, `DataType.fromJson` throws `scala.MatchError` or `java.util.NoSuchElementException` in some cases when the JSON input is invalid as below:
```scala
DataType.fromJson(""""abcd"""")
```
```
java.util.NoSuchElementException: key not found: abcd
at ...
```
```scala
DataType.fromJson("""{"abcd":"a"}""")
```
```
scala.MatchError: JObject(List((abcd,JString(a)))) (of class org.json4s.JsonAST$JObject)
at ...
```
```scala
DataType.fromJson("""{"fields": [{"a":123}], "type": "struct"}""")
```
```
scala.MatchError: JObject(List((a,JInt(123)))) (of class org.json4s.JsonAST$JObject)
at ...
```
After this PR,
```scala
DataType.fromJson(""""abcd"""")
```
```
java.lang.IllegalArgumentException: Failed to convert the JSON string 'abcd' to a data type.
at ...
```
```scala
DataType.fromJson("""{"abcd":"a"}""")
```
```
java.lang.IllegalArgumentException: Failed to convert the JSON string '{"abcd":"a"}' to a data type.
at ...
```
```scala
DataType.fromJson("""{"fields": [{"a":123}], "type": "struct"}""")
at ...
```
```
java.lang.IllegalArgumentException: Failed to convert the JSON string '{"a":123}' to a field.
```
## How was this patch tested?
Unit test added in `DataTypeSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17468 from HyukjinKwon/fromjson_exception.
## What changes were proposed in this pull request?
`BroadcastHint` should use child's statistics and set `isBroadcastable` to true.
## How was this patch tested?
Added a new stats estimation test for `BroadcastHint`.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17504 from wzhfy/broadcastHintEstimation.
## What changes were proposed in this pull request?
The query plan in an `AnalysisException` may be `null` when an `AnalysisException` object is serialized and then deserialized, since `plan` is marked `transient`. Or when someone throws an `AnalysisException` with a null query plan (which should not happen).
`def getMessage` is not tolerant of this and throws a `NullPointerException`, leading to loss of information about the original exception.
The fix is to add a `null` check in `getMessage`.
## How was this patch tested?
- Unit test
Author: Kunal Khamar <kkhamar@outlook.com>
Closes#17486 from kunalkhamar/spark-20164.
## What changes were proposed in this pull request?
Instead of iterating all expressions that can return null for null inputs, we can just check `NullIntolerant`.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17450 from cloud-fan/null.
…adoc
## What changes were proposed in this pull request?
Use recommended values for row boundaries in Window's scaladoc, i.e. `Window.unboundedPreceding`, `Window.unboundedFollowing`, and `Window.currentRow` (that were introduced in 2.1.0).
## How was this patch tested?
Local build
Author: Jacek Laskowski <jacek@japila.pl>
Closes#17417 from jaceklaskowski/window-expression-scaladoc.
## What changes were proposed in this pull request?
Commit 6c70a38 broke the build for scala 2.10. The commit uses some reflections which are not available in Scala 2.10. This PR fixes them.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#17473 from ueshin/issues/SPARK-19088.
### What changes were proposed in this pull request?
`FalseLiteral` and `TrueLiteral` should have been eliminated by optimizer rule `BooleanSimplification`, but null literals might be added by optimizer rule `NullPropagation`. For safety, our filter estimation should handle all the eligible literal cases.
Our optimizer rule BooleanSimplification is unable to remove the null literal in many cases. For example, `a < 0 or null`. Thus, we need to handle null literal in filter estimation.
`Not` can be pushed down below `And` and `Or`. Then, we could see two consecutive `Not`, which need to be collapsed into one. Because of the limited expression support for filter estimation, we just need to handle the case `Not(null)` for avoiding incorrect error due to the boolean operation on null. For details, see below matrix.
```
not NULL = NULL
NULL or false = NULL
NULL or true = true
NULL or NULL = NULL
NULL and false = false
NULL and true = NULL
NULL and NULL = NULL
```
### How was this patch tested?
Added the test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17446 from gatorsmile/constantFilterEstimation.
## What changes were proposed in this pull request?
This pr added `StructType.fromDDL` to convert a DDL format string into `StructType` for defining schemas in `functions.from_json`.
## How was this patch tested?
Added tests in `JsonFunctionsSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17406 from maropu/SPARK-20009.
## What changes were proposed in this pull request?
When we build the deserializer expression for map type, we will use `StaticInvoke` to call `ArrayBasedMapData.toScalaMap`, and declare the return type as `scala.collection.immutable.Map`. If the map is inside an Option, we will wrap this `StaticInvoke` with `WrapOption`, which requires the input to be `scala.collect.Map`. Ideally this should be fine, as `scala.collection.immutable.Map` extends `scala.collect.Map`, but our `ObjectType` is too strict about this, this PR fixes it.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17454 from cloud-fan/map.
## What changes were proposed in this pull request?
Join reorder algorithm should keep exactly the same order of output attributes in the top project.
For example, if user want to select a, b, c, after reordering, we should output a, b, c in the same order as specified by user, instead of b, a, c or other orders.
## How was this patch tested?
A new test case is added in `JoinReorderSuite`.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17453 from wzhfy/keepOrderInProject.
## What changes were proposed in this pull request?
TPCDS q45 fails becuase:
`ReorderJoin` collects all predicates and try to put them into join condition when creating ordered join. If a predicate with an IN subquery (`ListQuery`) is in a join condition instead of a filter condition, `RewritePredicateSubquery.rewriteExistentialExpr` would fail to convert the subquery to an `ExistenceJoin`, and thus result in error.
We should prevent push down of IN subquery to Join operator.
## How was this patch tested?
Add a new test case in `FilterPushdownSuite`.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17428 from wzhfy/noSubqueryInJoinCond.
## What changes were proposed in this pull request?
The current SessionState initialization code path is quite complex. A part of the creation is done in the SessionState companion objects, a part of the creation is one inside the SessionState class, and a part is done by passing functions.
This PR refactors this code path, and consolidates SessionState initialization into a builder class. This SessionState will not do any initialization and just becomes a place holder for the various Spark SQL internals. This also lays the ground work for two future improvements:
1. This provides us with a start for removing the `HiveSessionState`. Removing the `HiveSessionState` would also require us to move resource loading into a separate class, and to (re)move metadata hive.
2. This makes it easier to customize the Spark Session. Currently you will need to create a custom version of the builder. I have added hooks to facilitate this. A future step will be to create a semi stable API on top of this.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17433 from hvanhovell/SPARK-20100.
## What changes were proposed in this pull request?
In current stage, we don't have advanced statistics such as sketches or histograms. As a result, some operator can't estimate `nullCount` accurately. E.g. left outer join estimation does not accurately update `nullCount` currently. So for `IsNull` and `IsNotNull` predicates, we only estimate them when the child is a leaf node, whose `nullCount` is accurate.
## How was this patch tested?
A new test case is added in `FilterEstimationSuite`.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17438 from wzhfy/nullEstimation.
## What changes were proposed in this pull request?
The `CollapseWindow` is currently to aggressive when collapsing adjacent windows. It also collapses windows in the which the parent produces a column that is consumed by the child; this creates an invalid window which will fail at runtime.
This PR fixes this by adding a check for dependent adjacent windows to the `CollapseWindow` rule.
## How was this patch tested?
Added a new test case to `CollapseWindowSuite`
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17432 from hvanhovell/SPARK-20086.
## What changes were proposed in this pull request?
The `FailureSafeParser` is only used in sql core, it doesn't make sense to put it in catalyst module.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17408 from cloud-fan/minor.
## What changes were proposed in this pull request?
Constraint propagation can be computation expensive and block the driver execution for long time. For example, the below benchmark needs 30mins.
Compared with previous PRs #16998, #16785, this is a much simpler option: add a flag to disable constraint propagation.
### Benchmark
Run the following codes locally.
import org.apache.spark.ml.{Pipeline, PipelineStage}
import org.apache.spark.ml.feature.{OneHotEncoder, StringIndexer, VectorAssembler}
import org.apache.spark.sql.internal.SQLConf
spark.conf.set(SQLConf.CONSTRAINT_PROPAGATION_ENABLED.key, false)
val df = (1 to 40).foldLeft(Seq((1, "foo"), (2, "bar"), (3, "baz")).toDF("id", "x0"))((df, i) => df.withColumn(s"x$i", $"x0"))
val indexers = df.columns.tail.map(c => new StringIndexer()
.setInputCol(c)
.setOutputCol(s"${c}_indexed")
.setHandleInvalid("skip"))
val encoders = indexers.map(indexer => new OneHotEncoder()
.setInputCol(indexer.getOutputCol)
.setOutputCol(s"${indexer.getOutputCol}_encoded")
.setDropLast(true))
val stages: Array[PipelineStage] = indexers ++ encoders
val pipeline = new Pipeline().setStages(stages)
val startTime = System.nanoTime
pipeline.fit(df).transform(df).show
val runningTime = System.nanoTime - startTime
Before this patch: 1786001 ms ~= 30 mins
After this patch: 26392 ms = less than half of a minute
Related PRs: #16998, #16785.
## 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#17186 from viirya/add-flag-disable-constraint-propagation.
## What changes were proposed in this pull request?
Fix for typo in Analyzer
## How was this patch tested?
local build
Author: Jacek Laskowski <jacek@japila.pl>
Closes#17409 from jaceklaskowski/analyzer-typo.
## What changes were proposed in this pull request?
Since the state is tied a "group" in the "mapGroupsWithState" operations, its better to call the state "GroupState" instead of a key. This would make it more general if you extends this operation to RelationGroupedDataset and python APIs.
## How was this patch tested?
Existing unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#17385 from tdas/SPARK-20057.
## What changes were proposed in this pull request?
Currently, when we perform count with timestamp types, it prints the internal representation as the column name as below:
```scala
Seq(new java.sql.Timestamp(1)).toDF("a").groupBy("a").pivot("a").count().show()
```
```
+--------------------+----+
| a|1000|
+--------------------+----+
|1969-12-31 16:00:...| 1|
+--------------------+----+
```
This PR proposes to use external Scala value instead of the internal representation in the column names as below:
```
+--------------------+-----------------------+
| a|1969-12-31 16:00:00.001|
+--------------------+-----------------------+
|1969-12-31 16:00:...| 1|
+--------------------+-----------------------+
```
## How was this patch tested?
Unit test in `DataFramePivotSuite` and manual tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17348 from HyukjinKwon/SPARK-20018.
## What changes were proposed in this pull request?
This PR proposes to make `mode` options in both CSV and JSON to use `cass object` and fix some related comments related previous fix.
Also, this PR modifies some tests related parse modes.
## How was this patch tested?
Modified unit tests in both `CSVSuite.scala` and `JsonSuite.scala`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17377 from HyukjinKwon/SPARK-19949.
## What changes were proposed in this pull request?
Adding event time based timeout. The user sets the timeout timestamp directly using `KeyedState.setTimeoutTimestamp`. The keys times out when the watermark crosses the timeout timestamp.
## How was this patch tested?
Unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#17361 from tdas/SPARK-20030.
## What changes were proposed in this pull request?
Change the nullability of function `StringToMap` from `false` to `true`.
Author: zhaorongsheng <334362872@qq.com>
Closes#17350 from zhaorongsheng/bug-fix_strToMap_NPE.
## What changes were proposed in this pull request?
Support` ALTER TABLE ADD COLUMNS (...) `syntax for Hive serde and some datasource tables.
In this PR, we consider a few aspects:
1. View is not supported for `ALTER ADD COLUMNS`
2. Since tables created in SparkSQL with Hive DDL syntax will populate table properties with schema information, we need make sure the consistency of the schema before and after ALTER operation in order for future use.
3. For embedded-schema type of format, such as `parquet`, we need to make sure that the predicate on the newly-added columns can be evaluated properly, or pushed down properly. In case of the data file does not have the columns for the newly-added columns, such predicates should return as if the column values are NULLs.
4. For datasource table, this feature does not support the following:
4.1 TEXT format, since there is only one default column `value` is inferred for text format data.
4.2 ORC format, since SparkSQL native ORC reader does not support the difference between user-specified-schema and inferred schema from ORC files.
4.3 Third party datasource types that implements RelationProvider, including the built-in JDBC format, since different implementations by the vendors may have different ways to dealing with schema.
4.4 Other datasource types, such as `parquet`, `json`, `csv`, `hive` are supported.
5. Column names being added can not be duplicate of any existing data column or partition column names. Case sensitivity is taken into consideration according to the sql configuration.
6. This feature also supports In-Memory catalog, while Hive support is turned off.
## How was this patch tested?
Add new test cases
Author: Xin Wu <xinwu@us.ibm.com>
Closes#16626 from xwu0226/alter_add_columns.
## What changes were proposed in this pull request?
1. Improve documentation for class `Cost` and `JoinReorderDP` and method `buildJoin()`.
2. Change code structure of `buildJoin()` to make the logic clearer.
3. Add a debug-level log to record information for join reordering, including time cost, the number of items and the number of plans in memo.
## How was this patch tested?
Not related.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17353 from wzhfy/reorderFollow.
### What changes were proposed in this pull request?
SessionCatalog API setCurrentDatabase does not set the current database of the underlying ExternalCatalog. Thus, weird errors could come in the test suites after we call reset. We need to fix it.
So far, have not found the direct impact in the other code paths because we expect all the SessionCatalog APIs should always use the current database value we managed, unless some of code paths skip it. Thus, we fix it in the test-only function reset().
### How was this patch tested?
Multiple test case failures are observed in mvn and add a test case in SessionCatalogSuite.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17354 from gatorsmile/useDB.
## What changes were proposed in this pull request?
Currently JSON and CSV have exactly the same logic about handling bad records, this PR tries to abstract it and put it in a upper level to reduce code duplication.
The overall idea is, we make the JSON and CSV parser to throw a BadRecordException, then the upper level, FailureSafeParser, handles bad records according to the parse mode.
Behavior changes:
1. with PERMISSIVE mode, if the number of tokens doesn't match the schema, previously CSV parser will treat it as a legal record and parse as many tokens as possible. After this PR, we treat it as an illegal record, and put the raw record string in a special column, but we still parse as many tokens as possible.
2. all logging is removed as they are not very useful in practice.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Wenchen Fan <cloud0fan@gmail.com>
Closes#17315 from cloud-fan/bad-record2.
## What changes were proposed in this pull request?
A Bean serializer in `ExpressionEncoder` could change values when Beans having NULL. A concrete example is as follows;
```
scala> :paste
class Outer extends Serializable {
private var cls: Inner = _
def setCls(c: Inner): Unit = cls = c
def getCls(): Inner = cls
}
class Inner extends Serializable {
private var str: String = _
def setStr(s: String): Unit = str = str
def getStr(): String = str
}
scala> Seq("""{"cls":null}""", """{"cls": {"str":null}}""").toDF().write.text("data")
scala> val encoder = Encoders.bean(classOf[Outer])
scala> val schema = encoder.schema
scala> val df = spark.read.schema(schema).json("data").as[Outer](encoder)
scala> df.show
+------+
| cls|
+------+
|[null]|
| null|
+------+
scala> df.map(x => x)(encoder).show()
+------+
| cls|
+------+
|[null]|
|[null]| // <-- Value changed
+------+
```
This is because the Bean serializer does not have the NULL-check expressions that the serializer of Scala's product types has. Actually, this value change does not happen in Scala's product types;
```
scala> :paste
case class Outer(cls: Inner)
case class Inner(str: String)
scala> val encoder = Encoders.product[Outer]
scala> val schema = encoder.schema
scala> val df = spark.read.schema(schema).json("data").as[Outer](encoder)
scala> df.show
+------+
| cls|
+------+
|[null]|
| null|
+------+
scala> df.map(x => x)(encoder).show()
+------+
| cls|
+------+
|[null]|
| null|
+------+
```
This pr added the NULL-check expressions in Bean serializer along with the serializer of Scala's product types.
## How was this patch tested?
Added tests in `JavaDatasetSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17347 from maropu/SPARK-19980.
## What changes were proposed in this pull request?
After sort merge join for inner join, now we only keep left key ordering. However, after inner join, right key has the same value and order as left key. So if we need another smj on right key, we will unnecessarily add a sort which causes additional cost.
As a more complicated example, A join B on A.key = B.key join C on B.key = C.key join D on A.key = D.key. We will unnecessarily add a sort on B.key when join {A, B} and C, and add a sort on A.key when join {A, B, C} and D.
To fix this, we need to propagate all sorted information (equivalent expressions) from bottom up through `outputOrdering` and `SortOrder`.
## How was this patch tested?
Test cases are added.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17339 from wzhfy/sortEnhance.
## What changes were proposed in this pull request?
update `StatFunctions.multipleApproxQuantiles` to handle NaN/null
## How was this patch tested?
existing tests and added tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#16971 from zhengruifeng/quantiles_nan.
## What changes were proposed in this pull request?
Star schema consists of one or more fact tables referencing a number of dimension tables. In general, queries against star schema are expected to run fast because of the established RI constraints among the tables. This design proposes a join reordering based on natural, generally accepted heuristics for star schema queries:
- Finds the star join with the largest fact table and places it on the driving arm of the left-deep join. This plan avoids large tables on the inner, and thus favors hash joins.
- Applies the most selective dimensions early in the plan to reduce the amount of data flow.
The design document was included in SPARK-17791.
Link to the google doc: [StarSchemaDetection](https://docs.google.com/document/d/1UAfwbm_A6wo7goHlVZfYK99pqDMEZUumi7pubJXETEA/edit?usp=sharing)
## How was this patch tested?
A new test suite StarJoinSuite.scala was implemented.
Author: Ioana Delaney <ioanamdelaney@gmail.com>
Closes#15363 from ioana-delaney/starJoinReord2.
## What changes were proposed in this pull request?
This PR proposes to support an array of struct type in `to_json` as below:
```scala
import org.apache.spark.sql.functions._
val df = Seq(Tuple1(Tuple1(1) :: Nil)).toDF("a")
df.select(to_json($"a").as("json")).show()
```
```
+----------+
| json|
+----------+
|[{"_1":1}]|
+----------+
```
Currently, it throws an exception as below (a newline manually inserted for readability):
```
org.apache.spark.sql.AnalysisException: cannot resolve 'structtojson(`array`)' due to data type
mismatch: structtojson requires that the expression is a struct expression.;;
```
This allows the roundtrip with `from_json` as below:
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = ArrayType(StructType(StructField("a", IntegerType) :: Nil))
val df = Seq("""[{"a":1}, {"a":2}]""").toDF("json").select(from_json($"json", schema).as("array"))
df.show()
// Read back.
df.select(to_json($"array").as("json")).show()
```
```
+----------+
| array|
+----------+
|[[1], [2]]|
+----------+
+-----------------+
| json|
+-----------------+
|[{"a":1},{"a":2}]|
+-----------------+
```
Also, this PR proposes to rename from `StructToJson` to `StructsToJson ` and `JsonToStruct` to `JsonToStructs`.
## How was this patch tested?
Unit tests in `JsonFunctionsSuite` and `JsonExpressionsSuite` for Scala, doctest for Python and test in `test_sparkSQL.R` for R.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17192 from HyukjinKwon/SPARK-19849.
## What changes were proposed in this pull request?
When a key does not get any new data in `mapGroupsWithState`, the mapping function is never called on it. So we need a timeout feature that calls the function again in such cases, so that the user can decide whether to continue waiting or clean up (remove state, save stuff externally, etc.).
Timeouts can be either based on processing time or event time. This JIRA is for processing time, but defines the high level API design for both. The usage would look like this.
```
def stateFunction(key: K, value: Iterator[V], state: KeyedState[S]): U = {
...
state.setTimeoutDuration(10000)
...
}
dataset // type is Dataset[T]
.groupByKey[K](keyingFunc) // generates KeyValueGroupedDataset[K, T]
.mapGroupsWithState[S, U](
func = stateFunction,
timeout = KeyedStateTimeout.withProcessingTime) // returns Dataset[U]
```
Note the following design aspects.
- The timeout type is provided as a param in mapGroupsWithState as a parameter global to all the keys. This is so that the planner knows this at planning time, and accordingly optimize the execution based on whether to saves extra info in state or not (e.g. timeout durations or timestamps).
- The exact timeout duration is provided inside the function call so that it can be customized on a per key basis.
- When the timeout occurs for a key, the function is called with no values, and KeyedState.isTimingOut() set to true.
- The timeout is reset for key every time the function is called on the key, that is, when the key has new data, or the key has timed out. So the user has to set the timeout duration everytime the function is called, otherwise there will not be any timeout set.
Guarantees provided on timeout of key, when timeout duration is D ms:
- Timeout will never be called before real clock time has advanced by D ms
- Timeout will be called eventually when there is a trigger with any data in it (i.e. after D ms). So there is a no strict upper bound on when the timeout would occur. For example, if there is no data in the stream (for any key) for a while, then the timeout will not be hit.
Implementation details:
- Added new param to `mapGroupsWithState` for timeout
- Added new method to `StateStore` to filter data based on timeout timestamp
- Changed the internal map type of `HDFSBackedStateStore` from Java's `HashMap` to `ConcurrentHashMap` as the latter allows weakly-consistent fail-safe iterators on the map data. See comments in code for more details.
- Refactored logic of `MapGroupsWithStateExec` to
- Save timeout info to state store for each key that has data.
- Then, filter states that should be timed out based on the current batch processing timestamp.
- Moved KeyedState for `o.a.s.sql` to `o.a.s.sql.streaming`. I remember that this was a feedback in the MapGroupsWithState PR that I had forgotten to address.
## How was this patch tested?
New unit tests in
- MapGroupsWithStateSuite for timeouts.
- StateStoreSuite for new APIs in StateStore.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#17179 from tdas/mapgroupwithstate-timeout.
## What changes were proposed in this pull request?
If case classes have circular references below, it throws StackOverflowError;
```
scala> :pasge
case class classA(i: Int, cls: classB)
case class classB(cls: classA)
scala> Seq(classA(0, null)).toDS()
java.lang.StackOverflowError
at scala.reflect.internal.Symbols$Symbol.info(Symbols.scala:1494)
at scala.reflect.runtime.JavaMirrors$JavaMirror$$anon$1.scala$reflect$runtime$SynchronizedSymbols$SynchronizedSymbol$$super$info(JavaMirrors.scala:66)
at scala.reflect.runtime.SynchronizedSymbols$SynchronizedSymbol$$anonfun$info$1.apply(SynchronizedSymbols.scala:127)
at scala.reflect.runtime.SynchronizedSymbols$SynchronizedSymbol$$anonfun$info$1.apply(SynchronizedSymbols.scala:127)
at scala.reflect.runtime.Gil$class.gilSynchronized(Gil.scala:19)
at scala.reflect.runtime.JavaUniverse.gilSynchronized(JavaUniverse.scala:16)
at scala.reflect.runtime.SynchronizedSymbols$SynchronizedSymbol$class.gilSynchronizedIfNotThreadsafe(SynchronizedSymbols.scala:123)
at scala.reflect.runtime.JavaMirrors$JavaMirror$$anon$1.gilSynchronizedIfNotThreadsafe(JavaMirrors.scala:66)
at scala.reflect.runtime.SynchronizedSymbols$SynchronizedSymbol$class.info(SynchronizedSymbols.scala:127)
at scala.reflect.runtime.JavaMirrors$JavaMirror$$anon$1.info(JavaMirrors.scala:66)
at scala.reflect.internal.Mirrors$RootsBase.getModuleOrClass(Mirrors.scala:48)
at scala.reflect.internal.Mirrors$RootsBase.getModuleOrClass(Mirrors.scala:45)
at scala.reflect.internal.Mirrors$RootsBase.getModuleOrClass(Mirrors.scala:45)
at scala.reflect.internal.Mirrors$RootsBase.getModuleOrClass(Mirrors.scala:45)
at scala.reflect.internal.Mirrors$RootsBase.getModuleOrClass(Mirrors.scala:45)
```
This pr added code to throw UnsupportedOperationException in that case as follows;
```
scala> :paste
case class A(cls: B)
case class B(cls: A)
scala> Seq(A(null)).toDS()
java.lang.UnsupportedOperationException: cannot have circular references in class, but got the circular reference of class B
at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor(ScalaReflection.scala:627)
at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$9.apply(ScalaReflection.scala:644)
at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$9.apply(ScalaReflection.scala:632)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
```
## How was this patch tested?
Added tests in `DatasetSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17318 from maropu/SPARK-19896.
## What changes were proposed in this pull request?
We have some concerns about removing size in the cost model [in the previous pr](https://github.com/apache/spark/pull/17240). It's a tradeoff between code structure and algorithm completeness. I tend to keep the size and thus create this new pr without changing cost model.
What this pr does:
1. We only consider consecutive inner joinable items, thus excluding cartesian products in reordering procedure. This significantly reduces the search space and memory overhead of memo. Otherwise every combination of items will exist in the memo.
2. This pr also includes a bug fix: if a leaf item is a project(_, child), current solution will miss the project.
## How was this patch tested?
Added test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17286 from wzhfy/joinReorder3.
## What changes were proposed in this pull request?
This pr added entries in `FunctionRegistry` and supported `from_json` in SQL.
## How was this patch tested?
Added tests in `JsonFunctionsSuite` and `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17320 from maropu/SPARK-19967.
## What changes were proposed in this pull request?
Allows null values of the pivot column to be included in the pivot values list without throwing NPE
Note this PR was made as an alternative to #17224 but preserves the two phase aggregate operation that is needed for good performance.
## How was this patch tested?
Additional unit test
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#17226 from aray/pivot-null.
## What changes were proposed in this pull request?
Currently `SessionCatalogSuite` is only for `InMemoryCatalog`, there is no suite for `HiveExternalCatalog`.
And there are some ddl function is not proper to test in `ExternalCatalogSuite`, because some logic are not full implement in `ExternalCatalog`, these ddl functions are full implement in `SessionCatalog`(e.g. merge the same logic from `ExternalCatalog` up to `SessionCatalog` ).
It is better to test it in `SessionCatalogSuite` for this situation.
So we should add a test suite for `SessionCatalog` with `HiveExternalCatalog`
The main change is that in `SessionCatalogSuite` add two functions:
`withBasicCatalog` and `withEmptyCatalog`
And replace the code like `val catalog = new SessionCatalog(newBasicCatalog)` with above two functions
## How was this patch tested?
add `HiveExternalSessionCatalogSuite`
Author: windpiger <songjun@outlook.com>
Closes#17287 from windpiger/sessioncatalogsuit.
### 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.
## 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.
## 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.
## 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.
## 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.
## 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.
## 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.
## 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.
## 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.
## What changes were proposed in this pull request?
Commit 4ce970d714 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.
## 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.
## 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.
## What changes were proposed in this pull request?
- Timestamp hashing is done as per [TimestampWritable.hashCode()](ff67cdda1c/serde/src/java/org/apache/hadoop/hive/serde2/io/TimestampWritable.java (L406)) in Hive
- Interval hashing is done as per [HiveIntervalDayTime.hashCode()](ff67cdda1c/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.
## 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.
## Summary of changes
Add a new configuration option that allows Spark SQL to infer a case-sensitive schema from a Hive Metastore table's data files when a case-sensitive schema can't be read from the table properties.
- Add spark.sql.hive.caseSensitiveInferenceMode param to SQLConf
- Add schemaPreservesCase field to CatalogTable (set to false when schema can't
successfully be read from Hive table props)
- Perform schema inference in HiveMetastoreCatalog if schemaPreservesCase is
false, depending on spark.sql.hive.caseSensitiveInferenceMode
- Add alterTableSchema() method to the ExternalCatalog interface
- Add HiveSchemaInferenceSuite tests
- Refactor and move ParquetFileForamt.meregeMetastoreParquetSchema() as
HiveMetastoreCatalog.mergeWithMetastoreSchema
- Move schema merging tests from ParquetSchemaSuite to HiveSchemaInferenceSuite
[JIRA for this change](https://issues.apache.org/jira/browse/SPARK-19611)
## How was this patch tested?
The tests in ```HiveSchemaInferenceSuite``` should verify that schema inference is working as expected. ```ExternalCatalogSuite``` has also been extended to cover the new ```alterTableSchema()``` API.
Author: Budde <budde@amazon.com>
Closes#16944 from budde/SPARK-19611.
## What changes were proposed in this pull request?
If we create a external datasource table with a non-qualified location , we should qualified it to store in catalog.
```
CREATE TABLE t(a string)
USING parquet
LOCATION '/path/xx'
CREATE TABLE t1(a string, b string)
USING parquet
PARTITIONED BY(b)
LOCATION '/path/xx'
```
when we get the table from catalog, the location should be qualified, e.g.'file:/path/xxx'
## How was this patch tested?
unit test added
Author: windpiger <songjun@outlook.com>
Closes#17095 from windpiger/tablepathQualified.
## What changes were proposed in this pull request?
A follow up to SPARK-19859:
- extract the calculation of `delayMs` and reuse it.
- update EventTimeWatermarkExec
- use the correct `delayMs` in EventTimeWatermark
## How was this patch tested?
Jenkins.
Author: uncleGen <hustyugm@gmail.com>
Closes#17221 from uncleGen/SPARK-19859.
Forking a newSession() from SparkSession currently makes a new SparkSession that does not retain SessionState (i.e. temporary tables, SQL config, registered functions etc.) This change adds a method cloneSession() which creates a new SparkSession with a copy of the parent's SessionState.
Subsequent changes to base session are not propagated to cloned session, clone is independent after creation.
If the base is changed after clone has been created, say user registers new UDF, then the new UDF will not be available inside the clone. Same goes for configs and temp tables.
Unit tests
Author: Kunal Khamar <kkhamar@outlook.com>
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16826 from kunalkhamar/fork-sparksession.
## What changes were proposed in this pull request?
Add a output mode parameter to `flatMapGroupsWithState` and just define `mapGroupsWithState` as `flatMapGroupsWithState(Update)`.
`UnsupportedOperationChecker` is modified to disallow unsupported cases.
- Batch mapGroupsWithState or flatMapGroupsWithState is always allowed.
- For streaming (map/flatMap)GroupsWithState, see the following table:
| Operators | Supported Query Output Mode |
| ------------- | ------------- |
| flatMapGroupsWithState(Update) without aggregation | Update |
| flatMapGroupsWithState(Update) with aggregation | None |
| flatMapGroupsWithState(Append) without aggregation | Append |
| flatMapGroupsWithState(Append) before aggregation | Append, Update, Complete |
| flatMapGroupsWithState(Append) after aggregation | None |
| Multiple flatMapGroupsWithState(Append)s | Append |
| Multiple mapGroupsWithStates | None |
| Mxing mapGroupsWithStates and flatMapGroupsWithStates | None |
| Other cases of multiple flatMapGroupsWithState | None |
## How was this patch tested?
The added unit tests. Here are the tests related to (map/flatMap)GroupsWithState:
```
[info] - batch plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on batch relation: supported (1 millisecond)
[info] - batch plan - flatMapGroupsWithState - multiple flatMapGroupsWithState(Append)s on batch relation: supported (0 milliseconds)
[info] - batch plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on batch relation: supported (0 milliseconds)
[info] - batch plan - flatMapGroupsWithState - multiple flatMapGroupsWithState(Update)s on batch relation: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation without aggregation in update mode: supported (2 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation without aggregation in append mode: not supported (7 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation without aggregation in complete mode: not supported (5 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation with aggregation in Append mode: not supported (11 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation with aggregation in Update mode: not supported (5 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation with aggregation in Complete mode: not supported (5 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation without aggregation in append mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation without aggregation in update mode: not supported (6 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation before aggregation in Append mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation before aggregation in Update mode: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation before aggregation in Complete mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation after aggregation in Append mode: not supported (6 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on streaming relation after aggregation in Update mode: not supported (4 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on streaming relation in complete mode: not supported (2 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on batch relation inside streaming relation in Append output mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Append) on batch relation inside streaming relation in Update output mode: supported (1 millisecond)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on batch relation inside streaming relation in Append output mode: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - flatMapGroupsWithState(Update) on batch relation inside streaming relation in Update output mode: supported (0 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - multiple flatMapGroupsWithStates on streaming relation and all are in append mode: supported (2 milliseconds)
[info] - streaming plan - flatMapGroupsWithState - multiple flatMapGroupsWithStates on s streaming relation but some are not in append mode: not supported (7 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation without aggregation in append mode: not supported (3 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation without aggregation in complete mode: not supported (3 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation with aggregation in Append mode: not supported (6 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation with aggregation in Update mode: not supported (3 milliseconds)
[info] - streaming plan - mapGroupsWithState - mapGroupsWithState on streaming relation with aggregation in Complete mode: not supported (4 milliseconds)
[info] - streaming plan - mapGroupsWithState - multiple mapGroupsWithStates on streaming relation and all are in append mode: not supported (4 milliseconds)
[info] - streaming plan - mapGroupsWithState - mixing mapGroupsWithStates and flatMapGroupsWithStates on streaming relation: not supported (4 milliseconds)
```
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17197 from zsxwing/mapgroups-check.
## What changes were proposed in this pull request?
Fix for SQL round function that modifies original column when underlying data frame is created from a local product.
import org.apache.spark.sql.functions._
case class NumericRow(value: BigDecimal)
val df = spark.createDataFrame(Seq(NumericRow(BigDecimal("1.23456789"))))
df.show()
+--------------------+
| value|
+--------------------+
|1.234567890000000000|
+--------------------+
df.withColumn("value_rounded", round('value)).show()
// before
+--------------------+-------------+
| value|value_rounded|
+--------------------+-------------+
|1.000000000000000000| 1|
+--------------------+-------------+
// after
+--------------------+-------------+
| value|value_rounded|
+--------------------+-------------+
|1.234567890000000000| 1|
+--------------------+-------------+
## How was this patch tested?
New unit test added to existing suite `org.apache.spark.sql.MathFunctionsSuite`
Author: Wojtek Szymanski <wk.szymanski@gmail.com>
Closes#17075 from wojtek-szymanski/SPARK-19727.
### What changes were proposed in this pull request?
Observed by felixcheung in https://github.com/apache/spark/pull/16739, when users use the shuffle-enabled `repartition` API, they expect the partition they got should be the exact number they provided, even if they call shuffle-disabled `coalesce` later.
Currently, `CollapseRepartition` rule does not consider whether shuffle is enabled or not. Thus, we got the following unexpected result.
```Scala
val df = spark.range(0, 10000, 1, 5)
val df2 = df.repartition(10)
assert(df2.coalesce(13).rdd.getNumPartitions == 5)
assert(df2.coalesce(7).rdd.getNumPartitions == 5)
assert(df2.coalesce(3).rdd.getNumPartitions == 3)
```
This PR is to fix the issue. We preserve shuffle-enabled Repartition.
### How was this patch tested?
Added a test case
Author: Xiao Li <gatorsmile@gmail.com>
Closes#16933 from gatorsmile/CollapseRepartition.
## What changes were proposed in this pull request?
Since we have a `View` node now, we can remove the view identifier in `SubqueryAlias`, which was used to indicate a view node before.
## How was this patch tested?
Update the related test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#17210 from jiangxb1987/SubqueryAlias.
## What changes were proposed in this pull request?
Reorder the joins using a dynamic programming algorithm (Selinger paper):
First we put all items (basic joined nodes) into level 1, then we build all two-way joins at level 2 from plans at level 1 (single items), then build all 3-way joins from plans at previous levels (two-way joins and single items), then 4-way joins ... etc, until we build all n-way joins and pick the best plan among them.
When building m-way joins, we only keep the best plan (with the lowest cost) for the same set of m items. E.g., for 3-way joins, we keep only the best plan for items {A, B, C} among plans (A J B) J C, (A J C) J B and (B J C) J A. Thus, the plans maintained for each level when reordering four items A, B, C, D are as follows:
```
level 1: p({A}), p({B}), p({C}), p({D})
level 2: p({A, B}), p({A, C}), p({A, D}), p({B, C}), p({B, D}), p({C, D})
level 3: p({A, B, C}), p({A, B, D}), p({A, C, D}), p({B, C, D})
level 4: p({A, B, C, D})
```
where p({A, B, C, D}) is the final output plan.
For cost evaluation, since physical costs for operators are not available currently, we use cardinalities and sizes to compute costs.
## How was this patch tested?
add test cases
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#17138 from wzhfy/joinReorder.
Previously, we were using the mirror of passed in `TypeTag` when reflecting to build an encoder. This fails when the outer class is built in (i.e. `Seq`'s default mirror is based on root classloader) but inner classes (i.e. `A` in `Seq[A]`) are defined in the REPL or a library.
This patch changes us to always reflect based on a mirror created using the context classloader.
Author: Michael Armbrust <michael@databricks.com>
Closes#17201 from marmbrus/replSeqEncoder.
## What changes were proposed in this pull request?
The new watermark should override the old one. Otherwise, we just pick up the first column which has a watermark, it may be unexpected.
## How was this patch tested?
The new test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17199 from zsxwing/SPARK-19859.
## What changes were proposed in this pull request?
Jira : https://issues.apache.org/jira/browse/SPARK-19843
Created wrapper classes (`IntWrapper`, `LongWrapper`) to wrap the result of parsing (which are primitive types). In case of problem in parsing, the method would return a boolean.
## How was this patch tested?
- Added new unit tests
- Ran a prod job which had conversion from string -> int and verified the outputs
## Performance
Tiny regression when all strings are valid integers
```
conversion to int: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
--------------------------------------------------------------------------------
trunk 502 / 522 33.4 29.9 1.0X
SPARK-19843 493 / 503 34.0 29.4 1.0X
```
Huge gain when all strings are invalid integers
```
conversion to int: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------
trunk 33913 / 34219 0.5 2021.4 1.0X
SPARK-19843 154 / 162 108.8 9.2 220.0X
```
Author: Tejas Patil <tejasp@fb.com>
Closes#17184 from tejasapatil/SPARK-19843_is_numeric_maybe.
## What changes were proposed in this pull request?
This pr added entries in `FunctionRegistry` and supported `to_json` in SQL.
## How was this patch tested?
Added tests in `JsonFunctionsSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#16981 from maropu/SPARK-19637.
## What changes were proposed in this pull request?
1. support boolean type in binary expression estimation.
2. deal with compound Not conditions.
3. avoid convert BigInt/BigDecimal directly to double unless it's within range (0, 1).
4. reorganize test code.
## How was this patch tested?
modify related test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#17148 from wzhfy/fixFilter.
## What changes were proposed in this pull request?
Before this pr, LocalLimit/GlobalLimit/Sample propagates the same row count and column stats from its child, which is incorrect.
We can get the correct rowCount in Statistics for GlobalLimit/Sample whether cbo is enabled or not.
We don't know the rowCount for LocalLimit because we don't know the partition number at that time. Column stats should not be propagated because we don't know the distribution of columns after Limit or Sample.
## How was this patch tested?
Added test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16696 from wzhfy/limitEstimation.
## What changes were proposed in this pull request?
Currently we don't explicitly forbid the following behaviors:
1. The statement CREATE VIEW AS INSERT INTO throws the following exception:
```
scala> spark.sql("CREATE VIEW testView AS INSERT INTO tab VALUES (1, \"a\")")
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: org.apache.hadoop.hive.ql.metadata.HiveException: at least one column must be specified for the table;
scala> spark.sql("CREATE VIEW testView(a, b) AS INSERT INTO tab VALUES (1, \"a\")")
org.apache.spark.sql.AnalysisException: The number of columns produced by the SELECT clause (num: `0`) does not match the number of column names specified by CREATE VIEW (num: `2`).;
```
2. The statement INSERT INTO view VALUES throws the following exception from checkAnalysis:
```
scala> spark.sql("INSERT INTO testView VALUES (1, \"a\")")
org.apache.spark.sql.AnalysisException: Inserting into an RDD-based table is not allowed.;;
'InsertIntoTable View (`default`.`testView`, [a#16,b#17]), false, false
+- LocalRelation [col1#14, col2#15]
```
After this PR, the behavior changes to:
```
scala> spark.sql("CREATE VIEW testView AS INSERT INTO tab VALUES (1, \"a\")")
org.apache.spark.sql.catalyst.parser.ParseException: Operation not allowed: CREATE VIEW ... AS INSERT INTO;
scala> spark.sql("CREATE VIEW testView(a, b) AS INSERT INTO tab VALUES (1, \"a\")")
org.apache.spark.sql.catalyst.parser.ParseException: Operation not allowed: CREATE VIEW ... AS INSERT INTO;
scala> spark.sql("INSERT INTO testView VALUES (1, \"a\")")
org.apache.spark.sql.AnalysisException: `default`.`testView` is a view, inserting into a view is not allowed;
```
## How was this patch tested?
Add a new test case in `SparkSqlParserSuite`;
Update the corresponding test case in `SQLViewSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#17125 from jiangxb1987/insert-with-view.
## What changes were proposed in this pull request?
Currently we treat the location of table/partition/database as URI string.
It will be safer if we can make the type of location as java.net.URI.
In this PR, there are following classes changes:
**1. CatalogDatabase**
```
case class CatalogDatabase(
name: String,
description: String,
locationUri: String,
properties: Map[String, String])
--->
case class CatalogDatabase(
name: String,
description: String,
locationUri: URI,
properties: Map[String, String])
```
**2. CatalogStorageFormat**
```
case class CatalogStorageFormat(
locationUri: Option[String],
inputFormat: Option[String],
outputFormat: Option[String],
serde: Option[String],
compressed: Boolean,
properties: Map[String, String])
---->
case class CatalogStorageFormat(
locationUri: Option[URI],
inputFormat: Option[String],
outputFormat: Option[String],
serde: Option[String],
compressed: Boolean,
properties: Map[String, String])
```
Before and After this PR, it is transparent for user, there is no change that the user should concern. The `String` to `URI` just happened in SparkSQL internally.
Here list some operation related location:
**1. whitespace in the location**
e.g. `/a/b c/d`
For both table location and partition location,
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b c/d'` ,
then `DESC EXTENDED t ` show the location is `/a/b c/d`,
and the real path in the FileSystem also show `/a/b c/d`
**2. colon(:) in the location**
e.g. `/a/b:c/d`
For both table location and partition location,
when `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b:c/d'` ,
**In linux file system**
`DESC EXTENDED t ` show the location is `/a/b:c/d`,
and the real path in the FileSystem also show `/a/b:c/d`
**in HDFS** throw exception:
`java.lang.IllegalArgumentException: Pathname /a/b:c/d from hdfs://iZbp1151s8hbnnwriekxdeZ:9000/a/b:c/d is not a valid DFS filename.`
**while** After `INSERT INTO TABLE t PARTITION(a="a:b") SELECT 1`
then `DESC EXTENDED t ` show the location is `/xxx/a=a%3Ab`,
and the real path in the FileSystem also show `/xxx/a=a%3Ab`
**3. percent sign(%) in the location**
e.g. `/a/b%c/d`
For both table location and partition location,
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b%c/d'` ,
then `DESC EXTENDED t ` show the location is `/a/b%c/d`,
and the real path in the FileSystem also show `/a/b%c/d`
**4. encoded(%25) in the location**
e.g. `/a/b%25c/d`
For both table location and partition location,
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION '/a/b%25c/d'` ,
then `DESC EXTENDED t ` show the location is `/a/b%25c/d`,
and the real path in the FileSystem also show `/a/b%25c/d`
**while** After `INSERT INTO TABLE t PARTITION(a="%25") SELECT 1`
then `DESC EXTENDED t ` show the location is `/xxx/a=%2525`,
and the real path in the FileSystem also show `/xxx/a=%2525`
**Additionally**, except the location, there are two other factors will affect the location of the table/partition. one is the table name which does not allowed to have special characters, and the other is `partition name` which have the same actions with `partition value`, and `partition name` with special character situation has add some testcase and resolve a bug in [PR](https://github.com/apache/spark/pull/17173)
### Summary:
After `CREATE TABLE t... (PARTITIONED BY ...) LOCATION path`,
the path which we get from `DESC TABLE` and `real path in FileSystem` are all the same with the `CREATE TABLE` command(different filesystem has different action that allow what kind of special character to create the path, e.g. HDFS does not allow colon, but linux filesystem allow it ).
`DataBase` also have the same logic with `CREATE TABLE`
while if the `partition value` has some special character like `%` `:` `#` etc, then we will get the path with encoded `partition value` like `/xxx/a=A%25B` from `DESC TABLE` and `real path in FileSystem`
In this PR, the core change code is using `new Path(str).toUri` and `new Path(uri).toString`
which transfrom `str to uri `or `uri to str`.
for example:
```
val str = '/a/b c/d'
val uri = new Path(str).toUri --> '/a/b%20c/d'
val strFromUri = new Path(uri).toString -> '/a/b c/d'
```
when we restore table/partition from metastore, or get the location from `CREATE TABLE` command, we can use it as above to change string to uri `new Path(str).toUri `
## How was this patch tested?
unit test added.
The `current master branch` also `passed all the test cases` added in this PR by a litter change.
https://github.com/apache/spark/pull/17149/files#diff-b7094baa12601424a5d19cb930e3402fR1764
here `toURI` -> `toString` when test in master branch.
This can show that this PR is transparent for user.
Author: windpiger <songjun@outlook.com>
Closes#17149 from windpiger/changeStringToURI.
## What changes were proposed in this pull request?
This PR adds a new `Once` analysis rule batch consists of a single analysis rule `LookupFunctions` that performs simple existence check over `UnresolvedFunctions` without actually resolving them.
The benefit of this rule is that it doesn't require function arguments to be resolved first and therefore doesn't rely on relation resolution, which may incur potentially expensive partition/schema discovery cost.
Please refer to [SPARK-19737][1] for more details about the motivation.
## How was this patch tested?
New test case added in `AnalysisErrorSuite`.
[1]: https://issues.apache.org/jira/browse/SPARK-19737
Author: Cheng Lian <lian@databricks.com>
Closes#17168 from liancheng/spark-19737-lookup-functions.
## What changes were proposed in this pull request?
Hive hash to support Decimal datatype. [Hive internally normalises decimals](4ba713ccd8/storage-api/src/java/org/apache/hadoop/hive/common/type/HiveDecimalV1.java (L307)) and I have ported that logic as-is to HiveHash.
## How was this patch tested?
Added unit tests
Author: Tejas Patil <tejasp@fb.com>
Closes#17056 from tejasapatil/SPARK-17495_decimal.
## What changes were proposed in this pull request?
This PR proposes to both,
**Do not allow json arrays with multiple elements and return null in `from_json` with `StructType` as the schema.**
Currently, it only reads the single row when the input is a json array. So, the codes below:
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = StructType(StructField("a", IntegerType) :: Nil)
Seq(("""[{"a": 1}, {"a": 2}]""")).toDF("struct").select(from_json(col("struct"), schema)).show()
```
prints
```
+--------------------+
|jsontostruct(struct)|
+--------------------+
| [1]|
+--------------------+
```
This PR simply suggests to print this as `null` if the schema is `StructType` and input is json array.with multiple elements
```
+--------------------+
|jsontostruct(struct)|
+--------------------+
| null|
+--------------------+
```
**Support json arrays in `from_json` with `ArrayType` as the schema.**
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = ArrayType(StructType(StructField("a", IntegerType) :: Nil))
Seq(("""[{"a": 1}, {"a": 2}]""")).toDF("array").select(from_json(col("array"), schema)).show()
```
prints
```
+-------------------+
|jsontostruct(array)|
+-------------------+
| [[1], [2]]|
+-------------------+
```
## How was this patch tested?
Unit test in `JsonExpressionsSuite`, `JsonFunctionsSuite`, Python doctests and manual test.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16929 from HyukjinKwon/disallow-array.
## What changes were proposed in this pull request?
This pr is to support Seq, Map, and Struct in functions.lit; it adds a new IF named `lit2` with `TypeTag` for avoiding type erasure.
## How was this patch tested?
Added tests in `LiteralExpressionSuite`
Author: Takeshi Yamamuro <yamamuro@apache.org>
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#16610 from maropu/SPARK-19254.
## What changes were proposed in this pull request?
This is a follow-up pr of #16308 and #16750.
This pr enables timezone support in partition values.
We should use `timeZone` option introduced at #16750 to parse/format partition values of the `TimestampType`.
For example, if you have timestamp `"2016-01-01 00:00:00"` in `GMT` which will be used for partition values, the values written by the default timezone option, which is `"GMT"` because the session local timezone is `"GMT"` here, are:
```scala
scala> spark.conf.set("spark.sql.session.timeZone", "GMT")
scala> val df = Seq((1, new java.sql.Timestamp(1451606400000L))).toDF("i", "ts")
df: org.apache.spark.sql.DataFrame = [i: int, ts: timestamp]
scala> df.show()
+---+-------------------+
| i| ts|
+---+-------------------+
| 1|2016-01-01 00:00:00|
+---+-------------------+
scala> df.write.partitionBy("ts").save("/path/to/gmtpartition")
```
```sh
$ ls /path/to/gmtpartition/
_SUCCESS ts=2016-01-01 00%3A00%3A00
```
whereas setting the option to `"PST"`, they are:
```scala
scala> df.write.option("timeZone", "PST").partitionBy("ts").save("/path/to/pstpartition")
```
```sh
$ ls /path/to/pstpartition/
_SUCCESS ts=2015-12-31 16%3A00%3A00
```
We can properly read the partition values if the session local timezone and the timezone of the partition values are the same:
```scala
scala> spark.read.load("/path/to/gmtpartition").show()
+---+-------------------+
| i| ts|
+---+-------------------+
| 1|2016-01-01 00:00:00|
+---+-------------------+
```
And even if the timezones are different, we can properly read the values with setting corrent timezone option:
```scala
// wrong result
scala> spark.read.load("/path/to/pstpartition").show()
+---+-------------------+
| i| ts|
+---+-------------------+
| 1|2015-12-31 16:00:00|
+---+-------------------+
// correct result
scala> spark.read.option("timeZone", "PST").load("/path/to/pstpartition").show()
+---+-------------------+
| i| ts|
+---+-------------------+
| 1|2016-01-01 00:00:00|
+---+-------------------+
```
## How was this patch tested?
Existing tests and added some tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#17053 from ueshin/issues/SPARK-18939.
## What changes were proposed in this pull request?
When we resolve inline tables in analyzer, we will evaluate the expressions of inline tables.
When it evaluates a `TimeZoneAwareExpression` expression, an error will happen because the `TimeZoneAwareExpression` is not associated with timezone yet.
So we need to resolve these `TimeZoneAwareExpression`s with time zone when resolving inline tables.
## 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#17114 from viirya/resolve-timeawareexpr-inline-table.
## What changes were proposed in this pull request?
`MetastoreRelation` is used to represent table relation for hive tables, and provides some hive related information. We will resolve `SimpleCatalogRelation` to `MetastoreRelation` for hive tables, which is unnecessary as these 2 are the same essentially. This PR merges `SimpleCatalogRelation` and `MetastoreRelation`
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17015 from cloud-fan/table-relation.
## What changes were proposed in this pull request?
This PR proposes to fix the lint-breaks as below:
```
[ERROR] src/test/java/org/apache/spark/network/TransportResponseHandlerSuite.java:[29,8] (imports) UnusedImports: Unused import - org.apache.spark.network.buffer.ManagedBuffer.
[ERROR] src/main/java/org/apache/spark/unsafe/types/UTF8String.java:[156,10] (modifier) ModifierOrder: 'Nonnull' annotation modifier does not precede non-annotation modifiers.
[ERROR] src/main/java/org/apache/spark/SparkFirehoseListener.java:[122] (sizes) LineLength: Line is longer than 100 characters (found 105).
[ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java:[164,78] (coding) OneStatementPerLine: Only one statement per line allowed.
[ERROR] src/test/java/test/org/apache/spark/JavaAPISuite.java:[1157] (sizes) LineLength: Line is longer than 100 characters (found 121).
[ERROR] src/test/java/org/apache/spark/streaming/JavaMapWithStateSuite.java:[149] (sizes) LineLength: Line is longer than 100 characters (found 113).
[ERROR] src/test/java/test/org/apache/spark/streaming/Java8APISuite.java:[146] (sizes) LineLength: Line is longer than 100 characters (found 122).
[ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[32,8] (imports) UnusedImports: Unused import - org.apache.spark.streaming.Time.
[ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[611] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[1317] (sizes) LineLength: Line is longer than 100 characters (found 102).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetAggregatorSuite.java:[91] (sizes) LineLength: Line is longer than 100 characters (found 102).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[113] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[164] (sizes) LineLength: Line is longer than 100 characters (found 110).
[ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[212] (sizes) LineLength: Line is longer than 100 characters (found 114).
[ERROR] src/test/java/org/apache/spark/mllib/tree/JavaDecisionTreeSuite.java:[36] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java:[26,8] (imports) UnusedImports: Unused import - com.amazonaws.regions.RegionUtils.
[ERROR] src/test/java/org/apache/spark/streaming/kinesis/JavaKinesisStreamSuite.java:[20,8] (imports) UnusedImports: Unused import - com.amazonaws.regions.RegionUtils.
[ERROR] src/test/java/org/apache/spark/streaming/kinesis/JavaKinesisStreamSuite.java:[94] (sizes) LineLength: Line is longer than 100 characters (found 103).
[ERROR] src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java:[30,8] (imports) UnusedImports: Unused import - org.apache.spark.sql.api.java.UDF1.
[ERROR] src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java:[72] (sizes) LineLength: Line is longer than 100 characters (found 104).
[ERROR] src/main/java/org/apache/spark/examples/mllib/JavaRankingMetricsExample.java:[121] (sizes) LineLength: Line is longer than 100 characters (found 101).
[ERROR] src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java:[28,8] (imports) UnusedImports: Unused import - org.apache.spark.api.java.JavaRDD.
[ERROR] src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java:[29,8] (imports) UnusedImports: Unused import - org.apache.spark.api.java.JavaSparkContext.
```
## How was this patch tested?
Manually via
```bash
./dev/lint-java
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17072 from HyukjinKwon/java-lint.
## What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/16395. It fixes some code style issues, naming issues, some missing cases in pattern match, etc.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17065 from cloud-fan/follow-up.
### What changes were proposed in this pull request?
As explained in Hive JIRA https://issues.apache.org/jira/browse/HIVE-12224, HOLD_DDLTIME was broken as soon as it landed. Hive 2.0 removes HOLD_DDLTIME from the API. In Spark SQL, we always set it to FALSE. Like Hive, we should also remove it from our Catalog APIs.
### How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17063 from gatorsmile/removalHoldDDLTime.
## What changes were proposed in this pull request?
Currently we can only check the estimated stats in logical plans by debugging. We need to provide an easier and more efficient way for developers/users.
In this pr, we add EXPLAIN COST command to show stats in the optimized logical plan.
E.g.
```
spark-sql> EXPLAIN COST select count(1) from store_returns;
...
== Optimized Logical Plan ==
Aggregate [count(1) AS count(1)#24L], Statistics(sizeInBytes=16.0 B, rowCount=1, isBroadcastable=false)
+- Project, Statistics(sizeInBytes=4.3 GB, rowCount=5.76E+8, isBroadcastable=false)
+- Relation[sr_returned_date_sk#3,sr_return_time_sk#4,sr_item_sk#5,sr_customer_sk#6,sr_cdemo_sk#7,sr_hdemo_sk#8,sr_addr_sk#9,sr_store_sk#10,sr_reason_sk#11,sr_ticket_number#12,sr_return_quantity#13,sr_return_amt#14,sr_return_tax#15,sr_return_amt_inc_tax#16,sr_fee#17,sr_return_ship_cost#18,sr_refunded_cash#19,sr_reversed_charge#20,sr_store_credit#21,sr_net_loss#22] parquet, Statistics(sizeInBytes=28.6 GB, rowCount=5.76E+8, isBroadcastable=false)
...
```
## How was this patch tested?
Add test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#16594 from wzhfy/showStats.
## What changes were proposed in this pull request?
Fixed the line ending of `FilterEstimation.scala` (It's still using `\n\r`). Also improved the tests to cover the cases where the literals are on the left side of a binary operator.
## How was this patch tested?
Existing unit tests.
Author: Shuai Lin <linshuai2012@gmail.com>
Closes#17051 from lins05/fix-cbo-filter-file-encoding.
## What changes were proposed in this pull request?
This PR adds tests hive-hash by comparing the outputs generated against Hive 1.2.1. Following datatypes are covered by this PR:
- null
- boolean
- byte
- short
- int
- long
- float
- double
- string
- array
- map
- struct
Datatypes that I have _NOT_ covered but I will work on separately are:
- Decimal (handled separately in https://github.com/apache/spark/pull/17056)
- TimestampType
- DateType
- CalendarIntervalType
## How was this patch tested?
NA
Author: Tejas Patil <tejasp@fb.com>
Closes#17049 from tejasapatil/SPARK-17495_remaining_types.
## What changes were proposed in this pull request?
We traverse predicate and evaluate the logical expressions to compute the selectivity of a FILTER operator.
## How was this patch tested?
We add a new test suite to test various logical operators.
Author: Ron Hu <ron.hu@huawei.com>
Closes#16395 from ron8hu/filterSelectivity.
## What changes were proposed in this pull request?
This PR adds a special streaming deduplication operator to support `dropDuplicates` with `aggregation` and watermark. It reuses the `dropDuplicates` API but creates new logical plan `Deduplication` and new physical plan `DeduplicationExec`.
The following cases are supported:
- one or multiple `dropDuplicates()` without aggregation (with or without watermark)
- `dropDuplicates` before aggregation
Not supported cases:
- `dropDuplicates` after aggregation
Breaking changes:
- `dropDuplicates` without aggregation doesn't work with `complete` or `update` mode.
## How was this patch tested?
The new unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16970 from zsxwing/dedup.
## What changes were proposed in this pull request?
This PR is a small follow-up on https://github.com/apache/spark/pull/16804. This PR also adds support for nested char/varchar fields in orc.
## How was this patch tested?
I have added a regression test to the OrcSourceSuite.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17030 from hvanhovell/SPARK-19459-follow-up.
## What changes were proposed in this pull request?
This pr fixed a class-cast exception below;
```
scala> spark.range(10).selectExpr("cast (id as decimal) as x").selectExpr("percentile(x, 0.5)").collect()
java.lang.ClassCastException: org.apache.spark.sql.types.Decimal cannot be cast to java.lang.Number
at org.apache.spark.sql.catalyst.expressions.aggregate.Percentile.update(Percentile.scala:141)
at org.apache.spark.sql.catalyst.expressions.aggregate.Percentile.update(Percentile.scala:58)
at org.apache.spark.sql.catalyst.expressions.aggregate.TypedImperativeAggregate.update(interfaces.scala:514)
at org.apache.spark.sql.execution.aggregate.AggregationIterator$$anonfun$1$$anonfun$applyOrElse$1.apply(AggregationIterator.scala:171)
at org.apache.spark.sql.execution.aggregate.AggregationIterator$$anonfun$1$$anonfun$applyOrElse$1.apply(AggregationIterator.scala:171)
at org.apache.spark.sql.execution.aggregate.AggregationIterator$$anonfun$generateProcessRow$1.apply(AggregationIterator.scala:187)
at org.apache.spark.sql.execution.aggregate.AggregationIterator$$anonfun$generateProcessRow$1.apply(AggregationIterator.scala:181)
at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator.processInputs(ObjectAggregationIterator.scala:151)
at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator.<init>(ObjectAggregationIterator.scala:78)
at org.apache.spark.sql.execution.aggregate.ObjectHashAggregateExec$$anonfun$doExecute$1$$anonfun$2.apply(ObjectHashAggregateExec.scala:109)
at
```
This fix simply converts catalyst values (i.e., `Decimal`) into scala ones by using `CatalystTypeConverters`.
## How was this patch tested?
Added a test in `DataFrameSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17028 from maropu/SPARK-19691.
## What changes were proposed in this pull request?
This pr comes from #16928 and fixed a json behaviour along with the CSV one.
## How was this patch tested?
Added tests in `JsonSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17023 from maropu/SPARK-19695.
### What changes were proposed in this pull request?
Currently, if `NumPartitions` is not set in RepartitionByExpression, we will set it using `spark.sql.shuffle.partitions` during Planner. However, this is not following the general resolution process. This PR is to set it in `Parser` and then `Optimizer` can use the value for plan optimization.
### How was this patch tested?
Added a test case.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#16988 from gatorsmile/resolveRepartition.
## What changes were proposed in this pull request?
This PR proposes to fix two.
**Skip a property without a getter in beans**
Currently, if we use a JavaBean without the getter as below:
```java
public static class BeanWithoutGetter implements Serializable {
private String a;
public void setA(String a) {
this.a = a;
}
}
BeanWithoutGetter bean = new BeanWithoutGetter();
List<BeanWithoutGetter> data = Arrays.asList(bean);
spark.createDataFrame(data, BeanWithoutGetter.class).show();
```
- Before
It throws an exception as below:
```
java.lang.NullPointerException
at org.spark_project.guava.reflect.TypeToken.method(TypeToken.java:465)
at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$2.apply(JavaTypeInference.scala:126)
at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$2.apply(JavaTypeInference.scala:125)
```
- After
```
++
||
++
||
++
```
**Supports empty bean in encoder creation**
```java
public static class EmptyBean implements Serializable {}
EmptyBean bean = new EmptyBean();
List<EmptyBean> data = Arrays.asList(bean);
spark.createDataset(data, Encoders.bean(EmptyBean.class)).show();
```
- Before
throws an exception as below:
```
java.lang.UnsupportedOperationException: Cannot infer type for class EmptyBean because it is not bean-compliant
at org.apache.spark.sql.catalyst.JavaTypeInference$.org$apache$spark$sql$catalyst$JavaTypeInference$$serializerFor(JavaTypeInference.scala:436)
at org.apache.spark.sql.catalyst.JavaTypeInference$.serializerFor(JavaTypeInference.scala:341)
```
- After
```
++
||
++
||
++
```
## How was this patch tested?
Unit test in `JavaDataFrameSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17013 from HyukjinKwon/SPARK-19666.
## What changes were proposed in this pull request?
This is a small change to make GeneratorOuter always unresolved. It is mostly no-op change but makes it more clear since GeneratorOuter shouldn't survive analysis phase.
This requires also handling in ResolveAliases rule.
## How was this patch tested?
Existing generator tests.
Author: Bogdan Raducanu <bogdan@databricks.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#17026 from bogdanrdc/PR16958.
## What changes were proposed in this pull request?
The Range was modified to produce "recordsRead" metric instead of "generated rows". The tests were updated and partially moved to SQLMetricsSuite.
## How was this patch tested?
Unit tests.
Author: Ala Luszczak <ala@databricks.com>
Closes#16960 from ala/range-records-read.
## What changes were proposed in this pull request?
If a new option `wholeFile` is set to `true` the JSON reader will parse each file (instead of a single line) as a value. This is done with Jackson streaming and it should be capable of parsing very large documents, assuming the row will fit in memory.
Because the file is not buffered in memory the corrupt record handling is also slightly different when `wholeFile` is enabled: the corrupt column will contain the filename instead of the literal JSON if there is a parsing failure. It would be easy to extend this to add the parser location (line, column and byte offsets) to the output if desired.
These changes have allowed types other than `String` to be parsed. Support for `UTF8String` and `Text` have been added (alongside `String` and `InputFormat`) and no longer require a conversion to `String` just for parsing.
I've also included a few other changes that generate slightly better bytecode and (imo) make it more obvious when and where boxing is occurring in the parser. These are included as separate commits, let me know if they should be flattened into this PR or moved to a new one.
## How was this patch tested?
New and existing unit tests. No performance or load tests have been run.
Author: Nathan Howell <nhowell@godaddy.com>
Closes#16386 from NathanHowell/SPARK-18352.
- Move external/java8-tests tests into core, streaming, sql and remove
- Remove MaxPermGen and related options
- Fix some reflection / TODOs around Java 8+ methods
- Update doc references to 1.7/1.8 differences
- Remove Java 7/8 related build profiles
- Update some plugins for better Java 8 compatibility
- Fix a few Java-related warnings
For the future:
- Update Java 8 examples to fully use Java 8
- Update Java tests to use lambdas for simplicity
- Update Java internal implementations to use lambdas
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#16871 from srowen/SPARK-19493.
## What changes were proposed in this pull request?
Jira: https://issues.apache.org/jira/browse/SPARK-19618
Moved the check for validating number of buckets from `DataFrameWriter` to `BucketSpec` creation
## How was this patch tested?
- Added more unit tests
Author: Tejas Patil <tejasp@fb.com>
Closes#16948 from tejasapatil/SPARK-19618_max_buckets.
## What changes were proposed in this pull request?
This is a follow-up pr of #16308.
This pr enables timezone support in CSV/JSON parsing.
We should introduce `timeZone` option for CSV/JSON datasources (the default value of the option is session local timezone).
The datasources should use the `timeZone` option to format/parse to write/read timestamp values.
Notice that while reading, if the timestampFormat has the timezone info, the timezone will not be used because we should respect the timezone in the values.
For example, if you have timestamp `"2016-01-01 00:00:00"` in `GMT`, the values written with the default timezone option, which is `"GMT"` because session local timezone is `"GMT"` here, are:
```scala
scala> spark.conf.set("spark.sql.session.timeZone", "GMT")
scala> val df = Seq(new java.sql.Timestamp(1451606400000L)).toDF("ts")
df: org.apache.spark.sql.DataFrame = [ts: timestamp]
scala> df.show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
scala> df.write.json("/path/to/gmtjson")
```
```sh
$ cat /path/to/gmtjson/part-*
{"ts":"2016-01-01T00:00:00.000Z"}
```
whereas setting the option to `"PST"`, they are:
```scala
scala> df.write.option("timeZone", "PST").json("/path/to/pstjson")
```
```sh
$ cat /path/to/pstjson/part-*
{"ts":"2015-12-31T16:00:00.000-08:00"}
```
We can properly read these files even if the timezone option is wrong because the timestamp values have timezone info:
```scala
scala> val schema = new StructType().add("ts", TimestampType)
schema: org.apache.spark.sql.types.StructType = StructType(StructField(ts,TimestampType,true))
scala> spark.read.schema(schema).json("/path/to/gmtjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
scala> spark.read.schema(schema).option("timeZone", "PST").json("/path/to/gmtjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
```
And even if `timezoneFormat` doesn't contain timezone info, we can properly read the values with setting correct timezone option:
```scala
scala> df.write.option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").option("timeZone", "JST").json("/path/to/jstjson")
```
```sh
$ cat /path/to/jstjson/part-*
{"ts":"2016-01-01T09:00:00"}
```
```scala
// wrong result
scala> spark.read.schema(schema).option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").json("/path/to/jstjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 09:00:00|
+-------------------+
// correct result
scala> spark.read.schema(schema).option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").option("timeZone", "JST").json("/path/to/jstjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
```
This pr also makes `JsonToStruct` and `StructToJson` `TimeZoneAwareExpression` to be able to evaluate values with timezone option.
## How was this patch tested?
Existing tests and added some tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#16750 from ueshin/issues/SPARK-18937.
Move `SQLViewSuite` from `sql/hive` to `sql/core`, so we can test the view supports without hive metastore. Also moved the test cases that specified to hive to `HiveSQLViewSuite`.
Improve the test coverage of SQLViewSuite, cover the following cases:
1. view resolution(possibly a referenced table/view have changed after the view creation);
2. handle a view with user specified column names;
3. improve the test cases for a nested view.
Also added a test case for cyclic view reference, which is a known issue that is not fixed yet.
N/A
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#16674 from jiangxb1987/view-test.
## What changes were proposed in this pull request?
A follow-up to disallow space as the delimiter in broadcast hint.
## How was this patch tested?
Jenkins test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16941 from viirya/disallow-space-delimiter.
## What changes were proposed in this pull request?
Support cardinality estimation and stats propagation for all join types.
Limitations:
- For inner/outer joins without any equal condition, we estimate it like cartesian product.
- For left semi/anti joins, since we can't apply the heuristics for inner join to it, for now we just propagate the statistics from left side. We should support them when other advanced stats (e.g. histograms) are available in spark.
## How was this patch tested?
Add a new test suite.
Author: Zhenhua Wang <wzh_zju@163.com>
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16228 from wzhfy/joinEstimate.
## What changes were proposed in this pull request?
A small update to https://github.com/apache/spark/pull/16925
1. Rename SubstituteHints -> ResolveHints to be more consistent with rest of the rules.
2. Added more documentation in the rule and be more defensive / future proof to skip views as well as CTEs.
## How was this patch tested?
This pull request contains no real logic change and all behavior should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#16939 from rxin/SPARK-16475.
## What changes were proposed in this pull request?
The reason for test failure is that the property “oracle.jdbc.mapDateToTimestamp” set by the test was getting converted into all lower case. Oracle database expects this property in case-sensitive manner.
This test was passing in previous releases because connection properties were sent as user specified for the test case scenario. Fixes to handle all option uniformly in case-insensitive manner, converted the JDBC connection properties also to lower case.
This PR enhances CaseInsensitiveMap to keep track of input case-sensitive keys , and uses those when creating connection properties that are passed to the JDBC connection.
Alternative approach PR https://github.com/apache/spark/pull/16847 is to pass original input keys to JDBC data source by adding check in the Data source class and handle case-insensitivity in the JDBC source code.
## How was this patch tested?
Added new test cases to JdbcSuite , and OracleIntegrationSuite. Ran docker integration tests passed on my laptop, all tests passed successfully.
Author: sureshthalamati <suresh.thalamati@gmail.com>
Closes#16891 from sureshthalamati/jdbc_case_senstivity_props_fix-SPARK-19318.
## What changes were proposed in this pull request?
This pull request introduces a simple hint infrastructure to SQL and implements broadcast join hint using the infrastructure.
The hint syntax looks like the following:
```
SELECT /*+ BROADCAST(t) */ * FROM t
```
For broadcast hint, we accept "BROADCAST", "BROADCASTJOIN", and "MAPJOIN", and a sequence of relation aliases can be specified in the hint. A broadcast hint plan node will be inserted on top of any relation (that is not aliased differently), subquery, or common table expression that match the specified name.
The hint resolution works by recursively traversing down the query plan to find a relation or subquery that matches one of the specified broadcast aliases. The traversal does not go past beyond any existing broadcast hints, subquery aliases. This rule happens before common table expressions.
Note that there was an earlier patch in https://github.com/apache/spark/pull/14426. This is a rewrite of that patch, with different semantics and simpler test cases.
## How was this patch tested?
Added a new unit test suite for the broadcast hint rule (SubstituteHintsSuite) and new test cases for parser change (in PlanParserSuite). Also added end-to-end test case in BroadcastSuite.
Author: Reynold Xin <rxin@databricks.com>
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#16925 from rxin/SPARK-16475-broadcast-hint.
What changes were proposed in this pull request?
Support CREATE [EXTERNAL] TABLE LIKE LOCATION... syntax for Hive serde and datasource tables.
In this PR,we follow SparkSQL design rules :
supporting create table like view or physical table or temporary view with location.
creating a table with location,this table will be an external table other than managed table.
How was this patch tested?
Add new test cases and update existing test cases
Author: ouyangxiaochen <ou.yangxiaochen@zte.com.cn>
Closes#16868 from ouyangxiaochen/spark19115.
## What changes were proposed in this pull request?
This PR proposes to support type coercion between `ArrayType`s where the element types are compatible.
**Before**
```
Seq(Array(1)).toDF("a").selectExpr("greatest(a, array(1D))")
org.apache.spark.sql.AnalysisException: cannot resolve 'greatest(`a`, array(1.0D))' due to data type mismatch: The expressions should all have the same type, got GREATEST(array<int>, array<double>).; line 1 pos 0;
Seq(Array(1)).toDF("a").selectExpr("least(a, array(1D))")
org.apache.spark.sql.AnalysisException: cannot resolve 'least(`a`, array(1.0D))' due to data type mismatch: The expressions should all have the same type, got LEAST(array<int>, array<double>).; line 1 pos 0;
sql("SELECT * FROM values (array(0)), (array(1D)) as data(a)")
org.apache.spark.sql.AnalysisException: incompatible types found in column a for inline table; line 1 pos 14
Seq(Array(1)).toDF("a").union(Seq(Array(1D)).toDF("b"))
org.apache.spark.sql.AnalysisException: Union can only be performed on tables with the compatible column types. ArrayType(DoubleType,false) <> ArrayType(IntegerType,false) at the first column of the second table;;
sql("SELECT IF(1=1, array(1), array(1D))")
org.apache.spark.sql.AnalysisException: cannot resolve '(IF((1 = 1), array(1), array(1.0D)))' due to data type mismatch: differing types in '(IF((1 = 1), array(1), array(1.0D)))' (array<int> and array<double>).; line 1 pos 7;
```
**After**
```scala
Seq(Array(1)).toDF("a").selectExpr("greatest(a, array(1D))")
res5: org.apache.spark.sql.DataFrame = [greatest(a, array(1.0)): array<double>]
Seq(Array(1)).toDF("a").selectExpr("least(a, array(1D))")
res6: org.apache.spark.sql.DataFrame = [least(a, array(1.0)): array<double>]
sql("SELECT * FROM values (array(0)), (array(1D)) as data(a)")
res8: org.apache.spark.sql.DataFrame = [a: array<double>]
Seq(Array(1)).toDF("a").union(Seq(Array(1D)).toDF("b"))
res10: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [a: array<double>]
sql("SELECT IF(1=1, array(1), array(1D))")
res15: org.apache.spark.sql.DataFrame = [(IF((1 = 1), array(1), array(1.0))): array<double>]
```
## How was this patch tested?
Unit tests in `TypeCoercion` and Jenkins tests and
building with scala 2.10
```scala
./dev/change-scala-version.sh 2.10
./build/mvn -Pyarn -Phadoop-2.4 -Dscala-2.10 -DskipTests clean package
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16777 from HyukjinKwon/SPARK-19435.
## What changes were proposed in this pull request?
This PR proposes to fix the error message when some data types are compatible and others are not in set/union operation.
Currently, the code below:
```scala
Seq((1,("a", 1))).toDF.union(Seq((1L,("a", "b"))).toDF)
```
throws an exception saying `LongType` and `IntegerType` are incompatible types. It should say something about `StructType`s with more readable format as below:
**Before**
```
Union can only be performed on tables with the compatible column types.
LongType <> IntegerType at the first column of the second table;;
```
**After**
```
Union can only be performed on tables with the compatible column types.
struct<_1:string,_2:string> <> struct<_1:string,_2:int> at the second column of the second table;;
```
*I manually inserted a newline in the messages above for readability only in this PR description.
## How was this patch tested?
Unit tests in `AnalysisErrorSuite`, manual tests and build wth Scala 2.10.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16882 from HyukjinKwon/SPARK-19544.
## What changes were proposed in this pull request?
Currently the udf `to_date` has different return value with an invalid date input.
```
SELECT to_date('2015-07-22', 'yyyy-dd-MM') -> return `2016-10-07`
SELECT to_date('2014-31-12') -> return null
```
As discussed in JIRA [SPARK-19496](https://issues.apache.org/jira/browse/SPARK-19496), we should return null in both situations when the input date is invalid
## How was this patch tested?
unit test added
Author: windpiger <songjun@outlook.com>
Closes#16870 from windpiger/to_date.
## What changes were proposed in this pull request?
Reading from an existing ORC table which contains `char` or `varchar` columns can fail with a `ClassCastException` if the table metadata has been created using Spark. This is caused by the fact that spark internally replaces `char` and `varchar` columns with a `string` column.
This PR fixes this by adding the hive type to the `StructField's` metadata under the `HIVE_TYPE_STRING` key. This is picked up by the `HiveClient` and the ORC reader, see https://github.com/apache/spark/pull/16060 for more details on how the metadata is used.
## How was this patch tested?
Added a regression test to `OrcSourceSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#16804 from hvanhovell/SPARK-19459.
## What changes were proposed in this pull request?
Using from_json on a column with an empty string results in: java.util.NoSuchElementException: head of empty list.
This is because `parser.parse(input)` may return `Nil` when `input.trim.isEmpty`
## How was this patch tested?
Regression test in `JsonExpressionsSuite`
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#16881 from brkyvz/json-fix.
## What changes were proposed in this pull request?
With the new approach of view resolution, we can get rid of SQL generation on view creation, so let's remove SQL builder for operators.
Note that, since all sql generation for operators is defined in one file (org.apache.spark.sql.catalyst.SQLBuilder), it’d be trivial to recover it in the future.
## How was this patch tested?
N/A
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#16869 from jiangxb1987/SQLBuilder.
## What changes were proposed in this pull request?
Set currentVars to null in GenerateOrdering.genComparisons before genCode is called. genCode ignores INPUT_ROW if currentVars is not null and in genComparisons we want it to use INPUT_ROW.
## How was this patch tested?
Added test with 2 queries in WholeStageCodegenSuite
Author: Bogdan Raducanu <bogdan.rdc@gmail.com>
Closes#16852 from bogdanrdc/SPARK-19512.
## What changes were proposed in this pull request?
Previously range operator could not be interrupted. For example, using DAGScheduler.cancelStage(...) on a query with range might have been ineffective.
This change adds periodic checks of TaskContext.isInterrupted to codegen version, and InterruptibleOperator to non-codegen version.
I benchmarked the performance of codegen version on a sample query `spark.range(1000L * 1000 * 1000 * 10).count()` and there is no measurable difference.
## How was this patch tested?
Adds a unit test.
Author: Ala Luszczak <ala@databricks.com>
Closes#16872 from ala/SPARK-19514b.
## What changes were proposed in this pull request?
SPARK-19265 had made table relation cache general; this follow-up aims to make `tableRelationCache`'s maximum size configurable.
In order to do sanity-check, this patch also adds a `checkValue()` method to `TypedConfigBuilder`.
## How was this patch tested?
new test case: `test("conf entry: checkValue()")`
Author: Liwei Lin <lwlin7@gmail.com>
Closes#16736 from lw-lin/conf.
## What changes were proposed in this pull request?
Hive metastore is not case-preserving and keep partition columns with lower case names. If Spark SQL creates a table with upper-case partition column names using `HiveExternalCatalog`, when we rename partition, it first calls the HiveClient to renamePartition, which will create a new lower case partition path, then Spark SQL renames the lower case path to upper-case.
However, when we rename a nested path, different file systems have different behaviors. e.g. in jenkins, renaming `a=1/b=2` to `A=2/B=2` will success, but leave an empty directory `a=1`. in mac os, the renaming doesn't work as expected and result to `a=1/B=2`.
This PR renames the partition directory recursively from the first partition column in `HiveExternalCatalog`, to be most compatible with different file systems.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16837 from cloud-fan/partition.
### What changes were proposed in this pull request?
`table.schema` is always not empty for partitioned tables, because `table.schema` also contains the partitioned columns, even if the original table does not have any column. This PR is to fix the issue.
### How was this patch tested?
Added a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16848 from gatorsmile/inferHiveSerdeSchema.
## What changes were proposed in this pull request?
`mapGroupsWithState` is a new API for arbitrary stateful operations in Structured Streaming, similar to `DStream.mapWithState`
*Requirements*
- Users should be able to specify a function that can do the following
- Access the input row corresponding to a key
- Access the previous state corresponding to a key
- Optionally, update or remove the state
- Output any number of new rows (or none at all)
*Proposed API*
```
// ------------ New methods on KeyValueGroupedDataset ------------
class KeyValueGroupedDataset[K, V] {
// Scala friendly
def mapGroupsWithState[S: Encoder, U: Encoder](func: (K, Iterator[V], KeyedState[S]) => U)
def flatMapGroupsWithState[S: Encode, U: Encoder](func: (K, Iterator[V], KeyedState[S]) => Iterator[U])
// Java friendly
def mapGroupsWithState[S, U](func: MapGroupsWithStateFunction[K, V, S, R], stateEncoder: Encoder[S], resultEncoder: Encoder[U])
def flatMapGroupsWithState[S, U](func: FlatMapGroupsWithStateFunction[K, V, S, R], stateEncoder: Encoder[S], resultEncoder: Encoder[U])
}
// ------------------- New Java-friendly function classes -------------------
public interface MapGroupsWithStateFunction<K, V, S, R> extends Serializable {
R call(K key, Iterator<V> values, state: KeyedState<S>) throws Exception;
}
public interface FlatMapGroupsWithStateFunction<K, V, S, R> extends Serializable {
Iterator<R> call(K key, Iterator<V> values, state: KeyedState<S>) throws Exception;
}
// ---------------------- Wrapper class for state data ----------------------
trait State[S] {
def exists(): Boolean
def get(): S // throws Exception is state does not exist
def getOption(): Option[S]
def update(newState: S): Unit
def remove(): Unit // exists() will be false after this
}
```
Key Semantics of the State class
- The state can be null.
- If the state.remove() is called, then state.exists() will return false, and getOption will returm None.
- After that state.update(newState) is called, then state.exists() will return true, and getOption will return Some(...).
- None of the operations are thread-safe. This is to avoid memory barriers.
*Usage*
```
val stateFunc = (word: String, words: Iterator[String, runningCount: KeyedState[Long]) => {
val newCount = words.size + runningCount.getOption.getOrElse(0L)
runningCount.update(newCount)
(word, newCount)
}
dataset // type is Dataset[String]
.groupByKey[String](w => w) // generates KeyValueGroupedDataset[String, String]
.mapGroupsWithState[Long, (String, Long)](stateFunc) // returns Dataset[(String, Long)]
```
## How was this patch tested?
New unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16758 from tdas/mapWithState.
## What changes were proposed in this pull request?
The optimizer tries to remove redundant alias only projections from the query plan using the `RemoveAliasOnlyProject` rule. The current rule identifies removes such a project and rewrites the project's attributes in the **entire** tree. This causes problems when parts of the tree are duplicated (for instance a self join on a temporary view/CTE) and the duplicated part contains the alias only project, in this case the rewrite will break the tree.
This PR fixes these problems by using a blacklist for attributes that are not to be moved, and by making sure that attribute remapping is only done for the parent tree, and not for unrelated parts of the query plan.
The current tree transformation infrastructure works very well if the transformation at hand requires little or a global contextual information. In this case we need to know both the attributes that were not to be moved, and we also needed to know which child attributes were modified. This cannot be done easily using the current infrastructure, and solutions typically involves transversing the query plan multiple times (which is super slow). I have moved around some code in `TreeNode`, `QueryPlan` and `LogicalPlan`to make this much more straightforward; this basically allows you to manually traverse the tree.
This PR subsumes the following PRs by windpiger:
Closes https://github.com/apache/spark/pull/16267
Closes https://github.com/apache/spark/pull/16255
## How was this patch tested?
I have added unit tests to `RemoveRedundantAliasAndProjectSuite` and I have added integration tests to the `SQLQueryTestSuite.union` and `SQLQueryTestSuite.cte` test cases.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#16757 from hvanhovell/SPARK-18609.
## What changes were proposed in this pull request?
This pull request adds two new user facing functions:
- `to_date` which accepts an expression and a format and returns a date.
- `to_timestamp` which accepts an expression and a format and returns a timestamp.
For example, Given a date in format: `2016-21-05`. (YYYY-dd-MM)
### Date Function
*Previously*
```
to_date(unix_timestamp(lit("2016-21-05"), "yyyy-dd-MM").cast("timestamp"))
```
*Current*
```
to_date(lit("2016-21-05"), "yyyy-dd-MM")
```
### Timestamp Function
*Previously*
```
unix_timestamp(lit("2016-21-05"), "yyyy-dd-MM").cast("timestamp")
```
*Current*
```
to_timestamp(lit("2016-21-05"), "yyyy-dd-MM")
```
### Tasks
- [X] Add `to_date` to Scala Functions
- [x] Add `to_date` to Python Functions
- [x] Add `to_date` to SQL Functions
- [X] Add `to_timestamp` to Scala Functions
- [x] Add `to_timestamp` to Python Functions
- [x] Add `to_timestamp` to SQL Functions
- [x] Add function to R
## How was this patch tested?
- [x] Add Functions to `DateFunctionsSuite`
- Test new `ParseToTimestamp` Expression (*not necessary*)
- Test new `ParseToDate` Expression (*not necessary*)
- [x] Add test for R
- [x] Add test for Python in test.py
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: anabranch <wac.chambers@gmail.com>
Author: Bill Chambers <bill@databricks.com>
Author: anabranch <bill@databricks.com>
Closes#16138 from anabranch/SPARK-16609.
## What changes were proposed in this pull request?
I have a frequency distribution table with following entries
Age, No of person
21, 10
22, 15
23, 18
..
..
30, 14
Moreover it is common to have data in frequency distribution format to further calculate Percentile, Median. With current implementation
It would be very difficult and complex to find the percentile.
Therefore i am proposing enhancement to current Percentile and Approx Percentile implementation to take frequency distribution column into consideration
## How was this patch tested?
1) Enhanced /sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/aggregate/PercentileSuite.scala to cover the additional functionality
2) Run some performance benchmark test with 20 million row in local environment and did not see any performance degradation
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: gagan taneja <tanejagagan@gagans-MacBook-Pro.local>
Closes#16497 from tanejagagan/branch-18940.
## What changes were proposed in this pull request?
It often happens that a complex object (struct/map/array) is created only to get elements from it in an subsequent expression. We can add an optimizer rule for this.
## How was this patch tested?
unit-tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Eyal Farago <eyal@nrgene.com>
Author: eyal farago <eyal.farago@gmail.com>
Closes#16043 from eyalfa/SPARK-18601.
### What changes were proposed in this pull request?
The removed codes for `IN` are not reachable, because the previous rule `InConversion` already resolves the type coercion issues.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16783 from gatorsmile/typeCoercionIn.
## What changes were proposed in this pull request?
The SQL parser can mistake a `WHEN (...)` used in `CASE` for a function call. This happens in cases like the following:
```sql
select case when (1) + case when 1 > 0 then 1 else 0 end = 2 then 1 else 0 end
from tb
```
This PR fixes this by re-organizing the case related parsing rules.
## How was this patch tested?
Added a regression test to the `ExpressionParserSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#16821 from hvanhovell/SPARK-19472.
## What changes were proposed in this pull request?
The current way of resolving `InsertIntoTable` and `CreateTable` is convoluted: sometimes we replace them with concrete implementation commands during analysis, sometimes during planning phase.
And the error checking logic is also a mess: we may put it in extended analyzer rules, or extended checking rules, or `CheckAnalysis`.
This PR simplifies the data source analysis:
1. `InsertIntoTable` and `CreateTable` are always unresolved and need to be replaced by concrete implementation commands during analysis.
2. The error checking logic is mainly in 2 rules: `PreprocessTableCreation` and `PreprocessTableInsertion`.
## How was this patch tested?
existing test.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16269 from cloud-fan/ddl.
## What changes were proposed in this pull request?
DataFrame.except doesn't work for UDT columns. It is because `ExtractEquiJoinKeys` will run `Literal.default` against UDT. However, we don't handle UDT in `Literal.default` and an exception will throw like:
java.lang.RuntimeException: no default for type
org.apache.spark.ml.linalg.VectorUDT3bfc3ba7
at org.apache.spark.sql.catalyst.expressions.Literal$.default(literals.scala:179)
at org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys$$anonfun$4.apply(patterns.scala:117)
at org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys$$anonfun$4.apply(patterns.scala:110)
More simple fix is just let `Literal.default` handle UDT by its sql type. So we can use more efficient join type on UDT.
Besides `except`, this also fixes other similar scenarios, so in summary this fixes:
* `except` on two Datasets with UDT
* `intersect` on two Datasets with UDT
* `Join` with the join conditions using `<=>` on UDT columns
## 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#16765 from viirya/df-except-for-udt.
## What changes were proposed in this pull request?
This PR proposes to
- remove unused `findTightestCommonType` in `TypeCoercion` as suggested in https://github.com/apache/spark/pull/16777#discussion_r99283834
- rename `findTightestCommonTypeOfTwo ` to `findTightestCommonType`.
- fix comments accordingly
The usage was removed while refactoring/fixing in several JIRAs such as SPARK-16714, SPARK-16735 and SPARK-16646
## How was this patch tested?
Existing tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16786 from HyukjinKwon/SPARK-19446.
## What changes were proposed in this pull request?
In `ExpressionEncoder.toRow` and `fromRow`, we catch the exception and output `treeString` of serializer/deserializer expressions in the error message. However, encoder can be very complex and the serializer/deserializer expressions can be very large trees and blow up the log files(e.g. generate over 500mb logs for this single error message.) As a first attempt, this PR try to use `simpleString` instead.
**BEFORE**
```scala
scala> :paste
// Entering paste mode (ctrl-D to finish)
case class TestCaseClass(value: Int)
import spark.implicits._
Seq(TestCaseClass(1)).toDS().collect()
// Exiting paste mode, now interpreting.
java.lang.RuntimeException: Error while decoding: java.lang.NullPointerException
newInstance(class TestCaseClass)
+- assertnotnull(input[0, int, false], - field (class: "scala.Int", name: "value"), - root class: "TestCaseClass")
+- input[0, int, false]
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.fromRow(ExpressionEncoder.scala:303)
...
```
**AFTER**
```scala
...
// Exiting paste mode, now interpreting.
java.lang.RuntimeException: Error while decoding: java.lang.NullPointerException
newInstance(class TestCaseClass)
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.fromRow(ExpressionEncoder.scala:303)
...
```
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#16701 from dongjoon-hyun/SPARK-18909-EXPR-ERROR.
## What changes were proposed in this pull request?
There is a metadata introduced before to mark the optional columns in merged Parquet schema for filter predicate pushdown. As we upgrade to Parquet 1.8.2 which includes the fix for the pushdown of optional columns, we don't need this metadata now.
## 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#16756 from viirya/remove-optional-metadata.
## What changes were proposed in this pull request?
This PR proposes three things as below:
- Support LaTex inline-formula, `\( ... \)` in Scala API documentation
It seems currently,
```
\( ... \)
```
are rendered as they are, for example,
<img width="345" alt="2017-01-30 10 01 13" src="https://cloud.githubusercontent.com/assets/6477701/22423960/ab37d54a-e737-11e6-9196-4f6229c0189c.png">
It seems mistakenly more backslashes were added.
- Fix warnings Scaladoc/Javadoc generation
This PR fixes t two types of warnings as below:
```
[warn] .../spark/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala:335: Could not find any member to link for "UnsupportedOperationException".
[warn] /**
[warn] ^
```
```
[warn] .../spark/sql/core/src/main/scala/org/apache/spark/sql/internal/VariableSubstitution.scala:24: Variable var undefined in comment for class VariableSubstitution in class VariableSubstitution
[warn] * `${var}`, `${system:var}` and `${env:var}`.
[warn] ^
```
- Fix Javadoc8 break
```
[error] .../spark/mllib/target/java/org/apache/spark/ml/PredictionModel.java:7: error: reference not found
[error] * E.g., {link VectorUDT} for vector features.
[error] ^
[error] .../spark/mllib/target/java/org/apache/spark/ml/PredictorParams.java:12: error: reference not found
[error] * E.g., {link VectorUDT} for vector features.
[error] ^
[error] .../spark/mllib/target/java/org/apache/spark/ml/Predictor.java:10: error: reference not found
[error] * E.g., {link VectorUDT} for vector features.
[error] ^
[error] .../spark/sql/hive/target/java/org/apache/spark/sql/hive/HiveAnalysis.java:5: error: reference not found
[error] * Note that, this rule must be run after {link PreprocessTableInsertion}.
[error] ^
```
## How was this patch tested?
Manually via `sbt unidoc` and `jeykil build`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16741 from HyukjinKwon/warn-and-break.
### What changes were proposed in this pull request?
Currently, the function `to_json` allows users to provide options for generating JSON. However, it does not pass it to `JacksonGenerator`. Thus, it ignores the user-provided options. This PR is to fix it. Below is an example.
```Scala
val df = Seq(Tuple1(Tuple1(java.sql.Timestamp.valueOf("2015-08-26 18:00:00.0")))).toDF("a")
val options = Map("timestampFormat" -> "dd/MM/yyyy HH:mm")
df.select(to_json($"a", options)).show(false)
```
The current output is like
```
+--------------------------------------+
|structtojson(a) |
+--------------------------------------+
|{"_1":"2015-08-26T18:00:00.000-07:00"}|
+--------------------------------------+
```
After the fix, the output is like
```
+-------------------------+
|structtojson(a) |
+-------------------------+
|{"_1":"26/08/2015 18:00"}|
+-------------------------+
```
### How was this patch tested?
Added test cases for both `from_json` and `to_json`
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16745 from gatorsmile/toJson.
## What changes were proposed in this pull request?
During canonicalization, `NOT(...(l, r))` should not expect such cases that `l.hashcode > r.hashcode`.
Take the rule `case NOT(GreaterThan(l, r)) if l.hashcode > r.hashcode` for example, it should never be matched since `GreaterThan(l, r)` itself would be re-written as `GreaterThan(r, l)` given `l.hashcode > r.hashcode` after canonicalization.
This patch consolidates rules like `case NOT(GreaterThan(l, r)) if l.hashcode > r.hashcode` and `case NOT(GreaterThan(l, r))`.
## How was this patch tested?
This patch expanded the `NOT` test case to cover both cases where:
- `l.hashcode > r.hashcode`
- `l.hashcode < r.hashcode`
Author: Liwei Lin <lwlin7@gmail.com>
Closes#16719 from lw-lin/canonicalize.
## What changes were proposed in this pull request?
This PR fixes both,
javadoc8 break
```
[error] .../spark/sql/hive/target/java/org/apache/spark/sql/hive/FindHiveSerdeTable.java:3: error: reference not found
[error] * Replaces {link SimpleCatalogRelation} with {link MetastoreRelation} if its table provider is hive.
```
and the example in `StructType` as a self-contained example as below:
```scala
import org.apache.spark.sql._
import org.apache.spark.sql.types._
val struct =
StructType(
StructField("a", IntegerType, true) ::
StructField("b", LongType, false) ::
StructField("c", BooleanType, false) :: Nil)
// Extract a single StructField.
val singleField = struct("b")
// singleField: StructField = StructField(b,LongType,false)
// If this struct does not have a field called "d", it throws an exception.
struct("d")
// java.lang.IllegalArgumentException: Field "d" does not exist.
// ...
// Extract multiple StructFields. Field names are provided in a set.
// A StructType object will be returned.
val twoFields = struct(Set("b", "c"))
// twoFields: StructType =
// StructType(StructField(b,LongType,false), StructField(c,BooleanType,false))
// Any names without matching fields will throw an exception.
// For the case shown below, an exception is thrown due to "d".
struct(Set("b", "c", "d"))
// java.lang.IllegalArgumentException: Field "d" does not exist.
// ...
```
```scala
import org.apache.spark.sql._
import org.apache.spark.sql.types._
val innerStruct =
StructType(
StructField("f1", IntegerType, true) ::
StructField("f2", LongType, false) ::
StructField("f3", BooleanType, false) :: Nil)
val struct = StructType(
StructField("a", innerStruct, true) :: Nil)
// Create a Row with the schema defined by struct
val row = Row(Row(1, 2, true))
```
Also, now when the column is missing, it throws an exception rather than ignoring.
## How was this patch tested?
Manually via `sbt unidoc`.
- Scaladoc
<img width="665" alt="2017-01-26 12 54 13" src="https://cloud.githubusercontent.com/assets/6477701/22297905/1245620e-e362-11e6-9e22-43bb8d9871af.png">
- Javadoc
<img width="722" alt="2017-01-26 12 54 27" src="https://cloud.githubusercontent.com/assets/6477701/22297899/0fd87e0c-e362-11e6-9033-7590bda1aea6.png">
<img width="702" alt="2017-01-26 12 54 32" src="https://cloud.githubusercontent.com/assets/6477701/22297900/0fe14154-e362-11e6-9882-768381c53163.png">
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16703 from HyukjinKwon/SPARK-12970.
## What changes were proposed in this pull request?
This pr added a variable for a UDF name in `ScalaUDF`.
Then, if the variable filled, `DataFrame#explain` prints the name.
## How was this patch tested?
Added a test in `UDFSuite`.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#16707 from maropu/SPARK-19338.
## What changes were proposed in this pull request?
As of Spark 2.1, Spark SQL assumes the machine timezone for datetime manipulation, which is bad if users are not in the same timezones as the machines, or if different users have different timezones.
We should introduce a session local timezone setting that is used for execution.
An explicit non-goal is locale handling.
### Semantics
Setting the session local timezone means that the timezone-aware expressions listed below should use the timezone to evaluate values, and also it should be used to convert (cast) between string and timestamp or between timestamp and date.
- `CurrentDate`
- `CurrentBatchTimestamp`
- `Hour`
- `Minute`
- `Second`
- `DateFormatClass`
- `ToUnixTimestamp`
- `UnixTimestamp`
- `FromUnixTime`
and below are implicitly timezone-aware through cast from timestamp to date:
- `DayOfYear`
- `Year`
- `Quarter`
- `Month`
- `DayOfMonth`
- `WeekOfYear`
- `LastDay`
- `NextDay`
- `TruncDate`
For example, if you have timestamp `"2016-01-01 00:00:00"` in `GMT`, the values evaluated by some of timezone-aware expressions are:
```scala
scala> val df = Seq(new java.sql.Timestamp(1451606400000L)).toDF("ts")
df: org.apache.spark.sql.DataFrame = [ts: timestamp]
scala> df.selectExpr("cast(ts as string)", "year(ts)", "month(ts)", "dayofmonth(ts)", "hour(ts)", "minute(ts)", "second(ts)").show(truncate = false)
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
|ts |year(CAST(ts AS DATE))|month(CAST(ts AS DATE))|dayofmonth(CAST(ts AS DATE))|hour(ts)|minute(ts)|second(ts)|
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
|2016-01-01 00:00:00|2016 |1 |1 |0 |0 |0 |
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
```
whereas setting the session local timezone to `"PST"`, they are:
```scala
scala> spark.conf.set("spark.sql.session.timeZone", "PST")
scala> df.selectExpr("cast(ts as string)", "year(ts)", "month(ts)", "dayofmonth(ts)", "hour(ts)", "minute(ts)", "second(ts)").show(truncate = false)
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
|ts |year(CAST(ts AS DATE))|month(CAST(ts AS DATE))|dayofmonth(CAST(ts AS DATE))|hour(ts)|minute(ts)|second(ts)|
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
|2015-12-31 16:00:00|2015 |12 |31 |16 |0 |0 |
+-------------------+----------------------+-----------------------+----------------------------+--------+----------+----------+
```
Notice that even if you set the session local timezone, it affects only in `DataFrame` operations, neither in `Dataset` operations, `RDD` operations nor in `ScalaUDF`s. You need to properly handle timezone by yourself.
### Design of the fix
I introduced an analyzer to pass session local timezone to timezone-aware expressions and modified DateTimeUtils to take the timezone argument.
## How was this patch tested?
Existing tests and added tests for timezone aware expressions.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#16308 from ueshin/issues/SPARK-18350.
## What changes were proposed in this pull request?
acceptType() in UDT will no only accept the same type but also all base types
## How was this patch tested?
Manual test using a set of generated UDTs fixing acceptType() in my user defined types
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: gmoehler <moehler@de.ibm.com>
Closes#16660 from gmoehler/master.
## What changes were proposed in this pull request?
This PR will report proper error messages when a subquery expression contain an invalid plan. This problem is fixed by calling CheckAnalysis for the plan inside a subquery.
## How was this patch tested?
Existing tests and two new test cases on 2 forms of subquery, namely, scalar subquery and in/exists subquery.
````
-- TC 01.01
-- The column t2b in the SELECT of the subquery is invalid
-- because it is neither an aggregate function nor a GROUP BY column.
select t1a, t2b
from t1, t2
where t1b = t2c
and t2b = (select max(avg)
from (select t2b, avg(t2b) avg
from t2
where t2a = t1.t1b
)
)
;
-- TC 01.02
-- Invalid due to the column t2b not part of the output from table t2.
select *
from t1
where t1a in (select min(t2a)
from t2
group by t2c
having t2c in (select max(t3c)
from t3
group by t3b
having t3b > t2b ))
;
````
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#16572 from nsyca/18863.
## What changes were proposed in this pull request?
This PR fixes the code in Optimizer phase where the NULL-aware expression of a NOT IN query is expanded in Rule `RewritePredicateSubquery`.
Example:
The query
select a1,b1
from t1
where (a1,b1) not in (select a2,b2
from t2);
has the (a1, b1) = (a2, b2) rewritten from (before this fix):
Join LeftAnti, ((isnull((_1#2 = a2#16)) || isnull((_2#3 = b2#17))) || ((_1#2 = a2#16) && (_2#3 = b2#17)))
to (after this fix):
Join LeftAnti, (((_1#2 = a2#16) || isnull((_1#2 = a2#16))) && ((_2#3 = b2#17) || isnull((_2#3 = b2#17))))
## How was this patch tested?
sql/test, catalyst/test and new test cases in SQLQueryTestSuite.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#16467 from nsyca/19017.
## What changes were proposed in this pull request?
Spark SQL follows MySQL to do the implicit type conversion for binary comparison: http://dev.mysql.com/doc/refman/5.7/en/type-conversion.html
However, this may return confusing result, e.g. `1 = 'true'` will return true, `19157170390056973L = '19157170390056971'` will return true.
I think it's more reasonable to follow postgres in this case, i.e. cast string to the type of the other side, but return null if the string is not castable to keep hive compatibility.
## How was this patch tested?
newly added tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15880 from cloud-fan/compare.
## What changes were proposed in this pull request?
CataLogTable's partitionSchema should check if each column name in partitionColumnNames must match one and only one field in schema, if not we should throw an exception
and CataLogTable's partitionSchema should keep order with partitionColumnNames
## How was this patch tested?
N/A
Author: windpiger <songjun@outlook.com>
Closes#16606 from windpiger/checkPartionColNameWithSchema.
## What changes were proposed in this pull request?
Hive will expand the view text, so it needs 2 fields: originalText and viewText. Since we don't expand the view text, but only add table properties, perhaps only a single field `viewText` is enough in CatalogTable.
This PR brought in the following changes:
1. Remove the param `viewOriginalText` from `CatalogTable`;
2. Update the output of command `DescribeTableCommand`.
## How was this patch tested?
Tested by exsiting test cases, also updated the failed test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#16679 from jiangxb1987/catalogTable.
## What changes were proposed in this pull request?
To implement DDL commands, we added several analyzer rules in sql/hive module to analyze DDL related plans. However, our `Analyzer` currently only have one extending interface: `extendedResolutionRules`, which defines extra rules that will be run together with other rules in the resolution batch, and doesn't fit DDL rules well, because:
1. DDL rules may do some checking and normalization, but we may do it many times as the resolution batch will run rules again and again, until fixed point, and it's hard to tell if a DDL rule has already done its checking and normalization. It's fine because DDL rules are idempotent, but it's bad for analysis performance
2. some DDL rules may depend on others, and it's pretty hard to write `if` conditions to guarantee the dependencies. It will be good if we have a batch which run rules in one pass, so that we can guarantee the dependencies by rules order.
This PR adds a new extending interface in `Analyzer`: `postHocResolutionRules`, which defines rules that will be run only once in a batch runs right after the resolution batch.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16645 from cloud-fan/analyzer.
## What changes were proposed in this pull request?
As I pointed out in https://github.com/apache/spark/pull/15807#issuecomment-259143655 , the current subexpression elimination framework has a problem, it always evaluates all common subexpressions at the beginning, even they are inside conditional expressions and may not be accessed.
Ideally we should implement it like scala lazy val, so we only evaluate it when it gets accessed at lease once. https://github.com/apache/spark/issues/15837 tries this approach, but it seems too complicated and may introduce performance regression.
This PR simply stops common subexpression elimination for conditional expressions, with some cleanup.
## How was this patch tested?
regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16659 from cloud-fan/codegen.
### What changes were proposed in this pull request?
It is weird to create Hive source tables when using InMemoryCatalog. We are unable to operate it. This PR is to block users to create Hive source tables.
### How was this patch tested?
Fixed the test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16587 from gatorsmile/blockHiveTable.
## What changes were proposed in this pull request?
PythonUDF is unevaluable, which can not be used inside a join condition, currently the optimizer will push a PythonUDF which accessing both side of join into the join condition, then the query will fail to plan.
This PR fix this issue by checking the expression is evaluable or not before pushing it into Join.
## How was this patch tested?
Add a regression test.
Author: Davies Liu <davies@databricks.com>
Closes#16581 from davies/pyudf_join.
## What changes were proposed in this pull request?
Sort in a streaming plan should be allowed only after a aggregation in complete mode. Currently it is incorrectly allowed when present anywhere in the plan. It gives unpredictable potentially incorrect results.
## How was this patch tested?
New test
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16662 from tdas/SPARK-19314.
## What changes were proposed in this pull request?
Change non-cbo estimation behavior of aggregate:
- If groupExpression is empty, we can know row count (=1) and the corresponding size;
- otherwise, estimation falls back to UnaryNode's computeStats method, which should not propagate rowCount and attributeStats in Statistics because they are not estimated in that method.
## How was this patch tested?
Added test case
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16631 from wzhfy/aggNoCbo.
## What changes were proposed in this pull request?
We have a table relation plan cache in `HiveMetastoreCatalog`, which caches a lot of things: file status, resolved data source, inferred schema, etc.
However, it doesn't make sense to limit this cache with hive support, we should move it to SQL core module so that users can use this cache without hive support.
It can also reduce the size of `HiveMetastoreCatalog`, so that it's easier to remove it eventually.
main changes:
1. move the table relation cache to `SessionCatalog`
2. `SessionCatalog.lookupRelation` will return `SimpleCatalogRelation` and the analyzer will convert it to `LogicalRelation` or `MetastoreRelation` later, then `HiveSessionCatalog` doesn't need to override `lookupRelation` anymore
3. `FindDataSourceTable` will read/write the table relation cache.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16621 from cloud-fan/plan-cache.
## What changes were proposed in this pull request?
On CREATE/ALTER a view, it's no longer needed to generate a SQL text string from the LogicalPlan, instead we store the SQL query text、the output column names of the query plan, and current database to CatalogTable. Permanent views created by this approach can be resolved by current view resolution approach.
The main advantage includes:
1. If you update an underlying view, the current view also gets updated;
2. That gives us a change to get ride of SQL generation for operators.
Major changes of this PR:
1. Generate the view-specific properties(e.g. view default database, view query output column names) during permanent view creation and store them as properties in the CatalogTable;
2. Update the commands `CreateViewCommand` and `AlterViewAsCommand`, get rid of SQL generation from them.
## How was this patch tested?
Existing tests.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#16613 from jiangxb1987/view-write-path.
## What changes were proposed in this pull request?
remove ununsed imports and outdated comments, and fix some minor code style issue.
## How was this patch tested?
existing ut
Author: uncleGen <hustyugm@gmail.com>
Closes#16591 from uncleGen/SPARK-19227.
## What changes were proposed in this pull request?
Added outer_explode, outer_posexplode, outer_inline functions and expressions.
Some bug fixing in GenerateExec.scala for CollectionGenerator. Previously it was not correctly handling the case of outer with empty collections, only with nulls.
## How was this patch tested?
New tests added to GeneratorFunctionSuite
Author: Bogdan Raducanu <bogdan.rdc@gmail.com>
Closes#16608 from bogdanrdc/SPARK-13721.
## What changes were proposed in this pull request?
Remove duplicate call of reset() function in CurrentOrigin.withOrigin().
## How was this patch tested?
Existing test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#16615 from jiangxb1987/dummy-code.
### What changes were proposed in this pull request?
Empty partition column values are not valid for partition specification. Before this PR, we accept users to do it; however, Hive metastore does not detect and disallow it too. Thus, users hit the following strange error.
```Scala
val df = spark.createDataFrame(Seq((0, "a"), (1, "b"))).toDF("partCol1", "name")
df.write.mode("overwrite").partitionBy("partCol1").saveAsTable("partitionedTable")
spark.sql("alter table partitionedTable drop partition(partCol1='')")
spark.table("partitionedTable").show()
```
In the above example, the WHOLE table is DROPPED when users specify a partition spec containing only one partition column with empty values.
When the partition columns contains more than one, Hive metastore APIs simply ignore the columns with empty values and treat it as partial spec. This is also not expected. This does not follow the actual Hive behaviors. This PR is to disallow users to specify such an invalid partition spec in the `SessionCatalog` APIs.
### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16583 from gatorsmile/disallowEmptyPartColValue.
## What changes were proposed in this pull request?
This PR is a follow-up to address the comments https://github.com/apache/spark/pull/16233/files#r95669988 and https://github.com/apache/spark/pull/16233/files#r95662299.
We try to wrap the child by:
1. Generate the `queryOutput` by:
1.1. If the query column names are defined, map the column names to attributes in the child output by name;
1.2. Else set the child output attributes to `queryOutput`.
2. Map the `queryQutput` to view output by index, if the corresponding attributes don't match, try to up cast and alias the attribute in `queryOutput` to the attribute in the view output.
3. Add a Project over the child, with the new output generated by the previous steps.
If the view output doesn't have the same number of columns neither with the child output, nor with the query column names, throw an AnalysisException.
## How was this patch tested?
Add new test cases in `SQLViewSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#16561 from jiangxb1987/alias-view.
## What changes were proposed in this pull request?
When we convert a string to integral, we will convert that string to `decimal(20, 0)` first, so that we can turn a string with decimal format to truncated integral, e.g. `CAST('1.2' AS int)` will return `1`.
However, this brings problems when we convert a string with large numbers to integral, e.g. `CAST('1234567890123' AS int)` will return `1912276171`, while Hive returns null as we expected.
This is a long standing bug(seems it was there the first day Spark SQL was created), this PR fixes this bug by adding the native support to convert `UTF8String` to integral.
## How was this patch tested?
new regression tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16550 from cloud-fan/string-to-int.
## What changes were proposed in this pull request?
Pivoting adds backticks (e.g. 3_count(\`c\`)) in column names and, in some cases,
thes causes analysis exceptions like;
```
scala> val df = Seq((2, 3, 4), (3, 4, 5)).toDF("a", "x", "y")
scala> df.groupBy("a").pivot("x").agg(count("y"), avg("y")).na.fill(0)
org.apache.spark.sql.AnalysisException: syntax error in attribute name: `3_count(`y`)`;
at org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute$.e$1(unresolved.scala:134)
at org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute$.parseAttributeName(unresolved.scala:144)
...
```
So, this pr proposes to remove these backticks from column names.
## How was this patch tested?
Added a test in `DataFrameAggregateSuite`.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#14812 from maropu/SPARK-17237.
## What changes were proposed in this pull request?
In this pr, we add more test cases for project and aggregate estimation.
## How was this patch tested?
Add test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16551 from wzhfy/addTests.
## What changes were proposed in this pull request?
This patch simplifies slightly the logical plan statistics cache implementation, as discussed in https://github.com/apache/spark/pull/16529
## How was this patch tested?
N/A - this has no behavior change.
Author: Reynold Xin <rxin@databricks.com>
Closes#16544 from rxin/SPARK-19149.
## What changes were proposed in this pull request?
We should be able to resolve a nested view. The main advantage is that if you update an underlying view, the current view also gets updated.
The new approach should be compatible with older versions of SPARK/HIVE, that means:
1. The new approach should be able to resolve the views that created by older versions of SPARK/HIVE;
2. The new approach should be able to resolve the views that are currently supported by SPARK SQL.
The new approach mainly brings in the following changes:
1. Add a new operator called `View` to keep track of the CatalogTable that describes the view, and the output attributes as well as the child of the view;
2. Update the `ResolveRelations` rule to resolve the relations and views, note that a nested view should be resolved correctly;
3. Add `viewDefaultDatabase` variable to `CatalogTable` to keep track of the default database name used to resolve a view, if the `CatalogTable` is not a view, then the variable should be `None`;
4. Add `AnalysisContext` to enable us to still support a view created with CTE/Windows query;
5. Enables the view support without enabling Hive support (i.e., enableHiveSupport);
6. Fix a weird behavior: the result of a view query may have different schema if the referenced table has been changed. After this PR, we try to cast the child output attributes to that from the view schema, throw an AnalysisException if cast is not allowed.
Note this is compatible with the views defined by older versions of Spark(before 2.2), which have empty `defaultDatabase` and all the relations in `viewText` have database part defined.
## How was this patch tested?
1. Add new tests in `SessionCatalogSuite` to test the function `lookupRelation`;
2. Add new test case in `SQLViewSuite` to test resolve a nested view.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#16233 from jiangxb1987/resolve-view.
## What changes were proposed in this pull request?
Currently we have two sets of statistics in LogicalPlan: a simple stats and a stats estimated by cbo, but the computing logic and naming are quite confusing, we need to unify these two sets of stats.
## How was this patch tested?
Just modify existing tests.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#16529 from wzhfy/unifyStats.
## What changes were proposed in this pull request?
This PR allow update mode for non-aggregation streaming queries. It will be same as the append mode if a query has no aggregations.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16520 from zsxwing/update-without-agg.
## What changes were proposed in this pull request?
Support cardinality estimation of aggregate operator
## How was this patch tested?
Add test cases
Author: Zhenhua Wang <wzh_zju@163.com>
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16431 from wzhfy/aggEstimation.
## What changes were proposed in this pull request?
Support cardinality estimation for project operator.
## How was this patch tested?
Add a test suite and a base class in the catalyst package.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#16430 from wzhfy/projectEstimation.
## What changes were proposed in this pull request?
Added a `to` call at the end of the code generated by `ScalaReflection.deserializerFor` if the requested type is not a supertype of `WrappedArray[_]` that uses `CanBuildFrom[_, _, _]` to convert result into an arbitrary subtype of `Seq[_]`.
Care was taken to preserve the original deserialization where it is possible to avoid the overhead of conversion in cases where it is not needed
`ScalaReflection.serializerFor` could already be used to serialize any `Seq[_]` so it was not altered
`SQLImplicits` had to be altered and new implicit encoders added to permit serialization of other sequence types
Also fixes [SPARK-16815] Dataset[List[T]] leads to ArrayStoreException
## How was this patch tested?
```bash
./build/mvn -DskipTests clean package && ./dev/run-tests
```
Also manual execution of the following sets of commands in the Spark shell:
```scala
case class TestCC(key: Int, letters: List[String])
val ds1 = sc.makeRDD(Seq(
(List("D")),
(List("S","H")),
(List("F","H")),
(List("D","L","L"))
)).map(x=>(x.length,x)).toDF("key","letters").as[TestCC]
val test1=ds1.map{_.key}
test1.show
```
```scala
case class X(l: List[String])
spark.createDataset(Seq(List("A"))).map(X).show
```
```scala
spark.sqlContext.createDataset(sc.parallelize(List(1) :: Nil)).collect
```
After adding arbitrary sequence support also tested with the following commands:
```scala
case class QueueClass(q: scala.collection.immutable.Queue[Int])
spark.createDataset(Seq(List(1,2,3))).map(x => QueueClass(scala.collection.immutable.Queue(x: _*))).map(_.q.dequeue).collect
```
Author: Michal Senkyr <mike.senkyr@gmail.com>
Closes#16240 from michalsenkyr/sql-caseclass-list-fix.
## What changes were proposed in this pull request?
Today we have different syntax to create data source or hive serde tables, we should unify them to not confuse users and step forward to make hive a data source.
Please read https://issues.apache.org/jira/secure/attachment/12843835/CREATE-TABLE.pdf for details.
TODO(for follow-up PRs):
1. TBLPROPERTIES is not added to the new syntax, we should decide if we wanna add it later.
2. `SHOW CREATE TABLE` should be updated to use the new syntax.
3. we should decide if we wanna change the behavior of `SET LOCATION`.
## How was this patch tested?
new tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16296 from cloud-fan/create-table.
## What changes were proposed in this pull request?
There are many locations in the Spark repo where the same word occurs consecutively. Sometimes they are appropriately placed, but many times they are not. This PR removes the inappropriately duplicated words.
## How was this patch tested?
N/A since only docs or comments were updated.
Author: Niranjan Padmanabhan <niranjan.padmanabhan@gmail.com>
Closes#16455 from neurons/np.structure_streaming_doc.
## What changes were proposed in this pull request?
Now all aggregation functions support partial aggregate, we can remove the `supportsPartual` flag in `AggregateFunction`
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16461 from cloud-fan/partial.
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/16402 we made a mistake that, when double/float is infinity, the `Literal` codegen will output boxed value and cause wrong result.
This PR fixes this by special handling infinity to not output boxed value.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16469 from cloud-fan/literal.
### What changes were proposed in this pull request?
The data in the managed table should be deleted after table is dropped. However, if the partition location is not under the location of the partitioned table, it is not deleted as expected. Users can specify any location for the partition when they adding a partition.
This PR is to delete partition location when dropping managed partitioned tables stored in `InMemoryCatalog`.
### How was this patch tested?
Added test cases for both HiveExternalCatalog and InMemoryCatalog
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16448 from gatorsmile/unsetSerdeProp.
## What changes were proposed in this pull request?
Currently collect_set/collect_list aggregation expression don't support partial aggregation. This patch is to enable partial aggregation for them.
## 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#16371 from viirya/collect-partial-support.
## What changes were proposed in this pull request?
We add a cbo configuration to switch between default stats and estimated stats.
We also define a new statistics method `planStats` in LogicalPlan with conf as its parameter, in order to pass the cbo switch and other estimation related configurations in the future. `planStats` is used on the caller sides (i.e. in Optimizer and Strategies) to make transformation decisions based on stats.
## How was this patch tested?
Add a test case using a dummy LogicalPlan.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#16401 from wzhfy/cboSwitch.
### What changes were proposed in this pull request?
Remove useless `databaseName ` from `SimpleCatalogRelation`.
### How was this patch tested?
Existing test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16438 from gatorsmile/removeDBFromSimpleCatalogRelation.
### What changes were proposed in this pull request?
Fixed non-thread-safe functions used in SessionCatalog:
- refreshTable
- lookupRelation
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16437 from gatorsmile/addSyncToLookUpTable.
## What changes were proposed in this pull request?
`Literal` can use `CodegenContex.addReferenceObj` to implement codegen, instead of `CodegenFallback`. This can also simplify the generated code a little bit, before we will generate: `((Expression) references[1]).eval(null)`, now it's just `references[1]`.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16402 from cloud-fan/minor.
## What changes were proposed in this pull request?
Currently we implement `Aggregator` with `DeclarativeAggregate`, which will serialize/deserialize the buffer object every time we process an input.
This PR implements `Aggregator` with `TypedImperativeAggregate` and avoids to serialize/deserialize buffer object many times. The benchmark shows we get about 2 times speed up.
For simple buffer object that doesn't need serialization, we still go with `DeclarativeAggregate`, to avoid performance regression.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16383 from cloud-fan/aggregator.
## What changes were proposed in this pull request?
Statistics in LogicalPlan should use attributes to refer to columns rather than column names, because two columns from two relations can have the same column name. But CatalogTable doesn't have the concepts of attribute or broadcast hint in Statistics. Therefore, putting Statistics in CatalogTable is confusing.
We define a different statistic structure in CatalogTable, which is only responsible for interacting with metastore, and is converted to statistics in LogicalPlan when it is used.
## How was this patch tested?
add test cases
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#16323 from wzhfy/nameToAttr.
## What changes were proposed in this pull request?
SortPartitions and RedistributeData logical operators are not actually used and can be removed. Note that we do have a Sort operator (with global flag false) that subsumed SortPartitions.
## How was this patch tested?
Also updated test cases to reflect the removal.
Author: Reynold Xin <rxin@databricks.com>
Closes#16381 from rxin/SPARK-18973.
## What changes were proposed in this pull request?
Made update mode public. As part of that here are the changes.
- Update DatastreamWriter to accept "update"
- Changed package of InternalOutputModes from o.a.s.sql to o.a.s.sql.catalyst
- Added update mode state removing with watermark to StateStoreSaveExec
## How was this patch tested?
Added new tests in changed modules
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16360 from tdas/SPARK-18234.
## What changes were proposed in this pull request?
When we append data to an existing table with `DataFrameWriter.saveAsTable`, we will do various checks to make sure the appended data is consistent with the existing data.
However, we get the information of the existing table by matching the table relation, instead of looking at the table metadata. This is error-prone, e.g. we only check the number of columns for `HadoopFsRelation`, we forget to check bucketing, etc.
This PR refactors the error checking by looking at the metadata of the existing table, and fix several bugs:
* SPARK-18899: We forget to check if the specified bucketing matched the existing table, which may lead to a problematic table that has different bucketing in different data files.
* SPARK-18912: We forget to check the number of columns for non-file-based data source table
* SPARK-18913: We don't support append data to a table with special column names.
## How was this patch tested?
new regression test.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16313 from cloud-fan/bug1.
## What changes were proposed in this pull request?
Currently `ImplicitTypeCasts` doesn't handle casts between `ArrayType`s, this is not convenient, we should add a rule to enable casting from `ArrayType(InternalType)` to `ArrayType(newInternalType)`.
Goals:
1. Add a rule to `ImplicitTypeCasts` to enable casting between `ArrayType`s;
2. Simplify `Percentile` and `ApproximatePercentile`.
## How was this patch tested?
Updated test cases in `TypeCoercionSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#16057 from jiangxb1987/implicit-cast-complex-types.
## What changes were proposed in this pull request?
percentile_approx is the name used in Hive, and approx_percentile is the name used in Presto. approx_percentile is actually more consistent with our approx_count_distinct. Given the cost to alias SQL functions is low (one-liner), it'd be better to just alias them so it is easier to use.
## How was this patch tested?
Technically I could add an end-to-end test to verify this one-line change, but it seemed too trivial to me.
Author: Reynold Xin <rxin@databricks.com>
Closes#16300 from rxin/SPARK-18892.
## What changes were proposed in this pull request?
Check whether Aggregation operators on a streaming subplan have aggregate expressions with isDistinct = true.
## How was this patch tested?
Added unit test
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16289 from tdas/SPARK-18870.
## What changes were proposed in this pull request?
Right now, once a user set the comment of a column with create table command, he/she cannot update the comment. It will be useful to provide a public interface (e.g. SQL) to do that.
This PR implements the following SQL statement:
```
ALTER TABLE table [PARTITION partition_spec]
CHANGE [COLUMN] column_old_name column_new_name column_dataType
[COMMENT column_comment]
[FIRST | AFTER column_name];
```
For further expansion, we could support alter `name`/`dataType`/`index` of a column too.
## How was this patch tested?
Add new test cases in `ExternalCatalogSuite` and `SessionCatalogSuite`.
Add sql file test for `ALTER TABLE CHANGE COLUMN` statement.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15717 from jiangxb1987/change-column.
## What changes were proposed in this pull request?
After the bug fix in SPARK-18854, TreeNode.apply now returns TreeNode[_] rather than a more specific type. It would be easier for interactive debugging to introduce a function that returns the BaseType.
## How was this patch tested?
N/A - this is a developer only feature used for interactive debugging. As long as it compiles, it should be good to go. I tested this in spark-shell.
Author: Reynold Xin <rxin@databricks.com>
Closes#16288 from rxin/SPARK-18869.
## What changes were proposed in this pull request?
This is a bug introduced by subquery handling. numberedTreeString (which uses generateTreeString under the hood) numbers trees including innerChildren (used to print subqueries), but apply (which uses getNodeNumbered) ignores innerChildren. As a result, apply(i) would return the wrong plan node if there are subqueries.
This patch fixes the bug.
## How was this patch tested?
Added a test case in SubquerySuite.scala to test both the depth-first traversal of numbering as well as making sure the two methods are consistent.
Author: Reynold Xin <rxin@databricks.com>
Closes#16277 from rxin/SPARK-18854.
## What changes were proposed in this pull request?
This patch reduces the default number element estimation for arrays and maps from 100 to 1. The issue with the 100 number is that when nested (e.g. an array of map), 100 * 100 would be used as the default size. This sounds like just an overestimation which doesn't seem that bad (since it is usually better to overestimate than underestimate). However, due to the way we assume the size output for Project (new estimated column size / old estimated column size), this overestimation can become underestimation. It is actually in general in this case safer to assume 1 default element.
## How was this patch tested?
This should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#16274 from rxin/SPARK-18853.
## What changes were proposed in this pull request?
Move the checking of GROUP BY column in correlated scalar subquery from CheckAnalysis
to Analysis to fix a regression caused by SPARK-18504.
This problem can be reproduced with a simple script now.
Seq((1,1)).toDF("pk","pv").createOrReplaceTempView("p")
Seq((1,1)).toDF("ck","cv").createOrReplaceTempView("c")
sql("select * from p,c where p.pk=c.ck and c.cv = (select avg(c1.cv) from c c1 where c1.ck = p.pk)").show
The requirements are:
1. We need to reference the same table twice in both the parent and the subquery. Here is the table c.
2. We need to have a correlated predicate but to a different table. Here is from c (as c1) in the subquery to p in the parent.
3. We will then "deduplicate" c1.ck in the subquery to `ck#<n1>#<n2>` at `Project` above `Aggregate` of `avg`. Then when we compare `ck#<n1>#<n2>` and the original group by column `ck#<n1>` by their canonicalized form, which is #<n2> != #<n1>. That's how we trigger the exception added in SPARK-18504.
## How was this patch tested?
SubquerySuite and a simplified version of TPCDS-Q32
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#16246 from nsyca/18814.
## What changes were proposed in this pull request?
`OverwriteOptions` was introduced in https://github.com/apache/spark/pull/15705, to carry the information of static partitions. However, after further refactor, this information becomes duplicated and we can remove `OverwriteOptions`.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15995 from cloud-fan/overwrite.
## What changes were proposed in this pull request?
Change the statement `SHOW TABLES [EXTENDED] [(IN|FROM) database_name] [[LIKE] 'identifier_with_wildcards'] [PARTITION(partition_spec)]` to the following statements:
- SHOW TABLES [(IN|FROM) database_name] [[LIKE] 'identifier_with_wildcards']
- SHOW TABLE EXTENDED [(IN|FROM) database_name] LIKE 'identifier_with_wildcards' [PARTITION(partition_spec)]
After this change, the statements `SHOW TABLE/SHOW TABLES` have the same syntax with that HIVE has.
## How was this patch tested?
Modified the test sql file `show-tables.sql`;
Modified the test suite `DDLSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#16262 from jiangxb1987/show-table-extended.
## What changes were proposed in this pull request?
Fixes compile errors in generated code when user has case class with a `scala.collections.immutable.Map` instead of a `scala.collections.Map`. Since ArrayBasedMapData.toScalaMap returns the immutable version we can make it work with both.
## How was this patch tested?
Additional unit tests.
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#16161 from aray/fix-map-codegen.
The value of the "isSrcLocal" parameter passed to Hive's loadTable and
loadPartition methods needs to be set according to the user query (e.g.
"LOAD DATA LOCAL"), and not the current code that tries to guess what
it should be.
For existing versions of Hive the current behavior is probably ok, but
some recent changes in the Hive code changed the semantics slightly,
making code that sets "isSrcLocal" to "true" incorrectly to do the
wrong thing. It would end up moving the parent directory of the files
into the final location, instead of the file themselves, resulting
in a table that cannot be read.
I modified HiveCommandSuite so that existing "LOAD DATA" tests are run
both in local and non-local mode, since the semantics are slightly different.
The tests include a few new checks to make sure the semantics follow
what Hive describes in its documentation.
Tested with existing unit tests and also ran some Hive integration tests
with a version of Hive containing the changes that surfaced the problem.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#16179 from vanzin/SPARK-18752.
## What changes were proposed in this pull request?
During column stats collection, average and max length will be null if a column of string/binary type has only null values. To fix this, I use default size when avg/max length is null.
## How was this patch tested?
Add a test for handling null columns
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#16243 from wzhfy/nullStats.
## What changes were proposed in this pull request?
1. In SparkStrategies.canBroadcast, I will add the check plan.statistics.sizeInBytes >= 0
2. In LocalRelations.statistics, when calculate the statistics, I will change the size to BigInt so it won't overflow.
## How was this patch tested?
I will add a test case to make sure the statistics.sizeInBytes won't overflow.
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#16175 from huaxingao/spark-17460.
## What changes were proposed in this pull request?
Typo fixes
## How was this patch tested?
Local build. Awaiting the official build.
Author: Jacek Laskowski <jacek@japila.pl>
Closes#16144 from jaceklaskowski/typo-fixes.
## What changes were proposed in this pull request?
`makeRootConverter` is only called with a `StructType` value. By making this method less general we can remove pattern matches, which are never actually hit outside of the test suite.
## How was this patch tested?
The existing tests.
Author: Nathan Howell <nhowell@godaddy.com>
Closes#16084 from NathanHowell/SPARK-18654.
## What changes were proposed in this pull request?
Fixes AnalysisException for pivot queries that have group by columns that are expressions and not attributes by substituting the expressions output attribute in the second aggregation and final projection.
## How was this patch tested?
existing and additional unit tests
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#16177 from aray/SPARK-17760.
## What changes were proposed in this pull request?
I jumped the gun on merging https://github.com/apache/spark/pull/16120, and missed a tiny potential problem. This PR fixes that by changing a val into a def; this should prevent potential serialization/initialization weirdness from happening.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#16170 from hvanhovell/SPARK-18634.
(Link to Jira issue: https://issues.apache.org/jira/browse/SPARK-18572)
## What changes were proposed in this pull request?
Currently Spark answers the `SHOW PARTITIONS` command by fetching all of the table's partition metadata from the external catalog and constructing partition names therefrom. The Hive client has a `getPartitionNames` method which is many times faster for this purpose, with the performance improvement scaling with the number of partitions in a table.
To test the performance impact of this PR, I ran the `SHOW PARTITIONS` command on two Hive tables with large numbers of partitions. One table has ~17,800 partitions, and the other has ~95,000 partitions. For the purposes of this PR, I'll call the former table `table1` and the latter table `table2`. I ran 5 trials for each table with before-and-after versions of this PR. The results are as follows:
Spark at bdc8153, `SHOW PARTITIONS table1`, times in seconds:
7.901
3.983
4.018
4.331
4.261
Spark at bdc8153, `SHOW PARTITIONS table2`
(Timed out after 10 minutes with a `SocketTimeoutException`.)
Spark at this PR, `SHOW PARTITIONS table1`, times in seconds:
3.801
0.449
0.395
0.348
0.336
Spark at this PR, `SHOW PARTITIONS table2`, times in seconds:
5.184
1.63
1.474
1.519
1.41
Taking the best times from each trial, we get a 12x performance improvement for a table with ~17,800 partitions and at least a 426x improvement for a table with ~95,000 partitions. More significantly, the latter command doesn't even complete with the current code in master.
This is actually a patch we've been using in-house at VideoAmp since Spark 1.1. It's made all the difference in the practical usability of our largest tables. Even with tables with about 1,000 partitions there's a performance improvement of about 2-3x.
## How was this patch tested?
I added a unit test to `VersionsSuite` which tests that the Hive client's `getPartitionNames` method returns the correct number of partitions.
Author: Michael Allman <michael@videoamp.com>
Closes#15998 from mallman/spark-18572-list_partition_names.
## What changes were proposed in this pull request?
As reported in the Jira, there are some weird issues with exploding Python UDFs in SparkSQL.
The following test code can reproduce it. Notice: the following test code is reported to return wrong results in the Jira. However, as I tested on master branch, it causes exception and so can't return any result.
>>> from pyspark.sql.functions import *
>>> from pyspark.sql.types import *
>>>
>>> df = spark.range(10)
>>>
>>> def return_range(value):
... return [(i, str(i)) for i in range(value - 1, value + 1)]
...
>>> range_udf = udf(return_range, ArrayType(StructType([StructField("integer_val", IntegerType()),
... StructField("string_val", StringType())])))
>>>
>>> df.select("id", explode(range_udf(df.id))).show()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/spark/python/pyspark/sql/dataframe.py", line 318, in show
print(self._jdf.showString(n, 20))
File "/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
File "/spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value py4j.protocol.Py4JJavaError: An error occurred while calling o126.showString.: java.lang.AssertionError: assertion failed
at scala.Predef$.assert(Predef.scala:156)
at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:120)
at org.apache.spark.sql.execution.GenerateExec.consume(GenerateExec.scala:57)
The cause of this issue is, in `ExtractPythonUDFs` we insert `BatchEvalPythonExec` to run PythonUDFs in batch. `BatchEvalPythonExec` will add extra outputs (e.g., `pythonUDF0`) to original plan. In above case, the original `Range` only has one output `id`. After `ExtractPythonUDFs`, the added `BatchEvalPythonExec` has two outputs `id` and `pythonUDF0`.
Because the output of `GenerateExec` is given after analysis phase, in above case, it is the combination of `id`, i.e., the output of `Range`, and `col`. But in planning phase, we change `GenerateExec`'s child plan to `BatchEvalPythonExec` with additional output attributes.
It will cause no problem in non wholestage codegen. Because when evaluating the additional attributes are projected out the final output of `GenerateExec`.
However, as `GenerateExec` now supports wholestage codegen, the framework will input all the outputs of the child plan to `GenerateExec`. Then when consuming `GenerateExec`'s output data (i.e., calling `consume`), the number of output attributes is different to the output variables in wholestage codegen.
To solve this issue, this patch only gives the generator's output to `GenerateExec` after analysis phase. `GenerateExec`'s output is the combination of its child plan's output and the generator's output. So when we change `GenerateExec`'s child, its output is still correct.
## How was this patch tested?
Added test cases to PySpark.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16120 from viirya/fix-py-udf-with-generator.
## What changes were proposed in this pull request?
This is kind of a long-standing bug, it's hidden until https://github.com/apache/spark/pull/15780 , which may add `AssertNotNull` on top of `LambdaVariable` and thus enables subexpression elimination.
However, subexpression elimination will evaluate the common expressions at the beginning, which is invalid for `LambdaVariable`. `LambdaVariable` usually represents loop variable, which can't be evaluated ahead of the loop.
This PR skips expressions containing `LambdaVariable` when doing subexpression elimination.
## How was this patch tested?
updated test in `DatasetAggregatorSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16143 from cloud-fan/aggregator.
## What changes were proposed in this pull request?
We currently have function input_file_name to get the path of the input file, but don't have functions to get the block start offset and length. This patch introduces two functions:
1. input_file_block_start: returns the file block start offset, or -1 if not available.
2. input_file_block_length: returns the file block length, or -1 if not available.
## How was this patch tested?
Updated existing test cases in ColumnExpressionSuite that covered input_file_name to also cover the two new functions.
Author: Reynold Xin <rxin@databricks.com>
Closes#16133 from rxin/SPARK-18702.
## What changes were proposed in this pull request?
Fix for SPARK-18091 which is a bug related to large if expressions causing generated SpecificUnsafeProjection code to exceed JVM code size limit.
This PR changes if expression's code generation to place its predicate, true value and false value expressions' generated code in separate methods in context so as to never generate too long combined code.
## How was this patch tested?
Added a unit test and also tested manually with the application (having transformations similar to the unit test) which caused the issue to be identified in the first place.
Author: Kapil Singh <kapsingh@adobe.com>
Closes#15620 from kapilsingh5050/SPARK-18091-IfCodegenFix.
## What changes were proposed in this pull request?
This fix puts an explicit list of operators that Spark supports for correlated subqueries.
## How was this patch tested?
Run sql/test, catalyst/test and add a new test case on Generate.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#16046 from nsyca/spark18455.0.
## What changes were proposed in this pull request?
This fixes the parser rule to match named expressions, which doesn't work for two reasons:
1. The name match is not coerced to a regular expression (missing .r)
2. The surrounding literals are incorrect and attempt to escape a single quote, which is unnecessary
## How was this patch tested?
This adds test cases for named expressions using the bracket syntax, including one with quoted spaces.
Author: Ryan Blue <blue@apache.org>
Closes#16107 from rdblue/SPARK-18677-fix-json-path.
### What changes were proposed in this pull request?
Added a test case for using joins with nested fields.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16110 from gatorsmile/followup-18674.
## What changes were proposed in this pull request?
Two bugs are addressed here
1. INSERT OVERWRITE TABLE sometime crashed when catalog partition management was enabled. This was because when dropping partitions after an overwrite operation, the Hive client will attempt to delete the partition files. If the entire partition directory was dropped, this would fail. The PR fixes this by adding a flag to control whether the Hive client should attempt to delete files.
2. The static partition spec for OVERWRITE TABLE was not correctly resolved to the case-sensitive original partition names. This resulted in the entire table being overwritten if you did not correctly capitalize your partition names.
cc yhuai cloud-fan
## How was this patch tested?
Unit tests. Surprisingly, the existing overwrite table tests did not catch these edge cases.
Author: Eric Liang <ekl@databricks.com>
Closes#16088 from ericl/spark-18659.
## What changes were proposed in this pull request?
This replaces uses of `TextOutputFormat` with an `OutputStream`, which will either write directly to the filesystem or indirectly via a compressor (if so configured). This avoids intermediate buffering.
The inverse of this (reading directly from a stream) is necessary for streaming large JSON records (when `wholeFile` is enabled) so I wanted to keep the read and write paths symmetric.
## How was this patch tested?
Existing unit tests.
Author: Nathan Howell <nhowell@godaddy.com>
Closes#16089 from NathanHowell/SPARK-18658.
## What changes were proposed in this pull request?
SPARK-18429 introduced count-min sketch aggregate function for SQL, but the implementation and testing is more complicated than needed. This simplifies the test cases and removes support for data types that don't have clear equality semantics:
1. Removed support for floating point and decimal types.
2. Removed the heavy randomized tests. The underlying CountMinSketch implementation already had pretty good test coverage through randomized tests, and the SPARK-18429 implementation is just to add an aggregate function wrapper around CountMinSketch. There is no need for randomized tests at three different levels of the implementations.
## How was this patch tested?
A lot of the change is to simplify test cases.
Author: Reynold Xin <rxin@databricks.com>
Closes#16093 from rxin/SPARK-18663.
## What changes were proposed in this pull request?
This PR makes `ExpressionEncoder.serializer.nullable` for flat encoder for a primitive type `false`. Since it is `true` for now, it is too conservative.
While `ExpressionEncoder.schema` has correct information (e.g. `<IntegerType, false>`), `serializer.head.nullable` of `ExpressionEncoder`, which got from `encoderFor[T]`, is always false. It is too conservative.
This is accomplished by checking whether a type is one of primitive types. If it is `true`, `nullable` should be `false`.
## How was this patch tested?
Added new tests for encoder and dataframe
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#15780 from kiszk/SPARK-18284.
## What changes were proposed in this pull request?
The current error message of USING join is quite confusing, for example:
```
scala> val df1 = List(1,2,3).toDS.withColumnRenamed("value", "c1")
df1: org.apache.spark.sql.DataFrame = [c1: int]
scala> val df2 = List(1,2,3).toDS.withColumnRenamed("value", "c2")
df2: org.apache.spark.sql.DataFrame = [c2: int]
scala> df1.join(df2, usingColumn = "c1")
org.apache.spark.sql.AnalysisException: using columns ['c1] can not be resolved given input columns: [c1, c2] ;;
'Join UsingJoin(Inner,List('c1))
:- Project [value#1 AS c1#3]
: +- LocalRelation [value#1]
+- Project [value#7 AS c2#9]
+- LocalRelation [value#7]
```
after this PR, it becomes:
```
scala> val df1 = List(1,2,3).toDS.withColumnRenamed("value", "c1")
df1: org.apache.spark.sql.DataFrame = [c1: int]
scala> val df2 = List(1,2,3).toDS.withColumnRenamed("value", "c2")
df2: org.apache.spark.sql.DataFrame = [c2: int]
scala> df1.join(df2, usingColumn = "c1")
org.apache.spark.sql.AnalysisException: USING column `c1` can not be resolved with the right join side, the right output is: [c2];
```
## How was this patch tested?
updated tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16100 from cloud-fan/natural.
## What changes were proposed in this pull request?
Due to confusion between URI vs paths, in certain cases we escape partition values too many times, which causes some Hive client operations to fail or write data to the wrong location. This PR fixes at least some of these cases.
To my understanding this is how values, filesystem paths, and URIs interact.
- Hive stores raw (unescaped) partition values that are returned to you directly when you call listPartitions.
- Internally, we convert these raw values to filesystem paths via `ExternalCatalogUtils.[un]escapePathName`.
- In some circumstances we store URIs instead of filesystem paths. When a path is converted to a URI via `path.toURI`, the escaped partition values are further URI-encoded. This means that to get a path back from a URI, you must call `new Path(new URI(uriTxt))` in order to decode the URI-encoded string.
- In `CatalogStorageFormat` we store URIs as strings. This makes it easy to forget to URI-decode the value before converting it into a path.
- Finally, the Hive client itself uses mostly Paths for representing locations, and only URIs occasionally.
In the future we should probably clean this up, perhaps by dropping use of URIs when unnecessary. We should also try fixing escaping for partition names as well as values, though names are unlikely to contain special characters.
cc mallman cloud-fan yhuai
## How was this patch tested?
Unit tests.
Author: Eric Liang <ekl@databricks.com>
Closes#16071 from ericl/spark-18635.
## What changes were proposed in this pull request?
For input object of non-flat type, we can't encode it to row if it's null, as Spark SQL doesn't allow the entire row to be null, only its columns can be null. That's the reason we forbid users to use top level null objects in https://github.com/apache/spark/pull/13469
However, if users wrap non-flat type with `Option`, then we may still encoder top level null object to row, which is not allowed.
This PR fixes this case, and suggests users to wrap their type with `Tuple1` if they do wanna top level null objects.
## How was this patch tested?
new test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15979 from cloud-fan/option.
## What changes were proposed in this pull request?
Currently we haven't implemented `SHOW TABLE EXTENDED` in Spark 2.0. This PR is to implement the statement.
Goals:
1. Support `SHOW TABLES EXTENDED LIKE 'identifier_with_wildcards'`;
2. Explicitly output an unsupported error message for `SHOW TABLES [EXTENDED] ... PARTITION` statement;
3. Improve test cases for `SHOW TABLES` statement.
## How was this patch tested?
1. Add new test cases in file `show-tables.sql`.
2. Modify tests for `SHOW TABLES` in `DDLSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15958 from jiangxb1987/show-table-extended.
### What changes were proposed in this pull request?
The `constraints` of an operator is the expressions that evaluate to `true` for all the rows produced. That means, the expression result should be neither `false` nor `unknown` (NULL). Thus, we can conclude that `IsNotNull` on all the constraints, which are generated by its own predicates or propagated from the children. The constraint can be a complex expression. For better usage of these constraints, we try to push down `IsNotNull` to the lowest-level expressions (i.e., `Attribute`). `IsNotNull` can be pushed through an expression when it is null intolerant. (When the input is NULL, the null-intolerant expression always evaluates to NULL.)
Below is the existing code we have for `IsNotNull` pushdown.
```Scala
private def scanNullIntolerantExpr(expr: Expression): Seq[Attribute] = expr match {
case a: Attribute => Seq(a)
case _: NullIntolerant | IsNotNull(_: NullIntolerant) =>
expr.children.flatMap(scanNullIntolerantExpr)
case _ => Seq.empty[Attribute]
}
```
**`IsNotNull` itself is not null-intolerant.** It converts `null` to `false`. If the expression does not include any `Not`-like expression, it works; otherwise, it could generate a wrong result. This PR is to fix the above function by removing the `IsNotNull` from the inference. After the fix, when a constraint has a `IsNotNull` expression, we infer new attribute-specific `IsNotNull` constraints if and only if `IsNotNull` appears in the root.
Without the fix, the following test case will return empty.
```Scala
val data = Seq[java.lang.Integer](1, null).toDF("key")
data.filter("not key is not null").show()
```
Before the fix, the optimized plan is like
```
== Optimized Logical Plan ==
Project [value#1 AS key#3]
+- Filter (isnotnull(value#1) && NOT isnotnull(value#1))
+- LocalRelation [value#1]
```
After the fix, the optimized plan is like
```
== Optimized Logical Plan ==
Project [value#1 AS key#3]
+- Filter NOT isnotnull(value#1)
+- LocalRelation [value#1]
```
### How was this patch tested?
Added a test
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16067 from gatorsmile/isNotNull2.
## What changes were proposed in this pull request?
The result of a `sum` aggregate function is typically a Decimal, Double or a Long. Currently the output dataType is based on input's dataType.
The `FunctionArgumentConversion` rule will make sure that the input is promoted to the largest type, and that also ensures that the output uses a (hopefully) sufficiently large output dataType. The issue is that sum is in a resolved state when we cast the input type, this means that rules assuming that the dataType of the expression does not change anymore could have been applied in the mean time. This is what happens if we apply `WidenSetOperationTypes` before applying the casts, and this breaks analysis.
The most straight forward and future proof solution is to make `sum` always output the widest dataType in its class (Long for IntegralTypes, Decimal for DecimalTypes & Double for FloatType and DoubleType). This PR implements that solution.
We should move expression specific type casting rules into the given Expression at some point.
## How was this patch tested?
Added (regression) tests to SQLQueryTestSuite's `union.sql`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#16063 from hvanhovell/SPARK-18622.
## What changes were proposed in this pull request?
`AggregateFunction` currently implements `ImplicitCastInputTypes` (which enables implicit input type casting). There are actually quite a few situations in which we don't need this, or require more control over our input. A recent example is the aggregate for `CountMinSketch` which should only take string, binary or integral types inputs.
This PR removes `ImplicitCastInputTypes` from the `AggregateFunction` and makes a case-by-case decision on what kind of input validation we should use.
## How was this patch tested?
Refactoring only. Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#16066 from hvanhovell/SPARK-18632.
## What changes were proposed in this pull request?
ExistenceJoin should be treated the same as LeftOuter and LeftAnti, not InnerLike and LeftSemi. This is not currently exposed because the rewrite of [NOT] EXISTS OR ... to ExistenceJoin happens in rule RewritePredicateSubquery, which is in a separate rule set and placed after the rule PushPredicateThroughJoin. During the transformation in the rule PushPredicateThroughJoin, an ExistenceJoin never exists.
The semantics of ExistenceJoin says we need to preserve all the rows from the left table through the join operation as if it is a regular LeftOuter join. The ExistenceJoin augments the LeftOuter operation with a new column called exists, set to true when the join condition in the ON clause is true and false otherwise. The filter of any rows will happen in the Filter operation above the ExistenceJoin.
Example:
A(c1, c2): { (1, 1), (1, 2) }
// B can be any value as it is irrelevant in this example
B(c1): { (NULL) }
select A.*
from A
where exists (select 1 from B where A.c1 = A.c2)
or A.c2=2
In this example, the correct result is all the rows from A. If the pattern ExistenceJoin around line 935 in Optimizer.scala is indeed active, the code will push down the predicate A.c1 = A.c2 to be a Filter on relation A, which will incorrectly filter the row (1,2) from A.
## How was this patch tested?
Since this is not an exposed case, no new test cases is added. The scenario is discovered via a code review of another PR and confirmed to be valid with peer.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#16044 from nsyca/spark-18614.
## What changes were proposed in this pull request?
This PR implements a new Aggregate to generate count min sketch, which is a wrapper of CountMinSketch.
## How was this patch tested?
add test cases
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#15877 from wzhfy/cms.
## What changes were proposed in this pull request?
This PR make `sbt unidoc` complete with Java 8.
This PR roughly includes several fixes as below:
- Fix unrecognisable class and method links in javadoc by changing it from `[[..]]` to `` `...` ``
```diff
- * A column that will be computed based on the data in a [[DataFrame]].
+ * A column that will be computed based on the data in a `DataFrame`.
```
- Fix throws annotations so that they are recognisable in javadoc
- Fix URL links to `<a href="http..."></a>`.
```diff
- * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] model for regression.
+ * <a href="http://en.wikipedia.org/wiki/Decision_tree_learning">
+ * Decision tree (Wikipedia)</a> model for regression.
```
```diff
- * see http://en.wikipedia.org/wiki/Receiver_operating_characteristic
+ * see <a href="http://en.wikipedia.org/wiki/Receiver_operating_characteristic">
+ * Receiver operating characteristic (Wikipedia)</a>
```
- Fix < to > to
- `greater than`/`greater than or equal to` or `less than`/`less than or equal to` where applicable.
- Wrap it with `{{{...}}}` to print them in javadoc or use `{code ...}` or `{literal ..}`. Please refer https://github.com/apache/spark/pull/16013#discussion_r89665558
- Fix `</p>` complaint
## How was this patch tested?
Manually tested by `jekyll build` with Java 7 and 8
```
java version "1.7.0_80"
Java(TM) SE Runtime Environment (build 1.7.0_80-b15)
Java HotSpot(TM) 64-Bit Server VM (build 24.80-b11, mixed mode)
```
```
java version "1.8.0_45"
Java(TM) SE Runtime Environment (build 1.8.0_45-b14)
Java HotSpot(TM) 64-Bit Server VM (build 25.45-b02, mixed mode)
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16013 from HyukjinKwon/SPARK-3359-errors-more.
## What changes were proposed in this pull request?
For the following workflow:
1. I have a column called time which is at minute level precision in a Streaming DataFrame
2. I want to perform groupBy time, count
3. Then I want my MemorySink to only have the last 30 minutes of counts and I perform this by
.where('time >= current_timestamp().cast("long") - 30 * 60)
what happens is that the `filter` gets pushed down before the aggregation, and the filter happens on the source data for the aggregation instead of the result of the aggregation (where I actually want to filter).
I guess the main issue here is that `current_timestamp` is non-deterministic in the streaming context and shouldn't be pushed down the filter.
Does this require us to store the `current_timestamp` for each trigger of the streaming job, that is something to discuss.
Furthermore, we want to persist current batch timestamp and watermark timestamp to the offset log so that these values are consistent across multiple executions of the same batch.
brkyvz zsxwing tdas
## How was this patch tested?
A test was added to StreamingAggregationSuite ensuring the above use case is handled. The test injects a stream of time values (in seconds) to a query that runs in complete mode and only outputs the (count) aggregation results for the past 10 seconds.
Author: Tyson Condie <tcondie@gmail.com>
Closes#15949 from tcondie/SPARK-18339.
## What changes were proposed in this pull request?
This is absolutely minor. PR https://github.com/apache/spark/pull/15595 uses `dt1.asNullable == dt2.asNullable` expressions in a few places. It is however more efficient to call `dt1.sameType(dt2)`. I have replaced every instance of the first pattern with the second pattern (3/5 were introduced by #15595).
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#16041 from hvanhovell/SPARK-18058.
## What changes were proposed in this pull request?
In #15764 we added a mechanism to detect if a function is temporary or not. Hive functions are treated as non-temporary. Of the three hive functions, now "percentile" has been implemented natively, and "hash" has been removed. So we should update the list.
## How was this patch tested?
Unit tests.
Author: Shuai Lin <linshuai2012@gmail.com>
Closes#16049 from lins05/update-temp-function-detect-hive-list.
## What changes were proposed in this pull request?
Implement percentile SQL function. It computes the exact percentile(s) of expr at pc with range in [0, 1].
## How was this patch tested?
Add a new testsuite `PercentileSuite` to test percentile directly.
Updated related testcases in `ExpressionToSQLSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Author: 蒋星博 <jiangxingbo@meituan.com>
Author: jiangxingbo <jiangxingbo@meituan.com>
Closes#14136 from jiangxb1987/percentile.
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/15704 will fail if we use int literal in `DROP PARTITION`, and we have reverted it in branch-2.1.
This PR reverts it in master branch, and add a regression test for it, to make sure the master branch is healthy.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16036 from cloud-fan/revert.
## What changes were proposed in this pull request?
We currently push down join conditions of a Left Anti join to both sides of the join. This is similar to Inner, Left Semi and Existence (a specialized left semi) join. The problem is that this changes the semantics of the join; a left anti join filters out rows that matches the join condition.
This PR fixes this by only pushing down conditions to the left hand side of the join. This is similar to the behavior of left outer join.
## How was this patch tested?
Added tests to `FilterPushdownSuite.scala` and created a SQLQueryTestSuite file for left anti joins with a regression test.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#16026 from hvanhovell/SPARK-18597.
## What changes were proposed in this pull request?
The `CollapseWindow` optimizer rule changes the order of output attributes. This modifies the output of the plan, which the optimizer cannot do. This also breaks things like `collect()` for which we use a `RowEncoder` that assumes that the output attributes of the executed plan are equal to those outputted by the logical plan.
## How was this patch tested?
I have updated an incorrect test in `CollapseWindowSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#16027 from hvanhovell/SPARK-18604.
## What changes were proposed in this pull request?
Janino can optimize `true ? a : b` into `a` or `false ? a : b` into `b`, or if/else with literal condition, so we should use literal as `ev.isNull` if possible.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#16008 from ueshin/issues/SPARK-18585.
### What changes were proposed in this pull request?
Currently, the name validation checks are limited to table creation. It is enfored by Analyzer rule: `PreWriteCheck`.
However, table renaming and database creation have the same issues. It makes more sense to do the checks in `SessionCatalog`. This PR is to add it into `SessionCatalog`.
### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16018 from gatorsmile/nameValidate.
## What changes were proposed in this pull request?
Currently, `OuterReference` is not `NamedExpression`. So, it raises 'ClassCastException` when it used in projection lists of IN correlated subqueries. This PR aims to support that by making `OuterReference` as `NamedExpression` to show correct error messages.
```scala
scala> sql("CREATE TEMPORARY VIEW t1 AS SELECT * FROM VALUES 1, 2 AS t1(a)")
scala> sql("CREATE TEMPORARY VIEW t2 AS SELECT * FROM VALUES 1 AS t2(b)")
scala> sql("SELECT a FROM t1 WHERE a IN (SELECT a FROM t2)").show
java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.OuterReference cannot be cast to org.apache.spark.sql.catalyst.expressions.NamedExpression
```
## How was this patch tested?
Pass the Jenkins test with new test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#16015 from dongjoon-hyun/SPARK-17251-2.
## What changes were proposed in this pull request?
The nullability of `InputFileName` should be `false`.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#16007 from ueshin/issues/SPARK-18583.
## What changes were proposed in this pull request?
In `HyperLogLogPlusPlus`, if the relative error is so small that p >= 19, it will cause ArrayIndexOutOfBoundsException in `THRESHOLDS(p-4)` . We should check `p` and when p >= 19, regress to the original HLL result and use the small range correction they use.
The pr also fixes the upper bound in the log info in `require()`.
The upper bound is computed by:
```
val relativeSD = 1.106d / Math.pow(Math.E, p * Math.log(2.0d) / 2.0d)
```
which is derived from the equation for computing `p`:
```
val p = 2.0d * Math.log(1.106d / relativeSD) / Math.log(2.0d)
```
## How was this patch tested?
add test cases for:
1. checking validity of parameter relatvieSD
2. estimation with smaller relative error so that p >= 19
Author: Zhenhua Wang <wzh_zju@163.com>
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#15990 from wzhfy/hllppRsd.
## What changes were proposed in this pull request?
This PR only tries to fix things that looks pretty straightforward and were fixed in other previous PRs before.
This PR roughly fixes several things as below:
- Fix unrecognisable class and method links in javadoc by changing it from `[[..]]` to `` `...` ``
```
[error] .../spark/sql/core/target/java/org/apache/spark/sql/streaming/DataStreamReader.java:226: error: reference not found
[error] * Loads text files and returns a {link DataFrame} whose schema starts with a string column named
```
- Fix an exception annotation and remove code backticks in `throws` annotation
Currently, sbt unidoc with Java 8 complains as below:
```
[error] .../java/org/apache/spark/sql/streaming/StreamingQuery.java:72: error: unexpected text
[error] * throws StreamingQueryException, if <code>this</code> query has terminated with an exception.
```
`throws` should specify the correct class name from `StreamingQueryException,` to `StreamingQueryException` without backticks. (see [JDK-8007644](https://bugs.openjdk.java.net/browse/JDK-8007644)).
- Fix `[[http..]]` to `<a href="http..."></a>`.
```diff
- * [[https://blogs.oracle.com/java-platform-group/entry/diagnosing_tls_ssl_and_https Oracle
- * blog page]].
+ * <a href="https://blogs.oracle.com/java-platform-group/entry/diagnosing_tls_ssl_and_https">
+ * Oracle blog page</a>.
```
`[[http...]]` link markdown in scaladoc is unrecognisable in javadoc.
- It seems class can't have `return` annotation. So, two cases of this were removed.
```
[error] .../java/org/apache/spark/mllib/regression/IsotonicRegression.java:27: error: invalid use of return
[error] * return New instance of IsotonicRegression.
```
- Fix < to `<` and > to `>` according to HTML rules.
- Fix `</p>` complaint
- Exclude unrecognisable in javadoc, `constructor`, `todo` and `groupname`.
## How was this patch tested?
Manually tested by `jekyll build` with Java 7 and 8
```
java version "1.7.0_80"
Java(TM) SE Runtime Environment (build 1.7.0_80-b15)
Java HotSpot(TM) 64-Bit Server VM (build 24.80-b11, mixed mode)
```
```
java version "1.8.0_45"
Java(TM) SE Runtime Environment (build 1.8.0_45-b14)
Java HotSpot(TM) 64-Bit Server VM (build 25.45-b02, mixed mode)
```
Note: this does not yet make sbt unidoc suceed with Java 8 yet but it reduces the number of errors with Java 8.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15999 from HyukjinKwon/SPARK-3359-errors.
## What changes were proposed in this pull request?
- Raise Analysis exception when correlated predicates exist in the descendant operators of either operand of a Full outer join in a subquery as well as in a FOJ operator itself
- Raise Analysis exception when correlated predicates exists in a Window operator (a side effect inadvertently introduced by SPARK-17348)
## How was this patch tested?
Run sql/test catalyst/test and new test cases, added to SubquerySuite, showing the reported incorrect results.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#16005 from nsyca/FOJ-incorrect.1.
## What changes were proposed in this pull request?
The current implementation of column stats uses the base64 encoding of the internal UnsafeRow format to persist statistics (in table properties in Hive metastore). This is an internal format that is not stable across different versions of Spark and should NOT be used for persistence. In addition, it would be better if statistics stored in the catalog is human readable.
This pull request introduces the following changes:
1. Created a single ColumnStat class to for all data types. All data types track the same set of statistics.
2. Updated the implementation for stats collection to get rid of the dependency on internal data structures (e.g. InternalRow, or storing DateType as an int32). For example, previously dates were stored as a single integer, but are now stored as java.sql.Date. When we implement the next steps of CBO, we can add code to convert those back into internal types again.
3. Documented clearly what JVM data types are being used to store what data.
4. Defined a simple Map[String, String] interface for serializing and deserializing column stats into/from the catalog.
5. Rearranged the method/function structure so it is more clear what the supported data types are, and also moved how stats are generated into ColumnStat class so they are easy to find.
## How was this patch tested?
Removed most of the original test cases created for column statistics, and added three very simple ones to cover all the cases. The three test cases validate:
1. Roundtrip serialization works.
2. Behavior when analyzing non-existent column or unsupported data type column.
3. Result for stats collection for all valid data types.
Also moved parser related tests into a parser test suite and added an explicit serialization test for the Hive external catalog.
Author: Reynold Xin <rxin@databricks.com>
Closes#15959 from rxin/SPARK-18522.
## What changes were proposed in this pull request?
In Spark SQL, some expression may output safe format values, e.g. `CreateArray`, `CreateStruct`, `Cast`, etc. When we compare 2 values, we should be able to compare safe and unsafe formats.
The `GreaterThan`, `LessThan`, etc. in Spark SQL already handles it, but the `EqualTo` doesn't. This PR fixes it.
## How was this patch tested?
new unit test and regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15929 from cloud-fan/type-aware.
## What changes were proposed in this pull request?
This PR proposes throwing an `AnalysisException` with a proper message rather than `NoSuchElementException` with the message ` key not found: TimestampType` when unsupported types are given to `reflect` and `java_method` functions.
```scala
spark.range(1).selectExpr("reflect('java.lang.String', 'valueOf', cast('1990-01-01' as timestamp))")
```
produces
**Before**
```
java.util.NoSuchElementException: key not found: TimestampType
at scala.collection.MapLike$class.default(MapLike.scala:228)
at scala.collection.AbstractMap.default(Map.scala:59)
at scala.collection.MapLike$class.apply(MapLike.scala:141)
at scala.collection.AbstractMap.apply(Map.scala:59)
at org.apache.spark.sql.catalyst.expressions.CallMethodViaReflection$$anonfun$findMethod$1$$anonfun$apply$1.apply(CallMethodViaReflection.scala:159)
...
```
**After**
```
cannot resolve 'reflect('java.lang.String', 'valueOf', CAST('1990-01-01' AS TIMESTAMP))' due to data type mismatch: arguments from the third require boolean, byte, short, integer, long, float, double or string expressions; line 1 pos 0;
'Project [unresolvedalias(reflect(java.lang.String, valueOf, cast(1990-01-01 as timestamp)), Some(<function1>))]
+- Range (0, 1, step=1, splits=Some(2))
...
```
Added message is,
```
arguments from the third require boolean, byte, short, integer, long, float, double or string expressions
```
## How was this patch tested?
Tests added in `CallMethodViaReflection`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15694 from HyukjinKwon/SPARK-18179.
## What changes were proposed in this pull request?
Fixes the inconsistency of error raised between data source and hive serde
tables when schema is specified in CTAS scenario. In the process the grammar for
create table (datasource) is simplified.
**before:**
``` SQL
spark-sql> create table t2 (c1 int, c2 int) using parquet as select * from t1;
Error in query:
mismatched input 'as' expecting {<EOF>, '.', 'OPTIONS', 'CLUSTERED', 'PARTITIONED'}(line 1, pos 64)
== SQL ==
create table t2 (c1 int, c2 int) using parquet as select * from t1
----------------------------------------------------------------^^^
```
**After:**
```SQL
spark-sql> create table t2 (c1 int, c2 int) using parquet as select * from t1
> ;
Error in query:
Operation not allowed: Schema may not be specified in a Create Table As Select (CTAS) statement(line 1, pos 0)
== SQL ==
create table t2 (c1 int, c2 int) using parquet as select * from t1
^^^
```
## How was this patch tested?
Added a new test in CreateTableAsSelectSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#15968 from dilipbiswal/ctas.
## What changes were proposed in this pull request?
While this behavior is debatable, consider the following use case:
```sql
UNCACHE TABLE foo;
CACHE TABLE foo AS
SELECT * FROM bar
```
The command above fails the first time you run it. But I want to run the command above over and over again, and I don't want to change my code just for the first run of it.
The issue is that subsequent `CACHE TABLE` commands do not overwrite the existing table.
Now we can do:
```sql
UNCACHE TABLE IF EXISTS foo;
CACHE TABLE foo AS
SELECT * FROM bar
```
## How was this patch tested?
Unit tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15896 from brkyvz/uncache.
## What changes were proposed in this pull request?
This PR blocks an incorrect result scenario in scalar subquery where there are GROUP BY column(s)
that are not part of the correlated predicate(s).
Example:
// Incorrect result
Seq(1).toDF("c1").createOrReplaceTempView("t1")
Seq((1,1),(1,2)).toDF("c1","c2").createOrReplaceTempView("t2")
sql("select (select sum(-1) from t2 where t1.c1=t2.c1 group by t2.c2) from t1").show
// How can selecting a scalar subquery from a 1-row table return 2 rows?
## How was this patch tested?
sql/test, catalyst/test
new test case covering the reported problem is added to SubquerySuite.scala
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#15936 from nsyca/scalarSubqueryIncorrect-1.
## What changes were proposed in this pull request?
Technically map type is not orderable, but can be used in equality comparison. However, due to the limitation of the current implementation, map type can't be used in equality comparison so that it can't be join key or grouping key.
This PR makes this limitation explicit, to avoid wrong result.
## How was this patch tested?
updated tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15956 from cloud-fan/map-type.
## What changes were proposed in this pull request?
The nullabilities of `MapObject` can be made more strict by relying on `inputObject.nullable` and `lambdaFunction.nullable`.
Also `ExternalMapToCatalyst.dataType` can be made more strict by relying on `valueConverter.nullable`.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#15840 from ueshin/issues/SPARK-18398.
## What changes were proposed in this pull request?
This pr extracts method for preparing arguments from `StaticInvoke`, `Invoke` and `NewInstance` and modify to short circuit if arguments have `null` when `propageteNull == true`.
The steps are as follows:
1. Introduce `InvokeLike` to extract common logic from `StaticInvoke`, `Invoke` and `NewInstance` to prepare arguments.
`StaticInvoke` and `Invoke` had a risk to exceed 64kb JVM limit to prepare arguments but after this patch they can handle them because they share the preparing code of NewInstance, which handles the limit well.
2. Remove unneeded null checking and fix nullability of `NewInstance`.
Avoid some of nullabilty checking which are not needed because the expression is not nullable.
3. Modify to short circuit if arguments have `null` when `needNullCheck == true`.
If `needNullCheck == true`, preparing arguments can be skipped if we found one of them is `null`, so modified to short circuit in the case.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#15901 from ueshin/issues/SPARK-18467.
## What changes were proposed in this pull request?
This PR adds code generation to `Generate`. It supports two code paths:
- General `TraversableOnce` based iteration. This used for regular `Generator` (code generation supporting) expressions. This code path expects the expression to return a `TraversableOnce[InternalRow]` and it will iterate over the returned collection. This PR adds code generation for the `stack` generator.
- Specialized `ArrayData/MapData` based iteration. This is used for the `explode`, `posexplode` & `inline` functions and operates directly on the `ArrayData`/`MapData` result that the child of the generator returns.
### Benchmarks
I have added some benchmarks and it seems we can create a nice speedup for explode:
#### Environment
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.11.6
Intel(R) Core(TM) i7-4980HQ CPU 2.80GHz
```
#### Explode Array
##### Before
```
generate explode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate explode array wholestage off 7377 / 7607 2.3 439.7 1.0X
generate explode array wholestage on 6055 / 6086 2.8 360.9 1.2X
```
##### After
```
generate explode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate explode array wholestage off 7432 / 7696 2.3 443.0 1.0X
generate explode array wholestage on 631 / 646 26.6 37.6 11.8X
```
#### Explode Map
##### Before
```
generate explode map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate explode map wholestage off 12792 / 12848 1.3 762.5 1.0X
generate explode map wholestage on 11181 / 11237 1.5 666.5 1.1X
```
##### After
```
generate explode map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate explode map wholestage off 10949 / 10972 1.5 652.6 1.0X
generate explode map wholestage on 870 / 913 19.3 51.9 12.6X
```
#### Posexplode
##### Before
```
generate posexplode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate posexplode array wholestage off 7547 / 7580 2.2 449.8 1.0X
generate posexplode array wholestage on 5786 / 5838 2.9 344.9 1.3X
```
##### After
```
generate posexplode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate posexplode array wholestage off 7535 / 7548 2.2 449.1 1.0X
generate posexplode array wholestage on 620 / 624 27.1 37.0 12.1X
```
#### Inline
##### Before
```
generate inline array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate inline array wholestage off 6935 / 6978 2.4 413.3 1.0X
generate inline array wholestage on 6360 / 6400 2.6 379.1 1.1X
```
##### After
```
generate inline array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate inline array wholestage off 6940 / 6966 2.4 413.6 1.0X
generate inline array wholestage on 1002 / 1012 16.7 59.7 6.9X
```
#### Stack
##### Before
```
generate stack: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate stack wholestage off 12980 / 13104 1.3 773.7 1.0X
generate stack wholestage on 11566 / 11580 1.5 689.4 1.1X
```
##### After
```
generate stack: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
generate stack wholestage off 12875 / 12949 1.3 767.4 1.0X
generate stack wholestage on 840 / 845 20.0 50.0 15.3X
```
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#13065 from hvanhovell/SPARK-15214.
## What changes were proposed in this pull request?
The previous documentation and example for DateDiff was wrong.
## How was this patch tested?
Doc only change.
Author: Reynold Xin <rxin@databricks.com>
Closes#15937 from rxin/datediff-doc.
## What changes were proposed in this pull request?
The nullability of `WrapOption` should be `false`.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#15887 from ueshin/issues/SPARK-18442.
### What changes were proposed in this pull request?
Currently, when CTE is used in RunnableCommand, the Analyzer does not replace the logical node `With`. The child plan of RunnableCommand is not resolved. Thus, the output of the `With` plan node looks very confusing.
For example,
```
sql(
"""
|CREATE VIEW cte_view AS
|WITH w AS (SELECT 1 AS n), cte1 (select 2), cte2 as (select 3)
|SELECT n FROM w
""".stripMargin).explain()
```
The output is like
```
ExecutedCommand
+- CreateViewCommand `cte_view`, WITH w AS (SELECT 1 AS n), cte1 (select 2), cte2 as (select 3)
SELECT n FROM w, false, false, PersistedView
+- 'With [(w,SubqueryAlias w
+- Project [1 AS n#16]
+- OneRowRelation$
), (cte1,'SubqueryAlias cte1
+- 'Project [unresolvedalias(2, None)]
+- OneRowRelation$
), (cte2,'SubqueryAlias cte2
+- 'Project [unresolvedalias(3, None)]
+- OneRowRelation$
)]
+- 'Project ['n]
+- 'UnresolvedRelation `w`
```
After the fix, the output is as shown below.
```
ExecutedCommand
+- CreateViewCommand `cte_view`, WITH w AS (SELECT 1 AS n), cte1 (select 2), cte2 as (select 3)
SELECT n FROM w, false, false, PersistedView
+- CTE [w, cte1, cte2]
: :- SubqueryAlias w
: : +- Project [1 AS n#16]
: : +- OneRowRelation$
: :- 'SubqueryAlias cte1
: : +- 'Project [unresolvedalias(2, None)]
: : +- OneRowRelation$
: +- 'SubqueryAlias cte2
: +- 'Project [unresolvedalias(3, None)]
: +- OneRowRelation$
+- 'Project ['n]
+- 'UnresolvedRelation `w`
```
BTW, this PR also fixes the output of the view type.
### How was this patch tested?
Manual
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15854 from gatorsmile/cteName.
## What changes were proposed in this pull request?
Small fix, fix the errors caused by lint check in Java
- Clear unused objects and `UnusedImports`.
- Add comments around the method `finalize` of `NioBufferedFileInputStream`to turn off checkstyle.
- Cut the line which is longer than 100 characters into two lines.
## How was this patch tested?
Travis CI.
```
$ build/mvn -T 4 -q -DskipTests -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install
$ dev/lint-java
```
Before:
```
Checkstyle checks failed at following occurrences:
[ERROR] src/main/java/org/apache/spark/network/util/TransportConf.java:[21,8] (imports) UnusedImports: Unused import - org.apache.commons.crypto.cipher.CryptoCipherFactory.
[ERROR] src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java:[516,5] (modifier) RedundantModifier: Redundant 'public' modifier.
[ERROR] src/main/java/org/apache/spark/io/NioBufferedFileInputStream.java:[133] (coding) NoFinalizer: Avoid using finalizer method.
[ERROR] src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeMapData.java:[71] (sizes) LineLength: Line is longer than 100 characters (found 113).
[ERROR] src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java:[112] (sizes) LineLength: Line is longer than 100 characters (found 110).
[ERROR] src/test/java/org/apache/spark/sql/catalyst/expressions/HiveHasherSuite.java:[31,17] (modifier) ModifierOrder: 'static' modifier out of order with the JLS suggestions.
[ERROR]src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java:[64] (sizes) LineLength: Line is longer than 100 characters (found 103).
[ERROR] src/main/java/org/apache/spark/examples/ml/JavaInteractionExample.java:[22,8] (imports) UnusedImports: Unused import - org.apache.spark.ml.linalg.Vectors.
[ERROR] src/main/java/org/apache/spark/examples/ml/JavaInteractionExample.java:[51] (regexp) RegexpSingleline: No trailing whitespace allowed.
```
After:
```
$ build/mvn -T 4 -q -DskipTests -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install
$ dev/lint-java
Using `mvn` from path: /home/travis/build/ConeyLiu/spark/build/apache-maven-3.3.9/bin/mvn
Checkstyle checks passed.
```
Author: Xianyang Liu <xyliu0530@icloud.com>
Closes#15865 from ConeyLiu/master.
## What changes were proposed in this pull request?
This PR aims to improve DataSource option keys to be more case-insensitive
DataSource partially use CaseInsensitiveMap in code-path. For example, the following fails to find url.
```scala
val df = spark.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
df.write.format("jdbc")
.option("UrL", url1)
.option("dbtable", "TEST.SAVETEST")
.options(properties.asScala)
.save()
```
This PR makes DataSource options to use CaseInsensitiveMap internally and also makes DataSource to use CaseInsensitiveMap generally except `InMemoryFileIndex` and `InsertIntoHadoopFsRelationCommand`. We can not pass them CaseInsensitiveMap because they creates new case-sensitive HadoopConfs by calling newHadoopConfWithOptions(options) inside.
## How was this patch tested?
Pass the Jenkins test with newly added test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15884 from dongjoon-hyun/SPARK-18433.
## What changes were proposed in this pull request?
it's weird that every session can set its own warehouse path at runtime, we should forbid it and make it a static conf.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15825 from cloud-fan/warehouse.
## What changes were proposed in this pull request?
Commit f14ae4900a broke the scala 2.10 build. This PR fixes this by simplifying the used pattern match.
## How was this patch tested?
Tested building manually. Ran `build/sbt -Dscala-2.10 -Pscala-2.10 package`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15891 from hvanhovell/SPARK-18300-scala-2.10.
## What changes were proposed in this pull request?
The `FoldablePropagation` optimizer rule, pulls foldable values out from under an `Expand`. This breaks the `Expand` in two ways:
- It rewrites the output attributes of the `Expand`. We explicitly define output attributes for `Expand`, these are (unfortunately) considered as part of the expressions of the `Expand` and can be rewritten.
- Expand can actually change the column (it will typically re-use the attributes or the underlying plan). This means that we cannot safely propagate the expressions from under an `Expand`.
This PR fixes this and (hopefully) other issues by explicitly whitelisting allowed operators.
## How was this patch tested?
Added tests to `FoldablePropagationSuite` and to `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15857 from hvanhovell/SPARK-18300.
### What changes were proposed in this pull request?
When the exception is an invocation exception during function lookup, we return a useless/confusing error message:
For example,
```Scala
df.selectExpr("concat_ws()")
```
Below is the error message we got:
```
null; line 1 pos 0
org.apache.spark.sql.AnalysisException: null; line 1 pos 0
```
To get the meaningful error message, we need to get the cause. The fix is exactly the same as what we did in https://github.com/apache/spark/pull/12136. After the fix, the message we got is the exception issued in the constuctor of function implementation:
```
requirement failed: concat_ws requires at least one argument.; line 1 pos 0
org.apache.spark.sql.AnalysisException: requirement failed: concat_ws requires at least one argument.; line 1 pos 0
```
### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15878 from gatorsmile/functionNotFound.
This PR adds a new method `withWatermark` to the `Dataset` API, which can be used specify an _event time watermark_. An event time watermark allows the streaming engine to reason about the point in time after which we no longer expect to see late data. This PR also has augmented `StreamExecution` to use this watermark for several purposes:
- To know when a given time window aggregation is finalized and thus results can be emitted when using output modes that do not allow updates (e.g. `Append` mode).
- To minimize the amount of state that we need to keep for on-going aggregations, by evicting state for groups that are no longer expected to change. Although, we do still maintain all state if the query requires (i.e. if the event time is not present in the `groupBy` or when running in `Complete` mode).
An example that emits windowed counts of records, waiting up to 5 minutes for late data to arrive.
```scala
df.withWatermark("eventTime", "5 minutes")
.groupBy(window($"eventTime", "1 minute") as 'window)
.count()
.writeStream
.format("console")
.mode("append") // In append mode, we only output finalized aggregations.
.start()
```
### Calculating the watermark.
The current event time is computed by looking at the `MAX(eventTime)` seen this epoch across all of the partitions in the query minus some user defined _delayThreshold_. An additional constraint is that the watermark must increase monotonically.
Note that since we must coordinate this value across partitions occasionally, the actual watermark used is only guaranteed to be at least `delay` behind the actual event time. In some cases we may still process records that arrive more than delay late.
This mechanism was chosen for the initial implementation over processing time for two reasons:
- it is robust to downtime that could affect processing delay
- it does not require syncing of time or timezones between the producer and the processing engine.
### Other notable implementation details
- A new trigger metric `eventTimeWatermark` outputs the current value of the watermark.
- We mark the event time column in the `Attribute` metadata using the key `spark.watermarkDelay`. This allows downstream operations to know which column holds the event time. Operations like `window` propagate this metadata.
- `explain()` marks the watermark with a suffix of `-T${delayMs}` to ease debugging of how this information is propagated.
- Currently, we don't filter out late records, but instead rely on the state store to avoid emitting records that are both added and filtered in the same epoch.
### Remaining in this PR
- [ ] The test for recovery is currently failing as we don't record the watermark used in the offset log. We will need to do so to ensure determinism, but this is deferred until #15626 is merged.
### Other follow-ups
There are some natural additional features that we should consider for future work:
- Ability to write records that arrive too late to some external store in case any out-of-band remediation is required.
- `Update` mode so you can get partial results before a group is evicted.
- Other mechanisms for calculating the watermark. In particular a watermark based on quantiles would be more robust to outliers.
Author: Michael Armbrust <michael@databricks.com>
Closes#15702 from marmbrus/watermarks.
## What changes were proposed in this pull request?
Return an Analysis exception when there is a correlated non-equality predicate in a subquery and the correlated column from the outer reference is not from the immediate parent operator of the subquery. This PR prevents incorrect results from subquery transformation in such case.
Test cases, both positive and negative tests, are added.
## How was this patch tested?
sql/test, catalyst/test, hive/test, and scenarios that will produce incorrect results without this PR and product correct results when subquery transformation does happen.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#15763 from nsyca/spark-17348.
## What changes were proposed in this pull request?
This removes the serialization test from RegexpExpressionsSuite and
replaces it by serializing all expressions in checkEvaluation.
This also fixes math constant expressions by making LeafMathExpression
Serializable and fixes NumberFormat values that are null or invalid
after serialization.
## How was this patch tested?
This patch is to tests.
Author: Ryan Blue <blue@apache.org>
Closes#15847 from rdblue/SPARK-18387-fix-serializable-expressions.
## What changes were proposed in this pull request?
As of current 2.1, INSERT OVERWRITE with dynamic partitions against a Datasource table will overwrite the entire table instead of only the partitions matching the static keys, as in Hive. It also doesn't respect custom partition locations.
This PR adds support for all these operations to Datasource tables managed by the Hive metastore. It is implemented as follows
- During planning time, the full set of partitions affected by an INSERT or OVERWRITE command is read from the Hive metastore.
- The planner identifies any partitions with custom locations and includes this in the write task metadata.
- FileFormatWriter tasks refer to this custom locations map when determining where to write for dynamic partition output.
- When the write job finishes, the set of written partitions is compared against the initial set of matched partitions, and the Hive metastore is updated to reflect the newly added / removed partitions.
It was necessary to introduce a method for staging files with absolute output paths to `FileCommitProtocol`. These files are not handled by the Hadoop output committer but are moved to their final locations when the job commits.
The overwrite behavior of legacy Datasource tables is also changed: no longer will the entire table be overwritten if a partial partition spec is present.
cc cloud-fan yhuai
## How was this patch tested?
Unit tests, existing tests.
Author: Eric Liang <ekl@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15814 from ericl/sc-5027.
## What changes were proposed in this pull request?
This PR corrects several partition related behaviors of `ExternalCatalog`:
1. default partition location should not always lower case the partition column names in path string(fix `HiveExternalCatalog`)
2. rename partition should not always lower case the partition column names in updated partition path string(fix `HiveExternalCatalog`)
3. rename partition should update the partition location only for managed table(fix `InMemoryCatalog`)
4. create partition with existing directory should be fine(fix `InMemoryCatalog`)
5. create partition with non-existing directory should create that directory(fix `InMemoryCatalog`)
6. drop partition from external table should not delete the directory(fix `InMemoryCatalog`)
## How was this patch tested?
new tests in `ExternalCatalogSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15797 from cloud-fan/partition.
## What changes were proposed in this pull request?
This makes the result value both transient and lazy, so that if the RegExpReplace object is initialized then serialized, `result: StringBuffer` will be correctly initialized.
## How was this patch tested?
* Verified that this patch fixed the query that found the bug.
* Added a test case that fails without the fix.
Author: Ryan Blue <blue@apache.org>
Closes#15834 from rdblue/SPARK-18368-fix-regexp-replace.