## 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?
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 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?
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 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.
## 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?
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?
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?
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?
`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?
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 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?
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?
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?
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?
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?
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?
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?
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?
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?
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?
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?
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?
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?
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.
