## What changes were proposed in this pull request?
In the current test case for CombineTypedFilters, we lack the test of FilterFunction, so let's add it.
In addition, in TypedFilterOptimizationSuite's existing test cases, Let's extract a common LocalRelation.
## How was this patch tested?
add new test cases.
Author: caoxuewen <cao.xuewen@zte.com.cn>
Closes#20482 from heary-cao/TypedFilterOptimizationSuite.
## What changes were proposed in this pull request?
This PR is to fix the `ReplaceExceptWithFilter` rule when the right's Filter contains the references that are not in the left output.
Before this PR, we got the error like
```
java.util.NoSuchElementException: key not found: a
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)
```
After this PR, `ReplaceExceptWithFilter ` will not take an effect in this case.
## How was this patch tested?
Added tests
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20444 from gatorsmile/fixReplaceExceptWithFilter.
## What changes were proposed in this pull request?
It is a common pattern to apply multiple transforms to a `Dataset` (using `Dataset.withColumn` for example. This is currently quite expensive because we run `CheckAnalysis` on the full plan and create an encoder for each intermediate `Dataset`.
This PR extends the usage of the `AnalysisBarrier` to include `CheckAnalysis`. By doing this we hide the already analyzed plan from `CheckAnalysis` because barrier is a `LeafNode`. The `AnalysisBarrier` is in the `FinishAnalysis` phase of the optimizer.
We also make binding the `Dataset` encoder lazy. The bound encoder is only needed when we materialize the dataset.
## How was this patch tested?
Existing test should cover this.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#20402 from hvanhovell/SPARK-23223.
## What changes were proposed in this pull request?
Currently, all Aggregate operations will go into RemoveRepetitionFromGroupExpressions, but there is no group expression or there is no duplicate group expression in group expression, we not need copy for logic plan.
## How was this patch tested?
the existed test case.
Author: caoxuewen <cao.xuewen@zte.com.cn>
Closes#20375 from heary-cao/RepetitionGroupExpressions.
## What changes were proposed in this pull request?
The `GenArrayData.genCodeToCreateArrayData` produces illegal java code when code splitting is enabled. This is used in `CreateArray` and `CreateMap` expressions for complex object arrays.
This issue is caused by a typo.
## How was this patch tested?
Added a regression test in `complexTypesSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#20391 from hvanhovell/SPARK-23208.
## What changes were proposed in this pull request?
This PR is repaired as follows
1、update y -> x in "left outer join" test case ,maybe is mistake.
2、add a new test case："left outer join and left sides are limited"
3、add a new test case："left outer join and right sides are limited"
4、add a new test case: "right outer join and right sides are limited"
5、add a new test case: "right outer join and left sides are limited"
6、Remove annotations without code implementation
## How was this patch tested?
add new unit test case.
Author: caoxuewen <cao.xuewen@zte.com.cn>
Closes#20381 from heary-cao/LimitPushdownSuite.
## What changes were proposed in this pull request?
Typo fixes
## How was this patch tested?
Local build / Doc-only changes
Author: Jacek Laskowski <jacek@japila.pl>
Closes#20344 from jaceklaskowski/typo-fixes.
## What changes were proposed in this pull request?
When there is an operation between Decimals and the result is a number which is not representable exactly with the result's precision and scale, Spark is returning `NULL`. This was done to reflect Hive's behavior, but it is against SQL ANSI 2011, which states that "If the result cannot be represented exactly in the result type, then whether it is rounded or truncated is implementation-defined". Moreover, Hive now changed its behavior in order to respect the standard, thanks to HIVE-15331.
Therefore, the PR propose to:
- update the rules to determine the result precision and scale according to the new Hive's ones introduces in HIVE-15331;
- round the result of the operations, when it is not representable exactly with the result's precision and scale, instead of returning `NULL`
- introduce a new config `spark.sql.decimalOperations.allowPrecisionLoss` which default to `true` (ie. the new behavior) in order to allow users to switch back to the previous one.
Hive behavior reflects SQLServer's one. The only difference is that the precision and scale are adjusted for all the arithmetic operations in Hive, while SQL Server is said to do so only for multiplications and divisions in the documentation. This PR follows Hive's behavior.
A more detailed explanation is available here: https://mail-archives.apache.org/mod_mbox/spark-dev/201712.mbox/%3CCAEorWNAJ4TxJR9NBcgSFMD_VxTg8qVxusjP%2BAJP-x%2BJV9zH-yA%40mail.gmail.com%3E.
## How was this patch tested?
modified and added UTs. Comparisons with results of Hive and SQLServer.
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20023 from mgaido91/SPARK-22036.
## What changes were proposed in this pull request?
Previously, PR #19201 fix the problem of non-converging constraints.
After that PR #19149 improve the loop and constraints is inferred only once.
So the problem of non-converging constraints is gone.
However, the case below will fail.
```
spark.range(5).write.saveAsTable("t")
val t = spark.read.table("t")
val left = t.withColumn("xid", $"id" + lit(1)).as("x")
val right = t.withColumnRenamed("id", "xid").as("y")
val df = left.join(right, "xid").filter("id = 3").toDF()
checkAnswer(df, Row(4, 3))
```
Because `aliasMap` replace all the aliased child. See the test case in PR for details.
This PR is to fix this bug by removing useless code for preventing non-converging constraints.
It can be also fixed with #20270, but this is much simpler and clean up the code.
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#20278 from gengliangwang/FixConstraintSimple.
## What changes were proposed in this pull request?
There are already quite a few integration tests using window frames, but the unit tests coverage is not ideal.
In this PR the already existing tests are reorganized, extended and where gaps found additional cases added.
## How was this patch tested?
Automated: Pass the Jenkins.
Author: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Closes#20019 from gaborgsomogyi/SPARK-22361.
## What changes were proposed in this pull request?
Problem: it throw TempTableAlreadyExistsException and output "Temporary table '$table' already exists" when we create temp view by using org.apache.spark.sql.catalyst.catalog.GlobalTempViewManager#create, it's improper.
So fix improper information about TempTableAlreadyExistsException when create temp view:
change "Temporary table" to "Temporary view"
## How was this patch tested?
test("rename temporary view - destination table already exists, with: CREATE TEMPORARY view")
test("rename temporary view - destination table with database name,with:CREATE TEMPORARY view")
Author: xubo245 <601450868@qq.com>
Closes#20227 from xubo245/fixDeprecated.
## What changes were proposed in this pull request?
Add support for `Null` type in the `schemaFor` method for Scala reflection.
## How was this patch tested?
Added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20219 from mgaido91/SPARK-23025.
## What changes were proposed in this pull request?
(courtesy of liancheng)
Spark SQL supports both global aggregation and grouping aggregation. Global aggregation always return a single row with the initial aggregation state as the output, even there are zero input rows. Spark implements this by simply checking the number of grouping keys and treats an aggregation as a global aggregation if it has zero grouping keys.
However, this simple principle drops the ball in the following case:
```scala
spark.emptyDataFrame.dropDuplicates().agg(count($"*") as "c").show()
// +---+
// | c |
// +---+
// | 1 |
// +---+
```
The reason is that:
1. `df.dropDuplicates()` is roughly translated into something equivalent to:
```scala
val allColumns = df.columns.map { col }
df.groupBy(allColumns: _*).agg(allColumns.head, allColumns.tail: _*)
```
This translation is implemented in the rule `ReplaceDeduplicateWithAggregate`.
2. `spark.emptyDataFrame` contains zero columns and zero rows.
Therefore, rule `ReplaceDeduplicateWithAggregate` makes a confusing transformation roughly equivalent to the following one:
```scala
spark.emptyDataFrame.dropDuplicates()
=> spark.emptyDataFrame.groupBy().agg(Map.empty[String, String])
```
The above transformation is confusing because the resulting aggregate operator contains no grouping keys (because `emptyDataFrame` contains no columns), and gets recognized as a global aggregation. As a result, Spark SQL allocates a single row filled by the initial aggregation state and uses it as the output, and returns a wrong result.
To fix this issue, this PR tweaks `ReplaceDeduplicateWithAggregate` by appending a literal `1` to the grouping key list of the resulting `Aggregate` operator when the input plan contains zero output columns. In this way, `spark.emptyDataFrame.dropDuplicates()` is now translated into a grouping aggregation, roughly depicted as:
```scala
spark.emptyDataFrame.dropDuplicates()
=> spark.emptyDataFrame.groupBy(lit(1)).agg(Map.empty[String, String])
```
Which is now properly treated as a grouping aggregation and returns the correct answer.
## How was this patch tested?
New unit tests added
Author: Feng Liu <fengliu@databricks.com>
Closes#20174 from liufengdb/fix-duplicate.
## What changes were proposed in this pull request?
This pr fixed the issue when casting structs into strings;
```
scala> val df = Seq(((1, "a"), 0), ((2, "b"), 0)).toDF("a", "b")
scala> df.write.saveAsTable("t")
scala> sql("SELECT CAST(a AS STRING) FROM t").show
+-------------------+
| a|
+-------------------+
|[0,1,1800000001,61]|
|[0,2,1800000001,62]|
+-------------------+
```
This pr modified the result into;
```
+------+
| a|
+------+
|[1, a]|
|[2, b]|
+------+
```
## How was this patch tested?
Added tests in `CastSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20176 from maropu/SPARK-22981.
## What changes were proposed in this pull request?
This PR fixes a longstanding correctness bug in `GenerateUnsafeRowJoiner`. This class was introduced in https://github.com/apache/spark/pull/7821 (July 2015 / Spark 1.5.0+) and is used to combine pairs of UnsafeRows in TungstenAggregationIterator, CartesianProductExec, and AppendColumns.
### Bugs fixed by this patch
1. **Incorrect combining of null-tracking bitmaps**: when concatenating two UnsafeRows, the implementation "Concatenate the two bitsets together into a single one, taking padding into account". If one row has no columns then it has a bitset size of 0, but the code was incorrectly assuming that if the left row had a non-zero number of fields then the right row would also have at least one field, so it was copying invalid bytes and and treating them as part of the bitset. I'm not sure whether this bug was also present in the original implementation or whether it was introduced in https://github.com/apache/spark/pull/7892 (which fixed another bug in this code).
2. **Incorrect updating of data offsets for null variable-length fields**: after updating the bitsets and copying fixed-length and variable-length data, we need to perform adjustments to the offsets pointing the start of variable length fields's data. The existing code was _conditionally_ adding a fixed offset to correct for the new length of the combined row, but it is unsafe to do this if the variable-length field has a null value: we always represent nulls by storing `0` in the fixed-length slot, but this code was incorrectly incrementing those values. This bug was present since the original version of `GenerateUnsafeRowJoiner`.
### Why this bug remained latent for so long
The PR which introduced `GenerateUnsafeRowJoiner` features several randomized tests, including tests of the cases where one side of the join has no fields and where string-valued fields are null. However, the existing assertions were too weak to uncover this bug:
- If a null field has a non-zero value in its fixed-length data slot then this will not cause problems for field accesses because the null-tracking bitmap should still be correct and we will not try to use the incorrect offset for anything.
- If the null tracking bitmap is corrupted by joining against a row with no fields then the corruption occurs in field numbers past the actual field numbers contained in the row. Thus valid `isNullAt()` calls will not read the incorrectly-set bits.
The existing `GenerateUnsafeRowJoinerSuite` tests only exercised `.get()` and `isNullAt()`, but didn't actually check the UnsafeRows for bit-for-bit equality, preventing these bugs from failing assertions. It turns out that there was even a [GenerateUnsafeRowJoinerBitsetSuite](03377d2522/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala) but it looks like it also didn't catch this problem because it only tested the bitsets in an end-to-end fashion by accessing them through the `UnsafeRow` interface instead of actually comparing the bitsets' bytes.
### Impact of these bugs
- This bug will cause `equals()` and `hashCode()` to be incorrect for these rows, which will be problematic in case`GenerateUnsafeRowJoiner`'s results are used as join or grouping keys.
- Chained / repeated invocations of `GenerateUnsafeRowJoiner` may result in reads from invalid null bitmap positions causing fields to incorrectly become NULL (see the end-to-end example below).
- It looks like this generally only happens in `CartesianProductExec`, which our query optimizer often avoids executing (usually we try to plan a `BroadcastNestedLoopJoin` instead).
### End-to-end test case demonstrating the problem
The following query demonstrates how this bug may result in incorrect query results:
```sql
set spark.sql.autoBroadcastJoinThreshold=-1; -- Needed to trigger CartesianProductExec
create table a as select * from values 1;
create table b as select * from values 2;
SELECT
t3.col1,
t1.col1
FROM a t1
CROSS JOIN b t2
CROSS JOIN b t3
```
This should return `(2, 1)` but instead was returning `(null, 1)`.
Column pruning ends up trimming off all columns from `t2`, so when `t2` joins with another table this triggers the bitmap-copying bug. This incorrect bitmap is subsequently copied again when performing the final join, causing the final output to have an incorrectly-set null bit for the first field.
## How was this patch tested?
Strengthened the assertions in existing tests in GenerateUnsafeRowJoinerSuite. Also verified that the end-to-end test case which uncovered this now passes.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#20181 from JoshRosen/SPARK-22984-fix-generate-unsaferow-joiner-bitmap-bugs.
## What changes were proposed in this pull request?
**The current shuffle planning logic**
1. Each operator specifies the distribution requirements for its children, via the `Distribution` interface.
2. Each operator specifies its output partitioning, via the `Partitioning` interface.
3. `Partitioning.satisfy` determines whether a `Partitioning` can satisfy a `Distribution`.
4. For each operator, check each child of it, add a shuffle node above the child if the child partitioning can not satisfy the required distribution.
5. For each operator, check if its children's output partitionings are compatible with each other, via the `Partitioning.compatibleWith`.
6. If the check in 5 failed, add a shuffle above each child.
7. try to eliminate the shuffles added in 6, via `Partitioning.guarantees`.
This design has a major problem with the definition of "compatible".
`Partitioning.compatibleWith` is not well defined, ideally a `Partitioning` can't know if it's compatible with other `Partitioning`, without more information from the operator. For example, `t1 join t2 on t1.a = t2.b`, `HashPartitioning(a, 10)` should be compatible with `HashPartitioning(b, 10)` under this case, but the partitioning itself doesn't know it.
As a result, currently `Partitioning.compatibleWith` always return false except for literals, which make it almost useless. This also means, if an operator has distribution requirements for multiple children, Spark always add shuffle nodes to all the children(although some of them can be eliminated). However, there is no guarantee that the children's output partitionings are compatible with each other after adding these shuffles, we just assume that the operator will only specify `ClusteredDistribution` for multiple children.
I think it's very hard to guarantee children co-partition for all kinds of operators, and we can not even give a clear definition about co-partition between distributions like `ClusteredDistribution(a,b)` and `ClusteredDistribution(c)`.
I think we should drop the "compatible" concept in the distribution model, and let the operator achieve the co-partition requirement by special distribution requirements.
**Proposed shuffle planning logic after this PR**
(The first 4 are same as before)
1. Each operator specifies the distribution requirements for its children, via the `Distribution` interface.
2. Each operator specifies its output partitioning, via the `Partitioning` interface.
3. `Partitioning.satisfy` determines whether a `Partitioning` can satisfy a `Distribution`.
4. For each operator, check each child of it, add a shuffle node above the child if the child partitioning can not satisfy the required distribution.
5. For each operator, check if its children's output partitionings have the same number of partitions.
6. If the check in 5 failed, pick the max number of partitions from children's output partitionings, and add shuffle to child whose number of partitions doesn't equal to the max one.
The new distribution model is very simple, we only have one kind of relationship, which is `Partitioning.satisfy`. For multiple children, Spark only guarantees they have the same number of partitions, and it's the operator's responsibility to leverage this guarantee to achieve more complicated requirements. For example, non-broadcast joins can use the newly added `HashPartitionedDistribution` to achieve co-partition.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19080 from cloud-fan/exchange.
## What changes were proposed in this pull request?
The following SQL query should return zero rows, but in Spark it actually returns one row:
```
SELECT 1 from (
SELECT 1 AS z,
MIN(a.x)
FROM (select 1 as x) a
WHERE false
) b
where b.z != b.z
```
The problem stems from the `PushDownPredicate` rule: when this rule encounters a filter on top of an Aggregate operator, e.g. `Filter(Agg(...))`, it removes the original filter and adds a new filter onto Aggregate's child, e.g. `Agg(Filter(...))`. This is sometimes okay, but the case above is a counterexample: because there is no explicit `GROUP BY`, we are implicitly computing a global aggregate over the entire table so the original filter was not acting like a `HAVING` clause filtering the number of groups: if we push this filter then it fails to actually reduce the cardinality of the Aggregate output, leading to the wrong answer.
In 2016 I fixed a similar problem involving invalid pushdowns of data-independent filters (filters which reference no columns of the filtered relation). There was additional discussion after my fix was merged which pointed out that my patch was an incomplete fix (see #15289), but it looks I must have either misunderstood the comment or forgot to follow up on the additional points raised there.
This patch fixes the problem by choosing to never push down filters in cases where there are no grouping expressions. Since there are no grouping keys, the only columns are aggregate columns and we can't push filters defined over aggregate results, so this change won't cause us to miss out on any legitimate pushdown opportunities.
## How was this patch tested?
New regression tests in `SQLQueryTestSuite` and `FilterPushdownSuite`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#20180 from JoshRosen/SPARK-22983-dont-push-filters-beneath-aggs-with-empty-grouping-expressions.
## What changes were proposed in this pull request?
This patch adds additional escaping in `from_utc_timestamp` / `to_utc_timestamp` expression codegen in order to a bug where invalid timezones which contain special characters could cause generated code to fail to compile.
## How was this patch tested?
New regression tests in `DateExpressionsSuite`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#20182 from JoshRosen/SPARK-22985-fix-utc-timezone-function-escaping-bugs.
## What changes were proposed in this pull request?
This pr fixed the issue when casting maps into strings;
```
scala> Seq(Map(1 -> "a", 2 -> "b")).toDF("a").write.saveAsTable("t")
scala> sql("SELECT cast(a as String) FROM t").show(false)
+----------------------------------------------------------------+
|a |
+----------------------------------------------------------------+
|org.apache.spark.sql.catalyst.expressions.UnsafeMapData38bdd75d|
+----------------------------------------------------------------+
```
This pr modified the result into;
```
+----------------+
|a |
+----------------+
|[1 -> a, 2 -> b]|
+----------------+
```
## How was this patch tested?
Added tests in `CastSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20166 from maropu/SPARK-22973.
## What changes were proposed in this pull request?
This pr modified `elt` to output binary for binary inputs.
`elt` in the current master always output data as a string. But, in some databases (e.g., MySQL), if all inputs are binary, `elt` also outputs binary (Also, this might be a small surprise).
This pr is related to #19977.
## How was this patch tested?
Added tests in `SQLQueryTestSuite` and `TypeCoercionSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20135 from maropu/SPARK-22937.
## What changes were proposed in this pull request?
#19201 introduced the following regression: given something like `df.withColumn("c", lit(2))`, we're no longer picking up `c === 2` as a constraint and infer filters from it when joins are involved, which may lead to noticeable performance degradation.
This patch re-enables this optimization by picking up Aliases of Literals in Projection lists as constraints and making sure they're not treated as aliased columns.
## How was this patch tested?
Unit test was added.
Author: Adrian Ionescu <adrian@databricks.com>
Closes#20155 from adrian-ionescu/constant_constraints.
## What changes were proposed in this pull request?
This pr fixed the issue when casting arrays into strings;
```
scala> val df = spark.range(10).select('id.cast("integer")).agg(collect_list('id).as('ids))
scala> df.write.saveAsTable("t")
scala> sql("SELECT cast(ids as String) FROM t").show(false)
+------------------------------------------------------------------+
|ids |
+------------------------------------------------------------------+
|org.apache.spark.sql.catalyst.expressions.UnsafeArrayData8bc285df|
+------------------------------------------------------------------+
```
This pr modified the result into;
```
+------------------------------+
|ids |
+------------------------------+
|[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]|
+------------------------------+
```
## How was this patch tested?
Added tests in `CastSuite` and `SQLQuerySuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20024 from maropu/SPARK-22825.
## What changes were proposed in this pull request?
This pr is a follow-up to fix a bug left in #19977.
## How was this patch tested?
Added tests in `StringExpressionsSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20149 from maropu/SPARK-22771-FOLLOWUP.
## What changes were proposed in this pull request?
`FoldablePropagation` is a little tricky as it needs to handle attributes that are miss-derived from children, e.g. outer join outputs. This rule does a kind of stop-able tree transform, to skip to apply this rule when hit a node which may have miss-derived attributes.
Logically we should be able to apply this rule above the unsupported nodes, by just treating the unsupported nodes as leaf nodes. This PR improves this rule to not stop the tree transformation, but reduce the foldable expressions that we want to propagate.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#20139 from cloud-fan/foldable.
## What changes were proposed in this pull request?
Currently, we do not guarantee an order evaluation of conjuncts in either Filter or Join operator. This is also true to the mainstream RDBMS vendors like DB2 and MS SQL Server. Thus, we should also push down the deterministic predicates that are after the first non-deterministic, if possible.
## How was this patch tested?
Updated the existing test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20069 from gatorsmile/morePushDown.
## What changes were proposed in this pull request?
The `analyze` method in `implicit class DslLogicalPlan` already includes `EliminateSubqueryAliases`. So there's no need to call `EliminateSubqueryAliases` again after calling `analyze` in some test code.
## How was this patch tested?
Existing tests.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#20122 from wzhfy/redundant_code.
## What changes were proposed in this pull request?
This pr modified `concat` to concat binary inputs into a single binary output.
`concat` in the current master always output data as a string. But, in some databases (e.g., PostgreSQL), if all inputs are binary, `concat` also outputs binary.
## How was this patch tested?
Added tests in `SQLQueryTestSuite` and `TypeCoercionSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#19977 from maropu/SPARK-22771.
## What changes were proposed in this pull request?
The issue has been raised in two Jira tickets: [SPARK-21657](https://issues.apache.org/jira/browse/SPARK-21657), [SPARK-16998](https://issues.apache.org/jira/browse/SPARK-16998). Basically, what happens is that in collection generators like explode/inline we create many rows from each row. Currently each exploded row contains also the column on which it was created. This causes, for example, if we have a 10k array in one row that this array will get copy 10k times - to each of the row. this results a qudratic memory consumption. However, it is a common case that the original column gets projected out after the explode, so we can avoid duplicating it.
In this solution we propose to identify this situation in the optimizer and turn on a flag for omitting the original column in the generation process.
## How was this patch tested?
1. We added a benchmark test to MiscBenchmark that shows x16 improvement in runtimes.
2. We ran some of the other tests in MiscBenchmark and they show 15% improvements.
3. We ran this code on a specific case from our production data with rows containing arrays of size ~200k and it reduced the runtime from 6 hours to 3 mins.
Author: oraviv <oraviv@paypal.com>
Author: uzadude <ohad.raviv@gmail.com>
Author: uzadude <15645757+uzadude@users.noreply.github.com>
Closes#19683 from uzadude/optimize_explode.
## What changes were proposed in this pull request?
The PR introduces a new method `addImmutableStateIfNotExists ` to `CodeGenerator` to allow reusing and sharing the same global variable between different Expressions. This helps reducing the number of global variables needed, which is important to limit the impact on the constant pool.
## How was this patch tested?
added UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19940 from mgaido91/SPARK-22750.
## What changes were proposed in this pull request?
This PR is follow-on of #19518. This PR tries to reduce the number of constant pool entries used for accessing mutable state.
There are two directions:
1. Primitive type variables should be allocated at the outer class due to better performance. Otherwise, this PR allocates an array.
2. The length of allocated array is up to 32768 due to avoiding usage of constant pool entry at access (e.g. `mutableStateArray[32767]`).
Here are some discussions to determine these directions.
1. [[1]](https://github.com/apache/spark/pull/19518#issuecomment-346690464), [[2]](https://github.com/apache/spark/pull/19518#issuecomment-346690642), [[3]](https://github.com/apache/spark/pull/19518#issuecomment-346828180), [[4]](https://github.com/apache/spark/pull/19518#issuecomment-346831544), [[5]](https://github.com/apache/spark/pull/19518#issuecomment-346857340)
2. [[6]](https://github.com/apache/spark/pull/19518#issuecomment-346729172), [[7]](https://github.com/apache/spark/pull/19518#issuecomment-346798358), [[8]](https://github.com/apache/spark/pull/19518#issuecomment-346870408)
This PR modifies `addMutableState` function in the `CodeGenerator` to check if the declared state can be easily initialized compacted into an array. We identify three types of states that cannot compacted:
- Primitive type state (ints, booleans, etc) if the number of them does not exceed threshold
- Multiple-dimensional array type
- `inline = true`
When `useFreshName = false`, the given name is used.
Many codes were ported from #19518. Many efforts were put here. I think this PR should credit to bdrillard
With this PR, the following code is generated:
```
/* 005 */ class SpecificMutableProjection extends org.apache.spark.sql.catalyst.expressions.codegen.BaseMutableProjection {
/* 006 */
/* 007 */ private Object[] references;
/* 008 */ private InternalRow mutableRow;
/* 009 */ private boolean isNull_0;
/* 010 */ private boolean isNull_1;
/* 011 */ private boolean isNull_2;
/* 012 */ private int value_2;
/* 013 */ private boolean isNull_3;
...
/* 10006 */ private int value_4999;
/* 10007 */ private boolean isNull_5000;
/* 10008 */ private int value_5000;
/* 10009 */ private InternalRow[] mutableStateArray = new InternalRow[2];
/* 10010 */ private boolean[] mutableStateArray1 = new boolean[7001];
/* 10011 */ private int[] mutableStateArray2 = new int[1001];
/* 10012 */ private UTF8String[] mutableStateArray3 = new UTF8String[6000];
/* 10013 */
...
/* 107956 */ private void init_176() {
/* 107957 */ isNull_4986 = true;
/* 107958 */ value_4986 = -1;
...
/* 108004 */ }
...
```
## How was this patch tested?
Added a new test case to `GeneratedProjectionSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19811 from kiszk/SPARK-18016.
## What changes were proposed in this pull request?
Equi-height histogram is one of the state-of-the-art statistics for cardinality estimation, which can provide better estimation accuracy, and good at cases with skew data.
This PR is to improve join estimation based on equi-height histogram. The difference from basic estimation (based on ndv) is the logic for computing join cardinality and the new ndv after join.
The main idea is as follows:
1. find overlapped ranges between two histograms from two join keys;
2. apply the formula `T(A IJ B) = T(A) * T(B) / max(V(A.k1), V(B.k1))` in each overlapped range.
## How was this patch tested?
Added new test cases.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#19594 from wzhfy/join_estimation_histogram.
## What changes were proposed in this pull request?
We should not operate on `references` directly in `Expression.doGenCode`, instead we should use the high-level API `addReferenceObj`.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19962 from cloud-fan/codegen.
## What changes were proposed in this pull request?
The query execution/optimization does not guarantee the expressions are evaluated in order. We only can combine them if and only if both are deterministic. We need to update the optimizer rule: CombineFilters.
## How was this patch tested?
Updated the existing tests.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19947 from gatorsmile/combineFilters.
## What changes were proposed in this pull request?
SPARK-22543 fixes the 64kb compile error for deeply nested expression for non-wholestage codegen. This PR extends it to support wholestage codegen.
This patch brings some util methods in to extract necessary parameters for an expression if it is split to a function.
The util methods are put in object `ExpressionCodegen` under `codegen`. The main entry is `getExpressionInputParams` which returns all necessary parameters to evaluate the given expression in a split function.
This util methods can be used to split expressions too. This is a TODO item later.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19813 from viirya/reduce-expr-code-for-wholestage.
## What changes were proposed in this pull request?
We have two methods to reference an object `addReferenceMinorObj` and `addReferenceObj `. The latter creates a new global variable, which means new entries in the constant pool.
The PR unifies the two method in a single `addReferenceObj` which returns the code to access the object in the `references` array and doesn't add new mutable states.
## How was this patch tested?
added UTs.
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19916 from mgaido91/SPARK-22716.
## What changes were proposed in this pull request?
Histogram is effective in dealing with skewed distribution. After we generate histogram information for column statistics, we need to adjust filter estimation based on histogram data structure.
## How was this patch tested?
We revised all the unit test cases by including histogram data structure.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Ron Hu <ron.hu@huawei.com>
Closes#19783 from ron8hu/supportHistogram.
## What changes were proposed in this pull request?
Some objects functions are using global variables which are not needed. This can generate some unneeded entries in the constant pool.
The PR replaces the unneeded global variables with local variables.
## How was this patch tested?
added UTs
Author: Marco Gaido <mgaido@hortonworks.com>
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#19908 from mgaido91/SPARK-22696.
## What changes were proposed in this pull request?
GenerateSafeProjection is defining a mutable state for each struct, which is not needed. This is bad for the well known issues related to constant pool limits.
The PR replace the global variable with a local one.
## How was this patch tested?
added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#19914 from mgaido91/SPARK-22699.
## What changes were proposed in this pull request?
This PR accomplishes the following two items.
1. Reduce # of global variables from two to one for generated code of `Case` and `Coalesce` and remove global variables for generated code of `In`.
2. Make lifetime of global variable local within an operation
Item 1. reduces # of constant pool entries in a Java class. Item 2. ensures that an variable is not passed to arguments in a method split by `CodegenContext.splitExpressions()`, which is addressed by #19865.
## How was this patch tested?
Added new tests into `PredicateSuite`, `NullExpressionsSuite`, and `ConditionalExpressionSuite`.
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19901 from kiszk/SPARK-22705.
## What changes were proposed in this pull request?
CreateNamedStruct and InSet are using a global variable which is not needed. This can generate some unneeded entries in the constant pool.
The PR removes the unnecessary mutable states and makes them local variables.
## How was this patch tested?
added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19896 from mgaido91/SPARK-22693.
## What changes were proposed in this pull request?
ScalaUDF is using global variables which are not needed. This can generate some unneeded entries in the constant pool.
The PR replaces the unneeded global variables with local variables.
## How was this patch tested?
added UT
Author: Marco Gaido <mgaido@hortonworks.com>
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#19900 from mgaido91/SPARK-22695.
## What changes were proposed in this pull request?
This PR accomplishes the following two items.
1. Reduce # of global variables from two to one
2. Make lifetime of global variable local within an operation
Item 1. reduces # of constant pool entries in a Java class. Item 2. ensures that an variable is not passed to arguments in a method split by `CodegenContext.splitExpressions()`, which is addressed by #19865.
## How was this patch tested?
Added new test into `ArithmeticExpressionSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19899 from kiszk/SPARK-22704.
## What changes were proposed in this pull request?
The SQL `Analyzer` goes through a whole query plan even most part of it is analyzed. This increases the time spent on query analysis for long pipelines in ML, especially.
This patch adds a logical node called `AnalysisBarrier` that wraps an analyzed logical plan to prevent it from analysis again. The barrier is applied to the analyzed logical plan in `Dataset`. It won't change the output of wrapped logical plan and just acts as a wrapper to hide it from analyzer. New operations on the dataset will be put on the barrier, so only the new nodes created will be analyzed.
This analysis barrier will be removed at the end of analysis stage.
## How was this patch tested?
Added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19873 from viirya/SPARK-20392-reopen.
## What changes were proposed in this pull request?
As a simple example:
```
spark-sql> create table base (a int, b int) using parquet;
Time taken: 0.066 seconds
spark-sql> create table relInSubq ( x int, y int, z int) using parquet;
Time taken: 0.042 seconds
spark-sql> explain select a from base where a in (select x from relInSubq);
== Physical Plan ==
*Project [a#83]
+- *BroadcastHashJoin [a#83], [x#85], LeftSemi, BuildRight
:- *FileScan parquet default.base[a#83,b#84] Batched: true, Format: Parquet, Location: InMemoryFileIndex[hdfs://100.0.0.4:9000/wzh/base], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<a:int,b:int>
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint)))
+- *Project [x#85]
+- *FileScan parquet default.relinsubq[x#85] Batched: true, Format: Parquet, Location: InMemoryFileIndex[hdfs://100.0.0.4:9000/wzh/relinsubq], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<x:int>
```
We only need column `a` in table `base`, but all columns (`a`, `b`) are fetched.
The reason is that, in "Operator Optimizations" batch, `ColumnPruning` first produces a `Project` on table `base`, but then it's removed by `removeProjectBeforeFilter`. Because at that time, the predicate subquery is in filter form. Then, in "Rewrite Subquery" batch, `RewritePredicateSubquery` converts the subquery into a LeftSemi join, but this batch doesn't have the `ColumnPruning` rule. This results in reading all columns for the `base` table.
## How was this patch tested?
Added a new test case.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#19855 from wzhfy/column_pruning_subquery.
## What changes were proposed in this pull request?
It turns out that `HashExpression` can pass around some values via parameter when splitting codes into methods, to save some global variable slots.
This can also prevent a weird case that global variable appears in parameter list, which is discovered by https://github.com/apache/spark/pull/19865
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19878 from cloud-fan/minor.
## What changes were proposed in this pull request?
Repartitioning by empty set of expressions is currently possible, even though it is a case which is not handled properly. Indeed, in `HashExpression` there is a check to avoid to run it on an empty set, but this check is not performed while repartitioning.
Thus, the PR adds a check to avoid this wrong situation.
## How was this patch tested?
added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#19870 from mgaido91/SPARK-22665.
## What changes were proposed in this pull request?
#19696 replaced the deprecated usages for `Date` and `Waiter`, but a few methods were missed. The PR fixes the forgotten deprecated usages.
## How was this patch tested?
existing UTs
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19875 from mgaido91/SPARK-22473_FOLLOWUP.
## What changes were proposed in this pull request?
This PR introduces a way to explicitly range-partition a Dataset. So far, only round-robin and hash partitioning were possible via `df.repartition(...)`, but sometimes range partitioning might be desirable: e.g. when writing to disk, for better compression without the cost of global sort.
The current implementation piggybacks on the existing `RepartitionByExpression` `LogicalPlan` and simply adds the following logic: If its expressions are of type `SortOrder`, then it will do `RangePartitioning`; otherwise `HashPartitioning`. This was by far the least intrusive solution I could come up with.
## How was this patch tested?
Unit test for `RepartitionByExpression` changes, a test to ensure we're not changing the behavior of existing `.repartition()` and a few end-to-end tests in `DataFrameSuite`.
Author: Adrian Ionescu <adrian@databricks.com>
Closes#19828 from adrian-ionescu/repartitionByRange.
## What changes were proposed in this pull request?
This PR adds an optimization rule that infers join conditions using propagated constraints.
For instance, if there is a join, where the left relation has 'a = 1' and the right relation has 'b = 1', then the rule infers 'a = b' as a join predicate. Only semantically new predicates are appended to the existing join condition.
Refer to the corresponding ticket and tests for more details.
## How was this patch tested?
This patch comes with a new test suite to cover the implemented logic.
Author: aokolnychyi <anton.okolnychyi@sap.com>
Closes#18692 from aokolnychyi/spark-21417.
## What changes were proposed in this pull request?
This PR reduces # of global variables in generated code by replacing a global variable with a local variable with an allocation of an object every time. When a lot of global variables were generated, the generated code may meet 64K constant pool limit.
This PR reduces # of generated global variables in the following three operations:
* `Cast` with String to primitive byte/short/int/long
* `RegExpReplace`
* `CreateArray`
I intentionally leave [this part](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala#L595-L603). This is because this variable keeps a class that is dynamically generated. In other word, it is not possible to reuse one class.
## How was this patch tested?
Added test cases
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19797 from kiszk/SPARK-22570.
## What changes were proposed in this pull request?
Currently, in the optimize rule `PropagateEmptyRelation`, the following cases is not handled:
1. empty relation as right child in left outer join
2. empty relation as left child in right outer join
3. empty relation as right child in left semi join
4. empty relation as right child in left anti join
5. only one empty relation in full outer join
case 1 / 2 / 5 can be treated as **Cartesian product** and cause exception. See the new test cases.
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#19825 from gengliangwang/SPARK-22615.
## What changes were proposed in this pull request?
Code generation is disabled for CaseWhen when the number of branches is higher than `spark.sql.codegen.maxCaseBranches` (which defaults to 20). This was done to prevent the well known 64KB method limit exception.
This PR proposes to support code generation also in those cases (without causing exceptions of course). As a side effect, we could get rid of the `spark.sql.codegen.maxCaseBranches` configuration.
## How was this patch tested?
existing UTs
Author: Marco Gaido <mgaido@hortonworks.com>
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#19752 from mgaido91/SPARK-22520.
## What changes were proposed in this pull request?
This PR changes `FormatString` code generation to place generated code for expressions for arguments into separated methods if these size could be large.
This PR passes variable arguments by using an `Object` array.
## How was this patch tested?
Added new test cases into `StringExpressionSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19817 from kiszk/SPARK-22603.
## What changes were proposed in this pull request?
This PR reduces the number of fields in the test case of `CastSuite` to fix an issue that is pointed at [here](https://github.com/apache/spark/pull/19800#issuecomment-346634950).
```
java.lang.OutOfMemoryError: GC overhead limit exceeded
java.lang.OutOfMemoryError: GC overhead limit exceeded
at org.codehaus.janino.UnitCompiler.findClass(UnitCompiler.java:10971)
at org.codehaus.janino.UnitCompiler.findTypeByName(UnitCompiler.java:7607)
at org.codehaus.janino.UnitCompiler.getReferenceType(UnitCompiler.java:5758)
at org.codehaus.janino.UnitCompiler.getType2(UnitCompiler.java:5732)
at org.codehaus.janino.UnitCompiler.access$13200(UnitCompiler.java:206)
at org.codehaus.janino.UnitCompiler$18.visitReferenceType(UnitCompiler.java:5668)
at org.codehaus.janino.UnitCompiler$18.visitReferenceType(UnitCompiler.java:5660)
at org.codehaus.janino.Java$ReferenceType.accept(Java.java:3356)
at org.codehaus.janino.UnitCompiler.getType(UnitCompiler.java:5660)
at org.codehaus.janino.UnitCompiler.buildLocalVariableMap(UnitCompiler.java:2892)
at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:2764)
at org.codehaus.janino.UnitCompiler.compileDeclaredMethods(UnitCompiler.java:1262)
at org.codehaus.janino.UnitCompiler.compileDeclaredMethods(UnitCompiler.java:1234)
at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:538)
at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:890)
at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:894)
at org.codehaus.janino.UnitCompiler.access$600(UnitCompiler.java:206)
at org.codehaus.janino.UnitCompiler$2.visitMemberClassDeclaration(UnitCompiler.java:377)
at org.codehaus.janino.UnitCompiler$2.visitMemberClassDeclaration(UnitCompiler.java:369)
at org.codehaus.janino.Java$MemberClassDeclaration.accept(Java.java:1128)
at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:369)
at org.codehaus.janino.UnitCompiler.compileDeclaredMemberTypes(UnitCompiler.java:1209)
at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:564)
at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:890)
at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:894)
at org.codehaus.janino.UnitCompiler.access$600(UnitCompiler.java:206)
at org.codehaus.janino.UnitCompiler$2.visitMemberClassDeclaration(UnitCompiler.java:377)
at org.codehaus.janino.UnitCompiler$2.visitMemberClassDeclaration(UnitCompiler.java:369)
at org.codehaus.janino.Java$MemberClassDeclaration.accept(Java.java:1128)
at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:369)
at org.codehaus.janino.UnitCompiler.compileDeclaredMemberTypes(UnitCompiler.java:1209)
at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:564)
...
```
## How was this patch tested?
Used existing test case
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19806 from kiszk/SPARK-22595.
## What changes were proposed in this pull request?
When I played with codegen in developing another PR, I found the value of `CodegenContext.INPUT_ROW` is not reliable. Under wholestage codegen, it is assigned to null first and then suddenly changed to `i`.
The reason is `GenerateOrdering` changes `CodegenContext.INPUT_ROW` but doesn't restore it back.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19800 from viirya/SPARK-22591.
## What changes were proposed in this pull request?
A frequently reported issue of Spark is the Java 64kb compile error. This is because Spark generates a very big method and it's usually caused by 3 reasons:
1. a deep expression tree, e.g. a very complex filter condition
2. many individual expressions, e.g. expressions can have many children, operators can have many expressions.
3. a deep query plan tree (with whole stage codegen)
This PR focuses on 1. There are already several patches(#15620#18972#18641) trying to fix this issue and some of them are already merged. However this is an endless job as every non-leaf expression has this issue.
This PR proposes to fix this issue in `Expression.genCode`, to make sure the code for a single expression won't grow too big.
According to maropu 's benchmark, no regression is found with TPCDS (thanks maropu !): https://docs.google.com/spreadsheets/d/1K3_7lX05-ZgxDXi9X_GleNnDjcnJIfoSlSCDZcL4gdg/edit?usp=sharing
## How was this patch tested?
existing test
Author: Wenchen Fan <wenchen@databricks.com>
Author: Wenchen Fan <cloud0fan@gmail.com>
Closes#19767 from cloud-fan/codegen.
## What changes were proposed in this pull request?
This PR changes `cast` code generation to place generated code for expression for fields of a structure into separated methods if these size could be large.
## How was this patch tested?
Added new test cases into `CastSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19730 from kiszk/SPARK-22500.
## What changes were proposed in this pull request?
This PR changes `elt` code generation to place generated code for expression for arguments into separated methods if these size could be large.
This PR resolved the case of `elt` with a lot of argument
## How was this patch tested?
Added new test cases into `StringExpressionsSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19778 from kiszk/SPARK-22550.
## What changes were proposed in this pull request?
This PR changes `GenerateUnsafeRowJoiner.create()` code generation to place generated code for statements to operate bitmap and offset into separated methods if these size could be large.
## How was this patch tested?
Added a new test case into `GenerateUnsafeRowJoinerSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19737 from kiszk/SPARK-22508.
## What changes were proposed in this pull request?
This PR changes `concat_ws` code generation to place generated code for expression for arguments into separated methods if these size could be large.
This PR resolved the case of `concat_ws` with a lot of argument
## How was this patch tested?
Added new test cases into `StringExpressionsSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19777 from kiszk/SPARK-22549.
## What changes were proposed in this pull request?
This PR changes `concat` code generation to place generated code for expression for arguments into separated methods if these size could be large.
This PR resolved the case of `concat` with a lot of argument
## How was this patch tested?
Added new test cases into `StringExpressionsSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19728 from kiszk/SPARK-22498.
## What changes were proposed in this pull request?
This PR changes `In` code generation to place generated code for expression for expressions for arguments into separated methods if these size could be large.
## How was this patch tested?
Added new test cases into `PredicateSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19733 from kiszk/SPARK-22501.
## What changes were proposed in this pull request?
Both `Coalesce` and `AtLeastNNonNulls` can cause the 64KB limit exception when used with a lot of arguments and/or complex expressions.
This PR splits their expressions in order to avoid the issue.
## How was this patch tested?
Added UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19720 from mgaido91/SPARK-22494.
## What changes were proposed in this pull request?
This PR changes `least` and `greatest` code generation to place generated code for expression for arguments into separated methods if these size could be large.
This PR resolved two cases:
* `least` with a lot of argument
* `greatest` with a lot of argument
## How was this patch tested?
Added a new test case into `ArithmeticExpressionsSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19729 from kiszk/SPARK-22499.
## What changes were proposed in this pull request?
This fixes a problem caused by #15880
`select '1.5' > 0.5; // Result is NULL in Spark but is true in Hive.
`
When compare string and numeric, cast them as double like Hive.
Author: liutang123 <liutang123@yeah.net>
Closes#19692 from liutang123/SPARK-22469.
## What changes were proposed in this pull request?
This PR changes `AND` or `OR` code generation to place condition and then expressions' generated code into separated methods if these size could be large. When the method is newly generated, variables for `isNull` and `value` are declared as an instance variable to pass these values (e.g. `isNull1409` and `value1409`) to the callers of the generated method.
This PR resolved two cases:
* large code size of left expression
* large code size of right expression
## How was this patch tested?
Added a new test case into `CodeGenerationSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#18972 from kiszk/SPARK-21720.
## What changes were proposed in this pull request?
Because of the memory leak issue in `scala.reflect.api.Types.TypeApi.<:<` (https://github.com/scala/bug/issues/8302), creating an encoder may leak memory.
This PR adds `cleanUpReflectionObjects` to clean up these leaking objects for methods calling `scala.reflect.api.Types.TypeApi.<:<`.
## How was this patch tested?
The updated unit tests.
Author: Shixiong Zhu <zsxwing@gmail.com>
Closes#19687 from zsxwing/SPARK-19644.
## What changes were proposed in this pull request?
One powerful feature of `Dataset` is, we can easily map SQL rows to Scala/Java objects and do runtime null check automatically.
For example, let's say we have a parquet file with schema `<a: int, b: string>`, and we have a `case class Data(a: Int, b: String)`. Users can easily read this parquet file into `Data` objects, and Spark will throw NPE if column `a` has null values.
However the null checking is left behind for top-level primitive values. For example, let's say we have a parquet file with schema `<a: Int>`, and we read it into Scala `Int`. If column `a` has null values, we will get some weird results.
```
scala> val ds = spark.read.parquet(...).as[Int]
scala> ds.show()
+----+
|v |
+----+
|null|
|1 |
+----+
scala> ds.collect
res0: Array[Long] = Array(0, 1)
scala> ds.map(_ * 2).show
+-----+
|value|
+-----+
|-2 |
|2 |
+-----+
```
This is because internally Spark use some special default values for primitive types, but never expect users to see/operate these default value directly.
This PR adds null check for top-level primitive values
## How was this patch tested?
new test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19707 from cloud-fan/bug.
Continuation of PR#19528 (https://github.com/apache/spark/pull/19529#issuecomment-340252119)
The problem with the maven build in the previous PR was the new tests.... the creation of a spark session outside the tests meant there was more than one spark session around at a time.
I was using the spark session outside the tests so that the tests could share data; I've changed it so that each test creates the data anew.
Author: Nathan Kronenfeld <nicole.oresme@gmail.com>
Author: Nathan Kronenfeld <nkronenfeld@uncharted.software>
Closes#19705 from nkronenfeld/alternative-style-tests-2.
## What changes were proposed in this pull request?
We're building a data lineage tool in which we need to monitor the metadata changes in ExternalCatalog, current ExternalCatalog already provides several useful events like "CreateDatabaseEvent" for custom SparkListener to use. But still there's some event missing, like alter database event and alter table event. So here propose to and new ExternalCatalogEvent.
## How was this patch tested?
Enrich the current UT and tested on local cluster.
CC hvanhovell please let me know your comments about current proposal, thanks.
Author: jerryshao <sshao@hortonworks.com>
Closes#19649 from jerryshao/SPARK-22405.
## What changes were proposed in this pull request?
For a class with field name of special characters, e.g.:
```scala
case class MyType(`field.1`: String, `field 2`: String)
```
Although we can manipulate DataFrame/Dataset, the field names are encoded:
```scala
scala> val df = Seq(MyType("a", "b"), MyType("c", "d")).toDF
df: org.apache.spark.sql.DataFrame = [field$u002E1: string, field$u00202: string]
scala> df.as[MyType].collect
res7: Array[MyType] = Array(MyType(a,b), MyType(c,d))
```
It causes resolving problem when we try to convert the data with non-encoded field names:
```scala
spark.read.json(path).as[MyType]
...
[info] org.apache.spark.sql.AnalysisException: cannot resolve '`field$u002E1`' given input columns: [field 2, fie
ld.1];
[info] at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
...
```
We should use decoded field name in Dataset schema.
## How was this patch tested?
Added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19664 from viirya/SPARK-22442.
## What changes were proposed in this pull request?
Since SPARK-21939, Apache Spark uses `TimeLimits` instead of the deprecated `Timeouts`. This PR fixes the build warning `BufferHolderSparkSubmitSuite.scala` introduced at [SPARK-22222](https://github.com/apache/spark/pull/19460/files#diff-d8cf6e0c229969db94ec8ffc31a9239cR36) by removing the redundant `Timeouts`.
```scala
trait Timeouts in package concurrent is deprecated: Please use org.scalatest.concurrent.TimeLimits instead
[warn] with Timeouts {
```
## How was this patch tested?
N/A
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19697 from dongjoon-hyun/SPARK-22222.
## What changes were proposed in this pull request?
It's not safe in all cases to push down a LIMIT below a FULL OUTER
JOIN. If the limit is pushed to one side of the FOJ, the physical
join operator can not tell if a row in the non-limited side would have a
match in the other side.
*If* the join operator guarantees that unmatched tuples from the limited
side are emitted before any unmatched tuples from the other side,
pushing down the limit is safe. But this is impractical for some join
implementations, e.g. SortMergeJoin.
For now, disable limit pushdown through a FULL OUTER JOIN, and we can
evaluate whether a more complicated solution is necessary in the future.
## How was this patch tested?
Ran org.apache.spark.sql.* tests. Altered full outer join tests in
LimitPushdownSuite.
Author: Henry Robinson <henry@cloudera.com>
Closes#19647 from henryr/spark-22211.
forward-port https://github.com/apache/spark/pull/19622 to master branch.
This bug doesn't exist in master because we've added hive bucketing support and the hive bucketing metadata can be recognized by Spark, but we should still port it to master: 1) there may be other unsupported hive metadata removed by Spark. 2) reduce code difference between master and 2.2 to ease the backport in the feature.
***
When we alter table schema, we set the new schema to spark `CatalogTable`, convert it to hive table, and finally call `hive.alterTable`. This causes a problem in Spark 2.2, because hive bucketing metedata is not recognized by Spark, which means a Spark `CatalogTable` representing a hive table is always non-bucketed, and when we convert it to hive table and call `hive.alterTable`, the original hive bucketing metadata will be removed.
To fix this bug, we should read out the raw hive table metadata, update its schema, and call `hive.alterTable`. By doing this we can guarantee only the schema is changed, and nothing else.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19644 from cloud-fan/infer.
## What changes were proposed in this pull request?
Fix three deprecation warnings introduced by move to ANTLR 4.7:
* Use ParserRuleContext.addChild(TerminalNode) in preference to
deprecated ParserRuleContext.addChild(Token) interface.
* TokenStream.reset() is deprecated in favour of seek(0)
* Replace use of deprecated ANTLRInputStream with stream returned by
CharStreams.fromString()
The last item changed the way we construct ANTLR's input stream (from
direct instantiation to factory construction), so necessitated a change
to how we override the LA() method to always return an upper-case
char. The ANTLR object is now wrapped, rather than inherited-from.
* Also fix incorrect usage of CharStream.getText() which expects the rhs
of the supplied interval to be the last char to be returned, i.e. the
interval is inclusive, and work around bug in ANTLR 4.7 where empty
streams or intervals may cause getText() to throw an error.
## How was this patch tested?
Ran all the sql tests. Confirmed that LA() override has coverage by
breaking it, and noting that tests failed.
Author: Henry Robinson <henry@apache.org>
Closes#19578 from henryr/spark-21983.
## What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/17075 , to fix the bug in codegen path.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19576 from cloud-fan/bug.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-22333
In current version, users can use CURRENT_DATE() and CURRENT_TIMESTAMP() without specifying braces.
However, when a table has columns named as "current_date" or "current_timestamp", it will still be parsed as function call.
There are many such cases in our production cluster. We get the wrong answer due to this inappropriate behevior. In general, ColumnReference should get higher priority than timeFunctionCall.
## How was this patch tested?
unit test
manul test
Author: donnyzone <wellfengzhu@gmail.com>
Closes#19559 from DonnyZone/master.
## What changes were proposed in this pull request?
Adds a new optimisation rule 'ReplaceExceptWithNotFilter' that replaces Except logical with Filter operator and schedule it before applying 'ReplaceExceptWithAntiJoin' rule. This way we can avoid expensive join operation if one or both of the datasets of the Except operation are fully derived out of Filters from a same parent.
## How was this patch tested?
The patch is tested locally using spark-shell + unit test.
Author: Sathiya <sathiya.kumar@polytechnique.edu>
Closes#19451 from sathiyapk/SPARK-22181-optimize-exceptWithFilter.
## What changes were proposed in this pull request?
SPARK-18016 introduced `NestedClass` to avoid that the many methods generated by `splitExpressions` contribute to the outer class' constant pool, making it growing too much. Unfortunately, despite their definition is stored in the `NestedClass`, they all are invoked in the outer class and for each method invocation, there are two entries added to the constant pool: a `Methodref` and a `Utf8` entry (you can easily check this compiling a simple sample class with `janinoc` and looking at its Constant Pool). This limits the scalability of the solution with very large methods which are split in a lot of small ones. This means that currently we are generating classes like this one:
```
class SpecificUnsafeProjection extends org.apache.spark.sql.catalyst.expressions.UnsafeProjection {
...
public UnsafeRow apply(InternalRow i) {
rowWriter.zeroOutNullBytes();
apply_0(i);
apply_1(i);
...
nestedClassInstance.apply_862(i);
nestedClassInstance.apply_863(i);
...
nestedClassInstance1.apply_1612(i);
nestedClassInstance1.apply_1613(i);
...
}
...
private class NestedClass {
private void apply_862(InternalRow i) { ... }
private void apply_863(InternalRow i) { ... }
...
}
private class NestedClass1 {
private void apply_1612(InternalRow i) { ... }
private void apply_1613(InternalRow i) { ... }
...
}
}
```
This PR reduce the Constant Pool size of the outer class by adding a new method to each nested class: in this method we invoke all the small methods generated by `splitExpression` in that nested class. In this way, in the outer class there is only one method invocation per nested class, reducing by orders of magnitude the entries in its constant pool because of method invocations. This means that after the patch the generated code becomes:
```
class SpecificUnsafeProjection extends org.apache.spark.sql.catalyst.expressions.UnsafeProjection {
...
public UnsafeRow apply(InternalRow i) {
rowWriter.zeroOutNullBytes();
apply_0(i);
apply_1(i);
...
nestedClassInstance.apply(i);
nestedClassInstance1.apply(i);
...
}
...
private class NestedClass {
private void apply_862(InternalRow i) { ... }
private void apply_863(InternalRow i) { ... }
...
private void apply(InternalRow i) {
apply_862(i);
apply_863(i);
...
}
}
private class NestedClass1 {
private void apply_1612(InternalRow i) { ... }
private void apply_1613(InternalRow i) { ... }
...
private void apply(InternalRow i) {
apply_1612(i);
apply_1613(i);
...
}
}
}
```
## How was this patch tested?
Added UT and existing UTs
Author: Marco Gaido <mgaido@hortonworks.com>
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#19480 from mgaido91/SPARK-22226.
## What changes were proposed in this pull request?
Support unit tests of external code (i.e., applications that use spark) using scalatest that don't want to use FunSuite. SharedSparkContext already supports this, but SharedSQLContext does not.
I've introduced SharedSparkSession as a parent to SharedSQLContext, written in a way that it does support all scalatest styles.
## How was this patch tested?
There are three new unit test suites added that just test using FunSpec, FlatSpec, and WordSpec.
Author: Nathan Kronenfeld <nicole.oresme@gmail.com>
Closes#19529 from nkronenfeld/alternative-style-tests-2.
## What changes were proposed in this pull request?
Rewritten error message for clarity. Added extra information in case of attribute name collision, hinting the user to double-check referencing two different tables
## How was this patch tested?
No functional changes, only final message has changed. It has been tested manually against the situation proposed in the JIRA ticket. Automated tests in repository pass.
This PR is original work from me and I license this work to the Spark project
Author: Ruben Berenguel Montoro <ruben@mostlymaths.net>
Author: Ruben Berenguel Montoro <ruben@dreamattic.com>
Author: Ruben Berenguel <ruben@mostlymaths.net>
Closes#17100 from rberenguel/SPARK-13947-error-message.
## What changes were proposed in this pull request?
For performance reason, we should resolve in operation on an empty list as false in the optimizations phase, ad discussed in #19522.
## How was this patch tested?
Added UT
cc gatorsmile
Author: Marco Gaido <marcogaido91@gmail.com>
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19523 from mgaido91/SPARK-22301.
## What changes were proposed in this pull request?
The current implementation of `ApproxCountDistinctForIntervals` is `ImperativeAggregate`. The number of `aggBufferAttributes` is the number of total words in the hllppHelper array. Each hllppHelper has 52 words by default relativeSD.
Since this aggregate function is used in equi-height histogram generation, and the number of buckets in histogram is usually hundreds, the number of `aggBufferAttributes` can easily reach tens of thousands or even more.
This leads to a huge method in codegen and causes error:
```
org.codehaus.janino.JaninoRuntimeException: Code of method "apply(Lorg/apache/spark/sql/catalyst/InternalRow;)Lorg/apache/spark/sql/catalyst/expressions/UnsafeRow;" of class "org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection" grows beyond 64 KB.
```
Besides, huge generated methods also result in performance regression.
In this PR, we change its implementation to `TypedImperativeAggregate`. After the fix, `ApproxCountDistinctForIntervals` can deal with more than thousands endpoints without throwing codegen error, and improve performance from `20 sec` to `2 sec` in a test case of 500 endpoints.
## How was this patch tested?
Test by an added test case and existing tests.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#19506 from wzhfy/change_forIntervals_typedAgg.
## What changes were proposed in this pull request?
This is an effort to reduce the difference between Hive and Spark. Spark supports case-sensitivity in columns. Especially, for Struct types, with `spark.sql.caseSensitive=true`, the following is supported.
```scala
scala> sql("select named_struct('a', 1, 'A', 2).a").show
+--------------------------+
|named_struct(a, 1, A, 2).a|
+--------------------------+
| 1|
+--------------------------+
scala> sql("select named_struct('a', 1, 'A', 2).A").show
+--------------------------+
|named_struct(a, 1, A, 2).A|
+--------------------------+
| 2|
+--------------------------+
```
And vice versa, with `spark.sql.caseSensitive=false`, the following is supported.
```scala
scala> sql("select named_struct('a', 1).A, named_struct('A', 1).a").show
+--------------------+--------------------+
|named_struct(a, 1).A|named_struct(A, 1).a|
+--------------------+--------------------+
| 1| 1|
+--------------------+--------------------+
```
However, types are considered different. For example, SET operations fail.
```scala
scala> sql("SELECT named_struct('a',1) union all (select named_struct('A',2))").show
org.apache.spark.sql.AnalysisException: Union can only be performed on tables with the compatible column types. struct<A:int> <> struct<a:int> at the first column of the second table;;
'Union
:- Project [named_struct(a, 1) AS named_struct(a, 1)#57]
: +- OneRowRelation$
+- Project [named_struct(A, 2) AS named_struct(A, 2)#58]
+- OneRowRelation$
```
This PR aims to support case-insensitive type equality. For example, in Set operation, the above operation succeed when `spark.sql.caseSensitive=false`.
```scala
scala> sql("SELECT named_struct('a',1) union all (select named_struct('A',2))").show
+------------------+
|named_struct(a, 1)|
+------------------+
| [1]|
| [2]|
+------------------+
```
## How was this patch tested?
Pass the Jenkins with a newly add test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#18460 from dongjoon-hyun/SPARK-21247.
## What changes were proposed in this pull request?
For non-deterministic expressions, they should be considered as not contained in the [[ExpressionSet]].
This is consistent with how we define `semanticEquals` between two expressions.
Otherwise, combining expressions will remove non-deterministic expressions which should be reserved.
E.g.
Combine filters of
```scala
testRelation.where(Rand(0) > 0.1).where(Rand(0) > 0.1)
```
should result in
```scala
testRelation.where(Rand(0) > 0.1 && Rand(0) > 0.1)
```
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#19475 from gengliangwang/non-deterministic-expressionSet.
## What changes were proposed in this pull request?
Currently percentile_approx never returns the first element when percentile is in (relativeError, 1/N], where relativeError default 1/10000, and N is the total number of elements. But ideally, percentiles in [0, 1/N] should all return the first element as the answer.
For example, given input data 1 to 10, if a user queries 10% (or even less) percentile, it should return 1, because the first value 1 already reaches 10%. Currently it returns 2.
Based on the paper, targetError is not rounded up, and searching index should start from 0 instead of 1. By following the paper, we should be able to fix the cases mentioned above.
## How was this patch tested?
Added a new test case and fix existing test cases.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#19438 from wzhfy/improve_percentile_approx.
## What changes were proposed in this pull request?
Current `CodeGeneraor.splitExpressions` splits statements into methods if the total length of statements is more than 1024 characters. The length may include comments or empty line.
This PR excludes comment or empty line from the length to reduce the number of generated methods in a class, by using `CodeFormatter.stripExtraNewLinesAndComments()` method.
## How was this patch tested?
Existing tests
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#18966 from kiszk/SPARK-21751.
## What changes were proposed in this pull request?
We should not break the assumption that the length of the allocated byte array is word rounded:
https://github.com/apache/spark/blob/master/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java#L170
So we want to use `Integer.MAX_VALUE - 15` instead of `Integer.MAX_VALUE - 8` as the upper bound of an allocated byte array.
cc: srowen gatorsmile
## How was this patch tested?
Since the Spark unit test JVM has less than 1GB heap, here we run the test code as a submit job, so it can run on a JVM has 4GB memory.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Feng Liu <fengliu@databricks.com>
Closes#19460 from liufengdb/fix_array_max.
## What changes were proposed in this pull request?
`monotonically_increasing_id` doesn't work in Structured Streaming. We should throw an exception if a streaming query uses it.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19336 from viirya/SPARK-21947.
## What changes were proposed in this pull request?
By definition the table name in Spark can be something like `123x`, `25a`, etc., with exceptions for literals like `12L`, `23BD`, etc. However, Spark SQL has a special byte length literal, which stops users to use digits followed by `b`, `k`, `m`, `g` as identifiers.
byte length literal is not a standard sql literal and is only used in the `tableSample` parser rule. This PR move the parsing of byte length literal from lexer to parser, so that users can use it as identifiers.
## How was this patch tested?
regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19392 from cloud-fan/parser-bug.
## What changes were proposed in this pull request?
This pr added code to check actual bytecode size when compiling generated code. In #18810, we added code to give up code compilation and use interpreter execution in `SparkPlan` if the line number of generated functions goes over `maxLinesPerFunction`. But, we already have code to collect metrics for compiled bytecode size in `CodeGenerator` object. So,we could easily reuse the code for this purpose.
## How was this patch tested?
Added tests in `WholeStageCodegenSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#19083 from maropu/SPARK-21871.
## What changes were proposed in this pull request?
Allow one-sided outer joins between two streams when a watermark is defined.
## How was this patch tested?
new unit tests
Author: Jose Torres <jose@databricks.com>
Closes#19327 from joseph-torres/outerjoin.
## What changes were proposed in this pull request?
Users could hit `java.lang.NullPointerException` when the tables were created by Hive and the table's owner is `null` that are got from Hive metastore. `DESC EXTENDED` failed with the error:
> SQLExecutionException: java.lang.NullPointerException at scala.collection.immutable.StringOps$.length$extension(StringOps.scala:47) at scala.collection.immutable.StringOps.length(StringOps.scala:47) at scala.collection.IndexedSeqOptimized$class.isEmpty(IndexedSeqOptimized.scala:27) at scala.collection.immutable.StringOps.isEmpty(StringOps.scala:29) at scala.collection.TraversableOnce$class.nonEmpty(TraversableOnce.scala:111) at scala.collection.immutable.StringOps.nonEmpty(StringOps.scala:29) at org.apache.spark.sql.catalyst.catalog.CatalogTable.toLinkedHashMap(interface.scala:300) at org.apache.spark.sql.execution.command.DescribeTableCommand.describeFormattedTableInfo(tables.scala:565) at org.apache.spark.sql.execution.command.DescribeTableCommand.run(tables.scala:543) at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:66) at
## How was this patch tested?
Added a unit test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19395 from gatorsmile/desc.
## What changes were proposed in this pull request?
The `percentile_approx` function previously accepted numeric type input and output double type results.
But since all numeric types, date and timestamp types are represented as numerics internally, `percentile_approx` can support them easily.
After this PR, it supports date type, timestamp type and numeric types as input types. The result type is also changed to be the same as the input type, which is more reasonable for percentiles.
This change is also required when we generate equi-height histograms for these types.
## How was this patch tested?
Added a new test and modified some existing tests.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#19321 from wzhfy/approx_percentile_support_types.
## What changes were proposed in this pull request?
#### Architecture
This PR implements stream-stream inner join using a two-way symmetric hash join. At a high level, we want to do the following.
1. For each stream, we maintain the past rows as state in State Store.
- For each joining key, there can be multiple rows that have been received.
- So, we have to effectively maintain a key-to-list-of-values multimap as state for each stream.
2. In each batch, for each input row in each stream
- Look up the other streams state to see if there are matching rows, and output them if they satisfy the joining condition
- Add the input row to corresponding stream’s state.
- If the data has a timestamp/window column with watermark, then we will use that to calculate the threshold for keys that are required to buffered for future matches and drop the rest from the state.
Cleaning up old unnecessary state rows depends completely on whether watermark has been defined and what are join conditions. We definitely want to support state clean up two types of queries that are likely to be common.
- Queries to time range conditions - E.g. `SELECT * FROM leftTable, rightTable ON leftKey = rightKey AND leftTime > rightTime - INTERVAL 8 MINUTES AND leftTime < rightTime + INTERVAL 1 HOUR`
- Queries with windows as the matching key - E.g. `SELECT * FROM leftTable, rightTable ON leftKey = rightKey AND window(leftTime, "1 hour") = window(rightTime, "1 hour")` (pseudo-SQL)
#### Implementation
The stream-stream join is primarily implemented in three classes
- `StreamingSymmetricHashJoinExec` implements the above symmetric join algorithm.
- `SymmetricsHashJoinStateManagers` manages the streaming state for the join. This essentially is a fault-tolerant key-to-list-of-values multimap built on the StateStore APIs. `StreamingSymmetricHashJoinExec` instantiates two such managers, one for each join side.
- `StreamingSymmetricHashJoinExecHelper` is a helper class to extract threshold for the state based on the join conditions and the event watermark.
Refer to the scaladocs class for more implementation details.
Besides the implementation of stream-stream inner join SparkPlan. Some additional changes are
- Allowed inner join in append mode in UnsupportedOperationChecker
- Prevented stream-stream join on an empty batch dataframe to be collapsed by the optimizer
## How was this patch tested?
- New tests in StreamingJoinSuite
- Updated tests UnsupportedOperationSuite
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#19271 from tdas/SPARK-22053.
## What changes were proposed in this pull request?
This work is a part of [SPARK-17074](https://issues.apache.org/jira/browse/SPARK-17074) to compute equi-height histograms. Equi-height histogram is an array of bins. A bin consists of two endpoints which form an interval of values and the ndv in that interval.
This PR creates a new aggregate function, given an array of endpoints, counting distinct values (ndv) in intervals among those endpoints.
This PR also refactors `HyperLogLogPlusPlus` by extracting a helper class `HyperLogLogPlusPlusHelper`, where the underlying HLLPP algorithm locates.
## How was this patch tested?
Add new test cases.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#15544 from wzhfy/countIntervals.
#### What changes were proposed in this pull request?
This PR enhances the TRIM function support in Spark SQL by allowing the specification
of trim characters set. Below is the SQL syntax :
``` SQL
<trim function> ::= TRIM <left paren> <trim operands> <right paren>
<trim operands> ::= [ [ <trim specification> ] [ <trim character set> ] FROM ] <trim source>
<trim source> ::= <character value expression>
<trim specification> ::=
LEADING
| TRAILING
| BOTH
<trim character set> ::= <characters value expression>
```
or
``` SQL
LTRIM (source-exp [, trim-exp])
RTRIM (source-exp [, trim-exp])
```
Here are the documentation link of support of this feature by other mainstream databases.
- **Oracle:** [TRIM function](http://docs.oracle.com/cd/B28359_01/olap.111/b28126/dml_functions_2126.htm#OLADM704)
- **DB2:** [TRIM scalar function](https://www.ibm.com/support/knowledgecenter/en/SSMKHH_10.0.0/com.ibm.etools.mft.doc/ak05270_.htm)
- **MySQL:** [Trim function](http://dev.mysql.com/doc/refman/5.7/en/string-functions.html#function_trim)
- **Oracle:** [ltrim](https://docs.oracle.com/cd/B28359_01/olap.111/b28126/dml_functions_2018.htm#OLADM594)
- **DB2:** [ltrim](https://www.ibm.com/support/knowledgecenter/en/SSEPEK_11.0.0/sqlref/src/tpc/db2z_bif_ltrim.html)
This PR is to implement the above enhancement. In the implementation, the design principle is to keep the changes to the minimum. Also, the exiting trim functions (which handles a special case, i.e., trimming space characters) are kept unchanged for performane reasons.
#### How was this patch tested?
The unit test cases are added in the following files:
- UTF8StringSuite.java
- StringExpressionsSuite.scala
- sql/SQLQuerySuite.scala
- StringFunctionsSuite.scala
Author: Kevin Yu <qyu@us.ibm.com>
Closes#12646 from kevinyu98/spark-14878.
## What changes were proposed in this pull request?
If there are two projects like as follows.
```
Project [a_with_metadata#27 AS b#26]
+- Project [a#0 AS a_with_metadata#27]
+- LocalRelation <empty>, [a#0, b#1]
```
Child Project has an output column with a metadata in it, and the parent Project has an alias that implicitly forwards the metadata. So this metadata is visible for higher operators. Upon applying CollapseProject optimizer rule, the metadata is not preserved.
```
Project [a#0 AS b#26]
+- LocalRelation <empty>, [a#0, b#1]
```
This is incorrect, as downstream operators that expect certain metadata (e.g. watermark in structured streaming) to identify certain fields will fail to do so. This PR fixes it by preserving the metadata of top-level aliases.
## How was this patch tested?
New unit test
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#19240 from tdas/SPARK-22018.
# What changes were proposed in this pull request?
UDF to_json only supports converting `StructType` or `ArrayType` of `StructType`s to a json output string now.
According to the discussion of JIRA SPARK-21513, I allow to `to_json` support converting `MapType` and `ArrayType` of `MapType`s to a json output string.
This PR is for SQL and Scala API only.
# How was this patch tested?
Adding unit test case.
cc viirya HyukjinKwon
Author: goldmedal <liugs963@gmail.com>
Author: Jia-Xuan Liu <liugs963@gmail.com>
Closes#18875 from goldmedal/SPARK-21513.
## What changes were proposed in this pull request?
Improve QueryPlanConstraints framework, make it robust and simple.
In https://github.com/apache/spark/pull/15319, constraints for expressions like `a = f(b, c)` is resolved.
However, for expressions like
```scala
a = f(b, c) && c = g(a, b)
```
The current QueryPlanConstraints framework will produce non-converging constraints.
Essentially, the problem is caused by having both the name and child of aliases in the same constraint set. We infer constraints, and push down constraints as predicates in filters, later on these predicates are propagated as constraints, etc..
Simply using the alias names only can resolve these problems. The size of constraints is reduced without losing any information. We can always get these inferred constraints on child of aliases when pushing down filters.
Also, the EqualNullSafe between name and child in propagating alias is meaningless
```scala
allConstraints += EqualNullSafe(e, a.toAttribute)
```
It just produces redundant constraints.
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#19201 from gengliangwang/QueryPlanConstraints.
## What changes were proposed in this pull request?
`JacksonUtils.verifySchema` verifies if a data type can be converted to JSON. For `MapType`, it now verifies the key type. However, in `JacksonGenerator`, when converting a map to JSON, we only care about its values and create a writer for the values. The keys in a map are treated as strings by calling `toString` on the keys.
Thus, we should change `JacksonUtils.verifySchema` to verify the value type of `MapType`.
## How was this patch tested?
Added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19167 from viirya/test-jacksonutils.
## What changes were proposed in this pull request?
We have many optimization rules now in `Optimzer`. Right now we don't have any checks in the optimizer to check for the structural integrity of the plan (e.g. resolved). When debugging, it is difficult to identify which rules return invalid plans.
It would be great if in test mode, we can check whether a plan is still resolved after the execution of each rule, so we can catch rules that return invalid plans.
## How was this patch tested?
Added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18956 from viirya/SPARK-21726.
## What changes were proposed in this pull request?
Add an assert in logical plan optimization that the isStreaming bit stays the same, and fix empty relation rules where that wasn't happening.
## How was this patch tested?
new and existing unit tests
Author: Jose Torres <joseph.torres@databricks.com>
Author: Jose Torres <joseph-torres@databricks.com>
Closes#19056 from joseph-torres/SPARK-21765-followup.
## What changes were proposed in this pull request?
SQL predicates don't have complete expression description. This patch goes to complement the description by adding arguments, examples.
This change also adds related test cases for the SQL predicate expressions.
## How was this patch tested?
Existing tests. And added predicate test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18869 from viirya/SPARK-21654.
…build; fix some things that will be warnings or errors in 2.12; restore Scala 2.12 profile infrastructure
## What changes were proposed in this pull request?
This change adds back the infrastructure for a Scala 2.12 build, but does not enable it in the release or Python test scripts.
In order to make that meaningful, it also resolves compile errors that the code hits in 2.12 only, in a way that still works with 2.11.
It also updates dependencies to the earliest minor release of dependencies whose current version does not yet support Scala 2.12. This is in a sense covered by other JIRAs under the main umbrella, but implemented here. The versions below still work with 2.11, and are the _latest_ maintenance release in the _earliest_ viable minor release.
- Scalatest 2.x -> 3.0.3
- Chill 0.8.0 -> 0.8.4
- Clapper 1.0.x -> 1.1.2
- json4s 3.2.x -> 3.4.2
- Jackson 2.6.x -> 2.7.9 (required by json4s)
This change does _not_ fully enable a Scala 2.12 build:
- It will also require dropping support for Kafka before 0.10. Easy enough, just didn't do it yet here
- It will require recreating `SparkILoop` and `Main` for REPL 2.12, which is SPARK-14650. Possible to do here too.
What it does do is make changes that resolve much of the remaining gap without affecting the current 2.11 build.
## How was this patch tested?
Existing tests and build. Manually tested with `./dev/change-scala-version.sh 2.12` to verify it compiles, modulo the exceptions above.
Author: Sean Owen <sowen@cloudera.com>
Closes#18645 from srowen/SPARK-14280.
## What changes were proposed in this pull request?
Allows `BinaryComparison` operators to work on any data type that actually supports ordering as verified by `TypeUtils.checkForOrderingExpr` instead of relying on the incomplete list `TypeCollection.Ordered` (which is removed by this PR).
## How was this patch tested?
Updated unit tests to cover structs and arrays.
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#18818 from aray/SPARK-21110.
## What changes were proposed in this pull request?
This PR bumps the ANTLR version to 4.7, and fixes a number of small parser related issues uncovered by the bump.
The main reason for upgrading is that in some cases the current version of ANTLR (4.5) can exhibit exponential slowdowns if it needs to parse boolean predicates. For example the following query will take forever to parse:
```sql
SELECT *
FROM RANGE(1000)
WHERE
TRUE
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
AND NOT upper(DESCRIPTION) LIKE '%FOO%'
```
This is caused by a know bug in ANTLR (https://github.com/antlr/antlr4/issues/994), which was fixed in version 4.6.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#19042 from hvanhovell/SPARK-21830.
## What changes were proposed in this pull request?
With the check for structural integrity proposed in SPARK-21726, it is found that the optimization rule `PullupCorrelatedPredicates` can produce unresolved plans.
For a correlated IN query looks like:
SELECT t1.a FROM t1
WHERE
t1.a IN (SELECT t2.c
FROM t2
WHERE t1.b < t2.d);
The query plan might look like:
Project [a#0]
+- Filter a#0 IN (list#4 [b#1])
: +- Project [c#2]
: +- Filter (outer(b#1) < d#3)
: +- LocalRelation <empty>, [c#2, d#3]
+- LocalRelation <empty>, [a#0, b#1]
After `PullupCorrelatedPredicates`, it produces query plan like:
'Project [a#0]
+- 'Filter a#0 IN (list#4 [(b#1 < d#3)])
: +- Project [c#2, d#3]
: +- LocalRelation <empty>, [c#2, d#3]
+- LocalRelation <empty>, [a#0, b#1]
Because the correlated predicate involves another attribute `d#3` in subquery, it has been pulled out and added into the `Project` on the top of the subquery.
When `list` in `In` contains just one `ListQuery`, `In.checkInputDataTypes` checks if the size of `value` expressions matches the output size of subquery. In the above example, there is only `value` expression and the subquery output has two attributes `c#2, d#3`, so it fails the check and `In.resolved` returns `false`.
We should not let `In.checkInputDataTypes` wrongly report unresolved plans to fail the structural integrity check.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18968 from viirya/SPARK-21759.
## What changes were proposed in this pull request?
When json_tuple in extracting values from JSON it returns null values within repeated columns except the first one as below:
``` scala
scala> spark.sql("""SELECT json_tuple('{"a":1, "b":2}', 'a', 'b', 'a')""").show()
+---+---+----+
| c0| c1| c2|
+---+---+----+
| 1| 2|null|
+---+---+----+
```
I think this should be consistent with Hive's implementation:
```
hive> SELECT json_tuple('{"a": 1, "b": 2}', 'a', 'a');
...
1 1
```
In this PR, we located all the matched indices in `fieldNames` instead of returning the first matched index, i.e., indexOf.
## How was this patch tested?
Added test in JsonExpressionsSuite.
Author: Jen-Ming Chung <jenmingisme@gmail.com>
Closes#19017 from jmchung/SPARK-21804.
## What changes were proposed in this pull request?
The getAliasedConstraints fuction in LogicalPlan.scala will clone the expression set when an element added,
and it will take a long time. This PR add a function to add multiple elements at once to reduce the clone time.
Before modified, the cost of getAliasedConstraints is:
100 expressions: 41 seconds
150 expressions: 466 seconds
After modified, the cost of getAliasedConstraints is:
100 expressions: 1.8 seconds
150 expressions: 6.5 seconds
The test is like this:
test("getAliasedConstraints") {
val expressionNum = 150
val aggExpression = (1 to expressionNum).map(i => Alias(Count(Literal(1)), s"cnt$i")())
val aggPlan = Aggregate(Nil, aggExpression, LocalRelation())
val beginTime = System.currentTimeMillis()
val expressions = aggPlan.validConstraints
println(s"validConstraints cost: ${System.currentTimeMillis() - beginTime}ms")
// The size of Aliased expression is n * (n - 1) / 2 + n
assert( expressions.size === expressionNum * (expressionNum - 1) / 2 + expressionNum)
}
(Please fill in changes proposed in this fix)
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Run new added test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: 10129659 <chen.yanshan@zte.com.cn>
Closes#19022 from eatoncys/getAliasedConstraints.
## What changes were proposed in this pull request?
All streaming logical plans will now have isStreaming set. This involved adding isStreaming as a case class arg in a few cases, since a node might be logically streaming depending on where it came from.
## How was this patch tested?
Existing unit tests - no functional change is intended in this PR.
Author: Jose Torres <joseph-torres@databricks.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#18973 from joseph-torres/SPARK-21765.
## What changes were proposed in this pull request?
``` scala
scala> Seq(("""{"Hyukjin": 224, "John": 1225}""")).toDS.selectExpr("json_tuple(value, trim(null))").show()
...
java.lang.NullPointerException
at ...
```
Currently the `null` field name will throw NullPointException. As a given field name null can't be matched with any field names in json, we just output null as its column value. This PR achieves it by returning a very unlikely column name `__NullFieldName` in evaluation of the field names.
## How was this patch tested?
Added unit test.
Author: Jen-Ming Chung <jenmingisme@gmail.com>
Closes#18930 from jmchung/SPARK-21677.
## What changes were proposed in this pull request?
This pr sorted output attributes on their name and exprId in `AttributeSet.toSeq` to make the order consistent. If the order is different, spark possibly generates different code and then misses cache in `CodeGenerator`, e.g., `GenerateColumnAccessor` generates code depending on an input attribute order.
## How was this patch tested?
Added tests in `AttributeSetSuite` and manually checked if the cache worked well in the given query of the JIRA.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18959 from maropu/SPARK-18394.
## What changes were proposed in this pull request?
Close the whole stage codegen when the function lines is longer than the maxlines which will be setted by
spark.sql.codegen.MaxFunctionLength parameter, because when the function is too long , it will not get the JIT optimizing.
A benchmark test result is 10x slower when the generated function is too long :
ignore("max function length of wholestagecodegen") {
val N = 20 << 15
val benchmark = new Benchmark("max function length of wholestagecodegen", N)
def f(): Unit = sparkSession.range(N)
.selectExpr(
"id",
"(id & 1023) as k1",
"cast(id & 1023 as double) as k2",
"cast(id & 1023 as int) as k3",
"case when id > 100 and id <= 200 then 1 else 0 end as v1",
"case when id > 200 and id <= 300 then 1 else 0 end as v2",
"case when id > 300 and id <= 400 then 1 else 0 end as v3",
"case when id > 400 and id <= 500 then 1 else 0 end as v4",
"case when id > 500 and id <= 600 then 1 else 0 end as v5",
"case when id > 600 and id <= 700 then 1 else 0 end as v6",
"case when id > 700 and id <= 800 then 1 else 0 end as v7",
"case when id > 800 and id <= 900 then 1 else 0 end as v8",
"case when id > 900 and id <= 1000 then 1 else 0 end as v9",
"case when id > 1000 and id <= 1100 then 1 else 0 end as v10",
"case when id > 1100 and id <= 1200 then 1 else 0 end as v11",
"case when id > 1200 and id <= 1300 then 1 else 0 end as v12",
"case when id > 1300 and id <= 1400 then 1 else 0 end as v13",
"case when id > 1400 and id <= 1500 then 1 else 0 end as v14",
"case when id > 1500 and id <= 1600 then 1 else 0 end as v15",
"case when id > 1600 and id <= 1700 then 1 else 0 end as v16",
"case when id > 1700 and id <= 1800 then 1 else 0 end as v17",
"case when id > 1800 and id <= 1900 then 1 else 0 end as v18")
.groupBy("k1", "k2", "k3")
.sum()
.collect()
benchmark.addCase(s"codegen = F") { iter =>
sparkSession.conf.set("spark.sql.codegen.wholeStage", "false")
f()
}
benchmark.addCase(s"codegen = T") { iter =>
sparkSession.conf.set("spark.sql.codegen.wholeStage", "true")
sparkSession.conf.set("spark.sql.codegen.MaxFunctionLength", "10000")
f()
}
benchmark.run()
/*
Java HotSpot(TM) 64-Bit Server VM 1.8.0_111-b14 on Windows 7 6.1
Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
max function length of wholestagecodegen: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
codegen = F 443 / 507 1.5 676.0 1.0X
codegen = T 3279 / 3283 0.2 5002.6 0.1X
*/
}
## How was this patch tested?
Run the unit test
Author: 10129659 <chen.yanshan@zte.com.cn>
Closes#18810 from eatoncys/codegen.
This version fixes a few issues in the import order checker; it provides
better error messages, and detects more improper ordering (thus the need
to change a lot of files in this patch). The main fix is that it correctly
complains about the order of packages vs. classes.
As part of the above, I moved some "SparkSession" import in ML examples
inside the "$example on$" blocks; that didn't seem consistent across
different source files to start with, and avoids having to add more on/off blocks
around specific imports.
The new scalastyle also seems to have a better header detector, so a few
license headers had to be updated to match the expected indentation.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18943 from vanzin/SPARK-21731.
## What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/15900 , to fix one more bug:
When table schema is empty and need to be inferred at runtime, we should not resolve parent plans before the schema has been inferred, or the parent plans will be resolved against an empty schema and may get wrong result for something like `select *`
The fix logic is: introduce `UnresolvedCatalogRelation` as a placeholder. Then we replace it with `LogicalRelation` or `HiveTableRelation` during analysis, so that it's guaranteed that we won't resolve parent plans until the schema has been inferred.
## How was this patch tested?
regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18907 from cloud-fan/bug.
## What changes were proposed in this pull request?
While discovering optimization rules and their test coverage, I did not find any tests for `CheckCartesianProducts` in the Catalyst folder. So, I decided to create a new test suite. Once I finished, I found a test in `JoinSuite` for this functionality so feel free to discard this change if it does not make much sense. The proposed test suite covers a few additional use cases.
Author: aokolnychyi <anton.okolnychyi@sap.com>
Closes#18909 from aokolnychyi/check-cartesian-join-tests.
## What changes were proposed in this pull request?
This patch removes the unused SessionCatalog.getTableMetadataOption and ExternalCatalog. getTableOption.
## How was this patch tested?
Removed the test case.
Author: Reynold Xin <rxin@databricks.com>
Closes#18912 from rxin/remove-getTableOption.
## What changes were proposed in this pull request?
Push filter predicates through EventTimeWatermark if they're deterministic and do not reference the watermarked attribute. (This is similar but not identical to the logic for pushing through UnaryNode.)
## How was this patch tested?
unit tests
Author: Jose Torres <joseph-torres@databricks.com>
Closes#18790 from joseph-torres/SPARK-21587.
## What changes were proposed in this pull request?
This PR is to add the spark version info in the table metadata. When creating the table, this value is assigned. It can help users find which version of Spark was used to create the table.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18709 from gatorsmile/addVersion.
I have discovered that "full_outer" name option is working in Spark 2.0, but it is not printed in exception. Please verify.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: BartekH <bartekhamielec@gmail.com>
Closes#17985 from BartekH/patch-1.
## What changes were proposed in this pull request?
This pr (follow-up of #18772) used `UnresolvedSubqueryColumnAliases` for `visitTableName` in `AstBuilder`, which is a new unresolved `LogicalPlan` implemented in #18185.
## How was this patch tested?
Existing tests
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18857 from maropu/SPARK-20963-FOLLOWUP.
## What changes were proposed in this pull request?
This pr added parsing rules to support column aliases for join relations in FROM clause.
This pr is a sub-task of #18079.
## How was this patch tested?
Added tests in `AnalysisSuite`, `PlanParserSuite,` and `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18772 from maropu/SPARK-20963-2.
## What changes were proposed in this pull request?
OneRowRelation is the only plan that is a case object, which causes some issues with makeCopy using a 0-arg constructor. This patch changes it from a case object to a case class.
This blocks SPARK-21619.
## How was this patch tested?
Should be covered by existing test cases.
Author: Reynold Xin <rxin@databricks.com>
Closes#18839 from rxin/SPARK-21634.
## What changes were proposed in this pull request?
Hive `pmod(3.13, 0)`:
```:sql
hive> select pmod(3.13, 0);
OK
NULL
Time taken: 2.514 seconds, Fetched: 1 row(s)
hive>
```
Spark `mod(3.13, 0)`:
```:sql
spark-sql> select mod(3.13, 0);
NULL
spark-sql>
```
But the Spark `pmod(3.13, 0)`:
```:sql
spark-sql> select pmod(3.13, 0);
17/06/25 09:35:58 ERROR SparkSQLDriver: Failed in [select pmod(3.13, 0)]
java.lang.NullPointerException
at org.apache.spark.sql.catalyst.expressions.Pmod.pmod(arithmetic.scala:504)
at org.apache.spark.sql.catalyst.expressions.Pmod.nullSafeEval(arithmetic.scala:432)
at org.apache.spark.sql.catalyst.expressions.BinaryExpression.eval(Expression.scala:419)
at org.apache.spark.sql.catalyst.expressions.UnaryExpression.eval(Expression.scala:323)
...
```
This PR make `pmod(number, 0)` to null.
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18413 from wangyum/SPARK-21205.
## What changes were proposed in this pull request?
Currently, StructType.merge() only reports data types of conflicting fields when merging two incompatible schemas. It would be nice to also report the field names for easier debugging.
## How was this patch tested?
Unit test in DataTypeSuite.
Print exception message when conflict is triggered.
Author: bravo-zhang <mzhang1230@gmail.com>
Closes#16365 from bravo-zhang/spark-18950.
## What changes were proposed in this pull request?
This pr added parsing rules to support subquery column aliases in FROM clause.
This pr is a sub-task of #18079.
## How was this patch tested?
Added tests in `PlanParserSuite` and `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18185 from maropu/SPARK-20962.
## What changes were proposed in this pull request?
Long values can be passed to `rangeBetween` as range frame boundaries, but we silently convert it to Int values, this can cause wrong results and we should fix this.
Further more, we should accept any legal literal values as range frame boundaries. In this PR, we make it possible for Long values, and make accepting other DataTypes really easy to add.
This PR is mostly based on Herman's previous amazing work: 596f53c339
After this been merged, we can close#16818 .
## How was this patch tested?
Add new tests in `DataFrameWindowFunctionsSuite` and `TypeCoercionSuite`.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#18540 from jiangxb1987/rangeFrame.
## What changes were proposed in this pull request?
When the code that is generated is greater than 64k, then Janino compile will fail and CodeGenerator.scala will log the entire code at Error level.
SPARK-20871 suggests only logging the code at Debug level.
Since, the code is already logged at debug level, this Pull Request proposes not including the formatted code in the Error logging and exception message at all.
When an exception occurs, the code will be logged at Info level but truncated if it is more than 1000 lines long.
## How was this patch tested?
Existing tests were run.
An extra test test case was added to CodeFormatterSuite to test the new maxLines parameter,
Author: pj.fanning <pj.fanning@workday.com>
Closes#18658 from pjfanning/SPARK-20871.
## What changes were proposed in this pull request?
When we list partitions from hive metastore with a partial partition spec, we are expecting exact matching according to the partition values. However, hive treats dot specially and match any single character for dot. We should do an extra filter to drop unexpected partitions.
## How was this patch tested?
new regression test.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18671 from cloud-fan/hive.
## What changes were proposed in this pull request?
Address scapegoat warnings for:
- BigDecimal double constructor
- Catching NPE
- Finalizer without super
- List.size is O(n)
- Prefer Seq.empty
- Prefer Set.empty
- reverse.map instead of reverseMap
- Type shadowing
- Unnecessary if condition.
- Use .log1p
- Var could be val
In some instances like Seq.empty, I avoided making the change even where valid in test code to keep the scope of the change smaller. Those issues are concerned with performance and it won't matter for tests.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#18635 from srowen/Scapegoat1.
## What changes were proposed in this pull request?
This PR changes the direction of expression transformation in the DecimalPrecision rule. Previously, the expressions were transformed down, which led to incorrect result types when decimal expressions had other decimal expressions as their operands. The root cause of this issue was in visiting outer nodes before their children. Consider the example below:
```
val inputSchema = StructType(StructField("col", DecimalType(26, 6)) :: Nil)
val sc = spark.sparkContext
val rdd = sc.parallelize(1 to 2).map(_ => Row(BigDecimal(12)))
val df = spark.createDataFrame(rdd, inputSchema)
// Works correctly since no nested decimal expression is involved
// Expected result type: (26, 6) * (26, 6) = (38, 12)
df.select($"col" * $"col").explain(true)
df.select($"col" * $"col").printSchema()
// Gives a wrong result since there is a nested decimal expression that should be visited first
// Expected result type: ((26, 6) * (26, 6)) * (26, 6) = (38, 12) * (26, 6) = (38, 18)
df.select($"col" * $"col" * $"col").explain(true)
df.select($"col" * $"col" * $"col").printSchema()
```
The example above gives the following output:
```
// Correct result without sub-expressions
== Parsed Logical Plan ==
'Project [('col * 'col) AS (col * col)#4]
+- LogicalRDD [col#1]
== Analyzed Logical Plan ==
(col * col): decimal(38,12)
Project [CheckOverflow((promote_precision(cast(col#1 as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) AS (col * col)#4]
+- LogicalRDD [col#1]
== Optimized Logical Plan ==
Project [CheckOverflow((col#1 * col#1), DecimalType(38,12)) AS (col * col)#4]
+- LogicalRDD [col#1]
== Physical Plan ==
*Project [CheckOverflow((col#1 * col#1), DecimalType(38,12)) AS (col * col)#4]
+- Scan ExistingRDD[col#1]
// Schema
root
|-- (col * col): decimal(38,12) (nullable = true)
// Incorrect result with sub-expressions
== Parsed Logical Plan ==
'Project [(('col * 'col) * 'col) AS ((col * col) * col)#11]
+- LogicalRDD [col#1]
== Analyzed Logical Plan ==
((col * col) * col): decimal(38,12)
Project [CheckOverflow((promote_precision(cast(CheckOverflow((promote_precision(cast(col#1 as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) as decimal(26,6))) * promote_precision(cast(col#1 as decimal(26,6)))), DecimalType(38,12)) AS ((col * col) * col)#11]
+- LogicalRDD [col#1]
== Optimized Logical Plan ==
Project [CheckOverflow((cast(CheckOverflow((col#1 * col#1), DecimalType(38,12)) as decimal(26,6)) * col#1), DecimalType(38,12)) AS ((col * col) * col)#11]
+- LogicalRDD [col#1]
== Physical Plan ==
*Project [CheckOverflow((cast(CheckOverflow((col#1 * col#1), DecimalType(38,12)) as decimal(26,6)) * col#1), DecimalType(38,12)) AS ((col * col) * col)#11]
+- Scan ExistingRDD[col#1]
// Schema
root
|-- ((col * col) * col): decimal(38,12) (nullable = true)
```
## How was this patch tested?
This PR was tested with available unit tests. Moreover, there are tests to cover previously failing scenarios.
Author: aokolnychyi <anton.okolnychyi@sap.com>
Closes#18583 from aokolnychyi/spark-21332.
## What changes were proposed in this pull request?
Follow up to a few comments on https://github.com/apache/spark/pull/17150#issuecomment-315020196 that couldn't be addressed before it was merged.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#18646 from srowen/SPARK-19810.2.
## What changes were proposed in this pull request?
This PR fixes a wrong comparison for `BinaryType`. This PR enables unsigned comparison and unsigned prefix generation for an array for `BinaryType`. Previous implementations uses signed operations.
## How was this patch tested?
Added a test suite in `OrderingSuite`.
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#18571 from kiszk/SPARK-21344.
## What changes were proposed in this pull request?
- Remove Scala 2.10 build profiles and support
- Replace some 2.10 support in scripts with commented placeholders for 2.12 later
- Remove deprecated API calls from 2.10 support
- Remove usages of deprecated context bounds where possible
- Remove Scala 2.10 workarounds like ScalaReflectionLock
- Other minor Scala warning fixes
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#17150 from srowen/SPARK-19810.
## What changes were proposed in this pull request?
This pr made it more consistent to handle column name duplication. In the current master, error handling is different when hitting column name duplication:
```
// json
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq("""{"a":1, "a":1}"""""").toDF().coalesce(1).write.mode("overwrite").text("/tmp/data")
scala> spark.read.format("json").schema(schema).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Reference 'a' is ambiguous, could be: a#12, a#13.;
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:287)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:181)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:153)
scala> spark.read.format("json").load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Duplicate column(s) : "a" found, cannot save to JSON format;
at org.apache.spark.sql.execution.datasources.json.JsonDataSource.checkConstraints(JsonDataSource.scala:81)
at org.apache.spark.sql.execution.datasources.json.JsonDataSource.inferSchema(JsonDataSource.scala:63)
at org.apache.spark.sql.execution.datasources.json.JsonFileFormat.inferSchema(JsonFileFormat.scala:57)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:176)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:176)
// csv
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq("a,a", "1,1").toDF().coalesce(1).write.mode("overwrite").text("/tmp/data")
scala> spark.read.format("csv").schema(schema).option("header", false).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Reference 'a' is ambiguous, could be: a#41, a#42.;
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:287)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:181)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:153)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:152)
// If `inferSchema` is true, a CSV format is duplicate-safe (See SPARK-16896)
scala> spark.read.format("csv").option("header", true).load("/tmp/data").show
+---+---+
| a0| a1|
+---+---+
| 1| 1|
+---+---+
// parquet
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq((1, 1)).toDF("a", "b").coalesce(1).write.mode("overwrite").parquet("/tmp/data")
scala> spark.read.format("parquet").schema(schema).option("header", false).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Reference 'a' is ambiguous, could be: a#110, a#111.;
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:287)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:181)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:153)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:152)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
```
When this patch applied, the results change to;
```
// json
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq("""{"a":1, "a":1}"""""").toDF().coalesce(1).write.mode("overwrite").text("/tmp/data")
scala> spark.read.format("json").schema(schema).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in datasource: "a";
at org.apache.spark.sql.util.SchemaUtils$.checkColumnNameDuplication(SchemaUtil.scala:47)
at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtil.scala:33)
at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:186)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:368)
scala> spark.read.format("json").load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in datasource: "a";
at org.apache.spark.sql.util.SchemaUtils$.checkColumnNameDuplication(SchemaUtil.scala:47)
at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtil.scala:33)
at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:186)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:368)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:156)
// csv
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq("a,a", "1,1").toDF().coalesce(1).write.mode("overwrite").text("/tmp/data")
scala> spark.read.format("csv").schema(schema).option("header", false).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in datasource: "a";
at org.apache.spark.sql.util.SchemaUtils$.checkColumnNameDuplication(SchemaUtil.scala:47)
at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtil.scala:33)
at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:186)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:368)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
scala> spark.read.format("csv").option("header", true).load("/tmp/data").show
+---+---+
| a0| a1|
+---+---+
| 1| 1|
+---+---+
// parquet
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq((1, 1)).toDF("a", "b").coalesce(1).write.mode("overwrite").parquet("/tmp/data")
scala> spark.read.format("parquet").schema(schema).option("header", false).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in datasource: "a";
at org.apache.spark.sql.util.SchemaUtils$.checkColumnNameDuplication(SchemaUtil.scala:47)
at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtil.scala:33)
at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:186)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:368)
```
## How was this patch tested?
Added tests in `DataFrameReaderWriterSuite` and `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17758 from maropu/SPARK-20460.
## What changes were proposed in this pull request?
Since we do not set active sessions when parsing the plan, we are unable to correctly use SQLConf.get to find the correct active session. Since https://github.com/apache/spark/pull/18531 breaks the build, I plan to revert it at first.
## How was this patch tested?
The existing test cases
Author: Xiao Li <gatorsmile@gmail.com>
Closes#18568 from gatorsmile/revert18531.
## What changes were proposed in this pull request?
This pr modified code to use string types by default if `array` and `map` in functions have no argument. This behaviour is the same with Hive one;
```
hive> CREATE TEMPORARY TABLE t1 AS SELECT map();
hive> DESCRIBE t1;
_c0 map<string,string>
hive> CREATE TEMPORARY TABLE t2 AS SELECT array();
hive> DESCRIBE t2;
_c0 array<string>
```
## How was this patch tested?
Added tests in `DataFrameFunctionsSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18516 from maropu/SPARK-21281.
## What changes were proposed in this pull request?
un-aliased subquery is supported by Spark SQL for a long time. Its semantic was not well defined and had confusing behaviors, and it's not a standard SQL syntax, so we disallowed it in https://issues.apache.org/jira/browse/SPARK-20690 .
However, this is a breaking change, and we do have existing queries using un-aliased subquery. We should add the support back and fix its semantic.
This PR fixes the un-aliased subquery by assigning a default alias name.
After this PR, there is no syntax change from branch 2.2 to master, but we invalid a weird use case:
`SELECT v.i from (SELECT i FROM v)`. Now this query will throw analysis exception because users should not be able to use the qualifier inside a subquery.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18559 from cloud-fan/sub-query.
## What changes were proposed in this pull request?
When data type is struct, InSet now uses TypeUtils.getInterpretedOrdering (similar to EqualTo) to build a TreeSet. In other cases it will use a HashSet as before (which should be faster). Similarly, In.eval uses Ordering.equiv instead of equals.
## How was this patch tested?
New test in SQLQuerySuite.
Author: Bogdan Raducanu <bogdan@databricks.com>
Closes#18455 from bogdanrdc/SPARK-21228.
## What changes were proposed in this pull request?
Add missing test cases back and revise code style
Follow up the previous PR: https://github.com/apache/spark/pull/18479
## How was this patch tested?
Unit test
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#18548 from gengliangwang/stat_propagation_revise.
### What changes were proposed in this pull request?
This PR removes SQLConf parameters from the optimizer rules
### How was this patch tested?
The existing test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18533 from gatorsmile/rmSQLConfOptimizer.
### What changes were proposed in this pull request?
This PR is to remove SQLConf parameters from the parser-related classes.
### How was this patch tested?
The existing test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18531 from gatorsmile/rmSQLConfParser.
## What changes were proposed in this pull request?
support to create [temporary] function with the keyword 'OR REPLACE' and 'IF NOT EXISTS'
## How was this patch tested?
manual test and added test cases
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: ouyangxiaochen <ou.yangxiaochen@zte.com.cn>
Closes#17681 from ouyangxiaochen/spark-419.
## What changes were proposed in this pull request?
Currently `RowEncoder` doesn't preserve nullability of `ArrayType` or `MapType`.
It returns always `containsNull = true` for `ArrayType`, `valueContainsNull = true` for `MapType` and also the nullability of itself is always `true`.
This pr fixes the nullability of them.
## How was this patch tested?
Add tests to check if `RowEncoder` preserves array/map nullability.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#13873 from ueshin/issues/SPARK-16167.
## What changes were proposed in this pull request?
`ExternalMapToCatalyst` should null-check map key prior to converting to internal value to throw an appropriate Exception instead of something like NPE.
## How was this patch tested?
Added a test and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18524 from ueshin/issues/SPARK-21300.
### What changes were proposed in this pull request?
SQLConf is moved to Catalyst. We are adding more and more test cases for verifying the conf-specific behaviors. It is nice to add a helper function to simplify the test cases.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18469 from gatorsmile/withSQLConf.
## What changes were proposed in this pull request?
Looking at the code in `SessionCatalog.registerFunction`, the parameter `ignoreIfExists` is a wrong name. When `ignoreIfExists` is true, we will override the function if it already exists. So `overrideIfExists` should be the corrected name.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18510 from cloud-fan/minor.
## What changes were proposed in this pull request?
We currently implement statistics propagation directly in logical plan. Given we already have two different implementations, it'd make sense to actually decouple the two and add stats propagation using mixin. This would reduce the coupling between logical plan and statistics handling.
This can also be a powerful pattern in the future to add additional properties (e.g. constraints).
## How was this patch tested?
Should be covered by existing test cases.
Author: Reynold Xin <rxin@databricks.com>
Closes#18479 from rxin/stats-trait.
## What changes were proposed in this pull request?
For performance reasons, `UnsafeRow.getString`, `getStruct`, etc. return a "pointer" that points to a memory region of this unsafe row. This makes the unsafe projection a little dangerous, because all of its output rows share one instance.
When we implement SQL operators, we should be careful to not cache the input rows because they may be produced by unsafe projection from child operator and thus its content may change overtime.
However, when we updating values of InternalRow(e.g. in mutable projection and safe projection), we only copy UTF8String, we should also copy InternalRow, ArrayData and MapData. This PR fixes this, and also fixes the copy of vairous InternalRow, ArrayData and MapData implementations.
## How was this patch tested?
new regression tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18483 from cloud-fan/fix-copy.
### What changes were proposed in this pull request?
Function argument should not be named expressions. It could cause two issues:
- Misleading error message
- Unexpected query results when the column name is `distinct`, which is not a reserved word in our parser.
```
spark-sql> select count(distinct c1, distinct c2) from t1;
Error in query: cannot resolve '`distinct`' given input columns: [c1, c2]; line 1 pos 26;
'Project [unresolvedalias('count(c1#30, 'distinct), None)]
+- SubqueryAlias t1
+- CatalogRelation `default`.`t1`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [c1#30, c2#31]
```
After the fix, the error message becomes
```
spark-sql> select count(distinct c1, distinct c2) from t1;
Error in query:
extraneous input 'c2' expecting {')', ',', '.', '[', 'OR', 'AND', 'IN', NOT, 'BETWEEN', 'LIKE', RLIKE, 'IS', EQ, '<=>', '<>', '!=', '<', LTE, '>', GTE, '+', '-', '*', '/', '%', 'DIV', '&', '|', '||', '^'}(line 1, pos 35)
== SQL ==
select count(distinct c1, distinct c2) from t1
-----------------------------------^^^
```
### How was this patch tested?
Added a test case to parser suite.
Author: Xiao Li <gatorsmile@gmail.com>
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18338 from gatorsmile/parserDistinctAggFunc.
## What changes were proposed in this pull request?
Invalidate spark's stats after data changing commands:
- InsertIntoHadoopFsRelationCommand
- InsertIntoHiveTable
- LoadDataCommand
- TruncateTableCommand
- AlterTableSetLocationCommand
- AlterTableDropPartitionCommand
## How was this patch tested?
Added test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#18449 from wzhfy/removeStats.
## What changes were proposed in this pull request?
Move elimination of Distinct clause from analyzer to optimizer
Distinct clause is useless after MAX/MIN clause. For example,
"Select MAX(distinct a) FROM src from"
is equivalent of
"Select MAX(a) FROM src from"
However, this optimization is implemented in analyzer. It should be in optimizer.
## How was this patch tested?
Unit test
gatorsmile cloud-fan
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#18429 from gengliangwang/distinct_opt.
## What changes were proposed in this pull request?
`isTableSample` and `isGenerated ` were introduced for SQL Generation respectively by https://github.com/apache/spark/pull/11148 and https://github.com/apache/spark/pull/11050
Since SQL Generation is removed, we do not need to keep `isTableSample`.
## How was this patch tested?
The existing test cases
Author: Xiao Li <gatorsmile@gmail.com>
Closes#18379 from gatorsmile/CleanSample.
## What changes were proposed in this pull request?
Currently we do a lot of validations for subquery in the Analyzer. We should move them to CheckAnalysis which is the framework to catch and report Analysis errors. This was mentioned as a review comment in SPARK-18874.
## How was this patch tested?
Exists tests + A few tests added to SQLQueryTestSuite.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#17713 from dilipbiswal/subquery_checkanalysis.
## What changes were proposed in this pull request?
After wiring `SQLConf` in logical plan ([PR 18299](https://github.com/apache/spark/pull/18299)), we can remove the need of passing `conf` into `def stats` and `def computeStats`.
## How was this patch tested?
Covered by existing tests, plus some modified existing tests.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#18391 from wzhfy/removeConf.
## What changes were proposed in this pull request?
Currently we have several tens of test sqls in catalyst will fail at `SimpleAnalyzer.checkAnalysis`, we should make sure they are valid.
This PR makes the following changes:
1. Apply `checkAnalysis` on plans that tests `Optimizer` rules, but don't require the testcases for `Parser`/`Analyzer` pass `checkAnalysis`;
2. Fix testcases for `Optimizer` that would have fall.
## How was this patch tested?
Apply `SimpleAnalyzer.checkAnalysis` on plans in `PlanTest.comparePlans`, update invalid test cases.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15417 from jiangxb1987/cptest.
### What changes were proposed in this pull request?
We should not silently ignore `DISTINCT` when they are not supported in the function arguments. This PR is to block these cases and issue the error messages.
### How was this patch tested?
Added test cases for both regular functions and window functions
Author: Xiao Li <gatorsmile@gmail.com>
Closes#18340 from gatorsmile/firstCount.
## What changes were proposed in this pull request?
Built-in SQL Function UnaryMinus/UnaryPositive support string type, if it's string type, convert it to double type, after this PR:
```sql
spark-sql> select positive('-1.11'), negative('-1.11');
-1.11 1.11
spark-sql>
```
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18173 from wangyum/SPARK-20948.
## What changes were proposed in this pull request?
This PR adds built-in SQL function `BIT_LENGTH()`, `CHAR_LENGTH()`, and `OCTET_LENGTH()` functions.
`BIT_LENGTH()` returns the bit length of the given string or binary expression.
`CHAR_LENGTH()` returns the length of the given string or binary expression. (i.e. equal to `LENGTH()`)
`OCTET_LENGTH()` returns the byte length of the given string or binary expression.
## How was this patch tested?
Added new test suites for these three functions
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#18046 from kiszk/SPARK-20749.
## What changes were proposed in this pull request?
This pull-request exclusively includes the class splitting feature described in #16648. When code for a given class would grow beyond 1600k bytes, a private, nested sub-class is generated into which subsequent functions are inlined. Additional sub-classes are generated as the code threshold is met subsequent times. This code includes 3 changes:
1. Includes helper maps, lists, and functions for keeping track of sub-classes during code generation (included in the `CodeGenerator` class). These helper functions allow nested classes and split functions to be initialized/declared/inlined to the appropriate locations in the various projection classes.
2. Changes `addNewFunction` to return a string to support instances where a split function is inlined to a nested class and not the outer class (and so must be invoked using the class-qualified name). Uses of `addNewFunction` throughout the codebase are modified so that the returned name is properly used.
3. Removes instances of the `this` keyword when used on data inside generated classes. All state declared in the outer class is by default global and accessible to the nested classes. However, if a reference to global state in a nested class is prepended with the `this` keyword, it would attempt to reference state belonging to the nested class (which would not exist), rather than the correct variable belonging to the outer class.
## How was this patch tested?
Added a test case to the `GeneratedProjectionSuite` that increases the number of columns tested in various projections to a threshold that would previously have triggered a `JaninoRuntimeException` for the Constant Pool.
Note: This PR does not address the second Constant Pool issue with code generation (also mentioned in #16648): excess global mutable state. A second PR may be opened to resolve that issue.
Author: ALeksander Eskilson <alek.eskilson@cerner.com>
Closes#18075 from bdrillard/class_splitting_only.
## What changes were proposed in this pull request?
It is really painful to not have configs in logical plan and expressions. We had to add all sorts of hacks (e.g. pass SQLConf explicitly in functions). This patch exposes SQLConf in logical plan, using a thread local variable and a getter closure that's set once there is an active SparkSession.
The implementation is a bit of a hack, since we didn't anticipate this need in the beginning (config was only exposed in physical plan). The implementation is described in `SQLConf.get`.
In terms of future work, we should follow up to clean up CBO (remove the need for passing in config).
## How was this patch tested?
Updated relevant tests for constraint propagation.
Author: Reynold Xin <rxin@databricks.com>
Closes#18299 from rxin/SPARK-21092.
## What changes were proposed in this pull request?
Since `stack` function generates a table with nullable columns, it should allow mixed null values.
```scala
scala> sql("select stack(3, 1, 2, 3)").printSchema
root
|-- col0: integer (nullable = true)
scala> sql("select stack(3, 1, 2, null)").printSchema
org.apache.spark.sql.AnalysisException: cannot resolve 'stack(3, 1, 2, NULL)' due to data type mismatch: Argument 1 (IntegerType) != Argument 3 (NullType); line 1 pos 7;
```
## How was this patch tested?
Pass the Jenkins with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#17251 from dongjoon-hyun/SPARK-19910.
## What changes were proposed in this pull request?
This patch fixes a bug that can cause NullPointerException in LikeSimplification, when the pattern for like is null.
## How was this patch tested?
Added a new unit test case in LikeSimplificationSuite.
Author: Reynold Xin <rxin@databricks.com>
Closes#18273 from rxin/SPARK-21059.
The PR contains a tiny change to fix the way Spark parses string literals into timestamps. Currently, some timestamps that contain nanoseconds are corrupted during the conversion from internal UTF8Strings into the internal representation of timestamps.
Consider the following example:
```
spark.sql("SELECT cast('2015-01-02 00:00:00.000000001' as TIMESTAMP)").show(false)
+------------------------------------------------+
|CAST(2015-01-02 00:00:00.000000001 AS TIMESTAMP)|
+------------------------------------------------+
|2015-01-02 00:00:00.000001 |
+------------------------------------------------+
```
The fix was tested with existing tests. Also, there is a new test to cover cases that did not work previously.
Author: aokolnychyi <anton.okolnychyi@sap.com>
Closes#18252 from aokolnychyi/spark-17914.
## What changes were proposed in this pull request?
add test case to MathExpressionsSuite as #17906
## How was this patch tested?
unit test cases
Author: liuxian <liu.xian3@zte.com.cn>
Closes#18082 from 10110346/wip-lx-0524.
## What changes were proposed in this pull request?
Currently, hive's stats are read into `CatalogStatistics`, while spark's stats are also persisted through `CatalogStatistics`. As a result, hive's stats can be unexpectedly propagated into spark' stats.
For example, for a catalog table, we read stats from hive, e.g. "totalSize" and put it into `CatalogStatistics`. Then, by using "ALTER TABLE" command, we will store the stats in `CatalogStatistics` into metastore as spark's stats (because we don't know whether it's from spark or not). But spark's stats should be only generated by "ANALYZE" command. This is unexpected from this command.
Secondly, now that we have spark's stats in metastore, after inserting new data, although hive updated "totalSize" in metastore, we still cannot get the right `sizeInBytes` in `CatalogStatistics`, because we respect spark's stats (should not exist) over hive's stats.
A running example is shown in [JIRA](https://issues.apache.org/jira/browse/SPARK-21031).
To fix this, we add a new method `alterTableStats` to store spark's stats, and let `alterTable` keep existing stats.
## How was this patch tested?
Added new tests.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#18248 from wzhfy/separateHiveStats.
## What changes were proposed in this pull request?
add more datatype for some unit tests
## How was this patch tested?
unit tests
Author: liuxian <liu.xian3@zte.com.cn>
Closes#17880 from 10110346/wip_lx_0506.
### What changes were proposed in this pull request?
The precision and scale of decimal values are wrong when the input is BigDecimal between -1.0 and 1.0.
The BigDecimal's precision is the digit count starts from the leftmost nonzero digit based on the [JAVA's BigDecimal definition](https://docs.oracle.com/javase/7/docs/api/java/math/BigDecimal.html). However, our Decimal decision follows the database decimal standard, which is the total number of digits, including both to the left and the right of the decimal point. Thus, this PR is to fix the issue by doing the conversion.
Before this PR, the following queries failed:
```SQL
select 1 > 0.0001
select floor(0.0001)
select ceil(0.0001)
```
### How was this patch tested?
Added test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#18244 from gatorsmile/bigdecimal.
### What changes were proposed in this pull request?
Currently, the unquoted string of a function identifier is being used as the function identifier in the function registry. This could cause the incorrect the behavior when users use `.` in the function names. This PR is to take the `FunctionIdentifier` as the identifier in the function registry.
- Add one new function `createOrReplaceTempFunction` to `FunctionRegistry`
```Scala
final def createOrReplaceTempFunction(name: String, builder: FunctionBuilder): Unit
```
### How was this patch tested?
Add extra test cases to verify the inclusive bug fixes.
Author: Xiao Li <gatorsmile@gmail.com>
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18142 from gatorsmile/fuctionRegistry.
## What changes were proposed in this pull request?
Removed a duplicate case in "SPARK-20854: select hint syntax with expressions"
## How was this patch tested?
Existing tests.
Author: Bogdan Raducanu <bogdan@databricks.com>
Closes#18217 from bogdanrdc/SPARK-20854-2.
## What changes were proposed in this pull request?
`HintInfo.isBroadcastable` is actually not an accurate name, it's used to force the planner to broadcast a plan no matter what the data size is, via the hint mechanism. I think `forceBroadcast` is a better name.
And `isBroadcastable` only have 2 possible values: `Some(true)` and `None`, so we can just use boolean type for it.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18189 from cloud-fan/stats.
## What changes were proposed in this pull request?
SQL hint syntax:
* support expressions such as strings, numbers, etc. instead of only identifiers as it is currently.
* support multiple hints, which was missing compared to the DataFrame syntax.
DataFrame API:
* support any parameters in DataFrame.hint instead of just strings
## How was this patch tested?
Existing tests. New tests in PlanParserSuite. New suite DataFrameHintSuite.
Author: Bogdan Raducanu <bogdan@databricks.com>
Closes#18086 from bogdanrdc/SPARK-20854.
## What changes were proposed in this pull request?
Add build-int SQL function - UUID.
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18136 from wangyum/SPARK-20910.
## What changes were proposed in this pull request?
We changed the parser to reject unaliased subqueries in the FROM clause in SPARK-20690. However, the error message that we now give isn't very helpful:
scala> sql("""SELECT x FROM (SELECT 1 AS x)""")
org.apache.spark.sql.catalyst.parser.ParseException:
mismatched input 'FROM' expecting {<EOF>, 'WHERE', 'GROUP', 'ORDER', 'HAVING', 'LIMIT', 'LATERAL', 'WINDOW', 'UNION', 'EXCEPT', 'MINUS', 'INTERSECT', 'SORT', 'CLUSTER', 'DISTRIBUTE'}(line 1, pos 9)
We should modify the parser to throw a more clear error for such queries:
scala> sql("""SELECT x FROM (SELECT 1 AS x)""")
org.apache.spark.sql.catalyst.parser.ParseException:
The unaliased subqueries in the FROM clause are not supported.(line 1, pos 14)
## How was this patch tested?
Modified existing tests to reflect this change.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18141 from viirya/SPARK-20916.
## What changes were proposed in this pull request?
Add build-int SQL function - DAYOFWEEK
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18134 from wangyum/SPARK-20909.
## What changes were proposed in this pull request?
This PR adds built-in SQL function `(REPLACE(<string_expression>, <search_string> [, <replacement_string>])`
`REPLACE()` return that string that is replaced all occurrences with given string.
## How was this patch tested?
added new test suites
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#18047 from kiszk/SPARK-20750.
## What changes were proposed in this pull request?
See class doc of `ConstantPropagation` for the approach used.
## How was this patch tested?
- Added unit tests
Author: Tejas Patil <tejasp@fb.com>
Closes#17993 from tejasapatil/SPARK-20758_const_propagation.
## What changes were proposed in this pull request?
This pr added parsing rules to support table column aliases in FROM clause.
## How was this patch tested?
Added tests in `PlanParserSuite`, `SQLQueryTestSuite`, and `PlanParserSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18079 from maropu/SPARK-20841.
### What changes were proposed in this pull request?
In Cache manager, the plan matching should ignore Hint.
```Scala
val df1 = spark.range(10).join(broadcast(spark.range(10)))
df1.cache()
spark.range(10).join(spark.range(10)).explain()
```
The output plan of the above query shows that the second query is not using the cached data of the first query.
```
BroadcastNestedLoopJoin BuildRight, Inner
:- *Range (0, 10, step=1, splits=2)
+- BroadcastExchange IdentityBroadcastMode
+- *Range (0, 10, step=1, splits=2)
```
After the fix, the plan becomes
```
InMemoryTableScan [id#20L, id#23L]
+- InMemoryRelation [id#20L, id#23L], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
+- BroadcastNestedLoopJoin BuildRight, Inner
:- *Range (0, 10, step=1, splits=2)
+- BroadcastExchange IdentityBroadcastMode
+- *Range (0, 10, step=1, splits=2)
```
### How was this patch tested?
Added a test.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#18131 from gatorsmile/HintCache.
## What changes were proposed in this pull request?
spark-sql>SELECT ceil(cast(12345.1233 as float));
spark-sql>12345
For this case, the result we expected is `12346`
spark-sql>SELECT floor(cast(-12345.1233 as float));
spark-sql>-12345
For this case, the result we expected is `-12346`
Because in `Ceil` or `Floor`, `inputTypes` has no FloatType, so it is converted to LongType.
## How was this patch tested?
After the modification:
spark-sql>SELECT ceil(cast(12345.1233 as float));
spark-sql>12346
spark-sql>SELECT floor(cast(-12345.1233 as float));
spark-sql>-12346
Author: liuxian <liu.xian3@zte.com.cn>
Closes#18103 from 10110346/wip-lx-0525-1.
## What changes were proposed in this pull request?
Add built-in SQL function `CH[A]R`:
For `CHR(bigint|double n)`, returns the ASCII character having the binary equivalent to `n`. If n is larger than 256 the result is equivalent to CHR(n % 256)
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18019 from wangyum/SPARK-20748.
## What changes were proposed in this pull request?
It is reported that there is performance downgrade when applying ML pipeline for dataset with many columns but few rows.
A big part of the performance downgrade comes from some operations (e.g., `select`) on DataFrame/Dataset which re-create new DataFrame/Dataset with a new `LogicalPlan`. The cost can be ignored in the usage of SQL, normally.
However, it's not rare to chain dozens of pipeline stages in ML. When the query plan grows incrementally during running those stages, the total cost spent on re-creation of DataFrame grows too. In particular, the `Analyzer` will go through the big query plan even most part of it is analyzed.
By eliminating part of the cost, the time to run the example code locally is reduced from about 1min to about 30 secs.
In particular, the time applying the pipeline locally is mostly spent on calling transform of the 137 `Bucketizer`s. Before the change, each call of `Bucketizer`'s transform can cost about 0.4 sec. So the total time spent on all `Bucketizer`s' transform is about 50 secs. After the change, each call only costs about 0.1 sec.
<del>We also make `boundEnc` as lazy variable to reduce unnecessary running time.</del>
### Performance improvement
The codes and datasets provided by Barry Becker to re-produce this issue and benchmark can be found on the JIRA.
Before this patch: about 1 min
After this patch: about 20 secs
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17770 from viirya/SPARK-20392.
## What changes were proposed in this pull request?
This is a follow-up to SPARK-20857 to move the broadcast hint from Statistics into a new HintInfo class, so we can be more flexible in adding new hints in the future.
## How was this patch tested?
Updated test cases to reflect the change.
Author: Reynold Xin <rxin@databricks.com>
Closes#18087 from rxin/SPARK-20867.
## What changes were proposed in this pull request?
This patch renames BroadcastHint to ResolvedHint (and Hint to UnresolvedHint) so the hint framework is more generic and would allow us to introduce other hint types in the future without introducing new hint nodes.
## How was this patch tested?
Updated test cases.
Author: Reynold Xin <rxin@databricks.com>
Closes#18072 from rxin/SPARK-20857.
## What changes were proposed in this pull request?
As srowen pointed in 609ba5f2b9 (commitcomment-22221259), the previous tests are not proper.
This follow-up is going to fix the tests.
## 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#18048 from viirya/SPARK-20399-follow-up.
### What changes were proposed in this pull request?
After we adding a new field `stats` into `CatalogTable`, we should not expose Hive-specific Stats metadata to `MetastoreRelation`. It complicates all the related codes. It also introduces a bug in `SHOW CREATE TABLE`. The statistics-related table properties should be skipped by `SHOW CREATE TABLE`, since it could be incorrect in the newly created table. See the Hive JIRA: https://issues.apache.org/jira/browse/HIVE-13792
Also fix the issue to fill Hive-generated RowCounts to our stats.
This PR is to handle Hive-specific Stats metadata in `HiveClientImpl`.
### How was this patch tested?
Added a few test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#14971 from gatorsmile/showCreateTableNew.
### What changes were proposed in this pull request?
Currently, we have a bug when we specify `IF NOT EXISTS` in `INSERT OVERWRITE` data source tables. For example, given a query:
```SQL
INSERT OVERWRITE TABLE $tableName partition (b=2, c=3) IF NOT EXISTS SELECT 9, 10
```
we will get the following error:
```
unresolved operator 'InsertIntoTable Relation[a#425,d#426,b#427,c#428] parquet, Map(b -> Some(2), c -> Some(3)), true, true;;
'InsertIntoTable Relation[a#425,d#426,b#427,c#428] parquet, Map(b -> Some(2), c -> Some(3)), true, true
+- Project [cast(9#423 as int) AS a#429, cast(10#424 as int) AS d#430]
+- Project [9 AS 9#423, 10 AS 10#424]
+- OneRowRelation$
```
This PR is to fix the issue to follow the behavior of Hive serde tables
> INSERT OVERWRITE will overwrite any existing data in the table or partition unless IF NOT EXISTS is provided for a partition
### How was this patch tested?
Modified an existing test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18050 from gatorsmile/insertPartitionIfNotExists.
## What changes were proposed in this pull request?
spark-sql>SELECT ceil(1234567890123456);
1234567890123456
spark-sql>SELECT ceil(12345678901234567);
12345678901234568
spark-sql>SELECT ceil(123456789012345678);
123456789012345680
when the length of the getText is greater than 16. long to double will be precision loss.
but mysql handle the value is ok.
mysql> SELECT ceil(1234567890123456);
+------------------------+
| ceil(1234567890123456) |
+------------------------+
| 1234567890123456 |
+------------------------+
1 row in set (0.00 sec)
mysql> SELECT ceil(12345678901234567);
+-------------------------+
| ceil(12345678901234567) |
+-------------------------+
| 12345678901234567 |
+-------------------------+
1 row in set (0.00 sec)
mysql> SELECT ceil(123456789012345678);
+--------------------------+
| ceil(123456789012345678) |
+--------------------------+
| 123456789012345678 |
+--------------------------+
1 row in set (0.00 sec)
## How was this patch tested?
Supplement the unit test.
Author: caoxuewen <cao.xuewen@zte.com.cn>
Closes#18016 from heary-cao/ceil_long.
## What changes were proposed in this pull request?
spark-sql>select month("1582-09-28");
spark-sql>10
For this case, the expected result is 9, but it is 10.
spark-sql>select day("1582-04-18");
spark-sql>28
For this case, the expected result is 18, but it is 28.
when the date before "1582-10-04", the function of `month` and `day` return the value which is not we expected.
## How was this patch tested?
unit tests
Author: liuxian <liu.xian3@zte.com.cn>
Closes#17997 from 10110346/wip_lx_0516.
## What changes were proposed in this pull request?
GenerateUnsafeProjection.writeStructToBuffer() did not honor the assumption that the caller must make sure that a value is not null before using the getter. This could lead to various errors. This change fixes that behavior.
Example of code generated before:
```scala
/* 059 */ final UTF8String fieldName = value.getUTF8String(0);
/* 060 */ if (value.isNullAt(0)) {
/* 061 */ rowWriter1.setNullAt(0);
/* 062 */ } else {
/* 063 */ rowWriter1.write(0, fieldName);
/* 064 */ }
```
Example of code generated now:
```scala
/* 060 */ boolean isNull1 = value.isNullAt(0);
/* 061 */ UTF8String value1 = isNull1 ? null : value.getUTF8String(0);
/* 062 */ if (isNull1) {
/* 063 */ rowWriter1.setNullAt(0);
/* 064 */ } else {
/* 065 */ rowWriter1.write(0, value1);
/* 066 */ }
```
## How was this patch tested?
Adds GenerateUnsafeProjectionSuite.
Author: Ala Luszczak <ala@databricks.com>
Closes#18030 from ala/fix-generate-unsafe-projection.
## What changes were proposed in this pull request?
In the previous approach we used `aliasMap` to link an `Attribute` to the expression with potentially the form `f(a, b)`, but we only searched the `expressions` and `children.expressions` for this, which is not enough when an `Alias` may lies deep in the logical plan. In that case, we can't generate the valid equivalent constraint classes and thus we fail at preventing the recursive deductions.
We fix this problem by collecting all `Alias`s from the logical plan.
## How was this patch tested?
No additional test case is added, but do modified one test case to cover this situation.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#18020 from jiangxb1987/inferConstrants.
## What changes were proposed in this pull request?
We add missing attributes into Filter in Analyzer. But we shouldn't do it through subqueries like this:
select 1 from (select 1 from onerow t1 LIMIT 1) where t1.c1=1
This query works in current codebase. However, the outside where clause shouldn't be able to refer `t1.c1` attribute.
The root cause is we allow subqueries in FROM have no alias names previously, it is confusing and isn't supported by various databases such as MySQL, Postgres, Oracle. We shouldn't support it 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#17935 from viirya/SPARK-20690.
## What changes were proposed in this pull request?
Hive allows inserting data to bucketed table without guaranteeing bucketed and sorted-ness based on these two configs : `hive.enforce.bucketing` and `hive.enforce.sorting`.
What does this PR achieve ?
- Spark will disallow users from writing outputs to hive bucketed tables by default (given that output won't adhere with Hive's semantics).
- IF user still wants to write to hive bucketed table, the only resort is to use `hive.enforce.bucketing=false` and `hive.enforce.sorting=false` which means user does NOT care about bucketing guarantees.
Changes done in this PR:
- Extract table's bucketing information in `HiveClientImpl`
- While writing table info to metastore, `HiveClientImpl` now populates the bucketing information in the hive `Table` object
- `InsertIntoHiveTable` allows inserts to bucketed table only if both `hive.enforce.bucketing` and `hive.enforce.sorting` are `false`
Ability to create bucketed tables will enable adding test cases to Spark while I add more changes related to hive bucketing support. Design doc for hive hive bucketing support : https://docs.google.com/document/d/1a8IDh23RAkrkg9YYAeO51F4aGO8-xAlupKwdshve2fc/edit#
## How was this patch tested?
- Added test for creating bucketed and sorted table.
- Added test to ensure that INSERTs fail if strict bucket / sort is enforced
- Added test to ensure that INSERTs can go through if strict bucket / sort is NOT enforced
- Added test to validate that bucketing information shows up in output of DESC FORMATTED
- Added test to ensure that `SHOW CREATE TABLE` works for hive bucketed tables
Author: Tejas Patil <tejasp@fb.com>
Closes#17644 from tejasapatil/SPARK-17729_create_bucketed_table.
## What changes were proposed in this pull request?
This pr added a new Optimizer rule to combine nested Concat. The master supports a pipeline operator '||' to concatenate strings in #17711 (This pr is follow-up). Since the parser currently generates nested Concat expressions, the optimizer needs to combine the nested expressions.
## How was this patch tested?
Added tests in `CombineConcatSuite` and `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17970 from maropu/SPARK-20730.
## What changes were proposed in this pull request?
This PR proposes three things as below:
- Use casting rules to a timestamp in `to_timestamp` by default (it was `yyyy-MM-dd HH:mm:ss`).
- Support single argument for `to_timestamp` similarly with APIs in other languages.
For example, the one below works
```
import org.apache.spark.sql.functions._
Seq("2016-12-31 00:12:00.00").toDF("a").select(to_timestamp(col("a"))).show()
```
prints
```
+----------------------------------------+
|to_timestamp(`a`, 'yyyy-MM-dd HH:mm:ss')|
+----------------------------------------+
| 2016-12-31 00:12:00|
+----------------------------------------+
```
whereas this does not work in SQL.
**Before**
```
spark-sql> SELECT to_timestamp('2016-12-31 00:12:00');
Error in query: Invalid number of arguments for function to_timestamp; line 1 pos 7
```
**After**
```
spark-sql> SELECT to_timestamp('2016-12-31 00:12:00');
2016-12-31 00:12:00
```
- Related document improvement for SQL function descriptions and other API descriptions accordingly.
**Before**
```
spark-sql> DESCRIBE FUNCTION extended to_date;
...
Usage: to_date(date_str, fmt) - Parses the `left` expression with the `fmt` expression. Returns null with invalid input.
Extended Usage:
Examples:
> SELECT to_date('2016-12-31', 'yyyy-MM-dd');
2016-12-31
```
```
spark-sql> DESCRIBE FUNCTION extended to_timestamp;
...
Usage: to_timestamp(timestamp, fmt) - Parses the `left` expression with the `format` expression to a timestamp. Returns null with invalid input.
Extended Usage:
Examples:
> SELECT to_timestamp('2016-12-31', 'yyyy-MM-dd');
2016-12-31 00:00:00.0
```
**After**
```
spark-sql> DESCRIBE FUNCTION extended to_date;
...
Usage:
to_date(date_str[, fmt]) - Parses the `date_str` expression with the `fmt` expression to
a date. Returns null with invalid input. By default, it follows casting rules to a date if
the `fmt` is omitted.
Extended Usage:
Examples:
> SELECT to_date('2009-07-30 04:17:52');
2009-07-30
> SELECT to_date('2016-12-31', 'yyyy-MM-dd');
2016-12-31
```
```
spark-sql> DESCRIBE FUNCTION extended to_timestamp;
...
Usage:
to_timestamp(timestamp[, fmt]) - Parses the `timestamp` expression with the `fmt` expression to
a timestamp. Returns null with invalid input. By default, it follows casting rules to
a timestamp if the `fmt` is omitted.
Extended Usage:
Examples:
> SELECT to_timestamp('2016-12-31 00:12:00');
2016-12-31 00:12:00
> SELECT to_timestamp('2016-12-31', 'yyyy-MM-dd');
2016-12-31 00:00:00
```
## How was this patch tested?
Added tests in `datetime.sql`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17901 from HyukjinKwon/to_timestamp_arg.
## What changes were proposed in this pull request?
spark-sql>select bround(12.3, 2);
spark-sql>NULL
For this case, the expected result is 12.3, but it is null.
So ,when the second parameter is bigger than "decimal.scala", the result is not we expected.
"round" function has the same problem. This PR can solve the problem for both of them.
## How was this patch tested?
unit test cases in MathExpressionsSuite and MathFunctionsSuite
Author: liuxian <liu.xian3@zte.com.cn>
Closes#17906 from 10110346/wip_lx_0509.
## What changes were proposed in this pull request?
The new SQL parser is introduced into Spark 2.0. All string literals are unescaped in parser. Seems it bring an issue regarding the regex pattern string.
The following codes can reproduce it:
val data = Seq("\u0020\u0021\u0023", "abc")
val df = data.toDF()
// 1st usage: works in 1.6
// Let parser parse pattern string
val rlike1 = df.filter("value rlike '^\\x20[\\x20-\\x23]+$'")
// 2nd usage: works in 1.6, 2.x
// Call Column.rlike so the pattern string is a literal which doesn't go through parser
val rlike2 = df.filter($"value".rlike("^\\x20[\\x20-\\x23]+$"))
// In 2.x, we need add backslashes to make regex pattern parsed correctly
val rlike3 = df.filter("value rlike '^\\\\x20[\\\\x20-\\\\x23]+$'")
Follow the discussion in #17736, this patch adds a config to fallback to 1.6 string literal parsing and mitigate migration issue.
## 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#17887 from viirya/add-config-fallback-string-parsing.
## What changes were proposed in this pull request?
This pr added parsing rules to support aliases in table value functions.
The previous pr (#17666) has been reverted because of the regression. This new pr fixed the regression and add tests in `SQLQueryTestSuite`.
## How was this patch tested?
Added tests in `PlanParserSuite` and `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17928 from maropu/SPARK-20311-3.
## What changes were proposed in this pull request?
In filter estimation, we update column stats for those columns in filter condition. However, if the number of rows decreases after the filter (i.e. the overall selectivity is less than 1), we need to update (scale down) the number of distinct values (NDV) for all columns, no matter they are in filter conditions or not.
This pr also fixes the inconsistency of rounding mode for ndv and rowCount.
## How was this patch tested?
Added new tests.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17918 from wzhfy/scaleDownNdvAfterFilter.
## What changes were proposed in this pull request?
The query
```
SELECT 1 FROM (SELECT COUNT(*) WHERE FALSE) t1
```
should return a single row of output because the subquery is an aggregate without a group-by and thus should return a single row. However, Spark incorrectly returns zero rows.
This is caused by SPARK-16208 / #13906, a patch which added an optimizer rule to propagate EmptyRelation through operators. The logic for handling aggregates is wrong: it checks whether aggregate expressions are non-empty for deciding whether the output should be empty, whereas it should be checking grouping expressions instead:
An aggregate with non-empty grouping expression will return one output row per group. If the input to the grouped aggregate is empty then all groups will be empty and thus the output will be empty. It doesn't matter whether the aggregation output columns include aggregate expressions since that won't affect the number of output rows.
If the grouping expressions are empty, however, then the aggregate will always produce a single output row and thus we cannot propagate the EmptyRelation.
The current implementation is incorrect and also misses an optimization opportunity by not propagating EmptyRelation in the case where a grouped aggregate has aggregate expressions (in other words, `SELECT COUNT(*) from emptyRelation GROUP BY x` would _not_ be optimized to `EmptyRelation` in the old code, even though it safely could be).
This patch resolves this issue by modifying `PropagateEmptyRelation` to consider only the presence/absence of grouping expressions, not the aggregate functions themselves, when deciding whether to propagate EmptyRelation.
## How was this patch tested?
- Added end-to-end regression tests in `SQLQueryTest`'s `group-by.sql` file.
- Updated unit tests in `PropagateEmptyRelationSuite`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#17929 from JoshRosen/fix-PropagateEmptyRelation.
## What changes were proposed in this pull request?
This pr added parsing rules to support aliases in table value functions.
## How was this patch tested?
Added tests in `PlanParserSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17666 from maropu/SPARK-20311.
## What changes were proposed in this pull request?
Fix build warnings primarily related to Breeze 0.13 operator changes, Java style problems
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#17803 from srowen/SPARK-20523.
## What changes were proposed in this pull request?
A fix for the same problem was made in #17693 but ignored `JsonToStructs`. This PR uses the same fix for `JsonToStructs`.
## How was this patch tested?
Regression test
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#17826 from brkyvz/SPARK-20549.
## What changes were proposed in this pull request?
Add support for the SQL standard distinct predicate to SPARK SQL.
```
<expression> IS [NOT] DISTINCT FROM <expression>
```
## How was this patch tested?
Tested using unit tests, integration tests, manual tests.
Author: ptkool <michael.styles@shopify.com>
Closes#17764 from ptkool/is_not_distinct_from.
## What changes were proposed in this pull request?
Currently, when the type string is invalid, it looks printing empty parentheses. This PR proposes a small improvement in an error message by removing it in the parse as below:
```scala
spark.range(1).select($"col".cast("aa"))
```
**Before**
```
org.apache.spark.sql.catalyst.parser.ParseException:
DataType aa() is not supported.(line 1, pos 0)
== SQL ==
aa
^^^
```
**After**
```
org.apache.spark.sql.catalyst.parser.ParseException:
DataType aa is not supported.(line 1, pos 0)
== SQL ==
aa
^^^
```
## How was this patch tested?
Unit tests in `DataTypeParserSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17784 from HyukjinKwon/SPARK-20492.
## What changes were proposed in this pull request?
Relax the requirement that a `TimeZoneAwareExpression` has to have its `timeZoneId` set to be considered resolved.
With this change, a `Cast` (which is a `TimeZoneAwareExpression`) can be considered resolved if the `(fromType, toType)` combination doesn't require time zone information.
Also de-relaxed test cases in `CastSuite` so Casts in that test suite don't get a default`timeZoneId = Option("GMT")`.
## How was this patch tested?
Ran the de-relaxed`CastSuite` and it's passing. Also ran the SQL unit tests and they're passing too.
Author: Kris Mok <kris.mok@databricks.com>
Closes#17777 from rednaxelafx/fix-catalyst-cast-timezone.
## What changes were proposed in this pull request?
change to using Jackson's `com.fasterxml.jackson.core.JsonFactory`
public JsonParser createParser(String content)
## How was this patch tested?
existing unit tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Eric Wasserman <ericw@sgn.com>
Closes#17693 from ewasserman/SPARK-20314.
## What changes were proposed in this pull request?
This PR avoids an exception in the case where `scala.math.BigInt` has a value that does not fit into long value range (e.g. `Long.MAX_VALUE+1`). When we run the following code by using the current Spark, the following exception is thrown.
This PR keeps the value using `BigDecimal` if we detect such an overflow case by catching `ArithmeticException`.
Sample program:
```
case class BigIntWrapper(value:scala.math.BigInt)```
spark.createDataset(BigIntWrapper(scala.math.BigInt("10000000000000000002"))::Nil).show
```
Exception:
```
Error while encoding: java.lang.ArithmeticException: BigInteger out of long range
staticinvoke(class org.apache.spark.sql.types.Decimal$, DecimalType(38,0), apply, assertnotnull(assertnotnull(input[0, org.apache.spark.sql.BigIntWrapper, true])).value, true) AS value#0
java.lang.RuntimeException: Error while encoding: java.lang.ArithmeticException: BigInteger out of long range
staticinvoke(class org.apache.spark.sql.types.Decimal$, DecimalType(38,0), apply, assertnotnull(assertnotnull(input[0, org.apache.spark.sql.BigIntWrapper, true])).value, true) AS value#0
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:290)
at org.apache.spark.sql.SparkSession$$anonfun$2.apply(SparkSession.scala:454)
at org.apache.spark.sql.SparkSession$$anonfun$2.apply(SparkSession.scala:454)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.immutable.List.map(List.scala:285)
at org.apache.spark.sql.SparkSession.createDataset(SparkSession.scala:454)
at org.apache.spark.sql.Agg$$anonfun$18.apply$mcV$sp(MySuite.scala:192)
at org.apache.spark.sql.Agg$$anonfun$18.apply(MySuite.scala:192)
at org.apache.spark.sql.Agg$$anonfun$18.apply(MySuite.scala:192)
at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:68)
at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163)
at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175)
...
Caused by: java.lang.ArithmeticException: BigInteger out of long range
at java.math.BigInteger.longValueExact(BigInteger.java:4531)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:140)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:434)
at org.apache.spark.sql.types.Decimal.apply(Decimal.scala)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:287)
... 59 more
```
## How was this patch tested?
Add new test suite into `DecimalSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#17684 from kiszk/SPARK-20341.
## What changes were proposed in this pull request?
It is often useful to be able to track changes to the `ExternalCatalog`. This PR makes the `ExternalCatalog` emit events when a catalog object is changed. Events are fired before and after the change.
The following events are fired per object:
- Database
- CreateDatabasePreEvent: event fired before the database is created.
- CreateDatabaseEvent: event fired after the database has been created.
- DropDatabasePreEvent: event fired before the database is dropped.
- DropDatabaseEvent: event fired after the database has been dropped.
- Table
- CreateTablePreEvent: event fired before the table is created.
- CreateTableEvent: event fired after the table has been created.
- RenameTablePreEvent: event fired before the table is renamed.
- RenameTableEvent: event fired after the table has been renamed.
- DropTablePreEvent: event fired before the table is dropped.
- DropTableEvent: event fired after the table has been dropped.
- Function
- CreateFunctionPreEvent: event fired before the function is created.
- CreateFunctionEvent: event fired after the function has been created.
- RenameFunctionPreEvent: event fired before the function is renamed.
- RenameFunctionEvent: event fired after the function has been renamed.
- DropFunctionPreEvent: event fired before the function is dropped.
- DropFunctionPreEvent: event fired after the function has been dropped.
The current events currently only contain the names of the object modified. We add more events, and more details at a later point.
A user can monitor changes to the external catalog by adding a listener to the Spark listener bus checking for `ExternalCatalogEvent`s using the `SparkListener.onOtherEvent` hook. A more direct approach is add listener directly to the `ExternalCatalog`.
## How was this patch tested?
Added the `ExternalCatalogEventSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17710 from hvanhovell/SPARK-20420.
## What changes were proposed in this pull request?
A cast expression with a resolved time zone is not equal to a cast expression without a resolved time zone. The `ResolveAggregateFunction` assumed that these expression were the same, and would fail to resolve `HAVING` clauses which contain a `Cast` expression.
This is in essence caused by the fact that a `TimeZoneAwareExpression` can be resolved without a set time zone. This PR fixes this, and makes a `TimeZoneAwareExpression` unresolved as long as it has no TimeZone set.
## How was this patch tested?
Added a regression test to the `SQLQueryTestSuite.having` file.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17641 from hvanhovell/SPARK-20329.
## What changes were proposed in this pull request?
Apply Complementation Laws during boolean expression simplification.
## How was this patch tested?
Tested using unit tests, integration tests, and manual tests.
Author: ptkool <michael.styles@shopify.com>
Author: Michael Styles <michael.styles@shopify.com>
Closes#17650 from ptkool/apply_complementation_laws.
## What changes were proposed in this pull request?
If a plan has multi-level successive joins, e.g.:
```
Join
/ \
Union t5
/ \
Join t4
/ \
Join t3
/ \
t1 t2
```
Currently we fail to reorder the inside joins, i.e. t1, t2, t3.
In join reorder, we use `OrderedJoin` to indicate a join has been ordered, such that when transforming down the plan, these joins don't need to be rerodered again.
But there's a problem in the definition of `OrderedJoin`:
The real join node is a parameter, but not a child. This breaks the transform procedure because `mapChildren` applies transform function on parameters which should be children.
In this patch, we change `OrderedJoin` to a class having the same structure as a join node.
## How was this patch tested?
Add a corresponding test case.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17668 from wzhfy/recursiveReorder.
## What changes were proposed in this pull request?
Replace non-existent `repartitionBy` with `distribute` in `CollapseRepartitionSuite`.
## How was this patch tested?
local build and `catalyst/testOnly *CollapseRepartitionSuite`
Author: Jacek Laskowski <jacek@japila.pl>
Closes#17657 from jaceklaskowski/CollapseRepartitionSuite.
## What changes were proposed in this pull request?
This patch fixes a bug in the way LIKE patterns are translated to Java regexes. The bug causes any character following an escaped backslash to be escaped, i.e. there is double-escaping.
A concrete example is the following pattern:`'%\\%'`. The expected Java regex that this pattern should correspond to (according to the behavior described below) is `'.*\\.*'`, however the current situation leads to `'.*\\%'` instead.
---
Update: in light of the discussion that ensued, we should explicitly define the expected behaviour of LIKE expressions, especially in certain edge cases. With the help of gatorsmile, we put together a list of different RDBMS and their variations wrt to certain standard features.
| RDBMS\Features | Wildcards | Default escape [1] | Case sensitivity |
| --- | --- | --- | --- |
| [MS SQL Server](https://msdn.microsoft.com/en-us/library/ms179859.aspx) | _, %, [], [^] | none | no |
| [Oracle](https://docs.oracle.com/cd/B12037_01/server.101/b10759/conditions016.htm) | _, % | none | yes |
| [DB2 z/OS](http://www.ibm.com/support/knowledgecenter/SSEPEK_11.0.0/sqlref/src/tpc/db2z_likepredicate.html) | _, % | none | yes |
| [MySQL](http://dev.mysql.com/doc/refman/5.7/en/string-comparison-functions.html) | _, % | none | no |
| [PostreSQL](https://www.postgresql.org/docs/9.0/static/functions-matching.html) | _, % | \ | yes |
| [Hive](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF) | _, % | none | yes |
| Current Spark | _, % | \ | yes |
[1] Default escape character: most systems do not have a default escape character, instead the user can specify one by calling a like expression with an escape argument [A] LIKE [B] ESCAPE [C]. This syntax is currently not supported by Spark, however I would volunteer to implement this feature in a separate ticket.
The specifications are often quite terse and certain scenarios are undocumented, so here is a list of scenarios that I am uncertain about and would appreciate any input. Specifically I am looking for feedback on whether or not Spark's current behavior should be changed.
1. [x] Ending a pattern with the escape sequence, e.g. `like 'a\'`.
PostreSQL gives an error: 'LIKE pattern must not end with escape character', which I personally find logical. Currently, Spark allows "non-terminated" escapes and simply ignores them as part of the pattern.
According to [DB2's documentation](http://www.ibm.com/support/knowledgecenter/SSEPGG_9.7.0/com.ibm.db2.luw.messages.sql.doc/doc/msql00130n.html), ending a pattern in an escape character is invalid.
_Proposed new behaviour in Spark: throw AnalysisException_
2. [x] Empty input, e.g. `'' like ''`
Postgres and DB2 will match empty input only if the pattern is empty as well, any other combination of empty input will not match. Spark currently follows this rule.
3. [x] Escape before a non-special character, e.g. `'a' like '\a'`.
Escaping a non-wildcard character is not really documented but PostgreSQL just treats it verbatim, which I also find the least surprising behavior. Spark does the same.
According to [DB2's documentation](http://www.ibm.com/support/knowledgecenter/SSEPGG_9.7.0/com.ibm.db2.luw.messages.sql.doc/doc/msql00130n.html), it is invalid to follow an escape character with anything other than an escape character, an underscore or a percent sign.
_Proposed new behaviour in Spark: throw AnalysisException_
The current specification is also described in the operator's source code in this patch.
## How was this patch tested?
Extra case in regex unit tests.
Author: Jakob Odersky <jakob@odersky.com>
This patch had conflicts when merged, resolved by
Committer: Reynold Xin <rxin@databricks.com>
Closes#15398 from jodersky/SPARK-17647.
## What changes were proposed in this pull request?
Currently when estimating predicates like col > literal or col = literal, we will update min or max in column stats based on literal value. However, literal value is of Catalyst type (internal type), while min/max is of external type. Then for the next predicate, we again need to do type conversion to compare and update column stats. This is awkward and causes many unnecessary conversions in estimation.
To solve this, we use Catalyst type for min/max in `ColumnStat`. Note that the persistent format in metastore is still of external type, so there's no inconsistency for statistics in metastore.
This pr also fixes a bug for boolean type in `IN` condition.
## How was this patch tested?
The changes for ColumnStat are covered by existing tests.
For bug fix, a new test for boolean type in IN condition is added
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17630 from wzhfy/refactorColumnStat.
### 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?
This PR proposes to run Spark unidoc to test Javadoc 8 build as Javadoc 8 is easily re-breakable.
There are several problems with it:
- It introduces little extra bit of time to run the tests. In my case, it took 1.5 mins more (`Elapsed :[94.8746569157]`). How it was tested is described in "How was this patch tested?".
- > One problem that I noticed was that Unidoc appeared to be processing test sources: if we can find a way to exclude those from being processed in the first place then that might significantly speed things up.
(see joshrosen's [comment](https://issues.apache.org/jira/browse/SPARK-18692?focusedCommentId=15947627&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15947627))
To complete this automated build, It also suggests to fix existing Javadoc breaks / ones introduced by test codes as described above.
There fixes are similar instances that previously fixed. Please refer https://github.com/apache/spark/pull/15999 and https://github.com/apache/spark/pull/16013
Note that this only fixes **errors** not **warnings**. Please see my observation https://github.com/apache/spark/pull/17389#issuecomment-288438704 for spurious errors by warnings.
## How was this patch tested?
Manually via `jekyll build` for building tests. Also, tested via running `./dev/run-tests`.
This was tested via manually adding `time.time()` as below:
```diff
profiles_and_goals = build_profiles + sbt_goals
print("[info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments: ",
" ".join(profiles_and_goals))
+ import time
+ st = time.time()
exec_sbt(profiles_and_goals)
+ print("Elapsed :[%s]" % str(time.time() - st))
```
produces
```
...
========================================================================
Building Unidoc API Documentation
========================================================================
...
[info] Main Java API documentation successful.
...
Elapsed :[94.8746569157]
...
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17477 from HyukjinKwon/SPARK-18692.
## 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?
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?
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?
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?
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?
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?
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?
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.
…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?
`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?
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?
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?
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?
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?
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?
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?
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?
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?
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?
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?
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?
- 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.
## 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.
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.
## 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 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?
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 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.
