### What changes were proposed in this pull request?
RocksDB provides backward compatibility but it doesn't always provide forward compatibility. It's better to store the RocksDB format version in the checkpoint so that it would give us more information to provide the rollback guarantee when we upgrade the RocksDB version that may introduce incompatible change in a new Spark version.
A typical case is when a user upgrades their query to a new Spark version, and this new Spark version has a new RocksDB version which may use a new format. But the user hits some bug and decide to rollback. But in the old Spark version, the old RocksDB version cannot read the new format.
In order to handle this case, we will write the RocksDB format version to the checkpoint. When restarting from a checkpoint, we will force RocksDB to use the format version stored in the checkpoint. This will ensure the user can rollback their Spark version if needed.
We also provide a config `spark.sql.streaming.stateStore.rocksdb.formatVersion` for users who don't need to rollback their Spark versions to overwrite the format version specified in the checkpoint.
### Why are the changes needed?
Provide the Spark version rollback guarantee for streaming queries when a new RocksDB introduces an incompatible format change.
### Does this PR introduce _any_ user-facing change?
No. RocksDB state store is a new feature in Spark 3.2, which has not yet released.
### How was this patch tested?
The new unit tests.
Closes#33749 from zsxwing/SPARK-36519.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit ea4919801a)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
The implement of https://github.com/apache/spark/pull/33665 make `make_timestamp` could accepts integer type as the seconds parameter.
This PR let `make_timestamp` accepts `decimal(16,6)` type as the seconds parameter and cast integer to `decimal(16,6)` is safe, so we can simplify the code.
### Why are the changes needed?
Simplify `make_timestamp`.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
New tests.
Closes#33775 from beliefer/SPARK-36428-followup.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
(cherry picked from commit 707eefa3c7)
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Revert [[SPARK-35028][SQL] ANSI mode: disallow group by aliases ](https://github.com/apache/spark/pull/32129)
### Why are the changes needed?
It turns out that many users are using the group by alias feature. Spark has its precedence rule when alias names conflict with column names in Group by clause: always use the table column. This should be reasonable and acceptable.
Also, external DBMS such as PostgreSQL and MySQL allow grouping by alias, too.
As we are going to announce ANSI mode GA in Spark 3.2, I suggest allowing the group by alias in ANSI mode.
### Does this PR introduce _any_ user-facing change?
No, the feature is not released yet.
### How was this patch tested?
Unit tests
Closes#33758 from gengliangwang/revertGroupByAlias.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
(cherry picked from commit 8bfb4f1e72)
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Add new type `AnsiIntervalType` to `AbstractDataType.scala`, and extend it by `YearMonthIntervalType` and by `DayTimeIntervalType`
### Why are the changes needed?
To improve code maintenance. The change will allow to replace checking of both `YearMonthIntervalType` and `DayTimeIntervalType` by a check of `AnsiIntervalType`, for instance:
```scala
case _: YearMonthIntervalType | _: DayTimeIntervalType => false
```
by
```scala
case _: AnsiIntervalType => false
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By existing test suites.
Closes#33753 from MaxGekk/ansi-interval-type-trait.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit 82a31508af)
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Disallow comparison between Interval and String in the default type coercion rules.
### Why are the changes needed?
If a binary comparison contains interval type and string type, we can't decide which
interval type the string should be promoted as. There are many possible interval
types, such as year interval, month interval, day interval, hour interval, etc.
### Does this PR introduce _any_ user-facing change?
No, the new interval type is not released yet.
### How was this patch tested?
Existing UT
Closes#33750 from gengliangwang/disallowCom.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit 26d6b952dc)
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This patch supports dynamic gap duration in session window.
### Why are the changes needed?
The gap duration used in session window for now is a static value. To support more complex usage, it is better to support dynamic gap duration which determines the gap duration by looking at the current data. For example, in our usecase, we may have different gap by looking at the certain column in the input rows.
### Does this PR introduce _any_ user-facing change?
Yes, users can specify dynamic gap duration.
### How was this patch tested?
Modified existing tests and new test.
Closes#33691 from viirya/dynamic-session-window-gap.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
(cherry picked from commit 8b8d91cf64)
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Normalize pushed down aggregate col names and group by col names ...
### Why are the changes needed?
to handle case sensitive col names
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Modify existing test
Closes#33739 from huaxingao/normalize.
Authored-by: Huaxin Gao <huaxin_gao@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit 3f8ec0dae4)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
If a binary operation contains interval type and string literal, we can't decide which interval type the string literal should be promoted as. There are many possible interval types, such as year interval, month interval, day interval, hour interval, etc.
The related binary operation for Interval contains
- Add
- Subtract
- Comparisions
Note that `Interval Multiple/Divide StringLiteral` is valid as them is not binary operators(the left and right are not of the same type). This PR also add tests for them.
### Why are the changes needed?
Avoid ambiguously implicit casting string literals to interval types.
### Does this PR introduce _any_ user-facing change?
No, the ANSI type coercion is not released yet.
### How was this patch tested?
New tests.
Closes#33737 from gengliangwang/disallowStringAndInterval.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
(cherry picked from commit ecdea91602)
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Test the following valid operations:
```
year-month interval * null
null * year-month interval
year-month interval / null
```
and invalid operations:
```
null / interval
int / interval
```
### Why are the changes needed?
Improve test coverage
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass CI
Closes#33729 from gengliangwang/addTest.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit eb6be7f1ee)
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
With ANSI mode, `SELECT make_timestamp(1, 1, 1, 1, 1, 1)` fails, because the 'seconds' parameter needs to be of type DECIMAL(8,6), and INT can't be implicitly casted to DECIMAL(8,6) under ANSI mode.
```
org.apache.spark.sql.AnalysisException
cannot resolve 'make_timestamp(1, 1, 1, 1, 1, 1)' due to data type mismatch: argument 6 requires decimal(8,6) type, however, '1' is of int type.; line 1 pos 7
```
We should update the function `make_timestamp` to allow integer type 'seconds' parameter.
### Why are the changes needed?
Make `make_timestamp` could accepts integer as 'seconds' parameter.
### Does this PR introduce _any_ user-facing change?
'Yes'.
`make_timestamp` could accepts integer as 'seconds' parameter.
### How was this patch tested?
New tests.
Closes#33665 from beliefer/SPARK-36428.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 7d82336734)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This PR fixes an existing correctness issue where a non-deterministic With-CTE can be executed multiple times producing different results, by deferring the inline of With-CTE to after the analysis stage. This fix also provides the future opportunity of performance improvement by executing deterministic With-CTEs only once in some circumstances.
The major changes include:
1. Added new With-CTE logical nodes: `CTERelationDef`, `CTERelationRef`, `WithCTE`. Each `CTERelationDef` has a unique ID and the mapping between CTE def and CTE ref is based on IDs rather than names. `WithCTE` is a resolved version of `With`, only that: 1) `WithCTE` is a multi-children logical node so that most logical rules can automatically apply to CTE defs; 2) In the main query and each subquery, there can only be at most one `WithCTE`, which means nested With-CTEs are combined.
2. Changed `CTESubstitution` rule so that if NOT in legacy mode, CTE defs will not be inlined immediately, but rather transformed into a `CTERelationRef` per reference.
3. Added new With-CTE rules: 1) `ResolveWithCTE` - to update `CTERelationRef`s with resolved output from corresponding `CTERelationDef`s; 2) `InlineCTE` - to inline deterministic CTEs or non-deterministic CTEs with only ONE reference; 3) `UpdateCTERelationStats` - to update stats for `CTERelationRef`s that are not inlined.
4. Added a CTE physical planning strategy to plan `CTERelationRef`s as an independent shuffle with round-robin partitioning so that such CTEs will only be materialized once and different references will later be a shuffle reuse.
A current limitation is that With-CTEs mixed with SQL commands or DMLs will still go through the old inline code path because of our non-standard language specs and not-unified command/DML interfaces.
This is a correctness issue. Non-deterministic CTEs should produce the same output regardless of how many times it is referenced/used in query, while under the current implementation there is no such guarantee and would lead to incorrect query results.
No.
Added UTs.
Regenerated golden files for TPCDS plan stability tests. There is NO change to the `simplified.txt` files, the only differences are expression IDs.
Closes#33671 from maryannxue/spark-36447.
Authored-by: Maryann Xue <maryann.xue@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 29b1e394c6)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, `null + interval` will become `cast(cast(null as timestamp) + interval) as null`. This is a unexpected behavior and the result should not be of null type.
This weird behavior applies to `null - interval`, `interval + null`, `interval - null` as well.
To change it, I propose to cast the null as the same data type of the other element in the add/subtract:
```
null + interval => cast(null as interval) + interval
null - interval => cast(null as interval) - interval
interval + null=> interval + cast(null as interval)
interval - null => interval - cast(null as interval)
```
### Why are the changes needed?
Change the confusing behavior of `Interval +/- NULL` and `NULL +/- Interval`
### Does this PR introduce _any_ user-facing change?
No, the new interval type is not released yet.
### How was this patch tested?
Existing UT
Closes#33727 from gengliangwang/intervalTypeCoercion.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit d4466d55ca)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Promote more string literal in subtractions. In the ANSI type coercion rule, we already promoted
```
string - timestamp => cast(string as timestamp) - timestamp
```
This PR is to promote the following string literals:
```
string - date => cast(string as date) - date
date - string => date - cast(date as string)
timestamp - string => timestamp
```
It is very straightforward to cast the string literal as the data type of the other side in the subtraction.
2. Merge the string promotion logic from the rule `StringLiteralCoercion`:
```
date_sub(date, string) => date_sub(date, cast(string as int))
date_add(date, string) => date_add(date, cast(string as int))
```
### Why are the changes needed?
1. Promote the string literal in the subtraction as the data type of the other side. This is straightforward and consistent with PostgreSQL
2. Certerize all the string literal promotion in the ANSI type coercion rule
### Does this PR introduce _any_ user-facing change?
No, the new ANSI type coercion rules are not released yet.
### How was this patch tested?
Existing UT
Closes#33724 from gengliangwang/datetimeTypeCoercion.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
(cherry picked from commit 48e333af54)
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This is a followup of #33140
It turns out that we may be able to complete the AQE and columnar execution integration without the AQE post stage creation extension. The rule `ApplyColumnarRulesAndInsertTransitions` can add to-columnar transition if the shuffle/broadcast supports columnar.
### Why are the changes needed?
remove APIs that are not needed.
### Does this PR introduce _any_ user-facing change?
No, the APIs are not released yet.
### How was this patch tested?
existing and manual tests
Closes#33701 from cloud-fan/aqe.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 124d011ee7)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fixes a bug in `DisableUnnecessaryBucketedScan`.
When running any aggregate function, without any grouping keys, on a table with a single bucket, multiple rows are returned.
This happens because the aggregate function satisfies the `AllTuples` distribution, no `Exchange` will be planned, and the bucketed scan will be disabled.
### Why are the changes needed?
Bug fixing. Aggregates over no grouping keys should return a single row.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added new test in `DisableUnnecessaryBucketedScanSuite`.
Closes#33711 from IonutBoicuAms/fix-bug-disableunnecessarybucketedscan.
Authored-by: IonutBoicuAms <ionut.boicu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 2b665751d9)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds more datetime tests in `date.sql` and `timestamp.sql`, especially for string promotion.
### Why are the changes needed?
improve test coverage
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
N/A
Closes#33707 from cloud-fan/test.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 00a4364f38)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to remove the filter applying to input rows against watermark in SessionWindowStateStoreSaveExec, since SessionWindowStateStoreSaveExec is expected to store all inputs into state store, and apply eviction later.
### Why are the changes needed?
The code is logically not right, though I can't reproduce the actual problem.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests. I can't come up with broken case failing on previous code, but we can review the logic instead.
Closes#33708 from HeartSaVioR/SPARK-36480.
Authored-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit fac4e5eb3e)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Implement a new rule for the date-time operations in the ANSI type coercion system:
1. Date will be converted to Timestamp when it is in the subtraction with Timestmap.
2. Promote string literals in date_add/date_sub/time_add
### Why are the changes needed?
Currently the type coercion rule `DateTimeOperations` doesn't match the design of the ANSI type coercion system:
1. For date_add/date_sub, if the input is timestamp type, Spark should not convert it into date type since date type is narrower than the timestamp type.
2. For date_add/date_sub/time_add, string value can be implicit cast to date/timestamp only when it is literal.
Thus, we need to have a new rule for the date-time operations in the ANSI type coercion system.
### Does this PR introduce _any_ user-facing change?
No, the ANSI type coercion rules are not releaesd.
### How was this patch tested?
New UT
Closes#33666 from gengliangwang/datetimeOp.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
(cherry picked from commit 3029e62a82)
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This PR proposes to prohibit update mode in streaming aggregation with session window.
UnsupportedOperationChecker will check and prohibit the case. As a side effect, this PR also simplifies the code as we can remove the implementation of iterator to support outputs of update mode.
This PR also cleans up test code via deduplicating.
### Why are the changes needed?
The semantic of "update" mode for session window based streaming aggregation is quite unclear.
For normal streaming aggregation, Spark will provide the outputs which can be "upsert"ed based on the grouping key. This is based on the fact grouping key won't be changed.
This doesn't hold true for session window based streaming aggregation, as session range is changing.
If end users leverage their knowledge about streaming aggregation, they will consider the key as grouping key + session (since they'll specify these things in groupBy), and it's high likely possible that existing row is not updated (overwritten) and ended up with having different rows.
If end users consider the key as grouping key, there's a small chance for end users to upsert the session correctly, though only the last updated session will be stored so it won't work with event time processing which there could be multiple active sessions.
### Does this PR introduce _any_ user-facing change?
No, as we haven't released this feature.
### How was this patch tested?
Updated tests.
Closes#33689 from HeartSaVioR/SPARK-36463.
Authored-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
(cherry picked from commit ed60aaa9f1)
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Currently, when `set spark.sql.timestampType=TIMESTAMP_NTZ`, the behavior is different between `from_json` and `from_csv`.
```
-- !query
select from_json('{"t":"26/October/2015"}', 't Timestamp', map('timestampFormat', 'dd/MMMMM/yyyy'))
-- !query schema
struct<from_json({"t":"26/October/2015"}):struct<t:timestamp_ntz>>
-- !query output
{"t":null}
```
```
-- !query
select from_csv('26/October/2015', 't Timestamp', map('timestampFormat', 'dd/MMMMM/yyyy'))
-- !query schema
struct<>
-- !query output
java.lang.Exception
Unsupported type: timestamp_ntz
```
We should make `from_json` throws exception too.
This PR fix the discussion below
https://github.com/apache/spark/pull/33640#discussion_r682862523
### Why are the changes needed?
Make the behavior of `from_json` more reasonable.
### Does this PR introduce _any_ user-facing change?
'Yes'.
from_json throwing Exception when we set spark.sql.timestampType=TIMESTAMP_NTZ.
### How was this patch tested?
Tests updated.
Closes#33684 from beliefer/SPARK-36429-new.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 186815be1c)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Support TypeCoercion of ANSI intervals with different fields
### Why are the changes needed?
Support TypeCoercion of ANSI intervals with different fields
### Does this PR introduce _any_ user-facing change?
After this pr user can
- use comparison function with different fields of DayTimeIntervalType/YearMonthIntervalType such as `INTERVAL '1' YEAR` > `INTERVAL '11' MONTH`
- support different field of ansi interval type in collection function such as `array(INTERVAL '1' YEAR, INTERVAL '11' MONTH)`
- support different field of ansi interval type in `coalesce` etc..
### How was this patch tested?
Added UT
Closes#33661 from AngersZhuuuu/SPARK-SPARK-36431.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit 89d8a4eacf)
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Retain `spark.sql.catalog.*` confs when resolving view.
### Why are the changes needed?
Currently, if a view in default catalog ref a table in another catalog (e.g. jdbc), `org.apache.spark.sql.AnalysisException: Table or view not found: cat.t` will be thrown on accessing the view if the catalog has not been loaded yet.
### Does this PR introduce _any_ user-facing change?
Yes, bug fix.
### How was this patch tested?
Add UT.
Closes#33692 from pan3793/SPARK-36466.
Authored-by: Cheng Pan <chengpan@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 7f56b73cad)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, v2 ALTER TABLE REPLACE COLUMNS does not check duplicates for the user specified columns. For example,
```
spark.sql(s"CREATE TABLE $t (id int) USING $v2Format")
spark.sql(s"ALTER TABLE $t REPLACE COLUMNS (data string, data string)")
```
doesn't fail the analysis, and it's up to the catalog implementation to handle it.
### Why are the changes needed?
To check the duplicate columns during analysis.
### Does this PR introduce _any_ user-facing change?
Yes, now the above will command will print out the following:
```
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in the user specified columns: `data`
```
### How was this patch tested?
Added new unit tests
Closes#33676 from imback82/replace_cols_duplicates.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit e1a5d94117)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
not push down partition filter to `ORCScan` for DSv2
### Why are the changes needed?
Seems to me that partition filter is only used for partition pruning and shouldn't be pushed down to `ORCScan`. We don't push down partition filter to ORCScan in DSv1
```
== Physical Plan ==
*(1) Filter (isnotnull(value#19) AND NOT (value#19 = a))
+- *(1) ColumnarToRow
+- FileScan orc [value#19,p1#20,p2#21] Batched: true, DataFilters: [isnotnull(value#19), NOT (value#19 = a)], Format: ORC, Location: InMemoryFileIndex(1 paths)[file:/private/var/folders/pt/_5f4sxy56x70dv9zpz032f0m0000gn/T/spark-c1..., PartitionFilters: [isnotnull(p1#20), isnotnull(p2#21), (p1#20 = 1), (p2#21 = 2)], PushedFilters: [IsNotNull(value), Not(EqualTo(value,a))], ReadSchema: struct<value:string>
```
Also, we don't push down partition filter for parquet in DSv2.
https://github.com/apache/spark/pull/30652
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing test suites
Closes#33680 from huaxingao/orc_filter.
Authored-by: Huaxin Gao <huaxin_gao@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit b04330cd38)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR fixes a performance regression introduced in https://github.com/apache/spark/pull/33172
Before #33172 , the target size is adaptively calculated based on the default parallelism of the spark cluster. Sometimes it's very small and #33172 sets a min partition size to fix perf issues. Sometimes the calculated size is reasonable, such as dozens of MBs.
After #33172 , we no longer calculate the target size adaptively, and by default always coalesce the partitions into 1 MB. This can cause perf regression if the adaptively calculated size is reasonable.
This PR brings back the code that adaptively calculate the target size based on the default parallelism of the spark cluster.
### Why are the changes needed?
fix perf regression
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests
Closes#33655 from cloud-fan/minor.
Lead-authored-by: Wenchen Fan <wenchen@databricks.com>
Co-authored-by: Wenchen Fan <cloud0fan@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 9a539d5846)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Unify DataSource V1 insert schema check field name before prepare writer.
And in this PR we add check for avro V1 insert too.
### Why are the changes needed?
Unify code and add check for avro V1 insert too.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#33566 from AngersZhuuuu/SPARK-36271.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit f3e079b09b)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Spark should check result plan's output schema name
### Why are the changes needed?
In current code, some optimizer rule may change plan's output schema, since in the code we always use semantic equal to check output, but it may change the plan's output schema.
For example, for SchemaPruning, if we have a plan
```
Project[a, B]
|--Scan[A, b, c]
```
the origin output schema is `a, B`, after SchemaPruning. it become
```
Project[A, b]
|--Scan[A, b]
```
It change the plan's schema. when we use CTAS, the schema is same as query plan's output.
Then since we change the schema, it not consistent with origin SQL. So we need to check final result plan's schema with origin plan's schema
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existed UT
Closes#33583 from AngersZhuuuu/SPARK-36352.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit e051a540a1)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/33140 to propose a simpler idea for integrating columnar execution into AQE.
Instead of making the `ColumnarToRowExec` and `RowToColumnarExec` dynamic to handle `AdaptiveSparkPlanExec`, it's simpler to let the consumer decide if it needs columnar output or not, and pass a boolean flag to `AdaptiveSparkPlanExec`.
For Spark vendors, they can set the flag differently in their custom columnar parquet writing command when the input plan is `AdaptiveSparkPlanExec`.
One argument is if we need to look at the final plan of AQE and consume the data differently (either row or columnar format). I can't think of a use case and I think we can always statically know if the AQE plan should output row or columnar data.
### Why are the changes needed?
code simplification.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
manual test
Closes#33624 from cloud-fan/aqe.
Lead-authored-by: Wenchen Fan <wenchen@databricks.com>
Co-authored-by: Wenchen Fan <cloud0fan@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 8714eefe6f)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR reverts the change in SPARK-36429 (#33654).
See [conversation](https://github.com/apache/spark/pull/33654#issuecomment-894160037).
### Why are the changes needed?
To recover CIs.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
N/A
Closes#33670 from sarutak/revert-SPARK-36429.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
(cherry picked from commit e17612d0bf)
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
### What changes were proposed in this pull request?
Currently, references to grouping sets are reported as errors after aggregated expressions, e.g.
```
SELECT count(name) c, name
FROM VALUES ('Alice'), ('Bob') people(name)
GROUP BY name GROUPING SETS(name);
```
Error in query: expression 'people.`name`' is neither present in the group by, nor is it an aggregate function. Add to group by or wrap in first() (or first_value) if you don't care which value you get.;;
### Why are the changes needed?
Fix the map anonymous function in the constructAggregateExprs function does not use underscores to avoid
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit tests.
Closes#33574 from gaoyajun02/SPARK-36339.
Lead-authored-by: gaoyajun02 <gaoyajun02@gmail.com>
Co-authored-by: gaoyajun02 <gaoyajun02@meituan.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 888f8f03c8)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR updates a golden to comply with the change in SPARK-36429 (#33654).
### Why are the changes needed?
To recover GA failure.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
GA itself.
Closes#33663 from sarutak/followup-SPARK-36429.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 63c7d1847d)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, when `set spark.sql.timestampType=TIMESTAMP_NTZ`, the behavior is different between `from_json` and `from_csv`.
```
-- !query
select from_json('{"t":"26/October/2015"}', 't Timestamp', map('timestampFormat', 'dd/MMMMM/yyyy'))
-- !query schema
struct<from_json({"t":"26/October/2015"}):struct<t:timestamp_ntz>>
-- !query output
{"t":null}
```
```
-- !query
select from_csv('26/October/2015', 't Timestamp', map('timestampFormat', 'dd/MMMMM/yyyy'))
-- !query schema
struct<>
-- !query output
java.lang.Exception
Unsupported type: timestamp_ntz
```
We should make `from_json` throws exception too.
This PR fix the discussion below
https://github.com/apache/spark/pull/33640#discussion_r682862523
### Why are the changes needed?
Make the behavior of `from_json` more reasonable.
### Does this PR introduce _any_ user-facing change?
'Yes'.
from_json throwing Exception when we set spark.sql.timestampType=TIMESTAMP_NTZ.
### How was this patch tested?
Tests updated.
Closes#33654 from beliefer/SPARK-36429.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently `datetime.sql` contains a lot of tests and will be run 3 times: default mode, ansi mode, ntz mode. It wastes the test time and also large test files are hard to read.
This PR proposes to split it into smaller ones:
1. `date.sql`, which contains date literals, functions and operations. It will be run twice with default and ansi mode.
2. `timestamp.sql`, which contains timestamp (no ltz or ntz suffix) literals, functions and operations. It will be run 4 times: default mode + ans off, defaul mode + ansi on, ntz mode + ansi off, ntz mode + ansi on.
3. `datetime_special.sql`, which create datetime values whose year is outside of [0, 9999]. This is a separated file as JDBC doesn't support them and need to ignore this test file. It will be run 4 times as well.
4. `timestamp_ltz.sql`, which contains timestamp_ltz literals and constructors. It will be run twice with default and ntz mode, to make sure its result doesn't change with the timestamp mode. Note that, operations with ltz are tested by `timestamp.sql`
5. `timestamp_ntz.sql`, which contains timestamp_ntz literals and constructors. It will be run twice with default and ntz mode, to make sure its result doesn't change with the timestamp mode. Note that, operations with ntz are tested by `timestamp.sql`
### Why are the changes needed?
reduce test run time.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
N/A
Closes#33640 from cloud-fan/test.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This PR fixes the issue that `ConfigEntry` to be introduced to the doc field directly without calling `.key`, which causes malformed documents on the web site and in the result of `SET -v`
1. https://spark.apache.org/docs/3.1.2/configuration.html#static-sql-configuration - spark.sql.hive.metastore.jars
2. set -v
### Why are the changes needed?
bugfix
### Does this PR introduce _any_ user-facing change?
no, but contains doc fix
### How was this patch tested?
new tests
Closes#33647 from yaooqinn/SPARK-36421.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit c7fa3c9090)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This reverts SPARK-31475, as there are always more concurrent jobs running in AQE mode, especially when running multiple queries at the same time. Currently, the broadcast timeout does not record accurately for the BroadcastQueryStageExec only, but also including the time waiting for being scheduled. If all the resources are currently being occupied for materializing other stages, it timeouts without a chance to run actually.
The default value is 300s, and it's hard to adjust the timeout for AQE mode. Usually, you need an extremely large number for real-world cases. As you can see in the example, above, the timeout we used for it was 1800s, and obviously, it needed 3x more or something
### Why are the changes needed?
AQE is default now, we can make it more stable with this PR
### Does this PR introduce _any_ user-facing change?
yes, broadcast timeout now is not used for AQE
### How was this patch tested?
modified test
Closes#33636 from yaooqinn/SPARK-36414.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 0c94e47aec)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
RemoveNoopOperators should keep output schema
### Why are the changes needed?
Expand function
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Not need
Closes#33587 from AngersZhuuuu/SPARK-36355.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 02810eecbf)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add the Resolver to `checkColumnNotExists` to check name exist in case sensitive.
### Why are the changes needed?
At now the resolver is `_ == _` of `findNestedField` called by `checkColumnNotExists`
Add `alter.conf.resolver` to it.
[SPARK-36381](https://issues.apache.org/jira/browse/SPARK-36381)
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Add ut tests
Closes#33618 from Peng-Lei/sensitive-cloumn-name.
Authored-by: PengLei <peng.8lei@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 87d49cbcb1)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to ban `YearMonthIntervalType` and `DayTimeIntervalType` at the analysis phase while creating a table using a built-in filed-based datasource or writing a dataset to such datasource. In particular, add the following case:
```scala
case _: DayTimeIntervalType | _: YearMonthIntervalType => false
```
to all methods that override either:
- V2 `FileTable.supportsDataType()`
- V1 `FileFormat.supportDataType()`
### Why are the changes needed?
To improve user experience with Spark SQL, and output a proper error message at the analysis phase.
### Does this PR introduce _any_ user-facing change?
Yes but ANSI interval types haven't released yet. So, for users this is new behavior.
### How was this patch tested?
By running the affected test suites:
```
$ build/sbt -Phive-2.3 "test:testOnly *HiveOrcSourceSuite"
```
Closes#33580 from MaxGekk/interval-ban-in-ds.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit 67cbc93263)
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR adds test considering the case `spark.sql.legacy.interval.enabled` is `true` for SPARK-35815.
### Why are the changes needed?
SPARK-35815 (#33456) changes `Dataset.withWatermark` to accept ANSI interval literals as `delayThreshold` but I noticed the change didn't work with `spark.sql.legacy.interval.enabled=true`.
We can't detect this issue because there is no test which considers the legacy interval type at that time.
In SPARK-36323 (#33551), this issue was resolved but it's better to add test.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#33606 from sarutak/test-watermark-with-legacy-interval.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit 92cdb17d1a)
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/30494
This PR proposes a new way to optimize the final query stage in AQE. We first collect the effective user-specified repartition (semantic-wise, user-specified repartition is only effective if it's the root node or under a few simple nodes), and get the required distribution for the final plan. When we optimize the final query stage, we skip certain `AQEShuffleReadRule` if it breaks the required distribution.
### Why are the changes needed?
The current solution for optimizing the final query stage is pretty hacky and overkill. As an example, the newly added rule `OptimizeSkewInRebalancePartitions` can hardly apply as it's very common that the query plan has shuffles with origin `ENSURE_REQUIREMENTS`, which is not supported by `OptimizeSkewInRebalancePartitions`.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
updated tests
Closes#33541 from cloud-fan/aqe.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit dd80457ffb)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This a followup of the recent work such as https://github.com/apache/spark/pull/33200
For `ALTER TABLE` commands, the logical plans do not have the common `AlterTable` prefix in the name and just use names like `SetTableLocation`. This PR proposes to follow the same naming rule in `ALTER TABE ... COLUMN` commands.
This PR also moves these AlterTable commands to a individual file and give them a base trait.
### Why are the changes needed?
name simplification
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing test
Closes#33609 from cloud-fan/dsv2.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit 7cb9c1c241)
Signed-off-by: Max Gekk <max.gekk@gmail.com>
This PR proposes to fail properly so JSON parser can proceed and parse the input with the permissive mode.
Previously, we passed `null`s as are, the root `InternalRow`s became `null`s, and it causes the query fails even with permissive mode on.
Now, we fail explicitly if `null` is passed when the input array contains `null`.
Note that this is consistent with non-array JSON input:
**Permissive mode:**
```scala
spark.read.json(Seq("""{"a": "str"}""", """null""").toDS).collect()
```
```
res0: Array[org.apache.spark.sql.Row] = Array([str], [null])
```
**Failfast mode**:
```scala
spark.read.option("mode", "failfast").json(Seq("""{"a": "str"}""", """null""").toDS).collect()
```
```
org.apache.spark.SparkException: Malformed records are detected in record parsing. Parse Mode: FAILFAST. To process malformed records as null result, try setting the option 'mode' as 'PERMISSIVE'.
at org.apache.spark.sql.catalyst.util.FailureSafeParser.parse(FailureSafeParser.scala:70)
at org.apache.spark.sql.DataFrameReader.$anonfun$json$7(DataFrameReader.scala:540)
at scala.collection.Iterator$$anon$11.nextCur(Iterator.scala:484)
```
To make the permissive mode to proceed and parse without throwing an exception.
**Permissive mode:**
```scala
spark.read.json(Seq("""[{"a": "str"}, null]""").toDS).collect()
```
Before:
```
java.lang.NullPointerException
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
```
After:
```
res0: Array[org.apache.spark.sql.Row] = Array([null])
```
NOTE that this behaviour is consistent when JSON object is malformed:
```scala
spark.read.schema("a int").json(Seq("""[{"a": 123}, {123123}, {"a": 123}]""").toDS).collect()
```
```
res0: Array[org.apache.spark.sql.Row] = Array([null])
```
Since we're parsing _one_ JSON array, related records all fail together.
**Failfast mode:**
```scala
spark.read.option("mode", "failfast").json(Seq("""[{"a": "str"}, null]""").toDS).collect()
```
Before:
```
java.lang.NullPointerException
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
```
After:
```
org.apache.spark.SparkException: Malformed records are detected in record parsing. Parse Mode: FAILFAST. To process malformed records as null result, try setting the option 'mode' as 'PERMISSIVE'.
at org.apache.spark.sql.catalyst.util.FailureSafeParser.parse(FailureSafeParser.scala:70)
at org.apache.spark.sql.DataFrameReader.$anonfun$json$7(DataFrameReader.scala:540)
at scala.collection.Iterator$$anon$11.nextCur(Iterator.scala:484)
```
Manually tested, and unit test was added.
Closes#33608 from HyukjinKwon/SPARK-36379.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(cherry picked from commit 0bbcbc6508)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
For added UT, without this patch will failed as below
```
[info] - SHOW TABLES V2: SPARK-36086: CollapseProject project replace alias should use origin column name *** FAILED *** (4 seconds, 935 milliseconds)
[info] java.lang.RuntimeException: After applying rule org.apache.spark.sql.catalyst.optimizer.CollapseProject in batch Operator Optimization before Inferring Filters, the structural integrity of the plan is broken.
[info] at org.apache.spark.sql.errors.QueryExecutionErrors$.structuralIntegrityIsBrokenAfterApplyingRuleError(QueryExecutionErrors.scala:1217)
[info] at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:229)
[info] at scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
[info] at scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
[info] at scala.collection.immutable.List.foldLeft(List.scala:91)
[info] at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:208)
[info] at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:200)
[info] at scala.collection.immutable.List.foreach(List.scala:431)
[info] at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:200)
[info] at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:179)
[info] at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88)
```
CollapseProject project replace alias should use origin column name
### Why are the changes needed?
Fix bug
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#33576 from AngersZhuuuu/SPARK-36086.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit f3173956cb)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change the `NullType.simpleString` to "void" to set "void" as the formal type name of `NullType`
### Why are the changes needed?
This PR is intended to address the type name discussion in PR #28833. Here are the reasons:
1. The type name of NullType is displayed everywhere, e.g. schema string, error message, document. Hence it's not possible to hide it from users, we have to choose a proper name
2. The "void" is widely used as the type name of "NULL", e.g. Hive, pgSQL
3. Changing to "void" can enable the round trip of `toDDL`/`fromDDL` for NullType. (i.e. make `from_json(col, schema.toDDL)`) work
### Does this PR introduce _any_ user-facing change?
Yes, the type name of "NULL" is changed from "null" to "void". for example:
```
scala> sql("select null as a, 1 as b").schema.catalogString
res5: String = struct<a:void,b:int>
```
### How was this patch tested?
existing test cases
Closes#33437 from linhongliu-db/SPARK-36224-void-type-name.
Authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 2f700773c2)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, v2 ALTER TABLE ADD COLUMNS does not check duplicates for the user specified columns. For example,
```
spark.sql(s"CREATE TABLE $t (id int) USING $v2Format")
spark.sql("ALTER TABLE $t ADD COLUMNS (data string, data string)")
```
doesn't fail the analysis, and it's up to the catalog implementation to handle it. For v1 command, the duplication is checked before invoking the catalog.
### Why are the changes needed?
To check the duplicate columns during analysis and be consistent with v1 command.
### Does this PR introduce _any_ user-facing change?
Yes, now the above will command will print out the fllowing:
```
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in the user specified columns: `data`
```
### How was this patch tested?
Added new unit tests
Closes#33600 from imback82/alter_add_duplicate_columns.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 3b713e7f61)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Changes in this PR:
- `AdaptiveSparkPlanExec` has new methods `finalPlanSupportsColumnar` and `doExecuteColumnar` to support adaptive queries where the final query stage produces columnar data.
- `SessionState` now has a new set of injectable rules named `finalQueryStagePrepRules` that can be applied to the final query stage.
- `AdaptiveSparkPlanExec` can now safely be wrapped by either `RowToColumnarExec` or `ColumnarToRowExec`.
A Spark plugin can use the new rules to remove the root `ColumnarToRowExec` transition that is inserted by previous rules and at execution time can call `finalPlanSupportsColumnar` to see if the final query stage is columnar. If the plan is columnar then the plugin can safely call `doExecuteColumnar`. The adaptive plan can be wrapped in either `RowToColumnarExec` or `ColumnarToRowExec` to force a particular output format. There are fast paths in both of these operators to avoid any redundant transitions.
### Why are the changes needed?
Without this change it is necessary to use reflection to get the final physical plan to determine whether it is columnar and to execute it is a columnar plan. `AdaptiveSparkPlanExec` only provides public methods for row-based execution.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I have manually tested this patch with the RAPIDS Accelerator for Apache Spark.
Closes#33140 from andygrove/support-columnar-adaptive.
Authored-by: Andy Grove <andygrove73@gmail.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
(cherry picked from commit 0f538402fb)
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
This PR proposes to implement `distributed-sequence` index in Scala side.
### Why are the changes needed?
- Avoid unnecessary (de)serialization
- Keep the nullability in the input DataFrame when `distributed-sequence` is enabled. During the serialization, all fields are being nullable for now (see https://github.com/apache/spark/pull/32775#discussion_r645882104)
### Does this PR introduce _any_ user-facing change?
No to end users since pandas API on Spark is not released yet.
```python
import pyspark.pandas as ps
ps.set_option('compute.default_index_type', 'distributed-sequence')
ps.range(1).spark.print_schema()
```
Before:
```
root
|-- id: long (nullable = true)
```
After:
```
root
|-- id: long (nullable = false)
```
### How was this patch tested?
Manually tested, and existing tests should cover them.
Closes#33570 from HyukjinKwon/SPARK-36338.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit c6140d4d0a)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/33352 , to simplify the JDBC aggregate pushdown:
1. We should get the schema of the aggregate query by asking the JDBC server, instead of calculating it by ourselves. This can simplify the code a lot, and is also more robust: the data type of SUM may vary in different databases, it's fragile to assume they are always the same as Spark.
2. because of 1, now we can remove the `dataType` property from the public `Sum` expression.
This PR also contains some small improvements:
1. Spark should deduplicate the aggregate expressions before pushing them down.
2. Improve the `toString` of public aggregate expressions to make them more SQL.
### Why are the changes needed?
code and API simplification
### Does this PR introduce _any_ user-facing change?
this API is not released yet.
### How was this patch tested?
existing tests
Closes#33579 from cloud-fan/dsv2.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit 387a251a68)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Since we have add log about commit time, I think this useful and we can make user know it directly in SQL tab's UI.
### Why are the changes needed?
Make user can directly know commit duration.
### Does this PR introduce _any_ user-facing change?
User can see file commit duration in SQL tab's SQL plan graph
### How was this patch tested?
Mannul tested
Closes#33553 from AngersZhuuuu/SPARK-34399-FOLLOWUP.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
