### What changes were proposed in this pull request?
Follow comment of https://github.com/apache/spark/pull/25854#discussion_r342383272
### Why are the changes needed?
NO
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
ADD TEST CASE
Closes#26406 from AngersZhuuuu/SPARK-29145-FOLLOWUP.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
SPARK-29397 added new interfaces for creating driver and executor
plugins. These were added in a new, more isolated package that does
not pollute the main o.a.s package.
The old interface is now redundant. Since it's a DeveloperApi and
we're about to have a new major release, let's remove it instead of
carrying more baggage forward.
Closes#26390 from vanzin/SPARK-29399.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
All tooltips message will display in centre.
### Why are the changes needed?
Some time tooltips will hide the data of column and tooltips display position will be inconsistent in UI.
### Does this PR introduce any user-facing change?
yes.
### How was this patch tested?
Manual test.
Closes#26263 from 07ARB/SPARK-29570.
Lead-authored-by: Ankitraj <8948111+07ARB@users.noreply.github.com>
Co-authored-by: 07ARB <ankitrajboudh@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Adjust improper unpersist timing on RDD.
### Why are the changes needed?
Improper unpersist timing will result in memory waste
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manually
Closes#26469 from Icysandwich/SPARK-29844.
Authored-by: DongWang <cqwd123@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
When creating v2 expressions, we have public java APIs, as well as interval scala APIs. All of these APIs take a string column name and parse it to `NamedReference`.
This is convenient for end-users, but not for interval development. For example, the query plan already contains the parsed partition/bucket column names, and it's tricky if we need to quote the names before creating v2 expressions.
This PR proposes to change the interval scala APIs to take `NamedReference` directly, with a new method to create `NamedReference` with the exact name parts. The public java APIs are not changed.
### Why are the changes needed?
fix a bug, and make it easier to create v2 expressions correctly in the future.
### Does this PR introduce any user-facing change?
yes, now v2 CREATE TABLE works as expected.
### How was this patch tested?
a new test
Closes#26425 from cloud-fan/extract.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Ryan Blue <blue@apache.org>
### What changes were proposed in this pull request?
When whole stage codegen `HashAggregateExec`, create the hash map when we begin to process inputs.
### Why are the changes needed?
Sort-merge join completes directly if the left side table is empty. If there is an aggregate in the right side, the aggregate will not be triggered at all, but its hash map is created during codegen and can't be released.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
a new test
Closes#26471 from cloud-fan/memory.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
```sql
-- !query 83
select -integer '7'
-- !query 83 schema
struct<7:int>
-- !query 83 output
7
-- !query 86
select -date '1999-01-01'
-- !query 86 schema
struct<DATE '1999-01-01':date>
-- !query 86 output
1999-01-01
-- !query 87
select -timestamp '1999-01-01'
-- !query 87 schema
struct<TIMESTAMP('1999-01-01 00:00:00'):timestamp>
-- !query 87 output
1999-01-01 00:00:00
```
the integer should be -7 and the date and timestamp results are confusing which should throw exceptions
### Why are the changes needed?
bug fix
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
ADD UTs
Closes#26479 from yaooqinn/SPARK-29855.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Executor's heartbeat will send synchronously to BlockManagerMaster to let it know that the block manager is still alive. In a heavy cluster, it will timeout and cause block manager re-register unexpected.
This improvement will separate a heartbeat endpoint from the driver endpoint. In our production environment, this was really helpful to prevent executors from unstable up and down.
### Why are the changes needed?
`BlockManagerMasterEndpoint` handles many events from executors like `RegisterBlockManager`, `GetLocations`, `RemoveShuffle`, `RemoveExecutor` etc. In a heavy cluster/app, it is always busy. The `BlockManagerHeartbeat` event also was handled in this endpoint. We found it may timeout when it's busy. So we add a new endpoint `BlockManagerMasterHeartbeatEndpoint` to handle heartbeat separately.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Exist UTs
Closes#25971 from LantaoJin/SPARK-29298.
Lead-authored-by: lajin <lajin@ebay.com>
Co-authored-by: Alan Jin <lajin@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Print events that take too long time to process, to help find out what type of events is slow.
Introduce two extra configs:
* **spark.scheduler.listenerbus.logSlowEvent.enabled** Whether to enable log the events that are slow
* **spark.scheduler.listenerbus.logSlowEvent.threshold** The time threshold of whether an event is considered to be slow.
### How was this patch tested?
N/A
Closes#25702 from jiangxb1987/SPARK-29001.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add ShowTablePropertiesStatement and make SHOW TBLPROPERTIES go through the same catalog/table resolution framework of v2 commands.
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing end-users. e.g.
USE my_catalog
DESC t // success and describe the table t from my_catalog
SHOW TBLPROPERTIES t // report table not found as there is no table t in the session catalog
### Does this PR introduce any user-facing change?
yes. When running SHOW TBLPROPERTIES Spark fails the command if the current catalog is set to a v2 catalog, or the table name specified a v2 catalog.
### How was this patch tested?
Unit tests.
Closes#26176 from planga82/feature/SPARK-29519_SHOW_TBLPROPERTIES_datasourceV2.
Authored-by: Pablo Langa <soypab@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch adds `JsonDeserialize` annotation for the field which type is `Option[Long]` in LogInfo/AttemptInfoWrapper. It hits https://github.com/FasterXML/jackson-module-scala/wiki/FAQ#deserializing-optionint-and-other-primitive-challenges - other existing json models take care of this, but we missed to add annotation to these classes.
### Why are the changes needed?
Without this change, SHS will throw ClassNotFoundException when rebuilding App UI.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually tested.
Closes#26397 from HeartSaVioR/SPARK-29755.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
This patch fixes the edge case of streaming left/right outer join described below:
Suppose query is provided as
`select * from A join B on A.id = B.id AND (A.ts <= B.ts AND B.ts <= A.ts + interval 5 seconds)`
and there're two rows for L1 (from A) and R1 (from B) which ensures L1.id = R1.id and L1.ts = R1.ts.
(we can simply imagine it from self-join)
Then Spark processes L1 and R1 as below:
- row L1 and row R1 are joined at batch 1
- row R1 is evicted at batch 2 due to join and watermark condition, whereas row L1 is not evicted
- row L1 is evicted at batch 3 due to join and watermark condition
When determining outer rows to match with null, Spark applies some assumption commented in codebase, as below:
```
Checking whether the current row matches a key in the right side state, and that key
has any value which satisfies the filter function when joined. If it doesn't,
we know we can join with null, since there was never (including this batch) a match
within the watermark period. If it does, there must have been a match at some point, so
we know we can't join with null.
```
But as explained the edge-case earlier, the assumption is not correct. As we don't have any good assumption to optimize which doesn't have edge-case, we have to track whether such row is matched with others before, and match with null row only when the row is not matched.
To track the matching of row, the patch adds a new state to streaming join state manager, and mark whether the row is matched to others or not. We leverage the information when dealing with eviction of rows which would be candidates to match with null rows.
This approach introduces new state format which is not compatible with old state format - queries with old state format will be still running but they will still have the issue and be required to discard checkpoint and rerun to take this patch in effect.
### Why are the changes needed?
This patch fixes a correctness issue.
### Does this PR introduce any user-facing change?
No for compatibility viewpoint, but we'll encourage end users to discard the old checkpoint and rerun the query if they run stream-stream outer join query with old checkpoint, which might be "yes" for the question.
### How was this patch tested?
Added UT which fails on current Spark and passes with this patch. Also passed existing streaming join UTs.
Closes#26108 from HeartSaVioR/SPARK-26154-shorten-alternative.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
This unblocks the event handling thread, which should help avoid dropped
events when large queries are running.
Existing unit tests should already cover this code.
Closes#26405 from vanzin/SPARK-29766.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Enable nested schema pruning and nested pruning on expressions by default. We have been using those features in production in Apple for couple months with great success. For some jobs, we reduce the data reading by more than 8x and 21x faster in wall clock time.
### Why are the changes needed?
Better performance.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#26443 from dbtsai/enableNestedSchemaPrunning.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
1,ML models should extend toString method to expose basic information.
Current some algs (GBT/RF/LoR) had done this, while others not yet.
2,add `val numFeatures` in `BisectingKMeansModel`/`GaussianMixtureModel`/`KMeansModel`/`AFTSurvivalRegressionModel`/`IsotonicRegressionModel`
### Why are the changes needed?
ML models should extend toString method to expose basic information.
### Does this PR introduce any user-facing change?
yes
### How was this patch tested?
existing testsuites
Closes#26439 from zhengruifeng/models_toString.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
For better test coverage, this pr is to add join-related configs in `inner-join.sql` and `postgreSQL/join.sql`. These join related configs were just copied from ones in the other join-related tests in `SQLQueryTestSuite` (e.g., https://github.com/apache/spark/blob/master/sql/core/src/test/resources/sql-tests/inputs/natural-join.sql#L2-L4).
### Why are the changes needed?
Better test coverage.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#26459 from maropu/AddJoinConds.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
With the latest string to literal optimization https://github.com/apache/spark/pull/26256, some interval strings can not be cast when there are some spaces between signs and unit values. After state `PARSE_SIGN`, it directly goes to `PARSE_UNIT_VALUE` when takes a space character as the end. So when there are some white spaces come before the real unit value, it fails to parse, we should add a new state like `TRIM_VALUE` to trim all these spaces.
How to re-produce, which aim the revisions since https://github.com/apache/spark/pull/26256 is merged
```sql
select cast(v as interval) from values ('+ 1 second') t(v);
select cast(v as interval) from values ('- 1 second') t(v);
```
### Why are the changes needed?
bug fix
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
1. ut
2. new benchmark test
Closes#26449 from yaooqinn/SPARK-29605.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Hive support STORED AS new file format syntax:
```sql
CREATE TABLE tbl(a int) STORED AS TEXTFILE;
CREATE TABLE tbl2 LIKE tbl STORED AS PARQUET;
```
We add a similar syntax for Spark. Here we separate to two features:
1. specify a different table provider in CREATE TABLE LIKE
2. Hive compatibility
In this PR, we address the first one:
- [ ] Using `USING provider` to specify a different table provider in CREATE TABLE LIKE.
- [ ] Using `STORED AS file_format` in CREATE TABLE LIKE to address Hive compatibility.
### Why are the changes needed?
Use CREATE TABLE tb1 LIKE tb2 command to create an empty table tb1 based on the definition of table tb2. The most user case is to create tb1 with the same schema of tb2. But an inconvenient case here is this command also copies the FileFormat from tb2, it cannot change the input/output format and serde. Add the ability of changing file format is useful for some scenarios like upgrading a table from a low performance file format to a high performance one (parquet, orc).
### Does this PR introduce any user-facing change?
Add a new syntax based on current CTL:
```sql
CREATE TABLE tbl2 LIKE tbl [USING parquet];
```
### How was this patch tested?
Modify some exist UTs.
Closes#26097 from LantaoJin/SPARK-29421.
Authored-by: lajin <lajin@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose new expression `MakeInterval` and register it as the function `make_interval`. The function accepts the following parameters:
- `years` - the number of years in the interval, positive or negative. The parameter is multiplied by 12, and added to interval's `months`.
- `months` - the number of months in the interval, positive or negative.
- `weeks` - the number of months in the interval, positive or negative. The parameter is multiplied by 7, and added to interval's `days`.
- `hours`, `mins` - the number of hours and minutes. The parameters can be negative or positive. They are converted to microseconds and added to interval's `microseconds`.
- `seconds` - the number of seconds with the fractional part in microseconds precision. It is converted to microseconds, and added to total interval's `microseconds` as `hours` and `minutes`.
For example:
```sql
spark-sql> select make_interval(2019, 11, 1, 1, 12, 30, 01.001001);
2019 years 11 months 8 days 12 hours 30 minutes 1.001001 seconds
```
### Why are the changes needed?
- To improve user experience with Spark SQL, and allow users making `INTERVAL` columns from other columns containing `years`, `months` ... `seconds`. Currently, users can make an `INTERVAL` column from other columns only by constructing a `STRING` column and cast it to `INTERVAL`. Have a look at the `IntervalBenchmark` as an example.
- To maintain feature parity with PostgreSQL which provides such function:
```sql
# SELECT make_interval(2019, 11);
make_interval
--------------------
2019 years 11 mons
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
- By new tests for the `MakeInterval` expression to `IntervalExpressionsSuite`
- By tests in `interval.sql`
Closes#26446 from MaxGekk/make_interval.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Provide Ellipses in Statement column , just like description in Jobs page .
### Why are the changes needed?
When a query is executed the whole query statement is displayed no matter how big it is. When bigger queries are executed, it covers a large portion of the page display, when we have multiple queries it is difficult to scroll down to view all.
### Does this PR introduce any user-facing change?
No
Before:
After:
### How was this patch tested?
Manual
Closes#26364 from PavithraRamachandran/ellipse_JDBC.
Authored-by: Pavithra Ramachandran <pavi.rams@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to enable [GitHub Action Cache on Maven local repository](https://github.com/actions/cache/blob/master/examples.md#java---maven) for the following goals.
1. To reduce the chance of failure due to the Maven download flakiness.
2. To speed up the build a little bit.
Unfortunately, due to the GitHub Action Cache limitation, it seems that we cannot put all into a single cache. It's ignored like the following.
- **.m2/repository is 680777194 bytes**
```
/bin/tar -cz -f /home/runner/work/_temp/01f162c3-0c78-4772-b3de-b619bb5d7721/cache.tgz -C /home/runner/.m2/repository .
3
##[warning]Cache size of 680777194 bytes is over the 400MB limit, not saving cache.
```
### Why are the changes needed?
Not only for the speed up, but also for reducing the Maven download flakiness, we had better enable caching on local maven repository. The followings are the failure examples in these days.
- https://github.com/apache/spark/runs/295869450
```
[ERROR] Failed to execute goal on project spark-streaming-kafka-0-10_2.12:
Could not resolve dependencies for project org.apache.spark:spark-streaming-kafka-0-10_2.12🫙spark-367716:
Could not transfer artifact com.fasterxml.jackson.datatype:jackson-datatype-jdk8:jar:2.10.0
from/to central (https://repo.maven.apache.org/maven2):
Failed to transfer file https://repo.maven.apache.org/maven2/com/fasterxml/jackson/datatype/
jackson-datatype-jdk8/2.10.0/jackson-datatype-jdk8-2.10.0.jar with status code 503 -> [Help 1]
...
[ERROR] mvn <args> -rf :spark-streaming-kafka-0-10_2.12
```
```
[ERROR] Failed to execute goal on project spark-tools_2.12:
Could not resolve dependencies for project org.apache.spark:spark-tools_2.12🫙3.0.0-SNAPSHOT:
Failed to collect dependencies at org.clapper:classutil_2.12🫙1.5.1:
Failed to read artifact descriptor for org.clapper:classutil_2.12🫙1.5.1:
Could not transfer artifact org.clapper:classutil_2.12:pom:1.5.1 from/to central (https://repo.maven.apache.org/maven2):
Connection timed out (Read failed) -> [Help 1]
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually check the GitHub Action log of this PR.
```
Cache restored from key: 1.8-hadoop-2.7-maven-com-5b4a9fb13c5f5ff78e65a20003a3810796e4d1fde5f24d397dfe6e5153960ce4
Cache restored from key: 1.8-hadoop-2.7-maven-org-5b4a9fb13c5f5ff78e65a20003a3810796e4d1fde5f24d397dfe6e5153960ce4
```
Closes#26456 from dongjoon-hyun/SPARK-29820.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
- `SqlBase.g4` is modified to support a negative sign `-` in the interval type constructor from a string and in interval literals
- Negate interval in `AstBuilder` if a sign presents.
- Interval related SQL statements are moved from `inputs/datetime.sql` to new file `inputs/interval.sql`
For example:
```sql
spark-sql> select -interval '-1 month 1 day -1 second';
1 months -1 days 1 seconds
spark-sql> select -interval -1 month 1 day -1 second;
1 months -1 days 1 seconds
```
### Why are the changes needed?
For feature parity with PostgreSQL which supports that:
```sql
# select -interval '-1 month 1 day -1 second';
?column?
-------------------------
1 mon -1 days +00:00:01
(1 row)
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
- Added tests to `ExpressionParserSuite`
- by `interval.sql`
Closes#26438 from MaxGekk/negative-interval.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR aims to upgrade `scala-maven-plugin` to `4.3.0` for Scala `2.13.1`.
We tried 4.2.4, but it's reverted due to Windows build issue. Now, `4.3.0` has a Window fix.
### Why are the changes needed?
Scala 2.13.1 seems to break the binary compatibility.
We need to upgrade `scala-maven-plugin` to bring the the following fixes for the latest Scala 2.13.1.
- https://github.com/davidB/scala-maven-plugin/issues/363
- https://github.com/sbt/zinc/issues/698
Also, `4.3.0` has the following Window fix.
- https://github.com/davidB/scala-maven-plugin/issues/370 (4.2.4 throws error on Windows)
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
- For now, we don't support Scala-2.13. This PR at least needs to pass the existing Jenkins with Maven to get prepared for Scala-2.13.
- `AppVeyor` passed. (https://ci.appveyor.com/project/ApacheSoftwareFoundation/spark/builds/28745383)
Closes#26457 from dongjoon-hyun/SPARK-29528.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In the PR, I propose an enumeration for interval units with the value `YEAR`, `MONTH`, `WEEK`, `DAY`, `HOUR`, `MINUTE`, `SECOND`, `MILLISECOND`, `MICROSECOND` and `NANOSECOND`.
### Why are the changes needed?
- This should prevent typos in interval unit names
- Stronger type checking of unit parameters.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By existing test suites `ExpressionParserSuite` and `IntervalUtilsSuite`
Closes#26455 from MaxGekk/interval-unit-enum.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
Add AlterViewAsStatement and make ALTER VIEW ... QUERY go through the same catalog/table resolution framework of v2 commands.
It's important to make all the commands have the same table resolution behavior, to avoid confusing end-users. e.g.
```
USE my_catalog
DESC v // success and describe the view v from my_catalog
ALTER VIEW v SELECT 1 // report view not found as there is no view v in the session catalog
```
Yes. When running ALTER VIEW ... QUERY, Spark fails the command if the current catalog is set to a v2 catalog, or the view name specified a v2 catalog.
unit tests
Closes#26453 from huaxingao/spark-29730.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
The Spark metrics system produces many different metrics and not all of them are used at the same time. This proposes to introduce a configuration parameter to allow disabling the registration of metrics in the "static sources" category.
### Why are the changes needed?
This allows to reduce the load and clutter on the sink, in the cases when the metrics in question are not needed. The metrics registerd as "static sources" are under the namespaces CodeGenerator and HiveExternalCatalog and can produce a significant amount of data, as they are registered for the driver and executors.
### Does this PR introduce any user-facing change?
It introduces a new configuration parameter `spark.metrics.register.static.sources.enabled`
### How was this patch tested?
Manually tested.
```
$ cat conf/metrics.properties
*.sink.prometheusServlet.class=org.apache.spark.metrics.sink.PrometheusServlet
*.sink.prometheusServlet.path=/metrics/prometheus
master.sink.prometheusServlet.path=/metrics/master/prometheus
applications.sink.prometheusServlet.path=/metrics/applications/prometheus
$ bin/spark-shell
$ curl -s http://localhost:4040/metrics/prometheus/ | grep Hive
metrics_local_1573330115306_driver_HiveExternalCatalog_fileCacheHits_Count 0
metrics_local_1573330115306_driver_HiveExternalCatalog_filesDiscovered_Count 0
metrics_local_1573330115306_driver_HiveExternalCatalog_hiveClientCalls_Count 0
metrics_local_1573330115306_driver_HiveExternalCatalog_parallelListingJobCount_Count 0
metrics_local_1573330115306_driver_HiveExternalCatalog_partitionsFetched_Count 0
$ bin/spark-shell --conf spark.metrics.static.sources.enabled=false
$ curl -s http://localhost:4040/metrics/prometheus/ | grep Hive
```
Closes#26320 from LucaCanali/addConfigRegisterStaticMetrics.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Explicitly clear registered metrics when `MetricsSystem` shuts down.
### Why are the changes needed?
See https://issues.apache.org/jira/browse/SPARK-29795 for a complete explanation. The TL;DR is there is some evidence this could leak resources after Spark is shut down, and that may be a minor issue in Spark 3+ for apps or tests that re-start SparkContexts in the same JVM.
### Does this PR introduce any user-facing change?
The possible difference here is that, after Spark is stopped, metrics are no longer available. It's unclear to me whether this is intended behavior anyway.
### How was this patch tested?
See https://issues.apache.org/jira/browse/SPARK-29795 for more context:
- Spark 3 already passes tests without this change
- Spark 2.4 does too, as exists in branch-2.4 now
- Spark 2.4 fails tests if metrics 4.x is used, without this change
The last point is not directly relevant, as Spark 2.4 will not use metrics 4.x. It's evidence that it addresses some potential issue, however.
Closes#26427 from srowen/SPARK-29795.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
As it has been discussed in https://github.com/apache/spark/pull/26397#discussion_r343726691 `FsHistoryProvider` import section has to be cleaned up.
### Why are the changes needed?
Unused imports.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Closes#26436 from gaborgsomogyi/SPARK-29755.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Fixing the typos in SQL reference document.
### Why are the changes needed?
For user readability
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Tested manually.
Closes#26424 from jobitmathew/typo.
Authored-by: Jobit Mathew <jobit.mathew@huawei.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Remove the duplicate code
See the build failure: https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Compile/job/spark-master-compile-maven-hadoop-3.2/986/
### Why are the changes needed?
Fix the compilation
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
The existing tests
Closes#26445 from gatorsmile/hotfixPraser.
Authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
This PR supports MERGE INTO in the parser and add the corresponding logical plan. The SQL syntax likes,
```
MERGE INTO [ds_catalog.][multi_part_namespaces.]target_table [AS target_alias]
USING [ds_catalog.][multi_part_namespaces.]source_table | subquery [AS source_alias]
ON <merge_condition>
[ WHEN MATCHED [ AND <condition> ] THEN <matched_action> ]
[ WHEN MATCHED [ AND <condition> ] THEN <matched_action> ]
[ WHEN NOT MATCHED [ AND <condition> ] THEN <not_matched_action> ]
```
where
```
<matched_action> =
DELETE |
UPDATE SET * |
UPDATE SET column1 = value1 [, column2 = value2 ...]
<not_matched_action> =
INSERT * |
INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 ...])
```
### Why are the changes needed?
This is a start work for introduce `MERGE INTO` support for the builtin datasource, and the design work for the `MERGE INTO` support in DSV2.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
New test cases.
Closes#26167 from xianyinxin/SPARK-28893.
Authored-by: xy_xin <xianyin.xxy@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
On the worker we express lambda functions as strings and then eval them to create a "mapper" function. This make the code hard to read & limits the # of arguments a udf can support to 256 for python <= 3.6.
This PR rewrites the mapper functions as nested functions instead of "lambda strings" and allows passing in more than 255 args.
### Why are the changes needed?
The jira ticket associated with this issue describes how MLflow uses udfs to consume columns as features. This pattern isn't unique and a limit of 255 features is quite low.
### Does this PR introduce any user-facing change?
Users can now pass more than 255 cols to a udf function.
### How was this patch tested?
Added a unit test for passing in > 255 args to udf.
Closes#26442 from MrBago/replace-lambdas-on-worker.
Authored-by: Bago Amirbekian <bago@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
### What changes were proposed in this pull request?
Replace qualifiedName with multipartIdentifier in parser rules of DDL commands.
### Why are the changes needed?
There are identifiers in some DDL rules we use `qualifiedName`. We should use `multipartIdentifier` because it can capture wrong identifiers such as `test-table`, `test-col`.
### Does this PR introduce any user-facing change?
Yes. Wrong identifiers such as test-table, will be captured now after this change.
### How was this patch tested?
Unit tests.
Closes#26419 from viirya/SPARK-29680-followup2.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
This PR proposes to infer bytes as binary types in Python 3. See https://github.com/apache/spark/pull/25749 for discussions. I have also checked that Arrow considers `bytes` as binary type, and PySpark UDF can also accepts `bytes` as a binary type.
Since `bytes` is not a `str` anymore in Python 3, it's clear to call it `BinaryType` in Python 3.
### Why are the changes needed?
To respect Python 3's `bytes` type and support Python's primitive types.
### Does this PR introduce any user-facing change?
Yes.
**Before:**
```python
>>> spark.createDataFrame([[b"abc"]])
Traceback (most recent call last):
File "/.../spark/python/pyspark/sql/types.py", line 1036, in _infer_type
return _infer_schema(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1062, in _infer_schema
raise TypeError("Can not infer schema for type: %s" % type(row))
TypeError: Can not infer schema for type: <class 'bytes'>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/session.py", line 787, in createDataFrame
rdd, schema = self._createFromLocal(map(prepare, data), schema)
File "/.../spark/python/pyspark/sql/session.py", line 445, in _createFromLocal
struct = self._inferSchemaFromList(data, names=schema)
File "/.../spark/python/pyspark/sql/session.py", line 377, in _inferSchemaFromList
schema = reduce(_merge_type, (_infer_schema(row, names) for row in data))
File "/.../spark/python/pyspark/sql/session.py", line 377, in <genexpr>
schema = reduce(_merge_type, (_infer_schema(row, names) for row in data))
File "/.../spark/python/pyspark/sql/types.py", line 1064, in _infer_schema
fields = [StructField(k, _infer_type(v), True) for k, v in items]
File "/.../spark/python/pyspark/sql/types.py", line 1064, in <listcomp>
fields = [StructField(k, _infer_type(v), True) for k, v in items]
File "/.../spark/python/pyspark/sql/types.py", line 1038, in _infer_type
raise TypeError("not supported type: %s" % type(obj))
TypeError: not supported type: <class 'bytes'>
```
**After:**
```python
>>> spark.createDataFrame([[b"abc"]])
DataFrame[_1: binary]
```
### How was this patch tested?
Unittest was added and manually tested.
Closes#26432 from HyukjinKwon/SPARK-29798.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
It adds a note about the required port of a master url in Kubernetes.
Currently a port needs to be specified for the Kubernetes API. Also in case the API is hosted on the HTTPS port. Else the driver might fail with https://medium.com/kidane.weldemariam_75349/thanks-james-on-issuing-spark-submit-i-run-into-this-error-cc507d4f8f0d
Yes, a change to the "Running on Kubernetes" guide.
None - Documentation change
Closes#26426 from Tapped/patch-1.
Authored-by: Emil Sandstø <emilalexer@hotmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
interval type support >, >=, <, <=, =, <=>, order by, min,max..
### Why are the changes needed?
Part of SPARK-27764 Feature Parity between PostgreSQL and Spark
### Does this PR introduce any user-facing change?
yes, we now support compare intervals
### How was this patch tested?
add ut
Closes#26337 from yaooqinn/SPARK-29679.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
avg aggregate support interval type values
### Why are the changes needed?
Part of SPARK-27764 Feature Parity between PostgreSQL and Spark
### Does this PR introduce any user-facing change?
yes, we can do avg on intervals
### How was this patch tested?
add ut
Closes#26347 from yaooqinn/SPARK-29688.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Spark org.apache.spark.sql.functions do not have `if` function so conditions are expressed using `when-otherwise` function. However `If` (which is available in SQL) has more efficient code gen. This pr rewrites `when-otherwise` conditions to `If` if it is possible (`when-otherwise` with single branch)
### Why are the changes needed?
It is an optimization enhancement. Here is a simple performance comparison (tested in local mode (with 4 cores)):
```
val df = spark.range(10000000000L).withColumn("x", rand)
val resultA = df.withColumn("r", when($"x" < 0.5, lit(1)).otherwise(lit(0))).agg(sum($"r"))
val resultB = df.withColumn("r", expr("if(x < 0.5, 1, 0)")).agg(sum($"r"))
resultA.collect() // takes 56s to finish
resultB.collect() // takes 30s to finish
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
New test is added.
Closes#26294 from davidvrba/spark-28477_rewriteCaseWhenToIf.
Authored-by: davidvrba <vrba.dave@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1,unpersist intermediate rdd `wordCounts`
2,if the `dataset` is already persisted, we do not need to persist rdd `input`
3,if both `minDF`&`maxDF` are gteq or lt than 1, we can compare & check them af first.
### Why are the changes needed?
we should unpersit unused rdd ASAP
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing testsuites
Closes#26398 from zhengruifeng/CountVectorizer_unpersist_wordCounts.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
V2 catalog support namespace, we should add `withNamespace` like `withDatabase`.
### Why are the changes needed?
Make test easy.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Add UT.
Closes#26411 from ulysses-you/Add-test-with-namespace.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Move method add/subtract/negate from CalendarInterval to IntervalUtils
### Why are the changes needed?
https://github.com/apache/spark/pull/26410#discussion_r343125468 suggested here
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
add uts and move some
Closes#26423 from yaooqinn/SPARK-29787.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Kafka producers are now closed when `spark.kafka.producer.cache.timeout` reached which could be significant problem when processing big SQL queries. The workaround was to increase `spark.kafka.producer.cache.timeout` to a number where the biggest SQL query can be finished.
In this PR I've adapted similar solution which already exists on the consumer side, namely applies Apache Commons Pool on the producer side as well. Main advantages choosing this solution:
* Producers are not closed until they're in use
* No manual reference counting needed (which may be error prone)
* Thread-safe by design
* Provides jmx connection to the pool where metrics can be fetched
What this PR contains:
* Introduced producer side parameters to configure pool
* Renamed `InternalKafkaConsumerPool` to `InternalKafkaConnectorPool` and made it abstract
* Created 2 implementations from it: `InternalKafkaConsumerPool` and `InternalKafkaProducerPool`
* Adapted `CachedKafkaProducer` to use `InternalKafkaProducerPool`
* Changed `KafkaDataWriter` and `KafkaDataWriteTask` to release producer even in failure scenario
* Added several new tests
* Extended `KafkaTest` to clear not only producers but consumers as well
* Renamed `InternalKafkaConsumerPoolSuite` to `InternalKafkaConnectorPoolSuite` where only consumer tests are checking the behavior (please see comment for reasoning)
What this PR not yet contains(but intended when the main concept is stable):
* User facing documentation
### Why are the changes needed?
Kafka producer closed after 10 minutes (with default settings).
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing + additional unit tests.
Cluster tests being started.
Closes#25853 from gaborgsomogyi/SPARK-21869.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This PR proposes to add **Single threading model design (pinned thread model)** mode which is an experimental mode to sync threads on PVM and JVM. See https://www.py4j.org/advanced_topics.html#using-single-threading-model-pinned-thread
### Multi threading model
Currently, PySpark uses this model. Threads on PVM and JVM are independent. For instance, in a different Python thread, callbacks are received and relevant Python codes are executed. JVM threads are reused when possible.
Py4J will create a new thread every time a command is received and there is no thread available. See the current model we're using - https://www.py4j.org/advanced_topics.html#the-multi-threading-model
One problem in this model is that we can't sync threads on PVM and JVM out of the box. This leads to some problems in particular at some codes related to threading in JVM side. See:
7056e004ee/core/src/main/scala/org/apache/spark/SparkContext.scala (L334)
Due to reusing JVM threads, seems the job groups in Python threads cannot be set in each thread as described in the JIRA.
### Single threading model design (pinned thread model)
This mode pins and syncs the threads on PVM and JVM to work around the problem above. For instance, in the same Python thread, callbacks are received and relevant Python codes are executed. See https://www.py4j.org/advanced_topics.html#the-single-threading-model
Even though this mode can sync threads on PVM and JVM for other thread related code paths,
this might cause another problem: seems unable to inherit properties as below (assuming multi-thread mode still creates new threads when existing threads are busy, I suspect this issue already exists when multiple jobs are submitted in multi-thread mode; however, it can be always seen in single threading mode):
```bash
$ PYSPARK_PIN_THREAD=true ./bin/pyspark
```
```python
import threading
spark.sparkContext.setLocalProperty("a", "hi")
def print_prop():
print(spark.sparkContext.getLocalProperty("a"))
threading.Thread(target=print_prop).start()
```
```
None
```
Unlike Scala side:
```scala
spark.sparkContext.setLocalProperty("a", "hi")
new Thread(new Runnable {
def run() = println(spark.sparkContext.getLocalProperty("a"))
}).start()
```
```
hi
```
This behaviour potentially could cause weird issues but this PR currently does not target this fix this for now since this mode is experimental.
### How does this PR fix?
Basically there are two types of Py4J servers `GatewayServer` and `ClientServer`. The former is for multi threading and the latter is for single threading. This PR adds a switch to use the latter.
In Scala side:
The logic to select a server is encapsulated in `Py4JServer` and use `Py4JServer` at `PythonRunner` for Spark summit and `PythonGatewayServer` for Spark shell. Each uses `ClientServer` when `PYSPARK_PIN_THREAD` is `true` and `GatewayServer` otherwise.
In Python side:
Simply do an if-else to switch the server to talk. It uses `ClientServer` when `PYSPARK_PIN_THREAD` is `true` and `GatewayServer` otherwise.
This is disabled by default for now.
## How was this patch tested?
Manually tested. This can be tested via:
```python
PYSPARK_PIN_THREAD=true ./bin/pyspark
```
and/or
```bash
cd python
./run-tests --python-executables=python --testnames "pyspark.tests.test_pin_thread"
```
Also, ran the Jenkins tests with `PYSPARK_PIN_THREAD` enabled.
Closes#24898 from HyukjinKwon/pinned-thread.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This aims to exclude the `preview` release to recover `HiveExternalCatalogVersionsSuite`. Currently, new preview release breaks `branch-2.4` PRBuilder since yesterday. New release (especially `preview`) should not affect `branch-2.4`.
- https://github.com/apache/spark/pull/26417 (Failed 4 times)
### Why are the changes needed?
**BEFORE**
```scala
scala> scala.io.Source.fromURL("https://dist.apache.org/repos/dist/release/spark/").mkString.split("\n").filter(_.contains("""<li><a href="spark-""")).map("""<a href="spark-(\d.\d.\d)/">""".r.findFirstMatchIn(_).get.group(1))
java.util.NoSuchElementException: None.get
```
**AFTER**
```scala
scala> scala.io.Source.fromURL("https://dist.apache.org/repos/dist/release/spark/").mkString.split("\n").filter(_.contains("""<li><a href="spark-""")).filterNot(_.contains("preview")).map("""<a href="spark-(\d.\d.\d)/">""".r.findFirstMatchIn(_).get.group(1))
res5: Array[String] = Array(2.3.4, 2.4.4)
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
This should pass the PRBuilder.
Closes#26428 from dongjoon-hyun/SPARK-HiveExternalCatalogVersionsSuite.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
```java
public static final int YEARS_PER_DECADE = 10;
public static final int YEARS_PER_CENTURY = 100;
public static final int YEARS_PER_MILLENNIUM = 1000;
public static final byte MONTHS_PER_QUARTER = 3;
public static final int MONTHS_PER_YEAR = 12;
public static final byte DAYS_PER_WEEK = 7;
public static final long DAYS_PER_MONTH = 30L;
public static final long HOURS_PER_DAY = 24L;
public static final long MINUTES_PER_HOUR = 60L;
public static final long SECONDS_PER_MINUTE = 60L;
public static final long SECONDS_PER_HOUR = MINUTES_PER_HOUR * SECONDS_PER_MINUTE;
public static final long SECONDS_PER_DAY = HOURS_PER_DAY * SECONDS_PER_HOUR;
public static final long MILLIS_PER_SECOND = 1000L;
public static final long MILLIS_PER_MINUTE = SECONDS_PER_MINUTE * MILLIS_PER_SECOND;
public static final long MILLIS_PER_HOUR = MINUTES_PER_HOUR * MILLIS_PER_MINUTE;
public static final long MILLIS_PER_DAY = HOURS_PER_DAY * MILLIS_PER_HOUR;
public static final long MICROS_PER_MILLIS = 1000L;
public static final long MICROS_PER_SECOND = MILLIS_PER_SECOND * MICROS_PER_MILLIS;
public static final long MICROS_PER_MINUTE = SECONDS_PER_MINUTE * MICROS_PER_SECOND;
public static final long MICROS_PER_HOUR = MINUTES_PER_HOUR * MICROS_PER_MINUTE;
public static final long MICROS_PER_DAY = HOURS_PER_DAY * MICROS_PER_HOUR;
public static final long MICROS_PER_MONTH = DAYS_PER_MONTH * MICROS_PER_DAY;
/* 365.25 days per year assumes leap year every four years */
public static final long MICROS_PER_YEAR = (36525L * MICROS_PER_DAY) / 100;
public static final long NANOS_PER_MICROS = 1000L;
public static final long NANOS_PER_MILLIS = MICROS_PER_MILLIS * NANOS_PER_MICROS;
public static final long NANOS_PER_SECOND = MILLIS_PER_SECOND * NANOS_PER_MILLIS;
```
The above parameters are defined in IntervalUtils, DateTimeUtils, and CalendarInterval, some of them are redundant, some of them are cross-referenced.
### Why are the changes needed?
To simplify code, enhance consistency and reduce risks
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
modified uts
Closes#26399 from yaooqinn/SPARK-29757.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
remove the leading "interval" in `CalendarInterval.toString`.
### Why are the changes needed?
Although it's allowed to have "interval" prefix when casting string to int, it's not recommended.
This is also consistent with pgsql:
```
cloud0fan=# select interval '1' day;
interval
----------
1 day
(1 row)
```
### Does this PR introduce any user-facing change?
yes, when display a dataframe with interval type column, the result is different.
### How was this patch tested?
updated tests.
Closes#26401 from cloud-fan/interval.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose new function `stringToInterval()` in `IntervalUtils` for converting `UTF8String` to `CalendarInterval`. The function is used in casting a `STRING` column to an `INTERVAL` column.
### Why are the changes needed?
The proposed implementation is ~10 times faster. For example, parsing 9 interval units on JDK 8:
Before:
```
9 units w/ interval 14004 14125 116 0.1 14003.6 0.0X
9 units w/o interval 13785 14056 290 0.1 13784.9 0.0X
```
After:
```
9 units w/ interval 1343 1344 1 0.7 1343.0 0.3X
9 units w/o interval 1345 1349 8 0.7 1344.6 0.3X
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
- By new tests for `stringToInterval` in `IntervalUtilsSuite`
- By existing tests
Closes#26256 from MaxGekk/string-to-interval.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Handle the inconsistence dividing zeros between literals and columns.
fix the null issue too.
### Why are the changes needed?
BUG FIX
### 1 Handle the inconsistence dividing zeros between literals and columns
```sql
-- !query 24
select
k,
v,
cast(k as interval) / v,
cast(k as interval) * v
from VALUES
('1 seconds', 1),
('2 seconds', 0),
('3 seconds', null),
(null, null),
(null, 0) t(k, v)
-- !query 24 schema
struct<k:string,v:int,divide_interval(CAST(k AS INTERVAL), CAST(v AS DOUBLE)):interval,multiply_interval(CAST(k AS INTERVAL), CAST(v AS DOUBLE)):interval>
-- !query 24 output
1 seconds 1 interval 1 seconds interval 1 seconds
2 seconds 0 interval 0 microseconds interval 0 microseconds
3 seconds NULL NULL NULL
NULL 0 NULL NULL
NULL NULL NULL NULL
```
```sql
-- !query 21
select interval '1 year 2 month' / 0
-- !query 21 schema
struct<divide_interval(interval 1 years 2 months, CAST(0 AS DOUBLE)):interval>
-- !query 21 output
NULL
```
in the first case, interval ’2 seconds ‘ / 0, it produces `interval 0 microseconds `
in the second case, it is `null`
### 2 null literal issues
```sql
-- !query 20
select interval '1 year 2 month' / null
-- !query 20 schema
struct<>
-- !query 20 output
org.apache.spark.sql.AnalysisException
cannot resolve '(interval 1 years 2 months / NULL)' due to data type mismatch: differing types in '(interval 1 years 2 months / NULL)' (interval and null).; line 1 pos 7
-- !query 22
select interval '4 months 2 weeks 6 days' * null
-- !query 22 schema
struct<>
-- !query 22 output
org.apache.spark.sql.AnalysisException
cannot resolve '(interval 4 months 20 days * NULL)' due to data type mismatch: differing types in '(interval 4 months 20 days * NULL)' (interval and null).; line 1 pos 7
-- !query 23
select null * interval '4 months 2 weeks 6 days'
-- !query 23 schema
struct<>
-- !query 23 output
org.apache.spark.sql.AnalysisException
cannot resolve '(NULL * interval 4 months 20 days)' due to data type mismatch: differing types in '(NULL * interval 4 months 20 days)' (null and interval).; line 1 pos 7
```
dividing or multiplying null literals, error occurs; where in column is fine as the first case
### Does this PR introduce any user-facing change?
NO, maybe yes, but it is just a follow-up
### How was this patch tested?
add uts
cc cloud-fan MaxGekk maropu
Closes#26410 from yaooqinn/SPARK-29387.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch leverages V2 continuous memory stream to extract tests from Kafka micro-batch sink suite and continuous sink suite and deduplicate them. These tests are basically doing the same, except how to run and verify the result.
### Why are the changes needed?
We no longer have same tests spotted on two places - brings 300 lines deletion.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UTs.
Closes#26292 from HeartSaVioR/SPARK-29635.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
