### What changes were proposed in this pull request?
Corrected ShortType and ByteType mapping to SmallInt and TinyInt, corrected setter methods to set ShortType and ByteType as setShort() and setByte(). Changes in JDBCUtils.scala
Fixed Unit test cases to where applicable and added new E2E test cases in to test table read/write using ShortType and ByteType.
#### Problems
- In master in JDBCUtils.scala line number 547 and 551 have a problem where ShortType and ByteType are set as Integers rather than set as Short and Byte respectively.
```
case ShortType =>
(stmt: PreparedStatement, row: Row, pos: Int) =>
stmt.setInt(pos + 1, row.getShort(pos))
The issue was pointed out by maropu
case ByteType =>
(stmt: PreparedStatement, row: Row, pos: Int) =>
stmt.setInt(pos + 1, row.getByte(pos))
```
- Also at line JDBCUtils.scala 247 TinyInt is interpreted wrongly as IntergetType in getCatalystType()
``` case java.sql.Types.TINYINT => IntegerType ```
- At line 172 ShortType was wrongly interpreted as IntegerType
``` case ShortType => Option(JdbcType("INTEGER", java.sql.Types.SMALLINT)) ```
- All thru out tests, ShortType and ByteType were being interpreted as IntegerTypes.
### Why are the changes needed?
A given type should be set using the right type.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Corrected Unit test cases where applicable. Validated in CI/CD
Added a test case in MsSqlServerIntegrationSuite.scala, PostgresIntegrationSuite.scala , MySQLIntegrationSuite.scala to write/read tables from dataframe with cols as shorttype and bytetype. Validated by manual as follows.
```
./build/mvn install -DskipTests
./build/mvn test -Pdocker-integration-tests -pl :spark-docker-integration-tests_2.12
```
Closes#26301 from shivsood/shorttype_fix_maropu.
Authored-by: shivsood <shivsood@microsoft.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
`saveAsTable` had an oversight where write options were not considered in the append save mode.
### Why are the changes needed?
Address the bug so that write options can be considered during appends.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Unit test added that looks in the logic plan of `AppendData` for the existing write options.
Closes#26474 from SpaceRangerWes/master.
Authored-by: Wesley Hoffman <wesleyhoffman109@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR adds a SQL Conf: `spark.sql.streaming.stopActiveRunOnRestart`. When this conf is `true` (by default it is), an already running stream will be stopped, if a new copy gets launched on the same checkpoint location.
### Why are the changes needed?
In multi-tenant environments where you have multiple SparkSessions, you can accidentally start multiple copies of the same stream (i.e. streams using the same checkpoint location). This will cause all new instantiations of the new stream to fail. However, sometimes you may want to turn off the old stream, as the old stream may have turned into a zombie (you no longer have access to the query handle or SparkSession).
It would be nice to have a SQL flag that allows the stopping of the old stream for such zombie cases.
### Does this PR introduce any user-facing change?
Yes. Now by default, if you launch a new copy of an already running stream on a multi-tenant cluster, the existing stream will be stopped.
### How was this patch tested?
Unit tests in StreamingQueryManagerSuite
Closes#26225 from brkyvz/stopStream.
Lead-authored-by: Burak Yavuz <brkyvz@gmail.com>
Co-authored-by: Burak Yavuz <burak@databricks.com>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
### What changes were proposed in this pull request?
rename EveryAgg/AnyAgg to BoolAnd/BoolOr
### Why are the changes needed?
Under ansi mode, `every`, `any` and `some` are reserved keywords and can't be used as function names. `EveryAgg`/`AnyAgg` has several aliases and I think it's better to not pick reserved keywords as the primary name.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#26486 from cloud-fan/naming.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
rename the config to address the comment: https://github.com/apache/spark/pull/24594#discussion_r285431212
improve the config description, provide a default value to simplify the code.
### Why are the changes needed?
make the config more understandable.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#26395 from cloud-fan/config.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The current parse and analyze flow for DELETE is: 1, the SQL string will be firstly parsed to `DeleteFromStatement`; 2, the `DeleteFromStatement` be converted to `DeleteFromTable`. However, the SQL string can be parsed to `DeleteFromTable` directly, where a `DeleteFromStatement` seems to be redundant.
It is the same for UPDATE.
This pr removes the unnecessary `DeleteFromStatement` and `UpdateTableStatement`.
### Why are the changes needed?
This makes the codes for DELETE and UPDATE cleaner, and keep align with MERGE INTO.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existed tests and new tests.
Closes#26464 from xianyinxin/SPARK-29835.
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?
Currently, `SupportsNamespaces.dropNamespace` drops a namespace only if it is empty. Thus, to implement a cascading drop, one needs to iterate all objects (tables, view, etc.) within the namespace (including its sub-namespaces recursively) and drop them one by one. This can have a negative impact on the performance when there are large number of objects.
Instead, this PR proposes to change the default behavior of dropping a namespace to cascading such that implementing cascading/non-cascading drop is simpler without performance penalties.
### Why are the changes needed?
The new behavior makes implementing cascading/non-cascading drop simple without performance penalties.
### Does this PR introduce any user-facing change?
Yes. The default behavior of `SupportsNamespaces.dropNamespace` is now cascading.
### How was this patch tested?
Added new unit tests.
Closes#26476 from imback82/drop_ns_cascade.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add 3 interval functions justify_days, justify_hours, justif_interval to support justify interval values
### Why are the changes needed?
For feature parity with postgres
add three interval functions to justify interval values.
justify_days(interval) | interval | Adjust interval so 30-day time periods are represented as months | justify_days(interval '35 days') | 1 mon 5 days
-- | -- | -- | -- | --
justify_hours(interval) | interval | Adjust interval so 24-hour time periods are represented as days | justify_hours(interval '27 hours') | 1 day 03:00:00
justify_interval(interval) | interval | Adjust interval using justify_days and justify_hours, with additional sign adjustments | justify_interval(interval '1 mon -1 hour') | 29 days 23:00:00
### Does this PR introduce any user-facing change?
yes. new interval functions are added
### How was this patch tested?
add ut
Closes#26465 from yaooqinn/SPARK-29390.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### 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>
### 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?
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?
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 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?
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?
- `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?
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?
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?
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?
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?
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?
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?
Update `LocalShuffleReaderExec.outputPartitioning` to use attributes from `ReusedQueryStage`.
This also removes the override `doCanonicalize` in local/coalesced shuffle reader, as these 2 operators change the output partitioning. It's not safe to strip them in the canonicalized query plan.
### Why are the changes needed?
We will have an invalid output partitioning if we don fix it.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#26400 from cloud-fan/aqe.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
This patch fixes the bug that `ContinuousMemoryStream[String]` throws error regarding ClassCastException - cast String to UTFString. This is because ContinuousMemoryStream and ContinuousRecordEndpoint uses origin input as it is for underlying data structure of Row, and encoding is missing here.
To force encoding, this patch changes the element type of underlying array to UnsafeRow instead of Any for ContinuousRecordEndpoint - ContinuousMemoryStream and TextSocketContinuousStream are modified to reflect the change.
### Why are the changes needed?
Above section describes the bug.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Add new UT to check for availability on couple of types.
Closes#26300 from HeartSaVioR/SPARK-29642.
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?
instead of checking the exact number of local shuffle readers, we should check whether the number of shuffles is equal to the number of local readers.
### Why are the changes needed?
AQE is known to have randomness. We may pick different build side for broadcast join depending on which query stage finishes first. The decision to build side may add/remove shuffles downstream, so it's flaky to check the exact number of local shuffle readers.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
test only PR.
Closes#26394 from cloud-fan/test.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Added UT for the classes `ThriftServerPage.scala` and `ThriftServerSessionPage.scala`
### Why are the changes needed?
Currently, there are no UTs for testing Thriftserver UI page
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
UT
Closes#26403 from shahidki31/ut.
Authored-by: shahid <shahidki31@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
During creation of array, if CreateArray does not gets any children to set data type for array, it will create an array of null type .
### Why are the changes needed?
When empty array is created, it should be declared as array<null>.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Tested manually
Closes#26324 from amanomer/29462.
Authored-by: Aman Omer <amanomer1996@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch removes v1 ALTER TABLE CHANGE COLUMN syntax.
### Why are the changes needed?
Since in v2 we have ALTER TABLE CHANGE COLUMN and ALTER TABLE RENAME COLUMN, this old syntax is not necessary now and can be confusing.
The v2 ALTER TABLE CHANGE COLUMN should fallback to v1 AlterTableChangeColumnCommand (#26354).
### Does this PR introduce any user-facing change?
Yes, the old v1 ALTER TABLE CHANGE COLUMN syntax is removed.
### How was this patch tested?
Unit tests.
Closes#26338 from viirya/SPARK-29680.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Added new expressions `MultiplyInterval` and `DivideInterval` to multiply/divide an interval by a numeric. Updated `TypeCoercion.DateTimeOperations` to turn the `Multiply`/`Divide` expressions of `CalendarIntervalType` and `NumericType` to `MultiplyInterval`/`DivideInterval`.
To support new operations, added new methods `multiply()` and `divide()` to `CalendarInterval`.
### Why are the changes needed?
- To maintain feature parity with PostgreSQL which supports multiplication and division of intervals by doubles:
```sql
# select interval '1 hour' / double precision '1.5';
?column?
----------
00:40:00
```
- To conform the SQL standard which defines those operations: `numeric * interval`, `interval * numeric` and `interval / numeric`. See [4.5.3 Operations involving datetimes and intervals](http://www.contrib.andrew.cmu.edu/~shadow/sql/sql1992.txt).
- Improve Spark SQL UX and allow users to adjust interval columns. For example:
```sql
spark-sql> select (timestamp'now' - timestamp'yesterday') * 1.3;
interval 2 days 10 hours 39 minutes 38 seconds 568 milliseconds 900 microseconds
```
### Does this PR introduce any user-facing change?
Yes, previously the following query fails with the error:
```sql
spark-sql> select interval 1 hour 30 minutes * 1.5;
Error in query: cannot resolve '(interval 1 hours 30 minutes * 1.5BD)' due to data type mismatch: differing types in '(interval 1 hours 30 minutes * 1.5BD)' (interval and decimal(2,1)).; line 1 pos 7;
```
After:
```sql
spark-sql> select interval 1 hour 30 minutes * 1.5;
interval 2 hours 15 minutes
```
### How was this patch tested?
- Added tests for the `multiply()` and `divide()` methods to `CalendarIntervalSuite.java`
- New test suite `IntervalExpressionsSuite`
- by tests for `Multiply` -> `MultiplyInterval` and `Divide` -> `DivideInterval` in `TypeCoercionSuite`
- updated `datetime.sql`
Closes#26132 from MaxGekk/interval-mul-div.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add AlterTableSerDePropertiesStatement and make ALTER TABLE ... SET SERDE/SERDEPROPERTIES 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
ALTER TABLE t SET SERDE 'org.apache.class' // 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 ALTER TABLE ... SET SERDE/SERDEPROPERTIES, 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#26374 from huaxingao/spark_29695.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Disallow creating a permanent view that references a temporary view in **expressions**.
### Why are the changes needed?
Creating a permanent view that references a temporary view is currently disallowed. For example,
```SQL
# The following throws org.apache.spark.sql.AnalysisException
# Not allowed to create a permanent view `per_view` by referencing a temporary view `tmp`;
CREATE VIEW per_view AS SELECT t1.a, t2.b FROM base_table t1, (SELECT * FROM tmp) t2"
```
However, the following is allowed.
```SQL
CREATE VIEW per_view AS SELECT * FROM base_table WHERE EXISTS (SELECT * FROM tmp);
```
This PR fixes the bug where temporary views used inside expressions are not checked.
### Does this PR introduce any user-facing change?
Yes. Now the following SQL query throws an exception as expected:
```SQL
# The following throws org.apache.spark.sql.AnalysisException
# Not allowed to create a permanent view `per_view` by referencing a temporary view `tmp`;
CREATE VIEW per_view AS SELECT * FROM base_table WHERE EXISTS (SELECT * FROM tmp);
```
### How was this patch tested?
Added new unit tests.
Closes#26361 from imback82/spark-29630.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR introduces a new SQL command: `SHOW CURRENT NAMESPACE`.
### Why are the changes needed?
Datasource V2 supports multiple catalogs/namespaces and having `SHOW CURRENT NAMESPACE` to retrieve the current catalog/namespace info would be useful.
### Does this PR introduce any user-facing change?
Yes, the user can perform the following:
```
scala> spark.sql("SHOW CURRENT NAMESPACE").show
+-------------+---------+
| catalog|namespace|
+-------------+---------+
|spark_catalog| default|
+-------------+---------+
scala> spark.sql("USE testcat.ns1.ns2").show
scala> spark.sql("SHOW CURRENT NAMESPACE").show
+-------+---------+
|catalog|namespace|
+-------+---------+
|testcat| ns1.ns2|
+-------+---------+
```
### How was this patch tested?
Added unit tests.
Closes#26379 from imback82/show_current_catalog.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This patch adds the option to clean up files which are completed in previous batch.
`cleanSource` -> "archive" / "delete" / "off"
The default value is "off", which Spark will do nothing.
If "delete" is specified, Spark will simply delete input files. If "archive" is specified, Spark will require additional config `sourceArchiveDir` which will be used to move input files to there. When archiving (via move) the path of input files are retained to the archived paths as sub-path.
Note that it is only applied to "micro-batch", since for batch all input files must be kept to get same result across multiple query executions.
## How was this patch tested?
Added UT. Manual test against local disk as well as HDFS.
Closes#22952 from HeartSaVioR/SPARK-20568.
Lead-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Co-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Co-authored-by: Jungtaek Lim <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This pr proposes to be case insensitive when matching dialects via jdbc url prefix.
When I use jdbc url such as: ```jdbc: MySQL://localhost/db``` to query data through sparksql, the result is wrong, but MySQL supports such url writing.
because sparksql matches MySQLDialect by prefix ```jdbc:mysql```, so ```jdbc: MySQL``` is not matched with the correct dialect. Therefore, it should be case insensitive when identifying the corresponding dialect through jdbc url
https://issues.apache.org/jira/browse/SPARK-28552
## How was this patch tested?
UT.
Closes#25287 from teeyog/sql_dialect.
Lead-authored-by: yong.tian1 <yong.tian1@dmall.com>
Co-authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Co-authored-by: Chris Martin <chris@cmartinit.co.uk>
Co-authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Co-authored-by: Kent Yao <yaooqinn@hotmail.com>
Co-authored-by: teeyog <teeyog@gmail.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
`SampleExec` has a bug that it sets `needCopyResult` to false as long as the `withReplacement` parameter is false. This causes problems if its child needs to copy the result, e.g. a join.
### Why are the changes needed?
to fix a correctness issue
### Does this PR introduce any user-facing change?
Yes, the result will be corrected.
### How was this patch tested?
a new test
Closes#26387 from cloud-fan/sample-bug.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Current checkstyle checking folder can't cover all folder.
Since for support multi version hive, we have some divided hive folder.
We should check it too.
### Why are the changes needed?
Fix build bug
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
NO
Closes#26385 from AngersZhuuuu/SPARK-29742.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
sum support interval 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, sum can evaluate intervals
### How was this patch tested?
add ut
Closes#26325 from yaooqinn/SPARK-29663.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add AlterTableAddPartitionStatement and make ALTER TABLE ... ADD PARTITION 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
ALTER TABLE t ADD PARTITION (id=1) // 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 ALTER TABLE ... ADD PARTITION, 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#26369 from imback82/spark-29678.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, JDBC/ODBC tab in the WEBUI doesn't support hiding table. Other tabs in the web ui like, Jobs, stages, SQL etc supports hiding table (refer https://github.com/apache/spark/pull/22592).
In this PR, added the support for hide table in the jdbc/odbc tab also.
### Why are the changes needed?
Spark ui about the contents of the form need to have hidden and show features, when the table records very much. Because sometimes you do not care about the record of the table, you just want to see the contents of the next table, but you have to scroll the scroll bar for a long time to see the contents of the next table.
### Does this PR introduce any user-facing change?
No, except support of hide table
### How was this patch tested?
Manually tested
Closes#26353 from shahidki31/hideTable.
Authored-by: shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
- Retry the tests for special date-time values on failure. The tests can potentially fail when reference values were taken before midnight and test code resolves special values after midnight. The retry can guarantees that the tests run during the same day.
- Simplify getting of the current timestamp via `Instant.now()`. This should avoid any issues of converting current local datetime to an instance. For example, the same local time can be mapped to 2 instants when clocks are turned backward 1 hour on daylight saving date.
- Extract common code to SQLHelper
- Set the tested zoneId to the session time zone in `DateTimeUtilsSuite`.
### Why are the changes needed?
To make the tests more stable.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By existing test suites `Date`/`TimestampFormatterSuite` and `DateTimeUtilsSuite`.
Closes#26380 from MaxGekk/retry-on-fail.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
If the resolved table is v1 table, AlterTableAlterColumnStatement fallbacks to v1 AlterTableChangeColumnCommand.
### Why are the changes needed?
To make the catalog/table lookup logic consistent.
### Does this PR introduce any user-facing change?
Yes, a ALTER TABLE ALTER COLUMN command previously fails on v1 tables. After this, it falls back to v1 AlterTableChangeColumnCommand.
### How was this patch tested?
Unit test.
Closes#26354 from viirya/SPARK-29353.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to changed `CalendarInterval.toString`:
- to skip the `week` unit
- to convert `milliseconds` and `microseconds` as the fractional part of the `seconds` unit.
### Why are the changes needed?
To improve readability.
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
- By `CalendarIntervalSuite` and `IntervalUtilsSuite`
- `literals.sql`, `datetime.sql` and `interval.sql`
Closes#26367 from MaxGekk/interval-to-string-format.
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 is somewhat a complement of https://github.com/apache/spark/pull/21853.
The `Sort` without `Limit` operator in `Join` subquery is useless, it's the same case in `GroupBy` when the aggregation function is order irrelevant, such as `count`, `sum`.
This PR try to remove this kind of `Sort` operator in `SQL Optimizer`.
### Why are the changes needed?
For example, `select count(1) from (select a from test1 order by a)` is equal to `select count(1) from (select a from test1)`.
'select * from (select a from test1 order by a) t1 join (select b from test2) t2 on t1.a = t2.b' is equal to `select * from (select a from test1) t1 join (select b from test2) t2 on t1.a = t2.b`.
Remove useless `Sort` operator can improve performance.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Adding new UT `RemoveSortInSubquerySuite.scala`
Closes#26011 from WangGuangxin/remove_sorts.
Authored-by: wangguangxin.cn <wangguangxin.cn@bytedance.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Support non-reversed keywords to be used in high order functions.
### Why are the changes needed?
the keywords are non-reversed.
### Does this PR introduce any user-facing change?
yes, all non-reversed keywords can be used in high order function correctly
### How was this patch tested?
add uts
Closes#26366 from yaooqinn/SPARK-29722.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The `assertEquals` method of JUnit Assert requires the first parameter to be the expected value. In this PR, I propose to change the order of parameters when the expected value is passed as the second parameter.
### Why are the changes needed?
Wrong order of assert parameters confuses when the assert fails and the parameters have special string representation. For example:
```java
assertEquals(input1.add(input2), new CalendarInterval(5, 5, 367200000000L));
```
```
java.lang.AssertionError:
Expected :interval 5 months 5 days 101 hours
Actual :interval 5 months 5 days 102 hours
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By existing tests.
Closes#26377 from MaxGekk/fix-order-in-assert-equals.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
re-arrange the parser rules to make it clear that multiple unit TO unit statement like `SELECT INTERVAL '1-1' YEAR TO MONTH '2-2' YEAR TO MONTH` is not allowed.
### Why are the changes needed?
This is definitely an accident that we support such a weird syntax in the past. It's not supported by any other DBs and I can't think of any use case of it. Also no test covers this syntax in the current codebase.
### Does this PR introduce any user-facing change?
Yes, and a migration guide item is added.
### How was this patch tested?
new tests.
Closes#26285 from cloud-fan/syntax.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
###What changes were proposed in this pull request?
Add AlterTableDropPartitionStatement and make ALTER TABLE/VIEW ... DROP PARTITION 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
ALTER TABLE t DROP PARTITION (id=1) // 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 ALTER TABLE/VIEW ... DROP PARTITION, 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#26303 from huaxingao/spark-29643.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Current CalendarInterval has 2 fields: months and microseconds. This PR try to change it
to 3 fields: months, days and microseconds. This is because one logical day interval may
have different number of microseconds (daylight saving).
### Why are the changes needed?
One logical day interval may have different number of microseconds (daylight saving).
For example, in PST timezone, there will be 25 hours from 2019-11-2 12:00:00 to
2019-11-3 12:00:00
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
unit test and new added test cases
Closes#26134 from LinhongLiu/calendarinterval.
Authored-by: Liu,Linhong <liulinhong@baidu.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add AlterTableRenamePartitionStatement and make ALTER TABLE ... RENAME TO PARTITION 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
ALTER TABLE t PARTITION (id=1) RENAME TO PARTITION (id=2) // 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 ALTER TABLE ... RENAME TO PARTITION, 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#26350 from huaxingao/spark_29676.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
Fix JDBC metrics counter data type. Related pull request [26109](https://github.com/apache/spark/pull/26109).
### Why are the changes needed?
Avoid overflow.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Exists UT.
Closes#26346 from ulysses-you/SPARK-29687.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Now we use JDBC api and set an Illegal isolationLevel option, spark will throw a `scala.MatchError`, it's not friendly to user. So we should add an IllegalArgumentException.
### Why are the changes needed?
Make exception friendly to user.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Add UT.
Closes#26334 from ulysses-you/SPARK-29675.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
Bring back https://github.com/apache/spark/pull/25955
### What changes were proposed in this pull request?
This adds a new rule, `V2ScanRelationPushDown`, to push filters and projections in to a new `DataSourceV2ScanRelation` in the optimizer. That scan is then used when converting to a physical scan node. The new relation correctly reports stats based on the scan.
To run scan pushdown before rules where stats are used, this adds a new optimizer override, `earlyScanPushDownRules` and a batch for early pushdown in the optimizer, before cost-based join reordering. The other early pushdown rule, `PruneFileSourcePartitions`, is moved into the early pushdown rule set.
This also moves pushdown helper methods from `DataSourceV2Strategy` into a util class.
### Why are the changes needed?
This is needed for DSv2 sources to supply stats for cost-based rules in the optimizer.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
This updates the implementation of stats from `DataSourceV2Relation` so tests will fail if stats are accessed before early pushdown for v2 relations.
Closes#26341 from cloud-fan/back.
Lead-authored-by: Wenchen Fan <wenchen@databricks.com>
Co-authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
[PR#25295](https://github.com/apache/spark/pull/25295) already implement the rule of converting the shuffle reader to local reader for the `BroadcastHashJoin` in probe side. This PR support converting the shuffle reader to local reader in build side.
### Why are the changes needed?
Improve performance
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing unit tests
Closes#26289 from JkSelf/supportTwoSideLocalReader.
Authored-by: jiake <ke.a.jia@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is code cleanup PR for https://github.com/apache/spark/pull/25600, aiming to remove an unnecessary condition and to correct a code comment.
### Why are the changes needed?
For code cleanup only.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Passed existing tests.
Closes#26328 from maryannxue/dpp-followup.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
- Added `getDuration()` to calculate interval duration in specified time units assuming provided days per months
- Added `isNegative()` which return `true` is the interval duration is less than 0
- Fix checking negative intervals by using `isNegative()` in structured streaming classes
- Fix checking of `year-months` intervals
### Why are the changes needed?
This fixes incorrect checking of negative intervals. An interval is negative when its duration is negative but not if interval's months **or** microseconds is negative. Also this fixes checking of `year-month` interval support because the `month` field could be negative.
### Does this PR introduce any user-facing change?
Should not
### How was this patch tested?
- Added tests for the `getDuration()` and `isNegative()` methods to `IntervalUtilsSuite`
- By existing SS tests
Closes#26177 from MaxGekk/interval-is-positive.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Update `AlterTableSetLocationStatement` to store `partitionSpec` and make `ALTER TABLE a.b.c PARTITION(...) SET LOCATION 'loc'` fail if `partitionSpec` is set with unsupported message.
### 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
ALTER TABLE t PARTITION(...) SET LOCATION 'loc' // report set location with partition spec is not supported.
```
### Does this PR introduce any user-facing change?
yes. When running ALTER TABLE (set partition location), 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?
New unit tests
Closes#26304 from imback82/alter_table_partition_loc.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add ShowColumnsStatement and make SHOW COLUMNS 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 COLUMNS FROM 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 COLUMNS 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#26182 from planga82/feature/SPARK-29523_SHOW_COLUMNS_datasourceV2.
Authored-by: Unknown <soypab@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to extract parsing of the seconds interval units to the private method `parseNanos` in `IntervalUtils` and modify the code to correctly parse the fractional part of the seconds unit of intervals in the cases:
- When the fractional part has less than 9 digits
- The seconds unit is negative
### Why are the changes needed?
The changes are needed to fix the issues:
```sql
spark-sql> select interval '10.123456 seconds';
interval 10 seconds 123 microseconds
```
The correct result must be `interval 10 seconds 123 milliseconds 456 microseconds`
```sql
spark-sql> select interval '-10.123456789 seconds';
interval -9 seconds -876 milliseconds -544 microseconds
```
but the whole interval should be negated, and the result must be `interval -10 seconds -123 milliseconds -456 microseconds`, taking into account the truncation to microseconds.
### Does this PR introduce any user-facing change?
Yes. After changes:
```sql
spark-sql> select interval '10.123456 seconds';
interval 10 seconds 123 milliseconds 456 microseconds
spark-sql> select interval '-10.123456789 seconds';
interval -10 seconds -123 milliseconds -456 microseconds
```
### How was this patch tested?
By existing and new tests in `ExpressionParserSuite`.
Closes#26313 from MaxGekk/fix-interval-nanos-parsing.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This adds a new rule, `V2ScanRelationPushDown`, to push filters and projections in to a new `DataSourceV2ScanRelation` in the optimizer. That scan is then used when converting to a physical scan node. The new relation correctly reports stats based on the scan.
To run scan pushdown before rules where stats are used, this adds a new optimizer override, `earlyScanPushDownRules` and a batch for early pushdown in the optimizer, before cost-based join reordering. The other early pushdown rule, `PruneFileSourcePartitions`, is moved into the early pushdown rule set.
This also moves pushdown helper methods from `DataSourceV2Strategy` into a util class.
### Why are the changes needed?
This is needed for DSv2 sources to supply stats for cost-based rules in the optimizer.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
This updates the implementation of stats from `DataSourceV2Relation` so tests will fail if stats are accessed before early pushdown for v2 relations.
Closes#25955 from rdblue/move-v2-pushdown.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Ryan Blue <blue@apache.org>
### What changes were proposed in this pull request?
To push the built jars to maven release repository, we need to remove the 'SNAPSHOT' tag from the version name.
Made the following changes in this PR:
* Update all the `3.0.0-SNAPSHOT` version name to `3.0.0-preview`
* Update the sparkR version number check logic to allow jvm version like `3.0.0-preview`
**Please note those changes were generated by the release script in the past, but this time since we manually add tags on master branch, we need to manually apply those changes too.**
We shall revert the changes after 3.0.0-preview release passed.
### Why are the changes needed?
To make the maven release repository to accept the built jars.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
### What changes were proposed in this pull request?
MICROS_PER_MONTH = DAYS_PER_MONTH * MICROS_PER_DAY
### Why are the changes needed?
fix bug
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
add ut
Closes#26321 from yaooqinn/SPARK-29653.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch fixes the issue that external listeners are not initialized properly when `spark.sql.hive.metastore.jars` is set to either "maven" or custom list of jar.
("builtin" is not a case here - all jars in Spark classloader are also available in separate classloader)
The culprit is lazy initialization (lazy val or passing builder function) & thread context classloader. HiveClient leverages IsolatedClientLoader to properly load Hive and relevant libraries without issue - to not mess up with Spark classpath it uses separate classloader with leveraging thread context classloader.
But there's a messed-up case - SessionState is being initialized while HiveClient changed the thread context classloader from Spark classloader to Hive isolated one, and streaming query listeners are loaded from changed classloader while initializing SessionState.
This patch forces initializing SessionState in SparkSQLEnv to avoid such case.
### Why are the changes needed?
ClassNotFoundException could occur in spark-sql with specific configuration, as explained above.
### Does this PR introduce any user-facing change?
No, as I don't think end users assume the classloader of external listeners is only containing jars for Hive client.
### How was this patch tested?
New UT added which fails on master branch and passes with the patch.
The error message with master branch when running UT:
```
java.lang.IllegalArgumentException: Error while instantiating 'org.apache.spark.sql.hive.HiveSessionStateBuilder':;
org.apache.spark.sql.AnalysisException: java.lang.IllegalArgumentException: Error while instantiating 'org.apache.spark.sql.hive.HiveSessionStateBuilder':;
at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:109)
at org.apache.spark.sql.hive.HiveExternalCatalog.databaseExists(HiveExternalCatalog.scala:221)
at org.apache.spark.sql.internal.SharedState.externalCatalog$lzycompute(SharedState.scala:147)
at org.apache.spark.sql.internal.SharedState.externalCatalog(SharedState.scala:137)
at org.apache.spark.sql.hive.thriftserver.SparkSQLEnv$.init(SparkSQLEnv.scala:59)
at org.apache.spark.sql.hive.thriftserver.SparkSQLEnvSuite.$anonfun$new$2(SparkSQLEnvSuite.scala:44)
at org.apache.spark.sql.hive.thriftserver.SparkSQLEnvSuite.withSystemProperties(SparkSQLEnvSuite.scala:61)
at org.apache.spark.sql.hive.thriftserver.SparkSQLEnvSuite.$anonfun$new$1(SparkSQLEnvSuite.scala:43)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:186)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:149)
at org.scalatest.FunSuiteLike.invokeWithFixture$1(FunSuiteLike.scala:184)
at org.scalatest.FunSuiteLike.$anonfun$runTest$1(FunSuiteLike.scala:196)
at org.scalatest.SuperEngine.runTestImpl(Engine.scala:286)
at org.scalatest.FunSuiteLike.runTest(FunSuiteLike.scala:196)
at org.scalatest.FunSuiteLike.runTest$(FunSuiteLike.scala:178)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterEach$$super$runTest(SparkFunSuite.scala:56)
at org.scalatest.BeforeAndAfterEach.runTest(BeforeAndAfterEach.scala:221)
at org.scalatest.BeforeAndAfterEach.runTest$(BeforeAndAfterEach.scala:214)
at org.apache.spark.SparkFunSuite.runTest(SparkFunSuite.scala:56)
at org.scalatest.FunSuiteLike.$anonfun$runTests$1(FunSuiteLike.scala:229)
at org.scalatest.SuperEngine.$anonfun$runTestsInBranch$1(Engine.scala:393)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:381)
at org.scalatest.SuperEngine.runTestsInBranch(Engine.scala:376)
at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:458)
at org.scalatest.FunSuiteLike.runTests(FunSuiteLike.scala:229)
at org.scalatest.FunSuiteLike.runTests$(FunSuiteLike.scala:228)
at org.scalatest.FunSuite.runTests(FunSuite.scala:1560)
at org.scalatest.Suite.run(Suite.scala:1124)
at org.scalatest.Suite.run$(Suite.scala:1106)
at org.scalatest.FunSuite.org$scalatest$FunSuiteLike$$super$run(FunSuite.scala:1560)
at org.scalatest.FunSuiteLike.$anonfun$run$1(FunSuiteLike.scala:233)
at org.scalatest.SuperEngine.runImpl(Engine.scala:518)
at org.scalatest.FunSuiteLike.run(FunSuiteLike.scala:233)
at org.scalatest.FunSuiteLike.run$(FunSuiteLike.scala:232)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:56)
at org.scalatest.BeforeAndAfterAll.liftedTree1$1(BeforeAndAfterAll.scala:213)
at org.scalatest.BeforeAndAfterAll.run(BeforeAndAfterAll.scala:210)
at org.scalatest.BeforeAndAfterAll.run$(BeforeAndAfterAll.scala:208)
at org.apache.spark.SparkFunSuite.run(SparkFunSuite.scala:56)
at org.scalatest.tools.SuiteRunner.run(SuiteRunner.scala:45)
at org.scalatest.tools.Runner$.$anonfun$doRunRunRunDaDoRunRun$13(Runner.scala:1349)
at org.scalatest.tools.Runner$.$anonfun$doRunRunRunDaDoRunRun$13$adapted(Runner.scala:1343)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.scalatest.tools.Runner$.doRunRunRunDaDoRunRun(Runner.scala:1343)
at org.scalatest.tools.Runner$.$anonfun$runOptionallyWithPassFailReporter$24(Runner.scala:1033)
at org.scalatest.tools.Runner$.$anonfun$runOptionallyWithPassFailReporter$24$adapted(Runner.scala:1011)
at org.scalatest.tools.Runner$.withClassLoaderAndDispatchReporter(Runner.scala:1509)
at org.scalatest.tools.Runner$.runOptionallyWithPassFailReporter(Runner.scala:1011)
at org.scalatest.tools.Runner$.run(Runner.scala:850)
at org.scalatest.tools.Runner.run(Runner.scala)
at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.runScalaTest2(ScalaTestRunner.java:133)
at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.main(ScalaTestRunner.java:27)
Caused by: java.lang.IllegalArgumentException: Error while instantiating 'org.apache.spark.sql.hive.HiveSessionStateBuilder':
at org.apache.spark.sql.SparkSession$.org$apache$spark$sql$SparkSession$$instantiateSessionState(SparkSession.scala:1054)
at org.apache.spark.sql.SparkSession.$anonfun$sessionState$2(SparkSession.scala:156)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.SparkSession.sessionState$lzycompute(SparkSession.scala:154)
at org.apache.spark.sql.SparkSession.sessionState(SparkSession.scala:151)
at org.apache.spark.sql.SparkSession.$anonfun$new$3(SparkSession.scala:105)
at scala.Option.map(Option.scala:230)
at org.apache.spark.sql.SparkSession.$anonfun$new$1(SparkSession.scala:105)
at org.apache.spark.sql.internal.SQLConf$.get(SQLConf.scala:164)
at org.apache.spark.sql.hive.client.HiveClientImpl.newState(HiveClientImpl.scala:183)
at org.apache.spark.sql.hive.client.HiveClientImpl.<init>(HiveClientImpl.scala:127)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:300)
at org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:421)
at org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:314)
at org.apache.spark.sql.hive.HiveExternalCatalog.client$lzycompute(HiveExternalCatalog.scala:68)
at org.apache.spark.sql.hive.HiveExternalCatalog.client(HiveExternalCatalog.scala:67)
at org.apache.spark.sql.hive.HiveExternalCatalog.$anonfun$databaseExists$1(HiveExternalCatalog.scala:221)
at scala.runtime.java8.JFunction0$mcZ$sp.apply(JFunction0$mcZ$sp.java:23)
at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:99)
... 58 more
Caused by: java.lang.ClassNotFoundException: test.custom.listener.DummyQueryExecutionListener
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at org.apache.spark.util.Utils$.classForName(Utils.scala:206)
at org.apache.spark.util.Utils$.$anonfun$loadExtensions$1(Utils.scala:2746)
at scala.collection.TraversableLike.$anonfun$flatMap$1(TraversableLike.scala:245)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at scala.collection.TraversableLike.flatMap(TraversableLike.scala:245)
at scala.collection.TraversableLike.flatMap$(TraversableLike.scala:242)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:108)
at org.apache.spark.util.Utils$.loadExtensions(Utils.scala:2744)
at org.apache.spark.sql.util.ExecutionListenerManager.$anonfun$new$1(QueryExecutionListener.scala:83)
at org.apache.spark.sql.util.ExecutionListenerManager.$anonfun$new$1$adapted(QueryExecutionListener.scala:82)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.sql.util.ExecutionListenerManager.<init>(QueryExecutionListener.scala:82)
at org.apache.spark.sql.internal.BaseSessionStateBuilder.$anonfun$listenerManager$2(BaseSessionStateBuilder.scala:293)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.internal.BaseSessionStateBuilder.listenerManager(BaseSessionStateBuilder.scala:293)
at org.apache.spark.sql.internal.BaseSessionStateBuilder.build(BaseSessionStateBuilder.scala:320)
at org.apache.spark.sql.SparkSession$.org$apache$spark$sql$SparkSession$$instantiateSessionState(SparkSession.scala:1051)
... 80 more
```
Closes#26258 from HeartSaVioR/SPARK-29604.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
```
postgres=# select date '2001-09-28' + integer '7';
?column?
------------
2001-10-05
(1 row)postgres=# select integer '7';
int4
------
7
(1 row)
```
Add support for typed integer literal expression from postgreSQL.
### Why are the changes needed?
SPARK-27764 Feature Parity between PostgreSQL and Spark
### Does this PR introduce any user-facing change?
support typed integer lit in SQL
### How was this patch tested?
add uts
Closes#26291 from yaooqinn/SPARK-29629.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Now, `RepartitionByExpression` is allowed at Dataset method `Dataset.repartition()`. But in spark sql, we do not have an equivalent functionality.
In hive, we can use `distribute by`, so it's worth to add a hint to support such function.
Similar jira [SPARK-24940](https://issues.apache.org/jira/browse/SPARK-24940)
## Why are the changes needed?
Make repartition hints consistent with repartition api .
## Does this PR introduce any user-facing change?
This pr intends to support quries below;
```
// SQL cases
- sql("SELECT /*+ REPARTITION(c) */ * FROM t")
- sql("SELECT /*+ REPARTITION(1, c) */ * FROM t")
- sql("SELECT /*+ REPARTITION_BY_RANGE(c) */ * FROM t")
- sql("SELECT /*+ REPARTITION_BY_RANGE(1, c) */ * FROM t")
```
## How was this patch tested?
UT
Closes#25464 from ulysses-you/SPARK-28746.
Lead-authored-by: ulysses <youxiduo@weidian.com>
Co-authored-by: ulysses <646303253@qq.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to move all static methods from the `CalendarInterval` class to the `IntervalUtils` object. All those methods are rewritten from Java to Scala.
### Why are the changes needed?
- For consistency with other helper methods. Such methods were placed to the helper object `IntervalUtils`, see https://github.com/apache/spark/pull/26190
- Taking into account that `CalendarInterval` will be fully exposed to users in the future (see https://github.com/apache/spark/pull/25022), it would be nice to clean it up by moving service methods to an internal object.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
- By moved tests from `CalendarIntervalSuite` to `IntervalUtilsSuite`
- By existing test suites
Closes#26261 from MaxGekk/refactoring-calendar-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?
Add AlterTableRecoverPartitionsStatement and make ALTER TABLE ... RECOVER PARTITIONS 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
ALTER TABLE t RECOVER PARTITIONS // 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 ALTER TABLE ... RECOVER PARTITIONS 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#26269 from huaxingao/spark-29612.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
To push the built jars to maven release repository, we need to remove the 'SNAPSHOT' tag from the version name.
Made the following changes in this PR:
* Update all the `3.0.0-SNAPSHOT` version name to `3.0.0-preview`
* Update the PySpark version from `3.0.0.dev0` to `3.0.0`
**Please note those changes were generated by the release script in the past, but this time since we manually add tags on master branch, we need to manually apply those changes too.**
We shall revert the changes after 3.0.0-preview release passed.
### Why are the changes needed?
To make the maven release repository to accept the built jars.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26243 from jiangxb1987/3.0.0-preview-prepare.
Lead-authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
There're some issues observed in `HiveUserDefinedTypeSuite."Support UDT in Hive UDF"`:
1) Neither function (TestUDF) nor test take "nullable" point column into account.
2) ExamplePointUDT. sqlType is ArrayType which doesn't provide information how many elements are expected. RandomDataGenerator may provide less elements than needed.
This patch fixes `HiveUserDefinedTypeSuite."Support UDT in Hive UDF"` to change the type of "point" column to be non-nullable, as well as not use RandomDataGenerator to create row for UDT backed by ArrayType.
### Why are the changes needed?
CI builds are failing in high occurrences.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually tested by running tests locally multiple times.
Closes#26287 from HeartSaVioR/SPARK-28158-FOLLOWUP.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds `DROP NAMESPACE` support for V2 catalogs.
### Why are the changes needed?
Currently, you cannot drop namespaces for v2 catalogs.
### Does this PR introduce any user-facing change?
The user can now perform the following:
```SQL
CREATE NAMESPACE mycatalog.ns
DROP NAMESPACE mycatalog.ns
SHOW NAMESPACES IN mycatalog # Will show no namespaces
```
to drop a namespace `ns` inside `mycatalog` V2 catalog.
### How was this patch tested?
Added unit tests.
Closes#26262 from imback82/drop_namespace.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add LoadDataStatement and make LOAD DATA INTO TABLE 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
LOAD DATA INPATH 'filepath' INTO TABLE 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 LOAD DATA INTO TABLE, 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#26178 from viirya/SPARK-29521.
Lead-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Co-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In this PR, extend the support of pagination to session table in `JDBC/PDBC` .
### Why are the changes needed?
Some times we may connect a lot client and there a many session info shown in session tab.
make it can be paged for better view.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manuel verify.
After pr:
<img width="1440" alt="Screen Shot 2019-10-25 at 4 19 27 PM" src="https://user-images.githubusercontent.com/46485123/67555133-50ae9900-f743-11e9-8724-9624a691f232.png">
<img width="1434" alt="Screen Shot 2019-10-25 at 4 19 38 PM" src="https://user-images.githubusercontent.com/46485123/67555165-5906d400-f743-11e9-819e-73f86a333dd3.png">
Closes#26253 from AngersZhuuuu/SPARK-29599.
Lead-authored-by: angerszhu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
After this PR, we can create and register Hive UDFs to accept UDT type, like `VectorUDT` and `MatrixUDT`. These UDTs are widely used in Spark machine learning.
## How was this patch tested?
add new ut
Closes#24961 from uncleGen/SPARK-28158.
Authored-by: uncleGen <hustyugm@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
### Why are the changes needed?
When make the `LocalShuffleReaderExec` to leaf node, there exists a potential issue: the leaf node will hide the running query stage and make the unfinished query stage as finished query stage when creating its parent query stage.
This PR make the leaf node to unary node.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing tests
Closes#26250 from JkSelf/updateLeafNodeofLocalReaderToUnaryExecNode.
Authored-by: jiake <ke.a.jia@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This pr is to fix wrong code to check parameter lengths of split methods in `subexpressionEliminationForWholeStageCodegen`.
### Why are the changes needed?
Bug fix.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#26267 from maropu/SPARK-29008-FOLLOWUP.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
The `DateTimeUtilsSuite` and `TimestampFormatterSuite` assume constant time difference between `timestamp'yesterday'`, `timestamp'today'` and `timestamp'tomorrow'` which is wrong on daylight switching day - day length can be 23 or 25 hours. In the PR, I propose to use Java 8 time API to calculate instances of `yesterday` and `tomorrow` timestamps.
### Why are the changes needed?
The changes fix test failures and make the tests tolerant to daylight time switching.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By existing test suites `DateTimeUtilsSuite` and `TimestampFormatterSuite`.
Closes#26273 from MaxGekk/midnight-tolerant.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In the PR https://github.com/apache/spark/pull/26215, we supported pagination for sqlstats table in JDBC/ODBC server page. In this PR, we are extending the support of pagination to sqlstats session table by making use of existing pagination classes in https://github.com/apache/spark/pull/26215.
### Why are the changes needed?
Support pagination for sqlsessionstats table in JDBC/ODBC server page in the WEBUI. It will easier for user to analyse the table and it may fix the potential issues like oom while loading the page, that may occur similar to the SQL page (refer #22645)
### Does this PR introduce any user-facing change?
There will be no change in the sqlsessionstats table in JDBC/ODBC server page execpt pagination support.
### How was this patch tested?
Manually verified.
Before:
After:
Closes#26246 from shahidki31/SPARK_29589.
Authored-by: shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Reset the `WritableColumnVector` when getting "next" ColumnarBatch in `RowToColumnarExec`
### Why are the changes needed?
When converting `Iterator[InternalRow]` to `Iterator[ColumnarBatch]`, the vectors used to create a new `ColumnarBatch` should be reset in the iterator's "next()" method.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26137 from rongma1997/reset-WritableColumnVector.
Authored-by: rongma1997 <rong.ma@intel.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
```
hive> select version();
OK
3.1.1 rf4e0529634b6231a0072295da48af466cf2f10b7
Time taken: 2.113 seconds, Fetched: 1 row(s)
```
### Why are the changes needed?
From hive behavior and I guess it is useful for debugging and developing etc.
### Does this PR introduce any user-facing change?
add a misc func
### How was this patch tested?
add ut
Closes#26209 from yaooqinn/SPARK-29554.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/24557 to fix `since` version.
### Why are the changes needed?
This is found during 3.0.0-preview preparation.
The version will be exposed to our SQL document like the following. We had better fix this.
- https://spark.apache.org/docs/latest/api/sql/#array_min
### Does this PR introduce any user-facing change?
Yes. It's exposed at `DESC FUNCTION EXTENDED` SQL command and SQL doc, but this is new at 3.0.0.
### How was this patch tested?
Manual.
```
spark-sql> DESC FUNCTION EXTENDED min_by;
Function: min_by
Class: org.apache.spark.sql.catalyst.expressions.aggregate.MinBy
Usage: min_by(x, y) - Returns the value of `x` associated with the minimum value of `y`.
Extended Usage:
Examples:
> SELECT min_by(x, y) FROM VALUES (('a', 10)), (('b', 50)), (('c', 20)) AS tab(x, y);
a
Since: 3.0.0
```
Closes#26264 from dongjoon-hyun/SPARK-27653.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add ShowCreateTableStatement and make SHOW CREATE TABLE 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 CREATE TABLE 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 CREATE TABLE, 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#26184 from viirya/SPARK-29527.
Lead-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Co-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch adds the functionality to measure records being written for JDBC writer. In reality, the value is meant to be a number of records being updated from queries, as per JDBC spec it will return updated count.
### Why are the changes needed?
Output metrics for JDBC writer are missing now. The value of "bytesWritten" is also missing, but we can't measure it from JDBC API.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Unit test added.
Closes#26109 from HeartSaVioR/SPARK-29461.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
First, a bit of background on the code being changed. The current code tracks
metric updates for each task, recording which metrics the task is monitoring
and the last update value.
Once a SQL execution finishes, then the metrics for all the stages are
aggregated, by building a list with all (metric ID, value) pairs collected
for all tasks in the stages related to the execution, then grouping by metric
ID, and then calculating the values shown in the UI.
That is full of inefficiencies:
- in normal operation, all tasks will be tracking and updating the same
metrics. So recording the metric IDs per task is wasteful.
- tracking by task means we might be double-counting values if you have
speculative tasks (as a comment in the code mentions).
- creating a list of (metric ID, value) is extremely inefficient, because now
you have a huge map in memory storing boxed versions of the metric IDs and
values.
- same thing for the aggregation part, where now a Seq is built with the values
for each metric ID.
The end result is that for large queries, this code can become both really
slow, thus affecting the processing of events, and memory hungry.
The updated code changes the approach to the following:
- stages track metrics by their ID; this means the stage tracking code
naturally groups values, making aggregation later simpler.
- each metric ID being tracked uses a long array matching the number of
partitions of the stage; this means that it's cheap to update the value of
the metric once a task ends.
- when aggregating, custom code just concatenates the arrays corresponding to
the matching metric IDs; this is cheaper than the previous, boxing-heavy
approach.
The end result is that the listener uses about half as much memory as before
for tracking metrics, since it doesn't need to track metric IDs per task.
I captured heap dumps with the old and the new code during metric aggregation
in the listener, for an execution with 3 stages, 100k tasks per stage, 50
metrics updated per task. The dumps contained just reachable memory - so data
kept by the listener plus the variables in the aggregateMetrics() method.
With the old code, the thread doing aggregation references >1G of memory - and
that does not include temporary data created by the "groupBy" transformation
(for which the intermediate state is not referenced in the aggregation method).
The same thread with the new code references ~250M of memory. The old code uses
about ~250M to track all the metric values for that execution, while the new
code uses about ~130M. (Note the per-thread numbers include the amount used to
track the metrics - so, e.g., in the old case, aggregation was referencing
about ~750M of temporary data.)
I'm also including a small benchmark (based on the Benchmark class) so that we
can measure how much changes to this code affect performance. The benchmark
contains some extra code to measure things the normal Benchmark class does not,
given that the code under test does not really map that well to the
expectations of that class.
Running with the old code (I removed results that don't make much
sense for this benchmark):
```
[info] Java HotSpot(TM) 64-Bit Server VM 1.8.0_181-b13 on Linux 4.15.0-66-generic
[info] Intel(R) Core(TM) i7-6820HQ CPU 2.70GHz
[info] metrics aggregation (50 metrics, 100k tasks per stage): Best Time(ms) Avg Time(ms)
[info] --------------------------------------------------------------------------------------
[info] 1 stage(s) 2113 2118
[info] 2 stage(s) 4172 4392
[info] 3 stage(s) 7755 8460
[info]
[info] Stage Count Stage Proc. Time Aggreg. Time
[info] 1 614 1187
[info] 2 620 2480
[info] 3 718 5069
```
With the new code:
```
[info] Java HotSpot(TM) 64-Bit Server VM 1.8.0_181-b13 on Linux 4.15.0-66-generic
[info] Intel(R) Core(TM) i7-6820HQ CPU 2.70GHz
[info] metrics aggregation (50 metrics, 100k tasks per stage): Best Time(ms) Avg Time(ms)
[info] --------------------------------------------------------------------------------------
[info] 1 stage(s) 727 886
[info] 2 stage(s) 1722 1983
[info] 3 stage(s) 2752 3013
[info]
[info] Stage Count Stage Proc. Time Aggreg. Time
[info] 1 408 177
[info] 2 389 423
[info] 3 372 660
```
So the new code is faster than the old when processing task events, and about
an order of maginute faster when aggregating metrics.
Note this still leaves room for improvement; for example, using the above
measurements, 600ms is still a huge amount of time to spend in an event
handler. But I'll leave further enhancements for a separate change.
Tested with benchmarking code + existing unit tests.
Closes#26218 from vanzin/SPARK-29562.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Track timing info for each rule in optimization phase using `QueryPlanningTracker` in Structured Streaming
### Why are the changes needed?
In Structured Streaming we only track rule info in analysis phase, not in optimization phase.
### Does this PR introduce any user-facing change?
No
Closes#25914 from wenxuanguan/spark-29227.
Authored-by: wenxuanguan <choose_home@126.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add UncacheTableStatement and make UNCACHE TABLE 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
UNCACHE TABLE 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 UNCACHE TABLE, 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?
New unit tests
Closes#26237 from imback82/uncache_table.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Remove the requirement of fetch_size>=0 from JDBCOptions to allow negative fetch size.
### Why are the changes needed?
Namely, to allow data fetch in stream manner (row-by-row fetch) against MySQL database.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Unit test (JDBCSuite)
This closes#26230 .
Closes#26244 from fuwhu/SPARK-21287-FIX.
Authored-by: fuwhu <bestwwg@163.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
# What changes were proposed in this pull request?
Add description for ignoreNullFields, which is commited in #26098 , in DataFrameWriter and readwriter.py.
Enable user to use ignoreNullFields in pyspark.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
run unit tests
Closes#26227 from stczwd/json-generator-doc.
Authored-by: stczwd <qcsd2011@163.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Only use antlr4 to parse the interval string, and remove the duplicated parsing logic from `CalendarInterval`.
### Why are the changes needed?
Simplify the code and fix inconsistent behaviors.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Pass the Jenkins with the updated test cases.
Closes#26190 from cloud-fan/parser.
Lead-authored-by: Wenchen Fan <wenchen@databricks.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Recent timezone definition changes in very new JDK 8 (and beyond) releases cause test failures. The below was observed on JDK 1.8.0_232. As before, the easy fix is to allow for these inconsequential variations in test results due to differing definition of timezones.
### Why are the changes needed?
Keeps test passing on the latest JDK releases.
### Does this PR introduce any user-facing change?
None
### How was this patch tested?
Existing tests
Closes#26236 from srowen/SPARK-29578.
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?
Supports pagination for SQL Statisitcs table in the JDBC/ODBC tab using existing Spark pagination framework.
### Why are the changes needed?
It will easier for user to analyse the table and it may fix the potential issues like oom while loading the page, that may occur similar to the SQL page (refer https://github.com/apache/spark/pull/22645)
### Does this PR introduce any user-facing change?
There will be no change in the `SQLStatistics` table in JDBC/ODBC server page execpt pagination support.
### How was this patch tested?
Manually verified.
Before PR:
After PR:
Closes#26215 from shahidki31/jdbcPagination.
Authored-by: shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Support SparkSQL use iN/EXISTS with subquery in JOIN condition.
### Why are the changes needed?
Support SQL use iN/EXISTS with subquery in JOIN condition.
### Does this PR introduce any user-facing change?
This PR is for enable user use subquery in `JOIN`'s ON condition. such as we have create three table
```
CREATE TABLE A(id String);
CREATE TABLE B(id String);
CREATE TABLE C(id String);
```
we can do query like :
```
SELECT A.id from A JOIN B ON A.id = B.id and A.id IN (select C.id from C)
```
### How was this patch tested?
ADDED UT
Closes#25854 from AngersZhuuuu/SPARK-29145.
Lead-authored-by: angerszhu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Reimplement the iterator in UnsafeExternalRowSorter in database style. This can be done by reusing the `RowIterator` in our code base.
### Why are the changes needed?
During the job in #26164, after involving a var `isReleased` in `hasNext`, there's possible that `isReleased` is false when calling `hasNext`, but it becomes true before calling `next`. A safer way is using database-style iterator: `advanceNext` and `getRow`.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes#26229 from xuanyuanking/SPARK-21492-follow-up.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add CacheTableStatement and make CACHE TABLE 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
CACHE TABLE 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 CACHE TABLE, 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#26179 from viirya/SPARK-29522.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a follow-up of #24052 to correct assert condition.
### Why are the changes needed?
To test IllegalArgumentException condition..
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manual Test (during fixing of SPARK-29453 find this issue)
Closes#26234 from 07ARB/SPARK-29571.
Authored-by: 07ARB <ankitrajboudh@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/26189 to regenerate the result on EC2.
### Why are the changes needed?
This will be used for the other PR reviews.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A.
Closes#26233 from dongjoon-hyun/SPARK-29533.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
`OptimizeLocalShuffleReader` rule is very conservative and gives up optimization as long as there are extra shuffles introduced. It's very likely that most of the added local shuffle readers are fine and only one introduces extra shuffle.
However, it's very hard to make `OptimizeLocalShuffleReader` optimal, a simple workaround is to run this rule again right before executing a query stage.
### Why are the changes needed?
Optimize more shuffle reader to local shuffle reader.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing ut
Closes#26207 from JkSelf/resolve-multi-joins-issue.
Authored-by: jiake <ke.a.jia@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add RefreshTableStatement and make REFRESH TABLE 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
REFRESH TABLE 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 REFRESH TABLE, 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?
New unit tests
Closes#26183 from imback82/refresh_table.
Lead-authored-by: Terry Kim <yuminkim@gmail.com>
Co-authored-by: Terry Kim <terryk@terrys-mbp-2.lan>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
As described in [SPARK-29542](https://issues.apache.org/jira/browse/SPARK-29542) , the descriptions of `spark.sql.files.*` are confused.
In this PR, I make their descriptions be clearly.
### Why are the changes needed?
It makes the descriptions of `spark.sql.files.*` be clearly.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes#26200 from turboFei/SPARK-29542-partition-maxSize.
Authored-by: turbofei <fwang12@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This moves the tracking of active queries from a per SparkSession state, to the shared SparkSession for better safety in isolated Spark Session environments.
### Why are the changes needed?
We have checks to prevent the restarting of the same stream on the same spark session, but we can actually make that better in multi-tenant environments by actually putting that state in the SharedState instead of SessionState. This would allow a more comprehensive check for multi-tenant clusters.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added tests to StreamingQueryManagerSuite
Closes#26018 from brkyvz/sharedStreamingQueryManager.
Lead-authored-by: Burak Yavuz <burak@databricks.com>
Co-authored-by: Burak Yavuz <brkyvz@gmail.com>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
### What changes were proposed in this pull request?
This PR adds `CREATE NAMESPACE` support for V2 catalogs.
### Why are the changes needed?
Currently, you cannot explicitly create namespaces for v2 catalogs.
### Does this PR introduce any user-facing change?
The user can now perform the following:
```SQL
CREATE NAMESPACE mycatalog.ns
```
to create a namespace `ns` inside `mycatalog` V2 catalog.
### How was this patch tested?
Added unit tests.
Closes#26166 from imback82/create_namespace.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add ShowPartitionsStatement and make SHOW PARTITIONS 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.
### Does this PR introduce any user-facing change?
Yes. When running SHOW PARTITIONS, 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#26198 from huaxingao/spark-29539.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
Add TruncateTableStatement and make TRUNCATE TABLE 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
TRUNCATE TABLE 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 TRUNCATE TABLE, 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#26174 from viirya/SPARK-29517.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
We shall have a new mechanism that the downstream operators may notify its parents that they may release the output data stream. In this PR, we implement the mechanism as below:
- Add function named `cleanupResources` in SparkPlan, which default call children's `cleanupResources` function, the operator which need a resource cleanup should rewrite this with the self cleanup and also call `super.cleanupResources`, like SortExec in this PR.
- Add logic support on the trigger side, in this PR is SortMergeJoinExec, which make sure and call the `cleanupResources` to do the cleanup job for all its upstream(children) operator.
### Why are the changes needed?
Bugfix for SortMergeJoin memory leak, and implement a general framework for SparkPlan resource cleanup.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
UT: Add new test suite JoinWithResourceCleanSuite to check both standard and code generation scenario.
Integrate Test: Test with driver/executor default memory set 1g, local mode 10 thread. The below test(thanks taosaildrone for providing this test [here](https://github.com/apache/spark/pull/23762#issuecomment-463303175)) will pass with this PR.
```
from pyspark.sql.functions import rand, col
spark.conf.set("spark.sql.join.preferSortMergeJoin", "true")
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", -1)
# spark.conf.set("spark.sql.sortMergeJoinExec.eagerCleanupResources", "true")
r1 = spark.range(1, 1001).select(col("id").alias("timestamp1"))
r1 = r1.withColumn('value', rand())
r2 = spark.range(1000, 1001).select(col("id").alias("timestamp2"))
r2 = r2.withColumn('value2', rand())
joined = r1.join(r2, r1.timestamp1 == r2.timestamp2, "inner")
joined = joined.coalesce(1)
joined.explain()
joined.show()
```
Closes#26164 from xuanyuanking/SPARK-21492.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR test `ThriftServerQueryTestSuite` in an asynchronous way.
### Why are the changes needed?
The default value of `spark.sql.hive.thriftServer.async` is `true`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
```
build/sbt "hive-thriftserver/test-only *.ThriftServerQueryTestSuite" -Phive-thriftserver
build/mvn -Dtest=none -DwildcardSuites=org.apache.spark.sql.hive.thriftserver.ThriftServerQueryTestSuite test -Phive-thriftserver
```
Closes#26172 from wangyum/SPARK-29516.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
This PR remove unnecessary orc version and hive version in doc.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A.
Closes#26146 from denglingang/SPARK-24576.
Lead-authored-by: denglingang <chitin1027@gmail.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
As I have comment in [SPARK-29516](https://github.com/apache/spark/pull/26172#issuecomment-544364977)
SparkSession.sql() method parse process not under current sparksession's conf, so some configuration about parser is not valid in multi-thread situation.
In this pr, we add a SQLConf parameter to AbstractSqlParser and initial it with SessionState's conf.
Then for each SparkSession's parser process. It will use's it's own SessionState's SQLConf and to be thread safe
### Why are the changes needed?
Fix bug
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
NO
Closes#26187 from AngersZhuuuu/SPARK-29530.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Only invoke `checkAndGlobPathIfNecessary()` when we have to use `InMemoryFileIndex`.
### Why are the changes needed?
Avoid unnecessary function invocation.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass Jenkins.
Closes#26196 from Ngone51/dev-avoid-unnecessary-invocation-on-globpath.
Authored-by: wuyi <ngone_5451@163.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
I extended `ExtractBenchmark` to support the `INTERVAL` type of the `source` parameter of the `date_part` function.
### Why are the changes needed?
- To detect performance issues while changing implementation of the `date_part` function in the future.
- To find out current performance bottlenecks in `date_part` for the `INTERVAL` type
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running the benchmark and print out produced values per each `field` value.
Closes#26175 from MaxGekk/extract-interval-benchmark.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Added new benchmark `IntervalBenchmark` to measure performance of interval related functions. In the PR, I added benchmarks for casting strings to interval. In particular, interval strings with `interval` prefix and without it because there is special code for this da576a737c/common/unsafe/src/main/java/org/apache/spark/unsafe/types/CalendarInterval.java (L100-L103) . And also I added benchmarks for different number of units in interval strings, for example 1 unit is `interval 10 years`, 2 units w/o interval is `10 years 5 months`, and etc.
### Why are the changes needed?
- To find out current performance issues in casting to intervals
- The benchmark can be used while refactoring/re-implementing `CalendarInterval.fromString()` or `CalendarInterval.fromCaseInsensitiveString()`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running the benchmark via the command:
```shell
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.IntervalBenchmark"
```
Closes#26189 from MaxGekk/interval-from-string-benchmark.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This pr refine the code in ThriftServerQueryTestSuite.blackList to reuse the black list of SQLQueryTestSuite instead of duplicating all test cases from SQLQueryTestSuite.blackList.
### Why are the changes needed?
To reduce code duplication.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26188 from fuwhu/SPARK-TBD.
Authored-by: fuwhu <bestwwg@163.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
`CatalogTable` to `HiveTable` will change the table's ownership. How to reproduce:
```scala
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.catalog.{CatalogStorageFormat, CatalogTable, CatalogTableType}
import org.apache.spark.sql.types.{LongType, StructType}
val identifier = TableIdentifier("spark_29498", None)
val owner = "SPARK-29498"
val newTable = CatalogTable(
identifier,
tableType = CatalogTableType.EXTERNAL,
storage = CatalogStorageFormat(
locationUri = None,
inputFormat = None,
outputFormat = None,
serde = None,
compressed = false,
properties = Map.empty),
owner = owner,
schema = new StructType().add("i", LongType, false),
provider = Some("hive"))
spark.sessionState.catalog.createTable(newTable, false)
// The owner is not SPARK-29498
println(spark.sessionState.catalog.getTableMetadata(identifier).owner)
```
This PR makes it set the `HiveTable`'s owner to `CatalogTable`'s owner if it's owner is not empty when converting `CatalogTable` to `HiveTable`.
### Why are the changes needed?
We should not change the ownership of the table when converting `CatalogTable` to `HiveTable`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
unit test
Closes#26160 from wangyum/SPARK-29498.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
```
bit_and(expression) -- The bitwise AND of all non-null input values, or null if none
bit_or(expression) -- The bitwise OR of all non-null input values, or null if none
```
More details:
https://www.postgresql.org/docs/9.3/functions-aggregate.html
### Why are the changes needed?
Postgres, Mysql and many other popular db support them.
### Does this PR introduce any user-facing change?
add two bit agg
### How was this patch tested?
add ut
Closes#26155 from yaooqinn/SPARK-27879.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fix Fix the associated location already exists in `SQLQueryTestSuite`:
```
build/sbt "~sql/test-only *SQLQueryTestSuite -- -z postgreSQL/join.sql"
...
[info] - postgreSQL/join.sql *** FAILED *** (35 seconds, 420 milliseconds)
[info] postgreSQL/join.sql
[info] Expected "[]", but got "[org.apache.spark.sql.AnalysisException
[info] Can not create the managed table('`default`.`tt3`'). The associated location('file:/root/spark/sql/core/spark-warehouse/org.apache.spark.sql.SQLQueryTestSuite/tt3') already exists.;]" Result did not match for query #108
```
### Why are the changes needed?
Fix bug.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26181 from wangyum/TestError.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add RepairTableStatement and make REPAIR TABLE 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
MSCK REPAIR TABLE 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 MSCK REPAIR TABLE, 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?
New unit tests
Closes#26168 from imback82/repair_table.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/25241 .
The typed interval expression should fail for invalid format.
### Why are the changes needed?
Te be consistent with the typed timestamp/date expression
### Does this PR introduce any user-facing change?
Yes. But this feature is not released yet.
### How was this patch tested?
updated test
Closes#26151 from cloud-fan/bug.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
* Adding an additional check in `stringToTimestamp` to handle cases where the input has trailing ':'
* Added a test to make sure this works.
### Why are the changes needed?
In a couple of scenarios while converting from String to Timestamp `DateTimeUtils.stringToTimestamp` throws an array out of bounds exception if there is trailing ':'. The behavior of this method requires it to return `None` in case the format of the string is incorrect.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added a test in the `DateTimeTestUtils` suite to test if my fix works.
Closes#26143 from rahulsmahadev/SPARK-29494.
Lead-authored-by: Rahul Mahadev <rahul.mahadev@databricks.com>
Co-authored-by: Rahul Shivu Mahadev <51690557+rahulsmahadev@users.noreply.github.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Current Spark SQL `SHOW FUNCTIONS` don't show `!=`, `<>`, `between`, `case`
But these expressions is truly functions. We should show it in SQL `SHOW FUNCTIONS`
### Why are the changes needed?
SHOW FUNCTIONS show '!=', '<>' , 'between', 'case'
### Does this PR introduce any user-facing change?
SHOW FUNCTIONS show '!=', '<>' , 'between', 'case'
### How was this patch tested?
UT
Closes#26053 from AngersZhuuuu/SPARK-29379.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The `date_part()` function can accept the `source` parameter of the `INTERVAL` type (`CalendarIntervalType`). The following values of the `field` parameter are supported:
- `"MILLENNIUM"` (`"MILLENNIA"`, `"MIL"`, `"MILS"`) - number of millenniums in the given interval. It is `YEAR / 1000`.
- `"CENTURY"` (`"CENTURIES"`, `"C"`, `"CENT"`) - number of centuries in the interval calculated as `YEAR / 100`.
- `"DECADE"` (`"DECADES"`, `"DEC"`, `"DECS"`) - decades in the `YEAR` part of the interval calculated as `YEAR / 10`.
- `"YEAR"` (`"Y"`, `"YEARS"`, `"YR"`, `"YRS"`) - years in a values of `CalendarIntervalType`. It is `MONTHS / 12`.
- `"QUARTER"` (`"QTR"`) - a quarter of year calculated as `MONTHS / 3 + 1`
- `"MONTH"` (`"MON"`, `"MONS"`, `"MONTHS"`) - the months part of the interval calculated as `CalendarInterval.months % 12`
- `"DAY"` (`"D"`, `"DAYS"`) - total number of days in `CalendarInterval.microseconds`
- `"HOUR"` (`"H"`, `"HOURS"`, `"HR"`, `"HRS"`) - the hour part of the interval.
- `"MINUTE"` (`"M"`, `"MIN"`, `"MINS"`, `"MINUTES"`) - the minute part of the interval.
- `"SECOND"` (`"S"`, `"SEC"`, `"SECONDS"`, `"SECS"`) - the seconds part with fractional microsecond part.
- `"MILLISECONDS"` (`"MSEC"`, `"MSECS"`, `"MILLISECON"`, `"MSECONDS"`, `"MS"`) - the millisecond part of the interval with fractional microsecond part.
- `"MICROSECONDS"` (`"USEC"`, `"USECS"`, `"USECONDS"`, `"MICROSECON"`, `"US"`) - the total number of microseconds in the `second`, `millisecond` and `microsecond` parts of the given interval.
- `"EPOCH"` - the total number of seconds in the interval including the fractional part with microsecond precision. Here we assume 365.25 days per year (leap year every four years).
For example:
```sql
> SELECT date_part('days', interval 1 year 10 months 5 days);
5
> SELECT date_part('seconds', interval 30 seconds 1 milliseconds 1 microseconds);
30.001001
```
### Why are the changes needed?
To maintain feature parity with PostgreSQL (https://www.postgresql.org/docs/11/functions-datetime.html#FUNCTIONS-DATETIME-EXTRACT)
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
- Added new test suite `IntervalExpressionsSuite`
- Add new test cases to `date_part.sql`
Closes#25981 from MaxGekk/extract-from-intervals.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
A followup of [#25295](https://github.com/apache/spark/pull/25295).
1) change the logWarning to logDebug in `OptimizeLocalShuffleReader`.
2) update the test to check whether query stage reuse can work well with local shuffle reader.
### Why are the changes needed?
make code robust
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#26157 from JkSelf/followup-25295.
Authored-by: jiake <ke.a.jia@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The handling of the catalog across plans should be as follows ([SPARK-29014](https://issues.apache.org/jira/browse/SPARK-29014)):
* The *current* catalog should be used when no catalog is specified
* The default catalog is the catalog *current* is initialized to
* If the *default* catalog is not set, then *current* catalog is the built-in Spark session catalog.
This PR addresses the issue where *current* catalog usage is not followed as describe above.
### Why are the changes needed?
It is a bug as described in the previous section.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Unit tests added.
Closes#26120 from imback82/cleanup_catalog.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add `AnalyzeTableStatement` and `AnalyzeColumnStatement`, and make ANALYZE TABLE 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
ANALYZE TABLE 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 ANALYZE TABLE, 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?
new tests
Closes#26129 from cloud-fan/analyze-table.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
