### 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?
- `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?
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?
```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?
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?
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?
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?
- 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?
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>
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?
- 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?
```
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?
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?
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?
```
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?
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?
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?
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?
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 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?
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?
```
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?
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?
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?
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>
### Why are the changes needed?
As mentioned in jira, sometimes we need to be able to support the retention of null columns when writing JSON.
For example, sparkmagic(used widely in jupyter with livy) will generate sql query results based on DataSet.toJSON and parse JSON to pandas DataFrame to display. If there is a null column, it is easy to have some column missing or even the query result is empty. The loss of the null column in the first row, may cause parsing exceptions or loss of entire column data.
### Does this PR introduce any user-facing change?
Example in spark-shell.
scala> spark.sql("select null as a, 1 as b").toJSON.collect.foreach(println)
{"b":1}
scala> spark.sql("set spark.sql.jsonGenerator.struct.ignore.null=false")
res2: org.apache.spark.sql.DataFrame = [key: string, value: string]
scala> spark.sql("select null as a, 1 as b").toJSON.collect.foreach(println)
{"a":null,"b":1}
### How was this patch tested?
Add new test to JacksonGeneratorSuite
Closes#26098 from stczwd/json.
Lead-authored-by: stczwd <qcsd2011@163.com>
Co-authored-by: Jackey Lee <qcsd2011@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Regularize all the shuffle configurations related to adaptive execution.
2. Add default value for `BlockStoreShuffleReader.shouldBatchFetch`.
### Why are the changes needed?
It's a follow-up PR for #26040.
Regularize the existing `spark.sql.adaptive.shuffle` namespace in SQLConf.
### Does this PR introduce any user-facing change?
Rename one released user config `spark.sql.adaptive.minNumPostShufflePartitions` to `spark.sql.adaptive.shuffle.minNumPostShufflePartitions`, other changed configs is not released yet.
### How was this patch tested?
Existing UT.
Closes#26147 from xuanyuanking/SPARK-9853.
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 proposes a few typos:
1. Sparks => Spark's
2. parallize => parallelize
3. doesnt => doesn't
Closes#26140 from plusplusjiajia/fix-typos.
Authored-by: Jiajia Li <jiajia.li@intel.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
BIT_COUNT(N) - Returns the number of bits that are set in the argument N as an unsigned 64-bit integer, or NULL if the argument is NULL
### Why are the changes needed?
Supported by MySQL，Microsoft SQL Server ，etc.
### Does this PR introduce any user-facing change?
add a built-in function
### How was this patch tested?
add uts
Closes#26139 from yaooqinn/SPARK-29491.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This PR takes over #19788. After we split the shuffle fetch protocol from `OpenBlock` in #24565, this optimization can be extended in the new shuffle protocol. Credit to yucai, closes#19788.
### What changes were proposed in this pull request?
This PR adds the support for continuous shuffle block fetching in batch:
- Shuffle client changes:
- Add new feature tag `spark.shuffle.fetchContinuousBlocksInBatch`, implement the decision logic in `BlockStoreShuffleReader`.
- Merge the continuous shuffle block ids in batch if needed in ShuffleBlockFetcherIterator.
- Shuffle server changes:
- Add support in `ExternalBlockHandler` for the external shuffle service side.
- Make `ShuffleBlockResolver.getBlockData` accept getting block data by range.
- Protocol changes:
- Add new block id type `ShuffleBlockBatchId` represent continuous shuffle block ids.
- Extend `FetchShuffleBlocks` and `OneForOneBlockFetcher`.
- After the new shuffle fetch protocol completed in #24565, the backward compatibility for external shuffle service can be controlled by `spark.shuffle.useOldFetchProtocol`.
### Why are the changes needed?
In adaptive execution, one reducer may fetch multiple continuous shuffle blocks from one map output file. However, as the original approach, each reducer needs to fetch those 10 reducer blocks one by one. This way needs many IO and impacts performance. This PR is to support fetching those continuous shuffle blocks in one IO (batch way). See below example:
The shuffle block is stored like below:
The ShuffleId format is s"shuffle_$shuffleId_$mapId_$reduceId", referring to BlockId.scala.
In adaptive execution, one reducer may want to read output for reducer 5 to 14, whose block Ids are from shuffle_0_x_5 to shuffle_0_x_14.
Before this PR, Spark needs 10 disk IOs + 10 network IOs for each output file.
After this PR, Spark only needs 1 disk IO and 1 network IO. This way can reduce IO dramatically.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Add new UT.
Integrate test with setting `spark.sql.adaptive.enabled=true`.
Closes#26040 from xuanyuanking/SPARK-9853.
Lead-authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Co-authored-by: yucai <yyu1@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
bool_or(x) <=> any/some(x) <=> max(x)
bool_and(x) <=> every(x) <=> min(x)
Args:
x: boolean
### Why are the changes needed?
PostgreSQL, Presto and Vertica, etc also support this feature:
### Does this PR introduce any user-facing change?
add new functions support
### How was this patch tested?
add ut
Closes#26126 from yaooqinn/SPARK-27880.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Proposed new expression `SubtractDates` which is used in `date1` - `date2`. It has the `INTERVAL` type, and returns the interval from `date1` (inclusive) and `date2` (exclusive). For example:
```sql
> select date'tomorrow' - date'yesterday';
interval 2 days
```
Closes#26034
### Why are the changes needed?
- To conform the SQL standard which states the result type of `date operand 1` - `date operand 2` must be the interval type. 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 mixing date and timestamp in subtractions. For example: `select timestamp'now' + (date'2019-10-01' - date'2019-09-15')`
### Does this PR introduce any user-facing change?
Before the query below returns number of days:
```sql
spark-sql> select date'2019-10-05' - date'2018-09-01';
399
```
After it returns an interval:
```sql
spark-sql> select date'2019-10-05' - date'2018-09-01';
interval 1 years 1 months 4 days
```
### How was this patch tested?
- by new tests in `DateExpressionsSuite` and `TypeCoercionSuite`.
- by existing tests in `date.sql`
Closes#26112 from MaxGekk/date-subtract.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
Change Literal.sql to output CAST('fpValue' AS FLOAT) instead of CAST(fpValue AS FLOAT) as the SQL for a floating point literal.
### Why are the changes needed?
The old version doesn't work for very small floating point numbers; the value will fail to parse if it doesn't fit in a DECIMAL(38).
This doesn't apply to doubles because they have special literal syntax.
### Does this PR introduce any user-facing change?
Not really.
### How was this patch tested?
New unit tests.
Closes#26114 from jose-torres/fpliteral.
Authored-by: Jose Torres <joseph.torres@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Updating univocity-parsers version to 2.8.3, which adds support for multiple character delimiters
Moving univocity-parsers version to spark-parent pom dependencyManagement section
Adding new utility method to build multi-char delimiter string, which delegates to existing one
Adding tests for multiple character delimited CSV
### What changes were proposed in this pull request?
Adds support for parsing CSV data using multiple-character delimiters. Existing logic for converting the input delimiter string to characters was kept and invoked in a loop. Project dependencies were updated to remove redundant declaration of `univocity-parsers` version, and also to change that version to the latest.
### Why are the changes needed?
It is quite common for people to have delimited data, where the delimiter is not a single character, but rather a sequence of characters. Currently, it is difficult to handle such data in Spark (typically needs pre-processing).
### Does this PR introduce any user-facing change?
Yes. Specifying the "delimiter" option for the DataFrame read, and providing more than one character, will no longer result in an exception. Instead, it will be converted as before and passed to the underlying library (Univocity), which has accepted multiple character delimiters since 2.8.0.
### How was this patch tested?
The `CSVSuite` tests were confirmed passing (including new methods), and `sbt` tests for `sql` were executed.
Closes#26027 from jeff303/SPARK-24540.
Authored-by: Jeff Evans <jeffrey.wayne.evans@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
When inserting a value into a column with the different data type, Spark performs type coercion. Currently, we support 3 policies for the store assignment rules: ANSI, legacy and strict, which can be set via the option "spark.sql.storeAssignmentPolicy":
1. ANSI: Spark performs the type coercion as per ANSI SQL. In practice, the behavior is mostly the same as PostgreSQL. It disallows certain unreasonable type conversions such as converting `string` to `int` and `double` to `boolean`. It will throw a runtime exception if the value is out-of-range(overflow).
2. Legacy: Spark allows the type coercion as long as it is a valid `Cast`, which is very loose. E.g., converting either `string` to `int` or `double` to `boolean` is allowed. It is the current behavior in Spark 2.x for compatibility with Hive. When inserting an out-of-range value to a integral field, the low-order bits of the value is inserted(the same as Java/Scala numeric type casting). For example, if 257 is inserted to a field of Byte type, the result is 1.
3. Strict: Spark doesn't allow any possible precision loss or data truncation in store assignment, e.g., converting either `double` to `int` or `decimal` to `double` is allowed. The rules are originally for Dataset encoder. As far as I know, no mainstream DBMS is using this policy by default.
Currently, the V1 data source uses "Legacy" policy by default, while V2 uses "Strict". This proposal is to use "ANSI" policy by default for both V1 and V2 in Spark 3.0.
### Why are the changes needed?
Following the ANSI SQL standard is most reasonable among the 3 policies.
### Does this PR introduce any user-facing change?
Yes.
The default store assignment policy is ANSI for both V1 and V2 data sources.
### How was this patch tested?
Unit test
Closes#26107 from gengliangwang/ansiPolicyAsDefault.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Implement a rule in the new adaptive execution framework introduced in [SPARK-23128](https://issues.apache.org/jira/browse/SPARK-23128). This rule is used to optimize the shuffle reader to local shuffle reader when smj is converted to bhj in adaptive execution.
## How was this patch tested?
Existing tests
Closes#25295 from JkSelf/localShuffleOptimization.
Authored-by: jiake <ke.a.jia@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
move the statement logical plans that were created for v2 commands to a new file `statements.scala`, under the same package of `v2Commands.scala`.
This PR also includes some minor cleanups:
1. remove `private[sql]` from `ParsedStatement` as it's in the private package.
2. remove unnecessary override of `output` and `children`.
3. add missing classdoc.
### Why are the changes needed?
Similar to https://github.com/apache/spark/pull/26111 , this is to better organize the logical plans of data source v2.
It's a bit weird to put the statements in the package `org.apache.spark.sql.catalyst.plans.logical.sql` as `sql` is not a good sub-package name in Spark SQL.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#26125 from cloud-fan/statement.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Refine the document of v2 session catalog config, to clearly explain what it is, when it should be used and how to implement it.
### Why are the changes needed?
Make this config more understandable
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Pass the Jenkins with the newly updated test cases.
Closes#26071 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?
This PR adds JSON serialization for Spark external Rows.
### Why are the changes needed?
This is to be used for observable metrics where the `StreamingQueryProgress` contains a map of observed metrics rows which needs to be serialized in some cases.
### Does this PR introduce any user-facing change?
Yes, a user can call `toJson` on rows returned when collecting a DataFrame to the driver.
### How was this patch tested?
Added a new test suite: `RowJsonSuite` that should test this.
Closes#26013 from hvanhovell/SPARK-29347.
Authored-by: herman <herman@databricks.com>
Signed-off-by: herman <herman@databricks.com>
### What changes were proposed in this pull request?
move the v2 command logical plans from `basicLogicalOperators.scala` to a new file `v2Commands.scala`
### Why are the changes needed?
As we keep adding v2 commands, the `basicLogicalOperators.scala` grows bigger and bigger. It's better to have a separated file for them.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
not needed
Closes#26111 from cloud-fan/command.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In the PR, I propose to move interval parsing to `CalendarInterval.fromCaseInsensitiveString()` which throws an `IllegalArgumentException` for invalid strings, and reuse it from `CalendarInterval.fromString()`. The former one handles `IllegalArgumentException` only and returns `NULL` for invalid interval strings. This will allow to support interval strings without the `interval` prefix in casting strings to intervals and in interval type constructor because they use `fromString()` for parsing string intervals.
For example:
```sql
spark-sql> select cast('1 year 10 days' as interval);
interval 1 years 1 weeks 3 days
spark-sql> SELECT INTERVAL '1 YEAR 10 DAYS';
interval 1 years 1 weeks 3 days
```
### Why are the changes needed?
To maintain feature parity with PostgreSQL which supports interval strings without prefix:
```sql
# select interval '2 months 1 microsecond';
interval
------------------------
2 mons 00:00:00.000001
```
and to improve Spark SQL UX.
### Does this PR introduce any user-facing change?
Yes, previously parsing of interval strings without `interval` gives `NULL`:
```sql
spark-sql> select interval '2 months 1 microsecond';
NULL
```
After:
```sql
spark-sql> select interval '2 months 1 microsecond';
interval 2 months 1 microseconds
```
### How was this patch tested?
- Added new tests to `CalendarIntervalSuite.java`
- A test for casting strings to intervals in `CastSuite`
- Test for interval type constructor from strings in `ExpressionParserSuite`
Closes#26079 from MaxGekk/interval-str-without-prefix.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, `SHOW NAMESPACES` and `SHOW DATABASES` are separate code paths. This PR merges two implementations.
### Why are the changes needed?
To remove code/behavior duplication
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added new unit tests.
Closes#26006 from imback82/combine_show.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Revert this commit 18b7ad2fc5.
### Why are the changes needed?
See https://github.com/apache/spark/pull/16304#discussion_r92753590
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
There is no test for that.
Closes#26101 from MaxGekk/revert-mean-seconds-per-month.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Update ANSI mode related config names in comments as "spark.sql.ansi.enabled"
### Why are the changes needed?
The removed configuration `spark.sql.parser.ansi.enabled` and `spark.sql.failOnIntegralTypeOverflow` still exist in code comments.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Grep the whole code to ensure the remove config names no longer exist.
```
git grep "parser.ansi.enabled"
git grep failOnIntegralTypeOverflow
git grep decimalOperationsNullOnOverflow
git grep ANSI_SQL_PARSER
git grep FAIL_ON_INTEGRAL_TYPE_OVERFLOW
git grep DECIMAL_OPERATIONS_NULL_ON_OVERFLOW
```
Closes#26067 from gengliangwang/spark-28989-followup.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Use `.sameElements` to compare (non-nested) arrays, as `Arrays.deep` is removed in 2.13 and wasn't the best way to do this in the first place.
### Why are the changes needed?
To compile with 2.13.
### Does this PR introduce any user-facing change?
None.
### How was this patch tested?
Existing tests.
Closes#26073 from srowen/SPARK-29416.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Replace `Unit` with equivalent `()` where code refers to the `Unit` companion object.
### Why are the changes needed?
It doesn't compile otherwise in Scala 2.13.
- https://github.com/scala/scala/blob/v2.13.0/src/library/scala/Unit.scala#L30
### Does this PR introduce any user-facing change?
Should be no behavior change at all.
### How was this patch tested?
Existing tests.
Closes#26070 from srowen/SPARK-29411.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
