### What changes were proposed in this pull request?
In the PR, I propose to update an existing item in the SQL migration guide, and mention that Spark 3.2 supports foldable special datetime values as well.
<img width="1292" alt="Screenshot 2021-08-25 at 23 29 51" src="https://user-images.githubusercontent.com/1580697/130860184-27f0ba56-6c2d-4a5a-91a8-195f2f8aa5da.png">
### Why are the changes needed?
To inform users about actual Spark SQL behavior introduced by https://github.com/apache/spark/pull/33816
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By generating docs, and checking results manually.
Closes#33840 from MaxGekk/special-datetime-cast-migr-guide.
Authored-by: Max 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 the update the SQL migration guide about the changes introduced by the PRs https://github.com/apache/spark/pull/33709 and https://github.com/apache/spark/pull/33769.
<img width="1011" alt="Screenshot 2021-08-23 at 11 40 35" src="https://user-images.githubusercontent.com/1580697/130419710-640f20b3-6a38-4eb1-a6d6-2e069dc5665c.png">
### Why are the changes needed?
To inform users about the upcoming changes in parsing datetime strings. This should help users to migrate on the new release.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By generating the doc, and checking by eyes:
```
$ SKIP_API=1 SKIP_RDOC=1 SKIP_PYTHONDOC=1 SKIP_SCALADOC=1 bundle exec jekyll build
```
Closes#33809 from MaxGekk/datetime-cast-migr-guide.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Bugfix: link to correction location of Pyspark Dataframe documentation
### Why are the changes needed?
Current website returns "Not found"
### Does this PR introduce _any_ user-facing change?
Website fix
### How was this patch tested?
Documentation change
Closes#33420 from dominikgehl/feature/SPARK-36209.
Authored-by: Dominik Gehl <dog@open.ch>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Rework [PR](https://github.com/apache/spark/pull/33212) with suggestions.
This PR make `spark.read.json()` has the same behavior with Datasource API `spark.read.format("json").load("path")`. Spark should turn a non-nullable schema into nullable when using API `spark.read.json()` by default.
Here is an example:
```scala
val schema = StructType(Seq(StructField("value",
StructType(Seq(
StructField("x", IntegerType, nullable = false),
StructField("y", IntegerType, nullable = false)
)),
nullable = true
)))
val testDS = Seq("""{"value":{"x":1}}""").toDS
spark.read
.schema(schema)
.json(testDS)
.printSchema()
spark.read
.schema(schema)
.format("json")
.load("/tmp/json/t1")
.printSchema()
// root
// |-- value: struct (nullable = true)
// | |-- x: integer (nullable = true)
// | |-- y: integer (nullable = true)
```
Before this pr:
```
// output of spark.read.json()
root
|-- value: struct (nullable = true)
| |-- x: integer (nullable = false)
| |-- y: integer (nullable = false)
```
After this pr:
```
// output of spark.read.json()
root
|-- value: struct (nullable = true)
| |-- x: integer (nullable = true)
| |-- y: integer (nullable = true)
```
- `spark.read.csv()` also has the same problem.
- Datasource API `spark.read.format("json").load("path")` do this logical when resolve relation.
c77acf0bbc/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala (L415-L421)
### Does this PR introduce _any_ user-facing change?
Yes, `spark.read.json()` and `spark.read.csv()` not respect the user-given schema and always turn it into a nullable schema by default.
### How was this patch tested?
New test.
Closes#33436 from cfmcgrady/SPARK-35912-v3.
Authored-by: Fu Chen <cfmcgrady@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR modifies comment for `UTF8String.trimAll` and`sql-migration-guide.mld`.
The comment for `UTF8String.trimAll` says like as follows.
```
Trims whitespaces ({literal <=} ASCII 32) from both ends of this string.
```
Similarly, `sql-migration-guide.md` mentions about the behavior of `cast` like as follows.
```
In Spark 3.0, when casting string value to integral types(tinyint, smallint, int and bigint),
datetime types(date, timestamp and interval) and boolean type,
the leading and trailing whitespaces (<= ASCII 32) will be trimmed before converted to these type values,
for example, `cast(' 1\t' as int)` results `1`, `cast(' 1\t' as boolean)` results `true`,
`cast('2019-10-10\t as date)` results the date value `2019-10-10`.
In Spark version 2.4 and below, when casting string to integrals and booleans,
it does not trim the whitespaces from both ends; the foregoing results is `null`,
while to datetimes, only the trailing spaces (= ASCII 32) are removed.
```
But SPARK-32559 (#29375) changed the behavior and only whitespace ASCII characters will be trimmed since Spark 3.0.1.
### Why are the changes needed?
To follow the previous change.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Confirmed the document built by the following command.
```
SKIP_API=1 bundle exec jekyll build
```
Closes#33287 from sarutak/fix-utf8string-trim-issue.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR allow the parser to parse unit list interval literals like `'3' day '10' hours '3' seconds` or `'8' years '3' months` as `YearMonthIntervalType` or `DayTimeIntervalType`.
### Why are the changes needed?
For ANSI compliance.
### Does this PR introduce _any_ user-facing change?
Yes. I noted the following things in the `sql-migration-guide.md`.
* Unit list interval literals are parsed as `YearMonthIntervaType` or `DayTimeIntervalType` instead of `CalendarIntervalType`.
* `WEEK`, `MILLISECONS`, `MICROSECOND` and `NANOSECOND` are not valid units for unit list interval literals.
* Units of year-month and day-time cannot be mixed like `1 YEAR 2 MINUTES`.
### How was this patch tested?
New tests and modified tests.
Closes#32949 from sarutak/day-time-multi-units.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to update the SQL migration guide, in particular the section about the migration from Spark 2.4 to 3.0. New item informs users about the following issue:
**What**: Spark doesn't detect encoding (charset) in CSV files with BOM correctly. Such files can be read only in the multiLine mode when the CSV option encoding matches to the actual encoding of CSV files. For example, Spark cannot read UTF-16BE CSV files when encoding is set to UTF-8 which is the default mode. This is the case of the current ES ticket.
**Why**: In previous Spark versions, encoding wasn't propagated to the underlying library that means the lib tried to detect file encoding automatically. It could success for some encodings that require BOM presents at the beginning of files. Starting from the versions 3.0, users can specify file encoding via the CSV option encoding which has UTF-8 as the default value. Spark propagates such default to the underlying library (uniVocity), and as a consequence this turned off encoding auto-detection.
**When**: Since Spark 3.0. In particular, the commit 2df34db586 causes the issue.
**Workaround**: Enabling the encoding auto-detection mechanism in uniVocity by passing null as the value of CSV option encoding. A more recommended approach is to set the encoding option explicitly.
### Why are the changes needed?
To improve user experience with Spark SQL. This should help to users in their migration from Spark 2.4.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Should be checked by building docs in GA/jenkins.
Closes#33300 from MaxGekk/csv-encoding-migration-guide.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
As described in #32831, Spark has compatible issues when querying a view created by an
older version. The root cause is that Spark changed the auto-generated alias name. To avoid
this in the future, we could ask the user to specify explicit column names when creating
a view.
### Why are the changes needed?
Avoid compatible issue when querying a view
### Does this PR introduce _any_ user-facing change?
Yes. User will get error when running query below after this change
```
CREATE OR REPLACE VIEW v AS SELECT CAST(t.a AS INT), to_date(t.b, 'yyyyMMdd') FROM t
```
### How was this patch tested?
not yet
Closes#32832 from linhongliu-db/SPARK-35686-no-auto-alias.
Authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR changes the unionByName with null filling logic to append new nested struct fields from the right side of the union to the schema versus sorting fields alphabetically. It removes the need to use UpdateField expressions, and just directly projects new nested structs from each side of the union with the correct schema. This changes the union'd schema from being alphabetically sorted previously to now "left dominant", where the fields from the left side of the union are included and then the missing ones from the right are added in the same order found originally.
### Why are the changes needed?
Certain nested structs would cause unionByName with null filling to error out due to part of the logic for rewriting the expression tree to sort the structs.
### Does this PR introduce _any_ user-facing change?
Yes, nested struct fields will be in a different order after unionByName with null filling than before, though shouldn't cause much effective difference.
### How was this patch tested?
Updated existing tests based on the new StructField ordering and added a new test for the case that was broken originally.
Closes#33040 from Kimahriman/union-by-name-struct-order.
Authored-by: Adam Binford <adamq43@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/32513
It's hard to keep the command execution name for `DataFrameWriter`, as the command logical plan is a bit messy (DS v1, file source and hive and different command logical plans) and sometimes it's hard to distinguish "insert" and "save".
However, `DataFrameWriterV2` only produce v2 commands which are pretty clean. It's easy to keep the command execution name for them.
### Why are the changes needed?
less breaking changes.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
N/A
Closes#32919 from cloud-fan/follow.
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 upgrades built-in Hive to 2.3.9. Hive 2.3.9 changes:
- [HIVE-17155] - findConfFile() in HiveConf.java has some issues with the conf path
- [HIVE-24797] - Disable validate default values when parsing Avro schemas
- [HIVE-24608] - Switch back to get_table in HMS client for Hive 2.3.x
- [HIVE-21200] - Vectorization: date column throwing java.lang.UnsupportedOperationException for parquet
- [HIVE-21563] - Improve Table#getEmptyTable performance by disabling registerAllFunctionsOnce
- [HIVE-19228] - Remove commons-httpclient 3.x usage
### Why are the changes needed?
Fix regression caused by AVRO-2035.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#32750 from wangyum/SPARK-34512.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Currently, Spark eagerly executes commands on the caller side of `QueryExecution`, which is a bit hacky as `QueryExecution` is not aware of it and leads to confusion.
For example, if you run `sql("show tables").collect()`, you will see two queries with identical query plans in the web UI.
The first query is triggered at `Dataset.logicalPlan`, which eagerly executes the command.
The second query is triggered at `Dataset.collect`, which is the normal query execution.
From the web UI, it's hard to tell that these two queries are caused by eager command execution.
This PR proposes to move the eager command execution to `QueryExecution`, and turn the command plan to `CommandResult` to indicate that command has been executed already. Now `sql("show tables").collect()` still triggers two queries, but the quey plans are not identical. The second query becomes:
In addition to the UI improvements, this PR also has other benefits:
1. Simplifies code as caller side no need to worry about eager command execution. `QueryExecution` takes care of it.
2. It helps https://github.com/apache/spark/pull/32442 , where there can be more plan nodes above commands, and we need to replace commands with something like local relation that produces unsafe rows.
### Why are the changes needed?
Explained above.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing tests
Closes#32513 from beliefer/SPARK-35378.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Co-authored-by: Wenchen Fan <cloud0fan@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Override `getJDBCType` method in `MySQLDialect` so that `FloatType` is mapped to `FLOAT` instead of `REAL`
### Why are the changes needed?
MySQL treats `REAL` as a synonym to `DOUBLE` by default (see https://dev.mysql.com/doc/refman/8.0/en/numeric-types.html). Therefore, when creating a table with a column of `REAL` type, it will be created as `DOUBLE`. However, currently, `MySQLDialect` does not provide an implementation for `getJDBCType`, and will thus ultimately fall back to `JdbcUtils.getCommonJDBCType`, which maps `FloatType` to `REAL`. This change is needed so that we can properly map the `FloatType` to `FLOAT` for MySQL.
### Does this PR introduce _any_ user-facing change?
Prior to this PR, when writing a dataframe with a `FloatType` column to a MySQL table, it will create a `DOUBLE` column. After the PR, it will create a `FLOAT` column.
### How was this patch tested?
Added a test case in `JDBCSuite` that verifies the mapping.
Closes#32605 from mariosmeim-db/SPARK-35446.
Authored-by: Marios Meimaris <marios.meimaris@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to support special datetime values introduced by #25708 and by #25716 only in typed literals, and don't recognize them in parsing strings to dates/timestamps. The following string values are supported only in typed timestamp literals:
- `epoch [zoneId]` - `1970-01-01 00:00:00+00 (Unix system time zero)`
- `today [zoneId]` - midnight today.
- `yesterday [zoneId]` - midnight yesterday
- `tomorrow [zoneId]` - midnight tomorrow
- `now` - current query start time.
For example:
```sql
spark-sql> SELECT timestamp 'tomorrow';
2019-09-07 00:00:00
```
Similarly, the following special date values are supported only in typed date literals:
- `epoch [zoneId]` - `1970-01-01`
- `today [zoneId]` - the current date in the time zone specified by `spark.sql.session.timeZone`.
- `yesterday [zoneId]` - the current date -1
- `tomorrow [zoneId]` - the current date + 1
- `now` - the date of running the current query. It has the same notion as `today`.
For example:
```sql
spark-sql> SELECT date 'tomorrow' - date 'yesterday';
2
```
### Why are the changes needed?
In the current implementation, Spark supports the special date/timestamp value in any input strings casted to dates/timestamps that leads to the following problems:
- If executors have different system time, the result is inconsistent, and random. Column values depend on where the conversions were performed.
- The special values play the role of distributed non-deterministic functions though users might think of the values as constants.
### Does this PR introduce _any_ user-facing change?
Yes but the probability should be small.
### How was this patch tested?
By running existing test suites:
```
$ build/sbt "sql/testOnly org.apache.spark.sql.SQLQueryTestSuite -- -z interval.sql"
$ build/sbt "sql/testOnly org.apache.spark.sql.SQLQueryTestSuite -- -z date.sql"
$ build/sbt "sql/testOnly org.apache.spark.sql.SQLQueryTestSuite -- -z timestamp.sql"
$ build/sbt "test:testOnly *DateTimeUtilsSuite"
```
Closes#32714 from MaxGekk/remove-datetime-special-values.
Lead-authored-by: Max Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
CTAS with location clause acts as an insert overwrite. This can cause problems when there are subdirectories within a location directory.
This causes some users to accidentally wipe out directories with very important data. We should not allow CTAS with location to a non-empty directory.
### Why are the changes needed?
Hive already handled this scenario: HIVE-11319
Steps to reproduce:
```scala
sql("""create external table `demo_CTAS`( `comment` string) PARTITIONED BY (`col1` string, `col2` string) STORED AS parquet location '/tmp/u1/demo_CTAS'""")
sql("""INSERT OVERWRITE TABLE demo_CTAS partition (col1='1',col2='1') VALUES ('abc')""")
sql("select* from demo_CTAS").show
sql("""create table ctas1 location '/tmp/u2/ctas1' as select * from demo_CTAS""")
sql("select* from ctas1").show
sql("""create table ctas2 location '/tmp/u2' as select * from demo_CTAS""")
```
Before the fix: Both create table operations will succeed. But values in table ctas1 will be replaced by ctas2 accidentally.
After the fix: `create table ctas2...` will throw `AnalysisException`:
```
org.apache.spark.sql.AnalysisException: CREATE-TABLE-AS-SELECT cannot create table with location to a non-empty directory /tmp/u2 . To allow overwriting the existing non-empty directory, set 'spark.sql.legacy.allowNonEmptyLocationInCTAS' to true.
```
### Does this PR introduce _any_ user-facing change?
Yes, if the location directory is not empty, CTAS with location will throw AnalysisException
```
sql("""create table ctas2 location '/tmp/u2' as select * from demo_CTAS""")
```
```
org.apache.spark.sql.AnalysisException: CREATE-TABLE-AS-SELECT cannot create table with location to a non-empty directory /tmp/u2 . To allow overwriting the existing non-empty directory, set 'spark.sql.legacy.allowNonEmptyLocationInCTAS' to true.
```
`CREATE TABLE AS SELECT` with non-empty `LOCATION` will throw `AnalysisException`. To restore the behavior before Spark 3.2, need to set `spark.sql.legacy.allowNonEmptyLocationInCTAS` to `true`. , default value is `false`.
Updated SQL migration guide.
### How was this patch tested?
Test case added in SQLQuerySuite.scala
Closes#32411 from vinodkc/br_fixCTAS_nonempty_dir.
Authored-by: Vinod KC <vinod.kc.in@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Generally, we would expect that x = y => hash( x ) = hash( y ). However +-0 hash to different values for floating point types.
```
scala> spark.sql("select hash(cast('0.0' as double)), hash(cast('-0.0' as double))").show
+-------------------------+--------------------------+
|hash(CAST(0.0 AS DOUBLE))|hash(CAST(-0.0 AS DOUBLE))|
+-------------------------+--------------------------+
| -1670924195| -853646085|
+-------------------------+--------------------------+
scala> spark.sql("select cast('0.0' as double) == cast('-0.0' as double)").show
+--------------------------------------------+
|(CAST(0.0 AS DOUBLE) = CAST(-0.0 AS DOUBLE))|
+--------------------------------------------+
| true|
+--------------------------------------------+
```
Here is an extract from IEEE 754:
> The two zeros are distinguishable arithmetically only by either division-byzero ( producing appropriately signed infinities ) or else by the CopySign function recommended by IEEE 754 /854. Infinities, SNaNs, NaNs and Subnormal numbers necessitate four more special cases
From this, I deduce that the hash function must produce the same result for 0 and -0.
### Why are the changes needed?
It is a correctness issue
### Does this PR introduce _any_ user-facing change?
This changes only affect to the hash function applied to -0 value in float and double types
### How was this patch tested?
Unit testing and manual testing
Closes#32496 from planga82/feature/spark35207_hashnegativezero.
Authored-by: Pablo Langa <soypab@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR extends `ADD FILE/JAR/ARCHIVE` commands to be able to take multiple path arguments like Hive.
### Why are the changes needed?
To make those commands more useful.
### Does this PR introduce _any_ user-facing change?
Yes. In the current implementation, those commands can take a path which contains whitespaces without enclose it by neither `'` nor `"` but after this change, users need to enclose such paths.
I've note this incompatibility in the migration guide.
### How was this patch tested?
New tests.
Closes#32205 from sarutak/add-multiple-files.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
### What changes were proposed in this pull request?
Add note in migration guide about DayTimeIntervalType/YearMonthIntervalType show different between Hive SerDe and row format delimited
### Why are the changes needed?
Add note
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Not need
Closes#32343 from AngersZhuuuu/SPARK-35220-FOLLOWUP.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Parse the year-month interval literals like `INTERVAL '1-1' YEAR TO MONTH` to values of `YearMonthIntervalType`, and day-time interval literals to `DayTimeIntervalType` values. Currently, Spark SQL supports:
- DAY TO HOUR
- DAY TO MINUTE
- DAY TO SECOND
- HOUR TO MINUTE
- HOUR TO SECOND
- MINUTE TO SECOND
All such interval literals are converted to `DayTimeIntervalType`, and `YEAR TO MONTH` to `YearMonthIntervalType` while loosing info about `from` and `to` units.
**Note**: new behavior is under the SQL config `spark.sql.legacy.interval.enabled` which is `false` by default. When the config is set to `true`, the interval literals are parsed to `CaledarIntervalType` values.
Closes#32176
### Why are the changes needed?
To conform the ANSI SQL standard which assumes conversions of interval literals to year-month or day-time interval but not to mixed interval type like Catalyst's `CalendarIntervalType`.
### Does this PR introduce _any_ user-facing change?
Yes.
Before:
```sql
spark-sql> SELECT INTERVAL '1 01:02:03.123' DAY TO SECOND;
1 days 1 hours 2 minutes 3.123 seconds
spark-sql> SELECT typeof(INTERVAL '1 01:02:03.123' DAY TO SECOND);
interval
```
After:
```sql
spark-sql> SELECT INTERVAL '1 01:02:03.123' DAY TO SECOND;
1 01:02:03.123000000
spark-sql> SELECT typeof(INTERVAL '1 01:02:03.123' DAY TO SECOND);
day-time interval
```
### How was this patch tested?
1. By running the affected test suites:
```
$ ./build/sbt "test:testOnly *.ExpressionParserSuite"
$ SPARK_GENERATE_GOLDEN_FILES=1 build/sbt "sql/testOnly *SQLQueryTestSuite -- -z interval.sql"
$ SPARK_GENERATE_GOLDEN_FILES=1 build/sbt "sql/testOnly *SQLQueryTestSuite -- -z create_view.sql"
$ SPARK_GENERATE_GOLDEN_FILES=1 build/sbt "sql/testOnly *SQLQueryTestSuite -- -z date.sql"
$ SPARK_GENERATE_GOLDEN_FILES=1 build/sbt "sql/testOnly *SQLQueryTestSuite -- -z timestamp.sql"
```
2. PostgresSQL tests are executed with `spark.sql.legacy.interval.enabled` is set to `true` to keep compatibility with PostgreSQL output:
```sql
> SELECT interval '999' second;
0 years 0 mons 0 days 0 hours 16 mins 39.00 secs
```
Closes#32209 from MaxGekk/parse-ansi-interval-literals.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Support no-serde mode script transform use ArrayType/MapType/StructStpe data.
### Why are the changes needed?
Make user can process array/map/struct data
### Does this PR introduce _any_ user-facing change?
Yes, user can process array/map/struct data in script transform `no-serde` mode
### How was this patch tested?
Added UT
Closes#30957 from AngersZhuuuu/SPARK-31937.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: angerszhu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Normal function parameters should not support alias, hive not support too
In this pr we forbid use alias in `TRANSFORM`'s inputs
### Why are the changes needed?
Fix bug
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#32165 from AngersZhuuuu/SPARK-35070.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add change of `DESC NAMESPACE`'s schema to migration guide
### Why are the changes needed?
Update doc
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Not need
Closes#32155 from AngersZhuuuu/SPARK-34577-followup.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
CREATE TABLE LIKE should respect the reserved properties of tables and fail if specified, using `spark.sql.legacy.notReserveProperties` to restore.
### Why are the changes needed?
Make DDLs consistently treat reserved properties
### Does this PR introduce _any_ user-facing change?
YES, this is a breaking change as using `create table like` w/ reserved properties will fail.
### How was this patch tested?
new test
Closes#32025 from yaooqinn/SPARK-34935.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR removes the description that `||` and `&&` can be used as logical operators from the migration guide.
### Why are the changes needed?
At the `Compatibility with Apache Hive` section in the migration guide, it describes that `||` and `&&` can be used as logical operators.
But, in fact, they cannot be used as described.
AFAIK, Hive also doesn't support `&&` and `||` as logical operators.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I confirmed that `&&` and `||` cannot be used as logical operators with both Hive's interactive shell and `spark-sql`.
I also built the modified document and confirmed that the modified document doesn't break layout.
Closes#32023 from sarutak/modify-hive-compatibility-doc.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Modify the `SubtractTimestamps` expression to return values of `DayTimeIntervalType` when `spark.sql.legacy.interval.enabled` is set to `false` (which is the default).
### Why are the changes needed?
To conform to the ANSI SQL standard which requires ANSI intervals as the result of timestamps subtraction, see
<img width="656" alt="Screenshot 2021-03-29 at 19 09 34" src="https://user-images.githubusercontent.com/1580697/112866455-7e2f0d00-90c2-11eb-96e6-3feb7eea7e09.png">
### Does this PR introduce _any_ user-facing change?
Yes.
### How was this patch tested?
By running new tests:
```
$ build/sbt "test:testOnly *DateTimeUtilsSuite"
$ build/sbt "test:testOnly *DateExpressionsSuite"
$ build/sbt "test:testOnly *ColumnExpressionSuite"
```
and some tests from `SQLQueryTestSuite`:
```
$ build/sbt "sql/testOnly *SQLQueryTestSuite -- -z timestamp.sql"
$ build/sbt "sql/testOnly *SQLQueryTestSuite -- -z datetime.sql"
$ build/sbt "sql/testOnly *SQLQueryTestSuite -- -z interval.sql"
```
Closes#32016 from MaxGekk/subtract-timestamps-to-intervals.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
1. Add the SQL config `spark.sql.legacy.interval.enabled` which will control when Spark SQL should use `CalendarIntervalType` instead of ANSI intervals.
2. Modify the `SubtractDates` expression to return values of `DayTimeIntervalType` when `spark.sql.legacy.interval.enabled` is set to `false` (which is the default).
### Why are the changes needed?
To conform to the ANSI SQL standard which requires ANSI intervals as the result of dates subtraction, see
<img width="656" alt="Screenshot 2021-03-29 at 19 09 34" src="https://user-images.githubusercontent.com/1580697/112866455-7e2f0d00-90c2-11eb-96e6-3feb7eea7e09.png">
### Does this PR introduce _any_ user-facing change?
Yes.
### How was this patch tested?
By running new tests:
```
$ build/sbt "test:testOnly *DateExpressionsSuite"
$ build/sbt "test:testOnly *ColumnExpressionSuite"
```
and some tests from `SQLQueryTestSuite`:
```
$ build/sbt "sql/testOnly *SQLQueryTestSuite -- -z date.sql"
$ build/sbt "sql/testOnly *SQLQueryTestSuite -- -z datetime.sql"
$ build/sbt "sql/testOnly *SQLQueryTestSuite -- -z interval.sql"
```
Closes#31996 from MaxGekk/subtract-dates-to-intervals.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Use resolved attributes instead of data-frame fields for replacing values.
### Why are the changes needed?
dataframe.na.replace() does not work for column having a dot in the name
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
Added unit tests for the same
Closes#31769 from amandeep-sharma/master.
Authored-by: Amandeep Sharma <happyaman91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1 add a sapce between words
2 unify the initials' case
### Why are the changes needed?
correct spelling issues for better user experience
### Does this PR introduce _any_ user-facing change?
yes.
### How was this patch tested?
manually
Closes#31748 from hopefulnick/doc_rectify.
Authored-by: nickhliu <nickhliu@tencent.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Hive support type constructed value as partition spec value, spark should support too.
### Why are the changes needed?
Support TypeConstructed partition spec value keep same with hive
### Does this PR introduce _any_ user-facing change?
Yes, user can use TypeConstruct value as partition spec value such as
```
CREATE TABLE t1(name STRING) PARTITIONED BY (part DATE)
INSERT INTO t1 PARTITION(part = date'2019-01-02') VALUES('a')
CREATE TABLE t2(name STRING) PARTITIONED BY (part TIMESTAMP)
INSERT INTO t2 PARTITION(part = timestamp'2019-01-02 11:11:11') VALUES('a')
CREATE TABLE t4(name STRING) PARTITIONED BY (part BINARY)
INSERT INTO t4 PARTITION(part = X'537061726B2053514C') VALUES('a')
```
### How was this patch tested?
Added UT
Closes#30421 from AngersZhuuuu/SPARK-33474.
Lead-authored-by: angerszhu <angers.zhu@gmail.com>
Co-authored-by: Angerszhuuuu <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?
Add `table_identifier` in sql-migration-guide for SHOW CREATE TABLE.
### Why are the changes needed?
To make document more readable.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing test suites.
Closes#31608 from Karl-WangSK/sqldoc.
Lead-authored-by: Karl-WangSK <shikai.wang@linkflowtech.com>
Co-authored-by: ShiKai Wang <wskqing@gmail.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
### What changes were proposed in this pull request?
This PR fix an issue that `java.sql.RowId` is mapped to `LongType` and prefer `StringType`.
In the current implementation, JDBC RowID type is mapped to `LongType` except for `OracleDialect`, but there is no guarantee to be able to convert RowID to long.
`java.sql.RowId` declares `toString` and the specification of `java.sql.RowId` says
> _all methods on the RowId interface must be fully implemented if the JDBC driver supports the data type_
(https://docs.oracle.com/javase/8/docs/api/java/sql/RowId.html)
So, we should prefer StringType to LongType.
### Why are the changes needed?
This seems to be a potential bug.
### Does this PR introduce _any_ user-facing change?
Yes. RowID is mapped to StringType rather than LongType.
### How was this patch tested?
New test and the existing test case `SPARK-32992: map Oracle's ROWID type to StringType` in `OracleIntegrationSuite` passes.
Closes#31491 from sarutak/rowid-type.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
### What changes were proposed in this pull request?
This PR changes the type mapping for `money` and `money[]` types for PostgreSQL.
Currently, those types are tried to convert to `DoubleType` and `ArrayType` of `double` respectively.
But the JDBC driver seems not to be able to handle those types properly.
https://github.com/pgjdbc/pgjdbc/issues/100https://github.com/pgjdbc/pgjdbc/issues/1405
Due to these issue, we can get the error like as follows.
money type.
```
[info] org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0) (192.168.1.204 executor driver): org.postgresql.util.PSQLException: Bad value for type double : 1,000.00
[info] at org.postgresql.jdbc.PgResultSet.toDouble(PgResultSet.java:3104)
[info] at org.postgresql.jdbc.PgResultSet.getDouble(PgResultSet.java:2432)
[info] at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$5(JdbcUtils.scala:418)
```
money[] type.
```
[info] org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0) (192.168.1.204 executor driver): org.postgresql.util.PSQLException: Bad value for type double : $2,000.00
[info] at org.postgresql.jdbc.PgResultSet.toDouble(PgResultSet.java:3104)
[info] at org.postgresql.jdbc.ArrayDecoding$5.parseValue(ArrayDecoding.java:235)
[info] at org.postgresql.jdbc.ArrayDecoding$AbstractObjectStringArrayDecoder.populateFromString(ArrayDecoding.java:122)
[info] at org.postgresql.jdbc.ArrayDecoding.readStringArray(ArrayDecoding.java:764)
[info] at org.postgresql.jdbc.PgArray.buildArray(PgArray.java:310)
[info] at org.postgresql.jdbc.PgArray.getArrayImpl(PgArray.java:171)
[info] at org.postgresql.jdbc.PgArray.getArray(PgArray.java:111)
```
For money type, a known workaround is to treat it as string so this PR do it.
For money[], however, there is no reasonable workaround so this PR remove the support.
### Why are the changes needed?
This is a bug.
### Does this PR introduce _any_ user-facing change?
Yes. As of this PR merged, money type is mapped to `StringType` rather than `DoubleType` and the support for money[] is stopped.
For money type, if the value is less than one thousand, `$100.00` for instance, it works without this change so I also updated the migration guide because it's a behavior change for such small values.
On the other hand, money[] seems not to work with any value but mentioned in the migration guide just in case.
### How was this patch tested?
New test.
Closes#31442 from sarutak/fix-for-money-type.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
### What changes were proposed in this pull request?
Passing around the output attributes should have more benefits like keeping the exprID unchanged to avoid bugs when we apply more operators above the command output DataFrame.
This PR did 2 things ：
1. After this pr, a `SHOW TBLPROPERTIES` clause's output shows `key` and `value` columns whether you specify the table property `key`. Before this pr, a `SHOW TBLPROPERTIES` clause's output only show a `value` column when you specify the table property `key`..
2. Keep `SHOW TBLPROPERTIES` command's output attribute exprId unchanged.
### Why are the changes needed?
1. Keep `SHOW TBLPROPERTIES`'s output schema consistence
2. Keep `SHOW TBLPROPERTIES` command's output attribute exprId unchanged.
### Does this PR introduce _any_ user-facing change?
After this pr, a `SHOW TBLPROPERTIES` clause's output shows `key` and `value` columns whether you specify the table property `key`. Before this pr, a `SHOW TBLPROPERTIES` clause's output only show a `value` column when you specify the table property `key`.
Before this PR:
```
sql > SHOW TBLPROPERTIES tabe_name('key')
value
value_of_key
```
After this PR
```
sql > SHOW TBLPROPERTIES tabe_name('key')
key value
key value_of_key
```
### How was this patch tested?
Added UT
Closes#31378 from AngersZhuuuu/SPARK-34240.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The current implement of some DDL not unify the output and not pass the output properly to physical command.
Such as: The `ShowTables` output attributes `namespace`, but `ShowTablesCommand` output attributes `database`.
As the query plan, this PR pass the output attributes from `ShowTables` to `ShowTablesCommand`, `ShowTableExtended ` to `ShowTablesCommand`.
Take `show tables` and `show table extended like 'tbl'` as example.
The output before this PR:
`show tables`
|database|tableName|isTemporary|
-- | -- | --
| default| tbl| false|
If catalog is v2 session catalog, the output before this PR:
|namespace|tableName|
-- | --
| default| tbl
`show table extended like 'tbl'`
|database|tableName|isTemporary| information|
-- | -- | -- | --
| default| tbl| false|Database: default...|
The output after this PR:
`show tables`
|namespace|tableName|isTemporary|
-- | -- | --
| default| tbl| false|
`show table extended like 'tbl'`
|namespace|tableName|isTemporary| information|
-- | -- | -- | --
| default| tbl| false|Database: default...|
### Why are the changes needed?
This PR have benefits as follows:
First, Unify schema for the output of SHOW TABLES.
Second, pass the output attributes could keep the expr ID unchanged, so that avoid bugs when we apply more operators above the command output dataframe.
### Does this PR introduce _any_ user-facing change?
Yes.
The output schema of `SHOW TABLES` replace `database` by `namespace`.
### How was this patch tested?
Jenkins test.
Closes#31245 from beliefer/SPARK-34157.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/26006
In #26006 , we merged the v1 and v2 SHOW DATABASES/NAMESPACES commands, but we missed a behavior change that the output schema of SHOW DATABASES becomes different.
This PR adds a legacy config to restore the old schema, with a migration guide item to mention this behavior change.
### Why are the changes needed?
Improve backward compatibility
### Does this PR introduce _any_ user-facing change?
No (the legacy config is false by default)
### How was this patch tested?
a new test
Closes#31474 from cloud-fan/command-schema.
Lead-authored-by: Wenchen Fan <cloud0fan@gmail.com>
Co-authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Correct the version of SQL configuration `spark.sql.legacy.parseNullPartitionSpecAsStringLiteral` from 3.2.0 to 3.0.2.
Also, revise the documentation and test case.
### Why are the changes needed?
The release version in https://github.com/apache/spark/pull/31421 was wrong.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit tests
Closes#31434 from gengliangwang/reviseVersion.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In spark, the `count(table.*)` may cause very weird result, for example:
```
select count(*) from (select 1 as a, null as b) t;
output: 1
select count(t.*) from (select 1 as a, null as b) t;
output: 0
```
This is because spark expands `t.*` while converts `*` to count(1), this will confuse
users. After checking the ANSI standard, `count(*)` should always be `count(1)` while `count(t.*)`
is not allowed. What's more, this is also not allowed by common databases, e.g. MySQL, Oracle.
So, this PR proposes to block the ambiguous behavior and print a clear error message for users.
### Why are the changes needed?
to avoid ambiguous behavior and follow ANSI standard and other SQL engines
### Does this PR introduce _any_ user-facing change?
Yes, `count(table.*)` behavior will be blocked and output an error message.
### How was this patch tested?
newly added and existing tests
Closes#31286 from linhongliu-db/fix-table-star.
Authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a follow up for https://github.com/apache/spark/pull/30538.
It adds a legacy conf `spark.sql.legacy.parseNullPartitionSpecAsStringLiteral` in case users wants the legacy behavior.
It also adds document for the behavior change.
### Why are the changes needed?
In case users want the legacy behavior, they can set `spark.sql.legacy.parseNullPartitionSpecAsStringLiteral` as true.
### Does this PR introduce _any_ user-facing change?
Yes, adding a legacy configuration to restore the old behavior.
### How was this patch tested?
Unit test.
Closes#31421 from gengliangwang/legacyNullStringConstant.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add document for the behavior change in SPARK-34052, in SQL migration guide.
### Why are the changes needed?
Document behavior change for Spark users.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes#31351 from sunchao/SPARK-34052-followup.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This is a follow up of the PRs https://github.com/apache/spark/pull/31066 and https://github.com/apache/spark/pull/31304 that changed behavior of some commands regarding to table cache refreshing. The PR updates the SQL migration guide, in particular, the item which describes new behavior.
### Why are the changes needed?
To inform users about command behavior changes.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes#31309 from MaxGekk/refreshTable-sql-migration-guide.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR changes cache refreshing of v1 tables in v1 commands. In particular, v1 table dependents are not removed from the cache after this PR. Comparing to current implementation, we just clear cached data of all dependents and keep them in the cache. So, the next actions will fill in the cached data of the original v1 table and its dependents. In more details:
1. Modified the `CatalogImpl.refreshTable()` method to use `recacheByPlan()` instead of `lookupCachedData()`, `uncacheQuery()` and `cacheQuery()`. Users can call this method via public API like `spark.catalog.refreshTable()`.
2. Rewritten the part in `CatalogImpl.refreshTable()` which was responsible for table meta-data refreshing because this code stopped to work properly after removing of the second `sparkSession.table(tableIdent)`.
3. Added new private method `invalidateCachedTable()` to `SessionCatalog`. Comparing to the existing `SessionCatalog.refreshTable`, it invalidates the relation cache only. If we called `SessionCatalog.refreshTable` from `CatalogImpl.refreshTable()`, we would refresh temporary and global temporary views twice (that could lead to refreshing file index twice).
### Why are the changes needed?
1. This should improve user experience with table/view caching. For example, let's imagine that an user has cached v1 table and cached view based on the table. And the user passed the table to external library which drops/renames/adds partitions in the v1 table. Unfortunately, the user gets the view uncached after that even he/she hasn't uncached the view explicitly.
2. To improve code maintenance.
3. To reduce the amount of calls to Hive external catalog.
4. Also this should speed up table recaching.
5. To have the same behavior as for v2 tables supported by https://github.com/apache/spark/pull/31172
### Does this PR introduce _any_ user-facing change?
From the view of the correctness of query results, there are no behavior changes but the changes might influence on consuming memory and query execution time. For example:
Before:
```scala
scala> sql("CREATE TABLE tbl (c int)")
scala> sql("CACHE TABLE tbl")
scala> sql("CREATE VIEW v AS SELECT * FROM tbl")
scala> sql("CACHE TABLE v")
scala> spark.catalog.isCached("v")
res6: Boolean = true
scala> spark.catalog.refreshTable("tbl")
scala> spark.catalog.isCached("v")
res8: Boolean = false
```
After:
```scala
scala> spark.catalog.refreshTable("tbl")
scala> spark.catalog.isCached("v")
res8: Boolean = true
```
### How was this patch tested?
1. Added new unit tests that create a view, a temporary view and a global temporary view on top of v1/v2 tables, and refresh the base table via `ALTER TABLE .. ADD/DROP/RENAME PARTITION`.
2. By running the unified test suites:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableAddPartitionSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableDropPartitionSuite"
# build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableRenamePartitionSuite"
```
Closes#31206 from MaxGekk/refreshTable-recache-by-plan.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Hive 2.3.8 changes:
HIVE-19662: Upgrade Avro to 1.8.2
HIVE-24324: Remove deprecated API usage from Avro
HIVE-23980: Shade Guava from hive-exec in Hive 2.3
HIVE-24436: Fix Avro NULL_DEFAULT_VALUE compatibility issue
HIVE-24512: Exclude calcite in packaging.
HIVE-22708: Fix for HttpTransport to replace String.equals
HIVE-24551: Hive should include transitive dependencies from calcite after shading it
HIVE-24553: Exclude calcite from test-jar dependency of hive-exec
### Why are the changes needed?
Upgrade Avro and Parquet to latest version.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing test add test try to upgrade Parquet to 1.11.1 and Avro to 1.10.1: https://github.com/apache/spark/pull/30517Closes#30657 from wangyum/SPARK-33696.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR aims to strip auto-generated cast. The main logic is:
1. Add tag if Cast is specified by user.
2. Wrap `PrettyAttribute` in usePrettyExpression.
### Why are the changes needed?
Make sql consistent with dsl. Here is an inconsistent example before this PR:
```
-- output field name: FLOOR(1)
spark.emptyDataFrame.select(floor(lit(1)))
-- output field name: FLOOR(CAST(1 AS DOUBLE))
spark.sql("select floor(1)")
```
Note that, we don't remove the `Cast` so the auto-generated `Cast` can still work. The only changed place is `usePrettyExpression`, we use `PrettyAttribute` replace `Cast` to give a better sql string.
### Does this PR introduce _any_ user-facing change?
Yes, the default field name may change.
### How was this patch tested?
Add test and pass exists test.
Closes#31034 from ulysses-you/SPARK-33989.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a followup PR for SPARK-33690 (#30647) .
In addition to the original PR, this PR intends to escape the following meta-characters in `Dataset#showString`.
* `\r` (carrige ret)
* `\f` (form feed)
* `\b` (backspace)
* `\u000B` (vertical tab)
* `\u0007` (bell)
### Why are the changes needed?
To avoid breaking the layout of `Dataset#showString`.
`\u0007` does not break the layout of `Dataset#showString` but it's noisy (beeps for each row) so it should be also escaped.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Modified the existing tests.
I also build the documents and check the generated html for `sql-migration-guide.md`.
Closes#31144 from sarutak/escape-metacharacters-in-getRows.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Update migration guide according to https://github.com/apache/spark/pull/30942#issuecomment-755054562
### Why are the changes needed?
update migration guide.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Not need
Closes#31051 from AngersZhuuuu/SPARK-32685-FOLLOW-UP.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
For same SQL
```
SELECT TRANSFORM(a, b, c, null)
ROW FORMAT DELIMITED
USING 'cat'
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '&'
FROM (select 1 as a, 2 as b, 3 as c) t
```
In hive:
```
hive> SELECT TRANSFORM(a, b, c, null)
> ROW FORMAT DELIMITED
> USING 'cat'
> ROW FORMAT DELIMITED
> FIELDS TERMINATED BY '&'
> FROM (select 1 as a, 2 as b, 3 as c) t;
OK
123\N NULL
Time taken: 14.519 seconds, Fetched: 1 row(s)
hive> packet_write_wait: Connection to 10.191.58.100 port 32200: Broken pipe
```
In Spark
```
Spark master: local[*], Application Id: local-1609225830376
spark-sql> SELECT TRANSFORM(a, b, c, null)
> ROW FORMAT DELIMITED
> USING 'cat'
> ROW FORMAT DELIMITED
> FIELDS TERMINATED BY '&'
> FROM (select 1 as a, 2 as b, 3 as c) t;
1 2 3 null NULL
Time taken: 4.297 seconds, Fetched 1 row(s)
spark-sql>
```
We should keep same. Change default ROW FORMAT FIELD DELIMIT to `\u0001`
In hive default value is '1' to char is '\u0001'
```
bucket_count -1
column.name.delimiter ,
columns
columns.comments
columns.types
file.inputformat org.apache.hadoop.hive.ql.io.NullRowsInputFormat
```
### Why are the changes needed?
Keep same behavior with hive
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#30958 from AngersZhuuuu/SPARK-33930.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Update the SQL migration guide about the changes made by:
- https://github.com/apache/spark/pull/30778
- https://github.com/apache/spark/pull/30711
- https://github.com/apache/spark/pull/30866
### Why are the changes needed?
To inform users about the recent changes in the upcoming releases.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes#30925 from MaxGekk/sql-migr-guide-hiveclientimpl.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Addressed comments in PR #30567, including:
1. add test case for SPARK-33647 and SPARK-33142
2. add migration guide
3. add `getRawTempView` and `getRawGlobalTempView` to return the raw view info (i.e. TemporaryViewRelation)
4. other minor code clean
### Why are the changes needed?
Code clean and more test cases
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing and newly added test cases
Closes#30666 from linhongliu-db/SPARK-33142-followup.
Lead-authored-by: Linhong Liu <linhong.liu@databricks.com>
Co-authored-by: Linhong Liu <67896261+linhongliu-db@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR intends to escape meta-characters (e.g., \n and \t) in `Dataset.showString`.
Before this PR:
```
scala> Seq("aaa\nbbb\t\tccccc").toDF("value").show()
+--------------+
| value|
+--------------+
|aaa
bbb ccccc|
+--------------+
```
After this PR:
```
+-----------------+
| value|
+-----------------+
|aaa\nbbb\t\tccccc|
+-----------------+
```
### Why are the changes needed?
For better output.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added a unit test.
Closes#30647 from maropu/EscapeMetaInShow.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
