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From Ryan Blue <rb...@netflix.com.INVALID>
Subject Re: [DISCUSS] Syntax for table DDL
Date Mon, 01 Oct 2018 18:20:43 GMT
What do you mean by consistent with the syntax in SqlBase.g4? These aren’t
currently defined, so we need to decide what syntax to support. There are
more details below, but the syntax I’m proposing is more standard across
databases than Hive, which uses confusing and non-standard syntax.

I doubt that we want to support Hive syntax for a few reasons. Hive uses
the same column CHANGE statement for multiple purposes, so it ends up with
strange patterns for simple tasks, like updating the column’s type:

ALTER TABLE t CHANGE a1 a1 INT;

The column name is doubled because old name, new name, and type are always
required. So you have to know the type of a column to change its name and
you have to double up the name to change its type. Hive also allows a
couple other oddities:

   - Column reordering with FIRST and AFTER keywords. Column reordering is
   tricky to get right so I’m not sure we want to add it.
   - RESTRICT and CASCADE to signal whether to change all partitions or
   not. Spark doesn’t support partition-level schemas except through Hive, and
   even then I’m not sure how reliable it is.

I know that we wouldn’t necessarily have to support these features from
Hive, but I’m pointing them out to ask the question: why copy Hive’s syntax
if it is unlikely that Spark will implement all of the “features”? I’d
rather go with SQL syntax from databases like PostgreSQL or others that are
more standard and common.

The more “standard” versions of these statements are like what I’ve
proposed:

   - ALTER TABLE ident ALTER COLUMN qualifiedName TYPE dataType: ALTER is
   used by SQL Server, Access, DB2, and PostgreSQL; MODIFY by MySQL and
   Oracle. COLUMN is optional in Oracle and TYPE is omitted by databases
   other than PosgreSQL. I think we could easily add MODIFY as an
   alternative to the second ALTER (and maybe alternatives like UPDATE and
   CHANGE) and make both TYPE and COLUMN optional.
   - ALTER TABLE ident RENAME COLUMN qualifiedName TO qualifiedName: This
   syntax is supported by PostgreSQL, Oracle, and DB2. MySQL uses the same
   syntax as Hive and it appears that SQL server doesn’t have this statement.
   This also match the table rename syntax, which uses TO.
   - ALTER TABLE ident DROP (COLUMN | COLUMNS) qualifiedNameList: This
   matches PostgreSQL, Oracle, DB2, and SQL server. MySQL makes COLUMN
   optional. Most don’t allow deleting multiple columns, but it’s a reasonable
   extension.

While we’re on the subject of ALTER TABLE DDL, I should note that all of
the databases use ADD COLUMN syntax that differs from Hive (and currently,
Spark):

   - ALTER TABLE ident ADD COLUMN qualifiedName dataType (',' qualifiedName
   dataType)*: All other databases I looked at use ADD COLUMN, but not all
   of them support adding multiple columns at the same time. Hive requires (
   and ) enclosing the columns and uses the COLUMNS keyword instead of
   COLUMN. I think that Spark should be updated to make the parens optional
   and to support both keywords, COLUMN and COLUMNS.

What does everyone think? Is it reasonable to use the more standard syntax
instead of using Hive as a base?

rb

On Fri, Sep 28, 2018 at 11:07 PM Xiao Li <gatorsmile@gmail.com> wrote:

> Are they consistent with the current syntax defined in SqlBase.g4? I think
> we are following the Hive DDL syntax:
> https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-AlterTable/Partition/Column
>
> Ryan Blue <rblue@netflix.com.invalid> 于2018年9月28日周五 下午3:47写道:
>
>> Hi everyone,
>>
>> I’m currently working on new table DDL statements for v2 tables. For
>> context, the new logical plans for DataSourceV2 require a catalog interface
>> so that Spark can create tables for operations like CTAS. The proposed
>> TableCatalog API also includes an API for altering those tables so we can
>> make ALTER TABLE statements work. I’m implementing those DDL statements,
>> which will make it into upstream Spark when the TableCatalog PR is merged.
>>
>> Since I’m adding new SQL statements that don’t yet exist in Spark, I want
>> to make sure that the syntax I’m using in our branch will match the syntax
>> we add to Spark later. I’m basing this proposed syntax on PostgreSQL
>> <https://www.postgresql.org/docs/current/static/ddl-alter.html>.
>>
>>    - *Update data type*: ALTER TABLE tableIdentifier ALTER COLUMN
>>    qualifiedName TYPE dataType.
>>    - *Rename column*: ALTER TABLE tableIdentifier RENAME COLUMN
>>    qualifiedName TO qualifiedName
>>    - *Drop column*: ALTER TABLE tableIdentifier DROP (COLUMN | COLUMNS)
>>    qualifiedNameList
>>
>> A few notes:
>>
>>    - Using qualifiedName in these rules allows updating nested types,
>>    like point.x.
>>    - Updates and renames can only alter one column, but drop can drop a
>>    list.
>>    - Rename can’t move types and will validate that if the TO name is
>>    qualified, that the prefix matches the original field.
>>    - I’m also changing ADD COLUMN to support adding fields to nested
>>    columns by using qualifiedName instead of identifier.
>>
>> Please reply to this thread if you have suggestions based on a different
>> SQL engine or want this syntax to be different for another reason. Thanks!
>>
>> rb
>> --
>> Ryan Blue
>> Software Engineer
>> Netflix
>>
>

-- 
Ryan Blue
Software Engineer
Netflix

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