Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

[SPARK-33641][SQL][DOC][FOLLOW-UP] Add migration guide for CHAR VARCHAR types #30654

Closed
wants to merge 5 commits into from
Closed

[SPARK-33641][SQL][DOC][FOLLOW-UP] Add migration guide for CHAR VARCHAR types #30654

Changes from 2 commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
898172b
[SPARK-33641][SQL][DOC][FOLLOW-UP] Add migration guide for CHAR VARCH…
yaooqinn Dec 7, 2020
3cb8724
[SPARK-33641][SQL][DOC][FOLLOW-UP] Add migration guide for CHAR VARCH…
yaooqinn Dec 7, 2020
a50e5ec
[SPARK-33641][SQL][DOC][FOLLOW-UP] Add migration guide for CHAR VARCH…
yaooqinn Dec 8, 2020
80d4421
[SPARK-33641][SQL][DOC][FOLLOW-UP] Add migration guide for CHAR VARCH…
yaooqinn Dec 8, 2020
dc88a0b
typo
yaooqinn Dec 9, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 2 additions & 0 deletions docs/sql-migration-guide.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,8 @@ license: |

- In Spark 3.1, creating or altering a view will capture runtime SQL configs and store them as view properties. These configs will be applied during the parsing and analysis phases of the view resolution. To restore the behavior before Spark 3.1, you can set `spark.sql.legacy.useCurrentConfigsForView` to `true`.

- In Spark 3.1, CHAR/CHARACTER and VARCHAR types become individual types from string. By default, they can only be used in table schema, not functions/operators. To restore the behavior before Spark 3.1, where treats them as string with length parameter simply ignored, you can set `spark.sql.legacy.charVarcharAsString` to `true`.
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

how about CHAR/CHARACTER and VARCHAR types become individual types from string -> we support CHAR/CHARACTER and VARCHAR types in our type system framework instead of replacing them with STRING types?

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

nit: table schema -> a table schema

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

, where treats them as string with length parameter simply ignored, -> , which treats them as STRING types and ignores a length parameter (e.g., CHAR(4)), ?

Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

thanks, updated


## Upgrading from Spark SQL 3.0 to 3.0.1

- In Spark 3.0, JSON datasource and JSON function `schema_of_json` infer TimestampType from string values if they match to the pattern defined by the JSON option `timestampFormat`. Since version 3.0.1, the timestamp type inference is disabled by default. Set the JSON option `inferTimestamp` to `true` to enable such type inference.
Expand Down