From dc88a0bafcf630a14792d6cb736a6d2fb21b2169 Mon Sep 17 00:00:00 2001 From: Kent Yao Date: Wed, 9 Dec 2020 14:38:46 +0800 Subject: [PATCH] typo --- docs/sql-migration-guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/sql-migration-guide.md b/docs/sql-migration-guide.md index 0a31b73405233..2bc04a0a79995 100644 --- a/docs/sql-migration-guide.md +++ b/docs/sql-migration-guide.md @@ -54,7 +54,7 @@ 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, we support CHAR/CHARACTER and VARCHAR types in our type system framework instead of replacing them with STRING types. Currently, they can only be used in a table schema, not functions/operators. To restore the behavior before Spark 3.1, which treats them as STRING types and ignores a length parameter, e.g. `CHAR(4)`, you can set `spark.sql.legacy.charVarcharAsString` to `true`. + - Since Spark 3.1, CHAR/CHARACTER and VARCHAR types are supported in the table schema. Table scan/insertion will respect the char/varchar semantic. If char/varchar is used in places other than table schema, an exception will be thrown (CAST is an exception that simply treats char/varchar as string like before). To restore the behavior before Spark 3.1, which treats them as STRING types and ignores a length parameter, e.g. `CHAR(4)`, you can set `spark.sql.legacy.charVarcharAsString` to `true`. ## Upgrading from Spark SQL 3.0 to 3.0.1