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[SPARK-32018][FOLLOWUP][Doc] Add migration guide for decimal value overflow in sum aggregation #29458

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[SPARK-32018][FOLLOWUP][Doc] Add migration guide for decimal value overflow in sum aggregation #29458

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2 changes: 2 additions & 0 deletions docs/sql-migration-guide.md
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Expand Up @@ -36,6 +36,8 @@ license: |

- In Spark 3.1, NULL elements of structures, arrays and maps are converted to "null" in casting them to strings. In Spark 3.0 or earlier, NULL elements are converted to empty strings. To restore the behavior before Spark 3.1, you can set `spark.sql.legacy.castComplexTypesToString.enabled` to `true`.

- In Spark 3.1, when `spark.sql.ansi.enabled` is false, Spark always returns null if the sum of decimal type column overflows. In Spark 3.0 or earlier, when `spark.sql.ansi.enabled` is false, the sum of decimal type column may return null or incorrect result, or even fails at runtime (depending on the actual query plan execution).

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@maropu maropu Aug 18, 2020

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nit: we need to describe spark.sql.ansi.enabled is false two times? I think its okay just to describe it like this;

In Spark 3.0 or earlier, the sum of...

or

In Spark 3.0 or earlier, in the case, the sum of...

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@gengliangwang gengliangwang Aug 19, 2020

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@maropu Thanks

## 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.
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