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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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gengliangwang Aug 18, 2020
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4 changes: 4 additions & 0 deletions docs/sql-migration-guide.md
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Expand Up @@ -36,6 +36,10 @@ 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, sum aggregation of decimal type column always returns `null` on decimal value overflow. In Spark 3.0 or earlier, when `spark.sql.ansi.enabled` is false and decimal value overflow happens in sum aggregation of decimal type column:
- If it is hash aggregation with `group by` clause, a runtime exception is thrown.
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not many users know the physical nodes. How about

In Spark 3.1, Spark always returns null if the sum of decimal overflows under non-ANSI
mode (`spark.sql.ansi.enabled` is false). In Spark 3.0 or earlier, the sum of decimal may
fail at runtime under non-ANSI mode (when the query has GROUP BY and is planned as hash aggregate)

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not many users know the physical nodes. How about

In Spark 3.1, Spark always returns null if the sum of decimal overflows under non-ANSI
mode (`spark.sql.ansi.enabled` is false). In Spark 3.0 or earlier, the sum of decimal may
fail at runtime under non-ANSI mode (when the query has GROUP BY and is planned as hash aggregate)

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@gengliangwang gengliangwang Aug 18, 2020 •
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The name "non-ANSI mode" is a bit wired.
Also, we have to mention that Spark 3.0 or earlier returns null under certain conditions.

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@cloud-fan cloud-fan Aug 18, 2020 •
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We can use "default mode".

I don't see a difference between "may fail at runtime" or "may return null". They are mutually exclusive.

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Thanks, I have updated the doc and screenshot

- Otherwise, null is returned.

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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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@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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