[GitHub] viirya commented on a change in pull request #23388: [SPARK-26448][SQL] retain the difference between 0.0 and -0.0

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viirya commented on a change in pull request #23388: [SPARK-26448][SQL] retain the difference between 0.0 and -0.0
URL: https://github.com/apache/spark/pull/23388#discussion_r244084657
 
 

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 File path: docs/sql-migration-guide-upgrade.md
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 @@ -25,8 +25,6 @@ displayTitle: Spark SQL Upgrading Guide
 
   - In Spark version 2.4 and earlier, `Dataset.groupByKey` results to a grouped dataset with key attribute wrongly named as "value", if the key is non-struct type, e.g. int, string, array, etc. This is counterintuitive and makes the schema of aggregation queries weird. For example, the schema of `ds.groupByKey(...).count()` is `(value, count)`. Since Spark 3.0, we name the grouping attribute to "key". The old behaviour is preserved under a newly added configuration `spark.sql.legacy.dataset.nameNonStructGroupingKeyAsValue` with a default value of `false`.
 
-  - In Spark version 2.4 and earlier, float/double -0.0 is semantically equal to 0.0, but users can still distinguish them via `Dataset.show`, `Dataset.collect` etc. Since Spark 3.0, float/double -0.0 is replaced by 0.0 internally, and users can't distinguish them any more.
 
 Review comment:
   The motivation of this fix is to avoid this behavior change?

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