Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/23141#discussion_r236200834
--- Diff: docs/sql-migration-guide-upgrade.md ---
@@ -17,14 +17,16 @@ displayTitle: Spark SQL Upgrading Guide
- Since Spark 3.0, the `from_json` functions supports two modes -
`PERMISSIVE` and `FAILFAST`. The modes can be set via the `mode` option. The
default mode became `PERMISSIVE`. In previous versions, behavior of `from_json`
did not conform to either `PERMISSIVE` nor `FAILFAST`, especially in processing
of malformed JSON records. For example, the JSON string `{"a" 1}` with the
schema `a INT` is converted to `null` by previous versions but Spark 3.0
converts it to `Row(null)`.
- - In Spark version 2.4 and earlier, the `from_json` function produces
`null`s for JSON strings and JSON datasource skips the same independetly of its
mode if there is no valid root JSON token in its input (` ` for example). Since
Spark 3.0, such input is treated as a bad record and handled according to
specified mode. For example, in the `PERMISSIVE` mode the ` ` input is
converted to `Row(null, null)` if specified schema is `key STRING, value INT`.
+ - In Spark version 2.4 and earlier, the `from_json` function produces
`null`s for JSON strings and JSON datasource skips the same independetly of its
mode if there is no valid root JSON token in its input (` ` for example). Since
Spark 3.0, such input is treated as a bad record and handled according to
specified mode. For example, in the `PERMISSIVE` mode the ` ` input is
converted to `Row(null, null)` if specified schema is `key STRING, value INT`.
- The `ADD JAR` command previously returned a result set with the single
value 0. It now returns an empty result set.
- In Spark version 2.4 and earlier, users can create map values with map
type key via built-in function like `CreateMap`, `MapFromArrays`, etc. Since
Spark 3.0, it's not allowed to create map values with map type key with these
built-in functions. Users can still read map values with map type key from data
source or Java/Scala collections, though they are not very useful.
-
+
- 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.
--- End diff --
I ran few simple queries on Hive 2.1.
Simple comparison seems ok:
```
hive> select 1 where 0.0=-0.0;
OK
1
Time taken: 0.047 seconds, Fetched: 1 row(s)
hive> select 1 where -0.0<0.0;
OK
Time taken: 0.053 seconds
```
But group by behavior seems not correct:
```
hive> select * from test;
OK
0.0
-0.0
0.0
Time taken: 0.11 seconds, Fetched: 3 row(s)
hive> select * from test;
OK
0.0
-0.0
0.0
Time taken: 0.11 seconds, Fetched: 3 row(s)
hive> select a, count(*) from test group by a;
-0.0 3
Time taken: 1.308 seconds, Fetched: 1 row(s)
```
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