Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/18865#discussion_r137917938
--- Diff: docs/sql-programming-guide.md ---
@@ -1542,6 +1542,10 @@ options.
# Migration Guide
+## Upgrading From Spark SQL 2.2 to 2.3
+
+ - The queries which select only `spark.sql.columnNameOfCorruptRecord`
column are disallowed now. Notice that the queries which have only the column
after column pruning (e.g. filtering on the column followed by a counting
operation) are also disallowed. If you want to select only the corrupt records,
you should cache or save the underlying Dataset and DataFrame before running
such queries.
--- End diff --
> Since Spark 2.3, the queries from raw JSON/CSV files are disallowed when
the referenced columns only include the internal corrupt record column (named
`_corrupt_column` by default). For example,
`spark.read.schema(schema).json(file).filter($"_corrupt_record".isNotNull).count()`
and `spark.read.schema(schema).json(file).select("_corrupt_record").show()`.
Instead, you can cache or save the parsed results and then send the same query.
For example, `val df = spark.read.schema(schema).json(file).cache()` and then
`df.filter($"_corrupt_record".isNotNull).count()`.
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