Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/22237#discussion_r214302343
--- Diff: docs/sql-programming-guide.md ---
@@ -1897,6 +1897,7 @@ working with timestamps in `pandas_udf`s to get the
best performance, see
- In version 2.3 and earlier, CSV rows are considered as malformed if at
least one column value in the row is malformed. CSV parser dropped such rows in
the DROPMALFORMED mode or outputs an error in the FAILFAST mode. Since Spark
2.4, CSV row is considered as malformed only when it contains malformed column
values requested from CSV datasource, other values can be ignored. As an
example, CSV file contains the "id,name" header and one row "1234". In Spark
2.4, selection of the id column consists of a row with one column value 1234
but in Spark 2.3 and earlier it is empty in the DROPMALFORMED mode. To restore
the previous behavior, set `spark.sql.csv.parser.columnPruning.enabled` to
`false`.
- Since Spark 2.4, File listing for compute statistics is done in
parallel by default. This can be disabled by setting
`spark.sql.parallelFileListingInStatsComputation.enabled` to `False`.
- Since Spark 2.4, Metadata files (e.g. Parquet summary files) and
temporary files are not counted as data files when calculating table size
during Statistics computation.
+ - Since Spark 2.4, 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.
--- End diff --
nit: from_json -> `` `from_json` ``.
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