Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/21909#discussion_r210765672
--- Diff: docs/sql-programming-guide.md ---
@@ -1894,6 +1894,7 @@ 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, text-based datasources like CSV and JSON don't parse
input lines if the required schema pushed down to the datasources is empty. The
schema can be empty in the case of the count() action. For example, Spark 2.3
and earlier versions failed on JSON files with invalid encoding but Spark 2.4
returns total number of lines in the file. To restore the previous behavior
when the underlying parser is always invoked even for the empty schema, set
`true` to `spark.sql.legacy.bypassParserForEmptySchema`. This option will be
removed in Spark 3.0.
--- End diff --
Is it right based on what you said
https://github.com/apache/spark/pull/21909#discussion_r210704902?
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