Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/18792#discussion_r130523169
--- Diff: docs/sql-programming-guide.md ---
@@ -1903,6 +1903,23 @@ releases of Spark SQL.
Hive can optionally merge the small files into fewer large files to
avoid overflowing the HDFS
metadata. Spark SQL does not support that.
+**Hive UDF/UDTF/UDAF**
+
+Not all the APIs of the Hive UDF/UDTF/UDAF are supported by Spark SQL.
Below are the unsupported APIs:
+
+* `getRequiredJars` and `getRequiredFiles` (`UDF` and `GenericUDF`) are
functions to automatically
+ include additional resources required by this UDF.
+* `initialize(StructObjectInspector)` in `GenericUDTF` is not supported
yet. Spark SQL currently uses
+ a deprecated interface `initialize(ObjectInspector[])` only.
+* `configure` (`GenericUDF`, `GenericUDTF`, and `GenericUDAFEvaluator`) is
a function to initialize
+ functions with `MapredContext`, which is inapplicable to Spark. But,
Spark SQL does not use `MapredContext` internally.
--- End diff --
nit: `But` looks redundant here, because there's `inapplicable` before.
Looks like negative to negative...
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