Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/8835#discussion_r40133051
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala ---
@@ -329,7 +329,13 @@ case class EvaluatePython(
/**
* :: DeveloperApi ::
* Uses PythonRDD to evaluate a [[PythonUDF]], one partition of tuples at
a time.
- * The input data is zipped with the result of the udf evaluation.
+ *
+ * Python evaluation works by sending the necessary (projected) input data
via a socket to an
+ * external Python process, and combine the result from the Python process
with the original row.
+ *
+ * For each row we send to Python, we also put it in a queue. For each
output row from Python,
+ * we drain the queue to find the original input row. Note that if the
Python process is way too
+ * slow, this could lead to the queue growing unbounded and eventually run
out of memory.
--- End diff --
Could we mitigate this by using a
[LinkedBlockingDeque](https://docs.oracle.com/javase/7/docs/api/java/util/concurrent/LinkedBlockingDeque.html)
to have the producer-side block on inserts once the queue grows to a certain
size?
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