icexelloss commented on a change in pull request #22305:
[SPARK-24561][SQL][Python] User-defined window aggregation functions with
Pandas UDF (bounded window)
URL: https://github.com/apache/spark/pull/22305#discussion_r240686604
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/python/WindowInPandasExec.scala
##########
@@ -27,17 +27,62 @@ import
org.apache.spark.api.python.{ChainedPythonFunctions, PythonEvalType}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.execution.{GroupedIterator, SparkPlan,
UnaryExecNode}
+import org.apache.spark.sql.catalyst.plans.physical.{AllTuples,
ClusteredDistribution, Distribution, Partitioning}
+import org.apache.spark.sql.execution.{ExternalAppendOnlyUnsafeRowArray,
SparkPlan}
import org.apache.spark.sql.execution.arrow.ArrowUtils
-import org.apache.spark.sql.types.{DataType, StructField, StructType}
+import org.apache.spark.sql.execution.window._
+import org.apache.spark.sql.types._
import org.apache.spark.util.Utils
+/**
+ * This class calculates and outputs windowed aggregates over the rows in a
single partition.
+ *
+ * This is similar to [[WindowExec]]. The main difference is that this node
doesn't not compute
+ * any window aggregation values. Instead, it computes the lower and upper
bound for each window
+ * (i.e. window bounds) and pass the data and indices to python work to do the
actual window
+ * aggregation.
+ *
+ * It currently materializes all data associated with the same partition key
and passes them to
+ * Python worker. This is not strictly necessary for sliding windows and can
be improved (by
+ * possibly slicing data into overlapping chunks and stitch them together).
Review comment:
Fixed
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