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_r242302664
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/python/WindowInPandasExec.scala
##########
@@ -27,17 +27,65 @@ 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
does 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 worker 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 stitching them together).
+ *
+ * This class groups window expressions by their window boundaries so that
window expressions
+ * with the same window boundaries can share the same window bounds. The
window bounds are
+ * prepended to the data passed to the python worker.
+ *
+ * For example, if we have:
+ * avg(v) over specifiedwindowframe(RowFrame, -5, 5),
+ * avg(v) over specifiedwindowframe(RowFrame, UnboundedPreceding,
UnboundedFollowing),
+ * avg(v) over specifiedwindowframe(RowFrame, -3, 3),
+ * max(v) over specifiedwindowframe(RowFrame, -3, 3)
+ *
+ * The python input will look like:
+ * (lower_bound_w1, upper_bound_w1, lower_bound_w3, upper_bound_w3, v)
+ *
+ * where w1 is specifiedwindowframe(RowFrame, -5, 5)
+ * w2 is specifiedwindowframe(RowFrame, UnboundedPreceding,
UnboundedFollowing)
+ * w3 is specifiedwindowframe(RowFrame, -3, 3)
+ *
+ * Note that w2 doesn't have bound indices in the python input because it's
unbounded window
+ * so it's bound indices will always be the same.
+ *
+ * Bounded window and Unbounded window are evaluated differently in Python
worker:
+ * (1) Bounded window takes the window bound indices in addition to the input
columns.
+ * Unbounded window takes only input columns.
+ * (2) Bounded window evaluates the udf once per input row.
+ * Unbounded window evaluates the udf once per window partition. (Because
the udf
+ * is deterministic and window bounds are the same for all input rows)
Review comment:
SGTM. Removed for now.
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