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https://issues.apache.org/jira/browse/SPARK-24561?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16717418#comment-16717418
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ASF GitHub Bot commented on SPARK-24561:
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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_r240667535
##########
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).
+ *
+ * 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 its
unbounded window
+ * so it's bound indices will always be the same.
+ *
+ * Unbounded window also have a different eval type, because:
+ * (1) It doesn't have bound indices as input
+ * (2) The udf only needs to be evaluated once the in python worker (because
the udf is
+ * deterministic and window bounds are the same for all windows)
Review comment:
Oh this relates to the statement "The udf only needs to be evaluated once".
If the udf is not deterministic, even the window bounds are the same, we still
need to evaluate it once per window
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> User-defined window functions with pandas udf (bounded window)
> --------------------------------------------------------------
>
> Key: SPARK-24561
> URL: https://issues.apache.org/jira/browse/SPARK-24561
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark
> Affects Versions: 2.3.1
> Reporter: Li Jin
> Priority: Major
>
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