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https://issues.apache.org/jira/browse/FLINK-5047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15898184#comment-15898184
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ASF GitHub Bot commented on FLINK-5047:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3364#discussion_r104522529
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/DataSetSlideTimeWindowAggFlatMapFunction.scala
---
@@ -0,0 +1,93 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.flink.table.runtime.aggregate
+
+import org.apache.flink.api.common.functions.RichFlatMapFunction
+import org.apache.flink.api.common.typeinfo.TypeInformation
+import org.apache.flink.api.java.typeutils.ResultTypeQueryable
+import org.apache.flink.configuration.Configuration
+import org.apache.flink.streaming.api.windowing.windows.TimeWindow
+import org.apache.flink.types.Row
+import org.apache.flink.util.{Collector, Preconditions}
+
+
+/**
+ * It is used for sliding windows on batch for time-windows. It takes a
prepared input row,
+ * aligns the window start, and replicates or omits records for different
panes of a sliding
+ * window. It is used for non-incremental aggregations.
+ *
+ * @param aggregates aggregate functions
+ * @param groupingKeysLength number of grouping keys
+ * @param windowSize window size of the sliding window
+ * @param windowSlide window slide of the sliding window
+ * @param returnType return type of this function
+ */
+class DataSetSlideTimeWindowAggFlatMapFunction(
+ private val aggregates: Array[Aggregate[_]],
+ private val groupingKeysLength: Int,
+ private val timeFieldPos: Int,
+ private val windowSize: Long,
+ private val windowSlide: Long,
+ @transient private val returnType: TypeInformation[Row])
+ extends RichFlatMapFunction[Row, Row]
+ with ResultTypeQueryable[Row] {
+
+ private var aggregateBuffer: Row = _
+ private var outWindowStartIndex: Int = _
+
+ override def open(config: Configuration) {
+ Preconditions.checkNotNull(aggregates)
--- End diff --
Move everything to the constructor and remove `open()`?
> Add sliding group-windows for batch tables
> ------------------------------------------
>
> Key: FLINK-5047
> URL: https://issues.apache.org/jira/browse/FLINK-5047
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: Jark Wu
> Assignee: Timo Walther
>
> Add Slide group-windows for batch tables as described in
> [FLIP-11|https://cwiki.apache.org/confluence/display/FLINK/FLIP-11%3A+Table+API+Stream+Aggregations].
> There are two ways to implement sliding windows for batch:
> 1. replicate the output in order to assign keys for overlapping windows. This
> is probably the more straight-forward implementation and supports any
> aggregation function but blows up the data volume.
> 2. if the aggregation functions are combinable / pre-aggregatable, we can
> also find the largest tumbling window size from which the sliding windows can
> be assembled. This is basically the technique used to express sliding windows
> with plain SQL (GROUP BY + OVER clauses). For a sliding window Slide(10
> minutes, 2 minutes) this would mean to first compute aggregates of
> non-overlapping (tumbling) 2 minute windows and assembling consecutively 5 of
> these into a sliding window (could be done in a MapPartition with sorted
> input). The implementation could be done as an optimizer rule to split the
> sliding aggregate into a tumbling aggregate and a SQL WINDOW operator. Maybe
> it makes sense to implement the WINDOW clause first and reuse this for
> sliding windows.
> 3. There is also a third, hybrid solution: Doing the pre-aggregation on the
> largest non-overlapping windows (as in 2) and replicating these results and
> processing those as in the 1) approach. The benefits of this is that it a) is
> based on the implementation that supports non-combinable aggregates (which is
> required in any case) and b) that it does not require the implementation of
> the SQL WINDOW operator. Internally, this can be implemented again as an
> optimizer rule that translates the SlidingWindow into a pre-aggregating
> TublingWindow and a final SlidingWindow (with replication).
> see FLINK-4692 for more discussion
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