[
https://issues.apache.org/jira/browse/FLINK-5658?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15926473#comment-15926473
]
ASF GitHub Bot commented on FLINK-5658:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3386#discussion_r106183250
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/UnboundedEventTimeOverProcessFunction.scala
---
@@ -0,0 +1,283 @@
+/*
+ * 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 java.io.{ByteArrayInputStream, ByteArrayOutputStream}
+import java.util
+
+import org.apache.flink.api.common.typeinfo.TypeInformation
+import org.apache.flink.configuration.Configuration
+import org.apache.flink.types.Row
+import org.apache.flink.streaming.api.functions.{ProcessFunction}
+import org.apache.flink.util.{Collector, Preconditions}
+import org.apache.flink.api.common.state._
+import org.apache.flink.api.common.typeutils.TypeSerializer
+import org.apache.flink.api.common.typeutils.base.StringSerializer
+import org.apache.flink.api.java.functions.KeySelector
+import org.apache.flink.api.java.tuple.Tuple
+import org.apache.flink.core.memory.{DataInputViewStreamWrapper,
DataOutputViewStreamWrapper}
+import org.apache.flink.runtime.state.{FunctionInitializationContext,
FunctionSnapshotContext}
+import org.apache.flink.streaming.api.checkpoint.CheckpointedFunction
+import org.apache.flink.streaming.api.operators.TimestampedCollector
+import org.apache.flink.streaming.api.windowing.windows.TimeWindow
+import org.apache.flink.table.functions.{Accumulator, AggregateFunction}
+
+import scala.collection.mutable.ArrayBuffer
+
+/**
+ * A ProcessFunction to support unbounded event-time over-window
+ *
+ * @param aggregates the aggregate functions
+ * @param aggFields the filed index which the aggregate functions use
+ * @param forwardedFieldCount the input fields count
+ * @param interMediateType the intermediate row tye which the state saved
+ * @param keySelector the keyselector
+ * @param keyType the key type
+ *
+ */
+class UnboundedEventTimeOverProcessFunction(
+ private val aggregates: Array[AggregateFunction[_]],
+ private val aggFields: Array[Int],
+ private val forwardedFieldCount: Int,
+ private val interMediateType: TypeInformation[Row],
+ private val keySelector: KeySelector[Row, Tuple],
+ private val keyType: TypeInformation[Tuple])
+ extends ProcessFunction[Row, Row]
+ with CheckpointedFunction{
+
+ Preconditions.checkNotNull(aggregates)
+ Preconditions.checkNotNull(aggFields)
+ Preconditions.checkArgument(aggregates.length == aggFields.length)
+
+ private var output: Row = _
+ private var state: MapState[TimeWindow, Row] = _
+ private val aggregateWithIndex: Array[(AggregateFunction[_], Int)] =
aggregates.zipWithIndex
+
+ /** Sorted list per key for choose the recent result and the records
need retraction **/
+ private val timeSectionsMap: java.util.HashMap[Tuple,
java.util.LinkedList[TimeWindow]] =
+ new java.util.HashMap[Tuple, java.util.LinkedList[TimeWindow]]
+
+ /** For store timeSectionsMap **/
+ private var timeSectionsState: ListState[String] = _
+ private var inputKeySerializer: TypeSerializer[Tuple] = _
+ private var timeSerializer: TypeSerializer[TimeWindow] = _
+
+ override def open(config: Configuration) {
+ output = new Row(forwardedFieldCount + aggregates.length)
+ val valueSerializer: TypeSerializer[Row] =
+
interMediateType.createSerializer(getRuntimeContext.getExecutionConfig)
+ timeSerializer = new TimeWindow.Serializer
+ val stateDescriptor: MapStateDescriptor[TimeWindow, Row] =
+ new MapStateDescriptor[TimeWindow, Row]("rowtimeoverstate",
timeSerializer, valueSerializer)
+ inputKeySerializer =
keyType.createSerializer(getRuntimeContext.getExecutionConfig)
+ state = getRuntimeContext.getMapState[TimeWindow, Row](stateDescriptor)
+ }
+
+ override def processElement(
+ input: Row,
+ ctx: ProcessFunction[Row, Row]#Context,
+ out: Collector[Row]): Unit = {
+
+ val key = keySelector.getKey(input)
+ val timeSections = if (timeSectionsMap.containsKey(key))
timeSectionsMap.get(key)
+ else new util.LinkedList[TimeWindow]()
+
+ expire(key, ctx.timerService.currentWatermark, timeSections)
+
+ // discard later record
+ if (ctx.timestamp() >= ctx.timerService().currentWatermark()) {
+
+ timeSectionsMap.put(key, timeSections)
+
+ // find the last accumulator with the same key before current
timestamp
+ // and find the accumulators need to retraction
+ val (closestTimeOption: Option[TimeWindow],
+ newTimeSection: TimeWindow,
+ retractions: Array[TimeWindow]) =
+ resolveTimeSection(ctx.timestamp,timeSections)
+
+ val newAccumulators = new Row(forwardedFieldCount +
aggregates.length)
+ aggregateWithIndex.foreach { case (agg, i) =>
--- End diff --
Use `while` loops to iterate over the aggregates. Scala's `foreach` loops
have overhead
> Add event time OVER RANGE BETWEEN UNBOUNDED PRECEDING aggregation to SQL
> ------------------------------------------------------------------------
>
> Key: FLINK-5658
> URL: https://issues.apache.org/jira/browse/FLINK-5658
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: Fabian Hueske
> Assignee: Yuhong Hong
>
> The goal of this issue is to add support for OVER RANGE aggregations on event
> time streams to the SQL interface.
> Queries similar to the following should be supported:
> {code}
> SELECT
> a,
> SUM(b) OVER (PARTITION BY c ORDER BY rowTime() RANGE BETWEEN UNBOUNDED
> PRECEDING AND CURRENT ROW) AS sumB,
> MIN(b) OVER (PARTITION BY c ORDER BY rowTime() RANGE BETWEEN UNBOUNDED
> PRECEDING AND CURRENT ROW) AS minB
> FROM myStream
> {code}
> The following restrictions should initially apply:
> - All OVER clauses in the same SELECT clause must be exactly the same.
> - The PARTITION BY clause is optional (no partitioning results in single
> threaded execution).
> - The ORDER BY clause may only have rowTime() as parameter. rowTime() is a
> parameterless scalar function that just indicates processing time mode.
> - bounded PRECEDING is not supported (see FLINK-5655)
> - FOLLOWING is not supported.
> The restrictions will be resolved in follow up issues. If we find that some
> of the restrictions are trivial to address, we can add the functionality in
> this issue as well.
> This issue includes:
> - Design of the DataStream operator to compute OVER ROW aggregates
> - Translation from Calcite's RelNode representation (LogicalProject with
> RexOver expression).
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
