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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-5658) Add event time OVER RANGE BETWEEN UNBOUNDED PRECEDING aggregation to SQL
Date Thu, 23 Mar 2017 06:39:41 GMT

    [ https://issues.apache.org/jira/browse/FLINK-5658?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15937817#comment-15937817
] 

ASF GitHub Bot commented on FLINK-5658:
---------------------------------------

Github user hongyuhong commented on a diff in the pull request:

    https://github.com/apache/flink/pull/3386#discussion_r107597005
  
    --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/UnboundedEventTimeOverProcessFunction.scala
---
    @@ -0,0 +1,201 @@
    +/*
    + * 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.util
    +
    +import org.apache.flink.api.common.typeinfo.{BasicTypeInfo, 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.java.tuple.Tuple2
    +import org.apache.flink.api.java.typeutils.TupleTypeInfo
    +import org.apache.flink.streaming.api.operators.TimestampedCollector
    +import org.apache.flink.table.functions.{Accumulator, AggregateFunction}
    +
    +
    +/**
    +  * 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 inputType the input row tye which the state saved
    +  *
    +  */
    +class UnboundedEventTimeOverProcessFunction(
    +    private val aggregates: Array[AggregateFunction[_]],
    +    private val aggFields: Array[Int],
    +    private val forwardedFieldCount: Int,
    +    private val intermediateType: TypeInformation[Row],
    +    private val inputType: TypeInformation[Row])
    +  extends ProcessFunction[Row, Row]{
    +
    +  Preconditions.checkNotNull(aggregates)
    +  Preconditions.checkNotNull(aggFields)
    +  Preconditions.checkArgument(aggregates.length == aggFields.length)
    +
    +  private var output: Row = _
    +  private var accumulatorState: ValueState[Row] = _
    +  private var rowState: ListState[Tuple2[Long, Row]] = _
    +
    +
    +  override def open(config: Configuration) {
    +    output = new Row(forwardedFieldCount + aggregates.length)
    +    val stateDescriptor: ValueStateDescriptor[Row] =
    +      new ValueStateDescriptor[Row]("accumulatorstate", intermediateType)
    +    accumulatorState = getRuntimeContext.getState[Row](stateDescriptor)
    +
    +    val tuple2Type: TypeInformation[Tuple2[Long, Row]] =
    +      new TupleTypeInfo(BasicTypeInfo.LONG_TYPE_INFO, inputType)
    +        .asInstanceOf[TypeInformation[Tuple2[Long, Row]]]
    +    val tupleStateDescriptor: ListStateDescriptor[Tuple2[Long, Row]] =
    +      new ListStateDescriptor[Tuple2[Long, Row]]("rowliststate", tuple2Type)
    +    rowState = getRuntimeContext.getListState[Tuple2[Long, Row]](tupleStateDescriptor)
    +
    +  }
    +
    +  /**
    +    * Process one element from the input stream, not emit the output
    +    *
    +    * @param input The input value.
    +    * @param ctx   The ctx to register timer or get current time
    +    * @param out   The collector for returning result values.
    +    *
    +    */
    +  override def processElement(
    +     input: Row,
    +     ctx:  ProcessFunction[Row, Row]#Context,
    +     out: Collector[Row]): Unit = {
    +
    +    // discard later record
    +    if (ctx.timestamp() >= ctx.timerService().currentWatermark()) {
    +      // ensure every key just register on timer
    +      ctx.timerService.registerEventTimeTimer(ctx.timerService.currentWatermark + 1)
    +
    +      rowState.add(new Tuple2(ctx.timestamp, input))
    +    }
    +  }
    +
    +  /**
    +    * Called when a timer set fires, sort current records according the timestamp
    +    * and emit the output
    +    *
    +    * @param timestamp The timestamp of the firing timer.
    +    * @param ctx       The ctx to register timer or get current time
    +    * @param out       The collector for returning result values.
    +    */
    +  override def onTimer(
    +      timestamp: Long,
    +      ctx: ProcessFunction[Row, Row]#OnTimerContext,
    +      out: Collector[Row]): Unit = {
    +
    +    val rowList = rowState.get.iterator
    +    if (rowList.hasNext) {
    +      val curWatermark = ctx.timerService.currentWatermark
    +      val sortList = new util.LinkedList[Tuple2[Long, Row]]()
    --- End diff --
    
    As my understanding,  even we use RocksDBState, when we use MapState.get or ListState.get,
it will malloc enough memory to store the deserialize value, this part memory is the same
to sortList, then how should we control the memory of this part? Cause we need some structure
to help us do the sort action, currently i can not think some better state to do this.
    If you have any better solution, i'm very appreciate to know that.
    Thanks very much.


> 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).



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