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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-5655) Add event time OVER RANGE BETWEEN x PRECEDING aggregation to SQL
Date Tue, 28 Mar 2017 16:39:43 GMT

    [ https://issues.apache.org/jira/browse/FLINK-5655?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15945515#comment-15945515
] 

ASF GitHub Bot commented on FLINK-5655:
---------------------------------------

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

    https://github.com/apache/flink/pull/3629#discussion_r108467310
  
    --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/RangeClauseBoundedOverProcessFunction.scala
---
    @@ -0,0 +1,213 @@
    +/*
    + * 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.{List => JList, ArrayList => JArrayList}
    +
    +import org.apache.flink.api.common.state._
    +import org.apache.flink.api.common.typeinfo.{BasicTypeInfo, TypeInformation}
    +import org.apache.flink.api.java.typeutils.{ListTypeInfo, RowTypeInfo}
    +import org.apache.flink.configuration.Configuration
    +import org.apache.flink.streaming.api.functions.ProcessFunction
    +import org.apache.flink.table.functions.{Accumulator, AggregateFunction}
    +import org.apache.flink.types.Row
    +import org.apache.flink.util.{Collector, Preconditions}
    +
    +/**
    + * Process Function for RANGE clause event-time bounded OVER window
    + *
    + * @param aggregates           the list of all [[AggregateFunction]] used for this aggregation
    + * @param aggFields            the position (in the input Row) of the input value for
each aggregate
    + * @param forwardedFieldCount  the count of forwarded fields.
    + * @param aggregationStateType the row type info of aggregation
    + * @param inputRowType         the row type info of input row
    + * @param precedingOffset      the preceding offset
    + */
    +class RangeClauseBoundedOverProcessFunction(
    +    private val aggregates: Array[AggregateFunction[_]],
    +    private val aggFields: Array[Int],
    +    private val forwardedFieldCount: Int,
    +    private val aggregationStateType: RowTypeInfo,
    +    private val inputRowType: RowTypeInfo,
    +    private val precedingOffset: Long)
    +  extends ProcessFunction[Row, Row] {
    +
    +  Preconditions.checkNotNull(aggregates)
    +  Preconditions.checkNotNull(aggFields)
    +  Preconditions.checkArgument(aggregates.length == aggFields.length)
    +  Preconditions.checkNotNull(forwardedFieldCount)
    +  Preconditions.checkNotNull(aggregationStateType)
    +  Preconditions.checkNotNull(precedingOffset)
    +
    +  private var output: Row = _
    +
    +  // the state which keeps the last triggering timestamp
    +  private var lastTriggeringTsState: ValueState[Long] = _
    +
    +  // the state which used to materialize the accumulator for incremental calculation
    +  private var accumulatorState: ValueState[Row] = _
    +
    +  // the state which keeps all the data that are not expired.
    +  // The first element (as the mapState key) of the tuple is the time stamp. Per each
time stamp,
    +  // the second element of tuple is a list that contains the entire data of all the rows
belonging
    +  // to this time stamp.
    +  private var dataState: MapState[Long, JList[Row]] = _
    +
    +  override def open(config: Configuration) {
    +
    +    output = new Row(forwardedFieldCount + aggregates.length)
    +
    +    val lastTriggeringTsDescriptor: ValueStateDescriptor[Long] =
    +      new ValueStateDescriptor[Long]("lastTriggeringTsState", classOf[Long])
    +    lastTriggeringTsState = getRuntimeContext.getState(lastTriggeringTsDescriptor)
    +
    +    val accumulatorStateDescriptor =
    +      new ValueStateDescriptor[Row]("accumulatorState", aggregationStateType)
    +    accumulatorState = getRuntimeContext.getState(accumulatorStateDescriptor)
    +
    +    val keyTypeInformation: TypeInformation[Long] =
    +      BasicTypeInfo.LONG_TYPE_INFO.asInstanceOf[TypeInformation[Long]]
    +    val valueTypeInformation: TypeInformation[JList[Row]] = new ListTypeInfo[Row](inputRowType)
    +
    +    val mapStateDescriptor: MapStateDescriptor[Long, JList[Row]] =
    +      new MapStateDescriptor[Long, JList[Row]](
    +        "dataState",
    +        keyTypeInformation,
    +        valueTypeInformation)
    +
    +    dataState = getRuntimeContext.getMapState(mapStateDescriptor)
    +
    +  }
    +
    +  override def processElement(
    +    input: Row,
    +    ctx: ProcessFunction[Row, Row]#Context,
    +    out: Collector[Row]): Unit = {
    +
    +    // triggering timestamp for trigger calculation
    +    val triggeringTs = ctx.timestamp
    +
    +    val lastTriggeringTs = lastTriggeringTsState.value
    +
    +    // check if the data is expired, if not, save the data and register event time timer
    +    if (triggeringTs > lastTriggeringTs) {
    +      val data = dataState.get(triggeringTs)
    +      if (null != data) {
    +        data.add(input)
    +        dataState.put(triggeringTs, data)
    +      } else {
    +        val data = new JArrayList[Row]
    +        data.add(input)
    +        dataState.put(triggeringTs, data)
    +        // register event time timer
    +        ctx.timerService.registerEventTimeTimer(triggeringTs)
    +      }
    +    }
    +  }
    +
    +  override def onTimer(
    +    timestamp: Long,
    +    ctx: ProcessFunction[Row, Row]#OnTimerContext,
    +    out: Collector[Row]): Unit = {
    +    // gets all window data from state for the calculation
    +    val inputs: JList[Row] = dataState.get(timestamp)
    +
    +    if (null != inputs) {
    +
    +      var accumulators = accumulatorState.value
    +      var dataListIndex = 0
    +      var aggregatesIndex = 0
    +
    +      // initialize when first run or failover recovery per key
    +      if (null == accumulators) {
    +        accumulators = new Row(aggregates.length)
    +        aggregatesIndex = 0
    +        while (aggregatesIndex < aggregates.length) {
    +          accumulators.setField(aggregatesIndex, aggregates(aggregatesIndex).createAccumulator())
    +          aggregatesIndex += 1
    +        }
    +      }
    +
    +      // keep up timestamps of retract data
    +      val retractTsList: JList[Long] = new JArrayList[Long]
    +
    +      val dataTimestampIt = dataState.keys.iterator
    +      while (dataTimestampIt.hasNext) {
    +        val dataTs: Long = dataTimestampIt.next()
    +        val offset = timestamp - dataTs
    +        if (offset > precedingOffset) {
    +          val retractDataList = dataState.get(dataTs)
    +          dataListIndex = 0
    +          while (dataListIndex < retractDataList.size()) {
    +            aggregatesIndex = 0
    +            while (aggregatesIndex < aggregates.length) {
    +              val accumulator = accumulators.getField(aggregatesIndex).asInstanceOf[Accumulator]
    +              aggregates(aggregatesIndex)
    +                .retract(accumulator, retractDataList.get(dataListIndex).getField(aggFields(aggregatesIndex)))
    +              aggregatesIndex += 1
    +            }
    +            dataListIndex += 1
    +            retractTsList.add(dataTs)
    +          }
    +        }
    +      }
    +
    +      // remove the data that has been retracted
    +      dataListIndex = 0
    +      while (dataListIndex < retractTsList.size) {
    +        dataState.remove(retractTsList.get(dataListIndex))
    +        dataListIndex += 1
    +      }
    +
    +      // copy forwarded fields to output row
    +      aggregatesIndex = 0
    +      while (aggregatesIndex < forwardedFieldCount) {
    +        output.setField(aggregatesIndex, inputs.get(0).getField(aggregatesIndex))
    +        aggregatesIndex += 1
    +      }
    +
    +      dataListIndex = 0
    +      while (dataListIndex < inputs.size()) {
    +        // accumulate current row and set aggregate in output row
    +        aggregatesIndex = 0
    +        while (aggregatesIndex < aggregates.length) {
    +          val index = forwardedFieldCount + aggregatesIndex
    +          val accumulator = accumulators.getField(aggregatesIndex).asInstanceOf[Accumulator]
    +          aggregates(aggregatesIndex).accumulate(accumulator, inputs.get(dataListIndex).getField(aggFields(aggregatesIndex)))
    +          if (dataListIndex >= (inputs.size() - 1)) {
    --- End diff --
    
    can't we do this after both loops terminated?


> Add event time OVER RANGE BETWEEN x PRECEDING aggregation to SQL
> ----------------------------------------------------------------
>
>                 Key: FLINK-5655
>                 URL: https://issues.apache.org/jira/browse/FLINK-5655
>             Project: Flink
>          Issue Type: Sub-task
>          Components: Table API & SQL
>            Reporter: Fabian Hueske
>            Assignee: sunjincheng
>
> 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 INTERVAL '1' HOUR PRECEDING
AND CURRENT ROW) AS sumB,
>   MIN(b) OVER (PARTITION BY c ORDER BY rowTime() RANGE BETWEEN INTERVAL '1' HOUR 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.
> - UNBOUNDED PRECEDING is not supported (see FLINK-5658)
> - 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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