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From fhueske <...@git.apache.org>
Subject [GitHub] flink pull request #3585: [FLINK-5990][table]Add event time OVER ROWS BETWEE...
Date Wed, 22 Mar 2017 15:01:45 GMT
Github user fhueske commented on a diff in the pull request:

    https://github.com/apache/flink/pull/3585#discussion_r107434657
  
    --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/RowsClauseBoundedOverProcessFunction.scala
---
    @@ -0,0 +1,207 @@
    +/*
    + * 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.{ArrayList, List => JList}
    +
    +import org.apache.flink.api.common.state._
    +import org.apache.flink.api.java.typeutils.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 ROWS clause event-time bounded OVER window
    +  *
    +  * @param aggregates           the list of all [[org.apache.flink.table.functions.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 precedingOffset      the preceding offset
    +  */
    +class RowsClauseBoundedOverProcessFunction(
    +    private val aggregates: Array[AggregateFunction[_]],
    +    private val aggFields: Array[Int],
    +    private val forwardedFieldCount: Int,
    +    private val aggregationStateType: RowTypeInfo,
    +    private val precedingOffset: Int)
    +  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 keeps the count of data
    +  private var dataCountState: ValueState[Long] = null
    +
    +  // 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 dataCountStateDescriptor =
    +      new ValueStateDescriptor[Long]("dataCountState", classOf[Long])
    +    dataCountState = getRuntimeContext.getState(dataCountStateDescriptor)
    +
    +    val accumulatorStateDescriptor =
    +      new ValueStateDescriptor[Row]("accumulatorState", aggregationStateType)
    +    accumulatorState = getRuntimeContext.getState(accumulatorStateDescriptor)
    +
    +    val mapStateDescriptor: MapStateDescriptor[Long, JList[Row]] =
    +      new MapStateDescriptor[Long, JList[Row]](
    +        "dataState",
    +        classOf[Long],
    +        classOf[JList[Row]])
    +
    +    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 && triggeringTs > ctx.timerService.currentWatermark)
{
    +      if (dataState.contains(triggeringTs)) {
    +        val data = dataState.get(triggeringTs)
    +        data.add(input)
    +        dataState.put(triggeringTs, data)
    +      } else {
    +        val data = new ArrayList[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 j: Int = 0
    +      while (j < inputs.size) {
    +        val input = inputs.get(j)
    +        var accumulators = accumulatorState.value
    +
    +        // initialize when first run or failover recovery per key
    +        if (null == accumulators) {
    +          accumulators = new Row(aggregates.length)
    +          var i = 0
    +          while (i < aggregates.length) {
    +            accumulators.setField(i, aggregates(i).createAccumulator)
    +            i += 1
    +          }
    +        }
    +
    +        var dataCount = dataCountState.value + 1
    +        dataCountState.update(dataCount)
    +
    +        var lastExpiredRow: Row = null
    +
    +        if (dataCount > precedingOffset) {
    +          val dataTimestampIt = dataState.keys.iterator
    +          var expiredDataTs: Long = Long.MaxValue
    --- End diff --
    
    If we move to a single timer implementation, I would split the keys into two sorted lists
of keys to accumulate and keys to retract. That way we have fast access to all records.


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