flink-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-5047) Add sliding group-windows for batch tables
Date Tue, 07 Mar 2017 17:19:38 GMT

    [ https://issues.apache.org/jira/browse/FLINK-5047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15899795#comment-15899795
] 

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_r104725017
  
    --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/DataSetSlideCountWindowAggReduceGroupFunction.scala
---
    @@ -0,0 +1,113 @@
    +/*
    + * 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.lang.Iterable
    +import java.sql.Timestamp
    +
    +import org.apache.calcite.runtime.SqlFunctions
    +import org.apache.flink.api.common.functions.RichGroupReduceFunction
    +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 count-windows. It takes a prepared input
row,
    +  * pre-aggregates (pre-tumbles) rows, aligns the window start, and replicates or omits
records
    +  * for different panes of a sliding window.
    +  *
    +  * @param aggregates aggregate functions
    +  * @param groupingKeysLength number of grouping keys
    +  * @param preTumblingSize number of records to be aggregated (tumbled) before emission
    +  * @param windowSize window size of the sliding window
    +  * @param windowSlide window slide of the sliding window
    +  * @param returnType return type of this function
    +  */
    +class DataSetSlideCountWindowAggReduceGroupFunction(
    +    private val aggregates: Array[Aggregate[_]],
    +    private val groupingKeysLength: Int,
    +    private val preTumblingSize: Long,
    +    private val windowSize: Long,
    +    private val windowSlide: Long,
    +    @transient private val returnType: TypeInformation[Row])
    +  extends RichGroupReduceFunction[Row, Row]
    +  with ResultTypeQueryable[Row] {
    +
    +  private var output: Row = _
    +  private var outWindowStartIndex: Int = _
    +
    +  override def open(config: Configuration) {
    +    Preconditions.checkNotNull(aggregates)
    +    // add one field to store window start count
    +    val partialRowLength = groupingKeysLength +
    +      aggregates.map(_.intermediateDataType.length).sum + 1
    +    output = new Row(partialRowLength)
    +    outWindowStartIndex = partialRowLength - 1
    +  }
    +
    +  override def reduce(records: Iterable[Row], out: Collector[Row]): Unit = {
    +    var count: Long = 0
    +
    +    val iterator = records.iterator()
    +
    +    while (iterator.hasNext) {
    +      val record = iterator.next()
    +      // reset aggregates after completed tumbling
    +      if (count % preTumblingSize == 0) {
    +        // initiate intermediate aggregate value.
    +        aggregates.foreach(_.initiate(output))
    +      }
    +
    +      // merge intermediate aggregate value to buffer.
    +      aggregates.foreach(_.merge(record, output))
    +
    +      count += 1
    +
    +      // trigger tumbling evaluation
    +      if (count % preTumblingSize == 0) {
    --- End diff --
    
    But shouldn't batch be the reference for streaming?


> 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



--
This message was sent by Atlassian JIRA
(v6.3.15#6346)

Mime
View raw message