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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-5047) Add sliding group-windows for batch tables
Date Wed, 08 Mar 2017 14:33:38 GMT

    [ https://issues.apache.org/jira/browse/FLINK-5047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15901340#comment-15901340
] 

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_r104925916
  
    --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/AggregateUtil.scala
---
    @@ -186,6 +200,130 @@ object AggregateUtil {
       }
     
       /**
    +    * Create a [[org.apache.flink.api.common.functions.GroupReduceFunction]] that prepares
for
    +    * partial aggregates of sliding windows (time and count-windows).
    +    * It requires a prepared input (with intermediate aggregate fields and aligned rowtime
for
    +    * pre-tumbling in case of time-windows), pre-aggregates (pre-tumbles) rows, aligns
the
    +    * window-start, and replicates or omits records for different panes of a sliding
window.
    +    *
    +    * The output of the function contains the grouping keys, the intermediate aggregate
values of
    +    * all aggregate function and the aligned window start. Window start must not be a
timestamp,
    +    * but can also be a count value for count-windows.
    +    *
    +    * The output is stored in Row by the following format:
    +    *
    +    * {{{
    +    *                      avg(x) aggOffsetInRow = 2      count(z) aggOffsetInRow = 5
    +    *                            |                          |
    +    *                            v                          v
    +    *        +---------+---------+--------+--------+--------+--------+-------------+
    +    *        |groupKey1|groupKey2|  sum1  | count1 |  sum2  | count2 | windowStart |
    +    *        +---------+---------+--------+--------+--------+--------+-------------+
    +    *                                              ^                 ^
    +    *                                              |                 |
    +    *                                 sum(y) aggOffsetInRow = 4    window start for pane
mapping
    +    * }}}
    +    *
    +    * NOTE: this function is only used for sliding windows with partial aggregates on
batch tables.
    +    */
    +  def createDataSetSlideWindowPrepareGroupReduceFunction(
    +      window: LogicalWindow,
    +      namedAggregates: Seq[CalcitePair[AggregateCall, String]],
    +      groupings: Array[Int],
    +      inputType: RelDataType,
    +      isParserCaseSensitive: Boolean)
    +    : RichGroupReduceFunction[Row, Row] = {
    +
    +    val aggregates = transformToAggregateFunctions(
    +      namedAggregates.map(_.getKey),
    +      inputType,
    +      needRetraction = false)._2
    +
    +    val returnType: RowTypeInfo = createDataSetAggregateBufferDataType(
    +      groupings,
    +      aggregates,
    +      inputType,
    +      Some(Array(BasicTypeInfo.LONG_TYPE_INFO)))
    +
    +    window match {
    +      case EventTimeSlidingGroupWindow(_, _, size, slide) if isTimeInterval(size.resultType)
=>
    +        // sliding time-window
    +        // for partial aggregations
    +        new DataSetSlideTimeWindowAggReduceCombineFunction(
    +          aggregates,
    +          groupings.length,
    +          returnType.getArity - 1,
    +          asLong(size),
    +          asLong(slide),
    +          returnType)
    +
    +      case _ =>
    +        throw new UnsupportedOperationException(s"$window is currently not supported
on batch.")
    +    }
    +  }
    +
    +  /**
    +    * Create a [[org.apache.flink.api.common.functions.FlatMapFunction]] that prepares
for
    +    * non-incremental aggregates of sliding windows (time-windows).
    +    *
    +    * It requires a prepared input (with intermediate aggregate fields), aligns the
    +    * window-start, and replicates or omits records for different panes of a sliding
window.
    +    *
    +    * The output of the function contains the grouping keys, the intermediate aggregate
values of
    +    * all aggregate function and the aligned window start.
    +    *
    +    * The output is stored in Row by the following format:
    +    *
    +    * {{{
    +    *                      avg(x) aggOffsetInRow = 2      count(z) aggOffsetInRow = 5
    +    *                            |                          |
    +    *                            v                          v
    +    *        +---------+---------+--------+--------+--------+--------+-------------+
    +    *        |groupKey1|groupKey2|  sum1  | count1 |  sum2  | count2 | windowStart |
    +    *        +---------+---------+--------+--------+--------+--------+-------------+
    +    *                                              ^                 ^
    +    *                                              |                 |
    +    *                                 sum(y) aggOffsetInRow = 4      window start for
pane mapping
    +    * }}}
    +    *
    +    * NOTE: this function is only used for time-based sliding windows on batch tables.
    +    */
    +  def createDataSetSlideWindowPrepareFlatMapFunction(
    +      window: LogicalWindow,
    +      namedAggregates: Seq[CalcitePair[AggregateCall, String]],
    --- End diff --
    
    remove unnecessary parameters `namedAggregates` and `groupings`


> 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



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