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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-3226) Translate optimized logical Table API plans into physical plans representing DataSet programs
Date Mon, 08 Feb 2016 16:47:40 GMT

    [ https://issues.apache.org/jira/browse/FLINK-3226?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15137205#comment-15137205
] 

ASF GitHub Bot commented on FLINK-3226:
---------------------------------------

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

    https://github.com/apache/flink/pull/1600#discussion_r52192508
  
    --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/api/table/plan/nodes/logical/FlinkAggregate.scala
---
    @@ -58,19 +58,23 @@ class FlinkAggregate(
         )
       }
     
    -  override def computeSelfCost (planner: RelOptPlanner): RelOptCost = {
    -
    -    val origCosts = super.computeSelfCost(planner)
    -    val deltaCost = planner.getCostFactory.makeHugeCost()
    -
    -    // only prefer aggregations with transformed Avg
    -    aggCalls.toList.foldLeft[RelOptCost](origCosts){
    -      (c: RelOptCost, a: AggregateCall) =>
    -        if (a.getAggregation.isInstanceOf[SqlAvgAggFunction]) {
    -          c.plus(deltaCost)
    -        } else {
    -          c
    -        }
    -    }
    -  }
    +//
    +// DO NOT ASSIGN HUGE COSTS TO PLANS WITH AVG AGGREGATIONS
    +//   ONLY NECESSARY IF AggregateReduceFunctionsRule IS ENABLED.  
    +//  
    +//  override def computeSelfCost (planner: RelOptPlanner): RelOptCost = {
    +//
    +//    val origCosts = super.computeSelfCost(planner)
    +//    val deltaCost = planner.getCostFactory.makeHugeCost()
    +//
    +//    // only prefer aggregations with transformed Avg
    +//    aggCalls.toList.foldLeft[RelOptCost](origCosts){
    +//      (c: RelOptCost, a: AggregateCall) =>
    +//        if (a.getAggregation.isInstanceOf[SqlAvgAggFunction]) {
    +//          c.plus(deltaCost)
    +//        } else {
    +//          c
    +//        }
    +//    }
    +//  }
    --- End diff --
    
    Yes, we decided to disable `AggregateReduceFunctionsRule` temporarily, thus the note on
top.
    I could remove it completely if you think it'd be better.


> Translate optimized logical Table API plans into physical plans representing DataSet
programs
> ---------------------------------------------------------------------------------------------
>
>                 Key: FLINK-3226
>                 URL: https://issues.apache.org/jira/browse/FLINK-3226
>             Project: Flink
>          Issue Type: Sub-task
>          Components: Table API
>            Reporter: Fabian Hueske
>            Assignee: Chengxiang Li
>
> This issue is about translating an (optimized) logical Table API (see FLINK-3225) query
plan into a physical plan. The physical plan is a 1-to-1 representation of the DataSet program
that will be executed. This means:
> - Each Flink RelNode refers to exactly one Flink DataSet or DataStream operator.
> - All (join and grouping) keys of Flink operators are correctly specified.
> - The expressions which are to be executed in user-code are identified.
> - All fields are referenced with their physical execution-time index.
> - Flink type information is available.
> - Optional: Add physical execution hints for joins
> The translation should be the final part of Calcite's optimization process.
> For this task we need to:
> - implement a set of Flink DataSet RelNodes. Each RelNode corresponds to one Flink DataSet
operator (Map, Reduce, Join, ...). The RelNodes must hold all relevant operator information
(keys, user-code expression, strategy hints, parallelism).
> - implement rules to translate optimized Calcite RelNodes into Flink RelNodes. We start
with a straight-forward mapping and later add rules that merge several relational operators
into a single Flink operator, e.g., merge a join followed by a filter. Timo implemented some
rules for the first SQL implementation which can be used as a starting point.
> - Integrate the translation rules into the Calcite optimization process



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