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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 14:37:40 GMT

    [ https://issues.apache.org/jira/browse/FLINK-3226?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15137006#comment-15137006
] 

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

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

    https://github.com/apache/flink/pull/1600#discussion_r52175572
  
    --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/api/table/plan/functions/aggregate/MaxAggregate.scala
---
    @@ -0,0 +1,136 @@
    +/*
    + * 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.api.table.plan.functions.aggregate
    +
    +abstract class MaxAggregate[T] extends Aggregate[T]{
    +
    +}
    +
    +class TinyIntMaxAggregate extends MaxAggregate[Byte] {
    +  private var max = Byte.MinValue
    +
    +  override def initiateAggregate: Unit = {
    +    max = Byte.MinValue
    +  }
    +
    +  override def aggregate(value: Any): Unit = {
    +    val current = value.asInstanceOf[Byte]
    +    if (current > max) {
    +      max = current
    +    }
    +  }
    +
    +  override def getAggregated(): Byte = {
    +    max
    +  }
    +}
    +
    +class SmallIntMaxAggregate extends MaxAggregate[Short] {
    +  private var max = Short.MinValue
    +
    +  override def initiateAggregate: Unit = {
    +    max = Short.MinValue
    +  }
    +
    +  override def aggregate(value: Any): Unit = {
    +    val current = value.asInstanceOf[Short]
    +    if (current > max) {
    +      max = current
    +    }
    +  }
    +
    +  override def getAggregated(): Short = {
    +    max
    +  }
    +}
    +
    +class IntMaxAggregate extends MaxAggregate[Int] {
    +  private var max = Int.MinValue
    +
    +  override def initiateAggregate: Unit = {
    +    max = Int.MinValue
    +  }
    +
    +  override def aggregate(value: Any): Unit = {
    +    val current = value.asInstanceOf[Int]
    +    if (current > max) {
    +      max = current
    +    }
    +  }
    +
    +  override def getAggregated(): Int = {
    +    max
    +  }
    +}
    +
    +class LongMaxAggregate extends MaxAggregate[Long] {
    +  private var max = Long.MinValue
    +
    +  override def initiateAggregate: Unit = {
    +    max = Int.MinValue
    +  }
    +
    +  override def aggregate(value: Any): Unit = {
    +    val current = value.asInstanceOf[Long]
    +    if (current > max) {
    +      max = current
    +    }
    +  }
    +
    +  override def getAggregated(): Long = {
    +    max
    +  }
    +}
    +
    +class FloatMaxAggregate extends MaxAggregate[Float] {
    +  private var max = Float.MinValue
    +
    +  override def initiateAggregate: Unit = {
    +    max = Int.MinValue
    +  }
    +
    +  override def aggregate(value: Any): Unit = {
    +    val current = value.asInstanceOf[Float]
    +    if (current > max) {
    +      max = current
    +    }
    +  }
    +
    +  override def getAggregated(): Float = {
    +    max
    +  }
    +}
    +
    +class DoubleMaxAggregate extends MaxAggregate[Double] {
    +  private var max = Double.MinValue
    +
    +  override def initiateAggregate: Unit = {
    +    max = Int.MaxValue
    --- End diff --
    
    Why `Int.MaxValue` here?


> 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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