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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_r52349128 --- Diff: flink-libraries/flink-table/src/main/scala/org/apache/flink/api/table/runtime/aggregate/AvgAggregate.scala --- @@ -0,0 +1,145 @@ +/* + * 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.runtime.aggregate + +abstract class AvgAggregate[T] extends Aggregate[T] { + +} + +// TinyInt average aggregate return Int as aggregated value. +class TinyIntAvgAggregate extends AvgAggregate[Byte] { + private var sum: Long = 0 + private var count: Int = 0 + + override def initiateAggregate: Unit = { + sum = 0 + count = 0 + } + + override def aggregate(value: Any): Unit = { + count += 1 + sum += value.asInstanceOf[Byte] --- End diff -- What about simply adding `0.5`? > 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332)