聊聊flink Table的Distinct Aggregation

序

本文主要研究一下flink Table的Distinct Aggregation

實例

//Distinct can be applied to GroupBy Aggregation, GroupBy Window Aggregation and Over Window Aggregation.
Table orders = tableEnv.scan("Orders");
// Distinct aggregation on group by
Table groupByDistinctResult = orders
    .groupBy("a")
    .select("a, b.sum.distinct as d");
// Distinct aggregation on time window group by
Table groupByWindowDistinctResult = orders
    .window(Tumble.over("5.minutes").on("rowtime").as("w")).groupBy("a, w")
    .select("a, b.sum.distinct as d");
// Distinct aggregation on over window
Table result = orders
    .window(Over
        .partitionBy("a")
        .orderBy("rowtime")
        .preceding("UNBOUNDED_RANGE")
        .as("w"))
    .select("a, b.avg.distinct over w, b.max over w, b.min over w");

//User-defined aggregation function can also be used with DISTINCT modifiers
Table orders = tEnv.scan("Orders");
// Use distinct aggregation for user-defined aggregate functions
tEnv.registerFunction("myUdagg", new MyUdagg());
orders.groupBy("users").select("users, myUdagg.distinct(points) as myDistinctResult");

• Distinct Aggregation能夠用於內置的及自定義的aggregation function；內置的aggregation function諸如GroupBy Aggregation、GroupBy Window Aggregation、Over Window Aggregation

AggregateFunction

flink-table_2.11-1.7.0-sources.jar!/org/apache/flink/table/functions/AggregateFunction.scala

/**
  * Base class for User-Defined Aggregates.
  *
  * The behavior of an [[AggregateFunction]] can be defined by implementing a series of custom
  * methods. An [[AggregateFunction]] needs at least three methods:
  *  - createAccumulator,
  *  - accumulate, and
  *  - getValue.
  *
  *  There are a few other methods that can be optional to have:
  *  - retract,
  *  - merge, and
  *  - resetAccumulator
  *
  * All these methods must be declared publicly, not static and named exactly as the names
  * mentioned above. The methods createAccumulator and getValue are defined in the
  * [[AggregateFunction]] functions, while other methods are explained below.
  *
  *
  * {{{
  * Processes the input values and update the provided accumulator instance. The method
  * accumulate can be overloaded with different custom types and arguments. An AggregateFunction
  * requires at least one accumulate() method.
  *
  * @param accumulator           the accumulator which contains the current aggregated results
  * @param [user defined inputs] the input value (usually obtained from a new arrived data).
  *
  * def accumulate(accumulator: ACC, [user defined inputs]): Unit
  * }}}
  *
  *
  * {{{
  * Retracts the input values from the accumulator instance. The current design assumes the
  * inputs are the values that have been previously accumulated. The method retract can be
  * overloaded with different custom types and arguments. This function must be implemented for
  * datastream bounded over aggregate.
  *
  * @param accumulator           the accumulator which contains the current aggregated results
  * @param [user defined inputs] the input value (usually obtained from a new arrived data).
  *
  * def retract(accumulator: ACC, [user defined inputs]): Unit
  * }}}
  *
  *
  * {{{
  * Merges a group of accumulator instances into one accumulator instance. This function must be
  * implemented for datastream session window grouping aggregate and dataset grouping aggregate.
  *
  * @param accumulator  the accumulator which will keep the merged aggregate results. It should
  *                     be noted that the accumulator may contain the previous aggregated
  *                     results. Therefore user should not replace or clean this instance in the
  *                     custom merge method.
  * @param its          an [[java.lang.Iterable]] pointed to a group of accumulators that will be
  *                     merged.
  *
  * def merge(accumulator: ACC, its: java.lang.Iterable[ACC]): Unit
  * }}}
  *
  *
  * {{{
  * Resets the accumulator for this [[AggregateFunction]]. This function must be implemented for
  * dataset grouping aggregate.
  *
  * @param accumulator  the accumulator which needs to be reset
  *
  * def resetAccumulator(accumulator: ACC): Unit
  * }}}
  *
  *
  * @tparam T   the type of the aggregation result
  * @tparam ACC the type of the aggregation accumulator. The accumulator is used to keep the
  *             aggregated values which are needed to compute an aggregation result.
  *             AggregateFunction represents its state using accumulator, thereby the state of the
  *             AggregateFunction must be put into the accumulator.
  */
abstract class AggregateFunction[T, ACC] extends UserDefinedFunction {
  /**
    * Creates and init the Accumulator for this [[AggregateFunction]].
    *
    * @return the accumulator with the initial value
    */
  def createAccumulator(): ACC

  /**
    * Called every time when an aggregation result should be materialized.
    * The returned value could be either an early and incomplete result
    * (periodically emitted as data arrive) or the final result of the
    * aggregation.
    *
    * @param accumulator the accumulator which contains the current
    *                    aggregated results
    * @return the aggregation result
    */
  def getValue(accumulator: ACC): T

    /**
    * Returns true if this AggregateFunction can only be applied in an OVER window.
    *
    * @return true if the AggregateFunction requires an OVER window, false otherwise.
    */
  def requiresOver: Boolean = false

  /**
    * Returns the TypeInformation of the AggregateFunction's result.
    *
    * @return The TypeInformation of the AggregateFunction's result or null if the result type
    *         should be automatically inferred.
    */
  def getResultType: TypeInformation[T] = null

  /**
    * Returns the TypeInformation of the AggregateFunction's accumulator.
    *
    * @return The TypeInformation of the AggregateFunction's accumulator or null if the
    *         accumulator type should be automatically inferred.
    */
  def getAccumulatorType: TypeInformation[ACC] = null
}

• AggregateFunction繼承了UserDefinedFunction；它有兩個泛型，一個T表示value的泛型，一個ACC表示Accumulator的泛型；它定義了createAccumulator、getValue、getResultType、getAccumulatorType方法(這幾個方法中子類必須實現createAccumulator、getValue方法)
• 對於AggregateFunction，有一個accumulate方法這裏沒定義，可是須要子類定義及實現，該方法接收ACC,T兩個參數，返回void；另外還有retract、merge、resetAccumulator三個方法是可選的，須要子類根據狀況去定義及實現
• 對於datastream bounded over aggregate操做，要求實現restract方法，該方法接收ACC,T兩個參數，返回void；對於datastream session window grouping aggregate以及dataset grouping aggregate操做，要求實現merge方法，該方法接收ACC,java.lang.Iterable<T>兩個參數，返回void；對於dataset grouping aggregate操做，要求實現resetAccumulator方法，該方法接收ACC參數，返回void

小結

• Table的Distinct Aggregation能夠用於內置的及自定義的aggregation function；內置的aggregation function諸如GroupBy Aggregation、GroupBy Window Aggregation、Over Window Aggregation
• AggregateFunction繼承了UserDefinedFunction；它有兩個泛型，一個T表示value的泛型，一個ACC表示Accumulator的泛型；它定義了createAccumulator、getValue、getResultType、getAccumulatorType方法(這幾個方法中子類必須實現createAccumulator、getValue方法)
• 對於AggregateFunction，有一個accumulate方法這裏沒定義，可是須要子類定義及實現，該方法接收ACC,T兩個參數，返回void；另外還有retract、merge、resetAccumulator三個方法是可選的，須要子類根據狀況去定義及實現(對於datastream bounded over aggregate操做，要求實現restract方法，該方法接收ACC,T兩個參數，返回void；對於datastream session window grouping aggregate以及dataset grouping aggregate操做，要求實現merge方法，該方法接收ACC,java.lang.Iterable\<T\>兩個參數，返回void；對於dataset grouping aggregate操做，要求實現resetAccumulator方法，該方法接收ACC參數，返回void)

doc

• Aggregations