Flink JOIN 执行计划

代码：

    val table1 = env.fromElements((1, "hello")).toTable(tEnv, 'a, 'b)    val table2 = env.fromElements((1, "hello")).toTable(tEnv, 'c, 'd)    val table = table1.join(table2).where("b = d").select("a, c")

执行计划：

== Abstract Syntax Tree ==LogicalProject(a=[$0], c=[$2])  LogicalFilter(condition=[=($1, $3)])    LogicalJoin(condition=[true], joinType=[inner])      LogicalTableScan(table=[[_DataSetTable_0]])      LogicalTableScan(table=[[_DataSetTable_1]])== Optimized Logical Plan ==DataSetCalc(select=[a, c])  DataSetJoin(where=[=(b, d)], join=[a, b, c, d], joinType=[InnerJoin])    DataSetScan(table=[[_DataSetTable_0]])    DataSetScan(table=[[_DataSetTable_1]])== Physical Execution Plan ==Stage 4 : Data Source    content : collect elements with CollectionInputFormat    Partitioning : RANDOM_PARTITIONED    Stage 3 : Map        content : from: (a, b)        ship_strategy : Forward        exchange_mode : PIPELINED        driver_strategy : Map        Partitioning : RANDOM_PARTITIONEDStage 6 : Data Source    content : collect elements with CollectionInputFormat    Partitioning : RANDOM_PARTITIONED    Stage 5 : Map        content : from: (c, d)        ship_strategy : Forward        exchange_mode : PIPELINED        driver_strategy : Map        Partitioning : RANDOM_PARTITIONED        Stage 2 : Join            content : where: (=(b, d)), join: (a, b, c, d)            ship_strategy : Hash Partition on [1]            exchange_mode : PIPELINED            driver_strategy : Hybrid Hash (build: from: (a, b) (id: 3))            Partitioning : RANDOM_PARTITIONED            Stage 1 : FlatMap                content : select: (a, c)                ship_strategy : Forward                exchange_mode : PIPELINED                driver_strategy : FlatMap                Partitioning : RANDOM_PARTITIONED                Stage 0 : Data Sink                    content : org.apache.flink.api.java.io.DiscardingOutputFormat                    ship_strategy : Forward                    exchange_mode : PIPELINED                    Partitioning : RANDOM_PARTITIONED

Flink优化器的深度优先遍历：

    /**      * Plan.accept     * Traverses the job depth first from all data sinks on towards the sources.     *      * @see Visitable#accept(Visitor)     */    @Override    public void accept(Visitor<Operator<?>> visitor) {        for (GenericDataSinkBase<?> sink : this.sinks) {            sink.accept(visitor);        }    }    /**      * GenericDataSinkBase.accept     * Accepts the visitor and applies it this instance. This method applies the visitor in a depth-first traversal.     * The visitors pre-visit method is called and, if returning      * <tt>true</tt>, the visitor is recursively applied on the single input. After the recursion returned,     * the post-visit method is called.     *      * @param visitor The visitor.     *       * @see org.apache.flink.util.Visitable#accept(org.apache.flink.util.Visitor)     */    @Override    public void accept(Visitor<Operator<?>> visitor) {        boolean descend = visitor.preVisit(this);        if (descend) {            this.input.accept(visitor);            visitor.postVisit(this);        }    }