Flink Batch Job DAG构造以及物理执行计划的生成

• Flink 通过Dataset API来定义Batch Job， 实现上主要涉及如下几个internal数据结构
  1. [flink-java] org.apache.flink.api.java.operators.Operator
  2. [flink-core] org.apache.flink.api.common.operators.Operator
  3. [flink-optimizer] org.apache.flink.optimizer.dag.OptimizerNode
  4. [flink-optimizer] org.apache.flink.optimizer.plan.PlanNode /Channel
  5. [flink-runtime] org.apache.flink.runtime.jobgraph.JobVertex / IntermediateDataSet / JobEdge
• 整个流程如下
  1. DataSet Api => ExecutionEnvironment:List<DataSink<?>> sinks
  2. ExecutionEnvironment#execute => Plan:List<GenericDataSinkBase<?>> sinks
  3. PlanExecutor#executePlan(p) => RemoteExecutor#executePlan => RemoteExecutor#executePlanWithJars => ClusterClient#run => ClusterClient#getOptimizedPlan => Optimizer#compile:OptimizedPlan
     a. GraphCreatingVisitor => (Plan) program.accept(graphCreator); => OptimizerNode [（DagConnection:ExecutionMode, InterestingProperties , ShipStrategyType,TempMode)（AbstractOperatorDescriptor）]
     b. IdAndEstimatesVisitor=> (OptimizerNode) rootNode.accept(new IdAndEstimatesVisitor(this.statistics));
     c. BranchesVisitor => rootNode.accept(branchingVisitor);
     d. InterestingPropertyVisitor => rootNode.accept(propsVisitor);
     e. List<PlanNode> bestPlan = rootNode.getAlternativePlans(this.costEstimator); => PlanNode (DriverStrategy, LocalProperties, GlobalProperties)
     f. PlanFinalizer#createFinalPlan(bestPlanSinks, program.getJobName(), program);
     g. BinaryUnionReplacer => plan.accept(new BinaryUnionReplacer());
     h. RangePartitionRewriter => plan.accept(new RangePartitionRewriter(plan, executionConfig));
  4. ClusterClient#getJobGraph => jobGraphGenerator#compileJobGraph((OptimizedPlan) optPlan); JobGraph(JobVertex / IntermediateDataSet / JobEdge)
     a. create the job vertex and sets driver strategy.
     b. connects all of the current node's predecessors to the current node.
• internal数据结构详细解析
  1. [flink-java] org.apache.flink.api.java.operators.Operator
     • 由DataSet API 对应构造
     • 各个java.operators.Operator实现类的translateToDataFlow方法定义了转换到common.operators.Operator 的逻辑
  2. [flink-core] org.apache.flink.api.common.operators.Operator
     • 描述Batch DAG中的计算算子
     • 并发，配置，资源规格，标志(id,name)，CompilerHints(hints to the compiler)，OperatorInformation输出类型(TypeInformation<OUT>)是其中的核心成员
  3. [flink-optimizer] org.apache.flink.optimizer.dag.OptimizerNode
     • Optmizer DAG中的计算算子表示
     • 根据common.operators.Operator (almost) one-to-one 对应翻译，包含了一下optimizer需要的additional information。
     • GraphCreatingVisitor负责common.operators.Operator 到OptimizerNode的转换
     • 核心成员：
       • DagConnection:ExecutionMode, InterestingProperties, ShipStrategyType,TempMode
       • AbstractOperatorDescriptor
  4. [flink-optimizer] org.apache.flink.optimizer.plan.PlanNode /Channel
     • Batch Job的物理执行 DAG
     • OptimizerNode到PlanNode的转换依赖OptimizerNode#getAlternativePlans方法，其中依赖AbstractOperatorDescriptor的两个子类的instantiate方法
       • OperatorDescriptorSingle#instantiate(Channel in, SingleInputNode node)
       • OperatorDescriptorDual#instantiate(Channel in1, Channel in2, TwoInputNode node)
     • 核心成员：

       OptimizerNode template;
       DriverStrategy driverStrategy;
       LocalProperties localProps; GlobalProperties globalProps;(ship strategy/ local strategy)
       Iterable<Channel> getInputs()
       List<NamedChannel> broadcastInputs
       List<Channel> outChannels;
       

• 其他的比较重要的数据结构
  • DriverStrategy
  • LocalProperties； GlobalProperties
  • DamBehavior