1.1 系统层次架构

graph TB
    subgraph "应用层 Application Layer"
        A1[Flink SQL]
        A2[Table API]
        A3[DataStream API]
        A4[DataSet API]
        A5[ProcessFunction API]
    end
    
    subgraph "核心运行时 Core Runtime"
        B1[JobManager]
        B2[TaskManager]
        B3[ResourceManager]
        B4[Dispatcher]
    end
    
    subgraph "存储层 Storage Layer"
        C1[State Backend]
        C2[Checkpoint Storage]
        C3[Savepoint Storage]
    end
    
    subgraph "部署层 Deployment Layer"
        D1[Standalone]
        D2[YARN]
        D3[Kubernetes]
        D4[Mesos]
    end
    
    A1 --> A2
    A2 --> A3
    A3 --> A5
    A4 --> A5
    
    A3 --> B1
    A4 --> B1
    A5 --> B1
    
    B1 --> B2
    B1 --> B3
    B1 --> B4
    
    B2 --> C1
    B1 --> C2
    B1 --> C3
    
    B1 --> D1
    B1 --> D2
    B1 --> D3
    B1 --> D4

1.2 运行时组件架构

graph TB
    subgraph "Client 客户端"
        Client[Flink Client]
    end
    
    subgraph "Flink Master"
        subgraph "JobManager Cluster"
            JM1[JobManager Leader]
            JM2[JobManager Standby]
            JM3[JobManager Standby]
        end
        
        subgraph "ResourceManager"
            RM[Resource Manager]
        end
        
        subgraph "Dispatcher"
            DISP[Dispatcher]
        end
        
        subgraph "WebUI"
            WEB[Web Dashboard]
        end
    end
    
    subgraph "TaskManager Cluster"
        TM1[TaskManager 1]
        TM2[TaskManager 2]
        TM3[TaskManager N]
    end
    
    subgraph "External Storage"
        HDFS[HDFS/S3]
        ZK[ZooKeeper]
    end
    
    Client --> DISP
    DISP --> JM1
    JM1 --> RM
    RM --> TM1
    RM --> TM2
    RM --> TM3
    
    JM1 --> TM1
    JM1 --> TM2
    JM1 --> TM3
    
    JM1 --> HDFS
    JM1 --> ZK
    TM1 --> HDFS
    TM2 --> HDFS
    TM3 --> HDFS
    
    WEB --> JM1

1.3 数据流处理架构

graph LR
    subgraph "Data Sources 数据源"
        S1[Kafka]
        S2[File System]
        S3[Database]
        S4[Socket]
    end
    
    subgraph "Flink Streaming Runtime"
        subgraph "Source Operators"
            SO1[Source 1]
            SO2[Source 2]
        end
        
        subgraph "Transformation Operators"
            TO1[Map]
            TO2[Filter]
            TO3[KeyBy]
            TO4[Window]
            TO5[Reduce]
        end
        
        subgraph "Sink Operators"
            SK1[Sink 1]
            SK2[Sink 2]
        end
    end
    
    subgraph "Data Sinks 数据汇"
        D1[Kafka]
        D2[File System]
        D3[Database]
        D4[Dashboard]
    end
    
    S1 --> SO1
    S2 --> SO1
    S3 --> SO2
    S4 --> SO2
    
    SO1 --> TO1
    SO2 --> TO1
    TO1 --> TO2
    TO2 --> TO3
    TO3 --> TO4
    TO4 --> TO5
    
    TO5 --> SK1
    TO5 --> SK2
    
    SK1 --> D1
    SK1 --> D2
    SK2 --> D3
    SK2 --> D4

2. 作业提交和执行时序图

2.1 作业提交流程

sequenceDiagram
    participant Client as Flink Client
    participant Dispatcher as Dispatcher
    participant JM as JobManager
    participant RM as ResourceManager
    participant TM as TaskManager
    
    Client->>Dispatcher: submitJob(JobGraph)
    Dispatcher->>Dispatcher: createJobManagerRunner
    Dispatcher->>JM: startJobManager
    
    JM->>JM: scheduleJob
    JM->>JM: createExecutionGraph
    JM->>RM: requestSlots(SlotRequests)
    
    RM->>RM: allocateSlots
    RM->>TM: requestSlot
    TM->>RM: offerSlot
    RM->>JM: slotAllocated
    
    JM->>TM: deployTask(TaskDeploymentDescriptor)
    TM->>TM: createTask
    TM->>TM: startTask
    TM->>JM: taskRunning
    
    JM->>Client: jobSubmitted

2.2 检查点执行流程

sequenceDiagram
    participant JM as JobManager
    participant Coordinator as CheckpointCoordinator
    participant TM1 as TaskManager 1
    participant TM2 as TaskManager 2
    participant Storage as StateBackend
    
    JM->>Coordinator: triggerCheckpoint
    Coordinator->>TM1: triggerCheckpoint(checkpointId)
    Coordinator->>TM2: triggerCheckpoint(checkpointId)
    
    TM1->>TM1: snapshotState
    TM1->>Storage: storeState
    Storage->>TM1: stateHandle
    TM1->>Coordinator: acknowledgeCheckpoint
    
    TM2->>TM2: snapshotState
    TM2->>Storage: storeState
    Storage->>TM2: stateHandle
    TM2->>Coordinator: acknowledgeCheckpoint
    
    Coordinator->>Coordinator: completeCheckpoint
    Coordinator->>Storage: storeCheckpointMetadata
    Coordinator->>JM: checkpointCompleted

2.3 故障恢复流程

sequenceDiagram
    participant JM as JobManager
    participant TM1 as TaskManager 1
    participant TM2 as TaskManager 2
    participant Storage as StateBackend
    participant RM as ResourceManager
    
    TM1->>JM: taskFailed
    JM->>JM: handleTaskFailure
    JM->>JM: restartStrategy.canRestart()
    
    JM->>TM1: cancelTask
    JM->>TM2: cancelTask
    
    JM->>RM: releaseSlots
    JM->>Storage: getLatestCheckpoint
    Storage->>JM: checkpointMetadata
    
    JM->>RM: requestSlots
    RM->>JM: slotsAllocated
    
    JM->>TM1: deployTask(withStateHandle)
    JM->>TM2: deployTask(withStateHandle)
    
    TM1->>Storage: restoreState
    TM2->>Storage: restoreState
    
    TM1->>JM: taskRunning
    TM2->>JM: taskRunning

3. 数据流执行时序图

3.1 流处理执行流程

sequenceDiagram
    participant User as 用户程序
    participant Env as StreamExecutionEnvironment
    participant Graph as StreamGraphGenerator
    participant Optimizer as StreamGraphOptimizer
    participant Scheduler as JobScheduler
    participant Task as StreamTask
    
    User->>Env: addSource()
    User->>Env: transform()
    User->>Env: addSink()
    User->>Env: execute()
    
    Env->>Graph: generateStreamGraph()
    Graph->>Graph: addOperators
    Graph->>Graph: connectOperators
    
    Graph->>Optimizer: optimize(StreamGraph)
    Optimizer->>Optimizer: chainOperators
    Optimizer->>Optimizer: setParallelism
    
    Optimizer->>Scheduler: scheduleJob(JobGraph)
    Scheduler->>Scheduler: createExecutionGraph
    Scheduler->>Task: deployTasks
    
    Task->>Task: openOperators
    Task->>Task: processElements
    Task->>Task: closeOperators

3.2 窗口处理时序图

sequenceDiagram
    participant Source as SourceOperator
    participant KeyBy as KeyByOperator
    participant Window as WindowOperator
    participant Sink as SinkOperator
    participant Timer as TimerService
    
    Source->>KeyBy: emit(element, timestamp)
    KeyBy->>Window: processElement(element, key)
    
    Window->>Window: assignToWindows
    Window->>Window: addToWindowState
    Window->>Timer: registerTimer(windowEnd)
    
    Timer->>Window: onEventTime(timestamp)
    Window->>Window: triggerWindow
    Window->>Window: applyWindowFunction
    Window->>Sink: emit(result)
    
    Window->>Window: clearWindowState

3.3 状态管理时序图

sequenceDiagram
    participant Operator as StreamOperator
    participant State as KeyedState
    participant Backend as StateBackend
    participant Checkpoint as CheckpointStorage
    
    Operator->>State: get(key)
    State->>Backend: getState(key)
    Backend->>State: stateValue
    State->>Operator: value
    
    Operator->>State: update(key, newValue)
    State->>Backend: putState(key, newValue)
    
    Note over Operator,Checkpoint: Checkpoint Trigger
    
    Operator->>State: snapshot()
    State->>Backend: createSnapshot()
    Backend->>Checkpoint: storeSnapshot()
    Checkpoint->>Backend: snapshotHandle
    Backend->>State: snapshotHandle
    State->>Operator: snapshotHandle

4. 模块交互架构图

4.1 核心模块依赖关系

graph TB
    subgraph "API Layer"
        API1[flink-streaming-java]
        API2[flink-table]
        API3[flink-java]
        API4[flink-scala]
    end
    
    subgraph "Runtime Layer"
        RT1[flink-runtime]
        RT2[flink-runtime-web]
    end
    
    subgraph "Core Layer"
        CORE1[flink-core]
        CORE2[flink-annotations]
    end
    
    subgraph "Connector Layer"
        CONN1[flink-connectors]
        CONN2[flink-formats]
    end
    
    subgraph "Infrastructure Layer"
        INFRA1[flink-filesystems]
        INFRA2[flink-state-backends]
        INFRA3[flink-metrics]
    end
    
    API1 --> RT1
    API2 --> RT1
    API3 --> RT1
    API4 --> RT1
    
    RT1 --> CORE1
    RT2 --> RT1
    
    API1 --> CORE1
    API2 --> CORE1
    API3 --> CORE1
    API4 --> CORE1
    
    CONN1 --> API1
    CONN2 --> API1
    
    RT1 --> INFRA1
    RT1 --> INFRA2
    RT1 --> INFRA3
    
    CORE1 --> CORE2

4.2 运行时组件交互图

graph TB
    subgraph "JobManager Components"
        JM[JobManager]
        Scheduler[JobScheduler]
        Coordinator[CheckpointCoordinator]
        Graph[ExecutionGraph]
    end
    
    subgraph "TaskManager Components"
        TM[TaskManager]
        TaskSlot[Task Slot]
        StreamTask[Stream Task]
        StateBackend[State Backend]
    end
    
    subgraph "Network Layer"
        Network[Network Stack]
        Buffer[Buffer Pool]
        Partition[Result Partition]
        Gate[Input Gate]
    end
    
    subgraph "Memory Management"
        Memory[Memory Manager]
        Segment[Memory Segment]
    end
    
    JM --> Scheduler
    JM --> Coordinator
    JM --> Graph
    
    Scheduler --> TM
    TM --> TaskSlot
    TaskSlot --> StreamTask
    StreamTask --> StateBackend
    
    StreamTask --> Network
    Network --> Buffer
    Network --> Partition
    Network --> Gate
    
    TM --> Memory
    Memory --> Segment
    Buffer --> Segment

5. 数据流图转换过程

5.1 图转换流程

graph LR
    subgraph "用户程序"
        UserCode[User Code]
    end
    
    subgraph "API层转换"
        StreamGraph[StreamGraph]
        JobGraph[JobGraph]
        ExecutionGraph[ExecutionGraph]
        PhysicalGraph[Physical Graph]
    end
    
    subgraph "运行时执行"
        Tasks[Parallel Tasks]
    end
    
    UserCode --> StreamGraph
    StreamGraph --> JobGraph
    JobGraph --> ExecutionGraph
    ExecutionGraph --> PhysicalGraph
    PhysicalGraph --> Tasks
    
    StreamGraph -.-> |"算子链接<br/>优化"| JobGraph
    JobGraph -.-> |"并行化<br/>调度"| ExecutionGraph
    ExecutionGraph -.-> |"资源分配<br/>部署"| PhysicalGraph

5.2 算子链接优化

graph TB
    subgraph "优化前 Before Chaining"
        A1[Source] --> B1[Map]
        B1 --> C1[Filter]
        C1 --> D1[Sink]
    end
    
    subgraph "优化后 After Chaining"
        Chain[Source->Map->Filter] --> D2[Sink]
    end
    
    A1 -.-> Chain
    B1 -.-> Chain
    C1 -.-> Chain

6. 内存管理架构

6.1 TaskManager 内存分布

graph TB
    subgraph "TaskManager JVM Heap"
        subgraph "Flink Memory"
            TaskHeap[Task Heap Memory]
            Framework[Framework Memory]
        end
        
        subgraph "Network Memory"
            NetworkBuffers[Network Buffers]
        end
        
        subgraph "Managed Memory"
            ManagedMem[Managed Memory]
            StateBackend[State Backend]
        end
    end
    
    subgraph "Off-Heap Memory"
        DirectMem[Direct Memory]
        MetaSpace[Metaspace]
    end
    
    TaskHeap --> |"用户代码执行"| Framework
    NetworkBuffers --> |"网络通信"| ManagedMem
    ManagedMem --> |"状态存储"| StateBackend

6.2 网络栈架构

graph TB
    subgraph "Producer Side"
        RecordWriter[RecordWriter]
        ResultPartition[ResultPartition]
        BufferPool1[Buffer Pool]
    end
    
    subgraph "Network Layer"
        NettyServer[Netty Server]
        NettyClient[Netty Client]
    end
    
    subgraph "Consumer Side"
        InputGate[InputGate]
        BufferPool2[Buffer Pool]
        RecordReader[RecordReader]
    end
    
    RecordWriter --> ResultPartition
    ResultPartition --> BufferPool1
    BufferPool1 --> NettyServer
    
    NettyServer --> NettyClient
    
    NettyClient --> InputGate
    InputGate --> BufferPool2
    BufferPool2 --> RecordReader

7. 容错机制架构

7.1 检查点机制

graph TB
    subgraph "Checkpoint Coordinator"
        Trigger[Checkpoint Trigger]
        Coordinator[Checkpoint Coordinator]
        Scheduler[Checkpoint Scheduler]
    end
    
    subgraph "Task Level"
        Task1[Task 1]
        Task2[Task 2]
        TaskN[Task N]
    end
    
    subgraph "State Storage"
        StateBackend1[State Backend]
        CheckpointStorage[Checkpoint Storage]
        SavepointStorage[Savepoint Storage]
    end
    
    Trigger --> Coordinator
    Coordinator --> Scheduler
    
    Scheduler --> Task1
    Scheduler --> Task2
    Scheduler --> TaskN
    
    Task1 --> StateBackend1
    Task2 --> StateBackend1
    TaskN --> StateBackend1
    
    StateBackend1 --> CheckpointStorage
    StateBackend1 --> SavepointStorage

7.2 故障检测和恢复

graph TB
    subgraph "Failure Detection"
        HeartBeat[HeartBeat Monitor]
        FailureDetector[Failure Detector]
    end
    
    subgraph "Recovery Strategy"
        RestartStrategy[Restart Strategy]
        FailoverStrategy[Failover Strategy]
    end
    
    subgraph "Recovery Process"
        StateRestore[State Restore]
        TaskRestart[Task Restart]
        JobRestart[Job Restart]
    end
    
    HeartBeat --> FailureDetector
    FailureDetector --> RestartStrategy
    RestartStrategy --> FailoverStrategy
    
    FailoverStrategy --> StateRestore
    FailoverStrategy --> TaskRestart
    FailoverStrategy --> JobRestart

这个架构图和时序图文档提供了 Flink 系统的全面视图，从整体架构到具体的执行流程，帮助深入理解 Flink 的设计原理和运行机制。