21张图彻底搞懂 Git 进阶用法

21张图彻底搞懂 Git 进阶用法

  • git commit -a 相当于运行 git add 把所有当前目录下的文件加入暂存区域再运行。git commit.

  • git commit files 进行一次包含最后一次提交加上工作目录中文件快照的提交。并且文件被添加到暂存区域。

  • git checkout HEAD – files 回滚到复制最后一次提交。

约定

==

后文中以下面的形式使用图片。

21张图彻底搞懂 Git 进阶用法

绿色的5位字符表示提交的ID,分别指向父节点。分支用橘色显示,分别指向特定的提交。当前分支由附在其上的HEAD标识。这张图片里显示最后5次提交,ed489是最新提交。main分支指向此次提交,另一个stable分支指向祖父提交节点。

命令详解

====

Diff

====

有许多种方法查看两次提交之间的变动。下面是一些示例。

21张图彻底搞懂 Git 进阶用法

Commit

======

提交时,git用暂存区域的文件创建一个新的提

### Flink Exactly-Once Semantics Explained In the context of stream processing, ensuring that each record is processed only once (exactly-once) without any loss or duplication becomes critical for applications requiring high accuracy and reliability. For this purpose, Apache Flink implements sophisticated mechanisms to guarantee exactly-once delivery semantics. #### Importance of Exactly-Once Processing Exactly-once processing ensures every message is consumed precisely one time by downstream systems, preventing both data loss and duplicate records[^3]. This level of assurance is particularly important when dealing with financial transactions, billing information, or other scenarios where even a single error can lead to significant issues. #### Implementation Mechanisms To achieve exactly-once guarantees, Flink employs several key technologies: 1. **Checkpointing**: Periodic snapshots are taken across all operators within a job graph at consistent points in time. These checkpoints serve as recovery states which allow jobs to resume from these saved positions upon failure. 2. **Two-phase commit protocol**: When interacting with external systems like databases or messaging queues through sinks, Flink uses an extended version of the two-phase commit transaction mechanism. During checkpoint creation, pre-commit actions prepare changes; after successful completion of the checkpoint process, global commits finalize those operations[^4]. ```mermaid graph LR; A[Start Transaction] --> B{Prepare Changes}; B --> C(Pre-Commit); C --> D{All Pre-commits Succeed?}; D -->|Yes| E(Global Commit); D -->|No| F(Abort); ``` This diagram illustrates how the two-phase commit works during sink operations. Each operator prepares its part before confirming globally whether everything has been successfully prepared. Only then does it proceed with committing or aborting based on consensus among participants. #### Barrier Insertion & Propagation For maintaining consistency between different parts of computation while taking periodic snapshots, barriers play a crucial role. They act as synchronization markers inserted into streams periodically according to configured intervals. As they propagate along with events throughout the topology, they ensure that no new elements enter until previous ones have completed their respective stages up till the barrier point. ```mermaid sequenceDiagram participant Source participant OperatorA participant OperatorB Note over Source: Time advances... Source->>OperatorA: Data Element 1 Source->>OperatorA: Checkpoint Barrier X Source->>OperatorA: Data Element 2 OperatorA->>OperatorB: Forwarded Elements + Barrier X Note right of OperatorB: Process pending items\nbefore handling next element post-barrier ``` The sequence above shows how barriers travel alongside regular data flow but enforce order so that computations remain synchronized despite asynchronous nature inherent in distributed environments. --related questions-- 1. What challenges arise when implementing exactly-once semantics in real-world applications? 2. How do checkpointing frequencies impact performance versus fault tolerance trade-offs? 3. Can you explain what happens if some nodes fail midway through a two-phase commit operation? 4. Are there alternative methods besides using barriers for achieving similar levels of consistency? 5. In practice, under what circumstances might at-least-once be preferred over exactly-once semantics?
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