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Fork-Join（分叉-合并）
将任务分叉为并行的子任务，并在完成后合并各自的结果
复杂度: high并行化
In 30 seconds
- What
- 把一个任务拆成互相独立的并行子任务，等全部完成后再合并结果。
- When to use
- 可以分解为独立工作的大型问题，并行执行比顺序处理更节省时间。
- Watch out
- 同步开销和结果合并的复杂度，可能在小任务或高度耦合的任务上抵消掉全部提速收益。
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将任务分叉为并行的子任务，并在完成后合并各自的结果
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模式： 递归分解任务、并行执行、再合并结果
原因： 通过分治与动态负载均衡实现最优并行度
关键洞察： Task → [Subtask1, Subtask2, ...] → Parallel_Execute → Join → Result
Discover how Fork-Join relates to other patterns
Prerequisites, next steps, and learning progression
Foundation parallel processing pattern with chunking and aggregation
💡 Understanding structured parallel processing helps with fork-join decomposition
Complex workflows with dependencies and state management
💡 Natural evolution for complex parallel workflows with dependencies
Structured parallel processing without recursive decomposition
💡 Better for non-recursive problems with clear data partitioning
Service-oriented parallel processing without recursion
💡 Simpler approach for service-based parallel processing
Recursive parallel analysis for complex financial computations
Hierarchical parallel processing of structured knowledge
Recursive parallel processing for code analysis and optimization
通过这些精选资源加深理解