Patterns
⏳

Async-Await

Non-blocking asynchronous execution with promise-based coordination

Complexity: mediumParallelization

🎯 30-Second Overview

Pattern: Non-blocking asynchronous execution with promise-based coordination

Why: Improves throughput and responsiveness while maintaining code readability

Key Insight: Promise β†’ async/await β†’ [concurrent_operations] β†’ await_results

⚑ Quick Implementation

1Identify:Find async operations (I/O, network, etc.)
2Wrap:Use promises with error handling & timeouts
3Async/Await:Write sequential-looking async code
4Concurrent:Run multiple operations in parallel
5Coordinate:Use Promise.all/race for orchestration
Example: async_ops β†’ [Promise.all([op1, op2, op3])] β†’ await_results β†’ process

πŸ“‹ Do's & Don'ts

βœ…Use Promise.all for independent concurrent operations
βœ…Apply Promise.race for timeout and first-response scenarios
βœ…Implement proper error boundaries and catch blocks
βœ…Set appropriate timeouts to prevent hanging
βœ…Use async iterators for streaming data
❌Forget to await async operations
❌Create async/await cascades eliminating concurrency
❌Ignore error handling in promise chains
❌Block event loop with CPU-intensive operations
❌Skip timeout handling for long operations

🚦 When to Use

Use When

  • β€’ I/O intensive operations
  • β€’ Multiple independent API calls
  • β€’ Non-blocking background tasks
  • β€’ Event-driven architectures

Avoid When

  • β€’ Simple synchronous operations
  • β€’ CPU-intensive tasks
  • β€’ Critical sequential execution order
  • β€’ Deterministic timing requirements

πŸ“Š Key Metrics

Response Time Improvement
Latency reduction vs sequential execution
Throughput Increase
Operations per unit time with parallelism
Error Handling Effectiveness
Success rate of graceful recovery
Resource Utilization
CPU and memory efficiency gains
Promise Resolution Rate
% operations completing within timeout
Concurrency Efficiency
Parallel operations vs coordination overhead

πŸ’‘ Top Use Cases

API Integration: Concurrent service calls β†’ gather responses β†’ process results
File Processing: Parallel file I/O β†’ process data streams β†’ aggregate output
Database Operations: Concurrent queries β†’ merge results β†’ return dataset
Microservice Coordination: Parallel service calls β†’ orchestrate responses
Real-time Processing: Stream processing β†’ concurrent analysis β†’ live results

Pattern Relationships

Discover how Async-Await relates to other patterns

Prerequisites, next steps, and learning progression

Prerequisites

No prerequisites needed - great for beginners!

Next Steps

(3)
πŸ“‘
Scatter-Gather
mediumparallelization

Structured service orchestration with result aggregation

πŸ’‘ Natural evolution for systematic multi-service coordination

πŸ—ΊοΈ
Map-Reduce
mediumparallelization

Structured parallel processing with chunking and reduction

πŸ’‘ Advanced parallel processing for large-scale data operations

🍴
Fork-Join
mediumparallelization

Recursive parallel decomposition with work stealing

πŸ’‘ Sophisticated recursive parallel processing with load balancing

Alternatives

(1)
⛓️
Sequential Chaining
lowprompt chaining

Linear processing when concurrency isn't needed

πŸ’‘ Simpler approach when operations must be sequential

Industry Applications

Financial Services

Concurrent financial data processing and API integration

πŸ“ŠMulti-Criteria Decision Making
βš–οΈLLM-as-Judge

Content & Knowledge

Concurrent content processing and knowledge retrieval

πŸ“šAdvanced RAG
πŸ—‚οΈHierarchical Planning

Software Development

Concurrent code execution and testing workflows

πŸ’»Code Execution
πŸ”§SWE-Bench Suite

References & Further Reading

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