System Design Fundamentals: Load Balancing, Caching, and Idempotent APIs
You are tasked with designing a backend service intended for deployment across multiple instances, operating behind a load balancer. Address the following core system design aspects:
1. Load Balancing
- Define what a load balancer is and articulate its primary purposes in a distributed system.
- Compare and contrast common load balancing routing policies, including:
- Round-robin and Weighted Round-robin
- Least Connections
- Consistent Hashing
- Latency/Health-based Routing
- Sticky Sessions (Session Affinity)
- Discuss the distinctions between L4 (Transport Layer) and L7 (Application Layer) load balancing, providing scenarios where each would be the preferred choice.
2. Caching
- Identify various strategic points where caching can be effectively applied within a system architecture (e.g., client, CDN, edge, reverse proxy, application, database).
- Explain different cache read/write patterns, such as cache-aside, read-through, write-through, and write-back.
- Describe mechanisms for handling cache invalidation, Time-To-Live (TTL) settings, and managing data staleness.
- Detail strategies to prevent or mitigate the "cache stampede" or "thundering herd" problem.
3. Idempotency Support
Design an API and supporting system that robustly handles idempotent requests, using examples like a payment charge or order creation.
- Define idempotency and explain its critical importance in distributed systems.
- Propose a design for an idempotency key: specify its origin, format, and typical lifecycle/expiration.
- Describe how the service would store and check the state associated with an idempotency key.
- Address challenges related to concurrency (when the same key arrives simultaneously), request retries, and handling partial failures in an idempotent manner.
- Discuss how idempotency interacts with load balancing and considerations for distributed deployments.
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Core dilemma
Demonstrating a foundational understanding of distributed system reliability, performance, and correctness mechanisms, and their interconnected tradeoffs across various architectural layers.
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System Design Fundamentals: Load Balancing, Caching, and Idempotent APIs
You are tasked with designing a backend service intended for deployment across multiple instances, operating behind a load balancer. Address the following core system design aspects:
1. Load Balancing
- Define what a load balancer is and articulate its primary purposes in a distributed system.
- Compare and contrast common load balancing routing policies, including:
- Round-robin and Weighted Round-robin
- Least Connections
- Consistent Hashing
- Latency/Health-based Routing
- Sticky Sessions (Session Affinity)
- Discuss the distinctions between L4 (Transport Layer) and L7 (Application Layer) load balancing, providing scenarios where each would be the preferred choice.
2. Caching
- Identify various strategic points where caching can be effectively applied within a system architecture (e.g., client, CDN, edge, reverse proxy, application, database).
- Explain different cache read/write patterns, such as cache-aside, read-through, write-through, and write-back.
- Describe mechanisms for handling cache invalidation, Time-To-Live (TTL) settings, and managing data staleness.
- Detail strategies to prevent or mitigate the "cache stampede" or "thundering herd" problem.
3. Idempotency Support
Design an API and supporting system that robustly handles idempotent requests, using examples like a payment charge or order creation.
- Define idempotency and explain its critical importance in distributed systems.
- Propose a design for an idempotency key: specify its origin, format, and typical lifecycle/expiration.
- Describe how the service would store and check the state associated with an idempotency key.
- Address challenges related to concurrency (when the same key arrives simultaneously), request retries, and handling partial failures in an idempotent manner.
- Discuss how idempotency interacts with load balancing and considerations for distributed deployments.