Designs

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Designs with worked examples 6

Ordered by design number

Company tags are community-reported. Counts on cards show how many people reported that design.

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2. Design a Distributed Rate Limiter
Enforce tenant quotas across servers while making burst behavior and outage policy explicit.
ClassicMedium
Caching Databases Concurrency
+8
Distributed systems API gateways AWS Lua Rate limiting Redis Reliability Token bucket
Worked design
Practice
3. Design a Distributed Cache
Partition cached data, survive node loss and keep cache misses from overwhelming the source of truth.
ClassicMedium
Caching Sharding Databases
+9
Distributed systems AWS Consistency Consistent hashing Hot keys LRU eviction Memcached Redis Replication
Worked design
Practice
29. Cache-Aside (Lazy Loading)
A read-heavy service sends every request to a database that is slower and more expensive per read than memory, and the same few rows are asked for over and over.
Infrastructure & SREMedium
Caching Databases Read paths
+1
Redis
Worked design
Practice
32. Token Bucket Rate Limiter
A limit enforced per server is not a limit: ten servers each allowing a hundred requests a minute allow a thousand. And a counter per fixed window lets twice the limit through across a window boundary.
Infrastructure & SREMedium
Caching Databases Lua
+3
Rate limiting Redis Token bucket
Worked design
Practice
66. Design for Read Scale: A Product Page Read a Million Times a Second
Serve a product page a million times a second: a cheap query, replicas, Redis and the edge, with hot keys, stampedes and invalidation handled.
ClassicMedium
Caching Databases Aurora
+4
AWS CDNs Read replicas Redis
Worked design
Practice
70. Design an In-Memory Data Store like Redis
An in-memory data store like Redis, from one event loop to Redis Cluster to a durable platform.
ClassicMedium
Caching Sharding Databases
+5
Distributed systems AWS In memory Redis Replication
Worked design
Practice

Each design brings together the brief, practice, examples and discussion.

Designs with worked examples 6

2. Design a Distributed Rate Limiter

3. Design a Distributed Cache

29. Cache-Aside (Lazy Loading)

32. Token Bucket Rate Limiter

66. Design for Read Scale: A Product Page Read a Million Times a Second

70. Design an In-Memory Data Store like Redis