System design interviews can feel overwhelming due to their open-ended nature. Having a structured mental framework and quick access to core operational metrics gives you immediate traction.
Here is a condensed, high-yield cheat sheet covering the foundational topics every engineer should know before an architecture review or interview.
1. Estimation Cheat Sheet & Core Metrics
Latency Scale
- L1 Cache / RAM Access: ~1 ns to 100 ns (Use for high-frequency, ultra-low latency memory operations).
- NVMe SSD Read: ~100 µs (1,000 times slower than RAM).
- Network Read (Same Region): ~0.5 ms to 2 ms.
- Cross-Continental Packet Roundtrip: ~150 ms (Justifies placing edge caches/CDNs close to users).
QPS & Capacity Multipliers
- 100M Daily Active Users (DAU): approx 1,200 Average QPS (assuming 10 requests/user/day).
- Peak Traffic: Always multiply average QPS by 2 times to 5 times to size infrastructure for bursts.
- Storage Rule: 1 Byte/sec = approx 86.4 KB/day = approx 31.5 MB/year.
2. Database Selection Matrix
| Database Type | Key Examples | Primary Use Case | When to Avoid |
|---|---|---|---|
| Relational (SQL) | PostgreSQL, MySQL | ACID transactions, strict schema, complex relational joins | Write throughput exceeding $50k QPS on single nodes |
| Document | MongoDB, DynamoDB | Rapidly evolving schemas, hierarchical catalog data | Deep multi-table transactional aggregations |
| Key-Value | Redis, Memcached | Session stores, distributed locks, transient caching | Queries requiring filtering by non-key attributes |
| Wide-Column | Cassandra, ScyllaDB | High-volume time-series, IoT telemetry, append-only logs | Frequent updates/deletes to existing records |
| Search Engine | Elasticsearch | Full-text fuzzy search, inverted indexing, log analytics | Primary source-of-truth transactional storage |
3. Caching & Data Storage Strategies
Caching Strategies
- Cache-Aside (Lazy Loading): App reads from cache; on miss, reads from DB, updates cache, and returns. Best for standard read-heavy workloads.
- Write-Through: App writes to cache and DB synchronously. Ensures strong consistency at the cost of higher write latency.
- Write-Back (Write-Behind): App writes directly to cache; cache asynchronously updates DB in batches. Ultra-high write performance, but risks data loss on cache failure.
Common Cache Pitfalls
- Cache Stampede (Thundering Herd): Concurrent requests miss cache simultaneously, overwhelming the DB. Fix: Distributed locks or proactive cache warming.
- Cache Penetration: Non-existent keys repeatedly bypass cache to hit the DB. Fix: Cache null responses or use Bloom Filters.
4. Networking Protocols & Load Balancing
API Protocols
- HTTP/REST: Standard request-response over TCP. Best for public, client-facing APIs.
- gRPC (HTTP/2): High-performance, binary-serialized RPC protocol. Best for low-latency microservice-to-microservice calls.
- WebSockets: Persistent, bi-directional TCP stream. Best for real-time applications (chat, live gaming, collaborative tools).
- Server-Sent Events (SSE): Unidirectional streaming from server to client over HTTP. Best for live dashboards and AI text streaming.
Load Balancing
- Layer 4 (Transport): Routes traffic based on IP and TCP/UDP ports without payload inspection (ultra-fast).
-
Layer 7 (Application): Inspects HTTP headers, cookies, and URLs for intelligent routing (e.g., routing
/api/v1/paymentsto dedicated payment servers).
5. Async Pipelines & Distributed Transactions
Messaging Infrastructure
- Message Queues (RabbitMQ, AWS SQS): Point-to-point worker queues where messages are deleted once processed. Best for background task processing.
- Event Streams (Apache Kafka): Distributed, append-only log where consumers track their own offsets. Best for real-time analytics pipelines and event replayability.
Handling Distributed Consistency
- Two-Phase Commit (2PC): Blocking consensus protocol for cross-database ACID guarantees. Avoid in highly distributed systems due to bottleneck locks.
- Saga Pattern: A sequence of local microservice transactions coordinated via events, using compensating operations to roll back on failure. Best for complex distributed workflows.
6. Resilience & Fault Tolerance Patterns
- Circuit Breaker: Automatically trips and fails fast when a downstream dependency experiences degradation, preventing cascading failure.
- Rate Limiter (Token Bucket): Enforces request caps to protect services from traffic surges and abuse.
- Exponential Backoff with Jitter: Adds randomized delay intervals to retries to prevent "retry storms" on recovering systems.
📌 Note: This cheat sheet covers the foundational concepts. The full 10-page System Design Interview Guide contains end-to-end architectural diagrams, detailed mathematical derivations, step-by-step trade-off frameworks, and deep-dive case studies for Uber, Netflix, and Twitter.
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