System Design Concepts QA Engineers Encounter in Practice

A fast, beginner-friendly guide to system design concepts interviewers and employers actually care about.

If you’re starting with system design, the real struggle isn’t complexity.

It’s fragmentation.

One blog explains CAP. Another explains caching. A third talks about queues—without context.

This guide fixes that.

You’ll learn 50+ essential system design concepts in one structured place—covering scalability, reliability, networking, databases, caching, messaging, observability, and security.

No academic fluff. No copied definitions. Just clear explanations, real-world intuition, and interview-ready thinking.

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Why This System Design Guide Works

• Written for beginners and early engineers
• Optimized for system design interviews (FAANG-style)
• Focused on real-world trade-offs, not textbook theory
• Designed to be read in ~15 minutes

If you understand these 50 ideas, you’ll think about systems the way senior engineers do.

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Core Scaling & Performance

1. Vertical Scaling – Increase CPU, RAM, disk on single machine. Simple but limited by hardware ceilings.​

2. Horizontal Scaling – Add machines, distribute load via load balancers. Complex but infinitely scalable.​

3. Latency – Time to serve one request (p99 < 200ms ideal). Critical for user experience.​

4. Throughput – Requests/second handled. Measures system capacity (10k RPS).​

5. Amdahl’s Law – System speed limited by slowest serial component. Parallelization has diminishing returns.​

6. Load Balancer – Distributes traffic across servers using algorithms. Single point of failure risk.​

7. LB Algorithms – Round Robin (simple), Least Connections (smart), IP Hash (sticky sessions).​

8. Reverse Proxy – Sits before servers for routing, SSL termination, security (Nginx, HAProxy).​

9. API Gateway – Single entry for microservices: auth, rate limiting, metrics (Kong, AWS API Gateway).​

10. CDN – Edge servers cache static content near users (Cloudflare, CloudFront).​

11. Stateless Services – No session data in memory. Easy horizontal scaling, any instance handles any request.​

12. Stateful Services – Maintain sessions/connections. Sticky sessions or external storage required.​

13. Caching – Store hot data in fast memory (Redis). Cache hit ratio > 90% target.​

14. Cache Strategies – Cache-Aside (app manages), Write-Through (sync), Write-Back (async).​

15. Eviction Policies – LRU (least recently used), LFU (least frequently), FIFO. Balance staleness vs memory.​

Consistency & Data Models

16. CAP Theorem – Network partition: choose Consistency OR Availability (not both).​

17. PACELC – Even without partitions, trade Latency for Consistency.​

18. ACID – Atomicity, Consistency, Isolation, Durability. SQL transaction guarantees.​

19. BASE – Basically Available, Soft state, Eventual consistency. NoSQL scalability model.​

20. Eventual Consistency – Data synchronizes over time, not immediately.​

21. Replication – Master-Slave (read scaling) vs Master-Master (write scaling).​

22. Sharding – Split data across nodes by range/hash. Hash minimizes hotspots.​

23. Consistent Hashing – Add/remove nodes with minimal data movement. Virtual nodes balance load.​

24. Indexing – B-Tree speeds reads, slows writes. Choose indexes wisely.​

25. WAL (Write-Ahead Logging) – Log changes before disk commit. Crash recovery guarantee.​

Data Organization

26. Normalization – Eliminate redundancy, ensure data integrity (3NF ideal).​

27. Denormalization – Duplicate data for read performance. Write complexity increases.​

28. Polyglot Persistence – Multiple DBs per use case (SQL analytics, NoSQL documents, graphs relationships).​

29. Bloom Filter – Probabilistic “does not exist” check. 1% false positive, zero false negatives.​

30. Vector DBs – Store embeddings for AI similarity search (Pinecone, Weaviate).​

31. Connection Pooling – Reuse DB connections. Pool size = concurrent users × queries/user.​

32. Database Per Service – Microservices own their data. Eventual consistency across boundaries.​

Networking & Protocols

33. DNS – Domain → IP resolution. TTL caching critical for performance.​

34. TCP – Reliable, ordered delivery with handshake. Web, databases.​

35. UDP – Fast, unordered. Video streaming, gaming.​

36. HTTP/2 – Multiplexing, header compression, server push.​

37. HTTP/3 – QUIC over UDP. Faster connection, better mobile.​

38. REST vs gRPC – JSON human-readable vs Protocol Buffers high-performance.​

39. WebSocket – Bidirectional persistent connection. Chat, gaming.​

Architecture Patterns

40. Monolith – Single deployable unit. Simple dev, hard to scale teams.​

41. Microservices – Independent services by domain. Operational complexity.​

42. Serverless – Event-driven functions (AWS Lambda). No server management.​

43. Saga Pattern – Distributed transactions via choreography/orchestration.​

44. CQRS – Separate Command (write) and Query (read) models.​

45. Event Sourcing – Store state changes as event stream.​

46. BFF (Backend for Frontend) – API per client type (mobile, web).​

47. Strangler Fig – Gradually extract microservices from monolith.​

Reliability & Resilience

48. Rate Limiting – Token Bucket (bursts), Leaky Bucket (smooth). Fixed vs Sliding windows.​

49. Circuit Breaker – Closed→Open→Half-Open. Prevents cascading failures.​

50. Bulkhead – Resource isolation per service (thread pools).​

51. Retry with Backoff – Exponential backoff + jitter prevents thundering herd.​

52. Idempotency – Same request yields same result. Safe retries.​

53. Heartbeat – Health monitoring. 3 missed = failure.​

54. Graceful Degradation – Partial functionality during failures.​

55. Fault Tolerance – Fail-fast, retry, circuit break strategies.​

Messaging & Async

56. Message Queue – Async point-to-point (RabbitMQ, SQS).​

57. Pub/Sub – Event broadcasting (Kafka, Redis Pub/Sub).​

58. Dead Letter Queue – Failed message storage for debugging.​

Observability

59. Distributed Tracing – Request correlation across services (Jaeger).​

60. SLA/SLO/SLI – SLA (customer), SLO (target), SLI (measurement).​

61. Golden Signals – Latency, Traffic, Errors, Saturation.​

62. Log Aggregation – Centralized structured logs (ELK, Loki).​

Security

63. Zero Trust – Never trust, always verify identity + context.​

64. Defense in Depth – Multiple security layers (firewall + WAF + encryption).​

65. Service Mesh – Sidecar proxies for security/observability (Istio).​

Quick Reference Matrix

Problem	Solution	Trade-off
High Latency	CDN + Caching	Cache invalidation
Data Loss Risk	Replication + WAL	Storage cost
Cascading Failures	Circuit Breaker + Bulkhead	Complexity
Uneven Load	Consistent Hashing	Rebalance cost
Too Many Requests	Rate Limiting + Queue	User experience

Frequently Asked Questions (FAQs)

1. Vertical vs Horizontal—which should I use first?
Vertical for simplicity, horizontal for scale. Most systems outgrow vertical limits.​

2. When does microservices make sense vs monolith?

50 engineers, multiple teams, different tech stacks. Otherwise monolith wins.​

3. Redis vs Memcached?
Redis: persistence + pub/sub. Memcached: pure speed.​

4. How much caching is too much?
Cache hit ratio >85%, stale data <5%. Monitor both metrics.​

5. Circuit Breaker vs Retry?
Circuit Breaker first (stop the bleeding), Retry second (with jitter).​

6. SQL vs NoSQL decision matrix?
Joins/ACID → SQL. Scale/flexible schema → NoSQL.​

7. What’s the single most important reliability metric?
MTTR (Mean Time To Recovery). Fast recovery > perfect prevention.​

8. Eventual consistency—how eventual?
<100ms for hot data, <5s for cold data. Define your SLA.​

9. gRPC vs REST—when to choose each?
Internal/high-throughput → gRPC. External/human-readable → REST.​

10. Zero Trust implementation priority?

Identity, 2. Least privilege, 3. Assume breach mindset.​

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Summary | 50 P0 Critical System Design Concepts — Short & Clear Explanations

Below is a quick-reference version of all 50 concepts. Use this for fast revision, interviews, or mental models.

1. Vertical Scaling – Increasing CPU, RAM, or disk on a single machine. Simple but limited.
2. Horizontal Scaling – Adding more machines and distributing load. Complex but highly scalable.
3. CAP Theorem – During network failure, choose between Consistency or Availability.
4. PACELC – Even without failure, systems trade Latency for Consistency.
5. ACID – Strong guarantees for correctness in transactions.
6. BASE – Availability-first model with eventual consistency.
7. Latency – Time taken to serve one request.
8. Throughput – Number of requests handled per unit time.
9. Amdahl’s Law – The slowest part limits overall system speed.
10. Eventual Consistency – Data becomes consistent over time.
11. Stateless Services – No user data stored in memory; easy to scale.
12. Stateful Services – Maintain session or state; harder to distribute.
13. Monolith – Single deployable unit; simple but harder to scale teams.
14. Microservices – Independent services; scalable but operationally heavy.
15. Serverless – Run code without managing servers; event-driven.
16. Load Balancer – Distributes traffic across servers.
17. LB Algorithms – Decide how traffic is routed (Round Robin, Least Conn).
18. Reverse Proxy – Sits in front of servers for routing and security.
19. API Gateway – Central entry point for APIs.
20. CDN – Serves static content closer to users.
21. DNS – Resolves domain names to IP addresses.
22. TCP – Reliable, ordered communication.
23. UDP – Fast but unreliable communication.
24. HTTP/2 & HTTP/3 – Faster web protocols with multiplexing.
25. REST vs gRPC – Human-readable APIs vs high-performance RPC.
26. Sharding – Splitting data across nodes.
27. Replication – Copying data for fault tolerance.
28. Consistent Hashing – Minimizes rebalancing when nodes change.
29. Indexing – Speeds up reads at the cost of writes.
30. WAL – Logs writes before committing to disk.
31. Normalization – Reduce redundancy for correctness.
32. Denormalization – Duplicate data for performance.
33. Polyglot Persistence – Multiple databases for different needs.
34. Bloom Filter – Fast probabilistic existence check.
35. Vector DBs – Power similarity search for AI systems.
36. Rate Limiting – Controls request flow.
37. Circuit Breaker – Stops cascading failures.
38. Bulkhead – Isolates failures.
39. Retry with Backoff – Safe retry strategy.
40. Idempotency – Same request = same result.
41. Caching – Store frequently accessed data.
42. Cache Strategies – Cache-aside, write-through, write-back.
43. Eviction Policies – Decide what data to remove.
44. Message Queue – Async communication.
45. Pub/Sub – Event broadcasting.
46. Dead Letter Queue – Captures failed messages.
47. Distributed Tracing – Track request flow across services.
48. SLA/SLO/SLI – Reliability metrics and contracts.
49. Zero Trust – Always verify, never trust.
50. Defense in Depth – Multiple security layers.

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Key Takeaways

• System design is about trade-offs, not tools
• Simplicity scales better than cleverness
• Failures are normal—design for them
• Clear thinking beats memorized definitions

If you understand these 50 concepts, you already think like a system designer.

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