Beginner LevelSystem Design & Architecture
Explain the CAP Theorem and PACELC Theorem with real-world database examples.
❓ Interview Prompt: What are Consistency, Availability, and Partition Tolerance? How does PACELC expand CAP when no network partition exists?
The CAP Theorem states that a distributed system can only provide at most 2 out of 3 guarantees during a network partition:
•
CP (Consistency + Partition Tolerance): Rejects writes/reads if data sync cannot be guaranteed across nodes (e.g. HBase, MongoDB with strict write concerns).
•
AP (Availability + Partition Tolerance): Returns stale data rather than erroring (e.g. Cassandra, DynamoDB).
PACELC Theorem:
Extends CAP:
•
If Partition (P): Choose between Availability (A) or Consistency (C);
•
Else (E): Choose between Latency (L) or Consistency (C).
Example: DynamoDB defaults to PA/EL (prioritizes availability during partitions, low latency normally).
Code Implementation Example
markdown
PACELC Decision Matrix:
┌────────────────────────────────────────────────────────┐
│ PACELC │
├───────────────────────────┬────────────────────────────┤
│ If Network Partition │ Else (Normal) │
├─────────────┬─────────────┼──────────────┬─────────────┤
│ Availability│ Consistency │ Low Latency │ Consistency │
│ (AP) │ (CP) │ (EL) │ (EC) │
└─────────────┴─────────────┴──────────────┴─────────────┘💡PACELC database trade-off matrix.
🎯 Interview Key Takeaway
System design requires balancing consistency against availability and network latency depending on business SLAs.
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