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Key Characteristics of Distributed Systems - FAANG Interview Guide
Table of Contents
- 🔷 Key Characteristics of Distributed Systems
- 🧠 Big Picture
- 1. Scalability = "Can system grow?"
- 2. Reliability = "Does it work correctly over time?"
- ⚡ Fault Tolerance (Closely Related)
- 3. Availability = "Is system UP?"
- Reliability vs Availability (SUPER IMPORTANT)
- Efficiency = "How fast + how much?"
- Real Interview Insight (Most Important)
- Interview Script (Memorize This)
- Start
- While explaining
- Trade-off line (VERY POWERFUL)
- 🧠 Final Cheat Sheet
- 💡 Golden Line
🔷 Key Characteristics of Distributed Systems
🧠 Big Picture
"These are the qualities that decide whether your system survives real-world scale."
1. Scalability = "Can system grow?"
Simple
👉 Handle more users/data without breaking
Types
Horizontal (BEST 🔥)
- Add more machines
- Example: adding more servers to handle traffic
Vertical
- Increase power of one machine (CPU/RAM)
- Limited + expensive
Key Insight
"Real systems scale horizontally"
2. Reliability = "Does it work correctly over time?"
Simple
👉 No data loss, correct results
Example
- Add to cart → should NEVER disappear
How achieved
- Replication
- Redundancy
Key Insight
"System can fail internally, but user should not feel it"
⚡ Fault Tolerance (Closely Related)
Simple
👉 System continues EVEN IF parts fail
Example
- One server crashes → another takes over
🔥 Difference (Important)
- Reliability → correct results over time
- Fault tolerance → survives failures
3. Availability = "Is system UP?"
Simple
👉 % of time system is working
- 99.9% → ~8 hours downtime/year
- 99.99% → ~1 hour
Example
- Website loads vs doesn't load
⚠️ Important Insight
"System can be available but still wrong"
Example:
- Wrong data but site is working → available ❌ reliable
Reliability vs Availability (SUPER IMPORTANT)
| Concept | Meaning |
|---|---|
| Reliability | Correctness |
| Availability | Uptime |
Example
💳 Banking:
- Must be reliable + available
📱 Social media:
- Availability > strict correctness
Efficiency = "How fast + how much?"
Two Metrics
Latency
👉 Time to respond
- Example: API response = 100ms
Throughput
👉 Work per second
- Example: 10K requests/sec
Key Insight
"Low latency + high throughput = good system"
Real Interview Insight (Most Important)
"These characteristics conflict with each other → trade-offs"
Examples:
- High availability → weaker consistency (CAP theorem)
- High reliability → more cost (replication)
- High scalability → more complexity
Interview Script (Memorize This)
Start
"I'll define key system characteristics like scalability, availability, and reliability."
While explaining
Scalability
"System should scale horizontally to handle millions of users."
Reliability
"Data should remain correct even in case of failures."
Availability
"We target around 99.9% uptime."
Efficiency
"We aim for low latency and high throughput."
Trade-off line (VERY POWERFUL)
"We'll need to balance these properties based on system requirements."
🧠 Final Cheat Sheet
| Property | Meaning | Example |
|---|---|---|
| Scalability | Handle growth | Add servers |
| Reliability | Correct results | No data loss |
| Availability | System uptime | 99.9% uptime |
| Efficiency | Fast + high load | Low latency |
| Manageability | Easy ops | Monitoring |
💡 Golden Line
"A good distributed system is not perfect in all aspects — it balances scalability, reliability, and availability based on use case."