"I'll design a Snowflake-style ID generator with these components:

1. ID Format (64-bit):
   - 41 bits: Timestamp (milliseconds)
   - 5 bits: Datacenter ID (32 datacenters)
   - 5 bits: Worker ID (32 workers per DC)
   - 12 bits: Sequence (4,096 IDs/ms)

2. Key Properties:
   - No coordination between nodes
   - Time-ordered IDs
   - High throughput (10K+ IDs/sec per node)
   - Low latency (<1ms)

3. Architecture:
   - Stateless ID generator nodes
   - Load balancer for distribution
   - Optional coordination service for worker IDs
   - Monitoring and metrics"

┌─────────────┐
│   Clients   │
└──────┬──────┘
       │
┌──────┴──────┐
│Load Balancer│
└──────┬──────┘
       │
   ┌───┴───┬───────┬───────┐
   │       │       │       │
┌──┴──┐ ┌──┴──┐ ┌──┴──┐ ┌──┴──┐
│Node1│ │Node2│ │Node3│ │NodeN│
│WID:1│ │WID:2│ │WID:3│ │WID:N│
└─────┘ └─────┘ └─────┘ └─────┘

"Let me explain the 64-bit ID structure:

Bit Layout:
┌─────────────────────────────────────────────────────┐
│ 0 │ 41-bit Timestamp │ 5-bit DC │ 5-bit Worker │ 12-bit Seq │
└─────────────────────────────────────────────────────┘

Timestamp (41 bits):
- Milliseconds since custom epoch (2020-01-01)
- 2^41 ms = 69.7 years
- Provides time-ordering
- Allows efficient time-range queries

Datacenter ID (5 bits):
- 32 datacenters maximum
- Geographic distribution
- Regional isolation
- Disaster recovery

Worker ID (5 bits):
- 32 workers per datacenter
- 1,024 total workers globally
- No coordination between workers
- Unique per node

Sequence (12 bits):
- 4,096 IDs per millisecond per worker
- Resets each millisecond
- Handles burst traffic
- Overflow waits for next millisecond"

"Here's the core generation algorithm:

def generate_id(self):
    timestamp = current_time_ms()
    
    # Handle clock regression
    if timestamp < self.last_timestamp:
        raise ClockRegressionError()
    
    # Same millisecond - increment sequence
    if timestamp == self.last_timestamp:
        self.sequence = (self.sequence + 1) & 0xFFF
        
        # Sequence overflow - wait
        if self.sequence == 0:
            timestamp = wait_next_ms()
    else:
        # New millisecond - reset sequence
        self.sequence = 0
    
    self.last_timestamp = timestamp
    
    # Build ID: [timestamp][dc][worker][seq]
    return ((timestamp - EPOCH) << 22) | \
           (self.datacenter_id << 17) | \
           (self.worker_id << 12) | \
           self.sequence

Key points:
- Lock-free with atomic operations
- Handles clock regression
- Manages sequence overflow
- Sub-millisecond latency"

"Great question! Clock regression is a critical issue.

Detection:
- Compare current time with last timestamp
- If current < last, clock moved backwards

Handling Options:

1. Refuse to Generate (Preferred):
   - Throw error immediately
   - Alert operations team
   - Wait for clock to catch up
   - Prevents duplicate IDs

2. Use Last Timestamp:
   - Continue with last known time
   - Increment sequence
   - Risk of sequence overflow
   - Temporary solution

3. Wait It Out:
   - If regression < 5ms, wait
   - Sleep until clock catches up
   - Resume normal operation
   - Acceptable for small regressions

Prevention:
- Multiple NTP servers
- Monitor clock drift
- Alert on drift > 100ms
- Refuse generation if drift > 1 second"

"Uniqueness is guaranteed by the combination of:

1. Timestamp:
   - Unique per millisecond
   - Monotonically increasing
   - 41 bits = 69 years

2. Datacenter + Worker ID:
   - Unique per node
   - 10 bits = 1,024 unique nodes
   - No two nodes share same ID

3. Sequence:
   - Unique within millisecond
   - 12 bits = 4,096 per ms
   - Resets each millisecond

Mathematical Proof:
- ID = f(timestamp, datacenter, worker, sequence)
- Each component unique in its dimension
- Combination guarantees global uniqueness
- Collision probability: 0 (with proper worker ID management)

Edge Cases:
- Clock regression: Detected and prevented
- Worker ID conflicts: Coordination service prevents
- Sequence overflow: Wait for next millisecond"

"Excellent scaling question!

Current Capacity:
- Single worker: 4,096 IDs/ms = 4M IDs/sec theoretical
- Practical: 10K IDs/sec per worker
- 100 workers: 1M IDs/sec
- 1,000 workers: 10M IDs/sec

Scaling Strategy:

1. Horizontal Scaling:
   - Add more workers
   - Linear scaling
   - No coordination overhead
   - Each worker independent

2. Geographic Distribution:
   - Workers across datacenters
   - Reduced latency
   - Regional isolation
   - Disaster recovery

3. Load Balancing:
   - Round-robin distribution
   - Geographic routing
   - Health-based routing
   - Auto-scaling

Bottlenecks:
- Worker ID space: 1,024 workers max
- Solution: Use 128-bit IDs for unlimited workers
- Sequence overflow: 4,096/ms per worker
- Solution: Add more workers

Cost:
- $100/worker/month
- 1M IDs/sec = 100 workers = $10K/month
- $0.01 per million IDs
- Very cost-effective"

"We'll use a database sequence"
→ Shows lack of understanding of distributed systems

"Clock synchronization isn't important"
→ Critical for time-based IDs

"We don't need to handle clock regression"
→ Will cause duplicate IDs

"Worker IDs can be random"
→ Defeats the purpose of uniqueness guarantee

"We can generate unlimited IDs per millisecond"
→ Ignores sequence bit limitations

"We'll use a Snowflake-style approach for coordination-free generation"
→ Shows knowledge of industry standards

"Clock synchronization via NTP is critical, with monitoring for drift"
→ Demonstrates understanding of dependencies

"We need to detect and handle clock regression to prevent duplicates"
→ Shows attention to edge cases

"Worker IDs must be unique and managed via coordination service"
→ Understands the uniqueness guarantee

"We're limited to 4,096 IDs per millisecond per worker, so we scale horizontally"
→ Understands capacity and scaling

0-5 min: Requirements clarification
- Ask about scale, latency, ordering
- Clarify constraints (64-bit vs 128-bit)
- Understand use cases

5-15 min: High-level design
- Draw architecture diagram
- Explain ID format
- Discuss key components
- Cover basic algorithm

15-30 min: Deep dive
- Detailed algorithm
- Handle edge cases
- Discuss scaling
- Cover monitoring

30-40 min: Advanced topics
- Security considerations
- Alternative approaches
- Tradeoffs discussion
- Follow-up questions

40-45 min: Wrap up
- Summarize design
- Highlight key decisions
- Discuss next steps

✓ No coordination required
✓ Sub-millisecond latency
✓ Linear horizontal scaling
✓ Time-ordered IDs
✓ 69-year lifetime
✓ 99.99% availability

✓ Clock regression handling
✓ Sequence overflow management
✓ Worker ID coordination
✓ Comprehensive monitoring
✓ Graceful degradation
✓ Disaster recovery

✓ 10K IDs/sec per worker
✓ 1,024 workers globally
✓ 10M IDs/sec theoretical
✓ Geographic distribution
✓ Auto-scaling support
✓ Cost-effective ($0.01/M IDs)

1. "Explain how Snowflake IDs work"
2. "What are the advantages over database sequences?"
3. "How do you ensure uniqueness?"
4. "What happens if the clock goes backwards?"

5. "How do you handle sequence overflow?"
6. "How do you assign worker IDs?"
7. "What's the maximum throughput?"
8. "How do you monitor the system?"

9. "How do you migrate from auto-increment to Snowflake?"
10. "What if you need more than 1,024 workers?"
11. "How do you handle leap seconds?"
12. "What are the security considerations?"

□ ID format and bit allocation
□ Uniqueness guarantee
□ Clock synchronization
□ Worker ID management
□ Sequence overflow handling
□ Scaling strategy
□ Monitoring and alerting
□ Edge cases (clock regression, etc.)
□ Alternative approaches
□ Tradeoffs and decisions

Strong Performance Indicators:
✓ Clear explanation of ID format
✓ Handled edge cases proactively
✓ Discussed tradeoffs
✓ Showed production awareness
✓ Scaled the design appropriately
✓ Considered monitoring and operations

Areas for Improvement:
✗ Missed clock regression handling
✗ Didn't discuss worker ID management
✗ Ignored sequence overflow
✗ No monitoring discussion
✗ Didn't consider alternatives
✗ Weak on scaling strategy