Initial: 10 dispatchers
After scaling: 20 dispatchers

Benefits:
- 2x event processing capacity
- Better fault tolerance
- Reduced latency

Process:
1. Add new dispatcher instances
2. Subscribe to Kafka partitions
3. Start processing events
4. Auto-rebalancing

Downtime: Zero

Initial: 100 workers, 10K deliveries/second
After scaling: 200 workers, 20K deliveries/second

Auto-Scaling Triggers:
- Queue depth > 10,000 → Scale up
- Queue depth < 1,000 → Scale down
- Worker utilization > 80% → Scale up
- Worker utilization < 30% → Scale down

Scaling Strategy:
- Scale up: Add 20% capacity
- Scale down: Remove 10% capacity
- Cooldown: 5 minutes
- Max workers: 10,000

Benefits:
- Linear scaling
- Handle traffic spikes
- Cost optimization

HTTP Connection Pool:
- Min connections: 10 per worker
- Max connections: 100 per worker
- Idle timeout: 60 seconds
- Connection reuse: 1000 requests

Benefits:
- Reduce connection overhead
- Faster request processing
- Lower latency
- Better throughput

Configuration:
http_client:
  connection_pool:
    max_connections: 100
    max_connections_per_host: 10
    idle_timeout: 60
    connection_lifetime: 300

Event Batching:
- Accumulate events for 100ms
- Batch size: 100 events
- Single webhook call with array
- Reduce overhead by 100x

Delivery Batching:
- Group deliveries by endpoint
- Send multiple events together
- Reduce network calls
- Higher throughput

Trade-off:
- Latency: +100ms
- Throughput: +100x
- Complexity: +Medium

Recommendation: Optional batching for high-volume webhooks

Multi-Level Cache:

L1 (In-Memory):
- Hot webhooks (top 1%)
- Size: 100 MB per dispatcher
- TTL: 60 seconds
- Hit rate: 80%

L2 (Redis):
- Warm webhooks (top 10%)
- Size: 10 GB cluster
- TTL: 300 seconds
- Hit rate: 95%

L3 (Database):
- All webhooks
- Size: 10 GB
- No TTL
- Hit rate: 100%

Cache Warming:
- Pre-load popular webhooks
- Refresh before expiration
- Predictive caching

Regions:
- US-East (Primary)
- US-West (Secondary)
- EU-West (Primary)
- Asia-Pacific (Primary)

Event Routing:
- Events published to nearest region
- Webhooks delivered from nearest region
- Cross-region replication for redundancy

Latency:
- Same region: <500ms
- Cross-region: <2 seconds
- Global: <5 seconds

Benefits:
- Lower latency
- Better reliability
- Compliance (data residency)

Event Replication:
- Async replication via Kafka MirrorMaker
- 100-500ms lag acceptable
- Eventual consistency

Webhook Configuration:
- Sync replication (strong consistency)
- <1 second propagation
- Raft consensus

Delivery Coordination:
- Regional delivery workers
- No cross-region coordination
- Independent operation

Round Robin:
- Simple, even distribution
- No state required

Least Connections:
- Route to least busy worker
- Better load distribution

Weighted Distribution:
- Larger workers get more load
- Proportional to capacity

Recommendation: Least connections for workers

Partition by Webhook ID:
- hash(webhook_id) % num_partitions
- Same webhook → same partition
- Ordered delivery per webhook
- Parallel processing across webhooks

Partition by Priority:
- High priority → Dedicated partitions
- Medium priority → Shared partitions
- Low priority → Best effort partitions

Benefits:
- Isolation
- Priority enforcement
- Scalability

Adaptive Backoff:
- Monitor endpoint health
- Adjust retry delays dynamically
- Faster recovery for healthy endpoints

Endpoint Health Tracking:
if endpoint.success_rate > 0.95:
    retry_delay = base_delay * 0.5  // Faster retry
elif endpoint.success_rate < 0.5:
    retry_delay = base_delay * 2    // Slower retry
else:
    retry_delay = base_delay

Circuit Breaker Integration:
- Open: Stop retrying for 5 minutes
- Half-Open: Try 10% of requests
- Closed: Normal operation

Benefits:
- Faster recovery
- Reduced load on failing endpoints
- Better resource utilization

Separate Queues by Delay:
- retry_queue_1s: Retry in 1 second
- retry_queue_2s: Retry in 2 seconds
- retry_queue_4s: Retry in 4 seconds
- retry_queue_8s: Retry in 8 seconds
- retry_queue_16s: Retry in 16 seconds

Benefits:
- Efficient scheduling
- No polling overhead
- Precise timing
- Scalable

Current (Year 1):
- 1M webhooks
- 100M events/day
- 2.5B deliveries/day
- $144K/month

Year 2 (2x growth):
- 2M webhooks
- 200M events/day
- 5B deliveries/day
- $288K/month

Year 3 (3x growth):
- 3M webhooks
- 300M events/day
- 7.5B deliveries/day
- $432K/month

Linear scaling with traffic

Per 1K deliveries/second:
- Workers: 10
- Dispatchers: 1
- Queue brokers: 1
- Database: 100 GB
- Cost: $14K/month

Breakdown:
- Workers: 85% ($11.9K)
- Dispatchers: 5% ($700)
- Queue: 5% ($700)
- Database: 5% ($700)

Delivery Metrics:
- Deliveries/second
- Success rate
- Response time (P50, P99)
- Retry rate
- DLQ size

Endpoint Metrics:
- Response time per endpoint
- Success rate per endpoint
- Circuit breaker state
- Rate limit hits

System Metrics:
- Queue depth
- Worker utilization
- Dispatcher lag
- Database performance

Scale Up Workers:
- Queue depth > 10,000 for 5 minutes
- Worker utilization > 80% for 5 minutes
- Delivery latency P99 > 5s
- Failed deliveries > 10%

Scale Down Workers:
- Queue depth < 1,000 for 15 minutes
- Worker utilization < 30% for 15 minutes
- All metrics healthy

Cooldown Periods:
- Scale up: 60 seconds
- Scale down: 300 seconds

Max/Min Limits:
- Min workers: 10
- Max workers: 10,000
- Scale increment: 20%

Over-Provisioned:
- 200 workers × $100 = $20K
- 40% utilization
- Wasted: $12K

Right-Sized:
- 120 workers × $100 = $12K
- 70% utilization
- Savings: $8K (40%)

Strategies:
- Monitor actual usage
- Scale down off-peak
- Use spot instances (70% savings)
- Reserved instances (40% savings)

Fast Endpoints (<1s):
- Use lightweight workers
- Higher concurrency
- Lower cost per delivery

Slow Endpoints (>5s):
- Use dedicated workers
- Lower concurrency
- Prevent blocking

Batch Deliveries:
- Group events by endpoint
- Single HTTP call
- Reduce cost by 10x

Hot/Warm/Cold Tiering:
- Hot (SSD): Last 7 days, $1K/month
- Warm (HDD): 8-90 days, $2K/month
- Cold (S3): >90 days, $500/month

Retention Policies:
- Delete successful deliveries after 30 days
- Keep failed deliveries for 90 days
- Archive to S3 after 90 days
- Compress historical data (10:1 ratio)

Savings: 80% storage cost reduction

1. Slow Endpoints:
   - Response time >5 seconds
   - Mitigation: Timeout, circuit breaker

2. Retry Storm:
   - Many webhooks retrying simultaneously
   - Mitigation: Jitter, rate limiting

3. Queue Throughput:
   - 100K events/s limit
   - Mitigation: More partitions, brokers

4. Database Writes:
   - 10K writes/s per shard
   - Mitigation: Batch writes, sharding

5. Network Bandwidth:
   - 10 Gbps per server
   - Mitigation: Compression, regional deployment

Slow Endpoints:
- Implement timeout (30s default)
- Use circuit breaker
- Dedicated worker pool
- Alert webhook owner

Retry Storm:
- Add jitter to retry delays
- Rate limit retries per endpoint
- Gradual retry ramp-up
- Monitor retry rate

Queue Bottleneck:
- Add Kafka partitions
- Add more brokers
- Increase replication factor
- Optimize consumer lag

Database Bottleneck:
- Batch writes (100 per batch)
- Add read replicas
- Shard by webhook_id
- Use time-series DB for metrics