Performance & Scaling

Learn how Mesrai achieves sub-second response times and scales to handle thousands of concurrent reviews.

Performance Overview

Mesrai delivers exceptional performance:

• Review Speed: <5 seconds for standard PRs
• Throughput: 10,000+ reviews/hour
• Availability: 99.9% uptime SLA
• Latency: Sub-5ms API responses

Architecture

Edge Network

Global CDN distribution:

User Request
    ↓
Cloudflare Edge (100+ locations)
    ↓
Nearest Regional Cluster
    ↓
Mesrai Workers

Benefits:

• Reduced latency by 60-80%
• Automatic failover
• DDoS protection

Worker Architecture

Distributed processing model:

GitHub Webhook
    ↓
Load Balancer
    ↓
Worker Pool (100+ workers)
    ├─ Code Analysis Workers
    ├─ LLM Request Workers
    └─ Post-Processing Workers

Horizontal Scaling:

• Auto-scales based on load
• Handles 10x traffic spikes
• Zero-downtime deployments

Caching Strategy

Multi-layer caching:

L1: CDN Edge Cache (100ms TTL)
    ↓
L2: Redis Cluster (1hr TTL)
    ↓
L3: Database Cache (24hr TTL)

Cache Hit Rates:

• Static assets: 99%
• API responses: 85%
• Code analysis: 70%

Review Performance

Processing Pipeline

Optimized review stages:

Optimization Techniques

1. Parallel Processing

Multiple workers process simultaneously:

await Promise.all([
  fetchCodeChanges(),
  buildCallGraph(),
  analyzeDependencies(),
  loadContextFiles()
]);

Speed Improvement: 3x faster

2. Incremental Analysis

Only analyze changed code:

• First Review: Full codebase scan (10s)
• Subsequent Reviews: Delta only (3s)

Token Savings: 60-80%

3. Smart Context Selection

Prioritized file loading:

High Priority (always loaded):
  - Changed files
  - Direct imports
  - Test files

Medium Priority (if time permits):
  - Related utilities
  - Type definitions

Low Priority (cached or skipped):
  - Vendor code
  - Documentation

4. Streaming Responses

Stream results as they’re ready:

1. Security issues → Post immediately
2. Critical bugs → Post next
3. Suggestions → Post last

User Experience: See results 2x faster

Scalability

Auto-Scaling

Dynamic resource allocation:

scaling:
  min_workers: 10
  max_workers: 500
 
  scale_up:
    metric: queue_depth
    threshold: 100
    increment: 50
 
  scale_down:
    metric: idle_time
    threshold: 300s
    decrement: 10

Result: Handle traffic spikes without manual intervention

Load Balancing

Intelligent request distribution:

• Round Robin: Even distribution
• Least Connections: Avoid overload
• Geographic: Route to nearest region
• Health Checks: Skip unhealthy workers

Database Scaling

Read Replicas

Multiple read replicas for queries:

Primary (Write)
  ├─ Replica 1 (Read - US East)
  ├─ Replica 2 (Read - US West)
  ├─ Replica 3 (Read - EU)
  └─ Replica 4 (Read - Asia)

Query Performance: 4x faster reads

Sharding

Data partitioned by organization:

Shard 1: Orgs 1-1000
Shard 2: Orgs 1001-2000
Shard 3: Orgs 2001-3000
...

Scalability: Support millions of organizations

Queue Management

Async job processing:

Priority Queues:
  1. Critical (security issues)
  2. High (active PRs)
  3. Normal (standard reviews)
  4. Low (background tasks)

Fair Processing: No queue starvation

Performance Monitoring

Metrics Dashboard

Real-time performance tracking:

Review Metrics:
  - P50 latency: 3.2s
  - P95 latency: 6.1s
  - P99 latency: 12.4s
  - Success rate: 99.8%

System Metrics:
  - CPU usage: 45%
  - Memory usage: 62%
  - Queue depth: 23
  - Active workers: 87

Alerting

Automated alerts for issues:

alerts:
  - name: High Latency
    condition: p95 > 10s
    action: Scale up workers
 
  - name: Error Rate
    condition: errors > 1%
    action: Notify on-call
 
  - name: Queue Backup
    condition: queue_depth > 500
    action: Emergency scale

Optimization Best Practices

1. Reduce PR Size

Smaller PRs = faster reviews:

• Optimal: 50-200 lines changed
• Good: 200-500 lines
• Acceptable: 500-1000 lines
• Slow: 1000+ lines

Recommendation: Split large PRs into smaller chunks

2. Optimize Context

Control what gets analyzed:

# .mesrai.yml
context:
  max_files: 30
  max_file_size: 100000
  exclude:
    - "dist/**"
    - "node_modules/**"

3. Use Quick Reviews

For minor changes:

@mesrai review --quick

Speed: 2-3x faster than standard reviews

4. Enable Caching

Let Mesrai cache common patterns:

caching:
  enabled: true
  ttl: 3600

Performance Gain: 40-60% faster repeat reviews

5. Parallel PRs

Don’t wait for reviews to complete:

• Open multiple PRs simultaneously
• Mesrai processes in parallel
• No queue blocking

Regional Performance

Data Centers

Global presence for low latency:

Geographic Routing

Automatic routing to nearest region:

User in California
  → US West (Oregon)
  → 52ms latency

User in London
  → Europe (Frankfurt)
  → 38ms latency

Load Testing Results

Stress Test

Recent load test results:

Duration: 1 hour
Concurrent Users: 10,000
Total Reviews: 250,000

Results:
  - Average latency: 3.8s
  - Max latency: 11.2s
  - Success rate: 99.95%
  - Throughput: 4,167 reviews/min

Spike Test

Handling sudden traffic:

Normal Load: 100 reviews/min
Spike: 2,000 reviews/min (20x increase)

Auto-scaling response:
  - Detection time: 15s
  - Scale-up time: 45s
  - Recovery: Full capacity in 60s

Performance Comparison

Vs. Manual Review

Vs. Competitors

Troubleshooting

Slow Reviews

Symptoms: Reviews taking >10 seconds

Causes:

• Large PR size (1000+ lines)
• Many context files
• Deep dependency tree
• Rate limiting

Solutions:

1. Split large PRs
2. Reduce max_files in config
3. Use --quick mode
4. Contact support if persists

Timeout Errors

Symptoms: “Review timed out” error

Causes:

• Extremely large PR
• Complex codebase
• Network issues

Solutions:

1. Retry review
2. Use @mesrai review --quick
3. Exclude large files
4. Check Status Page

Queue Delays

Symptoms: Review pending for >5 minutes

Causes:

• High system load
• Many concurrent reviews
• Regional outage

Solutions:

1. Check status page
2. Wait for auto-scaling
3. Retry after 5 minutes
4. Contact support if urgent

Future Optimizations

Planned improvements:

Q1 2025

• Streaming Reviews: Real-time results
• Edge Computing: 50% latency reduction
• Smart Caching: 80% cache hit rate

Q2 2025

• Multi-Model: Multiple LLMs in parallel
• Predictive Scaling: ML-based auto-scaling
• WebSockets: Real-time updates

Q3 2025

• Local Processing: On-premise option
• Offline Mode: Cache-based reviews
• Custom Models: BYOM support

API Performance

Rate Limits

Generous API limits:

Response Times

Typical API latencies:

GET /api/v1/reviews      → <5ms
POST /api/v1/review      → 3-6s
GET /api/v1/usage        → <10ms
POST /api/v1/webhook     → <50ms

SLA & Guarantees

Uptime SLA

Service level agreement:

• Standard: 99.5% uptime
• Pro: 99.9% uptime
• Enterprise: 99.99% uptime

Performance SLA

Response time guarantees:

• P95 Latency: <10 seconds
• P99 Latency: <20 seconds
• API Response: <100ms

Credits

SLA violations result in service credits:

Uptime < 99.9%: 10% credit
Uptime < 99.5%: 25% credit
Uptime < 99.0%: 50% credit

Monitoring & Status

• Status Page
• Performance Dashboard
• Incident History

Next Steps

• View Performance Dashboard
• Read Architecture Overview
• Configure Optimization
• Contact Performance Team