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How to scale the system for Black Friday Traffic

5 min read

Scaling for a massive, predictable traffic spike like Black Friday requires a multi-layered strategy. It’s not just about adding more servers; it’s about making the entire system more efficient and resilient, especially for a read-heavy service like an e-commerce platform. Here’s a comprehensive approach:

The Core Principle: Optimize for Read Operations

First, recognize the traffic pattern:

  • 99%+ Read Operations (GET): Users will be browsing, searching, and viewing products.
  • <1% Write Operations (POST, PUT): Only a few admin users might be updating product details, and this should be discouraged or frozen during peak hours.

This read-heavy nature means our primary goal is to serve read requests as fast as possible without ever touching the primary database.

Strategy 1: Aggressive and Multi-layered Caching

  1. CDN Caching: Use a Content Delivery Network (CDN) to cache static assets (images, CSS, JS) and even dynamic content where possible. CDNs can offload a significant portion of traffic from your origin servers. (TTL: 1-5 minutes for dynamic content, longer for static assets))
  2. Application-level Caching: Implement caching at the application layer using tools like Redis or Memcached. Cache frequently accessed data such as product listings, categories, and user sessions. (TTL: 30 seconds to 5 minutes))
  3. Database Query Caching: Use database-level caching for read-heavy queries. Many databases support query caching natively, or you can use an external caching layer.
  4. Edge Caching: If using a microservices architecture, consider edge caching for services that serve read-heavy data.

How it works: When a user requests a product page, the system first checks the CDN. If the content is not there, it checks the application cache. If it’s still not found, it queries the database. This layered approach ensures that most requests are served from the fastest possible source.

Strategy 2: Scale the Database Intelligently

Database will be the main bottleneck. You cannot simply increase the size of the database server indefinitely(vertical scaling); instead, you need to scale it out and optimize it for read operations.

  1. Use Read Replicas: This is the most critical database scaling technique for this workload.
    • Set up one or more read-only copies (replicas) of your primary database.
    • Configure your application’s data access layer to direct all SELECT queries (reads) to the read replicas.
    • Direct all INSERT, UPDATE, DELETE queries (writes) to the single primary database.
    • This isolates the heavy read traffic from the write database, preventing slowdowns.
  2. Optimize Queries and Indexes:
    • Before the event, analyze your most frequent queries using a tool like EXPLAIN.
    • Ensure all columns used in WHERE clauses, JOINs, and ORDER BY clauses are properly indexed. A missing index under heavy load can bring the entire database to its knees.
    • Consider denormalization for read-heavy tables to reduce the need for complex joins.
  3. Database Sharding: If the dataset is massive, consider sharding the database. This involves splitting the database into smaller, more manageable pieces (shards) that can be distributed across multiple servers.
  4. Connection Pooling: Use connection pooling to manage database connections efficiently. This reduces the overhead of establishing connections and helps maintain performance under load.
  5. Use a High-performance Database: Consider using databases optimized for read-heavy workloads, such as NoSQL databases (e.g., Cassandra, MongoDB) or NewSQL databases that can handle high throughput.
  6. Read Consistency: Understand the consistency model of your read replicas. Some databases offer eventual consistency, which might be acceptable for product listings but not for inventory counts.

Strategy 3: Scale the Application Layer Horizontally

Your API code needs to be able to handle thousands of concurrent requests.

  1. Horizontal Scaling (Autoscaling):
    • Instead of one giant server (vertical), run your application on multiple smaller servers/containers (horizontal).
    • Place them behind a Load Balancer (like NGINX, AWS ALB) that distributes incoming traffic evenly across all instances.
    • Configure an Autoscaling Group in your cloud provider. Set rules to automatically add more application instances when CPU utilization or request count goes above a threshold (e.g., 70%), and remove them when traffic subsides.
  2. Stateless Application Design: Ensure your application is stateless, meaning any instance can handle any request without relying on local session data. Use distributed caches or databases for session management.
  3. Optimize Application Performance: - Profile your application to identify bottlenecks. - Optimize code paths, database access patterns, and third-party API calls. - Use asynchronous processing for non-critical tasks (e.g., sending emails, logging).

Strategy 4: Pre-emptive and Operational Readiness

What you do before Black Friday is as important as the technology itself.

  1. Load Testing: Simulate Black Friday traffic using tools like JMeter, Locust, or Gatling. Identify bottlenecks and optimize accordingly. Start with expected traffic and gradually increase to 2-3x that amount.
  2. Database Maintenance: - Run database maintenance tasks (e.g., vacuuming, reindexing) well before the event. - Ensure backups are up-to-date and tested.
  3. Warm the Cache:
    • Preload caches with popular product data before the event starts to avoid cache misses during peak traffic.
  4. Freeze Non-essential Changes: Implement a code freeze period before Black Friday to prevent last-minute changes that could introduce bugs or performance issues.
  5. Monitoring and Alerting: - Set up comprehensive monitoring (using tools like Prometheus, Grafana, Datadog) to track application performance, database health, cache hit rates, and server load. - Configure alerts for critical metrics so your team can respond quickly to any issues. - API: Request latency (p95, p99), error rate (5xx errors). - Database: CPU utilization, number of active connections, slow query logs. - Cache: Hit/miss ratio. A low hit ratio is a major red flag.
  6. Feature Flags: Use feature flags to quickly disable non-essential features if they start causing issues under load.

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