When developing APIs for services in a company with a large user base, scalability becomes a critical consideration. Scaling an API in production for such a company requires keeping certain fundamentals in mind. Today, I’ll discuss a few key points that you should also keep in mind when approaching this challenge. Here’s a breakdown of some key techniques and strategies you can use:

API Optimizations

  • Reduce Payload Size: Send only the necessary data in responses and avoid including unnecessary properties, as every byte contributes to network overhead, even if it’s just a single byte.

  • Response Compression: Enable compression techniques like Gzip or Brotli to reduce payload size and accelerate data transfer.

  • Pagination and Filtering: For large datasets, implement pagination, filtering, and sorting mechanisms to limit the amount of data sent in a single request.

  • Stateless Applications: Design application servers to be stateless, meaning they only process request-specific information and do not store or persist data locally. All state-related information, such as user sessions, should be managed in external systems like databases, caches (e.g., Redis), or session stores. This approach ensures that application servers can be easily scaled up or down during deployment to handle varying request loads, improving flexibility, fault tolerance, and scalability.

  • Concurrency: Leverage concurrency to optimize performance when handling multiple tasks simultaneously. For instance, if an API request requires data from two or more services, you can make those service calls concurrently instead of sequentially. This reduces the overall response time by executing tasks in parallel, making better use of system resources.

Database Optimizations

  • Indexing: Ensure that all frequently queried columns are properly indexed. This helps speed up SELECT queries by reducing the amount of data scanned. Avoid unnecessary indexing so that your write performance does not become bottleneck. Always explain the high RPS queries before putting in production server.

  • Query Optimization: Avoid complex JOINs and subqueries that slow down read and write operations. Also select the columns that are needed, avoid using * even if you have fewer or single column. Use EXPLAIN to analyze query performance and look for slow points.

  • Use Proper Data Types: Choose appropriate data types and sizes for database columns to minimize storage requirements and reduce overhead. For example, use TINYINT instead of INT for small numeric ranges, and VARCHAR with a defined maximum length instead of a generic TEXT column for variable-length strings. Avoid allocating unnecessary space by matching the data type to the actual range of values needed, which helps improve query performance and reduces memory and disk usage.

  • Avoid N+1 Queries: This issue occurs when a query fetches a list of items (1 query), and for each item, another query is executed (N queries). For example, fetching a list of users and then querying their associated orders individually leads to N+1 queries. To prevent this, use techniques like eager loading (e.g., JOIN statements or SELECT … IN queries) to retrieve related data in a single query. Avoiding N+1 queries reduces redundant database calls and significantly improves API response times.

  • Working with Active Data: Keep only active and frequently accessed data in your main database tables to maintain optimal query performance. Archive unnecessary or historical data to separate tables or storage systems to reduce table size and improve query efficiency. This practice helps minimize index and table scan times, ensuring faster reads and writes.

  • Precompute Data: Precompute data for complex or frequently used calculations and store the results in the database. This reduces the need for on-the-fly computations during queries, improving response times and lowering database load. Use techniques like materialized views, denormalized tables, or scheduled background jobs to prepare precomputed data, especially for reports, summaries, or analytics.

  • Connection Pooling: Manage and optimize database connections with a connection pool to handle high concurrency, avoiding excessive connection overhead. Close connections that are no longer in use, and tune timeout settings to manage idle connections efficiently.

  • Primary-Replica Setup: Use a primary-replica (master-slave) setup where the primary database handles writes, and replicas handle read traffic. Load balancers or application logic can direct read requests to replicas and write requests to the primary.

  • Read Replicas: Distribute read requests across multiple read replicas to scale horizontally and reduce read latency.

  • Partitioning: Partition tables based on certain fields (e.g., date, location) to split large tables into smaller, more manageable parts. This speeds up both reads and writes.

Caching Layers

  • In-Memory Cache: Use Redis or Memcached to cache frequent reads. This offloads load from the database for common queries and reduces latency.

  • Database Query Cache: For complex queries that cannot be cached at the application layer, some RDBMS systems offer built-in query caching.

  • HTTP Caching: For REST APIs, implement HTTP caching headers like ETag, Last-Modified, and Cache-Control to minimize repeated reads from the database.

Optimizing Writes

  • Batching and Bulk Operations: Instead of writing one row at a time, batch inserts, updates, or deletes into bulk operations. Many RDBMS systems optimize for bulk operations, which reduces transaction overhead.

  • Asynchronous Writes: Offload some non-critical writes (e.g., logging, analytics) to message queues like RabbitMQ or Kafka, and process them asynchronously.

  • Debouncing and Throttling: For high-frequency writes (e.g., metrics or real-time events), use debouncing or throttling to limit writes per second or group them before committing.

Horizontal Scaling

  • Sharding: Divide the data into smaller, more manageable databases (shards) based on some criteria (e.g., user ID). Sharding reduces the size of each database, helping maintain performance at scale.

  • Microservices with Independent Databases: If you’re using a microservices architecture, each service can have its database. This ensures that high read/write loads for one service do not affect others. Distribute incoming API requests across multiple servers using a load balancer to avoid overloading a single server and ensure high availability.

API Rate Limiting and Throttling

  • Gateway: Use an API gateway to centralize routing, authentication, rate limiting, and monitoring. Tools like Kong, AWS API Gateway, or Traefik can help manage APIs effectively.

  • Rate Limiting: Set limits on API requests to prevent abuse and reduce excessive database loads. Implement IP-based rate limiting or user-based quotas.

  • Backpressure Mechanism: For high-traffic APIs, implement backpressure strategies (like retry with exponential backoff) to prevent overloading your database.

Monitoring and Tuning

  • Database Monitoring: Use monitoring tools (e.g., Prometheus, Grafana, or APM tools like Datadog) to track query performance, slow queries, CPU, memory usage, and I/O operations.

  • Load Testing: Perform load and stress testing to identify bottlenecks and tune settings before the system goes live. Tools like Apache JMeter, and k6 can be helpful.

  • Tracing Tools: Use tools like OpenTelemetry (Otel) for API tracing to identify bottlenecks and performance issues in your APIs. Tracing helps you monitor and analyze the flow of requests across services, pinpointing delays or errors caused by downstream or upstream dependencies, such as network latency, service timeouts, or database query slowness. By integrating tracing into your system, you can gain valuable insights into your API’s behavior and resolve issues efficiently.

By following these steps, you can effectively manage large read and write operations in your REST API with an RDBMS, ensuring that it scales to meet demand while maintaining performance.