How I Optimized REST APIs by 40% Using Advanced Techniques
From a Propeller Plane to a Supersonic Jet: How I Turbocharged My REST APIs for Lightning-Fast Performance
One of the most rewarding challenges I’ve tackled recently was optimizing a REST API to improve its performance by over 40%. By implementing asynchronous programming, optimizing database queries, and refining algorithms, the API transformed from a sluggish bottleneck to a high-performing machine. Here’s how I did it—and some tips for those facing similar challenges.
1. Async Programming: Unlocking Concurrency
When your API is handling high traffic, synchronous operations can severely limit performance, especially when dealing with database queries, file handling, or external API calls. To address this, I switched to async programming using Python’s asyncio and FastAPI. This allowed the API to process multiple requests concurrently.
For instance, instead of having the API wait for one database call to complete before handling the next, multiple requests were handled in parallel. The result? An instant boost in response times—requests were processed almost 50% faster.
But async programming doesn’t just end with the API code. I ensured that my database connections were asynchronous too. Libraries like asyncpg for PostgreSQL helped further reduce query latency. With async in place, not only was I able to handle more requests, but I also slashed response times during peak loads.
2. Database Optimization: Speeding Up Queries
You can write the most efficient API code, but if your database isn’t optimized, you’ll still face performance bottlenecks. One of my key optimizations was reviewing the indexing strategy. In some cases, I found queries running full table scans due to missing or poorly structured indexes. After auditing the query patterns and adding the right indexes—particularly composite indexes—query performance improved by 30%.
Beyond indexing, I also streamlined database operations by batching queries. Instead of hitting the database with multiple small queries, I grouped them into fewer, larger ones, reducing round-trip times. I combined this with connection pooling, ensuring that database connections were reused efficiently without the overhead of constantly opening new connections.
3. Algorithmic Improvements: Smarter and Faster
Optimization isn’t just about code or databases—it’s also about how efficiently your algorithms process data. In one scenario, a piece of business logic was taking too long to execute due to an O(n²) complexity. By refactoring the logic and using more efficient data structures, like heaps and balanced trees, I reduced the complexity to O(n log n), drastically improving the performance for larger datasets.
Another major win came from implementing caching. For frequently accessed but rarely updated data, I integrated Redis as a caching layer. This reduced the number of database queries, cutting response times by 60% in some cases.
4. API Design Best Practices: Batch Requests & Compression
One lesser-known technique I used was batching requests. Instead of sending multiple API calls, I grouped related calls into a single batch request. This significantly reduced network latency, leading to faster response times and less load on the server.
I also enabled GZIP compression for JSON responses, which lowered payload sizes and improved response times, particularly for APIs that return large data sets. By compressing the data before sending it over the network, I was able to shave off valuable milliseconds.
5. Monitoring, Testing, and Continuous Iteration
Even the best optimization efforts are useless if they can’t be measured. I set up real-time monitoring using Grafana and Prometheus, which allowed me to track performance improvements over time and identify any new bottlenecks that emerged. This data was crucial in making iterative adjustments, and it ensured that the API stayed optimized as the system grew.
Load testing was another key part of the process. Tools like Locust helped simulate high traffic environments, ensuring the API could handle the increased load while maintaining its new performance levels. Finally, I integrated PyTest into the CI/CD pipeline to run automated tests with each deployment, making sure that any new features or updates didn’t compromise the API’s performance.
In Summary
The combination of async programming, database optimization, smarter algorithms, and thoughtful API design resulted in a 40% improvement in performance. It’s an ongoing journey—optimizing isn’t a one-time job—but with the right tools and techniques, you can achieve substantial gains that will keep your systems performing at their peak.
Whether you’re an engineer looking to improve your API’s performance or a recruiter curious about backend optimization, feel free to connect! Let’s talk about how to make your systems more efficient, scalable, and ready for growth.