Speed Matters: A Deep Dive into API Optimization

In Today’s world, time isn’t just money anymore; it’s a luxury. Everything is sold with one promise: “It’s faster.” Think about it: flights over trains, watching movies in theaters instead of waiting for OTT releases, or buying YouTube Premium to skip ads. We’re all chasing speed. A few years ago, deliveries took days. Then came same-day deliveries. Now, with the rise of quick commerce, we’ve moved to deliveries in mere minutes. And guess what? Nobody’s complaining about things being too fast.

From a software perspective, the same rules apply. Even a few milliseconds of delay in your page’s loading time can drastically impact the customer experience. Everything must be optimized for a seamless user journey. While there are countless ways to optimize software for speed, this blog will focus on APIs.

What are APIs?

API stands for Application Programming Interface. Simply put, it’s the interface between your application (client-side) and your program (server-side).

Let’s break it down with an analogy: Imagine you’re at a toy store. You ask the shopkeeper to show you toys for your niece’s sixth birthday. The shopkeeper takes a moment to think, finds toys suitable for a six-year-old, and shows them to you. Here, the toy store’s inventory is like a database, your request is the client-side query, and the shopkeeper processes and returns the results. That’s essentially what an API does.

Now that we know what APIs are, let’s discuss why their execution speed is crucial.

Why Execution Speed Is a Game-Changer

Imagine you’re running late for a party. You ask the shopkeeper for a gift, but they take a few minutes to show you the options. Then, when you refine your request (“Something football-related”), it takes them even longer. This delay hampers your shopping experience.

Similarly, imagine a user visiting your website to search for a specific medicine. A slight delay in showing results might make them abandon your app and switch to a competitor. The situation worsens if the user has a slow or limited internet connection. Poor performance impacts the user experience and, ultimately, your conversion rates.

Execution speed matters. Now that we’ve identified the problem, let’s talk about the solutions.

Mastering the Art of Speed Optimization

To optimize for speed, you must first identify areas of improvement. Analyze the entire timeline of a request—from when the user clicks the search button to when the application displays the results. Let’s explore some scenarios and their solutions.

Load Balancing During High Traffic Scenarios

Sometimes, occasional traffic spikes can degrade performance. Imagine a toy store used to handling five customers suddenly gets 100 during a festive sale. As the shop owner, you’d hire more shopkeepers to handle the crowd. Similarly, you can horizontally scale your application by increasing the number of servers and using tools like Nginx to distribute traffic evenly. This ensures consistent user experience even during traffic surges.

Caching Responses for Faster Access

What if the delay wasn’t due to a lack of shopkeepers but the inefficiency in how they fetched toys? To address this, the shopkeeper could prepare a list of the most commonly requested toys, enabling them to respond faster to customer requests. Similarly, in the world of APIs, caching serves as this quick-access list. By storing frequently requested responses temporarily, caching avoids the need to recompute or refetch data each time. For instance, if you’re fetching static information, like medicine details that don’t change often, caching can significantly reduce response times. When updates occur, the cache can be purged to ensure the data remains accurate and relevant.

Optimizing Computationally Expensive DB queries

Now imagine you’ve picked a big football kit but want it gift-wrapped in a specific paper. You are also looking to pay via credit card because you forgot to carry enough cash and your phone’s battery is dead. The shopkeeper needs to check with the packaging team for availability and the accounting team for payment options. This back-and-forth increases wait time. What if instead of asking multiple teams, the shopkeeper had limited info about whether or not a specific toy can be packed, acceptable payment modes for a gift without requiring to check with the respective teams?

One way to do this is to start storing this information along with the toy with the help of an additional note on top of it. Or you can just divide concerns. the shopkeeper will only be concerned with helping your customer select the gifts. for packaging/payment the customer may go to respective counters.

In database terms, this means reducing joins by denormalizing data wherever possible. For instance, instead of repeatedly querying a “cold-storage items” table to check if an order is temperature-sensitive, you could add a “cold-order” flag directly to the orders table. Reducing unnecessary joins significantly improves query performance. Unoptimized queries are a bottlenecks in the performance of many APIs. It is a complicated problem to solve considering the simplicity of fetching from multiple tables instead of identifying what improvements can be made to the current database schema.

The Magical Wand of Indexing

A disorganized toy warehouse makes finding specific toys time-consuming. But if the toys are sorted by age group (e.g., Shelf A for ages 1-2, Shelf B for ages 2-3), the shopkeeper’s job becomes easier. This is indexing in databases. Indexing improves query execution time by narrowing the search scope. However, it comes with a trade-off: while read operations become faster, write operations may slow down since data must be correctly placed in the index.

Indexes are especially useful for read-heavy databases but may not be ideal for write-heavy systems. Understanding when and how to use indexing is crucial for optimizing API performance.

Identifying the Right Metrics

So far we have talked about our problems and identifying our solution. But it is also important to analyze whether or not our solutions actually work. There are multiple metrics to measure the performance of an API. Some of them are as follows:

1. Average Duration Time: The mean time to process a request.

2. P50 Latency: The time it takes to process 50% of requests.

3. P99 Latency: The time within which 99% of requests are processed.

4. Min/Max Latency: The shortest and longest processing times.

Monitoring these metrics helps ensure our optimizations deliver the desired results.

Conclusion

To wrap up, we’ve covered what APIs are, why their execution speed matters, and some practical ways to improve it. Remember, building efficient systems is only half the job; the other half is continuously monitoring and optimizing them.

I hope you found this blog insightful and engaging. For those wondering about the writing style—no, I didn’t ask an AI to “rewrite this like a five-year-old.” My goal is to simplify technical concepts with relatable analogies. Thanks for reading. See you next time!