Speed for a website is synonymous with competitive ability. Websites with faster load times are prioritized by search engines, thus bringing more traffic, meaning more revenue. In fact, if your website loading time is between 1 and 2 seconds, the conversion rate will be around 3x higher, according to a Portent study. Scalability is all about handling traffic surges, be it due to a sale, viral content, successful marketing efforts, and so on. If your website slows down during this time, it will be a huge financial loss. So, while we’ll look at fixing the technical side of speed and scalability, this directly translates into business metrics and financial success.
When we think about the behind-the-scenes of the smooth and fast user experience, backend development services encompass a wide range of functions. They are:
- user authorization & authentication,
- database management,
- various APIs /endpoints/microservices,
- server optimization,
- codebase efficiency,
- security & logging,
- DevOps practices, and
- caching strategies.
Each of these points is often critical for website operations and directly impacts speed and scalability.
Backend Optimization Strategies: Tips for Enhancing Performance and Efficiency
In terms of definition, your website speed is how fast users can see the results of client-side requests, be it requesting some data, logging in a user, or sending some user input, such as a search filter to receive a response. Website speed is often calculated as the average loading of several different pages, while scale means measuring speed at different loads. For instance, below you can see the results of measuring the speed of retrieving data from different databases based on the number of key-value pairs (scale). Similarly, a backend web development company would conduct speed testing for functional code blocks, requests for large content pieces, submitting data, and so on.
However, at a more abstract level, a website’s speed and scalability depend on the choice of technology stack, patterns, and coding practices.
Tip 1
Match the use case with the selected language or adapt the existing architecture to optimize for the use case. For instance, most startups opt for a common web app stack with NodeJS for the server-side. NodeJS is great for real-time functionality, like real-time collaboration tools or messaging. However, imagine you want to start tracking user actions such as clicks, time spent on a particular product page, and trending items, and then process it to quickly offer personalized recommendations on an e-commerce app. At a small scale, NodeJS can handle it; however, at a larger scale, you should add a microservice written in a different language, such as Python, Rust, or Go.
Tip 2
Make sure that querying tools are used strategically. Some developers often opt for ORMs and ODMs simply because they make their code more readable, enhance reliability, and aid in creating queries. However, it does introduce an intermediary between the backend and the database. Therefore, it increases the processing speed. When your app is gaining momentum and you choose to introduce microservices in your architecture, it might be best to open direct interaction between a separate microservice and a database.
Tip 3
Choose use-case appropriate frameworks. For instance, the most common framework for Node.js is Express. It supports a variety of use cases. However, there is also Fastify, and in certain scenarios it outperforms Express by 5-6x times. For example, one developer published results of testing a simple text-based scenario, and the results for Express were 20,309 requests/second while Fastify managed 114,195 requests/second. Fastify appeared to be 5.6x times faster.
Maximizing Website Performance: The Power of Caching Mechanisms
These types of caching are essential to back-end development services for your website’s speed and scalability:
- Edge caching with CDN;
- In-memory caching for authentication tokens, product catalogs, flash sales, and so on.
Here, a backend web development company should not only use best-matching tools, but also develop policies for:
- Which data to cache,
- Duration of caching,
- Cache invalidation policies.
How Dedicated Backend Developers Improve Website Performance with Optimized Database Queries
Data is at the core of any current web or mobile application, and it is only going to grow, largely for personalization purposes. With SQL and NoSQL databases, the initial step of ensuring high speed and performance is to ensure fast query processing, whether it is read, update, write, or delete operations. Depending on requirements for data integrity and scale, the initial choice of the database will determine crucial performance indicators. Generally, for startups, a NoSQL database such as MongoDB is preferred for its document-oriented structure, which can flexibly change and service fast read/write operations.
Regardless of the type of database, one way a backend development company can improve speed lies in the indexing of frequent queries. It helps to immensely speed up database querying operations that include joining, filtering, and sorting operations. For instance, here is a dev sharing the experience of introducing indexing for a db of a healthcare app: “The queries that were taking seconds are now down to .2 MS. Some of the queries experienced a 2,000% increase in speed…. Insertion barely took a hit — nothing noticeable at all.” The latter is essential to consider: indexing speeds up one kind of operation but bears costs for others. Any operations that insert or delete records will lead to reindexing. Therefore, custom backend development always considers the unique needs of each app to strategically index the database column to gain the most speed boost, but remain scalable for other operations.
More strategies employed by a backend app development company to ensure the best speed and scalability are:
- Optimization to avoid subqueries in favor of joins: due to technical implementations and, especially, newer versions of databases, joins can be done much faster through indexing and hashes, while subqueries might run for every outer query call and require storing intermediary results on disk.
- Operations batching: Instead of updating or creating each record on its own, consider grouping them in batches. This also helps to avoid the N+1 Query problem, which sends multiple queries one by one in a loop.
- Sharding or partitioning, with the first one for NoSQL and the other for SQL databases. The essence of it lies in breaking down large databases into portions so that they can be stored across multiple servers and accessed faster through subsets.
