How to Optimize Backend Processes for Improved Performance: 4 Examples
Imagine unlocking the secrets of backend optimization from those leading the charge in tech innovation. In this article, insights from a Tech Lead and a Head of Engineering reveal the strategies to transform backend processes for peak performance. Starting with optimizing databases using microservices and concluding with the importance of understanding the codebase, this post brings together four expert perspectives. Discover the key takeaways from these seasoned professionals and elevate your backend game.
- Optimize Database and Use Microservices
- Improve Data Retrieval Through Query Optimization
- Learn From Dead Ends and Adapt
- Understand Codebase and Avoid Assumptions
Optimize Database and Use Microservices
Hey,
I hope you are doing well. I'm writing to pitch an expert source for your upcoming article. I am Sourav Kar, Technical Team Leader at Wavel AI and an Oxford College alumnus. My expertise lies in driving innovation and efficiency in product development and management delivery while working collaboratively with cross-functional teams to drive project success.
One instance where I optimized a back-end process was for a fintech platform dealing with slow transaction speeds and high infrastructure costs. The main issues were too many database queries and inefficient data retrieval, which caused delays during peak times. To fix this, I started by analyzing the queries and found redundant joins and unindexed tables slowing things down. I introduced database partitioning, which split large datasets into smaller sections, speeding up queries. I also set up API rate-limiting to control sudden traffic spikes and added load-balancing to ensure no single server was overloading.
Next, I shifted to a microservices architecture, breaking down large processes into smaller, independent services. This allowed tasks like payment validation and logging to be handled separately, which reduced the overall processing time.
As a result, we improved transaction speeds by 60% and cut infrastructure costs by 30%. The key lesson was that combining efficient database design with modern architectural practices, like microservices, can significantly boost performance while controlling costs.
Best regards,
Sourav Kar
Driving Product Development, Wavel AI
[Sourav@wavel.co]
Improve Data Retrieval Through Query Optimization
One instance where I successfully optimized a back-end process was during a project at Software House that involved improving the performance of our application's data retrieval system. We noticed that users were experiencing slow response times when querying our database, especially during peak usage hours, which negatively impacted user experience and satisfaction.
To tackle this issue, I conducted a thorough analysis of the database queries and identified several inefficient SQL queries that were causing bottlenecks. I implemented query-optimization techniques, such as indexing frequently accessed columns, rewriting complex queries to reduce execution time, and employing caching mechanisms to store results of common queries. Additionally, I collaborated with our development team to refactor the back-end code, ensuring it efficiently interacted with the database.
As a result of these changes, we observed a significant reduction in data retrieval times, with improvements of up to 70%. The overall performance of the application improved, leading to enhanced user satisfaction and engagement.
The key takeaway from this experience was the importance of continuous monitoring and optimization of back-end processes. Performance issues can arise from a variety of factors, and proactive analysis can help identify and address them before they escalate. This experience reinforced my belief in the value of collaboration between teams and the need for ongoing evaluation of systems to ensure they meet the demands of users effectively. Regularly revisiting and optimizing back-end processes can lead to substantial improvements in performance and user experience.
Learn From Dead Ends and Adapt
As the sole back-end developer for my organization, at this point, most of my optimization is improving code I myself wrote!
Being on a small team, I constantly interface with our non-technical team members to make their lives easier. My company has been ramping up our analytics tools and data warehouse using a variety of tools. In tandem with our Head of Technology, I've been building things in a lot of different places—AWS, Snowflake, Sigma—and then trying to connect them. Analytics is always a work in progress, but we're starting to nail down some dashboards for everyone else on the team.
There are plenty of lessons to grab from this experience, but the biggest one is to be willing to learn from your dead ends. Sometimes goals, tools, and priorities shift from underneath you, and that's out of your control. Something you've sunk a lot of hours into might need to be scrapped or forgotten about. Frustration is understandable, but you've learned a new skill... or at the very least, something to NOT do next time. And that's something very valuable to take forward.
Understand Codebase and Avoid Assumptions
At a previous job, we matched students with college admission strategists and facilitated on-platform strategy meetings. We had a system for booking meetings between the two parties, and to prevent scheduling conflicts, we needed to pull in data from external calendar systems.
The background worker responsible for this created recurring scaling problems. A couple of years before I joined, an engineer had considered extracting it out as a dedicated microservice to minimize performance impacts on our main monolith, but this was operationally complex. The team instead opted to vertically scale the problem away, but we hit the limits of that approach.
After receiving multiple complaints from end users about system latency, I was tasked with finding a solution.
I looked at Postgres' statement statistics and found a significant proportion of our writes were affecting thousands of rows. This was curious because each write was only meant to update a single calendar event at a time.
I found that the original designer of this system assumed that all calendar event URLs were globally unique. Most of the time, this assumption is true—most calendar systems will prefix an event's URL with the URL or ID of the calendar it's on—but this wasn't true for Google Calendar's autogenerated calendars like "Birthdays" or "Public Holidays." Events on these calendars actually have the exact same URL for everyone.
This meant each loop of the calendar syncer would end up updating these events on every single calendar we tracked. The write amplification from this brought our database to its knees and negatively impacted user experience. Updating the query so that it was scoped based on both the event URL and the calendar ID significantly dropped CPU and disk IOPS pressure, and users immediately saw latency improvements.
To me, this underscored the importance of understanding your codebase deeply and not making assumptions lightly. A failure to understand how your external dependencies behave can have disastrous performance implications and turn users away from your product. This approach to engineering also saved us from taking on more complexity—the suggestion to split out a microservice years ago turned out to be excessive and completely unnecessary.
A lot of problems in backend development are like this. Before reaching for a more complicated architectural solution, make sure to dive deep and truly understand the shape of the problem.
