How We Scaled Our Backend with Read Replicas | Guide

As full stack developers, we often get so caught up in adding new features that we forget to check whether our backend can actually scale when traffic increases. Building scalable backend systems is a must to ensure that the applications remain functional and reliable with user growth.  

We ran into that exact situation in one of our projects recently. There are quite a few tricks up our sleeves to fix this problem, but Redis is usually our first choice for scaling backends and databases. 

However, this time proved to be different. Redis didn’t really cut the job.  Our team had already integrated Redis for performance, but at a certain point it wasn’t enough. 

So, in this blog, we want to share how our developers improvised and tackled read performance issues in our app with read replicas. It will explain where Redis fell short, and how read replicas made the difference.   

The Problem: Redis wasn’t fast enough 

At first, Redis felt like magic. We cached user data, transactions, and dashboard stats to improve scalability in our full stack application. The APIs responded in milliseconds. The load on SQL Server dropped. Everyone was happy. But then a few cracks started to appear: 

• We had to manually keep Redis updated 
• Some Redis keys returned stale data 
• When filters or dynamic queries were needed, Redis couldn’t handle them 
• For certain views, Redis was not worth caching with too many combinations 

It became obvious: Redis was great for speed, but it wasn’t built for complex or dynamic queries. And since Redis didn’t sync automatically with SQL Server, it added operational overhead. 

Plan B: Database replication 

Most of the projects our development team has worked on so far involved short-term scaling, like handling sudden traffic spikes. But this time, we needed the data to be consistent and durable across multiple domains for long-term scaling.  

After some deliberation, we changed course and picked database replication as our plan B. Database replication creates multiple copies of the database. However, these copies are used to handle the read-only operations, while the primary database handles the write operations. Hence, the name read replicas.  

Read replicas scale applications easily since you can add as many replicas as your traffic grows. This improves performance because more servers can handle read traffic, which reduces response time.  

Twitter, or now known as X, uses read replicas to handle user feeds. New tweets from users are written to the primary database, while fetching feeds, which is a read operation, are done through replicas. 

The turning point: Adding read replicas for consistent, complex Reads 

To solve the gap that Redis couldn’t fill, we started using read replicas of our SQL Server database. The change was simple as we kept our primary DB for writes and routed all read-heavy API traffic, especially filtered and paginated data, to a read-only replica. No caching. No manual sync. Just clean, fresh data at scale. 

Our team configured the app to: 

• Use Redis for fast, fixed patterns (like transactions:userId) 
• Use Read Replicas for anything dynamic (like filters, searches, ranges) 
• Fallback to the primary DB if replica wasn’t available 

This hybrid setup gave us both speed and accuracy without compromising on developer effort. 

Real-world application: Solving slow filters on the dashboard 

So far, we haven’t talked about the project that led us to change our course. We had a dashboard showing user transactions with filters for date, amount, and type. Such dashboards are widely used in FinTech, e-commerce stores, and ERP systems. 

Now, let’s see what the problems with Redis were in creating this dashboard.  

Cache explosion 

When users applied filters like date, amount, or type, each unique combination of those filters produced a different result. For example: 

Transactions from, or over $500, or both January and over $500 

Each of those results needed its own cache entry in Redis. So instead of one general cache, we had to create and store hundreds of separate keys. One for every possible filter combination. The more filters users had, the more cache keys we needed to manage. 

Messy cache management 

Whenever the underlying data changed, like when a new transaction was added or an existing one was updated, we had to find and delete all the related cache entries in Redis, so the dashboard wouldn’t show outdated information. 

But since there were hundreds of cache keys, figuring out which ones to remove and when became tricky. Sometimes we cleared too many and lost useful cached data; other times, we missed some, and users saw stale results. Over time, this invalidation and refreshing process became hard to maintain and easy to break. Over time, this invalidation and refreshing process became hard to maintain — a reminder of how crucial effective data management tools are for keeping systems organized, consistent, and scalable.

Filter changes 

Redis caching only helped when users applied the exact same filters that were already stored in the cache. But if they changed the filter even a little, that specific result didn’t exist in Redis. Since Redis couldn’t “adapt” or combine cached data for new filter combinations, it became useless in those cases. 

That meant the system still had to query the main database to get fresh results, which defeated the purpose of caching and limited the performance benefit Redis was supposed to provide. 

How read replicas proved to be life savers 

Let’s see how read replicas solved each of these bottlenecks. 

1. We just passed filters in the SQL query 

With read replicas, we didn’t have to pre-cache every possible combination of filters. Instead, we simply ran our SQL queries, including any filters, directly on the replica database. Because replicas were designed to handle read-heavy workloads, they processed those queries efficiently without slowing down the main system.  

This meant no more managing hundreds of Redis keys or worrying about what’s cached and what isn’t. 

2. No extra caching or invalidation 

The entire process of constant cache refreshes disappeared with read replicas. The replica database automatically stayed in sync with the primary one, so the data we read was always up to date, sometimes within seconds depending on replication lag. We also didn’t need custom scripts or complex cache invalidation logic. The replication mechanism took care of keeping data current. This simplicity made maintenance easier and ensured our results were always consistent with the latest data. 

3. Consistent results for recent data 

Because read replicas were synced from the primary database, every transaction or update eventually appeared on the replica automatically. This ensured users always saw nearly real-time information, even for the most recent activity.  

Unlike Redis, which showed stale data until manually refreshed, replicas continuously updated in the background. As a result, our dashboard data remained reliable and accurate without extra engineering effort to keep it that way. 

4. No overloading of primary DB 

One of the biggest advantages of read replicas was performance offloading. Our primary database handled all the writes, while replicas handled the reads. That meant dashboards, data analytics, and user queries ran on replicas instead of competing with business-critical write operations.  

This separation prevented slowdowns, reduced query contention, and kept the primary database healthy even under heavy traffic. We could also add more replicas as our user base grew, scaling read capacity without touching our main database. 

Overall, this greatly improved our team’s productivity. Streamlining workflows and reducing manual coding helped us focus more on building features rather than fixing infrastructure — a mindset that also aligns with how no-code and low-code platforms work. To give you an idea, it used to take us around 2 days to debug the above-mentioned problems in Redis. But it took us just two hours to configure read replicas that solved all these challenges.

Lessons learned and best practices 

Continuous professional development (CDP) is the hallmark of any top professional, whether in programming or any other trade in life. This project was a great learning experience for us. And these were the most illuminating lessons we gained from it.  

1. Redis isn’t always the right tool 

If your data changes often, or your reads involve filtering, joining, or pagination, Redis is too rigid. You’ll end up building your own syncing system, which is fragile at scale. 

2. Read replicas are great for consistency 

They stay synced automatically and are perfect for real-time dashboards or search features where caching doesn’t help. 

3. Know when to combine both 

We still use Redis for hot data that doesn’t change often, like dropdown options, user profiles, or most-viewed items. But for anything user-specific or filterable, replicas make more sense. 

4. Test for replication lag 

Read replicas aren’t always real-time. If your app shows the latest activity instantly, test for delays. In our case, a few seconds of lag were acceptable for most features. 

Final thoughts 

Redis is still one of our favorite tools. It is excellent for fast reads, rate limiting, and sessions. But like all great tools, it has its limitations. Redis helped us scale fast, but when we hit complexity, it wasn’t enough. Adding read replicas filled that gap, reduced technical debt, and made the system more stable. 

Therefore, if you’re building real-time features, dashboards, or anything with filters, don’t rely solely on Redis. It’s great for speed, but not for structure. 

Read replicas brought balance to our stack. They are great for complex queries and can handle heavy analytics much better than Redis. But why choose one when you can have best of both worlds? Speed from Redis, and consistency from the database. And together, they helped us scale without burning out on edge-case bugs. 

Let Redis handle the repeated reads. Let replicas handle the real data. 
And let yourself focus on building features that matter. 

If you want such in-depth expertise to handle your mobile or web app development projects, get in touch with Xavor to create reliable, scalable, fast, and high-performing applications. Partner with us by drop a line at [email protected].