There is a common saying in development circles that full stack developers are like a Swiss Army Knife. They may not be the sharpest at every development task, but they are capable enough to handle almost anything during software development.
Whether it’s cross platform mobile development or custom web development, full stack developers are expected to innovate and find ingenious solutions for every development hurdle.
In full stack development, we can fully attest to that statement. Creating software is a continuous learning process. Our team often faces new challenges and learns new things during the job. For example, we’ve often hit a wall with performance bottlenecks, especially when scaling read-heavy features. One recent challenge led us down the path of Redis integration, and it ended up being a game-changer for both speed and stability.
In this blog, we’ll walk you through the problems we faced, how Redis helped, what issues we encountered during implementing Redis, and give some practical tips, so you can apply Redis in your own projects.
The problem: Lagging performance and costly DB calls
We were building a dashboard using React, Node.js, and SQL Server. We used React on the frontend, Node.js on the backend, and SQL Server for the database.
While building the dashboard using these three technologies, we noticed a few recurring issues:
- Slow API response times when traffic spiked, which meant pages took longer to load
- Redundant DB queries for frequently accessed data
- High database CPU usage, even though the data rarely changed
- A sense that “everything worked… until more users showed up”
Overall, it felt like our project worked, but only for a few users and that too it wouldn’t scale. These problems made async tasks like cron jobs, filters, and pagination painfully slow and unpredictable. And we knew the current setup wouldn’t scale as the user count grew.
Why scalability issues arise in the first place
Scalability is a common obstacle in the creation of modern software. Application development services providers have put a great deal of effort and thought into resolving it. Here is a breakdown of the major reasons why it happens:
1. Too many repeated database calls
Every time a user opens the dashboard or clicks something, the app asks the database for the same data again and again, even if nothing has changed.
Imagine if thousands of people ask the same question at once. The database has to do the same work a thousand times, which overloads it.
2. More processing due to no caching layer
Without any caching layer, there is no place to hold onto recently used data. That means every request goes straight to the main database, doing full calculations every time.
It’s like recalculating a math problem from scratch each time instead of just writing down the answer and reusing it.
3. Async tasks added more pressure
Async tasks are background jobs, such as pulling data, updating summaries, and the likes. These jobs run at the same time as user traffic and hit the same database, which makes things even slower during peak usage.
4. Legacy patterns in modern workloads
The original design works fine with a small number of users. But it is built with the idea that the database will always be available and fast. However, as usage grows, the app starts to break under the weight of all those live requests, especially because it doesn’t have systems like caching or load balancing in place.
The solution: Introducing Redis as a read-first cache layer
Instead of hitting the SQL database directly for every request, we decided to introduce Redis as a read-first layer.
Redis is an open-source, in-memory data store often used as a cache, database, and message broker. It’s known for being extremely fast because it keeps data in memory (RAM) rather than reading from disk-like traditional databases.
Developers use Redis in several ways, including:
- Quickly storing and retrieving data that’s used often
- Reducing the load on your main database
- Improving response times for users
- Coordinating background jobs or tasks between services
Here’s how we changed our app’s architecture using Redis:
The updated flow
- A cron job ran every few minutes to sync fresh data from SQL Server into Redis.
- All API routes read from Redis, not the database itself.
- Redis data were stored in structured keys (e.g. transactions:<userId>) for faster access.
Tools and stack
We used the following tools for this project:
- Node.js (Express) for backend APIs
- TypeORM to pull data from SQL
- ioredis for Redis operations
- React for frontend
- BullMQ for background tasks
- Datadog to monitor latency and cache hit/miss ratio
Challenges with Redis and how we solved them
Redis is an excellent tool, but it also has its complexities. It makes copies of your data, and you need to make sure those copies are always up to date. Furthermore, Redis’s memory can run out of space, and some keys may not work.
Here is what we found challenging working with Redis and how we solved these problems.
1. Cache invalidation logic
Problem: How to keep Redis in sync with SQL data?
Solution: We built a cron job that ran every 5 minutes, pulled updated records, and overwrote Redis keys in batches (5000 records per chunk using async promises). This ensured freshness while keeping memory usage in check.
2. Memory usage exploded
Problem: Redis kept growing uncontrollably with millions of records.
Solution: We implemented a TTL (time-to-live) for non-critical keys, used compressed values (e.g., JSON stringified arrays), and filtered out unnecessary columns before caching.
3. Debugging cache misses
Problem: Sometimes Redis returned empty even though the DB had data.
Solution: We added Datadog APM logs + Winston to log every cache miss. This helped us find issues in key naming and incorrect JSON parsing.
Real impact: case study
We had a backend endpoint /api/users/:id/transactions that supported filtering, sorting, and pagination. Previously these situations persisted:
- Cold response time was 2.5–3.2 seconds
- Frequent 504 Gateway Timeouts during peak usage were common
- We faced DB CPU spikes during filter queries
After Redis:
- Cold response was reduced to ~200ms
- We faced no more SQL hits on read
- There were zero DB overloads during load test (1000 concurrent users)
Lessons learned and tips
- Redis is fast, but it’s not magic
We still needed proper structure. Use predictable keys, compressed values, and avoid caching things that change too often. - Async task batching is a superpower
Using Promise.all with batch sizes (e.g., 20 promises for 5000 records each) helped sync huge datasets without choking the system. - Monitoring is non-negotiable
Without Datadog (or similar), we wouldn’t have spotted the silent Redis bugs. Always log cache hits/misses for observability. - Keep fallbacks in place
If Redis is unavailable, your app should still serve users. Use DB as a backup only when cache fails.
Final thoughts
The demand for full stack developers has grown by 35% each year. It will remain one of the most in-demand tech roles in the future. But it is a continuously evolving role where your learning on the job never ends.
If you’re a developer scaling a full stack app in 2025, Redis isn’t optional anymore. It’s a must-have for fast, scalable systems. Whether you’re building with Node, Python, or React, caching strategies like Redis-first read patterns will save your app’s performance and you your sanity).
For us, this integration didn’t just improve performance; it helped us learn real-world async batching, observability, and system design, all of which are in demand at every serious company today. Let Redis do the heavy lifting. You focus on building better features.
Xavor offers top-notch mobile app development services that can build high-performing, scalable apps that can handle any workload under all conditions. Our developers are proficient in Flutter, React, Node.js, Xamarin and other major frameworks and tools to create the best possible mobile applications.
Get a free consultation session with our developers by contacting us at [email protected].
