There’s a widespread saying in growth circles that full stack builders are like a Swiss Military Knife. They will not be the sharpest at each growth activity, however they’re succesful sufficient to deal with virtually something throughout software program growth.

Whether or not it’s cross platform mobile development or customized internet growth, full stack builders are anticipated to innovate and discover ingenious options for each growth hurdle.

In full stack development, we will totally attest to that assertion. Creating software program is a steady studying course of. Our workforce typically faces new challenges and learns new issues throughout the job. For instance, we’ve typically hit a wall with efficiency bottlenecks, particularly when scaling read-heavy options. One latest problem led us down the trail of Redis integration, and it ended up being a game-changer for each velocity and stability.

On this weblog, we’ll stroll you thru the issues we confronted, how Redis helped, what points we encountered throughout implementing Redis, and provides some sensible ideas, so you may apply Redis in your individual initiatives.

The issue: Lagging efficiency and dear DB calls

We had been constructing a dashboard utilizing React, Node.js, and SQL Server. We used React on the frontend, Node.js on the backend, and SQL Server for the database.

Whereas constructing the dashboard utilizing these three applied sciences, we seen just a few recurring points:

• Gradual API response instances when site visitors spiked, which meant pages took longer to load
• Redundant DB queries for incessantly accessed knowledge
• Excessive database CPU utilization, although the info hardly ever modified
• A way that “every thing labored… till extra customers confirmed up”

General, it felt like our venture labored, however just for just a few customers and that too it wouldn’t scale. These issues made async duties like cron jobs, filters, and pagination painfully sluggish and unpredictable. And we knew the present setup wouldn’t scale because the consumer depend grew.

Why scalability points come up within the first place

Scalability is a standard impediment within the creation of recent software program. Utility growth companies suppliers have put quite a lot of effort and thought into resolving it. Here’s a breakdown of the most important the reason why it occurs:

1. Too many repeated database calls

Each time a consumer opens the dashboard or clicks one thing, the app asks the database for a similar knowledge time and again, even when nothing has modified.

Think about if 1000’s of individuals ask the identical query without delay. The database has to do the identical work a thousand instances, which overloads it.

2. Extra processing resulting from no caching layer

With none caching layer, there isn’t a place to carry onto lately used knowledge. Which means each request goes straight to the principle database, doing full calculations each time.

It’s like recalculating a math drawback from scratch every time as a substitute of simply writing down the reply and reusing it.

3. Async duties added extra strain

Async duties are background jobs, similar to pulling knowledge, updating summaries, and the likes. These jobs run concurrently consumer site visitors and hit the identical database, which makes issues even slower throughout peak utilization.

4. Legacy patterns in trendy workloads

The unique design works high-quality with a small variety of customers. However it’s constructed with the concept the database will all the time be out there and quick. Nonetheless, as utilization grows, the app begins to interrupt below the burden of all these reside requests, particularly as a result of it doesn’t have techniques like caching or load balancing in place.

The answer: Introducing Redis as a read-first cache layer

As a substitute of hitting the SQL database straight for each request, we determined to introduce Redis as a read-first layer.

Redis is an open-source, in-memory knowledge retailer typically used as a cache, database, and message dealer. It’s recognized for being extraordinarily quick as a result of it retains knowledge in reminiscence (RAM) reasonably than studying from disk-like conventional databases.

Builders use Redis in a number of methods, together with:

• Rapidly storing and retrieving knowledge that’s used typically
• Lowering the load in your primary database
• Enhancing response instances for customers
• Coordinating background jobs or duties between companies

Right here’s how we modified our app’s structure utilizing Redis:

The up to date movement

1. A cron job ran each couple of minutes to sync contemporary knowledge from SQL Server into Redis.
2. All API routes learn from Redis, not the database itself.
3. Redis knowledge had been saved in structured keys (e.g. transactions:) for quicker entry.

Instruments and stack

We used the next instruments for this venture:

• Node.js (Specific) for backend APIs
• TypeORM to tug knowledge from SQL
• ioredis for Redis operations
• React for frontend
• BullMQ for background duties
• Datadog to observe latency and cache hit/miss ratio

Challenges with Redis and the way we solved them

Redis is a wonderful software, however it additionally has its complexities. It makes copies of your knowledge, and you must be certain these copies are all the time updated. Moreover, Redis’s reminiscence can run out of area, and a few keys might not work.

Here’s what we discovered difficult working with Redis and the way we solved these issues.

1. Cache invalidation logic

Drawback: Methods to preserve Redis in sync with SQL knowledge?

Resolution: We constructed a cron job that ran each 5 minutes, pulled up to date data, and overwrote Redis keys in batches (5000 data per chunk utilizing async guarantees). This ensured freshness whereas holding reminiscence utilization in test.

2. Reminiscence utilization exploded

Drawback: Redis stored rising uncontrollably with hundreds of thousands of data.

Resolution: We carried out a TTL (time-to-live) for non-critical keys, used compressed values (e.g., JSON stringified arrays), and filtered out pointless columns earlier than caching.

3. Debugging cache misses

Drawback: Typically Redis returned empty although the DB had knowledge.

Resolution: We added Datadog APM logs + Winston to log each cache miss. This helped us discover points in key naming and incorrect JSON parsing.

Actual impression: case examine

We had a backend endpoint /api/customers/:id/transactions that supported filtering, sorting, and pagination. Beforehand these conditions persevered:

• Chilly response time was 2.5–3.2 seconds
• Frequent 504 Gateway Timeouts throughout peak utilization had been widespread
• We confronted DB CPU spikes throughout filter queries

After Redis:

• Chilly response was decreased to ~200ms
• We confronted no extra SQL hits on learn
• There have been zero DB overloads throughout load check (1000 concurrent customers)

Classes realized and ideas

• Redis is quick, however it’s not magic
  We nonetheless wanted correct construction. Use predictable keys, compressed values, and keep away from caching issues that change too typically.
• Async activity batching is a superpower
  Utilizing Promise.all with batch sizes (e.g., 20 guarantees for 5000 data every) helped sync enormous datasets with out choking the system.
• Monitoring is non-negotiable
  With out Datadog (or comparable), we wouldn’t have noticed the silent Redis bugs. All the time log cache hits/misses for observability.
• Maintain fallbacks in place
  If Redis is unavailable, your app ought to nonetheless serve customers. Use DB as a backup solely when cache fails.

Last ideas

The demand for full stack builders has grown by 35% annually. It’s going to stay one of the crucial in-demand tech roles sooner or later. However it’s a repeatedly evolving function the place your studying on the job by no means ends.

When you’re a developer scaling a full stack app in 2025, Redis isn’t optionally available anymore. It’s a must have for quick, scalable techniques. Whether or not you’re constructing with Node, Python, or React, caching methods like Redis-first learn patterns will save your app’s efficiency and also you your sanity).

For us, this integration didn’t simply enhance efficiency; it helped us study real-world async batching, observability, and system design, all of that are in demand at each critical firm at the moment. Let Redis do the heavy lifting. You deal with constructing higher options.

Xavor provides top-notch cell app growth companies that may construct high-performing, scalable apps that may deal with any workload below all circumstances. Our builders are proficient in Flutter, React, Node.js, Xamarin and different main frameworks and instruments to create the absolute best cell functions.

Get a free session session with our builders by contacting us at [email protected].