There’s a frequent saying in growth circles that full stack builders are like a Swiss Military Knife. They will not be the sharpest at each growth job, 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 net growth, full stack builders are anticipated to innovate and discover ingenious options for each growth hurdle.

In full stack development, we are able to totally attest to that assertion. Creating software program is a steady studying course of. Our workforce usually faces new challenges and learns new issues in the course of the job. For instance, we’ve usually hit a wall with efficiency bottlenecks, particularly when scaling read-heavy options. One current 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 suggestions, so you may apply Redis in your personal 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 observed a number of recurring points:

• Gradual API response occasions when visitors spiked, which meant pages took longer to load
• Redundant DB queries for ceaselessly accessed information
• Excessive database CPU utilization, despite the fact that the information hardly ever modified
• A way that “every thing labored… till extra customers confirmed up”

Total, it felt like our venture labored, however just for a number of 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. Software growth providers suppliers have put a substantial amount of effort and thought into resolving it. Here’s a breakdown of the key explanation 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 information time and again, even when nothing has modified.

Think about if 1000’s of individuals ask the identical query directly. The database has to do the identical work a thousand occasions, which overloads it.

2. Extra processing as a consequence of no caching layer

With none caching layer, there is no such thing as a place to carry onto not too long ago used information. Which means each request goes straight to the principle database, doing full calculations each time.

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

3. Async duties added extra strain

Async duties are background jobs, similar to pulling information, updating summaries, and the likes. These jobs run similtaneously consumer 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. Nevertheless, as utilization grows, the app begins to interrupt underneath the burden of all these stay requests, particularly as a result of it doesn’t have programs 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 immediately for each request, we determined to introduce Redis as a read-first layer.

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

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

• Shortly storing and retrieving information that’s used usually
• Decreasing the load in your most important database
• Bettering response occasions for customers
• Coordinating background jobs or duties between providers

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

The up to date movement

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

Instruments and stack

We used the next instruments for this venture:

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

Challenges with Redis and the way we solved them

Redis is a superb instrument, however it additionally has its complexities. It makes copies of your information, and you have to be sure that these copies are all the time updated. Moreover, Redis’s reminiscence can run out of house, 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

Downside: The way to preserve Redis in sync with SQL information?

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

2. Reminiscence utilization exploded

Downside: Redis stored rising uncontrollably with hundreds of thousands of information.

Answer: 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

Downside: Generally Redis returned empty despite the fact that the DB had information.

Answer: 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 frequent
• We confronted DB CPU spikes throughout filter queries

After Redis:

• Chilly response was diminished 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 suggestions

• 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 usually.
• Async job batching is a superpower
  Utilizing Promise.all with batch sizes (e.g., 20 guarantees for 5000 information every) helped sync big datasets with out choking the system.
• Monitoring is non-negotiable
  With out Datadog (or related), we wouldn’t have noticed the silent Redis bugs. At all times log cache hits/misses for observability.
• Preserve fallbacks in place
  If Redis is unavailable, your app ought to nonetheless serve customers. Use DB as a backup solely when cache fails.

Remaining ideas

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

If you happen to’re a developer scaling a full stack app in 2025, Redis isn’t optionally available anymore. It’s essential for quick, scalable programs. 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 give attention to constructing higher options.

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

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