What happens if Redis goes down?

Built properly, your application slows down but keeps working, falling back to the database while Redis is unavailable. Built carelessly, it stops entirely. Which of those you get is a design decision made long before the outage happens.

Is data safe in Redis?

Safe enough for what belongs there, which is nothing irreplaceable. Persistence options exist and we configure them, but they reduce the window of loss rather than eliminating it. Anything you would miss should live in your primary database.

How much memory will we need?

Less than most people assume, since a cache holds what is asked for often rather than everything. We size it from your actual access patterns and set a limit with an eviction policy, so it never grows until something breaks.

What is Valkey, and does it affect us?

Redis changed its licence in 2024 and Valkey emerged as an open fork, now offered by several cloud providers as a drop-in replacement. You may already be running it without knowing. For most applications the difference is invisible, but it is worth checking.

Redis
Development Services

Stateside Digitals provides Redis development services, adding caching, queues, and session handling that make an application feel faster without changing the database sitting underneath it.

Redis holds data in memory rather than on disk, which is why it answers in microseconds where a traditional database takes milliseconds. That gap sounds small and compounds quickly. A page making forty database calls feels sluggish to the person waiting, and the same page reading those answers from memory feels immediate instead.

For a business, Redis rarely replaces anything. It sits in front of what you already run, holding the answers your application asks for repeatedly so the database underneath is not asked the same question several thousand times an hour. It also handles jobs a relational database does poorly: queues, counters, short-lived locks, and rate limits.

Answers In Microseconds

Memory, Not Disk

Data is held where it can be read immediately, removing the wait that makes an otherwise well-built application feel slow to use.

Takes Pressure Off

Your Database Does Less Work

Repeated queries get answered without touching your primary database, which often delays the day you need to pay for a larger one.

More Than A Cache

Queues, Locks, And Counters

The same system handles background jobs, usage limits, and coordination between services, rather than adding a separate tool for each.

Session And Token Storage

Keeping people signed in across several servers, so logging in once works no matter which machine handles the next request.

Background Job Queues

Sending email, processing uploads, and generating reports away from the request that triggered them, so users are never left watching a page while work happens behind it.

Rate And Usage Limits

Counting requests per user or per key fast enough to enforce limits without the check itself becoming the slow part of the request.

This is the mistake worth avoiding and it is a common one. Redis holds data in memory, which is what makes it fast and also what makes it the wrong home for anything you cannot afford to lose. It can write to disk, but that persistence is a safety net rather than a guarantee, and a badly timed restart can still cost you the most recent writes.

So the rule we work to is that nothing lives only in Redis. Whatever it holds should be rebuildable from your primary database, whether that is a cached query result, a session that can be re-established, or a queue that can be repopulated. Treated that way, losing the Redis instance is an inconvenience rather than an incident, which is exactly what a cache ought to be.

We add Redis to existing applications and tune the instances already running, covering cache strategy and invalidation, eviction policies, and memory sizing. Queue work integrates with BullMQ, Sidekiq, or Celery depending on your stack. High availability runs through Sentinel or Cluster, on ElastiCache, MemoryStore, Azure Cache, or servers you manage yourself.

Redis
Valkey
BullMQ
Sidekiq
Celery
Sentinel
Cluster
ElastiCache
MemoryStore
Azure Cache
Eviction Policies
Memory Sizing

Eviction Policy Chosen

Memory Limits Set

Nothing Stored Only Here

Failover Actually Tested

An eviction policy is set deliberately, because the default is to start refusing writes once memory fills, which takes the application down with it.

A hard limit is configured and sized from real access patterns, so the instance never grows quietly until something else on the box suffers.

Everything in Redis is rebuildable from your primary database, which turns losing the instance into an inconvenience rather than an incident.

We take the primary away on purpose and watch what the application does, because failover that has never been exercised is a assumption rather than a feature.

Eviction Policy Chosen

Memory Limits Set

Nothing Stored Only Here

Failover Actually Tested

Not always. If your database is keeping up and pages load quickly, adding Redis introduces a system to run for no gain. It earns its place when the same queries repeat constantly, when sessions must be shared across servers, or when work needs queueing.

Redis
Development Services