What is Redis?
Redis is an open-source, in-memory data store used for caching, session storage and real-time features. By keeping data in memory rather than on disk, it serves and updates information in microseconds, which makes applications dramatically faster and more responsive under heavy load.
How does Redis work?
Redis stores data in memory rather than on disk, which is the source of its speed - reading from memory is orders of magnitude faster than reading from a disk-based database. It is often described as a data structure store, because instead of only key-value pairs it supports rich structures like lists, sets, sorted sets, hashes and counters, each with operations optimised for that structure. Although data lives in memory, Redis can persist to disk in the background, so it survives restarts without losing everything.
In a typical application, Redis sits alongside a primary database. The main database remains the authoritative source of truth, while Redis holds frequently accessed or time-sensitive data so the application can serve it instantly without hitting the slower database every time.
Why Redis matters
As an application grows, the database often becomes the bottleneck - every request that queries it competes for limited capacity. Redis matters because it absorbs that pressure: caching common queries and sessions in memory means the database is consulted far less often, so the whole system stays fast and handles more users on the same hardware. For real-time features and high-traffic flows, this difference between memory speed and disk speed is decisive.
What is Redis used for?
Common use cases include:
- Caching - storing frequent query results to relieve the database.
- Session storage - holding user session data for fast access.
- Rate limiting - counting requests to enforce usage limits.
- Leaderboards and counters - sorted sets for real-time rankings.
- Message queues and pub/sub - passing events between components.
Redis best practices
Treat Redis as a fast complement to a durable database, not as the single source of truth for critical data. Set sensible expiry on cached entries so stale data does not linger, and plan how the cache is invalidated when underlying data changes. Watch memory usage closely, since memory is finite and Redis fails differently when it runs out. And choose the right data structure for each job - using sorted sets for rankings or counters for limits is what unlocks its real performance.
How PixelForce approaches Redis
At PixelForce, caching and data-store decisions are made in Phase 1 - Scoping and Design, where our in-house Adelaide team identifies where performance will matter before the bottleneck appears in production. Redis is a common choice for caching, sessions and real-time features, and it is configured as part of our AWS DevOps consulting work to keep applications fast and reliable under load. We add it where it genuinely earns its place rather than by reflex - introducing caching too early can add complexity without benefit. Matching infrastructure honestly to the product's needs is how we have kept 100+ products performant at 99.99% reliability.
Where this applies
The PixelForce services where Redis matters most - explore how we put it to work in client products.
Frequently asked questions
PostgreSQL is a disk-based relational database built to be the durable, authoritative source of truth, with strong consistency and complex queries. Redis is an in-memory store built for speed, typically used for caching, sessions and real-time features rather than as the primary record. In most applications they work together: PostgreSQL holds the canonical data, while Redis serves frequently accessed information instantly to relieve load and improve responsiveness.
No. Caching is its most common use, but Redis is far more versatile. Its rich data structures make it well suited to session storage, rate limiting, real-time leaderboards and counters, and lightweight message queues or publish/subscribe messaging between components. Treating it purely as a cache underuses it. That said, caching remains the use case where it delivers the most obvious and immediate value for many applications.
Not necessarily. Although Redis keeps data in memory, it offers persistence options that write data to disk in the background, so it can recover after a restart. The trade-off is that persistence settings balance durability against performance. For genuinely critical data, the safer pattern is to treat a durable database as the source of truth and use Redis for fast access, so a Redis restart never risks losing irreplaceable information.
Add Redis when performance demands it - typically when the database becomes a bottleneck under load, when sessions or frequently accessed data need to be served faster, or when real-time features like rate limiting or leaderboards are required. Introducing it too early can add complexity without real benefit. The honest approach is to identify the genuine performance need first, then add Redis where it clearly relieves pressure or unlocks a feature.
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