What does it do?
This library lets Python programs talk to Redis, a database that keeps almost everything in memory rather than on a hard drive, making it extraordinarily fast for the kinds of short-lived, frequently accessed data apps need constantly: session logins, shopping cart contents, rate limits, live leaderboards. Where a traditional database is built to safely store data long-term, Redis is built for speed, often answering requests in a fraction of a millisecond. This Python library is simply the messenger between your code and a running Redis server, letting you save and fetch that fast-access data with simple commands. It’s frequently used as a temporary cache sitting in front of a slower main database, absorbing repeat requests so the main database doesn’t have to.
See it in action
This code stores a piece of information in a super-fast Redis database, reads it back, and then increases a counter by one.
import redis
r = redis.Redis(host="localhost", port=6379, decode_responses=True)
r.set("username", "alice")
print(r.get("username"))
r.incr("visit_count")
print(r.get("visit_count"))Why would a non-developer care?
Every time an app feels instantly responsive — remembering you’re logged in across pages, showing a live view count, enforcing ‘you’ve hit your limit for today’ — there’s a good chance something like Redis is quietly making that fast. Without a caching layer like it, applications would need to hit their slower main database far more often, and everything would feel a little more sluggish. It’s one of the more invisible pieces of infrastructure behind software that feels snappy.
Real-world examples
Twitter, GitHub, and Snapchat have all publicly discussed using Redis at various points to handle things like caching, real-time counters, and session storage at massive scale. It’s also extremely common as the backing store for background job queues in Python web applications, holding tasks waiting to be processed. Without something like Redis in the stack, features like real-time notifications or live counters would need to be rebuilt on slower, less suitable storage.
Who uses it
Backend developers who need extremely fast, temporary storage for things like sessions, caches, queues, or real-time counters.
How it compares to alternatives
Memcached is Redis’s older, simpler rival, offering pure caching without Redis’s extra data structures like lists, sets, and sorted sets that make it more versatile. Compared to a traditional database like PostgreSQL, Redis trades long-term durability guarantees for raw speed. It’s rarely a replacement for a main database, more often a fast companion sitting alongside one.
Fun fact
Redis stands for REmote DIctionary Server and was originally created by Italian developer Salvatore Sanfilippo to scale the real-time analytics behind his own startup.