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antirez
redis
C2009–2020
antirez / redis

redis

Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes, Streams, HyperLogLogs, Bitmaps.

142Distinct people
165Mentions
3Explicit recommenders
169GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes, Streams, HyperLogLogs, Bitmaps.

GitHub

Where it is used

  • Sidekiq — Sidekiq uses Redis as the storage engine for all job and operational data. It connects to Redis by default and provides guidance for production configuration. Source

What people are saying

Ah I see. I didn't realize it was old, I just picked server.c since it just sounded like it'd have a decent mix of stuff. I think the only 'taste' things I mentioned is the…

mehrdadn used a recommendation phrase. 3 indexed comments match. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 2:59:53 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year165 dated mentions

Observed mentions: 2009-09-03 00:45:38 UTC to 2020-06-30 21:42:49 UTC.

01020302009-01-01 00:00:00 UTC to 2010-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments20092010-01-01 00:00:00 UTC to 2011-01-01 00:00:00 UTC (exclusive): 6 mentions; 1 submissions, 5 comments2011-01-01 00:00:00 UTC to 2012-01-01 00:00:00 UTC (exclusive): 12 mentions; 3 submissions, 9 comments2012-01-01 00:00:00 UTC to 2013-01-01 00:00:00 UTC (exclusive): 18 mentions; 5 submissions, 13 comments2013-01-01 00:00:00 UTC to 2014-01-01 00:00:00 UTC (exclusive): 12 mentions; 0 submissions, 12 comments2014-01-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 7 mentions; 2 submissions, 5 comments20142015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 17 mentions; 3 submissions, 14 comments2016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 26 mentions; 5 submissions, 21 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 16 mentions; 2 submissions, 14 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 27 mentions; 8 submissions, 19 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 16 mentions; 3 submissions, 13 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 6 mentions; 0 submissions, 6 comments2020

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20092009-01-01 00:00:00 UTC2010-01-01 00:00:00 UTC211
20102010-01-01 00:00:00 UTC2011-01-01 00:00:00 UTC615
20112011-01-01 00:00:00 UTC2012-01-01 00:00:00 UTC1239
20122012-01-01 00:00:00 UTC2013-01-01 00:00:00 UTC18513
20132013-01-01 00:00:00 UTC2014-01-01 00:00:00 UTC12012
20142014-01-01 00:00:00 UTC2015-01-01 00:00:00 UTC725
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC17314
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC26521
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC16214
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC27819
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC16313
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC606

From the project README

An excerpt from the repository, fetched Nov 13, 2026.

Read the project’s introduction
This document serves as both a quick start guide to Redis and a detailed resource for building it from source. New to Redis? Start with What is Redis and Getting Started Ready to build from source? Jump to Build Redis from Source Want to contribute? See the Code contributions section and CONTRIBUTING.md Looking for detailed documentation? Navigate to redis.io/docs Table of contents What is Redis? Key use cases Why choose Redis? What is Redis Open Source? Getting started Redis starter projects Using Redis with client libraries Using Redis with redis-cli Using Redis with Redis Insight Redis data types, processing engines, and capabilities Community Build Redis from source Build and run Redis with all data structures - Ubuntu 20.04 (Focal) Build and run Redis with all data structures - Ubuntu 22.04 (Jammy) / 24.04 (Noble) Build and run Redis with all data structures - Debian 11 (Bullseye) / 12 (Bookworm) Build and run Redis with all data structures - AlmaLinux 8.10 / Rocky Linux 8.10 Build and run Redis with all data structures - AlmaLinux 9.5 / Rocky Linux 9.5 Build and run Redis with all data structures - macOS 13 (Ventura) and macOS 14 (Sonoma) Build and run Redis with all data structures - macOS 15 (Sequoia) Building Redis - flags and general notes Fixing build problems with dependencies or cached build options Fixing problems building 32 bit binaries Allocator Monotonic clock Verbose build Running Redis with TLS Code contributions Redis Trademarks What is Redis? For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine. Key use cases Redis excels in various applications, including: Caching: Supports multiple eviction policies, key expiration, and hash-field expiration. Distributed Session Store: Offers flexible session data modeling (string, JSON, hash). Data Structure Server: Provides low-level data structures (strings, lists, sets, hashes, sorted sets, JSON, etc.) with high-level semantics (counters, queues, leaderboards, rate limiters) and supports transactions & scripting. NoSQL Data Store: Key-value, document, and time series data storage. Search and Query Engine: Indexing for hash/JSON documents, supporting vector search, full-text search, geospatial queries, ranking, and aggregations via Redis Query Engine. Event Store & Message Broker: Implements queues (lists), priority queues (sorted sets), event deduplication (sets), streams, and pub/sub with probabilistic stream processing capabilities. Vector Store for GenAI: Integrates with AI applications (e.g. LangGraph, mem0) for short-term memory, long-term memory, LLM response caching (semantic caching), and retrieval augmented generation (RAG). Real-Time Analytics: Powers personalization, recommendations, fraud detection, and risk assessment. Why choose Redis? Redis is a popular choice for developers worldwide due to its combination of speed, flexibility, and rich feature set. Here's why people choose Redis for: Performance: Because Redis keeps data primarily in memory and uses efficient data structures, it achieves extremely low latency (often sub-millisecond) for both read and write operations. This makes it ideal for applications demanding real-time responsiveness. Flexibility: Redis isn't just a key-value store, it provides native support for a wide range of data structures and capabilities listed in What is Redis? Extensibility: Redis is not limited to the built-in data structures, it has a modules API that makes it possible to extend Redis functionality and rapidly implement new Redis commands Simplicity: Redis has a simple, text-based protocol and well-documented command set Ubiquity: Redis is battle tested in production workloads at a massive scale. There is a good chance you indirectly interact with Redis several times daily Versatility: Redis is the de facto standard for use cases such as: Caching: quickly access frequently used data without needing to query your primary database Session management: read and write user session data without hurting user experience or slowing down every API call Querying, sorting, and analytics: perform deduplication, full text search, and secondary indexing on in-memory data as fast as possible Messaging and interservice communication: job queues, message brokering, pub/sub, and streams for communicating between services Vector operations: Long-term and short-term LLM memory, RAG content retrieval, semantic caching, semantic routing, and vector similarity search In summary, Redis provides a powerful, fast, and flexible toolkit for solving a wide variety of data management challenges. If you want to know more, here is a list of starting points: Introduction to Redis data types The full list of Redis commands Redis for AI Redis documentation What is Redis Open Source? Redis Community Edition (Redis CE) was renamed Redis Open Source with the v8.0 release. Redis Ltd. also offers Redis Software, a self-managed software with additional compliance, reliability, and resiliency for enterprise scaling, and Redis Cloud, a fully managed service integrated with Google Cloud, Azure, and AWS for production-ready apps. Read more about the differences between Redis Open Source and Redis here. Getting started If you want to get up and running with Redis quickly without needing to build from source, use one of the following methods: Redis Cloud Official Redis Docker images (Alpine/Debian) docker run -d -p 6379:6379 redis:latest Redis binary distributions Snap Homebrew RPM Debian Redis quick start guides If you prefer to build Redis from source - see instructions below. Redis starter projects To get started as quickly as possible in your language of choice, use one of the following starter projects: Python (redis-py) C#/.NET (NRedisStack/StackExchange.Redis) Go (go-redis) JavaScript (node-redis) Java/Spring (Jedis) Using Redis with client libraries T
Read the full README on GitHub

What Hacker News says

12 sources
Don't write a cv/resume. This advice applies to any person that is effectively "unknown" in the tech scene - that's 99% of us. A cv is useless for conveying who you are and what value you bring relative to all the other thousands of people out there. A paper is a paper is a paper. Therefore the first crucial piece for us unknown developers is to have Open Source projects on GitHub. Companies don't want to see a GitHub profile because they love GitHub. GitHub profiles show _exactly_ what you can do, and provide proof that you've actually done it, as well as the how, why, when, and where. You have to understand how powerful and uncommon it is to have working software out in the wild that a) works, and b) is Open Source for everyone to see. a) is important because all employers need a person who can ship ; a doer. b) is important because it
Read the rest
is proof of your knowledge and skillset. This includes design decisions, documentation, git commits, api documentation, design patterns, proper testing, and so on. You are doing yourself a disservice by not having projects on GitHub, not because it's trendy, but because it is the only credible, fool-proof, tangible way to measure someone's coding ability _at a glance_. By the time anyone invests time to have you answer programming questions or code on a white-board, they are already interested in you, so this is about getting them interested in the first place. "Hello I am Salvatore Sanfilippo, I created https://github.com/antirez/redis You see how completely badass that is? The next crucial piece for we unknown developers is honesty. I think a good software developer gets stuff done. If they don't know how to do something they can learn on their own. They are motivated to keep learning and keep doing a better job because they are passionate about their work. I think employers agree with me and they look for this kind of candidate. Therefore if you love programming, just be honest. If you don't know some things, just be honest. If you really want to work for _____, just be honest. If you think you are not good enough for a particular position but you really want that position, just be honest. I think having an honest conversation with a tech-lead at any company will go millions of miles beyond submitting some piece of paper to HR. Just be honest. Are you looking for a job doing javascript? Think you are good at jquery but want to get into more complex stuff like backbone? The problem is they don't know you and you don't know them. So offer to intern, offer to come on board for 2 months to prove yourself. Software is a great field to be in, because its largely merit based. So use that to your advantage. But you can't prove yourself through words. You have to show it and do it. best of luck.
In Ask HN: No money, can't find any jobs, what am I doing wrong?
Best of all, lookups in hashes are still O(1), making them very quick. Based on the zipmap code (linked below), a zipmap is implemented as an array of adjacent (key, value) pairs. Lookup in a zipmap is actually linear search. There is no hashing. The lookup will run in time proportional to the number of entries in the map. We found this setting was best around 1000; any higher and the HSET commands would cause noticeable CPU activity This shouldn't be surprising either, as the redis documentation states that "If a specially encoded value will overflow the configured max size, Redis will automatically convert it into normal encoding." Their higher level hashing strategy of dividing the media id by 1000 guarantees that 1000 is the maximum number of entries in any zipmap. Setting hash-max-zipmap-entries to anything lower than 1000 means
Read the rest
some of their zipmaps will be converted to normal redis key/value encodings. https://github.com/antirez/redis/blob/unstable/src/zipmap.c
In Storing hundreds of millions of simple key-value pairs in Redis
The the code the article links to for zipmap.c (https://github.com/antirez/redis/blob/unstable/src/zipmap.c) is rather literate. I haven't dug extremely deeply into the sources for many F/OSS projects; the code I'm interested in reading has been inevitably opaque (at least to my inexperience). This particular source file (and maybe the rest of Redis?) is really good. I think I'll be taking many more looks at Redis (code- and usage-wise) in the future.
In Storing hundreds of millions of simple key-value pairs in Redis

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