🧱 Redis
A data structure server, not a cache and not a database — strings, hashes, sets and the sorted sets that make leaderboards, rate limits and schedules the same structure. Then the question the whole track asks: if this process died now, what exactly would you lose?
No lessons published yet — this is the roadmap, so you can see exactly what the course will cover before it lands.
Before you start: NoSQL Concepts — Series 1, and Series 2. The key-value shape and its bargain — microsecond reads in exchange for only ever asking by key — are taught there for every engine. This track spends its time on what makes Redis more than that shape.
Short on time? The 14 episodes marked core are the shortest path to being productive at work. Start there and pick up the rest whenever a job asks for them.
Series 1 · The Data Structures
what Redis actually stores, and what each one is for
- 01
In Memory and Single-Threaded — Why One Slow Command Blocks Everyone
coreintermediatecoming soon - 02
Strings, INCR and TTL — Atomic Counters, and Keys That Clean Themselves
coreintermediatecoming soon - 03
Hashes and Sets — an Object Under One Key, and Membership in Constant Time
coreintermediatecoming soon - 04
Lists — a Work Queue in Two Commands, and the Job You Lose on a Crash
coreintermediatecoming soon - 05
Sorted Sets — Leaderboards, Rate Limits and Schedules Are the Same Structure
coreintermediatecoming soon - 06
Project — Five Requirements, Five Structures, and Why Each Fits
coreintermediate#projectcoming soon
Series 2 · The Patterns
caching, queues, locks and rate limits — with their failure modes
- 01
Cache-Aside and the Stampede — When a Cache Working Correctly Takes You Down
coreadvancedcoming soon - 02
Keyspace Design — Prefixes, SCAN Not KEYS, and Every Key Gets a TTL
coreadvancedcoming soon - 03
Pub/Sub — Fire and Forget, and Who That Is Right For
coreadvancedcoming soon - 04
Streams and Consumer Groups — Acknowledgement, Redelivery, and Where Kafka Begins
coreadvancedcoming soon - 05
Distributed Locks — the TTL Is a Guess, So the Work Must Be Idempotent
advancedcoming soon - 06
Rate Limiting — Fixed Window, Sliding Window, and What Happens When Redis Is Down
advancedcoming soon - 07
Project — A Cache With TTLs, Jitter and a Stampede You Can Demonstrate
coreadvanced#projectcoming soon
Series 3 · What You Are Trusting It With
persistence, eviction, replication — and the fork
- 01
RDB and AOF — Exactly How Much You Would Lose, and Whether That Is Fine
coreadvancedcoming soon - 02
maxmemory and Eviction — Why a Cache and a Queue Must Not Share an Instance
coreadvancedcoming soon - 03
Replication, Sentinel and Cluster — Hash Slots, and Why Multi-Key Stops Working
advancedcoming soon - 04
Pipelining, MULTI and Lua — and Why MULTI Is Not a Rollback
advancedcoming soon - 05
Redis and Valkey — the Licence Change, and Checking What You Actually Run
advancedcoming soon - 06
Project — Audit an Instance and State Exactly What a Crash Would Cost
coreadvanced#projectcoming soon
Not here, on purpose
- Why key-value at all, and how it differs from the other shapes — covered in NoSQL Concepts, Series 2 · 00, which this course assumes
- Caching as a general architectural idea — covered in BI & Data Visualization, Series 3 · 22 for a dashboard's cache. In this course, Series 2 · 00 is the caching patterns and the failure modes they have
- Running the server — provisioning, backups, monitoring, capacity — covered in Data Ops for the discipline. In this course, Series 3 covers only what changes the durability and availability of your data
- Redis as a message broker at scale — covered in Apache Kafka, which is a log with retention and consumer groups built for it. In this course, Series 2 · 15 covers Streams and states plainly where the boundary is
- The modules — search, JSON, time series, probabilistic types — not covered. They are a separate product surface with their own licences and availability, and an episode about them would age faster than anything else here