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Ideas you will meet

Distributed Systems

Consensus, time, failure and the fundamentals of scale-out.

What we already know about this

Our own lessons on these ideas. Read them here before you start, so it makes sense.

• Core Concepts: How Things Actually Work · 8 minWhy Redis is fast, and Redis versus MemcachedIn-memory storage, a single-threaded event loop and rich data structures, plus how persistence and replication work.Read hereClose

  Why it is fast

  1. Everything lives in memory. No disk seek on the read path.
  2. Simple, efficient data structures (hash tables, skip lists, compact encodings) with predictable costs.
  3. A single-threaded event loop for command execution: no locks between threads, no context switching, and each command runs atomically. I/O multiplexing lets one thread serve thousands of connections.
  4. A lean protocol and in-process memory management.

  (Newer versions use extra threads for network I/O, but command execution stays ordered.)

  Image: Mixabest · Public domain · Source · Wikimedia Commons: Dual-channel DDR memory use 6026

  How the pieces fit

  100%

  Figure SDA-CORE-09-1 · Redis: event loop, memory, persistence and replication · © forcsguy.com

  More than a cache

  Redis stores typed values:

  Type	Use
  String	cached values, counters, flags
  Hash	objects with fields (a user profile)
  List	queues, recent items
  Set	unique members, tags, set maths
  Sorted set	leaderboards, priority queues, time-ordered data
  Others	streams, bitmaps, HyperLogLog, geospatial

  Because operations run on the server, you can increment, push, pop or intersect without a read-modify-write race.

  100%

  Figure SDA-CORE-09-2 · Redis data types and what they are for · © forcsguy.com

  Durability options

  Memory is volatile, so Redis offers:

  • Snapshots (RDB): periodic point-in-time dumps. Compact, but recent writes can be lost.
  • Append-only file (AOF): logs every write; replays on restart. Safer, bigger and a little slower.
  • Or none at all when it is purely a cache.

  Scaling and availability

  • Replication: a primary copies to replicas (asynchronously), serving reads and enabling failover.
  • Sentinel / managed failover promotes a replica when the primary dies.
  • Cluster: splits keys across nodes by hash slot, for data larger than one machine.

  Redis versus Memcached

  	Redis	Memcached
  Data types	many	strings only
  Persistence	optional	none
  Replication	yes	not built in
  Threading	one for commands	multi-threaded
  Best for	cache plus data structures, queues, counters	a very simple, large, pure cache

  For most new projects Redis is the more flexible choice. Memcached remains fine for a straightforward cache that must use many CPU cores.

  Pitfalls

  • Memory is finite: set a maxmemory and an eviction policy (for example LRU).
  • Avoid slow commands on huge collections.
  • A very hot key still concentrates load on one node.

  More on this site

  Related reading

  • Redis DocumentationRedis · Redis Ltd.Documentation
  • Redis BlogRedis engineers · Redis Ltd.Blogs
  • Redis UniversityRedis · Redis Ltd.Courses
  • RedisRedis · Redis Ltd.GitHub

  Show 6 moreShow fewer

  • Redis persistenceRedis · Redis Ltd.Documentation
  • Memory Optimization in Redis at WattpadWattpad engineers · WattpadCase Studies
  • Scale with Redis ClusterRedis · Redis Ltd.Documentation
  • Scaling Job Queue with Redis at SlackSlack engineers · SlackCase Studies
  • Moving persistent data out of Redis at GithubGithub engineers · GithubCase Studies
  • PayPal Developer Community BlogPaypal engineers · PaypalBlogs

  Open this lesson with its quiz and exercise