LiveJournal Architecture

LiveJournal Architecture

Mon, 07/09/2007 - 16:57 — Todd Hoff

• LiveJournal Architecture (2449)

A fascinating and detailed story of how LiveJournal evolved their system to scale. LiveJournal was an early player in the free blog service race and faced issues from quickly adding a large number of users. Blog posts come fast and furious which causes a lot of writes and writes are particularly hard to scale. Understanding how LiveJournal faced their scaling problems will help any aspiring website builder.

Site: http://www.livejournal.com/

Information Sources

LiveJournal - Behind The Scenes Scaling Storytime

Google Video

Tokyo Video

2005 version

Platform

Linux

MySql

Perl

Memcached

MogileFS

Apache

What's Inside?

Scaling from 1, 2, and 4 hosts to cluster of servers.

Avoid single points of failure.

Using MySQL replication only takes you so far.

Becoming IO bound kills scaling.

Spread out writes and reads for more parallelism.

You can't keep adding read slaves and scale.

Shard storage approach, using DRBD , for maximal throughput. Allocate shards based on roles.

Caching to improve performance with memcached. Two-level hashing to distributed RAM.

Perlbal for web load balancing.

MogileFS, a distributed file system, for parallelism.

TheSchwartz and Gearman for distributed job queuing to do more work in parallel.

Solving persistent connection problems.

Lessons Learned

Don't be afraid to write your own software to solve your own problems. LiveJournal as provided incredible value to the community through their efforts.

Sites can evolve from small 1, 2 machine setups to larger systems as they learn about their users and what their system really needs to do.

Parallelization is key to scaling. Remove choke points by caching, load balancing, sharding, clustering file systems, and making use of more disk spindles.

Replication has a cost. You can't just keep adding more and more read slaves and expect to scale.

Low level issues like which OS event notification mechanism to use, file system and disk interactions, threading and even models, and connection types, matter at scale.

Large sites eventually turn to a distributed queuing and scheduling mechanism to distribute large work loads across a grid.