This was the first architecture profile on HighScalability. IMHO LiveJournal was really the start of the openness on how to build stuff at scale, setting the whole industry off with an excellent role model. They wrote about their architecture, they open sourced their tools, they showed that success wasn't based on keeping secrets, and they set forth principles still followed by our rather amazing industry. No other industry is so open and cooperative, with their eyes cast so far forward, intent on building cool stuff. When all around seems dark it would be good to keep this little bit of light in mind...
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.
