Todd Hoff’s picture
Tue, 07/17/2007 - 20:20 — Todd Hoff
* YouTube Architecture (2446)
YouTube grew incredibly fast, to over 100 million video views per day, with only a handful of people responsible for scaling the site. How did they manage to deliver all that video to all those usersé And how have they evolved since being acquired by Googleé
Recipe for handling rapid growth
This loop runs many times a day.
- More disks serving content which means more speed.
Headroom. If a machine goes down others can take over.
There are online backups.
- Apache had too much overhead.
Uses epoll to wait on multiple fds.
Switched from single process to multiple process configuration to handle more connections.
- CDNs replicate content in multiple places. There’s a better chance of content being closer to the user, with fewer hops, and content will run over a more friendly network.
CDN machines mostly serve out of memory because the content is so popular there’s little thrashing of content into and out of memory.
- There’s a long tail effect. A video may have a few plays, but lots of videos are being played. Random disks blocks are being accessed.
Caching doesn’t do a lot of good in this scenario, so spending money on more cache may not make sense. This is a very interesting point. If you have a long tail product caching won’t always be your performance savior.
Tune RAID controller and pay attention to other lower level issues to help.
Tune memory on each machine so there’s not too much and not too little.
Serving Video Key Points
- Lots of disk seeks and problems with inode caches and page caches at OS level.
Ran into per directory file limit. Ext3 in particular. Moved to a more hierarchical structure. Recent improvements in the 2.6 kernel may improve Ext3 large directory handling up to 100 times, yet storing lots of files in a file system is still not a good idea.
A high number of requests/sec as web pages can display 60 thumbnails on page.
Under such high loads Apache performed badly.
Used squid (reverse proxy) in front of Apache. This worked for a while, but as load increased performance eventually decreased. Went from 300 requests/second to 20.
Tried using lighttpd but with a single threaded it stalled. Run into problems with multiprocesses mode because they would each keep a separate cache.
With so many images setting up a new machine took over 24 hours.
Rebooting machine took 6-10 hours for cache to warm up to not go to disk.
- Avoids small file problem because it clumps files together.
Fast, fault tolerant. Assumes its working on a unreliable network.
Lower latency because it uses a distributed multilevel cache. This cache works across different collocation sites.
For more information on BigTable take a look at Google Architecture, GoogleTalk Architecture, and BigTable.
- Use MySQL to store meta data like users, tags, and descriptions.
Served data off a monolithic RAID 10 Volume with 10 disks.
Living off credit cards so they leased hardware. When they needed more hardware to handle load it took a few days to order and get delivered.
They went through a common evolution : single server, went to a single master with multiple read slaves, then partitioned the database, and then settled on a sharding approach.
Suffered from replica lag. The master is multi-threaded and runs on a large machine so it can handle a lot of work. Slaves are single threaded and usually run on lesser machines and replication is asynchronous, so the slaves can lag significantly behind the master.
Updates cause cache misses which goes to disk where slow I/O causes slow replication.
Using a replicating architecture you need to spend a lot of money for incremental bits of write performance.
One of their solutions was prioritize traffic by splitting the data into two clusters : a video watch pool and a general cluster. The idea is that people want to watch video so that function should get the most resources. The social networking features of YouTube are less important so they can be routed to a less capable cluster.
- Went to database partitioning.
Split into shards with users assigned to different shards.
Spreads writes and reads.
Much better cache locality which means less IO.
Resulted in a 30% hardware reduction.
Reduced replica lag to 0.
Can now scale database almost arbitrarily.
Data Center Strategy
looks at different metrics to know who is closest.
- Software : DB, caching
OS : disk I/O
Hardware : memory, RAID