How Instagram Feeds Work: Celery and RabbitMQ(blogs.vmware.com)
blogs.vmware.com
How Instagram Feeds Work: Celery and RabbitMQ
http://blogs.vmware.com/vfabric/2013/04/how-instagram-feeds-work-celery-and-rabbitmq.html
6 comments
In short, the feeds are heavily denormalized and are constructed when new photos are added by people you follow, rather than at request time.
They seem to favor using disk space and saving on processing time and memory.
They seem to favor using disk space and saving on processing time and memory.
Which was where Twitter had to go in their first major refactor, as well. Originally timeline views would be built on-demand, but that approach proved unscalable very quickly, so they moved to preemptively building timelines by copying tweets everywhere they needed to go at publish-time rather than request-time.
It's basically just an inversion; instead of the cache being a read-through layer over the database, the database becomes a write-through layer over the cache.
It's basically just an inversion; instead of the cache being a read-through layer over the database, the database becomes a write-through layer over the cache.
It seems that these problems would be better solved at the database level. It is just that the open source databases lack good implementations of materialized views and data replication to slaves. Instead you have this more fragile solution involving the Postgres, RabbitMQ, and Redis to accomplish nearly the same thing.
This post is great.. but its from April 2013. I wonder how their architecture has changed since then?
They've since moved from EC2 to Facebook infrastructure, see their blog post here:
http://instagram-engineering.tumblr.com/post/89992572022/mig...
http://instagram-engineering.tumblr.com/post/89992572022/mig...
Were they only using two RabbitMQ brokers? I have a habit of running one per machine, which allows every app to simply connect to localhost; but on virtualized clusters, RabbitMQ gets a network partition almost every other day, and having just two would obviously cut down on the number of partitions.
If they are using EC2 hosts, I wonder why they don't use AWS SQS/SNS for their queue systems and Dynamo for their materialized views.
Probably due to lock in.
Isn't the cost of those services also a function of the number of requests? E.g. they bill on a request basis, not an hourly cpu time basis?
The article states:
'directly fetch all the photos that the user followed from a single, monolithic data store, sort them by creation time and then only display the latest 10'
That isn't how the query would work. It is implying that the query would return all the results, clearly it would only return 10. I haven't read all the details, I'm sure Instagram does something better then this basic SQL now but it is silly in a technical article to overstate the problem in such an obviously incorrect way.