From various readings and conversations I had, my understanding of Facebook's current architecture is:
- Web
front-end written in PHP. Facebook's HipHop [1] then converts it to C++
and compiles it using g++, thus providing a high performance templating
and Web logic execution layer
- Business logic is exposed as
services using Thrift [2]. Some of these services are implemented in
PHP, C++ or Java depending on service requirements (some other
languages are probably used...)
- Services implemented in Java
don't use any usual enterprise application server but rather use
Facebook's custom application server. At first this can look as wheel
reinvented but as these services are exposed and consumed only (or
mostly) using Thrift, the overhead of Tomcat, or even Jetty, was
probably too high with no significant added value for their need.
- Persistence
is done using MySQL, Memcached [3], Facebook's Cassandra [4], Hadoop's
HBase [5]. Memcached is used as a cache for MySQL as well as a general
purpose cache. Facebook engineers admit that their use of Cassandra is
currently decreasing as they now prefer HBase for its simpler
consistency model and its MapReduce ability.
- Offline processing is done using Hadoop and Hive
- Data
such as logging, clicks and feeds transit using Scribe [6] and are
aggregating and stored in HDFS using Scribe-HDFS [7], thus allowing
extended analysis using MapReduce
- BigPipe [8] is their custom technology to accelerate page rendering using a pipelining logic
- Varnish Cache [9] is used for HTTP proxying. They've prefered it for its high performance and efficiency [10].
- The
storage of the billions of photos posted by the users is handled by
Haystack, an ad-hoc storage solution developed by Facebook which brings
low level optimizations and append-only writes [11].
- Facebook
Messages is using its own architecture which is notably based on
infrastructure sharding and dynamic cluster management. Business logic
and persistence is encapsulated in so-called 'Cell'. Each Cell handles
a part of users ; new Cells can be added as popularity grows [12].
Persistence is achieved using HBase [13].
- Facebook Messages' search engine is built with an inverted index stored in HBase [14]
- Facebook Search Engine's implementation details are unknown as far as I know
- The typeahead search uses a custom storage and retrieval logic [15]
- Chat is based on an Epoll server developed in Erlang and accessed using Thrift [16]
About the resources provisioned for each of these components, some information and numbers are known:
- Facebook
is estimated to own more than 60,000 servers [17]. Their recent
datacenter in Prineville, Oregon is based on entirely self-designed
hardware [18] that was recently unveiled as Open Compute Project [19].
- 300 TB of data is stored in Memcached processes [20]
- Their
Hadoop and Hive cluster is made of 3000 servers with 8 cores, 32 GB
RAM, 12 TB disks that is a total of 24k cores, 96 TB RAM and 36 PB
disks [20]
- 100 billion hits per day, 50 billion photos, 3 trillion objects cached, 130 TB of logs per day as of july 2010 [21]
[1]
HipHop for PHP:
http://developers.facebook.com/b...[2]
Thrift:
[3]
Memcached:
[4]
Cassandra:
[5]
HBase:
[6]
Scribe:
[7]
Scribe-HDFS:
http://hadoopblog.blogspot.com/2...[8]
BigPipe:
[9]
Varnish Cache:
[10]
Facebook goes for Varnish:
re.com/...[11]
Needle in a haystack: efficient storage of billions of photos:
[12]
Scaling the Messages Application Back End:
[13]
The Underlying Technology of Messages:
[14]
The Underlying Technology of Messages Tech Talk:
[15]
Facebook's typeahead search architecture:
[16]
Facebook Chat:
[17]
Who has the most Web Servers?:
[18] B
uilding Efficient Data Centers with the Open Compute Project:
[19]
Open Compute Project:
[20]
Facebook's architecture presentation at Devoxx 2010:
http://www.devoxx.com[21]
Scaling Facebook to 500 millions users and beyond:
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