Rainbows! is like unicorn, but Different...

While Rainbows! depends on unicorn for its process/socket management, HTTP parser and configuration language; Rainbows! is more ambitious.

Architectural Diagrams

unicorn uses a 1:1 mapping of processes to clients

unicorn master
\_ unicorn worker[0]
|  \_ client[0]
\_ unicorn worker[1]
|  \_ client[1]
\_ unicorn worker[2]
|  \_ client[2]
...
\_ unicorn worker[M]
   \_ client[M]

Rainbows! uses a M:N mapping of processes to clients

rainbows master
 \_ rainbows worker[0]
 |  \_ client[0,0]
 |  \_ client[0,1]
 |  \_ client[0,2]
 |  ...
 |  \_ client[0,N]
 \_ rainbows worker[1]
 |  \_ client[1,0]
 |  \_ client[1,1]
 |  \_ client[1,2]
 |  \_ client[1,3]
 |  ...
 |  \_ client[1,N]
 \_ rainbows worker[2]
 |  \_ client[2,0]
 |  \_ client[2,1]
 |  \_ client[2,2]
 |  ...
 |  \_ client[2,N]
 ...
 \_ rainbows worker[M]
    \_ client[M,0]
    \_ client[M,1]
    \_ client[M,2]
    ...
    \_ client[M,N]

In both cases, workers share common listen sockets with the master and pull connections off the listen queue only if the worker has resources available.

Differences from unicorn

• log rotation is handled immediately in Rainbows! whereas unicorn has the luxury of delaying it until the current request is finished processing to prevent log entries for one request to be split across files.

• load balancing between workers is imperfect, certain worker processes may be servicing more requests than others so it is important to not set worker_connections too high. unicorn worker processes can never be servicing more than one request at once.

• speculative, non-blocking accept() is not used, this is to help load balance between multiple worker processes.

• HTTP pipelining and keepalive may be used for GET and HEAD requests.

• Less heavily-tested and inherently more complex.

Similarities with unicorn

While some similarities are obvious (we depend on and subclass off unicorn code), some things are not:

• Does not attempt to accept() connections when pre-configured limits are hit (worker_connections). This will first help balance load to different worker processes, and if your listen() :backlog is overflowing: to other machines in your cluster.

• Accepts the same signals for process management, so you can share scripts to manage them (and nginx, too).

• supports per-process listeners, allowing an external load balancer like haproxy or nginx to be used to balance between multiple worker processes.

• Exposes a streaming "rack.input" to the Rack application that reads data off the socket as the application reads it (while retaining rewindable semantics as required by Rack). This allows Rack-compliant apps/middleware to implement things such as real-time upload progress monitoring.

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