DESIGN

== Design

* Simplicity: Unicorn is a traditional UNIX prefork web server.
  No threads are used at all, this makes applications easier to debug
  and fix.  When your application goes awry, a BOFH can just
  "kill -9" the runaway worker process without worrying about tearing
  all clients down, just one.  Only UNIX-like systems supporting
  fork() and file descriptor inheritance are supported.

* The Ragel+C HTTP parser is taken from Mongrel.  This is the
  only non-Ruby part and there are no plans to add any more
  non-Ruby components.

* All HTTP parsing and I/O is done much like Mongrel:
    1. read/parse HTTP request headers in full
    2. call Rack application
    3. write HTTP response back to the client

* Like Mongrel, neither keepalive nor pipelining are supported.
  These aren't needed since Unicorn is only designed to serve
  fast, low-latency clients directly.  Do one thing, do it well;
  let nginx handle slow clients.

* Configuration is purely in Ruby and eval().  Ruby is less
  ambiguous than YAML and lets lambdas for
  before_fork/after_fork/before_exec hooks be defined inline.  An
  optional, separate config_file may be used to modify supported
  configuration changes (and also gives you plenty of rope if you RTFS
  :>)

* One master process spawns and reaps worker processes.  The
  Rack application itself is called only within the worker process (but
  can be loaded within the master).  A copy-on-write friendly garbage
  collector like the one found in Ruby 2.0.0dev or Ruby Enterprise Edition
  can be used to minimize memory usage along with the "preload_app true"
  directive (see Unicorn::Configurator).

* The number of worker processes should be scaled to the number of
  CPUs, memory or even spindles you have.  If you have an existing
  Mongrel cluster on a single-threaded app, using the same amount of
  processes should work.  Let a full-HTTP-request-buffering reverse
  proxy like nginx manage concurrency to thousands of slow clients for
  you.  Unicorn scaling should only be concerned about limits of your
  backend system(s).

* Load balancing between worker processes is done by the OS kernel.
  All workers share a common set of listener sockets and does
  non-blocking accept() on them.  The kernel will decide which worker
  process to give a socket to and workers will sleep if there is
  nothing to accept().

* Since non-blocking accept() is used, there can be a thundering
  herd when an occasional client connects when application
  *is not busy*.  The thundering herd problem should not affect
  applications that are running all the time since worker processes
  will only select()/accept() outside of the application dispatch.

* Additionally, thundering herds are much smaller than with
  configurations using existing prefork servers.  Process counts should
  only be scaled to backend resources, _never_ to the number of expected
  clients like is typical with blocking prefork servers.  So while we've
  seen instances of popular prefork servers configured to run many
  hundreds of worker processes, Unicorn deployments are typically only
  2-4 processes per-core.

* On-demand scaling of worker processes never happens automatically.
  Again, Unicorn is concerned about scaling to backend limits and should
  never configured in a fashion where it could be waiting on slow
  clients.  For extremely rare circumstances, we provide TTIN and TTOU
  signal handlers to increment/decrement your process counts without
  reloading.  Think of it as driving a car with manual transmission:
  you have a lot more control if you know what you're doing.

* Blocking I/O is used for clients.  This allows a simpler code path
  to be followed within the Ruby interpreter and fewer syscalls.
  Applications that use threads continue to work if Unicorn
  is only serving LAN or localhost clients.

* SIGKILL is used to terminate the timed-out workers from misbehaving apps
  as reliably as possible on a UNIX system.  The default timeout is a
  generous 60 seconds (same default as in Mongrel).

* The poor performance of select() on large FD sets is avoided
  as few file descriptors are used in each worker.
  There should be no gain from moving to highly scalable but
  unportable event notification solutions for watching few
  file descriptors.

* If the master process dies unexpectedly for any reason,
  workers will notice within :timeout/2 seconds and follow
  the master to its death.

* There is never any explicit real-time dependency or communication
  between the worker processes nor to the master process.
  Synchronization is handled entirely by the OS kernel and shared
  resources are never accessed by the worker when it is servicing
  a client.