blob 46d79230ca20159f2a57977eb8f9ecef049718d6 4620 bytes (raw)
name: DESIGN # note: path name is non-authoritative(*)
* 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.
* 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 mainline Ruby 2.0.0 and later
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
* 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
* 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
solving 46d7923 ...
found 46d7923 in https://yhbt.net/unicorn.git/
(*) Git path names are given by the tree(s) the blob belongs to.
Blobs themselves have no identifier aside from the hash of its contents.^
Code repositories for project(s) associated with this public inbox
This is a public inbox, see mirroring instructions
for how to clone and mirror all data and code used for this inbox;
as well as URLs for read-only IMAP folder(s) and NNTP newsgroup(s).