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App Server Arena

Comparison of Ruby Application Servers

Lonestar Ruby Conference, Austin, TX: July 19th, 2013

This repository contains code, notes and other assets pertaining to my talk entitled "App Server Arena" that was/is to be given at Lonestar Ruby Conf in 2013.

The app

There's a very simple Sinatra application here designed to have some basic capabilities:

  • Spit out the name of the app server running it at the moment and other process/env info
  • Compute Pi to 7,500 decimal places
  • Hit Twitter and get the last 10 tweets from @devops_borat
  • Sleep for 1 second
  • Randomly do one of the above with a 50% chance to do the first (except Twitter because of API rate limitations)

The idea here is to provide diagnostic information, do some mildly computationaly expensive task, and then have a way to see how things perform when waiting on network response.

The slides

You can see the slides from this presentation at

Note that the formatting is off due to the fact that Slideshare doesn't like Keynote documents, so I had to export this as PPT. How lame is that?

An introduction to the gladiators

In this talk we'll examine four Ruby application servers:

  • Passenger, one of the easiest to configure and very common
  • Thin, an EventMachine based server
  • Unicorn, a very straight-forward app server for fast clients and responses
  • Puma, a truly concurrent application server

The application servers in question here were compared in several categories:

  • Mode of Operation (Fighting Style)
  • Use Cases (Strategy)
  • Configuration (Training)
  • Performance (Combat)


Phusion Passenger is an application server by the folks at Phusion that is compiled either as an Apache module when using Apache, or compiled directly along with the nginx source code (because nginx doesn't contain a plugin architecture like Apache does). It excels in situations that need multiple low traffic applications running on the same machine.

Mode of Operation (Fighting Style)

Phusion Passenger operates by embedding itself into nginx or Apache. In this example, I only examined nginx simply because Engine Yard does not use Apache in our stack.

There's one interesting side effect of using nginx with Passenger, however: because nginx doesn't have a "plugin-like" architecture, similar to the way Apache does, nginx has to be recompiled from scratch using Phusion's nginx source code with their modifications to compile and run Passenger. This is usually not a big deal, and to Phusion's credit they generally do a pretty good job with it, but it should be noted that, when running Passenger, instead of being able to get nginx straight from its maintainers, you'll have to get it through Phusion for their updates (unless you're merging nginx and Passenger code yourself, which, let's face it, is kinda crazy).

Once compiled and installed, Passenger is basically a part of nginx. You then configure Passenger by modifying nginx configuration - usually somewhere like /etc/nginx/ or /opt/nginx.

Passenger has a huge array of configuration options to choose from to suit nearly any environment. However, it should be noted that under its default configuration, Passenger will use an elastic worker spawning method that waits until a worker is necessary before starting one. This has numerous benefits and unintended side effects, however, that we'll get into later.

When Passenger is configured, it's set as a "location" in nginx configuration. Further configuration instructs nginx to forward requests to that location, which is how Passenger then takes over. It takes a given request, finds and available worker, and then forwards the request to it. There are two ways to configure this request routing internally:

  • per-worker
  • pool-wide (global queue)

With the per-worker feature, Passenger maintains a separate queue for each worker. This can be problematic if a request comes in that takes a while and other requests are queued up behind it in the same worker.

The second option, a global queue, allows Passenger to put all requests on the same queue "stack". Workers then are given whatever is next on this stack, thus making the long request situation a little less problematic.

Use Cases (Stragegy)

Passenger is best used in situations where its unique spawning method can really shine. Passenger has the ability to kill off workers that don't have enough traffic to justify their existence (so, zero traffic) to conserve memory, and dynamically respawn them as needed.

This has several benefits and side effects. For an always-on, dedicated application, this behavior can be a real pain. While it can be changed, the problem with this method for large, dedicated applications is that, during periods of low traffic (during the night, for example), workers will be killed off, and then when traffic picks up in the morning, requests will hang inside Passenger while it tries to launch new worker processes in memory. This also happens after nginx is restarted.

There is a significant benefit for machines with multiple applications that have low to moderate traffic, however: with this spawning method, you can literally get more bang for your buck by allowing an "elastic" and automatic management of small applications by Passenger. This means you can deploy more applications to the same hardware, so long as their traffic is relatively minimal.

In the performance tests that follow, I profiled Passenger version 3 on Engine Yard Cloud. I didn't do anything with Passenger 4 yet simply because we don't have access to it on Cloud just yet (though that's in the works).

There are several features that other application servers have that Passenger doesn't - unless you pay for them. For example, Passenger 3 can't run multi-threaded. With Passenger 4, you can do this with the Enterprise version (which is a paid annual license to Phusion). Additionally, Passenger doesnt offer zero-downtime deploys (restarting one worker after another seemlessly) in its standard edition product, but that feature is available under the Enterprise license, as are several others.

In short, Passenger can be great for digital agencies looking to host multiple applications on medium-sized virtual machines.

Configuration (Training)

Passenger has a dizzying array of configuration options detailed at Some of the more salient ones are listed here.

  • passenger_ruby - tell Passenger which ruby interpreter to use
  • passenger_spawn_method - smart or direct; smart caches code on spawn, direct doesn't. Direct is more compatible with some applications in some cases, but is slower than smart.
  • passenger_max_pool_size - max number of application workers that can exist across ALL applications.
  • passenger_min_instances - the miniumum number of application workers per application.
  • passenger_pool_idle_time - how long, in seconds, a worker can remain idle before being shut down. Set this to zero to disable worker shutdown except for extenuating circumstances.
  • passenger_pre_start_url - By default, Passenger won't start a worker until it has requests for it. This option lets you pre-start workers based on the URL the app is going to be accessed at during nginx startup. Passenger spoofs a dummy request to the URI given during start up to "jump start" itself, sorta.

All Passenger configuration belongs in nginx configuration. Varioud directives can exist in http, server, or location blocks.


Unicorn is an application server for fast clients and applications that has a really great operational infrastructure under the hood, relying on in-memory forking to recover crashed workers and pulling requests from a unix socket instead of a primary router process or thread. It can be configured to call Ruby blocks before_fork and after_fork during its operation, and can do zero downtime deploys when the master receives a HUP signal or a USR2+QUIT.

Mode of Operation (Fighting Style)

Unicorn, by contrast to Passenger, doesn't have an internal "tie-in" with nginx, nor does it have a single router process. Instead, Unicorn launches a master process that contains one single copy of your application in memory, and then forks itself into worker processes. The number of worker processes depends on how Unicorn is configured; it could be one to as many as the machine can reasonably hold.

Unicorn is then configured to bind to a unix socket on the local machine. Requests from nginx, then, are placed in this socket. Each worker then, of its own volition, dips into the socket, finds the next request to handle, works it, then returns a response to nginx.

The Unicorn master process, in this case, stands by and observes each of its workers. If any worker becomes unresponsive, the master kills the worker and simply forks itself again. In this way, Unicorn's overall architecture is rather stable, and allows for hot restarts - restarting only one worker at a time after having code deployed.

Use Cases (Strategy)

When Unicorn starts up with an appropriate configuration, it will first read your application into memory as one master process. Then, once it's done that, it will read that configuration and run its before_fork block. After that, Unicorn will fork itself into N number of workers as defined in its configuration, and then each worker will execute its own after_fork block. Unicorn's master process then sits back, grabs a cup of coffee and a newspaper, and plays "manager", just watching the other processes, ready to kill one off via SIGKILL if it takes too long to execute a request. If that happens, or if the process crashes, Unicorn simply forks itself to replace it.

Unicorn is built to cut off long running worker processes after a default of 30 seconds via SIGKILL. This is why Unicorn says it's built for "fast clients" - because anything beyond that cut-off is subject to termination. This can be changed in Unicorn's configuration, however.

Unlike Passenger (even version 4 - except enterprise), Unicorn is capable of a zero downtime deploy. By sending the Unicorn master a HUP signal, it will reload itself and its workers based off your most recent code deploy. Note that if using the preload_app feature, a USR2 + QUIT has to be sent to the master process. This tells the master to load up another copy of itself, and once verified that copy is running, kill off its old workers. Either case can effect a zero-downtime deploy, though the second of these can cause a temporary memory usage spike depending on the size of the application.

Unicorn is best used in situations with one specific application on a host, as it will spawn several workers as configured and maintain them at all times. Ergo, memory consumption could be a problem if you launch more workers than you have memory to reasonably support.

At Engine Yard, we've generally found that for dedicated applications, Unicorn is a better choice over Passenger for several reasons. First, even though Passenger is, for all intents and purposes, a quality product, we've seen several cases with version 3 where Passenger has crashed, failed, had a process become unresponsive or taken on some other extremely strange behavior that isn't logged anywhere and makes absolutely no sense. The term "gremlins" truly applies here. We see far less of this with Unicorn, and from our experience, we find that Unicorn tends to be quite a bit more stable. However, it should be noted again that this is with Passenger 3. We have yet to see this same degree of comparison versus Passenger 4, especially at high load, and I'm hopeful that this kind of comparison will no longer be valid with Passenger 4.

Unicorn is probably not well suited to applications that have long-running requests, large uploads, or long-polling/websockets etc. as it doesn't run a threaded or evented architecture. It's very simple and straight-forward, finding any request it can, working it as fast as it can, and repeating the loop. Any request that takes longer than the configured cut-off will just be terminated.

All around, we find that Unicorn's best use case is with a single, dedicated application running at all times behind a reverse proxy (nginx) and load balancer (haproxy or ELB on Engine Yard) that stick to a regular, request/response application flow cycle. It generally processes requests very fast and has a highly stable architecture.

Configuration (Training)

Unicorn can use a configuration file that details, in Ruby, what to do before_fork, after_fork, and so on. For example, before_forking the master into a worker process, you want to disconnect ActiveRecord if it's got an open database connection; otherwise that could get pretty funky when you have several worker processes using the same database connection later on down the road. You can then re-establish a new connection inside the worker process by calling the appropriate method in an after_fork block in the Unicorn configuration.

Unicorn is generally set to listen to one specific socket on the local machine. You'd then have nginx put all its requests there as a location block in its configuration. Unicorn workers then proactively, of their own volition, examine that socket and grab requests to work from it directly, instead of having a single thread or process push requests to each worker independently.

Some of Unicorn's more interesting configuration options:

  • listen: tells Unicorn where to put the socket for requests
  • timeout: number of seconds to wait on a worker before forcibly killing it with a SIGKILL.
  • worker_processes: how many worker processes to fork. In memory, you'll have N+1 - the master isn't counted among the total here. (Example: worker_processes: 6 means you have 7 Unicorn processes - the master and 6 workers).
  • stdout/stderr_path: logs output from stdout/stderr directly to the log file locations for each option

I generally recommend running multiple Unicorn workers in production. Generally, I would take the number of CPU cores on a machine and multiply by 1.5, then round up. This ensures you have enough processes to continue allowing workers to process requests if one happens to be stuck with a long running request, or is waiting on I/O or network response for an abnormal amount of time. This is because, unlike Thin or Puma, Unicorn is neither evented nor is it multi-threaded.


Thin is an EventMachine-based application server for Ruby that claims to be the "most secure, stable, fast and extensible Ruby web server". It's similar to Unicorn in that it launches multiple workers and listens to a socket, but different in that it has no master controlling process and has one specific socket per worker process.

Mode of Operation (Fighting Style)

Thin works much like Unicorn, except that when started in cluster mode, each Thin worker opens its own socket, or is bound to its own port. Nginx can then be configured to "round-robin" balance requests between as many Thin sockets/ports as you have configured to start. It doesn't have a master process (by default) that runs and monitors the workers like Unicorn does, but it is capable of a "hot restart" using the "onebyone" option in configuration, which restarts one worker at a time for zero-downtime deploys.

Under the hood, Thin relies on an EventMachine-based architecture. This is not a fully asynchronous architecture like Puma, launching new requests in threads, but in theory it should allow for significant speed improvements by taking action only when enough data has been received from the client, for example (when an "event" fires as opposed to waiting for something to finish).

Use Cases (Strategy)

Thin isn't yet available on Engine Yard Cloud (let us know if you'd like to see this change), though it can be installed via custom chef recipes, which I've done for this talk (and the source code can be seen in the cookbooks/ directory).

Like Unicorn, Thin is best suited to a single application running at all times on a given host. Since our platform doesn't support Thin at this point in time, we can't offer up any comparisons of Thin versus any of the other application servers discussed here.

Thin runs based on EventMachine under the hood. This means that applications that can benefit from an evented architecture - for example, a long-polling application - may benefit significantly from Thin.

Configuration (Training)

Thin's overall configuration is similar to Unicorn, though instead of just one socket, Thin has a socket for each individual worker when launched in cluster mode. This means that your reverse proxy (nginx in our case) needs to balance requests between those sockets. If you have, for example, 5 Thin workers, you should have 5 sockets and 5 entries in an nginx upstream:

upstream thin_upstream {
  server unix:/var/run/engineyard/thin.example.1.sock fail_timeout=0;
  server unix:/var/run/engineyard/thin.example.2.sock fail_timeout=0;
  server unix:/var/run/engineyard/thin.example.3.sock fail_timeout=0;
  server unix:/var/run/engineyard/thin.example.4.sock fail_timeout=0;
  server unix:/var/run/engineyard/thin.example.5.sock fail_timeout=0;

Thin can be booted with a configuration file in YAML format. Some of the more interesting options:

  • environment - "production", "staging", etc.
  • servers: number of application workers you want to run
  • onebyone: boolean; whether or not to do hot restarts, restarting each worker "one by one"
  • tag: - a special tag that will be seen in ps output on the machine. "thin-MyAppName" for example

Even though Thin is evented, I would still recommend the same basic configuration for Thin's number of production workers as I mentioned for Unicorn: (num_cores * 1.5).round. This should provide you with more than enough workers in the unlikely event that one gets tied down somehow.


Puma is an Engine Yard sponsored project. Its primary strength is that it's a truly concurrent application server, unlike any of the other examples mentioned here (Thin's concurrency support is labeled "experimental" at present).

Mode of Operation (Fighting Style)

Puma can be configured to bind to ports, or to pull from a socket, just like Thin and Unicorn. However, unlike Thin and Unicorn, Puma will open a new thread for each incoming request. This means that blocking actions that aren't necessarily heavy on CPU usage should not be a problem for Puma.

However, when discussing threading, we must constantly be aware of Ruby's Global VM Lock (GVL). This is a limitation (possibly a feature?) of the language interpreter that ensures that even when launching new threads, except in specific cases, Ruby will only execute one ruby code instruction at a time. MRI can still hand off async instructions to underlying C-based drivers, for example, but as for executing actual Ruby code, the GVL ensures that only one instruction is processed at a time per each Ruby process.

For this reason, Puma will run best under JRuby or Rubinius. Unfortunately, I didn't have time to profile Puma under either of these interpreters; instead performance benchmarking below is based on MRI 2.0. This is, admittedly, somewhat unfair to Puma, but maintains comparison parity with the other application servers mentioned here.

Use Cases (Strategy)

Unlike the above referenced app servers, Puma is a fully threaded application server. It doesn't rely on EventMachine like Thin, and like Unicorn it can run several workers pulling requests off of a centralized unix socket, or it can bind a worker to a port. Puma doesn't maintain a master process like Unicorn, however.

Because Puma is threaded, it can benefit more from JRuby or Rubinius than the other items here. Unfortunately there wasn't enough time to profile results under JRuby/Rubinius for Puma, but an application may see a performance gain even on MRI 2.0, depending on what's going on.

Puma is best suited to single applications running on a host, just like Thin and Unicorn. However, because it's multi-threaded, if you run a Ruby implementation without an internal GVL (JRuby/Rubinius), you can theoretically run a single Puma process on your machine instead of one worker per core plus a few for good measure as you generally have to for the other application servers mentioned here. This is because Puma can request a new thread from the operating system for each incoming request, drop execution of that thread while waiting on external events (disk I/O, database driver access, network I/O, etc.), pick up other threads while this is going on, and and then pick up execution of the other thread after the "wait" event is finished.

Under MRI, because of the GVL, Puma can't quite shine as well as it would under a non-GVL-addled interpreter. However, even still, there are several cases where Puma may be a good fit. If you have an application that isn't particularly CPU bound, but does execute multiple external requests to databases, APIs, and disk I/O, Puma may provide a performance boost.

Configuration (Training)

Like Thin, Puma can take arguments on the command line or through a configuration file. However, since Puma is threaded, it has multiple other options designed to give you control over those threading capabilities.

Like Unicorn, Puma maintains a single socket for nginx to pull requests from.

Some of Puma's more interesting options:

  • -e environment: "production", "staging", etc.
  • --pidfile - where to put the pidfile
  • -t X:Y - minimum number of threads, maximum number of threads, per process. Example: -t 4:16
  • -w X - how many workers to spawn. Theoretically not necessary on JRuby/Rubinius, but with MRI you should spawn the same number you would for Thin or Unicorn
  • -b: where to bind to. Can be either a unix socket or a port.

The Arena

The arena in which all these application servers will do battle is the application in this repository. It's a basic Sinatra app with some purely academic examples, not designed to do real, meaningful work, but instead to attempt a fair (or "fair enough") set of tasks for each application server to process and repeat multiple times.


This action spits out information about the application server itself (pid of the worker, name of app server processing the request), and some information about the request and response environment variables. It's designed to be as close to the "metal" as reasonably possible, and does not reference any external data store.


Computes pi to 5,000 decimal places. Initially, I was doing 20k, but that turned out to cause all application servers to fall over very quickly into my tests, leaving them without enough sample size to be considered even borederline reliable.

This task is designed to simulate mildly-computaitonally expensive work and see how each does under that particular type of strain.


I am so excite! The /borat resource fetches the last 10 tweets from @devops_borat. The intent here was to generate a test that would give a fair estimate of how various servers performed with real-world network latency, but unfortunately, Twitter's API rate limitations prevented me from testing this as thoroughly as I would have liked. I also was unable to include it in my /random test for that very reason.


Sleeps for 1 second then returns a response. Spoofs unusually high request queuing and shows how each processes requests while stuck in a "wait" like situation.


Does one of the above at random with a 50% chance to do the first. The idea would be to simulate load based on several users attacking the application at once, all doing different things.


In no case can any of the performance tests here be taken as "gospel", or in any way interpreted that these simplistic use cases are in any way all-encompassing. These are really straight-forward and simple tests that show different servers under different situations, but are not generalizable and applicable to even a majority of applications out there, especially those in complex problem domains.

In other words, your mileage may vary.

Testing hardware

This app was deployed to four different environments on Engine Yard Cloud backed by Amazon EC2 in US East 1. All VMs are High CPU Mediums. They come with 2 VCPU cores each and 1.7GB of memory. No database was used and no external data store (with the exception of the Twitter example, since a REST API could be considered a data store) was used in the app because I didn't want to artificially slow down tests because the app server was waiting on, for example, the database driver for whatever reason - that'd just skew the tests in a weird way.

Testing methodology

I used a tool called Siege by Jeff Fulmer. I'd had major problems getting apache bench to work right and stumbled on Siege, which has a lot of very interesting options.

(Tip: you can install this crazy easy on OS X via brew install siege. If you don't have Homebrew, you really should get it.)

I used the following command/syntax to perform these tests, then put the results in flat files you can read under the "performance" subdirectory:

siege -r 1000 -c 100 -b -q http://asa-puma/sleep > puma.txt 2>&1

I'm not concerned with doing anything too fancy here, I just want a straight up, "how fast can you do it?" test. With the /borat example hitting the Twitter API, I left off the -b (benchmark) option to allow siege to internally throttle requests so as not to get blacklisted from Twitter.

The arguments used:

  • -r repetitions. Do this test N number of times, in this case, 1000.
  • -c concurrency. How many simultaneous requests are we doing? In this case, 100 simultaneous requests, a thousdand times over.
  • -b benchmark. Removes internal throttling from siege. Really tries to hit the server crazy hard.
  • -q quiet. Suppresses output that otherwise shows every. single. get. request. ever. made. throughout the entire test.

Other than that, I used the default settings for siege:

jaustinhughey:~/ $ siege -C                                                                                                                                                                                                        [14:45:02]
JoeDog/1.00 [en] (X11; I; Siege 3.0.0)
Edit the resource file to change the settings.
version:                        3.0.0
verbose:                        true
quiet:                          false
debug:                          false
protocol:                       HTTP/1.1
get method:                     HEAD
connection:                     close
concurrent users:               15
time to run:                    n/a
repetitions:                    n/a
socket timeout:                 30
delay:                          1 sec
internet simulation:            false
benchmark mode:                 false
failures until abort:           1024
named URL:                      none
URLs file:                      /usr/local/Cellar/siege/3.0.0/etc/urls.txt
logging:                        true
log file:                       /usr/local/var/siege.log
resource file:                  /usr/local/Cellar/siege/3.0.0/etc/siegerc
timestamped output:             false
comma separated output:         false
allow redirects:                true
allow zero byte data:           true
allow chunked encoding:         true
upload unique files:            true

A quick note about the "concurrency" rating you'll see in these tests: the higher it is, the worse the app server performed. This is because the number in the concurrency rating is how many requests are being processed at the same time, on average. If performance is high, very few things will be processed "at the same time" because formerly issued instructions (requests) will have already been processed. So, higher concurrency, lower performance according to siege output.

Results & Analysis


Performed reasonably well all around. Fell over hard on the pi test and failed to complete /server. Didn't even make it a quarter of the way through /sleep. Roughly on-par with the other contestants when doing simple tasks under mild to moderate load. Highest rate of concurrency on /borat test.

Outperformed Puma in the /random test and maintained a very high degree of concurrency during the same. Failed to complete the /random test however, and had the longest "shortest duration" metric. Average. Appears to be a solid contender for generic use cases. Will likely outperform the others here in a low-to-moderate traffic, multi-application deployment due to its elastic loading of applications into memory, thus making your dollar go farther.


Faster than Passenger or Thin with /borat, has second highest transaction rate for the same. Shortest duration of both longest and shortest transaction with /borat as well. One of only two that actually finished the /pi test, and did so faster than Thin. Maintained highest concurrency and transaction rates on /pi of all contestants. Had the shortest "longest transaction" on /pi. Completed entire /server test. Was second in total duration only to Thin. Only contestant to complete all /sleep tests and still managed to have the shortest "longest transaction" duration.

Zero failed transactions on /random with highest concurrency rating.


One of only two to complete the /pi test, though it was slightly slower than Unicorn. Had the shortest transaction under /pi and quickest "longest transaction" under /server. Maintained highest concurrency rating and transaction rate under /server. Failed miserably with /sleep, however, only completing 66 requests!

One of only two to finish the /random test, though it took much longer than Unicorn and had the lowest transaction rate. Thin appears to be an excellent choice for any application that doesn't rely on lots of wait-like scenarios or that don't have much request queueing.


Fastest to finish /borat. Failed miserably with the /pi test. On par with Unicorn's speed for finishing the /server test, had second highest transaction rate for the same. Failed the /sleep test horribly, finishing fewer than 25% of the test.

Failed the /random test horribly, finishing less than 25%. I found this extremely odd so I went for a second round for Puma on the /random test, thinking that perhaps over my planned 100,000 requests, it was thrown an inordinate number of "pi" tests (because it is random, after all). The results were repeated the second time around. While it's still possible that it was hit with an unusually large number of "pi" tests, it's not as likely as it just plain doesn't work well when presented with computationally expensive tasks while processing multiple other incoming requests.

In all fairness, due to Ruby's GVL, I should have at least run more Puma workers. Instead, in the interest of research, I ran only as many as there are CPU cores on the machine (2) because I wanted to see, even with a 1:1 worker:core ratio how Puma performed compared to its contemporaries. In most cases, the /pi and /random tests being notable exceptions, it performs on-par with the others tested here.


  • Multiple low traffic apps on limited hardware? Passenger.
  • Using live streaming in Rails 4? Puma or Thin.
  • Single app, fast request/response cycle (maybe an API for example)? Unicorn.
  • This test wasn't as fair as it should have been to Puma because of the GVL in MRI. The test should be repeated under JRuby and/or Rubinius to see how it performs when it has all the tools it's supposed to.
  • Passenger 4 may blow Passenger 3 out of the water - we'll just have to wait and see.
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