Many types of performance problems can result from the load created by concurrent users of web applications, and all too often these scalability bottlenecks go undetected until the application has been deployed in production. Load-testing, the generation of simulated user requests, is a great way to catch these types of issues before they get out of hand. Last month I presented about load testing with Canonical's Corey Goldberg at the Boston Python Meetup last week and thought the topic deserved blog discussion as well.
In this two-part series, I'll walk through generating load using the Python multi-mechanize load-testing framework, then collect and analyze data about app performance using Tracelytics.
Also, a request: there's mechanize documentation available, but I unfortunately haven't found any full documentation of the python mechanize API online-post a comment if you know where to find it!
Meet the app: Reddit
The web app that I'll be using for all the examples in this post is an open source reddit running on a single node in EC2. You don't need to understand how it works in order to enjoy this post, but if you do want to play along, there is a super-easy install script that sets up a whole stack.
Generating the data
Performance testing can start off simple: hit pages in your app, and monitor how long they take to load. You can automate this using something like the mechanize library in Python, or even something more low level like httplib/urllib2. This is a good start, but today we're looking for concurrency as well.
Enter multi-mechanize. Multi-mechanize takes simple request or transaction simulation scripts you've written and fires them repeatedly from many threads simultaneously in configurable patterns. It's as easy as writing a few scripts that simulate users doing different actions on your website (login, browse, submit comment, etc.) and then writing a short config that tells multi-mechanize how to play them back.
Since our site is reddit, I'm going to write a few scripts that read discussion threads, one that posts comments, and one that votes on comments. I'll walk through writing to of the scripts: a simple read-only request, and more complicated one that logs in and submits a comment. The rest of them are available in the full source of the load tests on github.
A simple mechanize script
First, the test is wrapped in a Transaction class-this is how multi-mechanize will run each of your scripts. The __init__() is called once per worker thread, then run() will be invoked repeatedly to generate the requests of your transaction. For development and debugging, it's easier to just run the scripts individually, so the __main__ block at the end provides that functionality.
All of the work here is happening in the run() method. A mechanize browser is instantiated, and our simple request for the front page of Reddit is performed. Finally, we make sure that a valid page was returned.
There's one more thing: custom timers. Multi-mechanize can collect timing information about the requests it performs. If you store that information in a correctly-named dict, it will be able to generate charts of the data later.
A more complicated script
Now, let's take a look at a slightly more complicated script. This one posts a comment on a particular story, so it'll have to take the following actions:
- Log in as a user
- Open thread page on Reddit
- Post comment
It's a bit longer, so I've broken it up according to the bullets above with accompanying explanation. The full version of the example can be found here: https://gist.github.com/1529242
The first thing that's happening here is familiar: pulling up a page in our mechanize browser. In order to login, though, we need to start interacting with forms on the page, and this means tweaking some of the default mechanize browser settings. We need to set three attributes on our browser: follow 30x redirects (lets the login redirect back), specify the Referrer page header (validation for comment post), and ignore robots.txt rules (Reddit doesn't like robots playing human).
After that, it's on to forms. The mechanize browser interface with forms is pretty simple: you can list all the forms on the page with browser.forms, select a form to interact with using select_form, and then manipulate the fields of the form using the browser.form object.
select_form can take a variety of selection predicates, most of which revolve around using attributes such as the form's CSS ID. Our example, Reddit, doesn't have much identifying information associated with forms, so I've used numeric selection to grab them. The login form happens to be form 1.
Pretty straightforward: head to the thread page now that we're logged in. Now we want to actually submit the comment. Here's the heavy lifting:
In this simple example, user credentials are provided in __init__. However, a more realistic example might involve many different users logging in. In the code on github, I've written auser pool implementation that takes care of this problem by instantiating a pool of logged in users for each script (then, each invocation of run can check out a different user).
(Debugging note for those playing along: if comments are not showing up in the thread but are showing up in the users profile, that means that some of the background jobs may not be running correctly. The site re-caches the comment tree asynchronously after posts.)
Running the full load test
After writing a few individual mechanize scripts, the final step is putting them all together with a multi-mechanize config. Multi-mechanize organizes load tests in terms of "projects" which are represented by subdirectories of a directory called projects. Each project contains a config file and a directory called test_scripts which contains your individual load tests scripts. It should look like this:
The config file specifies how long the load test should run for, whether it should ramp up the amount of pain or keep it constant, a few output and statistics settings, and of course the number of threads and scripts you want to run. Here's an example config:
Runtime sets the duration of the test, in seconds. Ramp up, if nonzero, tolls multi-mechanize to linearly increase the number of threads up to the specified numbers during the wrapup.
And here's how to invoke them, finally:
Learning from our load tests
Multi-mechanize collects statistics about timing information that you provide in your tests (custom_timers) and dumps the output in a results subdirectory of your project. This can easily be plotted in your favorite graphics package. Here's an example of the average times from a read load increasing over 30 minutes:
Ok, so it's getting slower, but why?? These timers treat the application like a black box-they'll show you that it can be slow, but you won't know why or what layers of the stack are slow. In the next post, we'll talk about how to gather actionable data from your load tests.