Welcome!

Open Source Cloud Authors: Yeshim Deniz, Pat Romanski, Elizabeth White, Liz McMillan, Zakia Bouachraoui

Related Topics: Microservices Expo, Java IoT, Industrial IoT, Open Source Cloud, Machine Learning , Python

Microservices Expo: Article

Performing Under Pressure | Part 1

Load-Testing with Multi-Mechanize

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.

load-testing

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:

Comment submission is a little bit different because it works via AJAX.  The mechanized browser doesn't process JavaScript, meaning that we'll have to take things into our own hands here.  So, we inspect two forms to grab the state information that JavaScript on the page would use to submit the form, and we construct our own request manually.  (Form 0 provides the ‘uh' value in a hidden field; form 12 is the top-level comment submit form.)

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 article, we'll talk about how to gather actionable data from your load tests.

Related Articles

Performing under pressure, pt. 2: Collecting and visualizing load-test performance data

Python and Gevent

Tracing Python - An API

More Stories By Dan Kuebrich

Dan Kuebrich is a web performance geek, currently working on Application Performance Management at AppNeta. He was previously a founder of Tracelytics (acquired by AppNeta), and before that worked on AmieStreet/Songza.com.

IoT & Smart Cities Stories
Early Bird Registration Discount Expires on August 31, 2018 Conference Registration Link ▸ HERE. Pick from all 200 sessions in all 10 tracks, plus 22 Keynotes & General Sessions! Lunch is served two days. EXPIRES AUGUST 31, 2018. Ticket prices: ($1,295-Aug 31) ($1,495-Oct 31) ($1,995-Nov 12) ($2,500-Walk-in)
Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life settlement products to hedge funds and investment banks. After, he co-founded a revenue cycle management company where he learned about Bitcoin and eventually Ethereal. Andrew's role at ConsenSys Enterprise is a mul...
Nicolas Fierro is CEO of MIMIR Blockchain Solutions. He is a programmer, technologist, and operations dev who has worked with Ethereum and blockchain since 2014. His knowledge in blockchain dates to when he performed dev ops services to the Ethereum Foundation as one the privileged few developers to work with the original core team in Switzerland.
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...
The challenges of aggregating data from consumer-oriented devices, such as wearable technologies and smart thermostats, are fairly well-understood. However, there are a new set of challenges for IoT devices that generate megabytes or gigabytes of data per second. Certainly, the infrastructure will have to change, as those volumes of data will likely overwhelm the available bandwidth for aggregating the data into a central repository. Ochandarena discusses a whole new way to think about your next...
CloudEXPO | DevOpsSUMMIT | DXWorldEXPO are the world's most influential, independent events where Cloud Computing was coined and where technology buyers and vendors meet to experience and discuss the big picture of Digital Transformation and all of the strategies, tactics, and tools they need to realize their goals. Sponsors of DXWorldEXPO | CloudEXPO benefit from unmatched branding, profile building and lead generation opportunities.
Dynatrace is an application performance management software company with products for the information technology departments and digital business owners of medium and large businesses. Building the Future of Monitoring with Artificial Intelligence. Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more busine...
All in Mobile is a place where we continually maximize their impact by fostering understanding, empathy, insights, creativity and joy. They believe that a truly useful and desirable mobile app doesn't need the brightest idea or the most advanced technology. A great product begins with understanding people. It's easy to think that customers will love your app, but can you justify it? They make sure your final app is something that users truly want and need. The only way to do this is by ...
DXWorldEXPO LLC announced today that Big Data Federation to Exhibit at the 22nd International CloudEXPO, colocated with DevOpsSUMMIT and DXWorldEXPO, November 12-13, 2018 in New York City. Big Data Federation, Inc. develops and applies artificial intelligence to predict financial and economic events that matter. The company uncovers patterns and precise drivers of performance and outcomes with the aid of machine-learning algorithms, big data, and fundamental analysis. Their products are deployed...