If you have a Python string that contains the representation of escaped characters (say, a backslash \, an x, and two hexadecimal digits), and you want to decode those escapes back to the actual character they represent, you can use codecs.escape_decode.
The dramatic increase in Learnosity users during the back-to-school period each year challenges our engineering teams to find new approaches to ensuring rock-solid reliability at all times.
Stability is a core part of Learnosity’s offering. Prior to back-to-school (known as “BTS” internally) we load-test our system to handle a 5x to 10x increase on current usage. That might sound excessive, but it accounts for the surge of first-time users that new customers bring to the fold as well as the additional users that existing customers bring.
Since the BTS traffic spike occurs from mid-August to mid-October, we start preparing in March. We test our infrastructure and apps to find and remove any bottlenecks.
Last year, a larger client ramped up their testing. This created a 3x usage increase of our Events API. In the process, several of our monitoring thresholds were breached and the message delivery latency increased to an unacceptable level.
As a result, we poured resources into testing and ensuring our system was stable even under exceptional stress. To detail the process, I’ve broken the post into two parts:
Creating the load with Locust (this piece)
Running the load test (in part two, coming soon).
Here’s a snapshot of what I cover in this post:
Our target metrics.
How we wrote a Locust script to generate load for a Publish/Subscribe system.
Our observations that:
The load test must reflect real user behaviours and interactions
Load testing alone doesn’t validate system behaviour against target metrics. It’s better to measure this separately while the system is under load.