LiveCheck: End-to-end test for production/staging.¶
What is the problem with unit tests? What is difficult about maintaining integration tests? Why testing in production makes sense. Bucket testing, slow deploy of new tests will give confidence of a release. A staging environment is still desirable.
Enables you to:
track requests as they travel through your micro service architecture.
define contracts that should be met at every step
all by writing a class that looks like a regular unit test.
This is a passive observer, so will be able to detect and complain when subsystems are down. Tests are executed based on probability, so you can run tests for every requests, or for just 30%, 50%, or even 0.1% of your requests.
This is not just for micro service architectures, it’s for any asynchronous system. A monolith sending celery tasks is a good example, you could track and babysit at every step of a work flow to make sure things progress as they should.
Every stage of your production pipeline could be tested for things such as “did the account debt exceed a threshold after this change”, or “did the account earn a lot of credits after this change”.
This means LiveCheck can be used to monitor and alert on anomalies happening in your product, as well as for testing general reliability and the consistency of your distributed system.
Every LiveCheck test case is a stream processor, and so can use tables to store data.
Tutorial¶
LiveCheck Example.
First start an instance of the stock ordering system in a new terminal:
$ python examples/livecheck.py worker -l info
Then in a new terminal, start a LiveCheck instance for this app
$ python examples/livecheck.py livecheck -l info
Then visit
http://localhost:6066/order/init/sell/in your browser.Alternatively you can use the
post_ordercommand:$ python examples/livecheck.py post_order --side=sell
The probability of a test execution happening is 50% so you have to do this at least twice to see activity happening in the LiveCheck instance terminal.
