So you're tasked with building a service that facades your company's legacy user management system. You need to do this as your part of the company needs to handle the legacy system going down.
To add resiliency you are required to save a user's information in case the legacy system is down. You need to be able to handle restarts so this cache will need to be persistent.
You're going to use Cassandra as your persistence, so how do test this? You sit down your your analyst and QA and come up with the following feature:
How would you implement these features? Assuming that the legacy system is a HTTP service you can use Wiremock to mock it being up and down.
For example here is how to mock the legacy system being up with Wiremock:
And an example of it being down:
So the next requirement is that Cassandra being down doesn't make your service fail i.e.
The first one you could stop Cassandra, perhaps using the great tool CCM. However this is slow, and you need to write code to make sure it is back up/down, all of this in a different process. And how about the next test? How do we make Cassandra return the result slowly? Or produce a Write Timeout Exception?
That is where Stubbed Cassandra comes in handy. To get is started you can add some code like this to start it before the tests and close it after tests:
Now implementing the step definition to mimic Cassandra being down is as easy as:
Which is a lot quicker than turning off a real Cassandra, and starting it back up in the @Before is also very quick.
Now to mimic Write time outs in Cassandra:
Very similar things could be done for read timeouts and unavailable exceptions.
This article gave you an insight to how you can behaviour drive features relating to Cassandra being down. The full code and running tests are
. Full information on how to use Stubbed Cassandra is
, you'll probably want to documentation for the Java client for Scassandra which is