TL;DR notes from two articles I read today.
The best ways to test your serverless applications
- For the serverless functions you write, test for each of the following risks: configuration (databases, tables, access rights), technical workflow (parsing and using incoming requests, handling of successful responses and errors), business logic and integration (reading incoming request structures, storage order in databases).
- Break up functions into hexagonal architecture (ports and adapters) with separation of concerns through layers of responsibility.
- For unit tests, use a local adapter or mock as an adapter to test the function business layer in isolation.
- Use adapters to simulate to test integration with third-party end services. Save memory and time by testing not for full integration but file storage integration with the in-memory adapter.
- For proper monitoring of integrations, use back-end tools such as IOpipe, Thundra, Dashbird, Epsagon, etc., and front-end tools such as Sentry or Rollbar. You can also use an open-source error tracking app such as Desole that you install in your AWS account.
Full post here, 10 mins read
How to measure the reliability of your software throughout the CI/CD workflow
- In addition to CI/CD, you should consider incorporating continuous reliability in the workflow. This may mean more focus on troubleshooting than on writing code.
- Consider whether to automate every step, or even whether some steps should be automated more than others.
- Look beyond log files and testing to determine quality of code: set up advanced quality gates to block problematic code from passing to the next stage and use feedback loops to inform more comprehensive testing.
- In addition to log aggregators and performance monitoring tools, get a more granular understanding of app quality by ensuring you can access the source code, variable state and stack trace at the time of an error. Aggregate this data across the app, library, class, deployment or another boundary for an insight into the functional quality of the code.
- Based on this data, you can categorise known, reintroduced and unknown errors, classify events, understand frequency and failure rates, enabling you to write more comprehensive tests for development and pre-production environments alike, driving the higher code quality.
Full post here, 6 mins read
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