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When we write tests, we've inevitably got to choose an interface to write against - for a unit test, this is the interface of whatever unit is under test, usually the type signature of a function or the public methods on a class. These unit interfaces tend to change often in response to refactoring, optimisations, new requirements and so on, and they should be able to change quickly too, or we make any of these important improvements.

Instead what we want to do is write our tests against interfaces that seldom change, and thus we should target public, external interfaces, which are (generally) better designed and much slower to change than the non-exposed interfaces on units. For example, in our scenario above, we could treat our system as a black box and use its HTTP API as the interface that we use to test it (e.g. using supertest), use a mocked HTTP API for the web service it calls (e.g. using nock), and run it against a real database that we reset after each test.

Python standard library offers 3 concurrency APIs.

How do you know which API to use in your project?

In this tutorial you will discover helpful step-by-step procedure and helpful questions to guide you to the most appropriate concurrency API.

After reading this guide, you will also know how to choose the right Python concurrency API for current and future projects.

The JSON format remains one of the most popular text data formats for Data-in-Transition. You can encounter JSON data on every stack level of your application: from the database to UI, from IoT sensors data to the mobile app’s payload. And it is not a coincidence; the format has a good balance between being convenient for developers and decent payload density. In Rust ecosystem, the de-facto standard for dealing with JSON is Serde. Although it is the best choice for most cases, there can be alternative approaches that can work best for your application. One of these approaches we are going to cover in this article.

By default Flask writes logs to the console in plain-text format. This can be limiting if you intend to store your logs in a text file and periodically send them to a central monitoring service. For example, Kibana, only accepts JSON logs by default.

You might also want to enrich your logs with additional metadata, e.g. timestamps, method names, log type (Warn, Debug, etc.). In this post we will use the Python logging library to modify Flask's logging format and write them to a text file. In the end we will see how to periodically send these logs to an external service using Flume.