Grafana is a data visualization and monitoring tool allowing you to show metrics stored for example in InfluxDB as charts. It gives you possibility to create dashboards to link charts or histograms into connected groups. Thanks to built-in InfluxDB support it automatically handles provided aggregation functions. Query editor supports also tag or field completion.
Mocking is just pretending to be and simulating specific behavior, we can replace our code’s parts with fake objects and verify how system behaves. At some point our system’s code starts to be complicated and have so many dependencies what makes it impossible to test without mocks. To cover all corner cases or to freely control our test scenario flow we have to simulate part of functionality – we have to force it to behave in exact way we want. For this purpose python uses unitest.mock library which is a standard library starting from Python 3.3 (for older versions installation is required).
JMeter is an open-source JAVA-based load testing tool for measuring and analyzing performance of services and applications. It simulates user behavior by sending requests to server. It can be used for testing variety of protocols and services like HTTP, HTTPS, SOAP / REST Webservices, FTP, JDBC, LDAP, SMTP, POP3, IMAP and many others.
InfluxDB it is a database oriented on time series. It was designed to accept large number of queries and write requests. It is perfect for storing timestamped data – data we would like to query by timestamp rather than index. It supports SQL-like language to easily pull data off as well as JSON requests through HTTP API. Influx uses tags and fields - tags to easily index series of data (you can query by it after where clause) and fields for keeping values (select field_1, field_2 from … )
In my professional career I used to write tests with both frameworks: unittest and pytest. Both are great tools with certain pros and cons, but pytest is significantly more popular these days. In this short blogpost I am going to share with you a couple of pytest’s features, that in my opinion, provide the answer for the heading question.
Parametrized tests are a lot easier to maintain. Usually when single requirement changes it’s enough to change one variable in the code. You don’t have to read the whole implementation of the test (If you know what your variable means).
Although it's quite powerful tool, you need to customize it to get the full benefit of it. One of these improvements is usage of async/await commands (from Node 7.6 version). In this article I am going to show you how to adjust your code to make this commands work.
Are you tired of scrolling through dozens of red and green files in code reviews, only to click “Reviewed” after a few minutes of staring at the screen? You should be. Because this is not the point of reviewing the code.
In this article I will show you how to write unit tests for Node.js modules and its dependencies from scratch.