Our great sponsors
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
Well, the service is done. I've shown how easy it is to do things with Beanie. You can stop thinking about parsing and validating database data and just focus on your project. Certainly, I didn't use all the possible functions. You can find the whole list of methods in the project description here
But that's a bit boring, isn't it? Now let's get to the interesting part - the usage examples. It will show how handy this tool is. I'm deliberately omitting some additional imports and helpers here, so as not to overload the picture and focus only on the important things. The entire working app from this article is in my GitHub repo beanie-fastapi-demo.
The main component of Beanie is Pydantic - a popular library for data parsing and validation. It helps to implement the main feature - data structuring. Beanie Document - is an abstraction over the Pydantic BaseModel that allows working with Python objects at the application level and JSON objects at the database level. In the general case, one MongoDB collection is associated with one Beanie Document. This brings predictability when working with the database, and at the same time preserves all the flexibility of MongoDB documents - it is possible to represent any data structure with the Pydantic model (or even a group of structures with Optional and Union annotations).