db.knowledgeHutCollection.find().pretty() When we use the find command, we fetch all the data set from the collection as follows. Let us check out an example to understand better how projections work:įirstly, get familiar with how MongoDB stores data, as MongoDB is schemaless it stores data in separate documents, here table is denoted as "Collection", a row is "Document" and the column means "Field".Ĭonsider the following collection for a MongoDB projection example, where we have a data set of KnowledgeHut inventory with some documents and fields associated with them. Filter data without impacting the overall database performance.For Example, If a Document contains 10 fields and only 5 fields are to be shown the same can be achieved using the Projections. MongoDB Projection is a special feature allowing you to select only the necessary data rather than selecting the whole set of data from the document. To overcome such issues, MongoDB provides a special feature known as MongoDB Projection. Though when you want to fetch selective information from a huge number of records, MongoDB will process a whole lot of unnecessary data and at the same time put lots of pressure on the overall database performance. The reason that MongoDB is widely used if you want to make the most of this tool, do check MongoDB professional certification. It is faster than RDBMS due to its efficient storage and indexing techniques and being a NoSQL solution it is designed to handle a large amount of data. Review the Building a CRUD App with FastAPI, MongoDB, and Beanie tutorial to see how you can leverage Beanie ODM, which provides an additional abstraction layer over Motor, making it much easier to interact with collections inside a Mongo database.Ĭheck out the Test-Driven Development with FastAPI and Docker course to learn more about testing and setting up CI/CD for a FastAPI app.Mongo DB is a popular NoSQL and open-source document-oriented database which allows a highly scalable and flexible document structure.Configure a static IP on Heroku with Fixie Socks and restrict access to the MongoDB Atlas database.Create a GitHub repo for your application and configure CI/CD with GitHub Actions.Set up unit and integration tests with pytest.You can find the code used in this tutorial on GitHub. Perform a quick self-check by reviewing the objectives at the beginning of the tutorial. In this tutorial, you learned how to create a CRUD app with FastAPI and MongoDB and deploy it to Heroku. You have successfully deployed your application to Heroku. Run heroku open to open your app in your default browser. Set the default database to "students" as well. Once done, grab the database connection information from your cluster by clicking the "Connect" button:Ĭlick on the second option, "Connect to your application":Ĭopy the connection URL, making sure to update the password. For a production app you'll want to restrict access to a static IP. MongoDB Atlasīefore deploying, we need to set up MongoDB Atlas, a cloud database service for MongoDB to host our database.įollow the Getting Started guide where you'll create an account, deploy a free tier cluster, set up a user, and whitelist an IP address.įor testing purposes, use 0.0.0.0/0 for the whitelisted IP to allow access from anywhere. In this section, we'll deploy the app to Heroku and configure a cloud database for MongoDB. Remove any remaining students and test out the read routes again, ensuring the responses are appropriate for an empty database. Retrieve the ID of the user you created earlier and test the delete route: , le = 4.0 ) class Config : schema_extra = doesn't exist". , gt = 0, lt = 9 ) gpa : float = Field (. From typing import Optional from pydantic import BaseModel, EmailStr, Field class StudentSchema ( BaseModel ): fullname : str = Field (.
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