MongoDB VS SqlKata Query Builder

MongoDB — a database service

MongoDB is an open source NoSQL Document database which horizontally scales utilizing clustered servers in replica-sets. A great option for testing and performing local development with MongoDB is using Docker and Docker Compose. The following docker compose spec will create a local docker container running MongoDB with the data persisted to a docker volume.

MongoDB (Mongoose)

Databases Backend developers use databases like MySQL, PostgreSQL, MongoDB (NoSQL), or Redis (an in-memory database) to store and manage data. It is important for persistent data storage and retrieval in web applications.

MongoDB{:target="_blank"} is a document-oriented database with high scalability and flexibility for handling unstructured or semi-structured data. It offers rich querying capabilities, indexing options, and support for distributed data storage with a technique called “sharding”.

NoSQL databases are non-relational databases with flexible schema designed for high performance at a massive scale. Unlike traditional relational databases, which use tables and predefined schemas, NoSQL databases use a variety of data models. There are 4 main types of NoSQL databases - document, graph, key-value, and column-oriented databases. NoSQL databases generally are well-suited for unstructured data, large-scale applications, and agile development processes. The most popular examples of NoSQL databases are MongoDB (document), Memgraph (graph), Redis (key-value store) and Apache HBase (column-oriented).

Our example application will be based on the ME*N stack where M is a MongoDB (but it could be MySQL, or any other) database. E is for Express, which is a backend framework that sits on top of Node.JS. There are others but Express is extremely common and will meet our needs well. N is for the JavaScript runtime Node.js but Deno and Bun are possible alternatives. Finally the * is for the frontend framework such as Angular (MEAN), React (MERN) or Vue (MEVN), to name a few. However, in our example we will not be using a framework (to keep it agnostic and besides it is not that complicated), instead we will be using the native web technologies (HTML, CSS and JS).

MongoDB is a highly scalable document-type database. Its flexible schema approach allows developers to adapt and modify the data structure as needed, providing agility in application development.

MongoDB is an open-source, document-oriented database, meaning it stores data in a semi-structured format similar to JSON (JavaScript Object Notation), known as BSON (Binary JSON).

SqlKata is your friend.

Don't know if can be a good pick for the no-ORM requirement but I would take a look at SqlKata which is a nice query builder + execution engine, built on top of Dapper

That is basically the description of an object mapper, with all the guarantees of an object mapper :). It seems if you actually use the query builder as such, no guarantees exist.

I'm pretty picky regarding query builders and ORM's, to the extent of having written several of them over the years, in different languages (both dynamic and strong typed, unfortunately closed-source). I'm a strong advocate of schema-first design, and usually a query builder will allow you to design your queries explicitly, but having some internal behaviors (such as string concatenation, identifier quoting and automatic in-order separation of parameters and values to be bound) taken care of. As good examples of this, I'd mention golang's goqu (https://github.com/doug-martin/goqu) and - to some extent - C# SqlKata (https://sqlkata.com/). Following my frustrations with Python ORMs, I built my own toy project, sort-of-in-beta, called rickdb (https://github.com/oddbit-project/rick_db).

Less about the exact syntax and more about the tool, for example: https://github.com/sqlkata/querybuilder. I just chose that since it was on top of a search but the idea is the same. Your code generates raw SQL, so it's 100% interchangeable with writing SQL yourself however the builder library deals with the syntax, proper ordering, quoting, full attribute names, etc. Some such libraries even let you define your schema in code to make your SQL generation type safe.