How does a query language like SQL differ from a document query language?

SQL and document query languages differ primarily in their data models, query structures, and use cases. SQL is designed for relational databases, which organize data into tables with fixed schemas and relationships. Document query languages, like MongoDB’s query syntax, work with semi-structured or hierarchical data stored in formats like JSON. These differences influence how developers interact with data and solve specific problems.

The first major distinction is the data model. SQL assumes a rigid, tabular structure where data is normalized into tables with rows and columns. For example, a user’s data might be split into separate users and orders tables linked by foreign keys. Document databases store data as self-contained documents (e.g., JSON objects) with nested structures. A user document might embed their orders as an array within the same document, avoiding the need for joins. This flexibility allows document databases to handle evolving schemas but can make complex relational queries less efficient.

Query syntax and capabilities also differ. SQL uses declarative statements like SELECT * FROM users WHERE age > 30 JOIN orders ON users.id = orders.user_id to fetch and combine data across tables. Document query languages often use method chaining or JSON-like filters, such as db.users.find({ age: { $gt: 30 } }) in MongoDB, which operates on a single collection. While SQL excels at joins and transactions, document queries focus on retrieving and manipulating nested data within a single document. For example, updating a subfield in a document might use db.users.updateOne({ _id: 1 }, { $set: { "address.city": "Paris" } }), whereas SQL would require updating a separate addresses table.

Finally, scalability and use cases vary. SQL databases prioritize ACID transactions and consistency, making them ideal for applications like banking systems. Document databases prioritize horizontal scalability and flexible schemas, suiting scenarios like content management systems where data formats change frequently. For instance, an e-commerce app might use SQL for order processing but a document database for product catalogs with varying attributes. Developers choose between them based on whether their problem demands strict structure or adaptability.