JSON Schema is a lightweight data interchange format that generates clear, easy-to-understand documentation, making validation and testing easier.
JSON Schema is used to describe the structure and validation constraints of JSON documents.
Some have called it “the future for well-developed systems that have nested structures”.
There’s some weight to those claims; it’s definitely become a go-to tool for those who get past its steep learning curve.
It’s easy for humans to read and write, and equally easy for machines to parse and generate.
JSON Schema is a declarative language for validating the format and structure of a JSON Object.
It describes how data should look for a specific application and how it can be modified.
There are three main parts to JSON Schema:
This is the specification where the terminology for a schema is defined.
The JSON Schema validation is a document that explains how validation constraints may be defined. It lists and defines the set of keywords that can be used to specify validations for a JSON API.
This is where keywords associated with hyperlinks and hypermedia are defined.
Schemas in general are used to validate files before use to prevent (or at least lower the risk of) software failing in unexpected ways.
If there’s an error in the data, the schema fails immediately. Schemas can serve as an extra quality filter for client-supplied data.
Using JSON Schema solves most of the communication problems between the front-end and the back-end, as well as between ETL (Extract, Transform and Load) and data consumption flows.
It creates a process for detailing the format of JSON messages in a language both humans and machines understand. This is especially useful in test automation.
The primary strength of JSON Schema is that it generates clear, human- and machine-readable documentation.
It’s easy to accurately describe the structure of data in a way that developers can use for automated validation.
This makes work easier for developers and testers, but the benefits go beyond productivity.
Clearer language allows developers to spot potential problems faster, and good documentation leads to more economical maintenance over time.
JSON Schema has a surprisingly sharp learning curve.
Some developers feel it’s hard to work with, dismissing it as “too verbose”. Because of the criticism, it isn’t well known.
Using JSON Schema makes projects grow quickly. For example, every nested level of JSON adds two levels of JSON Schema to the project.
This is a weakness common to schemas, though, and depending on the project it may be outweighed by the benefits. It’s also worth considering that JSON Schema has features that keep the size expansion down.
For example, objects can be described in the “definitions section” and simply referenced later.
Some developers prefer to use Mongoose, an Object Document Mapper (ODM) that allows them to define schemas, then create models based on those schemas.
The obvious drawback is that an extra abstraction layer delivers a hit to performance.
Sometimes developers jump into a new MongoDB with a very flexible schema. This inevitably dooms them to “schema hell”, where they lose control as the project grows.
Performance is undeniably important. However, there are times when the cost of recovering from mistakes is far higher than the cost of taking the speed hit that comes with schema validation.
For those times the performance drop isn’t large enough to justify the risk of bad data entering the system, and that’s where JSON Schema comes into play.
JSON Schema is proving itself as a development option, but there’s no single “best tool” for every project. Concepta takes pride in designing a business-oriented solution that focuses on delivering value for our clients.
To see what that solution might look like for your company, reserve your free consultation today!