How do I validate optional keys in a map with Schema?

Use (s/optional-key :key) in map schemas, as shown in the README with examples like {(s/optional-key :foo) s/Keyword}. This allows keys to be missing without failing validation, and you can combine them with required keys for flexible data shapes.

Schema vs clojure.spec: which one should I use?

Schema is more lightweight and declarative, focusing on data shapes with rich error messages, while clojure.spec is more powerful for specifications and generative testing. Choose Schema for simplicity and seamless integration with existing Clojure code, especially in teams prioritizing documentation.

Can Schema generate random data for testing?

Yes, through the experimental schema-generators library, which integrates with clojure.test.check to produce data matching your schemas. This is useful for property-based testing, though it's noted as experimental in the README.

How to handle recursive data structures in Schema?

Use s/recursive with a var reference, as demonstrated with Tree schemas in the README (e.g., {:value s/Int :children [(s/recursive #'Tree)]}). This allows you to define schemas for nested or infinite data types without manual repetition.

Is Schema compatible with ClojureScript projects?

Yes, Schema supports both Clojure and ClojureScript, with cross-platform schemas like s/Str and s/Num, and macros that work in both environments. The README includes ClojureScript-specific usage notes, such as :include-macros true.

What's the performance impact of enabling Schema validation in production?

Validation adds runtime overhead, so it's recommended to use it selectively—perhaps only in development or tests—or optimize with s/validator for repeated checks. The README advises using *assert* flags to control code generation and reduce size.

Prismatic's schema — Clojure Data Validation Library

What is Prismatic's schema?

Schema is a Clojure and ClojureScript library for declarative data description and validation. It provides a lightweight way to define the expected shape of data—like nested maps, lists, or specific value types—and validate that data matches those expectations. It solves the problem of unclear data contracts in Clojure codebases, making functions easier to understand and debug.

Target Audience

Clojure and ClojureScript developers working on teams or projects where data shape clarity, API robustness, and maintainability are important. It's especially useful for those building services, APIs, or applications with complex data flows.

Value Proposition

Developers choose Schema because it offers a simple, idiomatic way to document and validate data without the overhead of a full type system. Its seamless integration with Clojure, rich error messages, and powerful features like coercion and test data generation make it a practical tool for improving code quality.

Clojure(Script) library for declarative data description and validation

Use Cases

Best For

Documenting the expected shape of function inputs and outputs in Clojure
Validating data from external sources like JSON APIs or databases
Adding runtime type checking to Clojure applications without heavy tools
Coercing loosely-typed data (e.g., from HTTP requests) into domain-specific types
Generating test data from schemas for property-based testing
Improving team onboarding by making data contracts explicit and readable

Not Ideal For

Projects requiring compile-time type safety and static analysis, as Schema operates purely at runtime.
High-performance applications where validation overhead must be minimized, due to Schema's runtime checks.
Teams already using clojure.spec or core.typed extensively, since Schema might duplicate or conflict with those systems.
Scenarios with highly polymorphic or dynamic data shapes that are difficult to predefine with static schemas.

Pros & Cons

Pros

Idiomatic Data Description

Schemas are plain Clojure data structures that mirror your data, making them easy to read and write, as shown in examples like {:a {:b s/Str :c s/Int}}.

Rich Error Messages

Validation failures provide descriptive, programmer-friendly errors that pinpoint exact issues, such as 'not (instance? java.lang.String :b)' for type mismatches.

Seamless Function Integration

Macros like s/defn allow annotating function arguments and return values with schemas, enabling optional runtime validation without disrupting code flow.

Flexible Data Coercion

Schema can automatically transform input data, like converting JSON strings to keywords, using coercion matchers that simplify API handling.

Cons

Runtime Performance Overhead

Enabling validation adds computational cost at runtime, which can be significant in performance-critical paths, and requires careful management with features like s/validator.

No Compile-Time Guarantees

Unlike full type systems, Schema does not provide static type checking; errors are only caught at runtime, which might delay bug detection.

Experimental Features

Some utilities, such as data generation and partial datum completion, are labeled as experimental in the README, indicating they may be unstable or lack long-term support.

Frequently Asked Questions

What is Prismatic's schema?

Target Audience

Value Proposition

Use Cases

Best For

Documenting the expected shape of function inputs and outputs in Clojure
Validating data from external sources like JSON APIs or databases
Adding runtime type checking to Clojure applications without heavy tools
Coercing loosely-typed data (e.g., from HTTP requests) into domain-specific types
Generating test data from schemas for property-based testing
Improving team onboarding by making data contracts explicit and readable

Not Ideal For

Projects requiring compile-time type safety and static analysis, as Schema operates purely at runtime.
High-performance applications where validation overhead must be minimized, due to Schema's runtime checks.
Teams already using clojure.spec or core.typed extensively, since Schema might duplicate or conflict with those systems.
Scenarios with highly polymorphic or dynamic data shapes that are difficult to predefine with static schemas.

Pros & Cons

Pros

Idiomatic Data Description

Schemas are plain Clojure data structures that mirror your data, making them easy to read and write, as shown in examples like {:a {:b s/Str :c s/Int}}.

Rich Error Messages

Validation failures provide descriptive, programmer-friendly errors that pinpoint exact issues, such as 'not (instance? java.lang.String :b)' for type mismatches.

Seamless Function Integration

Macros like s/defn allow annotating function arguments and return values with schemas, enabling optional runtime validation without disrupting code flow.

Flexible Data Coercion

Schema can automatically transform input data, like converting JSON strings to keywords, using coercion matchers that simplify API handling.

Cons

Runtime Performance Overhead

Enabling validation adds computational cost at runtime, which can be significant in performance-critical paths, and requires careful management with features like s/validator.

No Compile-Time Guarantees

Unlike full type systems, Schema does not provide static type checking; errors are only caught at runtime, which might delay bug detection.

Experimental Features

Some utilities, such as data generation and partial datum completion, are labeled as experimental in the README, indicating they may be unstable or lack long-term support.

Frequently Asked Questions

Prismatic's schema

What is Prismatic's schema?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?

Prismatic's schema

What is Prismatic's schema?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?