JSON (JavaScript Object Notation) has become the standard of data transmission on the web due to its simplicity, readability, and lightweight nature. Its language independence and seamless integration with web technologies further enhance its widespread adoption across various platforms. 

For a language like Clojure, which is renowned for its robustness in building web applications, having efficient tools is essential to maintain popularity and suitability for any project. 

Clojure excels in data manipulation with its rich set of built-in data structures, making it inherently well-suited for handling JSON. This type of transmission can be effortlessly decoded into Clojure’s native data structures, such as maps and vectors, allowing developers to seamlessly navigate, manipulate, and transform JSON data within their applications.

Effective JSON Parsing and Generation Libraries in Clojure

When working with JSON in Clojure, developers have several robust libraries at their disposal to speed up the development and ensure consistency and efficiency. Among the most popular are Cheshire, Jsonista, and data.json, each offering unique features and benefits suited to different needs and use cases.


Cheshire is probably the most widely used Clojure library for JSON processing, meeting extensive demand within the Clojure ecosystem. Thanks to leveraging the highly optimized Jackson data processing library for Java, Cheshire is feature-rich and pretty good in terms of performance. It supports features like custom encoders and decoders, pretty printing, parsing various binary formats, lazy decoding, and streaming.

However, Jackson's substantial size and feature set may introduce unnecessary dependencies for projects requiring more lightweight solutions.

Let’s look at the example of encoding and decoding JSON with Cheshire:

1;;; Encoding
2(cheshire.core/generate-string {:foo "bar" :baz 5}) ;; => "{\"foo\":\"bar\",\"baz\":5}"
3;; pretty print
4(cheshire.core/generate-string {:foo "bar"} {:pretty true}) ;; => "{\n \"foo\" : \"bar\"\n}"
5;; write to a stream
6(cheshire.core/generate-stream {:foo "bar" :baz 5} ( "/tmp/foo"))
7;;; Decoding
8(cheshire.core/parse-string "{\"foo\":\"bar\"}") ;; => {"foo" "bar"}
9;; keywordize keys
10(cheshire.core/parse-string "{\"foo\":\"bar\"}" true) ;; => {:foo "bar"}
11;; apply custom functions to keys 
12(cheshire.core/parse-string "{\"foo\":\"bar\"}" (fn [k] (keyword (.toUpperCase k)))) ;; => {:FOO "bar"}
13;; parse a stream from a file
14(cheshire.core/parse-stream ( "/tmp/foo")) ;; => {"foo" "bar", "baz" 5}


Jsonista is a relatively new library that distinguishes itself through exceptional performance in encoding and decoding JSON. Benchmarks indicate that Jsonista significantly outperforms its competitors across all payload sizes. 

Jsonista is also based on Jackson and offers an extensive set of customizations via the configuration of the Jackson ObjectMapper, including custom value encoders and decoders. 

Despite lacking some streaming support, which may be a limitation for some applications, the library offers its performance benefits. With its high-speed processing capabilities, Jsonista has the potential to become the go-to JSON library for the Clojure ecosystem in the future.

Here’s an example of JSON processing with Jsonista:

1;;; Encoding
2(jsonista.core/write-value-as-string {:foo "bar"}) ;; => "{\"foo\":\"bar\"}"
3;; pretty print
4(jsonista.core/write-value-as-string {:foo "bar"} (jsonista.core/object-mapper {:pretty true}));; => "{\n \"foo\" : \"bar\"\n}"
5;; write to a stream
6(jsonista.core/write-value ( "/tmp/bar") {:foo "bar" :baz 5})
7;;; Decoding
8(jsonista.core/read-value "{\"foo\":\"bar\"}") ;; => {"foo" "bar"}
9;; keywordize keys
10(jsonista.core/read-value "{\"foo\":\"bar\"}" jsonista.core/keyword-keys-object-mapper);; => {:foo "bar"}
11;; apply custom functions to keys
12(jsonista.core/read-value "{\"foo\":\"bar\"}" (jsonista.core/object-mapper
13{:decode-key-fn (fn [k] (keyword (.toUpperCase k)))}))
14;; parse a stream from a file
15(jsonista.core/read-value ( "/tmp/bar")) ;; => {"foo" "bar", "baz" 5})


data.json is a simpler library developed and maintained by the core Clojure team. It aims to provide a pure-Clojure JSON processing solution with no external dependencies. 

Although data.json is not as fast or feature-rich as Cheshire or Jsonista, it offers essential decoding, encoding, and streaming capabilities, making it a suitable choice for projects with smaller payloads and less demanding performance requirements.

Here’s how data.json handles JSON:

1;;; Encoding
2;; generate some json
3( {:foo "bar" :baz 5}) ;; => "{\"foo\":\"bar\",\"baz\":5}"
4;; pretty print
5( {:foo "bar" :baz 5} :indent true) ;; => "{\n \"foo\": \"bar\",\n \"baz\": 5\n}"
6;; write some json to a stream
7(with-open [out ( "/tmp/baz")]
8( {:foo "bar" :baz 5} out)) ;; => "{\"foo\":\"bar\",\"baz\":5}"
9;;; Decoding
10( "{\"foo\":\"bar\"}") ;; => {"foo" "bar"}
11;; keywordize keys
12( "{\"foo\":\"bar\"}" :key-fn keyword) ;; => {:foo "bar"}
13;; apply custom functions to keys
14( "{\"foo\":\"bar\"}" :key-fn (fn [k] (keyword (.toUpperCase k)))) ;; => {:FOO "bar"}
15;; parse a stream from a file
16( ( "/tmp/baz")) ;; => {"foo" "bar", "baz" 5}

In summary, Clojure developers have a range of options for JSON processing, from the feature-rich and widely used Cheshire, to the high-performance Jsonista, and the lightweight, dependency-free data.json. Each library has its strengths and is best suited to specific use cases, allowing developers to choose the right tool for their project's needs.

Exploring JSON Schema Validation in Clojure

Validating incoming JSON data is essential for maintaining data integrity and security in modern applications. Ensuring that JSON documents conform to expected structures and constraints can prevent a multitude of issues down the line

JSON Schema is a powerful tool for defining the JSON documents’ structure, content, and semantics. It is widely used for validating that JSON data meets the predefined expectations. In Clojure, the json-schema library provides a straightforward way to leverage JSON Schema for validation. With a single public function, ~validate~, the library returns the data if no errors are found. This simplicity makes it easy to incorporate JSON validation into the pipelines. Additionally, the library supports the generation of JSON Schemas, facilitating the creation of schemas that can be shared and reused.

For those who prefer to use Clojure's seamless Java interoperability, there are Java libraries such as the json-schema-validator. While these libraries offer robust validation capabilities, developers may need to implement Clojure wrapper functions themselves to integrate them smoothly into their Clojure projects.

Alternatively, Clojure developers can use one of the language's specialized data validation libraries, such as clojure.spec, schema, or malli. These libraries offer extensive features and can handle complex validation tasks beyond the scope of JSON Schema. 

For instance, clojure.spec provides powerful capabilities for describing the shape of data, generative testing, and data conforming. Here’s an example of JSON schema validation with clojure.spec: 

1(require '[clojure.spec.alpha :as s])
2(s/def ::name string?)
3(s/def ::age int?)
4(s/def ::person (s/keys :req [::name ::age]))
5(s/valid? ::person {::name "John", ::age 25}) ;; => true
6(s/valid? ::person {::name "John", ::age "25"}) ;; => false

On the other hand, schema offers a more straightforward approach to data validation and coercion: 

(require '[schema.core :as s])
(s/defschema Person
{:name s/Str, :age s/Int})
{:name "John"
:age 25}) ;; => {:name "John", :age 25}
{:name "John"
:age "25"}) ;; => Value does not match schema: {:age (not (integer? "25"))}

And the third option, malli, combines the best of both worlds with an expressive schema language and comprehensive validation tools. Let’s look at the implementation example:

1(require '[malli.core :as m])
2(def Person
4[:name :string]
5[:age :int]])
6(m/validate Person {:name "John", :age 25}) ;; => true
7(m/validate Person {:name "John", :age "25"}) ;; => false

Each approach has its strengths, allowing developers to choose the most suitable method based on their project's requirements and complexity.

Integrating JSON with Clojure-based Web APIs and Services

Integrating JSON with Clojure-based web APIs and services is straightforward and efficient, thanks to the rich ecosystem of libraries and tools available within the Clojure community. By leveraging libraries such as ring-json, compojure-api, cheshire, and others, developers can build robust and scalable web APIs that seamlessly consume and produce JSON data.

Generating JSON responses to client requests is a common requirement when building web APIs in Clojure. Libraries like ring-json and compojure-api offer convenient solutions for handling JSON serialization and deserialization. 

ring-json integrates with the Ring framework to automatically encode and decode JSON in HTTP requests and responses, simplifying the process of working with JSON payloads. compojure-api, built on top of the Compojure routing library, extends these capabilities by providing comprehensive support for creating RESTful APIs, complete with JSON validation, coercion, and documentation generation.

To further enhance JSON handling in Clojure web applications, developers can utilize middleware and utility libraries that streamline common tasks. For instance, ring-middleware-format provides content negotiation and automatic format detection, ensuring that APIs can seamlessly handle different data formats, including JSON. Here’s an example:

1(require '[ring.middleware.json :refer [wrap-json-response wrap-json-body]]
2'[ring.util.response :refer [response]])
3(defn handler [request]
4(response {:foo "bar"}))
5(def app
6(-> handler

These libraries empower high-performance web services that are both robust and easy to maintain, Clojure developers can create efficient and scalable web APIs that leverage the full potential of JSON. Moreover, this choice is already proven by real-world applications and use cases of JSON in Clojure development, from web APIs and data processing pipelines to real-time applications and machine learning, making Clojure an excellent choice for modern development. 


Is JSON manipulation efficient in Clojure compared to other programming languages?


Yes, JSON manipulation in Clojure is generally efficient due to its rich set of built-in data structures and libraries. Although performance can vary based on specific use cases and implementation, Clojure's emphasis on immutability and functional programming often leads to concise and expressive JSON manipulation code.

Yes, you can use Clojure's native data structures like maps and vectors directly with JSON. Libraries for JSON processing in Clojure seamlessly handle conversion between Clojure data structures and JSON format, allowing for natural and idiomatic data manipulation, and leveraging Clojure’s power and flexibility.

JSON data validation in Clojure can be achieved using libraries like json-schema, clojure.spec, schema, and malli. The json-schema library allows developers to define JSON schemas and validate data against them easily. Alternatively, clojure.spec, schema, and malli arefeature-rich libraries that offer comprehensive validation capabilities, allowing developers to specify data schemas and validate JSON structures with custom predicates and rules.

Cheshire is a popular Clojure library for JSON processing that leverages the highly optimized Jackson library for performance. It offers features like custom encoders and decoders, pretty printing, and streaming. Jsonista, on the other hand, focuses on performance and is based on Jackson as well, excelling in decoding and encoding speed. data.json, maintained by the core Clojure team, provides basic JSON processing capabilities without external dependencies. It is suitable for smaller payloads but less efficient for large datasets compared to Cheshire and Jsonista.

JSON serialization and deserialization perfomance in Clojure can be imporoved by:

  • Minimizing unnecessary conversions between Clojure data structures and JSON.

  • Batching serializtion/deserialization operations where possible.

  • Using streaming or lazy processing for large datasets.

Oleksandr Druk
Clojure Developer

Self-taught developer. Programming languages design enthusiast. 3 years of experience with Clojure.

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