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From Web Development to Data Science: 10 Uses of Clojure Development and Our Recent Experience with It

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Clojure isn’t the mainstream programming language.

However, being a product engineering company, we regularly deliver projects with this powerful and convenient solution.

We know you might have a question like what’s so unique about Clojure?

Well, the decision to use Clojure is not based on a single reason alone. And that’s precisely why we have dedicated this entire blog to it.

In this blog, we’ll explore its real-world applications and how it is addressing the modern challenges of software product engineering.

But before that, let us share a success story that revolves around our client’s experience with Clojure development.

Client Spotlight: Azilen’s Clojure Development Journey and its Impactful Outcomes

Our expertise in software product engineering and our decision to leverage Clojure for the client’s online policy system resulted in a remarkable transformation.

Here is a quick overview of the client’s success with Clojure.

Problem Analysis:

The client’s online policy system, initially built with Node.js, faced significant challenges as their customer base grew and demands increased.

These challenges can be summarized as follows:

1. Scalability Storm:

The system struggled to handle the increasing number of concurrent requests.

As a result, it led to performance bottlenecks and dissatisfied customers during peak times.

2. Maintenance Quicksand:

As the system evolved and more features were added, the codebase became complex and difficult to maintain.

3. Concurrency Whirlpool:

The system encountered issues related to concurrent operations, such as data races and threading problems, which put data integrity at risk.

4. Ecosystem Constraints:

Despite being a rich ecosystem, Node.js fell short of catering to the client’s requirements for functional programming and robust concurrency support.

How Clojure Helped?

We recognized that the challenges faced by our client required a robust and scalable solution.

After careful analysis, we proposed migrating the system to Clojure, which effectively tackled each of these challenges.

Let’s explore how Clojure addressed them.

1. Scalability:

Leveraged Clojure’s concurrency support and lightweight threads for better scalability.

2. Maintenance:

Clojure’s simplicity and functional programming reduced complexity, making maintenance easier and hassle-free.

3. Concurrency:

Utilized Clojure’s Software Transactional Memory (STM) to ensure data integrity and handle concurrent operations.

4. Ecosystem:

Combined Clojure’s ecosystem with seamless integration of existing Java resources.

Now, Let’s Take a Closer Look into Programming

We’re sharing a few samples of the code base so you can see how Clojure helped us overcome the client’s challenges.

1. Concurrent Policy Fetch

(ns my-insurance-system.policy
(:require [clojure.core.async :as async :refer [go <! chan buffer]]))

;; Simulate fetching policy data from a remote API
(defn fetch-policies []
[{:id 1 :premium 500}
{:id 2 :premium 750}
{:id 3 :premium 1000}
{:id 4 :premium 900}
{:id 5 :premium 600}])

;; Fetch and process policy data concurrently
(defn update-policy-in-some-way [policy]
(assoc policy :discounted true))

(defn get-policy-data []
(let [policies (fetch-policies)
policy-chan (chan 5 (buffer 10))]
(async/pipeline-async 5
(fn [policy] (update-policy-in-some-way policy))
(map :id policies))
(async/<!! (async/into [] policy-chan))))


In this example, the insurance company employs Clojure for its implementation, which utilizes core.async to concurrently fetch and process policy data.

The fetch-policies function simulates fetching policy data from a remote API. Meanwhile, get-policy-data performs the concurrent retrieval and processing of policies.

Now, they are currently enjoying the benefits of efficient concurrent data processing.

2. Calculating Total Premiums

(ns my-insurance-system.calculations)

;; Calculate total premiums for policies using reduce and pure functions
(defn calculate-total-premium [policies]
(reduce (fn [sum policy] (+ sum (:premium policy))) 0 policies))


In this example, the insurance company utilizes Clojure’s functional programming paradigm to calculate the total premiums for policies.

The calculate-total-premium function uses reduce and pure functions to aggregate the premium amounts, ensuring efficient and maintainable code.

3. Navigating the Polylithic Architecture

Our journey took an exciting turn when we encountered the concept of Polylithic Architecture – a groundbreaking approach to structuring our system.

Note: The code examples for Polylithic Architecture are more conceptual and less implementation-focused.

The following snippets illustrate the principles of component-based development.

Policy Component

(ns my-insurance-system.components.policy
(:require [my-insurance-system.db :as db]))

(defn create-policy [data]
(db/save-policy data))

(defn get-policy [id]
(db/get-policy id))

(defn update-policy [id data]
(db/update-policy id data))

(defn delete-policy [id]
(db/delete-policy id))

This example showcases how the Policy component contains functions associated with managing policies.

It delegates the actual database operations to the my-insurance-system.db namespace, following the component-based development approach.

In which, each component focuses on a specific domain and offers a well-defined interface for interacting with the system.

Claim Component

(ns my-insurance-system.components.claim
(:require [my-insurance-system.db :as db]))

(defn create-claim [data]
(db/save-claim data))

(defn get-claim [id]
(db/get-claim id))

(defn update-claim [id data]
(db/update-claim id data))

(defn delete-claim [id]
(db/delete-claim id))

Much like the Policy component, the Claim component encompasses operations related to claim management.

It adheres to the same component-based development principles, providing clear and isolated functionality for interacting with claim data.

4. CQRS Pattern

Embracing the CQRS pattern further optimized our system’s performance and overall user experience.

Command Handlers

(ns my-insurance-system.commands.policy
(:require [my-insurance-system.components.policy :as policy]))

(defn create-policy [data]
(policy/create-policy data))

(defn update-policy [id data]
(policy/update-policy id data))

(defn delete-policy [id]
(policy/delete-policy id))

In a system following the CQRS principles, command handlers are responsible for accepting user commands and triggering corresponding actions within the domain.

In this example, the Command Handlers for policies delegate the command operations to the Policy component, ensuring proper command execution.

Query Handlers

(ns my-insurance-system.queries.policy
(:require [my-insurance-system.components.policy :as policy]))

(defn get-policy [id]
(policy/get-policy id))

(defn get-all-policies []

On the read side, the Query Handlers are responsible for processing read requests and returning data to users.

In this example, the Query Handlers for policies retrieve policy data from the Policy component and return it to the users, ensuring separation between read and write operations.

The Exceptional Outcome!

By utilizing the capabilities of Clojure development, we were able to implement a robust, scalable, and efficient system that met our client’s growing customer needs and set sail for a bright future.

Now, let’s explore the real-world applications of Clojure.

What is Clojure Used For? 10 Remarkable Use Cases of Clojure

Clojure offers a wide range of use cases due to its unique features and flexibility.

Here are some common use cases of Clojure.

1. Web Development

Clojure is well-suited for developing web applications across different domains.

Its Ring library provides a standard interface for handling HTTP requests and responses.

Furthermore, frameworks like Luminus and Compojure make it easy to build web applications with Clojure, offering robust support for routing, middleware integration, and template rendering.

2. Big Data and Data Analysis

The functional programming paradigm and superior data handling capabilities of Clojure make it suitable for big data processing and data analysis tasks.

Moreover, its Incanter library offers powerful data manipulation and visualization capabilities for data exploration and processing.

3. Distributed Systems

Clojure focuses on concurrency and immutability.

This makes it well-suited for building distributed systems, such as –

  • Distributed databases
  • Message brokers
  • Real-time systems

4. Scripting and Automation

For writing scripts and automating repetitive tasks, Clojure is a perfect solution due to its robust functional programming and concise syntax.

5. Artificial Intelligence and Machine Learning

Clojure has several libraries and frameworks such as Cortex, Encog, and clj-ml.

This enables developers to build and train artificial neural networks and machine learning algorithms.

6. Game Development

Clojure can be used for game development with the help of its LibGDX library.

This enables developers to build cross-platform games for desktop and mobile devices.

7. Internet of Things (IoT)

The use of Clojure in the IoT domain is relatively niche.

However, its lightweight footprint, JVM compatibility, and rapid prototyping make it an ideal solution for IoT devices and embedded systems.

8. DevOps and Infrastructure Automation

Clojure can be widely used for DevOps and infrastructure automation tasks, such as –

  • Automation scripts
  • Infrastructure as Code (IaC)
  • Integration with existing tools
  • REPL-driven development for infrastructure tasks
  • Configuration management
  • Cloud orchestration

9. Prototyping and Rapid Development

Clojure offers unique functionalities like REPL-driven development, concise syntax, a code-as-data approach, and a rich collection of libraries.

This enables developers to prototype and experiment with the code at a rapid pace, making it ideal for agile and iterative development processes.

10. Financial and Statistical Applications

Clojure is gaining popularity in the financial and statistical domain due to its powerful data manipulation capabilities, robust support for mathematical computations, and numerical libraries.

As a result, it is being used in a wide range of financial and statistical applications, such as –

Now the Question is – Which is the Better Fit for Your Backend Requirements? Node.js or Clojure?

In the world of software product engineering, Node.js and Clojure stand out as ideal solutions for back-end development.

Node.js, a JavaScript runtime, is known for its event-driven approach and vast ecosystem.

Meanwhile, Clojure, a functional language on the JVM, offers immutable data structures and powerful concurrency support.

So, let’s compare Node.js and Clojure, highlighting their strengths and weaknesses.

HTML Table Generator
Runtime environment for JavaScript Functional programming language for the JVM
Extensive npm package ecosystem Limited library availability compared to Node.js
Single-threaded event loop (non-blocking I/O)
Built-in support for immutable data and parallelism
Language Paradigm
Asynchronous, event-driven, imperative
Functional programming with immutable data
Generally faster due to the V8 engine
Performance can be slower due to JVM overhead
Learning Curve
Easier for developers familiar with JavaScript
The steeper learning curve for functional programming
Community Support
Large and active community
Smaller but dedicated community
Error Handling
Callbacks, Promises, and async/await
Software Transactional Memory (STM) and Try/Catch
Tools & Libraries
Abundant tools and libraries for various tasks
Fewer specialized libraries compared to Node.js
Web Development
Popular for building web applications
Can be used for web development but is less common
Scalable with cluster modules or microservices
Can scale with multi-threading and STM
- Large ecosystem & community support
- Easy for JavaScript developers to adapt
- Asynchronous I/O for high concurrency
- Powerful concurrency with immutable data
- Functional programming paradigm
- JVM's stability and performance
- Callback hell & potential callback issues
- May have performance issues with heavy computation
- JavaScript's dynamic typing can lead to errors
- Smaller library ecosystem
- The learning curve for imperative developers
- Overhead of JVM and garbage collection

Elevate Your Projects with Azilen’s Clojure Development Expertise

Clojure has opened a world of possibilities for software product development.

And being a product engineering company, we believe that it is more than just a programming language – it’s a gateway to unlock the full potential of functional programming.

At Azilen, we take pride in our deep understanding of functional programming paradigms.

Our team of experienced developers not only excels in Clojure development but also holds a proven mastership in building elegant, ingenious, and future-proof software products.

Whether you are a startup or an established enterprise, our agile and flexible approach ensures that we adapt to your project’s specific needs and deliver exceptional results.

Let us be your guide in the world of Clojure development and together, we’ll create NextGen products that establish new standards of excellence!

Explore Our Software Product Development Offerings

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