I’ve been thinking about a persistent challenge in modern application development. We often find ourselves building systems that need to handle different kinds of data—structured documents, complex relationships, and simple key-value pairs—all at once. We then stitch together separate databases and services, creating a fragile architecture. This complexity led me to explore a different path: combining Feathers.js with ArangoDB. This pairing offers a way to build real-time applications with a single, flexible data engine. Let’s look at how this works and why it might change how you approach your next project.
Think about a typical social feed. You have user profiles (documents), follower networks (graphs), and session data (key-value). Traditionally, this might involve MongoDB for profiles, Neo4j for networks, and Redis for sessions. Managing three databases, keeping data in sync, and pushing real-time updates becomes a major task. What if one system could handle it all, and a framework could manage the real-time layer seamlessly? That’s the promise here.
Feathers.js operates on a simple service concept. Each service, whether for users, posts, or comments, provides a consistent interface over your data. ArangoDB, as a multi-model database, doesn’t force you to choose one data model. You can store a user as a JSON document in one collection and the “follows” relationships as edges in a separate graph, all within the same database. The integration connects these two ideas.
Setting up a basic service is straightforward. First, you’d install the necessary packages.
npm install @feathersjs/feathers @feathersjs/express @feathersjs/socketio feathers-arangodbThen, you can create a Feathers service that hooks into an ArangoDB collection. This service instantly gives you CRUD endpoints and real-time events.
const { Service } = require('feathers-arangodb');
const { Database } = require('arangojs');
// Connect to ArangoDB
const db = new Database();
db.useDatabase('myApp');
db.useBasicAuth('username', 'password');
const collection = db.collection('users');
// Create a Feathers service for the 'users' collection
class UserService extends Service {
constructor(options) {
super({
Model: collection,
...options
});
}
}
app.use('/users', new UserService());Now, you have a /users endpoint. Creating a new user via a POST request will automatically broadcast a created event to all connected WebSocket clients. But where does the multi-model part come in? This is where ArangoDB’s query language, AQL, becomes essential. Feathers services can be customized with hooks. Imagine you want to find all users who liked a specific post. This is a graph traversal problem.
You could add a custom method to your service. Inside, you’d use AQL to traverse the graph from a post document, through ‘liked’ edges, to reach user documents.
// Inside a custom service method or hook
const aql = require('arangojs').aql;
async findUsersWhoLiked(postId) {
const cursor = await db.query(aql`
FOR vertex IN 1..1 OUTBOUND ${postId} likes
RETURN vertex
`);
return cursor.all();
}This single query jumps from a document to a graph and back. The real power is that both the posts collection (documents) and the likes edge collection (graph) are in the same database. You’re not moving data between systems. Have you considered how much simpler your data layer could be if you didn’t need separate connectors for different database types?
Performance is a common concern. ArangoDB executes these multi-model queries in a single engine. For read-heavy, real-time applications like dashboards or collaborative tools, this consolidation reduces latency. Feathers.js complements this by efficiently managing client connections and only pushing the data that has changed. When a document linked in a graph is updated, Feathers can notify every client subscribed to that data stream.
Let’s consider a practical scenario: a project management tool. Tasks are documents. The dependencies between tasks form a graph. When you update a task’s deadline, you might need to recalculate the timeline for all dependent tasks. An AQL query can traverse the dependency graph from the updated task. Once the new dates are calculated, Feathers.js services can emit patched events for each affected task. Every team member’s interface updates instantly, showing the new schedule.
This approach does require a shift in thinking. You design your data schema not just as isolated collections, but with potential relationships in mind from the start. Which collections will be pure documents? Where will you need edges? Planning this structure early makes writing the complex, powerful queries much easier later.
The development experience feels clean. You write your data logic once in AQL, leveraging joins, traversals, and full-text search as needed. Feathers.js then handles the HTTP and WebSocket layers, authentication, and authorization around it. You avoid writing boilerplate code for REST endpoints or manual WebSocket message passing.
I find this combination liberates me to focus on application logic rather than infrastructure glue. It’s particularly effective for prototypes and MVPs where requirements are fluid. You aren’t locked into a rigid data model. If you suddenly need to add a recommendation engine (a classic graph problem) to your content app, you can create a new edge collection and start writing traversal queries without introducing a new database.
Of course, no technology is a silver bullet. For extremely high-volume, simple key-value operations, a dedicated cache might still be better. For massively large graph-only problems, a pure graph database could have an edge. But for the vast middle ground of applications that need flexibility, real-time features, and relational logic, this duo is compelling.
The integration of Feathers.js and ArangoDB addresses a real need for simpler, more cohesive full-stack architecture. It reduces moving parts and lets you model your data in a way that naturally fits your problem domain. The result is an application that is easier to build, reason about, and maintain as it grows.
What feature in your current app is held back by database limitations? Could modeling it as a graph or mixing data types within a single query unlock new possibilities? I encourage you to try this stack for a small internal tool or a new feature branch. Experiment with storing some data as documents and connecting it with edges. See how the real-time updates feel.
If you’ve built something with Feathers.js or ArangoDB, what was your experience? Share your thoughts or questions in the comments below. If this exploration of a unified real-time and multi-model approach was helpful, please like and share it with other developers who might be wrestling with similar architectural puzzles. Let’s discuss how we can build less complex, more powerful applications.
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