When I Accidentally Fell Into Graphs and Ended Up Solving Stuff With Neo4j

So here’s the thing. I didn’t wake up one day thinking, “you know what’s missing in my life? Graph databases.” I was just trying to connect some dots in a project. Literally, dots. Data points that were linked to each other in ways that felt too complex for my poor relational tables. I had a bunch of users, some interactions, some relationships, and the classic “how do these people connect to each other” problem.

At first I tried to brute-force it in SQL. You know the drill. Multiple JOINs. Nested queries that look like ancient scrolls written to confuse mortals. Every time I wanted to answer a simple question like “who are the second-degree connections of this person,” I ended up with a query that looked like a conspiracy wall in a crime show. I was adding indexes like free candy but the performance still dragged.

That’s when I stumbled on Neo4j. Someone casually mentioned “graph database” in a forum, and my initial reaction was “oh great, yet another shiny tech toy.” But curiosity won. I downloaded it. The interface popped up with its little graph explorer and I was hooked in like two minutes.

The Aha Moment

In SQL, I had been thinking in rows and columns. In Neo4j, I was suddenly thinking in nodes and relationships. I could say, “this user knows this user,” or “this user likes this course,” and Neo4j stored that as an actual connection. Not some abstract join condition, but a direct link. And the kicker? When I asked Neo4j, “show me all the friends of friends of this person,” it actually gave me the result instantly. No ten-line SQL monster, just a neat little Cypher query.

For example, I could write something as simple as:

MATCH (a:User)-[:FRIEND]->(b:User)-[:FRIEND]->(c:User) 
WHERE a.name = "Alice" 
RETURN c

That’s it. That’s the whole query. Compare that with the three days I wasted writing ugly SQL for the same thing.

Where I Actually Used It

I ended up using Neo4j in a side project where I wanted to recommend courses to students based on what other “similar” students were taking. Think of it like Netflix’s “you may also like” but for learning.

The structure was simple:

• Users were nodes.
• Courses were nodes.
• A :TOOK relationship connected them.
• Then I could connect users indirectly through shared courses.
  
  

With that graph, I could ask Neo4j: “Show me the courses that are connected to users who are similar to this one.” And it worked beautifully. Instead of building some heavyweight recommendation engine, I got decent recommendations with a few graph traversals.

SQL would have required insane JOINs across users and courses, plus extra logic for weighting. With Neo4j, it just felt natural. Traversing relationships was literally what it was made for.

What Surprised Me

Performance Even with thousands of nodes and relationships, queries were fast. Like scary fast. While SQL slowed down with every extra join, Neo4j seemed to thrive on connections.

Visualization The built-in graph viewer made debugging fun. Instead of staring at rows, I could see bubbles with lines between them. When I spotted a weird connection, it was easy to fix.

Cypher Language It’s like SQL but more… human. You actually draw the relationship in text. (a)-[:LIKES]->(b) feels way more intuitive than writing “JOIN ON user.id = likes.user_id.”

Why I’m Sticking With It

Now, I’m not saying Neo4j replaces SQL for everything. If you just want to store products and prices, stick with relational. But if your data is naturally about relationships, like networks, recommendations, or hierarchies, then graphs are just better.

For me, the killer feature is that it thinks like I do when I try to solve these problems. Instead of bending my brain to fit relational tables, I just map my real-world scenario into nodes and relationships.

I’ve only scratched the surface with things like shortest path algorithms and community detection. Neo4j has built-in graph algorithms that can tell you clusters of users, influencer detection, all that social network magic. I didn’t even plan to get into that, but now it’s on my list.

Final Thoughts

Neo4j feels like one of those tools you don’t think you need until you try it. And then suddenly you see problems differently. I didn’t “learn a database,” I kind of learned a new way to think about data.

Will I use it everywhere? No. But now whenever someone says “recommendations,” “network,” or “relationships,” my brain immediately goes “that’s a graph problem.” And that shift alone feels worth it.

So yeah, I basically stumbled into Neo4j, and it solved a problem that had been eating my time for weeks. If you’re drowning in JOINs, maybe it’s time you tried drawing your data instead of tabling it.