How a side project connecting Event Sourcing to Hazelcast sat unfinished for years — and why I decided to bring it back with an AI collaborator.
In my previous post, I shared some of my thinking about Event-Driven Microservices — the coupling problems, the mental shift toward thinking in events, and the patterns (Event Sourcing, CQRS, materialized views) that make it all work. That post was conceptual. This one is personal.
I’ve been playing around with design concepts in this area for some time. While I was an employee of Hazelcast, I frequently worked with customers and prospects to show how Hazelcast Jet — an event stream processing engine built into the Hazelcast platform — could be used to build event processing solutions that would scale while continuing to provide low latency. These conversations were always framed around stream processing, though. Even when the intended use case was around microservices, we didn’t explicitly get into the Event Sourcing pattern. As someone coming from a background that was database-centric, the concept of events as the source of truth was a bit much for me.
The Light Bulb Moment
It was a light bulb moment when I realized that Hazelcast Jet could fit naturally into an Event Sourcing architecture — and that Hazelcast IMDG (the in-memory data grid, or caching layer) could concurrently maintain materialized views representing the current state of domain objects.
Think about it: Event Sourcing needs an event log and a processing pipeline. Hazelcast Jet is a processing pipeline. CQRS needs a fast read-side store that’s kept in sync with the event stream. Hazelcast IMDG is a fast read-side store. Event Sourcing + CQRS maps beautifully onto Jet + IMDG (even though that acronym is officially retired — it’s all just “Hazelcast” now).
And from there, I really wanted to demonstrate this. The original Microservices Framework project began.
Version 1: The Proof of Concept
The first version was focused on proving the core idea worked. Could I wire up a Hazelcast Jet pipeline to process domain events, persist them to an event store, and update materialized views — all in a way that was generic enough to work across different services?
The answer was yes. The central pattern that emerged was
straightforward: a service’s handleEvent() method writes incoming
events to a PendingEvents map, which triggers a Jet pipeline that
persists events to the EventStore, updates materialized views, and
publishes to an event bus for other services to consume. It worked,
and it was fast.
Now, the central components of the architecture — the domain object, event class, controller, and pipeline — have survived relatively intact through multiple iterations of the implementation. The bones were good. But a lot of the specific implementation choices I made around those bones haven’t aged all that well.
You know how it goes with side projects. Technical debt accumulates quietly, one “I’ll fix this later” at a time, until you’re looking at a codebase where you know you’d make different choices if you were starting over — but the sunk cost of time already invested keeps you from actually doing it. It’s the software equivalent of a kitchen renovation where you keep patching the old cabinets because ripping them out feels like too big a project for a weekend.
That version of the framework is still hanging around on GitHub, although I decided not to link to it here as I may take it down at any time. (Upcoming posts will link to the improved version, so embedding links to the original will inevitably lead to someone grabbing the wrong one.)
I got it to a working state, but there was a long list of things I wanted to add. Saga patterns for coordinating multi-service transactions. Observability dashboards. Comprehensive tests. Documentation that went beyond “read the code.” Each of these was a meaningful chunk of work, and progress slowed to a crawl.
The Stall
Let’s be honest about what happened: the project stalled. Not dramatically — it wasn’t ever really abandoned. It just… stopped moving. Every few months I’d open the codebase, when I had some extra time, and make a few minor, inconsequential changes while thinking of the more ambitious refactorings or added features that I’d get to when time permitted.
If you’ve ever maintained a passion project alongside a day job, you know this feeling. The ideas don’t go away — they sit in the back of your mind, periodically surfacing with a pang of “I should really get back to that.” But the activation energy to restart is high, especially when the next step isn’t a fun new feature but the grind of scaffolding, configuration, and test coverage. So you close the laptop and tell yourself next month will be different. (It won’t be.)
Enter AI-Assisted Development
In early 2025, I started using Claude for various coding tasks and was genuinely surprised by the results. This wasn’t autocomplete on steroids — I could describe an architectural pattern and get back code that understood the why, not just the what. I could say “this needs to work like an event journal with replay capability” and get something that actually accounted for ordering guarantees and idempotency.
That’s when the thought crystallized: what if I could use this to break through the stall?
Here’s the thing — the stuff that had been blocking me wasn’t the hard design work. I knew what the architecture should look like. The bottleneck was the sheer volume of implementation grind: scaffolding new services, writing comprehensive tests, wiring up Docker configurations, producing documentation. Exactly the kind of work where you need focused hours, and a side project never has enough of those.
Now, I want to be clear about what I mean here, because “AI wrote my code” carries a lot of baggage. This wasn’t about handing off the project and checking back in when it was done. It was about having a collaborator who could take high-level design direction and turn it into working code at a pace that made the project viable again. I’d provide the domain expertise, the architectural decisions, and the quality bar. The AI would provide the throughput.
Making the Decision
I decided to move forward with a clean reimplementation rather than trying to evolve the existing codebase. The core patterns from the original work — the Jet pipeline architecture, the event store design, the materialized view update strategy — were proven and would carry forward. But the project structure, package naming, dependency versions, and framework abstractions would start fresh. Sometimes the best way to fix a kitchen is to actually rip out the cabinets.
The plan was to use Claude’s desktop interface for iterative design discussions (requirements, architecture, implementation planning) and then hand off to Claude Code for the actual coding. Design first, then build — with comprehensive documentation at every step so the AI would have rich context to work from.
What happened next — the design phase, the handoff to Claude Code, and the surprises along the way — is the subject of the next post.
Nodes and Edges 