Raphael Koh

In our last Domain-Driven Design discussion, we learned how to group similar business components into application Bounded Contexts, which were separated folders in our Rails apps. This segregated cohesive groups of application code into separate folder structures (bounded contexts), and gave us a jumping-off point to drawing more explicit boundaries between domains in our monolith. Now it’s time for us to re-think how to communicate from one context to another. Here’s how:

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As a consulting firm that’s been around for 17 years, Carbon Five has a unique perspective on trends in digital product development. In this post we look back on 2016 and reflect on what we’ve seen in the industry, and where we think it’s headed. Here are the the top five trends we saw in 2016.

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In part one, we looked at querying 100,000 International Statistical Classification of Diseases (ICD) code records for 3-7 character codes. In part two of three, we’ll cover full-text search of the record descriptions using Postgres and Arel.

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For a recent project, we built a live-search for over 60,000 records which used both simple pattern matching and full-text search. The records we needed to search were diagnoses that are used to assign patients to a therapy. I had never done full-text search or anything real-time with that many records, so I ended up doing a lot of experimentation. These posts will cover my experience, and I hope they’ll be of value to anyone implementing their own Postgres search.

The Problem

The records we were searching were International Statistical Classification of Diseases (ICD) codes. Each record consists of a 3-7 character code, a short description averaging 11 words, and a few other data and association fields (for this post, I generated 100,000 records matching the real ICD format). We needed to be able to search by code and description, and users would be changing their search query quickly to find the right record, so it needed to be responsive. In part one, I’ll cover the code search where a user enters one or more codes (which may be partial).

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Your Rails application has become a monolith. Your test suite takes 30 minutes to run, your models grow to several hundred (or thousand!) lines, and feature development slows to a crawl. New members of your team must read through massive amounts of code before they can feel confident making changes. You’ve watched Uncle Bob’s Architecture the Lost Years, Matt Wynne’s Hexagonal Rails and read Martin Fowler’s Microservices and are convinced that the time has come to start breaking things up into smaller, simpler, faster components – but you’re unsure of where to begin.

In this article, I will demonstrate an approach to breaking a monolithic-style Rails application apart into microservices using plain Ruby classes and a simple RPC framework – all deployed to Heroku. I’ll show you how to work through this migration incrementally; you’ll be able to decompose things down as far as makes sense for your codebase. As a special bonus, I’ll show you how you can use Barrister RPC to alleviate much of the pain associated with documenting and creating API clients for the components of your now-distributed system. I’m skipping the advocacy; if you’re here it’s because you already love yourself some microservices but don’t know how to implement them yourself.

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