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The architecture overview in Ilya Mikhasik’s “Our System Series” describes a four-tier microservices design, running from frontend to application services, registry services, and database. Its most distinctive choice is how it stores relationships: instead of a separate table for every kind of connection between objects, it uses one general links structure built from entities and links.
The four tiers, in order
The author lists the architecture in this sequence: Frontend, Application Services, Registry Services, Database. Each tier has one stated responsibility, and the tiers are meant to be read as a stack in which each one serves the layer above it.
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| Tier | Responsibility, as the author describes it | What the overview does not say |
|---|---|---|
| Frontend | Handles user interaction. | Technologies used are not named. |
| Application Services | Implement business and supporting workflows. | Individual services and their boundaries are not listed. |
| Registry Services | Provide reusable database operations that workflows can call. | Operation names, interfaces, and call protocol are not shown. |
| Database | Stores entities and the relationships between them. | The database engine and exact schema are not named. |
The overview is a conceptual description. It contains no component diagram, deployment layout, or code, so it does not establish whether each tier runs as a separate process or on a separate host. Read the “microservices” label as the author’s term for the overall design, not as a confirmed deployment pattern.
Why registry services are a separate tier
The main reason the author gives for the registry layer is reuse. Without it, each application workflow would have to implement the same database operations on its own. With it, workflows call shared registry operations, and the logic for reading and writing entities and links lives in one reusable place. The overview treats this as a design rationale; it does not report any measured effect on development effort or defects.
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How relationships are modelled
The data model is graph-like and built from two concepts:
- Entities are application objects. The author’s examples include users, projects, and accounts, plus other objects an application might need.
- Links represent relationships between entities.
The key design decision is that links do not get a dedicated table for each possible pairing of entity types. Instead, the design uses a general links structure that can hold any relationship. According to the overview, each link record carries:
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- identifiers for the two connected entities (its endpoints);
- a direction;
- a type;
- a weight;
- optional additional data, stored as JSON.
The author notes that this structure can represent complex networks of connected objects. The overview does not explain how links are queried, indexed, or validated.
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The stated motivation is flexibility. In the author’s words: “This approach allows us to introduce new entity types and relationships without redesigning the entire database schema.” (Ilya Mikhasik, DEV Community article.) In other words, adding a new kind of object or relationship is meant to be a data-model change rather than a schema redesign. That is the rationale the author offers. It is not a benchmark result, and the overview provides no measurements of schema change effort, query speed, data integrity, or how the approach behaves at scale.
Trade-offs worth testing against your own workload
The overview does not compare this design with alternatives, so the points below are questions to evaluate rather than conclusions drawn from the article.
| Question | What the design favours | What to check before adopting a similar model |
|---|---|---|
| Schema flexibility versus database-enforced structure | New entity types and relationships can be added without a schema redesign. | Which rules the database enforces directly, and which would move into application or registry code. |
| Reusable registry operations versus domain-specific workflow logic | Common read and write operations are shared across workflows. | Whether workflows still need specialised logic that a generic operation cannot express. |
| Easy representation of new relationship types versus validation and querying | A new relationship type needs no new table. | How type, direction, and JSON content are validated, and how queries over a single generic links structure perform for your access patterns. |
What this overview does not establish
The article is a system description, not an independent architecture review, and it does not claim the pattern suits every workload. Several details a reader would normally need are absent:
- programming languages and frameworks;
- the database engine and exact schema;
- the service communication protocol;
- deployment topology and scaling strategy;
- transaction handling and access-control design;
- availability, latency, and any measured performance.
The overview is also not a statement of a general best practice. The series introduction makes the same point: every system reflects its requirements, constraints, and history, and a design explained in one context may not transfer unchanged to another.
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The overview promises further explanation of how application services and registry services divide their responsibilities, using a signup workflow as the example. The series introduction describes the broader aim: to explain how technologies work together in a real application, why decisions were made in their original context, and what could be improved. Details of the signup flow are not part of this overview and should be read in the later articles.
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Source for this article: Ilya Mikhasik, “Our System Series: Architecture Overview,” DEV Community, and the series introduction by the same author.
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