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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteHow do you manage shared reference data across microservices? Give one service clear ownership of its meaning and changes, then let consumers receive updates in ways that fit their read patterns. In Denis Toropov’s account, three services stored balances separately for good reasons, but each maintained overlapping reference data. The resulting drift was not just a synchronization problem: different systems could interpret the same balance differently because no single owner governed the shared business semantics.
Why shared reference data caused a problem
Toropov describes three services with separate balance databases: one supported an account display, one held customer-level balances, and one held current account balances. Their different read patterns, performance needs, and data representations justified keeping their balance stores separate; the case is not an argument for merging them.
All three nevertheless depended on reference entities such as account types, statuses, product attributes, and classifiers. Those entities provide context for interpreting a balance. As Toropov puts it, “A balance by itself is just a number.” If one service assigns a different meaning or category to that number, the views can disagree even when the underlying balance value is the same.
The services refreshed their copies differently: one on a schedule, another in response to an event, and a third through a separate integration flow. An update therefore did not necessarily reach all three together. Each service could operate with a different version or mapping, making inconsistencies harder to identify and reconcile. Investigating a discrepancy could mean tracing three databases, three services, separate update histories, and multiple teams. Toropov describes prolonged diagnosis and recurring operational pain, but reports no measured incident rate or migration outcome.
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Three ways to manage the data
Toropov considered three broad options. They differ less in whether data is copied than in who controls the model, rules, and changes.
| Option | Ownership and updates | Main trade-off |
|---|---|---|
| Keep local copies and improve synchronization | Each consumer retains its copy and read path; synchronization is improved among services. | Preserves local reads and autonomy, but duplication and the question of who owns shared meaning remain. |
| Use a shared reference database | Consumers access centrally stored data. | Centralizes storage, but direct schema coupling and duplicated interpretation logic can persist if no owner governs the contract and behavior. |
| Create a dedicated reference data service | A service owns the reference model, its rules and versions, and how changes are published. | Makes ownership explicit, while adding a component with its own availability and operational obligations. |
His team chose the dedicated service because it saw competing ownership of business semantics as the underlying issue—not simply a need to deliver updates faster. A shared database alone would centralize where data lived without necessarily establishing who could change its meaning or how consumers should interpret it.
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What a dedicated reference data service should own
A dedicated service should be more than a thin CRUD layer. Its responsibility is to define and govern the reference model, while making changes understandable and consumable by dependent systems.
- The model: fields, relationships, constraints, and lifecycle rules.
- Versions: an explicit way to identify which version of the reference data is active.
- Change distribution: predictable publication through an API, events, snapshots, or a hybrid suited to consumers.
- Validation and audit: checks on updates and a traceable record of changes.
- Freshness and delivery health: monitoring for stale data, failed updates, and consumer lag.
These responsibilities make the service the source of truth for the reference entity’s model and lifecycle. They do not require it to serve every online read.
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Centralize write ownership without forcing every read through one service
Consumers may keep local, read-optimized projections when their workloads or latency requirements call for them. The important distinction is between who is authorized to define and change shared reference data and where every consumer reads at runtime. Toropov summarizes it this way: “The important nuance is that we centralized ownership, not necessarily every online read.”
A synchronous call to the owner for every balance view can make that service a dependency on the critical path. If it becomes slow or unavailable, degradation may cascade to consumers that otherwise could have answered from local data. Asynchronous distribution or snapshots can reduce that runtime dependency, but consumers then need a way to track which version they have and how far behind they are. The design choice should account for each consumer’s freshness needs, read latency, availability expectations, and ability to operate with a known older version.
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Decide whether a dedicated service fits your case
The dedicated-service pattern is most compelling when multiple systems use the same entities, changes to those entities affect business interpretation, and disagreements about ownership or update behavior are costly to resolve. It is not an automatic answer for every shared dataset. Sam Newman’s Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith (O’Reilly Media, 2019) discusses alternatives including duplication, a dedicated schema, a shared library, and a dedicated service.
Before choosing, make the trade-offs explicit across these questions:
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- Model ownership: Who approves and implements a change to fields, relationships, or meaning?
- Version awareness: How can a consumer tell which reference-data version it is using?
- Distribution: Do consumers need an API, events, snapshots, or a combination?
- Runtime dependency: What happens to reads if the owner is slow or unavailable?
- Autonomy and isolation: Which consumers need local reads or the ability to continue through a service outage?
- Freshness and observability: How will teams detect stale projections, failed updates, and lag?
- Operational effort: Will a central owner make audits and discrepancy investigations easier enough to justify running another service?
These are decision criteria, not a measured scorecard. Toropov’s account does not establish that the migration reduced incidents, latency, cost, or reconciliation time. It presents a single team’s rationale and reports clearer ownership and fewer collisions as consequences, without quantified before-and-after results.
Who is the source of truth for a reference entity?
The source of truth should be the component with explicit authority over the entity’s model, validation, lifecycle, and change publication—not merely whichever database happens to contain a copy. Consumers can hold projections for their own reads, but should know how those projections are updated, which version they represent, and how to surface lag or failed delivery. In this case, the dedicated service was chosen to establish that explicit owner.
What if one system uses the new version while another is still on the old one?
That is a version and freshness problem that the design must make visible rather than assume away. Publish changes with identifiable versions, monitor consumer lag and update failures, and give dependent systems a defined way to handle a version mismatch. Depending on the business rules, that may mean continuing with a known version, delaying an operation, or rejecting a change until data is aligned. The case account calls for explicit versions and monitoring but does not prescribe one universal mismatch policy; that policy should reflect the consequences of interpreting the entity inconsistently.
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