1. Introduction
Eventual consistency is a core requirement in distributed systems where data flows across multiple services, queues, storage systems, and event-driven pipelines. When systems scale across microservices, different versions of the same entity may temporarily become inconsistent. Without monitoring and automated detection, these inconsistencies may accumulate and impact order processing, billing, analytics, and customer experience.
This article provides a complete architecture and implementation guide for building an Eventual Consistency Monitoring System. It includes monitoring loops, reconciliation engines, drift detection patterns, and dashboards, with a practical example based on .NET and SQL.
2. Business Problem
In microservices, the same business object may exist in:
Order Service
Payment Service
Shipment Service
Inventory Service
Analytics Service
Whenever an update occurs in one service, it may take time before other services receive and apply the change. Failures in message delivery, partial commits, or queue failures can result in permanent inconsistency.
3. System Goals
Detect data mismatches between services
Auto-heal inconsistent records
Provide reconciliation reports
Surface real-time drift metrics
Enable scalable periodic scanning
4. High-Level Architecture
Source systems: Microservices, SQL/NoSQL stores, event logs
Monitoring engine: Periodic job that pulls snapshots
Drift detection rules: Match conditions and validation logic
Reconciliation engine: Fix or re-publish events
Dashboard + alerts: Grafana or Angular UI
5. Data Drift Detection Patterns
5.1 Snapshot Comparison
Take periodic snapshots from different services and compare key fields.
5.2 Event Key Correlation
Compare latest events from each service using message IDs.
5.3 Hash-Based Comparison
Store hash of entity state. If hash differs, drift occurred.
5.4 Dual Writes Checker
When a write should happen in two systems, confirm both applied it.
6. Technical Design
6.1 DriftRule
DriftRuleId
ServiceA
ServiceB
EntityType
MatchKey
CheckFields (JSON)
AutoFixEnabled
6.2 DriftRecord
DriftId
RuleId
EntityId
ValueA
ValueB
DriftStatus
DetectedOn
FixedOn
6.3 Reconciliation Job
A .NET background worker uses:
SQL queries
API calls
Message replay
7. Implementation – Background Worker
public class DriftMonitor : BackgroundService
{
private readonly DriftService _service;
public DriftMonitor(DriftService service)
{
_service = service;
}
protected override async Task ExecuteAsync(CancellationToken stoppingToken)
{
while (!stoppingToken.IsCancellationRequested)
{
await _service.CheckAllRulesAsync();
await Task.Delay(TimeSpan.FromMinutes(5), stoppingToken);
}
}
}
8. Angular Monitoring Dashboard
Drift list component
Drilldown view for each mismatch
Charts for drift frequency
Filters (entity, service, date)
9. Auto-Fix Strategies
10. Production Recommendations
Use durable logs for auditing
Include metrics into Prometheus
Maintain reconciliation history for compliance
Provide selective ignore rules