Event Consistency Checker

A tiny interactive tool to compare two JSON events and surface basic inconsistencies between their fields, naming, and types.

This project is part of my Systems of Trust Series, exploring how distributed systems maintain truth, alignment, and consistency across event flows and microservices.

The goal of this checker is to provide a simple, legible way to understand how small mismatches in event structure can create downstream confusion, drift, or data-quality issues.

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Purpose

Even when two events describe the same user action, small inconsistencies — different names, types, or missing fields — can create downstream ambiguity.
These inconsistencies introduce:

• misaligned contract assumptions
• data-quality issues
• difficulties reconciling logs
• incorrect dashboards and analytics
• inconsistent interpretations of “what actually happened”

This checker surfaces these mismatches clearly and early.

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Features (MVP)

This prototype includes:

• Input boxes for Event A and Event B
• Basic field comparison (presence/absence)
• Naming differences (e.g., user_id vs userId)
• Simple type checks ("123" vs 123)
• Summary of consistency level
• Human-readable list of mismatches

The tool is intentionally minimal and focused on conceptual clarity.

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Example

Event A:

{
  "user_id": 123,
  "action": "login",
  "timestamp": "2025-11-22T15:00:00Z"
}

Event B:

{
  "userId": "123",
  "type": "LOGIN",
  "timestamp": "2025-11-22T15:00:00Z"
}

Output:

Inconsistencies Detected:
- Field name mismatch: user_id (Event A) vs userId (Event B)
- Type mismatch for user/user_id: number vs string
- Field only in Event A: action
- Field only in Event B: type

Consistency: Low
Issues: 4

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Demo Screenshot

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Consistency Check Flow Diagram

       [Event A JSON]       [Event B JSON]
              |                     |
              v                     v
        Extract Fields        Extract Fields
              \               /
               \             /
                v           v
           Field Presence Comparator
      (only in A, only in B, shared keys)
                |
                v
          Naming Comparator
   (user_id vs userId vs userid, etc.)
                |
                v
            Type Comparator
        ("123" vs 123, enum drift)
                |
                v
        Semantic/Meaning Drift Check
 (value interpretation differences, e.g. login vs LOGIN)
                |
                v
            Consistency Summary
      (pass/fail, mismatch count, notes)

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Why Event Consistency Matters

Even small inconsistencies can ripple through distributed systems:

• misaligned schemas between services
• difficulty reconciling logs
• incorrect analytics and dashboards
• inconsistent business logic
• harder debugging and incident review
• model drift in ML systems

This checker provides a small, understandable way to visualize how event drift and schema mismatches show up in practice.

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How This Maps to Real Systems

Even though it's simple, each part corresponds to real-world architecture:

Field Naming

Teams often evolve naming conventions independently (user_id, userId, userid).
Standardization prevents integration failures.

Type Consistency

"123" vs 123 can break joins, dashboards, and ETL jobs.

Missing Fields

One service writing action, another writing type — this is a common cause of “silent divergence.”

Consistency Summary

In safety-critical or regulated environments, teams need a quick way to verify whether two events are describing the same conceptual action.

This tool represents a tiny, legible slice of that workflow.

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Part of the Systems of Trust Series

Main repo:
https://github.com/rtfenter/Systems-of-Trust-Series

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Status

MVP implemented and active.
This checker is intentionally lightweight and focuses on core mechanics, not production-grade validation.

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Local Use

No installation required.

To run locally:

1. Clone the repo
2. Open index.html in your browser

Everything runs client-side.