Schema Validation

Overview

What it means in plain English

Schema Validation checks data against a declared schema for types, required fields, constraints, formats, object structure, and allowed alternatives. It can be applied to API requests, responses, webhook payloads, configuration, and event messages. It does not replace authentication or business validation because structurally valid data can still be unauthorized, inconsistent, or operationally unsafe. In production, teams should define ownership and apply versioned schemas, strict but compatible rules, clear error paths, size limits, unknown-field policy, and monitoring of validation failures. The main risks include rejecting compatible extensions, accepting coerced invalid data, schema drift, denial of service from complex payloads, and incomplete error handling.

How it works and why it matters

Schema Validation checks data against a declared schema for types, required fields, constraints, formats, object structure, and allowed alternatives. It does not replace authentication or business validation because structurally valid data can still be unauthorized, inconsistent, or operationally unsafe.

The main risks include rejecting compatible extensions, accepting coerced invalid data, schema drift, denial of service from complex payloads, and incomplete error handling. It can be applied to API requests, responses, webhook payloads, configuration, and event messages. Data handled by Schema Validation should use documented types, units, timestamp rules, nullable fields, and enum behavior to prevent silent interpretation errors.

In production, teams should define ownership and apply versioned schemas, strict but compatible rules, clear error paths, size limits, unknown-field policy, and monitoring of validation failures. For Schema Validation, the application should preserve a correlation path from request construction through response, asynchronous completion, and final business state.

Useful measures include validation failure rate, top failed fields, schema-version usage, drift incidents, and false rejection count. Schema Validation is closely connected to Request Schema, Response Schema, and Webhook Payload Validation. Observability for Schema Validation should record operation, version, principal, request ID, latency, retry count, error code, and final state without exposing secrets.

Testing Schema Validation should cover malformed input, denied access, rate limits, timeouts, duplicates, asynchronous completion, and version changes. The Schema Validation owner should publish stable error semantics and guidance on whether correction, retry, status lookup, or escalation is appropriate.

The Schema Validation contract should define identifiers, validation, authorization, status semantics, errors, limits, and version behavior at the same boundary.

Worked example

A payout API receives an object with amount, asset, network and destination. Validation rejects an absent network, invalid decimal format or unexpected data type before the request reaches irreversible payout logic. A separate business-rule check evaluates balances and allowed recipients.

Practical checks and common confusion

Valid JSON is not the same as a valid payment request. Define required fields, units, allowed values and version compatibility. Schema validation cannot replace authorization, balance checks, address validation or fraud controls.

Learn the related concepts

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