Structured data testing practice should cover the two most common syntaxes: JSON-LD and Microdata. Each has trade-offs and practical implications for testing, rendering, and maintenance. This page explains how to design tests that handle both syntaxes, how to validate conversions between them, and how to choose the approach that fits your platform and performance constraints.
JSON-LD is preferred for many modern implementations because it separates machine-readable markup from HTML structure, is easier to generate server-side, and is resilient to DOM changes. Microdata embeds properties directly in HTML elements and can be easier for CMS-driven templates that already render content in place. Your structured data testing practice should account for the chosen syntax and be prepared to validate either or both depending on legacy content and platform constraints.
Maintain equivalent canonical examples for JSON-LD and Microdata for each content type so you can compare expected outputs. These examples should be functionally identical—same types, properties, and values—so tests can assert semantic parity between syntaxes. When migrating from Microdata to JSON-LD or vice versa, use these canonical pairs to verify that data consumers see equivalent structured information.
Different tools parse JSON-LD and Microdata differently. JSON-LD validators focus on JSON structure and nested objects, while Microdata validators must traverse the DOM and relate properties to the nearest item scope. Include a mix of tools in your testing practice and run both syntax-specific validators and schema-aware linters. For pages that include both syntaxes, ensure parsers yield consistent term expansion and entity relationships.
Client-side frameworks often inject or hydrate JSON-LD or build Microdata dynamically. Use headless browsers in tests to capture the final rendered DOM after JavaScript execution. Tests should wait for network activity to settle or for specific DOM signals that indicate rendering is complete. For single-page apps, simulate navigation flows and ensure that structured data for each virtual page is present and updated correctly on route changes.
When migrating from Microdata to JSON-LD, maintain both versions in a staging environment and run parity tests to ensure no loss of information. Map Microdata itemprop attributes to JSON-LD properties and validate nested relationships. Keep a rollback plan that reinstates the previous syntax until the migration is validated across search consoles and consumer tests.
Products, events, and recipes frequently contain nested objects such as offers, location, author, or ingredient lists. Design tests that assert presence and correctness of these nested structures. For JSON-LD, validate that arrays are present and ordered if order matters (for example, steps in a recipe). For Microdata, validate that nested itemscopes are attached correctly to the parent items and that itemid or itemref attributes resolve as expected.
Handle language tags, localized values, and multi-currency offers carefully. Test that language-specific properties such as name and description include appropriate language tags and that numerical formats use the required decimal and currency conventions. Ensure that the chosen syntax preserves localization metadata and does not strip language attributes during rendering or serialization.
Prefer JSON-LD where possible for easier generation and separation from presentation. Maintain a small library of helper functions or server-side templates to generate JSON-LD consistently. For Microdata, centralize attribute generation in shared components to reduce duplication. Document both patterns in your testing practice so new contributors can follow established conventions and tests can be written from canonical examples.
A mature structured data testing practice covers both JSON-LD and Microdata with canonical examples, syntax-specific validators, headless browser tests for client-side rendering, and migration plans for syntax changes. These elements ensure consistent, accurate markup regardless of rendering approach and reduce the chance of losing rich results during refactors or platform changes.