Safely Converting YAML, XML, JSON, and CSV in the Browser

Define the data contract before converting

YAML, XML, JSON, and CSV can express related information, but their type systems are not equivalent. JSON has explicit nulls, booleans, numbers, arrays, and objects; CSV is primarily rows of text cells. XML distinguishes elements, attributes, namespaces, and text nodes, while YAML adds indentation, aliases, and implicit scalar types.

The first decision is therefore not the destination format but the meaning that must survive. Document field types, empty values, order, duplicate keys, dates, and sensitive-data rules before conversion; otherwise a syntactically successful result can still lose information.

• Keep a small representative fixture.
• Version source and target schemas.
• Distinguish null, empty string, and absent fields.
• Compare record counts before and after.

Parse YAML and XML without executing data

Safe YAML handling should reject duplicate keys, bound alias expansion, and avoid executing custom tags. ByteQuant uses those safe parsing controls, but it cannot know the target application's schema or every scalar convention.

DOMParser validates XML syntax, not XSD, namespace contracts, external entities, signatures, or business rules. Readable formatting does not establish semantic validity. Use a separate qualified validation chain whenever an XSD or signed document matters.

• Bound YAML aliases and document size.
• Do not resolve XML external entities.
• Never silently turn parser errors into plain text.
• Test production configuration in the target application.

Use JSON Pointer for lossless paths

Dot paths become ambiguous when keys contain dots or brackets. RFC 6901 JSON Pointer defines escaping for tilde and slash, making it a more reliable intermediate form for inventories and structural comparisons.

During reconstruction, a numeric path segment can be ambiguous between an array index and a numeric object key. Test empty arrays and objects, preserve ordering requirements, and make path collisions fail explicitly rather than overwriting values.

• Test root primitives and empty containers.
• Do not hand-edit path escaping.
• Document whether array order is meaningful.
• Validate reconstructed JSON against a schema.

Choose CSV identity and normalization deliberately

A duplicate CSV row only exists relative to defined key columns. Ignoring surrounding space and letter case can help for email addresses, but the same rule may incorrectly merge product codes. Keeping the first or last record likewise depends on trustworthy ordering and source semantics.

After cleaning, retain source-row, unique-key, and removed-row counts. Mask personal data before sharing tables and run destructive cleanup only against a recoverable copy.