XMLify — Simplify Your Data Exchange with Automatic XML Conversion

XMLify Tips: Best Practices for Generating Valid XML from JSONConverting JSON to XML is a common task in data integration, API gateways, and legacy system interoperability. XMLify—whether it’s a tool, library, or internal function—makes that conversion easier, but there are important best practices to ensure the resulting XML is valid, robust, and maintainable. This article covers principles, practical tips, and examples you can apply when using XMLify to turn JSON into production-ready XML.

Why JSON → XML conversions matter

Many modern systems speak JSON, while enterprise applications, message brokers, and older APIs still rely on XML. Converting JSON to XML preserves existing investments and enables interoperability without rewriting systems. However, differences in typing, ordering, and schema expectations mean naive conversions often produce invalid or semantically confusing XML. XMLify helps bridge these differences—but only when used with attention to XML norms.

Understand the semantic differences

JSON is typeless and primarily object/array oriented; XML is tree-structured with attributes, elements, and mixed content.
JSON arrays map to repeated XML elements; JSON objects map to nested elements or attributes depending on your rules.
JSON has booleans, numbers, and nulls. XML represents everything as text unless you apply schemas (XSD) or type annotations.

Plan how you want to represent types and structure before converting. Decide whether to preserve JSON types (via attributes or type hints) or to normalize everything as strings.

Design a clear mapping strategy

Define a consistent mapping between JSON constructs and XML constructs. Common patterns:

JSON object -> XML element with child elements for each key.
JSON array -> repeated XML child elements with the same tag name (or a wrapper element containing children).
JSON primitive -> element text content or an attribute value.
JSON null -> empty element or omitted element, depending on schema requirements.
Special keys (like @attributes) -> attributes in XML.

Example mapping choices:

Use a wrapper element for arrays: {“items”:[…]} -> …
Or use repeated elements: {“tag”:[a,b]} -> ab

Document whichever conventions you choose and use them consistently across XMLify configurations.

Preserve ordering when it matters

JSON object key order is not guaranteed by the standard (though most implementations preserve insertion order). XML consumers, however, may rely on element order. If ordering is important:

Use arrays in JSON to express order explicitly.
Configure XMLify to respect array ordering and to map objects into ordered element lists when needed.
When converting objects with significant order, include an explicit ordering key or index.

Handle attributes vs elements deliberately

Attributes are best for metadata or small, identifying values; elements are better for large content or structured data.

Conventions:

Map JSON keys prefixed with “@” (or another agreed marker) to XML attributes.
Map normal keys to child elements.
Avoid overusing attributes for complex data—attributes cannot contain child elements.

Example: JSON: { “book”: {

"@id": "bk101", "title": "XML Basics", "authors": ["A. Smith", "B. Jones"]

} } XML:
XML Basics
A. SmithB. Jones

Deal with namespaces and prefixes

XML namespaces avoid element name collisions. When converting:

Decide how JSON will express namespaces (e.g., keys with colon-separated prefixes like “ns:element” or a dedicated namespace mapping object).
Provide a namespace mapping configuration to XMLify so prefixes resolve to URIs.
Ensure default namespaces and prefixed elements are declared in root or appropriate elements.

Example JSON approach: { “xmlns”: {“x”: “http://example.com/x”}, “x:element”: “value” }

Resulting XML: value/x:element

Representing types and schema validation

If the XML consumer requires types (e.g., via XSD):

Use type hints in JSON (e.g., {“price”: {“#text”: “12.34”, “@type”: “decimal”}}) and map them to xsi:type attributes or typed content where appropriate.
Alternatively, perform a post-conversion pass to validate against an XSD and adjust conversions to match schema expectations.
Consider generating an XSD from your canonical XML structure so you can validate XMLify output automatically.

Escape special characters and handle CDATA

XML has reserved characters (<, >, &, ‘, “). Ensure XMLify properly escapes text values. For content that may contain markup or needs to remain unescaped, wrap it in CDATA sections.

Rules:

Escape by default.
Use CDATA for literal markup or when the consumer expects unescaped content.
Beware that CDATA cannot contain the sequence “]]>”.

Example: <![CDATA[Some bold text & more]]>

Nulls, empty strings, and optional elements

Decide and document how to represent JSON nulls and empty strings:

Omit elements for nulls when absence equals null.
Use empty elements () for empty strings or explicit emptiness.
Use an explicit attribute (e.g., xsi:nil=“true”) when working with XSDs that require nil.

Example with xsi:nil:

Large payloads and streaming

For big JSON payloads:

Use streaming conversion to avoid high memory usage—parse JSON and write XML incrementally.
Prefer SAX/streaming writers in XMLify (if available) instead of constructing full DOM in memory.
Chunk arrays and process elements one-by-one to keep memory bounded.

Maintain readability and pretty printing

While minimal XML is compact, pretty-printed XML improves debugging and diffs. Configure XMLify to:

Emit indented XML for logs and development.
Switch to compact (no extra whitespace) for production where size matters.

Error handling and diagnostics

Provide clear error messages for:

Invalid characters that can’t be converted.
Namespace conflicts.
Schema validation failures.

Include contextual details (path to JSON property, expected type) to speed debugging. Log examples of problematic JSON fragments, not entire payloads, to avoid log bloat and leaking sensitive data.

Security considerations

Sanitize content that may be interpreted as XML entities to prevent entity expansion (XXE) attacks.
Disable DTD processing when parsing XML consumers or validators.
Validate or whitelist incoming JSON keys if keys are used as element names (to avoid creating unexpected element names).

Testing and contract verification

Create fixtures that cover arrays, nested objects, nulls, attributes, namespaces, and edge cases.
Use round-trip tests: JSON -> XML -> JSON (or XML -> JSON -> XML) to ensure conversions preserve intent.
Validate output against XSDs where available.
Include performance tests for large arrays and deeply nested structures.

Example conversion patterns

Simple object JSON: { “user”: {“id”: 1, “name”: “Anna”} } XML: 1Anna
Array as repeated elements JSON: {“tags”: [“x”,“y”]} XML: xy
Attributes and text node JSON: {“note”: {“@lang”: “en”, “#text”: “Hello”}} XML: Hello
Null to xsi:nil JSON: {“price”: null} XML:

Tools and libraries

Many languages have libraries to help:

JavaScript/Node: xmlbuilder2, jsontoxml, fast-xml-parser
Java: Jackson (XML module), XStream
Python: dicttoxml, xmltodict (bidirectional), lxml for validation
Go: encoding/xml with custom marshaling

Choose a library that supports the mapping patterns and streaming features you need, and that lets you configure namespaces, attributes, and type hints.

Operational tips

Version your conversion rules/configuration as part of your API contract.
Provide samples and a converter playground for consumers to test.
Log conversion metrics (time, size, errors) to monitor regressions.

Conclusion

Converting JSON to valid XML reliably requires clear mapping conventions, careful handling of types, namespaces, and special values, and attention to streaming and security. XMLify can automate much of the work, but success comes from designing predictable conventions, validating against schemas when needed, and testing edge cases. Apply the patterns above to produce XML that is both valid and maintainable across systems.