JSON Data Quality Error Catalog
A troubleshooting catalog for syntax, encoding, type, schema, date, number, and interoperability mistakes commonly found in JSON workflows.
Parse, then validate meaning
Valid syntax does not prove that types, required fields, date formats, or business rules are correct.
Preserve a minimal failing sample
Reduce the problem to the smallest safe payload that reproduces the issue. Remove secrets and personal data.
Treat producers and consumers as one contract
A formatter can repair whitespace, but data contracts require agreement about keys, types, nulls, ranges, and versions.
How to read the downloadable table
The table is organized around “Error class” and “Example signal.” Begin with the row labeled “Trailing comma,” then read across the full row before comparing it with another case. The cells are designed to preserve context; copying a single number or phrase without its row label can change the meaning.
Use the last row, “Circular source object,” as a completeness check rather than as an automatic conclusion. Where the table contains scores, thresholds, examples, or suggested actions, they apply only under the method and limitations stated on this page.
A repeatable application workflow
Define the decision, collect verified inputs, apply the table consistently, and save enough evidence for another reviewer to reproduce the result. Compare more than one scenario before making a final decision.
For json data quality error catalog, keep a short review log containing the date, page or file tested, input values, result, reviewer notes, and any source that changed the interpretation. This turns a one-time check into an auditable workflow.
What evidence to preserve
Save the exact version of the input, a screenshot or exported result where appropriate, the source URL, and the date accessed. If the result depends on software, include the browser, library, encoder, calculator version, or device conditions that could affect reproduction.
When publishing a conclusion, distinguish an observation from an inference. An observation reports what the documented test produced; an inference explains what that result may mean. Readers should be able to see which is which.
Maintenance and citation practice
Cite this resource using the full title, ToolnixHub, the reviewed date, the canonical report URL, and the relevant table or section. Link to the report page rather than an isolated download so readers can see the methodology and limitations.
Recheck the underlying primary references before using the resource for a time-sensitive decision. Standards, rates, browser support, product behavior, and official guidance can change after the reviewed date, even when the general workflow remains useful.
| Error class | Example signal | Detection | Repair principle |
|---|---|---|---|
| Trailing comma | Unexpected token before } or ] | Parser | Remove unsupported trailing delimiter |
| Single quotes | Strings use apostrophes | Parser | Use double-quoted JSON strings |
| Unquoted key | {name:"A"} | Parser | Quote property names |
| Comment | // note | Parser | Move comments outside JSON or use supported format |
| Duplicate key | Same key twice | Schema/linter | Define unique keys and resolve conflict |
| Wrong type | "42" instead of 42 | Schema | Normalize producer or validation |
| Null ambiguity | null means unknown or empty | Data contract | Document semantic meaning |
| Date ambiguity | 03/04/26 | Schema/business rule | Use documented ISO-style representation |
| Unsafe integer | Very large ID becomes rounded | Runtime test | Represent identifiers safely |
| NaN/Infinity | Non-JSON numeric values | Parser | Use null/string/error contract |
| Encoding | Invalid UTF-8 | Byte validation | Normalize encoding |
| Control character | Raw newline in string | Parser | Escape characters |
| Root mismatch | Array expected, object received | Schema | Align contract |
| Required field | Key absent | Schema | Supply or revise requirement |
| Enum mismatch | Unexpected status value | Schema | Use allowed vocabulary |
| Circular source object | Serializer fails | Runtime | Remove cycle or define reference model |
Methodology
The catalog separates parser errors, schema errors, encoding errors, and semantic contract failures.
Limitations
Exact messages vary by language, parser, schema system, and runtime.
Questions about this report
Can a formatter fix every JSON problem?
No. It may expose syntax issues but cannot infer the intended data model.
Are duplicate keys always rejected?
Behavior varies; avoiding them prevents silent overwrites and interoperability problems.
How often should this resource be reviewed?
Review it whenever a primary reference, rate, standard, browser behavior, tool implementation, or decision context changes. For time-sensitive use, verify the sources on the day of the decision.
May a publisher reuse the dataset?
Publishers may quote or summarize a reasonable portion with clear attribution and a link to the canonical report. Republishing the complete dataset as a substitute for this resource requires permission.