Record Array Unique Key Indexer
Build a unique typed-scalar record index as entries; numeric and string keys remain distinct, missing and duplicate keys fail.
Description
Build a unique typed-scalar record index as entries; numeric and string keys remain distinct, missing and duplicate keys fail.
A record index maps unique scalar keys back to their original positions. Keeping keys as values avoids coercing numeric and textual keys into identical object-property names.
When to use this tool
Build a unique typed-scalar record index as entries; numeric and string keys remain distinct, missing and duplicate keys fail.
- Prepare the supplied recordsJson, keyField fields for this operation.
- Compare the complete example with the representation used by your own input before carrying the result into another step.
How to use the tool and how it works
- Enter recordsJson, keyField. Use the exact text syntax, units and index conventions explained below.
- Run the tool. If input validation fails, correct the supplied fields before interpreting any result.
- Read result.index. Interpret false, null and empty lists using the distinctions below; they can be valid outputs.
Require every record to contain an own scalar keyField and reject duplicate typed keys. Return an index array of key and original recordIndex pairs.1
| Input field | Meaning and representation | Accepted input |
|---|---|---|
| recordsJson | JSON text, entered as one text field. The decoded structure is illustrated below; it is not a separate file. | Text |
| keyField | key field. | Text; at most 8,192 UTF-16 code units, subject to the grammar above |
For the JSON text fields, the following decoded structures show the example records before they are serialized into input strings. Keep field names, list nesting and value types as shown.
[
{
"field": "recordsJson",
"decoded": [
{
"id": 1
},
{
"id": "1"
}
]
}
]| Output field | Interpretation |
|---|---|
| result | Object containing the named outputs below. |
| result.index | The index list in the ordering and representation described above. |
{
"input": {
"recordsJson": "[{\"id\":1},{\"id\":\"1\"}]",
"keyField": "id"
},
"output": {
"result": {
"index": [
{
"key": 1,
"recordIndex": 0
},
{
"key": "1",
"recordIndex": 1
}
]
}
}
}Limitations and assumptions
- Number 1 and string "1" remain different keys. This is a separate lookup index, not an object keyed by coerced strings or a reordered record collection.
- Decoded JSON is bounded to 20,000 nodes and 32 nesting levels; record arrays have at most 128 records. Parsed number values use JavaScript floating point rather than arbitrary-precision decimal arithmetic.
- Every declared top-level field is required and unexpected fields are rejected. Numeric inputs must be finite; ordinary text fields are bounded to 8,192 UTF-16 code units, with tighter operation-specific limits where stated.
- Ordinary string lists have at most 128 entries of at most 128 code units each; numeric lists and matrices have separate bounded size checks. Nonfinite results and excessively large serialized outputs are rejected.
- Indexes are zero-based and spans have an exclusive end unless explicitly stated otherwise. A valid false, null or empty-list result is distinct from malformed input.
Alternative or Complementary methods
For document-wide requirements, combine these structural inventories with an explicit JSON Schema. Preserve the distinction between missing properties and null values, and between numbers and numeric-looking strings when building joins or indexes.
References
Similar or alternative tools
- JSON Array Homogeneity Audit
Audit immediate element-type homogeneity for every JSON array; an empty array is vacuously homogeneous, not evidence of a particular type.
- json array length policy check
Audit all JSON array lengths against inclusive bounds; no inferred schema or scalar-array coercion.