Record Field Union Profiler
Profile first-seen record field union, presence counts and observed top-level types; no inferred required schema.
Description
Profile first-seen record field union, presence counts and observed top-level types; no inferred required schema.
A schema-union profile summarizes which fields occur across a record collection and which immediate value types appear there. Presence and null values are separate observations.
When to use this tool
Profile first-seen record field union, presence counts and observed top-level types; no inferred required schema.
- Prepare the supplied recordsJson 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. 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.fields. Interpret false, null and empty lists using the distinctions below; they can be valid outputs.
Collect keys in first-seen enumeration order. For each key, count records containing it as an own property and list distinct types observed among those present values.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 |
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": [
{
"a": 1
},
{
"a": null,
"b": "x"
}
]
}
]| Output field | Interpretation |
|---|---|
| result | Object containing the named outputs below. |
| result.fields | The fields list in the ordering and representation described above. |
{
"input": {
"recordsJson": "[{\"a\":1},{\"a\":null,\"b\":\"x\"}]"
},
"output": {
"result": {
"fields": [
{
"key": "a",
"presentCount": 2,
"types": [
"number",
"null"
]
},
{
"key": "b",
"presentCount": 1,
"types": [
"string"
]
}
]
}
}
}Limitations and assumptions
- This profiles the supplied sample only. It does not infer required fields for unseen records, inspect nested schemas or treat null as an absent property.
- 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
- 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.
- Record Equijoin Cardinality Counter
Count exact typed-scalar equijoin pairs with full duplicate multiplicity; absent members do not join, explicit null values can join.