Random JSON Duplicate ID Dataset Generator

Description

Construct linked records with an exact duplicate insertion count and retain missing, unique and ambiguous reference candidate indices.

A parser corpus pairs literal input spellings or structured documents with observable parser results or explicitly constructed expectations. Successful parsing, semantic validity, and exact round-trip preservation are separate properties, so test records retain the relevant evidence.

Construct linked records with an exact duplicate insertion count and retain missing, unique and ambiguous reference candidate indices.

When to use this tool

Use this generator to create reproducible json duplicate id dataset cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply ids, duplicateCount, missingControl in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read records, references, duplicateCount, duplicateRate, pretty. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Retain each distinct input ID once, append duplicate sampled IDs, and draw references to generated IDs, optionally planting one missing target. For each reference, report every matching record index and missing, unique, or ambiguous status.^[1]

Input field	Meaning and units	Accepted input
ids	Ids	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 128.; minimum items 1; maximum items 16.
duplicateCount	Duplicate Count	Required. Safe integer; minimum 0; maximum 16.
missingControl	Missing Control	Required. Boolean.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
records	Records	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 32.
records[].id	Literal identifier used to match records, independently of their display labels.	Required. Text; maximum UTF-16 length 65536.
records[].ref	Ref	Required. Text; maximum UTF-16 length 65536.
references	References	Required. Array of Object; no unknown keys.; minimum items 0; maximum items 32.
references[].recordIndex	Record Index	Required. Safe integer; minimum 0; maximum 31.
references[].target	Target	Required. Text; maximum UTF-16 length 65536.
references[].candidates	Candidates	Required. Array of Safe integer; minimum 0; maximum 31.; minimum items 0; maximum items 32.
references[].status	Named outcome; inspect null and error fields before interpreting associated values.	Required. Text; one of "missing", "unique", "ambiguous".
duplicateCount	Duplicate Count	Required. Safe integer; minimum 0; maximum 16.
duplicateRate	Duplicate Rate	Required. Finite number; minimum 0; maximum 1.
pretty	Pretty	Required. Text; maximum UTF-16 length 65536.
randomness	Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.	Required. Object; no unknown keys.
randomness.seed	Optional numeric replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "ids": [
      "a",
      "b"
    ],
    "duplicateCount": 1,
    "missingControl": false,
    "seed": 42
  },
  "output": {
    "records": [
      {
        "id": "a",
        "ref": "b"
      },
      {
        "id": "b",
        "ref": "b"
      },
      {
        "id": "b",
        "ref": "b"
      }
    ],
    "references": [
      {
        "recordIndex": 0,
        "target": "b",
        "candidates": [
          1,
          2
        ],
        "status": "ambiguous"
      },
      {
        "recordIndex": 1,
        "target": "b",
        "candidates": [
          1,
          2
        ],
        "status": "ambiguous"
      },
      {
        "recordIndex": 2,
        "target": "b",
        "candidates": [
          1,
          2
        ],
        "status": "ambiguous"
      }
    ],
    "duplicateCount": 1,
    "duplicateRate": 0.3333333333333333,
    "pretty": "[\n  {\n    \"id\": \"a\",\n    \"ref\": \"b\"\n  },\n  {\n    \"id\": \"b\",\n    \"ref\": \"b\"\n  },\n  {\n    \"id\": \"b\",\n    \"ref\": \"b\"\n  }\n]\n",
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

duplicateCount counts appended records, not duplicate groups. A reference to a repeated ID is ambiguous even if its values appear identical. This generates integrity controls rather than repairing identifiers.
A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random JSON Duplicate ID Dataset Generator — tool.io

Similar or alternative tools

Random JSON Flattening Round-Trip Corpus Generator
Reuse the random nested-document generator for its safe dot-path/contiguous-array subset, then actually flatten and rebuild every generated document with both existing cores; retain intermediate leaves and original/rebuilt forms, excluding sparse/mixed containers, reserved segments, literal-dot keys and empty-container loss cases rather than claiming arbitrary JSON round trips.
Random JSON Merge Conflict Scenario Generator
Construct flat base/left/right edits and report true three-way conflicts with explicit left, right or base resolution policy.