Random Top K Frequency Workload Generator

Description

Construct repeated token counts with optional sampled extras, shuffle the stream and retain exact top-k answers under explicit bank, UTF-16 lexical or first-occurrence tie ordering.

An operation trace pairs an ordered request stream with the state after each step. Empty operations, ties, evictions, and boundary indexes are part of the modeled contract rather than incidental display details.

Construct repeated token counts with optional sampled extras, shuffle the stream and retain exact top-k answers under explicit bank, UTF-16 lexical or first-occurrence tie ordering.

When to use this tool

Use this generator to create reproducible top k frequency workload 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 tokens, repetitionsPerToken, extraCount, k, tiePolicy 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 stream, ranking, topKeys. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Repeat every distinct bank token the requested number of times, add sampled extras and shuffle the stream. Count frequencies and sort by descending count with explicit bank, UTF-16 lexical or first-occurrence tie policy, then return top k keys.^[1]

Input field	Meaning and units	Accepted input
tokens	Tokens	Required. Array of Text; maximum UTF-16 length 64.; minimum items 2; maximum items 16.
repetitionsPerToken	Repetitions Per Token	Required. Safe integer; minimum 1; maximum 4.
extraCount	Extra Count	Required. Safe integer; minimum 0; maximum 16.
k	K	Required. Safe integer; minimum 1; maximum 16.
tiePolicy	Tie Policy	Required. Text; one of "bank", "lexicographic", "first".
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
stream	Stream	Required. Array of Text; maximum UTF-16 length 64.; minimum items 0; maximum items 64.
ranking	Ranking	Required. Array of Object; no unknown keys.; minimum items 0; maximum items 16.
ranking[].key	Key	Required. Text; maximum UTF-16 length 64.
ranking[].count	Requested count; the mechanics explain whether one draw expands into several returned records.	Required. Safe integer; minimum 1; maximum 64.
topKeys	Top Keys	Required. Array of Text; maximum UTF-16 length 64.; minimum items 0; maximum items 16.
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": {
    "tokens": [
      "b",
      "a"
    ],
    "repetitionsPerToken": 2,
    "extraCount": 0,
    "k": 1,
    "tiePolicy": "lexicographic",
    "seed": 42
  },
  "output": {
    "stream": [
      "b",
      "a",
      "b",
      "a"
    ],
    "ranking": [
      {
        "key": "a",
        "count": 2
      },
      {
        "key": "b",
        "count": 2
      }
    ],
    "topKeys": [
      "a"
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

The constructed stream is bounded to 64 entries and k must fit the token bank. The tie convention is part of the answer; this is an exact small workload, not a streaming approximation benchmark.
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 Top K Frequency Workload Generator — tool.io

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