Random Hash Table Collision Workload Generator

Description

Solve residues for the caller's toy linear modular hash, construct distinct planted colliding integer keys and bounded noise, then retain bucket counts and every actual collision pair; no cryptographic collision claim.

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.

When to use this tool

Use this generator to create reproducible hash table collision 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 bucketCount, targetBucket, multiplier, offset, collidingCount, noiseCount 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 keys, buckets, plantedCollisionCount, bucketCounts, collisionPairs. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Solve residues reaching the target bucket under the supplied affine modular hash, draw distinct quotient cycles for planted collisions, and append replacement-sampled noise keys. Compute bucket counts and every same-bucket index pair using exact modular arithmetic.^[1]

Input field	Meaning and units	Accepted input
bucketCount	Bucket Count	Required. Safe integer; minimum 1; maximum 32.
targetBucket	Target Bucket	Required. Safe integer; minimum 0; maximum 31.
multiplier	Multiplier	Required. Safe integer; minimum 1; maximum 32.
offset	Offset	Required. Safe integer.
collidingCount	Colliding Count	Required. Safe integer; minimum 1; maximum 32.
noiseCount	Noise Count	Required. Safe integer; minimum 0; maximum 32.
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
keys	Keys	Required. Array of Safe integer; minimum 0; maximum 32031.; minimum items 0; maximum items 64.
buckets	Buckets	Required. Array of Safe integer; minimum 0; maximum 31.; minimum items 0; maximum items 64.
plantedCollisionCount	Planted Collision Count	Required. Safe integer; minimum 1; maximum 32.
bucketCounts	Bucket Counts	Required. Array of Safe integer; minimum 0; maximum 64.; minimum items 0; maximum items 32.
collisionPairs	Collision Pairs	Required. Array of Array of Safe integer; minimum 0; maximum 63.; minimum items 2; maximum items 2.; minimum items 0; maximum items 2016.
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": {
    "bucketCount": 3,
    "targetBucket": 1,
    "multiplier": 1,
    "offset": 0,
    "collidingCount": 2,
    "noiseCount": 1,
    "seed": 42
  },
  "output": {
    "keys": [
      1804,
      1345,
      5
    ],
    "buckets": [
      1,
      1,
      2
    ],
    "plantedCollisionCount": 2,
    "bucketCounts": [
      0,
      2,
      1
    ],
    "collisionPairs": [
      [
        0,
        1
      ]
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Noise can add further collisions or repeat keys. plantedCollisionCount counts constructed keys rather than all collision pairs, and an unreachable target bucket is rejected; this is not a cryptographic hash attack model.
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 Hash Table Collision Workload Generator — tool.io

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