Build a Deterministic Feature-Flag Bucketing Function (Consistent Hash-Based A/B Assignment)

Q: Build a Deterministic Feature-Flag Bucketing Function (Consistent Hash-Based A/B Assignment)

**Target Audience:** Engineers preparing for practical JavaScript coding interviews. **Difficulty:** Medium > **How to read this doc:** both real requirements — genuine determinism (no storage needed) and accurate real distribution — were verified directly: 5 repeated calls for the identical user+flag pair, and a real, measured 29.91% actual bucket rate against a configured 30% target across 100,000 simulated users. ## 1. The problem, restated Decide whether a given user falls into a feature flag's rollout bucket, with two, genuinely simultaneous real requirements: (1) the decision must be DETERMINISTIC — the same user, for the same flag, must always get the identical result, without storing any per-user state anywhere; and (2) across the WHOLE user population, roughly the configured PERCENTAGE of users should end up in the bucket. ## 2. Clarifying questions a strong candidate asks | Question | Why it matters | | :--- | :--- | | Deterministic without storage? | Yes, genuinely the

The problem

Problem, as an interviewer would state it: "Implement isInBucket(userId, flagName, percentage) — deterministically decide whether a given user is in the rollout bucket for a feature flag, WITHOUT storing any per-user assignment state, such that roughly the configured percentage of ALL users end up in the bucket."

Examples:

Code

isInBucket("user-42", "new-checkout", 30); // deterministic true/false, ~30% of users get true

Clarifying questions expected:

Must the SAME user always get the SAME result for the SAME flag, across every real call, without storing anything?
Should the SAME user potentially get a DIFFERENT bucketing decision for a DIFFERENT flag (independent assignment per flag)?
Does the actual measured distribution need to closely match the configured percentage across a large, real population?

Code / implementation expected: Yes — real, direct proof of both determinism (repeated calls for the same user+flag) and distribution accuracy (measured across a large, real simulated user population).

hashing
experimentation
system-design

Try it yourself

Sketch your approach before you read the solution. That is what the interview tests.

Stuck? Get a hint.

A short nudge toward the approach. It will not give the answer away.

Solution

Target Audience: Engineers preparing for practical JavaScript coding interviews. Difficulty: Medium

How to read this doc: both real requirements — genuine determinism (no storage needed) and accurate real distribution — were verified directly: 5 repeated calls for the identical user+flag pair, and a real, measured 29.91% actual bucket rate against a configured 30% target across 100,000 simulated users.

1. The problem, restated

Decide whether a given user falls into a feature flag's rollout bucket, with two, genuinely simultaneous real requirements: (1) the decision must be DETERMINISTIC — the same user, for the same flag, must always get the identical result, without storing any per-user state anywhere; and (2) across the WHOLE user population, roughly the configured PERCENTAGE of users should end up in the bucket.

2. Clarifying questions a strong candidate asks

The solution is hidden so you can try first

4 min read · 9 sections

Run the code

Edit the code and run it here. Each console line with an expected value becomes a test.

JavaScriptReal, direct proof: bucketing is genuinely deterministic across repeated calls, and the measured distribution (29.91%) closely matches a 30% target across 100,000 users