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Architecture Reference Blueprint

High-Performance Feature Flagging & Experimentation Engine

Architect a resilient, low-latency feature flagging and A/B experimentation platform capable of evaluating billions of daily flags in under 1 millisecond.

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System Constraints

Non-Negotiable Architecture Constraints

✓Sub-millisecond local in-memory flag evaluation with zero network roundtrips
✓Instant global rule propagation (< 2 seconds) on flag toggle in admin console
✓Deterministic MurmurHash3 user bucketing ensuring consistent user variant exposure
✓Zero application downtime if the central flag server goes offline (offline-first fallback)

Component Topology

System Components & Technologies

Modular subsystems designed with decoupled responsibilities, clear contracts, and scalable storage layers.

3D Isometric Architecture

High-Performance Feature Flagging & Experimentation Engine Stack Topology

Live Telemetry Active
Tier 1: ManagementTier 2: EdgeTier 3: In-MemoryTier 4: Experimentation
01

Management Admin Console

< 15ms
Role: Creating flags, setting rollout percentages, and targeting user segmentsNext.js 15 + PostgreSQL
02

Edge Streaming Distribution

< 35ms
Role: Pushing flag rule updates to thousands of application servers over SSEServer-Sent Events (SSE) / Redis PubSub
03

In-Memory Client SDK

< 5ms
Role: Evaluating targeting rules locally in application memory with zero latencyGo / TypeScript / Python SDKs
04

Experimentation Analytics

< 1ms
Role: Computing Bayesian statistical significance on conversion metricsClickHouse + dbt
Subsystem 01

Management Admin Console

Creating flags, setting rollout percentages, and targeting user segments

Production Stack:

Next.js 15 + PostgreSQL

Subsystem 02

Edge Streaming Distribution

Pushing flag rule updates to thousands of application servers over SSE

Production Stack:

Server-Sent Events (SSE) / Redis PubSub

Subsystem 03

In-Memory Client SDK

Evaluating targeting rules locally in application memory with zero latency

Production Stack:

Go / TypeScript / Python SDKs

Subsystem 04

Experimentation Analytics

Computing Bayesian statistical significance on conversion metrics

Production Stack:

ClickHouse + dbt

Data Lifecycle

End-to-End Data Flow Sequence

1

Engineer toggles feature rollout to 20% of users in the Next.js Management Console.

2

Update is written to PostgreSQL and broadcast over Redis PubSub to edge SSE servers.

3

Application SDKs maintain persistent SSE connections, updating local in-memory rule caches in < 500ms.

4

When user requests a page, SDK computes MurmurHash3(user_id + flag_key) % 100 in 0.05ms locally.

5

Evaluation event is buffered and flushed asynchronously to ClickHouse for statistical significance tracking.

Reliability & Resilience

Failure modes & automated mitigations

Failure Mode 01

Management Server Outage

Mitigation Architecture

Client SDKs cache rule sets on local disk, continuing to evaluate flags flawlessly during central server outages.

Failure Mode 02

Network Partition During Flag Toggle

Mitigation Architecture

SDKs implement automatic exponential backoff reconnection with fallback to last-known-good rule state.

Failure Mode 03

Stale Flag Code Accumulation

Mitigation Architecture

Integrate automated AST linters in GitHub CI/CD to detect and report deprecated feature flag conditionals.

Architecture FAQs

Frequently asked blueprint questions

Making a network API call for every feature flag check adds 50–100ms latency to every request. Evaluating flags in memory takes less than 0.1 milliseconds.

MurmurHash3 is a deterministic hashing algorithm that produces the exact same hash output for a given user ID, guaranteeing users stay in their assigned experiment bucket.

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