← Back to all projects

LEARN FEATURE FLAG ENGINEERING

In the early days of web development, deploying meant releasing. If you pushed code, the users saw it. If there was a bug, you had to roll back the entire deployment. Feature flags (or Feature Toggles) decouple these two events.

Learn Feature Flag Management: From Zero to Feature Engineering Master

Goal: Deeply understand the architecture of high-performance feature flag systems. You will transition from simple boolean toggles to building a distributed, low-latency evaluation engine capable of complex targeting, deterministic rollouts, and global “kill switch” capabilities, ensuring you can manage production risk at scale.

Why Feature Flag Management Matters

In the early days of web development, “deploying” meant “releasing.” If you pushed code, the users saw it. If there was a bug, you had to roll back the entire deployment. Feature flags (or Feature Toggles) decouple these two events.

Companies like Facebook, Netflix, and Flickr pioneered this to enable Continuous Deployment. They can deploy code to production 50 times a day, but only “turn on” features when they are ready.

What understanding this unlocks:

  • Risk Mitigation: Turn off a broken feature in milliseconds without a re-deploy.
  • Canary Releases: Show a feature to 1% of users first to monitor performance.
  • Trunk-Based Development: Merge incomplete features into main safely behind a flag.
  • A/B Testing: Use flags to measure which version of a feature performs better.
  • Entitlements: Manage “Pro” vs “Basic” features through the same system.

Core Concept Analysis

The Decoupling Architecture

[ Control Plane (Admin UI) ]  <-- Product Manager sets rules
         |
         v
[ Distribution Layer (API/CDN) ] <-- Rules pushed to the edge
         |
         v
[ SDK / Evaluation Engine ]    <-- Lives INSIDE your app
         |
         v
[ Your Application Code ]      <-- if (sdk.isEnabled("new_ui")) { ... }

Fundamental Building Blocks

1. The Evaluation Engine

This is the “brain.” It takes a Flag Definition (rules) and a User Context (ID, email, country) and returns a boolean or value. CRITICAL: For performance, evaluation happens locally in the SDK, not via a network call to the server for every if statement.

2. Deterministic Hashing (For Rollouts)

How do you ensure user “123” always sees the same version of a 10% rollout? You hash the UserID + FlagKey and check the modulo:

hash("user123" + "new_button_color") % 100 < 10  => User is in the 10% group

3. Targeting Rules

The engine must support complex logic:

  • user_id is in [1, 5, 10] (Beta testers)
  • email ends with @company.com (Internal employees)
  • version >= 2.0.0 (App version targeting)
  • country == US (Geo-fencing)

4. The Kill Switch

A global override that bypasses all rules to force a flag to false. This is the “Panic Button” for SREs.

The Feature Flag Lifecycle

1. DEFINE  -> "New Checkout" flag created in UI.
2. TARGET  -> Rules: "Internal users only" or "10% of Canada".
3. DEPLOY  -> Code merged to main and deployed to production (Flag is OFF).
4. EXPOSE  -> Toggle ON for specific segments.
5. MONITOR -> Check error rates/latency for the flagged group.
6. ROLLOUT -> Gradually increase 10% -> 50% -> 100%.
7. CLEANUP -> Remove the `if` statement from code (The "Technical Debt" phase).

Concept Summary Table

Concept Cluster What You Need to Internalize
Local Evaluation Flags are evaluated in-memory within the app to avoid millisecond latencies on every check.
State Sync How the SDK stays updated (Polling vs. Streaming/SSE) without overloading the server.
Contextual Targeting Passing user attributes into the engine to make granular decisions.
Deterministic Rollouts Using consistent hashing to ensure a user’s experience doesn’t flicker.
Default Fallbacks What happens if the FF server is unreachable? Your code must have a safe default.

Deep Dive Reading by Concept

Foundation

Concept Book & Chapter
Feature Toggles “Continuous Delivery” by Jez Humble & David Farley — Ch. 12: “Managing Infrastructure and Environments”
Decoupling Release “Accelerate” by Nicole Forsgren — Ch. 5: “Architecture”

Systems & Performance

Concept Book & Chapter
Hashing & Distribution “Designing Data-Intensive Applications” by Martin Kleppmann — Ch. 6: “Partitioning” (for understanding deterministic distribution)
Local Caching “High Performance Browser Networking” by Ilya Grigorik — Ch. 16: “HTTP Cache”

Essential Reading Order

  1. The Core Philosophy (Day 1):
  2. Strategy (Week 1):
    • Continuous Delivery Ch. 12 (Humble/Farley)
  3. Engineering Reality (Week 2):
    • LaunchDarkly’s Engineering blog on “Consistent Hashing”
    • Pete Hodgson’s patterns for Feature Toggles.

Project List

Projects are ordered from fundamental logic to distributed systems complexity.

Project 1: The Hardcoded Toggle (Understanding State)

  • File: LEARN_FEATURE_FLAG_ENGINEERING.md
  • Main Programming Language: Go
  • Alternative Programming Languages: C#, TypeScript, Rust
  • Coolness Level: Level 1: Pure Corporate Snoozefest
  • Business Potential: 1. The “Resume Gold”
  • Difficulty: Level 1: Beginner
  • Knowledge Area: State Management
  • Software or Tool: Standard Library
  • Main Book: “Code Complete, 2nd Edition” by Steve McConnell

What you’ll build: A simple configuration-driven feature toggle system where flags are loaded from a local file at startup and checked via a central FeatureManager.

Why it teaches FF: It introduces the core “interface” of a feature flag. You learn that your application code shouldn’t know how a flag is decided, only that it exists.

Core challenges you’ll face:

  • Interface Design: Designing a clean IsEnabled(flagName) API.
  • Hot-Reloading: Trying to change the config file and having the app update without restarting (Signal handling or polling).
  • Type Safety: How do you handle non-existent flags without crashing?

Key Concepts:

  • Configuration Management: Code Complete Ch. 18 (External Configuration)
  • Singleton/Service Pattern: Design Patterns by Gamma et al. (For the Manager)

Difficulty: Beginner Time estimate: 2-4 hours Prerequisites: Basic file I/O and JSON/YAML parsing.

Real World Outcome

You will have a program that behaves differently based on a configuration file. You can “release” a new feature by simply editing a text file while the program is running.

Example Output:

# config.json: {"new_dashboard": false}
$ ./my_app
[INFO] Welcome to the Classic Dashboard.

# (Edit config.json: set new_dashboard to true)
$ kill -HUP $(pgrep my_app) # Trigger a reload
[INFO] Welcome to the NEW V2 DASHBOARD!

The Core Question You’re Answering

“How can I change my application’s behavior without changing its code?”

Before you write any code, sit with this question. Most developers think of code as static once compiled. A feature flag turns code into a dynamic system where logic paths can be swapped at runtime.

Project 2: The Contextual Rule Engine (Targeting)

  • File: LEARN_FEATURE_FLAG_ENGINEERING.md
  • Main Programming Language: TypeScript (Node.js)
  • Alternative Programming Languages: Python, Java
  • Coolness Level: Level 2: Practical but Forgettable
  • Business Potential: 2. The “Micro-SaaS / Pro Tool”
  • Difficulty: Level 2: Intermediate
  • Knowledge Area: Logical Parsing
  • Software or Tool: JSON Logic or custom parser
  • Main Book: “Compilers: Principles, Techniques, and Tools” (The Dragon Book)

What you’ll build: A system where flags aren’t just true/false, but based on Rules. e.g., “Flag is ON if user.email ends with @company.com”.

Why it teaches FF: This is the heart of “Targeting.” You’ll learn how to represent complex business logic as data structures (JSON) and evaluate them against a “Context” object.

Core challenges you’ll face:

  • Rule Evaluator: Implementing AND, OR, IN, CONTAINS logic in a generic way.
  • Context Injection: How to pass the current user’s data into the engine without polluting your entire codebase.
  • Performance: Evaluating 100 rules in < 1ms.

Key Concepts:

  • Predicate Logic: How to represent (A and B) or C in JSON.
  • Dynamic Evaluation: Building a safe “interpreter” for your rules.

Difficulty: Intermediate Time estimate: 1 weekend Prerequisites: Understanding of JSON, recursion, and boolean logic.

Real World Outcome

You’ll build a CLI tool where you can “test” user profiles against a rule set.

Example Output:

$ ./ff_tester --rules rules.json --user "{ \"email\": \"alice@google.com\", \"country\": \"US\" }"

Checking flag: "enable_beta_ui"...
Rule: user.email endsWith "@google.com"
Result: ENABLED (User is internal)

$ ./ff_tester --rules rules.json --user "{ \"email\": \"bob@other.com\", \"country\": \"UK\" }"
Result: DISABLED (No rules matched)

Project 3: The Deterministic Rollout (Hashing)

  • File: LEARN_FEATURE_FLAG_ENGINEERING.md
  • Main Programming Language: Rust
  • Alternative Programming Languages: Go, C++
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 1. The “Resume Gold”
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Mathematics / Hashing
  • Software or Tool: MurmurHash3 or SHA-1
  • Main Book: “Designing Data-Intensive Applications” by Martin Kleppmann

What you’ll build: A percentage-based rollout engine. Given a flag set to “20%”, the same UserID must always get the same result, but across 1000 users, exactly ~200 should have it enabled.

Why it teaches FF: This teaches you why simple random numbers (Math.random() < 0.2) are dangerous for feature flags (they cause “flickering”). You’ll master deterministic distribution.

Core challenges you’ll face:

  • Hash Collisions: Ensuring “Flag A” and “Flag B” don’t enable for the exact same 20% of users (Salted hashing).
  • Floating Point Math: Converting a large hash integer into a 0-100 percentage accurately.
  • Stickiness: Verifying that a user’s experience stays the same even if other users are added.

Key Concepts:

  • Consistent Hashing: Using salts to differentiate flags.
  • Probability Distribution: Verifying your hash function is “uniform.”

Difficulty: Advanced Time estimate: 1 weekend Prerequisites: Basic understanding of hashing (MD5/SHA/Murmur).

Real World Outcome

A simulation tool that runs 10,000 “users” through a 15% rollout and prints a histogram showing the distribution.

Example Output:

$ ./rollout_sim --percentage 15 --flag "payment_v2"
Processing 10,000 users...

User 1 (id: "u1"): OFF
User 2 (id: "u2"): ON
...
Summary:
- Enabled: 1,504 (15.04%)
- Disabled: 8,496 (84.96%)
- Stickiness Check: User "u2" re-run => ON (Verified)

Project 4: The Evaluation SDK (Concurrency & Fallbacks)

  • File: LEARN_FEATURE_FLAG_ENGINEERING.md
  • Main Programming Language: Java or C#
  • Alternative Programming Languages: Go, Swift
  • Coolness Level: Level 2: Practical but Forgettable
  • Business Potential: 4. The “Open Core” Infrastructure
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Software Architecture / Thread Safety
  • Software or Tool: Thread-safe Maps (ConcurrentHashMap)
  • Main Book: “Java Concurrency in Practice” by Brian Goetz

What you’ll build: A thread-safe client library (SDK) that can be embedded in an app. It must store flag data in memory, allow for atomic updates, and provide “default values” when a flag is missing.

Why it teaches FF: You’ll learn the difference between “raw logic” and a “production-ready library.” You’ll deal with race conditions (updating flags while evaluating them) and the “Bootstrapping” problem.

Core challenges you’ll face:

  • Thread Safety: Reading from the flag cache while a background thread is updating it.
  • Bootstrapping: What does the app do in the first 50ms before the flags have loaded?
  • Error Boundaries: If the SDK throws an exception, the host app must not crash.

Key Concepts:

  • Read-Write Locks: Optimizing for high-read, low-write scenarios.
  • Graceful Degradation: The “Safety Net” pattern.

Difficulty: Advanced Time estimate: 1-2 weeks Prerequisites: Multi-threading basics, synchronization primitives.

Project 5: The Management API (Control Plane)

  • File: LEARN_FEATURE_FLAG_ENGINEERING.md
  • Main Programming Language: Go (Gin or Echo)
  • Alternative Programming Languages: Node.js (Express), Python (FastAPI)
  • Coolness Level: Level 2: Practical but Forgettable
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 2: Intermediate
  • Knowledge Area: Backend API Design
  • Software or Tool: PostgreSQL
  • Main Book: “Designing Web APIs” by Brenda Jin

What you’ll build: A RESTful API that allows administrators to create flags, define rules, and manage rollouts. This is the “brain” that stores the source of truth.

Why it teaches FF: You’ll learn how to model feature flags as database entities and how to structure the data for efficient distribution to thousands of SDKs.

Core challenges you’ll face:

  • Schema Design: How to store hierarchical rules (JSONB vs. Relational tables).
  • Validation: Ensuring rules are logically sound before saving (e.g., no infinite loops).
  • Security: Preventing unauthorized users from toggling production flags.

Key Concepts:

  • Resource Versioning: Incrementing a version field every time a flag changes.
  • Audit Logs: Tracking WHO changed WHAT and WHY.

Difficulty: Intermediate Time estimate: 1 week Prerequisites: SQL, REST API fundamentals.

Real World Outcome

A set of API endpoints where you can manage your system.

Example Output:

$ curl -X POST /api/v1/flags -d '{"key": "promo_banner", "state": "OFF"}'
$ curl -X GET /api/v1/flags/promo_banner
{
  "key": "promo_banner",
  "state": "OFF",
  "version": 1,
  "updated_at": "2025-12-28T10:00:00Z"
}

Project 6: Real-time Sync with SSE (The Distribution Layer)

  • File: LEARN_FEATURE_FLAG_ENGINEERING.md
  • Main Programming Language: Node.js or Go
  • Alternative Programming Languages: Python (Aiohttp)
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 4. The “Open Core” Infrastructure
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Real-time Networking
  • Software or Tool: Server-Sent Events (SSE)
  • Main Book: “High Performance Browser Networking” by Ilya Grigorik

What you’ll build: A system that pushes flag updates to connected SDKs in real-time. When a flag is toggled in the API, all running apps should update their local cache within < 500ms.

Why it teaches FF: You’ll understand the tradeoff between Polling (simple but heavy) and Streaming (complex but efficient). This is how professional tools like LaunchDarkly work.

Core challenges you’ll face:

  • Connection Management: Handling thousands of persistent SSE connections.
  • Reconnection Logic: Ensuring SDKs don’t miss updates when the internet drops.
  • Fan-out: Efficiently broadcasting a single change to 10,000 clients.

Key Concepts:

  • Server-Sent Events (SSE): Why it’s better than WebSockets for uni-directional updates.
  • Pub/Sub (Redis): Bridging the API to the Streaming server.

Difficulty: Advanced Time estimate: 1-2 weeks Prerequisites: Understanding of HTTP persistent connections and Pub/Sub patterns.

Real World Outcome

You’ll see a log of updates being pushed to “SDK Clients” instantly.

Example Output:

[API] Flag "new_checkout" toggled ON.
[STREAM] Broadcasting change to 142 clients...
[CLIENT 1] Received update: new_checkout=ON (Version 5)
[CLIENT 2] Received update: new_checkout=ON (Version 5)
# Latency: 12ms

Project 7: The Admin Dashboard (UI)

  • File: LEARN_FEATURE_FLAG_ENGINEERING.md
  • Main Programming Language: React or Vue
  • Alternative Programming Languages: Svelte, Alpine.js
  • Coolness Level: Level 2: Practical but Forgettable
  • Business Potential: 2. The “Micro-SaaS / Pro Tool”
  • Difficulty: Level 2: Intermediate
  • Knowledge Area: Frontend / UX
  • Software or Tool: Tailwind CSS
  • Main Book: “Refactoring UI” by Adam Wathan & Steve Schoger

What you’ll build: A visual interface for your management API. It needs a “Kill Switch” button, rule builders, and an activity log.

Why it teaches FF: You’ll learn that Feature Flags are a product management tool, not just an engineering tool. The UI must make complex rules understandable to non-coders.

Core challenges you’ll face:

  • Complex Form State: Building a UI for nested rules (e.g., “If A and (B or C)”).
  • Safety UX: Adding “Double Confirmation” or “Protected Windows” for critical flags.
  • Real-time Feedback: Showing that a flag is currently “Active” in production.

Key Concepts:

  • Visual Programming: Representing logic as UI components.
  • State Feedback: Reflecting backend changes in the UI immediately.

Difficulty: Intermediate Time estimate: 1 week Prerequisites: Modern Frontend framework (React/Vue).

Real World Outcome

A polished dashboard where a Product Manager can safely toggle a “Kill Switch” for the entire site.

Example Output:

  • A big red “PANIC: DISABLE ALL FLAGS” button.
  • A visual breakdown of a 20% rollout (Pie chart).
  • A searchable list of all flags with their current production status.

Project 8: The Edge Proxy (Performance at Scale)

  • File: LEARN_FEATURE_FLAG_ENGINEERING.md
  • Main Programming Language: Rust or C++
  • Alternative Programming Languages: OpenResty (Lua), Go
  • Coolness Level: Level 4: Hardcore Tech Flex
  • Business Potential: 5. The “Industry Disruptor”
  • Difficulty: Level 4: Expert
  • Knowledge Area: Systems Programming / CDNs
  • Software or Tool: Redis or Cloudflare Workers
  • Main Book: “Systems Performance” by Brendan Gregg

What you’ll build: A proxy that sits between your SDKs and your API. It caches flag data at the “Edge” and serves it with sub-millisecond latency. It should also perform some evaluation logic to reduce the payload sent to the client.

Why it teaches FF: This teaches you the Global Scale problem. How do you serve flags to 1 million users in Japan when your database is in New York?

Core challenges you’ll face:

  • Cache Invalidation: How do you “purge” the edge cache instantly when a flag changes?
  • Read-Through Caching: Efficiently fetching from the source of truth only when necessary.
  • Data Locality: Using Redis as a distributed cache.

Key Concepts:

  • Distributed Caching: Eventual vs. Strong consistency in flag delivery.
  • Edge Computing: Moving logic closer to the user.

Difficulty: Expert Time estimate: 2 weeks Prerequisites: Experience with Caching, Proxies, and basic Systems programming.

Project 9: The Domain-Specific Language (Advanced Rules)

  • File: LEARN_FEATURE_FLAG_ENGINEERING.md
  • Main Programming Language: Rust or ANTLR (any language)
  • Alternative Programming Languages: Go, Python (Lark)
  • Coolness Level: Level 5: Pure Magic (Super Cool)
  • Business Potential: 5. The “Industry Disruptor”
  • Difficulty: Level 4: Expert
  • Knowledge Area: Compilers / Language Design
  • Software or Tool: ANTLR or custom Lexer/Parser
  • Main Book: “Language Implementation Patterns” by Terence Parr

What you’ll build: A custom, human-readable language for defining flag rules. Instead of complex JSON, users write: IF country == "UK" AND (device == "mobile" OR user_id % 10 == 0) THEN "v2" ELSE "v1".

Why it teaches FF: This is how enterprise-grade FF platforms (like Optimizely) handle massive rule sets. You’ll learn how to transform text into an Abstract Syntax Tree (AST) that the evaluation engine can execute instantly.

Core challenges you’ll face:

  • Lexing & Parsing: Converting a string of text into logical tokens.
  • Type Checking: Ensuring the rule doesn’t compare a “string” to an “integer” incorrectly.
  • AST Execution: Traversing the tree to get a result.

Key Concepts:

  • AST (Abstract Syntax Tree): The internal representation of logic.
  • Recursive Descent Parsing: A common technique for building parsers.

Difficulty: Expert Time estimate: 2 weeks Prerequisites: Data structures (Trees), basic parsing concepts.

Real World Outcome

A rule engine that accepts a string and a context, returning the decision.

Example Output:

$ ./rule_engine --eval "user.age > 18 AND user.verified == true" --context '{"age": 21, "verified": true}'
Result: TRUE

$ ./rule_engine --eval "user.age > 18 AND user.verified == true" --context '{"age": 15, "verified": true}'
Result: FALSE

Project 10: Impression Tracking (Analytics Integration)

  • File: LEARN_FEATURE_FLAG_ENGINEERING.md
  • Main Programming Language: Go or Node.js
  • Alternative Programming Languages: Java, Rust
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 2. The “Micro-SaaS / Pro Tool”
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Data Pipelines / Observability
  • Software or Tool: Apache Kafka or RabbitMQ
  • Main Book: “Designing Data-Intensive Applications” by Martin Kleppmann

What you’ll build: A system that records exactly WHICH user saw WHICH flag at WHICH time. This “Impression” data is vital for A/B testing and debugging.

Why it teaches FF: You’ll learn the importance of Observability. A feature flag without tracking is a blind switch. You need to know if the “New UI” is actually being shown and if it’s causing errors.

Core challenges you’ll face:

  • High Volume: Handling 100,000 “impressions” per second without slowing down the app.
  • Buffering & Batching: Sending data from the SDK in batches to save network bandwidth.
  • De-duplication: Ensuring you don’t record the same user/flag pair 1000 times a second.

Key Concepts:

  • Event Sourcing: Treating impressions as a stream of facts.
  • Batching Algorithms: Trade-off between data freshness and network efficiency.

Difficulty: Advanced Time estimate: 1-2 weeks Prerequisites: Message queues (Kafka/RMQ), basic data pipeline knowledge.

Real World Outcome

A dashboard showing “Live Impressions” as users navigate your app.

Example Output:

[IMP] User "u123" exposed to "payment_v2" (Control Group)
[IMP] User "u456" exposed to "payment_v2" (Treatment Group)
[STATS] payment_v2: 5,000 exposures in last 60s.

Project 11: The Distributed Kill Switch (Panic Mode)

  • File: LEARN_FEATURE_FLAG_ENGINEERING.md
  • Main Programming Language: Go or Erlang
  • Alternative Programming Languages: Elixir, Rust
  • Coolness Level: Level 4: Hardcore Tech Flex
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Distributed Systems / Reliability
  • Software or Tool: Redis or Etcd
  • Main Book: “Site Reliability Engineering” by Google (The O’Reilly book)

What you’ll build: A global override system. When an SRE hits the “Panic” button, a signal is sent through a separate, high-priority channel (bypassing normal flag logic) to force specific flags to OFF across all global instances.

Why it teaches FF: This is about Safety. You’ll learn how to build systems that work even when the main control plane is failing.

Core challenges you’ll face:

  • Out-of-band Signaling: Using a lightweight channel (like Redis Pub/Sub or UDP) for speed.
  • State Conflict: What happens if the API says “ON” but the Panic Button says “OFF”?
  • Propagation Speed: Getting the “Kill” signal to 1000 servers in < 100ms.

Key Concepts:

  • Fail-Safe Design: Defaulting to the safest state.
  • Control Theory: Feedback loops in distributed systems.

Difficulty: Advanced Time estimate: 1 week Prerequisites: Network sockets, concurrency, SRE mindset.

Real World Outcome

A CLI command that instantly disables a feature across a mock cluster of 10 nodes.

Example Output:

$ ./panic --kill "login_v2"
[PANIC] Broadcasted KILL login_v2 to 10 nodes.
[NODE 1] ACK: login_v2 is now FORCED OFF.
[NODE 2] ACK: login_v2 is now FORCED OFF.
...
# All nodes disabled in 45ms.

Project 12: Secret Management & SDK Security

  • File: LEARN_FEATURE_FLAG_ENGINEERING.md
  • Main Programming Language: TypeScript (React Native or Browser)
  • Alternative Programming Languages: Swift, Kotlin
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Cybersecurity
  • Software or Tool: JWT or HMAC
  • Main Book: “Foundations of Information Security” by Jason Andress

What you’ll build: A secure distribution system for “Client-side” SDKs (mobile/web). It must ensure that users cannot “see” flags they aren’t supposed to know about (e.g., hidden internal features) by sniffing network traffic.

Why it teaches FF: You’ll learn the security difference between Server-side SDKs (safe) and Client-side SDKs (unsafe). You’ll implement “Flag Scrubbing” and signed payloads.

Core challenges you’ll face:

  • Data Scrubbing: Only sending the flags relevant to the current user to the client.
  • Payload Signing: Using HMAC to ensure the flags haven’t been tampered with.
  • Secret Leaks: Preventing the “API Key” from being stolen and used to toggle flags.

Key Concepts:

  • Least Privilege: Don’t send data the client doesn’t need.
  • HMAC (Hash-based Message Authentication Code): Verifying integrity.

Difficulty: Advanced Time estimate: 1 week Prerequisites: Basic cryptography (Hashing, HMAC), JWT knowledge.

Project Comparison Table

Project Difficulty Time Depth of Understanding Fun Factor
1. Hardcoded Toggle Level 1 4h ⭐ 🟢
2. Contextual Rules Level 2 2d ⭐⭐ 🟢🟢
3. Deterministic Hashing Level 3 2d ⭐⭐⭐⭐ 🟢🟢🟢
4. Evaluation SDK Level 3 1w ⭐⭐⭐ 🟢🟢
5. Management API Level 2 1w ⭐⭐ 🟢
6. Real-time Sync Level 3 2w ⭐⭐⭐⭐ 🟢🟢🟢🟢
7. Admin Dashboard Level 2 1w ⭐ 🟢🟢
8. Edge Proxy Level 4 2w ⭐⭐⭐⭐⭐ 🟢🟢🟢🟢
9. DSL Rules Level 4 2w ⭐⭐⭐⭐⭐ 🟢🟢🟢🟢🟢
10. Impression Tracking Level 3 1w ⭐⭐⭐ 🟢🟢
11. Panic Button Level 3 1w ⭐⭐⭐⭐ 🟢🟢🟢🟢
12. SDK Security Level 3 1w ⭐⭐⭐ 🟢🟢

Recommendation

Where to start?

  • If you are a Backend Engineer: Start with Project 3 (Hashing) and Project 6 (SSE Sync). These cover the most important distributed systems concepts of FF.
  • If you are a Full-stack Developer: Build Project 2 (Contextual Rules) followed by Project 7 (Dashboard) to see the full “Control Plane to Evaluation” flow.
  • If you want to understand “Systems”: Jump straight to Project 8 (Edge Proxy) and Project 9 (DSL).

Final Overall Project: “FlagForge” - The Enterprise Platform

What you’ll build: A complete, self-hosted feature flag platform. It includes:

  1. A Go-based API with Postgres for flag storage.
  2. A React Dashboard with a visual rule builder.
  3. A Rust Edge Proxy that serves flags via SSE.
  4. An SDK Implementation in at least two languages (e.g., TypeScript and Go) that supports deterministic rollouts, local evaluation, and impression tracking.

Why it teaches FF: This forces you to integrate every single concept. You’ll deal with the Distribution Problem: how to get a rule changed in the UI, saved in DB, pushed to the Proxy, and received by the SDK in under 1 second.

Success Criteria:

  • Toggle a flag in the UI; see the “New Feature” appear in a running app within 1 second.
  • Run a 20% rollout and verify exactly ~20% of users see the feature.
  • Hit the “Panic” button and see all apps revert to “Default” immediately.
  • Zero network calls to the API/Proxy during isEnabled() checks (verified with a profiler).

Summary

This learning path covers Feature Flag Engineering through 12 hands-on projects.

# Project Name Main Language Difficulty Time Estimate
1 Hardcoded Toggle Go Level 1 4h
2 Contextual Rules TypeScript Level 2 2d
3 Deterministic Hashing Rust Level 3 2d
4 Evaluation SDK Java Level 3 1w
5 Management API Go Level 2 1w
6 Real-time Sync (SSE) Node.js Level 3 2w
7 Admin Dashboard React Level 2 1w
8 Edge Proxy Rust Level 4 2w
9 DSL Rule Language Rust Level 4 2w
10 Impression Tracking Go Level 3 1w
11 Distributed Kill Switch Go Level 3 1w
12 SDK Security TypeScript Level 3 1w

For beginners: Start with projects #1, #2, #5, #7. For intermediate: Focus on projects #3, #4, #6, #10. For advanced: Master projects #8, #9, #11, #12.

Expected Outcomes

After completing these projects, you will:

  • Understand the difference between Server-side and Client-side evaluation.
  • Be able to implement deterministic rollouts using consistent hashing.
  • Build low-latency distribution systems using SSE and Edge Caching.
  • Understand the security implications of feature flag distribution.
  • Be capable of building or maintaining an enterprise-grade FF platform.

You’ll have built a portfolio of working components that demonstrate deep mastery of production release management from first principles.

```