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Distributed Key Value Store : DurableKV

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$99.00
One-time · 12 months of access
Or access with subscription Preview a lesson free — no account needed Day 3: Persist Data to Disk with a Write-Ahead…

This course includes

  • Hands-on coding exercises
  • Downloadable resources & code
  • Certificate of completion
  • 12 months of access

Why This Course?

Most backend development courses teach you how to build a service that works. This course teaches you how to build a service that survives. The gap between a functional prototype and a production-grade system that sustains hundreds of millions of requests per second, endures network partitions, and recovers gracefully from hardware failure is vast. It’s filled with subtle, often counter-intuitive trade-offs, operational nightmares, and the hard-won lessons of engineers who've carried the pager through the night.

This course bridges that gap. We won't just implement algorithms; we'll break them. We won't just deploy services; we'll watch them fail under duress and learn to diagnose the symptoms. You'll move beyond "happy path" development to understand the brutal realities of distributed systems: the lies fsync can tell, why retries are often the leading cause of cascading failure, how clock skew silently breaks lease-based leadership, and why "eventually consistent" is a promise, not a guarantee. You'll emerge with the ability to design, build, and — critically — operate backend systems that are not just correct, but resilient, scalable, and observable under real-world conditions.

What You'll Build

You will continuously evolve a single, production-inspired distributed key-value store, which we'll call DurableKV. Starting as a simple, in-memory HTTP service on Day 1, DurableKV will progressively gain persistence, replication, tunable consistency, partitioning, dynamic cluster membership, and advanced observability. By Day 30, your DurableKV cluster will be capable of surviving multiple node failures, network partitions, and resource saturation events, maintaining data availability and consistency guarantees, and providing rich operational insights on your local machine, mimicking the architecture of systems like Dynamo and Cassandra.

Who Should Take This Course?

This course is meticulously designed for a broad spectrum of technical professionals seeking to understand the deep mechanics of production backend systems:

Software Engineers & Developers: You'll gain a concrete understanding of distributed systems patterns, build hands-on experience with resilience techniques, and learn to write code that performs reliably under load, preparing you for senior and staff-level backend roles.
Systems Programmers & Engineers: Deepen your understanding of how high-performance, fault-tolerant systems are constructed from first principles, from network protocols to storage engines.
Software Designers & Architects: Acquire a practical framework for evaluating architectural trade-offs (consistency vs. availability, latency vs. durability) with real-world implications, moving beyond theoretical diagrams to actionable design decisions.
Product Managers: Develop the vocabulary and intuition to make informed decisions about product features involving data consistency, availability, and operational cost, understanding the engineering complexities behind "eventual consistency" or "high availability."
SREs & DevOps Engineers: Learn to anticipate failure modes by building and breaking systems yourself. You'll gain practical experience in designing for observability, crafting effective alerts, and building robust runbooks for common distributed system failures.
QA Engineers: Understand the complex failure scenarios in distributed systems, enabling you to design more comprehensive test plans that go beyond functional correctness to stress resilience and data integrity.
Engineering Managers: Gain a deeper appreciation for the technical challenges and operational overhead of building and maintaining scalable backend infrastructure, empowering you to better support your teams and estimate project timelines.

What Makes This Course Different?

This course is built on a "learn by doing, break, and fix" philosophy. Every lesson is a hands-on lab where you write, run, test, and critically, break a real working system on your own machine. Unlike theoretical courses or "toy project" tutorials, you will continuously build one single, evolving production-grade system from Day 1 to Day 30. We follow a dependency-graph curriculum, ensuring each day's work builds directly on the previous, leading to a continuously integrated, fully functional artifact.

Crucially, this curriculum embeds dedicated failure days where you deliberately introduce chaos – killing processes, partitioning networks, corrupting data, saturating resources – to observe, diagnose, and fix the resulting outages. This experience is invaluable; engineers remember what they broke and fixed far more vividly than what they read. We also embrace a "production-honest" approach, including the often-overlooked but vital aspects of real systems: configuration management, schema evolution, rolling upgrades, backpressure mechanisms, capacity planning, and the on-call observability that makes operating these systems possible.

What's Included

📚
Video Lessons
Comprehensive content
💻
Hands-On Projects
Build real-world systems
📁
Source Code & Resources
Downloadable materials
🏆
Certificate
On completion
📅
12 Months Access
Learn at your own pace
📱
Any Device
Desktop, tablet & mobile
All Lessons
▼
▶ Day 1: Build a Basic HTTP Key-Value Service — and Store Nothing Durable
FREE
▶ Day 2: Implement Concurrent Request Handling with Go Goroutines and Channels — and Observe Race Conditions
FREE
▶ Day 3: Persist Data to Disk with a Write-Ahead Log — and Corrupt It
FREE
🔒 Day 4: Refine the API with Idempotent Operations — and Avoid Double Writes
PRO
🔒 Day 5: Add Structured Logging and Basic Metrics to Observe Service Health — and Identify Bottlenecks
PRO
🔒 Day 6: Establish Inter-Node Communication with gRPC — and Handle Network Glitches
PRO
🔒 Day 7: Implement Gossip for Dynamic Cluster Membership — and Watch Nodes Join/Leave
PRO
🔒 Day 8: Replicate Writes Asynchronously to Multiple Nodes with a WAL Stream — and Accept Divergence
PRO
🔒 Day 9: Implement Tunable Read and Write Quorums (N, W, R) — and Measure Consistency Trade-offs
PRO
🔒 Day 10: Kill a Replica Mid-Write — and Observe Divergent State
PRO
🔒 Day 11: Repair Data Divergence with Read Repair on GET Requests — and Restore Consistency on Demand
PRO
🔒 Day 12: Replicate the Write-Ahead Log to a Second Node — and Watch It Fall Behind
PRO
🔒 Day 13: Implement Anti-Entropy with Merkle Trees for Background Data Repair — and Reduce Repair Time
PRO
🔒 Day 14: Build a Client-Side Load Balancer with Retries and Timeouts — and Survive Node Failures Gracefully
PRO
🔒 Day 15: Partition the Network Between Replicas — and Observe Stale Reads
PRO
🔒 Day 16: Hash Keys to Distribute Data Across Partitions — and Understand Data Locality
PRO
🔒 Day 17: Implement Consistent Hashing for Elastic Cluster Membership — and Minimize Data Movement
PRO
🔒 Day 18: Build a Request Router to Direct Traffic to the Correct Partition Owner — and Avoid Cross-Node Hops
PRO
🔒 Day 19: Handle High Concurrency with Connection Pools and Backpressure — and Prevent Resource Exhaustion
PRO
🔒 Day 20: Grow the Cluster: Add a New Node and Initiate Data Rebalancing — and Observe Performance Degradation
PRO
🔒 Day 21: Implement Hinted Handoff to Buffer Writes When a Replica is Temporarily Down — and Maximize Availability
PRO
🔒 Day 22: Build a Coordination Service for Cluster-Wide Configuration Changes — and Prevent Split-Brain Scenarios
PRO
🔒 Day 23: Implement Snapshotting and Compacting the Write-Ahead Log — and Limit Disk Usage and Recovery Time
PRO
🔒 Day 24: Saturate a Node's CPU/Disk/Network — and Watch Latency Spike
PRO
🔒 Day 25: Implement a Rolling Upgrade Strategy for the Service Protocol — and Ensure Zero Downtime
PRO
🔒 Day 26: Add Distributed Tracing to Follow Requests End-to-End — and Pinpoint Latency Hotspots
PRO
🔒 Day 27: Perform Capacity Planning: Benchmark Throughput and Latency Under Realistic Load — and Find the Breaking Point
PRO
🔒 Day 28: Define Service Level Objectives (SLOs) and Configure Anomaly Detection Alerts — and Get Paged Proactively
PRO
🔒 Day 29: Introduce a Subtle Bug or Configuration Error — and Observe Cascading Failure
PRO
🔒 Day 30: Conduct a Post-Mortem and Document System Architecture with Playbooks — and Learn from Our Failures
PRO

Prerequisites

  • Basic understanding of programming
  • Willingness to learn
Educational content image for Distributed Key Value Store : DurableKV
$99.00
One-time · 12 months of access
Or access with subscription Preview a lesson free — no account needed Day 3: Persist Data to Disk with a Write-Ahead…

This course includes

  • Hands-on coding exercises
  • Downloadable resources & code
  • Certificate of completion
  • 12 months of access
Course Content 30 lessons
✅ 3 free lessons available — no account needed
▶ Day 1: Build a Basic HTTP Key-Value Service — and Store Nothing Durable FREE ▶ Day 2: Implement Concurrent Request Handling with Go Goroutines and Channels — and Observe Race Conditions FREE ▶ Day 3: Persist Data to Disk with a Write-Ahead Log — and Corrupt It FREE 🔒 Day 4: Refine the API with Idempotent Operations — and Avoid Double Writes PRO 🔒 Day 5: Add Structured Logging and Basic Metrics to Observe Service Health — and Identify Bottlenecks PRO 🔒 Day 6: Establish Inter-Node Communication with gRPC — and Handle Network Glitches PRO 🔒 Day 7: Implement Gossip for Dynamic Cluster Membership — and Watch Nodes Join/Leave PRO 🔒 Day 8: Replicate Writes Asynchronously to Multiple Nodes with a WAL Stream — and Accept Divergence PRO 🔒 Day 9: Implement Tunable Read and Write Quorums (N, W, R) — and Measure Consistency Trade-offs PRO 🔒 Day 10: Kill a Replica Mid-Write — and Observe Divergent State PRO 🔒 Day 11: Repair Data Divergence with Read Repair on GET Requests — and Restore Consistency on Demand PRO 🔒 Day 12: Replicate the Write-Ahead Log to a Second Node — and Watch It Fall Behind PRO 🔒 Day 13: Implement Anti-Entropy with Merkle Trees for Background Data Repair — and Reduce Repair Time PRO 🔒 Day 14: Build a Client-Side Load Balancer with Retries and Timeouts — and Survive Node Failures Gracefully PRO 🔒 Day 15: Partition the Network Between Replicas — and Observe Stale Reads PRO 🔒 Day 16: Hash Keys to Distribute Data Across Partitions — and Understand Data Locality PRO 🔒 Day 17: Implement Consistent Hashing for Elastic Cluster Membership — and Minimize Data Movement PRO 🔒 Day 18: Build a Request Router to Direct Traffic to the Correct Partition Owner — and Avoid Cross-Node Hops PRO 🔒 Day 19: Handle High Concurrency with Connection Pools and Backpressure — and Prevent Resource Exhaustion PRO 🔒 Day 20: Grow the Cluster: Add a New Node and Initiate Data Rebalancing — and Observe Performance Degradation PRO 🔒 Day 21: Implement Hinted Handoff to Buffer Writes When a Replica is Temporarily Down — and Maximize Availability PRO 🔒 Day 22: Build a Coordination Service for Cluster-Wide Configuration Changes — and Prevent Split-Brain Scenarios PRO 🔒 Day 23: Implement Snapshotting and Compacting the Write-Ahead Log — and Limit Disk Usage and Recovery Time PRO 🔒 Day 24: Saturate a Node's CPU/Disk/Network — and Watch Latency Spike PRO 🔒 Day 25: Implement a Rolling Upgrade Strategy for the Service Protocol — and Ensure Zero Downtime PRO 🔒 Day 26: Add Distributed Tracing to Follow Requests End-to-End — and Pinpoint Latency Hotspots PRO 🔒 Day 27: Perform Capacity Planning: Benchmark Throughput and Latency Under Realistic Load — and Find the Breaking Point PRO 🔒 Day 28: Define Service Level Objectives (SLOs) and Configure Anomaly Detection Alerts — and Get Paged Proactively PRO 🔒 Day 29: Introduce a Subtle Bug or Configuration Error — and Observe Cascading Failure PRO 🔒 Day 30: Conduct a Post-Mortem and Document System Architecture with Playbooks — and Learn from Our Failures PRO

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