Masterclass: High Concurrency Systems & B2B Commerce#
Have you ever experienced a system crash precisely during the most critical moment of a Mega Sale event? Are your PostgreSQL databases buckling under the weight of locking issues when too many users attempt to place orders simultaneously?
Welcome to the High Concurrency Systems Masterclass.
About this Masterclass
This series distills 17+ years of production experience, drawing directly from the battlefield of building resilient, high-traffic e-commerce systems as an Independent Consultant. It provides practical, battle-tested blueprints for managing 25 million requests per month with Go and Microservices architecture.
🎯 Architecture Review & Consulting (Hire Me)#
If your enterprise e-commerce or B2B platform is struggling with slow database queries, checkout timeouts, or scaling bottlenecks, don’t let it jeopardize your business revenue.
👉 Book a 1:1 Architecture Consultation this week with Lê Tuấn Anh (Vesviet) to identify bottlenecks and implement proven scaling strategies.
📚 Core Curriculum#
Forget generic, theoretical scaling advice. This curriculum tackles the exact concurrency challenges faced in production:
The Reality of C10M: Surviving Extreme Traffic — Exec Summary
An overview for Tech Leads & Architects: Why traditional scaling fails at millions of requests and how to build high-concurrency systems using Golang.
Chapter 1: How Systems Handle Millions of Requests/s (C10M)? Lessons from Shopee & Alipay
A deep dive into how modern distributed systems break the C10M barrier using stateless APIs, multi-level caching, and Go.
Chapter 2: The 3 Caching Vulnerabilities (Penetration, Breakdown, Avalanche) & Go Singleflight
Learn how to defend against Cache Penetration, Avalanche, and Breakdown using Bloom Filters, TTL jittering, and Golang singleflight.
Chapter 3: Distributed Rate Limiting with Redis & GCRA Algorithm
Discover why local rate limiters fail in Microservices and how Redis Lua scripts powering the GCRA algorithm solve distributed throttling.
Chapter 4: Solving the Dual-Write Problem with Transactional Outbox Pattern
Master the Transactional Outbox Pattern using GORM and CDC to eliminate Dual-Write data inconsistencies in event-driven systems.
Chapter 5: Optimizing Golang Database Connection Pools
*Tune your sql.DB connection pool parameters (MaxOpenConns, MaxIdleConns) and implement PgBouncer to maximize Go database performance.
Chapter 6: API Gateway vs Service Mesh in Microservices Architecture
Understand the clear boundaries between North-South traffic (API Gateway) and East-West traffic (Service Mesh) in large Go architectures.
Chapter 7: Designing Idempotency APIs for Payment Systems
Prevent double-charging customers by implementing robust Idempotency Keys and Atomic Redis locks in your HTTP POST transactions.
Chapter 8: Distributed Locking — Redlock vs ZooKeeper
Master distributed synchronization by comparing Redis Redlock algorithms against strongly consistent Apache ZooKeeper locks.
Chapter 9: Database Sharding & Read/Write Splitting
Scale your relational database infinitely using GORM dbresolver for Read/Write splitting and Consistent Hashing for massive Sharding.
Stop guessing why your system is failing under load. Contact me today for a comprehensive Technical Audit and start scaling with confidence.
Essential tooling for diagnosing and validating high-concurrency systems in production:
FAQ#
How do you handle inventory race conditions in a high-concurrency Go system?
Use Optimistic Concurrency Control (OCC) at the database layer instead of pessimistic locks. The pattern: UPDATE inventory SET reserved_stock = reserved_stock + $qty, version = version + 1 WHERE sku_id = $id AND (total_stock - reserved_stock) >= $qty AND version = $current_version. If RowsAffected == 0, another goroutine won the race — retry or return stock-unavailable. This eliminates SELECT FOR UPDATE contention that serializes all concurrent orders on the same row.
What is the Transactional Outbox Pattern and why is it needed?
The Transactional Outbox Pattern solves the dual-write problem: if your service writes to PostgreSQL and then publishes to Kafka, a crash between those two steps loses the event permanently. The fix: write both the business state change and an outbox event record in the same database transaction. A CDC process (Debezium or TiCDC) reads the event_outbox table and publishes to Kafka. Either both succeed (transaction commits) or neither does (transaction rolls back). Zero dual-write risk.
How do Go goroutine pools prevent OOM in high-traffic systems?
Unbounded goroutine creation is the primary OOM cause in Go microservices. A bounded worker pool limits concurrency using a semaphore channel: sem := make(chan struct{}, maxWorkers). Each goroutine acquires a slot (sem <- struct{}{}), processes one item, then releases it (<-sem). If all maxWorkers slots are taken, new goroutines block at the send rather than spawning unconstrained. At 50,000 messages/burst, this prevents 50,000 concurrent database connections from exhausting the PostgreSQL pool.
When should I use Dapr Workflow vs Dapr Pub/Sub Saga choreography?
Use Pub/Sub choreography (each service reacts to events independently) for linear 2–4 step Sagas where any developer can reason about the full flow at a glance. Switch to Dapr Workflow Orchestration (a single durable orchestrator function) when your Saga has 5+ steps, complex conditional branching (approval gates, multi-warehouse allocation), or compensation logic that requires reading 4+ service codebases to trace. Dapr Workflow persists state after each step — a crash mid-saga replays from the last checkpoint, not from the beginning.
Prerequisite: Before reading this chapter, please ensure you have read the previous article in this series: Chapter 8: Distributed Locking — Redlock vs ZooKeeper.
When your application reaches tens of millions of users, the Database becomes the ultimate bottleneck. CPU maxes out at 100%, RAM depletes, and queries take seconds instead of milliseconds. This is the stage where you must deploy distributed database strategies.
1. Read/Write Splitting Because 80% of traffic is Read-only, separate your DB into a Write Master and Read Slaves. Use GORM’s dbresolver plugin to route queries automatically without altering business logic.
...
Prerequisite: Before reading this chapter, please ensure you have read the previous article in this series: Chapter 7: Fortifying Payment Systems with Idempotent APIs.
In a standalone Go application, preventing two Goroutines from overwriting the same data (Race Condition) is achieved via sync.Mutex. However, when your system scales out to 10 servers behind a Load Balancer, sync.Mutex is useless because it only locks local RAM. You need a Distributed Lock.
...
Prerequisite: Before reading this chapter, please ensure you have read the previous article in this series: Chapter 6: API Gateway vs Service Mesh in Microservices Architecture.
In E-commerce or Fintech, the ultimate nightmare is not a system crash, but charging a customer twice for a single order. This is usually caused by network lag, an impatient user double-clicking “Pay”, or automated app retry logic.
The mandatory solution for any transactional API (Payment/Order) is Idempotency.
...
Prerequisite: Before reading this chapter, please ensure you have read the previous article in this series: Chapter 5: Unlocking Database Performance via Connection Pooling.
When your Golang application scales from dozens to hundreds of Microservices, managing communication becomes a macro-level challenge. You will constantly encounter two tightly coupled concepts: API Gateway and Service Mesh.
Many engineers ask: “If I already deploy Istio (Service Mesh), do I still need Kong (API Gateway)?” The answer lies in the fundamental difference between North-South and East-West traffic.
...
Prerequisite: Before reading this chapter, please ensure you have read the previous article in this series: Chapter 4: Solving the Dual-Write Problem with Transactional Outbox Pattern.
If your Golang system processes business logic blazingly fast but chokes at the Database layer, 90% of the time, it is due to an incorrectly configured *sql.DB.
1. Understanding *sql.DB In Golang, sql.Open() does NOT create a direct database connection. It instantiates a thread-safe Connection Pool manager. You must initialize the db variable only once during app startup.
...
Prerequisite: Before reading this chapter, please ensure you have read the previous article in this series: Chapter 3: Distributed Rate Limiting with Redis & GCRA Algorithm.
When your Golang application migrates from a Monolith to event-driven Microservices, you will immediately face an architectural nightmare: the Dual-Write Problem.
1. What is the Dual-Write Problem? Dual-Write occurs when an app attempts to write to a Database and publish to a Message Broker (Kafka) simultaneously. Without a distributed transaction, network failures will cause the two systems to fall out of sync.
...
Prerequisite: Before reading this chapter, please ensure you have read the previous article in this series: Chapter 2: The 3 Caching Vulnerabilities (Penetration, Breakdown, Avalanche) & Go Singleflight.
If caching is the shield protecting your database, Rate Limiting is the armor guarding your API servers from DDoS attacks and resource exhaustion caused by abusive clients.
1. Why Local Rate Limiting Fails in Microservices Local RAM limiters fail because Load Balancers distribute traffic across multiple nodes. A user allowed 100 req/sec can exploit a 5-node cluster by sending 500 req/sec, bypassing the intended limit. Centralized state via Redis is required.
...
Prerequisite: Before reading this chapter, please ensure you have read the previous article in this series: Chapter 1: How Systems Handle Millions of Requests/s (C10M)? Lessons from Shopee & Alipay.
Caching is the ultimate shield for databases in distributed systems. However, poorly implemented caches can become the exact reason your system crashes. In this chapter, we dissect three classic caching phenomenons and how to defend against them using Golang.
1. Cache Penetration & Bloom Filter Mathematics Cache penetration occurs when attackers query non-existent IDs, bypassing the cache entirely. Defend against it by caching NULL values or utilizing Bloom Filters at the memory level.
...
Prerequisite: Before reading this chapter, please ensure you have read the previous article in this series: Executive Summary: The Reality of C10M: Surviving Extreme Traffic.
To build a system capable of handling millions of Requests Per Second (RPS) — known as the C10M problem — vertical scaling is never enough. It requires a meticulously designed Distributed Architecture.
1. The Shift from C10K to C10M While C10K (10,000 concurrent connections) was solved by non-blocking I/O (like NGINX using epoll or kqueue), C10M shifts the bottleneck entirely to the operating system kernel. Systems must bypass the kernel using technologies like DPDK or XDP to handle 10 million connections efficiently.
...
Prerequisite: This is the executive summary and introductory overview of the High Concurrency Systems series. No prior reading is required to start here. You can view the full series roadmap at the Series Hub.
Despite the massive advancements in cloud computing, enterprise applications facing explosive traffic growth inevitably hit a brutal wall: the Database and the Network layer. The root cause lies not in the hardware, but in the Architecture. We attempt to solve the “Millions of Requests per Second” (C10M) problem by simply throwing more servers at it (Vertical/Horizontal Scaling), only to realize that stateful bottlenecks, cache stampedes, and dual-write inconsistencies bring the entire cluster to its knees.
...