Chapter 2: Flash Sale Engine - The Mystery Behind Redis and Hot Keys

Flash Sale events are the ultimate stress test for system architecture. When an iPhone is sold for $1, millions of users will smash the "Buy Now" button in the exact same millisecond. If this massive spike hits a MySQL database directly, the system will instantly crash due to Row Locks and Deadlocks.

1. The Hot Key Problem and Two-Tier Caching

A highly discounted product is known as a Hot Key.
Many developers mistakenly believe that "just putting inventory in Redis" solves everything. However, a single Redis node has Network Bandwidth and CPU limits (typically maxing out at ~100k Ops/sec). One million clicks on a single key will saturate the network interface card (NIC) of that Redis node.

Shopee's Solution: Multi-Level Caching

Tier 1 (Local Cache): Built directly into the RAM of the Golang Application Servers (using tools like sync.Map or BigCache). This local cache only stores a boolean flag: "Is the item still in stock?". It has a TTL of just 1-2 seconds but successfully blocks 90% of useless traffic from hitting the network once the item is sold out.
Tier 2 (Distributed Cache - Redis): Only when the Local Cache reports that the item is available does the request proceed to the Redis cluster.

2. Preventing Overselling with Atomic Lua Scripts

When a user buys an item, the system must deduct the inventory. But if you use standard commands: Read stock (GET) -> Check if > 0 -> Write new stock (SET), you will face a critical Race Condition. Two parallel threads might both read a stock value of 1, both decrement it, and result in selling two items when only one existed (Overselling).

The Solution: Shopee wraps the inventory deduction logic inside Lua Scripts running natively within Redis. Because Redis is fundamentally single-threaded, executing a Lua script acts as an Atomic Transaction—no other requests can interrupt it mid-execution.

-- Example Lua Script for Inventory Deduction
local stock_key = KEYS[1]
local stock = tonumber(redis.call('GET', stock_key))

if stock and stock > 0 then
    redis.call('DECR', stock_key)
    return 1 -- Purchase Successful
else
    return 0 -- Out of Stock
end

Fail Fast: Thanks to this mechanism, if the Lua script returns 0, the request is immediately rejected and the user sees an "Out of Stock" message. This RAM-level operation takes mere microseconds.

3. Inventory Sharding

For mega-campaigns, a single Hot Key on a single Redis Node is still too risky. Shopee employs Inventory Sharding.
If there are 1,000 iPhones, they do not store the number 1,000 in a single key iphone_stock. Instead, they slice it into 10 shards: iphone_stock_1 to iphone_stock_10. Each key holds 100 items and is distributed across 10 different physical Redis Nodes.

A load balancer or router randomly routes incoming user traffic to one of those 10 keys, instantly dividing the massive system pressure by 10.

sequenceDiagram
    participant User
    participant App as Golang Server<br/>(Local Cache)
    participant Redis as Redis Cluster<br/>(Sharded)
    participant Worker as Kafka Worker

    User->>App: Click "Buy Now"
    Note over App: Check Local Cache.<br/>Block if Out of Stock
    App->>Redis: Route to shard (e.g. stock_3)
    Note over Redis: Execute Atomic Lua Script
    Redis-->>App: If 0: Return Error
    Redis-->>Worker: If 1: Push Order Event to Queue
    Worker-->>User: Process Order Asynchronously

Developer Takeaway: RAM and caching are your strongest weapons against heavy traffic. However, do not blindly rely on a Distributed Cache. Combine it with Local Caches on the App Server to save network bandwidth, and always use Lua Scripts to guarantee data consistency when handling sensitive numbers like inventory or wallet balances.

References & Further Reading

This post was originally published on my blog at Chapter 2: Flash Sale Engine - Solving Overselling and Hot Keys.

Hi, I'm Lê Tuấn Anh (vesviet) 👋
I am a Senior Go Backend Architect & Distributed Systems Engineer with 17+ years of experience building high-traffic platforms (25M+ requests/month).
If you enjoyed this deep-dive, let's connect on LinkedIn or explore my consulting services at tanhdev.com/hire.