We Hit 6 Billion MongoDB Documents (And Lived to Tell the Tale)

The “Oh Crap” Moment

Avluz.com reached 6 billion documents in its MongoDB cluster this year, encountering performance issues as query times climbed to 8 seconds and carefully tuned indexes became ineffective. The team addressed this by migrating from AWS to OVH and utilizing GenSpark AI for optimization.

Why This Matters

Ideal database models assume predictable workloads, but real-world scaling reveals unforeseen challenges. Failing to address these can lead to unacceptable latency and escalating costs; Avluz.com’s AWS bill reached $7,500/month before intervention.

Key Insights

• OVH cost savings: $2,180/month compared to $7,500/month on AWS for the same hardware configuration.
• Compound sharding keys: Distributing data effectively is critical; hashing user IDs alongside timestamps avoids hot shards.
• AI-assisted optimization: GenSpark AI identified index issues and provided a critical sharding strategy, saving an estimated 7-8 weeks of engineering time.

Working Example

sh.shardCollection("avluz.events", {
user_id: "hashed",
timestamp: 1
})

Practical Applications

• E-commerce catalog: Large product catalogs with frequent updates can utilize similar sharding strategies for performance.
• Pitfall: Overly aggressive connection pooling can overwhelm a database, causing connection errors and performance degradation.

References:

• https://dev.to/milinda_biswas_fb9eeb2a8a/we-hit-6-billion-mongodb-documents-and-lived-to-tell-the-tale-26pj