Distributed Sharding and Scaling Quadtrees to Billions of Points

Sharding by Region
Divide world into coarse regions. Each region is a shard containing a quadtree of that area. A query for Manhattan goes to the NYC shard. A query for London goes to the London shard. Each shard is independent, enabling parallel scaling.
Region boundaries cause problems. A radius query near a boundary needs data from multiple shards. The coordinator must fan out to both shards, merge results, and deduplicate. Boundary queries are slower than interior queries.
Cell-Based Sharding
Assign quadtree cells to shards using consistent hashing or range partitioning. Fine-grained cells distribute load more evenly than coarse regions. Hot cells can be split across multiple shards with replication.
Cell assignment should balance load. Naive assignment puts all downtown cells on one shard. Randomized or hash-based assignment spreads load but loses locality. Hybrid approaches: group nearby cells, then hash groups to shards.
Consistency Challenges
Distributed quadtrees face consistency issues. A point moves from cell A on shard 1 to cell B on shard 2. During transition, the point might appear in both, neither, or be stale. Strong consistency requires distributed transactions or coordination.
Eventual consistency is often acceptable for location data. A rider position being slightly stale rarely matters. Design for tolerance: queries accept that results may be seconds old. Critical operations (pickup confirmation) use separate strongly consistent path.
💡 Key Insight: Distributed spatial indexing is hard because spatial locality conflicts with distribution. Points near each other should be queried together but distributing them reduces load. Balance locality (fewer cross-shard queries) against distribution (even load).

💡 Key Takeaways

✓Region sharding: each region has independent quadtree, simple but boundary queries span shards

✓Cell-based sharding: fine-grained distribution but loses locality benefits

✓Hot cell problem: downtown cells get most traffic, need replication or splitting

✓Moving points across shards cause consistency challenges; eventual consistency usually acceptable

✓Balance locality (query efficiency) against distribution (load balancing)

📌 Interview Tips

1Explain boundary problem: query 1 km from NYC-NJ border needs both shards, doubling latency

2For hot spots like airports, recommend cell splitting: replicate hot cell data across multiple shards

3Acknowledge consistency trade-off: stale location data is acceptable, pickup confirmation needs strong consistency

← Back to Quadtree & Spatial Indexing Overview