Data Partitioning

Design partitioning strategies (sharding, bucketing) to distribute data across multiple databases or nodes, enabling horizontal scaling.

Context

You are designing data partitioning for large-scale systems. The user faces single database bottlenecks (throughput or storage). Read their data volume, access patterns, and consistency requirements.

Domain Context

Based on designing data-intensive applications and partition strategies from Dynamo/Cassandra:

• Range Partitioning: Partition by key range (UserID 1-1000 on DB1, 1001-2000 on DB2). Simple but uneven load.
• Hash Partitioning: Partition by hash(key). Even distribution, but range queries require scatter-gather.
• Directory-Based: Maintain mapping of key → partition. Flexible, adds lookup cost.
• Rebalancing: When adding nodes, some partitions migrate. Strategy affects availability.
• Hot Partitions: Some keys receive disproportionate traffic. Mitigate by splitting hot partition or using caching.

Instructions

1. Choose Partition Key: What key distributes evenly? UserID is obvious for user-centric apps. For IoT, might be (SensorType, RegionID). Avoid keys that hotspot (Country or Status).

2. Select Partitioning Scheme:

   • Range: If range queries common (time-series data). Risk: uneven distribution.
   • Hash: If uniform access. Range queries require scatter-gather.
   • Directory: If rebalancing needed frequently. Cost: lookup on every request.

3. Plan Rebalancing: How do you add capacity? Manual assignment or automatic? How long to rebalance? Require zero-downtime.

4. Handle Hot Partitions: If UserID=1 gets 1000x traffic, split further (UserID=1-A, UserID=1-B). Or use cache in front.

5. Define Consistency Model: Within partition, can be strongly consistent. Across partitions, typically eventual consistency. Define what queries require multi-partition reads.

Anti-Patterns

• Partition Key Too Fine-Grained: Each request hits different partition. Result: no cache hits, high latency. Guard: Partition key should group frequently-accessed data.
• Uneven Partition Distribution: One partition holds 90% of data. Result: that node becomes bottleneck. Guard: Measure partition size distribution; rebalance if skew > 2x.
• Cross-Partition Transactions: Rely on distributed transactions for consistency. Result: complex, slow, failure-prone. Guard: Keep transactions within single partition; accept eventual consistency across partitions.
• No Plan for Rebalancing: Cannot add capacity without downtime. Guard: Design rebalancing strategy before going to production.

Further Reading

• Designing Data-Intensive Applications by Martin Kleppmann — comprehensive partitioning strategies
• Cassandra: The Definitive Guide — practical partitioning in distributed systems
• Google Spanner — consistency and partitioning at scale