partitioningscalabilitymulti-tenantdata-model
Sharding Strategies
Partition data/work across shards to scale throughput and storage while controlling skew.
Definition
Sharding splits datasets and traffic across multiple partitions by hash, range, tenant, or hybrid keys.
When To Use
- Datasets exceed single-node throughput/storage limits.
- Need independent scaling of hotspots or tenant groups.
- Large multi-region systems requiring partition-local operations.
When Not To Use
- Small workloads where vertical scaling remains sufficient.
- Without a plan for cross-shard queries and transactions.
- When access patterns are unknown and key choice is premature.
Tradeoffs
- Improves horizontal scale, but complicates rebalancing and cross-shard operations.
- Can isolate noisy tenants, with extra routing and metadata cost.
- Enables regional locality, while increasing operational burden.
Common Failure Modes
- Hot partition overload due to poor shard key entropy.
- Rebalance operations create long tail latency and cache invalidation storms.
- Cross-shard joins degrade query performance unpredictably.
Interview Framing
Use this structure when the interviewer asks for this pattern explicitly.
Explain shard key rationale, hotspot mitigation, rebalancing plan, and multi-tenant isolation impact.
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Related Concepts
Consistent Hashing
Distribute keys across nodes while minimizing remapped keys during node add/remove events.
Backpressure
Control producer rate based on downstream capacity to avoid queue explosions and cascading failures.
Quorum Consistency
Use read/write quorum sizes to balance consistency, availability, and latency in replicated stores.