Sharding in System Design Interviews w/ Meta Staff Engineer

In summary, sharding is a critical horizontal scaling strategy for system design interviews that requires carefully selecting a high-cardinality shard key aligned with query patterns, adopting hash-based distribution with consistent hashing, and proactively addressing operational challenges like hotspots, cross-shard queries, and distributed transactions rather than applying it reflexively without data-driven justification.

• Sharding splits data across multiple standalone database instances to overcome vertical scaling limits in storage and throughput.
• A strong shard key must have high cardinality, enable even data distribution, and align with primary query patterns.
• Hash-based sharding combined with consistent hashing is the industry standard for handling data rebalancing during scale-out.
• Hotspots often occur with skewed data or high-traffic users, solvable via compound shard keys or dedicated high-capacity shards.
• Cross-shard queries introduce latency and complexity, typically mitigated through caching, precomputation, or strategic data denormalization.
• Distributed transactions across shards are fragile; the Saga pattern is preferred over two-phase commit for maintaining eventual consistency.
• In interviews, only propose sharding after mathematically justifying storage or throughput bottlenecks, avoiding reflexive scaling.
• Always address the four core interview components: shard key selection, distribution strategy, trade-offs, and future scaling plans.