CodeMosa

Master LeetCode Patterns

Advanced

Database Sharding

Data & Consistency

Partition data across multiple databases to scale write throughput and storage

Core Idea

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Split data into shards by hash, range, tenant, or geography. Route requests to the correct shard and rebalance online as data grows.

When to Use

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When a single database cannot handle write volume or dataset size, or you need tenant/geo isolation.

Recognition Cues

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Indicators that this pattern might be the right solution
  • Primary bottlenecked by write IOPS
  • Large tables exceed manageable size
  • Hot partitions or tenants dominate load

Pattern Variants & Approaches

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Overview

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Route requests based on a shard key to the correct shard; rebalance as data/hotspots evolve.

Overview Architecture

Shard Key⚙️Application⚖️Shard Router💾Shard 1💾Shard 2💾Shard 3

When to Use This Variant

  • Single node limits reached
  • Skewed hot keys
  • Need parallelism across many nodes

Use Case

User/content stores and large multi-tenant datasets.

Advantages

  • Linear scale with shards
  • Isolation of hotspots
  • Geo-partitioning options

Implementation Example

# Shard router sketch
shard = hash(user_id) % N
conn = pools[shard]
conn.query(...)

Tradeoffs

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Pros

  • Scales writes and storage horizontally
  • Tenant and geo isolation options
  • Operational flexibility with shard moves

Cons

  • Application and query complexity
  • Cross-shard consistency challenges
  • Operationally heavy resharding

Common Pitfalls

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  • Poor shard key causing hotspots
  • Cross-shard joins/transactions with high latency
  • Complex online resharding with downtime risk
  • Uneven shard sizes and skew

Design Considerations

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  • Shard key selection and over-sharding
  • Directory service or consistent hashing
  • Dual-writes and backfills for resharding
  • Throttled migrations and rebalancing
  • Isolation per tenant/region when required

Real-World Examples

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Twitter

Timeline and user shards

Hundreds of millions of users
Instagram

Range/hash sharding for media and users

Billions of media objects
YouTube

Content and metadata sharded globally

Exabyte-scale datasets

Complexity Analysis

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Scalability

High - Many shards in parallel

Implementation Complexity

High - Routing and rebalancing

Cost

Medium to High - More nodes

Related Patterns

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Database ReplicationCQRSConsistent HashingService Discovery