Database Sharding Summary
Sharding is a database architecture strategy that horizontally partitions data across multiple servers or “shards.” Each shard contains a subset of the total data and operates as an independent database, allowing the system to distribute load and scale horizontally.
Key Concepts
- Horizontal Partitioning: Data is split based on specific values within a column (shard key)
- Shard Key Selection: Critical for even data distribution and query efficiency
- Distributed Queries: Queries may need to access multiple shards for complete results
- Data Rebalancing: Process of redistributing data when adding or removing shards
Benefits
- Improves scalability by distributing load across multiple machines
- Enhances performance by reducing index size and contention
- Increases fault tolerance when implemented with proper redundancy
- Allows for geographic distribution of data
Real-Life Examples
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Instagram: Uses sharding based on user IDs to manage billions of photos and videos across thousands of servers.
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MongoDB: Implements auto-sharding capabilities to distribute data across multiple machines, with automatic load balancing.
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Google Bigtable: Shards data by row keys, enabling Google to handle petabytes of data across thousands of commodity servers.
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Uber: Shards trip data geographically to optimize for local queries and manage their enormous real-time data processing needs.
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Pinterest: Utilizes sharding with MySQL to handle over 100 million active users and billions of pins.
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Shopify: Implements a multi-tenant architecture with sharded databases to support millions of online stores.
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GitHub: Uses multiple MySQL shards to distribute repository data and handle high-volume developer activity.
Each of these implementations tailors sharding strategies to their specific workload patterns, query requirements, and scaling needs.