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What is database sharding?

Database sharding is a method of horizontal scaling that increases solution performance and availability across global, distributed systems.
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Database sharding defined

Database sharding refers to the process of dividing a large database into smaller, more manageable segments known as shards. It supports horizontal scaling so that applications and systems can handle growing data and user volumes without affecting performance or availability. Organizations use sharding strategies to overcome storage constraints and other challenges associated with scaling databases in modern cloud environments.

  • Database sharding is defined as a method of horizontal scaling that splits a database into independent shards running on a shared-nothing architecture (SNA).
  • Data is divided among shards using shard keys, which allow applications to route requests to appropriate shards.
  • Data is commonly divided among shards using numeric values, hash functions, lookup directories, or geographic locations.
  • Advantages of database sharding include improved query performance, support for horizontal scalability, and greater fault tolerance.
  • Sharding use cases include banking, e-commerce, and social media platforms, which must often handle rapid growth or surges in data or users.

How does database sharding work?

Database sharding is a technique used to scale large databases horizontally by dividing them into smaller, independent pieces called shards. Each shard operates as a separate database that contains a distinct subset of the overall data but maintains the same schema. This allows the shards to collectively form a single, logical database even though they run on separate servers.

Database sharding follows a shared-nothing architecture (SNA), a distributed computing model where each shard operates on its own without sharing processing, memory, or storage resources with other shards.

A shard key—a specific field or combination of fields in the data—determines which data will be stored on each shard. When an application needs to read or write data, it uses the shard key to route the request to the appropriate shard.

Sharding strategies

Organizations manage data distributed across multiple shards in different ways. Here are some common strategies:

  • Range-based or dynamic sharding: Data is divided by ranges of values in the shard key. For example, one shard in an e-commerce database can be assigned customer ID numbers 1–1,000,000, and another shard can be assigned customer ID numbers 1,000,001–2,000,000. This method is efficient for range queries but can result in uneven distribution if certain ranges are accessed more frequently.
  • Hash-based sharding:Data is distributed evenly across shards by applying a mathematical formula called a hash function to each shard key. This approach helps prevent overloading but may scatter related data across multiple shards, making range queries less efficient.
  • Directory-based or lookup-based sharding: A shard map indicates which shard keys correspond to which data. This strategy offers flexibility to quickly add more shard keys as needed but adds complexity and potential overhead.
  • Geo-sharding: Data is partitioned based on geographic location, reducing latency for users in different regions and supporting data residency requirements. Many platforms pair geo-sharding with database replication for greater resilience.

Learn more about databases.

What are the main benefits of database sharding?

Database sharding requires careful selection of shard keys and a sharding strategy as well as query routing optimization. Here are some key advantages of database sharding:

  • Improves query performance:By distributing data across multiple shards, databases can more quickly retrieve the dataset needed to execute each query. This results in faster response times.
  • Supports horizontal scalability: Rather than scale up resource capacity on a single server, organizations can accommodate growing data and workload volumes by scaling out and adding more shards (and servers). Learn more about scaling up versus scaling out.
  • Strengthens fault tolerance: Because sharding employs an SNA approach, each shard is isolated from the others. Any issue that affects one shard doesn’t impact the others, enhancing system reliability. Data replication across shards further protects availability.
  • Drives cost efficiency:Data shards have fewer hardware and software requirements than large databases, which often require expensive, high-end servers.

Helps optimize AI workloads: Complex, monolithic databases can slow the ability of AI to access data, make predictions, and learn over time. Sharding supports creation of a distributed, data-rich environment where AI can function more effectively.

Industry-specific examples of database sharding

Database sharding is especially valuable for cloud-based solutions that experience rapid growth or unpredictable spikes in data or user traffic. The following use cases demonstrate ways that sharding can be applied across industries:

  • E-commerce: Retail platforms split customer and order data across shards, helping ensure fast product searches and smooth checkout experiences during peak shopping periods.
  • Financial services: Records for banking and other financial transactions are split by date or account number, allowing efficient queries and secure, scalable storage.
  • Online gaming: Player or session data is divided by region and user ID, balancing loads across shards and minimizing latency (the time required for data to travel to its destination) for global audiences.
  • Social media: Social media platforms divide user profile and activity feed data by user ID or time range. This approach allows the platform to rapidly query recent posts and support millions of concurrent users.
  • Software-as-a-service (SaaS): SaaS apps with multiple tenants commonly divide data by tenant ID, which isolates customer data and simplifies management as the user base grows. They also can divide it by tenant workload or geographic location.

Learn more about how big data analytics work.

The future of database sharding

By distributing data across multiple, independent servers, sharding reduces the load on any single server, minimizes bottlenecks, and improves system availability and responsiveness. The advantages of using database sharding in modern cloud environments will only increase as AI and other related technologies continue to evolve.

Trends to watch include the following:

AI-assisted sharding workflows

Intelligent features, such as automated shard key selection, workload rebalancing, and query routing, will become more mainstream.

Advantage: Simplified and optimized operations and unprecedented scalability

Zero-downtime resharding

Modern data platforms will increasingly support shard key changes without requiring any data downtime or complex data migrations.

Advantage: Greater flexibility to update sharding strategies as workloads evolve

Sharding combined with serverless databases

Serverless databases independently provision and manage servers for each shard. They also scale capacity up or down on demand based on event-driven queries.

Advantage: Reduction in operational overhead

Adaptive, policy-driven geosharding

Automated sharding workflows automatically route data across geographic shards based on geo-specific policy rules embedded in code. Advantage: Quicker compliance with changing regulatory and privacy requirements

Learn more about cloud computing.

RESOURCES

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Access a wide range of learning resources for students and professionals covering the latest in database technology, including sharding in Microsoft Azure SQL Database.
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Frequently asked questions

  • Database sharding is the process of horizontally scaling a large database across multiple servers, each hosting a shard with a distinct subset of the overall data. Sharding is used to efficiently handle growing data volumes and user traffic by spreading queries and storage across machines rather than relying on a single large server.
  • Both sharding and partitioning optimize database operations by breaking up a large database into smaller subsets. However, while partitioning splits data on the same server, sharding distributes the data across multiple servers. Replication refers to maintaining multiple copies of the same dataset across different servers for redundancy. 
  • Yes, database sharding improves application performance and scalability. By distributing data and workloads across shards, sharding reduces query response times and increases fault tolerance. Organizations can also scale apps horizontally Choice of shard keys and shard strategies are essential to optimizing data sharding.
  • Yes, Azure and other cloud platforms offer built-in support for database sharding. For example, Azure SQL Database provides several tools for optimizing database scaling, including a client library that simplifies shard creation and management.