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Database Sharding: How It Works and Its Benefits

October 12, 2024 by Jing

What is Sharding?

Sharding is an optimization technique in database management systems that splits the data of a large table into multiple smaller tables by rows or columns. These smaller tables are called "shards" or "partitions". In the case of horizontal sharding, the new table has the same schema but contains different rows; while in vertical sharding, the new table only contains some of the columns in the original table, forming a subset of the schema.

Through sharding, the data of the original table is divided into multiple shards, horizontal sharding is split by rows, and vertical sharding is split by columns.

Why Sharding?

Sharding is a technique used to achieve database scalability by splitting large tables into smaller parts (logical shards) and distributing these parts to different server nodes to achieve horizontal expansion, thereby improving system performance. When the sharded data is placed in different physical locations, these logical shards become physical shards.

When a database runs on a single server, its computing power and its ability to process data are limited by hardware. By adopting a horizontal expansion approach, a more resilient database architecture can be created, which is mainly reflected in two aspects:

• Leveraging the power of massive parallel processing, each node in the cluster can independently process the data in its shard, so that queries can be executed in parallel and all computing resources in the cluster can be fully utilized.
• Because the amount of data after sharding is relatively small, each node only needs to scan fewer data rows when executing a query, which speeds up the query response.

Horizontal sharding is particularly effective when the query focuses on a subset of data rows that are usually closely related. For example, if queries often filter data based on a short time interval, these queries will be limited to a few shards instead of spanning the entire database.

On the other hand, vertical sharding can be useful when queries primarily request a subset of specific columns rather than an entire row of data. For example, if some queries only require access to name information, while others require address information, the name and address information can be stored on different servers.

Sharding can also improve database availability. Without sharding, if the database fails, the entire application will be affected. In a sharded database, only the parts that depend on the failed shard will be affected. To further mitigate this impact, shard data is usually replicated on other nodes as a backup, thereby reducing the risk of service interruption due to a single point of failure.

Database Sharding vs. Partitioning

Both sharding and partitioning are approaches to handling large data sets by dividing the data into smaller, more manageable subsets. The difference is that sharding refers to storing data across multiple different machines, while partitioning refers to dividing the data within a single database instance on the same server.

Often, the terms sharding and partitioning are used interchangeably, especially when referring to the "horizontal" and "vertical" approaches. "Horizontal sharding" and "horizontal partitioning" usually refer to the same concept of dividing data by rows, so that rows with similar characteristics are grouped together.

So, in general:

Database Sharding

• Involves splitting the database into smaller, independently managed units (shards), which are usually deployed on different servers.
• Each shard contains a subset of the data and manages a specific range of data or attributes.
• This approach is often used in distributed systems to increase scalability and performance.
• There needs to be a mechanism to ensure that queries and transactions are directed to the correct shard.

Database Partitioning

• Splits the database into smaller logical parts, but these parts are not necessarily autonomous units.
• Partitions can choose to stay on the same server or within a single database instance.
• The goal is to improve manageability and performance by organizing data, and common partitioning methods include attributes such as date, geographic location, or key value range.
• Partitions do not necessarily need to be distributed across multiple servers, although this is sometimes done to maintain data availability in the event of a node failure.

In summary, while both sharding and partitioning aim to organize data, sharding specifically focuses on distribution across multiple servers for scalability, although partitioning generally does the same thing.

Sharding Types

You can shard data into different shards based on different criteria. The criteria you choose usually depends on the needs of your application, the structure of your data, the architecture of your system, the geographic distribution, and your expectations for scalability. Here are the four main types of sharding:

1. Range-based sharding (sometimes called dynamic sharding)

Range-based sharding is a way of partitioning data by specific data intervals or intervals, such as date ranges, numeric fields, or alphanumeric identifiers. This approach is suitable when the data has a natural ordering property and queries are usually for a specific range. For example, an e-commerce application can use range-based sharding to distribute order data by date ranges.

The advantages of range-based sharding include:

• Because the data is distributed in order, range queries can be performed very efficiently.
• Data archiving and purging can be achieved by simply removing entire shards.
• This sharding approach is well suited for processing time series data or historical records.

The challenges of range-based sharding include:

• If the data is unevenly distributed, it may result in different shard sizes.
• Data skew needs to be addressed.
• It may not be flexible enough when faced with non-uniform data access patterns.

2. Key-based Sharding

Key-based Sharding uses a hash function to determine which shard a particular data item belongs to. The function takes as input a partial or full attribute of the data and maps it to a shard ID. This approach is often chosen when the data itself does not have a natural ordering property or when uniform distribution of the data is of concern. For example, Hazelcast Platform uses a hash algorithm to distribute data across its partitions (i.e., shards).

Key-based Sharding has the following advantages:

• Evenly distributes data to avoid hot spots or uneven roads.
• Suitable for situations where data order does not need to be maintained.
• The solution is highly scalable and easy to implement.

Some challenges faced by Key-based Sharding are:

• Querying a specific range of data may become complicated.
• Rebalancing data in shards may become more difficult as the amount of data continues to grow.
• Data may need to be redistributed when shards are added or removed.

3. Directory-based sharding

Directory-based sharding, sometimes also called metadata-based sharding, uses a separate service or metadata store to track the relationship between data and the shard to which it belongs. In this model, each data record is accompanied by metadata or attributes that indicate the shard to which it belongs. The directory-based sharding approach provides the ability to flexibly distribute data based on a variety of criteria, such as business logic or data characteristics.

The advantages of directory-based sharding include:

• It can flexibly respond to complex sharding requirements.
• It can simplify shard management and data rebalancing.
• It supports dynamic adjustment of data distribution rules without interrupting service.

Challenges faced by directory-based sharding include:

• It introduces additional complexity because a separate metadata service needs to be maintained.
• There may be additional performance costs for metadata lookups.
• There is a risk that the metadata service becomes a single point of failure.

4. Geographic-based sharding

Geographic-based sharding is particularly important for distributed systems and applications that need to operate globally. This approach distributes data based on the source location of the data or the geographical location of the user, ensuring that users can access data copies that are closer to them, thereby reducing network latency and improving performance. Geographic sharding is often used in content delivery networks (CDNs) and applications that require global coverage.

The benefits of geographic sharding include:

• Reduced response time for applications around the world, improving user experience.
• Particularly suitable for processing geospatial queries and applications that require location awareness.
• Disaster recovery and increased fault tolerance can be supported through geographic redundancy.

The challenges of geographic sharding include:

• Implementation is more complex because it is necessary to determine the specific location of data.
• Maintaining data consistency across different geographic regions can be a challenge.
• Very sensitive to changes in the geographic distribution of user groups and changes in access patterns.

Benefits of sharding

Database sharding brings several significant benefits:

• Scalability: Sharding enables a database to scale horizontally by spreading data across multiple servers. As data volume and user activity increase, this growth can be accommodated by adding new shards to keep the system's performance stable.
• Improved performance: By spreading out data and workload, sharding can greatly improve query execution efficiency and improve response times. Because requests can be processed by multiple shards in parallel, users get the data they need faster.
• Fault tolerance: Sharding provides an inherent fault tolerance mechanism. Even if one shard or server fails, the system can still continue to operate because other shards are still available. This ensures high system availability and data durability.
• Efficient resource utilization: Sharding optimizes resource usage by evenly distributing data and workload. This approach reduces the probability of resource bottlenecks and maximizes the utilization efficiency of hardware resources.
• Data isolation: Sharding can also achieve data isolation for easy management and protection. Different shards can implement their own access control policies and security configurations.

The challenge of sharding

Sharding also faces some challenges:

• Complexity: Sharding brings complexity to the database architecture. It requires careful planning, monitoring and maintenance. Additionally, choosing the right sharing key and method can be challenging.
• Data distribution issues: Ensuring that data is evenly distributed across shards can be tricky, especially when dealing with skewed data access patterns. Poor data distribution can lead to unbalanced shard sizes.
• Shard Management: Managing a large number of shards can become cumbersome. Shard creation, removal, and rebalancing require careful coordination and automation.
• Data consistency: Maintaining data consistency across multiple shards is challenging. Distributed transactions and ensuring strong data consistency can be complex and can impact performance.
• Query complexity: Some queries may span multiple shards, requiring coordination mechanisms to retrieve, merge, and present data coherently. Complex queries can affect query performance.
• Single points of failure: Certain sharding architectures may introduce single points of failure, especially when using directory-based sharding and centralized metadata services. Ensuring high availability becomes critical.

How to implement sharding?

Implementing sharding in a data management system usually requires the following key steps:

• Data modeling: Choose a shard key, an attribute or combination of attributes that determines how data is distributed across shards. The choice of shared keys has a significant impact on performance and data distribution.
• Shard creation: Set up and configure the shards that will be used to distribute data. A shard can be a physical server, virtual machine, or container, depending on the overall architecture of the system.
• Data migration: Migrate existing data to the corresponding shards according to the selected shard key. Data migration tools and scripts can simplify this process.
• Query routing: Design and implement a query routing mechanism to direct user query requests and transactions to the correct shards based on the shard key. This usually requires a middleware layer dedicated to routing functionality.
• Sharding management: Deploy shard management tools and processes, including but not limited to adding or removing shards, data rebalancing, and handling shard failures.
• Monitoring and maintenance: Establish monitoring and maintenance processes to ensure the health status and performance level of the sharded database. This includes monitoring for potential issues such as load imbalance across shards, high query latency, and hardware failures.

Sharding in Real Applications

Sharding technology is widely used in multiple fields and industries to meet the requirements of scalability and performance. Here are some typical application examples:

• Social Media Platforms: Social media companies use sharding technology to handle massive amounts of user-generated content such as posts, pictures, and videos. Sharding ensures that users can quickly access their data and provides high availability.
• E-commerce: Online retailers use sharding technology to manage and store large amounts of product information and cope with high traffic access during peak hours. Sharding is essential for processing order information and inventory management.
• Gaming Industry: Online gaming platforms use sharding technology to distribute game states and player profile data. Sharding helps provide low-latency gaming experience in globally distributed multiplayer games.
• Financial Services: Financial institutions rely on sharding technology to manage large amounts of transaction records, customer profiles, and financial history data. Sharding not only improves performance, but also enhances data security.
• Content Delivery Network (CDN): CDN uses geo-based sharding technology to cache web content and efficiently distribute it to users around the world. Data is distributed to edge servers close to users, reducing latency.
• IoT and Telemetry Data Processing: Internet of Things (IoT) platforms use sharding to manage the large amounts of data generated by sensors and devices. Sharding helps in real-time processing and analysis of telemetry data.

Conclusion

Sharding is a powerful technique used to enhance the scalability and performance of database systems. It enables large applications to handle large data volumes, high user loads, and concurrent operations by splitting data into smaller and manageable parts and distributing these parts across multiple servers or storage systems.

Understanding the different types of sharding approaches such as range-based sharding, key-based sharding, directory-based sharding, and geo-based sharding is critical to choosing the sharding strategy that best suits a particular application. While sharding brings many benefits, it also introduces complexity and challenges that require careful planning, monitoring, and management. If implemented properly, sharding is one of the key drivers for building high-performance distributed systems.

Sharding is essentially a method of distributing and storing a single logical data set across multiple databases. Sharding enables horizontal scaling by distributing data across multiple machines, effectively improving the performance of applications that rely on large-scale databases.

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