What is Database Sharding?

What is Database Sharding?

Database sharding is a technique for horizontal scaling of databases, where the data is split across multiple database instances, or shards, to improve performance and reduce the impact of large amounts of data on a single database. In this article, we will explain the concept of database sharding, its advantages and disadvantages, how it works, and the different types of sharding architectures.

Why Database Sharding?

As an application grows, it has more active users, more features, and generates more data every day. The database becomes a bottleneck if the data volume becomes too large and too many users attempt to use the application to read or save information simultaneously. The application slows down and affects customer experience.

Database sharding is one of the methods to solve this problem because it enables parallel processing of smaller datasets across shards. By distributing the data across multiple machines, a sharded database can handle more requests than a single machine can. Sharding is a form of scaling known as horizontal scaling or scale-out, as additional nodes are brought on to share the load. Horizontal scaling allows for near-limitless scalability to handle big data and intense workloads.

In contrast, vertical scaling refers to increasing the power of a single machine or single server through a more powerful CPU, increased RAM, or increased storage capacity. Vertical scaling has its limitations, such as cost, availability, and performance degradation.

Advantages and Disadvantages of Database Sharding

Database sharding has several benefits and challenges that need to be considered before implementing it. Some of the advantages are:

• Improved response time: Data retrieval takes longer on a single large database. The database management system needs to search through many rows to retrieve the correct data. By contrast, data shards have fewer rows than the entire database. Therefore, it takes less time to retrieve specific information, or run a query, from a sharded database.
• Avoided total service outage: If the computer hosting the database fails, the application that depends on the database fails too. Database sharding prevents this by distributing parts of the database into different computers. Failure of one of the computers does not shut down the application because it can operate with other functional shards. Sharding is also often done in combination with data replication across shards. So, if one shard becomes unavailable, the data can be accessed and restored from an alternate shard.
• Scaled efficiently: A growing database consumes more computing resources and eventually reaches storage capacity. Organizations can use database sharding to add more computing resources to support database scaling. They can add new shards at runtime without shutting down the application for maintenance.

Some of the disadvantages are:

• Increased complexity: Database sharding adds complexity to the design and management of the database and the application. It requires careful planning and implementation of the sharding strategy, such as choosing the right shard key, balancing the load across shards, handling cross-shard queries and transactions, ensuring data consistency and integrity, and monitoring and troubleshooting issues.
• Reduced functionality: Database sharding limits some of the functionality that is available on a single database, such as joins, foreign keys, aggregations, and transactions. These operations become more difficult or impossible to perform across shards, especially if they involve data that is not co-located on the same shard. Therefore, database sharding may require changes in the application logic or compromise on some features.
• Potential data loss: Database sharding increases the risk of data loss or corruption if one or more shards fail or become inaccessible. This can happen due to hardware failures, network issues, human errors, or malicious attacks. To prevent this, database sharding should be combined with data backup and recovery mechanisms.

How Does Database Sharding Work?

A database stores information in multiple datasets consisting of columns and rows. Database sharding splits a single dataset into partitions or shards. Each shard contains unique rows of information that can be stored separately across multiple computers, called nodes.

The process of database sharding involves three main steps:

1. Choosing a shard key: A shard key is a column or a set of columns that determines how the data is distributed across shards. The shard key should have high cardinality (many possible values) and low skew (even distribution of values) to ensure that each shard has roughly equal amounts of data and load. For example, a shard key could be based on user ID, geographic location, date range, product category, etc.
2. Applying a sharding algorithm: A sharding algorithm is a function that maps each shard key value to a specific shard. The sharding algorithm should be consistent (always return the same shard for the same value) and deterministic (return only one shard for each value). For example, a sharding algorithm could be based on hashing (applying a hash function to the shard key value), range (assigning values within certain ranges to different shards), or list (assigning values from predefined lists to different shards).
3. Routing queries: A query router is a component that directs queries from the application to the appropriate shard based on the shard key value. The query router can be implemented as a proxy server, a middleware layer, or a library within the application. The query router should be able to handle cross-shard queries (queries that involve data from multiple shards) and transactions (queries that need to be executed atomically across shards).

Sharding Architecture and Types

There are different ways to design and implement a sharded database architecture, depending on the requirements and constraints of the application. Some of the common types of sharding architectures are:

• Single-shard: In this type, each shard is stored on a single node. This is the simplest and most efficient way to shard a database, as it avoids the overhead of replication and synchronization across nodes. However, it also exposes the database to single points of failure and limits the scalability of each shard.
• Multi-shard: In this type, each shard is replicated across multiple nodes for redundancy and availability. This improves the reliability and fault tolerance of the database, as it can survive node failures and provide backup copies of the data. However, it also introduces the challenges of maintaining data consistency and resolving conflicts across nodes.
• Shared-nothing: In this type, each node is independent and self-contained, meaning that it stores only one shard and does not share any resources with other nodes. This maximizes the scalability and performance of the database, as it eliminates any contention or interference between nodes. However, it also requires more hardware and network resources and makes cross-shard operations more difficult.
• Shared-everything: In this type, each node is interconnected and interdependent, meaning that it can store and access any shard and share resources with other nodes. This reduces the hardware and network costs and makes cross-shard operations easier. However, it also reduces the scalability and performance of the database, as it creates more contention and overhead between nodes.

FAQs

Here are some frequently asked questions about database sharding:

• What is the difference between sharding and partitioning? Sharding and partitioning are both techniques for dividing a large dataset into smaller subsets. However, sharding refers to splitting data across multiple physical machines or servers, while partitioning refers to splitting data within a single machine or server. Partitioning can be done for various reasons, such as improving query performance, optimizing storage space, or facilitating backups. Sharding is a specific form of partitioning that is done for horizontal scaling purposes.

• What are some examples of applications that use database sharding? Database sharding is commonly used by applications that need to handle large amounts of data and high user concurrency. Some examples are social media platforms (such as Facebook, Twitter, Instagram), e-commerce platforms (such as Amazon, eBay, Alibaba), online gaming platforms (such as Fortnite, PUBG, Minecraft), and cloud computing platforms (such as AWS, Azure, Google Cloud).

• What are some challenges or drawbacks of database sharding? Database sharding has several challenges or drawbacks that need to be addressed before implementing it. Some of them are:

  • Choosing a suitable shard key that balances the load across shards and minimizes cross-shard operations
  • Implementing a consistent and deterministic sharding algorithm that assigns each shard key value to a specific shard
  • Designing a query router that directs queries from the application to the appropriate shard based on the shard key value
  • Handling cross-shard queries and transactions that involve data from multiple shards
  • Ensuring data consistency and integrity across shards
  • Monitoring and troubleshooting issues across shards
  • Migrating or rebalancing data across shards when adding or removing nodes

Conclusion

Database sharding is a technique for horizontal scaling of databases, where the data is split across multiple database instances, or shards, to improve performance and reduce the impact of large amounts of data on a single database. Database sharding has several advantages and disadvantages that need to be considered before implementing it. Database sharding involves choosing a shard key, applying a sharding algorithm, and routing queries to the appropriate shard. There are different types of sharding architectures that can be used depending on the requirements and constraints of the application.

We hope this article has helped you understand what database sharding is, why it is important, how it works, and what are its benefits and challenges.

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