What Is Database Sharding?

As your application grows, so do the challenges it faces. Choosing the right infrastructure becomes crucial in addressing these challenges effectively. A key aspect of this decision is selecting a database architecture that can support the increasing size and traffic of your application.

Database sharding offers a scalable architecture that enables applications to grow horizontally. It facilitates the management of larger storage requirements and enhances request handling efficiency.

This tutorial explores the concept of database sharding, highlighting its advantages and disadvantages. It includes practical examples of sharding strategies to consider, alongside alternative approaches.

What Is Database Sharding?

Database sharding involves distributing a single dataset across multiple databases. It utilizes a horizontal partitioning database architecture, where each database shares a schema but contains distinct sets of data rows. Unlike vertical partitioning, which divides a database table into columns, sharding splits it into rows across multiple servers.

This approach allows additional server nodes to handle request loads, thereby enhancing both storage capacity and server responsiveness. The diagram below illustrates a typical sharding setup:

In this example, the initial database (example_db) is partitioned into two databases (example_db_part_1 and example_db_part_2). Each partition, or “shard,” contains specific rows from the original database.

Pros and Cons of Database Sharding

Database sharding supports application growth by increasing storage capacity and improving request efficiency. However, it’s not universally suitable and comes with its own set of limitations.

The following sections discuss when to consider implementing sharding in your application, as well as scenarios where exploring alternative approaches might be more appropriate.

Reasons to Shard

Sharding is aligned with horizontal scaling, also known as scaling out, which involves adding more nodes to enhance servers’ capacity for managing increased traffic and storage demands.

In line with these horizontal scaling principles, database sharding offers performance advantages for growing applications, including:

• Increased Storage Capacity: Traditional single-machine setups have practical storage limits, but sharding mitigates this by distributing data across multiple machines.

• Improved Response Times: Sharded databases often operate on smaller database instances, reducing the time required to locate data and respond to queries.

• Enhanced Reliability: Distributed data in sharded databases improves resilience. Outages affecting individual shards do not necessarily impact the entire database, bolstering overall service reliability.

Reasons not to Shard

Database sharding is tailored for specific needs, offering scalability to enhance storage capabilities and performance. However, it’s not universally applicable and introduces its own set of challenges.

Consider these potential reasons against sharding your database when evaluating its benefits:

• Increased Complexity: Sharding inherently increases the number of nodes required to support a database. This expansion leads to greater administrative and maintenance overhead. Additionally, the cost associated with scaling infrastructure needs careful consideration.

• Increased Request Overhead: Sharded databases rely on routers to direct requests to the appropriate shards. This routing process adds overhead to each request. Requests that involve data aggregation from multiple shards further intensify this overhead, as routers must query each relevant shard to fulfill such requests.

Approaches to Database Sharding

There are several options for organizing database shards, each defining how and where data is partitioned among them. Ensuring consistent data division is crucial for the effectiveness of a sharded database.

Here are three commonly utilized sharding architectures:

• Key-Based Sharding: Also known as hash-based sharding, this method involves taking values from a chosen column, applying a hash function to them, and then distributing the data into shards based on the hash results. The column used for hashing is typically referred to as the hash key, and its values serve as the shard’s identifiers.

The diagram below illustrates a database where the id column undergoes hashing, depicted by the example_db_hash table. Subsequently, the database is sharded according to the hash values:

Note: The hashes themselves are not stored in a separate table but are derived from a function used to distribute data among shards. The table representation is for visualization purposes only.

• Range-Based Sharding: This approach divides shards based on specific value ranges within a designated column. For instance, a database sharded on a date column might allocate data where date < 2010-01-01 to one shard and data where date >= 2010-01-01 to another shard.

Adding a pub_year column to the previous example facilitates this type of sharding. In this scenario, the database is sharded with data where pub_year < 1900 in one shard and pub_year >= 1900 in another:

• Directory-Based Sharding: This method utilizes a sharding lookup table to associate data with specific shards based on predefined categories. These categories are typically tracked in a designated column, and the sharding mechanism maps different values from that column to their respective shards.

In the following example, directory-based sharding is implemented using the type column, which categorizes entries into two distinct values: paperback and hardcover. The lookup table ensures that entries of each type are appropriately assigned to their respective shards:

Alternatives to Database Sharding

Considering both the advantages and disadvantages of sharding, you might determine that it’s not the optimal choice for your application. If you’re exploring alternative solutions, here are a few options to consider that cater to different scaling needs:

• Vertical Scaling: When expanding horizontally, such as through sharding, isn’t viable, vertical scaling might be the better approach. This involves increasing the capacity of your database server directly, enhancing storage and processing power in a single instance.

• Utilizing Specialized Services: For instance, if your database handles binary file data, consider migrating this storage to a cloud storage provider. This approach ensures efficient handling of specific types of data, leveraging specialized services for optimal performance.

• Implementing Database Replication: Ideal for scenarios with high read-to-write ratios, database replication involves creating duplicates of a database to serve read requests. Performance can be further optimized through strategies like load balancing, ensuring efficient distribution of request loads across replicated instances.

Conclusion

You should now have an understanding of the concept of database sharding, and why it may or may not be right for your application. With examples of different sharding methods, along with some alternatives, you’re now prepared to decide on the best solution for your application’s needs.