Companies of all sizes and across industries are struggling to cope with an explosion of data never before seen in the short history of computing. As applications reach new levels of sophistication and become deeply interconnected, these companies find themselves increasingly overworked, overheated, and at their wits’ end, desperately trying to squeeze just a bit more performance and availability out of their aging database architectures.

Enter sharding, a powerful database architecture pattern that offers a solution to these challenges. Sharding scales out databases as data volume and user load grow, providing performance and high availability by spreading a database’s data across multiple servers.

Sharding is supported by MongoDB, Cassandra, MySQL, and PostgreSQL. However, each tool has its own set of advantages and limitations. Specifically, MongoDB features built-in sharding capabilities that come with automatic balancing. Cassandra uses a decentralized architecture which makes it particularly adept at managing large amounts of data distributed across many nodes.

Considering your consistency needs and the models that are supported by your database management system is essential when implementing sharding. While some systems such as Mongo DB and PostgreSQL support ACID compliance in various ways to ensure transactional data integrity, others like Cassandra opt for an eventually consistent model. This model may compromise immediate consistency in favor of improved partition tolerance and availability.

In this article, we’ll cover the basics of sharding, its benefits and drawbacks, and the best practices for adding it to your database architecture. We’ll wrap up by looking at a real-world use case to learn how sharding performs in action.

Understanding Database Sharding

Database sharding represents a form of horizontal or logical partitioning that breaks a monolithic table into a collection of smaller objects, each referred to as a ‘shard’, each of which holds a subset of the overall data for storage in the database server or node. Splitting the workload of the database across multiple servers results in several advantages: performance, scalability, and cost-effectiveness for dealing with large data sets. It also has drawbacks which will be covered later in this article.

The main upside of sharding here is that it addresses the problems of traditional database architectures. By distributing clients’ queries among multiple servers, and running the queries in parallel on different shards, sharding lowers the load on any given server instance and reduces query-response times. If your dataset is expanding rapidly and you anticipate a significant influx of data, you can introduce additional shards to the cluster. This will enable horizontal scaling of the cluster, empowering it to accommodate the escalating volumes of data and workload with ease.

The representation that follows illustrates how the process can go, into the architecture of a sharded database partitions and distributed data over several servers and shards.

First, the table shows the transition from a monolithic database (A) to a sharded database (B), which allows data to be spread out over several shards, for improved performance and scale. The sharded database (B) is divided into two shards: the first shard (C) and the second shard (D), which are hosted on the first server (G) and the second server (H) respectively. Each server handles a portion of the data: the first server has a subset of data for the first shard (K), while the second server has a subset of data for the second shard (L).

Sharding Strategies: Choosing the Right Approach

The specifics of the sharding strategy will depend on the exact requirements of the application and its inherent characteristics. The sharding strategy is how you want to partition a database into the different shards. In this section I will introduce some of the most common approaches:

Key-based Sharding

Key-based sharding revolves around the careful selection of shard keys, which act as the linchpin for distributing data across multiple shards. These keys, typically representing attributes or values within the dataset, are strategically chosen based on the application’s access patterns and data characteristics.

Once the shard keys are identified, data is partitioned across shards based on these keys. This ensures that related data is grouped together within the same shard, facilitating efficient querying and data retrieval.

For example, we will consider a scenario within an e-commerce site where user registrations and orders start to increase steadily over time. To minimize performance degradation with the continuous influx of user and order data into the system, the platform decides to use key-based sharding to split its user and order data into multiple logical databases.

Shard Key Selection: The platform chooses the user ID as the shard key for user data and the order ID as the shard key for order data. Related pieces of data can be grouped in the same shard.

Shard Distribution: User IDs and order IDs are hashed to determine which of the shards they go into 1- 2000 users into Shard 1, 2001-4000 users into Shard 2… the same for order IDs.

Example User Data Sharding:

Example Order Data Sharding:

The tables above illustrates the data that will go into each shard which holds a certain range of user IDs or order Ids. As new users register or new orders are placed, the system dynamically assigned them to the appropriate shard using the ID assigned by the system. Thus, the data increase and workload can be handled properly.

Range-based Sharding

Partition criteria are based on a range of values, encompassing multiple dates or number ranges. We can only query data within these ranges.

For example, a social-media community on the Internet decides to shard its user data across geographical regions to speed up data access for users in various geographical regions to minimize latency.

Shard Key selection: The platform uses the user’s geographic location as the shard key.

Shard Distribution: The platform breaks up the space based on geographic regions into ranges and uses a range tag to assign each range to a single shard.

User Data Sharding

In the above representation each shard stores the user data of a defined geographic range of users. For example , users from Latin America can be found on the second shard, Europe on the fourth shard, and so on. Since those corresponding shards are in different locations in the world, by sharding according to the geographic regions, the social media company could enhance the user experience by providing the best data accessibility to its users and reducing the latency.

Hash-based Sharding

Hash based sharding divides data into shards based on the result of a hash function applied to a key value. This will provide a fairly random distribution of data in most cases.

Consider a messaging application that is sharding its message data to serve growing user interactions more efficiently.

Shard Key Selection: The platform picks a message identifier, such as a message ID, as the shard key for message data.

Shard Distribution: The platform takes a hash of the message ID to figure out the matching shard. The hash function hashes the message IDs into a range of values, while each shard is responsible for a segment of hashed values.

Message Data Sharding

.This process utilizes a hash function that accepts an input key, such as a user ID or message ID. The resulting output is typically within the range of 0 to the number of shards minus one and determines which shard should receive the data entry. This technique guarantees even distribution across all accessible shards while reducing overload on any particular node for more robust performance and scalability.

Despite its benefits, hash-based sharding poses challenges that must be addressed. Uneven data distribution and hotspots can occur due to unfortunate outcomes of the hashing function. Adding new shards can also result in complex and resource-intensive efforts to rebalance existing data. Thus, careful consideration of the hash function’s design and ongoing monitoring of shard performance are essential to maintain an efficient system.

Directory-based Sharding

It Maintains A look-up table or directory that maps data to a shard providing greater flexibility in placement or management of data. The centralized reference table facilitates dynamic adjustment of data distribution, eliminating the need for extensive reconfiguration in sharding strategy.

For instance, a healthcare platform might want to shard its data on patients by medical specialties, for better data management and access for healthcare providers.

Shard Key Selection: The Platform chooses a medical specialty as a shard key of patient data.

Shard Distribution: Rather than hash the shard key directly to produce the shard, this system maintains a central directory or lookup table that assigns each specialty to a shard.

Directory

… (and so on for other medical specialties)

The given directory maps each medical specialist to its related shard. For instance, each patient’s data regarding dermatology is stored in the first shard, each patient’s data regarding cardiology is stored in the second shard , etc. Through the directory-based method, the healthcare platform can self-manage the data distribution through medical specialties and organize patient’s medical data by specialists for healthcare providers.

The type of shard key or strategy selected will depend on the needs of the application’s data access pattern, expected traffic growth, and requirements for performance. Getting this right is very important because it can make or break the scalability and performance of the system.

Best Practices and Challenges

Sharding is a complex concept to implement. A deep understanding of its fundamental logic will be beneficial. Here are some common best practices:

Data modeling: Careful data modeling is crucial for effective sharding. This is a key step to identify the key entities in the application and determine the best ways to distribute entities across shards to maximize performance and avoid expensive cross-shard queries.

Picking the correct shard key: We need to select the right shard key that makes data evenly distributed across shards and thus provides optimal query performance. Deciding how to split the data is a fundamental question that is closely related to spatial and temporal granularity.

Assign the proper number of shards: The number of shards we build will vary depending on the system’s expected growth, its scalability strategy, and the hardware limitations. Overprovisioning shard count can result in performance degradation and wasted resources, while under provisioning can lead to poor load balancing, chronic latency issues, and scaling problems.

Monitoring and Rebalancing: Once a sharded database is running, it should be monitored periodically to detect common signs of performance degradation. Inadequate shard key selection or uneven data distribution can lead to data hotspots, where certain shards become overloaded with requests. Keeping the size of each shard within its pre-set scale limits is also recommended so that each shard can focus on the actual work it’s assigned.

Benefits of Sharding

While the most obvious benefits of sharding relate to scalability, performance, and cost-efficiency, there are some hidden benefits that are worth mentioning:

These benefits can be an added incentive for organizations to choose optimal data management solutions.

Some Drawbacks of sharding

Sharding is one of those database management techniques that offers hope to organizations and companies that need to scale in response to expanding data loads, but the reality is complex and fraught with peril. It’s up to database architecture experts to turn up the lights so that organizations can better find a way through.

Complexity of Shard Management

One of the complexities associated with sharded databases is the lifecycle management of the shards themselves: creating new shards, scaling shards, and deleting shards. As the system grows, adding new shards to handle increased data volumes or changing data distribution across shards could also become a complex task.

Metadata management such as maintaining routing tables across the shards, and keeping all the shards in sync, requires sophisticated orchestration mechanisms and good tooling. We also need to dynamically adjust our shard management to keep pace with the dynamic nature of modern applications. For instance, as the workloads change over time, scaling the sharded database must become agile enough to adjust to these changing workload patterns.

Data Migration Challenges

Migrating data between shards or re-sharding existing data can be challenging for enterprises that operate in sharded environments. Regardless of the purpose of the migration – whether it’s rebalancing data because it’s unevenly distributed, adding shards to accommodate growth, or consolidating shards to optimize the use of resources – this process can have major implications.

Enterprises that need to re-shard data need to develop a robust plan for data migration. Data migration across shards requires enterprise customers to come up with robust strategies that minimize disruption to operations, maintain consistency across the data, and safeguard query performance during the data migration.

Vendor Lock-In

Sharding solutions from specific vendors or proprietary sharding technologies entail the risk of vendor lock-in. Enterprises that rely on such services or technologies could become dependent on vendor-specific features, APIs, or proprietary protocols, which could limit flexibility and hinder interoperability with other systems and tools. After all, migrating to another vendor’s sharding product is often expensive and time-consuming – it involves moving the data, refactoring applications, and rearchitecting the system. Organizations can reduce the risk of vendor lock-in by preferentially adopting solutions based on open standards, leveraging modular architectures, and designing clear exit strategies to switch to another vendor or migrate to a different technology if required.

Although sharding can be a great option when it comes to scaling up and optimizing database performance, enterprises must study and deal with the disadvantages and difficulties associated with it.

Considerations and Challenges of Sharding

Moreover, while various tools facilitate sharding, it’s important to note that the number of compatible tools can be limited. Although MongoDB, Cassandra and MySQL databases provide support for sharding features; not all database systems uniformly endorse this feature. As such, companies must assess their individual requirements and available resources carefully before implementing an effective and interoperable approach. This approach involves partitioning large data sets across multiple nodes or shards within their system architecture.

Additionally, sharding has the potential to complicate adherence to ACID compliance. Even minor adjustments can require complex coordination of cross-region transaction to support transactions.

Maintaining data integrity and consistency across distributed shards can be challenging due to the increased complexity it brings. Hence, it is crucial to conduct comprehensive research and planning for successful integration of sharding into enterprise architecture. This approach enables businesses to benefit from sharding while effectively managing any associated challenges.

A Sharding Use Case

Let’s say we’re an e-commerce platform that has suddenly gotten a lot more business and is reaching the limit of what our monolithic database can handle. In pursuit of accelerating retrieval times for our busy customers, we opt to implement sharding to distribute our product catalog across multiple shards.

Efficient query processing can be achieved by implementing key-based sharding, where each shard is responsible for managing a specific range of product IDs. To ensure consistency and easy management, we will use the same DBMS across all shards. We plan to transfer existing product data to their corresponding shards using a reliable routing layer that directs queries based on product IDs.

Multiple database servers will be established to host the shards, enabling load balancing and reducing query response times. Resultant monitoring tools will aid in tracking performance metrics which will enable us to spot potential issues before they occur and anticipate future scaling needs.

Drawbacks Encountered

Changing from a monolithic architecture to a sharded one adds a great deal of complexity and overhead. The platform must restructure its data model, re-factor its application code, and deploy some new infrastructure components that are needed to support sharding. All of this planning, testing, and investment of resources can be a lengthy process.

Ensuring data integrity across shards leads to atomicity and isolation problems when transactions span multiple shards. Maintaining the necessary coordination might lead to considerable performance overhead and complexity in dealing with edge cases.

Since data is sharded across different shards, some queries might execute faster than others. Cross-shard queries will usually take longer to respond to because more work is required to coordinate the data across shards, then fetch and aggregate the data. Such sharded systems can sometimes stumble on more complex queries that require joining multiple shards and take quite a while, with little obvious to the users. All of which results in poor user experience and system responsiveness.

Mitigation Strategies

Despite encountering these drawbacks, the e-commerce platform employs several techniques to handle the challenge of sharding effectively:

Automated Monitoring and Management

Making it operational also includes implementing strong monitoring and management control feedback to track the health and performance of the sharded database. Automated proactive alerting corrects performance issues as they’re detected and keeps the system operating at scale, without the need for scheduled downtime

Optimized Shard Key Selection

This process starts by analyzing data access patterns and query requirements. It aims to understand how users will interact with the platform, and which data sets they query the most. Examples of candidate shard keys could include the most commonly queried fields or fields that exhibit a relatively uniform distribution of data, such as user ID, product_category, date, and similar attributes.

For each candidate shard key, the platform estimates how much it can mitigate skew and distribution imbalance. Some metrics, like the data distribution variance and the query load distribution variance, quantify the skewness associated with each shard key.

The initial selection of the shard key may be incorrect, leading to performance issues. For instance, if a shard key like product_category results in uneven distribution—where some categories have far more products than others—it can create hotspots that degrade performance.

To address this, the platform iteratively refines its shard key selection through testing and validation with realistic simulations. Realistic simulations of multiple shard key candidates are evaluated on query performance and resource utilization and used as the system’s feedback to guide the evolution of its function.

The goal is to identify the shard key selection that provides optimal query performance and hardware utilization while maintaining balanced data distribution across shards.

The e-commerce platform ensures enhanced query performance and optimized use of resources across a sharded database environment by selecting shard keys that can minimize data skew and distribution imbalance. The selection of shard keys with this awareness translates to better system scalability, reduced likelihood of performance bottlenecks, and a wonderful user experience for our customers. Below outlines the workflow for optimal shard key selection in our sharded environment.

The workflow begins with Identifying the most important data accessing patterns and querying behaviors. Then we determine the candidate shard key, which is the most suitable for skew and distribution imbalance removal. Through multiple tests and validations, we iteratively refine the shard key.

The optimal shard key is eventually selected to deliver optimal query performance, maximize the utilization of hardware, and best distribute data among shards.

Query Routing

To implement query routing, our e-commerce platform adopts a key-based sharding strategy, for storing a range of product IDs. To materialize this, we maintain a dynamic routing table, mapping product IDs to the shards they belong to. Furthermore, we built a routing service that handles incoming queries and then parses those queries into individual attributes and the relevant key sought (ie product ID to query about). The parsed product ID is then used to look up the routing table to determine which shard is the target of this query.

With Error-handling mechanisms baked into the routing service, we attempt to handle any issues that might arise from shard unavailability or query failures. The routing service is put through rigorous testing in load testing to ensure both its functionality and performance as a scaling mechanism. With all these measures, we can support e-commerce sales of the largest product catalog, while maintaining an optimized performance level as the underlying data chunk grows beyond a dozen nodes. Below outlines the workflow for our query routing with some explanations :

Continuous Optimization and Refinement

This involves timely optimizations and tuning of its sharding strategy. It includes using evolving workload patterns and performance metrics to identify areas for improvement. It also entails periodically reviewing shard distribution, data partitioning techniques, and query optimization techniques to improve the entire strategy. Over time, we refine the sharding strategy to mitigate the drawbacks of a sharded cluster. As a result, the system can become increasingly efficient.

Conclusion

Database sharding allows us to break up a monolithic database into varied partitions (n shards) that can be located across data centers and distributed across individual servers, allowing system administrators to scale the system and lower the cost of providing high-performance computing.

Database sharding addresses the issues of scaling up resources and performance concerns of databases. As a result of this transformation, systems such as search engines can handle large volumes of users as well as queries without exhausting server resources.

Sharding presents inherent complexity and challenges. Mitigation strategies such as continuously optimizing and fine‑tuning, accurate and automated monitoring and management, and more, can be applied to provide effective means for dealing with the challenges presented by database sharding.