Mastering Database Sharding: Strategies and Best Practices

Database sharding is a database architecture pattern where a large dataset is divided into smaller, more manageable pieces called "shards." Each shard is stored on a separate database server to improve performance, scalability, and availability.

This approach helps distribute the load and ensures that no single server becomes a bottleneck, allowing the system to handle more data and higher transaction volumes efficiently. Each shard operates independently, but together they form a single logical database system. Moreover, database sharding can improve fault tolerance by isolating failures to individual shards, allowing the rest of the system to continue operating smoothly.

In this post, we'll break down the benefits of database sharding, how it compares to other methods of database organization, and how it helps keep your data safe and accessible.

It's common knowledge that businesses are facing an unprecedented explosion of data. The volume, velocity, and variety of information that companies need to process and analyze grows at an exponential rate every year.

Research shows the amount of data created and replicated globally is expected to nearly double between 2021 and 2025, reaching a staggering 181 zettabytes (that's 181 billion terabytes). This massive influx of data presents both opportunities and challenges. While it offers the potential for deeper insights and more informed decision-making, it also puts immense pressure on database systems. As data volumes grow, traditional database architectures often struggle to keep up, resulting in slower query times, reduced performance, and potential system failures.

This is where database sharding comes in. By distributing data across multiple servers, sharding allows businesses to horizontally scale their databases, effectively keeping pace with the ever-increasing data demands. It enables companies to maintain high performance and quick response times, even as their data footprint expands.

Think of database sharding as a weight distribution. Let's say you need to carry several bags up a flight of stairs that weigh a collective one hundred pounds. While it's more than possible for some people to carry the bags themselves, others will have trouble. Splitting up the bags between multiple people means everyone can carry a lighter load. There'll be much less strain since no single person has to carry such a heavy weight.

Database sharding is much the same. Sharing the load puts less strain on the servers, freeing them up to work more efficiently for everyone involved.

Database sharding is vital for businesses who handle large volumes of data and need to access them on a rolling basis. Without database sharding, a server can run too slowly and cause a frustrating experience for users.

Have you ever found your computer slowing down when you have too many programs open? Database servers face a similar effect when they have too much data and too many people trying to access it. This large data volume mixed with high activity results in a slower response time and, in worst-case scenarios, server shutdowns.

Database sharding is a smart way of dividing up information so businesses can both store and quickly access large amounts of data. Here are some other specific benefits of a sharded database.

If you've ever worried about running out of database storage, multiple shards enable you to scale. You can simply add more shards as you reach your data limit, preventing data bottlenecks or possible shutdowns. You can also get more mileage out of your database servers by dividing up your data sets. You reduce the risk of overloading a specific server while still bringing in more data on a rolling basis.

The most notable benefit of a sharded database is faster response times. Another helpful metaphor is to think of your database management system like a bookshelf.

Imagine you're in a library trying to find a specific book. Would you rather search through a bookshelf filled with a thousand books or one hundred? Database sharding uses the same amount of information, but is divided into smaller rows. This feature means your database management system can retrieve information more quickly, resulting in a faster experience.

Too much data processed in a short amount of time can overload your database management system. One of the most common results is a service outage, resulting in hours or even days of lost business productivity.

Database sharding prevents this from happening by reducing strain on your system and preventing too much dependence on one form of storage. This means that, even if one shard suddenly becomes unavailable, the other shards can still work independently.

To understand how database sharding works, it helps to think of your data as a series of organized shelves. Your database stores your information using a mixture of rows and columns called a dataset.

When you split up these shards across multiple computers, you've created a node. All of your shards are divided up into multiple nodes, though they all contain the same information about the entire database. Your dataset is then split into shards, a shard key, and what's known as a shared-nothing architecture.

The technical term for each of your divided chunks of data is a "logical shard." The physical computer storing these logical shards is called a "physical shard" or sometimes a "database node."

You can think of shards -- or, specifically, logical shards -- like all the different books on your database's shelves. Each one contains unique information and it's up to you where and how you want to store them.

Well-organized data is key to smoothly running a business. A shard key is how you properly organize data by type, reducing wasted time trying to find the data you need.

Each dataset comes in columns filled with rows. A shard key is how developers decide which rows in each dataset should be grouped together into a shard. These shard keys can come from existing columns or brand new ones. Selecting the right shard key is crucial for the efficiency of your sharded database. A well-chosen shard key ensures that data is evenly distributed across shards, preventing any single shard from becoming a bottleneck.

It also helps maintain query performance, as it allows the system to quickly locate the relevant shards when a query is executed. Shard keys are often based on frequently accessed or logically grouped data, such as customer IDs, geographic regions, or timestamps.

By grouping related data together, shard keys can improve read and write operations, making data retrieval and management more efficient. However, choosing a poor shard key can lead to uneven data distribution, increased load on certain nodes, and ultimately degrade performance.

A shared-nothing architecture is a database management system that operates with several independent parts. That means every physical shard you create will only operate on the data it contains — it can't pull data from another physical shard.

However, it's possible to create a shard system where multiple shards can pull data from other sources. Creating a software layer is one way you can coordinate your data storage and provide access to multiple shards at once.

While sharding is an incredibly effective way of improving response times and shared access, there are still drawbacks. The size of your business and how often you retrieve large volumes of data will determine if sharding is the method for you.

Database sharding significantly increases infrastructure costs due to the need for multiple servers or nodes to distribute data. This multiplication of hardware not only raises initial equipment expenses but also leads to higher ongoing costs for power consumption, data center space, and networking.

Additionally, the complexity of managing a sharded system often necessitates more skilled personnel or additional training, further adding to operational costs. These increased expenses can be substantial. But for many businesses dealing with large data volumes, the improved scalability and performance can justify the investment.

Another difficult aspect of database sharding is the level of complexity it adds to your business operations. Instead of managing a single database, you have to split your attention between multiple physical shards (or nodes).

Smaller businesses who don't require large volumes of data yet may find sharding to be unnecessarily complex. However, small businesses with plans to scale would eventually benefit from sharding.

Uneven distribution of data across shards is a significant challenge in database sharding. This imbalance can lead to performance bottlenecks on overloaded shards, wasted resources on underutilized ones, and increased complexity in system management.

When one shard becomes a "hot spot" for queries, it can struggle to keep up with demand, undermining the primary goal of sharding: to distribute load evenly for optimal performance.

However, advanced database systems often offer automatic balancing features. These systems can detect uneven distribution and redistribute data across shards to maintain balance, ensuring consistent performance and efficient resource utilization without manual intervention.

Choosing the right database system, such as InterSystems IRIS, is crucial in mitigating these challenges. These systems have built-in balancers that can watch shard loads and automatically change where data is stored. This lets you focus on using your data instead of managing how it's distributed.

Database sharding is fundamentally flexible, giving businesses more control over their data and how it's organized. However, there are a few main methods you should look into before starting.

Also known as dynamic sharding, range-based sharding divides database rows based on its value. Whatever range you decide on using becomes a shard key for quick and easy access.

For example, if you decide to divide your customers by their industry, you can use a shard key to quickly find them in the database. The application you use will automatically categorize and store the customer's information on a specific node. You can also do reverse matching if you need to find an even more specific record.

Range-based sharding is easy to implement and closely replicates working with a spreadsheet of well-organized data. However, it's easy to accidentally overload too much data on one node.

Practical Use Case: Range-based sharding is ideal for e-commerce platforms categorizing products by price ranges or customers by registration dates. It's also suitable for financial applications managing transactions within specific date ranges.

When you want to enjoy a fine level of control over smaller details, turn to hashed sharding. This sharding method works by assigning a shard key to a specific row in the database through a "hash function."

The hash function automatically takes information from the designated row and creates a "hash value." This hash value works as your shard key and stores information on the physical shard you choose.

Hashed sharding is favored for how evenly it distributes data across physical shards, reducing the risk of overloading a specific machine. However, it can't distinguish information based on deeper meaning, so you'll still need to apply some oversight. Hashed sharding is especially useful for social media platforms or large web applications where user data needs to be spread out evenly to keep one server from being too busy.

Another form of sharding that's similar to a spreadsheet is directory sharding. This accessible method provides a lookup table to let you link database columns to shard keys. Any application that stores information based on a specific detail, such as color or date, refers to the lookup table first.

Directory sharding is popular among database managers for how effectively it organizes information based on important details. There's no range limit and each shard provides more meaning beyond numbers. The only downside is the potential for wrongful organization if the lookup table contains inaccurate information.

Practical Use Case: Directory sharding is well-suited for content management systems (CMS) or inventory management systems where items need to be quickly found based on specific attributes like category or tag.

This sharding method is crucial for businesses that gather a large volume of geographical data. Geo-sharding divides up information by details such as town, city, district, or neighborhood.

This sharding method also has an advantage based on where the physical shards are located. A specific city or town can act as a shard key, storing customer information based on how close they are to a physical shard. This method results in faster response times. That said, geo sharding's benefits only work if there's a shorter physical distance between the customer and the physical shard. There's also a risk of uneven data distribution if there are a larger amount of customers in one area than another.

Practical Use Case: Geo sharding is ideal for logistics and delivery services, ride-sharing apps, or any application where user experience depends on low-latency and localized data access.

Also known as entity-based sharding, relationship-based sharding groups similar data on the same physical shard. This method is unique from other sharding applications since you don't have to separate quite as much data.

As a result, relationship-based sharding reduces the computing power needed to retrieve similar data together. Its main downside is its complexity and the possibility of accidentally grouping dissimilar data together.

Practical Use Case: Relationship-based sharding is perfect for customer relationship management (CRM) systems or any application that benefits from grouping related entities, such as orders and customers, or products and categories, together to improve query performance and reduce retrieval time.

Sharding a database isn't as complicated as it sounds. Much like putting together a new spreadsheet, you need to figure out your end goal and how sharding can help you get there.

Do you need to organize specific information more effectively? How about speeding up response times for customers who live closer to physical shards? No matter which sharding method you choose, there's a specific process for getting started:

1. Pick your sharding scheme — ask yourself about the data you're splitting up. Why do you want to split up this data and how?
2. Determine your organization method — while there are many sharding methods, consider choosing from the common ones above.
3. Pick your target infrastructure — narrow down the servers you'll be creating shards on and make an estimate on how much data you'll be storing.
4. Make a unique routing layer — you need to determine how your application will store data and query it later.

Execute your migration plan — lastly, you need to decide how you'll migrate all this information with minimal downtime. Many modern data management solutions streamline this process by building it into their software offerings.

You may be wondering if there are other ways you can organize, store, and retrieve your information. While database sharding is quickly becoming the go-to method for larger businesses, you can also try the following.

If you simply need faster response times, consider optimizing your business operations with vertical scaling. This straightforward approach simply adds more RAM or CPU to your database server to handle more traffic.

This option is less costly than database sharding, though it also doesn't have the same flexibility with organizing data.

Replication is a more old-fashioned way of organizing your database. Unlike database sharding’s customized algorithm, replication duplicates exact copies of your database and stores them on separate servers.

Since database sharding doesn't create copies of information, replication may be more suitable to your business model. You may be more concerned with losing your data than you are with organizing it.

Lastly, partitioning is a method that splits up a database table into different groups. Horizontal partitioning splits up data into rows, while vertical partitioning splits up data into columns.

Database sharding is similar since it also splits up data into different groups with unique rows. However, it stores this information across different nodes. Partitioning splits up this information all on the same computer.

Database sharding is an incredibly helpful way to reduce downtime and more effectively organize information. However, it does come with a learning curve and may be too complex for some business models.

One thing is for sure: businesses who want to grow need to have an intentional strategy for how they store and distribute data.

Over 90% of organizations in a recent survey achieved measurable value after a data-based investment. Whether you're trying to save time retrieving data or are worried about overloading your database server, optimizing your database will pay dividends in the future.

You don't have to figure out data distribution by yourself. InterSystems IRIS provides you with several cutting-edge tools to take out the heavy lifting while helping your business scale.

What sets InterSystems IRIS apart is its remarkable flexibility in scaling. Whether you need to scale up by adding more resources to a single server, or scale out by distributing data across multiple servers, InterSystems IRIS adapts to your needs.

This flexibility allows you to start small and grow your database infrastructure in line with your business requirements, without the need for major overhauls or migrations.

InterSystems IRIS also offers seamless transitions between different scaling approaches. You can easily switch from vertical to horizontal scaling as your data volumes increase, ensuring that your database performance keeps pace with your business growth.

This scalability, combined with InterSystems IRIS intelligent data distribution and automatic balancing features, makes it an ideal choice for businesses of all sizes looking to future-proof their data infrastructure.