What Is Sharding? Real-World Applications and Best Practices

I think I’m addicted to massively multiplayer online role-playing games (MMORPGs). I’d literally miss my own wedding playing “Final Fantasy XIV Online”. Just don’t let my future bride-to-be read this!

What I love the most about this game is that its data centers in North America can house up to 18,000 simultaneous logins! The secret? Something called sharding.

Sharding is a method of splitting up a database into smaller, more manageable pieces. We call these pieces “shards.” Each shard is like its own mini database that handles a specific chunk of the data.

This technology is actually pretty awesome if you learn how it works. Let’s dive deeper into the details, shall we?

I’ll give you an example of a big online store on Black Friday, Cyber Monday, or any other peak shopping day. Millions of people are browsing the store. You’ve got people adding stuff to their carts, claiming discounts, and making purchases at the same time.

If all that data sits in one big database, it’s going to slow things down. And if you don’t do anything about it, you risk losing your customers. That’s where sharding comes to your rescue.

This method breaks down your database into shards. Each shard takes in specific chunk of the data. For example, one shard will be like:

“Hey store owner, don’t worry about users from Europe. I gotchu!”

Another shard will be like:

“Watsup my friend! Need help with North American customers? I’ll handle them.”

My point here is that sharding is just a fancy name for delegating database tasks to different teams. The goal is to make sure these databases aren’t being overwhelmed. And the reward is faster processing times for queries.

Don’t worry about queries at the moment — I’ll tell you more about them a little later.

The core idea of sharding is simple: take one massive dataset and split it into smaller, manageable chunks. Then, spread these shards across multiple servers. That’s it!

That way, each server focuses on its assigned shard. We even experience this as humans; we tend to be more productive when we focus on individual tasks.

Sharding isn’t the only way to handle large datasets. You can decide to replicate or partition your dataset, for example. But don’t be too quick to choose either of these two alternatives. Here’s why.

Replication means creating copies of the same dataset on multiple servers. That works if you want a redundant system. By “redundant,” I mean a system that’s always available. In this kind of system, when one server fails, another takes over.

But I wouldn’t rely on replication if performance is my primary concern. That’s because replication doesn’t make the dataset smaller. Instead, it just creates identical copies.

Back in college, I’d create partitions for my desktop computer. One partition would handle all my school stuff (boring!), and the other the fun stuff (games).

Partitioning splits the dataset into segments stored on one server. One may argue that it’s similar to sharding. I can see that. But the key difference here is that all partitions usually stay on a single machine. It’s like dividing books into sections but keeping them all in one library.

Now imagine you’ve got a huge library with every book ever written. Sounds exciting for a bookworm, but it can also be quite overwhelming.

Now, let’s split the collection into categories. Fiction goes to one bookcase, science books to another, and so on. Each book case becomes a “shard,” focused on its category. Visitors can go straight to the book case they need. This way, it’s easier to find the book they’re looking for. On top of that, they’ll find it faster than they would’ve had they searched the entire library.

So far, you’ve seen what sharding is. Now I’ll shift focus to exactly how it works.

Shard Keys

The shard key is basically a field in your database. A good example would be a user ID or a region code. This is what decides where each piece of data goes.

Ever been to a hotel with a bunch of keys hanging on the wall behind the front desk in the lobby? Once you’ve checked in, the attendant hands you the keys to a hotel room that’s readily available.

That’s pretty much how a sharding key works. It knows what database is available and which system should use it.

Data Distribution Strategies

Sharding isn’t just about creating shards. You actually need a strategy for this. Fortunately, you’ve got a couple of options here.

• Range-Based Sharding: Here, you divide data into ranges. For example, you may split customer IDs from 1 to 1000 in one shard and then 1001–2000 in another.
• Hash-Based Sharding: What this means is that you create some sort of algorithm that assigns data to different shards. You’ll then assign this algorithm to the shard key. The good thing about this option is that it helps avoid uneven workloads. On the downside, though, it makes it harder to predict the location of specific data.
• Directory-Based Sharding: A lookup table maps each piece of data to its shard. It’s a flexible alternative, but it can become a little complicated since you’ll need to manage and update each directly.

The key takeaway here is that each strategy has its pros and cons. Therefore, the right choice for you will depend on your needs.

Query Routing

Let’s briefly talk about something called “query routing.” As the name implies, you’re basically asking the system to tell you which shard has the data you’re looking for.

In other words, it “routes” your query to the system. Then, it uses the shard key to identify the right shard.

Splitting databases into shards is obviously beneficial. But how? And to what extent? Here’s what to keep in mind.

Sharding is awesome for handling big data, but that doesn’t mean it’s a perfect system. Here are some potential challenges you may have to deal with at some point.

Complexity in Design

Sharding isn’t something you can slap together overnight. That’s just not how it works. I talked about distribution strategies earlier in this article. You need a strategy to make sharding work.

To be more specific, you’ll need to carefully design how data gets split across the shards. If not done right, you could end up with shards that are uneven or, worse, a nightmare to manage.

I’d start with picking the right key to divide your data. That’s because not picking the right key could create issues like hot spots, where one shard gets overloaded while others sit idle.

You also have to think about the future for a moment. Do you expect your systems to grow? Most people do. If so, that’ll make planning even trickier, but not impossible.

If you don’t plan for the future now, you might need to redesign the whole setup later. And trust me on this; that’s no fun at all.

Data Rebalancing

As your system grows, so does your data. And guess what? That means reshuffling data between shards.

It sounds simple, yes, but it can get tricky fast. This is especially true if you’re trying to avoid downtime.

Rebalancing can also make your system a little sluggish. That’s because moving data around takes time.

Then, there’s the challenge of figuring out how to split and reassign data without breaking things or losing consistency. It’s even more challenging if you’ve got a massive dataset. The bigger the data set, the more resources you’ll need.

Cross-Shard Queries

If a query involves several shards, it takes longer to process. And if we’re looking at a high number of shards, you’ll need to brace yourself for some serious latency.

Not to mention, you’ll have to figure out how to merge the results from different shards.

Even simple operations, like counting rows, can become complicated when shards are involved. You might also face challenges in maintaining ACID properties across shards.

If you’re wondering what ACID is, it’s a short form for Atomicity, Consistency, Isolation, and Durability. This is a set of principles that ensure the reliable processing of database transactions.

Also, query optimization becomes a whole new ball game. That is because you need to coordinate across multiple systems instead of just one.

Operational Overheads

More shards mean more stuff to monitor, maintain, and keep consistent. You’re lookin’ at backups, security, and the constant need to sync changes.

Don’t forget that each shard adds to the workload, which makes operations even more time-consuming.

Monitoring tools, in addition, need to grow with your shards. And as if that’s not stressful enough, you’ll need to check multiple locations to debug issues.

Thinking of backing up and restoring data? Great idea, but I hate to break it to you; it’s more complicated than you think. The reason? You’re dealing with multiple databases, not just one.

Here comes the fun part — a look into where sharding fits in the real world.

Databases

We’ve talked about this countless times. Sharding is a great solution for databases when they start bursting at the seams.

In fact, big names in the database industry, MongoDB and MySQL, all have built-in sharding options to handle huge datasets. This means faster queries and better performance, even with millions of records.

In addition, it’s one way of avoiding the dreaded “out of space” message when your server gets overwhelmed.

Large-Scale Web Applications

Ever wondered how social media platforms like X, Instagram, and Facebook, can handle billions of users? You guessed it — sharding plays a huge role. Imagine an app like Instagram storing every photo, comment, and like in one place. That could result in total chaos.

Sharding splits user data into manageable chunks, such as regions. This way, users in Europe won’t slow down those in the U.S. and vice versa.

Large-scale web applications use sharding to split user data into more manageable chunks. This leads to faster loading and a better user experience.

eCommerce sites love this, too. Remember when everyone was refreshing online stores like Best Buy and Amazon for that PS5? That’s because demand was high and supply low. Without sharding, these platforms wouldn’t be able to handle the heavy traffic load.

Blockchain Technology

Decentralized systems, like Ethereum 2.0, rely on sharding to keep things scalable. Instead of every node handling all transactions, for instance, sharding breaks the blockchain into smaller pieces.

This means faster transaction speeds and lower fees. Ask any crypto fan out there, and they’ll tell you that this technology is a big deal to them. It also keeps blockchains decentralized while supporting more users and applications. Without it, blockchains would crumble under heavy traffic.

Gaming

Sharding makes online games lag-free (well, most of the time). Imagine a massively multiplayer online game like “World of Warcraft”, where millions of players are exploring the same world. Without sharding, the servers would crash.

Game developers use shards to split player data and game states across servers. One shard might handle North America, another South America, another Europe, and so on.

Game servers take advantage of sharding by splitting player data and game states across multiple servers.

Also, thanks to sharding, developers don’t need to rework the entire system when updating the game.

Sharding can transform how your system handles data. But you’ll first need to get it right. I’ll share some tips to get you started.

Choose the Right Shard Key

I’ve said this before, and I’ll say it again: the shard key is the key to unlocking the true potential of sharding. Usually, it decides how it’ll distribute your data across shards.

Imagine you’re organizing a library, and the shard key is the method you use to sort the books by genre, author, or publication year. If you pick a shard key that clusters too much data in one shard (like sorting by a popular author), you’ll overload one section while others sit nearly empty.

A good shard key distributes data evenly. eCommerce platforms, for instance, might use customer IDs or order IDs because they tend to be unique and spread out naturally.

Avoid keys like timestamps; they’ll send too much data to one shard. A good example of such a scenario would be a lightning sale that starts at 11 PM and ends at midnight. Data collected within this time bracket will go into one shard, leaving the other shards unoccupied.

Monitor Shard Performance

It’s good to check up on your shards every now and then. It’s like boiling milk — you blink, and everything boils over. You want to make sure everything works. And by monitoring, I mean using tools like Grafana or Prometheus.

These tools can track shard loads, query times, and storage usage. This way, you’ll be able to identify shards that are overburdened or those that still have room for more data. When you identify an overloaded shard, you decide the next move. Maybe rebalance data. If not, split the shard into smaller ones. Whichever works for you.

Plan for Growth

Sharding isn’t just about solving today’s problems. In fact, one might argue that it’s more about preparing for tomorrow. You need to prepare for growth. And given that sharding is a scalable solution, you’ll have plenty of room to scale.

One way of preparing for the future is by using a consistent hashing algorithm. This makes it simpler to add or remove shards without redistributing all the data. The algorithm sets the rules, and the system follows. Reserve extra capacity in your infrastructure to handle more data. Also, keep scaling strategies in place to avoid downtime during peak traffic.

Test Cross-Shard Queries

At some point, you may end up dealing with cross-shard queries. If you’ve ever participated in a group project, you know that sometimes, coordinating multiple parts can get tricky. That’s exactly how cross-shard queries feel. It’s even worse when a query spans multiple shards. It’ll need to combine the results, which could make things even more complex.

Let’s say you’re trying to calculate the total sales across regions stored in separate shards. You, being the data-driven store owner that you are, want to know how shoppers in the UK, U.S., and Canada interacted with your online store.

First, your system needs to pull data from each shard representing these three regions. Then, it’ll have to merge the data before presenting it to you. See how complex that could potentially turn out to be?

To make sure everything works and that you trust the data delivered to you, keep these tips in mind:

• Test these interactions under real-world conditions
• Optimize indexes and query patterns to reduce data retrieval times.
• Use tools like distributed query planners to streamline the process.

Simulating high-traffic scenarios is a proven way of making sure your system handles multi-shard queries without slowing down.

Sharding, as I mentioned earlier, isn’t the only solution to scaling challenges. But that also doesn’t mean you should completely shut the door to other alternatives. It’s worth knowing how they work and if they can pair up with sharding.

Replication

Replication creates multiple copies of your data across servers. The goal here is to make sure that if a server fails, others can step in and get the job done.

This alternative works if you just want to create a redundant network. But if your goal is to improve performance, it’s not the best candidate for this role.

Here’s the catch: replication doesn’t divide the data into smaller pieces like sharding. Instead, it just copies the same data everywhere.

This works great when reading the data. A good example is when people are scrolling through your blog posts.

But when they start making changes to the data at the same time — like commenting on posts — that could potentially slow down the system. That’s because every change needs to update all the copies.

Partitioning

Partitioning is also an alternative, but not as effective as sharding. That’s because it involves dividing data into smaller pieces that are easier to manage.

The biggest issue here is that partitioning keeps all partitions on the same database. As a result, databases tend to run out of space.

The good news is that you don’t necessarily need to completely rule out partitioning. It can, in fact, work side by side with sharding.

For example, if I’m running a small business, I can use sharding to separate customer orders by region. Then, as data grows, I’d combine partitioning with sharding to scale the database.

Caching

Caching temporarily keeps the data you frequently access in memory.

As a result, it reduces the need to query the database and loads the content faster. As you can imagine, this creates a better user experience (no one wants to wait ages for a page to load).

Combining caching and sharding gives you the best of both worlds. To be more specific, caching complements sharding by reducing the load on shards.

Databases are not just about storing stuff anymore. We’re in a new era of technology where everything seems to revolve around performance.

I learned this while shopping for laptops. Why does a laptop with one terabyte hard disk cost less than one with a 250-gigabyte solid-state drive? Performance, ladies and gentlemen.

It’s the same story with databases. Anything that improves their performance will always catch people’s attention. I’m not saying sharding is the only way to make your database perform better, but it’s up there with the best solutions.