What Is Database Scalability and How to Scale a Database?

Ever dreamed of making an app that’s as massive and popular as Facebook? Well, realistically, you have to start with a much smaller app. If your user base grows, so do your database needs. But what exactly does that mean?

Scaling a database is inevitable when you get more users. A database is scaled to meet the changing needs of application usage. You need more processing power, storage space, and network capacity to scale.

So how does database scalability work? And how does it benefits your application? We’ll also talk about how developers like yourself can solve database scalability challenges.

Why Is Database Scalability Needed?

Think you can just launch an application and be done with it? It’s a little more complex than that.. After going live, you must ensure that your application can serve relevant user traffic. A user base can jump from hundreds to millions in a couple of hours. But, your app shouldn’t suffer scalability issues.

Even large companies can make that mistake and suffer from it. In 2012, EA released The Simpsons: Tapped Out, and it faced problems right after launch.

Due to massive user demand, the application could not handle user requests or traffic.

For an app to succeed, it needs to perform well and be available. So, you need to scale both the software and hardware of the application to tackle traffic. This ensures 24/7 application availability, no matter how many users you have.

What is Database Scalability?

Database scalability adds or removes database resources on demand. It aims to meet the changing needs of users on live servers.

A database can scale in two ways:

Vertical scaling

Horizontal scaling

Vertical Database Scaling

Vertical database scaling adds CPU, storage space, and memory resources to the same server. It improves the performance of individual databases.

Vertical scaling is better if the application doesn’t need massive memory and processing. Generally, it doesn’t change the app’s code. Vertical database scaling updates the hardware specifications mostly.

A machine’s hardware resources limit the extent of vertical scaling. Beyond this limit, an application would need extra servers. More servers bring complexity, and they are expensive.

Horizontal Database Scaling

As mentioned above, vertical scaling adds more computer resources to the same server. However, the horizontal database scaling technique adds more servers to the system. It distributes the database across various machines.

Data requests from the users go directly to the corresponding server that stores the data. This process is also known as data sharding.

A horizontally scalable database increases database performance. But both the hardware and code must be upgraded. In horizontal scaling, the app’s downtime is minimum. If one server is down, others can handle data requests.

On the downside, data sharding is a complex process. So, maintaining multiple servers can be difficult for database administrators.

How to Scale Up Database: 5 Database Scaling Techniques

Various database scaling methods are available. Each method is different depending on the database and the goal of scalability.

These are 5 techniques that can be used to scale the database for better performance.

Replication

Sharding

Cache

Indexing

Session storage

1. Replication

Replication is a horizontal database scaling technique. It duplicates or makes copies of computer resources. The developer can create and maintain a copy of the database using the replication technique. Typically, more than one server contains a copy of the information. This means better accessibility, reliability, and fault tolerance.

Due to replication, when a user requests data, the answer to the search query is not limited to one server. If one server goes down, others can still answer the request. So, the system’s capacity to handle database read requests improves.

However, data needs to be consistent on these extra servers so that the response to a user query is always the same.

2. Sharding

This is another horizontal database scaling technique. It manages the system load by splitting the main database into smaller databases called shards.

Sometimes sharding is also referred to as table partitioning. Generally, managing smaller shards is easier and faster.

There are two main sharding techniques.

Vertical sharding

Horizontal sharding

Horizontal sharding organizes data tables in separate machines or servers. The resulting tables have identical columns but different rows. For instance, if a database table has 100 rows, we can split them into two horizontal shards of 50 rows each.

Vertical sharding is more complex. Each partition is independent, with distinct rows and columns in each shard.

Example:
What happens if one user requests a name and the other requests an address from an employee record? We can split the name and address columns into separate shards.

Sharded databases mean better processing speed and resilience against failures. In case of downtime, a sharded database will continue to serve user requests. The affected shard is dealt with in isolation. But, a non-sharded database can cause a system-wide outage.

Sharding is flexible and offers great benefits. But, it is complex and expensive. The implementation and maintenance costs are high. So, this technique is usually considered after other scaling techniques are already tried.

3. Cache

Caching database queries is the simplest technique to handle database load. Caching scales the database by storing previous database results in temporary memory.

When a user requests information, the system checks whether or not the cache contains it. The saved data is retrieved from the cache instead of the main storage. This allows faster access to information.

Caching also offers fast processing and better resiliency. If the main database is unavailable, the cache can still provide output to the user.

4. Indexing

Database indexing speeds up data retrieval from a database. A database index is like a table of contents or a book’s index.

Every time a user requests data, indexes find the data quickly. It saves time spent on searching the entire database table to find the relevant record.

There are two types of database indexes:

Clustered index

Non-clustered index

A clustered index points to unique data records (the actual data). A data record represents the actual data values. A non-clustered index points to the relevant data address. A data address represents a position in the memory where the data is saved.

Due to quicker search results, database load is managed, and users get a better experience using your app.

But adding an index table is costly. If your table requires frequent updates, you’ll have to update the corresponding index table as well. So, if indexes are not properly managed, they can lead to performance degradation.

5. Session Storage

Session storage is a technique that works with how your application handles user sessions.

Most applications store sessions in browser cookies or on database servers. Generally, the browser stores session data on the client machine in the form of cookies which offers quick access but is limited in size.

However, cookies can be removed easily. And they offer limited memory, so there’s always a risk of session data loss. To solve this problem, we can temporarily save the session data on server storage. On the server, sessions are stored for a limited period, so they don’t affect the overall speed of the server or the database.

Ready to Learn More?

In summary, these are 5 common database scaling methods:

Replication: Create duplicate database servers to handle user load

Sharding: Distribute data among multiple smaller databases, known as shards

Cache: Store the result of previous user data requests in memory

Indexing: Create fast loop-up tables to enable quicker querying on database tables

Session storage: Store session data to manage user load

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