4 Database Scaling Solutions You Need to Know

You have launched your application with an intuitive, user-friendly UI. But if your application is experiencing a load problem, it will frustrate your end customers while using it. Chances are the issue is not within the application but with the database. According to a survey, 38% of database professionals reported database downtime as the significant issue that kept them up at night. The downtime could be due to any number of issues, including improper database configuration, poor load handling, database queries being timed out, and so on. 

In this article, we will discuss whether you need to scale your database and how to tackle database scalability issues. Let’s begin with the most obvious question — why database scaling? 

Why Scale Your Database?

Your application's database should be able to expand or contract its compute resources to match the dynamic needs of the application. For example, your database should be able to scale up to handle a sudden surge in traffic. Also, when not in condition, your database should be able to contract to save resources. One of the best ways to ensure good database scalability is to choose the proper database according to your requirements. With physical servers, database expansion and contraction can be a headache. Cloud database solutions can do the trick.

Database scalability is a resource-intensive and challenging task. Hence, before starting the project, you need to ensure that your product needs a scalable database. First, determine whether your product will experience a surge in traffic in the foreseeable future. If not, you can still do it with your old database.

If your business is a startup, there is no point in investing resources in acquiring a scalable database. You can do so once your application hits critical mass and expect a good surge in traffic.

Here are a few scenarios in which database scalability is necessary:

• Your application is outdated, and you want to migrate to a cloud system

• Your application experiences high loads

• Your application needs to comply with the regulations

• You want a balanced workload that can serve users all over the world

Database Scaling Solutions

1. Cache Database Queries

One of the most straightforward ways to improve your database’s load-handling capability is to cache database queries. Usually, an application has only a handful of queries that make up the bulk of requests made. You can cache such queries so that, in the future, these requests are read from the cache. This eliminates the need to fetch data from the database every time such common requests are made. The user is served with the required data quickly. This way, caching helps in improving the performance of your database.

Amazon ElastiCache is a caching service that helps you in caching your database. With Amazon ElastiCache, you can scale with in-memory caching. Amazon ElastiCache supports real-time use cases. The following are the ideal use cases for Amazon ElastiCache:

• Gaming leaderboards

• Analytics

• Streaming

2. Database Indexes

Scaling a database doesn’t always mean adding more databases to the existing setup. Sometimes, by optimizing the current database, you can potentially scale to some extent. This is where database indexing comes into play. Using the database indexing technique, you are structuring the data to improve the speed of data retrieval from the database table.

3. Data Replication 

Data replication is a strategy used to make identical copies of the database to create additional machines. A replication strategy is beneficial in tackling peak loads. As the data is replicated, queries can be spread across multiple databases, and this, in turn, will reduce the load on a single database. Moreover, in the unlikely event that a storage device fails, the replicated data will come in handy, and the system will still be fully functional.

4. Sharding

One of the major bottlenecks in scaling your database is the improperly designed database. To ensure that you do not face this bottleneck, it is essential to start by choosing the correct database for the correct business application. For example:

• A bank should choose relational DBMS to ensure ACID (atomicity, consistency, isolation, durability) for its structured data

• An online multiplayer game can rely on key-value no SQL database

No matter which database you select, ensure that it has a sharding feature in it. Sharding means splitting a single large chunk of the database into smaller chunks of data, known as shards, that can be stored across multiple databases. There are two types of sharding:

• Horizontal sharding

• Vertical sharding

Horizontal sharding proves effective when your database queries return a subset of rows of data. These rows are often grouped together. For instance, the queries in which the data is filtered are based on short date ranges.

Vertical sharding proves effective when your database queries return a subset of columns of data. For instance, if some database queries request only names while others want only cities, then vertical sharding proves effective in such cases.

Shards have two major advantages:

• The system's overall storage capacity is directly proportional to the number of database shards.  

• If one shard is offline, you can still rely on the shard pool to retrieve and store your data. When a shard goes offline, only a portion of the overall dataset is unavailable at the moment. Therefore, it won’t majorly impact the operation of the system. 

Conclusion

The database is a crucial element of any application. If you want to scale your application, you can’t do it without scaling the database. Fortunately, due to technological advancements in recent years, we have all the tools we need to make the scaling process seamless and effortless. One can leverage cloud service providers like Azure, AWS, or Google Cloud to scale their application. 

However, one needs to consider certain factors before jumping into scalability. I hope this article has served you well in understanding the fundamental problems associated with scaling and how to tackle them. In case I have missed some crucial points, do let me know in the comments below.