What is Data Sharding? Scaling Databases in SaaS Applications

While a single database server can handle a couple of hundred active users in the case of a new software startup, as the application grows to millions of worldwide users, it will eventually be overloaded. 

The large number of queries will make loading slower, cause random crashes of the system, and reduce the quality of the user experience. In order to bypass these limitations of physical servers, one needs to use a special technology called data sharding.

Database Sharding: What is it? 

In order to understand what this means, one should think about classic vertical scaling when an organization gets a more powerful server with increased RAM and a processor to increase the computing resources of its application. At some point, even the most powerful server will reach its physical limitation.

In contrast to vertical scaling, which increases the capabilities of a single machine, horizontal scaling divides a huge database into several parts called “shards” that work independently on their own servers.

Techniques for Horizontal Scaling

Distribution of records across various servers necessitates the use of a well-defined mathematical formula called a shard key:

• Range-Based Sharding: The partitioning of data occurs depending on value ranges, such as customer accounts by postal code or registration date.
• Hash-Based Sharding: A hash function uses an identifier, such as customer ID, to equally distribute data records among available servers.
• Directory-Based Sharding: The directory maps individual customers’ records or the whole enterprise tenant directly to the corresponding database shard.

Advantages of the Approach for SaaS Applications

Using a design approach that incorporates distributed databases allows for certain architecture benefits for scalable software:

• Improving SaaS Efficiency: The query processing time is greatly reduced since each server processes its small database rather than a multi-terabyte one.
• Improving System Availability: In case of a failure of one of the database nodes or maintenance procedures, only the users of that particular shard are affected, whereas others can continue using the application.
• Scalability without Limitation: The engineering team will be able to keep database scaling as the size of their subscriber base grows by simply installing more database servers in the cluster.

Also Read: What is SaaS Data Migration? Process & Best Practices

Conclusion

It is important for developing software today to have an infrastructure that scales easily along with the growth of users. Data sharding enables cloud companies to avoid being limited by hardware with single-server infrastructures and any future system bottlenecks. 

It helps them break down huge datasets, improve saas performance, and distribute them efficiently within network clusters.