Database Scaling

FAQs

Yes. MongoDB allows you to scale your clusters vertically by adding more virtual resources to the cluster, or horizontally by partitioning the data via sharding.

Databases are scaled either vertically (by adding more virtual resources to existing machines) or horizontally (by adding more machines, distributing data, and processing across those machines).

Scaling in DBMS is the ability to expand the capacity of a database system in order to support larger amounts of requests and/or store more data without sacrificing performance.

Scalability issues are problems caused by a system's inability to support growing demand on virtual resources, such as storage/memory, processing, and network bandwidth. These are usually manifested as a system's degraded performance, errors, or unresponsiveness.

Scalability is usually tested through load testing, e.g., simulating a real-life large number of requests and ensuring that the system can support those requests and adapt to differing amounts of load.

Yes. However, relational databases are not as easily scaled as more modern, non-relational databases. MongoDB, for example, is built from the ground up to scale massively and has high availability.

NoSQL databases are usually built by design for a distributed database environment, allowing them to take advantage of more availability and partition networking built-in solutions, which sometimes comes as a tradeoff for consistency. Most relational database management systems RDBMS), such as SQL Server and Oracle, choose consistency over availability. These systems often focus on storing business transaction information, and so consistency is critical to their operation. On the other hand, most non-relational and NoSQL databases choose availability over consistency because their main focus is the ability to support large amounts of users and data volume even when some of the database nodes go down. This assists in the support of scalability using the “partition of data” approach. MongoDB has both availability and consistency thanks to replica sets and multi-document transactions. This helps make it an ACID-compliant database and reduces the tradeoffs developers normally have to make.

Generally, you start by scaling vertically by adding more storage, CPUs, and memory. You could also enable replication and serve some of the read requests from different nodes in the cluster. However, this may require that the application be aware of the different nodes.

Horizontal scaling involves adding additional servers and partitioning the system dataset and load over those servers. Vertical scaling involves expanding the resources used by a single server/replica set.

Neither is better. You should choose the type of database scaling that meets your use case. Vertical scaling is generally simpler but more limited. Horizontal scaling is more complex but supports larger loads in terms of the number of requests served as well as total data storage.

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Database Scaling

FAQs

Is MongoDB scalable?

How do you scale a database?

What is scaling in DBMS?

What are scalability issues?

How do you test scalability?

Are relational databases scalable?

Why are NoSQL databases more scalable than RDBMS databases?

How do I scale a SQL database?

How do I scale a database horizontally?

Which is better: horizontal or vertical scaling?

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