When to pick a NoSQL Database?
Handling a large number of read-write operations
NoSQL databases are built to handle a large number of read-write operations due to the eventual consistency model. With the ability to add nodes on the fly, they can handle more concurrent traffic, enabling us to scale fast.
They are also built to handle big data with minimal latency. Pick a NoSQL database if you are looking to scale fast and willing to give up on strong consistency.
Flexibility with data modelling
A NoSQL DB is a good fit if you are not sure about your data model during the initial phases of development and things are expected to change at a rapid pace. NoSQL databases offer us more flexibility.
Eventual consistency over strong consistency
NoSQL databases are a good pick when we do not need ACID transactions and are okay to give up strong consistency.
A good example of this is a microblogging site like Twitter. When a celebrity’s tweet blows up and everyone likes and re-tweets it from across the world, does it matter if the like count goes up or down a tad bit for a short while?
The celebrity certainly wouldn’t care if instead of the actual 5 million 500 likes, the system shows the like count as 5 million 250 for a short while.
When a large application is deployed on hundreds of servers spread across the globe, the geographically distributed nodes take some time to reach a global consensus.
Until they reach a consensus, the value of the entity is inconsistent. The value of the entity eventually becomes consistent after a short while. This is what eventual consistency is.
However, the inconsistency does not mean any sort of data loss. It just means that the data takes a short while to travel across the globe via the internet cables under the ocean to reach a global consensus and become consistent.
We experience this behavior all the time, especially on YouTube. Often you might see a video with 10 views and 15 likes. How is this even possible?
It’s not. The actual views are already more than the likes. It’s just the count of views is inconsistent and takes a short while to get updated. I will discuss eventual consistency in more detail in the upcoming lessons.
Running data analytics
NoSQL databases also fit best for data analytics use cases, where we have to deal with an influx of massive amounts of data.
There are dedicated databases for use cases like this, such as time-series databases, wide-column, document-oriented databases, etc. I’ll talk about each of them later in this chapter.
In the next lesson, let’s look into the performance comparison of SQL and NoSQL technology.