Difference between columnar and row-based databases.

There are many types of databases present and there is a lot of confusion(for beginners) to use which one in which cases. To add to the confusion there are columnar databases. In this article, we will look at what is the difference between row-based and columnar databases and where we can use columnar databases.

Difference between columnar and row-based databases

The main difference between these two is the way they save the data on the disk. In row-based databases, each row is stored in a disk as consecutive disk spaces.

In the case of a columnar database, each column is stored in consecutive locations on disk.

Difference between columnar and row-based databases.

Now, let’s take a look what are advantages and disadvantages of these.

In terms of write

Writes will be easier in row-based databases, why? because there will be less seek operation and just writes will be there on the disk, because all the locations to write is the next locations in the disk.

Writes will be more latent in the columnar database since it has to find the new location of the row and the offset at which it can write the next data of the row.

In terms of reads

Read depends on what you want to read. If your use case of row-wise reads, then certainly rows-wise databases is the choice but if you want to read whole data of anyone column, in that scenario columnar databases are awesome to use.

Now you must be thinking in what scenario we will read the whole column data, well these use cases come in data analytics with big data.

Look at the given problem.

Given data of users as name, age and address, find the average age, draw the five-point plot of the ages. If you look at these use cases this is the one that matches our case. So where ever you see this kind of use case you can use the columnar databases.

Few columnar databases: Cassandra, Redshift

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Gaurav Yadav

Gaurav is cloud infrastructure engineer and a full stack web developer and blogger. Sportsperson by heart and loves football. Scale is something he loves to work for and always keen to learn new tech. Experienced with CI/CD, distributed cloud infrastructure, build systems and lot of SRE Stuff.

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