Data Locality

As per the name depiction, Data Locality looks like knowing the location of the data; it is partially relevant.

Traditionally, to use data for analysis, at the backend the data is fetched from storage/ data nodes into master nodes, for computation. There is a complete history of how initially whole data was pulled then blocks were pulled then the columnar pull concept came-in, etc. so we won't go into detail about how technology has evolved to process data.

Since semi and unstructured data has become a necessary part of the Data World, it comes with huge storage requirements i.e., in TB and even in PB. To host this humongous data, clusters of 100s of storage/ data nodes are set up, so it is not possible to pull this size of data for computation in master nodes which are normally few. It was a critical requirement to process queries where data is residing.

With the introduction of Big Data/ Hadoop/ HDFS where data is stored in the shape of blocks, the concept surfaced i.e., instead of fetching data for computation, let's take the query to the storage/ data nodes, process it there and fetch only processed data. This process of moving the computation close to where the actual data resides on the storage/ data node, instead of moving large data to computation, is called Data Locality.

In recent times, a new concept of separating computing and storage is surfacing.

In nutshell, when query runs on the same node where data is located, it is called Data Locality.