Definition

Data locality is the principle of placing computation (queries, processing) close to where the data resides, rather than moving large data sets across the network.

Core concept

By co-locating computation with data, systems reduce network transfer and latency. For example, in Hadoop/Spark, tasks are scheduled on nodes that already store the data block.

Use cases

Big data processing (Hadoop, Spark), edge computing, and distributed databases optimize by moving small computations to data (e.g., map tasks) rather than shuffling large volumes of data.

Trade-offs

To exploit locality, data must be partitioned effectively. In some cases, data might be replicated to achieve locality. If data is poorly distributed, locality gains are limited.

Example

A Spark job runs its map tasks on nodes holding the input files (HDFS blocks).

References

“Data locality is the process of moving computation to the node where the data resides”[[33]](https://www.thoughtworks.com/insights/decoder/d/data-locality "Data locality | Thoughtworks").