States, consistency and sessions

Hadoop is a distributed computing framework that is designed to process large amounts of data in parallel across a cluster of computers. To ensure that data processing is efficient and accurate, Hadoop employs several key concepts, including states, consistency, and sessions.

States refer to the condition of the data being processed in Hadoop. In Hadoop, data is divided into smaller chunks, and each chunk is processed independently across multiple nodes in the cluster. The state of each chunk of data is tracked to ensure that it is processed correctly and consistently.

Consistency refers to the accuracy and correctness of the data being processed in Hadoop. Hadoop employs various mechanisms to ensure consistency, such as replication and fault tolerance, to ensure that data processing is accurate and reliable.

Sessions refer to the duration of a user’s interaction with Hadoop. A session typically begins when a user logs into Hadoop and ends when the user logs out. During a session, the user can perform various tasks, such as submitting jobs, monitoring job progress, and accessing data.

Overall, states, consistency, and sessions are critical concepts in Hadoop that ensure efficient and accurate data processing in a distributed computing environment.