In what way does Hadoop handle the processing of records that are split between different block boundaries?

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Hadoop handles the processing of records that are split between different block boundaries by using a concept called Input Splits. Input Splits are logical divisions of the input data that Hadoop uses to distribute work across the nodes in the cluster.

When a file is input into a Hadoop cluster, it is physically split into fixed-size blocks and distributed across the nodes in the cluster. Input Splits are then created for each block, with each split containing one or more complete records.

If a record spans across multiple blocks, Hadoop will identify it and include it in the Input Splits that correspond to those blocks, ensuring that the entire record is processed by a single Map task. This is done by using special record delimiters or markers that are defined by the user, so that Hadoop knows when a record starts and ends.

Hadoop ensures that each Map task receives a complete set of input records by ensuring that all Input Splits are processed by the same Map task. This enables Hadoop to process large datasets efficiently and in parallel, without losing any data or processing incomplete records.