Which feature allows tasks to be executed in parallel across different nodes in Spark?

The feature that enables tasks to be executed in parallel across different nodes in Spark is distributed computing. This concept is fundamental to Spark’s architecture, as it allows the processing of large datasets across multiple machines, leveraging the hardware capabilities of each node.

In a distributed computing environment, data can be split into smaller chunks that can be processed simultaneously on different nodes, significantly speeding up computation times and improving performance for large-scale data processing tasks. Each node operates independently, allowing for efficient resource utilization and reduced processing time.

Batch processing refers to the execution of a series of jobs or tasks at once rather than continuously, but it does not inherently imply parallel execution across nodes. MapReduce, a programming model for processing large data sets, is often associated with distributed computing; however, it is not a feature exclusive to Spark and does not define Spark’s parallel processing capabilities. Single-thread processing restricts execution to one thread, negating the benefits of parallelism inherent in a distributed system.

Thus, distributed computing is the defining feature that facilitates parallel task execution across heterogeneous nodes in Spark’s architecture.