H13-711_V3.5 · Question #340
An Executor of a Spark task can run multiple tasks at the same time
The correct answer is A. True. Option A is correct because a Spark Executor is a JVM process that holds a thread pool, and each thread can run an independent task concurrently - the degree of parallelism within a single Executor is controlled by spark.executor.cores, which defaults to more than 1 in many…
Question
An Executor of a Spark task can run multiple tasks at the same time
Options
- ATrue
- BFalse
How the community answered
(31 responses)- A81% (25)
- B19% (6)
Explanation
Option A is correct because a Spark Executor is a JVM process that holds a thread pool, and each thread can run an independent task concurrently - the degree of parallelism within a single Executor is controlled by spark.executor.cores, which defaults to more than 1 in many configurations. This means a single Executor can process multiple partitions simultaneously rather than sequentially.
Option B is wrong because it conflates an Executor (a long-lived worker process) with a single-threaded worker - Spark intentionally allocates multiple cores per Executor to maximize CPU utilization on each node.
Memory tip: Think of an Executor as a manager with a team of workers (threads/cores). One manager can direct multiple workers at the same time - executor.cores is the team size.
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