H13-711_V3.5 · Question #341
Spark Streaming computing is based on DStream, which decomposes streaming computing into a series of short batch jobs.
The correct answer is A. True. A (True) is correct because Spark Streaming's core abstraction, DStream (Discretized Stream), works by breaking a continuous data stream into small, fixed-time-interval micro-batches, each of which is processed as a standard Spark RDD batch job - this is literally the defining…
Question
Spark Streaming computing is based on DStream, which decomposes streaming computing into a series of short batch jobs.
Options
- ATrue
- BFalse
How the community answered
(57 responses)- A72% (41)
- B28% (16)
Explanation
A (True) is correct because Spark Streaming's core abstraction, DStream (Discretized Stream), works by breaking a continuous data stream into small, fixed-time-interval micro-batches, each of which is processed as a standard Spark RDD batch job - this is literally the defining characteristic of the micro-batch architecture.
B is wrong because it contradicts the fundamental design of Spark Streaming; the micro-batch approach is not just an implementation detail but the architectural foundation that distinguishes Spark Streaming from true event-at-a-time streaming engines like Apache Flink.
Memory tip: Think of DStream as a "Discretized Stream" - discretized means broken into discrete chunks (batches). If you remember that "D" stands for discrete/discretized, you'll always recall that Spark Streaming slices continuous streams into small batch jobs rather than processing events one-by-one.
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