H13-711_V3.5 · Question #361
Flink can not only provide real-time computing that supports high throughput and exactly-once semantics, but also batch data processing.
The correct answer is A. True. A is correct because Apache Flink is a unified stream-and-batch processing framework - it was designed from the ground up to handle both paradigms under a single runtime. Flink's DataStream API handles real-time streaming with high throughput and exactly-once semantics via its…
Question
Flink can not only provide real-time computing that supports high throughput and exactly-once semantics, but also batch data processing.
Options
- ATrue
- BFalse
How the community answered
(69 responses)- A84% (58)
- B16% (11)
Explanation
A is correct because Apache Flink is a unified stream-and-batch processing framework - it was designed from the ground up to handle both paradigms under a single runtime. Flink's DataStream API handles real-time streaming with high throughput and exactly-once semantics via its checkpointing mechanism, while its DataSet API (and now the unified Table/SQL API) handles bounded batch workloads. B is wrong because it falsely implies Flink is limited to streaming only, which contradicts Flink's core design philosophy of treating batch as a special case of streaming (a bounded data stream).
Memory tip: Think of Flink's logo - a squirrel that can move fast (streaming) and store/process nuts in bulk (batch). The tagline "stateful computations over data streams" is broad enough to encompass both unbounded (streaming) and bounded (batch) data.
Topics
Community Discussion
No community discussion yet for this question.