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Answer: Spark buffers past inputs as a streaming state for both input streams, enabling every future input to be matched with past inputs.
In stream-stream joins, Spark buffers past inputs as a streaming state for both input streams. This allows every future input to be matched with past inputs. The state can be limited by using watermarks. Reference: [Introducing Stream-Stream Joins in Apache Spark 2.3](https://www.databricks.com/blog/2018/03/13/introducing-stream-stream-joins-in-apache-spark-2-3.html).
Author: LeetQuiz Editorial Team
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What is the correct statement about streaming state in Stream-Stream Joins?
A
Stream-Stream Joins are stateless, meaning Spark does not buffer past inputs as a streaming state for the input streams.
B
Spark buffers past inputs as a streaming state for the left input stream only, to match future right inputs with past left inputs.
C
Spark buffers past inputs as a streaming state for the right input stream only, to match future left inputs with past right inputs.
D
Spark buffers past inputs as a streaming state for both input streams, enabling every future input to be matched with past inputs.
E
Stream-Stream Joins do not support limiting the state information using watermarks.
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