Answer-first summary for fast verification
Answer: Checkpointing and Write-ahead Logs
## Explanation Structured Streaming uses two key mechanisms to reliably track processing progress and handle failures: **1. Checkpointing** - Stores the current state of the streaming query, including: - Progress information (which offsets have been processed) - Aggregation state (for stateful operations) - Metadata about the query **2. Write-ahead Logs (WAL)** - Records the offset ranges being processed in each trigger before the actual processing begins. This ensures: - Exactly-once processing semantics - If a failure occurs during processing, the system can replay from the last recorded offset - No data loss or duplication **Why other options are incorrect:** - **Option B**: Incorrect - Structured Streaming can and does record offset ranges - **Option C**: Replayable sources and idempotent sinks are important concepts but not the specific mechanisms for recording offset ranges - **Option D**: Write-ahead logs are correct, but idempotent sinks alone don't track offset ranges - **Option E**: Checkpointing is correct, but idempotent sinks don't track offset ranges The combination of checkpointing and write-ahead logs provides the fault tolerance and exactly-once semantics that Structured Streaming is known for.
Author: Keng Suppaseth
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In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?
A
Checkpointing and Write-ahead Logs
B
Structured Streaming cannot record the offset range of the data being processed in each trigger.
C
Replayable Sources and Idempotent Sinks
D
Write-ahead Logs and Idempotent Sinks
E
Checkpointing and Idempotent Sinks