Databricks Certified Data Engineer - Associate

Explanation

In Apache Spark Structured Streaming, the mechanism used to record the offset range of data being processed in each trigger is Checkpointing and Write-ahead Logs.

Key Concepts:

1. Checkpointing:

   • Stores the progress information of a streaming query, including the offset ranges processed
   • Enables fault tolerance by allowing the query to restart from where it left off
   • Typically stored in a reliable storage system (like HDFS, S3, or DBFS)

2. Write-ahead Logs (WAL):

   • Records the offset ranges being processed before the actual processing begins
   • Ensures that even if the processing fails, the system knows what data was being processed
   • Works in conjunction with checkpointing to provide end-to-end exactly-once semantics

Why the other options are incorrect:

• Option B (Replayable Sources and Idempotent Sinks): While replayable sources and idempotent sinks are important for end-to-end exactly-once processing, they don't directly track the offset ranges during processing.
• Option C (Write-ahead Logs and Idempotent Sinks): Idempotent sinks ensure that duplicate writes don't cause issues, but they don't track offset ranges.
• Option D (Checkpointing and Idempotent Sinks): Similar to option C, idempotent sinks don't track offset ranges.

How it works:

1. When a trigger occurs, Structured Streaming records the offset range to be processed in the write-ahead log
2. The system then processes the data
3. After successful processing, the progress (including the offset range) is committed to the checkpoint
4. If a failure occurs, the system can restart from the last checkpoint and use the write-ahead log to determine what needs to be reprocessed

This combination ensures reliable progress tracking and fault tolerance in Structured Streaming applications.