Exam Certified Data Engineer Associate topic 1 question 27 discussion

E. Checkpointing and Idempotent Sinks

If failure by restarting and/or reprocessing then choose E If the worker running the task crashes then choose A

E is correct

Correct answer is E

In Structured Streaming, Spark uses the following two mechanisms to reliably track the progress of the stream and ensure fault tolerance: Checkpointing: Spark maintains metadata about the processing state, including the offset range of the data processed in each trigger. This metadata is stored in a reliable storage system like HDFS, AWS S3, or Azure Data Lake. If a failure occurs, Spark can recover and resume processing from the last recorded state in the checkpoint. Idempotent Sinks: Idempotent sinks ensure that output operations (e.g., writing data to storage or a database) can be re-executed without causing duplicate data or errors. By combining idempotent sinks with checkpointing, Spark ensures that reprocessing data due to a failure does not compromise data integrity.

Why the correct answer is E. Checkpointing and Idempotent Sinks: Checkpointing: Spark Structured Streaming uses checkpointing to track the state of the data being processed. Checkpoints allow the system to restart processing from where it left off in case of failure, ensuring reliability. Idempotent Sinks: Idempotent sinks ensure that reprocessing the same data multiple times (in case of a failure or restart) doesn’t lead to duplicate results. The sink can handle repeated writes of the same data without issues. Why A. Checkpointing and Write-ahead Logs is incorrect: Spark Structured Streaming does not use Write-ahead Logs (WAL) for tracking offsets or ensuring fault tolerance. While WALs are used in some systems for durability, Spark Structured Streaming relies on checkpointing and the concept of idempotent operations to ensure consistency and fault tolerance.

The correct answer is: E. Checkpointing and Idempotent Sinks In Structured Streaming, Spark uses checkpointing to reliably track the progress of the streaming data. Checkpointing saves the state of the streaming computation to a reliable storage system. Idempotent sinks ensure that even if data is reprocessed, the results remain consistent and correct, preventing duplicate data from being written.

Checkpointing: Spark saves metadata, including offsets, in a checkpoint directory, allowing it to recover from failures by replaying data starting from the last checkpoint. Write-ahead Logs (WAL): Spark writes information about the data being processed to a log before the data is written to the sink. This ensures that even if a failure occurs, Spark can recover and reprocess the data from the log.

A is the correct answer.

The answer is A "Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. ... Finally, the system ensures end-to-end exactly-once fault-tolerance guarantees through checkpointing and Write-Ahead Logs." - Apache Spark Structured Streaming Programming Guide

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The answer is Checkpointing and idempotent sinks (E) How does structured streaming achieves end to end fault tolerance: • First, Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval. • Next, the streaming sinks are designed to be _idempotent_—that is, multiple writes of the same data (as identified by the offset) do not result in duplicates being written to the sink. Taken together, replayable data sources and idempotent sinks allow Structured Streaming to ensure end-to-end, exactly-once semantics under any failure condition

1 checkpointing and write ahead logs to record the offset range of data being processed 2 checkpointing and idempotent sinks achieve end to end fault tolerance

Correct is A

The answer is Checkpointing and idempotent sinks How does structured streaming achieves end to end fault tolerance: First, Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval. Next, the streaming sinks are designed to be _idempotent_—that is, multiple writes of the same data (as identified by the offset) do not result in duplicates being written to the sink. Taken together, replayable data sources and idempotent sinks allow Structured Streaming to ensure end-to-end, exactly-once semantics under any failure condition.

A. Checkpointing and Write-ahead Logs To reliably track the exact progress of processing and handle failures in Spark Structured Streaming, Spark uses both checkpointing and write-ahead logs. Checkpointing allows Spark to periodically save the state of the streaming application to a reliable distributed file system, which can be used for recovery in case of failures. Write-ahead logs are used to record the offset range of data being processed, ensuring that the system can recover and reprocess data from the last known offset in the event of a failure.

A: The engine uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. -- in the link search for "The engine uses " youll find the answer. https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#:~:text=The%20engine%20uses%20checkpointing%20and,being%20processed%20in%20each%20trigger.