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How would you ensure exactly-once processing for Kafka consumers in your Spark job?

Spark/Big Datahard0.5 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Meesho
Key Concepts Tested
optimizationpartitionsparksql
Expert AnswerPremium
96 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Exactly-once Kafka + Spark: (1) Use `readStream.format('kafka')` with `kafka.group.id`. (2) Write to idempotent sink (e.g., Delta with merge, or transactional DB). (3) Use Kafka 0.11+ transactions: enable `spark.sql.streaming.checkpointLocation`; use `kafka.bootstrap.servers` with transactional producer....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Meesho. The answer also includes follow-up discussion points that interviewers commonly explore.

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