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How does Kafka ensure message durability and reliability?

Spark/Big Datahard0.5 min readPremium
Frequency
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Asked at 1 company
Category
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questions in Spark/Big Data
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88E|81M|283H
in this category
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1,863
across 7 categories
Asked at these companies
Fragma Data Systems
Key Concepts Tested
optimizationpartition
Expert AnswerPremium
104 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Kafka ensures durability via: (1) Replication—configurable `replication.factor` (default 1, prod often 3). (2) Acks—`acks=all` waits for all in-sync replicas. (3) Persistence—messages written to disk, not just memory. (4) Consumer offsets—committed to `__consumer_offsets` for at-least-once....
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 Fragma Data Systems. The answer also includes follow-up discussion points that interviewers commonly explore.

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