Explain De-duplication Strategies.
De-duplication is the process of detecting and removing duplicate data or messages so each unique item is stored or processed only once—crucial for reliability and efficiency in distributed systems.
When to use/Use Cases
- In data pipelines or backups to reduce redundant storage.
- In message queues (Kafka, RabbitMQ) to ensure each event is processed once.
- In APIs or payment systems to prevent duplicate transactions during retries.
Example
A payment API assigns each request a unique transaction ID.
If the same ID reappears, the system skips processing to avoid double-charging.
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Why Is It Important
De-duplication minimizes wasted storage, reduces processing overhead, and prevents inconsistent outcomes in distributed systems where network retries or replication can reintroduce the same data.
Interview Tips
- Relate it to idempotency and message delivery semantics (at-least-once, exactly-once).
- Discuss techniques like hashing, unique IDs, and Bloom filters.
- Mention trade-offs between accuracy and performance.
Trade-offs
- Inline vs. post-process: Inline saves space early but adds latency; post-process avoids delay but consumes more temporary storage.
- Memory vs. speed: Caching duplicates boosts detection speed but increases memory use.
Pitfalls
- Hash collisions causing false positives.
- Unexpired duplicate markers bloating in-memory tracking.
- Over-aggressive filtering leading to missed valid data.
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