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LetsGit.IT/Categories/Microservices
Microserviceshard

Kafka ordering: what ordering guarantees do you get and how do you design for ordering?

Tags
#microservices#kafka#ordering#events#partitioning
Back to categoryPractice quiz

Answer

Kafka guarantees ordering only within a single partition. To keep events for an entity in order, publish them with the same partition key (e.g., `orderId`) so they land in the same partition. There is no global ordering across partitions, and consumers should still handle duplicates/retries.

Advanced answer

Deep dive

Expanding on the short answer — what usually matters in practice:

  • Context (tags): microservices, kafka, ordering, events, partitioning
  • Scaling: what scales horizontally vs vertically, where bottlenecks appear.
  • Reliability: retries/circuit breakers/idempotency, observability (logs/metrics/traces).
  • Evolution: keep changes cheap (boundaries, contracts, tests).
  • Explain the "why", not just the "what" (intuition + consequences).
  • Trade-offs: what you gain/lose (time, memory, complexity, risk).
  • Edge cases: empty inputs, large inputs, invalid inputs, concurrency.

Examples

A tiny example (an explanation template):

// Example: discuss trade-offs for "kafka-ordering:-what-ordering-guarantees-do-you-"
function explain() {
  // Start from the core idea:
  // Kafka guarantees ordering only within a single partition. To keep events for an entity in 
}

Common pitfalls

  • Too generic: no concrete trade-offs or examples.
  • Mixing average-case and worst-case (e.g., complexity).
  • Ignoring constraints: memory, concurrency, network/disk costs.

Interview follow-ups

  • When would you choose an alternative and why?
  • What production issues show up and how do you diagnose them?
  • How would you test edge cases?

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