In system design interviews, focus on showing structured thinking and curiosity rather than perfection. Proactively identify key challenges and explain your reasoning clearly, even if your solution isn’t fully complete. Treat the interview as a collaborative discussion—ask clarifying questions, check in with the interviewer, and think aloud as you refine ideas. What matters most is demonstrating how you approach problems, explore trade-offs, and communicate effectively under uncertainty.

Side note: 

1. Prepare common deep dive topics such as kafka, postgres and redis.
2. Prepare common function and non-functional requirements for your targeted company

Areas of Strength

• Enumerated all functional and non-functional requirements clearly

• Proactively identified challenges of double booking with idempotency

Areas for Improvement

Communication & Depth — examples you can say

• Proactively surface risks (matching/state/faults):
  “We have two sources of truth for order state (payment vs. inventory). To prevent mismatched status, I’ll define a canonical state machine with allowed transitions (e.g., CREATED → PAID → FULFILLED → CLOSED) and enforce it at the write path with DB constraints plus idempotent handlers. For faults, retries are exponential with jitter, capped, and guarded by idempotency keys; timeouts degrade to a compensating ‘cancel’ flow.”
  
  
• Drive deep dives:
  “There are three hairy spots we should unpack: (1) cross-service status transitions, (2) idempotency + retries under partial failure, and (3) backpressure when downstream is slow. I can start with (1) unless you prefer to jump to (3).”
• Justify a choice (concise, trade-off aware):
  “I’m choosing Kafka for the delivery bus because it gives durable, ordered logs per key and high throughput. That lets producers and consumers scale independently. We’ll trade off simpler ops from SQS/Rabbit for stronger ordering and replay.”

Collaboration & Engagement — examples you can say

• Make it a conversation:
  “Would you like me to go deeper on the storage schema and constraints, or on the retry/idempotency design?”
  “Is it helpful if I sketch a quick swimlane of producer → Kafka → consumer → DB control flow?”
  “I can compare DynamoDB vs. Postgres here—do you want to learn more about it?”
• Periodic check-ins to calibrate:
  “I’ve outlined the write path; before I continue with reads and indexing, any concerns on consistency or failover you want me to address?”

Technical Depth & Practical Insight — examples you can say

• Go beyond tool names (Redis example):
  “We’ll cache frequently accessed product metadata in Redis with TTL-based eviction. Target ≥90% hit rate to cut DB read QPS by ~10×. We’ll use request-coalescing to avoid stampedes and a negative-cache TTL for known-missing keys. ”
• Name limitations & scaling:
  “Postgres gives us ACID and constraints, but horizontal writes require partitioning/sharding. We’ll start with partitioning by order_id, add read replicas, and pre-compute hot aggregates. If write QPS approaches limits, we’ll split tenants across shards with a routing tier.”