Q1. What is the CAP theorem and how does it guide decisions?
A distributed store can guarantee only two of three during a network partition: Consistency (every read sees the latest write), Availability (every request gets a non-err...
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Q2. What is the cache-aside pattern and how do you keep the cache consistent?
In cache-aside the application checks the cache first. On a miss it reads from the database, writes the value into the cache with a TTL, and returns it. On writes, the co...
Q3. How do you invalidate cached data?
Invalidation is the hardest part of caching. Options, often combined: - TTL expiry: every entry has a lifetime; simple and self-healing, but data can be stale until it ex...
Q4. Explain write concern and read concern in MongoDB.
Write concern controls how many nodes must acknowledge a write before it counts as successful. w:1 means the primary acknowledged it; w:"majority" means a majority of rep...
Q5. How do you keep a cache consistent with the database?
Perfect consistency is not achievable without giving up caching; aim for bounded staleness and correct behavior. Practical rules: - On write, commit to the database first...
Q6. How does the CAP theorem affect microservice design?
CAP states that during a network partition a distributed system must choose between consistency (every read sees the latest write) and availability (every request gets a ...
Q7. How do you handle a distributed transaction without two-phase commit?
Two-phase commit is rarely used in microservices because it needs a coordinating transaction manager, holds locks across services, blocks on coordinator failure and hurts...