Swiggy Incident Response & Production Debugging interview questions

Established traffic keeps working while new connections die at random, which is exactly why this one confuses people. The table is full of flows that will not be needed again for days.

A two-second dependency should cost you latency, not three services. The mechanics are arithmetic: Little's law plus retry amplification. The fix is a budget, not a ban.

One saturated core on a proxy is rarely about raw traffic volume. It is a parallelism failure with three usual suspects, and the per-thread view names the culprit in minutes.

The reflex is rollback and it is usually right. Shared state, changed credentials and unavailable old artifacts can make an undo incomplete or unsafe. Verify those dependencies before choosing the recovery.

The box looks CPU-starved but the CPUs are starving themselves. Reading the reclaim loop correctly separates people who have sat on the console from people who have read about it.

The backup is doing its job and the database is paying for it. Attribution first, then enforcement that actually binds on modern kernels, because priority support depends on the active kernel and IO scheduler.

A public registry throttles by source IP and your whole fleet shares one. Why fifty nodes count as a single anonymous user, and authentication and caching changes that reduce upstream demand.

Purges act on exact cache keys, and the edge is full of variants the purge never named. Verify per variant at real POPs, then fix the key model instead of chasing URLs forever.

Stop the source before tuning anything: kill the runner, revoke its credentials, shed test-shaped traffic at the edge, protect checkout. Forensics and guardrails only after customers stop feeling it.

Two commands disagreeing is the diagnosis, not the mystery. A deleted file with an open handle is invisible to du and fully charged by df, and rm made it worse rather than better.

The pod's resolver is configured to try five search domains before the real one. An uncached external hostname can trigger several search-expanded queries before its absolute lookup, and the failures are what you are paying for.

One partition has one consumer, by design. If the key distribution sends a third of the traffic to one partition, the parallelism you bought is unavailable to the work that needs it.

A deceptively deep question, because there are at least six places a version can get stuck and they span the pipeline, the cluster and the edge. Work the path in order.

An idle database with a starved application is the tell. The connections are held, not used. More database capacity does not release leaked connections.

Status codes describe an HTTP failure category. Locate the component that emitted the response, then use its logs to distinguish connection, capacity and application failures.

Three places, and which one depends on how the thing was started. Knowing which logs the runtime collects and which the application writes itself, and that journald may not persist across reboots, saves the first twenty minutes.

A common scenario question, and the one where a list of commands most often stands in for an argument about what each one eliminates. Empty logs is the clue, not the obstacle.

The gentlest scheduling question there is, and it still separates people who read the events from people who guess. Check whether the pod has a node before choosing between scheduling and startup diagnosis.