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How does database indexing improve performance?

Database indexing improves performance by creating a data structure, typically a B-tree or hash based structure, that allows the database engine to locate rows quickly without scanning the entire table.

Without an index, queries often require full table scans, which are O(n). With a proper index, lookups can be reduced to O(log n) or even O(1) depending on the index type.

However, indexes come with trade offs. They consume additional storage and can slow down write operations because the index must be updated whenever the data changes. Therefore, indexes should be added selectively based on query patterns.

How do you prioritize tasks when everything seems urgent?

When multiple tasks are urgent, I first evaluate impact and urgency separately. I prioritize work that affects customers or production stability the most.

I also communicate early with stakeholders to align expectations and avoid silent trade offs. If necessary, I break work into smaller deliverables to show progress quickly.

This structured prioritization helps ensure that the most critical issues are addressed first while maintaining transparency with the team.

What about negative numbers?

What is the difference between REST and RPC?

REST is an architectural style that models interactions around resources using standard HTTP methods such as GET, POST, PUT, and DELETE. It emphasizes stateless communication and resource oriented design.

RPC, or Remote Procedure Call, focuses on invoking actions or functions on a remote server as if they were local calls. It is typically more operation focused rather than resource focused.

REST is generally preferred for public web APIs due to its simplicity and cache friendliness, while RPC based approaches like gRPC are often used in internal microservices where performance and strong contracts are more important.

How do you approach debugging a system you did not build?

When debugging an unfamiliar system, I start by understanding the high level architecture and the data flow between components.

Next, I reproduce the issue in a controlled environment and use logs, metrics, and traces to narrow down where the failure occurs. I form hypotheses and validate them incrementally rather than making broad changes.

Throughout the process, I document findings and communicate with team members who have context. This structured approach helps reduce guesswork and speeds up root cause identification.

Tell me about a time you failed.

How would you detect a memory leak in a production system?

I would start by monitoring memory usage trends over time using application metrics and system level tools. A steadily increasing memory footprint without release is usually a strong signal.

Next, I would capture heap dumps or use profiling tools to identify objects that are not being garbage collected. From there, I would trace back to the code paths retaining those references.

After fixing the root cause, I would add monitoring and alerts to detect abnormal memory growth early in the future. Prevention through observability is just as important as the fix itself.

How would you design a cache system?

I would begin by clarifying access patterns, data volatility, and consistency requirements.

At a high level, the system would include a fast in-memory cache layer such as Redis in front of the primary database. I would define an eviction policy like LRU to manage memory usage and implement TTLs for data freshness.

I would also plan for cache invalidation strategies, cache stampede protection, and monitoring of hit rate. The design trade-off typically balances freshness, consistency, and performance.

Something here is wrong.

Tell me about a time you disagreed with a teammate.

In a previous project, we disagreed on introducing a new caching layer. I raised concerns about cache invalidation complexity and proposed we measure current latency first. We ran benchmarks together and agreed on a simpler optimization that met our needs.