Software Engineer Interview Questions

I'd start with a high-level architecture: load balancers, application servers, databases, and caching layers. For URL encoding, I'd use base62 encoding with a counter or hash approach to generate unique short codes. The database would store mappings between short codes and original URLs, partitioned by hash of the short code for horizontal scaling. I'd implement Redis caching for frequently accessed URLs to reduce database load. For 100M URLs daily, that's about 1,200 requests per second, so I'd need multiple application servers behind load balancers. I'd also add analytics tracking, rate limiting to prevent abuse, and CDN for global performance. Key considerations include handling duplicate URLs, expiration policies, and monitoring for popular links that might cause hot spots.

Last year, our e-commerce API started returning 500 errors for 20% of checkout requests during Black Friday traffic. I immediately checked our monitoring dashboards and saw database connection timeouts spiking. I coordinated with the team lead to implement a temporary fix by increasing connection pool size while I investigated the root cause. Through log analysis, I discovered a new feature deployed that week wasn't properly closing database connections. I rolled back that specific change, which restored service within 30 minutes. Post-incident, I implemented connection leak detection in our testing pipeline and added alerts for connection pool exhaustion. I also led a blameless post-mortem where we identified gaps in our code review process for database interactions. The incident taught me the importance of monitoring connection health and having rollback procedures ready for critical periods.

First, I'd analyze the query execution plan to identify bottlenecks like full table scans or inefficient joins. I'd check if appropriate indexes exist on columns used in WHERE clauses, JOIN conditions, and ORDER BY statements. For a 50M record table, missing indexes are often the primary culprit. I'd also examine if the query can be rewritten more efficiently - perhaps breaking complex joins into smaller queries or using EXISTS instead of IN for subqueries. If indexes exist but aren't being used, I'd check for issues like data type mismatches or functions applied to indexed columns. Other optimizations include: adding covering indexes to avoid key lookups, partitioning the table if queries typically filter by date ranges, updating table statistics, or implementing query result caching for frequently accessed data. I'd measure performance after each change to ensure improvements and avoid over-indexing which can slow down writes.

When our team decided to migrate from REST to GraphQL for our mobile API, I had only basic GraphQL knowledge but needed to become proficient within two weeks. I started by reading the official documentation and working through the tutorial to understand core concepts like schemas, resolvers, and queries. I then built a small personal project mirroring our existing API structure to practice schema design and resolver implementation. I joined GraphQL community forums and watched conference talks to learn best practices around performance optimization and security. Most importantly, I paired with a senior developer who had GraphQL experience for the first few implementation tasks. Within the deadline, I successfully implemented three new GraphQL endpoints and helped document our team's GraphQL conventions. This experience taught me that combining hands-on practice with community resources and mentorship is the most effective way to rapidly acquire new technical skills while maintaining code quality.

I'll use the expand-around-centers approach. For each possible center (including between characters for even-length palindromes), I'll expand outward while characters match. Here's the implementation: ```python def longestPalindrome(s): if not s: return '' start, max_len = 0, 1 for i in range(len(s)): # Check odd-length palindromes left, right = i, i while left >= 0 and right < len(s) and s[left] == s[right]: current_len = right - left + 1 if current_len > max_len: start, max_len = left, current_len left -= 1 right += 1 # Check even-length palindromes left, right = i, i + 1 while left >= 0 and right < len(s) and s[left] == s[right]: current_len = right - left + 1 if current_len > max_len: start, max_len = left, current_len left -= 1 right += 1 return s[start:start + max_len] ``` Time complexity is O(n²) where n is string length, as we check each of the n centers and potentially expand n times. Space complexity is O(1) excluding the output string.

Our team was planning to implement real-time notifications using long polling, but I believed WebSockets would be more efficient for our high-frequency trading application where latency is critical. Instead of immediately opposing the decision, I researched both approaches thoroughly, creating a comparison document with performance benchmarks, connection overhead analysis, and implementation complexity for our specific use case. I requested a team meeting to present my findings objectively, showing that WebSockets would reduce our average message latency from 200ms to under 50ms with lower server resource usage. I also acknowledged the benefits of long polling, like easier debugging and better compatibility with corporate firewalls. The team appreciated the data-driven approach, and after discussion, we agreed to prototype both solutions. The WebSocket prototype performed significantly better in our testing environment, so we adopted that approach. The key was presenting facts rather than opinions and remaining open to the team's collective decision.

I would immediately communicate the situation to my manager and stakeholders, clearly outlining the bug's impact and potential risks of not addressing it. I'd propose three options: 1) Implement a temporary workaround to meet the deadline while scheduling proper bug fixes for the next sprint, 2) Request a deadline extension to fix both issues properly, or 3) Reduce feature scope to accommodate bug fixes within the timeline. I'd recommend option 1 if the workaround doesn't compromise security or user experience, as it balances business needs with technical integrity. I'd document the technical debt created and ensure it's prioritized in upcoming sprints. Throughout this process, I'd keep all stakeholders informed about trade-offs and ensure we have monitoring in place to catch any issues early.

I would first have a private, constructive conversation with the team member to understand any underlying challenges they might be facing - whether it's unclear requirements, lack of familiarity with our coding standards, or personal circumstances affecting their work. I'd offer specific, actionable feedback with examples from recent PRs and provide resources like our style guide or pair programming sessions. If needed, I'd suggest code review sessions where we walk through best practices together. I'd also propose interim solutions like having them create smaller, more focused PRs for easier review, or implementing automated code quality tools to catch issues earlier. I'd set clear expectations and a timeline for improvement while ensuring they feel supported. If the pattern persists after these interventions, I'd involve our engineering manager to discuss additional support or training opportunities, always focusing on team success rather than individual blame.

I'd design the order processing service using an event-driven architecture with the Saga pattern for distributed transactions. The flow would start with order validation, then orchestrate calls to inventory, payment, and fulfillment services. For payment failures, I'd implement a retry mechanism with exponential backoff and circuit breakers, ensuring idempotency through unique transaction IDs. Inventory would be handled with optimistic locking and eventual consistency - we'd reserve inventory items with TTL and release them if payment fails. For data consistency, I'd use the outbox pattern with event sourcing, ensuring each service publishes events atomically with database changes. The order service would maintain its own state machine (pending → paid → fulfilled → shipped) and compensate for failures by publishing reversal events. I'd implement monitoring with distributed tracing to track order flows across services and use message queues with dead letter queues for handling persistent failures. Database-wise, each service would own its data with API-based communication only.

I'd start by analyzing the query execution plan to identify bottlenecks like table scans, expensive joins, or missing indexes. Using tools like EXPLAIN ANALYZE, I'd measure current performance metrics including execution time, rows processed, and I/O costs. Next, I'd examine the database schema and existing indexes to identify opportunities for optimization. My approach would include: 1) Adding appropriate indexes on frequently filtered and joined columns, 2) Rewriting the query to eliminate unnecessary joins or subqueries, 3) Consider query restructuring with CTEs or temp tables for complex logic, 4) Implementing pagination if the query returns large result sets. For immediate relief, I'd consider read replicas for reporting queries or query result caching for frequently accessed data. I'd also analyze if the data model itself needs optimization - perhaps denormalization for read-heavy workloads. Throughout the process, I'd benchmark each change against the baseline, monitoring both the specific query performance and overall database health to ensure improvements don't negatively impact other operations.

At my previous company, I advocated for adopting a new JavaScript framework for our customer dashboard rewrite, believing it would significantly improve development speed and user experience. I researched the framework, created a proof of concept, and convinced the team it was the right choice. However, three months into development, we discovered the framework had poor mobile performance and limited community support for issues we encountered. Rather than continuing down the wrong path, I immediately raised concerns with my team and manager. We made the difficult decision to migrate back to our proven stack, which delayed our release by six weeks. I took full ownership of the decision and worked extra hours to minimize the impact. I organized knowledge-sharing sessions so the team could learn from my research process mistakes - particularly around evaluating framework maturity and mobile performance testing. This experience taught me to be more thorough in technical evaluations, especially around non-functional requirements, and to build in earlier validation checkpoints for major technical decisions.

I believe sustainable high performance comes from working smart rather than just working long hours. I maintain productivity through several strategies: First, I prioritize ruthlessly using frameworks like Eisenhower Matrix to focus on high-impact work and avoid context switching. I use time-blocking techniques to dedicate focused periods for deep work like coding or system design. During challenging phases, I communicate early and often with my team about realistic timelines and potential roadblocks, rather than suffering in silence. I've learned to recognize my own warning signs of burnout - like decreased code quality or procrastination - and take proactive breaks. I maintain boundaries by having dedicated offline time for learning, exercise, and personal interests, which actually makes me more creative and productive at work. When facing intense deadlines, I focus on delivering MVP solutions first, then iterating, rather than pursuing perfectionist solutions that delay delivery. I also make sure to celebrate wins with my team and take time to reflect on lessons learned after challenging projects, which helps maintain motivation and continuous improvement.