Backend Engineering & Performance Topics
Backend system optimization, performance tuning, memory management, and engineering proficiency. Covers system-level performance, remote support tools, and infrastructure optimization.
System Monitoring and Performance Tuning
Operational monitoring and continuous tuning of system and infrastructure resources to maintain performance and reliability. Topics include key system health and performance metrics such as central processing unit usage memory utilization disk input output and latency network bandwidth process counts system load latency and throughput and queries per second, establishing baselines and normal ranges, anomaly detection and root cause triage, instrumentation and metric collection for system health, reading monitoring dashboards and recognizing common failure patterns, interpreting system logs and using diagnostic commands and tools, setting alert thresholds and prioritization and escalation pathways, capacity planning and remediation steps, resource tuning to remove bottlenecks, and knowing when to escalate to deeper engineering investigation. Candidates should be able to connect observed symptoms to likely causes describe basic troubleshooting workflows and propose mitigation and prevention measures.
Performance Profiling and Optimization
Comprehensive skills and methodology for profiling, diagnosing, and optimizing runtime performance across services, applications, and platforms. Involves measuring baseline performance using monitoring and profiling tools, capturing central processing unit, memory, input output, and network metrics, and interpreting flame graphs and execution traces to find hotspots. Requires a reproducible measure first approach to isolate root causes, distinguish central processing unit time from graphical processing unit time, and separate application bottlenecks from system level issues. Covers platform specific profilers and techniques such as frame time budgeting for interactive applications, synthetic benchmarks and production trace replay, and instrumentation with metrics, logs, and distributed traces. Candidates should be familiar with common root causes including lock contention, garbage collection pauses, disk saturation, cache misses, and inefficient algorithms, and be able to prioritize changes by expected impact. Optimization techniques included are algorithmic improvements, parallelization and concurrency control, memory management and allocation strategies, caching and batching, hardware acceleration, and focused micro optimizations. Also includes validating improvements through before and after measurements, regression and degradation analysis, reasoning about trade offs between performance, maintainability, and complexity, and creating reproducible profiling hooks and tests.
System Resource and Input Output Optimization
Techniques for managing system resources and optimizing input output including memory management, buffer and cache tuning, storage tiering and device selection, disk access patterns and throughput trade offs, central processing unit utilization, contention resolution, and diagnosing resource bottlenecks. Candidates should discuss monitoring and observability, trade offs between latency and throughput, caching strategies, memory pooling and fragmentation mitigation, and platform specific constraints when optimizing resource usage.
Performance Optimization and Latency Engineering
Covers systematic approaches to measuring and improving system performance and latency at architecture and code levels. Topics include profiling and tracing to find where time is actually spent, forming and testing hypotheses, optimizing critical paths, and validating improvements with measurable metrics. Candidates should be able to distinguish central processing unit bound work from input output bound work, analyze latency versus throughput trade offs, evaluate where caching and content delivery networks help or hurt, recognize database and network constraints, and propose strategies such as query optimization, asynchronous processing patterns, resource pooling, and load balancing. Also includes performance testing methodologies, reasoning about trade offs and risks, and describing end to end optimisation projects and their business impact.
System Resource Management and Monitoring
Monitor and manage operating system and hardware level resources to ensure application performance and stability. Topics include central processing unit utilization and context switching, system load trends, memory usage including heap and stack behavior, paging and swapping effects, disk input output operations and free space, and network bandwidth utilization and packet loss. Know diagnostic tools and commands for observing these signals, recognize patterns of resource contention and exhaustion such as out of memory and high input output wait, and understand mitigation techniques including tuning, resource limits, throttling, caching, capacity planning, and vertical or horizontal scaling.
Performance Tuning and Trade Offs
Covers practical techniques and the decision making involved in improving system and database performance. Topics include identifying bottlenecks through profiling and monitoring, the performance tuning lifecycle of measure diagnose implement and verify, and common optimizations such as indexing strategies, query restructuring, denormalization, caching layers, materialized views, and appropriate use of query hints. Also includes understanding performance related trade offs such as CPU versus memory, read versus write optimization, latency versus throughput, and complexity versus maintainability. Emphasizes prioritizing optimizations based on business impact and return on investment, cost considerations, and when to avoid premature optimization. Candidates should demonstrate how they measure improvements, validate results, and align technical changes with product and business goals.
Scaling and Performance Optimization
Centers on diagnosing performance issues and planning for growth, including capacity planning, profiling and bottleneck analysis, caching strategies, load testing, latency and throughput trade offs, and cost versus performance considerations. Interviewers will look for pragmatic approaches to scale systems incrementally while maintaining reliability and user experience.