Backend Engineering & Performance Topics
Backend system optimization, performance tuning, memory management, and engineering proficiency. Covers system-level performance, remote support tools, and infrastructure optimization.
Performance Engineering and Cost Optimization
Engineering practices and trade offs for meeting performance objectives while controlling operational cost. Topics include setting latency and throughput targets and latency budgets; benchmarking profiling and tuning across application database and infrastructure layers; memory compute serialization and batching optimizations; asynchronous processing and workload shaping; capacity estimation and right sizing for compute and storage to reduce cost; understanding cost drivers in cloud environments including network egress and storage tiering; trade offs between real time and batch processing; and monitoring to detect and prevent performance regressions. Candidates should describe measurement driven approaches to optimization and be able to justify trade offs between cost complexity and user experience.
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.
Performance Optimization and Reliability Improvements
Optimizing infrastructure for performance and cost. Topics include profiling, identifying bottlenecks, making trade-off decisions, monitoring improvements, and preventing regressions. Discussion of measurable impact (reduced latency, lower costs, improved reliability). Understanding when optimization is worthwhile vs. premature.
Linux Troubleshooting and Diagnostics
In depth troubleshooting and diagnostic techniques for complex Linux issues at the system and kernel level. Includes advanced use of strace, ltrace, perf, ftrace, and reading proc and sys filesystems, root cause analysis of memory leaks and resource exhaustion, diagnosing intermittent failures and I O bottlenecks, log analysis, service debugging, containerized environment troubleshooting, and strategies for progressive isolation, replication, and remediation of production incidents. Senior level expectations include understanding kernel interactions, tracing user space to kernel transitions, and designing observability approaches to prevent recurrence.
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.
Performance Optimization and Bottleneck Analysis
Focuses on identifying and resolving performance bottlenecks across pipelines, infrastructure and applications. Candidates should be able to analyze metrics and traces, run profiling and load tests, identify hotspots in CPU, memory, disk and network usage, optimize resource requests and limits, tune autoscaling and load balancing, and improve continuous integration and continuous delivery pipeline speed through caching and parallelization. The topic also covers trade offs between vertical and horizontal scaling, database and cache tuning, capacity planning, and how to measure and communicate the impact of optimizations.
Application Performance and Latency Analysis
Techniques for diagnosing and improving application performance and latency across the stack. Topics include identifying latency sources at the application network and storage layers, interpreting latency distributions and percentiles including tail latency, using distributed tracing and request correlation to trace latency across services, profiling and hot path analysis, optimizing database queries and indexes, caching and cache invalidation strategies, batching and concurrency controls, instrumentation and sampling strategies, and defining latency objectives. Candidates should be able to walk through a performance regression investigation and propose targeted mitigations.