Backend Engineering & Performance Topics
Backend system optimization, performance tuning, memory management, and engineering proficiency. Covers system-level performance, remote support tools, and infrastructure optimization.
Scalability Analysis and Bottleneck Identification
Techniques for analyzing existing systems to find and prioritize bottlenecks and to validate scaling hypotheses. Topics include profiling and benchmarking strategies instrumentation and monitoring of latency throughput error rates and resource utilization; identification of common bottlenecks such as database write throughput central processing unit saturation memory pressure disk input output limits and network bandwidth constraints; designing experiments and load tests to reproduce issues and validate mitigations; proposing incremental fixes such as caching partitioning asynchronous processing or connection pooling; and measuring impact with clear metrics and iteration. Interviewers will probe the candidate on moving from observations to root cause and on designing low risk experiments to validate improvements.
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 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.
Performance Strategy and Resource Efficiency
High level strategy for balancing performance, resource constraints, and cost. Topics include trade off analysis, when to optimize versus accept costs, algorithm and data structure selection under resource constraints, power and energy trade offs, memory and storage budgets, and cost aware performance design. Candidates should discuss prioritization, measurement driven decision making, and resource efficient system design.
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.
Optimization Under Constraints
Technical approaches for optimizing code and systems when operating under constraints such as limited memory, strict frame or latency budgets, network bandwidth limits, or device specific limitations. Topics include profiling and instrumentation to identify bottlenecks, algorithmic complexity improvements, memory and data structure trade offs, caching and data locality strategies, parallelism and concurrency considerations, and platform specific tuning. Emphasize measurement driven optimization, benchmarking, risk of premature optimization, graceful degradation strategies, and communicating performance trade offs to product and engineering stakeholders.
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.