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Scalability Fundamentals Questions

Core concepts and back of the envelope estimation techniques for junior to intermediate engineers. This includes converting business requirements into technical metrics such as requests per second, data volume, and bandwidth; understanding when a single machine is insufficient and when to move to distributed systems; basic vertical versus horizontal scaling trade offs; basic sharding, replication, and caching patterns; monitoring signals to track capacity such as CPU trends and disk usage growth; and considerations for backup and recovery times and maintenance windows. Emphasis is on foundational calculations and practical guidelines for when and how to scale.

MediumTechnical
36 practiced
Case study: migrating from 3 large instances to 10 small instances improved p95 response time by 30% but increased monthly infra cost by 25%. Break down the factors you would evaluate to decide whether to keep this change, including SLO impact, error budget consumption, operational complexity, autoscaling behavior, and cost optimization alternatives.
EasyTechnical
43 practiced
Write a short Python script that computes the number of web server instances required given: peak_rps, per_instance_rps_capacity, redundancy_factor (minimum copies of capacity required, e.g., 2 for N+1), and safety_margin (percentage >0). The script should round up and print explanation for rounding choices. Include a sample invocation and output in comments.
MediumTechnical
64 practiced
Write a Python function that takes (incoming_rps, cache_hit_rate, downstream_requests_per_miss, instance_capacity) and returns backend_rps and number_of_instances_needed. Include unit tests for example cases and explain assumptions (e.g., independence of misses).
HardSystem Design
36 practiced
Design an observability dashboard and alerting strategy to detect early capacity degradation across a microservices fleet. Specify derived metrics to surface (e.g., rate-of-change, rolling-window percentiles), how to combine multiple signals for higher-precision alerts, and how to present runbook actions to on-call engineers.
EasyTechnical
43 practiced
Explain the trade-offs between vertical scaling (bigger machines) and horizontal scaling (more machines) for a stateful service that stores session data in memory. Cover operational complexity, cost behavior, single-point-of-failure risk, performance, and the point where horizontal scaling becomes preferable.

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