Automation Relevant Coding Problems Questions
Practice problems involving: parsing and transforming log data, working with strings and regular expressions, counting and aggregating data, basic system operations like file handling and simple data processing, sorting and searching through configuration data. These often appear in SRE interviews to test automation-relevant thinking and real-world problem application.
HardTechnical
0 practiced
Implement a windowed unique-user counter using HyperLogLog (HLL). Provide functions add(user_id, timestamp) and estimate_unique(window_seconds, now). Approach: use multiple HLL sketches for time buckets, union them for queries, and expire old buckets. Implement core management code in Python or Go and explain memory/accuracy trade-offs.
EasyTechnical
0 practiced
Given a newline-delimited stream of JSON log objects (one JSON object per line) containing at least 'level' and 'message', implement a Python program to compute the top N most frequent error messages (where level == 'ERROR') and output them with counts. Requirements: stream input line-by-line, memory-efficient top-N implementation for very large cardinality, and stable ordering for tie counts.
HardTechnical
0 practiced
Design and implement an external sorting utility that sorts a massive log file ( >100GB ) by timestamp. Requirements: allow specifying memory limit (e.g., 500MB), implement chunking, local sort per chunk, and k-way multi-file merge. Provide core code for chunk creation, temp files, and merge phase and describe IO and CPU trade-offs.
HardTechnical
0 practiced
Implement a thread-safe in-memory rate limiter that supports token-bucket and leaky-bucket algorithms. Requirements: per-key limits, a global fallback limit, high concurrency (many goroutines), and low latency. Provide a Go implementation with appropriate locking or sharding and describe trade-offs between algorithms.
HardTechnical
0 practiced
Implement a parser that reads Prometheus text exposition format (/metrics) and converts it into a structured in-memory representation: for each metric name with labels, store samples and support histogram buckets and summary types. Requirements: streaming parse, ignore comments, and support repeated metric occurrences.
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