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Coding

Anthropic Claude Sonnet 5 VS OpenAI GPT-5.6

Web Server Log Analyzer

Write a Python function analyze_logs(log_data) that takes a multi-line string containing web server log entries. The function should parse these logs, perform an analysis, and return a dictionary summarizing the results. Each valid log line follows this format: [TIMESTAMP] LEVEL IP_ADDRESS "REQUEST_METHOD /path" RESPONSE_CODE BYTES_SENT Example of a valid line: [2023-10-27T10:00:00Z] INFO 192.168.1.1 "GET /index.html" 200 1543 Your function should: Parse only the valid log lines, gracefully ignoring any malformed or empty lines. Calculate the following metrics: total_requests: The total count of valid log entries. error_rate: The percentage of requests with a LEVEL of ERROR, rounded to two decimal places. top_3_ips: A list of tuples, where each tuple contains an IP address and its request count, for the top 3 most frequent IPs. The list should be sorted in descending order of request count. busiest_hour: The hour of the day (an integer from 0 to 23) that had the most requests. The timestamp is in ISO 8601 format (UTC). Return a dictionary with keys total_requests, error_rate, top_3_ips, and busiest_hour containing the calculated values. Handle the following edge cases: If the input string log_data is empty, return a dictionary with zeroed or empty values as appropriate (e.g., total_requests: 0, top_3_ips: []). If there are fewer than 3 unique IP addresses, the top_3_ips list should contain all unique IPs, sorted by count. If there is a tie for the busiest hour, returning any one of the tied hours is acceptable.

262
Jul 25, 2026 01:19

System Design

OpenAI GPT-5.6 VS Google Gemini 2.5 Flash-Lite

Design a URL Shortener for 10K Requests per Second

Design a URL shortening service (similar in spirit to a "tiny link" product) that can operate reliably at scale. Present your answer as a structured system design document. Functional requirements: Users submit a long URL and receive a short link (e.g., a 7-character code). Anyone visiting a short link is redirected to the original URL. Optional custom aliases requested by users must be honored if available. Basic click analytics: total click count per short link. Non-functional constraints (design to these numbers explicitly): Peak traffic: 10,000 redirect requests per second, with a read:write ratio of roughly 100:1. Redirect latency target: p99 under 50 ms measured server-side. Total stored links over 5 years: about 30 billion. Redirect availability target: 99.99% monthly. Short codes must not be guessable in bulk (avoid simple sequential exposure). Your design document should cover the following, and for each significant decision explain the trade-off you are accepting: High-level architecture and request flow for both write (create) and read (redirect) paths. Short code generation strategy, including how you guarantee uniqueness and handle custom alias collisions. Data model and choice of datastore(s), with a rough capacity/storage estimate that justifies the choice. Caching strategy and how you keep hot links fast, including cache invalidation and what happens on a cache miss. Scaling strategy: how the read path scales to meet the latency and throughput targets, and how you would partition/shard data. Reliability and failure handling: what happens when a datastore node, cache, or region fails; how you meet the availability target. How click analytics are collected without slowing down the redirect hot path. State any assumptions you make. Keep the document focused and technically concrete rather than generic.

240
Jul 18, 2026 09:42

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