Start by clarifying the requirements: file size, error handling, and performance needs. Then demonstrate a clean, idiomatic solution using json.load() for files, with proper context management and exception handling. Finally, discuss trade-offs and potential improvements for production use.
Pro tip: Mention that for large files, json.load() loads the entire file into memory, so you might use ijson or process incrementally if memory is a concern. Also, always specify encoding='utf-8' when opening the file to avoid platform-dependent defaults.
Ask about file size, expected structure, error handling needs, and whether the data is trusted. This shows you think about context before coding.
Use a with statement to open the file and json.load() to parse it. Show a minimal working example.
Wrap the parsing in try-except blocks to catch FileNotFoundError, json.JSONDecodeError, and PermissionError. Explain how you would log or propagate errors.
Mention that json.load() reads the entire file into memory. For large files, suggest streaming approaches like ijson or line-delimited JSON.
If the JSON comes from an untrusted source, validate the schema and avoid using eval(). Mention that json.loads() is safe by default.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Also basic, but I fumbled slightly explaining error handling.
Start by clarifying the requirements: which library (requests vs httpx), error handling, timeouts, and response parsing. Then write a clean, production-ready function that makes the GET request, handles common exceptions, and returns the response. Finally, discuss trade-offs like synchronous vs asynchronous, retries, and idempotency.
Pro tip: Demonstrate awareness of Stripe's API best practices: always set a timeout, use exponential backoff for retries, and handle rate limits (429) gracefully. Mention that GET requests should be idempotent and safe, aligning with REST principles.
Ask about the expected response format (JSON, text), error handling needs, timeout, and whether retries or async are required. Confirm the library preference (e.g., requests, httpx).
Use the chosen library to perform a GET request with a timeout. For example, with requests: `response = requests.get(url, timeout=5)`. Then call `response.raise_for_status()` to catch HTTP errors.
Wrap the request in a try-except block to catch exceptions like `requests.exceptions.Timeout`, `ConnectionError`, and `HTTPError`. Consider retry logic with exponential backoff for transient failures.
Parse the response based on content type (e.g., `response.json()` for JSON). Return the parsed data or handle it as needed. Optionally, log status codes and response times.
Mention alternatives like using `httpx` for async support, adding retries with `urllib3` or `tenacity`, and considering idempotency keys for safe retries. Highlight the importance of timeouts and rate limit handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: input JSON schema, expected map output format, and any constraints on libraries or performance. Then outline a pipeline: parse and validate the JSON, transform ride data into markers and path overlays, and render the map image using a mapping library or custom drawing. Discuss trade-offs and edge cases like large datasets or invalid coordinates.
Pro tip: Mention that you would validate the JSON schema and handle malformed data gracefully, and consider using a streaming parser for large files to avoid memory issues. Also, discuss how you would test the rendering with unit tests for parsing and visual regression tests for the map output.
Ask about the JSON structure, expected map output (format, resolution), and any constraints like performance or library restrictions. Confirm whether the map should be interactive or a static image.
Outline steps to parse JSON, validate data (e.g., coordinates, timestamps), and transform into a normalized format for markers and paths. Consider error handling for missing or invalid fields.
Decide on a mapping library (e.g., Leaflet, Mapbox) or custom canvas/SVG rendering. Discuss trade-offs: ease of use vs. control, dependencies, and performance for large datasets.
Describe how to generate markers (e.g., start/end points) and path overlays (e.g., polylines) from the parsed data. Mention coordinate projection and styling options.
Address edge cases like empty rides, invalid coordinates, or huge files. Explain testing strategy: unit tests for parsing, integration tests for rendering, and visual checks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.