Q. How is user persistence handled (i.e. storing registered users)?
A: In the blog’s simple hands-on setup, users are stored in an in-memory array (or similar ephemeral store). That works for demonstration, but in production you must use a database (e.g. MongoDB, PostgreSQL, MySQL). On signup, you would insert a user record (with hashed password). On login, verify credentials against the DB, then create a […]
Q. How do I ensure security, e.g. cookie hijacking, session fixation, etc.?
Answer: Some recommended practices: Use HTTP-only cookies (so JavaScript cannot read the session cookie) Use Secure flag (only send cookie over HTTPS) Use SameSite setting to mitigate CSRF (Cross-Site Request Forgery) Regenerate session IDs after login (to avoid session fixation) Set session expiration (e.g. maxAge) or destroy on logout Use a robust session store (not […]
Q. Which Node.js / Express packages are needed to implement session authentication?
A: Typical packages include: express-session (to manage session creation, persistence, cookie settings) A session store (in-memory is default but not production-safe; e.g. connect-redis, connect-mongo, or another external store) Body parsing middleware (e.g. body-parser) to parse req.body for login credentials A view/template engine (the blog uses Jade / Pug) or static HTML to render login/signup forms […]
Q. What is session-based authentication and how does it differ from token (JWT) based?
A: Session-based authentication uses server-side storage of session state. When a user logs in, a session is created on the server (or in a session store), and a session ID is stored in a cookie (usually HTTP-only) in the client’s browser. On each request, the cookie sends the session ID; the server looks it up […]
Q. How do I handle large query results or large data transfers?
A: Use pagination or chunked fetches (fetchmany) to avoid huge memory loads. Use Snowflake’s output to CSV or stage data (e.g. COPY INTO) and then download via Python. Use efficient data types and batching for inserts. Use stream_results=True in the connector to stream large SELECT results rather than loading all at once. Monitor query performance […]
Q. How do I execute queries and handle results in Python?
A: Once connected, use a cursor object to execute() SQL statements (e.g. SELECT, INSERT). Use fetchone(), fetchall() or iterate over the cursor to retrieve results. Manage exceptions (e.g. SqlSnowflake errors). Close cursor and connection when done. The blog likely walks through sample code.
Q. How do I authenticate and configure the connection securely?
A: Use environment variables or secret management to store credentials (username, password, account, warehouse, database, schema). Avoid hardcoding them. Optionally, use key pair authentication or OAuth depending on Snowflake setup. Always restrict permissions (principle of least privilege).
Q. Which Python client or library do I use to connect to Snowflake?
A: You can use the official snowflake-connector-python library. This library handles authentication, session creation, executing SQL, and fetching results. The blog likely demonstrates its installation (e.g. via pip install snowflake-connector-python) and usage patterns.
Q. What is Snowflake and why connect it with Python?
A: Snowflake is a cloud-based data warehouse platform, offering scalability, separation of compute and storage, performance, and ease of use. The blog notes that Snowflake is popular in ELT and data analytics contexts. Connecting Python to Snowflake allows executing SQL queries, data ingestion/export, data transformations, and integration with data science workflows.
Q. What are best practices and caveats when using PM2 in production?
Answer: Use pm2 save after starting processes and use pm2 startup for auto-restart on reboot. Watch memory leaks — restart processes before limits reached. Use pm2-runtime instead of plain pm2 in Docker containers to keep process in the foreground. Avoid excessive customization — let PM2 handle environment variables and ecosystem files. Be cautious with environment […]