SQL Query Analyzer

SELECT * fetches all columns, including large text/blob columns you may not need, increasing I/O, network transfer, and memory usage. It also breaks if the table schema changes. Always select only the columns you need.

A query without a WHERE clause on a large table performs a full table scan — it reads every row. On a table with millions of rows this can take minutes and cause lock contention. Always add WHERE conditions indexed columns for production queries.

Without LIMIT, a query returns every matching row. If a bug causes a WHERE clause to be too broad (or missing), the application may receive millions of rows, causing memory exhaustion and timeouts. LIMIT provides a safety boundary.

An N+1 problem occurs when you run one query to get N records, then run 1 more query per record (N queries total). For example: get all users, then for each user get their orders. This is extremely slow at scale. Fix by using JOINs or batch queries.

When you compare a column to a value of a different type (e.g., WHERE user_id = '123' when user_id is INT), the database must cast every value in the column to compare them, bypassing indexes. Always match types in WHERE conditions.