MongoDB Query Optimization: How Do Indexes Improve Query Efficiency?

MongoDB indexes are the core of query optimization, designed to address slow queries caused by full table scans when dealing with large datasets. Essentially, they are mapping structures (similar to directories) that associate field values with document locations, transforming queries from O(n) full table scans into O(log n) fast lookups and significantly improving efficiency. To create an index, use `createIndex({field: sortOrder})`, for example: `db.students.createIndex({age: 1})`. Common index types include single-field, compound (combining multiple fields with order adjusted based on query frequency), unique (ensuring field uniqueness), and text indexes (supporting fuzzy search). To verify if an index is effective, use `explain("executionStats")` to check the execution plan. Focus on `executionTimeMillis` (execution time) and `totalDocsExamined` (number of documents examined). If the latter equals the result count, the index is working. Important considerations: More indexes are not always better. Excessive indexes consume storage and slow down write operations. Prioritize indexing fields with high query frequency and avoid indexing fields with low query rates or high repetition. Properly using indexes ensures MongoDB maintains efficient responses as data grows.