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.
Read MoreLearn MongoDB Indexing: Boost Your Query Speed by 10x
MongoDB indexes are used to improve query performance, solving the inefficiency of "full table scan" (with a time complexity of O(n)) when no index is present. With an index, the complexity is reduced to O(log n), similar to using a library catalog to locate books. An index is a special data structure (based on B-tree/B+ tree) that stores mappings between field values and document positions. Basic types include single-field indexes (the most common, e.g., `db.users.createIndex({age:1})`); compound indexes (multi-field, e.g., `{age:1, gender:1}`, which must follow the "leftmost prefix principle"). There are also advanced types such as multikey, geospatial, and text indexes. Indexes are created using `createIndex()`, and verification is done using `explain()` to view the execution plan. It is recommended to create indexes on frequently queried, sorted, or compound query fields. Indexes are not suitable for small datasets, extremely frequent writes, low-cardinality, or highly repeated fields. Avoid over-indexing and duplicate indexes. Use `explain` to verify effectiveness and prevent index failure due to field type mismatches.
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