使用 MongoDB 的 explain 命令可以深入了解查询的执行计划，从而帮助你优化和调试查询性能。下面是详细的步骤和代码示例，展示如何使用 explain 命令深入分析查询。
1. 基本使用
最基本的 explain 命令可以提供查询的执行计划。
示例：基本explain使用
db.students.find({ studentId: 12345 }).explain();
2. explain() 的模式
explain 命令有三种模式：
示例：不同模式的explain
// queryPlanner 模式 db.students.find({ studentId: 12345 }).explain("queryPlanner"); // executionStats 模式 db.students.find({ studentId: 12345 }).explain("executionStats"); // allPlansExecution 模式 db.students.find({ studentId: 12345 }).explain("allPlansExecution");
3. explain() 输出解读
示例输出（executionStats 模式）
{ "queryPlanner": { "plannerVersion": 1, "namespace": "school.students", "indexFilterSet": false, "parsedQuery": { "studentId": { "$eq": 12345 } }, "winningPlan": { "stage": "FETCH", "inputStage": { "stage": "IXSCAN", "keyPattern": { "studentId": 1 }, "indexName": "studentId_1", "direction": "forward", "indexBounds": { "studentId": [ "[12345, 12345]" ] } } }, "rejectedPlans": [] }, "executionStats": { "executionSuccess": true, "nReturned": 1, "executionTimeMillis": 2, "totalKeysExamined": 1, "totalDocsExamined": 1, "executionStages": { "stage": "FETCH", "nReturned": 1, "executionTimeMillisEstimate": 0, "works": 2, "advanced": 1, "needTime": 0, "needYield": 0, "saveState": 0, "restoreState": 0, "isEOF": 1, "invalidates": 0, "docsExamined": 1, "alreadyHasObj": 0, "inputStage": { "stage": "IXSCAN", "nReturned": 1, "executionTimeMillisEstimate": 0, "works": 2, "advanced": 1, "needTime": 0, "needYield": 0, "saveState": 0, "restoreState": 0, "isEOF": 1, "invalidates": 0, "keyPattern": { "studentId": 1 }, "indexName": "studentId_1", "isMultiKey": false, "multiKeyPaths": { "studentId": [] }, "indexBounds": { "studentId": [ "[12345, 12345]" ] }, "keysExamined": 1, "seeks": 1, "dupsTested": 0, "dupsDropped": 0 } } }, "serverInfo": { "host": "localhost", "port": 27017, "version": "4.4.6", "gitVersion": "22c124145fa3bfdaeafb3f6d1b5f3d4e8391fe86" } }
关键字段解读
queryPlanner:namespace: 查询的命名空间，即数据库和集合的名称。parsedQuery: MongoDB 解析后的查询条件。winningPlan: 查询的执行计划，包括使用的索引和操作步骤。rejectedPlans: 被拒绝的其他执行计划。executionStats:executionSuccess: 查询是否成功。nReturned: 查询返回的文档数量。executionTimeMillis: 查询的执行时间（毫秒）。totalKeysExamined: 扫描的索引键数量。totalDocsExamined: 扫描的文档数量。数字越小越好。executionStages: 查询的执行步骤及其统计信息。executionStages:stage: 执行阶段名称，例如FETCH, IXSCAN。nReturned: 该阶段返回的文档数量。executionTimeMillisEstimate: 该阶段的估计执行时间。keysExamined: 该阶段扫描的索引键数量。docsExamined: 该阶段扫描的文档数量。inputStage: 下一个输入阶段的信息。
4. 示例：复合索引和多条件查询
假设有一个复合索引 { lastName: 1, firstName: 1 }，并执行多条件查询。
创建复合索引
db.students.createIndex({ lastName: 1, firstName: 1 });
查询及执行计划分析
db.students.find({ lastName: "Smith", firstName: "John" }).explain("executionStats");
示例输出及解读
{ "queryPlanner": { "plannerVersion": 1, "namespace": "school.students", "indexFilterSet": false, "parsedQuery": { "lastName": { "$eq": "Smith" }, "firstName": { "$eq": "John" } }, "winningPlan": { "stage": "FETCH", "inputStage": { "stage": "IXSCAN", "keyPattern": { "lastName": 1, "firstName": 1 }, "indexName": "lastName_1_firstName_1", "direction": "forward", "indexBounds": { "lastName": [ "[\"Smith\", \"Smith\"]" ], "firstName": [ "[\"John\", \"John\"]" ] } } }, "rejectedPlans": [] }, "executionStats": { "executionSuccess": true, "nReturned": 1, "executionTimeMillis": 1, "totalKeysExamined": 1, "totalDocsExamined": 1, "executionStages": { "stage": "FETCH", "nReturned": 1, "executionTimeMillisEstimate": 0, "works": 2, "advanced": 1, "needTime": 0, "needYield": 0, "saveState": 0, "restoreState": 0, "isEOF": 1, "invalidates": 0, "docsExamined": 1, "alreadyHasObj": 0, "inputStage": { "stage": "IXSCAN", "nReturned": 1, "executionTimeMillisEstimate": 0, "works": 2, "advanced": 1, "needTime": 0, "needYield": 0, "saveState": 0, "restoreState": 0, "isEOF": 1, "invalidates": 0, "keyPattern": { "lastName": 1, "firstName": 1 }, "indexName": "lastName_1_firstName_1", "isMultiKey": false, "multiKeyPaths": { "lastName": [], "firstName": [] }, "indexBounds": { "lastName": [ "[\"Smith\", \"Smith\"]" ], "firstName": [ "[\"John\", \"John\"]" ] }, "keysExamined": 1, "seeks": 1, "dupsTested": 0, "dupsDropped": 0 } } } }
通过以上示例和解读，您可以深入了解 MongoDB 查询的执行计划，并根据执行计划中的信息优化查询和索引设计。合理使用 explain 命令，可以显著提升查询性能，确保数据库的高效运行。
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