平时做技术实践时，很多问题不是概念不会，而是细节没串起来。拿“MongoDB采用explain命令的步骤和代码示例”来说，它看着像小点，放到项目里常会牵出环境、配置、兼容性和维护成本。下面按实际采用顺序，把思路、关键写法和容易踩坑的地方讲清楚，便于大家直接对照操作。
采用 MongoDB 的 explain 命令能够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示例代码，展示如何采用 explain 命令深入分析查询。
1. 基本采用
最基本的 explain 命令能够提供查询的执行计划。
示例：基本explain采用
db.students.find({ studentId: 12345 }).explain();2. explain() 的模式
explain 命令有三种模式：
- queryPlanner: 得到查询的逻辑计划和索引采用情况。
- executionStats: 得到查询的逻辑计划、索引采用情况以及执行统计信息。
- allPlansExecution: 得到查询的逻辑计划、所有备选计划以及每个计划的执行统计信息。
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，包括采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: 查询得到的文档数量。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，比如FETCH,IXSCAN。nReturned: 该阶段得到的文档数量。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 }，同时执行多条件查询。
新建复合索引
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
}
}
}
}从实现思路看，借助以上示例和解读，您能够深入了解 MongoDB 查询的执行计划，同时根据执行计划中的信息优化查询和索引设计。合理采用 explain 命令，能够显著提升查询性能，确保数据库的高效运行。
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