我正在尝试使用 PyMongo 构建一个 Python 脚本，该脚本将能够命中一个 Mongo 数据库，该数据库可以精确匹配数据库中可能存在的 n 个对象。目前，我有这个设置:

db.entries.find({'$or': [<list-of-objects]})

对象列表看起来像这样:

[{'email': 'some@email.com', 'zip': '11111'}, {'email': 'another@email.com', 'zip': '11112'}, ...]

当我在列表中有 10 个左右的项目时，使用 $or 可以正常工作。我现在正在用 100 测试，需要很长时间才能返回。我考虑过使用多个 $in 过滤器，但我不知道这是否是最佳选择。

我确信有更好的方法来处理这个问题，但我对 Mongo 还很陌生。

编辑:.explain() 的输出如下:

{
    "executionStats": {
        "executionTimeMillis": 228734,
        "nReturned": 2,
        "totalKeysExamined": 0,
        "allPlansExecution": [],
        "executionSuccess": true,
        "executionStages": {
            "needYield": 0,
            "saveState": 43556,
            "restoreState": 43556,
            "isEOF": 1,
            "inputStage": {
                "needYield": 0,
                "saveState": 43556,
                "restoreState": 43556,
                "isEOF": 1,
                "inputStage": {
                    "needYield": 0,
                    "direction": "forward",
                    "saveState": 43556,
                    "restoreState": 43556,
                    "isEOF": 1,
                    "docsExamined": 5453000,
                    "nReturned": 2,
                    "needTime": 5452999,
                    "filter": {
                        "$or": [{
                            "$and": [{
                                "email": {
                                    "$eq": "some@email.com"
                                }
                            }, {
                                "zipcode": {
                                    "$eq": "11111"
                                }
                            }]
                        }, {
                            "$and": [{
                                "email": {
                                    "$eq": "another@email.com"
                                }
                            }, {
                                "zipcode": {
                                    "$eq": "11112"
                                }
                            }]
                        }]
                    },
                    "executionTimeMillisEstimate": 208083,
                    "invalidates": 0,
                    "works": 5453002,
                    "advanced": 2,
                    "stage": "COLLSCAN"
                },
                "nReturned": 2,
                "needTime": 5452999,
                "executionTimeMillisEstimate": 211503,
                "transformBy": {
                    "_id": false
                },
                "invalidates": 0,
                "works": 5453002,
                "advanced": 2,
                "stage": "PROJECTION"
            },
            "nReturned": 2,
            "needTime": 5452999,
            "executionTimeMillisEstimate": 213671,
            "invalidates": 0,
            "works": 5453002,
            "advanced": 2,
            "stage": "SUBPLAN"
        },
        "totalDocsExamined": 5453000
    },
    "queryPlanner": {
        "parsedQuery": {
            "$or": [{
                "$and": [{
                    "email": {
                        "$eq": "some@email.com"
                    }
                }, {
                    "zipcode": {
                        "$eq": "11111"
                    }
                }]
            }, {
                "$and": [{
                    "email": {
                        "$eq": "another@email.com"
                    }
                }, {
                    "zipcode": {
                        "$eq": "11112"
                    }
                }]
            }]
        },
        "rejectedPlans": [],
        "namespace": "db.entries",
        "winningPlan": {
            "inputStage": {
                "transformBy": {
                    "_id": false
                },
                "inputStage": {
                    "filter": {
                        "$or": [{
                            "$and": [{
                                "email": {
                                    "$eq": "some@email.com"
                                }
                            }, {
                                "zipcode": {
                                    "$eq": "11111"
                                }
                            }]
                        }, {
                            "$and": [{
                                "email": {
                                    "$eq": "another@email.com"
                                }
                            }, {
                                "zipcode": {
                                    "$eq": "11112"
                                }
                            }]
                        }]
                    },
                    "direction": "forward",
                    "stage": "COLLSCAN"
                },
                "stage": "PROJECTION"
            },
            "stage": "SUBPLAN"
        },
        "indexFilterSet": false,
        "plannerVersion": 1
    },
    "ok": 1.0,
    "serverInfo": {
        "host": "somehost",
        "version": "3.4.6",
        "port": 27017,
        "gitVersion": "c55eb86ef46ee7aede3b1e2a5d184a7df4bfb5b5"
    }
}

最佳答案

我建议创建一个新索引(复合索引)，因为您使用两个字段进行搜索:

db.entries.createIndex( {"email": 1, "zip": 1} )

现在运行您的查询并在您的查询中附加 explain() 命令，您应该看到它开始使用 IXSCAN 而不是 COLLSCAN。

关于MongoDB 在多个文档字段上完全匹配，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/45514068/