Question

我定期检查我的MongoDB日志文件以查找慢查询并解决它。今天，我看到一个查询花费了124毫秒，而且非常高！

2018-09-29T18:19:57.016+0330 I COMMAND  [conn17674] command DataCrm.Ticket command: find { find: "Ticket", filter: { Jobs: { $elemMatch: { User: ObjectId('577370ae55477005ec9b657c') } } }, sort: { TicketNumber: -1 }, skip: 0, limit: 100, lsid: { id: UUID("dcf13400-816e-4b50-8591-654ac4d1daab") }, $db: "DataCrm" } planSummary: IXSCAN { Jobs.User: 1 } keysExamined:6496 docsExamined:6496 hasSortStage:1 cursorExhausted:1 numYields:53 nreturned:100 reslen:246821 locks:{ Global: { acquireCount: { r: 108 } }, Database: { acquireCount: { r: 54 } }, Collection: { acquireCount: { r: 54 } } } protocol:op_query 124ms

如您所见，我在“ Jobs.User”上有一个索引。所以我在shell中测试了这个查询：

db.getCollection("Ticket").find({ Jobs: { $elemMatch: { User: ObjectId('577370ae55477005ec9b657c') } } }).sort({ TicketNumber: -1 }).skip(0).limit(100).explain("executionStats");

结果如下：

{ 
"queryPlanner" : {
    "plannerVersion" : 1.0, 
    "namespace" : "DataCrm.Ticket", 
    "indexFilterSet" : false, 
    "parsedQuery" : {
        "Jobs" : {
            "$elemMatch" : {
                "User" : {
                    "$eq" : ObjectId("577370ae55477005ec9b657c")
                }
            }
        }
    }, 
    "winningPlan" : {
        "stage" : "LIMIT", 
        "limitAmount" : 100.0, 
        "inputStage" : {
            "stage" : "FETCH", 
            "filter" : {
                "Jobs" : {
                    "$elemMatch" : {
                        "User" : {
                            "$eq" : ObjectId("577370ae55477005ec9b657c")
                        }
                    }
                }
            }, 
            "inputStage" : {
                "stage" : "IXSCAN", 
                "keyPattern" : {
                    "TicketNumber" : 1.0
                }, 
                "indexName" : "TicketNumber_1", 
                "isMultiKey" : false, 
                "multiKeyPaths" : {
                    "TicketNumber" : [

                    ]
                }, 
                "isUnique" : true, 
                "isSparse" : false, 
                "isPartial" : false, 
                "indexVersion" : 2.0, 
                "direction" : "backward", 
                "indexBounds" : {
                    "TicketNumber" : [
                        "[MaxKey, MinKey]"
                    ]
                }
            }
        }
    }, 
    "rejectedPlans" : [
        {
            "stage" : "SORT", 
            "sortPattern" : {
                "TicketNumber" : -1.0
            }, 
            "limitAmount" : 100.0, 
            "inputStage" : {
                "stage" : "SORT_KEY_GENERATOR", 
                "inputStage" : {
                    "stage" : "FETCH", 
                    "filter" : {
                        "Jobs" : {
                            "$elemMatch" : {
                                "User" : {
                                    "$eq" : ObjectId("577370ae55477005ec9b657c")
                                }
                            }
                        }
                    }, 
                    "inputStage" : {
                        "stage" : "IXSCAN", 
                        "keyPattern" : {
                            "Jobs.User" : 1.0
                        }, 
                        "indexName" : "Jobs.User_1", 
                        "isMultiKey" : true, 
                        "multiKeyPaths" : {
                            "Jobs.User" : [
                                "Jobs"
                            ]
                        }, 
                        "isUnique" : false, 
                        "isSparse" : false, 
                        "isPartial" : false, 
                        "indexVersion" : 2.0, 
                        "direction" : "forward", 
                        "indexBounds" : {
                            "Jobs.User" : [
                                "[ObjectId('577370ae55477005ec9b657c'), ObjectId('577370ae55477005ec9b657c')]"
                            ]
                        }
                    }
                }
            }
        }
    ]
}, 
"executionStats" : {
    "executionSuccess" : true, 
    "nReturned" : 100.0, 
    "executionTimeMillis" : 13.0, 
    "totalKeysExamined" : 640.0, 
    "totalDocsExamined" : 640.0, 
    "executionStages" : {
        "stage" : "LIMIT", 
        "nReturned" : 100.0, 
        "executionTimeMillisEstimate" : 0.0, 
        "works" : 641.0, 
        "advanced" : 100.0, 
        "needTime" : 540.0, 
        "needYield" : 0.0, 
        "saveState" : 10.0, 
        "restoreState" : 10.0, 
        "isEOF" : 1.0, 
        "invalidates" : 0.0, 
        "limitAmount" : 100.0, 
        "inputStage" : {
            "stage" : "FETCH", 
            "filter" : {
                "Jobs" : {
                    "$elemMatch" : {
                        "User" : {
                            "$eq" : ObjectId("577370ae55477005ec9b657c")
                        }
                    }
                }
            }, 
            "nReturned" : 100.0, 
            "executionTimeMillisEstimate" : 0.0, 
            "works" : 640.0, 
            "advanced" : 100.0, 
            "needTime" : 540.0, 
            "needYield" : 0.0, 
            "saveState" : 10.0, 
            "restoreState" : 10.0, 
            "isEOF" : 0.0, 
            "invalidates" : 0.0, 
            "docsExamined" : 640.0, 
            "alreadyHasObj" : 0.0, 
            "inputStage" : {
                "stage" : "IXSCAN", 
                "nReturned" : 640.0, 
                "executionTimeMillisEstimate" : 0.0, 
                "works" : 640.0, 
                "advanced" : 640.0, 
                "needTime" : 0.0, 
                "needYield" : 0.0, 
                "saveState" : 10.0, 
                "restoreState" : 10.0, 
                "isEOF" : 0.0, 
                "invalidates" : 0.0, 
                "keyPattern" : {
                    "TicketNumber" : 1.0
                }, 
                "indexName" : "TicketNumber_1", 
                "isMultiKey" : false, 
                "multiKeyPaths" : {
                    "TicketNumber" : [

                    ]
                }, 
                "isUnique" : true, 
                "isSparse" : false, 
                "isPartial" : false, 
                "indexVersion" : 2.0, 
                "direction" : "backward", 
                "indexBounds" : {
                    "TicketNumber" : [
                        "[MaxKey, MinKey]"
                    ]
                }, 
                "keysExamined" : 640.0, 
                "seeks" : 1.0, 
                "dupsTested" : 0.0, 
                "dupsDropped" : 0.0, 
                "seenInvalidated" : 0.0
            }
        }
    }
}, 
"serverInfo" : {
    "host" : "VM1", 
    "port" : 27017.0, 
    "version" : "3.6.2", 
    "gitVersion" : "489d177dbd0f0420a8ca04d39fd78d0a2c539420"
}, 
"ok" : 1.0
}

您可以看到执行时间仅为13ms，这是一个合理的时间。我不了解消耗124毫秒的记录查询会发生什么情况！有人有主意吗？

Answer 1

您会看到获胜的计划是LIMIT -> IXSCAN。这意味着mongodb引擎决定使用index(TicketNumber: 1)进行排序，并继续检查匹配项，直到达到限制数为止。

我的错误是我在TicketNumber字段上有一个升序索引，并且查询试图对降序进行排序。
现在，我添加了index(TicketNumber: -1)，现在执行时间减少到只有26ms！

MongoDB：日志文件中存在慢查询，但是当我进行解释说明时，它很快

1 个答案: