以下是聚合查询：
[
{
"$match": {
"UserId": {
"$in": [
5
]
},
"WorkflowStartTime": {
"$gte": ISODate('2015-04-09T00:00:00.000Z'),
"$lte": ISODate('2015-04-16T00:00:00.000Z')
}
}
},
{
"$group": {
"_id": {
"Task": "$TaskId",
"WorkflowId": "$WorkflowInstanceId"
},
"TaskName": {
"$first": "$Task"
},
"StartTime": {
"$first": "$StartTime"
},
"EndTime": {
"$last": "$EndTime"
},
"LastExecutionTime": {
"$last": "$StartTime"
},
"WorkflowName": {
"$first": "$WorkflowName"
}
}
},
{
"$project": {
"_id": 1,
"LastExecutionTime": 1,
"TaskName": 1,
"AverageExecutionTime": {
"$subtract": [
"$EndTime",
"$StartTime"
]
},
"WorkflowName": 1
}
},
{
"$group": {
"_id": "$_id.Task",
"LastExecutionTime": {
"$last": "$LastExecutionTime"
},
"AverageExecutionTime": {
"$avg": "$AverageExecutionTime"
},
"TaskName": {
"$first": "$TaskName"
},
"TotalInstanceCount": {
"$sum": 1
},
"WorkflowName": {
"$first": "$WorkflowName"
}
}
},
{
"$project": {
"Id": "$_id",
"_id": 0,
"Name": "$TaskName",
"LastExecutionDate": {
"$substr": [
"$LastExecutionTime",
0,
30
]
},
"AverageExecutionTimeInMilliSeconds": "$AverageExecutionTime",
"TotalInstanceCount": "$TotalInstanceCount",
"WorkflowName": 1
}
}
]
我的收集文件如下：
{
"_id" : ObjectId("550ff07ce4b09bf056df4ac1"),
"OutputData" : "xyz",
"InputData" : null,
"Location" : null,
"ChannelName" : "XYZ",
"UserId" : 5,
"TaskId" : 95,
"ChannelId" : 5,
"Status" : "Success",
"TaskTypeId" : 7,
"WorkflowId" : 37,
"Task" : "XYZ",
"WorkflowStartTime" : ISODate("2015-03-23T05:09:26Z"),
"EndTime" : ISODate("2015-03-23T05:22:44Z"),
"StartTime" : ISODate("2015-03-23T05:22:44Z"),
"TaskType" : "TRIGGER",
"WorkflowInstanceId" : "23-3-2015-95d17f17-2580-4fe3-b627-12e862af08ce",
"StackTrace" : null,
"WorkflowName" : "XYZ data workflow"
}
我有 {WorkflowStartTime：1，UserId：1，StartTime：1}
的索引他们在收集中几乎没有900000个记录，因为我在使用日期范围进行查询时使用的是数据子集，所以它需要大约1.5到1.7秒。我已经使用聚合框架和其他具有大量数据的集合，并且性能非常好。不知道这个查询有什么问题，因为它显示出非常慢的输出，我希望它作为一个实时分析查询在工厂中。 赞赏它上面的任何指针。
{explain：true} 添加到聚合查询
时的输出{
"stages": [
{
"$cursor": {
"query": {
"UserId": {
"$in": [
5
]
},
"WorkflowStartTime": {
"$gte": "ISODate(2015-04-09T00:00:00Z)",
"$lte": "ISODate(2015-04-16T00:00:00Z)"
}
},
"fields": {
"EndTime": 1,
"StartTime": 1,
"Task": 1,
"TaskId": 1,
"WorkflowInstanceId": 1,
"WorkflowName": 1,
"_id": 0
},
"plan": {
"cursor": "BtreeCursor ",
"isMultiKey": false,
"scanAndOrder": false,
"indexBounds": {
"WorkflowStartTime": [
[
"ISODate(2015-04-16T00:00:00Z)",
"ISODate(2015-04-09T00:00:00Z)"
]
],
"UserId": [
[
5,
5
]
]
},
"allPlans": [
{
"cursor": "BtreeCursor ",
"isMultiKey": false,
"scanAndOrder": false,
"indexBounds": {
"WorkflowStartTime": [
[
"ISODate(2015-04-16T00:00:00Z)",
"ISODate(2015-04-09T00:00:00Z)"
]
],
"UserId": [
[
5,
5
]
]
}
}
]
}
}
},
{
"$group": {
"_id": {
"Task": "$TaskId",
"WorkflowId": "$WorkflowInstanceId"
},
"TaskName": {
"$first": "$Task"
},
"StartTime": {
"$first": "$StartTime"
},
"EndTime": {
"$last": "$EndTime"
},
"LastExecutionTime": {
"$last": "$StartTime"
},
"WorkflowName": {
"$first": "$WorkflowName"
}
}
},
{
"$project": {
"_id": true,
"LastExecutionTime": true,
"TaskName": true,
"AverageExecutionTime": {
"$subtract": [
"$EndTime",
"$StartTime"
]
},
"WorkflowName": true
}
},
{
"$group": {
"_id": "$_id.Task",
"LastExecutionTime": {
"$last": "$LastExecutionTime"
},
"AverageExecutionTime": {
"$avg": "$AverageExecutionTime"
},
"TaskName": {
"$first": "$TaskName"
},
"TotalInstanceCount": {
"$sum": {
"$const": 1
}
},
"WorkflowName": {
"$first": "$WorkflowName"
}
}
},
{
"$project": {
"_id": false,
"Id": "$_id",
"Name": "$TaskName",
"LastExecutionDate": {
"$substr": [
"$LastExecutionTime",
{
"$const": 0
},
{
"$const": 30
}
]
},
"AverageExecutionTimeInMilliSeconds": "$AverageExecutionTime",
"TotalInstanceCount": "$TotalInstanceCount",
"WorkflowName": true
}
}
],
"ok": 1
}
答案 0 :(得分：2)
聚合不使用任何索引。 您需要创建一个新索引：
{UserId:1,WorkflowStartTime:1}
如果一切都好，那么agregation + explain必须出现在这一行：
"winningPlan" :...