我们将mongodb用于所有分析查询。下面，我粘贴了其中一个查询的查询计划。该集合大约有130万个条目，并且该查询在一个映射（最多可包含4万个条目）上运行，该映射在match管道中指定为 assignment_supply_entity_mapping_id 。尽管在 assignment_supply_entity_mapping_id 上进行了IXSCAN，该查询当前仍耗时553ms。
我的问题是，考虑到要查询的数据量，所花费的时间不是很多吗？如果是这样，我该如何对其进行优化？我怀疑嵌套过滤器是原因。这是真的吗？
{
"op":"command",
"ns":"analytics_new.leads",
"command":{
"aggregate":"leads",
"pipeline":[
{
"$project":{
"logs":0
}
},
{
"$match":{
"$and":[
{
"assignment_supply_entity_mapping_id":{
"$in":[
1011,
850,
998,
840
]
}
},
{
"tags.is_duplicate":{
"$ne":true
}
},
{
"tags.is_abandoned":{
"$ne":true
}
},
{
"tags.is_archived":{
"$ne":true
}
},
{
"id":{
"$nin":[
]
}
},
{
"$or":[
{
"all_agents":{
"$in":[
2765,
2766,
193,
994,
1007,
1188,
1376,
1769,
2421,
2422,
2423,
2424,
115,
133,
149,
193,
441,
747,
884,
994,
1003,
1007,
1100,
1188,
1378,
1612,
1769,
1964,
1966,
1967,
1968,
1969,
1970,
1971,
1973,
2333,
2364,
3170,
3318,
3320,
3757,
4071,
4294,
4297,
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1188,
994,
193,
715,
1599,
2333,
3170,
2424,
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1007,
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1377,
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1100,
649,
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1376,
441,
1612,
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337,
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712,
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2392,
115,
708,
1037,
1964,
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1956,
3320,
2364,
978,
1974,
149,
996,
1003,
1951,
2610,
2422,
884
]
}
},
{
"$and":[
{
"agent.id":{
"$exists":false
}
},
{
"$or":[
{
"$and":[
{
"current_status.id":5
},
{
"$or":[
{
"recently_contacted_agent_ids":{
"$exists":false
}
},
{
"recently_contacted_agent_ids":{
"$in":[
2765,
2766,
193,
994,
1007,
1188,
1376,
1769,
2421,
2422,
2423,
2424,
115,
133,
149,
193,
441,
747,
884,
994,
1003,
1007,
1100,
1188,
1378,
1612,
1769,
1964,
1966,
1967,
1968,
1969,
1970,
1971,
1973,
2333,
2364,
3170,
3318,
3320,
3757,
4071,
4294,
4297,
1965,
1188,
994,
193,
715,
1599,
2333,
3170,
2424,
1972,
1007,
2423,
1377,
1378,
1970,
1100,
649,
1950,
1376,
441,
1612,
939,
1966,
1971,
133,
1952,
1969,
337,
1973,
712,
3318,
2392,
115,
708,
1037,
1964,
2421,
1967,
537,
1769,
1968,
1956,
3320,
2364,
978,
1974,
149,
996,
1003,
1951,
2610,
2422,
884
]
}
}
]
}
]
},
{
"current_status.id":{
"$in":[
4,
1,
2,
12
]
}
}
]
}
]
}
]
}
]
}
},
{
"$group":{
"_id":{
"channel":"$lead_origin"
},
"booking_done":{
"$sum":{
"$cond":[
{
"$and":[
{
"$ne":[
{
"$type":"$status_timestamps.booking_done_timestamp"
},
"missing"
]
},
{
"$gt":[
"$status_timestamps.booking_done_timestamp",
"$status_timestamps.failed_timestamp"
]
}
]
},
1,
0
]
}
},
"failed":{
"$sum":{
"$cond":[
{
"$ne":[
{
"$type":"$status_timestamps.failed_timestamp"
},
"missing"
]
},
1,
0
]
}
},
"failed_after_visit":{
"$sum":{
"$cond":[
{
"$and":[
{
"$and":[
{
"$ne":[
{
"$type":"$status_timestamps.failed_timestamp"
},
"missing"
]
},
{
"$ne":[
{
"$type":"$status_timestamps.visit_done_timestamp"
},
"missing"
]
}
]
},
{
"$gt":[
"$status_timestamps.failed_timestamp",
"$status_timestamps.visit_done_timestamp"
]
}
]
},
1,
0
]
}
},
"junk":{
"$sum":{
"$cond":[
{
"$ne":[
{
"$type":"$status_timestamps.junk_timestamp"
},
"missing"
]
},
1,
0
]
}
},
"received":{
"$sum":1
},
"visit_done":{
"$sum":{
"$cond":[
{
"$ne":[
{
"$type":"$status_timestamps.visit_done_timestamp"
},
"missing"
]
},
1,
0
]
}
}
}
}
],
"cursor":{
},
"$db":"analytics_new"
},
"keysExamined":31080,
"docsExamined":31076,
"cursorExhausted":true,
"numYield":249,
"locks":{
"Global":{
"acquireCount":{
"r":NumberLong(508)
}
},
"Database":{
"acquireCount":{
"r":NumberLong(254)
}
},
"Collection":{
"acquireCount":{
"r":NumberLong(254)
}
}
},
"nreturned":6,
"responseLength":905,
"protocol":"op_query",
"millis":553,
"planSummary":"IXSCAN { assignment_supply_entity_mapping_id: 1, created_at: -1 }",
"ts": ISODate("2019-03-04T08:28:57.143 Z"),
"client":"172.31.18.209",
"allUsers":[
],
"user":""
}