我有Mongodb集合，其中包含大约700万个代表地点的文档。
我运行一个查询，搜索名称以特定位置附近的前缀开头的地方。
我们有一个复合索引，如下所述，以加快搜索速度。
当搜索查询找到匹配（即使只有一个）时，查询执行速度非常快（~20 milisec）。但是当没有匹配时，查询执行可能需要30秒。
请协助。
详细信息：
每个地方（geoData）都有以下字段：
"loc" - a GeoJSON point that represent the location
"categoriesIds" - array of int ids
"name" - the name of the placee
此集合定义了以下索引：
{
"loc" : "2dsphere",
"categoriesIds" : 1,
"name" : 1
}
查询是：
db.geoData.find({
"loc":{
"$near":{
"$geometry":{
"type": "Point" ,
"coordinates": [ -0.10675191879272461 , 51.531600743186644]
},
"$maxDistance": 5000.0
}
},
"categoriesIds":{
"$in": [ 1 , 2 , 71 , 70 , 74 , 72 , 73 , 69 , 44 , 26 , 27 , 33 , 43 , 45 , 53 , 79]
},
"name":{ "$regex": "^Cafe Ne"}
})
执行统计 （Link to the whole explain result）
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 1,
"executionTimeMillis" : 169,
"totalKeysExamined" : 14333,
"totalDocsExamined" : 1,
"executionStages" : {
"stage" : "GEO_NEAR_2DSPHERE",
"nReturned" : 1,
"executionTimeMillisEstimate" : 60,
"works" : 14354,
"advanced" : 1,
"needTime" : 14351,
"needFetch" : 0,
"saveState" : 361,
"restoreState" : 361,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"loc" : "2dsphere",
"categoriesIds" : 1,
"name" : 1
},
"indexName" : "loc_2dsphere_categoriesIds_1_name_1",
"searchIntervals" : [
{
"minDistance" : 0,
"maxDistance" : 3408.329295346151,
"maxInclusive" : false
},
{
"minDistance" : 3408.329295346151,
"maxDistance" : 5000,
"maxInclusive" : true
}
],
"inputStages" : [
{
"stage" : "FETCH",
"nReturned" : 1,
"executionTimeMillisEstimate" : 20,
"works" : 6413,
"advanced" : 1,
"needTime" : 6411,
"needFetch" : 0,
"saveState" : 361,
"restoreState" : 361,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 1,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"filter" : {
"TwoDSphereKeyInRegionExpression" : true
},
"nReturned" : 1,
"executionTimeMillisEstimate" : 20,
"works" : 6413,
"advanced" : 1,
"needTime" : 6411,
"needFetch" : 0,
"saveState" : 361,
"restoreState" : 361,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"loc" : "2dsphere",
"categoriesIds" : 1,
"name" : 1
},
"indexName" : "loc_2dsphere_categoriesIds_1_name_1",
"isMultiKey" : true,
"direction" : "forward",
"indexBounds" : {
"loc" : [
"[\"2f1003230\", \"2f1003230\"]",
"[\"2f10032300\", \"2f10032300\"]",
"[\"2f100323000\", \"2f100323000\"]",
"[\"2f1003230001\", \"2f1003230001\"]",
"[\"2f10032300012\", \"2f10032300013\")",
"[\"2f1003230002\", \"2f1003230002\"]",
"[\"2f10032300021\", \"2f10032300022\")",
"[\"2f10032300022\", \"2f10032300023\")",
"[\"2f100323003\", \"2f100323003\"]",
"[\"2f1003230031\", \"2f1003230031\"]",
"[\"2f10032300311\", \"2f10032300312\")",
"[\"2f10032300312\", \"2f10032300313\")",
"[\"2f10032300313\", \"2f10032300314\")",
"[\"2f1003230032\", \"2f1003230032\"]",
"[\"2f10032300320\", \"2f10032300321\")",
"[\"2f10032300321\", \"2f10032300322\")"
],
"categoriesIds" : [
"[1.0, 1.0]",
"[2.0, 2.0]",
"[26.0, 26.0]",
"[27.0, 27.0]",
"[33.0, 33.0]",
"[43.0, 43.0]",
"[44.0, 44.0]",
"[45.0, 45.0]",
"[53.0, 53.0]",
"[69.0, 69.0]",
"[70.0, 70.0]",
"[71.0, 71.0]",
"[72.0, 72.0]",
"[73.0, 73.0]",
"[74.0, 74.0]",
"[79.0, 79.0]"
],
"name" : [
"[\"Cafe Ne\", \"Cafe Nf\")",
"[/^Cafe Ne/, /^Cafe Ne/]"
]
},
"keysExamined" : 6412,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0,
"matchTested" : 1
}
},
{
"stage" : "FETCH",
"nReturned" : 0,
"executionTimeMillisEstimate" : 40,
"works" : 7922,
"advanced" : 0,
"needTime" : 7921,
"needFetch" : 0,
"saveState" : 261,
"restoreState" : 261,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 0,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"filter" : {
"TwoDSphereKeyInRegionExpression" : true
},
"nReturned" : 0,
"executionTimeMillisEstimate" : 40,
"works" : 7922,
"advanced" : 0,
"needTime" : 7921,
"needFetch" : 0,
"saveState" : 261,
"restoreState" : 261,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"loc" : "2dsphere",
"categoriesIds" : 1,
"name" : 1
},
"indexName" : "loc_2dsphere_categoriesIds_1_name_1",
"isMultiKey" : true,
"direction" : "forward",
"indexBounds" : {
"loc" : [
"[\"2f1003230\", \"2f1003230\"]",
"[\"2f10032300\", \"2f10032300\"]",
"[\"2f100323000\", \"2f100323000\"]",
"[\"2f1003230001\", \"2f1003230001\"]",
"[\"2f10032300011\", \"2f10032300012\")",
"[\"2f10032300012\", \"2f10032300013\")",
"[\"2f1003230002\", \"2f1003230002\"]",
"[\"2f10032300021\", \"2f10032300022\")",
"[\"2f10032300022\", \"2f10032300023\")",
"[\"2f100323003\", \"2f100323003\"]",
"[\"2f1003230031\", \"2f1003230032\")",
"[\"2f1003230032\", \"2f1003230032\"]",
"[\"2f10032300320\", \"2f10032300321\")",
"[\"2f10032300321\", \"2f10032300322\")",
"[\"2f10032300322\", \"2f10032300323\")"
],
"categoriesIds" : [
"[1.0, 1.0]",
"[2.0, 2.0]",
"[26.0, 26.0]",
"[27.0, 27.0]",
"[33.0, 33.0]",
"[43.0, 43.0]",
"[44.0, 44.0]",
"[45.0, 45.0]",
"[53.0, 53.0]",
"[69.0, 69.0]",
"[70.0, 70.0]",
"[71.0, 71.0]",
"[72.0, 72.0]",
"[73.0, 73.0]",
"[74.0, 74.0]",
"[79.0, 79.0]"
],
"name" : [
"[\"Cafe Ne\", \"Cafe Nf\")",
"[/^Cafe Ne/, /^Cafe Ne/]"
]
},
"keysExamined" : 7921,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0,
"matchTested" : 0
}
}
]
},
搜索＆＃34; CafeNeeNNN＆＃34;时的执行统计数据而不是＆＃34; Cafe Ne＆＃34; （Link to the whole explain result）
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 0,
"executionTimeMillis" : 2537,
"totalKeysExamined" : 232259,
"totalDocsExamined" : 162658,
"executionStages" : {
"stage" : "FETCH",
"filter" : {
"$and" : [
{
"name" : /^CafeNeeNNN/
},
{
"categoriesIds" : {
"$in" : [
1,
2,
26,
27,
33,
43,
44,
45,
53,
69,
70,
71,
72,
73,
74,
79
]
}
}
]
},
"nReturned" : 0,
"executionTimeMillisEstimate" : 1330,
"works" : 302752,
"advanced" : 0,
"needTime" : 302750,
"needFetch" : 0,
"saveState" : 4731,
"restoreState" : 4731,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 70486,
"alreadyHasObj" : 70486,
"inputStage" : {
"stage" : "GEO_NEAR_2DSPHERE",
"nReturned" : 70486,
"executionTimeMillisEstimate" : 1290,
"works" : 302751,
"advanced" : 70486,
"needTime" : 232264,
"needFetch" : 0,
"saveState" : 4731,
"restoreState" : 4731,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"loc" : "2dsphere"
},
"indexName" : "loc_2dsphere",
"searchIntervals" : [
{
"minDistance" : 0,
"maxDistance" : 3408.329295346151,
"maxInclusive" : false
},
{
"minDistance" : 3408.329295346151,
"maxDistance" : 5000,
"maxInclusive" : true
}
],
"inputStages" : [
{
"stage" : "FETCH",
"nReturned" : 44540,
"executionTimeMillisEstimate" : 110,
"works" : 102690,
"advanced" : 44540,
"needTime" : 58149,
"needFetch" : 0,
"saveState" : 4731,
"restoreState" : 4731,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 44540,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"filter" : {
"TwoDSphereKeyInRegionExpression" : true
},
"nReturned" : 44540,
"executionTimeMillisEstimate" : 90,
"works" : 102690,
"advanced" : 44540,
"needTime" : 58149,
"needFetch" : 0,
"saveState" : 4731,
"restoreState" : 4731,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"loc" : "2dsphere"
},
"indexName" : "loc_2dsphere",
"isMultiKey" : false,
"direction" : "forward",
"indexBounds" : {
"loc" : [
"[\"2f1003230\", \"2f1003230\"]",
"[\"2f10032300\", \"2f10032300\"]",
"[\"2f100323000\", \"2f100323000\"]",
"[\"2f1003230001\", \"2f1003230001\"]",
"[\"2f10032300012\", \"2f10032300013\")",
"[\"2f1003230002\", \"2f1003230002\"]",
"[\"2f10032300021\", \"2f10032300022\")",
"[\"2f10032300022\", \"2f10032300023\")",
"[\"2f100323003\", \"2f100323003\"]",
"[\"2f1003230031\", \"2f1003230031\"]",
"[\"2f10032300311\", \"2f10032300312\")",
"[\"2f10032300312\", \"2f10032300313\")",
"[\"2f10032300313\", \"2f10032300314\")",
"[\"2f1003230032\", \"2f1003230032\"]",
"[\"2f10032300320\", \"2f10032300321\")",
"[\"2f10032300321\", \"2f10032300322\")"
]
},
"keysExamined" : 102689,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0,
"matchTested" : 44540
}
},
{
"stage" : "FETCH",
"nReturned" : 47632,
"executionTimeMillisEstimate" : 250,
"works" : 129571,
"advanced" : 47632,
"needTime" : 81938,
"needFetch" : 0,
"saveState" : 2556,
"restoreState" : 2556,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 47632,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"filter" : {
"TwoDSphereKeyInRegionExpression" : true
},
"nReturned" : 47632,
"executionTimeMillisEstimate" : 230,
"works" : 129571,
"advanced" : 47632,
"needTime" : 81938,
"needFetch" : 0,
"saveState" : 2556,
"restoreState" : 2556,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"loc" : "2dsphere"
},
"indexName" : "loc_2dsphere",
"isMultiKey" : false,
"direction" : "forward",
"indexBounds" : {
"loc" : [
"[\"2f1003230\", \"2f1003230\"]",
"[\"2f10032300\", \"2f10032300\"]",
"[\"2f100323000\", \"2f100323000\"]",
"[\"2f1003230001\", \"2f1003230001\"]",
"[\"2f10032300011\", \"2f10032300012\")",
"[\"2f10032300012\", \"2f10032300013\")",
"[\"2f1003230002\", \"2f1003230002\"]",
"[\"2f10032300021\", \"2f10032300022\")",
"[\"2f10032300022\", \"2f10032300023\")",
"[\"2f100323003\", \"2f100323003\"]",
"[\"2f1003230031\", \"2f1003230032\")",
"[\"2f1003230032\", \"2f1003230032\"]",
"[\"2f10032300320\", \"2f10032300321\")",
"[\"2f10032300321\", \"2f10032300322\")",
"[\"2f10032300322\", \"2f10032300323\")"
]
},
"keysExamined" : 129570,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0,
"matchTested" : 47632
}
}
]
}
},
集合上的索引
{
"0" : {
"v" : 1,
"key" : {
"_id" : 1
},
"name" : "_id_",
"ns" : "wego.geoData"
},
"1" : {
"v" : 1,
"key" : {
"srcId" : 1
},
"name" : "srcId_1",
"ns" : "wego.geoData"
},
"2" : {
"v" : 1,
"key" : {
"loc" : "2dsphere"
},
"name" : "loc_2dsphere",
"ns" : "wego.geoData",
"2dsphereIndexVersion" : 2
},
"3" : {
"v" : 1,
"key" : {
"name" : 1
},
"name" : "name_1",
"ns" : "wego.geoData"
},
"4" : {
"v" : 1,
"key" : {
"loc" : "2dsphere",
"categoriesIds" : 1,
"name" : 1
},
"name" : "loc_2dsphere_categoriesIds_1_name_1",
"ns" : "wego.geoData",
"2dsphereIndexVersion" : 2
},
"5" : {
"v" : 1,
"key" : {
"loc" : "2dsphere",
"categoriesIds" : 1,
"keywords" : 1
},
"name" : "loc_2dsphere_categoriesIds_1_keywords_1",
"ns" : "wego.geoData",
"2dsphereIndexVersion" : 2
}
}
答案 0 :(得分：10)
我将在这里推测一下，然后评论一下你的设计。
首先，当您在key上创建一个索引，该索引在一个值上有一个数组时，您将为该数组的每个元素创建一个记录：
要索引包含数组值的字段，MongoDB会创建一个索引 数组中每个元素的键。
这是MongoDB own documentation about indecies。
所以，如果你的典型记录超过一手牌，并且你有700万条记录， 你的索引是巨大的，扫描索引本身也需要时间来发现索引不包含你要查找的内容。它还是 比收集扫描更快，但与查找现有记录的速度相比，它的速度很慢。
现在，让我评论一下您的架构设计。这是一个风格问题，所以可以随意忽略这一部分。
您的记录可能属于17个类别。这有点压倒性，而且滥用了术语category。类别是特定的
分裂，一种快速将事物与一组事物联系起来的方法。属于这么多群体的东西是什么？
我们以您的记录Cafe Ne为例。我假设在现实世界中 - 请记住，编程和应用程序充其量只能解决现实世界的问题 - Cafe Ne，无论是餐厅，咖啡馆，爵士酒吧，
晚餐。肯定不是车库（除非，咖啡馆意味着用我不知道的语言的汽车）。我很难想象这是一家银行或牙科诊所。我必须真正努力，找到超过10个有意义的类别，用户搜索咖啡馆。
我的观点是，即使mongodb允许你设计这样的东西，但这并不意味着你必须这样做。尝试缩小您拥有的类别数量和您寻找的类别数量，您将获得更好的表现。
答案 1 :(得分：2)
正如JohnnyHK在评论中提出的那样，Oz123在他的回答中指出，这里的问题似乎是一个已经变得如此之大以至于它作为索引表现不佳的索引。我相信除了已经指出的类别扩展问题之外，索引中字段的排序也会产生麻烦。复合索引为built according to the order of fields，在name之后放置categoriesIds会使name上的查询成本更高。
很明显，您需要调整索引。具体如何调整它们取决于您期望支持的查询类型。特别是，我不确定您是否会从loc和name的复合索引中看到更好的效果，或者如果您从单个索引中看到更好的效果，用于loc，一个用于name。当使用复合索引时最好使用单个索引并依赖索引交集时，Mongo本身是a little vague。
我的直觉说个别索引会表现更好，但我会测试两种情况。
如果您预计还需要按类别进行查询，如果没有可能缩小查询范围的name或loc字段，则最好创建单独的categoriesIds指数。
答案 2 :(得分：1)
复合索引中字段的顺序非常重要。在没有访问真实数据和使用模式的情况下很难诊断，但是这个密钥可能会增加仅使用索引匹配（或不匹配）文档的几率：
{
"loc" : "2dsphere",
"name" : 1,
"categoriesIds" : 1
}
答案 3 :(得分：0)
不确定是否是完全相同的问题，但是在没有找到结果的情况下，多键索引的性能却很相似。
这实际上是v3.3.8中修复的Mongo错误。 https://jira.mongodb.org/browse/SERVER-15086
我们在升级Mongo并重建索引后解决了问题。