MongoDB之几种情况下的索引选择策略
一、MongoDB如何选择索引
如果我们在Collection建了5个index，那么当我们查询的时候，MongoDB会根据查询语句的筛选条件、sort排序等来定位可以使用的index作为候选索引；然后MongoDB会创建对应数量的查询计划，并分别使用不同线程执行查询计划，最终会选择一个执行最快的index；但是这个选择也不是一成不变的，后续还会有一段时间根据实际执行情况动态调整；
二、数据准备
for(let i = 0;i<1000000;i++){
db.users.insertOne({
"id":i,
"name":'user'+i,
"age":Math.floor(Math.random()*120),
"created":new Date(ISODate().getTime() - 1000 * 60*i)
});
}
三、正则对index的使用
MongoDB支持正则查询，在特定的情况其也是可以利用index获得查询性能的提升；
虽然MongDB执行正则会最大限度的使用index，但是不同的用法还是会影响对index的利用程度的；
执行以下普通正则表达式
从queryPlanner.winningPlan部分的COLLSCAN，可以看到正则表达式默认会进行全表的扫描；
从executionStats.executionStages部分可以看到COLLSCAN共扫描了1000000个文档，并返回1111个文档，总耗时794ms；
db.users.find({
name:/user999/
}).explain('executionStats')
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.users",
"indexFilterSet" : false,
"winningPlan" : {
"stage" : "COLLSCAN",
"filter" : {
"name" : {
"$regex" : "user999"
}
},
"direction" : "forward"
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 1111,
"executionTimeMillis" : 909,
"totalKeysExamined" : 0,
"totalDocsExamined" : 1000000,
"executionStages" : {
"stage" : "COLLSCAN",
"filter" : {
"name" : {
"$regex" : "user999"
}
},
"nReturned" : 1111,
"executionTimeMillisEstimate" : 794,
"works" : 1000002,
"advanced" : 1111,
"needTime" : 998890,
"needYield" : 0,
"saveState" : 7830,
"restoreState" : 7830,
"isEOF" : 1,
"invalidates" : 0,
"direction" : "forward",
"docsExamined" : 1000000
}
}
}
创建一个包含name的index；
db.users.createIndex({name:1})
再次执行上边的查询，可以看到使用了我们新建的name_1索引；但是从执行状态来看，还是扫描了全体的索引的key，并不能很好的利用index；
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.users",
"indexFilterSet" : false,
"parsedQuery" : {
"name" : {
"$regex" : "user999"
}
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"filter" : {
"name" : {
"$regex" : "user999"
}
},
"keyPattern" : {
"name" : 1
},
"indexName" : "name_1"
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 1111,
"executionTimeMillis" : 971,
"totalKeysExamined" : 1000000,
"totalDocsExamined" : 1111,
"executionStages" : {
"stage" : "FETCH",
"nReturned" : 1111,
"executionTimeMillisEstimate" : 887,
"docsExamined" : 1111,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"filter" : {
"name" : {
"$regex" : "user999"
}
},
"nReturned" : 1111,
"executionTimeMillisEstimate" : 876,
"keyPattern" : {
"name" : 1
},
"indexName" : "name_1",
"keysExamined" : 1000000
}
}
}
}
使用前缀匹配的话可以最大限度的利用index，从执行状态可以看到只检测了1111个index key；
db.users.find({
name:/^user999/
}).explain('executionStats')
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.users",
"indexFilterSet" : false,
"parsedQuery" : {
"name" : {
"$regex" : "^user999"
}
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"name" : 1
},
"indexName" : "name_1"
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 1111,
"executionTimeMillis" : 2,
"totalKeysExamined" : 1111,
"totalDocsExamined" : 1111,
"executionStages" : {
"stage" : "FETCH",
"nReturned" : 1111,
"executionTimeMillisEstimate" : 0
"docsExamined" : 1111
"inputStage" : {
"stage" : "IXSCAN",
"nReturned" : 1111,
"executionTimeMillisEstimate" : 0,
"indexName" : "name_1",
"keysExamined" : 1111
}
}
}
}
即使是前缀匹配，如果忽略大小写的话也无法充分利用index了；
db.users.find({
name:/^user999/i
}).explain('executionStats')
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.users",
"indexFilterSet" : false,
"parsedQuery" : {
"name" : {
"$regex" : "user999",
"$options" : "i"
}
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"filter" : {
"name" : {
"$regex" : "user999",
"$options" : "i"
}
},
"keyPattern" : {
"name" : 1
},
"indexName" : "name_1"
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 1111,
"executionTimeMillis" : 943,
"totalKeysExamined" : 1000000,
"totalDocsExamined" : 1111,
"executionStages" : {
"stage" : "FETCH",
"nReturned" : 1111,
"executionTimeMillisEstimate" : 833,
"works" : 1000001,
"inputStage" : {
"stage" : "IXSCAN",
"filter" : {
"name" : {
"$regex" : "user999",
"$options" : "i"
}
},
"nReturned" : 1111,
"executionTimeMillisEstimate" : 833,
"keyPattern" : {
"name" : 1
},
"indexName" : "name_1"
"keysExamined" : 1000000
}
}
}
}
四、$or从句对索引的利用
MongoDB执行$or从句的时候，会将所有的从句作为逻辑的整体，要不就都使用index，要不就都进行全表扫描；
执行以下的查询语句；
db.users.find({
$or:[
{name:/^user666/},
{age:{$gte:80}}
]
}).explain('executionStats')
在只有name_1这个index的时候，我们可以看到MongoDB进行了全表扫描，全表扫描的时候进行$or从句的过滤；
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.users",
"indexFilterSet" : false,
"parsedQuery" : {
"$or" : [
{
"age" : {
"$gte" : 20
}
},
{
"name" : {
"$regex" : "^user666"
}
}
]
},
"winningPlan" : {
"stage" : "SUBPLAN",
"inputStage" : {
"stage" : "COLLSCAN",
"filter" : {
"$or" : [
{
"age" : {
"$gte" : 20
}
},
{
"name" : {
"$regex" : "^user666"
}
}
]
},
"direction" : "forward"
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 833995,
"executionTimeMillis" : 576,
"totalKeysExamined" : 0,
"totalDocsExamined" : 1000000,
"executionStages" : {
"stage" : "SUBPLAN",
"nReturned" : 833995,
"executionTimeMillisEstimate" : 447,
"inputStage" : {
"stage" : "COLLSCAN",
"filter" : {
"$or" : [
{
"age" : {
"$gte" : 20
}
},
{
"name" : {
"$regex" : "^user666"
}
}
]
},
"nReturned" : 833995,
"executionTimeMillisEstimate" : 447,
"docsExamined" : 1000000
}
}
}
}
我们对name字段新建一个index；
db.users.createIndex({age:1})
再次执行以上的查询语句，这次可以看到每个从句都利用了index，并且每个从句会单独执行并最终进行or操作；
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.users",
"indexFilterSet" : false,
"parsedQuery" : {
"$or" : [
{
"age" : {
"$gte" : 80
}
},
{
"name" : {
"$regex" : "^user666"
}
}
]
},
"winningPlan" : {
"stage" : "SUBPLAN",
"inputStage" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "OR",
"inputStages" : [
{
"stage" : "IXSCAN",
"keyPattern" : {
"name" : 1
},
"indexName" : "name_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"name" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "forward",
"indexBounds" : {
"name" : [
"[\"user666\", \"user667\")",
"[/^user666/, /^user666/]"
]
}
},
{
"stage" : "IXSCAN",
"keyPattern" : {
"age" : 1
},
"indexName" : "age_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"age" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "forward",
"indexBounds" : {
"age" : [
"[80.0, inf.0]"
]
}
}
]
}
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 333736,
"executionTimeMillis" : 741,
"totalKeysExamined" : 334102,
"totalDocsExamined" : 333736,
"executionStages" : {
"stage" : "SUBPLAN",
"nReturned" : 333736,
"executionTimeMillisEstimate" : 703,
"inputStage" : {
"stage" : "FETCH",
"nReturned" : 333736,
"executionTimeMillisEstimate" : 682
"docsExamined" : 333736,
"inputStage" : {
"stage" : "OR",
"nReturned" : 333736,
"executionTimeMillisEstimate" : 366,
"inputStages" : [
{
"stage" : "IXSCAN",
"nReturned" : 1111,
"executionTimeMillisEstimate" : 0,
"keyPattern" : {
"name" : 1
},
"indexName" : "name_1",
"indexBounds" : {
"name" : [
"[\"user666\", \"user667\")",
"[/^user666/, /^user666/]"
]
},
"keysExamined" : 1112
},
{
"stage" : "IXSCAN",
"nReturned" : 332990,
"executionTimeMillisEstimate" : 212,
"keyPattern" : {
"age" : 1
},
"indexName" : "age_1",
"indexBounds" : {
"age" : [
"[80.0, inf.0]"
]
},
"keysExamined" : 332990
}
]
}
}
}
}
}
五、sort对索引的利用
如果sort操作无法利用index，则MongoDB就会在内存中排序数据，并且数据量一大就会报错；
db.users.find().sort({created: -1}).explain('executionStats')
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.users",
"indexFilterSet" : false,
"parsedQuery" : {
},
"winningPlan" : {
"stage" : "SORT",
"sortPattern" : {
"created" : -1
},
"inputStage" : {
"stage" : "SORT_KEY_GENERATOR",
"inputStage" : {
"stage" : "COLLSCAN",
"direction" : "forward"
}
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : false,
"errorMessage" : "Exec error resulting in state FAILURE :: caused by :: Sort operation used more than the maximum 33554432 bytes of RAM. Add an index, or specify a smaller limit.",
"errorCode" : 96,
"nReturned" : 0,
"executionTimeMillis" : 959,
"totalKeysExamined" : 0,
"totalDocsExamined" : 361996,
"executionStages" : {
"stage" : "SORT",
"nReturned" : 0,
"executionTimeMillisEstimate" : 922,
"sortPattern" : {
"created" : -1
},
"memUsage" : 33554518,
"memLimit" : 33554432,
"inputStage" : {
"stage" : "SORT_KEY_GENERATOR",
"nReturned" : 361996,
"executionTimeMillisEstimate" : 590,
"inputStage" : {
"stage" : "COLLSCAN",
"nReturned" : 361996,
"executionTimeMillisEstimate" : 147,
"direction" : "forward",
"docsExamined" : 361996
}
}
}
}
}
如果是单字段index，sort从两个方向都可以充分利用index；可以看到MongoDB直接按照index的顺序返回结果，直接就没有sort阶段了；
db.users.find().sort({name: -1}).explain('executionStats')
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.users",
"indexFilterSet" : false,
"parsedQuery" : {
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"name" : 1
},
"indexName" : "name_1",
"direction" : "backward",
"indexBounds" : {
"name" : [
"[MaxKey, MinKey]"
]
}
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 1000000,
"executionTimeMillis" : 1317,
"totalKeysExamined" : 1000000,
"totalDocsExamined" : 1000000,
"executionStages" : {
"stage" : "FETCH",
"nReturned" : 1000000,
"executionTimeMillisEstimate" : 1180,
"inputStage" : {
"stage" : "IXSCAN",
"nReturned" : 1000000,
"executionTimeMillisEstimate" : 560,
"keyPattern" : {
"name" : 1
},
"indexName" : "name_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"name" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "backward",
"indexBounds" : {
"name" : [
"[MaxKey, MinKey]"
]
},
"keysExamined" : 1000000,
"seeks" : 1,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0
}
}
}
}
对于复合索引，sort除了可以从整体上从两个方向利用index，也可以利用index的前缀索引和非前缀局部索引；
新建复合索引
db.users.createIndex({created:-1, name:1, age:1})
按照复合索引的反方向进行整体排序；
db.users.find().sort({created:1, name:-1, age:-1}).explain('executionStats')
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.users",
"indexFilterSet" : false,
"parsedQuery" : {
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"created" : -1,
"name" : 1,
"age" : 1
},
"indexName" : "created_-1_name_1_age_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"created" : [ ],
"name" : [ ],
"age" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "backward",
"indexBounds" : {
"created" : [
"[MinKey, MaxKey]"
],
"name" : [
"[MaxKey, MinKey]"
],
"age" : [
"[MaxKey, MinKey]"
]
}
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 1000000,
"executionTimeMillis" : 1518,
"totalKeysExamined" : 1000000,
"totalDocsExamined" : 1000000,
"executionStages" : {
"stage" : "FETCH",
"nReturned" : 1000000,
"executionTimeMillisEstimate" : 1364,
"docsExamined" : 1000000,
"inputStage" : {
"stage" : "IXSCAN",
"nReturned" : 1000000,
"executionTimeMillisEstimate" : 816,
"keyPattern" : {
"created" : -1,
"name" : 1,
"age" : 1
},
"indexName" : "created_-1_name_1_age_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"created" : [ ],
"name" : [ ],
"age" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "backward",
"indexBounds" : {
"created" : [
"[MinKey, MaxKey]"
],
"name" : [
"[MaxKey, MinKey]"
],
"age" : [
"[MaxKey, MinKey]"
]
},
"keysExamined" : 1000000
}
}
}
}
排序使用索引前缀，也需要保证字段的顺序，但是可以反方向排序；
db.users.find().sort({created:1, name:-1, age:-1}).explain('executionStats')
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.users",
"indexFilterSet" : false,
"parsedQuery" : {
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"created" : -1,
"name" : 1,
"age" : 1
},
"indexName" : "created_-1_name_1_age_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"created" : [ ],
"name" : [ ],
"age" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "backward",
"indexBounds" : {
"created" : [
"[MinKey, MaxKey]"
],
"name" : [
"[MaxKey, MinKey]"
],
"age" : [
"[MaxKey, MinKey]"
]
}
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 1000000,
"executionTimeMillis" : 1487,
"totalKeysExamined" : 1000000,
"totalDocsExamined" : 1000000,
"executionStages" : {
"stage" : "FETCH",
"nReturned" : 1000000,
"executionTimeMillisEstimate" : 1339,
"works" : 1000001,
"advanced" : 1000000,
"needTime" : 0,
"needYield" : 0,
"saveState" : 7845,
"restoreState" : 7845,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 1000000,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"nReturned" : 1000000,
"executionTimeMillisEstimate" : 769,
"works" : 1000001,
"advanced" : 1000000,
"needTime" : 0,
"needYield" : 0,
"saveState" : 7845,
"restoreState" : 7845,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"created" : -1,
"name" : 1,
"age" : 1
},
"indexName" : "created_-1_name_1_age_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"created" : [ ],
"name" : [ ],
"age" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "backward",
"indexBounds" : {
"created" : [
"[MinKey, MaxKey]"
],
"name" : [
"[MaxKey, MinKey]"
],
"age" : [
"[MaxKey, MinKey]"
]
},
"keysExamined" : 1000000,
"seeks" : 1,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0
}
}
}
}
排序如果使用的是非前缀的局部字典排序，name需要保证前边的字段是等值筛选操作才行；
db.users.find({created:new Date("2021-10-30T08:17:01.184Z")}).sort({name:-1}).explain('executionStats')
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.users",
"indexFilterSet" : false,
"parsedQuery" : {
"created" : {
"$eq" : ISODate("2021-10-30T08:17:01.184Z")
}
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"created" : -1,
"name" : 1,
"age" : 1
},
"indexName" : "created_-1_name_1_age_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"created" : [ ],
"name" : [ ],
"age" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "backward",
"indexBounds" : {
"created" : [
"[new Date(1635581821184), new Date(1635581821184)]"
],
"name" : [
"[MaxKey, MinKey]"
],
"age" : [
"[MaxKey, MinKey]"
]
}
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 0,
"executionTimeMillis" : 0,
"totalKeysExamined" : 0,
"totalDocsExamined" : 0,
"executionStages" : {
"stage" : "FETCH",
"nReturned" : 0,
"executionTimeMillisEstimate" : 0,
"works" : 1,
"advanced" : 0,
"needTime" : 0,
"needYield" : 0,
"saveState" : 0,
"restoreState" : 0,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 0,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"nReturned" : 0,
"executionTimeMillisEstimate" : 0,
"works" : 1,
"advanced" : 0,
"needTime" : 0,
"needYield" : 0,
"saveState" : 0,
"restoreState" : 0,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"created" : -1,
"name" : 1,
"age" : 1
},
"indexName" : "created_-1_name_1_age_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"created" : [ ],
"name" : [ ],
"age" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "backward",
"indexBounds" : {
"created" : [
"[new Date(1635581821184), new Date(1635581821184)]"
],
"name" : [
"[MaxKey, MinKey]"
],
"age" : [
"[MaxKey, MinKey]"
]
},
"keysExamined" : 0,
"seeks" : 1,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0
}
}
}
}
六、搜索数据对索引命中的影响
MongoDB对index的选择是受到实际场景的数据影响比较大的，即与实际数据的分布规律有关，也跟实际筛选出来的数据有关系；所以我们对索引的优化和测试都需要考虑实际的数据场景才行；
由于name的字段值筛选出来的key太多，不能充分利用index，所以MongoDB拒绝了name_1并选择了age_1;
db.users.find({
name:/^user/,
age:{$gte:110}
}).explain('executionStats')
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.users",
"indexFilterSet" : false,
"parsedQuery" : {
"$and" : [
{
"age" : {
"$gte" : 110
}
},
{
"name" : {
"$regex" : "^user"
}
}
]
},
"winningPlan" : {
"stage" : "FETCH",
"filter" : {
"name" : {
"$regex" : "^user"
}
},
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"age" : 1
},
"indexName" : "age_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"age" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "forward",
"indexBounds" : {
"age" : [
"[110.0, inf.0]"
]
}
}
},
"rejectedPlans" : [
{
"stage" : "FETCH",
"filter" : {
"age" : {
"$gte" : 110
}
},
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"name" : 1
},
"indexName" : "name_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"name" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "forward",
"indexBounds" : {
"name" : [
"[\"user\", \"uses\")",
"[/^user/, /^user/]"
]
}
}
}
]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 83215,
"executionTimeMillis" : 246,
"totalKeysExamined" : 83215,
"totalDocsExamined" : 83215,
"executionStages" : {
"stage" : "FETCH",
"filter" : {
"name" : {
"$regex" : "^user"
}
},
"nReturned" : 83215,
"executionTimeMillisEstimate" : 232,
"works" : 83216,
"advanced" : 83215,
"needTime" : 0,
"needYield" : 0,
"saveState" : 658,
"restoreState" : 658,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 83215,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"nReturned" : 83215,
"executionTimeMillisEstimate" : 43,
"works" : 83216,
"advanced" : 83215,
"needTime" : 0,
"needYield" : 0,
"saveState" : 658,
"restoreState" : 658,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"age" : 1
},
"indexName" : "age_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"age" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "forward",
"indexBounds" : {
"age" : [
"[110.0, inf.0]"
]
},
"keysExamined" : 83215,
"seeks" : 1,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0
}
}
}
}
我们修改一下name筛选条件的值，进一步缩小命中的范围，可以看到这次MongoDB选择了name_1;
db.users.find({
name:/^user8888/,
age:{$gte:110}
}).explain('executionStats')
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.users",
"indexFilterSet" : false,
"parsedQuery" : {
"$and" : [
{
"age" : {
"$gte" : 110
}
},
{
"name" : {
"$regex" : "^user8888"
}
}
]
},
"winningPlan" : {
"stage" : "FETCH",
"filter" : {
"age" : {
"$gte" : 110
}
},
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"name" : 1
},
"indexName" : "name_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"name" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "forward",
"indexBounds" : {
"name" : [
"[\"user8888\", \"user8889\")",
"[/^user8888/, /^user8888/]"
]
}
}
},
"rejectedPlans" : [
{
"stage" : "FETCH",
"filter" : {
"name" : {
"$regex" : "^user8888"
}
},
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"age" : 1
},
"indexName" : "age_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"age" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "forward",
"indexBounds" : {
"age" : [
"[110.0, inf.0]"
]
}
}
}
]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 10,
"executionTimeMillis" : 0,
"totalKeysExamined" : 112,
"totalDocsExamined" : 111,
"executionStages" : {
"stage" : "FETCH",
"filter" : {
"age" : {
"$gte" : 110
}
},
"nReturned" : 10,
"executionTimeMillisEstimate" : 0,
"works" : 114,
"advanced" : 10,
"needTime" : 102,
"needYield" : 0,
"saveState" : 1,
"restoreState" : 1,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 111,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"nReturned" : 111,
"executionTimeMillisEstimate" : 0,
"works" : 113,
"advanced" : 111,
"needTime" : 1,
"needYield" : 0,
"saveState" : 1,
"restoreState" : 1,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"name" : 1
},
"indexName" : "name_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"name" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "forward",
"indexBounds" : {
"name" : [
"[\"user8888\", \"user8889\")",
"[/^user8888/, /^user8888/]"
]
},
"keysExamined" : 112,
"seeks" : 2,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0
}
}
}
}
MongoDB之几种情况下的索引选择策略的更多相关文章
- oracle数据库什么情况下创建索引比较好
索引就好象一本字典的目录.凭借字典的目录,我们可以非常迅速的找到我们所需要的条目.数据库也是如此.凭借Oracle数据库的索引,相关语句可以迅速的定位记录的位置,而不必去定位整个表. 虽 然说,在表中 ...
- 数据库表设计时一对一关系存在的必要性 数据库一对一、一对多、多对多设计 面试逻辑题3.31 sql server 查询某个表被哪些存储过程调用 DataTable根据字段去重 .Net Core Cors中间件解析 分析MySQL中哪些情况下数据库索引会失效
数据库表设计时一对一关系存在的必要性 2017年07月24日 10:01:07 阅读数:694 在表设计过程中,我无意中觉得一对一关系觉得好没道理,直接放到一张表中不就可以了吗?真是说,网上信息什么都 ...
- 五种情况下会刷新控件状态（刷新所有子FWinControls的显示）——从DFM读取数据时、新增加子控件时、重新创建当前控件的句柄时、设置父控件时、显示状态被改变时
五种情况下会刷新控件状态(刷新控件状态才能刷新所有子FWinControls的显示): 在TWinControls.PaintControls中,对所有FWinControls只是重绘了边框,而没有整 ...
- create-react-app：reject和不reject(使用react-app-rewired)这2种情况下的antd组件按需引入配置
create-react-app:eject和不eject(使用react-app-rewired)这2种情况下的antd组件按需引入配置: 不eject(使用react-app-rewired)配置 ...
- Oracle redo 日志损坏的几种情况下的恢复
Oracle redo 日志损坏的几种情况下的恢复 第一:损坏的redo为非正在使用的redo log 1.归档模式,不是当前正在日志损坏,数据库打开模式. 模拟损坏:SQL> select * ...
- Java实现PV操作 | 读者与写者（在三种情况下进行讨论）
注 :本文应结合[天勤笔记]进行学习. 1.读者优先 设置rmutex信号量来对readcount变量进行互斥访问.mutex信号量对写者与读者进行同步. static syn rmutex=new ...
- Atitit.有分区情况下的表查询策略流程
Atitit.有分区情况下的表查询策略流程 1. 分区表查询策略流程1 2. 常见数据库oracle mysql的分区查询语句1 2.1. 跨分区查询(oracle)1 2.2. 单分区查询 (ora ...
- varchar int 查询 到底什么情况下走索引？
一个字符类型的.一个int类型的,查询的时候到底会不会走索引,其实很多工作了几年的开发人员有时也会晕,下面就用具体事例来测试一下. 1. 准备工作 先准备2张表,以备后续测试使用. 表1:创建表te ...
- virtualBox中有线和无线两种情况下centos虚拟机和本地机互ping的方案
之前写微信点餐系统的时候,刚开始是无线连接,然后每次进去虚拟机ip和本地ip都会改变,所以每次都需要配置一下nginx,还有本地的路径.之后换有线连接,就研究了一下桥接模式有线情况下虚拟机静态ip设置 ...
随机推荐
- live2d
原文来自https://www.fghrsh.net/post/123.html Live2D 看板娘 v1.4 / Demo 3 - 内置 waifu-tips.json (博客园等网站引用推荐) ...
- linux查看磁盘SN
ls -l /dev/disk/by-id/ | grep -iE <SN>
- SQL优化一例：通过改变分组条件（减少计算次数）来提高效率
#与各人授权日期相关,所以有十万用户,就有十万次查询(相关子查询) @Run.ExecuteSql("更新各人应听正课数",@"update bi_data.study_ ...
- mdf与ldf文件如何还原到SQL Server数据库
1,首先新建两个数据库(Db1,Db2) 在Db1数据库中添加一个表: 然后点击数据库右键-属性-文件-找到文件路径 然后复制这个路径中的Db1的数据,把这两个文件放到其他文件之下 复制之前需要关闭S ...
- SpringBoot项目给静态变量static注入@Value
@value不能直接注入值给静态属性,spring 不允许/不支持把值注入到静态变量中:spring支持set方法注入,我们可以利用非静态setter 方法注入静态变量 @Component publ ...
- IDEA安装vue.js插件后，new没有Vue component
首先要安装vue相关的插件vue.js 但是很多人安装vue.js右键发现没有vue Componment,解决方法如下 Settings>Editor>File and Code Tem ...
- OnZoom 基于Apache Hudi的流批一体架构实践
1. 背景 OnZoom是Zoom新产品,是基于Zoom Meeting的一个独一无二的在线活动平台和市场.作为Zoom统一通信平台的延伸,OnZoom是一个综合性解决方案,为付费的Zoom用户提供创 ...
- 【LeetCode】1402. 做菜顺序 Reducing Dishes
作者: 负雪明烛 id: fuxuemingzhu 个人博客:http://fuxuemingzhu.cn/ 目录 题目描述 题目大意 解题方法 贪心 日期 题目地址:https://leetcode ...
- 【LeetCode】674. Longest Continuous Increasing Subsequence 解题报告（Python）
作者: 负雪明烛 id: fuxuemingzhu 个人博客: http://fuxuemingzhu.cn/ 目录 题目描述 题目大意 解题方法 动态规划 空间压缩DP 日期 题目地址:https: ...
- The Monkey King(hdu5201)
The Monkey King Time Limit: 8000/4000 MS (Java/Others) Memory Limit: 65536/65536 K (Java/Others)T ...