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MongoDB 索引相关知识

背景:

      MongoDB和MySQL一样,都会产生慢查询,所以都需要对其进行优化:包括创建索引、重构查询等。现在就说明在MongoDB下的索引相关知识点,可以通过这篇文章MongoDB 查询优化分析了解MongoDB慢查询的一些特点。

执行计划分析:

      因为MongoDB也是BTree索引,所以使用上和MySQL大致一样。通过explain查看一个query的执行计划,来判断如何加索引,explain在3.0版本的时候做了一些改进,现在针对这2个版本进行分析:

3.0之前:

zjy:PRIMARY> db.newtask.find({"b":"CYHS1301942"}).explain()
{
"cursor" : "BtreeCursor b_1_date_1", #游标类型:BasicCursor(全表扫描)、BtreeCursor(BTree索引扫描)、GeoSearchCursor(地理空间索引扫描)。
"isMultiKey" : false,
"n" : 324, #返回的结果数,count()。
"nscannedObjects" : 324, #扫描的对象
"nscanned" : 324, #扫描的索引数
"nscannedObjectsAllPlans" : 324, #代表所有尝试执行的计划所扫描的对象
"nscannedAllPlans" : 324, #代表所有尝试执行的计划所扫描的索引
"scanAndOrder" : false, #True:对文档进行排序,false:对索引进行排序
"indexOnly" : false, #对查询的结果进行排序不需要搜索其他文档,查询和返回字段使用同一索引
"nYields" : 0, #为了让写操作执行而让出读锁的次数
"nChunkSkips" : 0, #忽略文档数
"millis" : 1, #执行查询消耗的时间
"indexBounds" : { #索引扫描中使用的最大/小值。
"b" : [
[
"CYHS1301942",
"CYHS1301942"
]
],
"date" : [
[
{
"$minElement" : 1
},
{
"$maxElement" : 1
}
]
]
},
"server" : "db-mongo1:27017"
}

​​3.0​​之后:在explain()里有三个参数:"queryPlanner", "executionStats", and "allPlansExecution",默认是:queryPlanner。具体的含义见​​官方文档​​。

zjy:PRIMARY> db.newtask.find({"b":"CYHS1301942"}).explain()
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "cde.newtask", #集合
"indexFilterSet" : false,
"parsedQuery" : {
"b" : {
"$eq" : "CYHS1301942"
}
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN", #索引扫描,COLLSCAN表示全表扫描。
"keyPattern" : {
"b" : 1,
"date" : 1
},
"indexName" : "b_1_date_1", #索引名
"isMultiKey" : false,
"direction" : "forward",
"indexBounds" : {
"b" : [
"[\"CYHS1301942\", \"CYHS1301942\"]"
],
"date" : [
"[MinKey, MaxKey]"
]
}
}
},
"rejectedPlans" : [ ]
},
"serverInfo" : {
"host" : "mongo1",
"port" : 27017,
"version" : "3.0.4",
"gitVersion" : "0481c958daeb2969800511e7475dc66986fa9ed5"
},
"ok" : 1
}

3.0要是查看更详细的执行计划请看其他2个参数:

MongoDB 索引相关知识_expainMongoDB 索引相关知识_mongodb_02

zjy:PRIMARY> db.newtask.find({"b":"CYHS1301942"}).explain("allPlansExecution")
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "cde.newtask",
"indexFilterSet" : false,
"parsedQuery" : {
"b" : {
"$eq" : "CYHS1301942"
}
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"b" : 1,
"date" : 1
},
"indexName" : "b_1_date_1",
"isMultiKey" : false,
"direction" : "forward",
"indexBounds" : {
"b" : [
"[\"CYHS1301942\", \"CYHS1301942\"]"
],
"date" : [
"[MinKey, MaxKey]"
]
}
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 1,
"executionTimeMillis" : 0,
"totalKeysExamined" : 1,
"totalDocsExamined" : 1,
"executionStages" : {
"stage" : "FETCH",
"nReturned" : 1,
"executionTimeMillisEstimate" : 0,
"works" : 2,
"advanced" : 1,
"needTime" : 0,
"needFetch" : 0,
"saveState" : 0,
"restoreState" : 0,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 1,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"nReturned" : 1,
"executionTimeMillisEstimate" : 0,
"works" : 2,
"advanced" : 1,
"needTime" : 0,
"needFetch" : 0,
"saveState" : 0,
"restoreState" : 0,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"b" : 1,
"date" : 1
},
"indexName" : "b_1_date_1",
"isMultiKey" : false,
"direction" : "forward",
"indexBounds" : {
"b" : [
"[\"CYHS1301942\", \"CYHS1301942\"]"
],
"date" : [
"[MinKey, MaxKey]"
]
},
"keysExamined" : 1,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0,
"matchTested" : 0
}
},
"allPlansExecution" : [ ]
},
"serverInfo" : {
"host" : "mongo1",
"port" : 27017,
"version" : "3.0.4",
"gitVersion" : "0481c958daeb2969800511e7475dc66986fa9ed5"
},
"ok" : 1
}

View Code

MongoDB 索引相关知识_expainMongoDB 索引相关知识_mongodb_02

zjy:PRIMARY> db.newtask.find({"b":"CYHS1301942"}).explain("executionStats")
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "cde.newtask",
"indexFilterSet" : false,
"parsedQuery" : {
"b" : {
"$eq" : "CYHS1301942"
}
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"b" : 1,
"date" : 1
},
"indexName" : "b_1_date_1",
"isMultiKey" : false,
"direction" : "forward",
"indexBounds" : {
"b" : [
"[\"CYHS1301942\", \"CYHS1301942\"]"
],
"date" : [
"[MinKey, MaxKey]"
]
}
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 1,
"executionTimeMillis" : 0,
"totalKeysExamined" : 1,
"totalDocsExamined" : 1,
"executionStages" : {
"stage" : "FETCH",
"nReturned" : 1,
"executionTimeMillisEstimate" : 0,
"works" : 2,
"advanced" : 1,
"needTime" : 0,
"needFetch" : 0,
"saveState" : 0,
"restoreState" : 0,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 1,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"nReturned" : 1,
"executionTimeMillisEstimate" : 0,
"works" : 2,
"advanced" : 1,
"needTime" : 0,
"needFetch" : 0,
"saveState" : 0,
"restoreState" : 0,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"b" : 1,
"date" : 1
},
"indexName" : "b_1_date_1",
"isMultiKey" : false,
"direction" : "forward",
"indexBounds" : {
"b" : [
"[\"CYHS1301942\", \"CYHS1301942\"]"
],
"date" : [
"[MinKey, MaxKey]"
]
},
"keysExamined" : 1,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0,
"matchTested" : 0
}
}
},
"serverInfo" : {
"host" : "mongo1",
"port" : 27017,
"version" : "3.0.4",
"gitVersion" : "0481c958daeb2969800511e7475dc66986fa9ed5"
},
"ok" : 1
}

View Code

上面介绍了如何查看执行计划,那么下面介绍下如何管理索引。

​​索引管理​​,具体请看[权威指南第5章]

1)查看/显示集合的索引:db.collectionName.getIndexes()或则db.system.indexes.find()

zjy:PRIMARY> db.data.getIndexes()
[
{
"v" : 1,
"key" : {
"_id" : 1
},
"name" : "_id_", #索引名
"ns" : "survey.data" #集合名
},
{
"v" : 1,
"unique" : true, #唯一索引
"key" : {
"sid" : 1,
"user" : 1
},
"name" : "sid_1_user_1",
"ns" : "survey.data"
},
{
"v" : 1,
"key" : {
"sid" : 1,
"cdate" : -1
},
"name" : "sid_1_cdate_-1",
"ns" : "survey.data"
},
{
"v" : 1,
"key" : {
"sid" : 1,
"created" : -1
},
"name" : "sid_1_created_-1",
"ns" : "survey.data"
},
{
"v" : 1,
"key" : {
"sid" : 1,
"user" : 1,
"modified" : 1
},
"name" : "sid_1_user_1_modified_1",
"ns" : "survey.data"
}
]
zjy:PRIMARY> db.system.indexes.find({"ns":"survey.data"})
{ "v" : 1, "key" : { "_id" : 1 }, "name" : "_id_", "ns" : "survey.data" }
{ "v" : 1, "unique" : true, "key" : { "sid" : 1, "user" : 1 }, "name" : "sid_1_user_1", "ns" : "survey.data" }
{ "v" : 1, "key" : { "sid" : 1, "cdate" : -1 }, "name" : "sid_1_cdate_-1", "ns" : "survey.data" }
{ "v" : 1, "key" : { "sid" : 1, "created" : -1 }, "name" : "sid_1_created_-1", "ns" : "survey.data" }
{ "v" : 1, "key" : { "sid" : 1, "user" : 1, "modified" : 1 }, "name" : "sid_1_user_1_modified_1", "ns" : "survey.data" }

2)创建索引:db.collections.ensureIndex({...})

普通索引

zjy:PRIMARY> db.comments.ensureIndex({"name":1})  #name字段上创建索引,升序。倒序为-1。
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 2,
"numIndexesAfter" : 3,
"ok" : 1
}

zjy:PRIMARY> db.comments.ensureIndex({"account.name":1}) #内嵌文档上创建索引。
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 3,
"numIndexesAfter" : 4,
"ok" : 1
}

zjy:PRIMARY> db.comments.ensureIndex({"age":1},{"name":"idx_name"}) #指定索引名称
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 4,
"numIndexesAfter" : 5,
"ok" : 1
}

zjy:PRIMARY> db.comments.ensureIndex({"name":1,"age":1},{"name":"idx_name_age","background":true}) #后台创建复合索引
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 5,
"numIndexesAfter" : 6,
"ok" : 1
}

zjy:PRIMARY> db.comments.ensureIndex({"name":1,"age":1},{"name":"uk_name_age","background":true,"unique":true}) #后台创建唯一索引
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
}
zjy:PRIMARY> db.comments.ensureIndex({"name":1,"age":1},{"unique":true,"dropDups":true,"name":"uk_name_age"}) #删除重复数据创建唯一索引,dropDups在3.0里废弃。
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
}

​​哈希索引​​:hashed

zjy:PRIMARY> db.abc.ensureIndex({"a":"hashed"})
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
}
zjy:PRIMARY> db.abc.getIndexes()
[
{
"v" : 1,
"key" : {
"_id" : 1
},
"name" : "_id_",
"ns" : "test.abc"
},
{
"v" : 1,
"key" : {
"a" : "hashed"
},
"name" : "a_hashed",
"ns" : "test.abc"
}
]

这里还有2个比较特殊的索引:​​稀疏索引(​​sparse)和​​TTL索引​​(expireAfterSeconds)

TTL索引是一种特定的数据块,请求赋予时间范围的方式,它指定一个时间点,超过该时间点数据变成无效。

zjy:PRIMARY> db.comments.find()
{ "_id" : ObjectId("55ae6b99313fd7b879b5296c"), "name" : "zhoujy", "age" : 22, "ts" : ISODate("2015-07-21T15:56:09.651Z") }
{ "_id" : ObjectId("55ae6b9a313fd7b879b5296d"), "name" : "zhoujy", "age" : 22, "ts" : ISODate("2015-07-21T15:56:10.739Z") }
{ "_id" : ObjectId("55ae6b9b313fd7b879b5296e"), "name" : "zhoujy", "age" : 22, "ts" : ISODate("2015-07-21T15:56:11.555Z") }
{ "_id" : ObjectId("55ae6b9c313fd7b879b5296f"), "name" : "zhoujy", "age" : 22, "ts" : ISODate("2015-07-21T15:56:12.267Z") }
{ "_id" : ObjectId("55ae6b9c313fd7b879b52970"), "name" : "zhoujy", "age" : 22, "ts" : ISODate("2015-07-21T15:56:12.899Z") }
zjy:PRIMARY> db.comments.ensureIndex({"ts":1},{expireAfterSeconds:60}) #创建TTL索引,过期时间60秒,即60秒时间生成的数据会被删除。
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
}
zjy:PRIMARY> db.comments.find()
{ "_id" : ObjectId("55ae6b99313fd7b879b5296c"), "name" : "zhoujy", "age" : 22, "ts" : ISODate("2015-07-21T15:56:09.651Z") }
{ "_id" : ObjectId("55ae6b9a313fd7b879b5296d"), "name" : "zhoujy", "age" : 22, "ts" : ISODate("2015-07-21T15:56:10.739Z") }
{ "_id" : ObjectId("55ae6b9b313fd7b879b5296e"), "name" : "zhoujy", "age" : 22, "ts" : ISODate("2015-07-21T15:56:11.555Z") }
{ "_id" : ObjectId("55ae6b9c313fd7b879b5296f"), "name" : "zhoujy", "age" : 22, "ts" : ISODate("2015-07-21T15:56:12.267Z") }
{ "_id" : ObjectId("55ae6b9c313fd7b879b52970"), "name" : "zhoujy", "age" : 22, "ts" : ISODate("2015-07-21T15:56:12.899Z") }

zjy:PRIMARY> db.comments.getIndexes()
[
{
"v" : 1,
"key" : {
"_id" : 1
},
"name" : "_id_",
"ns" : "test.comments"
},
{
"v" : 1,
"key" : {
"ts" : 1
},
"name" : "ts_1",
"ns" : "test.comments",
"expireAfterSeconds" : 60
}
]

zjy:PRIMARY> db.comments.find() #60秒之后查看,数据已经没有

最后有一类索引是text index ​​文本索引​​​:更多的信息见 [MongoDB大数据处理权威指南第八章]和这里

测试数据:

MongoDB 索引相关知识_expainMongoDB 索引相关知识_mongodb_02

db.comments.insert({"name":"abc","mem":"You can create a text index on the field or fields whose value is a string or an array of string elements","ts":new Date()})

db.comments.insert({"name":"def","mem":"When creating a text index on multiple fields, you can specify the individual fields or you can use wildcard specifier ($**)","ts":new Date()})

db.comments.insert({"name":"ghi","mem":"This text index catalogs all string data in the subject field and the content field, where the field value is either a string or an array of string elements.","ts":new Date()})

db.comments.insert({"name":"jkl","mem":"To allow for text search on all fields with string content, use the wildcard specifier ($**) to index all fields that contain string content.","ts":new Date()})

db.comments.insert({"name":"mno","mem":"The following example indexes any string value in the data of every field of every document in collection and names the index TextIndex:","ts":new Date()})

View Code

​​创建​​:

> db.comments.ensureIndex({"mem":"text"})   #创建text索引
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
}

使用:​​$text 操作符​​

> db.comments.find({$text:{$search:"specifier"}}).pretty()
{
"_id" : ObjectId("55aee886a782f35b366926ef"),
"name" : "jkl",
"mem" : "To allow for text search on all fields with string content, use the wildcard specifier ($**) to index all fields that contain string content.",
"ts" : ISODate("2015-07-22T00:49:10.350Z")
}
{
"_id" : ObjectId("55aee886a782f35b366926ed"),
"name" : "def",
"mem" : "When creating a text index on multiple fields, you can specify the individual fields or you can use wildcard specifier ($**)",
"ts" : ISODate("2015-07-22T00:49:10.346Z")
}


> db.comments.runCommand("text",{search:"specifier"}) #3.0之前可以使用,之后无效。
{
"results" : [
{
"score" : 0.8653846153846153,
"obj" : {
"_id" : ObjectId("55aee886a782f35b366926ed"),
"name" : "def",
"mem" : "When creating a text index on multiple fields, you can specify the individual fields or you can use wildcard specifier ($**)",
"ts" : ISODate("2015-07-22T00:49:10.346Z")
}
},
{
"score" : 0.5357142857142857,
"obj" : {
"_id" : ObjectId("55aee886a782f35b366926ef"),
"name" : "jkl",
"mem" : "To allow for text search on all fields with string content, use the wildcard specifier ($**) to index all fields that contain string content.",
"ts" : ISODate("2015-07-22T00:49:10.350Z")
}
}
],
"stats" : {
"nscanned" : NumberLong(2),
"nscannedObjects" : NumberLong(2),
"n" : 2,
"timeMicros" : 173
},
"ok" : 1
}

上面大致介绍了各类索引的介绍和使用,具体的信息和注意事项可以找​​官方文档​​里查看,特别是要注意text和ttl索引的使用。

3)删除索引:dropIndex

zjy:PRIMARY> db.abc.getIndexes()    #查看索引
[
{
"v" : 1,
"key" : {
"_id" : 1
},
"name" : "_id_",
"ns" : "test.abc"
},
{
"v" : 1,
"key" : { #索引字段
"a" : "hashed"
},
"name" : "a_hashed", #索引名
"ns" : "test.abc"
},
{
"v" : 1,
"key" : {
"b" : 1
},
"name" : "b_1",
"ns" : "test.abc"
},
{
"v" : 1,
"key" : {
"c" : 1
},
"name" : "idx_c",
"ns" : "test.abc"
}
]
zjy:PRIMARY> db.abc.dropIndex({"a" : "hashed"}) #删除索引,指定"key"
{ "nIndexesWas" : 4, "ok" : 1 }
zjy:PRIMARY> db.abc.dropIndex({"b" : 1}) #删除索引,指定"key"
{ "nIndexesWas" : 3, "ok" : 1 }
zjy:PRIMARY> db.abc.dropIndex("idx_c") #删除索引,指定"name"
{ "nIndexesWas" : 2, "ok" : 1 }
zjy:PRIMARY> db.abc.getIndexes()
[
{
"v" : 1,
"key" : {
"_id" : 1
},
"name" : "_id_",
"ns" : "test.abc"
}
]

zjy:PRIMARY> db.abc.dropIndex("*") #删除索引,删除集合的全部索引
{
"nIndexesWas" : 4,
"msg" : "non-_id indexes dropped for collection",
"ok" : 1
}

4)重建索引:索引出现损坏需要重建。reindex

zjy:PRIMARY> db.abc.reIndex()   #执行
{
"nIndexesWas" : 1,
"nIndexes" : 1,
"indexes" : [
{
"key" : {
"_id" : 1
},
"name" : "_id_",
"ns" : "test.abc"
}
],
"ok" : 1
}

5)强制使用指定索引。hint

db.abc.find({"c":1,"b":2}).hint("b_1")  #hint里面是"索引字段"或则"索引名"

 

总结:

      索引可以加快检索、排序等操作的效率,但是对于增删改的操作却有一定的开销,所以不要一味的加索引,在必要的字段上加合适的索引才是需要的。更多的信息请参考​​官方文档​​。

 

~~~~~~~~~~~~~~~ 万物之中,希望至美 ~~~~~~~~~~~~~~~

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