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" : { "$eq" : "CYHS1301942" } }, "winningPlan" : { "stage" : "FETCH", "inputStage" : { "stage" : "IXSCAN", #索引扫描，COLLSCAN表示全表扫描。 "keyPattern" : { "b" : 1, "date" : 1 }, "indexName" : "b_1_date_1", #索引名 "isMultiKey" : false, "direction" : "forward", "indexBounds" : { "b" : [ "["CYHS1301942", "CYHS1301942"]" ], "date" : [ "[MinKey, MaxKey]" ] } } }, "rejectedPlans" : [ ] }, "serverInfo" : { "host" : "mongo1", "port" : 27017, "version" : "3.0.4", "gitVersion" : "0481c958daeb2969800511e7475dc66986fa9ed5" }, "ok" : 1 }
3.0要是查看更详细的执行计划请看其他2个参数：
zjy:PRIMARY> db.newtask.find({"b":"CYHS1301942"}).explain("allPlansExecution") { "queryPlanner" : { "plannerVersion" : 1, "namespace" : "cde.newtask", "indexFilterSet" : false, "parsedQuery" : { "b" : { "$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 }
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 }
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：
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 ($**) to index all fields that contain string content.","ts":new Date()})
db.comments.insert({"name":"mno","mem":"The following example indexes any string value in the data of every field of every document in collection and names the index TextIndex:","ts":new Date()})
创建:
> db.comments.ensureIndex({"mem":"text"}) #创建text索引
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
}
使用：$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。