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：

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。