M201: MongoDB Performance chapter 2 Mongodb Indexes学习记录
运行环境
操作系统：windows 10 家庭中文版
Mongodb ：Mongodb 3.4
Mongodb安装路径：E:>MongoDB\Server\3.4\bin\
Mongodb存储路径：E:>MongoDB\data
课后问题
lab1
In this lab you’re going to determine which queries are able to successfully use a given index for both filtering and sorting.
Given the following index:
{ "first_name": 1, "address.state": -1, "address.city": -1, "ssn": 1 }Which of the following queries are able to use it for both filtering and sorting?
Check all that apply:
- db.people.find({“address.city”: “West Cindy”}).sort({“address.city” : -1 })
- db.people.find({“first_name”: {$gt: “J”}}).sort({“address.city”: -1})
- db.people.find({“first_name”:”Jessica”}).sort({“address.state”: 1,”address.city” : 1})
- db.people.find({“first_name”: “Jessica”,”address.state”: {$lt:”S”}}).sort({“address.state”:1})
- db.people.find({“address.state”:”South Dakota”,”first_name”: “Jessica”}).sort({“address.city”: -1})
解答
启动mongod守护进程：
C:\Users\Shinelon>e:
E:\>MongoDB\Server\3.4\bin\mongod.exe --dbpath MongoDB\data
2018-04-16T08:43:34.604-0700 I CONTROL [initandlisten] MongoDB starting : pid=7500 port=27017 dbpath=MongoDB\data 64-bit host=DESKTOP-MP9NVQ7
2018-04-16T08:43:34.605-0700 I CONTROL [initandlisten] targetMinOS: Windows 7/Windows Server 2008 R2
2018-04-16T08:43:34.605-0700 I CONTROL [initandlisten] db version v3.4.6
2018-04-16T08:43:34.606-0700 I CONTROL [initandlisten] git version: c55eb86ef46ee7aede3b1e2a5d184a7df4bfb5b5
2018-04-16T08:43:34.606-0700 I CONTROL [initandlisten] OpenSSL version: OpenSSL 1.0.1u-fips 22 Sep 2016
2018-04-16T08:43:34.606-0700 I CONTROL [initandlisten] allocator: tcmalloc
2018-04-16T08:43:34.606-0700 I CONTROL [initandlisten] modules: none
2018-04-16T08:43:34.606-0700 I CONTROL [initandlisten] build environment:
2018-04-16T08:43:34.606-0700 I CONTROL [initandlisten] distmod: 2008plus-ssl
2018-04-16T08:43:34.606-0700 I CONTROL [initandlisten] distarch: x86_64
2018-04-16T08:43:34.606-0700 I CONTROL [initandlisten] target_arch: x86_64
2018-04-16T08:43:34.606-0700 I CONTROL [initandlisten] options: { storage: { dbPath: "MongoDB\data" } }
2018-04-16T08:43:34.634-0700 I - [initandlisten] Detected data files in MongoDB\data created by the 'wiredTiger' storage engine, so setting the active storage engine to 'wiredTiger'.
2018-04-16T08:43:34.635-0700 I STORAGE [initandlisten] wiredtiger_open config: create,cache_size=7656M,session_max=20000,eviction=(threads_min=4,threads_max=4),config_base=false,statistics=(fast),log=(enabled=true,archive=true,path=journal,compressor=snappy),file_manager=(close_idle_time=100000),checkpoint=(wait=60,log_size=2GB),statistics_log=(wait=0),
2018-04-16T08:43:35.494-0700 I CONTROL [initandlisten]
2018-04-16T08:43:35.495-0700 I CONTROL [initandlisten] ** WARNING: Access control is not enabled for the database.
2018-04-16T08:43:35.495-0700 I CONTROL [initandlisten] ** Read and write access to data and configuration is unrestricted.
2018-04-16T08:43:35.495-0700 I CONTROL [initandlisten]
2018-04-16T23:43:35.738+0800 I FTDC [initandlisten] Initializing full-time diagnostic data capture with directory 'MongoDB/data/diagnostic.data'
2018-04-16T23:43:35.741+0800 I NETWORK [thread1] waiting for connections on port 27017进入mongo shell：
E:\>MongoDB\Server\3.4\bin\mongo.exe
MongoDB shell version v3.4.6
connecting to: mongodb://127.0.0.1:27017
MongoDB server version: 3.4.6
Server has startup warnings:
2018-04-16T08:43:35.494-0700 I CONTROL [initandlisten]
2018-04-16T08:43:35.495-0700 I CONTROL [initandlisten] ** WARNING: Access control is not enabled for the database.
2018-04-16T08:43:35.495-0700 I CONTROL [initandlisten] ** Read and write access to data and configuration is unrestricted.
2018-04-16T08:43:35.495-0700 I CONTROL [initandlisten]切换至m201库：
> use m201
switched to db m201创建题目给出的索引：
> db.people.ensureIndex({ "first_name": 1, "address.state": -1, "address.city": -1, "ssn": 1 })
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
}依次查看5个查询的执行计划：
- 第一个：
> db.people.find({"address.city": "West Cindy"}).sort({"address.city" : -1 }).explain()
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "m201.people",
"indexFilterSet" : false,
"parsedQuery" : {
"address.city" : {
"$eq" : "West Cindy"
}
},
"winningPlan" : {
"stage" : "SORT",
"sortPattern" : {
"address.city" : -1
},
"inputStage" : {
"stage" : "SORT_KEY_GENERATOR",
"inputStage" : {
"stage" : "COLLSCAN",
"filter" : {
"address.city" : {
"$eq" : "West Cindy"
}
},
"direction" : "forward"
}
}
},
"rejectedPlans" : [ ]
},
"serverInfo" : {
"host" : "DESKTOP-MP9NVQ7",
"port" : 27017,
"version" : "3.4.6",
"gitVersion" : "c55eb86ef46ee7aede3b1e2a5d184a7df4bfb5b5"
},
"ok" : 1
}注意到使用的是COLLSCAN（全表扫描）
第二个
> db.people.find({"first_name": {$gt: "J"}}).sort({"address.city": -1}).explain()
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "m201.people",
"indexFilterSet" : false,
"parsedQuery" : {
"first_name" : {
"$gt" : "J"
}
},
"winningPlan" : {
"stage" : "SORT",
"sortPattern" : {
"address.city" : -1
},
"inputStage" : {
"stage" : "SORT_KEY_GENERATOR",
"inputStage" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"first_name" : 1,
"address.state" : -1,
"address.city" : -1,
"ssn" : 1
},
"indexName" : "first_name_1_address.state_-1_address.city_-1_ssn_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"first_name" : [ ],
"address.state" : [ ],
"address.city" : [ ],
"ssn" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "forward",
"indexBounds" : {
"first_name" : [
"(\"J\", {})"
],
"address.state" : [
"[MaxKey, MinKey]"
],
"address.city" : [
"[MaxKey, MinKey]"
],
"ssn" : [
"[MinKey, MaxKey]"
]
}
}
}
}
},
"rejectedPlans" : [ ]
},
"serverInfo" : {
"host" : "DESKTOP-MP9NVQ7",
"port" : 27017,
"version" : "3.4.6",
"gitVersion" : "c55eb86ef46ee7aede3b1e2a5d184a7df4bfb5b5"
},
"ok" : 1
}注意只有sorting使用的是SORT_KEY_GENERATOR（内存排序），filtering用的IXSCAN（索引）
第三个：
> db.people.find({"first_name":"Jessica"}).sort({"address.state": 1,"address.city" : 1}).explain()
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "m201.people",
"indexFilterSet" : false,
"parsedQuery" : {
"first_name" : {
"$eq" : "Jessica"
}
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"first_name" : 1,
"address.state" : -1,
"address.city" : -1,
"ssn" : 1
},
"indexName" : "first_name_1_address.state_-1_address.city_-1_ssn_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"first_name" : [ ],
"address.state" : [ ],
"address.city" : [ ],
"ssn" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "backward",
"indexBounds" : {
"first_name" : [
"[\"Jessica\", \"Jessica\"]"
],
"address.state" : [
"[MinKey, MaxKey]"
],
"address.city" : [
"[MinKey, MaxKey]"
],
"ssn" : [
"[MaxKey, MinKey]"
]
}
}
},
"rejectedPlans" : [ ]
},
"serverInfo" : {
"host" : "DESKTOP-MP9NVQ7",
"port" : 27017,
"version" : "3.4.6",
"gitVersion" : "c55eb86ef46ee7aede3b1e2a5d184a7df4bfb5b5"
},
"ok" : 1
}符合题意
第四个：
> db.people.find({"first_name": "Jessica","address.state": {$lt:"S"}}).sort({"address.state":1}).explain()
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "m201.people",
"indexFilterSet" : false,
"parsedQuery" : {
"$and" : [
{
"first_name" : {
"$eq" : "Jessica"
}
},
{
"address.state" : {
"$lt" : "S"
}
}
]
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"first_name" : 1,
"address.state" : -1,
"address.city" : -1,
"ssn" : 1
},
"indexName" : "first_name_1_address.state_-1_address.city_-1_ssn_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"first_name" : [ ],
"address.state" : [ ],
"address.city" : [ ],
"ssn" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "backward",
"indexBounds" : {
"first_name" : [
"[\"Jessica\", \"Jessica\"]"
],
"address.state" : [
"[\"\", \"S\")"
],
"address.city" : [
"[MinKey, MaxKey]"
],
"ssn" : [
"[MaxKey, MinKey]"
]
}
}
},
"rejectedPlans" : [ ]
},
"serverInfo" : {
"host" : "DESKTOP-MP9NVQ7",
"port" : 27017,
"version" : "3.4.6",
"gitVersion" : "c55eb86ef46ee7aede3b1e2a5d184a7df4bfb5b5"
},
"ok" : 1
}符合题意
第五个：
> db.people.find({"address.state":"South Dakota","first_name": "Jessica"}).sort({"address.city": -1}).explain()
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "m201.people",
"indexFilterSet" : false,
"parsedQuery" : {
"$and" : [
{
"address.state" : {
"$eq" : "South Dakota"
}
},
{
"first_name" : {
"$eq" : "Jessica"
}
}
]
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"first_name" : 1,
"address.state" : -1,
"address.city" : -1,
"ssn" : 1
},
"indexName" : "first_name_1_address.state_-1_address.city_-1_ssn_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"first_name" : [ ],
"address.state" : [ ],
"address.city" : [ ],
"ssn" : [ ]
},
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "forward",
"indexBounds" : {
"first_name" : [
"[\"Jessica\", \"Jessica\"]"
],
"address.state" : [
"[\"South Dakota\", \"South Dakota\"]"
],
"address.city" : [
"[MaxKey, MinKey]"
],
"ssn" : [
"[MinKey, MaxKey]"
]
}
}
},
"rejectedPlans" : [ ]
},
"serverInfo" : {
"host" : "DESKTOP-MP9NVQ7",
"port" : 27017,
"version" : "3.4.6",
"gitVersion" : "c55eb86ef46ee7aede3b1e2a5d184a7df4bfb5b5"
},
"ok" : 1
}符合题意
所以3，4，5为所求
lab2
In this lab you’re going to examine several example queries and determine which compound index will best service them.
> db.people.find({
"address.state": "Nebraska",
"last_name": /^G/,
"job": "Police officer"
})> db.people.find({
"job": /^P/,
"first_name": /^C/,
"address.state": "Indiana"
}).sort({ "last_name": 1 })> db.people.find({
"address.state": "Connecticut",
"birthday": {
"$gte": ISODate("2010-01-01T00:00:00.000Z"),
"$lt": ISODate("2011-01-01T00:00:00.000Z")
}
})If you had to build one index on the people collection, which of the following indexes would best sevice all 3 queries?
Choose the best answer:
- { “job”: 1, “address.state”: 1 }
- { “job”: 1, “address.state”: 1 ,”last_name”: 1}
- {“address.state”: 1, “job”: 1}
- {“address.state”: 1, “job”: 1, “first_name”: 1}
- {“address.state”: 1,”last_name”: 1, “job”: 1}
- { “job”: 1, “address.state”: 1 ,”first_name”: 1}
解答
分析：
第一个查询只包含范围查询，无排序。索引包含address.state，last_name，job这3个字段即可，适合的索引为：
5=2>1=3>4=6第二个查询需要根据last_name排序，所以最适合他的索引是包含job，first_name，address.state，last_name字段，并且last_name字段排在最后的索引,适合的索引为：
2>5>4=6>1=3第三个查询需要根据birthday排序（无相关索引），且要包含address.state字段，适合的索引为：
3>4=5>1=2=6综上分析，只有5是最合适的