索引

在认识索引的之前我们先建立一张表，并往其中插入200万条数据。

// test.js
//生成随机数
function GetRandomNum(min,max){let range = max-min;   //得到随机数区间let rand = Math.random(); //得到随机值return (min + Math.round(rand *range)); //最小值+随机数取整
}//console.log(GetRandomNum(10000,99999));//生成随机用户名
function GetRadomUserName(min,max){let tempStringArray= "123456789qwertyuiopasdfghjklzxcvbnm".split("");//构造生成时的字母库数组let outPuttext = ""; //最后输出的变量//进行循环，随机生产用户名的长度，这里需要生成随机数方法的配合for(let i=1 ;i<GetRandomNum(min,max);i++){//随机抽取字母，拼装成需要的用户名outPuttext=outPuttext+tempStringArray[GetRandomNum(0,tempStringArray.length)]}return outPuttext;
}var db = connect('company');
db.randomInfo.drop();
var  tempInfo = [];
for (let i=0;i<2000000;i++){tempInfo.push({username:GetRadomUserName(7,16),regeditTime:new Date(),randNum0:GetRandomNum(100000,999999),randNum1:GetRandomNum(100000,999999),randNum2:GetRandomNum(100000,999999),randNum3:GetRandomNum(100000,999999),randNum4:GetRandomNum(100000,999999),randNum5:GetRandomNum(100000,999999),randNum6:GetRandomNum(100000,999999),randNum7:GetRandomNum(100000,999999),randNum8:GetRandomNum(100000,999999),randNum8:GetRandomNum(100000,999999),})
}db.randomInfo.insert(tempInfo);

> mongo
> load("./test.js")
connecting to: mongodb://127.0.0.1:27017/company
MongoDB server version: 3.4.10
···
// 这个过程可能需要2分钟左右> use company
switched to db company
> db.randomInfo.stats()            // 使用这个查看插入了几条数据
{"ns" : "company.randomInfo","size" : 421908971,"count" : 1835000,"avgObjSize" : 229,"storageSize" : 188686336,"capped" : false,"wiredTiger" : {"metadata" : {"formatVersion" : 1},"creationString" : "access_pattern_hint=none,allocation_size=4KB,app_metadata=(formatVersion=1),block_allocation=best,block_compressor=snappy,cache_resident=false,checksum=on,colgroups=,collator=,columns=,dictionary=0,encryption=(keyid=,name=),exclusive=false,extractor=,format=btree,huffman_key=,huffman_value=,ignore_in_memory_cache_size=false,immutable=false,internal_item_max=0,internal_key_max=0,internal_key_truncate=true,internal_page_max=4KB,key_format=q,key_gap=10,leaf_item_max=0,leaf_key_max=0,leaf_page_max=32KB,leaf_value_max=64MB,log=(enabled=true),lsm=(auto_throttle=true,bloom=true,bloom_bit_count=16,bloom_config=,bloom_hash_count=8,bloom_oldest=false,chunk_count_limit=0,chunk_max=5GB,chunk_size=10MB,merge_max=15,merge_min=0),memory_page_max=10m,os_cache_dirty_max=0,os_cache_max=0,prefix_compression=false,prefix_compression_min=4,source=,split_deepen_min_child=0,split_deepen_per_child=0,split_pct=90,type=file,value_format=u","type" : "file","uri" : "statistics:table:collection-0-5869292382622143333","LSM" : {"bloom filter false positives" : 0,"bloom filter hits" : 0,"bloom filter misses" : 0,"bloom filter pages evicted from cache" : 0,"bloom filter pages read into cache" : 0,"bloom filters in the LSM tree" : 0,"chunks in the LSM tree" : 0,"highest merge generation in the LSM tree" : 0,"queries that could have benefited from a Bloom filter that did not exist" : 0,"sleep for LSM checkpoint throttle" : 0,"sleep for LSM merge throttle" : 0,"total size of bloom filters" : 0},"block-manager" : {"allocations requiring file extension" : 15471,"blocks allocated" : 15475,"blocks freed" : 39,"checkpoint size" : 188481536,"file allocation unit size" : 4096,"file bytes available for reuse" : 188416,"file magic number" : 120897,"file major version number" : 1,"file size in bytes" : 188686336,"minor version number" : 0},"btree" : {"btree checkpoint generation" : 20,"column-store fixed-size leaf pages" : 0,"column-store internal pages" : 0,"column-store variable-size RLE encoded values" : 0,"column-store variable-size deleted values" : 0,"column-store variable-size leaf pages" : 0,"fixed-record size" : 0,"maximum internal page key size" : 368,"maximum internal page size" : 4096,"maximum leaf page key size" : 2867,"maximum leaf page size" : 32768,"maximum leaf page value size" : 67108864,"maximum tree depth" : 4,"number of key/value pairs" : 0,"overflow pages" : 0,"pages rewritten by compaction" : 0,"row-store internal pages" : 0,"row-store leaf pages" : 0},"cache" : {"bytes currently in the cache" : 502018875,"bytes read into cache" : 0,"bytes written from cache" : 437640755,"checkpoint blocked page eviction" : 0,"data source pages selected for eviction unable to be evicted" : 12,"hazard pointer blocked page eviction" : 0,"in-memory page passed criteria to be split" : 130,"in-memory page splits" : 62,"internal pages evicted" : 0,"internal pages split during eviction" : 1,"leaf pages split during eviction" : 56,"modified pages evicted" : 56,"overflow pages read into cache" : 0,"overflow values cached in memory" : 0,"page split during eviction deepened the tree" : 1,"page written requiring lookaside records" : 0,"pages read into cache" : 0,"pages read into cache requiring lookaside entries" : 0,"pages requested from the cache" : 2232017,"pages written from cache" : 15472,"pages written requiring in-memory restoration" : 0,"tracked dirty bytes in the cache" : 0,"unmodified pages evicted" : 0},"cache_walk" : {"Average difference between current eviction generation when the page was last considered" : 0,"Average on-disk page image size seen" : 0,"Clean pages currently in cache" : 0,"Current eviction generation" : 0,"Dirty pages currently in cache" : 0,"Entries in the root page" : 0,"Internal pages currently in cache" : 0,"Leaf pages currently in cache" : 0,"Maximum difference between current eviction generation when the page was last considered" : 0,"Maximum page size seen" : 0,"Minimum on-disk page image size seen" : 0,"On-disk page image sizes smaller than a single allocation unit" : 0,"Pages created in memory and never written" : 0,"Pages currently queued for eviction" : 0,"Pages that could not be queued for eviction" : 0,"Refs skipped during cache traversal" : 0,"Size of the root page" : 0,"Total number of pages currently in cache" : 0},"compression" : {"compressed pages read" : 0,"compressed pages written" : 15312,"page written failed to compress" : 0,"page written was too small to compress" : 158,"raw compression call failed, additional data available" : 0,"raw compression call failed, no additional data available" : 0,"raw compression call succeeded" : 0},"cursor" : {"bulk-loaded cursor-insert calls" : 0,"create calls" : 3,"cursor-insert key and value bytes inserted" : 429166606,"cursor-remove key bytes removed" : 0,"cursor-update value bytes updated" : 0,"insert calls" : 1835000,"next calls" : 162051,"prev calls" : 1,"remove calls" : 0,"reset calls" : 30748,"restarted searches" : 0,"search calls" : 0,"search near calls" : 1227,"truncate calls" : 0,"update calls" : 0},"reconciliation" : {"dictionary matches" : 0,"fast-path pages deleted" : 0,"internal page key bytes discarded using suffix compression" : 31112,"internal page multi-block writes" : 4,"internal-page overflow keys" : 0,"leaf page key bytes discarded using prefix compression" : 0,"leaf page multi-block writes" : 66,"leaf-page overflow keys" : 0,"maximum blocks required for a page" : 242,"overflow values written" : 0,"page checksum matches" : 209,"page reconciliation calls" : 171,"page reconciliation calls for eviction" : 57,"pages deleted" : 1},"session" : {"object compaction" : 0,"open cursor count" : 3},"transaction" : {"update conflicts" : 0}},"nindexes" : 1,"totalIndexSize" : 18272256,"indexSizes" : {"_id_" : 18272256},"ok" : 1
}

// 执行
> db.randomInfo.getIndexes()
[{"v" : 2,"key" : {"_id" : 1},"name" : "_id_","ns" : "company.randomInfo"}
]
// 这是默认的索引，我们一般不会使用这个索引的

建立一个索引

> db.randomInfo.ensureIndex({username: 1})
{"createdCollectionAutomatically" : false,"numIndexesBefore" : 1,"numIndexesAfter" : 2,"ok" : 1
}
> db.randomInfo.getIndexes()            // 然后查看发现有两条索引了
[{"v" : 2,"key" : {"_id" : 1},"name" : "_id_","ns" : "company.randomInfo"},{"v" : 2,"key" : {"username" : 1},"name" : "username_1","ns" : "company.randomInfo"}
]
>

//test1.js
var startTime = new Date().getTime()  //得到程序运行的开始时间
var  db = connect('company')          //链接数据库
var   rs=db.randomInfo.find({username:"tfruhjy8k"})  //根据用户名查找用户
rs.forEach(rs=>{printjson(rs)})                     //循环输出
var  runTime = new Date().getTime()-startTime;      //得到程序运行时间
print('[SUCCESS]This run time is:'+runTime+'ms')    //打印出运行时间

// 执行查找
> load('./test1.js')
connecting to: mongodb://127.0.0.1:27017/company
MongoDB server version: 3.4.10
{"_id" : ObjectId("5ac8b73b5646d96c6db3e1a8"),"username" : "od2umr6kec","regeditTime" : ISODate("2018-04-07T12:18:44.292Z"),"randNum0" : 577322,"randNum1" : 961443,"randNum2" : 999621,"randNum3" : 968291,"randNum4" : 834839,"randNum5" : 637084,"randNum6" : 172311,"randNum7" : 219693,"randNum8" : 617081
}
[SUCCESS]This run time is:11ms   // 关键看这里，你会发现时间缩短了好多呢
true
>

无论是在关系型数据库还是文档数据库，建立索引都是非常重要的。前边讲了，索引这东西是要消耗硬盘和内存资源的，所以还是要根据程序需要进行建立了。MongoDB也给我们进行了限制，只允许我们建立64个索引值。

复合索引

复合索引就是两条以上的索引

// 在建立一个索引
> db.randomInfo.ensureIndex({randNum0: 1});
{"createdCollectionAutomatically" : false,"numIndexesBefore" : 2,"numIndexesAfter" : 3,"ok" : 1
}

> db.randomInfo.getIndexes();
[{"v" : 2,"key" : {"_id" : 1},"name" : "_id_","ns" : "company.randomInfo"},{"v" : 2,"key" : {"username" : 1},"name" : "username_1","ns" : "company.randomInfo"},{"v" : 2,"key" : {"randNum0" : 1},"name" : "randNum0_1","ns" : "company.randomInfo"}
]
>

我们同时查询两个索引的值，看看效果是怎么样的。

//
var startTime=new Date().getTime();
var db = connect('company');var  rs= db.randomInfo.find({username:'7xwb8y3',randNum0:565509});rs.forEach(rs=>{printjson(rs)});var runTime = new Date().getTime()-startTime;
print('[Demo]this run time is '+runTime+'ms');
// 从性能上看并没有什么特殊的变化，查询时间还是在10ms左右。MongoDB的复合查询是按照我们的索引顺序进行查询的。就是我们用db.randomInfo.getIndexes()查询出的数组。

指定索引查找

//
var  rs= db.randomInfo.find({username:'7xwb8y3',randNum0:565509}).hint({randNum0:1});

删除索引

db.randomInfo.dropIndex('randNum0_1');//索引的唯一ID

这里需要注意的是删除时填写的值，并不是我们的字段名称（key），而是我们索引查询表中的name值。这是一个小坑。

全文索引

有些时候需要在大篇幅的文章中搜索关键词，比如我写的文章每篇都在万字以上，这时候你想搜索关键字是非常不容易的，MongoDB为我们提供了全文索引。

// 插入两条数据
db.info.insert({contextInfo:"I am a programmer, I love life, love family. Every day after work, I write a diary."})
db.info.insert({contextInfo:"I am a programmer, I love PlayGame, love drink. Every day after work, I playGame and drink."})

建立全文索引

db.info.ensureIndex({contextInfo:'text'});
//需要注意的是这里使用text关键词来代表全文索引，我们在这里就不建立数据模型了。

全文索引查找

// $text:表示要在全文索引中查东西。这里的$test指的就是contextInfo
// $search:后边跟查找的内容。
db.info.find({$text:{$search:"programmer"}})； // 查找contextInfo中含有programmer关键字的

查找多个词

// 比如我们希望查找数据中有programmer，family，diary，drink的数据（这是或的关系），所以两条数据都会出现。
db.info.find({$text:{$search:"programmer family diary drink"}})// 如果我们这时候希望不查找出来有drink这个单词的记录，我们可以使用“-”减号来取消。
db.info.find({$text:{$search:"programmer family diary -drink"}})// 全文搜索中是支持转义符的，比如我们想搜索的是两个词（love PlayGame和drink），这时候需要使用\斜杠来转意。
db.info.find({$text:{$search:"\"love PlayGame\" drink"}})

全文索引在工作还是经常使用的，比如博客文章的搜索，长文件的关键词搜索，这些都需要使用全文索引来进行。

到这里Mongodb的基本知识就基本结束了，下一节我们将会学习如何管理Mongodb

• 参考文献

技术胖