摘要：插入兩條數據建立全文索引需要注意的是這里使用關鍵詞來代表全文索引，我們在這里就不建立數據模型了。全文索引查找表示要在全文索引中查東西。全文索引在工作還是經常使用的，比如博客文章的搜索，長文件的關鍵詞搜索，這些都需要使用全文索引來進行。

索引

在認識索引的之前我們先建立一張表，并往其中插入200萬條數據。

// test.js
//生成隨機數
function GetRandomNum(min,max){
    let range = max-min;   //得到隨機數區間
    let rand = Math.random(); //得到隨機值
    return (min + Math.round(rand *range)); //最小值+隨機數取整
}

//console.log(GetRandomNum(10000,99999));

//生成隨機用戶名
function GetRadomUserName(min,max){
    let tempStringArray= "123456789qwertyuiopasdfghjklzxcvbnm".split("");//構造生成時的字母庫數組
    let outPuttext = ""; //最后輸出的變量
    //進行循環，隨機生產用戶名的長度，這里需要生成隨機數方法的配合
    for(let i=1 ;i
> mongo
> load("./test.js")
connecting to: mongodb://127.0.0.1:27017/company
MongoDB server version: 3.4.10
···
// 這個過程可能需要2分鐘左右

> use company
switched to db company
> db.randomInfo.stats()            // 使用這個查看插入了幾條數據
{
        "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
}

// 執行
> db.randomInfo.getIndexes()
[
        {
                "v" : 2,
                "key" : {
                        "_id" : 1
                },
                "name" : "_id_",
                "ns" : "company.randomInfo"
        }
]
// 這是默認的索引，我們一般不會使用這個索引的
建立一個索引
> db.randomInfo.ensureIndex({username: 1})
{
        "createdCollectionAutomatically" : false,
        "numIndexesBefore" : 1,
        "numIndexesAfter" : 2,
        "ok" : 1
}
> db.randomInfo.getIndexes()            // 然后查看發現有兩條索引了
[
        {
                "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()  //得到程序運行的開始時間
var  db = connect("company")          //鏈接數據庫
var   rs=db.randomInfo.find({username:"tfruhjy8k"})  //根據用戶名查找用戶
rs.forEach(rs=>{printjson(rs)})                     //循環輸出
var  runTime = new Date().getTime()-startTime;      //得到程序運行時間
print("[SUCCESS]This run time is:"+runTime+"ms")    //打印出運行時間
// 執行查找
> 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   // 關鍵看這里，你會發現時間縮短了好多呢
true
>
無論是在關系型數據庫還是文檔數據庫，建立索引都是非常重要的。前邊講了，索引這東西是要消耗硬盤和內存資源的，所以還是要根據程序需要進行建立了。MongoDB也給我們進行了限制，只允許我們建立64個索引值。
復合索引
復合索引就是兩條以上的索引
// 在建立一個索引
> 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"
        }
]
>
我們同時查詢兩個索引的值，看看效果是怎么樣的。
// 
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");
// 從性能上看并沒有什么特殊的變化，查詢時間還是在10ms左右。MongoDB的復合查詢是按照我們的索引順序進行查詢的。就是我們用db.randomInfo.getIndexes()查詢出的數組。
指定索引查找
// 
var  rs= db.randomInfo.find({username:"7xwb8y3",randNum0:565509}).hint({randNum0:1});
刪除索引
db.randomInfo.dropIndex("randNum0_1");//索引的唯一ID
這里需要注意的是刪除時填寫的值，并不是我們的字段名稱（key），而是我們索引查詢表中的name值。這是一個小坑。
全文索引
有些時候需要在大篇幅的文章中搜索關鍵詞，比如我寫的文章每篇都在萬字以上，這時候你想搜索關鍵字是非常不容易的，MongoDB為我們提供了全文索引。
// 插入兩條數據
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"});
//需要注意的是這里使用text關鍵詞來代表全文索引，我們在這里就不建立數據模型了。
全文索引查找
// $text:表示要在全文索引中查東西。這里的$test指的就是contextInfo
// $search:后邊跟查找的內容。
db.info.find({$text:{$search:"programmer"}})； // 查找contextInfo中含有programmer關鍵字的
查找多個詞
// 比如我們希望查找數據中有programmer，family，diary，drink的數據（這是或的關系），所以兩條數據都會出現。
db.info.find({$text:{$search:"programmer family diary drink"}})

// 如果我們這時候希望不查找出來有drink這個單詞的記錄，我們可以使用“-”減號來取消。
db.info.find({$text:{$search:"programmer family diary -drink"}})

// 全文搜索中是支持轉義符的，比如我們想搜索的是兩個詞（love PlayGame和drink），這時候需要使用斜杠來轉意。
db.info.find({$text:{$search:""love PlayGame" drink"}})
全文索引在工作還是經常使用的，比如博客文章的搜索，長文件的關鍵詞搜索，這些都需要使用全文索引來進行。

到這里Mongodb的基本知識就基本結束了，下一節我們將會學習如何管理Mongodb
參考文獻
技術胖