MongoDB （ 五 ）高级_索引

摘要：插入两条数据建立全文索引需要注意的是这里使用关键词来代表全文索引，我们在这里就不建立数据模型了。全文索引查找表示要在全文索引中查东西。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。

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
> 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