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
