### 1. term filter 與keyword
測試數據
~~~
POST /forum/article/_bulk
{ "index": { "_id": 1 }}
{ "articleID" : "XHDK-A-1293-#fJ3", "userID" : 1, "hidden": false, "postDate": "2017-01-01" }
{ "index": { "_id": 2 }}
{ "articleID" : "KDKE-B-9947-#kL5", "userID" : 1, "hidden": false, "postDate": "2017-01-02" }
{ "index": { "_id": 3 }}
{ "articleID" : "JODL-X-1937-#pV7", "userID" : 2, "hidden": false, "postDate": "2017-01-01" }
{ "index": { "_id": 4 }}
{ "articleID" : "QQPX-R-3956-#aD8", "userID" : 2, "hidden": true, "postDate": "2017-01-02" }
~~~
1. 匹配text
~~~
GET forum/_search
{
"query": {
"constant_score": { # 固定分值，就不用計算相關度，過濾唄
"filter": {
"term": {
"articleID": "KDKE-B-9947-#kL5"
}
},
"boost": 1.2
}
}
}
~~~
> articleID被分詞成：kdke,b,9947,kl5，而term過濾是精準過濾，KDKE-B-9947-#kL5不會被分詞，還是用KDKE-B-9947-#kL5去查詢過濾，所以這條過濾不能得到document。
結果啥也沒搜到
~~~
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 0,
"max_score": null,
"hits": []
}
}
~~~
2. 再用articleID.keyword去過濾
keyword參考elasticsearch基礎 type=keyword
~~~
GET forum/_search
{
"query": {
"constant_score": {
"filter": {
"term": {
"articleID.keyword": "KDKE-B-9947-#kL5"
}
},
"boost": 1.2 # 固定分值，不指定默認為1
}
}
}
~~~
結果
~~~
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 1.2,
"hits": [
{
"_index": "forum",
"_type": "article",
"_id": "2",
"_score": 1.2,
"_source": {
"articleID": "KDKE-B-9947-#kL5",
"userID": 1,
"hidden": false,
"postDate": "2017-01-02"
}
}
]
}
}
~~~
或者這么寫
~~~
GET forum/_search
{
"query": {
"bool": {
"filter": {
"term": {
"articleID.keyword": "KDKE-B-9947-#kL5"
}
}
}
}
}
~~~
## 2. 組合查詢
~~~
GET forum/_search
{
"query": {
"bool": {
"should": [
{"term": {"postDate":"2017-01-01"}},
{"term": {"articleID":"QQPX-R-3956-#aD8"}} # 文檔被分詞，查詢條件（不被分詞）無法精準匹配到QQPX-R-3956-#aD8的文檔
]
}
}
}
~~~
~~~
GET forum/_search
{
"query": {
"bool": {
"should": [
{"term": {"postDate":"2017-01-01"}},
{"term": {"articleID.keyword":"QQPX-R-3956-#aD8"}} # text 字段keyword屬性，不分詞，查詢到QQPX-R-3956-#aD8
]
}
}
}
~~~
## 3. terms 查詢
1. 查找articleID是QQPX-R-3956-#aD8 或者KDKE-B-9947-#kL5的文檔
~~~
GET forum/_search
{
"query": {
"terms": {
"articleID.keyword": [
"QQPX-R-3956-#aD8",
"KDKE-B-9947-#kL5"
]
}
}
}
~~~
## 4. range
1. range查詢
~~~
GET forum/_search
{
"query": {
"range": {
"view_cnt": {
"gte": 50,
"lte": 90
}
}
}
}
~~~
2. range過濾
~~~
GET forum/_search
{
"query": {
"constant_score": {
"filter": {
"range": {
"view_cnt": {
"gte": 50,
"lte": 90
}
}
}
}
}
}
~~~
* 同樣的效果range過濾比range查詢要快
## 5. 分組聚合
穿件index和映射
~~~
PUT /tvs
{
"mappings": {
"sales": {
"properties": {
"price": {
"type": "long"
},
"color": {
"type": "keyword"
},
"brand": {
"type": "keyword"
},
"sold_date": {
"type": "date"
}
}
}
}
}
~~~
數據
~~~
POST /tvs/sales/_bulk
{ "index": {}}
{ "price" : 1000, "color" : "紅色", "brand" : "長虹", "sold_date" : "2016-10-28" }
{ "index": {}}
{ "price" : 2000, "color" : "紅色", "brand" : "長虹", "sold_date" : "2016-11-05" }
{ "index": {}}
{ "price" : 3000, "color" : "綠色", "brand" : "小米", "sold_date" : "2016-05-18" }
{ "index": {}}
{ "price" : 1500, "color" : "藍色", "brand" : "TCL", "sold_date" : "2016-07-02" }
{ "index": {}}
{ "price" : 1200, "color" : "綠色", "brand" : "TCL", "sold_date" : "2016-08-19" }
{ "index": {}}
{ "price" : 2000, "color" : "紅色", "brand" : "長虹", "sold_date" : "2016-11-05" }
{ "index": {}}
{ "price" : 8000, "color" : "紅色", "brand" : "三星", "sold_date" : "2017-01-01" }
{ "index": {}}
{ "price" : 2500, "color" : "藍色", "brand" : "小米", "sold_date" : "2017-02-12" }
~~~
1. 按照color分組
~~~
GET /tvs/_search
{
"size": 0, # 只顯示聚合結果
"aggs": {
"group_by_color": {
"terms": {
"field": "color",
"size": 10000 # 控制返回結果數量
}
}
}
}
~~~
得到
~~~
"buckets": [
{
"key": "紅色",
"doc_count": 4
},
{
"key": "綠色",
"doc_count": 2
},
{
"key": "藍色",
"doc_count": 2
}
]
}
}
~~~
2. 求每種顏色電視銷售價格的平均值
~~~
GET /tvs/_search
{
"size": 0, # 只顯示聚合結果
"aggs": {
"group_by_color": {
"terms": {
"field": "color",
"size": 1000
},
"aggs": {
"avg_price": {
"avg": {
"field": "price"
}
}
}
}
}
}
~~~
color》brand》avg
~~~
GET tvs/_search
{
"size": 0,
"aggs": {
"group_by_color": {
"terms": {
"field": "color",
"size": 10
},
"aggs": {
"group_by_brand": {
"terms": {
"field": "brand",
"size": 10
},
"aggs": {
"avg_price": {
"avg": {
"field": "price"
}
}
}
}
}
}
}
}
~~~
3. 求顏色下的價格最高,最小值，價格總數
~~~
GET /tvs/_search
{
"size": 0,
"aggs": {
"group_by_color": {
"terms": {
"field": "color",
"size": 10000
},
"aggs": {
"max_price": {
"max": {
"field": "price"
}
},
"min_price":{
"min": {
"field": "price"
}
},
"sum_price":{
"sum": {
"field": "price"
}
}
}
}
}
}
~~~
得到
~~~
"buckets": [
{
"key": "紅色",
"doc_count": 4,
"max_price": {
"value": 8000
},
"min_price": {
"value": 1000
},
"sum_price": {
"value": 13000
}
},
{
"key": "綠色",
"doc_count": 2,
"max_price": {
"value": 3000
},
"min_price": {
"value": 1200
},
"sum_price": {
"value": 4200
}
},
{
"key": "藍色",
"doc_count": 2,
"max_price": {
"value": 2500
},
"min_price": {
"value": 1500
},
"sum_price": {
"value": 4000
}
~~~
## 6. histogram
1. 求以2000為步長，電視的銷售總價
~~~
GET /tvs/_search
{
"size": 0,
"aggs": {
"group_by_price": {
"histogram": {
"field": "price",
"interval": 2000
},
"aggs": {
"NAME": {
"sum": {
"field": "price"
}
}
}
}
}
}
~~~
得到
~~~
"group_by_price": {
"buckets": [
{
"key": 0,
"doc_count": 3,
"NAME": {
"value": 3700
}
},
{
"key": 2000,
"doc_count": 4,
"NAME": {
"value": 9500
}
},
{
"key": 4000,
"doc_count": 0,
"NAME": {
"value": 0
}
},
{
"key": 6000,
"doc_count": 0,
"NAME": {
"value": 0
}
},
{
"key": 8000,
"doc_count": 1,
"NAME": {
"value": 8000
}
}
]
~~~
2. 求每個月的銷售總價
## 7. 求季度每個品牌的銷售額
~~~
GET tvs/_search
{
"size": 0,
"aggs": {
"sales": {
"date_histogram": {
"field": "sold_date",
"interval": "quarter",
"format": "yyyy-MM-dd",
"min_doc_count": 0,
"extended_bounds":{
"min":"2016-01-01",
"max":"2017-12-12"
}
},
"aggs": {
"group_price_month": {
"terms": {
"field": "brand"
},
"aggs": {
"sales_sum": {
"sum": {
"field": "price"
}
}
}
}
}
}
}
}
~~~
~~~
},
{
"key_as_string": "2016-07-01",
"key": 1467331200000,
"doc_count": 2,
"group_price_month": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "TCL",
"doc_count": 2,
"sales_sum": {
"value": 2700
}
}
]
}
},
{
"key_as_string": "2016-10-01",
"key": 1475280000000,
"doc_count": 3,
"group_price_month": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "長虹",
"doc_count": 3,
"sales_sum": {
"value": 5000
}
}
]
}
},
{
~~~
## 8. filter與aggression
查找電視價格大于2500的銷售平均價格
首先把價格大于2500的電視過濾出來，在聚合
~~~
GET /tvs/_search
{
"size": 0,
"query": {
"constant_score": {
"filter": {
"range": {
"price": {
"gt": 2500
}
}
}
}
},
"aggs": {
"avg_lgt1200": {
"avg": {
"field": "price"
}
}
}
}
~~~
得到
~~~
"hits": []
},
"aggregations": {
"avg_lgt1200": {
"value": 5500
}
}
}
~~~
## 9. 最近一個月某品牌的銷售總價
~~~
GET /tvs/_search
{
"size": 0,
"query": {
"constant_score": {
"filter": {
"term": {
"brand": "長虹"
}
}
}
},
"aggs": {
"recent_moth":{
"filter": { # 在這里過濾，因為只在查詢的結果中過濾，性能最優
"range": {
"sold_date": {
"gte": "now-30d" # now-30d 從現在開始，減30天
}
}
},
"aggs": {
"sum_price": {
"sum": {
"field": "price"
}
}
}
}
}
}
~~~
得到
~~~
"aggregations": {
"recent_moth": {
"doc_count": 3,
"sum_price": {
"value": 5000
}
}
}
}
~~~
## 10. 聚合排序
1. 一個桶排序
~~~
GET /tvs/_search
{
"size": 0,
"aggs": {
"groupbycolor": {
"terms": {
"field": "color",
"order": {
"avg_price": "asc" # 指定按照avg_price排序
}
},
"aggs": {
"avg_price": {
"avg": {
"field": "price"
}
}
}
}
}
}
~~~
2. 2個桶排序
~~~
GET /tvs/sales/_search
{
"size": 0,
"aggs": {
"group_by_color": {
"terms": {
"field": "color",
"order": {
"_count": "asc" # 大桶按照doc count升序
}
},
"aggs": {
"group_by_brand": {
"terms": {
"field": "brand",
"order": {
"avg_price": "desc" # 小桶降序
}
},
"aggs": {
"avg_price": {
"avg": {
"field": "price"
}
}
}
}
}
}
}
}
~~~
## 11. 網站訪問時間統計
~~~
PUT /website
{
"mappings": {
"logs":{
"properties": {
"latency":{
"type": "long"
},
"province":{
"type": "keyword"
},
"timestamp":{
"type": "date"
}
}
}
}
}
~~~
~~~
POST /website/logs/_bulk
{ "index": {}}
{ "latency" : 105, "province" : "江蘇", "timestamp" : "2016-10-28" }
{ "index": {}}
{ "latency" : 83, "province" : "江蘇", "timestamp" : "2016-10-29" }
{ "index": {}}
{ "latency" : 92, "province" : "江蘇", "timestamp" : "2016-10-29" }
{ "index": {}}
{ "latency" : 112, "province" : "江蘇", "timestamp" : "2016-10-28" }
{ "index": {}}
{ "latency" : 68, "province" : "江蘇", "timestamp" : "2016-10-28" }
{ "index": {}}
{ "latency" : 76, "province" : "江蘇", "timestamp" : "2016-10-29" }
{ "index": {}}
{ "latency" : 101, "province" : "新疆", "timestamp" : "2016-10-28" }
{ "index": {}}
{ "latency" : 275, "province" : "新疆", "timestamp" : "2016-10-29" }
{ "index": {}}
{ "latency" : 166, "province" : "新疆", "timestamp" : "2016-10-29" }
{ "index": {}}
{ "latency" : 654, "province" : "新疆", "timestamp" : "2016-10-28" }
{ "index": {}}
{ "latency" : 389, "province" : "新疆", "timestamp" : "2016-10-28" }
{ "index": {}}
{ "latency" : 302, "province" : "新疆", "timestamp" : "2016-10-29" }
~~~
~~~
需求：比如有一個網站，記錄下了每次請求的訪問的耗時，需要統計tp50，tp90，tp99
tp50：50%的請求的耗時最長在多長時間
tp90：90%的請求的耗時最長在多長時間
tp99：99%的請求的耗時最長在多長時間
~~~
~~~
GET /website/_search
{
"aggs": {
"latency_percentiles": {
"percentiles": {
"field": "latency",
"percents": [
50,
99
]
}
}
}
}
~~~
得到
~~~
},
"aggregations": {
"latency_percentiles": {
"values": {
"50.0": 108.5, # 50%的請求在108以內
"99.0": 624.8500000000001 # 99%的請求在624以內
}
}
}
}
~~~
2. 統計各省的訪問情況
* 一個aggs內可以包含一個aggs
* 一個aggs可以包含多個不同的metric（指標統計：平均值，最大值，，，），但是記得起名字
~~~
GET /website/logs/_search
{
"size": 0,
"aggs": {
"group_by_province": {
"terms": {
"field": "province"
},
"aggs": {
"latency_percentiles": {
"percentiles": {
"field": "latency",
"percents": [
50,
95,
99
]
}
},
"latency_avg": {
"avg": {
"field": "latency"
}
}
}
}
}
}
~~~
得到，新疆的網絡不行啊
~~~
"buckets": [
{
"key": "新疆",
"doc_count": 6,
"latency_avg": {
"value": 314.5
},
"latency_percentiles": {
"values": {
"50.0": 288.5,
"95.0": 587.75,
"99.0": 640.75
}
}
},
{
"key": "江蘇",
"doc_count": 6,
"latency_avg": {
"value": 89.33333333333333
},
"latency_percentiles": {
"values": {
"50.0": 87.5,
"95.0": 110.25,
"99.0": 111.65
}
}
}
]
}
}
}
~~~
## 12. percentile_ranks(求某數值所占比例)
> SLA：就是你提供的服務的標準
> 我們的網站的提供的訪問延時的SLA，確保所有的請求100%，都必須在200ms以內，大公司內，一般都是要求100%在200ms以內
> 如果超過1s，則需要升級到A級故障，代表網站的訪問性能和用戶體驗急劇下降
> 需求：在200ms以內的，有百分之多少，在1000毫秒以內的有百分之多少，percentile ranks metric
> 這個percentile ranks，其實比pencentile還要常用
> 按照品牌分組，計算，電視機，售價在1000占比，2000占比，3000占比
~~~
GET /website/_search
{
"size": 0,
"aggs": {
"groupby_province": {
"terms": {
"field": "province"
},
"aggs": {
"percentile_s": {
"percentile_ranks": {
"field": "latency",
"values": [
100,
300,
500
]
}
}
}
}
}
}
~~~
得到
~~~
"buckets": [
{
"key": "新疆",
"doc_count": 6,
"percentile_s": {
"values": {
"100.0": 8.076923076923077,
"300.0": 53.3625730994152,
"500.0": 65.31446540880503
}
}
},
{
"key": "江蘇",
"doc_count": 6,
"percentile_s": {
"values": {
"100.0": 46.42857142857142,
"300.0": 100,
"500.0": 100
}
}
}
]
}
}
}
~~~
> 新疆：
> 響應時間小于100毫秒占46.42%
> 響應時間小于300毫秒占53.42%
> 響應時間小于500毫秒占65.42%
>
## 13. 文檔模型
1. 建立索引，已經文件路徑分詞器
~~~
PUT /fs
{
"settings": {
"analysis": {
"analyzer": {
"paths": {
"tokenizer": "path_hierarchy"
}
}
}
}
}
~~~
path_hierarchy：路徑分詞器
2. 創建映射
~~~
PUT /fs/_mapping/file
{
"properties": {
"name": {
"type": "keyword"
},
"path": {
"type": "keyword", # 不分詞的路徑，用于精準匹配
"fields": {
"tree": {
"type": "text",
"analyzer": "paths" # 分詞子field，用于所有路徑的搜索
}
}
}
}
}
~~~
3. 插入一條數據
~~~
PUT /fs/file/1
{
"name": "README.txt",
"path": "/workspace/projects/helloworld",
"contents": "這是我的第一個elasticsearch程序"
}
~~~
4. 搜索文件
1. 查找/workspace/projects/helloworld路徑下的elasticsearch文件
~~~
GET /fs/file/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"contents": "elasticsearch"
}
},
{
"constant_score": {
"filter": {
"term": {
"path": "/workspace/projects/helloworld"
}
}
}
}
]
}
}
}
~~~
~~~
"hits": [
{
"_index": "fs",
"_type": "file",
"_id": "1",
"_score": 1.284885,
"_source": {
"name": "README.txt",
"path": "/workspace/projects/helloworld",
"contents": "這是我的第一個elasticsearch程序"
}
}
]
}
}
~~~
5. 查找/workspace目錄下的所有elasticsearch文件
~~~
GET /fs/file/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"contents": "elasticsearch"
}
},
{
"constant_score": {
"filter": {
"term": {
"path.tree": "/workspace" # 用可分詞的field，過濾所有路徑
}
}
}
}
]
}
}
}
~~~
- Docker
- 什么是docker
- Docker安裝、組件啟動
- docker網絡
- docker命令
- docker swarm
- dockerfile
- mesos
- 運維
- Linux
- Linux基礎
- Linux常用命令_1
- Linux常用命令_2
- ip命令
- 什么是Linux
- SELinux
- Linux GCC編譯警告：Clock skew detected. 錯誤解決辦法
- 文件描述符
- find
- 資源統計
- LVM
- Linux相關配置
- 服務自啟動
- 服務器安全
- 字符集
- shell腳本
- shell命令
- 實用腳本
- shell 數組
- 循環與判斷
- 系統級別進程開啟和停止
- 函數
- java調用shell腳本
- 發送郵件
- Linux網絡配置
- Ubuntu
- Ubuntu發送郵件
- 更換apt-get源
- centos
- 防火墻
- 虛擬機下配置網絡
- yum重新安裝
- 安裝mysql5.7
- 配置本地yum源
- 安裝telnet
- 忘記root密碼
- rsync+ crontab
- Zabbix
- Zabbix監控
- Zabbix安裝
- 自動報警
- 自動發現主機
- 監控MySQL
- 安裝PHP常見錯誤
- 基于nginx安裝zabbix
- 監控Tomcat
- 監控redis
- web監控
- 監控進程和端口號
- zabbix自定義監控
- 觸發器函數
- zabbix監控mysql主從同步狀態
- Jenkins
- 安裝Jenkins
- jenkins+svn+maven
- jenkins執行shell腳本
- 參數化構建
- maven區分環境打包
- jenkins使用注意事項
- nginx
- nginx認證功能
- ubuntu下編譯安裝Nginx
- 編譯安裝
- Nginx搭建本地yum源
- 文件共享
- Haproxy
- 初識Haproxy
- haproxy安裝
- haproxy配置
- virtualbox
- virtualbox 復制新的虛擬機
- ubuntu下vitrualbox安裝redhat
- centos配置雙網卡
- 配置存儲
- Windows
- Windows安裝curl
- VMware vSphere
- 磁盤管理
- 增加磁盤
- gitlab
- 安裝
- tomcat
- Squid
- bigdata
- FastDFS
- FastFDS基礎
- FastFDS安裝及簡單實用
- api介紹
- 數據存儲
- FastDFS防盜鏈
- python腳本
- ELK
- logstash
- 安裝使用
- kibana
- 安準配置
- elasticsearch
- elasticsearch基礎_1
- elasticsearch基礎_2
- 安裝
- 操作
- java api
- 中文分詞器
- term vector
- 并發控制
- 對text字段排序
- 倒排和正排索引
- 自定義分詞器
- 自定義dynamic策略
- 進階練習
- 共享鎖和排它鎖
- nested object
- 父子關系模型
- 高亮
- 搜索提示
- Redis
- redis部署
- redis基礎
- redis運維
- redis-cluster的使用
- redis哨兵
- redis腳本備份還原
- rabbitMQ
- rabbitMQ安裝使用
- rpc
- RocketMQ
- 架構概念
- 安裝
- 實例
- 好文引用
- 知乎
- ACK
- postgresql
- 存儲過程
- 編程語言
- 計算機網絡
- 基礎_01
- tcp/ip
- http轉https
- Let's Encrypt免費ssl證書（基于haproxy負載）
- what's the http?
- 網關
- 網絡IO
- http
- 無狀態網絡協議
- Python
- python基礎
- 基礎數據類型
- String
- List
- 遍歷
- Python基礎_01
- python基礎_02
- python基礎03
- python基礎_04
- python基礎_05
- 函數
- 網絡編程
- 系統編程
- 類
- Python正則表達式
- pymysql
- java調用python腳本
- python操作fastdfs
- 模塊導入和sys.path
- 編碼
- 安裝pip
- python進階
- python之setup.py構建工具
- 模塊動態導入
- 內置函數
- 內置變量
- path
- python模塊
- 內置模塊_01
- 內置模塊_02
- log模塊
- collections
- Twisted
- Twisted基礎
- 異步編程初探與reactor模式
- yield-inlineCallbacks
- 系統編程
- 爬蟲
- urllib
- xpath
- scrapy
- 爬蟲基礎
- 爬蟲種類
- 入門基礎
- Rules
- 反反爬蟲策略
- 模擬登陸
- problem
- 分布式爬蟲
- 快代理整站爬取
- 與es整合
- 爬取APP數據
- 爬蟲部署
- collection for ban of web
- crawlstyle
- API
- 多次請求
- 向調度器發送請求
- 源碼學習
- LinkExtractor源碼分析
- 構建工具-setup.py
- selenium
- 基礎01
- 與scrapy整合
- Django
- Django開發入門
- Django與MySQL
- java
- 設計模式
- 單例模式
- 工廠模式
- java基礎
- java位移
- java反射
- base64
- java內部類
- java高級
- 多線程
- springmvc-restful
- pfx數字證書
- 生成二維碼
- 項目中使用log4j
- 自定義注解
- java發送post請求
- Date時間操作
- spring
- 基礎
- spring事務控制
- springMVC
- 注解
- 參數綁定
- springmvc+spring+mybatis+dubbo
- MVC模型
- SpringBoot
- java配置入門
- SpringBoot基礎入門
- SpringBoot web
- 整合
- SpringBoot注解
- shiro權限控制
- CommandLineRunner
- mybatis
- 靜態資源
- SSM整合
- Aware
- Spring API使用
- Aware接口
- mybatis
- 入門
- mybatis屬性自動映射、掃描
- 問題
- @Param 注解在Mybatis中的使用 以及傳遞參數的三種方式
- mybatis-SQL
- 逆向生成dao、model層代碼
- 反向工程中Example的使用
- 自增id回顯
- SqlSessionDaoSupport
- invalid bound statement(not found)
- 脈絡
- beetl
- beetl是什么
- 與SpringBoot整合
- shiro
- 什么是shiro
- springboot+shrio+mybatis
- 攔截url
- 枚舉
- 圖片操作
- restful
- java項目中日志處理
- JSON
- 文件工具類
- KeyTool生成證書
- 兼容性問題
- 開發規范
- 工具類開發規范
- 壓縮圖片
- 異常處理
- web
- JavaScript
- 基礎語法
- 創建對象
- BOM
- window對象
- DOM
- 閉包
- form提交-文件上傳
- td中內容過長
- 問題1
- js高級
- js文件操作
- 函數_01
- session
- jQuery
- 函數01
- data()
- siblings
- index()與eq()
- select2
- 動態樣式
- bootstrap
- 表單驗證
- 表格
- MUI
- HTML
- iframe
- label標簽
- 規范編程
- layer
- sss
- 微信小程序
- 基礎知識
- 實踐
- 自定義組件
- 修改自定義組件的樣式
- 基礎概念
- appid
- 跳轉
- 小程序發送ajax
- 微信小程序上下拉刷新
- if
- 工具
- idea
- Git
- maven
- svn
- Netty
- 基礎概念
- Handler
- SimpleChannelInboundHandler 與 ChannelInboundHandler
- 網絡編程
- 網絡I/O
- database
- oracle
- 游標
- PLSQL Developer
- mysql
- MySQL基準測試
- mysql備份
- mysql主從不同步
- mysql安裝
- mysql函數大全
- SQL語句
- 修改配置
- 關鍵字
- 主從搭建
- centos下用rpm包安裝mysql
- 常用sql
- information_scheme數據庫
- 值得學的博客
- mysql學習
- 運維
- mysql權限
- 配置信息
- 好文mark
- jsp
- jsp EL表達式
- C
- test