代码调整
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#当前服务的基本信息
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microservice:
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ename: @artifactId@
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name: '@name@'
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version: @version@
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sentinel:
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url: @sentinel.url@
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gateway:
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url: @gateway.url@
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server:
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port: 10210
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#feign接口开启服务熔断降级处理
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feign:
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sentinel:
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enabled: true
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spring:
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application:
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name: @artifactId@
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#nacos注册中心以及配置中心的指定
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cloud:
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nacos:
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discovery:
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ip: @service.server.url@
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server-addr: @nacos.url@
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namespace: @nacos.namespace@
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config:
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server-addr: @nacos.url@
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namespace: @nacos.namespace@
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file-extension: yaml
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shared-configs:
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- data-id: share-config.yaml
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refresh: true
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- data-Id: algorithm-config.yaml
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refresh: true
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main:
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allow-bean-definition-overriding: true
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#项目日志的配置
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logging:
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config: http://@nacos.url@/nacos/v1/cs/configs?tenant=@nacos.namespace@&group=DEFAULT_GROUP&dataId=logback.xml
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level:
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root: info
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#mybatis配置信息
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mybatis-plus:
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#别名扫描
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type-aliases-package: com.njcn.algorithm.pojo
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mqtt:
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client-id: @artifactId@${random.value}
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@@ -1,172 +0,0 @@
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> 前言：
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>
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> 为了方便开发中使用influx时序数据库，摒弃了在代码中拼接查询sql语句，确保查询结果的准确性之余提高代码的阅读性。同时优化处理了部分开发过程遇到的问题：
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>
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> * 时区问题：开发者无需关注，默认为 tz('Asia/Shanghai')
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> * 返回的Instant类型的时间，在序列化到前端页面时，格式不对，添加：@JsonSerialize(using = InstantDateSerializer.class)处理解决
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> * 同时由于influx针对or查询时，超过100个连接是性能会有比较严重的影响，提供了influx的正则表达式的查询方法：InfluxQueryWrapper.regular()，此方案还待测试检查
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#### 1、快速上手
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目前没有采用泛型的方式，是因为想返回实体更灵活，可以指定任意想要映射的实体。
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```java
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//两个构造方法：
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/***
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* 返回和查询用的同一个实体
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* @param measurement 查询目标表
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*/
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public InfluxQueryWrapper(Class measurement) {
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this.measurement = measurement;
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this.resultEntity = measurement;
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this.initSql();
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}
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/***
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* 返回和查询用的不是同一个实体
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* @param measurement 查询目标表
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* @param resultEntity 返回映射实体
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*/
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public InfluxQueryWrapper(Class measurement, Class resultEntity) {
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this.measurement = measurement;
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this.resultEntity = resultEntity;
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this.initSql();
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}
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```
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初始化完成后，便已经确定目标measurement（influx表名）以及返回实体。
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###### 示例：
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```java
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InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataFlicker.class);
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//生成 select * from "data_flicker" tz('Asia/Shanghai')
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```
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#### 2、函数使用
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##### 2.1、select
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select(ICFunction<T, R>... fieldsStr)：此函数指定查询返回的那几个字段
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###### 示例：
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```java
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InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataFlicker.class);
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influxQueryWrapper.select(DataFlicker::getTime);
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//生成 select time from "data_flicker" tz('Asia/Shanghai')
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```
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##### 2.2、count统计
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count(ICFunction<T, R> columnName)：累计总数；
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count(ICFunction<T, R> columnName, ICFunction<T, R> resultName)：累计总数，返回属性名不同
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```java
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InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataFlicker.class);
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influxQueryWrapper.count(DataFlicker::getTime,StatisticsResult::getValue);
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//生成 select COUNT("time") as value from "data_flicker" tz('Asia/Shanghai')
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```
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> 类似的还有：mean平均值、median中位数、mode最常出现的值、spread最大与最小值的差、sum求和、abs绝对值、max最大值、min最小值
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##### 2.3、top最大值的几条记录
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top(ICFunction<T, R> columnName, int num)：最大值的一个结果集合
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```java
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InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataFlicker.class);
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influxQueryWrapper.top(DataFlicker::getTime,5);
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//生成最大的前5某个属性的集合
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//select TOP("time",5) as value from "data_flicker" tz('Asia/Shanghai')
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```
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> 类似的还有：bottom最小值的几条记录
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##### 2.4、maxSamePrefixAndSuffix针对前缀或后缀相同的属性批量处理最大值
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maxSamePrefixAndSuffix(String prefix, String suffix, List<Object> diffContent)：开发过程中遇到表字段前后缀一致时，需要批量处理的。
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```java
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InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataV.class);
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influxQueryWrapper.maxSamePrefixAndSuffix("v_","",timesList);
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//生成最大的前5某个属性的集合
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//select MAX("v_1") as v_1,MAX("v_2") as v_2,MAX("v_3") as v_3,MAX("v_4") as v_4 as value from "data_v" tz('Asia/Shanghai')
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```
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> 类似的还有：minSamePrefixAndSuffix针对前缀或后缀相同的属性批量处理最小值
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##### 2.5、percentile百分之？最大值
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目前灿能项目常用于cp95最大值
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```java
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InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataFlicker.class);
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influxQueryWrapper.percentile(DataFlicker::getTime,95);
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//select PERCENTILE("time",95) as value from "data_flicker" tz('Asia/Shanghai')
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```
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##### 2.6、between区间
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between(ICFunction<T, R> fieldName, Object val1, Object val2)：作为条件区间语句生成
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```java
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InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataFlicker.class);
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influxQueryWrapper.between(DataFlicker::getTime,"2022-04-30 16:00:00","2022-05-30 16:00:00");
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//select * from "data_flicker" where time >='2022-04-30 16:00:00' AND time <='2022-05-30 16:00:00' tz('Asia/Shanghai')
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```
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##### 2.7、eq等于
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eq(ICFunction<T, R> columnName, Object columnValue)：指定某字段等于xxx
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```java
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InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataFlicker.class);
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influxQueryWrapper.eq(DataFlicker::getTime,"2022-05-30 16:00:00");
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//select * from "data_flicker" where time ='2022-04-30 16:00:00' tz('Asia/Shanghai')
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```
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> 类似的还有：ne不等于、gt大于、ge大于等于、lt小于、le小于等于
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##### 2.8、or拼接多条件
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or(ICFunction<T, R> fieldName, List<Object> columnValues)：查询某字段等于集合内的值时的结果，类似于关系数据库的in函数；
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```java
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InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataV.class);
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influxQueryWrapper.or(DataV::getLineId,lineList);
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//select * from "data_v" where (line_id = "111" or line_id = "222") tz('Asia/Shanghai')
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```
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##### 2.9、regular正则模糊查询
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regular(ICFunction<T, R> fieldName, List<String> columnValues)：查询条件有多个选项时，效果等同于关系型数据库的in，同时or的内容如果超过100个选项时会带来严重的性能问题，查询很慢，可以调整为正则模糊查询
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```java
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InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataV.class);
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influxQueryWrapper.regular(DataV::getLineId,lineList);
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//select * from "data_v" where line_id =~/^111|222|333$/ tz('Asia/Shanghai')
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```
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##### 2.10、groupBy分组
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> influxdb只能针对tag去分组
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groupBy(ICFunction<T, R>... columnName)：以tag去分组；
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```java
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InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataV.class);
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influxQueryWrapper.groupBy(DataV::getLineId,DataV::getGualityFlag);
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//select * from "data_v" group by line_id,quality_flag tz('Asia/Shanghai')
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```
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##### 2.11、timeDesc以时间降序
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```java
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InfluxQueryWrapper influxQueryWrapper = new InfluxQueryWrapper(DataV.class);
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influxQueryWrapper.timeDesc();
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//select * from "data_v" order by time desc tz('Asia/Shanghai')
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```
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> 类似的还有：timeAsc以时间升序
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