使用InfluxDB的连续查询解决聚合性能问题

==背景==

数据库：我们的生产环境中有一个设备运行的数据库使用的是InfluxDB，这里面存储了所有设备上报上来的实时运行数据，数据量增速较快。

功能需求：产品有一个叫趋势分析的功能，用来按照不同的算子（mean、max等），不同的时间段（1分钟、30分钟）等对数据进行聚合。

==版本==

1.7.1、单机版

==问题==

经过压力测试之后，发现当聚合时间选择1分钟、5分钟等细粒度的时间的是偶，聚合的速度非常的慢。

概括一句话：基于原始数据进行实时聚合，不合理

==解决思路==

InfluxDB提供了连续查询的高级功能，尝试在每天凌晨的时候将数据聚合好，

官方文档：https://docs.influxdata.com/influxdb/v1.7/query_language/continuous_queries/

强烈建议把官方文档从头到尾浏览一遍，是学习一门技术最好的入门方法。

==初次尝试==

1、创建存储聚合结果的数据库

create database rexel_analysis

2、为数据库创建保存策略

设置数据留存时间为1年（365天）。

create retention policy one_year on rexel_analysis duration 365d replication 1 default

3、创建数据库权限

出于安全考虑，为数据库做了ACL权限。

GRANT read ON rexel_analysis TO devread
GRANT write ON rexel_analysis TO devwrite
GRANT all ON rexel_analysis TO devall

4、创建一个连续查询

CREATE CONTINUOUS QUERY cq_mean_1m ON rexel_private BEGIN SELECT mean(*) INTO rexel_analysis.one_year.data_up_1m FROM rexel_private.one_year.device_data_up GROUP BY time(1m) END

5、查看已有连续查询

SHOW CONTINUOUS QUERIES

6、查看连续查询的计算结果

从结果上可以看到，连续查询按照我预设的每分钟执行1次，并将结果插入到了另一个数据库中。

use rexel_analysis
show measurements
select mean_AI01_0001, mean_AR03_0256 from data_up_1m order by desc tz('Asia/Shanghai')

7、删除连续查询

DROP CONTINUOUS QUERY cq_mean_1m ON rexel_private

8、修改连续查询

根据官网的介绍，创建CQ之后，无法进行更改，如果需要更改需要drop掉之后重新create。

9、查询连续查询的日志

待补充

==初次尝试体验==

以上是初次尝试InfluxDB的连续查询的过程，有几个体验：

【好的体验】

1、可以看到连续查询会按照指定的时间计划对数据进行聚合，并将结果保存到指定的地方，是一个很好的解决性能的思路。

2、表中的字段有好几千个，使用带有通配符（*）的函数和INTO查询的反向引用语法，可以自动对数据库中所有度量和数字字段中的数据进行降采样。

【不好的体验】

1、每次连续查询时间间隔很短（时间间隔 = now() - group by time()）

2、查询结果的字段别名比较恶心，比如原来字段叫AI01_0001，因为计算的是mean，结果库中的字段名就变为了mean_AI01_0001。

==配置采样频率与时间范围==

连续查询提供了高级语法：RESAMPLE EVERY FOR

CREATE CONTINUOUS QUERY <cq_name> ON <database_name> 
[RESAMPLE [EVERY <interval>] [FOR <interval>]] 
BEGIN SELECT <function>(<stuff>)[,<function>(<stuff>)] INTO <different_measurement> 
FROM <current_measurement> [WHERE <stuff>] GROUP BY time(<interval>)[,<stuff>] 
END

RESAMPLE EVERY ：采样执行频次。如RESAMPLE EVERY 30m：表示30分钟执行一次。

RESAMPLE FOR ：采样时间范围。如RESAMPLE FOR 60m：时间范围 = now() - for间隔（60m）。

RESAMPLE EVERY 30m FOR 60m：表示每30分钟执行一次60分钟内的数据计算。

注意：

如果此时在<cq_query>中使用了GROUP BY time duration，那么FOR定义的duration必须大于或者等于GROUP BY指定的time duration，不然就会报错。

反过来，如果EVERY定义的duration 大于GROUP BY指定的time duration，那么执行将按照EVERY定义的duration来执行。

例如：如果GROUP BY time(5m)且EVERY间隔为10m，则CQ每十分钟执行一次

==语句样例==

每1分钟执行1次平均值计算，时间范围1分钟
CREATE CONTINUOUS QUERY cq_mean_1m ON rexel_private  BEGIN SELECT mean(*) INTO rexel_analysis.one_year.data_up_1m FROM rexel_private.one_year.device_data_up GROUP BY time(1m) END

每1分钟执行1次平均值计算，时间范围1天
CREATE CONTINUOUS QUERY cq_mean_1m ON rexel_private RESAMPLE FOR 1d BEGIN SELECT mean(*) INTO rexel_analysis.one_year.data_up_1m FROM rexel_private.one_year.device_data_up GROUP BY time(1m) END

==项目实践==

经过上面一番体验之后，对连续查询已经有了基本的了解，那么实际中如何使用呢？

我们的场景：

1、可选的时间组（共8个）：1分钟、5分钟、30分钟、1小时、6小时、12小时、1天、1周

2、可选的聚合模式（共8个）：最老值（last）、最新值（first）、最大值（max）、最小值（min）、平均值（mean）、中间值（median）、极差值（spread）、累加值（sum）

3、时间范围：最多3个月

那么，连续查询策略该如何设计呢？

【方案一】

按照时间组和聚合模式的排列组合创建查询策略。如下图所示，这种方案一共需要创建64个连续查询，感觉有些啰嗦。

【方案二】

按照和时间组创建查询策略。如下图所以，每一行的查询策略是一样的，各个聚合方法的结果放在同一张表中。

这样减少了连续查询的数量，维护也方便了很多。

表中的数据大概是这个样子的

【方案三】

将方案二工具化，在mysql中创建一个关于influxdb连续查询的字典表，根据这个表来自动创建连续查询。（思想：让机器做的更多）

建表语句及数据如下：

SET NAMES utf8mb4;
SET FOREIGN_KEY_CHECKS = 0;

-- ----------------------------
-- Table structure for influx_cq_dict
-- ----------------------------
DROP TABLE IF EXISTS `influx_cq_dict`;
CREATE TABLE `influx_cq_dict`  (
  `cq_name` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT '连续查询的名称',
  `from_database` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT '源数据库',
  `from_retention_policy` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT '源存储策略',
  `from_measurement` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT '源表名',
  `to_database` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT '目标数据库',
  `to_retention_policy` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT '目标存储策略',
  `to_measurement` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT '目标表名',
  `for_interval` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT '时间间隔',
  `every` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NULL DEFAULT NULL COMMENT '执行频率',
  `field` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT '查询字段',
  `func` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT '聚合功能',
  `group_by_time` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT 'GROUP BY指定的time duration',
  `fill` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT '空白填充方式',
  `is_delete` varchar(1) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL DEFAULT '0' COMMENT '是否删除 0,未删除；1:删除',
  PRIMARY KEY (`cq_name`) USING BTREE
) ENGINE = InnoDB AUTO_INCREMENT = 146 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_general_ci COMMENT = 'InfluxDB连续查询字典表' ROW_FORMAT = Dynamic;

-- ----------------------------
-- Records of influx_cq_dict
-- ----------------------------
INSERT INTO `influx_cq_dict` VALUES ('cq_device_data_up_12h', 'rexel_online', 'one_year', 'device_data_up', 'rexel_online_analysis', 'one_year', 'device_data_up_12h', '1d', '1d', '*', 'last,first,max,min,mean,median,spread,sum', '12h', 'none', '0');
INSERT INTO `influx_cq_dict` VALUES ('cq_device_data_up_1d', 'rexel_online', 'one_year', 'device_data_up', 'rexel_online_analysis', 'one_year', 'device_data_up_1d', '1d', '1d', '*', 'last,first,max,min,mean,median,spread,sum', '1d', 'none', '0');
INSERT INTO `influx_cq_dict` VALUES ('cq_device_data_up_1h', 'rexel_online', 'one_year', 'device_data_up', 'rexel_online_analysis', 'one_year', 'device_data_up_1h', '1d', '1d', '*', 'last,first,max,min,mean,median,spread,sum', '1h', 'none', '0');
INSERT INTO `influx_cq_dict` VALUES ('cq_device_data_up_1m', 'rexel_online', 'one_year', 'device_data_up', 'rexel_online_analysis', 'one_year', 'device_data_up_1m', '1d', '1d', '*', 'last,first,max,min,mean,median,spread,sum', '1m', 'none', '0');
INSERT INTO `influx_cq_dict` VALUES ('cq_device_data_up_1w', 'rexel_online', 'one_year', 'device_data_up', 'rexel_online_analysis', 'one_year', 'device_data_up_1w', '1d', '1d', '*', 'last,first,max,min,mean,median,spread,sum', '1w', 'none', '0');
INSERT INTO `influx_cq_dict` VALUES ('cq_device_data_up_30m', 'rexel_online', 'one_year', 'device_data_up', 'rexel_online_analysis', 'one_year', 'device_data_up_30m', '1d', '1d', '*', 'last,first,max,min,mean,median,spread,sum', '30m', 'none', '0');
INSERT INTO `influx_cq_dict` VALUES ('cq_device_data_up_5m', 'rexel_online', 'one_year', 'device_data_up', 'rexel_online_analysis', 'one_year', 'device_data_up_5m', '1d', '1d', '*', 'last,first,max,min,mean,median,spread,sum', '5m', 'none', '0');
INSERT INTO `influx_cq_dict` VALUES ('cq_device_data_up_5m_test', 'rexel_online', 'one_year', 'device_data_up', 'rexel_online_analysis', 'one_year', 'device_data_up_5m_test', '1h', '5m', 'AI01_0001,AI01_0002', 'last,first,max,min,mean,median,spread,sum', '5m', 'none', '1');
INSERT INTO `influx_cq_dict` VALUES ('cq_device_data_up_6h', 'rexel_online', 'one_year', 'device_data_up', 'rexel_online_analysis', 'one_year', 'device_data_up_6h', '1d', '1d', '*', 'last,first,max,min,mean,median,spread,sum', '6h', 'none', '0');

SET FOREIGN_KEY_CHECKS = 1;

==Java代码==

1、Controller类

package com.rexel.backstage.project.tool.init.controller;

import com.rexel.backstage.project.tool.init.service.IInfluxCqDictService;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import com.rexel.backstage.framework.web.controller.BaseController;
import com.rexel.backstage.framework.web.domain.AjaxResult;

/**
 * InfluxDB连续查询Controller
 *
 * @date 2020-07-30
 */
@RestController
@RequestMapping("/rexel/tool/influx/continuousQuery")
public class InfluxCqDictController extends BaseController {
    private IInfluxCqDictService influxCqDictService;

    @Autowired
    public void setInfluxCqDictService(IInfluxCqDictService influxCqDictService) {
        this.influxCqDictService = influxCqDictService;
    }

    /**
     * 创建InfluxDB连续查询
     */
    @PostMapping("/refresh")
    public AjaxResult refresh(@RequestParam("type") String type) {
        return AjaxResult.success(influxCqDictService.refreshContinuousQuery(type));
    }
}

2、Service接口类

package com.rexel.backstage.project.tool.init.service;

import com.alibaba.fastjson.JSONObject;

/**
 * InfluxDB连续查询Service接口
 *
 * @author admin
 * @date 2020-07-30
 */
public interface IInfluxCqDictService {
    /**
     * 刷新InfluxDB连续查询
     *
     * @param type create/drop
     * @return 结果
     */
    JSONObject refreshContinuousQuery(String type);
}

3、Service实现类

package com.rexel.backstage.project.tool.init.service.impl;

import com.alibaba.fastjson.JSONArray;
import com.alibaba.fastjson.JSONObject;
import com.rexel.backstage.project.tool.init.domain.InfluxCqDict;
import com.rexel.backstage.project.tool.init.mapper.InfluxCqDictMapper;
import com.rexel.backstage.project.tool.init.service.IInfluxCqDictService;
import com.rexel.influxdb.InfluxUtils;
import com.rexel.influxdb.constans.InfluxSql;
import java.util.ArrayList;
import java.util.List;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

/**
 * InfluxDB连续查询Service业务层处理
 * 
 * @author admin
 * @date 2020-07-30
 */
@Service
@Slf4j
public class InfluxCqDictServiceImpl implements IInfluxCqDictService {
    private InfluxUtils influxUtils = InfluxUtils.getInstance();
    private InfluxCqDictMapper influxCqDictMapper;
    private List<InfluxCqDict> influxCqDictList;
    private final static String INIT = "init";
    private final static String DROP = "drop";
    private final static String CREATE = "create";

    @Autowired
    public void setInfluxCqDictMapper(InfluxCqDictMapper influxCqDictMapper) {
        this.influxCqDictMapper = influxCqDictMapper;
    }

    /**
     * 刷新InfluxDB连续查询
     * 
     * @return 结果
     */
    @Override
    public JSONObject refreshContinuousQuery(String type) {
        influxCqDictList = influxCqDictMapper.selectInfluxCqDictList();

        // 首次
        if (INIT.toLowerCase().equals(type.toLowerCase())) {
            recreateDatabase();
            dropAllCp();
            createAllCp();
        }

        // 删除
        if (DROP.toLowerCase().equals(type.toLowerCase())) {
            dropAllCp();
        }

        // 创建
        if (CREATE.toLowerCase().equals(type.toLowerCase())) {
            dropAllCp();
            createAllCp();
        }

        return new JSONObject();
    }

    /**
     * 获取源数据库列表
     *
     * @return 列表
     */
    private List<String> getDatabaseFrom() {
        List<String> result = new ArrayList<>();
        for(InfluxCqDict influxCqDict : influxCqDictList) {
            String database = influxCqDict.getFromDatabase();
            if (!result.contains(database)) {
                result.add(database);
            }
        }
        return result;
    }

    /**
     * 获取目标数据库列表
     *
     * @return 列表
     */
    private List<String> getDatabaseTo() {
        List<String> result = new ArrayList<>();
        for(InfluxCqDict influxCqDict : influxCqDictList) {
            String database = influxCqDict.getToDatabase();
            if (!result.contains(database)) {
                result.add(database);
            }
        }
        return result;
    }

    /**
     * 重新创建database
     */
    private void recreateDatabase() {
        List<String> dbList = getDatabaseTo();
        for(String database : dbList) {
            influxUtils.dropDatabase(database);
            influxUtils.createDatabase(database);
            influxUtils.createRetentionPolicy(database);
        }
    }

    /**
     * 删除所有连续查询
     */
    private void dropAllCp() {
        JSONArray jsonArray = influxUtils.getContinuousQueries();
        List<String> dbList =  getDatabaseFrom();
        for(String database : dbList) {
            for (int i = 0; i < jsonArray.size(); i++) {
                JSONObject jsonObject = jsonArray.getJSONObject(i);
                influxUtils.dropContinuousQuery(jsonObject.getString("name"), database);
            }
        }
    }

    /**
     * 创建所有连续查询
     */
    private void createAllCp() {
        for(InfluxCqDict influxCqDict : influxCqDictList) {
            String createCqStr = makeOneCqStr(influxCqDict);
            influxUtils.createContinuousQuery(createCqStr);
        }
    }

    /**
     * 生成单个连续查询语句
     *
     * @param influxCqDict InfluxCqDict
     * @return 连续查询语句
     */
    private String makeOneCqStr(InfluxCqDict influxCqDict) {
        String every = makeEvery(influxCqDict);
        String fields = makeFields(influxCqDict);
        String groupBy = makeGroupBy(influxCqDict);

        JSONObject paramJson = new JSONObject();
        paramJson.put("cqName", influxCqDict.getCqName());
        paramJson.put("onDatabase", influxCqDict.getFromDatabase());
        paramJson.put("every", every);
        paramJson.put("forInterval", influxCqDict.getForInterval());
        paramJson.put("fields", fields);
        paramJson.put("toDatabase", influxCqDict.getToDatabase());
        paramJson.put("toRetentionPolicy", influxCqDict.getToRetentionPolicy());
        paramJson.put("toMeasurement", influxCqDict.getToMeasurement());
        paramJson.put("fromDatabase", influxCqDict.getFromDatabase());
        paramJson.put("fromRetentionPolicy", influxCqDict.getFromRetentionPolicy());
        paramJson.put("fromMeasurement", influxCqDict.getFromMeasurement());
        paramJson.put("groupBy", groupBy);
        paramJson.put("fill", influxCqDict.getFill());
        return InfluxUtils.formatSql(InfluxSql.CREATE_CONTINUOUS_QUERY, paramJson);
    }

    /**
     * 生成语句Field字段
     *
     * @param influxCqDict InfluxCqDict
     * @return Field字段
     */
    private String makeFields(InfluxCqDict influxCqDict) {
        String[] fields = influxCqDict.getField().split(",");
        String[] funcs = influxCqDict.getFunc().split(",");

        StringBuilder sb = new StringBuilder();
        for (String field : fields) {
            for (String func : funcs) {
                sb.append(func).append("(").append(field).append("),");
            }
        }
        return sb.substring(0, sb.length() - 1);
    }

    /**
     * 生成GroupBy字段
     *
     * @param influxCqDict InfluxCqDict
     * @return GroupBy字段
     */
    private String makeGroupBy(InfluxCqDict influxCqDict) {
        List<String> tagKeys = influxUtils.getMeasurementTagKeys(
            influxCqDict.getFromDatabase(), influxCqDict.getFromMeasurement());

        StringBuilder sb = new StringBuilder();
        sb.append("time(").append(influxCqDict.getGroupByTime()).append(")");
        if (tagKeys.size() > 0) {
            sb.append(",");
        }
        for (String tagKey : tagKeys) {
            sb.append(tagKey).append(",");
        }
        return sb.substring(0, sb.length() - 1);
    }

    /**
     * 生成EVERY字段
     *
     * @param influxCqDict InfluxCqDict
     * @return EVERY字段
     */
    private String makeEvery(InfluxCqDict influxCqDict) {
        String every = influxCqDict.getEvery();
        if (every != null && !every.isEmpty()) {
            return " EVERY " + every;
        }
        return "";
    }
}

4、Domain类

package com.rexel.backstage.project.tool.init.domain;

import lombok.Data;

/**
 * InfluxDB连续查询domain类
 *
 * @author admin
 * @date 2020-07-30
 */
@Data
public class InfluxCqDict {
    /** 连续查询的名称 */
    private String cqName;

    /** 源数据库 */
    private String fromDatabase;

    /** 源存储策略 */
    private String fromRetentionPolicy;

    /** 源表名 */
    private String fromMeasurement;

    /** 目标数据库 */
    private String toDatabase;

    /** 目标存储策略 */
    private String toRetentionPolicy;

    /** 目标表名 */
    private String toMeasurement;

    /** 时间间隔 */
    private String forInterval;

    /** 执行频率 */
    private String every;

    /** 查询字段 */
    private String field;

    /** 聚合功能 */
    private String func;

    /** GROUP BY指定的time duration */
    private String groupByTime;

    /** 空白填充方式 */
    private String fill;
}

5、Mapper类

package com.rexel.backstage.project.tool.init.mapper;

import com.rexel.backstage.project.tool.init.domain.InfluxCqDict;
import java.util.List;
import org.springframework.stereotype.Repository;

/**
 * InfluxDB连续查询Mapper接口
 *
 * @author admin
 * @date 2020-07-30
 */
@Repository
public interface InfluxCqDictMapper {
    /**
     * 查询InfluxDB连续查询
     *
     * @return InfluxDB连续查询列表
     */
     List<InfluxCqDict> selectInfluxCqDictList();

    /**
     * 新增InfluxDB连续查询
     *
     * @param influxCqDict InfluxDB连续查询
     * @return 结果
     */
    int insertInfluxCqDict(InfluxCqDict influxCqDict);

    /**
     * 删除InfluxDB连续查询
     *
     * @param database 源数据库名
     * @return 结果
     */
    int deleteInfluxCqDictByDatabase(String database);
}

6、MyBatis的XML文件

<?xml version="1.0" encoding="UTF-8" ?>
<!DOCTYPE mapper
PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN"
"http://mybatis.org/dtd/mybatis-3-mapper.dtd">
<mapper namespace="com.rexel.backstage.project.tool.init.mapper.InfluxCqDictMapper">
    
    <resultMap type="com.rexel.backstage.project.tool.init.domain.InfluxCqDict" id="InfluxCqDictResult">
        <result property="cqName" column="cq_name"/>
        <result property="fromDatabase" column="from_database"/>
        <result property="fromRetentionPolicy" column="from_retention_policy"/>
        <result property="fromMeasurement" column="from_measurement"/>
        <result property="toDatabase" column="to_database"/>
        <result property="toRetentionPolicy" column="to_retention_policy"/>
        <result property="toMeasurement" column="to_measurement"/>
        <result property="forInterval" column="for_interval"/>
        <result property="every" column="every"/>
        <result property="field" column="field"/>
        <result property="func" column="func"/>
        <result property="groupByTime" column="group_by_time"/>
        <result property="fill" column="fill"/>
    </resultMap>

    <sql id="selectInfluxCqDictVo">
        select cq_name, from_database, from_retention_policy, from_measurement, to_database, to_retention_policy, to_measurement, for_interval, every, field, func, group_by_time, fill from influx_cq_dict
    </sql>

    <select id="selectInfluxCqDictList" resultMap="InfluxCqDictResult">
        <include refid="selectInfluxCqDictVo"/>
        where is_delete = 0;
    </select>

    <insert id="insertInfluxCqDict" parameterType="com.rexel.backstage.project.tool.init.domain.InfluxCqDict">
        insert into influx_cq_dict
        <trim prefix="(" suffix=")" suffixOverrides=",">
            <if test="cqName != null  and cpName != ''">cq_name,</if>
            <if test="fromDatabase != null  and fromDatabase != ''">from_database,</if>
            <if test="fromRetentionPolicy != null  and fromRetentionPolicy != ''">from_retention_policy,</if>
            <if test="fromMeasurement != null  and fromMeasurement != ''">from_measurement,</if>
            <if test="toDatabase != null  and toDatabase != ''">to_database,</if>
            <if test="toRetentionPolicy != null  and toRetentionPolicy != ''">to_retention_policy,</if>
            <if test="toMeasurement != null  and toMeasurement != ''">to_measurement,</if>
            <if test="for != null  and for != ''">for,</if>
            <if test="every != null  and every != ''">every,</if>
            <if test="field != null  and field != ''">field,</if>
            <if test="func != null  and func != ''">func,</if>
            <if test="groupByTime != null  and groupByTime != ''">group_by_time,</if>
            <if test="fill != null  and fill != ''">fill,</if>
        </trim>
        <trim prefix="values (" suffix=")" suffixOverrides=",">
            <if test="cqName != null  and cqName != ''">#{qpName},</if>
            <if test="fromDatabase != null  and fromDatabase != ''">#{fromDatabase},</if>
            <if test="fromRetentionPolicy != null  and fromRetentionPolicy != ''">#{fromRetentionPolicy},</if>
            <if test="fromMeasurement != null  and fromMeasurement != ''">#{fromMeasurement},</if>
            <if test="toDatabase != null  and toDatabase != ''">#{toDatabase},</if>
            <if test="toRetentionPolicy != null  and toRetentionPolicy != ''">#{toRetentionPolicy},</if>
            <if test="toMeasurement != null  and toMeasurement != ''">#{toMeasurement},</if>
            <if test="for != null  and for != ''">#{for},</if>
            <if test="every != null  and every != ''">#{every},</if>
            <if test="field != null  and field != ''">#{field},</if>
            <if test="func != null  and func != ''">#{func},</if>
            <if test="groupByTime != null  and groupByTime != ''">#{groupByTime},</if>
            <if test="fill != null  and fill != ''">#{fill},</if>
        </trim>
    </insert>

    <delete id="deleteInfluxCqDictByDatabase" parameterType="String">
        delete from influx_cq_dict where from_database = #{fromDatabase}
    </delete>
</mapper>

7、InfluxUtils类

package com.rexel.influxdb;

import com.alibaba.fastjson.JSONArray;
import com.alibaba.fastjson.JSONObject;
import com.rexel.influxdb.constans.InfluxSql;
import com.rexel.influxdb.query.QueryDeviceMeta;
import com.rexel.influxdb.query.QueryDeviceMetaResult;
import com.rexel.influxdb.query.QueryProductMeta;
import com.rexel.influxdb.query.QueryProductMetaResult;
import com.rexel.utils.times.TimeUtils;
import java.util.HashMap;
import java.util.Map;
import java.util.Map.Entry;
import java.util.Set;
import lombok.extern.slf4j.Slf4j;
import okhttp3.OkHttpClient;
import org.influxdb.InfluxDB;
import org.influxdb.InfluxDBFactory;
import java.io.IOException;
import java.io.InputStream;
import java.util.ArrayList;
import java.util.List;
import java.util.Properties;
import java.util.concurrent.TimeUnit;
import org.influxdb.dto.Query;
import org.influxdb.dto.QueryResult;
import org.influxdb.dto.QueryResult.Result;
import org.influxdb.dto.QueryResult.Series;

/**
 * @ClassName InfluxUtils
 * @Description InfluxDB共通类
 * @Author: chunhui.qu
 * @Date: 2020/6/26
 */
@Slf4j
public class InfluxUtils {
    private InfluxDB influxDb;
    private volatile Map<String, JSONObject> productMetaData = new HashMap<>();
    private volatile Map<String, JSONObject> deviceMetaData = new HashMap<>();

    /**
     * 构造函数
     */
    private InfluxUtils() {
        // do nothing
    }

    /**
     * 单例模式
     */
    private static class SingletonInstance {
        private static final InfluxUtils INSTANCE = new InfluxUtils();
    }

    /**
     * 获取对象句柄
     */
    public static InfluxUtils getInstance() {
        return SingletonInstance.INSTANCE;
    }

    /**
     * 创建InfluxDB连接
     *
     * @return InfluxDB
     */
    public InfluxDB connect() {
        if (influxDb != null) {
            return influxDb;
        }

        Properties properties = getProperties();
        String url = properties.getProperty("influx.url");
        String username = properties.getProperty("influx.username");
        String password = properties.getProperty("influx.password");
        log.info("influx.url=" + url);
        log.info("influx.username=" + username);
        log.info("influx.password=" + password);

        OkHttpClient.Builder client =
            new OkHttpClient.Builder().readTimeout(100, TimeUnit.SECONDS);
        influxDb = InfluxDBFactory.connect(url, username, password, client);

        return influxDb;
    }

    /**
     * 创建database
     *
     * @param database database
     */
    public void createDatabase(String database) {
        connect();
        JSONObject params = new JSONObject();
        params.put("database", database);
        String sql =  formatSql(InfluxSql.CREATE_DATA_BASE, params);
        QueryResult queryResult = influxDb.query(new Query(sql));
        log.info(queryResult.toString());
    }

    /**
     * 删除database
     *
     * @param database database
     */
    public void dropDatabase(String database) {
        connect();
        JSONObject params = new JSONObject();
        params.put("database", database);
        String sql =  formatSql(InfluxSql.DROP_DATA_BASE, params);
        QueryResult queryResult = influxDb.query(new Query(sql));
        log.info(queryResult.toString());
    }

    /**
     * 创建数据保存策略
     *
     * @param database database
     */
    public void createRetentionPolicy(String database) {
        connect();
        JSONObject params = new JSONObject();
        params.put("database", database);
        String sql =  formatSql(InfluxSql.CREATE_RETENTION_POLICY, params);
        QueryResult queryResult = influxDb.query(new Query(sql));
        log.info(queryResult.toString());
    }

    /**
     * 查询连续查询
     *
     * @return 结果
     */
    public JSONArray getContinuousQueries() {
        connect();
        QueryResult queryResult = influxDb.query(new Query(InfluxSql.SHOW_CONTINUOUS_QUERIES));
        return convert(queryResult, false);
    }

    /**
     * 删除指定连续查询
     *
     * @param cpName 连续查询名称
     * @param database database
     */
    public void dropContinuousQuery(String cpName, String database) {
        connect();
        JSONObject params = new JSONObject();
        params.put("cpName", cpName);
        params.put("database", database);
        String sql =  formatSql(InfluxSql.DROP_CONTINUOUS_QUERY, params);
        QueryResult queryResult = influxDb.query(new Query(sql));
        log.info(queryResult.toString());
    }

    /**
     * 创建连续查询
     *
     * @param createCqStr 创建语句
     */
    public void createContinuousQuery(String createCqStr) {
        connect();
        QueryResult queryResult = influxDb.query(new Query(createCqStr));
        log.info(queryResult.toString());
    }

    /**
     * 查询指定measurement的tag key
     *
     * @param database database
     * @param measurement measurement
     * @return tag key列表
     */
    public List<String> getMeasurementTagKeys(String database, String measurement) {
        connect();
        JSONObject params = new JSONObject();
        params.put("database", database);
        params.put("measurement", measurement);
        String sql =  formatSql(InfluxSql.SHOW_TAG_KEYS, params);
        QueryResult queryResult = influxDb.query(new Query(sql));
        JSONArray jsonArray = convert(queryResult, false);

        List<String> tagKeys = new ArrayList<>();
        for (int i = 0; i < jsonArray.size(); i++) {
            JSONObject jsonObject = jsonArray.getJSONObject(i);
            String tagKey = jsonObject.getString("tagKey");
            if (!tagKeys.contains(tagKey)) {
                tagKeys.add(tagKey);
            }
        }
        return tagKeys;
    }

    /**
     * InfluxQL格式化
     *
     * @param sql 原始SQL
     * @param params 参数
     * @return 格式化结果
     */
    public static String formatSql(String sql, JSONObject params) {
        Set<Entry<String, Object>> set = params.entrySet();
        for (Entry<String, Object> entry : set) {
            String param = "{" + entry.getKey() + "}";
            sql = sql.replace(param, String.valueOf(entry.getValue()));
        }
        return sql;
    }

    /**
     * 转换QueryResult
     *
     * @param queryResult QueryResult
     * @return JSONArray
     */
    public static JSONArray convert(QueryResult queryResult, boolean removeTime) {
        JSONArray jsonArray = new JSONArray();
        List<Result> results = queryResult.getResults();
        for (Result result : results) {
            List<Series> seriesList = result.getSeries();
            if (seriesList == null) {
                continue;
            }
            for (Series series : seriesList) {
                List<List<Object>> valuesList = series.getValues();
                if (valuesList == null) {
                    continue;
                }
                for (List<Object> values : valuesList) {
                    List<String> columns = series.getColumns();
                    JSONObject jsonObject = new JSONObject();
                    for (int i = 0; i < columns.size(); i++) {
                        String column = columns.get(i);
                        if ("time".equals(column)) {
                            if (!removeTime) {
                                jsonObject.put(column, TimeUtils.time8ToDateString(values.get(i).toString()));
                            }
                        } else {
                            Object value = values.get(i);
                            if (value != null) {
                                jsonObject.put(column, value);
                            }
                        }
                    }
                    jsonArray.add(jsonObject);
                }
            }
        }

        return jsonArray;
    }
/**
     * 读取资源文件
     *
     * @return Properties
     */
    private Properties getProperties() {
        Properties props = new Properties();
        try(InputStream is = InfluxUtils.class
            .getClassLoader().getResourceAsStream("application.properties")) {
            props.load(is);
        } catch (IOException e) {
            log.error("[读取资源文件异常:]",e);
        }
        return props;
    }
}

8、接口地址

http://localhost:9200/rexel/tool/influx/continuousQuery/refresh?type=init
http://localhost:9200/rexel/tool/influx/continuousQuery/refresh?type=drop
http://localhost:9200/rexel/tool/influx/continuousQuery/refresh?type=create

9、实现结果

==相关配置==

在整个过程中有几个相关的配置需要注意一下：

1、coordinator

query-timeout = "0s"

不要设置查询超时时间（因为首次查询90天的数据，是很有可能超时的，后面按需再设置）

2、continuous_queries

enabled = true：开启连续查询

log-enabled = true：开启连续查询日志

query-stats-enabled = true：将使用有关连续查询的运行时间及其持续时间的信息来写入数据_internal

==遇到的坑==

【坑1】

发生时间：2020年7月31日

问题描述：查看连续查询的日志（（/var/log/messages）），存在error=timeout的问题，

我在配置文件中已经把query-timeout设置为0了，依然出现这个问题。暂时还不知道原因。。。。很是惆怅。

2020年8月3日 追记：

未能在组件本身上找到原因及解决办法，尝试着将一个大的连续查询拆解为多个小的连续查询之后，问题得以解决。

拆解前：

CREATE CONTINUOUS QUERY cq_device_data_up_sum_6h ON rexel_online RESAMPLE EVERY 1d FOR 1d BEGIN SELECT first(*), last(*), max(*), mean(*), median(*), min(*), spread(*), sum(*) INTO rexel_online_analysis.one_year.device_data_up_sum_6h FROM rexel_online.one_year.device_data_up GROUP BY time(6h), deviceName, event, productKey fill(none) END

拆解后：

CREATE CONTINUOUS QUERY cq_device_data_up_sum_6h ON rexel_online RESAMPLE EVERY 1d FOR 1d BEGIN SELECT sum(*) INTO rexel_online_analysis.one_year.device_data_up_sum_6h FROM rexel_online.one_year.device_data_up GROUP BY time(6h), deviceName, event, productKey fill(none) END

--END--