二百四十五、海豚调度器——用DolphinScheduler调度执行复杂的HiveSQL(HQL包含多种海豚无法正确识别的符号)

天地风雷水火山泽2024-07-25 17:08

一、目的

在Hive中完成复杂JSON，既有对象还有数组而且数组中包含数组的解析后，原本以为没啥问题了，结果在DolphinScheduler中调度又出现了大问题，搞了一天、试了很多种方法、死了无数脑细胞，才解决了这个问题！

二、HiveSQL

复制代码 
insert  overwrite  table  hurys_dc_dwd.dwd_json_statistics partition(day)
select
        t1.device_no,
        source_device_type,
        sn,
        model,
        create_time,
        cycle,
        get_json_object(coil_list,'$.laneNo')  lane_no,
        get_json_object(coil_list,'$.laneType')           lane_type,
        section_no,
        get_json_object(coil_list,'$.coilNo')             coil_no,
        get_json_object(coil_list,'$.volumeSum')          volume_sum,
        get_json_object(coil_list,'$.volumePerson')       volume_person,
        get_json_object(coil_list,'$.volumeCarNon')       volume_car_non,
        get_json_object(coil_list,'$.volumeCarSmall')     volume_car_small,
        get_json_object(coil_list,'$.volumeCarMiddle')    volume_car_middle,
        get_json_object(coil_list,'$.volumeCarBig')       volume_car_big,
        get_json_object(coil_list,'$.speedAvg')           speed_avg,
        get_json_object(coil_list,'$.speed85')            speed_85,
        get_json_object(coil_list,'$.timeOccupancy')      time_occupancy,
        get_json_object(coil_list,'$.averageHeadway')     average_headway,
        get_json_object(coil_list,'$.averageGap')         average_gap,
        substr(create_time,1,10) day
from (select
       get_json_object(statistics_json,'$.deviceNo')          device_no,
       get_json_object(statistics_json,'$.sourceDeviceType')  source_device_type,
       get_json_object(statistics_json,'$.sn')                sn,
       get_json_object(statistics_json,'$.model')             model,
       get_json_object(statistics_json,'$.createTime')        create_time ,
       get_json_object(statistics_json,'$.data.cycle')        cycle,
       get_json_object(replace(replace(section_list,':{',':[{'),'}}','}]}'),'$.sectionNo') section_no,
       section_list
from hurys_dc_ods.ods_statistics
lateral view explode(split(replace(replace(replace(get_json_object(statistics_json,'$.data.sectionList'),
    '[',''),']',''),'},{"sectionNo"','}|{"sectionNo"'),"\\|")) tf as section_list
    where day='2024-07-18' --  date_sub(current_date(), 1)   -- '2024-07-18' --
    ) as t1
lateral view explode(split(replace(replace(replace(get_json_object(replace(replace(section_list,
    ':{',':[{'),'}}','}]}'),'$.coilList'),'[',''),']',''),'},','}|'),"\\|")) tf1 as coil_list
    where substr(create_time,1,10) =  '2024-07-18' --date_sub(current_date(), 1)   --'2024-07-17'
;

三、原先海豚任务的调度方式

在shell脚本里添加HiveSQL语句

#! /bin/bash

source /etc/profile

nowdate=`date --date='0 days ago' "+%Y%m%d"`

yesdate=`date -d yesterday +%Y-%m-%d`

hive -e "

use hurys_dc_dwd;

set hive.vectorized.execution.enabled=false;

set hive.exec.dynamic.partition=true;

set hive.exec.dynamic.partition.mode=nonstrict;

set hive.exec.max.dynamic.partitions.pernode=1000;

set hive.exec.max.dynamic.partitions=1500;

with t1 as(

select

get_json_object(statistics_json,'$.deviceNo') device_no,

get_json_object(statistics_json,'$.sourceDeviceType') source_device_type,

get_json_object(statistics_json,'$.sn') sn,

get_json_object(statistics_json,'$.model') model,

get_json_object(statistics_json,'$.createTime') create_time ,

get_json_object(statistics_json,'$.data.cycle') cycle,

get_json_object(replace(replace(section_list,':{',':[{'),'}}','}]}'),'$.sectionNo') section_no,

section_list

from hurys_dc_ods.ods_statistics

lateral view explode(split(replace(replace(replace(get_json_object(statistics_json,'$.data.sectionList'),'[',''),']',''),'},{"sectionNo"','}|{"sectionNo"'),"\\\\|")) tf as section_list

where day='$yesdate'

)

insert overwrite table hurys_dc_dwd.dwd_json_statistics partition(day)

select

t1.device_no,

source_device_type,

sn,

model,

substr(create_time,1,19) create_time ,

cycle,

get_json_object(coil_list,'$.laneNo') lane_no,

get_json_object(coil_list,'$.laneType') lane_type,

section_no,

get_json_object(coil_list,'$.coilNo') coil_no,

get_json_object(coil_list,'$.volumeSum') volume_sum,

get_json_object(coil_list,'$.volumePerson') volume_person,

get_json_object(coil_list,'$.volumeCarNon') volume_car_non,

get_json_object(coil_list,'$.volumeCarSmall') volume_car_small,

get_json_object(coil_list,'$.volumeCarMiddle') volume_car_middle,

get_json_object(coil_list,'$.volumeCarBig') volume_car_big,

get_json_object(coil_list,'$.speedAvg') speed_avg,

get_json_object(coil_list,'$.speed85') speed_85,

get_json_object(coil_list,'$.timeOccupancy') time_occupancy,

get_json_object(coil_list,'$.averageHeadway') average_headway,

get_json_object(coil_list,'$.averageGap') average_gap,

substr(create_time,1,10) day

from t1

lateral view explode(split(replace(replace(replace(get_json_object(replace(replace(section_list,':{',':[{'),'}}','}]}'),'$.coilList'),'[',''),']',''),'},','}|'),"\\\\|")) tf1 as coil_list

where substr(create_time,1,10) ='$yesdate'

"

四、原先方式报错日志

海豚无法正确识别HiveSQL里解析复杂JSON的多种符号

五、解决方式

把HiveSQL放在一个SQL文件里，然后在脚本里是执行Hive的sourceSQL文件

1 SQL文件

--使用hurys_dc_ods数据库

use hurys_dc_dwd;

--hive调优（必须先执行调优语句，否则部分复杂SQL运行会有问题）

set hive.vectorized.execution.enabled=false;

--开启动态分区功能（默认 true，开启）

set hive.exec.dynamic.partition=true;

--设置为非严格模式 nonstrict 模式表示允许所有的分区字段都可以使用动态分区

set hive.exec.dynamic.partition.mode=nonstrict;

--在每个执行 MR 的节点上，最大可以创建多少个动态分区

set hive.exec.max.dynamic.partitions.pernode=1000;

--在所有执行 MR 的节点上，最大一共可以创建多少个动态分区。默认 1000

set hive.exec.max.dynamic.partitions=1500;

insert overwrite table hurys_dc_dwd.dwd_json_statistics partition(day)

select

t1.device_no,

source_device_type,

sn,

model,

create_time,

cycle,

get_json_object(coil_list,'$.laneNo') lane_no,

get_json_object(coil_list,'$.laneType') lane_type,

section_no,

get_json_object(coil_list,'$.coilNo') coil_no,

get_json_object(coil_list,'$.volumeSum') volume_sum,

get_json_object(coil_list,'$.volumePerson') volume_person,

get_json_object(coil_list,'$.volumeCarNon') volume_car_non,

get_json_object(coil_list,'$.volumeCarSmall') volume_car_small,

get_json_object(coil_list,'$.volumeCarMiddle') volume_car_middle,

get_json_object(coil_list,'$.volumeCarBig') volume_car_big,

get_json_object(coil_list,'$.speedAvg') speed_avg,

get_json_object(coil_list,'$.speed85') speed_85,

get_json_object(coil_list,'$.timeOccupancy') time_occupancy,

get_json_object(coil_list,'$.averageHeadway') average_headway,

get_json_object(coil_list,'$.averageGap') average_gap,

substr(create_time,1,10) day

from (select

get_json_object(statistics_json,'$.deviceNo') device_no,

get_json_object(statistics_json,'$.sourceDeviceType') source_device_type,

get_json_object(statistics_json,'$.sn') sn,

get_json_object(statistics_json,'$.model') model,

get_json_object(statistics_json,'$.createTime') create_time ,

get_json_object(statistics_json,'$.data.cycle') cycle,

get_json_object(replace(replace(section_list,':{',':[{'),'}}','}]}'),'$.sectionNo') section_no,

section_list

from hurys_dc_ods.ods_statistics

lateral view explode(split(replace(replace(replace(get_json_object(statistics_json,'$.data.sectionList'),'[',''),']',''),'},{"sectionNo"','}|{"sectionNo"'),"\\|")) tf as section_list
where day= date_sub(current_date(), 1)

) as t1

lateral view explode(split(replace(replace(replace(get_json_object(replace(replace(section_list,':{',':[{'),'}}','}]}'),'$.coilList'),'[',''),']',''),'},','}|'),"\\|")) tf1 as coil_list
where substr(create_time,1,10) = date_sub(current_date(), 1)

;

2 海豚任务执行脚本

#! /bin/bash

source /etc/profile

nowdate=`date --date='0 days ago' "+%Y-%m-%d"`

yesdate=`date -d yesterday +%Y-%m-%d`

hive -e "

source dwd_json_statistics.sql

"

3 执行任务，验证结果

终于解决了，以后碰到类似调度器识别不了SQL里符号的问题，实在不行就用这个方法，把SQL放在SQL文件里，然后在脚本里执行这个SQL文件就行了，这样就能规避这类问题了

相关推荐
semantist@语校15 分钟前
第五十四篇｜从事实字段到推理边界：名古屋国际外语学院Prompt生成中的过度推断防御设计
大数据·linux·服务器·人工智能·百度·语言模型·prompt
秋刀鱼 ..36 分钟前
第二届电气、自动化与人工智能国际学术会议（ICEAAI 2026）
大数据·运维·人工智能·机器人·自动化
2401_878820471 小时前
Elasticsearch（ES）搜索引擎
大数据·elasticsearch·搜索引擎
数智顾问1 小时前
（102页PPT）数字化转型，从战略到执行（附下载方式）
大数据·人工智能·物联网
成长之路5141 小时前
【工具变量】全国地级市旧海关DID数据（1842-1949年）
大数据
starfalling10241 小时前
doris压缩方法_测试实践
大数据
G皮T2 小时前
【Elasticsearch】大慢查询隔离（二）：选择插件
大数据·elasticsearch·搜索引擎·全文检索·插件·性能·查询
zhaodiandiandian2 小时前
生成式AI重构内容创作生态：人机协同成核心竞争力
大数据·人工智能·重构
小猪佩奇TONY2 小时前
常用软件工具的使用(1) ---- git 的安装和基础操作
大数据·git·elasticsearch
财经三剑客2 小时前
东风集团股份：11月生产量达21.6万辆 销量19.6万辆
大数据·人工智能·汽车
热门推荐
01GitHub 镜像站点02【超详细教程】手把手教你从微软官网免费下载Windows 10官方原版ISO镜像（2025最新版）03【AutoGLM部署】本地私有化部署AI手机Agent04UV安装并设置国内源05Open-AutoGLM Windows 安装部署教程06Linux下V2Ray安装配置指南07安娜的档案(Anna’s Archive) 镜像网站/国内最新可访问入口（持续更新）08BongoCat - 跨平台键盘猫动画工具09Cursor 又偷偷更新，这个功能太实用：Visual Editor for Cursor Browser10Windows 11 官方系统安装与重装完整教程（2025年最新版）