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)

news2024/11/15 21:44:35

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

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)
;