二百二十九、离线数仓——离线数仓Hive从Kafka、MySQL到ClickHouse的完整开 ...

一、目的

为了整理离线数仓开辟的全流程，算是温故知新吧
离线数仓的数据源是Kafka和MySQL数据库，Kafka存业务数据，MySQL存维度数据
采集工具是Kettle和Flume，Flume采集Kafka数据，Kettle采集MySQL数据
离线数仓是Hive
目标数据库是ClickHouse
任务调度器是海豚
二、数据采集

（一）Flume采集Kafka数据

1、Flume设置文件

## agent a1
a1.sources = s1
a1.channels = c1
a1.sinks = k1
## configure source s1
a1.sources.s1.type = org.apache.flume.source.kafka.KafkaSource
a1.sources.s1.kafka.bootstrap.servers = 192.168.0.27:9092
a1.sources.s1.kafka.topics = topic_b_queue
a1.sources.s1.kafka.consumer.group.id = queue_group
a1.sources.s1.kafka.consumer.auto.offset.reset = latest
a1.sources.s1.batchSize = 1000
## configure channel c1
## a1.channels.c1.type = memory
## a1.channels.c1.capacity = 10000
## a1.channels.c1.transactionCapacity = 1000
a1.channels.c1.type = file
a1.channels.c1.checkpointDir = /home/data/flumeData/checkpoint/queue
a1.channels.c1.dataDirs = /home/data/flumeData/flumedata/queue
## configure sink k1
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path = hdfs://hurys23:8020/user/hive/warehouse/hurys_dc_ods.db/ods_queue/day=%Y-%m-%d/
a1.sinks.k1.hdfs.filePrefix = queue
a1.sinks.k1.hdfs.fileSuffix = .log
a1.sinks.k1.hdfs.round = true
a1.sinks.k1.hdfs.roundValue = 10
a1.sinks.k1.hdfs.roundUnit = second
a1.sinks.k1.hdfs.rollSize = 1200000000
a1.sinks.k1.hdfs.rollCount = 0
a1.sinks.k1.hdfs.rollInterval = 0
a1.sinks.k1.hdfs.idleTimeout = 60
a1.sinks.k1.hdfs.minBlockReplicas = 1
a1.sinks.k1.hdfs.fileType = SequenceFile
a1.sinks.k1.hdfs.codeC = gzip
## Bind the source and sink to the channel
a1.sources.s1.channels = c1
a1.sinks.k1.channel = c1

2、用海豚调度Flume任务

#!/bin/bash
source /etc/profile
/usr/local/hurys/dc_env/flume/flume190/bin/flume-ng agent -n a1 -f /usr/local/hurys/dc_env/flume/flume190/conf/queue.properties

3、目标路径

（二）Kettle采集MySQL维度数据

1、Kettle任务设置

2、用海豚调度Kettle任务

#!/bin/bash
source /etc/profile
/usr/local/hurys/dc_env/kettle/data-integration/pan.sh -rep=hurys_linux_kettle_repository -user=admin -pass=admin -dir=/mysql_to_hdfs/ -trans=23_MySQL_to_HDFS_tb_radar_lane level=Basic >>/home/log/kettle/23_MySQL_to_HDFS_tb_radar_lane_`date +%Y%m%d`.log 

3、目标路径

三、ODS层

（一）业务数据表

1. use hurys_dc_ods;
3. create external table  if not exists  ods_queue(
4.     queue_json  string
5. )
6. comment '静态排队数据表——静态分区'
7. partitioned by (day string)
8. stored as SequenceFile
9. ;

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1. --刷新表分区
2. msck repair table ods_queue;
3. --查看表分区
4. show partitions ods_queue;
5. --查看表数据
6. select * from ods_queue;

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（二）维度数据表

1. use hurys_dc_basic;
3. create  external  table  if not exists  tb_device_scene(
4.     id        int      comment '主键id',
5.     device_no string   comment '设备编号',
6.     scene_id  string   comment '场景编号'
7. )
8. comment '雷达场景表'
9. row format delimited fields terminated by ','
10. stored as  textfile  location '/data/tb_device_scene'
11. tblproperties("skip.header.line.count"="1") ;
12. --查看表数据
13. select * from hurys_dc_basic.tb_device_scene;

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四、DWD层

（一）业务数据清洗

1、业务数据的JSON有多层

1. --1、静态排队数据内部表——动态分区  dwd_queue
2. create  table  if not exists  dwd_queue(
3.     device_no    string          comment '设备编号',
4.     lane_num     int             comment '车道数量',
5.     create_time  timestamp       comment '创建时间',
6.     lane_no      int             comment '车道编号',
7.     lane_type    int             comment '车道类型 0:渠化1:来向2:出口3:去向4:左弯待转区5:直行待行区6:右转专用道99:未定义车道',
8.     queue_count  int             comment '排队车辆数',
9.     queue_len    decimal(10,2)   comment '排队长度(m)',
10.     queue_head   decimal(10,2)   comment '排队第一辆车距离停止线距离（m）',
11.     queue_tail   decimal(10,2)   comment '排队最后一辆车距离停止线距离（m）'
12. )
13. comment '静态排队数据表——动态分区'
14. partitioned by (day string)
15. stored as orc
16. ;
17. --动态插入数据
19. with t1 as(
20. select
21.        get_json_object(queue_json,'$.deviceNo')   device_no,
22.        get_json_object(queue_json,'$.createTime') create_time,
23.        get_json_object(queue_json,'$.laneNum')    lane_num,
24.        get_json_object(queue_json,'$.queueList')  queue_list
25. from hurys_dc_ods.ods_queue
26.     )
27. insert  overwrite  table  hurys_dc_dwd.dwd_queue partition(day)
28. select
29.         t1.device_no,
30.         t1.lane_num,
31.         substr(create_time,1,19)                                               create_time ,
32.         get_json_object(list_json,'$.laneNo')                                  lane_no,
33.         get_json_object(list_json,'$.laneType')                                lane_type,
34.         get_json_object(list_json,'$.queueCount')                              queue_count,
35.         cast(get_json_object(list_json,'$.queueLen')   as decimal(10,2))       queue_len,
36.         cast(get_json_object(list_json,'$.queueHead')  as decimal(10,2))       queue_head,
37.         cast(get_json_object(list_json,'$.queueTail')  as decimal(10,2))       queue_tail,
38.         date(t1.create_time) day
39. from t1
40. lateral view explode(split(regexp_replace(regexp_replace(queue_list,
41.                                                 '\\[|\\]','') ,   --将json数组两边的中括号去掉
42.                             '\\}\\,\\{','\\}\\;\\{'),  --将json数组元素之间的逗号换成分号
43.                   '\\;') --以分号作为分隔符(split函数以分号作为分隔)
44.           )list_queue as list_json
45. where  device_no is not null  and create_time is not null and  get_json_object(list_json,'$.queueLen') between 0 and 500
46. and  get_json_object(list_json,'$.queueHead')  between 0 and 500 and  get_json_object(list_json,'$.queueTail')  between 0 and 500 and  get_json_object(list_json,'$.queueCount') between 0 and 100
47. group by t1.device_no, t1.lane_num, substr(create_time,1,19), get_json_object(list_json,'$.laneNo'), get_json_object(list_json,'$.laneType'), get_json_object(list_json,'$.queueCount'), cast(get_json_object(list_json,'$.queueLen')   as decimal(10,2)), cast(get_json_object(list_json,'$.queueHead')  as decimal(10,2)), cast(get_json_object(list_json,'$.queueTail')  as decimal(10,2)), date(t1.create_time)
48. ;
49. --查看分区
50. show partitions dwd_queue;
51. --查看数据
52. select * from dwd_queue
53. where day='2024-03-11';
54. --删掉表分区
55. alter table hurys_dc_dwd.dwd_queue drop partition (day='2024-03-11');

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2、业务数据的JSON只有一层

1. --2、转向比数据内部表——动态分区  dwd_turnratio
2. create  table  if not exists  dwd_turnratio(
3.     device_no       string        comment '设备编号',
4.     cycle           int           comment '转向比数据周期' ,
5.     create_time     timestamp     comment '创建时间',
6.     volume_sum      int           comment '指定时间段内通过路口的车辆总数',
7.     speed_avg       decimal(10,2) comment '指定时间段内通过路口的所有车辆速度的平均值',
8.     volume_left     int           comment '指定时间段内通过路口的左转车辆总数',
9.     speed_left      decimal(10,2) comment '指定时间段内通过路口的左转车辆速度的平均值',
10.     volume_straight int           comment '指定时间段内通过路口的直行车辆总数',
11.     speed_straight  decimal(10,2) comment '指定时间段内通过路口的直行车辆速度的平均值',
12.     volume_right    int           comment '指定时间段内通过路口的右转车辆总数',
13.     speed_right     decimal(10,2) comment '指定时间段内通过路口的右转车辆速度的平均值',
14.     volume_turn     int           comment '指定时间段内通过路口的掉头车辆总数',
15.     speed_turn      decimal(10,2) comment '指定时间段内通过路口的掉头车辆速度的平均值'
16. )
17. comment '转向比数据表——动态分区'
18. partitioned by (day string)   --分区字段不能是表中已经存在的数据，可以将分区字段看作表的伪列。
19. stored as orc                 --表存储数据格式为orc
20. ;
21. --动态插入数据
22. --解析json字段、去重、非空、volumeSum>=0
23. --speed_avg、speed_left、speed_straight、speed_right、speed_turn 等字段保留两位小数
24. --0<=volume_sum<=1000、0<=speed_avg<=150、0<=volume_left<=1000、0<=speed_left<=100、0<=volume_straight<=1000
25. --0<=speed_straight<=150、0<=volume_right<=1000、0<=speed_right<=100、0<=volume_turn<=100、0<=speed_turn<=100
26. with t1 as(
27. select
28.         get_json_object(turnratio_json,'$.deviceNo')        device_no,
29.         get_json_object(turnratio_json,'$.cycle')           cycle,
30.         get_json_object(turnratio_json,'$.createTime')      create_time,
31.         get_json_object(turnratio_json,'$.volumeSum')       volume_sum,
32.         cast(get_json_object(turnratio_json,'$.speedAvg')     as decimal(10,2))    speed_avg,
33.         get_json_object(turnratio_json,'$.volumeLeft')      volume_left,
34.         cast(get_json_object(turnratio_json,'$.speedLeft')    as decimal(10,2))    speed_left,
35.         get_json_object(turnratio_json,'$.volumeStraight')  volume_straight,
36.         cast(get_json_object(turnratio_json,'$.speedStraight')as decimal(10,2))    speed_straight,
37.         get_json_object(turnratio_json,'$.volumeRight')     volume_right,
38.         cast(get_json_object(turnratio_json,'$.speedRight')   as decimal(10,2))    speed_right ,
39.         case when  get_json_object(turnratio_json,'$.volumeTurn')  is null then 0 else get_json_object(turnratio_json,'$.volumeTurn')  end as   volume_turn ,
40.         case when  get_json_object(turnratio_json,'$.speedTurn')   is null then 0 else cast(get_json_object(turnratio_json,'$.speedTurn')as decimal(10,2))   end as   speed_turn
41. from hurys_dc_ods.ods_turnratio)
42. insert overwrite table hurys_dc_dwd.dwd_turnratio partition (day)
43. select
44.        t1.device_no,
45.        cycle,
46.        substr(create_time,1,19)              create_time ,
47.        volume_sum,
48.        speed_avg,
49.        volume_left,
50.        speed_left,
51.        volume_straight,
52.        speed_straight ,
53.        volume_right,
54.        speed_right ,
55.        volume_turn,
56.        speed_turn,
57.        date(create_time) day
58. from t1
59. where device_no is not null and volume_sum between 0 and 1000 and speed_avg between 0 and 150 and volume_left  between 0 and 1000
60. and speed_left between 0 and 100 and volume_straight between 0 and 1000 and speed_straight between 0 and 150
61. and volume_right between 0 and 1000 and speed_right between 0 and 100 and volume_turn between 0 and 100 and speed_turn between 0 and 100
62. group by t1.device_no, cycle, substr(create_time,1,19), volume_sum, speed_avg, volume_left, speed_left, volume_straight, speed_straight, volume_right, speed_right, volume_turn, speed_turn, date(create_time)
63. ;
64. --查看分区
65. show partitions dwd_turnratio;
66. --查看数据
67. select * from hurys_dc_dwd.dwd_turnratio
68. where day='2024-03-11';
69. --删掉表分区
70. alter table hurys_dc_dwd.dwd_turnratio drop partition (day='2024-03-11');

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（二）维度数据清洗

1. create table if not exists  dwd_radar_lane(
2.     device_no         string  comment '雷达编号',
3.     lane_no           string  comment '车道编号',
4.     lane_id           string  comment '车道id',
5.     lane_direction    string  comment '行驶方向',
6.     lane_type         int     comment '车道类型 0渠化，1来向路段，2出口，3去向路段，4路口，5非路口路段，6其他',
7.     lane_length       float   comment '车道长度',
8.     lane_type_name    string  comment '车道类型名称'
9. )
10. comment '雷达车道信息表'
11. stored as orc
12. ;
13. --create table if not exists  dwd_radar_lane  stored as orc as
14. --加载数据
15. insert overwrite table  hurys_dc_dwd.dwd_radar_lane
16. select
17. device_no, lane_no, lane_id, lane_direction, lane_type,lane_length ,
18.        case when lane_type='0' then '渠化'
19.             when lane_type='1' then '来向路段'
20.             when lane_type='2' then '出口'
21.             when lane_type='3' then '去向路段'
22.        end as lane_type_name
23. from hurys_dc_basic.tb_radar_lane
24. where lane_length is not null
25. group by device_no, lane_no, lane_id, lane_direction, lane_type, lane_length
26. ;
27. --查看表数据
28. select * from hurys_dc_dwd.dwd_radar_lane;

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五、DWS层

1. create  table  if not exists  dws_statistics_volume_1hour(
2.     device_no        string         comment '设备编号',
3.     scene_name       string         comment '场景名称',
4.     lane_no          int            comment '车道编号',
5.     lane_direction   string         comment '车道流向',
6.     section_no       int            comment '断面编号',
7.     device_direction string         comment '雷达朝向',
8.     sum_volume_hour  int            comment '每小时总流量',
9.     start_time       timestamp      comment '开始时间'
10. )
11. comment '统计数据流量表——动态分区——1小时周期'
12. partitioned by (day string)
13. stored as orc
14. ;
15. --动态加载数据  --两个一起 1m41s 、 convert.join=false  1m43s、
16. --注意字段顺序  查询语句中字段顺序与建表字段顺序一致
17. insert  overwrite  table  hurys_dc_dws.dws_statistics_volume_1hour  partition(day)
18. select
19.        dwd_st.device_no,
20.        dwd_sc.scene_name,
21.        dwd_st.lane_no,
22.        dwd_rl.lane_direction,
23.        dwd_st.section_no,
24.        dwd_rc.device_direction,
25.        sum(volume_sum) sum_volume_hour,
26.        concat(substr(create_time, 1, 14), '00:00') start_time,
27.        day
28. from hurys_dc_dwd.dwd_statistics as dwd_st
29.     right join hurys_dc_dwd.dwd_radar_lane as dwd_rl
30.               on dwd_rl.device_no=dwd_st.device_no and dwd_rl.lane_no=dwd_st.lane_no
31.     right join hurys_dc_dwd.dwd_device_scene as dwd_ds
32.               on dwd_ds.device_no=dwd_st.device_no
33.     right join hurys_dc_dwd.dwd_scene as dwd_sc
34.               on dwd_sc.scene_id = dwd_ds.scene_id
35.     right join hurys_dc_dwd.dwd_radar_config as dwd_rc
36.               on dwd_rc.device_no=dwd_st.device_no
37. where dwd_st.create_time is not null
38. group by dwd_st.device_no, dwd_sc.scene_name, dwd_st.lane_no, dwd_rl.lane_direction, dwd_st.section_no, dwd_rc.device_direction, concat(substr(create_time, 1, 14), '00:00'), day
39. ;
40. --查看分区
41. show partitions dws_statistics_volume_1hour;
42. --查看数据
43. select * from hurys_dc_dws.dws_statistics_volume_1hour
44. where day='2024-02-29';

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六、ADS层

这里的ADS层，其实就是用Kettle把Hive的DWS层效果数据同步到ClickHouse中，也是一个Kettle任务而已

如许用海豚进行调度每一层的任务，整个离线数仓流程就跑起来了
七、海豚调度任务（除了2个采集任务外）

（一）delete_stale_data（根据删除策略删除ODS层原始数据）

#! /bin/bash
source /etc/profile
nowdate=`date --date='0 days ago' "+%Y%m%d"`
day_30_ago_date=`date -d "30 day ago " +%Y-%m-%d`
#静态列队数据
hadoop fs -test -e /user/hive/warehouse/hurys_dc_ods.db/ods_queue/day=${day_30_ago_date}
if [ $? -ne 0 ]; then
    echo "文件不存在"
else 
    hdfs dfs -rm -r /user/hive/warehouse/hurys_dc_ods.db/ods_queue/day=${day_30_ago_date}
fi
#轨迹数据
hadoop fs -test -e /user/hive/warehouse/hurys_dc_ods.db/ods_track/day=${day_30_ago_date}
if [ $? -ne 0 ]; then
    echo "文件不存在"
else 
    hdfs dfs -rm -r /user/hive/warehouse/hurys_dc_ods.db/ods_track/day=${day_30_ago_date}
fi
#动态列队数据
hadoop fs -test -e /user/hive/warehouse/hurys_dc_ods.db/ods_queue_dynamic/day=${day_30_ago_date}
if [ $? -ne 0 ]; then
    echo "文件不存在"
else 
    hdfs dfs -rm -r /user/hive/warehouse/hurys_dc_ods.db/ods_queue_dynamic/day=${day_30_ago_date}
fi
#区域数据
hadoop fs -test -e /user/hive/warehouse/hurys_dc_ods.db/ods_area/day=${day_30_ago_date}
if [ $? -ne 0 ]; then
    echo "文件不存在"
else 
    hdfs dfs -rm -r /user/hive/warehouse/hurys_dc_ods.db/ods_area/day=${day_30_ago_date}
fi
#事件数据
hadoop fs -test -e /user/hive/warehouse/hurys_dc_ods.db/ods_event/day=${day_30_ago_date}
if [ $? -ne 0 ]; then
    echo "文件不存在"
else 
    hdfs dfs -rm -r /user/hive/warehouse/hurys_dc_ods.db/ods_event/day=${day_30_ago_date}
fi
#删除表分区
hive -e "
use hurys_dc_ods;
alter table hurys_dc_ods.ods_area drop partition (day='$day_30_ago_date');
alter table hurys_dc_ods.ods_event drop partition (day='$day_30_ago_date');
alter table hurys_dc_ods.ods_queue drop partition (day='$day_30_ago_date');
alter table hurys_dc_ods.ods_queue_dynamic drop partition (day='$day_30_ago_date');
alter table hurys_dc_ods.ods_track drop partition (day='$day_30_ago_date')
"
（二）flume（Flume采集Kafka业务数据）

（三）create_database_table（主动创建Hive和ClickHouse的库表）

1、创建Hive库表

#! /bin/bash
source /etc/profile
hive -e "
source  1_dws.sql
"

2、创建ClickHouse库表

#! /bin/bash
source /etc/profile
clickhouse-client --user default --password hurys@123 -d default --multiquery <1_ads.sql

（四）hive_dws（DWS层任务）

#! /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_dws;
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=2000;
    
            
insert  overwrite  table  hurys_dc_dws.dws_statistics_volume_1hour  partition(day='$yesdate')
select
       dwd_st.device_no,
       dwd_sc.scene_name,
       dwd_st.lane_no,
       dwd_rl.lane_direction,
       dwd_st.section_no,
       dwd_rc.device_direction,
       sum(volume_sum) sum_volume_hour,
       concat(substr(create_time, 1, 14), '00:00') start_time
from hurys_dc_dwd.dwd_statistics as dwd_st
    right join hurys_dc_dwd.dwd_radar_lane as dwd_rl
              on dwd_rl.device_no=dwd_st.device_no and dwd_rl.lane_no=dwd_st.lane_no
    right join hurys_dc_dwd.dwd_device_scene as dwd_ds
              on dwd_ds.device_no=dwd_st.device_no
    right join hurys_dc_dwd.dwd_scene as dwd_sc
              on dwd_sc.scene_id = dwd_ds.scene_id
    right join hurys_dc_dwd.dwd_radar_config as dwd_rc
              on dwd_rc.device_no=dwd_st.device_no
where dwd_st.create_time is not null  and  day= '$yesdate'
group by dwd_st.device_no, dwd_sc.scene_name, dwd_st.lane_no, dwd_rl.lane_direction, dwd_st.section_no, dwd_rc.device_direction, concat(substr(create_time, 1, 14), '00:00')    
"
（五）hive_basic（维度表基础库）

#! /bin/bash
source /etc/profile
hive -e "
set hive.vectorized.execution.enabled=false;
use hurys_dc_basic
"

（六）dolphinscheduler_log（删除海豚日记文件）

#! /bin/bash
source /etc/profile
nowdate=`date --date='0 days ago' "+%Y%m%d"`
yesdate=`date -d yesterday +%Y-%m-%d`
cd  /usr/local/hurys/dc_env/dolphinscheduler/dolphin/logs/
rm -rf dolphinscheduler-api.$yesdate*.log
rm -rf dolphinscheduler-master.$yesdate*.log
rm -rf dolphinscheduler-worker.$yesdate*.log

（七）Kettle_Hive_to_ClickHouse（Kettle采集Hive的DWS层数据同步到ClickHouse的ADS层中）

#!/bin/bash
source /etc/profile
/usr/local/hurys/dc_env/kettle/data-integration/pan.sh -rep=hurys_linux_kettle_repository -user=admin -pass=admin -dir=/hive_to_clickhouse/ -trans=17_Hive_to_ClickHouse_ads_avg_volume_15min level=Basic >>/home/log/kettle/17_Hive_to_ClickHouse_ads_avg_volume_15min_`date +%Y%m%d`.log 

（八）Kettle_MySQL_to_HDFS（Kettle采集MySQL维度表数据到HDFS中）

（九）hive_dwd（DWD层任务）

1、业务数据的JSON有多层

#! /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(queue_json,'$.deviceNo')   device_no,
       get_json_object(queue_json,'$.createTime') create_time,
       get_json_object(queue_json,'$.laneNum')    lane_num,
       get_json_object(queue_json,'$.queueList')  queue_list
from hurys_dc_ods.ods_queue
where date(get_json_object(queue_json,'$.createTime')) = '$yesdate'
    )
insert  overwrite  table  hurys_dc_dwd.dwd_queue partition(day='$yesdate')
select
        t1.device_no,
        t1.lane_num,
        substr(create_time,1,19)                                               create_time ,
        get_json_object(list_json,'$.laneNo')                                  lane_no,
        get_json_object(list_json,'$.laneType')                                lane_type,
        get_json_object(list_json,'$.queueCount')                              queue_count,
        cast(get_json_object(list_json,'$.queueLen')   as decimal(10,2))       queue_len,
        cast(get_json_object(list_json,'$.queueHead')  as decimal(10,2))       queue_head,
        cast(get_json_object(list_json,'$.queueTail')  as decimal(10,2))       queue_tail
from t1
lateral view explode(split(regexp_replace(regexp_replace(queue_list,
                                                '\\\\[|\\\\]','') ,      --将json数组两边的中括号去掉
                                 '\\\\}\\\\,\\\\{','\\\\}\\\\;\\\\{'),   --将json数组元素之间的逗号换成分号
                   '\\\\;')   --以分号作为分隔符(split函数以分号作为分隔)
          )list_queue as list_json
where  device_no is not null  and  get_json_object(list_json,'$.queueLen') between 0 and 500 and  get_json_object(list_json,'$.queueHead')  between 0 and 500 and  get_json_object(list_json,'$.queueTail')  between 0 and 500 and  get_json_object(list_json,'$.queueCount') between 0 and 100
group by t1.device_no, t1.lane_num, substr(create_time,1,19), get_json_object(list_json,'$.laneNo'), get_json_object(list_json,'$.laneType'), get_json_object(list_json,'$.queueCount'), cast(get_json_object(list_json,'$.queueLen')   as decimal(10,2)), cast(get_json_object(list_json,'$.queueHead')  as decimal(10,2)), cast(get_json_object(list_json,'$.queueTail')  as decimal(10,2))
"
2、业务数据的JSON单层

#! /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(turnratio_json,'$.deviceNo')        device_no,
        get_json_object(turnratio_json,'$.cycle')           cycle,
        get_json_object(turnratio_json,'$.createTime')      create_time,
        get_json_object(turnratio_json,'$.volumeSum')       volume_sum,
        cast(get_json_object(turnratio_json,'$.speedAvg')     as decimal(10,2))    speed_avg,
        get_json_object(turnratio_json,'$.volumeLeft')      volume_left,
        cast(get_json_object(turnratio_json,'$.speedLeft')    as decimal(10,2))    speed_left,
        get_json_object(turnratio_json,'$.volumeStraight')  volume_straight,
        cast(get_json_object(turnratio_json,'$.speedStraight')as decimal(10,2))    speed_straight,
        get_json_object(turnratio_json,'$.volumeRight')     volume_right,
        cast(get_json_object(turnratio_json,'$.speedRight')   as decimal(10,2))    speed_right ,
        case when  get_json_object(turnratio_json,'$.volumeTurn')  is null then 0 else get_json_object(turnratio_json,'$.volumeTurn')  end as   volume_turn ,
        case when  get_json_object(turnratio_json,'$.speedTurn')   is null then 0 else cast(get_json_object(turnratio_json,'$.speedTurn')as decimal(10,2))   end as   speed_turn
from hurys_dc_ods.ods_turnratio
where date(get_json_object(turnratio_json,'$.createTime')) = '$yesdate'
)
insert overwrite table hurys_dc_dwd.dwd_turnratio partition (day='$yesdate')
select
       t1.device_no,
       cycle,
       substr(create_time,1,19)              create_time ,
       volume_sum,
       speed_avg,
       volume_left,
       speed_left,
       volume_straight,
       speed_straight ,
       volume_right,
       speed_right ,
       volume_turn,
       speed_turn
from t1
where device_no is not null and volume_sum between 0 and 1000 and speed_avg between 0 and 150 and volume_left  between 0 and 1000 and speed_left between 0 and 100 and volume_straight between 0 and 1000 and speed_straight between 0 and 150 and volume_right between 0 and 1000 and speed_right between 0 and 100 and volume_turn between 0 and 100 and speed_turn between 0 and 100
group by t1.device_no, cycle, substr(create_time,1,19), volume_sum, speed_avg, volume_left, speed_left, volume_straight, speed_straight, volume_right, speed_right, volume_turn, speed_turn
"
3、维度数据

#! /bin/bash
source /etc/profile
hive -e "
use hurys_dc_dwd;
set hive.vectorized.execution.enabled=false;
insert overwrite table hurys_dc_dwd.dwd_holiday
select
day, holiday,year
from hurys_dc_basic.tb_holiday
group by day, holiday, year
"
（十）hive_ods（ODS层任务）

#! /bin/bash
source /etc/profile
hive -e "
use hurys_dc_ods;
msck repair table ods_queue;
msck repair table ods_turnratio;
msck repair table ods_queue_dynamic;
msck repair table ods_statistics;
msck repair table ods_area;
msck repair table ods_pass;
msck repair table ods_track;
msck repair table ods_evaluation;
msck repair table ods_event;
"

目前，整个离线数仓的流程大抵就是如许，有题目的后面再完善！

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