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这篇文章主要为大家展示了“怎么使用SQL实现车流量的计算”,内容简而易懂,条理清晰,希望能够帮助大家解决疑惑,下面让小编带领大家一起研究并学习一下“怎么使用SQL实现车流量的计算”这篇文章吧。
将数据导入hive,通过SparkSql编写sql,实现不同业务的数据计算实现,主要讲述车辆卡口转换率,卡口转化率:主要计算不同卡口下车辆之间的流向,求出之间的转换率。
select car, monitor_id, action_time, ROW_NUMBER () OVER (PARTITION by car ORDER by action_time) as n1 FROM traffic.hive_flow_action
此结果做为表1,方便后面错位连接使用
通过表1的结果,与自身进行错位链接,并以车牌为分区,拼接经过卡口的过程
(select t1.car, t1.monitor_id, concat(t1.monitor_id, "->", t2.monitor_id) as way from ( select car, monitor_id, action_time, ROW_NUMBER () OVER (PARTITION by car ORDER by action_time) as n1 FROM traffic.hive_flow_action) t1 left join ( select car, monitor_id, action_time, ROW_NUMBER () OVER (PARTITION by car ORDER by action_time) as n1 FROM traffic.hive_flow_action) t2 on t1.car = t2.car and t1.n1 = t2.n1-1 where t2.action_time is not null)
获取到每辆车的一个行车记录,经过的卡口
获取卡口1~卡口2,…等的车辆数有哪些,即拿上面的行车记录字段进行分区在进行统计
(select s1.way, COUNT(1) sumCar from --行车过程 (select t1.car, t1.monitor_id, concat(t1.monitor_id, "->", t2.monitor_id) as way from ( select car, monitor_id, action_time, ROW_NUMBER () OVER (PARTITION by car ORDER by action_time) as n1 FROM traffic.hive_flow_action) t1 left join ( select car, monitor_id, action_time, ROW_NUMBER () OVER (PARTITION by car ORDER by action_time) as n1 FROM traffic.hive_flow_action) t2 on t1.car = t2.car and t1.n1 = t2.n1-1 where t2.action_time is not null)s1 group by way)
获取每个卡口最初的车辆数,方便后面拿行车轨迹车辆数/总车辆数,得出卡口之间的转换率
select monitor_id , COUNT(1) sumall from traffic.hive_flow_action group by monitor_id
select s2.way, s2.sumCar / s3.sumall zhl from ( select s1.way, COUNT(1) sumCar from --行车过程 ( select t1.car, t1.monitor_id, concat(t1.monitor_id, "->", t2.monitor_id) as way from ( select car, monitor_id, action_time, ROW_NUMBER () OVER (PARTITION by car ORDER by action_time) as n1 FROM traffic.hive_flow_action) t1 left join ( select car, monitor_id, action_time, ROW_NUMBER () OVER (PARTITION by car ORDER by action_time) as n1 FROM traffic.hive_flow_action) t2 on t1.car = t2.car and t1.n1 = t2.n1-1 where t2.action_time is not null)s1 group by way)s2 left join --每个卡口总车数 ( select monitor_id , COUNT(1) sumall from traffic.hive_flow_action group by monitor_id) s3 on split(s2.way, "->")[0]= s3.monitor_id
以上是“怎么使用SQL实现车流量的计算”这篇文章的所有内容,感谢各位的阅读!相信大家都有了一定的了解,希望分享的内容对大家有所帮助,如果还想学习更多知识,欢迎关注亿速云行业资讯频道!
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