Weather Station（字符串）
一、数据源设计（4 张表） 1. 骑手表（rider.txt） plaintext R001,张三,站点A,2年 R002,李四,站点B,1年 R003,王五,站点A,3年 R004,赵六,站点C,0.5年 字段说明：rid（骑手 ID）、rname（骑手姓名）、station（所属站点）、experience（从业经验） 2. 订单表（order.txt） plaintext O001,U001,S001,2025-06-20 12:00:00,北京市朝阳区建国路88号,2025-06-20 12:30:00,2025-06-20 12:25:00,R001,1.5 O002,U002,S002,2025-06-20 18:30:00,北京市海淀区中关村大街5号,2025-06-20 19:00:00,2025-06-20 19:10:00,R002,2.0 O003,U003,S001,2025-06-20 19:15:00,北京市朝阳区建国路88号,2025-06-20 19:45:00,2025-06-20 19:35:00,R001,1.5 O004,U004,S003,2025-06-21 12:45:00,北京市丰台区南三环西路1号,2025-06-21 13:15:00,2025-06-21 13:00:00,R003,3.0 O005,U005,S002,2025-06-21 18:00:00,北京市海淀区中关村大街5号,2025-06-21 18:30:00,2025-06-21 18:45:00,R004,2.0 字段说明：oid（订单 ID）、uid（用户 ID）、sid（商家 ID）、order_time（下单时间）、address（配送地址）、expect_time（预计送达时间）、actual_time（实际送达时间）、rid（骑手 ID）、distance（配送距离 / 公里） 3. 站点表（station.txt） plaintext 站点A,朝阳区建国路88号,3 站点B,海淀区中关村大街5号,2 站点C,丰台区南三环西路1号,1 字段说明：station（站点名称）、location（站点位置）、rider_num（站点骑手数量） 4. 天气表（weather.txt） plaintext 2025-06-20,朝阳区,晴天,25℃ 2025-06-20,海淀区,多云,28℃ 2025-06-21,朝阳区,小雨,22℃ 2025-06-21,丰台区,中雨,20℃ 字段说明：date（日期）、area（区域）、weather（天气）、temperature（温度） 二、Pig 分析解决方案 案例 1：加载四张表数据并定义字段结构 pig grunt> rider = LOAD '/export/data/rider.txt' USING PigStorage(',') AS (rid:chararray, rname:chararray, station:chararray, experience:chararray); grunt> order = LOAD '/export/data/order.txt' USING PigStorage(',') AS (oid:chararray, uid:chararray, sid:chararray, order_time:chararray, address:chararray, expect_time:chararray, actual_time:chararray, rid:chararray, distance:double); grunt> station = LOAD '/export/data/station.txt' USING PigStorage(',') AS (station:chararray, location:chararray, rider_num:int); grunt> weather = LOAD '/export/data/weather.txt' USING PigStorage(',') AS (date:chararray, area:chararray, weather:chararray, temperature:chararray); 案例 2：多表关联查询配送效率数据 订单与骑手表关联 pig grunt> order_rider = JOIN order BY rid, rider BY rid; 关联站点表获取站点信息 pig grunt> order_rider_station = JOIN order_rider BY order_rider::station, station BY station; 提取订单日期与区域，关联天气表 pig grunt> order_date_area = FOREACH order_rider_station GENERATE oid, order_time, actual_time, expect_time, distance, rid, rname, experience, station, rider_num, CONCAT(SUBSTRING(order_time, 0, 10), '') AS order_date, -- 提取日期 EXTRACT(area FROM address) AS order_area; -- 简化提取区域（实际需更复杂文本处理） grunt> order_weather = JOIN order_date_area BY (order_date, order_area), weather BY (date, area); 案例 3：计算配送耗时与效率指标 pig grunt> delivery_efficiency = FOREACH order_weather GENERATE oid, rid, rname, station, rider_num, distance, weather, temperature, -- 计算配送耗时（分钟） (TOUNIXTIME(actual_time) - TOUNIXTIME(expect_time)) / 60 AS delay_minutes, -- 计算准时率（实际≤预计为准时，>0为延误） IF((TOUNIXTIME(actual_time) - TOUNIXTIME(expect_time)) <= 0, '准时', '延误') AS is_on_time; 案例 4：按骑手分组分析平均配送效率 pig grunt> rider_group = GROUP delivery_efficiency BY (rid, rname, station, experience); grunt> rider_efficiency = FOREACH rider_group GENERATE group::rid, group::rname, group::station, group::experience, AVG(distance) AS avg_distance, AVG(delay_minutes) AS avg_delay, COUNT(oid) AS order_count, SUM(IF(is_on_time == '准时', 1, 0)) / COUNT(oid) AS on_time_rate; 案例 5：按天气与站点分析效率差异 pig grunt> weather_station_group = GROUP delivery_efficiency BY (weather, station); grunt> weather_station_efficiency = FOREACH weather_station_group GENERATE group::weather, group::station, AVG(delay_minutes) AS avg_delay, AVG(distance) AS avg_distance, SUM(IF(is_on_time == '准时', 1, 0)) / COUNT(oid) AS on_time_rate; 案例 6：存储高效骑手分析结果（准时率 > 90%） pig grunt> efficient_riders = FILTER rider_efficiency BY on_time_rate >= 0.9; grunt> STORE efficient_riders INTO '/export/data/delivery_efficiency_analysis'; 在vm软件里面怎么把这些代码展示出来帮我写一个详细步骤，一步一步来
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