SELECT COALESCE(storage_in.in_date, storage_out.out_date) AS date, COALESCE(storage_in.product_name, storage_out.product_name) AS product_name, IFNULL(storage_in.quantity, 0) AS IN, IFNULL(storage_out.quantity, 0) AS OUT, SUM(IFNULL(storage_in.quantity, 0) - IFNULL(storage_out.quantity, 0)) OVER (PARTITION BY COALESCE(storage_in.in_date, storage_out.out_date), COALESCE(storage_in.product_name, storage_out.product_name) ORDER BY COALESCE(storage_in.in_date, storage_out.out_date)) AS END FROM storage_in FULL OUTER JOIN storage_out ON storage_in.product_name = storage_out.pproduct_name AND storage_in.in_date = storage_out.out_date ORDER BY 1,2;

这是一个 SQL 查询语句，用于从两个表中获取产品进出库存信息，并计算每个产品在每个日期的总库存。其中使用了 COALESCE 函数来处理空值，IFNULL 函数来处理 NULL 值。JOIN 条件是产品名称和日期相等，使用了 FULL OUTER JOIN 来保证左右两个表都包含在结果中。最后使用了 OVER 子句来计算每个产品在每个日期的总库存。

相关问题

delimiter // CREATE PROCEDURE area_pro2 ( IN x INT, OUT alarm_count INT, OUT over_spped INT, OUT over_spped_10 INT, OUT over_spped_20 INT, OUT over_spped_50 INT, OUT untreated INT, OUT processed INT ) BEGIN WITH cte AS ( SELECT organization_id FROM `user` WHERE organization_id = x OR father LIKE CONCAT( COALESCE ( ( SELECT father FROM `user` WHERE organization_id = x ), "" ), x, "-%" ) AND is_del = 0 ) SELECT count( location_alarm.id ) INTO alarm_count, COUNT( CASE WHEN location_alarm.max_speed > location_alarm.speed_limit * 10 THEN 0 END ), count( CASE WHEN location_alarm.max_speed / location_alarm.speed_limit > 11 THEN 0 END ) INTO over_spped_10, count( CASE WHEN location_alarm.max_speed / location_alarm.speed_limit > 12 THEN 0 END ) INTO over_spped_20, count( CASE WHEN location_alarm.max_speed / location_alarm.speed_limit > 15 THEN 0 END ) INTO over_spped_50, count( CASE WHEN location_alarm.alarm_process_id IS NULL THEN 0 END ) INTO untreated, count( location_alarm.alarm_process_id ) INTO processed FROM cte INNER JOIN car ON car.organization_id = cte.organization_id AND car.is_del = 0 INNER JOIN location_alarm ON car.id = location_alarm.car_id; END // 我这个存储过程为什么报错了

你的存储过程报错可能是因为你没有指定 DELIMITER。在你的存储过程中，你使用了分号 (;) 来分隔语句，而这与 MySQL 默认使用的分隔符相同。因此，你需要使用 DELIMITER 命令来改变分隔符。

下面是修改后的代码：

DELIMITER //
CREATE PROCEDURE area_pro2 (
  IN x INT,
  OUT alarm_count INT,
  OUT over_spped INT,
  OUT over_spped_10 INT,
  OUT over_spped_20 INT,
  OUT over_spped_50 INT,
  OUT untreated INT,
  OUT processed INT
)
BEGIN
  WITH cte AS (
    SELECT
      organization_id
    FROM
      `user`
    WHERE
      organization_id = x
      OR father LIKE CONCAT(
        COALESCE((SELECT father FROM `user` WHERE organization_id = x), ''),
        x,
        '-%'
      )
      AND is_del = 0
  )
  SELECT
    count(location_alarm.id) INTO alarm_count,
    COUNT(CASE WHEN location_alarm.max_speed > location_alarm.speed_limit * 10 THEN 0 END),
    count(CASE WHEN location_alarm.max_speed / location_alarm.speed_limit > 11 THEN 0 END) INTO over_spped_10,
    count(CASE WHEN location_alarm.max_speed / location_alarm.speed_limit > 12 THEN 0 END) INTO over_spped_20,
    count(CASE WHEN location_alarm.max_speed / location_alarm.speed_limit > 15 THEN 0 END) INTO over_spped_50,
    count(CASE WHEN location_alarm.alarm_process_id IS NULL THEN 0 END) INTO untreated,
    count(location_alarm.alarm_process_id) INTO processed
  FROM
    cte
    INNER JOIN car ON car.organization_id = cte.organization_id
      AND car.is_del = 0
    INNER JOIN location_alarm ON car.id = location_alarm.car_id;
END//
DELIMITER ;

在这里，我们使用了 DELIMITER // 命令来指定新的分隔符，并在存储过程的结尾使用了 END// 来结束存储过程定义。最后，我们使用 DELIMITER ; 命令来将分隔符改回默认值。

val userrdd = sc.textFile("C:\\Users\\20430\\Desktop\\spark课设\\users.dat").map(_.split("::")).map(x => (x(0), (x(1), x(2), x(3)))) //用户ID 用户性别 用户年龄 用户职业 val rdd5 = rdd4.join(userrdd) // rdd5.collect().foreach(println) val data: RDD[sex] = rdd5.map(x => sex(x._1, x._2._1._1, x._2._1._2, x._2._2._3, x._2._2._2, x._2._2._1)) import spark.implicits._ val df = data.toDF() //创建dataFrame df.createOrReplaceTempView("sex2") //临时表 val resultDF: DataFrame = spark.sql("select userid,movieid,labe,rating,zhiye,sex from sex2 lateral view explode(split(schema,'\\\\|')) tmp as labe") resultDF.coalesce(1).write.csv("C:\\Users\\20430\\Desktop\\spark课设\\输出") resultDF.show()

这段代码主要是将用户信息、电影信息和评分信息进行整合，并输出到CSV文件中。具体来说，代码第一行通过 `sc.textFile()` 方法将指定路径下的users.dat文件以文本形式读入，然后通过 `map()` 方法对每一行进行切割，得到一个数组，其中数组的第0个元素是用户ID，第1个元素是用户性别，第2个元素是用户年龄，第3个元素是用户职业。然后通过 `map()` 方法将每个元素转换成 Tuple 的形式，即 (userId, (userGender, userAge, userOccupation))。

代码第二行是将电影评分数据和用户信息进行组合，通过 `join()` 方法将rdd4和userrdd进行join操作，得到rdd5。其中，rdd4是已经处理好的电影评分数据和电影类型数据，每个元素是一个Tuple，形如 (userId, (movieId, rating))，userrdd是已经处理好的用户数据，每个元素是一个Tuple，形如 (userId, (userGender, userAge, userOccupation))。

代码第三行是将rdd5中的元素进行转换，将元素的各个部分提取出来，构成一个新的样例类sex的对象，其中sex样例类的各个属性分别对应用户ID、电影ID、评分、职业、性别和年龄。返回一个新的RDD data，其中每个元素都是一个sex对象。

代码第四行将RDD转换成DataFrame类型，然后为DataFrame创建一个名为sex2的临时表。

代码第五行是通过Spark SQL查询的方式，将sex2表展开，得到一个新的DataFrame resultDF，其中每个元素都是一个Tuple，形如 (userId, movieId, label, rating, occupation, gender)。其中，label是指电影类型，通过explode和split函数将sex2表中的schema列展开为多行，每行对应一个电影类型。结果将DataFrame输出到CSV文件中。

代码最后一行是将DataFrame以表格形式展示出来。