Shulou(Shulou.com)06/03 Report--

Products A table of changes in commodity prices, orders orders for goods, recording each purchase and date

Based on the non-equivalent join in Spark SQL, the matching of orders and products is realized, and the goods in each order are counted corresponding to the current price.

A slowly changing price list

Wang Zai milk, there has been a price change.

Scala > val products = sc.parallelize (Array (("Wang Zai Milk", "2017-01-01", "2018-01-01", 4), | ("Wang Zai Milk", "2018-01-02", "2020-01-01", 5), | ("Wang Laoji", "2017-01-02", "2019-01-01", 5), | ("Weilong spicy Gluten", "2010-01-01") "2020-01-01", 2) |). ToDF ("name", "startDate", "endDate", "price") products: org.apache.spark.sql.DataFrame = [name: string, startDate: string... 2 more fields] scala > products.show () +-+ | name | startDate | endDate | price | +-+ | Wangzai Milk | 2017-01-01 | 2018-01-01 | 4 | Wangzai Milk | 2018-01-02 | 2020-01-01 | 5 | Wang Laoji | 2017-01-02 | 2019-01-01 | 5 | Weilong spicy gluten | 2010 -01-01 | 2020-01-01 | 2 | +-+

Order form (commodity name, order date)

Wang Tsai milk had an order in different price periods.

Scala > val orders = sc.parallelize (Array (| ("2017-06-01", "Wang Zai Milk"), | ("2017-07-01", "Wang Laoji"), | ("2018-03-01", "Wang Zai Milk") |) .toDF ("date", "product") orders: org.apache.spark.sql.DataFrame = [date: string " Product: string] scala > orders.show+-+-+ | date | product | +-+-+ | 2017-06-01 | Wang Zai Milk | | 2017-07-01 | Wang Laoji | | 2018-03-01 | Wang Zai Milk | +-+-+

Calculate the current commodity price of each order through an unequal connection

Check out the prices of the two orders in different periods of time.

Scala > orders.join (products, $"product" = $"name" & & $"date" > = $"startDate" & & $"date"

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