Question

输入数据的格式如下：

+--------------------+-------------+--------------------+
|           date     |       user  |           product  |
+--------------------+-------------+--------------------+
|        2016-10-01  |        Tom  |           computer |
+--------------------+-------------+--------------------+
|        2016-10-01  |        Tom  |           iphone   |
+--------------------+-------------+--------------------+
|        2016-10-01  |       Jhon  |             book   |
+--------------------+-------------+--------------------+
|        2016-10-02  |        Tom  |             pen    |
+--------------------+-------------+--------------------+
|        2016-10-02  |       Jhon  |             milk   |
+--------------------+-------------+--------------------+

输出格式如下：

+-----------+-----------------------+
|     user  |        products       |
+-----------------------------------+
|     Tom   |   computer,iphone,pen |
+-----------------------------------+
|     Jhon  |          book,milk    |  
+-----------------------------------+

输出显示每个用户按日期订购的所有产品。

我想使用Spark处理这些数据，你能帮我吗？谢谢。

Answer 1

最好使用map-reduceBykey（）组合而不是groupBy ..还假设数据没有

#Read the data using val ordersRDD = sc.textFile("/file/path")
val ordersRDD = sc.parallelize( List(("2016-10-01","Tom","computer"), 
    ("2016-10-01","Tom","iphone"), 
    ("2016-10-01","Jhon","book"), 
    ("2016-10-02","Tom","pen"), 
    ("2016-10-02","Jhon","milk")))

#group by (date, user), sort by key & reduce by user & concatenate products
val dtusrGrpRDD = ordersRDD.map(rec => ((rec._2, rec._1), rec._3))
   .sortByKey().map(x=>(x._1._1, x._2))
   .reduceByKey((acc, v) => acc+","+v)

#if needed, make it to DF
scala> dtusrGrpRDD.toDF("user", "product").show()
+----+-------------------+
|user|            product|
+----+-------------------+
| Tom|computer,iphone,pen|
|Jhon|          book,milk|
+----+-------------------+

Answer 2

如果您正在使用HiveContext（您应该使用）：

使用python的示例：

from pyspark.sql.functions import collect_set

df = ... load your df ...
new_df = df.groupBy("user").agg(collect_set("product").alias("products"))

如果您不希望重复产品中的结果列表，则可以使用collect_list。

Answer 3

对于数据框，它是两层的：

GroupBy将为您提供分组的用户集帖子，您可以对其进行迭代并在分组的数据集上进行collect_list或collect_set。

如何使用spark中的逗号分隔符将相同的列值连接到新列

3 个答案: