Apache spark 比较两个数据集并获得更改的字段

我正在使用Java开发spark，我将从api下载数据并与mongodb数据进行比较，而下载的json有15-20个字段，但数据库有300个字段

现在，我的任务是将下载的JSON与mongodb数据进行比较，并获取与过去数据更改的任何字段

样本数据集 从API下载的数据

Mongodb数据

由于列的长度，我不能使用except

预期产量

---

请找到下面相同的源代码。这里我以唯一的电话号码条件为例

val list = List((1,"tony",123,"a@g.com"), (2,"stark",456,"b@g.com") 
       (3,"spidy",789,"c@g.com"))
val df1 = list.toDF("StudentId","Name","Phone","Email")
     .select('StudentId as "StudentId_1", 'Name as "Name_1",'Phone as "Phone_1", 
     'Email as "Email_1")

df1.show()

val list1 = List((1,"tony",1234,"a@g.com","NY","Nowhere"), 
     (2,"stark",456,"bg@g.com", "NY", "Nowhere"),
     (3,"spidy",789,"c@g.com","OH","Nowhere"))

val df2 = list1.toDF("StudentId","Name","Phone","Email","State","City")
      .select('StudentId as "StudentId_2", 'Name as "Name_2", 'Phone as "Phone_2",
         'Email as "Email_2", 'State as "State_2", 'City as "City_2")

df2.show()

val df3 = df1.join(df2, df1("StudentId_1") === 
         df2("StudentId_2")).where(df1("Phone_1") =!= df2("Phone_2"))

df3.withColumnRenamed("Phone_1", "Past_Phone").show()

+-----------+------+-------+-------+
|StudentId_1|Name_1|Phone_1|Email_1|
+-----------+------+-------+-------+
|          1|  tony|    123|a@g.com|
|          2| stark|    456|b@g.com|
|          3| spidy|    789|c@g.com|
+-----------+------+-------+-------+

+-----------+------+-------+--------+-------+-------+
|StudentId_2|Name_2|Phone_2| Email_2|State_2| City_2|
+-----------+------+-------+--------+-------+-------+
|          1|  tony|   1234| a@g.com|     NY|Nowhere|
|          2| stark|    456|bg@g.com|     NY|Nowhere|
|          3| spidy|    789| c@g.com|     OH|Nowhere|
+-----------+------+-------+--------+-------+-------+

+-----------+------+----------+-------+-----------+------+-------+-------+-------+-------+
|StudentId_1|Name_1|Past_Phone|Email_1|StudentId_2|Name_2|Phone_2|Email_2|State_2| City_2|
+-----------+------+----------+-------+-----------+------+-------+-------+-------+-------+
|          1|  tony|       123|a@g.com|          1|  tony|   1234|a@g.com|     NY|Nowhere|
+-----------+------+----------+-------+-----------+------+-------+-------+-------+-------+

---

假设您的数据位于2个数据帧中。我们可以为它们创建临时视图，如下所示

api_df.createOrReplaceTempView("api_data")
mongo_df.createOrReplaceTempView("mongo_data")

接下来我们可以使用Spark SQL。在这里，我们使用

StudentId

列将这两个视图连接起来，然后在它们上面使用一个case语句来计算过去的电话号码和电子邮件

spark.sql("""
select a.*
, case when a.Phone = b.Phone then '' else b.Phone end as Past_phone
, case when a.Email = b.Email then '' else b.Email end as Past_Email
from api_data a
join mongo_data b
on a.StudentId = b.StudentId
order by a.StudentId""").show()

输出：

+---------+-----+-----+-------+----------+----------+
|StudentId| Name|Phone|  Email|Past_phone|Past_Email|
+---------+-----+-----+-------+----------+----------+
|        1| tony|  123|a@g.com|      1234|          |
|        2|stark|  456|b@g.com|          |  bg@g.com|
|        3|spidy|  789|c@g.com|          |          |
+---------+-----+-----+-------+----------+----------+

我们有：

df1.show
+-----------+------+-------+-------+
|StudentId_1|Name_1|Phone_1|Email_1|
+-----------+------+-------+-------+
|          1|  tony|    123|a@g.com|
|          2| stark|    456|b@g.com|
|          3| spidy|    789|c@g.com|
+-----------+------+-------+-------+

df2.show

+-----------+------+-------+--------+-------+-------+
|StudentId_2|Name_2|Phone_2| Email_2|State_2| City_2|
+-----------+------+-------+--------+-------+-------+
|          1|  tony|   1234| a@g.com|     NY|Nowhere|
|          2| stark|    456|bg@g.com|     NY|Nowhere|
|          3| spidy|    789| c@g.com|     OH|Nowhere|
+-----------+------+-------+--------+-------+-------+

加入后：

var jn = df2.join(df1,df1("StudentId_1")===df2("StudentId_2"))

然后

参考：

下一步：

ans.select(ans("StudentId_2") as "StudentId",ans("Name_2") as "Name",ans("Phone_2") as "Phone",ans("Email_2") as "Email",ans("Past_Email"),ans("Past_Phone")).show

+---------+-----+-----+--------+----------+----------+
|StudentId| Name|Phone|   Email|Past_Email|Past_Phone|
+---------+-----+-----+--------+----------+----------+
|        1| tony| 1234| a@g.com|          |       123|
|        2|stark|  456|bg@g.com|   b@g.com|          |
|        3|spidy|  789| c@g.com|          |          |
+---------+-----+-----+--------+----------+----------+

---

谢谢您的快速回答，但是，我期望的表不应该有state，city。如果不是空的话，应该只显示更改的字段。我提出，我不知道df1中的哪些字段正在更改，所以它应该是dynamicOkay。我们是否有任何特定的列始终存在？

df1.show
+-----------+------+-------+-------+
|StudentId_1|Name_1|Phone_1|Email_1|
+-----------+------+-------+-------+
|          1|  tony|    123|a@g.com|
|          2| stark|    456|b@g.com|
|          3| spidy|    789|c@g.com|
+-----------+------+-------+-------+

df2.show

+-----------+------+-------+--------+-------+-------+
|StudentId_2|Name_2|Phone_2| Email_2|State_2| City_2|
+-----------+------+-------+--------+-------+-------+
|          1|  tony|   1234| a@g.com|     NY|Nowhere|
|          2| stark|    456|bg@g.com|     NY|Nowhere|
|          3| spidy|    789| c@g.com|     OH|Nowhere|
+-----------+------+-------+--------+-------+-------+

var jn = df2.join(df1,df1("StudentId_1")===df2("StudentId_2"))

var ans = jn.withColumn("Past_Phone", when(jn("Phone_2").notEqual(jn("Phone_1")),jn("Phone_1")).otherwise("")).withColumn("Past_Email", when(jn("Email_2").notEqual(jn("Email_1")),jn("Email_1")).otherwise(""))

ans.select(ans("StudentId_2") as "StudentId",ans("Name_2") as "Name",ans("Phone_2") as "Phone",ans("Email_2") as "Email",ans("Past_Email"),ans("Past_Phone")).show

+---------+-----+-----+--------+----------+----------+
|StudentId| Name|Phone|   Email|Past_Email|Past_Phone|
+---------+-----+-----+--------+----------+----------+
|        1| tony| 1234| a@g.com|          |       123|
|        2|stark|  456|bg@g.com|   b@g.com|          |
|        3|spidy|  789| c@g.com|          |          |
+---------+-----+-----+--------+----------+----------+