我有下面的例子
val df_temp1 = Seq(
("1","Adam","Angra", "Anastasia")
).toDF("id","fname", "mname", "lname")
df_temp1.createOrReplaceTempView("df_temp1")
val df1 = spark.sql("""select id,named_struct('opi1',array(named_struct('data_description','fname','data_details',fname),named_struct('data_description','mname','data_details',mname),named_struct('data_description','lname','data_details',lname))) as pi, array(named_struct('data_description','fname','data_details',fname),named_struct('data_description','mname','data_details',mname), named_struct('data_description','lname','data_details',lname)) as opi2 from df_temp1""")
df1.printSchema
df1.show(false)
df1.createOrReplaceTempView("df1")
這給出了以下輸出模式
root
|-- id: string (nullable = true)
|-- pi: struct (nullable = false)
| |-- opi1: array (nullable = false)
| | |-- element: struct (containsNull = false)
| | | |-- data_description: string (nullable = false)
| | | |-- data_details: string (nullable = true)
|-- opi2: array (nullable = false)
| |-- element: struct (containsNull = false)
| | |-- data_description: string (nullable = false)
| | |-- data_details: string (nullable = true)
下面的結果
--- ----------------------------------------------------- ---------------------------------------------------
|id |pi |opi2 |
--- ----------------------------------------------------- ---------------------------------------------------
|1 |{[{fname, Adam}, {mname, Angra}, {lname, Anastasia}]}|[{fname, Adam}, {mname, Angra}, {lname, Anastasia}]|
--- ----------------------------------------------------- ---------------------------------------------------
我希望將 opi2 與 opi1 一起包含在 pi 中,因此預期的架構應該如下所示
root
|-- id: string (nullable = true)
|-- pi: struct (nullable = false)
| |-- opi1: array (nullable = false)
| | |-- element: struct (containsNull = false)
| | | |-- data_description: string (nullable = false)
| | | |-- data_details: string (nullable = true)
|----|-- opi2: array (nullable = false)
| | |-- element: struct (containsNull = false)
| | |-- |--data_description: string (nullable = false)
| | |---- |--data_details: string (nullable = true)
預期輸出將是 pi 內的兩個陣列 opi1 和 opi2 ,如下所示
--- ----------------------------------------------------- ---------------------------------------------------
|id |pi |
--- ----------------------------------------------------- ---------------------------------------------------
|1 |{[{fname, Adam}, {mname, Angra}, {lname, Anastasia}],[{fname, Adam}, {mname, Angra}, {lname, Anastasia}]}|
--- ----------------------------------------------------- ---------------------------------------------------
所以基本上將現有列添加到結構中(順便說一下,我使用的是 Spark 2.3,因此無法使用 Spark 2.4 中的任何函式)
uj5u.com熱心網友回復:
只需從pi.opi1and創建一個新結構opi2
val df2 = spark.sql("select id, named_struct('opi1',pi.opi1, 'opi2', opi2) as pi from df1")
df2.show(false)
df2.printSchema
--- ----------------------------------------------------------------------------------------------------------
|id |pi |
--- ----------------------------------------------------------------------------------------------------------
|1 |{[{fname, Adam}, {mname, Angra}, {lname, Anastasia}], [{fname, Adam}, {mname, Angra}, {lname, Anastasia}]}|
--- ----------------------------------------------------------------------------------------------------------
root
|-- id: string (nullable = true)
|-- pi: struct (nullable = false)
| |-- opi1: array (nullable = false)
| | |-- element: struct (containsNull = false)
| | | |-- data_description: string (nullable = false)
| | | |-- data_details: string (nullable = true)
| |-- opi2: array (nullable = false)
| | |-- element: struct (containsNull = false)
| | | |-- data_description: string (nullable = false)
| | | |-- data_details: string (nullable = true)
轉載請註明出處,本文鏈接:https://www.uj5u.com/net/442679.html
上一篇:無法使用PySpark在Databricks上與apachespark函式to_timestamp()連接并添加一列
