在 Spark 中将 String 转换为 Map

Question

Below data in csv file with delimeter | , I want to convert string to Map for PersonalInfo column data so that I can extract required information.

I try to convert below csv to parquet format with String to Map using Cast I got datatype mismatch error.

Below is data for your ref. Your help is much appreciated.

Empcode EmpName PersonalInfo
1       abc     """email"":""abc@gmail.com"",""Location"":""India"",""Gender"":""Male"""
2       xyz     """email"":""xyz@gmail.com"",""Location"":""US"""
3       pqr     """email"":""abc@gmail.com"",""Gender"":""Female"",""Location"":""Europe"",""Mobile"":""1234"""

Thanks

Answer 1

One simple way is to use str_to_map function after you get rid of the double quotes from PersonalInfo column:

val df1 = df.withColumn(
  "PersonalInfo",
  expr("str_to_map(regexp_replace(PersonalInfo, '\"', ''))")
)

df1.show(false)

//+-------+-------+------------------------------------------------------------------------------+
//|Empcode|EmpName|PersonalInfo                                                                  |
//+-------+-------+------------------------------------------------------------------------------+
//|1      |abc    |{email -> abc@gmail.com, Location -> India, Gender -> Male}                   |
//|2      |xyz    |{email -> xyz@gmail.com, Location -> US}                                      |
//|3      |pqr    |{email -> abc@gmail.com, Gender -> Female, Location -> Europe, Mobile -> 1234}|
//+-------+-------+------------------------------------------------------------------------------+

Answer 2

If you want to create a map from PersonalInfo column, from Spark 3.0 you can proceed as follows:

• Split your string according to "","" using split function
• For each element of your obtained string array, create sub-arrays according to "":"" using split function
• Remove all "" from elements of sub-arrays using regexp_replace function
• Build map entries using struct function
• Use map_from_entries to build map from your array of entries

Complete code is as follows:

import org.apache.spark.sql.functions.{col, map_from_entries, regexp_replace, split, struct, transform}

val result = data.withColumn("PersonalInfo",
  map_from_entries(
    transform(
      split(col("PersonalInfo"), "\"\",\"\""),
      item => struct(
        regexp_replace(split(item, "\"\":\"\"")(0), "\"\"", ""),
        regexp_replace(split(item, "\"\":\"\"")(1), "\"\"", "")
      )
    )
  )
)

With the following input_dataframe :

+-------+-------+---------------------------------------------------------------------------------------------+
|Empcode|EmpName|PersonalInfo                                                                                 |
+-------+-------+---------------------------------------------------------------------------------------------+
|1      |abc    |""email"":""abc@gmail.com"",""Location"":""India"",""Gender"":""Male""                       |
|2      |xyz    |""email"":""xyz@gmail.com"",""Location"":""US""                                              |
|3      |pqr    |""email"":""abc@gmail.com"",""Gender"":""Female"",""Location"":""Europe"",""Mobile"":""1234""|
+-------+-------+---------------------------------------------------------------------------------------------+

You get the following result dataframe:

+-------+-------+------------------------------------------------------------------------------+
|Empcode|EmpName|PersonalInfo                                                                  |
+-------+-------+------------------------------------------------------------------------------+
|1      |abc    |{email -> abc@gmail.com, Location -> India, Gender -> Male}                   |
|2      |xyz    |{email -> xyz@gmail.com, Location -> US}                                      |
|3      |pqr    |{email -> abc@gmail.com, Gender -> Female, Location -> Europe, Mobile -> 1234}|
+-------+-------+------------------------------------------------------------------------------+