Handling Invalid Column Names in Spark: A Step-by-Step Guide

In data processing, it’s common to encounter files where the first line contains invalid or dummy column names, which can disrupt the reading of data into a structured format. This post explores how to handle such scenarios using Apache Spark.

The Problem: Dummy Column Names

Consider the following input file:

DummyCol1,DummyCol2
EmployeeId,LastName,FirstName,Title
1,Adams,Andrew,General Manager
2,Edwards,Nancy,Sales Manager
3,Peacock,Jane,Sales Support Agent
4,Park,Margaret,Sales Support Agent
5,Johnson,Steve,Sales Support Agent
6,Mitchell,Michael,IT Manager
7,King,Robert,IT Staff
8,Callahan,Laura,IT Staff

In this file, the actual column names start from the second row. The first row contains dummy columns that aren’t useful and can cause issues when reading the file into a DataFrame in Spark.

Attempting to Read the File

Let’s first see what happens when we try to read this file directly using Spark’s csv reader:

val df = spark.read.csv("data_dump.csv")
df.show()

The resulting DataFrame might look like this:

+----------+---------+
|       _c0|      _c1|
+----------+---------+
| DummyCol1|DummyCol2|
|EmployeeId| LastName|
|         1|    Adams|
|         2|  Edwards|
|         3|  Peacock|
|         4|     Park|
|         5|  Johnson|
|         6| Mitchell|
|         7|     King|
|         8| Callahan|
+----------+---------+

As you can see, Spark has incorrectly inferred the schema, leading to only two columns being read, with the actual data not being properly parsed.

The Solution: Custom Handling of Column Names

To correctly read all the columns and ignore the dummy ones, you can follow these steps:

1. Read the Entire File as Text: First, read the file as a text file so that each line is treated as a single string.
2. Generate Row IDs: Add a unique identifier to each row using monotonically_increasing_id.
3. Extract and Set Correct Column Names: Use the second row to extract the actual column names.
4. Split the Data into Columns: Finally, split each line into columns based on commas and apply the correct column names.

Here’s how you can implement this in Spark:

import org.apache.spark.sql.functions._

val df = spark.read.text("data_dump.csv").withColumn("row_id",monotonically_increasing_id)

val cols=df.select("value").filter($"row_id"===1).first.mkString.split(",")  

val df2 = df.filter($"row_id">1).withColumn("temp", split(col("value"), ",")).
 select((0 until cols.length).map(i => col("temp").getItem(i).as(cols.apply(i))): _*)

df2.show()

Final Output

The final DataFrame, after applying the above steps, will look like this:

+----------+--------+---------+-------------------+
|EmployeeId|LastName|FirstName|              Title|
+----------+--------+---------+-------------------+
|         1|   Adams|   Andrew|    General Manager|
|         2| Edwards|    Nancy|      Sales Manager|
|         3| Peacock|     Jane|Sales Support Agent|
|         4|    Park| Margaret|Sales Support Agent|
|         5| Johnson|    Steve|Sales Support Agent|
|         6|Mitchell|  Michael|         IT Manager|
|         7|    King|   Robert|           IT Staff|
|         8|Callahan|    Laura|           IT Staff|
+----------+--------+---------+-------------------+

Conclusion

By reading the file as text and manually processing the first few lines, you can effectively handle files with invalid or dummy column names in Spark. This method ensures that your DataFrame accurately represents the data, with all columns properly named and parsed.