Reading a multiple line JSON with pyspark

I hope this solution well find you.

To read a JSON file in Python with PySpark when it contains multiple records with each variable on a different line, you can use a custom approach to handle the file format. Here is a potential solution:

Read the file using the textFile() method to load it as an RDD (Resilient Distributed Dataset). This will allow you to process each line individually.

lines = spark.sparkContext.textFile("file.json")
Use the map() function to transform each line, ensuring that each line is a single JSON object. You can achieve this by appending a comma (,) at the end of each line except the last line, effectively concatenating all the lines into a single JSON array.

import json

processed_lines = lines.map(lambda line: line + "," if not line.endswith("}") else line)
json_array = "[" + processed_lines.reduce(lambda a, b: a + b) + "]"
Convert the JSON array string back to an RDD using parallelize().

json_rdd = spark.sparkContext.parallelize([json_array])
Use the read() method to read the JSON data as a DataFrame.

df = spark.read.json(json_rdd)
If necessary, select the desired columns from the DataFrame to obtain the desired output.

output_df = df.select("firstname", "id", "name")
output_df.show()
This approach processes each line individually and concatenates them into a single JSON array, which can be read into a DataFrame using spark.read.json(). By applying these steps, you should be able to parse the JSON file with multiple records on different lines and obtain the expected output.

Note: Keep in mind that processing large JSON files can be memory-intensive. Ensure that your Spark cluster has enough memory to handle the size of the file. Additionally, consider partitioning the data or using other optimization techniques if needed.