Hello @Shivap,

To write a file as a single file, you can use the appropriate options in the write method to control the output format. The default behavior is generating multiple files such as _committed, _started, _SUCCESS, and part files because the underlying operation defaults to saving the data in a distributed manner.

Here are the steps to ensure you write a single file:

1. Configure the Output Path: Specify the exact file path where you want the single file to be written.
2. Use Coalesce or Repartition: If you're working with a DataFrame, use coalesce(1) to collect all data into one partition before writing. This will force Spark to write the data out as a single file.
3. Save the File with dbutils.fs.cp: Write the DataFrame to a temporary path, then use dbutils.fs.cp to copy the resultant part file to the desired single file path.

Here is an example using these steps:

# Example DataFrame

df = spark.createDataFrame([(1, "a"), (2, "b"), (3, "c")], ["id", "value"])

# Write DataFrame to a temporary directory

temp_path = "dbfs:/tmp/output"

(df.coalesce(1) # Reduce to one partition

.write

.mode('overwrite')

.option('header', 'true')

.csv(temp_path))

# List the files in the temporary directory

files = dbutils.fs.ls(temp_path)

part_file = [file.path for file in files if file.name.startswith("part")][0]

# Define the final output path

single_file_path = "dbfs:/path/to/final_output.csv"

# Copy the part file to the final output path

dbutils.fs.cp(part_file, single_file_path)

# Clean up temporary directory

dbutils.fs.rm(temp_path, True)

This approach ensures that the data is written to a single file named final_output.csv in your specified location.

Remember, this technique might not be optimal for very large datasets due to the overhead of collecting data to a single partition. For large datasets, consider using appropriate partitioning strategies or handling multiple part files appropriately on the consuming side.