Transform a file into a bytes format without using BlobServiceClient.

[LiLO]

Hello everyone,

I would like to know if it was possible to transform, with PySpark, a flat file stored in a directory in Azure Blob storage into bytes format to be able to parse it, while using the connection already integrated into the cluster between databricks and Azure Blob storage , I already found some code that uses BlobServiceClient but I would like to do that using the already integrated connection.

regards,

[Kaniz_Fatma]

Hi @LiLO, You can achieve this using PySpark and the integrated connection between Databricks and Azure Blob storage. Let’s break down the steps:

1. Read the Flat File from Azure Blob Storage:

   • Use the integrated connection to read the flat file directly from Azure Blob storage into a PySpark DataFrame. You can specify the path to the directory where your flat file is stored.
   • For example, if your flat file is in a directory called mydata within your Azure Blob storage container, you can read it like this:

     from pyspark.sql import SparkSession
     
     spark = SparkSession.builder.appName("AzureBlobReader").getOrCreate()
     
     # Read the flat file into a DataFrame
     flat_file_df = spark.read.csv("abfss://<container-name>@<storage-account-name>.dfs.core.windows.net/mydata")
     

2. Convert DataFrame to Bytes Format:

   • Once you have the DataFrame, you can convert it to bytes format. PySpark DataFrames are already represented in a binary format internally, so you don’t need to explicitly convert them to bytes.
   • If you need to extract the raw bytes from the DataFrame, you can use the collect() method to retrieve the data as a list of rows, where each row is a tuple of values. Then, you can serialize the rows to bytes using any serialization method (e.g., JSON, Avro, Parquet).

3. Parsing the Data:

   • Now that you have the data in bytes format, you can parse it according to your requirements.
   • For example, if your flat file is in CSV format, you can parse it using PySpark’s built-in CSV reader or custom logic.

Here’s a simplified example of reading a CSV file from Azure Blob storage and converting it to bytes format:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("AzureBlobReader").getOrCreate()

# Read the flat file into a DataFrame
flat_file_df = spark.read.csv("abfss://<container-name>@<storage-account-name>.dfs.core.windows.net/mydata")

# Convert DataFrame to bytes (optional, depending on your use case)
# For example, serialize the DataFrame to JSON bytes
json_bytes = flat_file_df.toJSON().collect()[0].encode("utf-8")

# Now you can parse the JSON bytes as needed
# (e.g., deserialize it back to a DataFrame or process it further)

Remember to replace <container-name> and <storage-account-name> with your actual container and storage account names. Adjust the code according to your specific file format and requirements.

Feel free to adapt this example to your use case, and let me know if you need further assistance! 😊