How To Read Csv File From Local In Databricks

Reading a CSV File from Local in Databricks

Databricks does not directly support reading local files using its standard read_csv method because it relies on Spark, which requires files to be accessible by all Spark workers in the cluster. However, you can work around this limitation by uploading your local CSV file to a distributed file system like Databricks File System (DBFS) or cloud storage like AWS S3 or Azure Blob Storage. Here’s how you can do it:

Step 1: Upload the CSV File to DBFS

You can upload your CSV file to DBFS using the Databricks CLI or the Databricks UI. Here’s how to do it using the CLI:

      databricks fs cp /path/to/local/file.csv dbfs:/path/to/upload/
    

Step 2: Read the CSV File in Databricks

After uploading the file, you can read it using Spark’s read.csv method in Databricks:

      from pyspark.sql import SparkSession

      # Initialize SparkSession
      spark = SparkSession.builder.getOrCreate()

      # Read the CSV file
      df = spark.read.format("csv") 
                  .option("header", "true") 
                  .option("delimiter", ",") 
                  .load("dbfs:/path/to/upload/file.csv")

      # Display the DataFrame
      df.show()
    

Frequently Asked Questions

• Q: Can I read a CSV file directly from my local machine using Databricks?
  
  A: No, Databricks cannot directly read local files because Spark requires files to be accessible by all workers in the cluster. You need to upload the file to a distributed file system first.
• Q: How do I handle malformed CSV records in Databricks?
  
  A: You can use the mode option to handle malformed records. Options include PERMISSIVE, DROPMALFORMED, and FAILFAST.
• Q: What is the difference between using read.csv and read.format("csv") in Spark?
  
  A: Both methods read CSV files, but read.format("csv") provides more flexibility by allowing you to specify additional options like headers and delimiters.
• Q: Can I specify a schema when reading a CSV file in Databricks?
  
  A: Yes, you can specify a schema using the schema option when reading a CSV file.
• Q: How do I upload multiple CSV files to DBFS at once?
  
  A: You can use the Databricks CLI with a loop to upload multiple files, or use the Databricks UI to upload them individually.
• Q: What is the badRecordsPath option used for?
  
  A: The badRecordsPath option is used to write malformed records to a file for further inspection.
• Q: Can I use SQL to read CSV files in Databricks?
  
  A: Yes, you can use SQL with the read_files function to read CSV files in Databricks, but it requires Databricks Runtime 13.3 LTS or above.

Bottom Line

Reading a CSV file from a local machine in Databricks requires uploading the file to a distributed file system like DBFS first. After uploading, you can use Spark’s read.format("csv") method to read the file. This approach ensures that the file is accessible to all Spark workers in the cluster.

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