Reading a CSV File from DBFS in Databricks
To read a CSV file from the Databricks File System (DBFS), you can use either PySpark or SQL. Here are the steps for both methods:
Using PySpark
First, ensure you have a SparkSession initialized. Then, you can read a CSV file using the `spark.read.csv()` method.
from pyspark.sql import SparkSession
# Initialize SparkSession
spark = SparkSession.builder.appName('Read CSV').getOrCreate()
# Read CSV file
df = spark.read.csv("dbfs:/path/to/your/file.csv", header=True, inferSchema=True)
# Display the DataFrame
df.show()
Using SQL with `read_files` Function
Databricks recommends using the `read_files` table-valued function for SQL users. This function is available in Databricks Runtime 13.3 LTS and above.
SELECT * FROM read_files(
'dbfs:/path/to/your/file.csv',
format => 'csv',
header => true,
mode => 'PERMISSIVE'
)
Frequently Asked Questions
- Q: What is the default mode for handling malformed records in CSV files?
A: The default mode is PERMISSIVE, which inserts nulls for fields that could not be parsed correctly.
- Q: How do I specify a custom schema for a CSV file?
A: You can specify a custom schema using the `schema` option in PySpark or by defining it in SQL when using `read_files` with a schema.
- Q: Can I upload a CSV file directly to DBFS from a local machine?
A: Yes, you can upload a CSV file to DBFS using the Databricks UI or the Databricks CLI.
- Q: What is the purpose of the `badRecordsPath` option?
A: The `badRecordsPath` option allows you to write malformed records to a specified file, which takes precedence over including them in the DataFrame with the `_corrupt_record` column.
- Q: How do I handle different delimiters in a CSV file?
A: You can specify a different delimiter using the `delimiter` option in PySpark.
- Q: Can I use SQL to read CSV files without `read_files`?
A: Yes, but you cannot specify data source options or schema without using temporary views or `read_files`.
- Q: What is the difference between `DROPMALFORMED` and `FAILFAST` modes?
A: `DROPMALFORMED` drops lines with malformed records, while `FAILFAST` aborts the reading process if any malformed data is found.
Bottom Line: Reading CSV files from DBFS in Databricks is straightforward using either PySpark or SQL with the `read_files` function. Both methods offer flexibility in handling malformed records and specifying custom schemas or delimiters.