Read and write text files

The text format reads each line of a text file as a row in a DataFrame with a single value column of type StringType. Azure Databricks users commonly use it for log parsing, ingesting raw data before further processing, or any workflow that requires line-by-line access to file content. Azure Databricks supports reading and writing text files with Apache Spark, including write compression.

Prerequisites

Azure Databricks does not require additional configuration to use text files. However, to stream text files, you need Auto Loader.

Options

Use the .option() and .options() methods of DataFrameReader and DataFrameWriter to configure text data sources. For a complete list of supported options, see DataFrameReader text options and DataFrameWriter text options.

Usage

The following examples use the Wanderbricks dataset to demonstrate reading and writing text files using the Spark DataFrame API and SQL.

Read text files using SQL

To query text files without registering a table, use read_files. Unity Catalog permissions on the external location apply automatically.

SELECT * FROM read_files(
  '/Volumes/<catalog>/<schema>/<volume>/review_comments',
  format => 'text'
)

Read and write text files

The text format requires a DataFrame with a single StringType column. The following examples write Wanderbricks review comments as a text file, then read them back.

Python

from pyspark.sql.functions import col

# Write wanderbricks review comments as a text file
df = spark.read.table("samples.wanderbricks.reviews").select(col("comment").alias("value"))
df.write.format("text").save("/Volumes/<catalog>/<schema>/<volume>/review_comments")

# Read a text file — each line becomes a row in the "value" column
df = spark.read.format("text").load("/Volumes/<catalog>/<schema>/<volume>/review_comments")
display(df)

Scala

import org.apache.spark.sql.functions.col

// Write wanderbricks review comments as a text file
val df = spark.read.table("samples.wanderbricks.reviews").select(col("comment").alias("value"))
df.write.format("text").save("/Volumes/<catalog>/<schema>/<volume>/review_comments")

// Read a text file — each line becomes a row in the "value" column
val text = spark.read.format("text").load("/Volumes/<catalog>/<schema>/<volume>/review_comments")
text.show()

Additional resources

• Read and write CSV files: If your text data is delimited or tabular, CSV provides structured parsing with schema inference, header support, and configurable delimiters.