Text

ProphecySparkBasicsPython0.0.1+ProphecySparkBasicsScala0.0.1+Databricks UC Single Cluster14.3+Databricks UC Shared14.3+Livy3.2.0+

The Text file type is:

• Easy to read from, write to, and share.
• Compatible with many programs, and easy to exchange data.

Parameters

Parameter	Tab	Description
Location	Location	File path to read from or write to the Text file.
Schema	Properties	Schema to apply on the loaded data. In the Source gem, you can define or edit the schema visually or in JSON code. In the Target gem, you can view the schema visually or as JSON code.

Source

The Source gem reads data from Text files and allows you to optionally specify the following additional properties.

Source properties

Property name	Description	Default
Description	Description of your dataset.	None
Enforce schema	Whether to use the schema you define.	true
Read file as single row	Whether to read each file from input path as a single row.	false
Line Separator	Sets a separator for each field and value. The separator can be one or more characters.	\r, \r\n, and \n
Recursive File Lookup	Whether to recursively load files and disable partition inferring. If the data source explicitly specifies the partitionSpec when therecursiveFileLookup is true, the Source gem throws an exception.	false

Example

Compiled code

tip

To see the compiled code of your project, switch to the Code view in the project header.

Python

def read_avro(spark: SparkSession) -> DataFrame:
    return spark.read\
        .format("text")\
        .text("dbfs:/FileStore/customers.txt", wholetext = False, lineSep = "\n")

Scala

object read_avro {

  def apply(spark: SparkSession): DataFrame =
    spark.read
        .format("text")
        .option("lineSep", "\n")
        .save("dbfs:/FileStore/customers.txt")

}

---

Target

The Target gem writes data to Text files and allows you to optionally specify the following additional properties.

Target properties

Property name	Description	Default
Description	Description of your dataset.	None
Write Mode	How to handle existing data. For a list of the possible values, see Supported write modes.	error
Partition Columns	List of columns to partition the Text files by. The Text file type only supports a single column apart from the partition columns. If the DataFrame contains more than one column apart from partition columns as the input DataFrame, the Target gem throws an AnalysisException error.	None
Compression Codec	Compression codec when writing to the Text file. The Text file supports the following codecs: none, bzip2, gzip, lz4, snappy and deflate.	None
Line Separator	Defines the line separator to use for parsing.	\n

Supported write modes

Write mode	Description
error	If the data already exists, throw an exception.
overwrite	If the data already exists, overwrite the data with the contents of the DataFrame.
append	If the data already exists, append the contents of the DataFrame.
ignore	If the data already exists, do nothing with the contents of the DataFrame. This is similar to the CREATE TABLE IF NOT EXISTS clause in SQL.

Example

Compiled code

tip

To see the compiled code of your project, switch to the Code view in the project header.

Python

def write_text(spark: SparkSession, in0: DataFrame):
    in0.write\
        .format("text")\
        .mode("overwrite")\
        .text("dbfs:/FileStore/customers.txt", compression = "gzip", lineSep = "\n")

Scala

object write_text {
  def apply(spark: SparkSession, in: DataFrame): Unit =
    in.write
      .format("text")
      .mode("overwrite")
      .option("compression", "gzip")
      .option("lineSep", "\n")
      .save("dbfs:/FileStore/customers.txt")
}