CSV

Allows you to read or write delimited files such as CSV (Comma-separated Values) or TSV (Tab-separated Values).

Source

Source Parameters

CSV Source supports all the available Spark read options for CSV.

The below list contains the additional parameters to read a CSV file:

Parameter		Description	Required
Dataset Name		Name of the Dataset	True
Location		Location of the file(s) to be loaded E.g.: dbfs:/data/test.csv	True
Schema		Schema to applied on the loaded data. Can be defined/edited as JSON or inferred using Infer Schema button.	True

Example

Step 1 - Create Source Component

Generated Code

Python

def load_csv(spark: SparkSession) -> DataFrame:
    return spark.read\
        .schema(
          StructType([
            StructField("order_id", IntegerType(), True),
            StructField("customer_id", IntegerType(), True),
            StructField("order_status", StringType(), True),
            StructField("order_category", StringType(), True),
            StructField("order_date", DateType(), True),
            StructField("amount", DoubleType(), True)
        ])
        )\
        .option("header", True)\
        .option("quote", "\"")\
        .option("sep", ",")\
        .csv("dbfs:/Prophecy/anshuman@simpledatalabs.com/OrdersDatasetInput.csv")

Scala

object load_csv {

  def apply(spark: SparkSession): DataFrame =
    spark.read
      .format("csv")
      .option("header", true)
      .option("quote",  "\"")
      .option("sep",    ",")
      .schema(
        StructType(
          Array(
            StructField("order_id",       IntegerType, true),
            StructField("customer_id",    IntegerType, true),
            StructField("order_status",   StringType,  true),
            StructField("order_category", StringType,  true),
            StructField("order_date",     DateType,    true),
            StructField("amount",         DoubleType,  true)
          )
        )
      )
      .load("dbfs:/Prophecy/anshuman@simpledatalabs.com/OrdersDatasetInput.csv")

}

---

Target

Target Parameters

CSV Target supports all the available Spark write options for CSV.

The below list contains the additional parameters to write a CSV file:

Parameter	Description	Required
Dataset Name	Name of the Dataset	True
Location	Location of the file(s) to be loaded E.g.: dbfs:/data/output.csv	True
Write Mode	How to handle existing data. See this table for a list of available options.	False

Supported Write Modes

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

Example

Step 1 - Create Target Component

Generated Code

Python

def write_as_csv(spark: SparkSession, in0: DataFrame):
    in0.write\
        .option("header", True)\
        .option("sep", ",")\
        .mode("error")\
        .option("separator", ",")\
        .option("header", True)\
        .csv("dbfs:/Prophecy/anshuman@simpledatalabs.com/output.csv")

Scala

object write_as_csv {
  def apply(spark: SparkSession, in: DataFrame): Unit =
    in.write
      .format("csv")
      .option("header", true)
      .option("sep",    ",")
      .mode("error")
      .save("dbfs:/Prophecy/anshuman@simpledatalabs.com/output.csv")
}

Producing a single output file

Because of Spark's distributed nature, output files are written as multiple separate partition files. If you need a single output file for some reason (such as reporting or exporting to an external system), use a Repartition Gem in Coalesce mode with 1 output partition:

caution

Note: This is not recommended for extremely large data sets as it may overwhelm the worker node writing the file.