overwriting a spark output using pyspark

Try:

spark_df.write.format('com.databricks.spark.csv') \
  .mode('overwrite').option("header", "true").save(self.output_file_path)

Spark 1.4 and above has a built in csv function for the dataframewriter

https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrameWriter

e.g.

spark_df.write.csv(path=self.output_file_path, header="true", mode="overwrite", sep="\t")

Which is syntactic sugar for

spark_df.write.format("csv").mode("overwrite").options(header="true",sep="\t").save(path=self.output_file_path)

I think what is confusing is finding where exactly the options are available for each format in the docs.

These write related methods belong to the DataFrameWriter class: https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrameWriter

The csv method has these options available, also available when using format("csv"): https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrameWriter.csv

The way you need to supply parameters also depends on if the method takes a single (key, value) tuple or keyword args. It's fairly standard to the way python works generally though, using (*args, **kwargs), it just differs from the Scala syntax.

For example The option(key, value) method takes one option as a tuple like option(header,"true") and the .options(**options) method takes a bunch of keyword assignments e.g. .options(header="true",sep="\t")

EDIT 2021

The docs have had a huge facelift which may be good from the perspective of new users discovering functionality from a requirement perspective, but does need some adjusting to.

DataframeReader and DataframeWriter are now part of the Input/Output in the API docs: https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql.html#input-and-output

The DataframeWriter.csv callable is now here https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrameWriter.csv.html#pyspark.sql.DataFrameWriter.csv