How to export a table dataframe in PySpark to csv?

Question

I am using Spark 1.3.1 (PySpark) and I have generated a table using a SQL query. I now have an object that is a DataFrame. I want to export this DataFrame object (I have called it "table") to a csv file so I can manipulate it and plot the columns. How do I export the DataFrame "table" to a csv file?

Thanks!

Answer 1

If data frame fits in a driver memory and you want to save to local files system you can convert Spark DataFrame to local Pandas DataFrame using toPandas method and then simply use to_csv:

df.toPandas().to_csv('mycsv.csv')

Otherwise you can use spark-csv:

• Spark 1.3

  df.save('mycsv.csv', 'com.databricks.spark.csv')
  

• Spark 1.4+

  df.write.format('com.databricks.spark.csv').save('mycsv.csv')
  

In Spark 2.0+ you can use csv data source directly:

df.write.csv('mycsv.csv')

Answer 2

For Apache Spark 2+, in order to save dataframe into single csv file. Use following command

query.repartition(1).write.csv("cc_out.csv", sep='|')

Here 1 indicate that I need one partition of csv only. you can change it according to your requirements.

Answer 3

If you cannot use spark-csv, you can do the following:

df.rdd.map(lambda x: ",".join(map(str, x))).coalesce(1).saveAsTextFile("file.csv")

If you need to handle strings with linebreaks or comma that will not work. Use this:

import csv
import cStringIO

def row2csv(row):
    buffer = cStringIO.StringIO()
    writer = csv.writer(buffer)
    writer.writerow([str(s).encode("utf-8") for s in row])
    buffer.seek(0)
    return buffer.read().strip()

df.rdd.map(row2csv).coalesce(1).saveAsTextFile("file.csv")

Answer 4

You need to repartition the Dataframe in a single partition and then define the format, path and other parameter to the file in Unix file system format and here you go,

df.repartition(1).write.format('com.databricks.spark.csv').save("/path/to/file/myfile.csv",header = 'true')

Read more about the repartition function Read more about the save function

However, repartition is a costly function and toPandas() is worst. Try using .coalesce(1) instead of .repartition(1) in previous syntax for better performance.

Read more on repartition vs coalesce functions.

Answer 5

How about this (in you don't want an one liner) ?

for row in df.collect():
    d = row.asDict()
    s = "%d\t%s\t%s\n" % (d["int_column"], d["string_column"], d["string_column"])
    f.write(s)

f is a opened file descriptor. Also the separator is a TAB char, but it's easy to change to whatever you want.