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How To Save DataFrame as Different Formats in PySpark (Json, Parquet, ORC, Avro, CSV) ?


This post explains Sample Code - How To Save DataFrame as Different Formats in PySpark (Json, Parquet, ORC, Avro,CSV) . We will consider the below file formats -

  • JSON
  • Parquet
  • ORC
  • Avro
  • CSV
  • HDFS File
  First we will build the basic Spark Session which will be needed in all the code blocks.  

1.  Save DataFrame as CSV File:

We can use the DataFrameWriter class and the method within it - DataFrame.write.csv() to save or write as Dataframe as a CSV file. DataFrame.write.csv() has three main arguments viz -

  • Path
  • Separator
  • Header
 


df.write.csv(path='OUTPUT\_DIR', 
             header=True, 
             sep=',')
  Alternatively


df.write.format("com.databricks.spark.csv")
  .option("header", "true")
  .save("output.csv")
 

2.  Save DataFrame as ORC File:

To save or write a DataFrame as a ORC file, we can use write.orc() within the DataFrameWriter class.


df.write.orc(path='OUTPUT\_DIR')
   

3.  Save DataFrame as JSON File:

To save or write a DataFrame as a JSON file, we can use write.json() within the DataFrameWriter class.


df.write.json(path='OUTPUT\_DIR')
 

4.  Save DataFrame as Parquet File:

To save or write a DataFrame as a Parquet file, we can use write.parquet() within the DataFrameWriter class.


df.write.parquet(path='OUTPUT\_DIR')
 

5.  Save DataFrame as AVRO File:

 


df.write.format("com.databricks.spark.avro").save(outputPath)
 

6.  Save DataFrame to HDFS :

 


import os
from pyspark.sql import SparkSession

os.environ\["HADOOP\_USER\_NAME"\] = "hdfs"
os.environ\["PYTHON\_VERSION"\] = "3.6"

# Spark Session and DataFrame creation
sparkSession = SparkSession
                  .builder
                  .appName("hdfsapp")
                  .getOrCreate()

data = \[('NY', 1), ('London', 2), ('Singapore', 3), ('Bangalore', 4), ('Paris', 5)\]

df = sparkSession.createDataFrame(data)

# Write into HDFS
df.write.csv("hdfs://cluster/user/hdfs/output.csv")
     

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