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How To Read Various File Formats in PySpark (Json, Parquet, ORC, Avro) ?


This post explains Sample Code - How To Read Various File Formats in PySpark (Json, Parquet, ORC, Avro). We will consider the below file formats -

  • JSON
  • Parquet
  • ORC
  • Avro
  • CSV
We will use SparkSQL to load the file , read it and then print some data of it.   First we will build the basic Spark Session which will be needed in all the code blocks.


import org.apache.spark.sql.SparkSession

val spark \= SparkSession
  .builder()
  .appName("Various File Read")
  .config("spark.some.config.option", "some-value")
  .getOrCreate()

// For implicit conversions like converting RDDs to DataFrames
import spark.implicits.\_
sc = spark.sparkContext
   

1. JSON File :

 


OPTION 1 - 
============

# Json File
path \= "anydir/customerData.json"
inputDF \= spark.read.json(path)

\# Visualize the schema using the printSchema() method
inputDF.printSchema()

\# Creates a temporary view using the DataFrame
inputDF.createOrReplaceTempView("customer")

\# Use SQL statements 
listDF \= spark.sql("SELECT name FROM customer WHERE rank BETWEEN 1 AND 10")
listDF.show()


OPTION 2 -
============
\# A DataFrame can also be created for a JSON dataset using RDD Object
jsonStrings \= \['{"name":"Smith","address":{"city":"NYC","building":"rockstarforth"}}'\]
dataRDD \= sc.parallelize(jsonStrings)
dataDf \= spark.read.json(dataRDD)
dataDf.show()
   

2. Parquet File :

We will first read a json file , save it as parquet format and then read the parquet file.


inputDF \= spark.read.json("somedir/customerdata.json")

\# Save DataFrames as Parquet files which maintains the schema information.
inputDF.write.parquet("input.parquet")

\# Read above Parquet file. This gives a Dataframe.
dataParquet \= spark.read.parquet("input.parquet")

\# Parquet files can also be used to create a temporary view and then used in SQL statements.
dataParquet.createOrReplaceTempView("tableParquet")
students \= spark.sql("SELECT name FROM tableParquet WHERE age >= 13 AND age <= 19")
students.show()
 

3. Avro File :

Avro formatis supported in Spark Sql from Spark 2.4.

The spark-avro module is not internal . And hence not part of  spark-submit or spark-shell . We need to add the Avro dependency i.e. spark-avro_2.12  through --packages while submitting spark jobs with  spark-submit . Example below -


./bin/spark-submit --packages org.apache.spark:spark-avro_2.12:2.4.4 ...
If you want to add the Avro package to to spark-shell,  use below command while launching the spark-shell  -


./bin/spark-shell --packages org.apache.spark:spark-avro_2.12:2.4.4 ...
  First lets create a avro format file


inputDF = spark.read.json("somedir/customerdata.json")

`inputDF.select("name", "city").write.format("avro").save("customerdata.avro")`
Now use below code to read the Avro file df = spark.read.format("avro").load("customerdata.avro")  

4. ORC File :


#OPTION 1 -

val orcfile \= "FILE\_PATH\_OF\_THE\_ORC\_FILE"
val df \= sqlContext.read.format("orc").load(orcfile)
df.show

#OPTION 2 -
val orcfile = "FILE\_PATH\_OF\_THE\_ORC\_FILE"
val dataOrc \= spark.read.option("inferSchema", true).orc(orcfile)
dataOrc.show()

#If you want to load from Multiple Paths 
val dataDF \= sqlContext.read.format("orc").load("hdfs://localhost:8020/Dir1/\*","hdfs://localhost:8020/Dir2/\*/part-r-\*.orc")
   

5. CSV Files:

Case 1: - Let's say - we have to create the schema of the CSV file to be read.


from pyspark.sql.types import \*

id = StructField("id",StringType(),True)

Occupation = StructField("Occupation",StringType(), True)

columnList = \[id, Occupation\]

dfSchema = StructType(columnList)


#==============================
Let's print the schema details #==============================

dfSchema

StructType(List(StructField(id,StringType,true),
                StructField(Occupation))
)

#=====================================
# use the Schema to read the CSV File#=====================================

df = spark.read.csv('inputFile.csv',
                     header=True,
                     schema=dfSchema)


#Print the data
df.show(5)

#Print the schema
df.printSchema()
  Case 2: - Let's say - we want Spark to  infer the schema instead of creating the schema ourselves.


df = spark.read.csv('inputFile.csv',
                     header=True,
                     inferSchema=True)


#Print the data
df.show(5)

#Print the schema
df.printSchema()
  This ends up a concise summary as How to Read Various File Formats in PySpark (Json, Parquet, ORC, Avro). Hope this helps .  

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