Roi Zaig

How to create a Spark DataFrame in different ways DataFrames are a table-like interface in Spark. The DataFrame structure is a distributed collection of data, build on Dataset. Since DataFrame have a defined schema and types it allows Spark to utilize different optimization on top of the schema and to execute calculations faster. Below we have initial code to instantiate SparkContext , SQLContext and SparkSession code. from pyspark.sql import Row from pyspark import SparkContext from pyspark import SQLContext sc = SparkContext() sqlContext = SQLContext(sc) from pyspark.sql import SparkSession spark = SparkSession . builder \ . master( 'local' ) \ . appName( 'Create DataFrame' ) \ . getOrCreate() toDF method Perhaps the most simple way to create a DataFrame is to use the toDF method. All you need is to supply a list of column names (strings) and a list of tuples as data. If you don't specify the column names Spark generate the...

First thing to notice it the file structure. Its actually a collection of json objects *each one in a single line*. { "field_name": "value", "field_name": "value", ... } { "field_name": "value", "field_name": "value", ... } { "field_name": "value", "field_name": "value", ... } Unlike csv() method the json() method does not have inferSchema parameter. The schema is inferred from the json types. from pyspark.sql import SparkSession import pyspark.sql.types as t import pyspark.sql.functions as f spark = SparkSession . builder \ . master( 'local' ) \ . appName( 'Read JSON file' ) \ . getOrCreate() df = spark . read \ . json( 'data.json' , multiLine = True ) df . printSchema() df . show(truncate = False ) root |-- Address: string (nullable = true) |-- Age: long (nullable = true) |-- Birth Date: string (nu...