schemaRDD

Steps for creating DataFrames, SchemaRDD and performing operations using SparkSQL

Spark SQL: SparkSQL is a Spark module for Structured data processing. One use of SparkSQL is to execute SQL queries using a basic SQL syntax. There are several ways to interact with Spark SQL including SQL, the dataframes API,dataset API. The backbone for all these operation is Dataframes and SchemaRDD. DataFrames A dataFrame is a distributed collection of data organised into named columns. It is conceptually equivalent to a table in a relational database. SchemaRDD SchemaRDDs are made of row objects along with the metadata information. Spark SQL needs SQLcontext object,which is created from existing SparkContext. Steps for creating Dataframes,SchemaRDD and performing some operations using the sql methods provided by sqlContext. Step 1: start the spark shell by using the following command....

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