SparkContext,
JavaSparkContext,
SQLContext
and SparkSession
?SparkSession
?SparkSession
?SQLContext
, SparkContext
, and JavaSparkContext
also in SparkSession
?parallelize
have different behaviors in SparkContext
and JavaSparkContext
. How do they behave in SparkSession
?How can I create the following using a SparkSession
?
RDD
JavaRDD
JavaPairRDD
Dataset
Is there a method to transform a JavaPairRDD
into a Dataset
or a Dataset
into a JavaPairRDD
?
sparkContext
is a Scala implementation entry point and JavaSparkContext
is a java wrapper of sparkContext
.
SQLContext
is entry point of SparkSQL which can be received from sparkContext
.Prior to 2.x.x, RDD ,DataFrame and Data-set were three different data abstractions.Since Spark 2.x.x, All three data abstractions are unified and SparkSession
is the unified entry point of Spark.
An additional note is , RDD meant for unstructured data, strongly typed data and DataFrames are for structured and loosely typed data. You can check
Is there any method to convert or create Context using Sparksession ?
yes. its sparkSession.sparkContext()
and for SQL, sparkSession.sqlContext()
Can I completely replace all the Context using one single entry SparkSession ?
yes. you can get respective contexs from sparkSession.
Does all the functions in SQLContext, SparkContext,JavaSparkContext etc are added in SparkSession?
Not directly. you got to get respective context and make use of it.something like backward compatibility
How to use such function in SparkSession?
get respective context and make use of it.
How to create the following using SparkSession?
sparkSession.sparkContext.parallelize(???)
sparkSession.sparkContext.parallelize(???).map(//making your data as key-value pair here is one way)