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  1. What is the difference between SparkContext, JavaSparkContext, SQLContext and SparkSession?
  2. Is there any method to convert or create a Context using a SparkSession?
  3. Can I completely replace all the Contexts using one single entry SparkSession?
  4. Are all the functions in SQLContext, SparkContext, and JavaSparkContext also in SparkSession?
  5. Some functions like parallelize have different behaviors in SparkContext and JavaSparkContext. How do they behave in SparkSession?

  6. 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?

Balaji Reddy
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Manikandan Balasubramanian
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5 Answers5

45

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?

  1. RDD can be created from sparkSession.sparkContext.parallelize(???)
  2. JavaRDD same applies with this but in java implementation
  3. JavaPairRDD sparkSession.sparkContext.parallelize(???).map(//making your data as key-value pair here is one way)
  4. Dataset what sparkSession returns is Dataset if it is structured data.
altayseyhan
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Balaji Reddy
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16

Explanation from spark source code under branch-2.1

SparkContext: Main entry point for Spark functionality. A SparkContext represents the connection to a Spark cluster, and can be used to create RDDs, accumulators and broadcast variables on that cluster.

Only one SparkContext may be active per JVM. You must stop() the active SparkContext before creating a new one. This limitation may eventually be removed; see SPARK-2243 for more details.

JavaSparkContext: A Java-friendly version of [[org.apache.spark.SparkContext]] that returns [[org.apache.spark.api.java.JavaRDD]]s and works with Java collections instead of Scala ones.

Only one SparkContext may be active per JVM. You must stop() the active SparkContext before creating a new one. This limitation may eventually be removed; see SPARK-2243 for more details.

SQLContext: The entry point for working with structured data (rows and columns) in Spark 1.x.

As of Spark 2.0, this is replaced by [[SparkSession]]. However, we are keeping the class here for backward compatibility.

SparkSession: The entry point to programming Spark with the Dataset and DataFrame API.

Deanzz
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7

I will talk about Spark version 2.x only.

SparkSession: It's a main entry point of your spark Application. To run any code on your spark, this is the first thing you should create. 

from pyspark.sql import SparkSession
spark = SparkSession.builder.master("local").appName("Word Count")\
.config("spark.some.config.option", "some-value")\
.getOrCreate()

SparkContext: It's a inner Object (property) of SparkSession. It's used to interact with Low-Level API Through SparkContext you can create RDD, accumlator and Broadcast variables.

for most cases you won't need SparkContext. You can get SparkContext from SparkSession

val sc = spark.sparkContext
Gaurang Shah
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2

Spark Context is Class in Spark API which is the first stage to build the spark application. Functionality of the spark context is to create memory in RAM we call this as driver memory, allocation of number of executers and cores in short its all about the cluster management. Spark Context can be used to create RDD and shared variables. To access this we need to create object of it.

This way we can create Spark Context :: var sc=new SparkContext()

Spark Session this is new Object added since spark 2.x which is replacement of Sql Context and Hive Context. Earlier we had two options like one is Sql Context which is way to do sql operation on Dataframe and second is Hive Context which manage the Hive connectivity related stuff and fetch/insert the data from/to the hive tables.

Since 2.x came We can create SparkSession for the SQL operation on Dataframe and if you have any Hive related work just call the Method enablehivesupport() then you can use the SparkSession for the both Dataframe and hive related SQL operations.

This way we can create SparkSession for Sql operation on Dataframe

val sparksession=SparkSession.builder().getOrCreate();

Second way is to create SparkSession for Sql operation on Dataframe as well as Hive Operation.

val sparkSession=SparkSession.builder().enableHiveSupport().getOrCreate()

Nilesh Shinde
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0

Spark Context:

Since Spark 1.x, Spark SparkContext is an entry point to Spark and defined in org. apache. spark package and used to programmatically create Spark RDD, accumulators, and broadcast variables on the cluster. Its object sc is the default variable available in spark-shell and it can be programmatically created using SparkContext class.

SparkContext is a client of spark's execution environment. SparkContext is the entry point of the spark execution job. SparkContext acts as the master of the spark application.

Hope you will find this Apache SparkContext Examples site useful.

SparkSession:

Since Spark 2.0, SparkSession has become an entry point to Spark to work with RDD, DataFrame, and Dataset. Prior to 2.0, SparkContext used to be an entry point. Here, I will mainly focus on explaining what is SparkSession by defining and describing how to create Spark Session and using the default Spark Session ‘spark’ variable from spark-shell.

Apache spark2.0 onwards, spark session is the new entry point for spark applications. All the functionalities provided by spark context are available in the Spark session. spark session Provides API(s) to work on Datasets and Dataframes. Prior to Spark2.0:

Spark Context was the entry point for spark jobs. RDD was one of the main APIs then, and it was created and manipulated using spark Context. For every other APIs, different Contexts were required - For SQL, SQL Context was required.

You can find more real-time examples on Apache SparkSession.

SQLContext: In Spark Version 1.0 SQLContext (org.apache.spark.sql.SQLContext ) is an entry point to SQL in order to work with structured data (rows and columns) however with 2.0 SQLContext has been replaced with SparkSession. Apache Spark SQLContext is the entry point to SparkSQL which is a Spark module for structured data (rows and columns) in Spark 1.x. processing. Spark SQLContext is initialized. apache-spark SQL context is the entry point of Spark SQL which can be received from spark context JavaSparkContext:

JavaSparkContext For JAVARDD same as above is done but in java implementation.

JavaSparkContext Java-friendly version of [[org.apache.spark.SparkContext]] that returns [[org.apache.spark.api.java.JavaRDD]]s and works with Java collections instead of Scala ones.

Kumar Spark
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