Apache Spark SQL Commands
Apache Spark SQL Commands, welcome to the world of Apache Spark Basic SQL commands. Are you the one who is looking forward to knowing the Apache Spark SQL commands List which comes under Apache Spark? Or the one who is very keen to explore the list of all the SQL commands in Apache Spark with examples that are available? Then you’ve landed on the Right path which provides the standard and Basic Apache Spark SQL Commands.
If you are the one who is keen to learn the technology then learn the advanced certification course from the best Apache Spark training institute who can help guide you about the course from the 0 Level to Advanced level. So don’t just dream to become the certified Pro Developer Achieve it by choosing the best World classes Apache Spark Training Institute in Bangalore which consists of World-class Trainers.
We, Prwatech listed some of the Top Apache Spark SQL Commands which Every Spark Developer should know about. So follow the Below Mentioned Apache Spark Basic SQL Commands and Learn the Advanced Apache Spark course from the best Spark Trainer like a Pro.
Spark context(sc) : To initialize the functionalities of Spark SQL
To create a spark context
Check the context created
Data set: Optimized version of RDD which uses interpreter and optimizer for processing.
Data Frame: A data frame is a Data set organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python.
Schema of a table: Using the below-mentioned command we find out the schema of Data set
Show data: Using below-mentioned command we display the values inside our data set
Reading files using spark session: Using the below-mentioned command we can read data from an external source by providing their path of existence.
Show value: To Display the data
Selecting a particular column: Using below-mentioned command we can display all the data from a particular column
Selecting more than one column: Using below-mentioned command we can display all the data from two selected column
Incrementing the value of column: Using below-mentioned command we can increment the data with the given value
Alias: Using the below-mentioned command we can display columns as other names.
Filter: Using below-mentioned command we can filter out the value from using different parameters
Data frames are also transformational in nature and they are immutable
Various Functions in Group By
Count: Using this Group by function to count the given data set.
Max: Using this Group by function to find out the maximum values for given datasets.
Min: Using this Group by function to find out the minimum values for given datasets.
Average: Using this Group by function to find out the average values for given datasets.
Write: Using the below-mentioned command we can write and store data on the mentioned location as per user requirement.
To perform query we create a template view
Now we can use command per operations using SQL queries
Create dataset: Dataset is only created using (seq) object
Data set with metadata info
Now we can perform SQL query
UDF allows us to register custom functions to call within SQL. These are a very popular way to expose advanced functionality to SQL users in an organization so that these users can call into it without writing code
Test Case 1: Converting Celsius into Fahrenheit
Register UDF: Using the below-mentioned command we are registering new functions as per user need.
Test case 2: Lower to Upper case