Apache Spark is a lightning-fast cluster computing framework designed for fast computation. It is of the most successful projects in the Apache Software Foundation. Spark SQL is a new module in Spark which integrates relational processing with Spark’s functional programming API. It supports querying data either via SQL or via the Hive Query Language.

Why Spark SQL Came Into Picture?

Spark SQL originated as Apache Hive to run on top of Spark and is now integrated with the Spark stack. Apache Hive had certain limitations as mentioned below. Spark SQL was built to overcome these drawbacks and replace Apache Hive.

Limitations With Hive:

• Hive launches MapReduce jobs internally for executing the ad-hoc queries. MapReduce lags in the performance when it comes to the analysis of medium sized datasets (10 to 200 GB).
• Hive has no resume capability. This means that if the processing dies in the middle of a workflow, you cannot resume from where it got stuck.
• Hive cannot drop encrypted databases in cascade when trash is enabled and leads to an execution error. To overcome this, users have to use Purge option to skip trash instead of drop.

These drawbacks gave way to the birth of Spark SQL.

Spark SQL Overview

Spark SQL integrates relational processing with Spark’s functional programming. It provides support for various data sources and makes it possible to weave SQL queries with code transformations thus resulting in a very powerful tool.

Let us explore, what Spark SQL has to offer. Spark SQL blurs the line between RDD and relational table. It offers much tighter integration between relational and procedural processing, through declarative DataFrame APIs which integrates with Spark code. It also provides higher optimization. DataFrame API and Datasets API are the ways to interact with Spark SQL.

With Spark SQL, Apache Spark is accessible to more users and improves optimization for the current ones. Spark SQL provides DataFrame APIs which perform relational operations on both external data sources and Spark’s built-in distributed collections. It introduces extensible optimizer called Catalyst as it helps in supporting a wide range of data sources and algorithms in Big-data.

Spark runs on both Windows and UNIX-like systems (e.g. Linux, Microsoft, Mac OS). It is easy to run locally on one machine — all you need is to have java installed on your system PATH, or the JAVA_HOME environment variable pointing to a Java installation.

Figure: Architecture of Spark SQL.

Spark SQL Libraries

Spark SQL has the following four libraries which are used to interact with relational and procedural processing:

1. Data Source API (Application Programming Interface):
   

   This is a universal API for loading and storing structured data.

   • It has built in support for Hive, Avro, JSON, JDBC, Parquet, etc.
   • Supports third party integration through Spark packages
   • Support for smart sources.

2. DataFrame API:

   A DataFrame is a distributed collection of data organized into named column. It is equivalent to a relational table in SQL used for storing data into tables.

   • It is a Data Abstraction and Domain Specific Language (DSL) applicable on structure and semi structured data.
   • DataFrame API is distributed collection of data in the form of named column and row.
   • It is lazily evaluated like Apache Spark Transformations and can be accessed through SQL Context and Hive Context.
   • It processes the data in the size of Kilobytes to Petabytes on a single-node cluster to multi-node clusters.
   • Supports different data formats (Avro, CSV, Elastic Search and Cassandra) and storage systems (HDFS, HIVE Tables, MySQL, etc.).
   • Can be easily integrated with all Big Data tools and frameworks via Spark-Core.
   • Provides API for Python, Java, Scala, and R Programming.

   • SQL Interpreter And Optimizer:

     SQL Interpreter and Optimizer is based on functional programming constructed in Scala.

     • It is the newest and most technically evolved component of SparkSQL.
     • It provides a general framework for transforming trees, which is used to perform analysis/evaluation, optimization, planning, and run time code spawning.
     • This supports cost based optimization (run time and resource utilization is termed as cost) and rule based optimization, making queries run much faster than their RDD (Resilient Distributed Dataset) counterparts.

     e.g. Catalyst is a modular library which is made as a rule based system. Each rule in framework focuses on the distinct optimization.

   • SQL Service:

     SQL Service is the entry point for working with structured data in Spark. It allows the creation of DataFrame objects as well as the execution of SQL queries.

     Features Of Spark SQL

     The following are the features of Spark SQL:

     1.Integration With Spark

     Spark SQL queries are integrated with Spark programs. Spark SQL allows us to query structured data inside Spark programs, using SQL or a DataFrame API which can be used in Java, Scala, Python and R. To run streaming computation, developers simply write a batch computation against the DataFrame / Dataset API, and Spark automatically increments the computation to run it in a streaming fashion. This powerful design means that developers don’t have to manually manage state, failures, or keeping the application in sync with batch jobs. Instead, the streaming job always gives the same answer as a batch job on the same data.

     2.Uniform Data Access
     DataFrames and SQL support a common way to access a variety of data sources, like Hive, Avro, Parquet, ORC, JSON, and JDBC. This joins the data across these sources. This is very helpful to accommodate all the existing users into Spark SQL.

     3.Hive Compatibility

     Spark SQL runs unmodified Hive queries on current data. It rewrites the Hive front-end and meta store, allowing full compatibility with current Hive data, queries, and UDFs.

     4.Standard Connectivity
     Connection is through JDBC or ODBC. JDBC and ODBC are the industry norms for connectivity for business intelligence tools.

     5.Performance And Scalability
     Spark SQL incorporates a cost-based optimizer, code generation and columnar storage to make queries agile alongside computing thousands of nodes using the Spark engine, which provides full mid-query fault tolerance. The interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Internally, Spark SQL uses this extra information to perform extra optimization. Spark SQL can directly read from multiple sources (files, HDFS, JSON/Parquet files, existing RDDs, Hive, etc.). It ensures fast execution of existing Hive queries.

     The image below depicts the performance of Spark SQL when compared to Hadoop. Spark SQL executes upto 100x times faster than Hadoop.

     

     1. User Defined Functions
        Spark SQL has language integrated User-Defined Functions (UDFs). UDF is a feature of Spark SQL to define new Column-based functions that extend the vocabulary of Spark SQL’s DSL for transforming Datasets. UDFs are black boxes in their execution.

        The example below defines a UDF to convert a given text to upper case.

        Code explanation:
        1. Creating a dataset “hello world”
        2. Defining a function ‘upper’ which converts a string into upper case.
        3. We now import the ‘udf’ package into Spark.
        4. Defining our UDF, ‘upperUDF’ and importing our function ‘upper’.
        5. Displaying the results of our User Defined Function in a new column ‘upper’.

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        val dataset = Seq((0, "hello"),(1, "world")).toDF("id","text") val upper: String => String =_.toUpperCase import org.apache.spark.sql.functions.udf val upperUDF = udf(upper) dataset.withColumn("upper", upperUDF('text)).show

     Figure:Demonstration of a User Defined Function, upperUDF

     Code explanation:
     1. We now register our function as ‘myUpper’
     2. Cataloging our UDF among the other functions.

     1 2	spark.udf.register("myUpper", (input:String) => input.toUpperCase) spark.catalog.listFunctions.filter('name like "%upper%").show(false)

     Figure:Results of the User Defined Function, upperUDF

     Querying Using Spark SQL

     We will now start querying using Spark SQL. Note that the actual SQL queries are similar to the ones used in popular SQL clients.

     Starting the Spark Shell. Go to the Spark directory and execute ./bin/spark-shell in the terminal to being the Spark Shell.

     For the querying examples shown in the blog, we will be using two files, ’employee.txt’ and ’employee.json’. The images below show the content of both the files. Both these files are stored at ‘examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala’ inside the folder containing the Spark installation (~/Downloads/spark-2.0.2-bin-hadoop2.7). So, all of you who are executing the queries, place them in this directory or set the path to your files in the lines of code below.

     

     Figure:Contents of employee.txt

     

     Code explanation:
     1. We first import a Spark Session into Apache Spark.
     2. Creating a Spark Session ‘spark’ using the ‘builder()’ function.
     3. Importing the Implicts class into our ‘spark’ Session.
     4. We now create a DataFrame ‘df’ and import data from the ’employee.json’ file.
     5. Displaying the DataFrame ‘df’. The result is a table of 5 rows of ages and names from our ’employee.json’ file.

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     import org.apache.spark.sql.SparkSession val spark = SparkSession.builder().appName("Spark SQL basic example").config("spark.some.config.option", "some-value").getOrCreate() import spark.implicits._ val df = spark.read.json("examples/src/main/resources/employee.json") df.show()

     Figure:Starting a Spark Session and displaying DataFrame of employee.json

     Code explanation:
     1. Importing the Implicts class into our ‘spark’ Session.
     2. Printing the schema of our ‘df’ DataFrame.
     3. Displaying the names of all our records from ‘df’ DataFrame.

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     import spark.implicits._ df.printSchema() df.select("name").show()

     Figure:Schema of a DataFrame

     Code explanation:
     1. Displaying the DataFrame after incrementing everyone’s age by two years.
     2. We filter all the employees above age 30 and display the result.

     1 2	df.select($"name", $"age" + 2).show() df.filter($"age" > 30).show()

     Figure:Basic SQL operations on employee.json

     Code explanation:
     1. Counting the number of people with the same ages. We use the ‘groupBy’ function for the same.
     2. Creating a temporary view ’employee’ of our ‘df’ DataFrame.
     3. Perform a ‘select’ operation on our ’employee’ view to display the table into ‘sqlDF’.
     4. Displaying the results of ‘sqlDF’.

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     df.groupBy("age").count().show() df.createOrReplaceTempView("employee") val sqlDF = spark.sql("SELECT * FROM employee") sqlDF.show()

     Figure:SQL operations on employee.json

Source: Spark SQL Tutorial | Understanding Spark SQL With Examples | Edureka