Apache Spark SQL Introduction, Welcome to the world of Apache spark SQL tutorial. In these Tutorials, one can explore an introduction to Apache Spark SQL, features of Spark SQL and Uses of Spark SQL. Learn More advanced tutorials on Apache Spark SQL introduction for beginners from India’s Leading Apache Spark Training institute which Provides Advanced Apache Spark Course for those tech enthusiasts who wanted to explore the technology from scratch to advanced level like a Pro.
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Introduction to Apache Spark SQL
Spark SQL supports distributed in-memory computations on a huge scale. It is a spark module for structured data processing. It gives information about the structure of both data & computation takes place. This extra information helps SQL to perform extra optimizations. The major aspect of Spark SQL is that we can execute SQL queries. It can also be used to read data from an existing hive installation. When SQL runs in another programming language, then results come as dataset/data frame. By using the command-line or over JDBC/ODBC, we can interact with the SQL interface.
Spark SQL offers three main capabilities for using structured and semi-structured data. They are following below:
Spark SQL provides a data frame abstraction in Python, Java, and Scala. It simplifies working with structured datasets. In Spark, SQL data frames are the same as tables in a relational database.
Spark SQL can read and write data in various structured formats, such as JSON, hive tables, and parquet.
By using SQL, we can query the data, both inside a Spark program and from external tools that connect to Spark SQL.
It is an Optimized version of RDD which uses an interpreter and optimizer for processing. It is an interface, provides the advantages of RDDs with the comfort of Spark SQL’s execution engine. It is a distributed collection of data to construct a dataset, we can use JVM objects. Afterward, it can manipulate using functional transformations such as a map, flatMap, filter, etc. and many more. In two languages, dataset API is available like Scala and Java. R and Python do not support dataset API. But, Python is very dynamic in nature. It provides many of the benefits of the dataset API, such as we can access the field of a row by name naturally row.columnName.
A DataFrame is a Dataset organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood. DataFrames can be constructed from a wide array of sources such as structured data files, tables in Hive, external databases, or existing RDDs. The DataFrame API is available in Scala, Java, Python, and R. In Scala and Java, a DataFrame is represented by a Dataset of Rows. In the Scala API, DataFrame is simply a type alias of Dataset[Row]. While, in Java API, users need to use Dataset<Row> to represent a DataFrame.
Throughout this document, we will often refer to Scala/Java Datasets of Rows as DataFrames.
Features of Spark SQL DataFrame
Few characteristics of a data frame in spark are:
It provides the ability to process the data in the size of kilobytes to petabytes. Even on a single node cluster to a large cluster.
Dataframes support different data formats, such as Avro, CSV, elastic search, and Cassandra. It also supports storage systems like HDFS, HIVE tables, MySQL, etc.
By using Spark-core, it can be easily integrated with all big data tools and frameworks.
Data frames provide API for Python, Java, Scala, as well as R programming.
Unified data access
For working with structured data, Schema-RDDs provide a single interface. Also, it includes Apache Hive tables, parquet files, and JSON files.
Uses of Spark SQL
Most importantly, it executes SQL queries.
We can read data from existing Hive installation by using SparkSQL.
While we run SQL, at another programming language, it results in a dataset/data frame.