Spark SQL is a Spark extension for structured data processing. Unlike the basic Spark RDD API, the API interfaces provided by Spark SQL provide the engine Spark with more insight into the internal structure of both the computation and data being processed. Spark SQL uses this insight to perform extra optimizations. There are several ways to interact with Spark SQL including SQL, Dataset, and even interoperability with RDD API.
Spark SQL may be used to execute SQL queries. This opens the door for those who already know SQL to apply it to Spark. When running SQL from within another programming language the results will be returned as a Dataset/DataFrame.
A Dataset is a distributed collection of data. A DataFrame is a Dataset organized into named columns. The Dataset interface was added in Spark 1.6. Dataset provides the benefits of RDDs such as strong typing and lambda functions with the benefits of an optimized execution engine. A DataFrame is a Dataset organized into named columns. Conceptually, a DataFrame is similar to a table in a relational database or a data frame in R or Python.
Spark SQL provides two different mechanisms for converting existing RDDs into Datasets. The first method uses reflection to infer the schema. The reflection approach can work well if the data source schema is known.
The second method for creating Datasets is through a programmatic interface that allows you to construct a schema and then apply it to an existing Spark RDD. While this method is more verbose, it allows you to construct Datasets when the columns and their types are not known until runtime.
You should be familiar with Python, Scala, or Java in order to use Spark SQL, as these three languages have APIs.
If you’d rather utilize Python, the PySpark API for Spark SQL gives a Python interface to Spark SQL. The PySpark API allows you to write Spark SQL queries in Python and handle structured and semi-structured data using DataFrame and SQL API.
You can utilize the Spark SQL API offered by the Spark Core API if you want to use Scala or Java. For structured data processing, the Spark SQL API includes a DataFrame API and a SQL API. These APIs allow you to write Spark SQL queries in Scala or Java. The tutorials below are for Scala or Java.
Thus, while it is feasible to use Spark SQL without understanding Python, Scala, or Java languages, it should be considered necessary to be proficient in at least one of them in order to work well with Spark SQL.
Spark Read JSON is a powerful capability allowing developers to read and query JSON files using Apache Spark. JSON, or JavaScript Object Notation, is a lightweight data-interchange format commonly used for data transfer. With Spark read JSON, users can easily load JSON data into Spark DataFrames, which can then be manipulated using Spark’s powerful APIs. … Read more
In this Spark Read JDBC tutorial, we will cover using Spark SQL with a mySQL database. Spark’s read JDBC methods allows us to read data and create DataFrames from a relational database supporting JDBC connectivity. It is useful for a variety of reasons including leveraging Spark’s distributed computing capabilities for processing data stored in a … Read more
In this Spark Read CSV in Scala tutorial, we will create a DataFrame from a CSV source and query it with Spark SQL. Both simple and advanced examples will be explored and cover topics such as inferring schema from the header row of a CSV file. ** Updated April 2023 ** Starting in Spark … Read more
