Intro to Spark SQL

1. Intro to Spark SQL

Welcome back! We’ll now learn how Spark SQL enables us to use Python and SQL in the same PySpark environment.

2. What is Spark SQL

Let’s explore Spark SQL, a powerful component of Apache Spark that integrates seamlessly with its ecosystem. It enables processing of structured and semi-structured data using SQL syntax. Why choose Spark SQL over other tools? First, it leverages Spark’s distributed computing power, handling massive datasets effortlessly by distributing computations across a cluster. Second, Spark SQL simplifies querying with familiar SQL syntax, making it accessible to analysts and engineers. Third, its integration with PySpark DataFrames enables blending SQL and programmatic operations for complex workflows. In short, Spark SQL delivers the flexibility, speed, and scalability needed for working with large, diverse datasets.

3. Creating temp tables

To work with SQL in PySpark, start by registering a DataFrame as a temporary view. We can use the `createDataFrame()` method to make the DataFrame or load the data from a flat file. This allows interaction with the DataFrame using SQL syntax via the `.createOrReplaceTempView()` method, where you pass the view name as a string. For example, we'll create a view called people Temporary views exist only for the current session, making them ideal for quick, session-based exploration. For instance, to find people older than 30, use the `spark.sql()` function and pass your SQL query as a string. This query retrieves the Name and Age columns from the people view, filtering to return only rows where Age exceeds 30. The result is another DataFrame, which you can continue processing with PySpark outside this session.

4. Deeper into temp views

Let’s explore how to load data from CSV files into DataFrames, register them as temporary views, run SQL queries, and blend SQL with DataFrame commands for advanced manipulation. To query a DataFrame with SQL, we first load the data into a DataFrame using methods like `spark.read.csv()`. After loading, we register the DataFrame as a temporary view, allowing SQL-based interaction while safeguarding the underlying data (and data source) during analysis. These views are session-scoped, lasting only as long as the Spark session is active. For extended use, global temporary views or permanent tables must be created.

5. Combining SQL and DataFrame operations

One of the most powerful aspects of Spark SQL is its seamless integration with DataFrame operations. After running an SQL query, the results are returned as a DataFrame, allowing us to apply additional transformations programmatically. Here, we used SQL to filter high-earning employees and then added a Bonus column using DataFrame operations.

6. Let's practice!

Let's go seamlessly blend SQL and PySpark in single scripts.

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