SQL Built-in Functions in Spark

Spark SQL Function Introduction

Spark SQL functions are a set of built-in functions provided by Apache Spark for performing various operations on DataFrame and Dataset objects in Spark SQL. These functions enable users to manipulate and analyze data within Spark SQL queries, providing a wide range of functionalities similar to those found in traditional SQL databases.

Spark also includes more built-in functions that are less common and are not defined here. You can also access them using the expr() API and calling them through a SQL expression string. regr_count is an example that is built-in but not defined here, because it is less commonly used. To invoke it, use expr("regr_count(yCol, xCol)").

Utilize these standard library functions provided by Spark SQL whenever feasible. They offer enhanced compile-time safety, manage null values efficiently, and generally deliver superior performance compared to User-Defined Functions (UDFs). In scenarios where performance is crucial, it’s advisable to minimize reliance on custom UDFs, as their performance is not assured.

Spark groups all these SQL functions into the below categories.

• String Functions
• Date & Time Functions
• Collection Functions
• Math Functions
• Aggregate Functions
• Window Functions
• Sorting Functions

Importing SQL Functions in Scala

In Spark with Scala, all these are part of org.apache.spark.sql.functions and return org.apache.spark.sql.Column type.

In order to use these, you need to use the following import.

// Import all SQL Functions
import org.apache.spark.sql.functions._

Alternatively, you can import a specific in Scala using the snippet below.

// Import a specific function
// This imports col() function
import org.apache.spark.sql.functions.col

String Functions

String functions are used to manipulate string data within DataFrame and Dataset objects. With functions like substring, concat, and length, you can extract substrings, concatenate strings, and determine string lengths, among other operations.

These enable efficient string manipulation in Spark SQL queries, facilitating tasks such as data cleansing, transformation, and analysis. Leveraging these ensures concise, readable code and efficient processing of string data within Apache Spark’s distributed computing framework.

These are grouped as “ string_funcs” in spark SQL.

Date and Time Functions

These Date and Time functions enable users to perform operations like date operations, formatting, extracting components (e.g., year, month), and converting between different date formats. These support various date and time units, including days, months, years, hours, minutes, and seconds.

The default date format is “yyyy-MM-dd” for date data types. When you work with date columns in Spark DataFrames, dates are typically represented and manipulated using this default format.

Timestamps represent date and time together. The default timestamp format in Spark is “yyyy-MM-dd HH:mm:ss.” This format includes both date and time components, with hours (HH), minutes (mm), and seconds (ss) represented in the 24-hour clock format.

SQL Collection Functions

Collection functions in Spark SQL are used when working with array and map columns in DataFrames.These functions enable users to perform various operations on array and map columns efficiently, such as filtering, transforming, aggregating, and accessing elements.

Log Functions

Aggregate Functions

Window Functions

Spark SQL window functions are used for performing calculations across a group of rows, known as a window, within a DataFrame. These functions allow users to perform aggregations, ranking, and analytical calculations over partitions of data defined by a window specification. Window functions operate on a set of rows related to the current row, rather than the entire dataset.

Sorting Functions

Conclusion

In conclusion, this article has provided a comprehensive overview of Spark SQL functions, showcasing their versatility and power in data processing tasks within the Apache Spark framework. By using these functions you can efficiently manipulate and transform data using Spark SQL.

Happy Learning !!

Related Articles

• Spark Date and Time Functions
• Spark String Functions
• Spark Array Functions
• Spark Map Functions
• Spark Aggregate Functions
• Spark Window Functions
• Spark Sort Functions