How Do I Query Complex Data Types (Arrays, Maps, Structs, UDTs) in Spark SQL?

Querying complex data types in Spark SQL

Introduction

Spark SQL supports querying data with complex data types, such as maps and arrays. This document provides guidance on efficiently accessing and manipulating these complex types.

Query Array

Access array elements:

• Column.getItem: Gets the element at a specific index.
• Hive Square Brackets: Use square brackets to retrieve elements.
• UDF: Create user-defined functions (UDFs) to apply custom logic.

Query Mapping

Access mapping value:

• Column.getField: Get the value of a specific key.
• Hive Square Brackets: Use square brackets to retrieve values.
• Dot syntax: Use the full path with dot syntax.
• UDF: Create a UDF to perform operations on a map.

Query structure

Structure fields can be accessed using dot syntax:

• For DataFrame API: df.select($"struct_name.field_name")
• For SQL: SELECT struct_name.field_name FROM df

Structure array

Fields in a structure array can be accessed using the following methods:

• Dot syntax: Directly access the field name.
• Standard Column Methods: Use methods like getItem and getField.

User-Defined Type (UDT)

Use UDF to access UDT fields. For more information, see the Spark SQL documentation.

Performance Notes

• There may be performance limitations with nested values.
• Consider flattening mode or expanding collections for best performance.
• Dot syntax can be used in conjunction with the wildcard character (*) to select multiple fields.

Additional functions

Spark SQL supports a variety of built-in functions for complex types:

• Array functions: array_max, array_sum, arrays_zip, array_union
• Mapping function: map_keys, map_values

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