Let's Get Aggregated: Custom UDAFs in Spark

Go beyond standard functions in Spark. Build custom, type-safe aggregations to execute complex logic with only a single data shuffle.

#1about 2 minutes

Going beyond standard aggregations in Spark

Standard functions like sum and count are useful, but custom aggregations are required for specific business logic and performance on large datasets.

#2about 4 minutes

Understanding the Spark Aggregator interface

The Aggregator interface requires implementing zero, reduce, merge, and finish methods to support Spark's distributed execution model of pre-aggregation and shuffling.

#3about 2 minutes

Analyzing a standard word count solution's inefficiency

A typical word count solution using built-in functions and window functions results in an inefficient execution plan with two separate data shuffles.

#4about 2 minutes

Designing a UDAF for an efficient word count

The high-level design for a custom word count aggregator involves using a local frequency map as a buffer to pre-aggregate data before a single shuffle and merge step.

#5about 5 minutes

A step-by-step implementation of the UDAF methods

This walkthrough covers the Scala implementation of the Aggregator interface, including defining type parameters and coding the zero, reduce, merge, and finish logic.

#6about 1 minute

Analyzing the performance gains of the custom UDAF

Applying the custom aggregator and inspecting its execution plan reveals a significant performance improvement, reducing the process to a single data shuffle.

#7about 2 minutes

Leveraging complex data structures in UDAFs

UDAFs can handle complex, structured data types like case classes for both intermediate buffers and final outputs, enabling sophisticated, multi-part aggregations.

#8about 3 minutes

Key use cases for custom aggregation functions

Custom aggregators are ideal for complex business logic, performance optimization, code reusability across teams, and integration with the Spark SQL API.