Data-Centric Metaprogramming

Explore data-centric metaprogramming techniques to optimize data structure storage and improve performance in Scala programming. Learn how to efficiently model and store data structures like relational employee tables using Vector[Employee], and discover how to instruct the compiler to optimize memory layout automatically. Understand the benefits of this approach in avoiding premature optimization and allowing developers to use their preferred abstractions while still achieving performance gains. Examine real-world applications, including potential 40% speedups in Spark. Dive into topics such as inefficient representations, bypass methods, performance architectures, and the limitations of metaprogramming. Gain insights from this Scala Days Berlin 2016 conference talk to enhance your Scala programming skills and optimize your code for better performance.

Introduction
Motivation
The problem
The transformation
Open world
Representations
Transformations
Applications
Conclusion
Inefficient representation
Bypass methods
Performance
Architectures
DollyLinker
Limitations of metaprogramming