Scaling Datalog for Machine Learning on Big Data

TL;DR

This paper advocates for using Datalog, a declarative query language, to unify and optimize machine learning workflows on big data, demonstrating promising performance and flexibility.

Contribution

It introduces a declarative approach using Datalog to program, optimize, and execute diverse machine learning models on large-scale data systems.

Findings

Declarative Datalog-based models can effectively represent ML algorithms.

Optimized execution plans improve performance on large clusters.

The approach offers increased flexibility and ease of programming.

Abstract

In this paper, we present the case for a declarative foundation for data-intensive machine learning systems. Instead of creating a new system for each specific flavor of machine learning task, or hardcoding new optimizations, we argue for the use of recursive queries to program a variety of machine learning systems. By taking this approach, database query optimization techniques can be utilized to identify effective execution plans, and the resulting runtime plans can be executed on a single unified data-parallel query processing engine. As a proof of concept, we consider two programming models--Pregel and Iterative Map-Reduce-Update---from the machine learning domain, and show how they can be captured in Datalog, tuned for a specific task, and then compiled into an optimized physical plan. Experiments performed on a large computing cluster with real data demonstrate that this declarative approach can provide very good performance while offering both increased generality and programming ease.