SQLFlow: A Bridge between SQL and Machine Learning

TL;DR

SQLFlow is a system that bridges SQL and machine learning, enabling developers to write ML workflows directly in SQL across various databases and ML engines, and deploy them efficiently on cloud platforms.

Contribution

It introduces a novel SQL extension and parsing algorithm to support diverse ML techniques and integrates with multiple database systems and ML engines for streamlined workflows.

Findings

Supports a wide range of ML techniques including deep learning and tree models.

Enables deployment as Kubernetes-native workflows for fault tolerance.

Used by major industrial companies like Alibaba and DiDi.

Abstract

Industrial AI systems are mostly end-to-end machine learning (ML) workflows. A typical recommendation or business intelligence system includes many online micro-services and offline jobs. We describe SQLFlow for developing such workflows efficiently in SQL. SQL enables developers to write short programs focusing on the purpose (what) and ignoring the procedure (how). Previous database systems extended their SQL dialect to support ML. SQLFlow (https://sqlflow.org/sqlflow ) takes another strategy to work as a bridge over various database systems, including MySQL, Apache Hive, and Alibaba MaxCompute, and ML engines like TensorFlow, XGBoost, and scikit-learn. We extended SQL syntax carefully to make the extension working with various SQL dialects. We implement the extension by inventing a collaborative parsing algorithm. SQLFlow is efficient and expressive to a wide variety of ML techniques -- supervised and unsupervised learning; deep networks and tree models; visual model explanation in addition to training and prediction; data processing and feature extraction in addition to ML. SQLFlow compiles a SQL program into a Kubernetes-native workflow for fault-tolerable execution and on-cloud deployment. Current industrial users include Ant Financial, DiDi, and Alibaba Group.