University of Illinois Urbana-Champaign

MojoFrame: dataframe library in Mojo language

Huang, Arthur

Permalink

https://hdl.handle.net/2142/129945

Description

Title
MojoFrame: dataframe library in Mojo language
Author(s)
Huang, Arthur
Issue Date
2025-07-18
Director of Research (if dissertation) or Advisor (if thesis)
Park, Yongjoo
Department of Study
Siebel School Comp & Data Sci
Discipline
Computer Science
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • Mlir
  • Jit Compilation
  • Systems For Data Science
  • Systems For Machine Learning
  • Dataframe
  • Database Management Systems
Language
eng
Abstract
Mojo is an emerging programming language built on MLIR (Multi-Level Intermediate Representation) and JIT compilation. It enables transparent optimizations with respect to the underlying hardware (e.g., CPUs, GPUs), while allowing users to express their logic using Python-like user-friendly syntax. Mojo has been shown to offer great performance in tensor operations; however, its performance has not been tested for relational operations (e.g., filtering, join, and group-by), which are common in data science workflows. To date, no dataframe implementation exists in the Mojo ecosystem. In this work, we introduce the first Mojo-native dataframe library, called MojoFrame, that supports core relational operations and user-defined functions (UDFs). MojoFrame is built on top of Mojo’s tensor to achieve fast operations on numeric columns, while utilizing a cardinality-aware approach to effectively integrate non-numeric columns for flexible data representation. To achieve high efficiency, MojoFrame takes significantly different approaches than existing libraries. MojoFrame supports all operations for TPC-H queries, and achieves up to 2.97× speedup versus existing dataframe libraries in other programming languages. Nevertheless, there remain optimization opportunities for MojoFrame (and the Mojo language), particularly in data loading and dictionary operations.
Graduation Semester
2025-08
Type of Resource
Text
Handle URL
https://hdl.handle.net/2142/129945
Copyright and License Information
Copyright 2025 Arthur Huang

Owning Collections

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois
Dissertations and Theses - Computer Science
Dissertations and Theses from the Siebel School of Computer Science