MojoFrame: dataframe library in Mojo language

Huang, Arthur

Permalink

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

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

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

Thesis

Handle URL

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

Copyright and License Information

Copyright 2025 Arthur Huang

Owning Collections