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Operon: Incremental Construction of Ragged Data via Named Dimensions

Sungbin Moon, Jiho Park, Suyoung Hwang, Donghyun Koh, Seunghyun Moon, Minhyeong Lee

TL;DR

Operon tackles the challenge of processing ragged data in large-scale pipelines by introducing a formalism of named dimensions and partial shapes. It provides a Rust-based DSL that statically verifies pipeline correctness and a runtime that incrementally constructs data shapes while dynamically scheduling tasks, ensuring deterministic and confluent parallel execution. The work formalizes dimensions, resolutions, coordinates, and arrays, and proves progress, termination, and determinism under a fixed oracle for shape lengths. Empirically, Operon dramatically reduces baseline overhead (about 14.94x) versus Prefect and maintains near-linear throughput as workloads scale, while enabling robust persistence and recovery through a database-backed state. The approach enables robust data-centric, ragged-data pipelines for machine learning and data generation, with future work on richer type systems and broader workflow expressivity.

Abstract

Modern data processing workflows frequently encounter ragged data: collections with variable-length elements that arise naturally in domains like natural language processing, scientific measurements, and autonomous AI agents. Existing workflow engines lack native support for tracking the shapes and dependencies inherent to ragged data, forcing users to manage complex indexing and dependency bookkeeping manually. We present Operon, a Rust-based workflow engine that addresses these challenges through a novel formalism of named dimensions with explicit dependency relations. Operon provides a domain-specific language where users declare pipelines with dimension annotations that are statically verified for correctness, while the runtime system dynamically schedules tasks as data shapes are incrementally discovered during execution. We formalize the mathematical foundation for reasoning about partial shapes and prove that Operon's incremental construction algorithm guarantees deterministic and confluent execution in parallel settings. The system's explicit modeling of partially-known states enables robust persistence and recovery mechanisms, while its per-task multi-queue architecture achieves efficient parallelism across heterogeneous task types. Empirical evaluation demonstrates that Operon outperforms an existing workflow engine with 14.94x baseline overhead reduction while maintaining near-linear end-to-end output rates as workloads scale, making it particularly suitable for large-scale data generation pipelines in machine learning applications.

Operon: Incremental Construction of Ragged Data via Named Dimensions

TL;DR

Operon tackles the challenge of processing ragged data in large-scale pipelines by introducing a formalism of named dimensions and partial shapes. It provides a Rust-based DSL that statically verifies pipeline correctness and a runtime that incrementally constructs data shapes while dynamically scheduling tasks, ensuring deterministic and confluent parallel execution. The work formalizes dimensions, resolutions, coordinates, and arrays, and proves progress, termination, and determinism under a fixed oracle for shape lengths. Empirically, Operon dramatically reduces baseline overhead (about 14.94x) versus Prefect and maintains near-linear throughput as workloads scale, while enabling robust persistence and recovery through a database-backed state. The approach enables robust data-centric, ragged-data pipelines for machine learning and data generation, with future work on richer type systems and broader workflow expressivity.

Abstract

Modern data processing workflows frequently encounter ragged data: collections with variable-length elements that arise naturally in domains like natural language processing, scientific measurements, and autonomous AI agents. Existing workflow engines lack native support for tracking the shapes and dependencies inherent to ragged data, forcing users to manage complex indexing and dependency bookkeeping manually. We present Operon, a Rust-based workflow engine that addresses these challenges through a novel formalism of named dimensions with explicit dependency relations. Operon provides a domain-specific language where users declare pipelines with dimension annotations that are statically verified for correctness, while the runtime system dynamically schedules tasks as data shapes are incrementally discovered during execution. We formalize the mathematical foundation for reasoning about partial shapes and prove that Operon's incremental construction algorithm guarantees deterministic and confluent execution in parallel settings. The system's explicit modeling of partially-known states enables robust persistence and recovery mechanisms, while its per-task multi-queue architecture achieves efficient parallelism across heterogeneous task types. Empirical evaluation demonstrates that Operon outperforms an existing workflow engine with 14.94x baseline overhead reduction while maintaining near-linear end-to-end output rates as workloads scale, making it particularly suitable for large-scale data generation pipelines in machine learning applications.

Paper Structure

This paper contains 31 sections, 33 theorems, 25 equations, 8 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Motivating Example
  3. Challenges
  4. Unintuitive code structure
  5. Ambiguity in repetition
  6. Late discovery of tasks and data lengths
  7. Our Design
  8. Contributions
  9. Ragged and Named Dimensions
  10. Dimensions
  11. Shapes and Coordinates
  12. Arrays
  13. Incremental Resolutions
  14. Partial Shapes
  15. Incremental Construction
  16. ...and 16 more sections

Key Result

lemma 1

A subspace $\mathcal{E} \subseteq \mathcal{D}$ is convex if and only if it is an order-convex subposet, that is, if $d, e \in \mathcal{E}$, $f \in \mathcal{D}$, and $d \preceq f \preceq e$, then $f \in \mathcal{E}$.

Figures (8)

  • Figure 1: Workflows for scientific figure captioning. Rounded boxes denote data entries, and rectangles denote processing tasks. (a) Original SciCap+ pipeline yang2024scicap+ extracts a single paragraph $K_\text{text}$ per figure $I$ using regex matching. (b) Our pipeline introduces a vision-language model (VLM) agent to assess and gather multiple relevant paragraphs $K_\text{text}'$.
  • Figure 2: Operon pipeline definitions for the motivating example. Dimensions are explicitly declared and tracked through the pipeline. Angle brackets denote iteration and aggregation axes.
  • Figure 3: A resolution map $R$ on the dimension space $\left\{\textcolor{gray}{p}, \textcolor{red}{s}, \textcolor{violet}{g}, \textcolor{Green4}{f}\right\}$ from Example \ref{['ex:motex_dimension_space']} defining a ragged profile. For the single paper shown, there are 3 figures and 5 sections; each section contains 4, 3, 2, 0, and 3 paragraphs, respectively. This configuration uniquely defines the 36 possible positions for relevance scores, which are computed for each paragraph and each figure.
  • Figure 4: Syntax of the Operon domain-specific language.
  • Figure 5: Static checking rules for the DSL.
  • ...and 3 more figures

Theorems & Definitions (49)

  • definition 1: Dimension spaces
  • definition 2: Structure of dimension spaces
  • lemma 1
  • corollary 1
  • definition 3: Resolutions
  • definition 4: In-bounds condition
  • definition 5: Shapes
  • definition 6: Coordinates
  • definition 7: Subcoordinates
  • proposition 1
  • ...and 39 more