An Empirical Study of Developers' Challenges in Implementing Workflows as Code: A Case Study on Apache Airflow
- URL: http://arxiv.org/abs/2406.00180v1
- Date: Fri, 31 May 2024 20:16:03 GMT
- Title: An Empirical Study of Developers' Challenges in Implementing Workflows as Code: A Case Study on Apache Airflow
- Authors: Jerin Yasmin, Jiale Wang, Yuan Tian, Bram Adams,
- Abstract summary: We study Stack Overflow posts derived from 9,591 Airflow-related questions to understand developers' challenges and root causes.
We find that the most significant obstacles arise when defining and executing their workflow.
Our analysis identifies 10 root causes behind the challenges, including incorrect configuration, complex environmental setup, and a lack of basic knowledge about Airflow and the external systems that it interacts with.
- Score: 9.189463227291377
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Workflows as Code paradigm is becoming increasingly essential to streamline the design and management of complex processes within data-intensive software systems. These systems require robust capabilities to process, analyze, and extract insights from large datasets. Workflow orchestration platforms such as Apache Airflow are pivotal in meeting these needs, as they effectively support the implementation of the Workflows as Code paradigm. Nevertheless, despite its considerable advantages, developers still face challenges due to the specialized demands of workflow orchestration and the complexities of distributed execution environments. In this paper, we manually study 1,000 sampled Stack Overflow posts derived from 9,591 Airflow-related questions to understand developers' challenges and root causes while implementing Workflows as Code. Our analysis results in a hierarchical taxonomy of Airflow-related challenges that contains 7 high-level categories and 14 sub-categories. We find that the most significant obstacles for developers arise when defining and executing their workflow. Our in-depth analysis identifies 10 root causes behind the challenges, including incorrect workflow configuration, complex environmental setup, and a lack of basic knowledge about Airflow and the external systems that it interacts with. Additionally, our analysis of references shared within the collected posts reveals that beyond the frequently cited Airflow documentation, documentation from external systems and third-party providers is also commonly referenced to address Airflow-related challenges.
Related papers
- Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows? [73.81908518992161]
We introduce Spider2-V, the first multimodal agent benchmark focusing on professional data science and engineering.
Spider2-V features real-world tasks in authentic computer environments and incorporating 20 enterprise-level professional applications.
These tasks evaluate the ability of a multimodal agent to perform data-related tasks by writing code and managing the GUI in enterprise data software systems.
arXiv Detail & Related papers (2024-07-15T17:54:37Z) - FlowBench: Revisiting and Benchmarking Workflow-Guided Planning for LLM-based Agents [64.1759086221016]
We present FlowBench, the first benchmark for workflow-guided planning.
FlowBench covers 51 different scenarios from 6 domains, with knowledge presented in diverse formats.
Results indicate that current LLM agents need considerable improvements for satisfactory planning.
arXiv Detail & Related papers (2024-06-21T06:13:00Z) - On The Importance of Reasoning for Context Retrieval in Repository-Level Code Editing [82.96523584351314]
We decouple the task of context retrieval from the other components of the repository-level code editing pipelines.
We conclude that while the reasoning helps to improve the precision of the gathered context, it still lacks the ability to identify its sufficiency.
arXiv Detail & Related papers (2024-06-06T19:44:17Z) - Efficient Orchestrated AI Workflows Execution on Scale-out Spatial Architecture [17.516934379812994]
We present "Orchestrated AIs," an approach that integrates various tasks with logic-driven decisions into dynamic, sophisticated AIs.
We find that the intrinsic Dual Dynamicity of Orchestrated AIs can be effectively represented using the Orchestrated spatial Graph.
Our evaluations demonstrate that significantly outperforms traditional architectures in handling the dynamic demands of Orchestrated AIs.
arXiv Detail & Related papers (2024-05-21T14:09:31Z) - High-level Stream Processing: A Complementary Analysis of Fault Recovery [1.3398445165628463]
We focus on robust deployment setups inspired by requirements for near real-time analytics of a large cloud observability platform.
The results indicate significant potential for improving fault recovery and performance.
New abstractions for transparent configuration tuning are also needed for large-scale industry setups.
arXiv Detail & Related papers (2024-05-13T16:48:57Z) - Large Language Models to the Rescue: Reducing the Complexity in
Scientific Workflow Development Using ChatGPT [11.410608233274942]
Scientific systems are increasingly popular for expressing and executing complex data analysis pipelines over large datasets.
However, implementing is difficult due to the involvement of many blackbox tools and the deep infrastructure stack necessary for their execution.
We investigate the efficiency of Large Language Models, specifically ChatGPT, to support users when dealing with scientific domains.
arXiv Detail & Related papers (2023-11-03T10:28:53Z) - Octopus: Embodied Vision-Language Programmer from Environmental Feedback [59.772904419928054]
Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning.
In this paper, we introduce Octopus, a novel VLM designed to proficiently decipher an agent's vision and textual task objectives.
Our design allows the agent to adeptly handle a wide spectrum of tasks, ranging from mundane daily chores in simulators to sophisticated interactions in complex video games.
arXiv Detail & Related papers (2023-10-12T17:59:58Z) - Reusability Challenges of Scientific Workflows: A Case Study for Galaxy [56.78572674167333]
This study examined the reusability of existing and exposed several challenges.
The challenges preventing reusability include tool upgrading, tool support, design flaws, incomplete, failure to load a workflow, etc.
arXiv Detail & Related papers (2023-09-13T20:17:43Z) - A Logic Programming Approach to Global Logistics in a Co-Design
Environment [0.0]
This paper considers the challenge of creating and optimizing a global logistics system for the construction of a passenger aircraft.
The product in question is an aircraft, comprised of multiple components, manufactured at multiple sites worldwide.
The goal is to find an optimal way to build the aircraft taking into consideration the requirements for its industrial system.
arXiv Detail & Related papers (2023-08-30T09:06:34Z) - Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow [49.724842920942024]
Industries such as finance, meteorology, and energy generate vast amounts of data daily.
We propose Data-Copilot, a data analysis agent that autonomously performs querying, processing, and visualization of massive data tailored to diverse human requests.
arXiv Detail & Related papers (2023-06-12T16:12:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.