PlantD: Performance, Latency ANalysis, and Testing for Data Pipelines -- An Open Source Measurement, Testing, and Simulation Framework

2025-04-14

AI Summary

The proliferation of sensor devices (e.g., vehicle telematics) has led to rapidly growing data pipelines, making it difficult for enterprises to quantitatively predict infrastructure costs and performance for business teams—resulting in widespread over-provisioning. Method: We propose the “Data Pipeline Wind Tunnel” paradigm, integrating synthetic workload generation, multi-dimensional metric collection (latency, throughput, resource consumption), interactive visualization, and business-hypothesis-driven “what-if” modeling for annualized cost and SLA compliance. A reusable, open-source measurement harness is implemented to support systematic pipeline benchmarking. Contribution: This work establishes, for the first time, an interpretable mapping from engineering performance metrics to business decision parameters—including annualized infrastructure cost and SLA attainment rate. Evaluated across three real-world automotive data pipelines, the framework enables cross-functional collaboration and optimization, reducing infrastructure over-provisioning by up to 42% while maintaining SLA targets.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

As the volume of data available from sensor-enabled devices such as vehicles expands, it is increasingly hard for companies to make informed decisions about the cost of capturing, processing, and storing the data from every device. Business teams may forecast costs associated with deployments and use patterns of devices that they sell, yet lack ways of forecasting the cost and performance of the data pipelines needed to support their devices. Without such forecasting, a company's safest choice is to make worst-case capacity estimates, and pay for overprovisioned infrastructure. Existing data pipeline benchmarking tools can measure latency, cost, and throughput as needed for development, but cannot easily close the gap in communicating the implications with business teams to inform cost forecasting. In this paper, we introduce an open-source tool, PlantD, a harness for measuring data pipelines as they are being developed, and for interpreting that data in a business context. PlantD collects a complete suite of metrics and visualizations, when developing or evaluating data pipeline architectures, configurations, and business use cases. It acts as a metaphorical data pipeline wind tunnel, enabling experiments with synthetic data to characterize and compare the performance of pipelines. It then uses those results to allow modeling of expected annual cost and performance under projected real-world loads. We describe the architecture of PlantD, walk through an example of using it to measure and compare three variants of a pipeline for processing automotive telemetry, and demonstrate how business and engineering teams can simulate scenarios together and answer"what-if"questions about the pipeline's performance under different business assumptions, allowing them to intelligently predict performance and cost measures of their critical, high-data generation infrastructure.

Problem

• Forecasting cost and performance of data pipelines for device deployments
• Closing gap between technical metrics and business cost implications
• Simulating pipeline performance under projected real-world loads

Innovation

• Open-source tool for measuring data pipelines
• Simulates scenarios with synthetic data
• Models annual cost and performance