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

TL;DR

PlantD introduces an open-source wind-tunnel framework for data pipelines that enables end-to-end measurement of performance and cost under synthetic, business-driven loads. The system combines a management interface, load generator, data collection via OpenTelemetry, a data generator, and a business-analysis module that builds a digital twin to simulate annual load. A Honda telematics case study compares three pipeline variants and uses traffic projections to predict annual cost and latency under nominal and high demand. The results show that measurement-driven design trade-offs can be quantified to guide architecture decisions and prevent overprovisioning through informed forecasting.

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.

Figures (8)

• Figure 1: The data pipeline wind tunnel is a tool for instrumenting a data pipeline, subjecting it to load from incoming data and/or queries, and capturing a complete suite of metrics from it, in a form useful to engineers, managers, and business analysts.
• Figure 2: Screenshot of user interface, showing recently run experiments and their status.
• Figure 3: General system overview of PlantD with its major components, implemented as Kubernetes Custom Resources. PlantD Core (left) manages user-facing resources such as the user interface (PlantD-Studio) and the Prometheus and Redis repositories of collected data. Schema and DataSet describe the data format that must be synthesized to feed a pipeline-under-test; LoadPattern describes the timing and quantity of data fed to the pipeline; Pipeline describes the endpoint and protocol of a pipeline; finally Experiment ties these together, manages a scheduled experiment, and points to the data collected from it.
• Figure 4: Conceptual flow of the research method. Engineering experiments (left) send synthetic data to a real pipeline, and measure its characteristics. Business analysis (right) models pipeline cost and performance, and applies business projections to extrapolate performance over a future year.
• Figure 5: Correction factors for each month (top), for each hour within a week (center), and resulting Nominal and High projections (bottom), assuming no net change (Nominal) or growth (High). Red is daily maximum of Nominal project; orange is daily maximum of High projection, and green is the daily minimum of both projections.