Northlight: Declarative and Optimized Analysis of Atmospheric Datasets in SparkSQL

TL;DR

Northlight integrates NetCDF atmospheric data processing into SparkSQL, offering automatic optimizations that significantly improve performance and scalability for complex Earth science analytics.

Contribution

It introduces Northlight, a system that combines NetCDF processing with SparkSQL, featuring automatic optimizations tailored for atmospheric datasets.

Findings

Outperforms state-of-the-art pipelines by up to 6x in speed.

Scales gracefully with analysis task selectivity.

Provides an easy-to-use, optimized platform for Earth science data analysis.

Abstract

Performing data-intensive analytics is an essential part of modern Earth science. As such, research in atmospheric physics and meteorology frequently requires the processing of very large observational and/or modeled datasets. Typically, these datasets (a) have high dimensionality, i.e. contain various measurements per spatiotemporal point, (b) are extremely large, containing observations over a long time period. Additionally, (c) the analytical tasks being performed on these datasets are structurally complex. Over the years, the binary format NetCDF has been established as a de-facto standard in distributing and exchanging such multi-dimensional datasets in the Earth science community -- along with tools and APIs to visualize, process, and generate them. Unfortunately, these access methods typically lack either (1) an easy-to-use but rich query interface or (2) an automatic optimization pipeline tailored towards the specialities of these datasets. As such, researchers from the field of Earth sciences (which are typically not computer scientists) unnecessarily struggle in efficiently working with these datasets on a daily basis. Consequently, in this work, we aim at resolving the aforementioned issues. Instead of proposing yet another specialized tool and interface to work with atmospheric datasets, we integrate sophisticated NetCDF processing capabilities into the established SparkSQL dataflow engine -- resulting in our system Northlight. In contrast to comparable systems, Northlight introduces a set of fully automatic optimizations specifically tailored towards NetCDF processing. We experimentally show that Northlight scales gracefully with the selectivity of the analysis tasks and outperforms the comparable state-of-the-art pipeline by up to a factor of 6x.