Big data scalability for high throughput processing and analysis of vehicle engineering data

Abstract: "Sympathy for Data" is a platform that is utilized for Big Data automation analytics. It is based on visual interface and workflow configurations. The main purpose of the platform is to reuse parts of code for structured analysis of vehicle engineering data. However, there are some performance issues on a single machine for processing a large amount of data in Sympathy for Data. There are also disk and CPU IO intensive issues when the data is oversized and the platform need fits comfortably in memory. In addition, for data over the TB or PB level, the Sympathy for data needs separate functionality for efficient processing simultaneously and scalable for distributed computation functionality. This paper focuses on exploring the possibilities and limitations in using the Sympathy for Data platform in various data analytic scenarios within the Volvo Cars vision and strategy. This project re-writes the CDE workflow for over 300 nodes into pure Python script code and make it executable on the Apache Spark and Dask infrastructure. We explore and compare both distributed computing frameworks implemented on Amazon Web Service EC2 used for 4 machine with a 4x type for distributed cluster measurement. However, the benchmark results show that Spark is superior to Dask from performance perspective. Apache Spark and Dask will combine with Sympathy for Data products for a Big Data processing engine to optimize the system disk and CPU IO utilization. There are several challenges when using Spark and Dask to analyze large-scale scientific data on systems. For instance, parallel file systems are shared among all computing machines, in contrast to shared-nothing architectures. Moreover, accessing data stored in commonly used scientific data formats, such as HDF5 is not tentatively supported in Spark. This report presents research carried out on the next generation of Big Data platforms in the automotive industry called "Sympathy for Data". The research questions focusing on improving the I/O performance and scalable distributed function to promote Big Data analytics. During this project, we used the Dask.Array parallelism features for interpretation the data sources as a raster shows in table format, and Apache Spark used as data processing engine for parallelism to load data sources to memory for improving the big data computation capacity. The experiments chapter will demonstrate 640GB of engineering data benchmark for single node and distributed computation mode to evaluate the Sympathy for Data Disk CPU and memory metrics. Finally, the outcome of this project improved the six times performance of the original Sympathy for data by developing a middleware SparkImporter. It is used in Sympathy for Data for distributed computation and connected to the Apache Spark for data processing through the maximum utilization of the system resources. This improves its throughput, scalability, and performance. It also increases the capacity of the Sympathy for data to process Big Data and avoids big data cluster infrastructures.