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Evaluation of NoSQL in the Energy Marketplace with GraphQL Optimization

Michael Howard

TL;DR

This study evaluates the NoSQL vs. SQL solutions through a comparison of Google Cloud Firestore and Cloud SQL MySQL offerings to determine whether Firestore can support the energy marketplace storage needs and if the introduction of a GraphQL middleware layer can overcome its deficiencies.

Abstract

The growing popularity of electric vehicles in the United States requires an ever-expanding infrastructure of commercial DC fast charging stations. The U.S. Department of Energy estimates 33,355 publicly available DC fast charging stations as of September 2023. Range anxiety is an important impediment to the adoption of electric vehicles and is even more relevant in underserved regions in the country. The peer-to-peer energy marketplace helps fill the demand by allowing private home and small business owners to rent their 240 Volt, level-2 charging facilities. The existing, publicly accessible outlets are wrapped with a Cloud-connected microcontroller managing security and charging sessions. These microcontrollers act as Edge devices communicating with a Cloud message broker, while both buyer and seller users interact with the framework via a web-based user interface. The database storage used by the marketplace framework is a key component in both the cost of development and the performance that contributes to the user experience. A traditional storage solution is the SQL database. However, difficulty in scaling across multiple nodes and cost of its server-based compute have resulted in a trend in the last 20 years towards other NoSQL, serverless approaches. In this study, we evaluate the NoSQL vs. SQL solutions through a comparison of Google Cloud Firestore and Cloud SQL MySQL offerings. The comparison pits Google's serverless, document-model, non-relational, NoSQL against the server-base, table-model, relational, SQL service. The evaluation is based on query latency, flexibility/scalability, and cost criteria. Through benchmarking and analysis of the architecture, we determine whether Firestore can support the energy marketplace storage needs and if the introduction of a GraphQL middleware layer can overcome its deficiencies.

Evaluation of NoSQL in the Energy Marketplace with GraphQL Optimization

TL;DR

This study evaluates the NoSQL vs. SQL solutions through a comparison of Google Cloud Firestore and Cloud SQL MySQL offerings to determine whether Firestore can support the energy marketplace storage needs and if the introduction of a GraphQL middleware layer can overcome its deficiencies.

Abstract

The growing popularity of electric vehicles in the United States requires an ever-expanding infrastructure of commercial DC fast charging stations. The U.S. Department of Energy estimates 33,355 publicly available DC fast charging stations as of September 2023. Range anxiety is an important impediment to the adoption of electric vehicles and is even more relevant in underserved regions in the country. The peer-to-peer energy marketplace helps fill the demand by allowing private home and small business owners to rent their 240 Volt, level-2 charging facilities. The existing, publicly accessible outlets are wrapped with a Cloud-connected microcontroller managing security and charging sessions. These microcontrollers act as Edge devices communicating with a Cloud message broker, while both buyer and seller users interact with the framework via a web-based user interface. The database storage used by the marketplace framework is a key component in both the cost of development and the performance that contributes to the user experience. A traditional storage solution is the SQL database. However, difficulty in scaling across multiple nodes and cost of its server-based compute have resulted in a trend in the last 20 years towards other NoSQL, serverless approaches. In this study, we evaluate the NoSQL vs. SQL solutions through a comparison of Google Cloud Firestore and Cloud SQL MySQL offerings. The comparison pits Google's serverless, document-model, non-relational, NoSQL against the server-base, table-model, relational, SQL service. The evaluation is based on query latency, flexibility/scalability, and cost criteria. Through benchmarking and analysis of the architecture, we determine whether Firestore can support the energy marketplace storage needs and if the introduction of a GraphQL middleware layer can overcome its deficiencies.
Paper Structure (40 sections, 9 equations, 17 figures, 14 tables)

This paper contains 40 sections, 9 equations, 17 figures, 14 tables.

Table of Contents

  1. Introduction
  2. Motivations
  3. Research Question
  4. Context of the Study
  5. Objectives and Contributions
  6. Overview of the Paper
  7. Background
  8. EV Charging
  9. Related Work
  10. Performance Evaluation of IoT Data Management
  11. Benchmarking with YCSB
  12. Google Firestore
  13. Oracle MySQL
  14. Sharding a Relational Database
  15. Google Spanner
  16. ...and 25 more sections

Figures (17)

  • Figure 1: The energy marketplace supporting: 1. Device registration, 2. Data storage, 3. Device search and reserve, 4. Session management, and 5. Session details.
  • Figure 2: Top 10 fast charging companies sorted by number of locations Evadaoption2022. The data was sourced from the US Department of Energy as of Dec 31, 2021 Doechargers2022.
  • Figure 3: A Cloud framework to implement the energy marketplace. A seller registers their IoT device allowing buyers to search and reserve.
  • Figure 4: Simplified diagram of the Firestore stack Kesavan2023.
  • Figure 5: Query processing through the Real-time Cache Kesavan2023.
  • ...and 12 more figures