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With the rapid development of intelligent cars, the traditional distributed electronic electrical architecture is beginning to iterate towards a more centralized architecture, driving intelligent cars to have stronger centralized computing power and data management requirements. At the same time, the maturilization of Large Language Models/LLMs, autonomous driving, human-vehicle interaction, and intelligent diagnostics, etc on intelligent cars place higher demands on overall data management.
We deployed a set of end-to-end embedded time-series database in mass-produced vehicles, which is well integrated with GreptimeDB's cloud platform. This seamless solution maximizes security and efficiency of vehicle-cloud integrated time-series data management.
Real-life Challenges Faced by Intelligent Car Manufacturer
High Data-related Costs
Each car costs hundreds of dollars annually:
- Significant data volume leads to high usage costs even after general compression, resulting in situations where the cost outweigh the benefits;
- Traditional block and SSD storage incur high costs; and the tightly coupled storage and computing makes horizontal scaling difficult;
- Uploading local data to the cloud incurs significant data traffic costs;
- Large amount of computing resources are consumed during data format conversion and analysis process on the cloud
Weak Write, Query and Analysis Capabilities at the Vehicle End
Existing time-series databases in the market are unable to meet the computing constraints of the embedded environment in vehicles, resulting in a lack or very weak writing, querying, and analyzing capabilities.
Data Inaccuracy Makes it Difficult to Obtain Valuable Insights
Due to performance and cost concerns, data can only be obtained at minute or second frequency in the cloud, which often fails to meet business requirements. For example, in the automotive fault diagnosis scenario, it is necessary to trace data at a point of time when the fault occurred. However, due to insufficient data precision, engineers need to spend a lot of time reproducing and debugging the fault scenario.
How GreptimeDB Resolves Challenges Above
GreptimeDB lowers the cost of data usage through technical innovations:
- Over 30x data loseless compression greatly reduces storage and bandwidth costs;
- Cloud-native architecture supports object storage, elastic scaling, cross-cloud deployment, etc, significantly reducing cloud resource cost, and the storage and computing separation achieves unlimited horizontal scaling;
- No need for data conversion when data flows from the vehicle to the cloud. GreptimeDB on the cloud can directly read the data uploaded to object storage from the vehicle end, reducing cloud resource costs.
Vehicle-end Lightweight Deployment for Full Function Database
By deploying a complete function databases on the vehicles, the following benefits are realized:
- Lightweight deployment of full-featured, multi-mode databases achieves high availability of complete functions under low computational resource consumption on the vehicle end;
- Embedded database on the vehicle supports machine learning capabilities, which enables data on vehicles to be directly connected to LLMs, autonomous driving applications, etc, boosting end-to-end capability from data processing to analysis;
- The Embedded database drastically reduces the development workload required to improve data quality, laying a solid foundation for data-driven applications
Vehicle-Cloud Integrated Management for Full-scale Time-series Data
Vehicle-Cloud Integrated TSDB has achieved the following capabilities for car manufacturers:
- Achieving millisecond-level precision processing and usage of time series data for all vehicles with controlled costs;
- Equipped with end-to-end data monitoring capabilities, reducing operational and maintenance costs;
- A complete, multi-mode database integrated with vehicles, bringing new opportunities for in-vehicle data processing and analysis, and helping address data compliance and privacy requirements.
Greptime Vehicle-Cloud Integrated TSDB Product Architecture
The architecture consists of three parts: GreptimeDB Edge, GreptimeDB on the cloud, and GreptimeDB Edge Manager.
GreptimeDB Edge, the "database in vehicle", is specifically designed for the in-vehicle environment with optimized storage and computing environments. This version has very low resource requirements and can fully utilize in-vehicle computing resources to develop in-vehicle applications. Highly compressed data files can be directly synchronized to object storage for queries in the cloud, significantly reducing bandwidth costs.
GreptimeDB on the cloud, is a cloud-native distributed time-series database with built-in SQL analysis capabilities and compatibility with upstream and downstream ecosystems, such as MySQL protocol, monitoring ecosystem protocols, visualization tools, etc.
This cloud version is based on object storage, the storage-compute separation architecture makes it easy to scale elastically, with low operation and storage costs. The Vehicle-Cloud Integrated TSDB is specially optimized for in-vehicle cloud scenarios, significantly improving the reading speed of data uploaded from the vehicle and supporting large-scale vehicle terminals.
GreptimeDB Edge Manager is a control plane which increases O&M efficiency for edge devices, data models, data quality, and upload tasks.
Case Study for Integrated Vehicle & Cloud Platform
A prominent international electric car manufacturer has integrated GreptimeDB's Vehicle-Cloud Integrated TSDB solution into its fleet of mass-produced vehicles, saving over one hundred dollars each car annually. This solution also provides a secure and efficient foundation for data-driven applications, bringing excellent product and user experiences.
About Greptime
We help industries that generate large amounts of time-series data, such as Connected Vehicles (CV), IoT, and Observability, to efficiently uncover the hidden value of data in real-time.
Visit the latest version to get started and get the most out of your data.
GreptimeDB, written in Rust, is a distributed, open-source time-series database designed for unlimited horizontal scaling, high performance, and integrated analytics. We provide GreptimeDB Enterprise for corporate users which supports more enterprise features and customized services. Contact us here for more information.
GreptimeCloud is a fully-managed cloud database-as-a-service (DBaaS) solution built on GreptimeDB. It efficiently supports applications in fields such as observability, IoT, and finance. The built-in observability solution, GreptimeAI, helps users comprehensively monitor the cost, performance, traffic, and security of LLM applications.
The Vehicle-Cloud Integrated TSDB is a finely tailored solution that aligns closely with the specific business scenarios of automotive companies, addressing the challenges posed by the exponential growth of vehicle data. The multimodal vehicle-side database, combined with the cloud-based GreptimeDB Enterprise, greatly reduces traffic, computing, and storage costs, and boosts data timeliness and business insight capabilities.
If anything above draws your attention, don't hesitate to star us on GitHub or join GreptimeDB Community on Slack. Also, you can go to our contribution page to find some interesting issues to start with.
