/PRZWT/Today, with the rapid development of Internet of Things (iot) technology, data is growing exponentially, data types are becoming increasingly diverse, and scenarios are becoming more complex and changing rapidly. A truly user-friendly database must possess strong adaptability and the ability to continuously evolve. From architecture design to performance optimization, it should comprehensively enhance its own capabilities to meet the data management demands of the Internet of Things era.
On November 12th, at the "Data Insights into the Future, Embracing the All-Domain Intelligence of the Internet of Things - Inspur KaiwuDB New Product Release Conference", Inspur released the brand-new version of its self-developed distributed multi-model database, KaiWUDB V3.0, and the Inspur Kaiwu Internet of Things Industry Digital Intelligence Brain K-Mind, aiming to integrate data technology with cutting-edge technologies such as AI large models Accelerate the improvement of the product and service system to comprehensively empower the intelligent upgrade of iot users. Among them, the core product KaiwuDB V3.0 integrates multiple capabilities such as high-performance timing processing, multi-mode fusion, distribution, security features, and AI, providing enterprises with a one-stop data management solution that is high-performance, highly reliable, low-cost, and easy to operate and maintain - helping enterprises effectively address the management challenges of massive, real-time, and multi-mode data in the Internet of Things era Achieve the dual goals of cost reduction and efficiency improvement as well as data-driven business innovation.
I. Technological Innovation: Centered on multi-mode, comprehensively enhance data processing capabilities
The multi-mode integration for the Internet of Things, as a core technical barrier of KaiwuDB, has been continuously enhanced in version 3.0. Meanwhile, the enhancement of stream computing and data distribution functions provides users with more flexible ways to implement business logic, further expanding the application scenarios of the database.
The KaiwuDB V3.0 relational engine has added support for large objects, enabling efficient management of both binary data and text information generated by sensors. Meanwhile, it supports the parallel processing of efficient cross-mode connection operators and time series operators. Compared with version 2.2, the cross-mode query performance has been improved by 5 to 10 times, breaking the barriers between different data models and providing the possibility for the fusion analysis of multi-source heterogeneous data in the Internet of Things.
Through stream computing, KaiwuDB V3.0 can capture subtle changes in data in real time and quickly preprocess and analyze the data according to pre-set rules, improving data quality, saving storage and computing resources, and supporting business decisions at the second to millisecond level.
In the industrial Internet of Things, the operational data of production equipment needs to be simultaneously transmitted to multiple departments of an enterprise, such as its production management system and quality monitoring system. The data distribution function of KaiwuDB V3.0 can flexibly configure the distribution objects, perform data flow and synchronization as needed, and help achieve data collaboration and efficient operation among various systems.
2. Outstanding performance: Efficient write queries to handle the challenges of massive real-time data
In view of the strong real-time and large throughput characteristics of Internet of Things (iot) data, KaiwuDB V3.0 has been comprehensively optimized in terms of performance, with significant advantages in data writing and query performance, resource utilization, and real-time data processing capabilities.
KaiwuDB V3.0 supports writing millions of data points in seconds and fully exploits the high bandwidth feature of sequential disk writes through append writes. Real-time generated data is efficiently written into the database to enhance I/O efficiency and ensure that massive amounts of data can be stored promptly and stably. Compared with version 2.2, the single-machine write performance of KaiwuDB V3.0 has increased by 40% to 216%, and the distributed write performance has improved by 20% to 50%.
KaiwuDB V3.0 adopts intelligent computing pushdown technology, pushing all time series operators down to the data storage node. This innovative measure has greatly reduced the amount of data transmitted over the network, avoiding the delay and bandwidth occupation caused by the back-and-forth transmission of a large amount of data in the network. It also has an inbuilt read cache dedicated to storing the latest device data for each table, helping users quickly understand the current status of their devices. Compared with version 2.2, the average query performance of KaiwuDB V3.0 has been improved by 50%, with a maximum improvement of 600%.
Iot applications often involve complex computing tasks. The flexible Pipeline model of KaiwuDB V3.0 can decompose and process complex tasks in parallel, while I/O operations and the computing process do not block each other, significantly improving the utilization rate of cluster resources and execution efficiency.
Three. Flexible and easy to Use: Distributed +AI, stable system operation and simplified operation
To adapt to the diverse deployment environments of the Internet of Things, KaiwuDB V3.0 has done a lot of work in terms of availability and ease of use.
The optimization of the distributed architecture has led to a dual improvement in the stability and performance of KaiwuDB V3.0. The data distribution optimization strategy has effectively enhanced the write performance of the multi-replica cluster, meeting the demand for high concurrent writes in the Internet of Things scenarios. The data synchronization monitoring function can view the data synchronization delay between the master replica and the slave replica, promptly identify and solve potential problems, and ensure data consistency and system availability.
The intelligent operation and maintenance tool KAT achieves efficient database connection through the MCP protocol and combines LLM technology to deeply integrate natural language processing with database operation and maintenance. This enables complex operation and maintenance tasks that originally required a large amount of manual input to be executed efficiently and automatically and managed intelligently, significantly reducing operation and maintenance costs.
The newly added compatibility adaptation covers more domestic deployment scenarios, enabling KaiwuDB V3.0 to run stably in different hardware and software environments. It supports bidirectional data flow with big data components such as Flink, seamlessly integrating into stream computing scenarios, providing strong support for complex computing and in-depth analysis in the Internet of Things.
Four. Security and Trustworthiness: Multiple protections ensure the security of user data
Data security is the foundation of Internet of Things (iot) applications. The data of the industrial, energy, government, and transportation sectors served by KaiwuDB is the lifeblood of a nation. KaiwuDB V3.0 has established a comprehensive security protection system to safeguard user data security.
The mandatory access control mechanism, with its meticulous permission management strategy, strictly restricts users' access scope and operation permissions to data, eliminating the risks of illegal access and unauthorized operations.
Non-transparent storage encryption technology is like putting on an "invisible armor" for data, allowing data to exist in the form of ciphertext in the storage medium. Even if storage devices are stolen or illegally accessed, attackers cannot obtain valuable information.
The SQL anti-injection function is like the "immune system" of a database. By strictly filtering and verifying the input content, the injection and execution of malicious SQL code are prevented to ensure the stable operation of the database.
Security measures such as strengthened password policies, national cipher encryption, and GSSAPI third-party authentication have further enhanced the confidentiality and integrity of data, providing reliable guarantees for secure access to the database.
Gartner predicts that all enterprises that have invested heavily in the AI field need to extend their data management capabilities into the AI field to support long-term business needs. "AI+" has become a key area that almost all enterprises are focusing on nowadays. How to organically integrate AI, large models and data technology to make AI practical and data valuable has become the key. The newly released distributed multi-mode database KaiwuDB V3.0 and the industry digital intelligence brain K-Mind are significant steps for Inspur in building the "Internet of Things Digital infrastructure", better meeting the strict requirements of large-scale data processing and management in the Internet of Things scenarios. Inspur KaiwuDB will continue to evolve in the future, providing strong support for the digital transformation of more iot enterprises and jointly creating a smarter, more efficient and secure digital future.
