OceanBase Embraces AI, Its Value Continues to Rise
By Dongmei, InfoQ
October 25, 2024
The rise of AI applications has driven a revolution in data processing. 2023 was widely considered the year of large language models, witnessing unprecedented leaps in AI technology. As we enter 2024, AI-native applications born from large language models are emerging like mushrooms after a spring rain.
More and more AIapplications are no longer limited to pure text generation and answering, but are gradually evolving into multimodal applications. Take the typical multi-modal hybrid query as an example: when a consumer searches in an AI application for recommend a milk tea shop within 500 meters, with an average consumption of less than 24 yuan, a rating of 4.5 stars or above, and no queues, the database needs to process different types of data simultaneously, including GIS data (distance), relational data (price, rating), and vector data (no queues). This means that traditional data processing methods can no longer meet the complex demands brought by new AI applications.
According to statistics from the Italian PXR Research Institute, the global volume of data/information created, captured, replicated, and consumed has grown from 2ZB in 2010 to 64.2ZB in 2020. It is estimated that the total global data volume will exceed 181ZB by 2025. This data includes both dynamic, real-time data streams and static, historical data storage; it includes both structured database records and unstructuredspeech, images, and videos.
This massive and complex data presents new challenges for databases:
- Real-time performance and low latency: With the increasing demand for real-time data analysis and decision-making, databases must be able to process large amounts of data quickly to support real-time applications such as online recommendationsand dynamic pricing. This places higher demands on traditional databases in terms of performance and response time.
- Massive data and diversity: AI applications generate large volumes of data of diverse types (structured, semi-structured, and unstructured). Databases need to have the ability to process and store different types of data, requiring support for multi-modal architecture to meet multi-modal data needs.
- Complex fusion queries: AI applications place higher demands on complex data analysis. Databases need to support complex SQL query optimization to improve query performance and meet the training and prediction needs of deep learning and machine learning models.
- Multi-source data fusion:Data from different sources needs to be fused and processed to obtain more comprehensive and accurate information. For example, fusing data from different businesses and workloads within an enterprise, or even ecological business data and industry data, to analyze the company’s market competitiveness and development trends.
Driven by AI technology, the era of single, structured,and static data architecture has come to an end. A revolution in database architecture is on the horizon. So, in terms of technology, what kind of database can meet these challenges and handle the massive data processing demands brought by the explosion of AI applications?
OceanBase has presented its solution. At the 2024OceanBase Global Developer Conference held on October 23rd, …
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