A major online media company manages vast data across scenarios like real-time public opinion monitoring, content verification, and multidimensional analysis. As data grows, they face significant challenges, including handling high concurrency during peak traffic periods, ensuring the accuracy of data and insights, and maintaining high performance. To overcome these hurdles, the company urgently requires a unified solution that can efficiently manage these complexities while providing the scalability and flexibility needed for future growth.
High recall rate requirements
The business relies on high recall rates to ensure accurate data analysis. A recall rate below 98% leads to inaccurate insights, affecting overall business precision.
Lack of real-time data access
Immediate data retrieval upon ingestion is essential for real-time monitoring in public sentiment analysis. Without this capability, the business risks missing critical insights.
Limited scalability for high concurrent reads/writes
The current system struggles to support the high concurrent demands of tens of thousands of QPS and 2000 TPS while maintaining a 99% recall rate, impacting performance during peak loads.
Inefficient data management
The business primarily relies on the last 7 days of hot data for vector searches, but without proper data lifecycle management, handling and accessing this data becomes inefficient.
The Relyt AI-ready Data Cloud revolutionizes data processing and analytics through cutting-edge vector storage and retrieval capabilities. This solution converts unstructured data into vectors using advanced embedding models. These vectors, along with structured data, are securely stored in specialized vector databases, facilitating real-time hybrid search and retrieval of both structured data and unstructured data.
Customer Benefits
High recall rate
ensuring comprehensive retrieval and accurate decision-making.
High concurrency
with a peak write TPS of 2,000 and a peak QPS of 13,000.
Auto data management
with a default data retention period of 7 days.
Hundred billion-scale
vectors
200 million
queries per day
10 ms
average latency
98%
recall rate
13,000
QPS
2,000
TPS