Log Analysis
During the operation of business systems, servers, databases, and containers generate a large amount of logs and monitoring data. These are dispersed, diverse, and massive in scale, making retrieval and analysis difficult. ES, through rich data collection tools and distributed storage, facilitates unified log management and real-time monitoring of indicators. The one-stop full observation advantage helps users quickly pinpoint issues and improve operational efficiency.
Information Retrieval
Elasticsearch is ideal for website search, mobile app search, and other scenarios, especially when dealing with large data volumes, high concurrency, and high requirements for search flexibility and relevance. It can return search results in milliseconds from PB-scale structured and unstructured data using flexible keywords, query conditions, and fuzzy matching.
Vector Search
Vector Retrieval is a retrieval technique based on the Vector Space Model. It converts data such as text, images, and videos into numerical vectors to conduct similarity searches in the vector space, overcoming the limitations of traditional text searches which can only be based on keywords and not on semantic searches. ES provides a one-stop solution from Vector Generation to Vector Indexing, Storage, and Retrieval, helping users efficiently build applications such as Semantic Search, Image Search, and Product Recommendation.
Retrieval-Augmented Generation (RAG)
Large Language Models (LLMs) face numerous challenges in enterprise applications, including the lack of enterprise private domain knowledge, hallucinations, and knowledge updates. RAG combines retrieval and generation technology, utilizing inputs from the enterprise knowledge base to improve the accuracy of LLM responses. ES provides one-stop services around RAG, including data slicing, vector retrieval, text and vector hybrid search, rerank, and large model integration, surpassing the single-point solution of traditional Tencent Cloud VectorDB and helping enterprises easily build AI assistants, knowledge Q&A, and other scenarios.
Data analysis
In the context of data-driven operations, industries such as e-commerce, mobile applications, and advertising media need to leverage data analysis and data mining to assist business decisions. The massive scale of business data brings significant challenges to statistical analysis. ES has structured query capabilities, supporting complex filtering and aggregation statistical features, helping clients efficiently perform personalized statistical analysis on massive data, identify problems and opportunities, and assist in business decisions, thus realizing the true value of data.
Database Query Acceleration
Relational databases tend to focus on transactional queries and often face challenges such as insufficient query performance and poor scalability in scenarios with massive data scale. ES offers elastic scalability and high concurrency as well as low latency query capabilities for massive data, using data synchronization tools to maintain database synchronization. It supports SQL capabilities to meet clients' database query acceleration needs, addressing the shortcomings of traditional databases.