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Milvus Highlights
cloud native
Milvus supports running on public cloud, private cloud or hybrid cloud, which allows users to choose the appropriate cloud environment for deployment according to their needs and preferences. Cloud-native features make Milvus more flexible and convenient.
Simple and easy to use
Milvus provides easy-to-use Python, Java, Go and C++ SDKs, as well as RESTful API interfaces. These tools and interfaces make it easier for users to interact and integrate with Milvus, lowering the development and usage threshold.
Milvus Features
Supports multiple data types and complex searches
Milvus' data record fields support multiple data types and support complex searches on one or more fields, such as filtering, sorting, and aggregation. This flexibility enables Milvus to handle more complex data query and analysis tasks.
High scalability and reliability
Milvus supports distributed deployment, and users can expand cluster capacity and improve reliability by adding nodes. This high scalability enables Milvus to meet the challenges of large-scale data processing and search.
Milvus function
Comprehensive similarity metrics
Milvus supports a variety of similarity calculation indicators, including but not limited to Euclidean distance, inner product, Hamming distance and Jaccard distance. The selection of these indicators depends on your specific application needs, allowing Milvus to meet the similarity search requirements in various scenarios.
Industry-leading performance
Based on highly optimized Approximate Nearest Neighbor Search (ANNS) index libraries such as faiss, annoy and hnswlib, Milvus provides excellent search performance. Users can choose the most suitable index type according to different usage scenarios to achieve the best search results.
Dynamic data management
Milvus allows users to insert, delete, search and update data at any time without worrying about the limitations of static data. This dynamic data management feature makes Milvus more flexible and efficient in handling real-time data updates and searches.
Near real-time search
After inserting or updating data, Milvus can search the new data almost immediately, ensuring the accuracy of search results and data consistency. This near-real-time search capability is critical for application scenarios that require fast response.
Highly cost effective
Milvus makes full use of the parallel computing capabilities of modern processors, enabling millisecond-level searches of billions of data to be completed on a single general-purpose server. This high performance and low hardware requirements make Milvus highly cost-effective.
Milvus FAQ
What should I do if the query results are incorrect?
Possible reasons include Milvus internal cache and data inconsistency, metadata synchronization issues, uneven data sharding, network problems, configuration errors, services not running, improper index selection, insufficient hardware configuration, or excessive query requests.
Solutions include restarting the Milvus service to reload the cache and synchronize metadata, check the network connection and service status, verify whether the connection parameters are correct, select an appropriate index structure, upgrade hardware to improve performance, and optimize query requests.
What should I do if the data insertion or update speed is too slow?
Ensure that the data inserted or updated conforms to the data format required by Milvus.
Check whether the data size exceeds the limit of Milvus and try to insert or update the data in batches to improve efficiency.
You can consider optimizing the hardware environment or adjusting the configuration parameters of Milvus to improve the insertion speed.
Milvus update log:
1: Brand new interface, refreshing, simple and efficient
2: Performance is getting better and better
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