- Does Milvus 0.7.0 support data files from previous versions of Milvus?
- Does Milvus 0.7.0 support server configuration files from previous versions of Milvus?
- Does Milvus 0.7.0 support applications built by clients from previous versions of Milvus?
- What is Milvus?
- When is Milvus a good choice?
- How to use Milvus?
- How easy is it to use Milvus?
- Is Milvus highly available?
- Can Milvus handle datasets with 10-billion or 100-billion scale?
- How does Milvus work?
- Which index methods are supported?
- Does Milvus support simultaneous inserting and searching?
- Where are the data stored?
- How does Milvus compare to other vector search tools?
- Is Milvus an end-to-end product?
- Have questions that were not answered?
No. Milvus 0.7.0 cannot directly use data files from previous versions of Milvus. You must import data again.
No, Milvus 0.7.0 does not support server configuration files (
server_config.yaml) from previous versions of Milvus.
No. The client interface in Milvus 0.7.0 have been updated. Applications based on previous versions of Milvus must also be updated before they can support Milvus 0.7.0.
Milvus is an open source similarity search engine for massive-scale feature vectors. It is built with heterogeneous computing architecture for the best performance and cost efficiency. Searches over billion-scale vectors take only milliseconds with minimum computing resources. It can be easily deployed on both bare metal and cloud platforms with Linux operating systems.
Milvus is best suited for applications that require reliable and efficient similarity search of large-scale vectors, and millisecond response times, regardless of scale.
Milvus returns single-row reads in 0.6 ms or less and single-row writes in approximately 0.03 ms, and supports a variety of indexes for optimizing query performance. It can also be used in hybrid search for both structured and unstructured data.
Milvus provides various clients and supports all gRPC communication types.
Milvus can be easily installed with docker images. You can use APIs for vector insertion, deletion, and search. For more details, see Install Milvus.
To start your first vector search program, please go to Milvus example code.
Milvus supports write-ahead logging (WAL), which ensures the atomicity and durability of data operations. In distributed scenarios, Milvus ensures continuous service capability in case of any single point of failure.
Milvus provides Mishards, a sharding middleware for Milvus, to establish an orchestrated cluster, which can process datasets with 10-billion or 100-billion scale. However, Mishards is still in the experimental phase and is not recommended for production. Refer to Mishards Readme for more information.
When vectors are imported into Milvus, they will be stored and indexed. Each vector is assigned a unique ID. User-defined vector IDs are also supported. When vector are searched, IDs of the most similar vectors will be returned.
Please refer to Index Types for supported index methods.
Vectors that have been imported into Milvus are stored in your local disk. Metadata can be stored either in MySQL or SQLite 3.
Milvus is the only one that is a high-performance and easy-to-use vector search engine and scales easily.
Not yet. Milvus accepts vectors as input and returns vectors through queries. You cannot use Milvus to extract features from unstructured data.
If you still have questions that are not covered in this list, you can take the following steps to find an answer: