Milvus, an LF AI & Data Foundation Incubation Project, has released version 1.1! Milvus is an open source vector database that is highly flexible, reliable, and blazing fast. It supports adding, deleting, updating, and near real-time search of vectors on a trillion-byte scale.
In version 1.1, Milvus adds a variety of improvements. Highlights include:
- #4756 Improves the performance of the get_entity_by_id() method call.
- #4856 Upgrades hnswlib to v0.5.0.
- #4958 Improves the performance of IVF index training.
- #4564 Supports specifying partition in a get_entity_by_id() method call.
- #4806 Supports specifying partition in a delete_entity_by_id() method call.
- #4905 Adds the release_collection() method, which unloads a specific collection from cache.
- #4778 Fails to access vector index in Mishards.
- #4797 The system returns false results after merging search requests with different topK parameters.
- #4838 The server does not respond immediately to an index building request on an empty collection.
- #4858 For GPU-enabled Milvus, the system crashes on a search request with a large topK (> 2048).
- #4862 A read-only node merges segments during startup.
- #4894 The capacity of a Bloom filter does not equal to the row count of the segment it belongs to.
- #4908 The GPU cache is not cleaned up after a collection is dropped.
- #4933 It takes a long while for the system to build index for a small segment.
- #4952 Fails to set timezone as “UTC + 5:30”.
- #5008 The system crashes randomly during continuous, concurrent delete, insert, and search operations.
- #5010 For GPU-enabled Milvus, query fails on IVF_PQ if nbits ≠ 8.
- #5050 get_collection_stats() returns false index type for segments still in the process of index building.
- #5063 The system crashes when an empty segment is flushed.
- #5078 For GPU-enabled Milvus, the system crashes when creating an IVF index on vectors of 2048, 4096, or 8192 dimensions.
As usual, there is strong support for Milvus from our fantastic open source community! We thank the following individuals for making their pull request part of Milvus 1.1:
To learn more about the Milvus 1.1 release, check out the full release notes. Want to get involved with Milvus? Be sure to join the Milvus-Announce and Milvus-Technical-Discuss mailing lists to join the community and stay connected on the latest updates.
Congratulations to the Milvus team and we look forward to continued growth and success as part of the LF AI & Data Foundation! To learn about hosting an open source project with us, visit the LF AI & Data Foundation website.
Milvus Key Links
LF AI & Data Resources