What's New in Milvus 2.2.10 and 2.2.11

We are excited to announce the release of Milvus versions 2.2.10 and 2.2.11! These latest versions come loaded with numerous enhancements that significantly improve the product's functionality and user experience. We have incorporated feedback from our community of users and made updates to enhance the performance and user experiences further! This blog post will highlight some of the more prominent features. For a complete list of changes, please check the release notes.
📦 PyPI: https://pypi.org/project/milvus/
📚 Docs: https://milvus.io/docs
🛠️ Release Notes:
🐳 Docker Image: docker pull
🚀 Release: milvus-2.2.11
We are continuously enhancing RBAC to expand the security functionalities across different aspects of the system. In version 2.2.10, we introduced the 'FlushAll' function and Database API to the RBAC capabilities. These updates offer improved control over permissions and access to sensitive operations, ultimately enhancing the system's overall security.
Additionally, both versions, 2.2.10 and 2.2.11, introduced several optimizations and upgrades. Notably, in version 2.2.11, we optimized disk usage for RocksMq by enabling zstd compression starting from level 2. This enhancement significantly improves storage efficiency. Additionally, in version 2.2.10, we made a major improvement by replacing the CGO payload writer with a Go payload writer. This change reduced memory usage, leading to improved system performance and stability.
Please note that there are several bug fixes and performance enhancements in the Milvus 2.2.10 & 2.2.1 releases, so check out the release notes for more details.
Summary
In addition to all of the features listed above, Milvus 2.2.10 & 2.2.11 include several bug fixes and improvements. To learn more:
See the release notes for version 2.2.10 & version 2.2.11 for the complete list of changes Download Milvus and get started.
- Summary
Content
Start Free, Scale Easily
Try the fully-managed vector database built for your GenAI applications.
Try Zilliz Cloud for FreeKeep Reading

Milvus + Surveillance: How Vector Databases Transform Multi-Camera Tracking
See how Milvus vector database enhances multi-camera tracking with similarity-based matching for better surveillance in retail, warehouses and transport hubs.

Bringing AI to Legal Tech: The Role of Vector Databases in Enhancing LLM Guardrails
Discover how vector databases enhance AI reliability in legal tech, ensuring accurate, compliant, and trustworthy AI-powered legal solutions.

Matryoshka Representation Learning Explained: The Method Behind OpenAI’s Efficient Text Embeddings
Matryoshka Representation Learning (MRL) is a method for generating hierarchical, nested embeddings that capture information at multiple levels of abstraction.