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.
Steffi LiSteffi is the Director of Product Marketing at Zilliz, with prior experience leading the GTM strategies for open-source data technologies such as Apache Kafka and Apache Airflow. Passionate about travel, yoga, and non-fiction reading, she brings a balanced and enriched worldview to her professional pursuits.
- Summary
Content
Start Free, Scale Easily
Try the fully-managed vector database built for your GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How to Build an Enterprise-Ready RAG Pipeline on AWS with Bedrock, Zilliz Cloud, and LangChain
Build production-ready enterprise RAG with AWS Bedrock, Nova models, Zilliz Cloud, and LangChain. Complete tutorial with deployable code.
8 Latest RAG Advancements Every Developer Should Know
Explore eight advanced RAG variants that can solve real problems you might be facing: slow retrieval, poor context understanding, multimodal data handling, and resource optimization.
Why DeepSeek V3 is Taking the AI World by Storm: A Developer’s Perspective
Explore how DeepSeek V3 achieves GPT-4 level performance at fraction of the cost. Learn about MLA, MoE, and MTP innovations driving this open-source breakthrough.