---
title: "𝗧𝗵𝗲 𝗠𝗶𝗹𝘃𝘂𝘀 𝟯.𝟬 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗶𝘀 𝗵𝗲𝗿𝗲 — and we’re incredibly excited to share what’..."
source_name: "Milvus(@milvusio)"
original_url: "https://x.com/milvusio/status/2049878946414542986"
canonical_url: "https://www.traeai.com/articles/ac1cbc82-8e17-499b-9527-e44b531e831b"
content_type: "tweet"
language: "中文"
score: 8
tags: ["Milvus","VectorSearch","DataInfrastructure","SemanticQuery","ZillizCloud"]
published_at: "2026-04-30T15:50:00+00:00"
created_at: "2026-05-01T02:15:51.599056+00:00"
---

# 𝗧𝗵𝗲 𝗠𝗶𝗹𝘃𝘂𝘀 𝟯.𝟬 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗶𝘀 𝗵𝗲𝗿𝗲 — and we’re incredibly excited to share what’...

Canonical URL: https://www.traeai.com/articles/ac1cbc82-8e17-499b-9527-e44b531e831b
Original source: https://x.com/milvusio/status/2049878946414542986

## Summary

Milvus 3.0 路线图发布，将支持更多数据湖原位搜索、语义查询引擎功能及 Zilliz Cloud Lakebase 工作流，助力AI团队实现从服务到发现的闭环。

## Key Takeaways

- Milvus 3.0 扩展至三大方向：原地数据搜索、更丰富的语义查询引擎、Lakebase工作流基础。
- 新增功能包括外部集合、Iceberg等格式支持、语义排序与聚合、FAISS索引家族等。
- Zilliz Cloud Lakebase集成流处理、导入导出、在线查询等功能，促进AI数据基础设施循环。

## Content

Title: Milvus on X: "𝗧𝗵𝗲 𝗠𝗶𝗹𝘃𝘂𝘀 𝟯.𝟬 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗶𝘀 𝗵𝗲𝗿𝗲 — and we’re incredibly excited to share what’s next.

For years, Milvus has been known for production vector search: high QPS, low latency, and 100B+ scale for online AI applications. 

But AI teams are now asking for more https://t.co/vxQZrMwV8H" / X

URL Source: http://x.com/milvusio/status/2049878946414542986

Markdown Content:
Milvus on X: "𝗧𝗵𝗲 𝗠𝗶𝗹𝘃𝘂𝘀 𝟯.𝟬 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗶𝘀 𝗵𝗲𝗿𝗲 — and we’re incredibly excited to share what’s next. For years, Milvus has been known for production vector search: high QPS, low latency, and 100B+ scale for online AI applications. But AI teams are now asking for more https://t.co/vxQZrMwV8H" / X

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𝗧𝗵𝗲 𝗠𝗶𝗹𝘃𝘂𝘀 𝟯.𝟬 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗶𝘀 𝗵𝗲𝗿𝗲 — and we’re incredibly excited to share what’s next. For years, Milvus has been known for production vector search: high QPS, low latency, and 100B+ scale for online AI applications. But AI teams are now asking for more than serving. They still need fast vector search in production. They also need to explore, search, deduplicate, cluster, and analyze massive embedding datasets that already live in data lakes — without copying everything into another system first. That is the next chapter Milvus 3.0 is moving toward. This roadmap expands Milvus in three major directions: 𝟭. 𝗦𝗲𝗮𝗿𝗰𝗵𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝘄𝗵𝗲𝗿𝗲 𝗶𝘁 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗹𝗶𝘃𝗲𝘀 External Collection, open-format support with Iceberg, Vortex, Lance, and Parquet, Snapshot, Spark Connector, Flexible Schema, and TEXT LOB all move Milvus closer to a world where lake-resident data can become searchable, semantic data without heavy migration. 𝟮. 𝗔 𝗿𝗶𝗰𝗵𝗲𝗿 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗾𝘂𝗲𝗿𝘆 𝗲𝗻𝗴𝗶𝗻𝗲 Order By, Aggregation, StructList, FAISS family indexes, MinHash, Null Vector, Entity TTL, RLS, and related features extend Milvus beyond nearest-neighbor search into a more complete execution engine for semantic search workloads. 𝟯. 𝗔 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗟𝗮𝗸𝗲𝗯𝗮𝘀𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗼𝗻 𝗭𝗶𝗹𝗹𝗶𝘇 𝗖𝗹𝗼𝘂𝗱 (𝗺𝗮𝗻𝗮𝗴𝗲𝗱 𝗠𝗶𝗹𝘃𝘂𝘀) With streaming, import, external tables, online queries, on-demand queries, and production jobs such as deduplication, anomaly detection, and clustering, Zilliz Cloud Lakebase connects serving and discovery on a shared data layer. This is the bigger shift behind Milvus 3.0: Vector search is no longer just an online serving path. It is becoming part of the data infrastructure loop that helps AI teams serve, discover, improve, and serve again. Milvus 3.0 and Lakebase are coming soon. Stay tuned — we can’t wait to share more with the Milvus community.

[![Image 2: Image](https://pbs.twimg.com/media/HHJS-SlboAAV2vd?format=jpg&name=small)](http://x.com/milvusio/status/2049878946414542986/photo/1)

[3:50 PM · Apr 30, 2026](http://x.com/milvusio/status/2049878946414542986)

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