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Milvus Project Roadmap and Time Schedule



Time Note
2.
22022.10.28Experiment2.32022.12.
3.02023.8.23
30
Stable
2.4
2023.4.30Experiment
2024.2.
5
08Under development
2023.6.30Stable
3.0
-RC
2023
2024.
9
4.30
Experiment3.0-GA2023.10.30Stable

...

2.2

  1. Refactor Segment Assignment in Query coord
  2. Support Disk ANN Index
  3. Support RBAC 
  4. Support BulkInsert Data
  5. Support Rate Limiter → Memory protection, Insertion/Search throughput
  6. Support collection level data TTL
  7. Primary/Backup coordinator
  8. Refine and fully tested pymilvus, java and go SDK.
  9. Release birdwatcher, milvus meta inspect and hacking tool
  10. Better Monitoring, collection level DML metrics supported

     

2.3

  1. Refactor On Disk Data format 
  2. Performance Improving on both memory/disk index
  3. Faster failure recovery spped.
  4. Resource group for querynode physical isolation
  5. Merge Index coordinator/ Data coordinator into 1 role
  6. Integrate with new vector search engine, Knowhere 2.0
  7. Support Range Search
  8. Refine Alias functionality
  9. Backup support
  10. Rolling Upgrade
  11. Better Data Partition
  12. Incremental data subscription
  13. Milvus benchmark tool
  14. Fully tested Restful API
  15. Intergated with Huggingface, OpenAI, Paddle paddle

2.4

  1. Support GPU index
  2. ARM Support
  3. Refactor Scalar search engine with DuckDB/Velox?
  4. Better Hybrid search speed
  5. Remove Datanode, all compaction/bulkload functionality moved into indexnode, streaming functionalities moved into log node.
  6. Better Query pagination
  7. Primary/Backup milvus cluster
  8. Improve multi memory replica availability
  9. Intergate with more inference generation system
  10. Tracing
  11. Support offline deployment

2.5 ‘

  1. Support ScaNN Index
  2. Support more datatype，map，list...
  3. Compaction based on vector classification and scalar value
  4. Better streaming data search performance
  5. Segment LRU load for memory index 
  6. Index parameter optimization tool

3.0 

  1. SQL Support 
  2. Optimizer for vector database
  3. Modify collection schema
  4. Support entity Update
  5. GPU index build
  6. Fully tested Cpp/Rust API
  7. New Milvus bootcamp

    

...

Under

Design


Welcome to the Milvus Roadmap! Our journey is an ongoing adventure in improving and evolving Milvus, and we're excited to share with you our achievements, our upcoming plans, and our vision for the future. This roadmap is not just a list of features; it's a reflection of our commitment to innovation and community collaboration. We invite you to explore, provide feedback, and join us in shaping Milvus' future!

Roadmap

Category

Milvus 2.3.x (Achieved in recent releases)

Milvus 2.4.0(End of CY23 release)

Roadmap(3.0)

Scalability and Performance
World-class scalability and performance

Growing Index
Index for streaming-in data

ScaNN Index
20% faster than HNSW

Bulk Insert Optimazation
Easier import for larger data

Scalar Fields Index
Indexing for specific scalar fields

Holds More Collections/Partitions
Support 10000+ collections in small size cluster

Inverted Index for JSON

GPU Acceleration

Full test for 10 billion+ vectors

Ease of Use
Provide flexible usabilities and maintainance

Cluster Rolling Update
Update affected window <30s

Delete by Expression
Easy to delete useless data

Upsert
Update embeddings for ambiguous scenarios

Support More Datatypes
Datetime and various vector types(fp16, bf16)

Add/Delete Collection Fields

Support SQL Syntax

Upgrade SDK

Enterprise Offering
Features for production-ready

RBAC
Role based access control

Partition Key
Performance enhancement for large datasets

Accesslog Enhancement
Detailed info recorded for audit and tracing

Compaction Optimization
Improve system stability

CDC for Online/Cross-Cloud Migration

AI-augment Capability Designed features for AI applications

Dynamic Schema
Provide flexibility by schema-free

Group By for Grouping Requests
Aggregate split embeddings for one set

Multi-Vectors(beta)
Multiplex recall and reranking framework

Sparse Vector(beta)
Local feature extraction or keyword search

Scenario-oriented Vector Search Patterns

Spark Connector for large scale data processing

Multi-Vectors GA

Sparse Vector GA


Notes:

  • Our roadmap is dynamic and adapts based on new learnings and community feedback.

  • Community engagement is highly encouraged. To provide feedback or raise issues, visit Milvus GitHub.

  • Detailed information on recent releases, please refer to release notes.

How to Contribute


As an open-source project, Milvus thrives on community contributions. Here's how you can be a part of our journey.

Share Feedback

  • Issue Reporting: Encounter a bug or have a suggestion? Open an issue on our GitHub page.

  • Feature Suggestions: Have ideas for new features or improvements? We'd love to hear them!

Code Contributions

  • Pull Requests: Contribute directly to our codebase. Whether it's fixing bugs, adding features, or improving documentation, your contributions are welcome.

  • Development Guide: Check our Contributor's Guide for guidelines on code contributions.

Spread the Word

  • Social Sharing: Love Milvus? Share your use cases and experiences on social media and tech blogs.

  • Star Us on GitHub: Show your support by starring our GitHub repository.

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