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Qdrant 2023 Roadmap

Hi! This document is our plan for Qdrant development in 2023. Previous year roadmap is available here:

Goals of the release:

  • Maintain easy upgrades - we plan to keep backward compatibility for at least one major version back.
    • That means that you can upgrade Qdrant without any downtime and without any changes in your client code within one major version.
    • Storage should be compatible between any two consequent versions, so you can upgrade Qdrant with automatic data migration between consecutive versions.
  • Make billion-scale serving cheap - qdrant already can serve billions of vectors, but we want to make it even more affordable.
  • Easy scaling - our plan is to make it easy to dynamically scale Qdrant, so you could go from 1 to 1B vectors seamlessly.
  • Various similarity search scenarios - we want to support more similarity search scenarios, e.g. sparse search, grouping requests, diverse search, etc.

How to contribute

If you are a Qdrant user - Data Scientist, ML Engineer, or MLOps, the best contribution would be the feedback on your experience with Qdrant. Let us know whenever you have a problem, face an unexpected behavior, or see a lack of documentation. You can do it in any convenient way - create an issue, start a discussion, or drop up a message. If you use Qdrant or Metric Learning in your projects, we'd love to hear your story! Feel free to share articles and demos in our community.

For those familiar with Rust - check out our contribution guide. If you have problems with code or architecture understanding - reach us at any time. Feeling confident and want to contribute more? - Come to work with us!

Milestones

  • ⚛️ Quantization support
    • Scalar quantization f32 -> u8 (4x compression)
    • Advanced quantization (8x and 16x compression)
    • Support for binary vectors

  • ⏫ Scalability
    • Automatic replication factor adjustment
    • Automatic shard distribution on cluster scaling
    • Repartitioning support

  • 👀 Search scenarios
    • Diversity search - search for vectors that are different from each other
    • Sparse vectors search - search for vectors with a small number of non-zero values
    • Grouping requests - search within payload-defined groups
    • Different scenarios for recommendation API

  • Additionally
    • Extend full-text filtering support
      • Support for phrase queries
      • Support for logical operators
    • Simplify update of collection parameters