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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 minor version back.
- That means that you can upgrade Qdrant without any downtime and without any changes in your client code within one minor 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
- :atom_symbol: Quantization support
- Scalar quantization f32 -> u8 (4x compression)
- Product quantization (4x, 8x, 16x, 32x, and 64x compression)
- Binary quantization (32x compression, 40x speedup)
- Support for binary vectors
- :arrow_double_up: Scalability
- Automatic replication factor adjustment
- Automatic shard distribution on cluster scaling
- Repartitioning support
- :eyes: Search scenarios
- Diversity search - search for vectors that are different from each other
- Discovery search - constrain the space in which the search is performed
- 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
- Extend full-text filtering support