milvus-io/milvus v2.3.0-beta

milvus-2.3.0 beta

on GitHub

2.3.0 beta

Release date: 20 March, 2023

The latest release of Milvus introduced a new feature that will please many users: Nvidia GPU support. This new feature brings the ability to support heterogeneous computing, which can significantly accelerate specialized workloads. With GPU support, users can expect faster and more efficient vector data searches, ultimately improving productivity and performance.

Features

GPU support

Milvus now supports two GPU-based IVF indexes: RAFT and FAISS. According to a benchmark on RAFT's GPU-based IVF-series indexes, GPU indexing achieves a 10x increase in search performance on large NQ cases.

• Benchmark

  We have compared RAFT-IVF-Flat with IVF-Flat and HNSW at a recall rate of 95%, and obtained the following results.

  Datasets	SIFT	GIST	GLOVE	Deep
  HNSW (VPS)	14,537	791	1516	5761
  IVF-Flat (VPS)	3097	142	791	723
  RAFT-IVF-Flat (VPS)	121,568	5737	20,163	16,557

  Also we benchmarked RAFT-IVF-PQ comparing Knowhere's fastest index HNSW at 80% recall.

  Datasets	SIFT	GIST	GLOVE	Deep
  HNSW(VPS)	20,809	2593	8005	13,291
  RAFT-IVF-PQ(VPS)	271,885	7448	38,989	80,363

  These benchmarks run against Knowhere on a host with an 8-core CPU, 32 GB of RAM, and an Nvidia A100 GPU with an NQ of 100.

  For details on these benchmarks, refer to the release notes of Knowhere v2.1.0.

  Special thanks go to @wphicks and @cjnolet from Nvidia for their contributions to the RAFT code.

Memory-mapped (mmap) file I/O

In scenarios where there is not sufficient memory for large datasets and it is insensitive to query performance, Milvus uses mmap to allow the system to treat parts of a file as if they were in memory. This can reduce memory usage and improve performance if all data is held in the system page cache.

Range search

The range search method returns all vectors within a certain radius around the query point, as opposed to the k-nearest ones. Range search is a valuable tool for querying vectors within a specific distance, for use cases such as anomaly detection and object distinction.

Upsert

Milvus now supports record upsert, similar to that in a relational database. This operation atomically deletes the original entity with the primary key (PK) and inserts a new entity. Note that upserts can only be applied to a given primary key.

Change Data Capture(CDC)

Change Data Capture is a process that identifies and tracks changes to data in a database. Milvus CDC provides real-time subscriptions to data and database events as they occur.

In addition to the aforementioned features, later release 2.3 of Milvus will also introduce new features such as accurate count support, Feder visualization support and growing segment indexing.

Milvus later will offer Dynamic Partitioning, which allows users to conveniently create and load a partition without releasing the collection. In addition, Milvus 2.3.0 will improve memory management, performance, and manageability under multi-partition cases.

Now, you can download Milvus and get started.