We decided to focus on developing a more simple and fast Vald as a Cloud-Native ANN search engine. Along with this decision, we also chose to deprecate some internal components that do not fit the way our going.

This change will make Vald more usable and more maintainable for users.

Deprecated Components

These components will be removed in the next minor release(v1.2.0).

[Vald Meta]

  • Saving metadata including UUID and vector into user-defined external Databases.

[Vald Meta Gateway]

  • Forwarding metadata to Vald Meta.

[Vald Backup Manager]

  • Saving the set of UUID, vector, Agent Pod IPs into the user-defined external Database.

[Vald Backup Gateway]

How to deploy Vald on your k3d within 5 minutes

Photo by Sebastian Unrau on Unsplash

In this post, we’ll show a quick and easy way to perform a similarity search using Vald.


Before we start, we need to install 4 tools.

If you have Homebrew installed, you can install them by the following command.

$ brew install helm
$ brew install jq
$ brew install k3d
$ brew install kubectl

Setting up a k3d cluster

Create a k3d cluster by executing the following command. It creates a cluster with 4 nodes (1 server + 3 agents).

$ k3d cluster create vald --api-port…

Why we need the ANN vector search engine?

As you know, in recent decades, information technology has been extremely evolved. The technology evolution such as Smart Phone, 5G Network, Cloud Computing, has been affecting our lifestyle. We can not only search for what you’d like to know but also buy anything on the Internet, sharing and saving images, videos, memories, and more digital content. Along with those changes, the demand for searching with object data has become stronger. The most popular search technology is the k-NN (k nearest neighbor). It is a very simple, fast, and high-precision algorithm. However, considering using a vectorized object data, the dimension of…

The State of Data Retrieval

Nowadays, the need for information retrieval is increasingly based on text and various types of data, including text, images, video, and audio. These technologies are getting more and more advanced every year. The types of data and ways to interpret them are also diversifying. Currently, these technologies are helpful in a wide range of areas, such as recommendation search, which includes the user’s behaviour in context, and image search, which uses image feature vector to perform similar searches. As these technologies and contents develop, the scale of these technologies is increasing every year. It is not easy to search these…


A highly scalable distributed fast approximate nearest neighbor dense vector search engine.

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