Introduction
Vald is an open-source high-performance vector similarity search system developed by Yahoo! JAPAN Research. It is designed to efficiently search and retrieve similar vectors from large-scale datasets. In this detailed explanation, we will explore 20 features of Vald search, including its architecture, performance, code examples for indexing and searching, as well as information on performance and scalability.
Features
1. Vector Similarity Search:
Vald is primarily designed for vector similarity search. It allows users to find similar vectors based on a distance metric, such as cosine similarity or Euclidean distance.
2. High Scalability:
Vald is built to handle large-scale datasets efficiently. It supports the indexing and searching of billions of vectors with low latency and high throughput.
3. Distributed Architecture:
Vald adopts a distributed architecture to achieve scalability. It uses a distributed key-value store to store and retrieve vectors, allowing horizontal scaling across multiple nodes.
4. Indexing Algorithm:
Vald uses various indexing algorithms, such as Product Quantization (PQ), Inverted Multi-Index (IMI), and Annoy. These algorithms enable fast indexing and retrieval of vectors.
5. Annoy Support:
Annoy is a popular approximate nearest neighbor library. Vald provides seamless integration with Annoy, allowing users to utilize its indexing capabilities within the Vald system.
6. Multi-Language Support:
Vald supports multiple programming languages, including Go, Python, and Java, making it accessible to a wide range of developers.
7. RESTful API:
Vald provides a RESTful API, allowing users to interact with the system through HTTP requests. This makes it easy to integrate Vald into existing applications or services.
8. GPU Acceleration:
Vald leverages GPU acceleration to speed up vector similarity calculations. This enables faster search and retrieval, especially for large-scale datasets.
9. Approximate Nearest Neighbor (ANN) Search:
Vald supports approximate nearest neighbor search algorithms, which trade-off accuracy for improved performance. This is useful in scenarios where real-time or near-real-time search is required.
10. Dimensionality Reduction:
Vald supports dimensionality reduction techniques, such as Principal Component Analysis (PCA), to reduce the dimensionality of vectors. This helps in improving search efficiency and reducing storage requirements.
11. High Availability:
Vald is designed to provide high availability even in the presence of node failures. It achieves this by replicating data across multiple nodes and leveraging distributed consensus protocols.
12. Data Compression:
Vald supports data compression techniques to reduce storage requirements. By compressing vectors, it optimizes storage efficiency while maintaining search accuracy.
13. Customizable Distance Metrics:
Vald allows users to define custom distance metrics based on their specific use cases. This flexibility enables the system to handle a wide range of similarity search scenarios.
14. Automatic Rebalancing:
Vald automatically rebalances data across nodes to ensure load balancing and optimal performance. This feature helps in maintaining scalability and mitigating hotspots.
15. Query Filtering:
Vald supports query filtering, allowing users to define filters to narrow down search results based on specific criteria. This feature improves search accuracy and efficiency.
16. Multi-Modal Search:
Vald extends its capabilities beyond vector similarity search to support multi-modal search, where different types of data, such as text and images, can be indexed and searched together.
17. Query Routing:
Vald implements efficient query routing mechanisms to direct search queries to the appropriate nodes. This minimizes network overhead and improves overall search performance.
18. Dynamic Data Updates:
Vald supports dynamic updates to indexed data, allowing real-time modifications without interrupting the search process. This feature is crucial in scenarios where data frequently changes.
19. Monitoring and Metrics:
Vald provides built-in monitoring and metrics collection features, enabling users to monitor the
system’s performance, resource utilization, and health status.
20. Extensible Plugin System:
Vald offers an extensible plugin system that allows users to customize and extend the functionality of the system. This enables integration with external components and the addition of new features.
Architecture
Performance
Code Example – Indexing
```go
package main
import (
"context"
"fmt"
"log"
vald "github.com/vdaas/vald-client-go/v0/vald"
)
func main() {
// Create a Vald client
client, err := vald.NewClient(context.Background(), vald.WithHost("localhost"), vald.WithPort(8081))
if err != nil {
log.Fatalf("Failed to create Vald client: %v", err)
}
// Define vectors to be indexed
vectors := [][]float32{
{0.5, 0.1, 0.9},
{0.8, 0.2, 0.7},
{0.3, 0.6, 0.4},
}
// Index vectors
err = client.Insert(context.Background(), vectors)
if err != nil {
log.Fatalf("Failed to insert vectors: %v", err)
}
fmt.Println("Vectors indexed successfully")
}
```Code Example – Searching
```go
package main
import (
"context"
"fmt"
"log"
vald "github.com/vdaas/vald-client-go/v0/vald"
)
func main() {
// Create a Vald client
client, err := vald.NewClient(context.Background(), vald.WithHost("localhost"), vald.WithPort(8081))
if err != nil {
log.Fatalf("Failed to create Vald client: %v",
err)
}
// Define the query vector
query := []float32{0.7, 0.3, 0.5}
// Search for similar vectors
results, err := client.Search(context.Background(), query, 5)
if err != nil {
log.Fatalf("Failed to search for vectors: %v", err)
}
fmt.Println("Similar vectors:")
for _, result := range results {
fmt.Println(result.Vector)
}
}
```Conclusion
Vald search is a high-performance vector similarity search system designed for large-scale datasets. Its distributed architecture, support for various indexing algorithms, and GPU acceleration contribute to its scalability and efficient search capabilities. The system provides a RESTful API, supports multiple programming languages, and offers features like approximate nearest neighbor search, dimensionality reduction, and query filtering. With its automatic rebalancing, high availability, and customizability, Vald is a versatile solution for a wide range of similarity search use cases.
