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Vespa documentation

Vespa is a powerful and scalable engine for low-latency computation over large data sets. It is designed for real-time applications that require a combination of search, recommendation, and personalization. Vespa allows developers to build applications that can handle high volumes of queries and data writes while maintaining fast response times.

Key Features

Vespa offers a rich set of features for building sophisticated search and recommendation applications:

  • Real-time Indexing and Search: Vespa provides low-latency CRUD (Create, Read, Update, Delete) operations, making data searchable in milliseconds after being fed.
  • Approximate Nearest Neighbor (ANN) Search: Vespa includes a highly efficient implementation of the HNSW algorithm for fast and accurate vector search, which can be combined with traditional filters.
  • Flexible Ranking and Relevance: Ranking is a first-class citizen in Vespa. It supports complex, multi-phase ranking expressions and can integrate with machine-learned models (e.g., ONNX, TensorFlow, XGBoost, LightGBM) to deliver highly relevant results.
  • Scalability and Elasticity: Vespa is designed to scale horizontally. Content clusters can be grown or shrunk on the fly without service interruptions, and data is automatically redistributed to maintain a balanced load.
  • Rich Data Modeling: Vespa supports a variety of data types, including structured data, unstructured text, and tensors for vector embeddings. It also supports parent-child relationships to model complex data hierarchies.
  • Comprehensive Query Language: Vespa's query language (YQL) allows for a combination of keyword search, structured filtering, and nearest neighbor search in a single query.
  • Component-Based Architecture: Vespa's container clusters host custom Java components (Searchers, Document Processors) that allow for extensive customization of query and data processing pipelines.

Architecture

A Vespa application consists of two main types of clusters:

  • Stateless Container Clusters: These clusters handle incoming queries and data writes. They host application components that process requests and responses, perform query rewriting, and federate to backend services.
  • Stateful Content Clusters: These clusters are responsible for storing, indexing, and searching data. They automatically manage data distribution and redundancy.

Vespa's architecture is designed for high availability and fault tolerance. When a node fails, the system automatically re-routes traffic and re-distributes data to maintain service.

Application Package

A Vespa application is defined by an application package, which contains all the necessary configuration, schemas, components, and machine-learned models. This self-contained package allows for atomic deployments and ensures consistency between code and configuration.

Key files in an application package include:

  • services.xml: Defines the services and clusters that make up the application, including their topology and resource allocation.
  • schemas/*.sd: Defines the document types, their fields, and how they should be indexed and searched. Rank profiles are also defined within schemas.

APIs and Interfaces

Vespa provides a comprehensive set of APIs for interacting with the system:

  • Document API (/document/v1/): A REST API for performing CRUD operations on documents.
  • Query API (/search/): A powerful API for querying data using YQL, with extensive options for ranking, grouping, and presentation.
  • Configuration and Deployment APIs: REST APIs for deploying application packages and managing system configuration.

This overview provides a glimpse into the capabilities of the Vespa Search engine. For more in-depth information, please refer to each of the documentation links below.