ArangoSearch vs Milvus: What are the differences?

Introduction

ArangoSearch and Milvus are both powerful tools for handling and searching through big data. However, there are some key differences between them that set them apart in terms of features and functionalities.

1. Data Structure and Purpose: ArangoSearch is a full-text search engine that is integrated into the ArangoDB database. It allows users to perform complex text searches on their data by using different search analyzers and ranking models. On the other hand, Milvus is an open-source vector database that is designed specifically for similarity search and vector storage. It focuses on handling high-dimensional vector data.

2. Query Capabilities: ArangoSearch provides users with a wide range of search capabilities, including exact matches, phrase searches, fuzzy matching, and wildcard searches. It also supports advanced search features like relevance ranking and combining multiple search conditions. In contrast, Milvus offers efficient similarity search algorithms, such as Euclidean distance and cosine similarity, to perform nearest neighbor searches on vector data.

3. Scaling and Distribution: ArangoSearch is built to scale horizontally by distributing data across multiple nodes in a cluster. It offers automatic sharding and replication, enabling high availability and fault tolerance. On the other hand, Milvus provides built-in support for distributed deployment, allowing users to scale their vector databases by adding more servers and utilizing the power of parallel computing.

4. Indexing Techniques: ArangoSearch uses an inverted index structure combined with tokenization and stemming techniques for efficient full-text search. It supports different index types, including hash index, skiplist index, and persistent indexes. In comparison, Milvus employs various indexing techniques such as IVF (Inverted File with Vectorization), NSG (Navigating Spreading Graph), and HNSW (Hierarchical Navigable Small World) to accelerate similarity search on vector data.

5. Data Model: ArangoSearch operates on structured data, which means that the underlying data needs to be stored in a defined schema. It supports various data types, including strings, numbers, and arrays. Milvus, on the other hand, focuses on unstructured and high-dimensional vector data. It does not enforce strict schema requirements, allowing users to store and search vector data without predefined structures.

6. Integration and Ecosystem: ArangoSearch is tightly integrated with the ArangoDB database, which provides a wide range of data management and querying capabilities. It can be easily used alongside other ArangoDB technologies like graph and key-value stores. Milvus, on the contrary, is developed as a standalone vector database and supports integration with popular machine learning frameworks and libraries such as TensorFlow and PyTorch.

In Summary, ArangoSearch is a full-text search engine integrated into ArangoDB, providing advanced search capabilities on structured data, while Milvus is a vector database designed for similarity search on unstructured high-dimensional vector data. ArangoSearch focuses on text search with powerful indexing techniques, while Milvus emphasizes efficient similarity search algorithms and scalability for vector data.