Milvus vs Solr: What are the differences?

Introduction:

This Markdown code presents the key differences between Milvus and Solr. Milvus is an open-source similarity search engine while Solr is an open-source search platform based on Apache Lucene. The differences between Milvus and Solr are outlined below.

1. Inverted Indexing vs Vector Similarity Searching: Solr is primarily focused on full-text search capabilities with support for inverted indexing. In contrast, Milvus is designed specifically for similarity search, using vector similarity searching algorithms for efficient retrieval of similar items based on their feature vectors.

2. Scalability: Both Milvus and Solr are designed to handle large-scale data, but they differ in their underlying architecture. Milvus adopts the distributed architecture, allowing for easy scalability and high availability. Solr, on the other hand, is built on a master-slave architecture, where scalability depends on adding additional nodes to the cluster manually.

3. Supported Data Types: Solr provides support for a wide range of data types, including text, numbers, dates, and more. It also supports faceted search, filtering, and geo-spatial search. Milvus, being focused on similarity search, is specifically optimized for handling vector data types, such as high-dimensional feature vectors commonly used in machine learning and deep learning tasks.

4. Query Capabilities: Solr offers rich query capabilities with support for boolean search, fuzzy search, wildcard search, and complex queries using query parsers. Milvus, on the other hand, provides similarity search operations based on vector embeddings. Its query capabilities are centered around finding items that are most similar or have the highest similarity score to a given query vector.

5. Use Cases: Solr is widely used in various applications for text search, document indexing, and retrieval. It is commonly used in enterprise search, e-commerce platforms, and content management systems. Milvus is specifically designed for similarity search applications, making it suitable for image search, recommendation systems, natural language processing, and other tasks where finding similar items is crucial.

6. Community and Ecosystem: Solr enjoys a large and active community with a vast ecosystem of plugins, extensions, and documentation. It has been widely adopted and proven in numerous production systems. Milvus, being relatively new, is rapidly growing its community and ecosystem. However, its focus on similarity search means that it may have a smaller user base but a more targeted and specialized community.

In Summary, Milvus and Solr differ in their approach to search. Milvus is optimized for similarity search using vector similarity searching algorithms, while Solr is focused on full-text search capabilities with support for inverted indexing. They differ in scalability, supported data types, query capabilities, use cases, and the size and focus of their respective communities.