elastic vs. milvus on Scalability
No. Only scale at the server level.
Yes. At the component level (which provides more fine-grained scalability).
Dynamic segment placement
With static sharding, if your data grows beyond the capacity of your server, you will need to add more machines to the cluster and re-shard all of your data. This can be a time-consuming and complex process. Additionally, imbalanced shards can introduce bottlenecks and reduce the efficiency of your system.
Regarding scalability, Milvus uses worker nodes for each type of action (components to handle connections, data nodes to handle ingestion, index nodes to index, and query nodes to search). Each node has its own assigned CPU and memory resources. Milvus can dynamically allocate new nodes to an action group, speeding up operations or reducing the number of nodes, thus freeing resources for other actions. Dynamically allocating nodes allows for easier scaling and resource planning and guarantees latency and throughput.
elastic vs. milvus on Functionality
Performance is the biggest challenge with vector databases as the number of unstructured data elements stored in a vector database grows into hundreds of millions or billions, and horizontal scaling across multiple nodes becomes paramount.
Furthermore, differences in insert rate, query rate, and underlying hardware may result in different application needs, making overall system tunability a mandatory feature for vector databases.
Yes. (combine vector and traditional search)
Yes with Scalar filtering
11 (FLAT, IVS_FLAT, IVF_SQ8, IVF_PQ, HNSW, BIN_FLAT, BIN_IVF_FLAT, DiskANN, GPU_IVF_FLAT, GPU_IVF_PQ, and ScaNN)
Elasticsearch uses reverse index and builds vector search capability on top of the exsting search architecture. Elasticsearch is good at text search, but the whole architecture is not purpose-built for vector search.
- Milvus supports multiple in-memory indexes and table-level partitions results in the high performance required for real-time information retrieval systems.
- RBAC support is a requirement for enterprise-grade applications.
- In regards to partitions, by limiting searches to one or several subsets of the database, partitions can provide a more efficient way to filter data compared to static sharding, which can introduce bottlenecks and require re-sharding as data grows beyond server capacity. Partitions are a great way to manage your data by grouping it into subsets based on categories or time ranges. This can help you to easily filter and search through large amounts of data, without having to search through the entire database every time.
- No single Index type can fit all use cases since each use case will have different tradeoffs With more index types supported, you have more flexibility to find the balance between accuracy, performance and cost.
elastic vs. milvus on Purpose-built
What’s your vector database for?
A vector database is a fully managed solution for storing, indexing, and searching across a massive dataset of unstructured data that leverages the power of embeddings from machine learning models. A vector database should have the following features:
- Scalability and tunability
- Multi-tenancy and data isolation
- A complete suite of APIs
- An intuitive user interface/administrative console
Python, Java, Go, C++, Node.js, Rust, Ruby, .NET (C#), PHP, Perl
Python, Java, Go, C++, Node.js, Ruby
elastic vs. milvus: what’s right for me?
Elasticsearch is built on Apache Lucene and was first released in 2010 by Elastic.
License: Dual-licensed Server Side Public License (SSPL) or the Elastic License
Milvus is a fully open source and independent project, maintained by a number of companies and individuals, some of whom also offer commercial services and support. Graduate of LF AI Data.
License: Apache-2.0 license