Elastic vs. Redis
Compare Elastic vs. Redis by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Elastic vs. Redis on Scalability
No. Redis primarily operates by keeping data in memory. The classic architecture of Redis does not inherently follow the storage-compute separation architecture. Instead, it tightly couples data storage and computation in the same node or instance to ensure data access performance.
No. Only scale at the server level.
No. It only scales at the server level. In addition, you need to reshard all the data when scaling out a Redis cluster.
Yes - built-in replication; HA provided in an additional layer by Reis Cluster or Redis Sentinel)
Static sharding
With Redis Enteprise
Elastic scalability
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.
Redis Scalability
High Availabiltiy can be achieved with Redis Enterprise.
Elastic vs. Redis 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 - Pre-filtering documents against an index containing searchable fields
1 (HNSW)
HNSW & IVFFlat
Elastic functionality
Elasticsearch uses an inverted indexand 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.
Redis
Redis has supprt for similarity queries search with the use of vector fields; It is important to note that the k default LIMIT is 10.
Elastic vs. Redis 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
Add on to Redis
No
Python, Java, Go, C++, Node.js, Rust, Ruby, .NET (C#), PHP, Perl
Python for Vector Search
Elastic vs. Redis: what’s right for me?
Elasticsearch
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
Redis is an in-memory data structure store used as a database, cache, message broker, and streaming engine that has a vector field type for the storage, querying and indexing of vectors.
License: BSD License