Rockset vs Elasticsearch
Compare and contrast Rockset and Elasticsearch by architecture, ingestion, queries, performance, and scalability.
Rockset vs Elasticsearch Ingestion
Rockset is built for streaming data and is a mutable database, supporting in-place updates for vectors and metadata. As a real-time search and analytics database, Rockset supports searches across large-scale data, including vector embeddings and metadata.
Elasticsearch supports both streaming and bulk ingestion. It recommends using fewer Lucene segments and avoiding updates and reindexing to save on compute costs. Elasticsearch supports searches across large-scale data, including vector embeddings and metadata.
Rockset vs Elasticsearch Indexing
Rockset supports KNN and ANN search using FAISS indexing algorithms. Rockset consolidates search, vector search, columnar and row indexes into a Converged Index to support a wide range of query patterns out of the box. Vectorization is used to speed up query execution
Elasticsearch supports KNN and ANN search using HNSW indexing algorithms. Elasticsearch provides inverted indexes and vector search indexes and uses vectorization to speed up query execution. Users are responsible for index maintenance.
Rockset vs Elasticsearch Querying
Rockset supports pre-filtering and applying a filter during an approximate kNN search. Rockset supports SQL and REST APIs. Rockset applies a filter during an approximate kNN search.
Elasticsearch supports REST APIs.
Rockset vs Elasticsearch Ecosystem
Rockset vs Elasticsearch Architecture
Rockset is built for the cloud and separates compute-storage and compute-compute. The compute used for ingestion and indexing of vector embeddings is isolates from the compute used for query serving. Rockset is used by enterprises including Allianz, JetBlue and Whatnot.
Elasticsearch is built for on-prem with a tightly coupled architecture. Scaling Elasticsearch requires data and infrastructure expertise and management. Elasticsearch is used by enterprises including Booking.com and Cisco.