Rockset vs Elasticsearch for real-time analytics.

Elasticsearch was designed to monitor and search on immuntable data using logs. Rockset is designed for real-time analytics where relationships matter, data is constantly being updated and analyzed, and cloud-native effciencies are built for scale and cost effectiveness.

Data flowing to Rockset logo
  • 4x Faster Ingestion. When dealing with high-velocity streaming data, Rockset uses Log Structured Merge Trees that are mutable and at an individual field level. This architecture outperforms Elasticsearch with any and all streaming data types.

  • 44% Lower Infrastructure Cost. Rockset seperates storage, ingest, and query compute so you don’t end up overprovisioning. In addition, we can save you 100x on storage costs by using SQL-based roll-ups.

  • 20x Faster Development Time. Rockset’s cloud-native structure saves your team time by not needing to manage clusters, nodes, shards, and indexes. Additionally, Rockset’s Coverged Index enables ad hoc searches and analytics without any index managment.
The most compute-efficient way to build user-facing analytics.

Elasticsearch was designed to monitor and search on immuntable data using logs. Rockset is designed for real-time analytics where relationships matter, data is constantly being updated and analyzed, and cloud-native effciencies are built for scale and cost effectiveness.

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SQL Joins

Sub-second SQL search, aggregations and joins.
Database with brush icon

No Data Preparation

Search and analyze deeply nested JSON data.
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Sub-Second Queries

Rockset’s Converged Index delivers millisecond-latency queries.

Rockset delivered true real-time ingestion and queries, with sub-50 millisecond end-to-end latency. That didn’t just match Elasticsearch, but did so at much lower operational effort and cost, while handling a much higher volume and variety of data, and enabling more complex analytics – all in SQL.

Emmanuel Fuentes, Whatnot