Rockset versus Elasticsearch
for Real-Time Analytics

Elasticsearch was designed for log analytics and optimized for text search. Rockset, a leading Elasticsearch alternative, is designed for real-time search, aggregations and joins on event streams and CDC streams. Reduce infrastructure costs using real-time data connectors, efficient upserts and compute-storage separation. Save ops time with a fully managed cloud-native real-time analytics database.


Faster Ingestion

Ingest high volume event streams and CDC streams. Rockset’s Converged Index is mutable at an individual field level, with highly efficient upserts. Achieve better performance at scale with full isolation between ingest compute and query compute


Lower Infrastructure Costs

Eliminate hardware over-provisioning with compute-storage separation. Additionally, Rockset achieves 10-100x reduction in storage with streaming aggregations in the form of SQL-based rollups during ingestion


Faster Development Time

Elasticsearch was built for the datacenter era, demanding constant capacity planning, cluster management, re-indexing and re-sharding. Deploy real-time analytics 20x faster with Rockset’s fully managed cloud-native real-time analytics database



Run standard SQL, including complex JOINs on deeply nested JSON, with Rockset’s compute-efficient Converged Index. No denormalization required. The lack of performant JOINs in Elasticsearch is a huge constraint.

Whatnot logo

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.

Xin Xia, Marketplace and Discovery

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Command Alkon logo

We chose Rockset over Elasticsearch for our application. We now look to use Rockset for any search and analytics feature on any data. We absolutely love Rockset. It’s a game changer for us.

Doug Moore, VP of Cloud

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SQL-style JOINs
JOINs on elasticsearch are prohibitively expensive.
You’ll need to denormalize data, perform application-side joins, use nested objects or parent-child relationships, each of which is expensive and complex.
Streaming Ingest
Elasticsearch users batch updates to minimize the cost.
When an update is made to a document in Elasticsearch, the old document is deleted and the new one is buffered and merged into a new segment. With frequent inserts and updates, expect to budget 70% of your CPU on merge operations. When you’re at tens of terabytes of data, this is a very slow and expensive process to execute a few times a week.
Compute and Storage
Elasticsearch's tightly coupled architecture leads to inefficient resource utilization.
Elasticsearch clusters contain both compute and storage, which cannot be scaled independently. Because clusters are responsible for ingestion and queries, writes and reads can interfere with each other. At peak usage, compute contention renders apps unresponsive.
Operational Burden
Even managed Elasticsearch requires in-depth knowledge to control for costs
Elasticsearch is a highly-complex distributed database that requires data management, query DSL, data processing and cluster management. One user estimated a 6 month roadmap for their application on Elasticsearch and 3 full-time engineers to manage the system.
9 Signs You Should Migrate Off Elasticsearch
Learn from real engineering teams who’ve tried and failed to scale real-time analytics on Elasticsearch.

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The Elasticsearch database is designed for log analytics. Use Rockset for real-time analytics with complex search, aggregations and JOINs on event streams and CDC streams. Scale with cloud-native efficiency.


Reduce infra costs by 44%

Increase ingest speeds by 4x

Full SQL, including joins

20x faster development