Real-Time Alternative
to Elasticsearch with
SQL Joins

SQL joins are not supported in Elasticsearch. To workaround the lack of join support, you’ll need to either denormalize data, perform application-side joins, or use nested objects or parent-child relationships. But there’s a much more efficient way to join data with Rockset for real-time analytics.

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  • There are four common approaches to joins in Elasticsearch: denormalization, application-side joins, nested objects, parent-child relationships. Each of these options adds complexity, including data duplication, managing data changes, manually tuning for performance and indexing time.

  • Rockset stores data in a document data model while supporting full SQL, including joins. You can ingest deeply nested JSON and run sub-second joins. No data preparation required.

  • Rockset achieves fast JOINs with its Converged Index, an index inspired by search indexes and column stores. Furthermore, Rockset’s massively distributed query execution engine adds to both scalability and speed.
Get $300 in Free Credits

SQL joins are not supported in Elasticsearch. To workaround the lack of join support, you’ll need to either denormalize data, perform application-side joins, or use nested objects or parent-child relationships. But there’s a much more efficient way to join data with Rockset for real-time analytics.

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SQL Support

Sub-second SQL search, aggregations and joins.
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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.

Elasticsearch doesn’t support joins, so we were constantly denormalizing our data to get around this. Data that would occupy 1 TB in Elasticsearch now takes up 10 GB in Rockset.

Jake Quist, Head of Engineering at Sequoia Capital