RethinkDB vs Hazelcast

Compare features, pricing, and capabilities to find which solution is best for your needs.

RethinkDB

RethinkDB is a free and open-source, distributed document database designed for building real-time web applications.

Open Source

Platforms: Mac OS X Windows Linux BSD Discontinued

Screenshots:

Alternatives Details Website

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Hazelcast

Hazelcast is a leading open-source distributed in-memory data grid and stream processing platform designed for high-performance, low-latency applications. It provides incredibly fast data access and processing by keeping data in RAM across a cluster of machines, enabling real-time responsiveness. by Hazelcast, Inc.

Open Source

Platforms: Mac OS X Windows Linux

Alternatives Details Website

Comparison Summary

RethinkDB and Hazelcast are both powerful solutions in their space. RethinkDB offers rethinkdb is a free and open-source, distributed document database designed for building real-time web applications., while Hazelcast provides hazelcast is a leading open-source distributed in-memory data grid and stream processing platform designed for high-performance, low-latency applications. it provides incredibly fast data access and processing by keeping data in ram across a cluster of machines, enabling real-time responsiveness.. Compare their features and pricing to find the best match for your needs.

Pros & Cons Comparison

RethinkDB

RethinkDB

Analysis & Comparison

Advantages

Excellent real-time data pushing capabilities (changefeeds).

Elegant and programmatic query language (ReQL).

Flexible JSON document model.

Designed for distributed deployments and scalability.

Active open-source community maintaining the project.

Limitations

Original commercial company is no longer operational.

Reliance on community for support and future development.

Smaller ecosystem compared to larger databases like MongoDB or PostgreSQL.

Steeper learning curve for ReQL compared to SQL for some developers.

Hazelcast

Hazelcast

Analysis & Comparison

Advantages

Extremely low latency due to in-memory data storage.

Highly scalable and fault-tolerant architecture automatically handles data distribution and replication.

Integrated stream processing engine for real-time analytics and event processing.

Offers familiar distributed data structures simplifying development.

Pure Java implementation integrates well with Java ecosystems.

Flexible deployment options including cloud and containers.

Limitations

In-memory nature means data size is limited by available RAM.

Requires operational knowledge to effectively manage and tune large clusters.

Steeper learning curve initially compared to simpler caching solutions.

Persistence requires additional configuration and can introduce complexity.

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