RethinkDB vs Apache Cassandra

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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Apache Cassandra icon

Apache Cassandra

Apache Cassandra is a highly scalable, distributed NoSQL database designed to handle large amounts of data across many commodity servers, providing high availability without a single point of failure.

Open Source

Platforms: Mac OS X Windows Linux BSD

Alternatives Details Website

Comparison Summary

RethinkDB and Apache Cassandra 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 Apache Cassandra provides apache cassandra is a highly scalable, distributed nosql database designed to handle large amounts of data across many commodity servers, providing high availability without a single point of failure.. 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.

Apache Cassandra

Apache Cassandra

Analysis & Comparison

Advantages

High availability with no single point of failure.

Excellent linear scalability to handle increasing data volumes.

Fault-tolerant and resilient to node failures.

Designed for high write throughput and low-latency reads.

Tunable consistency allows balancing performance and consistency.

Limitations

Data modeling requires a different approach compared to RDBMS.

Complex transactions like multi-keyspace ACID are not supported.

Operational overhead can be high for large clusters.

CQL is not a full SQL implementation; complex queries may be challenging.

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