MapD vs RethinkDB: What are the differences?
MapD: Open source GPU-Powered Database. Interactively query and visualize massive datasets with the parallel power of GPUs; RethinkDB: JSON. Scales to multiple machines with very little effort. Open source. RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.
MapD and RethinkDB belong to "Databases" category of the tech stack.
Some of the features offered by MapD are:
- column store
On the other hand, RethinkDB provides the following key features:
- JSON data model and immediate consistency.
- Distributed joins, subqueries, aggregation, atomic updates.
- Secondary, compound, and arbitrarily computed indexes.
"Super fast, and the approach taken" is the top reason why over 2 developers like MapD, while over 46 developers mention "Powerful query language" as the leading cause for choosing RethinkDB.
MapD and RethinkDB are both open source tools. It seems that RethinkDB with 22.4K GitHub stars and 1.74K forks on GitHub has more adoption than MapD with 1.91K GitHub stars and 271 GitHub forks.
What is MapD?
What is RethinkDB?
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What are the cons of using MapD?
What are the cons of using RethinkDB?
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We initially chose RethinkDB because of the schema-less document store features, and better durability resilience/story than MongoDB In the end, it didn't work out quite as we expected: there's plenty of scalability issues, it's near impossible to run analytical workloads and small community makes working with Rethink a challenge. We're in process of migrating all our workloads to PostgreSQL and hopefully, we will be able to decommission our RethinkDB deployment soon.
High-speed update-aware storage used in our region server infrastructure; provides a good middle layer for storage of rapidly modified information.
Main database, using it in multiple datacenters in an active-active configuration.