HBase vs TokuMX: What are the differences?
Developers describe HBase as "The Hadoop database, a distributed, scalable, big data store". Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop. On the other hand, TokuMX is detailed as "A high-performance, concurrent, compressing, drop-in replacement engine for MongoDB". TokuMX is a drop-in replacement for MongoDB, and offers 20X performance improvements, 90% reduction in database size, and support for ACID transactions with MVCC. TokuMX has the same binaries, supports the same drivers, data model, and features of MongoDB, because it shares much of its code with MongoDB.
HBase and TokuMX belong to "Databases" category of the tech stack.
"Performance" is the primary reason why developers consider HBase over the competitors, whereas "When your two-week MongoDB love affair ends, try this" was stated as the key factor in picking TokuMX.
HBase and TokuMX are both open source tools. It seems that HBase with 2.91K GitHub stars and 2.01K forks on GitHub has more adoption than TokuMX with 679 GitHub stars and 90 GitHub forks.
What is HBase?
What is TokuMX?
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What are the cons of using HBase?
What are the cons of using TokuMX?
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The final output is inserted into HBase to serve the experiment dashboard. We also load the output data to Redshift for ad-hoc analysis. For real-time experiment data processing, we use Storm to tail Kafka and process data in real-time and insert metrics into MySQL, so we could identify group allocation problems and send out real-time alerts and metrics.