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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
> Why is it faster? Is it algorithmic, or some neat trick, or just a much more efficient implementation?
Scylla is written in C++ (versus Java for Cassandra) and uses the high-performance Seastar[0] framework.
> And are they closely equivalent? Would you use one or the other for the same thing, or do they make different CAP promises?
Scylla claims to be a drop-in replacement for Cassandra.
0. http://seastar.io/
In part it's also simply a demonstration of different priorities. Scylla's USP is performance, so a lot of elbow grease is spent there. The Apache Cassandra community is focused primarily on operating at scale, as that's its USP.
Performance is adequate for Cassandra, so the community has (for several years) primarily focused elsewhere. It will be a priority again in future, but in the meantime there are more important things. With many huge scale users out there the community has focused on guaranteeing correctness and stability at scale. For example, the Harry[1] toolkit for validating huge databases, and an adversarial cluster simulator[2] for exposing distributed and other complex bugs. Also a huge amount of behind-the-scenes work that isn't so easy to call out.
The community is now focusing on expanding the utility of the database for these use cases. For example the recently proposed enhancement to bring state-of-the-art general purpose transactions[3] to Apache Cassandra.
[1] https://github.com/apache/cassandra-harry