tempesta

https://github.com/tempesta-tech/webshield/), a small open-source Python daemon: * periodically executes analytic queries to detect spikes in traffic (requests or bytes per second), response delays, surges in HTTP error codes, and other anomalies; * upon detecting a spike, classifies the clients and validates the current model; * if the model is validated, automatically blocks malicious clients by IP, TLS fingerprints, or HTTP fingerprints. To simplify and accelerate classification — whether automatic or manual — we introduced a new TLS fingerprinting method. WebShield is a small and simple daemon, yet it is effective against multi-thousand-IP botnets.

ngle server!), while for all other compared systems corresponding metric could be evaluated as nearly 0 by construction, that gives Anna a huge edge. In multi-node benchmark, they claim 10x over Cassandra. ScyllaDB (https://www.scylladb.com/) claims the same, while being ACID and linearizable, unlike Anna. Also, Anna achieves stronger consistency levels by holding off reads, that kills latency, given 100 millisecond gossip granularity. If it applies only to their multi-key consistency (Read Committed/Uncommitted) it's probably OK, because I suppose that there is no magic bullet that allows to preserve super low latencies and providing similar consistency in Scylla either. But if Anna needs to hold off reads for any of their claimed single-key consistency levels (all of which are weaker than linearizable), that's worse than Scylla. The authors of the paper didn't detail the algorithm for each consistency level. Seems like the authors don't benchmark multi-node scalability of Anna on any consistency levels except the weakest, simple eventual consistency. It would be interesting to see if Anna scales as well on stronger consistency levels. To me, the main outcome of this paper is another confirmation that shared-nothing, thread-per-core, message passing designs are beneficial in the modern computing environment. This is not new, however, see H-Store, Scylla/Seastar, Tarantool (https://github.com/tarantool/tarantool), Aeron (https://github.com/real-logic/aeron), Tempesta (https://github.com/tempesta-tech/tempesta), etc. Novelty is the framework that generalizes thread/node scalability, different consistency levels reusing the same codebase, and having just a single knob - gossip granularity. Practical applications are limited. Certain techniques are probably going to be cherry-picked by systems such as Redis Cluster and In-Memory Data Grids.