Replica selection in Apache Cassandra : Reducing the tail latency for reads using the C3 algorithm

Abstract: Keeping response times low is crucial in order to provide a good user experience. Especially the tail latency proves to be a challenge to keep low as size, complexity and overall use of services scale up. In this thesis we look at reducing the tail latency for reads in the Apache Cassandra database system by implementing the new replica selection algorithm called C3, recently developed by Lalith Suresh, Marco Canini, Stefan Schmid and Anja Feldmann. Through extensive benchmarks with several stress tools, we find that C3 indeed decreases the tail latencies of Cassandra on generated load. However, when evaluating C3 on production load, results does not show any particular improvement. We argue that this is mostly due to the variable size records in the data set and token awareness in the production client. We also present a client-side implementation of C3 in the DataStax Java driver in an attempt to remove the caveat of token aware clients. The client-side implementation did give positive results, but as the benchmark results showed a lot of variance we deem the results to be too inconclusive to confirm that the implementation works as intended. We conclude that the server-side C3 algorithm will work effectively for systems with homogeneous row sizes where the clients are not token aware.