Popular is cheaper: curtailing memory costs in interactive analytics engines
Popular is cheaper: curtailing memory costs in interactive analytics engines Ghosh et al., EuroSys’18
(If you don’t have ACM Digital Library access, the paper can be accessed either by following the link above directly from The Morning Paper blog site).
We’re sticking with the optimisation of data analytics today, but at the other end of the spectrum to the work on smart arrays that we looked at yesterday. Getafix (extra points for the Asterix-inspired name, especially as it works with Yahoo!’s Druid cluster) is aimed at reducing the memory costs for large-scale in-memory data analytics, without degrading performance of course. It does this through an intelligent placement strategy that decides on replication level and data placement for data segments based on the changing popularity of those segments over time. Experiments with workloads from Yahoo!’s production Druid cluster that Getafix can reduce memory footprint by 1.45-2.15x while maintaining comparable average and tail latencies. If you translate that into a public cloud setting, and assuming a 100TB hot dataset size — a conservative estimate in the Yahoo! case — we’re looking at savings on the order of $10M per year.
Real-time analytics is projected to Continue reading
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