Characterizing, modeling, and benchmarking RocksDB key-value workloads at Facebook

Characterizing, modeling, and benchmarking RocksDB key-value workloads at Facebook, Cao et al., FAST'20 You get good at what you practice. Or in the case of key-value stores, what you benchmark. So if you want to design a system that will offer good real-world performance, it's really useful to have benchmarks that accurately represent real-world workloads. ... Continue Reading

Building an elastic query engine on disaggregated storage

Building an elastic query engine on disaggregated storage, Vuppalapati, NSDI'20 This paper describes the design decisions behind the Snowflake cloud-based data warehouse. As the saying goes, 'all snowflakes are special' - but what is it exactly that's special about this one? When I think about cloud-native architectures, I think about disaggregation (enabling each resource type ... Continue Reading

Narrowing the gap between serverless and its state with storage functions

Narrowing the gap between serverless and its state with storage functions, Zhang et al., SoCC'19 "Narrowing the gap" was runner-up in the SoCC'19 best paper awards. While being motivated by serverless use cases, there's nothing especially serverless about the key-value store, Shredder, this paper reports on. Shredder's novelty lies in a new implementation of an ... Continue Reading

Declarative recursive computation on an RDBMS

Declarative recursive computation on an RDBMS... or, why you should use a database for distributed machine learing Jankov et al., VLDB'19 If you think about a system like Procella that’s combining transactional and analytic workloads on top of a cloud-native architecture, extensions to SQL for streaming, dataflow based materialized views (see e.g. Naiad, Noria, Multiverses, ... Continue Reading