LazyBase: Trading Freshness for Performance in a Scalable Database

摘要

The LazyBase scalable database system is specialized for the growing class of data analysis applications that extract knowledge from large, rapidly chang ing data sets. It pro-vides the scalability of popular NoSQL systems without the query-time complexity associated with their eventual consis-tency models, offering a clear consistency model and explicit per-query control over the trade-off between latency and re-sult freshness. With an architecture designed around batch-ing and pipelining of updates, LazyBase simultaneously in-gests atomic batches of updates at a very high throughput and offers quick read queries to a stale-but-consistent ver-sion of the data. Although slightly stale results are sufficient for many analysis queries, fully up-to-date results can be ob-tained when necessary by also scanning updates still in the pipeline. Compared to the Cassandra NoSQL system. Lazy-Base provides 4X-5X faster update throughput and 4X faster read query throughput for range queries while remaining competitive for point queries. We demonstrate LazyBase's tradeoff between query latency and result freshness as well as the benefits of its consistency model. We also demon-strate specific cases where Cassandra's consistency model is weaker than LazyBase's.