Predictable performance and high query concurrency for data analytics

Conventional data warehouses employ the query-at-a-time model, which maps each query to a distinct physical plan. When several queries execute concurrently, this model introduces contention and thrashing, because the physical plans-unaware of each other-compete for access to the underlying I/O and computation resources. As a result, while modern systems can efficiently optimize and evaluate a single complex data analysis query, their performance suffers significantly and can be highly erratic when multiple complex queries run at the same time. We present in this paper Cjoin, a new design that substantially improves throughput in large-scale data analytics systems processing many concurrent join queries. In contrast to the conventional query-at-a-time model our approach employs a single physical plan that shares I/O, computation, and tuple storage across all in-flight join queries. We use an "always on" pipeline of non-blocking operators, managed by a controller that continuously examines the current query mix and optimizes the pipeline on the fly. Our design enables data analytics engines to scale gracefully to large data sets, provide predictable execution times, and reduce contention. We implemented Cjoin as an extension to the PostgreSQL DBMS. This prototype outperforms conventional commercial systems by an order of magnitude for tens to hundreds of concurrent queries.