Tastes Great, Less Filling: Low-Impact OLAP MapReduce Queries on High-Performance OLTP Systems
Xin Jia, Andrew Pavlo, Stanley B. Zdonik · 2013
The previous decade saw the rise of separate, dedicated database management systems (DBMS) for online transaction processing (OLTP) and online analytical processing (OLAP) workloads [3]. The former are focused on executing short-lived, small-footprint transactions with high throughput and strong consistency guarantees. OLAP DBMSs typically target longer running and more complex queries that examine the database after it is offloaded from the front-end OLTP DBMS. For many, the latency overhead of transferring data between these two systems, as well as their administrative costs, is too onerous. A burgeoning alternative is to use a hybrid approach where an OLTP system is able execute OLAP-style queries alongside the transactional workload [1]. This provides users the ability to execute business intelligence and other analytical queries in “real-time ” (i.e., without waiting for data to be copied to the OLAP system). Such an approach has its own drawbacks, however, especially in a clustered environment. If the data is spread across multiple machines, then the OLAP queries must be executed as heavy-weight distributed transactions, which are well-known to significantly reduce the overall throughput of an OLTP system [2].