Recently, parallel search engines have been implemented based on scalable distributed file systems such as Google File System. However, we claim that building a massively-parallel search engine using a parallel DBMS can be an attractive alternative since it supports a higher-level (i.e., SQL-level) interface than that of a distributed file system for easy and less error-prone application development while providing scalability. Regarding higher-level functionality, we can draw a parallel with the traditional O/S file system vs. DBMS. In this paper, we propose a new approach of building a massively-parallel search engine using a DB-IR tightly-integrated parallel DBMS. To estimate the performance, we propose a hybrid (i.e., analytic and experimental) performance model for the parallel search engine. We argue that the model can accurately estimate the performance of a massively-parallel (e.g., 300-node) search engine using the experimental results obtained from a small-scale (e.g., 5-node) one. We show that the estimation error between the model and the actual experiment is less than 2.13% by observing that the bulk of the query processing time is spent at the slave (vs. at the master and network) and by estimating the time spent at the slave based on actual measurement. Using our model, we demonstrate a commercial-level scalability and performance of our architecture. Our proposed system ODYS is capable of handling 1 billion queries per day (81 queries/sec) for 30 billion Web pages by using only 43,472 nodes with an average query response time of 194 ms. By using twice as many (86,944) nodes, ODYS can provide an average query response time of 148 ms. These results show that building a massively-parallel search engine using a parallel DBMS is a viable approach with advantages of supporting the high-level (i.e., DBMS-level), SQL-like programming interface.
