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Recently, parallel search engines have been implemented based on scalabledistributed file systems such as Google File System. However, we claim thatbuilding a massively-parallel search engine using a parallel DBMS can be anattractive alternative since it supports a higher-level (i.e., SQL-level)interface than that of a distributed file system for easy and less error-proneapplication development while providing scalability. In this paper, we proposea new approach of building a massively-parallel search engine using a DB-IRtightly-integrated parallel DBMS and demonstrate its commercial-levelscalability and performance. In addition, we present a hybrid (i.e., analyticand experimental) performance model for the parallel search engine. We havebuilt a five-node parallel search engine according to the proposed architectureusing a DB-IR tightly-integrated DBMS. Through extensive experiments, we showthe correctness of the model by comparing the projected output with theexperimental results of the five-node engine. Our model demonstrates that ODYSis capable of handling 1 billion queries per day (81 queries/sec) for 30billion web pages by using only 43,472 nodes with an average query responsetime of 211 ms, which is equivalent to or better than those of commercialsearch engines. We also show that, by using twice as many (86,944) nodes, ODYScan provide an average query response time of 162 ms, which is significantlylower than those of commercial search engines.

ODYS: A Massively-Parallel Search Engine Using a DB-IR Tightly-Integrated Parallel DBMS