  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. In this paper, we propose
a new approach of building a massively-parallel search engine using a DB-IR
tightly-integrated parallel DBMS and demonstrate its commercial-level
scalability and performance. In addition, we present a hybrid (i.e., analytic
and experimental) performance model for the parallel search engine. We have
built a five-node parallel search engine according to the proposed architecture
using a DB-IR tightly-integrated DBMS. Through extensive experiments, we show
the correctness of the model by comparing the projected output with the
experimental results of the five-node engine. Our model demonstrates that 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 211 ms, which is equivalent to or better than those of commercial
search engines. We also show that, by using twice as many (86,944) nodes, ODYS
can provide an average query response time of 162 ms, which is significantly
lower than those of commercial search engines.
