This study demonstrates a way to build large-scale recommender systems by just writing a series of SQL-like queries. In order to efficiently run recommendation logics on a cluster of computers, we implemented a variety of recommendation algorithms and common recommendation functions (e.g., efficient similarity computation, top-k retrieval, and evaluation measures) asHive user-defined functions (UDFs) in Apache Hivemall. We demonstrate that how Apache Hivemall can easily be used for building a scalable recommendation system with satisfying business requirements such as scalability, latency, and stability.

In this paper, we propose a non-blocking buffer management scheme based on a lock-free variant of the GCLOCK page replacement algorithm. Concurrent access to the buffer management module is a major factor that prevents database scalability to processors. Therefore, we propose a non-blocking scheme for bufferfix operations that fix buffer frames for requested pages without locks by combining Nb-GCLOCK and a non-blocking hash table. Our experimental results revealed that our scheme can obtain nearly linear scalability to processors up to 64 processors, although the existing locking-based schemes do not scale beyond 16 processors.