Research of Massive Internet Text Data Real-Time Loading and Index System

Weihong Han, Yan Jia, Shuqiang Yang
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Abstract

With rapid development of the Internet and communication technology, massive text data has been accumulated in Internet, including text data on network pages, emails, instant messengers and etc. Requirements on increasing data volume, real-time data-loading and creating text indexes pose enormous challenges to data-loading techniques. This paper presents a data loading system in real time, Text-loader that is used in ITSR (Internet Text Data Real-time Storage and Retrieval System). Text-loader consists of an efficient algorithm for bulk data loading and exchange partition mechanism, increasing text index creation algorithm, optimized parallelism, and guidelines for system tuning. Performance studies show the positive effects of these techniques with loading speed of every Cluster, increasing from 220 million records per day to 1.2 billion per day, and achieving the top loading speed of 6TB data when 10 Clusters are in parallel. This framework offers a promising approach for loading other large and complex text databases.
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海量网络文本数据实时加载与索引系统研究
随着互联网和通信技术的快速发展,互联网上积累了大量的文本数据,包括网页、电子邮件、即时通讯工具等文本数据。不断增长的数据量、实时数据加载和创建文本索引的需求对数据加载技术提出了巨大的挑战。本文介绍了一种用于互联网文本数据实时存储与检索系统(ITSR)的实时数据加载系统——Text-loader。文本加载器包括用于批量数据加载和交换分区机制的高效算法、增加文本索引创建算法、优化的并行性和系统调优指南。性能研究表明,这些技术对每个集群的加载速度都有积极影响,从每天2.2亿条记录增加到每天12亿条记录,并在10个集群并行时实现6TB数据的最高加载速度。该框架为加载其他大型和复杂的文本数据库提供了一种很有前途的方法。
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