HDFS日志数据分析中的块异常检测探索
Exploring Block Anomaly Detection In HDFS Log Data Analysis
AI总结：
本研究针对HDFS日志人工异常检测复杂枯燥的问题，提出结合LLM-BiLSTM模型与Kafka流式管道的检测方案，实现HDFS块异常的快速准确检测。
AI中文摘要：
近年来，随着大数据技术的发展，越来越多的公司使用HDFS进行数据处理与存储，分布式文件系统的维护已成为数据管理中极为重要的部分。由于服务器系统功能日益多样化、服务愈发复杂，记录实时事件的日志能帮助系统运维人员定位服务器系统中发生的故障与错误，以保障服务器始终可用。包含大型数据集的分布式文件系统HDFS会记录大量日志，且这些日志并非总是结构化数据，稳定性也不足。然而，逐一检查日志以检测系统中出现的问题，对系统运维人员而言是复杂且枯燥的工作。利用机器学习技术与自然语言处理技术检测HDFS块异常，将帮助系统运维人员快速准确地定位并修复异常。本文提出一种流式HDFS日志块异常检测工作流，它帮助维护人员使用并行计算网络处理历史日志，构建LLM-BiLSTM混合深度学习模型检测HDFS中的异常块，再基于Kafka构建流式日志管道，提供实时HDFS日志块异常检测解决方案。
英文摘要：
In recent years, with the development of big data technology, increasingly more companies use HDFS for data processing and storage. As a result, the maintenance of distributed file systems has become an extremely important part of data management. As the function of server systems is becoming increasingly diversified and their services are becoming complex, the logs, recording real-time events make it easier for system operators to locate the failures and errors that happened in the server systems to make server always available. HDFS, a distributed file system, which contains large data sets, will record a large number of logs. Moreover, the logs are not always structured data, they are not stable as well. However, to detect the problems that occur in the system by checking one log by one log, it's complicated and boring work for the system operators. Using machine learning techniques and natural language processing techniques to detect the HDFS block anomaly will help the system operators to locate and fix the anomaly rapidly and accurately. This paper proposes a streaming HDFS log block anomaly workflow. It helps maintenance practitioners to use parallel computing network in processing historical log, and construct LLM-BiLSTM hybrid deep learning model to detect anomaly block in HDFS, then build streaming log pipeline based on Kafka to give one real-time HDFS log block anomaly detection solution.
