Dataset Card for logfit-project/HDFS_v1

Dataset Summary

The HDFS v1 log dataset captures Hadoop Distributed File System (HDFS) console logs that were collected from a private cloud deployment while benchmark workloads were executed. Each log line can be associated with one or more block identifiers; block-level anomaly labels were generated by manually crafted rules. This script preserves the raw line structure while attaching a binary anomaly flag for downstream anomaly detection research.

Supported Tasks and Leaderboards

• anomaly-detection: binary classification of logs.

Dataset Structure

• date: Six-digit date stamp from the original console output (YYMMDD).
• time: Six-digit time stamp (HHMMSS).
• pid: Process identifier extracted from the log line.
• level: Log level (e.g., INFO).
• component: Java/daemon component emitting the log entry.
• content: Verbose message content for the event.
• block_id: Space-separated block identifiers discovered in the log line (empty if none present).
• anomaly: Binary indicator derived from block-level labels (1 = anomalous block).

Source Data

• Homepage: https://github.com/logpai/loghub/tree/master/HDFS#hdfs_v1
• Original Maintainers: The LogPAI team (https://logpai.com/).

Dataset Creation

The raw logs were parsed in a streaming fashion using a deterministic regular expression so that large-scale HDFS deployments can be transformed without exhausting memory. Block-level labels are joined on the fly by searching for block identifiers in each line.

Uses

Suitable for supervised and semi-supervised anomaly detection across distributed system logs, log template mining, and benchmarking log representation learning.

Citation

• Wei Xu, Ling Huang, Armando Fox, David Patterson, Michael Jordan. "Detecting Large-Scale System Problems by Mining Console Logs", SOSP 2009.
• Jieming Zhu, Shilin He, Pinjia He, Jinyang Liu, Michael R. Lyu. "Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics", ISSRE 2023.

Dataset Statistics

• Number of log lines: 11175629