Datasets:
File size: 2,314 Bytes
e4dc76a da85a01 fe8d2ee 8f07e7b 4661cad da85a01 e4dc76a fe8d2ee fc8a7c5 fe8d2ee 8b4928e e9b1e5a fe8d2ee 8b4928e fe8d2ee da85a01 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
---
pretty_name: HDFS_v1
dataset_name: logfit-project/HDFS_v1
task_categories:
- text-classification
language:
- en
size_categories:
- 10M<n<20M
annotations_creators:
- logfit-project
license: other
---
# 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
|