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---
language: en
tags:
- log-analysis
- hdfs
- anomaly-detection
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: event_encoded
dtype: string
- name: tokenized_block
sequence: int64
- name: block_id
dtype: string
- name: label
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1159074302
num_examples: 460048
- name: validation
num_bytes: 145089712
num_examples: 57506
- name: test
num_bytes: 144844752
num_examples: 57507
download_size: 173888975
dataset_size: 1449008766
---
# HDFS Logs Train/Val/Test Splits
This dataset contains preprocessed HDFS log sequences split into train, validation, and test sets for anomaly detection tasks.
## Dataset Description
The dataset is derived from the HDFS log dataset, which contains system logs from a Hadoop Distributed File System (HDFS).
Each sequence represents a block of log messages, labeled as either normal or anomalous. The dataset has been preprocessed
using the Drain algorithm to extract structured fields and identify event types.
### Data Fields
- `block_id`: Unique identifier for each HDFS block, used to group log messages into blocks
- `event_encoded`: The preprocessed log sequence with event IDs and parameters
- `tokenized_block`: The tokenized log sequence, used for training
- `label`: Classification label ('Normal' or 'Anomaly')
### Data Splits
- Training set: 460,049 sequences (80%)
- Validation set: 57,506 sequences (10%)
- Test set: 57,506 sequences (10%)
The splits are stratified by the Label field to maintain class distribution across splits.
## Source Data
Original data source: https://zenodo.org/records/8196385/files/HDFS_v1.zip?download=1
## Preprocessing
We preprocess the logs using the Drain algorithm to extract structured fields and identify event types.
We then encode the logs using a pretrained tokenizer and add special tokens to separate event types. This
dataset should be immediately usable for training and testing models for log-based anomaly detection.
## Intended Uses
This dataset is designed for:
- Training log anomaly detection models
- Evaluating log sequence prediction models
- Benchmarking different approaches to log-based anomaly detection
see [honicky/pythia-14m-hdfs-logs](https://huggingface.co/honicky/pythia-14m-hdfs-logs) for an example model.
## Citation
If you use this dataset, please cite the original HDFS paper:
```bibtex
@inproceedings{xu2009detecting,
title={Detecting large-scale system problems by mining console logs},
author={Xu, Wei and Huang, Ling and Fox, Armando and Patterson, David and Jordan, Michael I},
booktitle={Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles},
pages={117--132},
year={2009}
}
```
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