README.md

---
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}
}
```