Datasets:
File size: 3,996 Bytes
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license: other
task_categories:
- text-classification
tags:
- anomaly-detection
- log-analysis
- hdfs
pretty_name: HDFS v1 Block-Level Logs
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: block_id
dtype: large_string
- name: text
dtype: large_string
- name: label
dtype: int8
splits:
- name: train
num_bytes: 1106771219
num_examples: 460048
- name: dev
num_bytes: 138246934
num_examples: 57506
- name: test
num_bytes: 138610465
num_examples: 57507
download_size: 211196536
dataset_size: 1383628618
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
# HDFS v1 Block-Level Dataset
**Resources:**
- [Video Explainer](https://youtu.be/k-94oCJ_WJo)
- [GitHub Repo](https://github.com/ShawhinT/rlvr-hdfs-classification)
- [Dataset](https://huggingface.co/datasets/shawhin/HDFS_v1_blocks)
## Dataset Summary
This dataset is a **block-level transformation** of the [HDFS_v1](https://huggingface.co/datasets/logfit-project/HDFS_v1) log dataset. While the original dataset contains individual log lines (~11M rows), this version aggregates all log entries belonging to the same block into a single text sequence, making it suitable for LLM-based anomaly classification.
Each row represents a unique HDFS block with its complete log history concatenated in chronological order.
## Supported Tasks
- **anomaly-detection**: Binary text classification to predict whether a block experienced an anomaly based on its log sequence.
## Dataset Structure
### Data Splits
| Split | Examples | Normal | Anomaly |
|-------|----------|--------|---------|
| train | 460,048 | 446,578 | 13,470 |
| dev | 57,506 | 55,822 | 1,684 |
| test | 57,507 | 55,823 | 1,684 |
### Data Fields
| Field | Type | Description |
|-------|------|-------------|
| `block_id` | string | Unique HDFS block identifier (e.g., `blk_-1608999687919862906`) |
| `text` | string | Concatenated log entries for the block, newline-separated |
| `label` | int | Binary anomaly label (1 = anomalous, 0 = normal) |
### Text Format
Each log line within `text` follows the format:
```
<LEVEL> <COMPONENT>: <CONTENT>
```
Example:
```
INFO dfs.DataNode$DataXceiver: Receiving block blk_-1608999687919862906 src: /10.251.73.220:42557 dest: /10.251.73.220:50010
INFO dfs.DataNode$DataXceiver: Receiving block blk_-1608999687919862906 src: /10.251.73.220:55213 dest: /10.251.73.220:50010
INFO dfs.FSNamesystem: BLOCK* NameSystem.allocateBlock: /mnt/hadoop/mapred/system/job_200811092030_0001/job.jar. blk_-1608999687919862906
INFO dfs.DataNode$PacketResponder: PacketResponder 1 for block blk_-1608999687919862906 terminating
INFO dfs.DataNode$PacketResponder: Received block blk_-1608999687919862906 of size 67108864 from /10.251.73.220
```
## Source Data
- **Original Dataset**: [logfit-project/HDFS_v1](https://huggingface.co/datasets/logfit-project/HDFS_v1)
- **Original Source**: [LogPAI/loghub](https://github.com/logpai/loghub/tree/master/HDFS#hdfs_v1)
## Dataset Creation
1. Loaded the original line-level HDFS_v1 dataset
2. Formatted each log line as `<LEVEL> <COMPONENT>: <CONTENT>`
3. Grouped by `block_id` and concatenated log entries (ordered by line number)
4. Aggregated anomaly labels (max per block)
5. Created stratified 80/10/10 train/dev/test splits preserving class distribution
## Citation
```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},
booktitle={SOSP 2009}
}
@inproceedings{zhu2023loghub,
title={Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics},
author={Zhu, Jieming and He, Shilin and He, Pinjia and Liu, Jinyang and Lyu, Michael R.},
booktitle={ISSRE 2023}
}
```
## License
See original dataset license.
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