| | --- |
| | 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} |
| | } |
| | ``` |
| | |
