--- 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} } ```