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Datasets:
shawhin
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HDFS_v1_blocks

Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Size:
100K - 1M
Tags:
anomaly-detection
log-analysis
hdfs
Libraries:
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Dask
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HDFS_v1_blocks / README.md
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metadata 
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:

Dataset Summary

This dataset is a block-level transformation of the 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

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 

@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.