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logfit-project
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Thunderbird

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Text Classification
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metadata
pretty_name: Thunderbird
dataset_name: logfit-project/Thunderbird
task_categories:
  - text-classification
language:
  - en
size_categories:
  - 100M<n<200M
annotations_creators:
  - logfit-project
license: other

Dataset Card for logfit-project/Thunderbird

Dataset Summary

The Thunderbird dataset captures console logs from the Thunderbird supercomputer deployed at Sandia National Laboratories. Entries include alert and non-alert events flagged via category tags, enabling research into alert detection, failure prediction, and broader log analytics workflows for large-scale clusters.

Supported Tasks and Leaderboards

  • anomaly-detection: distinguish alert-tagged events from routine operational logs.
  • log-parsing: template discovery and sequence modeling for large-scale HPC environments.

Dataset Structure

  • label: Alert category tag (- denotes non-alert informational entries).
  • timestamp: Unix epoch timestamp for the log event.
  • date: Calendar date formatted as YYYY.MM.DD.
  • user: Host account name associated with the emitted log.
  • month: Short month string extracted from the raw message.
  • day: Day-of-month integer from the message header.
  • time: Local time-of-day string (HH:MM:SS).
  • location: Combined source identifier conveying host and logger context.
  • component: Process or subsystem identifier preceding the message body.
  • pid: Numeric process identifier when provided (set to -1 if absent).
  • content: Human-readable message body for the event.
  • anomaly: Binary indicator (1 for alert labels, 0 when label is -).

Source Data

Dataset Creation

Raw Thunderbird logs are streamed line-by-line using a deterministic parser consistent with the published structured sample. PIDs are extracted from bracketed segments when present, while messages lacking PIDs are assigned -1. Parsing avoids loading the full source file into memory so the complete corpus can be processed efficiently.

Uses

Applicable to supervised alert detection, incident trend analysis, failure prediction, and validation of log parsing algorithms for large distributed systems.

Citation

  • Adam J. Oliner, Jon Stearley. "What Supercomputers Say: A Study of Five System Logs", DSN 2007.
  • Jieming Zhu, Shilin He, Pinjia He, Jinyang Liu, Michael R. Lyu. "Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics", ISSRE 2023.

Dataset Statistics

  • Number of log lines: 211212192