# logpai/loghub

A large collection of system log datasets for AI-driven log analytics [ISSRE'23]

Repository: https://github.com/logpai/loghub
Canonical: https://ross.abutalabs.com/products/loghub
License: NOASSERTION
License Family: other
Topics: logs, unstructured-logs, log-analysis, log-parsing, datasets, anomaly-detection, log-intelligence
Last push: 2026-08-26T04:38:05+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 3738, "days_push": 7, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2807, forks 790 (observed 2026-08-28T04:07:22.821608+00:00)

## What it is
Loghub is a curated collection of raw system log datasets (HDFS, Hadoop, Spark, OpenStack, and more) for AI-driven log analytics research. It provides labeled and unlabeled logs of varying sizes, freely available for academic and research use.

## Use cases
- benchmark log parsing algorithms on real system logs
- train anomaly detection models on labeled log data
- evaluate log-based deep learning models
- research log template extraction and structured logging
- build datasets for log intelligence and AIOps experiments

## When to choose
- you need real, unsanitized system logs for log analytics research
- you want labeled datasets for anomaly detection benchmarking
- you need diverse log sources across distributed systems and supercomputers

## When to avoid
- you need production-ready log management or monitoring tooling
- you require sanitized or privacy-compliant data for commercial use
- you need small toy datasets for quick tutorials

## Facets
- artifact type: dataset
- maturity: active
- function: machine-learning, nlp, analytics, data-science
- domain: machine-learning, data-science, big-data, analytics, security
- platform: cross-platform
- tags: log-datasets, log-parsing, anomaly-detection, log-analytics, research-datasets, system-logs

## Member repositories
- logpai/loghub (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:22.821608+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:14:27.596313+00:00, confidence not recorded.
  - readme: https://github.com/logpai/loghub (fetched 2026-08-28T04:07:22.821608+00:00, sha dbe378c236e7)
- Data as of 2026-08-30T08:39:29.467469+00:00.