{
  "slug": "boom-2025",
  "title": "BOOM: Benchmark of Observability Metrics",
  "canonical_source_url": "https://huggingface.co/datasets/Datadog/BOOM",
  "official_code_url": "https://github.com/DataDog/toto/tree/main/boom",
  "official_leaderboard_url": "https://huggingface.co/spaces/Datadog/BOOM",
  "introducing_source": "wiki/sources/toto-2025.md",
  "dataset_type": "observability metrics forecasting benchmark",
  "temporal_structure": "metric-query time series from Datadog internal pre-production monitoring, with each query represented as a univariate or multivariate time series of up to 100 variates",
  "actions_or_interventions": "none; passive forecasting benchmark without deployments, rollbacks, autoscaling, remediation, or other operator-action channels",
  "suitability_note": "important benchmark for high-cardinality observability forecasting and hundreds-of-variates HDTSF; not sufficient by itself for action-conditioned operations world models",
  "license_note": "Hugging Face dataset page lists Apache-2.0; Datadog states the data was generated from internal monitoring of pre-production environments and excludes customer data",
  "reported_scale": {
    "points": 350000000,
    "metric_queries": 2807,
    "variates": 32887,
    "max_variates_per_entry": 100
  },
  "taxonomy": {
    "metric_types": ["gauge", "rate", "distribution", "count"],
    "domains": ["application usage", "infrastructure", "database", "networking", "security"]
  },
  "subset_notes": {
    "boomlet": "smaller representative subset from the Toto paper: 32 metric queries, 1,627 variates, and about 23 million observation points"
  },
  "created": "2026-05-15",
  "updated": "2026-05-15"
}