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River Auto-Profiler

Anomaly detection that actually works out of the box starts with knowing what normal looks like for each series in your data. The River Auto-Profiler does that automatically, running inside InfluxDB 3 as a Processing Engine plugin, classifying every incoming series as stable, noisy, trending, seasonal, or bursty, and calibrating per-series thresholds as data arrives. Pair it with the River Anomaly Detector and the pipeline self-tunes from first write

Configuration

Arguments can be passed inline (space-separated lists) or via TOML config file (native TOML lists). Parameter names are the same in both modes.

Argument Required Default Description
include_fields No ”” Fields to profile (space-separated). If set, only these numeric fields are processed
exclude_fields No ”” Fields to exclude from profiling (space-separated)
exclude_tables No ”” Tables to skip when using all_tables trigger (space-separated)
string_fields No ”” String columns that are fields, not tags (space-separated). All other strings become tags
max_series No 1000 Max unique series to track (LRU eviction)
initial_fading_factor No 0.3 Initial fading factor for EW stats. Auto-adapted based on write frequency after calibration
seasonal_period No “hourly” Seasonal period for variance buckets: "hourly" (24 buckets) or "weekly" (168 buckets)
profile_write_interval No 50 Write profile every N observations per series
min_observations No 50 Observations needed before profile is considered mature
log_profiles No false Log profile updates to server log
checkpoint_interval_seconds No 1800 Seconds between model checkpoints to the database
max_checkpoint_age_hours No 24 Ignore checkpoints older than this when restoring
min_checkpoint_observations No 10 Skip checkpointing profiles with fewer observations than this (avoids persisting cold-start state)
config_file_path No Path to TOML config file. Supports absolute paths or relative paths (resolved via PLUGIN_DIR)

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