usage: continuous Ollama Cloud usage sampling + delta report
- scripts/ollama_usage_poll.py: per-minute cron sample into db/ollama_usage.sqlite, write-on-change; meta table records every poll so a data gap can be told apart from a failed or missed poll - scripts/ollama_usage_report.py: delta report keyed on per-model request_count (limits.*.usage has 0.1 % resolution, short-interval deltas are noise) - SKILL.md: Continuous sampling section
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nanobot
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skills/usage/scripts/ollama_usage_poll.py
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112
skills/usage/scripts/ollama_usage_poll.py
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#!/usr/bin/env python3
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"""Sample Ollama Cloud usage into db/ollama_usage.sqlite. Run from cron every minute.
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Writes a `samples` row only when something changed; `meta` records every poll so a
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gap in `samples` can be told apart from a poll that failed or never ran.
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"""
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from __future__ import annotations
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import json
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import sqlite3
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import sys
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import urllib.error
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import urllib.request
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from datetime import UTC, datetime
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from pathlib import Path
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from ollama_usage import API_URL, load_api_key
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WORKSPACE = Path(__file__).resolve().parents[3]
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DB_PATH = WORKSPACE / "db" / "ollama_usage.sqlite"
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SCHEMA = """
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CREATE TABLE IF NOT EXISTS samples (
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ts TEXT PRIMARY KEY,
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session_usage REAL NOT NULL,
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weekly_usage REAL NOT NULL,
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session_models TEXT NOT NULL,
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weekly_models TEXT NOT NULL
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);
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CREATE TABLE IF NOT EXISTS meta (
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id INTEGER PRIMARY KEY CHECK (id = 1),
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last_ts TEXT NOT NULL,
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last_status TEXT NOT NULL
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);
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"""
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def fetch_usage() -> dict:
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request = urllib.request.Request(API_URL, headers={
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"Authorization": f"Bearer {load_api_key()}",
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"Accept": "application/json",
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})
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with urllib.request.urlopen(request, timeout=15) as response:
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return json.load(response)
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def canonical_models(models: list[dict]) -> str:
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"""Stable JSON for change detection — the API returns an unordered list."""
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return json.dumps(
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{m["name"]: m["request_count"] for m in models},
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sort_keys=True,
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)
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def connect() -> sqlite3.Connection:
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DB_PATH.parent.mkdir(parents=True, exist_ok=True)
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conn = sqlite3.connect(DB_PATH)
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conn.executescript(SCHEMA)
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return conn
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def record_poll(conn: sqlite3.Connection, ts: str, status: str) -> None:
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conn.execute(
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"INSERT INTO meta (id, last_ts, last_status) VALUES (1, ?, ?) "
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"ON CONFLICT(id) DO UPDATE SET last_ts = excluded.last_ts, "
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"last_status = excluded.last_status",
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(ts, status),
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)
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conn.commit()
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def latest_sample(conn: sqlite3.Connection) -> tuple | None:
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row = conn.execute(
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"SELECT session_usage, weekly_usage, session_models, weekly_models "
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"FROM samples ORDER BY ts DESC LIMIT 1"
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).fetchone()
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return row
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def main() -> None:
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now = datetime.now(UTC).replace(microsecond=0).isoformat()
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conn = connect()
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try:
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data = fetch_usage()
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except urllib.error.HTTPError as e:
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record_poll(conn, now, f"http_{e.code}")
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print(f"{now} ollama.com API error: HTTP {e.code}", file=sys.stderr)
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return
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except urllib.error.URLError as e:
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record_poll(conn, now, "unreachable")
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print(f"{now} ollama.com unreachable: {e.reason}", file=sys.stderr)
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return
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limits = data.get("limits", {})
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session = limits.get("session", {})
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weekly = limits.get("weekly", {})
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sample = (
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session.get("usage", 0.0),
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weekly.get("usage", 0.0),
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canonical_models(session.get("models", [])),
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canonical_models(weekly.get("models", [])),
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)
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if sample != latest_sample(conn):
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conn.execute("INSERT INTO samples VALUES (?, ?, ?, ?, ?)", (now, *sample))
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record_poll(conn, now, "ok")
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if __name__ == "__main__":
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main()
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123
skills/usage/scripts/ollama_usage_report.py
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skills/usage/scripts/ollama_usage_report.py
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#!/usr/bin/env python3
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"""Delta report over db/ollama_usage.sqlite collected by ollama_usage_poll.py.
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Request counts are the exact axis; `limits.*.usage` has a resolution of 0.1 %,
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so per-sample percentage deltas are mostly quantization noise and are shown only
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as a running level, plus one aggregate for the whole window.
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"""
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from __future__ import annotations
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import argparse
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import itertools
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import json
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import sqlite3
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import sys
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from datetime import UTC, datetime, timedelta
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from pathlib import Path
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WORKSPACE = Path(__file__).resolve().parents[3]
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DB_PATH = WORKSPACE / "db" / "ollama_usage.sqlite"
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DEFAULT_WINDOW = timedelta(hours=24)
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GAP_MINUTES = 15
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--since", help="ISO 8601 UTC start (default: 24 h ago)")
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parser.add_argument("--until", help="ISO 8601 UTC end (default: now)")
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return parser.parse_args()
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def resolve_window(args: argparse.Namespace) -> tuple[str, str]:
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now = datetime.now(UTC).replace(microsecond=0)
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since = args.since or (now - DEFAULT_WINDOW).isoformat()
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until = args.until or now.isoformat()
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return since, until
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def load_samples(conn: sqlite3.Connection, since: str, until: str) -> list[tuple]:
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return conn.execute(
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"SELECT ts, session_usage, weekly_usage, session_models, weekly_models "
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"FROM samples WHERE ts >= ? AND ts <= ? ORDER BY ts",
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(since, until),
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).fetchall()
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def count_deltas(previous: dict[str, int], current: dict[str, int]) -> dict[str, int]:
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names = set(previous) | set(current)
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deltas = {n: current.get(n, 0) - previous.get(n, 0) for n in names}
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return {n: d for n, d in sorted(deltas.items()) if d}
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def format_deltas(deltas: dict[str, int]) -> str:
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return ", ".join(f"{name} +{count}" for name, count in deltas.items()) or "-"
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def minutes_between(earlier: str, later: str) -> float:
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delta = datetime.fromisoformat(later) - datetime.fromisoformat(earlier)
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return delta.total_seconds() / 60
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def is_window_reset(previous: tuple, current: tuple) -> bool:
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"""Session window rolled over: its usage or its request total went down."""
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previous_total = sum(json.loads(previous[3]).values())
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current_total = sum(json.loads(current[3]).values())
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return current[1] < previous[1] or current_total < previous_total
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def print_rows(samples: list[tuple]) -> dict[str, int]:
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total: dict[str, int] = {}
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for previous, current in itertools.pairwise(samples):
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gap = minutes_between(previous[0], current[0])
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if gap > GAP_MINUTES:
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print(f" … {gap:.0f} min with no recorded change (idle or poller down)")
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if is_window_reset(previous, current):
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print(" ── session window reset ──")
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deltas = count_deltas(json.loads(previous[4]), json.loads(current[4]))
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for name, count in deltas.items():
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total[name] = total.get(name, 0) + count
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print(
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f"{current[0]} session {current[1] * 100:5.1f} % "
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f"weekly {current[2] * 100:5.1f} % {format_deltas(deltas)}"
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)
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return total
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def print_summary(samples: list[tuple], total: dict[str, int]) -> None:
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first, last = samples[0], samples[-1]
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print()
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print(f"Window: {first[0]} → {last[0]} ({len(samples)} samples)")
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print(f"Weekly usage: {first[2] * 100:.1f} % → {last[2] * 100:.1f} %")
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print(f"Requests: {format_deltas(total)}")
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def print_poll_status(conn: sqlite3.Connection) -> None:
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row = conn.execute("SELECT last_ts, last_status FROM meta WHERE id = 1").fetchone()
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if row:
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print(f"Last poll: {row[0]} ({row[1]})")
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def main() -> None:
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if not DB_PATH.is_file():
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sys.exit(f"no samples yet: {DB_PATH} does not exist")
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args = parse_args()
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since, until = resolve_window(args)
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conn = sqlite3.connect(DB_PATH)
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samples = load_samples(conn, since, until)
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if len(samples) < 2:
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print(f"Not enough samples between {since} and {until}.")
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print_poll_status(conn)
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return
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total = print_rows(samples)
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print_summary(samples, total)
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print_poll_status(conn)
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if __name__ == "__main__":
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main()
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