76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
"""SQLite-backed tracking of active + historical streams.
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Backed by a file on the shared `appdata` PV (mounted at STATE_DB_PATH), so
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state survives pod restarts/redeploys. "Active" streams are just history
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rows with end IS NULL — no separate table needed, which also gives free
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crash recovery (if the pod dies mid-stream, the row is simply still open).
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"""
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import os
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import sqlite3
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import threading
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from datetime import datetime, timezone
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def _now_iso():
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return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
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SCHEMA = """
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CREATE TABLE IF NOT EXISTS streams (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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path TEXT NOT NULL,
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source_type TEXT,
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start TEXT NOT NULL,
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end TEXT
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);
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CREATE INDEX IF NOT EXISTS idx_streams_active ON streams (path) WHERE end IS NULL;
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"""
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class StreamState:
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def __init__(self, db_path, history_limit=50):
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self._db_path = db_path
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self._history_limit = history_limit
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self._lock = threading.Lock()
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os.makedirs(os.path.dirname(db_path) or ".", exist_ok=True)
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with self._connect() as conn:
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conn.executescript(SCHEMA)
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def _connect(self):
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conn = sqlite3.connect(self._db_path)
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conn.row_factory = sqlite3.Row
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return conn
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def on_ready(self, path, source_type=None):
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with self._lock, self._connect() as conn:
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conn.execute(
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"INSERT INTO streams (path, source_type, start, end) VALUES (?, ?, ?, NULL)",
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(path, source_type, _now_iso()),
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)
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def on_not_ready(self, path):
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with self._lock, self._connect() as conn:
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row = conn.execute(
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"SELECT id FROM streams WHERE path = ? AND end IS NULL ORDER BY id DESC LIMIT 1",
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(path,),
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).fetchone()
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if row:
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conn.execute(
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"UPDATE streams SET end = ? WHERE id = ?",
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(_now_iso(), row["id"]),
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)
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def active(self):
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with self._connect() as conn:
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rows = conn.execute(
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"SELECT path, source_type, start FROM streams WHERE end IS NULL ORDER BY id DESC"
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).fetchall()
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return [dict(r) for r in rows]
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def history(self):
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with self._connect() as conn:
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rows = conn.execute(
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"SELECT path, source_type, start, end FROM streams ORDER BY id DESC LIMIT ?",
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(self._history_limit,),
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).fetchall()
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return [dict(r) for r in rows]
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