microdao-daarion/services/matrix-bridge-dagi/app/event_store.py

"""
event_store — M2.3: Persistent event deduplication via SQLite.

Stores processed Matrix event_ids so that bridge restarts do not reprocess
events still returned by /sync (within TTL window).

Schema:
  processed_events (room_id, event_id, ts, sender_hash)
    PK: (room_id, event_id)
    IDX: idx_processed_events_ts (ts)

Design notes:
  - Uses aiosqlite for non-blocking async access from the ingress event loop.
  - Prune is best-effort: failures are logged but do NOT abort processing.
  - If the DB is unavailable (init error, corruption), EventStore degrades to
    a no-op: is_processed() returns False, mark_processed() is a no-op.
    The in-memory LRU dedupe (H1) continues to protect within a single run.
  - WAL mode is enabled for better concurrent read performance.
"""
from __future__ import annotations

import logging
import time
from pathlib import Path
from typing import Optional, Tuple

try:
    import aiosqlite
    _AIOSQLITE_OK = True
except ImportError:  # pragma: no cover
    aiosqlite = None  # type: ignore
    _AIOSQLITE_OK = False

logger = logging.getLogger(__name__)

_SCHEMA = """
CREATE TABLE IF NOT EXISTS processed_events (
    room_id     TEXT NOT NULL,
    event_id    TEXT NOT NULL,
    ts          INTEGER NOT NULL,
    sender_hash TEXT,
    PRIMARY KEY (room_id, event_id)
);
CREATE INDEX IF NOT EXISTS idx_processed_events_ts ON processed_events (ts);
"""


class EventStore:
    """
    Async SQLite-backed deduplication store for Matrix event_ids.

    Usage:
        store = EventStore("/app/data/matrix_bridge.db", ttl_h=48)
        await store.open()
        ...
        hit = await store.is_processed(room_id, event_id)
        if not hit:
            await store.mark_processed(room_id, event_id, sender_hash)
        ...
        pruned = await store.prune(batch=5000)
        await store.close()
    """

    def __init__(
        self,
        db_path: str,
        ttl_h: int = 48,
        prune_batch: int = 5000,
    ) -> None:
        self.db_path = db_path
        self.ttl_h = ttl_h
        self.prune_batch = prune_batch
        self._db: Optional["aiosqlite.Connection"] = None
        self._ok: bool = False
        self._last_prune_at: Optional[float] = None
        self._pruned_rows_last: int = 0

    # ── Lifecycle ─────────────────────────────────────────────────────────────

    async def open(self) -> bool:
        """
        Open the SQLite connection and apply schema.

        Returns True on success; False on failure (degraded mode).
        """
        if not _AIOSQLITE_OK:
            logger.warning("aiosqlite not available — persistent dedupe disabled")
            return False
        try:
            Path(self.db_path).parent.mkdir(parents=True, exist_ok=True)
            self._db = await aiosqlite.connect(self.db_path)
            # WAL mode: better concurrent read, non-blocking writes
            await self._db.execute("PRAGMA journal_mode=WAL")
            await self._db.execute("PRAGMA synchronous=NORMAL")
            await self._db.executescript(_SCHEMA)
            await self._db.commit()
            self._ok = True
            logger.info("EventStore opened: %s (ttl_h=%d)", self.db_path, self.ttl_h)
            return True
        except Exception as exc:
            logger.error("EventStore.open failed — degraded: %s", exc)
            self._ok = False
            return False

    async def close(self) -> None:
        """Close the SQLite connection gracefully."""
        if self._db is not None:
            try:
                await self._db.close()
            except Exception as exc:  # pragma: no cover
                logger.warning("EventStore.close error: %s", exc)
            self._db = None
        self._ok = False

    # ── Core operations ───────────────────────────────────────────────────────

    async def is_processed(self, room_id: str, event_id: str) -> bool:
        """
        Return True if (room_id, event_id) has already been processed.

        Safe to call even when degraded (returns False → no false deduplication).
        """
        if not self._ok or self._db is None:
            return False
        try:
            async with self._db.execute(
                "SELECT 1 FROM processed_events WHERE room_id=? AND event_id=? LIMIT 1",
                (room_id, event_id),
            ) as cursor:
                row = await cursor.fetchone()
            return row is not None
        except Exception as exc:
            logger.warning("EventStore.is_processed error (degraded): %s", exc)
            return False

    async def mark_processed(
        self,
        room_id: str,
        event_id: str,
        sender_hash: str = "",
    ) -> bool:
        """
        Insert (room_id, event_id) as processed.

        Returns True on success, False if already exists or on error.
        Uses INSERT OR IGNORE to avoid duplicates without raising.
        """
        if not self._ok or self._db is None:
            return False
        ts = int(time.time())
        try:
            await self._db.execute(
                "INSERT OR IGNORE INTO processed_events (room_id, event_id, ts, sender_hash) "
                "VALUES (?, ?, ?, ?)",
                (room_id, event_id, ts, sender_hash or None),
            )
            await self._db.commit()
            return True
        except Exception as exc:
            logger.warning("EventStore.mark_processed error (degraded): %s", exc)
            return False

    # ── Prune ─────────────────────────────────────────────────────────────────

    async def prune(self, batch: Optional[int] = None) -> int:
        """
        Delete events older than ttl_h.

        Returns the number of rows deleted (0 on error or degraded).
        Uses LIMIT batch to avoid long locks on large tables.
        """
        if not self._ok or self._db is None:
            return 0

        cutoff = int(time.time()) - self.ttl_h * 3600
        effective_batch = batch or self.prune_batch
        deleted = 0

        try:
            # SQLite DELETE with LIMIT requires compiling with SQLITE_ENABLE_UPDATE_DELETE_LIMIT,
            # which may not be available. Use a subquery approach instead.
            await self._db.execute(
                "DELETE FROM processed_events "
                "WHERE rowid IN ("
                "  SELECT rowid FROM processed_events WHERE ts < ? LIMIT ?"
                ")",
                (cutoff, effective_batch),
            )
            await self._db.commit()
            # Estimate rows deleted from changes()
            async with self._db.execute("SELECT changes()") as cursor:
                row = await cursor.fetchone()
            deleted = row[0] if row else 0
            self._last_prune_at = time.time()
            self._pruned_rows_last = deleted
            if deleted:
                logger.info("EventStore pruned %d rows (cutoff=%d)", deleted, cutoff)
        except Exception as exc:
            logger.warning("EventStore.prune error: %s", exc)

        return deleted

    # ── Health / introspection ─────────────────────────────────────────────────

    def as_health_dict(self) -> dict:
        return {
            "enabled": self._ok,
            "db_path": self.db_path,
            "ttl_h": self.ttl_h,
            "ok": self._ok,
            "last_prune_at": self._last_prune_at,
            "pruned_rows_last": self._pruned_rows_last,
        }