Refactor scheduler lock implementation with heartbeat mechanism

- Add heartbeat-based lock renewal in scheduler_lock_heartbeat.py
- Update scheduler_lock.py with improved lock management
- Add comprehensive tests for scheduler lock functionality
- Update deployment and task documentation

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Docs/Deployment.md

@@ -18,7 +18,65 @@ If fail2ban goes offline but the backend always returns 200, Docker treats the c
 ---
 
 
-## Resource Allocation
+## Scheduler Lock
+
+In multi-instance deployments (e.g., Kubernetes, Docker Swarm), the scheduler lock prevents duplicate execution of background tasks by ensuring only one instance runs the scheduler at a time.
+
+### How It Works
+
+The lock is stored in the SQLite database and enforced via:
+
+1. **Lock Acquisition** — At startup, each instance tries to insert a lock record. Only one succeeds; others reject startup with a clear error message.
+2. **Heartbeat** — The lock-holding instance sends a heartbeat every 5 seconds to prove it's still alive.
+3. **Stale Lock Cleanup** — On startup, any lock older than 60 seconds (without a heartbeat) is automatically deleted, allowing recovery from instance crashes.
+
+### Configuration
+
+| Parameter | Value | Rationale |
+|-----------|-------|-----------|
+| **Heartbeat Interval** | 5 seconds | Allows ~12 missed heartbeats before lock expires |
+| **Lock TTL** | 60 seconds | Time before a lock without heartbeat is considered abandoned |
+| **Min Safe Ratio** | 12x (TTL / interval) | Robust protection against temporary delays or high load |
+
+With a 60-second TTL and 5-second heartbeat interval, the lock survives even if the instance becomes unresponsive for up to ~55 seconds. This provides strong protection against false positives while still detecting genuine crashes.
+
+### Monitoring
+
+Check logs for these key events:
+
+- `scheduler_lock_acquired` — Lock successfully acquired at startup (INFO)
+- `scheduler_lock_heartbeat_updated` — Heartbeat successfully updated (DEBUG)
+- `scheduler_lock_heartbeat_failed` — Heartbeat update failed; lock may be lost (WARNING)
+- `scheduler_lock_heartbeat_timeout` — Heartbeat exceeded 5-second timeout (ERROR)
+- `scheduler_lock_held_by_other_instance` — Another instance holds the lock (WARNING at startup)
+
+### Troubleshooting: "Blocklist import runs twice"
+
+**Symptom:** Blocklist import task executes simultaneously in two instances, causing duplicate entries or data corruption.
+
+**Cause:** The scheduler lock was released prematurely (e.g., instance crash, database timeout) while a task was still running.
+
+**Solution:**
+
+1. **Check heartbeat timing** — Ensure the instance isn't hanging for >60 seconds (monitor CPU/memory/disk).
+2. **Verify database health** — Run `SELECT * FROM scheduler_lock;` to see if a stale lock exists. If present, delete it: `DELETE FROM scheduler_lock;`
+3. **Review logs** — Look for `scheduler_lock_heartbeat_failed` or `scheduler_lock_heartbeat_timeout` errors in the time window when duplication occurred.
+4. **Increase resource limits** — If the backend is memory/CPU constrained, increase limits in `docker-compose.yml` to prevent slowdowns that trigger false lock timeouts.
+5. **Check database performance** — Slow database queries can delay heartbeat updates. Run `PRAGMA integrity_check;` to check for corruption.
+
+If duplication occurs frequently, consider migrating to Redis-backed locking (see Advanced section below) for higher reliability.
+
+### Advanced: Migrating to Redis
+
+For very high-traffic deployments with strict data consistency requirements, you can replace the SQLite-backed lock with Redis:
+
+- **Why:** Redis is single-threaded and atomic by design; clock skew and timeout issues are eliminated.
+- **How:** Install `redlock-py` or `aioredis`, replace `scheduler_lock.py` with a Redis implementation, update heartbeat interval to 2-3 seconds.
+- **Trade-off:** Adds a Redis dependency but eliminates database lock contention and provides microsecond-precision atomicity.
+
+This is not required for typical deployments but is recommended if you see frequent scheduler conflicts in logs.
+
+---
 
 All containers have hard limits (max usage) and soft reservations (guaranteed allocation). This ensures:
 - **Isolation**: A misbehaving container cannot crash others or the host

Docs/Tasks.md

@@ -1,50 +1,3 @@
-## [IMPORTANT] Database transactions lack explicit isolation
-
-**Where found**
-
-- `backend/app/repositories/session_repo.py:40-60` — multiple queries without `BEGIN TRANSACTION`
-- Similar pattern in multi-step operations across repositories
-
-**Why this is needed**
-
-Without explicit boundaries, concurrent requests can race: Thread A checks if exists → not found, Thread B checks same → not found, Thread A inserts → succeeds, Thread B inserts → duplicate error or silent overwrite.
-
-**Goal**
-
-Wrap all multi-step operations in explicit transactions with appropriate isolation level.
-
-**What to do**
-
-1. Use explicit `BEGIN IMMEDIATE` transaction:
-   ```python
-   await db.execute("BEGIN IMMEDIATE")
-   try:
-       await db.execute("INSERT INTO sessions ...")
-       await db.commit()
-   except Exception:
-       await db.rollback()
-       raise
-   ```
-
-2. Use `IMMEDIATE` mode to lock immediately for writes
-3. Document transaction boundaries clearly
-
-**Possible traps and issues**
-
-- Nested transactions (SAVEPOINTs) may be needed
-- Locks held too long cause contention
-- Deadlocks possible with concurrent writers
-
-**Docs changes needed**
-
-- Add section in `Docs/Backend-Development.md` § Database Transactions
-
-**Doc references**
-
-- `Docs/Backend-Development.md` (database design)
-
----
-
 ## [IMPORTANT] Scheduler lock race condition
 
 **Where found**