# Remote H2 Database Query via SSH When an MCP server needs to read from an H2 database on a remote host (e.g., Traccar on app2), use this pattern: ## Architecture ``` MCP tool call → SSH into remote host (key-based, no password prompt) → scp the .mv.db file to a local temp file → Java H2 Shell query (org.h2.tools.Shell) → Parse pipe-delimited output → Delete temp file → Return structured JSON ``` ## Why this pattern - **H2 databases lock.** You cannot query a live H2 database from two processes simultaneously. Copying with `scp` creates a read-only snapshot — the copy succeeds even while the DB is in use, and you query the copy. - **Java is needed.** H2's file format is proprietary. Python libraries like `jaydebeapi` exist but are brittle; the H2 Shell jar is self-contained and reliable. - **30-second cache.** The SCP + Java query takes 3-5 seconds. Caching avoids this overhead on repeated calls. ## Prerequisites On the local host: ```bash apt-get install -y default-jre-headless # Copy H2 jar from the remote host or the container scp root@remote:/tmp/h2.jar /tmp/h2.jar # Test: java -cp /tmp/h2.jar org.h2.tools.Shell --help ``` On the remote host (the H2 jar must exist): ```bash # If inside a Docker container: docker cp container_name:/opt/app/lib/h2-*.jar /tmp/h2.jar ``` ## Implementation Template ```python import subprocess, tempfile, os, time HOST = "152.53.xxx.xxx" DB_PATH = "/root/docker/app/data/database" CACHE_TTL = 30 _cache = {"data": None, "ts": 0} def query_devices(): if _cache["data"] and (time.time() - _cache["ts"]) < CACHE_TTL: return _cache["data"] tmp_db = tempfile.mktemp(suffix=".mv.db") # 1. Copy DB snapshot from remote subprocess.run( ["scp", "-i", "/root/.ssh/itpp-infra", "-o", "StrictHostKeyChecking=no", f"root@{HOST}:{DB_PATH}.mv.db", tmp_db], capture_output=True, timeout=15, check=True ) # 2. Query with Java H2 Shell base = tmp_db.replace(".mv.db", "") result = subprocess.run( ["java", "-cp", "/tmp/h2.jar", "org.h2.tools.Shell", "-url", f"jdbc:h2:{base}", "-user", "sa", "-password", "", "-sql", "SELECT id, name, latitude, longitude FROM tc_devices;"], capture_output=True, text=True, timeout=20 ) os.unlink(tmp_db) # 3. Parse pipe-delimited output (H2 Shell format) # Output: "ID | NAME | LATITUDE | LONGITUDE\n1 | foo | 10.4 | -75.5" rows = [] for line in result.stdout.strip().split('\n'): parts = [p.strip() for p in line.split('|')] if len(parts) >= 4 and parts[0].strip().isdigit(): rows.append({ "id": int(parts[0]), "name": parts[1] if parts[1] != "null" else None, }) data = {"rows": rows, "count": len(rows)} _cache["data"] = data _cache["ts"] = time.time() return data ``` ## Traccar Schema Reference (GPS Tracking) These are the live table schemas used by Traccar 6.x. All queries go through the H2 database at `/root/docker/traccar/traccar-data/database.mv.db` on app2 (152.53.39.202). ### tc_devices | Column | Type | Notes | |---|---|---| | id | INT | Primary key | | name | VARCHAR | User-assigned device name | | uniqueid | VARCHAR | Device identifier (IMEI, serial) | | lastupdate | TIMESTAMP | Last position received | | status | VARCHAR | "online", "offline", or "unknown" | ### tc_positions | Column | Type | Notes | |---|---|---| | id | BIGINT | Auto-increment | | deviceid | INT | FK → tc_devices.id | | latitude | DOUBLE | Decimal degrees | | longitude | DOUBLE | Decimal degrees | | speed | DOUBLE | Knots (× 1.15078 for mph) | | devicetime | TIMESTAMP | Device-reported time | ### Common Queries ```sql -- All devices with latest position (one query) SELECT d.id, d.name, d.uniqueid, d.lastupdate, d.status, p.latitude, p.longitude, p.speed, p.devicetime FROM tc_devices d LEFT JOIN tc_positions p ON p.id = ( SELECT MAX(id) FROM tc_positions WHERE deviceid = d.id ) ORDER BY d.lastupdate DESC; -- Position history for one device SELECT latitude, longitude, speed, devicetime FROM tc_positions WHERE deviceid = 1 ORDER BY devicetime ASC; -- Stats: distance (haversine), max speed, active time SELECT COUNT(*) as positions, MAX(speed) * 1.15078 as max_speed_mph FROM tc_positions WHERE deviceid = 1 AND devicetime >= NOW() - INTERVAL '24' HOUR; ``` ## FastAPI Proxy Integration When the MCP server is slow (3-5s per SCP+Java query), serve data through a FastAPI proxy with 30-second caching. The ops portal at `/opt/ops-portal/server.py` uses this pattern: ```python # /api/ft360/status endpoint proxies Traccar data @app.get("/api/ft360/status") async def traccar_status(): return _get_traccar_data() # cached 30s, SCP+Java underneath ``` The JavaScript dashboard fetches from `/api/ft360/status` every 30 seconds — same TTL as the cache. No authentication needed; the proxy handles the SSH and Java complexity. - **Java must be on PATH.** Install `default-jre-headless` before using. The `java` binary in Docker containers (e.g., `/opt/traccar/jre/bin/java`) isn't on PATH by default. - **H2 jar must be on the same host and compatible.** Copy it from the container or remote host — don't download a different version from Maven Central. - **H2 Shell output format.** The first line is column headers (`ID | NAME | ...`), subsequent lines are data. Filter with `.isdigit()` on the first column. - **SCP key must work.** The SSH key path must be absolute, and `StrictHostKeyChecking=no` avoids prompt-freezing on first connection. - **Temp file cleanup.** Always `os.unlink(tmp_db)` in a `finally` block or immediately after the query. A 176KB file won't cause issues if left, but it's sloppy. - **Cache invalidation.** If you stop+start the remote container (restarting the DB), the cache may return stale data until the TTL expires. For write operations, skip the cache.