# Test Infrastructure: MS SQL 2022 Database

## Overview

The test database uses Microsoft SQL Server 2022 Developer Edition running in Docker. It provides two empty databases for ScadaLink — schema creation is handled by EF Core migrations at application startup (dev mode).

## Image & Ports

- **Image**: `mcr.microsoft.com/mssql/server:2022-latest`
- **Port**: 1433
- **Edition**: Developer (free, full-featured)

## Credentials

| Account | Username | Password | Purpose |
|---------|----------|----------|---------|
| SA | `sa` | `ScadaLink_Dev1#` | Server admin (setup only) |
| App | `scadalink_app` | `ScadaLink_Dev1#` | Application login (db_owner on both databases) |

## Databases

| Database | Purpose |
|----------|---------|
| `ScadaLinkConfig` | Configuration Database component — templates, deployments, users, audit log |
| `ScadaLinkMachineData` | Machine/operational data storage |

Both databases are created by `infra/mssql/setup.sql`. EF Core migrations populate the `ScadaLinkConfig` schema. The `ScadaLinkMachineData` database is seeded with sample tables and stored procedures by `infra/mssql/machinedata_seed.sql`.

## Data Persistence

SQL data is stored in the named Docker volume `scadalink-mssql-data`. Data survives container restarts and `docker compose down`. To reset the database completely:

```bash
docker compose down -v
docker compose up -d
# Re-run setup.sql after the container starts
```

## First-Time Setup

After the first `docker compose up -d`, run the setup script:

```bash
docker exec -i scadalink-mssql /opt/mssql-tools18/bin/sqlcmd \
  -S localhost -U sa -P 'ScadaLink_Dev1#' -C \
  -i /docker-entrypoint-initdb.d/setup.sql
```

This creates the databases and the `scadalink_app` login. Then seed the Machine Data database with sample tables, stored procedures, and data:

```bash
docker exec -i scadalink-mssql /opt/mssql-tools18/bin/sqlcmd \
  -S localhost -U sa -P 'ScadaLink_Dev1#' -C \
  -i /docker-entrypoint-initdb.d/machinedata_seed.sql
```

You only need to run these once (or again after deleting the volume).

## Connection Strings

For `appsettings.Development.json`:

```
Server=localhost,1433;Database=ScadaLinkConfig;User Id=scadalink_app;Password=ScadaLink_Dev1#;TrustServerCertificate=true
```

```
Server=localhost,1433;Database=ScadaLinkMachineData;User Id=scadalink_app;Password=ScadaLink_Dev1#;TrustServerCertificate=true
```

## Verification

1. Check the container is running:

```bash
docker ps --filter name=scadalink-mssql
```

2. Query using `sqlcmd` inside the container:

```bash
docker exec -it scadalink-mssql /opt/mssql-tools18/bin/sqlcmd \
  -S localhost -U sa -P 'ScadaLink_Dev1#' -C \
  -Q "SELECT name FROM sys.databases"
```

3. Verify the app login:

```bash
docker exec -it scadalink-mssql /opt/mssql-tools18/bin/sqlcmd \
  -S localhost -U scadalink_app -P 'ScadaLink_Dev1#' -C \
  -d ScadaLinkConfig \
  -Q "SELECT DB_NAME()"
```

## CLI Tool

The `infra/tools/mssql_tool.py` script provides a convenient CLI for interacting with the SQL Server.

**Install dependencies** (one-time):
```bash
pip install -r infra/tools/requirements.txt
```

**Commands**:

```bash
# Check connectivity and verify expected databases exist
python infra/tools/mssql_tool.py check

# Run the first-time setup script (uses autocommit mode for CREATE DATABASE)
python infra/tools/mssql_tool.py setup --script infra/mssql/setup.sql

# List tables in a database
python infra/tools/mssql_tool.py tables --database ScadaLinkConfig

# Run an ad-hoc query
python infra/tools/mssql_tool.py query --database ScadaLinkConfig --sql "SELECT name FROM sys.tables"
```

Use `--host`, `--port`, `--user`, `--password` to override defaults (localhost:1433, sa, ScadaLink_Dev1#). Run with `--help` for full usage.

## Machine Data Tables & Stored Procedures

The `machinedata_seed.sql` script creates the following in `ScadaLinkMachineData`:

**Tables**:

| Table | Description | Sample Data |
|-------|-------------|-------------|
| `TagHistory` | Time-series tag values from OPC UA / custom protocols | Pressure, flow, level, temperature, speed readings for SiteA/SiteB |
| `ProductionCounts` | Shift/line production totals (good, reject, efficiency) | 3 days of 2-shift data across 2 sites |
| `EquipmentEvents` | State changes, faults, maintenance, alarm events | Pump faults, belt inspections, batch starts |
| `BatchRecords` | Production batch tracking (start, complete, abort) | 5 batches including one in-progress |
| `AlarmHistory` | Historical alarm activations, acks, and clears | Active, acknowledged, and cleared alarms |

**Stored Procedures**:

| Procedure | Description |
|-----------|-------------|
| `usp_GetTagHistory` | Get tag values for a tag path within a date range |
| `usp_GetProductionSummary` | Aggregate production by line over a date range |
| `usp_InsertBatchRecord` | Insert a new batch (for `CachedWrite` testing) |
| `usp_CompleteBatch` | Complete or abort a batch |
| `usp_GetEquipmentEvents` | Get recent equipment events with optional filters |
| `usp_GetActiveAlarms` | Get active/acknowledged alarms by severity |

## Relevance to ScadaLink Components

- **Configuration Database** — primary consumer; EF Core context targets `ScadaLinkConfig`.
- **Deployment Manager** — reads/writes deployment records in `ScadaLinkConfig`.
- **Template Engine** — reads/writes template definitions in `ScadaLinkConfig`.
- **Security & Auth** — user/role data stored in `ScadaLinkConfig`.
- **External System Gateway** — scripts use `Database.Connection("machineDataConnection")` to query `ScadaLinkMachineData` tables and stored procedures.
- **Site Runtime** — scripts call stored procedures via `Database.Connection()` and `Database.CachedWrite()` for batch recording and data queries.
- **Inbound API** — methods can query machine data via named database connections.

## Notes

- The `sa` password must meet SQL Server complexity requirements (uppercase, lowercase, digit, special character, 8+ characters).
- If the container fails to start, check Docker has at least 2GB RAM allocated (SQL Server minimum requirement).
- The setup script is idempotent — safe to run multiple times.