#!/usr/bin/env python3
"""
Explore Scrutiny's InfluxDB schema to understand how SMART data is stored.
Run this first to understand the data structure before running the main logger.
"""

import os
from influxdb_client import InfluxDBClient
from dotenv import load_dotenv

load_dotenv()

def explore_influxdb():
    """Connect to InfluxDB and explore the schema."""
    url = os.getenv('INFLUXDB_URL')
    token = os.getenv('INFLUXDB_TOKEN')
    org = os.getenv('INFLUXDB_ORG', 'scrutiny')
    bucket = os.getenv('INFLUXDB_BUCKET', 'metrics')

    print(f"Connecting to InfluxDB at {url}")
    print(f"Organization: {org}")
    print(f"Bucket: {bucket}\n")

    client = InfluxDBClient(url=url, token=token, org=org)
    query_api = client.query_api()

    # 1. List all measurements in the bucket
    print("=" * 80)
    print("STEP 1: Discovering measurements in bucket")
    print("=" * 80)

    query_measurements = f'''
    import "influxdata/influxdb/schema"
    schema.measurements(bucket: "{bucket}")
    '''

    try:
        result = query_api.query(query_measurements)
        measurements = []
        for table in result:
            for record in table.records:
                measurement = record.get_value()
                measurements.append(measurement)
                print(f"  - {measurement}")

        if not measurements:
            print("  No measurements found!")
            return

        print(f"\nFound {len(measurements)} measurement(s)\n")

        # 2. For each measurement, show field keys
        print("=" * 80)
        print("STEP 2: Discovering fields for each measurement")
        print("=" * 80)

        for measurement in measurements[:5]:  # Limit to first 5 measurements
            print(f"\nMeasurement: {measurement}")
            query_fields = f'''
            import "influxdata/influxdb/schema"
            schema.measurementFieldKeys(
                bucket: "{bucket}",
                measurement: "{measurement}"
            )
            '''

            result = query_api.query(query_fields)
            for table in result:
                for record in table.records:
                    field = record.get_value()
                    print(f"  Field: {field}")

        # 3. Show sample data from the first measurement
        print("\n" + "=" * 80)
        print("STEP 3: Sample data from first measurement")
        print("=" * 80)

        first_measurement = measurements[0]
        query_sample = f'''
        from(bucket: "{bucket}")
            |> range(start: -24h)
            |> filter(fn: (r) => r._measurement == "{first_measurement}")
            |> limit(n: 10)
        '''

        print(f"\nSample records from '{first_measurement}':\n")
        result = query_api.query(query_sample)
        for table in result:
            for record in table.records:
                print(f"Time: {record.get_time()}")
                print(f"  Measurement: {record.get_measurement()}")
                print(f"  Field: {record.get_field()}")
                print(f"  Value: {record.get_value()}")
                print(f"  Tags: {record.values}")
                print()

    except Exception as e:
        print(f"Error querying InfluxDB: {e}")
        import traceback
        traceback.print_exc()

    finally:
        client.close()


if __name__ == '__main__':
    if not os.getenv('INFLUXDB_URL'):
        print("ERROR: Please create a .env file with your InfluxDB configuration")
        print("Copy .env.example to .env and fill in your details")
        exit(1)

    explore_influxdb()