WesandClaude Sonnet 4.5
27afedb32e
Fetches SMART data from Scrutiny's InfluxDB and calculates failure probabilities using the exact BackBlaze tables from SnapRAID source. Key features: - Calculates both v12 (with attr 193) and v13 (without attr 193) algorithms - v12 matches SnapRAID pre-v13.0 (includes Load Cycle Count) - v13 matches modern SnapRAID v13.0+ (excludes Load Cycle Count) - Stores both values for trending and comparison - Correctly applies bit masks (16-bit for 187/188, 32-bit for others) - Annualizes monthly rates and applies Poisson distribution - Logs to PostgreSQL with device metadata Solved the mystery: SnapRAID v12 uses attribute 193 which was removed in v13.0. Load cycle count has massive impact on failure predictions for some drives (80% vs 4% difference). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
113 lines
3.5 KiB
Python
Executable File
113 lines
3.5 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Explore Scrutiny's InfluxDB schema to understand how SMART data is stored.
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Run this first to understand the data structure before running the main logger.
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"""
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import os
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from influxdb_client import InfluxDBClient
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from dotenv import load_dotenv
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load_dotenv()
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def explore_influxdb():
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"""Connect to InfluxDB and explore the schema."""
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url = os.getenv('INFLUXDB_URL')
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token = os.getenv('INFLUXDB_TOKEN')
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org = os.getenv('INFLUXDB_ORG', 'scrutiny')
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bucket = os.getenv('INFLUXDB_BUCKET', 'metrics')
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print(f"Connecting to InfluxDB at {url}")
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print(f"Organization: {org}")
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print(f"Bucket: {bucket}\n")
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client = InfluxDBClient(url=url, token=token, org=org)
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query_api = client.query_api()
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# 1. List all measurements in the bucket
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print("=" * 80)
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print("STEP 1: Discovering measurements in bucket")
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print("=" * 80)
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query_measurements = f'''
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import "influxdata/influxdb/schema"
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schema.measurements(bucket: "{bucket}")
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'''
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try:
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result = query_api.query(query_measurements)
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measurements = []
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for table in result:
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for record in table.records:
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measurement = record.get_value()
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measurements.append(measurement)
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print(f" - {measurement}")
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if not measurements:
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print(" No measurements found!")
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return
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print(f"\nFound {len(measurements)} measurement(s)\n")
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# 2. For each measurement, show field keys
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print("=" * 80)
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print("STEP 2: Discovering fields for each measurement")
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print("=" * 80)
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for measurement in measurements[:5]: # Limit to first 5 measurements
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print(f"\nMeasurement: {measurement}")
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query_fields = f'''
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import "influxdata/influxdb/schema"
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schema.measurementFieldKeys(
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bucket: "{bucket}",
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measurement: "{measurement}"
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)
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'''
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result = query_api.query(query_fields)
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for table in result:
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for record in table.records:
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field = record.get_value()
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print(f" Field: {field}")
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# 3. Show sample data from the first measurement
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print("\n" + "=" * 80)
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print("STEP 3: Sample data from first measurement")
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print("=" * 80)
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first_measurement = measurements[0]
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query_sample = f'''
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from(bucket: "{bucket}")
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|> range(start: -24h)
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|> filter(fn: (r) => r._measurement == "{first_measurement}")
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|> limit(n: 10)
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'''
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print(f"\nSample records from '{first_measurement}':\n")
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result = query_api.query(query_sample)
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for table in result:
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for record in table.records:
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print(f"Time: {record.get_time()}")
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print(f" Measurement: {record.get_measurement()}")
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print(f" Field: {record.get_field()}")
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print(f" Value: {record.get_value()}")
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print(f" Tags: {record.values}")
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print()
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except Exception as e:
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print(f"Error querying InfluxDB: {e}")
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import traceback
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traceback.print_exc()
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finally:
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client.close()
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if __name__ == '__main__':
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if not os.getenv('INFLUXDB_URL'):
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print("ERROR: Please create a .env file with your InfluxDB configuration")
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print("Copy .env.example to .env and fill in your details")
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exit(1)
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explore_influxdb()
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