Grafana is an open-source visualization and monitoring tool — it turns logs, metrics, and time-series data into dashboards and alerts. We can use it to see how the system is performing its work properly.

Let’s build a IndexCalc → QC → Grafana monitoring pipeline step-by-step to illustrate potential of using it in our workflow.

First the Goal is to Automatically:

• Visualize & alert in Grafana (e.g., if QC fails or job runs late).
• Run your IndexCalc daily at 11:00 AM.
• Perform QC checks after completion.
• Log results (pass/fail, duration, timestamp) into a database.

            ┌──────────────────────────┐
            │     APScheduler Job      │
            │ (runs IndexCalc at 11AM) │
            └────────────┬─────────────┘
                         │
                         ▼
           ┌──────────────────────────┐
           │     IndexCalc Engine     │
           │  (data fetch + calc)     │
           └────────────┬─────────────┘
                         │
                         ▼
           ┌──────────────────────────┐
           │      QC Validator        │
           │ (check completeness, etc.)│
           └────────────┬─────────────┘
                         │
                         ▼
           ┌──────────────────────────┐
           │   Database (SQLite/Postgres) │
           │   job_log table:              │
           │   timestamp, status, reason   │
           └────────────┬─────────────┘
                         │
                         ▼
           ┌──────────────────────────┐
           │        Grafana           │
           │  Dashboard + Alert Rules │
           └──────────────────────────┘

Now the concrete set up, Step 1. Scheduler + IndexCalc + QC (Python)

from apscheduler.schedulers.blocking import BlockingScheduler
from datetime import datetime
import sqlite3
import random

DB_FILE = "indexcalc_jobs.db"

def run_indexcalc():
    start = datetime.now()
    print(f"[{start}] Running IndexCalc...")

    # Simulate index calculation
    success = random.choice([True, True, True, False])  # fail sometimes
    reason = None if success else "Missing data from source"

    # Simulate duration
    duration = round(random.uniform(20, 40), 2)

    # Log QC result
    with sqlite3.connect(DB_FILE) as conn:
        conn.execute("""
            CREATE TABLE IF NOT EXISTS job_log (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                timestamp TEXT,
                status TEXT,
                duration REAL,
                reason TEXT
            )
        """)
        conn.execute(
            "INSERT INTO job_log (timestamp, status, duration, reason) VALUES (?, ?, ?, ?)",
            (start.isoformat(), "PASS" if success else "FAIL", duration, reason)
        )
        conn.commit()

    print(f"Job completed: {'PASS' if success else 'FAIL'} (Duration {duration}s)")

scheduler = BlockingScheduler()
scheduler.add_job(run_indexcalc, 'cron', hour=11, minute=0)
print("Scheduler started. Will run at 11:00 AM daily.")
scheduler.start()

Step 2. Connect Grafana Option A: Connect directly to SQLite (via plugin): Grafana supports SQLite with a plugin — install from the Grafana Marketplace: Plugin: frser-sqlite-datasource

Step 3. Create a Dashboard

Example panels:

The dashboard is a web-based UI dashboard that Grafana provides. After you install or start Grafana (either self-hosted or cloud), it launches a web server that runs a UI — your dashboards live there.

The visual could be

+————————————————————-+
| IndexCalc QC Monitor |
+————————————————————-+
| Last Run: PASS ✅ | Avg Duration: 31.2s | Fail Count: 1 |
+————————————————————-+
| Job Duration (line chart over time) |
| |
| ●●●●●●●●●●●●●●● |
+————————————————————-+
| QC Failures by Reason (bar chart) |
| Missing Data ▇▇▇▇▇▇▇▇▇▇ |
| API Timeout ▇▇▇ |
+————————————————————-+