add readme documentation about background scheduler

documentation/background-scheduler/README.md

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+# Background Scheduler Documentation
+
+## What is a Background Scheduler?
+
+A Background Scheduler is a tool that allows you to run tasks or functions at specific intervals, asynchronously,
+without blocking the main application process. It is often used to perform background tasks that need to run
+periodically, such as data synchronization, sending notifications, running maintenance tasks, or cleaning up resources.
+
+**APScheduler** (Advanced Python Scheduler) is a popular Python library that provides powerful functionality for
+scheduling jobs in the background. It supports various job stores, execution options, and flexibility in scheduling.
+
+## How Background Scheduler is Typically Used
+
+1. **Running Periodic Tasks**: Background schedulers are commonly used for tasks that need to be executed at regular
+   intervals, like cleaning up expired data from a database or updating a cache.
+2. **Scheduled Notifications**: Sending scheduled emails, reminders, or push notifications at set intervals or times.
+3. **Data Syncing**: Automatically fetching or syncing data from external services (e.g., APIs) at regular intervals to
+   ensure fresh and up-to-date data.
+4. **Database Maintenance**: Running database cleanup jobs, backup tasks, or archiving operations periodically.
+
+## APScheduler Components
+
+APScheduler works by scheduling jobs and running them either once or on a repeating schedule. Here are some of the key
+components:
+
+- **Job**: A task or function that you want to schedule. This can be a function or method.
+- **Trigger**: Specifies when a job should be executed. Commonly used triggers include `interval` (to run periodically),
+  `date` (to run once at a specific time), and `cron` (to run based on a cron expression).
+- **Job Store**: Where the job metadata is stored (in-memory, file, or a database).
+- **Executor**: Specifies how the job should be executed (e.g., in a thread or in a process).
+
+### Basic Example of Using APScheduler
+
+```python
+from apscheduler.schedulers.background import BackgroundScheduler
+import time
+
+
+# A simple function to be scheduled
+def job():
+    print("Running scheduled task")
+
+
+# Create a scheduler instance
+scheduler = BackgroundScheduler()
+
+# Add a job to run every 10 seconds
+scheduler.add_job(job, 'interval', seconds=10)
+
+# Start the scheduler in the background
+scheduler.start()
+
+# Keep the script running to allow the scheduler to run in the background
+try:
+    while True:
+        time.sleep(2)
+except (KeyboardInterrupt, SystemExit):
+    scheduler.shutdown()  # Shut down the scheduler when exiting the app
+```
+
+In this example:
+
+- The `job` function will be executed every 10 seconds without blocking the main program.
+- The `BackgroundScheduler` allows the scheduler to run in the background while the main application continues to
+  operate.
+
+### Common Use Cases for Background Schedulers
+
+- **Web Applications**: Running tasks in the background to offload work from the main application, such as updating
+  data, sending notifications, or managing queues.
+- **Data Pipelines**: Running periodic data aggregation or transformation tasks in the background without user
+  intervention.
+- **Monitoring Systems**: Scheduling tasks to monitor services or systems and send alerts or take action when certain
+  conditions are met.
+- **Maintenance Jobs**: Scheduling cleanup tasks, backups, or other administrative tasks that need to run regularly
+  without user interaction.
+
+## How Background Scheduler is Used in This Route
+
+In the provided route, the **APScheduler** is used to automate the updating of programming language tags from the Stack
+Overflow API at regular intervals (every 24 hours). This approach ensures that the data in the database remains
+up-to-date without having to rely on manual updates or requests to trigger the update process.
+
+### Key Functions Using Background Scheduler
+
+1. `update_tags_in_db` Function:
+    - This function fetches the latest tags from the Stack Overflow API and updates the database with the new data.
+    - It is an asynchronous function, allowing it to run in the background without blocking other operations of the
+      application.
+
+2. `start_scheduler` Function:
+    - This function is responsible for initializing the BackgroundScheduler and scheduling the update_tags_in_db
+      function to run every 24 hours.
+    - It sets the trigger type to 'interval', which means the job will run at fixed intervals, specifically every 24
+      hours in this case.
+    - The scheduler runs as a background process, meaning the main application does not need to stop or wait for it to
+      complete.
+
+#### How It Works in the Application
+
+- **Scheduled Update**: The scheduler runs automatically upon the application's startup and ensures that the Stack
+  Overflow tags are updated every 24 hours without user intervention.
+- **Non-Blocking**: Since the scheduler runs in the background, the main FastAPI application is free to handle user
+  requests and other processes without being blocked by this scheduled task.
+- **Database Update**: Every 24 hours, the `update_tags_in_db` function fetches the latest tags from the Stack Overflow
+  API and updates the database with the new tag data, replacing the older entries.
+
+```python
+# Starts the scheduler to update Stack Overflow tags every 24 hours
+def start_scheduler():
+    scheduler = BackgroundScheduler()
+
+    # Add a job to run the update_tags_in_db function every 24 hours
+    scheduler.add_job(lambda: asyncio.run(update_tags_in_db()), 'interval', hours=24)
+
+    # Start the scheduler in the background
+    scheduler.start()
+```
+
+### Advantages of Using APScheduler in This Route:
+
+- **Automation**: The update process is fully automated, so no manual triggers are needed to keep the data fresh.
+- **Efficiency**: Since the data is updated every 24 hours, API calls are limited, reducing the risk of hitting the
+  quota limit for the Stack Overflow API.
+- **Scalability**: As the application scales, the scheduler will continue to handle periodic updates without additional
+  complexity.
+- **Separation of Concerns**: The main application remains focused on handling user requests, while the scheduler
+  manages the background task of updating the database.
+
+### Other Use Cases of Background Scheduler in FastAPI:
+
+- **Sending Automated Emails**: You can use APScheduler to schedule email reminders or notifications based on user
+  actions or time intervals.
+- **Daily Data Backups**: Schedule regular backups of your database to prevent data loss.
+- **Periodic Cache Refreshing**: In applications that rely on external data sources, background schedulers can
+  periodically refresh the cache to ensure the data is always fresh and up-to-date.
+- **Maintenance Tasks**: Tasks such as clearing old logs, cleaning up expired sessions, or recalculating metrics can be
+  scheduled during off-peak hours.
+
+## Conclusion
+
+A **Background Scheduler** is a powerful tool for handling automated, periodic tasks within your applications. In the
+context of your FastAPI application, **APScheduler** is used to periodically update data from the Stack Overflow API,
+ensuring the system stays current without manual intervention. This enables efficient, scalable, and automated
+management of tasks that need to be performed regularly in the background.