I am working on a real-time IoT application where sensor readings (including a timestamp, sensor ID, and measurement value) are stored in GridDB. To reduce the query load and improve response times for frequently accessed data, I want to maintain a local cache in my Python application. This cache should automatically refresh every few seconds with only the latest sensor data (for example, data from the last minute).

My challenges are:

1. Continuously polling GridDB without blocking the main application thread.
2. Updating the local cache with only the most recent sensor reading per sensor.
3. Ensuring that the cache is refreshed in an “event-driven” manner (simulated here by a timed polling mechanism).

Below is a sample code snippet illustrating my approach:

import threading
import time
from datetime import datetime, timedelta
from griddb_python import StoreFactory

# Connect to GridDB
factory = StoreFactory.get_instance()
gridstore = factory.get_store(
    host="192.168.0.10", 
    port=31999, 
    cluster_name="defaultCluster", 
    username="admin", 
    password="Farwa_Admin"
)
container = gridstore.get_container("sensor_data")

# Local cache to store latest reading per sensor_id
local_cache = {}

# Define cache duration and refresh interval
CACHE_DURATION = timedelta(minutes=1)
REFRESH_INTERVAL = 10  # seconds

def refresh_cache():
    global local_cache
    now = datetime.now()
    start_time = now - CACHE_DURATION
    
    # Use a prepared statement to query sensor data from the last minute
    query = container.query("SELECT * WHERE timestamp >= ?")
    query.set_parameters([start_time])
    
    rs = query.fetch(False)
    updated_cache = {}
    
    # Update cache: keep only the most recent reading for each sensor
    while rs.has_next():
        row = rs.next()  # assume row is a dict-like object with keys 'sensor_id', 'timestamp', 'value'
        sensor_id = row['sensor_id']
        # If sensor_id already exists, compare timestamps and update if this reading is more recent
        if sensor_id in updated_cache:
            if row['timestamp'] > updated_cache[sensor_id]['timestamp']:
                updated_cache[sensor_id] = row
        else:
            updated_cache[sensor_id] = row

    local_cache = updated_cache
    print(f"Cache refreshed at {now.strftime('%Y-%m-%d %H:%M:%S')} with {len(local_cache)} entries.")

    # Schedule the next cache refresh
    threading.Timer(REFRESH_INTERVAL, refresh_cache).start()

# Start the event-driven cache refresh process
refresh_cache()

# Main application loop can continue doing other work without being blocked
while True:
    # Example: Access the cache for quick sensor data retrieval
    # (In a real application, replace this with actual logic)
    time.sleep(REFRESH_INTERVAL)
    print("Current cache snapshot:", local_cache)