### 3. Connection Management

*   **Connection Pooling:** Always use connection pooling (e.g., `JedisPool` in Java, `redis-py` has built-in connection pooling) to efficiently manage connections and reduce overhead.
*   **Error Handling:** Implement robust error handling for connection failures, timeouts, and Redis-specific exceptions.
*   **Configuration Management:** Store Redis connection details (host, port, password) securely in environment variables or a configuration service, not hardcoded in your application.

## Configuration Parameters

### TTL (Time-To-Live)

The user-specified `ttl` for this workflow is **3600 seconds (1 hour)**.

*   **Purpose:** The TTL determines how long a key-value pair will remain in the cache before Redis automatically evicts it. This is critical for managing data freshness and cache size.
*   **Setting TTL:**
    *   **At creation:** Use `SETEX key seconds value` (as shown in code examples).
    *   **After creation:** Use `EXPIRE key seconds` or `PEXPIRE key milliseconds`.
    *   **To remove TTL:** Use `PERSIST key`.
*   **Considerations for TTL:**
    *   **Data Freshness:** A shorter TTL means fresher data but potentially more cache misses and higher load on the backend.
    *   **Cache Hit Ratio:** A longer TTL increases the likelihood of cache hits but risks serving stale data.
    *   **Memory Usage:** Longer TTLs can lead to higher memory consumption if not managed with `maxmemory` and eviction policies.
    *   **Dynamic TTLs:** Consider using different TTLs for different types of data based on their update frequency and criticality. For example, user profile data might have a 1-hour TTL, while a trending news feed might have a 5-minute TTL.

### Other Critical Redis Configurations

| Parameter           | Description