Determining a Rolling Moving Average in Python Scheduled Time Event with SQL Select Statement

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As a technical blogger, I’ve encountered numerous questions and issues from developers who struggle to determine the rolling moving average of data stored in a database. In this article, we’ll delve into the problem presented by the Stack Overflow user and explore the possible solutions.

Understanding the Problem

The issue at hand is with a Python script that reports the rolling 24-hour moving average every hour using sched.scheduler. The script fetches data from a price database and calculates the daily moving average. However, after 24 hours, it begins to report “None” as the daily MA, suggesting that the time being evaluated is not recalculating dynamically each time the scheduled event fires.

Initial Analysis

The initial analysis by the user indicates that replacing the SQL now() variable with a Python variable to capture the current time does not solve the issue. This suggests that the problem lies elsewhere in the code, possibly related to how the cursor is managing records or the timing of the scheduled event.

Solution Overview

To address this issue, we need to reconsider the approach taken by the user and explore alternative methods for determining the rolling moving average. We’ll examine how cursors handle record management and discuss possible adjustments to ensure accurate calculations.

Alternative Approach: Utilizing the cursor Object

The Stack Overflow answer suggests updating the code to approximately:

def get_daily_ma():
    cnx = mysql.connector.connect(...)
    cursor = cnx.cursor()
    try:
        cursor.execute("SELECT AVG(price) AS DailyAvg FROM price_log.price WHERE date_time >=(now() - INTERVAL 24 HOUR)")
        get_daily_ma = cursor.fetchone()
        for row in get_daily_ma:
            print('Daily MA:' + str(row))
    except Exception as error:
        if error is not None:
            print('Caught this error: ' + repr(error))
        else:
            print('Connected successfully to database')
        connect()
    finally:
        cursor.close()
        cnx.close()

This approach closes the cursor and connection after executing the query, which might resolve the issue of stale records.

The Role of Cursors in Record Management

When using a cursor object to manage records from a database, it’s essential to understand how cursors handle record management. Here are some key points to consider:

• Statement caching: When executing a query with a cursor, MySQL caches the results for subsequent executions. This means that if you execute the same query multiple times without closing and reopening the cursor, the results will remain cached.
• Record fetching: When fetching records from the database using a cursor, it’s essential to remember that the fetchone(), fetchmany(), or fetchall() methods return only a single record, multiple records, or all records, respectively. However, if you execute another query on the same cursor without clearing its cache, the previous results will be returned again.
• Cursor management: Properly managing cursors is crucial to avoid memory leaks and performance issues. Closing the cursor and connection after use helps free up resources.

Reconsidering the SQL Query

The original SQL query used now() with an interval of 24 hours, which may not be suitable for all scenarios. Here are some alternatives:

• Using a fixed timestamp: Instead of using now() with an interval, you can use a fixed timestamp that represents the start of the 24-hour period.
• Date-based filtering: You can filter records based on specific dates or date ranges to achieve the desired result.

Example: Using a Fixed Timestamp

Here’s an example of how you might modify the SQL query to use a fixed timestamp:

def get_daily_ma():
    cnx = mysql.connector.connect(...)
    cursor = cnx.cursor()
    try:
        # Use a fixed timestamp for the 24-hour period
        start_date = datetime.now() - timedelta(hours=24)
        cursor.execute("SELECT AVG(price) AS DailyAvg FROM price_log.price WHERE date_time >= %s", (start_date,))
        get_daily_ma = cursor.fetchone()
        for row in get_daily_ma:
            print('Daily MA:' + str(row))
    except Exception as error:
        if error is not None:
            print('Caught this error: ' + repr(error))
        else:
            print('Connected successfully to database')
        connect()
    finally:
        cursor.close()
        cnx.close()

This approach provides more control over the 24-hour period and ensures accurate calculations.

Best Practices for Determining Rolling Moving Averages

When determining rolling moving averages, keep the following best practices in mind:

• Use the correct data: Ensure that you’re using the most recent data available to calculate the moving average.
• Consider time intervals: Adjust your time interval based on the specific requirements of your application or dataset.
• Implement proper cursor management: Close and reopen cursors as needed to avoid memory leaks and performance issues.

Conclusion

Determining rolling moving averages can be a complex task, especially when dealing with large datasets and scheduled events. By understanding how cursors handle record management and adjusting the SQL query accordingly, you can ensure accurate calculations and avoid common pitfalls. Remember to implement proper cursor management and adjust your approach based on specific requirements.

Last modified on 2023-07-22