Introduction

Warning: this content is generated from the ChatGPT's answer, but I think this explanation is pretty simpler than the GCP documentation.

The question that I asked ChatGPT to answer:

• explain GCP alert policy's rolling window function "sum" option, sum vs count.
• create example array that contain objects, each object contain the metric values are 2, 3, 4 and corresponding time.

In Google Cloud Platform (GCP), alert policies allow you to monitor your resources and get notifications when certain conditions are met. One of the features of alert policies is the use of rolling windows, which help in aggregating metric data over a specific time interval to evaluate conditions. Within these rolling windows, you can apply different aggregation functions, such as "sum" and "count." Here’s a detailed example of these options:

Explain by example

Let's create an example array in JSON format that contains objects. Each object will represent a metric value and its corresponding timestamp. Here, we'll use timestamps that are one minute apart for simplicity.

[
    {
        "metric_value": 2,
        "timestamp": "2024-06-27T10:00:00Z"
    },
    {
        "metric_value": 3,
        "timestamp": "2024-06-27T10:01:00Z"
    },
    {
        "metric_value": 4,
        "timestamp": "2024-06-27T10:02:00Z"
    },
    {
        "metric_value": 1,
        "timestamp": "2024-06-27T10:03:00Z"
    }
]

Explanation:

• metric_value: Represents the value of the metric at a given time.
• timestamp: Represents the time at which the metric value was recorded.

This array contains four objects with metric values 2, 3, 4, and 1, recorded at 10:00, 10:01, 10:02, and 10:03 UTC respectively. You can use this array to simulate the metric data for various operations like aggregation using "sum" or "count" in a rolling window.

Aggregation Example:

• Sum: If you sum the metric_value over this array:

2 + 3 + 4 + 1 = 10

• Count: If you count the number of metric values in this array:

4 (since there are four objects in the array)

You can adjust the metric values and timestamps according to your specific use case or time intervals.

Definition

Sum

The "sum" function adds up all the metric values over the rolling window.

Purpose: It gives the total cumulative value over a specified period.

Use Cases:

• Monitoring the total number of events or transactions.
• Aggregating total resource usage (e.g., CPU, memory).
• Tracking the total amount of data transferred or processed.

Example:

• Alert on Total Usage: Set an alert if the total CPU usage exceeds a certain threshold over a rolling window of 5 minutes.
• Alert on Total Data Processed: Trigger an alert if the total data processed exceeds a certain limit in a given time frame.

Count

The "count" function counts the number of data points or occurrences of a metric over the rolling window.

Purpose: It gives the number of instances or samples of the metric over the specified period.

Use Cases:

• Counting the number of occurrences of a specific event.
• Monitoring the number of metric samples received.
• Tracking the number of error occurrences or alerts triggered.

Practical Application in Alert Policies

Sum

• Alert on Total Usage: Set an alert if the total CPU usage exceeds a certain threshold over a rolling window of 5 minutes.

• Alert on Total Data Processed: Trigger an alert if the total data processed exceeds a certain limit in a given time frame.

Count

• Alert on Number of Errors: Trigger an alert if the number of error occurrences exceeds a threshold within a 10-minute window.

• Alert on Request Rate: Set an alert if the number of requests drops below a certain threshold, indicating potential downtime or reduced service usage.

By understanding the difference between the "sum" and "count" functions, you can configure more effective and meaningful alert policies tailored to the specific monitoring needs of your GCP environment.