add lookback delta prometheus

Daniel Parker

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22-02-2025

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Prometheus, a popular open-source monitoring and alerting system, doesn't natively support a "lookback delta" function. A lookback delta calculates the difference between a metric's current value and its value at a specific point in the past. This is crucial for understanding trends and detecting anomalies. This article will explore how to achieve lookback delta calculations within the Prometheus ecosystem, leveraging PromQL (Prometheus Query Language) and potentially external tools.

Understanding the Need for Lookback Delta

Monitoring system metrics often involves understanding not just the current value, but also how that value has changed over time. A simple example is CPU utilization. Knowing the current CPU usage is helpful, but knowing the change in CPU usage over the last hour, day, or week provides much richer context. This is where the lookback delta becomes invaluable. It allows you to:

• Identify Trends: Quickly spot upward or downward trends in metrics.
• Detect Anomalies: Flag significant deviations from the expected change over time.
• Improve Alerting: Trigger alerts based on the rate of change rather than just absolute values.

Achieving Lookback Delta with PromQL (Limitations)

While PromQL offers powerful querying capabilities, it doesn't have a direct "lookback delta" function. However, we can approximate this behavior using a combination of techniques:

1. Using offset and subtraction: This is the most straightforward approach. The offset modifier shifts the time series back in time. We then subtract the offset value from the current value.

metric_name - metric_name offset 1h

This query calculates the difference between the current value of metric_name and its value one hour ago. Replace 1h with your desired lookback period (e.g., 5m, 1d).

Limitations of the offset method:

• Irregular Data: If your metric has gaps in its data, the offset method might yield inaccurate results. The query will compare the current value to the last available data point within the lookback period, not necessarily exactly one hour ago.
• Sparse Data: If the data is very sparse, the lookback will be less meaningful.

2. Using changes (for counters): If your metric is a counter (e.g., total requests served), the changes function can provide the increase over a time range. However, this doesn't directly give you a delta relative to a specific past point in time.

changes(metric_name[1h])

This shows the change in the counter value over the last hour.

Limitations of the changes method:

• Only for counters: This is only applicable to counter metrics; not for gauges.
• Not a point-in-time comparison: Doesn't give the delta to a specific past point, but the increase over an interval.

Enhancing Lookback Delta with External Tools

For more sophisticated lookback delta calculations, especially when dealing with irregular or sparse data, consider using external tools that can pre-process your Prometheus data:

• Grafana: Grafana allows for more complex calculations and visualizations within its dashboards. You can use Grafana's built-in functions or write custom panels to calculate and display lookback deltas.

• Custom Scripting: You can write scripts (e.g., Python) to fetch data from Prometheus's /api/v1/query_range endpoint, perform the lookback delta calculation, and then visualize the results using a charting library. This offers maximum flexibility but requires more development effort.

Example: Calculating CPU Utilization Delta

Let's say you have a metric cpu_usage. To calculate the delta in CPU usage over the last 5 minutes using the offset method:

cpu_usage - cpu_usage offset 5m

This query would give you the difference between the current CPU usage and the CPU usage 5 minutes ago. Remember to adapt this query and choose the most appropriate method based on the nature of your specific metric.

Conclusion

While Prometheus doesn't offer a direct lookback delta function in PromQL, approximations can be made using the offset and changes functions. For more complex scenarios or better handling of irregular data, leveraging external tools like Grafana or custom scripting provides a robust solution for calculating and visualizing lookback deltas in your Prometheus metrics. Understanding the limitations of each approach is key to selecting the best method for your use case. Remember to always consider the nature of your metric (gauge or counter) when selecting the appropriate PromQL functions or external tools.