Troubleshooting Grafana Alert Rules With GitHub Data

Are you encountering issues while trying to create alert rules in Grafana using a GitHub datasource? You're not alone! Many users have reported a frustrating error message: input data must be a wide series but got type long (input refid). This article delves into the potential causes of this issue and provides troubleshooting steps to help you resolve it, ensuring you can effectively monitor your GitHub repositories with Grafana alerts. We'll explore common pitfalls and offer solutions to get your alerts up and running. So, let's dive in and troubleshoot this problem together, making your Grafana and GitHub integration seamless.

Understanding the Problem: Wide Series vs. Long Type

The core of the issue lies in how Grafana expects data to be formatted for alert rules compared to how the GitHub datasource is delivering it. Grafana's alerting engine often requires data in a "wide series" format, which essentially means that each data point has a timestamp and multiple value columns. This format is ideal for visualizing trends and setting thresholds based on multiple metrics. However, the GitHub datasource, depending on the query, might return data in a "long type" format, where each data point has a timestamp and a single value column. This mismatch causes the alert rule creation process to fail.

To put it simply, imagine Grafana expecting a spreadsheet with columns for time, CPU usage, and memory usage (wide series). Instead, it receives a simple list of time and a single value like number of commits (long type). This discrepancy leads to the input data must be a wide series error. To effectively create alert rules, you need to transform or reshape the data from the GitHub datasource into a format that Grafana's alerting engine can understand.

Understanding the Data Format Discrepancy

The error message input data must be a wide series but got type long (input refid) indicates a mismatch between the data format expected by Grafana's alerting engine and the format provided by the GitHub datasource. Grafana's alerting system typically requires data in a wide series format, which includes a timestamp and multiple value columns. This format is suitable for visualizing trends and setting thresholds based on various metrics. In contrast, the GitHub datasource might return data in a long type format, where each data point consists of a timestamp and a single value column.

Data Transformation Requirements

To resolve this issue, you need to transform or reshape the data from the GitHub datasource into a format compatible with Grafana's alerting engine. This transformation involves converting the long type data into a wide series format, ensuring that the data includes the necessary columns for Grafana to process. Consider using Grafana's transformation features to reshape the data before creating alert rules. This might involve aggregating data, pivoting tables, or using other transformation functions to create the desired wide series format.

Common Causes and Troubleshooting Steps

Let's explore the most common reasons why you might be facing this issue and how to address them:

1. Query Type Incompatibility:

• The Problem: Not all query types available in the GitHub datasource are inherently suitable for alerting. Some queries might return data that is too granular or doesn't lend itself well to threshold-based alerting.
• The Solution: Experiment with different query types. Instead of focusing on individual workflow runs, try aggregating data over a period. For example, query the total number of commits per day, the average build time per week, or the number of open pull requests. These aggregated metrics are more likely to be compatible with Grafana's alerting system. Consider using Grafana's transformation features to aggregate or reshape the data before creating the alert rule.

2. Data Aggregation and Transformation:

• The Problem: Raw data from the GitHub datasource might not be in the correct format for alerting. You need to aggregate and transform it into a time series that Grafana can understand.

• The Solution: Utilize Grafana's built-in transformation capabilities. Here's how:

  • Aggregate: Use functions like sum(), count(), mean(), min(), and max() to aggregate data over time. For example, if you're tracking workflow runs, you could count the number of runs per hour.
  • Group By: Group data by time intervals (e.g., 1h, 1d, 1w) to create a time series. This allows you to monitor trends over time.
  • Reduce: The Reduce transformation can be particularly useful. It allows you to calculate a single value from a series, such as the average or maximum value over a specific period. This can be helpful for setting thresholds.
  • Transformations: Grafana offers various transformations that can reshape your data. Experiment with options like "Extract fields", "Merge series", and "Filter by name" to prepare the data for alerting.

3. Data Type Mismatch:

• The Problem: The data type returned by the GitHub datasource might not be compatible with Grafana's alerting engine. For instance, if the data is returned as a string, you won't be able to set numerical thresholds.
• The Solution: Ensure that the data being returned is numerical. If it's not, you might need to adjust your query or use Grafana's transformation features to convert the data type. For example, you could use the toNumber() function to convert a string representation of a number into an actual number.

4. Grafana Version Compatibility:

• The Problem: Older versions of Grafana or the GitHub datasource plugin might have bugs or limitations that cause compatibility issues.
• The Solution: Make sure you're running the latest versions of both Grafana and the GitHub datasource plugin. Updates often include bug fixes and improvements that can resolve compatibility problems.

5. Datasource Configuration:

• The Problem: Incorrect configuration of the GitHub datasource can lead to data retrieval issues.
• The Solution: Double-check your datasource configuration. Ensure that you have the correct API token, repository owner, and repository name. Verify that the token has the necessary permissions to access the data you're trying to query. Try creating a new API token with broader permissions to rule out permission-related issues.

Step-by-Step Troubleshooting Checklist

1. Verify Data Source Configuration:

   • Ensure that your GitHub datasource is correctly configured with the appropriate API token and repository details.
   • Confirm that the API token has the necessary permissions to access the data you are trying to query.

2. Inspect Query Results:

   • Use Grafana's Explore feature to inspect the raw data returned by your GitHub datasource query.
   • Check the data format and structure to identify any inconsistencies or issues that might be causing the error.

3. Apply Data Transformations:

   • Use Grafana's transformation features to reshape the data into a wide series format.
   • Aggregate data using functions like sum(), count(), or mean() to create a time series.

4. Check Data Types:

   • Ensure that the data types returned by the GitHub datasource are compatible with Grafana's alerting engine.
   • Convert data types if necessary using Grafana's transformation features.

5. Update Grafana and Plugins:

   • Make sure you are running the latest versions of Grafana and the GitHub datasource plugin.
   • Update to the latest versions to benefit from bug fixes and improvements.

Example Scenario and Solution

Let's say you want to create an alert when the number of failed workflow runs in your repository exceeds a certain threshold within a 24-hour period.

1. Query: Start by querying the GitHub datasource for the number of failed workflow runs.
2. Transformation: Use the Aggregate transformation to count the number of failed runs per hour. Group the data by 1 hour intervals.
3. Transformation: Use the Reduce transformation to calculate the sum of failed runs over a 24-hour period.
4. Alert Rule: Create an alert rule that triggers when the calculated sum exceeds your desired threshold.

By following these steps, you can transform the data into a format that Grafana's alerting engine can understand, resolving the input data must be a wide series error.

Alternative Solutions and Workarounds

If you're still struggling to get the GitHub datasource working with Grafana alerts, consider these alternative solutions:

• Use a Custom Script: Write a script that queries the GitHub API, transforms the data into the desired format, and pushes it to a time-series database like Prometheus. Then, use Prometheus as a datasource in Grafana and create your alert rules based on the data in Prometheus.
• Explore Other Datasources: Consider using other datasources that might provide similar data in a more compatible format. For example, some CI/CD platforms offer their own datasources that are specifically designed for monitoring and alerting.

Conclusion

Troubleshooting the input data must be a wide series error when using the GitHub datasource in Grafana can be challenging, but by understanding the underlying data format requirements and following the troubleshooting steps outlined in this article, you can effectively resolve the issue. Remember to experiment with different query types, utilize Grafana's transformation features, and ensure that your datasource is properly configured. With a little patience and persistence, you'll be able to create powerful alerts that help you monitor your GitHub repositories and stay on top of critical events.

If you want to learn more about Grafana alerting, check out the **Grafana documentation