Warden/Watcher Deployments: Why < 100% On Dashboards?

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Have you ever noticed that your Warden/Watcher dashboards aren't showing a full 100% deployment rate? You're not alone! This is a common issue, and in this article, we're going to dive deep into the reasons behind it. We'll explore why the number of SKRs with Warden/Watcher deployed might be lower than expected and what we can do to fix it. So, let's get started and figure out what's going on!

Understanding the Dashboard Metrics

First, let's break down the key metrics we're looking at on the Warden/Watcher dashboards. These dashboards provide a snapshot of the deployment status across your SKRs (SAP Kyma Runtimes). The critical data points include:

  • Number of SKRs: This is the total count of your SAP Kyma Runtimes.
  • Number of SKRs with Warden/Watcher deployed: This indicates how many of your SKRs have the Warden and Watcher components successfully deployed.
  • Number of unready Warden/Watcher deployments: This metric highlights any deployments that are not in a ready state, potentially indicating issues or failures.
  • Percentage of SKRs with Warden/Watcher deployed: This is the crucial metric that shows the deployment rate, and it's the one we're focusing on when we see it dip below 100%.

The main problem we're tackling is the discrepancy between the total number of SKRs and the number of SKRs with Warden/Watcher deployed. This difference directly impacts the percentage metric, causing it to fall below the expected 100%. During monitoring, it's often observed that the numbers match between Warden and Watcher charts, and the number of unready watcher deployments is typically zero. This suggests that the issue isn't necessarily with failed deployments, but rather something else. Previous observations have pointed to clusters in a deprovisioning state as a potential cause. Let's delve deeper into this and other possible reasons.

Investigating the Root Causes for Low Deployment Percentages

To truly understand why Warden/Watcher dashboards might be displaying less than 100% deployment, we need to put on our detective hats and investigate the potential root causes. There are several factors that could contribute to this discrepancy, and it's important to consider each one:

1. Clusters in Deprovisioning State

As mentioned earlier, one of the primary suspects is clusters that are in the process of being deprovisioned. When a cluster is being decommissioned, it's natural that Warden/Watcher might not be fully deployed or operational. These clusters shouldn't be counted in the overall metrics as clusters in active use, as they are intentionally being removed from the system. Identifying and excluding these clusters from the deployment calculations is a crucial step in getting an accurate view of the active deployment rate.

2. Deployment Failures and Rollbacks

Although less frequent, deployment failures or rollbacks can also lead to a lower percentage. If a Warden/Watcher deployment fails on a particular SKR, or if a rollback is initiated due to issues, the component might not be reported as deployed. It's essential to monitor deployment logs and alerts to catch these instances. However, the dashboards often show zero unready deployments, suggesting this isn't the main driver of the issue.

3. Newly Provisioned Clusters

Another factor to consider is newly provisioned clusters. There might be a delay between the creation of an SKR and the successful deployment of Warden/Watcher. This could be due to various reasons, such as provisioning scripts, network configurations, or resource availability. If the dashboards are capturing data in real-time, these newly provisioned clusters might temporarily skew the deployment percentage downwards until Warden/Watcher is fully deployed.

4. Reporting and Data Collection Issues

Sometimes, the issue might not be with the deployment itself, but with the reporting or data collection mechanisms. Glitches in the data pipeline, incorrect queries, or outdated data can all lead to inaccurate metrics. It's essential to verify the data sources and ensure that the queries used to populate the dashboards are correct and up-to-date.

5. Version Mismatches and Compatibility Issues

In some cases, version mismatches between Warden/Watcher and the underlying Kyma runtime can cause deployment problems. If a new version of Kyma is deployed without a compatible Warden/Watcher version, deployments might fail or become unstable. Ensuring compatibility between components is crucial for a smooth deployment process.

By systematically investigating these potential causes, we can pinpoint the exact reasons for the low deployment percentages and take appropriate action.

Strategies for Fixing the Dashboard and Improving Accuracy

Now that we've explored the possible reasons behind the dashboard discrepancies, let's talk about how we can fix them. The goal is to create a more accurate and informative view of the Warden/Watcher deployment status. Here are several strategies we can implement:

1. Exclude Deprovisioning Clusters

The most impactful fix is to exclude clusters in a deprovisioning state from the deployment calculations. These clusters should not be considered when determining the overall deployment percentage. To achieve this, we need to identify these clusters, potentially by querying a status flag or tag within the cluster metadata. Once identified, we can modify the dashboard queries to filter out these clusters, providing a more accurate representation of the deployment rate on active SKRs.

2. Differentiate Expected vs. Actual Deployments

To further refine the dashboard, we should aim to differentiate between the number of SKRs where Warden/Watcher is expected to be deployed and the number of SKRs with actual deployments. This will give us a clearer picture of the deployment gap. The dashboard should ideally display:

  • The total number of active SKRs where Warden/Watcher should be deployed.
  • The number of SKRs with Warden/Watcher successfully deployed.
  • The percentage of SKRs with Warden/Watcher deployed (calculated based on the above two metrics).

This distinction helps in isolating the issue and focusing on clusters where deployments are genuinely missing or failing.

3. Enhance Data Filtering and Querying

The queries used to populate the dashboard need to be robust and accurate. We should review the queries to ensure they are correctly filtering data and not including irrelevant information. For example, we might need to refine queries to consider the cluster status, component versions, and deployment timestamps. Using more precise queries will reduce the chances of misrepresentation and provide a clearer view of the deployment landscape.

4. Implement Real-time Monitoring and Alerts

To proactively address deployment issues, we should implement real-time monitoring and alerting. This involves setting up alerts for failed deployments, unready components, and other anomalies. By catching issues early, we can prevent them from impacting the overall deployment percentage and ensure a smoother deployment process. Tools like Prometheus and Grafana can be configured to provide such real-time insights.

5. Improve Data Visualization

The way data is presented on the dashboard can significantly impact its usefulness. We should ensure that the visualizations are clear, intuitive, and easy to understand. This might involve using different chart types, color-coding, or adding tooltips to provide additional context. A well-designed dashboard makes it easier to identify trends, spot anomalies, and make informed decisions.

6. Document Dashboard Logic and Procedures

Finally, it's crucial to document the dashboard logic and procedures. This includes explaining how the metrics are calculated, what data sources are used, and how to interpret the results. This documentation serves as a valuable resource for monitoring duty personnel and anyone else using the dashboard. It ensures consistency in interpretation and facilitates troubleshooting when issues arise.

By implementing these strategies, we can significantly improve the accuracy and usefulness of the Warden/Watcher dashboards, giving us a clearer understanding of the deployment status and enabling us to take proactive action when needed.

Step-by-Step Guide to Fixing the Dashboard (Easy Fix Scenario)

Okay, let's talk about the scenario where we've identified a relatively straightforward fix for the dashboard. This is the best-case scenario, where we can apply the fix directly and see immediate improvements. Here's a step-by-step guide to tackle an "easy fix" situation:

Step 1: Identify the Root Cause

The first step is always to pinpoint the exact reason for the discrepancy. This usually involves reviewing the metrics, logs, and configurations. If the investigation reveals that clusters in a deprovisioning state are the primary cause, we're on the right track for an easy fix. Other simple causes might include incorrect query filters or data aggregation issues.

Step 2: Develop a Solution

Once you know the cause, the next step is to devise a solution. For instance, if deprovisioning clusters are the issue, the solution might involve modifying the dashboard queries to exclude these clusters based on their status. If it's a query filter issue, you'll need to correct the filter logic. Ensure that the solution aligns with the overall goals of accuracy and clarity for the dashboard.

Step 3: Implement the Fix

Now comes the implementation phase. This typically involves making changes to the dashboard configuration, such as modifying SQL queries, updating filters, or adjusting data aggregation settings. Make sure to follow your organization's change management procedures, and always back up your configurations before making any changes. If you're working with a dashboarding tool like Grafana, you'll usually make these changes through the user interface or configuration files.

Step 4: Test the Solution

Testing is critical to ensure that your fix works as expected and doesn't introduce any new issues. Verify that the dashboard now displays the correct metrics, excluding deprovisioning clusters or correcting the data aggregation. Compare the new results with previous data to confirm the improvement. It's also a good idea to get a second pair of eyes to review the changes and validate the results.

Step 5: Document the Changes

Once you've confirmed that the fix is working, it's essential to document the changes you've made. This documentation should include:

  • The issue that was identified.
  • The solution that was implemented.
  • The steps taken to implement the fix.
  • The results of the testing.

This documentation serves as a valuable reference for future troubleshooting and ensures that others can understand the changes that were made.

Step 6: Update Monitoring Duty Documentation

Finally, update the monitoring duty documentation to include an explanation of the dashboard and what to do if the issue reappears. This ensures that the on-call team is aware of the fix and can quickly address the problem if it occurs again. The documentation should include:

  • A description of the dashboard metrics.
  • An explanation of how the dashboard works.
  • Instructions on how to troubleshoot common issues.
  • Contact information for the dashboard owners or subject matter experts.

By following these steps, you can quickly and effectively fix dashboard issues, ensuring that your monitoring data is accurate and reliable.

What to Do When It's Not an Easy Fix: Creating a Follow-Up

Sometimes, the investigation reveals that the issue is more complex than initially anticipated. This is where the "easy fix" approach won't cut it, and we need a more structured approach. If you find yourself in this situation, the best course of action is to create a follow-up task or ticket. Here’s how to do it:

1. Clearly Define the Problem

The first step is to clearly articulate the problem. This means summarizing what you've investigated so far, the symptoms you've observed, and the potential root causes you've identified. The problem statement should be specific and concise, so anyone reading it can quickly understand the issue. For example, instead of saying "Dashboard shows incorrect data," you might say, "Warden/Watcher deployment percentage on the dashboard is consistently below 100%, even though unready deployments are minimal."

2. Outline the Investigation Steps Taken

Next, outline the steps you've already taken to investigate the issue. This provides context for anyone who will be working on the follow-up. It prevents duplication of effort and ensures that the next person can pick up where you left off. Include details such as:

  • Metrics reviewed.
  • Logs analyzed.
  • Configurations checked.
  • Potential causes explored.

3. Identify Remaining Questions or Unknowns

Be clear about what you still don't know. Identifying the unknowns helps focus the next steps. For example, you might not be sure if the issue is due to a data collection problem or a deployment failure on specific clusters. List these unknowns explicitly.

4. Propose Next Steps

Based on your investigation, suggest specific actions that should be taken next. This might include:

  • Deeper log analysis.
  • Data source verification.
  • Query optimization.
  • Testing on a staging environment.
  • Consulting with subject matter experts.

Having a clear set of proposed next steps makes it easier for someone to take ownership of the follow-up.

5. Assign a Priority and Timeframe

Determine the priority of the follow-up based on the impact of the issue. If the inaccurate dashboard is impacting critical monitoring or decision-making, it should be a high priority. Set a realistic timeframe for the follow-up, considering the complexity of the issue and the resources required. This helps ensure that the follow-up doesn't get lost in the shuffle.

6. Create a Detailed Ticket or Task

Now, create a detailed ticket or task in your issue tracking system (e.g., Jira, Trello, GitHub Issues). Include all the information you've gathered, such as the problem statement, investigation steps, unknowns, proposed next steps, priority, and timeframe. The ticket should be self-contained, providing all the necessary information for someone to work on the issue.

7. Assign the Ticket to the Appropriate Person or Team

Assign the ticket to the person or team best suited to handle the follow-up. This might be a dashboard owner, a data engineer, a DevOps team member, or a subject matter expert. Make sure the assignee is aware of the ticket and the required timeframe.

8. Communicate the Follow-Up

Finally, communicate the follow-up to any relevant stakeholders. This might include the monitoring duty team, dashboard users, or other teams that rely on the data. Keeping stakeholders informed ensures that everyone is aware of the issue and the steps being taken to resolve it.

By following these steps, you can effectively manage complex issues and ensure that they are properly addressed, even if they don't have an easy fix.

Conclusion

So, guys, we've covered a lot of ground in this article! We started by understanding the key metrics on the Warden/Watcher dashboards and the common issue of deployment percentages falling below 100%. We then dived into the potential root causes, from clusters in deprovisioning to data collection glitches. Next, we explored strategies for fixing the dashboard, including excluding deprovisioning clusters and refining dashboard queries. We also discussed step-by-step guides for both easy fixes and what to do when a follow-up is needed. The main takeaway? Don't panic when you see those numbers dip! By systematically investigating the issue and applying the strategies we've discussed, you can keep your Warden/Watcher dashboards accurate and reliable, ensuring smooth monitoring and decision-making. Keep those deployments healthy, and keep those dashboards shining!