The Short Answer

To fix the “Trace” error in observability, advanced users can toggle off the automatic trace sampling in the settings, which reduces the sync time from 15 minutes to 30 seconds, and then refresh the page to apply the changes. This quick fix resolves the issue in most cases, but for more complex scenarios, a deeper configuration change may be required.

Why This Error Happens

• Reason 1: The most common cause of the “Trace” error is the misconfiguration of the trace sampling rate, which can lead to an overwhelming amount of data being sent to the observability platform, causing it to crash or become unresponsive. For example, if the sampling rate is set to 100% for a high-traffic application, it can result in over 10,000 traces being sent per minute, exceeding the platform’s capacity.
• Reason 2: An edge case cause of this error is the presence of a circular dependency in the service graph, which can cause the tracing system to enter an infinite loop, leading to a stack overflow error. This can occur when two or more services are calling each other recursively, creating a cycle that cannot be resolved.
• Impact: The “Trace” error can significantly impact monitoring capabilities, making it difficult to identify and troubleshoot issues in the application. This can lead to prolonged downtime, decreased user satisfaction, and increased support requests. For instance, a study by a leading IT research firm found that the average cost of downtime per hour is around $5,600, highlighting the need for prompt resolution of such errors.

Step-by-Step Solutions

Method 1: The Quick Fix

1. Go to Settings > Trace Configuration > Sampling Rate
2. Toggle Automatic Trace Sampling to Off, which will reduce the sampling rate from 100% to 10%, decreasing the amount of data being sent to the platform.
3. Refresh the page to apply the changes, which should take around 30 seconds to complete.

Method 2: The Command Line/Advanced Fix

For more complex scenarios, you can use the observability platform’s command-line interface to adjust the tracing configuration. Run the following command to set the sampling rate to 5%:

1

observability-cli config set tracing.sample-rate 0.05

This will reduce the amount of data being sent to the platform, allowing you to troubleshoot the issue without overwhelming the system. Note that this command requires administrative privileges and should be used with caution.

Prevention: How to Stop This Coming Back

To prevent the “Trace” error from occurring in the future, follow these best practices:

• Configure the trace sampling rate based on the application’s traffic and performance requirements, taking into account the platform’s capacity and limitations.
• Regularly monitor the service graph for circular dependencies and resolve them promptly, using tools such as graph visualization and dependency analysis.
• Implement a robust monitoring system that can detect and alert on tracing issues before they become critical, using metrics such as trace volume, error rates, and system resource utilization.

If You Can’t Fix It…

[!WARNING] If observability keeps crashing despite trying the above fixes, consider switching to New Relic which handles Distributed tracing natively without these errors. New Relic’s distributed tracing feature provides a more robust and scalable solution for tracing and monitoring, with features such as automatic trace sampling, service mapping, and error analysis.

FAQ

Q: Will I lose data fixing this? A: The quick fix method will not result in data loss, as it only adjusts the trace sampling rate. However, if you need to perform a more extensive configuration change, you may need to restart the observability platform, which could result in a temporary loss of data (approximately 5-10 minutes). To minimize data loss, it is recommended to schedule maintenance during periods of low traffic and to use data backup and recovery mechanisms.

Q: Is this a bug in observability? A: The “Trace” error is not a bug in the observability platform, but rather a configuration issue that can be resolved by adjusting the trace sampling rate or resolving circular dependencies. The platform’s documentation and release notes (version 2.5 and later) provide guidance on how to configure tracing and troubleshoot common issues. However, if you are experiencing persistent issues, it is recommended to check the platform’s version history and release notes to ensure that you are running the latest version with the latest bug fixes and feature updates.

📚 Continue Learning

Check out our guides on observability and Trace.

Quick Verdict

For teams with a strong focus on log analysis and a budget over $10,000 per year, Splunk APM is the better choice due to its robust log management capabilities. However, for smaller teams or those prioritizing ease of use and a more comprehensive observability platform, Datadog is a more suitable option. Ultimately, the decision depends on the specific needs and constraints of your organization.

Feature Comparison Table

When to Choose Splunk APM

• If you’re a 50-person SaaS company needing to analyze large volumes of log data from various sources, Splunk APM is a better fit due to its robust log management capabilities and scalability.
• For teams with existing investments in Splunk’s ecosystem, such as Splunk Enterprise Security or Splunk IT Service Intelligence, Splunk APM provides a more integrated and streamlined observability experience.
• When your organization requires advanced log analysis and machine learning-powered insights, Splunk APM’s specialized features make it a more suitable choice.
• For large enterprises with complex, distributed systems, Splunk APM’s ability to handle high volumes of log data and provide detailed visibility into system performance makes it a better option.

When to Choose Datadog

• If you’re a 10-person startup with a limited budget and a need for a comprehensive observability platform, Datadog’s more predictable costs and ease of use make it a better fit.
• For teams prioritizing ease of use and a user-friendly interface, Datadog’s intuitive design and streamlined onboarding process make it a more suitable choice.
• When your organization requires a broad range of monitoring and analytics capabilities, including infrastructure, application, and user experience monitoring, Datadog’s comprehensive platform provides a more unified observability experience.
• For small to medium-sized businesses with relatively simple systems and a focus on ease of use, Datadog’s more accessible pricing and features make it a better option.

Real-World Use Case: Observability

Let’s consider a scenario where a 50-person SaaS company needs to set up observability for its e-commerce platform. With Splunk APM, the setup complexity would be around 5-7 days, requiring significant expertise in log analysis and configuration. Ongoing maintenance would require approximately 10 hours per week. The cost breakdown for 100 users/actions would be around $3,000 per month. Common gotchas include the need for careful log data management and potential performance issues if not properly configured.

In contrast, Datadog would require around 2-3 days for setup, with a more user-friendly interface and streamlined onboarding process. Ongoing maintenance would require approximately 5 hours per week. The cost breakdown for 100 users/actions would be around $1,500 per month. Common gotchas include the need for careful agent configuration and potential limitations in log analysis capabilities.

Migration Considerations

If switching between Splunk APM and Datadog, consider the following:

• Data export/import limitations: Splunk APM’s data export capabilities are more comprehensive, but may require additional configuration. Datadog’s data import capabilities are more streamlined, but may have limitations in terms of data format and volume.
• Training time needed: Splunk APM requires significant expertise in log analysis and configuration, with a training time of around 2-3 weeks. Datadog’s training time is around 1-2 weeks, with a more user-friendly interface and streamlined onboarding process.
• Hidden costs: Splunk APM’s pricing model can lead to unexpected costs if log data volumes exceed expectations. Datadog’s pricing model is more predictable, but may have hidden costs associated with additional features or support.

FAQ

Q: Which tool is better for log analysis? A: Splunk APM is more specialized in log-focused observability, with advanced log analysis and machine learning-powered insights. However, Datadog’s log analysis capabilities are still robust and suitable for many use cases.

Q: Can I use both Splunk APM and Datadog together? A: Yes, it is possible to use both tools together, but it may require additional configuration and integration efforts. Consider using Splunk APM for log-focused observability and Datadog for more comprehensive monitoring and analytics.

Q: Which has better ROI for Observability? A: Based on a 12-month projection, Datadog’s more predictable costs and comprehensive platform provide a better ROI for small to medium-sized businesses. However, for large enterprises with complex systems and high log data volumes, Splunk APM’s specialized features and scalability may provide a better ROI.

Bottom Line: Choose Splunk APM for log-focused observability and large-scale deployments, and Datadog for comprehensive monitoring and analytics with a more predictable cost structure.

🔍 More Splunk APM Comparisons

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Quick Verdict

For small to medium-sized teams with limited budgets, OpenObserve is a more cost-effective option, offering a robust open-source platform for observability. However, larger teams with complex infrastructure may prefer Datadog’s comprehensive features and support. Ultimately, the choice between OpenObserve and Datadog depends on your team’s specific needs and scalability requirements.

Feature Comparison Table

When to Choose OpenObserve

• If you’re a 10-person startup with a limited budget and need a cost-effective observability solution, OpenObserve is a great choice.
• For teams with technical expertise and a desire for high customization, OpenObserve’s open-source nature provides flexibility and control.
• If you’re a 50-person SaaS company needing to monitor a small to medium-sized infrastructure, OpenObserve can provide a robust and affordable solution.
• For organizations with strict security and compliance requirements, OpenObserve’s self-hosted option ensures data sovereignty and control.

When to Choose Datadog

• If you’re a 100-person enterprise with a complex infrastructure and multiple teams, Datadog’s comprehensive features and support can provide a unified observability platform.
• For teams with limited technical expertise, Datadog’s user-friendly interface and extensive documentation make it easier to get started.
• If you’re a large e-commerce company needing to monitor a high-volume infrastructure, Datadog’s scalability and performance features can handle the load.
• For organizations with a large number of integrations and dependencies, Datadog’s extensive integration library can simplify monitoring and troubleshooting.

Real-World Use Case: Observability

Let’s consider a scenario where a 50-person SaaS company needs to monitor its infrastructure and applications. With OpenObserve, setup complexity would take around 2-3 days, with an ongoing maintenance burden of 1-2 hours per week. The cost breakdown for 100 users/actions would be $0, as OpenObserve is free and open-source. However, common gotchas include the need for technical expertise and potential scalability limitations.

In contrast, Datadog would require a setup time of 1-2 days, with an ongoing maintenance burden of 1 hour per week. The cost breakdown for 100 users/actions would be around $1,500 per month, depending on the features and hosts required. Common gotchas include the potential for costs to add up quickly and limited customization options.

Migration Considerations

If switching between OpenObserve and Datadog, data export/import limitations may apply, with OpenObserve requiring manual data migration and Datadog providing a more streamlined process. Training time needed would be around 1-2 weeks for OpenObserve and 1-3 days for Datadog. Hidden costs may include additional support or consulting fees for OpenObserve, while Datadog’s costs are more transparent.

FAQ

Q: What is the main difference between OpenObserve and Datadog? A: The main difference is that OpenObserve is an open-source platform, while Datadog is a commercial solution with a custom pricing model.

Q: Can I use both OpenObserve and Datadog together? A: Yes, you can use both tools together, but it may require additional integration and configuration efforts. OpenObserve can be used for specific use cases, such as monitoring a small infrastructure, while Datadog can be used for more comprehensive monitoring and analytics.

Q: Which has better ROI for Observability? A: Based on a 12-month projection, OpenObserve can provide a better ROI for small to medium-sized teams, with estimated costs of $0-$5,000 per year. Datadog’s costs can range from $15,000 to $50,000 per year, depending on the features and hosts required. However, larger teams with complex infrastructure may find Datadog’s comprehensive features and support to be worth the additional cost.

Bottom Line: For teams with limited budgets and technical expertise, OpenObserve is a cost-effective and customizable option for observability, while larger teams with complex infrastructure may prefer Datadog’s comprehensive features and support.

🔍 More OpenObserve Comparisons

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Quick Verdict

For small to medium-sized teams with limited budgets, Grafana is a more cost-effective solution for observability, offering a wide range of integrations and a user-friendly interface. However, for larger teams with complex logging needs, Loki’s scalability and log-focused features make it a better choice. Ultimately, the decision between Grafana and Loki depends on your team’s specific needs and priorities.

Feature Comparison Table

When to Choose Grafana

• If you’re a 50-person SaaS company needing to monitor and analyze metrics from multiple sources, Grafana’s wide range of integrations and user-friendly interface make it a great choice.
• If you have a small team with limited logging needs, Grafana’s basic log management capabilities may be sufficient.
• If you’re already invested in the Prometheus ecosystem, Grafana’s native integration with Prometheus makes it a natural choice.
• If you prioritize a high degree of customization and flexibility in your observability tool, Grafana’s open-source nature and large community of developers make it a great option.

When to Choose Loki

• If you’re a large enterprise with complex logging needs, Loki’s advanced log management capabilities and scalability make it a better choice.
• If you’re looking for a cost-effective solution for log management, Loki’s open-source nature and lower enterprise edition pricing make it a great option.
• If you’re already using Prometheus and need a log-focused solution, Loki’s native integration with Prometheus and Kubernetes makes it a great choice.
• If you prioritize ease of use and a moderate learning curve, Loki’s more streamlined interface and simpler configuration make it a great option.

Real-World Use Case: Observability

Let’s say you’re a 100-person e-commerce company needing to monitor and analyze logs and metrics from your application. With Grafana, setup complexity would be around 2-3 days, with ongoing maintenance burden of 1-2 hours per week. Cost breakdown would be around $100/month for the enterprise edition, plus $500/month for hosting and support. With Loki, setup complexity would be around 1-2 days, with ongoing maintenance burden of 1 hour per week. Cost breakdown would be around $50/month for the enterprise edition, plus $300/month for hosting and support. Common gotchas include configuring data sources and setting up alerting rules.

Migration Considerations

If switching from Grafana to Loki, data export/import limitations include the need to reconfigure data sources and rewrite alerting rules. Training time needed would be around 1-2 weeks, with hidden costs including potential downtime and loss of productivity. If switching from Loki to Grafana, data export/import limitations include the need to reconfigure log management settings and rewrite dashboard configurations. Training time needed would be around 2-3 weeks, with hidden costs including potential downtime and loss of productivity.

FAQ

Q: Can I use both Grafana and Loki together? A: Yes, you can use both tools together, with Grafana handling metrics and Loki handling logs. This approach requires some additional configuration and setup, but can provide a comprehensive observability solution.

Q: Which has better ROI for Observability? A: Based on a 12-month projection, Loki’s lower enterprise edition pricing and reduced maintenance burden make it a more cost-effective solution for observability, with a potential ROI of 200-300%. However, Grafana’s wide range of integrations and customization options may provide additional value for teams with complex observability needs.

Q: How do I choose between Grafana and Loki for my team? A: Consider your team’s specific needs and priorities, including budget, logging needs, and metric management requirements. If you prioritize a wide range of integrations and customization options, Grafana may be a better choice. If you prioritize advanced log management capabilities and scalability, Loki may be a better choice.

Bottom Line: Ultimately, the choice between Grafana and Loki depends on your team’s specific needs and priorities, but for most use cases, Grafana’s wide range of integrations and user-friendly interface make it a great choice for observability.

🔍 More Grafana Comparisons

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