Common New Relic Mistakes to Avoid

Are you leveraging New Relic to its full potential for your technology stack? This powerful observability platform offers unparalleled insights, but many organizations stumble, leading to wasted resources and missed opportunities. Are you sure you’re not making these same mistakes?

Ignoring Proper Agent Configuration

One of the most frequent errors is neglecting the initial configuration of New Relic agents. A “set it and forget it” approach simply doesn’t work. The agents—which collect data from your applications and infrastructure—need careful tuning to ensure they’re capturing the right information without overwhelming the system.

Consider the language-specific nuances. For example, the Java agent offers extensive configuration options for transaction naming, allowing you to group similar requests for better analysis. If you leave the default settings, you might end up with hundreds of unique transaction names that make it impossible to identify performance bottlenecks. I’ve personally seen teams spend days trying to debug performance issues only to realize that the agent wasn’t properly configured to capture the relevant data.

Here are some critical aspects of agent configuration:

1. Transaction Naming: Define clear and consistent transaction names. Use patterns and regular expressions to group similar requests. For example, instead of seeing individual product IDs in your transaction names (e.g., `/product/123`, `/product/456`), use a pattern like `/product/{id}`.
2. Custom Attributes: Add custom attributes to your transactions and events to provide richer context. These attributes can include user IDs, session IDs, product categories, or any other relevant information. This enables you to segment and filter your data more effectively.
3. Error Handling: Configure the agent to capture and report errors accurately. Ensure that stack traces are included for easier debugging. Consider adding custom error attributes to provide additional context, such as the user’s input or the state of the application when the error occurred.
4. Sampling: Adjust the sampling rate to balance data accuracy and performance overhead. If you’re experiencing high traffic, you might need to reduce the sampling rate to prevent the agent from consuming too many resources. However, be careful not to reduce the sampling rate too much, as this can lead to inaccurate data.

_Based on internal New Relic data, organizations that invest in proper agent configuration see a 30% reduction in time spent troubleshooting performance issues._

Overlooking Service Level Indicators (SLIs) and Objectives (SLOs)

Many teams implement New Relic without clearly defining their Service Level Indicators (SLIs) and Service Level Objectives (SLOs). Without these metrics in place, it’s difficult to measure the effectiveness of your monitoring efforts or to identify areas that need improvement.

SLIs are the metrics you use to measure the performance and reliability of your services. Common SLIs include:

• Latency: The time it takes for a service to respond to a request.
• Error Rate: The percentage of requests that result in an error.
• Availability: The percentage of time that a service is available and functioning correctly.
• Throughput: The number of requests that a service can handle per unit of time.

SLOs are the targets you set for your SLIs. For example, you might set an SLO of 99.9% availability for your critical services.

Once you’ve defined your SLIs and SLOs, you can use New Relic to monitor them and alert you when they’re not being met. New Relic’s dashboards and alerting features make it easy to track your SLIs and SLOs over time and to identify trends that might indicate potential problems.

For example, let’s say your e-commerce website has an SLO of 200ms for the average response time of the product details page. You can create a New Relic dashboard that tracks the average response time of this page and set up an alert to notify you if it exceeds 200ms. This allows you to proactively identify and address performance issues before they impact your users.

Neglecting Synthetics Monitoring

Synthetic monitoring, often overlooked, is a crucial component of a robust observability strategy. It involves simulating user interactions with your application to proactively identify issues before they affect real users. Many teams rely solely on real user monitoring (RUM), which only captures data from actual user sessions. While RUM is valuable, it can’t detect issues that aren’t being triggered by real users.

Dynatrace, a competitor to New Relic, emphasizes the importance of combining synthetic and real user monitoring for complete visibility. New Relic Synthetics allows you to create various types of monitors, including:

• Simple Browser Monitors: Test the basic functionality of your website by simulating a user visiting a page and verifying that certain elements are present.
• Scripted Browser Monitors: Simulate more complex user interactions, such as logging in, adding items to a cart, and checking out.
• API Monitors: Test the performance and availability of your APIs.

By using synthetic monitoring, you can identify issues such as broken links, slow page load times, and API failures before they impact your users. You can also use synthetic monitoring to test the performance of your application under different conditions, such as during peak traffic hours or after a new release.

Consider a scenario where a new deployment introduces a subtle bug that only affects users in a specific geographic region. RUM might not detect this issue for hours or even days, as it relies on users in that region to trigger the bug. However, a synthetic monitor configured to run from that region would immediately detect the issue and alert your team.

Ignoring Alerting Best Practices

Effective alerting is critical for responding to issues quickly and efficiently. However, many teams struggle with alert fatigue, which occurs when they receive too many alerts, many of which are false positives or non-actionable. This can lead to teams ignoring alerts altogether, which defeats the purpose of monitoring.

To avoid alert fatigue, it’s essential to follow alerting best practices:

1. Define Clear Alerting Thresholds: Set thresholds that are meaningful and relevant to your business. Avoid setting thresholds that are too sensitive, as this will lead to false positives.
2. Use Multiple Conditions: Combine multiple conditions to trigger alerts. For example, instead of alerting when CPU utilization exceeds 80%, alert when CPU utilization exceeds 80% and memory utilization exceeds 90%. This reduces the likelihood of false positives.
3. Route Alerts to the Right People: Ensure that alerts are routed to the appropriate teams or individuals who are responsible for resolving the issue. Use New Relic’s notification channels to send alerts to email, Slack, PagerDuty, or other communication platforms.
4. Document Alerting Procedures: Create clear and concise documentation that outlines the steps to take when an alert is triggered. This helps ensure that everyone knows how to respond to alerts quickly and effectively.
5. Regularly Review and Tune Alerts: Continuously review and tune your alerts to ensure that they’re still relevant and effective. As your application and infrastructure evolve, your alerting needs will change.

_According to a 2025 report by the Uptime Institute, organizations that implement effective alerting strategies experience a 20% reduction in downtime._

Failing to Correlate Data from Different Sources

New Relic’s true power lies in its ability to correlate data from different sources, providing a holistic view of your application and infrastructure performance. Many teams, however, fail to take advantage of this capability, leading to fragmented insights and slower troubleshooting.

For example, you might be using New Relic APM to monitor the performance of your application, New Relic Infrastructure to monitor the performance of your servers, and New Relic Logs to collect and analyze your logs. If you’re not correlating data from these different sources, you might struggle to identify the root cause of performance issues.

Imagine a scenario where your application is experiencing slow response times. Without correlating data from different sources, you might spend hours trying to debug the application code, only to realize that the issue is caused by a network bottleneck on one of your servers. By correlating data from New Relic APM and New Relic Infrastructure, you could quickly identify the network bottleneck and resolve the issue.

To effectively correlate data from different sources, use New Relic’s features such as:

• Distributed Tracing: Track requests as they flow through your application and infrastructure. This allows you to identify bottlenecks and latency issues across different services.
• Logs in Context: View logs in the context of your application traces and metrics. This makes it easier to identify the root cause of errors and performance issues.
• Dashboards: Create custom dashboards that combine data from different sources. This allows you to visualize the relationships between different metrics and identify trends that might indicate potential problems.

Not Leveraging New Relic Query Language (NRQL) Effectively

Elastic, another player in the observability space, is known for its powerful query language. New Relic has similar capabilities with NRQL. NRQL is a powerful query language that allows you to extract and analyze data from New Relic. Many teams, however, only scratch the surface of NRQL’s capabilities, missing out on valuable insights.

NRQL allows you to:

• Aggregate Data: Calculate sums, averages, minimums, maximums, and other aggregate values.
• Filter Data: Filter data based on specific criteria.
• Group Data: Group data by one or more attributes.
• Create Custom Metrics: Define custom metrics based on existing data.
• Visualize Data: Create charts and graphs to visualize your data.

For example, let’s say you want to identify the top 10 most frequently occurring errors in your application. You can use the following NRQL query to achieve this:

“`nrql
SELECT count(*) FROM TransactionError FACET error.message LIMIT 10

This query will return a table showing the 10 most frequently occurring error messages and the number of times each error has occurred.

By mastering NRQL, you can unlock the full potential of New Relic and gain deeper insights into your application and infrastructure performance. New Relic offers extensive documentation and tutorials to help you learn NRQL.

Conclusion

Effectively using New Relic requires more than just installing the agents. By avoiding common pitfalls such as neglecting agent configuration, overlooking SLIs/SLOs, ignoring synthetic monitoring, mismanaging alerting, failing to correlate data, and underutilizing NRQL, you can maximize the value of your New Relic investment. Remember that proactive configuration, thoughtful monitoring, and data correlation are key to unlocking the full potential of New Relic for your technology stack. Start by reviewing your agent configuration and defining clear SLIs/SLOs today.