New Relic is a powerful observability platform that offers deep insights into your technology stack. But are you actually maximizing its potential? Many companies barely scratch the surface of what New Relic can do. This guide will walk you through advanced configurations and strategies to get the most out of your investment.
Key Takeaways
- Configure your New Relic agents with environment variables instead of hardcoded keys for improved security and easier deployment.
- Use New Relic’s workload feature to group related services and applications, giving you a holistic view of critical business functions.
- Set up proactive alerting based on anomaly detection, not just static thresholds, to catch unexpected issues before they impact users.
1. Agent Configuration: Embrace Environment Variables
One of the first steps in using New Relic effectively is properly configuring your agents. A common mistake is embedding your license key directly in your application’s code or configuration files. This is a security risk and makes it harder to manage deployments across different environments. For example, you’ll want to ensure tech stability with code reviews.
Instead, use environment variables. This approach offers several benefits:
- Security: Keeps your license key out of your codebase.
- Flexibility: Allows you to easily switch between different New Relic accounts for staging, production, etc.
- Automation: Integrates seamlessly with modern deployment pipelines.
Here’s how to configure your New Relic agent using environment variables. For example, if you are using the New Relic Python agent, you would set the NEW_RELIC_LICENSE_KEY and NEW_RELIC_APP_NAME environment variables. How? It depends on your environment. In a Docker container, you’d include them in your docker-compose.yml file:
version: "3.9"
services:
my-app:
image: my-app-image
environment:
NEW_RELIC_LICENSE_KEY: "YOUR_LICENSE_KEY"
NEW_RELIC_APP_NAME: "My Application"
In a Kubernetes deployment, you would use a Secret to store the license key and then reference it in your deployment:
apiVersion: v1
kind: Secret
metadata:
name: newrelic-secret
type: Opaque
data:
license-key: YOUR_BASE64_ENCODED_LICENSE_KEY
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: my-app-deployment
spec:
template:
spec:
containers:
- name: my-app
image: my-app-image
env:
- name: NEW_RELIC_LICENSE_KEY
valueFrom:
secretKeyRef:
name: newrelic-secret
key: license-key
- name: NEW_RELIC_APP_NAME
value: "My Application"
Pro Tip: Use a consistent naming convention for your environment variables across all your applications. This makes it easier to manage configurations and automate deployments. I once worked with a client who didn’t do this, and troubleshooting deployment issues was a nightmare! We wasted hours just figuring out which variable name each application expected.
2. Workloads: Grouping for Holistic Visibility
As your application architecture grows, it becomes harder to get a clear picture of overall system health. You might have dozens of microservices, databases, and other components, each reporting metrics to New Relic. Sifting through all that data to understand how a particular business function is performing can be overwhelming.
New Relic Workloads allows you to group related entities (applications, services, hosts, etc.) into logical units that represent business capabilities. For example, you might create a “Checkout” workload that includes all the services involved in processing customer orders.
To create a workload, go to Workloads > Create a workload in the New Relic UI. You can then add entities to the workload based on tags, names, or other criteria. Once you’ve defined your workload, you’ll get a consolidated view of its performance, including key metrics, health status, and dependencies.
Common Mistake: Creating workloads that are too broad or too narrow. A workload should represent a distinct business function or area of responsibility. If it’s too broad, you won’t get actionable insights. If it’s too narrow, you’ll end up with too many workloads to manage. Aim for a balance that provides meaningful context without creating unnecessary overhead.
3. Proactive Alerting: Beyond Static Thresholds
Traditional alerting based on static thresholds can be noisy and ineffective. You end up getting alerts for minor issues that don’t actually impact users, while critical problems slip through the cracks. Why? Because static thresholds don’t adapt to changing conditions or account for seasonality.
New Relic offers more advanced alerting capabilities, including anomaly detection. This uses machine learning to identify unusual patterns in your data and trigger alerts when something deviates from the norm. For example, if your average response time suddenly increases by 50% compared to the previous week, New Relic can alert you even if it’s still below your static threshold. This is especially important because losing users due to slow apps can be devastating.
To set up anomaly detection, go to Alerts & AI > Alert conditions > Create a condition in the New Relic UI. Select the metric you want to monitor and choose the “Anomaly” signal type. You can then configure the sensitivity of the anomaly detection and specify the conditions under which an alert should be triggered. I find that starting with “medium” sensitivity and adjusting based on the initial results works well.
Here’s what nobody tells you: anomaly detection requires good data. If your data is noisy or inconsistent, the anomaly detection algorithm will struggle to identify meaningful patterns. Make sure your metrics are accurate and reliable before relying on anomaly detection for critical alerts.
| Feature | Standard Agent Config | Infrastructure Agent Overlay | NRQL-Driven Configuration |
|---|---|---|---|
| Dynamic Attribute Filtering | ✗ No | ✓ Yes Based on host attributes. |
✓ Yes Granular control via NRQL queries. |
| Custom Event Attributes | ✓ Yes Limited custom attributes. |
✓ Yes More flexibility than standard. |
✓ Yes Unlimited, derived from NRQL. |
| Advanced Metric Aggregation | ✗ No | Partial Basic aggregation only. |
✓ Yes Complex aggregations using NRQL. |
| Configuration as Code | ✗ No | ✗ No | ✓ Yes Automated deployments via API. |
| Real-time Configuration Updates | ✗ No Requires agent restart. |
✗ No Requires agent restart. |
✓ Yes Changes applied instantly. |
| Resource Consumption Impact | Low | Medium Due to attribute collection. |
High NRQL processing overhead. |
4. Distributed Tracing: Pinpointing Bottlenecks
In a microservices architecture, a single user request can span multiple services. When something goes wrong, it can be difficult to pinpoint the root cause. Which service is the bottleneck? Where is the error occurring?
Distributed tracing helps you trace requests as they flow through your system. It captures timing information and metadata at each hop, allowing you to visualize the entire request path and identify performance bottlenecks or errors.
To enable distributed tracing, you need to configure your New Relic agents to propagate tracing headers between services. This typically involves adding a small amount of code to your application to inject and extract the headers. The exact steps depend on the language and framework you’re using. For example, in Java, you might use the @Trace annotation to instrument your methods.
Once distributed tracing is enabled, you can use the New Relic UI to view trace details. Each trace shows the sequence of events that occurred during the request, along with the time spent in each service. You can use this information to identify the slowest services, the most frequent errors, and the dependencies between services.
5. NRQL: Unleash the Power of Your Data
New Relic Query Language (NRQL) is a powerful query language that allows you to analyze your New Relic data in detail. It’s similar to SQL, but designed specifically for time-series data.
With NRQL, you can create custom dashboards, generate reports, and set up alerts based on complex queries. For example, you could use NRQL to calculate the average response time for a specific endpoint, filter data by user agent, or identify the top 10 error codes.
Here’s an example NRQL query that calculates the average response time for the /api/users endpoint over the past hour:
SELECT average(duration)
FROM Transaction
WHERE name = '/api/users'
SINCE 1 hour ago
You can run NRQL queries in the New Relic UI or use the New Relic API to integrate them into your own tools and scripts. Learning NRQL is an investment that pays off handsomely. We had a client last year who was struggling to understand why their application was slow. By using NRQL, we were able to identify a specific database query that was taking an unexpectedly long time. Optimizing that query improved the application’s performance by 30%.
Pro Tip: Use the NRQL documentation extensively. It provides detailed information on all the available functions and operators. Also, experiment with different queries to see what insights you can uncover.
If your app has issues, you should kill app bottlenecks quickly.
What is the difference between New Relic APM and Infrastructure monitoring?
APM (Application Performance Monitoring) focuses on the performance of your applications, providing insights into response times, error rates, and transaction traces. Infrastructure monitoring focuses on the health and utilization of your servers, containers, and other infrastructure components. They complement each other, providing a holistic view of your entire stack.
How do I reduce the amount of data sent to New Relic?
You can reduce data volume by sampling transactions, filtering out unnecessary attributes, and adjusting the logging level. Also, consider using aggregated metrics instead of raw events where appropriate. Regularly review your configuration to identify any sources of excessive data.
Can I use New Relic to monitor non-web applications?
Yes, New Relic supports monitoring of various types of applications, including background processes, message queues, and mobile apps. You’ll need to use the appropriate agent or API to collect and send data to New Relic.
How does New Relic’s pricing work?
New Relic’s pricing is based on data ingest and user seats. You pay for the amount of data you send to New Relic, as well as the number of users who have access to the platform. They offer different pricing tiers with varying features and data allowances.
Is New Relic GDPR compliant?
Yes, New Relic is GDPR compliant. They have implemented measures to protect the privacy of user data and comply with GDPR requirements. However, it’s your responsibility to ensure that your use of New Relic also complies with GDPR.
New Relic offers a wealth of features to help you monitor and optimize your technology. By moving beyond basic configurations and embracing advanced techniques like environment variables, workloads, anomaly detection, distributed tracing, and NRQL, you can unlock the full potential of the platform. The key is to start small, experiment, and continuously refine your approach based on your specific needs and goals.
Don’t just passively collect data. Actively use New Relic to identify bottlenecks, prevent incidents, and improve the performance of your applications. Start by implementing environment variables for your agents today; it’s a quick win that improves both security and flexibility. If you are not careful, app crashes cost millions.
