Interview Questions for New Relic

New Relic APM (Application Performance Monitoring) and Infrastructure Monitoring are distinct but complementary services. APM focuses on the performance of your applications, tracking things like transaction traces, code-level performance, and error rates. Think of it as a doctor’s examination of your application’s health, pinpointing issues within the code itself. Infrastructure Monitoring, on the other hand, provides visibility into the health and performance of your underlying servers, containers, and cloud infrastructure. It monitors things like CPU usage, memory consumption, disk I/O, and network traffic. This is like checking the vital signs of the machine your application runs on. While they are separate, they work together; a slow application might be caused by underlying infrastructure problems, and vice-versa. For instance, high CPU usage (Infrastructure) might directly correlate with slow response times in a specific application function (APM).

Troubleshooting slow database queries in New Relic typically involves a multi-step process. First, I’d navigate to the APM UI and pinpoint the slow transactions. New Relic’s transaction traces will show exactly which database calls are contributing most to the slowdown, highlighting the specific SQL query and its execution time. Then, I’d delve into New Relic’s database monitoring features to investigate the query itself. This includes examining execution plans to identify bottlenecks like missing indexes or inefficient joins. The ‘slow query’ feature can be invaluable here. I’d look at metrics like query execution time, the number of rows returned, and the amount of data read. The goal is to identify the root cause: Is it a poorly written query, a lack of indexing, insufficient database resources, or perhaps a problem with database connections? Once the bottleneck is identified, the solution might involve optimizing the query, adding indexes, upgrading database hardware, or addressing connection issues.

For example, if I see a consistently slow query selecting from a large table without an index, I’d recommend creating an index on the relevant columns. After implementing the fix, I would monitor the query’s performance in New Relic to ensure the improvement. The key is iterative improvement and close monitoring of results using New Relic’s powerful data visualization tools.

I have extensive experience building custom dashboards in New Relic to monitor key application and infrastructure metrics. I find the visualization capabilities very powerful. I typically create dashboards focused on specific areas like application performance, error rates, and infrastructure health. I leverage various visualization options such as charts, graphs, tables, and maps to present the data in a clear and concise manner. For example, I might create a dashboard with a line graph showing response times over time, a bar chart showing error rates by application, and a heatmap showing geographical distribution of user traffic. I also use dashboards to create custom alerts to trigger notifications based on specific thresholds and conditions. This ensures that I’m proactively alerted to any potential issues. In a recent project, I built a dashboard that integrated data from APM, Infrastructure, and Synthetics to provide a holistic view of the entire system, allowing for quicker identification and resolution of performance bottlenecks. I’ve found that well-designed dashboards are crucial for effective monitoring and proactive problem-solving.

Identifying and resolving performance bottlenecks using New Relic is a systematic process. I start by using the APM application overview to get a high-level view of application health, paying close attention to response times and error rates. Then, I use the transaction traces to drill down into the specific code sections and database calls that are causing performance problems. This lets me pinpoint the exact source of slowdowns. By analyzing call stacks and identifying long-running functions, I can determine whether the bottleneck stems from inefficient code, database issues, or external dependencies. For infrastructure issues, I’d consult the Infrastructure section, analyzing CPU, memory, and disk usage to see if resource constraints are impacting performance. For network-related issues, I’d use the network traces feature. After pinpointing the cause, I’ll apply the appropriate fix (optimize code, add database indexes, scale infrastructure, etc.), and then continuously monitor the metrics in New Relic to confirm that the performance issue has been resolved. The ability to correlate APM and Infrastructure data within New Relic is key for holistic performance optimization.

The Apdex (Application Performance Index) score in New Relic is a single metric summarizing user satisfaction with application response times. It’s a weighted average that considers the percentage of requests that fall within acceptable response time limits (usually defined by a ‘satisfied’ threshold, say under 1 second). Requests that exceed the satisfied threshold, but fall within a ‘tolerating’ threshold (say, under 4 seconds), are partially counted. Requests exceeding the tolerating threshold are counted as ‘frustrated’. An Apdex score ranges from 0 to 1, with 1 representing perfect user satisfaction (all requests met the ‘satisfied’ threshold). A lower score indicates that many requests are taking longer than expected, leading to user dissatisfaction. The significance of Apdex is that it gives you a concise, quantitative measure of your application’s overall performance from a user’s perspective. It helps focus on the impact on users rather than just raw metrics.

Configuring alerts and notifications in New Relic is a crucial aspect of proactive monitoring. I usually start by defining the critical metrics I want to monitor (e.g., response time, error rate, CPU utilization). Within New Relic, I set alert conditions based on thresholds (e.g., if response time exceeds 500ms for more than 5 minutes). Then, I choose the notification channels, such as email, SMS, or integrations with third-party collaboration tools like Slack. For instance, I might set an alert that sends an email to the team if the error rate for a specific API exceeds 1% for more than 10 minutes, or sends a PagerDuty alert if a server’s CPU utilization reaches 95%. The key is to avoid alert fatigue by being selective and setting appropriate thresholds, ensuring that alerts only trigger on truly significant events. Careful alert configuration ensures that the team is proactively informed about critical issues, enabling timely interventions.

New Relic Synthetics allows for proactive monitoring by simulating user interactions with your application from various locations across the globe. It involves creating monitors, essentially scripts that perform actions like accessing web pages, making API calls, and interacting with other services, mimicking real user behavior. This helps identify problems before end users experience them. For example, I might set up a Synthetics monitor to check the availability and responsiveness of a key API endpoint every 5 minutes from different geographical locations (New York, London, Tokyo). If the monitor detects an outage or slow response, it will trigger an alert. This proactive approach gives insights into performance from the user’s perspective, identifying regional outages or latency issues before they impact real users. I’ve used Synthetics to greatly improve our early warning system for potential performance and availability problems. It allowed us to quickly respond to events such as CDN failures or DNS propagation delays, preventing significant user impact.

New Relic Insights is a powerful tool for analyzing the vast amounts of data collected by New Relic. It allows you to go beyond simple dashboards and dive deep into your application’s performance, identifying trends and patterns that might otherwise be missed. Essentially, it lets you ask complex questions about your data and get insightful answers.

I typically utilize Insights by creating custom dashboards and visualizations based on NRQL (New Relic Query Language) queries. For instance, I might create a dashboard showing the average response time of my application over the past week, broken down by geographic location. To build this, I’d use NRQL to query the appropriate metrics and then use Insights’ visualization tools to display the results in a clear and concise manner. Another common use case is creating alerts based on specific thresholds. If the error rate exceeds a certain percentage, or if the response time consistently exceeds a predefined limit, Insights can automatically trigger alerts to notify the team.

Beyond dashboards, I leverage Insights for more complex analyses. This often involves building custom visualizations to understand relationships between different metrics. For example, I might analyze the correlation between database query performance and application response time to pinpoint database bottlenecks. This allows proactive optimization rather than just reactive troubleshooting.

Monitoring a microservices architecture with New Relic requires a strategic approach that leverages its capabilities for distributed tracing and service-level monitoring. The key is to gain a holistic view of how each service interacts with others while still identifying performance bottlenecks within individual services.

First, I would instrument each microservice with New Relic’s agents. This provides visibility into key metrics such as response time, throughput, error rates, and resource utilization (CPU, memory, etc.) for each individual service. Then, I’d utilize New Relic’s distributed tracing capabilities to follow requests as they travel through the different services. This is crucial for identifying latency issues caused by slow inter-service communication. Think of it like tracking a package – distributed tracing shows you every step of the journey and helps identify delays at specific points.

Furthermore, I would define custom dashboards and alerts specific to the needs of each microservice and the overall system health. This might involve monitoring key business transactions and setting alerts for significant drops in throughput or increases in error rates. Using dashboards for each service allows a quick view of their individual performance. A separate dashboard can monitor the overall system health by aggregating relevant metrics from all services. This approach ensures comprehensive monitoring and rapid response to potential issues.

Correlating metrics from different New Relic data sources is vital for a comprehensive understanding of your application’s performance. New Relic offers several ways to achieve this, primarily using NRQL and its data visualizations.

For example, let’s say you want to understand how database performance impacts your application’s overall response time. You could query your application performance data (e.g., using the Transaction entity) and your database performance data (e.g., using the Database entity) simultaneously in a single NRQL query. This allows you to see the relationship between the two. A simple example would be:

This query correlates the average transaction duration with the average database duration, faceted by hostname. You can then visualize this data in Insights, for example, using a line chart to visually see the correlation between them. Another approach is to use joins in NRQL to combine data from different entities.

By combining data from various sources – application performance, infrastructure monitoring, logs, and more – you gain a holistic understanding of your system and pinpoint areas needing optimization. This multi-faceted approach enables effective problem-solving and proactive performance tuning.

NRQL (New Relic Query Language) is the cornerstone of my New Relic data analysis. It’s a powerful SQL-like language that allows me to query the vast amounts of telemetry data collected by New Relic. My experience ranges from simple queries to complex analyses leveraging aggregations, joins, and time-series functions.

I regularly use NRQL for: building custom dashboards and visualizations, creating alerts based on specific metrics (e.g., high error rates), identifying trends and patterns in application performance, performing root cause analysis of performance bottlenecks, and generating reports to track key performance indicators (KPIs).

For example, to find the slowest transactions over the last hour, I’d use a query like: SELECT transactionName, average(duration) FROM Transaction FACET transactionName WHERE appName = 'MyApplication' SINCE 1 hour ago LIMIT 10 ORDER BY average(duration) DESC This query shows the top ten slowest transactions by average duration. This allows me to pinpoint bottlenecks for immediate attention. I regularly use more complex queries involving joins and aggregations to analyze relationships between different metrics and pinpoint performance issues across different parts of the application.

New Relic’s error tracking provides detailed insights into application errors, allowing for efficient troubleshooting. The process typically involves these steps:

• Identify the Error: Start by navigating to the Errors tab within New Relic. This provides a summary of errors, ranked by frequency and impact. Look for errors with a high count or significant user impact.
• Analyze the Error Details: Click on a specific error to see detailed information, including stack traces, affected users, and the surrounding code context. Stack traces are critical for pinpointing the exact location of the error within the codebase.
• Investigate the Root Cause: Using the error details, I would start by replicating the error to confirm the steps and identify the underlying issue. This might involve examining logs, inspecting database queries, or looking at other relevant New Relic metrics like CPU utilization or memory usage to find any unusual patterns correlating with the errors.
• Implement a Fix: Once the root cause is determined, the appropriate fix is implemented. This could involve fixing a bug in the code, adjusting configurations, or addressing a system-level issue.
• Monitor the Fix: After the fix is deployed, I monitor New Relic to confirm that the error rate decreases and the system is stable.

Using error tracking effectively minimizes downtime and ensures a smooth user experience. The detailed information provided by New Relic allows for rapid identification and resolution of application issues.

Monitoring serverless applications with New Relic presents unique challenges due to their ephemeral nature. However, New Relic’s capabilities allow for effective monitoring. The key is to leverage its integrations with various serverless platforms (like AWS Lambda, Azure Functions, Google Cloud Functions) and focus on key metrics that indicate performance and health.

I typically begin by integrating New Relic with the chosen serverless platform. This integration provides visibility into invocation duration, error rates, and other relevant metrics directly from the platform. Additionally, I’d use custom instrumentation within the serverless functions themselves to capture more granular performance data. This might include logging specific metrics like database query times or external API call durations.

Crucially, I would focus on dashboards that visualize key metrics, such as invocation duration (latency), error rates, and cold start times. Cold start time is particularly important for serverless functions, so I would closely monitor this. Alerts can be set up to notify the team of issues such as increased invocation times or high error rates. By analyzing these metrics, I can identify performance bottlenecks and address issues promptly, ensuring the serverless application remains highly available and responsive.

Optimizing database performance is crucial for application responsiveness. New Relic provides comprehensive tools for monitoring and improving database performance. My approach involves several key steps:

• Database Monitoring: I begin by integrating New Relic’s database monitoring tools with the database system (e.g., MySQL, PostgreSQL, etc.). This provides detailed insights into query performance, connection pools, slow queries, and overall resource utilization. I can easily identify slow queries that might be affecting application performance.
• Identifying Slow Queries: Using New Relic’s database dashboards, I focus on identifying slow-running queries, which are major performance bottlenecks. The database dashboards show metrics such as average query execution times and the number of slow queries over time.
• Analyzing Query Performance: I analyze slow queries to determine the root cause. This involves examining the query itself, the database schema, and the indexes to identify any areas for improvement. This may require working with database administrators and reviewing execution plans.
• Optimization Strategies: Based on the analysis, I implement optimization strategies that may include optimizing queries, adding or adjusting indexes, tuning database configurations (e.g., connection pools), or upgrading database hardware. I would also ensure database queries are efficient and that appropriate indexes are in place.
• Continuous Monitoring: After implementing optimizations, I continuously monitor database performance using New Relic to ensure that the changes have produced the desired results and proactively identify any new issues that may arise.

This systematic approach ensures the database runs efficiently, resulting in a highly responsive and performant application.

New Relic boasts robust integration capabilities with a wide array of monitoring tools. This allows for a unified view of your entire technology stack, avoiding the need to jump between disparate systems. I’ve personally worked with integrations including Splunk for log analysis, Datadog for infrastructure monitoring (though we sometimes use both for different purposes), and PagerDuty for incident management. The key to successful integration is understanding the strengths of each tool. For instance, New Relic excels at application performance monitoring (APM), while Splunk is superior for deep log analysis. By strategically combining these, we get a holistic view. Integrations are typically handled through APIs and pre-built plugins provided by New Relic or the third-party tool. These usually require configuration involving API keys and data mappings to ensure seamless data flow and avoid duplication or inconsistencies.

For example, in a past project, integrating New Relic APM with Splunk allowed us to correlate application errors reported by New Relic with related log entries from Splunk, significantly speeding up root cause analysis. The process involved configuring Splunk to receive data from New Relic’s API, then creating dashboards in Splunk to visually represent this combined data. This gave us a much clearer picture than either tool could offer in isolation.

Handling large volumes of data in New Relic effectively hinges on strategic data management. Simply collecting everything without planning leads to performance issues and high costs. My approach involves a multi-faceted strategy:

• Data Sampling: For less critical metrics, using data sampling reduces the volume of data sent to New Relic. This is a critical tradeoff between data completeness and performance. We carefully select which metrics require 100% sampling and which can tolerate lower rates.
• Data Retention Policies: Carefully setting data retention policies is crucial. Keeping data longer than necessary increases storage costs and query times. For example, production data might be retained for 90 days, whereas development data for a shorter period.
• Data Filtering and Aggregation: Using filters and aggregations within New Relic itself reduces the amount of raw data needing processing. For instance, rather than examining every individual request, we might group them by user agent or status code.
• Alerting Strategies: Well-defined alerts prevent unnecessary investigations. We avoid generating alerts on minor fluctuations and only trigger them when significant performance degradations occur.

Ignoring these considerations can lead to sluggish dashboards, high costs, and an inability to effectively analyze data. The goal is to balance data granularity with the practical limitations of system resources and cost.

Distributed tracing in New Relic is a powerful technique for understanding the flow of requests across multiple services in a microservices architecture. Imagine a request traveling through several services, like a relay race. Distributed tracing acts as a tracking device on that baton. It provides a complete picture of a request’s journey, pinpointing bottlenecks and performance issues at each stage.

Its benefits are significant:

• Improved Performance Debugging: By visualizing the entire request path, you quickly identify which service is causing slowdowns.
• Faster Troubleshooting: Errors are immediately linked to their root cause, instead of tedious debugging across multiple logs and services.
• Microservices Visibility: Understanding the interactions between your services is essential, and distributed tracing makes that interaction transparent.

In practice, we instrument our applications to capture trace data, and New Relic’s APM automatically generates traces which visually illustrate the flow of a request, its latency at each service, and any errors encountered. This visualization, often in the form of a timeline or graph, is invaluable for microservices monitoring and debugging.

Optimizing New Relic’s performance for my applications is a continuous process. Key strategies include:

• Minimizing instrumentation overhead: Overly extensive instrumentation can impact application performance. We carefully choose which aspects to monitor, focusing on the most critical areas.
• Efficiently using data sampling: As mentioned earlier, sampling is crucial for managing data volume and performance. I leverage it strategically for less critical metrics without compromising important monitoring aspects.
• Optimizing queries and dashboards: Complex or poorly written New Relic queries can significantly slow down dashboard loading times. Regular review and optimization are essential.
• Utilizing New Relic’s performance recommendations: New Relic provides recommendations within its platform to help identify potential performance bottlenecks. Following these suggestions often yields noticeable improvements.
• Regularly reviewing and cleaning up unused data and dashboards: Redundant data and dashboards needlessly consume resources. Periodic cleanup is essential for efficient performance.

The primary goal is to ensure that New Relic itself doesn’t become a performance bottleneck. A well-configured and managed system is a crucial ally, not an additional burden.

New Relic’s security features are paramount in securing sensitive application and infrastructure data. My experience includes leveraging several key aspects:

• Role-Based Access Control (RBAC): Implementing robust RBAC to restrict access to data based on roles and responsibilities. This is crucial to ensure only authorized personnel view sensitive information.
• Data Encryption: Utilizing New Relic’s encryption features to protect data both in transit and at rest. This safeguards against unauthorized access even if breaches occur.
• API Security: Securely managing API keys and access tokens to prevent unauthorized access to the New Relic platform. This also involves regular key rotation.
• Integration with Security Information and Event Management (SIEM) systems: Using New Relic’s integration capabilities with SIEM tools enables centralized security monitoring and incident response. This allows correlating application performance data with security events for faster investigation of potential security threats.
• Compliance and Audit Logs: Leveraging New Relic’s audit logs for regulatory compliance. Regular review of these logs is vital for identifying potential security incidents and maintaining accountability.

Security is an ongoing concern and not just a one-time configuration. Regular review of security settings and proactive implementation of New Relic’s security features are crucial to maintaining a secure environment.

New Relic provides a comprehensive view of infrastructure health and performance. We use it to monitor a range of metrics, including:

• Server Metrics: CPU utilization, memory usage, disk I/O, and network activity. This allows for proactive identification of resource constraints before they impact application performance.
• Network Performance: Monitoring network latency, packet loss, and bandwidth utilization to pinpoint network bottlenecks affecting application availability.
• Database Performance: Tracking query times, connection pools, and resource utilization of our databases to identify and optimize database performance.
• Log Monitoring: Using New Relic to correlate application logs with performance data for faster root cause analysis. This provides insights into application and infrastructure error conditions.

By integrating infrastructure monitoring with APM, we create a holistic view. For instance, a slow database query might be highlighted by both our database performance metrics and by its impact on application response times visible in the APM dashboards. This integrated view facilitates more efficient and accurate troubleshooting.

Creating custom dashboards and alerts in New Relic is straightforward and empowers you to tailor monitoring to your specific needs. Dashboards are created using a drag-and-drop interface, allowing easy organization of metrics and visualizations. You can add charts, tables, and other widgets to represent key performance indicators (KPIs) of your application and infrastructure. These dashboards provide a visual summary of your system’s health. Alerting is configured based on specific thresholds for chosen metrics. For instance, an alert might trigger if CPU utilization exceeds 90% for 5 consecutive minutes. The process involves selecting the relevant metric, defining the threshold, and choosing the notification method (email, PagerDuty, etc.).

For example, a custom dashboard might display metrics such as application response times, error rates, and server resource utilization. Alerts could be set to notify the operations team if response times exceed a certain threshold or if error rates spike significantly. Creating these alerts is often done through a combination of the UI and occasionally leveraging New Relic Query Language (NRQL) for more complex conditions. NRQL allows building custom queries to retrieve and visualize specific data points.

My experience with the New Relic API is extensive. I’ve used it extensively for automating tasks, integrating with other systems, and building custom dashboards and alerts. I’m proficient in using the REST API to retrieve data, create custom events, and manage New Relic entities programmatically. For example, I’ve built a script using the API to automatically generate reports on application performance and alert our team when key metrics exceed predefined thresholds. This automation significantly reduced manual effort and improved our response time to performance issues. I’m familiar with various API clients like Postman and cURL and understand how to handle authentication, pagination, and error handling effectively. I’ve also worked with the New Relic Synthetics API to create and manage synthetic monitors for our web applications, providing proactive detection of potential problems before our users are even affected.

A specific example involved using the Insights query language to analyze application performance across different environments. By creating custom queries, we were able to identify a bottleneck in a specific database query that was impacting response times and subsequently addressed it.

Data accuracy and integrity in New Relic are paramount. We ensure this through several key strategies. First, we meticulously configure our instrumentation to accurately capture relevant data points. This includes precise setup of agents across various components (servers, databases, applications) and careful definition of custom events and metrics. We validate the data by comparing it to other sources, such as logs or system metrics, ensuring consistency and identifying any discrepancies. Regular audits of instrumentation and data collection are essential to catch configuration drifts. Moreover, understanding the limitations of sampling and potential data loss inherent in any monitoring system is critical. We use techniques like data aggregation and error handling to minimise these effects. For example, we meticulously check that our custom instrumentation isn’t introducing bias or erroneous data into New Relic. If we notice inconsistencies, we investigate the root cause, making changes to configurations or code as needed. We also leverage New Relic’s features for data retention management to ensure that we keep the necessary historical context for analysis while optimizing storage costs.

Troubleshooting latency issues in New Relic involves a systematic approach. I start by identifying the affected component and time period using the APM application performance overview and the browser monitoring tools. The key is to follow the request flow, tracing the path from the user’s browser to the backend services. Using distributed tracing, I identify slow database queries, external API calls, or network bottlenecks that contribute to the latency. New Relic’s ability to pinpoint slow transactions, broken down by different tiers of an application, is invaluable here. For example, if I see high latency on a specific API endpoint, I can then drill down into the associated traces to identify precisely where the delay occurs – perhaps a slow database query within the endpoint. This allows me to focus on the precise code section causing the problem, as opposed to using trial and error approaches. Once the bottleneck is found, I work with the development team to address the underlying issues by optimizing database queries, improving code efficiency, or resolving network problems.

New Relic One Platform represents a unified view of our entire technology stack. My experience encompasses using it to correlate data across various services, including infrastructure, applications, and logs. This integrated view gives a holistic perspective on our system’s health, simplifying troubleshooting and analysis. We use dashboards within New Relic One to track key metrics and visualize correlations across different systems and services. For instance, we’ve configured dashboards that show relationships between infrastructure metrics like CPU utilization and application performance metrics, like request response times. This enables us to quickly pinpoint the effects of infrastructure issues on application performance. The platform’s capabilities for creating and sharing custom dashboards and alerting are essential for ensuring proactive issue identification and resolution, and greatly reduces the amount of time spent manually investigating potential problems.

Monitoring the performance of our mobile application with New Relic involves using Mobile application monitoring and browser monitoring. We instrument our application using the New Relic mobile SDK. This provides insights into app crashes, performance issues, and user experience. Key metrics like app load times, crash rates, and network usage are carefully monitored. We define custom events that track user interactions to better understand the usage patterns of our application and spot potential issues. The mobile monitoring capabilities integrate seamlessly with other New Relic tools, allowing correlation with backend performance metrics. For example, if we identify a large number of crashes on specific device versions, we can then cross-reference these findings with our backend logs to pinpoint the underlying cause and prioritize bug fixes.

New Relic provides substantial benefits for our organization. Firstly, it empowers proactive problem identification and resolution. Instead of reacting to user complaints, we proactively address issues before they impact our customers. Secondly, New Relic provides data-driven insights into application performance and user behavior, allowing us to make informed decisions regarding optimization and resource allocation. Thirdly, it significantly improves our operational efficiency by automating various tasks and providing centralized monitoring of our entire technology stack. Finally, it facilitates more effective collaboration between development, operations, and support teams by providing a single, integrated view of our system’s health. The ROI is demonstrably positive, as we’ve reduced incident resolution times, improved customer satisfaction, and optimized resource utilization.

Effective New Relic usage involves several best practices. Firstly, thoughtful instrumentation is key. Ensure your agents are correctly configured and capturing all relevant data. Second, create well-defined dashboards and alerts focused on key performance indicators that directly relate to business objectives. Avoid alert fatigue by setting appropriate thresholds and prioritizing alerts. Third, leverage New Relic’s powerful querying and visualization capabilities to gain deeper insights into your data. This includes using Insights for ad-hoc analysis and creating custom dashboards. Fourth, utilize the API to automate tasks and integrate New Relic with other systems. Lastly, regularly review and optimize your New Relic configuration to ensure that data collection and reporting are as efficient and effective as possible. This includes regular review of your dashboards and alerts, to ensure they remain relevant and effective.