Simplify Node.js performance monitoring with full-stack observability

Analyze Node.js performance in real-time to identify emerging issues

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Holistic View

Metrics that Matter

Troubleshoot Smarter

Performance In Context

Alert Intelligently

Holistic Performance Monitoring Across Node.js Applications and Their Resources

SolarWinds Observability SaaS^® removes the boundary between application and infrastructure to provide holistic Node.js monitoring. Auto instrumentation and AI-powered analytics connect Node.js application performance metrics to the services, databases, and networks that support them.

Unified interactive dashboards visually connect and contextualize Node.js performance data to provide actionable intelligence. Deep drilldowns allow you to go from top-level performance metrics to relevant supporting traces, logs, and database queries. This capability allows developers to immediately view the context of uncaught exceptions and application crashes. At the same time, availability and real user metrics connect application performance to the end-user experience. The unified view ensures scheduled tasks run successfully and their resource consumption.

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Metrics That Provide Deep Insights Into Node.js Application Performance

SolarWinds Observability SaaS provides instant intelligence via an overall health score for Node.js applications based on golden signals, along with the ability to track performance using logical entity groups containing hosts, services, databases, and other related resources.

Detailed application throughput, traffic, errors, and saturation metrics deliver a comprehensive view of Node.js application health and performance. The collected telemetry also includes detailed metrics on tracked exceptions, such as error rate and count. Resource metrics such as disk reads, available memory, and CPU utilization allow you to analyze underlying infrastructure health metrics in the same interface. While load times, page views, and APEX scores deliver insights into the end-user experience.

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Intelligence to Identify Bottlenecks and Troubleshoot With Precision

SolarWinds Observability SaaS offers out-of-the-box support for Node.js apps such support includes auto-instrumented traces and Node.js-specific metrics. You can quickly begin collecting performance data, monitoring health and performance, and troubleshooting with minimal setup needed.

SolarWinds Observability SaaS combines application and infrastructure metrics into a single view. Whenever you encounter an issue with Node.js performance—or issues with another language or framework—the metrics and performance views provided by SolarWinds Observability SaaS allow you to quickly identify slow queries, bugs, inefficient code, resource saturation, or other problems within the underlying infrastructure. The platform automatically correlates logs with transaction traces, providing immediate context for debugging.

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Connect Node.js Application and Infrastructure Performance

Through a unified view, SolarWinds Observability SaaS enables you to see real-time performance in context. Using Interactive topology maps, you can visually explore relationships and dependencies. Detailed transaction tracing reaches beyond the application level to follow requests across hosts in the cloud and data center. The tracing can be manually or automatically extended to monitor cron and background job processes for complete operational visibility.

High-fidelity metrics provide a top-level, time-dependent view of performance trends while live code profiling pinpoints performance issues down to the line of code. Flexible entity groups allow consolidating data from multiple sources for customized performance monitoring, and a top-level health score surfaces emerging issues at a glance. Advanced configuration and customization features include log filtering and integration with other systems via the OpenTelemetry standard.

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Prioritize Alerts on Key Metrics

Work smarter not harder with unified alerts that consolidate alert creation and management to reduce repetition. AI-powered alerting along with a color-coded alert list focuses attention on real issues and filters out the noise. Out-of-the-box alerts, as well as customizable alert thresholds, surface important issues. Built-in notification integration with common communication services, including Slack, Teams, and email allows teams to easily bring notifications into existing work streams.

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Get More on Node.js monitoring?

Do you find yourself asking…

What is Node.js monitoring?
What is the relationship between Node.js and infrastructure resources?
How do we tackle Node.js performance issues and high latency?
My monitoring tool shows "No Data", or I suspect a memory leak—what is the next step?
How does distributed tracing identify the exact code blocking the event loop?

Node.js monitoring refers to application performance monitoring specifically for Node.js frameworks and modules. A Node.js monitoring solution allows you to keep track of performance metrics unique to your Node.js application environment and detect performance issues. A full-stack application performance management (APM) solution like SolarWinds Observability SaaS can combine Node.js monitoring with on-premises resource collection so you can monitor Node.js CPU usage, RAM usage, and other metrics with the potential to affect end-user performance.

The issue with Node.js stems from its single-threaded design. When you notice high latency, it indicates that a function is blocking the thread. Because everything operates on the same thread, all incoming requests must wait, causing your application to stall.

This is how tracing reveals the culprit:

1. Tracing for complete traceability

Distributed tracing automatically instruments your app to assign a unique ID to each user request as soon as it arrives. This identifier tracks the request throughout your entire back end and every service it interacts with—think of it like a GPS for your data.

The APM tool breaks down the request’s path into spans, with each span representing an operation, such as a function execution, a database query, or an external API call.
By combining these spans into a single waterfall chart, your visualization displays the response time for the entire workflow, allowing you to instantly identify which service or internal call is the slowest.

2. Linking latency with sevent loop delays

In a distributed trace, you can observe the total latency for a specific endpoint. If a trace highlights a slow span and your service health metrics simultaneously reveal a spike in event loop lag, you’ve established the link: the slow operation is blocking the thread.

3. Pinpointing the blocking function with profiling

This is where the top Node.js monitoring tools come into play. After the trace pinpoints the slow span (e.g., a function called processHeavyData), the next step is to investigate what occurred within that span.

Our tools can continuously capture a live code profile and correlate it with the identified slow trace.
This profile examines the execution stack during the slow operation, highlighting the line of JavaScript code responsible for most of the processing time—often revealing a synchronous task that should be asynchronous.
For instance, you might discover the slow span resulted from an intensive synchronous loop or a blocking filesystem operation that could have used its asynchronous version.

By connecting high-level distributed tracing (showing what slowed down and where) with low-level code profiling (explaining why it was slow), you can eliminate guesswork and go straight to the root cause—often pinpointing the exact line—so you can optimize right away.

Node.js monitoring refers to application performance monitoring specifically for Node.js frameworks and modules. A Node.js monitoring solution allows you to keep track of performance metrics unique to your Node.js application environment and detect performance issues. A full-stack application performance management (APM) solution like SolarWinds Observability SaaS can combine Node.js monitoring with on-premises resource collection so you can monitor Node.js CPU usage, RAM usage, and other metrics with the potential to affect end-user performance.
The issue with Node.js stems from its single-threaded design. When you notice high latency, it indicates that a function is blocking the thread. Because everything operates on the same thread, all incoming requests must wait, causing your application to stall.

This is how tracing reveals the culprit:

1. Tracing for complete traceability

Distributed tracing automatically instruments your app to assign a unique ID to each user request as soon as it arrives. This identifier tracks the request throughout your entire back end and every service it interacts with—think of it like a GPS for your data.

The APM tool breaks down the request’s path into spans, with each span representing an operation, such as a function execution, a database query, or an external API call.

By combining these spans into a single waterfall chart, your visualization displays the response time for the entire workflow, allowing you to instantly identify which service or internal call is the slowest.

2. Linking latency with sevent loop delays

In a distributed trace, you can observe the total latency for a specific endpoint. If a trace highlights a slow span and your service health metrics simultaneously reveal a spike in event loop lag, you’ve established the link: the slow operation is blocking the thread.

3. Pinpointing the blocking function with profiling

This is where the top Node.js monitoring tools come into play. After the trace pinpoints the slow span (e.g., a function called processHeavyData), the next step is to investigate what occurred within that span.

Our tools can continuously capture a live code profile and correlate it with the identified slow trace.

This profile examines the execution stack during the slow operation, highlighting the line of JavaScript code responsible for most of the processing time—often revealing a synchronous task that should be asynchronous.

For instance, you might discover the slow span resulted from an intensive synchronous loop or a blocking filesystem operation that could have used its asynchronous version.

By connecting high-level distributed tracing (showing what slowed down and where) with low-level code profiling (explaining why it was slow), you can eliminate guesswork and go straight to the root cause—often pinpointing the exact line—so you can optimize right away.