Yoann Robin
As your organization grows, you may operate in increasingly complex environments and manage more services and larger teams to maintain them. Evolution like this can lead to an explosion of telemetry data from across your stack, including metrics, traces, logs, and frontend interactions. The benefit of greater visibility is often outweighed by the challenge of acting on the data you collect, and you can easily fall behind on implementing the fixes your services require to operate reliably and efficiently.
By analyzing telemetry data from Application Performance Monitoring (APM), Real User Monitoring (RUM), Continuous Profiler, and Database Monitoring (DBM), Proactive App Recommendations automatically identifies performance and reliability issues—and crucially, suggests how to fix them.
In this post, we’ll explore how Proactive App Recommendations helps you:
- Detect and remediate backend inefficiencies
- Resolve frontend issues linked to user frustration
- Apply code-level fixes informed by profiling data
Detect and fix backend performance issues
Inefficiencies in your backend services can cause latency or user-facing errors that degrade the performance of upstream services and applications. This could be due to an antipattern such as sequential calls to a database or API that could be optimized into a single call, and this may not be evident until these inefficiencies affect service performance.
When Proactive App Recommendations detects an inefficient pattern such as this, you'll see a summary of the issue that visualizes the relevant requests and proposes a code fix. In the following screenshot, Proactive App Recommendations has detected a service that is making sequential API calls. It describes the problem and shows a flame graph that illustrates the performance impact.
The recommendation also suggests a resolution. Here, Proactive App Recommendations has identified the source code file at the root of the issue and suggests updating the code to parallelize the calls to the downstream service, optimizing the latency of the operation.
You can accept the change directly in Proactive App Recommendations, and Datadog will open a pull request (PR). Once the change is merged, the recommendation is automatically marked as resolved, and Datadog won't surface the same issue again. You can filter your view to show only open recommendations to highlight pending opportunities, or you can include resolved ones to view a history of findings and resolutions.
Improve frontend responsiveness with user frustration signals
Frontend issues can have an immediate impact on user satisfaction and retention. Datadog RUM detects frustration signals such as dead clicks (when a user's click produces no action) and rage clicks (when a user clicks the same element more than three times within one second). Frustration signals often indicate that a feature is slow to respond or broken entirely, and detecting and fixing them quickly is key to keeping users engaged.
When RUM detects a frustration signal, Proactive App Recommendations surfaces it, including the number of views affected and the number of users who experienced the issue. You'll also see a Session Replay of the affected user journey, related error messages from your application and a proposed fix for the underlying issue.
If your team already has a fix in progress, you can ignore a recommendation. Otherwise, you can assign it to a team member in Datadog Case Management and track its progress there. Or you can accept Proactive App Recommendations' suggested fix and merge the proposed change to quickly resolve the corresponding issue.
Apply code-level fixes based on profiling insights
Some performance issues originate deep in your service code. Problems such as inefficient loops, memory-intensive functions, and blocking operations can be hard to detect, but their impact on resource usage and service performance can quickly become evident.
Datadog’s Continuous Profiler collects code-level performance data in production, and Proactive App Recommendations suggests fixes to code that contributes to CPU, memory, and I/O bottlenecks. When the Profiler detects a performance issue that's attributable to a code inefficiency, you'll receive a recommendation with a detailed summary of the performance impact, a link to the relevant code, and a proposed fix with the option to open a PR directly.
Proactive App Recommendations are automatically filtered by team. This minimizes noise, and enables you to focus on recommendations that ensure the performance and reliability of services and applications under your team's ownership.
Turn telemetry data into action with Proactive App Recommendations
As your organization scales, so does your telemetry—and the time required to extract value from it. Proactive App Recommendations helps teams reduce that overhead by identifying issues and proposing optimizations, all from within the Datadog platform.
Proactive App Recommendations is available at no extra cost beyond your existing APM, RUM, DBM, or Continuous Profiler subscriptions. See the documentation to learn more about Proactive App Recommendations, or sign up for a 14-day free trial to get started.
