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Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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RUM now offers React Native Crash Reporting and Error Tracking
2022-11-17 · via Datadog | The Monitor blog

React Native has become the predominant development framework for cross-platform mobile applications. By interacting with native APIs largely under the hood and requiring only a fractional proportion of platform-specific code, it allows you to build applications for iOS, Android, and the browser using the same declarative JavaScript. But this cross-platform adaptability has its downsides. Because errors in React Native applications may originate either in the JavaScript in which the framework is written or in native iOS or Android code, detecting and troubleshooting these errors can be complicated. Error logs from mobile applications typically refer to inscrutable minified or unsymbolicated code, presenting a significant hurdle for troubleshooting. But getting a handle on these errors is critically important, particularly when it comes to crashes: the correlation between unstable applications and churn is well-established, making crash-free sessions a cardinal objective for mobile developers.

We’re pleased to announce that Datadog Crash Reporting and Error Tracking are now available for React Native via Real User Monitoring (RUM). RUM now generates comprehensive human-readable error reports, automatically detects crashes, provides analytics, and alerts on error trends for applications built in React Native. These features supply a wealth of contextual insight, both pinpointing bugs in your JavaScript, native iOS, and native Android code and helping you understand precisely when and where errors have occurred for your users. Onboarding is easy: with a single command to our CLI, source maps for your code will automatically be generated, detected and uploaded to Datadog upon each build of your application.

In this post, we’ll provide an overview of tracking recurring errors and pinpointing bugs with readable reports, as well as alerting on critical errors with Crash Reporting for React Native.

Track recurring errors and pinpoint bugs with readable reports

Once you’ve set up the RUM React Native SDK, Error Tracking will be enabled by default, and the Error Tracking Explorer will provide a detailed overview of any and all errors in your application. Error Tracking enables you to easily identify important application errors by automatically grouping closely related errors into issues.

Datadog Error Tracking now renders stack traces for React Native applications as easy-to-read error reports. With the Datadog React Native Wizard, all relevant source maps (including those for JavaScript, as well as dSYMs for native iOS code and Proguard mapping files for obfuscated native Android code) will be automatically uploaded to Datadog with each build of your React Native application. These source maps allow Datadog to symbolicate your stack traces and unminify your code, creating clear reports that point you directly to the sources of any errors.

Quickly track errors to their source with readable reports

RUM provides further insight, giving you detailed visibility into the precise context in which any errors have occurred within the affected user sessions.

RUM provides detailed context for each individual error

The Sessions & Replays view allows you to home in on errors and extensively slice and dice error data by isolating facets. In the context of React Native applications, filtering by the error.source_type facet can be particularly illuminating, since errors in this context may stem from a variety of sources.

RUM allows you to filter React Native errors based on whether they originate in JavaScript, native iOS, or native Android code

Where other tools may only detect JavaScript or native iOS or Android errors, Datadog Error Tracking ensures comprehensive oversight of every potential error source for React Native applications.

Alert on critical errors with Crash Reporting

Vigilance toward crashes, which are a major source of user frustration and churn, is critically important for developers of mobile applications. By enabling Crash Reporting in RUM, you can help to ensure a timely and thoroughgoing response to the most urgent errors in your React Native applications. You can configure Error Tracking Monitors to detect and alert you to new and escalating issues. And RUM Application Overviews can provide valuable insights into crash data to help you determine the precise queries on which to build the most effective monitors.

Assess critical, crash-causing errors with Error Tracking

Quickly get to the root of errors in React Native applications

Debugging React Native applications can be challenging. Crash Reporting and Error Tracking in RUM provide crucial visibility for these applications, with comprehensive, cross-platform error detection and readable error reports. Get started with RUM today to simplify and expedite critical troubleshooting and gain continuous, comprehensive insight into the overall usage and performance of your React Native applications. If you’re brand new to Datadog get started with a 14-day free trial.