Motadata ObserveOps error and exception tracking captures unhandled exceptions and faulted transactions from running application code and links each one to the trace and service that produced it.
Error and Exception Tracking in Motadata ObserveOps Application Performance Monitoring (APM) is error tracking software built around one question: which transaction broke, and where. It captures the failures that happen inside your application code as it runs. Unhandled exceptions, faulted transactions, and error responses are recorded at the point they occur, not reconstructed afterward from scattered log lines.
The APM module watches application behavior only. When an error surfaces, it is tied to the trace and the service where it was thrown, so an investigation moves straight from a raised exception to the request path that produced it. Infrastructure health, such as node and memory conditions, stays with the Hybrid Infrastructure module, and browser-side script errors stay with Real User Monitoring. ObserveOps links these views so a failure reads alongside its host context or its front-end symptom without any single module overstating its scope.
The dedicated Exception tab gives errors a home of their own inside APM, alongside the Overview and Database tabs, so teams read failures as first-class application events rather than scattered log lines. Saved Views in APM Explorer let a recurring investigation, such as a known failure signature, be reopened in one click instead of rebuilt from scratch each time it resurfaces.
Instrumentation runs through a single unified MotaAgent and is built on OpenTelemetry standards, so the error and exception data collected follows the same open model as every other trace ObserveOps captures.
Unhandled exceptions and faulted transactions are recorded directly from instrumented application code as they occur.
A purpose-built Exception tab presents application errors as first-class events inside APM, alongside the Overview and Database tabs.
Every captured exception links to the distributed trace where it was thrown, so you move from the error record to the exact request path and service without switching tools.
Errors and exceptions are captured from applications written in Java, .NET, PHP, Node.js, Python, and Go, along with C++ and Ruby on Host/VM and Docker.
Error rate is tracked per transaction and per service alongside P99 latency and throughput, so a rise in failures reads against the performance of the code that produced it.
A severity-based heatmap gives a real-time visual read on error rate alongside response time, built on existing APM counters with no additional telemetry setup required.
An alert configured during APM service registration fires when no traces arrive from a service within a defined period, surfacing a silent or mis-instrumented service before its errors go unseen entirely.
eBPF/OBI-based instrumentation discovers and monitors services automatically, with no code changes and no agent install, capturing request count, latency, and error rate alongside traces and spans in the existing APM error views.
Single-click filters on the API Endpoint Summary tab isolate traffic by HTTP method such as GET, POST, PUT, PATCH, DELETE, HEAD, or OPTIONS, so an error pattern concentrated on one verb is easy to isolate.
OTel-instrumented microservices can be tagged into named Business Services such as "Checkout" or "Payment Processing," giving error investigation a domain-level view instead of a service-by-service one.
Because each exception is tied to its trace and service, investigation starts at the request that broke instead of a raw log search.
Application exceptions from all supported languages are captured in the same APM view, so teams work from a single place rather than per-language tooling.
Application exceptions live next to traces, service topology, and endpoint analysis, so root-cause work stays in one place rather than jumping across modules.
Error rate sits beside P99 latency and throughput, and the same counter drives the severity-based heatmap, so teams judge a release by how its failures and its speed move together.
A trace-linked exception shows the surrounding spans, so the conditions that led to the failure are visible, not only the moment it was thrown.
The No Trace Received alert surfaces a service that has stopped reporting, so a quiet integration gap is not mistaken for a healthy one with nothing to report.
The single unified MotaAgent instruments application code across the supported languages: Java 8+, .NET 8/9, PHP 8.1 to 8.4, Node.js 18.19+/20.6+, Python 3.9+, and Go 1.18+. As each transaction runs, unhandled exceptions and error responses are captured and attached to the active trace. For Go, database tracing is not automatic: it requires QueryContext or ExecContext plus the otelsql wrapper, and once that instrumentation is in place database-related errors carry the same trace context. Code-free eBPF/OBI instrumentation adds a second collection path, discovering services and their error rate with no agent install and no code changes.
For C++ and Ruby, error and exception capture runs on Host/VM and Docker.
Error rate is computed per transaction and per service, and each error is correlated to the distributed trace and the service that raised it, so the analysis connects a failure to the request path, the endpoint, and the timing around it. A No Trace Received check runs against configured services, flagging any that go quiet within a defined window. Where microservices are tagged into Business Services, error rate rolls up to that domain level as well as the individual service.
The Exception tab presents captured application errors, and selecting an error opens the linked trace so the failing request reads as one timeline from entry point to the raised exception. The API Endpoint Summary tab's HTTP method filters narrow that view to a single verb, and the severity-based heatmap gives a fast visual read on where error rate is concentrated across services.
Error and exception capture follows application code wherever it runs. For Java, .NET, PHP, Node.js, Python, and Go, that means Host/VM, Docker, and Kubernetes, including managed distributions on AWS EKS, Oracle OKE, and Google GKE, plus Rancher-managed clusters and serverless AWS EKS Fargate, where node-level DaemonSet deployment is not possible.
Because collection runs through the same single unified MotaAgent and OpenTelemetry foundation everywhere, the Exception tab, trace-linked errors, and error-rate heatmap read the same way whether a service sits on a virtual machine, in a container, or across a cloud-native cluster. A team does not relearn error tracking each time an application moves to a new environment.
Error and Exception Tracking stays inside its application-code lane so the picture around a failure stays accurate rather than blurred across layers. APM owns the exception itself and the trace and service that produced it. When the question turns to the host or cluster underneath, such as memory pressure or node health, that belongs to the Hybrid Infrastructure module. When the question is what a real visitor saw in the browser, that belongs to Real User Monitoring.
ObserveOps links these views so a browser-side error can connect to the backend exception that caused it, and a spike in application errors can be read against the infrastructure it ran on, all without any one module claiming ground it does not own.
Error and Exception Tracking in Motadata ObserveOps APM makes application failures a tracked, first-class part of performance monitoring. By capturing exceptions from running code, tying each one to the trace that produced it, and adding a heatmap over error rate plus an alert for services that go quiet, the Exception tab lets teams move from a raised error, or a missing one, to its root cause without leaving the application view.
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