Java application monitoring that traces every request end to end on OpenTelemetry standards, from JVM garbage collection and heap pressure to the exact database call behind a slow transaction.
Java application monitoring shows how your Java 8+ services behave in production, from the code paths inside each request to the health of the JVM running them. Motadata ObserveOps instruments Java applications on OpenTelemetry standards and turns raw traces into a clear view of latency, errors, and throughput, so Java application performance monitoring becomes a matter of reading traces instead of tailing logs.
The Application Performance Monitoring (APM) module records application code behavior only: distributed traces, JVM internals, and the calls each service makes to its dependencies. Host and node resource health belongs to the Hybrid Infrastructure module, and browser-side experience belongs to Real User Monitoring, both of which cross-link into the same platform.
Whether a Java service runs on a bare Host or VM, inside Docker, or across a Kubernetes cluster, ObserveOps applies the same instrumentation model so the data reads the same way everywhere. That consistency matters when a service migrates from a virtual machine into a managed cluster, because its traces, endpoint breakdowns, and JVM metrics carry across without a change in how you read them. Teams typically reach for it once a microservice call chain outgrows what log tailing can explain, or once a Java workload starts moving across VM, container, and Kubernetes environments.
Every Java request is traced across services using a single unified MotaAgent, with distributed tracing built on OpenTelemetry standards so spans, errors, and dependencies read as one timeline instead of disconnected log lines.
JVM Analysis surfaces garbage collection behavior, heap usage, and thread activity for each Java 8+ service, so a latency spike can be checked against a GC pause or a growing thread count in the same view instead of two separate tools.
The APM agent auto-identifies Java applications ready for monitoring and enables instrumentation with a single click, so onboarding a newly deployed Java workload does not require manual wiring.
Java monitoring runs across Host and VM, Docker, and Kubernetes, including AWS EKS, EKS Fargate, OKE, GKE, and Rancher clusters, so containerized and serverless pod workloads are covered under one model.
API endpoint analysis breaks down per-endpoint behavior, HTTP method filters isolate traffic by verb, and directly discovered databases link into APM traces and service topology across 40+ database systems, giving visibility from a Java service to the database behind it.
OTel-instrumented Java microservices can be grouped into named Business Services, such as "Checkout" or "Payment Processing," analyzed and filtered as one unit. Business Service becomes a first-class dimension across APM dashboards, filters, and grouping views.
Compare view sets two Java services, or two Business Services, side by side with an independent time range each, so a post-deploy trace can be checked directly against the release before it.
The APM Database tab can be filtered by operation type, such as SELECT, INSERT, UPDATE, or DELETE, narrowing a Java service under investigation to the exact class of database call responsible for a slowdown.
Domain-specific attributes, KPIs, and identifiers can be pushed from Java source code into APM through the OTel SDK, stored as indexable aggregate counters and made available in the Custom Attribute Manager.
APM data can be built into Chart, Grid, Top-N, Counter, and Aggregation report types, so recurring Java service reviews are saved and reused rather than rebuilt from scratch.
The single unified MotaAgent instruments Java 8+ applications on OpenTelemetry standards, collecting distributed traces, spans, exceptions, API endpoint activity, and JVM metrics (garbage collection, heap, thread state) identically across Host and VM, Docker, and Kubernetes, including EKS, EKS Fargate, OKE, GKE, and Rancher. eBPF/OBI-based instrumentation discovers and monitors Java services automatically with no code changes or agent install, as a complementary, code-free option. A No Trace Received alert, configured at service registration, surfaces silence from a registered service.
Traces are analyzed for P99 latency, error rate, throughput, and Apdex at the transaction, service, endpoint, and Business Service level. JVM Analysis correlates runtime pressure with request behavior, the Database Operation Type filter narrows database analysis to a specific operation, and heatmaps give a severity-based read across counters without additional telemetry setup. Custom Business KPIs injected via the OTel SDK become additional counters in the same analysis.
APM Explorer, service maps, span views, and Compare view present traces and dependencies, while HTTP method filters and endpoint summaries narrow the view to the exact Java traffic under investigation. Consistent severity color indicators run across the APM view, and the trace scatter plot filters the trace grid below it. Saved Views preserve a search, its filters, and its layout for a recurring investigation, and frequently used trace fields can be pinned so they stay visible while a trace is inspected.
Java application monitoring in Motadata ObserveOps covers the full range of environments a Java workload runs in today. On a bare Host or VM, the MotaAgent instruments the JVM directly; in Docker, the same agent instruments containerized Java services; across Kubernetes, coverage extends to AWS EKS, EKS Fargate, Oracle OKE, Google GKE, and Rancher-managed clusters, so a Java service keeps identical tracing, JVM Analysis, and endpoint data in a self-managed cluster or a cloud provider's offering alike.
This matters most for hybrid estates, where the same Java codebase may run on a VM in one environment and inside a Kubernetes pod in another. EKS Fargate coverage extends visibility to serverless pod execution specifically, where node-level daemon deployments are not an option. Because the instrumentation model and metric set do not change across Host, VM, Docker, or any of the five Kubernetes flavors, a containerized migration does not require re-learning how to read Java performance data.
P99 latency, error rate, and throughput measured per service and per endpoint point to the exact Java transaction that is degrading.
Garbage collection, heap, and thread insights explain whether a slowdown comes from the code path or from the runtime itself.
Auto-discovery and one-click instrumentation shorten the path from deploying a Java service to seeing its traces; eBPF/OBI discovery offers a code-free alternative when an agent install is not practical.
One instrumentation model spans Host, VM, Docker, and every supported Kubernetes flavor, so a service keeps its monitoring as it moves from VM to cluster.
Directly discovered databases link with instrumented services across 40+ database systems, so a request can be followed from the Java entry point to the exact database call behind it.
Business Service Tagging groups related Java microservices into named units, and Compare view puts two services or two Business Services side by side with independent time ranges for a release or cross-team review.
Java application monitoring is scoped to application code behavior: traces, spans, exceptions, JVM internals, and the calls a Java service makes outward to its dependencies. It does not monitor the CPU, memory, or disk of the Host, VM, or Kubernetes node the service runs on; that is the scope of Hybrid Infrastructure Monitoring. A Java trace showing rising latency and a Hybrid Infrastructure view showing memory pressure on the same node point to one incident from two layers, without either module claiming the other's data.
Similarly, Java application monitoring stops at the server side of a request. What happens in the browser afterward, page load timing, rendering, and client-side interaction, is covered by Real User Monitoring. A slow API endpoint in Java APM and a slow page load in RUM are two ends of the same user journey, correlated through the platform rather than blended into a single module's claims.
Java Application Monitoring in Motadata ObserveOps connects request-level tracing with JVM runtime insight so Java teams can see both the code path and the virtual machine behind every transaction. Business Service Tagging and Compare view add a business-level lens on top of individual service traces, while the Database Operation Type filter, Custom Business KPI Injection, and Custom Reports turn that data into recurring, reusable analysis. With OpenTelemetry-based instrumentation and consistent coverage from Host and VM through full Kubernetes, it gives one grounded view of how Java services perform in production, wherever they run today or move to next.
Discover how Motadata ObserveOps can help you monitor your infrastructure in real-time and respond to issues instantly.