JBoss Performance Monitoring
Get end-to-end visibility into your JBoss performance with application monitoring tools. Gain insightful metrics on performance bottlenecks with Java monitoring to optimize your application.
Why jboss Production Issues Are Hard to Diagnose?
Undetected Cache Fragmentation
Infinispan entries evict prematurely under hot partition skew. Second-level cache misses spike database load untracked. Platform engineers overprovision nodes without hit ratio baselines.
Undertow Connector Blackouts
HTTP/2 streams pile up in acceptor threads during burst concurrency. NIO poller exhaustion drops persistent connections. SREs tune blindly across multi-port configs.
Clustered Domain Drift
JGroups multicast failures desync HA nodes without heartbeat profiles. Singleton deployments split-brain across partitions. Domain controllers mask member health until failover fails.
Silent Deadlock Propagation
MDBs deadlock on XA transactions spanning multiple datasources. Thread dumps bury EJB container locks in noise. Backend engineers kill processes instead of breaking cycles.
Untracked Datasource Leaks
Connection handles accumulate in JBossCLI data sources post-rollback. Max pool capacity hits zero under error storms. Platform teams recycle JNDI lookups without leak paths.
JMS Queue Backlogs
HornetQ persistence lags under high-volume producers without depth metrics. Message redelivery storms exhaust worker pools. SREs purge queues blind to consumer starvation.
Domain Controller Blindspots
Management realms lose audit trails across federated domains. CLI operations timeout without operation latency traces. Platform leads debug auth failures through console noise.
Immediate Runtime Telemetry
Connector metrics and thread states surface within seconds of agent attach. SREs correlate queue depth with JVM pauses instantly. Platform dashboards unify multi-instance health views.
Pinpoint JBoss Performance Bottlenecks Before They Affect Users
Get clear visibility into slow servlet execution, request handling delays, costly downstream calls, and production errors so your team can fix issues fast.
Request Time Lost Across Multiple Application Layers
JBoss applications often span servlets, services, and internal components, allowing latency to accumulate across layers without a single obvious hotspot.
Thread Utilization Issues During Concurrent Traffic
Under load, limited request-handling threads can become saturated and slow unrelated requests, even when CPU usage appears normal.
Persistence Operations Delaying Request Completion
Entity fetches and transactional operations can extend request lifecycles, and without request-linked visibility these delays remain hidden.
Failures Surfacing Far from the Original Trigger
Errors often occur deep in execution, far from the original request input, making it difficult to trace failures back to the initiating action.
Release Changes That Alter Runtime Behavior
Configuration or code changes can subtly shift execution paths, causing latency changes that teams often notice only after users are impacted.
Why Teams Choose Atatus for jboss Observability?
JBoss teams choose Atatus to examine real production request execution directly, instead of inferring behavior from disconnected data or averages.
Domain Instant Visibility
EJB metrics and undertow states surface cluster-wide post-attach. Platform controllers correlate node drifts instantly. HA health unifies across domain partitions.
Zero-Config Domain Coverage
JMX hooks instrument standalone and domain modes without standalone.xml edits. Backend fleets deploy without controller coordination. Full visibility hits prod Day Zero.
EJB-Level Stack Fidelity
Transaction rollbacks map to exact interceptor chains in prod dumps. Developers repro XA failures with container-equivalent traces. Debugging flows match runtime exactly.
Clustered Alert Correlation
Infinispan skew and pool exhaustion pagers trigger at 90% thresholds. SREs get cross-node runbooks per partition. Domain noise collapses to validated risks.
Observed Runtime Behavior
Debugging relies on what actually executed during live traffic, not assumptions about server or application flow.
Production Execution Evidence
Runtime evidence reflects real concurrency, real data, and real load patterns seen only in production.
Comparable Request Review
Failing and successful requests can be examined side by side using observed execution behavior.
Repeatable Issue Inspection
Recurring production issues can be reviewed consistently without unreliable local reproduction attempts.
Production-Driven Actions
Release, rollback, and mitigation actions are taken using observed production execution rather than intuition.
Unified Observability for Every Engineering Team
Atatus adapts to how engineering teams work across development, operations, and reliability.
Developers
Trace requests, debug errors, and identify performance issues at the code level with clear context.
DevOps
Track deployments, monitor infrastructure impact, and understand how releases affect application stability.
Release Engineer
Measure service health, latency, and error rates to maintain reliability and reduce production risk.
Frequently Asked Questions
Find answers to common questions about our platform