Jetty Monitoring
Monitor Jetty performance seamlessly with the Atatus Java agent, offering real-time visibility into your Jetty HTTP server, servlet engine, and JVM performance. Gain deep insights into request handling, transaction flow, and system health to ensure an efficient and high-performing server stack.
Where Jetty production visibility breaks
Request Handling Ambiguity
Connector configuration and handler chains make it difficult to confirm how requests were actually processed under live traffic.
Fragmented Execution Context
Errors surface without complete thread, request, or execution state, forcing engineers to reconstruct runtime conditions manually.
Slow Fault Localization
Determining whether failures originate in handlers, servlets, or downstream services takes longer as execution paths deepen.
Hidden Thread Contention
Thread pool saturation and scheduling delays develop gradually, remaining invisible until latency and error rates spike.
Dependency Timing Gaps
Upstream and downstream dependencies introduce delays that are difficult to correlate with Jetty request handling behavior.
Noisy Failure Signals
Alerts lack execution depth, pushing teams to respond to symptoms rather than the underlying processing breakdown.
Unclear Scale Dynamics
Rising concurrency and connection counts alter runtime behavior in ways teams cannot clearly observe or predict.
Declining Operational Confidence
Repeated blind investigations reduce trust in production understanding, slowing decision-making during high-impact incidents.
Gain Precise Metrics on Jetty Server Performance
Track how efficiently your Jetty servlet engine processes requests, uses threads, and manages sessions with real-time metrics so you can spot performance inefficiencies quickly.
Slow HTTP Request Handling Happens Silently
Without request processing metrics, it is difficult to see which endpoints or handlers add time to each HTTP request cycle.
High Thread Utilization Can Stall Throughput
Thread pools under sustained load can block new work, and visibility into thread usage shows when a Jetty instance approaches saturation.
Session Activity Affects Memory and Performance
Untracked session creation and expiration can increase memory usage or latency, and session metrics reveal how session behavior impacts performance.
Connector Load Spikes Reveal Bottlenecks
Monitoring queued connections and connector load highlights moments when the server struggles to keep up with incoming traffic.
GC Pauses Influence Throughput Consistency
Because Jetty runs on the JVM, garbage collection pauses can interrupt request processing, and correlating GC timing with request metrics shows when GC affects responsiveness.
Why Jetty teams standardize on Atatus
As Jetty deployments scale in traffic, concurrency, and ownership, understanding real production behavior becomes harder than operating the infrastructure itself. Teams standardize on Atatus to preserve execution clarity, align engineers around the same runtime reality, and maintain confidence as system complexity grows.
Clear Execution Flow
Engineers understand how requests move through handlers, threads, and execution stages without reconstructing Jetty internals.
Fast Team Alignment
Platform, SRE, and backend teams reach shared production understanding quickly, even during high-severity incidents.
Immediate Signal Confidence
Production signals are trusted early in investigations, enabling faster and more decisive response.
Lower Debug Overhead
Engineers spend less time correlating logs and thread behavior and more time isolating execution faults.
Predictable Incident Response
Incident handling follows consistent analytical patterns as traffic volume and system complexity increase.
Shared Runtime Reality
Teams reference the same execution evidence during outages and post-incident reviews.
Stable Under Concurrency
Production understanding remains intact as connection counts and parallelism rise.
Reduced On-Call Fatigue
Clear runtime insight shortens incident duration and reduces escalation loops for on-call engineers.
Long-Term Operational Trust
Teams continue scaling Jetty-based systems without fear of unseen production behavior.
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