Get end-to-end visibility into your Spring performance with application monitoring tools. Gain insightful metrics on performance bottlenecks with Java monitoring to optimize your application.
Teams lose hours chasing request flows across Spring Boot microservices without end-to-end visibility into async calls and service hops. Partial logs fail to link frontend errors to backend database stalls. Production incidents recur as engineers guess propagation paths.
Garbage collection pauses evade detection in high-load Spring apps. Heap dumps reveal leaks post-outage, but real-time profiling stays absent. Thread contention locks up endpoints unnoticed until throughput crashes.
N+1 queries overwhelm HikariCP pools in scaled Spring deployments. Index misses and unoptimized joins spike latency without query-level traces. SREs scramble to reproduce prod loads for fixes.
Tomcat threads block during peak traffic in Spring Boot clusters. CPU-bound handlers and memory thrashing degrade SLAs silently. Load balancers mask single-instance failures until cascading outages hit.
Circuit breaker trips in Feign clients go undetected across Spring services. External API timeouts cascade without failure propagation traces. Backend teams debug symptoms, not root service dependencies.
Horizontal scaling exposes weak session handling in Spring sessions. Stateful beans across instances cause data inconsistency under autoscaling. Platform engineers rework architectures post-scale failures.
Off-heap growth sustains in Spring Boot caches during long runs. Metaspace exhaustion halts JVMs without leak path profiling. Frequent restarts disrupt prod stability for backend teams.
Logs fail to correlate exceptions across distributed Spring transactions. Stack traces bury transaction rollbacks in noise. SREs pivot through siloed tools, delaying MTTR in complex deployments.
Real-time observability for Spring workloads that helps teams understand request flow, optimize performance, and resolve production issues faster.
See exactly how long each request takes across controllers, services, and internal processing layers. Quickly uncover slow execution paths affecting response times.
Track response times for dependent services, microservices, and external APIs. Identify latency introduced by downstream systems.
Analyze query execution time and database performance in real time. Eliminate inefficient data access slowing application performance.
Capture detailed exception information alongside correlated logs for every request. Debug faster with complete visibility across errors, traces, and performance data.
Spring teams choose Atatus when understanding real production execution matters more than interpreting summaries, averages, or assumptions.
Teams gain correlated traces and metrics within minutes of agent deploy, slashing blind debugging in Spring environments. Platform owners trust unified dashboards for cross-service health at scale.
Spring Boot jars instrument seamlessly without code changes or restarts, enabling rapid rollout across clusters. Backend devs adopt without retraining, focusing on code over config.
Precise exception snapshots and stack traces match IDE debugging, building trust in prod anomaly hunts. Engineers resolve issues faster with context-rich alerts tied to code lines.
Custom thresholds on JVM GC and thread metrics trigger targeted pagers, minimizing alert fatigue in high-scale Spring ops. Response teams act on prioritized signals alone.
End-to-end request maps reveal Spring microservice latencies, empowering leads to optimize service boundaries confidently. Scale decisions stem from trace-derived bottlenecks.
Real-time instance metrics validate Spring Boot horizontal growth, ensuring load distribution before SLAs break. Platform teams deploy expansions with data-backed assurance.
Automated heap analysis pinpoints Spring cache leaks, granting backend teams proactive memory governance. Outages drop as profilers expose root retention paths.
Correlated logs expose Spring at transactional failures across databases, accelerating fix validation in prod. SREs confirm atomicity without repro setups.
Unified Spring observability spans dev to prod, yielding MTTR under 10 minutes for engineering leads. Operational risks fade with verifiable performance baselines.
Atatus adapts to how engineering teams work across development, operations, and reliability.
Debug errors and slow paths with the full context of what happened.
Atatus for developersTrack deployments and see how each release affects stability.
Atatus for DevOpsWatch latency and error rates to ship with less production risk.
Atatus for release engineersPricing, deployment, data and the agent. If something is missing, a person will answer it on a demo call.
The Spring performance monitoring tool helps developers to optimize the Spring server performance.
A good Spring APM solution answers the following two questions:
You can easily use application performance monitoring tools to answer, detect and resolve the above issues before they could impact the end users.
In general, there are two types of monitoring:
Every organization should have a well-implemented Spring APM solution, which allows DevOps teams (and the organization as a whole) to resolve issues and performance bottlenecks efficiently and reduce Mean Time To Resolution (MTTR). This has a substantial impact on the bottom line of the business.
Now organizations do not have to deal with the unnecessary work involved in maintaining an extensive software analysis group.
Your Spring performance monitoring tools should be easy to use, offer actionable insights, and be able to:
Technical Benefits:
Business Benefits:
With the help of the database monitoring, you can easily identify the database performance bottlenecks.
