echo Performance Monitoring
Get end-to-end visibility into your Echo performance with application monitoring tools. Gain insightful metrics on performance bottlenecks with Go monitoring to optimize your application.
What caused echo production visibility to break first?
Context Propagation Breaks
Nested handlers drop request contexts, triggering cascading timeouts. Backend engineers chase phantom delays across call stacks.
Goroutine Channel Leaks
Unclosed streaming channels spawn memory hogs under load. Platform teams miss routine proliferation without runtime profiles.
Route Group Bloat
Hundreds of endpoints fragment visibility into silos. SREs manually map traffic patterns across ungrouped paths.
Dependency Injection Shadows
Global vars hide service bindings, breaking trace continuity. Backend debugging stalls on unmockable handler scopes.
Shutdown Resource Drains
Abrupt terminations leave DB handles and sockets open. Platform ops correlate crashes to unclean exits blindly.
Validation Latency Spikes
Heavy binders choke high RPS, masking schema mismatches. Engineers profile request parsing without aggregated timings.
Binder Overhead Explosion
Throughput increases mask saturation points. Systems fail gradually with no clear early indicators.
Route Growth Complexity
Expanding APIs increase runtime surface area. Identifying critical paths becomes increasingly difficult.
Inspect Performance Characteristics of Echo Requests
Break down execution across Echo route handlers, database access, cache reads, and outbound calls using request-level performance visibility.
Requests With Uneven Execution Time
Similar Echo routes can show wide timing variance under the same load, and request-level breakdowns reveal exactly where execution time diverges.
Database Time Dominating Request Duration
A small number of SQL calls can consume most of the request lifecycle, and query timing tied to traces shows where database cost accumulates.
Cache Reads Not Offsetting Database Load
Cache usage may exist but still fail to reduce total request time, and per-request cache timing shows whether reads are actually effective.
External Calls Extending Request Completion
Outbound HTTP calls can quietly extend total request duration, and dependency timing within traces identifies which call adds the most delay.
Latency Patterns Hidden in Aggregates
Average metrics hide slow outliers, while request-by-request traces expose recurring slow paths that summary views often miss.
Why Teams Choose Atatus?
Echo platform leads demand Atatus for runtime fidelity that accelerates triage over tool sprawl. Backend crews lock in observability from first deploy.
Handler Span Precision
Request flows map exact middleware latencies post-startup. Devs pinpoint ordering faults in live cycles instantly.
Stdlib Context Fidelity
Preserves native Echo contexts without wrapper overheads. SREs trace cancellations through unmodified stacks cleanly.
Routine Profile Accuracy
Goroutine trees expose leak sources by allocation site. Platform teams prune memory paths with line-level data.
Grouped Route Insights
Traffic aggregates reveal endpoint silos at scale. SREs balance loads across versioned path clusters precisely.
Binding Cost Breakdowns
Parser latencies split from handler execution cleanly. Devs optimize validators against real payload distributions.
Shutdown Event Capture
Graceful closures log open handles pre-crash. Platform ops audit resource hygiene across deploys reliably.
Service Binding Traces
Dependency scopes link to concrete implementations natively. Backend tests mirror production injection faithfully.
Consistent Runtime Interpretation
Signals align directly with execution behavior, reducing ambiguity during investigation.
Long-Term Confidence
As architectures, traffic patterns, and ownership change, teams retain continuity in how production systems are understood and operated.
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