Node.js Logs Monitoring & Observability
Effortlessly track Node.js logs, gaining instant insights into errors and refining logging for a more efficient and reliable application.
Achieve full visibility into Node.js application logs across Express, NestJS, and distributed runtime environments
Native Node.js stream log capture
Instrument process.stdout/stderr streams and async logger calls to capture unhandledRejection events, assertion failures, and module load errors from production Node.js processes.
Async operation log correlation
Parse structured logs from Winston, Pino transports, and custom async_hooks domains. Track Promise chain failures, EventEmitter memory leaks, and callback hell scenarios across distributed clusters.
Node.js cluster & worker insights
Monitor logs across primary/worker processes, PM2 instance groups, and Docker container fleets. Surface inter-process communication failures and load balancer health issues from Node.js runtime logs.
Event loop delay & heap analysis
Detect event loop lag warnings, V8 garbage collection pauses, heapdump triggers, and max-old-space-size exceeded conditions logged by Node.js diagnostics.
Database driver & queue failures
Capture Mongoose connection drops, Redis pub/sub disconnects, BullMQ job timeouts, and Prisma query failures embedded in Node.js application logs with full stack context.
Source map error resolution
Transform minified production stack traces back to original TypeScript/ESM source files. Link Node.js log entries directly to controllers, middleware functions, and service implementations.
Dependency version conflict alerts
Track npm audit warnings, peer dependency mismatches, and package-lock.json drift logged during Node.js startup and hot reload cycles across CI/CD deployments.
Microservices inter-service tracing
Correlate logs across NestJS microservices, Express API gateways, and GraphQL resolvers using traceparent headers and OpenTelemetry context propagation standards.
Understand What Your Node.js Logs Are Telling You in Real Time
Collect and centralize logs from your Node.js applications so you can parse, filter, and analyze them for faster troubleshooting and clearer operational insight.
Raw Logs Without Structure
Plain text Node.js logs make it difficult to spot patterns, and structuring them into searchable fields reveals meaningful trends quickly.
Searching Through Log Files Is Slow
Manually scanning logs across servers wastes time, and centralized ingestion with powerful search enables instant access to relevant entries.
Important Events Buried Under Volume
High log volume can drown out important signals, and custom filters with pipelines help focus attention on what matters most.
Context Switching Slows Debugging
Jumping between tools and views breaks investigation flow, and saved views help maintain focused debugging context.
Missing Alerts for Key Log Patterns
Without alerts, critical log patterns can go unnoticed, and configuring alerts on specific log conditions surfaces issues in real time.
Why Choose Atatus for Node.js Logs Monitoring?
Purpose-built Node.js log ingestion with Pino/Winston parsing and cluster-aware observability for high-scale JavaScript applications
Zero-config Winston/Pino support
Automatic detection and structured parsing of popular Node.js logging libraries without code changes or transport reconfiguration.
Cluster process synchronization
Unified timeline view across PM2 clusters, Docker swarms, and Kubernetes pods eliminates log silos in distributed Node.js deployments.
Async-safe log forwarding
Non-blocking UDP/HTTP batching prevents backpressure during traffic spikes and Node.js process restarts.
Performance overhead optimization
Sub-microsecond log appenders maintain Node.js event loop responsiveness under peak concurrent request volumes.
Multi-tenant log isolation
Namespace separation for serverless functions, API tenants, and microservice boundaries prevents cross-customer log leakage.
Long-term Node.js log retention
Cost-optimized storage preserves critical error patterns, security events, and performance baselines without data truncation.
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.
Unified Logs Monitoring & Observability Across Different Platforms
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