Centralize Go application logs
Collect logs written by Golang services using standard library logging and structured loggers such as zap or logrus across APIs and background processes.
Golang Logs Monitoring & Observability
Effortlessly track Golang logs, gaining instant insights into errors and refining logging for a more efficient and reliable application.
Challenges
Monitor Golang application logs and runtime metrics in production systems
Analyze HTTP and gRPC traffic
Inspect logs generated by net/http handlers and gRPC interceptors to understand request flow, response latency, and transport-level errors.
Observe goroutine behavior
Monitor runtime metrics related to goroutine counts and scheduling while correlating log events emitted during concurrent execution.
Track memory and garbage collection
Ingest Go runtime metrics for heap usage, allocation rates, and garbage collection pauses alongside application logs.
Correlate logs with request metrics
Link structured Go logs with request duration, status codes, and handler-level metrics to investigate performance regressions.
Monitor service startup and crashes
Capture startup logs, panic stack traces, and unexpected process exits to diagnose deployment and runtime failures.
Debug concurrency and blocking
Use logs and runtime signals together to identify goroutine leaks, blocking calls, and resource contention under load.
Support containerized Go services
Aggregate logs and metrics from Golang services running in containers or virtual machines for centralized operational visibility.
Goroutine and Execution Flow Visibility
- Track log generation across goroutines, service handlers, and background processes to understand concurrent execution behavior in Go applications.
- Correlate log entries with request context, service interactions, and execution paths across distributed workloads.
- Identify failures in concurrent task execution, service handling, and background processing affecting system reliability.
- Detect disruptions in request handling and goroutine scheduling impacting service responsiveness.
Runtime Errors and Panic Diagnostics
- Capture runtime errors, panic logs, and stack traces generated during Go application execution.
- Correlate panic events with affected services, execution paths, and deployment changes to identify root causes.
- Identify recurring failures caused by concurrency conflicts, dependency issues, and configuration problems.
- Detect hidden runtime failures affecting service stability and transaction processing.
Performance and Resource Behavior
- Analyze slow execution logs, resource contention messages, and concurrency-related timing signals affecting performance.
- Correlate execution behavior with database operations, API dependencies, and workload latency across services.
- Identify excessive logging impacting CPU usage, memory utilization, and I/O performance.
- Detect performance degradation through abnormal execution timing and irregular log volume patterns.
Security and Operational Monitoring
- Track authentication failures, suspicious traffic patterns, and unauthorized access attempts captured in Go service logs.
- Identify abnormal behavior across services indicating misuse, attacks, or operational risks.
- Correlate application logs with infrastructure activity for incident investigation and operational visibility.
- Detect reliability and security incidents affecting Go workloads using centralized log insights.
Why Atatus
Why choose Atatus for Golang logs monitoring?
01
Built for Go services
Atatus ingests structured and unstructured Go logs from production microservices without code changes.
02
Goroutine-aware context
Atatus preserves request and goroutine context to simplify concurrent execution tracing.
03
Structured log parsing
Atatus extracts fields like request IDs and service metadata for precise log analysis.
04
Panic and error grouping
Atatus indexes and groups Go panics and errors to accelerate root cause analysis.
05
High-volume ingestion
Atatus handles large log streams from high-throughput Go services without performance impact.
06
Centralized visibility
Atatus aggregates logs across containers, Kubernetes, and cloud environments in one place.
Teams
Unified Observability for Every Engineering Team
Atatus adapts to how engineering teams work across development, operations, and reliability.
Developers
Debug errors and slow paths with the full context of what happened.
Atatus for developersDevOps
Track deployments and see how each release affects stability.
Atatus for DevOpsRelease engineers
Watch latency and error rates to ship with less production risk.
Atatus for release engineers
FAQ
Frequently Asked Questions
Pricing, deployment, data and the agent. If something is missing, a person will answer it on a demo call.
Golang Logs Monitoring in Atatus is a comprehensive solution that allows users to track, analyze, and troubleshoot logs generated by Golang applications. It provides real-time visibility into log data, helping to identify issues, improve performance, and ensure the health of your Golang applications.
Atatus's Golang Logs Monitoring integrates seamlessly with your Golang applications, capturing log data and centralizing it for easy analysis. The solution offers powerful search and filter capabilities, making it simple to pinpoint specific events or issues within your logs. It also provides alerting features to notify you of critical events.
By offering real-time log streaming and advanced search capabilities, Golang Logs Monitoring enables quicker issue identification and resolution. The ability to correlate logs with performance metrics provides a contextual understanding, streamlining the troubleshooting process.
Yes, Atatus provides powerful log filtering and searching capabilities. Users can filter logs based on specific criteria, such as log level, timestamp, or custom keywords. The search functionality allows for efficient navigation through large log datasets.
Yes, Golang Logs Monitoring seamlessly integrates with other Atatus products, such as APM (Application Performance Monitoring). This integration allows for a holistic view of your Golang applications, combining logs with performance metrics such as Distributed Traces to provide a comprehensive understanding of your application's health.
Log data parsing prioritizes customer security by meticulously anonymizing Personally Identifiable Information (PII) data. Sensitive fields can be completely excluded during the parsing process, ensuring the privacy and compliance of customer information.
In Golang Logs Monitoring, log pipelines are customizable workflows that allow users to filter, parse, and fine-tune log data based on specific criteria.
Log pipelines can be customized by using diverse filters and defining custom parsing rules. Users have the flexibility to configure pipelines to meet their specific monitoring requirements, ensuring that log data is processed and organized according to their preferences.
Yes, log pipelines can be configured to filter log data based on various attributes such as log source, severity, timestamp, or any custom field. This customization allows users to focus on specific aspects of log data that are most relevant to their monitoring goals.
Saved Views in Golang Logs Monitoring enable you to create and save custom configurations for different troubleshooting scenarios. This feature simplifies collaboration, allows for quick context switching, and automates repetitive tasks, significantly improving overall efficiency.
If you exceed your log ingestion limits, we would contact you to discuss on stopping further processing new log data or upgrade your subscription.
You can choose to store logs in Atatus for a limited time (e.g., 7 days) or export them to external storage solutions like Amazon S3 for long-term retention.
To access historical log data beyond the retention period, you can rely on log data exports from Amazon S3, where you can push the logs into Atatus for further analysis.
Yes, Atatus provides users with the flexibility to customize log retention settings. Users can adjust retention periods based on their specific needs, aligning with compliance standards or internal data management policies.