Development on HUATUO Documentation

Collection Framework

Sun, 11 Jan 2026 00:00:00 +0000

HuaTuo framework provides three data collection modes: autotracing, event, and metrics, covering different monitoring scenarios, helping users gain comprehensive insights into system performance.

Collection Mode Comparison

Mode	Type	Trigger Condition	Data Output	Use Case
Autotracing	Event-driven	Triggered on system anomalies	ES + Local Storage, Prometheus (optional)	Non-routine operations, triggered on anomalies
Event	Event-driven	Continuously running, triggered on preset thresholds	ES + Local Storage, Prometheus (optional)	Continuous operations, directly dump context
Metrics	Metric collection	Passive collection	Prometheus format	Monitoring system metrics

Autotracing

Type: Event-driven (tracing).
Function: Automatically tracks system anomalies and dump context when anomalies occur.
Features:
- When a system anomaly occurs, autotracing is triggered automatically to dump relevant context.
- Data is stored to ES in real-time and stored locally for subsequent analysis and troubleshooting. It can also be monitored in Prometheus format for statistics and alerts.
- Suitable for scenarios with high performance overhead, such as triggering captures when metrics exceed a threshold or rise too quickly.
Integrated Features: CPU anomaly tracking (cpu idle), D-state tracking (dload), container contention (waitrate), memory burst allocation (memburst), disk anomaly tracking (iotracer).

Event

Type: Event-driven (tracing).
Function: Continuously operates within the system context, directly dump context when preset thresholds are met.
Features:
- Unlike autotracing, event continuously operates within the system context, rather than being triggered by anomalies.
- Data is also stored to ES and locally, and can be monitored in Prometheus format.
- Suitable for continuous monitoring and real-time analysis, enabling timely detection of abnormal behaviors. The performance impact of event collection is negligible.
Integrated Features: Soft interrupt anomalies (softirq), memory allocation anomalies (oom), soft lockups (softlockup), D-state processes (hungtask), memory reclamation (memreclaim), packet droped abnormal (dropwatch), network ingress latency (net_rx_latency).

Metrics

Type: Metric collection.
Function: Collects performance metrics from subsystems.
Features:
- Metric data can be sourced from regular procfs collection or derived from tracing (autotracing, event) data.
- Outputs in Prometheus format for easy integration into Prometheus monitoring systems.
- Unlike tracing data, metrics primarily focus on system performance metrics such as CPU usage, memory usage, and network traffic, etc.
- Suitable for monitoring system performance metrics, supporting real-time analysis and long-term trend observation.
Integrated Features: CPU (sys, usr, util, load, nr_running, etc.), memory (vmstat, memory_stat, directreclaim, asyncreclaim, etc.), IO (d2c, q2c, freeze, flush, etc.), network (arp, socket mem, qdisc, netstat, netdev, sockstat, etc.).

Multiple Purpose of Tracing Mode

Both autotracing and event belong to the tracing collection mode, offering the following dual purposes:

Add Metrics

Sun, 11 Jan 2026 00:00:00 +0000

Overview

The Metrics type is used to collect system performance and other indicator data. It can output in Prometheus format, serving as a data provider through the /metrics (curl localhost:<port>/metrics) .

Type：Metrics collection
Function：Collects performance metrics from various subsystems
Characteristics：
- Metrics are primarily used to collect system performance metrics such as CPU usage, memory usage, network statistics, etc. They are suitable for monitoring system performance and support real-time analysis and long-term trend observation.
- Metrics can come from regular procfs/sysfs collection or be generated from tracing types (autotracing, event).
- Outputs in Prometheus format for seamless integration into the Prometheus observability ecosystem.
Already Integrated：

Add Event

Sun, 11 Jan 2026 00:00:00 +0000

Overview

Type: Exception event-driven（tracing/event）
Function：Continuously runs in the system and captures context information when preset thresholds are reached
Characteristics:
- Unlike autotracing, event runs continuously rather than being triggered only when exceptions occur.
- Event data is stored locally in real-time and also sent to remote ES. You can also generate Prometheus metrics for observation.
- Suitable for continuous monitoring and real-time analysis, enabling timely detection of abnormal behaviors in the system. The performance impact of event type collection is negligible.
Already Integrated: Soft interrupt abnormalities（softirq）、abnormal memory allocation（oom）、soft lockups（softlockup）、D-state processes（hungtask）、memory reclaim（memreclaim）、abnormal packet loss（dropwatch）、network inbound latency (net_rx_latency), etc.

How to Add Event Metrics

Simply implement the ITracingEvent interface and complete registration to add events to the system.

Add Autotracing

Sun, 11 Jan 2026 00:00:00 +0000

Overview

Type：Exception event-driven（tracing/autotracing）
Function：Automatically tracks system abnormal states and triggers context information capture when exceptions occur
Characteristics：
- When system abnormalities occur, autotracing automatically triggers and captures relevant context information
- Event data is stored locally in real-time and also sent to remote ES, while you can also generate Prometheus metrics for observation
- Suitable for significant performance overhead， such as triggering capture when detecting metrics rising above certain thresholds or rising too rapidly
Already Integrated：abnormal usage tracking (cpu idle), D-state tracking (dload), container internal/external contention (waitrate), sudden memory allocation (memburst), disk abnormal tracking (iotracer)

How to Add Autotracing

AutoTracing only requires implementing the ITracingEvent interface and completing registration to add events to the system.