Indicator analysis – time-series indicator queries and visual monitoring

Join the community

Scan with WeChat
Join the official community group

Try Guance

Start online with usage-based pricing and a true cloud service.

Get started

Choose a Guance edition

Free Business Enterprise

Code repositories

GitHub Gitee

Indicator analysis solves what problems they solve

Continuously monitor service health, capacity, and business trends through metric analysis

When teams need to continuously assess service health, capacity constraints, or business metrics are abnormal, metric analytics provides query, aggregation, grouping, and visualization capabilities for time-series data.

01Are there many sources of indicators? First, centralize the collection of host, middleware, cloud, and business metricsWith DataKit and integrated configuration, teams can collect hosts, containers, databases, middleware, cloud services, applications, and custom business metrics.
02You need to quickly spot trends and be able to dig deeper using DQL / PromQLMetric analysis supports simple queries, expression queries, DQL queries, and PromQL queries.
03With more and more metrics, you need to be able to manage calibers, labels, and storage policiesThe indicator management entry supports searching indicator sets, viewing metrics and label details, maintenance units and descriptions, and configuring data retention strategies as needed.

Are there many sources of indicators? First, centralize the collection of host, middleware, cloud, and business metrics

With DataKit and integrated configuration, teams can collect hosts, containers, databases, middleware, cloud services, applications, and custom business metrics. After unified collection, health status and capacity changes are analyzed by service, environment, region, version, or responsible person.

Book a demo View documentation

Are there many sources of indicators? First, centralize the collection of host, middleware, cloud, and business metrics

You need to quickly spot trends and be able to dig deeper using DQL / PromQL

Metric analysis supports simple queries, expression queries, DQL queries, and PromQL queries. Teams can first use charts to quickly observe trends, then continue analyzing by labels, functions, aggregation methods, and time windows, suitable for service health, capacity planning, and business metric monitoring.

Book a demo View documentation

With more and more metrics, you need to be able to manage calibers, labels, and storage policies

The indicator management entry supports searching indicator sets, viewing metrics and label details, maintenance units and descriptions, and configuring data retention strategies as needed. For platform teams, a unified metric standard reduces misunderstandings and helps control long-term storage costs.

Book a demo View documentation

With more and more metrics, you need to be able to manage calibers, labels, and storage policies

Frequently asked questions

What data is suitable for monitoring indicator analysis?

Metric analysis is suitable for monitoring time-series data such as hosts, containers, databases, middleware, cloud services, application performance, and business metrics, used to observe health status, capacity trends, abnormal fluctuations, and business changes.

What query methods does Guance indicator analysis support?

Guance supports simple queries, expression queries, DQL queries, and PromQL queries. Teams can analyze metric changes by tag, time window, aggregation function, and grouping dimension.

How to manage the caliber when there are too many indicators?

Teams can view metric sets, fields, labels, units, and descriptions through the metrics management portal, and use naming conventions and storage strategies to reduce misreadings, duplicate collections, and cost waste.

Related reading

Link tracing visualization tool: flame diagrams

This article demonstrates through real-world cases how flame graphs can be used to analyze service performance, identify problems, and take corresponding optimization measures.

Product capabilities

Guance best practices for link tracking analysis

This article uses RuoYi-Vue, a front-end and back-end separation application deployed on K8s, as a case study to explain how to perform link tracing analysis through Guance.

Best practices

Python Flask containerized application chain is observable

This article uses a containerized deployment example of the Python Flask application to explain how to perform link tracing through Guance to achieve performance monitoring and problem localization for applications.

Best practices

JAVA Application Implementation of Automatic APM Injection (Docker Edition)

This article explains how to inject APM probes into Java applications deployed by Docker through Guance.

Best practices