Log Monitoring Platform

What problems does log monitoring solve?

Transform distributed logs into queryable, alertable, and traceable log monitoring platforms

Log monitoring is designed for troubleshooting, system auditing, business analysis, and security operations scenarios. Guance unified management of application logs, host logs, container logs, cloud platform logs, and custom logs, and through log retrieval, structured log parsing, cluster analysis, chart aggregation, anomaly detection, log alerts, and link association, it transforms raw text into data assets that support troubleshooting and decision-making.

01Centralized searchUnified query logs across applications, hosts, containers, and cloud platforms
02Field analysisAutomatically extract key fields, allowing logs to be filtered and aggregated
03Contextual associationJump from logs to Trace, metrics, hosts, and alert events
04Log alertQuery conditions are consolidated into monitors and exception notifications

True troubleshooting path

From a surge in anomaly logs to a verifiable chain of root cause evidence

First, use trends and clustering to narrow down the problem scope, then associate traces, hosts, and events along unified labels. Confirmed valid query conditions can also be directly consolidated into monitoring rules.

First, place the distributed logs into the same query entry

EvidenceService, environment, host, container, and error levels

ConclusionObtain repeatable time windows and filter criteria

Data path

DataKit Log Collection → Pipeline Parsing and Field Standardization → Search, Aggregation, Association, and Alerts

Best for

Suitable for R&D, operations, maintenance, SRE, and security teams with dispersed applications, hosts, containers, cloud platforms, and security logs.

Requirements

High-quality analysis depends on field parsing, temporal consistency, and unified tags; Long-term retention, permission, and cost strategies can be combined with log management platforms.

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Log fields are confused? Extract order numbers, interfaces, and error codes into analyzable fields

Through pipelines, field parsing, and log style recognition, Guance can extract key fields such as user ID, order number, interface path, error code, region, and service name from the original log. Once the fields are structured, logs can be filtered, aggregated, alerted, and analyzed accordingly, enabling true service troubleshooting and business analysis.

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Log fields are confused? Extract order numbers, interfaces, and error codes into analyzable fields

High-value queries can accumulate into log alerts, reducing redundant checks

Confirmed valid query conditions during troubleshooting can be converted into log monitors. Whether it's a sudden increase in error logs, outliers in key fields, or persistent security incidents, the team can turn experience into rules to help the next issue be discovered earlier.

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Frequently asked questions

What is the difference between log monitoring and log management?

Log monitoring focuses more on quickly detecting anomalies, locating faults, and triggering alarms through logs; Log management also includes the complete lifecycle of collection, parsing, storage, governance, and permissions. Guance also covers log query analysis and log management capabilities.

What capabilities does a log analysis platform need?

A high-quality log analysis platform needs to cover log collection, field parsing, full-text retrieval, aggregation analysis, alert rules, permission governance, and contextual association. Guance further integrate logs with Trace, metrics, RUM, events, and infrastructure data, helping teams trace from a single error log to the full impact chain.

What data can Guance logs relate?

Guance logs can be linked with application traces, host metrics, container status, network data, user access sessions, and alert events through unified tags and context, enabling teams to quickly trace the impact and root cause clues from a single log.

How to change from log queries to alert rules?

Teams can save high-value query conditions in the log viewer and convert them into monitors. When the number of error logs, key fields, or exception patterns meets set conditions, Guance triggers an alert through the configured notification channel.

What scenarios are suitable for log field parsing?

Field parsing is suitable for extracting structured fields from log content such as order number, user ID, interface path, error code, region, service name, etc., facilitating subsequent filtering, aggregation, chart analysis, and issue tracking.

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Resources and further reading

Choose the next step from product documentation, related solutions, and technical guidance.

Related reading

Selected product practices, troubleshooting guidance, and technical solutions

A powerful log viewer to support data linkage analysis

Guance viewer's powerful query filtering and search functions help users quickly and accurately retrieve data and locate faults. The log viewer not only helps us collect and manage various log files, but also enables correlation analysis with multi-party data, providing users with more comprehensive and accurate data information to support efficient decision-making.

Product capabilities

Leverage Guance to build a unified enterprise-level log center

Guance adheres to the philosophy of "unified collection, unified processing, and unified analysis," building an efficient observation data analysis platform. This article Guance how to help enterprises quickly and effectively build unified log centers through the various processes of log collection, processing, storage, and analysis.

Best practices

Pipeline helps you easily manage massive amounts of data!

Data governance has become crucial in today's information age. As the volume of data continues to grow and diversify, organizations need to effectively manage and utilize this data to support business decisions and innovative development. Whether for data analysis or parsing, using Pipeline helps organizations automate and standardize data governance; Providing organizations with clearer and more actionable data views to strongly support data governance.