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When logs are scattered across applications, containers, hosts, cloud services, security systems, and business platforms, teams struggle to restore full context when a failure occurs. Guance log management and analysis solutions integrate log collection, pipeline parsing, field retrieval, alerting, ansensitization, link association, and cost governance into a single platform, helping R&D, operations, and security teams pinpoint issues using the same evidence.
Log fragmentation, location link breakage:Application logs, container logs, cloud service logs, and security logs are all stored in one place, requiring repeated tool switching during troubleshooting, which easily causes loss of context.
Original logs cannot be directly analyzed:Inconsistent field formats, insufficient structure, and excessive noisy data lead to slow retrieval, difficulty in statistics, and difficulty in maintaining alarm rules.
Sensitive information and cost pressures coexist:Logs may contain user information, keys, or transaction fields, and massive volumes of logs also incur costs for storage, retrieval, and retention cycles.
There are many alerts, but the real issue is unclear:Repeated alarms, contextless alerts, and low-value logs can overwhelm critical anomalies and affect fault response efficiency.
Unified collection, log queries, and log alarms:Integrates applications, hosts, containers, cloud services, and business logs, quickly narrows down issues through field filtering, log queries, full-text retrieval, and visual analysis, and consolidates high-value conditions into log alerts.
Pipeline Analysis and Log Governance:Raw logs are parsed, cleaned, field extracted, anxious, and routed, turning logs from "storeable" to "analyzable and alertable."
Links, links, and indicator correlations:Jump from a single error log to Trace, service, host, Pod, and access sessions, reducing the time spent manually piecing context.
Alarm Noise Reduction and Collaboration Closed-loop:Based on key fields, exception patterns, and impact scope alerts, issue assignment, troubleshooting, and review are integrated into the same context.
- Efficient Query and Filtering:It offers powerful log retrieval and analysis functions, supporting fast data queries, filtering, and in-depth analysis to help you quickly locate issues.
- Structured Data Processing:Supports converting raw log data from various sources into structured formats, simplifying data analysis processes and improving analysis efficiency.
- Visual Dashboard:Build diverse visual dashboards using log data to intuitively display business operations and uncover the business value of logs.
- Comprehensive correlation analysis:Supports linking logs with other observable data for comprehensive analysis, rapid locating, and troubleshooting.
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- Real-time structured processing:Provides pipeline functionality for real-time structured log processing, supports over 50 mainstream text processing templates, and meets diverse needs.
- Blacklist filtering:Supports blacklist functionality, filters out invalid logs, solves log management challenges, and ensures data quality.
- Flexible storage strategies:Develop storage strategies for log data according to different needs, reduce storage costs, and improve resource utilization.
- Data forwarding capability:Collected log data can be flexibly forwarded to big data platforms, supporting multi-platform data collaboration and enhancing data value.
- Data Animization and Compliance:Through unified data desensitization functions, the security of sensitive information is ensured, improving overall data compliance.
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- Multidimensional data association:Supports linking logs with front-end, APM, container, host, and business data for rapid data linkage analysis and comprehensive system status.
- Snapshot and anomaly tracking:Use snapshots and anomaly tracking features to log key moments, helping teams quickly synchronize information, locate and resolve issues.
- Sharing and Collaboration:Supports snapshot external linking functionality, allowing logs within a specific range to be shared with third parties, promoting cross-team collaboration and problem-solving.
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- Automatically write Pipelines:Leveraging the natural language processing capabilities of large models, log content is automatically parsed, helping developers efficiently and accurately write pipeline scripts, simplifying data processing workflows and reducing labor costs.
- AI log error analysis:Using Obsy AI Agent, key information from logs is automatically extracted, combined with online search engines and operations knowledge bases to quickly analyze possible failure causes and provide preliminary solutions.
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Observability platform and unified monitoring platform
Unified metrics, logs, links, RUM, AI analytics, and infrastructure data.
Real user monitoring
Analyze page performance, errors, and conversion experiences with real visit experience data.
LLM observability
Analyze large model calls, token costs, latency, errors, and application chains.
Suitable for teams with scattered logs, low query efficiency, difficulty structuring raw logs, high alert noise, rising log costs, and lack of fault context, especially in microservices, containers, and multi-cloud environments.
Guance associate logs with APM, infrastructure, RUM, and alert events through subscripts such as services, hosts, containers, TraceId, and access sessions.
Field desensitization, filtering, routing, and retention policy settings can be performed during the collection and pipeline stages, allowing high-value logs to enter retrieval and analysis, reducing costs caused by invalid data.
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