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北极星因果指标产品已正式发布,快速了解
Kindling - OriginX
Fault Root Cause Reasoning Engine
Implementing Kernel Behavior Observability Based on eBPF
Using eBPF-based kernel behavior observability data, threading the needle to link application observability data, network observability data, and log observability data to achieve root cause analysis
Kindling - OriginX
Fault Root Cause Reasoning Engine
Empower everyone to have Expert Level Troubleshooting Skills
Everyone to locate the root cause within 5 minutes, helping enterprises to easily implement the 1-5-10 strategy
Expert experience meticulously organize various scattered monitoring indicators and logs to intelligently generate fault root cause reports
Kindling - OriginX
Fault Root Cause Reasoning Engine
Eliminate differences in capability and experience to achieve standardized troubleshooting process
Intelligent and automated unification of typical manual troubleshooting steps into a standardized troubleshooting process, addressing differences in personnel capabilities and experience through platform capabilities.
Eliminating differences and improving efficiency is more conducive to the accumulation and inheritance of knowledge within the team
Kindling - OriginX
Fault Root Cause Reasoning Engine
Maximize the value of observability data
Automatically concatenate observability data to maximize the extraction of observability data value
Utilize eBPF technology and automated Tracing analysis to generate intuitive and understandable fault root cause reports, eliminate usage barriers, and enhance the value of observability system construction
Kindling - OriginX
Fault Root Cause Reasoning Engine
ontinuously analyze and resolve the disconnect between AIOps fault root cause conclusions and observability from the kernel perspective
Implement the TSA methodology, automatically correlate relevant metrics based on the North Star indicator for troubleshooting, and perform intelligent data drilling
Generate interpretable root cause reports to bridge the gap between AIOps fault root cause conclusions and observability data
自动化
Tracing关联分析
自动关联和分析各类Tracing / Metrics / Logging数据,收集关键操作的执行路径与数据进行故障根因的推导分析,生成可视化图表和报告,既是推导的过程依据,又可方便进行二次分析
标准化排障方式
简化用户体验设计
在排障过程中,什么时候该看什么数据,已经根据专家经验在界面流程当中设计好了,排障的特异化需求就能被固化并同步下来,成为标准化的排障流程
自动化智能化
查询分析功能
如果只有文档化的标准排障理论,而没有通过机器自动化智能化分析故障,并将标准化排障过程当中所需的数据自动化关联查询出来,完全依赖人去落地标准化排障是非常难的。
智能化给出可解释结论
在报告中直接给出故障根因结论,同时将故障的完整推导过程所需的Tracing / Metrics / Logging都已查询集成,再也不用因某些数据查询慢而打断思路。
无缝集成主流可观测性技术栈
故障根因推理引擎, 自动化Tracing关联分析生成可解释的故障根因报告
解锁排障专家之道
基于eBPF技术
故障根因推理引擎, 自动化Tracing关联分析生成可解释的故障根因报告eBPF是一种先进的系统内核技术,基于其能够获取到传统监控无法采集到的内核层指标,实现更精准深入分析
创新型TraceProfiling
故障根因推理引擎, 自动化Tracing关联分析生成可解释的故障根因报告利用创新型Trace-Profiling技术,精准捕捉每一次调用,自动组织和关联高价值的故障关键数据
北极星排障指标体系
故障根因推理引擎, 自动化Tracing关联分析生成可解释的故障根因报告基于北极星排障指标体系结合专家经验及算法推导故障根因,帮助用户真正落地实践1-5-10
极少资源占用
故障根因推理引擎, 自动化Tracing关联分析生成可解释的故障根因报告对宿主节点各类资源占用及消耗极少
SaaS版永久免费
故障根因推理引擎, 自动化Tracing关联分析生成可解释的故障根因报告Kindling - OriginX SaaS版永久免费使用,并且包含全部完整功能
私有化存储
故障根因推理引擎, 自动化Tracing关联分析生成可解释的故障根因报告采用边缘节点架构设计,数据完全私有化存储,用户拥有对数据绝对控制权
以SLO完成告警闭环
故障推理引擎的出现带来很多理念的转变
故障根因推理引擎, 自动化Tracing关联分析生成可解释的故障根因报告