Today’s monitoring practices put too much burden on SREs and developers, who spend countless hours staring at “single pane of glasses”, making s

Raw logs in, insights out — log analysis with PacketAI

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2022-01-14 10:30:09

Today’s monitoring practices put too much burden on SREs and developers, who spend countless hours staring at “single pane of glasses”, making sure the “system behaves normally”. Using proprietary NLP (Natural Language Processing) techniques, PacketAI is able to detect 5 types of anomalies in log data in real time, and present insights to users to accelerate troubleshooting. Register here to try it out.

Today’s monitoring practices put too much burden on SREs and developers, the rarest resource in any software organisation. These teams spend countless hours staring at “single pane of glasses”, making sure the “system behaves normally” by looking at metrics and logs and chasing abnormalities. These abnormalities are mostly captured by setting manual rules and thresholds. PacketAI introduces a new observability paradigm, relying on ML algorithms to autonomously establish a baseline for system behaviour and indicate when anomalies happen.

Any given component of an infrastructure generate two types of data: metrics and logs. Metrics mainly describe the outer characteristics of a component and the environmental conditions in which it runs. For example, response time of an app component, throughput of a database component, queue size of streaming engine and cpu usage of python data app. These metrics give the observer “black box” information about the component, which is enough to do quick, somewhat superficial troubleshooting.