ThirdEye: LinkedIn’s business-wide monitoring platform
Who is this presentation for?
- Software engineers and business analysts
Mean time to detect (MTTD) and mean time to restore (MTTR) describe how long it takes to discover a problem and how long it takes you to restore the issue after it was detected. The shorter the MTTD and MTTR, the less time spent in outage and the more availability your product retains. Given that products and services inevitably break at some point, you need to be adept at detecting and restoring service as soon as possible. The issue triage and restoration lifecycle is made up of several steps: capturing metrics, detection (requiring monitoring and alerting), escalation, investigating, and remediation. Each segment of the triage needs to be measured for efficiency and effectiveness in order to keep these metrics as short as possible.
Akshay Rai walks you through ThirdEye, a self-service experience enabling anyone to rapidly identify and investigate deviations in business and system metrics. At LinkedIn, Third Eye is used by several teams spanning business analysts and engineers, and over 10K metrics are actively monitored. ThirdEye provides anomaly detection and collaborative dashboards for data analysis and brings together critical data that impacts metrics in a single place: holidays, deployments, company-wide issues and more. You’ll leave with an understanding of the concepts behind the open source ThirdEye project, how it’s built, and a look into ThirdEye’s insights and long-term plans. Akshay also gives you a powerful analysis of how ThirdEye helped detect and investigate some of the major issues that occurred on LinkedIn.
- General knowledge of monitoring and debugging issues
What you'll learn
- Learn how to build and leverage a generic domain-independent platform to detect and recover from business and operational issues by running anomaly detection and diagnosis on a variety of metrics and data
Akshay Rai is a senior software engineer at LinkedIn, whose primary focus is to reduce the mean time to detect issues and the mean time to resolve issues that arise at LinkedIn. He works on LinkedIn’s next-generation anomaly detection and diagnosis platform. Previously, he actively led the popular Dr. Elephant project at LinkedIn and helped open source it, and he worked on operational intelligence solutions for Hadoop and Spark by building real-time systems that enable monitoring, visualizing, and debugging of big data applications and Hadoop clusters.
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