The rapid adoption of cloud-native and containerized microservices, distributed data processing frameworks, and continuous delivery practices has led to a massive increase in application complexity. This rapid change has very quickly led to obsolescence of current monitoring solutions. Several monitoring tools have taken an approach to collect more metrics, and do so efficiently. However, they simply offer a means to plot these metrics in as several 100 graphs, sometimes allowing the user to manually group/tag some graphs, but still leaving the user to manually correlate and make sense of what is going on. Let’s look into it in a little more detail:
Traditional monitoring approaches fall short.
Most traditional and current monitoring and troubleshooting products emphasize one standard way of visualizing an application and troubleshooting failures – dashboards. These dashboards are used to visualize metrics and employ simple static threshold-based rules to alert when metrics go beyond their normal operating range.
Troubleshooting workflow using dashboards and alerts.
A dashboards- and alerts-driven troubleshooting workflow in a traditional monitoring product is structured in the following way:
The above workflow is standard process for most monitoring solutions available today. However, there are several issues with is approach, especially when dealing with web-scale applications:
A data-science approach for complex and distributed applications.
The OpsClarity platform has several analytics constructs that are specifically designed to manage the hyper-scale, hyper-change microservices architecture of modern web-scale applications. The platform was built with the specific goal of significantly improving the troubleshooting workflow for these applications. It was designed from the ground up to handle the massive volume of data generated by modern web-scale applications by applying data science and advanced correlation and anomaly algorithms. However, since every application and metric is different, the same algorithms or anomaly detection techniques cannot be applied to all the metrics that are collected. Based on the context and history of the application and metrics, the platform constantly learns system behavior, understands the context, and chooses the appropriate combination of algorithms to apply. This intelligence is built into the platform’s engine, called the Operational Knowledge Graph (OKG).
Powered by the intelligence and knowledge curated by the OKG, the OpsClarity approach to web-scale application monitoring and troubleshooting is summarized as follows:
Amit is co-founder and CTO at OpsClarity. As a seasoned technologist, Amit is adept at finding innovative solutions to hard problems across a diverse set of domains like operational analytics, web search and advertising platforms, and drives the technology roadmap at OpsClarity. Prior to founding OpsClarity, he built-large scale crawling, indexing and web-graph analysis systems for web-search at Google and Yahoo. Amit holds multiple patents and has authored research papers in the areas of web-search and machine learning. He holds a MS in Computer Science from Stony Brook University.
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