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Logz.io Simplifies Production Troubleshooting with Release of Log Patterns
[November 05, 2019]

Logz.io Simplifies Production Troubleshooting with Release of Log Patterns


BOSTON and TEL AVIV, Israel, Nov. 05, 2019 (GLOBE NEWSWIRE) -- Logz.io, an open-source observability platform for modern DevOps teams, today announced the release of Log Patterns, the newest AI capability in the company’s AIOps portfolio. Created to streamline investigation and troubleshooting so engineers can resolve issues quickly and directly from their Kibana interface, Log Patterns automatically analyzes millions of log messages in real-time and identifies recurring patterns. As a result, engineers save time on the tasks of troubleshooting and root cause analysis, enabling them to devote more time to improving their products.

Log Patterns is Logz.io’s third AIOps feature, joining Cognitive Insights and Application Insights, which uses AI to help engineers detect critical events in applications before they impact business. While engineers rely heavily on log data for performance diagnostics and root cause analysis, due to the tremendous volume of machine data produced in modern IT environments, troubleshooting has become an inefficient, time consumig, and costly process. Log Patterns solves this issue by using advanced clustering to dissect indexed log messages into variables and constants to identify recurring patterns. This enables engineers to improve productivity as well as identify and resolve issues when they arise. Together, Logz.io’s Cognitive Insights, Application Insights, and Log Patterns provide users with a complete AIOps toolset for monitoring and troubleshooting, giving engineers full visibility into their IT environments.



“With the explosion of machine data volume and velocity, gaining visibility into the performance of distributed cloud applications is a huge challenge for engineers. Oftentimes, the data produced consists of logs that are verbose, repetitive and unimportant for investigative work, making it difficult to identify and resolve issues efficiently,” says Logz.io CEO, Tomer Levy. “Log Patterns is a powerful tool to cut through noise and reduce complex log data, making it easy for engineers to gain the right insights and effectively identify issues in their systems.”

Built on top of state-of-the-art clustering mechanisms, Logz.io Log Patterns is scalable, automatic, and Kibana-native. As a result, users can ship as much data as they need and view log patterns inside Kibana’s Discover page, alongside regular log views. Furthermore, the easy-to-use platform constantly improves in response to usage patterns, continuously perfecting existing log patterns while adding new patterns as they recur.


Logz.io provides engineers with an observability platform, including a Log Analytics application that consists of a set of advanced analysis tools to make troubleshooting a much easier and faster process. With this new release, engineers will be able to sift through millions of log messages in just a few clicks to find an issue’s root cause quickly and easily. As the third feature in the company’s AIOps suite, Logz.io Log Patterns plays a critical role in Logz.io’s core mission of empowering engineers to be more effective by providing them with the open source monitoring, troubleshooting, and security tools they want to use, with the scalability, enterprise features and availability required by modern businesses.

For more information on Logz.io Log Patterns, contact [email protected].

About Logz.io

Logz.io is an intelligent observability platform built on open source and designed to empower modern DevOps teams to monitor, troubleshoot, and secure distributed cloud workloads more effectively. Engineering driven companies like VMWare, ZipRecruiter, and Unity use Logz.io to simplify monitoring and security workflows, increasing developer productivity, reducing time to resolve issues, and increasing the performance and security of their mission-critical applications.                            

Contact:

Lauren Sirt
[email protected]

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