ELK for KPI’s

Matt HerbertTechnical Lead, DevOps Automation, Products and TechnologiesJanuary 17, 2016

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● Matt Herbert● Home: New Hampshire, USA● Started in the industry in 1993● Specializing in Automation● At Red Hat since January 2015● Formerly with Cisco for 8 years

working on 802.1X, VPN, Identity● Love programming in Python● Hobbies: Gardening, Maple Syruping,

Boating, Travel

About Me

maherber@redhat.com https://www.linkedin.com/in/mattherbert

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That which is measured improves. That which is measured and reported improves exponentially.

- Karl Pearson

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Complexity of the Release Pipeline

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The Problem

● 100s of servers● Dozens of critical services● Hundreds of packages built every day● Hundreds of advisories released every week

Nothing was measured!

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The SolutionClick to add subtitle

Measure It!

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1. Uptime2. Average time to release3. Release success rate4. Release accuracy rate

What should we Measure?

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Challenges

● Tools not implemented for metrics● Lots of nuance● No data retention policies

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Data started trickling inGoogle sheets doesn’t scale

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Elastic SearchA distributed, RESTful search and analytics

engine capable of solving a growing number of use

cases.

LogstashData processing pipeline

that ingests and transforms data, and then sends it to your favorite

“stash.”.

KibanaVisualize your Elastic

search data and navigate the Elastic Stack.

Welcome ELK (aka Elastic Stack)Elastic Search + Logstash + Kibana

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OperationsCentralized OS and App level logging

QualityTest log storage

Automation + DevelopmentMetrics

Elastic Stack at Red Hat

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Typical SysOps Kibana Dashboard

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Jenkins for Orchestration

Collect Data Import to Elastic Search Kibana for Visualization

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ShipshiftShip Jenkins artifacts to ELK

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