We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Logging Apache Spark - How we made it easy

Formale Metadaten

Titel
Logging Apache Spark - How we made it easy
Serientitel
Anzahl der Teile
56
Autor
Mitwirkende
Lizenz
CC-Namensnennung 3.0 Unported:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
Identifikatoren
Herausgeber
Erscheinungsjahr
Sprache

Inhaltliche Metadaten

Fachgebiet
Genre
Abstract
Are you familiar with the following Scenario? You're running your Apache Spark app on EMR, and the log file gets pretty heavy. You try and open it through the AWS UI, or download it straight to your computer. You end up connecting to the server running your driver or any of your executors, relentlessly searching your logs while simultaneously looking at Ganglia and the Spark UI for additional logs and metrics. If you are, this talk is exactly for you. Let me tell you how made it all easy with just some bootstrap actions, some bash scripts, Beats and Elastic. Customizable per app logging, with less searching of big log files and more looking into useful Kibana dashboards. This architecture is not nice to have, it's essential.