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From logging to monitoring to reactive insights

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From logging to monitoring to reactive insights

Avatar for Christian Schneider

Christian Schneider

February 24, 2023

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  1. Christian Schneider » For the last 6 years: Open Source

    Architect at Talend » Since October: Computer scientist at Adobe. » Apache member and committer » Twitter @schneider_chris » Website liquid-reality.de
  2. What you will learn » Apache Karaf Decanter + Elastic

    Search + Kibana » Approaches for central monitoring and logging » Custom reactive processing to produce higher level informations
  3. Apache Decanter Collectors • Pax-Logging • JMX • EventAdmin •

    Camel • ActiveMQ • ... Events Appenders • File • DB • Elastic Search • Kafka • JMS • ...
  4. Decanter setup for single machine JMX Collector Log Collector Camel

    Collector Event Admin Elastic Search Appender
  5. Decanter for multiple servers Server 1 + Decanter Server 2

    + Decanter Kafka Cluster Kafka Cluster Elastic Search + Kibana Decanter
  6. Decanter + logstash Server 1 Karaf Decanter Server 2 Spring

    boot Beats Kafka Cluster Kafka Cluster Elastic Search + Kibana Logstash Server Server 3 Tomcat Beats
  7. CXF Message logging Client Server Request Out Request In Response

    Out Response In slf4j slf4j • Meta data as MDC properties • Message id and correlation id • Service name as logger name
  8. Extending Decanter » Collectors » Appenders » Filter, Enrich Events

    » “Complex” Event Processing using Reactive Components
  9. Event Processing use case » Compute “up time” of bundles

    by noting start and stop events and sending an event with the bundle runtime on stop » Alert when we we have more than x percent errors in logged CXF messages » Correlate request and response into one message sent through decanter » Only send aggregated events when your source produces Events very fast