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Logging and Metrics in Microservice Architectur...

Logging and Metrics in Microservice Architectures @Javaone

Alexander Heusingfeld

October 27, 2015
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  1. Don’t Fly Blind Logging and Metrics in Microservice Architectures Tammo

    van Lessen | [email protected] Alexander Heusingfeld | [email protected] #javaone #logging #metrics www.innoQ.com
  2. If you’re able to treat every Bounded Context as a

    separately deployable, independent component… © innoQ/Roman Stranghöner
  3. … you’ll have a self-contained system - which can lead

    to a 
 microservice architecture Introduction to self-contained systems: https://www.innoq.com/de/links/self-contained-systems-infodeck/
  4. Requirements > Apply a well-thought logging concept > Aggregate logs

    in different formats from different systems > Search & Correlate > Visualize & Drill-down > Alerting
  5. Use Thread Contexts / MDCs %-5p: [%X{loginId}] %m%n ThreadContext.put("loginId", login);

    logger.error("Something bad happened!"); ThreadContext.clear(); + Layout: ERROR: [John Doe] Something bad happened! Log:
  6. Use Thread Contexts / MDCs { "@version" => "1", "@timestamp"

    => "2014-04-29T14:21:14.988-07:00", "logger" => "com.example.LogStashExampleTest", "level" => "ERROR", "thread" => "Test worker", "message" => "Something bad happened!", "Properties" => { "loginId" => "John Doe" } } ThreadContext.put("loginId", login); logger.error("Something bad happened!"); ThreadContext.clear(); + JSON Layout Log:
  7. Define QoS for Log Messages > Log messages may have

    different QoS > Use Markers and Filters to enable fine- grained routing of messages to dedicated appenders > Use Filters and Lookups to dynamically configure logging https://www.innoq.com/en/blog/per-request-debugging-with-log4j2/
  8. Requirements > Apply a well-thought logging concept > Aggregate logs

    in different formats from different systems > Search & Correlate > Visualize & Drill-down > Alerting
  9. Distributed Logstash Setup Shipper / 
 Logstash Forwarder Broker Indexer

    Storage & Search Visualize https://www.elastic.co/products/logstash Push Pull
  10. Requirements > Apply a well-thought logging concept > Aggregate logs

    in different formats from different systems > Search & Correlate > Visualize & Drill-down > Alerting
  11. Requirements > Apply a well-thought logging concept > Aggregate logs

    in different formats from different systems > Search & Correlate > Visualize & Drill-down > Alerting
  12. Filter Log Stream For Alerts input { … } filter

    { if [message] =~ /.*(CRITICAL|FATAL|ERROR|EXCEPTION).*/ { mutate { add_tag => "alarm" } } if [message] =~ /.*(?i)ignoreme.*/ { mutate { remove_tag => "alarm" } } } output { if [type] == "production" { if "alarm" in [tags] { pagerduty { description => "%{host} - %{log_level}: %{log_message}" details => { "timestamp" => "%{@timestamp}" "host" => "%{host}" "log_level" => "%{log_level}" "message" => "%{log_message}" "path" => "%{path}" } … } } } }
  13. Logging is cool… And I can use it to collect

    metrics as well, right? © http://www.flickr.com/photos/dkeats/3128150892/
  14. Logging is cool… And I can use it to collect

    metrics as well, right? Watch out! © http://www.flickr.com/photos/dkeats/3128150892/
  15. Gauges A gauge is an instrument that measures a value.

    © https://secure.flickr.com/photos/profilerehab/4974589604/
  16. Counters A counter is a simple incrementing and decrementing integer.

    © https://secure.flickr.com/photos/mwichary/2273099939/
  17. Meters A meter measures the rate at which a set

    of events occur. © https://www.flickr.com/photos/springfieldhomer/1244320899
  18. Timers A timer is a histogram over a duration. ©

    https://secure.flickr.com/photos/psd/4686988937/
  19. Distributed Metrics Architecture Measure Collect & Sample Store Query &

    Graph Anomaly Detection Alerting CEP Dashboards
  20. Push vs. Pull + event-based de-/registration + routable event stream

    + producer decides when to push - producer aware of target - packet-loss might be missed + producer unaware of target + multiple targets possible + flexible interval - might miss short-lived services - requires service-discovery
  21. Some Recommendations > Think about what metrics are of importance

    for operating your application > Consider retention policies > Carefully design your dashboards > Think about non-standard graph types
  22. Conclusions > Create and document concepts for logging and metrics

    > Collect & aggregate distributed logs and metrics > Create dashboards tailored for your audience > Correlate your data to make conscious decisions > Don’t create your very own big data problem
  23. Prevent the apocalypse! Logging shows events. Metrics show state. Don't

    fly blind! © http://www.flickr.com/photos/pasukaru76/5067879762
  24. Thank you! Questions? Comments? Tammo van Lessen | @taval [email protected]

    Alexander Heusingfeld | @goldstift [email protected] innoQ Deutschland GmbH Krischerstr. 100 D-40789 Monheim am Rhein Germany Phone: +49 2173 3366-0 innoQ Schweiz GmbH Gewerbestr. 11 CH-6330 Cham Switzerland Phone: +41 41 743 0116 www.innoq.com Ohlauer Straße 43 D-10999 Berlin Germany Phone: +49 2173 3366-0 Ludwigstr. 180 E D-63067 Offenbach Germany Phone: +49 2173 3366-0 Kreuzstr. 16 D-80331 München Germany Telefon +49 2173 3366-0 https://www.innoq.com/en/talks/2015/10/javaone-2015-logging-metrics-microservices/