Monitoring JHipster applications
with the JHipster Console

Pierre Besson
JHipster Conf, June 21th 2018

Who am I ?

Pierre Besson



  • FullStack & DevOps @IpponTech
  • JHipster core team member
    • πŸƒ Spring
    • 🐳 Docker
    • ☸️ Kubernetes

Introduction to microservice monitoring



The JHipster Console

problems when monitoring microservices

  • Distributed system -> More complex to observe
  • Logs are dispersed in many log files
  • Hard to locate the microservice that caused a problem
  • Hard to follow the chain of requests
  • πŸ‡/🐒 Studying latency is hard
  • πŸ’‚ Resiliency mechanism are hard to configure properly

The solutions

the 3 ways of Observability

  • Logs : discrete event
  • Metrics : numerical values (business or technical)
  • Traces : chains of calls in the system (spans)

  • Store everything in one place, with proper metadata
  • Use a search engine to query and aggregate monitoring data
  • Visualize using graphs and dashboards
  • Navigate around highly correlated data

The JHipster Console
  • Setup the ZELK stack (Zipkin, Elasticsearch, Logstash, Kibana) in docker
  • Enable reporting from any JHipster app with a few properties
  • Logs + metrics forwarded with logback-logstash-encoder
  • Traces are forwarded to Zipkin using Spring Cloud Sleuth
  • Logs are enriched with trace information (Trace ID)
  • Data is stored in Elasticseach for a certain number of days (Curator)
JHipster Console Architecture

JHipster Console demo

Setting up the Console

Navigating around Kibana

Creating custom visualizations and dashboards

Graphing metrics, manipulating timeseries

Following call traces accross services (Kibana + Zipkin)

The future of JHipster monitoring (Help wanted !)

  • Migrate from Dropmizard Metrics to Micrometer
  • Provide an alerting solution
  • Improve Prometheus support and add Grafana dashboards
  • Support cloud monitoring solutions: Datadog, Google Stackdriver, ...
  • Investigate new monitoring products: OpenCensus, Apache Skywalking, ...

Thank you !