Observability

• La formation se compose de travaux pratiques (mises en situation, débats, exercices).
• Une évaluation quotidienne de l'acquisition des connaissances de la veille est effectuée.
• Une synthèse est proposée en fin de formation.
• Une évaluation à chaud sera proposée au stagiaire à la fin du cours.
• Un support de cours (version électronique) sera remis à chaque participant comprenant les slides sur la théorie, les exercices.
• Une feuille d'émargement par demi-journée de présence est fournie en fin de formation ainsi qu'une attestation de fin de formation si le stagiaire a bien assisté à la totalité de la session.

System observability is a major issue in modern-day computing. This discipline makes it possible to understand the behavior of a system in production and to be able to react quickly in the event of a problem. Observability covers several practices: Logging, metrics, and tracing. This training will allow you to understand the challenges of observability and to put in place the necessary tools to have good visibility on your systems. We will illustrate the concepts of the course in Cloud infrastructures (VMs) but also in a Kubernetes infrastructure.

Objectives

Understand the three pillars of observability: Logs, Metrics, and Traces

Observe applications (containerized or not)

Observe infrastructures

Understand architectures and collection tools

Understanding the Observability Software Ecosystem

Elasticsearch offering: Filebeat, Metricbeat, Logstash, Elasticsearch, Kibana

Opensource is not to be outdone: Fluentd, Prometheus, Exporters, Grafana, Opentelemetry, Jaeger, etc.

Using dashboards

Deploy an observability solution in Kubernetes

• Understand the fundamentals of observability
• Implement the three pillars: logs, metrics and traces
• Deploy monitoring solutions with Prometheus and Grafana
• Set up centralized logging
• Analyze and troubleshoot production issues

• Tout public

Le formateur est un expert du domaine qui intervient sur le sujet depuis plusieurs années en formation mais aussi en conseil.

• Basic knowledge of Linux and application deployment
• Familiarity with containerization (Docker, Kubernetes)
• In order to validate the skills and prerequisites of each participant, prior to the training, the trainer organizes a phone interview. He then confirms that the participant has the required level and that the content meets their expectations.

Programme

• Overview of Observability
• The Three Pillars of Observability: Logs, Metrics, and Traces
• Efficiently generate application logs
• Technical vs. Application Metrics (KPIs)
• Traces: The New Kid on the Block in Observability
• Exercises: analysis of log architectures and different configuration files: syslog, log4j, kafka, etc.
• Demonstration of tracingAnalysis of interesting metrics according to the systems
• Log management
• Manage application logs according to programming languages=Log4j, Logback, etc. etc
• Exploitation of third-party software logs (Kafka, Postgresql, etc.) etc)
• Transform and structure logs for better usability
• The different collection tools: Filebeat, Fluentd, Logstash, etc.
• The various storage tools: Elasticsearch, Splunk, etc. The different operating tools: Kibana, Grafana, etc.
• Exercises: Collecting, transforming, storing, and exploiting logs from a Java application - Collecting, storing, and exploiting Nginx (or Apache) logsCollecting, storing, and exploiting Kafka logsCollecting, storing, and exploiting Postgresql logs
• Metrics Management
• What is a metric? Metric Generation: No Standard
• JMX and Jolokia for JVM-based applications (Springboot, Kafka, etc.)
• Operating system metrics (CPU, memory, IO, etc.)
• The metrics of Kafka / Postgresql / Nginx / Apache / etc. Storing metrics: Prometheus or Elasticsearch? Leverage metrics with Grafana or Kibana
• Exercises: Collecting metrics from a Java, Nginx, Kafka and postgresql application, storing in Prometheus, visualizing in Grafana
• Using the Metricbeat / Elasticsearch / Kibana suite to leverage metrics from multiple systems (OS / Application / Kafka / Postgresql / etc.)
• Trace Management
• What is a trace? Difference with logs A distributed debugging mechanism
• Trace standards: OpenTracing, OpenTelemetry, Jaeger, Zipkin, etc.
• Collection and storage tools: Jaeger, Zipkin Exploit and traces: Jaeger, GrafanaLinking traces and logs
• Exercises: Setting up a trace system with Opentelemetry, jaeger and Grafana for an Angular microservice application and Springboot
• Linking Traces and Logs
• Observability in Kubernetes
• Kubernetes Log Architecture and Metrics
• Collect node and pod logs
• Collect node and pod metrics
• Traceability of your applications in Kubernetes
• Exercises: Collecting logs from a Pod, storing them in Elasticsearch, exploiting them in Kibana - Automating pod scraping by prometheus

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