IBM Log Analysis with LogDNA allows you to capture your application and environment logs, filter out noisy or irrelevant log lines, alert, search, and archive your log data. You can build real-time dashboards with highly interactive graphs, including Counters, Gauges, Tables, and Time-Shifted Graphs.
This exercise shows how the IBM Log Analysis with LogDNA service can be used to configure and access logs of a Kubernetes application that is deployed on IBM Cloud. You will deploy a Spring Java application to a cluster provisioned on IBM Cloud Kubernetes Service, configure a LogDNA agent, generate application logs and access worker logs, pod logs or network logs. Then, you will search, filter and visualize those logs through Log Analysis with LogDNA Web UI.
The diagram below illustrates the service landscape used for this repo.
Adopted from repo Spring PetClinic Microservice example running on Kubernetes(in Korean), Analyze logs and monitor application health with LogDNA and Sysdig and IBM Cloud Patterns.
- an IBM Cloud account
- a Kubernetes cluster running in IBM cloud (IKS)
Note: it's highly recommended to enable
private endpoints(or private service endpoint) when you create your Kubernetes cluster in IBM Cloud. Theprivate endpointscan also be enabled after the cluster is created. This provides the option to use theprivate endpointswhen configuring LogDNA to collect logs from your application deployed to IKS cluster.
A simplified version of petclinic application is used in this repo. It includes four microservice components.
- api-gateway
- customers
- vets
- visits
The complete version of petclinic application with microservice architecture is available here.
- Lab 1 - Prepare exercise environment
- Lab 2 - Deploy sample application to IKS cluster
- Lab 3 - Deploy and Setup IBM Log Analysis with LogDNA
- Lab 4 - Generate application log entries
- Lab 5 - Work with your logs via LogDNA
- Lab 6 - Log streaming via LogDNA
There is lots of great information, tutorials, articles, etc on the IBM Developer site as well as broader web. Here are a subset of good examples related to data understanding, visualization and processing: