Splunk, one of the most popular commercial aggregation services. Where you can get both forwarding and aggregation end points all under one roof. In aggregating logs, there are two main options:Ĭommercial SaaS service – Splunk, Logz.io, etc are commonly used. The best way to work through a logging solution is by understanding what you are collecting, re-structuring the data (normalizing), and selecting a scalable location to aggregate the data.

Cloud - whether its use of AKS, EKS, etc you will need to potentially analyze against these components also.

Application - output from each service needs to be collected properly.

Kubernetes cluster - Nodes, Pods, containers, K8S APIs, etc.

With Kubernetes, logs have to be aggregated for: The abundance of logs and the significant amount of information requires a proper architecture and plan to aggregate and analyze this information. Every developer and/or DevOps lead collects and analyzes logs for applications and their environments.

Logging is the most used and well known component in Observability. We will reference articles in the industry and from our own site CloudJourney.io. This blog will provide a quick overview of observability, and work through each of these components. These three constitute what is known in the cloud native world as Observability. However together these three categories can provide powerful analysis mechanism for application issues. Individually each category can help specific issues. Popular open source tools include Zipkin and Jaeger. Tracing - Tells an end to end story for an application by “stitching” bits of a flow into an easily digestible format. However there is a significant amount of this data and you have to you log aggregation tools like Elas ticsearch, Splunk etc to index and analyze the information. Logs - Logs are easy human readable, structured bits of information from a specific point in time. Hence, metrics are useful for monitoring but more powerful when enabled with analysis mechanisms such as correlation and anomaly detection. And you can use mathematical modeling and prediction to derive the behavior of the system over an internal of time - either present or the future. Metrics - are a numeric representation of data measured over intervals of time. These options cover both open source, and commercial options and cover three main categories: Remember to restart Apache to apply the new configuration: sudo /opt/bitnami/ctlscript.In managing applications deployed on Kubernetes, developers have a significant number of options to choose from. Include '/opt/bitnami/apps/myapp/conf/nf' In order to have your custom app at / your configuration file '/opt/bitnami/apps/myapp/conf/nf' should look like: Include '/opt/bitnami/apps/myapp/conf/nf'Īnd your configuration file /opt/bitnami/apps/myapp/conf/nf: įinally, the file /opt/bitnami/apache2/conf/bitnami/nf should look like: Include '/opt/bitnami/apps/rockmongo/conf/nf' Include '/opt/bitnami/apps/rockmongo/conf/nf' If you want to serve RockMongo at /rockmongo your configuration file /opt/bitnami/apps/rockmongo/conf/nf should look like: Alias /rockmongo/ '/opt/bitnami/apps/rockmongo/htdocs/'Īlias /rockmongo '/opt/bitnami/apps/rockmongo/htdocs' I got some help from the Bitnami folks and here's the solution that fixed my issue on Google Cloud:

Bitnami mean stack passenger