A cloud-native approach to logs

Simplifying your application’s log emission process.

By Kevin Hoffman
August 10, 2016
Logs Logs (source: Skitterphoto via Pixabay)

In Beyond the Twelve-Factor App, I present a new set of guidelines that builds on Heroku’s original 12 factors and reflects today’s best practices for building cloud-native applications. I have changed the order of some to indicate a deliberate sense of priority, and added factors such as telemetry, security, and the concept of “API first” that should be considerations for any application that will be running in the cloud. These new 15-factor guidelines are:

  1. One codebase, one application
  2. API first
  3. Dependency management
  4. Design, build, release, and run
  5. Configuration, credentials, and code
  6. Logs
  7. Disposability
  8. Backing services
  9. Environment parity
  10. Administrative processes
  11. Port binding
  12. Stateless processes
  13. Concurrency
  14. Telemetry
  15. Authentication and authorization

Logs (the 11th of the original 12 factors) should be treated as event streams, that is, logs are a sequence of events emitted from an application in time-ordered sequence. The key point about dealing with logs in a cloud-native fashion is, as the original 12 factors indicate, a truly cloud-native application never concerns itself with routing or storage of its output stream.

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Sometimes this concept takes a little bit of getting used to. Application developers, especially those working in large enterprises, are often accustomed to rigidly controlling the shape and destination of their logs. Configuration files or config-related code set up the location on disk where the log files go, log rotation and rollover policies to deal with log file size and countless other minutiae.

Cloud applications can make no assumptions about the file system on which they run, other than the fact that it is ephemeral. A cloud-native application writes all of its log entries to stdout and stderr. This might scare a lot of people, fearing the loss of control that this implies.

You should consider the aggregation, processing, and storage of logs as a nonfunctional requirement that is satisfied not by your application, but by your cloud provider or some other tool suite running in cooperation with your platform. You can use tools like the ELK stack (ElasticSearch, Logstash, and Kibana), Splunk, Sumologic, or any number of other tools to capture and analyze your log emissions.

Embracing the notion that your application has less work to do in the cloud than it does in the enterprise can be a liberating experience.

When your applications are decoupled from the knowledge of log storage, processing, and analysis, your code becomes simpler, and you can rely on industry-standard tools and stacks to deal with logs. Moreover, if you need to change the way in which you store and process logs, you can do so without modifying the application.

One of the many reasons your application should not be controlling the ultimate destiny of its logs is due to elastic scalability. When you have a fixed number of instances on a fixed number of servers, storing logs on disk seems to make sense. However, when your application can dynamically go from 1 running instance to 100, and you have no idea where those instances are running, you need your cloud provider to deal with aggregating those logs on your behalf.

Simplifying your application’s log emission process allows you to reduce your codebase and focus more on your application’s core business value.

Post topics: Operations