This tutorial covers the following content:
- What are Docker Logs and Logging Drivers?
- What are the Docker Logging Challenges?
- How to Implement Effective Docker Logging Strategies?
The Docker logs assist in analyzing the actions in the stack layer. Also, these help in managing networking, storage layers, etc. The issues with the production applications are registered/analyzed at runtime and are written to the log file. This resultantly causes various limitations and becomes severe in the case of several instances executing in the application. In such scenarios, the “logging drivers” come into effect.
The disposable nature of Docker containers corresponds to losing all the logs/data generated while the existence of the particular Docker container when the container turns off. In such a case, there is a need to analyze the issue with the container(s).
This approach is challenging as it is not easy to deal with complex Docker environments with various containers running/executing. Also, it is required to map the log events with the corresponding containers/applications. This is highly complex, and considering these scenarios, it is vital to apply effective docker logging strategies.
Following are some of the most effective Docker logging strategies in Docker:
- Logging Directly Via the Application.
- Logging Via the Docker Logging Driver.
- Logging Using a Dedicated/Allocated Logging Container.
- Logging Using the Sidecar Approach.
- Logging Via Data Volumes.
Approach 1: Logging Directly Via the Application
In this approach, the applications within the containers manage their logging via a logging framework. This allows the programmers to control the logging event(s) effectively. This methodology has the following advantages and disadvantages, respectively:
- This approach is convenient to command if proficient with the logging frameworks.
- This approach imposes an additional load on the application process
- The logs should be moved to remote storage to avoid losing the data.
Approach 2: Logging Via the Docker Logging Driver
Docker comprises a log driver integrated in the container that acts/behaves as a log management system. The driver reads the container outcomes, formats the log and saves it to the file, thereby enhancing the performance.
- With this approach, there is no need for the container to write and read the log files internally.
- The containers rely on the host machine for this methodology to come into effect.
Approach 3: Logging Utilizing a Dedicated Logging Container
This methodology dedicates a container to collect/accumulate logs. Also, this container assists in handling the log files in the Docker environment. Moreover, this approach also enables collecting the logs via streams of Docker API data, log events, etc.
- In this approach, multiple logging containers can be deployed as per the requirement and the host machine is no longer dependent upon.
- The logging and the application containers should be defined/specified in a clear way.
Approach 4: Logging Using the Sidecar Approach
This approach also utilizes the logging containers, such as the “Dedicated Logging” approach. The core difference is that it pairs each app container with its dedicated/allocated logging container. It is such that it can be applied via the mounted volumes for sharing the logs between containers.
- This approach is easy to maintain as logging is managed automatically.
- It is difficult to set up and take up more resources.
Approach 5: Logging Via Data Volumes
A data volume corresponds to a marked directory within the container that comprises the shared data. It is such that even after the container turns off, the possibility of losing the logs/data is minimal.
- This approach is highly effective when centralizing and storing logs for a long-time span.
- It allows the programmers to share a single data volume with various containers.
- There is a risk of data loss while moving containers to different hosts.
The effective Docker logging strategies can be applied by logging directly via the “Application”, using the “Docker Logging Driver”, using a “Dedicated Logging Container”, utilizing the “Sidecar Approach”, or via “Data Volumes”.