Docker containers can suffer from numerous performance-related issues. I identify and resolve them as below:
High CPU and Memory: I monitor resource usage of the containers through Docker stats or Prometheus, among other monitoring software. Setting CPU and memory limits for containers ensures that a container does not hog resources and monopolize them. If an application were CPU-bound, I find out how to optimize my code. In case it is memory-intensive, I revisit how and when memory is allocated within the application and tweak appropriate Java or Node.js configs, if applicable.
Disk I/O Issues: Heavy read/write activities within containers may cause disk I/O slowness. Optimization techniques include using minimal disk-intensive drivers, such as overlay2 for Linux, and frequently rotating logs to prevent large log files from consuming excessive disk space.
Network Latency: Contained applications with network intensive operations (for example, microservices) suffer from latency. Configuring the correct networking mode, such as using host networking when low latency is critical would help. Decreasing number of hops for network paths and implementing a caching policy at the application level may also help reduce latency further.
Image size and build efficiency: large images delay the startup times, as well as take much storage. To handle this issue, I prevent unnecessary dependencies and apply multi-stage builds to separate runtime and build-time dependencies. Tools such as Docker Slim help in analyzing and reducing the image size, allowing faster builds and less consumption of resources.
