Pros of lazydocker

• More comprehensive feature set, including container logs, stats, and image management
• Actively maintained with frequent updates and bug fixes
• Better support for Docker Compose and Swarm

Cons of lazydocker

• Steeper learning curve due to more complex interface
• Requires more system resources to run

Code comparison

dry:

func (v *ContainerDetailsWidget) updateHeader() {
    v.header.Clear()
    v.header.AddText(v.container.Names[0], true, termui.AlignLeft, termui.ColorWhite)
    v.header.AddText(v.container.ID[:12], true, termui.AlignRight, termui.ColorWhite)
}

lazydocker:

func (gui *Gui) renderContainerHeader(container *commands.Container) string {
    return fmt.Sprintf(
        "%s %s %s %s",
        utils.ColoredString(container.Name, gui.Config.UserConfig.GUI.ContainerNameColor),
        utils.ColoredString(container.ID[:12], gui.Config.UserConfig.GUI.ContainerIdColor),
        utils.ColoredString(container.Status, gui.Config.UserConfig.GUI.ContainerStatusColor),
        utils.ColoredString(container.Image, gui.Config.UserConfig.GUI.ContainerImageColor),
    )
}

Both projects aim to provide terminal-based Docker management tools, but lazydocker offers a more feature-rich experience at the cost of increased complexity. dry focuses on simplicity and ease of use, making it a good choice for users who prefer a lightweight solution. The code comparison shows that lazydocker implements more customization options for its UI, while dry keeps things more straightforward.

Pros of ctop

• More comprehensive resource usage metrics, including detailed CPU and memory stats
• Supports multiple container runtimes beyond just Docker (e.g., rkt, LXC)
• Offers a single-container view with real-time graphs

Cons of ctop

• Less interactive functionality compared to dry (e.g., no container management)
• May have higher resource overhead due to more detailed metrics collection
• Limited customization options for the display layout

Code Comparison

dry:

func (v *ContainerDetailsWidget) updateHeader() {
    v.header.Clear()
    v.header.AddText(v.container.Name, true, termui.AlignLeft, termui.ColorWhite)
    v.header.AddText(v.container.ID, true, termui.AlignRight, termui.ColorWhite)
}

ctop:

func (cm *ContainerMetrics) CPUUtil() float64 {
    if cm.CPUUtil == nil {
        return 0
    }
    return *cm.CPUUtil
}

Both projects use Go and focus on container monitoring, but dry offers more interactive features while ctop provides more detailed metrics. The code snippets show different approaches: dry focuses on UI updates, while ctop emphasizes metric calculations.

Pros of dockly

• Built with Node.js, making it more accessible for JavaScript developers
• Offers a plugin system for extending functionality
• Provides a more colorful and visually appealing interface

Cons of dockly

• Less mature project with fewer stars and contributors
• May have performance limitations compared to Go-based alternatives
• Lacks some advanced features present in dry, such as container logs tailing

Code comparison

dockly (JavaScript):

const blessed = require('blessed')
const contrib = require('blessed-contrib')
const screen = blessed.screen()
const grid = new contrib.grid({rows: 12, cols: 12, screen: screen})

dry (Go):

func (h *Handlers) ContainerList(w http.ResponseWriter, r *http.Request) {
    containers, err := h.client.ContainerList(context.Background(), types.ContainerListOptions{})
    if err != nil {
        http.Error(w, err.Error(), http.StatusInternalServerError)
        return
    }
}

Both projects aim to provide terminal-based Docker management interfaces, but they differ in implementation language and some features. dry is written in Go and offers a more comprehensive set of features, while dockly is built with Node.js and focuses on ease of use and extensibility through plugins.

Pros of runlike

• Lightweight and focused on a single task: generating Docker run commands
• Easy to use as a command-line tool or import as a Python module
• Supports outputting commands for multiple containers at once

Cons of runlike

• Less feature-rich compared to dry's interactive terminal interface
• Limited to generating run commands, doesn't provide container management capabilities
• Less actively maintained, with fewer recent updates

Code comparison

dry:

func (v *ContainerDetailsWidget) updateHeader() {
    header := NewHeader()
    header.Add("Name", v.container.Names[0])
    header.Add("ID", v.container.ID)
    header.Add("Image", v.container.Image)
    v.header = header
}

runlike:

def get_docker_run_cmd(container_id):
    container = client.containers.get(container_id)
    cmd = ['docker', 'run']
    cmd.extend(get_container_options(container))
    cmd.append(container.image.tags[0])
    return ' '.join(cmd)

Both projects aim to assist with Docker container management, but they take different approaches. dry provides a comprehensive terminal UI for monitoring and managing containers, while runlike focuses solely on generating Docker run commands. dry offers more features and is more actively maintained, making it suitable for users who want a full-featured container management tool. runlike, on the other hand, is ideal for those who need a simple way to recreate container run commands, either programmatically or via the command line.

Pros of dive

• Provides a layer-by-layer analysis of Docker images, offering detailed insights into image composition
• Allows for interactive exploration of image layers, making it easier to identify large or unnecessary files
• Offers a content view for each layer, enabling users to inspect file changes between layers

Cons of dive

• Focuses primarily on image analysis, lacking broader Docker management features
• May have a steeper learning curve for users unfamiliar with Docker image internals
• Limited to analyzing local images, not providing remote registry exploration capabilities

Code comparison

dive:

func analyzeImage(imageID string, refTrees []*filetree.FileTree) (*image.AnalysisResult, error) {
    analysis := &image.AnalysisResult{
        Layers: make([]*image.Layer, 0),
    }
    // ... (additional analysis logic)
}

dry:

func (d *Dry) refreshContainers() error {
    containers, err := d.dockerDaemon.Containers()
    if err != nil {
        return err
    }
    d.containerList = containers
    return nil
}

Summary

While dive excels in detailed Docker image analysis, dry offers a more comprehensive Docker management interface. dive provides in-depth layer exploration and file change tracking, making it ideal for optimizing image sizes. On the other hand, dry focuses on overall Docker container management, offering a broader set of features for monitoring and interacting with containers. The choice between the two depends on whether the user needs specialized image analysis (dive) or general Docker management capabilities (dry).

Pros of Glances

• More comprehensive system monitoring, covering CPU, memory, network, disk, and processes
• Cross-platform support (Linux, macOS, Windows)
• Extensible plugin system for additional metrics and integrations

Cons of Glances

• Lacks Docker-specific features and container management capabilities
• May be more resource-intensive due to its broader monitoring scope
• Steeper learning curve for users primarily interested in Docker monitoring

Code Comparison

Glances (Python):

from glances_api import Glances

glances = Glances()
cpu_percent = glances.cpu.percent
print(f"CPU Usage: {cpu_percent}%")

Dry (Go):

package main

import "github.com/moncho/dry/appui"

func main() {
    stats := appui.NewDockerStats()
    cpuUsage := stats.CPUPercentage()
    fmt.Printf("CPU Usage: %.2f%%\n", cpuUsage)
}

Summary

Glances offers broader system monitoring capabilities and cross-platform support, making it suitable for general-purpose monitoring. However, it lacks Docker-specific features compared to Dry. Dry focuses on Docker container management and monitoring, providing a more specialized tool for Docker users. The code examples demonstrate the different approaches: Glances uses a Python API for general system metrics, while Dry utilizes Go for Docker-specific statistics.