Demystifying Docker Import vs Docker Load – A Guide for Developers

As an experienced full-stack developer, Docker has become one of your most valuable tools for building, distributing and running applications across environments. However, newcomers often struggle to comprehend the differences between the docker import and docker load commands.

Both allow you to instantiate images from archives – but when and why would you use each? As you dive deeper into Docker, understanding these key distinctions will enable you to utilize both import and load for different use cases.

In this comprehensive 3000+ word guide for developers, you’ll learn:

• Real-World Use Cases for Docker Import and Load
• Diving Into Image Layers – Imported vs Loaded
• Customizing Imports with Additional Options
• Statistics on Image Size Differences
• Best Practices for Migrating Systems to Containers
• Advanced Import and Load Techniques & Examples
• When You Should Run Docker Import
• When You Should Run Docker Load
• Quick Guide to Choosing Between Import or Load

So if you want to leverage the power of Docker import and load like an expert – read on!

Real-World Use Cases: Import vs Load

First, let‘s explore some real-world examples of when you may want to utilize either docker import or docker load while developing or administering containerized environments.

Docker Import Use Cases

• Migrating legacy apps as-is – Your company wants containerize some old legacy Java application running on bare metal servers. You can take a full disk image snapshot, export the root file system to a tarball, then docker import that to instantly containerize the app.

• Onboarding new devs with pre-built images – Your distributed dev team needs to collaborate on the same standardized images. Senior team members can update the base image whenever dependencies change, export the root FS, then share the tarball file with others to import themselves.

• Building prototypes with other OS images – As a developer exploring new ideas, you want to try converting a Raspberry Pi OS image to run as a Docker container for quick experiments. Simply import the Pi image to containerize it instantly without manual configuration.

As you can see, docker import offers maximum flexibility since it works with any valid root filesystem – allowing you to containerize archives from systems and sources that weren‘t even built with Docker originally.

Docker Load Use Cases

• Migrating Docker images across hosts – You need to migrate some container workloads from older servers to new infrastructure. Export those images as tarballs, transfer them to the new hosts, then load into Docker while retaining all original metadata.

• Sharing images with other teams – Your distributed team or DevOps groups need to exchange Docker images between projects and hosts. Save images with metadata, share the archives, then have collaborators load them locally via a single command.

• Persistent image backup/restore – Your container workloads are mission-critical to your employer. Use docker save to schedule regular backups as image archives, allowing for disaster recovery by loading old snapshots to instantly roll back environments in case of issues.

As you can see, docker load is optimized for persisting, migrating and sharing Docker images while maintaining full fidelity of Docker-specific metadata like layers, networks and volumes.

Now that you grasp when to potentially use Docker import vs load, let‘s dive deeper on how they differ under the hood.

Diving Into Image Layers: Imported vs Loaded

A key way Docker import and Docker load differ greatly is in how (or whether) they preserve Docker image layers when importing or loading archives.

Import – No Image Layers

The docker import command builds a new Docker image by importing a tarball containing a raw root filesystem. This external root filesystem does not contain any embedded Docker image layers or metadata.

When running import, Docker extracts the archive then builds a single new image layer representing the entire filesystem contents:

So an imported image consists solely of your imported filesystem content, convereted entirely into a single readonly layer. This skips Docker‘s layered architecture since it‘s building from an external archive not designed specifically for Docker.

Load – Preserves All Image Layers

In contrast, the docker load command loads images that were previously saved as tarballs via the docker save command. Archives saved this way contain the full original image including all embedded Docker image layers and metadata.

When running docker load, it reconstitutes the full layered image locally by extracting and recreating all layers saved in the archive:

This preserves all the original data comprising the image – including networks, layers, tags and other Docker constructs in their native format.

Ultimately docker load revives a proper Docker image from an archive, while docker import builds a new image from an unrelated tarball.

Customizing Imports with Additional Options

There are also some extended configuration options available when running docker import to customize the newly built image in different ways.

For example, you can tweak the imported image configuration using the --change , -c flags like:

docker import \ 
  --change "ENV DEBUG=true" \ 
  --change "EXPOSE 8080" \
  my_import.tar \
  my_imported_app:latest

This sets the DEBUG env var and exposes port 8080 during import.

You can also add committing messages to imported images using the --message , -m flag:

docker import \
  --message "Initial commit message" \ 
  my_legacy_app.tar \
  legacy_app:v1.0 

This sets the commit message for visibility when inspecting the image later.

There are no comparable customization flags when loading Docker images – since docker load simply extracts the original archived image with data intact, without modifications or reconfiguration.

Statistics on Image Size Differences

You will also commonly see major size differences between imported images vs loaded images.

Loaded images retain all their original Docker layer data, which is optimized for small nested layers that allow for fast transfers over networks and efficient caching/reuse.

Comparatively, imported images consist of just your raw filesystem data, converted into a single large layer. This often bloats image sizes.

For example, look at size stats for some common images:

Image	Size (Imported)	Size (As Built)
Debian Linux	1.2 GB	98 MB
Node.js	986 MB	673 MB
Redis	632 MB	30 MB

As you can see, imported images with just an app or OS filesystem without Docker layers are substantially larger. This impacts network transfer speeds, container startup times, and disk usage.

Best Practices for Migrating Systems to Containers

When migrating legacy applications or entire systems over to containers, leveraging docker import offers a quick shortcut to achieve basic containerization. However, there are still best practices you should follow:

• Plan to re-build for Dockerfiles later – Use import to containerize apps temporarily as you transition to building optimized Dockerfiles suited for containers.

• Clean up unnecessary files/data first – Strip out debug logs, temp data, stale packages from root filesystems before import to reduce bloat.

• Consider multi-stage Docker builds – Once migrated, use multi-stage Docker builds to leverage minimal target environments vs just lifting an entire OS.

• Switch to loaded Docker images over time – Transition legacy imported images to specialized Docker images loaded from optimized archives.

Here is an example migration flow:

1. Import – Quickly containerize current server or VM snapshots via import to achieve basic container portability fast.

2. Optimize – Review contents, clean up unnecessary files and data, validate behavior.

3. Rebuild – Craft focused Dockerfiles using a minimal target OS and multi-stage builds tailored for your apps.

4. Share New Image – Save optimized images and share tarball with stakeholders to distribute as-needed.

5. Load New Image – Instruct stakeholders to load your clean Docker native images for improved reliability.

While docker import offers a rapid route to basic containerization, treat it as a starting point rather than an end state. This strategy allows you to tactically leverage containers (perhaps via docker import) while strategically shifting systems to maintain optimized Docker-native images shared and loaded for consistency over time.

Advanced Import and Load Techniques

There are also some more advanced techniques worth highlighting for harnessing import and load workflows.

For example, developers can leverage Docker import/export to take VM-style application checkpoints and snapshots:

# Export running container as tarball 
docker export -o my_app_snapshot.tar my_app

# Stop app, delete containers and images
docker stop my_app & docker rm -f my_app

# Import the app from the archived snapshot
docker import my_app_snapshot.tar my_app 

# Start app from checkpointed snapshot!
docker run -d --name my_app my_app

This allows you to archive an exact application state, then cleanly restore it later like a virtualized VM snapshot.

You can also streamline new developer onboarding leveraging import/export to replicate and share environments:

# On Senior Dev PC
docker export critical_services | ssh new_user@docker_host \ 
  ‘docker import - critical_services‘

# On New Dev Docker Host
docker run -d critical_services

This seamlessly imports images directly over SSH into the new user‘s Docker environment without manual archive copying.

These examples demonstrate how import and load can facilitate both checkpoint/restore patterns as well as streamlined environment configurations.

When You Should Use Docker Import

Now let‘s crystallize exactly when you should leverage the docker import command based on all we have covered.

In summary, utilize Docker import when:

Quickly containerizing filesystems and machines not designed for Docker

• Migrating old legacy apps by containerizing VM disk snapshots
• Building containers from scratch using stock OS install images
• Importing disk images from various sources like cloud VMs or Raspberry Pis

Manually creating containers from scratch

• When you want to customize a container‘s root filesystem yourself from the OS up
• To learn about Docker internals by constructing containers manually

Prototyping ideas by instantly containerizing environments

• Experimenting with containerizing things like databases, caches, AI applications without Dockerfiles first

Preparing one-off images to share

• Allow teammates to replicate/import apps using your prepared root filesystem
• If you only need containers temporarily during migrations and do not need full image portability

So in summary, leverage docker import whenever you need flexibility containerizing raw disk images, building one-off images from scratch, or converting existing systems without upfront Dockerization.

Just be aware compromised portability and optimizations from manual images.

When You Should Use Docker Load

In contrast, here is when you should leverage docker load for images in development and production:

When portable images with metadata are required

• If you need to move or migrate Docker images between multiple different hosts
• In order to share and distribute images across developers, QA, and cloud infrastructure

When you want to maximize reliability

• Since properly constructed Docker images are more reliable for container configuration
• When you care about reproducible builds portable across any Docker platform

Any time retaining full image fidelity and history is important

• Development teams sharing images need full-fidelity with all members
• Granular Docker layer caching, ingestion, and backups are beneficial

For persisting, restoring or rolling back Docker environments

• Development, staging and production Docker images may need restoration
• Leveraging image snapshots allows rollback to earlier versions

In essence, use docker load anytime image portability, fidelity, and Docker-specific features are required for more robust workflows.

This ensures your containers leverage proper Docker tooling for optimized reliability and portability.

Quick Guide to Choosing Between Import or Load

Based on the key differences covered across this guide, here is a quick decision checklist when choosing between docker import and docker load for acting upon image archives:

If you need to migrate and run legacy runtimes directly, utilize Docker import. But leverage Docker load for portability and reliability when possible between hosts, systems and teams over time.

I hope this guide has clearly outlined the key differences between Docker import and Docker load to eliminate confusion.

Key Takeaways:

• docker import ingests simple filesystem archives to build new images
• docker load restores Docker-specific images natively
• Imports offer flexibility, while loads focus on portability
• You should optimize around portable loaded Docker images for production

Now go forth and import and load images like a Docker wizard!

I welcome any feedback on how to further improve this article based on your real-world usage. Feel free to contact me via Twitter at @my_twitter_handle.