How to deploy Mort using Docker in production?

Pull the official Docker image from ghcr.io, create a custom Dockerfile to add your configuration YAML, and run the container with appropriate ports and volumes, as outlined in the README's Docker section. Ensure you handle storage backends and caching setup separately.

Mort vs Imaginary: which is better for high-performance image processing?

Mort offers S3 compatibility and more storage backends, making it suitable for integrated storage solutions, while Imaginary is simpler and lighter but may lack features like distributed locking. Mort's request collapsing can handle high concurrency better in multi-instance setups.

Can Mort automatically convert images to WebP format on the fly?

Yes, Mort supports format conversion including WebP, and you can configure presets in the YAML file to automatically serve WebP images based on URL patterns, as shown in the demo examples with the 'webp' preset.

How does Mort handle security and access control for private images?

Mort supports S3-compatible keys for authentication and can be configured with bucket-level access controls, but it relies on proper network security and configuration, as it's designed to be deployed behind proxies or with custom headers for additional protection.

What are the monitoring options available in Mort for performance tracking?

Mort includes built-in Prometheus metrics and debug endpoints, allowing you to monitor cache hits, error rates, and processing times, which can be integrated into existing observability stacks for real-time insights.

How to add custom image filters or transformations in Mort?

Use the Tengo scripting language to write custom URL parsers, as documented in the README, or modify the Go code for more advanced integrations, but this requires familiarity with the modular architecture and configuration.

Open-Awesome

mort

MITGov0.37.0

An S3-compatible image processing server written in Go, supporting multiple storage backends and on-the-fly transformations.

GitHub

522 stars25 forks0 contributors

What is mort?

Mort is an S3-compatible image processing server written in Go that handles on-the-fly image transformations and storage across multiple backends. It solves the problem of offloading image processing workloads from main applications by providing a scalable, self-hosted solution for resizing, converting, and manipulating images in real-time.

Target Audience

Developers and DevOps engineers building applications that require scalable image processing, storage, and delivery, particularly those using S3-compatible storage or needing a self-hosted alternative to cloud-based image services.

Value Proposition

Developers choose Mort for its high performance, modular architecture, and extensive feature set including S3 compatibility, multiple storage backends, and Cloudinary-like APIs, all while being open-source and self-hostable.

Overview

Storage and image processing server written in Go

Use Cases

Best For

Building self-hosted image processing pipelines for web applications
Replacing cloud-based image services like Cloudinary or Imgix with an open-source alternative
Handling dynamic image transformations for e-commerce product catalogs
Serving optimized images from S3-compatible storage backends
Implementing distributed image processing across multiple server instances
Reducing application load by offloading image manipulation to a dedicated service

Not Ideal For

Organizations seeking a fully managed, hands-off image CDN with vendor SLAs and support.
Applications that require extensive video processing or AI-driven image enhancements beyond basic transformations.
Enterprises needing certified stability and long-term support for mission-critical image pipelines.

Pros & Cons

Pros

Multiple Storage Backends

Supports a wide range including disk, S3, HTTP, Azure, Google Cloud, Oracle, B2, and SFTP, enabling flexible integration into existing storage solutions as detailed in the features list.

Performance Optimizations

Implements request collapsing to prevent duplicate processing and offers response caching in memory or Redis, reducing server load and latency as highlighted in the README.

Cloudinary-Compatible API

Provides an API that mirrors Cloudinary's for image transformation and upload, making it easy for teams to migrate from or integrate with that service, as noted in the key features.

Modular Architecture

Designed with a fully modular architecture, allowing for custom URL parsers using Tengo and extensible configurations through environment variables, as emphasized in the philosophy and documentation.

Cons

Active Development Status

The project is explicitly marked as still in active development, leading to potential breaking changes and less stability compared to mature alternatives like Imaginary or Thumbor.

Complex Setup Requirements

Requires external dependencies like libvips and a Go environment for development, which can complicate deployment and maintenance compared to simpler, container-first solutions.

Limited Advanced Features

Focuses on core image operations; lacks built-in support for video processing, AI-based enhancements, or extensive third-party integrations found in commercial services like Cloudinary.

Open Source Alternative To

mort is an open-source alternative to the following products:

Cloudinary

Cloudinary is a cloud-based image and video management platform that provides upload, storage, manipulation, optimization, and delivery services. It offers APIs and SDKs for various programming languages.

Imgix

Imgix is a real-time image processing and delivery service that optimizes and transforms images on-the-fly via URL parameters for web and mobile apps.

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