Chapter 2
In-Depth Architecture of img
Peel back the layers of img to reveal a nuanced and elegant architecture purpose-built for the next era of image building. This chapter exposes the modular internals, innovative integration with BuildKit, and the advanced patterns that underpin img's speed, security, and extensibility-inviting you to look beyond surface commands and understand the true anatomy of modern container image tooling.
2.1 Architectural Overview of img
The img tool embodies a modular architecture that orchestrates container image creation through a clear separation of concerns among its primary components: the user interface layer, the build orchestration engine, and the backend execution environment. These layers collaborate via well-defined control flows and manage data lifecycles that preserve both efficiency and reproducibility throughout the build process. The figure below outlines the high-level blueprint of img, elucidating the interplay between user inputs, internal processing units, and external runtime dependencies.
User Interface Layer
The user interface layer provides a command-line interface (CLI) that acts as the gateway into img's functionality. User commands such as img build or img push are parsed and validated here. The CLI translates these commands into an internal representation that captures the build context, configurations, and target outputs. This translation ensures that subsequent layers operate on uniform and structured data formats, facilitating error handling and extensibility.
Build Orchestration Engine
At the core of img is the build orchestration engine, responsible for analyzing the build context and managing the sequence of operations required to produce a container image. This engine constructs a directed acyclic graph (DAG) representing build stages and dependencies, enabling parallelism where possible and enforcing strict ordering where necessary.
Each node in the DAG corresponds to a specific build step, such as extracting base image layers, running build instructions, or exporting final images. The orchestration engine ensures idempotency via content-addressable caching, avoiding redundant executions when source inputs and build configurations remain unchanged.
Conceptually, the DAG evaluation can be expressed as follows:
def evaluate_build_dag(node): for dependency in node.dependencies: evaluate_build_dag(dependency) if not cache.exists(node.hash): node_result = execute_build_step(node) cache.store(node.hash, node_result) return cache.retrieve(node.hash) This recursive approach guarantees that all dependencies are resolved and cached outputs are reused efficiently, minimizing build times and resource consumption.
Backend Execution Layer
The backend execution layer interacts with the underlying container runtime and filesystem to actualize the build steps prescribed by the orchestration engine. img supports multiple backends, including native containerd, BuildKit, and in certain configurations, direct manipulation of filesystem layers. This abstraction enables flexibility for running in diverse environments, from local workstations to server-side continuous integration systems.
The backend is responsible for:
- Layer management: creating, extracting, and composing filesystem layers.
- Execution sandboxing: isolating build steps within container contexts.
- Network and cache access: facilitating remote fetches and artifact caching.
Communication between the orchestration engine and backend is modeled as an instruction stream, where each element contains the action type, parameters, environment variables, and output directives.
{ "step": "RUN", "command": ["make", "all"], "env": {"BUILD_ENV": "production"}, "cache": true } Data Lifecycle and Cache Management
Throughout the build process, img manages various transient and persistent data forms, including build contexts, intermediate layers, and final images. This management is critical for reproducibility as well as incremental build speedups.
On receiving a build instruction, the system hashes the step definition combined with its dependency outputs to generate a unique key. Cache retrieval is attempted prior to execution, leveraging content digests to identify reusable artifacts. Executed steps update the cache and may also propagate metadata upstream to facilitate accurate invalidation on source changes.
Additionally, img employs aggressive layer deduplication by referencing immutable content hashes (e.g., SHA-256 digests), which ease storage burdens and accelerate push/pull operations with remote registries.
Control Flow...
