Fixing Dagger CI 'No Space Left On Device' Errors

Hey there, fellow developers! Ever been in that frustrating situation where your Dagger CI pipeline just gives up, throwing a cryptic no space left on device error? Yeah, it's a real head-scratcher, especially when you're just trying to get your awesome code deployed. This isn't just a Dagger problem, but it's a common headache in CI/CD environments, often compounded by the unique ways Dagger manages its internal filesystem snapshots. Today, we're diving deep into why this happens, focusing on those pesky node_modules folders, the quirks of GitHub Actions runners, and most importantly, how to fix it. We'll talk through strategies, provide practical cleanup steps, and get your Dagger pipelines running smoothly again. So grab a coffee, and let's tackle this beast together!

Understanding the "No Space Left on Device" Dagger CI Error

Alright, let's break down this no space left on device error when it pops up in your Dagger CI pipeline. At its core, this message means exactly what it says: the system—or more accurately, the specific filesystem Dagger is trying to write to—has run out of available storage. But it's not always your main hard drive; it's often a temporary volume, a Docker layer, or Dagger's internal snapshot storage hitting its limit. When you see something like failed to add directory "/app/node_modules": failed to copy source directory: failed to copy files: userspace copy failed: write /var/lib/dagger/worker/snapshots/.../node_modules/...: no space left on device, it's a strong indicator that Dagger's worker, which manages the execution of your pipeline steps within isolated environments, simply can't allocate any more disk space to persist the files it needs. This specific path, /var/lib/dagger/worker/snapshots/, points directly to Dagger's internal storage mechanism, where it creates and manages the immutable filesystem snapshots that are a core part of its power. Each step in your Dagger pipeline might generate new files or modify existing ones, and Dagger captures these changes as new layers. Over time, especially with large dependencies or many intermediate build artifacts, these layers can accumulate, consuming significant disk space. The issue often becomes particularly pronounced with JavaScript projects due to the sheer volume of files within node_modules. Tools like pnpm try to optimize this by using a content-addressable store, but when Dagger needs to copy those files into a container's working directory or create a new snapshot that includes them, that's when the disk usage can skyrocket. Imagine Dagger trying to create a new version of your /app directory, and it needs to duplicate all those gigabytes of node_modules just to get started—that's a recipe for disaster on a space-constrained environment. Common culprits for this problem include: excessively large dependency caches (like node_modules or vendor directories), lingering temporary build artifacts from previous runs, or even an accumulation of Docker images and volumes if Dagger is interacting with a Docker daemon that isn't regularly pruned. The immutable nature of Dagger's snapshots, while powerful for reproducibility, means that old layers aren't immediately discarded, leading to potential bloat if not managed proactively. This error isn't just a minor inconvenience; it's a hard stop, preventing your CI pipeline from completing its job and impacting your team's development velocity.

Why Dagger Builds Get So Chunky: The node_modules Monster

Let's get real for a second, guys. If you're working with JavaScript, you know the drill: the node_modules directory is often the biggest, most gargantuan folder in your entire project. We're talking hundreds of thousands of files, sometimes spanning gigabytes, all for a few dependencies. And when you factor in tools like pnpm, while they're fantastic for optimizing disk space locally by symlinking packages from a global content-addressable store, they can still become a challenge within a Dagger CI context. When Dagger interacts with your codebase, especially during a build step that involves installing dependencies, it needs to capture the state of your filesystem. This means that even if pnpm is using hard links or symlinks to its global store, Dagger often needs to copy or snapshot the resolved node_modules directory into its own internal representation. Each time you run an npm install, yarn install, or pnpm install, that potentially massive directory gets created or updated. If your Dagger pipeline rebuilds this directory in multiple steps, or in different contexts, each of those distinct node_modules structures can end up in different Dagger layers, consuming huge amounts of space. Imagine a scenario where you have a frontend and backend service, each with its own node_modules and Dagger steps. Even if they share some dependencies, Dagger might create separate snapshots for each, duplicating much of the data. The problem gets even worse with incremental builds or when caching strategies aren't perfectly aligned with Dagger's internal mechanisms. If Dagger isn't intelligently deduping or pruning old snapshots containing slightly different versions of node_modules, you end up with a growing collection of large, redundant directories within its internal /var/lib/dagger/worker/snapshots/ storage. This node_modules monster is undoubtedly one of the primary culprits when you encounter a no space left on device error, especially in a build process that involves many JavaScript-heavy projects or monorepos. Understanding this interaction between your package manager, the size of your dependencies, and Dagger's snapshotting mechanism is the first crucial step in effectively tackling these space-related issues.

The GitHub Runner Connection: A Common Bottleneck

Now, let's talk about the environment where many of us run our Dagger pipelines: GitHub Actions runners. These aren't infinite computing resources; they're virtual machines with finite limits on CPU, RAM, and crucially for us, disk space. When your Dagger CI pipeline kicks off on a GitHub Actions runner, it's operating within these constraints. The Dagger engine itself (the dagger daemon) runs on this runner, and its internal /var/lib/dagger/worker/snapshots/ directory, where all those filesystem layers and node_modules monsters live, is directly consuming the runner's allocated disk space. This is where the issue linked in the original problem, dagger/dagger/issues/6839, becomes highly relevant. That issue, and many others like it, highlight a common pain point: Dagger's internal storage can grow unchecked on a runner, especially if pipelines are frequently executed without proper cleanup. While Dagger is designed to be efficient with its storage, the sheer volume of data involved in modern builds, coupled with the ephemeral nature of CI runners, means that temporary files, old build artifacts, and stale Dagger cache entries can quickly pile up. Think about it: a typical GitHub Actions runner might have 100-200GB of disk space. That sounds like a lot, right? But if your build process involves installing 5GB of node_modules, then compiling it into a 2GB artifact, and Dagger is creating multiple immutable snapshots of these intermediate states, that space can evaporate surprisingly fast, especially if previous, failed, or partially completed runs have left behind orphaned Docker volumes or Dagger's own temporary directories. Moreover, if multiple Dagger pipelines are running on the same runner (either sequentially or even in parallel if your setup allows for it), they're all competing for that same pool of disk space. It's a race to the bottom, and your build often loses. The runner's lifecycle might not always include a comprehensive disk cleanup between jobs, or the default cleanup might not be aggressive enough to clear Dagger's specific cache locations. So, while Dagger is a powerful tool for defining your CI, its execution on a resource-constrained platform like a GitHub Runner demands a proactive approach to disk management. Understanding this interplay is key to not only fixing current failures but preventing future ones too.

Our Battle Plan: Smart Cleanup Strategies for Dagger CI

Alright, guys, enough talk about the problem; let's get down to solutions! The good news is that while the no space left on device error can be a major headache, there are concrete, actionable steps we can take to prevent it and keep our Dagger CI pipelines running smoothly. The key here is proactive cleanup and optimizing where and how Dagger stores its build artifacts and caches. It's about being smart with our disk space, not just blindly building everything. We need to integrate cleanup directly into our Dagger pipelines, ensuring that temporary files, old caches, and unnecessary build layers are pruned regularly. This isn't just a band-aid; it's a fundamental shift in how we manage resources in a CI/CD environment. By carefully planning our cleanup steps, we can significantly reduce the likelihood of encountering these dreaded space issues, especially when working with resource-intensive projects like those involving large JavaScript node_modules directories. It's time to take control back from that runaway disk usage!

Pruning Dagger's Cache

First things first: Dagger has its own cache management tools, and we should absolutely leverage them. The dagger cache prune command is your best friend here. What does it do? It's designed to clean up old, unused Dagger cache entries, reclaiming disk space that might be occupied by stale images or build layers. Think of it as spring cleaning for your Dagger workspace. You can run this command directly on your CI runner as part of your Dagger pipeline setup or tear-down. For example, you might add a step to run dagger cache prune --all --force before your main build steps to ensure maximum available space, or after a successful build to clean up any temporary artifacts that Dagger might have cached. Using --all ensures a thorough cleanup, while --force bypasses confirmation prompts, which is useful in automated CI environments. Integrating this command effectively can make a huge difference in keeping your runner's disk space healthy. Remember, Dagger's power comes from its caching and immutability, but without pruning, those caches can grow indefinitely.

Optimizing node_modules and Build Artifacts

Next up, let's tackle the node_modules monster directly. This is where most of your disk space issues likely originate. Several strategies can help here:

1. Pre-build Cleanup: Before you even run npm install or pnpm install in your Dagger container, consider running npm cache clean --force or pnpm store prune if you're dealing with a globally shared pnpm store on the runner. This ensures that only the absolutely necessary packages are downloaded and stored.
2. .dockerignore and .gitignore: If your Dagger pipeline is building Docker images, make sure your .dockerignore file is aggressive. Exclude node_modules, build artifacts, temporary files, and anything not strictly needed for the final image. Dagger respects these ignore files when copying contexts, significantly reducing the data it has to process and snapshot. Similarly, a well-configured .gitignore can prevent Dagger from tracking irrelevant files if it's directly working with your local repository.
3. Multi-stage Builds: This is a game-changer for Docker-based Dagger pipelines. Instead of installing node_modules and building your app in a single Docker layer, use multiple stages. The first stage installs dependencies and builds the application. The second (final) stage then copies only the necessary build artifacts and production dependencies from the first stage, leaving behind all the development dependencies and temporary build files. This drastically reduces the size of your final Docker image and, by extension, the intermediate Dagger snapshots. For instance, you could have a builder stage that installs node_modules and runs your build, and then a runner stage that copies FROM builder AS build-env and only picks up the dist folder and production node_modules.
4. Post-install Pruning: Sometimes, even after installation, there are temporary files or caches left behind. Within your Dagger build step, after npm install or pnpm install, you could add commands to delete unnecessary files or directories before creating the next Dagger snapshot. For example, RUN rm -rf /app/node_modules/**/*.map to remove source maps, or RUN find /app/node_modules -name "*.test.js" -delete for test files. Be careful with this, but it can save significant space.

General Runner Cleanup

Beyond Dagger's specific caches, sometimes the problem lies with the underlying runner environment. If you're explicitly using Docker commands within your Dagger pipeline or if previous jobs left Docker artifacts, you might need to clean those up:

1. Docker System Prune: If your CI runner is accumulating Docker images, containers, volumes, or networks, docker system prune -a --volumes is a powerful command that cleans up everything not currently in use. This should be run with caution and usually as part of a post-job cleanup script on the runner, rather than directly within Dagger unless you know exactly what you're doing. It can free up massive amounts of space.
2. Temporary Directories: Sometimes, builds generate temporary files outside of Dagger's direct control. Ensure your CI setup includes steps to clean up common temporary directories (/tmp, /var/tmp) if your build processes frequently dump large files there.
3. Consider Self-Hosted Runners: If you're constantly hitting disk space limits on managed CI runners, it might be time to consider self-hosted runners. These give you complete control over the underlying hardware, allowing you to provision machines with significantly more disk space, better IO, and custom cleanup scripts tailored to your specific needs.

A Proactive Approach: Monitoring Disk Usage

Finally, don't just wait for the no space left on device error to hit you. Be proactive! Add steps to your Dagger pipeline to monitor disk usage at critical junctures. A simple df -h command (disk free, human-readable) run at key points (e.g., before installing node_modules, after building, etc.) can provide invaluable insights into where your disk space is going. You can even add conditional checks to fail the build early if disk usage exceeds a certain threshold, allowing you to catch problems before they completely halt your pipeline. This diagnostic step is incredibly useful for identifying which parts of your build are the biggest offenders and for verifying that your cleanup strategies are actually working. By combining these smart cleanup strategies, guys, we can turn that no space left on device error from a recurring nightmare into a rare, easily preventable hiccup!

Practical Example: Integrating Cleanup into Your Dagger Pipeline

Let's get practical, shall we? Seeing these cleanup strategies in action within a Dagger pipeline makes a huge difference. While the exact code will depend on your Dagger client (Go, Python, TypeScript, etc.), the principles remain the same. We want to integrate these cleanup steps strategically to optimize disk usage. Here’s a conceptual look at how you might weave these ideas into a TypeScript-based Dagger pipeline, highlighting where you'd typically add these space-saving measures. This example assumes you're building a Node.js application, which is a common source of no space left errors due to large node_modules directories. The goal here isn't just to clean up after a problem, but to proactively manage space during the build process itself.

import { dag } from "./api.gen";

async function buildAndDeploy() {
  const src = dag.host().directory(".", { exclude: ["node_modules/", "dist/", ".git/"] });

  // Step 1: Proactive Runner Cleanup (if needed outside Dagger's direct control)
  // This would typically be a pre-job step in your GitHub Actions workflow file
  // For example: 
  // - name: Clean Docker artifacts on runner
  //   run: docker system prune -a --volumes --force || true 
  // We're focusing on Dagger internal cleanup here.

  // Step 2: Check initial disk usage (diagnostic step)
  console.log("--- Initial Disk Usage ---");
  await dag.container()
    .from("alpine/git") // A small base image for diagnostics
    .withExec(["sh", "-c", "df -h /var/lib/dagger || true"]) // Check Dagger's worker space if possible, or general root
    .withExec(["sh", "-c", "df -h /dev || true"]) // Also check common device mount points
    .stdout()
    .then(console.log);

  // Step 3: Set up the base Node.js container with optimized image for dependencies
  // Using a slim image or a specific Dagger caching mechanism is key.
  const nodejs = dag.container()
    .from("node:20-alpine")
    .withMountedDirectory("/app", src)
    .withWorkdir("/app");

  // Step 4: Install dependencies in a build stage (multi-stage concept applied within Dagger)
  const installDepsContainer = nodejs
    .withExec(["pnpm", "install", "--frozen-lockfile"]); // Or 'npm ci' or 'yarn install --immutable'

  // Optional: Prune pnpm store or npm cache *after* installation if it's external to the container
  // If pnpm store is on the host: await dag.host().shell().exec(["pnpm", "store", "prune"]).stdout().then(console.log);

  // Step 5: Build the application
  const buildContainer = installDepsContainer
    .withExec(["pnpm", "run", "build"]); // Or 'npm run build'

  // Step 6: Create a slim production image using artifacts from the build stage
  // This is the multi-stage Docker build equivalent for Dagger
  const productionImage = dag.container()
    .from("node:20-alpine") // Use a lightweight runtime image
    .withMountedDirectory("/app", buildContainer.directory("/app/dist")) // Only copy build output
    .withWorkdir("/app")
    .withEntrypoint(["node", "server.js"]); // Or your actual entrypoint

  // Step 7: Check disk usage again before final steps/publishing (diagnostic)
  console.log("--- Disk Usage After Build ---");
  await dag.container()
    .from("alpine/git")
    .withExec(["sh", "-c", "df -h /var/lib/dagger || true"]) 
    .stdout()
    .then(console.log);

  // Step 8: Publish the final image (e.g., to a registry)
  await productionImage.publish("your-registry/your-app:latest");

  // Step 9: Dagger Cache Pruning (crucial cleanup step)
  // This can be run after the job completes, or strategically during the job.
  // For GitHub Actions, you might put this in a separate step like:
  // - name: Prune Dagger cache
  //   run: dagger cache prune --all --force || true
  // Or directly in your Dagger function if you have the Dagger CLI available:
  // console.log("--- Pruning Dagger Cache ---");
  // await dag.host().shell().exec(["dagger", "cache", "prune", "--all", "--force"]).stdout().then(console.log);

  console.log("Pipeline completed successfully!");
}

In this example, we're not just adding a single cleanup step at the end. Instead, we're thinking about disk usage throughout the pipeline:

• We exclude unnecessary directories from being copied into the Dagger context initially using exclude filters. This saves Dagger from even seeing the node_modules on your host.
• We include diagnostic steps with df -h to understand disk consumption at different stages. This is invaluable for pinpointing where the bloat occurs.
• We model a multi-stage build by having installDepsContainer and buildContainer create their artifacts, and then productionImage selectively copies only what's needed, leaving behind the large development node_modules and intermediate build tools. This is probably the single most effective strategy for reducing final image size and Dagger's internal snapshot storage.
• We explicitly mention running dagger cache prune as an external step (or an internal one if your Dagger client supports calling the CLI), as this is the ultimate way to clear Dagger's own persistent cache on the runner. Running it with --all --force ensures a clean slate, especially if this is a dedicated Dagger runner.

Remember, guys, the goal is to be mindful of every byte your pipeline is using. By applying these techniques, you'll not only fix no space left errors but also speed up your builds and make your CI pipelines far more reliable and efficient. It's about working smarter, not just harder!

Conclusion

So there you have it, folks! The dreaded no space left on device error in Dagger CI, often exacerbated by the colossal node_modules directory and the finite resources of GitHub Actions runners, doesn't have to be a recurring nightmare. We've dug deep into why it happens, from Dagger's immutable snapshotting to the sheer volume of JavaScript dependencies, and explored the limitations of common CI environments. But more importantly, we've armed ourselves with a powerful set of strategies to combat it.

By proactively pruning Dagger's cache with dagger cache prune, optimizing our builds through smart .dockerignore files and multi-stage Dagger pipelines that selectively copy only essential artifacts, and performing general runner cleanup when necessary, we can dramatically improve the stability and efficiency of our CI/CD workflows. Adding diagnostic df -h steps is also a game-changer, allowing us to catch issues early and understand where our disk space is truly going. Remember, the key is proactive resource management and integrating these cleanup steps directly into your Dagger pipeline from the start, rather than waiting for failure. Your Dagger CI pipelines, and your sanity, will thank you for it. Keep building awesome stuff, guys, and keep that disk space clean!