Hey there, developer friend! 👋 Let’s talk about the worst part of CI/CD: waiting ages for your pipeline to install dependencies, rebuild the same files, and finally deploy. It’s like watching paint dry—except the paint is your productivity.
But what if I told you there’s a way to cut build times in half (or more) with a few simple tweaks? Spoiler: It’s all about dependency caching and avoiding redundant work. Let’s turn your sluggish pipeline into a speed demon.
Why Caching Matters: The Pain of Redundant Work
Imagine this:
- You push a tiny CSS fix.
- Your pipeline reinstalls all dependencies from scratch.
- It rebuilds the entire project.
- You wait 15 minutes for a 10-second change. 😤
The culprit? No caching. Every run starts from zero, wasting time and compute resources.
Step 1: Cache Dependency Managers
Most build times are eaten by dependency installation. Cache them!
Example: Node.js (npm/yarn)
# GitHub Actions
- name: Cache node_modules
uses: actions/cache@v3
with:
path: node_modules
key: ${{ runner.os }}-node-${{ hashFiles('package-lock.json') }}
# GitLab CI
cache:
key: $CI_COMMIT_REF_SLUG
paths:
- node_modules/
How it works:
- Key: Uniquely identifies the cache (e.g., OS + Node version + lockfile hash).
-
Path: The directory to cache (e.g.,
node_modules
,.venv
,.m2
).
Other Languages:
-
Python (pip): Cache
~/.cache/pip
or.venv
. -
Java (Maven/Gradle): Cache
~/.m2
or.gradle/caches
. -
Ruby (Bundler): Cache
vendor/bundle
.
Step 2: Cache Build Artifacts
Why rebuild everything when only one file changed?
Example: Frontend Builds (Webpack/Vite)
# GitHub Actions
- name: Cache build assets
uses: actions/cache@v3
with:
path: dist/
key: ${{ runner.os }}-build-${{ hashFiles('src/**') }}
# GitLab CI
cache:
key: build-$CI_COMMIT_REF_SLUG
paths:
- dist/
Pro Tip: Use hashFiles
to invalidate the cache when source files change.
Step 3: Multi-Stage Caching
Split your pipeline into stages and reuse caches across jobs:
# GitLab CI Example
stages:
- dependencies
- build
- test
install_deps:
stage: dependencies
script: npm install
cache:
key: $CI_COMMIT_REF_SLUG
paths:
- node_modules/
policy: push # Upload cache for later stages
build:
stage: build
script: npm run build
cache:
key: $CI_COMMIT_REF_SLUG
paths:
- node_modules/
- dist/
policy: pull # Reuse cache from dependencies stage
Why this rocks:
- Dependencies stage: Installs once, shares with all later jobs.
- No redundant work: Build/test jobs skip reinstalls.
Advanced Caching Strategies
- Fallback Caches: Use a base cache if no exact match exists.
# GitHub Actions
- name: Cache node_modules
uses: actions/cache@v3
with:
path: node_modules
key: ${{ runner.os }}-node-${{ hashFiles('package-lock.json') }}
restore-keys: |
${{ runner.os }}-node-
- Parallel Job Caching: Share caches across parallel matrix jobs.
jobs:
test:
strategy:
matrix:
os: [ubuntu-latest, macos-latest]
cache:
key: ${{ matrix.os }}-node-${{ hashFiles('package-lock.json') }}
- Ephemeral Caching: For monorepos, scope caches to subprojects.
key: frontend-${{ hashFiles('apps/frontend/package-lock.json') }}
Real-World Wins
-
Startup Saves 70% Build Time: Caching
node_modules
reduced their 10-minute pipeline to 3 minutes. - Enterprise Cuts Cloud Costs: Shared caches across 100+ microservices slashed monthly CI bills by 40%.
Common Pitfalls to Avoid
-
Over-Caching: Don’t cache huge directories (e.g.,
build/
with binaries). -
Stale Caches: Use
hashFiles
to auto-invalidate when dependencies change. -
Ignoring Cache Policies: In GitLab,
pull-push
vs.pull
can make or break performance.
Your Action Plan
- Audit Your Pipeline: Find jobs reinstalling dependencies or rebuilding artifacts.
- Add Caching: Start with dependency managers (npm, pip, etc.).
- Measure & Iterate: Use CI analytics to track time saved.
Key Takeaways
- Cache dependencies: Stop reinstalling npm/pip/Gradle packages every time.
- Cache build artifacts: Reuse compiled code where possible.
- Share caches: Across stages, jobs, and even pipelines.
Final Thought: Caching isn’t just a “nice-to-have”—it’s the secret sauce to CI/CD efficiency. Your future self (and your teammates) will thank you for the extra coffee breaks. ☕
Hit a snag? Drop a comment below—let’s optimize together! 🛠️
Top comments (4)
This is a pretty good article!
If we go back to first principles — a lot of tools are built for incremental work; and what’s how they work in our workstations.
Most CI just starts work from scratch all
the time.
I’m biased because I felt this problem needed to be addressed more fundamentally — which is what we do at Namespace (namespace.so); cache volumes bring you that incrementality with little to no friction.
Most folks on Namespace see dramatic performance improvements because of that. Just like you did.
Thanks so much for the kind words and absolutely spot-on about the core issue! You’re right most CI systems act like amnesiac robots, redoing work from scratch every time, while our local dev environments thrive on incremental builds. It’s wild how much time and energy that wastes.
What your team is doing at Namespace is exactly the kind of fundamental shift the ecosystem needs. Cache volumes that ‘just work’ without fiddling with YAML? That’s the dream! I’ve heard similar stories from devs who’ve cut their pipeline times by orders of magnitude with smarter caching—it’s awesome to see tools like yours pushing the envelope.
Would love to hear more about how teams are using Namespace in practice. Any surprising moments or edge cases you’ve solved? Keep up the great work!
One of the most interesting use cases is mixing Nix and cache volumes; in Nix every package lives in a content addressable nix store, and is immutable. That’s perfect for caching.
There are definitely caveats — for example pnpm works best when code and cache live in the same physical volume.
But overall cache volumes have been a net win in simplicity and performance.
Ooh, combining Nix with cache volumes is such a smart play! The immutability and content addressing of the Nix store feels almost tailor-made for caching like peanut butter and jelly for reproducibility. And you’re spot-on about the pnpm quirk. It’s a great reminder that even with trade-offs, the simplicity and speed gains from cache volumes are game-changing.
Have you found other tools or workflows that pair unexpectedly well with this setup? I’m always nerding out over these kinds of optimizations. Thanks for sharing the deep-dive this is gold for folks looking to squeeze every drop of efficiency out of their pipelines!