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Alex Aslam
Alex Aslam

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Optimizing CI/CD Pipelines: Slash Build Times with Smarter Dependency Caching ⚡

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:

  1. You push a tiny CSS fix.
  2. Your pipeline reinstalls all dependencies from scratch.
  3. It rebuilds the entire project.
  4. 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/  
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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/  
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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  
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Why this rocks:

  • Dependencies stage: Installs once, shares with all later jobs.
  • No redundant work: Build/test jobs skip reinstalls.

Advanced Caching Strategies

  1. 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-  
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  1. 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') }}  
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  1. Ephemeral Caching: For monorepos, scope caches to subprojects.
   key: frontend-${{ hashFiles('apps/frontend/package-lock.json') }}  
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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

  1. Over-Caching: Don’t cache huge directories (e.g., build/ with binaries).
  2. Stale Caches: Use hashFiles to auto-invalidate when dependencies change.
  3. Ignoring Cache Policies: In GitLab, pull-push vs. pull can make or break performance.

Your Action Plan

  1. Audit Your Pipeline: Find jobs reinstalling dependencies or rebuilding artifacts.
  2. Add Caching: Start with dependency managers (npm, pip, etc.).
  3. 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! 🛠️

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🐯 🚀 Timescale is now TigerData: Building the Modern PostgreSQL for the Analytical and Agentic Era

We’ve quietly evolved from a time-series database into the modern PostgreSQL for today’s and tomorrow’s computing, built for performance, scale, and the agentic future.

So we’re changing our name: from Timescale to TigerData. Not to change who we are, but to reflect who we’ve become. TigerData is bold, fast, and built to power the next era of software.

Read more

Top comments (4)

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20thr profile image
Hugo Santos

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.

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alex_aslam profile image
Alex Aslam

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!

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20thr profile image
Hugo Santos

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.

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alex_aslam profile image
Alex Aslam

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!

Tiger Data image

🐯 🚀 Timescale is now TigerData: Building the Modern PostgreSQL for the Analytical and Agentic Era

We’ve quietly evolved from a time-series database into the modern PostgreSQL for today’s and tomorrow’s computing, built for performance, scale, and the agentic future.

So we’re changing our name: from Timescale to TigerData. Not to change who we are, but to reflect who we’ve become. TigerData is bold, fast, and built to power the next era of software.

Read more

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