CI/CD Times and Our Daily Lives: Local vs Shared Build Cache

As a developer, one of the moments I most anticipate during the day is waiting for the CI/CD pipeline to finish after committing my code. Sometimes it’s five minutes, sometimes twenty. This waiting time isn’t just a technical delay; it’s a situation that directly impacts my mental flow, focus, and overall productivity. As a project’s build time increases, so do its development costs, debugging time, and even the team’s morale. Therefore, speeding up CI/CD processes has always been a priority for me.

Especially in large and multi-module projects, re-downloading all dependencies or recompiling all modules with every change leads to significant time loss. This is where build cache mechanisms come into play. I’ve spent a lot of time pondering this issue in my own side projects and in the production ERP system I worked on. Build cache allows us to skip these steps in subsequent builds by storing previously compiled or downloaded components. There are fundamentally two main approaches: local build cache and shared build cache. Although both serve the same purpose, they have significant differences in their implementation and the benefits they provide.

The Impact of CI/CD Times on Our Daily Lives

A fast CI/CD pipeline doesn’t just mean fast software delivery; it directly determines the quality of a developer’s daily workflow. Over the years, I’ve seen that reducing a build from 15 minutes to 5 minutes isn’t just a 10-minute saving; it means the developer doesn’t lose their “flow” state. Writing code while waiting 20 minutes for a build to finish often means getting distracted by Twitter or something else, leading to a loss of focus. This situation causes a serious drop in productivity, especially in teams that commit multiple times a day.

On one of my own side projects, a CI build for even a small change took 7-8 minutes. If I made five commits a day, this meant spending almost an hour just waiting for builds. These waits forced me to turn to other tasks, but the possibility of the build finishing before I could fully dive into those tasks always lingered in the back of my mind. This “context switching cost” is an often overlooked but very valuable loss of time. According to one study, it can take up to 20 minutes for a developer to switch from one task to another and regain full focus. If your builds frequently cause these switches, you’re spending a significant portion of your day just waiting and trying to regain focus.

Furthermore, long CI/CD times can also lead to delayed error detection. When you make a mistake, you only find out when the build breaks after 15-20 minutes. This, in turn, extends the debugging cycle. Short build times provide immediate feedback, allowing you to catch errors while they are still fresh. In my own Android spam app, I only realized a problem I had while integrating a native package after the full build was complete. If the build had taken 2-3 minutes, I could have found and fixed the error much faster. Therefore, CI/CD optimization is not just a technical issue but also a “life” issue directly related to developer well-being and work quality.

Local Build Cache: Quick Returns and First Steps

Local build cache, as the name suggests, is a mechanism that stores dependencies or intermediate compilation outputs used in build operations on the local machine. This typically occurs on the developer’s own computer or on the local file system of a CI/CD agent. Its primary goal is to shorten build times by preventing the same dependencies from being downloaded repeatedly or the same code from being compiled repeatedly.

Most modern build tools (like Maven, Gradle, npm, Yarn, Rust’s Cargo) have built-in local cache mechanisms. For example, in Maven, all the JAR files you download are stored in the ~/.m2/repository directory. npm uses the ~/.npm/_cacache directory. When you run npm install for the first time in a project, all dependencies are downloaded and saved to the cache. The second time you run it, if there’s no change in the package.json file, npm retrieves these dependencies from the cache, significantly shortening the download time.

# Find the npm cache directory
npm config get cache

# Clear the npm cache content
npm cache clean --force

On my own systems, especially with microservices I set up using Docker Compose, I heavily utilize this local cache. Caching dependencies in a separate layer within a service’s Dockerfile saves me from the hassle of re-downloading all dependencies with every code change. For example, in a Node.js application, copying the package.json and package-lock.json files with a separate COPY command and then running npm install ensures that the dependency layer is rebuilt only when these files change.

# Dockerfile example: Caching dependencies
FROM node:18-alpine

WORKDIR /app

# Copy package.json and package-lock.json files
COPY package*.json ./

# Install dependencies (this layer is rebuilt only if package.json changes)
RUN npm install

# Copy application code
COPY . .

CMD ["npm", "start"]

The biggest advantage of local cache is its ease of setup and low complexity. Each developer manages this cache on their own machine, or each CI/CD agent manages it on its own. However, in a CI/CD environment, this situation can become a disadvantage. With every new CI/CD run, if the agent is restarted or ephemeral, the cache is recreated from scratch. This results in a “cold cache” situation every time, leading to a slow initial build. We experienced this problem in the CI/CD pipeline of a manufacturing company’s ERP. Every new pipeline run took 3-4 minutes to re-download dependencies, unnecessarily extending the total build time. Local cache alone is insufficient at this point.

Shared Build Cache: Centralization and Team Productivity

Shared build cache is a method of storing build outputs or dependencies in a central location, typically on network storage (NFS, S3-compatible storage, or a repository manager like Artifactory or Nexus). This allows different developers or different CI/CD agents to use the same cache. When one person or a CI/CD pipeline compiles a component, the output is saved to the cache, and when another developer or pipeline needs the same component, they can retrieve it from the central cache instead of compiling it from scratch.

This approach provides significant productivity increases, especially in large teams and environments where multiple CI/CD agents are running. For example, in a monorepo structure, compiling a module can take a long time. With shared cache, this module is compiled once, and everyone can use this compiled output. Similarly, if your CI/CD pipeline has 10 different projects, and they all use the same core library, this library is downloaded and cached once, and the other 9 projects use it from the cache. At a client’s large e-commerce site, we reduced deployment times by up to 30% by switching to shared cache in a microservice architecture.

Shared cache is usually implemented through specialized tools or platforms. Tools like Bazel, Gradle Build Cache, and Nx offer built-in shared cache support. Additionally, such a structure can be established using Jenkins Shared Libraries or GitLab CI’s cache keyword. In GitLab CI/CD, we can control when the cache is rebuilt by specifying paths and key. With policy: pull-push, we can ensure the cache is downloaded and then restored.

# .gitlab-ci.yml Shared Cache example
stages:
  - build
  - test

variables:
  # Set cache key based on branch name
  # This way, different branches use their own caches
  # and the main branch cache doesn't get polluted.
  CACHE_KEY: "$CI_COMMIT_REF_SLUG"

build_job:
  stage: build
  image: node:18-alpine
  cache:
    key: "$CACHE_KEY"
    paths:
      - node_modules/
      - .npm/
    policy: pull-push # Download cache and upload it back after the job
  script:
    - npm ci --cache .npm --prefer-offline # Install dependencies, use cache
    - npm run build
  artifacts:
    paths:
      - dist/
    expire_in: 1 day # Keep compiled outputs for 1 day

test_job:
  stage: test
  image: node:18-alpine
  cache:
    key: "$CACHE_KEY"
    paths:
      - node_modules/
      - .npm/
    policy: pull # Only download cache
  script:
    - npm test

In this example, node_modules/ and .npm/ directories are cached. Thanks to the CACHE_KEY variable, each branch uses its own cache. With policy: pull-push, the cache is both downloaded and updated in the build job, while in the test job, it’s only downloaded. Setting up shared cache is more complex than local cache and requires a central storage solution. However, especially in projects with large teams and many developers, the benefits gained from this complexity far outweigh the initial setup costs.

Trade-offs and Decision Making: When to Use Which?

Choosing between local and shared build cache depends on many factors, including your project’s size, team structure, CI/CD infrastructure, and budget. Both approaches have their own advantages and disadvantages, and making the right decision requires a good understanding of these trade-offs.

In my experience, local cache is usually sufficient for small, solo projects. In my own side projects, since I work alone, local mechanisms like Docker layer caching and npm cache serve me well. My build times are already at acceptable levels (usually under 2 minutes). However, as the team grows or CI/CD pipelines become more complex, the benefits of shared cache become more apparent.

When making a decision, it’s beneficial to ask yourself these questions:

• How many developers are working on the code? The return on shared cache increases as the team grows.
• What are the current build times? If they are under 1-2 minutes, perhaps cache optimization is not your priority.
• Is your CI/CD environment ephemeral? If a new agent is spun up for every build, the impact of local cache will be limited.
• How often do your project’s dependencies change, and how large are they? Large and infrequently changing dependencies are good candidates for caching.
• What is your budget and capacity to manage operational complexity? Shared cache requires additional infrastructure and maintenance.

In my opinion, always utilize local cache mechanisms to their fullest for a small start. Optimize built-in features like Docker layer caching, npm cache, and maven local repo. If this is not enough and the need for build sharing between teams or CI/CD agents arises, then start evaluating shared cache solutions. Remember, every solution has a cost and complexity.

Practical Implementation and Things to Consider

I have encountered some practical challenges and learned lessons while implementing shared build cache. First, determining the right cache key strategy is crucial. If your cache key is too general, you’ll invalidate the cache unnecessarily or continue using old/faulty caches. If it’s too specific, your cache hit rate will decrease. I usually create a key by combining the branch name (like CI_COMMIT_REF_SLUG) and the hash of dependency definition files such as package.json or pom.xml. This provides branch-level isolation and guarantees that the cache is updated when dependencies change.

Second, storage cost and management. Shared cache requires storage space. If the cache size grows uncontrollably, your storage costs will increase. Object storage solutions like S3 are advantageous in terms of cost, but it’s important to manage the cache’s lifecycle (TTL - Time To Live) well. For example, setting policies to automatically delete old or unused caches is important. In an internal platform for a bank, we accumulated over 1 TB of unnecessary data because we forgot to clear the cache in test environments. Regular cleanup automation is essential for such situations.

Third, network latency. Using shared cache means you need to download the cache. If your CI/CD agent and cache server are in different geographical regions, this download process can become a bottleneck in itself. Therefore, keeping the cache server geographically close to your CI/CD agent or using solutions like CDN to improve performance is important. In one of my projects, cache download times reached up to 45 seconds due to accessing US-based S3 from European servers. Therefore, the location of the cache server needs to be carefully chosen.

# Example of measuring the time to download a cache file from S3 (pseudo-code)
# In a real CI/CD environment, these steps are automated

start_time=$(date +%s)
aws s3 cp s3://my-build-cache/my-project-cache.zip .
end_time=$(date +%s)
duration=$((end_time - start_time))
echo "Cache download time: $duration seconds"

Finally, monitoring. Continuously monitoring cache hit rates, cache size, and download times is critical to understanding how effective your cache strategy is. If your cache hit rate is below 50%, you may need to review your cache key strategy or the items being cached. In an ERP project, when our cache hit rate exceeded 90%, our average build time dropped from 8 minutes to 3 minutes. Metrics like these concretely demonstrate the success of optimization efforts. observability-metrikleri-ve-uygulama-performansi

Lessons from My Experience and the Future

I’ve been in the industry for twenty years, and during that time, I’ve wrestled with build processes on dozens of different projects. The effort I’ve spent on shortening build times, both in my own side projects and in large enterprise projects, has always paid off. Because fast builds are not just a technical success but also a factor that directly impacts team morale and daily work quality.

Once, in a production ERP system, I noticed that CI/CD times had suddenly increased during a new feature development cycle. Builds that averaged 12 minutes had stretched to 20 minutes. When we investigated the cause, we found that a newly added dependency had caused the cache key to be set incorrectly, and all dependencies were being re-downloaded with every build. When we fixed this simple error, the build time dropped back below 10 minutes. This incident once again showed me how important it is not only to set up cache mechanisms correctly but also to monitor and maintain them regularly.

In the future, I expect AI to become more integrated into build processes. For example, AI-powered systems could more intelligently predict which caches need to be invalidated by analyzing code changes, or optimize which steps can be run in parallel during a build. ai-destekli-operasyon-ve-pipeline-optimizasyonu Such automations will further speed up build processes, giving us developers more opportunities to focus on creative work.

Conclusion

Local and shared build cache mechanisms are indispensable tools for increasing the efficiency of CI/CD pipelines. Both approaches have their own advantages and disadvantages, and choosing the right one based on your project’s needs is critical. While local cache offers a great starting point for individual development and small projects, shared cache provides a scalable and centralized solution for large teams and complex CI/CD infrastructures.

Remember, fast CI/CD processes are not just a technical optimization but also an investment that improves developers’ daily workflow, reduces context switching costs, and generally offers a more enjoyable development experience. Making this investment correctly will positively impact both your project’s success and your team’s well-being. In my next post, I will discuss a critical data consistency issue we encountered in a client’s distributed systems and how we resolved it.