Slow builds in CI? How to use sccache to speed up

The invisible compilation bottleneck in CI

Anyone working with software development knows that waiting for a continuous integration pipeline to finish can be an exhausting task. In large projects written in languages like C++, Rust, or Go, even a minor code change often triggers a lengthy reprocessing cycle from scratch. In practice, this means the CI server repeats all the heavy lifting of turning human code into an executable binary on every single commit. This behavior consumes precious minutes and drastically reduces team agility.

When CI servers are destroyed and recreated on every run to ensure a clean environment, they lose the history of what has already been compiled. This exact scenario highlights the need to optimize workflows, which is just as critical as managing resources when a container accumulates digital clutter and slows down system performance. Fortunately, tools exist specifically designed to prevent this recurring computational waste.

What is sccache and how does it work

Created by Mozilla, sccache acts as a wrapper sitting between your build system and the actual compiler, such as GCC or Clang. When the system attempts to compile a code file, sccache intercepts the command, calculates a unique digital signature based on the file's content and compilation parameters, and checks if that result already exists in storage. If the file has not changed, it delivers the ready binary instantly.

Unlike traditional local caches that reside only on a developer's machine, sccache supports cloud-based remote storage. This means that if a teammate or a different CI server has already compiled that exact version of a library, the current pipeline can download the ready result instead of processing it again. This distributed approach turns build times into something predictable and extremely fast, eliminating recurring bottlenecks in the delivery pipeline.

Configuring remote storage in CI/CD

For cache to work in ephemeral CI environments like GitHub Actions or GitLab CI, sccache must save data in an externally accessible location. The most popular backends include Amazon S3, Google Cloud Storage, Redis, or S3-compatible storage servers like MinIO. Configuring this destination is the fundamental step to ensure persistence across distinct pipeline executions.

Below is a practical configuration example using environment variables to point sccache to a compatible S3 bucket:

export SCCACHE_BUCKET='my-ci-cache-bucket'export SCCACHE_REGION='us-east-1'export SCCACHE_S3_KEY_PREFIX='sccache/'export RUSTC_WRAPPER='/usr/local/bin/sccache'

With these variables active in the execution environment, any subsequent compiler call will be automatically redirected. Should any network issue or connection failure occur during the process, it is worth remembering the importance of monitoring network ports and sockets, techniques similar to those applied when we need to monitor active TCP connections on Linux to diagnose communication freezes.

Step-by-step guide to integrate sccache into the pipeline

Implementing the tool in an automated pipeline requires a logical sequence of installation, credential setup, and wrapper invocation. Follow the basic procedure to enable performance gains in your project:

1. Download and install the official sccache binary on the CI runner or agent using the appropriate package manager or GitHub Releases.
2. Configure remote storage access credentials (such as AWS access keys) securely using repository secrets.
3. Export the environment variable defining the compiler wrapper, such as RUSTC_WRAPPER for Rust projects or configuring the matching C/C++ compiler.
4. Execute your project's normal build command, allowing sccache to intercept and manage the generated artifacts.
5. Output usage statistics at the end of the pipeline to audit hit rates and the volume of saved data.

Following these steps, the pipeline starts reusing previous compilation artifacts with minimal maintenance effort. Speed gains are usually visible right on the second execution, when most external dependencies are already stored in the remote cache.

Monitoring efficiency and avoiding pitfalls

Installing the tool does not guarantee absolute success if the cache is constantly invalidated. Small environment changes, such as frequent shifts in the absolute paths of code files or dynamic compilation flags, can alter the hash generated by sccache and cause cache misses. Therefore, monitoring the report generated at the end of each run via statistics commands is vital.

Running sccache --show-stats at the end of the job reveals valuable metrics such as total requests, successfully retrieved files, and remote storage space used. Maintaining this oversight ensures that investment in storage infrastructure brings the expected productivity return to engineers. To explore these architectural concepts further in production, see our related technical analysis on Container hanging? How to audit syscalls with Strace.

Conclusion

Optimizing compilation times in continuous integration environments is no longer a luxury, but a requirement for teams seeking fast delivery cycles. Using sccache as a distributed cache elegantly solves the problem of computational rework, turning ephemeral servers into intelligent nodes that share knowledge with each other. By eliminating unnecessary waits, teams gain focus, reduce cloud infrastructure costs, and significantly accelerate software innovation pace.

Adopting this strategy requires only initial planning when choosing remote storage and properly configuring environment variables. With appropriate monitoring and validation steps, speed gains become evident right from the first few executions, benefiting both developers and overall application lifecycle stability.