Yeah we do some similar tricks with our registry[0]: pushes and pulls from inside AWS are served directly from AWS for maximum performance and no data transfer cost. Then when the client is outside AWS, we redirect all that to Tigris[1], also for maximum performance (CDN) and minimum data transfer cost (no cost from Tigris, just the cost to move content out of AWS once).

[0]: https://depot.dev/blog/introducing-depot-registry

[1]: https://www.tigrisdata.com/blog/depot-registry/

No worries, entirely valid question. There may be ways to tune page cache to be more like this, but my mental model for what we've done is effectively make reads and writes transparently redirect to the equivalent of a tmpfs, up to a certain size. If you reserve 2GB of memory for the cache, and the CI job's read and written files are less than 2GB, then _everything_ stays in RAM, at RAM throughput/IOPS. When you exceed the limit of the cache, blocks are moved to the physical disk in the background. Feels like we have more direct control here than page cache (and the page cache is still helping out in this scenario too, so it's more that we're using both).

No, it's more like swapping pages to disk when RAM is full, or like using RAM when the L2 cache is full.

Linux page cache exists to speed up access to the durable store which is the underlying block device (NVMe, SSD, HDD, etc).

The RAM-backed block device in question here is more like tmpfs, but with an ability to use the disk if, and only if, it overflows. There's no intention or need to store its whole contents on the durable "disk" device.

Hence you can do things entirely in RAM as long as your CI/CD job can fit all the data there, but if it can't fit, the job just gets slower instead of failing.

If you clearly understand your access patterns and memory requirements, you can often outperform the default OS page cache.

Consider a scenario where your VM has 4GB of RAM, but your build accesses a total of 6GB worth of files. Suppose your code interacts with 16GB of data, yet at any moment, its active working set is only around 2GB. If you preload all Docker images at the start of your build, they'll initially be cached in RAM. However, as your build progresses, the kernel will begin evicting these cached images to accommodate recently accessed data, potentially even files used infrequently or just once. And that's the key bit, to force caching of files you know are accessed more than once.

By implementing your own caching layer, you gain explicit control, allowing critical data to remain persistently cached in memory. In contrast, the kernel-managed page cache treats cached pages as opportunistic, evicting the least recently used pages whenever new data must be accommodated, even if this new data isn't frequently accessed.