Disk Images in Sequoia
When you create the disk image, macOS creates and attaches its container, and creates and mounts the file system within that. This is then saved to disk as a regular file occupying the full size of the disk image, plus the overhead incurred by the disk image container itself. No sparse files are involved at this stage.
When that disk image is mounted next, its container is attached through
diskarbitrationd
, then its file system is mounted. If that’s APFS (or HFS+), it undergoes Trimming, as with other mounts. That coalesces free storage blocks within the image to form one contiguous free space. The disk image is then saved in APFS sparse file format, skipping that contiguous free space. When the file system has been unmounted and the container detached, the space used to store the disk image has shrunk to the space actually used within the disk image, plus the container overhead. Unless the disk image is almost full, the amount of space required to store it on disk will be smaller than the full size of the disk image.[…]
The size of read-write disk images is therefore variable depending on the contents, the effectiveness of Trimming in coalescing free space, and the efficiency of APFS sparse file format.
[…]
Although read-write disk images stored as sparse files are efficient in their use of disk space, they’re still not as compact as sparse bundles.
Read speeds for sparse bundles and read-write disk images were high, whether the container was encrypted or not. On the internal SSD, encryption resulted in read speeds about 1 GB/s lower than those for unencrypted tests, but differences for the external SSD were small and not consistent.
Write speeds were only high for sparse bundles, where they showed similar effects with encryption. Read-write disk images showed write speeds consistently about 1 GB/s, whether on the internal or external SSD, and regardless of encryption.
When unencrypted, read and write speeds for sparse (disk) images were also slower. Although faster than read-write disk images when writing to the internal SSD, read speed was around 2.2 GB/s for both. Results for encrypted sparse images were by far the worst of all the tests performed, and ranged between 0.08 to 0.5 GB/s.
Surprisingly good results were obtained from a new-style virtual machine with FileVault enabled in its disk image. Although previous tests had found read and write speeds of 4.4 and 0.7 GB/s respectively, the Sequoia VM achieved 5.9 and 4.5 GB/s.
Sparse bundles generally have the best performance, though plain read-write images can be faster for reading. Single-file sparse disk images are slow.
If you’re going to use disk images of any type, then getting the right tool for the job is essential. This article considers the leading candidates:
- Disk Utility, bundled with macOS
- DropDMG, $24.99 from C-Command, or from the App Store
- Spundle, free from its Product Page here
hdiutil
, the command tool bundled with macOS.
Previously:
- DropDMG 3.6.8
- Disk Images in Sonoma
- Ventura File Encryption Problems
- Slow Mac Disk Image Writing
- APFS Sparse Bundle Bugs
- Data Loss on APFS Sparse Disk Images
Update (2024-10-18): Howard Oakley:
While this remarkable bug is present in macOS Sequoia 15.0 and 15.0.1, I’m afraid its days are numbered. If you want to experience the TARDIS sparse bundle, you’ve only got another week or two, as it appears to be fixed in the current beta of 15.1.
Update (2024-10-21): Howard Oakley:
- Sparse bundles are more complicated than read-write disk images (UDRW), with band size to be set, and compaction to be performed.
- Default band size appears to work well, and manually setting band size should seldom be necessary.
- Both types appear highly efficient in their use of disk space, with only small differences between them.
- Although it might be important to compact sparse bundles in some cases, the amount of free space returned by compaction is unlikely to be significant in many circumstances.
Perhaps this is because we don’t have tools to defragment the free space on APFS volumes.
Previously: