Add another performance example to README

README.md

@@ -53,6 +53,45 @@ than SquashFS compression**, it's **6 times faster to build the file
 system**, it's typically faster to access files on DwarFS and it uses
 less CPU resources.
 
+To give you an idea of what DwarFS is capable of, here's a quick comparison
+of DwarFS and SquashFS on a set of video files with a total size of 39 GiB.
+The twist is that each unique video file has two sibling files with a
+different set of audio streams (I didn't make this up, this is
+[an actual use case](https://github.com/mhx/dwarfs/discussions/63)). So
+there's redundancy in both the video and audio data, but as the streams
+are interleaved and identical blocks are typically very far apart, it's
+quite challenging to make use of that redundancy for compression. SquashFS
+essentially fails to compress the source data at all, whereas DwarFS is
+able to reduce the size by almost a factor of 3, which is close to the
+theoretical maximum:
+
+```
+$ du -hs dwarfs-video-test
+39G     dwarfs-video-test
+$ ls -lh dwarfs-video-test.*fs
+-rw-r--r-- 1 mhx users 14G Jul  2 13:01 dwarfs-video-test.dwarfs
+-rw-r--r-- 1 mhx users 39G Jul 12 09:41 dwarfs-video-test.squashfs
+```
+
+While this is already impressive, it gets even better. When mounting
+the SquashFS image and performing a random-read throughput test using
+[fio](https://github.com/axboe/fio/)-3.34, both `squashfuse` and
+`squashfuse_ll` top out at around 230 MiB/s:
+
+```
+$ fio --readonly --rw=randread --name=randread --bs=64k --direct=1 \
+      --opendir=mnt --numjobs=4 --ioengine=libaio --iodepth=32 \
+      --group_reporting --runtime=60 --time_based
+[...]
+   READ: bw=230MiB/s (241MB/s), 230MiB/s-230MiB/s (241MB/s-241MB/s), io=13.5GiB (14.5GB), run=60004-60004msec
+```
+
+DwarFS, however, manages to sustain **random read rates of 20 GiB/s**:
+
+```
+  READ: bw=20.2GiB/s (21.7GB/s), 20.2GiB/s-20.2GiB/s (21.7GB/s-21.7GB/s), io=1212GiB (1301GB), run=60001-60001msec
+```
+
 Distinct features of DwarFS are:
 
 - Clustering of files by similarity using a similarity hash function.