On 2016-11-17 13:51, Hans van Kranenburg wrote:
Hey,
On 11/17/2016 02:27 AM, Qu Wenruo wrote:
At 11/17/2016 04:30 AM, Hans van Kranenburg wrote:
In the last two days I've added the --blockgroup option to btrfs heatmap
to let it create pictures of block group internals.
Examples and more instructions are to be found in the README at:
https://github.com/knorrie/btrfs-heatmap/blob/master/README.md
To use the new functionality it needs a fairly recent python-btrfs for
the 'skinny' METADATA_ITEM_KEY to be present. Latest python-btrfs
release is v0.3, created yesterday.
Wow, really cool!
Thanks!
I always dream about a visualizing tool to represent the chunk and
extent level of btrfs.
This should really save me from reading the boring dec numbers from
btrfs-debug-tree.
Although IMHO the full fs output is mixing extent and chunk level
together, which makes it a little hard to represent multi-device case,
it's still an awesome tool!
The picture of a full filesystem just appends all devices together into
one big space, and then walks the dev_extent tree and associated
chunk/blockgroup items for the %used/greyscale value.
I don't see what displaying a blockgroup-level aggregate usage number
has to do with multi-device, except that the same %usage will appear
another time when using RAID1*.
When generating a picture of a file system with multiple devices,
boundaries between the separate devices are not visible now.
If someone has a brilliant idea about how to do this without throwing
out actual usage data...
The first thought that comes to mind for me is to make each device be a
different color, and otherwise obey the same intensity mapping
correlating to how much data is there. For example, if you've got a 3
device FS, the parts of the image that correspond to device 1 would go
from 0x000000 to 0xFF0000, the parts for device 2 could be 0x000000 to
0x00FF00, and the parts for device 3 could be 0x000000 to 0x0000FF.
This is of course not perfect (you can't tell what device each segment
of empty space corresponds to), but would probably cover most use cases.
(for example, with such a scheme, you could look at an image and tell
whether the data is relatively well distributed across all the devices
or you might need to re-balance).
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