Post Syndicated from Lennart Poettering original http://0pointer.net/blog/casync-a-tool-for-distributing-file-system-images.html

Introducing casync

In the past months I have been working on a new project:
casync. casync takes
inspiration from the popular rsync file
synchronization tool as well as the probably even more popular
git revision control system. It combines the
idea of the rsync algorithm with the idea of git-style
content-addressable file systems, and creates a new system for
efficiently storing and delivering file system images, optimized for
high-frequency update cycles over the Internet. Its current focus is
on delivering IoT, container, VM, application, portable service or OS
images, but I hope to extend it later in a generic fashion to become
useful for backups and home directory synchronization as well (but
more about that later).

The basic technological building blocks casync is built from are
neither new nor particularly innovative (at least not anymore),
however the way casync combines them is different from existing tools,
and that’s what makes it useful for a variety of usecases that other
tools can’t cover that well.

Why?

I created casync after studying how today’s popular tools store and
deliver file system images. To very incomprehensively and briefly name
a few: Docker has a layered tarball approach,
OSTree serves the
individual files directly via HTTP and maintains packed deltas to
speed up updates, while other systems operate on the block layer and
place raw squashfs images (or other archival file systems, such as
IS09660) for download on HTTP shares (in the better cases combined
with zsync data).

Neither of these approaches appeared fully convincing to me when used
in high-frequency update cycle systems. In such systems, it is
important to optimize towards a couple of goals:

1. Most importantly, make updates cheap traffic-wise (for this most tools use image deltas of some form)
2. Put boundaries on disk space usage on servers (keeping deltas between all version combinations clients might want to run updates between, would suggest keeping an exponentially growing amount of deltas on servers)
3. Put boundaries on disk space usage on clients
4. Be friendly to Content Delivery Networks (CDNs), i.e. serve neither too many small nor too many overly large files, and only require the most basic form of HTTP. Provide the repository administrator with high-level knobs to tune the average file size delivered.
5. Simplicity to use for users, repository administrators and developers

I don’t think any of the tools mentioned above are really good on more
than a small subset of these points.

Specifically: Docker’s layered tarball approach dumps the “delta”
question onto the feet of the image creators: the best way to make
your image downloads minimal is basing your work on an existing image
clients might already have, and inherit its resources, maintaing full
history. Here, revision control (a tool for the developer) is
intermingled with update management (a concept for optimizing
production delivery). As container histories grow individual deltas
are likely to stay small, but on the other hand a brand-new deployment
usually requires downloading the full history onto the deployment
system, even though there’s no use for it there, and likely requires
substantially more disk space and download sizes.

OSTree’s serving of individual files is unfriendly to CDNs (as many
small files in file trees cause an explosion of HTTP GET
requests). To counter that OSTree supports placing pre-calculated
delta images between selected revisions on the delivery servers, which
means a certain amount of revision management, that leaks into the
clients.

Delivering direct squashfs (or other file system) images is almost
beautifully simple, but of course means every update requires a full
download of the newest image, which is both bad for disk usage and
generated traffic. Enhancing it with zsync makes this a much better
option, as it can reduce generated traffic substantially at very
little cost of history/metadata (no explicit deltas between a large
number of versions need to be prepared server side). On the other hand
server requirements in disk space and functionality (HTTP Range
requests) are minus points for the usecase I am interested in.

(Note: all the mentioned systems have great properties, and it’s not
my intention to badmouth them. They only point I am trying to make is
that for the use case I care about — file system image delivery with
high high frequency update-cycles — each system comes with certain
drawbacks.)

Security & Reproducability

Besides the issues pointed out above I wasn’t happy with the security
and reproducability properties of these systems. In today’s world
where security breaches involving hacking and breaking into connected
systems happen every day, an image delivery system that cannot make
strong guarantees regarding data integrity is out of
date. Specifically, the tarball format is famously undeterministic:
the very same file tree can result in any number of different
valid serializations depending on the tool used, its version and the
underlying OS and file system. Some tar implementations attempt to
correct that by guaranteeing that each file tree maps to exactly
one valid serialization, but such a property is always only specific
to the tool used. I strongly believe that any good update system must
guarantee on every single link of the chain that there’s only one
valid representatin of the data to deliver, that can easily be
verified.

What casync Is

So much about the background why I created casync. Now, let’s have a
look what casync actually is like, and what it does. Here’s the brief
technical overview:

Encoding: Let’s take a large linear data stream, split it into
variable-sized chunks (the size of each being a function of the
chunk’s contents), and store these chunks in individual, compressed
files in some directory, each file named after a strong hash value of
its contents, so that the hash value may be used to as key for
retrieving the full chunk data. Let’s call this directory a “chunk
store”. At the same time, generate a “chunk index” file that lists
these chunk hash values plus their respective chunk sizes in a simple
linear array. The chunking algorithm is supposed to create variable,
but similarly sized chunks from the data stream, and do so in a way
that the same data results in the same chunks even if placed at
varying offsets. For more information see this blog
story.

Decoding: Let’s take the chunk index file, and reassemble the large
linear data stream by concatenating the uncompressed chunks retrieved
from the chunk store, keyed by the listed chunk hash values.

As an extra twist, we introduce a well-defined, reproducible,
random-access serialization format for file trees (think: a more
modern tar), to permit efficient, stable storage of complete file
trees in the system, simply by serializing them and then passing them
into the encoding step explained above.

Finally, let’s put all this on the network: for each image you want to
deliver, generate a chunk index file and place it on an HTTP
server. Do the same with the chunk store, and share it between the
various index files you intend to deliver.

Why bother with all of this? Streams with similar contents will result
in mostly the same chunk files in the chunk store. This means it is
very efficient to store many related versions of a data stream in the
same chunk store, thus minimizing disk usage. Moreover, when
transferring linear data streams chunks already known on the receiving
side can be made use of, thus minimizing network traffic.

Why is this different from rsync or OSTree, or similar tools? Well,
one major difference between casync and those tools is that we
remove file boundaries before chunking things up. This means that
small files are lumped together with their siblings and large files
are chopped into pieces, which permits us to recognize similarities in
files and directories beyond file boundaries, and makes sure our chunk
sizes are pretty evenly distributed, without the file boundaries
affecting them.

The “chunking” algorithm is based on a the buzhash rolling hash
function. SHA256 is used as strong hash function to generate digests
of the chunks. xz is used to compress the individual chunks.

Here’s a diagram, hopefully explaining a bit how the encoding process
works, wasn’t it for my crappy drawing skills:

The diagram shows the encoding process from top to bottom. It starts
with a block device or a file tree, which is then serialized and
chunked up into variable sized blocks. The compressed chunks are then
placed in the chunk store, while a chunk index file is written listing
the chunk hashes in order. (The original SVG of this graphic may be
found here.

Details

Note that casync operates on two different layers, depending on the
usecase of the user:

1. You may use it on the block layer. In this case the raw block data
   on disk is taken as-is, read directly from the block device, split
   into chunks as described above, compressed, stored and delivered.

2. You may use it on the file system layer. In this case, the
   file tree serialization format mentioned above comes into play:
   the file tree is serialized depth-first (much like tar would do
   it) and then split into chunks, compressed, stored and delivered.

The fact that it may be used on both the block and file system layer
opens it up for a variety of different usecases. In the VM and IoT
ecosystems shipping images as block-level serializations is more
common, while in the container and application world file-system-level
serializations are more typically used.

Chunk index files referring to block-layer serializations carry the
.caibx suffix, while chunk index files referring to file system
serializations carry the .caidx suffix. Note that you may also use
casync as direct tar replacement, i.e. without the chunking, just
generating the plain linear file tree serialization. Such files
carry the .catar suffix. Internally .caibx are identical to
.caidx files, the only difference is semantical: .caidx files
describe a .catar file, while .caibx files may describe any other
blob. Finally, chunk stores are directories carrying the .castr
suffix.

Features

Here are a couple of other features casync has:

1. When downloading a new image you may use casync‘s --seed=
   feature: each block device, file, or directory specified is processed
   using the same chunking logic described above, and is used as
   preferred source when putting together the downloaded image locally,
   avoiding network transfer of it. This of course is useful whenever
   updating an image: simply specify one or more old versions as seed and
   only download the chunks that truly changed since then. Note that
   using seeds requires no history relationship between seed and the new
   image to download. This has major benefits: you can even use it to
   speed up downloads of relatively foreign and unrelated data. For
   example, when downloading a container image built using Ubuntu you can
   use your Fedora host OS tree in /usr as seed, and casync will
   automatically use whatever it can from that tree, for example timezone
   and locale data that tends to be identical between
   distributions. Example: casync extract
http://example.com/myimage.caibx --seed=/dev/sda1 /dev/sda2. This
   will place the block-layer image described by the indicated URL in the
   /dev/sda2 partition, using the existing /dev/sda1 data as seeding
   source. An invocation like this could be typically used by IoT systems
   with an A/B partition setup. Example 2: casync extract
http://example.com/mycontainer-v3.caidx --seed=/srv/container-v1
--seed=/srv/container-v2 /src/container-v3, is very similar but
   operates on the file system layer, and uses two old container versions
   to seed the new version.

2. When operating on the file system level, the user has fine-grained
   control on the metadata included in the serialization. This is
   relevant since different usecases tend to require a different set of
   saved/restored metadata. For example, when shipping OS images, file
   access bits/ACLs and ownership matter, while file modification times
   hurt. When doing personal backups OTOH file ownership matters little
   but file modification times are important. Moreover different backing
   file systems support different feature sets, and storing more
   information than necessary might make it impossible to validate a tree
   against an image if the metadata cannot be replayed in full. Due to
   this, casync provides a set of --with= and --without= parameters
   that allow fine-grained control of the data stored in the file tree
   serialization, including the granularity of modification times and
   more. The precise set of selected metadata features is also always
   part of the serialization, so that seeding can work correctly and
   automatically.

3. casync tries to be as accurate as possible when storing file
   system metadata. This means that besides the usual baseline of file
   metadata (file ownership and access bits), and more advanced features
   (extended attributes, ACLs, file capabilities) a number of more exotic
   data is stored as well, including Linux
   chattr(1) file attributes, as
   well as FAT file
   attributes
   (you may wonder why the latter? — EFI is FAT, and /efi is part of
   the comprehensive serialization of any host). In the future I intend
   to extend this further, for example storing btrfs subvolume
   information where available. Note that as described above every single
   type of metadata may be turned off and on individually, hence if you
   don’t need FAT file bits (and I figure it’s pretty likely you don’t),
   then they won’t be stored.

4. The user creating .caidx or .caibx files may control the desired
   average chunk length (before compression) freely, using the
   --chunk-size= parameter. Smaller chunks increase the number of
   generated files in the chunk store and increase HTTP GET load on the
   server, but also ensure that sharing between similar images is
   improved, as identical patterns in the images stored are more likely
   to be recognized. By default casync will use a 64K average chunk
   size. Tweaking this can be particularly useful when adapting the
   system to specific CDNs, or when delivering compressed disk images
   such as squashfs (see below).

5. Emphasis is placed on making all invocations reproducible,
   well-defined and strictly deterministic. As mentioned above this is a
   requirement to reach the intended security guarantees, but is also
   useful for many other usecases. For example, the casync digest
   command may be used to calculate a hash value identifying a specific
   directory in all desired detail (use --with= and --without to pick
   the desired detail). Moreover the casync mtree command may be used
   to generate a BSD mtree(5) compatible manifest of a directory tree,
   .caidx or .catar file.

6. The file system serialization format is nicely composable. By this
   I mean that the serialization of a file tree is the concatenation of
   the serializations of all files and file subtrees located at the
   top of the tree, with zero metadata references from any of these
   serializations into the others. This property is essential to ensure
   maximum reuse of chunks when similar trees are serialized.

7. When extracting file trees or disk image files, casync
   will automatically create
   reflinks
   from any specified seeds if the underlying file system supports it
   (such as btrfs, ocfs, and future xfs). After all, instead of
   copying the desired data from the seed, we can just tell the file
   system to link up the relevant blocks. This works both when extracting
   .caidx and .caibx files — the latter of course only when the
   extracted disk image is placed in a regular raw image file on disk,
   rather than directly on a plain block device, as plain block devices
   do not know the concept of reflinks.

8. Optionally, when extracting file trees, casync can
   create traditional UNIX hardlinks for identical files in specified
   seeds (--hardlink=yes). This works on all UNIX file systems, and can
   save substantial amounts of disk space. However, this only works for
   very specific usecases where disk images are considered read-only
   after extraction, as any changes made to one tree will propagate to
   all other trees sharing the same hardlinked files, as that’s the
   nature of hardlinks. In this mode, casync exposes OSTree-like
   behaviour, which is built heavily around read-only hardlink trees.

9. casync tries to be smart when choosing what to include in file
   system images. Implicitly, file systems such as procfs and sysfs are
   exluded from serialization, as they expose API objects, not real
   files. Moreover, the “nodump” (+d)
   chattr(1) flag is honoured by
   default, permitting users to mark files to exclude from serialization.

10. When creating and extracting file trees casync may apply am
    automatic or explicit UID/GID shift. This is particularly useful when
    transferring container image for use with Linux user namespacing.

11. In addition to local operation, casync currently supports HTTP,
    HTTPS, FTP and ssh natively for downloading chunk index files and
    chunks (the ssh mode requires installing casync on the remote host,
    though, but an sftp mode not requiring that should be easy to
    add). When creating index files or chunks, only ssh is supported as
    remote backend.

12. When operating on block-layer images, you may expose locally or
    remotely stored images as local block devices. Example: casync mkdev
http://example.com/myimage.caibx exposes the disk image described by
    the indicated URL as local block device in /dev, which you then may
    use the usual block device tools on, such as mount or fdisk (only
    read-only though). Chunks are downloaded on access with high priority,
    and at low priority when idle in the background. Note that in this
    mode, casync also plays a role similar to “dm-verity”, as all blocks
    are validated against the strong digests in the chunk index file
    before passing them on to the kernel’s block layer. This feature is
    implemented though Linux’ NBD kernel facility.

13. Similar, when operating on file-system-layer images, you may mount
    locally or remotely stored images as regular file systems. Example:
    casync mount http://example.com/mytree.caidx /srv/mytree mounts the
    file tree image described by the indicated URL as a local directory
    /srv/mytree. This feature is implemented though Linux’ FUSE kernel
    facility. Note that special care is taken that the images exposed this
    way can be packed up again with casync make and are guaranteed to
    return the bit-by-bit exact same serialization again that it was
    mounted from. No data is lost or changed while passing thrings through
    FUSE (OK, strictly speaking this is a lie, we do lose ACLs, but that’s
    hopefully just a temporary gap to be fixed soon).

14. In IoT A/B fixed size partition setups the file systems placed in
    the two partitions are usually much shorter than the partition size,
    in order to keep some room for later, larger updates. casync is able
    to analyze the superblock of a number of common file systems in order
    to determine the actual size of a file system stored on a block
    device, so that writing a file system to such a partition and reading
    it back again will result in reproducible data. Moreover this speeds
    up the seeding process, as there’s little point in seeding the
    whitespace after the file system within the partition.

Example Command Lines

Here’s how to use casync, explained with a few examples:

$ casync make foobar.caidx /some/directory

This will create a chunk index file foobar.caidx in the local
directory, and populate the chunk store directory default.castr
located next to it with the chunks of the serialization (you can
change the name for the store directory with --store= if you
like). This command operates on the file-system level. A similar
command operating on the block level:

$ casync make foobar.caibx /dev/sda1

This command creates a chunk index file foobar.caibx in the local
directory describing the current contents of the /dev/sda1 block
device, and populates default.castr in the same way as above. Note
that you may as well read a raw disk image from a file instead of a
block device:

$ casync make foobar.caibx myimage.raw

To reconstruct the original file tree from the .caidx file and
the chunk store of the first command, use:

$ casync extract foobar.caidx /some/other/directory

And similar for the block-layer version:

$ casync extract foobar.caibx /dev/sdb1

or, to extract the block-layer version into a raw disk image:

$ casync extract foobar.caibx myotherimage.raw

The above are the most basic commands, operating on local data
only. Now let’s make this more interesting, and reference remote
resources:

$ casync extract http://example.com/images/foobar.caidx /some/other/directory

This extracts the specified .caidx onto a local directory. This of
course assumes that foobar.caidx was uploaded to the HTTP server in
the first place, along with the chunk store. You can use any command
you like to accomplish that, for example scp or
rsync. Alternatively, you can let casync do this directly when
generating the chunk index:

$ casync make ssh.example.com:images/foobar.caidx /some/directory

This will use ssh to connect to the ssh.example.com server, and then
places the .caidx file and the chunks on it. Note that this mode of
operation is “smart”: this scheme will only upload chunks currently
missing on the server side, and not retransmit what already is
available.

Note that you can always configure the precise path or URL of the
chunk store via the --store= option. If you do not do that, then the
store path is automatically derived from the path or URL: the last
component of the path or URL is replaced by default.castr.

Of course, when extracting .caidx or .caibx files from remote sources,
using a local seed is advisable:

$ casync extract http://example.com/images/foobar.caidx --seed=/some/exising/directory /some/other/directory

Or on the block layer:

$ casync extract http://example.com/images/foobar.caibx --seed=/dev/sda1 /dev/sdb2

When creating chunk indexes on the file system layer casync will by
default store metadata as accurately as possible. Let’s create a chunk
index with reduced metadata:

$ casync make foobar.caidx --with=sec-time --with=symlinks --with=read-only /some/dir

This command will create a chunk index for a file tree serialization
that has three features above the absolute baseline supported: 1s
granularity timestamps, symbolic links and a single read-only bit. In
this mode, all the other metadata bits are not stored, including
nanosecond timestamps, full unix permission bits, file ownership or
even ACLs or extended attributes.

Now let’s make a .caidx file available locally as a mounted file
system, without extracting it:

$ casync mount http://example.comf/images/foobar.caidx /mnt/foobar

And similar, let’s make a .caibx file available locally as a block device:

$ casync mkdev http://example.comf/images/foobar.caidx

This will create a block device in /dev and print the used device
node path to STDOUT.

As mentioned, casync is big about reproducability. Let’s make use of
that to calculate the a digest identifying a very specific version of
a file tree:

$ casync digest .

This digest will include all metadata bits casync and the underlying
file system know about. Usually, to make this useful you want to
configure exactly what metadata to include:

$ casync digest --with=unix .

This makes use of the --with=unix shortcut for selecting metadata
fields. Specifying --with-unix= selects all metadata that
traditional UNIX file systems support. It is a shortcut for writing out:
--with=16bit-uids --with=permissions --with=sec-time --with=symlinks
--with=device-nodes --with=fifos --with=sockets.

Note that when calculating digests or creating chunk indexes you may
also use the negative --without= option to remove specific features
but start from the most precise:

$ casync digest --without=flag-immutable

This generates a digest with the most accurate metadata, but leaves
one feature out: chattr(1)‘s
immutable (+i) file flag.

To list the contents of a .caidx file use a command like the following:

$ casync list http://example.com/images/foobar.caidx

or

$ casync mtree http://example.com/images/foobar.caidx

The former command will generate a brief list of files and
directories, not too different from tar t or ls -al in its
output. The latter command will generate a BSD
mtree(5) compatible
manifest. Note that casync actually stores substantially more file
metadata than mtree files can express, though.

What casync isn’t

1. casync is not an attempt to minimize serialization and downloaded
   deltas to the extreme. Instead, the tool is supposed to find a good
   middle ground, that is good on traffic and disk space, but not at the
   price of convenience or requiring explicit revision control. If you
   care about updates that are absolutely minimal, there are binary delta
   systems around that might be an option for you, such as Google’s
   Courgette.

2. casync is not a replacement for rsync, or git or zsync or
   anything like that. They have very different usecases and
   semantics. For example, rsync permits you to directly synchronize two
   file trees remotely. casync just cannot do that, and it is unlikely
   it every will.

Where next?

casync is supposed to be a generic synchronization tool. Its primary
focus for now is delivery of OS images, but I’d like to make it useful
for a couple other usecases, too. Specifically:

1. To make the tool useful for backups, encryption is missing. I have
   pretty concrete plans how to add that. When implemented, the tool
   would might become an alternative to
   restic or
   tarsnap.

2. Right now, if you want to deploy casync in real-life, you still
   need to validate the downloaded .caidx or .caibx file yourself, for
   example with some gpg signature. It is my intention to integrate with
   gpg in a minimal way so that signing and verifying chunk index files
   is done automatically.

3. In the longer run, I’d like to build an automatic synchronizer for
   $HOME between systems from this. Each $HOME instance would be
   stored automatically in regular intervals in the cloud using casync,
   and conflicts would be resolved locally.

4. casync is written in a shared library style, but it is not yet
   built as one. Specifically this means that almost all of casync‘s
   functionality is supposed to be available as C API soon, and
   applications can process casync files on every level. It is my
   intention to make this library useful enough so that it will be easy
   to write a module for GNOME’s gvfs subsystem in order to make remote
   or local .caidx files directly available to applications (as an
   alternative to casync mount). In fact the idea is to make this all
   flexible enough that even the remoting backends can be replaced
   easily, for example to replace casync‘s default HTTP/HTTPS backends
   built on CURL with GNOME’s own HTTP implementation, in order to share
   cookies, certificates, … There’s also an alternative method to
   integrate with casync in place already: simply invoke casync as a
   subprocess. casync will inform you about a certain set of state
   changes using a mechanism compatible with
   sd_notify(3). In
   future it will also propagate progress data this way and more.

5. I intend to a add a new seeding back-end that sources chunks from
   the local network. After downloading the new .caidx file off the
   Internet casync would then search for the listed chunks on the local
   network first before retrieving them from the Internet. This should
   speed things up on all installations that have multiple similar
   systems deployed in the same network.

Further plans are listed tersely in the
TODO file.

FAQ:

1. Is this a systemd project? — casync is hosted under the
   github systemd umbrella, and the
   projects share the same coding style. However, the codebases are
   distinct and without interdependencies, and casync works fine both
   on systemd systems and systems without it.

2. Is casync portable? — At the moment: no. I only run Linux and
   that’s what I code for. That said, I am open to accepting portability
   patches (unlike for systemd, which doesn’t really make sense on
   non-Linux systems), as long as they don’t interfere too much with the
   way casync works. Specifically this means that I am not too
   enthusiastic about merging portability patches for OSes lacking the
   openat(2) family
   of APIs.

3. Does casync require reflink-capable file systems to work, such
   as btrfs? No it doesn’t. The reflink magic in casync is
   employed when the file system permits it, and it’s good to have it,
   but it’s not a requirement, and casync will implicitly fall back to
   copying when it isn’t available. Note that casync supports a number
   of file system features on a variety of file systems that aren’t
   available everywhere, for example FAT’s system/hidden file flags or
   xfs‘s projinherit file flag.

4. Is casync stable? — I just tagged the first, initial
   release. While I have been working on it since quite some time and it
   is quite featureful, this is the first time I advertise it publicly,
   and it hence received very little testing outside of its own test
   suite. I am also not fully ready to commit to the stability of the
   current serialization or chunk index format. I don’t see any breakages
   coming for it though. casync is pretty light on documentation right
   now, and does not even have a man page. I also intend to correct that
   soon.

5. Are the .caidx/.caibx and .catar file formats open and
   documented? — casync is Open Source, so if you want to know the
   precise format, have a look at the sources for now. It’s definitely my
   intention to add comprehensive docs for both formats however. Don’t
   forget this is just the initial version right now.

6. casync is just like $SOMEOTHERTOOL! Why are you reinventing
   the wheel (again)? — Well, because casync isn’t “just like” some
   other tool. I am pretty sure I did my homework, and that there is no
   tool just like casync right now. The tools coming closest are probably
   rsync, zsync, tarsnap, restic, but they are quite different beasts
   each.

7. Why did you invent your own serialization format for file trees?
   Why don’t you just use tar? That’s a good question, and other
   systems — most prominently tarsnap — do that. However, as mentioned
   above tar doesn’t enforce reproducability. It also doesn’t really do
   random access: if you want to access some specific file you need to
   read every single byte stored before it in the tar archive to find
   it, which is of course very expensive. The serialization casync
   implements places a focus on reproducability, random access, and
   metadata control. Much like traditional tar it can still be
   generated and extracted in a stream fashion though.

8. Does casync save/restore SELinux/SMACK file labels? At the
   moment not. That’s not because I wouldn’t want it to, but simply
   because I am not a guru of either of these systems, and didn’t want to
   implement something I do not fully grok nor can test. If you look at
   the sources you’ll find that there’s already some definitions in place
   that keep room for them though. I’d be delighted to accept a patch
   implementing this fully.

9. What about delivering squashfs images? How well does chunking
   work on compressed serializations? – That’s a very good point!
   Usually, if you apply the a chunking algorithm to a compressed data
   stream (let’s say a tar.gz file), then changing a single bit at the
   front will propagate into the entire remainder of the file, so that
   minimal changes will explode into major changes. Thankfully this
   doesn’t apply that strictly to squashfs images, as it provides
   random access to files and directories and thus breaks up the
   compression streams in regular intervals to make seeking easy. This
   fact is beneficial for systems employing chunking, such as casync as
   this means single bit changes might affect their vicinity but will not
   explode in an unbounded fashion. In order achieve best results when
   delivering squashfs images through casync the block sizes of
   squashfs and the chunks sizes of casync should be matched up (using
   casync‘s --chunk-size= option). How precisely to choose both
   values is left to reasearch by the user, for now.

10. What does the name casync mean? – It’s a synchronizing
    tool, hence the -sync suffix, following rsync‘s naming. It makes
    use of the content-addressable concept of git hence the ca-
    prefix.

11. Where can I get this stuff? Is it already packaged? – Check
    out the sources on GitHub. I
    just tagged the first
    version. Martin
    Pitt has packaged casync for
    Ubuntu. There
    is also an ArchLinux
    package.

Should you care? Is this a tool for you?

Well, that’s up to you really. If you are involved with projects that
need to deliver IoT, VM, container, application or OS images, then
maybe this is a great tool for you — but other options exist, some of
which are linked above.

Note that casync is an Open Source project: if it doesn’t do exactly
what you need, prepare a patch that adds what you need, and we’ll
consider it.

If you are interested in the project and would like to talk about this
in person, I’ll be presenting casync soon at Kinvolk’s Linux
Technologies
Meetup
in Berlin, Germany. You are invited. I also intend to talk about it at
All Systems Go!, also in Berlin.