3.2. Data integrity¶
So far, we mastered quite a number of challenges:
Creating and populating a dataset with large and small files, modifying content and saving the changes to history, installing datasets, even as subdatasets within datasets, recording the impact of commands on a dataset with the
datalad run (manual) and
datalad rerun (manual) commands, and capturing plenty of provenance on the way.
We further noticed that when we modified content in
list_titles.sh, the modified content was in a text file.
We learned that this precise type of file, in conjunction with the initial configuration template
text2git we gave to
datalad create (manual), is meaningful:
As the text file is stored in Git and not git-annex, no content unlocking is necessary.
As we saw within the demonstrations of
datalad run, modifying content of non-text files, such as
.jpgs, typically requires the additional step of unlocking file content, either by hand with the
datalad unlock (manual) command, or within
datalad run using the
There is one detail about DataLad datasets that we have not covered yet. It is a crucial component to understanding certain aspects of a dataset, but it is also a potential source of confusion that we want to eradicate.
You might have noticed already that an
ls -l or
tree command in your dataset shows small arrows and quite cryptic paths following each non-text file.
Maybe your shell also displays these files in a different color than text files when listing them.
We’ll take a look together, using the
books/ directory as an example:
Dataset directories look different on Windows
First of all, the Windows
tree command lists only directories by default, unless you parametrize it with
And, secondly, even if you list the individual files, you would not see the symlinks shown below.
Due to insufficient support for symlinks on Windows, git-annex does not use them.
The Windows-wit on git-annex's adjusted mode has more on that.
$ # in the root of DataLad-101 $ cd books $ tree . ├── bash_guide.pdf -> ../.git/annex/objects/WF/Gq/✂/MD5E-s1198170--0ab2c121✂MD5.pdf ├── byte-of-python.pdf -> ../.git/annex/objects/xF/42/✂/MD5E-s4161086--c832fc13✂MD5.pdf ├── progit.pdf -> ../.git/annex/objects/G6/Gj/✂/MD5E-s12465653--05cd7ed5✂MD5.pdf └── TLCL.pdf -> ../.git/annex/objects/jf/3M/✂/MD5E-s2120211--06d1efcb✂MD5.pdf 0 directories, 4 files
If you do not know what you are looking at, this looks weird, if not worse: intimidating, wrong, or broken. First of all: no, it is all fine. But let’s start with the basics of what is displayed here to understand it.
-> symbol connecting one path (the book’s name) to another path (the weird
sequence of characters ending in
This means that the files that are in the locations in which you saved content
and are named as you named your files (e.g.,
do not actually contain your files’ content:
they just point to the place where the actual file content resides.
This sounds weird, and like an unnecessary complication of things. But we will get to why this is relevant and useful shortly. First, however, where exactly are the contents of the files you created or saved?
The start of the link path is
../.git. The section Create a dataset contained
a note that strongly advised that you to not tamper with
(or in the worst case, delete) the
repository in the root of any dataset. One reason
why you should not do this is because this
.git directory is where all of your file content
is actually stored.
But why is that? We have to talk a bit git-annex now in order to understand it.
When a file is saved into a dataset to be tracked, by default – that is in a dataset created without any configuration template – DataLad gives this file to git-annex. Exceptions to this behavior can be defined based on
and/or path/pattern, and thus, for example, file extensions, or names, or file types (e.g., text files, as with the
git-annex, in order to version control the data, takes the file content
and moves it under
.git/annex/objects – the so called object-tree.
It further renames the file into the sequence of characters you can see
in the path, and in its place
creates a symlink with the original file name, pointing to the new location.
This process is often referred to as a file being annexed, and the object
tree is also known as the annex of a dataset.
File content management on Windows (adjusted mode)
Windows has insufficient support for symlinks and revoking write permissions on files. Therefore, git-annex classifies it as a crippled file system and has to stray from its default behavior: it enters adjusted mode. While git-annex on Unix-based file operating systems stores data in the annex and creates a symlink in the data’s original place, on Windows it moves data into the annex and creates a copy of the data in its original place. This behavior is not specific to Windows, but is done for any impaired file system, such as a dataset on a USB-stick plugged into a Mac.
Why is that? Data needs to be in the annex for version control and transport logistics – the annex is able to store all previous versions of the data, and manage the transport to other storage locations if you want to publish your dataset. But as the Findoutmore in this section shows, the annex is a non-human readable tree structure, and data thus also needs to exist in its original location. Thus, it exists in both places: it has moved into the annex, and copied back into its original location. Once you edit an annexed file, the most recent version of the file is available in its original location, and past versions are stored and readily available in the annex. If you reset your dataset to a previous state, as is shown in the section Back and forth in time, the respective version of your data is taken from the annex and copied to replace the newer version, and vice versa.
But doesn’t a copy mean data duplication?
And that is a big downside to DataLad and git-annex on Windows.
If you have a dataset with annexed file contents (be that a dataset you created and populated yourself, or one that you cloned and got file contents with
datalad get from), it will take up more space than on a Unix-based system.
How much more?
Every file that exists in your file hierarchy exists twice.
A fresh dataset with one version of each file is thus twice as big as it would be on a Linux computer.
Any past version of data does not exist in duplication.
Let’s take a concrete example to explain the last point in more detail.
How much space, do you think, is taken up in your dataset by the resized
As a reminder: It exists in two versions, a 400 by 400 pixel version (about 250Kb in size), and a 450 by 450 pixel version (about 310Kb in size).
The 400 by 400 pixel version is the most recent one.
The answer is: about 810Kb (~0.8 MB).
The most recent 400x400px version exists twice (in the annex and as a copy), and the 450x450px copy exists once in the annex.
If you would reset your dataset to the state when we created the 450x450px version, this file would instead exist twice.
Can I at least get unused or irrelevant data out of the dataset?
Yes, either with convenience commands (e.g.,
git annex unused followed by
git annex dropunused), or by explicitly using
drop on files (or their past versions) that you don’t want to keep anymore.
Alternatively, you can transfer data you don’t need but want to preserve to a different storage location.
Later parts of the handbook will demonstrate each of these alternatives.
For a demonstration that this file path is not complete gibberish,
take the target path of any of the book’s symlinks and
open it, for example with
evince <path>, or any other PDF reader in exchange for
Even though the path looks cryptic, it works and opens the file. Whenever you
use a command like
evince TLCL.pdf, internally, programs will follow
the same cryptic symlink like the one you have just opened.
But why does this symlink-ing happen? Up until now, it still seems like a very unnecessary, superfluous thing to do, right?
The resulting symlinks that look like
your files but only point to the actual content in
small in size. An
ls -lh reveals that all of these symlinks have roughly the same,
small size of ~130 Bytes:
$ ls -lh total 16K lrwxrwxrwx 1 elena elena 131 2019-06-18 16:13 bash_guide.pdf -> ../.git/annex/objects/WF/Gq/✂/MD5E-s1198170--0ab2c121✂MD5.pdf lrwxrwxrwx 1 elena elena 131 2019-06-18 16:13 byte-of-python.pdf -> ../.git/annex/objects/xF/42/✂/MD5E-s4161086--c832fc13✂MD5.pdf lrwxrwxrwx 1 elena elena 133 2019-06-18 16:13 progit.pdf -> ../.git/annex/objects/G6/Gj/✂/MD5E-s12465653--05cd7ed5✂MD5.pdf lrwxrwxrwx 1 elena elena 131 2019-06-18 16:13 TLCL.pdf -> ../.git/annex/objects/jf/3M/✂/MD5E-s2120211--06d1efcb✂MD5.pdf
Here you can see the reason why content is symlinked: Small file size means that Git can handle those symlinks! Therefore, instead of large file content, only the symlinks are committed into Git, and the Git repository thus stays lean. Simultaneously, still, all files stored in Git as symlinks can point to arbitrarily large files in the object tree. Within the object tree, git-annex handles file content tracking, and is busy creating and maintaining appropriate symlinks so that your data can be version controlled just as any text file.
This comes with two very important advantages:
One, should you have copies of the same data in different places of your dataset, the symlinks of these files point to the same place - in order to understand why this is the case, you will need to read the Find-out-more about the object tree. Therefore, any amount of copies of a piece of data is only one single piece of data in your object tree. This, depending on how much identical file content lies in different parts of your dataset, can save you much disk space and time.
The second advantage is less intuitive but clear for users familiar with Git. Compared to copying and deleting huge data files, small symlinks can be written very very fast, for example, when switching dataset versions, or branches.
Speedy branch switches
Switching branches fast, even when they track vasts amounts of data, lets you work with data with the same routines as in software development.
This leads to a few conclusions:
The first is that you should not be worried to see cryptic looking symlinks in your repository – this is how it should look. You can read the find-out-more on why these paths look so weird and what all of this has to do with data integrity, if you want to. It’s additional information that can help to establish trust in that your data are safely stored and tracked, and understanding more about the object tree and knowing bits of the git-annex basics can make you more confident in working with your datasets.
The second is that it should now be clear to you why the
should not be deleted or in any way modified by hand. This place is where
your data are stored, and you can trust git-annex to be better able to
work with the paths in the object tree than you or any other human are.
Lastly, understanding that annexed files in your dataset are symlinked will be helpful to understand how common file system operations such as moving, renaming, or copying content translate to dataset modifications in certain situations. Later in this book, the section Miscellaneous file system operations will take a closer look at that.
Data integrity and annex keys
So how do these cryptic paths and names in the object tree come into existence? It’s not malicious intent that leads to these paths and file names - its checksums.
When a file is annexed, git-annex typically generates a key (or annex key) from the file content. It uses this key (in part) as a name for the file and as the path in the object tree. Thus, the key is associated with the content of the file (the value), and therefore, using this key, file content can be identified.
Most key types contain a checksum. This is a string of a fixed number of characters computed from some input, for example the content of a PDF file, by a hash function.
This checksum uniquely identifies a file’s content. A hash function will generate the same character sequence for the same file content, and once file content changes, the generated checksum changes, too. Basing the file name on its contents thus becomes a way of ensuring data integrity: File content cannot be changed without git-annex noticing, because the file’s checksum, and thus its key in its symlink, will change. Furthermore, if two files have identical checksums, the content in these files is identical. Consequently, if two files have the same symlink, and thus link the same file in the object-tree, they are identical in content. This can save disk space if a dataset contains many identical files: Copies of the same data only need one instance of that content in the object tree, and all copies will symlink to it. If you want to read more about the computer science basics about hash functions check out the Wikipedia page.
$ # take a look at the last part of the target path: $ ls -lh TLCL.pdf lrwxrwxrwx 1 elena elena 131 2019-06-18 16:13 TLCL.pdf -> ../.git/annex/objects/jf/3M/✂/MD5E-s2120211--06d1efcb✂MD5.pdf
Let’s take a closer look at the structure of the symlink. The key from the hash function is the last part of the name of the file the symlink links to (in which the actual data content is stored).
$ # compare it to the checksum (here of type md5sum) of the PDF file and the subdirectory name $ md5sum TLCL.pdf 06d1efcb✂MD5 TLCL.pdf
The extension (e.g.,
consisting of two letters each.
These two letters are derived from the md5sum of the key, and their sole purpose to exist is to avoid issues with too many files in one directory (which is a situation that certain file systems have problems with).
The next subdirectory in the symlink helps to prevent accidental deletions and changes, as it does not have write permissions, so that users cannot modify any of its underlying contents.
This is the reason that annexed files need to be unlocked prior to modifications, and this information will be helpful to understand some file system management operations such as removing files or datasets. Section Miscellaneous file system operations takes a look at that.
The next part of the symlink contains the actual checksum.
There are different annex key backends that use different checksums.
Depending on which is used, the resulting checksum has a certain length and structure, and the first part of the symlink actually states which hash function is used.
By default, DataLad uses the
MD5E git-annex backend (the
E adds file extensions to annex keys), but should you want to, you can change this default to one of many other types.
The reason why MD5E is used is the relatively short length of the underlying MD5 checksums – which facilitates cross-platform compatibility for sharing datasets also with users on operating systems that have restrictions on total path length, such as Windows.
The one remaining unidentified bit in the file name is the one after the checksum identifier. This part is the size of the content in bytes. An annexed file in the object tree thus has a file name following this structure (but see the git-annex documentation on keys for the complete details):
You now know a great deal more about git-annex and the object tree. Maybe you are as amazed as we are about some of the ingenuity used behind the scenes. Even more mesmerizing things about git-annex can be found in its documentation.