The text below is adapted from §7 of the article Software Heritage: Why and How to Preserve Software Source Code (in proceedings of iPRES 2017, 14th International Conference on Digital Preservation, by Roberto Di Cosmo and Stefano Zacchiroli), which also provides a more general description of Software Heritage for the digital preservation research community.
In any archival project the choice of the underlying data model—at the logical level, independently from how data is actually stored on physical media—is paramount. The data model adopted by Software Heritage to represent the information that it collects is centered around the notion of software artifact, described below.
It is important to notice that according to our principles, we must store with every software artifact full information on where it has been found (provenance), that is also captured in our data model, so we start by providing some basic information on the nature of this provenance information.
Source code hosting places#
Currently, Software Heritage uses of a curated list of source code hosting places to crawl. The most common entries we expect to place in such a list are popular collaborative development forges (e.g., GitHub, Bitbucket), package manager repositories that host source package (e.g., CPAN, npm), and FOSS distributions (e.g., Fedora, FreeBSD). But we may of course allow also more niche entries, such as URLs of personal or institutional project collections not hosted on major forges.
While currently entirely manual, the curation of such a list might easily be semi-automatic, with entries suggested by fellow archivists and/or concerned users that want to notify Software Heritage of the need of archiving specific pieces of endangered source code. This approach is entirely compatible with Web-wide crawling approaches: crawlers capable of detecting the presence of source code might enrich the list. In both cases the list will remain curated, with (semi-automated) review processes that will need to pass before a hosting place starts to be used.
Once the hosting places are known, they will need to be periodically looked at in order to add to the archive missing software artifacts. Which software artifacts will be found there?
In general, each software distribution mechanism hosts multiple releases of a given software at any given time. For VCS (Version Control Systems), this is the natural behaviour; for software packages, while a single version of a package is just a snapshot of the corresponding software product, one can often retrieve both current and past versions of the package from its distribution site.
By reviewing and generalizing existing VCS and source package formats, we have identified the following recurrent artifacts as commonly found at source code hosting places. They form the basic ingredients of the Software Heritage archive. As the terminology varies quite a bit from technology to technology, we provide below both the canonical name used in Software Heritage and popular synonyms.
- contents (AKA “blobs”)
the raw content of (source code) files as a sequence of bytes, without file names or any other metadata. File contents are often recurrent, e.g., across different versions of the same software, different directories of the same project, or different projects all together.
a list of named directory entries, each of which pointing to other artifacts, usually file contents or sub-directories. Directory entries are also associated to some metadata stored as permission bits.
- revisions (AKA “commits”)
software development within a specific project is essentially a time-indexed series of copies of a single “root” directory that contains the entire project source code. Software evolves when a developer modifies the content of one or more files in that directory and record their changes.
Each recorded copy of the root directory is known as a “revision”. It points to a fully-determined directory and is equipped with arbitrary metadata. Some of those are added manually by the developer (e.g., commit message), others are automatically synthesized (timestamps, preceding commit(s), etc).
- releases (AKA “tags”)
some revisions are more equals than others and get selected by developers as denoting important project milestones known as “releases”. Each release points to the last commit in project history corresponding to the release and carries metadata: release name and version, release message, cryptographic signatures, etc.
Additionally, the following crawling-related information are stored as provenance information in the Software Heritage archive:
code “hosting places” as previously described are usually large platforms that host several unrelated software projects. For software provenance purposes it is important to be more specific than that.
Software origins are fine grained references to where source code artifacts archived by Software Heritage have been retrieved from. They take the form of
(type, url)pairs, where
urlis a canonical URL (e.g., the address at which one can
git clonea repository or download a source tarball) and
typethe kind of software origin (e.g., git, svn, or dsc for Debian source packages).
any kind of software origin offers multiple pointers to the “current” state of a development project. In the case of VCS this is reflected by branches (e.g., master, development, but also so called feature branches dedicated to extending the software in a specific direction); in the case of package distributions by notions such as suites that correspond to different maturity levels of individual packages (e.g., stable, development, etc.).
A “snapshot” of a given software origin records all entry points found there and where each of them was pointing at the time. For example, a snapshot object might track the commit where the master branch was pointing to at any given time, as well as the most recent release of a given package in the stable suite of a FOSS distribution.
links together software origins with snapshots. Every time an origin is consulted a new visit object is created, recording when (according to Software Heritage clock) the visit happened and the full snapshot of the state of the software origin at the time.
This model currently records visits as a single point in time. However, the actual visit process is not instantaneous. Loaders can record successive changes to the state of the visit, as their work progresses, as updates to the visit object.
With all the bits of what we want to archive in place, the next question is how to organize them, i.e., which logical data structure to adopt for their storage. A key observation for this decision is that source code artifacts are massively duplicated. This is so for several reasons:
code hosting diaspora (i.e., project development moving to the most recent/cool collaborative development technology over time);
copy/paste (AKA “vendoring”) of parts or entire external FOSS software components into other software products;
large overlap between revisions of the same project: usually only a very small amount of files/directories are modified by a single commit;
emergence of DVCS (distributed version control systems), which natively work by replicating entire repository copies around. GitHub-style pull requests are the pinnacle of this, as they result in creating an additional repository copy at each change done by a new developer;
migration from one VCS to another—e.g., migrations from Subversion to Git, which are really popular these days—resulting in additional copies, but in a different distribution format, of the very same development histories.
These trends seem to be neither stopping nor slowing down, and it is reasonable to expect that they will be even more prominent in the future, due to the decreasing costs of storage and bandwidth.
For this reason we argue that any sustainable storage layout for archiving source code in the very long term should support deduplication, allowing to pay for the cost of storing source code artifacts that are encountered more than once only once. For storage efficiency, deduplication should be supported for all the software artifacts we have discussed, namely: file contents, directories, revisions, releases, snapshots.
Realizing that principle, the Software Heritage archive is conceptually a single (big) Merkle Direct Acyclic Graph (DAG), as depicted in Figure Software Heritage Merkle DAG. In such a graph each of the artifacts we have described—from file contents up to entire snapshots—correspond to a node. Edges between nodes emerge naturally: directory entries point to other directories or file contents; revisions point to directories and previous revisions, releases point to revisions, snapshots point to revisions and releases. Additionally, each node contains all metadata that are specific to the node itself rather than to pointed nodes; e.g., commit messages, timestamps, or file names. Note that the structure is really a DAG, and not a tree, due to the fact that the line of revisions nodes might be forked and merged back.
In a Merkle structure each node is identified by an intrinsic identifier computed as a cryptographic hash of the node content. In the case of Software Heritage identifiers are computed taking into account both node-specific metadata and the identifiers of child nodes.
Consider the revision node in the picture whose identifier starts with c7640e08d... it points to a directory (identifier starting with 45f0c078..), which has also been archived. That directory contains a full copy, at a specific point in time, of a software component—in the example the Hello World software component available on our forge. The revision node also points to the preceding revision node (43ef7dcd..) in the project development history. Finally, the node contains revision-specific metadata, such as the author and committer of the given change, its timestamps, and the message entered by the author at commit time.
The identifier of the revision node itself (c7640e08d..) is computed as a cryptographic hash of a (canonical representation of) all the information shown in figure. A change in any of them—metadata and/or pointed nodes—would result in an entirely different node identifier. All other types of nodes in the Software Heritage archive behave similarly.
The Software Heritage archive inherits useful properties from the underlying Merkle structure. In particular, deduplication is built-in. Any software artifacts encountered in the wild gets added to the archive only if a corresponding node with a matching intrinsic identifier is not already available in the graph—file content, commits, entire directories or project snapshots are all deduplicated incurring storage costs only once.
Furthermore, as a side effect of this data model choice, the entire development history of all the source code archived in Software Heritage—which ambitions to match all published source code in the world—is available as a unified whole, making emergent structures such as code reuse across different projects or software origins, readily available. Further reinforcing the Software Heritage use cases, this object could become a veritable “map of the stars” of our entire software commons.
Extended data model#
In addition to the artifacts detailed above used to represent original software artifacts, the Software Heritage archive stores information about these artifacts.
a relationship between an original identifier of an artifact, in its native/upstream environment, and a core SWHID <persistent-identifiers>, which is specific to Software Heritage. As such, it is a triple made of:
the external identifier, stored as bytes whose format is opaque to the data model
a type (a simple name and a version), to identify the type of relationship
the “target”, which is a core SWHID
- raw extrinsic metadata
an opaque bytestring, along with its format (a simple name), an identifier of the object the metadata is about and in which context (similar to a qualified SWHID <persistent-identifiers>), and provenance information (the authority who provided it, the fetcher tool used to get it, and the data it was discovered at).
It provides both a way to store information about an artifact contributed by external entities, after the artifact was created, and an escape hatch to store metadata that would not otherwise fit in the data model.