Using git repository as a database backend
I’m doing a project that deals with structured document database. I have a tree of categories (~1000 categories, up to ~50 categories on each level), each category contains several thousands (up to, say, ~10000) of structured documents. Each document is several kilobytes of data in some structured form (I’d prefer YAML, but it may just as well be JSON or XML).
Users of this systems do several types of operations:
- retrieving of these documents by ID
- searching for documents by some of the structured attributes inside them
- editing documents (i.e. adding/removing/renaming/merging); each edit operation should be recorded as a transaction with some comment
- viewing a history of recorded changes for particular document (including viewing who, when and why changed the document, getting earlier version – and probably reverting to this one if requested)
Of course, the traditional solution would be using some sort of document database (such as CouchDB or Mongo) for this problem – however, this version control (history) thing tempted me to a wild idea – why shouldn’t I use
git repository as a database backend for this application?
On the first glance, it could be solved like this:
- category = directory, document = file
- getting document by ID => changing directories + reading a file in a working copy
- editing documents with edit comments => making commits by various users + storing commit messages
- history => normal git log and retrieval of older transactions
- search => that’s a slightly trickier part, I guess it would require periodic export of a category into relational database with indexing of columns that we’ll allow to search by
Are there any other common pitfalls in this solution? Have anyone tried to implement such backend already (i.e. for any popular frameworks – RoR, node.js, Django, CakePHP)? Does this solution have any possible implications on performance or reliability – i.e. is it proven that git would be much slower than traditional database solutions or there would be any scalability/reliability pitfalls? I presume that a cluster of such servers that push/pull each other’s repository should be fairly robust & reliable.
Basically, tell me if this solution will work and why it will or won’t do?
5 Solutions collect form web for “Using git repository as a database backend”
Answering my own question is not the best thing to do, but, as I ultimately dropped the idea, I’d like to share on the rationale that worked in my case. I’d like to emphasize that this rationale might not apply to all cases, so it’s up to architect to decide.
Generally, the first main point my question misses is that I’m dealing with multi-user system that work in parallel, concurrently, using my server with a thin client (i.e. just a web browser). This way, I have to maintain state for all of them. There are several approaches to this one, but all of them are either too hard on resources or too complex to implement (and thus kind of kill the original purpose of offloading all the hard implementation stuff to git in the first place):
“Blunt” approach: 1 user = 1 state = 1 full working copy of a repository that server maintains for user. Even if we’re talking about fairly small document database (for example, 100s MiBs) with ~100K of users, maintaining full repository clone for all of them makes disc usage run through the roof (i.e. 100K of users times 100MiB ~ 10 TiB). What’s even worse, cloning 100 MiB repository each time takes several seconds of time, even if done in fairly effective maneer (i.e. not using by git and unpacking-repacking stuff), which is non acceptable, IMO. And even worse — every edit that we apply to a main tree should be pulled to every user’s repository, which is (1) resource hog, (2) might lead to unresolved edit conflicts in general case.
Basically, it might be as bad as O(number of edits × data × number of users) in terms of disc usage, and such disc usage automatically means pretty high CPU usage.
“Only active users” approach: maintain working copy only for active users. This way, you generally store not a full-repo-clone-per-user, but:
- As user logs in, you clone the repository. It takes several seconds and ~100 MiB of disc space per active user.
- As user continues to work on the site, he works with the given working copy.
- As user logs out, his repository clone is copied back to main repository as a branch, thus storing only his “unapplied changes”, if there are any, which is fairly space-efficient.
Thus, disc usage in this case peaks at O(number of edits × data × number of active users), which is usually ~100..1000 times less than number of total users, but it makes logging in/out more complicated and slower, as it involves cloning of a per-user branch on every login and pulling these changes back on logout or session expiration (which should be done transactionally => adds another layer of complexity). In absolute numbers, it drops 10 TiBs of disc usage down to 10..100 GiBs in my case, that might be acceptable, but, yet again, we’re now talking about fairly small database of 100 MiBs.
“Sparse checkout” approach: making “sparse checkout” instead of full-blown repo clone per active user doesn’t help a lot. It might save ~10x of disc space usage, but at expense of much higher CPU/disc load on history-involving operations, which kind of kills the purpose.
“Workers pool” approach: instead of doing full-blown clones every time for active person, we might keep a pool of “worker” clones, ready to be used. This way, every time a users logs in, he occupies one “worker”, pulling there his branch from main repo, and, as he logs out, he frees the “worker”, which does clever git hard reset to become yet again just a main repo clone, ready to be used by another user logging in. Does not help much with disc usage (it’s still pretty high — only full clone per active user), but at least it makes logging in/out faster, as expense of even more complexity.
That said, note that I intentionally calculated numbers of fairly small database and user base: 100K users, 1K active users, 100 MiBs total database + history of edits, 10 MiBs of working copy. If you’d look at more prominent crowd-sourcing projects, there are much higher numbers there:
│ │ Users │ Active users │ DB+edits │ DB only │ ├──────────────┼───────┼──────────────┼──────────┼─────────┤ │ MusicBrainz │ 1.2M │ 1K/week │ 30 GiB │ 20 GiB │ │ en.wikipedia │ 21.5M │ 133K/month │ 3 TiB │ 44 GiB │ │ OSM │ 1.7M │ 21K/month │ 726 GiB │ 480 GiB │
Obviously, for that amounts of data/activity, this approach would be utterly unacceptable.
Generally, it would have worked, if one could use web browser as a “thick” client, i.e. issuing git operations and storing pretty much the full checkout on client’s side, not on the server’s side.
There are also other points that I’ve missed, but they’re not that bad compared to the first one:
- The very pattern of having “thick” user’s edit state is controversial in terms of normal ORMs, such as ActiveRecord, Hibernate, DataMapper, Tower, etc.
- As much as I’ve searched for, there’s zero existing free codebase for doing that approach to git from popular frameworks.
- There is at least one service that somehow manages to do that efficiently — that is obviously github — but, alas, their codebase is closed source and I strongly suspect that they do not use normal git servers / repo storage techniques inside, i.e. they basically implemented alternative “big data” git.
So, bottom line: it is possible, but for most current usecases it won’t be anywhere near the optimal solution. Rolling up your own document-edit-history-to-SQL implementation or trying to use any existing document database would be probably a better alternative.
An interesting approach indeed. I would say that if you need to store data, use a database, not a source code repository, which is designed for a very specific task. If you could use Git out-of-the-box, then it’s fine, but you probably need to build a document repository layer over it. So you could build it over a traditional database as well, right? And if it’s built-in version control that you’re interested in, why not just use one of open source document repository tools? There are plenty to choose from.
Well, if you decide to go for Git backend anyway, then basically it would work for your requirements if you implemented it as described. But:
1) You mentioned “cluster of servers that push/pull each other” – I’ve thought about it for a while and still I’m not sure. You can’t push/pull several repos as an atomic operation. I wonder if there could be a possibility of some merge mess during concurrent work.
2) Maybe you don’t need it, but an obvious functionality of a document repository you did not list is access control. You could possibly restrict access to some paths(=categories) via submodules, but probably you won’t be able to grant access on document level easily.
my 2 pence worth. A bit longing but …… I had a similar requirement in one of my incubation projects. Similar to yours , my key requirements where a document database ( xml in my case),with document versioning. It was for a multi-user system with a lot of collaboration use cases. My preference was to use available opensource solutions that support most of the key requirements.
To cut to the chase, I could not find any one product that provided both, in a way that was scalable enough ( number of users, usage volumes, storage and compute resources).I was biased towards git for all the promising capability, and (probable) solutions one could craft out of it. As I toyed with git option more, moving from a single user perspective to a multi ( milli) user perspective became an obvious challenge. Unfortunately, I did not get to do substantial performance analysis like you did. ( .. lazy/ quit early ….for version 2, mantra) Power to you!. Anyway, my biased idea has since morphed to the next (still biased ) alternative: a mesh-up of tools that are the best in their separate spheres, databases and version control.
While still work in progress ( …and slightly neglected ) the morphed version is simply this .
- on the frontend: (userfacing ) use a database for the 1st level
storage ( interfacing with user applications )
- on the backend,
use a version control system (VCS)(like git ) to perform
versioning of the data objects in database
In essence it would amount to adding a version control plugin to the database, with some integration glue, which you may have to develop, but may be a lot much easier.
How it would (supposed to ) work is that the primary multi-user interface data exchanges are through the database. The DBMS will handle all the fun and complex issues such as multi-user , concurrency e, atomic operations etc. On the backend the VCS would perform version control on a single set of data objects ( no concurrency, or multi-user issues). For each effective transactions on the database, version control is only performed on the data records that would have effectively changed.
As for the interfacing glue, it will be in the form of a simple interworking function between the database and the VCS. In terms of design, as simple approach would be an event driven interface, with data updates from the database triggering the version control procedures ( hint : assuming Mysql, use of triggers and sys_exec() blah blah …) .In terms of implementation complexity, it will range from the simple and effective ( eg scripting ) to the complex and wonderful ( some programmed connector interface) . All depends on how crazy you want to go with it , and how much sweat capital you are willing to spend. I reckon simple scripting should do the magic. And to access the end result, the various data versions, a simple alternative is to populate a clone of the database ( more a clone of the database structure) with the data referenced by the version tag/id/hash in the VCS. again this bit will be a simple query/translate/map job of an interface.
There are still some challenges and unknowns to be dealt with, but I suppose the impact, and relevance of most of these will largely depend on your application requirements and use cases. Some may just end up being non issues. Some of the issues include performance matching between the 2 key modules, the database and the VCS, for an application with high frequency data update activity, Scaling of resources (storage and processing power ) over time on the git side as the data , and users grow: steady, exponential or eventually plateau’s
Of the cocktail above, here is what I’m currently brewing
- using Git for the VCS ( initially considered good old CVS for the due to the use of only changesets or deltas between 2 version )
- using mysql ( due to the highly structured nature of my data, xml with strict xml schemas )
- toying around with MongoDB (to try a NoSQl database, which closely matches the native database structure used in git )
Some fun facts
– git actually does clear things to optimize storage, such as compression, and storage of only deltas between revision of objects
– YES, git does store only changesets or deltas between revisions of data objects, where is it is applicable ( it knows when and how) . Reference : packfiles, deep in the guts of Git internals
– Review of the git’s object storage ( content-addressable filesystem), shows stricking similarities ( from the concept perspective) with noSQL databases such mongoDB. Again, at the expense of sweat capital, it may provide more interesting possibilities for integrating the 2, and performance tweaking
If you got this far, let me if the above may be applicable to your case, and assuming it would be , how it would square up to some of the aspect in your last comprehensive performance analysis
As you mentioned, the multi-user case is a bit trickier to handle. One possible solution would be to use user-specific Git index files resulting in
- no need for separate working copies (disk usage is restricted to changed files)
- no need for time-consuming preparatory work (per user session)
The trick is to combine Git’s
GIT_INDEX_FILE environmental variable with the tools to create Git commits manually:
- git hash-object
- git update-index
- git write-tree
- git commit-tree
A solution outline follows (actual SHA1 hashes omitted from the commands):
# Initialize the index # N.B. Use the commit hash since refs might changed during the session. $ GIT_INDEX_FILE=user_index_file git reset --hard <starting_commit_hash> # # Change data and save it to `changed_file` # # Save changed data to the Git object database. Returns a SHA1 hash to the blob. $ cat changed_file | git hash-object -t blob -w --stdin da39a3ee5e6b4b0d3255bfef95601890afd80709 # Add the changed file (using the object hash) to the user-specific index # N.B. When adding new files, --add is required $ GIT_INDEX_FILE=user_index_file git update-index --cacheinfo 100644 <changed_data_hash> path/to/the/changed_file # Write the index to the object db. Returns a SHA1 hash to the tree object $ GIT_INDEX_FILE=user_index_file git write-tree 8ea32f8432d9d4fa9f9b2b602ec7ee6c90aa2d53 # Create a commit from the tree. Returns a SHA1 hash to the commit object # N.B. Parent commit should the same commit as in the first phase. $ echo "User X updated their data" | git commit-tree <new_tree_hash> -p <starting_commit_hash> 3f8c225835e64314f5da40e6a568ff894886b952 # Create a ref to the new commit git update-ref refs/heads/users/user_x_change_y <new_commit_hash>
Depending on your data you could use a cron job to merge the new refs to
master but the conflict resolution is arguably the hardest part here.
Ideas to make it easier are welcome.
I implemented a Ruby library on top of
libgit2 which makes this pretty easy to implement and explore. There are some obvious limitations, but it’s also a pretty liberating system since you get the full git toolchain.
The documentation includes some ideas about performance, tradeoffs, etc.