I'm doing a web app, and I need to make a branch for some major changes, the thing is, these changes require changes to the database schema, so I'd like to put the entire database under git as well.
How do I do that? is there a specific folder that I can keep under a git repository? How do I know which one? How can I be sure that I'm putting the right folder?
I need to be sure, because these changes are not backward compatible; I can't afford to screw up.
The database in my case is PostgreSQL
Edit:
Someone suggested taking backups and putting the backup file under version control instead of the database. To be honest, I find that really hard to swallow.
There has to be a better way.
Update:
OK, so there' no better way, but I'm still not quite convinced, so I will change the question a bit:
I'd like to put the entire database under version control, what database engine can I use so that I can put the actual database under version control instead of its dump?
Would sqlite be git-friendly?
Since this is only the development environment, I can choose whatever database I want.
Edit2:
What I really want is not to track my development history, but to be able to switch from my "new radical changes" branch to the "current stable branch" and be able for instance to fix some bugs/issues, etc, with the current stable branch. Such that when I switch branches, the database auto-magically becomes compatible with the branch I'm currently on.
I don't really care much about the actual data.
Take a database dump, and version control that instead. This way it is a flat text file.
Personally I suggest that you keep both a data dump, and a schema dump. This way using diff it becomes fairly easy to see what changed in the schema from revision to revision.
If you are making big changes, you should have a secondary database that you make the new schema changes to and not touch the old one since as you said you are making a branch.
I'm starting to think of a really simple solution, don't know why I didn't think of it before!!
Duplicate the database, (both the schema and the data).
In the branch for the new-major-changes, simply change the project configuration to use the new duplicate database.
This way I can switch branches without worrying about database schema changes.
EDIT:
By duplicate, I mean create another database with a different name (like my_db_2); not doing a dump or anything like that.
Use something like LiquiBase this lets you keep revision control of your Liquibase files. you can tag changes for production only, and have lb keep your DB up to date for either production or development, (or whatever scheme you want).
Irmin (branching + time travel)
Flur.ee (immutable + time travel + graph query)
XTDB (formerly called 'CruxDB') (time travel + query)
TerminusDB (immutable + branching + time travel + Graph Query!)
DoltDB (branching + time-travel + SQL query)
Quadrable (branching + remote state verification)
EdgeDB (no real time travel, but migrations derived by the compiler after schema changes)
Migra (diffing for Postgres schemas/data. Auto-generate migration scripts, auto-sync db state)
ImmuDB (immutable + time-travel)
I've come across this question, as I've got a similar problem, where something approximating a DB based Directory structure, stores 'files', and I need git to manage it. It's distributed, across a cloud, using replication, hence it's access point will be via MySQL.
The gist of the above answers, seem to similarly suggest an alternative solution to the problem asked, which kind of misses the point, of using Git to manage something in a Database, so I'll attempt to answer that question.
Git is a system, which in essence stores a database of deltas (differences), which can be reassembled, in order, to reproduce a context. The normal usage of git assumes that context is a filesystem, and those deltas are diff's in that file system, but really all git is, is a hierarchical database of deltas (hierarchical, because in most cases each delta is a commit with at least 1 parents, arranged in a tree).
As long as you can generate a delta, in theory, git can store it. The problem is normally git expects the context, on which it's generating delta's to be a file system, and similarly, when you checkout a point in the git hierarchy, it expects to generate a filesystem.
If you want to manage change, in a database, you have 2 discrete problems, and I would address them separately (if I were you). The first is schema, the second is data (although in your question, you state data isn't something you're concerned about). A problem I had in the past, was a Dev and Prod database, where Dev could take incremental changes to the schema, and those changes had to be documented in CVS, and propogated to live, along with additions to one of several 'static' tables. We did that by having a 3rd database, called Cruise, which contained only the static data. At any point the schema from Dev and Cruise could be compared, and we had a script to take the diff of those 2 files and produce an SQL file containing ALTER statements, to apply it. Similarly any new data, could be distilled to an SQL file containing INSERT commands. As long as fields and tables are only added, and never deleted, the process could automate generating the SQL statements to apply the delta.
The mechanism by which git generates deltas is diff and the mechanism by which it combines 1 or more deltas with a file, is called merge. If you can come up with a method for diffing and merging from a different context, git should work, but as has been discussed you may prefer a tool that does that for you. My first thought towards solving that is this https://git-scm.com/book/en/v2/Customizing-Git-Git-Configuration#External-Merge-and-Diff-Tools which details how to replace git's internal diff and merge tool. I'll update this answer, as I come up with a better solution to the problem, but in my case I expect to only have to manage data changes, in-so-far-as a DB based filestore may change, so my solution may not be exactly what you need.
There is a great project called Migrations under Doctrine that built just for this purpose.
Its still in alpha state and built for php.
http://docs.doctrine-project.org/projects/doctrine-migrations/en/latest/index.html
Take a look at RedGate SQL Source Control.
http://www.red-gate.com/products/sql-development/sql-source-control/
This tool is a SQL Server Management Studio snap-in which will allow you to place your database under Source Control with Git.
It's a bit pricey at $495 per user, but there is a 28 day free trial available.
NOTE
I am not affiliated with RedGate in any way whatsoever.
I've released a tool for sqlite that does what you're asking for. It uses a custom diff driver leveraging the sqlite projects tool 'sqldiff', UUIDs as primary keys, and leaves off the sqlite rowid. It is still in alpha so feedback is appreciated.
Postgres and mysql are trickier, as the binary data is kept in multiple files and may not even be valid if you were able to snapshot it.
https://github.com/cannadayr/git-sqlite
I want to make something similar, add my database changes to my version control system.
I am going to follow the ideas in this post from Vladimir Khorikov "Database versioning best practices". In summary i will
store both its schema and the reference data in a source control system.
for every modification we will create a separate SQL script with the changes
In case it helps!
You can't do it without atomicity, and you can't get atomicity without either using pg_dump or a snapshotting filesystem.
My postgres instance is on zfs, which I snapshot occasionally. It's approximately instant and consistent.
I think X-Istence is on the right track, but there are a few more improvements you can make to this strategy. First, use:
$pg_dump --schema ...
to dump the tables, sequences, etc and place this file under version control. You'll use this to separate the compatibility changes between your branches.
Next, perform a data dump for the set of tables that contain configuration required for your application to operate (should probably skip user data, etc), like form defaults and other data non-user modifiable data. You can do this selectively by using:
$pg_dump --table=.. <or> --exclude-table=..
This is a good idea because the repo can get really clunky when your database gets to 100Mb+ when doing a full data dump. A better idea is to back up a more minimal set of data that you require to test your app. If your default data is very large though, this may still cause problems though.
If you absolutely need to place full backups in the repo, consider doing it in a branch outside of your source tree. An external backup system with some reference to the matching svn rev is likely best for this though.
Also, I suggest using text format dumps over binary for revision purposes (for the schema at least) since these are easier to diff. You can always compress these to save space prior to checking in.
Finally, have a look at the postgres backup documentation if you haven't already. The way you're commenting on backing up 'the database' rather than a dump makes me wonder if you're thinking of file system based backups (see section 23.2 for caveats).
What you want, in spirit, is perhaps something like Post Facto, which stores versions of a database in a database. Check this presentation.
The project apparently never really went anywhere, so it probably won't help you immediately, but it's an interesting concept. I fear that doing this properly would be very difficult, because even version 1 would have to get all the details right in order to have people trust their work to it.
This question is pretty much answered but I would like to complement X-Istence's and Dana the Sane's answer with a small suggestion.
If you need revision control with some degree of granularity, say daily, you could couple the text dump of both the tables and the schema with a tool like rdiff-backup which does incremental backups. The advantage is that instead of storing snapshots of daily backups, you simply store the differences from the previous day.
With this you have both the advantage of revision control and you don't waste too much space.
In any case, using git directly on big flat files which change very frequently is not a good solution. If your database becomes too big, git will start to have some problems managing the files.
Here is what i am trying to do in my projects:
separate data and schema and default data.
The database configuration is stored in configuration file that is not under version control (.gitignore)
The database defaults (for setting up new Projects) is a simple SQL file under version control.
For the database schema create a database schema dump under the version control.
The most common way is to have update scripts that contains SQL Statements, (ALTER Table.. or UPDATE). You also need to have a place in your database where you save the current version of you schema)
Take a look at other big open source database projects (piwik,or your favorite cms system), they all use updatescripts (1.sql,2.sql,3.sh,4.php.5.sql)
But this a very time intensive job, you have to create, and test the updatescripts and you need to run a common updatescript that compares the version and run all necessary update scripts.
So theoretically (and thats what i am looking for) you could
dumped the the database schema after each change (manually, conjob, git hooks (maybe before commit))
(and only in some very special cases create updatescripts)
After that in your common updatescript (run the normal updatescripts, for the special cases) and then compare the schemas (the dump and current database) and then automatically generate the nessesary ALTER Statements. There some tools that can do this already, but haven't found yet a good one.
What I do in my personal projects is, I store my whole database to dropbox and then point MAMP, WAMP workflow to use it right from there.. That way database is always up-to-date where ever I need to do some developing. But that's just for dev! Live sites is using own server for that off course! :)
Storing each level of database changes under git versioning control is like pushing your entire database with each commit and restoring your entire database with each pull.
If your database is so prone to crucial changes and you cannot afford to loose them, you can just update your pre_commit and post_merge hooks.
I did the same with one of my projects and you can find the directions here.
That's how I do it:
Since your have free choise about DB type use a filebased DB like e.g. firebird.
Create a template DB which has the schema that fits your actual branch and store it in your repository.
When executing your application programmatically create a copy of your template DB, store it somewhere else and just work with that copy.
This way you can put your DB schema under version control without the data. And if you change your schema you just have to change the template DB
We used to run a social website, on a standard LAMP configuration. We had a Live server, Test server, and Development server, as well as the local developers machines. All were managed using GIT.
On each machine, we had the PHP files, but also the MySQL service, and a folder with Images that users would upload. The Live server grew to have some 100K (!) recurrent users, the dump was about 2GB (!), the Image folder was some 50GB (!). By the time that I left, our server was reaching the limit of its CPU, Ram, and most of all, the concurrent net connection limits (We even compiled our own version of network card driver to max out the server 'lol'). We could not (nor should you assume with your website) put 2GB of data and 50GB of images in GIT.
To manage all this under GIT easily, we would ignore the binary folders (the folders containing the Images) by inserting these folder paths into .gitignore. We also had a folder called SQL outside the Apache documentroot path. In that SQL folder, we would put our SQL files from the developers in incremental numberings (001.florianm.sql, 001.johns.sql, 002.florianm.sql, etc). These SQL files were managed by GIT as well. The first sql file would indeed contain a large set of DB schema. We don't add user-data in GIT (eg the records of the users table, or the comments table), but data like configs or topology or other site specific data, was maintained in the sql files (and hence by GIT). Mostly its the developers (who know the code best) that determine what and what is not maintained by GIT with regards to SQL schema and data.
When it got to a release, the administrator logs in onto the dev server, merges the live branch with all developers and needed branches on the dev machine to an update branch, and pushed it to the test server. On the test server, he checks if the updating process for the Live server is still valid, and in quick succession, points all traffic in Apache to a placeholder site, creates a DB dump, points the working directory from 'live' to 'update', executes all new sql files into mysql, and repoints the traffic back to the correct site. When all stakeholders agreed after reviewing the test server, the Administrator did the same thing from Test server to Live server. Afterwards, he merges the live branch on the production server, to the master branch accross all servers, and rebased all live branches. The developers were responsible themselves to rebase their branches, but they generally know what they are doing.
If there were problems on the test server, eg. the merges had too many conflicts, then the code was reverted (pointing the working branch back to 'live') and the sql files were never executed. The moment that the sql files were executed, this was considered as a non-reversible action at the time. If the SQL files were not working properly, then the DB was restored using the Dump (and the developers told off, for providing ill-tested SQL files).
Today, we maintain both a sql-up and sql-down folder, with equivalent filenames, where the developers have to test that both the upgrading sql files, can be equally downgraded. This could ultimately be executed with a bash script, but its a good idea if human eyes kept monitoring the upgrade process.
It's not great, but its manageable. Hope this gives an insight into a real-life, practical, relatively high-availability site. Be it a bit outdated, but still followed.
Update Aug 26, 2019:
Netlify CMS is doing it with GitHub, an example implementation can be found here with all information on how they implemented it netlify-cms-backend-github
I say don't. Data can change at any given time. Instead you should only commit data models in your code, schema and table definitions (create database and create table statements) and sample data for unit tests. This is kinda the way that Laravel does it, committing database migrations and seeds.
I would recommend neXtep (Link removed - Domain was taken over by a NSFW-Website) for version controlling the database it has got a good set of documentation and forums that explains how to install and the errors encountered. I have tested it for postgreSQL 9.1 and 9.3, i was able to get it working for 9.1 but for 9.3 it doesn't seems to work.
Use a tool like iBatis Migrations (manual, short tutorial video) which allows you to version control the changes you make to a database throughout the lifecycle of a project, rather than the database itself.
This allows you to selectively apply individual changes to different environments, keep a changelog of which changes are in which environments, create scripts to apply changes A through N, rollback changes, etc.
I'd like to put the entire database under version control, what
database engine can I use so that I can put the actual database under
version control instead of its dump?
This is not database engine dependent. By Microsoft SQL Server there are lots of version controlling programs. I don't think that problem can be solved with git, you have to use a pgsql specific schema version control system. I don't know whether such a thing exists or not...
Use a version-controlled database, of which there are now several.
https://www.dolthub.com/blog/2021-09-17-database-version-control/
These products don't apply version control on top of another type of database -- they are their own database engines that support version control operations. So you need to migrate to them or start building on them in the first place.
I write one of them, DoltDB, which combines the interfaces of MySQL and Git. Check it out here:
https://github.com/dolthub/dolt
I wish it were simpler. Checking in the schema as a text file is a good start to capture the structure of the DB. For the content, however, I have not found a cleaner, better method for git than CSV files. One per table. The DB can then be edited on multiple branches and merges extremely well.
I'm running a very computationally intensive scientific job that spits out results every now and then. The job is basically to just simulate the same thing a whole bunch of times, so it's divided among several computers, which use different OSes. I'd like to direct the output from all these instances to the same file, since all the computers can see the same filesystem via NFS/Samba. Here are the constraints:
Must allow safe concurrent appends. Must block if some other instance on another computer is currently appending to the file.
Performance does not count. I/O for each instance is only a few bytes per minute.
Simplicity does count. The whole point of this (besides pure curiosity) is so I can stop having every instance write to a different file and manually merging these files together.
Must not depend on the details of the filesystem. Must work with an unknown filesystem on an NFS or Samba mount.
The language I'm using is D, in case that matters. I've looked, there's nothing in the standard lib that seems to do this. Both D-specific and general, language-agnostic answers are fully acceptable and appreciated.
Over NFS you face some problems with client side caching and stale data. I have written an OS independent lock module to work over NFS before. The simple idea of creating a [datafile].lock file does not work well over NFS. The basic idea to work around it is to create a lock file [datafile].lock which if present means file is NOT locked and a process that wants to acquire a lock renames the file to a different name like [datafile].lock.[hostname].[pid]. The rename is an atomic enough operation that works well enough over NFS to guarantee exclusivity of the lock. The rest is basically a bunch of fail safe, loops, error checking and lock retrieval in case the process dies before releasing the lock and renaming the lock file back to [datafile].lock
The classic solution is to use a lock file, or more accurately a lock directory. On all common OSs creating a directory is an atomic operation so the routine is:
try to create a lock directory with a fixed name in a fixed location
if the create failed, wait a second or so and try again - repeat until success
write your data to the real data file
delete the lock directory
This has been used by applications such as CVS for many years across many platforms. The only problem occurs in the rare cases when your app crashes while writing and before removing the lock.
Why not just build a simple server which sits between the file and the other computers?
Then if you ever wanted to change the data format, you would only have to modify the server, and not all of the clients.
In my opinion building a server would be much easier than trying to use a Network file system.
Lock File with a twist
Like other answers have mentioned, the easiest method is to create a lock file in the same directory as the datafile.
Since you want to be able to access the same file over multiple PC the best solution I can think of is to just include the identifier of the machine currently writing to the data file.
So the sequence for writing to the data file would be:
Check if there is a lock file present
If there is a lock file, see if I'm the one owning it by checking that its content has my identifier.
If that's the case, just write to the data file then delete the lock file.
If that's not the case, just wait a second or a small random length of time and try the whole cycle again.
If there is no lock file, create one with my identifier and try the whole cycle again to avoid race condition (re-check that the lock file is really mine).
Along with the identifier, I would record a timestamp in the lock file and check whether it's older than a given timeout value.
If the timestamp is too old, then assume that the lock file is stale and just delete it as it would mea one of the PC writing to the data file may have crashed or its connection may have been lost.
Another solution
If you are in control the format of the data file, could be to reserve a structure at the beginning of the file to record whether it is locked or not.
If you just reserve a byte for this purpose, you could assume, for instance, that 00 would mean the data file isn't locked, and that other values would represent the identifier of the machine currently writing to it.
Issues with NFS
OK, I'm adding a few things because Jiri Klouda correctly pointed out that NFS uses client-side caching that will result in the actual lock file being in an undetermined state.
A few ways to solve this issue:
mount the NFS directory with the noac or sync options. This is easy but doesn't completely guarantee data consistency between client and server though so there may still be issues although in your case it may be OK.
Open the lock file or data file using the O_DIRECT, the O_SYNC or O_DSYNC attributes. This is supposed to disable caching altogether.
This will lower performance but will ensure consistency.
You may be able to use flock() to lock the data file but its implementation is spotty and you will need to check if your particular OS actually uses the NFS locking service. It may do nothing at all otherwise.
If the data file is locked, then another client opening it for writing will fail.
Oh yeah, and it doesn't seem to work on SMB shares, so it's probably best to just forget about it.
Don't use NFS and just use Samba instead: there is a good article on the subject and why NFS is probably not the best answer to your usage scenario.
You will also find in this article various methods for locking files.
Jiri's solution is also a good one.
Basically, if you want to keep things simple, don't use NFS for frequently-updated files that are shared amongst multiple machines.
Something different
Use a small database server to save your data into and bypass the NFS/SMB locking issues altogether or keep your current multiple data files system and just write a small utility to concatenate the results.
It may still be the safest and simplest solution to your problem.
I don't know D, but I thing using a mutex file to do the jobe might work. Here's some pseudo-code you might find useful:
do {
// Try to create a new file to use as mutex.
// If it's already created, it will throw some kind of error.
mutex = create_file_for_writing('lock_file');
} while (mutex == null);
// Open your log file and write results
log_file = open_file_for_reading('the_log_file');
write(log_file, data);
close_file(log_file);
close_file(mutex);
// Free mutex and allow other processes to create the same file.
delete_file(mutex);
So, all processes will try to create the mutex file but only the one who wins will be able to continue. Once you write your output, close and delete the mutex so other processes can do the same.
It's a very common scenario: some process wants to drop a file on a server every 30 minutes or so. Simple, right? Well, I can think of a bunch of ways this could go wrong.
For instance, processing a file may take more or less than 30 minutes, so it's possible for a new file to arrive before I'm done with the previous one. I don't want the source system to overwrite a file that I'm still processing.
On the other hand, the files are large, so it takes a few minutes to finish uploading them. I don't want to start processing a partial file. The files are just tranferred with FTP or sftp (my preference), so OS-level locking isn't an option.
Finally, I do need to keep the files around for a while, in case I need to manually inspect one of them (for debugging) or reprocess one.
I've seen a lot of ad-hoc approaches to shuffling upload files around, swapping filenames, using datestamps, touching "indicator" files to assist in synchronization, and so on. What I haven't seen yet is a comprehensive "algorithm" for processing files that addresses concurrency, consistency, and completeness.
So, I'd like to tap into the wisdom of crowds here. Has anyone seen a really bulletproof way to juggle batch data files so they're never processed too early, never overwritten before done, and safely kept after processing?
The key is to do the initial juggling at the sending end. All the sender needs to do is:
Store the file with a unique filename.
As soon as the file has been sent, move it to a subdirectory called e.g. completed.
Assuming there is only a single receiver process, all the receiver needs to do is:
Periodically scan the completed directory for any files.
As soon as a file appears in completed, move it to a subdirectory called e.g. processed, and start working on it from there.
Optionally delete it when finished.
On any sane filesystem, file moves are atomic provided they occur within the same filesystem/volume. So there are no race conditions.
Multiple Receivers
If processing could take longer than the period between files being delivered, you'll build up a backlog unless you have multiple receiver processes. So, how to handle the multiple-receiver case?
Simple: Each receiver process operates exactly as before. The key is that we attempt to move a file to processed before working on it: that, and the fact the same-filesystem file moves are atomic, means that even if multiple receivers see the same file in completed and try to move it, only one will succeed. All you need to do is make sure you check the return value of rename(), or whatever OS call you use to perform the move, and only proceed with processing if it succeeded. If the move failed, some other receiver got there first, so just go back and scan the completed directory again.
If the OS supports it, use file system hooks to intercept open and close file operations. Something like Dazuko. Other operating systems may let you know about file operations in anoter way, for example Novell Open Enterprise Server lets you define epochs, and read list of files modified during an epoch.
Just realized that in Linux, you can use inotify subsystem, or the utilities from inotify-tools package
File transfers is one of the classics of system integration. I'd recommend you to get the Enterprise Integration Patterns book to build your own answer to these questions -- to some extent, the answer depends on the technologies and platforms you are using for endpoint implementation and for file transfer. It's a quite comprehensive collection of workable patterns, and fairly well written.