Can I in svn hooks for Windows to write a command which relocate automatically some folders to another location in repository?
Hook must run at server
For example: Users commit files in his working copy (C:svnworkingcopy\dev)
At server will run a hook and automatically relocated or copy this files into another folder of repository.(https://svnserver/onlyread)
Where this user have permission to read only.
Thnk !
svn switch --relocate a user's working copy with a hook script? Looks like you are confusing the terms. Nevertheless I advise you to check the following warning in SVNBook:
While hook scripts can do almost anything, there is one dimension in
which hook script authors should show restraint: do not modify a
commit transaction using hook scripts. While it might be tempting to
use hook scripts to automatically correct errors, shortcomings, or
policy violations present in the files being committed, doing so can
cause problems. Subversion keeps client-side caches of certain bits of
repository data, and if you change a commit transaction in this way,
those caches become indetectably stale. This inconsistency can lead to
surprising and unexpected behavior. Instead of modifying the
transaction, you should simply validate the transaction in the
pre-commit hook and reject the commit if it does not meet the desired
requirements. As a bonus, your users will learn the value of careful,
compliance-minded work habits.
Related
I have an azure blob container with data which I have not uploaded myself. The data is not locally on my computer.
Is it possible to use dvc to download the data to my computer when I haven’t uploaded the data with dvc? Is it possible with dvc import-url?
I have tried using dvc pull, but can only get it to work if I already have the data locally on the computer and have used dvc add and dvc push .
And if I do it that way, then the folders on azure are not human-readable. Is it possible to upload them in a human-readable format?
If it is not possible is there then another way to download data automatically from azure?
I'll build up on #Shcheklein's great answer - specifically on the 'external dependencies' proposal - and focus on your last question, i.e. "another way to download data automatically from Azure".
Assumptions
Let's assume the following:
We're using a DVC pipeline, specified in an existing dvc.yaml file. The first stage in the current pipeline is called prepare.
Our data is stored on some Azure blob storage container, in a folder named dataset/. This folder follows a structure of sub-folders that we'd like to keep intact.
The Azure blob storage container has been configured in our DVC environment as a DVC 'data remote', with name myazure (more info about DVC 'data remotes' here)
High-level idea
One possibility is to start the DVC pipeline by synchronizing a local dataset/ folder with the dataset/ folder on the remote container.
This can be achieved with a command-line tool called azcopy, which is available for Windows, Linux and macOS.
As recommended here, it is a good idea to add azcopy to your account or system path, so that you can call this application from any directory on your system.
The high-level idea is:
Add an initial update_dataset stage to the DVC pipeline that checks if changes have been made in the remote dataset/ directory (i.e., file additions, modifications or removals).
If changes are detected, the update_datset stage shall use the azcopy sync [src] [dst] command to apply the changes on the Azure blob storage container (the [src]) to the local dataset/ folder (the [dst])
Add a dependency between update_dataset and the subsequent DVC pipeline stage prepare, using a 'dummy' file. This file should be added to (a) the outputs of the update_dataset stage; and (b) the dependencies of the prepare stage.
Implementation
This procedure has been tested on Windows 10.
Add a simple update_dataset stage to the DVC pipeline by running:
$ dvc stage add -n update_dataset -d remote://myazure/dataset/ -o .dataset_updated azcopy sync \"https://[account].blob.core.windows.net/[container]/dataset?[sas token]\" \"dataset/\" --delete-destination=\"true\"
Notice how we specify the 'dummy' file .dataset_updated as an output of the stage.
Edit the dvc.yaml file directly to modify the command of the update_dataset stage. After the modifications, the command shall (a) create the .dataset_updated file after the azcopy command - touch .dataset_updated - and (b) pass the current date and time to the .dataset_updated file to guarantee uniqueness between different update events - echo %date%-%time% > .dataset_updated.
stages:
update_dataset:
cmd: azcopy sync "https://[account].blob.core.windows.net/[container]/dataset?[sas token]" "dataset/" --delete-destination="true" && touch .dataset_updated && echo %date%-%time% > .dataset_updated # updated command
deps:
- remote://myazure/dataset/
outs:
- .dataset_updated
...
I recommend editing the dvc.yaml file directly to modify the command, as I wasn't able to come up with a complete dvc add stage command that took care of everything in one go.
This is due to the use of multiple commands chained by &&, special characters in the Azure connection string, and the echo expression that needs to be evaluated dynamically.
To make the prepare stage depend on the .dataset_updated file, edit the dvc.yaml file directly to add the new dependency, e.g.:
stages:
prepare:
cmd: <some command>
deps:
- .dataset_updated # add new dependency here
- ... # all other dependencies
...
Finally, you can test different scenarios on your remote side - e.g., adding, modifying or deleting files - and check what happens when you run the DVC pipeline up till the prepare stage:
$ dvc repro prepare
Notes
The solution presented above is very similar to the example given in DVC's external dependencies documentation.
Instead of the az copy command, it uses azcopy sync.
The advantage of azcopy sync is that it only applies the differences between your local and remote folders, instead of 'blindly' downloading everything from the remote side when differences are detected.
This example relies on a full connection string with an SAS token, but you can probably do without it if you configure azcopy with your credentials or fetch the appropriate values from environment variables
When defining the DVC pipeline stage, I've intentionally left out an output dependency with the local dataset/ folder - i.e. the -o dataset part - as it was causing the azcopy command to fail. I think this is because DVC automatically clears the folders specified as output dependencies when you reproduce a stage.
When defining the azcopy command, I've included the --delete-destination="true" option. This allows synchronization of deleted files, i.e. files are deleted on your local dataset folder if deleted on the Azure container.
Please, bear with me, since you have a lot of questions. Answer needs a bit structure and background to be useful. Or skip to the very end to find some new ways of doing Is it possible to upload them in a human-readable format? :). Anyways, please let me know if that solves your problem, and in general would be great to have a better description of what you are trying to accomplish at the end (high level description).
You are right that by default DVC structures its remote in a content-addressable way (which makes it non human-readable). There are pros and cons to this. It's easy to deduplicate data, it's easy to enforce immutability and make sure that no one can touch it directly and remove something, directory names in projects make it connected to actual project and their meaning, etc.
Some materials on this: Versioning Data and Models, my answer of on how DVC structures its data, upcoming Data Management User Guide section (WIP still).
Saying that, it's clear there are downsides to this approach, especially when it comes to managing a lot of objects in the cloud (e.g. millions of images, etc). To name a few concerns that I see a lot as a pattern:
Data has been created (and being updated) by someone else. There is some ETL, third party tool, etc. We need to keep that format.
Third party tool expect to have data in "human" readable way. It doesn't integrate with DVC to being able to access it indirectly via Git. (one of the examples - Label Studio need direct links to S3).
It's not practical to move all of data into DVC, it doesn't make sense to instantiate all the files at once as one directory. Users need slices, usually based on some annotations (metadata), etc.
So, DVC has multiple features to deal with data in its own original layout:
dvc import-url - it'll download objects, it'll cache them, and will by default push (dvc push) to remote to again save them to guarantee reproducibility (this can be changed). This command creates a special file .dvc that is being used to detect changes in the cloud to see if DVC needs to download something again. It should cover the case for "to download data automatically from azure".
dvc get-url - this more or less wget or rclone or aws s3 cp, etc with multi cloud support. It just downloads objects.
A bit advanced thing (if you DVC pipelines):
Similar to import-url but for DVC pipelines - external dependencies
The the third (new) option. It's in beta phase, it's called "cloud versioning" and essentially it tries to keep the storage human readable while still benefit from using .dvc files in Git if you need them to reference an exact version of the data.
Cloud Versioning with DVC (it's WPI when I write this, if PR is merged it means you can find it in the docs
The document summarizes well the approach:
DVC supports the use of cloud object versioning for cases where users prefer to retain their original filenames and directory hierarchy in remote storage, in exchange for losing the de-duplication and performance benefits of content-addressable storage. When cloud versioning is enabled, DVC will store files in the remote according to their original directory location and
filenames. Different versions of a file will then be stored as separate versions of the corresponding object in cloud storage.
Our Jenkins job downloads some code from a Perforce server, using a pre-defined workspace. It
sometimes fails with the following error message:
Client 'xxxx' can only be used from host 'yyyy'.
When I look at the workspace ("client" is an obsolete name for workspace), I see that its settings don't mention host yyyy at all.
I suspect that people (or unknown scripts) change the workspace's settings, do some work and then change them back. If a Jenkins job is scheduled to run during that time, it fails.
How can I determine if I guessed correctly? Are there any logs on the Perforce server which report workspace changes? Maybe some server setting to record all changes to workspaces?
Workspace settings look like something I should be able to track and/or revert using version control; is this really the case?
First and foremost, you should set the locked option on the client if you don't want anyone else messing with it (and set its Owner to be the user who runs the Jenkins job, and ensure that this user is password-protected so that nobody else can impersonate Jenkins).
To track changes to client specs, you can set up a spec depot (just create a depot with Type: spec). This will cause every spec update to be saved in that depot as a revision of a text file, e.g. client xxxx will correspond to a text file called //spec/client/xxxx. You can run normal commands like p4 annotate on that file to see its change history, and you can pipe old versions of the file into the current client spec by doing, e.g.:
p4 print -q //spec/client/xxxx | p4 client -i
But again, first and foremost, persistent clients that automation depends on should simply be locked so that they can't be sabotaged (intentionally or unwittingly) by other users.
I'm looking for a way to prevent commits which have no correct format for the commit message.
I intend to use the following convention:
https://www.conventionalcommits.org/en/v1.0.0/
I found out there is a folder with bash scripts which may be the key to that solution: .git/hooks/
However, I'm not sure how to write the script to enforce the format on the commit messages.
Will edit accordingly, thank you in advance.
There is a list of Tools in the documentation.
My suggestion would be to:
Pick a tool.
Run it from the command line, and see if you can get it to check commit messages.
Put the commands you use to check commits messages in a git hook.
How to make Websphere to automatically clean up temp folders during wach start or restart ?
I found out how to manually delete them. But can't ask the customer to do it. Is there some parameter or something that can be set in order to delete the cache/temp files automatically ?
You weren't specific about what cache or temporary files you wanted to delete, but in general, there is no WAS setting to do so. The logging system can be configured to roll log files over, but those aren't temporary files and typically you would want to keep them for some period of time for audit purposes. You also typically don't want to delete caches like the OSGi class cache, unless specifically told to do so by IBM support, so I would't suggest doing it on a server start/restart. The configuration repository uses temporary files that could be deleted on server start/restart. see this IBM KnowledgeCenter topic for details on the location of the files. Having said all that, if you're sure you know what files to delete, I'd suggest wrapping calls to the startServer or stopServer files with your own script(s). These are either batch files on windows platforms, or shell files on other platforms and shouldn't be modified by users. In your wrapper, simply delete the files and then call startServer.
I built an Analysis that displayed Results, error free. All is well.
Then, I added some filters to existing criteria sets. I also copied an existing criteria set, pasted it, and modified it's filters. When I try to display results, I see a View Display Error.
I’d like to revert back to that earlier functional version of the analyses, hopefully without manually undoing the all of filter & criteria changes I made since then.
If you’ve seen a feature like this, I’d like to hear about it!
Micah-
Great question. There are many times in the past when we wished we had some simple SCM on the Oracle BI Web Catalog. There is currently no "out of the box" source control for the web catalog, but some simple work-arounds do exist.
If you have access server side where the web catalog lives you can start with the following approach.
Oracle BI Web Catalog Version Control Using GIT Server Side with CRON Job:
Make a backup of your web catalog!
Create a GIT Repository in the web cat
base directory where the root dir and root.atr file exist.
Initial commit eveything. ( git add -A; git commit -a -m
"initial commit"; git push )
Setup a CRON job to run a script Hourly,
Minutely, etc that will tell GIT to auto commit any
adds/deletes/modifications to your GIT repository. ( git add -A; git
commit -a -m "auto_commit_$(date +"%Y-%m-%d_%T")"; git push )
Here are the issues with this approach:
If the CRON runs hourly, and an Analysis changes 3 times in the hour
you'll be missing some versions in there.
No actual user submitted commit messages.
Object details such as the Objects pretty "Name" (caption), Description (user populated on Save Dialog), ACLs, and object custom properties are stored in a binary file format. These files have the .atr extension. The good news though is that the actual object definition is stored in a plain text file in XML (Without the .atr).
Take this as a baseline, and build upon it. Here is how you could step it up!
Use incron or other inotify based file monitoring such as ruby
based guard. Using this approach you could commit nearly
instantly anytime a user saves an object and the BI server updates
the file system.
Along with inotify, you could leverage the BI Soap API to retrieve the actual object details such as Description. This would allow you to create meaningfull commit messages. Or, parse the binary .atr file and pull the info out. Here are some good links to learn more about Web Cat ATR files: Link (Keep in mind this links are discussing OBI 10g. The binary format for 11G has changed slightly.)