What is SVN (Subversion) and what are its top alternatives?
Top Alternatives to SVN (Subversion)
Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...
Mercurial is dedicated to speed and efficiency with a sane user interface. It is written in Python. Mercurial's implementation and data structures are designed to be fast. You can generate diffs between revisions, or jump back in time within seconds. ...
It is an open-source Version Control System for data science and machine learning projects. It is designed to handle large files, data sets, machine learning models, and metrics as well as code. ...
- Plastic SCM
Plastic SCM is a distributed version control designed for big projects. It excels on branching and merging, graphical user interfaces, and can also deal with large files and even file-locking (great for game devs). It includes "semantic" features like refactor detection to ease diffing complex refactors. ...
It is an interface to the version control system Git, implemented as an Emacs package. It aspires to be a complete Git porcelain. While we cannot (yet) claim that it wraps and improves upon each and every Git command, it is complete enough to allow even experienced Git users to perform almost all of their daily version control tasks directly from within Emacs. While many fine Git clients exist, only deserve to be called porcelains. ...
Pijul is a free and open source (AGPL 3) distributed version control system. Its distinctive feature is to be based on a sound theory of patches, which makes it easy to learn and use, and really distributed. ...
- Git Reflow
Reflow automatically creates pull requests, ensures the code review is approved, and squash merges finished branches to master with a great commit message template. ...
SVN (Subversion) alternatives & related posts
- Distributed version control system1.4K
- Efficient branching and merging1.1K
- Open source845
- Better than svn726
- Great command-line application368
- Easy to use232
- Does not require server222
- Small & Fast22
- Feature based workflow18
- Staging Area15
- Most wide-spread VSC13
- Role-based codelines11
- Disposable Experimentation11
- Frictionless Context Switching7
- Data Assurance6
- Just awesome4
- Github integration3
- Easy branching and merging3
- Possible to lose history and commits2
- Rebase supported natively; reflog; access to plumbing1
- Team Integration1
- Fast, scalable, distributed revision control system1
- Flexible, easy, Safe, and fast1
- CLI is great, but the GUI tools are awesome1
- It's what you do1
- Hard to learn16
- Inconsistent command line interface11
- Easy to lose uncommitted work9
- Worst documentation ever possibly made7
- Awful merge handling5
- Unexistent preventive security flows3
- Rebase hell3
- When --force is disabled, cannot rebase2
- Ironically even die-hard supporters screw up badly2
- Doesn't scale for big data1
related Git posts
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis as preferred in-memory database/store (great for caching)
The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:
- Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
- Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
- Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
- Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
- Scalability: All-in-one framework for distributed systems.
- Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
For markup and style, I used Pug and Sass, since they’re the perfect match to me. I love the clean and strict syntax of both of them and even more that their structure is almost similar. Also, both of them come with an expanded functionality such as mixins, loops and so on related to their “siblings” (HTML and CSS). Both of them require nesting and prevent untidy code, which can be a huge advantage when working in teams. I used JSON to store data (since the data quantity on my website is moderate) – JSON works also good in combo with Pug, using for loops, based on the JSON Objects for example.
To send my contact form I used PHP, since sending emails using PHP is still relatively convenient, simple and easy done.
DevOps: Of course, I used Git to do my version management (which I even do in smaller projects like my website just have an additional backup of my code). On top of that I used GitHub since it now supports private repository for free accounts (which I am using for my own). I use Babel to use ES6 functionality such as arrow functions and so on, and still don’t losing cross browser compatibility.
Side note: I used npm for package management. 🎉
- A lot easier to extend than git18
- Easy-to-grasp system with nice tools17
- Works on windows natively without cygwin nonsense13
- Written in python11
- Better than Git6
- Best GUI6
- Better than svn4
- Hg inc2
- Good user experience2
- TortoiseHg - Unified free gui for all platforms2
- Consistent UI2
- Native support to all platforms2
- Free to use1
- Track single upstream only0
- Does not distinguish between local and remote head0
related Mercurial posts
I've been excited about Git ever since it got a built-in UI. It's the perfect combination of a really solid, simple data model, which allows an experienced user to predict precisely what a Git subcommand will do, often without needing to read the documentation (see the slides linked from the attached article for details). Most important to me as the lead developer of a large open source project (Zulip) is that it makes it possible to build a really clean, clear development history that I regularly use to understand details of our code history that are critical to making correct changes.
And it performs really, really well. In 2014, I managed Dropbox's migration from Mercurial to Git. And just switching tools made just about every common operation (
git commit etc.) 2-10x faster than with Mercurial. It makes sense if you think about it, since Git was designed to perform well with Linux, one of the largest open source projects out there, but it was still a huge productivity increase that we got basically for free.
If you're learning Git, I highly recommend reading the other sections of Zulip's Git Guide; we get a lot of positive feedback from developers on it being a useful resource even for their projects unrelated to Zulip.
- Full reproducibility2
- Coupling between orchestration and version control1
- Requires working locally with the data1
- Doesn't scale for big data1
related DVC posts
I already use DVC to keep track and store my datasets in my machine learning pipeline. I have also started to use MLflow to keep track of my experiments. However, I still don't know whether to use DVC for my model files or I use the MLflow artifact store for this purpose. Or maybe these two serve different purposes, and it may be good to do both! Can anyone help, please?
- Wanna do Branch per Task Dev? Plastic rocks it8
- No Size limite4
- File Locking2
- Simple, easy to use interfaces. Resilient and solid2
- Very fast1
- Always uses automatic conflict resolution first1
- Adds files with only changed timestamp to pending1
- Keyboard shortcuts are lacking1
- Can't place windows next to each other to save space1
- No dark theme1
- Doesn't have file staging1
related Plastic SCM posts
- Best parts of GUI and command line git clients combined1
- Word wise diff highlighting1
- Can be slow on big diffs1
related Magit posts
related isomorphic-git posts
- A VCS based on Category Theory? Awesome6
- Patch-based VCS4
- I like birds2
- Unrelated patches are commutative1
- The exact patch dependencies are tracked1
- Lack of hosting options1