What is Mercurial?
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.
Mercurial is a tool in the Version Control System category of a tech stack.
Who uses Mercurial?
44 companies reportedly use Mercurial in their tech stacks, including AO.com, Bitbucket, and Deveo.
158 developers on StackShare have stated that they use Mercurial.
Debian, SourceTree, Windows, DataGrip, and Fedora are some of the popular tools that integrate with Mercurial. Here's a list of all 31 tools that integrate with Mercurial.
Pros of Mercurial
A lot easier to extend than git
Easy-to-grasp system with nice tools
Works on windows natively without cygwin nonsense
Written in python
Better than Git
Better than svn
Good user experience
TortoiseHg - Unified free gui for all platforms
Native support to all platforms
Free to use
Mar 4 2020 at 5:14PM
See all jobs
Jobs that mention Mercurial as a desired skillset
Mercurial Alternatives & Comparisons
What are some alternatives to Mercurial?
See all alternatives
Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
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.
Subversion exists to be universally recognized and adopted as an open-source, centralized version control system characterized by its reliability as a safe haven for valuable data; the simplicity of its model and usage; and its ability to support the needs of a wide variety of users and projects, from individuals to large-scale enterprise operations.
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 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.