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| #format text_markdown # On development workflows for (experimental) code sharing One core aim of Sage (and subprojects like Sage-manifolds, Sage-Combinat, Sage-Words, ...) is to improve the open source mathematical system \texttt{Sage} as an extensible toolbox for computer exploration (in geometry, algebraic and enumerative combinatorics, combinatorics on words, etc), and foster code sharing between researchers in those areas. Over the years, many development workflows have been experimented with; the goal of this document is to discuss them toward recommending best practice. Specifically, the objectives of a development workflow are: 1. Support fast paced development within a group of researchers working on the same topic, or needing similar features. 2. Support rapid and modular dissemination of experimental features |
{{{#!rst ======================================================== On development workflows for sharing (experimental) code ======================================================== One core aim of Sage is to foster code sharing, and encourage groups of researchers, teachers, and other users to get together to develop new features they need either on top of or within Sage, and share them. Over the years, many development workflows have been experimented by various groups of people to improve Sage in certain areas, like Sage-Combinat for (algebraic) combinatorics, Sage-Words for combinatorics on words, Sage-manifolds for differential geometry, purple-sage for number theory, ... The goal of this document is to discuss the different workflow that have been tried, their pros and cons, to share best practices and brainstorm about what support and recommendations Sage could provide for various use cases. At this point this is a collection of notes by N. Thiéry; please hack in and contribute your own vision! Objectives of a development workflow ==================================== Of course the milleage will vary from project to project, but the objectives of a development workflow can typically be to: 1. Support *fast paced development* within a group of users working on the same topic, or needing similar features. 2. Support *rapid dissemination of experimental features* The goal is simultaneously to support users, and to get early feedback on the code. |
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| This is important to get early feedback on the code. | - Feature discovery: increasing the chances for someone to discover that a given feature is being implemented somewhere. |
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| 4. Foster intrinsic high quality code by providing an ecosystem where (experimental) code can live, compete with other implementations, mature and be selected or die, all at a fine granularity. 5. Strike a balance between the risks of code-bloat and of code death 6. Minimize maintenance overhead, and in particular code rotting |
4. Foster intrinsic high quality code by providing an *ecosystem* where (experimental) code can live, compete with other implementations, mature and be selected or die, all at a fine granularity. 5. Strike a balance between centralized and decentralized. In particular mitigate the risks of code-bloat of the Sage library versus the risks of death of code lying out somewhere on the web. 6. Minimize *maintenance* overhead, and in particular code rotting |
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| This eases feature discovery by users (things are at their expected place) and enable transparent migration of the code inside the Sage library if and when desired (no need to change the code itself, nor code using it). This also promotes coherent coding standards. |
This eases dynamic feature discovery by users (once installed, features can be found at their expected place) and enable transparent migration of code inside the Sage library if and when desired (no need to change the code itself, nor code using it). This also promotes coherent coding standards. |
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| See also the related: | See also: |
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| ## Direct integration into Sage In this workflow, sharing a feature goes by integrating it into Sage. |
Existing workflows ================== Direct integration into Sage ---------------------------- In this workflow, each feature is shared by integrating it directly into Sage. |
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| - Simplicity for Sage developers: no additional workflow to learn, no need to worry about distribution - Promote early integration of code and 3. |
- Simplicity for Sage developers: no additional workflow to learn - No need to worry about release, distribution, test infrastructure, ... - Promotes early integration of code and 3. |
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| ## Patch queue as used by Sage-Combinat between 2009 and 2013 TODO: description Pros: - Relatively good for 1. (except for 6.) - Relatively good for 2. (thanks to "sage -combinat install"), except for modularity and requiring some Sage recompilation - 8. is straightforward Cons: - Complexity of working at the meta level (version control on the patches) - Really bad at 6: Horrible maintenance overhead due to syntactic conflicts and lack of automatic merging - Introduces a strong bias toward code death, or at least non integration into Sage - Monolithic: one could not use several patch queues at once, so this did not support overlaping groups of people working on different topics; this introduced a non-natural barrier between Sage-Combinat and the rest of the world, and prevented rapid reconfiguration of projects around topics and groups of developers ## Experimental feature branches |
Experimental feature branches ----------------------------- In this workflow, experimental feature or feature sets are implemented as branches on the Sage sources. |
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| ## Using (pip) packages Pros: |
Patch queue as used by Sage-Combinat between 2009 and 2013 ---------------------------------------------------------- See also those old `design notes about the Sage-Combinat workflow <combinat/CodeSharingWorkflow>`_. TODO: description Pros: - Relatively good for 1. (except for 6.) - Relatively good for 2. (thanks to "sage -combinat install"), except for modularity and requiring some Sage recompilation - 8. is straightforward Cons: - Complexity of working at the meta level (version control on the patches) - Really bad at 6: Horrible maintenance overhead due to syntactic conflicts and lack of automatic merging - Introduces a strong bias toward code death, or at least non integration into Sage - Monolithic: one could not use several patch queues at once, so this did not support overlaping groups of people working on different topics; this introduced a non-natural barrier between Sage-Combinat and the rest of the world, and prevented rapid reconfiguration of projects around topics and groups of developers Standalone (pip) packages ------------------------- Here the idea is to implement feature sets as independent Python packages on top of Sage. Converting a bunch of Python files into such a package to make it `easy to install <https://python-packaging.readthedocs.io/en/latest/minimal.html>`_ is straightforward e.g. with pip. Examples: - `Sage-Manifolds <http://sagemanifolds.obspm.fr/>`_ - `slabbe-0.2.spkg <http://www.slabbe.org/blogue/categorie/slabbe-spkg/>`_ See also this `blog post <http://www.slabbe.org/blogue/2014/08/releasing-slabbe-my-own-sage-package/>`_ - `CHA <https://bitbucket.org/nborie/cha>`_ Pros: |
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| - Risk of code rotting or death | |
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## Using (pip) packages with an integration mission |
- Risk of code rotting (as Sage evolves over time) or death (if it's not maintained) - Requires coordination with Sage and related packages to not step on each other Standalone (pip) packages with an integration mission ----------------------------------------------------- |
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| Specific steps: - layout the code as in the Sage library, with top module called sage-blah instead of sage, and use *recursive monkey patching* (TODO: make a pip package for this, and add a link here) to insert all this code dynamically in the Sage library. |
Specifics: - Layout the code as in the Sage library, with top module called e.g. ``sage-blah`` instead of ``sage``. For example, to add a method to the Sage class Partition, one would put it in an otherwise empty class ``sage-blah.combinat.partition.Partition``. - Use *recursive monkey patching* (TODO: make a pip package for this, and add a link here) to insert all the code dynamically in the Sage library. |
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| queues; however this is done at the granularity of methods rather than lines in the source code. Pros: - Same as for usual (pip) packages |
queues; however this is done semantically at the granularity of methods rather than syntactically at the granularity of lines in the source code. Examples: - `Sage-semigroups <https://github.com/nthiery/sage-semigroups/>`_ (very preliminary!!!) Pros: - Same as above |
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Cons: - The concept has not yet been really battlefield tested! - Moving code into the Sage library is done by copy pasting. This makes for a clean diff showing just the addition of the new methods, but means that one looses the history and author tracking (that's not that different from history squashing as used by many projects) }}} |
On development workflows for sharing (experimental) code
One core aim of Sage is to foster code sharing, and encourage groups of researchers, teachers, and other users to get together to develop new features they need either on top of or within Sage, and share them.
Over the years, many development workflows have been experimented by various groups of people to improve Sage in certain areas, like Sage-Combinat for (algebraic) combinatorics, Sage-Words for combinatorics on words, Sage-manifolds for differential geometry, purple-sage for number theory, ...
The goal of this document is to discuss the different workflow that have been tried, their pros and cons, to share best practices and brainstorm about what support and recommendations Sage could provide for various use cases.
At this point this is a collection of notes by N. Thiéry; please hack in and contribute your own vision!
Objectives of a development workflow
Of course the milleage will vary from project to project, but the objectives of a development workflow can typically be to:
- Support fast paced development within a group of users working on the same topic, or needing similar features.
Support rapid dissemination of experimental features
The goal is simultaneously to support users, and to get early feedback on the code.
Typical needs:
- Using, for a given calculation, experimental features from different areas, developped by different groups of people
- Getting the latest version of a feature, without having to upgrade all of Sage (e.g. just before delivering a talk!!!)
- Feature discovery: increasing the chances for someone to discover that a given feature is being implemented somewhere.
- Foster high quality code by promoting documentation, tests, code reviews
- Foster intrinsic high quality code by providing an ecosystem where (experimental) code can live, compete with other implementations, mature and be selected or die, all at a fine granularity.
Strike a balance between centralized and decentralized.
In particular mitigate the risks of code-bloat of the Sage library versus the risks of death of code lying out somewhere on the web.
- Minimize maintenance overhead, and in particular code rotting
- Remain flexible between the all-in-one versus packages development models (simplifying things out: between Sage's model and GAP's model)
Promote extending existing Sage classes and modules with additional features.
This eases dynamic feature discovery by users (once installed, features can be found at their expected place) and enable transparent migration of code inside the Sage library if and when desired (no need to change the code itself, nor code using it). This also promotes coherent coding standards.
Note: subclassing is not always an option to extend a class, e.g. when a feature is to be added to an abstract base class of many concrete classes (subclassing each and every concrete class would be a pain)
See also:
Existing workflows
Direct integration into Sage
In this workflow, each feature is shared by integrating it directly into Sage.
Pros:
- Simplicity for the user: all stable features are directly available in Sage
- Simplicity for Sage developers: no additional workflow to learn
- No need to worry about release, distribution, test infrastructure, ...
- Promotes early integration of code and 3.
- is straightforward
Cons:
- Limited support for 2.
- Slows down the development: once a feature is in Sage, any change needs to be reviewed, refactoring of the public API requires taking care of backward compatibility. No good for 4.
- Getting the latest feature forces updating to the latest version of Sage
- Introduces a bias toward code bloat (in doubt, features tend to be added to Sage)
Experimental feature branches
In this workflow, experimental feature or feature sets are implemented as branches on the Sage sources.
Pros:
- is straightforward
- Encourages integration into Sage
- Development history is automatically kept upon integration into Sage
Cons:
- Branch needs to be regularly updated to prevent code rotting due to syntactical conflicts with changes in Sage (though automatic merges help).
- requires basic git knowledge from end-users.
- Lack of modularity for 2.: due to potential conflicts, it's not easy to combine features from several branches; upgrading to the latest version of a branch often forces a change of version of Sage
- Cherry picking certain mature features for integration in Sage is somewhat cumbersome (the granularity of branches and commits is orthogonal to the granularity of features).
- It's hard to strike the right granularity in terms of feature / feature set. We tried dependency tracking among branches as a way to build feature sets out of features, but this did not work well.
- Because of the above, this workflow does not work well for 4.
- Introduces a bias toward the all-in-one development model.
Patch queue as used by Sage-Combinat between 2009 and 2013
See also those old design notes about the Sage-Combinat workflow.
TODO: description
Pros:
- Relatively good for 1. (except for 6.)
- Relatively good for 2. (thanks to "sage -combinat install"), except for modularity and requiring some Sage recompilation
- is straightforward
Cons:
- Complexity of working at the meta level (version control on the patches)
- Really bad at 6: Horrible maintenance overhead due to syntactic conflicts and lack of automatic merging
- Introduces a strong bias toward code death, or at least non integration into Sage
- Monolithic: one could not use several patch queues at once, so this did not support overlaping groups of people working on different topics; this introduced a non-natural barrier between Sage-Combinat and the rest of the world, and prevented rapid reconfiguration of projects around topics and groups of developers
Standalone (pip) packages
Here the idea is to implement feature sets as independent Python packages on top of Sage. Converting a bunch of Python files into such a package to make it easy to install is straightforward e.g. with pip.
Examples:
- Sage-Manifolds
- slabbe-0.2.spkg See also this blog post
- CHA
Pros:
- Good for 1., 2., 4.,
Cons:
- Handling of compatibility with various versions of the dependencies (in particular Sage)
- Risk of code rotting (as Sage evolves over time) or death (if it's not maintained)
- Requires coordination with Sage and related packages to not step on each other
Standalone (pip) packages with an integration mission
This is a variant on the previous development workflow, with an explicit focus on easing (or even promoting) the integration of mature code into Sage.
Specifics:
Layout the code as in the Sage library, with top module called e.g. sage-blah instead of sage. For example, to add a method to the Sage class Partition, one would put it in an otherwise empty class sage-blah.combinat.partition.Partition.
Use recursive monkey patching (TODO: make a pip package for this, and add a link here) to insert all the code dynamically in the Sage library.
The effect is to patch the Sage library, as with branches or patch queues; however this is done semantically at the granularity of methods rather than syntactically at the granularity of lines in the source code.
Examples:
- Sage-semigroups (very preliminary!!!)
Pros:
- Same as above
- is straightforward
- Lighter maintenance overhead compared to branches or patch queues: one only needs to take care of semantic conflicts, not syntactic ones.
- The integration of mature code into Sage helps for 3 and for the maintenance as well: keeping the library as a "small layer" over Sage reduces the risks of irreversibly drifting away, and reduces the amount of updating.
- Depending on how strongly one pushes toward the integration of mature code, one can flexibly interpolate between the all-in-one model and the package model
Cons:
- The concept has not yet been really battlefield tested!
- Moving code into the Sage library is done by copy pasting. This makes for a clean diff showing just the addition of the new methods, but means that one looses the history and author tracking (that's not that different from history squashing as used by many projects)