repository-library-design.md

Python-tuf repository API proposal: minimal repository abstraction

This is an attachment to ADR 10: Repository library design built on top of Metadata API, and documents the design proposal in Dec 2021.

Design principles

Primary goals of this repository library design are

1. Support full range of repository implementations: from command line “repository editing” tools to production repositories like PyPI
2. Provide canonical solutions for the difficult repository problems but avoid making implementation decisions
3. Keep python-tuf maintenance burden in mind: less is more

Why does this design look so different from both legacy python-tuf code and other implementations?

• Most existing implementations are focused on a specific use case (typically a command line application): this is a valid design choice but severely limits goal #1
• The problem space contains many application decisions. Many implementations solve this by creating functions with 15 arguments: this design tries to find another way (#2)
• The Metadata API makes modifying individual pieces of metadata simpler. This, combined with good repository API design, should enable more variance in where things are implemented: The repository library does not have to implement every little detail as we can safely let specific implementations handle things, see goal #3
• This variance means we can start by implementing a minimal design: as experience from implementations is collected, we can then move implementation details into the library (goals #2, #3)

Design

Application and library components

The design expects a fully functional repository application to contain code at three levels:

• Repository library (abstract classes that are part of python-tuf)
  • The Repository abstract class provides an ergonomic abstract metadata editing API for all code levels to use. It also provides implementations for some core edit actions like snapshot update.
  • A small amount of related functionality is also provided (private key management API, maybe repository validation).
  • is a very small library: possibly a few hundred lines of code.
• Concrete Repository implementation (typically part of application code, implements interfaces provided by the repository API in python-tuf)
  • Contains the “application level” decisions that the Repository abstraction requires to operate: examples of application decisions include
    • When should “targets” metadata next expire when it is edited?
    • What is the current “targets” metadata version? Where do we load it from?
    • Where to store current “targets” after editing? Should the previous version be deleted from storage?
• Actual application
  • Uses the Repository API to do the repository actions it needs to do

For context here’s a trivial example showing what “ergonomic editing” means -- this key-adding code could be in the application (or later, if common patterns are found, in the python-tuf library):

with repository.edit(“targets”) as targets:
    # adds a key for role1 (as an example, arbitrary edits are allowed)
    targets.add_key(key, “role1”)

This code loads current targets metadata for editing, adds the key to a role, and handles version and expiry bumps before persisting the new targets version. The reason for the context manager style is that it manages two things simultaneously:

• Hides the complexity of loading and persisting metadata, and updating expiry and versions from the editing code (by putting it in the repository implementation that is defined in python-tuf but implemented by the application)
• Still allows completely arbitrary edits on the metadata in question: now the library does not need to anticipate what application wants to do and on the other hand library can still provide e.g. snapshot functionality without knowing about the application decisions mentioned in previous point.

Other designs do not seem to manage both of these.

How the components are used

The core idea here is that because editing is ergonomic enough, when new functionality (like “developer uploads new targets”) is added, it can be added at any level: the application might add a handle_new_target_files() method that adds a bunch of targets into the metadata, but one of the previous layers could offer that as a helper function as well: code in both cases would look similar as it would use the common editing interface.

The proposed design is purposefully spartan in that the library provides very few high-level actions (the prototype only provided sign and snapshot): everything else is left to implementer at this point. As we gain experience of common usage patterns we can start providing other features as well.

There are a few additional items worth mentioning:

• Private key management: the Repository API should come with a “keyring abstraction” -- a way for the application to provide roles’ private keys for the Repository to use. Some implementations could be provided as well.
• Validating repository state: the design is very much focused on enabling efficient editing of individual metadata. Implementations are also likely to be interested in validating (after some edits) that the repository is correct according to client workflow and that it contains the expected changes. The Repository API should provide some validation, but we should recognise that validation may be implementation specific.
• Improved metadata editing: There are a small number of improvements that could be made to metadata editing. These do not necessarily need to be part of the repository API: they could be part of Metadata API as well

It would make sense for python-tuf to ship with at least one concrete Repository implementation: possibly a repo.py look alike. This implementation should not be part of the library but an example.

Details

This section includes links to a Proof of Concept implementation in repository-editor-for-tuf: it should not be seen as the exact proposed API but a prototype of the ideas.

The ideas in this document map to POC components like this:

Concept	repository-editor-for-tuf implementation
Repository API	librepo/repo.py, librepo/keys.py
Example of repository implementation	git_repo.py
Application code	cli.py (command line app), keys_impl.py (keyring implementation)
Repository validation	verifier.py (very rough, not intended for python-tuf)
Improved Metadata editing	helpers.py

Repository API

Repository itself is a minimal abstract class: The value of this class is in defining the abstract method signatures (most importantly _load, _save(), edit()) that enable ergonomic metadata editing. The Repository class in this proposal includes concrete implementations only for the following:

• sign() -- signing without editing metadata payload
• snapshot() -- updates snapshot and timestamp metadata based on given input. Note that a concrete Repository implementation could provide an easier to use snapshot that does not require input (see example in git_repo.py)

More concrete method implementations (see cli.py for examples) could be added to Repository itself but none seem essential at this point.

The current prototype API defines five abstract methods that take care of access to metadata storage, expiry updates, version updates and signing. These must be implemented in the concrete implementation:

• keyring(): A property that returns the private key mapping that should be used for signing.

• _load(): Loads metadata from storage or cache. Is used by edit() and sign().

• _save(): Signs and persists metadata in cache/storage. Is used by edit() and sign().

• edit(): The ContextManager that enables ergonomic metadata editing by handling expiry and version number management.

• init_role(): initializes new metadata handling expiry and version number. (init_role is in a way a special case of edit and should potentially be integrated there).

The API requires a “Keyring” abstraction that the repository code can use to lookup a set of signers for a specific role. Specific implementations of Keyring could include a file-based keyring for testing, env-var keyring for CI use, etc. Some implementations should be provided in the python-tuf code base and more could be implemented in applications.

Prototype status: Prototype Repository and Keyring abstractions exist in librepo/repo.py.

Example concrete Repository implementation

The design decisions that the included example GitRepository makes are not important but provide an example of what is possible:

• Metadata versions are stored in files in git, with filenames that allow serving the metadata directory as is over HTTP
• Version bumps are made based on git status (so edits in staging area only bump version once)
• “Current version” when loading metadata is decided based on filenames on disk
• Files are removed once they are no longer part of the snapshot (to keep directory uncluttered)
• Expiry times are decided based on an application specific metadata field
• Private keys can be stored in a file or in environment variables (for CI use)

Note that GitRepository implementation is significantly larger than the Repository interface -- but all of the complexity in GitRepository is really related to the design decisions made there.

Prototype status: The GitRepository example exists in git_repo.py.

Validating repository state

This is mostly undesigned but something built on top of TrustedMetadataSet (currently ngclient component) might work as a way to easily check specific aspects like:

• Is top-level metadata valid according to client workflow
• Is a role included in the snapshot and the delegation tree

It’s likely that different implementations will have different needs though: a command line app for small repos might want to validate loading all metadata into memory, but a server application hosting tens of thousands of pieces of metadata is unlikely to do so.

Prototype status: A very rough implementation exists in verifier.py : this is unlikely to be very useful

Improved metadata editing

Currently the identified improvement areas are:

• Metadata initialization: this could potentially be improved by adding default argument values to Metadata API constructors
• Modifying and looking up data about roles in delegating metadata (root/targets): they do similar things but root and targets do not have identical API. This may be a very specific use case and not interesting for some applications

Prototype status: Some potential improvements have been collected in helpers.py