Contributing#

Naming#

When refering to conda-store it should always be written all lowercase with a dash in between. conda-store should also be lowercase when beginning a sentence.

Development#

Significant effort has been put into simplifying the development and deployment process of conda-store. There is a docker based development workflow along with a non-containerized workflow if you are using Linux.

Containerized development#

Install the following dependencies before developing on conda-store.

To deploy conda-store run the following command

docker-compose up --build -d

Important

Many of the conda-store docker images are built/tested for amd64(x86-64) there will be a performance impact when building and running on arm architectures. Otherwise this workflow has been shown to run and build on OSX. Notice the architecture: amd64 whithin the docker-compose.yaml files.

Warning

If you’re developing on a Mac and run into issues that complain about tcp 0.0.0.0:5000: bind: address already in use you might need to deactivate the Airplay Receiver service from the Sharing section in Control Center. Have a look at this discussion on Apple.com for more details.

The following resources will be available:

On a fast machine this deployment should only take 10 or so seconds assuming the docker images have been partially built before. If you are making and changes to conda-store-server and would like to see those changes in the deployment. Run.

docker-compose down -v # not always necessary
docker-compose up --build

Linux development#

Install the following dependencies before developing on conda-store.

Install the development dependencies and activate the environment.

conda env create -f conda-store-server/environment-dev.yaml
conda activate conda-store-server-dev

Running conda-store. --standalone mode launched celery as a subprocess of the web server.

python -m conda_store_server.server –standalone tests/assets/conda_store_standalone_config.py


Visit [localhost:5000](http://localhost:5000/)

### Changes to API

The REST API is considered somewhat stable. If any changes are made to
the API make sure the update the OpenAPI/Swagger specification in
`docs/_static/openapi.json`. This may be downloaded from the `/docs`
endpoint when running conda-store. Ensure that the
`c.CondaStoreServer.url_prefix` is set to `/` when generating the
endpoints.

## Documentation

Install the following dependencies before contributing to the
documentation.

 - [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/)

To build the documentation install the development environment via
Conda.

```shell
conda env create -f conda-store-server/environment-dev.yaml
conda activate conda-store-server-dev

Then go in the documentation directory docs and build the documentation.

cd docs
sphinx-build -b html . _build

Then open the documentation via your favorite web browser.

firefox _build/index.html

The documentation has been primarily written in markdown as to make it easier to contribute to the documentation.

Release process#

Choose the <version> number. It should follow Semantic Versioning and the established pattern of v<x>.<y>.<z>.

Ensure that CHANGELOG.md is up to date with all the changes since the last release following the template provided within the markdown file.

All docker images within docker/kubernetes should be updated to the release version. spec.template.spec.containers[0].image is the path within the YAML files.

Update the version number in conda-store-server/setup.py and conda-store/setup.py to reflect the release version.

Once those changes have been made make a commit titled bump to version <version>.

Finally create a new release within the GitHub interface. Do this instead of a git TAG since you can include release notes on the repository. The Release should be titled Release <version> - <month>/<day>/<year> with the description being the changelog markdown for the particular release.

Once you have create a release the GitHub actions with the build the release and make it available on PyPi, Conda, and DockerHub.

After the PyPi release a release on Conda-Forge and it located at conda-forge/conda-store-feedstock. A PR must be created that updates to the released version <version>.

conda-store has two PyPi packages conda-store-server and conda-store.

  • update recipies/meta.yaml with the new version {% set version = "<version>" %}

  • update recipies/meta.yaml with the appropriate sha256 for each package. The sha256 can be found at https://pypi.org/project/conda-store/#files by clicking the view button.

Once the PR has been created ensure that you request a rerender of the feedstock with the following comment @conda-forge-admin please rerender. An example of this can be found in PR #2

Architecture#

conda-store was designed with the idea of scalable enterprise management of reproducible Conda environments.

conda-store architecture diagram

Configuration#

Traitlets is used for all configuration of conda-store. In the beginning command line options were used but eventually we learned that there were too many options for the user. Traitlets provides a python configuration file that you can use to configure values of the applications. It is used for both the server and worker. See tests/assets/conda_store_config.py for a full example.

Workers and server#

conda-store can be broken into two components. The workers which have the following responsibilities:

  • build Conda environments from Conda environment.yaml specifications

  • build Conda pack archives

  • build Conda docker images

  • remove Conda builds

  • modify symlinks to point current environment to given build

  • generally any tasks that can take an unbounded amount of time

All of the worker logic is in conda_store_server/build.py and conda_store_server/worker/*.py. Celery is used for managing tasks so you will see the celery tasks defined in conda_store_server/worker/tasks.py which in turn usually call built in CondaStore functions in conda_store_server/app.py or conda_store_server/build.py.

The web server has several responsibilities:

  • serve a UI for interacting with Conda environments

  • serve a REST API for managing Conda environments

  • serve a programmatic Docker registry for interesting docker-conda abilities

The web server is based on FastAPI. Originally Flask was chosen due to it being battle tested and that conda-store is not doing any special things with the web server. However, over time the ability for robust input and output guarantees from the endpoints along with auto documentation made FastAPI appealing. The backend web app is defined in conda_store_server.server.app. There are several components to the server:

  • UI :: conda_store_server/server/views/ui.py

  • REST API :: conda_store_server/server/views/api.py

  • registry :: conda_store_server/server/views/registry.py

Both the worker and server need a connection to a SQLAchemy compatible database, Redis, and S3 compatible object storage. The S3 server is used to store all build artifacts for example logs, docker layers, and the Conda-Pack tarball. The PostgreSQL database is used for storing all states on environments and builds along with powering the conda-store web server UI, REST API, and Docker registry. Redis is used for keeping track of task state and results along with enabling locks and realtime streaming of logs.

Terminology#

conda-store terminology

conda_environment = f(open("environment.yaml"), datatime.utcnow())

  • namespace :: a way of providing scopes between environments. This prevents Joe’s environment named data-science from colliding from Alice’s environment name data-science.

  • environment :: a pointer to a current build of a given specification

  • specification :: a Conda environment.yaml file

  • build :: a attempt of conda env install -f environment.yaml at a given point in time

In order to understand why we have the complicated terminology for an environment it helps to understand how Conda builds a given environment.

Reproducibility of Conda#

name: example
channels:
  - defaults
  - conda-forge
dependencies:
  - python >=3.7

Suppose we have the given environment.yaml file. How does Conda perform a build?

  1. Conda downloads channeldata.json from each of the channels which list the available architectures.

  2. Conda then downloads repodata.json for each of the architectures it is interested in (specifically your compute architecture along with noarch). The repodata.json has fields like package name, version, and dependencies.

You may notice that the channels listed above do not have a url. This is because in general you can add https://conda.anaconda.org/<channel-name> to a non-url channel.

  1. Conda then performs a solve to determine the exact version and sha256 of each package that it will download

  2. The specific packages are downloaded

  3. Conda does magic to fix the path prefixes of the install

There are two spots that introduce issues to reproducibility. The first issue is tracking when an environment.yaml file has changes. This can be easily tracked by taking a sha256 of the file . This is what conda-store does but sorts the dependencies to make sure it has a way of not triggering a rebuild if the order of two packages changes in the dependencies list. In step (2) repodata.json is updated regularly. When Conda solves for a user’s environment it tries to use the latest version of each package. Since repodata.json could be updated the next minute the same solve for the same environment.yaml file can result in different solves.

Authentication Model#

Authentication was modeled after JupyterHub for implementation. There is a base class conda_store_server.server.auth.Authenticaiton. If you are extending and using a form of OAuth2 use the conda_store_server.server.auth.GenericOAuthAuthentication. Similar to JupyterHub all configuration is modified via Traitlets. Below shows an example of setting us OAuth2 via JupyterHub for conda-store.

c.CondaStoreServer.authentication_class = JupyterHubOAuthAuthentication
c.JupyterHubOAuthAuthentication.jupyterhub_url = "http://jupyterhub:8000"
c.JupyterHubOAuthAuthentication.client_id = "service-this-is-a-jupyterhub-client"
c.JupyterHubOAuthAuthentication.client_secret = "this-is-a-jupyterhub-secret"

Once a user is authenticated a cookie or JSON Web Token is created to store the user credentials to ensure that conda-store is as stateless as possible. At this current point in time conda-store does not differentiate between a service and user. Similar to JupyterHub conda_store_server.server.auth.Authentication has an authenticate method. This method is the primary way to customize authentication. It is responsible for checking that the user credentials to login are correct as well as returning a dictionary following the schema conda_store_server.schema.AuthenticationToken. This stores a primary_namespace for a given authenticated service or user. In addition a dictionary of <namespace>/<name> map to a set of roles. See the Authorization model to better understand the key to set of roles meaning.

Authorization Model#

conda-store implements role based authorization to supports a flexible authorization model. A user or service is either authenticated or not. There are a set of default permissions assigned to authenticated and unauthenticated users via Traitlets. These can all be modified in the configuration. These roles are inherited based on the authentication status of the user or service. To support hierarchies we map a key such as default/* to a set of roles. The / separates the <namespace> from the <name> and * signifies match any (zero or more) characters. This was chosen to support rich authorization models while also being easy and efficient to implement in a database. * are supported anywhere in the key such as *n*viron*/n*me. Configure the following Traitlets to modify the inherited permissions for authenticated and unauthenticated users.

c.RBACAuthorizationBackend.unauthenticated_role_bindings = {
    "default/*": {"viewer"},
}

c.RBACAuthorizationBackend.authenticated_role_bindings = {
    "default/*": {"viewer"},
    "filesystem/*": {"viewer"},
}

Once we have collected the role mappings that a given user has we then map roles to sets of permissions. Currently there are only a few permissions but conda-store is capable of adapting in the future.

class Permissions(enum.Enum):
    ENVIRONMENT_CREATE = "build::create"
    ENVIRONMENT_READ = "build::read"
    ENVIRONMENT_UPDATE = "build::update"
    ENVIRONMENT_DELETE = "build::delete"

The role name to permission is configured via a single trait shown below c.RBACAuthorizationBackend.role_mappings.

c.RBACAuthorizationBackend.role_mappings = {
    "viewer": {
        Permissions.ENVIRONMENT_READ
    },
    "developer": {
        Permissions.ENVIRONMENT_CREATE,
        Permissions.ENVIRONMENT_READ,
        Permissions.ENVIRONMENT_UPDATE,
    },
    "admin": {
        Permissions.ENVIRONMENT_CREATE,
        Permissions.ENVIRONMENT_READ,
        Permissions.ENVIRONMENT_UPDATE,
        Permissions.ENVIRONMENT_DELETE,
    },
}

Lets go through a few examples to make this more concrete and assume the default configuration of conda-store.

Suppose we have an unauthenticated user trying to view the environment quansight/datascience.

First since the user is unauthenticated they inherit the default role mappings.

{
    "default/*": {"viewer"},
}

We go through each role mapping and try to match the expression default/* to quansight/datascience. In this case this does not match and thus for the given environment quansight/datascience the unauthenticated user has no roles. This user does not have any roles for the given environment but if they did we would iterate through all roles and combine the permissions. The next example will show this. So for this example the user has permissions {} in the given environment. The action of viewing a given environment requires build::read which the unauthenticated user does not have.

Suppose we have an unauthenticated user trying to delete the environment default/web-dev.

First since the user is unauthenticated they inherit the default role mappings.

{
    "default/*": {"viewer"},
}

We go through each role mapping and try to match the expression default/* to default/web-dev. In this case this does match and thus for the given environment default/web-dev the unauthenticated user has a set of roles {viewer}. For each role we map the role to permissions. We get {build::read}. The delete environment action requires build::delete permissions and thus the user is not authenticated to perform the action.

Suppose we have an authenticated user trying to delete the environment default/web-dev.

First since the user is authenticated they inherit the default role mappings.

{
    "default/*": {"viewer"},
    "filesystem/*": {"viewer"},
}

In addition to the default role bindings the user was authenticated via the authenticate method and has the following bindings added.

{
    "*/*": {"admin"}
}

In total the user has the following bindings.

{
    "default/*": {"viewer"},
    "filesystem/*": {"viewer"},
    "*/*": {"admin"},
}

Following the same process as before we iterate through each binding if it matches we add the given roles. For this example we get {viewer, admin}. Next we iterate through each role and map it to permissions and we get the following {build::create, build::read, build::update, build::delete}. The delete environment action requires build::delete permissions which the user has thus the action is permitted.

Database#

Model#

At a high level the database model can be described in the image bellow.

high level diagram

Important things to note about the relationship:

  • An environment exists within a given namespace and always has a current build

  • A build belongs to a particular environment and has associated condapackage and buildartfacts

  • A buildartifact is a way for the database to keep track of external resources for example s3 artifacts, filesystem directories, etc

  • A condapackage is a representation of a given Conda package which belongs to a given condachannel

  • A specification is the environment.yaml using in conda env create -f <environment.yaml>

The following will generate the database model shown bellow. It was generated from the examples/docker example. You’ll see in the command that we are excluding several tables. These tables are managed by celery.

pip install eralchemy  # not available on conda-forge
eralchemy -i "postgresql+psycopg2://admin:password@localhost:5432/conda-store"  \
    -x celery_tasksetmeta celery_taskmeta kombu_queue kombu_message alembic_version \
    -o output.png

entity relationship diagram

Migrations#

conda-store relies on SQLAlchemy for ORM mapping, and on Alembic for DB migrations.

The procedure to modify the database is the following :

  • First, modify the ORM Model according to the changes you want to make

  • edit the file conda-store-server/alembic.ini and replace the value for entry sqlalchemy.url to match the connection URL of your database.

  • in your command line, run the following :

cd conda-store-server/conda_store_server
alembic revision --autogenerate -m "description of your changes"

  • You should have a new file in conda-store-server/conda_store_server/alembic/versions/ . Review it thoroughly. It contains the alembic operations (op) to actually modify the database, either when upgrading (upgrade function) or downgrading (downgrade)

  • You can migrate your data within these upgrade/downgrade functions, for example :

from alembic import op

# revision identifiers, used by Alembic.
revision = 'abcdef01234567'
down_revision = '987654321f0edc'
branch_labels = None
depends_on = None

def upgrade():

    # operations to modify the database structure
    # ...
    op.create_table(
        'new_table',
        Column('id', INTEGER, primary_key=True),
        Column('field1', VARCHAR(50), nullable=False),
        Column('field2', INTEGER),
        Column('timestamp', TIMESTAMP, server_default=func.now())
    )
    # ...

    op.execute('''INSERT INTO new_table (field1, field2)
                  SELECT field1, field2
                  FROM old_table''')

    # other operations to modify the database structure
    # ...


def downgrade():

    op.drop_table('new_table')

  • Once you’re sure about the changes generated, you can apply them by running :

alembic upgrade head