Policy Packages

Overview

Policy packages are separate Python packages that can be used to add experiment-specific customisations to Rucio. They typically customise Rucio's handling of permissions and schema as well as optionally adding their own algorithms for various purposes, such as lfn to pfn conversion and surl construction.

Policy packages may be installed from a Python package repository such as PyPi or they may simply be installed in a local directory. In the latter case this directory will need to be added to the Rucio server's PYTHONPATH environment variable.

The name of the policy package in use is specified by the package value in the policy section of the Rucio configuration file. Alternatively, the package can be specified by the RUCIO_POLICY_PACKAGE environment variable (if both are set, the environment variable takes priority). If no package is specified, a built in generic policy will be used. If a package is specified but cannot be loaded, Rucio will exit with an error.

Multi-VO Rucio installations can load a different policy package for each VO. In this case, the configuration parameter or environment variable name is suffixed with the VO name (for example, package-vo1 or RUCIO_POLICY_PACKAGE_VO1).

Creating a policy package

The structure of a policy package is very simple. It contains the following:

• A permission.py module implementing permission customisations.
• A schema.py module implementing schema customisations.
• An __init__.py file that can optionally return a dictionary of algorithms provided by the package.
• It should also contain a SUPPORTED_VERSION field.

The easiest way to create the permission.py and schema.py modules is to modify the generic versions from the Rucio codebase. These can be found in lib/rucio/core/permission/generic.py and lib/rucio/common/schema/generic.py respectively.

In the has_permission function you may wish to default to the generic permission checks if your experiment does not need specific functionality for a particular action, or so that new actions added to Rucio will work without your policy package having to be updated. This fallback can be implemented with code similar to the following:

import rucio.core.permission.generic
if action not in perm:
    return rucio.core.permission.generic.has_permission(issuer, action, kwargs)

__init__.py should include a SUPPORTED_VERSION field indicating the major version(s) of Rucio that your package supports. This is checked against the Rucio server version to ensure compatibility when loading the policy package. This field can be a string if the policy package only supports a single Rucio version, or a list of strings if it supports multiple versions. Example:

SUPPORTED_VERSION = [ "1.30", "1.31", "32" ]

It can also contain an optional function called get_algorithms that returns a dictionary of custom algorithms implemented within the package. In fact, this structure should be a "dictionary of dictionaries" where the outer dictionary contains algorithm types, and each inner dictionary contains all the algorithms provided by the package for that type. Currently supported types are surl for SURL algorithms, lfn2pfn for LFN2PFN algorithms, and scope for scope extraction algorithms.

Example:

def get_algorithms():
    return { 'surl':
             { 'voname_surl': construct_surl_voname },
         'lfn2pfn':
         { 'voname_lfn2pfn': lfn2pfn_voname },
         'scope':
         { 'voname_extract_scope': extract_scope_voname } }

In all cases the names used to register the functions must be prefixed with the name of the virtual organisation that owns the policy package, to avoid naming conflicts on multi-VO Rucio installations.

Adding a new algorithm class

The system for registering algorithms within policy packages is intended to be extensible so that new algorithm classes can be added relatively easily. The basic workflow is as follows:

• The get_algorithms function within the policy package (see above) should return a dictionary of functions of the new class, indexed by name
• The core Rucio code should maintain a dictionary of functions of the new class, ready to be called when required. The details of this will differ depending on what the new class actually does and how it integrates with the Rucio code, but typically the algorithm name to be used will be selected by a value in the config file, as for the current lfn2pfn and surl algorithm types.
• Before the algorithm is called for the first time, the core Rucio code should call rucio.common.utils.register_policy_package_algorithms to import the algorithms for this class from the policy package and store them in its internal dictionary. This function takes care of the complexities of interfacing with the policy package, such as importing the package if necessary, and dealing with multiple packages in multi-VO Rucio installations.

Deploying Policy Packages in containers

It is now common to deploy Rucio using containers managed by software such as Docker and Kubernetes. This section of the documentation is intended to give guidance on how policy packages can be deployed in this type of environment.

Broadly speaking, three things must happen in order for a policy package to be deployed successfully:

1. The policy package code must be available to the Rucio server (and possibly other components such as daemons).
2. The directory containing the policy package must be in the server's PYTHONPATH.
3. The policy package name must be set in the Rucio configuration file, or using the RUCIO_POLICY_PACKAGE environment variable.

Installing the policy package

There are a few possible ways to get the policy package code into the container where the server runs. One way is to build a custom experiment-specific container image based on the generic Rucio server image, and to install the policy package at build time in the Dockerfile, either by directly copying the files in, or by installing it from some sort of repository. For experiments that already customise the container image, this is likely to be the easiest option.

Alternatively, the standard Rucio container can be used and a volume containing the policy package files can be mounted at run time (using the -v or --volume command line flag). When using Kubernetes, there is also a third possibility: use an init container to install the policy package onto a shared volume, which is then mounted by the server container when it starts up.

Adding the policy package to the server's PYTHONPATH

It is possible to set environment variables within the container when starting it (using Docker's -e command line flag). This can be used to set PYTHONPATH, however this will replace the original value rather than appending to it, so there is a risk of removing other important items from the path. A safer option is to override Rucio's docker-entrypoint.sh script and instead use a script that appends the policy package's directory to PYTHONPATH before starting the HTTP server. This can be done either at build time in the Dockerfile, or at run time using the --entrypoint command line option.

When deploying using Kubernetes and Helm charts, it is possible to specify the policy package directory in the optional_config: section of values.yaml. This is then propagated to the container as an environment variable, which can be added to PYTHONPATH by the entry point script. For example, include this in values.yaml:

optional_config:
  policy_pkg_path: /opt/rucio/policy

This will appear in the container's environment as a variable called POLICY_PKG_PATH, which can be added to PYTHONPATH by the entry point script before starting the server:

if [ ! -z "$POLICY_PKG_PATH" ]; then
    export PYTHONPATH=${POLICY_PKG_PATH}:${PYTHONPATH:+:}${PYTHONPATH}
fi

Specifying the policy package in the configuration file

It is likely that most experiments are already customising the Rucio configuration file, in which case the policy package (package = name in the [policy] section) can simply be added to the existing customised file. Alternatively, the package name can be set in the RUCIO_POLICY_PACKAGE environment variable (see previous section for how to pass environment variables into the server container).

When deploying using Kubernetes and Helm charts, it is possible to specify configuration options in values.yaml. Values included in the config: section of this file are automatically merged into rucio.cfg by the docker-entrypoint.sh script, so the policy package can be set as:

config:
  policy:
    package: packagename