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OpenSAFELY job runner
A job runner is a service that encapsulates:
- the task of checking out an OpenSAFELY study repo;
- executing actions defined in its
project.yamlconfiguration file when requested via a jobs queue; and - storing its results in a particular locations.
Quickrefs:
End users will find more information in the OpenSAFELY documentation.
Operating principles
In production, this software runs as a loop on a secure server within the infrastructure of the primary data provider. It polls an OpenSAFELY job server, looking for requests to run jobs.
Jobs belong to a workspace. This describes the git repo containing the
OpenSAFELY-compliant project under execution; the git branch, and kind of
database to use. The workspace also acts as a kind of namespace for
partitioning outputs of its jobs.
An OpenSAFELY-compliant repo must provide a project.yaml file which
describes how a requested job should be converted into a command (& arguments)
that can be run in a subprocess on the secure server. It incorporates the idea
of dependencies, so an action that generates a chart might depend on an action
that extracts data from the database for that chart. See the
Actions reference for more information.
An action can define outputs; these are persisted on disk and made available
to subsequent actions in the workspace, and users who have permission to log
into the server and view the raw files.
The runner takes care of executing dependencies in order. By default, it skips re-running a dependency whose previous run produced output that still exists in the production environment. The runner also reports status back to the job server, redacting possibly-sensitive information.
The runner is bundled as part of the opensafely-cli tool so users can test their actions locally.
Job structure
The job server serves jobs as JSON in the following format. First, a job must belong to a workspace:
{
"workspace": {
"name": "my workspace",
"repo": "https://github.com/opensafely/job-integration-tests",
"branch": "master",
"db": "full"
}
}
Possible values for "db" are "full", "slice", and "dummy".
A workspace is a way of associating jobs related to a given combination of branch, repository and database. To enqueue a job, a client POSTs JSON like this:
{
"backend": "tpp",
"action_id": "do_thing",
"workspace_id": 1
}
Consuming jobs
A job runner is service installed on a machine that has access to a given
backend. It receives jobs from the server and consumes those whose backend value matches the
value of the current BACKEND environment variable.
It must also define three environment variables which are an RFC1838 connection
URL; these correspond to the db requested in the job's workspace definition,
and as such are named FULL_DATABASE_URL, SLICE_DATABASE_URL, and
DUMMY_DATABASE_URL.
When a job is found, the following happens:
- The corresponding repo is fetched. Private repos are accessed using
the
PRIVATE_REPO_ACCESS_TOKENsupplied in the environment. - Its
project.yamlis parsed:- Individual
actionsare extracted from this file - A dependency graph is calculated for the requested action; for example, an action might depend on three previous actions before it can be run
- Each action in the graph is checked to see if it needs to be run
- Actions that either: (a) already have output generated from a previous run; (b) are currently running; (c) failed on their last run do not need to be run
- If a dependency has failed, then the requested action fails
- If the dependency needs to be run, a new job is pushed to the queue, and the current job is postponed
- If an action has no dependencies needing to be run, then its
docker runis executed - On completion, a status code and message are reported back to the job server. On success, a list of output file locations are also posted. On failure, the message has any potentially-sensitive information redacted, and is associated with a unique string so that a user with requisite permissions can log into the production environment and examine the docker logs for the full error.
- Individual
Output locations
Every action defines a list of outputs which are persisted to a permanent
storage location. The project author must categorise these outputs as either
highly_sensitive or moderately_sensitive. Any pseudonymised data which may
be highly disclosive (e.g. without low number redaction) should be classed as
highly_sensitive; data which the author believes could be released following
review should be classed as moderately_sensitive. This design allows tiered
levels of permissions for collaborators to review data outputs. For example, the
study author would usually have access to highly_sensitive material for
debugging; but other collaborators could have access to moderately_sensitive
data to prepare it for release (for which it is planned to add a
minimally_sensitive category).
Outputs are therefore persisted to filesystem paths according to the following environment variables:
# A location where cohort CSVs (one row per patient) should be
# stored. This folder must exist.
HIGH_PRIVACY_STORAGE_BASE=/home/opensafely/high_security
# A location where script outputs (some for publication) should be
# stored
MEDIUM_PRIVACY_STORAGE_BASE=/tmp/outputs/medium_security
Project.yaml
A valid project file looks like this:
version: "3.0"
expectations:
population_size: 1000
actions:
generate_study_population:
run: cohortextractor:latest generate_cohort --study-definition study_definition
outputs:
highly_sensitive:
cohort: output/input.csv
run_model:
run: stata-mp:latest analysis/model.do
needs: [generate_study_population]
outputs:
moderately_sensitive:
model: models/cox-model.txt
figure: figures/survival-plot.png
See the project pipeline documentation for a detailed description of the project.yaml setup.
Local actions development
The cohortextractor command-line tool imports this library, and implements the action-parsing-and-running functionality as a series of
synchronous docker commands, rather than asynchronously via the job queue.
For developers
Please see the additional information.
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