The objective of optimization to remove as many tasks from the graph as possible, as efficiently as possible, thereby delivering useful results as quickly as possible. For example, ideally if only a test script is modified in a push, then the resulting graph contains only the corresponding test suite task.
A task is said to be "optimized" when it is either replaced with an equivalent, already-existing task, or dropped from the graph entirely.
Each task has a single named optimization strategy, and can provide an argument
to that strategy. Each strategy is defined as an OptimizationStrategy
instance in taskcluster/taskgraph/optimization.py.
Each task has a task.optimization property describing the optimization
strategy that applies, specified as a dictionary mapping strategy to argument. For
example:
task.optimization = {'skip-unless-changed': ['js/**', 'tests/**']}
Strategy implementations are shared across all tasks, so they may cache commonly-used information as instance variables.
In some cases, such as try pushes, tasks in the target task set have been
explicitly requested and are thus excluded from optimization. In other cases,
the target task set is almost the entire task graph, so targeted tasks are
considered for optimization. This behavior is controlled with the
optimize_target_tasks parameter.
Note
Because it is a mix of "what the push author wanted" and "what should run
when necessary", try pushes with the old option syntax (-b do -p all,
etc.) do optimize target tasks. This can cause unexpected results when
requested jobs are optimized away. If those jobs were actually necessary,
then a try push with try_task_config.json is the solution.
.. toctree::
optimization-process
optimization-schedules