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45 строки
1.7 KiB
ReStructuredText
45 строки
1.7 KiB
ReStructuredText
Optimization
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============
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The objective of optimization to remove as many tasks from the graph as
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possible, as efficiently as possible, thereby delivering useful results as
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quickly as possible. For example, ideally if only a test script is modified in
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a push, then the resulting graph contains only the corresponding test suite
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task.
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A task is said to be "optimized" when it is either replaced with an equivalent,
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already-existing task, or dropped from the graph entirely.
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Optimization Functions
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----------------------
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During the optimization phase of task-graph generation, each task is optimized
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in post-order, meaning that each task's dependencies will be optimized before
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the task itself is optimized.
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Each task has a ``task.optimizations`` property describing the optimization
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methods that apply. Each is specified as a list of method and arguments. For
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example::
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task.optimizations = [
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['seta'],
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['skip-unless-changed', ['js/**', 'tests/**']],
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]
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These methods are defined in ``taskcluster/taskgraph/optimize.py``. They are
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applied in order, and the first to return a success value causes the task to
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be optimized.
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Each method can return either a taskId (indicating that the given task can be
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replaced) or indicate that the task can be optimized away. If a task on which
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others depend is optimized away, task-graph generation will fail.
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Optimizing Target Tasks
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-----------------------
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In some cases, such as try pushes, tasks in the target task set have been
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explicitly requested and are thus excluded from optimization. In other cases,
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the target task set is almost the entire task graph, so targetted tasks are
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considered for optimization. This behavior is controlled with the
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``optimize_target_tasks`` parameter.
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