forked from mirrors/gecko-dev
52 lines
1.9 KiB
ReStructuredText
52 lines
1.9 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 Strategies
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-----------------------
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Each task has a single named optimization strategy, and can provide an argument
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to that strategy. Each strategy is defined as an ``OptimizationStrategy``
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instance in ``taskcluster/taskgraph/optimization.py``.
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Each task has a ``task.optimization`` property describing the optimization
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strategy that applies, specified as a dictionary mapping strategy to argument. For
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example::
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task.optimization = {'skip-unless-changed': ['js/**', 'tests/**']}
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Strategy implementations are shared across all tasks, so they may cache
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commonly-used information as instance variables.
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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 targeted 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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.. note::
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Because it is a mix of "what the push author wanted" and "what should run
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when necessary", try pushes with the old option syntax (``-b do -p all``,
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etc.) *do* optimize target tasks. This can cause unexpected results when
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requested jobs are optimized away. If those jobs were actually necessary,
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then a try push with ``try_task_config.json`` is the solution.
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More Information
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----------------
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.. toctree::
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optimization-process
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optimization-schedules
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