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.

Optimization Strategies

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.

Optimizing Target Tasks

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 targetted tasks are considered for optimization. This behavior is controlled with the optimize_target_tasks parameter.

Optimization Process

Optimization proceeds in three phases: removing tasks, replacing tasks, and finally generating a subgraph containing only the remaining tasks.

Assume the following task graph as context for these examples:

TC1 <--\     ,- UP1
      , B1 <--- T1a
I1 <-|       `- T1b
      ` B2 <--- T2a
TC2 <--/     |- T2b
             `- UP2

Removing Tasks

This phase begins with tasks on which nothing depends and follows the dependency graph backward from there – right to left in the diagram above. If a task is not removed, then nothing it depends on will be removed either. Thus if T1a and T1b are both removed, B1 may be removed as well. But if T2b is not removed, then B2 may not be removed either.

For each task with no remaining dependencies, the decision whether to remove is made by calling the optimization strategy’s should_remove_task method. If this method returns True, the task is removed.

The optimization process takes a do_not_optimize argument containing a list of tasks that cannot be removed under any circumstances. This is used to “force” running specific tasks.

Replacing Tasks

This phase begins with tasks having no dependencies and follows the reversed dependency graph from there – left to right in the diagram above. If a task is not replaced, then anything depending on that task cannot be replaced. Replacement is generally done on the basis of some hash of the inputs to the task. In the diagram above, if both TC1 and I1 are replaced with existing tasks, then B1 is a candidate for replacement. But if TC2 has no replacement, then replacement of B2 will not be considered.

It is possible to replace a task with nothing. This is similar to optimzing away, but is useful for utility tasks like UP1. If such a task is considered for replacement, then all of its dependencies (here, B1) have already been replaced and there is no utility in running the task and no need for a replacement task. It is an error for a task on which others depend to be replaced with nothing.

The do_not_optimize set applies to task replacement, as does an additional existing_tasks dictionary which allows the caller to supply as set of known, pre-existing tasks. This is used for action tasks, for example, where it contains the entire task-graph generated by the original decision task.

Subgraph Generation

The first two phases annotate each task in the existing taskgraph with their fate: removed, replaced, or retained. The tasks that are replaced also have a replacement taskId.

The last phase constructs a subgraph containing the retained tasks, and simultaneously rewrites all dependencies to refer to taskIds instead of labels. To do so, it assigns a taskId to each retained task and uses the replacement taskId for all replaced tasks.

The result is an optimized taskgraph with tasks named by taskId instead of label. At this phase, the edges in the task graph diverge from the task.dependencies attributes, as the latter may contain dependencies outside of the taskgraph (for replacement tasks).

As a side-effect, this phase also expands all {"task-reference": ".."} objects within the task definitions.