Pants v2: Fast, consistent builds for Python and more

Welcome to the Pants v2 documentation hub!

Pants v2 is a fast, scalable build system for growing codebases. It's currently focused on Python, with support for other languages coming soon.

Here you'll find guides to help you get started with Pants v2, comprehensive documentation on how to configure, run and customize Pants v2, and information on how to get help from the Pants community.

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Internal Architecture

Rule Graph Construction

Overview

Build logic in Pants is declared using collections of @rules with recursively memoized and invalidated results. This framework (known as Pants' "Engine") has similar goals to Bazel's Skyframe and the Salsa framework: users define logic using a particular API, and the framework manages tracking the dependencies between nodes in a runtime graph.

In order to maximize the amount of work that can be reused at runtime, Pants statically computes the memoization keys for the nodes of the runtime graph from the user specified @rules during startup: this process is known as "rule graph construction". See the Goals section for more information on the strategy and reasoning for this.

Concepts used in compilers, including live variable analysis and monomorphization, can also be useful in rule graph construction to minimize rule identities and pre-decide which versions of their dependencies they will use. See the "Open Issues" section for a call for assistance with improving the implementation of rule graph construction!

Concepts

A successfully constructed RuleGraph contains a graph where nodes have one of three types, Rules, Querys, and Params, which map fairly closely to what a Pants @rule author consumes. The edges between nodes represent dependencies: Querys are always roots of the graph, Params are always leaves, and Rules represent the end user logic making up all of the internal nodes of the graph.

Rules

A Rule is a function or coroutine with all of its inputs declared as part of its type signature. The end user type signature is made up of:

  1. the return type of the Rule
  2. the positional arguments to the Rule
  3. a set of Gets which declare the runtime requirements of a coroutine, of the form Get(output_type, input_type)

In the RuleGraph, these are encoded in a Rule trait, with a DependencyKey trait representing both the positional arguments (which have no provided Param) and the Gets (which provide their input type as a Param).

Rules never refer to one another by name (i.e., they do not call one another by name): instead, their signature declares their requirements in terms of input/output types, and rule graph construction decides which potential dependencies will provide those requirements.

Queries

The roots/entrypoints of a RuleGraph are Querys, which should correspond one-to-one to external callsites that use the engine to request that values are computed. A Query has an output type, and a series of input types: Query(output_type, (*input_types)).

If a user makes a request to the engine that does not have a corresponding Query declared, the engine fails rather than attempting to dynamically determine which Rules to use to answer the Query: how a RuleGraph is constructed should show why that is the case.

Params

Params are typed, comparable (eq/hash) values that represent both the inputs to Rules, and the building block of the runtime memoization key for a Rule. The set of Params (unique by type) that are consumed to create a Rule's inputs (plus the Rule's own identity) make up the memoization key for a runtime instance of the Rule.

Params are eventually used as positional args to Rules, but it's important to note that the Params in a Rule instance's identity/memoization-key will not always become the positional arguments to that Rule: in many cases, a Param will be used by a Rule's transitive dependencies in order to produce an output value that becomes either a positional argument to the Rule as it starts, or the result of a Get while a coroutine Rule runs.

The Params that are available to a Rule are made available by the Rule's dependees (its "callers"), but similar to how Rules are not called by name, neither are all of their Params passed explicitly at each use site. A Rule will be used to compute the output value for a DependencyKey: i.e., a positional argument, Get result, or Query result. Of these usage sites, only Query specifies the complete set of Params that will be available: the other two usages (positional arguments and Gets) are able to use any Param that will be "in scope" at the use site.

Params flow down the graph from Querys and the provided Params of Gets: their presence does not need to be re-declared at each intermediate callsite. When a Rule consumes a Param as a positional argument, that Param will no longer be available to that Rule's dependencies (but it might still be present in other subgraphs adjacent to that Rule).

Goals

The goals of RuleGraph construction are:

  1. decide which Rules to use to answer Querys (transitively, since Rules do not call one another by name); and
  2. determine the minimum set of Param inputs needed to satisfy the Rules below those Querys

If either of the goals were removed, RuleGraph construction might be more straightforward:

  1. If rather than being type-driven, Rules called one another by name, you could statically determine their input Params by walking the call graph of Rules by name, and collecting their transitive input Params.
  2. If rather than needing to compute a minimum set of Param inputs for the memoization key, we instead required that all usage sites explicitly declared all Params that their dependencies might need, we could relatively easily eliminate candidates based on the combination of Param types at a use site. And if we were willing to have very large memoization keys, we could continue to have simple callsites, but skip pruning the Params that pass from a dependee to a dependency at runtime, and include any Params declared in any of a Rules transitive dependees to be part of its identity.

But both of the goals are important because together they allow for an API that is easy to write Rules for, with minimal boilerplate required to get the inputs needed for a Rule to compute a value, and minimal invalidation. Because the identity of a Rule is computed from its transitive input Params rather than from its positional arguments, Rules can accept arbitrarily-many large input values (which don't need to implement hash) with no impact on its memoization hit rate.

Constraints

There are a few constraints that decide which Rules are able to provide dependencies for one another:

  • param_consumption - When a Rule directly uses a Param as a positional argument, that Param is removed from scope for any of that Rule's dependencies.
    • For example, for a Rule y with a positional argument A and a Get(B, C): if there is a Param A in scope at y and it is used to satisfy the positional argument, it cannot also be used to (transitively) to satisfy the Get(B, C) (i.e., a hyptothetical rule that consumes both A and C would not be eligible in that position).
    • On the other hand, for a Rule w with Get(B, C) and Get(D, E), if there is a Param A in scope at w, two dependency Rules that consume A (transitively) can be used to satisfy those Gets. Only consuming a Param as a positional argument removes it from scope.
  • provided_params - When deciding whether one Rule can use another Rule to provide the output type of a Get, a constraint is applied that the candidate depedency must (transitively) consume the Param that is provided by the Get.
    • For example: if a Rule z has a Get(A, B), only Rules that compute an A and (transitively) consume a B are eligible to be used. This also means that a Param A which is already in scope for Rule z is not eligible to be used, because it would trivially not consume B.

Implementation

As of 3a188a1e06, we construct a RuleGraph using a combination of data flow analysis and some homegrown (and likely problematic: see the "Issue Overview") node splitting on the call graph of Rules.

The construction algorithm is broken up into phases:

  1. initial_polymorphic - Builds a polymorphic graph while computing an "out-set" for each node in the graph by accounting for which Params are available at each use site. During this phase, nodes may have multiple dependency edges per DependencyKey, which is what makes them "polymorphic". Each of the possible ways to compute a dependency will likely have different input Param requirements, and each node in this phase represents all of those possibilities.
  2. live_param_labeled - Run live variable analysis on the polymorphic graph to compute the initial "in-set" of Params used by each node in the graph. Because nodes in the polymorphic graph have references to all possible sources of a particular dependency type, the computed set is conservative (i.e., overly large).
    • For example: if a Rule x has exactly one DependencyKey, but there are two potential dependencies to provide that DependencyKey with input Params {A,B} and {B,C} (respectively), then at this phase the input Params for x must be the union of all possibilities: {A,B,C}.
    • If we were to stop RuleGraph construction at this phase, it would be necessary to do a form of dynamic dispatch at runtime to decide which source of a dependency to use based on the Params that were currently in scope. And the sets of Params used in the memoization key for each Rule would still be overly large, causing excess invalidation.
  3. monomorphize - "Monomorphize" the polymorphic graph by using the out-set of available Params (initialized during initial_polymorphic) and the in-set of consumed Params (computed during live_param_labeled) to partition nodes (and their dependees) for each valid combination of their dependencies. Combinations of dependencies that would be invalid (see the Constraints section) are not generated, which causes some pruning of the graph to happen during this phase.
    • Continuing the example from above: the goal of monomorphize is to create one copy of Rule x per legal combination of its DependencyKey. Assuming that both of x's dependencies remain legal (i.e. that all of {A,B,C} are still in scope in the dependees of x, etc), then two copies of x will be created: one that uses the first dependency and has an in-set of {A,B}, and another that uses the second dependency and has an in-set of {B,C}.
  4. prune_edges - Once the monomorphic graph has converged, each node in the graph will ideally have exactly one source of each DependencyKey (with the exception of Querys, which are not monomorphized). This phase validates that, and chooses the smallest input Param set to use for each Query. In cases where a node has more that one dependency per DependencyKey, it is because given a particular set of input Params there was more than one valid way to compute a dependency. This can happen either because there were too many Params in scope, or because there were multiple Rules with the same Param requirements.
    • This phase is the only phase that renders errors: all of the other phases mark nodes and edges "deleted" for particular reasons, and this phase consumes that record. A node that has been deleted indicates that that node is unsatisfiable for some reason, while an edge that has been deleted indicates that the source node was not able to consume the target node for some reason.
    • If a node has too many sources of a DependencyKey, this phase will recurse to attempt to locate the node in the Rule graph where the ambiguity was introduced. Likewise, if a node has no source of a DependencyKey, this phase will recurse on deleted nodes (which are preserved by the other phases) to attempt to locate the bottom-most Rule that was missing a DependencyKey.
  5. finalize - After prune_edges the graph is known to be valid, and this phase generates the final static RuleGraph for all Rules reachable from Querys.

Updated 2 months ago


Internal Architecture


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