reactive_dag

A domain-agnostic reactive DAG engine for Elixir/Ash apps: a dirty frontier

• depth-ordered incremental drain + change propagation, plus leaf-reconcile and nested-expression lowering. Every node's results are ordinary Ash rows in that node's own resource; the library owns one table, the frontier. Extracted from two apps that independently grew the same engine (the Red Hook cascade pipeline and the u2i compliance portal's model_eval), and now shared by both.

Documentation: the guides are the front door — Getting started, Configuration, Authoring nodes, LLM nodes, Sources and scanning, and The seams. This README is the reference-style overview.

The substrate decides when and in what order cells recompute; it never decides how or what a value means. Each host brings its domain at the seams:

• ReactiveDag.RecomputeStrategy — how a cell recomputes (cascade: per-key Elixir that may call an LLM / parse a PDF; the portal: one set-based SQL join). Returns the keys that actually changed.
• ReactiveDag.KeyRule — how a change propagates to a parent (identity, a remap, or :all for a whole-cell recompute).
• dirties_on — make ordinary Ash writes trigger the cascade: a create/update/destroy on a leaf resource marks that record's key dirty, inside the write's own transaction. Opt-in; without it the host calls Frontier.mark_dirty/3 itself.

What the library owns

Layer	Module	What it provides
Node IR	ReactiveDag.Cell	domain-neutral node; op is an optional free-atom label (load-bearing only for an op-dispatching RecomputeStrategy like SetOp); app fields ride in meta (with an Access impl so cell[:field] reads meta transparently).
Compiled plan	ReactiveDag.Plan	pure data: cells / parents / depths.
Graph math	ReactiveDag.Graph	build/1 (validate + parent edges + longest-path depths + cycle check); dirty_parents/4 (propagation via the host KeyRule).
Dirty frontier	ReactiveDag.Frontier	claim-as-delete over the host's dirty table; mark_dirty / next_cell / claim / empty?.
Drain loop	ReactiveDag.Drain	depth-ordered incremental propagation; run/2 parameterized by the two seams, returning {:ok, %Drain.Report{}} — the processing trace (per-step cell/claimed/changed/triggered_by/duration_us + totals). An optional :on_step hook streams the same fields live.
Result reads	ReactiveDag.Node.Rows	a cell's own rows addressed by CELL KEY (all/1, status_histogram/1, keys_by_status/3) plus reconcile/3, the leaf-write skeleton (current − want → retired). The read side of what the payload loop writes, and what Insights, Verdict and union are built on.
Change detection	ReactiveDag.Node.Fingerprint	the one value that decides whether an observation MOVED — a field list (hashed) or (row -> value). Used by a source-fed leaf (so a re-crawl that only bumps last_seen_at/etag does not fire the cascade) and by per_key (to skip an expensive action). One concept, one implementation, two rungs.
Nested-expr lowering	ReactiveDag.Lowering	walk/3 — the nested op-expression → flat-cell recursion both DSLs grew, parameterized by host callbacks (id grammar, ref resolution, cell construction).
Compile pipeline	ReactiveDag.Dsl	compile / validate_cells — resolve → structural-validate, with a domain-validation hook.
Op contract	ReactiveDag.Op	the behaviour a cell's compute module implements (recompute(cell, keys) -> {:ok, changed}). An op writes its rows and returns its changed keys; nothing else is asked of it.
Node authoring	ReactiveDag.Node	the authoring surface — an Ash resource extension: a resource declares its op + dependencies + computation in a reactive do … end block. The resource is the node and its own payload table. ReactiveDag.Node.graph/2 assembles the Plan from the node resources.
Payload loop	ReactiveDag.Node.Payload	writes a combinator's row into the node's own resource (the default; omit upsert:).
Config validation	ReactiveDag.Config	validate!/0 at boot, reporting EVERY problem at once (missing :repo, a writer that doesn't implement the behaviour, a table name that isn't a SQL identifier) instead of raising at the first query, possibly a long way into a deploy. The host calls it; the library starts no application of its own.
Content digests	ReactiveDag.Basis	a versioned digest of a row set, so "is this still what I saw?" is answerable without storing a copy. Sign-off is the motivating use — store the digest with a signature and it lapses automatically when the rows move — but nothing in it knows about signatures. The versioning is the part worth not re-deriving: an unknown scheme degrades to "re-check", never to a crash, so introducing v2 cannot invalidate every stored digest on deploy.
Introspection	ReactiveDag.Insights	the engine viewed from outside, for a dashboard/mix task/health check: levels/1 + edges/1 (structure), cell_status/2 + summary/1 (status histogram, key count, failing sample — read from each node's own rows), pending/1 (what the next drain would do), and an opt-in rolling window of %Drain.Report{}s (record/1 / recent/1 / last_report/0). All reads, no UI dependency — reactive_dag_dashboard renders it.
Write triggers	dirties_on	make ordinary Ash writes trigger the cascade: a create/update/destroy on a leaf resource marks that record's key dirty, inside the write's own transaction (so a rollback leaves nothing, and a commit always leaves the mark). Opt-in; without it the host calls Frontier.mark_dirty/3 at every write site. Contrast Source, which polls state the datastore does not own.
Scanner declaration	scan Mod	a leaf names the ReactiveDag.Source that feeds it, making the scanner↔leaf pairing a fact of the graph: Node.graph/2 verifies the module implements the behaviour AND that its own leaf_cells/1 claims this leaf, and Source.poll_all/2 finds every scanner from the plan instead of a hand-kept list. Single-leaf; a multi-leaf source uses leaf_cells/1 + verify!/2.
Scanner seam	ReactiveDag.Source	the behaviour a scanner implements (id / leaf_cells / poll) — reads external state into a leaf in a poll phase outside the drain; verify!/2 checks every declared leaf resolves to a real cell.

The host owns its resources (every node's results are its own rows), its op algebra, and its recompute executor. The library owns the schedule and one table — the dirty frontier. Domain differences sit on named seams, not forks.

Authoring a node

A node is an Ash resource with the ReactiveDag.Node extension. The resource IS the node and its own payload table — its reactive block is the computation, its attributes are the rows it materializes. The library closes the payload loop: into returns a row and the lib writes it into this resource — no upsert: needed for the common case.

defmodule MyApp.BudgetRollups do
  use Ash.Resource, data_layer: AshPostgres.DataLayer,   # its OWN payload table
    extensions: [ReactiveDag.Node]

  attributes do
    attribute :fund, :string, primary_key?: true         # the row IS its identity —
    attribute :fy, :integer, primary_key?: true          # no :key column; the cell
    attribute :total, :float                             # key is "gf|2025", derived
  end
  actions do
    create :upsert do upsert?(true); accept([:fund, :fy, :total]) end
  end

  reactive do
    op :fold
    # ASH-FIRST: the library reads :fiscal_lines, groups by the attributes,
    # folds each group, upserts the row by its Ash IDENTITY, and reports only
    # the changed keys. `recompute_by` names the UNIT a change invalidates —
    # it supplies the edge, the grouping and the claim rule. Every slot has an
    # escape hatch when the shape outgrows attributes.
    recompute_by :fund, to: :fiscal_lines, from: :fund
    reduce group_by: [:fund, :fy],
           into: [sum: [amount: :total]]
  end
end

upsert: is an optional override — supply it only to write somewhere other than the node's own resource (e.g. an existing shadow table). A tableless node (data_layer: Ash.DataLayer.Simple, no attributes) either supplies upsert: or uses the compute Module escape hatch.

Authoring is Ash-first — start from what Ash expresses declaratively and step outward only as far as the shape demands. Each form writes the result set (into the node's resource, or a custom upsert:) and reports only the changed keys:

• aggregate — the datastore does it: group + aggregate a relationship (avg/sum/count/…) in ONE query — no rows cross into the BEAM. The node's resource is the group's resource; over is its has_many. Only for relationship aggregates (Ash has no arbitrary GROUP BY … → rows). Example: aggregate over: :readings, avg: [flow: :avg_flow], count: :day_count.
• recompute_by — THE declaration the engine cares about: what unit does a change invalidate? recompute_by :category, to: :expenses, from: :expense_cat supplies the input edge, the grouping, the claim resolution and the read scope, so it subsumes key_rule on combinator nodes. Four answers: omitted (key-for-key), from: (per unit, by lookup), from_key: true (per unit, purely from the key's segments), :cell (redo everything). It is the recompute unit, not the output's grain — percentiles recompute_by :day while their rows are keyed day+percentile. Consumed at compile time; never traversed at recompute, because consumers query the derived rows instead.
• reduce — an in-BEAM fold, declared: the library reads the over node's resource (primary or a named :read action), auto-scoped to the dirty keys; group_by: names attributes, into: declares the fold ([sum: [amount: :total], count: :n]), keys derive as "gf|2025" (key_prefix: namespaces). Escapes: query: shapes the read WITHOUT leaving Ash (fn q, dirty -> … end); fn group_by/key/into for computed shapes; expand: for the group → many-rows shape (self-:keyed rows).
• join — a left join over ONE input, declared: sides are attributes (left: :declared_id) or [key: :acct, where: [kind: "budget"]] discriminator splits; into: picks columns per side, absent sides yielding nils (the gap is information). fn escapes for computed side keys/columns.
• run :action — the Ash-native escape hatch: the recompute is a GENERIC action on the node's own resource ((keys, cell_id) -> changed keys; the action writes its domain, its returned keys propagate). Arguments, policies, Ash.run_action testability — the computation stays a first-class action.

Beyond Ash entirely — an LLM call, a PDF/Tigris fetch, a bespoke multi-input recompute — the outermost escape hatch is a module: compute MyOp where MyOp implements ReactiveDag.Op. (Mirrors Ash's calculate :x, :type, MyModule — the arbitrary case is an entity too, not a schema key beside the declarative ones.)

Input edges: ref (recompute) vs context (read-as-context)

An input is one of two kinds:

• ref :x (also depends_on [:x], or a combinator's over:) — a recompute edge: when x changes, this node is dirtied and recomputes. The normal edge.
• context :x — a context edge: the node READS x as settled context but is not recomputed when x changes. It's still a real input (validated, ordered by depth so x settles first, read at recompute) — it just doesn't propagate.

Use context when recompute is expensive/non-deterministic and consults mutable context it shouldn't be re-triggered by — e.g. an LLM step that looks up a human-curated table:

reactive do
  op :map
  compute MyApp.EnhanceMinutes   # an LLM pass
  ref :transcripts               # a transcript change RE-RUNS the LLM
  context :people                # a people edit does NOT — the LLM just reads
                                 # current people the next time it runs
end

So an edit to a context input updates it, but drives no regeneration; the consuming node picks up the current value whenever it next recomputes for its own (recompute-edge) reasons.

reactive do
  op :map
  compute MyApp.Ops.EventsExtract   # arbitrary recompute (LLM, fetch, …)
end

# assemble + run a Node-authored graph (no host-written dispatch):
plan = ReactiveDag.Node.graph([BudgetRollups, FiscalLines, …], for_each: &fetch/1)
{:ok, report} =
  ReactiveDag.Drain.run(plan,
    recompute: ReactiveDag.Node.Recompute,   # runs reduce/join/aggregate or compute:
    key_rule:  ReactiveDag.Node.KeyRule)       # reads :identity | :all from the block
# report is a ReactiveDag.Drain.Report — the processing trace: one step per
# recompute (cell, claimed, changed, triggered_by, duration_us) + run totals.

# config
config :reactive_dag,
  repo: MyApp.Repo,
  dirty_table: "my_dirty"

A host can also assemble cells by hand and bring its own strategy/key_rule — ReactiveDag.Graph.build(cells) + ReactiveDag.Drain.run(plan, recompute:, key_rule:) — which is how both apps ran before adopting the Node surface.

Verdicts are ordinary rows

A node whose answer is one word — a status — is a payload node like any other, writing a :status column.

defmodule MyApp.StoreEncrypted do
  use Ash.Resource, data_layer: AshPostgres.DataLayer, extensions: [ReactiveDag.Node]

  # … `key` and `status` attributes, an `:upsert` action …

  reactive do
    op :reconcile
    key_rule :all
    reduce over: :stores,
           group_by: :store,
           into: fn _store, [r | _] -> %{status: if(r.enc, do: "present", else: "failing")} end
  end
end

There used to be a second shape for this — verdict? true, with no table, writing the status straight into the coordination tuple. It saved a migration when the answer was one word, and cost a ceiling: the tuple's schema is fixed, so the moment a verdict wanted company (a headroom, a breached_at) the shape had nothing to offer and you abandoned it entirely. A row costs a migration and answers every later question, so verdicts are rows.

Rolling up many verdicts into one graph-wide table is what union from: […] is for.

Human input

Scanners feed leaves out-of-band; a human edit (a managed list, an approval) writes a leaf too — via whatever the host uses for writes (an Ash action, a plain upsert), then marks the affected cells dirty so the drain propagates the consequences.

The library previously shipped a command frontier — a second, seq-ordered frontier for INTENTS, with per-scope serialization, a blocked/answer human-in-the-loop state, and an audit table. It was removed: in both hosts the commands turned out to be straight CRUD drained inline (enqueue immediately followed by run), so nothing was ever actually queued. The serialization it offered was already provided by the database, the audit trail is better served by a change-log on the resource, and its scope-freeze turned a failed edit into a wedged queue. A deferred/approval-gated write — where a change genuinely waits, unapplied, for a human — is the case that would justify bringing it back.

Status: both hosts run on the substrate — the shared engine spans a per-key Elixir recompute (cascade) and a set-based SQL recompute (the portal), proven by both suites green. Cascade authors several ops via the Node reduce/join combinators; the standalone compliance app consumes tagged releases. See ADR-001 for the boundary, the seams, and the design law behind them.