EmboFlow/design/03-workflows/workflow-execution-model.md

EmboFlow Workflow Execution Model

Goal

Define how EmboFlow represents, validates, executes, and observes canvas workflows.

The workflow system is the product core. The canvas is only the editing surface. The real system of record is the versioned workflow definition and its immutable run snapshots.

Core Objects

• WorkflowDefinition Logical workflow identity under a project
• WorkflowVersion Immutable snapshot of nodes, edges, runtime defaults, and plugin references
• NodeInstance Concrete node on a workflow graph
• WorkflowRun One execution of one workflow version
• RunTask Executable unit derived from a node during one run
• Artifact Managed output from a task or run

Workflow Layers

Each workflow version contains three layers.

Visual Layer

Used only by the editor:

• node positions
• collapsed state
• groups
• zoom defaults
• viewport metadata

Logic Layer

Used for graph semantics:

• nodes
• edges
• input/output ports
• branch conditions
• merge semantics
• dependency graph

Runtime Layer

Used for execution:

• node config values
• executor settings
• runtime resource limits
• retry policy
• code hooks
• cache policy

Visual changes must not change workflow semantics. Runtime changes must produce a new workflow version.

The current V1 editor implementation keeps a mutable local draft that is initialized from the latest saved workflow version. Saving the draft creates a new immutable workflow version. Triggering a run from a dirty draft first saves a fresh workflow version, then creates the run from that saved snapshot. The V1 editor also requires binding at least one project asset before run creation, and the selected asset ids are persisted with the run snapshot.

Node Categories

V1 node categories:

• Source
• Transform
• Inspect
• Annotate
• Export
• Utility

V1 Built-In Node Families

• asset upload/import
• archive extract
• folder rename
• directory validation
• metadata validation
• video quality inspection
• dataset readers for RLDS, LeRobot, HDF5, Rosbag
• canonical mapping nodes
• dataset writers and exporters
• training config export
• Python processing node

Node Definition Contract

Each node definition must expose:

• id
• name
• category
• version
• description
• inputSchema
• outputSchema
• configSchema
• uiSchema
• executorType
• runtimeDefaults
• permissions
• capabilities
• codeHookSpec

Code Hook Spec

V1 supports user code hooks only on:

• Transform
• Inspect
• Utility

Hooks must use a constrained entrypoint instead of arbitrary script structure.

Example:

def process(input_data, context):
    return input_data

This keeps serialization, logging, and runtime control predictable.

Data Flow Contract

Tasks should exchange managed references, not loose file paths.

V1 reference types:

• assetRef
• datasetVersionRef
• artifactRef
• annotationTaskRef
• inlineConfig

Executors may materialize files internally, but the platform-level contract must remain reference-based.

Validation Stages

Workflow execution must validate in this order:

1. workflow version exists
2. referenced plugins exist and are enabled
3. node schemas are valid
4. edge connections are schema-compatible
5. runtime configuration is complete
6. referenced assets and datasets are accessible
7. code hooks pass static validation
8. executor and scheduler requirements are satisfiable

Validation failure must block run creation.

Run Lifecycle

When a user executes a workflow:

1. resolve workflow version
2. validate and snapshot all runtime-relevant inputs, including bound asset references
3. resolve plugin versions
4. freeze node config and code hooks
5. compile graph into a DAG
6. create WorkflowRun
7. create RunTask entries
8. enqueue ready tasks
9. collect outputs, logs, and task state
10. finalize run status and summary

Run State Model

WorkflowRun Status

• pending
• queued
• running
• success
• failed
• cancelled
• partial_success

RunTask Status

• pending
• queued
• running
• success
• failed
• cancelled
• skipped

partial_success is used for workflows where non-blocking nodes fail but the run still produces valid outputs.

Retry And Failure Policy

Each node instance may define:

• retry count
• retry backoff policy
• fail-fast behavior
• continue-on-error behavior
• manual retry eligibility

V1 should support:

• fail_fast
• continue_on_error
• retry_n_times
• manual_retry

Cache Model

V1 should support node-level cache reuse.

Recommended cache key inputs:

• workflow version
• node id
• upstream reference summary
• config summary
• code hook digest
• plugin version
• executor version

Cache hit behavior:

• reuse output artifact refs
• reuse output summaries
• retain previous logs reference
• mark task as cache-resolved in metadata

Execution Context

Each task receives a normalized execution context containing:

• workspace id
• project id
• workflow run id
• task id
• actor id
• node config
• code hook content
• input references
• storage context
• temp working directory
• runtime resource limits

This context must be available across Python, Docker, and HTTP executors.

Observability Requirements

Each task must emit:

• status transitions
• start time and finish time
• duration
• executor metadata
• resource request metadata
• stdout/stderr log stream
• structured task summary
• artifact refs

Current V1 Implementation Notes

The current codebase keeps the low-level contract tests in memory while the executable local runtime persists workflow state to MongoDB.

The persisted local runtime now covers:

• workspace and project bootstrap
• asset registration and probe reporting
• workflow definition and immutable version snapshots
• workflow runs and task creation with worker-consumable dependency snapshots
• workflow run asset bindings persisted on both runs and tasks
• project-scoped run history queries from Mongo-backed workflow_runs
• worker polling of queued tasks from Mongo-backed run_tasks
• run-task status transitions from queued/pending to running/success/failed
• downstream task promotion when upstream nodes succeed
• artifact registration and producer lookup
• task-level artifact creation by the worker runtime

The React workflow editor now loads the latest persisted version from the Mongo-backed API instead of rendering only a fixed starter graph. Draft edits are local editor state until the user saves, at which point the draft is serialized into a new workflow version document. Before a run is created, the editor loads project assets, requires one to be selected, and passes that binding to the API.

The runtime Runs workspace now loads recent runs for the active project. Run detail views poll active runs until they settle and let the operator inspect task-level artifacts directly through Explore links.

The worker-backed runtime now persists task execution summaries directly on run_tasks instead of treating artifacts as the only observable output. Each completed or failed task records:

• startedAt and finishedAt
• durationMs
• appended logLines
• structured summary with outcome, executor, asset count, artifact ids, and failure text when present
• lastResultPreview for a lightweight selected-task preview in the Runs workspace

This makes the run detail view stable even when artifacts are large or delayed and keeps task-level observability queryable without reopening every artifact payload.

The API and worker runtimes now both have direct integration coverage against a real Mongo runtime through mongodb-memory-server, in addition to the older in-memory contract tests.

The first web authoring surface already follows the three-pane layout contract with:

• left node library
• center workflow canvas
• right node configuration panel

The first explore surface currently includes built-in renderers for:

• JSON artifacts
• directory artifacts
• video artifacts

The UI must allow:

• graph-level run status
• node-level log inspection
• node-level artifact browsing
• task retry entrypoint
• direct navigation from a node to preview output

Canvas Interaction Rules

V1 editor behavior should enforce:

• port-level connection rules
• incompatible edge blocking
• dirty-state detection
• explicit save before publish/run if graph changed
• per-node validation badges
• run from latest saved version, not unsaved draft

Example V1 Pipelines

Delivery Normalization

Raw Folder Import
  -> Archive Extract
  -> Folder Rename
  -> Directory Validation
  -> Metadata Validation
  -> Video Quality Check
  -> Delivery Export

Dataset Conversion

Rosbag Reader
  -> Canonical Mapping
  -> Frame Filter
  -> Metadata Normalize
  -> LeRobot Writer
  -> Training Config Export

V1 Non-Goals

The V1 workflow engine does not need:

• loop semantics
• streaming execution
• unbounded dynamic fan-out
• event-driven triggers
• advanced distributed DAG partitioning

The V1 goal is a stable, observable DAG executor for data engineering workflows.