workflow-orchestration-patterns
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Design durable workflows with Temporal for distributed systems. Covers workflow vs activity separation, saga patterns, state management, and determinism constraints. Use when building long-running processes, distributed transactions, or microservice orchestration.

ID: workflow-orchestration-patterns
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Workflow Orchestration Patterns


Master workflow orchestration architecture with Temporal, covering fundamental design decisions, resilience patterns, and best practices for building reliable distributed systems.


When to Use Workflow Orchestration


Ideal Use Cases (Source: docs.temporal.io)


  • Multi-step processes spanning machines/services/databases
  • Distributed transactions requiring all-or-nothing semantics
  • Long-running workflows (hours to years) with automatic state persistence
  • Failure recovery that must resume from last successful step
  • Business processes: bookings, orders, campaigns, approvals
  • Entity lifecycle management: inventory tracking, account management, cart workflows
  • Infrastructure automation: CI/CD pipelines, provisioning, deployments
  • Human-in-the-loop systems requiring timeouts and escalations

When NOT to Use


  • Simple CRUD operations (use direct API calls)
  • Pure data processing pipelines (use Airflow, batch processing)
  • Stateless request/response (use standard APIs)
  • Real-time streaming (use Kafka, event processors)

Critical Design Decision: Workflows vs Activities


The Fundamental Rule (Source: temporal.io/blog/workflow-engine-principles):

  • Workflows = Orchestration logic and decision-making
  • Activities = External interactions (APIs, databases, network calls)

Workflows (Orchestration)


Characteristics:

  • Contain business logic and coordination
  • MUST be deterministic (same inputs ÔåÆ same outputs)
  • Cannot perform direct external calls
  • State automatically preserved across failures
  • Can run for years despite infrastructure failures

Example workflow tasks:

  • Decide which steps to execute
  • Handle compensation logic
  • Manage timeouts and retries
  • Coordinate child workflows

Activities (External Interactions)


Characteristics:

  • Handle all external system interactions
  • Can be non-deterministic (API calls, DB writes)
  • Include built-in timeouts and retry logic
  • Must be idempotent (calling N times = calling once)
  • Short-lived (seconds to minutes typically)

Example activity tasks:

  • Call payment gateway API
  • Write to database
  • Send emails or notifications
  • Query external services

Design Decision Framework


``

Does it touch external systems?  Activity

Is it orchestration/decision logic?  Workflow

`


Core Workflow Patterns


1. Saga Pattern with Compensation


Purpose: Implement distributed transactions with rollback capability


Pattern (Source: temporal.io/blog/compensating-actions-part-of-a-complete-breakfast-with-sagas):


`

For each step:

1. Register compensation BEFORE executing

2. Execute the step (via activity)

3. On failure, run all compensations in reverse order (LIFO)

`


Example: Payment Workflow

1. Reserve inventory (compensation: release inventory)

2. Charge payment (compensation: refund payment)

3. Fulfill order (compensation: cancel fulfillment)


Critical Requirements:

  • Compensations must be idempotent
  • Register compensation BEFORE executing step
  • Run compensations in reverse order
  • Handle partial failures gracefully

2. Entity Workflows (Actor Model)


Purpose: Long-lived workflow representing single entity instance


Pattern (Source: docs.temporal.io/evaluate/use-cases-design-patterns):

  • One workflow execution = one entity (cart, account, inventory item)
  • Workflow persists for entity lifetime
  • Receives signals for state changes
  • Supports queries for current state

Example Use Cases:

  • Shopping cart (add items, checkout, expiration)
  • Bank account (deposits, withdrawals, balance checks)
  • Product inventory (stock updates, reservations)

Benefits:

  • Encapsulates entity behavior
  • Guarantees consistency per entity
  • Natural event sourcing

3. Fan-Out/Fan-In (Parallel Execution)


Purpose: Execute multiple tasks in parallel, aggregate results


Pattern:

  • Spawn child workflows or parallel activities
  • Wait for all to complete
  • Aggregate results
  • Handle partial failures

Scaling Rule (Source: temporal.io/blog/workflow-engine-principles):

  • Don't scale individual workflows
  • For 1M tasks: spawn 1K child workflows ├ù 1K tasks each
  • Keep each workflow bounded

4. Async Callback Pattern


Purpose: Wait for external event or human approval


Pattern:

  • Workflow sends request and waits for signal
  • External system processes asynchronously
  • Sends signal to resume workflow
  • Workflow continues with response

Use Cases:

  • Human approval workflows
  • Webhook callbacks
  • Long-running external processes

State Management and Determinism


Automatic State Preservation


How Temporal Works (Source: docs.temporal.io/workflows):

  • Complete program state preserved automatically
  • Event History records every command and event
  • Seamless recovery from crashes
  • Applications restore pre-failure state

Determinism Constraints


Workflows Execute as State Machines:

  • Replay behavior must be consistent
  • Same inputs ÔåÆ identical outputs every time

Prohibited in Workflows (Source: docs.temporal.io/workflows):

  • ÔØî Threading, locks, synchronization primitives
  • ÔØî Random number generation (random())
  • ÔØî Global state or static variables
  • ÔØî System time (datetime.now())
  • ÔØî Direct file I/O or network calls
  • ÔØî Non-deterministic libraries

Allowed in Workflows:

  • Ô£à workflow.now() (deterministic time)
  • Ô£à workflow.random() (deterministic random)
  • Ô£à Pure functions and calculations
  • Ô£à Calling activities (non-deterministic operations)

Versioning Strategies


Challenge: Changing workflow code while old executions still running


Solutions:

1. Versioning API: Use workflow.get_version() for safe changes

2. New Workflow Type: Create new workflow, route new executions to it

3. Backward Compatibility: Ensure old events replay correctly


Resilience and Error Handling


Retry Policies


Default Behavior: Temporal retries activities forever


Configure Retry:

  • Initial retry interval
  • Backoff coefficient (exponential backoff)
  • Maximum interval (cap retry delay)
  • Maximum attempts (eventually fail)

Non-Retryable Errors:

  • Invalid input (validation failures)
  • Business rule violations
  • Permanent failures (resource not found)

Idempotency Requirements


Why Critical (Source: docs.temporal.io/activities):

  • Activities may execute multiple times
  • Network failures trigger retries
  • Duplicate execution must be safe

Implementation Strategies:

  • Idempotency keys (deduplication)
  • Check-then-act with unique constraints
  • Upsert operations instead of insert
  • Track processed request IDs

Activity Heartbeats


Purpose: Detect stalled long-running activities


Pattern:

  • Activity sends periodic heartbeat
  • Includes progress information
  • Timeout if no heartbeat received
  • Enables progress-based retry

Best Practices


Workflow Design


1. Keep workflows focused - Single responsibility per workflow

2. Small workflows - Use child workflows for scalability

3. Clear boundaries - Workflow orchestrates, activities execute

4. Test locally - Use time-skipping test environment


Activity Design


1. Idempotent operations - Safe to retry

2. Short-lived - Seconds to minutes, not hours

3. Timeout configuration - Always set timeouts

4. Heartbeat for long tasks - Report progress

5. Error handling - Distinguish retryable vs non-retryable


Common Pitfalls


Workflow Violations:

  • Using datetime.now() instead of workflow.now()`
  • Threading or async operations in workflow code
  • Calling external APIs directly from workflow
  • Non-deterministic logic in workflows

Activity Mistakes:

  • Non-idempotent operations (can't handle retries)
  • Missing timeouts (activities run forever)
  • No error classification (retry validation errors)
  • Ignoring payload limits (2MB per argument)

Operational Considerations


Monitoring:

  • Workflow execution duration
  • Activity failure rates
  • Retry attempts and backoff
  • Pending workflow counts

Scalability:

  • Horizontal scaling with workers
  • Task queue partitioning
  • Child workflow decomposition
  • Activity batching when appropriate

Additional Resources


Official Documentation:

  • Temporal Core Concepts: docs.temporal.io/workflows
  • Workflow Patterns: docs.temporal.io/evaluate/use-cases-design-patterns
  • Best Practices: docs.temporal.io/develop/best-practices
  • Saga Pattern: temporal.io/blog/saga-pattern-made-easy

Key Principles:

1. Workflows = orchestration, Activities = external calls

2. Determinism is non-negotiable for workflows

3. Idempotency is critical for activities

4. State preservation is automatic

5. Design for failure and recovery

Informacion

Estado
Activo
Origen upstream
Usos 3
Ultimo uso 23/01 16:57
Actualizado 09/01/2026 02:14

Archivo origen

Z:\Repositorios\core\upstream\plugins\backend-development\skills\workflow-orchestration-patterns\SKILL.md