Workflow Patterns

Overview

Workflow Patterns are proven, reusable solutions for common workflow scenarios. Using patterns reduces development time and improves reliability by leveraging battle-tested approaches.

✅ Battle-Tested Solutions

Patterns provide:

Proven solutions to common problems
Reusable implementations
Best practices embedded
Reduced development time
Improved reliability

Sequential Pattern

Workflow steps execute in order, each depending on previous:

Step 1 → Step 2 → Step 3 → Step 4 → Complete

Characteristics:
- Clear linear flow
- Each step waits for previous
- Easy to understand
- Slowest approach

Use when:
- Steps have dependencies
- Order matters
- Simplicity is priority

Sequential Example

Workflow: Order Processing

Step 1: [Validate Order]
  └─ Must succeed before payment

Step 2: [Process Payment]
  └─ Must succeed before fulfillment

Step 3: [Create Fulfillment]
  └─ Must succeed before notification

Step 4: [Send Confirmation]
  └─ Final step

Total time: Sum of all steps

Sequential Pattern

Independent operations execute simultaneously:

        ┌─ Task A ─┐
        │          ├─ All complete → Continue
        ├─ Task B ─┤
        │          │
        └─ Task C ─┘

Characteristics:
- Multiple operations at once
- Must be independent
- Fastest approach
- Synchronization required

Use when:
- Operations are independent
- Order doesn't matter
- Speed is priority

Sequential Example

Workflow: Get User Profile (Fetch Multiple Data Sources)

Step 1: [Get User Basic Info] ──┐
Step 2: [Get User Orders] ───────→ Sequential execution
Step 3: [Get User Preferences] ─┘

Step 4: [Merge Results]
  └─ Combine all data

Step 5: [Send Response]

Time: Max(Step 1, Step 2, Step 3) + Steps 4, 5
vs Sequential: Sum of all steps
Savings: 50-75% depending on step sizes

Fork-Join Pattern

Workflow splits into branches, then joins:

        ┌─→ Branch A → ┐
Start ──┤─→ Branch B → ├─→ Join → Complete
        └─→ Branch C → ┘

Characteristics:
- Start with single path
- Split into branches
- Join results before continuing
- Combines branching + sequential

Use when:
- Different paths for different users/data
- Need results from all branches
- Must synchronize before next step

Fork-Join Example

Workflow: Comprehensive User Verification

Step 1: [Get User]

Step 2: Split (Fork):
  
  Branch A:
  ├─ [Verify Email]
  ├─ [Check Email Score]
  └─ Result: email_valid
  
  Branch B:
  ├─ [Verify Phone]
  ├─ [Check Phone Score]
  └─ Result: phone_valid
  
  Branch C:
  ├─ [Check Fraud Score]
  └─ Result: fraud_score

Step 3: Join Results
  ├─ email_valid
  ├─ phone_valid
  └─ fraud_score

Step 4: [Make Decision]
  IF all_valid THEN approve
  ELSE reject

Time: Sequential time of branches + join time
Savings vs sequential: Time of longest branch vs sum of all

Loop/Iteration Pattern

Process multiple items in sequence:

Sequential Loop:
FOR each item:
  [Process] → item1, item2, item3...
Total: n × step_time

Sequential Loop:
FOR each item (sequential):
  [Process] → all at once
Total: n × step_time

Loop Examples

Sequential Loop

Workflow: Validate All User Emails

Emails to check: 1000

FOR each email:
  [API: Validate email]
  
  IF invalid:
    [Add to invalid_list]

Total time: 1000 × 0.2s = 200s

Batch Loop

Workflow: Batch Validate Emails

Emails to check: 1000
Batch size: 100

FOR each batch of 100:
  [API: Batch validate (accepts array)]
  
  [Process results]

Total time: 10 batches × 0.5s = 5s
Savings: 98% faster

Sequential Loop

Workflow: Sequential Email Validation

Emails: 1000
Batch workers: 8

FOR 125 emails (sequential):
  [Validate email]
  
  IF invalid:
    [Add to invalid_list]

Total time: 125 × 0.2s = 25s
Savings: 88% faster than sequential

Conditional/Branching Pattern

Different paths based on conditions:

        ┌─→ Path A
Input ──┤─→ Path B
        └─→ Path C
        
        All converge
        
        → Output

Use when:
- Different logic for different scenarios
- Feature flags/toggles
- Role-based execution
- Error handling paths

Conditional Example

Workflow: Process Order by Type

Step 1: [Get Order Data]

Step 2: Determine Order Type
  IF order.type == "digital":
    Step 3a: [Verify payment]
    Step 3b: [Send download link]
    
  ELSE IF order.type == "physical":
    Step 3c: [Verify payment]
    Step 3d: [Create shipment]
    Step 3e: [Send tracking]
    
  ELSE IF order.type == "subscription":
    Step 3f: [Setup recurring]
    Step 3g: [Send welcome]

Step 4: [Send confirmation email]

Step 5: [Complete]

Retry Pattern

Automatic retry with backoff:

Attempt 1 → Fail
  ↓
Wait (2s)
  ↓
Attempt 2 → Fail
  ↓
Wait (4s)
  ↓
Attempt 3 → Success → Continue

Use when:
- Transient failures expected
- Don't want to fail immediately
- Want to recover automatically

Retry Example

Workflow: Resilient API Call

[API: Get Data]
  Configuration:
    - Max attempts: 5
    - Initial delay: 2s
    - Backoff: Exponential
    - Max delay: 60s
    - Retry on: Timeout, 503, Connection reset
    - Don't retry: 404, 401, 403

Result: Usually succeeds without user knowing

Polling Pattern

Repeatedly check status until ready:

Check Status
  ↓
IF ready: Continue
  ↓
IF not ready AND time < timeout:
  Wait (2s)
  Loop back to Check Status
  ↓
IF timeout: Fail or use fallback

Use when:
- Status updates asynchronously
- Don't know how long to wait
- Need to verify completion

Polling Example

Workflow: Wait for Async Job

Step 1: [Start background job]
  Output: job_id

Step 2: Poll for Completion
  Max polls: 30
  Poll interval: 2s
  Max time: 60s
  
  FOR poll_count = 1 to 30:
    [API: Get job status {job_id}]
    
    IF status == "completed":
      [Success - break loop]
    ELSE IF status == "failed":
      [Failure - break loop]
    ELSE:
      [WAIT: 2 seconds]
      [Continue to next poll]
  
  IF poll_count == 30:
    [Timeout - job taking too long]

Step 3: [Process result]

Fallback/Degradation Pattern

Use alternative if primary fails:

TRY:
  [Primary approach]
  
CATCH:
  [Fallback approach 1]
  
  IF still fails:
    [Fallback approach 2]
    
    IF still fails:
      [Graceful degradation]

Use when:
- Have backup options
- Acceptable to degrade
- Want continuous availability

Fallback Example

Workflow: Get User Data with Fallback

Step 1: TRY Primary Source
  [API: Get from primary endpoint]
  
  IF success:
    Continue with result

STEP 2: IF failed, TRY Fallback 1
  [API: Get from secondary endpoint]
  
  IF success:
    Continue with result

STEP 3: IF failed, TRY Fallback 2
  [Data Hub: Retrieve cached data]
  
  IF exists:
    [Use cached, mark as stale]
    Continue

STEP 4: IF all failed
  [Graceful error]
  [Return: "Data temporarily unavailable"]

Aggregation/Accumulation Pattern

Collect and combine results:

FOR each item:
  [Process]
  [Add result to collection]

[Aggregate all results]
[Generate output]

Use when:
- Processing multiple items
- Need to combine results
- Generate summary/report

Aggregation Example

Workflow: Generate Performance Report

Step 1: Initialize
  workflow.metrics = {
    total: 0,
    passed: 0,
    failed: 0,
    duration: 0
  }

Step 2: Collect Metrics
  FOR each test:
    [Run test]
    
    workflow.metrics.total += 1
    
    IF test_passed:
      workflow.metrics.passed += 1
    ELSE:
      workflow.metrics.failed += 1
    
    workflow.metrics.duration += test_duration

Step 3: Calculate Aggregates
  workflow.pass_rate = 
    (workflow.metrics.passed / workflow.metrics.total) × 100
  
  workflow.avg_duration = 
    workflow.metrics.duration / workflow.metrics.total

Step 4: Generate Report
  [Create report with metrics and aggregates]

Actor Model Pattern

Distributed processing with message passing:

Request → Queue → Worker 1 ⟹ Process → Result
                │  Worker 2 ⟹ Process → Result
                └─ Worker 3 ⟹ Process → Result

Characteristics:
- Distributes work
- Scales horizontally
- Asynchronous
- Resilient to failures

Use when:
- High volume of similar tasks
- Need horizontal scaling
- Can afford async processing

Actor Pattern Example

Workflow: Send Emails to 10,000 Users

Sequential (too slow):
FOR 10000 users:
  [Send email] → 10000 × 2s = 20000s (5+ hours)

Actor Model:
[Queue: 10000 email tasks]

[Worker 1] ─→ Send 1000 emails
[Worker 2] ─→ Send 1000 emails
[Worker 3] ─→ Send 1000 emails
...
[Worker 8] ─→ Send 1000 emails

Total: 1000 × 2s / 8 workers = 250s (4 minutes)

Compensation/Saga Pattern

Handle distributed transactions:

Step 1 ✓ → Step 2 ✓ → Step 3 ✓ → Complete

If Step 3 fails:
  Compensate Step 2
    ↓
  Compensate Step 1
    ↓
  Rollback complete

Use when:
- Multiple dependent operations
- Need rollback capability
- Can't use traditional transactions

Saga Example

Workflow: Order Processing with Rollback

Step 1: Reserve Inventory
  [Reserve stock]
  Output: reservation_id
  
  (Compensation: Release inventory)

Step 2: Process Payment
  [Charge card]
  Output: transaction_id
  
  (Compensation: Refund charge)

Step 3: Ship Order
  [Create shipment]
  Output: shipment_id
  
  (Compensation: Cancel shipment)

If Step 2 fails:
  [Release inventory reservation from Step 1]
  [Order cancelled]

If Step 3 fails:
  [Refund payment from Step 2]
  [Release inventory from Step 1]
  [Order cancelled]

Pattern Selection Guide

Choose pattern based on:

Performance:
- Need speed? → Optimize workflow
- Speed not critical? → Sequential
- Variable time? → Polling

Reliability:
- Transient failures? → Retry
- Need backup? → Fallback
- Must succeed? → Saga/Compensation

Complexity:
- Simple flow? → Sequential
- Multiple paths? → Conditional
- Multiple items? → Loop/Aggregation

Scale:
- Single execution? → Sequential
- High volume? → Actor model
- Batch processing? → Loop/Batch

Real-World Scenarios

E-Commerce: Complex Order

Pattern: Fork-Join + Conditional + Retry

[Get Order]
  ↓
Fork:
  ├─ [Verify inventory] (retry)
  ├─ [Process payment] (retry + fallback)
  └─ [Check fraud] (polling)

Join:
  ├─ If all valid:
  │  └─ [Create shipment]
  └─ Else:
     └─ [Cancel order]

Time: Optimized through batching

API Testing: Multi-Scenario

Pattern: Loop + Batching + Aggregation

[Initialize test suite]
  ↓
Batch (8 workers):
  FOR each test scenario:
    [Setup test]
    [Execute test]
    [Verify results]
    [Collect metrics]

Join:
  [Aggregate all metrics]
  [Generate report]

Monitoring: Event Processing

Pattern: Actor model + Aggregation

[Queue incoming events]
  ↓
Workers (distributed):
  [Process event]
  [Store result]
  [Update metrics]

Periodic aggregation:
  [Calculate statistics]
  [Generate alerts if needed]

Conditional Execution - Decision logic
Custom Variables - Data management
Performance Optimization - Speed patterns
Error Handling - Retry/fallback patterns
Best Practices - Professional standards

Summary

Workflow Patterns are proven, reusable solutions
Sequential - Simple, clear, slow
Sequential - Straightforward for dependent operations
Conditional - Different paths for different scenarios
Retry/Polling - Handle timing and transient failures
Fallback - Continue when primary fails
Choose pattern based on performance, reliability, and scale

Next: Return to Advanced Topics Overview to explore other topics.

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Last updated 11 days ago

hashtagOverview

hashtagSequential Pattern

hashtagSequential Example

hashtagSequential Pattern

hashtagSequential Example

hashtagFork-Join Pattern

hashtagFork-Join Example

hashtagLoop/Iteration Pattern

hashtagLoop Examples

hashtagSequential Loop

hashtagBatch Loop

hashtagSequential Loop

hashtagConditional/Branching Pattern

hashtagConditional Example

hashtagRetry Pattern

hashtagRetry Example

hashtagPolling Pattern

hashtagPolling Example

hashtagFallback/Degradation Pattern

hashtagFallback Example

hashtagAggregation/Accumulation Pattern

hashtagAggregation Example

hashtagActor Model Pattern

hashtagActor Pattern Example

hashtagCompensation/Saga Pattern

hashtagSaga Example

hashtagPattern Selection Guide

hashtagReal-World Scenarios

hashtagE-Commerce: Complex Order

hashtagAPI Testing: Multi-Scenario

hashtagMonitoring: Event Processing

hashtagRelated Topics

hashtagSummary

Overview

Sequential Pattern

Sequential Example

Sequential Pattern

Sequential Example

Fork-Join Pattern

Fork-Join Example

Loop/Iteration Pattern

Loop Examples

Sequential Loop

Batch Loop

Sequential Loop

Conditional/Branching Pattern

Conditional Example

Retry Pattern

Retry Example

Polling Pattern

Polling Example

Fallback/Degradation Pattern

Fallback Example

Aggregation/Accumulation Pattern

Aggregation Example

Actor Model Pattern

Actor Pattern Example

Compensation/Saga Pattern

Saga Example

Pattern Selection Guide

Real-World Scenarios

E-Commerce: Complex Order

API Testing: Multi-Scenario

Monitoring: Event Processing

Related Topics

Summary