Parallel Processing in Pega – Architecture Patterns That Scale

Introduction

In enterprise-scale applications, waiting for sequential steps is a luxury you can’t afford. Whether it's meeting SLAs, orchestrating integrations, or optimizing case resolution—parallel processing is your best ally when used with precision. This article dives into real-world design patterns for parallelism in Pega, based on what works at scale—and what breaks if you don’t plan ahead.

Split-Join: Coordinated Parallelism - You want multiple subprocesses to run in parallel and wait for all/some/any to complete before resuming the main flow.

Example: In a loan approval system:

1. Run Credit Check
2. Fetch Risk Score
3. Validate Customer KYC

How to Configure:

1. Add a Split-Join shape in your flow
2. Configure join conditions: a. All: Waits for all subflows b. Any: Resumes after the first completes c. Some: Specify a minimum number required
3. Use the Join tab to configure behavior and set timeouts

Exception Handling:

1. Ensure each subflow has defined error paths
2. Inspect .pxFlow() pages post-join to catch failures
3. Use a decision or utility shape to handle flow errors gracefully

Tip : Watch out for silent subflow failures that block joins—especially in multi-threaded systems.

Split-For-Each: Data-Driven Parallelism - You need to run the same flow logic across each item in a Page List or Page Group.

Example: For an e-commerce case, launch a fulfillment subflow for each product in .OrderDetails()

How to Configure:

1. Reference the Page List in the Split-For-Each shape
2. Set the child flow to run for each item
3. Enable "Create a new subflow for each page"
4. Limit maximum active subflows to avoid node overload

Page List Tips:

1. Populate it early using Data Transforms or preprocessing logic
2. Ensure each item has the required fields to execute the child flow

Tip : Avoid large Page Lists unless you implement batching—unchecked loops can destroy performance.

Queue Processors: True Asynchronous Execution - To process work in the background without blocking user flows or main threads.

Example: After submitting a case:

1. Generate PDF
2. Initiate SLA
3. Send notifications

How to Configure:

1. Create a Queue Processor rule (e.g., ProcessPDFQueue)
2. Link to a lightweight activity (no UI or manual steps)
3. Use Queue-For-Processing method or utility to trigger it

Activity Best Practices:

1. Handle exceptions safely
2. Keep it atomic and efficient
3. Log execution paths for traceability

Error Handling:

1. Enable retries with exponential backoff
2. Use Dead Letter Queue (DLQ) for unprocessed items
3. Monitor via Admin Studio

Tip : Queue Processors outperform Agents for async scale—they're dynamic, fault-tolerant, and built for load

Job Scheduler + Data Flow: Scheduled Async Orchestration - For recurring batch processing or evaluation logic—especially after hours.

Example: Nightly re-evaluation of inactive leads to update eligibility or offers

How to Configure:

1. Create a Job Scheduler rule (e.g., ReprocessLeadsJob)
2. Set it to run daily or based on a CRON expression
3. Link to an activity that starts a Data Flow using pxStartDataFlow

Data Flow Benefits:

1. Supports decisioning, segmentation, transformations
2. Highly visual and monitorable
3. Can chain into strategies or case updates

Tip : Job Schedulers + Data Flows = smart batch automation without compromising performance.

Choosing the Right Use case - Pattern

1. Parallel child flows with coordination - Split-Join
2. Repeat logic over list items - Split-For-Each
3. Background async processing - Queue Processor
4. Scheduled recurring logic - Job Scheduler + Data Flow

Real-World Pitfall: We once had a case where a Split-For-Each flow looped through over 2,000 records with no subflow limit. It brought the node to a crawl and blocked threads. Switching to a Job Scheduler + Queue Processor model (batched in 100s) dropped processing time by 60% and freed up all async threads.

Final Thoughts Parallel processing in Pega is a superpower—but only when paired with intentional design. Get it wrong, and you risk:

1. Thread starvation
2. Memory leaks
3. Async collisions
4. Invisible failures that surface in prod

Get it right, and you unlock:

1. Faster SLAs
2. Happy users
3. Resilient async pipelines
4. Systems that scale confidently

Want a deep dive into one of these with screenshots, flows, or code samples? Drop a comment or connect—we’re building Execution Edge to share what works.

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