Troubleshooting Orphaned Sessions and Queue Failures in Blue Prism RPA

Details: Category: Automation; By Mindful Chase; 05.Aug; Hits: 21

Blue Prism is a leading Robotic Process Automation (RPA) platform designed for scalable, secure automation in enterprise environments. While it excels in integrating with legacy systems and automating rule-based tasks, organizations often encounter complex challenges that aren’t widely discussed—particularly around queue management, memory leaks during unattended execution, and session handling failures under load. These issues can compromise bot reliability, lead to stuck items in work queues, or trigger system-wide performance degradation. For architects and automation leads, understanding these silent failures and how to mitigate them is essential for ensuring Blue Prism delivers on its promise of stable, scalable automation.

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Blue Prism Architecture and Operational Flow

Core Components

Blue Prism comprises several critical elements: the Control Room, Runtime Resources (bots), Application Modeller, and Work Queues. Runtime Resources execute processes, often concurrently, fetching items from queues and interacting with external systems via surface automation or APIs.

Concurrency and Work Queues

Work Queues enable load balancing across multiple bots. However, when thousands of items are queued and bots run in parallel, contention, data locking, or orphaned sessions can occur, especially if exception handling isn’t granular or transactional integrity is compromised.

Common Automation Failures and Root Causes

1. Orphaned Sessions in Runtime Resources

Improper termination or network timeouts can leave sessions orphaned in the system, blocking bot redeployment and inflating license consumption.

2. Unstable Queue Transactions

Broken retry logic or missing exception handling leads to stuck items in queues. These are hard to trace unless explicitly monitored and can silently affect SLA adherence.

3. Memory Leaks During Long-Running Processes

Blue Prism processes running over extended periods without release/re-init of objects can result in memory buildup, causing slowdowns or crashes—especially when using surface automation with resource-heavy UI applications.

4. Uncaught Business Exceptions

Failing to distinguish between system exceptions and business exceptions results in inaccurate retry behavior, inflating error rates and audit discrepancies.

5. Database Latency or Locking Issues

The Blue Prism database, if poorly indexed or overloaded, can cause delays in updating session logs, queue items, or environment variables. This leads to poor Control Room performance or delayed dashboard updates.

Diagnostics and Monitoring Strategies

Session Log Analysis

Use Control Room logs and session history to identify repeated failure patterns. Look for processes with long idle durations or inconsistent completion times.

Work Queue Audits

Regularly export and analyze queue item statuses. Track metrics such as retry count, last updated timestamp, and exception types.

Resource Utilization Monitoring

Monitor CPU, memory, and process handle counts on bot machines using Windows Performance Monitor or external agents like Nagios or Dynatrace.

# PowerShell snippet to monitor memory usage per bot session
Get-Process | Where-Object { $_.ProcessName -like "BluePrism*" } | Select-Object ProcessName, WS, CPU

Step-by-Step Fixes

1. Cleanup Orphaned Sessions

-- SQL Script Example --
DELETE FROM BPASession WHERE Status = 'Running' AND LastUpdated < GETDATE()-1

Use cautiously. Always backup the database before running manual cleanups.

2. Refactor Long-Running Processes

Break processes into smaller, modular subprocesses with checkpointing logic. Avoid keeping applications open across long loops.

3. Implement Robust Exception Handling

Always use Try-Catch blocks. Log error details to queue item status and distinguish system vs. business exceptions.

4. Enable Queue Alerting

Integrate Control Room with external monitoring tools via API or SQL triggers to alert on thresholds like pending retries or system exceptions exceeding a limit.

5. Optimize Database Performance

Work with DBAs to index key tables (e.g., BPASessionLog_N, BPAWorkQueueItem) and ensure proper archiving of logs older than 90 days.

Best Practices for Enterprise-Scale Blue Prism

Set queue item SLA and retry configurations per process
Use environment locking when accessing shared resources
Deploy a centralized logging mechanism outside Blue Prism (e.g., ELK stack)
Schedule bot reboot cycles to clear memory and orphaned handles
Use version control for process templates to track changes across environments

Conclusion

Blue Prism offers industrial-strength RPA capabilities, but operational excellence requires more than just process design. Issues like orphaned sessions, memory leaks, and inconsistent queue behaviors can silently degrade automation ROI. Enterprises must treat Blue Prism infrastructure with the same rigor as traditional software systems—implementing observability, exception handling discipline, and scalable design practices. By taking a proactive approach to troubleshooting and optimizing RPA operations, teams can ensure reliable, resilient, and high-performing digital workforces.

FAQs

1. What causes queue items to get stuck in Blue Prism?

Improper exception handling or process crashes without releasing the item can result in stuck states. Ensure proper finalization logic and retries.

2. How do I detect orphaned sessions?

Check the Control Room for sessions that show as running but have no recent activity or session log updates. You can also query the database directly.

3. Can I monitor Blue Prism bots externally?

Yes, by integrating with monitoring tools via the Blue Prism API or querying the database to pull metrics into systems like Splunk or Grafana.

4. Why does my process slow down over time?

Likely due to memory leaks or unoptimized loops. Long-running sessions should release resources periodically and avoid keeping UI apps open indefinitely.

5. Is Blue Prism suitable for real-time automation?

Blue Prism is better suited for scheduled or batch automation. Real-time use cases require careful queue configuration and fast system responsiveness.

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