Managing Long-Running FME Server Jobs: Solutions and Best Practices

Encountering FME Server jobs that run for excessive periods, especially when triggered by incorrect input data, is a common challenge for administrators. This article explores practical solutions to manage and prevent indefinitely running jobs, ensuring efficient server operation and optimal resource utilization. Understanding effective Server Positions for job management is crucial for maintaining a healthy FME Server environment.

Understanding the Problem of Long-Running Jobs

In FME Server, jobs are designed to process data efficiently. However, scenarios arise where a job may unexpectedly run for an extended duration—hours, or even indefinitely. This often occurs when the input data deviates from the expected format or contains errors that the workspace is not designed to handle gracefully. For example, a workspace designed for specific shapefile structures might get stuck in an endless loop if provided with a corrupted or incompatible file. This can lead to several issues, including:

Resource Exhaustion: Long-running jobs consume engine resources, potentially preventing other jobs from being processed in a timely manner.
Performance Degradation: Server performance can be significantly impacted as engines are tied up with unproductive tasks.
Log File Bloat: Faulty processes can generate massive log files, consuming disk space and hindering troubleshooting.

Solution 1: Implementing Workspace-Based Timeouts

One approach to mitigate this issue is to build timeout mechanisms directly into your FME Workspaces. This involves incorporating transformers that track the job’s execution time and terminate it if it exceeds a predefined limit.

Technique:

TimeStamper: At the beginning of your workspace, use a TimeStamper transformer to record the job’s start time.
Conditional Loop and Time Check: Within a loop (if applicable in your workspace logic), or at strategic points in your workflow, use a DateTimeCalculator or similar transformer to calculate the elapsed time since the job started.
Terminate Transformer: Employ a Tester or Conditional Filter to check if the elapsed time surpasses your defined timeout threshold (e.g., 5 minutes). If the timeout is exceeded, use a Terminator transformer to immediately halt the job.

Considerations:

Workspace Modification: This method requires modifying each workspace where you want to implement a timeout.
Execution Overhead: Adding time-checking logic introduces a small overhead to workspace execution.
Timeout Accuracy: The actual termination time might be slightly longer than the set threshold due to the time taken to reach the time-check point in the workspace.

This workspace-centric approach provides granular control over job execution duration and can be tailored to specific workspace requirements.

Solution 2: Utilizing the FME Server REST API for External Job Monitoring and Termination

A more centralized and proactive solution involves using the FME Server REST API to monitor running jobs and terminate those exceeding a time limit. This method is particularly effective for managing overall server health and doesn’t require modifying individual workspaces.

Technique:

Scheduled Monitoring Workspace: Create a separate FME Workspace that runs periodically (e.g., every minute) on FME Server.
REST API Calls: Within this monitoring workspace, use the HTTPCaller transformer to interact with the FME Server REST API:
- Get Running Jobs: Use the /fmerest/v2/transformations/jobs/running endpoint to retrieve a list of currently running jobs. This response includes job start times.
- Identify Long-Running Jobs: Process the API response to identify jobs that have been running longer than your desired timeout (e.g., 5 minutes). Calculate the runtime by comparing the start time with the current time.
- Terminate Jobs: For each identified long-running job, use the /fmerest/v2/transformations/commands/remove/running endpoint, providing the job ID, to terminate the job.

Advantages:

Centralized Management: Timeout policies are managed externally, without workspace modifications.
Proactive Intervention: Jobs are monitored and terminated automatically at regular intervals.
Server-Wide Control: This method provides a server-level solution, applicable to all jobs.

Considerations:

Engine Utilization: The monitoring workspace itself consumes an engine during its execution.
API Knowledge: Requires familiarity with the FME Server REST API.
Priority Setting: To ensure timely monitoring, the scheduled monitoring workspace might need to be assigned a higher priority to prevent queue delays.

Solution 3: Leveraging FME Server’s Built-in “Running Job Expiry Time” (FME Server 2017.0+)

For users on FME Server 2017.0 and later versions, a built-in feature simplifies job timeout management. The “Running Job Expiry Time” parameter, configurable within FME Server settings, automatically terminates jobs that exceed a specified runtime.

Configuration:

FME Server Web UI: Access the FME Server Web UI as an administrator.
System Configuration: Navigate to “System Configuration” and then “Workspace Engines”.
Running Job Expiry Time: Locate the “Running Job Expiry Time” setting.
Set Timeout Value: Specify the desired timeout duration in minutes or hours.

Benefits:

Ease of Use: Simple configuration through the FME Server interface.
Server-Level Setting: Applies to all jobs running on the server (or engine, depending on configuration scope).
No Workspace Modification: No changes are needed to individual workspaces.

Considerations:

Version Dependency: Requires FME Server 2017.0 or newer.
Global Setting: The expiry time is a global setting, potentially affecting all jobs. Careful consideration is needed to choose an appropriate timeout value that balances preventing runaway jobs with allowing sufficient time for legitimate long-running processes.

Choosing the Right Solution for Server Positions

The optimal solution depends on your specific needs and FME Server environment.

Workspace-Based Timeouts: Best for targeted control within specific workspaces and for handling expected variations in processing times based on input data.
REST API Monitoring: Suitable for centralized, proactive management of server resources and for implementing consistent timeout policies across all jobs without workspace modifications.
“Running Job Expiry Time” Setting: The simplest and most efficient option for FME Server 2017.0+ users seeking a server-wide timeout mechanism with minimal configuration effort.

By strategically implementing one or a combination of these solutions, FME Server administrators can effectively manage long-running jobs, maintain server stability, and optimize resource allocation. Understanding these server positions for job control empowers you to create a more robust and efficient FME Server environment.