Manually Triggered Hangfire Job Succeeds in Cloud Database But Doesn’t Execute

Below are some potential causes and troubleshooting steps you can take:

1. Multiple Projects Sharing the Same Database

If several projects point to the same Hangfire MongoDB database, they might be sharing job queues. This can lead to confusion about which instance actually picked up a manually triggered job. Even when you trigger it, another service (or even an old instance) might “consume” the job and mark it as succeeded without calling the expected code.

What to do:

• Verify that your project’s worker is the only one watching the queues for that job.
• Check if there’s any misconfiguration in queue naming so that your triggered jobs don’t accidentally land in a queue processed by another project.

2. Difference in Job Processing for Scheduled vs. Manually Triggered Jobs

Since scheduled jobs work fine but manually triggered ones do not, it’s worth examining if there’s a configuration difference or if the manual trigger bypasses some expected configuration.

What to do:

• Inspect the job invocation logs on the server after you trigger a job manually.
• Confirm that there isn’t a filter or global attribute altering the behavior based on the trigger type.

3. Configuration or Connection String Differences

When switching between your local Docker-based instance and the cloud instance, there may be subtle differences in how the Mongo settings or Hangfire options are applied. This can include things like default queue names, serialization settings, or even timeouts that might affect manual job execution.

What to do:

• Double-check the connection string differences – ensure that database names, authentication, and options are consistent.
• Compare Hangfire storage and server configuration settings between environments.

4. Logging and Error Handling

Even if the status in the database is "Succeeded," there might be warnings or logs that indicate that the job did not execute as intended. Hangfire’s logs might tell you if the job was “lost,” rejected, or moved to the wrong queue.

What to do:

• Increase the verbosity of Hangfire logging in the cloud environment.
• Look for any anomalies or differences in logs for scheduled versus triggered jobs.

5. Code Execution Environment

It’s possible the triggered job is being executed in an environment where your debugging or breakpoints are not hit. For example, if multiple worker servers are involved, the triggered job might be processed on a different server than the one you’re attached to in Rider.

What to do:

• Confirm on which server the job is being executed.
• If possible, add a logging statement at the very beginning of your job method to verify execution regardless of debugging.

Additional Diagnostic Information to Gather

• Logs: Gather detailed logs from Hangfire both on the cloud and local instances.
• Queue Information: Check which queues the jobs are enqueued to and which servers are processing them.
• Server Configuration: Compare Hangfire server configuration and options in both environments.
• Job Metadata: Look at job parameters, metadata, and any custom filters to see if the manual trigger behaves differently.
• Environment Differences: Double-check the environment variables or build configurations that get applied for the cloud instance.

Conclusion

The problem likely stems from an environmental or configuration discrepancy between your local Docker and cloud MongoDB setups. Focus on verifying the correct queue processing when multiple projects share the same database, check the Hangfire configuration settings, and enhance your logging. These steps should help you pinpoint where the discrepancy in behavior is originating.