Participant messages not received for particular study/lab
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- Dominant language
- Python
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- Forks
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Description
**Description**
We're having reports that messages to participants sent via the Lookit messaging system are not going through. This is happening consistently for one particular study. The website reports that the message is sent and is recorded in the log, but the emails are not being received. I have verified this by sending test emails to myself from the study in question and from a similar test study (external/scheduled) on the production server.
**How to reproduce**
On the [motor learning games study](https://lookit.mit.edu/exp/studies/1773/), click Message Participants and try sending a message (can be any message type, and can be own or another account). The website reports that the message has been sent and there are no errors/warnings on the page, but the email is not received (also not showing up in spam folder).
**Expected behavior**
Emails should be sent/received. When I've tried this with a test study, I received the emails immediately.
**Additional context**
In order to test this specific study, I moved the production data onto my local machine and can see the emails sent from this study being printed to the terminal. So I believe the problem lies after the email request is received and added to RabbitMQ, and after the point where the local/debug code diverges from production. It could be a bug within the Celery component or a problem with the email-sending parameters (e.g. lab email) such that the message gets all the way to the point of sending but then the email doesn’t/can’t actually send.
On my test study on the production server, I also tried changing the lab and testing the email function. Messages were received when the test study's lab is set to Demo/Sandbox/ECCL, but NOT when the relevant lab is used (Kid Motor Learning Lab) so the emailing problem seems related to the lab account. (Though I can't say that the problem is specific to this one lab - it's possible that other lab accounts are having, or could potentially have, the same problem.)
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the participant messaging flow for study 1773, comparing the local terminal behavior with production after the email request enters RabbitMQ. Inspect the Celery delivery path and lab-specific email parameters mentioned in the report. Done means production messages are received, or the failure is isolated with evidence.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, rabbitmq
- Domain
- api, backend, distributed-systems
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100