With queues you need to code with idempotency in mind and expect and handle the ‘EntityAlreadyExists’ as a viable response.
As others have suggested, causes could be
- Multiple message in the queue with the same identifier.
- Are peeking for the message and not reading it form the queue and so not making them invisible.
- Not deleting the message because an exception was thrown before you can delete them.
- Taking too long to process the message so it cannot be deleted (because invisibility was timed out) and appears again
Without looking at the code I am guessing that it is either the 3 or 4 option that is occurring.
If you cannot detect the issue with a code review, you may consider adding time based logging and try/catch wrappers to get a better understanding.
Using queues effectively, in a multi-role environment, requires a slightly different mindset and running into such issues early is actually a blessing in disguise.
Appended 2/24
Just to clarify, modifying the invisibility time out is not a generic solution to this type of problem. Also, note that this feature although available on the REST API, may not be available on the queue client.
Other options involve writing to table storage in an asynchronous manner to speed up your processing time, but again this is a stop gap measures which does not really address the underlying paradigm of working with queues.
So, the bottom line is to be idempotent. You can try using the table storage upsert (update or insert) feature to avoid getting the ‘EntitiyAlreadyExists’ error, if that works for your code. If all you are doing is inserting new entities to azure table storage then the upsert should solve your problem with minimal code change.
If you are doing updates then it is a different ball game all together. One pattern is to pair updates with dummy inserts in the same table with the same partition key so as to error out if the update occurred previously and so skip the update. Later after the message is deleted, you can delete the dummy inserts. However, all this adds to the complexity, so it is much better to revisit the architecture of the product; for example, do you really need to insert/update into so many tables?