I do mailings via rabbitmq: I send a mailing list from the main application, the consumer reads it and sends it.
A broadcast may consist of different messages, which must be sent in the correct order.
In fact, a mailing list is a list of messages: [message_1, message_2, message_3, message_4]
Some of the messages can be sent and at some point the third-party service stops accepting requests.
I will describe the process of the consumer:
I take out the message from queue which contains distribution.
Sending: 1 part > 2 part
An error occurs. It remains to send 3 part > 4 part.
Acknowledge the original message from the queue.
Put a new one at the beginning of the same queue: [message 3, message 4].
Question 1: Is it good to send a new message (from consumer) created from parts of an old one to the same queue?
Question 2: Is it a good solution?
Are there any other solutions?
The sequence you posted loses a message if the handler process crashes between steps 4 and 5. So you have to switch the order of steps 4 and 5. But as soon as you do that you have to deal with the duplication of messages. If for some reason (like a bug) ack fails for a large percentage of messages you can end up with the same broadcast repeated multiple times in the queue. So if you want to avoid duplicated messages you have to use some external persistence to perform deduping. Also, RabbitMQ doesn't guarantee that messages are delivered in the same order. So you can end up in a situation where two messages for the same address are delivered out of order. So deduping should be on the level of individual parts, not entire messages.
Question 2: Is it a good solution? Are there any other solutions?
Consider using an orchestrator like temporal.io which eliminates most of the consistency problems I described.
I have a queue which is supposed to receive the messages sent by a lambda function. This function is supposed to send each different message once only. However, I saw a scary amount of receive count on the console:
Since I cannot find any explanation about receive count in the plain English, I need to consult StackOverflow Community. I have 2 theories to verify:
There are actually not so many messages and the reason why "receive count" is that high is simply because I polled the messages for a looooong time so the messages were captured more than once;
the function that sends the messages to the queue is SQS-triggered, those messages might be processed by multiple processors. Though I set VisibilityTimeout already, are the messages which are processed going to be deleted? If they aren't remained, there are no reasons for them to be caught and processed for a second time.
Any debugging suggestion will be appreciated!!
So, receive count is basically the amount of times the lambda (or any other consumer) has received the message. It can be that a consumer receives a message more than once (this is by design, and you should handle that in your logic).
That being said, the receive count also increases if your lambda fails to process the message (or even hits the execution limits). The default is 3 times, so if something with your lambda is wrong, you will have at least 3 receives per message.
Also, when you are polling the message, via the AWS console, you are basically increasing the receive count.
SQS sometimes stops receiving messages or allowing message consumption, then resumes after ~5 mins. Do you know if there is a setting that can produce this behavior? I was playing around with the settings but could not change this behavior.
Notice: When I send a message, I get the ID and the OK as it was received, but the message is not in the queue.
If you are getting an ID and message is not in the queue,I believe you are using FIFO and it ignores dupliate messages within a default time frame (5 min. ?). Whatever is feeding the queue need to use a good deduplication id in case if you want to process duplicate messages.
Read this
I am working on a project that will require multiple workers to access the same queue to get information about a file which they will manipulate. Files are ranging from size, from mere megabytes to hundreds of gigabytes. For this reason, a visibility timeout doesn't seem to make sense because I cannot be certain how long it will take. I have though of a couple of ways but if there is a better way, please let me know.
The message is deleted from the original queue and put into a
‘waiting’ queue. When the program finished processing the file, it
deletes it, otherwise the message is deleted from the queue and put
back into the original queue.
The message id is checked with a database. If the message id is
found, it is ignored. Otherwise the program starts processing the
message and inserts the message id into the database.
Thanks in advance!
Use the default-provided SQS timeout but take advantage of ChangeMessageVisibility.
You can specify the timeout in several ways:
When the queue is created (default timeout)
When the message is retrieved
By having the worker call back to SQS and extend the timeout
If you are worried that you do not know the appropriate processing time, use a default value that is good for most situations, but don't make it so big that things become unnecessarily delayed.
Then, modify your workers to make a ChangeMessageVisiblity call to SQS periodically to extend the timeout. If a worker dies, the message stops being extended and it will reappear on the queue to be processed by another worker.
See: MessageVisibility documentation
I know that it is possible to consume a SQS queue using multiple threads. I would like to guarantee that each message will be consumed once. I know that it is possible to change the visibility timeout of a message, e.g., equal to my processing time. If my process spend more time than the visibility timeout (e.g. a slow connection) other thread can consume the same message.
What is the best approach to guarantee that a message will be processed once?
What is the best approach to guarantee that a message will be processed once?
You're asking for a guarantee - you won't get one. You can reduce probability of a message being processed more than once to a very small amount, but you won't get a guarantee.
I'll explain why, along with strategies for reducing duplication.
Where does duplication come from
When you put a message in SQS, SQS might actually receive that message more than once
For example: a minor network hiccup while sending the message caused a transient error that was automatically retried - from the message sender's perspective, it failed once, and successfully sent once, but SQS received both messages.
SQS can internally generate duplicates
Simlar to the first example - there's a lot of computers handling messages under the covers, and SQS needs to make sure nothing gets lost - messages are stored on multiple servers, and can this can result in duplication.
For the most part, by taking advantage of SQS message visibility timeout, the chances of duplication from these sources are already pretty small - like fraction of a percent small.
If processing duplicates really isn't that bad (strive to make your message consumption idempotent!), I'd consider this good enough - reducing chances of duplication further is complicated and potentially expensive...
What can your application do to reduce duplication further?
Ok, here we go down the rabbit hole... at a high level, you will want to assign unique ids to your messages, and check against an atomic cache of ids that are in progress or completed before starting processing:
Make sure your messages have unique identifiers provided at insertion time
Without this, you'll have no way of telling duplicates apart.
Handle duplication at the 'end of the line' for messages.
If your message receiver needs to send messages off-box for further processing, then it can be another source of duplication (for similar reasons to above)
You'll need somewhere to atomically store and check these unique ids (and flush them after some timeout). There are two important states: "InProgress" and "Completed"
InProgress entries should have a timeout based on how fast you need to recover in case of processing failure.
Completed entries should have a timeout based on how long you want your deduplication window
The simplest is probably a Guava cache, but would only be good for a single processing app. If you have a lot of messages or distributed consumption, consider a database for this job (with a background process to sweep for expired entries)
Before processing the message, attempt to store the messageId in "InProgress". If it's already there, stop - you just handled a duplicate.
Check if the message is "Completed" (and stop if it's there)
Your thread now has an exclusive lock on that messageId - Process your message
Mark the messageId as "Completed" - As long as this messageId stays here, you won't process any duplicates for that messageId.
You likely can't afford infinite storage though.
Remove the messageId from "InProgress" (or just let it expire from here)
Some notes
Keep in mind that chances of duplicate without all of that is already pretty low. Depending on how much time and money deduplication of messages is worth to you, feel free to skip or modify any of the steps
For example, you could leave out "InProgress", but that opens up the small chance of two threads working on a duplicated message at the same time (the second one starting before the first has "Completed" it)
Your deduplication window is as long as you can keep messageIds in "Completed". Since you likely can't afford infinite storage, make this last at least as long as 2x your SQS message visibility timeout; there is reduced chances of duplication after that (on top of the already very low chances, but still not guaranteed).
Even with all this, there is still a chance of duplication - all the precautions and SQS message visibility timeouts help reduce this chance to very small, but the chance is still there:
Your app can crash/hang/do a very long GC right after processing the message, but before the messageId is "Completed" (maybe you're using a database for this storage and the connection to it is down)
In this case, "Processing" will eventually expire, and another thread could process this message (either after SQS visibility timeout also expires or because SQS had a duplicate in it).
Store the message, or a reference to the message, in a database with a unique constraint on the Message ID, when you receive it. If the ID exists in the table, you've already received it, and the database will not allow you to insert it again -- because of the unique constraint.
AWS SQS API doesn't automatically "consume" the message when you read it with API,etc. Developer need to make the call to delete the message themselves.
SQS does have a features call "redrive policy" as part the "Dead letter Queue Setting". You just set the read request to 1. If the consume process crash, subsequent read on the same message will put the message into dead letter queue.
SQS queue visibility timeout can be set up to 12 hours. Unless you have a special need, then you need to implement process to store the message handler in database to allow it for inspection.
You can use setVisibilityTimeout() for both messages and batches, in order to extend the visibility time until the thread has completed processing the message.
This could be done by using a scheduledExecutorService, and schedule a runnable event after half the initial visibility time. The code snippet bellow creates and executes the VisibilityTimeExtender every half of the visibilityTime with a period of half the visibility time. (The time should to guarantee the message to be processed, extended with visibilityTime/2)
private final ScheduledExecutorService scheduler = Executors.newScheduledThreadPool(1);
ScheduledFuture<?> futureEvent = scheduler.scheduleAtFixedRate(new VisibilityTimeExtender(..), visibilityTime/2, visibilityTime/2, TimeUnit.SECONDS);
VisibilityTimeExtender must implement Runnable, and is where you update the new visibility time.
When the thread is done processing the message, you can delete it from the queue, and call futureEvent.cancel(true) to stop the scheduled event.