Skip to main content

RabbitMQ retrying failed messages

We are a Hybrid cloud file server company and recently we had a requirement where we had to allow users to View a file with Google docs and upon saving the file in Google docs we need to download the file back and create a version in Cloud file server. Upon saving the file in google docs we insert a message in rabbitMQ from app nodes and then a background GoogleDocs consumer process pulls the file and create a version using REST api of the cloud file server.

As there are many components involved here there can be multiple failure scenarios from Google throttling us, to appservers going down for maintenance, to app servers throttling the background jobs if they are under heavy load.  The problem with rabbitMQ is that once a message is delivered to the consumer even if the consumer doesn't acknowledges it, RabbitMQ won't redeliver the unacked message to consumers until the channel is properly closed and reopened.  I tried checking rabbit transactions api to rollback the transaction in case of external component failure and also tryied basic_recover but it has its own issues and none of them worked.

So the best idea I came up was to start a timer thread in python that wakes up every 1 minute and polls Rabbitmq for pending messages and processes them and closes the channel/connection and  goes back to sleep for 1 minute again.  This way all unacked messages would be redelivered.

Comments

  1. That can be useful, I bet you have to do extra job in terms of upgrading your program.

    ReplyDelete
  2. Yes I think recently rabbitmq released basic_reject api but we havent upgraded to that version, upgrading to that probably will solve this.

    ReplyDelete
  3. Erm... There is a command named basic_reject which rejects a message; then rabbit tries to redeliver it... Are you sure you're using the right library?

    ReplyDelete

Post a Comment

Popular posts from this blog

Haproxy and tomcat JSESSIONID

One of the biggest problems I have been trying to solve at our startup is to put our tomcat nodes in HA mode. Right now if a customer comes, he lands on to a node and remains there forever. This has two major issues: 1) We have to overprovision each node with ability to handle worse case capacity. 2) If two or three high profile customers lands on to same node then we need to move them manually. 3) We need to cut over new nodes and we already have over 100+ nodes.  Its a pain managing these nodes and I waste lot of my time in chasing node specific issues. I loath when I know I have to chase this env issue. I really hate human intervention as if it were up to me I would just automate thing and just enjoy the fruits of automation and spend quality time on major issues rather than mundane task,call me lazy but thats a good quality. So Finally now I am at a stage where I can put nodes behing HAProxy in QA env. today we were testing the HA config and first problem I immediately

Adding Jitter to cache layer

Thundering herd is an issue common to webapp that rely on heavy caching where if lots of items expire at the same time due to a server restart or temporal event, then suddenly lots of calls will go to database at same time. This can even bring down the database in extreme cases. I wont go into much detail but the app need to do two things solve this issue. 1) Add consistent hashing to cache layer : This way when a memcache server is added/removed from the pool, entire cache is not invalidated.  We use memcahe from both python and Java layer and I still have to find a consistent caching solution that is portable across both languages. hash_ring and spymemcached both use different points for server so need to read/test more. 2) Add a jitter to cache or randomise the expiry time: We expire long term cache  records every 8 hours after that key was added and short term cache expiry is 2 hours. As our customers usually comes to work in morning and access the cloud file server it can happe

Spring 3.2 quartz 2.1 Jobs added with no trigger must be durable.

I am trying to enable HA on nodes and in that process I found that in a two test node setup a job that has a frequency of 10 sec was running into deadlock. So I tried upgrading from Quartz 1.8 to 2.1 by following the migration guide but I ran into an exception that says "Jobs added with no trigger must be durable.". After looking into spring and Quartz code I figured out that now Quartz is more strict and earlier the scheduler.addJob had a replace parameter which if passed to true would skip the durable check, in latest quartz this is fixed but spring hasnt caught up to this. So what do you do, well I jsut inherited the factory and set durability to true and use that public class DurableJobDetailFactoryBean extends JobDetailFactoryBean {     public DurableJobDetailFactoryBean() {         setDurability(true);     } } and used this instead of JobDetailFactoryBean in the spring bean definition     <bean id="restoreJob" class="com.xxx.infrastructure.quar