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It is a Design Problem which I am listing out here.

I have different set of business operations that are carried out for different business entities.

Operations:

  • Operation A
  • Operation B
  • Operation C

For Example I have an Entity A. Entity A's data could be in parts, for example:

  • Entity A's (Jan Data)
  • Entity B's (Feb Data) etc.

To complete a Use Case all Operations (A,B,C) should be performed. Now these operations are performed and are independent of each other and can be performed in parallel, the only condition is that they should be of different Entities. So Entity A can't have all the operations (A, B or C) executing in parallel. And these operations are running on server side.

How to scale this and provide a solution?

I am thinking of following solution and would like to have inputs from the community on this.

I am thinking of three Queues for operations which I mentioned above

  • Queue A carrying out Operation A
  • Queue B carrying out Operation B
  • Queue C carrying out Operation C

And all the Consumers will be listening to these Queues.

  • Consumer A (or multiple consumers)
  • Consumer B (or multiple consumers)
  • Consumer C (or multiple consumers)

And my server would be load balanced, and I will be having a Single Message Queue containing these three queues.

So it is possible that I have 2 servers running and on every server there are for example 5 threads (Consumers) running, so there will be 10 instances of Consumer A running in parallel picking the data from the Message Queue A.

As I stated earlier that for the same entity A(that is the business use case which I have) all these operations (Operation A, Operation B and Operation C) can't be running in parallel, they should be only of them being executed.

So what I am thinking is to have a database entry for the Entity A and all the consumers must check whether there is a Database Entry for Entity A,

  • if not then

    1. Make an entry to Database for Entity A
    2. Go and execute the Operation
    3. Remove the Entry from the Database for Entity A
  • if there is an entry in Database found

    1. Enqueue the Data for Entity A again from the Queue where it was picked.

Is there any better solution possible for such a design problem?

  • Or maybe just use an existing distributed message queue system? – rwong Dec 26 '14 at 1:52
1

You can partition your data easily with consistent hashing, in this case you would use the entity as the hash key. The consistent hash takes a key and # of "buckets" as input and gives you back the bucket for that key.

With multiple servers in mind, a simple solution would be to pick a number of partitions up front (lets say 6), which means you will have 6 queues. When producing messages, calculate the bucket [consistentHash(entity.id, 6)] and put the message on the queue (partition) that corresponds to that bucket. This gives you ordering of messages per entity.

On the consumer side, simply ensure you have exactly one consumer per queue (partition). You can have as many servers as you want as long as there are no competing consumers.

You can then take this one step further inside each server to improve parallelism. The consumer of each queue (partition) can be a router, which simply takes each message and does another consistent hash on the entity, to N buckets where N is the number of your worker threads you want for parallelism, then hand off the message to the thread for the calculated bucket.

This setup routes messages for the same entity consistently to the same server, and the server will consistently route the message to the same worker thread. Assuming the N worker threads is relatively high, you get excellent parallelization of your tasks with message ordering based on any key you want.

  • I guess your answer is excellent and suits the requirement. But there is one problem i see , i am not sure if there is a solution to this as well, the messaging queue which I am taking about is going to be a persistent one . I can't afford to loose message when i take the consumer as a the router , I will have to handle everything whether the consumers consumer has completed successfully or not and lot of other stuff. – Abhishek Jan 10 '15 at 15:33
  • @user3475665, I use this paradigm backed by Redis lists. You can come up with a solution using BRPOPLPUSH to atomically consume and push to a temp list, with a callback to del the temp entry after processing. A reaper could be used to pickup orphaned messages for reliability. Coupled with idempotent messages, it is a safe, robust solution – Alex Jan 10 '15 at 18:05
2

You don't need any DB operation for synchronization between the operations.

If you need all the 3 operations to be performed on your data sequentially :

Send your Data to Queue A, which has consumers which perform operation A and at the end of it, send it to Queue B. Consumers on Queue B will perform Operation B and send the data to Queue C where consumers will be waiting to perform operation C on it.

If you need all the 3 operations to be performed on your data, but sequence is not important :

Keep only one Queue with 10 Consumers for each Operation (5 on each machine) Add headers OpA=true, opB=true, opC=true to your data when adding it to the Queue for the first time. The Consumers for Operation A should only select those WHERE opA=true . Use Selective Consumer for this (http://camel.apache.org/selective-consumer.html) When a Consumer gets the message, it should perform it's respective operation, remove it's respective header and send the data back to the same Queue (only if there are any more headers after removing it's own header).

  • Thanks for your answer, but there is a possiblity that Entity A while getting processed in Queue B for example, there is one more record of Entity A being pushed to Queue A, and the Use Case is that I need to prevent Entity A being picked by Queue A, until Queue B finishes processing Entity A. The use case is that at one point of time only 1 Record related to Entity A should get processed by any of the consumer. – Abhishek Dec 27 '14 at 4:57
  • And your second solution is pretty good, but if there are multiple consumers consuming different instances of Entity A then it will fail. – Abhishek Dec 27 '14 at 5:16
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This sounds like a classic case for the routing slip pattern.

This approach is exactly as you described - separate queues for each consumer / message processor. Only one consumer can work on a given message (Entity) at a time, but the consumers are free to work on any messages that are in their queue.

The main benefit of this approach is that you don't have to know the message route when you're crafting the code. You can define run-time routing based upon the message type; load on the individual consumers; or whatever makes sense for your requirements.


The biggest advantages of this approach are:

  • Each consumer maintains its independence from the other consumers. A long running consumer process can be individually supplemented by adding additional resources. Or if your consumers support it, additional resource can be dynamically added or shifted based upon current workloads.

  • Avoiding the need for database entries and locking. Generally speaking, DBs don't handle large scale event messaging like this very well. You can make it work, but the solution tends to be brittle and prone to breaking when requirements change.

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