I've just recently gotten into using Rebus, and noticed that it always creates transactional msmq-queues resulting in heavy traffic to the HDD (0,5 - 5mb/sec). Is this intentional - and can something be done to avoid it?
It's correctly observed that Rebus (with its default MsmqMessageQueue transport) always creates transactional MSMQ queues. It will also refuse to work with non-transactional input queues, throwing an error at startup if you created a non-transactional queue yourself and attempt to use it.
This is because the philosophy in Rebus revolves around the idea that messages are important data, just as important as the data in your SQL Server or whichever database you're using.
And yes, the way MSMQ implements durability is that messages are written to disk when they're sent, so that probably explains the disk activity you're seeing.
If you have a different opinion as to how you'd like your system to treat its messages, there's nothing that prevents you from replacing Rebus' transport with something that can work with non-transactional MSMQ. Keep in mind though, that all of Rebus' delivery guarantees will be void if you do so ;)
We had the very same observation, the annoying aspect is that we have 300/500 KB/sec write on disk even when there are no message on the queue. It seems that only polling from the queue causes a constant write on disk.
Gian Maria.
Related
I frequently see queues in software architecture, especially those called "scalable" with prominent representative of Actor from Akka.io multi-actor platform. However, how can queue be scalable, if we have to synchronize placing messages in queue (and therefore operate in single thread vs multi thread) and again synchronize taking out messages from queue (to assure, that message it taken exactly once)? It get's even more complicated, when those messages can change state of (actor) system - in this case even after taking out message from queue, it cannot be load balanced, but still processed in single thread.
Is it correct, that putting messages in queue must be synchronized?
Is it correct, that putting messages out of queue must be synchronized?
If 1 or 2 is correct, then how is queue scalable? Doesn't synchronization to single thread immediately create bottleneck?
How can (actor) system be scalable, if it is statefull?
Does statefull actor/bean mean, that I have to process messages in single thread and in order?
Does statefullness mean, that I have to have single copy of bean/actor per entire system?
If 6 is false, then how do I share this state between instances?
When I am trying to connect my new P2P node to netowrk, I believe I have to have some "server" that will tell me, who are other peers, is that correct? When I am trying to download torrent, I have to connect to tracker - if there is "server" then we do we call it P2P? If this tracker will go down, then I cannot connect to peers, is that correct?
Is synchronization and statefullness destroying scalability?
Is it correct, that putting messages in queue must be synchronized?
Is it correct, that putting messages out of queue must be synchronized?
No.
Assuming we're talking about the synchronized java keyword then that is a reenetrant mutual exclusion lock on the object. Even multiple threads accessing that lock can be fast as long as contention is low. And each object has its own lock so there are many locks, each which only needs to be taken for a short time, i.e. it is fine-grained locking.
But even if it did, queues need not be implemented via mutual exclusion locks. Lock-free and even wait-free queue data structures exist. Which means the mere presence of locks does not automatically imply single-threaded execution.
The rest of your questions should be asked separately because they are not about message queuing.
Of course you are correct in that a single queue is not scalable. The point of the Actor Model is that you can have millions of Actors and therefore distribute the load over millions of queues—if you have so many cores in your cluster. Always remember what Carl Hewitt said:
One Actor is no actor. Actors come in systems.
Each single actor is a fully sequential and single-threaded unit of computation. The whole model is constructed such that it is perfectly suited to describe distribution, though; this means that you create as many actors as you need.
What are architectural patterns/solutions that make distributed queues tick?
Please share for both ordered and non-ordered types.
You can think of the backend of a queue as a replicated database. (I am assuming the queues you are talking about consider themselves as durable: when they accept a message, they guarantee at least once delivery.)
As a replicated database, the message queue backend uses a replication protocol to make sure the message is on at least N hosts before acknowledging receipt to the sender. Common replication protocols are 2PC, 3PC, and consensus protocols like Raft, Multi-Paxos, and Chain Replication.
To send a message to a receiver, you have to do almost the same replication with a message lease. The queue server reserves the message for a certain period of time; it sends the message to the receiver, and if/when the receiver ackowledges receipt of the message the server deletes the message. Otherwise, the servers will resend the message to the next available receiver.
Some message queues stop there, others add lots of bells and whistles. SQS is one queue implementation that doesn't add many bells and whistles so that it can scale more. It allows them, for example, to shard the queue so that one SQS queue is actually made of many—even thousands—of these queues as described above. As an aside, I once heard one SQS developer ask another "What does 'ordering' mean when you are accepting millions of messages per second?"
That being said, some queues do provide strong ordering guarantees. (I have implemented a couple of these types of systems.) The cost of this is less ability to scale. To maintain ordering the queue's complexity goes way up. The queue has to maintain an ordered log of all the messages, and have the same ordering replicated across its servers. This is much much harder than unordered replication. Ordered queue systems typically elect a master to maintain the ordering and all messages are routed to the master. They also tend to use the more complex protocols for replication.
Anyone know of a message bus implementation which offers granular control over consistency guarantees? Full ACID is too slow and no ACID is too wrong.
We're currently using Rhino ESB wrapping MSMQ for our messaging. When using durable, transactional messaging with distributed transactions, MSMQ can block the commit for considerable time while it waits on I/O completion.
Our messages fall into two general categories: business logic and denormalisation. The latter account for a significant percentage of message bus traffic.
Business logic messages require the guarantees of full ACID and MSMQ has proven quite adequate for this.
Denormalisation messages:
MUST be durable.
MUST NOT be processed until after the originating transaction completes.
MAY be processed multiple times.
MAY be processed even if the originating transaction rolls back, as long as 2) is adhered to.
(In some specific cases the durability requirements could probably be relaxed, but identifying and handling those cases as exceptions to the rule adds complexity.)
All denormalisation messages are handled in-process so there is no need for IPC.
If the process is restarted, all transactions may be assumed to have completed (committed or rolled back) and all denormalisation messages not yet processed must be recovered. It is acceptable to replay denormalisation messages which were already processed.
As far as I can tell, messaging systems which deal with transactions tend to offer a choice between full ACID or nothing, and ACID carries a performance penalty. We're seeing calls to TransactionScope#Commit() taking as long as a few hundred milliseconds in some cases depending on the number of messages sent.
Using a non-transactional message queue causes messages to be processed before their originating transaction completes, resulting in consistency problems.
Another part of our system which has similar consistency requirements but lower complexity is already using a custom implementation of something akin to a transaction log, and generalising that for this use case is certainly an option, but I'd rather not implement a low-latency, concurrent, durable, transactional messaging system myself if I don't have to :P
In case anyone's wondering, the reason for requiring durability of denormalisation messages is that detecting desyncs and fixing desyncs can be extremely difficult and extremely expensive respectively. People do notice when something's slightly wrong and a page refresh doesn't fix it, so ignoring desyncs isn't an option.
It's not exactly the answer you're looking for, but Jonathan Oliver has written extensively on how to avoid using distributed transactions in messaging and yet maintain transactional integrity:
http://blog.jonathanoliver.com/2011/04/how-i-avoid-two-phase-commit/
http://blog.jonathanoliver.com/2011/03/removing-2pc-two-phase-commit/
http://blog.jonathanoliver.com/2010/04/idempotency-patterns/
Not sure if this helps you but, hey.
It turns out that MSMQ+SQL+DTC don't even offer the consistency guarantees we need. We previously encountered a problem where messages were being processed before the distributed transaction which queued them had been committed to the database, resulting in out-of-date reads. This is a side-effect of using ReadCommitted isolation to consume the queue, since:
Start transaction A.
Update database table in A.
Queue message in A.
Request commit of A.
Message queue commits A
Start transaction B.
Read message in B.
Read database table in B, using ReadCommitted <- gets pre-A data.
Database commits A.
Our requirement is that B's read of the table block on A's commit, which requires Serializable transactions, which carries a performance penalty.
It looks like the normal thing to do is indeed to implement the necessary constraints and guarantees oneself, even though it sounds like reinventing the wheel.
Anyone got any comments on this?
If you want to do this by hand, here is a reliable approach. It satisfies (1) and (2), and it doesn't even need the liberties that you allow in (3) and (4).
Producer (business logic) starts transaction A.
Insert/update whatever into one or more tables.
Insert a corresponding message into PrivateMessageTable (part of the domain, and unshared, if you will). This is what will be distributed.
Commit transaction A. Producer has now simply and reliably performed its writes including the insertion of a message, or rolled everything back.
Dedicated distributer job queries a batch of unprocessed messages from PrivateMessageTable.
Distributer starts transaction B.
Mark the unprocessed messages as processed, rolling back if the number of rows modified is different than expected (two instances running at the same time?).
Insert a public representation of the messages into PublicMessageTable (a publically exposed table, in whatever way). Assign new, strictly sequential Ids to the public representations. Because only one process is doing these inserts, this can be guaranteed. Note that the table must be on the same host to avoid 2PC.
Commit transaction B. Distributor has now distributed each message to the public table exactly once, with strictly sequantial Ids.
A consumer (there can be several) queries the next batch of messages from PublicMessageTable with Id greater than its own LastSeenId.
Consumer starts transaction C.
Consumer inserts its own representation of the messages into its own table ConsumerMessageTable (thus advancing LastSeenId). Insert-ignore can help protect against multiple instances running. Note that this table can be in a completely different server.
Commit transaction C. Consumer has now consumed each message exactly once, in the same order the messages were made publically available, without ever skipping a message.
We can do whatever we want based on the consumed messages.
Of course, this requires very careful implementation.
It is even suitable for database clusters, as long as there is only a single write node, and both reads and writes perform causality checks. It may well be that having one of these is sufficient, but I'd have to consider the implications more carefully to make that claim.
How to I exactly get the acknowledgement from Kafka once the message is consumed or processed. Might sound stupid but is there any way to know the start and end offset of that message for which the ack has been received ?
What I found so far is in 0.8 they have introduced the following way to choose from the offset for reading ..
kafka.api.OffsetRequest.EarliestTime() finds the beginning of the data in the logs and starts streaming from there, kafka.api.OffsetRequest.LatestTime() will only stream new messages.
example code
https://cwiki.apache.org/confluence/display/KAFKA/0.8.0+SimpleConsumer+Example
Still not sure about the acknowledgement part
Kafka isn't really structured to do this. To understand why, review the design documentation here.
In order to provide an exactly-once acknowledgement, you would need to create some external tracking system for your application, where you explicitly write acknowledgements and implement locks over the transaction id's in order to ensure things are only ever processed once. The computational cost of implementing such as system is extraordinarily high, and is one of the main reasons that large transactional systems require comparatively exotic hardware and have arguably lower scalability than systems such as Kafka.
If you do not require strong durability semantics, you can use the groups API to keep rough track of when the last message was read. This ensures that every message is read at least once. Note that since the groups API does not provide you the ability to explicitly track your applications own processing logic, that your actual processing guarantees are fairly weak in this scenario. Schemes that rely on idempotent processing are common in this environment.
Alternatively, you may use the poorly-named SimpleConsumer API (it is quite complex to use), which enables you to explicitly track timestamps within your application. This is the highest level of processing guarantee that can be achieved through the native Kafka API's since it enables you to track your applications own processing of the data that is read from the queue.
I am trying to implement job queue with MSMQ to save up some time on me implementing it in SQL. After reading around I realized MSMQ might not offer what I am after. Could you please advice me if my plan is realistic using MSMQ or recommend an alternative ?
I have number of processes picking up jobs from a queue (I might need to scale out in the future), once job is picked up processing follows, during this time job is locked to other processes by status, if needed job is chucked back (status changes again) to the queue for further processing, but physically the job still sits in the queue until completed.
MSMQ doesn't let me to keep the message in the queue while working on it, eg I can peek or read. Read takes message out of queue and peek doesn't allow changing the message (status).
Thank you
Using MSMQ as a datastore is probably bad as it's not designed for storage at all. Unless the queues are transactional the messages may not even get written to disk.
Certainly updating queue items in-situ is not supported for the reasons you state.
If you don't want a full blown relational DB you could use an in-memory cache of some kind, like memcached, or a cheap object db like raven.
Take a look at RabbitMQ, or many of the other messages queues. Most offer this functionality out of the box.
For example. RabbitMQ calls what you are describing, Work Queues. Multiple consumers can pull from the same queue and not pull the same item. Furthermore, if you use acknowledgements and the processing fails, the item is not removed from the queue.
.net examples:
https://www.rabbitmq.com/tutorials/tutorial-two-dotnet.html
EDIT: After using MSMQ myself, it would probably work very well for what you are doing, as far as I can tell. The key is to use transactions and multiple queues. For example, each status should have it's own queue. It's fairly safe to "move" messages from one queue to another since it occurs within a transaction. This moving of messages is essentially your change of status.
We also use the Message Extension byte array for storing message metadata, like status. This way we don't have to alter the actual message when moving it to another queue.
MSMQ and queues in general, require a different set of patterns than what most programmers are use to. Keep that in mind.
Perhaps, if you can give more information on why you need to peek for messages that are currently in process, there would be a way to handle that scenario with MSMQ. You could always add a database for additional tracking.