We have a micro-services architecture, with Kafka used as the communication mechanism between the services. Some of the services have their own databases. Say the user makes a call to Service A, which should result in a record (or set of records) being created in that service’s database. Additionally, this event should be reported to other services, as an item on a Kafka topic. What is the best way of ensuring that the database record(s) are only written if the Kafka topic is successfully updated (essentially creating a distributed transaction around the database update and the Kafka update)?
We are thinking of using spring-kafka (in a Spring Boot WebFlux service), and I can see that it has a KafkaTransactionManager, but from what I understand this is more about Kafka transactions themselves (ensuring consistency across the Kafka producers and consumers), rather than synchronising transactions across two systems (see here: “Kafka doesn't support XA and you have to deal with the possibility that the DB tx might commit while the Kafka tx rolls back.”). Additionally, I think this class relies on Spring’s transaction framework which, at least as far as I currently understand, is thread-bound, and won’t work if using a reactive approach (e.g. WebFlux) where different parts of an operation may execute on different threads. (We are using reactive-pg-client, so are manually handling transactions, rather than using Spring’s framework.)
Some options I can think of:
Don’t write the data to the database: only write it to Kafka. Then use a consumer (in Service A) to update the database. This seems like it might not be the most efficient, and will have problems in that the service which the user called cannot immediately see the database changes it should have just created.
Don’t write directly to Kafka: write to the database only, and use something like Debezium to report the change to Kafka. The problem here is that the changes are based on individual database records, whereas the business significant event to store in Kafka might involve a combination of data from multiple tables.
Write to the database first (if that fails, do nothing and just throw the exception). Then, when writing to Kafka, assume that the write might fail. Use the built-in auto-retry functionality to get it to keep trying for a while. If that eventually completely fails, try to write to a dead letter queue and create some sort of manual mechanism for admins to sort it out. And if writing to the DLQ fails (i.e. Kafka is completely down), just log it some other way (e.g. to the database), and again create some sort of manual mechanism for admins to sort it out.
Anyone got any thoughts or advice on the above, or able to correct any mistakes in my assumptions above?
Thanks in advance!
I'd suggest to use a slightly altered variant of approach 2.
Write into your database only, but in addition to the actual table writes, also write "events" into a special table within that same database; these event records would contain the aggregations you need. In the easiest way, you'd simply insert another entity e.g. mapped by JPA, which contains a JSON property with the aggregate payload. Of course this could be automated by some means of transaction listener / framework component.
Then use Debezium to capture the changes just from that table and stream them into Kafka. That way you have both: eventually consistent state in Kafka (the events in Kafka may trail behind or you might see a few events a second time after a restart, but eventually they'll reflect the database state) without the need for distributed transactions, and the business level event semantics you're after.
(Disclaimer: I'm the lead of Debezium; funnily enough I'm just in the process of writing a blog post discussing this approach in more detail)
Here are the posts
https://debezium.io/blog/2018/09/20/materializing-aggregate-views-with-hibernate-and-debezium/
https://debezium.io/blog/2019/02/19/reliable-microservices-data-exchange-with-the-outbox-pattern/
first of all, I have to say that I’m no Kafka, nor a Spring expert but I think that it’s more a conceptual challenge when writing to independent resources and the solution should be adaptable to your technology stack. Furthermore, I should say that this solution tries to solve the problem without an external component like Debezium, because in my opinion each additional component brings challenges in testing, maintaining and running an application which is often underestimated when choosing such an option. Also not every database can be used as a Debezium-source.
To make sure that we are talking about the same goals, let’s clarify the situation in an simplified airline example, where customers can buy tickets. After a successful order the customer will receive a message (mail, push-notification, …) that is sent by an external messaging system (the system we have to talk with).
In a traditional JMS world with an XA transaction between our database (where we store orders) and the JMS provider it would look like the following: The client sets the order to our app where we start a transaction. The app stores the order in its database. Then the message is sent to JMS and you can commit the transaction. Both operations participate at the transaction even when they’re talking to their own resources. As the XA transaction guarantees ACID we’re fine.
Let’s bring Kafka (or any other resource that is not able to participate at the XA transaction) in the game. As there is no coordinator that syncs both transactions anymore the main idea of the following is to split processing in two parts with a persistent state.
When you store the order in your database you can also store the message (with aggregated data) in the same database (e.g. as JSON in a CLOB-column) that you want to send to Kafka afterwards. Same resource – ACID guaranteed, everything fine so far. Now you need a mechanism that polls your “KafkaTasks”-Table for new tasks that should be send to a Kafka-Topic (e.g. with a timer service, maybe #Scheduled annotation can be used in Spring). After the message has been successfully sent to Kafka you can delete the task entry. This ensures that the message to Kafka is only sent when the order is also successfully stored in application database. Did we achieve the same guarantees as we have when using a XA transaction? Unfortunately, no, as there is still the chance that writing to Kafka works but the deletion of the task fails. In this case the retry-mechanism (you would need one as mentioned in your question) would reprocess the task an sends the message twice. If your business case is happy with this “at-least-once”-guarantee you’re done here with a imho semi-complex solution that could be easily implemented as framework functionality so not everyone has to bother with the details.
If you need “exactly-once” then you cannot store your state in the application database (in this case “deletion of a task” is the “state”) but instead you must store it in Kafka (assuming that you have ACID guarantees between two Kafka topics). An example: Let’s say you have 100 tasks in the table (IDs 1 to 100) and the task job processes the first 10. You write your Kafka messages to their topic and another message with the ID 10 to “your topic”. All in the same Kafka-transaction. In the next cycle you consume your topic (value is 10) and take this value to get the next 10 tasks (and delete the already processed tasks).
If there are easier (in-application) solutions with the same guarantees I’m looking forward to hear from you!
Sorry for the long answer but I hope it helps.
All the approach described above are the best way to approach the problem and are well defined pattern. You can explore these in the links provided below.
Pattern: Transactional outbox
Publish an event or message as part of a database transaction by saving it in an OUTBOX in the database.
http://microservices.io/patterns/data/transactional-outbox.html
Pattern: Polling publisher
Publish messages by polling the outbox in the database.
http://microservices.io/patterns/data/polling-publisher.html
Pattern: Transaction log tailing
Publish changes made to the database by tailing the transaction log.
http://microservices.io/patterns/data/transaction-log-tailing.html
Debezium is a valid answer but (as I've experienced) it can require some extra overhead of running an extra pod and making sure that pod doesn't fall over. This could just be me griping about a few back to back instances where pods OOM errored and didn't come back up, networking rule rollouts dropped some messages, WAL access to an aws aurora db started behaving oddly... It seems that everything that could have gone wrong, did. Not saying Debezium is bad, it's fantastically stable, but often for devs running it becomes a networking skill rather than a coding skill.
As a KISS solution using normal coding solutions that will work 99.99% of the time (and inform you of the .01%) would be:
Start Transaction
Sync save to DB
-> If fail, then bail out.
Async send message to kafka.
Block until the topic reports that it has received the
message.
-> if it times out or fails Abort Transaction.
-> if it succeeds Commit Transaction.
I'd suggest to use a new approach 2-phase message. In this new approach, much less codes are needed, and you don't need Debeziums any more.
https://betterprogramming.pub/an-alternative-to-outbox-pattern-7564562843ae
For this new approach, what you need to do is:
When writing your database, write an event record to an auxiliary table.
Submit a 2-phase message to DTM
Write a service to query whether an event is saved in the auxiliary table.
With the help of DTM SDK, you can accomplish the above 3 steps with 8 lines in Go, much less codes than other solutions.
msg := dtmcli.NewMsg(DtmServer, gid).
Add(busi.Busi+"/TransIn", &TransReq{Amount: 30})
err := msg.DoAndSubmitDB(busi.Busi+"/QueryPrepared", db, func(tx *sql.Tx) error {
return AdjustBalance(tx, busi.TransOutUID, -req.Amount)
})
app.GET(BusiAPI+"/QueryPrepared", dtmutil.WrapHandler2(func(c *gin.Context) interface{} {
return MustBarrierFromGin(c).QueryPrepared(db)
}))
Each of your origin options has its disadvantage:
The user cannot immediately see the database changes it have just created.
Debezium will capture the log of the database, which may be much larger than the events you wanted. Also deployment and maintenance of Debezium is not an easy job.
"built-in auto-retry functionality" is not cheap, it may require much codes or maintenance efforts.
Related
I am trying to implement event sourcing/CQRS/DDD for the first time, mostly for learning purposes, where there is the idea of an event store and a message queue such as Apache Kafka, and you have events flowing from event store => Kafka Connect JDBC/Debezium CDC => Kafka.
I am wondering why there needs to be a separate event store when it sounds like its purpose can be fulfilled by Kafka itself with its main features and log compaction or configuring log retention for permanent storage. Should I store my events in a dedicated store like RDBMS to feed into Kafka or should I feed them straight into Kafka?
Much of the literature on event-sourcing and cqrs comes from the [domain driven design] community; in its earliest form, CQRS was called DDDD... Distributed domain driven design.
One of the common patterns in domain driven design is to have a domain model ensuring the integrity of the data in your durable storage, which is to say, ensuring that there are no internal contradictions...
I am wondering why there needs to be a separate event store when it sounds like its purpose can be fulfilled by Kafka itself with its main features and log compaction or configuring log retention for permanent storage.
So if we want an event stream with no internal contradictions, how do we achieve that? One way is to ensure that only a single process has permission to modify the stream. Unfortunately, that leaves you with a single point of failure -- the process dies, and everything comes to an end.
On the other hand, if you have multiple processes updating the same stream, then you have risk of concurrent writes, and data races, and contradictions being introduced because one writer couldn't yet see what the other one did.
With an RDBMS or an Event Store, we can solve this problem by using transactions, or compare and swap semantics; and attempt to extend the stream with new events is rejected if there has been a concurrent modification.
Furthermore, because of its DDD heritage, it is common for the durable store to be divided into many very fine grained partitions (aka "aggregates"). One single shopping cart might reasonably have four streams dedicated to it.
If Kafka lacks those capabilities, then it is going to be a lousy replacement for an event store. KAFKA-2260 has been open for more than four years now, so we seem to be lacking the first. From what I've been able to discern from the Kakfa literature, it isn't happy about fine grained streams either (although its been a while since I checked, perhaps things have changed).
See also: Jesper Hammarbäck writing about this 18 months ago, and reaching similar conclusions to those expressed here.
Kafka can be used as a DDD event store, but there are some complications if you do so due to the features it is missing.
Two key features that people use with event sourcing of aggregates are:
Load an aggregate, by reading the events for just that aggregate
When concurrently writing new events for an aggregate, ensure only one writer succeeds, to avoid corrupting the aggregate and breaking its invariants.
Kafka can't do either of these currently, since 1 fails since you generally need to have one stream per aggregate type (it doesn't scale to one stream per aggregate, and this wouldn't necessarily be desirable anyway), so there's no way to load just the events for one aggregate, and 2 fails since https://issues.apache.org/jira/browse/KAFKA-2260 has not been implemented.
So you have to write the system in such as way that capabilities 1 and 2 aren't needed. This can be done as follows:
Rather than invoking command handlers directly, write them to
streams. Have a command stream per aggregate type, sharded by
aggregate id (these don't need permanent retention). This ensures that you only ever process a single
command for a particular aggregate at a time.
Write snapshotting code for all your aggregate types
When processing a command message, do the following:
Load the aggregate snapshot
Validate the command against it
Write the new events (or return failure)
Apply the events to the aggregate
Save a new aggregate snapshot, including the current stream offset for the event stream
Return success to the client (via a reply message perhaps)
The only other problem is handling failures (such as the snapshotting failing). This can be handled during startup of a particular command processing partition - it simply needs to replay any events since the last snapshot succeeded, and update the corresponding snapshots before resuming command processing.
Kafka Streams appears to have the features to make this very simple - you have a KStream of commands that you transform into a KTable (containing snapshots, keyed by aggregate id) and a KStream of events (and possibly another stream containing responses). Kafka allows all this to work transactionally, so there is no risk of failing to update the snapshot. It will also handle migrating partitions to new servers, etc. (automatically loading the snapshot KTable into a local RocksDB when this happens).
there is the idea of an event store and a message queue such as Apache Kafka, and you have events flowing from event store => Kafka Connect JDBC/Debezium CDC => Kafka
In the essence of DDD-flavoured event sourcing, there's no place for message queues as such. One of the DDD tactical patterns is the aggregate pattern, which serves as a transactional boundary. DDD doesn't care how the aggregate state is persisted, and usually, people use state-based persistence with relational or document databases. When applying events-based persistence, we need to store new events as one transaction to the event store in a way that we can retrieve those events later in order to reconstruct the aggregate state. Thus, to support DDD-style event sourcing, the store needs to be able to index events by the aggregate id and we usually refer to the concept of the event stream, where such a stream is uniquely identified by the aggregate identifier, and where all events are stored in order, so the stream represents a single aggregate.
Because we rarely can live with a database that only allows us to retrieve a single entity by its id, we need to have some place where we can project those events into, so we can have a queryable store. That is what your diagram shows on the right side, as materialised views. More often, it is called the read side and models there are called read-models. That kind of store doesn't have to keep snapshots of aggregates. Quite the opposite, read-models serve the purpose to represent the system state in a way that can be directly consumed by the UI/API and often it doesn't match with the domain model as such.
As mentioned in one of the answers here, the typical command handler flow is:
Load one aggregate state by id, by reading all events for that aggregate. It already requires for the event store to support that kind of load, which Kafka cannot do.
Call the domain model (aggregate root method) to perform some action.
Store new events to the aggregate stream, all or none.
If you now start to write events to the store and publish them somewhere else, you get a two-phase commit issue, which is hard to solve. So, we usually prefer using products like EventStore, which has the ability to create a catch-up subscription for all written events. Kafka supports that too. It is also beneficial to have the ability to create new event indexes in the store, linking to existing events, especially if you have several systems using one store. In EventStore it can be done using internal projections, you can also do it with Kafka streams.
I would argue that indeed you don't need any messaging system between write and read sides. The write side should allow you to subscribe to the event feed, starting from any position in the event log, so you can build your read-models.
However, Kafka only works in systems that don't use the aggregate pattern, because it is essential to be able to use events, not a snapshot, as the source of truth, although it is of course discussable. I would look at the possibility to change the way how events are changing the entity state (fixing a bug, for example) and when you use events to reconstruct the entity state, you will be just fine, snapshots will stay the same and you'll need to apply correction events to fix all the snapshots.
I personally also prefer not to be tightly coupled to any infrastructure in my domain model. In fact, my domain models have zero dependencies on the infrastructure. By bringing the snapshotting logic to Kafka streams builder, I would be immediately coupled and from my point of view it is not the best solution.
Theoretically you can use Kafka for Event Store but as many people mentioned above that you will have several restrictions, biggest of those, only able to read event with the offset in the Kafka but no other criteria.
For this reason they are Frameworks there dealing with the Event Sourcing and CQRS part of the problem.
Kafka is only part of the toolchain which provides you the capability of replaying events and back pressure mechanism that are protecting you from overload.
If you want to see how all fits together, I have a blog about it
I would like to implement the event-sourcing pattern using kafka as an event store.
I want to keep it as simple as possible.
The idea:
My app contains a list of customers. Customers an be created and deleted. Very simple.
When a request to create a customer comes in, I am creating the event CUSTOMER_CREATED including the customer data and storing this in a kafka topic using a KafkaProducer. The same when a customer is deleted with the event CUSTOMER_DELETED.
Now when i want to list all customers, i have to replay all events that happened so far and then get the current state meaning a list of all customers.
I would create a temporary customer list, and then processing all the events one by one (create customer, create customer, delete customer, create customer etc). (Consuming these events with a KafkaConsumer). In the end I return the temporary list.
I want to keep it as simple as possible and it's just about giving me an understanding on how event-sourcing works in practice. Is this event-sourcing? And also: how do I create snapshots when implementing it this way?
when i want to list all customers, i have to replay all events that happened so far
You actually don't, or at least not after your app starts fresh and is actively collecting / tombstoning the data. I encourage you to lookup the "Stream Table Duality", which basically states that your table is the current state of the world in your system, and a snapshot in time of all the streamed events thus far, which would be ((customers added + customers modified) - customers deleted).
The way you implement this in Kafka would be to use a compacted Kafka topic for your customers, which can be read into a Kafka Streams KTable, and persisted in memory or spill to disk (backed by RocksDB). The message key would be some UUID for the customer, or some other identifiable record that cannot change (e.g. not name, email, phone, etc. as all this can change)
With that, you can implement Interactive Queries on it to scan or lookup a certain customer's details.
Theoretically you can do Event Sourcing with Kafka as you mentioned, replaying all Events in the application start but as you mentioned, if you have 100 000 Events to reach a State it is not practical.
As it is mentioned in the previous answers, you can use Kafka Streaming KTable for sense of Event Sourcing but while KTable is hosted in Key/Value database RockDB, querying the data will be quite limited (You can ask what is the State of the Customer Id: 123456789 but you can't ask give me all Customers with State CUSTOMER_DELETED).
To achieve that flexibility, we need help from another pattern Command Query Responsibility Segregation (CQRS), personally I advice you to use Kafka reliable, extremely performant Broker and give the responsibility for Event Sourcing dedicated framework like Akka (which Kafka synergies naturally) with Apache Cassandra persistence and Akka Finite State Machine for the Command part and Akka Projection for the Query part.
If you want to see a sample how all these technology stacks plays together, I have a blog for it. I hope it can help you.
I want to use Lagom to build a data processing pipeline. The first step in this pipeline is a service using a Twitter client to supscribe to a stream of Twitter messages. For each new message I want to persist the message in Cassandra.
What I dont understand is given I model my Aggregare root as a List of TwitterMessages for example, after running for some time this aggregare root will be several gigabytes in size. There is no need to store all the TwitterMessages in memory since the goal of this one service is just to persist each incomming message and then publish the message out to Kafka for the next service to process.
How would I model my aggregate root as Persistent Entitie for a stream of messages without it consuming unlimited resources? Are there any example code showing this usage if Lagom?
Event sourcing is a good default go to, but not the right solution for everything. In your case it may not be the right approach. Firstly, do you need the Tweets persisted, or is it ok to publish them directly to Kafka?
Assuming you need them persisted, aggregates should store in memory whatever they need to validate incoming commands and generate new events. From what you've described, your aggregate doesn't need any data to do that, so your aggregate would not be a list of Twitter messages, rather, it could just be NotUsed. Each time it gets a command it emits a new event for that Tweet. The thing here is, it's not really an aggregate, because you're not aggregating any state, you're just emitting events in response to commands with no invariants or anything. And so, you're not really using the Lagom persistent entity API for what it was made to be used for. Nevertheless, it may make sense to use it in this way anyway, it's a high level API that comes with a few useful things, including the streaming functionality. But there are also some gotchas that you should be aware of, you put all your Tweets in one entity, you limit your throughput to what one core on one node can do sequentially at a time. So maybe you could expect to handle 20 tweets a second, if you ever expect it to ever be more than that, then you're using the wrong approach, and you'll need to at a minimum distribute your tweets across multiple entities.
The other approach would be to simply store the messages directly in Cassandra yourself, and then publish directly to Kafka after doing that. This would be a lot simpler, a lot less mechanics involved, and it should scale very nicely, just make sure you choose your partition key columns in Cassandra wisely - I'd probably partition by user id.
We want to introduce a Kafka Event Bus which will contain some events like EntityCreated or EntityModified into our application so other parts of our system can consume from it. The main application uses an RDMS (i.e. postgres) under the hood to store the entities and their relationship.
Now the issue is how you make sure that you only send out EntityCreated events on Kafka if you successfully saved to the RDMS. If you don't make sure that this is the case, you end up with inconsistencies on the consumers.
I saw three solutions, of which none is convincing:
Don't care: Very dangerous, there can be something going wrong when inserting into an RDMS.
When saving the entity, also save the message which should be sent into a own table. Then have a separate process which consumes from this table and publishes to Kafka and after a success deleted from this table. This is quiet complex to implement and also looks like an anti-pattern.
Insert into the RDMS, keep the (SQL-) Transaction open until you wrote successfully to Kafka and only then commit. The problem is that you potentially keep the RDMS transaction open for some time. Don't know how big the problem is.
Do real CQRS which means that you don't save at all to the RDMS but construct the RDMS out of the Kafka queue. That seems like the ideal way but is difficult to retrofit to a service. Also there are problems with inconsistencies due to latencies.
I had difficulties finding good solutions on the internet.
Maybe this question is to broad, feel free to point me somewhere it fits better.
When saving the entity, also save the message which should be sent into a own table. Then have a separate process which consumes from this table and publishes to Kafka and after a success deleted from this table. This is quiet complex to implement and also looks like an anti-pattern.
This is, in fact, the solution described by Udi Dahan in his talk: Reliable Messaging without Distributed Transactions. It's actually pretty close to a "best practice"; so it may be worth exploring why you think it is an anti-pattern.
Do real CQRS which means that you don't save at all to the RDMS but construct the RDMS out of the Kafka queue.
Noooo! That's where the monster is hiding! (see below).
If you were doing "real CQRS", your primary use case would be that your writers make events durable in your book of record, and the consumers would periodically poll for updates. Think "Atom Feed", with the additional constraint that the entries, and the order of entries, is immutable; you can share events, and pages of events; cache invalidation isn't a concern because, since the state doesn't change, the event representations are valid "forever".
This also has the benefit that your consumers don't need to worry about message ordering; the consumers are reading documents of well ordered events with pointers to the prior and subsequent documents.
Furthermore, you've additionally gotten a solution to a versioning story: rather than broadcasting N different representations of the same event, you send out one representation, and then negotiate the content when the consumer polls you.
Now, polling does have latency issues; you can reduce the latency by broadcasting an announcement of the update, and notifying the consumers that new events are available.
If you want to reduce the rate of false polling (waking up a consumer for an event that they don't care about), then you can start adding more information into the notification, so that the consumer can judge whether to pull an update.
Notice that "wake up and maybe poll" is a process that is triggered by a single event in isolation. "Wake up and poll just this message" is another variation on the same idea. We broadcast a thin version of EmailDeliveryScheduled; and the service responsible for that calls back to ask for the email/an enhanced version of the event with the details needed to construct the email.
These are specializations of "wake up and consume the notification". If you have a use case where you can't afford the additional latency required to poll, you can use the state in the representation of the isolated event.
But trying to reproduce an ordered sequence of events when that information is already exposed as a sharable, cacheable document... That's a pretty unusual use case right there. I wouldn't worry about it as a general problem to solve -- my guess is that these cases are rare, and not easily generalized.
Note that all of the above is about messaging, not about Kafka. Notice that messaging and event sourcing are documented as different use cases. Jay Kreps wrote (2013)
I use the term "log" here instead of "messaging system" or "pub sub" because it is a lot more specific about semantics and a much closer description of what you need in a practical implementation to support data replication.
You can think of the log as acting as a kind of messaging system with durability guarantees and strong ordering semantics
The book of record should be the sole authority for the order of event messages. Any consumer that cares about order should be reading ordered documents from the book of record, rather than reading unordered documents and reconstructing the order.
In your current design....
Now the issue is how you make sure that you only send out EntityCreated events on Kafka if you successfully saved to the RDMS.
If the RDBMS is the book of record (the source of "truth"), then the Kafka log isn't (yet).
You can get there from here, over a number of gentle steps; roughly, you add events into the existing database, you read from the existing database to write into kafka's log; you use kafka's log as a (time delayed) source of truth to build a replica of the existing RDBMS, you migrate your read use cases to the replica, you migrate your write use cases to kafka, and you decommission the legacy database.
Kafka's log may or may not be the book of record you want. Greg Young has been developing Get Event Store for quite some time, and has enumerated some of the tradeoffs (2016). Horses for courses - I wouldn't expect it to be too difficult to switch the log from one of these to the other with a well written code base, but I can't speak at all to the additional coupling that might occur.
There is no perfect way to do this if your requirement is look SQL & kafka as a single node. So the question should be: "What bad things(power failure, hardware failure) I can afford if it happen? What the changes(programming, architecture) I can take if it must apply to my applications?"
For those points you mentioned:
What if the node fail after insert to kafka before delete from sql?
What if the node fail after insert to kafka before commit the sql transaction?
What if the node fail after insert to sql before commit the kafka offset?
All of them will facing the risk of data inconsistency(4 is slightly better if the data insert to sql can not success more than once such as they has a non database generated pk).
From the viewpoint of changes, 3 is smallest, however, it will decrease sql throughput. 4 is biggest due to your business logic model will facing two kinds of database when you coding(write to kafka by a data encoder, read from sql by sql sentence), it has more coupling than others.
So the choice is depend on what your business is. There is no generic way.
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.