I'm trying to understand how Spring Batch does transaction management. This is not a technical question but more of conceptual one: what approach does Spring Batch use and what are the consequences of that approach?
Let me try to clarify this question a bit. For instance, looking at the TaskletStep, I see that generally a step execution looks something like this:
several JobRepository transactions to prepare the step metadata
a business transaction for every chunk to process
more JobRepository transactions to update the step metadata with the results of chunk processing
This seems to make sense. But what about a failure between 2 and 3? This would mean the business transaction was committed but Spring Batch was unable to record that fact in its internal metadata. So a restart would reprocess the same items again even though they have already been committed. Right?
I'm looking for an explanation of these details and the consequences of the design decisions made in Spring Batch. Is this documented somewhere? The Spring Batch reference guide has very few details on this. It simply explains things from the application developer's point of view.
There are two fundamental types of steps in Spring Batch, a Tasklet Step and a chunk based step. Each has it's own transaction details. Let's look at each:
Tasklet Based Step
When a developer implements their own tasklet, the transactionality is pretty straight forward. Each call to the Tasklet#execute method is executed within a transaction. You are correct in that there are updates before and after a step's logic is executed. They are not technically wrapped in a transaction since rollback isn't something we'd want to support for the job repository updates.
Chunk Based Step
When a developer uses a chunk based step, there is a bit more complexity involved due to the added abilities for skip/retry. However, from a simple level, each chunk is processed in a transaction. You still have the same updates before and after a chunk based step that are non-transactional for the same reasons previously mentioned.
The "What if" scenario
In your question, you ask about what would happen if the business logic completed but the updates to the job repository failed for some reason. Would the previously updated items be re-processed on a restart. As in most things, that depends. If you are using stateful readers/writers like the FlatFileItemReader, with each commit of the business transaction, the job repository is updated with the current state of what has been processed (within the same transaction). So in that case, a restart of the job would pick up where it left off...in this case at the end, and process no additional records.
If you are not using stateful readers/writers or have save state turned off, then it is a bit of buyer beware and you may end up with the situation you describe. The default behavior in the framework is to save state so that restartability is preserved.
Related
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.
I am working on an application where multiple clients will be writing to a queue (or queues), and multiple workers will be processing jobs off the queue. The problem is that in some cases, jobs are dependent on each other. By 'dependent', I mean they need to be processed in order.
This typically happens when an entity is created by the user, then deleted shortly after. Obviously I want the first job (i.e. the creation) to take place before the deletion. The problem is that creation can take a lot longer than deletion, so I can't guarantee that it will be complete before the deletion job commences.
I imagine that this type of problem is reasonably common with asynchronous processing. What strategies are there to deal with it? I know that I can assign priorities to queues to have some control over the processing order, but this is not good enough in this case. I need concrete guarantees.
This may not fit your model, but the model I have used involves not providing the deletion functionality until the creation functionality is complete.
When Create_XXX command is completed, it is responsible for raising an XXX_Created event, which also gets put on the queue. This event can then be handled to enable the deletion functionality, allowing the deletion of the newly created item.
The process of a Command completing, then raising an event which is handled and creates another Command is a common method of ensuring Commands get processed in the desired order.
I think an handy feature for your use case is Job chaining:
https://laravel.com/docs/5.5/queues#job-chaining
I am writing a project that will be generating reports. It would read all requests from a database by making a rest call, based on the type of request it will make a rest call to an endpoint, after getting the response it will save the response in an object and save it back to the database by making a call to an endpoint.
I am using spring-batch to handle the batch work. So far what I came up with is a single job (reader, processor, writer) that will do the whole things. I am not sure if this is the correct design considering
I do not want to queue up requests if some request is taking a long time to get a response back. [not sure yet]
I do not want to hold up saving response until all the responses are received. [using commit-internal will help]
If the job crashes for some reason, how can I restart the job [maybe using batch-admin will help but what are my other options]
By using chunk oriented processing Reader, Processor and Writer get executed in order until Reader has nothing to return.
If you can read one item at a time, process it and send it back to the endpoint that handles the persistence this approach is handy.
If you must read ALL the information at once the reader will get a big collection with all items and pass it to processor. The processor will process all the items and send the result to the writer. You cannot send just a few to the writer so you would have to do the persistence directly from processor and that would be against the design.
So, as I understand this, you have two options:
Design a reader that can read one item at a time. Use the chunk oriented processing that you already started to read one item, process it and send it back for persistence. Have a look at how other readers are implemented (like JdbcCursorItemReader).
You create a tasklet that reads the whole collection of items process it and sends them back for processing. You can break this in different tasklets.
commit-interval only controls after how many items transaction is commited. So it will not help you as all the processing and persistence is done by calling rest services.
I have figured out a design and I think it will work fine.
As for the questions that I asked, following are the answers:
Using asynchronous processors will help avoiding any queue.
http://docs.spring.io/spring-batch/trunk/reference/html/springBatchIntegration.html#asynchronous-processors
using commit-internal will solve it
This thread has the answer - Spring batch :Restart a job and then start next job automatically
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.
I am trying to implement transactions for distributed services in java over REST. I have some questions to ask.
What happens when resources reply affirmatively and in phase 2 they fail to commit?
I tried to search but unfortunately I could not find a proper answer to what happens when rollback fails in 2PC protocol. I know that its a blocking protocol and it waits for response for infinite time, but what happens in real world scenario?
what are the other protocols for distributed transaction management?
I read about JTA for transaction implementation, but is there any other implementation which can be used to implement transactions?
Any reply will be helpful. Thanks in advance.
I don't have answers to these questions but I created a specific method for my specific case. So posting here if some one need transactions for the same cases.
Since In my case there is no change to current entries in database (or indexer, which is also running as a service) but there were only new entries in system at different places, so the false failures were not harmful but false success were. So for my particular case I followed following strategy:
i. All the resources adds a transaction id with the row in database. In first phase when coordinator ask resources, all resources makes entries in database with transaction id generated by coordinator.
ii. After phase 1, when all resources reply affirmatively that means resources have made changes to database, coordinator makes an entry in it's own log that transaction is successful and conveys the same to resources. All resources makes the transaction status successful in the row of data inserted.
iii. A service run continuously to search the database and correct the transaction status by asking the status from coordinator. If there is no entry or failure entry, transaction returns failure status, and same is updated on service. When fetching data, if there is an entry in database which has failure label, then it always checks the transaction status with coordinator, if there is no entry of failure it filters the results. Hence those data entries are not supplied for which there is no information or there is failure information. So the outcome is always consistent.
This strategy provides a way for atomicity for my case.