because we use solace as message broker, I'm studying to use debezium embedded engine for Mysql CDC.
In some scenarios, transaction metadata is necessary, I referred to a embedded engine sample:
this.debeziumEngine = DebeziumEngine.create(ChangeEventFormat.of(Connect.class))
.using(customerConnectorConfiguration.asProperties())
.notifying(this::handleChangeEvent)
.build();
...
private void handleChangeEvent(RecordChangeEvent<SourceRecord> sourceRecordRecordChangeEvent) {
SourceRecord sourceRecord = sourceRecordRecordChangeEvent.record();
...
}
with gtid_mode=ON, I can get gtid in the value of sourceRecord, but have no idea how to listen the transaction event.
START TRANSACTION;
INSERT INTO customerdb.customer (id, fullname, email) VALUES (19, 'gee', 'gee#example.com');
COMMIT;
by running above script, seems the engine cannot get transaction begin/end event?
Could anyone enlighten me or provide some sample? Thanks~
Solace has developed a Debezium connector to capture all the database transactions and make it available as an event in the Solace event broker.
Solace Debezium Connector is positioned as an open-source solution and we are happy to share the working solution for you to explore and position in your use case.
Please share your official email id, will share all the details.
Regards,
Related
I've a mongoDB instance with a collection holding calendar events. This is fed using a Kafka application.
These events need to feed into other downstream systems, using Kafka Streams, but what I'd like to invesitgate is whether is would be possible to only trigger an event to a downstream system when the event has just happened (rather then passing future events downstream).
So if an event is received and written to mongo for a date in the future, the downstream system will only know about it as that date is reached and not before.
I've looked at the normal connectors (mongoDB -> Kafka https://www.mongodb.com/kafka-connector) and that functionaility isn't provided.
One of the ways I thought about doing this would be to write a custom application which queries the mongo DB collection on a schedule between the "last run" and "now" to get all the events which occur within these times and create a downstream event into Kafka. (setting indexes on the query elements in the mongo document).
Is there any other way?
Many thanks for reading.
Jill
Instead of query the mongodb I would suggest to create a consumer group to the original kafka topic, which the mongodb data is ingested from and do if you recognize that the date is in the future -> create a rundeck / airflow scheduled task configured to that date, so your consumer logic will be simple.
Another solution you can try is to do some changes to the source connector that you found and try to merge it.
Good luck! Im here if you have any questions
Partly for testing and debugging but also to work around an issue we are seeing in a topic where we have are unable to change the producer I would like to be able to store the value as a string in a CLOB in a database table.
I have this working as a Java based consumer but I am looking at whether this could be achieved using Kafka Connect.
Everything I have read says you need a schema with the reasoning being that how else would it know how to process the data into columns (which makes sense) but I don't want to do any processing of the data (which could be JSON but might just be text) I just want to treat the whole value as a string and load it into one column.
Is there any way this can be done within the Connect config or am I looking at adding extra processing to update the message (in which case the Java client is probably going to end up being simpler)
No, the JDBC Sink connector requires a schema to work. You could modify the source code to add in this behaviour.
I would personally try to stick with Kafka Connect for streaming data to a database since it does all the difficult stuff (scale out, restarts, etc etc etc) very well. Depending on the processing that you're talking about, it could well be that Single Message Transform would be very applicable, since they fit into the Kafka Connect pipeline. Or for more complex processing, Kafka Streams or ksqlDB.
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'm currently in the process of integrating WSO2's Siddhi CEP and Kafka. I want to produce a Siddhi stream by receiving events from Kafka. The Kafka data being received is in JSON format, where each event looks something like this:
{
"event":{
"orderID":"1532538588320",
"timestamps":[
15325,
153
],
"earliestTime":1532538
}
}
The SiddhiApp that I'm trying to run in the WSO2 stream processor looks like this:
#App:name('KafkaSiddhi')
#App:description('Consume events from a Kafka Topic and print the output.')
-- Streams
#source(type='kafka',
topic.list = 'order-aggregates',
partition.no.list = '0',
threading.option = 'single.thread',
group.id = 'time-aggregates',
bootstrap.servers = 'localhost:9092, localhost:2181',
#map(type='json'))
define stream TimeAggregateStream (orderID string,timestamps
object,earliestTime long);
#sink(type="log")
define stream TimeAggregateResultStream (orderID string, timestamps
object, earliestTime long);
-- Queries
from TimeAggregateStream
select orderID, timestamps, earliestTime
insert into TimeAggregateResultStream;
Running this app should log all of the data being updated in the order-aggregates Kafka cluster that I'm listening to. But I see no output whatsoever when click run.
I can tell that there is some type of interaction between the WSO2 stream processor and the order-aggregates topic, because error messages are outputted in real-time whenever I run the application with inconsistent data types for my stream schema. The error messages look like this:
[2018-07-25_10-14-37_224] ERROR
{org.wso2.extension.siddhi.map.json.sourcemapper.JsonSourceMapper} -
Json message {"event":{"orderID":"210000000016183","timestamps":
[1532538627000],"earliestTime":1532538627000}} contains incompatible
attribute types and values. Value 210000000016183 is not compatible with
type LONG. Hence dropping the message. (Encoded)
However, when I have the schema setup correctly, I receive no output at all when I run the application. I really don't know how to make sense of this. When I try to debug this by putting a breakpoint into the line including 'insert into', the debugger never stops at that line.
Can anyone offer some insight on how to approach this issue?
We have added the object support for json mapper extension in the latest release of the extension. Please download the extension[1] and replace the siddhi-map-json jar in /lib.
[1] https://store.wso2.com/store/assets/analyticsextension/details/0e6a6b38-f1d1-49f5-a685-e8c16741494d
Best Regards,
Ramindu.
I am trying to implement an event driven architecture to handle distributed transactions. Each service has its own database and uses Kafka to send messages to inform other microservices about the operations.
An example:
Order service -------> | Kafka |------->Payment Service
| |
Orders MariaDB DB Payment MariaDB Database
Order receives an order request. It has to store the new Order in its DB and publish a message so that Payment Service realizes it has to charge for the item:
private OrderBusiness orderBusiness;
#PostMapping
public Order createOrder(#RequestBody Order order){
logger.debug("createOrder()");
//a.- Save the order in the DB
orderBusiness.createOrder(order);
//b. Publish in the topic so that Payment Service charges for the item.
try{
orderSource.output().send(MessageBuilder.withPayload(order).build());
}catch(Exception e){
logger.error("{}", e);
}
return order;
}
These are my doubts:
Steps a.- (save in Order DB) and b.- (publish the message) should be performed in a transaction, atomically. How can I achieve that?
This is related to the previous one: I send the message with: orderSource.output().send(MessageBuilder.withPayload(order).build()); This operations is asynchronous and ALWAYS returns true, no matter if the Kafka broker is down. How can I know that the message has reached the Kafka broker?
Steps a.- (save in Order DB) and b.- (publish the message) should be
performed in a transaction, atomically. How can I achieve that?
Kafka currently does not support transactions (and thus also no rollback or commit), which you'd need to synchronize something like this. So in short: you can't do what you want to do. This will change in the near-ish future, when KIP-98 is merged, but that might take some time yet. Also, even with transactions in Kafka, an atomic transaction across two systems is a very hard thing to do, everything that follows will only be improved upon by transactional support in Kafka, it will still not entirely solve your issue. For that you would need to look into implementing some form of two phase commit across your systems.
You can get somewhat close by configuring producer properties, but in the end you will have to chose between at least once or at most once for one of your systems (MariaDB or Kafka).
Let's start with what you can do in Kafka do ensure delivery of a message and further down we'll dive into your options for the overall process flow and what the consequences are.
Guaranteed delivery
You can configure how many brokers have to confirm receipt of your messages, before the request is returned to you with the parameter acks: by setting this to all you tell the broker to wait until all replicas have acknowledged your message before returning an answer to you. This is still no 100% guarantee that your message will not be lost, since it has only been written to the page cache yet and there are theoretical scenarios with a broker failing before it is persisted to disc, where the message might still be lost. But this is as good a guarantee as you are going to get.
You can further reduce the risk of data loss by lowering the intervall at which brokers force an fsync to disc (emphasized text and/or flush.ms) but please be aware, that these values can bring with them heavy performance penalties.
In addition to these settings you will need to wait for your Kafka producer to return the response for your request to you and check whether an exception occurred. This sort of ties into the second part of your question, so I will go into that further down.
If the response is clean, you can be as sure as possible that your data got to Kafka and start worrying about MariaDB.
Everything we have covered so far only addresses how to ensure that Kafka got your messages, but you also need to write data into MariaDB, and this can fail as well, which would make it necessary to recall a message you potentially already sent to Kafka - and this you can't do.
So basically you need to choose one system in which you are better able to deal with duplicates/missing values (depending on whether or not you resend partial failures) and that will influence the order you do things in.
Option 1
In this option you initialize a transaction in MariaDB, then send the message to Kafka, wait for a response and if the send was successful you commit the transaction in MariaDB. Should sending to Kafka fail, you can rollback your transaction in MariaDB and everything is dandy.
If however, sending to Kafka is successful and your commit to MariaDB fails for some reason, then there is no way of getting back the message from Kafka. So you will either be missing a message in MariaDB or have a duplicate message in Kafka, if you resend everything later on.
Option 2
This is pretty much just the other way around, but you are probably better able to delete a message that was written in MariaDB, depending on your data model.
Of course you can mitigate both approaches by keeping track of failed sends and retrying just these later on, but all of that is more of a bandaid on the bigger issue.
Personally I'd go with approach 1, since the chance of a commit failing should be somewhat smaller than the send itself and implement some sort of dupe check on the other side of Kafka.
This is related to the previous one: I send the message with:
orderSource.output().send(MessageBuilder.withPayload(order).build());
This operations is asynchronous and ALWAYS returns true, no matter if
the Kafka broker is down. How can I know that the message has reached
the Kafka broker?
Now first of, I'll admit I am unfamiliar with Spring, so this may not be of use to you, but the following code snippet illustrates one way of checking produce responses for exceptions.
By calling flush you block until all sends have finished (and either failed or succeeded) and then check the results.
Producer<String, String> producer = new KafkaProducer<>(myConfig);
final ArrayList<Exception> exceptionList = new ArrayList<>();
for(MessageType message : messages){
producer.send(new ProducerRecord<String, String>("myTopic", message.getKey(), message.getValue()), new Callback() {
#Override
public void onCompletion(RecordMetadata metadata, Exception exception) {
if (exception != null) {
exceptionList.add(exception);
}
}
});
}
producer.flush();
if (!exceptionList.isEmpty()) {
// do stuff
}
I think the proper way for implementing Event Sourcing is by having Kafka be filled directly from events pushed by a plugin that reads from the RDBMS binlog e.g using Confluent BottledWater (https://www.confluent.io/blog/bottled-water-real-time-integration-of-postgresql-and-kafka/) or more active Debezium (http://debezium.io/). Then consuming Microservices can listen to those events, consume them and act on their respective databases being eventually consistent with the RDBMS database.
Have a look here to my full answer for a guideline:
https://stackoverflow.com/a/43607887/986160