That way, by using events, we can recreate the data up to the point we desire. The whole snapshot exists only as a mere reflection of past events. An event contains only the name of the event, and the necessary fields such as the ID and the changing attribute. the account holder’s name, balance, registration date, and so on. TransferEvent - when someone transfers their money to someone else’s account.Īn event does not contain all of the data for an account, e.g.WithdrawEvent - when someone withdraws money from their account, and.DepositEvent - when someone deposits money to their account,.CreateEvent - when opening a new bank account,.We analyzed some contracts, and agreed that the events that need to be handled by microservices are the following: We decided that Kafka is good match for the job. We want to separate them somehow.įirst, we want the balance calculation logic to stay out of the gigantic, monolithic application running on a mainframe developed in 1987. The systems were interconnected, and massive. Our Banku Corp, a top banking corporation had an increase in clients and transactions. That way, we have two running Kafka brokers inside our machine. $ bin/kafka-server-start.sh config/server-2.properties $ bin/kafka-server-start.sh config/server-1.properties We’ll be using 0.10.1.0 in this tutorial. The version that you need to download is in the 0.10 family. Kafka can be downloaded from either Confluent’s or Apache’s website. In this section, we will see how to create a topic in Kafka. In that way, messages stay in Kafka longer, and they can be replayed. What makes the difference is that after consuming the log, Kafka doesn’t delete it. Kafka is usually compared to a queuing system such as RabbitMQ. This Producer-Broker orchestration is handled by an instance of Apache ZooKeeper, outside of Kafka. Upon writing the data, each leader then replicates the same message to a different Kafka broker, either synchronously or asynchronously, as desired by the producer. The commit log is then received by a unique Kafka broker, acting as the leader of the partition to which the message is sent. The system responsible for sending a commit log to a Kafka broker is called a producer. In other words, this is how Kafka handles load balancing.Įach message is produced somewhere outside of Kafka. Thanks to partitioning, each consumer in a consumer group can be assigned to a process in an entirely different partition. Partitioning is the the process through which Kafka allows us to do parallel processing. Each partition then holds different logs. As Kafka performance is guaranteed to be constant at O(1), each partition can hold thousands, millions, or even more commit logs, and still do a fine job. However, unlike a table in a SQL database, a topic should normally have more than one partition. In Kafka, the order of commit logs is important, so each one of them has an ever-increasing index number used as an offset. Each of the commit logs has an index, aka an offset. Each table can have data expressed as a row, while in Kafka, data is simply expressed as a commit log, which is a string. If we compare Kafka to a database, a table in a database is a topic in Kafka. If the system in question needs only basic decoupling from a larger system, event-driven design is probably a better option. Event sourcing is good for a system that needs audit trail and time travel. The two architectural patterns are that are key for creating a microservice-based solution are Command-Query Responsibility Segregation, and Event Sourcing, when it makes sense. It can be quite difficult to do a query like this when a customer and an order are two different services: One of the challenges is atomicity - a way of dealing with distributed data, inherent to microservice architecture. However, building a microservice can be challenging. a microservice to handle user management, a microservice to handle purchase, etc.), we can easily add new features to our application. By decomposing a big system and creating various microservices for handling specific functions (e.g. This is why people turn to microservices. As an application grows, it can be hard to maintain all the code and make changes to it easily. The most common argument that calls for microservices is scalability first and foremost. The whole application is delivered in Go. Finally, we’ll learn how to make our consumer redundant by using consumer group. Then, by using a pattern called Command-Query Responsibility Segregation ( CQRS), we can have a materialized view acting as the gate for data retrieval. In this tutorial, we will take a look at how Kafka can help us with handling distributed messaging, by using the Event Sourcing pattern that is inherently atomic.
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