Event-driven integrations give businesses the flexibility they need to adapt and adjust to rapid market and customer preference changes. Apache Kafka has emerged as the leading system for brokering messages across the enterprise. Adding TriggerMesh to Kafka provides a way for messages to be routed and transformed cloud natively across systems. DevOps teams, like the one at PNC Bank, use the TriggerMesh declarative API to define these event-driven integrations and manage them as part of their CI/CD pipeline.
Many modern applications are rapidly adopting an event-driven architecture (EDA). An EDA is used to loosely couple independent microservices and provide near real-time behavior. When coupled with a cloud native mindset and the use of containers and serverless functions, EDA modernizes the entire enterprise application landscape.
Over the last 10 years, starting with the DevOps movement, great emphasis has been put into gaining agility and reducing the time to market for new applications. Racing from development to production is seen as a true competitive advantage. With this in mind, breaking monolithic applications into microservices has been seen as a way to deploy independent services faster, giving each microservice its own lifecycle. Packaging each microservice and managing it in production gave rise to the container and Kubernetes era. However, coupling each microservice is still an open problem and that is where EDA comes back in full force. When you adopt an event-driven architecture, you can couple together your independent microservices through a messaging substrate like Kafka; and so gain the agility and scale you have been looking for.
#devops #microservices #contributed
Given a modern distributed system composed of multiple microservices, each possessing a sub-set of a domain’s aggregate data, that system will almost assuredly have some data duplication. Given this duplication, how do we maintain data consistency? In this two-part post, we will explore one possible solution to this challenge — Apache Kafka and the model of eventual consistency.
Apache Kafka is an open-source distributed event streaming platform capable of handling trillions of messages. According to Confluent, initially conceived as a messaging queue, Kafka is based on an abstraction of a distributed commit log. Since being created and open-sourced by LinkedIn in 2011, Kafka has quickly evolved from a messaging queue to a full-fledged event streaming platform.
Eventual consistency, according to Wikipedia, is a consistency model used in distributed computing to achieve high availability that informally guarantees that if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. I previously covered the topic of eventual consistency in a distributed system using RabbitMQ in the May 2017 post, Eventual Consistency: Decoupling Microservices with Spring AMQP and RabbitMQ. The post was featured on Pivotal’s RabbitMQ website.
#domain-driven-design #apache-kafka #event-driven-architecture #kafka #spring-boot
Since Confluent Platform version 5.5, Avro is no longer the only schema in town. Protobuf and JSON schemas are now supported as first-class citizens in Confluent universe. But before I go on explaining how to use Protobuf with Kafka, let’s answer one often-asked question:
When applications communicate through a pub-sub system, they exchange messages and those messages need to be understood and agreed upon by all the participants in the communication. Additionally, you would like to detect and prevent changes to the message format that would make messages unreadable for some of the participants.
That’s where a schema comes in — it represents a contract between the participants in communication, just like an API represents a contract between a service and its consumers. And just as REST APIs can be described using OpenAPI (Swagger) so the messages in Kafka can be described using Avro, Protobuf or Avro schemas.
Schemas describe the structure of the data by:
In addition, together with Schema Registry, schemas prevent a producer from sending poison messages - malformed data that consumers cannot interpret. Schema Registry will detect if breaking changes are about to be introduced by the producer and can be configured to reject such changes. An example of a breaking change would be deleting a mandatory field from the schema.
Similar to Apache Avro, Protobuf is a method of serializing structured data. A message format is defined in a .proto file and you can generate code from it in many languages including Java, Python, C++, C#, Go and Ruby. Unlike Avro, Protobuf does not serialize schema with the message. So, in order to deserialize the message, you need the schema in the consumer.
Here’s an example of a Protobuf schema containing one message type:
#integration #apache kafka #schema #apache kafka tutorial #protobuf
Diogo Souza explains using Apache Kafka with .NET including setting it up and creating apps to test sending messages asynchronously.
Have you ever used async processing for your applications? Whether for a web-based or a cloud-driven approach, asynchronous code seems inevitable when dealing with tasks that do not need to process immediately. Apache Kafka is one of the most used and robust open-source event streaming platforms out there. Many companies and developers take advantage of its power to create high-performance async processes along with streaming for analytics purposes, data integration for microservices, and great monitoring tools for app health metrics. This article explains the details of using Kafka with .NET applications. It also shows the installation and usage on a Windows OS and its configuration for an ASP.NET API.
The world produces data constantly and exponentially. To embrace such an ever-growing amount of data, tools like Kafka come into existence, providing robust and impressive architecture.
But how does Kafka work behind the scenes?
Kafka works as a middleman exchanging information from producers to consumers. They are the two main actors in each edge of this linear process.
Figure 1. Producers and consumers in Kafka
Kafka can also be configured to work in a cluster of one or more servers. Those servers are called Kafka brokers. You can benefit from multiple features such as data replication, fault tolerance, and high availability with brokers.
Figure 2. Kafka clusters
These brokers are managed by another tool called Zookeeper. In summary, it is a service that aims to keep configuration-like data synchronized and organized in distributed systems.
#dotnet #kafka #apache #apache-kafka #developer
XML messages and XML Schema are not very common in the Apache Kafka and Event Streaming world! Why? Many people call XML legacy. It is complex, verbose, and often associated with the ugly WS-* Hell (SOAP, WSDL, etc). On the other side, every company older than five years uses XML. It is well understood, provides a good structure, and is human- and machine-readable.
This post does not want to start another flame war between XML and other technologies such as JSON (which also provides JSON Schema now), Avro, or Protobuf. Instead, I will walk you through the three main approaches to integrate between Kafka and XML messages as there is still a vast demand for implementing this integration today (often for integrating legacy applications and middleware).
Extensible Markup Language (XML) is a markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. The World Wide Web Consortium’s XML 1.0 Specification of 1998 and several other related specifications — all of them free open standards — define XML.
The design goals of XML emphasize simplicity, generality, and usability across the Internet. It is a textual data format with strong support via Unicode for different human languages. Although the design of XML focuses on documents, the language is widely used for the representation of arbitrary data structures such as those used in web services. Several schema systems exist to aid in defining XML-based languages, while programmers have developed many application programming interfaces (APIs) to assist the processing of XML data.
#open source #big data #integration #xml #json #kafka #middleware #event streaming #kafka connect platform #kafka connectors
In this blog, we are going to use kinesis as a source and kafka as a consumer.
Let’s get started.
Apache Flink provides the kinesis and kafka connector dependencies. Let’s add them in our build.sbt:
name := "flink-demo" version := "0.1" scalaVersion := "2.12.8" libraryDependencies ++= Seq( "org.apache.flink" %% "flink-scala" % "1.10.0", "org.apache.flink" %% "flink-connector-kinesis" % "1.10.0", "org.apache.flink" %% "flink-connector-kafka" % "1.10.0", "org.apache.flink" %% "flink-streaming-scala" % "1.10.0" )
The next step is to create a pointer to the environment on which this program runs.
val env = StreamExecutionEnvironment.getExecutionEnvironment
Setting parallelism of x here will cause all operators (such as join, map, reduce) to run with x parallel instance.
I am using 1 as it is a demo application.
Disabling the aws cbor, as we are testing locally.
System.setProperty("com.amazonaws.sdk.disableCbor", "true") System.setProperty("org.apache.flink.kinesis.shaded.com.amazonaws.sdk.disableCbor", "true")
Defining Kinesis consumer properties.
Do not worry about the endpoint, it is set to http://localhost:4568 as we will test the kinesis using localstack.
#apache flink #flink #scala ##apache-flink ##kinesis #apache #flink streaming #kafka #scala