Developing real-time data pipelines with Spring and Kafkamarius_bogoevici
Talk given at the Apache Kafka NYC Meetup, October 20, 2015.
http://www.meetup.com/Apache-Kafka-NYC/events/225697500/
Kafka has emerged as a clear choice for a high-throughput, low latency messaging system that addresses the needs of high-performance streaming applications. The Spring Framework has been, in the last decade, the de-facto standard for developing enterprise Java applications, providing a simple and powerful programming model that allows developers to focus on the business needs, leaving the boilerplate and middleware integration to the framework itself. In fact, it has evolved into a rich and powerful ecosystem, with projects focusing on specific aspects of enterprise software development - like Spring Boot, Spring Data, Spring Integration, Spring XD, Spring Cloud Stream/Data Flow to name just a few.
In this presentation, Marius Bogoevici from the Spring team will take the perspective of the Kafka user, and show, with live demos, how the various projects in the Spring ecosystem address their needs:
- how to build simple data integration applications using Spring Integration Kafka;
- how to build sophisticated data pipelines with Spring XD and Kafka;
- how to build cloud native message-driven microservices using Spring Cloud Stream and Kafka, and how to orchestrate them using Spring Cloud Data Flow;
The session discusses on how companies are using Apache Kafka & also covers under the hood details like partitions, brokers, replication.
About apache kafka: Apache Kafka is a distributed a streaming platform, Apache Kafka provides low-latency, high-throughput, fault-tolerant publish and subscribe pipelines and is able to process streams of events. Kafka provides reliable, millisecond responses to support both customer-facing applications and connecting downstream systems with real-time data.
This document provides an introduction to Apache Kafka. It describes Kafka as a distributed messaging system with features like durability, scalability, publish-subscribe capabilities, and ordering. It discusses key Kafka concepts like producers, consumers, topics, partitions and brokers. It also summarizes use cases for Kafka and how to implement producers and consumers in code. Finally, it briefly outlines related tools like Kafka Connect and Kafka Streams that build upon the Kafka platform.
Spring Boot+Kafka: the New Enterprise PlatformVMware Tanzu
This document discusses how Spring Boot and Kafka can form the basis of a new enterprise application platform focused on continuous delivery, event-driven architectures, and streaming data. It provides examples of companies that have successfully adopted this approach, such as Netflix transitioning to Spring Boot and a banking brand building a new core banking system using Spring Streams and Kafka. The document advocates an "event-first" and microservices-oriented mindset enabled by a streaming data platform and suggests that Spring Boot, Kafka, and related technologies provide a turnkey solution for implementing this new application development approach at large enterprises.
Kafka Connect is a framework which connects Kafka with external Systems. It helps to move the data in and out of the Kafka. Connect makes it simple to use existing connector configuration for common source and sink Connectors.
Dennis Wittekind, Confluent, Senior Customer Success Engineer
Perhaps you have heard of Kafka Connect and think it would be a great fit in your application's architecture, but you like to know how things work before you propose them to your team? Perhaps you know enough Connect to be dangerous, but you haven't had the time to really understand all the moving pieces? This meetup talk is for you! We'll briefly introduce Connect to the uninitiated, and then jump in to underlying concepts and considerations you should make when running Connect in production! We'll even run a live demo! What could go wrong!?
https://www.meetup.com/Saint-Louis-Kafka-meetup-group/events/272687113/
This document provides an introduction to Apache Kafka, an open-source distributed event streaming platform. It discusses Kafka's history as a project originally developed by LinkedIn, its use cases like messaging, activity tracking and stream processing. It describes key Kafka concepts like topics, partitions, offsets, replicas, brokers and producers/consumers. It also gives examples of how companies like Netflix, Uber and LinkedIn use Kafka in their applications and provides a comparison to Apache Spark.
The document discusses microservices architecture and how to implement it using Spring Boot and Spring Cloud. It describes how microservices address challenges with monolithic architectures like scalability and innovation. It then covers how to create a microservices-based application using Spring Boot, register services with Eureka, communicate between services using RestTemplate and Feign, and load balance with Ribbon.
Developing real-time data pipelines with Spring and Kafkamarius_bogoevici
Talk given at the Apache Kafka NYC Meetup, October 20, 2015.
http://www.meetup.com/Apache-Kafka-NYC/events/225697500/
Kafka has emerged as a clear choice for a high-throughput, low latency messaging system that addresses the needs of high-performance streaming applications. The Spring Framework has been, in the last decade, the de-facto standard for developing enterprise Java applications, providing a simple and powerful programming model that allows developers to focus on the business needs, leaving the boilerplate and middleware integration to the framework itself. In fact, it has evolved into a rich and powerful ecosystem, with projects focusing on specific aspects of enterprise software development - like Spring Boot, Spring Data, Spring Integration, Spring XD, Spring Cloud Stream/Data Flow to name just a few.
In this presentation, Marius Bogoevici from the Spring team will take the perspective of the Kafka user, and show, with live demos, how the various projects in the Spring ecosystem address their needs:
- how to build simple data integration applications using Spring Integration Kafka;
- how to build sophisticated data pipelines with Spring XD and Kafka;
- how to build cloud native message-driven microservices using Spring Cloud Stream and Kafka, and how to orchestrate them using Spring Cloud Data Flow;
The session discusses on how companies are using Apache Kafka & also covers under the hood details like partitions, brokers, replication.
About apache kafka: Apache Kafka is a distributed a streaming platform, Apache Kafka provides low-latency, high-throughput, fault-tolerant publish and subscribe pipelines and is able to process streams of events. Kafka provides reliable, millisecond responses to support both customer-facing applications and connecting downstream systems with real-time data.
This document provides an introduction to Apache Kafka. It describes Kafka as a distributed messaging system with features like durability, scalability, publish-subscribe capabilities, and ordering. It discusses key Kafka concepts like producers, consumers, topics, partitions and brokers. It also summarizes use cases for Kafka and how to implement producers and consumers in code. Finally, it briefly outlines related tools like Kafka Connect and Kafka Streams that build upon the Kafka platform.
Spring Boot+Kafka: the New Enterprise PlatformVMware Tanzu
This document discusses how Spring Boot and Kafka can form the basis of a new enterprise application platform focused on continuous delivery, event-driven architectures, and streaming data. It provides examples of companies that have successfully adopted this approach, such as Netflix transitioning to Spring Boot and a banking brand building a new core banking system using Spring Streams and Kafka. The document advocates an "event-first" and microservices-oriented mindset enabled by a streaming data platform and suggests that Spring Boot, Kafka, and related technologies provide a turnkey solution for implementing this new application development approach at large enterprises.
Kafka Connect is a framework which connects Kafka with external Systems. It helps to move the data in and out of the Kafka. Connect makes it simple to use existing connector configuration for common source and sink Connectors.
Dennis Wittekind, Confluent, Senior Customer Success Engineer
Perhaps you have heard of Kafka Connect and think it would be a great fit in your application's architecture, but you like to know how things work before you propose them to your team? Perhaps you know enough Connect to be dangerous, but you haven't had the time to really understand all the moving pieces? This meetup talk is for you! We'll briefly introduce Connect to the uninitiated, and then jump in to underlying concepts and considerations you should make when running Connect in production! We'll even run a live demo! What could go wrong!?
https://www.meetup.com/Saint-Louis-Kafka-meetup-group/events/272687113/
This document provides an introduction to Apache Kafka, an open-source distributed event streaming platform. It discusses Kafka's history as a project originally developed by LinkedIn, its use cases like messaging, activity tracking and stream processing. It describes key Kafka concepts like topics, partitions, offsets, replicas, brokers and producers/consumers. It also gives examples of how companies like Netflix, Uber and LinkedIn use Kafka in their applications and provides a comparison to Apache Spark.
The document discusses microservices architecture and how to implement it using Spring Boot and Spring Cloud. It describes how microservices address challenges with monolithic architectures like scalability and innovation. It then covers how to create a microservices-based application using Spring Boot, register services with Eureka, communicate between services using RestTemplate and Feign, and load balance with Ribbon.
This tutorial covers advanced consumer topics like custom deserializers, ConsumerRebalanceListener to rewind to a certain offset, manual assignment of partitions to implement a "priority queue", “at least once” message delivery semantics Consumer Java example, “at most once” message delivery semantics Consumer Java example, “exactly once” message delivery semantics Consumer Java example, and a lot more.
Spring integration을 통해_살펴본_메시징_세계Wangeun Lee
[스프링캠프 2015] Spring Integration을 통해 살펴본 메시징 세계 발표자료 입니다.
예제 소스 저장소는 프리젠테이션 안에 링크 걸어놨습니다.
감사합니다.
-------------------------------------------------------------------
우리는 늘 누군가와 소통(Communication)을 합니다. 소통을 통하여 누군가에게 일을 시키기도 하고 내가 일을 받기도 합니다. 애플리케이션도 마찬가지로 이기종간의 애플리케이션끼리 데이터로 소통을 하며 할 일을 서로 분산 처리할 상황이 발생하기도 합니다.
이런 분산 처리 이전에는 소통이 전제되어야 합니다. 애플리케이션 간 소통에 대한 고민은 선구자들에 의해 Enterprise Integration Patterns로 탄생되었으며 Spring에서도 그 패턴화의 추상화 일원으로 Spring Integration을 탄생시켰습니다.
이 강연에서는 Spring Integration을 통해 애플리케이션 간에 어떻게 쉽고 편하게(?) 소통을 할 수 있게 되었는지 살펴보며 예제와 사례를 통해 Spring Integration 입문에 도움을 주고자 합니다.
Apache Kafka is a distributed publish-subscribe messaging system that allows for high volumes of data to be passed from endpoints to endpoints. It uses a broker-based architecture with topics that messages are published to and persisted on disk for reliability. Producers publish messages to topics that are partitioned across brokers in a Kafka cluster, while consumers subscribe to topics and pull messages from brokers. The ZooKeeper service coordinates the Kafka brokers and notifies producers and consumers of changes.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
ksqlDB is a stream processing SQL engine, which allows stream processing on top of Apache Kafka. ksqlDB is based on Kafka Stream and provides capabilities for consuming messages from Kafka, analysing these messages in near-realtime with a SQL like language and produce results again to a Kafka topic. By that, no single line of Java code has to be written and you can reuse your SQL knowhow. This lowers the bar for starting with stream processing significantly.
ksqlDB offers powerful capabilities of stream processing, such as joins, aggregations, time windows and support for event time. In this talk I will present how KSQL integrates with the Kafka ecosystem and demonstrate how easy it is to implement a solution using ksqlDB for most part. This will be done in a live demo on a fictitious IoT sample.
This document provides an overview of Kafka, a distributed streaming platform. It can publish and subscribe to streams of records, store streams durably across clusters, and process streams as they occur. The Kafka cluster stores streams of records in topics. It has four main APIs: Producer API to publish data, Consumer API to subscribe to topics, Streams API to transform streams, and Connector API to connect Kafka and other systems. Records in Kafka topics are partitioned and ordered with offsets for scalability and fault tolerance. Consumers subscribe to topics in consumer groups to process partitions in parallel.
Introduction to Spring WebFlux #jsug #sf_a1Toshiaki Maki
The document provides an introduction and overview of Spring WebFlux, a non-blocking web framework for Spring. It discusses the differences between blocking and non-blocking web stacks, and how Spring WebFlux uses reactive streams and programming. Code examples are provided showing how to build reactive controllers and streams in Spring WebFlux that support backpressure.
Kafka Connect & Streams - the ecosystem around KafkaGuido Schmutz
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
Flink vs. Spark: this is the slide deck of my talk at the 2015 Flink Forward conference in Berlin, Germany, on October 12, 2015. In this talk, we tried to compare Apache Flink vs. Apache Spark with focus on real-time stream processing. Your feedback and comments are much appreciated.
This document discusses test-driven development with Jasmine and Karma. It justifies TDD for JavaScript, provides an overview of TDD and its benefits. It then explains the basics of Jasmine including suites, specifications, matchers and spies. Finally it covers configuring Karma and using tools like PhantomJS for running tests and karma-coverage for generating code coverage reports.
The presentation from our online webinar "Design patterns for microservice architecture".
Full video from webinar available here: https://www.youtube.com/watch?v=826aAmG06KM
If you’re a CTO or a Lead Developer and you’re planning to design service-oriented architecture, it’s definitely a webinar tailored to your needs. Adrian Zmenda, our Lead Dev, will explain:
- when microservice architecture is a safe bet and what are some good alternatives
- what are the pros and cons of the most popular design patterns (API Gateway, Backend for Frontend and more)
- how to ensure that the communication between services is done right and what to do in case of connection issues
- why we’ve decided to use a monorepo (monolithic repository)
- what we’ve learned from using the remote procedure call framework gRPC
- how to monitor the efficiency of individual services and whole SOA-based systems.
Kafka is an open source messaging system that can handle massive streams of data in real-time. It is fast, scalable, durable, and fault-tolerant. Kafka is commonly used for stream processing, website activity tracking, metrics collection, and log aggregation. It supports high throughput, reliable delivery, and horizontal scalability. Some examples of real-time use cases for Kafka include website monitoring, network monitoring, fraud detection, and IoT applications.
This document discusses reactive programming and Spring Webflux. It begins with an introduction to reactive programming and why it is needed for applications with high user expectations. It then covers reactive streams and the Reactive Manifesto. It introduces Project Reactor and compares it to RxJava. It discusses how Spring Webflux allows for non-blocking reactive REST APIs. It concludes with an overview of a demo and next steps including support for SQL, web sockets, and MongoDB.
Watch this talk here: https://www.confluent.io/online-talks/from-zero-to-hero-with-kafka-connect-on-demand
Integrating Apache Kafka® with other systems in a reliable and scalable way is often a key part of a streaming platform. Fortunately, Apache Kafka includes the Connect API that enables streaming integration both in and out of Kafka. Like any technology, understanding its architecture and deployment patterns is key to successful use, as is knowing where to go looking when things aren't working.
This talk will discuss the key design concepts within Apache Kafka Connect and the pros and cons of standalone vs distributed deployment modes. We'll do a live demo of building pipelines with Apache Kafka Connect for streaming data in from databases, and out to targets including Elasticsearch. With some gremlins along the way, we'll go hands-on in methodically diagnosing and resolving common issues encountered with Apache Kafka Connect. The talk will finish off by discussing more advanced topics including Single Message Transforms, and deployment of Apache Kafka Connect in containers.
The document discusses Spring Framework's data access features. It covers DAO support, transaction management using JDBC, and exceptions. It also discusses embedded databases, initializing other data sources, and the Spring DataAccessException hierarchy. Additionally, it talks about using JdbcTemplate for data access and provides examples of querying for simple types, generic maps, and domain objects.
Microservices - java ee vs spring boot and spring cloudBen Wilcock
Spring Boot and Spring Cloud provide an easier and more productive framework for building cloud-native microservices compared to Java EE. Spring Boot simplifies the development, deployment, and management of microservices. Spring Cloud adds helpful capabilities for service discovery, external configuration, load balancing, and monitoring that are missing from Java EE. While Java EE adoption is declining, the use of Spring Boot and Spring Cloud is growing rapidly among developers.
Introduction To Functional Reactive Programming PoznanEliasz Sawicki
This document provides an introduction to functional reactive programming (FRP). It discusses key concepts of FRP including working with streams of asynchronous data, manipulating streams using functions like map and filter, and combining multiple streams. It also covers the ReactiveCocoa framework for FRP in iOS and OS X, including how to think in signals that represent events over time rather than mutable state. An example is provided of validating a form by combining streams of name, surname, and email validation.
This tutorial covers advanced consumer topics like custom deserializers, ConsumerRebalanceListener to rewind to a certain offset, manual assignment of partitions to implement a "priority queue", “at least once” message delivery semantics Consumer Java example, “at most once” message delivery semantics Consumer Java example, “exactly once” message delivery semantics Consumer Java example, and a lot more.
Spring integration을 통해_살펴본_메시징_세계Wangeun Lee
[스프링캠프 2015] Spring Integration을 통해 살펴본 메시징 세계 발표자료 입니다.
예제 소스 저장소는 프리젠테이션 안에 링크 걸어놨습니다.
감사합니다.
-------------------------------------------------------------------
우리는 늘 누군가와 소통(Communication)을 합니다. 소통을 통하여 누군가에게 일을 시키기도 하고 내가 일을 받기도 합니다. 애플리케이션도 마찬가지로 이기종간의 애플리케이션끼리 데이터로 소통을 하며 할 일을 서로 분산 처리할 상황이 발생하기도 합니다.
이런 분산 처리 이전에는 소통이 전제되어야 합니다. 애플리케이션 간 소통에 대한 고민은 선구자들에 의해 Enterprise Integration Patterns로 탄생되었으며 Spring에서도 그 패턴화의 추상화 일원으로 Spring Integration을 탄생시켰습니다.
이 강연에서는 Spring Integration을 통해 애플리케이션 간에 어떻게 쉽고 편하게(?) 소통을 할 수 있게 되었는지 살펴보며 예제와 사례를 통해 Spring Integration 입문에 도움을 주고자 합니다.
Apache Kafka is a distributed publish-subscribe messaging system that allows for high volumes of data to be passed from endpoints to endpoints. It uses a broker-based architecture with topics that messages are published to and persisted on disk for reliability. Producers publish messages to topics that are partitioned across brokers in a Kafka cluster, while consumers subscribe to topics and pull messages from brokers. The ZooKeeper service coordinates the Kafka brokers and notifies producers and consumers of changes.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
ksqlDB is a stream processing SQL engine, which allows stream processing on top of Apache Kafka. ksqlDB is based on Kafka Stream and provides capabilities for consuming messages from Kafka, analysing these messages in near-realtime with a SQL like language and produce results again to a Kafka topic. By that, no single line of Java code has to be written and you can reuse your SQL knowhow. This lowers the bar for starting with stream processing significantly.
ksqlDB offers powerful capabilities of stream processing, such as joins, aggregations, time windows and support for event time. In this talk I will present how KSQL integrates with the Kafka ecosystem and demonstrate how easy it is to implement a solution using ksqlDB for most part. This will be done in a live demo on a fictitious IoT sample.
This document provides an overview of Kafka, a distributed streaming platform. It can publish and subscribe to streams of records, store streams durably across clusters, and process streams as they occur. The Kafka cluster stores streams of records in topics. It has four main APIs: Producer API to publish data, Consumer API to subscribe to topics, Streams API to transform streams, and Connector API to connect Kafka and other systems. Records in Kafka topics are partitioned and ordered with offsets for scalability and fault tolerance. Consumers subscribe to topics in consumer groups to process partitions in parallel.
Introduction to Spring WebFlux #jsug #sf_a1Toshiaki Maki
The document provides an introduction and overview of Spring WebFlux, a non-blocking web framework for Spring. It discusses the differences between blocking and non-blocking web stacks, and how Spring WebFlux uses reactive streams and programming. Code examples are provided showing how to build reactive controllers and streams in Spring WebFlux that support backpressure.
Kafka Connect & Streams - the ecosystem around KafkaGuido Schmutz
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
Flink vs. Spark: this is the slide deck of my talk at the 2015 Flink Forward conference in Berlin, Germany, on October 12, 2015. In this talk, we tried to compare Apache Flink vs. Apache Spark with focus on real-time stream processing. Your feedback and comments are much appreciated.
This document discusses test-driven development with Jasmine and Karma. It justifies TDD for JavaScript, provides an overview of TDD and its benefits. It then explains the basics of Jasmine including suites, specifications, matchers and spies. Finally it covers configuring Karma and using tools like PhantomJS for running tests and karma-coverage for generating code coverage reports.
The presentation from our online webinar "Design patterns for microservice architecture".
Full video from webinar available here: https://www.youtube.com/watch?v=826aAmG06KM
If you’re a CTO or a Lead Developer and you’re planning to design service-oriented architecture, it’s definitely a webinar tailored to your needs. Adrian Zmenda, our Lead Dev, will explain:
- when microservice architecture is a safe bet and what are some good alternatives
- what are the pros and cons of the most popular design patterns (API Gateway, Backend for Frontend and more)
- how to ensure that the communication between services is done right and what to do in case of connection issues
- why we’ve decided to use a monorepo (monolithic repository)
- what we’ve learned from using the remote procedure call framework gRPC
- how to monitor the efficiency of individual services and whole SOA-based systems.
Kafka is an open source messaging system that can handle massive streams of data in real-time. It is fast, scalable, durable, and fault-tolerant. Kafka is commonly used for stream processing, website activity tracking, metrics collection, and log aggregation. It supports high throughput, reliable delivery, and horizontal scalability. Some examples of real-time use cases for Kafka include website monitoring, network monitoring, fraud detection, and IoT applications.
This document discusses reactive programming and Spring Webflux. It begins with an introduction to reactive programming and why it is needed for applications with high user expectations. It then covers reactive streams and the Reactive Manifesto. It introduces Project Reactor and compares it to RxJava. It discusses how Spring Webflux allows for non-blocking reactive REST APIs. It concludes with an overview of a demo and next steps including support for SQL, web sockets, and MongoDB.
Watch this talk here: https://www.confluent.io/online-talks/from-zero-to-hero-with-kafka-connect-on-demand
Integrating Apache Kafka® with other systems in a reliable and scalable way is often a key part of a streaming platform. Fortunately, Apache Kafka includes the Connect API that enables streaming integration both in and out of Kafka. Like any technology, understanding its architecture and deployment patterns is key to successful use, as is knowing where to go looking when things aren't working.
This talk will discuss the key design concepts within Apache Kafka Connect and the pros and cons of standalone vs distributed deployment modes. We'll do a live demo of building pipelines with Apache Kafka Connect for streaming data in from databases, and out to targets including Elasticsearch. With some gremlins along the way, we'll go hands-on in methodically diagnosing and resolving common issues encountered with Apache Kafka Connect. The talk will finish off by discussing more advanced topics including Single Message Transforms, and deployment of Apache Kafka Connect in containers.
The document discusses Spring Framework's data access features. It covers DAO support, transaction management using JDBC, and exceptions. It also discusses embedded databases, initializing other data sources, and the Spring DataAccessException hierarchy. Additionally, it talks about using JdbcTemplate for data access and provides examples of querying for simple types, generic maps, and domain objects.
Microservices - java ee vs spring boot and spring cloudBen Wilcock
Spring Boot and Spring Cloud provide an easier and more productive framework for building cloud-native microservices compared to Java EE. Spring Boot simplifies the development, deployment, and management of microservices. Spring Cloud adds helpful capabilities for service discovery, external configuration, load balancing, and monitoring that are missing from Java EE. While Java EE adoption is declining, the use of Spring Boot and Spring Cloud is growing rapidly among developers.
Introduction To Functional Reactive Programming PoznanEliasz Sawicki
This document provides an introduction to functional reactive programming (FRP). It discusses key concepts of FRP including working with streams of asynchronous data, manipulating streams using functions like map and filter, and combining multiple streams. It also covers the ReactiveCocoa framework for FRP in iOS and OS X, including how to think in signals that represent events over time rather than mutable state. An example is provided of validating a form by combining streams of name, surname, and email validation.
From object oriented to functional domain modelingMario Fusco
This document discusses moving from an object-oriented approach to a functional approach for domain modeling. It provides examples of modeling a bank account and salary calculator in both OOP and FP styles. Some key benefits of the functional approach highlighted include immutability, avoiding side effects, handling errors through monads instead of exceptions, and composing functions together through currying and composition. Overall the document advocates that while OOP and FP both have merits, modeling as functions can provide advantages in terms of understandability, testability, and extensibility of the code.
Spring Integration allows for building integration flows with Java code instead of XML configuration. It provides inbound and outbound adapters to connect to resources like databases, web services, and message queues. The integration flows define the business logic and transformations to process messages from the inbound to outbound adapters using common EAI patterns like routers, filters, and transformers.
Microservice Architecture with CQRS and Event SourcingBen Wilcock
This document provides an overview of microservice architecture using CQRS (Command Query Responsibility Segregation) and Event Sourcing patterns. It defines CQRS as separating the write and read functions of an application. Event Sourcing records all state changes as a series of events rather than just the current state. The benefits include scalability, simplicity, flexibility and a business focus. Popular companies using this architecture include those needing cost-effective scaling like microservices. The author provides resources and advocates for CQRS and Event Sourcing to solve common architectural challenges.
Integration Patterns and Anti-Patterns for Microservices ArchitecturesApcera
Integration Patterns and Anti-Patterns for Microservices Architectures
David Williams
Co-Founder and Partner, Williams Garcia
You can learn more about NATS at http://www.nats.io
Scaling wix with microservices architecture devoxx London 2015Aviran Mordo
Many small startups build their systems on top of a traditional toolset like Tomcat, Hibernate, and MySQL. These systems are used because they facilitate easy development and fast progress, but many of them are monolithic and have limited scalability. So as a startup grows, the team is confronted with the problem of how to evolve the system and make it scalable.
Facing the same dilemma, Wix.com grew from 0 to 60 million users in just a few years. Facing some interesting challenges, like performance and availability. Traditional performance solutions, such as caching, would not help due to a very long tail problem which causes caching to be highly inefficient. And because every minute of downtime means customers lose money, the product needed to have near 100% availability.
Solving these issues required some interesting and out-of-the-box thinking, and this talk will discuss some of these strategies: building a highly preformant, highly available and highly scalable system; and leveraging microservices architecture and multi-cloud platforms to help build a very efficient and cost-effective system.
This document discusses EAI (Enterprise Application Integration) patterns using Spring Integration. It provides an overview of messaging, pipes and filters, and common EAI patterns. It then demonstrates how Spring Integration implements these patterns through its API, with an emphasis on messaging channels. Examples are given for sending and receiving JMS, AMQP, HTTP, and email messages. Common patterns like filtering, routing, splitting, and aggregating messages are also explained.
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2. Introduction to Kafka with Spring
Integration
• Kafka (Mihail Yordanov)
• Spring integration (Borislav Markov)
• Students Example (Mihail & Borislav)
• Conclusion
3. Motivation
• Real time data being continuously generated
• Producers and consumers relationships
• Streaming systems
• Messaging systems
4. Apache Kafka
• Developed by LinkedIn
• “We built Apache Kafka at LinkedIn with a specific
purpose in mind: to serve as a central repository of
data streams.” - Jay Kreps
• Kafka improvements
– Transporting data between systems
– Richer analytical processing
• “A publish-subscribe messaging system rethought as a
distributed commit log”
5. Kafka Vocabulary
• Brokers
• Producers
• Consumers
• “A publish-subscribe messaging system
rethought as a distributed commit log”
6. Kafka Vocabulary
• Topics
• Partitions
• “A publish-subscribe messaging system rethought
as a distributed commit log”
10. Consumer groups
• Definition - “A publish-subscribe messaging
system rethought as a distributed commit log”
• Publish - Subscribe
• Queueing
11. Spring Integration
• Spring Integration enables lightweight
messaging
• Supports integration with external systems via
declarative adapters
• Primary goal is to provide a simple model for
building enterprise integration solutions
• Separation of concerns
• Maintainable, testable code
12. Features
• Implementation of most of the Enterprise Integration Patterns
• Endpoint
• Channel (Point-to-point and Publish/Subscribe)
• Aggregator
• Filter
• Transformer
• Control Bus
• …
• Integration with External Systems
• ReST/HTTP
• ...
13. The theory...
The patterns can be found at
http://www.enterpriseintegrationpatterns.com/patterns/messag
ing/index.html
14. Message
• Messages are objects that carry information between two applications
• They are constructed by the producer and they are consumed/deconstructed at
the consumer/subscriber
• Message consists of:
– payload
– header
public interface Message<T> {
T getPayload();
MessageHeaders getHeaders();
}
15. Message endpoints
Common endpoints:
• Service Activator - invokes a method on a
bean
• Message Bridge - couples different
messaging modes/adapters. Example: (P2P
with Publish/Subscribe)
• Message Enricher - enrich the incoming
message with additional information.
– Header Enricher - add header
attributes
– Payload Enricher - enrich payload
16. Message endpoints (continued)
• Gateway - Used when we don’t have knowledge for the
messaging system.
– Synchronous Gateway - void or returns T
– Asynchronous Gateway - returns Future<T>
• Delayer - introduce delay between sender and receiver
• Consumers
– Polling Consumers - poll messages in a timely fashion
– Polling Using Triggers - poll messages with PeriodicTrigger
and with CronTrigger
– Event-Driven Consumers - they wait for someone to
deliver the message (framework’s responsibility)
17. Message endpoints (continued)
• Channel Adapter - endpoint that connects a Message Channel
to some other system or transport.
–inbound
–outbound
21. Flow components
• Filters
– Custom filters are tied to the framework, can
be bean method returning boolean
– Framework MessageSelector - use of method
boolean accept(Message m)
– Using annotation - @Filter
• Routers
– PayloadTypeRouter
– HeaderValueRouter
– Custom Routers - any bean method can be
router, the result will be the channel name.
– Recipient List Router - the message goes to all
statically defined channels
22. Flow components (continued...)
• Splitters - splits a message into pieces
– Custom splitters - any bean method;
– Framework AbstractMessageSplitter - use of method Object splitMessage(Message
m);
– Using annotation - @Splitter ;
• Aggregator
– assemble multiple messages to create a single parent message
– Complex task - all messages of a set have to arrive before aggregators can start work
– CorrelationStrategy - defines the key for grouping of the messages, the default
grouping is based on CORRELATION_ID
– ReleaseStrategy - dictates at which point the group of messages should be sent or
released for aggregation. Default is SequenceSizeReleazeStrategy
– Message Store - aggregators hold messages until all of them arrived. Options are:
• in-memory
• external database
• Resequencer - fix order of the messages(work on SEQUENCE_NUMBER)
23. Transformers
• Built-in transformers
– String Transformers - invokes toString() method of the payload
– Map Transformers - transformes POJO to a name-value pair of
Map
– Serializing and Deserializing Transformers - converts payload
to byte array
– JSON Transformers - object-to-json converts POJO to readable
JSON format; json-to-object transformer needs additional
property “type”
– XML Transformers - requires Spring OXM (Object-to-XML);
org.springframework.oxm.Marshaller/Unmarshaller
24. Transformers(continued...)
• Built-in transformers
– XPath Transformers - decodes
XML using XPath expressions, ex.:
xpath-
expression=”/mytag/@prop”
• Custom Transformers - can be any
spring bean method
• Using @Transformer
25. Students Example
• Example application will demo usage
of Kafka and Spring Integration
• App is built with maven
• Ideal candidate for Microservice
• Idea: takes students from outside and
calculates their average score