Towards a UML Profile for Domain-driven Design of Microservice ArchitecturesFlorian Rademacher
In the context of Microservice Architecture (MSA), Domain-driven Design (DDD) denotes a model-driven approach for domain decomposition and service identification. However, DDD-based domain models are typically expressed as informal UML class diagrams, which hampers model operations like code generation. As a first step to overcome this limitation, we present a UML profile that formalizes DDD-based domain models and enables domain-driven Microservice design.
Delivering: from Kafka to WebSockets | Adam Warski, SoftwareMillHostedbyConfluent
Here's the challenge: we've got a Kafka topic, where services publish messages to be delivered to browser-based clients through web sockets.
Sounds simple? It might, but we're faced with an increasing number of messages, as well as a growing count of web socket clients. How do we scale our solution? As our system contains a larger number of servers, failures become more frequent. How to ensure fault tolerance?
There’s a couple possible architectures. Each websocket node might consume all messages. Otherwise, we need an intermediary, which redistributes the messages to the proper web socket nodes.
Here, we might either use a Kafka topic, or a streaming forwarding service. However, we still need a feedback loop so that the intermediary knows where to distribute messages.
We’ll take a look at the strengths and weaknesses of each solution, as well as limitations created by the chosen technologies (Kafka and web sockets).
FireFly is a platform developed by Hyperledger that simplifies web3 application development. It handles common web3 infrastructure problems like decentralized architecture, tokenomics, wallet security, data privacy, and transaction management so developers can focus on building applications. FireFly includes tools like an API generator, data exchange, and orchestration engine to connect applications to blockchains without having to reinvent the wheel each time. It is open source, cloud-ready, and designed for production use at an enterprise scale.
Leveraging Docker for Hadoop build automation and Big Data stack provisioningDataWorks Summit
Apache Bigtop as an open source Hadoop distribution, focuses on developing packaging, testing and deployment solutions that help infrastructure engineers to build up their own customized big data platform as easy as possible. However, packages deployed in production require a solid CI testing framework to ensure its quality. Numbers of Hadoop component must be ensured to work perfectly together as well. In this presentation, we'll talk about how Bigtop deliver its containerized CI framework which can be directly replicated by Bigtop users. The core revolution here are the newly developed Docker Provisioner that leveraged Docker for Hadoop deployment and Docker Sandbox for developer to quickly start a big data stack. The content of this talk includes the containerized CI framework, technical detail of Docker Provisioner and Docker Sandbox, a hierarchy of docker images we designed, and several components we developed such as Bigtop Toolchain to achieve build automation.
Towards a UML Profile for Domain-driven Design of Microservice ArchitecturesFlorian Rademacher
In the context of Microservice Architecture (MSA), Domain-driven Design (DDD) denotes a model-driven approach for domain decomposition and service identification. However, DDD-based domain models are typically expressed as informal UML class diagrams, which hampers model operations like code generation. As a first step to overcome this limitation, we present a UML profile that formalizes DDD-based domain models and enables domain-driven Microservice design.
Delivering: from Kafka to WebSockets | Adam Warski, SoftwareMillHostedbyConfluent
Here's the challenge: we've got a Kafka topic, where services publish messages to be delivered to browser-based clients through web sockets.
Sounds simple? It might, but we're faced with an increasing number of messages, as well as a growing count of web socket clients. How do we scale our solution? As our system contains a larger number of servers, failures become more frequent. How to ensure fault tolerance?
There’s a couple possible architectures. Each websocket node might consume all messages. Otherwise, we need an intermediary, which redistributes the messages to the proper web socket nodes.
Here, we might either use a Kafka topic, or a streaming forwarding service. However, we still need a feedback loop so that the intermediary knows where to distribute messages.
We’ll take a look at the strengths and weaknesses of each solution, as well as limitations created by the chosen technologies (Kafka and web sockets).
FireFly is a platform developed by Hyperledger that simplifies web3 application development. It handles common web3 infrastructure problems like decentralized architecture, tokenomics, wallet security, data privacy, and transaction management so developers can focus on building applications. FireFly includes tools like an API generator, data exchange, and orchestration engine to connect applications to blockchains without having to reinvent the wheel each time. It is open source, cloud-ready, and designed for production use at an enterprise scale.
Leveraging Docker for Hadoop build automation and Big Data stack provisioningDataWorks Summit
Apache Bigtop as an open source Hadoop distribution, focuses on developing packaging, testing and deployment solutions that help infrastructure engineers to build up their own customized big data platform as easy as possible. However, packages deployed in production require a solid CI testing framework to ensure its quality. Numbers of Hadoop component must be ensured to work perfectly together as well. In this presentation, we'll talk about how Bigtop deliver its containerized CI framework which can be directly replicated by Bigtop users. The core revolution here are the newly developed Docker Provisioner that leveraged Docker for Hadoop deployment and Docker Sandbox for developer to quickly start a big data stack. The content of this talk includes the containerized CI framework, technical detail of Docker Provisioner and Docker Sandbox, a hierarchy of docker images we designed, and several components we developed such as Bigtop Toolchain to achieve build automation.
Firebase provides various services including analytics, authentication, database, storage, hosting, testing, distribution and monetization. It allows for unlimited analytics reporting, audience segmentation, offline and realtime database access, secure file storage, A/B testing, error monitoring, and integration with Google services like AdWords and AdMob. Firebase services are customizable, scalable and aim to improve the user experience.
카카오 광고 플랫폼 MSA 적용 사례 및 API Gateway와 인증 구현에 대한 소개if kakao
황민호(robin.hwang) / kakao corp. DSP개발파트
---
최근 Spring Cloud와 Netflix OSS로 MSA를 구성하는 시스템 기반의 서비스들이 많아지는 추세입니다.
카카오에서도 작년에 오픈한 광고 플랫폼 모먼트에 Spring Cloud 기반의 MSA환경을 구성하여, API Gateway도 적용하였는데 1년 반 정도 운영한 경험을 공유할 예정입니다. 더불어 MSA 환경에서는 API Gateway를 통해 인증을 어떻게 처리하는지 알아보고 OAuth2 기반의 JWT Token을 이용한 인증에 대한 이야기도 함께 나눌 예정입니다.
Enable DPDK and SR-IOV for containerized virtual network functions with zunheut2008
Zun is an OpenStack service that manages containers as first-class resources without relying on virtual machines. The document discusses enabling DPDK and SR-IOV support in Zun to accelerate containerized network functions (NFV). It outlines challenges in using containers for NFV and how Zun addresses gaps. Benchmark tests show containers leveraging DPDK and SR-IOV through Zun can achieve near-physical server performance for networking workloads.
Disaster Recovery for Multi-Region Apache Kafka Ecosystems at Uberconfluent
Speaker: Yupeng Fu, Staff Engineer, Uber
High availability and reliability are important requirements to Uber services, and the services shall tolerate datacenter failures in a region and fail over to another region. In this talk, we will present the active-active Apache Kafka® at Uber and how it facilitates disaster discovery across regions for Uber services. In particular, we will highlight the key components including topic replication, topic aggregation, offsets sync and then walk through several use cases of their disaster recovery strategy using active-active Kafka. Lastly, we will present several interesting challenges and the future work planned.
Yupeng Fu is a staff engineer in Uber Data Org leading the streaming data platform. Previously, he worked at Alluxio and Palantir, building distributed data analysis and storage platforms. Yupeng holds a B.S. and an M.S. from Tsinghua University and did his Ph.D. research on databases at UCSD.
Scaling to millions of users with Amazon CloudFront - April 2017 AWS Online T...Amazon Web Services
Learning Objectives:
• Learn how to use CloudFront dynamic delivery features • See a live demo and learn how to take advantage of Cloud Front newest features
Traditionally, content delivery networks (CDNs) were designed to accelerate static content. Amazon CloudFront supports delivery of an entire website, including dynamic, static, streaming and interactive content using a global network of edge locations. CloudFront integrates with other AWS services that are built to scale massively. Together, the solution can automatically scale to millions of users by leveraging the global reach of CloudFront and the auto scaling capability of AWS platform. In this talk, we introduce you to various design patterns and best practices to build a massively scalable solution using CloudFront. We discuss how this scale can be achieved without compromising on availability, security or cost.
This document discusses various design patterns for distributed systems, including service orientation patterns and CQRS (Command Query Responsibility Segregation). It defines common patterns such as service gateway, remote facade, and data transport object. It also discusses anti-patterns and provides examples of how to properly design services and separate commands from queries. The document is intended as a lesson on these patterns and techniques for programming distributed systems.
Iceoryx is an open-source middleware developed by Eclipse that provides real-time data transport capabilities. It can be used as an alternative to ROS2's Fast-RTPS and Connext middleware implementations. Iceoryx uses shared memory and message queues for high-performance data transport between processes. However, it currently has some limitations including single point of failure if the central RouDi daemon crashes, fixed memory mapping, and lack of support for request/response calls and quality of service features.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/renesas/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Yoshio Sato, Senior Product Marketing Manager in the Industrial Business Unit at Renesas, presents the "Dynamically Reconfigurable Processor Technology for Vision Processing" tutorial at the May 2019 Embedded Vision Summit.
The Dynamically Reconfigurable Processing (DRP) block in the Arm Cortex-A9 based RZ/A2M MPU accelerates image processing algorithms with spatially pipelined, time-multiplexed, reconfigurable- hardware compute resources. This hybrid ARM/DRP architecture combines the economy, flexibility and ease-of-use of microprocessors with the high throughput and low latency of performance- optimized hardware.
DRP technology achieves silicon area efficiency by dividing large data paths into sub- blocks that can be swapped into the DRP hardware on each clock cycle to accelerate multiple complex algorithms while avoiding the cost and power penalties associated with large FPGAs. Pre-built libraries and a C-language programming environment deliver these benefits without the need for hardware design expertise. Designs can be iteratively enhanced through pre-production and even after mass-market deployment.
In this presentation, Sato examines the DRP block’s architecture and operation, presents benchmarks demonstrating performance up to 20x greater than traditional CPUs and introduces resources for developing DRP-based embedded vision systems with the RZ/A2M MPU.
- Stefan Streichsbier is the CEO of GuardRails and a professional white-hat hacker who has identified severe shortcomings in security processes and technologies, leading him to create GuardRails.
- The document discusses the evolution of DevOps and increasing complexity, the state of security and how it needs to fit within modern development workflows, and introduces the concept of DevSecOps to address shortcomings and better integrate security.
- Key aspects of DevSecOps discussed include how to create, test, and monitor secure applications and empower development teams to build security in from the start rather than see it as a separate function. Automated security tools and the need to reduce noise and improve usability for developers is also
Kubernetes와 Kubernetes on OpenStack 환경의 비교와 그 구축방법에 대해서 알아봅니다.
1. 클라우드 동향
2. Kubernetes vs Kubernetes on OpenStack
3. Kubernetes on OpenStack 구축 방벙
4. Kubernetes on OpenStack 운영 방법
Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안SANG WON PARK
Apache Kafak의 빅데이터 아키텍처에서 역할이 점차 커지고, 중요한 비중을 차지하게 되면서, 성능에 대한 고민도 늘어나고 있다.
다양한 프로젝트를 진행하면서 Apache Kafka를 모니터링 하기 위해 필요한 Metrics들을 이해하고, 이를 최적화 하기 위한 Configruation 설정을 정리해 보았다.
[Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안]
Apache Kafka 성능 모니터링에 필요한 metrics에 대해 이해하고, 4가지 관점(처리량, 지연, Durability, 가용성)에서 성능을 최적화 하는 방안을 정리함. Kafka를 구성하는 3개 모듈(Producer, Broker, Consumer)별로 성능 최적화를 위한 …
[Apache Kafka 모니터링을 위한 Metrics 이해]
Apache Kafka의 상태를 모니터링 하기 위해서는 4개(System(OS), Producer, Broker, Consumer)에서 발생하는 metrics들을 살펴봐야 한다.
이번 글에서는 JVM에서 제공하는 JMX metrics를 중심으로 producer/broker/consumer의 지표를 정리하였다.
모든 지표를 정리하진 않았고, 내 관점에서 유의미한 지표들을 중심으로 이해한 내용임
[Apache Kafka 성능 Configuration 최적화]
성능목표를 4개로 구분(Throughtput, Latency, Durability, Avalibility)하고, 각 목표에 따라 어떤 Kafka configuration의 조정을 어떻게 해야하는지 정리하였다.
튜닝한 파라미터를 적용한 후, 성능테스트를 수행하면서 추출된 Metrics를 모니터링하여 현재 업무에 최적화 되도록 최적화를 수행하는 것이 필요하다.
Firebase provides various services including analytics, authentication, database, storage, hosting, testing, distribution and monetization. It allows for unlimited analytics reporting, audience segmentation, offline and realtime database access, secure file storage, A/B testing, error monitoring, and integration with Google services like AdWords and AdMob. Firebase services are customizable, scalable and aim to improve the user experience.
카카오 광고 플랫폼 MSA 적용 사례 및 API Gateway와 인증 구현에 대한 소개if kakao
황민호(robin.hwang) / kakao corp. DSP개발파트
---
최근 Spring Cloud와 Netflix OSS로 MSA를 구성하는 시스템 기반의 서비스들이 많아지는 추세입니다.
카카오에서도 작년에 오픈한 광고 플랫폼 모먼트에 Spring Cloud 기반의 MSA환경을 구성하여, API Gateway도 적용하였는데 1년 반 정도 운영한 경험을 공유할 예정입니다. 더불어 MSA 환경에서는 API Gateway를 통해 인증을 어떻게 처리하는지 알아보고 OAuth2 기반의 JWT Token을 이용한 인증에 대한 이야기도 함께 나눌 예정입니다.
Enable DPDK and SR-IOV for containerized virtual network functions with zunheut2008
Zun is an OpenStack service that manages containers as first-class resources without relying on virtual machines. The document discusses enabling DPDK and SR-IOV support in Zun to accelerate containerized network functions (NFV). It outlines challenges in using containers for NFV and how Zun addresses gaps. Benchmark tests show containers leveraging DPDK and SR-IOV through Zun can achieve near-physical server performance for networking workloads.
Disaster Recovery for Multi-Region Apache Kafka Ecosystems at Uberconfluent
Speaker: Yupeng Fu, Staff Engineer, Uber
High availability and reliability are important requirements to Uber services, and the services shall tolerate datacenter failures in a region and fail over to another region. In this talk, we will present the active-active Apache Kafka® at Uber and how it facilitates disaster discovery across regions for Uber services. In particular, we will highlight the key components including topic replication, topic aggregation, offsets sync and then walk through several use cases of their disaster recovery strategy using active-active Kafka. Lastly, we will present several interesting challenges and the future work planned.
Yupeng Fu is a staff engineer in Uber Data Org leading the streaming data platform. Previously, he worked at Alluxio and Palantir, building distributed data analysis and storage platforms. Yupeng holds a B.S. and an M.S. from Tsinghua University and did his Ph.D. research on databases at UCSD.
Scaling to millions of users with Amazon CloudFront - April 2017 AWS Online T...Amazon Web Services
Learning Objectives:
• Learn how to use CloudFront dynamic delivery features • See a live demo and learn how to take advantage of Cloud Front newest features
Traditionally, content delivery networks (CDNs) were designed to accelerate static content. Amazon CloudFront supports delivery of an entire website, including dynamic, static, streaming and interactive content using a global network of edge locations. CloudFront integrates with other AWS services that are built to scale massively. Together, the solution can automatically scale to millions of users by leveraging the global reach of CloudFront and the auto scaling capability of AWS platform. In this talk, we introduce you to various design patterns and best practices to build a massively scalable solution using CloudFront. We discuss how this scale can be achieved without compromising on availability, security or cost.
This document discusses various design patterns for distributed systems, including service orientation patterns and CQRS (Command Query Responsibility Segregation). It defines common patterns such as service gateway, remote facade, and data transport object. It also discusses anti-patterns and provides examples of how to properly design services and separate commands from queries. The document is intended as a lesson on these patterns and techniques for programming distributed systems.
Iceoryx is an open-source middleware developed by Eclipse that provides real-time data transport capabilities. It can be used as an alternative to ROS2's Fast-RTPS and Connext middleware implementations. Iceoryx uses shared memory and message queues for high-performance data transport between processes. However, it currently has some limitations including single point of failure if the central RouDi daemon crashes, fixed memory mapping, and lack of support for request/response calls and quality of service features.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/renesas/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Yoshio Sato, Senior Product Marketing Manager in the Industrial Business Unit at Renesas, presents the "Dynamically Reconfigurable Processor Technology for Vision Processing" tutorial at the May 2019 Embedded Vision Summit.
The Dynamically Reconfigurable Processing (DRP) block in the Arm Cortex-A9 based RZ/A2M MPU accelerates image processing algorithms with spatially pipelined, time-multiplexed, reconfigurable- hardware compute resources. This hybrid ARM/DRP architecture combines the economy, flexibility and ease-of-use of microprocessors with the high throughput and low latency of performance- optimized hardware.
DRP technology achieves silicon area efficiency by dividing large data paths into sub- blocks that can be swapped into the DRP hardware on each clock cycle to accelerate multiple complex algorithms while avoiding the cost and power penalties associated with large FPGAs. Pre-built libraries and a C-language programming environment deliver these benefits without the need for hardware design expertise. Designs can be iteratively enhanced through pre-production and even after mass-market deployment.
In this presentation, Sato examines the DRP block’s architecture and operation, presents benchmarks demonstrating performance up to 20x greater than traditional CPUs and introduces resources for developing DRP-based embedded vision systems with the RZ/A2M MPU.
- Stefan Streichsbier is the CEO of GuardRails and a professional white-hat hacker who has identified severe shortcomings in security processes and technologies, leading him to create GuardRails.
- The document discusses the evolution of DevOps and increasing complexity, the state of security and how it needs to fit within modern development workflows, and introduces the concept of DevSecOps to address shortcomings and better integrate security.
- Key aspects of DevSecOps discussed include how to create, test, and monitor secure applications and empower development teams to build security in from the start rather than see it as a separate function. Automated security tools and the need to reduce noise and improve usability for developers is also
Kubernetes와 Kubernetes on OpenStack 환경의 비교와 그 구축방법에 대해서 알아봅니다.
1. 클라우드 동향
2. Kubernetes vs Kubernetes on OpenStack
3. Kubernetes on OpenStack 구축 방벙
4. Kubernetes on OpenStack 운영 방법
Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안SANG WON PARK
Apache Kafak의 빅데이터 아키텍처에서 역할이 점차 커지고, 중요한 비중을 차지하게 되면서, 성능에 대한 고민도 늘어나고 있다.
다양한 프로젝트를 진행하면서 Apache Kafka를 모니터링 하기 위해 필요한 Metrics들을 이해하고, 이를 최적화 하기 위한 Configruation 설정을 정리해 보았다.
[Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안]
Apache Kafka 성능 모니터링에 필요한 metrics에 대해 이해하고, 4가지 관점(처리량, 지연, Durability, 가용성)에서 성능을 최적화 하는 방안을 정리함. Kafka를 구성하는 3개 모듈(Producer, Broker, Consumer)별로 성능 최적화를 위한 …
[Apache Kafka 모니터링을 위한 Metrics 이해]
Apache Kafka의 상태를 모니터링 하기 위해서는 4개(System(OS), Producer, Broker, Consumer)에서 발생하는 metrics들을 살펴봐야 한다.
이번 글에서는 JVM에서 제공하는 JMX metrics를 중심으로 producer/broker/consumer의 지표를 정리하였다.
모든 지표를 정리하진 않았고, 내 관점에서 유의미한 지표들을 중심으로 이해한 내용임
[Apache Kafka 성능 Configuration 최적화]
성능목표를 4개로 구분(Throughtput, Latency, Durability, Avalibility)하고, 각 목표에 따라 어떤 Kafka configuration의 조정을 어떻게 해야하는지 정리하였다.
튜닝한 파라미터를 적용한 후, 성능테스트를 수행하면서 추출된 Metrics를 모니터링하여 현재 업무에 최적화 되도록 최적화를 수행하는 것이 필요하다.
簡化 JVM 上雲 - 透過 Azure Spring Cloud 提升開發、發佈及服務監控效率Shengyou Fan
Spring Boot 一直是 Java 開發生態系裡市佔率最高的框架,許多企業都採用其開發自身服務。隨著開發典範的轉移,即便 Spring 提供完整方案,開發者往往對架構修改及服務管理的工作怯步,是否移轉上雲也有所疑慮。在這場分享裡,將會介紹由 Azure 提供的 Spring Cloud 解決方案,並從最簡單的一個 Spring Boot 應用開始,逐步導入微服務架構、連接 Azure DB、藍綠部署到服務監控,讓開發者了解使用 Azure 運行 Spring 是一個簡單又有效率的體驗,加速將 JVM 應用上雲。
ASP.NET MVC 就快進入4了,您跟上了嗎? 如何將現有的 MVC3 如何升級到MVC4呢?無痛升級系列。以及ASP.NET MVC4 新增功能介紹。
課程內容:
ASP.NET MVC 3 升級到 ASP.NET MVC4 的示範與常見問題說明
Basic Project & Empty Project Template
從無到有,建置ASP.NET MVC4 Web API應用程式、How to self-host a web API
Display Modes
View Switcher
Bundling and Minification
Task Support for Asynchronous Controllers
Mobile Project Template
This document provides instructions for installing Ubuntu Desktop 14.04 in a VirtualBox virtual machine. It outlines steps to create the VM, install Ubuntu, upgrade packages using the APT package manager, install VM guest additions, reboot, and create a new user account. System configuration files like /etc/passwd and /etc/group are also mentioned.