This document discusses microservices architectures and emerging technologies to support them. It introduces Envoy proxy as a sidecar proxy that implements common microservices patterns like circuit breaking and load balancing. It then introduces Istio as a control plane that manages Envoy proxies and provides higher-level capabilities like traffic management, security, and observability across microservices. The presentation argues that 2018 will be the year of service meshes, with Istio being a prominent example for managing microservices communication using Envoy proxies.
KubeCon NA 2018: Evolution of Integration and Microservices with Service Mesh...Christian Posta
Cloud-native describes a way of building applications on a cloud platform to iteratively discover and deliver business value. We now have access to a lot of similar technology that the large internet companies pioneered and used to their advantage to dominate their respective markets. What challenges arise when we start building applications to take advantage of this new technology?
In this talk we'll explore the role of service meshes when building distributed systems, why they make sense, and where they don't make sense. We will look at a class of problem that crops up that service mesh cannot solve, but that frameworks and even new programming languages like Ballerina are aiming to solve
The Hardest Part of Microservices: Calling Your ServicesChristian Posta
When building microservices, you must solve for a number of critical functions, but the process can be incredibly complex and expensive to maintain. Christian Posta offers an overview of Envoy Proxy and Istio.io Service Mesh, explaining how they solve application networking problems more elegantly by pushing these concerns down to the infrastructure layer and demonstrating how it all works.
Atlanta Microservices Day: Istio Service MeshChristian Posta
The document discusses the evolution of application networking from individual microservices libraries to shared proxies like Envoy and service meshes like Istio. It notes that as applications adopt microservices architectures, many common concerns around distributed systems must be addressed, such as service discovery, load balancing, and fault tolerance. Initially, different frameworks offered individual libraries to handle these issues, but this led to inconsistencies and increased complexity. Envoy proxy and the Istio service mesh aim to provide a standardized and shared way to address these cross-cutting distributed system concerns for all services regardless of language or framework.
A presentation on why or why not microservices, why a platform is important, discovering how to break down a monolith and some of the challenges you'll face (data, transactions, boundaries, etc). Last section is on Istio and service mesh introductions. Follow on twitter @christianposta for updates and more details
Service-mesh technology promises to deliver a lot of value to a cloud-native application, but it doesn't come without some hype. In this talk, we'll look at what is a "service mesh", how it compares to similar technology (Netflix OSS, API Management, ESBs, etc) and what options for service mesh exist today.
API Gateways are going through an identity crisisChristian Posta
API Gateways provide functionality like rate limiting, authentication, request routing, reporting, and more. If you've been following the rise in service-mesh technologies, you'll notice there is a lot of overlap with API Gateways when solving some of the challenges of microservices. If service mesh can solve these same problems, you may wonder whether you really need a dedicated API Gateway solution?
The reality is there is some nuance in the problems solved at the edge (API Gateway) compared to service-to-service communication (service mesh) within a cluster. But with the evolution of cluster-deployment patterns, these nuances are becoming less important. What's more important is that the API Gateway is evolving to live at a layer above service mesh and not directly overlapping with it. In other words, API Gateways are evolving to solve application-level concerns like aggregation, transformation, and deeper context and content-based routing as well as fitting into a more self-service, GitOps style workflow.
In this talk we put aside the "API Gateway" infrastructure as we know it today and go back to first principles with the "API Gateway pattern" and revisit the real problems we're trying to solve. Then we'll discuss pros and cons of alternative ways to implement the API Gateway pattern and finally look at open source projects like Envoy, Kubernetes, and GraphQL to see how the "API Gateway pattern" actually becomes the API for our applications while coexisting nicely with a service mesh (if you adopt a service mesh).
The document discusses Christian Posta's journey with microservices architectures. It begins by explaining why organizations are moving to microservices and defines microservices. It then covers related topics like cloud platforms, container technologies like Kubernetes and OpenShift, benefits and drawbacks of microservices, and tools for developing microservices like Docker, Kubernetes, OpenShift, and Camel.
Christian Posta is a principal middleware specialist and architect who has worked with large microservices architectures. He discusses why companies are moving to microservices and cloud platforms like Kubernetes and OpenShift. He covers characteristics of microservices like small autonomous teams and decentralized decision making. Posta also discusses breaking applications into independent services, shedding dependencies between teams, and using contracts and APIs for communication between services.
Building Information Systems using Event Modeling (Bobby Calderwood, Evident ...confluent
"Event Modeling is a fairly new information system modeling discipline created by Adam Dymitruk that is heavily influenced by CQRS and Event Sourcing. Its lineage follows from Event Storming, Design Thinking, and other modeling practices from the Agile and Domain-Driven Design communities. The methodology emphasizes simplicity (there are only four model ingredients) and inclusion of non-developer participants.
Like other modeling disciplines, Event Modeling is sufficiently general to enable collaborative learning and knowledge exchange among UI/UX designers, software engineers and architects, and business domain experts. But it's also sufficiently expressive and specific to be directly actionable by the implementors of the information system described by the model.
During this talk, we'll:
* Build an Event Model of a simple information system, including wire-framing the UI/UX experience
* Explore how to proceed from model to implementation using Kafka, its Streams and Connect APIs, and KSQL
* Jump-start the implementation by generating code directly from the Event Model
* Track and measure the work of implementation by generating tasks directly from the Event Model"
We consider a microservices architecture to achieve an end goal, not because it's "the cool thing to do". Every organization looking to adopt this architecture must realize (and adhere) to a set of foundational principles. Guided by those principles, we can correctly choose the technology to help support a microservices architecture and meet our end goals. This talk explains those core principles and gives you the tools needed for your microservices journey.
Knative builds on Kubernetes and Istio to provide "PaaS-like abstractions" that raise the level of abstraction for specifying, running, and modifying applications. Knative includes building blocks like Knative Serving for autoscaling container workloads to zero, Knative Eventing for composing event-driven services, Knative Build for building containers from source, and Knative Pipelines for abstracting CI/CD pipelines. While Knative can run any type of container, its building blocks help enable serverless-style functions by allowing compute resources to scale to zero and be driven by event loads.
Docker right now provides great value in the enterprise but the value proposition is more about developer productivity than scale-out.
Docker benefits include resource management, environment management, continuous delivery, developer and operations collaboration, and hybrid workloads.
Take care in its introduction. Consider Docker as just part of an overall toolkit and you don't need to go "full stack" to gain value.
Microservices architecture is a very powerful way to build scalable systems optimized for speed of change. To do this, we need to build independent, autonomous services which by definition tend to minimize dependencies on other systems. One of the tenants of microservices, and a way to minimize dependencies, is “a service should own its own database”. Unfortunately this is a lot easier said than done. Why? Because: your data.
We’ve been dealing with data in information systems for 5 decades so isn’t this a solved problem? Yes and no. A lot of the lessons learned are still very relevant. Traditionally, we application developers have accepted the practice of using relational databases and relying on all of their safety guarantees without question. But as we build services architectures that span more than one database (by design, as with microservices), things get harder. If data about a customer changes in one database, how do we reconcile that with other databases (especially where the data storage may be heterogenous?).
For developers focused on the traditional enterprise, not only do we have to try to build fast-changing systems that are surrounded by legacy systems, the domains (finance, insurance, retail, etc) are incredibly complicated. Just copying with Netflix does for microservices may or may not be useful. So how do we develop and reason about the boundaries in our system to reduce complexity in the domain?
In this talk, we’ll explore these problems and see how Domain Driven Design helps grapple with the domain complexity. We’ll see how DDD concepts like Entities and Aggregates help reason about boundaries based on use cases and how transactions are affected. Once we can identify our transactional boundaries we can more carefully adjust our needs from the CAP theorem to scale out and achieve truly autonomous systems with strictly ordered eventual consistency. We’ll see how technologies like Apache Kafka, Apache Camel and Debezium.io can help build the backbone for these types of systems. We’ll even explore the details of a working example that brings all of this together.
Continuous Intelligence for Customer Service Using Kafka Event Streams | Simo...HostedbyConfluent
Today’s products - devices, software and services - are well instrumented to permit users, vendors and service providers to gather maximum insight into how they are used, when they need repair and many other operational insights. Ensuring that products can rapidly adapt to a constantly changing environment and changing customer needs requires that the events they generate are analyzed continuously and in context. Insights can be synthesized from many sources in context - geospatial and proximity, trajectory and even predicted future states.Customers, vendors and service providers need to analyze, learn, and predict directly from streaming events because data volumes are huge and automated responses must often be delivered in milliseconds. To achieve insights quickly, we need to build models on-the-fly whose predictions are accurate and in sync with the real world, often to support automation. Many insights depend on analyzing the joint evolution of data sources whose behavior is correlated in time or space.In this talk we present Swim, an Apache 2.0 licensed platform for continuous intelligence applications. Swim builds a fluid model of data sources and their changing relationships in real-time - Swim applications analyze, learn and predict directly from event data. Swim applications integrate with Apache Kafka for event streaming. Developers need nothing more than Java skills. Swim deploys native or in containers on k8s, with the same code in each instance. Instances link to build an application layer mesh that facilitates distribution and massive scale without sacrificing consistency. We will present several continuous intelligence applications in use today that depend on real-time analysis, learning and prediction to power automation and deliver responses that are in sync with the real-world. We will show how easy it is to build, deploy and run distributed, highly available event streaming applications that analyze data from hundreds of millions of sources - petabytes per day. The architecture is intuitively appealing and blazingly fast.
Transformation During a Global Pandemic | Ashish Pandit and Scott Lee, Univer...HostedbyConfluent
When the University of California, San Diego launched its largest investment in tech in 2018, they planned to future proof their business processes and systems. Unexpectedly, it also prepared them to handle a global pandemic that changed every norm for the campus. With shelter-in-place orders taking immediate effect, they needed to quickly set up a robust online learning platform - one with powerful analytics to track student success. And, for the times students and staff are on campus, a contact tracing application was essential for their safety. We’d like to offer a conversation with Scott Lee to tell you more about UC San Diego’s rapid transformation from a traditional, on-campus institution to one of the leading examples of remote learning, and the critical role data connectivity played in making this possible.
The document discusses microservices and APIs. It covers how microservices optimize for speed by shedding dependencies and having dependencies on demand through services and APIs. It discusses consumer contracts for APIs and service versioning. It also discusses using an API gateway pattern for scalability, security, monitoring and more. It promotes API management for benefits like access control, analytics, and monetization of microservices.
The Hardest Part of Microservices: Your Data - Christian Posta, Red HatAmbassador Labs
Christian Posta, principal architect at Red Hat discusses how to manage your data within a microservices architecture at the 2017 Microservices.com Practitioner Summit.
Evolution of integration and microservices patterns with service meshJudy Breedlove
The document discusses the evolution of integration and microservice patterns with service mesh technologies like Istio. It describes how service meshes provide decentralized application networking infrastructure between services through a data plane and control plane. This includes features like advanced load balancing, traffic control, observability, and policy enforcement that help improve resilience, security, and reliability of distributed applications.
Service-mesh technology promises to deliver a lot of value to a cloud-native application, but it doesn't come without some hype. In this talk, we'll look at what is a "service mesh", how it compares to similar technology (Netflix OSS, API Management, ESBs, etc) and what options for service mesh exist today.
This document provides an overview of microservices from Christian Posta, a chief architect at Red Hat. It discusses what microservices are, reasons for using them, common microservices patterns and frameworks, decomposing monolithic applications into microservices, and ensuring resilience between services. The presentation also covers using Kubernetes and OpenShift for microservices and demonstrates sample applications.
Role of Integration and Service Mesh in Cloud Native Architecture KubeCon 2108Ballerina
Building useful services across our collection of existing applications, microservices, and now functions, we see a common theme: services must be able to communicate with each other, and solve problems like data mediation, routing, policy enforcement, security, and others. Service mesh is a technology that has emerged in container-based environments to help solve some of these problems; however, not all of them can be solved by pushing the problems to a different abstraction. Understanding the role and responsibility of service mesh and application-integration frameworks can help you successfully build useful business services on a cloud native platform. This talk will help you understand those roles and responsibilities and how service mesh and application integration co-exist to build cloud native applications.
Techniques for scaling application with security and visibility in cloudAkshay Mathur
Akshay Mathur gives a presentation on techniques for scaling applications with security and visibility in the cloud. He discusses 8 growth phases applications typically go through including load balancing, gaining insights, content optimization, offloading services, content switching, preventing bot traffic and DDoS attacks, continuous delivery, and the need for a unified cloud application front end solution to manage these phases. He introduces Appcito CAFE as a service that provides capabilities across availability, performance, security and DevOps to simplify application scaling in the cloud.
Beyond DevOps: How Netflix Bridges the Gap?C4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1mv6Kpr.
Josh Evans uses the Netflix Operations Engineering as a case study to explore the challenges faced by centralized engineering teams and approaches to addressing those challenges. Filmed at qconsf.com.
Josh Evans is Director of Operations Engineering at Netflix, with experience in e-commerce, playback control services, infrastructure, tools, testing, and operations.
Effective Service Mesh to turbocharge Cloud ResiliencyLiang Gang Yu
This document discusses how service meshes can turbocharge cloud resiliency. It describes some key challenges with inter-service communication like failures, latency, routing, and observability. It then explains how a service mesh provides a dedicated infrastructure layer to address these issues through capabilities like traffic management, circuit breaking, telemetry, and fault injection. Specifically, it outlines how the service mesh provides resiliency through active health checks, timeouts, retries, and circuit breaking in the data plane. It also discusses capabilities for observability, tracing, and injecting faults to test resiliency.
This document discusses cloud-native data and patterns for managing data in microservices architectures. It describes using data services and APIs to interface with existing data sources. Patterns like caching data at the edge with various caching strategies are discussed. The document also covers using multiple small databases with each microservice rather than a shared database. Event sourcing and CQRS patterns are presented as ways to integrate data across services. Finally, the impact on roles like database administrators is considered in cloud-native data environments.
<November 2017 Updated from earlier presentations on Cloud-native Data>
Cloud-native applications form the foundation for modern, cloud-scale digital solutions, and the patterns and practices for cloud-native at the app tier are becoming widely understood – statelessness, service discovery, circuit breakers and more. But little has changed in the data tier. Our modern apps are often connected to monolithic shared databases that have monolithic practices wrapped around them. As a result, the autonomy promised by moving to a microservices application architecture is compromised.
What we need are patterns and practices for cloud-native data. The anti-patterns of shared databases and simple proxy-style web services to front them give way to approaches that include use of caches (Netflix calls caching their hidden microservice), database per service and polyglot persistence, modern versions of ETL and data integration and more. In this session, aimed at the application developer/architect, Cornelia will look at those patterns and see how they serve the needs of the cloud-native application.
JavaZone 2017 - Microservices in action at the Dutch National PoliceBert Jan Schrijver
3 DevOps teams at the Dutch National Police are building big data applications in a private cloud using microservices architecture. They develop independently but coordinate planning. Testing includes unit, integration, end-to-end, load, and mutation tests. Features are developed on branches and deployed continuously to test and production after sprints. Challenges include balancing security and performance for stateless services and preventing monolithic frontends. They aim to improve with zero downtime deployments, product teams, and automated security testing.
3 DevOps teams at the Dutch National Police are building big data applications in a private cloud using microservices architecture. They develop using techniques like continuous delivery, short feedback loops, and embrace of change. Challenges include balancing performance and security with stateless services, minimizing dependencies between teams, and preventing monolithic frontends. Future plans include zero downtime deployments, cross-functional product teams, and a modular frontend.
OpenValue meetup October 2017 - Microservices in action at the Dutch National...Bert Jan Schrijver
3 DevOps teams at the Dutch National Police are building big data applications in a private cloud using microservices architecture. They develop independently but coordinate planning. Testing includes unit, integration, end-to-end, load, and mutation tests. Features are developed on branches and merged/deployed continuously. Services register with Consul for service discovery. Challenges include balancing security and performance for stateless services and preventing monolithic frontends. Future plans include zero downtime deployments and cross-functional product teams.
This presentation will describe how to go beyond a "Hello world" stream application and build a real-time data-driven product. We will present architectural patterns, go through tradeoffs and considerations when deciding on technology and implementation strategy, and describe how to put the pieces together. We will also cover necessary practical pieces for building real products: testing streaming applications, and how to evolve products over time.
Presented at highloadstrategy.com 2016 by Øyvind Løkling (Schibsted Products & Technology), joint work with Lars Albertsson (independent, www.mapflat.com).
This talk focuses on how we used Amazon Kinesis to build the pub-sub infra at Lyft, that ingests more than a 100 billion events per day. We'll review the strengths and weaknesses of Kinesis as a choice for streaming events in realtime, at Lyft's scale; as well as the best practices and lessons learnt over time.
Speaker: Hafiz Hamid (Lyft)
Hafiz Hamid is a software engineer on the Pub-Sub/Streaming Platform team at Lyft. He has built some of the key pieces in the messaging & streaming infrastructure at Lyft. Previously, Hafiz was a technical lead at Bing Search where he worked on data pipelines, relevance and web crawlers.
Mike Spicer is the lead architect for the IBM Streams team. In his presentation, Mike provides an overview of the many key new features available in IBM Streams V4.1. Simpler development, simpler management, and Spark integration are a few of the capabilities included in IBM Streams V4.1.
This document discusses when a service mesh may be needed and provides an overview of the current service mesh landscape. It begins with why microservices are adopted and the challenges of operating distributed applications. It then describes a maturity journey where a service mesh is not initially needed but may become useful for applications that become more complex, distributed, and interdependent. The document outlines some current major service mesh implementations and notes that the technology is still new and changing rapidly. It recommends investigating service meshes through proof of concepts but cautions that production usage requires significant resources. It profiles F5 Aspen Mesh and NGINX solutions for service meshes and microservices.
Early Draft: Service Mesh allows developers to focus on business logic while the crosscutting network data layer code is handled by the Service Mesh. This is a boon because this code can be tricky to implement and hard to test all of the edge cases. Service Mesh takes this a few steps further than AOP or Servlet Filters or custom language-specific frameworks because it works regardless of the underlying programming language being used which is great for polyglot development shops. Thus standardizing how these layers work, while allowing teams to pick the best tools or languages for the job at hand. Kubernetes and Istio Service Mesh automate best practices for DevSecOps needs like: failover, scale-out, scalability, health checks, circuit breakers, rate limiters, metrics, observability, avoiding cascading failure, disaster recovery, and traffic routing; supporting CI/CD and microservices architecture.
Istio’s ability to automate and maintaining zero trust networks is its most important feature. In the age of high-profile data breaches, security is paramount. Companies want to avoid major brand issues that impact the bottom line and shrink market capitalization in an instant. Istio allows a standard way to do mTLS and auto certificate rotation which helps prevent a breach and limits the blast radius if a breach occurs. Istio also takes the concern of mTLS from microservices deployments and makes it easy to use taking the burden off of application developers.
Social Media, Cloud Computing, Machine Learning, Open Source, and Big Data An...Open Analytics
This document discusses using social media, cloud computing, machine learning, open source, and big data analytics to analyze Twitter data. It describes how to collect tweets using the Twitter API, classify tweets in real-time using machine learning models on AWS, store classified tweets in MongoDB on AWS, and present results. Cost estimates for real-time classification of 1 million tweets per day are provided. Use cases described include tracking food poisoning reports and disease occurrence. Future directions discussed include developing turnkey services and linking to additional open data sources.
This document discusses using social media, cloud computing, machine learning, open source, and big data analytics to analyze Twitter data. It describes how to collect tweets using the Twitter API, classify tweets in real-time using machine learning models on AWS, store classified tweets in MongoDB on AWS, and present results. Cost estimates for real-time classification of 1 million tweets per day are provided. Use cases described include tracking food poisoning reports and disease occurrence. Future directions discussed include developing turnkey services and linking to additional open data sources.
Bol.com Tech lab September 2017 - Microservices in action at the Dutch Nation...Bert Jan Schrijver
3 DevOps teams are building high tech big data web applications in a private cloud environment to support police work. These teams use microservices architectures, with separate front-end and back-end components developed independently but integrated via service discovery. Testing includes unit, integration, and load testing across feature branches in a continuous integration environment before production deployment. Future work includes improving zero downtime deployments, modularizing the front-end, and automating security testing.
Similar to Microservices and Integration: what's next with Istio service mesh (20)
Move Auth, Policy, and Resilience to the PlatformChristian Posta
Developer's time is the most crucial resource in an enterprise IT organization. Too much time is spent on undifferentiated heavy lifting and in the world of APIs and microservices much of that is spent on non-functional, cross-cutting networking requirements like security, observability, and resilience.
As organizations reconcile their DevOps practices into Platform Engineering, tools like Istio help alleviate developer pain. In this talk we dig into what that pain looks like, how much it costs, and how Istio has solved these concerns by examining three real-life use cases. As this space continues to emerge, and innovation has not slowed, we will also discuss the recently announced Istio sidecar-less mode which significantly reduces the hurdles to adopt Istio within Kubernetes or outside Kubernetes.
Comparing Sidecar-less Service Mesh from Cilium and IstioChristian Posta
Service mesh is a powerful pattern for implementing strong zero-trust networking practices, introducing better network observability, and allowing for more fine-grained traffic control. Up until now, the sidecar pattern was used to implement service-mesh capability but as the technology matures, a new pattern has emerged: sidecarless service mesh. Two prominent open-source networking projects, Cilium and Istio, have implemented a sidecar-free approach to service mesh but they both make interesting design decisions and tradeoffs. In this talk we review the architecture of both, focusing on the pros and cons of implementations such as mutual authentication, ingress, and observability.
Understanding Wireguard, TLS and Workload IdentityChristian Posta
Zero Trust Networking has become a standard marketing buzzword but the underlying principles are critical for modern microservice-style architectures. Authentication, authorizations, policy, etc. can be difficult to implement between services and do so in a maintainable way. Google invented their own transparent encryption and authorization protocol called "ALTS" back in 2007 to serve the application layer of Google's Borg workload scheduler, but we don't see others using it outside Google.
In this webinar we look at existing technology like TLS and newcomer Wireguard and see how these technologies come together to provide a secure foundation for workload identity and modern service-to-service networking.
Istio ambient mesh uses a sidecar-less data plane that focuses on ease of operations, incremental adoption, and separation of security boundaries for applications and mesh infrastructure.
In this webinar, we'll explore:
- The forces of modernization and compliance pressures,
- How Zero Trust Architecture (ZTA) can help, and
- How Istio ambient mesh lowers the barrier for establishing the properties necessary to achieve Zero Trust and compliance
The document discusses Cilium and Istio with Gloo Mesh. It provides an overview of Gloo Mesh, an enterprise service mesh for multi-cluster, cross-cluster and hybrid environments based on upstream Istio. Gloo Mesh focuses on ease of use, powerful best practices built in, security, and extensibility. It allows for consistent API for multi-cluster north-south and east-west policy, team tenancy with service mesh as a service, and driving everything through GitOps.
This document discusses service mesh patterns for connecting microservices across multiple clusters. It describes using Envoy proxy to provide service discovery, load balancing, security and resiliency. Patterns are presented for connecting services across clusters with flat, controlled or separate networks. Managing connectivity across clusters can increase operator burden. Gloo Mesh is presented as a way to simplify management across multiple clusters with a centralized control plane.
Multicluster Kubernetes and Service Mesh PatternsChristian Posta
Building applications for cloud-native infrastructure that are resilient, scalable, secure, and meet compliance and IT objectives gets complicated. Another wrinkle for the organizations with which we work is the fact they need to run across a hybrid deployment footprint, not just Kubernetes. At Solo.io, we build application networking technology on Envoy Proxy that helps solve difficult multi-deployment, multi-cluster, and even multi-mesh problems.
In this webinar, we’re going to explore different options and patterns for building secure, scalable, resilient applications using technology like Kubernetes and Service Mesh without leaving behind existing IT investments. We’ll see why and when to use multi-cluster topologies, how to build for high availability and team autonomy, and solve for things like service discovery, identity federation, traffic routing, and access control.
Cloud-Native Application Debugging with Envoy and Service MeshChristian Posta
Microservices have been great for accelerating the software innovation and delivery, but they also present new challenges, especially as abstractions and automated orchestration at every layer make pinpointing the issue seem like walking around a maze with a blindfold. Existing tools weren’t designed for distributed environments, and the new tools need to consider how to leverage these abstraction layers to better observe, test, and troubleshoot issues.
Christian Posta walks you through Envoy Proxy and service mesh architecture for L7 data plane, the key features in Envoy that can help in debugging and troubleshooting, chaos engineering as a testing methodology for microservices, how to approach a testing and debugging framework for microservices, and new open source tools that address these areas. You’ll explore a workflow to discover and resolve microservices issues, including injecting experiments for stress testing the applications, gathering requests in flight, recording and replaying them, and debugging them step by step without affecting production traffic.
Kubernetes Ingress to Service Mesh (and beyond!)Christian Posta
Kubernetes users need to allow traffic to flow into and within the cluster. Treating the application traffic separately from the business logic allows presents new possibilities in how service to service traffic is served, controlled and observed — and provides a transition to intra cluster networking like Service Mesh. With microservices, there is a concept of both North / South traffic (incoming requests from end users to the cluster) and East / West (intra cluster) communication between the services. In this talk we will explain how Envoy Proxy works in Kubernetes as a proxy for both of these traffic directions and how it can be leveraged to do things like traffic shaping, security, and integrate the north/south to east/west behavior.
Christian Posta (@christianposta) is Global Field CTO at Solo.io, former Chief Architect at Red Hat, and well known in the community for being an author (Istio in Action, Manning, Istio Service Mesh, O'Reilly 2018, Microservices for Java Developers, O’Reilly 2016), frequent blogger, speaker, open-source enthusiast and committer on various open-source projects including Istio, Kubernetes, and many others. Christian has spent time at both enterprises as well as web-scale companies and now helps companies create and deploy large-scale, cloud-native resilient, distributed architectures. He enjoys mentoring, training and leading teams to be successful with distributed systems concepts, microservices, devops, and cloud-native application design.
The exploration of service mesh for any organization comes with some serious questions. What data plane should I use? How does this tie in with my existing API infrastructure? What kind of overhead do sidecar proxies demand? As I've seen in my work with various organizations over the years "if you have a successful microservices deployment, then you have a service mesh whether it’s explicitly optimized as one or not."
In this talk, we seek to understand the role of the data plane and how to pick the right component for the problem context. We start off by establishing the spectrum of data-plane components from shared gateways to in-code libraries with service proxies being along that spectrum. We clearly identify which scenarios would benefit from which part of the data-plane spectrum and show how modern service meshes including Istio, Linkerd, and Consul enable these optimizations.
Deep Dive: Building external auth plugins for Gloo EnterpriseChristian Posta
Using the plugin framework for Ext. Auth Service in Gloo Enterprise, we can build any custom AuthN/AuthZ plugins to handle security requirements not provided out of the box.
Role of edge gateways in relation to service mesh adoptionChristian Posta
API Gateways provide functionality like rate limiting, authentication, request routing, reporting, and more. If you’ve been following the rise in service-mesh technologies, you’ll notice there is a lot of overlap with API Gateways when solving some of the challenges of microservices. If service mesh can solve these same problems, you may wonder whether you really need a dedicated API Gateway solution?
The reality is there is some nuance in the problems solved at the edge (API Gateway) compared to service-to-service communication (service mesh) within a cluster. But with the evolution of cluster-deployment patterns, these nuances are becoming less important. What’s more important is that the API Gateway is evolving to live at a layer above service mesh and not directly overlapping with it. In other words, API Gateways are evolving to solve application-level concerns like aggregation, transformation, and deeper context and content-based routing as well as fitting into a more self-service, GitOps style workflow.
In this talk we put aside the “API Gateway” infrastructure as we know it today and go back to first principles with the “API Gateway pattern” and revisit the real problems we’re trying to solve. Then we’ll discuss pros and cons of alternative ways to implement the API Gateway pattern and finally look at open source projects like Envoy, Kubernetes, and GraphQL to see how the “API Gateway pattern” actually becomes the API for our applications while coexisting nicely with a service mesh (if you adopt a service mesh).
Navigating the service mesh landscape with Istio, Consul Connect, and LinkerdChristian Posta
The document discusses various service mesh options including Linkerd, Consul Connect, Istio, and AWS App Mesh. It provides an overview of each solution, describing their key features and strengths/opportunities. It emphasizes that the service mesh approach is useful for managing inter-service communication and that implementations are still evolving. It recommends starting simply and iteratively adopting capabilities to match needs.
Distributed microservices introduce new challenges: failure modes are harder to anticipate and resolve. In this session, we present a “Chaos Debugging” framework enabled by three open source projects: Gloo Shot, Squash, and Loop to help you increase your microservices’ “immunity” to issues.
Gloo Shot integrates with any service mesh to implement advanced, realistic chaos experiments. Squash connects powerful and mature debuggers (gdb, dlv, java debugging) to your microservices while they run in Kubernetes. Loop extends the capability of your service mesh to observe your application and record full transactions for sandboxed replay and debugging.
Come to this demo-heavy talk to see how together, Squash, Gloo Shot, and Loop allow you to trigger, replay, and investigate failure modes of your microservices in a language agnostic and efficient manner without requiring any changes to your code.
Leveraging Envoy Proxy and GraphQL to Lower the Risk of Monolith to Microserv...Christian Posta
If you have an existing Java monolith, you know you must take care making changes to it or altering it in any negative way. Often times these monoliths are very valuable to the business and generate a lot of revenue. At the same time, since it’s difficult to make changes to the monolith it’s desirable to move to a microservices architecture. Unfortunately you cannot just do a big-bang migration to a greenfield architecture and will have to incrementally adopt microservices. In this talk, we’ll look at using Gloo proxy which is based on Envoy Proxy and GraphQL to do surgical, function-level traffic control and API aggregation to safely migrate your monolith to microservices and serverless functions.
Service-mesh options with Linkerd, Consul, Istio and AWS AppMeshChristian Posta
Service mesh abstracts the network from developers to solve three main pain points:
How do services communicate securely with one another
How can services implement network resilience
When things go wrong, can we identify what and why
Service mesh implementations usually follow a similar architecture: traffic flows through control points between services (usually service proxies deployed as sidecar processes) while an out-of-band set of nodes is responsible for defining the behavior and management of the control points. This loosely breaks out into an architecture of a "data plane" through which requests flow and a "control plane" for managing a service mesh.
Different service mesh implementations use different data planes depending on their use cases and familiarity with particular technology. The control plane implementations vary between service-mesh implementations as well. In this talk, we'll take a look at three different control plane implementations with Istio, Linkerd and Consul, their strengths, and their specific tradeoffs to see how they chose to solve each of the three pain points from above. We can use this information to make choices about a service mesh or to inform our journey if we choose to build a control plane ourselves.
The document summarizes the new features of Istio 1.1, an open-source service mesh. Some key highlights include improved performance and scalability, namespace isolation, multi-cluster capabilities, easier installation with Helm, and locality-aware load balancing. A new Sidecar resource was introduced to improve performance by configuring resources for individual proxies. The presentation demonstrates performance improvements with the Sidecar resource and highlights additional functionality in Istio like traffic control and metrics collection.
CViewSurvey Digitech Pvt Ltd that works on a proven C.A.A.G. model.bhatinidhi2001
CViewSurvey is a SaaS-based Web & Mobile application that provides digital transformation to traditional paper surveys and feedback for customer & employee experience, field & market research that helps you evaluate your customer's as well as employee's loyalty.
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What is OCR Technology and How to Extract Text from Any Image for FreeTwisterTools
Discover the fascinating world of Optical Character Recognition (OCR) technology with our comprehensive presentation. Learn how OCR converts various types of documents, such as scanned paper documents, PDFs, or images captured by a digital camera, into editable and searchable data. Dive into the history, modern applications, and future trends of OCR technology. Get step-by-step instructions on how to extract text from any image online for free using a simple tool, along with best practices for OCR image preparation. Ideal for professionals, students, and tech enthusiasts looking to harness the power of OCR.
NBFC Software: Optimize Your Non-Banking Financial CompanyNBFC Softwares
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Software development... for all? (keynote at ICSOFT'2024)miso_uam
Our world runs on software. It governs all major aspects of our life. It is an enabler for research and innovation, and is critical for business competitivity. Traditional software engineering techniques have achieved high effectiveness, but still may fall short on delivering software at the accelerated pace and with the increasing quality that future scenarios will require.
To attack this issue, some software paradigms raise the automation of software development via higher levels of abstraction through domain-specific languages (e.g., in model-driven engineering) and empowering non-professional developers with the possibility to build their own software (e.g., in low-code development approaches). In a software-demanding world, this is an attractive possibility, and perhaps -- paraphrasing Andy Warhol -- "in the future, everyone will be a developer for 15 minutes". However, to make this possible, methods are required to tweak languages to their context of use (crucial given the diversity of backgrounds and purposes), and the assistance to developers throughout the development process (especially critical for non-professionals).
In this keynote talk at ICSOFT'2024 I presented enabling techniques for this vision, supporting the creation of families of domain-specific languages, their adaptation to the usage context; and the augmentation of low-code environments with assistants and recommender systems to guide developers (professional or not) in the development process.
Ansys Mechanical enables you to solve complex structural engineering problems and make better, faster design decisions. With the finite element analysis (FEA) solvers available in the suite, you can customize and automate solutions for your structural mechanics problems and parameterize them to analyze multiple design scenarios. Ansys Mechanical is a dynamic tool that has a complete range of analysis tools.
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An MVP (Minimum Viable Product) mobile application is a streamlined version of a mobile app that includes only the core features necessary to address the primary needs of its users. The purpose of an MVP is to validate the app concept with minimal resources, gather user feedback, and identify any areas for improvement before investing in a full-scale development. This approach allows businesses to quickly launch their app, test its market viability, and make data-driven decisions for future enhancements, ensuring a higher likelihood of success and user satisfaction.
introduction of Ansys software and basic and advance knowledge of modelling s...sachin chaurasia
Ansys Mechanical enables you to solve complex structural engineering problems and make better, faster design decisions. With the finite element analysis (FEA) solvers available in the suite, you can customize and automate solutions for your structural mechanics problems and parameterize them to analyze multiple design scenarios. Ansys Mechanical is a dynamic tool that has a complete range of analysis tools.
Break data silos with real-time connectivity using Confluent Cloud Connectorsconfluent
Connectors integrate Apache Kafka® with external data systems, enabling you to move away from a brittle spaghetti architecture to one that is more streamlined, secure, and future-proof. However, if your team still spends multiple dev cycles building and managing connectors using just open source Kafka Connect, it’s time to consider a faster and cost-effective alternative.
Cultural Shifts: Embracing DevOps for Organizational TransformationMindfire Solution
Mindfire Solutions specializes in DevOps services, facilitating digital transformation through streamlined software development and operational efficiency. Their expertise enhances collaboration, accelerates delivery cycles, and ensures scalability using cloud-native technologies. Mindfire Solutions empowers businesses to innovate rapidly and maintain competitive advantage in dynamic market landscapes.
Responsibilities of Fleet Managers and How TrackoBit Can Assist.pdfTrackobit
What do fleet managers do? What are their duties, responsibilities, and challenges? And what makes a fleet manager effective and successful? This blog answers all these questions.
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2. Christian Posta
Chief Architect, cloud application development
Twitter: @christianposta
Blog: http://blog.christianposta.com
Email: christian@redhat.com
Slides: http://slideshare.net/ceposta
• Author “Microservices for Java developers”
and other soon to be announced books
• Committer/contributor lots of open-source projects
• Blogger, speaker
7. @christianposta
Scientific method anyone?
• Make observations
• Formulate hypothesis
• Design an experiment
• State the indicators you will evaluate
• Conduct the experiment
• Evaluate results
• Accept or reject hypothesis
• Make new hypothesis and continue…
8. @christianposta
Traditional user story “requirements”
• As A…. <role>
• I Want… <goal/desire>
• So That… <receive benefit>
https://barryoreilly.com/2013/10/21/how-to-implement-hypothesis-driven-development/
11. @christianposta
We can now assert with confidence that
high IT performance correlates with
strong business performance, helping
to boost productivity, profitability and
market share.
https://puppet.com/resources/whitepaper/2014-state-devops-report
27. • Require specific language to bring in new services
• A single language doesn’t fit for all use cases
• How do you patch/upgrade/manage lifecycle?
• Need strict control over application library choices
Some drawbacks to this approach?
32. Let’s abstract this functionality to a single
binary and apply to all services.
• Allow heterogeneous architectures
• Remove application-specific implementations of this
functionality
• Consistently enforce these properties
• Correctly enforce these properties
• Opt-in as well as safety nets
@christianposta
36. Envoy is…
• service proxy
• written in C++, highly parallel, non-blocking
• L3/4 network filter
• out of the box L7 filters
• HTTP 2, including gRPC
• baked in service discovery/health checking
• advanced load balancing
• stats, metrics, tracing
• dynamic configuration through xDS
42. “2018 is the year of the service mesh”
Clayton Coleman (@smarterclayton)
Red Hat OpenShift Platform Architect
@christianposta
43. A service mesh is decentralized application-
networking infrastructure between your services
that provides resiliency, security, observability,
and routing control.
A service mesh is comprised of a data plane
and control plane.
@christianposta
Time for definitions:
44. All traffic between our applications flows
through these proxies. The proxies make
up the “data plane”
@christianposta
60. Thanks!
BTW: Hand drawn diagrams made with Paper by FiftyThree.com
Twitter: @christianposta
Blog: http://blog.christianposta.com
Email: christian@redhat.com
Slides: http://slideshare.net/cepostaFollow up links:
• http://envoyproxy.io
• http://istio.io
• http://blog.christianposta.com/istio-workshop/slides/
• http://launch.openshift.io
• http://blog.openshift.com
• http://developers.redhat.com/blog
• https://www.redhat.com/en/open-innovation-labs
Editor's Notes
One large database!
We should focus on how we design our data models so that they can be sharded and distributed…. Focus on transactions, etc not 2PC
How many people are embarking on projects to drive innovation in their organizations?
How many people think those projects will succeed?
How many people can predict the future?
How many people believe their organization’s executives can predict the future?
Inherently unknown….
Unknown Unknowns…..
As we look to “IT” to lead innovation, we need to optimize IT to go fast, ask questions, experiment and learn….
This is where containers and APIs help tremendously….
One large database!
We should focus on how we design our data models so that they can be sharded and distributed…. Focus on transactions, etc not 2PC
…… new challenges….. Let’s come back to that…..
…… new challenge….. Let’s come back to that…..
One large database!
We should focus on how we design our data models so that they can be sharded and distributed…. Focus on transactions, etc not 2PC
One large database!
We should focus on how we design our data models so that they can be sharded and distributed…. Focus on transactions, etc not 2PC
One large database!
We should focus on how we design our data models so that they can be sharded and distributed…. Focus on transactions, etc not 2PC
One large database!
We should focus on how we design our data models so that they can be sharded and distributed…. Focus on transactions, etc not 2PC
This concept of defining language, developing models to describe a domain, implementing those models, enforcing assertions, etc all happen within a certain context, and that context is vitally important in software. In common language, we are smart enough to resolve these types of language conflicts within a sentence because of its context. The computer doesn’t have this context. We have to make it explicit. And any context needs to have explicit boundaries.
This model needs to be “useful” ie, it should be able to be implemented. Try to establish a model that’s both useful for discussion with the domain experts and is implementable. There are infinite ways to model/think about something. Balance both masters with the model you choose.
Large complex domains may need multiple models. And really the only way to understand a language and model is within a certain context. That context should have boundaries so it doesn’t bleed or force others to bleed definitions and semantics.
Bounded context: within this space, this is the context of the language. This is what it means and it’s not ambiguous.
Central thing about a model is the language you create to express the prblem and solution very crisply. Need clear language and need boundaries.
Anti corruption layers are translations between the different models that may exist in multiple bounded contexts. They keep an internal model consistent and pure without bleeding across the boundaries.
Bounded contexts tend to be “self contained systems” themselves with a complete vertical stack of the software including UI, business logic, data models, and database. They tend to not share databases across multiple models.
This concept of defining language, developing models to describe a domain, implementing those models, enforcing assertions, etc all happen within a certain context, and that context is vitally important in software. In common language, we are smart enough to resolve these types of language conflicts within a sentence because of its context. The computer doesn’t have this context. We have to make it explicit. And any context needs to have explicit boundaries.
This model needs to be “useful” ie, it should be able to be implemented. Try to establish a model that’s both useful for discussion with the domain experts and is implementable. There are infinite ways to model/think about something. Balance both masters with the model you choose.
Large complex domains may need multiple models. And really the only way to understand a language and model is within a certain context. That context should have boundaries so it doesn’t bleed or force others to bleed definitions and semantics.
Bounded context: within this space, this is the context of the language. This is what it means and it’s not ambiguous.
Central thing about a model is the language you create to express the prblem and solution very crisply. Need clear language and need boundaries.
Anti corruption layers are translations between the different models that may exist in multiple bounded contexts. They keep an internal model consistent and pure without bleeding across the boundaries.
Bounded contexts tend to be “self contained systems” themselves with a complete vertical stack of the software including UI, business logic, data models, and database. They tend to not share databases across multiple models.
This concept of defining language, developing models to describe a domain, implementing those models, enforcing assertions, etc all happen within a certain context, and that context is vitally important in software. In common language, we are smart enough to resolve these types of language conflicts within a sentence because of its context. The computer doesn’t have this context. We have to make it explicit. And any context needs to have explicit boundaries.
This model needs to be “useful” ie, it should be able to be implemented. Try to establish a model that’s both useful for discussion with the domain experts and is implementable. There are infinite ways to model/think about something. Balance both masters with the model you choose.
Large complex domains may need multiple models. And really the only way to understand a language and model is within a certain context. That context should have boundaries so it doesn’t bleed or force others to bleed definitions and semantics.
Bounded context: within this space, this is the context of the language. This is what it means and it’s not ambiguous.
Central thing about a model is the language you create to express the prblem and solution very crisply. Need clear language and need boundaries.
Anti corruption layers are translations between the different models that may exist in multiple bounded contexts. They keep an internal model consistent and pure without bleeding across the boundaries.
Bounded contexts tend to be “self contained systems” themselves with a complete vertical stack of the software including UI, business logic, data models, and database. They tend to not share databases across multiple models.
This concept of defining language, developing models to describe a domain, implementing those models, enforcing assertions, etc all happen within a certain context, and that context is vitally important in software. In common language, we are smart enough to resolve these types of language conflicts within a sentence because of its context. The computer doesn’t have this context. We have to make it explicit. And any context needs to have explicit boundaries.
This model needs to be “useful” ie, it should be able to be implemented. Try to establish a model that’s both useful for discussion with the domain experts and is implementable. There are infinite ways to model/think about something. Balance both masters with the model you choose.
Large complex domains may need multiple models. And really the only way to understand a language and model is within a certain context. That context should have boundaries so it doesn’t bleed or force others to bleed definitions and semantics.
Bounded context: within this space, this is the context of the language. This is what it means and it’s not ambiguous.
Central thing about a model is the language you create to express the prblem and solution very crisply. Need clear language and need boundaries.
Anti corruption layers are translations between the different models that may exist in multiple bounded contexts. They keep an internal model consistent and pure without bleeding across the boundaries.
Bounded contexts tend to be “self contained systems” themselves with a complete vertical stack of the software including UI, business logic, data models, and database. They tend to not share databases across multiple models.
This concept of defining language, developing models to describe a domain, implementing those models, enforcing assertions, etc all happen within a certain context, and that context is vitally important in software. In common language, we are smart enough to resolve these types of language conflicts within a sentence because of its context. The computer doesn’t have this context. We have to make it explicit. And any context needs to have explicit boundaries.
This model needs to be “useful” ie, it should be able to be implemented. Try to establish a model that’s both useful for discussion with the domain experts and is implementable. There are infinite ways to model/think about something. Balance both masters with the model you choose.
Large complex domains may need multiple models. And really the only way to understand a language and model is within a certain context. That context should have boundaries so it doesn’t bleed or force others to bleed definitions and semantics.
Bounded context: within this space, this is the context of the language. This is what it means and it’s not ambiguous.
Central thing about a model is the language you create to express the prblem and solution very crisply. Need clear language and need boundaries.
Anti corruption layers are translations between the different models that may exist in multiple bounded contexts. They keep an internal model consistent and pure without bleeding across the boundaries.
Bounded contexts tend to be “self contained systems” themselves with a complete vertical stack of the software including UI, business logic, data models, and database. They tend to not share databases across multiple models.
This concept of defining language, developing models to describe a domain, implementing those models, enforcing assertions, etc all happen within a certain context, and that context is vitally important in software. In common language, we are smart enough to resolve these types of language conflicts within a sentence because of its context. The computer doesn’t have this context. We have to make it explicit. And any context needs to have explicit boundaries.
This model needs to be “useful” ie, it should be able to be implemented. Try to establish a model that’s both useful for discussion with the domain experts and is implementable. There are infinite ways to model/think about something. Balance both masters with the model you choose.
Large complex domains may need multiple models. And really the only way to understand a language and model is within a certain context. That context should have boundaries so it doesn’t bleed or force others to bleed definitions and semantics.
Bounded context: within this space, this is the context of the language. This is what it means and it’s not ambiguous.
Central thing about a model is the language you create to express the prblem and solution very crisply. Need clear language and need boundaries.
Anti corruption layers are translations between the different models that may exist in multiple bounded contexts. They keep an internal model consistent and pure without bleeding across the boundaries.
Bounded contexts tend to be “self contained systems” themselves with a complete vertical stack of the software including UI, business logic, data models, and database. They tend to not share databases across multiple models.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.
Get back to first principles.
Focus on principles, patterns, methodologies.
Tools will help, but you cannot start with tools.