A little introduction of ASP.NET Core SignalR and How to connect it using official .Net Standard 2.0 C# client library in Xamarin.Forms project for Front-end Developer.
Modern software development is increasingly taking a “microservice” approach that has resulted in an explosion of complexity at the network level. We have more applications running distributed across different datacenters. Distributed tracing, events, and metrics are essential for observing and understanding modern microservice architectures.
This talk is a deep dive on how to monitor your distributed system. You will get tools, methodologies, and experiences that will help you to realize what your applications expose and how to get value out from all these information.
Gianluca Arbezzano, SRE at InfluxData will share how to monitor a distributed system, how to switch from a more traditional monitoring approach to observability. Stay focused on the server’s role and not on the hostname because it’s not really important anymore, our servers or containers are fast moving part and it’s easy to detach it from the right in case of trouble than call the server by name as a cute puppet. How to design a SLO for your core services and now to iterate on them. Instrument your services with tracing using tools like Zipkin or Jaeger to measure latency between in your network.
TouK Nussknacker is a GUI for Apache Flink that was created to address client needs for a more user-friendly interface. It allows defining data pipelines and business rules through expressions that are accessible for semi-technical users. The architecture uses a model-driven approach where the data, sources, sinks, processors and rules are defined through JSON configuration. It has been used successfully in production for over a year at one of the largest Polish telcos for real-time marketing and fraud detection applications.
Flink Forward Berlin 2017: Till Rohrmann - From Apache Flink 1.3 to 1.4Flink Forward
The document summarizes updates and new features in Apache Flink versions 1.3 to 1.4, and previews what is planned for 1.5 and beyond. Key points include: improved APIs, side outputs and state handling in 1.3; event-driven I/O, flow control and deployment changes in 1.4; and planned additions of side inputs, state management evolution, and state replication in 1.5. The document encourages attendees to learn more about Flink's internals by attending related talks at the event.
Kong 1.0 reached maturity with the addition of L4 proxying, service mesh capabilities using mTLS, and blue/green upgrade paths. Kong 1.1 introduced declarative configuration using a YAML file for DB-less deployments and importing configurations into databases. It also added tags for filtering entities. Upcoming versions will focus on performance, gRPC support, and Kong Enterprise features while continuing to consolidate the platform.
Flink Forward Berlin 2017: Piotr Wawrzyniak - Extending Apache Flink stream p...Flink Forward
Many stream processing applications can benefit from or need to rely on the prediction made with machine learning (ML) methods. In this presentation, new features of Apache Samoa are presented with a real data processing scenario. These features make Apache SAMOA fully accessible for Apache Flink users: (1) the data stream processed within Apache Flink is forwarded to Apache Samoa stream mining engine to perform predictions with stream-oriented ML models, (2) ML models evolve after every labelled instance and, at the same time, new predictions are sent back to Apache Flink. In both cases, Apache Kafka is used for data exchange. Hence, Apache Samoa is used as stream mining engine, provided with input data from, and sending predictions to Apache Flink. During the presentation, real life aspects are illustrated with code examples, such as input and prediction stream integration and monitoring latency of data processing and stream mining.
This document summarizes updates to the Oslo project in OpenStack. It discusses Oslo's mission to produce shared Python libraries for OpenStack projects. It outlines recent improvements to Oslo messaging and policy libraries. It previews upcoming features in OpenStack Queens and Rocky, such as policy depreciation and adding scope to policy rules. The document provides information on contributing to and giving feedback on the Oslo project.
Modern software development is increasingly taking a “microservice” approach that has resulted in an explosion of complexity at the network level. We have more applications running distributed across different datacenters. Distributed tracing, events, and metrics are essential for observing and understanding modern microservice architectures.
This talk is a deep dive on how to monitor your distributed system. You will get tools, methodologies, and experiences that will help you to realize what your applications expose and how to get value out from all these information.
Gianluca Arbezzano, SRE at InfluxData will share how to monitor a distributed system, how to switch from a more traditional monitoring approach to observability. Stay focused on the server’s role and not on the hostname because it’s not really important anymore, our servers or containers are fast moving part and it’s easy to detach it from the right in case of trouble than call the server by name as a cute puppet. How to design a SLO for your core services and now to iterate on them. Instrument your services with tracing using tools like Zipkin or Jaeger to measure latency between in your network.
TouK Nussknacker is a GUI for Apache Flink that was created to address client needs for a more user-friendly interface. It allows defining data pipelines and business rules through expressions that are accessible for semi-technical users. The architecture uses a model-driven approach where the data, sources, sinks, processors and rules are defined through JSON configuration. It has been used successfully in production for over a year at one of the largest Polish telcos for real-time marketing and fraud detection applications.
Flink Forward Berlin 2017: Till Rohrmann - From Apache Flink 1.3 to 1.4Flink Forward
The document summarizes updates and new features in Apache Flink versions 1.3 to 1.4, and previews what is planned for 1.5 and beyond. Key points include: improved APIs, side outputs and state handling in 1.3; event-driven I/O, flow control and deployment changes in 1.4; and planned additions of side inputs, state management evolution, and state replication in 1.5. The document encourages attendees to learn more about Flink's internals by attending related talks at the event.
Kong 1.0 reached maturity with the addition of L4 proxying, service mesh capabilities using mTLS, and blue/green upgrade paths. Kong 1.1 introduced declarative configuration using a YAML file for DB-less deployments and importing configurations into databases. It also added tags for filtering entities. Upcoming versions will focus on performance, gRPC support, and Kong Enterprise features while continuing to consolidate the platform.
Flink Forward Berlin 2017: Piotr Wawrzyniak - Extending Apache Flink stream p...Flink Forward
Many stream processing applications can benefit from or need to rely on the prediction made with machine learning (ML) methods. In this presentation, new features of Apache Samoa are presented with a real data processing scenario. These features make Apache SAMOA fully accessible for Apache Flink users: (1) the data stream processed within Apache Flink is forwarded to Apache Samoa stream mining engine to perform predictions with stream-oriented ML models, (2) ML models evolve after every labelled instance and, at the same time, new predictions are sent back to Apache Flink. In both cases, Apache Kafka is used for data exchange. Hence, Apache Samoa is used as stream mining engine, provided with input data from, and sending predictions to Apache Flink. During the presentation, real life aspects are illustrated with code examples, such as input and prediction stream integration and monitoring latency of data processing and stream mining.
This document summarizes updates to the Oslo project in OpenStack. It discusses Oslo's mission to produce shared Python libraries for OpenStack projects. It outlines recent improvements to Oslo messaging and policy libraries. It previews upcoming features in OpenStack Queens and Rocky, such as policy depreciation and adding scope to policy rules. The document provides information on contributing to and giving feedback on the Oslo project.
Webinar: 99 Ways to Enrich Streaming Data with Apache Flink - Konstantin KnaufVerverica
The need to enrich a fast, high volume data stream with slow-changing reference data is probably one of the most wide-spread requirements in stream processing applications. Apache Flink's built-in join functionalities and its flexible lower-level APIs support stream enrichment in various ways depending on the specific requirements of the use case at hand. In this webinar, I like to provide an overview of the basic methods to enrich a data stream with Apache Flink and highlight use cases, limitations, advantages and disadvantages of each.
Cortex: Prometheus as a Service, One Year OnKausal
Presented by Tom Wilkie at PromCon 2017
With Speaker Notes: https://goo.gl/V9qGva
At PromCon 2016, I presented "Project Frankenstein: A multitenant, horizontally scalable Prometheus as a service". It's now one year later, and lots has changed - not least the name! This talk will discuss what we've learnt running a Prometheus service for the past year, the architectural changes we made from the original design, and the improvements we've made to the Cortex user experience.
Practical tips and tricks for Apache Kafka messages integration | Francesco T...HostedbyConfluent
Interacting with Apache Kafka seems straightforward at first, you “just” push and pull messages. Yet it can quickly become a source of frustration as the user encounters timeouts, vague error descriptions and disappearing messages. Experience helps a lot and I’m here to share what I know.
In this talk you will learn the tips & tricks I wish I had known at the beginning of my Apache Kafka journey. We’ll discuss topics like producer acknowledgments, server and consumer parameters (auto_offset_reset anyone?) that are commonly overlooked causing lots of developer’s pain. I’ll share with you how to generate code that works as expected on the first run, making your first integration painless. These tips will kickstart your Apache Kafka experience in Python and save you hours of debugging.
KKBOX is a music streaming service founded in 2004 that uses PHP and handles around 4000 API requests per second. It uses an event-driven asynchronous architecture and tools like Gitlab for code hosting and reviews, Gitlab CI for testing, and Slack for communication. The document discusses KKBOX's technologies and processes for code management, testing, deployment, and communication between teams.
Flink Forward San Francisco 2019: Building production Flink jobs with Airstre...Flink Forward
AirStream is a realtime stream computation framework that supports Flink as one of its processing engines. It allows engineers and data scientists at Airbnb to easily leverage Flink to build real time data pipelines and feedback loops. Multiple mission critical applications have been built on top of it. In this talk, we will start with an overview of AirStream, and describe how we have designed Airstream to leverage SQL support in Flink to allow users to easily build real time data pipelines. We will go over a few production use cases such as building a user activity profiler and building user identity mapping in realtime. We will also cover how we have integrated Airstream into the data infrastructure ecosystem at Airbnb through easily configurable connectors such as Kafka and Hive that allow users to easily leverage these components in their pipelines.
Flink currently features different APIs for bounded/batch (DataSet) and streaming (DataStream) programs. And while the DataStream API can handle batch use cases, it is much less efficient in that compared to the DataSet API. The Table API was built as a unified API on top of both, to cover batch and streaming with the same API, and under the hood delegate to either DataSet or DataStream.
In this talk, we present the latest on the Flink community's efforts to rework the APIs and the stack for better unified batch & streaming experience. We will discuss:
- The future roles and interplay of DataSet, DataStream, and Table API
- The new Flink stack and the abstractions on which these APIs will build
- The new unified batch/streaming sources
- How batch and streaming optimizations differ in the runtime, and what the future interplay of batch and streaming execution could look like.
This document discusses PowerShell cmdlets for managing cloud resources on Microsoft Azure. It provides an overview of development tools and SDKs for Azure, describes how to use REST APIs and SDKs to manage resources from various devices and platforms. It also outlines several command line tools including PowerShell for Windows, Bash for Linux and Mac, and how they can be used to manage subscriptions, storage accounts, virtual machines, databases and other Azure resources through cmdlets. Syntax examples and references are provided for getting started with the PowerShell cmdlets.
This document discusses service discovery in Docker using NGINX and NGINX Plus with Consul. It begins with an introduction to service discovery and why it is important for microservices applications. It then covers how open source NGINX integrates with Consul using Consul Template, noting limitations around increased memory usage. The document also discusses two methods for NGINX Plus integration - using the dynamic reconfiguration API with Consul watches, and DNS resolution using SRV records from Consul. It provides comparisons between the two methods and links to demo repositories.
At Yelp we run hundreds of Flink jobs to power a wide range of applications: push notifications, data replication, ETL, sessionizing and more. Routine operations like deploys, restart, and savepointing for so many jobs would take quite a bit of developers’ time without the right degree of automation. The latest addition to our toolshed is a Kubernetes operator managing the deployment and the lifetime of Flink clusters on PaaSTA, Yelp’s Platform As A Service.
We replaced our deployment framework launching Flink clusters on top of AWS EMR with a Kubernetes operator managing fully Docker-ized Flink clusters. Compared to EMR, this architecture allowed us to both drastically reduce the deployment time of our Flink clusters and to share our hardware resources more efficiently. In addition, we now offer to our developers the same interface they are used to for running REST services, batch jobs and many other workloads on PaaSTA.
This talk will give a brief overview of Yelp’s PaaSTA before diving into the details of how the Kubernetes operator has been implemented and how it has been integrated with Yelp developers’ workflow (deploy, logs, savepoints, upgrades, etc), to end with a glimpse of the future features we are planning for the operator (Flink as a library, autoscaling, etc.).
CNTUG x SDN Meetup #33 Talk 1: 從 Cilium 認識 cgroup ebpf - RuianHanLing Shen
The document discusses Cilium and cgroup eBPF applications. It provides an overview of Cilium, a history of cgroup eBPF in Linux kernels dating back to 2016, and how Cilium uses cgroup eBPF to implement services load balancing and network policy enforcement in Kubernetes. Specifically, it describes how the Cilium agent programs cgroup eBPF maps and programs based on Kubernetes services and endpoints, and how cgroup eBPF programs handle socket calls like connect and getpeername to implement load balancing and network address translation.
This document contains biographical information about Emerson Macedo, a software engineer, including that he has 15 years of experience with TCP/HTTP and languages like Elixir. It also discusses the evolution of a software project from its initial state of high technical debt and issues to a redesigned version using modern practices like standardized protocols and infrastructure automation. Specifically, it describes how the original codebase had problems like a single file with 2,000 lines of code and business logic in database functions, while the new version implemented best practices such as separating concerns into independent services using HTTP and JSON. Finally, it notes there is still room for improvement, such as removing over-reliance on the database for integration between services.
This document discusses using Prometheus to monitor Kubernetes clusters. It provides background on Kubernetes and Prometheus architectures. It then describes challenges with the previous monitoring setup and proposes using the Prometheus operator to more easily monitor Kubernetes and application metrics. The Prometheus operator allows automatically generating target configurations based on Kubernetes labels and provides Custom Resource Definitions for Prometheus and Service Monitors.
Flink Connector Development Tips & TricksEron Wright
A look at some of the challenges and techniques for developing a connector for Apache Flink, covering the different types of connectors, lifecycle, metrics, event-time support, and fault tolerance.
Presentation video: https://www.youtube.com/watch?v=ZkbYO5S4z18
Streaming your Lyft Ride Prices - Flink Forward SF 2019Thomas Weise
At Lyft we dynamically price our rides with a combination of various data sources, machine learning models, and streaming infrastructure for low latency, reliability and scalability. Dynamic pricing allows us to quickly adapt to real world changes and be fair to drivers (by say raising rates when there's a lot of demand) and fair to passengers (by let’s say offering to return 10 mins later for a cheaper rate). The streaming platform powers pricing by bringing together the best of two worlds using Apache Beam; ML algorithms in Python and Apache Flink as the streaming engine.
https://sf-2019.flink-forward.org/conference-program#streaming-your-lyft-ride-prices
A new tool for measuring performance in Drupal 8 - Drupal Dev Days MontpellierLuca Lusso
Discovering software bottlenecks is always a difficult task, but detecting them in Drupal can be a real nightmare. A simple contrib module can cause a lot of database queries, service instantiations or events to be triggered.
Classic debug tools like xDebug or XHProf fail to report those kinds of problems because they work at a lower level and they don't have any knowledge of Drupal internal structures.
Luckly Drupal 8 is built on Symfony 2 components and one of those components (the HTTP Kernel) provides the infrastructure for build custom profilers.
In this talk we'll see how to build a profiler to analyze the internal data structures of Drupal 8 and how to exend the profiler to add new data collectors.
The code is available as Drupal 8 module here: http://www.drupal.org/project/webprofiler
LAMP is an acronym that stands for Linux, Apache, MySQL, and PHP which together provide an open source platform for developing dynamic web applications. Linux is a free open source operating system, Apache is a free web server software, MySQL is an open source database management system, and PHP is a programming language commonly used for server-side scripting and web development. Key advantages of the LAMP stack include rapid development and deployment of PHP and MySQL applications as well as low costs for setup and hosting.
Flink Forward Berlin 2017: Aljoscha Krettek - Talk Python to me: Stream Proce...Flink Forward
Flink is a great stream processor, Python is a great programming language, Apache Beam is a great programming model and portability layer. Using all three together is a great idea! We will demo and discuss writing Beam Python pipelines and running them on Flink. We will cover Beam's portability vision that led here, what you need to know about how Beam Python pipelines are executed on Flink, and where Beam's portability framework is headed next (hint: Python pipelines reading from non-Python connectors)
There’s a lot of exciting new stuff in .NET Core, and more on the way! We’ll take a look at some top features in 3.1, including Blazor, desktop support (WPF and Windows Forms), single file executables, language features, and more. We'll also take an early look at what's on the way in .NET 5, and how you can start planning for it today.
Real time Communication with Signalr (Android Client)Deepak Gupta
This document discusses real-time communication using SignalR. It begins with examples of real-time applications and techniques for implementing real-time functionality like polling, long polling, and web sockets. It then introduces SignalR as a library that provides real-time functionality in ASP.NET applications and supports cross-platform communication. Implementation details are covered for both the server-side Hub API in ASP.NET and client-side usage in JavaScript and Android apps. Common use cases for SignalR are also listed.
Webinar: 99 Ways to Enrich Streaming Data with Apache Flink - Konstantin KnaufVerverica
The need to enrich a fast, high volume data stream with slow-changing reference data is probably one of the most wide-spread requirements in stream processing applications. Apache Flink's built-in join functionalities and its flexible lower-level APIs support stream enrichment in various ways depending on the specific requirements of the use case at hand. In this webinar, I like to provide an overview of the basic methods to enrich a data stream with Apache Flink and highlight use cases, limitations, advantages and disadvantages of each.
Cortex: Prometheus as a Service, One Year OnKausal
Presented by Tom Wilkie at PromCon 2017
With Speaker Notes: https://goo.gl/V9qGva
At PromCon 2016, I presented "Project Frankenstein: A multitenant, horizontally scalable Prometheus as a service". It's now one year later, and lots has changed - not least the name! This talk will discuss what we've learnt running a Prometheus service for the past year, the architectural changes we made from the original design, and the improvements we've made to the Cortex user experience.
Practical tips and tricks for Apache Kafka messages integration | Francesco T...HostedbyConfluent
Interacting with Apache Kafka seems straightforward at first, you “just” push and pull messages. Yet it can quickly become a source of frustration as the user encounters timeouts, vague error descriptions and disappearing messages. Experience helps a lot and I’m here to share what I know.
In this talk you will learn the tips & tricks I wish I had known at the beginning of my Apache Kafka journey. We’ll discuss topics like producer acknowledgments, server and consumer parameters (auto_offset_reset anyone?) that are commonly overlooked causing lots of developer’s pain. I’ll share with you how to generate code that works as expected on the first run, making your first integration painless. These tips will kickstart your Apache Kafka experience in Python and save you hours of debugging.
KKBOX is a music streaming service founded in 2004 that uses PHP and handles around 4000 API requests per second. It uses an event-driven asynchronous architecture and tools like Gitlab for code hosting and reviews, Gitlab CI for testing, and Slack for communication. The document discusses KKBOX's technologies and processes for code management, testing, deployment, and communication between teams.
Flink Forward San Francisco 2019: Building production Flink jobs with Airstre...Flink Forward
AirStream is a realtime stream computation framework that supports Flink as one of its processing engines. It allows engineers and data scientists at Airbnb to easily leverage Flink to build real time data pipelines and feedback loops. Multiple mission critical applications have been built on top of it. In this talk, we will start with an overview of AirStream, and describe how we have designed Airstream to leverage SQL support in Flink to allow users to easily build real time data pipelines. We will go over a few production use cases such as building a user activity profiler and building user identity mapping in realtime. We will also cover how we have integrated Airstream into the data infrastructure ecosystem at Airbnb through easily configurable connectors such as Kafka and Hive that allow users to easily leverage these components in their pipelines.
Flink currently features different APIs for bounded/batch (DataSet) and streaming (DataStream) programs. And while the DataStream API can handle batch use cases, it is much less efficient in that compared to the DataSet API. The Table API was built as a unified API on top of both, to cover batch and streaming with the same API, and under the hood delegate to either DataSet or DataStream.
In this talk, we present the latest on the Flink community's efforts to rework the APIs and the stack for better unified batch & streaming experience. We will discuss:
- The future roles and interplay of DataSet, DataStream, and Table API
- The new Flink stack and the abstractions on which these APIs will build
- The new unified batch/streaming sources
- How batch and streaming optimizations differ in the runtime, and what the future interplay of batch and streaming execution could look like.
This document discusses PowerShell cmdlets for managing cloud resources on Microsoft Azure. It provides an overview of development tools and SDKs for Azure, describes how to use REST APIs and SDKs to manage resources from various devices and platforms. It also outlines several command line tools including PowerShell for Windows, Bash for Linux and Mac, and how they can be used to manage subscriptions, storage accounts, virtual machines, databases and other Azure resources through cmdlets. Syntax examples and references are provided for getting started with the PowerShell cmdlets.
This document discusses service discovery in Docker using NGINX and NGINX Plus with Consul. It begins with an introduction to service discovery and why it is important for microservices applications. It then covers how open source NGINX integrates with Consul using Consul Template, noting limitations around increased memory usage. The document also discusses two methods for NGINX Plus integration - using the dynamic reconfiguration API with Consul watches, and DNS resolution using SRV records from Consul. It provides comparisons between the two methods and links to demo repositories.
At Yelp we run hundreds of Flink jobs to power a wide range of applications: push notifications, data replication, ETL, sessionizing and more. Routine operations like deploys, restart, and savepointing for so many jobs would take quite a bit of developers’ time without the right degree of automation. The latest addition to our toolshed is a Kubernetes operator managing the deployment and the lifetime of Flink clusters on PaaSTA, Yelp’s Platform As A Service.
We replaced our deployment framework launching Flink clusters on top of AWS EMR with a Kubernetes operator managing fully Docker-ized Flink clusters. Compared to EMR, this architecture allowed us to both drastically reduce the deployment time of our Flink clusters and to share our hardware resources more efficiently. In addition, we now offer to our developers the same interface they are used to for running REST services, batch jobs and many other workloads on PaaSTA.
This talk will give a brief overview of Yelp’s PaaSTA before diving into the details of how the Kubernetes operator has been implemented and how it has been integrated with Yelp developers’ workflow (deploy, logs, savepoints, upgrades, etc), to end with a glimpse of the future features we are planning for the operator (Flink as a library, autoscaling, etc.).
CNTUG x SDN Meetup #33 Talk 1: 從 Cilium 認識 cgroup ebpf - RuianHanLing Shen
The document discusses Cilium and cgroup eBPF applications. It provides an overview of Cilium, a history of cgroup eBPF in Linux kernels dating back to 2016, and how Cilium uses cgroup eBPF to implement services load balancing and network policy enforcement in Kubernetes. Specifically, it describes how the Cilium agent programs cgroup eBPF maps and programs based on Kubernetes services and endpoints, and how cgroup eBPF programs handle socket calls like connect and getpeername to implement load balancing and network address translation.
This document contains biographical information about Emerson Macedo, a software engineer, including that he has 15 years of experience with TCP/HTTP and languages like Elixir. It also discusses the evolution of a software project from its initial state of high technical debt and issues to a redesigned version using modern practices like standardized protocols and infrastructure automation. Specifically, it describes how the original codebase had problems like a single file with 2,000 lines of code and business logic in database functions, while the new version implemented best practices such as separating concerns into independent services using HTTP and JSON. Finally, it notes there is still room for improvement, such as removing over-reliance on the database for integration between services.
This document discusses using Prometheus to monitor Kubernetes clusters. It provides background on Kubernetes and Prometheus architectures. It then describes challenges with the previous monitoring setup and proposes using the Prometheus operator to more easily monitor Kubernetes and application metrics. The Prometheus operator allows automatically generating target configurations based on Kubernetes labels and provides Custom Resource Definitions for Prometheus and Service Monitors.
Flink Connector Development Tips & TricksEron Wright
A look at some of the challenges and techniques for developing a connector for Apache Flink, covering the different types of connectors, lifecycle, metrics, event-time support, and fault tolerance.
Presentation video: https://www.youtube.com/watch?v=ZkbYO5S4z18
Streaming your Lyft Ride Prices - Flink Forward SF 2019Thomas Weise
At Lyft we dynamically price our rides with a combination of various data sources, machine learning models, and streaming infrastructure for low latency, reliability and scalability. Dynamic pricing allows us to quickly adapt to real world changes and be fair to drivers (by say raising rates when there's a lot of demand) and fair to passengers (by let’s say offering to return 10 mins later for a cheaper rate). The streaming platform powers pricing by bringing together the best of two worlds using Apache Beam; ML algorithms in Python and Apache Flink as the streaming engine.
https://sf-2019.flink-forward.org/conference-program#streaming-your-lyft-ride-prices
A new tool for measuring performance in Drupal 8 - Drupal Dev Days MontpellierLuca Lusso
Discovering software bottlenecks is always a difficult task, but detecting them in Drupal can be a real nightmare. A simple contrib module can cause a lot of database queries, service instantiations or events to be triggered.
Classic debug tools like xDebug or XHProf fail to report those kinds of problems because they work at a lower level and they don't have any knowledge of Drupal internal structures.
Luckly Drupal 8 is built on Symfony 2 components and one of those components (the HTTP Kernel) provides the infrastructure for build custom profilers.
In this talk we'll see how to build a profiler to analyze the internal data structures of Drupal 8 and how to exend the profiler to add new data collectors.
The code is available as Drupal 8 module here: http://www.drupal.org/project/webprofiler
LAMP is an acronym that stands for Linux, Apache, MySQL, and PHP which together provide an open source platform for developing dynamic web applications. Linux is a free open source operating system, Apache is a free web server software, MySQL is an open source database management system, and PHP is a programming language commonly used for server-side scripting and web development. Key advantages of the LAMP stack include rapid development and deployment of PHP and MySQL applications as well as low costs for setup and hosting.
Flink Forward Berlin 2017: Aljoscha Krettek - Talk Python to me: Stream Proce...Flink Forward
Flink is a great stream processor, Python is a great programming language, Apache Beam is a great programming model and portability layer. Using all three together is a great idea! We will demo and discuss writing Beam Python pipelines and running them on Flink. We will cover Beam's portability vision that led here, what you need to know about how Beam Python pipelines are executed on Flink, and where Beam's portability framework is headed next (hint: Python pipelines reading from non-Python connectors)
There’s a lot of exciting new stuff in .NET Core, and more on the way! We’ll take a look at some top features in 3.1, including Blazor, desktop support (WPF and Windows Forms), single file executables, language features, and more. We'll also take an early look at what's on the way in .NET 5, and how you can start planning for it today.
Real time Communication with Signalr (Android Client)Deepak Gupta
This document discusses real-time communication using SignalR. It begins with examples of real-time applications and techniques for implementing real-time functionality like polling, long polling, and web sockets. It then introduces SignalR as a library that provides real-time functionality in ASP.NET applications and supports cross-platform communication. Implementation details are covered for both the server-side Hub API in ASP.NET and client-side usage in JavaScript and Android apps. Common use cases for SignalR are also listed.
This document provides information about upgrading to ASP.NET Core 3.0, Blazor, gRPC, and SignalR. It includes links to documentation and tutorials about these technologies. Key topics covered include how Blazor works, using Blazor on the client or server, building a pizza store UI with Blazor, gRPC concepts like protocol buffers and remote procedure calls, demonstrating gRPC, advantages of gRPC like performance and multiple languages, and an overview of SignalR.
An introduction to SignalR
This deck was part of my presentation to Virtusa employees on an ASP.NET asynchronous, persistent signaling library known as SignalR
There is also a slide on how to use SignalR with SharePoint.
Date: August 2013
Follow / Tweet me: @ShehanPeruma
Asp.Net Core SignalR is a library that makes developing real-time web functionality easy and allows bi-directional communication between server and client. In this presentation we are going to take a look at interesting features part of the new versions of SignalR (as part of Asp.Net Core), so you can build real-time web applications with all the benefits of ASP.NET Core like better performance and cross-platform support.
Demo: https://github.com/VGGeorgiev/Talks/tree/master/PlovDev%20-%20Fancy%20Features%20in%20Asp.Net%20Core%20SignalR
Asynchrone Echtzeitanwendungen für SharePoint mit SignalR und knockout.jsChristian Heindel
The document discusses integrating real-time applications into SharePoint using SignalR and knockout.js. It begins with an overview of real-time applications and their goals, then covers push technologies like WebSockets. It introduces SignalR for creating real-time connections and hubs, and knockout.js for MVVM data binding in JavaScript. The document shows how to use events in SharePoint and integrate SignalR into SharePoint 2010, 2013, and Online by writing HTTP modules, handlers or using the SPSignalR library. It demonstrates building a real-time application with these technologies.
SignalR: Add real-time to your applicationsEugene Zharkov
SignalR allows adding real-time functionality to applications by using multiple transport methods for client-server communication like WebSockets, server-sent events, and long polling. It supports clients on various platforms through libraries like jQuery, Mono, and QT. The core abstractions in SignalR are the PersistentConnection for raw connections and the Hub for a higher-level API. Applications can create Hub objects on the server to define methods for clients to call, and subscribe to events from the client side in various languages like C#, JavaScript, and C#.
What you need to know about .NET Core 3.0 and beyondJon Galloway
The document provides an overview of .NET Core 3.0 including its top features, upcoming release schedule, and what is coming next. It discusses the key features in .NET Core 3.0 such as Windows desktop apps, microservices, gRPC, and machine learning. It also outlines the future of .NET with .NET 5 which will unify the different .NET implementations into a single platform.
Building high performance microservices in finance with Apache ThriftRX-M Enterprises LLC
Apache Roadshow Chicago Talk on May 14, 2019
In this talk we’ll look at the ways Apache Thrift can solve performance problems commonly facing next generation applications deployed in performance sensitive capital markets and banking environments. The talk will include practical examples illustrating the construction, performance and resource utilization benefits of Apache Thrift. Apache Thrift is a high-performance cross platform RPC and serialization framework designed to make it possible for organizations to specify interfaces and application wide data structures suitable for serialization and transport over a wide variety of schemes. Due to the unparalleled set of languages supported by Apache Thrift, these interfaces and structs have similar interoperability to REST type services with an order of magnitude improvement in performance. Apache Thrift services are also a perfect fit for container technology, using considerably fewer resources than traditional application server style deployments. Decomposing applications into microservices, packaging them into containers and orchestrating them on systems like Kubernetes can bring great value to an organization; however, it can also take a very fast monolithic application and turn it into a high latency web of slow, resource hungry services. Apache Thrift is a perfect solution to the performance and resource ills of many microservice based endeavors.
IoT with SignalR & .NET Gadgeteer - NetMF@WorkMirco Vanini
Internet of things è vista come una possibile evoluzione dell'uso della Rete. Gli oggetti si rendono riconoscibili e acquisiscono intelligenza grazie al fatto di poter comunicare dati su se stessi e accedere ad informazioni aggregate da parte di altri. Partendo da queste affermazioni il talk preenterà una soluzione per il controllo ed il monitoraggio centralizzato dello stato di un sistema embedded a cui sono collegati una rete di sensori e di attuatori di varia natura attraverso l'utilizzo di SignalR. Tramite questa tecnologia è possibile realizzare servizi di notifica "push" con una straordinaria reattività, il tutto utilizzando il puro e semplice http
The document provides an overview of the Hyperledger Composer architecture, which includes client-side and blockchain-side components. Client-side components like the playground and CLI allow developing and testing business networks. Blockchain-side components include the runtime, which exposes business networks on various blockchain platforms, and connectors that provide standardized interfaces to interact with networks. Key parts include business network definitions, deployment of networks and runtime to platforms, and use of connection profiles to select appropriate connectors.
44CON 2014 - Binary Protocol Analysis with CANAPE, James Forshaw44CON
44CON 2014 - Binary Protocol Analysis with CANAPE, James Forshaw
CANAPE is an open source network proxy written in .NET. It has been developed to aid in the analysis and exploitation of unknown application network protocols using a similar use case to common HTTP proxies such as Burp or CAT.
This workshop will go through the basics of analysing an unknown application protocol with hands on training examples. By the end of the workshop candidates should be able to better understand CANAPE’s functionality and be able to apply that to other protocols they come across.
SignalR is a library for adding real-time functionality to applications. It allows bi-directional communication between server and client. SignalR supports websockets, server-sent events, and long polling. It is open source and supported on .NET Framework 4.0 and above on Windows and Linux. The Hubs API provides a simple way to make remote procedure calls between clients and server. SignalR supports browsers like IE8+, Firefox, Chrome and platforms like Windows, Silverlight and JavaScript.
Stream Processing with Apache Kafka and .NETconfluent
Presentation from South Bay.NET meetup on 3/30.
Speaker: Matt Howlett, Software Engineer at Confluent
Apache Kafka is a scalable streaming platform that forms a key part of the infrastructure at many companies including Uber, Netflix, Walmart, Airbnb, Goldman Sachs and LinkedIn. In this talk Matt will give a technical overview of Kafka, discuss some typical use cases (from surge pricing to fraud detection to web analytics) and show you how to use Kafka from within your C#/.NET applications.
What's new with .NET Core 3 - covering features from C#, .NET Core, ASP.NET Core, WPF - including nullability, indices and ranges, switch expressions, enhanced pattern matching, changes with ASP.NET Core, Blazor server-side components, and WPF with .NET Core.
This document summarizes new features in .NET Framework 4.5, including improvements to WeakReferences, streams, ReadOnlyDictionary, compression, and large objects. It describes enhancements to server GC, asynchronous programming, the Task Parallel Library, ASP.NET, Entity Framework, WCF, WPF, and more. The .NET 4.5 update focuses on performance improvements, support for asynchronous code and parallel operations, and enabling modern app development patterns.
- SignalR provides a simple way to add real-time web functionality to applications. It allows for persistent connections and messaging between servers and clients.
- It abstracts away the various techniques for real-time communication like websockets, long polling, and server-sent events and chooses the best transport.
- SignalR uses hubs to facilitate two-way communication between clients and servers through methods. This allows for different message types and structures to be sent.
Similar to Xamarin Form using ASP.NET Core SignalR client (20)
The document discusses loading embedded SVG files using the "full name" method. It suggests using the complete file path and name to load SVG resources that are embedded within a website or application, rather than just referencing the file by its name. This fully qualifies the resource and ensures it is loaded correctly regardless of the current directory structure.
🏎️Tech Transformation: DevOps Insights from the Experts 👩💻campbellclarkson
Connect with fellow Trailblazers, learn from industry experts Glenda Thomson (Salesforce, Principal Technical Architect) and Will Dinn (Judo Bank, Salesforce Development Lead), and discover how to harness DevOps tools with Salesforce.
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DevOps Consulting Company | Hire DevOps Servicesseospiralmantra
Spiral Mantra excels in providing comprehensive DevOps services, including Azure and AWS DevOps solutions. As a top DevOps consulting company, we offer controlled services, cloud DevOps, and expert consulting nationwide, including Houston and New York. Our skilled DevOps engineers ensure seamless integration and optimized operations for your business. Choose Spiral Mantra for superior DevOps services.
https://www.spiralmantra.com/devops/
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developers need. Plus, we'll zoom in on the advanced capabilities of vector search and semantic caching in Java, showcasing these through a live demo with Redis libraries. Get ready to see how these powerful tools can change the game!
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Alluxio Webinar | 10x Faster Trino Queries on Your Data PlatformAlluxio, Inc.
Alluxio Webinar
June. 18, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Jianjian Xie (Staff Software Engineer, Alluxio)
As Trino users increasingly rely on cloud object storage for retrieving data, speed and cloud cost have become major challenges. The separation of compute and storage creates latency challenges when querying datasets; scanning data between storage and compute tiers becomes I/O bound. On the other hand, cloud API costs related to GET/LIST operations and cross-region data transfer add up quickly.
The newly introduced Trino file system cache by Alluxio aims to overcome the above challenges. In this session, Jianjian will dive into Trino data caching strategies, the latest test results, and discuss the multi-level caching architecture. This architecture makes Trino 10x faster for data lakes of any scale, from GB to EB.
What you will learn:
- Challenges relating to the speed and costs of running Trino in the cloud
- The new Trino file system cache feature overview, including the latest development status and test results
- A multi-level cache framework for maximized speed, including Trino file system cache and Alluxio distributed cache
- Real-world cases, including a large online payment firm and a top ridesharing company
- The future roadmap of Trino file system cache and Trino-Alluxio integration
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What to do when you have a perfect model for your software but you are constrained by an imperfect business model?
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Mobile App Development Company In Noida | Drona InfotechDrona Infotech
React.js, a JavaScript library developed by Facebook, has gained immense popularity for building user interfaces, especially for single-page applications. Over the years, React has evolved and expanded its capabilities, becoming a preferred choice for mobile app development. This article will explore why React.js is an excellent choice for the Best Mobile App development company in Noida.
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Photoshop Tutorial for Beginners (2024 Edition)alowpalsadig
Photoshop Tutorial for Beginners (2024 Edition)
Explore the evolution of programming and software development and design in 2024. Discover emerging trends shaping the future of coding in our insightful analysis."
Here's an overview:Introduction: The Evolution of Programming and Software DevelopmentThe Rise of Artificial Intelligence and Machine Learning in CodingAdopting Low-Code and No-Code PlatformsQuantum Computing: Entering the Software Development MainstreamIntegration of DevOps with Machine Learning: MLOpsAdvancements in Cybersecurity PracticesThe Growth of Edge ComputingEmerging Programming Languages and FrameworksSoftware Development Ethics and AI RegulationSustainability in Software EngineeringThe Future Workforce: Remote and Distributed TeamsConclusion: Adapting to the Changing Software Development LandscapeIntroduction: The Evolution of Programming and Software Development
Photoshop Tutorial for Beginners (2024 Edition)Explore the evolution of programming and software development and design in 2024. Discover emerging trends shaping the future of coding in our insightful analysis."Here's an overview:Introduction: The Evolution of Programming and Software DevelopmentThe Rise of Artificial Intelligence and Machine Learning in CodingAdopting Low-Code and No-Code PlatformsQuantum Computing: Entering the Software Development MainstreamIntegration of DevOps with Machine Learning: MLOpsAdvancements in Cybersecurity PracticesThe Growth of Edge ComputingEmerging Programming Languages and FrameworksSoftware Development Ethics and AI RegulationSustainability in Software EngineeringThe Future Workforce: Remote and Distributed TeamsConclusion: Adapting to the Changing Software Development LandscapeIntroduction: The Evolution of Programming and Software Development
The importance of developing and designing programming in 2024
Programming design and development represents a vital step in keeping pace with technological advancements and meeting ever-changing market needs. This course is intended for anyone who wants to understand the fundamental importance of software development and design, whether you are a beginner or a professional seeking to update your knowledge.
Course objectives:
1. **Learn about the basics of software development:
- Understanding software development processes and tools.
- Identify the role of programmers and designers in software projects.
2. Understanding the software design process:
- Learn about the principles of good software design.
- Discussing common design patterns such as Object-Oriented Design.
3. The importance of user experience (UX) in modern software:
- Explore how user experience can improve software acceptance and usability.
- Tools and techniques to analyze and improve user experience.
4. Increase efficiency and productivity through modern development tools:
- Access to the latest programming tools and languages used in the industry.
- Study live examples of applications
1. ASP.NET Core SignalR &
Xamarin.Forms
Connect ASP.NET Core SignalR service via .NET Standard 2.0 C#
client library in Xamarin.Forms
2. ASP.NET Core SignalR
• A re-designed SignalR(http://signalr.net/) library for ASP.NET
Core 2.1 to provide real-time communication:
http://github.com/aspnet/signalr
• Not compatible with old ASP.NET SignalR.
• Existing Application that use ASP.NET SignalR or ASP.NET Core
SignalR:
• Office 365 real-time document co-authoring: http://bit.ly/2KIZjrb
• Visual Studio Live Share: http://bit.ly/2Nl8Aak
3. ASP.NET Core SignalR Features
• Multiple transport mechanism:
• Websocket: http://bit.ly/2MLVwtL
• Server-Send event: http://bit.ly/2KuvkXW
• Long Polling: http://bit.ly/2lOvg6t
• Hub concepts for client & server communication:
• Hub protocols: text based JSON & binary based MessagePack (
http://msgpack.org/ )
• Support 3 RPC style:
1. Server RPC call
2. Server Streaming RPC call
3. Server Call Client defined function
4. ASP.NET Core SignalR Features
• Hub Protocol spec is opened:
http://github.com/aspnet/SignalR/blob/master/specs/HubProtocol.m
d
• Provide many language implementation of client library:
• JavaScript: http://www.npmjs.com/package/@aspnet/signalr
• C#: http://www.nuget.org/packages/Microsoft.AspNetCore.SignalR.Client/
• C++ & Java Client are under development and will release stable version with
ASP.NET Core 2.2 timeline.
• 3rd Party:
• Swift : http://github.com/moozzyk/SignalR-Client-Swift
• RxJs enabled Js client: http:///www.npmjs.com/package/@ssv/signalr-client
5. ASP.NET Core SignalR Features
• Hub support ASP.NET Core Authentication mechanism
• Cookie
• JWT Auth Token
• Service instance can be scale-out using Redis on-premises,
using Azure SignalR Service(https://aka.ms/signalr_service ) in
cloud.
• Note: ASP.NET SignalR can scale-out via Azure Service Bus, but
there’s no plan for ASP.NET Core SignalR right now:
https://github.com/aspnet/SignalR/issues/1676
6. Xamarin & ASP.NET Core SignalR
• Using ASP.NET Core SignalR technology, we can build a cross
platform/device broadcasting system via Xamarin.
• Demo code:
http://bit.ly/2KHv57D
7. Xamarin.Forms as Core SignalR client
Using following Nuget Package in Xamarin.Forms project:
• Microsoft.AspNetCore.SignalR.Client:
https://www.nuget.org/packages/Microsoft.AspNetCore.SignalR.Client/
• Microsoft.Extensions.Logging.Console:
(for iOS & Android logging)
https://www.nuget.org/packages/Microsoft.Extensions.Logging.Console/
• Microsoft.Extensions.Logging.Debug:
(for UWP logging)
https://www.nuget.org/packages/Microsoft.Extensions.Logging.Debug/
• System.Threading.Channels:
(for invoke Server Streaming RPC call)
https://www.nuget.org/packages/System.Threading.Channels/
8. Xamarin.Forms as Core SignalR client
• Hub: a connection joint that client & sever can call their defined
methods mutually.
Server
Client #1
Client #2
Client #3
Hub
9. Xamarin.Forms as Core SignalR client
• On Client side, use “HubConnection(http://bit.ly/2tRduE6)” to
access the Server Method defined in Hub.
• To create HubConnection object, using
“HubConnectionBuilder(http://bit.ly/2MLLGYC)” to create.
• HubConnectionBuilder has many Fluent-API style extension
method to configure it. (ASP.NET Core configuration style)
10.
11. Xamarin.Forms as Core SignalR client
• This kind of “Code as Configuration” coding style is very
modifiable based on demand, and can be refactored to a clear
style using Action Delegate:
http://www.tutorialsteacher.com/csharp/csharp-action-delegate
12. Xamarin.Forms as Core SignalR client
• Once HubConneciton instance is created, call StartAsync() to
connect to remote ASP.NET Core SignalR server, call
StopAsync() or DisposeAsync() to tear down connection,
HubConnection object cannot be re-used, must create it every
time when starting a new connection.
13. Xamarin.Forms as Core SignalR client
• Server RPC call : (http://bit.ly/2KKDXN8)
• Use HubConnectionExtensions.InvokeAsync<TResult>()
extension method(http://bit.ly/2MOpy03 ) to get RPC call result.
14. Xamarin.Forms as Core SignalR client
• Server Streaming RPC call: http://bit.ly/2tQLbp4
• Use
HubConnectionExtensions.StreamAsChannelAsync<TResult>()
extension method (http://bit.ly/2zfC1bc),
• Which needs additional New Type called
“ChannelReader(http://bit.ly/2MJARXi)” from .NET Core runtime, that
is not documented in official docs yet.
Channel(http://bit.ly/2KJAkXG) is a kind of Data Queue that
mimics Go’s “Buffered Channel(http://bit.ly/2IRo56K)” concept:
https://github.com/stephentoub/corefxlab/blob/master/src/Syst
em.Threading.Tasks.Channels/README.md
15. Xamarin.Forms as Core SignalR client
• Server Streaming RPC call can be cancelled from client via Set
Cancellation token parameter in
HubConnectionExtensions.StreamAsChannelAsync<TResult>()
16. Xamarin.Forms as Core SignalR client
• Server Call Client defined function: (http://bit.ly/2lT4cDf)
Register client function using HubConnection.On() or
HubConnectionExtensions.On(http://bit.ly/2IQ0Auw) API:
17. Other Notes
Xamarin Client need to use latest preview of Visual Studio 2017 15.8
preview 3 or Visual Studio for Mac 7.6 preview 3:
https://github.com/aspnet/Announcements/issues/305
• UWP on Windows 10 Mobile OS is not support since .NET Standard
2.0 support for Windows 10 UWP comes in 2017 Fall Creators
Update.
• UWP logging has to use AddDebug(), not AddConsole().
• JWT Authentication is configured on HubConnectionBuilder’s
WithUrl() extension method:
https://github.com/aspnet/SignalR/blob/948ebf34ece11918804f443b65c6a053dfc
8f35c/samples/JwtClientSample/Program.cs#L38
• Local debugging & testing can use ngrok(http://ngrok.com) for real
device connection.