Introduction to Akka.NET and Akka.Cluster
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Demands and expectations for .NET developers have never been higher. We're expected increase customer value against higher and higher expectations; deliver our services and content across a greater variety of devices; retain, analyze, and use ever-growing volumes of data faster; and to do all of this while being available 24/7. That's a tall order, but it's one that is being done successfully by .NET companies all over the world - right now. In this webinar, lead by Petabridge CEO Aaron Stannard, we're going to cover how the obvious ways of scaling software in the past are doomed to fail and how .NET shops are developing and deploying their own distributed systems to tackle these problems efficiently and effectively using Akka.NET and Akka.Cluster.
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This document discusses reactive and parallel programming using Akka. It introduces Akka, an open-source toolkit for building distributed, concurrent applications on the JVM. Akka supports actors, futures, software transactional memory, and message passing to handle concurrency. Akka cluster allows building fault-tolerant distributed systems. Examples demonstrate using Akka for microservices, streaming engines, authentication platforms, and IoT applications. The document concludes with an example of how Agile Lab uses Akka in their applications.
Reactive Streams - László van den Hoek
This document provides an overview of reactive programming concepts including Reactive Streams and implementations like Akka and Akka Streams. It discusses:
- Non-blocking processing with asynchronous event loops and back pressure to prevent OutOfMemoryErrors.
- Use cases for Reactive Streams like managing uneven producer/consumer rates, ordering requirements, and efficient resource usage.
- Key aspects of Reactive Streams including the Publisher, Subscriber, and Subscription interfaces.
- How Akka implements the actor model for building concurrent, distributed applications and provides features like ordered message delivery, location transparency, and high-level components.
- How Akka Streams implements Reactive Streams for building data pipelines
ReactiveStream-meetup-Jan102015ppt
Akka is a toolkit for building highly concurrent, distributed, and resilient message-driven applications on the JVM based on the actor model. It provides Akka Streams, which allows expressing and running a chain of asynchronous processing steps on a sequence of elements to provide back-pressured asynchronous stream processing according to the Reactive Streams initiative standard. Akka Streams handles concurrency behind the scenes, so the user describes the processing rather than the implementation.
Solr Compute Cloud - An Elastic SolrCloud Infrastructure
Scaling search platforms for serving hundreds of millions of documents with low latency and high throughput workloads at an optimized cost is an extremely hard problem. BloomReach has implemented Sc2, which is an elastic Solr infrastructure for Big Data applications, supporting heterogeneous workloads and hosted in the cloud. It dynamically grows/shrinks search servers to provide application and pipeline level isolation, NRT search and indexing, latency guarantees, and application-specific performance tuning. In addition, it provides various high availability features such as differential real-time streaming, disaster recovery, context aware replication, and automatic shard and replica rebalancing, all with a zero downtime guarantee for all consumers. This infrastructure currently serves hundreds of millions of documents in millisecond response times with a load ranging in the order of 200-300K QPS.
This presentation will describe an innovate implementation of scaling Solr in an elastic fashion. It will review the architecture and take a deep dive into how each of these components interact to make the infrastructure truly elastic, real time, and robust while serving latency needs.
Solr Lucene Conference 2014 - Nitin Presentation
The document discusses Solr Compute Cloud (SC2), an elastic Solr infrastructure developed by BloomReach to address challenges of scaling search platforms for big data applications. SC2 dynamically provisions Solr clusters in the cloud for pipelines and indexing jobs, providing isolation. It ensures latency guarantees, dynamic scaling, high availability and disaster recovery. SC2 addresses issues BloomReach faced with a shared cluster approach like throughput limitations, stability problems and indexing challenges.
Akka Microservices Architecture And Design
This document discusses using Akka and microservices architecture for building distributed applications. It covers key Akka concepts like actors and messaging. It also discusses domain-driven design patterns for modeling application domains and boundaries. The document recommends using asynchronous messaging between microservices. It provides an example of using Apache Camel for enterprise integration. Finally, it discusses using Akka Clustering and Persistence for building highly available stateful services in a distributed fashion.
Solr Compute Cloud – An Elastic Solr Infrastructure: Presented by Nitin Sharm...
Solr Compute Cloud (SC2) is an elastic Solr infrastructure that allows for dynamic provisioning of Solr clusters on demand. This allows each search pipeline or job to have its own isolated cluster, improving stability, throughput, and cost optimization. The key benefits of SC2 are pipeline isolation, dynamic scaling, production cluster safeguards, and built-in high availability and disaster recovery features through technologies like the Solr HAFT service.
Akka in Production - ScalaDays 2015
Everyone in the Scala world is using or looking into using Akka for low-latency, scalable, distributed or concurrent systems. I'd like to share my story of developing and productionizing multiple Akka apps, including low-latency ingestion and real-time processing systems, and Spark-based applications.
When does one use actors vs futures?
Can we use Akka with, or in place of, Storm?
How did we set up instrumentation and monitoring in production?
How does one use VisualVM to debug Akka apps in production?
What happens if the mailbox gets full?
What is our Akka stack like?
I will share best practices for building Akka and Scala apps, pitfalls and things we'd like to avoid, and a vision of where we would like to go for ideal Akka monitoring, instrumentation, and debugging facilities. Plus backpressure and at-least-once processing.
May 2013 HUG: Apache Sqoop 2 - A next generation of data transfer tools
This document provides an overview of Apache Sqoop and discusses the transition from Sqoop 1 to Sqoop 2. Sqoop is a tool for transferring data between relational databases and Hadoop. Sqoop 1 was connector-based and had challenges around usability and security. Sqoop 2 addresses these with a new architecture separating connections from jobs, centralized metadata management, and role-based security for database access. Sqoop 2 is the primary focus of ongoing development to improve ease of use, extensibility, and security of data transfers with Hadoop.
ApacheCon BigData - What it takes to process a trillion events a day?
This document discusses stream processing at LinkedIn and summarizes two case studies:
1. LinkedIn's communications platform processes over a trillion events per day using Apache Samza for real-time notifications, security monitoring, and other applications.
2. Activity tracking in the News Feed uses stream processing to track metrics like ads clicked and session durations in real-time for hundreds of applications across LinkedIn.
3. The document also outlines some of the hard problems in stream processing like state management, remote I/O, and deployment models that Samza addresses through techniques such as local state caching, asynchronous remote access, and running on YARN or embedded.
Akka and-kubernetes
Akka and Kubernetes, the beginning of a beautiful relationship
One of the best features of Akka is Akka Cluster. Akka cluster allows for building distributed applications, where one application or service spans multiple nodes. From its initial release in 2013, Akka Cluster needed a node management system to manage the Akka nodes and to provide resilience and elasticity. With Kubernetes Akka finally has the node management system that is has been waiting for. Akka Cluster has been designed to gracefully handle nodes leaving and joining a running cluster while continuing to run. Kubernetes adds and removes nodes as needed to increase capacity or to recover from failures. In effect, there is a perfect symbiosis between Akka Cluster and Kubernetes. In this talk, we will look at and demonstrate how Akka Cluster and Kubernetes work together and how together they form a beautiful relationship.
Akka-intro-training-public.pdf
Akka introductory training material produced by LLAAMA.
Includes:
-issues with current multi-threading programming,
-Actor model,
-Actor location,
-Actor hierarchy,
-Supervision,
-Exception handling,
-Reactive architecture,
-Domain-driven design,
-Cluster,
-Sharding,
-Singleton,
-Split-Brain resolution,
-Akka Streams : source, flow, sink, backpressure, materialization, graph, operators, etc.
Fundamentals of Akka - Webinar
Having a problem in writing concurrent programs? Having to deal with threads, locks, race conditions, and so on is highly error-prone and can lead to code that is difficult to read, test, and maintain.
The answer to your above-mentioned challenge is Akka Actors.
It allows you to write concurrent code but without much of the pain doing so compared to writing your code using threads and locks.
Watch this webinar to explore the ways; how Akka actors facilitate and simplify the implementation of concurrent, distributed applications.
Akka.Net Overview
An overview of Akka.Net
- Intro to Actor Model
- Asynchronous Design
Actor Lifecycle
- Actor Hierarchies
- Location transparencies
- Configuration
- Clustering
- Routing
- Persistence
Build Cloud Applications with Akka and Heroku
- Akka is a great fit for building scalable applications on Heroku due to its "let it crash" philosophy and ability to easily scale out by adding more dynos. Within each dyno, Akka actors can be used to build highly concurrent and resilient applications.
- While remoting and clustering between dynos is not supported due to Heroku's HTTP-only architecture, Akka actors work well within individual dynos to handle requests and background jobs.
- A simple Akka application can be deployed to Heroku with no code changes - Akka will automatically scale out by creating actor instances on each dyno. The talk
Streaming Analytics with Spark, Kafka, Cassandra and Akka
This document discusses a new approach to building scalable data processing systems using streaming analytics with Spark, Kafka, Cassandra, and Akka. It proposes moving away from architectures like Lambda and ETL that require duplicating data and logic. The new approach leverages Spark Streaming for a unified batch and stream processing runtime, Apache Kafka for scalable messaging, Apache Cassandra for distributed storage, and Akka for building fault tolerant distributed applications. This allows building real-time streaming applications that can join streaming and historical data with simplified architectures that remove the need for duplicating data extraction and loading.
Composable Futures with Akka 2.0
Writing concurrent programs that can run in multiple threads and on multiple cores is crucial but daunting. Futures provides a convenient abstraction for many problem domains. The online course "Intermediate Scala" includes an up-to-date discussion of futures and the parts of java.util.concurrent that underlie the Scala futures implementation. Unlike Java's futures, Scala futures supports composition, transformations and sophisticated callbacks.
The author is managing editor of http://scalacourses.com, which offers self-paced online courses that teach Introductory and Intermediate Scala and Play Framework.
Strimzi - Where Apache Kafka meets OpenShift - OpenShift Spain MeetUp
Apache Kafka is the most used data streaming broker by companies. It could manage millions of messages easily and it is the base of many architectures based in events, micro-services, orchestration, ... and now cloud environments. OpenShift is the most extended Platform as a Service (PaaS). It is based in Kubernetes and it helps the companies to deploy easily any kind of workload in a cloud environment. Thanks many of its features it is the base for many architectures based in stateless applications to build new Cloud Native Applications. Strimzi is an open source community that implements a set of Kubernetes Operators to help you to manage and deploy Apache Kafka brokers in OpenShift environments.
These slides will introduce you Strimzi as a new component on OpenShift to manage your Apache Kafka clusters.
Slides used at OpenShift Meetup Spain:
- https://www.meetup.com/es-ES/openshift_spain/events/261284764/
Sigmoid akka stream
Akka is a toolkit for building highly concurrent, distributed, and resilient message-driven applications on the JVM based on the actor model. Reactive Streams is a standard for asynchronous stream processing with non-blocking back pressure on the JVM. Akka Streams implements Reactive Streams and provides a way to express and run a chain of asynchronous processing steps acting on a sequence of elements with every step processed by one actor to support parallelism.
Similar to Introduction to Akka.NET and Akka.Cluster (20)
Akka and Kubernetes, the beginning of a beautiful relationship
Akka and Kubernetes, the beginning of a beautiful relationship
Solr Compute Cloud - An Elastic SolrCloud Infrastructure
Solr Compute Cloud - An Elastic SolrCloud Infrastructure
Solr Compute Cloud – An Elastic Solr Infrastructure: Presented by Nitin Sharm...
Solr Compute Cloud – An Elastic Solr Infrastructure: Presented by Nitin Sharm...
May 2013 HUG: Apache Sqoop 2 - A next generation of data transfer tools
May 2013 HUG: Apache Sqoop 2 - A next generation of data transfer tools
ApacheCon BigData - What it takes to process a trillion events a day?
ApacheCon BigData - What it takes to process a trillion events a day?
Streaming Analytics with Spark, Kafka, Cassandra and Akka
Streaming Analytics with Spark, Kafka, Cassandra and Akka
Strimzi - Where Apache Kafka meets OpenShift - OpenShift Spain MeetUp
Strimzi - Where Apache Kafka meets OpenShift - OpenShift Spain MeetUp
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This document discusses .NET systems programming and garbage collection. It covers garbage collection generations, modes, and considerations for minimizing allocations. It also discusses eliminating delegates to reduce allocations, using value types appropriately, avoiding empty collections, and optimizing for thread locality to reduce context switching overhead. Data structures and synchronization techniques are discussed, emphasizing the importance of choosing lock-free data structures when possible to improve performance.
.NET Conf 2019 When and How to Use the Actor Model: an Introduction to Akka...
Actors are lightweight concurrent objects that can send and receive messages asynchronously in an event-driven manner. They provide a way to build scalable and fault-tolerant distributed applications using an asynchronous message-passing model. Actors avoid shared state and mutable data which makes it easier to build concurrent and distributed systems. Resources on how to use actors with Akka.NET and how they can help build scalable and resilient applications are available online.
Introduction to Distributed Tracing
This document discusses distributed tracing and how it can help solve problems caused by microservices. It covers what distributed tracing is, how it works, popular implementations like OpenTracing and Zipkin, and best practices for using distributed tracing. OpenTracing is introduced as a vendor-neutral standard that helps library developers implement tracing and defines common formats for propagating traces between services. Code examples are provided for collecting trace data using OpenTracing and Zipkin.
Akka.NET: Concurrency without the Pain
The .NET platform's last major releases prominently feature improved concurrency tools and keywords, and that's no accident. In a world where developers are expected to build applications that are consistently responsive across a wide range of devices, user experiences, and workloads knowing how to take full advantage of the power of concurrent programming is essential.
But let's face it: even with nice tools like the TPL and async / await, multi-threaded concurrent programming has typically been a bottomless pit of despair, Heisenbugs, and lessons learned the hard way.
None of this is true with Akka.NET and the actor model: a powerful programming methodology that makes building concurrent applications easy, fun, and much more powerful than what we had before.
Automed .NET Performance Testing with NBench
Not long ago in Akka.NET-land we had an issue occur where users noticed a dramatic drop in throughput in Akka.Remote’s message processing pipeline - and to make matters worse, this occurred in a production release of AKka.NET!
Yikes, how did that happen?
The answer is that although you can use unit tests and code reviews to detect functional problems with code changes and pull requests, using those same mechanisms to detect performance problems with code is utterly ineffective. Even skilled developers who have detailed knowledge about the internals of the .NET framework and CLR are unable to correctly predict how changes to code will impact its performance.
Hence why we developed NBench - a .NET performance-testing, stress-testing, and benchmarking framework for .NET applications that works and feels a lot like a unit test.
The New .NET Enterprise Stack
The document introduces a new .NET enterprise stack that is designed to handle write-heavy workloads without fear. It consists of infrastructure components like Windows Azure, data stores like Cassandra and Spark Streaming, runtimes like CoreCLR or Mono/CLR, and a programming model using Akka.NET. Metrics on an initial project using this stack found the code footprint was 83% smaller, turnaround time was 6 months, and developer fun was 100,000% more. More information can be found at the listed URL.
Full-Stack, Message-oriented Programming w/ Akka.NET Actors
This document discusses Akka.NET, a framework for building distributed, concurrent applications using the actor model. It explains that in the actor model, everything is an actor that can send and receive immutable messages asynchronously. Actors in Akka.NET are lightweight threads that handle messages concurrently without shared state. The document provides examples of how to define actors and send messages in Akka.NET. It argues that the actor model enables building highly concurrent, scalable and fault-tolerant applications more easily than traditional threading approaches.
OSS From the Outside In - A Personal Journey With Akka.NET
(From .NET Unboxed Conference 2015)
A year ago, I'd never sent in an open-source PR in my life. Today, I collaborate on Akka.NET with awesome developers and end users in 20+ countries every week. How the heck did THAT happen?!
- How do you get involved in a project and build credibility?
- How do you grok all the new ideas you need?
- Once you're involved, how do you build community around the project so people
actually USE the damn thing?
This is a talk about how a complete outsider gets into open-source quickly, and what possibilities that opens up for you personally and in your career.
Concurrency, Without the Pain: An Intro to Akka.NET @ Bay.NET
This document contains the slides and notes from a presentation on Akka.NET and the actor model given by Andrew Skotzko to the Bay.NET User Group. It introduces the challenges of writing multithreaded and concurrent code, describes how the actor model addresses these issues through message passing between isolated actors, and provides an overview of Akka.NET's implementation of the actor model for .NET applications. The presentation includes examples of actor messaging and demonstrates Akka.NET's features like hierarchies, supervision, and behavior switching.
Akka.NET @ London.NET
The document discusses the actor model for concurrency and introduces Akka.NET as an implementation of the actor model for .NET applications. It explains that the actor model involves treating all entities as actors that communicate asynchronously via message passing. Actors can create other actors, send and receive messages, and change their behavior. Akka.NET allows building distributed, highly concurrent, fault-tolerant and resilient applications using the actor model on .NET.
Akka.NET Fundamentals — #ProgNet15
The document is a presentation on Akka.NET and the actor model. It begins by introducing the speaker and stating the topics that will be covered, including an overview of the actor model and hands-on experience with Akka.NET fundamentals. It then discusses why concurrency is important and the challenges of traditional multithreaded programming. The bulk of the presentation focuses on explaining the actor model and how Akka.NET implements it on the .NET platform, including how actors communicate through message passing and how this addresses issues with shared state. Examples of where the actor model can be applied are provided.
Slides - Intro to Akka.Cluster
This document provides an introduction to Akka.Cluster, including defining what a cluster is, the benefits of clustering, how to enable clustering in an application, and examples of using clusters. It describes how clusters are formed and maintain themselves, including initial node discovery, leader election, and ring formation. Location transparency is discussed, where the physical location of actors does not impact functionality.
Akka.NET: Concurrency Without the Pain (Intro to the Actor Model)
We will introduce and explain the mind-bending actor model from the ground up. This will teach the foundational concepts for understanding the processing framework (Akka, ported to .NET from the JVM) that powers organizations like Walmart, Hootsuite, Apache Spark, Twitter, LinkedIn, and many more.
Attendees will walk away understanding all the fundamental concepts of what makes an actor systems so scalable and powerful, and all the foundational knowledge they need to start creating and deploying actor systems in the wild to solve hard problems.
Distributed Transactions in Akka.NET
"How do I do transactions across a distributed system?" is a very common question. This talk taught the basic conceptual model, which can then be customized and applied to specific use cases and consistency requirements.
Streaming ETL With Akka.NET
The document discusses building a streaming ETL pipeline using Akka.NET. It introduces the actor model as an alternative to shared state programming that allows building concurrent and distributed systems by modeling the system as actors that communicate asynchronously by message passing. The document provides an overview of Akka.NET and the actor model, demonstrating how actors can send and receive messages to process data streams in a reactive and resilient manner.
2 12-2015 - Cassandra Day LA - Using DataStax Enterprise and Actor Systems fo...
Cassandra and DataStax Enterprise have made it a lot easier to build distributed applications by providing a solid persistence layer that distributes well, is highly available, and comes with an intuitive programming model.
So what if you could have all of the nice things DataStax Enterprise provides to you at your application layer too?
1. An intuitive programming model;
2. High availability;
3. Reliability; and
4. The ability to decouple application design from network topology.
Well it turns out you can do this easier than ever before using the Actor Model and Akka.NET. This talk explains how.
http://getakka.net/
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.NET Conf 2019 When and How to Use the Actor Model: an Introduction to Akka...
.NET Conf 2019 When and How to Use the Actor Model: an Introduction to Akka...
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OSS From the Outside In - A Personal Journey With Akka.NET
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Concurrency, Without the Pain: An Intro to Akka.NET @ Bay.NET
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Akka.NET: Concurrency Without the Pain (Intro to the Actor Model)
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Introduction to Akka.NET and Akka.Cluster
- 1. Introduction to Akka.NET and Akka.Cluster Aaron Stannard, Co-founder Akka.NET @Aaronontheweb
- 2. High Expectations for Backend Developers
- 3. And the obvious ways to scale are wrong.
- 5. Scenario 1: Surge in User Activity
- 7. Brittle
- 8. Scenario 2: Real-time User Interactivity
- 10. Reality
- 11. Why is Distributed Programming Hard?
- 13. Enter the Actor Model
- 14. Origins
- 15. Actor Model Core Concepts
- 16. Akka.NET: Actor Model for .NET
- 17. Akka.NET Modules • Akka – core actor library • Akka.Remote – cross-node actor deployment / communication • Akka.Cluster – elastic actor networks (HA) • Akka.Persistence – event-sourcing, durable actor state & recovery • Akka.Streams – streaming workflows • Akka.Cluster.Tools – cluster singleton, distributed pub sub • Akka.Cluster.Sharding – durable state partitioning • Akka.DData – eventually consistent data replication
- 18. What's an Actor?
- 19. Statefulness & Concurrency with Actors
- 20. Actors have internal state
- 21. Actor state is always private, modified when handling messages, only shared via immutable messages
- 22. Actors live in a family tree
- 23. Actors process one message at a time
- 24. Actors on the Network
- 25. Event and Message Driven Programming
- 29. Communicate with Actors via Reference
- 30. Actor References Have Transparent Locations
- 31. Akka.Remote & Akka.Cluster: Location Doesn't Matter
- 33. What is a "Cluster?"
- 34. Network View of a Cluster
- 35. When do we use clustering?
- 37. CAP Theorem
- 39. Clustering is for AP
- 40. How an Akka.NET Cluster Forms
- 41. Initial State
- 42. Seed Node: Initial, well-known nodes contacted by nodes attempting to join the cluster
- 44. Leader Election
- 45. Ring Formation
- 46. Gossip: How Nodes Discover Each Other
- 47. Leaders Act on Gossip Convergence
- 49. Node Leaves
- 50. Node Exits
- 51. Running Multiple Akka.NET Apps in a Cluster
- 52. Cluster Roles
- 54. Clustered Routers How (most) actors on one node send messages to each other.
- 56. Cluster Routers
- 58. Cluster Group Router HOCON
- 59. Clusters & State
- 61. Stateful Apps Serve Results from Memory
- 62. State Locality
- 64. State Makes Protocols Work
- 65. CAP & Protocols
- 66. State has to be Moved
- 67. Akka.Cluster Makes This Easy
- 68. How to Move State
- 69. Recovering State on the Fly
- 70. Petabridge