Akka Streams is a toolkit for processing of streams. It is an implementation of Reactive Streams Specification. Its purpose is to “formulate stream processing setups such that we can then execute them efficiently and with bounded resource usage.”
Akka Streams is an implementation of Reactive Streams, which is a standard for asynchronous stream processing with non-blocking backpressure on the JVM. In this talk we'll cover the rationale behind Reactive Streams, and explore the different building blocks available in Akka Streams. I'll use Scala for all coding examples, but Akka Streams also provides a full-fledged Java8 API.After this session you will be all set and ready to reap the benefits of using Akka Streams!
Akka Streams is an implementation of Reactive Streams, which is a standard for asynchronous stream processing with non-blocking backpressure on the JVM. In this talk we'll cover the rationale behind Reactive Streams, and explore the different building blocks available in Akka Streams. I'll use Scala for all coding examples, but Akka Streams also provides a full-fledged Java8 API.After this session you will be all set and ready to reap the benefits of using Akka Streams!
Akka Streams are an implementation of the Reactive Streams specification (http://reactive-streams.org/), a joint effort that aims at standardizing the exchange of streams of data across asynchronous boundaries in a fully non-blocking way while providing flow control and mediating back pressure. In this presentation we go into the details of what this new abstraction can be used for and what the guiding principles are behind its development. We then focus on one prominent use-case which is the upcoming Akka HTTP module: a fully stream-enabled, reactive HTTP server and client implementation.
Journey into Reactive Streams and Akka StreamsKevin Webber
Are streams just collections? What's the difference between Java 8 streams and Reactive Streams? How do I implement Reactive Streams with Akka? Pub/sub, dynamic push/pull, non-blocking, non-dropping; these are some of the other concepts covered. We'll also discuss how to leverage streams in a real-world application.
Stream processing from single node to a clusterGal Marder
Building data pipelines shouldn't be so hard, you just need to choose the right tools for the task.
We will review Akka and Spark streaming, how they work and how to use them and when.
Back-Pressure in Action: Handling High-Burst Workloads with Akka Streams & Ka...Reactivesummit
Akka Streams and its amazing handling of stream back-pressure should be no surprise to anyone. But it takes a couple of use cases to really see it in action - especially use cases where the amount of work increases as you process make you really value the back-pressure.
This talk takes a sample web crawler use case where each processing pass expands to a larger and larger workload to process, and discusses how we use the buffering capabilities in Kafka and the back-pressure with asynchronous processing in Akka Streams to handle such bursts.
In addition, we will also provide some constructive “rants” about the architectural components, the maturity, or immaturity you’ll expect, and tidbits and open source goodies like memory-mapped stream buffers that can be helpful in other Akka Streams and/or Kafka use cases.
Akka Streams is an implementation of Reactive Streams, which is a standard for asynchronous stream processing with non-blocking backpressure on the JVM. In this talk we'll cover the rationale behind Reactive Streams, and explore the different building blocks available in Akka Streams. I'll use Scala for all coding examples, but Akka Streams also provides a full-fledged Java8 API.After this session you will be all set and ready to reap the benefits of using Akka Streams!
Akka Streams is an implementation of Reactive Streams, which is a standard for asynchronous stream processing with non-blocking backpressure on the JVM. In this talk we'll cover the rationale behind Reactive Streams, and explore the different building blocks available in Akka Streams. I'll use Scala for all coding examples, but Akka Streams also provides a full-fledged Java8 API.After this session you will be all set and ready to reap the benefits of using Akka Streams!
Akka Streams are an implementation of the Reactive Streams specification (http://reactive-streams.org/), a joint effort that aims at standardizing the exchange of streams of data across asynchronous boundaries in a fully non-blocking way while providing flow control and mediating back pressure. In this presentation we go into the details of what this new abstraction can be used for and what the guiding principles are behind its development. We then focus on one prominent use-case which is the upcoming Akka HTTP module: a fully stream-enabled, reactive HTTP server and client implementation.
Journey into Reactive Streams and Akka StreamsKevin Webber
Are streams just collections? What's the difference between Java 8 streams and Reactive Streams? How do I implement Reactive Streams with Akka? Pub/sub, dynamic push/pull, non-blocking, non-dropping; these are some of the other concepts covered. We'll also discuss how to leverage streams in a real-world application.
Stream processing from single node to a clusterGal Marder
Building data pipelines shouldn't be so hard, you just need to choose the right tools for the task.
We will review Akka and Spark streaming, how they work and how to use them and when.
Back-Pressure in Action: Handling High-Burst Workloads with Akka Streams & Ka...Reactivesummit
Akka Streams and its amazing handling of stream back-pressure should be no surprise to anyone. But it takes a couple of use cases to really see it in action - especially use cases where the amount of work increases as you process make you really value the back-pressure.
This talk takes a sample web crawler use case where each processing pass expands to a larger and larger workload to process, and discusses how we use the buffering capabilities in Kafka and the back-pressure with asynchronous processing in Akka Streams to handle such bursts.
In addition, we will also provide some constructive “rants” about the architectural components, the maturity, or immaturity you’ll expect, and tidbits and open source goodies like memory-mapped stream buffers that can be helpful in other Akka Streams and/or Kafka use cases.
Abstract –
Spark 2 is here, while Spark has been the leading cluster computation framework for severl years, its second version takes Spark to new heights. In this seminar, we will go over Spark internals and learn the new concepts of Spark 2 to create better scalable big data applications.
Target Audience
Architects, Java/Scala developers, Big Data engineers, team leaders
Prerequisites
Java/Scala knowledge and SQL knowledge
Contents:
- Spark internals
- Architecture
- RDD
- Shuffle explained
- Dataset API
- Spark SQL
- Spark Streaming
Spark real world use cases and optimizationsGal Marder
Using Spark for BigData became the standard in the industry. The internet is
full with "hello world" examples, but when your Spark job meets production all hell breaks loose. We will cover real world use cases, how they were designed, why they didn't work and how we made them run fast
There are many programming languages that can be used on the JVM (Java, Scala, Groovy, Kotlin, ...). In this session, we'll outline the main differences between them and discuss advantages and disadvantages for each.
Build Real-Time Streaming ETL Pipelines With Akka Streams, Alpakka And Apache...Lightbend
Things were easier when all our data used to be offline, analyzed overnight in batches. Now our data is online, in motion, and generated constantly. For architects, developers and their businesses, this means that there is an urgent need for tools and applications that can deliver real-time (or near real-time) streaming ETL capabilities.
In this session by Konrad Malawski, author, speaker and Senior Akka Engineer at Lightbend, you will learn how to build these streaming ETL pipelines with Akka Streams, Alpakka and Apache Kafka, and why they matter to enterprises that are increasingly turning to streaming Fast Data applications.
Flink Forward SF 2017: Joe Olson - Using Flink and Queryable State to Buffer ...Flink Forward
Flink's streaming API can be used to construct a scalable, fault tolerant framework for buffering high frequency time series data, with the goal being to output larger, immutable blocks of data. As the data is being buffered into larger blocks, Flink's queryable state feature can be used to service requests for data still in the "buffering" state. The high frequency time series data set in this example is electro cardiogram data (EKG) that is buffered from a sample rate in millisecond into multi minute blocks.
Flink Forward SF 2017: Dean Wampler - Streaming Deep Learning Scenarios with...Flink Forward
As a low-latency streaming tool, Flink offers the possibility of using machine learning, even "deep learning" (neural networks), with low latency. The growing FlinkML library provides some of the infrastructure support required for this goal, combined with third-party tools. This talk is a progress report on several scenarios we are developing at Lightbend, which combine Flink, Deeplearning4J, Spark, and Kafka to analyze cluster telemetry for anomaly detection, predictive autoscaling, and other scenarios. I'll focus on the pragmatics of training deep learning models in a streaming context, using batch and mini-batch training, combined with low-latency application of those models. I'll discuss the architecture we're using and highlight trade offs of particular tools for certain design problems in the implementation. I'll discuss the drawbacks and workarounds of our design and finish with a look at how future developments in Flink could improve its support for scenarios like ours.
Flink Forward SF 2017: Malo Deniélou - No shard left behind: Dynamic work re...Flink Forward
The Apache Beam programming model is designed to support several advanced data processing features such as autoscaling and dynamic work rebalancing. In this talk, we will first explain how dynamic work rebalancing not only provides a general and robust solution to the problem of stragglers in traditional data processing pipelines, but also how it allows autoscaling to be truly effective. We will then present how dynamic work rebalancing works as implemented in the Google Cloud Dataflow runner and which path other Apache Beam runners link Apache Flink can follow to benefit from it.
Flink Forward SF 2017: Shaoxuan Wang_Xiaowei Jiang - Blinks Improvements to F...Flink Forward
Search and recommendation system for Alibaba’s e-commerce platform use batch and streaming processing heavily. Flink SQL and Table API (which is a SQL-like DSL) provide simple, flexible, and powerful language to express the data processing logic. More importantly, it opens the door to unify the semantics of batch and streaming jobs. To support our products, we made lots of improvements to Flink SQL & TableAPI. We added the support for User-Defined Table function (UDTF), User-Defined Aggregates (UDAGG), window aggregate, and streaming retraction, etc. We have contributed these improvements back to the Flink community in the last few months. In this talk, we present the design and implementation of these improvement. We will also share the experience of running large scale Flink SQL and TableAPI jobs in Alibaba Search.
Developing Secure Scala Applications With Fortify For ScalaLightbend
From banks to airlines to credit rating agencies, security continues to be a major focus for organizations across various industries. As the newspapers show, it’s heavily damaging to enterprises when security vulnerabilities in their code, infrastructure, or open source frameworks/libraries get exploited.
The good news is that your Scala development team now has a powerful ally for securing their applications. Co-developed by the Fortify team along with Lightbend, the upcoming Fortify for Scala Plugin is the only Static Application Security Testing (SAST) solution to use the official Scala compiler. This plugin automatically identifies code-level security vulnerabilities early in the SDLC, so you can confidently and reliably secure your mission-critical Scala-based applications.
In this webinar by Seth Tisue, Scala Committer and Senior Scala Engineer at Lightbend, and Poonam Yadav, Product Manager for Fortify at Micro Focus, you will learn about:
* Some of the more than 200 vulnerabilities that the Fortify plugin for Scala can catch and help you resolve,
* How the plugin works to analyze, identify and provide actionable recommendations,
* How to integrate it into your modern DevOps environment,
* Why this plugin was co-developed by Lightbend and the Fortify team, and how it benefits your organization’s security professionals / CISO office.
Flink Forward SF 2017: Feng Wang & Zhijiang Wang - Runtime Improvements in Bl...Flink Forward
In 2016, we introduced Alibaba’s compute engine Blink which was based on our private branch of flink. It enalbed many large scale applications in Alibaba’s core business, such as search, recommendation and ads. With the deep and close colaboration with the flink community, we are finally close to contribute our improvements back to the flink community. In this talk, we will present our key contributions to flink runtime recently, such as the new YARN cluster mode for Flip-6, fine-grained failover for Flip-1, async i/o for Flip-12, incremental checkpoint, and the further improvements plan from Alibaba in the near future. Moreover, we will show some production use cases to illustrate how flink works in Alibaba’s large scale online applications, which includes real-time ETL as well as online machine learning. This talk is presented by Alibaba.
Patterns of the Lambda Architecture -- 2015 April -- Hadoop Summit, EuropeFlip Kromer
This talk centers on two things: a set of patterns for the architecture of high-scale data systems; and a framework for understanding the tradeoffs we make in designing them.
Revitalizing Enterprise Integration with Reactive StreamsLightbend
With Viktor Klang, Deputy CTO Lightbend, Inc.
As software grows more and more interconnected, and with several generations of software having to interoperate, a new take on the integration of systems is needed—ad hoc, unversioned, and unreplicated scripts just won’t suffice, and the traditional Enterprise Service Bus (ESB) concept has experienced stability, reliability, performance, and scalability problems.
In this webinar, Viktor explores a new take on Enterprise Integration Patterns:
First, he will explore the Reactive Streams standard, an orchestration layer where transformations are standalone, composable, reusable, and—most importantly—using asynchronous flow-control—back pressure—to maintain predictable, stable, behavior over time.
Furthermore, he will go through how one-off workloads relate to continuous, and batch, workloads, and how they can be addressed by that very same orchestration layer.
Finally, he will review how this type of design achieves resilience, scalability, and ultimately—responsiveness.
Scala.js is a compiler that compiles Scala source code to equivalent Javascript code. That lets you write Scala code that you can run in a web browser, or other environments (Chrome plugins, Node.js, etc.) where Javascript is supported. This presentation is an introduction to ScalaJS.
Async library is an asynchronous programming facility for Scala that offers a direct API for working with Futures.
It was added in Scala version 2.10 and is implemented using macros. Its main constructs, async and await, are inspired by similar constructs introduced in C# 5.0.
Abstract –
Spark 2 is here, while Spark has been the leading cluster computation framework for severl years, its second version takes Spark to new heights. In this seminar, we will go over Spark internals and learn the new concepts of Spark 2 to create better scalable big data applications.
Target Audience
Architects, Java/Scala developers, Big Data engineers, team leaders
Prerequisites
Java/Scala knowledge and SQL knowledge
Contents:
- Spark internals
- Architecture
- RDD
- Shuffle explained
- Dataset API
- Spark SQL
- Spark Streaming
Spark real world use cases and optimizationsGal Marder
Using Spark for BigData became the standard in the industry. The internet is
full with "hello world" examples, but when your Spark job meets production all hell breaks loose. We will cover real world use cases, how they were designed, why they didn't work and how we made them run fast
There are many programming languages that can be used on the JVM (Java, Scala, Groovy, Kotlin, ...). In this session, we'll outline the main differences between them and discuss advantages and disadvantages for each.
Build Real-Time Streaming ETL Pipelines With Akka Streams, Alpakka And Apache...Lightbend
Things were easier when all our data used to be offline, analyzed overnight in batches. Now our data is online, in motion, and generated constantly. For architects, developers and their businesses, this means that there is an urgent need for tools and applications that can deliver real-time (or near real-time) streaming ETL capabilities.
In this session by Konrad Malawski, author, speaker and Senior Akka Engineer at Lightbend, you will learn how to build these streaming ETL pipelines with Akka Streams, Alpakka and Apache Kafka, and why they matter to enterprises that are increasingly turning to streaming Fast Data applications.
Flink Forward SF 2017: Joe Olson - Using Flink and Queryable State to Buffer ...Flink Forward
Flink's streaming API can be used to construct a scalable, fault tolerant framework for buffering high frequency time series data, with the goal being to output larger, immutable blocks of data. As the data is being buffered into larger blocks, Flink's queryable state feature can be used to service requests for data still in the "buffering" state. The high frequency time series data set in this example is electro cardiogram data (EKG) that is buffered from a sample rate in millisecond into multi minute blocks.
Flink Forward SF 2017: Dean Wampler - Streaming Deep Learning Scenarios with...Flink Forward
As a low-latency streaming tool, Flink offers the possibility of using machine learning, even "deep learning" (neural networks), with low latency. The growing FlinkML library provides some of the infrastructure support required for this goal, combined with third-party tools. This talk is a progress report on several scenarios we are developing at Lightbend, which combine Flink, Deeplearning4J, Spark, and Kafka to analyze cluster telemetry for anomaly detection, predictive autoscaling, and other scenarios. I'll focus on the pragmatics of training deep learning models in a streaming context, using batch and mini-batch training, combined with low-latency application of those models. I'll discuss the architecture we're using and highlight trade offs of particular tools for certain design problems in the implementation. I'll discuss the drawbacks and workarounds of our design and finish with a look at how future developments in Flink could improve its support for scenarios like ours.
Flink Forward SF 2017: Malo Deniélou - No shard left behind: Dynamic work re...Flink Forward
The Apache Beam programming model is designed to support several advanced data processing features such as autoscaling and dynamic work rebalancing. In this talk, we will first explain how dynamic work rebalancing not only provides a general and robust solution to the problem of stragglers in traditional data processing pipelines, but also how it allows autoscaling to be truly effective. We will then present how dynamic work rebalancing works as implemented in the Google Cloud Dataflow runner and which path other Apache Beam runners link Apache Flink can follow to benefit from it.
Flink Forward SF 2017: Shaoxuan Wang_Xiaowei Jiang - Blinks Improvements to F...Flink Forward
Search and recommendation system for Alibaba’s e-commerce platform use batch and streaming processing heavily. Flink SQL and Table API (which is a SQL-like DSL) provide simple, flexible, and powerful language to express the data processing logic. More importantly, it opens the door to unify the semantics of batch and streaming jobs. To support our products, we made lots of improvements to Flink SQL & TableAPI. We added the support for User-Defined Table function (UDTF), User-Defined Aggregates (UDAGG), window aggregate, and streaming retraction, etc. We have contributed these improvements back to the Flink community in the last few months. In this talk, we present the design and implementation of these improvement. We will also share the experience of running large scale Flink SQL and TableAPI jobs in Alibaba Search.
Developing Secure Scala Applications With Fortify For ScalaLightbend
From banks to airlines to credit rating agencies, security continues to be a major focus for organizations across various industries. As the newspapers show, it’s heavily damaging to enterprises when security vulnerabilities in their code, infrastructure, or open source frameworks/libraries get exploited.
The good news is that your Scala development team now has a powerful ally for securing their applications. Co-developed by the Fortify team along with Lightbend, the upcoming Fortify for Scala Plugin is the only Static Application Security Testing (SAST) solution to use the official Scala compiler. This plugin automatically identifies code-level security vulnerabilities early in the SDLC, so you can confidently and reliably secure your mission-critical Scala-based applications.
In this webinar by Seth Tisue, Scala Committer and Senior Scala Engineer at Lightbend, and Poonam Yadav, Product Manager for Fortify at Micro Focus, you will learn about:
* Some of the more than 200 vulnerabilities that the Fortify plugin for Scala can catch and help you resolve,
* How the plugin works to analyze, identify and provide actionable recommendations,
* How to integrate it into your modern DevOps environment,
* Why this plugin was co-developed by Lightbend and the Fortify team, and how it benefits your organization’s security professionals / CISO office.
Flink Forward SF 2017: Feng Wang & Zhijiang Wang - Runtime Improvements in Bl...Flink Forward
In 2016, we introduced Alibaba’s compute engine Blink which was based on our private branch of flink. It enalbed many large scale applications in Alibaba’s core business, such as search, recommendation and ads. With the deep and close colaboration with the flink community, we are finally close to contribute our improvements back to the flink community. In this talk, we will present our key contributions to flink runtime recently, such as the new YARN cluster mode for Flip-6, fine-grained failover for Flip-1, async i/o for Flip-12, incremental checkpoint, and the further improvements plan from Alibaba in the near future. Moreover, we will show some production use cases to illustrate how flink works in Alibaba’s large scale online applications, which includes real-time ETL as well as online machine learning. This talk is presented by Alibaba.
Patterns of the Lambda Architecture -- 2015 April -- Hadoop Summit, EuropeFlip Kromer
This talk centers on two things: a set of patterns for the architecture of high-scale data systems; and a framework for understanding the tradeoffs we make in designing them.
Revitalizing Enterprise Integration with Reactive StreamsLightbend
With Viktor Klang, Deputy CTO Lightbend, Inc.
As software grows more and more interconnected, and with several generations of software having to interoperate, a new take on the integration of systems is needed—ad hoc, unversioned, and unreplicated scripts just won’t suffice, and the traditional Enterprise Service Bus (ESB) concept has experienced stability, reliability, performance, and scalability problems.
In this webinar, Viktor explores a new take on Enterprise Integration Patterns:
First, he will explore the Reactive Streams standard, an orchestration layer where transformations are standalone, composable, reusable, and—most importantly—using asynchronous flow-control—back pressure—to maintain predictable, stable, behavior over time.
Furthermore, he will go through how one-off workloads relate to continuous, and batch, workloads, and how they can be addressed by that very same orchestration layer.
Finally, he will review how this type of design achieves resilience, scalability, and ultimately—responsiveness.
Scala.js is a compiler that compiles Scala source code to equivalent Javascript code. That lets you write Scala code that you can run in a web browser, or other environments (Chrome plugins, Node.js, etc.) where Javascript is supported. This presentation is an introduction to ScalaJS.
Async library is an asynchronous programming facility for Scala that offers a direct API for working with Futures.
It was added in Scala version 2.10 and is implemented using macros. Its main constructs, async and await, are inspired by similar constructs introduced in C# 5.0.
Aurelia is a next generation UI framework. It is for browser, mobile and desktop. • It can enable you to not only create amazing UI but do it in a way that is maintainable, testable and extensible.
Realm Mobile Database - An IntroductionKnoldus Inc.
Realm is a cross-platform mobile database.It is a data persistence solution designed specifically for mobile applications. Realm store data in a universal, table-based format
It is simple as data are directly exposed as objects and queryable by code, removing the need for ORM's maintenance issues. Realm is faster than raw SQLite on common operations, while maintaining an extremely rich feature set.
It is a mechanism that enables us to sew/embed/bind WORDS in between a processed/unprocessed string literal.
Here by the processed string literal we mean processing of meta-characters like escape sequences(\n, \t, \r etc.) in the string.
Kanban is a scheduling system for lean manufacturing and just-in-time manufacturing. Kanban is an inventory-control system to control the supply chain. Taiichi Ohno, an industrial engineer at Toyota, developed kanban to improve manufacturing efficiency.
Shapeless- Generic programming for ScalaKnoldus Inc.
"Introduction to Shapeless- Generic programming for Scala !". Broadly speaking, shapeless is about programming with types. Doing things at compile-time that would more commonly be done at runtime to ensure type-safety. A long list of features provided by Shapeless are explained in the enclosed presentation.
Quill provides a Quoted Domain Specific Language (QDSL) to express queries in Scala and execute them in a target language. The library's core is designed to support multiple target languages, currently featuring specializations for Structured Query Language (SQL) and Cassandra Query Language (CQL).
Scala macro is the feature introduced in scala version 2.10, and have an experimental status for now. They are the piece of code that is executed at compile-time. Macro definitions are similar to the normal functions except that the body of these functions starts with keyword macro.
Email infrastructure service offered as an add-on for MailChimp,
Used to send personalized, one-to-one e-commerce emails, or automated transactional emails.
Knockout is a JavaScript library that helps you to create responsive display(UI)
It is based on Model–view–viewmodel (MVVM) pattern
It provides a simple two-way data binding mechanism between your data model and UI
It was developed and is maintained as an open source project by Steve Sanderson, a Microsoft employee on July 5, 2010
Scalaz is a Scala library for functional programming.
It provides purely functional data structures to complement those from the Scala standard library. It defines a set of foundational type classes (e.g. Functor, Monad) and corresponding instances for a large number of data structures.
The presentation covers ANTLR and its testing. In the presentation we will discuss what is grammar and how its been parsed into its corresponding parse tree. Then we will focus on the stages of the process of parsing. We will then understand what is ANTLR and will see some of the companies exploring features of ANTLR. Towards the end of the discussion we discuss how to test weather an input string is correct with respect to a grammar or not using TestRig along with the demonstration.
You may refer following blog:
https://blog.knoldus.com/2016/04/29/testing-grammar-using-antlr4-testrig-grun/
HTML5, CSS, JavaScript Style guide and coding conventionsKnoldus Inc.
Coding conventions are style guidelines for any programming language. As, we are growing ourselves rapidly in learning new technology, the need for learning of the coding standards and conventions for the same language also arises.
So, here let us try to learn some coding guidelines for few frontend languages.
Functional programming is a paradigm which concentrates on computing results rather than on performing actions. That is, when you call a function, the only significant effect that the function has is usually to compute a value and return it.
As the world is growing, so is data and with that analysis of this data has become important. Akka Stream is built on top of Akka-actors to handle the streaming data. It is used to analyze the data, perform operations on it asynchronously. In the session, I'm going to discuss basic components in Akka-Streams, back pressure, materialization, fusion, etc.
As the world is growing, so is data and with that analysis of this data has become important. Akka Stream is built on top of Akka-actors to handle the streaming data. It is used to analyze the data, perform operations on it asynchronously. In the session, I'm going to discuss basic components in Akka-Streams, back pressure, materialization, fusion, etc.
Start programming in a more functional style in Java. This is the second in a two part series on lambdas and streams in Java 8 presented at the JoziJug.
Development of a Distributed Stream Processing System (DSPS) in node.js and ZeroMQ and demonstration of an application of trending topics with a dataset from Twitter.
Artificial Intelligence: Artificial Neural NetworksThe Integral Worm
This presentation covers artificial neural networks for artificial intelligence. Topics covered are as follows: artificial neural networks, basic representation, hidden units, exclusive OR problem, backpropagation, advantages of artificial neural networks, properties of artificial neural networks, and disadvantages of artificial neural networks.
Reactive by example - at Reversim Summit 2015Eran Harel
Explaining the reactive manifesto by a real world case study.
This is a cool story about the evolution of our monitoring infrastructure. From the naive approach to a super resilient system.
How do we manage to handle 4M metrics / minute, and over 1K concurrent connections?
What strategies did we try to apply and where did it fail?
What are the techniques and technologies we use in order to achieve this?
How do we handle errors, and failures at this scale?
What can we still improve?
Getting Started with Apache Spark (Scala)Knoldus Inc.
In this session, we are going to cover Apache Spark, the architecture of Apache Spark, Data Lineage, Direct Acyclic Graph(DAG), and many more concepts. Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
Secure practices with dot net services.pptxKnoldus Inc.
Securing .NET services is paramount for protecting applications and data. Employing encryption, strong authentication, and adherence to best coding practices ensures resilience against potential threats, enhancing overall cybersecurity posture.
Distributed Cache with dot microservicesKnoldus Inc.
A distributed cache is a cache shared by multiple app servers, typically maintained as an external service to the app servers that access it. A distributed cache can improve the performance and scalability of an ASP.NET Core app, especially when the app is hosted by a cloud service or a server farm. Here we will look into implementation of Distributed Caching Strategy with Redis in Microservices Architecture focusing on cache synchronization, eviction policies, and cache consistency.
Introduction to gRPC Presentation (Java)Knoldus Inc.
gRPC, which stands for Remote Procedure Call, is an open-source framework developed by Google. It is designed for building efficient and scalable distributed systems. gRPC enables communication between client and server applications by defining a set of services and message types using Protocol Buffers (protobuf) as the interface definition language. gRPC provides a way for applications to call methods on a remote server as if they were local procedures, making it a powerful tool for building distributed and microservices-based architectures.
Using InfluxDB for real-time monitoring in JmeterKnoldus Inc.
Explore the integration of InfluxDB with JMeter for real-time performance monitoring. This session will cover setting up InfluxDB to capture JMeter metrics, configuring JMeter to send data to InfluxDB, and visualizing the results using Grafana. Learn how to leverage this powerful combination to gain real-time insights into your application's performance, enabling proactive issue detection and faster resolution.
Intoduction to KubeVela Presentation (DevOps)Knoldus Inc.
KubeVela is an open-source platform for modern application delivery and operation on Kubernetes. It is designed to simplify the deployment and management of applications in a Kubernetes environment. KubeVela is a modern software delivery platform that makes deploying and operating applications across today's hybrid, multi-cloud environments easier, faster and more reliable. KubeVela is infrastructure agnostic, programmable, yet most importantly, application-centric. It allows you to build powerful software, and deliver them anywhere!
Stakeholder Management (Project Management) PresentationKnoldus Inc.
A stakeholder is someone who has an interest in or who is affected by your project and its outcome. This may include both internal and external entities such as the members of the project team, project sponsors, executives, customers, suppliers, partners and the government. Stakeholder management is the process of managing the expectations and the requirements of these stakeholders.
Introduction To Kaniko (DevOps) PresentationKnoldus Inc.
Kaniko is an open-source tool developed by Google that enables building container images from a Dockerfile inside a Kubernetes cluster without requiring a Docker daemon. Kaniko executes each command in the Dockerfile in the user space using an executor image, which runs inside a container, such as a Kubernetes pod. This allows building container images in environments where the user doesn’t have root access, like a Kubernetes cluster.
Efficient Test Environments with Infrastructure as Code (IaC)Knoldus Inc.
In the rapidly evolving landscape of software development, the need for efficient and scalable test environments has become more critical than ever. This session, "Streamlining Development: Unlocking Efficiency through Infrastructure as Code (IaC) in Test Environments," is designed to provide an in-depth exploration of how leveraging IaC can revolutionize your testing processes and enhance overall development productivity.
Exploring Terramate DevOps (Presentation)Knoldus Inc.
Terramate is a code generator and orchestrator for Terraform that enhances Terraform's capabilities by adding features such as code generation, stacks, orchestration, change detection, globals, and more . It's primarily designed to help manage Terraform code at scale more efficiently . Terramate is particularly useful for managing multiple Terraform stacks, providing support for change detection and code generation 2. It allows you to create relationships between stacks to improve your understanding and control over your infrastructure . One of the key features of Terramate is its ability to detect changes at both the stack and module level. This capability allows you to identify which stacks and resources have been altered and selectively determine where you should execute commands.
Clean Code in Test Automation Differentiating Between the Good and the BadKnoldus Inc.
This session focuses on the principles of writing clean, maintainable, and efficient code in the context of test automation. The session will highlight the characteristics that distinguish good test automation code from bad, ultimately leading to more reliable and scalable testing frameworks.
Integrating AI Capabilities in Test AutomationKnoldus Inc.
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2. Agenda
1) What is Akka Stream ?
2) Components of Akka Stream.
3) Working with Graphs.
4) Error handling.
5) Testing Streams.
3. Akka Stream
There are two basic problems with data processing -
1) Blocking (Pull Based System)
2) Back Pressure (Push Based System)
Akka Streams is a toolkit for processing of streams. It is an
implementation of Reactive Streams Specification. Its purpose
is to “formulate stream processing setups such that we can
then execute them efficiently and with bounded resource
usage.”
4. Components of Akka Streams
There are three main components of Akka Streams -
1) Source
2) Flow
3) Sink
5. Source
Source is a “processing stage with exactly one output,
emitting data elements whenever downstream
processing stages are ready to receive them.”
●
Various Sources -
6. Sink
Sink is a “processing stage with exactly one input,
requesting and accepting data elements possibly slowing
down the upstream producer of elements.”
Various Sinks -
7. Flow
Flow is a “processing stage which has exactly one input
and output, which connects its up- and down streams by
transforming the data elements flowing through it.”
Various Flows -
8. Working with Graphs
Graphs are built from simple Flows which serve as the
linear connections within the graphs as well as junctions
which serve as fan-in and fan-out points for Flows.
Various Junctions provided by Akka Streams -
●
Fan-out Junctions
●
Fan-in Junctions
9. Fan-out Junctions
Fan-out operations give us the ability to split a stream
into sub-streams.
●
Broadcast[T] – (1 input, N outputs)
Ingests elements from one input and emits duplicated
events across more than one output.
●
Balance[T] – (1 input, N outputs)
Ingests elements from one input and emits to the first
available output port.
10. Fan-in Junctions
Fan-in operations give us the ability to join multiple streams into a single
output stream.
●
Merge[In] – (N inputs , 1 output)
Picks randomly from inputs pushing them one by one to its output.
●
MergePreferred[In]
Similar to Merge but if elements are available on preferred port, it picks
from it, otherwise randomly from others.
●
Concat[A] – (2 inputs, 1 output)
Concatenates two streams (first consume one, then the second one)
11. Error Handling
Strategies for how to handle exceptions from processing stream
elements can be defined when materializing the stream.
There are three ways to handle Exceptions -
●
Stop – The stream is completed with failure.
●
Resume – The element is dropped and the stream continues.
●
Restart - The element is dropped and the stream continues after
restarting the stage. Restarting a stage means that any accumulated
state is cleared.
12. Testing Streams
There are 3 ways to test the stream -
●
Simple sources, sinks and flows.
●
Sources and Sinks in combination with TestProbe from
the akka-testkit module.
●
Sources and Sinks specifically crafted for writing tests
from the akka-stream-testkit module.