Error handling is critical for reactive systems, as expressed by the "resilient" trait. This talk examines strengths and weaknesses of existing approaches. I also consider preventing errors in the first place.
Reactive design: languages, and paradigmsDean Wampler
A talk first given at React 2014 and refined for YOW! LambdaJam 2014 that explores the meaning of Reactive Programming, as described in the Reactive Manifesto, and how well it is supported by general design paradigms, like Functional Programming, Object-Oriented Programming, and Domain Driven Design, and by particular design approaches, such as Functional Reactive Programming, Reactive Extensions, Actors, etc.
Out Of Memory - Analyze Your Java Heap
A short introduction on why memory usage might not be what you expect it to be and tools for going deeper. Different ways for acquiring memory snapshots and how to examine them with MAT.
Roger Lindsjö, Ericsson
Part 3: What you should know about Resiliency, Errors vs Failures, Isolation (and Containment), Delegation and Replication in Reactive systems
In the final webinar with live Q/A in the Reactive Revealed series, we explore the way that Reactive systems maintain resiliency with an infrastructural approach designed to welcome failure often and recover gracefully. Presented by Reactive Manifesto co-author, Akka creator and CTO at Typesafe, Inc., Jonas Bonér explores what you should know about:
What you should know about maintaining resiliency with monolithic systems compared to distributed systems
How Reactive systems handle errors and prevents catastrophic failures with isolation and containment, delegation and replication
How isolation (and containment) of error state and behavior works to block the ripple effect of cascading failures
How delegation of failure management and replication lets Reactive systems continue running in the face of failures using a different error handling context, on a different thread or thread pool, in a different process, or on a different network node or computing center
Previous
Part 1 - Asynchronous I/O, Back-pressure and the Message-driven vs. Event-driven approach in Reactive systems | presented by Konrad Malawski
Part 2 - Elasticity, Scalability and Location Transparency in Reactive Systems | presented by Viktor Klang
Reactive design: languages, and paradigmsDean Wampler
A talk first given at React 2014 and refined for YOW! LambdaJam 2014 that explores the meaning of Reactive Programming, as described in the Reactive Manifesto, and how well it is supported by general design paradigms, like Functional Programming, Object-Oriented Programming, and Domain Driven Design, and by particular design approaches, such as Functional Reactive Programming, Reactive Extensions, Actors, etc.
Out Of Memory - Analyze Your Java Heap
A short introduction on why memory usage might not be what you expect it to be and tools for going deeper. Different ways for acquiring memory snapshots and how to examine them with MAT.
Roger Lindsjö, Ericsson
Part 3: What you should know about Resiliency, Errors vs Failures, Isolation (and Containment), Delegation and Replication in Reactive systems
In the final webinar with live Q/A in the Reactive Revealed series, we explore the way that Reactive systems maintain resiliency with an infrastructural approach designed to welcome failure often and recover gracefully. Presented by Reactive Manifesto co-author, Akka creator and CTO at Typesafe, Inc., Jonas Bonér explores what you should know about:
What you should know about maintaining resiliency with monolithic systems compared to distributed systems
How Reactive systems handle errors and prevents catastrophic failures with isolation and containment, delegation and replication
How isolation (and containment) of error state and behavior works to block the ripple effect of cascading failures
How delegation of failure management and replication lets Reactive systems continue running in the face of failures using a different error handling context, on a different thread or thread pool, in a different process, or on a different network node or computing center
Previous
Part 1 - Asynchronous I/O, Back-pressure and the Message-driven vs. Event-driven approach in Reactive systems | presented by Konrad Malawski
Part 2 - Elasticity, Scalability and Location Transparency in Reactive Systems | presented by Viktor Klang
This session talks about how unit testing of Spark applications is done, as well as tells the best way to do it. This includes writing unit tests with and without Spark Testing Base package, which is a spark package containing base classes to use when writing tests with Spark.
Slides from my talk at the Feb 2011 Seattle Tech Startups meeting. More info here (along with powerpoint slides): http://www.startupmonkeys.com/2011/02/scala-frugal-mechanic/
Spark Streaming makes it easy to build scalable fault-tolerant streaming applications. In this webinar, developers will learn:
*How Spark Streaming works - a quick review.
*Features in Spark Streaming that help prevent potential data loss.
*Complementary tools in a streaming pipeline - Kafka and Akka.
*Design and tuning tips for Reactive Spark Streaming applications.
Effective testing for spark programs Strata NY 2015Holden Karau
This session explores best practices of creating both unit and integration tests for Spark programs as well as acceptance tests for the data produced by our Spark jobs. We will explore the difficulties with testing streaming programs, options for setting up integration testing with Spark, and also examine best practices for acceptance tests.
Unit testing of Spark programs is deceptively simple. The talk will look at how unit testing of Spark itself is accomplished, as well as factor out a number of best practices into traits we can use. This includes dealing with local mode cluster creation and teardown during test suites, factoring our functions to increase testability, mock data for RDDs, and mock data for Spark SQL.
Testing Spark Streaming programs has a number of interesting problems. These include handling of starting and stopping the Streaming context, and providing mock data and collecting results. As with the unit testing of Spark programs, we will factor out the common components of the tests that are useful into a trait that people can use.
While acceptance tests are not always part of testing, they share a number of similarities. We will look at which counters Spark programs generate that we can use for creating acceptance tests, best practices for storing historic values, and some common counters we can easily use to track the success of our job.
Relevant Spark Packages & Code:
https://github.com/holdenk/spark-testing-base / http://spark-packages.org/package/holdenk/spark-testing-base
https://github.com/holdenk/spark-validator
Booz Allen Hamilton created the Field Guide to Data Science to help organizations and missions understand how to make use of data as a resource. The Second Edition of the Field Guide, updated with new features and content, delivers our latest insights in a fast-changing field. http://bit.ly/1O78U42
Apache Storm vs. Spark Streaming - two stream processing platforms comparedGuido Schmutz
Storm as well as Spark Streaming are Open-Source Frameworks supporting distributed stream processing. Storm has been developed by Twitter and is a free and open source distributed real-time computation system that can be used with any programming language. It is written primarily in Clojure and supports Java by default. Spark is fast and general engine for large-scale data processing and has been designed to provide a more efficient alternative to Hadoop MapReduce. Spark Streaming brings Spark's language-integrated API to stream processing, letting you write streaming applications the same way you write batch jobs. It supports both Java and Scala. This presentation shows how you can implement stream processing solutions with the two frameworks, discusses how they compare and highlights the differences and similarities.
Understanding Zulu Garbage Collection by Matt Schuetze, Director of Product M...zuluJDK
Understanding Zulu Garbage Collection by Matt Schuetze, Director of Product Management at Azul Systems
Find him on Twitter at @schuetzematt
For all of your openJDK™, Java, and Azul Systems information, please find us on the web at:
http://www.zuluJDK.org
http://www.azulsystems.com
@zuluJDK.org
@azulsystems
Azul Product Manager Matt Schuetze's presentation on JVM memory details to the Philadelphia Java User Group.
This session dovetails with the March, 2014 PhillyJUG deep dive session topic focused on Java compiler code transformation and JVM runtime execution. That session exposes myths that Java is slow and Java uses too much memory. In this session we will take a deeper look at Java memory management. The dreaded Out of Memory (OOM) error is one problem. Garbage collector activity and spikes leading to long pauses is another. He covers the foundations of garbage collection and why historically Java gets a bad rap, even though GC provides a marvelous memory management paradigm.
OpenNebulaConf 2013 - OpenNebula in a Multi-Customer-Environment by Bernd ErkOpenNebula Project
NETWAYS is using OpenNebula in their multi-customer cloud for years now. Having the roots in a typical XEN replacement, more and more cloud functionalities are used in production now. The system is heavily coupled with Puppet and all other internal systems like monitoring, backup and accounting. The talk will focus on the hurdles taken in the last years starting with various design considerations and different steps made to achieve the “final” architecture NETWAYS is using today. It’ will also give a detailed view onthe current setup and connected subsystems.
Bio:
Bernd Erk, Managing Director, has overseen the Managed Services, Consulting and Development business areas at NETWAYS since 2007. Ensuring the success and smooth operation of all customer projects and business processes, Bernd’s technical expertise stretches across Systems Management, Managed Services and Software Development. A contributor to Linux Magazine and Linux Technical Review in Germany, Bernd regularly publishes articles and presents on open source topics ranging across Icinga monitoring, MySQL database monitoring, OpenNebula Cloud framework and performance tuning among others.
Bernd was previously Operating Systems Specialist at Quelle Schickedanz AG & Co., where he worked heavily with Solaris, HPUX and Oracle databases. After which, Bernd spent 8 years as Business Unit Manager at Ise-Informatik where he dealt with Oracle databases and service oriented architectures.
This session talks about how unit testing of Spark applications is done, as well as tells the best way to do it. This includes writing unit tests with and without Spark Testing Base package, which is a spark package containing base classes to use when writing tests with Spark.
Slides from my talk at the Feb 2011 Seattle Tech Startups meeting. More info here (along with powerpoint slides): http://www.startupmonkeys.com/2011/02/scala-frugal-mechanic/
Spark Streaming makes it easy to build scalable fault-tolerant streaming applications. In this webinar, developers will learn:
*How Spark Streaming works - a quick review.
*Features in Spark Streaming that help prevent potential data loss.
*Complementary tools in a streaming pipeline - Kafka and Akka.
*Design and tuning tips for Reactive Spark Streaming applications.
Effective testing for spark programs Strata NY 2015Holden Karau
This session explores best practices of creating both unit and integration tests for Spark programs as well as acceptance tests for the data produced by our Spark jobs. We will explore the difficulties with testing streaming programs, options for setting up integration testing with Spark, and also examine best practices for acceptance tests.
Unit testing of Spark programs is deceptively simple. The talk will look at how unit testing of Spark itself is accomplished, as well as factor out a number of best practices into traits we can use. This includes dealing with local mode cluster creation and teardown during test suites, factoring our functions to increase testability, mock data for RDDs, and mock data for Spark SQL.
Testing Spark Streaming programs has a number of interesting problems. These include handling of starting and stopping the Streaming context, and providing mock data and collecting results. As with the unit testing of Spark programs, we will factor out the common components of the tests that are useful into a trait that people can use.
While acceptance tests are not always part of testing, they share a number of similarities. We will look at which counters Spark programs generate that we can use for creating acceptance tests, best practices for storing historic values, and some common counters we can easily use to track the success of our job.
Relevant Spark Packages & Code:
https://github.com/holdenk/spark-testing-base / http://spark-packages.org/package/holdenk/spark-testing-base
https://github.com/holdenk/spark-validator
Booz Allen Hamilton created the Field Guide to Data Science to help organizations and missions understand how to make use of data as a resource. The Second Edition of the Field Guide, updated with new features and content, delivers our latest insights in a fast-changing field. http://bit.ly/1O78U42
Apache Storm vs. Spark Streaming - two stream processing platforms comparedGuido Schmutz
Storm as well as Spark Streaming are Open-Source Frameworks supporting distributed stream processing. Storm has been developed by Twitter and is a free and open source distributed real-time computation system that can be used with any programming language. It is written primarily in Clojure and supports Java by default. Spark is fast and general engine for large-scale data processing and has been designed to provide a more efficient alternative to Hadoop MapReduce. Spark Streaming brings Spark's language-integrated API to stream processing, letting you write streaming applications the same way you write batch jobs. It supports both Java and Scala. This presentation shows how you can implement stream processing solutions with the two frameworks, discusses how they compare and highlights the differences and similarities.
Understanding Zulu Garbage Collection by Matt Schuetze, Director of Product M...zuluJDK
Understanding Zulu Garbage Collection by Matt Schuetze, Director of Product Management at Azul Systems
Find him on Twitter at @schuetzematt
For all of your openJDK™, Java, and Azul Systems information, please find us on the web at:
http://www.zuluJDK.org
http://www.azulsystems.com
@zuluJDK.org
@azulsystems
Azul Product Manager Matt Schuetze's presentation on JVM memory details to the Philadelphia Java User Group.
This session dovetails with the March, 2014 PhillyJUG deep dive session topic focused on Java compiler code transformation and JVM runtime execution. That session exposes myths that Java is slow and Java uses too much memory. In this session we will take a deeper look at Java memory management. The dreaded Out of Memory (OOM) error is one problem. Garbage collector activity and spikes leading to long pauses is another. He covers the foundations of garbage collection and why historically Java gets a bad rap, even though GC provides a marvelous memory management paradigm.
OpenNebulaConf 2013 - OpenNebula in a Multi-Customer-Environment by Bernd ErkOpenNebula Project
NETWAYS is using OpenNebula in their multi-customer cloud for years now. Having the roots in a typical XEN replacement, more and more cloud functionalities are used in production now. The system is heavily coupled with Puppet and all other internal systems like monitoring, backup and accounting. The talk will focus on the hurdles taken in the last years starting with various design considerations and different steps made to achieve the “final” architecture NETWAYS is using today. It’ will also give a detailed view onthe current setup and connected subsystems.
Bio:
Bernd Erk, Managing Director, has overseen the Managed Services, Consulting and Development business areas at NETWAYS since 2007. Ensuring the success and smooth operation of all customer projects and business processes, Bernd’s technical expertise stretches across Systems Management, Managed Services and Software Development. A contributor to Linux Magazine and Linux Technical Review in Germany, Bernd regularly publishes articles and presents on open source topics ranging across Icinga monitoring, MySQL database monitoring, OpenNebula Cloud framework and performance tuning among others.
Bernd was previously Operating Systems Specialist at Quelle Schickedanz AG & Co., where he worked heavily with Solaris, HPUX and Oracle databases. After which, Bernd spent 8 years as Business Unit Manager at Ise-Informatik where he dealt with Oracle databases and service oriented architectures.
This is the slide deck I presented at the first CommCon event in the UK: it goes through some of the possible strategies for scaling WebRTC applications, mostly if you're using Janus but not only.
Protecting JavaScript source code using obfuscation - OWASP Europe Tour 2013 ...AuditMark
The goal of code obfuscation is to delay the understanding of what a program does. It can be used, for example, in scenarios where the code contains Intellectual Property (algorithms) or when the owner wants to prevent a competitor for stealing and reusing the code. To achieve it, an obfuscation transformation translates easy to understand code into a much harder to understand form. But in order to be resilient, obfuscation transformations need also to resist automatic reversal performed using static or dynamic code analysis techniques. This presentation focuses on the specific case of JavaScript source obfuscation, main usage cases, presents some obfuscation examples and their value in providing real protection against reverse-engineering.
This is a story of two types: GenericType and SpecificType, where GenericType is a
superclass of SpecificType. There are two types of explicit cast in C#:
The Prefix cast:
[01] GenericType g=...;
[02] SpecificType t=(SpecificType) g;
The as cast:
[03] GenericType g=...;
[04] SpecificType t=g as SpecificType;
Most programmers have a habit of using one or the other — this isn’t usually a
conscious decision, but more of a function of which form a programmer saw first. I,
for instance, programmed in Java before I learned C#, so I was already in the prefix
cast habit. People with a Visual Basic background often do the opposite. There are
real differences between the two casting operators
Microservices and functional programmingMichael Neale
A talk I did recently on microservices and functional programming. Microservices are small, single purpose apps that are run as a service, which are usually composed together to provide the real app.
This talk discusses Spark (http://spark.apache.org), the Big Data computation system that is emerging as a replacement for MapReduce in Hadoop systems, while it also runs outside of Hadoop. I discuss why the issues why MapReduce needs to be replaced and how Spark addresses them with better performance and a more powerful API.
While Hadoop is the dominant "Big Data" tool suite today, it's a first-generation technology. I discuss its strengths and weaknesses, then look at how we "should" be doing Big Data and currently-available alternative tools.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
ER(Entity Relationship) Diagram for online shopping - TAEHimani415946
https://bit.ly/3KACoyV
The ER diagram for the project is the foundation for the building of the database of the project. The properties, datatypes, and attributes are defined by the ER diagram.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.