Pain of growing up, and moving to large scale

Vladimir Dejanovic is the founder and leader of AmsterdamJUG. Software architect and Team Lead with long experience in developing high performance software in multiple programming languages and technologies with high load traffic. Always interested in cool new stuff, Free and Open Source software Speaker at JavaOne, Devoxx BE and Devoxx US

Although software is deployed for a long time now, not everyone is exposed to it. In big companies developers usually were not involved, while in small companies in most cases scale wasn’t big, so deploying also wasn’t thought so much as a big of a deal. With move to DevOps and having successful “startups” landscape is changing a lot, however a lot of companies and engineers are still struggling with deploying software, especially on a large scale were rules of the game are on completely different level. In case of transition from small to big successful company deployment process can easily be one of the key factors deciding if company will make it or not. Although tools like Kubernetis and similar are designed to solve lot of this problem,in most cases from my experience companies are still not using them in amount they should, and are still relying on “old” techniques for deploying software into production. Also very often articles and blog posts usually point out only good part…

You built your product, put it on server, and it flew like a rocket. Now you need to move to larger scale, but how ? How to go from here ? Deploying software isn’t always the first thing on developers mind, while they work on some software. However as you start moving to larger scale your deployment process can make you or break you. It comes with pain and cruel truth of how good your deployment process really is. Join me while I address pain, issue and problems of deploying software on Large Scale. I will talk about some known approaches and their pro’s and con’s. Also I will share some tips and tricks into making deploying software on large scale easier. All of this come from my personal experience while working on this type of systems.

High-Performance Hibernate

Vlad Mihalcea is a Hibernate Developer Advocate. He spoke at Devoxx France, Voxxed Days Bucharest, JavaZone. This is a list of presentations that he’s been giving in 2016: https://vladmihalcea.com/presentations/

Hibernate is the most popular JPA provider and knowing its inner-workings can make the difference between a high-performance enterprise application and one that barely crawls. This presentation goes through the most common performance bottlenecks issues like exotic mappings, too much flushing, inappropriate queries, JDBC batch updates misuse, and suboptimal result set fetching. Aside from common anti-patterns, I will also present some very useful Hibernate features like the enhanced identifier generators, caching strategies, and bytecode enhancements.

I have a stream: Going reactive with Spring Data

Mark Paluch is a Software Craftsman working as Spring Data Engineer at Pivotal and lettuce Redis driver Project Lead. He is a member of the CDI 2.0 expert group and passionate about open source software.

Data access and application scalability are closely related. Applications employ threads until they are done with their work while most of the time is waiting for I/O. Reactive infrastructure shifts responsibilities to where they can be handled best. It’s a move towards data streaming that does not require upfront fetching and therefore optimizes memory and computational resources. This talk covers what a stream is and how reactive data access leverages scalability bounds by applying the most natural way of data access with Spring Data and Project Reactor. If you are a developer looking to consume data in a functional reactive style, this is your chance to gain the experience how your application can benefit from streaming data access and to learn why not everything should be reactive.

Turbo Charge CPU Utilization in Fork/Join Using the ManagedBlocker

Heinz Kabutz writes the popular “The Java Specialists’ Newsletter” read by tens of thousands of enthusiastic fans in over 138 countries. To sign up, visit http://www.javaspecialists.eu

Fork/Join is a framework for parallelizing calculations using recursive decomposition, also called divide and conquer. These algorithms occasionally end up duplicating work, especially at the beginning of the run. We can reduce wasted CPU cycles by implementing a reserved caching scheme. Before a task starts its calculation, it tries to reserve an entry in the shared map. If it is successful, it immediately begins. If not, it blocks until the other thread has finished its calculation. Unfortunately this might result in a significant number of blocked threads, decreasing CPU utilization. In this talk we will demonstrate this issue and offer a solution in the form of the ManagedBlocker. Combined with the Fork/Join, it can keep parallelism at the desired level.

Developing modern web applications using Meteor and MongoDB

Aleksey Savateyev is a senior architect at MongoDB and works with partners and Fortune 100 companies on building their applications on top of MongoDB database. His primary focus is building web and cloud services – he served as a Director of Product Management for Yahoo’s private cloud, as well as senior software architect for Microsoft Azure, built startups and development teams in few companies and holds a master’s degree in Computer Science.

This talk will walk you through the process of building fully-featured application with Meteor and MongoDB and show tips and tricks to scale and optimize this application once it becomes widely used.

MongoDB is a leading NoSQL database in the world and a primary choice for web developers. Meteor is one of the most advanced full stack development frameworks that has MongoDB at its core (both on server and the client!). This workshop will walk you through the process of building fully-featured application with Meteor and MongoDB and show tips and tricks to scale and optimize this application once it becomes widely used.

10 things I learned after doing 2400 code reviews in 6 months

Patroklos Papapetrou is a chief software architect, addicted to software quality and an agile team leader with almost 20 years of experience in software development. He believes and invests in people and team spirit seeking quality excellence. He’s one of the authors of SonarQube in action book and he recently founded his own consulting and training company. He’s an occasional speaker giving talks about clean code, code quality, software gardening and other cool stuff he wants to share with fellow developers

I work remotely as part of a distributed company and I have the chance in my current job to review almost all pull requests submitted by several developers. During the last six months I learned a lot both as a reviewer and a developer and this what I want to share!

I will discuss about things I learned to improve myself as a reviewer, and I will present automation and tools integration ideas to facilitate the code review process. I will also explain the most common coding mistakes I have noticed, developer patterns , human habits and how all this made me a not only a better code reviewer but also a better developer. It’s going to be a fun talk with some real code review examples that I hope people will enjoy. After all code reviews should be fun and not a necessary evil.

Extending DevOps to Big Data Applications with Kubernetes

Nicola Ferraro is a senior software engineer at Red Hat. I’m an Apache Camel committer and a contributor of the Fabric8 project, a microservice development platform based on Docker and Kubernetes. I’ve a long background on Big Data systems, having built applications based on Spark, HBase, Kafka and Hadoop for years. I’m also the author of some open source projects related to Big Data application development.

DevOps, continuous delivery and modern architectural trends can incredibly speed up the software development process. Big Data applications cannot be an exception and need to keep the same pace. In this talk, Nicola Ferraro (Red Hat) will show an overview of the next generation cloud-native Big Data systems based on Docker and Kubernetes. Switching from static Big Data platforms to infrastructure as code allows releasing robust applications in minutes by minimizing the gap between all the environments (development, testing, staging and production), a typical problem in Big Data application development. The presentation goes through the full release cycle of a Spark application alongside a fleet of microservices exchanging data in near real time. A fully featured continuous delivery pipeline, provided by the Fabric8 platform, will automate every step of the build and release process. Spark clusters will come to life from nowhere and disappear when they are no longer needed…

Effective Design of RESTful APIs

Mohamed Taman Co-Founder & CTO of PaySky International, an Enterprise Architect, a Java Champions, Adopts Java EE 7, 8 & JavaFX, JCP member, JCP Executive Committee member, JSR 354, 363 & 373 Expert Group member, MoroccoJUG member, EGJUG leader, Oracle Egypt Architects Club board member, speaks Java, love mobile, international speaker, Books Author, Won Duke’s choice 2015, 2014 awards, and JCP outstanding adopt-a-jar participant 2013 awards, IoT Geek.

A frequent speaker at Java/Oracle User Groups and conferences worldwide Including JavaOne (four times), JDC Egypt, Tunis JUG Day (twice), JEEConf (twice), jMaghreb (twice), 33’s Degree Poland 2014.
Got this year the most interesting and best talks at both Tunisia Esprit JUG Day 2014, and JEEConf 2014.
Have talks in RigaDevDays 2015, JPoint Russia 2015, JEEConf 2013-2015, Tunis JUG Days 2015, DWX Germany 2015, Devoxx Morocco, Belgium, and UK, JavaOne 2012-2016, JavaDays Ukraine Karkiev, Kiev, Voxxed Istanbul 2016, JFokus 2016.Developers creating websites needs to know how to build RESTful APIs correctly. This session will help you plan and model your own APIs and understand the six REST design constraints that help guide your architecture. Including an example that will brush everything up.

Developers creating websites needs to know how to build RESTful APIs correctly. This session will help you plan and model your own APIs and understand the six REST design constraints that help guide your architecture. I will starts with a simple overview, including advice on identifying the users or “participants” of your system and the activities they might perform with it. I’ll help you paper test your model, validating the design before you build it. You’ll then explore the HTTP concepts and REST constraints needed to build your API. Topics include: The three approaches to adding an API, Modeling tips, Creating and grouping API methods, Mapping activities to, verbs and actions, Validating your API, Working with HTTP headers and response codes, Caching, Layered systems, Creating a uniform interface. All those explanations topics will be provided by examples to clear up the idea and demonstrate the concept. So what are you waiting for ?! Click to enroll.

Handling Billions Of Edges in a Graph Database

Michael Hackstein is a Senior Graph Specialist @ ArangoDB GmbH. He holds a Masters degree in Computer Science and is the creator of ArangoDBs graph capabilities. During his academic career he focused on complex algorithms and especially graph databases. Michael is an internationally experienced speaker who loves salad, cake and clean code.

Modern graph databases are designed to handle the complexity but still not the amount of data. When hitting a certain size of a graph, many dedicated graph dbs reach their limits in scalability. In this talk I’ll provide an overview about current approaches and their limits towards scalability.

The complexity and amount of data rises. Modern graph databases are designed to handle the complexity but still not for the amount of data. When hitting a certain size of a graph many dedicated graph databases reach their limits in vertical or, most common, horizontal scalability. In this talk I’ll provide a brief overview about current approaches and their limits towards scalability. Dealing with complex data in a complex system doesn’t make things easier… but more fun finding a solution. Join me on my journey to handle billions of edges in a graph database.

IoT and Edge Integration with Open Source Frameworks

Kai Waehner works as Technical Evangelist at TIBCO. Kai’s main area of expertise lies within the fields of Big Data, Analytics, Machine Learning, Integration, SOA, Microservices, BPM, Cloud, Java EE and Enterprise Architecture Management. He is regular speaker at international IT conferences such as JavaOne, ApacheCon or OOP, writes articles for professional journals, and shares his experiences with new technologies on his blog (www.kai-waehner.de/blog). Find more details and references (presentations, articles, blog posts) on his website: www.kai-waehner.de

This session shows and demos open source IoT frameworks such as Eclipse Kura, Node-RED or Flogo built to develop very lightweight microservices, which can be deployed on small devices or in serverless architectures and wire together all different kinds of hardware devices, APIs and online services..

Internet of Things (IoT) and edge integration is getting more important than ever before due to the massively growing number of connected devices year by year. This session shows open source frameworks built to develop very lightweight microservices, which can be deployed on small devices or in serverless architectures with very low resources and wire together all different kinds of hardware devices, APIs and online services. The focus of this session lies on showing open source projects such as Eclipse Kura, Node-RED or Flogo, which offer a framework plus zero-code environment with web IDE for building and deploying integration and data processing directly onto connected devices using IoT standards such as MQTT, WebSockets or CoaP, but also other interfaces such as Twitter feeds or REST services. The end of the session discusses the relation to other components in a IoT architecture including cloud IoT platforms and big data respectively streaming analytics solutions..