[OracleCode - SF] Distributed caching for your next node.js project
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Caching keeps the data in memory that either slow to calculate or originate from another underlying back-end system. In this presentation, you will see how to leverage Hazelcast as a distributed cache for Node.js applications. Hazelcast is an open source, elastic, self-balancing, self-healing and highly available in-memory computing platform provides fast, reliable access to scalable in-memory data. This hands-on talk will enable you to get started exploring Hazelcast for Node.js and give your project a jump start in speed and scale. In this talk, will be covered: - How distributed caching works in general. - Capabilities of Node.js client. - Real world use cases and best practices.
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Apache Flink Crash Course by Slim Baltagi and Srini Palthepu
In this hands-on Apache Flink presentation, you will learn in a step-by-step tutorial style about:
• How to setup and configure your Apache Flink environment: Local/VM image (on a single machine), cluster (standalone), YARN, cloud (Google Compute Engine, Amazon EMR, ... )?
• How to get familiar with Flink tools (Command-Line Interface, Web Client, JobManager Web Interface, Interactive Scala Shell, Zeppelin notebook)?
• How to run some Apache Flink example programs?
• How to get familiar with Flink's APIs and libraries?
• How to write your Apache Flink code in the IDE (IntelliJ IDEA or Eclipse)?
• How to test and debug your Apache Flink code?
• How to deploy your Apache Flink code in local, in a cluster or in the cloud?
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Tony Gibbs gave a presentation on Amazon Redshift covering its history, architecture, concepts, and parallelism. The presentation included details on Redshift's cluster architecture, node components, storage design, data distribution styles, and terminology. It also provided a deep dive on parallelism in Redshift, explaining how queries are compiled and executed through streams, segments, and steps to enable massively parallel processing across nodes.
Scala Matsuri 2017
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Containerizing Distributed Pipes
Hagen Toennies from Gaikai Inc. presented this deck at the 2017 HPC Advisory Council Stanford Conference.
"In this talk we will present how we enable distributed, Unix style programming using Docker and Apache Kafka. We will show how we can take the famous Unix Pipe Pattern and apply it to a Distributed Computing System. We will demonstrate the development of two simple applications with the focus on "Do One Thing and Do It Well." Afterwards we demonstrate how we make these two programs work to together using Apache Kafka. By encapsulating our applications in containers we will also show how that enables us to go from the limited resources of a development machine to cluster of computers in a data center without changing our applications or containers."
Watch the video: http://wp.me/p3RLHQ-goG
Learn more: http://www.hpcadvisorycouncil.com/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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AWS OpsWorks for Chef Automate provides a fully managed Chef server and suite of automation tools that give you workflow automation for continuous deployment, automated testing for compliance and security, and a user interface that gives you visibility into your nodes and their status. The Chef server gives you full stack automation by handling operational tasks such as software and operating system configurations, package installations, database setups, and more. The Chef server centrally stores your configuration tasks and provides them to each node in your compute environment at any scale, from a few nodes to thousands of nodes. OpsWorks for Chef Automate is completely compatible with tooling and cookbooks from the Chef community and automatically registers new nodes with your Chef server.
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Complex Event Processing with Esper
This document discusses Complex Event Processing (CEP) using Esper. It defines CEP as detecting patterns among events. Esper is an open source CEP engine that provides an SQL-like Event Processing Language (EPL) to define queries over event streams. The document outlines Esper's architecture, features like filtering, windows, aggregation, and joins. It provides examples of EPL queries for topics detection, continuous queries, and pattern matching.
Apex & Geode: In-memory streaming, storage & analytics
Apache Apex and Apache Geode are two of the most promising incubating open source projects. Combined, they promise to fill gaps of existing big data analytics platforms. Apache Apex is an enterprise grade native YARN big data-in-motion platform that unifies stream and batch processing. Apex is highly scalable, performant, fault tolerant, and strong in operability. Apache Geode provides a database-like consistency model, reliable transaction processing and a shared-nothing architecture to maintain very low latency performance with high concurrency processing. We will also look at some use cases where how these two projects can be used together to form distributed, fault tolerant, reliable in memory data processing layer.
Presented at Geode summit - https://2016.event.geodesummit.com/schedule/sessions/apex_geode_in_memory_streaming_storage_analytics.html
Deadly Code! (seriously) Blocking & Hyper Context Switching Pattern
ScalaMatsuri 2017
In Scala, it’s relatively simple to write asynchronous processing, but you might overlook the bigger picture happening on the frameworks (such as Play and Slick).In this session, I will illustrate seemingly elegant code that turns out to bring about total destruction.
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This document discusses using distributed caching for Node.js projects. It describes how caching can improve performance by offloading expensive parts of an application's architecture and enabling scaling out to multiple machines. The document introduces Hazelcast, an open-source in-memory data grid that provides distributed caching and other capabilities. It also promotes following the Twitter account @gamussa for more information and provides a GitHub link for sample code related to using Hazelcast with Cloud Foundry.
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Project Casquatch is a database abstraction layer with code generation designed to streamline Cassandra development. Out of the box it comes pre-tuned with high available policies including load balancing, geo-redundancy, connection pooling, etc., sitting on top of the DataStax driver using native APIs. All of this is abstracted behind the ever prevalent POJO. Instead of writing CQL, we utilize generic programming that allows you to simply pass a generated POJO to a save() method or populate with a getById(). This is the same code reportedly used by T-Mobile for multiple national platforms including the activation of the Apple Watch and Galaxy Watch, T-Mobile Payments, Digits, and many others.
As presented at DataStax Accelerate on May 22, 2019
Video available at https://www.youtube.com/watch?v=8yscE2CmPow&list=PLm-EPIkBI3YpJbuKUGDlZVNHzT0umcBSl&index=45&t=0s
The 2nd half. Scaling to the next^2
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Business Growth Is Fueled By Your Event-Centric Digital Strategy
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3) Examples of Netflix and Amazon successfully leveraging EDA for hyper-personalization to retain customers and increase sales.
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ScyllaDB is a perfect match for Rust. Similar to the Rust programming language and the Tokio framework, ScyllaDB is built on an asynchronous, non-blocking runtime that works extremely well for building highly-reliable low-latency distributed applications.
In this workshop, we’ll build a sample Rust application on our high performance native Rust client driver. By compiling and walking through the code, you’ll learn how to craft queries to a locally running ScyllaDB cluster.
We’ll cover how to:
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If you’re an application developer with an interest in Rust and Tokio, this workshop is for you!
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Intro to Azure SQL database
My presentation slides from #SQLSat712 in Christchurch. I was a last minute ring in speaker, so please forgive any typos.
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Cloud Formation
This document discusses cloud computing platforms and options for .NET development. It explores who created clouds, what they provide, and why developers care about them. It examines challenges with early cloud platforms and recommends AppHarbor as a good option for .NET developers that provides fast instance startup, testability, code quality practices and integration with CI/CD pipelines.
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The presentation will feature demos on how you too can build similar applications.
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Join us for a developer workshop where we’ll go hands-on to explore the affinities between Rust, the Tokio framework, and ScyllaDB. You’ll go live with our sample Rust application, built on our new, high performance native Rust client driver.
Triple Blitz Strike
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2. It describes how to run legacy CGI applications like Smokeping using modern technologies like PSGI and Plack without using Apache.
3. The document expresses that while NoSQL databases have advantages, SQL databases are still more proven, reliable, and scalable for primary data storage in most cases. Problems can occur when NoSQL solutions are used prematurely as the primary database.
Aerospike meetup july 2019 | Big Data Demystified
Building a low latency (sub millisecond), high throughput database that can handle big data AND linearly scale is not easy - but we did it anyway...
In this session we will get to know Aerospike, an enterprise distributed primary key database solution.
- We will do an introduction to Aerospike - basic terms, how it works and why is it widely used in mission critical systems deployments.
- We will understand the 'magic' behind Aerospike ability to handle small, medium and even Petabyte scale data, and still guarantee predictable performance of sub-millisecond latency
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We will also run a demo - showing a live example of the performance and self-healing technologies the database have to offer.
Part 4 Maximizing the utilization of GPU resources on-premise and in the cloud
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This document discusses scheduling GPUs on premise and in the cloud with Grid Engine. It covers challenges in using GPUs with Grid Engine, how applications interact with GPUs, configuring metadata for GPUs in Grid Engine, GPU and CPU binding, managing environments and containers for GPUs, accounting for GPU usage in Grid Engine, and an example workflow for setting everything up. It also previews upcoming improvements in Grid Engine for better support of GPUs like the A100 and MIGs.Similar to [OracleCode - SF] Distributed caching for your next node.js project (20)
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Part 4 Maximizing the utilization of GPU resources on-premise and in the cloud
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More from Viktor Gamov
[DataSciCon] Divide, distribute and conquer stream v. batch
Data is flowing everywhere around us, from phones, credit cards, sensor-equipped buildings, vending machines, thermostats, trains, buses, planes, posts to social media, digital pictures and video and so on....
http://www.datascicon.tech
[Philly JUG] Divide, Distribute and Conquer: Stream v. Batch
This document discusses stream processing versus batch processing. It notes that stream processing involves data that is in motion and processed in real-time using streaming platforms and directed acyclic graphs. Batch processing involves data at rest that is processed through queries on the full dataset. The document also discusses challenges of stream processing like out-of-order and late data, and how windowing can provide a finite view of infinite data streams.
Testing containers with TestContainers @ AJUG 7/18/2017
You never know how your application performs once deployed to the production. Sure, you have unit tests, and your test coverage is sky high. However, you might depend on external resources like databases, web services, and distributed caches. Moreover, without a proper integration testing, you cannot be confident about the stability of your production environment. A long time ago, long before the invention of Docker, configuring the environment for integration testing was incredibly complicated and tedious. Developers and Ops were using fake database implementations, mocking servers, and all kinds of weird tricks to implement the environment without the real environment, but it was ugly, bulky, faulty and not cross-platform as well. Thanks to Docker, those days are long gone, now we can quickly prepare the environment with Docker for our tests.
In this talk, I would like to spread the word about awesome project TestContainers – an open source library that exposes API for JUnit tests. It provides lightweight, disposable instances of shared databases, distributed caches or grids, or anything else that can run in a Docker container, all securely and reliably downloaded from your Docker Hub.
[Philly ETE] Java Puzzlers NG
The more we work with Java 8, the more we go into the rabbit hole. Did they add all those streams, lambdas, monads, Optionals and CompletableFutures only to confuse us? It surely looks so! And Java 9 that heads our way brings even more of what we like the most, more puzzlers, of course! In this season we as usual have a great batch of the best Java WTF, great jokes to present them and great prizes for the winners!
Распределяй и властвуй — 2: Потоки данных наносят ответный удар
Виктор возвращается с продолжением доклада про обработку данных в памяти! На этот раз он противопоставит «потоковый» подход «пакетному». Как мы знаем, информации, зараза, много! Мало того, что её много, так ещё никто не хочет сидеть и ждать, пока Hadoop-кластер посчитает всё. Big Data уже не интересно, Fast Data — наше новое «всё»! Так что же делать, если новые данные всё прибывают и прибывают по каналам связи (через сокет-сервер ли или модную нынче Кафку) ежесекундно (а может, даже и быстрее)? Можно было бы складывать их в какое-нибудь хранилище и считать по ночам MapReduce-ом. Или всё-таки прибегнем к потоковой обработке? У каждого подхода есть свои преимущества и недостатки: как очевидные, так и те, что не всегда лежат на поверхности. После небольшого введения и обзора терминов Виктор на основе примеров кода покажет, как использовать Hazelcast Jet для распределенной обработки потоковых данных.
[JBreak] Блеск И Нищета Распределенных Стримов - 04-04-2017
Вы из тех, кто гонится за производительностью, запихивая в любой цикл Collection.parallelStream()? Задумываетесь, как обойтись без глобальной переделки кода и решить все проблемы с помощью большего количества серверов? Как получить распределённую обработку с линейным ростом производительности? Если подобные вопросы звучат у вас в голове, тогда этот доклад для вас. Оказывается, лишь немногие могут запускать Streams в распределенном режиме — Oracle Coherence, Infinispan, Hazelcast Jet — кто-то за деньги, кто-то бесплатно. На примере Hazelcast Jet Виктор расскажет обо всех деталях распределённой обработки. Кроме того, познакомит слушателей со своим другом Ориентированным Ациклическим Графом и покажет, как с его помощью организована обработка информации поверх API Java 8 Stream. Распределенная! Высокопроизводительная! «В памяти»!
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[DataSciCon] Divide, distribute and conquer stream v. batch
[DataSciCon] Divide, distribute and conquer stream v. batch
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[Philly JUG] Divide, Distribute and Conquer: Stream v. Batch
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Распределяй и властвуй — 2: Потоки данных наносят ответный удар
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[JBreak] Блеск И Нищета Распределенных Стримов - 04-04-2017
[JBreak] Блеск И Нищета Распределенных Стримов - 04-04-2017
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
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June Patch Tuesday
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The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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[OracleCode - SF] Distributed caching for your next node.js project
- 1. Distributed Caching for Your Next Node.js Project
- 2. @gamussa @hazelcast #oraclecode Solutions Architect Developer Advocate @gamussa in internetz Please, follow me on Twitter I’m very interesting © > whoami
- 3. @gamussa @hazelcast #oraclecode Why cache?
- 6. @gamussa @hazelcast #oraclecode Business Application (instance 2) Business Application (instance 1) Service Service Service RDBMS Mainframe MongoDB NoSQL REST
- 8. @gamussa @hazelcast #oraclecode Cache is good for… ◇Performance ◇Offload expensive parts of your architecture ◇Scale up – get the most out of one machine
- 10. @gamussa @hazelcast #oraclecode Cache is good for… ◇Performance ◇Offload expensive parts of your architecture ◇Scale up – get the most out of one machine ◇Scale out – add more capacity with more machines
- 12. @gamussa @hazelcast #oraclecode Cache is good for… ◇Performance ◇Offload expensive parts of your architecture ◇Scale up – get the most out of one machine ◇Scale out – add more capacity with more machines ◇Usually very fast and easy to apply
- 14. @gamussa @hazelcast #oraclecode Polyglot planform @gAmUssA @hazelcast #jfokus #hazelcastjet Problem Data doesn’t fit just one machine
- 17. @gamussa @hazelcast #oraclecode Replication or Sharding? http://book.mixu.net/distsys/single-page.html
- 18. @gamussa @hazelcast #oraclecode Demo time! It’s #oraclecode! Show me some code, yay!
- 20. @gamussa @hazelcast #oraclecode Thanks! Any questions? You can find me at: ◇@gamussa ◇viktor@hazelcast.com