A quick walktrough map-reduce and introduction into the Hazelcast implementation of Google's Whitepaper given at the Hazelcast User Group London on 11th June 2014
How to Use HazelcastMQ for Flexible Messaging and MoreHazelcast
HazelcastMQ provides a simple messaging layer on top of the basic Queue and Topic data structures provided by Hazelcast. HazelcastMQ emphasizes simple configuration and reliable clustering while providing an understandable and flexible messaging API. Building on the core features of Hazelcast such as scalability and resilience, HazelcastMQ maintains a small footprint and few dependencies.
Highly concurrent event-driven approaches, addressed by the Reactive Manifesto, have been common for years. A modern and lightweight application platform like Vert-x allows developers to take advantage of these approaches in a clean, comprehensible and extensible way. In this talk you will hear an overview of the principles, motivations and the key features of this framework.
Real World Enterprise Reactive Programming using Vert.xMariam Hakobyan
The presentation is about real world and production ready example in reactive programming area, using Vert.x. It shows the best practices, event driven application architecture on the cloud and lessons learned.
How to Use HazelcastMQ for Flexible Messaging and MoreHazelcast
HazelcastMQ provides a simple messaging layer on top of the basic Queue and Topic data structures provided by Hazelcast. HazelcastMQ emphasizes simple configuration and reliable clustering while providing an understandable and flexible messaging API. Building on the core features of Hazelcast such as scalability and resilience, HazelcastMQ maintains a small footprint and few dependencies.
Highly concurrent event-driven approaches, addressed by the Reactive Manifesto, have been common for years. A modern and lightweight application platform like Vert-x allows developers to take advantage of these approaches in a clean, comprehensible and extensible way. In this talk you will hear an overview of the principles, motivations and the key features of this framework.
Real World Enterprise Reactive Programming using Vert.xMariam Hakobyan
The presentation is about real world and production ready example in reactive programming area, using Vert.x. It shows the best practices, event driven application architecture on the cloud and lessons learned.
Our previous talk "Intro to Reactive Programming" defined reactive programming and provided details around key initiatives such as Reactive Streams and ReactiveX. In this talk we'll focus on where we are today with building reactive web applications. We'll take a look at the choice of runtimes, how Reactive Streams may be applied to network I/O, and what the programming model may look like. While this is a forward looking talk, we'll spend plenty of time demoing code built with with back-pressure ready libraries available today.
Micro services, reactive manifesto and 12-factorsDejan Glozic
Learn how micro-services, Reactive Manifesto and 12-factors answer us the 'What, Why and How' questions of creating modern distributed systems. One concept, four tenets, twelve factors - rules to live by in cloud.
This is your one stop shop introduction to get oriented to the world of reactive programming. There are lots of such intros out there even manifestos. We hope this is the one where you don't get lost and it makes sense. Get a definition of what "reactive" means and why it matters. Learn about Reactive Streams and Reactive Extensions and the emerging ecosystem around them. Get a sense for what going reactive means for the programming model. See lots of hands-on demos introducing the basic concepts in composition libraries using RxJava and Reactor.
Navigating through a web application can be a nagging experience, especially when you have a limited data plan. When the data speed is sluggish, it is very agonizing to wait for the page to load.
Distributed Computing in Hazelcast - Geekout 2014 EditionChristoph Engelbert
Today’s amounts of collected data are showing a nearly exponential growth. More than 75% of all the data have been collected in the past 5 years. To store this data and process it in an appropriate time you need to partition the data and parallelize the processing of reports and analytics.
This talk will demonstrate how to parallelize data processing using Hazelcast and it’s underlying distributed data structures. With a quick introduction into the different terms and some short live coding examples we will make the journey into the distributed computing.
Sourcecode of the demonstrations are available here:
1. https://github.com/noctarius/hazelcast-mapreduce-presentation
2. https://github.com/noctarius/hazelcast-distributed-computing
Today higher heaps and bigger RAM amounts are typical for standard Java server applications. We often then get to the limit of performance, predictability or GC pause times and using Off-Heap technologies we can lower that pains.
Today’s amounts of collected data are showing nearly exponential growth. More than 75 percent of all collected data has been collected in the past five years. To store that data and process it within an appropriate time, you need to partition the data and parallelize the processing of reports and analytics. This session demonstrates how to quickly and easily parallelize data processing with Hazelcast and its underlying distributed data structures. By giving a few quick introductions to different terms and some short live coding sessions, the presentation takes you on a journey through distributed computing.
[OracleCode SF] In memory analytics with apache spark and hazelcastViktor Gamov
Apache Spark is a distributed computation framework optimized to work in-memory, and heavily influenced by concepts from functional programming languages.
Hazelcast - open source in-memory data grid capable of amazing feats of scale - provides wide range of distributed computing primitives computation, including ExecutorService, M/R and Aggregations frameworks.
The nature of data exploration and analysis requires data scientists be able to ask questions that weren't planned to be asked—and get an answer fast!
In this talk, Viktor will explore Spark and see how it works together with Hazelcast to provide a robust in-memory open-source big data analytics solution!
Java 8 introduced the Stream API as a modern, functional, and very powerful tool for processing collections of data. One of the main benefits of the Stream API is that it hides the details of iteration over the underlying data set, allowing for parallel processing within a single JVM, using a fork/join framework. I will talk about a Stream API implementation that enables parallel processing across many machines and many JVMs. With an explanation of internals of the implementation, I will give an introduction to the general design behind stream processing using DAG (directed acyclic graph) engines and how an actor-based implementation can provide in-memory performance while still leveraging industry-wide known frameworks as Java Streams API.
https://www.jfokus.se/jfokus/talks.jsp#RidingtheJetStreams
Our previous talk "Intro to Reactive Programming" defined reactive programming and provided details around key initiatives such as Reactive Streams and ReactiveX. In this talk we'll focus on where we are today with building reactive web applications. We'll take a look at the choice of runtimes, how Reactive Streams may be applied to network I/O, and what the programming model may look like. While this is a forward looking talk, we'll spend plenty of time demoing code built with with back-pressure ready libraries available today.
Micro services, reactive manifesto and 12-factorsDejan Glozic
Learn how micro-services, Reactive Manifesto and 12-factors answer us the 'What, Why and How' questions of creating modern distributed systems. One concept, four tenets, twelve factors - rules to live by in cloud.
This is your one stop shop introduction to get oriented to the world of reactive programming. There are lots of such intros out there even manifestos. We hope this is the one where you don't get lost and it makes sense. Get a definition of what "reactive" means and why it matters. Learn about Reactive Streams and Reactive Extensions and the emerging ecosystem around them. Get a sense for what going reactive means for the programming model. See lots of hands-on demos introducing the basic concepts in composition libraries using RxJava and Reactor.
Navigating through a web application can be a nagging experience, especially when you have a limited data plan. When the data speed is sluggish, it is very agonizing to wait for the page to load.
Distributed Computing in Hazelcast - Geekout 2014 EditionChristoph Engelbert
Today’s amounts of collected data are showing a nearly exponential growth. More than 75% of all the data have been collected in the past 5 years. To store this data and process it in an appropriate time you need to partition the data and parallelize the processing of reports and analytics.
This talk will demonstrate how to parallelize data processing using Hazelcast and it’s underlying distributed data structures. With a quick introduction into the different terms and some short live coding examples we will make the journey into the distributed computing.
Sourcecode of the demonstrations are available here:
1. https://github.com/noctarius/hazelcast-mapreduce-presentation
2. https://github.com/noctarius/hazelcast-distributed-computing
Today higher heaps and bigger RAM amounts are typical for standard Java server applications. We often then get to the limit of performance, predictability or GC pause times and using Off-Heap technologies we can lower that pains.
Today’s amounts of collected data are showing nearly exponential growth. More than 75 percent of all collected data has been collected in the past five years. To store that data and process it within an appropriate time, you need to partition the data and parallelize the processing of reports and analytics. This session demonstrates how to quickly and easily parallelize data processing with Hazelcast and its underlying distributed data structures. By giving a few quick introductions to different terms and some short live coding sessions, the presentation takes you on a journey through distributed computing.
[OracleCode SF] In memory analytics with apache spark and hazelcastViktor Gamov
Apache Spark is a distributed computation framework optimized to work in-memory, and heavily influenced by concepts from functional programming languages.
Hazelcast - open source in-memory data grid capable of amazing feats of scale - provides wide range of distributed computing primitives computation, including ExecutorService, M/R and Aggregations frameworks.
The nature of data exploration and analysis requires data scientists be able to ask questions that weren't planned to be asked—and get an answer fast!
In this talk, Viktor will explore Spark and see how it works together with Hazelcast to provide a robust in-memory open-source big data analytics solution!
Java 8 introduced the Stream API as a modern, functional, and very powerful tool for processing collections of data. One of the main benefits of the Stream API is that it hides the details of iteration over the underlying data set, allowing for parallel processing within a single JVM, using a fork/join framework. I will talk about a Stream API implementation that enables parallel processing across many machines and many JVMs. With an explanation of internals of the implementation, I will give an introduction to the general design behind stream processing using DAG (directed acyclic graph) engines and how an actor-based implementation can provide in-memory performance while still leveraging industry-wide known frameworks as Java Streams API.
https://www.jfokus.se/jfokus/talks.jsp#RidingtheJetStreams
Performance Optimization Case Study: Shattering Hadoop's Sort Record with Spa...Databricks
Performance Optimization Case Study: Shattering Hadoop's Sort Record with Spark and Scala
Talk given by Reynold Xin at Scala Days SF 2015
In this talk, Reynold talks about the underlying techniques used to achieve high performance sorting using Spark and Scala, among which are sun.misc.Unsafe, exploiting cache locality, high-level resource pipelining.
Ultra Fast Deep Learning in Hybrid Cloud using Intel Analytics Zoo & AlluxioAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Ultra Fast Deep Learning in Hybrid Cloud using Intel Analytics Zoo & Alluxio
Jennie Wang, Software Engineer (Intel)
Tsai Louie, Software Engineer (Intel)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
JavaFest. Grzegorz Piwowarek. Hazelcast - Hitchhiker’s GuideFestGroup
Most of you probably heard about the distributed caching being the most common application of Hazelcast - but I bet not many of you heard about CRDT, HyperLogLog, or CP Subsystem.
During this talk, we'll go for quick journey around Hazelcast's ecosystem, revise basic functionality and have a look at some of the hidden flavours.
Even if that’s way too much for your use case, there’s still a lot for an engineer to learn from studying these concepts.
Cassandra Summit 2014: Interactive OLAP Queries using Apache Cassandra and SparkDataStax Academy
Presenter: Evan Chan, Principal Software Engineer at Socrata Inc.
How do you rapidly derive complex insights on top of really big data sets in Cassandra? This session draws upon Evan's experience building a distributed, interactive, columnar query engine on top of Cassandra and Spark. We will start by surveying the existing query landscape of Cassandra and discuss ways to integrate Cassandra and Spark. We will dive into the design and architecture of a fast, column-oriented query architecture for Spark, and why columnar stores are so advantageous for OLAP workloads. I will present a schema for Parquet-like storage of analytical datasets on Cassandra. Find out why Cassandra and Spark are the perfect match for enabling fast, scalable, complex querying and storage of big analytical data.
AWS re:Invent 2016: How Mapbox Uses the AWS Edge to Deliver Fast Maps for Mob...Amazon Web Services
Ian Ward, Platform and Security Engineer from Mapbox, discusses how the AWS global edge network helps improve the availability and performance of delivering hundreds of billions of map tiles to hundreds of millions of end users across the globe on mobile devices, in cars, and over the web. In this session, Ian shares insights on how Mapbox manages day-to-day edge operations using Amazon CloudFront logs, dashboards, and ad hoc queries, and how Mapbox has configured CloudFront with dozens of behaviors and origins to customize their content delivery. Mapbox has grown from using a single AWS region to using several regions, so Ian also explains how his team uses Amazon Route 53 and open source tools to simplify complexity around regional failover, and how Mapbox leverages AWS WAF to deter attacks and abuse.
How do you rapidly derive complex insights on top of really big data sets in Cassandra? This session draws upon Evan's experience building a distributed, interactive, columnar query engine on top of Cassandra and Spark. We will start by surveying the existing query landscape of Cassandra and discuss ways to integrate Cassandra and Spark. We will dive into the design and architecture of a fast, column-oriented query architecture for Spark, and why columnar stores are so advantageous for OLAP workloads. I will present a schema for Parquet-like storage of analytical datasets onCassandra. Find out why Cassandra and Spark are the perfect match for enabling fast, scalable, complex querying and storage of big analytical data.
Spring One 2 GX 2014 - CACHING WITH SPRING: ADVANCED TOPICS AND BEST PRACTICESMichael Plöd
Caching is relevant for a wide range of business applications and there is a huge variety of products in the market ranging from easy to adopt local heap based caches to powerful distributed data grids. This talk addresses advanced usage of Spring’s caching abstraction such as integrating a cache provider that is not integrated by the default Spring Package. In addition to that I will also give an overview of the JCache Specification and it’s adoption in the Spring ecosystem. Finally the presentation will also address various best practices for integrating various caching solutions into enterprise grade applications that don’t have the luxury of having „eventual consistency“ as a non-functional requirement.
Distributed deep learning with spark on AWS - Vincent Van Steenbergen @ PAPIs...PAPIs.io
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. In this talk we'll show how to use an AWS Spark cluster to train a model quickly from a laptop at a very little cost (around 10€).
Vincent Van Steenbergen is a freelance (big) data engineer who's working on a range of international projects, implementing systems able to handle terabytes of data, usually involving Spark, Scala, Kafka, Hadoop and Cassandra. His main interest right now is applying these techniques to solve machine learning problems. Vincent was previously a technical architect at Property. Works, a real estate startup in London and before that an R&D engineer at IDAaaS.
So, you have heard about in-memory computing but did not have time to deep dive into it nor had a chance to get acquainted with it in a commercial project? This session will try to identify the biggest challenges that are awaiting us in the world of distributed data processing. On the example of Hazelcast and its most popular features we will try to tackle the above-mentioned challenges right away. We will also have a closer look at the data structure offered by Hazelcast, their programming models and learn how to properly use them in our daily work. Finally, we will deep dive into caching. Don't miss this talk mate!
For the last two decades, the amount of data we store, process, and analyze is ever growing. The last decade shows a higher focus on immediate feedback loop data pipeline, using technologies such as Complex Event Processing (CEP), Stream Processing, and Change Data Capture (CDC). Services such as Kafka or NATS are to be found in almost every new system (at least to some extent).
To build a data pipeline, the number of technologies, frameworks, and platforms are endless. Getting the initial grasp of it all is much harder than expected, but together we can tackle it!
Messages sind heutzutage überall. Egal ob JavaScript Frontends in Form von Events, oder Backends mit Kafka oder NATS Message Queues, wir wollen zwei Ziele erreichen, Separation of Concerns (unabhängige Einheiten) und Skalierbarkeit (oder in Frontends Freigabe von Resourcen).
Da heute alles Responsive sein muss, brauchen wir Event-basierte Systeme. Also lasst uns gemeinsam die darunterliegenden Systeme erforschen, verstehen und Einsatzbereiche erarbeiten.
Farms are simple. A farm, a building or two, maybe a barn. Done. You’d wish.
Monitoring farms and barns is a tedious task. No farm looks like the other and water distribution, next to other elements, has grown generically. A little bit like the good old legacy systems we all love. With the additional complication of keeping track of topology changes, typical building automation systems are out of the scope.
See how clevabit integrated neo4j, PostgreSQL and TimescaleDB to bring observability to farms and what I learned along the way. And there were a lot of “this time it works” moments.
What I learned about IoT Security ... and why it's so hard!Christoph Engelbert
Smart devices taking over our living rooms, our bed rooms, and, in general, our life. It has never been more important to build secure devices, but most companies seem to fail, and they fail hard. We (only) build systems for farms and barns, and still, I wanted security for Cow-stumers.
Building a mostly secure system is fairly simple. There is a good set of low-hanging fruits. Building a really locked down system is tough, though. Much harder than expected. Here is what I learned.
Time-series data, or data being associated with its respective time of occurrence, is everywhere. From the obvious cases, such as metrics, observability, IoT data, all the way to logs, invoicing, or payment records. While storing some of these in relational databases is standard practice, people often reach for specific time-series databases when volume gets high. But imagine if you could have all of them in the same database: PostgreSQL.
With Instana the "Classic" Observability is not the end of the line. Find out what Observability means and how it can help DevOps, Developers, SREs day-by-day.
Building, deploying and operating application systems for high scale and failure tolerance is the supreme field of software engineering. While Continuous Integration (CI) and oftentimes also Continuous Delivery (CD) have become a part of commonly used build pipelines, monitoring and observability is still often an afterthought or manually configured. To keep up with containers being started and stopped for version upgrades, scaling up and down or to mitigate failure situations, monitoring needs to automate all the tasks to react to infrastructure changes and find issues before users being impacted. People today expect “Oops-Less Operation”, or do you want your bank to be offline?
Continuous Integration, Continuous Delivery, Continuous Monitoring!
These days CI and CD are commonly used mechanics to achieve fast turn-around times for high-demand applications. Microservices architectures and highly dynamic envrionments (based on Kubernetes, Docker, …), however, come with a whole different set of problems.
Systems, that not only appear and disappear dynamically (e.g. autoscaling), but most commonly tend to be written using multiple different programming languages, are hard to monitor from the point of view that matters: User Requests and User Experience. but the answer is simple; Continuous Monitoring (CM).
Let's build a polyglot microservices infrastructure. A way to monitor and trace multi-service requests will be demonstrated using Instana’s automatic discovery system.
As we all know Java is the best language in the world, except there is Go. Go is just so much more, isn’t it? The syntax is so concise and meaningful, the compiler is so much more helpful and the rules are all over it.
We will uncovering the bitter truth, the 5 reasons, that every Java developer should know about Go. We’ll present why Go is just the better programming language and why the hype around Go is all real.
Let your eyes be to opened and your brain to explode. Sarcasm included.
Everyone knows there isn't just one way of doing things. This is also true for web-administrated Embedded Devices and a lot of different ways to attack the implementation were taken before the combination of Golang and Typescript manifested. Plenty of the tries started by missing knowledge, inability, the hate of some programming languages or just plainly on size requirements. Over Java and C/C++ to Go+Lua, Go+JavaScript and the final decision on Go and Typescript, we follow the adventure of an embedded framework and the arising problems. Pros and Cons but also the feeling for a Java developer and new horizons are given.
JSON, by now, became a regular part of most applications and services. Do we, how ever, really want to transfer human readable information or are we looking for a binary protocol to be as debuggable as JSON? CBOR the Concise Binary Object Representation offers the best of JSON + an extremely efficient, binary representation.
http://www.cbor.io
The days of JNI is counted, Project Panama is on the rise to tear down the walls between Java and C/C++ forever. FFI (Foreign Function Interface) technology finally arrives into the Java world.
The way from monolithic to micro service architectures can hard. Overall micro services are not the all holy grail to just solve all your issues. You need to be aware that you need the right developers and the right toolset. Oh and not to forget, moving state to authorization systems doesn't mean your application is really stateless :)
Anyhow micro services are a great architecture and this deck is a short introduction on why we need to change our application architectures and what pitfalls you you have when introducing the idea of micro services.
The future of Java is insight with Java 9 around the corner. Last year's discussions around the removal from sun.misc.Unsafe and the eventually presented compromise is history. Time to start looking forward to some details from what's coming, especially in terms of the Unsafe API replacement.
Reaching critical masses with your application systems becomes harder every day. Caching helps to provide low latency and high availability over slow calculation, networks, databases and any other kind of external resource.
In-Memory Computing - Distributed Systems - Devoxx UK 2015Christoph Engelbert
Today’s amounts of collected data are showing a nearly exponential growth. More than 75% of all the data have been collected in the past 5 years. To store this data and process it in an appropriate time you need to partition the data and parallelize the processing of reports and analytics. This talk will demonstrate how to parallelize data processing using Hazelcast and it’s underlying distributed data structures. With a quick introduction into the different terms and some short live coding examples we will make the journey into the distributed computing.
JCache - Caching Introduction - What is the idea, where are we coming from and where we want to go in the future. Why we need caching and why do we want to cache?
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Map Reduce in Hazelcast - Hazelcast User Group London Version
1. BIG DATA - FAST DATA
USING MAPREDUCE IN HAZELCAST
Source:
www.hazelcast.com
2. Christoph Engelbert(@noctarius2k)
8+ years of JavaWeirdoness
Performance, GC, traffic topics
Apache Committer
Gaming, TravelManagement, ...
CastMapRMapReduce for Hazelcast3
www.hazelcast.com
24. Dataare mapped /transformed in asetof key-value pairs
SOME PSEUDO CODE (1/3)
MAPPING
map(key:String,document:String):Void->
foreachw:Wordindocument:
emit(w,1)
www.hazelcast.com
25. Multiple values are combined to an
intermediate resultto preserve traffic
SOME PSEUDO CODE (2/3)
COMBINING
combine(word:Word,counts:List[Int]):Void->
emit(word,sum(counts))
www.hazelcast.com
26. Values are reduced /aggregated to the requested result
SOME PSEUDO CODE (3/3)
REDUCING
reduce(word:String,counts:List[Int]):Int->
returnsum(counts)
www.hazelcast.com