Extreme Network Performance with Hazelcast on Torusware
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How can you get: * Extreme Database Performance * Extreme In-Memory Transaction Processing * Extreme Messaging Throughput Without throwing money at expensive Infiniband, Exadata or Exalogic Elastic Cloud hardware? In-Memory Data Grid is a proven way to get Elastically Scalable In-Memory compute performance, and unlike Oracle Coherence and other solutions, Hazelcast is able to achieve extreme scale performance on standard (COTS) commodity scale-out hardware and JVMs, without resorting to custom protocols and proprietary hardware such as Exadata. Hazelcast can achieve significant performance boosts by using Torusware, the high-speed middleware by TORUS Software Solutions, a true plug & play product which implements multiple optimizations (optimized NIO sockets epoll, TCP/IP bypass, 10/40 Gbps ROCE, InfiniBand plus RDMA native support). Thus, Torusware improves Hazelcast extreme scalability by running on standard hardware with virtually zero communication overhead. We’ll cover these topics: *How Hazelcast and Torusware are 100% compatible *An overview of performance results of Hazelcast+Torusware *How Torusware helps Hazelcast to take full advantage of the underlying infrastructure. *Live Q&A Presenter: Guillermo Lopez Taboada, TORUS CEO and Co-founder *Inventor of Torus Technology, fast communications on low latency networks and servers. *12 years experience in HPC and Big Data within academia and now at TORUS working in projects for Hewlett-Packard, Amazon WS, NASA, ESA, and the financial, telco and IT industries. *PhD and BS in Computer Science
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Dhabaleswar K (DK) Panda, Professor and University Distinguished Scholar, The Ohio State University
Xiaoyi Lu, Research Scientist, The Ohio State University
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Hazelcast 3.6 Roadmap Preview
In this webinar
Hazelcast has pushed the In-Memory Data Grid category further by adding High-Density Caching and making great strides in performance – but what’s next? In this talk Hazelcast CEO Greg Luck will explain the direction of the Hazelcast platform in detail. He’ll share what’s planned for features of High-Density Caching as well for the In-Memory Computing platform at large in the areas of PaaS, IaaS, extensions and integrations along with a detailed list of features planned for 3.6.
We’ll cover these topics:
-High-level platform roadmap
-Detailed list of features planned for 3.6
-Live Q&A
Presenter: Greg Luck, CEO at Hazelcast
Greg has worked with Java for 15 years. He is spec lead of the recently completed JSR107: JCache and the founder of Ehcache. He is a JCP Executive Committee alumni. Prior to Hazelcast, Greg was CTO at Terracotta, Inc which was acquired by Software AG. He was also Chief Architect at Australian travel startup Wotif.com which went to IPO. Earlier roles include consultant at ThoughtWorks and KPMG, and CIO at Virgin Blue, Tempo Services, Stamford Hotels and Resorts, and Australian Resorts. Greg has a Master’s degree in Information Technology from QUT and a Bachelor of Commerce from the University of Queensland.
Time to Make the Move to In-Memory Data Grids
In this webinar
DRAM is dirt cheap. That’s why in-memory databases, analytics, and data grids are surging in popularity among firms that have an insatiable need for performance and scalability. But, databases, analytics platforms, and data grids target very different use cases. In-memory data grids, in particular, are often misunderstood because they support an extensive set of use cases that often overlap other technologies. Join guest speaker Mike Gualtieri, Principal Analyst at Forrester Research, Greg Luck, CEO of Hazelcast, and Ken Kolda, Software Architect of Ellie Mae on this radio-show style webinar to boost your in-memory IQ.
We’ll cover these topics:
- How in-memory data grid differs from other “in-memory” technologies
- The use cases best suited to in-memory data grids
- Real-world Hazelcast customer implementation story
The Power of the JVM: Applied Polyglot Projects with Java and JavaScript
In this webinar
JavaScript is a powerful, flexible, and dynamically typed language. JVM is proven to be reliable and stable production platform. The power of the JVM is an ability to run programs written in different languages, including JavaScript. You can short the development cycle by leveraging a scripting abilities of JavaScript. This presentation is about JVM’s scripting capabilities that allow to mix Java and JavaScript in the same application.
In this session you’ll get introduced to the latest state of the polyglot frameworks that use JavaScript and Java side-by-side.
We’ll cover these topics:
-The JDK8’s Nashorn benefits for the server and the client side developers
-Using JavaScript and NodeJS applications and libraries on the Java platform
-Real-world projects overview that leveraging on Java-JavaScript interactions
-Live Q&A
Presenter:
Viktor Gamov, Senior Solutions Architect at Hazelcast
Viktor joined Hazelcast with over 5 years experience of architecting and building the enterprise applications using open source technologies. At his previous roles, he helped the financial companies and startups with various Java and HTML5 projects. He holds MS in Computer Science. He is a co-author of the O’Reilly book «Enterprise Web Development. From Desktop To Mobile». Viktor presented at various international conferences (http://lanyrd.com/gamussa) on Java and JavaScript related topics. He tweets at @gamussa.
JCache - It's finally here
This document provides an overview of JCache (JSR107), the Java standard for caching. It discusses why caching is used to improve performance, introduces the key concepts and classes in JCache, and covers how to use caches, entry processors, listeners, loaders/writers, and annotations with JCache. It also discusses future directions for JCache like asynchronous APIs and integration with Java 8 features.
Speed Up Your Existing Relational Databases with Hazelcast and Speedment
In this webinar
How do you get data from your existing relational databases changed by third party applications into your Hazelcast maps? How do you accomplish this if you have several databases, located on different sites, that need to be aggregated into a global Hazelcast map? How is it possible to reflect data from a relational database that has ten thousand updates per second or more?
Speedment’s SQL Reflector makes it possible to integrate your existing relational data with continuous updates of Hazelcast data-maps in real-time. In this webinar, we will show a couple of real-world cases where database applications are speeded up using Hazelcast maps fed by Speedment. We will also demonstrate how easily your existing database can be “reverse engineered” by the Speedment software that automatically creates efficient Java POJOs that can be used directly by Hazelcast.
We’ll cover these topics:
-Joint solution case studies
-Demo
-Live Q&A
Presenter:
Per-Åke Minborg, CTO at Speedment
Per-Åke Minborg is founder and CTO at Speedment AB. He is a passionate Java developer, dedicated to OpenSource software and an expert in finding new ways of solving problems – the harder problem the better. As a result, he has 15+ US patent applications and invention disclosures. He has a deep understanding of in-memory databases, high-performance solutions, cloud technologies and concurrent programming. He has previously served as CTO and founder of Chilirec and the Phone Pages. Per-Åke has a M.Sc. in Electrical Engineering from Chalmers University of Technology and several years of studies in computer science and computer security at university and PhD level.
Shared Memory Performance: Beyond TCP/IP with Ben Cotton, JPMorgan
- OpenHFT provides solutions for improving Java data locality and inter-process communication (IPC) transport, enabling ultra-low latency real-time Java deployments.
- It includes Chronicle Map, an off-heap concurrent map that avoids garbage collection pauses compared to on-heap maps. It also provides faster IPC than UDP/TCP via shared memory.
- Tests show Chronicle Map accessed via shared memory IPC can be over 1000x faster than Red Hat Infinispan accessed via UDP for a distributed cache workload.
Applying Real-time SQL Changes in your Hazelcast Data Grid
This document discusses using Speedment and Hazelcast to automatically scale out existing SQL databases. Speedment monitors databases for changes and reflects those changes in distributed Hazelcast maps in real-time. It generates code to represent database tables as Java POJOs, managers to interact with the POJOs, and configuration to integrate everything with Hazelcast. Users can then access and modify database data using the Hazelcast maps without changing their code, achieving automatic horizontal scaling of the SQL data. The process involves connecting an existing database, running Speedment to generate the associated code, and adding a few lines to integrate with an existing Hazelcast instance.
WAN Replication: Hazelcast Enterprise Lightning Talk
There are cases where you need to synchronise multiple clusters to the same state. Synchronisation of clusters, also known as WAN (Wide Area Network) Replication, it is mainly used for replicating data of different clusters over WAN environments like the Internet. In this module we will demonstrate WAN Replication in action and how to configure it.
JAAS Security Suite: Hazelcast Enterprise Lightning Talk
In this webinar
Hazelcast has an extensible, JAAS based security feature which can be used to authenticate both cluster members and clients and to perform access control checks on client operations. Access control can be done according to endpoint principal and/or endpoint address. In this module you’ll learn how to connect Hazelcast to a third party authentication provider.
Hazelcast for Terracotta Users
In this webinar we will compare the complexities involved in Terracotta with the code/configuration changes to migrate to Hazelcast. You will learn about important features of Hazelcast such as IMDG capabilities, off-heap data storage, distributed collections, etc. and the feature-rich product portfolio of Hazelcast. We will cover how Hazelcast can scale up and out dynamically and without downtime against the static configuration of Terracotta. Expect to leave the webinar being more educated about Hazelcast in terms of architecture, important features and best practices.
We’ll cover these topics:
- Hazelcast architecture and features
- Terracotta distributed architecture
- Scale – Vertical + Horizontal = Showcase no downtime feature in Hazelcast
- BigMemory vs. HDC
- Ease of installation – two jars against multiple jars
- Config and Code changes – cache vs. maps, off-heap vs. HDC
- Portability of Client APIs – IMap, IQueue, Topics, etc.
- Added functionalities – Showcase IExecutorService, EntryProcessors, Multimap, etc.
- DSO – Showcase EntryProcessors taking place of DSO
- Live Q&A
Presenter:
Rahul Gupta, Senior Solutions Architect
Rahul is a technology-driven professional with 12+ years of experience in building and architecting highly scalable and concurrent, low latency business critical distributed infrastructure. His expertise lies in Big Data and Real Time Analytics space where he specializes in big data governing technologies and Enterprise Architecture. Rahul is an expert in working with decision makers across different business verticals within an organization and guiding them in right decision making through in-depth technical understanding, analysis and evaluation procedures to bring home critical deals with high business values.
Big Data, Simple and Fast: Addressing the Shortcomings of Hadoop
In this webinar
This talk identifies several shortcomings of Apache Hadoop and presents an alternative approach for building simple and flexible Big Data software stacks quickly, based on next generation computing paradigms, such as in-memory data/compute grids. The focus of the talk is on software architectures, but several code examples using Hazelcast will be provided to illustrate the concepts discussed.
We’ll cover these topics:
-Briefly explain why Hadoop is not a universal, or inexpensive, Big Data solution – despite the hype
-Lay out technical requirements for a flexible Big/Fast Data processing stack
-Present solutions thought to be alternatives to Hadoop
-Argue why In-Memory Data/Compute Grids are so attractive in creating future-proof Big/Fast Data applications
-Discuss how well Hazelcast meets the Big/Fast Data requirements vs Hadoop
-Present several code examples using Java and Hazelcast to illustrate concepts discussed
-Live Q&A Session
Presenter:
Jacek Kruszelnicki, President of Numatica Corporation
JAXLondon - Squeezing Performance of IMDGs
The document discusses various techniques for optimizing performance when working with in-memory data grids (IMDGs). It begins with an overview of IMDGs and how they partition and distribute data across multiple servers. It then walks through a shopping cart use case and compares the performance of different approaches like locking, transactions, compare and swap, moving operations closer to the data using entry processors. Overall it provides solutions for squeezing maximum performance out of IMDGs.
OrientDB & Hazelcast: In-Memory Distributed Graph Database
OrientDB uses Hazelcast to achieve a distributed configuration with multi-master replication. By using these together you can scale up horizontally by adding new servers without stopping or reconfigure the cluster. In this webinar, you’ll be introduced to OrientDB and how it compares to other NoSQL DBMS. You will also learn how and why Hazelcast is being used with OrientDB to achieve scale, elasticity and performance.
Both Hazelcast and Orient Technologies are providing professional open source support to their respective projects under the Apache software license.
How to Use HazelcastMQ for Flexible Messaging and More
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.
Devoxx UK 2014 High Performance In-Memory Java with Open Source
This document discusses Hazelcast, an in-memory data grid (IMDG) solution. It provides an overview of Hazelcast capabilities like distributed caching, computing using executors and entry processors, and integration with external data stores. It also gives advice on when to use an IMDG and tips for coding with Hazelcast.
JSR107 State of the Union JavaOne 2013
The document discusses Java caching (JCache), which is an effort to standardize caching for the Java platform. It describes JCache's key features like its ConcurrentMap-like API, atomic operations, and event listeners. It provides examples of using the JCache API to get, put, and invoke operations on cache entries. It also discusses cache managers, entry processors, and caching annotations. It announces that Oracle Coherence will fully support the JCache specification in 2014.
Jfokus - Hazlecast
Peter Veentjer is a senior developer and solution architect at Hazelcast who has 14 years of Java experience. He discusses what Hazelcast is, which is a leading open source in-memory data grid that simplifies building scalable and highly available distributed systems. Hazelcast provides various distributed data structures like maps, queues, topics and more. Companies from various industries use Hazelcast for applications involving caching, messaging, event processing and more.
In-memory No SQL- GIDS2014
This document provides an overview of Hazelcast, an open source in-memory data grid platform. It discusses why organizations are adopting NoSQL solutions like Hazelcast to overcome impedance mismatch between objects and relational databases. It provides details on Hazelcast's features, uses cases, customers, and roadmap. Hazelcast provides a distributed in-memory map for fast data access, clustering for high availability, and can be used for caching, session replication, and more.
In-memory Data Management Trends & Techniques
- Hardware trends like increasing cores/CPU and RAM sizes enable in-memory data management techniques. Commodity servers can now support terabytes of memory.
- Different levels of data storage have vastly different access times, from registers (<1ns) to disk (4-7ms). Caching data in faster levels of storage improves performance.
- Techniques to exploit data locality, cache hierarchies, tiered storage, parallelism and in-situ processing can help overcome hardware limitations and achieve fast, real-time processing. Emerging in-memory databases use these techniques to enable new types of operational analytics.
How to Speed up your Database
This document discusses how to use Hazelcast to speed up database queries and operations. It introduces Hazelcast as an in-memory data grid that provides horizontal scalability. The author then explains how Hazelcast can be used to cache database queries for faster reads by integrating a MapStore. Updates and inserts can also be made faster using Hazelcast's write-behind functionality, which stores records asynchronously and writes to the database in the background. The document walks through integrating Hazelcast into an application in 4 steps and includes examples of querying a Hazelcast map and performing write operations.
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The Power of the JVM: Applied Polyglot Projects with Java and JavaScript
The Power of the JVM: Applied Polyglot Projects with Java and JavaScript
Speed Up Your Existing Relational Databases with Hazelcast and Speedment
Speed Up Your Existing Relational Databases with Hazelcast and Speedment
Shared Memory Performance: Beyond TCP/IP with Ben Cotton, JPMorgan
Shared Memory Performance: Beyond TCP/IP with Ben Cotton, JPMorgan
Applying Real-time SQL Changes in your Hazelcast Data Grid
Applying Real-time SQL Changes in your Hazelcast Data Grid
WAN Replication: Hazelcast Enterprise Lightning Talk
WAN Replication: Hazelcast Enterprise Lightning Talk
JAAS Security Suite: Hazelcast Enterprise Lightning Talk
JAAS Security Suite: Hazelcast Enterprise Lightning Talk
Big Data, Simple and Fast: Addressing the Shortcomings of Hadoop
Big Data, Simple and Fast: Addressing the Shortcomings of Hadoop
OrientDB & Hazelcast: In-Memory Distributed Graph Database
OrientDB & Hazelcast: In-Memory Distributed Graph Database
How to Use HazelcastMQ for Flexible Messaging and More
How to Use HazelcastMQ for Flexible Messaging and More
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6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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What’s New in Odoo 17 – A Complete Roadmap
Odoo releases a new update every year. The latest version, Odoo 17, came out in October 2023. It brought many improvements to the user interface and user experience, along with new features in modules like accounting, marketing, manufacturing, websites, and more.
The Odoo 17 update has been a hot topic among startups, mid-sized businesses, large enterprises, and Odoo developers aiming to grow their businesses. Since it is now already the first quarter of 2024, you must have a clear idea of what Odoo 17 entails and what it can offer your business if you are still not aware of it.
This blog covers the features and functionalities. Explore the entire blog and get in touch with expert Odoo ERP consultants to leverage Odoo 17 and its features for your business too.
An Overview of Odoo ERP
Odoo ERP was first released as OpenERP software in February 2005. It is a suite of business applications used for ERP, CRM, eCommerce, websites, and project management. Ten years ago, the Odoo Enterprise edition was launched to help fund the Odoo Community version.
When you compare Odoo Community and Enterprise, the Enterprise edition offers exclusive features like mobile app access, Odoo Studio customisation, Odoo hosting, and unlimited functional support.
Today, Odoo is a well-known name used by companies of all sizes across various industries, including manufacturing, retail, accounting, marketing, healthcare, IT consulting, and R&D.
The latest version, Odoo 17, has been available since October 2023. Key highlights of this update include:
Enhanced user experience with improvements to the command bar, faster backend page loading, and multiple dashboard views.
Instant report generation, credit limit alerts for sales and invoices, separate OCR settings for invoice creation, and an auto-complete feature for forms in the accounting module.
Improved image handling and global attribute changes for mailing lists in email marketing.
A default auto-signature option and a refuse-to-sign option in HR modules.
Options to divide and merge manufacturing orders, track the status of manufacturing orders, and more in the MRP module.
Dark mode in Odoo 17.
Now that the Odoo 17 announcement is official, let’s look at what’s new in Odoo 17!
What is Odoo ERP 17?
Odoo 17 is the latest version of one of the world’s leading open-source enterprise ERPs. This version has come up with significant improvements explained here in this blog. Also, this new version aims to introduce features that enhance time-saving, efficiency, and productivity for users across various organisations.
Odoo 17, released at the Odoo Experience 2023, brought notable improvements to the user interface and added new functionalities with enhancements in performance, accessibility, data analysis, and management, further expanding its reach in the market.
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Extreme Network Performance with Hazelcast on Torusware
- 1. Extreme Network Performance with Hazelcast on Torusware
- 2. Speaking Guillermo Lopez Taboada, PhD TORUS CEO and Co-founder Low-latency communications expert +10 years experience in HPC and Big Data working for Hewlett-Packard, Amazon WS, NASA, ESA, and the financial, telco and IT industries. guillermo.lopez@torusware.com
- 3. Torusware by Torusware is TORUS fast sockets middleware which boosts the performance of IT infrastructure Torusware improves predictability and performance of low- latency applications info@torusware.comhttp://torusware.com +110% performance increase +250% performance increase +2000% performance increase
- 4. RAM is the new disk…
- 5. and Disk is the new Tape Jim Gray, 2008
- 6. Resilience & Fault Tolerance? Scalability?
- 7. Answer: In Memory Data Grids
- 8. The Future are RAM clusters
- 9. Logical vs Physical view
- 10. …where the network is key
- 11. Networks evolved slow… 2004: 1 Gig. Ethernet + TCP/IP 2014: 1 Gig. Ethernet + TCP/IP
- 12. …as disks and DB! 2004: tens of ms latency 2014: tens of ms latency
- 13. Hazelcast already achieves extreme network performance RTT/2 Latency: 80 us Throughput: 80k ops/second Saturate 1 Gig. Ethernet
- 14. Can we go further?
- 16. Even further?
- 17. Beat Exadata
- 18. The furthest??
- 19. Bare Metal Performance with @torusware
- 20. Torusware accelerates your software…
- 21. fully plug&play!
- 22. No more code rewritting! No more tedious parametrization!
- 23. High-speedRDMA network(ROCE/IB) / Sharedmemory Network driver / POSIX utilities Sockets TCP/IP Applications TORUSWARE (TCP/IP bypass, optimized epoll) Plug&play, user and application transparent, no code rewriting! Torusware –Fast Sockets
- 26. 0 100000 200000 300000 400000 500000 600000 Loopback 1 Gig. Ethernet InfiniBand JVM Sockets Torusware 26 Oracle Coherence Exabus TCP SocketBus (Exalogic) (MessageBusTest bench)
- 33. •Hazelcast on Torusware: 0.417 secs Graphs from “Raj Subramani (Quant School) “Comparing NoSQL Data Stores“ Hazelcast vs Popular NoSQL Data Stores
- 34. •Hazelcast on Torusware: 8.085 secs Graphs from “Raj Subramani (Quant School) “Comparing NoSQL Data Stores“ Hazelcast vs Popular NoSQL Data Stores
- 35. •Hazelcast on Torusware: 0.346 secs Graphs from “Raj Subramani (Quant School) “Comparing NoSQL Data Stores“ Hazelcast vs Popular NoSQL Data Stores
- 36. •Hazelcast on Torusware: 2.139 secs Graphs from “Raj Subramani (Quant School) “Comparing NoSQL Data Stores“ Hazelcast vs Popular NoSQL Data Stores
- 37. •Today all is about speed –No delays in applications –Speed as key competitive advantage •Hazelcast+Toruswareis the fastest solution in the market –Hazelcastprovides peak in-memory data grid performance –ToruwareFasts Sockets have the lowest latency ever Conclusions
- 38. info@torusware.com http://torusware.com For Further Information