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  • Des hackers exploiteraient le référencement de Google sur le faux-positif de McAfee pour diffuser le

    Mise à jour du 26/04/10 Les requêtes sur le faux-positif de McAfee utilisées pour diffuser des malwares D'après un concurrent de McAfee, des hackers insèreraient des liens malicieux dans le référencement de Google Le faux-positif de McAfee - qui a bloqué des milliers de PC suite à une mauvaise mise à jour (lire ci-avant) - aurait donné des idées aux hackers. Un des concurrents de McAfee, Sophos, affirme que des cybercriminels utiliseraient leurs connaissances des techniques de référencement de Google pour exploiter cette affaire et propager leurs propres malwares. Le principe est simple. Un utilisateur a...

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  • Des hackers exploiteraient le référencement de Google sur le faux-positif de McAfee pour diffuser le

    Mise à jour du 26/04/10 Les requêtes sur le faux-positif de McAfee utilisées pour diffuser des malwares D'après un concurrent de McAfee, des hackers insèreraient des liens malicieux dans le référencement de Google Le faux-positif de McAfee - qui a bloqué des milliers de PC suite à une mauvaise mise à jour (lire ci-avant) - aurait donné des idées aux hackers. Un des concurrents de McAfee, Sophos, affirme que des cybercriminels utiliseraient leurs connaissances des techniques de référencement de Google pour exploiter cette affaire et propager leurs propres malwares. Le principe est simple. Un utilisateur a...

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  • SPARC T4-4 Delivers World Record Performance on Oracle OLAP Perf Version 2 Benchmark

    - by Brian
    Oracle's SPARC T4-4 server delivered world record performance with subsecond response time on the Oracle OLAP Perf Version 2 benchmark using Oracle Database 11g Release 2 running on Oracle Solaris 11. The SPARC T4-4 server achieved throughput of 430,000 cube-queries/hour with an average response time of 0.85 seconds and the median response time of 0.43 seconds. This was achieved by using only 60% of the available CPU resources leaving plenty of headroom for future growth. The SPARC T4-4 server operated on an Oracle OLAP cube with a 4 billion row fact table of sales data containing 4 dimensions. This represents as many as 90 quintillion aggregate rows (90 followed by 18 zeros). Performance Landscape Oracle OLAP Perf Version 2 Benchmark 4 Billion Fact Table Rows System Queries/hour Users* Response Time (sec) Average Median SPARC T4-4 430,000 7,300 0.85 0.43 * Users - the supported number of users with a given think time of 60 seconds Configuration Summary and Results Hardware Configuration: SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 1 TB memory Data Storage 1 x Sun Fire X4275 (using COMSTAR) 2 x Sun Storage F5100 Flash Array (each with 80 FMODs) Redo Storage 1 x Sun Fire X4275 (using COMSTAR with 8 HDD) Software Configuration: Oracle Solaris 11 11/11 Oracle Database 11g Release 2 (11.2.0.3) with Oracle OLAP option Benchmark Description The Oracle OLAP Perf Version 2 benchmark is a workload designed to demonstrate and stress the Oracle OLAP product's core features of fast query, fast update, and rich calculations on a multi-dimensional model to support enhanced Data Warehousing. The bulk of the benchmark entails running a number of concurrent users, each issuing typical multidimensional queries against an Oracle OLAP cube consisting of a number of years of sales data with fully pre-computed aggregations. The cube has four dimensions: time, product, customer, and channel. Each query user issues approximately 150 different queries. One query chain may ask for total sales in a particular region (e.g South America) for a particular time period (e.g. Q4 of 2010) followed by additional queries which drill down into sales for individual countries (e.g. Chile, Peru, etc.) with further queries drilling down into individual stores, etc. Another query chain may ask for yearly comparisons of total sales for some product category (e.g. major household appliances) and then issue further queries drilling down into particular products (e.g. refrigerators, stoves. etc.), particular regions, particular customers, etc. Results from version 2 of the benchmark are not comparable with version 1. The primary difference is the type of queries along with the query mix. Key Points and Best Practices Since typical BI users are often likely to issue similar queries, with different constants in the where clauses, setting the init.ora prameter "cursor_sharing" to "force" will provide for additional query throughput and a larger number of potential users. Except for this setting, together with making full use of available memory, out of the box performance for the OLAP Perf workload should provide results similar to what is reported here. For a given number of query users with zero think time, the main measured metrics are the average query response time, the median query response time, and the query throughput. A derived metric is the maximum number of users the system can support achieving the measured response time assuming some non-zero think time. The calculation of the maximum number of users follows from the well-known response-time law N = (rt + tt) * tp where rt is the average response time, tt is the think time and tp is the measured throughput. Setting tt to 60 seconds, rt to 0.85 seconds and tp to 119.44 queries/sec (430,000 queries/hour), the above formula shows that the T4-4 server will support 7,300 concurrent users with a think time of 60 seconds and an average response time of 0.85 seconds. For more information see chapter 3 from the book "Quantitative System Performance" cited below. -- See Also Quantitative System Performance Computer System Analysis Using Queueing Network Models Edward D. Lazowska, John Zahorjan, G. Scott Graham, Kenneth C. Sevcik external local Oracle Database 11g – Oracle OLAP oracle.com OTN SPARC T4-4 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 11/2/2012.

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  • My Red Gate Experience

    - by Colin Rothwell
    I’m Colin, and I’ve been an intern working with Mike in publishing on Simple-Talk and SQLServerCentral for the past ten weeks. I’ve mostly been working “behind the scenes”, making improvements to the spam filtering, along with various other small tweaks. When I arrived at Red Gate, one of the first things Mike asked me was what I wanted to get out of the internship. It wasn’t a question I’d given a great deal of thought to, but my immediate response was the same as almost anybody: to support my growing family. Well, ok, not quite that, but money was certainly a motivator, along with simply making sure that I didn’t get bored over the summer. Three months is a long time to fill, and many of my friends end up getting bored, or worse, knitting obsessively. With the arrogance which seems fairly common among Cambridge people, I wasn’t expecting to really learn much here! In my mind, the part of the year where I am at Uni is the part where I learn things, whilst Red Gate would be an opportunity to apply what I’d learnt. Thankfully, the opposite is true: I’ve learnt a lot during my time here, and there has been a definite positive impact on the way I write code. The first thing I’ve really learnt is that test-driven development is, in general, a sensible way of working. Before coming, I didn’t really get it: how could you test something you hadn’t yet written? It didn’t make sense! My problem was seeing a test as having to test all the behaviour of a given function. Writing tests which test the bare minimum possible and building them up is a really good way of crystallising the direction the code needs to grow in, and ensures you never attempt to write too much code at time. One really good experience of this was early on in my internship when Mike and I were working on the query used to list active authors: I’d written something which I thought would do the trick, but by starting again using TDD we grew something which revealed that there were several subtle mistakes in the query I’d written. I’ve also been awakened to the value of pair programming. Whilst I could sort of see the point before coming, I also thought that it was impossible that two people would ever get more done at the same computer than if they were working separately. I still think that this is true for projects with pieces that developers can easily work on independently, and with developers who both know the codebase, but I’ve found that pair programming can be really good for learning a code base, and for building up small projects to the point where you can start working on separate components, as well as solving particularly difficult problems. Later on in my internship, for my down tools week project, I was working on adding Python support to Glimpse. Another intern and I we pair programmed the entire project, using ping pong pair programming as much as possible. One bonus that this brought which I wasn’t expecting was that I found myself less prone to distraction: with someone else peering over my shoulder, I didn’t have the ever-present temptation to open gmail, or facebook, or yammer, or twitter, or hacker news, or reddit, and so on, and so forth. I’m quite proud of this project: I think it’s some of the best code I’ve written. I’ve also been really won over to the value of descriptive variables names. In my pre-Red Gate life, as a lone-ranger style cowboy programmer, I’d developed a tendency towards laziness in variable names, sometimes abbreviating or, worse, using acronyms. I’ve swiftly realised that this is a bad idea when working with a team: saving a few key strokes is inevitably not worth it when it comes to reading code again in the future. Longer names also mean you can do away with a majority of comments. I appreciate that if you’ve come up with an O(n*log n) algorithm for something which seemed O(n^2), you probably want to explain how it works, but explaining what a variable name means is a big no no: it’s so very easy to change the behaviour of the code, whilst forgetting about the comments. Whilst at Red Gate, I took the opportunity to attend a code retreat, which really helped me to solidify all the things I’d learnt. To be completely free of any existing code base really lets you focus on best practises and think about how you write code. If you get a chance to go on a similar event, I’d highly recommend it! Cycling to Red Gate, I’ve also become much better at fitting inner tubes: if you’re struggling to get the tube out, or re-fit the tire, letting a bit of air out usually helps. I’ve also become quite a bit better at foosball and will miss having a foosball table! I’d like to finish off by saying thank you to everyone at Red Gate for having me. I’ve really enjoyed working with, and learning from, the team that brings you this web site. If you meet any of them, buy them a drink!

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  • The Joy Of Hex

    - by Jim Giercyk
    While working on a mainframe integration project, it occurred to me that some basic computer concepts are slipping into obscurity. For example, just about anyone can tell you that a 64-bit processor is faster than a 32-bit processer. A grade school child could tell you that a computer “speaks” in ‘1’s and ‘0’s. Some people can even tell you that there are 8 bits in a byte. However, I have found that even the most seasoned developers often can’t explain the theory behind those statements. That is not a knock on programmers; in the age of IntelliSense, what reason do we have to work with data at the bit level? Many computer theory classes treat bit-level programming as a thing of the past, no longer necessary now that storage space is plentiful. The trouble with that mindset is that the world is full of legacy systems that run programs written in the 1970’s.  Today our jobs require us to extract data from those systems, regardless of the format, and that often involves low-level programming. Because it seems knowledge of the low-level concepts is waning in recent times, I thought a review would be in order.       CHARACTER: See Spot Run HEX: 53 65 65 20 53 70 6F 74 20 52 75 6E DECIMAL: 83 101 101 32 83 112 111 116 32 82 117 110 BINARY: 01010011 01100101 01100101 00100000 01010011 01110000 01101111 01110100 00100000 01010010 01110101 01101110 In this example, I have broken down the words “See Spot Run” to a level computers can understand – machine language.     CHARACTER:  The character level is what is rendered by the computer.  A “Character Set” or “Code Page” contains 256 characters, both printable and unprintable.  Each character represents 1 BYTE of data.  For example, the character string “See Spot Run” is 12 Bytes long, exclusive of the quotation marks.  Remember, a SPACE is an unprintable character, but it still requires a byte.  In the example I have used the default Windows character set, ASCII, which you can see here:  http://www.asciitable.com/ HEX:  Hex is short for hexadecimal, or Base 16.  Humans are comfortable thinking in base ten, perhaps because they have 10 fingers and 10 toes; fingers and toes are called digits, so it’s not much of a stretch.  Computers think in Base 16, with numeric values ranging from zero to fifteen, or 0 – F.  Each decimal place has a possible 16 values as opposed to a possible 10 values in base 10.  Therefore, the number 10 in Hex is equal to the number 16 in Decimal.  DECIMAL:  The Decimal conversion is strictly for us humans to use for calculations and conversions.  It is much easier for us humans to calculate that [30 – 10 = 20] in decimal than it is for us to calculate [1E – A = 14] in Hex.  In the old days, an error in a program could be found by determining the displacement from the entry point of a module.  Since those values were dumped from the computers head, they were in hex. A programmer needed to convert them to decimal, do the equation and convert back to hex.  This gets into relative and absolute addressing, a topic for another day.  BINARY:  Binary, or machine code, is where any value can be expressed in 1s and 0s.  It is really Base 2, because each decimal place can have a possibility of only 2 characters, a 1 or a 0.  In Binary, the number 10 is equal to the number 2 in decimal. Why only 1s and 0s?  Very simply, computers are made up of lots and lots of transistors which at any given moment can be ON ( 1 ) or OFF ( 0 ).  Each transistor is a bit, and the order that the transistors fire (or not fire) is what distinguishes one value from  another in the computers head (or CPU).  Consider 32 bit vs 64 bit processing…..a 64 bit processor has the capability to read 64 transistors at a time.  A 32 bit processor can only read half as many at a time, so in theory the 64 bit processor should be much faster.  There are many more factors involved in CPU performance, but that is the fundamental difference.    DECIMAL HEX BINARY 0 0 0000 1 1 0001 2 2 0010 3 3 0011 4 4 0100 5 5 0101 6 6 0110 7 7 0111 8 8 1000 9 9 1001 10 A 1010 11 B 1011 12 C 1100 13 D 1101 14 E 1110 15 F 1111   Remember that each character is a BYTE, there are 2 HEX characters in a byte (called nibbles) and 8 BITS in a byte.  I hope you enjoyed reading about the theory of data processing.  This is just a high-level explanation, and there is much more to be learned.  It is safe to say that, no matter how advanced our programming languages and visual studios become, they are nothing more than a way to interpret bits and bytes.  There is nothing like the joy of hex to get the mind racing.

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  • SAP : "Il faut faire simple, rapide, et sur mesure", le co-CEO Bill McDermott revient sur les mutations en cours de l'éditeur allemand

    SAP : « Il faut faire simple, rapide, et sur mesure » Bill McDermott, co-CEO, revient sur les grandes mutations en cours de l'éditeur allemand De passage à Paris, Bill McDermott ? un des deux co-PDG de SAP - a fait le tour des sujets qui conditionnent l'avenir de l'éditeur allemand. La conférence de presse s'est tenue au SAP Forum qui s'est déroulé le 31 mai au CNIT de La Défense. Parmi la myriade de sujets, Bill McDermott a confirmé son ambition dans les bases de données. Avec le rachat de Sybase, SAP a un objectif clair : devenir le leader de ce secteur dominé actuellement (en valeur) par son grand concurrent Oracle et en unité par Microsoft. « Dans le mo...

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  • SQLite with two python processes accessing it: one reading, one writing

    - by BBnyc
    I'm developing a small system with two components: one polls data from an internet resource and translates it into sql data to persist it locally; the second one reads that sql data from the local instance and serves it via json and a restful api. I was originally planning to persist the data with postgresql, but because the application will have a very low-volume of data to store and traffic to serve, I thought that was overkill. Is SQLite up to the job? I love the idea of the small footprint and no need to maintain yet another sql server for this one task, but am concerned about concurrency. It seems that with write ahead logging enabled, concurrently reading and writing a SQLite database can happen without locking either process out of the database. Can a single SQLite instance sustain two concurrent processes accessing it, if only one reads and the other writes? I started writing the code but was wondering if this is a misapplication of SQLite.

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  • Les Google Apps s'enrichissent d'une galerie d'applications professionnelles qui vise à concurrencer

    Mise à jour du 10/03/10 Les Google Apps s'enrichissent d'une galerie d'applications A vocation professionnelle, elle vise à concurrencer Microsoft Office 2010 Google vient de lancer le Google Apps MarketPlace. L'annonce a eu lieu lors du Campfire One, la grande messe annuelle où la firme de Moutain View aime à communiquer sur ses nouveautés. Le Google Apps MarketPlace est une nouvelle galerie qui propose des applications à vocation professionnelle. Elle vise à compléter les Google Apps, la suite d'outils en ligne (mail, Agenda, suite bureautique, création de sites, etc) de Google qui se positionne de plus en plus comme un concurrent des outil...

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  • How much server bandwidth does an average RTS game require per month?

    - by Nat Weiss
    My friend and I are going to write a multiplayer, multiplatform RTS game and are currently analyzing the costs of going with a client-server architecture. The game will have a small map with mostly characters, not buildings (think of DotA or League of Legends). The authoritative game logic will run on the server and message packet sizes will be highly optimized. We'd like to know approximately how much server bandwidth our proposed RTS game would use on a monthly basis, considering these theoretical constants: 100 concurrent users maximum 8 players maximum per game 10 ticks per second Bonus: If you can tell us approximately how much server RAM this kind of game would use that would also help a great deal. Thanks in advance.

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  • New Whitepaper: Deploying E-Business Suite on Exadata and Exalogic

    - by Elke Phelps (Oracle Development)
    Our E-Business Suite Performance Team recently published a new whitepaper to assist you with deploying E-Business Suite on the Oracle Exalogic Elastic Cloud and Oracle Exadata Database Machine , also referred to as Exastack.  If you are considering a migration to Exastack, this new whitepaper will assist you understanding sizing requirements, deployment standards and migration strategies: Deploying Oracle E-Business Suite on Oracle Exalogic Elastic Cloud and Oracle Exadata Database Machine (Note 1460742.1) This whitepaper covers the following topics: Scalability and Sizing Examples - provides performance benchmark analysis with concurrent user counts, scaling analysis and sizing recommendations Deployment Standards - includes recommendations for deploying the various components of the E-Business Suite architecture on Exastack Migration Standards and Guidelines - includes an overview of methods for migrating from commodity hardware to Exastack References Our Maximum Availability Architecture (MAA) team has a number of whitepapers that provide additional information regarding Oracle E-Business Suite on the Oracle Exadata Database Machine.  Their library of whitepapers may be found here: MAA Best Practices - Oracle Applications Unlimited  Related Articles Running E-Business Suite on Exadata V2 Running Oracle E-Business Suite on Exalogic Elastic Cloud

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  • Intel et Nvidia signent un accord de partages de certaines de leurs technologies, afin d'enterrer un procès vieux de deux ans

    Intel et Nvidia signent un accord de partages de certaines de leurs technologies, afin d'enterrer un procès vieux de deux ans Intel vient de s'engager à verser, à l'amiable, la somme de 1.5 milliard de dollars à Nvidia. Pour quelle raison ? Afin de clôturer un litige qui avait débuté en février 2009 suite à une plainte d'Intel contre Nvidia (affirmant que son concurrent ne possédait pas la licence nécessaire pour fabriquer des chipsets de carte-mère pour ses derniers processeurs. L'affaire s'était poursuivie avec une contre-plainte de Nvidia, qui retirait à Intel l'accès à certains de ses brevets concernant les processeurs graphiques tout en invoquant une rupture de contrat. Et tout ceci s'était, bien sur, envenimé par voie ...

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  • Parleys Testimonial at GlassFish Community Event, JavaOne 2012

    - by arungupta
    Parleys.com is an e-learning platform that provide a unique experience of online and offline viewing presentations, with integrated movies and chaptering, from the top notch developer conferences and about 40 JUGs all around the world. Stephan Janssen (the Devoxx man and Parleys webmaster) presented at the GlassFish Community Event at JavaOne 2012 and shared why they moved from Tomcat to GlassFish. The move paid off as GlassFish was able to handle 2000 concurrent users very easily. Now they are also running Devoxx CFP and registration on this updated infrastructure. The GlassFish clustering, the asadmin CLI, application versioning, and JMS implementation are some of the features that made them a happy user. Recently they migrated their application from Spring to Java EE 6. This allows them to get locked into proprietary frameworks and also avoid 40MB WAR file deployments. Stateless application, JAX-RS, MongoDB, and Elastic Search is their magical forumla for success there. Watch the video below showing him in full action: More details about their infrastructure is available here.

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  • Coherence Data Guarantees for Data Reads - Basic Terminology

    - by jpurdy
    When integrating Coherence into applications, each application has its own set of requirements with respect to data integrity guarantees. Developers often describe these requirements using expressions like "avoiding dirty reads" or "making sure that updates are transactional", but we often find that even in a small group of people, there may be a wide range of opinions as to what these terms mean. This may simply be due to a lack of familiarity, but given that Coherence sits at an intersection of several (mostly) unrelated fields, it may be a matter of conflicting vocabularies (e.g. "consistency" is similar but different in transaction processing versus multi-threaded programming). Since almost all data read consistency issues are related to the concept of concurrency, it is helpful to start with a definition of that, or rather what it means for two operations to be concurrent. Rather than implying that they occur "at the same time", concurrency is a slightly weaker statement -- it simply means that it can't be proven that one event precedes (or follows) the other. As an example, in a Coherence application, if two client members mutate two different cache entries sitting on two different cache servers at roughly the same time, it is likely that one update will precede the other by a significant amount of time (say 0.1ms). However, since there is no guarantee that all four members have their clocks perfectly synchronized, and there is no way to precisely measure the time it takes to send a given message between any two members (that have differing clocks), we consider these to be concurrent operations since we can not (easily) prove otherwise. So this leads to a question that we hear quite frequently: "Are the contents of the near cache always synchronized with the underlying distributed cache?". It's easy to see that if an update on a cache server results in a message being sent to each near cache, and then that near cache being updated that there is a window where the contents are different. However, this is irrelevant, since even if the application reads directly from the distributed cache, another thread update the cache before the read is returned to the application. Even if no other member modifies a cache entry prior to the local near cache entry being updated (and subsequently read), the purpose of reading a cache entry is to do something with the result, usually either displaying for consumption by a human, or by updating the entry based on the current state of the entry. In the former case, it's clear that if the data is updated faster than a human can perceive, then there is no problem (and in many cases this can be relaxed even further). For the latter case, the application must assume that the value might potentially be updated before it has a chance to update it. This almost aways the case with read-only caches, and the solution is the traditional optimistic transaction pattern, which requires the application to explicitly state what assumptions it made about the old value of the cache entry. If the application doesn't want to bother stating those assumptions, it is free to lock the cache entry prior to reading it, ensuring that no other threads will mutate the entry, a pessimistic approach. The optimistic approach relies on what is sometimes called a "fuzzy read". In other words, the application assumes that the read should be correct, but it also acknowledges that it might not be. (I use the qualifier "sometimes" because in some writings, "fuzzy read" indicates the situation where the application actually sees an original value and then later sees an updated value within the same transaction -- however, both definitions are roughly equivalent from an application design perspective). If the read is not correct it is called a "stale read". Going back to the definition of concurrency, it may seem difficult to precisely define a stale read, but the practical way of detecting a stale read is that is will cause the encompassing transaction to roll back if it tries to update that value. The pessimistic approach relies on a "coherent read", a guarantee that the value returned is not only the same as the primary copy of that value, but also that it will remain that way. In most cases this can be used interchangeably with "repeatable read" (though that term has additional implications when used in the context of a database system). In none of cases above is it possible for the application to perform a "dirty read". A dirty read occurs when the application reads a piece of data that was never committed. In practice the only way this can occur is with multi-phase updates such as transactions, where a value may be temporarily update but then withdrawn when a transaction is rolled back. If another thread sees that value prior to the rollback, it is a dirty read. If an application uses optimistic transactions, dirty reads will merely result in a lack of forward progress (this is actually one of the main risks of dirty reads -- they can be chained and potentially cause cascading rollbacks). The concepts of dirty reads, fuzzy reads, stale reads and coherent reads are able to describe the vast majority of requirements that we see in the field. However, the important thing is to define the terms used to define requirements. A quick web search for each of the terms in this article will show multiple meanings, so I've selected what are generally the most common variations, but it never hurts to state each definition explicitly if they are critical to the success of a project (many applications have sufficiently loose requirements that precise terminology can be avoided).

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  • Intel et Nvidia signent un accord de partages de technologies, pour enterrer un procès vieux de deux ans

    Intel et Nvidia signent un accord de partages de certaines de leurs technologies, afin d'enterrer un procès vieux de deux ans Intel vient de s'engager à verser, à l'amiable, la somme de 1.5 milliard de dollars à Nvidia. Pour quelle raison ? Afin de clôturer un litige qui avait débuté en février 2009 suite à une plainte d'Intel contre Nvidia (affirmant que son concurrent ne possédait pas la licence nécessaire pour fabriquer des chipsets de carte-mère pour ses derniers processeurs. L'affaire s'était poursuivie avec une contre-plainte de Nvidia, qui retirait à Intel l'accès à certains de ses brevets concernant les processeurs graphiques tout en invoquant une rupture de contrat. Et tout ceci s'était, bien sur, envenimé par voie ...

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  • Le MPEG LA s'attaque au VP8 et au WebM de Google, le consortium cherche des brevets utilisés illégalement par le codec open-source

    Le MPEG LA s'attaque au VP8 Et au WebM de Google, le consortium cherche des brevets qui seraient utilisés illégalement par le codec open-source La lutte qui oppose les industriels soutenant le H.264 et les partisans du libre autour des codecs vidéo est sur le point de se transformer en confrontation devant les tribunaux. L'organisme MPEG LA en charge des droits sur le codec H.264 vient en effet de lancer un appel à tous les industriels qui estiment détenir des brevets potentiellement utilisés par le codec concurrent, le « VP8 », racheté et décliné sous licences libres par Google avec WebM. L'objectif de cet appel est d'étudier la possibilité de constituer une commun...

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  • T-SQL Tuesday #15 : Running T-SQL workloads remotely on multiple servers

    - by AaronBertrand
    This month's installment of T-SQL Tuesday is hosted by Pat Wright ( blog | twitter ). Pat says: "So the topic I have chosen for this month is Automation! It can be Automation with T-SQL or with Powershell or a mix of both. Give us your best tips/tricks and ideas for making our lives easier through Automation." In a recent project, we've had a need to run concurrent workloads on as many as 100 instances of SQL Server in a test environment. A goal, obviously, is to accomplish this without having to...(read more)

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  • BeautyBay.com Boosts its Web business with Endeca!

    - by Richard Lefebvre
    BeautyBay.com Boosts Webpage Views by 70%, Increases Items Placed in Shopping Baskets, and Runs 160 Concurrent Brand and Product Promotion. BeautyBay.com Ltd is the United Kingdom’s largest independent online luxury beauty-product retailer. The company sells more than 10,000 products from leading brands like Urban Decay, Paul & Joe, Mario Badescu, bareMinerals, and Dr Sebagh. It strives to stock consumers’ favorite brands and serve as a leading source of beauty information and product reviews. The company won an Online Retail Award in 2013 in the Beauty, Perfume & Cosmetics category. Read the success story, featuring the role of Oracle Endeca here

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  • T-SQL Tuesday #15 : Running T-SQL workloads remotely on multiple servers

    - by AaronBertrand
    This month's installment of T-SQL Tuesday is hosted by Pat Wright ( blog | twitter ). Pat says: "So the topic I have chosen for this month is Automation! It can be Automation with T-SQL or with Powershell or a mix of both. Give us your best tips/tricks and ideas for making our lives easier through Automation." In a project we are working on, we've had a need to run concurrent workloads on as many as 100 instances of SQL Server in a test environment. A goal, obviously, is to accomplish this without...(read more)

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  • Concurrency Utilities for Java EE 6: JSR 236 Rebooting

    - by arungupta
    JSR 166 added support for concurrency utilities in the Java platform. The JSR 236's, a.k.a Concurrency Utilities for Java EE, goal was to extend that support to the Java EE platform by adding asynchronous abilities to different application components. The EG was however stagnant since Dec 2003. Its coming back to life with the co-spec lead Anthony Lai's message to the JSR 236 EG (archived here). The JSR will be operating under JCP 2.8's transparency rules and can be tracked at concurrency-spec.java.net. All the mailing lists are archived here. The final release is expected in Q1 2013 and the APIs will live in the javax.enterprise.concurrent package. Please submit your nomination if you would like to join this EG.

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  • Windows 8 débutera timidement pour décoller en 2014, selon Forrester, qui met en évidence l'hétérogénéité du marché global des OS

    Windows 8 débutera timidement pour décoller en 2014 selon Forrester, qui met en évidence l'hétérogénéité du marché global des OS À quelques jours de la sortie grand public de Windows 8, le cabinet d'analyse Forrester livre sa vision du futur de l'OS de Microsoft. Selon l'analyste Frank Gillett, vice-président du cabinet Forrester Research, le système d'exploitation va démarrer de façon timide en 2013 sur les PC, puis les ventes vont décoller en 2014. Par contre, Microsoft se positionnera simplement comme un concurrent sur le marché des tablettes et comme un troisième acteur dans le secteur de mobile, loin derrière Android et l'iPhone. Pour Franck Gillett, Microsoft...

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  • Peut-on réaliser un bon design Web sans Web-designer ? Quelques pistes de réflexions pour tenter d'y arriver

    Le design des sites aujourd'hui est un point "critique". Et bien souvent les utilisateurs préfèreront un "beau" site à son concurrent moins "évolué" graphiquement. Malheureusement, il n'est pas toujours possible d'avoir à ses côtés un web designer. Dans ce cas, il faut se retrousser les manches et tenter de faire du mieux que l'on peut. Savoir créer un design attrayant ne s'apprend pas en quelques lignes. Cependant voici un petit guide pour débutant qui vous aidera dans la création de votre graphisme. Les contraintes sont nécessaires Même si ça peut sembler contre-intuitif, un bon design part toujours de contraintes bien établies. Si vous pensez que votre projet n'...

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  • Google devrait-il arrêter le développement de Chrome OS ? La séparation des équipes de Chrome et Android est jugée "stupide"

    Google devrait-il abandonner le développement de Chrome OS ? Un analyste de Bloomberg vient de publier un billet de blog plutôt provocateur. Il y traite Google d'imbécile, du fait de son organisation interne relative au développement de ses deux systèmes d'exploitation. En effet, il faut savoir qu'à Mountain View, une équipe travaille sur Chrome OS, tandis que l'autre s'occupe d'Android. Et ces deux groupes ne collaborent absolument pas, il régnerait même entre eux un fort esprit de compétition, d'après certains salariés de la firme. Pourtant, d'après Brad Stone, Android a largement démontré sa supériorité et sa plus grande popularité que son "concurrent" interne. Il estime donc que le staff ...

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  • Google offre son codec video VP8 sous licence open source, Firefox l'intègre déjà

    Google offre son codec video VP8 sous licence open source, Firefox l'intègre déjà A l'occasion d'I/O (Innovation et Ouverture), sa conférence annuelle pour les développeurs, Google a fait plusieurs annonces importantes. Il a ainsi révélé que son codec vidéo VP8 sera désormais disponible en open source et sans royalties. Il s'agit d'un concurrent libre, en opposition aux technologies propriétaires comme H.264, permettant un encodage vidéo de qualité pour une consommation de bande passante limitée. Tout ceci fait partie d'un projet autrement plus vaste, WebM, dont le but est la création d'un format multimédia ouvert hautement qualitatif. Pour cela, les d...

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  • C redevient le langage le plus utilisé devant Java et C++, d'après le classement des langages de pro

    Le C redevient le langage de programmation le plus utilisé Devant Java et le C++, d'après TIOBE Software TIOBE Software publie chaque mois son classement (le TIOBE Programming Community index) des langages de programmation. D'après cet index, pour la première fois depuis 4 ans, Java perd sa place de langage le plus populaire au profit du C qui retrouve donc le top du classement. Le C "est assez constant au fil des années, il varie entre 15% et 20% de parts de marché depuis presque 10 ans. Donc, la raison principale de cette place de numéro 1 n'est pas une progression du C, mais plutôt la baisse de son concurrent Java", explique l'analyse qui accompagne ce ...

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  • Using XA Transactions in Coherence-based Applications

    - by jpurdy
    While the costs of XA transactions are well known (e.g. increased data contention, higher latency, significant disk I/O for logging, availability challenges, etc.), in many cases they are the most attractive option for coordinating logical transactions across multiple resources. There are a few common approaches when integrating Coherence into applications via the use of an application server's transaction manager: Use of Coherence as a read-only cache, applying transactions to the underlying database (or any system of record) instead of the cache. Use of TransactionMap interface via the included resource adapter. Use of the new ACID transaction framework, introduced in Coherence 3.6.   Each of these may have significant drawbacks for certain workloads. Using Coherence as a read-only cache is the simplest option. In this approach, the application is responsible for managing both the database and the cache (either within the business logic or via application server hooks). This approach also tends to provide limited benefit for many workloads, particularly those workloads that either have queries (given the complexity of maintaining a fully cached data set in Coherence) or are not read-heavy (where the cost of managing the cache may outweigh the benefits of reading from it). All updates are made synchronously to the database, leaving it as both a source of latency as well as a potential bottleneck. This approach also prevents addressing "hot data" problems (when certain objects are updated by many concurrent transactions) since most database servers offer no facilities for explicitly controlling concurrent updates. Finally, this option tends to be a better fit for key-based access (rather than filter-based access such as queries) since this makes it easier to aggressively invalidate cache entries without worrying about when they will be reloaded. The advantage of this approach is that it allows strong data consistency as long as optimistic concurrency control is used to ensure that database updates are applied correctly regardless of whether the cache contains stale (or even dirty) data. Another benefit of this approach is that it avoids the limitations of Coherence's write-through caching implementation. TransactionMap is generally used when Coherence acts as system of record. TransactionMap is not generally compatible with write-through caching, so it will usually be either used to manage a standalone cache or when the cache is backed by a database via write-behind caching. TransactionMap has some restrictions that may limit its utility, the most significant being: The lock-based concurrency model is relatively inefficient and may introduce significant latency and contention. As an example, in a typical configuration, a transaction that updates 20 cache entries will require roughly 40ms just for lock management (assuming all locks are granted immediately, and excluding validation and writing which will require a similar amount of time). This may be partially mitigated by denormalizing (e.g. combining a parent object and its set of child objects into a single cache entry), at the cost of increasing false contention (e.g. transactions will conflict even when updating different child objects). If the client (application server JVM) fails during the commit phase, locks will be released immediately, and the transaction may be partially committed. In practice, this is usually not as bad as it may sound since the commit phase is usually very short (all locks having been previously acquired). Note that this vulnerability does not exist when a single NamedCache is used and all updates are confined to a single partition (generally implying the use of partition affinity). The unconventional TransactionMap API is cumbersome but manageable. Only a few methods are transactional, primarily get(), put() and remove(). The ACID transactions framework (accessed via the Connection class) provides atomicity guarantees by implementing the NamedCache interface, maintaining its own cache data and transaction logs inside a set of private partitioned caches. This feature may be used as either a local transactional resource or as logging XA resource. However, a lack of database integration precludes the use of this functionality for most applications. A side effect of this is that this feature has not seen significant adoption, meaning that any use of this is subject to the usual headaches associated with being an early adopter (greater chance of bugs and greater risk of hitting an unoptimized code path). As a result, for the moment, we generally recommend against using this feature. In summary, it is possible to use Coherence in XA-oriented applications, and several customers are doing this successfully, but it is not a core usage model for the product, so care should be taken before committing to this path. For most applications, the most robust solution is normally to use Coherence as a read-only cache of the underlying data resources, even if this prevents taking advantage of certain product features.

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