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  • Distributed Rendering in the UDK and Unity

    - by N0xus
    At the moment I'm looking at getting a game engine to run in a CAVE environment. So far, during my research I've seen a lot of people being able to get both Unity and the Unreal engine up and running in a CAVE (someone did get CryEngine to work in one, but there is little research data about it). As of yet, I have not cemented my final choice of engine for use in the next stage of my project. I've experience in both, so the learning curve will be gentle on both. And both of the engines offer stereoscopic rendering, either already inbuilt with ReadD (Unreal) or by doing it yourself (Unity). Both can also make use of other input devices as well, such as the kinect or other devices. So again, both engines are still on the table. For the last bit of my preliminary research, I was advised to see if either, or both engines could do distributed rendering. I was advised this, as the final game we make could go into a variety of differently sized CAVEs. The one I have access to is roughly 2.4m x 3m cubed, and have been duly informed that this one is a "baby" compared to others. So, finally onto my question: Can either the Unreal Engine, or Unity Engine make it possible for developers to allow distributed rendering? Either through in built devices, or by creating my own plugin / script?

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  • Communication Between Different Technologies in a Distributed Application

    - by sjtaheri
    I had to a incorporate several legacy applications and services in a network-distributed application. The existing services and applications are written using different languages and technologies, including: java, C#.Net and C++; all running on MS Windows machines. Now I'm wondering about the communication mechanism between them. What is the simple and standard way? Thanks! PS. communications include simple message sending and remote method invocations.

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  • SQL SERVER – Shard No More – An Innovative Look at Distributed Peer-to-peer SQL Database

    - by pinaldave
    There is no doubt that SQL databases play an important role in modern applications. In an ideal world, a single database can handle hundreds of incoming connections from multiple clients and scale to accommodate the related transactions. However the world is not ideal and databases are often a cause of major headaches when applications need to scale to accommodate more connections, transactions, or both. In order to overcome scaling issues, application developers often resort to administrative acrobatics, also known as database sharding. Sharding helps to improve application performance and throughput by splitting the database into two or more shards. Unfortunately, this practice also requires application developers to code transactional consistency into their applications. Getting transactional consistency across multiple SQL database shards can prove to be very difficult. Sharding requires developers to think about things like rollbacks, constraints, and referential integrity across tables within their applications when these types of concerns are best handled by the database. It also makes other common operations such as joins, searches, and memory management very difficult. In short, the very solution implemented to overcome throughput issues becomes a bottleneck in and of itself. What if database sharding was no longer required to scale your application? Let me explain. For the past several months I have been following and writing about NuoDB, a hot new SQL database technology out of Cambridge, MA. NuoDB is officially out of beta and they have recently released their first release candidate so I decided to dig into the database in a little more detail. Their architecture is very interesting and exciting because it completely eliminates the need to shard a database to achieve higher throughput. Each NuoDB database consists of at least three or more processes that enable a single database to run across multiple hosts. These processes include a Broker, a Transaction Engine and a Storage Manager.  Brokers are responsible for connecting client applications to Transaction Engines and maintain a global view of the network to keep track of the multiple Transaction Engines available at any time. Transaction Engines are in-memory processes that client applications connect to for processing SQL transactions. Storage Managers are responsible for persisting data to disk and serving up records to the Transaction Managers if they don’t exist in memory. The secret to NuoDB’s approach to solving the sharding problem is that it is a truly distributed, peer-to-peer, SQL database. Each of its processes can be deployed across multiple hosts. When client applications need to connect to a Transaction Engine, the Broker will automatically route the request to the most available process. Since multiple Transaction Engines and Storage Managers running across multiple host machines represent a single logical database, you never have to resort to sharding to get the throughput your application requires. NuoDB is a new pioneer in the SQL database world. They are making database scalability simple by eliminating the need for acrobatics such as sharding, and they are also making general administration of the database simpler as well.  Their distributed database appears to you as a user like a single SQL Server database.  With their RC1 release they have also provided a web based administrative console that they call NuoConsole. This tool makes it extremely easy to deploy and manage NuoDB processes across one or multiple hosts with the click of a mouse button. See for yourself by downloading NuoDB here. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: CodeProject, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology Tagged: NuoDB

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  • Discussion of a Distributed Data Storage implementation

    - by fegol
    I want to implement a distributed data storage using a client/server architecture. Each data item will be stored persistently in disk in one of several remote servers. The client uses a library to update and query the data, shielding the client from its actual location. This should allow a client to associate keys (String) to values(byte[]), much as a Map does. The system must ensure that the amount of data stored in each server is approximately the same. The set of servers is known beforehand by other servers and clients. Both the client and the server will be written in Java, using sockets, threads, and files. I open this topic with the objective of discussing the best way to implement this idea, assuming simplicity, what are the issues of this implementation, performance measurements and discussion of the limitations.

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  • Azure Futures - Distributed Computing and Number Crunching

    - by JoshReuben
    "the biggest Azure customers today are the ones using HPC on-premises at the current time" - http://www.zdnet.com/blog/microsoft/windows-azure-futures-turning-the-cloud-into-a-supercomputer/8592?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+zdnet%2Fmicrosoft+%28ZDNet+All+About+Microsoft%29&utm_content=Google+Reader   Orleans Framework for cloud computing - http://research.microsoft.com/en-us/projects/orleans     HPC on Azure - http://www.zdnet.com/blog/microsoft/microsoft-finalizes-its-latest-supercomputing-operating-system-release/7414   Dryad is Microsoft’s competitor to Google MapReduce and Apache Hadoop  - http://www.zdnet.com/blog/microsoft/microsoft-takes-a-step-toward-commercializing-its-dryad-distributed-computing-technologies/8255?tag=mantle_skin;content   SQL Server Analysis Services DataMining in the cloud - http://www.sqlmag.com/article/reporting2/azure-data-mining-in-the-cloud.aspx

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  • Distributed Computing - Hybrid Systems Considerations

    When the Cloud was new, it was often presented as an 'all or nothing' solution. Nowadays, the canny Systems Architect will exploit the best advantages of 'cloud' distributed computing in the right place, and use in-house services where most appropriate. So what are the issues that govern these architectural decisions? What can SQL Monitor 3.2 monitor?Whatever you think is most important. Use custom metrics to monitor and alert on data that's most important for your environment. Find out more.

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  • Distributed computing for a company? Is there such a 'free' thing?

    - by Jakub
    I am new to the whole distributed computing / cloud thing. But I had an idea at work for our multimedia stuff like movie encoding / cpu intensive things tasks (which sometimes take a few hours). Is there a 'free' (linux?) way to go about using a Windows machine, and offsetting those cpu cycles for that task to say 10 servers that are generally idle (cpu wise)? I'm just curious if there is a way to do this or am I just grasping at straws here. My thought is that a 'cloud' setup would achieve this, however like I stated initially, I am a total newbie when it comes to it. This is just an idea, looking for some thoughts? Anyone achieve this?

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  • Healthcare and Distributed Data Don't Mix

    - by [email protected]
    How many times have you heard the story?  Hard disk goes missing, USB thumb drive goes missing, laptop goes missing...Not a week goes by that we don't hear about our data going missing...  Healthcare data is a big one, but we hear about credit card data, pricing info, corporate intellectual property...  When I have spoken at Security and IT conferences part of my message is "Why do you give your users data to lose in the first place?"  I don't suggest they can't have access to it...in fact I work for the company that provides the premiere data security and desktop solutions that DO provide access.  Access isn't the issue.  'Keeping the data' is the issue.We are all human - we all make mistakes... I fault no one for having their car stolen or that they dropped a USB thumb drive. (well, except the thieves - I can certainly find some fault there)  Where I find fault is in policy (or lack thereof sometimes) that allows users to carry around private, and important, data with them.  Mr. Director of IT - It is your fault, not theirs.  Ms. CSO - Look in the mirror.It isn't like one can't find a network to access the data from.  You are on a network right now.  How many Wireless ones (wifi, mifi, cellular...) are there around you, right now?  Allowing employees to remove data from the confines of (wait for it... ) THE DATA CENTER is just plain indefensible when it isn't required.  The argument that the laptop had a password and the hard disk was encrypted is ridiculous.  An encrypted drive tells thieves that before they sell the stolen unit for $75, they should crack the encryption and ascertain what the REAL value of the laptop is... credit card info, Identity info, pricing lists, banking transactions... a veritable treasure trove of info people give away on an 'encrypted disk'.What started this latest rant on lack of data control was an article in Government Health IT that was forwarded to me by Denny Olson, an Oracle Principal Sales Consultant in Minnesota.  The full article is here, but the point was that a couple laptops went missing in a couple different cases, and.. well... no one knows where the data is, and yes - they were loaded with patient info.  What were you thinking?Obviously you can't steal data form a Sun Ray appliance... since it has no data, nor any storage to keep the data on, and Secure Global Desktop allows access from Macs, Linux and Windows client devices...  but in all cases, there is no keeping the data unless you explicitly allow for it in your policy.   Since you can get at the data securely from any network, why would you want to take personal responsibility for it?  Both Sun Rays and Secure Global Desktop are widely used in Healthcare... but clearly not widely enough.We need to do a better job of getting the message out -  Healthcare (or insert your business type here) and distributed data don't mix. Then add Hot Desking and 'follow me printing' and you have something that Clinicians (and CSOs) love.Thanks for putting up my blood pressure, Denny.

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  • Jini : single server with multiple clients

    - by user200340
    Hi all, I have a question about how to make multiple clients can access a single file located on server side and keep the file consistent. I have a simple PhoneBook server-client Jini program running at the moment, and server only provides some getter functions to clients, such as getName(String number), getNumber(String name) from a PhoneBook class(serializable), phonebook data are stored in a text file (phonebook.txt) at the moment. I have tried to implement some functions allowing to write a new records into the phonebook.txt file. If the writing record (name) is existing, an integer number will be added into the writing record. for example the existing phonebook.txt is .... John 01-01010101 .... if the writing record is "John 01-12345678",then "John_1 01-12345678" will be writen into phonebook.txt However, if i start with two clients A and B (on the same machine using localhost), and A tries to write "John 01-11111111", B tries to write "John 01-22222222". The early record will be overwritten later record. So, there must be something i did complete wrong. My client and server code are just like Jini HelloWorld example. My server side code is . 1. LookupDiscovery with parameter new String[]{""}; 2. DiscoveryListener for LookupDiscovery 3. registrations are saved into a HashTable 4. for every discovered lookup service, i use registrar to register the ServiceItem, ServiceItem contains a null attributeSets, a null serviceId, and a service. The client code has: 1. LookupDiscovery with parameter new String[]{""}; 2. DiscoveryListener for LookupDiscovery 3. a ServiceTemplate with null attributeSets, a null serviceId and a type, the type is the interface class. 4. for each found ServiceRegistrar, if it can find the looking for ServiceTemplate, the returned Object is cast into the type of the interface class. I have tried to google more details, and i found JavaSpace could be the one i missed. But i am still not sure about it (i only start Jini for a very short time). So any help would be greatly appreciated.

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  • A leader election algorithm for an oriented hypercube

    - by mick
    I'm stuck with some problem where I have to design a leader election algorithm for an oriented hypercube. This should be done by using a tournament with a number of rounds equal to the dimension D of the hypercube. In each stage d, with 1 <= d < D two candidate leaders of neighbouring d-dimensional hypercubes should compete to become the single candidate leader of the (d+1)-dimensional hypercube that is the union of their respective hypercubes.

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  • Odd company release cycle: Go Distributed Source Control?

    - by MrLane
    sorry about this long post, but I think it is worth it! I have just started with a small .NET shop that operates quite a bit differently to other places that I have worked. Unlike any of my previous positions, the software written here is targetted at multiple customers and not every customer gets the latest release of the software at the same time. As such, there is no "current production version." When a customer does get an update, they also get all of the features added to he software since their last update, which could be a long time ago. The software is highly configurable and features can be turned on and off: so called "feature toggles." Release cycles are very tight here, in fact they are not on a shedule: when a feature is complete the software is deployed to the relevant customer. The team only last year moved from Visual Source Safe to Team Foundation Server. The problem is they still use TFS as if it were VSS and enforce Checkout locks on a single code branch. Whenever a bug fix gets put out into the field (even for a single customer) they simply build whatever is in TFS, test the bug was fixed and deploy to the customer! (Myself coming from a pharma and medical devices software background this is unbeliveable!). The result is that half baked dev code gets put into production without being even tested. Bugs are always slipping into release builds, but often a customer who just got a build will not see these bugs if they don't use the feature the bug is in. The director knows this is a problem as the company is starting to grow all of a sudden with some big clients coming on board and more smaller ones. I have been asked to look at source control options in order to eliminate deploying of buggy or unfinished code but to not sacrifice the somewhat asyncronous nature of the teams releases. I have used VSS, TFS, SVN and Bazaar in my career, but TFS is where most of my experience has been. Previously most teams I have worked with use a two or three branch solution of Dev-Test-Prod, where for a month developers work directly in Dev and then changes are merged to Test then Prod, or promoted "when its done" rather than on a fixed cycle. Automated builds were used, using either Cruise Control or Team Build. In my previous job Bazaar was used sitting on top of SVN: devs worked in their own small feature branches then pushed their changes to SVN (which was tied into TeamCity). This was nice in that it was easy to isolate changes and share them with other peoples branches. With both of these models there was a central dev and prod (and sometimes test) branch through which code was pushed (and labels were used to mark builds in prod from which releases were made...and these were made into branches for bug fixes to releases and merged back to dev). This doesn't really suit the way of working here, however: there is no order to when various features will be released, they get pushed when they are complete. With this requirement the "continuous integration" approach as I see it breaks down. To get a new feature out with continuous integration it has to be pushed via dev-test-prod and that will capture any unfinished work in dev. I am thinking that to overcome this we should go down a heavily feature branched model with NO dev-test-prod branches, rather the source should exist as a series of feature branches which when development work is complete are locked, tested, fixed, locked, tested and then released. Other feature branches can grab changes from other branches when they need/want, so eventually all changes get absorbed into everyone elses. This fits very much down a pure Bazaar model from what I experienced at my last job. As flexible as this sounds it just seems odd to not have a dev trunk or prod branch somewhere, and I am worried about branches forking never to re-integrate, or small late changes made that never get pulled across to other branches and developers complaining about merge disasters... What are peoples thoughts on this? A second final question: I am somewhat confused about the exact definition of distributed source control: some people seem to suggest it is about just not having a central repository like TFS or SVN, some say it is about being disconnected (SVN is 90% disconnected and TFS has a perfectly functional offline mode) and others say it is about Feature Branching and ease of merging between branches with no parent-child relationship (TFS also has baseless merging!). Perhaps this is a second question!

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  • Any Open Source Pregel like framework for distributed processing of large Graphs?

    - by Akshay Bhat
    Google has described a novel framework for distributed processing on Massive Graphs. http://portal.acm.org/citation.cfm?id=1582716.1582723 I wanted to know if similar to Hadoop (Map-Reduce) are there any open source implementations of this framework? I am actually in process of writing a Pseudo distributed one using python and multiprocessing module and thus wanted to know if someone else has also tried implementing it. Since public information about this framework is extremely scarce. (A link above and a blog post at Google Research)

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  • Building a Distributed Commerce Infrastructure in the Cloud using Azure and Commerce Server

    - by Lewis Benge
    One of the biggest questions I routinely get asked is how scalable Commerce Server is. Of course the text book answer is the product has been around for 10 years, powers some of the largest e-Commerce websites in the world, so it scales horizontally extremely well. One argument however though is what if you can't predict the growth of demand required of your Commerce Platform, or need the ability to scale up during busy seasons such as Christmas for a retail environment but are hesitant on maintaining the infrastructure on a year-round basis? The obvious answer is to utilise the many elasticated cloud infrastructure providers that are establishing themselves in the ever-growing market, the problem however is Commerce Server is still product which has a legacy tightly coupled dependency on Windows and IIS components. Commerce Server 2009 codename "R2" however introduced to the concept of an n-tier deployment of Microsoft Commerce Server, meaning you are no longer tied to core objects API but instead have serializable Commerce Entity objects, and business logic allowing for Commerce Server to now be built into a WCF-based SOA architecture. Presentation layers no-longer now need to remain on the same physical machine as the application server, meaning you can now build the user experience into multiple-technologies and host them in multiple places – leveraging the transport benefits that a WCF service may bring, such as message queuing, security, and multiple end-points. All of this logic will still need to remain in your internal infrastructure, for two reasons. Firstly cloud based computing infrastructure does not support PCI security requirements, and secondly even though many of the legacy Commerce Server dependencies have been abstracted away within this version of the application, it is still not a fully supported to be deployed exclusively into the cloud. If you do wish to benefit from the scalability of the cloud however, you can still achieve a great Commerce Server and Azure setup by utilising both the Azure App Fabric in terms of the service bus, and authentication services and Windows Azure to host any online presence you may require. The architecture would be something similar to this: This setup would allow you to construct your Commerce Services as part of your on-site infrastructure. These services would contain all of the channels custom business logic, and provide the overall interface back into the underlying Commerce Server components. It would be recommended that services are constructed around the specific business domain of the application, which based on your business model would usually consist of separate services around Catalogue, Orders, Search, Profiles, and Marketing. The App Fabric service bus is then used to abstract and aggregate further the services, making them available to the cloud and subsequently secured by App Fabrics authentication services. These services are now available for consumption by any client, using any supported technology – not just .NET. Thus meaning you are now able to construct apps for IPhone, integrate with Java based POS Devices, and any many other potential uses. This aggregation is useful, and forms the basis of the further strategy around diversifying and enhancing the e-Commerce experience, but also provides the foundation for the scalability we want to gain from utilising a cloud-based application platform. The Windows Azure application platform is Microsoft solution to benefiting from the true economies of scale in terms of the elasticity of the cloud. Just before the launch of the Azure Platform – Domino's pizza actually managed to run their whole SuperBowl operation from the scalability of Windows Azure, and simply switching back to their traditional operation the next day with no residual infrastructure costs. The platform also natively can subscribe to services and messages exposed within the AppFabric service bus, making it an ideal solution to build and deploy a presentation layer which will need to support of scalable infrastructure – such as a high demand public facing e-Commerce portal, or a promotion element of a brand. Windows Azure has excellent support for ASP.NET, including its own caching providers meaning expensive operations such as catalogue queries can persist in memory on the application server, reducing the demand on internal infrastructure and prioritising it for more business critical operations such as receiving orders and processing payments. Windows Azure also supports other languages too, meaning utilising this approach you can technically build a Commerce Server presentation layer in Java, PHP, or Ruby – or equally in ASP.NET or Silverlight without having to change any of the underlying business or Commerce Server implementation. This SOA-style architecture is one of the primary differentiators for Commerce Server as a product in the e-Commerce market, and now with the introduction of a WCF capability in Commerce Server 2009/2009 R2 the opportunities for extensibility of the both the user experience, and integration into third parties, are drastically increased, all with no effect to the underlying channel logic. So if you are looking at deployment options for your e-Commerce application to help support demand in a cost effective way. I would highly recommend you consider looking at Windows Azure, and if you have any questions in-particular about this style of deployment, please feel free to get in touch!

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  • Distributed transactions and queues, ruby, erlang

    - by chrispanda
    I have a problem that involves several machines, message queues, and transactions. So for example a user clicks on a web page, the click sends a message to another machine which adds a payment to the user's account. There may be many thousands of clicks per second. All aspects of the transaction should be fault tolerant. I've never had to deal with anything like this before, but a bit of reading suggests this is a well known problem. So to my questions. Am I correct in assuming that secure way of doing this is with a two phase commit, but the protocol is blocking and so I won't get the required performance? It appears that DBs like redis and message queuing system like Rescue, RabbitMQ etc don't really help me a lot - even if I implement some sort of two phase commit, the data will be lost if redis crashes because it is essentially memory-only. All of this has led me to look at erlang - but before I wade in and start learning a new language, I would really like to understand better if this is worth the effort. Specifically, am I right in thinking that because of its parallel processing capabilities, erlang is a better choice for implementing a blocking protocol like two phase commit, or am I confused?

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  • Distributed C++ game server which use database.

    - by Slav
    Hello. My C++ turn-based game server (which uses database) does stand against current average amount of clients (players), so I want to expand it to multiple (more then one) amount of computers and databases where all clients still will remain within single game world (servers will must communicate with each other and use multiple databases). Is there some tutorials/books/common standards which explain how to do it in a best way?

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  • Entity Framework and distributed Systems

    - by Dirk Beckmann
    I need some help or maybe only a hint for the right direction. I've got a system that is sperated into two applications. An existing VB.NET desktop client using Entity Framework 5 with code first approach and a asp.net Web Api client in C# that will be refactored right yet. It should be possible to deliver OData. The system and the datamodel is still involving and so migrations will happen in undefined intervalls. So I'm now struggling how to manage my database access on the web api system. So my favourd approch would be us Entity Framework on both systems but I'm running into trouble while creating new migrations. Two solutions I've thought about: Shared Data Access dll The first idea was to separate the data access layer to a seperate project an reference from each of the systems. The context would be the same as long as the dll is up to date in each system. This way both soulutions would be able to make a migration. The main problem ist that it is much more complicate to update a web api system than it is with the client Click Once Update Solution and not every migration is important for the web api. This would couse more update trouble and out of sync libraries Database First on Web Api The second idea was just to use the database first approch an on web api side. But it seems that all annotations will be lost by each model update. Other solutions with stored procedures have been discarded because of missing OData support and maintainability. Does anyone run into same conflicts or has any advices how such a problem can be solved!

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  • How are Reads Distributed in a Workload

    - by Bill Graziano
    People have uploaded nearly one millions rows of trace data to TraceTune.  That’s enough data to start to look at the results in aggregate.  The first thing I want to look at is logical reads.  This is the easiest metric to identify and fix. When you upload a trace, I rank each statement based on the total number of logical reads.  I also calculate each statement’s percentage of the total logical reads.  I do the same thing for CPU, duration and logical writes.  When you view a statement you can see all the details like this: This single statement consumed 61.4% of the total logical reads on the system while we were tracing it.  I also wanted to see the distribution of reads across statements.  That graph looks like this: On average, the highest ranked statement consumed just under 50% of the reads on the system.  When I tune a system, I’m usually starting in one of two modes: this “piece” is slow or the whole system is slow.  If a given piece (screen, report, query, etc.) is slow you can usually find the specific statements behind it and tune it.  You can make that individual piece faster but you may not affect the whole system. When you’re trying to speed up an entire server you need to identity those queries that are using the most disk resources in aggregate.  Fixing those will make them faster and it will leave more disk throughput for the rest of the queries. Here are some of the things I’ve learned querying this data: The highest ranked query averages just under 50% of the total reads on the system. The top 3 ranked queries average 73% of the total reads on the system. The top 10 ranked queries average 91% of the total reads on the system. Remember these are averages across all the traces that have been uploaded.  And I’m guessing that people mainly upload traces where there are performance problems so your mileage may vary. I also learned that slow queries aren’t the problem.  Before I wrote ClearTrace I used to identify queries by filtering on high logical reads using Profiler.  That picked out individual queries but those rarely ran often enough to put a large load on the system. If you look at the execution count by rank you’d see that the highest ranked queries also have the highest execution counts.  The graph would look very similar to the one above but flatter.  These queries don’t look that bad individually but run so often that they hog the disk capacity. The take away from all this is that you really should be tuning the top 10 queries if you want to make your system faster.  Tuning individually slow queries will help those specific queries but won’t have much impact on the system as a whole.

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