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  • New DMV… not yet

    - by Michael Zilberstein
    Downloaded and installed new toy: And while reading BOL, stumbled upon new extremely useful DMV: sys.dm_exec_query_profiles . This DMV enables DBA to monitor query progress while it is being executed. Counters in the DMV are per operation per thread. So we’ll be able to monitor in real time which thread (even for parallel processing) processes which node in the plan. Or find heavy operations “post mortem”. We all know the uncomfortable feeling when some heavy query runs and the boss starts asking...(read more)

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  • MAXDOP in SQL Azure

    - by Herve Roggero
    In my search of better understanding the scalability options of SQL Azure I stumbled on an interesting aspect: Query Hints in SQL Azure. More specifically, the MAXDOP hint. A few years ago I did a lot of analysis on this query hint (see article on SQL Server Central:  http://www.sqlservercentral.com/articles/Configuring/managingmaxdegreeofparallelism/1029/).  Here is a quick synopsis of MAXDOP: It is a query hint you use when issuing a SQL statement that provides you control with how many processors SQL Server will use to execute the query. For complex queries with lots of I/O requirements, more CPUs can mean faster parallel searches. However the impact can be drastic on other running threads/processes. If your query takes all available processors at 100% for 5 minutes... guess what... nothing else works. The bottom line is that more is not always better. The use of MAXDOP is more art than science... and a whole lot of testing; it depends on two things: the underlying hardware architecture and the application design. So there isn't a magic number that will work for everyone... except 1... :) Let me explain. The rules of engagements are different. SQL Azure is about sharing. Yep... you are forced to nice with your neighbors.  To achieve this goal SQL Azure sets the MAXDOP to 1 by default, and ignores the use of the MAXDOP hint altogether. That means that all you queries will use one and only one processor.  It really isn't such a bad thing however. Keep in mind that in some of the largest SQL Server implementations MAXDOP is usually also set to 1. It is a well known configuration setting for large scale implementations. The reason is precisely to prevent rogue statements (like a SELECT * FROM HISTORY) from bringing down your systems (like a report that should have been running on a different in the first place) and to avoid the overhead generated by executing too many parallel queries that could cause internal memory management nightmares to the host Operating System. Is summary, forcing the MAXDOP to 1 in SQL Azure makes sense; it ensures that your database will continue to function normally even if one of the other tenants on the same server is running massive queries that would otherwise bring you down. Last but not least, keep in mind as well that when you test your database code for performance on-premise, make sure to set the DOP to 1 on your SQL Server databases to simulate SQL Azure conditions.

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  • SQLAuthority News – Presented at Bangalore DevCon August 4, 2012

    - by pinaldave
    Bangalore Devcon 2012 was a great fun. Earlier this month I was fortunate to be invited to present at Dev Con. The event was very well planned and had excellent response. There were more than 140 attendees at any time in the sessions. There were two tracks and both tracks were running parallel to each other in the Microsoft Bangalore building. The venue is fantastic and the enthusiasm of the community is impeccable. We had a total of 12 sessions during the day. I had decided to attend each session if I can. We have so many fantastic speakers and I did not want to miss any of the sessions. As sessions were running parallel, I attended every session for 30 minutes and switched to another session. I had fun doing this experiment as tit gave me a good idea about every session. I presented personally on the session of SQL Tips and Tricks for Web Developer. DBA is a very common word and every time when we say SQL Server – lots of people think of DBA in their mind, however, SQL Server is used by many developers as well. In this session I tried to cover a few of the simple concepts where developers must pay special attention while writing T-SQL code. Sometimes a very small mistake can be very fatal on performance in the future. Here are few of the photos of the event. Btw, the two sessions which clearly stand out were Vinod Kumar‘s session on Leadership and Lohith‘s session on Visual Studio Tips and Tricks. Additional Read: Following are the blog posts by community on the Bangalore DevCon Experience. I encourage you to read them all and leave a comment which one you liked the most. http://abhishekbhat.wordpress.com/2012/08/07/devcon-2012-experience/ http://praveenprajapati.wordpress.com/2012/08/07/devcon-2012-part-2/ http://tomsblogsspot.blogspot.in/2012/08/devcon-2012.html?view=classic https://manasdash.wordpress.com/2012/08/06/devcon-2012-by-bdotnet-4th-august-2012/ http://www.jagan-bhathri.com/2012/08/bangalore-developer-conference-2012-by.html Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Author Visit, SQLAuthority News, T SQL, Technology

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  • SPARC T4-4 Beats 8-CPU IBM POWER7 on TPC-H @3000GB Benchmark

    - by Brian
    Oracle's SPARC T4-4 server delivered a world record TPC-H @3000GB benchmark result for systems with four processors. This result beats eight processor results from IBM (POWER7) and HP (x86). The SPARC T4-4 server also delivered better performance per core than these eight processor systems from IBM and HP. Comparisons below are based upon system to system comparisons, highlighting Oracle's complete software and hardware solution. This database world record result used Oracle's Sun Storage 2540-M2 arrays (rotating disk) connected to a SPARC T4-4 server running Oracle Solaris 11 and Oracle Database 11g Release 2 demonstrating the power of Oracle's integrated hardware and software solution. The SPARC T4-4 server based configuration achieved a TPC-H scale factor 3000 world record for four processor systems of 205,792 QphH@3000GB with price/performance of $4.10/QphH@3000GB. The SPARC T4-4 server with four SPARC T4 processors (total of 32 cores) is 7% faster than the IBM Power 780 server with eight POWER7 processors (total of 32 cores) on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 36% better in price performance compared to the IBM Power 780 server on the TPC-H @3000GB Benchmark. The SPARC T4-4 server is 29% faster than the IBM Power 780 for data loading. The SPARC T4-4 server is up to 3.4 times faster than the IBM Power 780 server for the Refresh Function. The SPARC T4-4 server with four SPARC T4 processors is 27% faster than the HP ProLiant DL980 G7 server with eight x86 processors on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 52% faster than the HP ProLiant DL980 G7 server for data loading. The SPARC T4-4 server is up to 3.2 times faster than the HP ProLiant DL980 G7 for the Refresh Function. The SPARC T4-4 server achieved a peak IO rate from the Oracle database of 17 GB/sec. This rate was independent of the storage used, as demonstrated by the TPC-H @3000TB benchmark which used twelve Sun Storage 2540-M2 arrays (rotating disk) and the TPC-H @1000TB benchmark which used four Sun Storage F5100 Flash Array devices (flash storage). [*] The SPARC T4-4 server showed linear scaling from TPC-H @1000GB to TPC-H @3000GB. This demonstrates that the SPARC T4-4 server can handle the increasingly larger databases required of DSS systems. [*] The SPARC T4-4 server benchmark results demonstrate a complete solution of building Decision Support Systems including data loading, business questions and refreshing data. Each phase usually has a time constraint and the SPARC T4-4 server shows superior performance during each phase. [*] The TPC believes that comparisons of results published with different scale factors are misleading and discourages such comparisons. Performance Landscape The table lists the leading TPC-H @3000GB results for non-clustered systems. TPC-H @3000GB, Non-Clustered Systems System Processor P/C/T – Memory Composite(QphH) $/perf($/QphH) Power(QppH) Throughput(QthH) Database Available SPARC Enterprise M9000 3.0 GHz SPARC64 VII+ 64/256/256 – 1024 GB 386,478.3 $18.19 316,835.8 471,428.6 Oracle 11g R2 09/22/11 SPARC T4-4 3.0 GHz SPARC T4 4/32/256 – 1024 GB 205,792.0 $4.10 190,325.1 222,515.9 Oracle 11g R2 05/31/12 SPARC Enterprise M9000 2.88 GHz SPARC64 VII 32/128/256 – 512 GB 198,907.5 $15.27 182,350.7 216,967.7 Oracle 11g R2 12/09/10 IBM Power 780 4.1 GHz POWER7 8/32/128 – 1024 GB 192,001.1 $6.37 210,368.4 175,237.4 Sybase 15.4 11/30/11 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64/128 – 512 GB 162,601.7 $2.68 185,297.7 142,685.6 SQL Server 2008 10/13/10 P/C/T = Processors, Cores, Threads QphH = the Composite Metric (bigger is better) $/QphH = the Price/Performance metric in USD (smaller is better) QppH = the Power Numerical Quantity QthH = the Throughput Numerical Quantity The following table lists data load times and refresh function times during the power run. TPC-H @3000GB, Non-Clustered Systems Database Load & Database Refresh System Processor Data Loading(h:m:s) T4Advan RF1(sec) T4Advan RF2(sec) T4Advan SPARC T4-4 3.0 GHz SPARC T4 04:08:29 1.0x 67.1 1.0x 39.5 1.0x IBM Power 780 4.1 GHz POWER7 05:51:50 1.5x 147.3 2.2x 133.2 3.4x HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 08:35:17 2.1x 173.0 2.6x 126.3 3.2x Data Loading = database load time RF1 = power test first refresh transaction RF2 = power test second refresh transaction T4 Advan = the ratio of time to T4 time Complete benchmark results found at the TPC benchmark website http://www.tpc.org. Configuration Summary and Results Hardware Configuration: SPARC T4-4 server 4 x SPARC T4 3.0 GHz processors (total of 32 cores, 128 threads) 1024 GB memory 8 x internal SAS (8 x 300 GB) disk drives External Storage: 12 x Sun Storage 2540-M2 array storage, each with 12 x 15K RPM 300 GB drives, 2 controllers, 2 GB cache Software Configuration: Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Enterprise Edition Audited Results: Database Size: 3000 GB (Scale Factor 3000) TPC-H Composite: 205,792.0 QphH@3000GB Price/performance: $4.10/QphH@3000GB Available: 05/31/2012 Total 3 year Cost: $843,656 TPC-H Power: 190,325.1 TPC-H Throughput: 222,515.9 Database Load Time: 4:08:29 Benchmark Description The TPC-H benchmark is a performance benchmark established by the Transaction Processing Council (TPC) to demonstrate Data Warehousing/Decision Support Systems (DSS). TPC-H measurements are produced for customers to evaluate the performance of various DSS systems. These queries and updates are executed against a standard database under controlled conditions. Performance projections and comparisons between different TPC-H Database sizes (100GB, 300GB, 1000GB, 3000GB, 10000GB, 30000GB and 100000GB) are not allowed by the TPC. TPC-H is a data warehousing-oriented, non-industry-specific benchmark that consists of a large number of complex queries typical of decision support applications. It also includes some insert and delete activity that is intended to simulate loading and purging data from a warehouse. TPC-H measures the combined performance of a particular database manager on a specific computer system. The main performance metric reported by TPC-H is called the TPC-H Composite Query-per-Hour Performance Metric (QphH@SF, where SF is the number of GB of raw data, referred to as the scale factor). QphH@SF is intended to summarize the ability of the system to process queries in both single and multiple user modes. The benchmark requires reporting of price/performance, which is the ratio of the total HW/SW cost plus 3 years maintenance to the QphH. A secondary metric is the storage efficiency, which is the ratio of total configured disk space in GB to the scale factor. Key Points and Best Practices Twelve Sun Storage 2540-M2 arrays were used for the benchmark. Each Sun Storage 2540-M2 array contains 12 15K RPM drives and is connected to a single dual port 8Gb FC HBA using 2 ports. Each Sun Storage 2540-M2 array showed 1.5 GB/sec for sequential read operations and showed linear scaling, achieving 18 GB/sec with twelve Sun Storage 2540-M2 arrays. These were stand alone IO tests. The peak IO rate measured from the Oracle database was 17 GB/sec. Oracle Solaris 11 11/11 required very little system tuning. Some vendors try to make the point that storage ratios are of customer concern. However, storage ratio size has more to do with disk layout and the increasing capacities of disks – so this is not an important metric in which to compare systems. The SPARC T4-4 server and Oracle Solaris efficiently managed the system load of over one thousand Oracle Database parallel processes. Six Sun Storage 2540-M2 arrays were mirrored to another six Sun Storage 2540-M2 arrays on which all of the Oracle database files were placed. IO performance was high and balanced across all the arrays. The TPC-H Refresh Function (RF) simulates periodical refresh portion of Data Warehouse by adding new sales and deleting old sales data. Parallel DML (parallel insert and delete in this case) and database log performance are a key for this function and the SPARC T4-4 server outperformed both the IBM POWER7 server and HP ProLiant DL980 G7 server. (See the RF columns above.) See Also Transaction Processing Performance Council (TPC) Home Page Ideas International Benchmark Page SPARC T4-4 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Sun Storage 2540-M2 Array oracle.com OTN Disclosure Statement TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org. SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads.

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  • Plan Operator Tuesday round-up

    - by Rob Farley
    Eighteen posts for T-SQL Tuesday #43 this month, discussing Plan Operators. I put them together and made the following clickable plan. It’s 1000px wide, so I hope you have a monitor wide enough. Let me explain this plan for you (people’s names are the links to the articles on their blogs – the same links as in the plan above). It was clearly a SELECT statement. Wayne Sheffield (@dbawayne) wrote about that, so we start with a SELECT physical operator, leveraging the logical operator Wayne Sheffield. The SELECT operator calls the Paul White operator, discussed by Jason Brimhall (@sqlrnnr) in his post. The Paul White operator is quite remarkable, and can consume three streams of data. Let’s look at those streams. The first pulls data from a Table Scan – Boris Hristov (@borishristov)’s post – using parallel threads (Bradley Ball – @sqlballs) that pull the data eagerly through a Table Spool (Oliver Asmus – @oliverasmus). A scalar operation is also performed on it, thanks to Jeffrey Verheul (@devjef)’s Compute Scalar operator. The second stream of data applies Evil (I figured that must mean a procedural TVF, but could’ve been anything), courtesy of Jason Strate (@stratesql). It performs this Evil on the merging of parallel streams (Steve Jones – @way0utwest), which suck data out of a Switch (Paul White – @sql_kiwi). This Switch operator is consuming data from up to four lookups, thanks to Kalen Delaney (@sqlqueen), Rick Krueger (@dataogre), Mickey Stuewe (@sqlmickey) and Kathi Kellenberger (@auntkathi). Unfortunately Kathi’s name is a bit long and has been truncated, just like in real plans. The last stream performs a join of two others via a Nested Loop (Matan Yungman – @matanyungman). One pulls data from a Spool (my post – @rob_farley) populated from a Table Scan (Jon Morisi). The other applies a catchall operator (the catchall is because Tamera Clark (@tameraclark) didn’t specify any particular operator, and a catchall is what gets shown when SSMS doesn’t know what to show. Surprisingly, it’s showing the yellow one, which is about cursors. Hopefully that’s not what Tamera planned, but anyway...) to the output from an Index Seek operator (Sebastian Meine – @sqlity). Lastly, I think everyone put in 110% effort, so that’s what all the operators cost. That didn’t leave anything for me, unfortunately, but that’s okay. Also, because he decided to use the Paul White operator, Jason Brimhall gets 0%, and his 110% was given to Paul’s Switch operator post. I hope you’ve enjoyed this T-SQL Tuesday, and have learned something extra about Plan Operators. Keep your eye out for next month’s one by watching the Twitter Hashtag #tsql2sday, and why not contribute a post to the party? Big thanks to Adam Machanic as usual for starting all this. @rob_farley

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  • Slow Chat with Industry Experts: Developing Multithreaded Applications

    Sponsored by Intel Join the experts who created The Intel Guide for Developing Multithreaded Applications for a slow chat about multithreaded application development. Bring your questions about application threading, memory management, synchronization, programming tools and more and get answers from the parallel programming experts. Post your questions here

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  • Slow Chat with Industry Experts: Developing Multithreaded Applications

    Sponsored by Intel Join the experts who created The Intel Guide for Developing Multithreaded Applications for a slow chat about multithreaded application development. Bring your questions about application threading, memory management, synchronization, programming tools and more and get answers from the parallel programming experts. Post your questions here

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  • Innovation and the Role of Social Media

    - by Brian Dirking
    A very interesting post by Andy Mulholland of CAP Gemini this week – “The CIO is trapped between the CEO wanting innovation and the CFO needing compliance” – had many interesting points: “A successful move in one area won’t be recognized and rapidly implemented in other areas to multiply the benefits, or worse unsuccessful ideas will get repeated adding to the cost and time wasted. That’s where the need to really address the combination of social networking, collaboration, knowledge management and business information is required.” Without communicating what works and what doesn’t, the innovations of our organization may be lost, and the failures repeated. That makes sense. If you liked Andy Mulholland’s blog post, you need to hear Howard Beader’s presentation at Enterprise 2.0 Conference on innovation and the role of social media. (Howard will be speaking in the Market Leaders Session at 1 PM on Wednesday June 22nd). Some of the thoughts Howard will share include: • Innovation is more than just ideas, it’s getting ideas to market, and removing the obstacles that stand in the way • Innovation is about parallel processing – you can’t remove the obstacles one by one because you will get to market too late • Innovation can be about product innovation, but it can also be about process innovation This brings us to Andy’s second issue he raises: "..the need for integration with, and visibility of, processes to understand exactly how the enterprise functions and delivers on its policies…" Andy goes on to talk about this from the perspective of compliance and the CFO’s concerns. And it’s true: innovation can come both in product innovation, but also internal process innovation. And process innovation can have as much impact as product innovation.  New supply chain models can disrupt an industry overnight. Many people ignore process innovation as a benefit of social business, because it is perceived as a bottom line rather than top line impact. But it can actually impact your top line by changing your entire business model. Oracle WebCenter sits at this crossroads between product innovation and process innovation, enabling you to drive go-to-market innovations through internal social media tools, removing obstacles in parallel, and also providing you deep insight into your processes so you can identify bottlenecks and realize whole new ways of doing business. Learn more about how at the Enterprise 2.0 Conference, where Oracle will be in booth #213 showing Oracle WebCenter and Oracle Fusion Applications.

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  • SSAS Native v .net Provider

    - by ACALVETT
    Recently I was investigating why a new server which is in its parallel running phase was taking significantly longer to process the daily data than the server its due to replace. The server has SQL & SSAS installed so the problem was not likely to be in the network transfer as its using shared memory. As i dug around the SQL dmv’s i noticed in sys.dm_exec_connections that the SSAS connection had a packet size of 8000 bytes instead of the usual 4096 bytes and from there i found that the datasource...(read more)

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  • Union,Except and Intersect operator in Linq

    - by Jalpesh P. Vadgama
    While developing a windows service using Linq-To-SQL i was in need of something that will intersect the two list and return a list with the result. After searching on net i have found three great use full operators in Linq Union,Except and Intersect. Here are explanation of each operator. Union Operator: Union operator will combine elements of both entity and return result as third new entities. Except Operator: Except operator will remove elements of first entities which elements are there in second entities and will return as third new entities. Intersect Operator: As name suggest it will return common elements of both entities and return result as new entities. Let’s take a simple console application as  a example where i have used two string array and applied the three operator one by one and print the result using Console.Writeline. Here is the code for that. C#, using GeSHi 1.0.8.6 using System; using System.Collections.Generic; using System.Linq; using System.Text;     namespace ConsoleApplication1 {     class Program     {         static void Main(string[] args)         {             string[] a = { "a", "b", "c", "d" };             string[] b = { "d","e","f","g"};               var UnResult = a.Union(b);             Console.WriteLine("Union Result");               foreach (string s in UnResult)             {                 Console.WriteLine(s);                          }               var ExResult = a.Except(b);             Console.WriteLine("Except Result");             foreach (string s in ExResult)             {                 Console.WriteLine(s);             }               var InResult = a.Intersect(b);             Console.WriteLine("Intersect Result");             foreach (string s in InResult)             {                 Console.WriteLine(s);             }             Console.ReadLine();                        }          } }   Parsed in 0.022 seconds at 45.54 KB/s Here is the output of console application as Expected. Hope this will help you.. Technorati Tags: Linq,Except,InterSect,Union,C#

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  • Code for Parallelism Features Tour

    Last year I linked to a screencast that shows off many VS2010 features delivered by the Parallel Computing team.There have been requests for the code used to demonstrate the features. Like with all my screencasts, you can see all the code in action, so you could simply type it in. To save you doing that though, you may download the two files with the demo code here: MM.cs and Program.cs. HTH. Comments about this post welcome at the original blog.

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  • Files for .NET Montreal and VTCC4 conference

    - by Vincent Grondin
    Hi,  here are the files for both the .NET Montreal presentation made Sept the 24th and at the Vermont Code Camp #4 on Sept the 22nd regarding Architecture problems and solutions linked to EF4.0, Async-await keywords and the Task Parallel Library. This zip file includes both power points in french and english and the DemoApplication which is I REMIND YOU VERY DEMO-WARE and doesn't handle task level exception and context switching.  ZipFile Enjoy

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  • How Parallelism Works in SQL Server

    - by Paul White
    You might have noticed that January was a quiet blogging month for me.  Part of the reason was that I was working on a series of articles for Simple Talk, examining how parallel query execution really works.  The first part is published today at: http://www.simple-talk.com/sql/learn-sql-server/understanding-and-using-parallelism-in-sql-server/ . This introductory piece is not quite as deeply technical as my SQLblog posts tend to be, but I hope there be enough interesting material to make...(read more)

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  • Lessons learned from Word 2007 automation with c# 2008

    - by robertphyatt
    My organization has an ongoing project to take documents produced for internal regulations and such, change some of the formatting and then export it as PDF. Our requirements were that only one person would be doing this, but it has been painfully tedious and sometimes error-prone to do by hand. Enter the fearless developer to automate the situation! Since I am one of those guys that just plain does not like VB, I wanted to do the automation in the ever-so-much-more-familiar C#. While Microsoft had made a dll that makes such a task easier, documentation on MSDN is pretty lame and most of the forumns and posts on the internet had little to do with my task. So, I feel like I can give back to the community and make a post here of the things I have learned so far. I hope this is helpful to whoever stumbles upon it. Steps to do this: 1) First of all, make some sort of a project and use some sort of a means to get the filename of the word document you are trying to open. I got the filename the user wanted with an openFileDialog tied to a button that I labeled 'Browse':        private void btnBrowse_Click(object sender, EventArgs e)        {            try            {                DialogResult myResult = openFileDialog1.ShowDialog();                if (myResult.Equals(DialogResult.OK))                {                    if (openFileDialog1.SafeFileName.EndsWith(".doc"))                    {                        txtFileName.Text = openFileDialog1.SafeFileName;                        paramSourceDocPath = openFileDialog1.FileName;                        paramExportFilePath = openFileDialog1.FileName.Replace(".doc", ".pdf");                    }                    else                    {                        txtFileName.Text = "only something that end with .doc, please";                    }                }            }            catch (Exception err)            {                lblError.Text = err.Message;            }        }   2) Add in "using Microsoft.Office.Interop.Word;" after setting your project to reference Microsoft.Office.Core and Microsoft.Office.Interop.Word so that you don't have to add "Microsoft.Office.Interop.Word" to the front of everything. 3) Now you are ready to play. You will need to have a copy of word open and a copy of your word document that you want to modify open to be able to make the changes that are needed. The word interop dll likes using ref on all the parameters passed in, and likes to have them as objects. If you don't want to specify the parameter, you have to give it a "Type.Missing". I suggest creating some objects that you reuse all over the place to maintain sanity. object paramMissing = Type.Missing; ApplicationClass wordApplication = new ApplicationClass(); Document wordDocument = wordApplication.Documents.Open(                ref paramSourceDocPath, ref paramMissing, ref paramMissing,                ref paramMissing, ref paramMissing, ref paramMissing,                ref paramMissing, ref paramMissing, ref paramMissing,                ref paramMissing, ref paramMissing, ref paramMissing,                ref paramMissing, ref paramMissing, ref paramMissing,                ref paramMissing); 4) There are many ways to modify the text of the inside of the word document. One of the ways that was most effective for me was to break it down by paragraph and then do things on each paragraph by what style the particular paragraph had.            foreach (Paragraph thisParagraph in wordDocument.Content.Paragraphs)            {                string strStyleName = ((Style)thisParagraph.get_Style()).NameLocal;                string strText = thisParagraph.Range.Text;                //Do whatever you need to do            } 5) Sometimes you want to insert a new line character somewhere in the text or insert text into the document, etc.  There are a few ways you can do this: you can either modify the text of a paragraph by doing something like this ('\r' makes a new paragraph, '\v' will make a newline without making a new paragraph. If you remove a '\r' from the text, it will eliminate the paragraph you removed it from): thisParagraph.Range.Text = "A\vNew Paragraph!\r" + thisParagraph.Range.Text; OR you could select where you want to insert it and have it act like you were typing in Word like any normal user (note: if you do not collapse the range first, you will overwrite the thing you got the range from) object oCollapseDirectionEnd = WdCollapseDirection.wdCollapseEnd; object oCollapseDirectionStart = WdCollapseDirection.wdCollapseStart; Range rangeInsertAtBeginning = thisParagraph.Range; Range rangeInsertAtEnd = thisParagraph.Range; rangeInsertAtBeginning.Collapse(ref oCollapseDirectionStart); rangeInsertAtEnd.Collapse(ref oCollapseDirectionEnd); rangeInsertAtBeginning.Select(); wordApplication.Selection.TypeText("Blah Blah Blah"); rangeInsertAtEnd.Select(); wordApplication.Selection.TypeParagraph(); 6) If you want to make text columns, like a newspaper or newsletter, you have to modify the page layout of the document or a section of the document to make it happen. In my case, I only wanted a particular section to have that, and I wanted to have a black line before and after the newspaper-like text columns. First you need to do a section break on either side of what you wanted, then you take the section and modify the page layout. Then you can modify the borders of the section (or another object in the word document). I also show here how to modify the alignment of a paragraph.            object oSectionBreak = WdBreakType.wdSectionBreakContinuous;            //These ranges were set while I was going through the paragraphs of my document, like I was showing earlier            rangeHeaderStart.InsertBreak(ref oSectionBreak);            rangeHeaderEnd.InsertBreak(ref oSectionBreak);            //change the alignment to justify            object oRangeHeaderStart = rangeStartJustifiedAlignment.Start;            object oRangeHeaderEnd = rangeHeaderEnd.End;            Range rangeHeader = wordDocument.Range(ref oRangeHeaderStart, ref oRangeHeaderEnd);            rangeHeader.Paragraphs.Alignment = WdParagraphAlignment.wdAlignParagraphJustify;            //find the section break and make it into triple text columns            foreach (Section mySection in wordDocument.Sections)            {                if (mySection.Range.Start == rangeHeaderStart.Start)                {                    mySection.PageSetup.TextColumns.Add(ref paramMissing, ref paramMissing, ref paramMissing);                    mySection.PageSetup.TextColumns.Add(ref paramMissing, ref paramMissing, ref paramMissing);                    //I didn't like the default spacing and column widths. This is how I adjusted them.                    foreach (TextColumn txtc in mySection.PageSetup.TextColumns)                    {                        try                        {                            txtc.SpaceAfter = 151.6f;                            txtc.Width = 7;                        }                        catch (Exception)                        {                            txtc.Width = 151.6f;                        }                    }                }            } That is all  I have time for today! I hope this was helpful to someone!

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  • Visual Studio 2010 and .NET Framework 4 Training Kit April 2010 Release

    - by Harish Pavithran
    The Visual Studio 2010 and .NET Framework 4 Training Kit includes presentations, hands-on labs, and demos. This content is designed to help you learn how to utilize the Visual Studio 2010 features and a variety of framework technologies including: C# 4 Visual Basic 10 F# Parallel Extensions Windows Communication Foundation Windows Workflow Windows Presentation Foundation ASP.NET 4 Windows 7 Entity Framework ADO.NET Data Services Managed Extensibility Framework Visual Studio Team System This version of the Training Kit works with Visual Studio 2010 and .NET Framework 4.  Here is the link enjoy www.microsoft.com/downloads/details.aspx

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  • Téléchargez gratuitement l'ebook sur le développement d'applications 'Threaded' qui utilisent le har

    Téléchargez gratuitement l'ebook sur le développement d'applications ?Threaded' Les logiciels de développement Intel® Parallel Studio accélèrent le développement d'applications ?Threaded' qui utilisent le hardware des utilisateurs finaux, depuis le ?'supercomputer'' jusqu'à l'ordinateur portable ou les mobiles. Optimisez la performance de votre application sur architecture Intel® et obtenez plus des derniers processeurs multi-coeurs d'Intel®. Depuis la manière dont les produits fonctionnent ensemble jusqu'à leurs jeux de fonctionnalités uniques, le Threading est maintenant plus facile et plus viable que jamais. Les outils sont optimisés donc les novices peuvent facilement se former et les développeurs expérimentés peuvent aisément ...

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  • Téléchargez gratuitement l'ebook sur le développement d'applications 'Threaded' qui utilisent le har

    Téléchargez gratuitement l'ebook sur le développement d'applications ?Threaded' Les logiciels de développement Intel® Parallel Studio accélèrent le développement d'applications ?Threaded' qui utilisent le hardware des utilisateurs finaux, depuis le ?'supercomputer'' jusqu'à l'ordinateur portable ou les mobiles. Optimisez la performance de votre application sur architecture Intel® et obtenez plus des derniers processeurs multi-coeurs d'Intel®. Depuis la manière dont les produits fonctionnent ensemble jusqu'à leurs jeux de fonctionnalités uniques, le Threading est maintenant plus facile et plus viable que jamais. Les outils sont optimisés donc les novices peuvent facilement se former et les développeurs expérimentés peuvent aisément ...

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  • Windows Azure Recipe: Big Data

    - by Clint Edmonson
    As the name implies, what we’re talking about here is the explosion of electronic data that comes from huge volumes of transactions, devices, and sensors being captured by businesses today. This data often comes in unstructured formats and/or too fast for us to effectively process in real time. Collectively, we call these the 4 big data V’s: Volume, Velocity, Variety, and Variability. These qualities make this type of data best managed by NoSQL systems like Hadoop, rather than by conventional Relational Database Management System (RDBMS). We know that there are patterns hidden inside this data that might provide competitive insight into market trends.  The key is knowing when and how to leverage these “No SQL” tools combined with traditional business such as SQL-based relational databases and warehouses and other business intelligence tools. Drivers Petabyte scale data collection and storage Business intelligence and insight Solution The sketch below shows one of many big data solutions using Hadoop’s unique highly scalable storage and parallel processing capabilities combined with Microsoft Office’s Business Intelligence Components to access the data in the cluster. Ingredients Hadoop – this big data industry heavyweight provides both large scale data storage infrastructure and a highly parallelized map-reduce processing engine to crunch through the data efficiently. Here are the key pieces of the environment: Pig - a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. Mahout - a machine learning library with algorithms for clustering, classification and batch based collaborative filtering that are implemented on top of Apache Hadoop using the map/reduce paradigm. Hive - data warehouse software built on top of Apache Hadoop that facilitates querying and managing large datasets residing in distributed storage. Directly accessible to Microsoft Office and other consumers via add-ins and the Hive ODBC data driver. Pegasus - a Peta-scale graph mining system that runs in parallel, distributed manner on top of Hadoop and that provides algorithms for important graph mining tasks such as Degree, PageRank, Random Walk with Restart (RWR), Radius, and Connected Components. Sqoop - a tool designed for efficiently transferring bulk data between Apache Hadoop and structured data stores such as relational databases. Flume - a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large log data amounts to HDFS. Database – directly accessible to Hadoop via the Sqoop based Microsoft SQL Server Connector for Apache Hadoop, data can be efficiently transferred to traditional relational data stores for replication, reporting, or other needs. Reporting – provides easily consumable reporting when combined with a database being fed from the Hadoop environment. Training These links point to online Windows Azure training labs where you can learn more about the individual ingredients described above. Hadoop Learning Resources (20+ tutorials and labs) Huge collection of resources for learning about all aspects of Apache Hadoop-based development on Windows Azure and the Hadoop and Windows Azure Ecosystems SQL Azure (7 labs) Microsoft SQL Azure delivers on the Microsoft Data Platform vision of extending the SQL Server capabilities to the cloud as web-based services, enabling you to store structured, semi-structured, and unstructured data. See my Windows Azure Resource Guide for more guidance on how to get started, including links web portals, training kits, samples, and blogs related to Windows Azure.

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  • Visual Basic Book Excerpt: Useful Namespaces

    This chapter provides an overview of some of the most important system namespaces and gives more detailed examples that demonstrate regular expressions, XML, cryptography, reflection, threading, parallel programming, and Direct3D....Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Context Sensitive History. Part 1 of 2

    A Desktop and Silverlight user action management system, with undo, redo, and repeat. Allowing actions to be monitored, and grouped according to a context (such as a UI control), executed sequentially or in parallel, and even to be rolled back on failure.

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  • Benchmarking MySQL Replication with Multi-Threaded Slaves

    - by Mat Keep
    0 0 1 1145 6530 Homework 54 15 7660 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} The objective of this benchmark is to measure the performance improvement achieved when enabling the Multi-Threaded Slave enhancement delivered as a part MySQL 5.6. As the results demonstrate, Multi-Threaded Slaves delivers 5x higher replication performance based on a configuration with 10 databases/schemas. For real-world deployments, higher replication performance directly translates to: · Improved consistency of reads from slaves (i.e. reduced risk of reading "stale" data) · Reduced risk of data loss should the master fail before replicating all events in its binary log (binlog) The multi-threaded slave splits processing between worker threads based on schema, allowing updates to be applied in parallel, rather than sequentially. This delivers benefits to those workloads that isolate application data using databases - e.g. multi-tenant systems deployed in cloud environments. Multi-Threaded Slaves are just one of many enhancements to replication previewed as part of the MySQL 5.6 Development Release, which include: · Global Transaction Identifiers coupled with MySQL utilities for automatic failover / switchover and slave promotion · Crash Safe Slaves and Binlog · Optimized Row Based Replication · Replication Event Checksums · Time Delayed Replication These and many more are discussed in the “MySQL 5.6 Replication: Enabling the Next Generation of Web & Cloud Services” Developer Zone article  Back to the benchmark - details are as follows. Environment The test environment consisted of two Linux servers: · one running the replication master · one running the replication slave. Only the slave was involved in the actual measurements, and was based on the following configuration: - Hardware: Oracle Sun Fire X4170 M2 Server - CPU: 2 sockets, 6 cores with hyper-threading, 2930 MHz. - OS: 64-bit Oracle Enterprise Linux 6.1 - Memory: 48 GB Test Procedure Initial Setup: Two MySQL servers were started on two different hosts, configured as replication master and slave. 10 sysbench schemas were created, each with a single table: CREATE TABLE `sbtest` (    `id` int(10) unsigned NOT NULL AUTO_INCREMENT,    `k` int(10) unsigned NOT NULL DEFAULT '0',    `c` char(120) NOT NULL DEFAULT '',    `pad` char(60) NOT NULL DEFAULT '',    PRIMARY KEY (`id`),    KEY `k` (`k`) ) ENGINE=InnoDB DEFAULT CHARSET=latin1 10,000 rows were inserted in each of the 10 tables, for a total of 100,000 rows. When the inserts had replicated to the slave, the slave threads were stopped. The slave data directory was copied to a backup location and the slave threads position in the master binlog noted. 10 sysbench clients, each configured with 10 threads, were spawned at the same time to generate a random schema load against each of the 10 schemas on the master. Each sysbench client executed 10,000 "update key" statements: UPDATE sbtest set k=k+1 WHERE id = <random row> In total, this generated 100,000 update statements to later replicate during the test itself. Test Methodology: The number of slave workers to test with was configured using: SET GLOBAL slave_parallel_workers=<workers> Then the slave IO thread was started and the test waited for all the update queries to be copied over to the relay log on the slave. The benchmark clock was started and then the slave SQL thread was started. The test waited for the slave SQL thread to finish executing the 100k update queries, doing "select master_pos_wait()". When master_pos_wait() returned, the benchmark clock was stopped and the duration calculated. The calculated duration from the benchmark clock should be close to the time it took for the SQL thread to execute the 100,000 update queries. The 100k queries divided by this duration gave the benchmark metric, reported as Queries Per Second (QPS). Test Reset: The test-reset cycle was implemented as follows: · the slave was stopped · the slave data directory replaced with the previous backup · the slave restarted with the slave threads replication pointer repositioned to the point before the update queries in the binlog. The test could then be repeated with identical set of queries but a different number of slave worker threads, enabling a fair comparison. The Test-Reset cycle was repeated 3 times for 0-24 number of workers and the QPS metric calculated and averaged for each worker count. MySQL Configuration The relevant configuration settings used for MySQL are as follows: binlog-format=STATEMENT relay-log-info-repository=TABLE master-info-repository=TABLE As described in the test procedure, the slave_parallel_workers setting was modified as part of the test logic. The consequence of changing this setting is: 0 worker threads:    - current (i.e. single threaded) sequential mode    - 1 x IO thread and 1 x SQL thread    - SQL thread both reads and executes the events 1 worker thread:    - sequential mode    - 1 x IO thread, 1 x Coordinator SQL thread and 1 x Worker thread    - coordinator reads the event and hands it to the worker who executes 2+ worker threads:    - parallel execution    - 1 x IO thread, 1 x Coordinator SQL thread and 2+ Worker threads    - coordinator reads events and hands them to the workers who execute them Results Figure 1 below shows that Multi-Threaded Slaves deliver ~5x higher replication performance when configured with 10 worker threads, with the load evenly distributed across our 10 x schemas. This result is compared to the current replication implementation which is based on a single SQL thread only (i.e. zero worker threads). Figure 1: 5x Higher Performance with Multi-Threaded Slaves The following figure shows more detailed results, with QPS sampled and reported as the worker threads are incremented. The raw numbers behind this graph are reported in the Appendix section of this post. Figure 2: Detailed Results As the results above show, the configuration does not scale noticably from 5 to 9 worker threads. When configured with 10 worker threads however, scalability increases significantly. The conclusion therefore is that it is desirable to configure the same number of worker threads as schemas. Other conclusions from the results: · Running with 1 worker compared to zero workers just introduces overhead without the benefit of parallel execution. · As expected, having more workers than schemas adds no visible benefit. Aside from what is shown in the results above, testing also demonstrated that the following settings had a very positive effect on slave performance: relay-log-info-repository=TABLE master-info-repository=TABLE For 5+ workers, it was up to 2.3 times as fast to run with TABLE compared to FILE. Conclusion As the results demonstrate, Multi-Threaded Slaves deliver significant performance increases to MySQL replication when handling multiple schemas. This, and the other replication enhancements introduced in MySQL 5.6 are fully available for you to download and evaluate now from the MySQL Developer site (select Development Release tab). You can learn more about MySQL 5.6 from the documentation  Please don’t hesitate to comment on this or other replication blogs with feedback and questions. Appendix – Detailed Results

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