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  • Using FOR XML AUTO against a synonym

    - by Rick
    We've just switched to synonyms for linked server stuff, and noticed that our FOR XML output is no longer correct. When returning XML results from a view, we could alias the view and that would be assigned as the element name. With synonyms, however, it seems to ignore the alias? We're still mostly on SQL 2005 - this bug doesn't seem to happen on our 2008 instance. Is this a known problem, and any ideas for work-arounds? For example, this is what we used to be able to do: select top 3 number from Numbers as elementname for xml auto <elementname number="0"/><elementname number="1"/><elementname number="2"/> And this is what happens with a synonym: select top 3 number from Numbers_synonym as elementname for xml auto <dbo.Numbers number="0"/><dbo.Numbers number="1"/><dbo.Numbers number="2"/> As you can see, SQL Server seems to use the name of the actual referenced object instead of the alias. This gets worse for cross server queries, because you get the four-part name instead of the nice alias. (eg: <rick_server.rick_database.dbo.Numbers number="0"/>...)

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  • Immutability and shared references - how to reconcile?

    - by davetron5000
    Consider this simplified application domain: Criminal Investigative database Person is anyone involved in an investigation Report is a bit of info that is part of an investigation A Report references a primary Person (the subject of an investigation) A Report has accomplices who are secondarily related (and could certainly be primary in other investigations or reports These classes have ids that are used to store them in a database, since their info can change over time (e.g. we might find new aliases for a person, or add persons of interest to a report) If these are stored in some sort of database and I wish to use immutable objects, there seems to be an issue regarding state and referencing. Supposing that I change some meta-data about a Person. Since my Person objects immutable, I might have some code like: class Person( val id:UUID, val aliases:List[String], val reports:List[Report]) { def addAlias(name:String) = new Person(id,name :: aliases,reports) } So that my Person with a new alias becomes a new object, also immutable. If a Report refers to that person, but the alias was changed elsewhere in the system, my Report now refers to the "old" person, i.e. the person without the new alias. Similarly, I might have: class Report(val id:UUID, val content:String) { /** Adding more info to our report */ def updateContent(newContent:String) = new Report(id,newContent) } Since these objects don't know who refers to them, it's not clear to me how to let all the "referrers" know that there is a new object available representing the most recent state. This could be done by having all objects "refresh" from a central data store and all operations that create new, updated, objects store to the central data store, but this feels like a cheesy reimplementation of the underlying language's referencing. i.e. it would be more clear to just make these "secondary storable objects" mutable. So, if I add an alias to a Person, all referrers see the new value without doing anything. How is this dealt with when we want to avoid mutability, or is this a case where immutability is not helpful?

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  • Parsing HTML using HTTP Agility Pack

    - by Pajci
    Here is one table out of 5: <h3>marec - maj 2009</h3> <div class="graf_table"> <table summary="layout table"> <tr> <th>DATUM</th> <td class="datum">10.03.2009</td> <td class="datum">24.03.2009</td> <td class="datum">07.04.2009</td> <td class="datum">21.04.2009</td> <td class="datum">05.05.2009</td> <td class="datum">06.05.2009</td> </tr> <tr> <th>Maloprodajna cena [EUR/L]</th> <td>0,96000</td> <td>0,97000</td> <td>0,99600</td> <td>1,00800</td> <td>1,00800</td> <td>1,01000</td> </tr> <tr> <th>Maloprodajna cena [SIT/L]</th> <td>230,054</td> <td>232,451</td> <td>238,681</td> <td>241,557</td> <td>241,557</td> <td>242,036</td> </tr> <tr> <th>Prodajna cena brez dajatev</th> <td>0,33795</td> <td>0,34628</td> <td>0,36795</td> <td>0,37795</td> <td>0,37795</td> <td>0,37962</td> </tr> <tr> <th>Trošarina</th> <td>0,46205</td> <td>0,46205</td> <td>0,46205</td> <td>0,46205</td> <td>0,46205</td> <td>0,46205</td> </tr> <tr> <th>DDV</th> <td>0,16000</td> <td>0,16167</td> <td>0,16600</td> <td>0,16800</td> <td>0,16800</td> <td>0,16833</td> </tr> </table> </div> I have to extract out values, where table header is DATUM and Maloprodajna cena [EUR/L]. I am using Agility HTML pack. this.htmlDoc = new HtmlAgilityPack.HtmlDocument(); this.htmlDoc.OptionCheckSyntax = true; this.htmlDoc.OptionFixNestedTags = true; this.htmlDoc.OptionAutoCloseOnEnd = true; this.htmlDoc.OptionOutputAsXml = true; // is this necessary ?? this.htmlDoc.OptionDefaultStreamEncoding = System.Text.Encoding.Default; I had a lot of trouble with getting those values out. I started with: var query = from html in doc.DocumentNode.SelectNodes("//div[@class='graf_table']").Cast<HtmlNode>() from table in html.SelectNodes("//table").Cast<HtmlNode>() from row in table.SelectNodes("tr").Cast<HtmlNode>() from cell in row.SelectNodes("th|td").Cast<HtmlNode>() select new { Table = table.Id, CellText = cell.InnerHtml }; but could not figure out a way to select only values where table header is DATUM and Maloprodajna cena[EUR/L]. Is it possible to do that with where clause? Then I ended with those two queries: var date = (from d in htmlDoc.DocumentNode.SelectNodes("//div[@class='graf_table']//table//tr[1]/td") select DateTime.Parse(d.InnerText)).ToArray(); var price = (from p in htmlDoc.DocumentNode.SelectNodes("//div[@class='graf_table']//table//tr[2]/td") select double.Parse(p.InnerText)).ToArray(); Is it possible to combine those two queries? And how would I convert that to lambda expression? I just started to learn those things and I would like to know how it is done so that in the future I would not have those question. O, one more question ... does anybody know any graph control, cause I have to show those values in graph. I started with Microsoft Chart Controls, but I am having trouble with setting it. So if anyone has any experience with it I would like to know how to set it, so that x axle will show all values not every second ... example: if I have: 10.03.2009, 24.03.2009, 07.04.2009, 21.04.2009, 05.05.2009, 06.05.2009 it show only: 10.03.2009, 07.04.2009, 05.05.2009, ect. I bind data to graph like that: chart1.Series["Series1"].Points.DataBindXY(date, price); I lot of questions for my fist post ... hehe, hope that I was not indistinct or something. Thank's for any reply!

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  • SQL SERVER – Disable Clustered Index and Data Insert

    - by pinaldave
    Earlier today I received following email. “Dear Pinal, [Removed unrelated content] We looked at your script and found out that in your script of disabling indexes, you have only included non-clustered index during the bulk insert and missed to disabled all the clustered index. Our DBA[name removed] has changed your script a bit and included all the clustered indexes. Since our application is not working. When DBA [name removed] tried to enable clustered indexes again he is facing error incorrect syntax error. We are in deep problem [word replaced] [Removed Identity of organization and few unrelated stuff ]“ I have replied to my client and helped them fixed the problem. What really came to my attention is the concept of disabling clustered index. Let us try to learn a lesson from this experience. In this case, there was no need to disable clustered index at all. I had done necessary work when I was called in to work on tuning project. I had removed unused indexes, created few optimal indexes and wrote a script to disable few selected high cost indexes when bulk insert (and similar) operations are performed. There was another script which rebuild all the indexes as well. The solution worked till they included clustered index in disabling the script. Clustered indexes are in fact original table (or heap) physically ordered (any more things – not scope of this article) according to one or more keys(columns). When clustered index is disabled data rows of the disabled clustered index cannot be accessed. This means there will be no insert possible. When non clustered indexes are disabled all the data related to physically deleted but the definition of the index is kept in the system. Due to the same reason even reorganization of the index is not possible till the clustered index (which was disabled) is rebuild. Now let us come to the second part of the question, regarding receiving the error when clustered index is ‘enabled’. This is very common question I receive on the blog. (The following statement is written keeping the syntax of T-SQL in mind) Clustered indexes can be disabled but can not be enabled, they have to rebuild. It is intuitive to think that something which we have ‘disabled’ can be ‘enabled’ but the syntax for the same is ‘rebuild’. This issue has been explained here: SQL SERVER – How to Enable Index – How to Disable Index – Incorrect syntax near ‘ENABLE’. Let us go over this example where inserting the data is not possible when clustered index is disabled. USE AdventureWorks GO -- Create Table CREATE TABLE [dbo].[TableName]( [ID] [int] NOT NULL, [FirstCol] [varchar](50) NULL, CONSTRAINT [PK_TableName] PRIMARY KEY CLUSTERED ([ID] ASC) ) GO -- Create Nonclustered Index CREATE UNIQUE NONCLUSTERED INDEX [IX_NonClustered_TableName] ON [dbo].[TableName] ([FirstCol] ASC) GO -- Populate Table INSERT INTO [dbo].[TableName] SELECT 1, 'First' UNION ALL SELECT 2, 'Second' UNION ALL SELECT 3, 'Third' GO -- Disable Nonclustered Index ALTER INDEX [IX_NonClustered_TableName] ON [dbo].[TableName] DISABLE GO -- Insert Data should work fine INSERT INTO [dbo].[TableName] SELECT 4, 'Fourth' UNION ALL SELECT 5, 'Fifth' GO -- Disable Clustered Index ALTER INDEX [PK_TableName] ON [dbo].[TableName] DISABLE GO -- Insert Data will fail INSERT INTO [dbo].[TableName] SELECT 6, 'Sixth' UNION ALL SELECT 7, 'Seventh' GO /* Error: Msg 8655, Level 16, State 1, Line 1 The query processor is unable to produce a plan because the index 'PK_TableName' on table or view 'TableName' is disabled. */ -- Reorganizing Index will also throw an error ALTER INDEX [PK_TableName] ON [dbo].[TableName] REORGANIZE GO /* Error: Msg 1973, Level 16, State 1, Line 1 Cannot perform the specified operation on disabled index 'PK_TableName' on table 'dbo.TableName'. */ -- Rebuliding should work fine ALTER INDEX [PK_TableName] ON [dbo].[TableName] REBUILD GO -- Insert Data should work fine INSERT INTO [dbo].[TableName] SELECT 6, 'Sixth' UNION ALL SELECT 7, 'Seventh' GO -- Clean Up DROP TABLE [dbo].[TableName] GO I hope this example is clear enough. There were few additional posts I had written years ago, I am listing them here. SQL SERVER – Enable and Disable Index Non Clustered Indexes Using T-SQL SQL SERVER – Enabling Clustered and Non-Clustered Indexes – Interesting Fact Reference : Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Constraint and Keys, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • SQL SERVER – Understanding ALTER INDEX ALL REBUILD with Disabled Clustered Index

    - by pinaldave
    This blog is in response to the ongoing communication with the reader who had earlier asked the question of SQL SERVER – Disable Clustered Index and Data Insert. The same reader has asked me the difference between ALTER INDEX ALL REBUILD and ALTER INDEX REBUILD along with disabled clustered index. Instead of writing a big theory, we will go over the demo right away. Here are the steps that we intend to follow. 1) Create Clustered and Nonclustered Index 2) Disable Clustered and Nonclustered Index 3) Enable – a) All Indexes, b) Clustered Index USE tempdb GO -- Drop Table if Exists IF EXISTS (SELECT * FROM sys.objects WHERE OBJECT_ID = OBJECT_ID(N'[dbo].[TableName]') AND type IN (N'U')) DROP TABLE [dbo].[TableName] GO -- Create Table CREATE TABLE [dbo].[TableName]( [ID] [int] NOT NULL, [FirstCol] [varchar](50) NULL ) GO -- Create Clustered Index ALTER TABLE [TableName] ADD CONSTRAINT [PK_TableName] PRIMARY KEY CLUSTERED ([ID] ASC) GO -- Create Nonclustered Index CREATE UNIQUE NONCLUSTERED INDEX [IX_NonClustered_TableName] ON [dbo].[TableName] ([FirstCol] ASC) GO -- Check that all the indexes are enabled SELECT OBJECT_NAME(OBJECT_ID), Name, type_desc, is_disabled FROM sys.indexes WHERE OBJECT_NAME(OBJECT_ID) = 'TableName' GO Now let us disable both the indexes. -- Disable Indexes -- Disable Nonclustered Index ALTER INDEX [IX_NonClustered_TableName] ON [dbo].[TableName] DISABLE GO -- Disable Clustered Index ALTER INDEX [PK_TableName] ON [dbo].[TableName] DISABLE GO -- Check that all the indexes are disabled SELECT OBJECT_NAME(OBJECT_ID), Name, type_desc, is_disabled FROM sys.indexes WHERE OBJECT_NAME(OBJECT_ID) = 'TableName' GO Next, let us rebuild all the indexes and see the output. -- Test 1: ALTER INDEX ALL REBUILD -- Rebuliding should work fine ALTER INDEX ALL ON [dbo].[TableName] REBUILD GO -- Check that all the indexes are enabled SELECT OBJECT_NAME(OBJECT_ID), Name, type_desc, is_disabled FROM sys.indexes WHERE OBJECT_NAME(OBJECT_ID) = 'TableName' GO Now, once again disable indexes for the second test. -- Disable Indexes -- Disable Nonclustered Index ALTER INDEX [IX_NonClustered_TableName] ON [dbo].[TableName] DISABLE GO -- Disable Clustered Index ALTER INDEX [PK_TableName] ON [dbo].[TableName] DISABLE GO -- Check that all the indexes are disabled SELECT OBJECT_NAME(OBJECT_ID), Name, type_desc, is_disabled FROM sys.indexes WHERE OBJECT_NAME(OBJECT_ID) = 'TableName' GO Next, let us build only the clustered index and see the output of all the indexes. -- Test 2: ALTER INDEX REBUILD -- Rebuliding should work fine ALTER INDEX [PK_TableName] ON [dbo].[TableName] REBUILD GO -- Check that only clustered index is enabled SELECT OBJECT_NAME(OBJECT_ID), Name, type_desc, is_disabled FROM sys.indexes WHERE OBJECT_NAME(OBJECT_ID) = 'TableName' GO Let us do final clean up. -- Clean up DROP TABLE [TableName] GO From the example, it is very clear that if you have built only clustered index when the nonclustered index is disabled, it still remains disabled. Do let me know if the idea is clear. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Index, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Metro: Introduction to CSS 3 Grid Layout

    - by Stephen.Walther
    The purpose of this blog post is to provide you with a quick introduction to the new W3C CSS 3 Grid Layout standard. You can use CSS Grid Layout in Metro style applications written with JavaScript to lay out the content of an HTML page. CSS Grid Layout provides you with all of the benefits of using HTML tables for layout without requiring you to actually use any HTML table elements. Doing Page Layouts without Tables Back in the 1990’s, if you wanted to create a fancy website, then you would use HTML tables for layout. For example, if you wanted to create a standard three-column page layout then you would create an HTML table with three columns like this: <table height="100%"> <tr> <td valign="top" width="300px" bgcolor="red"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </td> <td valign="top" bgcolor="green"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </td> <td valign="top" width="300px" bgcolor="blue"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </td> </tr> </table> When the table above gets rendered out to a browser, you end up with the following three-column layout: The width of the left and right columns is fixed – the width of the middle column expands or contracts depending on the width of the browser. Sometime around the year 2005, everyone decided that using tables for layout was a bad idea. Instead of using tables for layout — it was collectively decided by the spirit of the Web — you should use Cascading Style Sheets instead. Why is using HTML tables for layout bad? Using tables for layout breaks the semantics of the TABLE element. A TABLE element should be used only for displaying tabular information such as train schedules or moon phases. Using tables for layout is bad for accessibility (The Web Content Accessibility Guidelines 1.0 is explicit about this) and using tables for layout is bad for separating content from layout (see http://CSSZenGarden.com). Post 2005, anyone who used HTML tables for layout were encouraged to hold their heads down in shame. That’s all well and good, but the problem with using CSS for layout is that it can be more difficult to work with CSS than HTML tables. For example, to achieve a standard three-column layout, you either need to use absolute positioning or floats. Here’s a three-column layout with floats: <style type="text/css"> #container { min-width: 800px; } #leftColumn { float: left; width: 300px; height: 100%; background-color:red; } #middleColumn { background-color:green; height: 100%; } #rightColumn { float: right; width: 300px; height: 100%; background-color:blue; } </style> <div id="container"> <div id="rightColumn"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </div> <div id="leftColumn"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </div> <div id="middleColumn"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </div> </div> The page above contains four DIV elements: a container DIV which contains a leftColumn, middleColumn, and rightColumn DIV. The leftColumn DIV element is floated to the left and the rightColumn DIV element is floated to the right. Notice that the rightColumn DIV appears in the page before the middleColumn DIV – this unintuitive ordering is necessary to get the floats to work correctly (see http://stackoverflow.com/questions/533607/css-three-column-layout-problem). The page above (almost) works with the most recent versions of most browsers. For example, you get the correct three-column layout in both Firefox and Chrome: And the layout mostly works with Internet Explorer 9 except for the fact that for some strange reason the min-width doesn’t work so when you shrink the width of your browser, you can get the following unwanted layout: Notice how the middle column (the green column) bleeds to the left and right. People have solved these issues with more complicated CSS. For example, see: http://matthewjamestaylor.com/blog/holy-grail-no-quirks-mode.htm But, at this point, no one could argue that using CSS is easier or more intuitive than tables. It takes work to get a layout with CSS and we know that we could achieve the same layout more easily using HTML tables. Using CSS Grid Layout CSS Grid Layout is a new W3C standard which provides you with all of the benefits of using HTML tables for layout without the disadvantage of using an HTML TABLE element. In other words, CSS Grid Layout enables you to perform table layouts using pure Cascading Style Sheets. The CSS Grid Layout standard is still in a “Working Draft” state (it is not finalized) and it is located here: http://www.w3.org/TR/css3-grid-layout/ The CSS Grid Layout standard is only supported by Internet Explorer 10 and there are no signs that any browser other than Internet Explorer will support this standard in the near future. This means that it is only practical to take advantage of CSS Grid Layout when building Metro style applications with JavaScript. Here’s how you can create a standard three-column layout using a CSS Grid Layout: <!DOCTYPE html> <html> <head> <style type="text/css"> html, body, #container { height: 100%; padding: 0px; margin: 0px; } #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100%; } #leftColumn { -ms-grid-column: 1; background-color:red; } #middleColumn { -ms-grid-column: 2; background-color:green; } #rightColumn { -ms-grid-column: 3; background-color:blue; } </style> </head> <body> <div id="container"> <div id="leftColumn"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </div> <div id="middleColumn"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </div> <div id="rightColumn"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </div> </div> </body> </html> When the page above is rendered in Internet Explorer 10, you get a standard three-column layout: The page above contains four DIV elements: a container DIV which contains a leftColumn DIV, middleColumn DIV, and rightColumn DIV. The container DIV is set to Grid display mode with the following CSS rule: #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100%; } The display property is set to the value “-ms-grid”. This property causes the container DIV to lay out its child elements in a grid. (Notice that you use “-ms-grid” instead of “grid”. The “-ms-“ prefix is used because the CSS Grid Layout standard is still preliminary. This implementation only works with IE10 and it might change before the final release.) The grid columns and rows are defined with the “-ms-grid-columns” and “-ms-grid-rows” properties. The style rule above creates a grid with three columns and one row. The left and right columns are fixed sized at 300 pixels. The middle column sizes automatically depending on the remaining space available. The leftColumn, middleColumn, and rightColumn DIVs are positioned within the container grid element with the following CSS rules: #leftColumn { -ms-grid-column: 1; background-color:red; } #middleColumn { -ms-grid-column: 2; background-color:green; } #rightColumn { -ms-grid-column: 3; background-color:blue; } The “-ms-grid-column” property is used to specify the column associated with the element selected by the style sheet selector. The leftColumn DIV is positioned in the first grid column, the middleColumn DIV is positioned in the second grid column, and the rightColumn DIV is positioned in the third grid column. I find using CSS Grid Layout to be just as intuitive as using an HTML table for layout. You define your columns and rows and then you position different elements within these columns and rows. Very straightforward. Creating Multiple Columns and Rows In the previous section, we created a super simple three-column layout. This layout contained only a single row. In this section, let’s create a slightly more complicated layout which contains more than one row: The following page contains a header row, a content row, and a footer row. The content row contains three columns: <!DOCTYPE html> <html> <head> <style type="text/css"> html, body, #container { height: 100%; padding: 0px; margin: 0px; } #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100px 1fr 100px; } #header { -ms-grid-column: 1; -ms-grid-column-span: 3; -ms-grid-row: 1; background-color: yellow; } #leftColumn { -ms-grid-column: 1; -ms-grid-row: 2; background-color:red; } #middleColumn { -ms-grid-column: 2; -ms-grid-row: 2; background-color:green; } #rightColumn { -ms-grid-column: 3; -ms-grid-row: 2; background-color:blue; } #footer { -ms-grid-column: 1; -ms-grid-column-span: 3; -ms-grid-row: 3; background-color: orange; } </style> </head> <body> <div id="container"> <div id="header"> Header, Header, Header </div> <div id="leftColumn"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </div> <div id="middleColumn"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </div> <div id="rightColumn"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </div> <div id="footer"> Footer, Footer, Footer </div> </div> </body> </html> In the page above, the grid layout is created with the following rule which creates a grid with three rows and three columns: #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100px 1fr 100px; } The header is created with the following rule: #header { -ms-grid-column: 1; -ms-grid-column-span: 3; -ms-grid-row: 1; background-color: yellow; } The header is positioned in column 1 and row 1. Furthermore, notice that the “-ms-grid-column-span” property is used to span the header across three columns. CSS Grid Layout and Fractional Units When you use CSS Grid Layout, you can take advantage of fractional units. Fractional units provide you with an easy way of dividing up remaining space in a page. Imagine, for example, that you want to create a three-column page layout. You want the size of the first column to be fixed at 200 pixels and you want to divide the remaining space among the remaining three columns. The width of the second column is equal to the combined width of the third and fourth columns. The following CSS rule creates four columns with the desired widths: #container { display: -ms-grid; -ms-grid-columns: 200px 2fr 1fr 1fr; -ms-grid-rows: 1fr; } The fr unit represents a fraction. The grid above contains four columns. The second column is two times the size (2fr) of the third (1fr) and fourth (1fr) columns. When you use the fractional unit, the remaining space is divided up using fractional amounts. Notice that the single row is set to a height of 1fr. The single grid row gobbles up the entire vertical space. Here’s the entire HTML page: <!DOCTYPE html> <html> <head> <style type="text/css"> html, body, #container { height: 100%; padding: 0px; margin: 0px; } #container { display: -ms-grid; -ms-grid-columns: 200px 2fr 1fr 1fr; -ms-grid-rows: 1fr; } #firstColumn { -ms-grid-column: 1; background-color:red; } #secondColumn { -ms-grid-column: 2; background-color:green; } #thirdColumn { -ms-grid-column: 3; background-color:blue; } #fourthColumn { -ms-grid-column: 4; background-color:orange; } </style> </head> <body> <div id="container"> <div id="firstColumn"> First Column, First Column, First Column </div> <div id="secondColumn"> Second Column, Second Column, Second Column </div> <div id="thirdColumn"> Third Column, Third Column, Third Column </div> <div id="fourthColumn"> Fourth Column, Fourth Column, Fourth Column </div> </div> </body> </html>   Summary There is more in the CSS 3 Grid Layout standard than discussed in this blog post. My goal was to describe the basics. If you want to learn more than you can read through the entire standard at http://www.w3.org/TR/css3-grid-layout/ In this blog post, I described some of the difficulties that you might encounter when attempting to replace HTML tables with Cascading Style Sheets when laying out a web page. I explained how you can take advantage of the CSS 3 Grid Layout standard to avoid these problems when building Metro style applications using JavaScript. CSS 3 Grid Layout provides you with all of the benefits of using HTML tables for laying out a page without requiring you to use HTML table elements.

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  • Working with PivotTables in Excel

    - by Mark Virtue
    PivotTables are one of the most powerful features of Microsoft Excel.  They allow large amounts of data to be analyzed and summarized in just a few mouse clicks. In this article, we explore PivotTables, understand what they are, and learn how to create and customize them. Note:  This article is written using Excel 2010 (Beta).  The concept of a PivotTable has changed little over the years, but the method of creating one has changed in nearly every iteration of Excel.  If you are using a version of Excel that is not 2010, expect different screens from the ones you see in this article. A Little History In the early days of spreadsheet programs, Lotus 1-2-3 ruled the roost.  Its dominance was so complete that people thought it was a waste of time for Microsoft to bother developing their own spreadsheet software (Excel) to compete with Lotus.  Flash-forward to 2010, and Excel’s dominance of the spreadsheet market is greater than Lotus’s ever was, while the number of users still running Lotus 1-2-3 is approaching zero.  How did this happen?  What caused such a dramatic reversal of fortunes? Industry analysts put it down to two factors:  Firstly, Lotus decided that this fancy new GUI platform called “Windows” was a passing fad that would never take off.  They declined to create a Windows version of Lotus 1-2-3 (for a few years, anyway), predicting that their DOS version of the software was all anyone would ever need.  Microsoft, naturally, developed Excel exclusively for Windows.  Secondly, Microsoft developed a feature for Excel that Lotus didn’t provide in 1-2-3, namely PivotTables.  The PivotTables feature, exclusive to Excel, was deemed so staggeringly useful that people were willing to learn an entire new software package (Excel) rather than stick with a program (1-2-3) that didn’t have it.  This one feature, along with the misjudgment of the success of Windows, was the death-knell for Lotus 1-2-3, and the beginning of the success of Microsoft Excel. Understanding PivotTables So what is a PivotTable, exactly? Put simply, a PivotTable is a summary of some data, created to allow easy analysis of said data.  But unlike a manually created summary, Excel PivotTables are interactive.  Once you have created one, you can easily change it if it doesn’t offer the exact insights into your data that you were hoping for.  In a couple of clicks the summary can be “pivoted” – rotated in such a way that the column headings become row headings, and vice versa.  There’s a lot more that can be done, too.  Rather than try to describe all the features of PivotTables, we’ll simply demonstrate them… The data that you analyze using a PivotTable can’t be just any data – it has to be raw data, previously unprocessed (unsummarized) – typically a list of some sort.  An example of this might be the list of sales transactions in a company for the past six months. Examine the data shown below: Notice that this is not raw data.  In fact, it is already a summary of some sort.  In cell B3 we can see $30,000, which apparently is the total of James Cook’s sales for the month of January.  So where is the raw data?  How did we arrive at the figure of $30,000?  Where is the original list of sales transactions that this figure was generated from?  It’s clear that somewhere, someone must have gone to the trouble of collating all of the sales transactions for the past six months into the summary we see above.  How long do you suppose this took?  An hour?  Ten?  Probably. If we were to track down the original list of sales transactions, it might look something like this: You may be surprised to learn that, using the PivotTable feature of Excel, we can create a monthly sales summary similar to the one above in a few seconds, with only a few mouse clicks.  We can do this – and a lot more too! How to Create a PivotTable First, ensure that you have some raw data in a worksheet in Excel.  A list of financial transactions is typical, but it can be a list of just about anything:  Employee contact details, your CD collection, or fuel consumption figures for your company’s fleet of cars. So we start Excel… …and we load such a list… Once we have the list open in Excel, we’re ready to start creating the PivotTable. Click on any one single cell within the list: Then, from the Insert tab, click the PivotTable icon: The Create PivotTable box appears, asking you two questions:  What data should your new PivotTable be based on, and where should it be created?  Because we already clicked on a cell within the list (in the step above), the entire list surrounding that cell is already selected for us ($A$1:$G$88 on the Payments sheet, in this example).  Note that we could select a list in any other region of any other worksheet, or even some external data source, such as an Access database table, or even a MS-SQL Server database table.  We also need to select whether we want our new PivotTable to be created on a new worksheet, or on an existing one.  In this example we will select a new one: The new worksheet is created for us, and a blank PivotTable is created on that worksheet: Another box also appears:  The PivotTable Field List.  This field list will be shown whenever we click on any cell within the PivotTable (above): The list of fields in the top part of the box is actually the collection of column headings from the original raw data worksheet.  The four blank boxes in the lower part of the screen allow us to choose the way we would like our PivotTable to summarize the raw data.  So far, there is nothing in those boxes, so the PivotTable is blank.  All we need to do is drag fields down from the list above and drop them in the lower boxes.  A PivotTable is then automatically created to match our instructions.  If we get it wrong, we only need to drag the fields back to where they came from and/or drag new fields down to replace them. The Values box is arguably the most important of the four.  The field that is dragged into this box represents the data that needs to be summarized in some way (by summing, averaging, finding the maximum, minimum, etc).  It is almost always numerical data.  A perfect candidate for this box in our sample data is the “Amount” field/column.  Let’s drag that field into the Values box: Notice that (a) the “Amount” field in the list of fields is now ticked, and “Sum of Amount” has been added to the Values box, indicating that the amount column has been summed. If we examine the PivotTable itself, we indeed find the sum of all the “Amount” values from the raw data worksheet: We’ve created our first PivotTable!  Handy, but not particularly impressive.  It’s likely that we need a little more insight into our data than that. Referring to our sample data, we need to identify one or more column headings that we could conceivably use to split this total.  For example, we may decide that we would like to see a summary of our data where we have a row heading for each of the different salespersons in our company, and a total for each.  To achieve this, all we need to do is to drag the “Salesperson” field into the Row Labels box: Now, finally, things start to get interesting!  Our PivotTable starts to take shape….   With a couple of clicks we have created a table that would have taken a long time to do manually. So what else can we do?  Well, in one sense our PivotTable is complete.  We’ve created a useful summary of our source data.  The important stuff is already learned!  For the rest of the article, we will examine some ways that more complex PivotTables can be created, and ways that those PivotTables can be customized. First, we can create a two-dimensional table.  Let’s do that by using “Payment Method” as a column heading.  Simply drag the “Payment Method” heading to the Column Labels box: Which looks like this: Starting to get very cool! Let’s make it a three-dimensional table.  What could such a table possibly look like?  Well, let’s see… Drag the “Package” column/heading to the Report Filter box: Notice where it ends up…. This allows us to filter our report based on which “holiday package” was being purchased.  For example, we can see the breakdown of salesperson vs payment method for all packages, or, with a couple of clicks, change it to show the same breakdown for the “Sunseekers” package: And so, if you think about it the right way, our PivotTable is now three-dimensional.  Let’s keep customizing… If it turns out, say, that we only want to see cheque and credit card transactions (i.e. no cash transactions), then we can deselect the “Cash” item from the column headings.  Click the drop-down arrow next to Column Labels, and untick “Cash”: Let’s see what that looks like…As you can see, “Cash” is gone. Formatting This is obviously a very powerful system, but so far the results look very plain and boring.  For a start, the numbers that we’re summing do not look like dollar amounts – just plain old numbers.  Let’s rectify that. A temptation might be to do what we’re used to doing in such circumstances and simply select the whole table (or the whole worksheet) and use the standard number formatting buttons on the toolbar to complete the formatting.  The problem with that approach is that if you ever change the structure of the PivotTable in the future (which is 99% likely), then those number formats will be lost.  We need a way that will make them (semi-)permanent. First, we locate the “Sum of Amount” entry in the Values box, and click on it.  A menu appears.  We select Value Field Settings… from the menu: The Value Field Settings box appears. Click the Number Format button, and the standard Format Cells box appears: From the Category list, select (say) Accounting, and drop the number of decimal places to 0.  Click OK a few times to get back to the PivotTable… As you can see, the numbers have been correctly formatted as dollar amounts. While we’re on the subject of formatting, let’s format the entire PivotTable.  There are a few ways to do this.  Let’s use a simple one… Click the PivotTable Tools/Design tab: Then drop down the arrow in the bottom-right of the PivotTable Styles list to see a vast collection of built-in styles: Choose any one that appeals, and look at the result in your PivotTable:   Other Options We can work with dates as well.  Now usually, there are many, many dates in a transaction list such as the one we started with.  But Excel provides the option to group data items together by day, week, month, year, etc.  Let’s see how this is done. First, let’s remove the “Payment Method” column from the Column Labels box (simply drag it back up to the field list), and replace it with the “Date Booked” column: As you can see, this makes our PivotTable instantly useless, giving us one column for each date that a transaction occurred on – a very wide table! To fix this, right-click on any date and select Group… from the context-menu: The grouping box appears.  We select Months and click OK: Voila!  A much more useful table: (Incidentally, this table is virtually identical to the one shown at the beginning of this article – the original sales summary that was created manually.) Another cool thing to be aware of is that you can have more than one set of row headings (or column headings): …which looks like this…. You can do a similar thing with column headings (or even report filters). Keeping things simple again, let’s see how to plot averaged values, rather than summed values. First, click on “Sum of Amount”, and select Value Field Settings… from the context-menu that appears: In the Summarize value field by list in the Value Field Settings box, select Average: While we’re here, let’s change the Custom Name, from “Average of Amount” to something a little more concise.  Type in something like “Avg”: Click OK, and see what it looks like.  Notice that all the values change from summed totals to averages, and the table title (top-left cell) has changed to “Avg”: If we like, we can even have sums, averages and counts (counts = how many sales there were) all on the same PivotTable! Here are the steps to get something like that in place (starting from a blank PivotTable): Drag “Salesperson” into the Column Labels Drag “Amount” field down into the Values box three times For the first “Amount” field, change its custom name to “Total” and it’s number format to Accounting (0 decimal places) For the second “Amount” field, change its custom name to “Average”, its function to Average and it’s number format to Accounting (0 decimal places) For the third “Amount” field, change its name to “Count” and its function to Count Drag the automatically created field from Column Labels to Row Labels Here’s what we end up with: Total, average and count on the same PivotTable! Conclusion There are many, many more features and options for PivotTables created by Microsoft Excel – far too many to list in an article like this.  To fully cover the potential of PivotTables, a small book (or a large website) would be required.  Brave and/or geeky readers can explore PivotTables further quite easily:  Simply right-click on just about everything, and see what options become available to you.  There are also the two ribbon-tabs: PivotTable Tools/Options and Design.  It doesn’t matter if you make a mistake – it’s easy to delete the PivotTable and start again – a possibility old DOS users of Lotus 1-2-3 never had. We’ve included an Excel that should work with most versions of Excel, so you can download to practice your PivotTable skills. Download Our Practice Excel File Similar Articles Productive Geek Tips Magnify Selected Cells In Excel 2007Share Access Data with Excel in Office 2010Make Excel 2007 Print Gridlines In Workbook FileMake Excel 2007 Always Save in Excel 2003 FormatConvert Older Excel Documents to Excel 2007 Format TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 PCmover Professional Ben & Jerry’s Free Cone Day, 3/23/10 New Stinger from McAfee Helps Remove ‘FakeAlert’ Threats Google Apps Marketplace: Tools & Services For Google Apps Users Get News Quick and Precise With Newser Scan for Viruses in Ubuntu using ClamAV Replace Your Windows Task Manager With System Explorer

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  • Cutting edge technology, a lone Movember ranger and a 5-a-side football club ...meet the team at Oracle’s Belfast Offices.

    - by user10729410
    Normal 0 false false false EN-IE X-NONE X-NONE MicrosoftInternetExplorer4 /* 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-qformat:yes; 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:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Normal 0 false false false EN-IE X-NONE X-NONE MicrosoftInternetExplorer4 /* 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-qformat:yes; 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:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} By Olivia O’Connell To see what’s in store at Oracle’s next Open Day which comes to Belfast this week, I visited the offices with some colleagues to meet the team and get a feel for what‘s in store on November 29th. After being warmly greeted by Frances and Francesca, who make sure Front of House and Facilities run smoothly, we embarked on a quick tour of the 2 floors Oracle occupies, led by VP Bo, it was time to seek out some willing volunteers to be interviewed/photographed - what a shy bunch! A bit of coaxing from the social media team was needed here! In a male-dominated environment, the few women on the team caught my eye immediately. I got chatting to Susan, a business analyst and Bronagh, a tech writer. It becomes clear during our chat that the male/female divide is not an issue – “everyone here just gets on with the job,” says Suzanne, “We’re all around the same age and have similar priorities and luckily everyone is really friendly so there are no problems. ” A graduate of Queen’s University in Belfast majoring in maths & computer science, Susan works closely with product management and the development teams to ensure that the final project delivered to clients meets and exceeds their expectations. Bronagh, who joined us following working for a tech company in Montreal and gaining her post-grad degree at University of Ulster agrees that the work is challenging but “the environment is so relaxed and friendly”. Normal 0 false false false EN-IE X-NONE X-NONE MicrosoftInternetExplorer4 /* 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-qformat:yes; 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:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Software developer David is taking the Movember challenge for the first time to raise vital funds and awareness for men’s health. Like other colleagues in the office, he is a University of Ulster graduate and works on Reference applications and Merchandising Tools which enable customers to establish e-shops using Oracle technologies. The social activities are headed up by Gordon, a software engineer on the commerce team who joined the team 4 years ago after graduating from the University of Strathclyde at Glasgow with a degree in Computer Science. Everyone is unanimous that the best things about working at Oracle’s Belfast offices are the casual friendly environment and the opportunity to be at the cutting edge of technology. We’re looking forward to our next trip to Belfast for some cool demos and meet candidates. And as for the camera-shyness? Look who came out to have their picture taken at the end of the day! Normal 0 false false false EN-IE X-NONE X-NONE MicrosoftInternetExplorer4 /* 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-qformat:yes; 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:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} The Oracle offices in Belfast are located on the 6th floor, Victoria House, Gloucester Street, Belfast BT1 4LS, UK View Larger Map Normal 0 false false false EN-IE X-NONE X-NONE MicrosoftInternetExplorer4 /* 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-qformat:yes; 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:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Open day takes place on Thursday, 29th November 4pm – 8pm. Visit the 5 Demo Stations to find out more about each teams' activities and projects to date. See live demos including "Engaging the Customer", "Managing Your Store", "Helping the Customer", "Shopping on-line" and "The Commerce Experience" processes. The "Working @Oracle" stand will give you the chance to connect with our recruitment team and get information about the Recruitment process and making your career path in Oracle. Register here.

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  • SQL SERVER – Challenge – Puzzle – Why does RIGHT JOIN Exists

    - by pinaldave
    I had interesting conversation with the attendees of the my SQL Server Performance Tuning course. I was asked if LEFT JOIN can do the same task as RIGHT JOIN by reserving the order of the tables in join, why does RIGHT JOIN exists? The definitions are as following: Left Join – select all the records from the LEFT table and then pick up any matching records from the RIGHT table   Right Join – select all the records from the RIGHT table and then pick up any matching records from the LEFT table Most of us read from LEFT to RIGHT so we are using LEFT join. Do you have any explaination why RIGHT JOIN exists or can you come up with example, where RIGHT JOIN is absolutely required and the task can not be achieved with LEFT JOIN. Other Puzzles: SQL SERVER – Puzzle – Challenge – Error While Converting Money to Decimal SQL SERVER – Challenge – Puzzle – Usage of FAST Hint Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Puzzle, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Difference between LASTDATE and MAX for semi-additive measures in #DAX

    - by Marco Russo (SQLBI)
    I recently wrote an article on SQLBI about the semi-additive measures in DAX. I included the formulas common calculations and there is an interesting point that worth a longer digression: the difference between LASTDATE and MAX (which is similar to FIRSTDATE and MIN – I just describe the former, for the latter just replace the correspondent names). LASTDATE is a dax function that receives an argument that has to be a date column and returns the last date active in the current filter context. Apparently, it is the same value returned by MAX, which returns the maximum value of the argument in the current filter context. Of course, MAX can receive any numeric type (including date), whereas LASTDATE only accepts a column of type date. But overall, they seems identical in the result. However, the difference is a semantic one. In fact, this expression: LASTDATE ( 'Date'[Date] ) could be also rewritten as: FILTER ( VALUES ( 'Date'[Date] ), 'Date'[Date] = MAX ( 'Date'[Date] ) ) LASTDATE is a function that returns a table with a single column and one row, whereas MAX returns a scalar value. In DAX, any expression with one row and one column can be automatically converted into the corresponding scalar value of the single cell returned. The opposite is not true. So you can use LASTDATE in any expression where a table or a scalar is required, but MAX can be used only where a scalar expression is expected. Since LASTDATE returns a table, you can use it in any expression that expects a table as an argument, such as COUNTROWS. In fact, you can write this expression: COUNTROWS ( LASTDATE ( 'Date'[Date] ) ) which will always return 1 or BLANK (if there are no dates active in the current filter context). You cannot pass MAX as an argument of COUNTROWS. You can pass to LASTDATE a reference to a column or any table expression that returns a column. The following two syntaxes are semantically identical: LASTDATE ( 'Date'[Date] ) LASTDATE ( VALUES ( 'Date'[Date] ) ) The result is the same and the use of VALUES is not required because it is implicit in the first syntax, unless you have a row context active. In that case, be careful that using in a row context the LASTDATE function with a direct column reference will produce a context transition (the row context is transformed into a filter context) that hides the external filter context, whereas using VALUES in the argument preserve the existing filter context without applying the context transition of the row context (see the columns LastDate and Values in the following query and result). You can use any other table expressions (including a FILTER) as LASTDATE argument. For example, the following expression will always return the last date available in the Date table, regardless of the current filter context: LASTDATE ( ALL ( 'Date'[Date] ) ) The following query recap the result produced by the different syntaxes described. EVALUATE     CALCULATETABLE(         ADDCOLUMNS(              VALUES ('Date'[Date] ),             "LastDate", LASTDATE( 'Date'[Date] ),             "Values", LASTDATE( VALUES ( 'Date'[Date] ) ),             "Filter", LASTDATE( FILTER ( VALUES ( 'Date'[Date] ), 'Date'[Date] = MAX ( 'Date'[Date] ) ) ),             "All", LASTDATE( ALL ( 'Date'[Date] ) ),             "Max", MAX( 'Date'[Date] )         ),         'Date'[Calendar Year] = 2008     ) ORDER BY 'Date'[Date] The LastDate columns repeat the current date, because the context transition happens within the ADDCOLUMNS. The Values column preserve the existing filter context from being replaced by the context transition, so the result corresponds to the last day in year 2008 (which is filtered in the external CALCULATETABLE). The Filter column works like the Values one, even if we use the FILTER instead of the LASTDATE approach. The All column shows the result of LASTDATE ( ALL ( ‘Date’[Date] ) ) that ignores the filter on Calendar Year (in fact the date returned is in year 2010). Finally, the Max column shows the result of the MAX formula, which is the easiest to use and only don’t return a table if you need it (like in a filter argument of CALCULATE or CALCULATETABLE, where using LASTDATE is shorter). I know that using LASTDATE in complex expressions might create some issue. In my experience, the fact that a context transition happens automatically in presence of a row context is the main reason of confusion and unexpected results in DAX formulas using this function. For a reference of DAX formulas using MAX and LASTDATE, read my article about semi-additive measures in DAX.

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  • More CPU cores may not always lead to better performance – MAXDOP and query memory distribution in spotlight

    - by sqlworkshops
    More hardware normally delivers better performance, but there are exceptions where it can hinder performance. Understanding these exceptions and working around it is a major part of SQL Server performance tuning.   When a memory allocating query executes in parallel, SQL Server distributes memory to each task that is executing part of the query in parallel. In our example the sort operator that executes in parallel divides the memory across all tasks assuming even distribution of rows. Common memory allocating queries are that perform Sort and do Hash Match operations like Hash Join or Hash Aggregation or Hash Union.   In reality, how often are column values evenly distributed, think about an example; are employees working for your company distributed evenly across all the Zip codes or mainly concentrated in the headquarters? What happens when you sort result set based on Zip codes? Do all products in the catalog sell equally or are few products hot selling items?   One of my customers tested the below example on a 24 core server with various MAXDOP settings and here are the results:MAXDOP 1: CPU time = 1185 ms, elapsed time = 1188 msMAXDOP 4: CPU time = 1981 ms, elapsed time = 1568 msMAXDOP 8: CPU time = 1918 ms, elapsed time = 1619 msMAXDOP 12: CPU time = 2367 ms, elapsed time = 2258 msMAXDOP 16: CPU time = 2540 ms, elapsed time = 2579 msMAXDOP 20: CPU time = 2470 ms, elapsed time = 2534 msMAXDOP 0: CPU time = 2809 ms, elapsed time = 2721 ms - all 24 cores.In the above test, when the data was evenly distributed, the elapsed time of parallel query was always lower than serial query.   Why does the query get slower and slower with more CPU cores / higher MAXDOP? Maybe you can answer this question after reading the article; let me know: [email protected].   Well you get the point, let’s see an example.   The best way to learn is to practice. To create the below tables and reproduce the behavior, join the mailing list by using this link: www.sqlworkshops.com/ml and I will send you the table creation script.   Let’s update the Employees table with 49 out of 50 employees located in Zip code 2001. update Employees set Zip = EmployeeID / 400 + 1 where EmployeeID % 50 = 1 update Employees set Zip = 2001 where EmployeeID % 50 != 1 go update statistics Employees with fullscan go   Let’s create the temporary table #FireDrill with all possible Zip codes. drop table #FireDrill go create table #FireDrill (Zip int primary key) insert into #FireDrill select distinct Zip from Employees update statistics #FireDrill with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --First serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) goThe query took 1011 ms to complete.   The execution plan shows the 77816 KB of memory was granted while the estimated rows were 799624.  No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 1912 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 799624.  The estimated number of rows between serial and parallel plan are the same. The parallel plan has slightly more memory granted due to additional overhead. Sort properties shows the rows are unevenly distributed over the 4 threads.   Sort Warnings in SQL Server Profiler.   Intermediate Summary: The reason for the higher duration with parallel plan was sort spill. This is due to uneven distribution of employees over Zip codes, especially concentration of 49 out of 50 employees in Zip code 2001. Now let’s update the Employees table and distribute employees evenly across all Zip codes.   update Employees set Zip = EmployeeID / 400 + 1 go update statistics Employees with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go   The query took 751 ms to complete.  The execution plan shows the 77816 KB of memory was granted while the estimated rows were 784707.  No Sort Warnings in SQL Server Profiler.   Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 661 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 784707.  Sort properties shows the rows are evenly distributed over the 4 threads. No Sort Warnings in SQL Server Profiler.    Intermediate Summary: When employees were distributed unevenly, concentrated on 1 Zip code, parallel sort spilled while serial sort performed well without spilling to tempdb. When the employees were distributed evenly across all Zip codes, parallel sort and serial sort did not spill to tempdb. This shows uneven data distribution may affect the performance of some parallel queries negatively. For detailed discussion of memory allocation, refer to webcasts available at www.sqlworkshops.com/webcasts.     Some of you might conclude from the above execution times that parallel query is not faster even when there is no spill. Below you can see when we are joining limited amount of Zip codes, parallel query will be fasted since it can use Bitmap Filtering.   Let’s update the Employees table with 49 out of 50 employees located in Zip code 2001. update Employees set Zip = EmployeeID / 400 + 1 where EmployeeID % 50 = 1 update Employees set Zip = 2001 where EmployeeID % 50 != 1 go update statistics Employees with fullscan go  Let’s create the temporary table #FireDrill with limited Zip codes. drop table #FireDrill go create table #FireDrill (Zip int primary key) insert into #FireDrill select distinct Zip       from Employees where Zip between 1800 and 2001 update statistics #FireDrill with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go The query took 989 ms to complete.  The execution plan shows the 77816 KB of memory was granted while the estimated rows were 785594. No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 1799 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 785594.  Sort Warnings in SQL Server Profiler.    The estimated number of rows between serial and parallel plan are the same. The parallel plan has slightly more memory granted due to additional overhead.  Intermediate Summary: The reason for the higher duration with parallel plan even with limited amount of Zip codes was sort spill. This is due to uneven distribution of employees over Zip codes, especially concentration of 49 out of 50 employees in Zip code 2001.   Now let’s update the Employees table and distribute employees evenly across all Zip codes. update Employees set Zip = EmployeeID / 400 + 1 go update statistics Employees with fullscan go Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go The query took 250  ms to complete.  The execution plan shows the 9016 KB of memory was granted while the estimated rows were 79973.8.  No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0.  --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 85 ms to complete.  The execution plan shows the 13152 KB of memory was granted while the estimated rows were 784707.  No Sort Warnings in SQL Server Profiler.    Here you see, parallel query is much faster than serial query since SQL Server is using Bitmap Filtering to eliminate rows before the hash join.   Parallel queries are very good for performance, but in some cases it can hinder performance. If one identifies the reason for these hindrances, then it is possible to get the best out of parallelism. I covered many aspects of monitoring and tuning parallel queries in webcasts (www.sqlworkshops.com/webcasts) and articles (www.sqlworkshops.com/articles). I suggest you to watch the webcasts and read the articles to better understand how to identify and tune parallel query performance issues.   Summary: One has to avoid sort spill over tempdb and the chances of spills are higher when a query executes in parallel with uneven data distribution. Parallel query brings its own advantage, reduced elapsed time and reduced work with Bitmap Filtering. So it is important to understand how to avoid spills over tempdb and when to execute a query in parallel.   I explain these concepts with detailed examples in my webcasts (www.sqlworkshops.com/webcasts), I recommend you to watch them. The best way to learn is to practice. To create the above tables and reproduce the behavior, join the mailing list at www.sqlworkshops.com/ml and I will send you the relevant SQL Scripts.   Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   Disclaimer and copyright information:This article refers to organizations and products that may be the trademarks or registered trademarks of their various owners. Copyright of this article belongs to R Meyyappan / www.sqlworkshops.com. You may freely use the ideas and concepts discussed in this article with acknowledgement (www.sqlworkshops.com), but you may not claim any of it as your own work. This article is for informational purposes only; you use any of the suggestions given here entirely at your own risk.   Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   R Meyyappan [email protected] LinkedIn: http://at.linkedin.com/in/rmeyyappan  

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  • SQL Monitor’s data repository: Alerts

    - by Chris Lambrou
    In my previous post, I introduced the SQL Monitor data repository, and described how the monitored objects are stored in a hierarchy in the data schema, in a series of tables with a _Keys suffix. In this post I had planned to describe how the actual data for the monitored objects is stored in corresponding tables with _StableSamples and _UnstableSamples suffixes. However, I’m going to postpone that until my next post, as I’ve had a request from a SQL Monitor user to explain how alerts are stored. In the SQL Monitor data repository, alerts are stored in tables belonging to the alert schema, which contains the following five tables: alert.Alert alert.Alert_Cleared alert.Alert_Comment alert.Alert_Severity alert.Alert_Type In this post, I’m only going to cover the alert.Alert and alert.Alert_Type tables. I may cover the other three tables in a later post. The most important table in this schema is alert.Alert, as each row in this table corresponds to a single alert. So let’s have a look at it. SELECT TOP 100 AlertId, AlertType, TargetObject, [Read], SubType FROM alert.Alert ORDER BY AlertId DESC;  AlertIdAlertTypeTargetObjectReadSubType 165550397:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,9:SqlServer,1,4:Name,s0:,10 265549387:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,7:Machine,1,4:Name,s0:,10 365548187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 465547157:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 565546147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 665545187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 765544157:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 865543147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 965542187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 1065541147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 11…     So what are we seeing here, then? Well, AlertId is an auto-incrementing identity column, so ORDER BY AlertId DESC ensures that we see the most recent alerts first. AlertType indicates the type of each alert, such as Job failed (6), Backup overdue (14) or Long-running query (12). The TargetObject column indicates which monitored object the alert is associated with. The Read column acts as a flag to indicate whether or not the alert has been read. And finally the SubType column is used in the case of a Custom metric (40) alert, to indicate which custom metric the alert pertains to. Okay, now lets look at some of those columns in more detail. The AlertType column is an easy one to start with, and it brings use nicely to the next table, data.Alert_Type. Let’s have a look at what’s in this table: SELECT AlertType, Event, Monitoring, Name, Description FROM alert.Alert_Type ORDER BY AlertType;  AlertTypeEventMonitoringNameDescription 1100Processor utilizationProcessor utilization (CPU) on a host machine stays above a threshold percentage for longer than a specified duration 2210SQL Server error log entryAn error is written to the SQL Server error log with a severity level above a specified value. 3310Cluster failoverThe active cluster node fails, causing the SQL Server instance to switch nodes. 4410DeadlockSQL deadlock occurs. 5500Processor under-utilizationProcessor utilization (CPU) on a host machine remains below a threshold percentage for longer than a specified duration 6610Job failedA job does not complete successfully (the job returns an error code). 7700Machine unreachableHost machine (Windows server) cannot be contacted on the network. 8800SQL Server instance unreachableThe SQL Server instance is not running or cannot be contacted on the network. 9900Disk spaceDisk space used on a logical disk drive is above a defined threshold for longer than a specified duration. 101000Physical memoryPhysical memory (RAM) used on the host machine stays above a threshold percentage for longer than a specified duration. 111100Blocked processSQL process is blocked for longer than a specified duration. 121200Long-running queryA SQL query runs for longer than a specified duration. 131400Backup overdueNo full backup exists, or the last full backup is older than a specified time. 141500Log backup overdueNo log backup exists, or the last log backup is older than a specified time. 151600Database unavailableDatabase changes from Online to any other state. 161700Page verificationTorn Page Detection or Page Checksum is not enabled for a database. 171800Integrity check overdueNo entry for an integrity check (DBCC DBINFO returns no date for dbi_dbccLastKnownGood field), or the last check is older than a specified time. 181900Fragmented indexesFragmentation level of one or more indexes is above a threshold percentage. 192400Job duration unusualThe duration of a SQL job duration deviates from its baseline duration by more than a threshold percentage. 202501Clock skewSystem clock time on the Base Monitor computer differs from the system clock time on a monitored SQL Server host machine by a specified number of seconds. 212700SQL Server Agent Service statusThe SQL Server Agent Service status matches the status specified. 222800SQL Server Reporting Service statusThe SQL Server Reporting Service status matches the status specified. 232900SQL Server Full Text Search Service statusThe SQL Server Full Text Search Service status matches the status specified. 243000SQL Server Analysis Service statusThe SQL Server Analysis Service status matches the status specified. 253100SQL Server Integration Service statusThe SQL Server Integration Service status matches the status specified. 263300SQL Server Browser Service statusThe SQL Server Browser Service status matches the status specified. 273400SQL Server VSS Writer Service statusThe SQL Server VSS Writer status matches the status specified. 283501Deadlock trace flag disabledThe monitored SQL Server’s trace flag cannot be enabled. 293600Monitoring stopped (host machine credentials)SQL Monitor cannot contact the host machine because authentication failed. 303700Monitoring stopped (SQL Server credentials)SQL Monitor cannot contact the SQL Server instance because authentication failed. 313800Monitoring error (host machine data collection)SQL Monitor cannot collect data from the host machine. 323900Monitoring error (SQL Server data collection)SQL Monitor cannot collect data from the SQL Server instance. 334000Custom metricThe custom metric value has passed an alert threshold. 344100Custom metric collection errorSQL Monitor cannot collect custom metric data from the target object. Basically, alert.Alert_Type is just a big reference table containing information about the 34 different alert types supported by SQL Monitor (note that the largest id is 41, not 34 – some alert types have been retired since SQL Monitor was first developed). The Name and Description columns are self evident, and I’m going to skip over the Event and Monitoring columns as they’re not very interesting. The AlertId column is the primary key, and is referenced by AlertId in the alert.Alert table. As such, we can rewrite our earlier query to join these two tables, in order to provide a more readable view of the alerts: SELECT TOP 100 AlertId, Name, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType ORDER BY AlertId DESC;  AlertIdNameTargetObjectReadSubType 165550Monitoring error (SQL Server data collection)7:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,9:SqlServer,1,4:Name,s0:,00 265549Monitoring error (host machine data collection)7:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,7:Machine,1,4:Name,s0:,00 365548Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 465547Log backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 565546Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 665545Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 765544Log backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 865543Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 965542Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 1065541Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 Okay, the next column to discuss in the alert.Alert table is TargetObject. Oh boy, this one’s a bit tricky! The TargetObject of an alert is a serialized string representation of the position in the monitored object hierarchy of the object to which the alert pertains. The serialization format is somewhat convenient for parsing in the C# source code of SQL Monitor, and has some helpful characteristics, but it’s probably very awkward to manipulate in T-SQL. I could document the serialization format here, but it would be very dry reading, so perhaps it’s best to consider an example from the table above. Have a look at the alert with an AlertID of 65543. It’s a Backup overdue alert for the SqlMonitorData database running on the default instance of granger, my laptop. Each different alert type is associated with a specific type of monitored object in the object hierarchy (I described the hierarchy in my previous post). The Backup overdue alert is associated with databases, whose position in the object hierarchy is root → Cluster → SqlServer → Database. The TargetObject value identifies the target object by specifying the key properties at each level in the hierarchy, thus: Cluster: Name = "granger" SqlServer: Name = "" (an empty string, denoting the default instance) Database: Name = "SqlMonitorData" Well, look at the actual TargetObject value for this alert: "7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,". It is indeed composed of three parts, one for each level in the hierarchy: Cluster: "7:Cluster,1,4:Name,s7:granger," SqlServer: "9:SqlServer,1,4:Name,s0:," Database: "8:Database,1,4:Name,s14:SqlMonitorData," Each part is handled in exactly the same way, so let’s concentrate on the first part, "7:Cluster,1,4:Name,s7:granger,". It comprises the following: "7:Cluster," – This identifies the level in the hierarchy. "1," – This indicates how many different key properties there are to uniquely identify a cluster (we saw in my last post that each cluster is identified by a single property, its Name). "4:Name,s14:SqlMonitorData," – This represents the Name property, and its corresponding value, SqlMonitorData. It’s split up like this: "4:Name," – Indicates the name of the key property. "s" – Indicates the type of the key property, in this case, it’s a string. "14:SqlMonitorData," – Indicates the value of the property. At this point, you might be wondering about the format of some of these strings. Why is the string "Cluster" stored as "7:Cluster,"? Well an encoding scheme is used, which consists of the following: "7" – This is the length of the string "Cluster" ":" – This is a delimiter between the length of the string and the actual string’s contents. "Cluster" – This is the string itself. 7 characters. "," – This is a final terminating character that indicates the end of the encoded string. You can see that "4:Name,", "8:Database," and "14:SqlMonitorData," also conform to the same encoding scheme. In the example above, the "s" character is used to indicate that the value of the Name property is a string. If you explore the TargetObject property of alerts in your own SQL Monitor data repository, you might find other characters used for other non-string key property values. The different value types you might possibly encounter are as follows: "I" – Denotes a bigint value. For example, "I65432,". "g" – Denotes a GUID value. For example, "g32116732-63ae-4ab5-bd34-7dfdfb084c18,". "d" – Denotes a datetime value. For example, "d634815384796832438,". The value is stored as a bigint, rather than a native SQL datetime value. I’ll describe how datetime values are handled in the SQL Monitor data repostory in a future post. I suggest you have a look at the alerts in your own SQL Monitor data repository for further examples, so you can see how the TargetObject values are composed for each of the different types of alert. Let me give one further example, though, that represents a Custom metric alert, as this will help in describing the final column of interest in the alert.Alert table, SubType. Let me show you the alert I’m interested in: SELECT AlertId, a.AlertType, Name, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType WHERE AlertId = 65769;  AlertIdAlertTypeNameTargetObjectReadSubType 16576940Custom metric7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s6:master,12:CustomMetric,1,8:MetricId,I2,02 An AlertType value of 40 corresponds to the Custom metric alert type. The Name taken from the alert.Alert_Type table is simply Custom metric, but this doesn’t tell us anything about the specific custom metric that this alert pertains to. That’s where the SubType value comes in. For custom metric alerts, this provides us with the Id of the specific custom alert definition that can be found in the settings.CustomAlertDefinitions table. I don’t really want to delve into custom alert definitions yet (maybe in a later post), but an extra join in the previous query shows us that this alert pertains to the CPU pressure (avg runnable task count) custom metric alert. SELECT AlertId, a.AlertType, at.Name, cad.Name AS CustomAlertName, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType JOIN settings.CustomAlertDefinitions cad ON a.SubType = cad.Id WHERE AlertId = 65769;  AlertIdAlertTypeNameCustomAlertNameTargetObjectReadSubType 16576940Custom metricCPU pressure (avg runnable task count)7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s6:master,12:CustomMetric,1,8:MetricId,I2,02 The TargetObject value in this case breaks down like this: "7:Cluster,1,4:Name,s7:granger," – Cluster named "granger". "9:SqlServer,1,4:Name,s0:," – SqlServer named "" (the default instance). "8:Database,1,4:Name,s6:master," – Database named "master". "12:CustomMetric,1,8:MetricId,I2," – Custom metric with an Id of 2. Note that the hierarchy for a custom metric is slightly different compared to the earlier Backup overdue alert. It’s root → Cluster → SqlServer → Database → CustomMetric. Also notice that, unlike Cluster, SqlServer and Database, the key property for CustomMetric is called MetricId (not Name), and the value is a bigint (not a string). Finally, delving into the custom metric tables is beyond the scope of this post, but for the sake of avoiding any future confusion, I’d like to point out that whilst the SubType references a custom alert definition, the MetricID value embedded in the TargetObject value references a custom metric definition. Although in this case both the custom metric definition and custom alert definition share the same Id value of 2, this is not generally the case. Okay, that’s enough for now, not least because as I’m typing this, it’s almost 2am, I have to go to work tomorrow, and my alarm is set for 6am – eek! In my next post, I’ll either cover the remaining three tables in the alert schema, or I’ll delve into the way SQL Monitor stores its monitoring data, as I’d originally planned to cover in this post.

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  • MD5 implementation notes

    - by vaasu
    While going through RFC1321, I came across the following paragraph: This step uses a 64-element table T[1 ... 64] constructed from the sine function. Let T[i] denote the i-th element of the table, which is equal to the integer part of 4294967296 times abs(sin(i)), where i is in radians. The elements of the table are given in the appendix. From what I understood from paragraph, it means T[i] = Integer_part(4294967296 times abs(sin(i))) We know the following is true for all x: 0 <= sin(x) <= 1 Since i is an integer, abs(sin(i)) may very well be 0 for all values of i. That means table will contain all zero values ( 4294967296 times 0 is 0). In the implementation, this is not true. Why is this so? Appendix contains just the raw values after calculation. It does not show how it is derived from the sine function.

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  • SQL SERVER – Solution – Puzzle – Statistics are not Updated but are Created Once

    - by pinaldave
    Earlier I asked puzzle why statistics are not updated. Read the complete details over here: Statistics are not Updated but are Created Once In the question I have demonstrated even though statistics should have been updated after lots of insert in the table are not updated.(Read the details SQL SERVER – When are Statistics Updated – What triggers Statistics to Update) In this example I have created following situation: Create Table Insert 1000 Records Check the Statistics Now insert 10 times more 10,000 indexes Check the Statistics – it will be NOT updated Auto Update Statistics and Auto Create Statistics for database is TRUE Now I have requested two things in the example 1) Why this is happening? 2) How to fix this issue? I have many answers – here is the how I fixed it which has resolved the issue for me. NOTE: There are multiple answers to this problem and I will do my best to list all. Solution: Create nonclustered Index on column City Here is the working example for the same. Let us understand this script and there is added explanation at the end. -- Execution Plans Difference -- Estimated Execution Plan Vs Actual Execution Plan -- Create Sample Database CREATE DATABASE SampleDB GO USE SampleDB GO -- Create Table CREATE TABLE ExecTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO CREATE NONCLUSTERED INDEX IX_ExecTable1 ON ExecTable (City); GO -- Insert One Thousand Records -- INSERT 1 INSERT INTO ExecTable (ID,FirstName,LastName,City) SELECT TOP 1000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%20 = 1 THEN 'New York' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 5 THEN 'San Marino' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 3 THEN 'Los Angeles' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 7 THEN 'La Cinega' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 13 THEN 'San Diego' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 17 THEN 'Las Vegas' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Select Statement SELECT FirstName, LastName, City FROM ExecTable WHERE City  = 'New York' GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Replace your Statistics over here DBCC SHOW_STATISTICS('ExecTable', IX_ExecTable1); GO -------------------------------------------------------------- -- Round 2 -- Insert One Thousand Records -- INSERT 2 INSERT INTO ExecTable (ID,FirstName,LastName,City) SELECT TOP 1000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%20 = 1 THEN 'New York' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 5 THEN 'San Marino' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 3 THEN 'Los Angeles' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 7 THEN 'La Cinega' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 13 THEN 'San Diego' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 17 THEN 'Las Vegas' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Select Statement SELECT FirstName, LastName, City FROM ExecTable WHERE City  = 'New York' GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Replace your Statistics over here DBCC SHOW_STATISTICS('ExecTable', IX_ExecTable1); GO -- Clean up Database DROP TABLE ExecTable GO When I created non clustered index on the column city, it also created statistics on the same column with same name as index. When we populate the data in the column the index is update – resulting execution plan to be invalided – this leads to the statistics to be updated in next execution of SELECT. This behavior does not happen on Heap or column where index is auto created. If you explicitly update the index, often you can see the statistics are updated as well. You can see this is for sure happening if you follow the tell of John Sansom. John Sansom‘s suggestion: That was fun! Although the column statistics are invalidated by the time the second select statement is executed, the query is not compiled/recompiled but instead the existing query plan is reused. It is the “next” compiled query against the column statistics that will see that they are out of date and will then in turn instantiate the action of updating statistics. You can see this in action by forcing the second statement to recompile. SELECT FirstName, LastName, City FROM ExecTable WHERE City = ‘New York’ option(RECOMPILE) GO Kevin Cross also have another suggestion: I agree with John. It is reusing the Execution Plan. Aside from OPTION(RECOMPILE), clearing the Execution Plan Cache before the subsequent tests will also work. i.e., run this before round 2: ————————————————————– – Clear execution plan cache before next test DBCC FREEPROCCACHE WITH NO_INFOMSGS; ————————————————————– Nice puzzle! Kevin As this was puzzle John and Kevin both got the correct answer, there was no condition for answer to be part of best practices. I know John and he is finest DBA around – his tremendous knowledge has always impressed me. John and Kevin both will agree that clearing cache either using DBCC FREEPROCCACHE and recompiling each query every time is for sure not good advice on production server. It is correct answer but not best practice. By the way, if you have better solution or have better suggestion please advise. I am open to change my answer and publish further improvement to this solution. On very separate note, I like to have clustered index on my Primary Key, which I have not mentioned here as it is out of the scope of this puzzle. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, Readers Contribution, Readers Question, SQL, SQL Authority, SQL Index, SQL Puzzle, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Statistics

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  • SQL Server 2000 tables

    - by user40766
    We currently have an SQL Server 2000 database with one table containing data for multiple users. The data is keyed by memberid which is an integer field. The table has a clustered index on memberid. The table is now about 200 million rows. Indexing and maintenance are becoming issues. We are debating splitting the table into one table per user model. This would imply that we would end up with a very large number of tables potentially upto the 2,147,483,647, considering just positive values. My questions: Does anyone have any experience with a SQL Server (2000/2005) installation with millions of tables? What are the implications of this architecture with regards to maintenance and access using Query Analyzer, Enterprise Manager etc. What are the implications to having such a large number of indexes in a database instance. All comments are appreciated. Thanks

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  • Metro: Introduction to CSS 3 Grid Layout

    - by Stephen.Walther
    The purpose of this blog post is to provide you with a quick introduction to the new W3C CSS 3 Grid Layout standard. You can use CSS Grid Layout in Metro style applications written with JavaScript to lay out the content of an HTML page. CSS Grid Layout provides you with all of the benefits of using HTML tables for layout without requiring you to actually use any HTML table elements. Doing Page Layouts without Tables Back in the 1990’s, if you wanted to create a fancy website, then you would use HTML tables for layout. For example, if you wanted to create a standard three-column page layout then you would create an HTML table with three columns like this: <table height="100%"> <tr> <td valign="top" width="300px" bgcolor="red"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </td> <td valign="top" bgcolor="green"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </td> <td valign="top" width="300px" bgcolor="blue"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </td> </tr> </table> When the table above gets rendered out to a browser, you end up with the following three-column layout: The width of the left and right columns is fixed – the width of the middle column expands or contracts depending on the width of the browser. Sometime around the year 2005, everyone decided that using tables for layout was a bad idea. Instead of using tables for layout — it was collectively decided by the spirit of the Web — you should use Cascading Style Sheets instead. Why is using HTML tables for layout bad? Using tables for layout breaks the semantics of the TABLE element. A TABLE element should be used only for displaying tabular information such as train schedules or moon phases. Using tables for layout is bad for accessibility (The Web Content Accessibility Guidelines 1.0 is explicit about this) and using tables for layout is bad for separating content from layout (see http://CSSZenGarden.com). Post 2005, anyone who used HTML tables for layout were encouraged to hold their heads down in shame. That’s all well and good, but the problem with using CSS for layout is that it can be more difficult to work with CSS than HTML tables. For example, to achieve a standard three-column layout, you either need to use absolute positioning or floats. Here’s a three-column layout with floats: <style type="text/css"> #container { min-width: 800px; } #leftColumn { float: left; width: 300px; height: 100%; background-color:red; } #middleColumn { background-color:green; height: 100%; } #rightColumn { float: right; width: 300px; height: 100%; background-color:blue; } </style> <div id="container"> <div id="rightColumn"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </div> <div id="leftColumn"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </div> <div id="middleColumn"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </div> </div> The page above contains four DIV elements: a container DIV which contains a leftColumn, middleColumn, and rightColumn DIV. The leftColumn DIV element is floated to the left and the rightColumn DIV element is floated to the right. Notice that the rightColumn DIV appears in the page before the middleColumn DIV – this unintuitive ordering is necessary to get the floats to work correctly (see http://stackoverflow.com/questions/533607/css-three-column-layout-problem). The page above (almost) works with the most recent versions of most browsers. For example, you get the correct three-column layout in both Firefox and Chrome: And the layout mostly works with Internet Explorer 9 except for the fact that for some strange reason the min-width doesn’t work so when you shrink the width of your browser, you can get the following unwanted layout: Notice how the middle column (the green column) bleeds to the left and right. People have solved these issues with more complicated CSS. For example, see: http://matthewjamestaylor.com/blog/holy-grail-no-quirks-mode.htm But, at this point, no one could argue that using CSS is easier or more intuitive than tables. It takes work to get a layout with CSS and we know that we could achieve the same layout more easily using HTML tables. Using CSS Grid Layout CSS Grid Layout is a new W3C standard which provides you with all of the benefits of using HTML tables for layout without the disadvantage of using an HTML TABLE element. In other words, CSS Grid Layout enables you to perform table layouts using pure Cascading Style Sheets. The CSS Grid Layout standard is still in a “Working Draft” state (it is not finalized) and it is located here: http://www.w3.org/TR/css3-grid-layout/ The CSS Grid Layout standard is only supported by Internet Explorer 10 and there are no signs that any browser other than Internet Explorer will support this standard in the near future. This means that it is only practical to take advantage of CSS Grid Layout when building Metro style applications with JavaScript. Here’s how you can create a standard three-column layout using a CSS Grid Layout: <!DOCTYPE html> <html> <head> <style type="text/css"> html, body, #container { height: 100%; padding: 0px; margin: 0px; } #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100%; } #leftColumn { -ms-grid-column: 1; background-color:red; } #middleColumn { -ms-grid-column: 2; background-color:green; } #rightColumn { -ms-grid-column: 3; background-color:blue; } </style> </head> <body> <div id="container"> <div id="leftColumn"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </div> <div id="middleColumn"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </div> <div id="rightColumn"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </div> </div> </body> </html> When the page above is rendered in Internet Explorer 10, you get a standard three-column layout: The page above contains four DIV elements: a container DIV which contains a leftColumn DIV, middleColumn DIV, and rightColumn DIV. The container DIV is set to Grid display mode with the following CSS rule: #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100%; } The display property is set to the value “-ms-grid”. This property causes the container DIV to lay out its child elements in a grid. (Notice that you use “-ms-grid” instead of “grid”. The “-ms-“ prefix is used because the CSS Grid Layout standard is still preliminary. This implementation only works with IE10 and it might change before the final release.) The grid columns and rows are defined with the “-ms-grid-columns” and “-ms-grid-rows” properties. The style rule above creates a grid with three columns and one row. The left and right columns are fixed sized at 300 pixels. The middle column sizes automatically depending on the remaining space available. The leftColumn, middleColumn, and rightColumn DIVs are positioned within the container grid element with the following CSS rules: #leftColumn { -ms-grid-column: 1; background-color:red; } #middleColumn { -ms-grid-column: 2; background-color:green; } #rightColumn { -ms-grid-column: 3; background-color:blue; } The “-ms-grid-column” property is used to specify the column associated with the element selected by the style sheet selector. The leftColumn DIV is positioned in the first grid column, the middleColumn DIV is positioned in the second grid column, and the rightColumn DIV is positioned in the third grid column. I find using CSS Grid Layout to be just as intuitive as using an HTML table for layout. You define your columns and rows and then you position different elements within these columns and rows. Very straightforward. Creating Multiple Columns and Rows In the previous section, we created a super simple three-column layout. This layout contained only a single row. In this section, let’s create a slightly more complicated layout which contains more than one row: The following page contains a header row, a content row, and a footer row. The content row contains three columns: <!DOCTYPE html> <html> <head> <style type="text/css"> html, body, #container { height: 100%; padding: 0px; margin: 0px; } #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100px 1fr 100px; } #header { -ms-grid-column: 1; -ms-grid-column-span: 3; -ms-grid-row: 1; background-color: yellow; } #leftColumn { -ms-grid-column: 1; -ms-grid-row: 2; background-color:red; } #middleColumn { -ms-grid-column: 2; -ms-grid-row: 2; background-color:green; } #rightColumn { -ms-grid-column: 3; -ms-grid-row: 2; background-color:blue; } #footer { -ms-grid-column: 1; -ms-grid-column-span: 3; -ms-grid-row: 3; background-color: orange; } </style> </head> <body> <div id="container"> <div id="header"> Header, Header, Header </div> <div id="leftColumn"> Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column, Left Column </div> <div id="middleColumn"> Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column, Middle Column </div> <div id="rightColumn"> Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column, Right Column </div> <div id="footer"> Footer, Footer, Footer </div> </div> </body> </html> In the page above, the grid layout is created with the following rule which creates a grid with three rows and three columns: #container { display: -ms-grid; -ms-grid-columns: 300px auto 300px; -ms-grid-rows: 100px 1fr 100px; } The header is created with the following rule: #header { -ms-grid-column: 1; -ms-grid-column-span: 3; -ms-grid-row: 1; background-color: yellow; } The header is positioned in column 1 and row 1. Furthermore, notice that the “-ms-grid-column-span” property is used to span the header across three columns. CSS Grid Layout and Fractional Units When you use CSS Grid Layout, you can take advantage of fractional units. Fractional units provide you with an easy way of dividing up remaining space in a page. Imagine, for example, that you want to create a three-column page layout. You want the size of the first column to be fixed at 200 pixels and you want to divide the remaining space among the remaining three columns. The width of the second column is equal to the combined width of the third and fourth columns. The following CSS rule creates four columns with the desired widths: #container { display: -ms-grid; -ms-grid-columns: 200px 2fr 1fr 1fr; -ms-grid-rows: 1fr; } The fr unit represents a fraction. The grid above contains four columns. The second column is two times the size (2fr) of the third (1fr) and fourth (1fr) columns. When you use the fractional unit, the remaining space is divided up using fractional amounts. Notice that the single row is set to a height of 1fr. The single grid row gobbles up the entire vertical space. Here’s the entire HTML page: <!DOCTYPE html> <html> <head> <style type="text/css"> html, body, #container { height: 100%; padding: 0px; margin: 0px; } #container { display: -ms-grid; -ms-grid-columns: 200px 2fr 1fr 1fr; -ms-grid-rows: 1fr; } #firstColumn { -ms-grid-column: 1; background-color:red; } #secondColumn { -ms-grid-column: 2; background-color:green; } #thirdColumn { -ms-grid-column: 3; background-color:blue; } #fourthColumn { -ms-grid-column: 4; background-color:orange; } </style> </head> <body> <div id="container"> <div id="firstColumn"> First Column, First Column, First Column </div> <div id="secondColumn"> Second Column, Second Column, Second Column </div> <div id="thirdColumn"> Third Column, Third Column, Third Column </div> <div id="fourthColumn"> Fourth Column, Fourth Column, Fourth Column </div> </div> </body> </html>   Summary There is more in the CSS 3 Grid Layout standard than discussed in this blog post. My goal was to describe the basics. If you want to learn more than you can read through the entire standard at http://www.w3.org/TR/css3-grid-layout/ In this blog post, I described some of the difficulties that you might encounter when attempting to replace HTML tables with Cascading Style Sheets when laying out a web page. I explained how you can take advantage of the CSS 3 Grid Layout standard to avoid these problems when building Metro style applications using JavaScript. CSS 3 Grid Layout provides you with all of the benefits of using HTML tables for laying out a page without requiring you to use HTML table elements.

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  • SQL SERVER – Puzzle – Statistics are not Updated but are Created Once

    - by pinaldave
    After having excellent response to my quiz – Why SELECT * throws an error but SELECT COUNT(*) does not?I have decided to ask another puzzling question to all of you. I am running this test on SQL Server 2008 R2. Here is the quick scenario about my setup. Create Table Insert 1000 Records Check the Statistics Now insert 10 times more 10,000 indexes Check the Statistics – it will be NOT updated Note: Auto Update Statistics and Auto Create Statistics for database is TRUE Expected Result – Statistics should be updated – SQL SERVER – When are Statistics Updated – What triggers Statistics to Update Now the question is why the statistics are not updated? The common answer is – we can update the statistics ourselves using UPDATE STATISTICS TableName WITH FULLSCAN, ALL However, the solution I am looking is where statistics should be updated automatically based on algorithm mentioned here. Now the solution is to ____________________. Vinod Kumar is not allowed to take participate over here as he is the one who has helped me to build this puzzle. I will publish the solution on next week. Please leave a comment and if your comment consist valid answer, I will publish with due credit. Here is the script to reproduce the scenario which I mentioned. -- Execution Plans Difference -- Create Sample Database CREATE DATABASE SampleDB GO USE SampleDB GO -- Create Table CREATE TABLE ExecTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Insert One Thousand Records -- INSERT 1 INSERT INTO ExecTable (ID,FirstName,LastName,City) SELECT TOP 1000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%20 = 1 THEN 'New York' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 5 THEN 'San Marino' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 3 THEN 'Los Angeles' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 7 THEN 'La Cinega' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 13 THEN 'San Diego' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 17 THEN 'Las Vegas' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Display statistics of the table - none listed sp_helpstats N'ExecTable', 'ALL' GO -- Select Statement SELECT FirstName, LastName, City FROM ExecTable WHERE City  = 'New York' GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Replace your Statistics over here -- NOTE: Replace your _WA_Sys with stats from above query DBCC SHOW_STATISTICS('ExecTable', _WA_Sys_00000004_7D78A4E7); GO -------------------------------------------------------------- -- Round 2 -- Insert Ten Thousand Records -- INSERT 2 INSERT INTO ExecTable (ID,FirstName,LastName,City) SELECT TOP 10000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%20 = 1 THEN 'New York' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 5 THEN 'San Marino' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 3 THEN 'Los Angeles' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 7 THEN 'La Cinega' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 13 THEN 'San Diego' WHEN  ROW_NUMBER() OVER (ORDER BY a.name)%20 = 17 THEN 'Las Vegas' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Select Statement SELECT FirstName, LastName, City FROM ExecTable WHERE City  = 'New York' GO -- Display statistics of the table sp_helpstats N'ExecTable', 'ALL' GO -- Replace your Statistics over here -- NOTE: Replace your _WA_Sys with stats from above query DBCC SHOW_STATISTICS('ExecTable', _WA_Sys_00000004_7D78A4E7); GO -- You will notice that Statistics are still updated with 1000 rows -- Clean up Database DROP TABLE ExecTable GO USE MASTER GO ALTER DATABASE SampleDB SET SINGLE_USER WITH ROLLBACK IMMEDIATE; GO DROP DATABASE SampleDB GO Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Index, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: SQL Statistics, Statistics

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  • Currency Conversion in Oracle BI applications

    - by Saurabh Verma
    Authored by Vijay Aggarwal and Hichem Sellami A typical data warehouse contains Star and/or Snowflake schema, made up of Dimensions and Facts. The facts store various numerical information including amounts. Example; Order Amount, Invoice Amount etc. With the true global nature of business now-a-days, the end-users want to view the reports in their own currency or in global/common currency as defined by their business. This presents a unique opportunity in BI to provide the amounts in converted rates either by pre-storing or by doing on-the-fly conversions while displaying the reports to the users. Source Systems OBIA caters to various source systems like EBS, PSFT, Sebl, JDE, Fusion etc. Each source has its own unique and intricate ways of defining and storing currency data, doing currency conversions and presenting to the OLTP users. For example; EBS stores conversion rates between currencies which can be classified by conversion rates, like Corporate rate, Spot rate, Period rate etc. Siebel stores exchange rates by conversion rates like Daily. EBS/Fusion stores the conversion rates for each day, where as PSFT/Siebel store for a range of days. PSFT has Rate Multiplication Factor and Rate Division Factor and we need to calculate the Rate based on them, where as other Source systems store the Currency Exchange Rate directly. OBIA Design The data consolidation from various disparate source systems, poses the challenge to conform various currencies, rate types, exchange rates etc., and designing the best way to present the amounts to the users without affecting the performance. When consolidating the data for reporting in OBIA, we have designed the mechanisms in the Common Dimension, to allow users to report based on their required currencies. OBIA Facts store amounts in various currencies: Document Currency: This is the currency of the actual transaction. For a multinational company, this can be in various currencies. Local Currency: This is the base currency in which the accounting entries are recorded by the business. This is generally defined in the Ledger of the company. Global Currencies: OBIA provides five Global Currencies. Three are used across all modules. The last two are for CRM only. A Global currency is very useful when creating reports where the data is viewed enterprise-wide. Example; a US based multinational would want to see the reports in USD. The company will choose USD as one of the global currencies. OBIA allows users to define up-to five global currencies during the initial implementation. The term Currency Preference is used to designate the set of values: Document Currency, Local Currency, Global Currency 1, Global Currency 2, Global Currency 3; which are shared among all modules. There are four more currency preferences, specific to certain modules: Global Currency 4 (aka CRM Currency) and Global Currency 5 which are used in CRM; and Project Currency and Contract Currency, used in Project Analytics. When choosing Local Currency for Currency preference, the data will show in the currency of the Ledger (or Business Unit) in the prompt. So it is important to select one Ledger or Business Unit when viewing data in Local Currency. More on this can be found in the section: Toggling Currency Preferences in the Dashboard. Design Logic When extracting the fact data, the OOTB mappings extract and load the document amount, and the local amount in target tables. It also loads the exchange rates required to convert the document amount into the corresponding global amounts. If the source system only provides the document amount in the transaction, the extract mapping does a lookup to get the Local currency code, and the Local exchange rate. The Load mapping then uses the local currency code and rate to derive the local amount. The load mapping also fetches the Global Currencies and looks up the corresponding exchange rates. The lookup of exchange rates is done via the Exchange Rate Dimension provided as a Common/Conforming Dimension in OBIA. The Exchange Rate Dimension stores the exchange rates between various currencies for a date range and Rate Type. Two physical tables W_EXCH_RATE_G and W_GLOBAL_EXCH_RATE_G are used to provide the lookups and conversions between currencies. The data is loaded from the source system’s Ledger tables. W_EXCH_RATE_G stores the exchange rates between currencies with a date range. On the other hand, W_GLOBAL_EXCH_RATE_G stores the currency conversions between the document currency and the pre-defined five Global Currencies for each day. Based on the requirements, the fact mappings can decide and use one or both tables to do the conversion. Currency design in OBIA also taps into the MLS and Domain architecture, thus allowing the users to map the currencies to a universal Domain during the implementation time. This is especially important for companies deploying and using OBIA with multiple source adapters. Some Gotchas to Look for It is necessary to think through the currencies during the initial implementation. 1) Identify various types of currencies that are used by your business. Understand what will be your Local (or Base) and Documentation currency. Identify various global currencies that your users will want to look at the reports. This will be based on the global nature of your business. Changes to these currencies later in the project, while permitted, but may cause Full data loads and hence lost time. 2) If the user has a multi source system make sure that the Global Currencies and Global Rate Types chosen in Configuration Manager do have the corresponding source specific counterparts. In other words, make sure for every DW specific value chosen for Currency Code or Rate Type, there is a source Domain mapping already done. Technical Section This section will briefly mention the technical scenarios employed in the OBIA adaptors to extract data from each source system. In OBIA, we have two main tables which store the Currency Rate information as explained in previous sections. W_EXCH_RATE_G and W_GLOBAL_EXCH_RATE_G are the two tables. W_EXCH_RATE_G stores all the Currency Conversions present in the source system. It captures data for a Date Range. W_GLOBAL_EXCH_RATE_G has Global Currency Conversions stored at a Daily level. However the challenge here is to store all the 5 Global Currency Exchange Rates in a single record for each From Currency. Let’s voyage further into the Source System Extraction logic for each of these tables and understand the flow briefly. EBS: In EBS, we have Currency Data stored in GL_DAILY_RATES table. As the name indicates GL_DAILY_RATES EBS table has data at a daily level. However in our warehouse we store the data with a Date Range and insert a new range record only when the Exchange Rate changes for a particular From Currency, To Currency and Rate Type. Below are the main logical steps that we employ in this process. (Incremental Flow only) – Cleanup the data in W_EXCH_RATE_G. Delete the records which have Start Date > minimum conversion date Update the End Date of the existing records. Compress the daily data from GL_DAILY_RATES table into Range Records. Incremental map uses $$XRATE_UPD_NUM_DAY as an extra parameter. Generate Previous Rate, Previous Date and Next Date for each of the Daily record from the OLTP. Filter out the records which have Conversion Rate same as Previous Rates or if the Conversion Date lies within a single day range. Mark the records as ‘Keep’ and ‘Filter’ and also get the final End Date for the single Range record (Unique Combination of From Date, To Date, Rate and Conversion Date). Filter the records marked as ‘Filter’ in the INFA map. The above steps will load W_EXCH_RATE_GS. Step 0 updates/deletes W_EXCH_RATE_G directly. SIL map will then insert/update the GS data into W_EXCH_RATE_G. These steps convert the daily records in GL_DAILY_RATES to Range records in W_EXCH_RATE_G. We do not need such special logic for loading W_GLOBAL_EXCH_RATE_G. This is a table where we store data at a Daily Granular Level. However we need to pivot the data because the data present in multiple rows in source tables needs to be stored in different columns of the same row in DW. We use GROUP BY and CASE logic to achieve this. Fusion: Fusion has extraction logic very similar to EBS. The only difference is that the Cleanup logic that was mentioned in step 0 above does not use $$XRATE_UPD_NUM_DAY parameter. In Fusion we bring all the Exchange Rates in Incremental as well and do the cleanup. The SIL then takes care of Insert/Updates accordingly. PeopleSoft:PeopleSoft does not have From Date and To Date explicitly in the Source tables. Let’s look at an example. Please note that this is achieved from PS1 onwards only. 1 Jan 2010 – USD to INR – 45 31 Jan 2010 – USD to INR – 46 PSFT stores records in above fashion. This means that Exchange Rate of 45 for USD to INR is applicable for 1 Jan 2010 to 30 Jan 2010. We need to store data in this fashion in DW. Also PSFT has Exchange Rate stored as RATE_MULT and RATE_DIV. We need to do a RATE_MULT/RATE_DIV to get the correct Exchange Rate. We generate From Date and To Date while extracting data from source and this has certain assumptions: If a record gets updated/inserted in the source, it will be extracted in incremental. Also if this updated/inserted record is between other dates, then we also extract the preceding and succeeding records (based on dates) of this record. This is required because we need to generate a range record and we have 3 records whose ranges have changed. Taking the same example as above, if there is a new record which gets inserted on 15 Jan 2010; the new ranges are 1 Jan to 14 Jan, 15 Jan to 30 Jan and 31 Jan to Next available date. Even though 1 Jan record and 31 Jan have not changed, we will still extract them because the range is affected. Similar logic is used for Global Exchange Rate Extraction. We create the Range records and get it into a Temporary table. Then we join to Day Dimension, create individual records and pivot the data to get the 5 Global Exchange Rates for each From Currency, Date and Rate Type. Siebel: Siebel Facts are dependent on Global Exchange Rates heavily and almost none of them really use individual Exchange Rates. In other words, W_GLOBAL_EXCH_RATE_G is the main table used in Siebel from PS1 release onwards. As of January 2002, the Euro Triangulation method for converting between currencies belonging to EMU members is not needed for present and future currency exchanges. However, the method is still available in Siebel applications, as are the old currencies, so that historical data can be maintained accurately. The following description applies only to historical data needing conversion prior to the 2002 switch to the Euro for the EMU member countries. If a country is a member of the European Monetary Union (EMU), you should convert its currency to other currencies through the Euro. This is called triangulation, and it is used whenever either currency being converted has EMU Triangulation checked. Due to this, there are multiple extraction flows in SEBL ie. EUR to EMU, EUR to NonEMU, EUR to DMC and so on. We load W_EXCH_RATE_G through multiple flows with these data. This has been kept same as previous versions of OBIA. W_GLOBAL_EXCH_RATE_G being a new table does not have such needs. However SEBL does not have From Date and To Date columns in the Source tables similar to PSFT. We use similar extraction logic as explained in PSFT section for SEBL as well. What if all 5 Global Currencies configured are same? As mentioned in previous sections, from PS1 onwards we store Global Exchange Rates in W_GLOBAL_EXCH_RATE_G table. The extraction logic for this table involves Pivoting data from multiple rows into a single row with 5 Global Exchange Rates in 5 columns. As mentioned in previous sections, we use CASE and GROUP BY functions to achieve this. This approach poses a unique problem when all the 5 Global Currencies Chosen are same. For example – If the user configures all 5 Global Currencies as ‘USD’ then the extract logic will not be able to generate a record for From Currency=USD. This is because, not all Source Systems will have a USD->USD conversion record. We have _Generated mappings to take care of this case. We generate a record with Conversion Rate=1 for such cases. Reusable Lookups Before PS1, we had a Mapplet for Currency Conversions. In PS1, we only have reusable Lookups- LKP_W_EXCH_RATE_G and LKP_W_GLOBAL_EXCH_RATE_G. These lookups have another layer of logic so that all the lookup conditions are met when they are used in various Fact Mappings. Any user who would want to do a LKP on W_EXCH_RATE_G or W_GLOBAL_EXCH_RATE_G should and must use these Lookups. A direct join or Lookup on the tables might lead to wrong data being returned. Changing Currency preferences in the Dashboard: In the 796x series, all amount metrics in OBIA were showing the Global1 amount. The customer needed to change the metric definitions to show them in another Currency preference. Project Analytics started supporting currency preferences since 7.9.6 release though, and it published a Tech note for other module customers to add toggling between currency preferences to the solution. List of Currency Preferences Starting from 11.1.1.x release, the BI Platform added a new feature to support multiple currencies. The new session variable (PREFERRED_CURRENCY) is populated through a newly introduced currency prompt. This prompt can take its values from the xml file: userpref_currencies_OBIA.xml, which is hosted in the BI Server installation folder, under :< home>\instances\instance1\config\OracleBIPresentationServicesComponent\coreapplication_obips1\userpref_currencies.xml This file contains the list of currency preferences, like“Local Currency”, “Global Currency 1”,…which customers can also rename to give them more meaningful business names. There are two options for showing the list of currency preferences to the user in the dashboard: Static and Dynamic. In Static mode, all users will see the full list as in the user preference currencies file. In the Dynamic mode, the list shown in the currency prompt drop down is a result of a dynamic query specified in the same file. Customers can build some security into the rpd, so the list of currency preferences will be based on the user roles…BI Applications built a subject area: “Dynamic Currency Preference” to run this query, and give every user only the list of currency preferences required by his application roles. Adding Currency to an Amount Field When the user selects one of the items from the currency prompt, all the amounts in that page will show in the Currency corresponding to that preference. For example, if the user selects “Global Currency1” from the prompt, all data will be showing in Global Currency 1 as specified in the Configuration Manager. If the user select “Local Currency”, all amount fields will show in the Currency of the Business Unit selected in the BU filter of the same page. If there is no particular Business Unit selected in that filter, and the data selected by the query contains amounts in more than one currency (for example one BU has USD as a functional currency, the other has EUR as functional currency), then subtotals will not be available (cannot add USD and EUR amounts in one field), and depending on the set up (see next paragraph), the user may receive an error. There are two ways to add the Currency field to an amount metric: In the form of currency code, like USD, EUR…For this the user needs to add the field “Apps Common Currency Code” to the report. This field is in every subject area, usually under the table “Currency Tag” or “Currency Code”… In the form of currency symbol ($ for USD, € for EUR,…) For this, the user needs to format the amount metrics in the report as a currency column, by specifying the currency tag column in the Column Properties option in Column Actions drop down list. Typically this column should be the “BI Common Currency Code” available in every subject area. Select Column Properties option in the Edit list of a metric. In the Data Format tab, select Custom as Treat Number As. Enter the following syntax under Custom Number Format: [$:currencyTagColumn=Subjectarea.table.column] Where Column is the “BI Common Currency Code” defined to take the currency code value based on the currency preference chosen by the user in the Currency preference prompt.

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  • SQL SERVER – Weekly Series – Memory Lane – #005

    - by pinaldave
    Here is the list of curetted articles of SQLAuthority.com across all these years. Instead of just listing all the articles I have selected a few of my most favorite articles and have listed them here with additional notes below it. Let me know which one of the following is your favorite article from memory lane. 2006 SQL SERVER – Cursor to Kill All Process in Database I indeed wrote this cursor and when I often look back, I wonder how naive I was to write this. The reason for writing this cursor was to free up my database from any existing connection so I can do database operation. This worked fine but there can be a potentially big issue if there was any important transaction was killed by this process. There is another way to to achieve the same thing where we can use ALTER syntax to take database in single user mode. Read more about that over here and here. 2007 Rules of Third Normal Form and Normalization Advantage – 3NF The rules of 3NF are mentioned here Make a separate table for each set of related attributes, and give each table a primary key. If an attribute depends on only part of a multi-valued key, remove it to a separate table If attributes do not contribute to a description of the key, remove them to a separate table. Correct Syntax for Stored Procedure SP Sometime a simple question is the most important question. I often see in industry incorrectly written Stored Procedure. Few writes code after the most outer BEGIN…END and few writes code after the GO Statement. In this brief blog post, I have attempted to explain the same. 2008 Switch Between Result Pan and Query Pan – SQL Shortcut Many times when I am writing query I have to scroll the result displayed in the result set. Most of the developer uses the mouse to switch between and Query Pane and Result Pane. There are few developers who are crazy about Keyboard shortcuts. F6 is the keyword which can be used to switch between query pane and tabs of the result pane. Interesting Observation – Use of Index and Execution Plan Query Optimization is a complex game and it has its own rules. From the example in the article we have discovered that Query Optimizer does not use clustered index to retrieve data, sometime non clustered index provides optimal performance for retrieving Primary Key. When all the rows and columns are selected Primary Key should be used to select data as it provides optimal performance. 2009 Interesting Observation – TOP 100 PERCENT and ORDER BY If you pull up any application or system where there are more than 100 SQL Server Views are created – I am very confident that at one or two places you will notice the scenario wherein View the ORDER BY clause is used with TOP 100 PERCENT. SQL Server 2008 VIEW with ORDER BY clause does not throw an error; moreover, it does not acknowledge the presence of it as well. In this article we have taken three perfect examples and demonstrated which clause we should use when. Comma Separated Values (CSV) from Table Column A Very common question – How to create comma separated values from a table in the database? The answer is also very common if we use XML. Check out this article for quick learning on the same subject. Azure Start Guide – Step by Step Installation Guide Though Azure portal has changed a quite bit since I wrote this article, the concept used in this article are not old. They are still valid and many of the functions are still working as mentioned in the article. I believe this one article will put you on the track to use Azure! Size of Index Table for Each Index – Solution Earlier I have posted a small question on this blog and requested help from readers to participate here and provide a solution. The puzzle was to write a query that will return the size for each index that is on any particular table. We need a query that will return an additional column in the above listed query and it should contain the size of the index. This article presents two of the best solutions from the puzzle. 2010 Well, this week in 2010 was the week of puzzles as I posted three interesting puzzles. Till today I am noticing pretty good interesting in the puzzles. They are tricky but for sure brings a great value if you are a database developer for a long time. I suggest you go over this puzzles and their answers. Did you really know all of the answers? I am confident that reading following three blog post will for sure help you enhance the experience with T-SQL. SQL SERVER – Challenge – Puzzle – Usage of FAST Hint SQL SERVER – Puzzle – Challenge – Error While Converting Money to Decimal SQL SERVER – Challenge – Puzzle – Why does RIGHT JOIN Exists 2011 DVM sys.dm_os_sys_info Column Name Changed in SQL Server 2012 Have you ever faced a situation where something does not work? When you try to fix it - you enjoy fixing it and started to appreciate the breaking changes. Well, this was exactly I felt yesterday. Before I begin my story, I want to candidly state that I do not encourage anybody to use * in the SELECT statement. Now the disclaimer is over – I suggest you read the original story – you will love it! Get Directory Structure using Extended Stored Procedure xp_dirtree Here is the question to you – why would you do something in SQL Server where you can do the same task in command prompt much easily. Well, the answer is sometime there are real use cases when we have to do such thing. This is a similar example where I have demonstrated how in SQL Server 2012 we can use extended stored procedure to retrieve directory structure. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Memory Lane, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Running a simple integration scenario using the Oracle Big Data Connectors on Hadoop/HDFS cluster

    - by hamsun
    Between the elephant ( the tradional image of the Hadoop framework) and the Oracle Iron Man (Big Data..) an english setter could be seen as the link to the right data Data, Data, Data, we are living in a world where data technology based on popular applications , search engines, Webservers, rich sms messages, email clients, weather forecasts and so on, have a predominant role in our life. More and more technologies are used to analyze/track our behavior, try to detect patterns, to propose us "the best/right user experience" from the Google Ad services, to Telco companies or large consumer sites (like Amazon:) ). The more we use all these technologies, the more we generate data, and thus there is a need of huge data marts and specific hardware/software servers (as the Exadata servers) in order to treat/analyze/understand the trends and offer new services to the users. Some of these "data feeds" are raw, unstructured data, and cannot be processed effectively by normal SQL queries. Large scale distributed processing was an emerging infrastructure need and the solution seemed to be the "collocation of compute nodes with the data", which in turn leaded to MapReduce parallel patterns and the development of the Hadoop framework, which is based on MapReduce and a distributed file system (HDFS) that runs on larger clusters of rather inexpensive servers. Several Oracle products are using the distributed / aggregation pattern for data calculation ( Coherence, NoSql, times ten ) so once that you are familiar with one of these technologies, lets says with coherence aggregators, you will find the whole Hadoop, MapReduce concept very similar. Oracle Big Data Appliance is based on the Cloudera Distribution (CDH), and the Oracle Big Data Connectors can be plugged on a Hadoop cluster running the CDH distribution or equivalent Hadoop clusters. In this paper, a "lab like" implementation of this concept is done on a single Linux X64 server, running an Oracle Database 11g Enterprise Edition Release 11.2.0.4.0, and a single node Apache hadoop-1.2.1 HDFS cluster, using the SQL connector for HDFS. The whole setup is fairly simple: Install on a Linux x64 server ( or virtual box appliance) an Oracle Database 11g Enterprise Edition Release 11.2.0.4.0 server Get the Apache Hadoop distribution from: http://mir2.ovh.net/ftp.apache.org/dist/hadoop/common/hadoop-1.2.1. Get the Oracle Big Data Connectors from: http://www.oracle.com/technetwork/bdc/big-data-connectors/downloads/index.html?ssSourceSiteId=ocomen. Check the java version of your Linux server with the command: java -version java version "1.7.0_40" Java(TM) SE Runtime Environment (build 1.7.0_40-b43) Java HotSpot(TM) 64-Bit Server VM (build 24.0-b56, mixed mode) Decompress the hadoop hadoop-1.2.1.tar.gz file to /u01/hadoop-1.2.1 Modify your .bash_profile export HADOOP_HOME=/u01/hadoop-1.2.1 export PATH=$PATH:$HADOOP_HOME/bin export HIVE_HOME=/u01/hive-0.11.0 export PATH=$PATH:$HADOOP_HOME/bin:$HIVE_HOME/bin (also see my sample .bash_profile) Set up ssh trust for Hadoop process, this is a mandatory step, in our case we have to establish a "local trust" as will are using a single node configuration copy the new public keys to the list of authorized keys connect and test the ssh setup to your localhost: We will run a "pseudo-Hadoop cluster", in what is called "local standalone mode", all the Hadoop java components are running in one Java process, this is enough for our demo purposes. We need to "fine tune" some Hadoop configuration files, we have to go at our $HADOOP_HOME/conf, and modify the files: core-site.xml hdfs-site.xml mapred-site.xml check that the hadoop binaries are referenced correctly from the command line by executing: hadoop -version As Hadoop is managing our "clustered HDFS" file system we have to create "the mount point" and format it , the mount point will be declared to core-site.xml as: The layout under the /u01/hadoop-1.2.1/data will be created and used by other hadoop components (MapReduce = /mapred/...) HDFS is using the /dfs/... layout structure format the HDFS hadoop file system: Start the java components for the HDFS system As an additional check, you can use the GUI Hadoop browsers to check the content of your HDFS configurations: Once our HDFS Hadoop setup is done you can use the HDFS file system to store data ( big data : )), and plug them back and forth to Oracle Databases by the means of the Big Data Connectors ( which is the next configuration step). You can create / use a Hive db, but in our case we will make a simple integration of "raw data" , through the creation of an External Table to a local Oracle instance ( on the same Linux box, we run the Hadoop HDFS one node cluster and one Oracle DB). Download some public "big data", I use the site: http://france.meteofrance.com/france/observations, from where I can get *.csv files for my big data simulations :). Here is the data layout of my example file: Download the Big Data Connector from the OTN (oraosch-2.2.0.zip), unzip it to your local file system (see picture below) Modify your environment in order to access the connector libraries , and make the following test: [oracle@dg1 bin]$./hdfs_stream Usage: hdfs_stream locationFile [oracle@dg1 bin]$ Load the data to the Hadoop hdfs file system: hadoop fs -mkdir bgtest_data hadoop fs -put obsFrance.txt bgtest_data/obsFrance.txt hadoop fs -ls /user/oracle/bgtest_data/obsFrance.txt [oracle@dg1 bg-data-raw]$ hadoop fs -ls /user/oracle/bgtest_data/obsFrance.txt Found 1 items -rw-r--r-- 1 oracle supergroup 54103 2013-10-22 06:10 /user/oracle/bgtest_data/obsFrance.txt [oracle@dg1 bg-data-raw]$hadoop fs -ls hdfs:///user/oracle/bgtest_data/obsFrance.txt Found 1 items -rw-r--r-- 1 oracle supergroup 54103 2013-10-22 06:10 /user/oracle/bgtest_data/obsFrance.txt Check the content of the HDFS with the browser UI: Start the Oracle database, and run the following script in order to create the Oracle database user, the Oracle directories for the Oracle Big Data Connector (dg1 it’s my own db id replace accordingly yours): #!/bin/bash export ORAENV_ASK=NO export ORACLE_SID=dg1 . oraenv sqlplus /nolog <<EOF CONNECT / AS sysdba; CREATE OR REPLACE DIRECTORY osch_bin_path AS '/u01/orahdfs-2.2.0/bin'; CREATE USER BGUSER IDENTIFIED BY oracle; GRANT CREATE SESSION, CREATE TABLE TO BGUSER; GRANT EXECUTE ON sys.utl_file TO BGUSER; GRANT READ, EXECUTE ON DIRECTORY osch_bin_path TO BGUSER; CREATE OR REPLACE DIRECTORY BGT_LOG_DIR as '/u01/BG_TEST/logs'; GRANT READ, WRITE ON DIRECTORY BGT_LOG_DIR to BGUSER; CREATE OR REPLACE DIRECTORY BGT_DATA_DIR as '/u01/BG_TEST/data'; GRANT READ, WRITE ON DIRECTORY BGT_DATA_DIR to BGUSER; EOF Put the following in a file named t3.sh and make it executable, hadoop jar $OSCH_HOME/jlib/orahdfs.jar \ oracle.hadoop.exttab.ExternalTable \ -D oracle.hadoop.exttab.tableName=BGTEST_DP_XTAB \ -D oracle.hadoop.exttab.defaultDirectory=BGT_DATA_DIR \ -D oracle.hadoop.exttab.dataPaths="hdfs:///user/oracle/bgtest_data/obsFrance.txt" \ -D oracle.hadoop.exttab.columnCount=7 \ -D oracle.hadoop.connection.url=jdbc:oracle:thin:@//localhost:1521/dg1 \ -D oracle.hadoop.connection.user=BGUSER \ -D oracle.hadoop.exttab.printStackTrace=true \ -createTable --noexecute then test the creation fo the external table with it: [oracle@dg1 samples]$ ./t3.sh ./t3.sh: line 2: /u01/orahdfs-2.2.0: Is a directory Oracle SQL Connector for HDFS Release 2.2.0 - Production Copyright (c) 2011, 2013, Oracle and/or its affiliates. All rights reserved. Enter Database Password:] The create table command was not executed. The following table would be created. CREATE TABLE "BGUSER"."BGTEST_DP_XTAB" ( "C1" VARCHAR2(4000), "C2" VARCHAR2(4000), "C3" VARCHAR2(4000), "C4" VARCHAR2(4000), "C5" VARCHAR2(4000), "C6" VARCHAR2(4000), "C7" VARCHAR2(4000) ) ORGANIZATION EXTERNAL ( TYPE ORACLE_LOADER DEFAULT DIRECTORY "BGT_DATA_DIR" ACCESS PARAMETERS ( RECORDS DELIMITED BY 0X'0A' CHARACTERSET AL32UTF8 STRING SIZES ARE IN CHARACTERS PREPROCESSOR "OSCH_BIN_PATH":'hdfs_stream' FIELDS TERMINATED BY 0X'2C' MISSING FIELD VALUES ARE NULL ( "C1" CHAR(4000), "C2" CHAR(4000), "C3" CHAR(4000), "C4" CHAR(4000), "C5" CHAR(4000), "C6" CHAR(4000), "C7" CHAR(4000) ) ) LOCATION ( 'osch-20131022081035-74-1' ) ) PARALLEL REJECT LIMIT UNLIMITED; The following location files would be created. osch-20131022081035-74-1 contains 1 URI, 54103 bytes 54103 hdfs://localhost:19000/user/oracle/bgtest_data/obsFrance.txt Then remove the --noexecute flag and create the external Oracle table for the Hadoop data. Check the results: The create table command succeeded. CREATE TABLE "BGUSER"."BGTEST_DP_XTAB" ( "C1" VARCHAR2(4000), "C2" VARCHAR2(4000), "C3" VARCHAR2(4000), "C4" VARCHAR2(4000), "C5" VARCHAR2(4000), "C6" VARCHAR2(4000), "C7" VARCHAR2(4000) ) ORGANIZATION EXTERNAL ( TYPE ORACLE_LOADER DEFAULT DIRECTORY "BGT_DATA_DIR" ACCESS PARAMETERS ( RECORDS DELIMITED BY 0X'0A' CHARACTERSET AL32UTF8 STRING SIZES ARE IN CHARACTERS PREPROCESSOR "OSCH_BIN_PATH":'hdfs_stream' FIELDS TERMINATED BY 0X'2C' MISSING FIELD VALUES ARE NULL ( "C1" CHAR(4000), "C2" CHAR(4000), "C3" CHAR(4000), "C4" CHAR(4000), "C5" CHAR(4000), "C6" CHAR(4000), "C7" CHAR(4000) ) ) LOCATION ( 'osch-20131022081719-3239-1' ) ) PARALLEL REJECT LIMIT UNLIMITED; The following location files were created. osch-20131022081719-3239-1 contains 1 URI, 54103 bytes 54103 hdfs://localhost:19000/user/oracle/bgtest_data/obsFrance.txt This is the view from the SQL Developer: and finally the number of lines in the oracle table, imported from our Hadoop HDFS cluster SQL select count(*) from "BGUSER"."BGTEST_DP_XTAB"; COUNT(*) ---------- 1151 In a next post we will integrate data from a Hive database, and try some ODI integrations with the ODI Big Data connector. Our simplistic approach is just a step to show you how these unstructured data world can be integrated to Oracle infrastructure. Hadoop, BigData, NoSql are great technologies, they are widely used and Oracle is offering a large integration infrastructure based on these services. Oracle University presents a complete curriculum on all the Oracle related technologies: NoSQL: Introduction to Oracle NoSQL Database Using Oracle NoSQL Database Big Data: Introduction to Big Data Oracle Big Data Essentials Oracle Big Data Overview Oracle Data Integrator: Oracle Data Integrator 12c: New Features Oracle Data Integrator 11g: Integration and Administration Oracle Data Integrator: Administration and Development Oracle Data Integrator 11g: Advanced Integration and Development Oracle Coherence 12c: Oracle Coherence 12c: New Features Oracle Coherence 12c: Share and Manage Data in Clusters Oracle Coherence 12c: Oracle GoldenGate 11g: Fundamentals for Oracle Oracle GoldenGate 11g: Fundamentals for SQL Server Oracle GoldenGate 11g Fundamentals for Oracle Oracle GoldenGate 11g Fundamentals for DB2 Oracle GoldenGate 11g Fundamentals for Teradata Oracle GoldenGate 11g Fundamentals for HP NonStop Oracle GoldenGate 11g Management Pack: Overview Oracle GoldenGate 11g Troubleshooting and Tuning Oracle GoldenGate 11g: Advanced Configuration for Oracle Other Resources: Apache Hadoop : http://hadoop.apache.org/ is the homepage for these technologies. "Hadoop Definitive Guide 3rdEdition" by Tom White is a classical lecture for people who want to know more about Hadoop , and some active "googling " will also give you some more references. About the author: Eugene Simos is based in France and joined Oracle through the BEA-Weblogic Acquisition, where he worked for the Professional Service, Support, end Education for major accounts across the EMEA Region. He worked in the banking sector, ATT, Telco companies giving him extensive experience on production environments. Eugen currently specializes in Oracle Fusion Middleware teaching an array of courses on Weblogic/Webcenter, Content,BPM /SOA/Identity-Security/GoldenGate/Virtualisation/Unified Comm Suite) throughout the EMEA region.

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  • AutoAudit 1.10c

    - by Paul Nielsen
    AutoAudit is a free SQL Server (2005, 2008) Code-Gen utility that creates Audit Trail Triggers with: · Created, Modified, and RowVersion (incrementing INT) columns to table · Creates View to reconstruct deleted rows · Creates UDF to reconstruct Row History · Schema Audit Trigger to track schema changes · Re-code-gens triggers when Alter Table changes the table Version 1.10c Adds: · Createdby and ModifiedBy columns. Pass the user to the column and AutoAudit records that username instead of the Suser_Sname...(read more)

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  • Oracle Linux Events in December

    - by Zeynep Koch
    December will be a busy month for Oracle Linux team. We will be showcasing Oracle Linux and Oracle VM in conferences all around the world. Here's a list of December events we will showcase Oracle Linux: Gartner Data Center – North America Oracle will have a session and booth - Register today Dec 3-6, Las Vegas, USA 0 0 1 66 377 Oracle Corporation 3 1 442 14.0 96 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:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman";} Oracle Open World Latin America Oracle OpenWorld Latin America December 4–6, Sao Paulo, Brazil 0 0 1 25 145 Oracle Corporation 1 1 169 14.0 96 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:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman";} HP Discover EMEA HP Discover – EMEA December 4 – 6, Messe Frankfurt, Germany (Oracle Platinum sponsor) 0 0 1 41 239 Oracle Corporation 1 1 279 14.0 96 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:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman";} Oracle Superior Solutions and Cost Savings with Oracle Linux and Oracle VM See Location Details and register Dec 4 Kansas City and Tampa Dec 6 Milwaukee and Miami Dec 11 Washington, DC Dec 13 Raleigh Visit our booth and you can grab an Oracle Linux/Oracle VM DVD Kit and talk to Oracle Linux experts.

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  • Creating an email notification system based on polling database rows

    - by Ashish Sharma
    I have to design an email notification system based on the following requirements: The email notifications would be created based on polling rows in a Mysql 5.5 DB table when they are in a particular 'Completed' state. The email notification should be sent out in no more than 5 minutes from the time the row was created in the DB table (At the time of DB table row creation the state of the row might not be 'Completed'). Once 5 minutes for the DB table row expire in reaching the 'Completed' state, separate email notification need to be sent (basically telling the user that the original email notification would be delayed) and then sending the email notification as and when the row state reaches to being 'Completed'. The rest of the system requirements are : Adding relevant checks to monitor the whole system via MBeans interface. The system should be scalable so that if the rate of DB table rows creation increases so does the Email notification system be able to ramp up. So I request suggestions on following lines: What approach should I take in solving the problem described from a programming/Design pattern point of view? Suggestion for any third party plugin/software that can be used to solve the problem described? Points to take care regarding scalability and monitoring the health of the system? Java is the language of preference but I am open to using off the shelf components that can be interfaced with Java language or provide standard ports for communication. Currently I do have an in house grown system (written in Java) that is catering to the specified requirements, but it's now crumbling under increased load and now I want to give the problem a fresh look. thanks in advance Ashish

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  • SQL SERVER – Identify Most Resource Intensive Queries – SQL in Sixty Seconds #029 – Video

    - by pinaldave
    There are a few questions I often get asked. I wonder how interesting is that in our daily life all of us have to often need the same kind of information at the same time. Here is the example of the similar questions: How many user created tables are there in the database? How many non clustered indexes each of the tables in the database have? Is table Heap or has clustered index on it? How many rows each of the tables is contained in the database? I finally wrote down a very quick script (in less than sixty seconds when I originally wrote it) which can answer above questions. I also created a very quick video to explain the results and how to execute the script. Here is the complete script which I have used in the SQL in Sixty Seconds Video. SELECT [schema_name] = s.name, table_name = o.name, MAX(i1.type_desc) ClusteredIndexorHeap, COUNT(i.TYPE) NoOfNonClusteredIndex, p.rows FROM sys.indexes i INNER JOIN sys.objects o ON i.[object_id] = o.[object_id] INNER JOIN sys.schemas s ON o.[schema_id] = s.[schema_id] LEFT JOIN sys.partitions p ON p.OBJECT_ID = o.OBJECT_ID AND p.index_id IN (0,1) LEFT JOIN sys.indexes i1 ON i.OBJECT_ID = i1.OBJECT_ID AND i1.TYPE IN (0,1) WHERE o.TYPE IN ('U') AND i.TYPE = 2 GROUP BY s.name, o.name, p.rows ORDER BY schema_name, table_name Related Tips in SQL in Sixty Seconds: Find Row Count in Table – Find Largest Table in Database Find Row Count in Table – Find Largest Table in Database – T-SQL Identify Numbers of Non Clustered Index on Tables for Entire Database Index Levels, Page Count, Record Count and DMV – sys.dm_db_index_physical_stats Index Levels and Delete Operations – Page Level Observation What would you like to see in the next SQL in Sixty Seconds video? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Database, Pinal Dave, PostADay, SQL, SQL Authority, SQL in Sixty Seconds, SQL Query, SQL Scripts, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL, Technology, Video Tagged: Excel

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  • SQL SERVER – Capturing Wait Types and Wait Stats Information at Interval – Wait Type – Day 5 of 28

    - by pinaldave
    Earlier, I have tried to cover some important points about wait stats in detail. Here are some points that we had covered earlier. DMV related to wait stats reset when we reset SQL Server services DMV related to wait stats reset when we manually reset the wait types However, at times, there is a need of making this data persistent so that we can take a look at them later on. Sometimes, performance tuning experts do some modifications to the server and try to measure the wait stats at that point of time and after some duration. I use the following method to measure the wait stats over the time. -- Create Table CREATE TABLE [MyWaitStatTable]( [wait_type] [nvarchar](60) NOT NULL, [waiting_tasks_count] [bigint] NOT NULL, [wait_time_ms] [bigint] NOT NULL, [max_wait_time_ms] [bigint] NOT NULL, [signal_wait_time_ms] [bigint] NOT NULL, [CurrentDateTime] DATETIME NOT NULL, [Flag] INT ) GO -- Populate Table at Time 1 INSERT INTO MyWaitStatTable ([wait_type],[waiting_tasks_count],[wait_time_ms],[max_wait_time_ms],[signal_wait_time_ms], [CurrentDateTime],[Flag]) SELECT [wait_type],[waiting_tasks_count],[wait_time_ms],[max_wait_time_ms],[signal_wait_time_ms], GETDATE(), 1 FROM sys.dm_os_wait_stats GO ----- Desired Delay (for one hour) WAITFOR DELAY '01:00:00' -- Populate Table at Time 2 INSERT INTO MyWaitStatTable ([wait_type],[waiting_tasks_count],[wait_time_ms],[max_wait_time_ms],[signal_wait_time_ms], [CurrentDateTime],[Flag]) SELECT [wait_type],[waiting_tasks_count],[wait_time_ms],[max_wait_time_ms],[signal_wait_time_ms], GETDATE(), 2 FROM sys.dm_os_wait_stats GO -- Check the difference between Time 1 and Time 2 SELECT T1.wait_type, T1.wait_time_ms Original_WaitTime, T2.wait_time_ms LaterWaitTime, (T2.wait_time_ms - T1.wait_time_ms) DiffenceWaitTime FROM MyWaitStatTable T1 INNER JOIN MyWaitStatTable T2 ON T1.wait_type = T2.wait_type WHERE T2.wait_time_ms > T1.wait_time_ms AND T1.Flag = 1 AND T2.Flag = 2 ORDER BY DiffenceWaitTime DESC GO -- Clean up DROP TABLE MyWaitStatTable GO If you notice the script, I have used an additional column called flag. I use it to find out when I have captured the wait stats and then use it in my SELECT query to SELECT wait stats related to that time group. Many times, I select more than 5 or 6 different set of wait stats and I find this method very convenient to find the difference between wait stats. In a future blog post, we will talk about specific wait stats. Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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