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  • How to get the right row in a sorted DataView?

    - by ZeissS
    Hi, I have a DataGrid containing a small table. Until now, I had a double click handler on this grid which iterated over all rows to find the selected one: DataTable table = (DataTable)this.dataGrid.DataSource; int selectedRow = -1; for (int i=0; i<table.Rows.Count; i++) if (this.dataGrid.IsSelected(i)) selectedRow = i; break; } if ( selectedRow != -1 ) { DataRow row = table.Rows[selectedRow]; // More code ... } Problem: When the user clicks on a column header and sort the table, table.Rows does not return the right rows. It still contains the unsorted rows. How can I get the right column?

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  • Handling missing data

    - by soppotare
    Say I have a simple helpdesk application which logs calls made by users. I would typically have such fields in a table relating to the call e.g. CallID, Description, CustomerID etc. I Would also have a table of customers including CustomerID, Username, Password, FullName etc. Now when a user is deleted from the customers table then the inner join between the calls table and the users table to find out historically which user logged a call would produce no results. How do people usually deal with this? Have seperate customer and useraccount tables Just disable the accounts so the data is still available Record the customers name in the calls table as a seperate field. or any other methods / suggestions?

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  • Problem using OLEDBCOMMANDBUILDER.

    - by Lullly
    So, here it goes: I need to copy data from table in access database, in another table from another access database. Column names from tables are the same, except the fact that the FROM table has 5 columns, the TO table has 6. here is my code: dsFrom.Clear() dsTO.Clear() daFrom = Nothing daTO = Nothing conn_string1 = "Provider=Microsoft.Jet.OLEDB.4.0;Data Source="etc.mdb;" conn_string2 = "Provider=Microsoft.Jet.OLEDB.4.0;Data Source="database.mdb;" query1 = "Select * from nomenclator_produse" query2 = "Select * from nomenclator_produse" Conn1 = New OleDbConnection(conn_string1) conn2 = New OleDbConnection(conn_string2) Conn1.Open() conn2.Open() daFrom = New OleDbDataAdapter(query1, Conn1) daTO = New OleDbDataAdapter(query2, conn2) daFrom.AcceptChangesDuringFill = False dsFrom.HasChanges() daFrom.Fill(dsFrom, "nomenclator_Produse") dsFrom.HasChanges() Dim cb = New OleDbCommandBuilder(daFrom) dsTO = dsFrom.Copy daTO.UpdateCommand = cb.GetUpdateCommand daTO.InsertCommand = cb.GetInsertCommand daTO.Update(dsTO, "nomenclator_produse") Because the FROM table has 5 rows and the other has 6, i'm trying to use the InsertCommand generated by the DataAdapter of the first table. It works, only that it inserts the data from the FROMTABLE in the same FROMTABLE, instead of TOTABLE. :| please help me :(

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  • jQuery: How to get the first “td” in all rows

    - by bobby
    I have a table, and I want get the first “td” in all rows. My jquery here: $("table.SimpleTable tr td:first-child").css('background-color','red'); and my HTML here: <table class='SimpleTable' border="1" ID="Table1"> <tr> <td>Left</td> <td>Right</td> </tr> <tr> <td>Left</td> <td>Right</td> </tr> <tr> <td>Left</td> <td>Right</td> </tr> <tr> <td>Left</td> <td> <table border="1" ID="Table2"> <tr> <td>AAA</td> <td>AAA</td> <td>AAA</td> </tr> </table> </td> </tr> <tr> <td>Left</td> <td> <table border="1" ID="Table3"> <tr> <td>BBB</td> <td>BBB</td> <td>BBB</td> </tr> </table> </td> </tr> </table> The problem here it get the first "td" in the nested table of the second "td". Please help me!

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  • comparing two tables

    - by sza
    I have two identical table ie all the columns are identical and one of the datatype is Text, one is varchar(255) and the rest are int. Lets say the table name is 'AAAAA'. Table AAAAA was processed and backed up earlier this month. Both the tables were storing data and now the second table is only storing data. I need to find unmatching records from the second table (BBBBB) which is storing data right now and add those records to Table AAAAA. Your help will be highly appreciated. I tried to use 'EXCEPT' but it does not support text datatype. I'm using SQL Server 2005.

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  • Why is [date] + [time] non-deterministic in SQL Server 2008?

    - by John Gietzen
    I'm trying to do the following for my IIS logs table: ALTER TABLE [W3CLog] ADD [LogTime] AS [date] + ([time] - '1900-01-01') PERSISTED However, SQL Server 2008 tells me: Computed column 'LogTime' in table 'W3CLog' cannot be persisted because the column is non-deterministic. The table has this definition: CREATE TABLE [dbo].[W3CLog]( [Id] [bigint] IDENTITY(1,1) NOT NULL, ... [date] [datetime] NULL, [time] [datetime] NULL, ... ) Why is that non-deterministic? I really need to index that field. The table currently has 1598170 rows, and it is a pain to query if we can't do an index seek on the full time. Since this is being UNION'd with some other log formats, we can't very easily just use the two columns separately.

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  • Mysql Constraint problem

    - by Bramjam
    this is my table /* oefenreeks leerplan */ CREATE TABLE leerplan_oefenreeks ( leerplan_oefenreeks_id INT PRIMARY KEY AUTO_INCREMENT NOT NULL, leerplan_id INT NOT NULL, oefenreeks_id INT NOT NULL, plaats INT NOT NULL ); /* fk */ ALTER TABLE leerplan_oefenreeks ADD CONSTRAINT fk_leerp_oefenr_leerplan FOREIGN KEY(leerplan_id) REFERENCES leerplan (leerplan_id) ON DELETE CASCADE; ALTER TABLE leerplan_oefenreeks ADD CONSTRAINT fk_leerp_oefenr_oefenreeks FOREIGN KEY(oefenreeks_id) REFERENCES oefenreeks (oefenreeks_id) ON DELETE CASCADE; /* unique s *//*when I execute the nexline, my fk_leerp_oefenr_leerplan constraint vanishes/disappears*/ ALTER TABLE leerplan_oefenreeks ADD CONSTRAINT un_leerp_oefenr UNIQUE(leerplan_id, oefenreeks_id); ALTER TABLE leerplan_oefenreeks ADD CONSTRAINT un_leerp_oefenr_plaats UNIQUE(leerplan_id, plaats); when I go and check only 3 constraints exist (fk_leerp_oefenr_leerplan is gone) I don't understand why this happens, plz tell me (if you need more info just ask ;)

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  • AngularJS databinding

    - by user3652865
    How can I add multiple values to one object in an Array. I am having Environment and Cluster, I am able to assign multiple clusters to one environment. Now I want to add application name to this environment and cluster pair. I am having page called "Add Application". Here I am using select menu to for environment and Cluster. My first question is, when I select environment then want to show only those clusters which are assigned to that environment name. And assign application name to that pair. Also should be able to edit the Application field. I am using environmentServices and clusterServices to store updated data. link of JSFiddle: http://jsfiddle.net/avinashMaddy/J2KLK/5/ Please if someone can help me in this. Below is my code: <div class="maincontent" ng-controller="manageApplicationController"> <div class="article"> <form> <section> <!-- Environment --> <div class="col-md-4"> <label>Environment:</label> <select ng-model="newApp.selectedEnvironment" class="form-control" ng-options="environment.name for environment in environments"> <option value='' disabled style='display:none;'> Select Environment </option> </select> <span> <select ng-switch-when="true" disabled ng-model="newApp.selectedEnvironment" class="form-control" ng-options="environment.name for environment in environments"> <option value='' disabled style='display:none;'> Select Environment </option> </select> </span> </div> <!-- Cluster --> <div class="col-md-4"> <label>Cluster:</label> <span ng-switch on="newApp.showCancel"> <select ng-switch-default ng-model="newApp.selectedCluster" class="form-control" ng-options="cluster for cluster in clusters"> <option value='' disabled style='display:none;'> Select Environment </option> </select> <select ng-switch-when="true" disabled ng-model="newApp.selectedCluster" class="form-control" ng-options="cluster for cluster in clusters"> <option value='' disabled style='display:none;'> Select Environment </option> </select> </span> </div> <!-- Application Name --> <div class="col-md-4"> <label>Application Name:</label> <input type="text" class="form-control" name="applicationName" placeholder="Application" ng-model="app.name" required> <br/> <input type="hidden" ng-model="app.id" /> </div> </section> <!-- submit button --> <section class="col-md-12"> <button type="button" class="btn btn-default pull-right" ng-click="saveNewApplicatons()">Save</button> </section> </form> </div> <!-- table --> <div class="article"> <table class="table table-bordered table-striped"> <tr> <th colspan="6"> <div class="pull-left">Cluster Info</div> </th> </tr> <tr> <th>Environment</th> <th>Cluster</th> <th>Application</th> <th>Edit</th> <th>Header Ifo</th> </tr> <tr ng-repeat="app in applications"> <td>{{app.environment}}</td> <td>{{app.cluster}}</td> <td>{{app.name}}</td> <td> <a href="" ng-click="edit(app.id)" title="Edit">edit</span></a> | <a href="" ng-click="remove(app.id)" title="Delete">delete</a> </td> <td> <!-- Add template --> <script type="text/ng-template" id="addHederInfo.html"> <div class="modal-header"> <h3>Add Header Info</h3> </div> <div class="modal-body"> <input type="text" class="form-control" name="eName" placeholder="Header Key" ng-model="$parent.header.key"> <br/> <input type="text" class="form-control" name="eName" placeholder="Header Value" ng-model="$parent.header.value"> <br /> <input type="hidden" ng-model="header.id" /> <section> <div class="pull-right"> <button class="btn btn-primary" ng-click="saveHeader()">Add</button> <button class="btn btn-warning" ng-click="cancel()">Close</button> </div> </section> </div> <div class="modal-footer"> <h3>Existing Header Info for </h3> <table class="table table-bordered table-striped"> <tr> <th>Header Key</th> <th>Header Vlaue</th> </tr> <tr ng-repeat="header in headers"> <td>{{header.key}}</td> <td>{{header.value}}</td> </tr> </table> </div> </script> <!-- /Add template --> <script type="text/ng-template" id="editHederInfo.html"> <div class="modal-header"> <h3>Edit Header Info</h3> </div> <div class="modal-body"> <input type="text" class="form-control" name="eName" placeholder="Header Key" ng-model="$parent.header.key"> <br/> <input type="text" class="form-control" name="eName" placeholder="Header Value" ng-model="$parent.header.value"> <br /> <input type="hidden" ng-model="header.id" /> <section> <div class="pull-right"> <button class="btn btn-primary" ng-click="saveHeader()">Update</button> <button class="btn btn-warning" ng-click="cancel()">Close</button> </div> </section> </div> <div class="modal-footer"> <h3>Existing Header Info for</h3> <table class="table table-bordered table-striped"> <tr> <th>Header Key</th> <th>Header Vlaue</th> <th>Edit</th> </tr> <tr ng-repeat="header in headers"> <td>{{header.key}}</td> <td>{{header.value}}</td> <td> <a href="" ng-click="editHeader(header.id)" title="Edit"><span class="glyphicon glyphicon-edit" ></span></a> | <a href="" ng-click="removeHeader(header.id)" title="Edit"><span class="glyphicon glyphicon-trash"></span></a> </td> </tr> </table> </div> </script> <!-- Add template --> <!-- /Add template --> <a href="" ng-click="addInfo()">Add</a> | <a href="" ng-click="editInfo()">Edit</a> </td> </tr> </table> </div> </div> Controller.js: var apsApp = angular.module('apsApp', []); apsApp.service('clusterService', function(){ var clusters=[]; //simply returns the environment list this.list = function () { return clusters; }; }); apsApp.service('environmentService', function(){ var environments=[ {name :'DEV',}, {name:'PROD',}, {name:'QA',}, {name:'Linux_Dev',} ]; //simply returns the environment list this.list = function () { return environments; }; apsApp.controller('manageApplicationController', function ($scope, environmentService, clusterService) { var uid = 0; $scope.environments= environmentService.list(); $scope.clusters= clusterService.list(); $scope.newApp = {}; $scope.newApp.selectedEnvironment = $scope.environments[0]; $scope.newApp.selectedCluster = $scope.clusters[0]; $scope.newApp.buttonLabel = 'Save'; $scope.newApp.showCancel = false; /*$scope.applications=[ {'name': 'Enterprice App Store' }, {'name': 'UsageGateway'}, {'name': 'Click 2 Fill'}, {'name': 'ATT SmartWiFi'} ];*/ //add new application $scope.saveNewApplicatons = function() { if ($scope.select.id == undefined) { //if this is new application, add it in applications array $scope.clusters.push({ id: uid++, cluster: $scope.newApp.cluster, environment: $scope.newApp.selectedEnvironment }); } else { $scope.clusters[$scope.select.id].cluster = $scope.select.cluster; $scope.newApp.id = undefined; $scope.newApp.buttonLabel = 'Save Cluster'; $scope.newApp.showCancel = false; }; //clear the add appplicaitons form $scope.newApp.selectedEnvironment = $scope.environments[0]; }; //delete application $scope.remove = function (id) { //search app with given id and delete it for (i in $scope.clusters) { if ($scope.clusters[i].id == id) { confirm("This Cluster will get deleted permanently"); $scope.clusters.splice(i, 1); $scope.clust = {}; } } }; $scope.cancelUpdate = function () { $scope.newApp.buttonLabel = 'Save Cluster'; $scope.newApp.showCancel = false; $scope.newApp.id = undefined; $scope.newApp.cluster = ""; $scope.newApp.selectedEnvironment = $scope.environments[0]; }; });

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  • Use a single freemarker template to display tables of arbitrary pojos

    - by Kevin Pauli
    Attention advanced Freemarker gurus: I want to use a single freemarker template to be able to output tables of arbitrary pojos, with the columns to display defined separately than the data. The problem is that I can't figure out how to get a handle to a function on a pojo at runtime, and then have freemarker invoke that function (lambda style). From skimming the docs it seems that Freemarker supports functional programming, but I can't seem to forumulate the proper incantation. I whipped up a simplistic concrete example. Let's say I have two lists: a list of people with a firstName and lastName, and a list of cars with a make and model. would like to output these two tables: <table> <tr> <th>firstName</th> <th>lastName</th> </tr> <tr> <td>Joe</td> <td>Blow</d> </tr> <tr> <td>Mary</td> <td>Jane</d> </tr> </table> and <table> <tr> <th>make</th> <th>model</th> </tr> <tr> <td>Toyota</td> <td>Tundra</d> </tr> <tr> <td>Honda</td> <td>Odyssey</d> </tr> </table> But I want to use the same template, since this is part of a framework that has to deal with dozens of different pojo types. Given the following code: public class FreemarkerTest { public static class Table { private final List<Column> columns = new ArrayList<Column>(); public Table(Column[] columns) { this.columns.addAll(Arrays.asList(columns)); } public List<Column> getColumns() { return columns; } } public static class Column { private final String name; public Column(String name) { this.name = name; } public String getName() { return name; } } public static class Person { private final String firstName; private final String lastName; public Person(String firstName, String lastName) { this.firstName = firstName; this.lastName = lastName; } public String getFirstName() { return firstName; } public String getLastName() { return lastName; } } public static class Car { String make; String model; public Car(String make, String model) { this.make = make; this.model = model; } public String getMake() { return make; } public String getModel() { return model; } } public static void main(String[] args) throws Exception { final Table personTableDefinition = new Table(new Column[] { new Column("firstName"), new Column("lastName") }); final List<Person> people = Arrays.asList(new Person[] { new Person("Joe", "Blow"), new Person("Mary", "Jane") }); final Table carTable = new Table(new Column[] { new Column("make"), new Column("model") }); final List<Car> cars = Arrays.asList(new Car[] { new Car("Toyota", "Tundra"), new Car("Honda", "Odyssey") }); final Configuration cfg = new Configuration(); cfg.setClassForTemplateLoading(FreemarkerTest.class, ""); cfg.setObjectWrapper(new DefaultObjectWrapper()); final Template template = cfg.getTemplate("test.ftl"); process(template, personTableDefinition, people); process(template, carTable, cars); } private static void process(Template template, Table tableDefinition, List<? extends Object> data) throws Exception { final Map<String, Object> dataMap = new HashMap<String, Object>(); dataMap.put("tableDefinition", tableDefinition); dataMap.put("data", data); final Writer out = new OutputStreamWriter(System.out); template.process(dataMap, out); out.flush(); } } All the above is a given for this problem. So here is the template I have been hacking on. Note the comment where I am having trouble. <table> <tr> <#list tableDefinition.columns as col> <th>${col.name}</th> </#list> </tr> <#list data as pojo> <tr> <#list tableDefinition.columns as col> <td><#-- what goes here? --></td> </#list> </tr> </#list> </table> So col.name has the name of the property I want to access from the pojo. I have tried a few things, such as pojo.col.name and <#assign property = col.name/> ${pojo.property} but of course these don't work, I just included them to help convey my intent. I am looking for a way to get a handle to a function and have freemarker invoke it, or perhaps some kind of "evaluate" feature that can take an arbitrary expression as a string and evaluate it at runtime.

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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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  • 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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  • 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 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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  • 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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