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  • Combining Data from two MySQL tables.

    - by Nick
    I'm trying to combine data from two tables in MySQL with PHP. I want to select all the data (id, title, post_by, content and created_at) from the "posts" table. Then I would like to select the comment_id COUNT from the "comments" table IF the comment_id equals the posts id. Finally, I would like to echo/print something on this order: <? echo $row->title; ?> Posted by <? echo $row->post_by; ?> on <? echo $row->created_at; ?> CST <? echo $row->content; ?> <? echo $row->comment_id; ?> comments | <a href="comment.php?id=<? echo $row->id; ?>">view/post comments</a> I'm uncertain as to how to "combine" the data from two tables. I have tried numerous things and have spent several evenings and have had no luck. Any help would be greatly appreciated!

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  • addEventListener pass caller as argument

    - by Ryan
    Hi, I have the following code. And my problem is that when clicking, the event is firing but the argument passed is not the correct one. It is passing the last dynamically created row i.e. dataStructure.length value. Anyone knows a solution how to get around this? var table = document.getElementById('output'); for(i =0; i<dataStructure.length; i++){ var row = document.createElement('tr'); row.setAttribute('id', i); var url = dataStructure[i].url; if(document.addEventListener) row.addEventListener('click', function(){handleRowClick(i);}, false); var obj = dataStructure[i]; var cellCount = 0; for(field in obj){ var cell = document.createElement('td'); cell.setAttribute('id', cellCount++); //cell.addEventListener('click', function(){window.open(dataStructureObj.links[i].url);}, false); cell.innerHTML = obj[field]; row.appendChild(cell); } cellCount = 0; table.appendChild(row); } } function handleRowClick(rowClicked){ var rowHTML = rowClicked.innerHTML; var cells = rowHTML.getElementsByTagName('td'); for(cell in cells) { alert(cell.value); } window.open(cells[1].innerHTML); }

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  • what exactly is this.id ?

    - by kwokwai
    Hi all, I was doing some dynamic effect on DIV using JQuery when I found that the returned value of this.id varied from function to function. I got two sets of simple parent-child DIV tags like this: <DIV ID="row"> <DIV ID="c1"> <Input type="radio" name="testing" id="testing" VALUE="1">testing1 </DIV> </DIV> <DIV ID="row"> <DIV ID="c2"> <Input type="radio" name="testing" id="testing" VALUE="2">testing2 </DIV> </DIV> Code 1. $('#row DIV').mouseover(function(){ radio_btns.each(function() { $('#row DIV).addClass('testing'); // worked }); }); Code 2. $('#row DIV').mouseover(function(){ var childDivID = this.id; radio_btns.each(function() { $('#'+childDivID).parent('DIV').addClass('testing'); // didn't work }); }); I don't understand why only the first code couldn work and highlighted all the "row" DIV, but the first code failed to do so?

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  • pre_replace multi-dimensional array problem

    - by Martin
    I want to replace word groups by links. I use a multi-dimensional array to define these (in the real world there will be thousands of them). Here's the code: $text = "<html><body><pre> Here is Foo in text. Now come Baz? and Bar-X. Replace nothing here: Foo (followed by brackets). </pre></body></html>"; $s = array( array("t" => "Foo", "u" => "http://www.foo.com", "c" => "foo"), array("t" => "Baz?", "u" => "http://www.baz.net", "c" => "test"), array("t" => "Bar-X", "u" => "http://www.baz.org", "c" => "test") ); foreach ($s as $i => $row) { $replaced = preg_replace('/(?=\Q'.$row["t"].'\E[^(]+$)\b\Q'.$row["t"].'\E\b/m', '<a href="'.$row["u"].'" class="'.$row["c"].'">'.$row["t"].'</a>', $text); } echo $replaced; ?> The problem is that only one array element is replaced and not all. It's something about $text in peg_replace(). Anyone got a hint for me? Thanks!

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  • Mapping individual buttons on ASP.NET MVC View to controller actions

    - by skb
    I have an application where I need the user to be able to update or delete rows of data from the database. The rows are displayed to the user using a foreach loop in the .aspx file of my view. Each row will have two text fields (txtName, txtDesc), an update button, and a delete button. What I'm not sure of, is how do I have the update button send the message to the controller for which row to update? I can see a couple way of doing this: Put each row within it's own form tag, then when the update button is clicked, it will submit the values for that row only (there will also be a hidden field with the rowId) and the controller class would take all the post values as parameters to the Update method on the controller. Somehow, have the button be scripted in a way to send back only the values for that row with a POST to the controller. Is there a way of doing this? One thing I am concerned about is if each row has different names for it's controls assigned by ASP.NET (txtName1, txtDesc1, txtName2, txtDesc2), then how will their values get mapped to the correct parameters of the Controller method?

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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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  • Adding a Way To preserve A Comma In A CSV To DataTable Function

    - by Nick LaMarca
    I have a function that converts a .csv file to a datatable. One of the columns I am converting is is a field of names that have a comma in them i.e. "Doe, John" when converting the function treats this as 2 seperate fields because of the comma. I need the datatable to hold this as one field Doe, John in the datatable. Function CSV2DataTable(ByVal filename As String, ByVal sepChar As String) As DataTable Dim reader As System.IO.StreamReader Dim table As New DataTable Dim colAdded As Boolean = False Try ''# open a reader for the input file, and read line by line reader = New System.IO.StreamReader(filename) Do While reader.Peek() >= 0 ''# read a line and split it into tokens, divided by the specified ''# separators Dim tokens As String() = System.Text.RegularExpressions.Regex.Split _ (reader.ReadLine(), sepChar) ''# add the columns if this is the first line If Not colAdded Then For Each token As String In tokens table.Columns.Add(token) Next colAdded = True Else ''# create a new empty row Dim row As DataRow = table.NewRow() ''# fill the new row with the token extracted from the current ''# line For i As Integer = 0 To table.Columns.Count - 1 row(i) = tokens(i) Next ''# add the row to the DataTable table.Rows.Add(row) End If Loop Return table Finally If Not reader Is Nothing Then reader.Close() End Try End Function

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  • php while loop throwing a error over a $var < 10 statement

    - by William
    Okay, so for some reason this is giving me a error as seen here: http://prime.programming-designs.com/test_forum/viewboard.php?board=0 However if I take away the '&& $cur_row < 10' it works fine. Why is the '&& $cur_row < 10' causing me a problem? $sql_result = mysql_query("SELECT post, name, trip, Thread FROM (SELECT MIN(ID) AS min_id, MAX(ID) AS max_id, MAX(Date) AS max_date FROM test_posts GROUP BY Thread ) t_min_max INNER JOIN test_posts ON test_posts.ID = t_min_max.min_id WHERE Board=".$board." ORDER BY max_date DESC", $db); $num_rows = mysql_num_rows($sql_result); $cur_row = 0; while($row = mysql_fetch_row($sql_result) && $cur_row < 10) { $sql_max_post_query = mysql_query("SELECT ID FROM test_posts WHERE Thread=".$row[3].""); $post_num = mysql_num_rows($sql_max_post_query); $post_num--; $cur_row++; echo''.$cur_row.'<br/>'; echo'<div class="postbox"><h4>'.$row[1].'['.$row[2].']</h4><hr />' .$row[0]. '<br /><hr />[<a href="http://prime.programming-designs.com/test_forum/viewthread.php?thread='.$row[3].'">Reply</a>] '.$post_num.' posts omitted.</div>'; }

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  • Return REF CURSOR to procedure generated data

    - by ThaDon
    I need to write a sproc which performs some INSERTs on a table, and compile a list of "statuses" for each row based on how well the INSERT went. Each row will be inserted within a loop, the loop iterates over a cursor that supplies some values for the INSERT statement. What I need to return is a resultset which looks like this: FIELDS_FROM_ROW_BEING_INSERTED.., STATUS VARCHAR2 The STATUS is determined by how the INSERT went. For instance, if the INSERT caused a DUP_VAL_ON_INDEX exception indicating there was a duplicate row, I'd set the STATUS to "Dupe". If all went well, I'd set it to "SUCCESS" and proceed to the next row. By the end of it all, I'd have a resultset of N rows, where N is the number of insert statements performed and each row contains some identifying info for the row being inserted, along with the "STATUS" of the insertion Since there is no table in my DB to store the values I'd like to pass back to the user, I'm wondering how I can return the info back? Temporary table? Seems in Oracle temporary tables are "global", not sure I would want a global table, are there any temporary tables that get dropped after a session is done?

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  • c# Display the data in the List view

    - by Kumu
    private void displayResultsButton_Click(object sender, EventArgs e) { gameResultsListView.Items.Clear(); //foreach (Game game in footballLeagueDatabase.games) //{ ListViewItem row = new ListViewItem(); row.SubItems.Add(game.HomeTeam.ToString()); row.SubItems.Add(game.HomeScore.ToString()); row.SubItems.Add(game.AwayTeam.ToString()); row.SubItems.Add(game.AwayScore.ToString()); gameResultsListView.Items.Add(row); // } //footballLeagueDatabase.games.Sort(); } } } This is the display button and the following code describes the add button. private void addGameButton_Click(object sender, EventArgs e) { if ((homeTeamTxt.Text.Length) == 0) MessageBox.Show("You must enter a Home Team"); else if (homeScoreUpDown.Maximum <= 9 && homeScoreUpDown.Minimum >= 0) MessageBox.Show("You must enter one digit between 0 and 9"); else if ((awayTeamTxt.Text.Length) == 0) MessageBox.Show("You must enter a Away Team"); else if (awayScoreUpDown.Maximum <= 9 && awayScoreUpDown.Minimum >= 0) MessageBox.Show("You must enter one digit between 0 to 9"); else { //checkGameInputFields(); game = new Game(homeTeamTxt.Text, int.Parse(homeScoreUpDown.Value.ToString()), awayTeamTxt.Text, int.Parse(awayScoreUpDown.Value.ToString())); MessageBox.Show("Home Team" + 't' + homeTeamTxt.Text + "Away Team" + awayTeamTxt.Text + "created"); footballLeagueDatabase.AddGame(game); //clearCreateStudentInputFields(); } } I need to insert data into the above text field and Numeric up down control and display them in the list view. But I dont know How to do it, because when I press the button "Display Results" it displays the error message. If you know how can I display the data in the list view, please let me know.This is the first time I am using List view.

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  • using a href (html)tag along with PHP

    - by dexter
    i have tried: <?php include("delete.php") ?> <?php .... .... .... if($result=mysql_query($sql)) { echo "<table><th>Id</th><th>Name</th><th>Description</th><th>Unit Price</th>"; while($row = mysql_fetch_array($result)) { echo "<tr><td>".$row['Id']."</td><td>".$row['Name']."</td><td>".$row['Description']."</td><td>".$row['UnitPrice']."</td> <td><a href='delproduct($row[Id])' onclick = 'return MsgOkCancel()'>Delete</a></td></tr>"; echo "<br/>"; } } ?> following javascript is in the same page: <script type="text/javascript" language="javascript"> function MsgOkCancel() { if (confirm("Are You Sure You Want to Delete?")) { return true } else {return false} } </script> where delproduct is a javascript function in delete.php written like: <script type="javascript"> function delproduct(Id) { alert('Id '+ Id); } <script> ** after ** clicking Delete a okcancel message-box appear asking conformation ** but ** after clicking 'ok' it should execute statements inside delproduct function but it doesn't it gives error like: Object Not Found :The requested URL was not found on this server. what would be the problem? pls help, thanks

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  • JSON can't read, key reading fail maybe

    - by Abdullah Al Mubarok
    I wonder why I can't read the JSON Object like this : { "1":{"bulan":"Januari","tahun":"2012","tagihan":"205000","status":"Lunas"}, "2":{"bulan":"Februari","tahun":"2012","tagihan":"180000","status":"Lunas"}, "3":{"bulan":"Maret","tahun":"2012","tagihan":"120000","status":"Lunas"}, "4":{"bulan":"April","tahun":"2012","tagihan":"230000","status":"Lunas"}, "5":{"bulan":"Mei","tahun":"2012","tagihan":"160000","status":"Lunas"}, "6":{"bulan":"Juni","tahun":"2012","tagihan":"150000","status":"Belum Lunas"}, "panjang":6 } with my android code like this : try { int length = jobj.getInt("panjang"); for(int n = 0; n < length; n++){ String m = Integer.toString(n) JSONObject row = jobj.getJSONObject(m); String bulan = row.getString("bulan"); String tahun = row.getString("tahun"); String tagihan = row.getString("tagihan"); String status = row.getString("status"); HashMap<String, String> map = new HashMap<String, String>(); map.put("bulan", bulan); map.put("tahun", tahun); map.put("tagihan", tagihan); map.put("status", status); list.add(map); } } catch (JSONException e) { e.printStackTrace(); } It always return nothing, but it works fine if I change the key m to specific key like if String m = "1"; and I can't use JSONObject row = jobj.getJSONObject(n); because getJSONObject() just accept string, not int. is there something wrong with my code?

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  • problem during data fetch

    - by nectar
    here is my code $sql="SELECT * FROM $tbl_name WHERE ownerId='$UserId'"; $result=mysql_query($sql,$link)or die(mysql_error()); $row = mysql_fetch_array($result, MYSQL_ASSOC); <?php while($row = mysql_fetch_array($result, MYSQL_ASSOC)) { echo "<tr>"; echo "<td>".$row['pinId']."</td>"; echo "<td>".$row['usedby']."</td>"; echo "<td>".$row['status']."</td>"; echo "</tr>"; } ?> it is ignoring the first record means if 4 rows are in $row its ignoring the 1st one rest three are coming on page. ownerId is not primary key.

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  • MySQL/PHP Search Efficiency

    - by iMaster
    Hi! I'm trying to create a small search for my site. I've tried using full-text index search, but I could never get it to work. Here is what I've come up with: if(isset($_GET['search'])) { $search = str_replace('-', ' ', $_GET['search']); $result = array(); $titles = mysql_query("SELECT title FROM Entries WHERE title LIKE '%$search%'"); while($row = mysql_fetch_assoc($titles)) { $result[] = $row['title']; } $tags = mysql_query("SELECT title FROM Entries WHERE tags LIKE '%$search%'"); while($row = mysql_fetch_assoc($tags)) { $result[] = $row['title']; } $text = mysql_query("SELECT title FROM Entries WHERE entry LIKE '%$search%'"); while($row = mysql_fetch_assoc($text)) { $result[] = $row['title']; } $result = array_unique($result); } So basically, it searches through all the titles, body-text, and tags of all the entries in the DB. This works decently well, but I'm just wondering how efficient would it be? This would only be for a small blog, too. Either way I'm just wondering if this could be made any more efficient.

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  • Need help with jquery json data transfer from php file

    - by Scarface
    Hey guys I am trying to return the latest 10 results of a query from a php file, through json format, to a jquery getjson function that prints results. I am getting weird problems though. For example I am only getting 8 entries returned, and some are disordered, and sometimes nothing is returned. I am not really sure what I am doing wrong, so if anyone has any ideas I would really appreciate it. This is my query ($res) SELECT time, user, message FROM comments WHERE topic_id='$topic_id' ORDER BY time DESC LIMIT 10 This is the processing of the results while($row = mysql_fetch_array($res)){ $message=$row['message']; $user=$row['user']; if($row['message'] AND $row['time'] > $_GET['time']) $data[] = $row; } $out = json_encode($data); print $out; And this is the retrieval where prepare is just a function that returns information into a div $.getJSON(files+"processing.php?action=load&time="+0+"&topic_id="+topic_id+"&t=" + (new Date()), function(json) { if(json.length) { for(i=0; i < 10; i++) { $('#comment-list').prepend(prepare(json[i])); $('#list-' + count).fadeIn(1500); } } }); function prepare(response) { count++; var string = '<li class="comment-list" id="list-'+count+'">' //organize info into a div +'</li>'; return string; }

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  • Is there a way to update the height of a single UITableViewCell, without recalculating the height for every cell?

    - by Chris Vasselli
    I have a UITableView with a few different sections. One section contains cells that will resize as a user types text into a UITextView. Another section contains cells that render HTML content, for which calculating the height is relatively expensive. Right now when the user types into the UITextView, in order to get the table view to update the height of the cell, I call [self.tableView beginUpdates]; [self.tableView endUpdates]; However, this causes the table to recalculate the height of every cell in the table, when I really only need to update the single cell that was typed into. Not only that, but instead of recalculating the estimated height using tableView:estimatedHeightForRowAtIndexPath:, it calls tableView:heightForRowAtIndexPath: for every cell, even those not being displayed. Is there any way to ask the table view to update just the height of a single cell, without doing all of this unnecessary work? Update I'm still looking for a solution to this. As suggested, I've tried using reloadRowsAtIndexPaths:, but it doesn't look like this will work. Calling reloadRowsAtIndexPaths: with even a single row will still cause heightForRowAtIndexPath: to be called for every row, even though cellForRowAtIndexPath: will only be called for the row you requested. In fact, it looks like any time a row is inserted, deleted, or reloaded, heightForRowAtIndexPath: is called for every row in the table cell. I've also tried putting code in willDisplayCell:forRowAtIndexPath: to calculate the height just before a cell is going to appear. In order for this to work, I would need to force the table view to re-request the height for the row after I do the calculation. Unfortunately, calling [self.tableView beginUpdates]; [self.tableView endUpdates]; from willDisplayCell:forRowAtIndexPath: causes an index out of bounds exception deep in UITableView's internal code. I guess they don't expect us to do this. I can't help but feel like it's a bug in the SDK that in response to [self.tableView endUpdates] it doesn't call estimatedHeightForRowAtIndexPath: for cells that aren't visible, but I'm still trying to find some kind of workaround. Any help is appreciated.

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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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  • 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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  • 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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  • 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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