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• CPU Utilization LAMP stack

  - by Max

  We've got an ec2 m2.4xlarge running Magento (centos 5.6, httpd 2.2, php 5.2.17 with eaccelerator 0.9.5.3, mysql 5.1.52). Right now we're getting a large traffic spike, and our top looks like this: top - 09:41:29 up 31 days, 1:12, 1 user, load average: 120.01, 129.03, 113.23 Tasks: 1190 total, 18 running, 1172 sleeping, 0 stopped, 0 zombie Cpu(s): 97.3%us, 1.8%sy, 0.0%ni, 0.5%id, 0.0%wa, 0.0%hi, 0.0%si, 0.4%st Mem: 71687720k total, 36898928k used, 34788792k free, 49692k buffers Swap: 880737784k total, 0k used, 880737784k free, 1586524k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 2433 mysql 15 0 23.6g 4.5g 7112 S 564.7 6.6 33607:34 mysqld 24046 apache 16 0 411m 65m 28m S 26.4 0.1 0:09.05 httpd 24360 apache 15 0 410m 60m 25m S 26.4 0.1 0:03.65 httpd 24993 apache 16 0 410m 57m 21m S 26.1 0.1 0:01.41 httpd 24838 apache 16 0 428m 74m 20m S 24.8 0.1 0:02.37 httpd 24359 apache 16 0 411m 62m 26m R 22.3 0.1 0:08.12 httpd 23850 apache 15 0 411m 64m 27m S 16.8 0.1 0:14.54 httpd 25229 apache 16 0 404m 46m 17m R 10.2 0.1 0:00.71 httpd 14594 apache 15 0 404m 63m 34m S 8.4 0.1 1:10.26 httpd 24955 apache 16 0 404m 50m 21m R 8.4 0.1 0:01.66 httpd 24313 apache 16 0 399m 46m 22m R 8.1 0.1 0:02.30 httpd 25119 apache 16 0 411m 59m 23m S 6.8 0.1 0:01.45 httpd Questions: Would giving msyqld more memory help it cache queries and react faster? If so, how? Other than splitting mysql and php to separate servers (which we're about to do) is there anything else we could/should be doing? Thanks! UPDATE: Here's our my.cnf along with the output of mysqltuner. It looks like a cache problem. Thanks again! # cat /etc/my.cnf [client] port = **** socket = /var/lib/mysql/mysql.sock [mysqld] datadir=/mnt/persistent/mysql port=**** socket=/var/lib/mysql/mysql.sock key_buffer = 512M max_allowed_packet = 64M table_cache = 1024 sort_buffer_size = 8M read_buffer_size = 4M read_rnd_buffer_size = 2M myisam_sort_buffer_size = 64M thread_cache_size = 128M tmp_table_size = 128M join_buffer_size = 1M query_cache_limit = 2M query_cache_size= 64M query_cache_type = 1 max_connections = 1000 thread_stack = 128K thread_concurrency = 48 log-bin=mysql-bin server-id = 1 wait_timeout = 300 innodb_data_home_dir = /mnt/persistent/mysql/ innodb_data_file_path = ibdata1:10M:autoextend innodb_buffer_pool_size = 20G innodb_additional_mem_pool_size = 20M innodb_log_file_size = 64M innodb_log_buffer_size = 8M innodb_flush_log_at_trx_commit = 1 innodb_lock_wait_timeout = 50 innodb_thread_concurrency = 48 ft_min_word_len=3 [myisamchk] ft_min_word_len=3 key_buffer = 128M sort_buffer_size = 128M read_buffer = 2M write_buffer = 2M # ./mysqltuner.pl >> MySQLTuner 1.2.0 - Major Hayden <[email protected]> >> Bug reports, feature requests, and downloads at http://mysqltuner.com/ >> Run with '--help' for additional options and output filtering -------- General Statistics -------------------------------------------------- [--] Skipped version check for MySQLTuner script [OK] Currently running supported MySQL version 5.1.52-log [OK] Operating on 64-bit architecture -------- Storage Engine Statistics ------------------------------------------- [--] Status: +Archive -BDB +Federated +InnoDB -ISAM -NDBCluster [--] Data in MyISAM tables: 2G (Tables: 26) [--] Data in InnoDB tables: 749M (Tables: 250) [!!] Total fragmented tables: 262 -------- Security Recommendations ------------------------------------------- -------- Performance Metrics ------------------------------------------------- [--] Up for: 31d 2h 30m 38s (680M q [253.371 qps], 2M conn, TX: 4825B, RX: 236B) [--] Reads / Writes: 89% / 11% [--] Total buffers: 20.6G global + 15.1M per thread (1000 max threads) [OK] Maximum possible memory usage: 35.4G (51% of installed RAM) [OK] Slow queries: 0% (35K/680M) [OK] Highest usage of available connections: 53% (537/1000) [OK] Key buffer size / total MyISAM indexes: 512.0M/457.2M [OK] Key buffer hit rate: 100.0% (9B cached / 264K reads) [OK] Query cache efficiency: 42.3% (260M cached / 615M selects) [!!] Query cache prunes per day: 4384652 [OK] Sorts requiring temporary tables: 0% (1K temp sorts / 38M sorts) [!!] Joins performed without indexes: 100404 [OK] Temporary tables created on disk: 17% (7M on disk / 45M total) [OK] Thread cache hit rate: 99% (537 created / 2M connections) [!!] Table cache hit rate: 0% (1K open / 946K opened) [OK] Open file limit used: 9% (453/5K) [OK] Table locks acquired immediately: 99% (758M immediate / 758M locks) [OK] InnoDB data size / buffer pool: 749.3M/20.0G -------- Recommendations ----------------------------------------------------- General recommendations: Run OPTIMIZE TABLE to defragment tables for better performance Enable the slow query log to troubleshoot bad queries Adjust your join queries to always utilize indexes Increase table_cache gradually to avoid file descriptor limits Variables to adjust: query_cache_size (> 64M) join_buffer_size (> 1.0M, or always use indexes with joins) table_cache (> 1024)

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• Building a jQuery Plug-in to make an HTML Table scrollable

  - by Rick Strahl

  Today I got a call from a customer and we were looking over an older application that uses a lot of tables to display financial and other assorted data. The application is mostly meta-data driven with lots of layout formatting automatically driven through meta data rather than through explicit hand coded HTML layouts. One of the problems in this apps are tables that display a non-fixed amount of data. The users of this app don't want to use paging to see more data, but instead want to display overflow data using a scrollbar. Many of the forms are very densely populated, often with multiple data tables that display a few rows of data in the UI at the most. This sort of layout does not lend itself well to paging, but works much better with scrollable data. Unfortunately scrollable tables are not easily created. HTML Tables are mangy beasts as anybody who's done any sort of Web development knows. Tables are finicky when it comes to styling and layout, and they have many funky quirks, especially when it comes to scrolling both of the table rows themselves or even the child columns. There's no built-in way to make tables scroll and to lock headers while you do, and while you can embed a table (or anything really) into a scrolling div with something like this: <div style="position:relative; overflow: hidden; overflow-y: scroll; height: 200px; width: 400px;"> <table id="table" style="width: 100%" class="blackborder" > <thead> <tr class="gridheader"> <th>Column 1</th> <th>Column 2</th> <th>Column 3</th> <th >Column 4</th> </tr> </thead> <tbody> <tr> <td>Column 1 Content</td> <td>Column 2 Content</td> <td>Column 3 Content</td> <td>Column 4 Content</td> </tr> <tr> <td>Column 1 Content</td> <td>Column 2 Content</td> <td>Column 3 Content</td> <td>Column 4 Content</td> </tr> … </tbody> </table> </div> </div> that won't give a very satisfying visual experience: Both the header and body scroll which looks odd. You lose context as soon as the header scrolls off the top and when you reach the bottom of the list the bottom outline of the table shows which also looks off. The the side bar shows all the way down the length of the table yet another visual miscue. In a pinch this will work, but it's ugly. What's out there? Before we go further here you should know that there are a few capable grid plug-ins out there already. Among them: Flexigrid (can work of any table as well as with AJAX data) jQuery Scrollable Table Plug-in (feature similar to what I need but not quite) jqGrid (mostly an Ajax Grid which is very powerful and works very well) But in the end none of them fit the bill of what I needed in this situation. All of these require custom CSS and some of them are fairly complex to restyle. Others are AJAX only or work better with AJAX loaded data. However, I need to actually try (as much as possible) to maintain the original styling of the tables without requiring extensive re-styling. Building the makeTableScrollable() Plug-in To make a table scrollable requires rearranging the table a bit. In the plug-in I built I create two <div> tags and split the table into two: one for the table header and one for the table body. The bottom <div> tag then contains only the table's row data and can be scrolled while the header stays fixed. Using jQuery the basic idea is pretty simple: You create the divs, copy the original table into the bottom, then clone the table, clear all content append the <thead> section, into new table and then copy that table into the second header <div>. Easy as pie, right? Unfortunately it's a bit more complicated than that as it's tricky to get the width of the table right to account for the scrollbar (by adding a small column) and making sure the borders properly line up for the two tables. A lot of style settings have to be made to ensure the table is a fixed size, to remove and reattach borders, to add extra space to allow for the scrollbar and so forth. The end result of my plug-in is a table with a scrollbar. Using the same table I used earlier the result looks like this: To create it, I use the following jQuery plug-in logic to select my table and run the makeTableScrollable() plug-in against the selector: $("#table").makeTableScrollable( { cssClass:"blackborder"} ); Without much further ado, here's the short code for the plug-in: (function ($) { $.fn.makeTableScrollable = function (options) { return this.each(function () { var $table = $(this); var opt = { // height of the table height: "250px", // right padding added to support the scrollbar rightPadding: "10px", // cssclass used for the wrapper div cssClass: "" } $.extend(opt, options); var $thead = $table.find("thead"); var $ths = $thead.find("th"); var id = $table.attr("id"); var cssClass = $table.attr("class"); if (!id) id = "_table_" + new Date().getMilliseconds().ToString(); $table.width("+=" + opt.rightPadding); $table.css("border-width", 0); // add a column to all rows of the table var first = true; $table.find("tr").each(function () { var row = $(this); if (first) { row.append($("<th>").width(opt.rightPadding)); first = false; } else row.append($("<td>").width(opt.rightPadding)); }); // force full sizing on each of the th elemnts $ths.each(function () { var $th = $(this); $th.css("width", $th.width()); }); // Create the table wrapper div var $tblDiv = $("<div>").css({ position: "relative", overflow: "hidden", overflowY: "scroll" }) .addClass(opt.cssClass); var width = $table.width(); $tblDiv.width(width).height(opt.height) .attr("id", id + "_wrapper") .css("border-top", "none"); // Insert before $tblDiv $tblDiv.insertBefore($table); // then move the table into it $table.appendTo($tblDiv); // Clone the div for header var $hdDiv = $tblDiv.clone(); $hdDiv.empty(); var width = $table.width(); $hdDiv.attr("style", "") .css("border-bottom", "none") .width(width) .attr("id", id + "_wrapper_header"); // create a copy of the table and remove all children var $newTable = $($table).clone(); $newTable.empty() .attr("id", $table.attr("id") + "_header"); $thead.appendTo($newTable); $hdDiv.insertBefore($tblDiv); $newTable.appendTo($hdDiv); $table.css("border-width", 0); }); } })(jQuery); Oh sweet spaghetti code :-) The code starts out by dealing the parameters that can be passed in the options object map: height The height of the full table/structure. The height of the outside wrapper container. Defaults to 200px. rightPadding The padding that is added to the right of the table to account for the scrollbar. Creates a column of this width and injects it into the table. If too small the rightmost column might get truncated. if too large the empty column might show. cssClass The CSS class of the wrapping container that appears to wrap the table. If you want a border around your table this class should probably provide it since the plug-in removes the table border. The rest of the code is obtuse, but pretty straight forward. It starts by creating a new column in the table to accommodate the width of the scrollbar and avoid clipping of text in the rightmost column. The width of the columns is explicitly set in the header elements to force the size of the table to be fixed and to provide the same sizing when the THEAD section is moved to a new copied table later. The table wrapper div is created, formatted and the table is moved into it. The new wrapper div is cloned for the header wrapper and configured. Finally the actual table is cloned and cleared of all elements. The original table's THEAD section is then moved into the new table. At last the new table is added to the header <div>, and the header <div> is inserted before the table wrapper <div>. I'm always amazed how easy jQuery makes it to do this sort of re-arranging, and given of what's happening the amount of code is rather small. Disclaimer: Your mileage may vary A word of warning: I make no guarantees about the code above. It's a first cut and I provided this here mainly to demonstrate the concepts of decomposing and reassembling an HTML layout :-) which jQuery makes so nice and easy. I tested this component against the typical scenarios we plan on using it for which are tables that use a few well known styles (or no styling at all). I suspect if you have complex styling on your <table> tag that things might not go so well. If you plan on using this plug-in you might want to minimize your styling of the table tag and defer any border formatting using the class passed in via the cssClass parameter, which ends up on the two wrapper div's that wrap the header and body rows. There's also no explicit support for footers. I rarely if ever use footers (when not using paging that is), so I didn't feel the need to add footer support. However, if you need that it's not difficult to add - the logic is the same as adding the header. The plug-in relies on a well-formatted table that has THEAD and TBODY sections along with TH tags in the header. Note that ASP.NET WebForm DataGrids and GridViews by default do not generate well-formatted table HTML. You can look at my Adding proper THEAD sections to a GridView post for more info on how to get a GridView to render properly. The plug-in has no dependencies other than jQuery. Even with the limitations in mind I hope this might be useful to some of you. I know I've already identified a number of places in my own existing applications where I will be plugging this in almost immediately. Resources Download Sample and Plug-in code Latest version in the West Wind Web & AJAX Toolkit Repository © Rick Strahl, West Wind Technologies, 2005-2011Posted in jQuery  HTML  ASP.NET  

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• Pre-rentrée Oracle Open World 2012 : à vos agendas

  - by Eric Bezille

  A maintenant moins d'un mois de l’événement majeur d'Oracle, qui se tient comme chaque année à San Francisco, fin septembre, début octobre, les spéculations vont bon train sur les annonces qui vont y être dévoilées... Et sans lever le voile, je vous engage à prendre connaissance des sujets des "Key Notes" qui seront tenues par Larry Ellison, Mark Hurd, Thomas Kurian (responsable des développements logiciels) et John Fowler (responsable des développements systèmes) afin de vous donner un avant goût. Stratégie et Roadmaps Oracle Bien entendu, au-delà des séances plénières qui vous donnerons  une vision précise de la stratégie, et pour ceux qui seront sur place, je vous engage à ne pas manquer les séances d'approfondissement qui auront lieu dans la semaine, dont voici quelques morceaux choisis : "Accelerate your Business with the Oracle Hardware Advantage" avec John Fowler, le lundi 1er Octobre, 3:15pm-4:15pm "Why Oracle Softwares Runs Best on Oracle Hardware" , avec Bradley Carlile, le responsable des Benchmarks, le lundi 1er Octobre, 12:15pm-13:15pm "Engineered Systems - from Vision to Game-changing Results", avec Robert Shimp, le lundi 1er Octobre 1:45pm-2:45pm "Database and Application Consolidation on SPARC Supercluster", avec Hugo Rivero, responsable dans les équipes d'intégration matériels et logiciels, le lundi 1er Octobre, 4:45pm-5:45pm "Oracle’s SPARC Server Strategy Update", avec Masood Heydari, responsable des développements serveurs SPARC, le mardi 2 Octobre, 10:15am - 11:15am "Oracle Solaris 11 Strategy, Engineering Insights, and Roadmap", avec Markus Flier, responsable des développements Solaris, le mercredi 3 Octobre, 10:15am - 11:15am "Oracle Virtualization Strategy and Roadmap", avec Wim Coekaerts, responsable des développement Oracle VM et Oracle Linux, le lundi 1er Octobre, 12:15pm-1:15pm "Big Data: The Big Story", avec Jean-Pierre Dijcks, responsable du développement produits Big Data, le lundi 1er Octobre, 3:15pm-4:15pm "Scaling with the Cloud: Strategies for Storage in Cloud Deployments", avec Christine Rogers,  Principal Product Manager, et Chris Wood, Senior Product Specialist, Stockage , le lundi 1er Octobre, 10:45am-11:45am Retours d'expériences et témoignages Si Oracle Open World est l'occasion de partager avec les équipes de développement d'Oracle en direct, c'est aussi l'occasion d'échanger avec des clients et experts qui ont mis en oeuvre  nos technologies pour bénéficier de leurs retours d'expériences, comme par exemple : "Oracle Optimized Solution for Siebel CRM at ACCOR", avec les témoignages d'Eric Wyttynck, directeur IT Multichannel & CRM  et Pascal Massenet, VP Loyalty & CRM systems, sur les bénéfices non seulement métiers, mais également projet et IT, le mercredi 3 Octobre, 1:15pm-2:15pm "Tips from AT&T: Oracle E-Business Suite, Oracle Database, and SPARC Enterprise", avec le retour d'expérience des experts Oracle, le mardi 2 Octobre, 11:45am-12:45pm "Creating a Maximum Availability Architecture with SPARC SuperCluster", avec le témoignage de Carte Wright, Database Engineer à CKI, le mercredi 3 Octobre, 11:45am-12:45pm "Multitenancy: Everybody Talks It, Oracle Walks It with Pillar Axiom Storage", avec le témoignage de Stephen Schleiger, Manager Systems Engineering de Navis, le lundi 1er Octobre, 1:45pm-2:45pm "Oracle Exadata for Database Consolidation: Best Practices", avec le retour d'expérience des experts Oracle ayant participé à la mise en oeuvre d'un grand client du monde bancaire, le lundi 1er Octobre, 4:45pm-5:45pm "Oracle Exadata Customer Panel: Packaged Applications with Oracle Exadata", animé par Tim Shetler, VP Product Management, mardi 2 Octobre, 1:15pm-2:15pm "Big Data: Improving Nearline Data Throughput with the StorageTek SL8500 Modular Library System", avec le témoignage du CTO de CSC, Alan Powers, le jeudi 4 Octobre, 12:45pm-1:45pm "Building an IaaS Platform with SPARC, Oracle Solaris 11, and Oracle VM Server for SPARC", avec le témoignage de Syed Qadri, Lead DBA et Michael Arnold, System Architect d'US Cellular, le mardi 2 Octobre, 10:15am-11:15am "Transform Data Center TCO with Oracle Optimized Servers: A Customer Panel", avec les témoignages notamment d'AT&T et Liberty Global, le mardi 2 Octobre, 11:45am-12:45pm "Data Warehouse and Big Data Customers’ View of the Future", avec The Nielsen Company US, Turkcell, GE Retail Finance, Allianz Managed Operations and Services SE, le lundi 1er Octobre, 4:45pm-5:45pm "Extreme Storage Scale and Efficiency: Lessons from a 100,000-Person Organization", le témoignage de l'IT interne d'Oracle sur la transformation et la migration de l'ensemble de notre infrastructure de stockage, mardi 2 Octobre, 1:15pm-2:15pm Echanges avec les groupes d'utilisateurs et les équipes de développement Oracle Si vous avez prévu d'arriver suffisamment tôt, vous pourrez également échanger dès le dimanche avec les groupes d'utilisateurs, ou tous les soirs avec les équipes de développement Oracle sur des sujets comme : "To Exalogic or Not to Exalogic: An Architectural Journey", avec Todd Sheetz - Manager of DBA and Enterprise Architecture, Veolia Environmental Services, le dimanche 30 Septembre, 2:30pm-3:30pm "Oracle Exalytics and Oracle TimesTen for Exalytics Best Practices", avec Mark Rittman, de Rittman Mead Consulting Ltd, le dimanche 30 Septembre, 10:30am-11:30am "Introduction of Oracle Exadata at Telenet: Bringing BI to Warp Speed", avec Rudy Verlinden & Eric Bartholomeus - Managers IT infrastructure à Telenet, le dimanche 30 Septembre, 1:15pm-2:00pm "The Perfect Marriage: Sun ZFS Storage Appliance with Oracle Exadata", avec Melanie Polston, directeur, Data Management, de Novation et Charles Kim, Managing Director de Viscosity, le dimanche 30 Septembre, 9:00am-10am "Oracle’s Big Data Solutions: NoSQL, Connectors, R, and Appliance Technologies", avec Jean-Pierre Dijcks et les équipes de développement Oracle, le lundi 1er Octobre, 6:15pm-7:00pm Testez et évaluez les solutions Et pour finir, vous pouvez même tester les technologies au travers du Oracle DemoGrounds, (1133 Moscone South pour la partie Systèmes Oracle, OS, et Virtualisation) et des "Hands-on-Labs", comme : "Deploying an IaaS Environment with Oracle VM", le mardi 2 Octobre, 10:15am-11:15am "Virtualize and Deploy Oracle Applications in Minutes with Oracle VM: Hands-on Lab", le mardi 2 Octobre, 11:45am-12:45pm (il est fortement conseillé d'avoir suivi le "Hands-on-Labs" précédent avant d'effectuer ce Lab. "x86 Enterprise Cloud Infrastructure with Oracle VM 3.x and Sun ZFS Storage Appliance", le mercredi 3 Octobre, 5:00pm-6:00pm "StorageTek Tape Analytics: Managing Tape Has Never Been So Simple", le mercredi 3 Octobre, 1:15pm-2:15pm "Oracle’s Pillar Axiom 600 Storage System: Power and Ease", le lundi 1er Octobre, 12:15pm-1:15pm "Enterprise Cloud Infrastructure for SPARC with Oracle Enterprise Manager Ops Center 12c", le lundi 1er Octobre, 1:45pm-2:45pm "Managing Storage in the Cloud", le mardi 2 Octobre, 5:00pm-6:00pm "Learn How to Write MapReduce on Oracle’s Big Data Platform", le lundi 1er Octobre, 12:15pm-1:15pm "Oracle Big Data Analytics and R", le mardi 2 Octobre, 1:15pm-2:15pm "Reduce Risk with Oracle Solaris Access Control to Restrain Users and Isolate Applications", le lundi 1er Octobre, 10:45am-11:45am "Managing Your Data with Built-In Oracle Solaris ZFS Data Services in Release 11", le lundi 1er Octobre, 4:45pm-5:45pm "Virtualizing Your Oracle Solaris 11 Environment", le mardi 2 Octobre, 1:15pm-2:15pm "Large-Scale Installation and Deployment of Oracle Solaris 11", le mercredi 3 Octobre, 3:30pm-4:30pm En conclusion, une semaine très riche en perspective, et qui vous permettra de balayer l'ensemble des sujets au coeur de vos préoccupations, de la stratégie à l'implémentation... Cette semaine doit se préparer, pour tailler votre agenda sur mesure, à travers les plus de 2000 sessions dont je ne vous ai fait qu'un extrait, et dont vous pouvez retrouver l'ensemble en ligne.

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• Building Queries Systematically

  - by Jeremy Smyth

  The SQL language is a bit like a toolkit for data. It consists of lots of little fiddly bits of syntax that, taken together, allow you to build complex edifices and return powerful results. For the uninitiated, the many tools can be quite confusing, and it's sometimes difficult to decide how to go about the process of building non-trivial queries, that is, queries that are more than a simple SELECT a, b FROM c; A System for Building Queries When you're building queries, you could use a system like the following:  Decide which fields contain the values you want to use in our output, and how you wish to alias those fields Values you want to see in your output Values you want to use in calculations . For example, to calculate margin on a product, you could calculate price - cost and give it the alias margin. Values you want to filter with. For example, you might only want to see products that weigh more than 2Kg or that are blue. The weight or colour columns could contain that information. Values you want to order by. For example you might want the most expensive products first, and the least last. You could use the price column in descending order to achieve that. Assuming the fields you've picked in point 1 are in multiple tables, find the connections between those tables Look for relationships between tables and identify the columns that implement those relationships. For example, The Orders table could have a CustomerID field referencing the same column in the Customers table. Sometimes the problem doesn't use relationships but rests on a different field; sometimes the query is looking for a coincidence of fact rather than a foreign key constraint. For example you might have sales representatives who live in the same state as a customer; this information is normally not used in relationships, but if your query is for organizing events where sales representatives meet customers, it's useful in that query. In such a case you would record the names of columns at either end of such a connection. Sometimes relationships require a bridge, a junction table that wasn't identified in point 1 above but is needed to connect tables you need; these are used in "many-to-many relationships". In these cases you need to record the columns in each table that connect to similar columns in other tables. Construct a join or series of joins using the fields and tables identified in point 2 above. This becomes your FROM clause. Filter using some of the fields in point 1 above. This becomes your WHERE clause. Construct an ORDER BY clause using values from point 1 above that are relevant to the desired order of the output rows. Project the result using the remainder of the fields in point 1 above. This becomes your SELECT clause. A Worked Example   Let's say you want to query the world database to find a list of countries (with their capitals) and the change in GNP, using the difference between the GNP and GNPOld columns, and that you only want to see results for countries with a population greater than 100,000,000. Using the system described above, we could do the following:  The Country.Name and City.Name columns contain the name of the country and city respectively.  The change in GNP comes from the calculation GNP - GNPOld. Both those columns are in the Country table. This calculation is also used to order the output, in descending order To see only countries with a population greater than 100,000,000, you need the Population field of the Country table. There is also a Population field in the City table, so you'll need to specify the table name to disambiguate. You can also represent a number like 100 million as 100e6 instead of 100000000 to make it easier to read. Because the fields come from the Country and City tables, you'll need to join them. There are two relationships between these tables: Each city is hosted within a country, and the city's CountryCode column identifies that country. Also, each country has a capital city, whose ID is contained within the country's Capital column. This latter relationship is the one to use, so the relevant columns and the condition that uses them is represented by the following FROM clause:  FROM Country JOIN City ON Country.Capital = City.ID The statement should only return countries with a population greater than 100,000,000. Country.Population is the relevant column, so the WHERE clause becomes:  WHERE Country.Population > 100e6  To sort the result set in reverse order of difference in GNP, you could use either the calculation, or the position in the output (it's the third column): ORDER BY GNP - GNPOld or ORDER BY 3 Finally, project the columns you wish to see by constructing the SELECT clause: SELECT Country.Name AS Country, City.Name AS Capital,        GNP - GNPOld AS `Difference in GNP`  The whole statement ends up looking like this:  mysql> SELECT Country.Name AS Country, City.Name AS Capital, -> GNP - GNPOld AS `Difference in GNP` -> FROM Country JOIN City ON Country.Capital = City.ID -> WHERE Country.Population > 100e6 -> ORDER BY 3 DESC; +--------------------+------------+-------------------+ | Country            | Capital    | Difference in GNP | +--------------------+------------+-------------------+ | United States | Washington | 399800.00 | | China | Peking | 64549.00 | | India | New Delhi | 16542.00 | | Nigeria | Abuja | 7084.00 | | Pakistan | Islamabad | 2740.00 | | Bangladesh | Dhaka | 886.00 | | Brazil | Brasília | -27369.00 | | Indonesia | Jakarta | -130020.00 | | Russian Federation | Moscow | -166381.00 | | Japan | Tokyo | -405596.00 | +--------------------+------------+-------------------+ 10 rows in set (0.00 sec) Queries with Aggregates and GROUP BY While this system might work well for many queries, it doesn't cater for situations where you have complex summaries and aggregation. For aggregation, you'd start with choosing which columns to view in the output, but this time you'd construct them as aggregate expressions. For example, you could look at the average population, or the count of distinct regions.You could also perform more complex aggregations, such as the average of GNP per head of population calculated as AVG(GNP/Population). Having chosen the values to appear in the output, you must choose how to aggregate those values. A useful way to think about this is that every aggregate query is of the form X, Y per Z. The SELECT clause contains the expressions for X and Y, as already described, and Z becomes your GROUP BY clause. Ordinarily you would also include Z in the query so you see how you are grouping, so the output becomes Z, X, Y per Z.  As an example, consider the following, which shows a count of  countries and the average population per continent:  mysql> SELECT Continent, COUNT(Name), AVG(Population)     -> FROM Country     -> GROUP BY Continent; +---------------+-------------+-----------------+ | Continent     | COUNT(Name) | AVG(Population) | +---------------+-------------+-----------------+ | Asia          |          51 |   72647562.7451 | | Europe        |          46 |   15871186.9565 | | North America |          37 |   13053864.8649 | | Africa        |          58 |   13525431.0345 | | Oceania       |          28 |    1085755.3571 | | Antarctica    |           5 |          0.0000 | | South America |          14 |   24698571.4286 | +---------------+-------------+-----------------+ 7 rows in set (0.00 sec) In this case, X is the number of countries, Y is the average population, and Z is the continent. Of course, you could have more fields in the SELECT clause, and  more fields in the GROUP BY clause as you require. You would also normally alias columns to make the output more suited to your requirements. More Complex Queries  Queries can get considerably more interesting than this. You could also add joins and other expressions to your aggregate query, as in the earlier part of this post. You could have more complex conditions in the WHERE clause. Similarly, you could use queries such as these in subqueries of yet more complex super-queries. Each technique becomes another tool in your toolbox, until before you know it you're writing queries across 15 tables that take two pages to write out. But that's for another day...

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• SQL SERVER – Guest Post – Architecting Data Warehouse – Niraj Bhatt

  - by pinaldave

  Niraj Bhatt works as an Enterprise Architect for a Fortune 500 company and has an innate passion for building / studying software systems. He is a top rated speaker at various technical forums including Tech·Ed, MCT Summit, Developer Summit, and Virtual Tech Days, among others. Having run a successful startup for four years Niraj enjoys working on – IT innovations that can impact an enterprise bottom line, streamlining IT budgets through IT consolidation, architecture and integration of systems, performance tuning, and review of enterprise applications. He has received Microsoft MVP award for ASP.NET, Connected Systems and most recently on Windows Azure. When he is away from his laptop, you will find him taking deep dives in automobiles, pottery, rafting, photography, cooking and financial statements though not necessarily in that order. He is also a manager/speaker at BDOTNET, Asia’s largest .NET user group. Here is the guest post by Niraj Bhatt. As data in your applications grows it’s the database that usually becomes a bottleneck. It’s hard to scale a relational DB and the preferred approach for large scale applications is to create separate databases for writes and reads. These databases are referred as transactional database and reporting database. Though there are tools / techniques which can allow you to create snapshot of your transactional database for reporting purpose, sometimes they don’t quite fit the reporting requirements of an enterprise. These requirements typically are data analytics, effective schema (for an Information worker to self-service herself), historical data, better performance (flat data, no joins) etc. This is where a need for data warehouse or an OLAP system arises. A Key point to remember is a data warehouse is mostly a relational database. It’s built on top of same concepts like Tables, Rows, Columns, Primary keys, Foreign Keys, etc. Before we talk about how data warehouses are typically structured let’s understand key components that can create a data flow between OLTP systems and OLAP systems. There are 3 major areas to it: a) OLTP system should be capable of tracking its changes as all these changes should go back to data warehouse for historical recording. For e.g. if an OLTP transaction moves a customer from silver to gold category, OLTP system needs to ensure that this change is tracked and send to data warehouse for reporting purpose. A report in context could be how many customers divided by geographies moved from sliver to gold category. In data warehouse terminology this process is called Change Data Capture. There are quite a few systems that leverage database triggers to move these changes to corresponding tracking tables. There are also out of box features provided by some databases e.g. SQL Server 2008 offers Change Data Capture and Change Tracking for addressing such requirements. b) After we make the OLTP system capable of tracking its changes we need to provision a batch process that can run periodically and takes these changes from OLTP system and dump them into data warehouse. There are many tools out there that can help you fill this gap – SQL Server Integration Services happens to be one of them. c) So we have an OLTP system that knows how to track its changes, we have jobs that run periodically to move these changes to warehouse. The question though remains is how warehouse will record these changes? This structural change in data warehouse arena is often covered under something called Slowly Changing Dimension (SCD). While we will talk about dimensions in a while, SCD can be applied to pure relational tables too. SCD enables a database structure to capture historical data. This would create multiple records for a given entity in relational database and data warehouses prefer having their own primary key, often known as surrogate key. As I mentioned a data warehouse is just a relational database but industry often attributes a specific schema style to data warehouses. These styles are Star Schema or Snowflake Schema. The motivation behind these styles is to create a flat database structure (as opposed to normalized one), which is easy to understand / use, easy to query and easy to slice / dice. Star schema is a database structure made up of dimensions and facts. Facts are generally the numbers (sales, quantity, etc.) that you want to slice and dice. Fact tables have these numbers and have references (foreign keys) to set of tables that provide context around those facts. E.g. if you have recorded 10,000 USD as sales that number would go in a sales fact table and could have foreign keys attached to it that refers to the sales agent responsible for sale and to time table which contains the dates between which that sale was made. These agent and time tables are called dimensions which provide context to the numbers stored in fact tables. This schema structure of fact being at center surrounded by dimensions is called Star schema. A similar structure with difference of dimension tables being normalized is called a Snowflake schema. This relational structure of facts and dimensions serves as an input for another analysis structure called Cube. Though physically Cube is a special structure supported by commercial databases like SQL Server Analysis Services, logically it’s a multidimensional structure where dimensions define the sides of cube and facts define the content. Facts are often called as Measures inside a cube. Dimensions often tend to form a hierarchy. E.g. Product may be broken into categories and categories in turn to individual items. Category and Items are often referred as Levels and their constituents as Members with their overall structure called as Hierarchy. Measures are rolled up as per dimensional hierarchy. These rolled up measures are called Aggregates. Now this may seem like an overwhelming vocabulary to deal with but don’t worry it will sink in as you start working with Cubes and others. Let’s see few other terms that we would run into while talking about data warehouses. ODS or an Operational Data Store is a frequently misused term. There would be few users in your organization that want to report on most current data and can’t afford to miss a single transaction for their report. Then there is another set of users that typically don’t care how current the data is. Mostly senior level executives who are interesting in trending, mining, forecasting, strategizing, etc. don’t care for that one specific transaction. This is where an ODS can come in handy. ODS can use the same star schema and the OLAP cubes we saw earlier. The only difference is that the data inside an ODS would be short lived, i.e. for few months and ODS would sync with OLTP system every few minutes. Data warehouse can periodically sync with ODS either daily or weekly depending on business drivers. Data marts are another frequently talked about topic in data warehousing. They are subject-specific data warehouse. Data warehouses that try to span over an enterprise are normally too big to scope, build, manage, track, etc. Hence they are often scaled down to something called Data mart that supports a specific segment of business like sales, marketing, or support. Data marts too, are often designed using star schema model discussed earlier. Industry is divided when it comes to use of data marts. Some experts prefer having data marts along with a central data warehouse. Data warehouse here acts as information staging and distribution hub with spokes being data marts connected via data feeds serving summarized data. Others eliminate the need for a centralized data warehouse citing that most users want to report on detailed data. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Best Practices, Business Intelligence, Data Warehousing, Database, Pinal Dave, PostADay, Readers Contribution, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• TechDays 2011 Sweden Videos

  - by Your DisplayName here!

  All the videos from the excellent Örebro event are now online. Dominick Baier: A Technical Introduction to the Windows Identity Foundation (watch) Dominick Baier & Christian Weyer: Securing REST-Services and Web APIs on the Windows Azure Platform (watch) Christian Weyer: Real World Azure - Elasticity from on-premise to the cloud and back (watch) Our interview with Robert (watch)

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• User Group Meeting Summary - April 2010

  - by Michael Stephenson

  Thanks to everyone who could make it to what turned out to be an excellent SBUG event.  First some thanks to:  Speakers: Anthony Ross and Elton Stoneman Host: The various people at Hitachi who helped to organise and arrange the venue.   Session 1 - Getting up and running with Windows Mobile and the Windows Azure Service Bus In this session Anthony discussed some considerations for using Windows Mobile and the Windows Azure Service Bus from a real-world project which Hitachi have been working on with EasyJet.  Anthony also walked through a simplified demo of the concepts which applied on the project.   In addition to the slides and demo it was also very interesting to discuss with the guys involved on this project to hear about their real experiences developing with the Azure Service Bus and some of the limitations they have had to work around in Windows Mobiles ability to interact with the service bus.   On the back of this session we will look to do some further activities around this topic and the guys offered to share their wish list of features for both Windows Mobile and Windows Azure which we will look to share for user group discussion.   Another interesting point was the cost aspects of using the ISB which were very low.   Session 2 - The Enterprise Cache In the second session Elton used a few slides which are based around one of his customer scenario's where they are looking into the concept of an Enterprise Cache within the organisation.  Elton discusses this concept and also a codeplex project he is putting together which allows you to take advantage of a cache with various providers such as Memcached, AppFabric Caching and Ncache.   Following the presentation it was interesting to hear peoples thoughts on various aspects such as the enterprise cache versus an out of process application cache.  Also there was interesting discussion around how people would like to search the cache in the future.   We will again look to put together some follow-up activity on this   Meeting Summary Following the meeting all slide decks are saved in the skydrive location where we keep content from all meetings: http://cid-40015ea59a1307c8.skydrive.live.com/browse.aspx/.Public/SBUG/SBUG%20Meetings/2010%20April   Remember that the details of all previous events are on the following page. http://uksoabpm.org/Events.aspx   Competition We had three copies of the Windows Identity Foundation Patterns and Practices book that were raffles on the night, it would be great to hear any feedback on the book from those who won it.   Recording The user group meeting was recorded and we will look to make this available online sometime soon.   UG Business The following things were discussed as general UG topics:   We will change the name of the user group to the UK Connected Systems User Group to we are more inline with other user groups who cover similar topics and we believe this will help us to attract more members.  The content or focus of the user group is not expected to change.   The next meeting is 26th May and can be registered at the following link: http://sbugmay2010.eventbrite.com/

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• Type of Blobs

  - by kaleidoscope

  With the release of Windows Azure November 2009 CTP, now we have two types of blobs. Block Blob - This blob type is in place since PDC 2008 and is optimized for streaming workloads. [Max Size allowed : 200GB] Page Blob - With November 2009 CTP release, a new blob type is added which is optimized for random read / writes called Page Blob. [Max Size allowed : 1TB] More details can be found at: http://geekswithblogs.net/IUnknown/archive/2009/11/16/azure-november-ctp-announced.aspx Amit, S

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• Cloud Computing = Elasticity * Availability

  - by Herve Roggero

  What is cloud computing? Is hosting the same thing as cloud computing? Are you running a cloud if you already use virtual machines? What is the difference between Infrastructure as a Service (IaaS) and a cloud provider? And the list goes on… these questions keep coming up and all try to fundamentally explain what “cloud” means relative to other concepts. At the risk of over simplification, answering these questions becomes simpler once you understand the primary foundations of cloud computing: Elasticity and Availability.   Elasticity The basic value proposition of cloud computing is to pay as you go, and to pay for what you use. This implies that an application can expand and contract on demand, across all its tiers (presentation layer, services, database, security…).  This also implies that application components can grow independently from each other. So if you need more storage for your database, you should be able to grow that tier without affecting, reconfiguring or changing the other tiers. Basically, cloud applications behave like a sponge; when you add water to a sponge, it grows in size; in the application world, the more customers you add, the more it grows. Pure IaaS providers will provide certain benefits, specifically in terms of operating costs, but an IaaS provider will not help you in making your applications elastic; neither will Virtual Machines. The smallest elasticity unit of an IaaS provider and a Virtual Machine environment is a server (physical or virtual). While adding servers in a datacenter helps in achieving scale, it is hardly enough. The application has yet to use this hardware.  If the process of adding computing resources is not transparent to the application, the application is not elastic.   As you can see from the above description, designing for the cloud is not about more servers; it is about designing an application for elasticity regardless of the underlying server farm.   Availability The fact of the matter is that making applications highly available is hard. It requires highly specialized tools and trained staff. On top of it, it's expensive. Many companies are required to run multiple data centers due to high availability requirements. In some organizations, some data centers are simply on standby, waiting to be used in a case of a failover. Other organizations are able to achieve a certain level of success with active/active data centers, in which all available data centers serve incoming user requests. While achieving high availability for services is relatively simple, establishing a highly available database farm is far more complex. In fact it is so complex that many companies establish yearly tests to validate failover procedures.   To a certain degree certain IaaS provides can assist with complex disaster recovery planning and setting up data centers that can achieve successful failover. However the burden is still on the corporation to manage and maintain such an environment, including regular hardware and software upgrades. Cloud computing on the other hand removes most of the disaster recovery requirements by hiding many of the underlying complexities.   Cloud Providers A cloud provider is an infrastructure provider offering additional tools to achieve application elasticity and availability that are not usually available on-premise. For example Microsoft Azure provides a simple configuration screen that makes it possible to run 1 or 100 web sites by clicking a button or two on a screen (simplifying provisioning), and soon SQL Azure will offer Data Federation to allow database sharding (which allows you to scale the database tier seamlessly and automatically). Other cloud providers offer certain features that are not available on-premise as well, such as the Amazon SC3 (Simple Storage Service) which gives you virtually unlimited storage capabilities for simple data stores, which is somewhat equivalent to the Microsoft Azure Table offering (offering a server-independent data storage model). Unlike IaaS providers, cloud providers give you the necessary tools to adopt elasticity as part of your application architecture.    Some cloud providers offer built-in high availability that get you out of the business of configuring clustered solutions, or running multiple data centers. Some cloud providers will give you more control (which puts some of that burden back on the customers' shoulder) and others will tend to make high availability totally transparent. For example, SQL Azure provides high availability automatically which would be very difficult to achieve (and very costly) on premise.   Keep in mind that each cloud provider has its strengths and weaknesses; some are better at achieving transparent scalability and server independence than others.    Not for Everyone Note however that it is up to you to leverage the elasticity capabilities of a cloud provider, as discussed previously; if you build a website that does not need to scale, for which elasticity is not important, then you can use a traditional host provider unless you also need high availability. Leveraging the technologies of cloud providers can be difficult and can become a journey for companies that build their solutions in a scale up fashion. Cloud computing promises to address cost containment and scalability of applications with built-in high availability. If your application does not need to scale or you do not need high availability, then cloud computing may not be for you. In fact, you may pay a premium to run your applications with cloud providers due to the underlying technologies built specifically for scalability and availability requirements. And as such, the cloud is not for everyone.   Consistent Customer Experience, Predictable Cost With all its complexities, buzz and foggy definition, cloud computing boils down to a simple objective: consistent customer experience at a predictable cost.  The objective of a cloud solution is to provide the same user experience to your last customer than the first, while keeping your operating costs directly proportional to the number of customers you have. Making your applications elastic and highly available across all its tiers, with as much automation as possible, achieves the first objective of a consistent customer experience. And the ability to expand and contract the infrastructure footprint of your application dynamically achieves the cost containment objectives.     Herve Roggero is a SQL Azure MVP and co-author of Pro SQL Azure (APress).  He is the co-founder of Blue Syntax Consulting (www.bluesyntax.net), a company focusing on cloud computing technologies helping customers understand and adopt cloud computing technologies. For more information contact herve at hroggero @ bluesyntax.net .

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• May 2010 Chicago Architects Group Meeting

  - by Tim Murphy

  The Chicago Architects Group will be holding its next meeting on May 18th.  Please come and join us and get involved in our architect community. Register Presenter: Scott Seely  Topic: Azure For Architects       Location: TechNexus 200 S. Wacker Dr., Suite 1500 Room A/B Chicago, IL 60606 Time: 5:30 - Doors open at 5:00 del.icio.us Tags: Chicago Architects Group,Azure,Scott Seely

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• Resolving data redundancy up front

  - by okeofs

  Introduction As all of us do when confronted with a problem, the resource of choice is to ‘Google it’. This is where the plot thickens. Recently I was asked to stage data from numerous databases which were to be loaded into a data warehouse. To make a long story short, I was looking for a manner in which to obtain the table names from each database, to ascertain potential overlap.   As the source data comes from a SQL database created from dumps of a third party product,  one could say that there were +/- 95 tables for each database.   Yes I know that first instinct is to use the system stored procedure “exec sp_msforeachdb 'select "?" AS db, * from [?].sys.tables'”. However, if one stops to think about this, it would be nice to have all the results in a temporary or disc based  table; which in itself , implies additional labour. This said,  I decided to ‘re-invent’ the wheel. The full code sample may be found at the bottom of this article.   Define a few temporary tables and variables   declare @SQL varchar(max); declare @databasename varchar(75) /* drop table ##rawdata3 drop table #rawdata1 drop table #rawdata11 */ -- A temp table to hold the names of my databases CREATE TABLE #rawdata1 (    database_name varchar(50) ,    database_size varchar(50),    remarks Varchar(50) )     --A temp table with the same database names as above, HOWEVER using an --Identity number (recNO) as a loop variable. --You will note below that I loop through until I reach 25 (see below) as at --that point the system databases, the reporting server database etc begin. --1- 24 are user databases. These are really what I was looking for. --Whilst NOT the best solution,it works and the code was meant as a quick --and dirty. CREATE TABLE #rawdata11 (    recNo int identity(1,1),    database_name varchar(50) ,    database_size varchar(50),    remarks Varchar(50) )   --My output table showing the database name and table name CREATE TABLE ##rawdata3 (    database_name varchar(75) ,    table_name varchar(75), )   Insert the database names into a temporary table I pull the database names using the system stored procedure sp_databases   INSERT INTO #rawdata1 EXEC sp_databases Go   Insert the results from #rawdata1 into a table containing a record number  #rawdata11 so that I can LOOP through the extract   INSERT into #rawdata11 select * from  #rawdata1   We now declare 3 more variables:  @kounter is used to keep track of our position within the loop. @databasename is used to keep track of the’ current ‘ database name being used in the current pass of the loop;  as inorder to obtain the tables for that database we  need to issue a ‘USE’ statement, an insert command and other related code parts. This is the challenging part. @sql is a varchar(max) variable used to contain the ‘USE’ statement PLUS the’ insert ‘ code statements. We now initalize @kounter to 1 .   declare @kounter int; declare @databasename varchar(75); declare @sql varchar(max); set @kounter = 1   The Loop The astute reader will remember that the temporary table #rawdata11 contains our  database names  and each ‘database row’ has a record number (recNo). I am only interested in record numbers under 25. I now set the value of the temporary variable @DatabaseName (see below) .Note that I used the row number as a part of the predicate. Now, knowing the database name, I can create dynamic T-SQL to be executed using the sp_sqlexec stored procedure (see the code in red below). Finally, after all the tables for that given database have been placed in temporary table ##rawdata3, I increment the counter and continue on. Note that I used a global temporary table to ensure that the result set persists after the termination of the run. At some stage, I plan to redo this part of the code, as global temporary tables are not really an ideal solution.    WHILE (@kounter < 25)  BEGIN  select @DatabaseName = database_name from #rawdata11 where recNo = @kounter  set @SQL = 'Use ' + @DatabaseName + ' Insert into ##rawdata3 ' + + ' SELECT table_catalog,Table_name FROM information_schema.tables' exec sp_sqlexec  @Sql  SET @kounter  = @kounter + 1  END   The full code extract   Here is the full code sample.   declare @SQL varchar(max); declare @databasename varchar(75) /* drop table ##rawdata3 drop table #rawdata1 drop table #rawdata11 */ CREATE TABLE #rawdata1 (    database_name varchar(50) ,    database_size varchar(50),    remarks Varchar(50) ) CREATE TABLE #rawdata11 (    recNo int identity(1,1),    database_name varchar(50) ,    database_size varchar(50),    remarks Varchar(50) ) CREATE TABLE ##rawdata3 (    database_name varchar(75) ,    table_name varchar(75), )   INSERT INTO #rawdata1 EXEC sp_databases go INSERT into #rawdata11 select * from  #rawdata1 declare @kounter int; declare @databasename varchar(75); declare @sql varchar(max); set @kounter = 1 WHILE (@kounter < 25)  BEGIN  select @databasename = database_name from #rawdata11 where recNo = @kounter  set @SQL = 'Use ' + @DatabaseName + ' Insert into ##rawdata3 ' + + ' SELECT table_catalog,Table_name FROM information_schema.tables' exec sp_sqlexec  @Sql  SET @kounter  = @kounter + 1  END    select * from ##rawdata3  where table_name like '%SalesOrderHeader%'

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• Cloud Computing: Start with the problem

  - by BuckWoody

  At one point in my life I would build my own computing system for home use. I wanted a particular video card, a certain set of drives, and a lot of memory. Not only could I not find those things in a vendor’s pre-built computer, but those were more expensive – by a lot. As time moved on and the computing industry matured, I actually find that I can buy a vendor’s system as cheaply – and in some cases far more cheaply – than I can build it myself.   This paradigm holds true for almost any product, even clothing and furniture. And it’s also held true for software… Mostly. If you need an office productivity package, you simply buy one or use open-sourced software for that. There’s really no need to write your own Word Processor – it’s kind of been done a thousand times over. Even if you need a full system for customer relationship management or other needs, you simply buy one. But there is no “cloud solution in a box”.  Sure, if you’re after “Software as a Service” – type solutions, like being able to process video (Windows Azure Media Services) or running a Pig or Hive job in Hadoop (Hadoop on Windows Azure) you can simply use one of those, or if you just want to deploy a Virtual Machine (Windows Azure Virtual Machines) you can get that, but if you’re looking for a solution to a problem your organization has, you may need to mix Software, Infrastructure, and perhaps even Platforms (such as Windows Azure Computing) to solve the issue. It’s all about starting from the problem-end first. We’ve become so accustomed to looking for a box of software that will solve the problem, that we often start with the solution and try to fit it to the problem, rather than the other way around.  When I talk with my fellow architects at other companies, one of the hardest things to get them to do is to ignore the technology for a moment and describe what the issues are. It’s interesting to monitor the conversation and watch how many times we deviate from the problem into the solution. So, in your work today, try a little experiment: watch how many times you go after a problem by starting with the solution. Tomorrow, make a conscious effort to reverse that. You might be surprised at the results.

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• Windows Cloud Services Aren’t Exclusive to Microsoft

  - by Ken Cox [MVP]

  The Windows Azure brand has captured mindshare for the buzzword-du-jour, ‘cloud computing’. However, Microsoft certainly isn’t the only option for cranking up virtual machines to meet unexpected or peak demands. For example, I see that OrcsWeb has released its Windows Cloud Servers product , starting at $99.99 a month*.  Competition is a good thing - and make sure you do some cost comparisons when researching cloud resources. Some of us were unpleasantly surprised by Azure’s pricing structure...(read more)

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• REPLACE Multiple Spaces with One

  Replacing multiple spaces with a single space is an old problem that people use loops, functions, and/or Tally tables for. Here's a set based method from MVP Jeff Moden. “Thanks for building such a useful and simple-to-use service”- Steve Harshbarger, CTO, 10th Magnitude. Get started with Red Gate Cloud Services and back up your SQL Azure databases to Azure Blob storage or Amazon S3 – download a free trial today.

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• Let&rsquo;s keep informed with &ldquo;Data Explorer&rdquo;

  - by Luca Zavarella

  At Pass Summit 2011 a new project was announced. It’s a Microsoft SQL Azure Lab and its codename is Microsoft “Data Explorer”. According to the official blog (http://blogs.msdn.com/b/dataexplorer/), this new tool provides an innovative way to acquire new knowledge from the data that interest you. In a nutshell, Data Explorer allows you to combine data from multiple sources, to publish and share the result. In addition, you can generate data streams in the RESTful open format (Open Data Protocol), and they can then be used by other applications. Nonetheless we can still use Excel or PowerPivot to analyze the results. Sources can be varied: Excel spreadsheets, text files, databases, Windows Azure Marketplace, etc.. For those who are not familiar with this resource, I strongly suggest you to keep an eye on the data services available to the Marketplace: https://datamarket.azure.com/browse/Data To tell the truth, as I read the above blog post, I was tempted to think of the Data Explorer as a "SSIS on Azure" addressed to the Power User. In fact, reading the response from Tim Mallalieu (Group Program Manager of Data Explorer) to the comment made to his post, I had a positive response to my first impression: “…we originally thinking of ourselves as Self-Service ETL. As we talked to more folks and started partnering with other teams we realized that would be an area that we can add value but that there were more opportunities emerging.” The typical operations of the ETL phase ( processing and organization of data in different formats) can be obtained thanks to Data Explorer Mashup. This is an image of the tool: The flexibility in the manipulation of information is given by Data Explorer Formula Language. This is a formula-based Excel-style specific language: Anyone wishing to know more can check the project page in addition to aforementioned blog: http://www.microsoft.com/en-us/sqlazurelabs/labs/dataexplorer.aspx In light of this new project, there is no doubt about the intention of Microsoft to get closer and closer to the Power User, providing him flexible and very easy to use tools for data analysis. The prime example of this is PowerPivot. The question that remains is always the same: having in a company more Power User will implicitly mean having different data models representing the same reality. But this would inevitably lead to anarchical data management... What do you think about that?

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• Microsoft Patch Tuesday Promises Critical Updates

  Microsoft revealed some plans last week for its upcoming Patch Tuesday release that should keep IT professionals busy. The latest Patch Tuesday falls on June 14 and it will bring with it 16 bulletins from Microsoft focused on fixing 34 vulnerabilities that stretch across several of the company s products.... Microsoft? Windows Azure Host, Scale, and Manage Web Apps In The Cloud. Learn More About Azure.

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• Ruby on Rails: url_for :back leads to NoMethodError for back_url

  - by Platinum Azure

  Hi all, I'm trying to use url_for(:back) to create a redirect leading back to a previous page upon a user's logging in. I've had it working successfully for when the user just goes to the login page on his or her own. However, when the user is redirected to the login page due to accessing a page requiring that the user be authenticated, the redirect sends the user back to the page before the one s/he had tried to access with insufficient permissions. I'm trying to modify my login controller action to deal with the redirect properly. My plan is to have a query string parameter "redirect" that is used when a forced redirect occurs. In the controller, if that parameter exists that URL is used; otherwise, url_for(:back) is used, or if that doesn't work (due to lack of HTTP_REFERER), then the user is redirected to the site's home page. Here is the code snippet which is supposed to implement this logic: if params[:redirect] @url = params[:redirect] else @url = url_for :back @url ||= url_for :controller => "home", :action => "index" end The error I get is: NoMethodError in UsersController#login undefined method `back_url' for # RAILS_ROOT: [obscured] Application Trace | Framework Trace | Full Trace vendor/rails/actionpack/lib/action_controller/polymorphic_routes.rb:112:in `__send__' vendor/rails/actionpack/lib/action_controller/polymorphic_routes.rb:112:in `polymorphic_url' vendor/rails/actionpack/lib/action_controller/base.rb:628:in `url_for' app/controllers/users_controller.rb:16:in `login' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel/rails.rb:76:in `process' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel/rails.rb:74:in `synchronize' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel/rails.rb:74:in `process' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:159:in `process_client' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:158:in `each' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:158:in `process_client' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:285:in `run' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:285:in `initialize' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:285:in `new' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:285:in `run' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:268:in `initialize' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:268:in `new' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:268:in `run' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel/configurator.rb:282:in `run' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel/configurator.rb:281:in `each' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel/configurator.rb:281:in `run' /var/lib/gems/1.8/gems/mongrel-1.1.5/bin/mongrel_rails:128:in `run' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel/command.rb:212:in `run' /var/lib/gems/1.8/gems/mongrel-1.1.5/bin/mongrel_rails:281 vendor/rails/actionpack/lib/action_controller/polymorphic_routes.rb:112:in `__send__' vendor/rails/actionpack/lib/action_controller/polymorphic_routes.rb:112:in `polymorphic_url' vendor/rails/actionpack/lib/action_controller/base.rb:628:in `url_for' vendor/rails/actionpack/lib/action_controller/base.rb:1256:in `send' vendor/rails/actionpack/lib/action_controller/base.rb:1256:in `perform_action_without_filters' vendor/rails/actionpack/lib/action_controller/filters.rb:617:in `call_filters' vendor/rails/actionpack/lib/action_controller/filters.rb:610:in `perform_action_without_benchmark' vendor/rails/actionpack/lib/action_controller/benchmarking.rb:68:in `perform_action_without_rescue' /usr/lib/ruby/1.8/benchmark.rb:293:in `measure' vendor/rails/actionpack/lib/action_controller/benchmarking.rb:68:in `perform_action_without_rescue' vendor/rails/actionpack/lib/action_controller/rescue.rb:136:in `perform_action_without_caching' vendor/rails/actionpack/lib/action_controller/caching/sql_cache.rb:13:in `perform_action' vendor/rails/activerecord/lib/active_record/connection_adapters/abstract/query_cache.rb:34:in `cache' vendor/rails/activerecord/lib/active_record/query_cache.rb:8:in `cache' vendor/rails/actionpack/lib/action_controller/caching/sql_cache.rb:12:in `perform_action' vendor/rails/actionpack/lib/action_controller/base.rb:524:in `send' vendor/rails/actionpack/lib/action_controller/base.rb:524:in `process_without_filters' vendor/rails/actionpack/lib/action_controller/filters.rb:606:in `process_without_session_management_support' vendor/rails/actionpack/lib/action_controller/session_management.rb:134:in `process' vendor/rails/actionpack/lib/action_controller/base.rb:392:in `process' vendor/rails/actionpack/lib/action_controller/dispatcher.rb:184:in `handle_request' vendor/rails/actionpack/lib/action_controller/dispatcher.rb:112:in `dispatch_unlocked' vendor/rails/actionpack/lib/action_controller/dispatcher.rb:125:in `dispatch' vendor/rails/actionpack/lib/action_controller/dispatcher.rb:124:in `synchronize' vendor/rails/actionpack/lib/action_controller/dispatcher.rb:124:in `dispatch' vendor/rails/actionpack/lib/action_controller/dispatcher.rb:134:in `dispatch_cgi' vendor/rails/actionpack/lib/action_controller/dispatcher.rb:41:in `dispatch' vendor/rails/activesupport/lib/active_support/dependencies.rb:142:in `load_without_new_constant_marking' vendor/rails/activesupport/lib/active_support/dependencies.rb:142:in `load' vendor/rails/activesupport/lib/active_support/dependencies.rb:521:in `new_constants_in' vendor/rails/activesupport/lib/active_support/dependencies.rb:142:in `load' vendor/rails/railties/lib/commands/servers/mongrel.rb:64 /usr/lib/ruby/1.8/rubygems/custom_require.rb:31:in `gem_original_require' /usr/lib/ruby/1.8/rubygems/custom_require.rb:31:in `require' vendor/rails/activesupport/lib/active_support/dependencies.rb:153:in `require' vendor/rails/activesupport/lib/active_support/dependencies.rb:521:in `new_constants_in' vendor/rails/activesupport/lib/active_support/dependencies.rb:153:in `require' vendor/rails/railties/lib/commands/server.rb:49 /usr/lib/ruby/1.8/rubygems/custom_require.rb:31:in `gem_original_require' /usr/lib/ruby/1.8/rubygems/custom_require.rb:31:in `require' script/server:3 vendor/rails/actionpack/lib/action_controller/polymorphic_routes.rb:112:in `__send__' vendor/rails/actionpack/lib/action_controller/polymorphic_routes.rb:112:in `polymorphic_url' vendor/rails/actionpack/lib/action_controller/base.rb:628:in `url_for' app/controllers/users_controller.rb:16:in `login' vendor/rails/actionpack/lib/action_controller/base.rb:1256:in `send' vendor/rails/actionpack/lib/action_controller/base.rb:1256:in `perform_action_without_filters' vendor/rails/actionpack/lib/action_controller/filters.rb:617:in `call_filters' vendor/rails/actionpack/lib/action_controller/filters.rb:610:in `perform_action_without_benchmark' vendor/rails/actionpack/lib/action_controller/benchmarking.rb:68:in `perform_action_without_rescue' /usr/lib/ruby/1.8/benchmark.rb:293:in `measure' vendor/rails/actionpack/lib/action_controller/benchmarking.rb:68:in `perform_action_without_rescue' vendor/rails/actionpack/lib/action_controller/rescue.rb:136:in `perform_action_without_caching' vendor/rails/actionpack/lib/action_controller/caching/sql_cache.rb:13:in `perform_action' vendor/rails/activerecord/lib/active_record/connection_adapters/abstract/query_cache.rb:34:in `cache' vendor/rails/activerecord/lib/active_record/query_cache.rb:8:in `cache' vendor/rails/actionpack/lib/action_controller/caching/sql_cache.rb:12:in `perform_action' vendor/rails/actionpack/lib/action_controller/base.rb:524:in `send' vendor/rails/actionpack/lib/action_controller/base.rb:524:in `process_without_filters' vendor/rails/actionpack/lib/action_controller/filters.rb:606:in `process_without_session_management_support' vendor/rails/actionpack/lib/action_controller/session_management.rb:134:in `process' vendor/rails/actionpack/lib/action_controller/base.rb:392:in `process' vendor/rails/actionpack/lib/action_controller/dispatcher.rb:184:in `handle_request' vendor/rails/actionpack/lib/action_controller/dispatcher.rb:112:in `dispatch_unlocked' vendor/rails/actionpack/lib/action_controller/dispatcher.rb:125:in `dispatch' vendor/rails/actionpack/lib/action_controller/dispatcher.rb:124:in `synchronize' vendor/rails/actionpack/lib/action_controller/dispatcher.rb:124:in `dispatch' vendor/rails/actionpack/lib/action_controller/dispatcher.rb:134:in `dispatch_cgi' vendor/rails/actionpack/lib/action_controller/dispatcher.rb:41:in `dispatch' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel/rails.rb:76:in `process' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel/rails.rb:74:in `synchronize' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel/rails.rb:74:in `process' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:159:in `process_client' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:158:in `each' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:158:in `process_client' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:285:in `run' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:285:in `initialize' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:285:in `new' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:285:in `run' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:268:in `initialize' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:268:in `new' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel.rb:268:in `run' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel/configurator.rb:282:in `run' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel/configurator.rb:281:in `each' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel/configurator.rb:281:in `run' /var/lib/gems/1.8/gems/mongrel-1.1.5/bin/mongrel_rails:128:in `run' /var/lib/gems/1.8/gems/mongrel-1.1.5/lib/mongrel/command.rb:212:in `run' /var/lib/gems/1.8/gems/mongrel-1.1.5/bin/mongrel_rails:281 vendor/rails/activesupport/lib/active_support/dependencies.rb:142:in `load_without_new_constant_marking' vendor/rails/activesupport/lib/active_support/dependencies.rb:142:in `load' vendor/rails/activesupport/lib/active_support/dependencies.rb:521:in `new_constants_in' vendor/rails/activesupport/lib/active_support/dependencies.rb:142:in `load' vendor/rails/railties/lib/commands/servers/mongrel.rb:64 /usr/lib/ruby/1.8/rubygems/custom_require.rb:31:in `gem_original_require' /usr/lib/ruby/1.8/rubygems/custom_require.rb:31:in `require' vendor/rails/activesupport/lib/active_support/dependencies.rb:153:in `require' vendor/rails/activesupport/lib/active_support/dependencies.rb:521:in `new_constants_in' vendor/rails/activesupport/lib/active_support/dependencies.rb:153:in `require' vendor/rails/railties/lib/commands/server.rb:49 /usr/lib/ruby/1.8/rubygems/custom_require.rb:31:in `gem_original_require' /usr/lib/ruby/1.8/rubygems/custom_require.rb:31:in `require' script/server:3 Request Parameters: None Show session dump --- :user: :csrf_id: 2927cca61bbbe97218362b5bcdb74c0f flash: !map:ActionController::Flash::FlashHash {} Response Headers: {"Content-Type"="", "cookie"=[], "Cache-Control"="no-cache"} Bear in mind that I had it working earlier-- url_for(:back) knew how to operate properly before I added this logic. Thanks in advance for any ideas!

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• SQL SERVER – Beginning of SQL Server Architecture – Terminology – Guest Post

  - by pinaldave

  SQL Server Architecture is a very deep subject. Covering it in a single post is an almost impossible task. However, this subject is very popular topic among beginners and advanced users.  I have requested my friend Anil Kumar who is expert in SQL Domain to help me write  a simple post about Beginning SQL Server Architecture. As stated earlier this subject is very deep subject and in this first article series he has covered basic terminologies. In future article he will explore the subject further down. Anil Kumar Yadav is Trainer, SQL Domain, Koenig Solutions. Koenig is a premier IT training firm that provides several IT certifications, such as Oracle 11g, Server+, RHCA, SQL Server Training, Prince2 Foundation etc. In this Article we will discuss about MS SQL Server architecture. The major components of SQL Server are: Relational Engine Storage Engine SQL OS Now we will discuss and understand each one of them. 1) Relational Engine: Also called as the query processor, Relational Engine includes the components of SQL Server that determine what your query exactly needs to do and the best way to do it. It manages the execution of queries as it requests data from the storage engine and processes the results returned. Different Tasks of Relational Engine: Query Processing Memory Management Thread and Task Management Buffer Management Distributed Query Processing 2) Storage Engine: Storage Engine is responsible for storage and retrieval of the data on to the storage system (Disk, SAN etc.). to understand more, let’s focus on the following diagram. When we talk about any database in SQL server, there are 2 types of files that are created at the disk level – Data file and Log file. Data file physically stores the data in data pages. Log files that are also known as write ahead logs, are used for storing transactions performed on the database. Let’s understand data file and log file in more details: Data File: Data File stores data in the form of Data Page (8KB) and these data pages are logically organized in extents. Extents: Extents are logical units in the database. They are a combination of 8 data pages i.e. 64 KB forms an extent. Extents can be of two types, Mixed and Uniform. Mixed extents hold different types of pages like index, System, Object data etc. On the other hand, Uniform extents are dedicated to only one type. Pages: As we should know what type of data pages can be stored in SQL Server, below mentioned are some of them: Data Page: It holds the data entered by the user but not the data which is of type text, ntext, nvarchar(max), varchar(max), varbinary(max), image and xml data. Index: It stores the index entries. Text/Image: It stores LOB ( Large Object data) like text, ntext, varchar(max), nvarchar(max),  varbinary(max), image and xml data. GAM & SGAM (Global Allocation Map & Shared Global Allocation Map): They are used for saving information related to the allocation of extents. PFS (Page Free Space): Information related to page allocation and unused space available on pages. IAM (Index Allocation Map): Information pertaining to extents that are used by a table or index per allocation unit. BCM (Bulk Changed Map): Keeps information about the extents changed in a Bulk Operation. DCM (Differential Change Map): This is the information of extents that have modified since the last BACKUP DATABASE statement as per allocation unit. Log File: It also known as write ahead log. It stores modification to the database (DML and DDL). Sufficient information is logged to be able to: Roll back transactions if requested Recover the database in case of failure Write Ahead Logging is used to create log entries Transaction logs are written in chronological order in a circular way Truncation policy for logs is based on the recovery model SQL OS: This lies between the host machine (Windows OS) and SQL Server. All the activities performed on database engine are taken care of by SQL OS. It is a highly configurable operating system with powerful API (application programming interface), enabling automatic locality and advanced parallelism. SQL OS provides various operating system services, such as memory management deals with buffer pool, log buffer and deadlock detection using the blocking and locking structure. Other services include exception handling, hosting for external components like Common Language Runtime, CLR etc. I guess this brief article gives you an idea about the various terminologies used related to SQL Server Architecture. In future articles we will explore them further. Guest Author  The author of the article is Anil Kumar Yadav is Trainer, SQL Domain, Koenig Solutions. Koenig is a premier IT training firm that provides several IT certifications, such as Oracle 11g, Server+, RHCA, SQL Server Training, Prince2 Foundation etc. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Security, SQL Server, SQL Tips and Tricks, SQL Training, T SQL, Technology

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• Ruby libraries for parsing .doc files?

  - by Platinum Azure

  Hi all, I was just wondering if anyone knew of any good libraries for parsing .doc files (and similar formats, like .odt) to extract text, yet also keep formatting information where possible for display on a website. Capability of doing similarly for PDFs would be a bonus, but I'm not looking as much for that. This is for a Rails project, if that helps at all. Thanks in advance!

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• SQLAuthority News – Storing Data and Files in Cloud – Dropbox – Personal Technology Tip

  - by pinaldave

  I thought long and hard about doing a Personal Technology Tips series for this blog.  I have so many tips I’d like to share.  I am on my computer almost all day, every day, so I have a treasure trove of interesting tidbits I like to share if given the chance.  The only thing holding me back – which tip to share first?  The first tip obviously has the weight of seeming like the most important.  But this would mean choosing amongst my favorite tricks and shortcuts.  This is a hard task. Source: Dropbox.com My Dropbox I have finally decided, though, and have determined that the first Personal Technology Tip may not be the most secret or even trickier to master – in fact, it is probably the easiest.  My today’s Personal Technology Tip is Dropbox. I hope that all of you are nodding along in recognition right now.  If you do not use Dropbox, or have not even heard of it before, get on the internet and find their site.  You won’t be disappointed.  A quick recap for those in the dark: Dropbox is an online storage site with a lot of additional syncing and cloud-computing capabilities.  Now that we’ve covered the basics, let’s explore some of my favorite options in Dropbox. Collaborate with All The first thing I love about Dropbox is the ability it gives you to collaborate with others.  You can share files easily with other Dropbox users, and they can alter them, share them with you, all while keeping track of different versions in on easy place.  I’d like to see anyone try to accomplish that key idea – “easily” – using e-mail versions and multiple computers.  It’s even difficult to accomplish using a shared network. Afraid that this kind of ease looks too good to be true?  Afraid that maybe there isn’t enough storage space, or the user interface is confusing?  Think again.  There is plenty of space – you can get 2 GB with just a free account, and upgrades are inexpensive and go up to 100 GB of storage.  And the user interface is so easy that anyone can learn to use it. What I use Dropbox for I love Dropbox because I give a lot of presentations and often they are far from home.  I can keep my presentations on Dropbox and have easy access to them anywhere, without needing to have my whole computer with me.  This is just one small way that you can use Dropbox. You can sync your entire hard drive, or hard drives if you have multiple computers (home, work, office, shared), and you can set Dropbox to automatically sync files on a certain timeline, or whenever Dropbox notices that they’ve been changed. Why I love Dropbox Dropbox has plenty of storage, but 2 GB still has a hard time competing with the average desktop’s storage space.  So what if you want to sync most of your files, but only the ones you use the most and share between work and home, and not all your files (especially large files like pictures and videos)?  You can use selective sync to choose which files to sync. Above all, my favorite feature is LanSync.  Dropbox will search your Local Area Network (LAN) for new files and sync them to Dropbox, as well as downloading the new version to all the shared files across the network.  That means that if move around on different computers at work or at home, you will have the same version of the file every time.  Or, other users on the LAN will have access to the new version, which makes collaboration extremely easy. Ref: rzfeeser.com Dropbox has so many other features that I feel like I could create a Personal Technology Tips series devoted entirely to Dropbox.  I’m going to create a bullet list here to make things shorter, but I strongly encourage you to look further into these into options if it sounds like something you would use. Theft Recover Home Security File Hosting and Sharing Portable Dropbox Sync your iCal calendar Password Storage What is your favorite tool and why? I could go on and on, but I will end here.  In summary – I strongly encourage everyone to investigate Dropbox to see if it’s something they would find useful.  If you use Dropbox and know of a great feature I failed to mention, please share it with me, I’d love to hear how everyone uses this program. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority News, T SQL, Technology Tagged: Personal Technology

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• Item 2, Scott Myers Effective C++ question

  - by user619818

  In Item2 on page 16, (Prefer consts, enums, and inlines to #defines), Scott says: 'Also, though good compilers won't set aside storage for const objects of integer types'. I don't understand this. If I define a const object, eg const int myval = 5; then surely the compiler must set aside some memory (of int size) to store the value 5? Or is const data stored in some special way? This is more a question of computer storage I suppose. Basically, how does the computer store const objects so that no storage is set aside?

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• Joins in single-table queries

  - by Rob Farley

  Tables are only metadata. They don’t store data. I’ve written something about this before, but I want to take a viewpoint of this idea around the topic of joins, especially since it’s the topic for T-SQL Tuesday this month. Hosted this time by Sebastian Meine (@sqlity), who has a whole series on joins this month. Good for him – it’s a great topic. In that last post I discussed the fact that we write queries against tables, but that the engine turns it into a plan against indexes. My point wasn’t simply that a table is actually just a Clustered Index (or heap, which I consider just a special type of index), but that data access always happens against indexes – never tables – and we should be thinking about the indexes (specifically the non-clustered ones) when we write our queries. I described the scenario of looking up phone numbers, and how it never really occurs to us that there is a master list of phone numbers, because we think in terms of the useful non-clustered indexes that the phone companies provide us, but anyway – that’s not the point of this post. So a table is metadata. It stores information about the names of columns and their data types. Nullability, default values, constraints, triggers – these are all things that define the table, but the data isn’t stored in the table. The data that a table describes is stored in a heap or clustered index, but it goes further than this. All the useful data is going to live in non-clustered indexes. Remember this. It’s important. Stop thinking about tables, and start thinking about indexes. So let’s think about tables as indexes. This applies even in a world created by someone else, who doesn’t have the best indexes in mind for you. I’m sure you don’t need me to explain Covering Index bit – the fact that if you don’t have sufficient columns “included” in your index, your query plan will either have to do a Lookup, or else it’ll give up using your index and use one that does have everything it needs (even if that means scanning it). If you haven’t seen that before, drop me a line and I’ll run through it with you. Or go and read a post I did a long while ago about the maths involved in that decision. So – what I’m going to tell you is that a Lookup is a join. When I run SELECT CustomerID FROM Sales.SalesOrderHeader WHERE SalesPersonID = 285; against the AdventureWorks2012 get the following plan: I’m sure you can see the join. Don’t look in the query, it’s not there. But you should be able to see the join in the plan. It’s an Inner Join, implemented by a Nested Loop. It’s pulling data in from the Index Seek, and joining that to the results of a Key Lookup. It clearly is – the QO wouldn’t call it that if it wasn’t really one. It behaves exactly like any other Nested Loop (Inner Join) operator, pulling rows from one side and putting a request in from the other. You wouldn’t have a problem accepting it as a join if the query were slightly different, such as SELECT sod.OrderQty FROM Sales.SalesOrderHeader AS soh JOIN Sales.SalesOrderDetail as sod on sod.SalesOrderID = soh.SalesOrderID WHERE soh.SalesPersonID = 285; Amazingly similar, of course. This one is an explicit join, the first example was just as much a join, even thought you didn’t actually ask for one. You need to consider this when you’re thinking about your queries. But it gets more interesting. Consider this query: SELECT SalesOrderID FROM Sales.SalesOrderHeader WHERE SalesPersonID = 276 AND CustomerID = 29522; It doesn’t look like there’s a join here either, but look at the plan. That’s not some Lookup in action – that’s a proper Merge Join. The Query Optimizer has worked out that it can get the data it needs by looking in two separate indexes and then doing a Merge Join on the data that it gets. Both indexes used are ordered by the column that’s indexed (one on SalesPersonID, one on CustomerID), and then by the CIX key SalesOrderID. Just like when you seek in the phone book to Farley, the Farleys you have are ordered by FirstName, these seek operations return the data ordered by the next field. This order is SalesOrderID, even though you didn’t explicitly put that column in the index definition. The result is two datasets that are ordered by SalesOrderID, making them very mergeable. Another example is the simple query SELECT CustomerID FROM Sales.SalesOrderHeader WHERE SalesPersonID = 276; This one prefers a Hash Match to a standard lookup even! This isn’t just ordinary index intersection, this is something else again! Just like before, we could imagine it better with two whole tables, but we shouldn’t try to distinguish between joining two tables and joining two indexes. The Query Optimizer can see (using basic maths) that it’s worth doing these particular operations using these two less-than-ideal indexes (because of course, the best indexese would be on both columns – a composite such as (SalesPersonID, CustomerID – and it would have the SalesOrderID column as part of it as the CIX key still). You need to think like this too. Not in terms of excusing single-column indexes like the ones in AdventureWorks2012, but in terms of having a picture about how you’d like your queries to run. If you start to think about what data you need, where it’s coming from, and how it’s going to be used, then you will almost certainly write better queries. …and yes, this would include when you’re dealing with regular joins across multiples, not just against joins within single table queries.

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• MySQL Connect Only 10 Days Away - Focus on InnoDB Sessions

  - by Bertrand Matthelié

  Time flies and MySQL Connect is only 10 days away! You can check out the full program here as well as in the September edition of the MySQL newsletter. Mat recently blogged about the MySQL Cluster sessions you’ll have the opportunity to attend, and below are those focused on InnoDB. Remember you can plan your schedule with Schedule Builder. Saturday, 1.00 pm, Room Golden Gate 3: 10 Things You Should Know About InnoDB—Calvin Sun, Oracle InnoDB is the default storage engine for Oracle’s MySQL as of MySQL Release 5.5. It provides the standard ACID-compliant transactions, row-level locking, multiversion concurrency control, and referential integrity. InnoDB also implements several innovative technologies to improve its performance and reliability. This presentation gives a brief history of InnoDB; its main features; and some recent enhancements for better performance, scalability, and availability. Saturday, 5.30 pm, Room Golden Gate 4: Demystified MySQL/InnoDB Performance Tuning—Dimitri Kravtchuk, Oracle This session covers performance tuning with MySQL and the InnoDB storage engine for MySQL and explains the main improvements made in MySQL Release 5.5 and Release 5.6. Which setting for which workload? Which value will be better for my system? How can I avoid potential bottlenecks from the beginning? Do I need a purge thread? Is it true that InnoDB doesn't need thread concurrency anymore? These and many other questions are asked by DBAs and developers. Things are changing quickly and constantly, and there is no “silver bullet.” But understanding the configuration setting’s impact is already a huge step in performance improvement. Bring your ideas and problems to share them with others—the discussion is open, just moderated by a speaker. Sunday, 10.15 am, Room Golden Gate 4: Better Availability with InnoDB Online Operations—Calvin Sun, Oracle Many top Web properties rely on Oracle’s MySQL as a critical piece of infrastructure for serving millions of users. Database availability has become increasingly important. One way to enhance availability is to give users full access to the database during data definition language (DDL) operations. The online DDL operations in recent MySQL releases offer users the flexibility to perform schema changes while having full access to the database—that is, with minimal delay of operations on a table and without rebuilding the entire table. These enhancements provide better responsiveness and availability in busy production environments. This session covers these improvements in the InnoDB storage engine for MySQL for online DDL operations such as add index, drop foreign key, and rename column. Sunday, 11.45 am, Room Golden Gate 7: Developing High-Throughput Services with NoSQL APIs to InnoDB and MySQL Cluster—Andrew Morgan and John Duncan, Oracle Ever-increasing performance demands of Web-based services have generated significant interest in providing NoSQL access methods to MySQL (MySQL Cluster and the InnoDB storage engine of MySQL), enabling users to maintain all the advantages of their existing relational databases while providing blazing-fast performance for simple queries. Get the best of both worlds: persistence; consistency; rich SQL queries; high availability; scalability; and simple, flexible APIs and schemas for agile development. This session describes the memcached connectors and examines some use cases for how MySQL and memcached fit together in application architectures. It does the same for the newest MySQL Cluster native connector, an easy-to-use, fully asynchronous connector for Node.js. Sunday, 1.15 pm, Room Golden Gate 4: InnoDB Performance Tuning—Inaam Rana, Oracle The InnoDB storage engine has always been highly efficient and includes many unique architectural elements to ensure high performance and scalability. In MySQL 5.5 and MySQL 5.6, InnoDB includes many new features that take better advantage of recent advances in operating systems and hardware platforms than previous releases did. This session describes unique InnoDB architectural elements for performance, new features, and how to tune InnoDB to achieve better performance. Sunday, 4.15 pm, Room Golden Gate 3: InnoDB Compression for OLTP—Nizameddin Ordulu, Facebook and Inaam Rana, Oracle Data compression is an important capability of the InnoDB storage engine for Oracle’s MySQL. Compressed tables reduce the size of the database on disk, resulting in fewer reads and writes and better throughput by reducing the I/O workload. Facebook pushes the limit of InnoDB compression and has made several enhancements to InnoDB, making this technology ready for online transaction processing (OLTP). In this session, you will learn the fundamentals of InnoDB compression. You will also learn the enhancements the Facebook team has made to improve InnoDB compression, such as reducing compression failures, not logging compressed page images, and allowing changes of compression level. Not registered yet? You can still save US$ 300 over the on-site fee – Register Now!

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• Die Tape Library, die mitwächst

  - by A&C Redaktion

  Mit der Storage Tek SL150 Modular Tape Library hat Oracle eine Archiv-Lösung entwickelt, die zusammen mit dem Unternehmen wachsen kann. Die Ziele waren hoch gesteckt: Die neue Bandbibliothek sollte nicht nur extrem skalierbar, sondern auch günstig sein, denn sie ist als Einstiegs-Library für kleinere, wachsende und mittelständische Firmen gedacht. Zum Launch der Tape Library legt Oracle beeindruckende Zahlen und Fakten vor: - 75% günstiger in der Anschaffung, als vergleichbare Produkte - platzsparend durch 40% höhere Dichte - höchste Sicherheitsstandards - erweiterbar von 30 auf bis zu 300 Slots, und damit 900 Terabyte - einfache Bedienung dank intuitiver Benutzeroberfläche auf Basis der Oracle Fusion Middleware und Oracle Linux - die Installation dauert nur 30 Minuten - unterstützt viele verschiedene Systemumgebungen Partner haben die Möglichkeit, zu diesem neuen Mitglied der Oracle Produktfamilie eigene Support Services anzubieten. Details zu den Resell und Support Anforderungen finden Sie hier (mit OPN-Login): SL150 Produktübersicht Partner Support Option mit StorageTek SL150 Modular Tape Library FAQ - Partner Support Option mit StorageTek SL150 Modular Tape Library Auch die englischsprachige Pressemitteilung zum Launch bietet ausführliche Informationen und Details, von den Maßen bis zum Energieverbrauch, finden Sie hier im Storage Tek SL150 Data Sheet. Natürlich wollen wir Ihnen die ersten Stimmen aus der deutschsprachigen Fachpresse zur Storage Tek SL 150 nicht vorenthalten: SpeicherguideIT SecCityIT AdministratorDOAG

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• Die Tape Library, die mitwächst

  - by A&C Redaktion

  Mit der Storage Tek SL150 Modular Tape Library hat Oracle eine Archiv-Lösung entwickelt, die zusammen mit dem Unternehmen wachsen kann. Die Ziele waren hoch gesteckt: Die neue Bandbibliothek sollte nicht nur extrem skalierbar, sondern auch günstig sein, denn sie ist als Einstiegs-Library für kleinere, wachsende und mittelständische Firmen gedacht. Zum Launch der Tape Library legt Oracle beeindruckende Zahlen und Fakten vor: - 75% günstiger in der Anschaffung, als vergleichbare Produkte - platzsparend durch 40% höhere Dichte - höchste Sicherheitsstandards - erweiterbar von 30 auf bis zu 300 Slots, und damit 900 Terabyte - einfache Bedienung dank intuitiver Benutzeroberfläche auf Basis der Oracle Fusion Middleware und Oracle Linux - die Installation dauert nur 30 Minuten - unterstützt viele verschiedene Systemumgebungen Partner haben die Möglichkeit, zu diesem neuen Mitglied der Oracle Produktfamilie eigene Support Services anzubieten. Details zu den Resell und Support Anforderungen finden Sie hier (mit OPN-Login): SL150 Produktübersicht Partner Support Option mit StorageTek SL150 Modular Tape Library FAQ - Partner Support Option mit StorageTek SL150 Modular Tape Library Auch die englischsprachige Pressemitteilung zum Launch bietet ausführliche Informationen und Details, von den Maßen bis zum Energieverbrauch, finden Sie hier im Storage Tek SL150 Data Sheet. Natürlich wollen wir Ihnen die ersten Stimmen aus der deutschsprachigen Fachpresse zur Storage Tek SL 150 nicht vorenthalten: SpeicherguideIT SecCityIT AdministratorDOAG

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