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• text extraction from video game dialogue files [on hold]

  - by wdwvt1

  As part of an academic project, I am trying to access the dialogue files (whether audio or text) from a variety of sports video games (Madden or NBA 2kX would be fantastic). I have searched extensively on other sites (scholarly text-mining publications, r/gaming, r/madden, modding sites, etc.) for guidance in how to extract dialogue files, but have been unsuccessful. Given that I don't have even the domain specific language to ask the right question (i.e. the resources I am seeking are out there, I just can't find them) I am asking the SE game dev community for help with the 2 following questions: Is there a canonical resource that I should study that would get me started with how to extract text or audio files from games? I am very fluent in python, which usually excels at mining information from sources, but I struggle with knowing where to start with a video game (as opposed to a more familiar database with a defined API). Is this even feasible, or are protections included with newer games (e.g. NBA 2k13) going to make extraction of these resources in a programmatic way impossible? Thank you for your help!

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• SQL get data out of BEGIN; ...; END; block in python

  - by Claudiu

  I want to run many select queries at once by putting them between BEGIN; END;. I tried the following: cur = connection.cursor() cur.execute(""" BEGIN; SELECT ...; END;""") res = cur.fetchall() However, I get the error: psycopg2.ProgrammingError: no results to fetch How can I actually get data this way? Likewise, if I just have many selects in a row, I only get data back from the latest one. Is there a way to get data out of all of them?

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• Turning PHP page calling Zend functions procedurally into Zend Framework MVC-help!

  - by Joel

  Hi guys, I posted much of this question, but if didn't include all the Zend stuff because I thought it'd be overkill, but now I'm thinking it's not easy to figure out an OO way of doing this without that code... So with that said, please forgive the verbose code. I'm learning how to use MVC and OO in general, and I have a website that is all in PHP but most of the pages are basic static pages. I have already converted them all to views in Zend Framework, and have the Controller and layout set. All is good there. The one remaining page I have is the main reason I did this...it in fact uses Zend library (for gData connection and pulling info from a Google Calendar and displaying it on the page. I don't know enough about this to know where to begin to refactor the code to fit in the Zend Framework MVC model. Any help would be greatly appreciated!! .phtml view page: <div id="dhtmltooltip" align="left"></div> <script src="../js/tooltip.js" type="text/javascript"> </script> <div id="container"> <div id="conten"> <a name="C4"></a> <?php function get_desc_second_part(&$value) { list(,$val_b) = explode('==',$value); $value = trim($val_b); } function filterEventDetails($contentText) { $data = array(); foreach($contentText as $row) { if(strstr($row, 'When: ')) { ##cleaning "when" string to get date in the format "May 28, 2009"## $data['duration'] = str_replace('When: ','',$row); list($when, ) = explode(' to ',$data['duration']); $data['when'] = substr($when,4); if(strlen($data['when'])>13) $data['when'] = trim(str_replace(strrchr($data['when'], ' '),'',$data['when'])); $data['duration'] = substr($data['duration'], 0, strlen($data['duration'])-4); //trimming time zone identifier (UTC etc.) } if(strstr($row, 'Where: ')) { $data['where'] = str_replace('Where: ','',$row); //pr($row); //$where = strstr($row, 'Where: '); //pr($where); } if(strstr($row, 'Event Description: ')) { $event_desc = str_replace('Event Description: ','',$row); //$event_desc = strstr($row, 'Event Description: '); ## Filtering event description and extracting venue, ticket urls etc from it. //$event_desc = str_replace('Event Description: ','',$contentText[3]); $event_desc_array = explode('|',$event_desc); array_walk($event_desc_array,'get_desc_second_part'); //pr($event_desc_array); $data['venue_url'] = $event_desc_array[0]; $data['details'] = $event_desc_array[1]; $data['tickets_url'] = $event_desc_array[2]; $data['tickets_button'] = $event_desc_array[3]; $data['facebook_url'] = $event_desc_array[4]; $data['facebook_icon'] = $event_desc_array[5]; } } return $data; } // load library require_once 'Zend/Loader.php'; Zend_Loader::loadClass('Zend_Gdata'); Zend_Loader::loadClass('Zend_Gdata_ClientLogin'); Zend_Loader::loadClass('Zend_Gdata_Calendar'); Zend_Loader::loadClass('Zend_Http_Client'); // create authenticated HTTP client for Calendar service $gcal = Zend_Gdata_Calendar::AUTH_SERVICE_NAME; $user = "[email protected]"; $pass = "xxxxxxxx"; $client = Zend_Gdata_ClientLogin::getHttpClient($user, $pass, $gcal); $gcal = new Zend_Gdata_Calendar($client); $query = $gcal->newEventQuery(); $query->setUser('[email protected]'); $secondary=true; $query->setVisibility('private'); $query->setProjection('basic'); $query->setOrderby('starttime'); $query->setSortOrder('ascending'); //$query->setFutureevents('true'); $startDate=date('Y-m-d h:i:s'); $endDate="2015-12-31"; $query->setStartMin($startDate); $query->setStartMax($endDate); $query->setMaxResults(30); try { $feed = $gcal->getCalendarEventFeed($query); } catch (Zend_Gdata_App_Exception $e) { echo "Error: " . $e->getResponse(); } ?> <h1><?php echo $feed->title; ?></h1> <?php echo $feed->totalResults; ?> event(s) found. <table width="90%" border="3" align="center"> <tr> <td width="20%" align="center" valign="middle"><b>;DATE</b></td> <td width="25%" align="center" valign="middle"><b>VENUE</b></td> <td width="20%" align="center" valign="middle"><b>CITY</b></td> <td width="20%" align="center" valign="middle"><b>DETAILS</b></td> <td width="15%" align="center" valign="middle"><b>LINKS</b></td> </tr> <?php if((int)$feed->totalResults>0) { //checking if at least one event is there in this date range foreach ($feed as $event) { //iterating through all events //pr($event);die; $contentText = stripslashes($event->content->text); //striping any escape character $contentText = preg_replace('/\<br \/\>[\n\t\s]{1,}\<br \/\>/','<br />',stripslashes($event->content->text)); //replacing multiple breaks with a single break //die(); $contentText = explode('<br />',$contentText); //splitting data by break tag $eventData = filterEventDetails($contentText); $when = $eventData['when']; $where = $eventData['where']; $duration = $eventData['duration']; $venue_url = $eventData['venue_url']; $details = $eventData['details']; $tickets_url = $eventData['tickets_url']; $tickets_button = $eventData['tickets_button']; $facebook_url = $eventData['facebook_url']; $facebook_icon = $eventData['facebook_icon']; $title = stripslashes($event->title); echo '<tr>'; echo '<td width="20%" align="center" valign="middle" nowrap="nowrap">'; echo $when; echo '</td>'; echo '<td width="20%" align="center" valign="middle">'; if($venue_url!='') { echo '<a href="'.$venue_url.'" target="_blank">'.$title.'</a>'; } else { echo $title; } echo '</td>'; echo '<td width="20%" align="center" valign="middle">'; echo $where; echo '</td>'; echo '<td width="20%" align="center" valign="middle">'; $details = str_replace("\n","<br>",htmlentities($details)); $duration = str_replace("\n","<br>",$duration); $detailed_description = "<b>When</b>: <br>".$duration."<br><br>"; $detailed_description .= "<b>Description</b>: <br>".$details; echo '<a href="javascript:void(0);" onmouseover="ddrivetip(\''.$detailed_description.'\')" onmouseout="hideddrivetip()" onclick="return false">View Details</a>'; echo '</td>'; echo '<td width="20%" valign="middle">'; if(trim($tickets_url) !='' && trim($tickets_button)!='') { echo '<a href="'.$tickets_url.'" target="_blank"><img src="'.$tickets_button.'" border="0" ></a>'; } if(trim($facebook_url) !='' && trim($facebook_icon)!='') { echo '<a href="'.$facebook_url.'" target="_blank"><img src="'.$facebook_icon.'" border="0" ></a>'; } else { echo '......'; } echo '</td>'; echo '</tr>'; } } else { //else show 'no event found' message echo '<tr>'; echo '<td width="100%" align="center" valign="middle" colspan="5">'; echo "No event found"; echo '</td>'; } ?> </table> <h3><a href="#pastevents">Scroll down for a list of past shows.</a></h3> <br /> <a name="pastevents"></a> <ul class="pastShows"> <?php $startDate='2005-01-01'; $endDate=date('Y-m-d'); /*$gcal = Zend_Gdata_Calendar::AUTH_SERVICE_NAME; $user = "[email protected]"; $pass = "silverroof10"; $client = Zend_Gdata_ClientLogin::getHttpClient($user, $pass, $gcal); $gcal = new Zend_Gdata_Calendar($client); $query = $gcal->newEventQuery(); $query->setUser('[email protected]'); $query->setVisibility('private'); $query->setProjection('basic');*/ $query->setOrderby('starttime'); $query->setSortOrder('descending'); $query->setFutureevents('false'); $query->setStartMin($startDate); $query->setStartMax($endDate); $query->setMaxResults(1000); try { $feed = $gcal->getCalendarEventFeed($query); } catch (Zend_Gdata_App_Exception $e) { echo "Error: " . $e->getResponse(); } if((int)$feed->totalResults>0) { //checking if at least one event is there in this date range foreach ($feed as $event) { //iterating through all events $contentText = stripslashes($event->content->text); //striping any escape character $contentText = preg_replace('/\<br \/\>[\n\t\s]{1,}\<br \/\>/','<br />',stripslashes($event->content->text)); //replacing multiple breaks with a single break $contentText = explode('<br />',$contentText); //splitting data by break tag $eventData = filterEventDetails($contentText); $when = $eventData['when']; $where = $eventData['where']; $duration = $eventData['duration']; $title = stripslashes($event->title); echo '<li class="pastShows">' . $when . " - " . $title . ", " . $where . '</li>'; } } ?> </div> </div>

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• Determine how much can I write into a filehandle; copying data from one FH to the other.

  - by Vi

  How to determine if I can write the given number of bytes to a filehandle (socket actually)? (Alternatively, how to "unread" the data I had read from other filehandle?) I want something like: n = how_much_can_I_write(w_handle); n = read(r_handle, buf, n); assert(n==write(w_handle, buf, n)); Both filehandles (r_handle and w_handle) have received ready status from epoll_wait. I want all data from r_handle to be copied to w_handle without using a "write debt" buffer. In general, how to copy the data from one filehandle to the other simply and reliably?

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• Is your team is a high-performing team?

  As a child I can remember looking out of the car window as my father drove along the Interstate in Florida while seeing prisoners wearing bright orange jump suits and prison guards keeping a watchful eye on them. The prisoners were taking part in a prison road gang. These road gangs were formed to help the state maintain the state highway infrastructure. The prisoner’s primary responsibilities are to pick up trash and debris from the roadway. This is a prime example of a work group or working group used by most prison systems in the United States. Work groups or working groups can be defined as a collection of individuals or entities working together to achieve a specific goal or accomplish a specific set of tasks. Typically these groups are only established for a short period of time and are dissolved once the desired outcome has been achieved. More often than not group members usually feel as though they are expendable to the group and some even dread that they are even in the group. "A team is a small number of people with complementary skills who are committed to a common purpose, performance goals, and approach for which they are mutually accountable." (Katzenbach and Smith, 1993) So how do you determine that a team is a high-performing team?  This can be determined by three base line criteria that include: consistently high quality output, the promotion of personal growth and well being of all team members, and most importantly the ability to learn and grow as a unit. Initially, a team can successfully create high-performing output without meeting all three criteria, however this will erode over time because team members will feel detached from the group or that they are not growing then the quality of the output will decline. High performing teams are similar to work groups because they both utilize a collection of individuals or entities to accomplish tasks. What distinguish a high-performing team from a work group are its characteristics. High-performing teams contain five core characteristics. These characteristics are what separate a group from a team. The five characteristics of a high-performing team include: Purpose, Performance Measures, People with Tasks and Relationship Skills, Process, and Preparation and Practice. A high-performing team is much more than a work group, and typically has a life cycle that can vary from team to team. The standard team lifecycle consists of five states and is comparable to a human life cycle. The five states of a high-performing team lifecycle include: Formulating, Storming, Normalizing, Performing, and Adjourning. The Formulating State of a team is first realized when the team members are first defined and roles are assigned to all members. This initial stage is very important because it can set the tone for the team and can ultimately determine its success or failure. In addition, this stage requires the team to have a strong leader because team members are normally unclear about specific roles, specific obstacles and goals that my lay ahead of them.  Finally, this stage is where most team members initially meet one another prior to working as a team unless the team members already know each other. The Storming State normally arrives directly after the formulation of a new team because there are still a lot of unknowns amongst the newly formed assembly. As a general rule most of the parties involved in the team are still getting used to the workload, pace of work, deadlines and the validity of various tasks that need to be performed by the group.  In this state everything is questioned because there are so many unknowns. Items commonly questioned include the credentials of others on the team, the actual validity of a project, and the leadership abilities of the team leader.  This can be exemplified by looking at the interactions between animals when they first meet.  If we look at a scenario where two people are walking directly toward each other with their dogs. The dogs will automatically enter the Storming State because they do not know the other dog. Typically in this situation, they attempt to define which is more dominating via play or fighting depending on how the dogs interact with each other. Once dominance has been defined and accepted by both dogs then they will either want to play or leave depending on how the dogs interacted and other environmental variables. Once the Storming State has been realized then the Normalizing State takes over. This state is entered by a team once all the questions of the Storming State have been answered and the team has been tested by a few tasks or projects.  Typically, participants in the team are filled with energy, and comradery, and a strong alliance with team goals and objectives.  A high school football team is a perfect example of the Normalizing State when they start their season.  The player positions have been assigned, the depth chart has been filled and everyone is focused on winning each game. All of the players encourage and expect each other to perform at the best of their abilities and are united by competition from other teams. The Performing State is achieved by a team when its history, working habits, and culture solidify the team as one working unit. In this state team members can anticipate specific behaviors, attitudes, reactions, and challenges are seen as opportunities and not problems. Additionally, each team member knows their role in the team’s success, and the roles of others. This is the most productive state of a group and is where all the time invested working together really pays off. If you look at an Olympic figure skating team skate you can easily see how the time spent working together benefits their performance. They skate as one unit even though it is comprised of two skaters. Each skater has their routine completely memorized as well as their partners. This allows them to anticipate each other’s moves on the ice makes their skating look effortless. The final state of a team is the Adjourning State. This state is where accomplishments by the team and each individual team member are recognized. Additionally, this state also allows for reflection of the interactions between team members, work accomplished and challenges that were faced. Finally, the team celebrates the challenges they have faced and overcome as a unit. Currently in the workplace teams are divided into two different types: Co-located and Distributed Teams. Co-located teams defined as the traditional group of people working together in an office, according to Andy Singleton of Assembla. This traditional type of a team has dominated business in the past due to inadequate technology, which forced workers to primarily interact with one another via face to face meetings.  Team meetings are primarily lead by the person with the highest status in the company. Having personally, participated in meetings of this type, usually a select few of the team members dominate the flow of communication which reduces the input of others in group discussions. Since discussions are dominated by a select few individuals the discussions and group discussion are skewed in favor of the individuals who communicate the most in meetings. In addition, Team members might not give their full opinions on a topic of discussion in part not to offend or create controversy amongst the team and can alter decision made in meetings towards those of the opinions of the dominating team members. Distributed teams are by definition spread across an area or subdivided into separate sections. That is exactly what distributed teams when compared to a more traditional team. It is common place for distributed teams to have team members across town, in the next state, across the country and even with the advances in technology over the last 20 year across the world. These teams allow for more diversity compared to the other type of teams because they allow for more flexibility regarding location. A team could consist of a 30 year old male Italian project manager from New York, a 50 year old female Hispanic from California and a collection of programmers from India because technology allows them to communicate as if they were standing next to one another.  In addition, distributed team members consult with more team members prior to making decisions compared to traditional teams, and take longer to come to decisions due to the changes in time zones and cultural events. However, team members feel more empowered to speak out when they do not agree with the team and to notify others of potential issues regarding the work that the team is doing. Virtual teams which are a subset of the distributed team type is changing organizational strategies due to the fact that a team can now in essence be working 24 hrs a day because of utilizing employees in various time zones and locations.  A primary example of this is with customer services departments, a company can have multiple call centers spread across multiple time zones allowing them to appear to be open 24 hours a day while all a employees work from 9AM to 5 PM every day. Virtual teams also allow human resources departments to go after the best talent for the company regardless of where the potential employee works because they will be a part of a virtual team all that is need is the proper technology to be setup to allow everyone to communicate. In addition to allowing employees to work from home, the company can save space and resources by not having to provide a desk for every team member. In fact, those team members that randomly come into the office can actually share one desk amongst multiple people. This is definitely a cost cutting plus given the current state of the economy. One thing that can turn a team into a high-performing team is leadership. High-performing team leaders need to focus on investing in ongoing personal development, provide team members with direction, structure, and resources needed to accomplish their work, make the right interventions at the right time, and help the team manage boundaries between the team and various external parties involved in the teams work. A team leader needs to invest in ongoing personal development in order to effectively manage their team. People have said that attitude is everything; this is very true about leaders and leadership. A team takes on the attitudes and behaviors of its leaders. This can potentially harm the team and the team’s output. Leaders must concentrate on self-awareness, and understanding their team’s group dynamics to fully understand how to lead them. In addition, always learning new leadership techniques from other effective leaders is also very beneficial. Providing team members with direction, structure, and resources that they need to accomplish their work collectively sounds easy, but it is not.  Leaders need to be able to effectively communicate with their team on how their work helps the company reach for its organizational vision. Conversely, the leader needs to allow his team to work autonomously within specific guidelines to turn the company’s vision into a reality.  This being said the team must be appropriately staffed according to the size of the team’s tasks and their complexity. These tasks should be clear, and be meaningful to the company’s objectives and allow for feedback to be exchanged with the leader and the team member and the leader and upper management. Now if the team is properly staffed, and has a clear and full understanding of what is to be done; the company also must supply the workers with the proper tools to achieve the tasks that they are asked to do. No one should be asked to dig a hole without being given a shovel.  Finally, leaders must reward their team members for accomplishments that they achieve. Awards could range from just a simple congratulatory email, a party to close the completion of a large project, or other monetary rewards. Managing boundaries is very important for team leaders because it can alter attitudes of team members and can add undue stress to the team which will force them to loose focus on the tasks at hand for the group. Team leaders should promote communication between team members so that burdens are shared amongst the team and solutions can be derived from hearing the opinions of multiple sources. This also reinforces team camaraderie and working as a unit. Team leaders must manage the type and timing of interventions as to not create an even bigger mess within the team. Poorly timed interventions can really deflate team members and make them question themselves. This could really increase further and undue interventions by the team leader. Typically, the best time for interventions is when the team is just starting to form so that all unproductive behaviors are removed from the team and that it can retain focus on its agenda. If an intervention is effectively executed the team will feel energized about the work that they are doing, promote communication and interaction amongst the group and improve moral overall. High-performing teams are very import to organizations because they consistently produce high quality output and develop a collective purpose for their work. This drive to succeed allows team members to utilize specific talents allowing for growth in these areas.  In addition, these team members usually take on a sense of ownership with their projects and feel that the other team members are irreplaceable. References: http://blog.assembla.com/assemblablog/tabid/12618/bid/3127/Three-ways-to-organize-your-team-co-located-outsourced-or-global.aspx Katzenbach, J.R. & Smith, D.K. (1993). The Wisdom of Teams: Creating the High-performance Organization. Boston: Harvard Business School.

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• Fragmented Log files could be slowing down your database

  - by Fatherjack

  Something that is sometimes forgotten by a lot of DBAs is the fact that database log files get fragmented in the same way that you get fragmentation in a data file. The cause is very different but the effect is the same – too much effort reading and writing data. Data files get fragmented as data is changed through normal system activity, INSERTs, UPDATEs and DELETEs cause fragmentation and most experienced DBAs are monitoring their indexes for fragmentation and dealing with it accordingly. However, you don’t hear about so many working on their log files. How can a log file get fragmented? I’m glad you asked. When you create a database there are at least two files created on the disk storage; an mdf for the data and an ldf for the log file (you can also have ndf files for extra data storage but that’s off topic for now). It is wholly possible to have more than one log file but in most cases there is little point in creating more than one as the log file is written to in a ‘wrap-around’ method (more on that later). When a log file is created at the time that a database is created the file is actually sub divided into a number of virtual log files (VLFs). The number and size of these VLFs depends on the size chosen for the log file. VLFs are also created in the space added to a log file when a log file growth event takes place. Do you have your log files set to auto grow? Then you have potentially been introducing many VLFs into your log file. Let’s get to see how many VLFs we have in a brand new database. USE master GO CREATE DATABASE VLF_Test ON ( NAME = VLF_Test, FILENAME = 'C:\Program Files\Microsoft SQL Server\MSSQL10.ROCK_2008\MSSQL\DATA\VLF_Test.mdf', SIZE = 100, MAXSIZE = 500, FILEGROWTH = 50 ) LOG ON ( NAME = VLF_Test_Log, FILENAME = 'C:\Program Files\Microsoft SQL Server\MSSQL10.ROCK_2008\MSSQL\DATA\VLF_Test_log.ldf', SIZE = 5MB, MAXSIZE = 250MB, FILEGROWTH = 5MB ); go USE VLF_Test go DBCC LOGINFO; The results of this are firstly a new database is created with specified files sizes and the the DBCC LOGINFO results are returned to the script editor. The DBCC LOGINFO results have plenty of interesting information in them but lets first note there are 4 rows of information, this relates to the fact that 4 VLFs have been created in the log file. The values in the FileSize column are the sizes of each VLF in bytes, you will see that the last one to be created is slightly larger than the others. So, a 5MB log file has 4 VLFs of roughly 1.25 MB. Lets alter the CREATE DATABASE script to create a log file that’s a bit bigger and see what happens. Alter the code above so that the log file details are replaced by LOG ON ( NAME = VLF_Test_Log, FILENAME = 'C:\Program Files\Microsoft SQL Server\MSSQL10.ROCK_2008\MSSQL\DATA\VLF_Test_log.ldf', SIZE = 1GB, MAXSIZE = 25GB, FILEGROWTH = 1GB ); With a bigger log file specified we get more VLFs What if we make it bigger again? LOG ON ( NAME = VLF_Test_Log, FILENAME = 'C:\Program Files\Microsoft SQL Server\MSSQL10.ROCK_2008\MSSQL\DATA\VLF_Test_log.ldf', SIZE = 5GB, MAXSIZE = 250GB, FILEGROWTH = 5GB ); This time we see more VLFs are created within our log file. We now have our 5GB log file comprised of 16 files of 320MB each. In fact these sizes fall into all the ranges that control the VLF creation criteria – what a coincidence! The rules that are followed when a log file is created or has it’s size increased are pretty basic. If the file growth is lower than 64MB then 4 VLFs are created If the growth is between 64MB and 1GB then 8 VLFs are created If the growth is greater than 1GB then 16 VLFs are created. Now the potential for chaos comes if the default values and settings for log file growth are used. By default a database log file gets a 1MB log file with unlimited growth in steps of 10%. The database we just created is 6 MB, let’s add some data and see what happens. USE vlf_test go -- we need somewhere to put the data so, a table is in order IF OBJECT_ID('A_Table') IS NOT NULL DROP TABLE A_Table go CREATE TABLE A_Table ( Col_A int IDENTITY, Col_B CHAR(8000) ) GO -- Let's check the state of the log file -- 4 VLFs found EXECUTE ('DBCC LOGINFO'); go -- We can go ahead and insert some data and then check the state of the log file again INSERT A_Table (col_b) SELECT TOP 500 REPLICATE('a',2000) FROM sys.columns AS sc, sys.columns AS sc2 GO -- insert 500 rows and we get 22 VLFs EXECUTE ('DBCC LOGINFO'); go -- Let's insert more rows INSERT A_Table (col_b) SELECT TOP 2000 REPLICATE('a',2000) FROM sys.columns AS sc, sys.columns AS sc2 GO 10 -- insert 2000 rows, in 10 batches and we suddenly have 107 VLFs EXECUTE ('DBCC LOGINFO'); Well, that escalated quickly! Our log file is split, internally, into 107 fragments after a few thousand inserts. The same happens with any logged transactions, I just chose to illustrate this with INSERTs. Having too many VLFs can cause performance degradation at times of database start up, log backup and log restore operations so it’s well worth keeping a check on this property. How do we prevent excessive VLF creation? Creating the database with larger files and also with larger growth steps and actively choosing to grow your databases rather than leaving it to the Auto Grow event can make sure that the growths are made with a size that is optimal. How do we resolve a situation of a database with too many VLFs? This process needs to be done when the database is under little or no stress so that you don’t affect system users. The steps are: BACKUP LOG YourDBName TO YourBackupDestinationOfChoice Shrink the log file to its smallest possible size DBCC SHRINKFILE(FileNameOfTLogHere, TRUNCATEONLY) * Re-size the log file to the size you want it to, taking in to account your expected needs for the coming months or year. ALTER DATABASE YourDBName MODIFY FILE ( NAME = FileNameOfTLogHere, SIZE = TheSizeYouWantItToBeIn_MB) * – If you don’t know the file name of your log file then run sp_helpfile while you are connected to the database that you want to work on and you will get the details you need. The resize step can take quite a while This is already detailed far better than I can explain it by Kimberley Tripp in her blog 8-Steps-to-better-Transaction-Log-throughput.aspx. The result of this will be a log file with a VLF count according to the bullet list above. Knowing when VLFs are being created By complete coincidence while I have been writing this blog (it’s been quite some time from it’s inception to going live) Jonathan Kehayias from SQLSkills.com has written a great article on how to track database file growth using Event Notifications and Service Broker. I strongly recommend taking a look at it as this is going to catch any sneaky auto grows that take place and let you know about them right away. Hassle free monitoring of VLFs If you are lucky or wise enough to be using SQL Monitor or another monitoring tool that let’s you write your own custom metrics then you can keep an eye on this very easily. There is a custom metric for VLFs (written by Stuart Ainsworth) already on the site and there are some others there are very useful so take a moment or two to look around while you are there. Resources MSDN – http://msdn.microsoft.com/en-us/library/ms179355(v=sql.105).aspx Kimberly Tripp from SQLSkills.com – http://www.sqlskills.com/BLOGS/KIMBERLY/post/8-Steps-to-better-Transaction-Log-throughput.aspx Thomas LaRock at Simple-Talk.com – http://www.simple-talk.com/sql/database-administration/monitoring-sql-server-virtual-log-file-fragmentation/ Disclosure I am a Friend of Red Gate. This means that I am more than likely to say good things about Red Gate DBA and Developer tools. No matter how awesome I make them sound, take the time to compare them with other products before you contact the Red Gate sales team to make your order.

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

  - by Brian

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

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• What's up with OCFS2?

  - by wcoekaer

  On Linux there are many filesystem choices and even from Oracle we provide a number of filesystems, all with their own advantages and use cases. Customers often confuse ACFS with OCFS or OCFS2 which then causes assumptions to be made such as one replacing the other etc... I thought it would be good to write up a summary of how OCFS2 got to where it is, what we're up to still, how it is different from other options and how this really is a cool native Linux cluster filesystem that we worked on for many years and is still widely used. Work on a cluster filesystem at Oracle started many years ago, in the early 2000's when the Oracle Database Cluster development team wrote a cluster filesystem for Windows that was primarily focused on providing an alternative to raw disk devices and help customers with the deployment of Oracle Real Application Cluster (RAC). Oracle RAC is a cluster technology that lets us make a cluster of Oracle Database servers look like one big database. The RDBMS runs on many nodes and they all work on the same data. It's a Shared Disk database design. There are many advantages doing this but I will not go into detail as that is not the purpose of my write up. Suffice it to say that Oracle RAC expects all the database data to be visible in a consistent, coherent way, across all the nodes in the cluster. To do that, there were/are a few options : 1) use raw disk devices that are shared, through SCSI, FC, or iSCSI 2) use a network filesystem (NFS) 3) use a cluster filesystem(CFS) which basically gives you a filesystem that's coherent across all nodes using shared disks. It is sort of (but not quite) combining option 1 and 2 except that you don't do network access to the files, the files are effectively locally visible as if it was a local filesystem. So OCFS (Oracle Cluster FileSystem) on Windows was born. Since Linux was becoming a very important and popular platform, we decided that we would also make this available on Linux and thus the porting of OCFS/Windows started. The first version of OCFS was really primarily focused on replacing the use of Raw devices with a simple filesystem that lets you create files and provide direct IO to these files to get basically native raw disk performance. The filesystem was not designed to be fully POSIX compliant and it did not have any where near good/decent performance for regular file create/delete/access operations. Cache coherency was easy since it was basically always direct IO down to the disk device and this ensured that any time one issues a write() command it would go directly down to the disk, and not return until the write() was completed. Same for read() any sort of read from a datafile would be a read() operation that went all the way to disk and return. We did not cache any data when it came down to Oracle data files. So while OCFS worked well for that, since it did not have much of a normal filesystem feel, it was not something that could be submitted to the kernel mail list for inclusion into Linux as another native linux filesystem (setting aside the Windows porting code ...) it did its job well, it was very easy to configure, node membership was simple, locking was disk based (so very slow but it existed), you could create regular files and do regular filesystem operations to a certain extend but anything that was not database data file related was just not very useful in general. Logfiles ok, standard filesystem use, not so much. Up to this point, all the work was done, at Oracle, by Oracle developers. Once OCFS (1) was out for a while and there was a lot of use in the database RAC world, many customers wanted to do more and were asking for features that you'd expect in a normal native filesystem, a real "general purposes cluster filesystem". So the team sat down and basically started from scratch to implement what's now known as OCFS2 (Oracle Cluster FileSystem release 2). Some basic criteria were : Design it with a real Distributed Lock Manager and use the network for lock negotiation instead of the disk Make it a Linux native filesystem instead of a native shim layer and a portable core Support standard Posix compliancy and be fully cache coherent with all operations Support all the filesystem features Linux offers (ACL, extended Attributes, quotas, sparse files,...) Be modern, support large files, 32/64bit, journaling, data ordered journaling, endian neutral, we can mount on both endian /cross architecture,.. Needless to say, this was a huge development effort that took many years to complete. A few big milestones happened along the way... OCFS2 was development in the open, we did not have a private tree that we worked on without external code review from the Linux Filesystem maintainers, great folks like Christopher Hellwig reviewed the code regularly to make sure we were not doing anything out of line, we submitted the code for review on lkml a number of times to see if we were getting close for it to be included into the mainline kernel. Using this development model is standard practice for anyone that wants to write code that goes into the kernel and having any chance of doing so without a complete rewrite or.. shall I say flamefest when submitted. It saved us a tremendous amount of time by not having to re-fit code for it to be in a Linus acceptable state. Some other filesystems that were trying to get into the kernel that didn't follow an open development model had a lot harder time and a lot harsher criticism. March 2006, when Linus released 2.6.16, OCFS2 officially became part of the mainline kernel, it was accepted a little earlier in the release candidates but in 2.6.16. OCFS2 became officially part of the mainline Linux kernel tree as one of the many filesystems. It was the first cluster filesystem to make it into the kernel tree. Our hope was that it would then end up getting picked up by the distribution vendors to make it easy for everyone to have access to a CFS. Today the source code for OCFS2 is approximately 85000 lines of code. We made OCFS2 production with full support for customers that ran Oracle database on Linux, no extra or separate support contract needed. OCFS2 1.0.0 started being built for RHEL4 for x86, x86-64, ppc, s390x and ia64. For RHEL5 starting with OCFS2 1.2. SuSE was very interested in high availability and clustering and decided to build and include OCFS2 with SLES9 for their customers and was, next to Oracle, the main contributor to the filesystem for both new features and bug fixes. Source code was always available even prior to inclusion into mainline and as of 2.6.16, source code was just part of a Linux kernel download from kernel.org, which it still is, today. So the latest OCFS2 code is always the upstream mainline Linux kernel. OCFS2 is the cluster filesystem used in Oracle VM 2 and Oracle VM 3 as the virtual disk repository filesystem. Since the filesystem is in the Linux kernel it's released under the GPL v2 The release model has always been that new feature development happened in the mainline kernel and we then built consistent, well tested, snapshots that had versions, 1.2, 1.4, 1.6, 1.8. But these releases were effectively just snapshots in time that were tested for stability and release quality. OCFS2 is very easy to use, there's a simple text file that contains the node information (hostname, node number, cluster name) and a file that contains the cluster heartbeat timeouts. It is very small, and very efficient. As Sunil Mushran wrote in the manual : OCFS2 is an efficient, easily configured, quickly installed, fully integrated and compatible, feature-rich, architecture and endian neutral, cache coherent, ordered data journaling, POSIX-compliant, shared disk cluster file system. Here is a list of some of the important features that are included : Variable Block and Cluster sizes Supports block sizes ranging from 512 bytes to 4 KB and cluster sizes ranging from 4 KB to 1 MB (increments in power of 2). Extent-based Allocations Tracks the allocated space in ranges of clusters making it especially efficient for storing very large files. Optimized Allocations Supports sparse files, inline-data, unwritten extents, hole punching and allocation reservation for higher performance and efficient storage. File Cloning/snapshots REFLINK is a feature which introduces copy-on-write clones of files in a cluster coherent way. Indexed Directories Allows efficient access to millions of objects in a directory. Metadata Checksums Detects silent corruption in inodes and directories. Extended Attributes Supports attaching an unlimited number of name:value pairs to the file system objects like regular files, directories, symbolic links, etc. Advanced Security Supports POSIX ACLs and SELinux in addition to the traditional file access permission model. Quotas Supports user and group quotas. Journaling Supports both ordered and writeback data journaling modes to provide file system consistency in the event of power failure or system crash. Endian and Architecture neutral Supports a cluster of nodes with mixed architectures. Allows concurrent mounts on nodes running 32-bit and 64-bit, little-endian (x86, x86_64, ia64) and big-endian (ppc64) architectures. In-built Cluster-stack with DLM Includes an easy to configure, in-kernel cluster-stack with a distributed lock manager. Buffered, Direct, Asynchronous, Splice and Memory Mapped I/Os Supports all modes of I/Os for maximum flexibility and performance. Comprehensive Tools Support Provides a familiar EXT3-style tool-set that uses similar parameters for ease-of-use. The filesystem was distributed for Linux distributions in separate RPM form and this had to be built for every single kernel errata release or every updated kernel provided by the vendor. We provided builds from Oracle for Oracle Linux and all kernels released by Oracle and for Red Hat Enterprise Linux. SuSE provided the modules directly for every kernel they shipped. With the introduction of the Unbreakable Enterprise Kernel for Oracle Linux and our interest in reducing the overhead of building filesystem modules for every minor release, we decide to make OCFS2 available as part of UEK. There was no more need for separate kernel modules, everything was built-in and a kernel upgrade automatically updated the filesystem, as it should. UEK allowed us to not having to backport new upstream filesystem code into an older kernel version, backporting features into older versions introduces risk and requires extra testing because the code is basically partially rewritten. The UEK model works really well for continuing to provide OCFS2 without that extra overhead. Because the RHEL kernel did not contain OCFS2 as a kernel module (it is in the source tree but it is not built by the vendor in kernel module form) we stopped adding the extra packages to Oracle Linux and its RHEL compatible kernel and for RHEL. Oracle Linux customers/users obviously get OCFS2 included as part of the Unbreakable Enterprise Kernel, SuSE customers get it by SuSE distributed with SLES and Red Hat can decide to distribute OCFS2 to their customers if they chose to as it's just a matter of compiling the module and making it available. OCFS2 today, in the mainline kernel is pretty much feature complete in terms of integration with every filesystem feature Linux offers and it is still actively maintained with Joel Becker being the primary maintainer. Since we use OCFS2 as part of Oracle VM, we continue to look at interesting new functionality to add, REFLINK was a good example, and as such we continue to enhance the filesystem where it makes sense. Bugfixes and any sort of code that goes into the mainline Linux kernel that affects filesystems, automatically also modifies OCFS2 so it's in kernel, actively maintained but not a lot of new development happening at this time. We continue to fully support OCFS2 as part of Oracle Linux and the Unbreakable Enterprise Kernel and other vendors make their own decisions on support as it's really a Linux cluster filesystem now more than something that we provide to customers. It really just is part of Linux like EXT3 or BTRFS etc, the OS distribution vendors decide. Do not confuse OCFS2 with ACFS (ASM cluster Filesystem) also known as Oracle Cloud Filesystem. ACFS is a filesystem that's provided by Oracle on various OS platforms and really integrates into Oracle ASM (Automatic Storage Management). It's a very powerful Cluster Filesystem but it's not distributed as part of the Operating System, it's distributed with the Oracle Database product and installs with and lives inside Oracle ASM. ACFS obviously is fully supported on Linux (Oracle Linux, Red Hat Enterprise Linux) but OCFS2 independently as a native Linux filesystem is also, and continues to also be supported. ACFS is very much tied into the Oracle RDBMS, OCFS2 is just a standard native Linux filesystem with no ties into Oracle products. Customers running the Oracle database and ASM really should consider using ACFS as it also provides storage/clustered volume management. Customers wanting to use a simple, easy to use generic Linux cluster filesystem should consider using OCFS2. To learn more about OCFS2 in detail, you can find good documentation on http://oss.oracle.com/projects/ocfs2 in the Documentation area, or get the latest mainline kernel from http://kernel.org and read the source. One final, unrelated note - since I am not always able to publicly answer or respond to comments, I do not want to selectively publish comments from readers. Sometimes I forget to publish comments, sometime I publish them and sometimes I would publish them but if for some reason I cannot publicly comment on them, it becomes a very one-sided stream. So for now I am going to not publish comments from anyone, to be fair to all sides. You are always welcome to email me and I will do my best to respond to technical questions, questions about strategy or direction are sometimes not possible to answer for obvious reasons.

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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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• Service Broker, not ETL

  - by jamiet

  I have been very quiet on this blog of late and one reason for that is I have been very busy on a client project that I would like to talk about a little here. The client that I have been working for has a website that runs on a distributed architecture utilising a messaging infrastructure for communication between different endpoints. My brief was to build a system that could consume these messages and produce analytical information in near-real-time. More specifically I basically had to deliver a data warehouse however it was the real-time aspect of the project that really intrigued me. This real-time requirement meant that using an Extract transformation, Load (ETL) tool was out of the question and so I had no choice but to write T-SQL code (i.e. stored-procedures) to process the incoming messages and load the data into the data warehouse. This concerned me though – I had no way to control the rate at which data would arrive into the system yet we were going to have end-users querying the system at the same time that those messages were arriving; the potential for contention in such a scenario was pretty high and and was something I wanted to minimise as much as possible. Moreover I did not want the processing of data inside the data warehouse to have any impact on the customer-facing website. As you have probably guessed from the title of this blog post this is where Service Broker stepped in! For those that have not heard of it Service Broker is a queuing technology that has been built into SQL Server since SQL Server 2005. It provides a number of features however the one that was of interest to me was the fact that it facilitates asynchronous data processing which, in layman’s terms, means the ability to process some data without requiring the system that supplied the data having to wait for the response. That was a crucial feature because on this project the customer-facing website (in effect an OLTP system) would be calling one of our stored procedures with each message – we did not want to cause the OLTP system to wait on us every time we processed one of those messages. This asynchronous nature also helps to alleviate the contention problem because the asynchronous processing activity is handled just like any other task in the database engine and hence can wait on another task (such as an end-user query). Service Broker it was then! The stored procedure called by the OLTP system would simply put the message onto a queue and we would use a feature called activation to pick each message off the queue in turn and process it into the warehouse. At the time of writing the system is not yet up to full capacity but so far everything seems to be working OK (touch wood) and crucially our users are seeing data in near-real-time. By near-real-time I am talking about latencies of a few minutes at most and to someone like me who is used to building systems that have overnight latencies that is a huge step forward! So then, am I advocating that you all go out and dump your ETL tools? Of course not, no! What this project has taught me though is that in certain scenarios there may be better ways to implement a data warehouse system then the traditional “load data in overnight” approach that we are all used to. Moreover I have really enjoyed getting to grips with a new technology and even if you don’t want to use Service Broker you might want to consider asynchronous messaging architectures for your BI/data warehousing solutions in the future. This has been a very high level overview of my use of Service Broker and I have deliberately left out much of the minutiae of what has been a very challenging implementation. Nonetheless I hope I have caused you to reflect upon your own approaches to BI and question whether other approaches may be more tenable. All comments and questions gratefully received! Lastly, if you have never used Service Broker before and want to kick the tyres I have provided below a very simple “Service Broker Hello World” script that will create all of the objects required to facilitate Service Broker communications and then send the message “Hello World” from one place to anther! This doesn’t represent a “proper” implementation per se because it doesn’t close down down conversation objects (which you should always do in a real-world scenario) but its enough to demonstrate the capabilities! @Jamiet ----------------------------------------------------------------------------------------------- /*This is a basic Service Broker Hello World app. Have fun! -Jamie */ USE MASTER GO CREATE DATABASE SBTest GO --Turn Service Broker on! ALTER DATABASE SBTest SET ENABLE_BROKER GO USE SBTest GO -- 1) we need to create a message type. Note that our message type is -- very simple and allowed any type of content CREATE MESSAGE TYPE HelloMessage VALIDATION = NONE GO -- 2) Once the message type has been created, we need to create a contract -- that specifies who can send what types of messages CREATE CONTRACT HelloContract (HelloMessage SENT BY INITIATOR) GO --We can query the metadata of the objects we just created SELECT * FROM   sys.service_message_types WHERE name = 'HelloMessage'; SELECT * FROM   sys.service_contracts WHERE name = 'HelloContract'; SELECT * FROM   sys.service_contract_message_usages WHERE  service_contract_id IN (SELECT service_contract_id FROM sys.service_contracts WHERE name = 'HelloContract') AND        message_type_id IN (SELECT message_type_id FROM sys.service_message_types WHERE name = 'HelloMessage'); -- 3) The communication is between two endpoints. Thus, we need two queues to -- hold messages CREATE QUEUE SenderQueue CREATE QUEUE ReceiverQueue GO --more querying metatda SELECT * FROM sys.service_queues WHERE name IN ('SenderQueue','ReceiverQueue'); --we can also select from the queues as if they were tables SELECT * FROM SenderQueue   SELECT * FROM ReceiverQueue   -- 4) Create the required services and bind them to be above created queues CREATE SERVICE Sender   ON QUEUE SenderQueue CREATE SERVICE Receiver   ON QUEUE ReceiverQueue (HelloContract) GO --more querying metadata SELECT * FROM sys.services WHERE name IN ('Receiver','Sender'); -- 5) At this point, we can begin the conversation between the two services by -- sending messages DECLARE @conversationHandle UNIQUEIDENTIFIER DECLARE @message NVARCHAR(100) BEGIN   BEGIN TRANSACTION;   BEGIN DIALOG @conversationHandle         FROM SERVICE Sender         TO SERVICE 'Receiver'         ON CONTRACT HelloContract WITH ENCRYPTION=OFF   -- Send a message on the conversation   SET @message = N'Hello, World';   SEND  ON CONVERSATION @conversationHandle         MESSAGE TYPE HelloMessage (@message)   COMMIT TRANSACTION END GO --check contents of queues SELECT * FROM SenderQueue   SELECT * FROM ReceiverQueue   GO -- Receive a message from the queue RECEIVE CONVERT(NVARCHAR(MAX), message_body) AS MESSAGE FROM ReceiverQueue GO --If no messages were received and/or you can't see anything on the queues you may wish to check the following for clues: SELECT * FROM sys.transmission_queue -- Cleanup DROP SERVICE Sender DROP SERVICE Receiver DROP QUEUE SenderQueue DROP QUEUE ReceiverQueue DROP CONTRACT HelloContract DROP MESSAGE TYPE HelloMessage GO USE MASTER GO DROP DATABASE SBTest GO

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• C# Performance Pitfall – Interop Scenarios Change the Rules

  - by Reed

  C# and .NET, overall, really do have fantastic performance in my opinion.  That being said, the performance characteristics dramatically differ from native programming, and take some relearning if you’re used to doing performance optimization in most other languages, especially C, C++, and similar.  However, there are times when revisiting tricks learned in native code play a critical role in performance optimization in C#. I recently ran across a nasty scenario that illustrated to me how dangerous following any fixed rules for optimization can be… The rules in C# when optimizing code are very different than C or C++.  Often, they’re exactly backwards.  For example, in C and C++, lifting a variable out of loops in order to avoid memory allocations often can have huge advantages.  If some function within a call graph is allocating memory dynamically, and that gets called in a loop, it can dramatically slow down a routine. This can be a tricky bottleneck to track down, even with a profiler.  Looking at the memory allocation graph is usually the key for spotting this routine, as it’s often “hidden” deep in call graph.  For example, while optimizing some of my scientific routines, I ran into a situation where I had a loop similar to: for (i=0; i<numberToProcess; ++i) { // Do some work ProcessElement(element[i]); } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This loop was at a fairly high level in the call graph, and often could take many hours to complete, depending on the input data.  As such, any performance optimization we could achieve would be greatly appreciated by our users. After a fair bit of profiling, I noticed that a couple of function calls down the call graph (inside of ProcessElement), there was some code that effectively was doing: // Allocate some data required DataStructure* data = new DataStructure(num); // Call into a subroutine that passed around and manipulated this data highly CallSubroutine(data); // Read and use some values from here double values = data->Foo; // Cleanup delete data; // ... return bar; Normally, if “DataStructure” was a simple data type, I could just allocate it on the stack.  However, it’s constructor, internally, allocated it’s own memory using new, so this wouldn’t eliminate the problem.  In this case, however, I could change the call signatures to allow the pointer to the data structure to be passed into ProcessElement and through the call graph, allowing the inner routine to reuse the same “data” memory instead of allocating.  At the highest level, my code effectively changed to something like: DataStructure* data = new DataStructure(numberToProcess); for (i=0; i<numberToProcess; ++i) { // Do some work ProcessElement(element[i], data); } delete data; Granted, this dramatically reduced the maintainability of the code, so it wasn’t something I wanted to do unless there was a significant benefit.  In this case, after profiling the new version, I found that it increased the overall performance dramatically – my main test case went from 35 minutes runtime down to 21 minutes.  This was such a significant improvement, I felt it was worth the reduction in maintainability. In C and C++, it’s generally a good idea (for performance) to: Reduce the number of memory allocations as much as possible, Use fewer, larger memory allocations instead of many smaller ones, and Allocate as high up the call stack as possible, and reuse memory I’ve seen many people try to make similar optimizations in C# code.  For good or bad, this is typically not a good idea.  The garbage collector in .NET completely changes the rules here. In C#, reallocating memory in a loop is not always a bad idea.  In this scenario, for example, I may have been much better off leaving the original code alone.  The reason for this is the garbage collector.  The GC in .NET is incredibly effective, and leaving the allocation deep inside the call stack has some huge advantages.  First and foremost, it tends to make the code more maintainable – passing around object references tends to couple the methods together more than necessary, and overall increase the complexity of the code.  This is something that should be avoided unless there is a significant reason.  Second, (unlike C and C++) memory allocation of a single object in C# is normally cheap and fast.  Finally, and most critically, there is a large advantage to having short lived objects.  If you lift a variable out of the loop and reuse the memory, its much more likely that object will get promoted to Gen1 (or worse, Gen2).  This can cause expensive compaction operations to be required, and also lead to (at least temporary) memory fragmentation as well as more costly collections later. As such, I’ve found that it’s often (though not always) faster to leave memory allocations where you’d naturally place them – deep inside of the call graph, inside of the loops.  This causes the objects to stay very short lived, which in turn increases the efficiency of the garbage collector, and can dramatically improve the overall performance of the routine as a whole. In C#, I tend to: Keep variable declarations in the tightest scope possible Declare and allocate objects at usage While this tends to cause some of the same goals (reducing unnecessary allocations, etc), the goal here is a bit different – it’s about keeping the objects rooted for as little time as possible in order to (attempt) to keep them completely in Gen0, or worst case, Gen1.  It also has the huge advantage of keeping the code very maintainable – objects are used and “released” as soon as possible, which keeps the code very clean.  It does, however, often have the side effect of causing more allocations to occur, but keeping the objects rooted for a much shorter time. Now – nowhere here am I suggesting that these rules are hard, fast rules that are always true.  That being said, my time spent optimizing over the years encourages me to naturally write code that follows the above guidelines, then profile and adjust as necessary.  In my current project, however, I ran across one of those nasty little pitfalls that’s something to keep in mind – interop changes the rules. In this case, I was dealing with an API that, internally, used some COM objects.  In this case, these COM objects were leading to native allocations (most likely C++) occurring in a loop deep in my call graph.  Even though I was writing nice, clean managed code, the normal managed code rules for performance no longer apply.  After profiling to find the bottleneck in my code, I realized that my inner loop, a innocuous looking block of C# code, was effectively causing a set of native memory allocations in every iteration.  This required going back to a “native programming” mindset for optimization.  Lifting these variables and reusing them took a 1:10 routine down to 0:20 – again, a very worthwhile improvement. Overall, the lessons here are: Always profile if you suspect a performance problem – don’t assume any rule is correct, or any code is efficient just because it looks like it should be Remember to check memory allocations when profiling, not just CPU cycles Interop scenarios often cause managed code to act very differently than “normal” managed code. Native code can be hidden very cleverly inside of managed wrappers

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• Oracle Flashback Technologies - Overview

  - by Sridhar_R-Oracle

  Oracle Flashback Technologies - IntroductionIn his May 29th 2014 blog, my colleague Joe Meeks introduced Oracle Maximum Availability Architecture (MAA) and discussed both planned and unplanned outages. Let’s take a closer look at unplanned outages. These can be caused by physical failures (e.g., server, storage, network, file deletion, physical corruption, site failures) or by logical failures – cases where all components and files are physically available, but data is incorrect or corrupt. These logical failures are usually caused by human errors or application logic errors. This blog series focuses on these logical errors – what causes them and how to address and recover from them using Oracle Database Flashback. In this introductory blog post, I’ll provide an overview of the Oracle Database Flashback technologies and will discuss the features in detail in future blog posts. Let’s get started. We are all human beings (unless a machine is reading this), and making mistakes is a part of what we do…often what we do best!  We “fat finger”, we spill drinks on keyboards, unplug the wrong cables, etc.  In addition, many of us, in our lives as DBAs or developers, must have observed, caused, or corrected one or more of the following unpleasant events: Accidentally updated a table with wrong values !! Performed a batch update that went wrong - due to logical errors in the code !! Dropped a table !! How do DBAs typically recover from these types of errors? First, data needs to be restored and recovered to the point-in-time when the error occurred (incomplete or point-in-time recovery).  Moreover, depending on the type of fault, it’s possible that some services – or even the entire database – would have to be taken down during the recovery process.Apart from error conditions, there are other questions that need to be addressed as part of the investigation. For example, what did the data look like in the morning, prior to the error? What were the various changes to the row(s) between two timestamps? Who performed the transaction and how can it be reversed?  Oracle Database includes built-in Flashback technologies, with features that address these challenges and questions, and enable you to perform faster, easier, and convenient recovery from logical corruptions. HistoryFlashback Query, the first Flashback Technology, was introduced in Oracle 9i. It provides a simple, powerful and completely non-disruptive mechanism for data verification and recovery from logical errors, and enables users to view the state of data at a previous point in time.Flashback Technologies were further enhanced in Oracle 10g, to provide fast, easy recovery at the database, table, row, and even at a transaction level.Oracle Database 11g introduced an innovative method to manage and query long-term historical data with Flashback Data Archive. The 11g release also introduced Flashback Transaction, which provides an easy, one-step operation to back out a transaction. Oracle Database versions 11.2.0.2 and beyond further enhanced the performance of these features. Note that all the features listed here work without requiring any kind of restore operation.In addition, Flashback features are fully supported with the new multi-tenant capabilities introduced with Oracle Database 12c, Flashback Features Oracle Flashback Database enables point-in-time-recovery of the entire database without requiring a traditional restore and recovery operation. It rewinds the entire database to a specified point in time in the past by undoing all the changes that were made since that time.Oracle Flashback Table enables an entire table or a set of tables to be recovered to a point in time in the past.Oracle Flashback Drop enables accidentally dropped tables and all dependent objects to be restored.Oracle Flashback Query enables data to be viewed at a point-in-time in the past. This feature can be used to view and reconstruct data that was lost due to unintentional change(s) or deletion(s). This feature can also be used to build self-service error correction into applications, empowering end-users to undo and correct their errors.Oracle Flashback Version Query offers the ability to query the historical changes to data between two points in time or system change numbers (SCN) Oracle Flashback Transaction Query enables changes to be examined at the transaction level. This capability can be used to diagnose problems, perform analysis, audit transactions, and even revert the transaction by undoing SQLOracle Flashback Transaction is a procedure used to back-out a transaction and its dependent transactions.Flashback technologies eliminate the need for a traditional restore and recovery process to fix logical corruptions or make enquiries. Using these technologies, you can recover from the error in the same amount of time it took to generate the error. All the Flashback features can be accessed either via SQL command line (or) via Enterprise Manager.  Most of the Flashback technologies depend on the available UNDO to retrieve older data. The following table describes the various Flashback technologies: their purpose, dependencies and situations where each individual technology can be used.   Example Syntax Error investigation related:The purpose is to investigate what went wrong and what the values were at certain points in timeFlashback Queries  ( select .. as of SCN | Timestamp )   - Helps to see the value of a row/set of rows at a point in timeFlashback Version Queries  ( select .. versions between SCN | Timestamp and SCN | Timestamp)  - Helps determine how the value evolved between certain SCNs or between timestamps Flashback Transaction Queries (select .. XID=)   - Helps to understand how the transaction caused the changes.Error correction related:The purpose is to fix the error and correct the problems,Flashback Table  (flashback table .. to SCN | Timestamp)  - To rewind the table to a particular timestamp or SCN to reverse unwanted updates Flashback Drop (flashback table ..  to before drop )  - To undrop or undelete a table Flashback Database (flashback database to SCN  | Restore Point )  - This is the rewind button for Oracle databases. You can revert the entire database to a particular point in time. It is a fast way to perform a PITR (point-in-time recovery). Flashback Transaction (DBMS_FLASHBACK.TRANSACTION_BACKOUT(XID..))  - To reverse a transaction and its related transactions Advanced use cases Flashback technology is integrated into Oracle Recovery Manager (RMAN) and Oracle Data Guard. So, apart from the basic use cases mentioned above, the following use cases are addressed using Oracle Flashback. Block Media recovery by RMAN - to perform block level recovery Snapshot Standby - where the standby is temporarily converted to a read/write environment for testing, backup, or migration purposes Re-instate old primary in a Data Guard environment – this avoids the need to restore an old backup and perform a recovery to make it a new standby. Guaranteed Restore Points - to bring back the entire database to an older point-in-time in a guaranteed way. and so on..I hope this introductory overview helps you understand how Flashback features can be used to investigate and recover from logical errors.  As mentioned earlier, I will take a deeper-dive into to some of the critical Flashback features in my upcoming blogs and address common use cases.

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• SQL SERVER – Create a Very First Report with the Report Wizard

  - by Pinal Dave

  This example is from the Beginning SSRS by Kathi Kellenberger. Supporting files are available with a free download from the www.Joes2Pros.com web site. What is the report Wizard? In today’s world automation is all around you. Henry Ford began building his Model T automobiles on a moving assembly line a century ago and changed the world. The moving assembly line allowed Ford to build identical cars quickly and cheaply. Henry Ford said in his autobiography “Any customer can have a car painted any color that he wants so long as it is black.” Today you can buy a car straight from the factory with your choice of several colors and with many options like back up cameras, built-in navigation systems and heated leather seats. The assembly lines now use robots to perform some tasks along with human workers. When you order your new car, if you want something special, not offered by the manufacturer, you will have to find a way to add it later. In computer software, we also have “assembly lines” called wizards. A wizard will ask you a series of questions, often branching to specific questions based on earlier answers, until you get to the end of the wizard. These wizards are used for many things, from something simple like setting up a rule in Outlook to performing administrative tasks on a server. Often, a wizard will get you part of the way to the end result, enough to get much of the tedious work out of the way. Once you get the product from the wizard, if the wizard is not capable of doing something you need, you can tweak the results. Create a Report with the Report Wizard Let’s get started with your first report!  Launch SQL Server Data Tools (SSDT) from the Start menu under SQL Server 2012. Once SSDT is running, click New Project to launch the New Project dialog box. On the left side of the screen expand Business Intelligence and select Reporting Services. Configure the properties as shown in . Be sure to select Report Server Project Wizard as the type of report and to save the project in the C:\Joes2Pros\SSRSCompanionFiles\Chapter3\Project folder. Click OK and wait for the Report Wizard to launch. Click Next on the Welcome screen.  On the Select the Data Source screen, make sure that New data source is selected. Type JProCo as the data source name. Make sure that Microsoft SQL Server is selected in the Type dropdown. Click Edit to configure the connection string on the Connection Properties dialog box. If your SQL Server database server is installed on your local computer, type in localhost for the Server name and select the JProCo database from the Select or enter a database name dropdown. Click OK to dismiss the Connection Properties dialog box. Check Make this a shared data source and click Next. On the Design the Query screen, you can use the query builder to build a query if you wish. Since this post is not meant to teach you T-SQL queries, you will copy all queries from files that have been provided for you. In the C:\Joes2Pros\SSRSCompanionFiles\Chapter3\Resources folder open the sales by employee.sql file. Copy and paste the code from the file into the Query string Text Box. Click Next. On the Select the Report Type screen, choose Tabular and click Next. On the Design the Table screen, you have to figure out the groupings of the report. How do you do this? Well, you often need to know a bit about the data and report requirements. I often draw the report out on paper first to help me determine the groups. In the case of this report, I could group the data several ways. Do I want to see the data grouped by Year and Month? Do I want to see the data grouped by Employee or Category? The only thing I know for sure about this ahead of time is that the TotalSales goes in the Details section. Let’s assume that the CIO asked to see the data grouped first by Year and Month, then by Category. Let’s move the fields to the right-hand side. This is done by selecting Page > Group or Details >, as shown in, and click Next. On the Choose the Table Layout screen, select Stepped and check Include subtotals and Enable drilldown, as shown in. On the Choose the Style screen, choose any color scheme you wish (unlike the Model T) and click Next. I chose the default, Slate. On the Choose the Deployment Location screen, change the Deployment folder to Chapter 3 and click Next. At the Completing the Wizard screen, name your report Employee Sales and click Finish. After clicking Finish, the report and a shared data source will appear in the Solution Explorer and the report will also be visible in Design view. Click the Preview tab at the top. This report expects the user to supply a year which the report will then use as a filter. Type in a year between 2006 and 2013 and click View Report. Click the plus sign next to the Sales Year to expand the report to see the months, then expand again to see the categories and finally the details. You now have the assembly line report completed, and you probably already have some ideas on how to improve the report. Tomorrow’s Post Tomorrow’s blog post will show how to create your own data sources and data sets in SSRS. If you want to learn SSRS in easy to simple words – I strongly recommend you to get Beginning SSRS book from Joes 2 Pros. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: Reporting Services, SSRS

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• Twitter ?? Nashorn ????(??)

  - by Homma

  ???? Nashorn ? Java ??????? Twitter ???????????????????? JavaFX ??????????????? ????? ??? jlaskey ??? Nashorn Blog ????????????? https://blogs.oracle.com/nashorn/entry/nashorn_in_the_twitterverse_continued ???????? ?? Twitter ???????????????????????? JavaFX ??????????????????????????????? Nashorn ?? JavaFX ??????????????JavaFX ???????????????????????????????????????Nashorn ? Java ????????????????????????????????????(JavaFX ?????????????????????)? ?????????????????????????????????????????????? Twitter ????????????????????????? var twitter4j = Packages.twitter4j; var TwitterFactory = twitter4j.TwitterFactory; var Query = twitter4j.Query; function getTrendingData() { var twitter = new TwitterFactory().instance; var query = new Query("nashorn OR nashornjs"); query.since("2012-11-21"); query.count = 100; var data = {}; do { var result = twitter.search(query); var tweets = result.tweets; for each (var tweet in tweets) { var date = tweet.createdAt; var key = (1900 + date.year) + "/" + (1 + date.month) + "/" + date.date; data[key] = (data[key] || 0) + 1; } } while (query = result.nextQuery()); return data; } ??????????????????getTrendingData() ??????????????(??????????Nashorn ???????? OpenJDK ?????? 2012 ? 11 ? 21 ???)??????????????????????????????????? ????JavaFX ? BarChart ??????????? var javafx = Packages.javafx; var Stage = javafx.stage.Stage var Scene = javafx.scene.Scene; var Group = javafx.scene.Group; var Chart = javafx.scene.chart.Chart; var FXCollections = javafx.collections.FXCollections; var ObservableList = javafx.collections.ObservableList; var CategoryAxis = javafx.scene.chart.CategoryAxis; var NumberAxis = javafx.scene.chart.NumberAxis; var BarChart = javafx.scene.chart.BarChart; var XYChart = javafx.scene.chart.XYChart; var Series = javafx.scene.chart.XYChart.Series; var Data = javafx.scene.chart.XYChart.Data; function graph(stage, data) { var root = new Group(); stage.scene = new Scene(root); var dates = Object.keys(data); var xAxis = new CategoryAxis(); xAxis.categories = FXCollections.observableArrayList(dates); var yAxis = new NumberAxis("Tweets", 0.0, 200.0, 50.0); var series = FXCollections.observableArrayList(); for (var date in data) { series.add(new Data(date, data[date])); } var tweets = new Series("Tweets", series); var barChartData = FXCollections.observableArrayList(tweets); var chart = new BarChart(xAxis, yAxis, barChartData, 25.0); root.children.add(chart); } ????????????????????????????????stage.scene = new Scene(root) ? stage.setScene(new Scene(root)) ????????????????????Nashorn ? stage ??????? scene ???????????????????(Dynalink ?????????)Java Beans ???????????????? (setScene()) ???????????????????????????????Nashorn ? FXCollections ??????????????????????????????observableArrayList(dates) ??????????Nashorn ? JavaScript ??? (dates) ? Java ???????????????????????????? JavaScript ?????????????????? Java ????????????????????????????????????????????????????????????? ????????????????????????????????? JavaFX ???????????????????????? JavaFX ??????????????javafx.application.Application ??????????????????????????? JavaFX ????????????????????????????????????????????????? import java.io.IOException; import java.io.InputStream; import java.io.InputStreamReader; import javafx.application.Application; import javafx.stage.Stage; import javax.script.ScriptEngine; import javax.script.ScriptEngineManager; import javax.script.ScriptException; public class TrendingMain extends Application { private static final ScriptEngineManager MANAGER = new ScriptEngineManager(); private final ScriptEngine engine = MANAGER.getEngineByName("nashorn"); private Trending trending; public static void main(String[] args) { launch(args); } @Override public void start(Stage stage) throws Exception { trending = (Trending) load("Trending.js"); trending.start(stage); } @Override public void stop() throws Exception { trending.stop(); } private Object load(String script) throws IOException, ScriptException { try (final InputStream is = TrendingMain.class.getResourceAsStream(script)) { return engine.eval(new InputStreamReader(is, "utf-8")); } } } ???? Nashorn ??????? JSR-223 ? javax.script ????????? private static final ScriptEngineManager MANAGER = new ScriptEngineManager(); private final ScriptEngine engine = MANAGER.getEngineByName("nashorn"); ????????? JavaScript ???????? Nashorn ???????????????????? load ???????????????????????engine ???????????????load ????????????? ???????????????Java ???????????????????????????????????????????????????? Java ????????????????JavaFX ???????? start ????? stop ?????????????????????????????????????? public interface Trending { public void start(Stage stage) throws Exception; public void stop() throws Exception; } ?????????????????????????????? function newTrending() { return new Packages.Trending() { start: function(stage) { var data = getTrendingData(); graph(stage, data); stage.show(); }, stop: function() { } } } newTrending(); ?????? Trending ?????????????????????start ????? stop ??????????????????????????????????? eval ???? Java ??????????????? trending = (Trending) load("Trending.js"); ????????????????Trending.js ??????? getTrendingData ???????????? newTrending ????????????????????? Java ?????????newTrending ????????? eval ????????? Trending ????????????????????????????????????????????????????????? trending.start(stage); ???????? ???? Nashorn ????????? http://www.myexpospace.com/JavaOne2012/SessionFiles/CON5251_PDF_5251_0001.pdf ???????? Dynalink ??????? https://github.com/szegedi/dynalink ????????

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• Gnome 3 gdm fails to start after preupgrade from fedora 14 to 15

  - by digital illusion

  I'm not able to boot fedora 15 in runlevel 5. After all services start, when the login screen should appear, gdm just show a mouse waiting cursor and keeps restarting itself. From /var/log/gdm/\:0-greeter.log Gtk-Message: Failed to load module "pk-gtk-module" /usr/bin/gnome-session: symbol lookup error: /usr/lib/gtk-3.0/modules/libatk-bridge.so: undefined symbol: atk_plug_get_type /usr/libexec/gnome-setting-daemon: symbol lookup error: /usr/lib/gtk-3.0modules/libatk-bridge.so: undefined symbol: atk_plug_get_type Where should atk_plug_get_type be defined? Edit: Here a better description of the error (system-config-network-gui:2643): Gnome-WARNING **: Accessibility: failed to find module 'libgail-gnome' which is needed to make this application accessible /usr/bin/python: symbol lookup error: /usr/lib/gtk-2.0/modules/libatk-bridge.so: undefined symbol: atk_plug_get_type Why there are still references to gtk2? Did preupgrade fail? Attaching upgrade log... it seems gdm was not added, but it is present in the users and groups list. May 26 11:25:52 sysimage sendmail[1076]: alias database /etc/aliases rebuilt by root May 26 11:25:52 sysimage sendmail[1076]: /etc/aliases: 77 aliases, longest 23 bytes, 795 bytes total May 26 11:46:09 sysimage useradd[1793]: failed adding user 'dbus', data deleted May 26 11:53:37 sysimage systemd-machine-id-setup[2443]: Initializing machine ID from D-Bus machine ID. May 26 11:55:28 sysimage useradd[2835]: failed adding user 'apache', data deleted May 26 11:55:38 sysimage useradd[2842]: failed adding user 'haldaemon', data deleted May 26 11:55:43 sysimage useradd[2848]: failed adding user 'smolt', data deleted May 26 11:57:32 sysimage sendmail[3032]: alias database /etc/aliases rebuilt by root May 26 11:57:32 sysimage sendmail[3032]: /etc/aliases: 77 aliases, longest 23 bytes, 795 bytes total May 26 11:57:46 sysimage groupadd[3066]: group added to /etc/group: name=cgred, GID=482 May 26 11:57:47 sysimage groupadd[3066]: group added to /etc/gshadow: name=cgred May 26 11:57:47 sysimage groupadd[3066]: new group: name=cgred, GID=482 May 26 11:58:42 sysimage useradd[3086]: failed adding user 'ntp', data deleted May 26 12:00:13 sysimage dbus: avc: received policyload notice (seqno=2) May 26 12:15:08 sysimage useradd[4950]: failed adding user 'gdm', data deleted May 26 12:24:39 sysimage dbus: avc: received policyload notice (seqno=3) May 26 12:25:24 sysimage useradd[5522]: failed adding user 'mysql', data deleted May 26 12:25:37 sysimage useradd[5533]: failed adding user 'rpcuser', data deleted May 26 12:26:31 sysimage useradd[5592]: failed adding user 'tcpdump', data deleted Any suggestions before I revert installation to F14?

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• Tomcat6 getting crashed at regular intervals installed in Ubuntu

  - by Milesh Rout

  I have installed Tomcat6 in Ubuntu OS and when I run my web application the server gets crashed at regular intervals. I have tried a lot but not getting the solution. I have increased the memory upto 2048mb but still getting such error. Following is the error I am getting. Any help would be really appreciated. org.apache.tomcat.util.http.Parameters processParametersINFO: Invalid chunk starting at byte [312] and ending at byte [312] with a value of [null] ignoredException in thread "Timer-1" Exception in thread "com.mchange.v2.async.ThreadPoolAsynchronousRunner$PoolThread-#0" Exception in thread "com.mchange.v2.async.ThreadPoolAsynchronousRunner$PoolThread-#2" Exception in thread "com.mchange.v2.async.ThreadPoolAsynchronousRunner$PoolThread-#1" Exception in thread "Timer-2" Exception in thread "http-8080-4" Exception in thread "http-8080-8" Exception in thread "http-8080-17" Exception in thread "org.hibernate.cache.StandardQueryCache.data" Exception in thread "org.hibernate.cache.UpdateTimestampsCache.data" Exception in thread "org.hibernate.cache.StandardQueryCache.data" Exception in thread "org.hibernate.cache.StandardQueryCache.data" Exception in thread "org.hibernate.cache.UpdateTimestampsCache.data" Exception in thread "org.hibernate.cache.StandardQueryCache.data" Exception in thread "org.hibernate.cache.StandardQueryCache.data" Exception in thread "org.hibernate.cache.UpdateTimestampsCache.data" Exception in thread "com.safenet.usermgmt.User.data" Exception in thread "http-8080-7" Exception in thread "http-8080-12" Exception in thread "http-8080-16" Exception in thread "http-8080-14" Exception in thread "http-8080-13" Exception in thread "http-8080-15" Exception in thread "http-8080-6" OpenJDK Client VM warning: Exception java.lang.OutOfMemoryError occurred dispatching signal SIGTERM to handler- the VM may need to be forcibly terminated

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• Slowdown upon router/modem setup change

  - by Ollie Saunders

  I’ve been using a Belkin FSD7632-4 modem router to connect to my TalkTalk provided ADSL internet connection for some time and been pretty happy with it. Recently, however, the connection has been failing and I decided to get a ASUS RT-N16 instead, which is also a much more capable router generally. The ASUS RT-N16 doesn’t come with a modem built-in so I purchased as Zoom modem as well. I’ve set them both up and am using them to post this message. But I’m a bit miffed to find that I get a significantly and consistently slower downstream rate from the new configuration than with the old Belkin. Belkin modem router: downstream: 3.45 mbps upstream: 0.73 mbps ASUS router + Zoom modem: downstream: 2.71 mbps upstream: 0.66 mbps Any ideas why this is? The really weird thing about this is that the Zoom supports ADSL2 and ADSL2+ but I don’t think the old Belkin does. At first I thought it might be due to the Zoom modem being limited to PPPoE instead of PPPoA, which my ISP supports, but then I tried using PPPoE with the Belkin and that still gave a high speed. I’m using VC-Mux encapsulation with both. VPI of 0 and VCI of 38. I pulled this data off the Zoom: Mode: ADSL2 Line Coding: Trellis On Status: No Defect Link Power State: L0 Downstream Upstream SNR Margin (dB): 12.3 11.8 Attenuation (dB): 43.0 24.9 Output Power (dBm): 12.9 0.0 Attainable Rate (Kbps): 3936 844 Rate (Kbps): 3194 840 MSGc (number of bytes in overhead channel message): 59 10 B (number of bytes in Mux Data Frame): 99 14 M (number of Mux Data Frames in FEC Data Frame): 2 16 T (Mux Data Frames over sync bytes): 1 8 R (number of check bytes in FEC Data Frame): 8 8 S (ratio of FEC over PMD Data Frame length): 1.9833 9.0594 L (number of bits in PMD Data Frame): 839 219 D (interleaver depth): 32 2 Delay (msec): 15 4 Super Frames: 15808 14078 Super Frame Errors: 0 4294967232 RS Words: 513778 111753 RS Correctable Errors: 126 4294967238 RS Uncorrectable Errors: 0 N/A HEC Errors: 0 4294967279 OCD Errors: 0 0 LCD Errors: 0 0 Total Cells: 1920175 237597 Data Cells: 205993 392 Bit Errors: 0 0 Total ES: 0 0 Total SES: 0 0 Total UAS: 34 0

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• Using Truecrypt to secure mySQL database, any pitfalls?

  - by Saul

  The objective is to secure my database data from server theft, i.e. the server is at a business office location with normal premises lock and burglar alarm, but because the data is personal healthcare data I want to ensure that if the server was stolen the data would be unavailable as encrypted. I'm exploring installing mySQL on a mounted Truecrypt encrypted volume. It all works fine, and when I power off, or just cruelly pull the plug the encrypted drive disappears. This seems a load easier than encrypting data to the database, and I understand that if there is a security hole in the web app , or a user gets physical access to a plugged in server the data is compromised, but as a sanity check , is there any good reason not to do this? @James I'm thinking in a theft scenario, its not going to be powered down nicely and so is likely to crash any DB transactions running. But then if someone steals the server I'm going to need to rely on my off site backup anyway. @tomjedrz, its kind of all sensitive, individual personal and address details linked to medical referrals/records. Would be as bad in our field as losing credit card data, but means that almost everything in the database would need encryption... so figured better to run the whole DB in an encrypted partition. If encrypt data in the tables there's got to be a key somewhere on the server I'm presuming, which seems more of a risk if the box walks. At the moment the app is configured to drop a dump of data (weekly full and then deltas only hourly using rdiff) into a directory also on the Truecrypt disk. I have an off site box running WS_FTP Pro scheduled to connect by FTPs and synch down the backup, again into a Truecrypt mounted partition.

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• MySQL : table organisation for very large sets with high update frequency

  - by Remiz

  I'm facing a dilemma in the choice of my MySQL schema application. So before I start here is a picture extremely simplified of my database : Schema here : http://i43.tinypic.com/2wp5lxz.png In one sentence : for each customer, the application harvest text data and attached tags to each data collected. As approximation of the usage of each table, here is what I expect : customer : ~5000, shouldn't grow fast data : 5 millions per customer, could double or triple for big customers. tag : ~1000, quite fixed size data_tag : hundred of millions per customer easily. Each data can be tagged a lot. The harvesting process is permanent, that means that around every 15 minutes new data come and are tagged, that require a very constant index refreshing. A lot of my queries are a SELECT COUNT of DATA between specific DATES and tagged with a specific TAG on a specific CUSTOMER (very rarely it will involve several customers). Here is the situation, you can imagine with this kind of volume of data I'm facing a challenge in term of data organization and indexing. Again, it's a very minimalistic and simplified version of my structure. My question is, is it better: to stick with this model and to manage crazy index optimization ? (which involves potentially having billions of rows in the data_tag table) change the schema and use one data table and one data_tag table per customer ? (which involves having 5000 tables on my database) I'm running all of this on a MySQL 5.0 dedicated server (quad-core, 8Go of ram) replicated. I only use InnoDB, I also have another server that run Sphinx. So knowing all of this, I can't wait to hear your opinion about this. Thanks.

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• Does image block (firefox addon) save internet bandwidth usage?

  - by dkjain

  Does image block save internet bandwidth usage. I have a data capped plan from my ISP ( 5GB at 2mbps and thereafter 256 kpbs / pm). I doubt if the addon or other similar addon actually saves bandwidht. Here is my point of view, pls correct if that is wrong. When a request is sent to the server, the server sends out whatever page it's requested to serve with all its text and images etc. So essentially my ISP has made his pipe available for the data to reach me thus he would count those bytes under my data plan. When the data arrives it's all first stored to my browser cache (folder) area which means all the data has actually been received by me/computer using my ISP's pipe. The browser then fetches those data from the cache and displays it. By hitting the stop button or blocking images via ur addon I am just choosing not to display the data which would remain in the cache or eventually be discarded if still on the network pipe after a timeout limit. The point is the data request have been completed by the ISP and so the data would be metered and thus using addon such as image block or hitting stop button while page is loading does not in any way save internet bandwidth. Your comments plz....... Regards dk.

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• Are my web server permissions for uploading correct?

  - by user1699176

  I'm on debian and I have my website in the directory /srv/www/mysite.com/public_html I set chown for www-data:www-data on /srv/www. I have root disabled and created a sudo user which is id 1000:1000. I would also like to use this user to upload to /srv/www so I added my sudo user to the www-data group. I originally got a message saying that I didn't have permissions to upload a file to that directory. After playing around with multiple permissions for a while I finally was able to upload properly, but I'm not sure if this set up is correct. I'm hesitant to change it for now since it actually works, so I thought I'd ask for advice. I think what I ended up doing was this: sudo chown -R www-data:www-data /srv/www sudo chmod g+s /srv/www sudo usermod -aG www-data myuser sudo chgrp -R www-data /srv/www sudo chmod -R g+w /srv/www When I was finally able to successfully upload a file (with FileZilla) it showed the owner as myuser myuser. Shouldn't it have been www-data myuser? My question is whether this is correct and if there are any potential security issues? For example, I wasn't sure if I was actually supposed to use "myuser" to own the /srv/www directory instead sudo chown -R myuser:myuser /srv/www or maybe sudo chown -R www-data:myuser /srv/www If you need more info, let me know, thanks.

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• Knockout.js - Filtering, Sorting, and Paging

  - by jtimperley

  Originally posted on: http://geekswithblogs.net/jtimperley/archive/2013/07/28/knockout.js---filtering-sorting-and-paging.aspxKnockout.js is fantastic! Maybe I missed it but it appears to be missing flexible filtering, sorting, and pagination of its grids. This is a summary of my attempt at creating this functionality which has been working out amazingly well for my purposes. Before you continue, this post is not intended to teach you the basics of Knockout. They have already created a fantastic tutorial for this purpose. You'd be wise to review this before you continue. http://learn.knockoutjs.com/ Please view the full source code and functional example on jsFiddle. Below you will find a brief explanation of some of the components. http://jsfiddle.net/JTimperley/pyCTN/13/ First we need to create a model to represent our records. This model is a simple container with defined and guaranteed members. function CustomerModel(data) { if (!data) { data = {}; } var self = this; self.id = data.id; self.name = data.name; self.status = data.status; } Next we need a model to represent the page as a whole with an array of the previously defined records. I have intentionally overlooked the filtering and sorting options for now. Note how the filtering, sorting, and pagination are chained together to accomplish all three goals. This strategy allows each of these pieces to be used selectively based on the page's needs. If you only need sorting, just sort, etc. function CustomerPageModel(data) { if (!data) { data = {}; } var self = this; self.customers = ExtractModels(self, data.customers, CustomerModel); var filters = […]; var sortOptions = […]; self.filter = new FilterModel(filters, self.customers); self.sorter = new SorterModel(sortOptions, self.filter.filteredRecords); self.pager = new PagerModel(self.sorter.orderedRecords); } The code currently supports text box and drop down filters. Text box filters require defining the current 'Value' and the 'RecordValue' function to retrieve the filterable value from the provided record. Drop downs allow defining all possible values, the current option, and the 'RecordValue' as before. Once defining these filters, they are automatically added to the screen and any changes to their values will automatically update the results, causing their sort and pagination to be re-evaluated. var filters = [ { Type: "text", Name: "Name", Value: ko.observable(""), RecordValue: function(record) { return record.name; } }, { Type: "select", Name: "Status", Options: [ GetOption("All", "All", null), GetOption("New", "New", true), GetOption("Recently Modified", "Recently Modified", false) ], CurrentOption: ko.observable(), RecordValue: function(record) { return record.status; } } ]; Sort options are more simplistic and are also automatically added to the screen. Simply provide each option's name and value for the sort drop down as well as function to allow defining how the records are compared. This mechanism can easily be adapted for using table headers as the sort triggers. That strategy hasn't crossed my functionality needs at this point. var sortOptions = [ { Name: "Name", Value: "Name", Sort: function(left, right) { return CompareCaseInsensitive(left.name, right.name); } } ]; Paging options are completely contained by the pager model. Because we will be chaining arrays between our filtering, sorting, and pagination models, the following utility method is used to prevent errors when handing an observable array to another observable array. function GetObservableArray(array) { if (typeof(array) == 'function') { return array; }   return ko.observableArray(array); }

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• When to implement: Together with or after the source product?

  - by Jeremy Oosthuizen

  Somebody recently relayed a prospect's question to me: How hard would it be to implement OUBI after the source product (CC&B, WAM or NMS) has already been implemented? Fact is that MOST non-OUBI Data Warehouse / Business Intelligence implementations take place after the source application(s) are in place and hopefully stable. If an organization decides that they need better reporting and management information, then the logical path (see The Data Warehouse Institute's Data Warehouse Maturity Model) is to a Data Warehouse -- no matter when their last applications were implemented. If there is a pre-built Data Warehouse for their specific application, or even for the desired business process in their industry, they're in luck. Else they have to design and build from scratch, using a toolset. The implementation of a toolset is unlike the implementation of OUBI which, like OBI Apps, contain pre-built ETL routines and user content. Much has been written before about the advantages of that. So, because OUBI is designed specifically for Oracle Utilities transactional products, we often implement them in parallel -- with OUBI lagging a little behind by necessity, like Reporting. Customers know from the start they're going to need the solution, and therefore purchase the products at the same time. My biggest argument FOR a parallel installation/implementation of OUBI with the source product is two-fold: - There could be things (which is the technical term for data elements) that customers figure out they need when implementing OUBI, which are often easier added to the source product's implementation project, than to add later; - OUBI's ETL often points out errors (severe or not) with converted data, which are easier to fix during the source product's implementation project, or it may even be impossible to fix afterwards. The Conversion routines sometimes miss these errors, because the source system can live with the not-quite-perfect converted data. If the data can't be properly extracted, i.e. the proper Dimensions linked to the Facts, then it can't get into OUBI. That means it can't be analyzed effectively along with the rest of the organization's data. Then there is also the throw-away-work argument, which may be significant. The operational / transactional system cannot go live without reports on Day 1. A lot of those reports would be taken care of by the implementation of OUBI. If OUBI is implemented after go-live, those reports STILL have to be built during the source product's implementation project, but they become throw-away after the OUBI implementation. I have sometimes been told that it is better to implement OUBI after the source product, because it cuts down on scope and risk for the source product's implementation project. All I can say to that, is bah humbug. No, seriously, given the arguments above, planning has to include the OUBI implementation and it has to be managed properly -- just like any other implementation. If so, it should not add any risk and it should be included in the scope from the start. The answer to the prospect's question is therefore that it is not that much more difficult; after all, most DW/BI implemenations are done like that. They just have to consider the points above.

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• SQL SERVER – Query Hint – Contest Win Joes 2 Pros Combo (USD 198) – Day 1 of 5

  - by pinaldave

  August 2011 we ran a contest where every day we give away one book for an entire month. The contest had extreme success. Lots of people participated and lots of give away. I have received lots of questions if we are doing something similar this month. Absolutely, instead of running a contest a month long we are doing something more interesting. We are giving away USD 198 worth gift every day for this week. We are giving away Joes 2 Pros 5 Volumes (BOOK) SQL 2008 Development Certification Training Kit every day. One copy in India and One in USA. Total 2 of the giveaway (worth USD 198). All the gifts are sponsored from the Koenig Training Solution and Joes 2 Pros. The books are available here Amazon | Flipkart | Indiaplaza How to Win: Read the Question Read the Hints Answer the Quiz in Contact Form in following format Question Answer Name of the country (The contest is open for USA and India residents only) 2 Winners will be randomly selected announced on August 20th. Question of the Day: Which of the following queries will return dirty data? a) SELECT * FROM Table1 (READUNCOMMITED) b) SELECT * FROM Table1 (NOLOCK) c) SELECT * FROM Table1 (DIRTYREAD) d) SELECT * FROM Table1 (MYLOCK) Query Hints: BIG HINT POST Most SQL people know what a “Dirty Record” is. You might also call that an “Intermediate record”. In case this is new to you here is a very quick explanation. The simplest way to describe the steps of a transaction is to use an example of updating an existing record into a table. When the insert runs, SQL Server gets the data from storage, such as a hard drive, and loads it into memory and your CPU. The data in memory is changed and then saved to the storage device. Finally, a message is sent confirming the rows that were affected. For a very short period of time the update takes the data and puts it into memory (an intermediate state), not a permanent state. For every data change to a table there is a brief moment where the change is made in the intermediate state, but is not committed. During this time, any other DML statement needing that data waits until the lock is released. This is a safety feature so that SQL Server evaluates only official data. For every data change to a table there is a brief moment where the change is made in this intermediate state, but is not committed. During this time, any other DML statement (SELECT, INSERT, DELETE, UPDATE) needing that data must wait until the lock is released. This is a safety feature put in place so that SQL Server evaluates only official data. Additional Hints: I have previously discussed various concepts from SQL Server Joes 2 Pros Volume 1. SQL Joes 2 Pros Development Series – Dirty Records and Table Hints SQL Joes 2 Pros Development Series – Row Constructors SQL Joes 2 Pros Development Series – Finding un-matching Records SQL Joes 2 Pros Development Series – Efficient Query Writing Strategy SQL Joes 2 Pros Development Series – Finding Apostrophes in String and Text SQL Joes 2 Pros Development Series – Wildcard – Querying Special Characters SQL Joes 2 Pros Development Series – Wildcard Basics Recap Next Step: Answer the Quiz in Contact Form in following format Question Answer Name of the country (The contest is open for USA and India) Bonus Winner Leave a comment with your favorite article from the “additional hints” section and you may be eligible for surprise gift. There is no country restriction for this Bonus Contest. Do mention why you liked it any particular blog post and I will announce the winner of the same along with the main contest. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Joes 2 Pros, PostADay, SQL, SQL Authority, SQL Puzzle, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• Oracle OpenWorld Update: Demo Pods and Hands-on Labs

  - by Doug Reid

  0 false 18 pt 18 pt 0 0 false false false /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin;} Less than one week away until the start of Oracle OpenWorld 2012 and the Data Integration Solutions team is ready to go!  We have an exciting line up for you this year which we have summarized for you in the Oracle OpenWorld Focus on Data Integration Solutions document. In past posts we have discussed session themes and our customer panel, but today I would like to summarize our Hands-on Labs and Demo Pods that we have available for attendees. For Oracle GoldenGate Hands-On Labs we have two labs that we are running this year. Deep Dive into Oracle GoldenGate Thursday October 4th at 11:15AM in the Marriott Marquis Salon 1/2 Oracle GoldenGate provides real-time log-based change data capture and delivery between heterogeneous systems. It enables cost-effective, low-impact, real-time data integration and continuous availability solutions. This session covers Oracle GoldenGate 11g’s internal product architecture and includes a hands-on lab that covers configuration examples for target database instantiation and real-time change data capture and delivery. The participants will configure Oracle GoldenGate to instantiate a secondary database that can be used for disaster recovery or a reporting instance. Come learn how easy it is to use and how this can be a very valuable and easy technology solution for your organization. Introduction to Oracle GoldenGate Veridata Wednesday October 3rd 10:15AM in the Marriott Marquis Sales 1/2 Oracle GoldenGate Veridata compares one set of data with another and identifies data that is out of synchronization. In this hands-on lab, you will be introduced to the key features of this product. Using the Oracle GoldenGate Veridata Web client, you will have the opportunity to configure comparison objects and rules, initiate a comparison, review the status and output of a comparison, and review out-of-sync data. As a bonus this year, we have recorded the labs and made them available on youtube.com/oraclegoldengate. These will be available the day of the labs. Our demo pods are an opportunity for attendees to see our products but more so to meet the product management and development teams. I would like to point out that we have two Oracle GoldenGate 11gR2 demo pods, one in the database camp and the other in the middleware camp. The one in the middleware camp will be focused on all platforms while the one in the database camp will have a focus on the Oracle platform. The other two I would like to point out are the Monitoring Oracle GoldenGate and the Oracle Enterprise Manager demo pods; both of these pods will focus on methods to monitor GoldenGate but the OEM demo pod will have a specific focus on the Oracle GoldenGate Management Pack plug-in for OEM. Below is a list of our demo pods and their locations. Monitoring Oracle GoldenGate for End-to-End Visibility Moscone South, Right - S-241 Oracle Data Integrator and Oracle GoldenGate for Oracle Applications Moscone South, Right - S-240 Oracle GoldenGate 11gR2 New Features Moscone South, Right - S-239 Oracle GoldenGate 11gR2: Real-Time, Transactional Database Replication     Moscone South, Left - S-027 Oracle GoldenGate Veridata and Adapters Moscone South, Right - S-242 Oracle Enterprise Manager Moscone South, Left - S-040 Keep tuned to our blog during the show for news and highlights from the Data Integration Solutions team. See you there.

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