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  • Alternatives to Google Website Optimizer

    - by yahelc
    What (affordable) alternatives are there to Google Website Optimizer for A/B and multivariate tests? The pro's with GWO are basically that its free and that it integrates with Google Analytics. The cons: The relative high time cost of setting up a test. Some alternatives I've seen so far: Optimizely.com VisualWebsiteOptimizer.com Genetify (wiki.github.com/gregdingle/genetify/) Free, open-source, but seems like there's no developer community committed to the project.

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  • Wierd Results A/B Test in Google Website Optimizer

    - by Yisroel
    I set up a test in Google Website Optimizer that has a 3 variations - original (A), B, and C. In order to further validate the results of the test, I added a variation C that is exactly the same as the original. And thats where the results get weird. 6 days in to the test, the best performing variation is C. It outperforms the original by 18.4%! How is that possible? Do I now discount the results of this test entirely?

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  • Wierd Results A/B Test in Google Website Optimizer

    - by Yisroel
    I set up a test in Google Website Optimizer that has a 3 variations - original (A), B, and C. In order to further validate the results of the test, I added a variation C that is exactly the same as the original. And thats where the results get weird. 6 days in to the test, the best performing variation is C. It outperforms the original by 18.4%! How is that possible? Do I now discount the results of this test entirely?

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  • Weird Results A/B Test in Google Website Optimizer

    - by Yisroel
    I set up a test in Google Website Optimizer that has a 3 variations - original (A), B, and C. In order to further validate the results of the test, I added a variation C that is exactly the same as the original. And thats where the results get weird. 6 days into the test, the best performing variation is C. It outperforms the original by 18.4%! How is that possible? Do I now discount the results of this test entirely?

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  • SQL Server Optimizer Malfunction?

    - by Tony Davis
    There was a sharp intake of breath from the audience when Adam Machanic declared the SQL Server optimizer to be essentially "stuck in 1997". It was during his fascinating "Query Tuning Mastery: Manhandling Parallelism" session at the recent PASS SQL Summit. Paraphrasing somewhat, Adam (blog | @AdamMachanic) offered a convincing argument that the optimizer often delivers flawed plans based on assumptions that are no longer valid with today’s hardware. In 1997, when Microsoft engineers re-designed the database engine for SQL Server 7.0, SQL Server got its initial implementation of a cost-based optimizer. Up to SQL Server 2000, the developer often had to deploy a steady stream of hints in SQL statements to combat the occasionally wilful plan choices made by the optimizer. However, with each successive release, the optimizer has evolved and improved in its decision-making. It is still prone to the occasional stumble when we tackle difficult problems, join large numbers of tables, perform complex aggregations, and so on, but for most of us, most of the time, the optimizer purrs along efficiently in the background. Adam, however, challenged further any assumption that the current optimizer is competent at providing the most efficient plans for our more complex analytical queries, and in particular of offering up correctly parallelized plans. He painted a picture of a present where complex analytical queries have become ever more prevalent; where disk IO is ever faster so that reads from disk come into buffer cache faster than ever; where the improving RAM-to-data ratio means that we have a better chance of finding our data in cache. Most importantly, we have more CPUs at our disposal than ever before. To get these queries to perform, we not only need to have the right indexes, but also to be able to split the data up into subsets and spread its processing evenly across all these available CPUs. Improvements such as support for ColumnStore indexes are taking things in the right direction, but, unfortunately, deficiencies in the current Optimizer mean that SQL Server is yet to be able to exploit properly all those extra CPUs. Adam’s contention was that the current optimizer uses essentially the same costing model for many of its core operations as it did back in the days of SQL Server 7, based on assumptions that are no longer valid. One example he gave was a "slow disk" bias that may have been valid back in 1997 but certainly is not on modern disk systems. Essentially, the optimizer assesses the relative cost of serial versus parallel plans based on the assumption that there is no IO cost benefit from parallelization, only CPU. It assumes that a single request will saturate the IO channel, and so a query would not run any faster if we parallelized IO because the disk system simply wouldn’t be able to handle the extra pressure. As such, the optimizer often decides that a serial plan is lower cost, often in cases where a parallel plan would improve performance dramatically. It was challenging and thought provoking stuff, as were his techniques for driving parallelism through query logic based on subsets of rows that define the "grain" of the query. I highly recommend you catch the session if you missed it. I’m interested to hear though, when and how often people feel the force of the optimizer’s shortcomings. Barring mistakes, such as stale statistics, how often do you feel the Optimizer fails to find the plan you think it should, and what are the most common causes? Is it fighting to induce it toward parallelism? Combating unexpected plans, arising from table partitioning? Something altogether more prosaic? Cheers, Tony.

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  • Oracle tuning optimizer index cost adj and optimizer index caching

    - by Darryl Braaten
    What is the correct way to set the optimizer index cost adj parameter for Oracle. As a developer I have observed huge performance improvements as this parameter is lowered. Common queries are reduced from 2 seconds to 200ms. There are lots of warnings on the net that lowering this value will cause dire issues with the database, but no detail is given on what will start going wrong. I am currently only seeing only an upside, much improved application performance and no downside. I need to better understand the possible negative repercussions of adjusting these parameters.

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  • MySQL Connect 9 Days Away – Optimizer Sessions

    - by Bertrand Matthelié
    72 1024x768 Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Following my previous blog post focusing on InnoDB talks at MySQL Connect, let us review today the sessions focusing on the MySQL Optimizer: Saturday, 11.30 am, Room Golden Gate 6: MySQL Optimizer Overview—Olav Sanstå, Oracle The goal of MySQL optimizer is to take a SQL query as input and produce an optimal execution plan for the query. This session presents an overview of the main phases of the MySQL optimizer and the primary optimizations done to the query. These optimizations are based on a combination of logical transformations and cost-based decisions. Examples of optimization strategies the presentation covers are the main query transformations, the join optimizer, the data access selection strategies, and the range optimizer. For the cost-based optimizations, an overview of the cost model and the data used for doing the cost estimations is included. Saturday, 1.00 pm, Room Golden Gate 6: Overview of New Optimizer Features in MySQL 5.6—Manyi Lu, Oracle Many optimizer features have been added into MySQL 5.6. This session provides an introduction to these great features. Multirange read, index condition pushdown, and batched key access will yield huge performance improvements on large data volumes. Structured explain, explain for update/delete/insert, and optimizer tracing will help users analyze and speed up queries. And last but not least, the session covers subquery optimizations in Release 5.6. Saturday, 7.00 pm, Room Golden Gate 4: BoF: Query Optimizations: What Is New and What Is Coming? This BoF presents common techniques for query optimization, covers what is new in MySQL 5.6, and provides a discussion forum in which attendees can tell the MySQL optimizer team which optimizations they would like to see in the future. Sunday, 1.15 pm, Room Golden Gate 8: Query Performance Comparison of MySQL 5.5 and MySQL 5.6—Øystein Grøvlen, Oracle MySQL Release 5.6 contains several improvements in the query optimizer that create improved performance for complex queries. This presentation looks at how MySQL 5.6 improves the performance of many of the queries in the DBT-3 benchmark. Based on the observed improvements, the presentation discusses what makes the specific queries perform better in Release 5.6. It describes the relevant new optimization techniques and gives examples of the types of queries that will benefit from these techniques. Sunday, 4.15 pm, Room Golden Gate 4: Powerful EXPLAIN in MySQL 5.6—Evgeny Potemkin, Oracle The EXPLAIN command of MySQL has long been a very useful tool for understanding how MySQL will execute a query. Release 5.6 of the MySQL database offers several new additions that give more-detailed information about the query plan and make it easier to understand at the same time. This presentation gives an overview of new EXPLAIN features: structured EXPLAIN in JSON format, EXPLAIN for INSERT/UPDATE/DELETE, and optimizer tracing. Examples in the session give insights into how you can take advantage of the new features. They show how these features supplement and relate to each other and to classical EXPLAIN and how and why the MySQL server chooses a particular query plan. You can check out the full program here as well as in the September edition of the MySQL newsletter. Not registered yet? You can still save US$ 300 over the on-site fee – Register Now!

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  • "Zend Optimizer not installed" after I updated to Ubuntu 10.04

    - by Eugene
    Hi guys, I've just updated from 9.10 to 10.04. Everything seems to run fine except for zend optimizer which is throwing "Zend Optimizer not installed" error. I went to php.ini and the following line is still there Code: zend_extension=/etc/php5/ZendOptimizer.so Also I checked that the file does exist and that the php.ini I am looking at is in fact the php.ini file that is being used by the server. Please let me know if you have any ideas about how to fix or debug this. Thanks, Eugene

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  • need recommendation for running PHP/Zend based optimizer

    - by senorsmile
    Firstly, I must admit that I don't know much about setting up PHP beyond the basics. I have an Ubuntu 10.04 server system (hosted) running primarily as an FTP store for a commercial store software. The server that the commercial store is installed on is unfortunately not very reliable, and would like to move that to this Ubuntu 10.04 server. (We've already received permission from the store vendor to do this.) My problem is that they use Zend optimizer which is only compatible with PHP 5.2. I have tried a couple of "hacks" to downgrade PHP to 5.2, but it breaks so many other things that it doesn't seem worth it. My idea is to install some sort of container of Ubuntu 8.04 (like OpenVZ) on the server to house a native install of PHP 5.2 to meet the dependency of the store software. However, it appears that OpenVZ is no longer supported on Ubuntu. Is there another solution similar that I could run on a hosted server to installed a separate "container-like" 8.04 system?

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  • SQLAuthority News – Statistics Used by the Query Optimizer in Microsoft SQL Server 2008 – Microsoft Whitepaper

    - by pinaldave
    I recently presented session on Statistics and Best Practices in Virtual Tech Days on Nov 22, 2010. The sessions was very popular and I got many questions right after the sessions. The number question I had received was where everybody can get the further information. I am very much happy that my sessions created some curiosity for one of the most important feature of the SQL Server. Statistics are the heart of the SQL Server. Microsoft has published a white paper on the subject how statistics are useful to Query Optimizer. Here is the abstract of the same white paper from Microsoft. Statistics Used by the Query Optimizer in Microsoft SQL Server 2008 Writer: Eric N. Hanson and Yavor Angelov Microsoft SQL Server 2008 collects statistical information about indexes and column data stored in the database. These statistics are used by the SQL Server query optimizer to choose the most efficient plan for retrieving or updating data. This paper describes what data is collected, where it is stored, and which commands create, update, and delete statistics. By default, SQL Server 2008 also creates and updates statistics automatically, when such an operation is considered to be useful. This paper also outlines how these defaults can be changed on different levels (column, table, and database). In addition, it presents how certain query language features, such as Transact-SQL variables, interact with use of statistics by the optimizer, and it provides guidance for using these features when writing queries so you can obtain good query performance. Link to white paper Statistics Used by the Query Optimizer in Microsoft SQL Server 2008 ?Reference: Pinal Dave (http://blog.SQLAuthority.com)   Filed under: Pinal Dave, SQL, SQL Authority, SQL Documentation, SQL Download, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL White Papers, SQLAuthority News, T SQL, Technology

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  • How to prevent Google Website Optimizer from making Google Analytics spike Direct Traffic and lower Bounce Rate?

    - by Scott
    I am using Google Website Optimizer (GWO) and Google Analytics. Whenever a person (Google Website Optimizer) does a javascript redirect, Google Analytics will change the referrer. When the referrer changes, the traffic source becomes yourself and is changed to Direct Traffic. For Example: A visitor goes to google: searches for my great service. Clicks the link that goes to website page: /home/ At this point, Google Analytics tracks the referrer as Google. However, /home/ has a GWO javacript redirect to a battery of A/B tests. /home-1/ or /home-2/ or /home-3/ When the redirect from /home/ occurs to /home-1/, google analytics on the /home-1/ page now thinks the referrer is yourself and converts the referrer to Direct Traffic since the Direct Traffic bucket is the unknown. I'm really surprised that GWO and GA do this when they both come from google. Now, How does one fix this to prevent the overwrite of the referrer using GWO?

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  • Last chance to vote in Optimizer Bumper Sticker Competition

    - by Maria Colgan
    There is still time to vote in our competition to find the best Optimizer bumper sticker, which we will give away at the Optimizer demo booth at this years Oracle Open World. Click here to vote for your favorite. Then stop by the Optimizer demo booth at Oracle Open World to claim your bumper sticker! Remember voting will close on June 30th and the winning slogan will be announced in early July. +Maria Colgan

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  • How is the gimbal locked problem solved using accumulative matrix transformations

    - by Luke San Antonio
    I am reading the online "Learning Modern 3D Graphics Programming" book by Jason L. McKesson As of now, I am up to the gimbal lock problem and how to solve it using quaternions. However right here, at the Quaternions page. Part of the problem is that we are trying to store an orientation as a series of 3 accumulated axial rotations. Orientations are orientations, not rotations. And orientations are certainly not a series of rotations. So we need to treat the orientation of the ship as an orientation, as a specific quantity. I guess this is the first spot I start to get confused, the reason is because I don't see the dramatic difference between orientations and rotations. I also don't understand why an orientation cannot be represented by a series of rotations... Also: The first thought towards this end would be to keep the orientation as a matrix. When the time comes to modify the orientation, we simply apply a transformation to this matrix, storing the result as the new current orientation. This means that every yaw, pitch, and roll applied to the current orientation will be relative to that current orientation. Which is precisely what we need. If the user applies a positive yaw, you want that yaw to rotate them relative to where they are current pointing, not relative to some fixed coordinate system. The concept, I understand, however I don't understand how if accumulating matrix transformations is a solution to this problem, how the code given in the previous page isn't just that. Here's the code: void display() { glClearColor(0.0f, 0.0f, 0.0f, 0.0f); glClearDepth(1.0f); glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT); glutil::MatrixStack currMatrix; currMatrix.Translate(glm::vec3(0.0f, 0.0f, -200.0f)); currMatrix.RotateX(g_angles.fAngleX); DrawGimbal(currMatrix, GIMBAL_X_AXIS, glm::vec4(0.4f, 0.4f, 1.0f, 1.0f)); currMatrix.RotateY(g_angles.fAngleY); DrawGimbal(currMatrix, GIMBAL_Y_AXIS, glm::vec4(0.0f, 1.0f, 0.0f, 1.0f)); currMatrix.RotateZ(g_angles.fAngleZ); DrawGimbal(currMatrix, GIMBAL_Z_AXIS, glm::vec4(1.0f, 0.3f, 0.3f, 1.0f)); glUseProgram(theProgram); currMatrix.Scale(3.0, 3.0, 3.0); currMatrix.RotateX(-90); //Set the base color for this object. glUniform4f(baseColorUnif, 1.0, 1.0, 1.0, 1.0); glUniformMatrix4fv(modelToCameraMatrixUnif, 1, GL_FALSE, glm::value_ptr(currMatrix.Top())); g_pObject->Render("tint"); glUseProgram(0); glutSwapBuffers(); } To my understanding, isn't what he is doing (modifying a matrix on a stack) considered accumulating matrices, since the author combined all the individual rotation transformations into one matrix which is being stored on the top of the stack. My understanding of a matrix is that they are used to take a point which is relative to an origin (let's say... the model), and make it relative to another origin (the camera). I'm pretty sure this is a safe definition, however I feel like there is something missing which is blocking me from understanding this gimbal lock problem. One thing that doesn't make sense to me is: If a matrix determines the difference relative between two "spaces," how come a rotation around the Y axis for, let's say, roll, doesn't put the point in "roll space" which can then be transformed once again in relation to this roll... In other words shouldn't any further transformations to this point be in relation to this new "roll space" and therefore not have the rotation be relative to the previous "model space" which is causing the gimbal lock. That's why gimbal lock occurs right? It's because we are rotating the object around set X, Y, and Z axes rather than rotating the object around it's own, relative axes. Or am I wrong? Since apparently this code I linked in isn't an accumulation of matrix transformations can you please give an example of a solution using this method. So in summary: What is the difference between a rotation and an orientation? Why is the code linked in not an example of accumulation of matrix transformations? What is the real, specific purpose of a matrix, if I had it wrong? How could a solution to the gimbal lock problem be implemented using accumulation of matrix transformations? Also, as a bonus: Why are the transformations after the rotation still relative to "model space?" Another bonus: Am I wrong in the assumption that after a transformation, further transformations will occur relative to the current? Also, if it wasn't implied, I am using OpenGL, GLSL, C++, and GLM, so examples and explanations in terms of these are greatly appreciated, if not necessary. The more the detail the better! Thanks in advance...

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  • Zend Optimizer not Functioning Correctly on Plesk 9.3.0 VPS

    - by dallasclark
    I have a new VPS running Plesk 9.3.0 without 'much' modifications to any settings. I've moved a site to this VPS and I'm receiving a page full of random 'gibbrish' characters like: Zend2003120702116268102798xù Ÿ2½}MŒ%ÇqæCwËg¸„ÖXXZ[ÆùÿCK¢FŠäš’(’¢-ÂÒèu¿zš6gºÇÝ=$Ec:-xá=èàƒÃ ôžL/`,¼'û$èdû$ð ›±OYïUUdfde½á›GâcWTfDdF|‘™‘QÕ_nN‡OÝ›Ÿ/ú9¾¢»"…çÎ =B³øo/=÷…?úúW?·/LX5¯ß½ ðtEÍ ãB„ð÷øìÞéåU®•òÊëZÈi^¿lN/NÎNoÞ›/šÅC׸”šÅLËÏåùÉ+Ü á¸a6Ê÷Ž..ϯrç…Õ–)Õþñòüvsz•{å mî!F³ã[çWsÖZ%k'-ÐÝ<¬þZ1B¡¼ "-ÏîH @/Ü´b.Ï›ù"ü tb¼Ò!”]œ¼ïŠ6–Ál \Ü;½hÎOößh®^“4#…s¡CÀ†æôUèP³Ð§3¦¬“; –j‡ìþb¤÷š»¶³Wçç7÷îÜ…w•bÞs«[ÆÎav,@ÿ´ÜéÖåÌfž¯þVÚlö‹½ÎÛØå#Èoòudñ^÷чW+ÕSsÐý¹w˜7Ÿò«{ò…?<Ìo1»èZÄN_ð³»·îqr÷Vs¾"ýµ¾§þˆ¡v Ù.j†Çï®#{îÞüÞú¿ºý²Q0âLõ$rv¥{»[à|sÝwxþðúy¯)þ • 7ÛŽ È^YËZá‘JV<|·g“l2£{µ«Ù›=é§eCÍîõÖ»ÓÖQtL´D?ε܃ÁªÇ3=ﯸ^=þAIÏjöÐÁ0¡ò¥ 2øÙŸÞçÝÊéqÔ€Lï÷*+Jo¬õLͺFøì x¨ÕìÛ'GH“æådD)ÿ:¨5¼q±¦rÖøLf“Ðj îÅõ¬éa÷[!_zöN?þ"™†á©›0Ý{ˆWóª‘ÁH4µx5+Ë^–Ž›·ÉöŠd1¹Õ¬ phpinfo() shows PHP is running on the Zend engine. This server is unmanaged so I cannot ask the hosting provider for assistance. Any help big or small will be appreciated. [root@vps ~]# php -v PHP 5.1.6 (cli) (built: Mar 31 2010 02:39:17) Copyright (c) 1997-2006 The PHP Group Zend Engine v2.1.0, Copyright (c) 1998-2006 Zend Technologies

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  • Zend Optimizer not Functioning Correctly on Plesk 9.3.0 VPS

    - by dallasclark
    I have a new VPS running Plesk 9.3.0 without 'much' modifications to any settings. I've moved a site to this VPS and I'm receiving a page full of random 'gibbrish' characters like: Zend2003120702116268102798xù Ÿ2½}MŒ%ÇqæCwËg¸„ÖXXZ[ÆùÿCK¢FŠäš’(’¢-ÂÒèu¿zš6gºÇÝ=$Ec:-xá=èàƒÃ ôžL/`,¼'û$èdû$ð ›±OYïUUdfde½á›GâcWTfDdF|‘™‘QÕ_nN‡OÝ›Ÿ/ú9¾¢»"…çÎ =B³øo/=÷…?úúW?·/LX5¯ß½ ðtEÍ ãB„ð÷øìÞéåU®•òÊëZÈi^¿lN/NÎNoÞ›/šÅC׸”šÅLËÏåùÉ+Ü á¸a6Ê÷Ž..ϯrç…Õ–)Õþñòüvsz•{å mî!F³ã[çWsÖZ%k'-ÐÝ<¬þZ1B¡¼ "-ÏîH @/Ü´b.Ï›ù"ü tb¼Ò!”]œ¼ïŠ6–Ál \Ü;½hÎOößh®^“4#…s¡CÀ†æôUèP³Ð§3¦¬“; –j‡ìþb¤÷š»¶³Wçç7÷îÜ…w•bÞs«[ÆÎav,@ÿ´ÜéÖåÌfž¯þVÚlö‹½ÎÛØå#Èoòudñ^÷чW+ÕSsÐý¹w˜7Ÿò«{ò…?<Ìo1»èZÄN_ð³»·îqr÷Vs¾"ýµ¾§þˆ¡v Ù.j†Çï®#{îÞüÞú¿ºý²Q0âLõ$rv¥{»[à|sÝwxþðúy¯)þ • 7ÛŽ È^YËZá‘JV<|·g“l2£{µ«Ù›=é§eCÍîõÖ»ÓÖQtL´D?ε܃ÁªÇ3=ﯸ^=þAIÏjöÐÁ0¡ò¥ 2øÙŸÞçÝÊéqÔ€Lï÷*+Jo¬õLͺFøì x¨ÕìÛ'GH“æådD)ÿ:¨5¼q±¦rÖøLf“Ðj îÅõ¬éa÷[!_zöN?þ"™†á©›0Ý{ˆWóª‘ÁH4µx5+Ë^–Ž›·ÉöŠd1¹Õ¬ phpinfo() shows PHP is running on the Zend engine. This server is unmanaged so I cannot ask the hosting provider for assistance. Any help big or small will be appreciated.

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  • Semi-blocking Transformations in SQL Server Integration Services SSIS

    In a SSIS data flow, there are multiple types of transformations. On one hand you have synchronous and asynchronous transformations, but on the other hand you have non-blocking, semi-blocking and fully-blocking components. In this tip, Koen Verbeeck takes a closer look on the performance impact of semi-blocking transformations in SSIS. Can 41,000 DBAs really be wrong? Join 41,000 other DBAs who are following the new series from the DBA Team: the 5 Worst Days in a DBA’s Life. Part 3, As Corrupt As It Gets, is out now – read it here.

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  • Optimizer Bumper Sticker Competition

    - by Maria Colgan
    For the last couple of months we have been running a competition on twitter (@SQLMaria) looking for the best Optimizer bumper sticker, which we will give away at the Optimizer demo booth at this years Oracle Open World. We got tons of excellent entries and had a tough time narrowing it down to just 5 but we have. So now we need your help to pick the final winner. Click here to vote for your favorite. Then stop by the Optimizer demo booth at Oracle Open World to claim your bumper sticker!

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  • SQL SERVER – Maximize Database Performance with DB Optimizer – SQL in Sixty Seconds #054

    - by Pinal Dave
    Performance tuning is an interesting concept and everybody evaluates it differently. Every developer and DBA have different opinion about how one can do performance tuning. I personally believe performance tuning is a three step process Understanding the Query Identifying the Bottleneck Implementing the Fix While, we are working with large database application and it suddenly starts to slow down. We are all under stress about how we can get back the database back to normal speed. Most of the time we do not have enough time to do deep analysis of what is going wrong as well what will fix the problem. Our primary goal at that time is to just fix the database problem as fast as we can. However, here is one very important thing which we need to keep in our mind is that when we do quick fix, it should not create any further issue with other parts of the system. When time is essence and we want to do deep analysis of our system to give us the best solution we often tend to make mistakes. Sometimes we make mistakes as we do not have proper time to analysis the entire system. Here is what I do when I face such a situation – I take the help of DB Optimizer. It is a fantastic tool and does superlative performance tuning of the system. Everytime when I talk about performance tuning tool, the initial reaction of the people is that they do not want to try this as they believe it requires lots of the learning of the tool before they use it. It is absolutely not true with the case of the DB optimizer. It is a very easy to use and self intuitive tool. Once can get going with the product, in no time. Here is a quick video I have build where I demonstrate how we can identify what index is missing for query and how we can quickly create the index. Entire three steps of the query tuning are completed in less than 60 seconds. If you are into performance tuning and query optimization you should download DB Optimizer and give it a go. Let us see the same concept in following SQL in Sixty Seconds Video: You can Download DB Optimizer and reproduce the same Sixty Seconds experience. Related Tips in SQL in Sixty Seconds: Performance Tuning – Part 1 of 2 – Getting Started and Configuration Performance Tuning – Part 2 of 2 – Analysis, Detection, Tuning and Optimizing What would you like to see in the next SQL in Sixty Seconds video? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Database, Pinal Dave, PostADay, SQL, SQL Authority, SQL in Sixty Seconds, SQL Interview Questions and Answers, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology, Video Tagged: Identity

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  • Google Website Optimizer - Multi Variant Testing - Make a specific page a test page for two experime

    - by wawawowo
    Im having a little issue with setting up Multi Variant Tests in Google Website Optimizer. I wish to have two tests. One being which is a header banner which appears on every page and the conversion for example would be if the visitor lands on the contact us page. This was very easy to set up. However when I intend to add another test, again this will be on a element which appears on every page and the conversion page is if the visitor lands on the checkout page. But I am now having problems installing the control script. I get the error: Expected to find: }(function(){var k='0651116117',d=docum Found on line 7: (function(){var k='2666211118',d=docum Im assuming I have this error because I now have two control scripts in the header - one for each experiment. However I cannot combine each variation into just one experiment because each one is different and has a different conversion page? Please advise, thanks.

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  • Google Web Optimizer -- How long until winning combination?

    - by Django Reinhardt
    I've had an A/B Test running in Google Web Optimizer for six weeks now, and there's still no end in sight. Google is still saying: "We have not gathered enough data yet to show any significant results. When we collect more data we should be able to show you a winning combination." Is there any way of telling how close Google is to making up its mind? (Does anyone know what algorithm does it use to decide if there's been any "high confidence winners"?) According to the Google help documentation: Sometimes we simply need more data to be able to reach a level of high confidence. A tested combination typically needs around 200 conversions for us to judge its performance with certainty. But all of our conversions have over 200 conversations at the moment: 230 / 4061 (Original) 223 / 3937 (Variation 1) 205 / 3984 (Variation 2) 205 / 4007 (Variation 3) How much longer is it going to have to run?? Thanks for any help.

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  • With Google Website Optimizer's multivariate testing, can I vary multiple css classes on a single di

    - by brahn
    I would like to use Google Website Optimizer (GWO)'s multivariate tests to test some different versions of a web page. I can change from version to version just by varying some class tags on a div, i.e. the different versions are of this form: <div id="testing" class="foo1 bar1">content</div> <div id="testing" class="foo1 bar2">content</div> <div id="testing" class="foo2 bar1">content</div> <div id="testing" class="foo2 bar2">content</div> In the ideal, I would be able to use GWO section code in place of each class, and google would just swap in the appropriate tags (foo1 or foo2, bar1 or bar2). However, naively doing this results in horribly malformed code because I would be trying to put <script> tags inside the div's class attribute: <div id="testing" class=" <script>utmx_section("foo-class")</script>foo1</noscript> <script>utmx_section("bar-class")</script>bar1</noscript> "> content </div> And indeed, the browser chokes all over it. My current best approach is just to use a different div for each variable in the test, as follows: <script>utmx_section("foo-class-div")</script> <div class="foo1"> </noscript> <script>utmx_section("bar-class-div")</script> <div class="bar1"> </noscript> content </div> </div> So testing multiple variables requires layer of div-nesting per variable, and it all seems rather awkward. Is there a better approach that I could use in which I just vary the classes on a single div?

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  • Inequality joins, Asynchronous transformations and Lookups : SSIS

    - by jamiet
    It is pretty much accepted by SQL Server Integration Services (SSIS) developers that synchronous transformations are generally quicker than asynchronous transformations (for a description of synchronous and asynchronous transformations go read Asynchronous and synchronous data flow components). Notice I said “generally” and not “always”; there are circumstances where using asynchronous transformations can be beneficial and in this blog post I’ll demonstrate such a scenario, one that is pretty common when building data warehouses. Imagine I have a [Customer] dimension table that manages information about all of my customers as a slowly-changing dimension. If that is a type 2 slowly changing dimension then you will likely have multiple rows per customer in that table. Furthermore you might also have datetime fields that indicate the effective time period of each member record. Here is such a table that contains data for four dimension members {Terry, Max, Henry, Horace}: Notice that we have multiple records per customer and that the [SCDStartDate] of a record is equivalent to the [SCDEndDate] of the record that preceded it (if there was one). (Note that I am on record as saying I am not a fan of this technique of storing an [SCDEndDate] but for the purposes of clarity I have included it here.) Anyway, the idea here is that we will have some incoming data containing [CustomerName] & [EffectiveDate] and we need to use those values to lookup [Customer].[CustomerId]. The logic will be: Lookup a [CustomerId] WHERE [CustomerName]=[CustomerName] AND [SCDStartDate] <= [EffectiveDate] AND [EffectiveDate] <= [SCDEndDate] The conventional approach to this would be to use a full cached lookup but that isn’t an option here because we are using inequality conditions. The obvious next step then is to use a non-cached lookup which enables us to change the SQL statement to use inequality operators: Let’s take a look at the dataflow: Notice these are all synchronous components. This approach works just fine however it does have the limitation that it has to issue a SQL statement against your lookup set for every row thus we can expect the execution time of our dataflow to increase linearly in line with the number of rows in our dataflow; that’s not good. OK, that’s the obvious method. Let’s now look at a different way of achieving this using an asynchronous Merge Join transform coupled with a Conditional Split. I’ve shown it post-execution so that I can include the row counts which help to illustrate what is going on here: Notice that there are more rows output from our Merge Join component than on the input. That is because we are joining on [CustomerName] and, as we know, we have multiple records per [CustomerName] in our lookup set. Notice also that there are two asynchronous components in here (the Sort and the Merge Join). I have embedded a video below that compares the execution times for each of these two methods. The video is just over 8minutes long. View on Vimeo  For those that can’t be bothered watching the video I’ll tell you the results here. The dataflow that used the Lookup transform took 36 seconds whereas the dataflow that used the Merge Join took less than two seconds. An illustration in case it is needed: Pretty conclusive proof that in some scenarios it may be quicker to use an asynchronous component than a synchronous one. Your mileage may of course vary. The scenario outlined here is analogous to performance tuning procedural SQL that uses cursors. It is common to eliminate cursors by converting them to set-based operations and that is effectively what we have done here. Our non-cached lookup is performing a discrete operation for every single row of data, exactly like a cursor does. By eliminating this cursor-in-disguise we have dramatically sped up our dataflow. I hope all of that proves useful. You can download the package that I demonstrated in the video from my SkyDrive at http://cid-550f681dad532637.skydrive.live.com/self.aspx/Public/BlogShare/20100514/20100514%20Lookups%20and%20Merge%20Joins.zip Comments are welcome as always. @Jamiet Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • SQL SERVER – DMV – sys.dm_exec_query_optimizer_info – Statistics of Optimizer

    - by pinaldave
    Incredibly, SQL Server has so much information to share with us. Every single day, I am amazed with this SQL Server technology. Sometimes I find several interesting information by just querying few of the DMV. And when I present this info in front of my client during performance tuning consultancy, they are surprised with my findings. Today, I am going to share one of the hidden gems of DMV with you, the one which I frequently use to understand what’s going on under the hood of SQL Server. SQL Server keeps the record of most of the operations of the Query Optimizer. We can learn many interesting details about the optimizer which can be utilized to improve the performance of server. SELECT * FROM sys.dm_exec_query_optimizer_info WHERE counter IN ('optimizations', 'elapsed time','final cost', 'insert stmt','delete stmt','update stmt', 'merge stmt','contains subquery','tables', 'hints','order hint','join hint', 'view reference','remote query','maximum DOP', 'maximum recursion level','indexed views loaded', 'indexed views matched','indexed views used', 'indexed views updated','dynamic cursor request', 'fast forward cursor request') All occurrence values are cumulative and are set to 0 at system restart. All values for value fields are set to NULL at system restart. I have removed a few of the internal counters from the script above, and kept only documented details. Let us check the result of the above query. As you can see, there is so much vital information that is revealed in above query. I can easily say so many things about how many times Optimizer was triggered and what the average time taken by it to optimize my queries was. Additionally, I can also determine how many times update, insert or delete statements were optimized. I was able to quickly figure out that my client was overusing the Query Hints using this dynamic management view. If you have been reading my blog, I am sure you are aware of my series related to SQL Server Views SQL SERVER – The Limitations of the Views – Eleven and more…. With this, I can take a quick look and figure out how many times Views were used in various solutions within the query. Moreover, you can easily know what fraction of the optimizations has been involved in tuning server. For example, the following query would tell me, in total optimizations, what the fraction of time View was “reference“. As this View also includes system Views and DMVs, the number is a bit higher on my machine. SELECT (SELECT CAST (occurrence AS FLOAT) FROM sys.dm_exec_query_optimizer_info WHERE counter = 'view reference') / (SELECT CAST (occurrence AS FLOAT) FROM sys.dm_exec_query_optimizer_info WHERE counter = 'optimizations') AS ViewReferencedFraction Reference : Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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