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  • Visual Studio hangs when deploying a cube

    - by Richie
    Hello All, I'm having an issue with an Analysis Services project in Visual Studio 2005. My project always builds but only occasionally deploys. No errors are reported and VS just hangs. This is my first Analysis Services project so I am hoping that there is something obvious that I am just missing. Here is the situation I have a cube that I have successfully deployed. I then make some change, e.g., adding a hierarchy to a dimension. When I try to deploy again VS hangs. I have to restart Analysis Services to regain control of VS so I can shut it down. I restart everything sometimes once, sometimes twice or more before the project will eventually deploy. This happens with any change I make there seems to be no pattern to this behaviour. Sometimes I have to delete the cube from Analysis Services before restarting everything to get a successful deploy. Also I have successfully deployed the cube, and then subsequently successfully reprocessed a dimension then when I open a query window in SQL Server Management Studio it says that it can find any cubes. As a test I have deployed a cube successfully. I have then deleted it in Analysis Services and attempted to redeploy it, without making any changes to the cube, only to have the same behaviour mentioned above. VS just hangs with no reason so I have no idea where to start hunting down the problem. It is taking 15-20 minutes to make a change as simple as setting the NameColumn of a dimension attribute. As you can imagine this is taking hours of my time so I would greatly appreciate any assistance anyone can give me.

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  • How does the Amazon Recommendation feature work?

    - by Rachel
    What technology goes in behind the screens of Amazon recommendation technology? I believe that Amazon recommendation is currently the best in the market, but how do they provide us with such relevant recommendations? Recently, we have been involved with similar recommendation kind of project, but would surely like to know about the in and outs of the Amazon recommendation technology from a technical standpoint. Any inputs would be highly appreciated. Update: This patent explains how personalized recommendations are done but it is not very technical, and so it would be really nice if some insights could be provided. From the comments of Dave, Affinity Analysis forms the basis for such kind of Recommendation Engines. Also here are some good reads on the Topic Demystifying Market Basket Analysis Market Basket Analysis Affinity Analysis Suggested Reading: Data Mining: Concepts and Technique

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  • SQL Server 2008 and SP1

    - by andrew007
    Hi, I have a server where I installed SQL Server 2008 and after I applied the SP1. Now, I want also to add the Analysis Services to this instance by using the "Add or remove features...". My questions are: Is it possible to add the Analysis Services on a server with SP1 already installed? How can I apply the SP1 also to the new Analysis Service feature? THANKS!

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  • An XEvent a Day (8 of 31) – Targets Week – synchronous_event_counter

    - by Jonathan Kehayias
    Yesterday’s post, Targets Week - Bucketizers , looked at the bucketizer Targets in Extended Events and how they can be used to simplify analysis and perform more targeted analysis based on their output.  Today’s post will be fairly short, by comparison to the previous posts, while we look at the synchronous_event_counter target, which can be used to test the impact of an Event Session without actually incurring the cost of Event collection. What is the synchronous_event_counter? The synchronous_event_count...(read more)

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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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  • Security in Software

    The term security has many meanings based on the context and perspective in which it is used. Security from the perspective of software/system development is the continuous process of maintaining confidentiality, integrity, and availability of a system, sub-system, and system data. This definition at a very high level can be restated as the following: Computer security is a continuous process dealing with confidentiality, integrity, and availability on multiple layers of a system. Key Aspects of Software Security Integrity Confidentiality Availability Integrity within a system is the concept of ensuring only authorized users can only manipulate information through authorized methods and procedures. An example of this can be seen in a simple lead management application.  If the business decided to allow each sales member to only update their own leads in the system and sales managers can update all leads in the system then an integrity violation would occur if a sales member attempted to update someone else’s leads. An integrity violation occurs when a team member attempts to update someone else’s lead because it was not entered by the sales member.  This violates the business rule that leads can only be update by the originating sales member. Confidentiality within a system is the concept of preventing unauthorized access to specific information or tools.  In a perfect world the knowledge of the existence of confidential information/tools would be unknown to all those who do not have access. When this this concept is applied within the context of an application only the authorized information/tools will be available. If we look at the sales lead management system again, leads can only be updated by originating sales members. If we look at this rule then we can say that all sales leads are confidential between the system and the sales person who entered the lead in to the system. The other sales team members would not need to know about the leads let alone need to access it. Availability within a system is the concept of authorized users being able to access the system. A real world example can be seen again from the lead management system. If that system was hosted on a web server then IP restriction can be put in place to limit access to the system based on the requesting IP address. If in this example all of the sales members where accessing the system from the 192.168.1.23 IP address then removing access from all other IPs would be need to ensure that improper access to the system is prevented while approved users can access the system from an authorized location. In essence if the requesting user is not coming from an authorized IP address then the system will appear unavailable to them. This is one way of controlling where a system is accessed. Through the years several design principles have been identified as being beneficial when integrating security aspects into a system. These principles in various combinations allow for a system to achieve the previously defined aspects of security based on generic architectural models. Security Design Principles Least Privilege Fail-Safe Defaults Economy of Mechanism Complete Mediation Open Design Separation Privilege Least Common Mechanism Psychological Acceptability Defense in Depth Least Privilege Design PrincipleThe Least Privilege design principle requires a minimalistic approach to granting user access rights to specific information and tools. Additionally, access rights should be time based as to limit resources access bound to the time needed to complete necessary tasks. The implications of granting access beyond this scope will allow for unnecessary access and the potential for data to be updated out of the approved context. The assigning of access rights will limit system damaging attacks from users whether they are intentional or not. This principle attempts to limit data changes and prevents potential damage from occurring by accident or error by reducing the amount of potential interactions with a resource. Fail-Safe Defaults Design PrincipleThe Fail-Safe Defaults design principle pertains to allowing access to resources based on granted access over access exclusion. This principle is a methodology for allowing resources to be accessed only if explicit access is granted to a user. By default users do not have access to any resources until access has been granted. This approach prevents unauthorized users from gaining access to resource until access is given. Economy of Mechanism Design PrincipleThe Economy of mechanism design principle requires that systems should be designed as simple and small as possible. Design and implementation errors result in unauthorized access to resources that would not be noticed during normal use. Complete Mediation Design PrincipleThe Complete Mediation design principle states that every access to every resource must be validated for authorization. Open Design Design PrincipleThe Open Design Design Principle is a concept that the security of a system and its algorithms should not be dependent on secrecy of its design or implementation Separation Privilege Design PrincipleThe separation privilege design principle requires that all resource approved resource access attempts be granted based on more than a single condition. For example a user should be validated for active status and has access to the specific resource. Least Common Mechanism Design PrincipleThe Least Common Mechanism design principle declares that mechanisms used to access resources should not be shared. Psychological Acceptability Design PrincipleThe Psychological Acceptability design principle refers to security mechanisms not make resources more difficult to access than if the security mechanisms were not present Defense in Depth Design PrincipleThe Defense in Depth design principle is a concept of layering resource access authorization verification in a system reduces the chance of a successful attack. This layered approach to resource authorization requires unauthorized users to circumvent each authorization attempt to gain access to a resource. When designing a system that requires meeting a security quality attribute architects need consider the scope of security needs and the minimum required security qualities. Not every system will need to use all of the basic security design principles but will use one or more in combination based on a company’s and architect’s threshold for system security because the existence of security in an application adds an additional layer to the overall system and can affect performance. That is why the definition of minimum security acceptably is need when a system is design because this quality attributes needs to be factored in with the other system quality attributes so that the system in question adheres to all qualities based on the priorities of the qualities. Resources: Barnum, Sean. Gegick, Michael. (2005). Least Privilege. Retrieved on August 28, 2011 from https://buildsecurityin.us-cert.gov/bsi/articles/knowledge/principles/351-BSI.html Saltzer, Jerry. (2011). BASIC PRINCIPLES OF INFORMATION PROTECTION. Retrieved on August 28, 2011 from  http://web.mit.edu/Saltzer/www/publications/protection/Basic.html Barnum, Sean. Gegick, Michael. (2005). Defense in Depth. Retrieved on August 28, 2011 from  https://buildsecurityin.us-cert.gov/bsi/articles/knowledge/principles/347-BSI.html Bertino, Elisa. (2005). Design Principles for Security. Retrieved on August 28, 2011 from  http://homes.cerias.purdue.edu/~bhargav/cs526/security-9.pdf

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  • BigData and Customer Experience: Happy Together

    - by Isabel F. Peñuelas
    The two big buzzes of the year may lay closer than it appears. Both concepts intersect at various points: BigData and Return of Investment of Marketing Campaigns On a recent post Big Data Is The Future Of Marketing Jeff Dachis explains very clearly how “Big data analytics finally allows marketers to identify, measure, and manage what is positively impacting their Brand”. Regression analysis applied to big data volumes coming from social media will substitute the failed attempts to justify marketing investments on social media in terms of followers and likes, he continues, “the measurement models applied by marketers on TV Campaigns don´t work on social”, we need to study the data with fresh eyes and maybe then we will start understanding and measuring brand engagemet. Social CRM and BigData The real value of Social CRM start by analyzing mass of big data from social media in order of applying social intelligence techniques that allow us to classify new customer niches and communities and define appropriated strategies to contact potential customers. Gartner Says that the Market for Social CRM is on pace to surpass $1 Billion in Revenue by Year-End 2012 but in words of Zach Hofer-Shall, Analyst at Forrester Research “Social customer relationship management is hard” (The Social CRM Arms Race Heats ). To succeed brands need three things: Investing in new social tools, investing in consultancy and investing in infrastructure for massive data storage and analysis. Neither CeX or BigData are easy and cheap wins. But what are the customer benefits of such investments? Big Data and Brand Engagement Time is the most valuable asset of todays consumers: tired of information overload, exhausted by the terabytes of offering, anxious because of not having the same fast multichannel experience with their services’ marketers or preferred goods providers than the one they found on their social media. Yes, I know you have read this before- me too. But is real. The motto of the Customer Experience philosophy of providing a consistent experience through multiple touchpoints that makes the relationship customer/brand easier and valuable finds it basis on understanding customer/s preferences and context for which BigData analysis is another imperative. In summary, I believe that using BigData Analysis in combination with appropriated CeX strategies and technologies is a promising direction for achieving: efficiency and marketing cost-savings; growing the customer base; and increasing customer conversion and retention. In a world: The Direction of Future Marketing.

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  • MySQL and Hadoop Integration - Unlocking New Insight

    - by Mat Keep
    “Big Data” offers the potential for organizations to revolutionize their operations. With the volume of business data doubling every 1.2 years, analysts and business users are discovering very real benefits when integrating and analyzing data from multiple sources, enabling deeper insight into their customers, partners, and business processes. As the world’s most popular open source database, and the most deployed database in the web and cloud, MySQL is a key component of many big data platforms, with Hadoop vendors estimating 80% of deployments are integrated with MySQL. The new Guide to MySQL and Hadoop presents the tools enabling integration between the two data platforms, supporting the data lifecycle from acquisition and organisation to analysis and visualisation / decision, as shown in the figure below The Guide details each of these stages and the technologies supporting them: Acquire: Through new NoSQL APIs, MySQL is able to ingest high volume, high velocity data, without sacrificing ACID guarantees, thereby ensuring data quality. Real-time analytics can also be run against newly acquired data, enabling immediate business insight, before data is loaded into Hadoop. In addition, sensitive data can be pre-processed, for example healthcare or financial services records can be anonymized, before transfer to Hadoop. Organize: Data is transferred from MySQL tables to Hadoop using Apache Sqoop. With the MySQL Binlog (Binary Log) API, users can also invoke real-time change data capture processes to stream updates to HDFS. Analyze: Multi-structured data ingested from multiple sources is consolidated and processed within the Hadoop platform. Decide: The results of the analysis are loaded back to MySQL via Apache Sqoop where they inform real-time operational processes or provide source data for BI analytics tools. So how are companies taking advantage of this today? As an example, on-line retailers can use big data from their web properties to better understand site visitors’ activities, such as paths through the site, pages viewed, and comments posted. This knowledge can be combined with user profiles and purchasing history to gain a better understanding of customers, and the delivery of highly targeted offers. Of course, it is not just in the web that big data can make a difference. Every business activity can benefit, with other common use cases including: - Sentiment analysis; - Marketing campaign analysis; - Customer churn modeling; - Fraud detection; - Research and Development; - Risk Modeling; - And more. As the guide discusses, Big Data is promising a significant transformation of the way organizations leverage data to run their businesses. MySQL can be seamlessly integrated within a Big Data lifecycle, enabling the unification of multi-structured data into common data platforms, taking advantage of all new data sources and yielding more insight than was ever previously imaginable. Download the guide to MySQL and Hadoop integration to learn more. I'd also be interested in hearing about how you are integrating MySQL with Hadoop today, and your requirements for the future, so please use the comments on this blog to share your insights.

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  • #MDX in London and speculation about future books

    - by Marco Russo (SQLBI)
    Chris Webb, who wrote the Expert Cube Development with Microsoft SQL Server 2008 Analysis Services book with me and Alberto , is preparing another Introduction to MDX course in London, this time from October 26th to 28th. It is now a three day course (previously it was two day) and you can find every other detail here . You might be wondering whether we are writing something else... well, we don't have plan to release a new edition of the Analysis Services book - after all, all the content of the...(read more)

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  • Understanding Data Science: Recent Studies

    - by Joe Lamantia
    If you need such a deeper understanding of data science than Drew Conway's popular venn diagram model, or Josh Wills' tongue in cheek characterization, "Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician." two relatively recent studies are worth reading.   'Analyzing the Analyzers,' an O'Reilly e-book by Harlan Harris, Sean Patrick Murphy, and Marck Vaisman, suggests four distinct types of data scientists -- effectively personas, in a design sense -- based on analysis of self-identified skills among practitioners.  The scenario format dramatizes the different personas, making what could be a dry statistical readout of survey data more engaging.  The survey-only nature of the data,  the restriction of scope to just skills, and the suggested models of skill-profiles makes this feel like the sort of exercise that data scientists undertake as an every day task; collecting data, analyzing it using a mix of statistical techniques, and sharing the model that emerges from the data mining exercise.  That's not an indictment, simply an observation about the consistent feel of the effort as a product of data scientists, about data science.  And the paper 'Enterprise Data Analysis and Visualization: An Interview Study' by researchers Sean Kandel, Andreas Paepcke, Joseph Hellerstein, and Jeffery Heer considers data science within the larger context of industrial data analysis, examining analytical workflows, skills, and the challenges common to enterprise analysis efforts, and identifying three archetypes of data scientist.  As an interview-based study, the data the researchers collected is richer, and there's correspondingly greater depth in the synthesis.  The scope of the study included a broader set of roles than data scientist (enterprise analysts) and involved questions of workflow and organizational context for analytical efforts in general.  I'd suggest this is useful as a primer on analytical work and workers in enterprise settings for those who need a baseline understanding; it also offers some genuinely interesting nuggets for those already familiar with discovery work. We've undertaken a considerable amount of research into discovery, analytical work/ers, and data science over the past three years -- part of our programmatic approach to laying a foundation for product strategy and highlighting innovation opportunities -- and both studies complement and confirm much of the direct research into data science that we conducted. There were a few important differences in our findings, which I'll share and discuss in upcoming posts.

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  • Using The Data Mining Query Task in SSIS

    SQL Server Integration Services (SSIS) is a Business Intelligence tool which can be used by database developers or administrators to perform Extract, Transform & Load (ETL) operations. In my previous article Using Analysis Services Processing Task & Analysis Services ... [Read Full Article]

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  • WildPackets Monitors Diverse Networks

    WildPackets offers portable network analysis products which are designed for use on enterprise networks and in test and measurement labs, plus distributed network analysis solutions for enterprise-wide applications.

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  • Parent-child hierarchies and unary operators in PowerPivot

    - by Marco Russo (SQLBI)
    Alberto wrote an excellent post describing how to implement the Unary Operator feature (which is present in Analysis Services) in PowerPivot (there was a previous post about parent-child hierarchies, too). I have to say that the solution is not so easy to implement as in Analysis Services, but it just works and, from a practical point of view, it is not so difficult to implement if you understand how it works and accept its limitations (only sum and subtractions are supported). I think that many...(read more)

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  • Search Engine Optimizing

    Search Engine Optimization is a process by which a web site is improved so that it can be more easily found by search engines, rank higher and be found by its target audience. The main components to SEO are: keyword analysis, content analysis, title and meta tags, relevant link building, search engine submission, and maintenance. Below are steps in the process.

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  • Powerful Lessons in Data from the Presidential Election

    - by Christina McKeon
    Now that we’ve had a few days to recover from the U.S. presidential election, it’s a good time to take a step back from politics and look for the customer experience lessons that we can take away. The most powerful lesson is that when you know more about your base, you will have an advantage over your competition. That advantage will translate into you winning and your competition losing. Michael Scherer of TIME was given access to Obama’s data analysts two days before the election. His account is documented in Inside the Secret World of the Data Crunchers Who Helped Obama Win. What we learned from Scherer’s inside view is how well Obama’s team did in getting the right data, analyzing it, and acting on it. This data team recognized how critical it was to break down data silos within the campaign. As Scherer noted, they created “a single system that merged information from pollsters, fundraisers, field workers, consumer databases, and social-media and mobile contacts with the main Democratic voter files in the swing states.” The Obama analysis was so meticulous that they knew which celebrity and which type of celebrity event would help them maximize campaign contributions. With a single system, their data models became more precise. They determined which messages were more successful with specific demographic groups and that who made the calls mattered. Data analysis also led to many other changes in Obama’s campaign including a new ad buying strategy, using social media and applications to tap into supporters’ friends, and using new social news sites. While we did not have that same inside view into Romney’s campaign, much of the post-mortem coverage indicates that Romney’s team did not have the right analysis. As Peter Hamby of CNN wrote in Analysis: Why Romney Lost, “Romney officials had modeled an electorate that looked something like a mix of 2004 and 2008….” That historical data did not account for the changing demographics in the U.S. Does your organization approach data like the Obama or Romney team? Do you really know your base? How well can you predict what is going to happen in your business? If you haven’t already put together a strategy and plan to know more, this week’s civics lesson is a powerful reason to do it sooner rather than later. Your competitors are probably thinking the same thing that you are!

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  • Create Sound and Video Presentation For Your Website

    The concept of web video production offers the business community an exciting opportunity to expand their reach. The technological advances in the speeds of internet transmission now make it possible to take the concept of marketing to another level. The availability of high quality video provides an efficient tool for business to reach an expanding customer base. This article will briefly discuss web video production.

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  • Botnet Malware Sleeps Eight Months Activation, Child Concerns

    Daily Safety Check experts used a computer forensic analysis of a significant botnet that consisted of Carberp and SpyEye malware to come up with the details for their report. The analysis found that the botnet profiled the behavior of the slave computers it infected, similar to surveillance techniques used by law enforcement agencies, for an average of eight months. During the eight months, the botnet analyzed each computer's users and assigned ratings to certain activities to form a complete profile for each. Doing so allowed those behind the scheme to determine which were the most favora...

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  • Tissue Specific Electrochemical Fingerprinting on the NetBeans Platform

    - by Geertjan
    Proteomics and metalloproteomics are rapidly developing interdisciplinary fields providing enormous amounts of data to be classified, evaluated, and interpreted. Approaches offered by bioinformatics and also by biostatistical data analysis and treatment are therefore becoming increasingly relevant. A bioinformatics tool has been developed at universities in Prague and Brno, in the Czech Republic, for analysis and visualization in this domain, on the NetBeans Platform: More info:  http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0049654

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  • Revamp Websites With Custom Design

    The concept of web design encompasses the task of developing hypertext or hypermedia content which a user can access through the World Wide Web. Through web design websites are created. The concept entails more on designing rather than development, i.e. taking care of the graphic elements, look and feel and other aesthetic feature. It is different from professional web development which often concentrates on the technical aspect of the website.

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  • Feature: Lead with Intelligence

    Business efficiency depends on business decisions, and business decisions depend on current, accurate information and powerful analysis. See how Oracle data warehousing, business intelligence, and enterprise performance management solutions deliver the information, analysis, and efficiencies to propel your business ahead of the competition.

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  • Legal Applications of Metamorphic Code

    - by V_P
    Firstly, I would like to state that I already understand the 'vx' applications for Metamorphic code. I am not here to ask a question related to any of those topics as that would be inappropriate in this context. I would like to know if anyone has ever used 'Metamorphic' code in practice, for purposes other than those previously stated, if so, what was the reasoning for using said concept. In essence I am trying to discover a purpose for this concept, if any, other than circumventing anti-virus scanners and the like.

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  • mod perl in apache 2.2 not parsing perl scripts

    - by futureelite7
    Hi, I've set up a fresh Apache 2.2.15 server on windows server 2008 R2 with mod_perl (mod perl v2.0.4 / perl v5.10.1). Mod_perl and Perl 5.10 has been installed and loaded without problems. However, despite my configuration, the mod_perl module is failing to recognize and execute my .pl file, instead opting to print out the perl source instead. What did I do wrong, and how do I make perl process my pl script instead of sending it to the client? My configuration: <VirtualHost *:80> ServerAdmin [email protected] DocumentRoot "C:\Program Files (x86)\AWStats\wwwroot" ServerName analysis.example.com ServerAlias analysis.example.com ErrorLog "logs/analysis.example.com-error.log" CustomLog "logs/analysis.example.com-access.log" common DirectoryIndex index.php index.htm index.html PerlSwitches -T <Directory "C:\Program Files (x86)\AWStats\wwwroot"> Options Indexes FollowSymLinks AllowOverride None Order allow,deny Allow from all </Directory> <Directory "C:\Program Files (x86)\AWStats\wwwroot\cgi-bin"> AllowOverride None Options None Order allow,deny Allow from all <FilesMatch "\.pl$"> SetHandler perl-script # #PerlResponseHandler ModPerl::Registry PerlOptions +ParseHeaders Options +ExecCGI </FilesMatch> </directory> </VirtualHost> Many many thanks for the help!

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  • The way I think about Diagnostic tools

    - by Daniel Moth
    Every software has issues, or as we like to call them "bugs". That is not a discussion point, just a mere fact. It follows that an important skill for developers is to be able to diagnose issues in their code. Of course we need to advance our tools and techniques so we can prevent bugs getting into the code (e.g. unit testing), but beyond designing great software, diagnosing bugs is an equally important skill. To diagnose issues, the most important assets are good techniques, skill, experience, and maybe talent. What also helps is having good diagnostic tools and what helps further is knowing all the features that they offer and how to use them. The following classification is how I like to think of diagnostics. Note that like with any attempt to bucketize anything, you run into overlapping areas and blurry lines. Nevertheless, I will continue sharing my generalizations ;-) It is important to identify at the outset if you are dealing with a performance or a correctness issue. If you have a performance issue, use a profiler. I hear people saying "I am using the debugger to debug a performance issue", and that is fine, but do know that a dedicated profiler is the tool for that job. Just because you don't need them all the time and typically they cost more plus you are not as familiar with them as you are with the debugger, doesn't mean you shouldn't invest in one and instead try to exclusively use the wrong tool for the job. Visual Studio has a profiler and a concurrency visualizer (for profiling multi-threaded apps). If you have a correctness issue, then you have several options - that's next :-) This is how I think of identifying a correctness issue Do you want a tool to find the issue for you at design time? The compiler is such a tool - it gives you an exact list of errors. Compilers now also offer warnings, which is their way of saying "this may be an error, but I am not smart enough to know for sure". There are also static analysis tools, which go a step further than the compiler in identifying issues in your code, sometimes with the aid of code annotations and other times just by pointing them at your raw source. An example is FxCop and much more in Visual Studio 11 Code Analysis. Do you want a tool to find the issue for you with code execution? Just like static tools, there are also dynamic analysis tools that instead of statically analyzing your code, they analyze what your code does dynamically at runtime. Whether you have to setup some unit tests to invoke your code at runtime, or have to manually run your app (and interact with it) under the tool, or have to use a script to execute your binary under the tool… that varies. The result is still a list of issues for you to address after the analysis is complete or a pause of the execution when the first issue is encountered. If a code path was not taken, no analysis for it will exist, obviously. An example is the GPU Race detection tool that I'll be talking about on the C++ AMP team blog. Another example is the MSR concurrency CHESS tool. Do you want you to find the issue at design time using a tool? Perform a code walkthrough on your own or with colleagues. There are code review tools that go beyond just diffing sources, and they help you with that aspect too. For example, there is a new one in Visual Studio 11 and searching with my favorite search engine yielded this article based on the Developer Preview. Do you want you to find the issue with code execution? Use a debugger - let’s break this down further next. This is how I think of debugging: There is post mortem debugging. That means your code has executed and you did something in order to examine what happened during its execution. This can vary from manual printf and other tracing statements to trace events (e.g. ETW) to taking dumps. In all cases, you are left with some artifact that you examine after the fact (after code execution) to discern what took place hoping it will help you find the bug. Learn how to debug dump files in Visual Studio. There is live debugging. I will elaborate on this in a separate post, but this is where you inspect the state of your program during its execution, and try to find what the problem is. More from me in a separate post on live debugging. There is a hybrid of live plus post-mortem debugging. This is for example what tools like IntelliTrace offer. If you are a tools vendor interested in the diagnostics space, it helps to understand where in the above classification your tool excels, where its primary strength is, so you can market it as such. Then it helps to see which of the other areas above your tool touches on, and how you can make it even better there. Finally, see what areas your tool doesn't help at all with, and evaluate whether it should or continue to stay clear. Even though the classification helps us think about this space, the reality is that the best tools are either extremely excellent in only one of this areas, or more often very good across a number of them. Another approach is to offer a toolset covering all areas, with appropriate integration and hand off points from one to the other. Anyway, with that brain dump out of the way, in follow-up posts I will dive into live debugging, and specifically live debugging in Visual Studio - stay tuned if that interests you. Comments about this post by Daniel Moth welcome at the original blog.

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