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  • Much Ado About Nothing: Stub Objects

    - by user9154181
    The Solaris 11 link-editor (ld) contains support for a new type of object that we call a stub object. A stub object is a shared object, built entirely from mapfiles, that supplies the same linking interface as the real object, while containing no code or data. Stub objects cannot be executed — the runtime linker will kill any process that attempts to load one. However, you can link to a stub object as a dependency, allowing the stub to act as a proxy for the real version of the object. You may well wonder if there is a point to producing an object that contains nothing but linking interface. As it turns out, stub objects are very useful for building large bodies of code such as Solaris. In the last year, we've had considerable success in applying them to one of our oldest and thorniest build problems. In this discussion, I will describe how we came to invent these objects, and how we apply them to building Solaris. This posting explains where the idea for stub objects came from, and details our long and twisty journey from hallway idea to standard link-editor feature. I expect that these details are mainly of interest to those who work on Solaris and its makefiles, those who have done so in the past, and those who work with other similar bodies of code. A subsequent posting will omit the history and background details, and instead discuss how to build and use stub objects. If you are mainly interested in what stub objects are, and don't care about the underlying software war stories, I encourage you to skip ahead. The Long Road To Stubs This all started for me with an email discussion in May of 2008, regarding a change request that was filed in 2002, entitled: 4631488 lib/Makefile is too patient: .WAITs should be reduced This CR encapsulates a number of cronic issues with Solaris builds: We build Solaris with a parallel make (dmake) that tries to build as much of the code base in parallel as possible. There is a lot of code to build, and we've long made use of parallelized builds to get the job done quicker. This is even more important in today's world of massively multicore hardware. Solaris contains a large number of executables and shared objects. Executables depend on shared objects, and shared objects can depend on each other. Before you can build an object, you need to ensure that the objects it needs have been built. This implies a need for serialization, which is in direct opposition to the desire to build everying in parallel. To accurately build objects in the right order requires an accurate set of make rules defining the things that depend on each other. This sounds simple, but the reality is quite complex. In practice, having programmers explicitly specify these dependencies is a losing strategy: It's really hard to get right. It's really easy to get it wrong and never know it because things build anyway. Even if you get it right, it won't stay that way, because dependencies between objects can change over time, and make cannot help you detect such drifing. You won't know that you got it wrong until the builds break. That can be a long time after the change that triggered the breakage happened, making it hard to connect the cause and the effect. Usually this happens just before a release, when the pressure is on, its hard to think calmly, and there is no time for deep fixes. As a poor compromise, the libraries in core Solaris were built using a set of grossly incomplete hand written rules, supplemented with a number of dmake .WAIT directives used to group the libraries into sets of non-interacting groups that can be built in parallel because we think they don't depend on each other. From time to time, someone will suggest that we could analyze the built objects themselves to determine their dependencies and then generate make rules based on those relationships. This is possible, but but there are complications that limit the usefulness of that approach: To analyze an object, you have to build it first. This is a classic chicken and egg scenario. You could analyze the results of a previous build, but then you're not necessarily going to get accurate rules for the current code. It should be possible to build the code without having a built workspace available. The analysis will take time, and remember that we're constantly trying to make builds faster, not slower. By definition, such an approach will always be approximate, and therefore only incremantally more accurate than the hand written rules described above. The hand written rules are fast and cheap, while this idea is slow and complex, so we stayed with the hand written approach. Solaris was built that way, essentially forever, because these are genuinely difficult problems that had no easy answer. The makefiles were full of build races in which the right outcomes happened reliably for years until a new machine or a change in build server workload upset the accidental balance of things. After figuring out what had happened, you'd mutter "How did that ever work?", add another incomplete and soon to be inaccurate make dependency rule to the system, and move on. This was not a satisfying solution, as we tend to be perfectionists in the Solaris group, but we didn't have a better answer. It worked well enough, approximately. And so it went for years. We needed a different approach — a new idea to cut the Gordian Knot. In that discussion from May 2008, my fellow linker-alien Rod Evans had the initial spark that lead us to a game changing series of realizations: The link-editor is used to link objects together, but it only uses the ELF metadata in the object, consisting of symbol tables, ELF versioning sections, and similar data. Notably, it does not look at, or understand, the machine code that makes an object useful at runtime. If you had an object that only contained the ELF metadata for a dependency, but not the code or data, the link-editor would find it equally useful for linking, and would never know the difference. Call it a stub object. In the core Solaris OS, we require all objects to be built with a link-editor mapfile that describes all of its publically available functions and data. Could we build a stub object using the mapfile for the real object? It ought to be very fast to build stub objects, as there are no input objects to process. Unlike the real object, stub objects would not actually require any dependencies, and so, all of the stubs for the entire system could be built in parallel. When building the real objects, one could link against the stub objects instead of the real dependencies. This means that all the real objects can be built built in parallel too, without any serialization. We could replace a system that requires perfect makefile rules with a system that requires no ordering rules whatsoever. The results would be considerably more robust. We immediately realized that this idea had potential, but also that there were many details to sort out, lots of work to do, and that perhaps it wouldn't really pan out. As is often the case, it would be necessary to do the work and see how it turned out. Following that conversation, I set about trying to build a stub object. We determined that a faithful stub has to do the following: Present the same set of global symbols, with the same ELF versioning, as the real object. Functions are simple — it suffices to have a symbol of the right type, possibly, but not necessarily, referencing a null function in its text segment. Copy relocations make data more complicated to stub. The possibility of a copy relocation means that when you create a stub, the data symbols must have the actual size of the real data. Any error in this will go uncaught at link time, and will cause tragic failures at runtime that are very hard to diagnose. For reasons too obscure to go into here, involving tentative symbols, it is also important that the data reside in bss, or not, matching its placement in the real object. If the real object has more than one symbol pointing at the same data item, we call these aliased symbols. All data symbols in the stub object must exhibit the same aliasing as the real object. We imagined the stub library feature working as follows: A command line option to ld tells it to produce a stub rather than a real object. In this mode, only mapfiles are examined, and any object or shared libraries on the command line are are ignored. The extra information needed (function or data, size, and bss details) would be added to the mapfile. When building the real object instead of the stub, the extra information for building stubs would be validated against the resulting object to ensure that they match. In exploring these ideas, I immediately run headfirst into the reality of the original mapfile syntax, a subject that I would later write about as The Problem(s) With Solaris SVR4 Link-Editor Mapfiles. The idea of extending that poor language was a non-starter. Until a better mapfile syntax became available, which seemed unlikely in 2008, the solution could not involve extentions to the mapfile syntax. Instead, we cooked up the idea (hack) of augmenting mapfiles with stylized comments that would carry the necessary information. A typical definition might look like: # DATA(i386) __iob 0x3c0 # DATA(amd64,sparcv9) __iob 0xa00 # DATA(sparc) __iob 0x140 iob; A further problem then became clear: If we can't extend the mapfile syntax, then there's no good way to extend ld with an option to produce stub objects, and to validate them against the real objects. The idea of having ld read comments in a mapfile and parse them for content is an unacceptable hack. The entire point of comments is that they are strictly for the human reader, and explicitly ignored by the tool. Taking all of these speed bumps into account, I made a new plan: A perl script reads the mapfiles, generates some small C glue code to produce empty functions and data definitions, compiles and links the stub object from the generated glue code, and then deletes the generated glue code. Another perl script used after both objects have been built, to compare the real and stub objects, using data from elfdump, and validate that they present the same linking interface. By June 2008, I had written the above, and generated a stub object for libc. It was a useful prototype process to go through, and it allowed me to explore the ideas at a deep level. Ultimately though, the result was unsatisfactory as a basis for real product. There were so many issues: The use of stylized comments were fine for a prototype, but not close to professional enough for shipping product. The idea of having to document and support it was a large concern. The ideal solution for stub objects really does involve having the link-editor accept the same arguments used to build the real object, augmented with a single extra command line option. Any other solution, such as our prototype script, will require makefiles to be modified in deeper ways to support building stubs, and so, will raise barriers to converting existing code. A validation script that rederives what the linker knew when it built an object will always be at a disadvantage relative to the actual linker that did the work. A stub object should be identifyable as such. In the prototype, there was no tag or other metadata that would let you know that they weren't real objects. Being able to identify a stub object in this way means that the file command can tell you what it is, and that the runtime linker can refuse to try and run a program that loads one. At that point, we needed to apply this prototype to building Solaris. As you might imagine, the task of modifying all the makefiles in the core Solaris code base in order to do this is a massive task, and not something you'd enter into lightly. The quality of the prototype just wasn't good enough to justify that sort of time commitment, so I tabled the project, putting it on my list of long term things to think about, and moved on to other work. It would sit there for a couple of years. Semi-coincidentally, one of the projects I tacked after that was to create a new mapfile syntax for the Solaris link-editor. We had wanted to do something about the old mapfile syntax for many years. Others before me had done some paper designs, and a great deal of thought had already gone into the features it should, and should not have, but for various reasons things had never moved beyond the idea stage. When I joined Sun in late 2005, I got involved in reviewing those things and thinking about the problem. Now in 2008, fresh from relearning for the Nth time why the old mapfile syntax was a huge impediment to linker progress, it seemed like the right time to tackle the mapfile issue. Paving the way for proper stub object support was not the driving force behind that effort, but I certainly had them in mind as I moved forward. The new mapfile syntax, which we call version 2, integrated into Nevada build snv_135 in in February 2010: 6916788 ld version 2 mapfile syntax PSARC/2009/688 Human readable and extensible ld mapfile syntax In order to prove that the new mapfile syntax was adequate for general purpose use, I had also done an overhaul of the ON consolidation to convert all mapfiles to use the new syntax, and put checks in place that would ensure that no use of the old syntax would creep back in. That work went back into snv_144 in June 2010: 6916796 OSnet mapfiles should use version 2 link-editor syntax That was a big putback, modifying 517 files, adding 18 new files, and removing 110 old ones. I would have done this putback anyway, as the work was already done, and the benefits of human readable syntax are obvious. However, among the justifications listed in CR 6916796 was this We anticipate adding additional features to the new mapfile language that will be applicable to ON, and which will require all sharable object mapfiles to use the new syntax. I never explained what those additional features were, and no one asked. It was premature to say so, but this was a reference to stub objects. By that point, I had already put together a working prototype link-editor with the necessary support for stub objects. I was pleased to find that building stubs was indeed very fast. On my desktop system (Ultra 24), an amd64 stub for libc can can be built in a fraction of a second: % ptime ld -64 -z stub -o stubs/libc.so.1 -G -hlibc.so.1 \ -ztext -zdefs -Bdirect ... real 0.019708910 user 0.010101680 sys 0.008528431 In order to go from prototype to integrated link-editor feature, I knew that I would need to prove that stub objects were valuable. And to do that, I knew that I'd have to switch the Solaris ON consolidation to use stub objects and evaluate the outcome. And in order to do that experiment, ON would first need to be converted to version 2 mapfiles. Sub-mission accomplished. Normally when you design a new feature, you can devise reasonably small tests to show it works, and then deploy it incrementally, letting it prove its value as it goes. The entire point of stub objects however was to demonstrate that they could be successfully applied to an extremely large and complex code base, and specifically to solve the Solaris build issues detailed above. There was no way to finesse the matter — in order to move ahead, I would have to successfully use stub objects to build the entire ON consolidation and demonstrate their value. In software, the need to boil the ocean can often be a warning sign that things are trending in the wrong direction. Conversely, sometimes progress demands that you build something large and new all at once. A big win, or a big loss — sometimes all you can do is try it and see what happens. And so, I spent some time staring at ON makefiles trying to get a handle on how things work, and how they'd have to change. It's a big and messy world, full of complex interactions, unspecified dependencies, special cases, and knowledge of arcane makefile features... ...and so, I backed away, put it down for a few months and did other work... ...until the fall, when I felt like it was time to stop thinking and pondering (some would say stalling) and get on with it. Without stubs, the following gives a simplified high level view of how Solaris is built: An initially empty directory known as the proto, and referenced via the ROOT makefile macro is established to receive the files that make up the Solaris distribution. A top level setup rule creates the proto area, and performs operations needed to initialize the workspace so that the main build operations can be launched, such as copying needed header files into the proto area. Parallel builds are launched to build the kernel (usr/src/uts), libraries (usr/src/lib), and commands. The install makefile target builds each item and delivers a copy to the proto area. All libraries and executables link against the objects previously installed in the proto, implying the need to synchronize the order in which things are built. Subsequent passes run lint, and do packaging. Given this structure, the additions to use stub objects are: A new second proto area is established, known as the stub proto and referenced via the STUBROOT makefile macro. The stub proto has the same structure as the real proto, but is used to hold stub objects. All files in the real proto are delivered as part of the Solaris product. In contrast, the stub proto is used to build the product, and then thrown away. A new target is added to library Makefiles called stub. This rule builds the stub objects. The ld command is designed so that you can build a stub object using the same ld command line you'd use to build the real object, with the addition of a single -z stub option. This means that the makefile rules for building the stub objects are very similar to those used to build the real objects, and many existing makefile definitions can be shared between them. A new target is added to the Makefiles called stubinstall which delivers the stub objects built by the stub rule into the stub proto. These rules reuse much of existing plumbing used by the existing install rule. The setup rule runs stubinstall over the entire lib subtree as part of its initialization. All libraries and executables link against the objects in the stub proto rather than the main proto, and can therefore be built in parallel without any synchronization. There was no small way to try this that would yield meaningful results. I would have to take a leap of faith and edit approximately 1850 makefiles and 300 mapfiles first, trusting that it would all work out. Once the editing was done, I'd type make and see what happened. This took about 6 weeks to do, and there were many dark days when I'd question the entire project, or struggle to understand some of the many twisted and complex situations I'd uncover in the makefiles. I even found a couple of new issues that required changes to the new stub object related code I'd added to ld. With a substantial amount of encouragement and help from some key people in the Solaris group, I eventually got the editing done and stub objects for the entire workspace built. I found that my desktop system could build all the stub objects in the workspace in roughly a minute. This was great news, as it meant that use of the feature is effectively free — no one was likely to notice or care about the cost of building them. After another week of typing make, fixing whatever failed, and doing it again, I succeeded in getting a complete build! The next step was to remove all of the make rules and .WAIT statements dedicated to controlling the order in which libraries under usr/src/lib are built. This came together pretty quickly, and after a few more speed bumps, I had a workspace that built cleanly and looked like something you might actually be able to integrate someday. This was a significant milestone, but there was still much left to do. I turned to doing full nightly builds. Every type of build (open, closed, OpenSolaris, export, domestic) had to be tried. Each type failed in a new and unique way, requiring some thinking and rework. As things came together, I became aware of things that could have been done better, simpler, or cleaner, and those things also required some rethinking, the seeking of wisdom from others, and some rework. After another couple of weeks, it was in close to final form. My focus turned towards the end game and integration. This was a huge workspace, and needed to go back soon, before changes in the gate would made merging increasingly difficult. At this point, I knew that the stub objects had greatly simplified the makefile logic and uncovered a number of race conditions, some of which had been there for years. I assumed that the builds were faster too, so I did some builds intended to quantify the speedup in build time that resulted from this approach. It had never occurred to me that there might not be one. And so, I was very surprised to find that the wall clock build times for a stock ON workspace were essentially identical to the times for my stub library enabled version! This is why it is important to always measure, and not just to assume. One can tell from first principles, based on all those removed dependency rules in the library makefile, that the stub object version of ON gives dmake considerably more opportunities to overlap library construction. Some hypothesis were proposed, and shot down: Could we have disabled dmakes parallel feature? No, a quick check showed things being build in parallel. It was suggested that we might be I/O bound, and so, the threads would be mostly idle. That's a plausible explanation, but system stats didn't really support it. Plus, the timing between the stub and non-stub cases were just too suspiciously identical. Are our machines already handling as much parallelism as they are capable of, and unable to exploit these additional opportunities? Once again, we didn't see the evidence to back this up. Eventually, a more plausible and obvious reason emerged: We build the libraries and commands (usr/src/lib, usr/src/cmd) in parallel with the kernel (usr/src/uts). The kernel is the long leg in that race, and so, wall clock measurements of build time are essentially showing how long it takes to build uts. Although it would have been nice to post a huge speedup immediately, we can take solace in knowing that stub objects simplify the makefiles and reduce the possibility of race conditions. The next step in reducing build time should be to find ways to reduce or overlap the uts part of the builds. When that leg of the build becomes shorter, then the increased parallelism in the libs and commands will pay additional dividends. Until then, we'll just have to settle for simpler and more robust. And so, I integrated the link-editor support for creating stub objects into snv_153 (November 2010) with 6993877 ld should produce stub objects PSARC/2010/397 ELF Stub Objects followed by the work to convert the ON consolidation in snv_161 (February 2011) with 7009826 OSnet should use stub objects 4631488 lib/Makefile is too patient: .WAITs should be reduced This was a huge putback, with 2108 modified files, 8 new files, and 2 removed files. Due to the size, I was allowed a window after snv_160 closed in which to do the putback. It went pretty smoothly for something this big, a few more preexisting race conditions would be discovered and addressed over the next few weeks, and things have been quiet since then. Conclusions and Looking Forward Solaris has been built with stub objects since February. The fact that developers no longer specify the order in which libraries are built has been a big success, and we've eliminated an entire class of build error. That's not to say that there are no build races left in the ON makefiles, but we've taken a substantial bite out of the problem while generally simplifying and improving things. The introduction of a stub proto area has also opened some interesting new possibilities for other build improvements. As this article has become quite long, and as those uses do not involve stub objects, I will defer that discussion to a future article.

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  • Customer Support Spotlight: Clemson University

    - by cwarticki
    I've begun a Customer Support Spotlight series that highlights our wonderful customers and Oracle loyalists.  A week ago I visited Clemson University.  As I travel to visit and educate our customers, I provide many useful tips/tricks and support best practices (as found on my blog and twitter). Most of all, I always discover an Oracle gem who deserves recognition for their hard work and advocacy. Meet George Manley.  George is a Storage Engineer who has worked in Clemson's Data Center all through college, partially in the Hardware Architecture group and partially in the Storage group. George and the rest of the Storage Team work with most all of the storage technologies that they have here at Clemson. This includes a wide array of different vendors' disk arrays, with the most of them being Oracle/Sun 2540's.  He also works with SAM/QFS, ACSLS, and our SL8500 Tape Libraries (all three Oracle/Sun products). (pictured L to R, Matt Schoger (Oracle), Mark Flores (Oracle) and George Manley) George was kind enough to take us for a data center tour.  It was amazing.  I rarely get to see the inside of data centers, and this one was massive. Clemson Computing and Information Technology’s physical resources include the main data center located in the Information Technology Center at the Innovation Campus and Technology Park. The core of Clemson’s computing infrastructure, the data center has 21,000 sq ft of raised floor and is powered by a 14MW substation. The ITC power capacity is 4.5MW.  The data center is the home of both enterprise and HPC systems, and is staffed by CCIT staff on a 24 hour basis from a state of the art network operations center within the ITC. A smaller business continuance data center is located on the main campus.  The data center serves a wide variety of purposes including HPC (supercomputing) resources which are shared with other Universities throughout the state, the state's medicaid processing system, and nearly all other needs for Clemson University. Yes, that's no typo (14,256 cores and 37TB of memory!!! Thanks for the tour George and thank you very much for your time.  The tour was fantastic. I enjoyed getting to know your team and I look forward to many successes from Clemson using Oracle products. -Chris WartickiGlobal Customer Management

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  • A New Threat To Web Applications: Connection String Parameter Pollution (CSPP)

    - by eric.maurice
    Hi, this is Shaomin Wang. I am a security analyst in Oracle's Security Alerts Group. My primary responsibility is to evaluate the security vulnerabilities reported externally by security researchers on Oracle Fusion Middleware and to ensure timely resolution through the Critical Patch Update. Today, I am going to talk about a serious type of attack: Connection String Parameter Pollution (CSPP). Earlier this year, at the Black Hat DC 2010 Conference, two Spanish security researchers, Jose Palazon and Chema Alonso, unveiled a new class of security vulnerabilities, which target insecure dynamic connections between web applications and databases. The attack called Connection String Parameter Pollution (CSPP) exploits specifically the semicolon delimited database connection strings that are constructed dynamically based on the user inputs from web applications. CSPP, if carried out successfully, can be used to steal user identities and hijack web credentials. CSPP is a high risk attack because of the relative ease with which it can be carried out (low access complexity) and the potential results it can have (high impact). In today's blog, we are going to first look at what connection strings are and then review the different ways connection string injections can be leveraged by malicious hackers. We will then discuss how CSPP differs from traditional connection string injection, and the measures organizations can take to prevent this kind of attacks. In web applications, a connection string is a set of values that specifies information to connect to backend data repositories, in most cases, databases. The connection string is passed to a provider or driver to initiate a connection. Vendors or manufacturers write their own providers for different databases. Since there are many different providers and each provider has multiple ways to make a connection, there are many different ways to write a connection string. Here are some examples of connection strings from Oracle Data Provider for .Net/ODP.Net: Oracle Data Provider for .Net / ODP.Net; Manufacturer: Oracle; Type: .NET Framework Class Library: - Using TNS Data Source = orcl; User ID = myUsername; Password = myPassword; - Using integrated security Data Source = orcl; Integrated Security = SSPI; - Using the Easy Connect Naming Method Data Source = username/password@//myserver:1521/my.server.com - Specifying Pooling parameters Data Source=myOracleDB; User Id=myUsername; Password=myPassword; Min Pool Size=10; Connection Lifetime=120; Connection Timeout=60; Incr Pool Size=5; Decr Pool Size=2; There are many variations of the connection strings, but the majority of connection strings are key value pairs delimited by semicolons. Attacks on connection strings are not new (see for example, this SANS White Paper on Securing SQL Connection String). Connection strings are vulnerable to injection attacks when dynamic string concatenation is used to build connection strings based on user input. When the user input is not validated or filtered, and malicious text or characters are not properly escaped, an attacker can potentially access sensitive data or resources. For a number of years now, vendors, including Oracle, have created connection string builder class tools to help developers generate valid connection strings and potentially prevent this kind of vulnerability. Unfortunately, not all application developers use these utilities because they are not aware of the danger posed by this kind of attacks. So how are Connection String parameter Pollution (CSPP) attacks different from traditional Connection String Injection attacks? First, let's look at what parameter pollution attacks are. Parameter pollution is a technique, which typically involves appending repeating parameters to the request strings to attack the receiving end. Much of the public attention around parameter pollution was initiated as a result of a presentation on HTTP Parameter Pollution attacks by Stefano Di Paola and Luca Carettoni delivered at the 2009 Appsec OWASP Conference in Poland. In HTTP Parameter Pollution attacks, an attacker submits additional parameters in HTTP GET/POST to a web application, and if these parameters have the same name as an existing parameter, the web application may react in different ways depends on how the web application and web server deal with multiple parameters with the same name. When applied to connections strings, the rule for the majority of database providers is the "last one wins" algorithm. If a KEYWORD=VALUE pair occurs more than once in the connection string, the value associated with the LAST occurrence is used. This opens the door to some serious attacks. By way of example, in a web application, a user enters username and password; a subsequent connection string is generated to connect to the back end database. Data Source = myDataSource; Initial Catalog = db; Integrated Security = no; User ID = myUsername; Password = XXX; In the password field, if the attacker enters "xxx; Integrated Security = true", the connection string becomes, Data Source = myDataSource; Initial Catalog = db; Integrated Security = no; User ID = myUsername; Password = XXX; Intergrated Security = true; Under the "last one wins" principle, the web application will then try to connect to the database using the operating system account under which the application is running to bypass normal authentication. CSPP poses serious risks for unprepared organizations. It can be particularly dangerous if an Enterprise Systems Management web front-end is compromised, because attackers can then gain access to control panels to configure databases, systems accounts, etc. Fortunately, organizations can take steps to prevent this kind of attacks. CSPP falls into the Injection category of attacks like Cross Site Scripting or SQL Injection, which are made possible when inputs from users are not properly escaped or sanitized. Escaping is a technique used to ensure that characters (mostly from user inputs) are treated as data, not as characters, that is relevant to the interpreter's parser. Software developers need to become aware of the danger of these attacks and learn about the defenses mechanism they need to introduce in their code. As well, software vendors need to provide templates or classes to facilitate coding and eliminate developers' guesswork for protecting against such vulnerabilities. Oracle has introduced the OracleConnectionStringBuilder class in Oracle Data Provider for .NET. Using this class, developers can employ a configuration file to provide the connection string and/or dynamically set the values through key/value pairs. It makes creating connection strings less error-prone and easier to manager, and ultimately using the OracleConnectionStringBuilder class provides better security against injection into connection strings. For More Information: - The OracleConnectionStringBuilder is located at http://download.oracle.com/docs/cd/B28359_01/win.111/b28375/OracleConnectionStringBuilderClass.htm - Oracle has developed a publicly available course on preventing SQL Injections. The Server Technologies Curriculum course "Defending Against SQL Injection Attacks!" is located at http://st-curriculum.oracle.com/tutorial/SQLInjection/index.htm - The OWASP web site also provides a number of useful resources. It is located at http://www.owasp.org/index.php/Main_Page

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  • I have an apache process that takes 98% CPU. How can I find what apache call it runs?

    - by Nir
    As you can see below, a single Apache process hangs and takes large amount of CPU resources. How can I find what http call this apache process runs? PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 12554 www-data 20 0 776m 285m 199m R 97 3.7 67:15.84 apache2 14580 www-data 20 0 748m 372m 314m S 4 4.8 0:13.60 apache2 12561 www-data 20 0 784m 416m 322m S 3 5.4 0:58.10 apache2 12592 www-data 20 0 785m 427m 332m S 2 5.6 0:57.06 apache2

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  • How one decision can turn web services to hell

    - by DigiMortal
    In this posting I will show you how one stupid decision may turn developers life to hell. There is a project where bunch of complex applications exchange data frequently and it is very hard to change something without additional expenses. Well, one analyst thought that string is silver bullet of web services. Read what happened. Bad bad mistake In the early stages of integration project there was analyst who also established architecture and technical design for web services. There was one very bad mistake this analyst made: All data must be converted to strings before exchange! Yes, that’s correct, this was the requirement. All integers, decimals and dates are coming in and going out as strings. There was also explanation for this requirement: This way we can avoid data type conversion errors! Well, this guy works somewhere else already and I hope he works in some burger restaurant – far away from computers. Consequences If you first look at this requirement it may seem like little annoying piece of crap you can easily survive. But let’s see the real consequences one stupid decision can cause: hell load of data conversions are done by receiving applications and SSIS packages, SSIS packages are not error prone and they depend heavily on strings they get from different services, there are more than one format per type that is used in different services, for larger amounts of data all these conversion tasks slow down the work of integration packages, practically all developers have been in hurry with some SSIS import tasks and some fields that are not used in different calculations in SSAS cube are imported without data conversions (by example, some prices are strings in format “1.021 $”). The most painful problem for developers is the part of data conversions because they don’t expect that there is such a stupid requirement stated and therefore they are not able to estimate the time their tasks take on these web services. Also developers must be prepared for cases when suddenly some service sends data that is not in acceptable format and they must solve the problems ASAP. This puts unexpected load on developers and they are not very happy with it because they can’t understand why they have to live with this horror if it is possible to fix. What to do if you see something like this? Well, explain the problem to customer and demand special tasks to project schedule to get this mess solved before going on with new developments. It is cheaper to solve the problems now that later.

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  • The SPARC SuperCluster

    - by Karoly Vegh
    Oracle has been providing a lead in the Engineered Systems business for quite a while now, in accordance with the motto "Hardware and Software Engineered to Work Together." Indeed it is hard to find a better definition of these systems.  Allow me to summarize the idea. It is:  Build a compute platform optimized to run your technologies Develop application aware, intelligently caching storage components Take an impressively fast network technology interconnecting it with the compute nodes Tune the application to scale with the nodes to yet unseen performance Reduce the amount of data moving via compression Provide this all in a pre-integrated single product with a single-pane management interface All these ideas have been around in IT for quite some time now. The real Oracle advantage is adding the last one to put these all together. Oracle has built quite a portfolio of Engineered Systems, to run its technologies - and run those like they never ran before. In this post I'll focus on one of them that serves as a consolidation demigod, a multi-purpose engineered system.  As you probably have guessed, I am talking about the SPARC SuperCluster. It has many great features inherited from its predecessors, and it adds several new ones. Allow me to pick out and elaborate about some of the most interesting ones from a technological point of view.  I. It is the SPARC SuperCluster T4-4. That is, as compute nodes, it includes SPARC T4-4 servers that we learned to appreciate and respect for their features: The SPARC T4 CPUs: Each CPU has 8 cores, each core runs 8 threads. The SPARC T4-4 servers have 4 sockets. That is, a single compute node can in parallel, simultaneously  execute 256 threads. Now, a full-rack SPARC SuperCluster has 4 of these servers on board. Remember the keyword demigod.  While retaining the forerunner SPARC T3's exceptional throughput, the SPARC T4 CPUs raise the bar with single performance too - a humble 5x better one than their ancestors.  actually, the SPARC T4 CPU cores run in both single-threaded and multi-threaded mode, and switch between these two on-the-fly, fulfilling not only single-threaded OR multi-threaded applications' needs, but even mixed requirements (like in database workloads!). Data security, anyone? Every SPARC T4 CPU core has a built-in encryption engine, that is, encryption algorithms cast into silicon.  A PCI controller right on the chip for customers who need I/O performance.  Built-in, no-cost Virtualization:  Oracle VM for SPARC (the former LDoms or Logical Domains) is not a server-emulation virtualization technology but rather a serverpartitioning one, the hypervisor runs in the server firmware, and all the VMs' HW resources (I/O, CPU, memory) are accessed natively, without performance overhead.  This enables customers to run a number of Solaris 10 and Solaris 11 VMs separated, independent of each other within a physical server II. For Database performance, it includes Exadata Storage Cells - one of the main reasons why the Exadata Database Machine performs at diabolic speed. What makes them important? They provide DB backend storage for your Oracle Databases to run on the SPARC SuperCluster, that is what they are built and tuned for DB performance.  These storage cells are SQL-aware.  That is, if a SPARC T4 database compute node executes a query, it doesn't simply request tons of raw datablocks from the storage, filters the received data, and throws away most of it where the statement doesn't apply, but provides the SQL query to the storage node too. The storage cell software speaks SQL, that is, it is able to prefilter and through that transfer only the relevant data. With this, the traffic between database nodes and storage cells is reduced immensely. Less I/O is a good thing - as they say, all the CPUs of the world do one thing just as fast as any other - and that is waiting for I/O.  They don't only pre-filter, but also provide data preprocessing features - e.g. if a DB-node requests an aggregate of data, they can calculate it, and handover only the results, not the whole set. Again, less data to transfer.  They support the magical HCC, (Hybrid Columnar Compression). That is, data can be stored in a precompressed form on the storage. Less data to transfer.  Of course one can't simply rely on disks for performance, there is Flash Storage included there for caching.  III. The low latency, high-speed backbone network: InfiniBand, that interconnects all the members with: Real High Speed: 40 Gbit/s. Full Duplex, of course. Oh, and a really low latency.  RDMA. Remote Direct Memory Access. This technology allows the DB nodes to do exactly that. Remotely, directly placing SQL commands into the Memory of the storage cells. Dodging all the network-stack bottlenecks, avoiding overhead, placing requests directly into the process queue.  You can also run IP over InfiniBand if you please - that's the way the compute nodes can communicate with each other.  IV. Including a general-purpose storage too: the ZFSSA, which is a unified storage, providing NAS and SAN access too, with the following features:  NFS over RDMA over InfiniBand. Nothing is faster network-filesystem-wise.  All the ZFS features onboard, hybrid storage pools, compression, deduplication, snapshot, replication, NFS and CIFS shares Storageheads in a HA-Cluster configuration providing availability of the data  DTrace Live Analytics in a web-based Administration UI Being a general purpose application data storage for your non-database applications running on the SPARC SuperCluster over whichever protocol they prefer, easily replicating, snapshotting, cloning data for them.  There's a lot of great technology included in Oracle's SPARC SuperCluster, we have talked its interior through. As for external scalability: you can start with a half- of full- rack SPARC SuperCluster, and scale out to several racks - that is, stacking not separate full-rack SPARC SuperClusters, but extending always one large instance of the size of several full-racks. Yes, over InfiniBand network. Add racks as you grow.  What technologies shall run on it? SPARC SuperCluster is a general purpose scaleout consolidation/cloud environment. You can run Oracle Databases with RAC scaling, or Oracle Weblogic (end enjoy the SPARC T4's advantages to run Java). Remember, Oracle technologies have been integrated with the Oracle Engineered Systems - this is the Oracle on Oracle advantage. But you can run other software environments such as SAP if you please too. Run any application that runs on Oracle Solaris 10 or Solaris 11. Separate them in Virtual Machines, or even Oracle Solaris Zones, monitor and manage those from a central UI. Here the key takeaways once again: The SPARC SuperCluster: Is a pre-integrated Engineered System Contains SPARC T4-4 servers with built-in virtualization, cryptography, dynamic threading Contains the Exadata storage cells that intelligently offload the burden of the DB-nodes  Contains a highly available ZFS Storage Appliance, that provides SAN/NAS storage in a unified way Combines all these elements over a high-speed, low-latency backbone network implemented with InfiniBand Can grow from a single half-rack to several full-rack size Supports the consolidation of hundreds of applications To summarize: All these technologies are great by themselves, but the real value is like in every other Oracle Engineered System: Integration. All these technologies are tuned to perform together. Together they are way more than the sum of all - and a careful and actually very time consuming integration process is necessary to orchestrate all these for performance. The SPARC SuperCluster's goal is to enable infrastructure operations and offer a pre-integrated solution that can be architected and delivered in hours instead of months of evaluations and tests. The tedious and most importantly time and resource consuming part of the work - testing and evaluating - has been done.  Now go, provide services.   -- charlie  

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  • Database Security Events in April

    - by Troy Kitch
    Wed, Apr 18, Executive Oracle Database Security Round Table - Tampa, FL Tue, Apr 24, ISC(2) Leadership Regional Event Series - San Diego, CA April 24 - May 17,  Independent Oracle Users Group Enterprise Data at Risk Seminar Series Tue, Apr 24 IOUG Enterprise Data at Risk Seminar Series - Toronto Wed, Apr 25 IOUG Enterprise Data at Risk Seminar Series - New York Thu, Apr 26 IOUG Enterprise Data at Risk Seminar Series - Boston Thu, Apr 26 ISC(2) Leadership Regional Event Series - San Jose, CA

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  • Hype and LINQ

    - by Tony Davis
    "Tired of querying in antiquated SQL?" I blinked in astonishment when I saw this headline on the LinqPad site. Warming to its theme, the site suggests that what we need is to "kiss goodbye to SSMS", and instead use LINQ, a modern query language! Elsewhere, there is an article entitled "Why LINQ beats SQL". The designers of LINQ, along with many DBAs, would, I'm sure, cringe with embarrassment at the suggestion that LINQ and SQL are, in any sense, competitive ways of doing the same thing. In fact what LINQ really is, at last, is an efficient, declarative language for C# and VB programmers to access or manipulate data in objects, local data stores, ORMs, web services, data repositories, and, yes, even relational databases. The fact is that LINQ is essentially declarative programming in a .NET language, and so in many ways encourages developers into a "SQL-like" mindset, even though they are not directly writing SQL. In place of imperative logic and loops, it uses various expressions, operators and declarative logic to build up an "expression tree" describing only what data is required, not the operations to be performed to get it. This expression tree is then parsed by the language compiler, and the result, when used against a relational database, is a SQL string that, while perhaps not always perfect, is often correctly parameterized and certainly no less "optimal" than what is achieved when a developer applies blunt, imperative logic to the SQL language. From a developer standpoint, it is a mistake to consider LINQ simply as a substitute means of querying SQL Server. The strength of LINQ is that that can be used to access any data source, for which a LINQ provider exists. Microsoft supplies built-in providers to access not just SQL Server, but also XML documents, .NET objects, ADO.NET datasets, and Entity Framework elements. LINQ-to-Objects is particularly interesting in that it allows a declarative means to access and manipulate arrays, collections and so on. Furthermore, as Michael Sorens points out in his excellent article on LINQ, there a whole host of third-party LINQ providers, that offers a simple way to get at data in Excel, Google, Flickr and much more, without having to learn a new interface or language. Of course, the need to be generic enough to deal with a range of data sources, from something as mundane as a text file to as esoteric as a relational database, means that LINQ is a compromise and so has inherent limitations. However, it is a powerful and beautifully compact language and one that, at least in its "query syntax" guise, is accessible to developers and DBAs alike. Perhaps there is still hope that LINQ can fulfill Phil Factor's lobster-induced fantasy of a language that will allow us to "treat all data objects, whether Word files, Excel files, XML, relational databases, text files, HTML files, registry files, LDAPs, Outlook and so on, in the same logical way, as linked databases, and extract the metadata, create the entities and relationships in the same way, and use the same SQL syntax to interrogate, create, read, write and update them." Cheers, Tony.

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  • PHP may be executing as a "privileged" group and user, which could be a serious security vulnerability

    - by Martin
    I ran some security tests on a Ubuntu 12.04 Server, and I've got these warnings : PHP may be executing as a "privileged" group, which could be a serious security vulnerability. PHP may be executing as a "privileged" user, which could be a serious security vulnerability. In /etc/apache2/envvars, I have this: export APACHE_RUN_USER=www-data export APACHE_RUN_GROUP=www-data And all files in /var/www are having these user/group: www-data:www-data Am I setting this correctly? What should I do to fix this problem?

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  • Azure Diagnostics: The Bad, The Ugly, and a Better Way

    - by jasont
    If you’re a .Net web developer today, no doubt you’ve enjoyed watching Windows Azure grow up over the past couple of years. The platform has scaled, stabilized (mostly), and added on a slew of great (and sometimes overdue) features. What was once just an endpoint to host a solution, developers today have tremendous flexibility and options in the platform. Organizations are building new solutions and offerings on the platform, and others have, or are in the process of, migrating existing applications out of their own data centers into the Azure cloud. Whether new application development or migrating legacy, every development shop and IT organization needs to monitor their applications in the cloud, the same as they do on premises. Azure Diagnostics has some capabilities, but what I constantly hear from users is that it’s either (a) not enough, or (b) too cumbersome to set up. Today, Stackify is happy to announce that we fully support Azure deployments, just the same as your on-premises deployments. Let’s take a look below and compare and contrast the options. Azure Diagnostics Let’s crack open the Windows Azure documentation on Azure Diagnostics and see just how easy it is to use. The high level steps are:   Step 1: Import the Diagnostics Oh, I’ve already deployed my app without the diagnostics module. Guess I can’t do anything until I do this and re-deploy. Step 2: Configure the Diagnostics (and multiple sub-steps) Do I want it all? Or just pieces of it? Whoops, forgot to include a specific performance counter, I guess I’ll have to deploy again. Wait a minute… I have to specifically code these performance counters into my role’s OnStart() method, compile and deploy again? And query and consume it myself? Step 3: (Optional) Permanently store diagnostic data Lucky for me, Azure storage has gotten pretty cheap. But how often should I move the data into storage? I want to see real-time data, so I guess that’s out now as well. Step 4: (Optional) View stored diagnostic data Optional? Of course I want to see it. Conveniently, Microsoft recommends 3 tools to do this with. Un-conveniently, none of these are web based and they all just give you access to raw data, and very little charting or real-time intelligence. Just….. data. Nevermind that one product seems to have gotten stale since a recent acquisition, and doesn’t even have screenshots!   So, let’s summarize: lots of diagnostics data is available, but think realistically. Think Dev Ops. What happens when you are in the middle of a major production performance issue and you don’t have the diagnostics you need? You are redeploying an application (and thankfully you have a great branching strategy, so you feel perfectly safe just willy-nilly launching code into prod, don’t you?) to get data, then shipping it to storage, and then digging through that data to find a needle in a haystack. Would you like to be able to troubleshoot a performance issue in the middle of the night, or on a weekend, from your iPad or home computer’s web browser? Forget it: the best you get is this spark line in the Azure portal. If it’s real pointy, you probably have an issue; but since there is no alert based on a threshold your customers have likely already let you know. And high CPU, Memory, I/O, or Network doesn’t tell you anything about where the problem is. The Better Way – Stackify Stackify supports application and server monitoring in real time, all through a great web interface. All of the things that Azure Diagnostics provides, Stackify provides for your on-premises deployments, and you don’t need to know ahead of time that you’ll need it. It’s always there, it’s always on. Azure deployments are essentially no different than on-premises. It’s a Windows Server (or Linux) in the cloud. It’s behind a different firewall than your corporate servers. That’s it. Stackify can provide the same powerful tools to your Azure deployments in two simple steps. Step 1 Add a startup task to your web or worker role and deploy. If you can’t deploy and need it right now, no worries! Remote Desktop to the Azure instance and you can execute a Powershell script to download / install Stackify.   Step 2 Log in to your account at www.stackify.com and begin monitoring as much as you want, as often as you want and see the results instantly. WMI? It’s there Event Viewer? You’ve got it. File System Access? Yes, please! Would love to make sure my web.config is correct.   IIS / App Pool Info? Yep. You can even restart it. Running Services? All of them. Start and Stop them to your heart’s content. SQL Database access? You bet’cha. Alerts and Notification? Of course! You should know before your customers let you know. … and so much more.   Conclusion Microsoft has shown, consistently, that they love developers, developers, developers. What every developer needs to realize from this is that they’ve given you a canvas, which is exactly what Azure is. It’s great infrastructure that is readily available, easy to manage, and fairly cost effective. However, the tooling is your responsibility. What you get, at best, is bare bones. App and server diagnostics should be available when you need them. While we, as developers, try to plan for and think of everything ahead of time, there will come times where we need to get data that just isn’t available. And having to go through a lot of cumbersome steps to get that data, and then have to find a friendlier way to consume it…. well, that just doesn’t make a lot of sense to me. I’d rather spend my time writing and developing features and completing bug fixes for my applications, than to be writing code to monitor and diagnose.

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  • Characteristics of a Web service that promote reusability and change

    Characteristics of a Web service that promote reusability and change:  Standardized Data Exchange Formats (XML, JSON) Standardized communication protocols (Soap, Rest) Promotes Loosely Coupled Systems  Standardized Data Exchange Formats (XML, JSON) XML W3.org defines Extensible Markup Language (XML) as a simplistic text format derived from SGML. XML was designed to solve challenges found in large-scale electronic publishing. In addition,  XML is playing an important role in the exchange of data primarily focusing on data exchange on the web. JSON JavaScript Object Notation (JSON) is a human-readable text-based standard designed for data interchange. This format is used for serializing and transmitting data over a network connection in a structured format. The primary use of JSON is to transmit data between a server and web application. JSON is an alternative to XML. Standardized communication protocols (Soap, Rest) Soap W3Scools.com defines SOAP as a simple XML-based protocol. This protocol lets applications exchange data over HTTP.  SOAP provides a way to communicate between applications running on different operating systems, with different technologies and programming languages. Rest In 2007, Stefan Tilkov defines Representational State Transfer (REST) as a set of principles that outlines how Web standards are supposed to be used.  Using REST in an application will ensure that it exploits the Web’s architecture to its benefit. Promotes Loosely Coupled Systems “Loose coupling as an approach to interconnecting the components in a system or network so that those components, also called elements, depend on each other to the least extent practicable. Coupling refers to the degree of direct knowledge that one element has of another.” (TechTarget.com, 2007) “Loosely coupled system can be easily broken down into definable elements. The extent of coupling in a system can be measured by mapping the maximum number of element changes that can occur without adverse effects. Examples of such changes include adding elements, removing elements, renaming elements, reconfiguring elements, modifying internal element characteristics and rearranging the way in which elements are interconnected.” (TechTarget.com, 2007) References: W3C. (2011). Extensible Markup Language (XML). Retrieved from W3.org: http://www.w3.org/XML/ W3Scools.com. (2011). SOAP Introduction. Retrieved from W3Scools.com: http://www.w3schools.com/soap/soap_intro.asp Tilkov, Stefan. (2007). A Brief Introduction to REST. Retrieved from Infoq.com: http://www.infoq.com/articles/rest-introduction TechTarget.com. (2011). loose coupling. Retrieved from TechTarget.com: http://searchnetworking.techtarget.com/definition/loose-coupling

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  • Is it important for reflection-based serialization maintain consistent field ordering?

    - by Matchlighter
    I just finished writing a packet builder that dynamically loads data into a data stream for eventual network transmission. Each builder operates by finding fields in a given class (and its superclasses) that are marked with a @data annotation. When I finishing my implementation, I remembered that getFields() does not return results in any specific order. Should reflection-based methods for serializing arbitrary data (like my packets) attempt to preserve a specific field ordering (such as alphabetical), and if so, how?

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  • The need for user-defined index types

    - by Greg Low
    Since the removal of the 8KB limit on serialization, the ability to define new data types using SQL CLR integration is now almost at a usable level, apart from one key omission: indexes. We have no ability to create our own types of index to support our data types. As a good example of this, consider that when Microsoft introduced the geometry and geography (spatial) data types, they did so as system CLR data types but also needed to introduce a spatial index as a new type of index. Those of us that...(read more)

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  • Isn't MVC anti OOP?

    - by m3th0dman
    The main idea behind OOP is to unify data and behavior in a single entity - the object. In procedural programming there is data and separately algorithms modifying the data. In the Model-View-Controller pattern the data and the logic/algorithms are placed in distinct entities, the model and the controller respectively. In an equivalent OOP approach shouldn't the model and the controller be placed in the same logical entity?

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  • laptop crashed: why?

    - by sds
    my linux (ubuntu 12.04) laptop crashed, and I am trying to figure out why. # last sds pts/4 :0 Tue Sep 4 10:01 still logged in sds pts/3 :0 Tue Sep 4 10:00 still logged in reboot system boot 3.2.0-29-generic Tue Sep 4 09:43 - 11:23 (01:40) sds pts/8 :0 Mon Sep 3 14:23 - crash (19:19) this seems to indicate a crash at 09:42 (= 14:23+19:19). as per another question, I looked at /var/log: auth.log: Sep 4 09:17:02 t520sds CRON[32744]: pam_unix(cron:session): session closed for user root Sep 4 09:43:17 t520sds lightdm: pam_unix(lightdm:session): session opened for user lightdm by (uid=0) no messages file syslog: Sep 4 09:24:19 t520sds kernel: [219104.819975] CPU0: Package power limit normal Sep 4 09:43:16 t520sds kernel: imklog 5.8.6, log source = /proc/kmsg started. kern.log: Sep 4 09:24:19 t520sds kernel: [219104.819969] CPU1: Package power limit normal Sep 4 09:24:19 t520sds kernel: [219104.819971] CPU2: Package power limit normal Sep 4 09:24:19 t520sds kernel: [219104.819974] CPU3: Package power limit normal Sep 4 09:24:19 t520sds kernel: [219104.819975] CPU0: Package power limit normal Sep 4 09:43:16 t520sds kernel: imklog 5.8.6, log source = /proc/kmsg started. Sep 4 09:43:16 t520sds kernel: [ 0.000000] Initializing cgroup subsys cpuset Sep 4 09:43:16 t520sds kernel: [ 0.000000] Initializing cgroup subsys cpu I had a computation running until 9:24, but the system crashed 18 minutes later! kern.log has many pages of these: Sep 4 09:43:16 t520sds kernel: [ 0.000000] total RAM covered: 8086M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 64K num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 128K num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 256K num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 512K num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 1M num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 2M num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 4M num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 8M num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 16M num_reg: 10 lose cover RAM: 38M Sep 4 09:43:16 t520sds kernel: [ 0.000000] *BAD*gran_size: 64K chunk_size: 32M num_reg: 10 lose cover RAM: -16M Sep 4 09:43:16 t520sds kernel: [ 0.000000] *BAD*gran_size: 64K chunk_size: 64M num_reg: 10 lose cover RAM: -16M Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 128M num_reg: 10 lose cover RAM: 0G Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 256M num_reg: 10 lose cover RAM: 0G Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 512M num_reg: 10 lose cover RAM: 0G Sep 4 09:43:16 t520sds kernel: [ 0.000000] gran_size: 64K chunk_size: 1G num_reg: 10 lose cover RAM: 0G Sep 4 09:43:16 t520sds kernel: [ 0.000000] *BAD*gran_size: 64K chunk_size: 2G num_reg: 10 lose cover RAM: -1G does this mean that my RAM is bad?! it also says Sep 4 09:43:16 t520sds kernel: [ 2.944123] EXT4-fs (sda1): INFO: recovery required on readonly filesystem Sep 4 09:43:16 t520sds kernel: [ 2.944126] EXT4-fs (sda1): write access will be enabled during recovery Sep 4 09:43:16 t520sds kernel: [ 3.088001] firewire_core: created device fw0: GUID f0def1ff8fbd7dff, S400 Sep 4 09:43:16 t520sds kernel: [ 8.929243] EXT4-fs (sda1): orphan cleanup on readonly fs Sep 4 09:43:16 t520sds kernel: [ 8.929249] EXT4-fs (sda1): ext4_orphan_cleanup: deleting unreferenced inode 658984 ... Sep 4 09:43:16 t520sds kernel: [ 9.343266] EXT4-fs (sda1): ext4_orphan_cleanup: deleting unreferenced inode 525343 Sep 4 09:43:16 t520sds kernel: [ 9.343270] EXT4-fs (sda1): 56 orphan inodes deleted Sep 4 09:43:16 t520sds kernel: [ 9.343271] EXT4-fs (sda1): recovery complete Sep 4 09:43:16 t520sds kernel: [ 9.645799] EXT4-fs (sda1): mounted filesystem with ordered data mode. Opts: (null) does this mean my HD is bad? As per FaultyHardware, I tried smartctl -l selftest, which uncovered no errors: smartctl 5.41 2011-06-09 r3365 [x86_64-linux-3.2.0-30-generic] (local build) Copyright (C) 2002-11 by Bruce Allen, http://smartmontools.sourceforge.net === START OF INFORMATION SECTION === Model Family: Seagate Momentus 7200.4 Device Model: ST9500420AS Serial Number: 5VJE81YK LU WWN Device Id: 5 000c50 0440defe3 Firmware Version: 0003LVM1 User Capacity: 500,107,862,016 bytes [500 GB] Sector Size: 512 bytes logical/physical Device is: In smartctl database [for details use: -P show] ATA Version is: 8 ATA Standard is: ATA-8-ACS revision 4 Local Time is: Mon Sep 10 16:40:04 2012 EDT SMART support is: Available - device has SMART capability. SMART support is: Enabled === START OF READ SMART DATA SECTION === SMART overall-health self-assessment test result: PASSED See vendor-specific Attribute list for marginal Attributes. General SMART Values: Offline data collection status: (0x82) Offline data collection activity was completed without error. Auto Offline Data Collection: Enabled. Self-test execution status: ( 0) The previous self-test routine completed without error or no self-test has ever been run. Total time to complete Offline data collection: ( 0) seconds. Offline data collection capabilities: (0x7b) SMART execute Offline immediate. Auto Offline data collection on/off support. Suspend Offline collection upon new command. Offline surface scan supported. Self-test supported. Conveyance Self-test supported. Selective Self-test supported. SMART capabilities: (0x0003) Saves SMART data before entering power-saving mode. Supports SMART auto save timer. Error logging capability: (0x01) Error logging supported. General Purpose Logging supported. Short self-test routine recommended polling time: ( 1) minutes. Extended self-test routine recommended polling time: ( 109) minutes. Conveyance self-test routine recommended polling time: ( 2) minutes. SCT capabilities: (0x103b) SCT Status supported. SCT Error Recovery Control supported. SCT Feature Control supported. SCT Data Table supported. SMART Attributes Data Structure revision number: 10 Vendor Specific SMART Attributes with Thresholds: ID# ATTRIBUTE_NAME FLAG VALUE WORST THRESH TYPE UPDATED WHEN_FAILED RAW_VALUE 1 Raw_Read_Error_Rate 0x000f 117 099 034 Pre-fail Always - 162843537 3 Spin_Up_Time 0x0003 100 100 000 Pre-fail Always - 0 4 Start_Stop_Count 0x0032 100 100 020 Old_age Always - 571 5 Reallocated_Sector_Ct 0x0033 100 100 036 Pre-fail Always - 0 7 Seek_Error_Rate 0x000f 069 060 030 Pre-fail Always - 17210154023 9 Power_On_Hours 0x0032 095 095 000 Old_age Always - 174362787320258 10 Spin_Retry_Count 0x0013 100 100 097 Pre-fail Always - 0 12 Power_Cycle_Count 0x0032 100 100 020 Old_age Always - 571 184 End-to-End_Error 0x0032 100 100 099 Old_age Always - 0 187 Reported_Uncorrect 0x0032 100 100 000 Old_age Always - 0 188 Command_Timeout 0x0032 100 100 000 Old_age Always - 1 189 High_Fly_Writes 0x003a 100 100 000 Old_age Always - 0 190 Airflow_Temperature_Cel 0x0022 061 043 045 Old_age Always In_the_past 39 (0 11 44 26) 191 G-Sense_Error_Rate 0x0032 100 100 000 Old_age Always - 84 192 Power-Off_Retract_Count 0x0032 100 100 000 Old_age Always - 20 193 Load_Cycle_Count 0x0032 099 099 000 Old_age Always - 2434 194 Temperature_Celsius 0x0022 039 057 000 Old_age Always - 39 (0 15 0 0) 195 Hardware_ECC_Recovered 0x001a 041 041 000 Old_age Always - 162843537 196 Reallocated_Event_Count 0x000f 095 095 030 Pre-fail Always - 4540 (61955, 0) 197 Current_Pending_Sector 0x0012 100 100 000 Old_age Always - 0 198 Offline_Uncorrectable 0x0010 100 100 000 Old_age Offline - 0 199 UDMA_CRC_Error_Count 0x003e 200 200 000 Old_age Always - 0 254 Free_Fall_Sensor 0x0032 100 100 000 Old_age Always - 0 SMART Error Log Version: 1 No Errors Logged SMART Self-test log structure revision number 1 Num Test_Description Status Remaining LifeTime(hours) LBA_of_first_error # 1 Extended offline Completed without error 00% 4545 - SMART Selective self-test log data structure revision number 1 SPAN MIN_LBA MAX_LBA CURRENT_TEST_STATUS 1 0 0 Not_testing 2 0 0 Not_testing 3 0 0 Not_testing 4 0 0 Not_testing 5 0 0 Not_testing Selective self-test flags (0x0): After scanning selected spans, do NOT read-scan remainder of disk. If Selective self-test is pending on power-up, resume after 0 minute delay. Googling for the messages proved inconclusive, I can't even figure out whether the messages are routine or catastrophic. So, what do I do now?

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  • logrotate isn't rotating a particular log file (and i think it should be)

    - by Max Williams
    Hi all. For a particular app, i have log files in two places. One of the places has just one log file that i want to use with logrotate, for the other location i want to use logrotate on all log files in that folder. I've set up an entry called millionaire-staging in /etc/logrotate.d and have been testing it by calling logrotate -f millionaire-staging. Here's my entry: #/etc/logrotate.d/millionaire-staging compress rotate 1000 dateext missingok sharedscripts copytruncate /var/www/apps/test.millionaire/log/staging.log { weekly } /var/www/apps/test.millionaire/shared/log/*log { size 40M } So, for the first folder, i want to rotate weekly (this seems to have worked fine). For the other, i want to rotate only when the log files get bigger than 40 meg. When i look in that folder (using the same locator as in the logrotate config), i can see a file in there that's 54M and which hasn't been rotated: $ ls -lh /var/www/apps/test.millionaire/shared/log/*log -rw-r--r-- 1 www-data root 33M 2010-12-29 15:00 /var/www/apps/test.millionaire/shared/log/test.millionaire.charanga.com.access-log -rw-r--r-- 1 www-data root 54M 2010-09-10 16:57 /var/www/apps/test.millionaire/shared/log/test.millionaire.charanga.com.debug-log -rw-r--r-- 1 www-data root 53K 2010-12-14 15:48 /var/www/apps/test.millionaire/shared/log/test.millionaire.charanga.com.error-log -rw-r--r-- 1 www-data root 3.8M 2010-12-29 14:30 /var/www/apps/test.millionaire/shared/log/test.millionaire.charanga.com.ssl.access-log -rw-r--r-- 1 www-data root 16K 2010-12-17 15:00 /var/www/apps/test.millionaire/shared/log/test.millionaire.charanga.com.ssl.error-log -rw-r--r-- 1 deploy deploy 0 2010-12-29 14:49 /var/www/apps/test.millionaire/shared/log/unicorn.stderr.log -rw-r--r-- 1 deploy deploy 0 2010-12-29 14:49 /var/www/apps/test.millionaire/shared/log/unicorn.stdout.log Some of the other log files in that folder have been rotated though: $ ls -lh /var/www/apps/test.millionaire/shared/log total 91M -rw-r--r-- 1 www-data root 33M 2010-12-29 15:05 test.millionaire.charanga.com.access-log -rw-r--r-- 1 www-data root 54M 2010-09-10 16:57 test.millionaire.charanga.com.debug-log -rw-r--r-- 1 www-data root 53K 2010-12-14 15:48 test.millionaire.charanga.com.error-log -rw-r--r-- 1 www-data root 3.8M 2010-12-29 14:30 test.millionaire.charanga.com.ssl.access-log -rw-r--r-- 1 www-data root 16K 2010-12-17 15:00 test.millionaire.charanga.com.ssl.error-log -rw-r--r-- 1 deploy deploy 0 2010-12-29 14:49 unicorn.stderr.log -rw-r--r-- 1 deploy deploy 41K 2010-12-29 11:03 unicorn.stderr.log-20101229.gz -rw-r--r-- 1 deploy deploy 0 2010-12-29 14:49 unicorn.stdout.log -rw-r--r-- 1 deploy deploy 1.1K 2010-10-15 11:05 unicorn.stdout.log-20101229.gz I think what might have happened is that i first ran this config with a pattern matching *.log, and that means it only rotated the two files that ended in .log (as opposed to -log). Then, when i changed the config and ran it again, it won't do any more since it think's its already had its weekly run, or something. Can anyone see what i'm doing wrong? Is it to do with those top folders being owned by root rather than deploy do you think? thanks, max

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  • logrotate isn't rotating a particular log file (and i think it should be)

    - by Max Williams
    Hi all. For a particular app, i have log files in two places. One of the places has just one log file that i want to use with logrotate, for the other location i want to use logrotate on all log files in that folder. I've set up an entry called millionaire-staging in /etc/logrotate.d and have been testing it by calling logrotate -f millionaire-staging. Here's my entry: #/etc/logrotate.d/millionaire-staging compress rotate 1000 dateext missingok sharedscripts copytruncate /var/www/apps/test.millionaire/log/staging.log { weekly } /var/www/apps/test.millionaire/shared/log/*log { size 40M } So, for the first folder, i want to rotate weekly (this seems to have worked fine). For the other, i want to rotate only when the log files get bigger than 40 meg. When i look in that folder (using the same locator as in the logrotate config), i can see a file in there that's 54M and which hasn't been rotated: $ ls -lh /var/www/apps/test.millionaire/shared/log/*log -rw-r--r-- 1 www-data root 33M 2010-12-29 15:00 /var/www/apps/test.millionaire/shared/log/test.millionaire.charanga.com.access-log -rw-r--r-- 1 www-data root 54M 2010-09-10 16:57 /var/www/apps/test.millionaire/shared/log/test.millionaire.charanga.com.debug-log -rw-r--r-- 1 www-data root 53K 2010-12-14 15:48 /var/www/apps/test.millionaire/shared/log/test.millionaire.charanga.com.error-log -rw-r--r-- 1 www-data root 3.8M 2010-12-29 14:30 /var/www/apps/test.millionaire/shared/log/test.millionaire.charanga.com.ssl.access-log -rw-r--r-- 1 www-data root 16K 2010-12-17 15:00 /var/www/apps/test.millionaire/shared/log/test.millionaire.charanga.com.ssl.error-log -rw-r--r-- 1 deploy deploy 0 2010-12-29 14:49 /var/www/apps/test.millionaire/shared/log/unicorn.stderr.log -rw-r--r-- 1 deploy deploy 0 2010-12-29 14:49 /var/www/apps/test.millionaire/shared/log/unicorn.stdout.log Some of the other log files in that folder have been rotated though: $ ls -lh /var/www/apps/test.millionaire/shared/log total 91M -rw-r--r-- 1 www-data root 33M 2010-12-29 15:05 test.millionaire.charanga.com.access-log -rw-r--r-- 1 www-data root 54M 2010-09-10 16:57 test.millionaire.charanga.com.debug-log -rw-r--r-- 1 www-data root 53K 2010-12-14 15:48 test.millionaire.charanga.com.error-log -rw-r--r-- 1 www-data root 3.8M 2010-12-29 14:30 test.millionaire.charanga.com.ssl.access-log -rw-r--r-- 1 www-data root 16K 2010-12-17 15:00 test.millionaire.charanga.com.ssl.error-log -rw-r--r-- 1 deploy deploy 0 2010-12-29 14:49 unicorn.stderr.log -rw-r--r-- 1 deploy deploy 41K 2010-12-29 11:03 unicorn.stderr.log-20101229.gz -rw-r--r-- 1 deploy deploy 0 2010-12-29 14:49 unicorn.stdout.log -rw-r--r-- 1 deploy deploy 1.1K 2010-10-15 11:05 unicorn.stdout.log-20101229.gz I think what might have happened is that i first ran this config with a pattern matching *.log, and that means it only rotated the two files that ended in .log (as opposed to -log). Then, when i changed the config and ran it again, it won't do any more since it think's its already had its weekly run, or something. Can anyone see what i'm doing wrong? Is it to do with those top folders being owned by root rather than deploy do you think? thanks, max

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  • How do I start the postgreSQL service upon boot?

    - by Homunculus Reticulli
    I am running PostgreSQL (v 8.4) on Ubuntu 10.0.4. The PG service currently starts on reboot (after I installed PG on my machine), however, I want the service to use a new data directory. Currently, after a reboot, I have to: Stop the currently running PG service manually type: /usr/local/pgsql/bin/pg_ctl start -D /my/preffered/data/directory -l /usr/local/pgsql/data/logfile Which file do I need to edit to ensure that I always have the service using the correct data folder?

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  • SQL Server Developer Tools &ndash; Codename Juneau vs. Red-Gate SQL Source Control

    - by Ajarn Mark Caldwell
    So how do the new SQL Server Developer Tools (previously code-named Juneau) stack up against SQL Source Control?  Read on to find out. At the PASS Community Summit a couple of weeks ago, it was announced that the previously code-named Juneau software would be released under the name of SQL Server Developer Tools with the release of SQL Server 2012.  This replacement for Database Projects in Visual Studio (also known in a former life as Data Dude) has some great new features.  I won’t attempt to describe them all here, but I will applaud Microsoft for making major improvements.  One of my favorite changes is the way database elements are broken down.  Previously every little thing was in its own file.  For example, indexes were each in their own file.  I always hated that.  Now, SSDT uses a pattern similar to Red-Gate’s and puts the indexes and keys into the same file as the overall table definition. Of course there are really cool features to keep your database model in sync with the actual source scripts, and the rename refactoring feature is now touted as being more than just a search and replace, but rather a “semantic-aware” search and replace.  Funny, it reminds me of SQL Prompt’s Smart Rename feature.  But I’m not writing this just to criticize Microsoft and argue that they are late to the party with this feature set.  Instead, I do see it as a viable alternative for folks who want all of their source code to be version controlled, but there are a couple of key trade-offs that you need to know about when you choose which tool set to use. First, the basics Both tool sets integrate with a wide variety of source control systems including the most popular: Subversion, GIT, Vault, and Team Foundation Server.  Both tools have integrated functionality to produce objects to upgrade your target database when you are ready (DACPACs in SSDT, integration with SQL Compare for SQL Source Control).  If you regularly live in Visual Studio or the Business Intelligence Development Studio (BIDS) then SSDT will likely be comfortable for you.  Like BIDS, SSDT is a Visual Studio Project Type that comes with SQL Server, and if you don’t already have Visual Studio installed, it will install the shell for you.  If you already have Visual Studio 2010 installed, then it will just add this as an available project type.  On the other hand, if you regularly live in SQL Server Management Studio (SSMS) then you will really enjoy the SQL Source Control integration from within SSMS.  Both tool sets store their database model in script files.  In SSDT, these are on your file system like other source files; in SQL Source Control, these are stored in the folder structure in your source control system, and you can always GET them to your file system if you want to browse them directly. For me, the key differentiating factors are 1) a single, unified check-in, and 2) migration scripts.  How you value those two features will likely make your decision for you. Unified Check-In If you do a continuous-integration (CI) style of development that triggers an automated build with unit testing on every check-in of source code, and you use Visual Studio for the rest of your development, then you will want to really consider SSDT.  Because it is just another project in Visual Studio, it can be added to your existing Solution, and you can then do a complete, or unified single check-in of all changes whether they are application or database changes.  This is simply not possible with SQL Source Control because it is in a different development tool (SSMS instead of Visual Studio) and there is no way to do one unified check-in between the two.  You CAN do really fast back-to-back check-ins, but there is the possibility that the automated build that is triggered from the first check-in will cause your unit tests to fail and the CI tool to report that you broke the build.  Of course, the automated build that is triggered from the second check-in which contains the “other half” of your changes should pass and so the amount of time that the build was broken may be very, very short, but if that is very, very important to you, then SQL Source Control just won’t work; you’ll have to use SSDT. Refactoring and Migrations If you work on a mature system, or on a not-so-mature but also not-so-well-designed system, where you want to refactor the database schema as you go along, but you can’t have data suddenly disappearing from your target system, then you’ll probably want to go with SQL Source Control.  As I wrote previously, there are a number of changes which you can make to your database that the comparison tools (both from Microsoft and Red Gate) simply cannot handle without the possibility (or probability) of data loss.  Currently, SSDT only offers you the ability to inject PRE and POST custom deployment scripts.  There is no way to insert your own script in the middle to override the default behavior of the tool.  In version 3.0 of SQL Source Control (Early Access version now available) you have that ability to create your own custom migration script to take the place of the commands that the tool would have done, and ensure the preservation of your data.  Or, even if the default tool behavior would have worked, but you simply know a better way then you can take control and do things your way instead of theirs. You Decide In the environment I work in, our automated builds are not triggered off of check-ins, but off of the clock (currently once per night) and so there is no point at which the automated build and unit tests will be triggered without having both sides of the development effort already checked-in.  Therefore having a unified check-in, while handy, is not critical for us.  As for migration scripts, these are critically important to us.  We do a lot of new development on systems that have already been in production for years, and it is not uncommon for us to need to do a refactoring of the database.  Because of the maturity of the existing system, that often involves data migrations or other additional SQL tasks that the comparison tools just can’t detect on their own.  Therefore, the ability to create a custom migration script to override the tool’s default behavior is very important to us.  And so, you can see why we will continue to use Red Gate SQL Source Control for the foreseeable future.

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  • Function Folding in #PowerQuery

    - by Darren Gosbell
    Originally posted on: http://geekswithblogs.net/darrengosbell/archive/2014/05/16/function-folding-in-powerquery.aspxLooking at a typical Power Query query you will noticed that it's made up of a number of small steps. As an example take a look at the query I did in my previous post about joining a fact table to a slowly changing dimension. It was roughly built up of the following steps: Get all records from the fact table Get all records from the dimension table do an outer join between these two tables on the business key (resulting in an increase in the row count as there are multiple records in the dimension table for each business key) Filter out the excess rows introduced in step 3 remove extra columns that are not required in the final result set. If Power Query was to execute a query like this literally, following the same steps in the same order it would not be overly efficient. Particularly if your two source tables were quite large. However Power Query has a feature called function folding where it can take a number of these small steps and push them down to the data source. The degree of function folding that can be performed depends on the data source, As you might expect, relational data sources like SQL Server, Oracle and Teradata support folding, but so do some of the other sources like OData, Exchange and Active Directory. To explore how this works I took the data from my previous post and loaded it into a SQL database. Then I converted my Power Query expression to source it's data from that database. Below is the resulting Power Query which I edited by hand so that the whole thing can be shown in a single expression: let     SqlSource = Sql.Database("localhost", "PowerQueryTest"),     BU = SqlSource{[Schema="dbo",Item="BU"]}[Data],     Fact = SqlSource{[Schema="dbo",Item="fact"]}[Data],     Source = Table.NestedJoin(Fact,{"BU_Code"},BU,{"BU_Code"},"NewColumn"),     LeftJoin = Table.ExpandTableColumn(Source, "NewColumn"                                   , {"BU_Key", "StartDate", "EndDate"}                                   , {"BU_Key", "StartDate", "EndDate"}),     BetweenFilter = Table.SelectRows(LeftJoin, each (([Date] >= [StartDate]) and ([Date] <= [EndDate])) ),     RemovedColumns = Table.RemoveColumns(BetweenFilter,{"StartDate", "EndDate"}) in     RemovedColumns If the above query was run step by step in a literal fashion you would expect it to run two queries against the SQL database doing "SELECT * …" from both tables. However a profiler trace shows just the following single SQL query: select [_].[BU_Code],     [_].[Date],     [_].[Amount],     [_].[BU_Key] from (     select [$Outer].[BU_Code],         [$Outer].[Date],         [$Outer].[Amount],         [$Inner].[BU_Key],         [$Inner].[StartDate],         [$Inner].[EndDate]     from [dbo].[fact] as [$Outer]     left outer join     (         select [_].[BU_Key] as [BU_Key],             [_].[BU_Code] as [BU_Code2],             [_].[BU_Name] as [BU_Name],             [_].[StartDate] as [StartDate],             [_].[EndDate] as [EndDate]         from [dbo].[BU] as [_]     ) as [$Inner] on ([$Outer].[BU_Code] = [$Inner].[BU_Code2] or [$Outer].[BU_Code] is null and [$Inner].[BU_Code2] is null) ) as [_] where [_].[Date] >= [_].[StartDate] and [_].[Date] <= [_].[EndDate] The resulting query is a little strange, you can probably tell that it was generated programmatically. But if you look closely you'll notice that every single part of the Power Query formula has been pushed down to SQL Server. Power Query itself ends up just constructing the query and passing the results back to Excel, it does not do any of the data transformation steps itself. So now you can feel a bit more comfortable showing Power Query to your less technical Colleagues knowing that the tool will do it's best fold all the  small steps in Power Query down the most efficient query that it can against the source systems.

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  • Building an MVC application using QuickBooks

    - by dataintegration
    RSSBus ADO.NET Providers can be used from many tools and IDEs. In this article we show how to connect to QuickBooks from an MVC3 project using the RSSBus ADO.NET Provider for QuickBooks. Although this example uses the QuickBooks Data Provider, the same process can be used with any of our ADO.NET Providers. Creating the Model Step 1: Download and install the QuickBooks Data Provider from RSSBus. Step 2: Create a new MVC3 project in Visual Studio. Add a data model to the Models folder using the ADO.NET Entity Data Model wizard. Step 3: Create a new RSSBus QuickBooks Data Source by clicking "New Connection", specify the connection string options, and click Next. Step 4: Select all the tables and views you need, and click Finish to create the data model. Step 5: Right click on the entity diagram and select 'Add Code Generation Item'. Choose the 'ADO.NET DbContext Generator'. Creating the Controller and the Views Step 6: Add a new controller to the Controllers folder. Give it a meaningful name, such as ReceivePaymentsController. Also, make sure the template selected is 'Controller with empty read/write actions'. Before adding new methods to the Controller, create views for your model. We will add the List, Create, and Delete views. Step 7: Right click on the Views folder and go to Add -> View. Here create a new view for each: List, Create, and Delete templates. Make sure to also associate your Model with the new views. Step 10: Now that the views are ready, go back and edit the RecievePayment controller. Update your code to handle the Index, Create, and Delete methods. Sample Project We are including a sample project that shows how to use the QuickBooks Data Provider in an MVC3 application. You may download the C# project here or download the VB.NET project here. You will also need to install the QuickBooks ADO.NET Data Provider to run the demo. You can download a free trial here. To use this demo, you will also need to modify the connection string in the 'web.config'.

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  • Medical Devices which supports Direct access through Bluetooth Low Energy [on hold]

    - by Suganthan
    I have went through this link and came to know that we can directly interact with BLE devices to read and write data, so I just want to know some medical device which supports direct access to third-party application (we can directly access the data from the medical device data). Is their any devices which supports direct access to the data Note: I already went through medical devices like Withings and Fitbit

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  • What types of objects are useful in SQL CLR?

    - by Greg Low
    I've had a number of people over the years ask about whether or not a particular type of object is a good candidate for SQL CLR integration. The rules that I normally apply are as follows: Database Object Transact-SQL Managed Code Scalar UDF Generally poor performance Good option when limited or no data-access Table-valued UDF Good option if data-related Good option when limited or no data-access Stored Procedure Good option Good option when external access is required or limited data access DML...(read more)

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  • Pragmas and exceptions

    - by Darryl Gove
    The compiler pragmas: #pragma no_side_effect(routinename) #pragma does_not_write_global_data(routinename) #pragma does_not_read_global_data(routinename) are used to tell the compiler more about the routine being called, and enable it to do a better job of optimising around the routine. If a routine does not read global data, then global data does not need to be stored to memory before the call to the routine. If the routine does not write global data, then global data does not need to be reloaded after the call. The no side effect directive indicates that the routine does no I/O, does not read or write global data, and the result only depends on the input. However, these pragmas should not be used on routines that throw exceptions. The following example indicates the problem: #include <iostream extern "C" { int exceptional(int); #pragma no_side_effect(exceptional) } int exceptional(int a) { if (a==7) { throw 7; } else { return a+1; } } int a; int c=0; class myclass { public: int routine(); }; int myclass::routine() { for(a=0; a<1000; a++) { c=exceptional(c); } return 0; } int main() { myclass f; try { f.routine(); } catch(...) { std::cout << "Something happened" << a << c << std::endl; } } The routine "exceptional" is declared as having no side effects, however it can throw an exception. The no side effects directive enables the compiler to avoid storing global data back to memory, and retrieving it after the function call, so the loop containing the call to exceptional is quite tight: $ CC -O -S test.cpp ... .L77000061: /* 0x0014 38 */ call exceptional ! params = %o0 ! Result = %o0 /* 0x0018 36 */ add %i1,1,%i1 /* 0x001c */ cmp %i1,999 /* 0x0020 */ ble,pt %icc,.L77000061 /* 0x0024 */ nop However, when the program is run the result is incorrect: $ CC -O t.cpp $ ./a.out Something happend00 If the code had worked correctly, the output would have been "Something happened77" - the exception occurs on the seventh iteration. Yet, the current code produces a message that uses the original values for the variables 'a' and 'c'. The problem is that the exception handler reads global data, and due to the no side effects directive the compiler has not updated the global data before the function call. So these pragmas should not be used on routines that have the potential to throw exceptions.

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  • Now Shipping! NetAdvantage for .NET 2010 Volume 3!

    The new NetAdvantage Ultimate includes all four Line of Business user interface control sets for ASP .NET, Windows Forms, WPF and Silverlight plus two advanced Data Visualization UI control sets for WPF and Silverlight. With six NetAdvantage products in one robust package, Infragistics® gives you hundreds of controls and infinite development possibilities. Unified XAML Product Strategy-Share Code, Get More Controls In the 10.3 release, Infragistics continues to deliver code parity between the XAML platforms, WPF and Silverlight. In the line of business toolsets, Infragistics introduces the new xamSchedule™, full-featured, Outlook® 2010-style schedule controls, and the new xamDataTree™, a data bound tree view that comfortably handles tens of thousands of tree nodes. Mimicking our Silverlight Drag and Drop Framework, the WPF Drag and Drop Framework CTP empowers you to add your own rich touches to your applications. Track Users' Behaviors New to all NetAdvantage Silverlight controls is the Infragistics Analytics Framework (IGAF), which empowers you to track user behavior in RIAs running on Silverlight 4. Building on the Microsoft® Silverlight Analytics Framework, with IGAF you can analyze the user's behaviors to ensure the experience you want to deliver. NetAdvantage for Windows Forms--New Office® 2010 Ribbon and Application Menu 2010 Create new experiences with Windows Forms. Now with Office 2010 styling, NetAdvantage for Windows Forms has new features such as Microsoft® Office 2010 ribbon and enhanced Infragistics.Excel to export the contents of the high performance WinGrid™ into Microsoft Excel® 2010. The new Windows Message Support enables Infragistics standalone editor controls to process numerous Windows® OS messages, allowing them to respond just like native controls to changes in the Windows environment. Create Faster Web 2.0 Experiences with NetAdvantage for ASP .NET Infragistics continues to push the envelope to deliver the fastest ASP .NET WebForms controls available on the market. Our lightning fast ASP .NET grids are now enhanced with XPS/PDF Exporting and Summary Rows. This release also includes support for jQuery Templating (as a CTP) within our WebDataGrid™ and WebDataTree™ controls allowing you to quickly cut down overall page size. Deliver Business Intelligence with Power, Flexibility and the Office 2010 Experience NetAdvantage for WPF Data Visualization and NetAdvantage for Silverlight Data Visualization help you deliver flexible, powerful and usable end user experiences in Business Intelligence applications. Both suites include the Pivot Grid that delivers the full power of online analytical processing (OLAP) to present multi-dimensional data, sliced and diced in cross-tabulated form for end users to drill down into, interact with and easily extract meaning from the data. Mapping Made Easy 10.3 marks the official release of the WPF Data Visualization xamMap™ control to map anything and everything from geographic to geo-spacial mapping data. Map layers allow you to add successive levels of detail, navigational panes for panning in all directions, color swatch panes that facilitate value scales like Choropleth shading, and scale panes allowing users to zoom-in and out. Both toolsets introduce the first of many relationship maps! With the xamOrgChart™ CTP you can map out organizational charts of up to 50K employees, competitive brackets (think World Cup) and any other relational, organizational map your application needs. http://www.infragistics.com span.fullpost {display:none;}

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