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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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• Oracle Database In-Memory - Powering The Real-Time Enterprise – Aug/Sep

  - by Catalin Teodor

  Encourage your partners to mark their calendars for these LIVE webcasts on Oracle Database In-Memory benefits, features, and leading edge architecture. Oracle Database In-Memory is Oracle’s new memory-optimized technology that transparently accelerates analytic, data warehousing, and reporting workloads, while also accelerating transaction processing (OLTP) workloads. Register here!

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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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• 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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• 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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• Attend COLLABORATE 11 Virtualy

  - by david.stokes(at)oracle.com

  Stay connected to one of the leading Oracle training and educational events - COLLABORATE 11 - IOUG Forum. Join virtually by attending Plug-In to Orlando for just $299.  Oracle and other leading industry experts will present over 40 hours of live presentations on topics such as Database, Development, Business Intelligence, Security, Data Warehousing and more.For a full list of scheduled Plug-in sessions, click here. Register now and enter the priority code PC07 to claim your group license

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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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• 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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• Oracle Magazine, July/August 2005

  Oracle Magazine July/August 2005 features articles on the IT challenges and solutions of small and midsize businesses, Linux clusters as data warehousing solutions, EJB 3.0, developing PHP applications against Oracle XML DB, Oracle LogMiner, Oracle JDeveloper and Oracle ADF, and much more.

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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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• Upcoming events : Hotsos Symposium 2011

  - by Maria Colgan

  This year for the first time, I will present at the Hotsos Symposium in Dallas Texas, March 7 - 9. I will present on two topics Top tips for Optimal SQL Execution and Implement Best Practices for Extreme Performance with Oracle Data Warehousing. I am really looking forward to attending some excellent sessions at the conference from folks like Tom Kyte, Cary Millsap, Doug Burns, and Dan Fink. Hope to see you there!

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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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• 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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• 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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• parallel_for_each from amp.h – part 1

  - by Daniel Moth

  This posts assumes that you've read my other C++ AMP posts on index<N> and extent<N>, as well as about the restrict modifier. It also assumes you are familiar with C++ lambdas (if not, follow my links to C++ documentation). Basic structure and parameters Now we are ready for part 1 of the description of the new overload for the concurrency::parallel_for_each function. The basic new parallel_for_each method signature returns void and accepts two parameters: a grid<N> (think of it as an alias to extent) a restrict(direct3d) lambda, whose signature is such that it returns void and accepts an index of the same rank as the grid So it looks something like this (with generous returns for more palatable formatting) assuming we are dealing with a 2-dimensional space: // some_code_A parallel_for_each( g, // g is of type grid<2> [ ](index<2> idx) restrict(direct3d) { // kernel code } ); // some_code_B The parallel_for_each will execute the body of the lambda (which must have the restrict modifier), on the GPU. We also call the lambda body the "kernel". The kernel will be executed multiple times, once per scheduled GPU thread. The only difference in each execution is the value of the index object (aka as the GPU thread ID in this context) that gets passed to your kernel code. The number of GPU threads (and the values of each index) is determined by the grid object you pass, as described next. You know that grid is simply a wrapper on extent. In this context, one way to think about it is that the extent generates a number of index objects. So for the example above, if your grid was setup by some_code_A as follows: extent<2> e(2,3); grid<2> g(e); ...then given that: e.size()==6, e[0]==2, and e[1]=3 ...the six index<2> objects it generates (and hence the values that your lambda would receive) are:    (0,0) (1,0) (0,1) (1,1) (0,2) (1,2) So what the above means is that the lambda body with the algorithm that you wrote will get executed 6 times and the index<2> object you receive each time will have one of the values just listed above (of course, each one will only appear once, the order is indeterminate, and they are likely to call your code at the same exact time). Obviously, in real GPU programming, you'd typically be scheduling thousands if not millions of threads, not just 6. If you've been following along you should be thinking: "that is all fine and makes sense, but what can I do in the kernel since I passed nothing else meaningful to it, and it is not returning any values out to me?" Passing data in and out It is a good question, and in data parallel algorithms indeed you typically want to pass some data in, perform some operation, and then typically return some results out. The way you pass data into the kernel, is by capturing variables in the lambda (again, if you are not familiar with them, follow the links about C++ lambdas), and the way you use data after the kernel is done executing is simply by using those same variables. In the example above, the lambda was written in a fairly useless way with an empty capture list: [ ](index<2> idx) restrict(direct3d), where the empty square brackets means that no variables were captured. If instead I write it like this [&](index<2> idx) restrict(direct3d), then all variables in the some_code_A region are made available to the lambda by reference, but as soon as I try to use any of those variables in the lambda, I will receive a compiler error. This has to do with one of the direct3d restrictions, where only one type can be capture by reference: objects of the new concurrency::array class that I'll introduce in the next post (suffice for now to think of it as a container of data). If I write the lambda line like this [=](index<2> idx) restrict(direct3d), all variables in the some_code_A region are made available to the lambda by value. This works for some types (e.g. an integer), but not for all, as per the restrictions for direct3d. In particular, no useful data classes work except for one new type we introduce with C++ AMP: objects of the new concurrency::array_view class, that I'll introduce in the post after next. Also note that if you capture some variable by value, you could use it as input to your algorithm, but you wouldn’t be able to observe changes to it after the parallel_for_each call (e.g. in some_code_B region since it was passed by value) – the exception to this rule is the array_view since (as we'll see in a future post) it is a wrapper for data, not a container. Finally, for completeness, you can write your lambda, e.g. like this [av, &ar](index<2> idx) restrict(direct3d) where av is a variable of type array_view and ar is a variable of type array - the point being you can be very specific about what variables you capture and how. So it looks like from a large data perspective you can only capture array and array_view objects in the lambda (that is how you pass data to your kernel) and then use the many threads that call your code (each with a unique index) to perform some operation. You can also capture some limited types by value, as input only. When the last thread completes execution of your lambda, the data in the array_view or array are ready to be used in the some_code_B region. We'll talk more about all this in future posts… (a)synchronous Please note that the parallel_for_each executes as if synchronous to the calling code, but in reality, it is asynchronous. I.e. once the parallel_for_each call is made and the kernel has been passed to the runtime, the some_code_B region continues to execute immediately by the CPU thread, while in parallel the kernel is executed by the GPU threads. However, if you try to access the (array or array_view) data that you captured in the lambda in the some_code_B region, your code will block until the results become available. Hence the correct statement: the parallel_for_each is as-if synchronous in terms of visible side-effects, but asynchronous in reality.   That's all for now, we'll revisit the parallel_for_each description, once we introduce properly array and array_view – coming next. Comments about this post by Daniel Moth welcome at the original blog.

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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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• Oracle Magazine, July/August 2009

  Oracle Magazine July/August features articles on business efficiency with Oracle data warehousing, business intelligence and enterprise performance management; Oracle Enterprise Linux and Oracle Unbreakable Linux support, Oracle OpenWorld preview, open source, Oracle Application Development Framework, best PL/SQL practices, security for Oracle Application Express applications, Microsoft Visual Studio for .NET and Oracle Database, Oracle Data Pump, Tom Kyte answering your questions and much more.

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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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• GlynnTucker.Cache

  - by csharp-source.net

  The GlynnTucker.Cache assembly provides a data structure for caching slow data retrievals, for example data retrieved from a database server over the network. Think of it as a Hashtable that can automatically expire its data after a set amount of time or a specified period of inactivity, on a per-object basis. It is written in C# and dual licensed under the GPL/MPL, it should work with any .NET language.

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