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• Big Data – Role of Cloud Computing in Big Data – Day 11 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the NewSQL. In this article we will understand the role of Cloud in Big Data Story What is Cloud? Cloud is the biggest buzzword around from last few years. Everyone knows about the Cloud and it is extremely well defined online. In this article we will discuss cloud in the context of the Big Data. Cloud computing is a method of providing a shared computing resources to the application which requires dynamic resources. These resources include applications, computing, storage, networking, development and various deployment platforms. The fundamentals of the cloud computing are that it shares pretty much share all the resources and deliver to end users as a service.  Examples of the Cloud Computing and Big Data are Google and Amazon.com. Both have fantastic Big Data offering with the help of the cloud. We will discuss this later in this blog post. There are two different Cloud Deployment Models: 1) The Public Cloud and 2) The Private Cloud Public Cloud Public Cloud is the cloud infrastructure build by commercial providers (Amazon, Rackspace etc.) creates a highly scalable data center that hides the complex infrastructure from the consumer and provides various services. Private Cloud Private Cloud is the cloud infrastructure build by a single organization where they are managing highly scalable data center internally. Here is the quick comparison between Public Cloud and Private Cloud from Wikipedia:   Public Cloud Private Cloud Initial cost Typically zero Typically high Running cost Unpredictable Unpredictable Customization Impossible Possible Privacy No (Host has access to the data Yes Single sign-on Impossible Possible Scaling up Easy while within defined limits Laborious but no limits Hybrid Cloud Hybrid Cloud is the cloud infrastructure build with the composition of two or more clouds like public and private cloud. Hybrid cloud gives best of the both the world as it combines multiple cloud deployment models together. Cloud and Big Data – Common Characteristics There are many characteristics of the Cloud Architecture and Cloud Computing which are also essentially important for Big Data as well. They highly overlap and at many places it just makes sense to use the power of both the architecture and build a highly scalable framework. Here is the list of all the characteristics of cloud computing important in Big Data Scalability Elasticity Ad-hoc Resource Pooling Low Cost to Setup Infastructure Pay on Use or Pay as you Go Highly Available Leading Big Data Cloud Providers There are many players in Big Data Cloud but we will list a few of the known players in this list. Amazon Amazon is arguably the most popular Infrastructure as a Service (IaaS) provider. The history of how Amazon started in this business is very interesting. They started out with a massive infrastructure to support their own business. Gradually they figured out that their own resources are underutilized most of the time. They decided to get the maximum out of the resources they have and hence  they launched their Amazon Elastic Compute Cloud (Amazon EC2) service in 2006. Their products have evolved a lot recently and now it is one of their primary business besides their retail selling. Amazon also offers Big Data services understand Amazon Web Services. Here is the list of the included services: Amazon Elastic MapReduce – It processes very high volumes of data Amazon DynammoDB – It is fully managed NoSQL (Not Only SQL) database service Amazon Simple Storage Services (S3) – A web-scale service designed to store and accommodate any amount of data Amazon High Performance Computing – It provides low-tenancy tuned high performance computing cluster Amazon RedShift – It is petabyte scale data warehousing service Google Though Google is known for Search Engine, we all know that it is much more than that. Google Compute Engine – It offers secure, flexible computing from energy efficient data centers Google Big Query – It allows SQL-like queries to run against large datasets Google Prediction API – It is a cloud based machine learning tool Other Players Besides Amazon and Google we also have other players in the Big Data market as well. Microsoft is also attempting Big Data with the Cloud with Microsoft Azure. Additionally Rackspace and NASA together have initiated OpenStack. The goal of Openstack is to provide a massively scaled, multitenant cloud that can run on any hardware. Thing to Watch The cloud based solutions provides a great integration with the Big Data’s story as well it is very economical to implement as well. However, there are few things one should be very careful when deploying Big Data on cloud solutions. Here is a list of a few things to watch: Data Integrity Initial Cost Recurring Cost Performance Data Access Security Location Compliance Every company have different approaches to Big Data and have different rules and regulations. Based on various factors, one can implement their own custom Big Data solution on a cloud. Tomorrow In tomorrow’s blog post we will discuss about various Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• World Record Oracle Business Intelligence Benchmark on SPARC T4-4

  - by Brian

  Oracle's SPARC T4-4 server configured with four SPARC T4 3.0 GHz processors delivered the first and best performance of 25,000 concurrent users on Oracle Business Intelligence Enterprise Edition (BI EE) 11g benchmark using Oracle Database 11g Release 2 running on Oracle Solaris 10. A SPARC T4-4 server running Oracle Business Intelligence Enterprise Edition 11g achieved 25,000 concurrent users with an average response time of 0.36 seconds with Oracle BI server cache set to ON. The benchmark data clearly shows that the underlying hardware, SPARC T4 server, and the Oracle BI EE 11g (11.1.1.6.0 64-bit) platform scales within a single system supporting 25,000 concurrent users while executing 415 transactions/sec. The benchmark demonstrated the scalability of Oracle Business Intelligence Enterprise Edition 11g 11.1.1.6.0, which was deployed in a vertical scale-out fashion on a single SPARC T4-4 server. Oracle Internet Directory configured on SPARC T4 server provided authentication for the 25,000 Oracle BI EE users with sub-second response time. A SPARC T4-4 with internal Solid State Drive (SSD) using the ZFS file system showed significant I/O performance improvement over traditional disk for the Web Catalog activity. In addition, ZFS helped get past the UFS limitation of 32767 sub-directories in a Web Catalog directory. The multi-threaded 64-bit Oracle Business Intelligence Enterprise Edition 11g and SPARC T4-4 server proved to be a successful combination by providing sub-second response times for the end user transactions, consuming only half of the available CPU resources at 25,000 concurrent users, leaving plenty of head room for increased load. The Oracle Business Intelligence on SPARC T4-4 server benchmark results demonstrate that comprehensive BI functionality built on a unified infrastructure with a unified business model yields best-in-class scalability, reliability and performance. Oracle BI EE 11g is a newer version of Business Intelligence Suite with richer and superior functionality. Results produced with Oracle BI EE 11g benchmark are not comparable to results with Oracle BI EE 10g benchmark. Oracle BI EE 11g is a more difficult benchmark to run, exercising more features of Oracle BI. Performance Landscape Results for the Oracle BI EE 11g version of the benchmark. Results are not comparable to the Oracle BI EE 10g version of the benchmark. Oracle BI EE 11g Benchmark System Number of Users Response Time (sec) 1 x SPARC T4-4 (4 x SPARC T4 3.0 GHz) 25,000 0.36 Results for the Oracle BI EE 10g version of the benchmark. Results are not comparable to the Oracle BI EE 11g version of the benchmark. Oracle BI EE 10g Benchmark System Number of Users 2 x SPARC T5440 (4 x SPARC T2+ 1.6 GHz) 50,000 1 x SPARC T5440 (4 x SPARC T2+ 1.6 GHz) 28,000 Configuration Summary Hardware Configuration: SPARC T4-4 server 4 x SPARC T4-4 processors, 3.0 GHz 128 GB memory 4 x 300 GB internal SSD Storage Configuration: "> Sun ZFS Storage 7120 16 x 146 GB disks Software Configuration: Oracle Solaris 10 8/11 Oracle Solaris Studio 12.1 Oracle Business Intelligence Enterprise Edition 11g (11.1.1.6.0) Oracle WebLogic Server 10.3.5 Oracle Internet Directory 11.1.1.6.0 Oracle Database 11g Release 2 Benchmark Description Oracle Business Intelligence Enterprise Edition (Oracle BI EE) delivers a robust set of reporting, ad-hoc query and analysis, OLAP, dashboard, and scorecard functionality with a rich end-user experience that includes visualization, collaboration, and more. The Oracle BI EE benchmark test used five different business user roles - Marketing Executive, Sales Representative, Sales Manager, Sales Vice-President, and Service Manager. These roles included a maximum of 5 different pre-built dashboards. Each dashboard page had an average of 5 reports in the form of a mix of charts, tables and pivot tables, returning anywhere from 50 rows to approximately 500 rows of aggregated data. The test scenario also included drill-down into multiple levels from a table or chart within a dashboard. The benchmark test scenario uses a typical business user sequence of dashboard navigation, report viewing, and drill down. For example, a Service Manager logs into the system and navigates to his own set of dashboards using Service Manager. The BI user selects the Service Effectiveness dashboard, which shows him four distinct reports, Service Request Trend, First Time Fix Rate, Activity Problem Areas, and Cost Per Completed Service Call spanning 2002 to 2005. The user then proceeds to view the Customer Satisfaction dashboard, which also contains a set of 4 related reports, drills down on some of the reports to see the detail data. The BI user continues to view more dashboards – Customer Satisfaction and Service Request Overview, for example. After navigating through those dashboards, the user logs out of the application. The benchmark test is executed against a full production version of the Oracle Business Intelligence 11g Applications with a fully populated underlying database schema. The business processes in the test scenario closely represent a real world customer scenario. See Also SPARC T4-4 Server oracle.com OTN Oracle Business Intelligence oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN WebLogic Suite oracle.com OTN Oracle Solaris oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 30 September 2012.

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• Identity Management Monday at Oracle OpenWorld

  - by Tanu Sood

  What a great start to Oracle OpenWorld! Did you catch Larry Ellison’s keynote last evening? As expected, it was a packed house and the keynote received a tremendous response both from the live audience as well as the online community as evidenced by the frequent spontaneous applause in house and the twitter buzz. Here’s but a sampling of some of the tweets that flowed in: @paulvallee: I freaking love that #oracle has been born again in it's interest in core tech #oow (so good for #pythian) @rwang0: MyPOV: #oracle just leapfrogged the competition on the tech front across the board. All they need is the content delivery network #oow12 @roh1: LJE more astute & engaging this year. Nice announcements this year with 12c the MTDB sounding real good. #oow12 @brooke: Cool to see @larryellison interrupted multiple times by applause from the audience. Great speaker. #OOW And there’s lot more to come this week. Identity Management sessions kick-off today. Here’s a quick preview of what’s in store for you today for Identity Management: CON9405: Trends in Identity Management 10:45 a.m. – 11:45 a.m., Moscone West 3003 Hear directly from subject matter experts from Kaiser Permanente and SuperValu who would share the stage with Amit Jasuja, Senior Vice President, Oracle Identity Management and Security, to discuss how the latest advances in Identity Management that made it in Oracle Identity Management 11g Release 2 are helping customers address emerging requirements for securely enabling cloud, social and mobile environments. CON9492: Simplifying your Identity Management Implementation 3:15 p.m. – 4:15 p.m., Moscone West 3008 Implementation experts from British Telecom, Kaiser Permanente and UPMC participate in a panel to discuss best practices, key strategies and lessons learned based on their own experiences. Attendees will hear first-hand what they can do to streamline and simplify their identity management implementation framework for a quick return-on-investment and maximum efficiency. This session will also explore the architectural simplifications of Oracle Identity Governance 11gR2, focusing on how these enhancements simply deployments. CON9444: Modernized and Complete Access Management 4:45 p.m. – 5:45 p.m., Moscone West 3008 We have come a long way from the days of web single sign-on addressing the core business requirements. Today, as technology and business evolves, organizations are seeking new capabilities like federation, token services, fine grained authorizations, web fraud prevention and strong authentication. This session will explore the emerging requirements for access management, what a complete solution is like, complemented with real-world customer case studies from ETS, Kaiser Permanente and TURKCELL and product demonstrations. HOL10478: Complete Access Management Monday, October 1, 1:45 p.m. – 2:45 p.m., Marriott Marquis - Salon 1/2 And, get your hands on technology today. Register and attend the Hands-On-Lab session that demonstrates Oracle’s complete and scalable access management solution, which includes single sign-on, authorization, federation, and integration with social identity providers. Further, the session shows how to securely extend identity services to mobile applications and devices—all while leveraging a common set of policies and a single instance. Product Demonstrations The latest technology in Identity Management is also being showcased in the Exhibition Hall so do find some time to visit our product demonstrations there. Experts will be at hand to answer any questions. DEMOS LOCATION EXHIBITION HALL HOURS Access Management: Complete and Scalable Access Management Moscone South, Right - S-218 Monday, October 1 9:30 a.m.–6:00 p.m. 9:30 a.m.–10:45 a.m. (Dedicated Hours) Tuesday, October 2 9:45 a.m.–6:00 p.m. 2:15 p.m.–2:45 p.m. (Dedicated Hours) Wednesday, October 3 9:45 a.m.–4:00 p.m. 2:15 p.m.–3:30 p.m. (Dedicated Hours) Access Management: Federating and Leveraging Social Identities Moscone South, Right - S-220 Access Management: Mobile Access Management Moscone South, Right - S-219 Access Management: Real-Time Authorizations Moscone South, Right - S-217 Access Management: Secure SOA and Web Services Security Moscone South, Right - S-223 Identity Governance: Modern Administration and Tooling Moscone South, Right - S-210 Identity Management Monitoring with Oracle Enterprise Manager Moscone South, Right - S-212 Oracle Directory Services Plus: Performant, Cloud-Ready Moscone South, Right - S-222 Oracle Identity Management: Closed-Loop Access Certification Moscone South, Right - S-221 We recommend you keep the Focus on Identity Management document handy. And don’t forget, if you are not on site, you can catch all the keynotes LIVE from the comfort of your desk on YouTube.com/Oracle. Keep the conversation going on @oracleidm. Use #OOW and #IDM and get engaged today. Photo Courtesy: @OracleOpenWorld

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• AWS: setting up auto-scale for EC2 instances

  - by Elton Stoneman

  Originally posted on: http://geekswithblogs.net/EltonStoneman/archive/2013/10/16/aws-setting-up-auto-scale-for-ec2-instances.aspxWith Amazon Web Services, there’s no direct equivalent to Azure Worker Roles – no Elastic Beanstalk-style application for .NET background workers. But you can get the auto-scale part by configuring an auto-scaling group for your EC2 instance. This is a step-by-step guide, that shows you how to create the auto-scaling configuration, which for EC2 you need to do with the command line, and then link your scaling policies to CloudWatch alarms in the Web console. I’m using queue size as my metric for CloudWatch,  which is a good fit if your background workers are pulling messages from a queue and processing them.  If the queue is getting too big, the “high” alarm will fire and spin up a new instance to share the workload. If the queue is draining down, the “low” alarm will fire and shut down one of the instances. To start with, you need to manually set up your app in an EC2 VM, for a background worker that would mean hosting your code in a Windows Service (I always use Topshelf). If you’re dual-running Azure and AWS, then you can isolate your logic in one library, with a generic entry point that has Start() and Stop()  functions, so your Worker Role and Windows Service are essentially using the same code. When you have your instance set up with the Windows Service running automatically, and you’ve tested it starts up and works properly from a reboot, shut the machine down and take an image of the VM, using Create Image (EBS AMI) from the Web Console: When that completes, you’ll have your own AMI which you can use to spin up new instances, and you’re ready to create your auto-scaling group. You need to dip into the command-line tools for this, so follow this guide to set up the AWS autoscale command line tool. Now we’re ready to go. 1. Create a launch configuration This launch configuration tells AWS what to do when a new instance needs to be spun up. You create it with the as-create-launch-config command, which looks like this: as-create-launch-config sc-xyz-launcher # name of the launch config --image-id ami-7b9e9f12 # id of the AMI you extracted from your VM --region eu-west-1 # which region the new instance gets created in --instance-type t1.micro # size of the instance to create --group quicklaunch-1 #security group for the new instance 2. Create an auto-scaling group The auto-scaling group links to the launch config, and defines the overall configuration of the collection of instances: as-create-auto-scaling-group sc-xyz-asg # auto-scaling group name --region eu-west-1 # region to create in --launch-configuration sc-xyz-launcher # name of the launch config to invoke for new instances --min-size 1 # minimum number of nodes in the group --max-size 5 # maximum number of nodes in the group --default-cooldown 300 # period to wait (in seconds) after each scaling event, before checking if another scaling event is required --availability-zones eu-west-1a eu-west-1b eu-west-1c # which availability zones you want your instances to be allocated in – multiple entries means EC@ will use any of them 3. Create a scale-up policy The policy dictates what will happen in response to a scaling event being triggered from a “high” alarm being breached. It links to the auto-scaling group; this sample results in one additional node being spun up: as-put-scaling-policy scale-up-policy # policy name -g sc-psod-woker-asg # auto-scaling group the policy works with --adjustment 1 # size of the adjustment --region eu-west-1 # region --type ChangeInCapacity # type of adjustment, this specifies a fixed number of nodes, but you can use PercentChangeInCapacity to make an adjustment relative to the current number of nodes, e.g. increasing by 50% 4. Create a scale-down policy The policy dictates what will happen in response to a scaling event being triggered from a “low” alarm being breached. It links to the auto-scaling group; this sample results in one node from the group being taken offline: as-put-scaling-policy scale-down-policy -g sc-psod-woker-asg "--adjustment=-1" # in Windows, use double-quotes to surround a negative adjustment value –-type ChangeInCapacity --region eu-west-1 5. Create a “high” CloudWatch alarm We’re done with the command line now. In the Web Console, open up the CloudWatch view and create a new alarm. This alarm will monitor your metrics and invoke the scale-up policy from your auto-scaling group, when the group is working too hard. Configure your metric – this example will fire the alarm if there are more than 10 messages in my queue for over a minute: Then link the alarm to the scale-up policy in your group: 6. Create a “low” CloudWatch alarm The opposite of step 4, this alarm will trigger when the instances in your group don’t have enough work to do (e.g fewer than 2 messages in the queue for 1 minute), and will invoke the scale-down policy. And that’s it. You don’t need your original VM as the auto-scale group has a minimum number of nodes connected. You can test out the scaling by flexing your CloudWatch metric – in this example, filling up a queue from a  stub publisher – and watching AWS create new nodes as required, then stopping the publisher and watch AWS kill off the spare nodes.

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• World Record Batch Rate on Oracle JD Edwards Consolidated Workload with SPARC T4-2

  - by Brian

  Oracle produced a World Record batch throughput for single system results on Oracle's JD Edwards EnterpriseOne Day-in-the-Life benchmark using Oracle's SPARC T4-2 server running Oracle Solaris Containers and consolidating JD Edwards EnterpriseOne, Oracle WebLogic servers and the Oracle Database 11g Release 2. The workload includes both online and batch workload. The SPARC T4-2 server delivered a result of 8,000 online users while concurrently executing a mix of JD Edwards EnterpriseOne Long and Short batch processes at 95.5 UBEs/min (Universal Batch Engines per minute). In order to obtain this record benchmark result, the JD Edwards EnterpriseOne, Oracle WebLogic and Oracle Database 11g Release 2 servers were executed each in separate Oracle Solaris Containers which enabled optimal system resources distribution and performance together with scalable and manageable virtualization. One SPARC T4-2 server running Oracle Solaris Containers and consolidating JD Edwards EnterpriseOne, Oracle WebLogic servers and the Oracle Database 11g Release 2 utilized only 55% of the available CPU power. The Oracle DB server in a Shared Server configuration allows for optimized CPU resource utilization and significant memory savings on the SPARC T4-2 server without sacrificing performance. This configuration with SPARC T4-2 server has achieved 33% more Users/core, 47% more UBEs/min and 78% more Users/rack unit than the IBM Power 770 server. The SPARC T4-2 server with 2 processors ran the JD Edwards "Day-in-the-Life" benchmark and supported 8,000 concurrent online users while concurrently executing mixed batch workloads at 95.5 UBEs per minute. The IBM Power 770 server with twice as many processors supported only 12,000 concurrent online users while concurrently executing mixed batch workloads at only 65 UBEs per minute. This benchmark demonstrates more than 2x cost savings by consolidating the complete solution in a single SPARC T4-2 server compared to earlier published results of 10,000 users and 67 UBEs per minute on two SPARC T4-2 and SPARC T4-1. The Oracle DB server used mirrored (RAID 1) volumes for the database providing high availability for the data without impacting performance. Performance Landscape JD Edwards EnterpriseOne Day in the Life (DIL) Benchmark Consolidated Online with Batch Workload System Rack Units BatchRate(UBEs/m) Online Users Users /Units Users /Core Version SPARC T4-2 (2 x SPARC T4, 2.85 GHz) 3 95.5 8,000 2,667 500 9.0.2 IBM Power 770 (4 x POWER7, 3.3 GHz, 32 cores) 8 65 12,000 1,500 375 9.0.2 Batch Rate (UBEs/m) — Batch transaction rate in UBEs per minute Configuration Summary Hardware Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 4 x 300 GB 10K RPM SAS internal disk 2 x 300 GB internal SSD 2 x Sun Storage F5100 Flash Arrays Software Configuration: Oracle Solaris 10 Oracle Solaris Containers JD Edwards EnterpriseOne 9.0.2 JD Edwards EnterpriseOne Tools (8.98.4.2) Oracle WebLogic Server 11g (10.3.4) Oracle HTTP Server 11g Oracle Database 11g Release 2 (11.2.0.1) Benchmark Description JD Edwards EnterpriseOne is an integrated applications suite of Enterprise Resource Planning (ERP) software. Oracle offers 70 JD Edwards EnterpriseOne application modules to support a diverse set of business operations. Oracle's Day in the Life (DIL) kit is a suite of scripts that exercises most common transactions of JD Edwards EnterpriseOne applications, including business processes such as payroll, sales order, purchase order, work order, and manufacturing processes, such as ship confirmation. These are labeled by industry acronyms such as SCM, CRM, HCM, SRM and FMS. The kit's scripts execute transactions typical of a mid-sized manufacturing company. The workload consists of online transactions and the UBE – Universal Business Engine workload of 61 short and 4 long UBEs. LoadRunner runs the DIL workload, collects the user’s transactions response times and reports the key metric of Combined Weighted Average Transaction Response time. The UBE processes workload runs from the JD Enterprise Application server. Oracle's UBE processes come as three flavors: Short UBEs < 1 minute engage in Business Report and Summary Analysis, Mid UBEs > 1 minute create a large report of Account, Balance, and Full Address, Long UBEs > 2 minutes simulate Payroll, Sales Order, night only jobs. The UBE workload generates large numbers of PDF files reports and log files. The UBE Queues are categorized as the QBATCHD, a single threaded queue for large and medium UBEs, and the QPROCESS queue for short UBEs run concurrently. Oracle's UBE process performance metric is Number of Maximum Concurrent UBE processes at transaction rate, UBEs/minute. Key Points and Best Practices Two JD Edwards EnterpriseOne Application Servers, two Oracle WebLogic Servers 11g Release 1 coupled with two Oracle Web Tier HTTP server instances and one Oracle Database 11g Release 2 database on a single SPARC T4-2 server were hosted in separate Oracle Solaris Containers bound to four processor sets to demonstrate consolidation of multiple applications, web servers and the database with best resource utilizations. Interrupt fencing was configured on all Oracle Solaris Containers to channel the interrupts to processors other than the processor sets used for the JD Edwards Application server, Oracle WebLogic servers and the database server. A Oracle WebLogic vertical cluster was configured on each WebServer Container with twelve managed instances each to load balance users' requests and to provide the infrastructure that enables scaling to high number of users with ease of deployment and high availability. The database log writer was run in the real time RT class and bound to a processor set. The database redo logs were configured on the raw disk partitions. The Oracle Solaris Container running the Enterprise Application server completed 61 Short UBEs, 4 Long UBEs concurrently as the mixed size batch workload. The mixed size UBEs ran concurrently from the Enterprise Application server with the 8,000 online users driven by the LoadRunner. See Also SPARC T4-2 Server oracle.com OTN JD Edwards EnterpriseOne oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Oracle Fusion Middleware oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 09/30/2012.

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• Building InstallShield based Installers using Team Build 2010

  - by jehan

  Last few weeks, I have been working on Application Packaging stuff using all the widely used tools like InstallShield, WISE, WiX and Visual Studio Installer. So, I thought it would be good to post about how to Build the Installers developed using these tools with Team Build 2010. This post will focus on how to build the InstallShield generated packages using Team Build 2010. For the release of VS2010, Microsoft has partnered with Flexera who are the makers of InstallShield to create InstallShield Limited Edition, especially for the customers of Visual Studio. First Microsoft planned to release WiX (Windows Installer Xml) with VS2010, but later Microsoft dropped  WiX from VS2010 due to reasons which are best known to them and partnered with InstallShield for Limited Edition. It disappointed lot of people because InstallShield Limited Edition provides only few features of InstallShield and it may not feasable to build complex installer packages using this and it also requires License, where as WiX is an open source with no license costs and it has proved efficient in building most complex packages. Only the last three features are available in InstallShield Limited Edition from the total features offered by InstallShield as shown in below list.                                                                                            Feature Limited Edition for Visual Studio 2010 Standalone Build System Maintain a clean build machine by using only the part of InstallShield that compiles the installations. InstallShield Best Practices Validation Suite Avoid common installation issues. Try and Die Functionality RCreate a fully functional trial version of your product. InstallShield Repackager Create Windows Installer setups from any legacy installation. Multilingual Support Present installation text in up to 35 languages. Microsoft App-V™ Support Deploy your applications as App-V virtual packages that run without conflict. Industry-Standard InstallScript Achieve maximum flexibility in your installations. Dialog Editor Modify the layout of existing end-user dialogs, create new custom dialogs, and more. Patch Creation Build updates and patches for your products. Setup Prerequisite Editor Easily control prerequisite restart behavior and source locations. String Editor View Control the localizable text strings displayed at run time with this spreadsheet-like table. Text File Changes View Configure search-and-replace actions for content in text files to be modified at run time. Virtual Machine Detection Block your installations from running on virtual machines. Unicode Support Improve multi-language installation development. Support for 64-Bit COM Extraction Extract COM data from a 64-bit COM server. Windows Installer Installation Chaining Add MSI packages to your main installation and chain them together. XML Support Save time by quickly testing XML configuration changes to installation projects. Billboard Support for Custom Branding Display Adobe Flash billboards and other graphic files during the install process. SaaS Support (IIS 7 and SSL Technologies) Easily deploy Windows-based Web applications. Project Assistant Jumpstart a project by using a simplified set of views. Support for Digital Signatures Save time by digitally signing all your files at build time. Easily Run Custom Actions Schedule a custom action to run at precisely the right moment in your installation. Installation Prerequisites Check for and install prerequisites before your installation is executed. To create a InstallShield project in Visual Studio and Build it using Team Build 2010, first you have to add the InstallShield Project template  to your Solution file. If you want to use InstallShield Limited edition you can add it from FileàNewà project àother Project Types àSetup and Deploymentà InstallShield LE and if you are using other versions of InstallShield, then you have to add it from  from FileàNewà project àInstallShield Projects. Here, I’m using  InstallShield 2011 Premier edition as I already have it Installed. I have created a simple package for TailSpin Application which has a Feature called Web, few components and a IIS Web Site for  TailSpin application.   Before started working on this, I thought I may need to build the package by calling invoke process activity in build process template or have to create a new custom activity. But, it got build without any changes to build process template. But, it was failing with below error message. C:\Program Files (x86)\MSBuild\InstallShield\2011\InstallShield.targets (68): The "InstallShield.Tasks.InstallShield" task could not be loaded from the assembly C:\Program Files (x86)\MSBuild\InstallShield\2010Limited\InstallShield.Tasks.dll. Could not load file or assembly 'file:///C:\Program Files(x86)\MSBuild\InstallShield\2011\InstallShield.Tasks.dll' or one of its dependencies. An attempt was made to load a program with an incorrect format. Confirm that the <UsingTask> declaration is correct, that the assembly and all its dependencies are available, and that the task contains a public class that implements Microsoft.Build.Framework.ITask. This error is due to 64-bit build machine which I’m using. This issue will be replicable if you are queuing a build on a 64-bit build machine. To avoid this you have to ensure that you configured the build definition for your InstallShield project to load the InstallShield.Tasks.dll file (which is a 32-bit file); otherwise, you will encounter this build error informing you that the InstallShield.Tasks.dll file could not be loaded. To select the 32-bit version of MSBuild, click the Process tab of your build definition in Team Explorer. Then, under the Advanced node, find the MSBuild Platform setting, and select x86. Note that if you are using a 32-bit build machine, you can select either Auto or x86 for the MSBuild Platform setting.  Once I did above changes, the build got successful.

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• An Alphabet of Eponymous Aphorisms, Programming Paradigms, Software Sayings, Annoying Alliteration

  - by Brian Schroer

  Malcolm Anderson blogged about “Einstein’s Razor” yesterday, which reminded me of my favorite software development “law”, the name of which I can never remember. It took much Wikipedia-ing to find it (Hofstadter’s Law – see below), but along the way I compiled the following list: Amara’s Law: We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run. Brook’s Law: Adding manpower to a late software project makes it later. Clarke’s Third Law: Any sufficiently advanced technology is indistinguishable from magic. Law of Demeter: Each unit should only talk to its friends; don't talk to strangers. Einstein’s Razor: “Make things as simple as possible, but not simpler” is the popular paraphrase, but what he actually said was “It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience”, an overly complicated quote which is an obvious violation of Einstein’s Razor. (You can tell by looking at a picture of Einstein that the dude was hardly an expert on razors or other grooming apparati.) Finagle's Law of Dynamic Negatives: Anything that can go wrong, will—at the worst possible moment. - O'Toole's Corollary: The perversity of the Universe tends towards a maximum. Greenspun's Tenth Rule: Any sufficiently complicated C or Fortran program contains an ad hoc, informally-specified, bug-ridden, slow implementation of half of Common Lisp. (Morris’s Corollary: “…including Common Lisp”) Hofstadter's Law: It always takes longer than you expect, even when you take into account Hofstadter's Law. Issawi’s Omelet Analogy: One cannot make an omelet without breaking eggs - but it is amazing how many eggs one can break without making a decent omelet. Jackson’s Rules of Optimization: Rule 1: Don't do it. Rule 2 (for experts only): Don't do it yet. Kaner’s Caveat: A program which perfectly meets a lousy specification is a lousy program. Liskov Substitution Principle (paraphrased): Functions that use pointers or references to base classes must be able to use objects of derived classes without knowing it Mason’s Maxim: Since human beings themselves are not fully debugged yet, there will be bugs in your code no matter what you do. Nils-Peter Nelson’s Nil I/O Rule: The fastest I/O is no I/O.    Occam's Razor: The simplest explanation is usually the correct one. Parkinson’s Law: Work expands so as to fill the time available for its completion. Quentin Tarantino’s Pie Principle: “…you want to go home have a drink and go and eat pie and talk about it.” (OK, he was talking about movies, not software, but I couldn’t find a “Q” quote about software. And wouldn’t it be cool to write a program so great that the users want to eat pie and talk about it?) Raymond’s Rule: Computer science education cannot make anybody an expert programmer any more than studying brushes and pigment can make somebody an expert painter.  Sowa's Law of Standards: Whenever a major organization develops a new system as an official standard for X, the primary result is the widespread adoption of some simpler system as a de facto standard for X. Turing’s Tenet: We shall do a much better programming job, provided we approach the task with a full appreciation of its tremendous difficulty, provided that we respect the intrinsic limitations of the human mind and approach the task as very humble programmers.  Udi Dahan’s Race Condition Rule: If you think you have a race condition, you don’t understand the domain well enough. These rules didn’t exist in the age of paper, there is no reason for them to exist in the age of computers. When you have race conditions, go back to the business and find out actual rules. Van Vleck’s Kvetching: We know about as much about software quality problems as they knew about the Black Plague in the 1600s. We've seen the victims' agonies and helped burn the corpses. We don't know what causes it; we don't really know if there is only one disease. We just suffer -- and keep pouring our sewage into our water supply. Wheeler’s Law: All problems in computer science can be solved by another level of indirection... Except for the problem of too many layers of indirection. Wheeler also said “Compatibility means deliberately repeating other people's mistakes.”. The Wrong Road Rule of Mr. X (anonymous): No matter how far down the wrong road you've gone, turn back. Yourdon’s Rule of Two Feet: If you think your management doesn't know what it's doing or that your organisation turns out low-quality software crap that embarrasses you, then leave. Zawinski's Law of Software Envelopment: Every program attempts to expand until it can read mail. Zawinski is also responsible for “Some people, when confronted with a problem, think 'I know, I'll use regular expressions.' Now they have two problems.” He once commented about X Windows widget toolkits: “Using these toolkits is like trying to make a bookshelf out of mashed potatoes.”

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• Login failed for user 'sa' because the account is currently locked out. The system administrator can

  - by cabhilash

  Login failed for user 'sa' because the account is currently locked out. The system administrator can unlock it. (Microsoft SQL Server, Error: 18486) SQL server has local password policies. If policy is enabled which locks down the account after X number of failed attempts then the account is automatically locked down.This error with 'sa' account is very common. sa is default administartor login available with SQL server. So there are chances that an ousider has tried to bruteforce your system. (This can cause even if a legitimate tries to access the account with wrong password.Sometimes a user would have changed the password without informing others. So the other users would try to lo) You can unlock the account with the following options (use another admin account or connect via windows authentication) Alter account & unlock ALTER LOGIN sa WITH PASSWORD='password' UNLOCK Use another account Almost everyone is aware of the sa account. This can be the potential security risk. Even if you provide strong password hackers can lock the account by providing the wrong password. ( You can provide extra security by installing firewall or changing the default port but these measures are not always practical). As a best practice you can disable the sa account and use another account with same privileges.ALTER LOGIN sa DISABLE You can edit the lock-ot options using gpedit.msc( in command prompt type gpedit.msc and press enter). Navigate to Account Lokout policy as shown in the figure The Following options are available Account lockout threshold This security setting determines the number of failed logon attempts that causes a user account to be locked out. A locked-out account cannot be used until it is reset by an administrator or until the lockout duration for the account has expired. You can set a value between 0 and 999 failed logon attempts. If you set the value to 0, the account will never be locked out. Failed password attempts against workstations or member servers that have been locked using either CTRL+ALT+DELETE or password-protected screen savers count as failed logon attempts. Account lockout duration This security setting determines the number of minutes a locked-out account remains locked out before automatically becoming unlocked. The available range is from 0 minutes through 99,999 minutes. If you set the account lockout duration to 0, the account will be locked out until an administrator explicitly unlocks it. If an account lockout threshold is defined, the account lockout duration must be greater than or equal to the reset time. Default: None, because this policy setting only has meaning when an Account lockout threshold is specified. Reset account lockout counter after This security setting determines the number of minutes that must elapse after a failed logon attempt before the failed logon attempt counter is reset to 0 bad logon attempts. The available range is 1 minute to 99,999 minutes. If an account lockout threshold is defined, this reset time must be less than or equal to the Account lockout duration. Default: None, because this policy setting only has meaning when an Account lockout threshold is specified.When creating SQL user you can set CHECK_POLICY=on which will enforce the windows password policy on the account. The following policies will be applied Define the Enforce password history policy setting so that several previous passwords are remembered. With this policy setting, users cannot use the same password when their password expires.  Define the Maximum password age policy setting so that passwords expire as often as necessary for your environment, typically, every 30 to 90 days. With this policy setting, if an attacker cracks a password, the attacker only has access to the network until the password expires.  Define the Minimum password age policy setting so that passwords cannot be changed until they are more than a certain number of days old. This policy setting works in combination with the Enforce password historypolicy setting. If a minimum password age is defined, users cannot repeatedly change their passwords to get around the Enforce password history policy setting and then use their original password. Users must wait the specified number of days to change their passwords.  Define a Minimum password length policy setting so that passwords must consist of at least a specified number of characters. Long passwords--seven or more characters--are usually stronger than short ones. With this policy setting, users cannot use blank passwords, and they have to create passwords that are a certain number of characters long.  Enable the Password must meet complexity requirements policy setting. This policy setting checks all new passwords to ensure that they meet basic strong password requirements.  Password must meet the following complexity requirement, when they are changed or created: Not contain the user's entire Account Name or entire Full Name. The Account Name and Full Name are parsed for delimiters: commas, periods, dashes or hyphens, underscores, spaces, pound signs, and tabs. If any of these delimiters are found, the Account Name or Full Name are split and all sections are verified not to be included in the password. There is no check for any character or any three characters in succession. Contain characters from three of the following five categories:  English uppercase characters (A through Z) English lowercase characters (a through z) Base 10 digits (0 through 9) Non-alphabetic characters (for example, !, $, #, %) A catch-all category of any Unicode character that does not fall under the previous four categories. This fifth category can be regionally specific.

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• Slow boot on Ubuntu 12.04

  - by Hailwood

  My Ubuntu is booting really slow (Windows is booting faster...). I am using Ubuntu a Dell Inspiron 1545 Pentium(R) Dual-Core CPU T4300 @ 2.10GHz, 4GB Ram, 500GB HDD running Ubuntu 12.04 with gnome-shell 3.4.1. After running dmesg the culprit seems to be this section, in particular the last three lines: [26.557659] ADDRCONF(NETDEV_UP): eth0: link is not ready [26.565414] ADDRCONF(NETDEV_UP): eth0: link is not ready [27.355355] Console: switching to colour frame buffer device 170x48 [27.362346] fb0: radeondrmfb frame buffer device [27.362347] drm: registered panic notifier [27.362357] [drm] Initialized radeon 2.12.0 20080528 for 0000:01:00.0 on minor 0 [27.617435] init: udev-fallback-graphics main process (1049) terminated with status 1 [30.064481] init: plymouth-stop pre-start process (1500) terminated with status 1 [51.708241] CE: hpet increased min_delta_ns to 20113 nsec [59.448029] eth2: no IPv6 routers present But I have no idea how to start debugging this. sudo lshw -C video $ sudo lshw -C video *-display description: VGA compatible controller product: RV710 [Mobility Radeon HD 4300 Series] vendor: Hynix Semiconductor (Hyundai Electronics) physical id: 0 bus info: pci@0000:01:00.0 version: 00 width: 32 bits clock: 33MHz capabilities: pm pciexpress msi vga_controller bus_master cap_list rom configuration: driver=fglrx_pci latency=0 resources: irq:48 memory:e0000000-efffffff ioport:de00(size=256) memory:f6df0000-f6dfffff memory:f6d00000-f6d1ffff After loading the propriety driver my new dmesg log is below (starting from the first major time gap): [2.983741] EXT4-fs (sda6): mounted filesystem with ordered data mode. Opts: (null) [25.094327] ADDRCONF(NETDEV_UP): eth0: link is not ready [25.119737] udevd[520]: starting version 175 [25.167086] lp: driver loaded but no devices found [25.215341] fglrx: module license 'Proprietary. (C) 2002 - ATI Technologies, Starnberg, GERMANY' taints kernel. [25.215345] Disabling lock debugging due to kernel taint [25.231924] wmi: Mapper loaded [25.318414] lib80211: common routines for IEEE802.11 drivers [25.318418] lib80211_crypt: registered algorithm 'NULL' [25.331631] [fglrx] Maximum main memory to use for locked dma buffers: 3789 MBytes. [25.332095] [fglrx] vendor: 1002 device: 9552 count: 1 [25.334206] [fglrx] ioport: bar 1, base 0xde00, size: 0x100 [25.334229] pci 0000:01:00.0: PCI INT A -> GSI 16 (level, low) -> IRQ 16 [25.334235] pci 0000:01:00.0: setting latency timer to 64 [25.337109] [fglrx] Kernel PAT support is enabled [25.337140] [fglrx] module loaded - fglrx 8.96.4 [Mar 12 2012] with 1 minors [25.342803] Adding 4189180k swap on /dev/sda7. Priority:-1 extents:1 across:4189180k [25.364031] type=1400 audit(1338241723.027:2): apparmor="STATUS" operation="profile_load" name="/sbin/dhclient" pid=606 comm="apparmor_parser" [25.364491] type=1400 audit(1338241723.031:3): apparmor="STATUS" operation="profile_load" name="/usr/lib/NetworkManager/nm-dhcp-client.action" pid=606 comm="apparmor_parser" [25.364760] type=1400 audit(1338241723.031:4): apparmor="STATUS" operation="profile_load" name="/usr/lib/connman/scripts/dhclient-script" pid=606 comm="apparmor_parser" [25.394328] wl 0000:0c:00.0: PCI INT A -> GSI 17 (level, low) -> IRQ 17 [25.394343] wl 0000:0c:00.0: setting latency timer to 64 [25.415531] acpi device:36: registered as cooling_device2 [25.416688] input: Video Bus as /devices/LNXSYSTM:00/device:00/PNP0A03:00/device:34/LNXVIDEO:00/input/input6 [25.416795] ACPI: Video Device [VID] (multi-head: yes rom: no post: no) [25.416865] [Firmware Bug]: Duplicate ACPI video bus devices for the same VGA controller, please try module parameter "video.allow_duplicates=1"if the current driver doesn't work. [25.425133] lib80211_crypt: registered algorithm 'TKIP' [25.448058] snd_hda_intel 0000:00:1b.0: PCI INT A -> GSI 21 (level, low) -> IRQ 21 [25.448321] snd_hda_intel 0000:00:1b.0: irq 47 for MSI/MSI-X [25.448353] snd_hda_intel 0000:00:1b.0: setting latency timer to 64 [25.738867] eth1: Broadcom BCM4315 802.11 Hybrid Wireless Controller 5.100.82.38 [25.761213] input: HDA Intel Mic as /devices/pci0000:00/0000:00:1b.0/sound/card0/input7 [25.761406] input: HDA Intel Headphone as /devices/pci0000:00/0000:00:1b.0/sound/card0/input8 [25.783432] dcdbas dcdbas: Dell Systems Management Base Driver (version 5.6.0-3.2) [25.908318] EXT4-fs (sda6): re-mounted. Opts: errors=remount-ro [25.928155] input: Dell WMI hotkeys as /devices/virtual/input/input9 [25.960561] udevd[543]: renamed network interface eth1 to eth2 [26.285688] init: failsafe main process (835) killed by TERM signal [26.396426] input: PS/2 Mouse as /devices/platform/i8042/serio2/input/input10 [26.423108] input: AlpsPS/2 ALPS GlidePoint as /devices/platform/i8042/serio2/input/input11 [26.511297] Bluetooth: Core ver 2.16 [26.511383] NET: Registered protocol family 31 [26.511385] Bluetooth: HCI device and connection manager initialized [26.511388] Bluetooth: HCI socket layer initialized [26.511391] Bluetooth: L2CAP socket layer initialized [26.512079] Bluetooth: SCO socket layer initialized [26.530164] Bluetooth: BNEP (Ethernet Emulation) ver 1.3 [26.530168] Bluetooth: BNEP filters: protocol multicast [26.553893] type=1400 audit(1338241724.219:5): apparmor="STATUS" operation="profile_replace" name="/sbin/dhclient" pid=928 comm="apparmor_parser" [26.554860] Bluetooth: RFCOMM TTY layer initialized [26.554866] Bluetooth: RFCOMM socket layer initialized [26.554868] Bluetooth: RFCOMM ver 1.11 [26.557910] type=1400 audit(1338241724.223:6): apparmor="STATUS" operation="profile_load" name="/usr/lib/lightdm/lightdm/lightdm-guest-session-wrapper" pid=927 comm="apparmor_parser" [26.559166] type=1400 audit(1338241724.223:7): apparmor="STATUS" operation="profile_replace" name="/usr/lib/NetworkManager/nm-dhcp-client.action" pid=928 comm="apparmor_parser" [26.559574] type=1400 audit(1338241724.223:8): apparmor="STATUS" operation="profile_replace" name="/usr/lib/connman/scripts/dhclient-script" pid=928 comm="apparmor_parser" [26.575519] type=1400 audit(1338241724.239:9): apparmor="STATUS" operation="profile_load" name="/usr/lib/telepathy/mission-control-5" pid=931 comm="apparmor_parser" [26.581100] type=1400 audit(1338241724.247:10): apparmor="STATUS" operation="profile_load" name="/usr/lib/telepathy/telepathy-*" pid=931 comm="apparmor_parser" [26.582794] type=1400 audit(1338241724.247:11): apparmor="STATUS" operation="profile_load" name="/usr/bin/evince" pid=929 comm="apparmor_parser" [26.605672] ppdev: user-space parallel port driver [27.592475] sky2 0000:09:00.0: eth0: enabling interface [27.604329] ADDRCONF(NETDEV_UP): eth0: link is not ready [27.606962] ADDRCONF(NETDEV_UP): eth0: link is not ready [27.852509] vesafb: mode is 1024x768x32, linelength=4096, pages=0 [27.852513] vesafb: scrolling: redraw [27.852515] vesafb: Truecolor: size=0:8:8:8, shift=0:16:8:0 [27.852523] mtrr: type mismatch for e0000000,400000 old: write-back new: write-combining [27.852527] mtrr: type mismatch for e0000000,200000 old: write-back new: write-combining [27.852531] mtrr: type mismatch for e0000000,100000 old: write-back new: write-combining [27.852534] mtrr: type mismatch for e0000000,80000 old: write-back new: write-combining [27.852538] mtrr: type mismatch for e0000000,40000 old: write-back new: write-combining [27.852541] mtrr: type mismatch for e0000000,20000 old: write-back new: write-combining [27.852544] mtrr: type mismatch for e0000000,10000 old: write-back new: write-combining [27.852548] mtrr: type mismatch for e0000000,8000 old: write-back new: write-combining [27.852551] mtrr: type mismatch for e0000000,4000 old: write-back new: write-combining [27.852554] mtrr: type mismatch for e0000000,2000 old: write-back new: write-combining [27.852558] mtrr: type mismatch for e0000000,1000 old: write-back new: write-combining [27.853154] vesafb: framebuffer at 0xe0000000, mapped to 0xffffc90005580000, using 3072k, total 3072k [27.853405] Console: switching to colour frame buffer device 128x48 [27.853426] fb0: VESA VGA frame buffer device [28.539800] fglrx_pci 0000:01:00.0: irq 48 for MSI/MSI-X [28.540552] [fglrx] Firegl kernel thread PID: 1168 [28.540679] [fglrx] Firegl kernel thread PID: 1169 [28.540789] [fglrx] Firegl kernel thread PID: 1170 [28.540932] [fglrx] IRQ 48 Enabled [29.845620] [fglrx] Gart USWC size:1236 M. [29.845624] [fglrx] Gart cacheable size:489 M. [29.845629] [fglrx] Reserved FB block: Shared offset:0, size:1000000 [29.845632] [fglrx] Reserved FB block: Unshared offset:fc21000, size:3df000 [29.845635] [fglrx] Reserved FB block: Unshared offset:1fffb000, size:5000 [59.700023] eth2: no IPv6 routers present

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• Do Not Optimize Without Measuring

  - by Alois Kraus

  Recently I had to do some performance work which included reading a lot of code. It is fascinating with what ideas people come up to solve a problem. Especially when there is no problem. When you look at other peoples code you will not be able to tell if it is well performing or not by reading it. You need to execute it with some sort of tracing or even better under a profiler. The first rule of the performance club is not to think and then to optimize but to measure, think and then optimize. The second rule is to do this do this in a loop to prevent slipping in bad things for too long into your code base. If you skip for some reason the measure step and optimize directly it is like changing the wave function in quantum mechanics. This has no observable effect in our world since it does represent only a probability distribution of all possible values. In quantum mechanics you need to let the wave function collapse to a single value. A collapsed wave function has therefore not many but one distinct value. This is what we physicists call a measurement. If you optimize your application without measuring it you are just changing the probability distribution of your potential performance values. Which performance your application actually has is still unknown. You only know that it will be within a specific range with a certain probability. As usual there are unlikely values within your distribution like a startup time of 20 minutes which should only happen once in 100 000 years. 100 000 years are a very short time when the first customer tries your heavily distributed networking application to run over a slow WIFI network… What is the point of this? Every programmer/architect has a mental performance model in his head. A model has always a set of explicit preconditions and a lot more implicit assumptions baked into it. When the model is good it will help you to think of good designs but it can also be the source of problems. In real world systems not all assumptions of your performance model (implicit or explicit) hold true any longer. The only way to connect your performance model and the real world is to measure it. In the WIFI example the model did assume a low latency high bandwidth LAN connection. If this assumption becomes wrong the system did have a drastic change in startup time. Lets look at a example. Lets assume we want to cache some expensive UI resource like fonts objects. For this undertaking we do create a Cache class with the UI themes we want to support. Since Fonts are expensive objects we do create it on demand the first time the theme is requested. A simple example of a Theme cache might look like this: using System; using System.Collections.Generic; using System.Drawing; struct Theme { public Color Color; public Font Font; } static class ThemeCache { static Dictionary<string, Theme> _Cache = new Dictionary<string, Theme> { {"Default", new Theme { Color = Color.AliceBlue }}, {"Theme12", new Theme { Color = Color.Aqua }}, }; public static Theme Get(string theme) { Theme cached = _Cache[theme]; if (cached.Font == null) { Console.WriteLine("Creating new font"); cached.Font = new Font("Arial", 8); } return cached; } } class Program { static void Main(string[] args) { Theme item = ThemeCache.Get("Theme12"); item = ThemeCache.Get("Theme12"); } } This cache does create font objects only once since on first retrieve of the Theme object the font is added to the Theme object. When we let the application run it should print “Creating new font” only once. Right? Wrong! The vigilant readers have spotted the issue already. The creator of this cache class wanted to get maximum performance. So he decided that the Theme object should be a value type (struct) to not put too much pressure on the garbage collector. The code Theme cached = _Cache[theme]; if (cached.Font == null) { Console.WriteLine("Creating new font"); cached.Font = new Font("Arial", 8); } does work with a copy of the value stored in the dictionary. This means we do mutate a copy of the Theme object and return it to our caller. But the original Theme object in the dictionary will have always null for the Font field! The solution is to change the declaration of struct Theme to class Theme or to update the theme object in the dictionary. Our cache as it is currently is actually a non caching cache. The funny thing was that I found out with a profiler by looking at which objects where finalized. I found way too many font objects to be finalized. After a bit debugging I found the allocation source for Font objects was this cache. Since this cache was there for years it means that the cache was never needed since I found no perf issue due to the creation of font objects. the cache was never profiled if it did bring any performance gain. to make the cache beneficial it needs to be accessed much more often. That was the story of the non caching cache. Next time I will write something something about measuring.

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• Retrieve Performance Data from SOA Infrastructure Database

  - by fip

  My earlier blog posting shows how to enable, retrieve and interpret BPEL engine performance statistics to aid performance troubleshooting. The strength of BPEL engine statistics at EM is its break down per request. But there are some limitations with the BPEL performance statistics mentioned in that blog posting: The statistics were stored in memory instead of being persisted. To avoid memory overflow, the data are stored to a buffer with limited size. When the statistic entries exceed the limitation, old data will be flushed out to give ways to new statistics. Therefore it can only keep the last X number of entries of data. The statistics 5 hour ago may not be there anymore. The BPEL engine performance statistics only includes latencies. It does not provide throughputs. Fortunately, Oracle SOA Suite runs with the SOA Infrastructure database and a lot of performance data are naturally persisted there. It is at a more coarse grain than the in-memory BPEL Statistics, but it does have its own strengths as it is persisted. Here I would like offer examples of some basic SQL queries you can run against the infrastructure database of Oracle SOA Suite 11G to acquire the performance statistics for a given period of time. You can run it immediately after you modify the date range to match your actual system. 1. Asynchronous/one-way messages incoming rates The following query will show number of messages sent to one-way/async BPEL processes during a given time period, organized by process names and states select composite_name composite, state, count(*) Count from dlv_message where receive_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and receive_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, state order by Count; 2. Throughput of BPEL process instances The following query shows the number of synchronous and asynchronous process instances created during a given time period. It list instances of all states, including the unfinished and faulted ones. The results will include all composites cross all SOA partitions select state, count(*) Count, composite_name composite, component_name,componenttype from cube_instance where creation_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype order by count(*) desc; 3. Throughput and latencies of BPEL process instances This query is augmented on the previous one, providing more comprehensive information. It gives not only throughput but also the maximum, minimum and average elapse time BPEL process instances. select composite_name Composite, component_name Process, componenttype, state, count(*) Count, trunc(Max(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MaxTime, trunc(Min(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MinTime, trunc(AVG(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) AvgTime from cube_instance where creation_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype, state order by count(*) desc;   4. Combine all together Now let's combine all of these 3 queries together, and parameterize the start and end time stamps to make the script a bit more robust. The following script will prompt for the start and end time before querying against the database: accept startTime prompt 'Enter start time (YYYY-MM-DD HH24:MI:SS)' accept endTime prompt 'Enter end time (YYYY-MM-DD HH24:MI:SS)' Prompt "==== Rejected Messages ===="; REM 2012-10-24 21:00:00 REM 2012-10-24 21:59:59 select count(*), composite_dn from rejected_message where created_time >= to_timestamp('&&StartTime','YYYY-MM-DD HH24:MI:SS') and created_time <= to_timestamp('&&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_dn; Prompt " "; Prompt "==== Throughput of one-way/asynchronous messages ===="; select state, count(*) Count, composite_name composite from dlv_message where receive_date >= to_timestamp('&StartTime','YYYY-MM-DD HH24:MI:SS') and receive_date <= to_timestamp('&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_name, state order by Count; Prompt " "; Prompt "==== Throughput and latency of BPEL process instances ====" select state, count(*) Count, trunc(Max(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MaxTime, trunc(Min(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MinTime, trunc(AVG(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) AvgTime, composite_name Composite, component_name Process, componenttype from cube_instance where creation_date >= to_timestamp('&StartTime','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype, state order by count(*) desc;  

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• Code Structure / Level Design: Plants vs Zombies game level dissection

  - by lalan

  Hi Friends, I am interested in learning the class structure of Plants vs Zombies, particularly level design; for those who haven't played it - this video contains nice play-through: http://www.youtube.com/watch?v=89DfdOIJ4xw. How would I go ahead and design the code, mostly structure & classes, which allows for maximum flexibility & clean development? I am familiar with data driven design concepts, and would use events to handle most of dynamic behavior. Dissection at macro level: (Once every Level) Load tilemap, props, etc -- basically build the map (Once every Level) Camera Movement - might consider it as short cut-scene (Once every Level) Show Enemies you'll face during present level (Once every Level) Unit Selection Window/Panel - selection of defensive plants (Once every Level) Camera Movement - might consider it as short cut-scene (Once every Level) HUD Creation - based on unit selection (Level Loop) Enemy creation - based on types of zombies allowed (Level Loop) Sun/Resource generation (Level Loop) Show messages like 'huge wave of zombies coming', 'final wave' (Level Loop) Other unique events - Spawn gifts, money, tombstones, etc (Once every Level) Unlock new plant Potential game scripts: a) Level definitions: Level_1_1.xml, Level_1_2.xml, etc. Level_1_1.xml :: Sample script <map> <tilemap>tilemapFrontLawn</tilemap> <SpawnPoints> tiles where particular type of zombies (land vs water) may spawn</spawnPoints> <props> position, entity array -- lawnmower, </props> </map> <zombies> <... list of zombies who gonna attack by ids...> </zombies> <plants> <... list by plants which are available for defense by ids...> </plants> <progression> <ZombieWave name='first wave' spawnScript='zombieLightWave.lua' unlock='null'> <startMessages time=1.5>Ready</startMessages> <endMessages time=1.5>Huge wave of zombies incoming</endMessages> </ZombieWave> </progression> b) Entities definitions: .xmls containing zombies, plants, sun, lawnmower, coins, etc description. Potential classes: //LevelManager - Based on the level under play, it will load level script. Few of the // functions it may have: class LevelManager { public: bool load(string levelFileName); bool enter(); bool update(float deltatime); bool exit(); private: LevelData* mLevelData; } // LevelData - Contains the details of level loaded by LevelManager. class LevelData { private: string file; // array of camera,dialog,attackwaves, etc in active level LevelCutSceneCamera** mArrayCutSceneCamera; LevelCutSceneDialog** mArrayCutSceneDialog; LevelAttackWave** mArrayAttackWave; .... // which camera,dialog,attackwave is active in level uint mCursorCutSceneCamera; uint mCursorCutSceneDialog; uint mCursorAttackWave; public: // based on cursor, get the next camera,dialog,attackwave,etc in active level // return false/true based on failure/success bool nextCutSceneCamera(LevelCutSceneCamera**); bool nextCutSceneDialog(LevelCutSceneDialog**); } // LevelUnderPlay- LevelManager class LevelUnderPlay { private: LevelCutSceneCamera* mCutSceneCamera; LevelCutSceneDialog* mCutSceneDialog; LevelAttackWave* mAttackWave; Entities** mSelectedPlants; Entities** mAllowedZombies; bool isCutSceneCameraActive; public: bool enter(); bool update(float deltatime); bool exit(); } I am totally confused.. :( Does it make sense of using class composition (have flat class hierarchy) for managing levels. Is it a good idea to just add/remove/update sprites (or any drawable stuff) to current scene from LevelManager or LevelUnderPlay? If I want to make non-linear level design, how should I go ahead? Perhaps I would need a LevelProgression class, which would decide what to do based on decision tree. Any suggestions would be appreciated very much. Thank for your time, lalan

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• Aggregating cache data from OCEP in CQL

  - by Manju James

  There are several use cases where OCEP applications need to join stream data with external data, such as data available in a Coherence cache. OCEP’s streaming language, CQL, supports simple cache-key based joins of stream data with data in Coherence (more complex queries will be supported in a future release). However, there are instances where you may need to aggregate the data in Coherence based on input data from a stream. This blog describes a sample that does just that. For our sample, we will use a simplified credit card fraud detection use case. The input to this sample application is a stream of credit card transaction data. The input stream contains information like the credit card ID, transaction time and transaction amount. The purpose of this application is to detect suspicious transactions and send out a warning event. For the sake of simplicity, we will assume that all transactions with amounts greater than $1000 are suspicious. The transaction history is available in a Coherence distributed cache. For every suspicious transaction detected, a warning event must be sent with maximum amount, total amount and total number of transactions over the past 30 days, as shown in the diagram below. Application Input Stream input to the EPN contains events of type CCTransactionEvent. This input has to be joined with the cache with all credit card transactions. The cache is configured in the EPN as shown below: <wlevs:caching-system id="CohCacheSystem" provider="coherence"/> <wlevs:cache id="CCTransactionsCache" value-type="CCTransactionEvent" key-properties="cardID, transactionTime" caching-system="CohCacheSystem"> </wlevs:cache> Application Output The output that must be produced by the application is a fraud warning event. This event is configured in the spring file as shown below. Source for cardHistory property can be seen here. <wlevs:event-type type-name="FraudWarningEvent"> <wlevs:properties type="tuple"> <wlevs:property name="cardID" type="CHAR"/> <wlevs:property name="transactionTime" type="BIGINT"/> <wlevs:property name="transactionAmount" type="DOUBLE"/> <wlevs:property name="cardHistory" type="OBJECT"/> </wlevs:properties </wlevs:event-type> Cache Data Aggregation using Java Cartridge In the output warning event, cardHistory property contains data from the cache aggregated over the past 30 days. To get this information, we use a java cartridge method. This method uses Coherence’s query API on credit card transactions cache to get the required information. Therefore, the java cartridge method requires a reference to the cache. This may be set up by configuring it in the spring context file as shown below: <bean class="com.oracle.cep.ccfraud.CCTransactionsAggregator"> <property name="cache" ref="CCTransactionsCache"/> </bean> This is used by the java class to set a static property: public void setCache(Map cache) { s_cache = (NamedCache) cache; } The code snippet below shows how the total of all the transaction amounts in the past 30 days is computed. Rest of the information required by CardHistory object is calculated in a similar manner. Complete source of this class can be found here. To find out more information about using Coherence's API to query a cache, please refer Coherence Developer’s Guide. public static CreditHistoryData(String cardID) { … Filter filter = QueryHelper.createFilter("cardID = :cardID and transactionTime :transactionTime", map); CardHistoryData history = new CardHistoryData(); Double sum = (Double) s_cache.aggregate(filter, new DoubleSum("getTransactionAmount")); history.setTotalAmount(sum); … return history; } The java cartridge method is used from CQL as seen below: select cardID, transactionTime, transactionAmount, CCTransactionsAggregator.execute(cardID) as cardHistory from inputChannel where transactionAmount1000 This produces a warning event, with history data, for every credit card transaction over $1000. That is all there is to it. The complete source for the sample application, along with the configuration files, is available here. In the sample, I use a simple java bean to load the cache with initial transaction history data. An input adapter is used to create and send transaction events for the input stream.

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• Autoscaling in a modern world&hellip;. Part 2

  - by Steve Loethen

  When we last left off, we had a web application spinning away in the cloud, and a local console application watching it and reacting to changes in demand.  Reactions that were specified by a set of rules.  Let’s talk about those rules. Constraints.  The first set of rules this application answered to were the constraints. Here is what they looked like: <constraintRules> <rule name="default" enabled="true" rank="1" description="The default constraint rule"> <actions> <range min="1" max="4" target="AutoscalingApplicationRole"/> </actions> </rule> </constraintRules> Pretty basic.  We have one role, the “AutoscalingApplicationRole”, and we have decided to have it live within a range of 1 to 4.  This rule does not adjust, but instead, set’s limits on what other rules can do.  It has a rank, so you can have you can specify other sets of constraints, perhaps based on time or date, to allow for deviations from this set.  But for now, let’s keep it simple.  In the real world, you would probably use the minimum to set a lower end SLA.  A common value might be a 2, to prevent the reactive rules from ever taking you down to 1 role.  The maximum is often used to keep a rule from driving the cost up, setting an upper limit to prevent you waking up one morning and find a bill for hundreds of instances you didn’t expect.  So, here we have the range we want our application to live inside.  This is good for our investigation and testing.  Next, let’s take a look at the reactive rules.  These rules are what you use to react (hence reactive rules) to changing demands on your application.  The HOL has two simple rules.  One that looks at a queue depth, and one that looks at a performance counter that reports cpu utilization.  the XML in the rules file looks like this: <reactiveRules> <rule name="ScaleUp" rank="10" description="Scale Up the web role" enabled="true"> <when> <any> <greaterOrEqual operand="Length_05_holqueue" than="10"/> <greaterOrEqual operand="CPU_05_holwebrole" than="65"/> </any> </when> <actions> <scale target="AutoscalingApplicationRole" by="1"/> </actions> </rule> <rule name="ScaleDown" rank="10" description="Scale down the web role" enabled="true"> <when> <all> <less operand="Length_05_holqueue" than="5"/> <less operand="CPU_05_holwebrole" than="40"/> </all> </when> <actions> <scale target="AutoscalingApplicationRole" by="-1"/> </actions> </rule> </reactiveRules> <operands> <performanceCounter alias="CPU_05_holwebrole" performanceCounterName="\Processor(_Total)\% Processor Time" source="AutoscalingApplicationRole" timespan="00:05:00" aggregate="Average" /> <queueLength alias="Length_05_holqueue" queue="hol-queue" timespan="00:05:00" aggregate="Average"/> </operands> These rules are currently contained in a file called rules.xml, that is in the root of the console application.  The console app, starts up, grabs the rules and starts watching the 2 operands.  When it detects a rule has been satisfied, it performs the desired action.  (here, scale up or down my 1). But I want to host the autoscaler  in the cloud.  For my first trick, I will move the rules (and another file called services.xml) to azure blob storage.  Look for part 3.

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• HPCM 11.1.2.2.x - How to find data in an HPCM Standard Costing database

  - by Jane Story

  Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} When working with a Hyperion Profitability and Cost Management (HPCM) Standard Costing application, there can often be a requirement to check data or allocated results using reporting tools e.g Smartview. To do this, you are retrieving data directly from the Essbase databases related to your HPCM model. For information, running reports is covered in Chapter 9 of the HPCM User documentation. The aim of this blog is to provide a quick guide to finding this data for reporting in the HPCM generated Essbase database in v11.1.2.2.x of HPCM. In order to retrieve data from an HPCM generated Essbase database, it is important to understand each of the following dimensions in the Essbase database and where data is located within them: Measures dimension – identifies Measures AllocationType dimension – identifies Direct Allocation Data or Genealogy Allocation data Point Of View (POV) dimensions – there must be at least one, maximum of four. Business dimensions: Stage Business dimensions – these will be identified by the Stage prefix. Intra-Stage dimension – these will be identified by the _Intra suffix. Essbase outlines and reporting is explained in the documentation here:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/ch09s02.html For additional details on reporting measures, please review this section of the documentation:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/apas03.html Reporting requirements in HPCM quite often start with identifying non balanced items in the Stage Balancing report. The following documentation link provides help with identifying some of the items within the Stage Balancing report:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/generatestagebalancing.html The following are some types of data upon which you may want to report: Stage Data: Direct Input Assigned Input Data Assigned Output Data Idle Cost/Revenue Unassigned Cost/Revenue Over Driven Cost/Revenue Direct Allocation Data Genealogy Allocation Data Stage Data Stage Data consists of: Direct Input i.e. input data, the starting point of your allocation e.g. in Stage 1 Assigned Input Data i.e. the cost/revenue received from a prior stage (i.e. stage 2 and higher). Assigned Output Data i.e. for each stage, the data that will be assigned forward is assigned post stage data. Reporting on this data is explained in the documentation here:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/ch09s03.html Dimension Selection Measures Direct Input: CostInput RevenueInput Assigned Input (from previous stages): CostReceivedPriorStage RevenueReceivedPriorStage Assigned Output (to subsequent stages): CostAssignedPostStage RevenueAssignedPostStage AllocationType DirectAllocation POV One member from each POV dimension Stage Business Dimensions Any members for the stage business dimensions for the stage you wish to see the Stage data for. All other Dimensions NoMember Idle/Unassigned/OverDriven To view Idle, Unassigned or Overdriven Costs/Revenue, first select which stage for which you want to view this data. If multiple Stages have unassigned/idle, resolve the earliest first and re-run the calculation as differences in early stages will create unassigned/idle in later stages. Dimension Selection Measures Idle: IdleCost IdleRevenue Unassigned: UnAssignedCost UnAssignedRevenue Overdriven: OverDrivenCost OverDrivenRevenue AllocationType DirectAllocation POV One member from each POV dimension Dimensions in the Stage with Unassigned/ Idle/OverDriven Cost All the Stage Business dimensions in the Stage with Unassigned/Idle/Overdriven. Zoom in on each dimension to find the individual members to find which members have Unassigned/Idle/OverDriven data. All other Dimensions NoMember Direct Allocation Data Direct allocation data shows the data received by a destination intersection from a source intersection where a direct assignment(s) exists. Reporting on direct allocation data is explained in the documentation here:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/ch09s04.html You would select the following to report direct allocation data Dimension Selection Measures CostReceivedPriorStage AllocationType DirectAllocation POV One member from each POV dimension Stage Business Dimensions Any members for the SOURCE stage business dimensions and the DESTINATION stage business dimensions for the direct allocations for the stage you wish to report on. All other Dimensions NoMember Genealogy Allocation Data Genealogy allocation data shows the indirect data relationships between stages. Genealogy calculations run in the HPCM Reporting database only. Reporting on genealogy data is explained in the documentation here:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/ch09s05.html Dimension Selection Measures CostReceivedPriorStage AllocationType GenealogyAllocation (IndirectAllocation in 11.1.2.1 and prior versions) POV One member from each POV dimension Stage Business Dimensions Any stage business dimension members from the STARTING stage in Genealogy Any stage business dimension members from the INTERMEDIATE stage(s) in Genealogy Any stage business dimension members from the ENDING stage in Genealogy All other Dimensions NoMember Notes If you still don’t see data after checking the above, please check the following Check the calculation has been run. Here are couple of indicators that might help them with that. Note the size of essbase cube before and after calculations ensure that a calculation was run against the database you are examing. Export the essbase data to a text file to confirm that some data exists. Examine the date and time on task area to see when, if any, calculations were run and what choices were used (e.g. Genealogy choices) If data does not exist in places where they are expecting, it could be that No calculations/genealogy were run No calculations were successfully run The model/data at feeder location were either absent or incompatible, resulting in no allocation e.g no driver data. Smartview Invocation from HPCM From version 11.1.2.2.350 of HPCM (this version will be GA shortly), it is possible to directly invoke Smartview from HPCM. There is guided navigation before the Smartview invocation and it is then possible to see the selected value(s) in SmartView. Click to Download HPCM 11.1.2.2.x - How to find data in an HPCM Standard Costing database (Right click or option-click the link and choose "Save As..." to download this pdf file)

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• SPARC T4-4 Delivers World Record First Result on PeopleSoft Combined Benchmark

  - by Brian

  Oracle's SPARC T4-4 servers running Oracle's PeopleSoft HCM 9.1 combined online and batch benchmark achieved World Record 18,000 concurrent users while executing a PeopleSoft Payroll batch job of 500,000 employees in 43.32 minutes and maintaining online users response time at < 2 seconds. This world record is the first to run online and batch workloads concurrently. This result was obtained with a SPARC T4-4 server running Oracle Database 11g Release 2, a SPARC T4-4 server running PeopleSoft HCM 9.1 application server and a SPARC T4-2 server running Oracle WebLogic Server in the web tier. The SPARC T4-4 server running the application tier used Oracle Solaris Zones which provide a flexible, scalable and manageable virtualization environment. The average CPU utilization on the SPARC T4-2 server in the web tier was 17%, on the SPARC T4-4 server in the application tier it was 59%, and on the SPARC T4-4 server in the database tier was 35% (online and batch) leaving significant headroom for additional processing across the three tiers. The SPARC T4-4 server used for the database tier hosted Oracle Database 11g Release 2 using Oracle Automatic Storage Management (ASM) for database files management with I/O performance equivalent to raw devices. This is the first three tier mixed workload (online and batch) PeopleSoft benchmark also processing PeopleSoft payroll batch workload. Performance Landscape PeopleSoft HR Self-Service and Payroll Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-2 (db) 18,000 0.944 0.503 43.32 64 Configuration Summary Application Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 512 GB memory 5 x 300 GB SAS internal disks 1 x 100 GB and 2 x 300 GB internal SSDs 2 x 10 Gbe HBA Oracle Solaris 11 11/11 PeopleTools 8.52 PeopleSoft HCM 9.1 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Java Platform, Standard Edition Development Kit 6 Update 32 Database Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 256 GB memory 3 x 300 GB SAS internal disks Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Web Tier Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 2 x 300 GB SAS internal disks 1 x 100 GB internal SSD Oracle Solaris 11 11/11 PeopleTools 8.52 Oracle WebLogic Server 10.3.4 Java Platform, Standard Edition Development Kit 6 Update 32 Storage Configuration: 1 x Sun Server X2-4 as a COMSTAR head for data 4 x Intel Xeon X7550, 2.0 GHz 128 GB memory 1 x Sun Storage F5100 Flash Array (80 flash modules) 1 x Sun Storage F5100 Flash Array (40 flash modules) 1 x Sun Fire X4275 as a COMSTAR head for redo logs 12 x 2 TB SAS disks with Niwot Raid controller Benchmark Description This benchmark combines PeopleSoft HCM 9.1 HR Self Service online and PeopleSoft Payroll batch workloads to run on a unified database deployed on Oracle Database 11g Release 2. The PeopleSoft HRSS benchmark kit is a Oracle standard benchmark kit run by all platform vendors to measure the performance. It's an OLTP benchmark where DB SQLs are moderately complex. The results are certified by Oracle and a white paper is published. PeopleSoft HR SS defines a business transaction as a series of HTML pages that guide a user through a particular scenario. Users are defined as corporate Employees, Managers and HR administrators. The benchmark consist of 14 scenarios which emulate users performing typical HCM transactions such as viewing paycheck, promoting and hiring employees, updating employee profile and other typical HCM application transactions. All these transactions are well-defined in the PeopleSoft HR Self-Service 9.1 benchmark kit. This benchmark metric is the weighted average response search/save time for all the transactions. The PeopleSoft 9.1 Payroll (North America) benchmark demonstrates system performance for a range of processing volumes in a specific configuration. This workload represents large batch runs typical of a ERP environment during a mass update. The benchmark measures five application business process run times for a database representing large organization. They are Paysheet Creation, Payroll Calculation, Payroll Confirmation, Print Advice forms, and Create Direct Deposit File. The benchmark metric is the cumulative elapsed time taken to complete the Paysheet Creation, Payroll Calculation and Payroll Confirmation business application processes. The benchmark metrics are taken for each respective benchmark while running simultaneously on the same database back-end. Specifically, the payroll batch processes are started when the online workload reaches steady state (the maximum number of online users) and overlap with online transactions for the duration of the steady state. Key Points and Best Practices Two Oracle PeopleSoft Domain sets with 200 application servers each on a SPARC T4-4 server were hosted in 2 separate Oracle Solaris Zones to demonstrate consolidation of multiple application servers, ease of administration and performance tuning. Each Oracle Solaris Zone was bound to a separate processor set, each containing 15 cores (total 120 threads). The default set (1 core from first and third processor socket, total 16 threads) was used for network and disk interrupt handling. This was done to improve performance by reducing memory access latency by using the physical memory closest to the processors and offload I/O interrupt handling to default set threads, freeing up cpu resources for Application Servers threads and balancing application workload across 240 threads. See Also Oracle PeopleSoft Benchmark White Papers oracle.com SPARC T4-2 Server oracle.com OTN SPARC T4-4 Server oracle.com OTN PeopleSoft Enterprise Human Capital Management oracle.com OTN PeopleSoft Enterprise Human Capital Management (Payroll) oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Oracle's PeopleSoft HR and Payroll combined benchmark, www.oracle.com/us/solutions/benchmark/apps-benchmark/peoplesoft-167486.html, results 09/30/2012.

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• Nesting Linq-to-Objects query within Linq-to-Entities query –what is happening under the covers?

  - by carewithl

  var numbers = new int[] { 1, 2, 3, 4, 5 }; var contacts = from c in context.Contacts where c.ContactID == numbers.Max() | c.ContactID == numbers.FirstOrDefault() select c; foreach (var item in contacts) Console.WriteLine(item.ContactID); Linq-to-Entities query is first translated into Linq expression tree, which is then converted by Object Services into command tree. And if Linq-to-Entities query nests Linq-to-Objects query, then this nested query also gets translated into an expression tree. a) I assume none of the operators of the nested Linq-to-Objects query actually get executed, but instead data provider for particular DB (or perhaps Object Services) knows how to transform the logic of Linq-to-Objects operators into appropriate SQL statements? b) Data provider knows how to create equivalent SQL statements only for some of the Linq-to-Objects operators? c) Similarly, data provider knows how to create equivalent SQL statements only for some of the non-Linq methods in the Net Framework class library? EDIT: I know only some Sql so I can't be completely sure, but reading Sql query generated for the above code it seems data provider didn't actually execute numbers.Max method, but instead just somehow figured out that numbers.Max should return the maximum value and then proceed to include in generated Sql query a call to TSQL's build-in MAX function. It also put all the values held by numbers array into a Sql query. SELECT CASE WHEN (([Project1].[C1] = 1) AND ([Project1].[C1] IS NOT NULL)) THEN '0X0X' ELSE '0X1X' END AS [C1], [Extent1].[ContactID] AS [ContactID], [Extent1].[FirstName] AS [FirstName], [Extent1].[LastName] AS [LastName], [Extent1].[Title] AS [Title], [Extent1].[AddDate] AS [AddDate], [Extent1].[ModifiedDate] AS [ModifiedDate], [Extent1].[RowVersion] AS [RowVersion], CASE WHEN (([Project1].[C1] = 1) AND ([Project1].[C1] IS NOT NULL)) THEN [Project1].[CustomerTypeID] END AS [C2], CASE WHEN (([Project1].[C1] = 1) AND ([Project1].[C1] IS NOT NULL)) THEN [Project1].[InitialDate] END AS [C3], CASE WHEN (([Project1].[C1] = 1) AND ([Project1].[C1] IS NOT NULL)) THEN [Project1].[PrimaryDesintation] END AS [C4], CASE WHEN (([Project1].[C1] = 1) AND ([Project1].[C1] IS NOT NULL)) THEN [Project1].[SecondaryDestination] END AS [C5], CASE WHEN (([Project1].[C1] = 1) AND ([Project1].[C1] IS NOT NULL)) THEN [Project1].[PrimaryActivity] END AS [C6], CASE WHEN (([Project1].[C1] = 1) AND ([Project1].[C1] IS NOT NULL)) THEN [Project1].[SecondaryActivity] END AS [C7], CASE WHEN (([Project1].[C1] = 1) AND ([Project1].[C1] IS NOT NULL)) THEN [Project1].[Notes] END AS [C8], CASE WHEN (([Project1].[C1] = 1) AND ([Project1].[C1] IS NOT NULL)) THEN [Project1].[RowVersion] END AS [C9], CASE WHEN (([Project1].[C1] = 1) AND ([Project1].[C1] IS NOT NULL)) THEN [Project1].[BirthDate] END AS [C10], CASE WHEN (([Project1].[C1] = 1) AND ([Project1].[C1] IS NOT NULL)) THEN [Project1].[HeightInches] END AS [C11], CASE WHEN (([Project1].[C1] = 1) AND ([Project1].[C1] IS NOT NULL)) THEN [Project1].[WeightPounds] END AS [C12], CASE WHEN (([Project1].[C1] = 1) AND ([Project1].[C1] IS NOT NULL)) THEN [Project1].[DietaryRestrictions] END AS [C13] FROM [dbo].[Contact] AS [Extent1] LEFT OUTER JOIN (SELECT [Extent2].[ContactID] AS [ContactID], [Extent2].[BirthDate] AS [BirthDate], [Extent2].[HeightInches] AS [HeightInches], [Extent2].[WeightPounds] AS [WeightPounds], [Extent2].[DietaryRestrictions] AS [DietaryRestrictions], [Extent3].[CustomerTypeID] AS [CustomerTypeID], [Extent3].[InitialDate] AS [InitialDate], [Extent3].[PrimaryDesintation] AS [PrimaryDesintation], [Extent3].[SecondaryDestination] AS [SecondaryDestination], [Extent3].[PrimaryActivity] AS [PrimaryActivity], [Extent3].[SecondaryActivity] AS [SecondaryActivity], [Extent3].[Notes] AS [Notes], [Extent3].[RowVersion] AS [RowVersion], cast(1 as bit) AS [C1] FROM [dbo].[ContactPersonalInfo] AS [Extent2] INNER JOIN [dbo].[Customers] AS [Extent3] ON [Extent2].[ContactID] = [Extent3].[ContactID]) AS [Project1] ON [Extent1].[ContactID] = [Project1].[ContactID] LEFT OUTER JOIN (SELECT TOP (1) [c].[C1] AS [C1] FROM (SELECT [UnionAll3].[C1] AS [C1] FROM (SELECT [UnionAll2].[C1] AS [C1] FROM (SELECT [UnionAll1].[C1] AS [C1] FROM (SELECT 1 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable1] UNION ALL SELECT 2 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable2]) AS [UnionAll1] UNION ALL SELECT 3 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable3]) AS [UnionAll2] UNION ALL SELECT 4 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable4]) AS [UnionAll3] UNION ALL SELECT 5 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable5]) AS [c]) AS [Limit1] ON 1 = 1 LEFT OUTER JOIN (SELECT TOP (1) [c].[C1] AS [C1] FROM (SELECT [UnionAll7].[C1] AS [C1] FROM (SELECT [UnionAll6].[C1] AS [C1] FROM (SELECT [UnionAll5].[C1] AS [C1] FROM (SELECT 1 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable6] UNION ALL SELECT 2 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable7]) AS [UnionAll5] UNION ALL SELECT 3 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable8]) AS [UnionAll6] UNION ALL SELECT 4 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable9]) AS [UnionAll7] UNION ALL SELECT 5 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable10]) AS [c]) AS [Limit2] ON 1 = 1 CROSS JOIN (SELECT MAX([UnionAll12].[C1]) AS [A1] FROM (SELECT [UnionAll11].[C1] AS [C1] FROM (SELECT [UnionAll10].[C1] AS [C1] FROM (SELECT [UnionAll9].[C1] AS [C1] FROM (SELECT 1 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable11] UNION ALL SELECT 2 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable12]) AS [UnionAll9] UNION ALL SELECT 3 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable13]) AS [UnionAll10] UNION ALL SELECT 4 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable14]) AS [UnionAll11] UNION ALL SELECT 5 AS [C1] FROM (SELECT 1 AS X) AS [SingleRowTable15]) AS [UnionAll12]) AS [GroupBy1] WHERE [Extent1].[ContactID] IN ([GroupBy1].[A1], (CASE WHEN ([Limit1].[C1] IS NULL) THEN 0 ELSE [Limit2].[C1] END)) Based on this, is it possible that Linq2Entities provider indeed doesn't execute non-Linq and Linq-to-Object methods, but instead creates equivalent SQL statements for some of them ( and for others it throws an exception )? Thank you in advance

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• IBM Keynote: (hardware,software)–>{IBM.java.patterns}

  - by Janice J. Heiss

  On Sunday evening, September 30, 2012, Jason McGee, IBM Distinguished Engineer and Chief Architect Cloud Computing, along with John Duimovich IBM Distinguished Engineer and Java CTO, gave an information- and idea-rich keynote that left Java developers with much to ponder.Their focus was on the challenges to make Java more efficient and productive given the hardware and software environments of 2012. “One idea that is very interesting is the idea of multi-tenancy,” said McGee, “and how we can move up the spectrum. In traditional systems, we ran applications on dedicated middleware, operating systems and hardware. A lot of customers still run that way. Now people introduce hardware virtualization and share the hardware. That is good but there is a lot more we can do. We can share middleware and the application itself.” McGee challenged developers to better enable the Java language to function in these higher density models. He spoke about the need to describe patterns that help us grasp the full environment that an application needs, whether it’s a web or full enterprise application. Developers need to understand the resources that an application interacts with in a way that is simple and straightforward. The task is to then automate that deployment so that the complexity of infrastructure can be by-passed and developers can live in a simpler world where the cloud can automatically configure the needed environment. McGee argued that the key, something IBM has been working on, is to use a simpler pattern that allows a cloud-based architecture to embrace the entire infrastructure required for an application and make it highly available, scalable and able to recover from failure. The cloud-based architecture would automate the complexity of setting up and managing the infrastructure. IBM has been trying to realize this vision for customers so they can describe their Java application environment simply and allow the cloud to automate the deployment and management of applications. “The point,” explained McGee, “is to package the executable used to describe applications, to drop it into a shared system and let that system provide some intelligence about how to deploy and manage those applications.”John Duimovich on Improvements in JavaMcGee then brought onstage IBM’s Distinguished Engineer and CTO for Java, John Duimovich, who showed the audience ways to deploy Java applications more efficiently.Duimovich explained that, “When you run lots of copies of Java in the cloud or any hypervisor virtualized system, there are a lot of duplications of code and jar files. IBM has a facility called ‘shared classes’ where we put shared code, read only artefacts in a cache that is sharable across hypervisors.” By putting JIT code in ahead of time, he explained that the application server will use 20% less memory and operate 30% faster.  He described another example of how the JVM allows for the maximum amount of sharing that manages the tenants and file sockets and memory use through throttling and control. Duimovich touched on the “thin is in” model and IBM’s Liberty Profile and lightweight runtime for the cloud, which allows for greater efficiency in interacting with the cloud.Duimovich discussed the confusion Java developers experience when, for example, the hypervisor tells them that that they have 8 and then 4 and then 16 cores. “Because hypervisors are virtualized, they can change based on resource needs across the hypervisor layer. You may have 10 instances of an operation system and you may need to reallocate memory, " explained Duimovich.  He showed how to resize LPARs, reallocate CPUs and migrate applications as needed. He explained how application servers can resize thread pools and better use resources based on information from the hypervisors.Java Challenges in Hardware and SoftwareMcGee ended the keynote with a summary of upcoming hardware and software challenges for the Java platform. He noted that one reason developers love Java is it allows them to ignore differences in hardware. He stated that the most important things happening in hardware were in network and storage – in developments such as the speed of SSD, the exploitation of high-speed, low-latency networking, and recent developments such as storage-class memory, and non-volatile main memory. “So we are challenged to maintain the benefits of Java and the abstraction it provides from hardware while still exploiting the new innovations in hardware,” said McGee.McGee discussed transactional messaging applications where developers send messages transactionally persist a message to storage, something traditionally done by backing messages on spinning disks, something mostly outdated. “Now,” he pointed out, “we would use SSD and store it in Flash and get 70,000 messages a second. If we stored it using a PCI express-based flash memory device, it is still Flash but put on a PCI express bus on a card closer to the CPU. This way I get 300,000 messages a second and 25% improvement in latency.” McGee’s central point was that hardware has a huge impact on the performance and scalability of applications. New technologies are enabling developers to build classes of Java applications previously unheard of. “We need to be able to balance these things in Java – we need to maintain the abstraction but also be able to exploit the evolution of hardware technology,” said McGee. According to McGee, IBM's current focus is on systems wherein hardware and software are shipped together in what are called Expert Integrated Systems – systems that are pre-optimized, and pre-integrated together. McGee closed IBM’s engaging and thought-provoking keynote by pointing out that the use of Java in complex applications is increasingly being augmented by a host of other languages with strong communities around them – JavaScript, JRuby, Scala, Python and so forth. Java developers now must understand the strengths and weaknesses of such newcomers as applications increasingly involve a complex interconnection of languages.

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• College Courses through distance learning

  - by Matt

  I realize this isn't really a programming question, but didn't really know where to post this in the stackexchange and because I am a computer science major i thought id ask here. This is pretty unique to the programmer community since my degree is about 95% programming. I have 1 semester left, but i work full time. I would like to finish up in December, but to make things easier i like to take online classes whenever I can. So, my question is does anyone know of any colleges that offer distance learning courses for computer science? I have been searching around and found a few potential classes, but not sure yet. I would like to gather some classes and see what i can get approval for. Class I need: Only need one C SC 437 Geometric Algorithms C SC 445 Algorithms C SC 473 Automata Only need one C SC 452 Operating Systems C SC 453 Compilers/Systems Software While i only need of each of the above courses i still need to take two more electives. These also have to be upper 400 level classes. So i can take multiple in each category. Some other classes I can take are: CSC 447 - Green Computing CSC 425 - Computer Networking CSC 460 - Database Design CSC 466 - Computer Security I hoping to take one or two of these courses over the summer. If not, then online over the regular semester would be ok too. Any help in helping find these classes would be awesome. Maybe you went to a college that offered distance learning. Some of these classes may be considered to be graduate courses too. Descriptions are listed below if you need. Thanks! Descriptions Computer Security This is an introductory course covering the fundamentals of computer security. In particular, the course will cover basic concepts of computer security such as threat models and security policies, and will show how these concepts apply to specific areas such as communication security, software security, operating systems security, network security, web security, and hardware-based security. Computer Networking Theory and practice of computer networks, emphasizing the principles underlying the design of network software and the role of the communications system in distributed computing. Topics include routing, flow and congestion control, end-to-end protocols, and multicast. Database Design Functions of a database system. Data modeling and logical database design. Query languages and query optimization. Efficient data storage and access. Database access through standalone and web applications. Green Computing This course covers fundamental principles of energy management faced by designers of hardware, operating systems, and data centers. We will explore basic energy management option in individual components such as CPUs, network interfaces, hard drives, memory. We will further present the energy management policies at the operating system level that consider performance vs. energy saving tradeoffs. Finally we will consider large scale data centers where energy management is done at multiple layers from individual components in the system to shutting down entries subset of machines. We will also discuss energy generation and delivery and well as cooling issues in large data centers. Compilers/Systems Software Basic concepts of compilation and related systems software. Topics include lexical analysis, parsing, semantic analysis, code generation; assemblers, loaders, linkers; debuggers. Operating Systems Concepts of modern operating systems; concurrent processes; process synchronization and communication; resource allocation; kernels; deadlock; memory management; file systems. Algorithms Introduction to the design and analysis of algorithms: basic analysis techniques (asymptotics, sums, recurrences); basic design techniques (divide and conquer, dynamic programming, greedy, amortization); acquiring an algorithm repertoire (sorting, median finding, strong components, spanning trees, shortest paths, maximum flow, string matching); and handling intractability (approximation algorithms, branch and bound). Automata Introduction to models of computation (finite automata, pushdown automata, Turing machines), representations of languages (regular expressions, context-free grammars), and the basic hierarchy of languages (regular, context-free, decidable, and undecidable languages). Geometric Algorithms The study of algorithms for geometric objects, using a computational geometry approach, with an emphasis on applications for graphics, VLSI, GIS, robotics, and sensor networks. Topics may include the representation and overlaying of maps, finding nearest neighbors, solving linear programming problems, and searching geometric databases.

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• MDM for Tax Authorities

  - by david.butler(at)oracle.com

  In last week’s MDM blog, we discussed MDM in the Public Sector. I want to continue that thread. After all, no industry faces tougher data quality problems than governmental organizations, and few industries suffer more significant down side consequences to poor operations than local, state and federal governments. One key challenge area is taxation. Tax Authorities face a multitude of IT challenges. Firstly, the data used in tax calculations is increasing in volume and complexity. They must improve service by introducing multi-channel contact centers and self-service capabilities. Security concerns necessitate increasingly sophisticated data protection procedures. And cost constraints are driving Tax Authorities to rely on off-the-shelf software for many of their functional areas. Compounding these issues is the fact that the IT architectures in operation at most revenue and collections agencies are very complex. They typically include multiple, disparate operational and analytical systems across which the sum total of data about individual constituents is fragmented. To make matters more complicated, taxation is not carried out by a single jurisdiction, and often sources of income including employers, investments and other sources of taxable income and deductions must also be tracked and shared among tax authorities. Collectively, these systems are involved in tax assessment and collections, risk analysis, scoring, tracking, auditing and investigation case management. The Problem of Constituent Data Management The infrastructure described above makes it very difficult to create a consolidated representation of a given party. Differing formats and data models mean that a constituent may be represented in one way in one system and in a different way in another. Individual records are frequently inaccurate, incomplete, out of date and/or inconsistent with other records relating to the same constituent. When constituent data must be aggregated and scored, information within each system must be rationalized and normalized so the agency can produce a constituent information file (CIF) that provides a single source of truth about that party. If information about that constituent changes, each system in turn must be updated. There have been many attempts to solve this problem with technology: from consolidating transactional systems to conducting manual systems integration projects and superimposing layers of business intelligence and analytics. All these approaches can be successful in solving a portion of the problem at a specific point in time, but without an enterprise perspective, anything gained is quickly lost again. Oracle Constituent Data Mastering for Tax Authorities: A Single View of the Constituent Oracle has a flexible and long-term solution to the problem of securely integrating and managing constituent data. The Oracle Solution for mastering Constituent Data for Tax Authorities is based on two core product offerings: Oracle Customer Hub and – optionally – Oracle Application Integration Architecture (AIA). Customer Hub is a master data management (MDM) product that centralizes, de-duplicates, and enriches constituent data. It unifies fragmented information without disrupting existing business processes or IT investments. Role based data access and privacy rules guarantee maximum security and privacy. Data is continuously and automatically synchronized with all source systems. With the Oracle Customer Hub managing the master constituent identity, every department can capture transaction activity against the same record, improving reporting accuracy, employee productivity, reliability of constituent analytics, and day-to-day constituent relationships. Oracle Application Integration Architecture provides a collection of core pre-built processes to support out of the box Master Data Governance across Oracle Customer Hub, Siebel CRM, and Oracle E-Business Suite. It also provides a framework to enable MDM integrations with other Oracle and non-Oracle applications. Oracle AIA removes some of the key inhibitors to implementing a service-oriented architecture (SOA) by providing a pre-built SOA-based middleware foundation as well as industry-optimized service oriented applications, all built around a SOA governance model that encourages effective design and reuse. I encourage you to read Oracle Solution for Mastering Constituents Data for Public Sector – Tax Authorities by Roberto Negro. It is an outstanding whitepaper that describes how the Oracle MDM solution allows you to create a unified, reconciled source of high-quality constituent data and gain an accurate single view of each constituent. This foundation enables you to lower the costs associated with data quality and integration and create a tax organization that is efficient, secure and constituent-centric. Also, don’t forget the upcoming webcast on Thursday, February 10th: Deliver Improved Services to Citizens at Lower Cost to your Organization Our Guest Speaker is Ruben Spekle, from Capgemini. He will also provide insight into Public Sector Master Data Management and Case Management implementations including one that was executed for a Dutch Government Agency. If you are interested in how governmental organizations from around the world are using MDM to advance their cause, click here to register for the webcast.

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• Extreme Optimization – Numerical Algorithm Support

  - by JoshReuben

  Function Delegates Many calculations involve the repeated evaluation of one or more user-supplied functions eg Numerical integration. The EO MathLib provides delegate types for common function signatures and the FunctionFactory class can generate new delegates from existing ones. RealFunction delegate - takes one Double parameter – can encapsulate most of the static methods of the System.Math class, as well as the classes in the Extreme.Mathematics.SpecialFunctions namespace: var sin = new RealFunction(Math.Sin); var result = sin(1); BivariateRealFunction delegate - takes two Double parameters: var atan2 = new BivariateRealFunction (Math.Atan2); var result = atan2(1, 2); TrivariateRealFunction delegate – represents a function takes three Double arguments ParameterizedRealFunction delegate - represents a function taking one Integer and one Double argument that returns a real number. The Pow method implements such a function, but the arguments need order re-arrangement: static double Power(int exponent, double x) { return ElementaryFunctions.Pow(x, exponent); } ... var power = new ParameterizedRealFunction(Power); var result = power(6, 3.2); A ComplexFunction delegate - represents a function that takes an Extreme.Mathematics.DoubleComplex argument and also returns a complex number. MultivariateRealFunction delegate - represents a function that takes an Extreme.Mathematics.LinearAlgebra.Vector argument and returns a real number. MultivariateVectorFunction delegate - represents a function that takes a Vector argument and returns a Vector. FastMultivariateVectorFunction delegate - represents a function that takes an input Vector argument and an output Matrix argument – avoiding object construction  The FunctionFactory class RealFromBivariateRealFunction and RealFromParameterizedRealFunction helper methods - transform BivariateRealFunction or a ParameterizedRealFunction into a RealFunction delegate by fixing one of the arguments, and treating this as a new function of a single argument. var tenthPower = FunctionFactory.RealFromParameterizedRealFunction(power, 10); var result = tenthPower(x); Note: There is no direct way to do this programmatically in C# - in F# you have partial value functions where you supply a subset of the arguments (as a travelling closure) that the function expects. When you omit arguments, F# generates a new function that holds onto/remembers the arguments you passed in and "waits" for the other parameters to be supplied. let sumVals x y = x + y     let sumX = sumVals 10     // Note: no 2nd param supplied.     // sumX is a new function generated from partially applied sumVals.     // ie "sumX is a partial application of sumVals." let sum = sumX 20     // Invokes sumX, passing in expected int (parameter y from original)  val sumVals : int -> int -> int val sumX : (int -> int) val sum : int = 30 RealFunctionsToVectorFunction and RealFunctionsToFastVectorFunction helper methods - combines an array of delegates returning a real number or a vector into vector or matrix functions. The resulting vector function returns a vector whose components are the function values of the delegates in the array. var funcVector = FunctionFactory.RealFunctionsToVectorFunction(     new MultivariateRealFunction(myFunc1),     new MultivariateRealFunction(myFunc2));  The IterativeAlgorithm<T> abstract base class Iterative algorithms are common in numerical computing - a method is executed repeatedly until a certain condition is reached, approximating the result of a calculation with increasing accuracy until a certain threshold is reached. If the desired accuracy is achieved, the algorithm is said to converge. This base class is derived by many classes in the Extreme.Mathematics.EquationSolvers and Extreme.Mathematics.Optimization namespaces, as well as the ManagedIterativeAlgorithm class which contains a driver method that manages the iteration process.  The ConvergenceTest abstract base class This class is used to specify algorithm Termination , convergence and results - calculates an estimate for the error, and signals termination of the algorithm when the error is below a specified tolerance. Termination Criteria - specify the success condition as the difference between some quantity and its actual value is within a certain tolerance – 2 ways: absolute error - difference between the result and the actual value. relative error is the difference between the result and the actual value relative to the size of the result. Tolerance property - specify trade-off between accuracy and execution time. The lower the tolerance, the longer it will take for the algorithm to obtain a result within that tolerance. Most algorithms in the EO NumLib have a default value of MachineConstants.SqrtEpsilon - gives slightly less than 8 digits of accuracy. ConvergenceCriterion property - specify under what condition the algorithm is assumed to converge. Using the ConvergenceCriterion enum: WithinAbsoluteTolerance / WithinRelativeTolerance / WithinAnyTolerance / NumberOfIterations Active property - selectively ignore certain convergence tests Error property - returns the estimated error after a run MaxIterations / MaxEvaluations properties - Other Termination Criteria - If the algorithm cannot achieve the desired accuracy, the algorithm still has to end – according to an absolute boundary. Status property - indicates how the algorithm terminated - the AlgorithmStatus enum values:NoResult / Busy / Converged (ended normally - The desired accuracy has been achieved) / IterationLimitExceeded / EvaluationLimitExceeded / RoundOffError / BadFunction / Divergent / ConvergedToFalseSolution. After the iteration terminates, the Status should be inspected to verify that the algorithm terminated normally. Alternatively, you can set the ThrowExceptionOnFailure to true. Result property - returns the result of the algorithm. This property contains the best available estimate, even if the desired accuracy was not obtained. IterationsNeeded / EvaluationsNeeded properties - returns the number of iterations required to obtain the result, number of function evaluations.  Concrete Types of Convergence Test classes SimpleConvergenceTest class - test if a value is close to zero or very small compared to another value. VectorConvergenceTest class - test convergence of vectors. This class has two additional properties. The Norm property specifies which norm is to be used when calculating the size of the vector - the VectorConvergenceNorm enum values: EuclidianNorm / Maximum / SumOfAbsoluteValues. The ErrorMeasure property specifies how the error is to be measured – VectorConvergenceErrorMeasure enum values: Norm / Componentwise ConvergenceTestCollection class - represent a combination of tests. The Quantifier property is a ConvergenceTestQuantifier enum that specifies how the tests in the collection are to be combined: Any / All  The AlgorithmHelper Class inherits from IterativeAlgorithm<T> and exposes two methods for convergence testing. IsValueWithinTolerance<T> method - determines whether a value is close to another value to within an algorithm's requested tolerance. IsIntervalWithinTolerance<T> method - determines whether an interval is within an algorithm's requested tolerance.

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• Do you know your ADF "grace period?"

  - by Chris Muir

  What does the term "support" mean to you in context of vendors such as Oracle giving your organization support with our products? Over the last few weeks I'm taken a straw poll to discuss this very question with customers, and I've received a wide array of answers much to my surprise (which I've paraphrased): "Support means my staff can access dedicated resources to assist them solve problems" "Support means I can call Oracle at anytime to request assistance" "Support means we can expect fixes and patches to bugs in Oracle software" The last expectation is the one I'd like to focus on in this post, keep it in mind while reading this blog. From Oracle's perspective as we're in the business of support, we in fact offer numerous services which are captured on the table in the following page. As the text under the table indicates, you should consult the relevant Oracle Lifetime Support brochures to understand the length of time Oracle will support Oracle products. As I'm a product manager for ADF that sits under the FMW tree of Oracle products, let's consider ADF in particular. The FMW brochure is found here. On page 8 and 9 you'll see the current "Application Development Framework 11gR1 (11.1.1.x)" and "Application Development Framework 11gR2 (11.1.2)" releases are supported out to 2017 for Extended Support. This timeframe is pretty standard for Oracle's current released products, though as new releases roll in we should see those dates extended. On page 8 of the PDF note the comment at the end of this page that refers to the Oracle Support document 209768.1: For more-detailed information on bug fix and patch release policies, please refer to the “Error Correction Support Policy” on MyOracle Support. This policy document is important as it introduces Oracle's Error Correction Support Policy which addresses "patches and fixes". You can find it attached the previous Oracle Support document 209768.1. Broadly speaking while Oracle does provide "generalized support" up to 2017 for ADF, the Error Correction Support Policy dictates when Oracle will provide "patches and fixes" for Oracle software, and this is where the concept of the "grace period" comes in. As Oracle releases different versions of Oracle software, say 11.1.1.4.0, you are fully supported for patches and fixes for that specific version. However when we release the next version, say 11.1.1.5.0, Oracle provides at minimum of 3 months to a maximum of 1 year "grace period" where we'll continue to provide patches and fixes for the previous version. This gives you time to move from 11.1.1.4.0 to 11.1.1.5.0 without being unsupported for patches and fixes. The last paragraph does generalize as I've attempted to highlight the concept of the grace period rather than the specific dates for any version. For specific ADF and FMW versions and their respective grace periods and when they terminated you must visit Oracle Support Note 1290894.1. I'd like to include a screenshot here of the relevant table from that Oracle Support Note but as it is will be frequently updated it's better I force you to visit that note. Be careful to heed the comment in the note: According to policy, the Grace Period has passed because a newer Patch Set has been released for more than a year. Its important to note that the Lifetime Support Policy and Error Correction Support Policy documents are the single source of truth, subject to change, and will provide exceptions when required. This My Oracle Support document is providing a summary of the Grace Period dates and time lines for planning purposes. So remember to return to the policy document for all definitions, note 1290894.1 is a summary only and not guaranteed to be up to date or correct. A last point from Oracle's perspective. Why doesn't Oracle provide patches and fixes for all releases as long as they're supported? Amongst other reasons, it's a matter of practicality. Consider JDeveloper 10.1.3 released in 2005. JDeveloper 10.1.3 is still currently supported to 2017, but since that version was released there has been just under 20 newer releases of JDeveloper. Now multiply that across all Oracle's products and imagine the number of releases Oracle would have to provide fixes and patches for, and maintain environments to test them, build them, staff to write them and more, it's simple beyond the capabilities of even a large software vendor like Oracle. So the "grace period" restricts that patches and fixes window to something manageable. In conclusion does the concept of the "grace period" matter to you? If you define support as "getting assistance from Oracle" then maybe not. But if patches and fixes are important to you, then you need to understand the "grace period" and operate within the bounds of Oracle's Error Correction Support Policy. Disclaimer: this blog post was written July 2012. Oracle Support policies do change from time to time so the emphasis is on you to double check the facts presented in this blog.

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• Integrating Windows Form Click Once Application into SharePoint 2007 &ndash; Part 2 of 4

  - by Kelly Jones

  In my last post, I explained why we decided to use a Click Once application to solve our business problem. To quickly review, we needed a way for our business users to upload documents to a SharePoint 2007 document library in mass, set the meta data, set the permissions per document, and to do so easily. Let’s look at the pieces that make up our solution.  First, we have the Windows Form application.  This app is deployed using Click Once and calls SharePoint web services in order to upload files and then calls web services to set the meta data (SharePoint columns and permissions).  Second, we have a custom action.  The custom action is responsible for providing our users a link that will launch the Windows app, as well as passing values to it via the query string.  And lastly, we have the web services that the Windows Form application calls.  For our solution, we used both out of the box web services and a custom web service in order to set the column values in the document library as well as the permissions on the documents. Now, let’s look at the technical details of each of these pieces.  (All of the code is downloadable from here: )   Windows Form application deployed via Click Once The Windows Form application, called “Custom Upload”, has just a few classes in it: Custom Upload -- the form FileList.xsd -- the dataset used to track the names of the files and their meta data values SharePointUpload -- this class handles uploading the file SharePointUpload uses an HttpWebRequest to transfer the file to the web server. We had to change this code from a WebClient object to the HttpWebRequest object, because we needed to be able to set the time out value.  public bool UploadDocument(string localFilename, string remoteFilename) { bool result = true; //Need to use an HttpWebRequest object instead of a WebClient object // so we can set the timeout (WebClient doesn't allow you to set the timeout!) HttpWebRequest req = (HttpWebRequest)WebRequest.Create(remoteFilename); try { req.Method = "PUT"; req.Timeout = 60 * 1000; //convert seconds to milliseconds req.AllowWriteStreamBuffering = true; req.Credentials = System.Net.CredentialCache.DefaultCredentials; req.SendChunked = false; req.KeepAlive = true; Stream reqStream = req.GetRequestStream(); FileStream rdr = new FileStream(localFilename, FileMode.Open, FileAccess.Read); byte[] inData = new byte[4096]; int bytesRead = rdr.Read(inData, 0, inData.Length); while (bytesRead > 0) { reqStream.Write(inData, 0, bytesRead); bytesRead = rdr.Read(inData, 0, inData.Length); } reqStream.Close(); rdr.Close(); System.Net.HttpWebResponse response = (HttpWebResponse)req.GetResponse(); if (response.StatusCode != HttpStatusCode.OK && response.StatusCode != HttpStatusCode.Created) { String msg = String.Format("An error occurred while uploading this file: {0}\n\nError response code: {1}", System.IO.Path.GetFileName(localFilename), response.StatusCode.ToString()); LogWarning(msg, "2ACFFCCA-59BA-40c8-A9AB-05FA3331D223"); result = false; } } catch (Exception ex) { LogException(ex, "{E9D62A93-D298-470d-A6BA-19AAB237978A}"); result = false; } return result; } The class also contains the LogException() and LogWarning() methods. When the application is launched, it parses the query string for some initial values.  The query string looks like this: string queryString = "Srv=clickonce&Sec=N&Doc=DMI&SiteName=&Speed=128000&Max=50"; This Srv is the path to the server (my Virtual Machine is name “clickonce”), the Sec is short for security – meaning HTTPS or HTTP, the Doc is the shortcut for which document library to use, and SiteName is the name of the SharePoint site.  Speed is used to calculate an estimate for download speed for each file.  We added this so our users uploading documents would realize how long it might take for clients in remote locations (using slow WAN connections) to download the documents. The last value, Max, is the maximum size that the SharePoint site will allow documents to be.  This allowed us to give users a warning that a file is too large before we even attempt to upload it. Another critical piece is the meta data collection.  We organized our site using SharePoint content types, so when the app loads, it gets a list of the document library’s content types.  The user then select one of the content types from the drop down list, and then we query SharePoint to get a list of the fields that make up that content type.  We used both an out of the box web service, and one that we custom built, in order to get these values. Once we have the content type fields, we then add controls to the form.  Which type of control we add depends on the data type of the field.  (DateTime pickers for date/time fields, etc)  We didn’t write code to cover every data type, since we were working with a limited set of content types and field data types. Here’s a screen shot of the Form, before and after someone has selected the content types and our code has added the custom controls:     The other piece of meta data we collect is the in the upper right corner of the app, “Users with access”.  This box lists the different SharePoint Groups that we have set up and by checking the boxes, the user can set the permissions on the uploaded documents. All of this meta data is collected and submitted to our custom web service, which then sets the values on the documents on the list.  We’ll look at these web services in a future post. In the next post, we’ll walk through the Custom Action we built.

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• Unexpected behaviour with glFramebufferTexture1D

  - by Roshan

  I am using render to texture concept with glFramebufferTexture1D. I am drawing a cube on non-default FBO with all the vertices as -1,1 (maximum) in X Y Z direction. Now i am setting viewport to X while rendering on non default FBO. My background is blue with white color of cube. For default FBO, i have created 1-D texture and attached this texture to above FBO with color attachment. I am setting width of texture equal to width*height of above FBO view-port. Now, when i render this texture to on another cube, i can see continuous white color on start or end of each face of the cube. That means part of the face is white and rest is blue. I am not sure whether this behavior is correct or not. I expect all the texels should be white as i am using -1 and 1 coordinates for cube rendered on non-default FBO. code: #define WIDTH 3 #define HEIGHT 3 GLfloat vertices8[]={ 1.0f,1.0f,1.0f, -1.0f,1.0f,1.0f, -1.0f,-1.0f,1.0f, 1.0f,-1.0f,1.0f,//face 1 1.0f,-1.0f,-1.0f, -1.0f,-1.0f,-1.0f, -1.0f,1.0f,-1.0f, 1.0f,1.0f,-1.0f,//face 2 1.0f,1.0f,1.0f, 1.0f,-1.0f,1.0f, 1.0f,-1.0f,-1.0f, 1.0f,1.0f,-1.0f,//face 3 -1.0f,1.0f,1.0f, -1.0f,1.0f,-1.0f, -1.0f,-1.0f,-1.0f, -1.0f,-1.0f,1.0f,//face 4 1.0f,1.0f,1.0f, 1.0f,1.0f,-1.0f, -1.0f,1.0f,-1.0f, -1.0f,1.0f,1.0f,//face 5 -1.0f,-1.0f,1.0f, -1.0f,-1.0f,-1.0f, 1.0f,-1.0f,-1.0f, 1.0f,-1.0f,1.0f//face 6 }; GLfloat vertices[]= { 0.5f,0.5f,0.5f, -0.5f,0.5f,0.5f, -0.5f,-0.5f,0.5f, 0.5f,-0.5f,0.5f,//face 1 0.5f,-0.5f,-0.5f, -0.5f,-0.5f,-0.5f, -0.5f,0.5f,-0.5f, 0.5f,0.5f,-0.5f,//face 2 0.5f,0.5f,0.5f, 0.5f,-0.5f,0.5f, 0.5f,-0.5f,-0.5f, 0.5f,0.5f,-0.5f,//face 3 -0.5f,0.5f,0.5f, -0.5f,0.5f,-0.5f, -0.5f,-0.5f,-0.5f, -0.5f,-0.5f,0.5f,//face 4 0.5f,0.5f,0.5f, 0.5f,0.5f,-0.5f, -0.5f,0.5f,-0.5f, -0.5f,0.5f,0.5f,//face 5 -0.5f,-0.5f,0.5f, -0.5f,-0.5f,-0.5f, 0.5f,-0.5f,-0.5f, 0.5f,-0.5f,0.5f//face 6 }; GLuint indices[] = { 0, 2, 1, 0, 3, 2, 4, 5, 6, 4, 6, 7, 8, 9, 10, 8, 10, 11, 12, 15, 14, 12, 14, 13, 16, 17, 18, 16, 18, 19, 20, 23, 22, 20, 22, 21 }; GLfloat texcoord[] = { 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0 }; glGenTextures(1, &id1); glBindTexture(GL_TEXTURE_1D, id1); glGenFramebuffers(1, &Fboid); glTexParameterf(GL_TEXTURE_1D, GL_TEXTURE_MIN_FILTER, GL_NEAREST); glTexParameterf(GL_TEXTURE_1D, GL_TEXTURE_MAG_FILTER, GL_NEAREST); glTexParameterf(GL_TEXTURE_1D, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE); glTexImage1D(GL_TEXTURE_1D, 0, GL_RGBA, WIDTH*HEIGHT , 0, GL_RGBA, GL_UNSIGNED_BYTE,0); glBindFramebuffer(GL_FRAMEBUFFER, Fboid); glFramebufferTexture1D(GL_DRAW_FRAMEBUFFER,GL_COLOR_ATTACHMENT0,GL_TEXTURE_1D,id1,0); draw_cube(); glBindFramebuffer(GL_FRAMEBUFFER, 0); draw(); } draw_cube() { glViewport(0, 0, WIDTH, HEIGHT); glClearColor(0.0f, 0.0f, 0.5f, 1.0f); glClear(GL_COLOR_BUFFER_BIT); glEnableVertexAttribArray(glGetAttribLocation(temp.psId,"position")); glVertexAttribPointer(glGetAttribLocation(temp.psId,"position"), 3, GL_FLOAT, GL_FALSE, 0,vertices8); glDrawArrays (GL_TRIANGLE_FAN, 0, 24); } draw() { glClearColor(1.0f, 0.0f, 0.0f, 1.0f); glClearDepth(1.0f); glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT); glEnableVertexAttribArray(glGetAttribLocation(shader_data.psId,"tk_position")); glVertexAttribPointer(glGetAttribLocation(shader_data.psId,"tk_position"), 3, GL_FLOAT, GL_FALSE, 0,vertices); nResult = GL_ERROR_CHECK((GL_NO_ERROR, "glVertexAttribPointer(position, 3, GL_FLOAT, GL_FALSE, 0,vertices);")); glEnableVertexAttribArray(glGetAttribLocation(shader_data.psId,"inputtexcoord")); glVertexAttribPointer(glGetAttribLocation(shader_data.psId,"inputtexcoord"), 2, GL_FLOAT, GL_FALSE, 0,texcoord); glBindTexture(*target11, id1); glDrawElements ( GL_TRIANGLES, 36,GL_UNSIGNED_INT, indices ); when i change WIDTH=HEIGHT=2, and call a glreadpixels with height, width equal to 4 in draw_cube() i can see first 2 pixels with white color, next two with blue(glclearcolor), next two white and then blue and so on.. Now when i change width parameter in glTeximage1D to 16 then ideally i should see alternate patches of white and blue right? But its not the case here. why so?

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• How to Tell If Your Computer is Overheating and What to Do About It

  - by Chris Hoffman

  Heat is a computer’s enemy. Computers are designed with heat dispersion and ventilation in mind so they don’t overheat. If too much heat builds up, your computer may become unstable or suddenly shut down. The CPU and graphics card produce much more heat when running demanding applications. If there’s a problem with your computer’s cooling system, an excess of heat could even physically damage its components. Is Your Computer Overheating? When using a typical computer in a typical way, you shouldn’t have to worry about overheating at all. However, if you’re encountering system instability issues like abrupt shut downs, blue screens, and freezes — especially while doing something demanding like playing PC games or encoding video — your computer may be overheating. This can happen for several reasons. Your computer’s case may be full of dust, a fan may have failed, something may be blocking your computer’s vents, or you may have a compact laptop that was never designed to run at maximum performance for hours on end. Monitoring Your Computer’s Temperature First, bear in mind that different CPUs and GPUs (graphics cards) have different optimal temperature ranges. Before getting too worried about a temperature, be sure to check your computer’s documentation — or its CPU or graphics card specifications — and ensure you know the temperature ranges your hardware can handle. You can monitor your computer’s temperatures in a variety of different ways. First, you may have a way to monitor temperature that is already built into your system. You can often view temperature values in your computer’s BIOS or UEFI settings screen. This allows you to quickly see your computer’s temperature if Windows freezes or blue screens on you — just boot the computer, enter the BIOS or UEFI screen, and check the temperatures displayed there. Note that not all BIOSes or UEFI screens will display this information, but it is very common. There are also programs that will display your computer’s temperature. Such programs just read the sensors inside your computer and show you the temperature value they report, so there are a wide variety of tools you can use for this, from the simple Speccy system information utility to an advanced tool like SpeedFan. HWMonitor also offer this feature, displaying a wide variety of sensor information. Be sure to look at your CPU and graphics card temperatures. You can also find other temperatures, such as the temperature of your hard drive, but these components will generally only overheat if it becomes extremely hot in the computer’s case. They shouldn’t generate too much heat on their own. If you think your computer may be overheating, don’t just glance as these sensors once and ignore them. Do something demanding with your computer, such as running a CPU burn-in test with Prime 95, playing a PC game, or running a graphical benchmark. Monitor the computer’s temperature while you do this, even checking a few hours later — does any component overheat after you push it hard for a while? Preventing Your Computer From Overheating If your computer is overheating, here are some things you can do about it: Dust Out Your Computer’s Case: Dust accumulates in desktop PC cases and even laptops over time, clogging fans and blocking air flow. This dust can cause ventilation problems, trapping heat and preventing your PC from cooling itself properly. Be sure to clean your computer’s case occasionally to prevent dust build-up. Unfortunately, it’s often more difficult to dust out overheating laptops. Ensure Proper Ventilation: Put the computer in a location where it can properly ventilate itself. If it’s a desktop, don’t push the case up against a wall so that the computer’s vents become blocked or leave it near a radiator or heating vent. If it’s a laptop, be careful to not block its air vents, particularly when doing something demanding. For example, putting a laptop down on a mattress, allowing it to sink in, and leaving it there can lead to overheating — especially if the laptop is doing something demanding and generating heat it can’t get rid of. Check if Fans Are Running: If you’re not sure why your computer started overheating, open its case and check that all the fans are running. It’s possible that a CPU, graphics card, or case fan failed or became unplugged, reducing air flow. Tune Up Heat Sinks: If your CPU is overheating, its heat sink may not be seated correctly or its thermal paste may be old. You may need to remove the heat sink and re-apply new thermal paste before reseating the heat sink properly. This tip applies more to tweakers, overclockers, and people who build their own PCs, especially if they may have made a mistake when originally applying the thermal paste. This is often much more difficult when it comes to laptops, which generally aren’t designed to be user-serviceable. That can lead to trouble if the laptop becomes filled with dust and needs to be cleaned out, especially if the laptop was never designed to be opened by users at all. Consult our guide to diagnosing and fixing an overheating laptop for help with cooling down a hot laptop. Overheating is a definite danger when overclocking your CPU or graphics card. Overclocking will cause your components to run hotter, and the additional heat will cause problems unless you can properly cool your components. If you’ve overclocked your hardware and it has started to overheat — well, throttle back the overclock! Image Credit: Vinni Malek on Flickr     

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